Download The Effect of Biotic and Abiotic Forces on Species

The Effect of Biotic and Abiotic Forces on Species Richness
Peter J.T. White
Faculty of Science, Department of Biology
McGill University
Montréal, Québec, Canada
A thesis submitted to McGill University in partial fulfillment of the
requirements of the degree of Doctor of Philosophy
© Peter J.T. White, 2011
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TABLE OF CONTENTS
Table of Contents………………………………………………………………………………………………..….. 2
List of Tables………………………………………………………………………….….……………………………. 5
List of Figures……………………………………………………………………………………….…….…….…….. 8
List of Appendices…………………………………………………………………………………………………….13
Preface………………………………………………………………………………………………………………….… 14
Thesis Format and Style…………………………………………………………………………………… 14
Contribution of Co-Authors……………………………………………………………………………… 15
Original Contributions to Knowledge………………………………………………………………..17
References………………………………………………………………………………………………………. 21
Acknowledgements…………………………………………………………………………………………. 23
Thesis Abstract………………………………………………………………………………………………………… 26
Résumé…………………………………………………………………………………………………………………….28
General Introduction………………………………………………………………………………………………..30
References……………………………………………………………………………………………….……… 45
Chapter 1: Detecting Changes in Forest Floor Habitat after Canopy Disturbances…… 53
Abstract……………………..……………………………………………………………………………………. 54
Introduction…………………………………………………………………………………………………….. 55
Local Consequences of Damage………………………………………………………………..55
Landscape and Regional Investigations……………………………………………….…… 56
Habitat Implications of Ice Storms………………………………………………………….…56
Using Remote Sensing to Measure Habitat Change…………………….…………… 58
Objective………………………………………………………………………………….……………… 59
Method……………………………………………………………………………………………….…………… 59
Study Area………………………………………………………………………………………..……… 59
Satellite Imagery Processing……………………………………………………………………..60
VI Calculation…………………………………………………………………………………………… 61
Predicting the Spatial Pattern of CWD Influx……………………………………………. 63
Independent Validation of CWD Predictions…………………………………….……… 64
Methods Evaluation……………………………………………………………………………….… 65
Categorizing CWD Habitat…………………………………………………………………………66
Results………………………………………………………………………………………………………………67
Performance of VIs for Predicting CWD Influx……………………………………..…… 67
Independent Validation of CWD Predictions………………………………….………… 68
Categorization of CWD Influx into Different Habitat Types………………….…… 68
Methods Evaluation………………………………………………………………………….……… 69
Discussion……………………………………………………………………………………………………….. 69
Accuracy of VIs in Predicting Spatial Pattern of Damage…………………..……… 69
CWD Benefits for Mont St Hilaire Species…………………………………………..….… 71
The Benefits of Different Habitat Types for Insects……………………………..…… 72
CWD Influx and Species Movements……………………………………………………..… 74
Conclusion………………………………….……………………………………………………………. 76
Acknowledgements……………………………………………………………….………………………… 77
References………………………………………………………………………………………………………. 78
Figures………………………………………………………………………………………………………………89
Tables……………………………………………………………………………………………………………… 92
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Linking Statement 1………………………………………………………………………………….………..…… 96
Chapter 2: Human-Disturbance and Caterpillars in Managed Forest Fragments.….…. 97
Abstract……………………………………………………………………………………………………….….. 98
Introduction……………………………………………………………………………………………….……. 99
Trailside Habitat in Forests………………………………………………………………………. 99
An Analogy of Forest Edge Habitat…………………………………………………………... 101
Hypotheses……………………………………………………………….….…….….….….………… 102
Method……………………………………………………………………………………………………………. 103
Study Area……………………………………………………………………………………………….. 103
Trail Index Calculation……………………………………………………………………………… 106
Caterpillar Surveying and Identification…………………………………………………… 107
Site Host Plant Availability……………………………………………………………………….. 108
Statistical Analysis……………………………………………………………………………………. 109
Results…………………………………………………………………………………………………………….. 110
Trail Index and Host Plant Availability……………………………………………………… 110
Random Versus Non-Random Distribution of Caterpillar Species…………….. 111
Discussion……………………………………………………………………………………………………….. 112
Possible Mechanisms of Negative Relationship……………………………………….. 112
Trails Versus Edges…………………………………………………………………………………… 114
Conclusion……………………………………………………………………………………………….. 116
Acknowledgements………………………………………………………………………………….……… 117
References………………………………………………………………………………………………………. 118
Figures……………………………………………………………………………………………………….……. 131
Tables……………………………………………………………………………………………………………… 134
Linking Statement 2………………………………………………………………………………………………… 136
Chapter 3: Testing Two Methods that Relate Herbivorous Insects to Host Plants….... 137
Abstract…………………………………………………………………………………………………………… 138
Introduction…………………………………………………………………………………………………….. 140
Overlooked in Conservation Planning…………………………………………………..…..140
The Relationship between Hosts and Lepidoptera………………………………..…. 141
Methods………………………………………………………………………………..….….….….…....…… 144
Study Area…………………………………………………………………………..…………………… 144
Caterpillar Survey and Identification………………………………………………………… 146
Controlling for Habitat Disturbance..…………………………………………………..…… 147
Analyses…………………………………………………………………………………………………… 147
Host Plant and Caterpillar Relationships..……………………………….…….….….….. 149
Testing Host Plant-Specific Preferences………………………………….…….….….….. 149
Results………………………………………………………………………………………………………….…. 150
Caterpillar Sampling…………………………………………………………………………………. 150
Host Plant and Caterpillar Relationships………………………………………………….. 151
CAPIr and CAPIa…………..………………………………………………………………………….. 151
Host Plant Importance Relative to Trail Index………………………………………….. 152
Discussion……………………………………………………………………………………………………..… 153
Biodiversity and Conservation….….….…………………………………………………….… 153
Low Richness and Abundance in Invasive Trees……………………………………….. 154
Mechanisms…..….….….….….….….……………………………………………….…..…..……..157
Conclusion……………………………………………………………………………………………..… 159
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Acknowledgements……………………………………………………………………………………..….. 161
References………………………………………………………………………………………………….…… 162
Figures………………………………………………………………………………………………………..…… 172
Tables………………………………………………………………………………………………………….…… 177
Linking Statement 3………………………………………………………………………………………………… 181
Chapter 4: Intra-Seasonal Relationships between Insect Herbivores and their Hosts. 182
Abstract……………………………………………………………………………………………………….….. 183
Introduction………………………………………………………………………………………………….…. 184
Bottom-Up Effects: Foliar Quality.….….……………………….…….….…………………. 184
Bottom-Up Effects: Foliar Toxins.….….….….….….….….….….………………………… 186
Top-Down Effects.….….….….….….….….….….….….….….….….….….….…………….… 187
Current Gaps in Foliar Quality Research.….….….………….…….….…………………. 188
Tri-Trophic Relationships……………….….….….….….….….….…….….…………………. 191
Objective…………………………………………………………………………………………………. 192
Methods……………………………………………………………………………………………………….…. 193
Study Area…………………………………………………………………………………………….…. 193
Caterpillar Survey and Identification………………………………………………………...195
Measuring the Bottom-Up Effect…………………………..…..….……………….…..…... 195
Measuring the Top-Down Effect…………………………….….….……………….…..…... 196
Analysis.….….….….….….….….….….….….………………….……………..….……….…..…... 198
Methodological Assumptions…………………………………………………………………... 199
Results……………………………………………………………………………………………….……………. 200
Bottom-Up Foliar Quality……………………….………………………………………………… 201
Top-Down Pressure…………………………………………………………………………………. 201
Seasonality of Bottom-Up Effects.….….……………………………………………………..201
Tri-Trophic Relationships.….….….….….…………………….….….……………….…..…... 203
Discussion…………………………………………………………………………………………….….….….. 204
Bottom-Up: Foliar Quality………………….…………………………………….…….…………204
Bottom-Up: Toxins……………………………..….….……………………………….…………... 207
Top Down: Parasitoids…………………………………………………………………….…....… 208
Conclusion…………………………………………………………………………………….……….… 210
Acknowledgements……………………………………………………………………………………….… 211
References…………………………………………………………………………………………………….… 212
Figures…………………………………………………………………………………………………………….. 223
Tables……………………………………………………………………………………………………………… 228
General Conclusion……………………………………………………………………………………………….… 232
References……………………………………………………………………………………………………………... 237
Appendices……………………………………………………………………………………………………………… 239
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LIST OF TABLES
CHAPTER 1:
Table 1 Landsat-5 satellite imagery used in calculation of vegetation indices
for Mont St. Hilaire in 1996 and 1998.
Table 2 Vegetation Indices (VIs) used to calculate forest damage after the
1998 ice storm at Mont St. Hilaire. The VIs incorporated four
wavelengths of light: blue (450 – 520 nm), green (520 – 600 nm), red
(630 – 690 nm) and near infrared (NIR, 760 – 900 nm).
Table 3 The data for each regression model were split into random training
and validation datasets (80/20 ratio, 66/16 plots) to create ten cross
validation trials for each Vegetation Index. The R2 fit of each
validation dataset is given respective of the regression coefficient and
intercept calculated in its training dataset. NDVI provides the best for
both training and validation datasets (R2 = 0.20 and 0.37,
respectively).
CHAPTER 2:
Table 1 Trail Index is a consistent negative predictor of caterpillar abundance
and caterpillar species richness both across and within study
sites.Host plant availability is rarely significantly linked to caterpillar
species abundance or richness, only explaining a large degree of
caterpillar variance at Mont Shefford. Near-significant (*) and
significant (**) p-values are marked.
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CHAPTER 3:
Table 1The relationship between host plants and caterpillars shows that (a)
host plant (Shannon’s) diversity is a significant descriptor of
caterpillar (Shannon’s) diversity when Trail disturbance is accounted
for (total model adjusted R2 = 0.45F3,68 = 9.9) but (b)host plant
richness is a non-significant descriptor of caterpillar richness when
Trail disturbance is accounted for (total model adjusted R2 = 0.27,
F3,68 = 20.1).
Table 2CAPIa and CAPIr values for host plant trees (sorted in order of
decreasing CAPIr values) are calculated as the difference between the
observed and the average caterpillar abundances and richness in host
plant tree species (see equations 1 and 2). Greater CAPI values
indicate that a host plant is more preferred by the caterpillar
assemblage.
Table 3Host plant frequencies (combined) explained (a) 21.0% of caterpillar
abundance, (b) 24.9% of caterpillar richness and (c) 27.9% of
caterpillar Shannon’s diversity among quadrats. This was independent
of trail index which, when combined with a F. americana interaction
term, explained (a) 19.8%, (b) 22.3% and (c) 18.7% of the variances.
CHAPTER 4:
Table 1 Foliar nutrient properties in broadleaf trees in southern Quebec and
Ontario in early June, early July and early August. Foliar nutrient data
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are averaged across 34 different common deciduous tree species.
This table is adapted from Ricklefs and Matthew’s (1982), Table 3.
Table 2 A list of trees sampled (where n > 10) across the 72 study sites, and
their associated caterpillar richness and abundance.
Table 3Parasitoid pressure on host plants in August.
Table 4 Emergence records for parasitoids in Ontario and Quebec
documented by Natural Resources Canada, 1937-1949.
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LIST OF FIGURES
CHAPTER 1:
Figure 1 Coarse woody debris influx at Mont St. Hilaire resulting from the
1998 ice storm. Mean coarse woody debris influx was 1.7 kg/m2
(standard deviation = 0.97 kg/m2), for a total estimated input of 16.8
metric tons per hectare. Coarse woody debris input values are
spatially correlated up to 450 meters.
Figure 2 The predicted biomass of coarse woody debris resulting from the
1998 ice storm is positively correlated to the amount of young
(category 1 and 2) coarse woody debris that was measured in the
summer of 2008 at 18 randomly located sites across Mont St. Hilaire.
Figure 3 Concentrations of coarse woody debris at Mont St. Hilaire in (a) dry
shaded habitat, (b), moist shaded habitat, (c) sun-exposed habitat
and (d) wet habitat.
CHAPTER 2:
Figure 1 We sampled at four sites in the St. Lawrence River valley of southern
Quebec, Canada (Figure adapted from Atlas of Canada 2010), each
progressively farther from the center of Montreal (dashed bound).
Urban development is highest in the western sites, giving way to
farmlands eastward in the St-Lawrence Lowland, and then extensive
forests in the Appalachian Highlands
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Figure 2 Trail index calculation for a given pixel p at distance d from trail t
that has a width w. Index shown untransformed (a) and logtransformed (b).
Figure 3 Trail index across our four study sites: (A) Mont Royal, (B) Mont St.
Bruno, (C) Mont St. Hilaire and (D1, D2, D3) Mont Shefford. The
geospatial arrangement in this Figure does not reflect the regional
geolocations of the sites (see Figure 1).
CHAPTER 3:
Figure 1 Caterpillars were collected from four sites in the St. Lawrence River
valley of southern Quebec, Canada (Figure adapted from Atlas of
Canada 2010). The matrix surrounding each site isdominated by
agricultural lands and urban development with the exception of Mont
Royal, which is a forest fragment in an exclusively urban setting.
Figure2There was no relationship between the number of caterpillars
reported to use a given host plant and either (a) CAPIr or (b) CAPIa
scores. These relationships are expected to be positive as a host
plant’s acceptability should be indicative of the caterpillar assemblage
preference of that host plant relative to other host plants in the
community.
Figure3 A host plant replacement simulation for (a) caterpillar species
richness and (b) caterpillar richness in the Mont Royal forest
fragment. In these simulations F. grandifolia and A. pensylvanicum
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were substituted for A. platanoides and R. cathartica (dashed line)
and O. virginiana was substituted for F. americana (dotted line). The
substituted species were chosen because they had high CAPIr and/or
CAPIa scores and commonly share the same general canopy position
as the species they replace. Replacement of invasive species with O.
virginiana and A. pensylvanicum resulted in an increase of 30% in
caterpillar species richness and 40% in caterpillar abundance.
Replacement of F. americana with F. grandifolia resulted in an
increase of 18% in caterpillar species richness and 37% in caterpillar
abundance.
CHAPTER 4:
Figure 1 Caterpillars were collected from four sites in the St. Lawrence River
valley of southern Quebec, Canada (Figure adapted from Atlas of
Canada 2010) at the northern edge of the deciduous forest biome in
eastern North America. The matrix surrounding each site isdominated
by agricultural lands and urban development with the exception of
Mont Royal, which is a forest fragment in an exclusively urban setting.
Forest patches across the region are shown in dark gray, urban areas
in light gray (including the City of Montreal at the left side of the
pane).
Figure 2 Average foliar qualities at quadrats in the months of June (Je), July
(Jy) and August (Au) for (a) % water content (b) % nitrogen content,
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(c) % phosphorus content, (d) toughness (grams), (e) % fiber content,
(f) % lignin content and (g) % polyphenol content. Bars represent
standard error. ANOVAs between months are significant for all foliar
qualities at p < 0.001. Average foliar qualities for each quadrat were
weighted based on the proportion of the total basal area occupied by
each sampled host plant species in the quadrat. Host plant-specific
foliar qualities were taken from Ricklefs and Matthew (1982).
Figure 3Regression tree analyses of the determinants of caterpillar
richness (left hand panels) and abundance (right panels) at the
quadrat level. Analyses are shown for June (a, b), July (c, d) and
August (e, f). The variables shown are % Phosphorus content
(P), % Polyphenol content (Phenol),% Fiber content (Fiber) and
% Lignin content (Lig). Water content, Nitrogen content and
Toughness were included, but were not significant. The clause
presented at each node is the condition corresponding to the
left hand fork (the right hand fork would be the opposite
condition). Each clause is paired with an R2 value associated
with that node in brackets; this value is equal to the complexity
parameter of the node. At each terminus the average caterpillar
richness or abundance is given (depending on the tree) along
with the number of quadrats that satisfy the conditions of the
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fork (in brackets). Trees are pruned and show splits
corresponding to R2> 0.05.
Figure 4 Regression tree analyses for the determinants of caterpillar
richness (a) and abundance (b) for the month of August. These
trees were created using the same data as for Figure 3e and 3f
(respectively) but with the addition of parasitoid pressure data
for each quadrat. In (a) parasitoid pressure supersedes % fiber
content as the most important determinant of caterpillar
richness. In (b) parasitoid pressure is less important and
supersedes % polyphenol content as the primary determinant of
caterpillar abundance.
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APPENDICES
CHAPTER 2:
Appendix A1 We surveyed 36 macrolepidopteran moth species across the
four sites in our study region Mount Royal (R), Mont St. Bruno (B),
Mont St. Hilaire (H) and Mont Shefford (S). All species IDs were based
on 5th or 6th instar larvae identified using Wagner (2005).
Appendix A2 Micromoth distribution.
Appendix A3 Complete list of host plant species documented in vegetation
surveys.
Appendix A4Randomness was tested using randomization goodness-of-fit
tests. P-values were calculated using 10,000 replicates of
randomization.
CHAPTER 3:
AppendixB1 Caterpillar collections were made from 38 host plant trees in 72
quadrats across the four study sites.
AppendixB2 A record of the macrolepidoptera and microlepidoptera
morphospecies that were surveyed. Microlepidoptera morphospecies
were given unique alphanumeric designations and subsequent
individuals were verified with digital images. All macrolepidoptera
were identified using Wagner (2005) and Handfield (1999).
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PREFACE
Thesis Format and Style
This thesis is in a manuscript-based format, consisting of a set of four papers.
The first chapter explores the geospatial pattern of damage and biological
implications of the 1998 ice storm on an old-growth forest. The second, third and
fourth chapters are focused on how disturbance and habitat quality impact
caterpillar assemblage richness and abundance. All four chapters focus on forest
fragments found in the same area of the St. Lawrence floodplain in southern
Quebec (i.e. the Monteregié). This thesis was initially intended to have a remotesensing and GIS theme in all four chapters but after NSERC funding opportunities
became available in the spring of 2008 the thesis evolved to centre on
lepidopteran ecology and extensive field work was conducted in 2008 and 2009.
Thus, the first chapter has a different focus than the final three chapters that
comprise a cohesive unit. The first chapter, however, does link with the second
chapter through the themes of natural disturbances (Chapter 1) and humanbased disturbances (Chapter 2) in forest fragment communities.
14
Contributions of Co-Authors
Each of the four chapters was prepared as a manuscript for publication in a peer
reviewed journal, generally focusing on biological reserve management for the
conservation of biological diversity. I received valuable guidance and intellectual
contributions from my supervisors B. McGill and M.J. Lechowicz. I was, however,
the primary person designing the research questions, organizing and conducting
the data collection, computing statistical analyses and writing the research for
each chapter. I had two research assistants – R. MacKenzie and M. VonButtlar
who helped me collect the woody debris, vegetation and Lepidoptera data used
throughout this thesis.
Chapter 1: M.J. Lechowicz and B. McGill were both valuable intellectual
contributors. M.J. Lechowicz provided several rounds of editorial comments and
suggestions as the manuscript was in varying stages of preparation. B. McGill
provided very valuable insights and advice for the statistical procedures that
were used. They both played key roles in sharpening my research focus and
suggesting that the research focus on biotic implications. Prior coarse woody
debris collections by M. Hooper in 1998were also instrumental in allowing a
geospatial model to be computed.
Chapter 2: M.J. Lechowicz and B. McGill played similar roles as in Chapter
1.
15
Chapter 3: This will be a single-authored manuscript. M.J. Lechowicz
provided comments on the manuscript and encouraged me to think about the
implications of my findings, particularly encouraging me to explore a post hoc
invasive-species replacement exercise. B. McGill was an invaluable
consultanthelping me construct the conceptual arguments I use in this chapter.
He also provided several rounds of review before it was finalized.
Chapter 4: This will also bea single-authored manuscript. B. McGill played
a similar role as in chapter 3. He also encouraged me to use many of the
statistical methods that were chosen.
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Original Contributions to Knowledge
Chapter 1:
There has been much research on the impacts of the 1998 ice storm (and similar
storms) on broadleaved forests. The bulk of this research has explored either the
geophysical correlates to damage or the local biotic implications. For example,
there is a wide array of studies that have mapped out the pattern of ice storm
damage (King et al. 2005, Millward and Kraft 2004) or explored the topographic
correlates to damage (Vandyke 1999) at a landscape orregional scale. These
types of studies are valuable, but do not allow us to understand the large-scale
biotic impacts resulting from amass coarse woody debris influx. Thus, the first
original contribution to knowledge is the geospatial mapping of different types
of coarse woody debris habitat (which support different biotic communities)
across a landscape using freely available remote sensing imagery. Second, it has
often been assumed that the Normalized Difference Vegetation Index (NDVI) is
the best index to use in ecological studies to measure changes in forest canopy
cover but tests of this assumption are rare. I provide empirical evidence to
support this claim by showing that NDVI outperforms six other popular remotely
sensed vegetation indices in a cross validation analyses predicting coarse woody
debris influx resulting from canopy damage.
17
Chapter 2:
The result of human disturbances on forest communities is an important
conservation concern. One of the primary ways that humans impact natural
communities is through the presence of recreational hiking trails. The impact of
such trails has been explored for birds (Miller and Hobbs 2000), small mammals
(Meaney et al. 2002) and ground-dwelling beetles (Grandchamp et al. 2000)
(among other taxa), but it has never been considered for forest-dwelling
caterpillar assemblages. In this chapter I show that there is a consistent and
negative relationship between recreational trail presence and caterpillar
assemblage richness and abundance. Furthermore, to accomplish this analysis I
develop a simple index that quantifies the effect of trails on forest habitat as a
function of trail width and trail proximity. This is a simple and effective tool that
can be readily applied to other forest conservation areas to quantify a geospatial
index of trail impact. The importance of this research extends to forest reserve
management where trails are generally viewed as positive management tools
because they direct and control the flow of pedestrian traffic. While the research
in this chapter certainly does not suggest the elimination of trails, it suggests
that trails need to be limited and managed to maximize the richness and
abundance of forest-dwelling caterpillar assemblages.
18
Chapter 3:
Insect herbivores have been linked to their host plants in two different ways
depending on whether the unit of examination is an entire insect herbivore
assemblageor an individualinsect herbivore species. Insect herbivore assemblage
richness is often tied to host plant richness under the premise that a species-rich
host plant assemblage provides a wider array of food and structural resources
than a species-poor one (Haddad et al. 2001, Summerville and Crist 2004). This
is contrasted by studies of individual insect herbivore species that often tie
insect herbivores to host plants based on plant identity – some host plants are
highly preferred while others are not (e.g.Delisle and Hardy 1997, Liebhold et al.
1995).Consumption of preferred host plant foliage often results in greater
fecundity and developmental gains. It is unknown how host plant preferences at
the individual species level scales up to an entire insect herbivore assemblage. In
this chapter I test between these two relationships that posit: (1) insect
herbivore assemblage richness is driven by host plant richness and (2) insect
herbivore assemblage richness is driven by the presence of highly preferred host
plants.I demonstrate that the latter is a much stronger descriptor of insect
herbivore richness than the former. In addition, I showed thatinvasive host
plants are strongly negatively linked to insect herbivore assemblage richness and
abundance. A host plant replacement simulation showed that insect herbivore
assemblage richness and abundance could increase by 30-40% if invasive host
plants were replaced with functionally comparable native host plants. This
19
provided an empirical basis for continued and stronger forest management to
eliminate and restrict the establishment of invasive plants.
Chapter 4:
Broadly speaking, there are two theories to explain the distribution of caterpillar
species among host tree species. The first is that caterpillar species are
distributed with respect to their foliar nutrient needs; the second is that
caterpillar species are distributed so as to minimize their chances of coming into
contact with their parasitoid enemies (Lill 2001). Certain caterpillar species have
also been documented to feed on foliage high in polyphenol content to
sequester toxins as a defense against parasitism (Turlings et al. 1995). Previous
research has documented that a change in distribution strategies in certain
caterpillar species may occur corresponding to the phenology of parasitoids (Lill
et al. 2002). However these two pressures (bottom-up and top-down) have only
rarely been considered in a seasonal context and almost never in the context of
an entire caterpillar assemblage. My research in this chapter indicated that
caterpillars are distributed amongst host plants with respect to foliar quality
early in the season but that late in the season parasitoid pressure supersedes the
importance of foliar qualityas an explanation for caterpillar richness and
abundance patterns. In the caterpillar assemblage I examined, there was no
evidence to suggest that polyphenol sequestration plays a large role in impacting
caterpillar host plant choices.
20
References
Delisle, J. and Hardy, M. 1997. Male larval nutrition influences the reproductive
success of both sexes of the Spruce Budworm, Choristoneura fumiferana
(Lepidoptera: Tortricidae). - Functional Ecology 11: 451-463.
Grandchamp, A. C., Niemelä, J. and Kotze, J. 2000. The effects of trampling on
assemblages of ground beetles (Coleoptera, Carabidae) in urban forests
in Helsinki, Finland. - Urban Ecosystems 4: 321-332.
Haddad, N. M., Tilman, D., Haarstad, J., Ritchie, M. and Knops, J. M. N. 2001.
Contrasting effects of plant richness and composition on insect
communities: a field experiment. – The American Naturalist 158: 17-35.
King, D. J., Olthof, I., Pellikka, P. K. E., Seed, E. D. and Butson, C. 2005. Modelling
and mapping damage to forests from an ice storm using remote sensing
and environmental data. - Natural Hazards 35: 321-342.
Liebhold, A. M., Gottschalk, K. W., Muzika, R., Montgomery, M. E., Young, R.,
O'Day, K. and Kelley, B. 1995. Suitability of North American Tree Species
to the Gypsy Moth: A Summary of Field and Laboratory Tests. United
States Department of Agriculture, Northeastern Forest Experiment
Station, Radnor, PA
Lill, J. T. 2001. Selection on herbivore life-history traits by the first and third
trophic levels: the devil and the deep blue sea revisited. - Evolution 55:
2236-2247.
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Lill, J. T., Marquis, R. J. and Ricklefs, R. E. 2002. Host plants influence parasitism
of forest caterpillars. - Nature 417: 170-173.
Meaney, C. A., Ruggles, A. K., Clippinger, N. W. and Lubow, B. C. 2002. The
impact of recreational trails and grazing on small mammals in the
Colorado Piedmont. - Prairie Naturalist 34: 115-136.
Miller, J. R. and Hobbs, N. T. 2000. Recreational trails, human activity, and nest
predation in lowland riparian areas. - Landscape and Urban Planning 50:
227-236.
Millward, A. A. and Kraft, C. E. 2004. Physical influences of landscape on a largeextent ecological disturbance: the northeastern North American ice
storm of 1998. - Landscape Ecology 19: 99-111.
Summerville, K. S. and Crist, T.O. 2004. Forest moth taxa as indicators of
lepidopteran richness and habitat disturbance: a preliminary assessment.
- Biological Conservation 116: 9-18.
Turlings, T. C., Loughrin, J. H., McCall, P. J., Röse, U. S., Lewis, W. J. and
Tumlinson, J. H. 1995. How caterpillar-damaged plants protect
themselves by attracting parasitic wasps. - Proceedings of the National
Academy of Sciences 92: 4169-4174.
Vandyke, O. 1999. A literature review of ice storm impacts on forests in Eastern
North America. SCSS Technical Report #112. - Ontario Ministry of Natural
Resources, Southcentral Sciences Section, pp. 1-29.
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Acknowledgements
My wife Blythe has stood by me throughout this journey and it would have been
immeasurably more difficult for me to complete this thesis without her loving
and unwavering support. She has brought such a fantastic richness to my life and
has made our marriage a source of refreshment and rest. Over the past five
years this thesis has been a major story that has been written across the pages of
my life; Blythe has been the one to fill those pages with colour.
MENTORS
Throughout this thesis journey I have had had two invaluable mentors: Dr. Marty
Lechowicz and Dr. Brian McGill. I am appreciative of all of the time and effort
they have spent on me. I am very thankful that they chose to invest in me and I
hope that I can live up to their expectations.
EDUCATORS
I would like to the two educators who inspired me to become a scientist. Robert
Cassibo was my grade high school science teacher. He made science come alive
in a way that forever changed my perception of the world in which we live.
Without his influence I would not have become a scientist.
Dr. Jeff Houlahan was my undergraduate ecology professor at the
University of Ottawa. Through Jeff’s teaching, ecology became a relevant science
and I developed a deep interest in the study of organisms and their
23
environments. Without his influence I certainly would not have pursued
graduate-level ecology research.
ENCOURAGERS
There were many people who encouraged and motivated me over the course of
my PhD. There are a few who merit special mention. (1) Richard Feldman and I
started our PhDs in the McGill lab at the same time. He has been an academic
brother to me encouraging me when I’ve been frustrated and challenging me to
be a better student and scientist. (2) David Syncox motivates people to achieve
great things. He has been a wonderful friend and has gone out of his way on
innumerable occasions to be a listening ear for my frustrations and to encourage
me towards reaching the goal of thesis completion. (3) I will also mention the
McGill Lab Group who provided many excellent reviews and encouragement for
my research: Sergio Estrada, Julie Messier and John Donoghue.
FAMILY
My family has been very supportive of my decision to pursue a PhD. My mother
Linda often called asking “how things are going” and has been a constant
reminder of how much I am loved by my family. My sister Mags has also very
quick to read my papers and has been full of support. I am also thankful for the
rest of my wonderful familyandthe encouragements they provided along the
way – specifically my son Quentin, my dad Richard, my brother Andrew, my
24
sister-in-law Bethany. my brother-in-law Mike, my father-in-law Gary, my
mother-in-law Beth, my brother-in-law Andy, my sister-in-law Steffani, my
godmother Sharon, my godfather Bob and my best friend Todd and his wife
Krista.
FINANCIAL SUPPORT
There were several sources of funding that allowed me to complete this degree.
Thank you to Dr. Brian McGill, Dr. Martin Lechowicz and Dr. Richard Tomlinson
via Dr. Brian Alters at the Tomlinson Project in University Level Science Education
for your funding and support. Thanks also to the National Sciences and
Engineering Research Council of Canada for two years of support.
COLLEAGUES AND FRIENDS
Finally, I have had many colleagues and friends at McGill University and at
Emmaus Anglican Church to whom I owe a debt of gratitude for their friendship,
encouragement and intellectual contributions. I dare not try to list them all in
fear that I may leave some out. Thanks to all.
25
Thesis Abstract
One central question in ecology is why some areas have many species and others
have few. Many explanations have been proposed and often theforcesthat drive
species richness are context-dependent. These forces aredivided into two
general categories: biotic drivers and abiotic drivers. Biotic drivers are most
commonly described in terms as top-down and bottom-up effects while abiotic
drivers are commonly described in terms of climate and habitat disturbance. The
objective of this thesis is to determine how these drivers affect species richness
in terrestrial ecosystems. To test this I examine an insect herbivore assemblage
in a disturbed forest fragment landscape in southern Canada.I use geographic
information systems techniques to determine the impact of a natural episodic
disturbance (i.e. an abiotic natural driver) and a chronic human disturbance (i.e.
an abiotic human driver) on forest habitat quality (Chapter 1) and on a forestdwelling caterpillar assemblage (Chapter 2). I show that ice stormsresult in a
heterogeneous pattern of spatial damage across a forest landscape, differing
depending on the type of coarse woody debris examined. These different types
of coarse woody debris provide habitat for a diversity of taxa. In contrast with
natural disturbance, I found that human-based disturbance do not have a
positive impact on caterpillar assemblages.Pursuant to this, I explore the concept
of habitat quality from the perspective of host plant identity (Chapter 3) and
host plant quality (Chapter 4). I found that caterpillar assemblages have strong
host plant preferences and that these preferences may depend on quadrat-scale
26
foliar qualities (i.e. a biotic bottom-up driver)and parasitoid densities (i.e. a biotic
top-down driver) at different times in the growing season. This thesis adds to a
growing body of literature aimed to better understand the drivers of insect
species richnessacross disturbed landscapes. In addition, this thesis develops
several management-specific tools for measuring forest disturbance and
provides valuable insight into how the selection of different tree species for
planting initiatives can have important impacts on forest communities.
Résumé
27
Les communautés forestières qui habitent les parcelles de forêts qui subsistent
aujourd'hui sont affectées par les perturbations ainsi que par la qualité de
l'habitat que leur procure les plantes-hôtes. Ces deux phénomènes ont un
impact particulièrement important dans les paysages modifiés par l'activité
humaine. Développer une meilleure compréhension de ces phénomènes va
faciliter la prise de décision et les efforts de conservations visant à préserver et
protéger la biodiversité des forêts. L’objectif global de cette thèse est d’étudier
les divers aspects reliés à la qualité de l’habitat dans les parcelles forestières des
collines montérégiennes du sud-est du Québec, Canada. J’utilise des techniques
en système d’information géographique pour déterminer l’impact d’une
perturbation naturelle épisodique (tempête de verglas) ainsi qu’une
perturbation anthropogénique chronique (sentiers récréationnels) sur la qualité
des habitats forestiers (Chapitre 1) et un assemblage de chenilles vivant en forêt
(Chapitre 2). J’ai démontré que les dégâts engendrés par les tempêtes de verglas
sont distribués de façon hétérogène à travers le paysage forestier, différant
selon le type de débris ligneux grossiers examiné. Ces différents types de débris
ligneux grossiers servent d’habitat à divers groupes taxonomiques. J’ai découvert
que les perturbations anthropogéniques, au contraire des perturbations
naturelles, n’ont pas eu d’impacts positifs sur les assemblages de chenilles
vivants en forêt. J’ai également exploré le concept de qualité d’habitat en
considérant l'identité de la plante hôte (Chapitre 3) et la qualité foliaire à
l'échelle du quadrat (Chapitre 4). J'ai découvert que l'assemblage de chenille
28
démontre des préférences marquées pour certaines plantes hôtes et que ces
préférences peuvent dépendre de différentes qualités foliaires à l'échelle du
quadrat à différentes périodes durant la saison de croissance. Cette thèse
contribue à la documentation croissante sur les facteurs qui affectent la richesse
spécifique des communautés d'insectes vivant dans les forêts des paysages
perturbés. De plus, cette thèse propose plusieurs outils spécifiques à la gestion
pour évaluer les perturbations en milieu forestier et donne un aperçu de l'impact
que peut avoir la sélection de différentes espèces d'arbres sur les communautés
forestières lors de l'élaboration d'initiatives de plantation d'arbres.
General Introduction
29
Since the beginning of modern ecology “species richness” has been one of the
central fociin research. It is the subject of some of the most important ecological
theories and has been associated with overall ecosystem health (Rapport et al
1999) and pristine habitat (Brooks et al. 2002). Species richness is also the
primary measurement used in conservation biology where the primary goal is to
protect species and their natural environments from extinction. Ehrlich and
Ehrlich (1992) argue that there are four reasons why conserving richness should
be a primary goal for humankind: (1) it is an ethical responsibility to preserve
diverse forms of life on earth, (2) there are recreational and aesthetic valuesof
high species richness, (3) there is a wide variety of medical, food and textile
resources offered by high species richness, and (4) there is a wealth ofimportant
ecological services offered by high species richness such as air filtration, water
filtration, nutrient cycle maintenance and soil renewal. Each of these four factors
may (arguably) be diminished with significantrichness losses. While similar
arguments have been echoed by other authors and are rarely refuted (Ghilarov
2000, Randall 1991), we live in an era where species extinction rates are
extraordinarily high, due in large part to human activities (Pimm et al., 1995).
These extinctions are particularly high for insect species (Conrad et al 2006,
Dunn 2005, Thomas et al 2004) and are often due to human-caused landscapescale habitat destruction for the purposes of timber exploitation, agriculture and
landscape development (Brooks et al 2002).
What Controls Richness?
30
Why are some areas high in species richness while others are impoverished? The
variance of richness within and among different habitats has long intrigued
ecologists. There have been dozens of different mechanistic drivers of species
richness proposed over the years and the strength of a proposed variable often
seems to be context-dependent (Rosenzweig 1995, Adams 2009). Mechanistic
drivers of richness identified at broad geographic scales tend to differ from those
identified at small geographic scales (Currie 1991, Hawkins et al 2003);
mechanistic drivers identified in disturbed habitat tend to differ from those
identified in undisturbed habitat (Armesto and Pickett 1985).At the most general
level these drivers fall into one of two categories: biotic mechanisms and abiotic
mechanisms.
Biotic mechanisms occur when living things affect the richness of a
taxonomic group. These mechanisms can be divided into two sub-categories:
top-down forces and bottom-up forces. Top-down forcesare observable when a
species or taxonomic group at a higher trophic level affects a species or
taxonomic group at a lower tropic level. Herbivores, for example, can suppress
the species richness of their food plants (Crawley 1983, Hay 1985, Hunter and
Price 1992) and predators/parasitoids can affect the species richness of their
prey (Bruno and Cardinale 2008, Lill et al 2002). Bottom-up forcesare the
opposite and are observable when a species or taxonomic group at a lower
trophic level affects a species or taxonomic group at a higher trophic level. For
example, grassland plots with many plant species can support more herbivorous
31
insect species than plots with few plant species (Siemann et al. 1998) and
systems rich in prey can often support more predator species than those that are
poor (Rosenzweig 1995). The direction of the effect (i.e. top-down vs. bottomup) often seems to be very system-dependent and opposing effects can be
observed in analogous trophic structures– e.g. in some systems the bottom-up
effect of plants on herbivores is dominant and in others the top-down effect of
herbivores on plants is dominant. A third categoryof driving biotic forces exists
where regulation within a trophic level occurs through competition or densitydependence. However this is more commonly observedat the species population
level when measuring species abundance and performance rather than at the
assemblage or community level when measuring species richness (e.g.
Antonovics and Levin 1980, Ostfeld et al 2003).
Abiotic mechanisms occur when non-living things affect the richness of a
taxonomic group. Theseoften include climate variables like temperature,
precipitation, solar radiation and habitat modification variables
likefragmentation, isolation and habitat loss. At very large spatial scales
precipitation and energy are strong correlates to species richness (Currie 1991,
Hawkins et al. 2003). At smaller spatial scales disturbance events can play more
important roles. Human disturbance through habitat modification and
destruction generally results in species richness loss (e.g. Andren 1994, Helm et
al. 2006, Ross et al. 2002) whereas natural disturbances like fire and severe
weather events can result in species richness gains or losses depending on the
32
species group and habitat affected (e.g. species richness gain –Shafi and
Yarranton1973, Facio 2003, Lafon 2004, Moretti et al 2004; species richness loss
– Nekola 2002, Saint-Germain and Mauffette 2001, Swengel 2006).
Thesis Objective
The objective of this thesis is to examine how biotic forces (top-down and
bottom-up effects) and abiotic forces (natural and human disturbances) drive
terrestrial species richness. Understanding how these factors drive richness is
particularly important in disturbed landscapes where native species richness is
declining and extinctions are common. Since the direction and magnitude of
these driving factors can be context-dependent, I aim to analyze them using a
single taxonomic group in a specific ecological habitat type. The criteria I use for
selecting a taxonomic group are: (1) it must be a group occupying mid-trophic
levels and therefore subject to both bottom-up and top-down pressure, (2) it
must be broad enough to be ecologically important to ecosystem function in
disturbed landscapes, (3) it must be narrow enough to occupy one, roughly
homogeneous, ecological guild, (4) there must be a plausible mechanism by
which it can be affected by both natural and human disturbances. The criteria I
use for selecting an ecological habitat are: (1) it must be subjected to periodic or
chronic human disturbance, (2) it must be subjected to natural disturbance
events, preferably where a recent large disturbance event has occurred, and (3)
it must have high conservation value.
33
Taxonomic Group of Investigation
One ecologically important mid-trophic level taxon that is often affected by
bottom-up and top-down effects is insect herbivores. It isone of the most
species-rich guilds in terrestrial ecosystems and is ecologically important for
many reasons. First, many insect species are indicators thatreflect habitat quality
and the impacts of environmental disturbances (Summerville et al. 2004, New
2004). Second, insect herbivores play critical roles in terrestrial ecosystems as
food sources for bats (Goiti et al. 2009), birds (Murray et al. 1980), small
mammals (Whitaker 1966), reptiles (Hamilton and Pollack 1956), amphibians
(Moore and Stickland 1954) and other insects (Lill and Marquis 2001). Third,
theyalso play a crucial role in ecosystem function by converting leaves into
nutrient-rich frass that is readily consumed by detritivores and soil-dwelling
organisms (Schowalter et al. 1986). Therefore, in many ways, the health of an
ecosystem is tied to the species richness of the insect herbivores assemblage
living therein. They are also commonly impacted by both bottom-up (Siemann et
al. 2008) and top-down (Lill et al. 2002) effects.
Conservation initiatives focusing on insects havetraditionallyusedone of
two approaches: a single-species approach,or an assemblage approach. In the
single-species approach, a threatened species is identified and steps are
recommended to improve its survival chances. This could include increasing the
abundance of important host plants or trying to control predator populations
34
(e.g. Schultz and Dlugosch 1999).Onedrawback of the single-species approach is
that it is limited to species whose natural history is well known. This is
particularly true for insect herbivores because, as a taxon, their natural history is
very poorly known (New 2004). In fact, it is suggested that the majority of insect
species in the world is currently undiscovered, let alone well-described (Gaston
1991). While most of these undiscovered species are in tropical ecosystems, the
majority of well-studied species in temperate habitats tend to be defoliators
capable of causing significant economic and aesthetic damage (e.g. the gypsy
moth, Lymantria dispar) or species with bright colouration (e.g. the monarch
butterfly, Danaus plexippus). Temperate non-defoliating species and cryptic
species are often overlooked. Because of this narrow spectrum of research it
becomes difficult to makeeffectiveconservation-oriented management decision
based on one (or a few) species when the status of the rest of the assemblage
isunknown.This approach has sometimes been justified by the “umbrella
species” concept, but it is not always clear that thisachieves the desired goal
(Andelman and Fagan 2000, Roberge and Angelstam 2004). Instead, the net
result of this approach can be an overly large management investment for the
benefit of few species. Conversely, an assemblage-based approach focuses on
identifying, conserving and protectinghabitat that is high in species richness(New
2004). This requires less knowledge of the natural history of specific species
because the variable of interest isspecies richnessin an assemblage rather than
abundance in a species. It asks: “What characteristics of the habitat are
35
synonymous with high richness?” rather than “What characteristics of the
habitat are synonymous with high abundance in one species?” The assemblagebased approach can be more effective on the whole but it requiresa contextspecific understanding of how assemblage richness responds to driving
mechanisms.
Geographic Extent of Study
There are dozens of different ecosystems that host insect herbivores and many
fit the desired criteria of being impacted by both human and natural
disturbances. One such ecosystem that meets both criteria is the broadleaved
forest of the Mixedwood Plains (MWP) in southern Canada. Historically this
forest covered the entirety of the MWP but over the past century ithas been
reduced to isolated fragments in a matrix of agricultural, industrial and urban
development; less than 15% the area is currently forested(Allen 2001). The small
amount of forest that remains is important habitat for threatened species yet
continues to be at risk from human landscape modification(Environment Canada
2007, 2010). Foremost amongst these remnant fragments are those located in
an areaof southern Quebec known as the Montérégiewhere less than 1%
offorested land is protected(Ministère des Ressources Naturelles 2002).Many of
these protected forest fragments tend to occupy the hilltops of the Monteregian
Hills, a set of nine laccolith-based hills rising up from the floodplain.
36
Conservation on these hills has become increasingly challenging with the
occurrence of episodic and chronic, human and natural disturbances across the
MWP landscape. Although these hilltop forest fragments have escaped
widespread deforestation, privately owned lots within the fragments have been
subjected to residential development with associated vehicle access roads and
hiking trails (CantonShefford 2010, Centre de la Nature Mont Saint Hilaire 2007,
Les Amis De La Montagne 2008, Parcs Quebec 2010, Parcs Quebec 2010).
Smaller-scale cutting (and subsequent replanting) and the periodical
establishment of sugarbushes (resulting in the selective removal of all but sugar
maple trees) has also occurred over the past century and has changed the forest
tree species composition. Many of these disturbance events occurred before the
forests on these hills were granted protected status. Additionally, periodic ice
storms that damage canopy trees have left legacies of coarse woody debris on
the forest floor, changing habitat quality for the forest insect community
(Hooper et al. 2001).
The persistence of forest habitat on these hills in a landscape of
agricultural development is tied tohistoric events. Up until 10,000 ypb the
Champlain Sea covered much of the St. Lawrence floodplain region; when it
receded (c. 8,000 ybp) it left nutrient-rich sediment on the floodplain while the
hilltops (which were islands in the Sea) remained comparatively nutrient-poor.
Because of this sub-optimal soil quality and because of their challenging
topography, the hills remained (in part or in whole) undeveloped when the rest
37
of the floodplain was converted for human-use throughout the 20th century.
Forest fragments on three of these hills (Mont Royal, Mont St. Bruno and Mont
St. Hilaire) are currently protected (to varying degrees) with mandates to
preserve the diversity within their forests for conservation targets and forthe
enjoyment of the general public. A fourth Hill (Mont Shefford) has designated
conservation areas without explicit conservation mandates.
Four Approaches to Determine Drivers of Richness
The Monteregian forest fragments present an ideal ecosystem in which to study
the effects ofbiotic and abiotic forcesoninsect species richness. I use four diverse
approaches to examine this relationship. First, I approach it by using remote
sensing and GIS tools to examine the consequences of an ice-storm disturbance
(i.e. a natural abiotic driver) on saproxylic insects. Since there is a direct link
between high quality habitat and guild species richness, determining the habitat
consequences of a major natural disturbance is an important step for making
conservation-oriented management decisions. In addition, I develop and test
assessment tools to help quantify habitat quality and disturbance impact. The
last major ice stormdisturbance in my study region occurred in 1998 when more
than 100 mm of ice fell on Monteregian forest fragments. Various canopydamage models have been developed to gauge the impact of such events (e.g.
Olthof et al. 2004, Proulx and Greene 2001, Stueve et al. 2007), but there is a
need to better connect forest damage with ecologically meaningful habitat
38
changes.In Chapter 1 I usesatellite-derived remotely sensed vegetation indices to
examine how this storm changed coarse woody debris habitat at Mont St.
Hilaire, a hill in the MWP of Quebec. I develop geospatial data to document the
influx of different types of coarse woody debris that are important saproxylic
insect communities.
The research goals of Chapter 1 are stated in the form of objectives rather
than hypotheses. They are as follows:
1. To model the biotic changes resulting from coarse woody debris influx
following a major canopy disturbance.
2. To use remotely sensed satellite images and geographic information
systems to study the spatial heterogeneity, volume and connectivity of
coarse woody debris habitat following a major canopy disturbance.
3. To test the performance of seven different vegetation indices in
predicting habitat change after a major canopy disturbance.
Second, I approach the question of how abiotic and biotic forces affect insect
species by examining howman-made recreational trails (i.e. a human abiotic
driver) impact forest-dwelling Lepidoptera assemblages. The biggest form of
human impactwithin forest fragments is chronic disturbance fromrecreational
hiking trails. Trails are particularly compelling to research because they are a
type of abiotic disturbance that can be controlled by management decisions
within forested parks and reserves. This sets them apart from other types of
39
disturbance that are either uncontrollable (e.g. ice storms) or extremely difficult
to control without more powerful conservation mandates (e.g. to stop
landscape-scale deforestation). There are often significant habitat modifications
caused by the introduction of trails in forests. They are associated with high
levels of vegetation trampling, a higher abundance of disturbance-tolerant
plants, soil compaction, water drainage changes, light level changes, wind
changes and temperature changes. As an analogy to trails, forest edge habitat
has often been observed to have a higher than expected Lepidopterarichness
due to a mixed assemblage – part forest-dwelling Lepidoptera species, part
open-habitat Lepidoptera species. Trails have been positively, negatively, or
neutrally associated with population abundances and species richness in certain
taxa (e.g. birds: Miller 1998), but rarely linked to insect herbivore assemblages. A
greater understanding of the impact of trails on Lepidoptera assemblages can be
used to better protect and preserve richness in disturbed forest fragments.
The hypotheses tested in chapter 2 are:
1. Trailside habitat will be beneficial for caterpillar richness and abundance.
2. The availability of acceptable host plants will be a determinant of
caterpillar richness and abundance.
Third, I approach the question of how abiotic and biotic forces affect insect
species by examining how tree species richness and identity (i.e. biotic bottomup mechanisms) drive Lepidoptera richness and abundance within forest
40
fragments. In studies of single insect herbivore species, insect performance and
distribution are strongly tied to host plant identity, indicating that some host
plants are more preferred than others. This is contrasted by studies of insect
herbivore assemblages that often equate high insect assemblage richness to high
tree species richness. While it is well known that single insect herbivore species
(even polyphages)can have strong host plant preferences (Liebhold et al. 1995,
Maufette et al. 1983, Wint 1983) it is not known how well the host preferences
of individual species scales to the assemblage level. The claim that higher host
plant richness leads to higher insect herbivore richness is rooted in the
assumption that richer communities have a greater diversity of foliar nutrients
and a more structurally heterogeneous set of resources. This foliar and structural
diversity would theoretically allow more insect herbivore species to coexist. On
the other hand, comparisons of the hosting capabilities of trees show that some
hosts are more highly preferred than others (Barbosa et al. 2000, Moran and
Southwood 1982). Based on this, it is possible that the insect richness in a forest
stand depends on having a high volume of preferred hosts. This assemblage-level
“preferred-host” concept may be especially applicable for Lepidoptera in the
MWP forests where a vast majority of species are polyphagic (Handfield1999). It
is therefore possible for one or few hosts to be preferred by the majority of the
assemblage.These two concepts are not necessarily mutually exclusive, but if the
second outperforms the first it has important implications for forest
management and suggests a revision in our understanding of the relationships
41
between insect herbivore assemblages and their environment. Does an
abundance of preferred host plants correlate to high insect herbivore richness or
is insect herbivore richness more closely related to host plant richness? In
chapter 3 I test between these two possibilities.
Hypotheses:
1. The diversity and richness of a Lepidoptera assemblage is locally driven
by host plant richness.
2. Because host plant choice are made by individual Lepidoptera species,
the relationships between the Lepidoptera assemblage and host plants
will be accurately described as a function of host plant identity and
abundance (i.e. host plant preferences are cumulative).
Fourth, I approach the question of how abiotic and biotic forces affect insect
species by examining how top-down and bottom-up pressures(i.e. two biotic
drivers) on insect herbivore assemblages vary over the course of a growing
season. The impacts of tri-trophic relationships on insect herbivores have been
well studied (Mayhew 1997), but never for an entire assemblage in specific
temporal windows over the course of a growing season. Individual insect
herbivorespecies often discriminate between available host trees based on
certain foliar nutrient qualities that change with leaf age (Feeny 1970, Ricklefs
and Matthew 1982). A host plant might therefore be well usedearly in the
season but be relatively unused late in the season due to a loss of nutritive value.
42
Top-down pressure applied by predators and parasitoids (Lill et al. 2002)
complicates the seasonal relationship between insect herbivores and treeuse.This top-down pressure is often more intense later in the growing season. A
full understanding of the drivers of insect herbivore richness needs to take into
account the both bottom-up and top-down pressurein the context of a tritrophic relationship.In Chapter 4 I examine intra-seasonal tri-trophic
relationships for a Lepidoptera assemblage in MWP forest fragments. The key
foliar nutrient properties that have been linked as drivers of insect herbivore
performance are nitrogen content, water content, phosphorus content, toxin
content, fiber content, lignin content and leaf toughness. The nutrients among
these that are positively linked to herbivore performance – nitrogen, phosphorus
and water – tend to peak early in the season. The other nutrients peak in the
middle or at the end of the season. Insect herbivore host preferences often
correlate to host plant quality but this can be disrupted by high parasitoid
abundances (Gratton and Denno 2003). Parasitoids are not typically evenly
distributed among all host plants – some have high parasite loads (enemypacked space) while others have low parasite loads (enemy-free space). Host
plant choices for insect herbivores thus become a delicate balance of
minimizingexposure to parasitoids by avoiding enemy-packed space while
maximizing the nutritive quality of the foliage consumed. Two important
questions have arisen in trying to better understand the drivers of insect
herbivore assemblage richness: (i) to what extent does bottom-up (foliar
43
nutrient quality) and top-down (parasitoid-presence) variables impact herbivore
richness and (ii) how do the nature of bottom-up and top-down effects change
from the beginning to the end of a growing season?
Hypothesis:
1. The impact of top-down and bottom-up pressures will vary over the
course of the growing season. Specifically:
a. Foliar nitrogen content, water content and phosphorus content
should be the primary drivers of Lepidoptera assemblage richness
and abundance early in the season.
b. Foliar toughness, fiber content and lignin content should be the
primary drivers of Lepidoptera assemblage richness and
abundance late in the season.
c. Parasitoid pressure should have a significant impact on
Lepidoptera assemblage richness and abundance late in the
season.
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52
CHAPTER 1: Detecting Changes in Forest Floor Habitat after Disturbance
Peter J.T. White1, Brian J. McGill2 and Martin J. Lechowicz1
1
McGill University, Department of Biology
1205 Dr. Penfield Ave., Montreal, Quebec
H3A 1B1, Canada
2
University of Maine, School of Biology & Ecology
Deering Hall 303, Orono, ME
04469, U.S.A.
Abstract
A massive ice storm hit northeastern North America in 1998, dropping more than
100 mm of freezing rain at its epicenter in southern Quebec, Canada. There has
53
been extensive study of which trees and areas received the most damage, but
the biodiversity consequences of this damage at landscape scales have not
received much attention. We assessed the effectiveness of seven remotelysensed vegetation indices - NDVI, EVI, DVI, RDVI, ARVI, NDVIgreen and VARI - for
modeling the coarse woody debris (CWD) influx in an old growth forest reserve
at the storm’s epicenter; NDVI was the best predictor of CWD influx. We
categorized the geospatial CWD predictions from the NDVI-derived model to
map the spatial distribution of sun-exposed, moist-shaded, dry-shaded and wet
CWD microhabitats on the forest floor. Moist-shaded, dry-shaded and wet
patches of CWD were large and well connected, but sun-exposed patches were
small and sparse. Since these microhabitats affect the distribution and
abundance of saproxylic insects, wood-rotting fungi, salamanders, birds, small
burrowing mammals and plant species dependent on nurse-logs for
establishment, the CWD influx from the 1998 ice storm may have revitalized
local populations of these taxa through increased habitat availability as well as
increased dispersal within the reserve.
Introduction
A catastrophic ice storm hit northeastern North America from January 4th to 10th,
1998. Southern Quebec was most heavily impacted with some areas receiving in
54
excess of 100 mm of freezing rain (Milton and Bourque 1999). The ice storm is
considered the worst ever recorded in Canada (Milton and Bourque 1999,
Vandyke 1999). The Quebec Ministry of Natural Resources estimated that 12% of
trees in southern Quebec had more than 75% of their canopy destroyed and
were unlikely to survive (Irland 1998).
Local Consequences of Damage
Much of the ecological research about the 1998 ice storm has focused on local
correlates of forest damage and the structural consequences of damage. Local
topography has been consistently linked to damage with specific elevations
(Irland 2000, Weeks et al. 2009), slope angles (Bragg et al. 2003, Rhoads et al.
2002) and slope aspects (Bragg et al. 2003) all emerging as significant
descriptors. The reported species-specific impacts of ice on trees have varied
from study to study (Vandyke 1999) and from locality to locality (Brommit et al.
2004, Dugay et al. 2001, Hooper et al. 2001, Irland 1998, Rhoads et al. 2002). The
damage caused by ice storms has been found to accelerate, slow, or have no
impact on succession depending on the specific forest stand being examined
(Boerner et al. 1988, Brommit et al. 2004, DeSteven et al. 1991, Takahashi et al.
2007).
Landscape and Regional Investigations
The landscape and regional scale impacts of ice storms have been best studied
by blending satellite reflectance data, digital topography data, and ice accretion
55
data (King et al. 2005, McNab and Roof 2006, Millward and Kraft 2004, Olthof et
al. 2004, Stueve et al. 2007). For the 1998 ice storm, Millward and Kraft (2004)
used the Normalized Difference Vegetation Index (NDVI) to map canopy damage
in a 2000 km2 forest in the eastern Adirondack region of New York State. They
reported a complex and heterogeneous damage pattern, spatially autocorrelated
at distances below 300 m. Olthof et al. (2004) used pre- and post- storm
remotely sensed forest reflectance to create a regional damage map for eastern
Ontario; this study is also documented in King et al. (2005). Their damage models
discriminated between areas of low and high damage, identifying forest areas in
need of management intervention. These few regional and landscape scale
studies stand out in a literature dominated by studies at much smaller scales,
often focusing on no more than several dozen plots.
Habitat Implications of Ice Storms
The biotic implications of a large coarse woody debris (CWD) influx that
necessarily follows a major ice storm have rarely been considered on a landscape
scale, even though a diverse and abundant CWD landscape can have profound
positive impacts on forest diversity (Harmon et al. 1986). CWD created in
different moisture, heat and sunlight environments can support significantly
unique assemblages of saproxylic (CWD loving) insects. For example, it is
common for many bark beetles to prefer sun-exposed CWD over shaded CWD.
The warmer habitat is often a more inviting micro climate, and the sun-exposure
56
can lead to a quicker volatization of tree host compounds making the substrate
more preferable for many species (seeBouget and Duelli 2004 for a review). In
wet and moist habitats, the quick colonization of brown rot and white rot fungi
can modify the lignin, cellulose and hemi-cellulose concentrations in decaying
CWD making it favourable habitat for a different beetle assemblage (Kaila et al.
1994, Yee et al. 2004). Many saproxylic hoverflies also prefer to breed in very
moist decaying wood (Rotheray and Stuke 1998). Yet, both of these assemblages
are usually different from the saproxylic assemblage that uses waterlogged and
floating CWD (Braccia and Batzer 2001). Aquatic insects (mainly in freshwater
systems) use CWD as a feeding platform, food source, refuge from predation and
substrate for oviposition (Cranston and McKie 2006, Harmon et al. 1986).
The size, areal density and connectivity of CWD habitat are also
important to consider. Large pieces of CWD decay at a significantly slower rate
than small pieces (Vanderwel et al. 2006) and consequently provide a more
stable habitat, which can be very important for taxa with limited dispersal
abilities (Yee et al. 2004). Species richness and population abundance can be
higher for small mammals, woodpeckers, herptofauna, fungi and plants in areas
with a high volume of CWD (Keisker 2000). Furthermore, the connectivity of
CWD patches can be vitally important as CWD can support more species when it
is well connected compared to when it has patchy or clumped distribution
(McMillan and Kaufman 1995, Schiegg 2000).
57
Using Remote Sensing to Measure Habitat Change
NDVI is the most popular remotely sensed vegetation index (VI) used in
ecological geospatial research, but there is often little rationale given for this
choice (cf.Birky 2001, Millward and Kraft 2004, Newton 2007, Pettorelli et al.
2005 among many others). NDVI was originally proposed by Rouse (Rouse 1973,
Rouse et al. 1974) and has a wide array of applications (Bannari et al. 1995); it is
positively correlated to the Leaf Area Index (Fassnacht et al. 1997) and leaf
biomass (Birky 2001) in temperate forests. In addition to NDVI, there have been
more than 40 other VIs developed since the early 1970s (Bannari et al. 1995).
Many of these indices were designed with specific purposes in mind, for
example, to delineate low versus very low levels of vegetation (Richardson and
Everitt 1992) or high versus very high levels of vegetation (Huete et al. 2002).
Since red light is typically absorbed by chlorophyll and the near infrared light is
reflected by foliage (Bannari et al. 1995), NDVI is a logical choice for measuring
vegetation levels. But relying on these two wavelengths alone may omit
important information: green light can be used to describe soil properties, blue
light can be used to account for atmospheric abnormalities that may be present
when using imagery from different dates. Because of these properties, it has
been suggested that complete analyses should test and/or incorporate the use
of more than one VI (Bannari et al. 1995, Liu et al. 2007, Payero et al. 2004).
Objective
58
The goal of this paper is to use remotely sensed satellite images and geographic
information systems to study the spatial heterogeneity, volume and connectivity
of CWD habitat resulting from the 1998 ice storm at Mont St. Hilaire, a
deciduous old-growth forest landscape; the secondary goal is to test the
performance of seven different VIs in predicting habitat change after the ice
storm. In this paper we use remotely sensed vegetation indices calculated from
remotely sensed images, topography data and field measurements to create and
validate a predictive landscape-level CWD influx model. Using hydrology data,
topography data and canopy cover data we then categorize the influx model into
four different CWD habitat types: sun-exposed, dry-shaded, moist-shaded and
wet. Finally, we discuss the results and implications of this CWD influx for forest
biodiversity.
Methods
Study Area
Mont St. Hilaire is a laccolith-based hill situated on the St. Lawrence Floodplain,
30 km east of Montreal, Quebec (45°33'N; 75°09'W). Its annual average daily
temperature is 6 oC fluctuating from a 21 oC monthly average in August to a -11
o
C monthly average in January. On average, the region receives 836 mm of rain
and 2140 mm of snow each year with 111 and 47 rainfall and snowfall days
respectively (National Climate Data and Information Archive 2008). Mont St.
Hilaire is topographically diverse in elevation, slope steepness and slope aspect
59
and covered in 10 km2 of old growth forest, primarily composed of Acer
saccharum and Fagus grandifolia with significant numbers of Quercus rubra,
Fraxinus americana,Tsuga canadensis spp. and Pinus strobus among other
species (Arii 2004).
Satellite Imagery Processing
To model CWD input, weused multiband Landsat-5 Thematic Mapper (TM)
imagery downloaded from the USGS EROS EarthExplorer (USGS 2007) for the
years 1996 and 1998 (Table 1). Satellite imagery was selected from August to
ensure that trees were at full leaf flush at the time of observation; scenes from
1997 could not be used due to high cloud cover in available images.
Prior to the calculation of VIs, the Landsat imagery for each band was
converted from digital numbers into radiometrically-corrected planetary
reflectance (ρ) units using the following equation (adapted fromChandler and
Markham 2003):
[1]
Gain and offset are band-specific rescaling values, Esunλ is the mean solar
exoatmospheric irradiance, d is the earth-sun distance, and θs is the solar zenith
angle.
To account for atmospheric differences between the two images, we
corrected satellite reflectance data using a relative radiometric normalization
and atmospheric correction procedure (Hajj et al. 2008). Each band in the 1998
60
data was transformed to match the atmospheric conditions of the 1996 data
using a linear regression equation derived by calculating the change in 150,497
invariant targets (i.e. pixels where no change occurred between years). All
invariant targets were pixels between 60 and 160 km north of the ice storm
epicenter where ice accretion was less than 5 mm and unlikely to cause
canopydamage (Proulx and Greene 2001).
VI Calculation
We chose six additional VIs to compare to NDVI in ability to model change in
CWD input from pre to post storm (Table 2). The additional VIs span the blue,
green, red and infrared reflectance spectrum but were analogous to NDVI in the
sense that (a) they are all simple ratio-based VIs and (b) they do not require any
accessory parameters to compute. For all of these indices, large localized
changes in VI values should correlate with canopy-level damage and gap
formation that characteristically generate large amounts of CWD. The Enhanced
Vegetation Index (EVI) was proposed as an improvement to the NDVI because it
is less prone to saturate in high biomass regions (Huete et al. 2002) and is less
affected by certain atmospheric and soil conditions that may degrade the NDVI
signal (Liu and Huete 1995). The Difference Vegetation Index (DVI) offers more
precision when measuring sparsely vegetated surfaces (Liu et al. 2007,
Richardson and Everitt 1992). We included it in our model because heavy
disturbance could uncover sparsely vegetated forest floor that may be better
61
measured with DVI. The Renormalized Difference Vegetation Index (RDVI) is an
index that combines the benefits of NDVI and DVI; it performs well in both dense
canopy and sparse vegetation (i.e. canopy gap) conditions (Roujean and Breon
1995). The Atmospherically Resistant Vegetation Index (ARVI) is a newer version
of NDVI that takes into account and corrects for atmospheric light-scattering
effects on the red band (Bannari et al. 1995, Kaufman and Tanre 1992). The ARVI
accomplishes this correction by subtracting the difference in red and blue bands
from the red band before final Index calculation. A parameter used in its
calculation, γ, is an atmospheric self-correcting factor and is generally set to 1.0
for most remote sensing applications (Kaufman and Tanre 1992). The Green
Normalized Difference Vegetation Index (NDVIgreen), has been found to be very
sensitive to leaf chlorophyll levels (Gitelson and Merzlyak 1998) and can be less
prone to atmospheric interference found in the red band of normal NDVI
(Wolter et al. 1995). Finally, using red, green and blue bands, the Visible
Atmospheric Resistant Index (VARI) was designed to be very resistant to
atmospheric effects, employing the use of the blue-band similar to ARVI
(Gitelson et al. 2002). Furthermore, this index omits near infrared light making it
less affected by leaf orientation and soil moisture fluctuations than other indices
(Zhan et al. 2007).
We calculated the change in each index from 1996 to 1998 using the following
equation:
[2]
62
Predicting the Spatial Pattern of CWD Influx
In the summer of 1998 Hooper et al. (2001) recorded the biomass of freshly
fallen CWD at 103 plots across Mont St. Hilaire. We used these data as reference
points to make our spatial predictions of CWD influx across the mountain. To
reduce the effect of unwanted spatial autocorrelation in the Hooper et al. data,
we omitted plots that were less than 30 m apart. Ice storms do not have highly
spatially contagious damage because most tree damage comes from direct ice
loading originating from storm-based precipitation (a contrasting example of this
is tree damage caused by fire or pests/pathogens where damage on one tree
results in damage on some or all of the surrounding trees). The exception to this
is collateral damage occurring when an ice loaded tree falls, damaging trees in its
immediate vicinity (Rhoads et al. 2002).However, given that the occurrence of
large tree fall at Hooper’s sites was very low, we decided that a 30 m buffer
would sufficiently minimize spatial autocorrelation of collateral damage. This
filtering process left 82 of 103 plots.
We used cross validation to test the ability of each VI to describe the
influx of CWD. In each cross validation case, 20% of plots were used as holdouts
to validate regression importance. This process was repeated ten times for each
VI with a different set of holdouts used for each regression computation. The
average regression coefficient, intercept and R2 were computed to describe the
properties and predictive power of each VI. We tested the VIs as individual
63
regressions because of the high degree of collinearity between indices. We chose
the highest performing VI (based on R2) to make our spatial map of CWD influx
using the average relationship predicted by the 10 training datasets of that VI.
Independent Validation of CWD Predictions
In the summer of 2008, we chose eighteen new plots at random locations on
Mont St. Hilaire to validate our mountain-wide CWD data predictions. At each
plot we measured CWD greater than 75 mm in diameter in two perpendicular
intersecting 60m x 60m transects. Each piece was scored into one of five decay
classes as defined by Canada’s National Forest Inventory Ground Sampling
Guidelines (Canadian ForestService 2008). CWD in the youngest of these classes
(i.e. category 1 and 2) are most likely to represent wood dating back to the 1998
ice storm (Vanderwel et al. 2006, Zielonka 2006). As such, we estimated the total
CWD volume at each site as the sum of diameters of CWD in both categories 1
and 2.
Methods Evaluation
In our method of computing CWD influx predictions we had to scale the CWD
values measured by Hooper et al. (2001) in 113 m2 plots to a 30 m x 30 m (900
m2) scale to correspond to the resolution of Landsat-5 TM imagery. To do this,
we superimposed 30 m x 30 m plots centered on the original Hooper et al. plots
and assigned each one the CWD value corresponding to the smaller 113 m2 plot
64
within it. Although this rescaled the CWD data to the desired resolution, the
original plots were not georeferenced with Landsat-5 imagery in mind and thus
the superimposed 30 m x 30 m pixels corners did not align with the 30 m x 30 m
VI data pixels. To adjust for this, we recalculated VI values for each
superimposed pixel as a weighted mean of the four overlapping VI pixels based
on the proportion of overlap.
To calculate the impact of this upscaling on the statistics of our models,
we mimicked the procedure using high resolution (1 m) multiple-return Light
Detection and Ranging (LIDAR) data collected in 2003 (collection method
described in below). With these data we created a high resolution binary map of
canopy cover (tree height > 10 m) (Popescu et al. 2002, St-Onge and Achaichia
2001) and calculated the actual canopy cover at each of our 82 test plots in a 6 m
radius (113 m2). The high resolution canopy cover data was then converted to 30
m x 30 m resolution with the same spatial referencing used in our VI data. Using
weighted means based on the proportion of overlap, we then calculated the
average canopy cover at each superimposed 30 m x 30 m site for a correlation
comparison. This gave us an empirically-driven approximation of how the scaling
and weighted scaling of canopy data and VI data (respectively) affected the
statistical fit of our models.
Categorizing CWD Habitat
65
We divided our mountain-wide CWD prediction data into four categories
denoting areas likely to contain dry-shaded CWD, moist-shaded CWD, sunexposed CWD and wet CWD.First, we created a watershed map of Mont St.
Hilaire by categorizing ponds and the area within 15 m buffer of stream line
features as wet habitat most likely to contain wet CWD. We then used high
resolution LIDAR data to create a canopy density map across Mont St. Hilaire. In
May of 2003 multiple-return LIDAR data were collected for the reserve from a
Piper Navajo C-GOVX aircraft from a height of 1300m (at a repetition rate of
50000 Hz, a scanning frequency of 37Hz and a scanning range +/- 15o).The total
number of above-ground LIDAR hits was divided by the total number of LIDAR
hits (above-ground plus last return) in 30 m x 30 m pixels to compute a canopy
density map measuring light penetration to the ground layer. Sun-exposed
habitat was categorized as non-wet habitat pixels with less than 75% canopy
density. Dry-shadedhabitat was categorized as non sun-exposed and non-wet
habitat pixels on slopes of a 20 degree incline or more (i.e. high drainage). Moistshaded habitat was categorized as non-wet habitat pixels with greater than 80%
canopy density, on slopes with less than a 10 degree incline. Slopes with an
inclination of between 10 and 20 degrees were filtered out, as were canopy
densities between 75% and 80%.We calculated the number of patches, average
patch size and average (Euclidian) interpatch distances for each CWD habitat
type using Fragstats (McGarigal et al. 2002).Slope steepness and slope aspect for
Mont St. Hilaire were derived from the Canadian Digital Elevation Data
66
(GovernmentofCanada 2000). Topography variables were scaled from a
resolution of 22.5 m to a resolution of 30 m to match the TM satellite imagery.
Results
Performance of VIs for Predicting CWD Influx
NDVI performed the best for both training and validation in our cross-validation
trials (Table 3). It explained an average of 20% of the variance in training
datasets and 37% of the variance in validation datasets. NDVIgreen was a close
second, explaining 19% and 35% of the variance in training and validation
datasets (respectively). The poorest performing VI was VARI, explaining 4% and
19% of the variance in training and validation sets (respectively). The average
relationship between NDVI and CWD in our training datasets was:
[3]
Applying this relationship to hill-wide NDVI data, we created a CWD influx
prediction map for Mont St. Hilaire (Figure 1). On average, the 1998 storm
created an influx of 1.7 kg/m2 (standard deviation = 0.97 kg/m2) of CWD across
Mont St. Hilaire (16.8 metric tons per hectare); 16057 tons of CWD were
produced across the entire hill.
Independent Validation of CWD Influx Predictions
The 18 independent validation sites were predicted, based on fitting a regression
to 1998 data, to gain between 0.75 kg/m2 and 3.2 kg/m2 of CWD from the ice
67
storm (mean = 1.6 kg/m2, standard deviation = 0.65 kg/m2). There was a
significant positive relationship between the measured amount of young CWD
and the predicted biomass of CWD across independent validation sites collected
in 2008 (Figure 2, R2 = 0.32, p < 0.02).
Categorization of CWD Influx into Different Habitat Types
In total, 59.5% of the Mont St. Hilaire was categorized as likely to contain sunexposed, dry shaded, moist shaded, or wet (Figure 3). The remaining 40.5% was
in pixels with intermediate topography or canopy cover and as such could not be
assigned to one of the four habitat types. There were 334 tons of sun-exposed
CWD across 22 hectares of sun-exposed habitat (average = 1.5 kg/m2). This
habitat was arranged in 130 patches (average area = 1700 m2); the average
interpatch distance was 128 m (standard deviation =95 m). There were 4559
tons of dry-shaded CWD across 310 hectares of dry-shaded habitat (average =
1.4 kg/m2). This habitat was arranged in 63 patches (average area = 48000 m2);
the average interpatch distance was 77 m (standard deviation = 32 m). There
were 2851 tons of moist-shaded CWD across 150 hectares of moist-shaded
habitat (average = 1.9 kg/m2). This habitat was arranged in 163 patches (average
area = 9100 m2); the average interpatch distance was 83 m (standard deviation =
34 m). There were 1057 tons of wet CWD across 70 hectares of wet habitat
(average = 1.5 kg/m2). This habitat was arranged in 71 patches (average area =
9500 m2); the average interpatch distance was 74 m (standard deviation = 65 m).
68
Methods Evaluation
According to the LIDAR canopy cover analysis, among the 82 training/validation
sites the maximum canopy cover at a 113 m2 resolution was 91%; the minimum
canopy cover was 2%; the mean percent cover was 32% (standard deviation =
20%). When canopy cover values were recalculated based on a geographically
weighted mean at a 900 m2 resolution, the maximum canopy cover among the
82 training/validation sites was 68%; the minimum percent cover was 4%;the
mean percent cover was 37% (standard deviation = 13%). These two datasets
were significantly positively correlated with a slope of 1.0 (p < 0.001, R2 = 0.44).
Discussion
Accuracy of VIs in Predicting Spatial Pattern of CWD
The use of VIs to map the CWD influx in a forest following an ice storm is largely
unexplored. The CWD influx we calculated (16.8 tons / ha) is very similar to the
Hooper et al. (2001) estimate of CWD increase at St. Hilaire following the ice
storm (19.9 tons / ha). Our NDVI validation dataset produced a moderate R2
value (a mean R2 of 0.37) but the Methods Evaluationsection indicates that the
correlation between data at 113 m2 and 900 m2 had an R2 = 0.44 (i.e. 44% of the
variance explained). Thus, even if the prediction data at 113 m2 was perfect (as
in the case of our LIDAR Methods Assessment), 56% of the explanatory power in
our prediction models is likely to be lost by changing resolution from 113 m2 to
69
900 m2. In this context, our predictive NDVI-based model performed very well,
accounting for 84% of the possible variance (i.e. 37% out of a projected possible
44%). The validity of this relationship was further supported by the relationship
between the predicted and observed CWD at our 18 independent sample sites
(R2 of 0.32). The unexplained variance in this relationship (68%) is in part
attributed to the accuracy of our NDVI prediction model (as explained above),
but it can also be attributed to the difficulty in determining the precise temporal
origin of a piece of CWD found in on the forest floor. Site microclimate
conditions like moisture, standing water, humidity and wind can slow or
accelerate decomposition (Harmon et al. 1986), weakening the relationships
apparent in our data. Damaged trees also can die but remain upright as snags
which can decay at different rates than fallen CWD depending on the groundlayer microclimate conditions (particularly moisture) and the relative presence of
saproxylic insects, microbes, birds and fungus contributing to decomposition and
mechanical breakdown (Harmon et al. 1986). Pieces of CWD of different sizes
and different wood densities also decay at different rates (Enrong et al. 2006,
Harmon et al. 1986).
CWD Benefits for Mont St. Hilaire Species
The massive influx of CWD provides extra habitat for a diversity of taxa across
the Mont St. Hilaire reserve. Perhaps the most obvious taxa to benefit from the
habitat increase are those that directly contribute to the decomposition of CWD
70
– white rot fungi, brown rot fungi and wood boring saproxylic insects – which
increase in abundance and diversity with increasing amounts of CWD (Jonsell et
al. 1998, Kaila et al. 1997, Kaila et al. 1994, Yee et al. 2004). As CWD goes
through escalating stages of decay large downed logs become important nursery
habitat for seedlings, herbs and ferns that may otherwise be unable to colonize
the forest floor (Clark et al. 1999). Throughout the decay process, CWD is also
important habitat for many vertebrate species. Among Mont St. Hilaire
mammals, the Woodland Jumping Mouse (Napeozapus insignis) and the
Southern Red-backed Vole (Clethrionomys gapperi), are known to use CWD
shelter habitat rather than relying primarily on tunnel-digging.Similarly, the Redbacked Salamander (Plethodon cinereus) is dependent on CWD, particularly in its
juvenile amphibian stages (Richmond and Trombulak 2009). Insectivorous bird
diversity is higher in heavily storm-damaged areas, but in many cases this is likely
an indirect response of bird populations to an increased overall abundance of
insects (Faccio 2003, Torgersen and Bull 1995). Because CWD directly benefits
some forest-dwelling species more than others, we expect many of these biotic
impacts to be spatially aligned with the areas of the reserve there the highest
storm impact occurred. Our results show that the biggest concentrations of CWD
influx are the eastern and southeastern slopes of the mountain and the low-lying
area west of Lac Hertel (Figure 1). In addition, there are pockets of relatively high
CWD influx scattered elsewhere throughout the reserve.
71
The creation of highly-connected CWD networks across the mountain can
have additional impacts on some forest-dwelling species.Our results indicated
that approximately 50% of the mountain received more than 1.7 kg/m2 of CWD
influx and nearly 85% of the mountain received at least 0.7 kg/m2. These
networks can act as transportation corridors for small mammals and saproxylic
insects within and between habitat patches allowing an increase in both foraging
territory size and dispersal distances (McMillan and Kaufman 1995, Schiegg
2000, Schiegg 2000).
The Benefits of Different Habitat Types to Insects
The high volume of CWD across a clear moisture gradient at Mont St. Hilaire may
further benefit saproxylic habitat specialist species. In total, we documented
4893 tons of new CWD in dry habitat (sun-exposed and shaded) and 3908 tons of
new CWD in moist and wet habitat. Ødegard (2006) showed that saproxylic
insects were more diverse and abundant in wet forest stands than in dry forests
stands. The very high volume of CWD in wet, moist and dry habitat at Mont St.
Hilaire may encourage the persistence of this gradient. The baseline volume of
CWD expected in old growth forests similar to Mont St. Hilaire is about 20 tons
per hectare (Muller 2003). If this baseline were combined with our calculation of
8800 tons of new CWD in 552 hectares of moisture-defined habitat it yields
~20000 tons of CWD at St. Hilaire in habitats that can be defined by moisture
content. It is unknown if this incredible volume of CWD results in a wider
72
moisture gradient or merely a taller distribution tightly clustered around the
mean. Continued research on saproxylic distributions, diversity and CWD
moisture conditions at Mont St. Hilaire is needed to shed more light on these
patterns.
The sparse sun-exposed habitat on the mountain (22 ha) and its
comparatively low cumulative CWD volume (334 tons) suggests that the 1998 ice
storm may not have had a profound long-term impact on saproxylic insects
specific to these habitats. Generally speaking, the richness and abundance of
sun-loving saproxylic beetles tends to vary with gap size. Larger gaps support
more species and more individuals due to the increase in sun-exposure, wood
volume, structural diversity and microhabitat diversity that they provide (Bouget
2005). In Sweden, 131 of 446 red-listed (i.e. endangered or threatened)
saproxylic coleoptera species exclusively used sun-exposed CWD habitat rather
than semi-shaded or shaded habitat (Jonsell et al. 1998). Bouget (2005) suggests
that gaps need to be a minimum of 0.50 ha in size in order to support
sustainable populations of habitat-specific saproxylic insect populations. Since
the average sun-exposed patch size (i.e. gap size) we documented was 0.17 ha,
the Mont St. Hilaire open habitat patches resultant from the storm may be too
small to support these types of saproxylic populations.
This small patch-size for sun-exposed CWD is radically different from
what persisted in the summer immediately following the storm. Arii and
Lechowicz (2007) showed that the opening of the canopy cover at Mont-St
73
Hilaire increased twofold compared to the pre-storm canopy cover measures
(from an average of 11% to 22%). However, they and others also show that this
canopy opening is short lived, and that the canopy recovers to pre-storm
conditions within 3 to 7 years of disturbance (Arii and Lechowicz 2007, Beaudet
et al. 2007, Darwin et al. 2004). It is unclear whether or not a short-term burst of
sun-exposed CWD habitat like this can result in significant benefits for sun-loving
saproxylic species. The LIDAR data from 2003 shows that the canopy at Mont St.
Hilaire is very closed. In fact, at a 30m resolution, only two pixels (0.18 ha) had a
canopy density of less than 50% and an additional nine pixels (0.81 ha) had a
canopy density of less than 60%.
CWD Influx and Species Movements
In terms of dispersal, the increase in CWD at Mont St. Hilaire is more likely to
benefit animal populations within the mountain than to encourage extramountain colonization from other forest fragments. Mont St. Hilaire is an
isolated forest habitat situated within a matrix of agriculture, urban settlement
and roads. The closest large forested areas are Mont Rougemont (15 km2 of
forest, 7 km to the southeast) and Mont St. Bruno (4 km2 of forest, 9 km to the
west). There are several smaller fragments within 2-3 kilometers of the reserve,
but it is improbable that these small fragments would act as source populations
for colonization to Mont St. Hilaire. Dispersal distances of a few to several
kilometers are unrealistic for most vertebrates that use CWD (Bowman et al.
74
2002) – not to mention the extra difficulty they face of dispersing through
suboptimal (matrix) habitat. This is almost certainly true for salamanders, which
are not known as long-distance dispersers. For flightless saproxylic insects,
colonization over distances greater than 500 m is uncommon (den Boer 1970).
Even for full-winged beetles, depending on air currents, weather conditions and
matrix properties, dispersal of more than a few kilometers is rare (Rainus 2006).
Data from Burke and Goulet (1998) show that the diversity of native beetle
species in deciduous forest fragments is linked forest fragment size, particularly
for patches that are isolated at distances of more than 2 km from source
populations. Among animals, the obvious exception to dispersal limitation in this
case are avifauna where the distance between Mont St. Hilaire and surrounding
habitat patches is within the natal and breeding dispersal ranges of many bird
species, particularly migrants (Greenwood and Harvey 1982, Paradis et al. 1998).
Within the Mont St. Hilaire, many species dependent on CWD as habitat won’t
necessarily be constrained to the CWD habitat types as we have defined them;
for species that are constrained to these specific habitats, our analyses suggests
that within-mountain dispersal is a strong possibility. The average distance
between moist-shaded debris patches (83 m) and dry-shaded CWD patches (77
m) are manageable dispersal distances for many forest-dwelling species. In
addition, because the matrix-habitat in these cases is often very similar to
preferred-habitat (i.e. old-growth temperate forest habitat) successful migration
from one patch to another may occur more frequently.
75
Conclusion
We have shown that the geospatial patterns of ice storm damage can be
accurately predicted across forest landscapes using remotely sensed NDVI. The
1998 ice storm provided a large influx of CWD, which provided an array of CWD
habitats across local environmental gradients. Although canopy cover has long
since returned to pre-storm levels, the complex geospatial CWD pattern that was
produced across the Mont St. Hilaire shows a change in forest floor habitat
dynamics that favour deadwood-reliant fungi, insects, birds, small mammals and
salamanders.
Acknowledgements
We are very grateful to M.C. Hooper and K. Arii who conducted the bulk of the
data collection and conducted many of the initial Mont St. Hilaire 1998 ice storm
studies. We would like to thank T. Work, M. Kalacska, R. Feldman, J. Messier and
S. Estrada for their helpful reviews and suggestions. M. VonButtlar and R.
MacKenzie helped with the deadwood collection data and L. Herzig helped with
76
the LIDAR data compilation and computations. This research was made possible
by funds provided by the Natural Sciences and Engineering Research Council of
Canada; PW would also like to thank Richard Tomlinson, T-PULSE and B. Alters
for generously providing supplementary funding opportunities.
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Figure 1Coarse woody debris influx at Mont St. Hilaire resulting from the 1998
ice storm. Mean coarse woody debris influx was 1.7 kg/m2 (standard deviation =
0.97 kg/m2), for a total estimated input of 16.8 metric tons per hectare. Coarse
woody debris input values are spatially correlated up to 450 meters.
89
Figure 2The predicted biomass of coarse woody debris resulting from the 1998
ice storm is positively correlated to the amount of young (category 1 and 2)
coarse woody debris that was measured in the summer of 2008 at 18 randomly
located sites across Mont St. Hilaire.
90
Figure 3Concentrations of coarse woody debris at Mont St. Hilaire in (a) dry
shaded habitat, (b), moist shaded habitat, (c) sun-exposed habitat and (d) wet
habitat.
91
Table 1Landsat-5 satellite imagery used in calculation of vegetation indices for
Mont St. Hilaire in 1996 and 1998.
Scene Identifier
Sun Elevation
Sun Azimuth
LT50140281996218XXX02
Acquisition
Date
8/05/1996
51.0 o
128.4 o
LT50140281998239XXX02
8/27/1998
48.5 o
142.6 o
92
Table 2Vegetation Indices (VIs) used to calculate forest damage after the 1998 ice storm at Mont St. Hilaire. The VIs incorporated
four wavelengths of light: blue (450 – 520 nm), green (520 – 600 nm), red (630 – 690 nm) and near infrared (NIR, 760 – 900 nm).
Name
Equation
Normalized Difference Vegetation Index (NDVI)
Enhanced Vegetation Index (EVI)
Difference Vegetation Index (DVI)
Renormalized Difference Vegetation Index (RDVI)
Atmospherically Resistant Vegetation Index (ARVI)
Green Normalized Difference Vegetation Index (NDVIgreen)
Visible Atmospheric Resistant Index (VARI)
93
Table 3The data for each regression model were split into random training and
validation datasets (80/20 ratio, 66/16 plots) to create ten cross validation trials for
each Vegetation Index. The R2 fit of each validation dataset is given respective of the
regression coefficient and intercept calculated in its training dataset. NDVI provides the
best for both training and validation datasets (R2 = 0.20 and 0.37, respectively).
94
Cross
Validation
Trial #
1
2
3
4
5
6
7
8
9
10
Average
Training Dataset R2
Validation Dataset R2
NDVI
EVI
DVI
RDVI
ARVI
NDVIgreen
VARI
NDVI
EVI
DVI
RDVI
ARVI
NDVIgreen
VARI
0.15
0.07
0.21
0.24
019
0.23
0.29
0.24
0.24
0.16
0.20
0.08
0.02
0.13
0.16
0.11
0.16
0.18
0.15
0.17
0.09
0.13
0.03
0.01
0.10
0.13
0.10
0.14
0.15
0.13
0.14
0.05
0.10
0.09
0.02
014
0.18
0.14
0.18
0.22
0.18
0.19
0.09
0.14
0.10
0.03
0.18
0.21
0.13
0.20
0.23
0.20
0.17
0.09
0.15
0.14
0.04
0.19
0.28
0.19
0.22
0.32
0.22
0.23
0.11
0.19
0.02
0.02
0.03
0.08
0.04
0.03
0.03
0.04
0.03
0.04
0.04
0.56
0.69
0.34
0.19
0.38
0.33
0.20
0.24
0.28
0.46
0.37
0.54
0.69
0.28
0.06
0.31
0.18
011
0.23
0.14
0.41
0.30
0.54
0.71
0.28
0.14
0.25
0.05
0.14
0.16
0.08
0.40
0.28
0.57
0.74
0.32
0.16
0.34
0.11
0.17
0.21
0.15
0.47
0.32
0.53
0.55
0.16
0.07
037
0.04
0.18
0.18
0.25
0.43
0.28
0.61
0.70
0.45
0.02
0.35
0.25
0.03
0.25
0.25
0.54
0.35
0.30
0.10
0.24
0.00
0.12
0.36
0.24
0.18
0.22
0.15
0.19
95
Linking Statement 1
In Chapter 1 I examined the geospatial and biotic impacts of an abiotic natural
disturbance on a remnant forest. I generated detailed biotic impact data and it
was evident that the patterns of influx for different types of coarse woody
debris were highly heterogeneous. The long-term impact of this will be variable
for different saproxylic guilds and other taxa reliant on specific forms of coarse
woody debris. Remnant forest fragments are subjected to many forms of
disturbance, both natural and human in origin. To best understand how
disturbances shape forest communities the second category of abiotic
disturbances, human-caused, needs to be examined. In Chapter 2 I explore this
by analyzing the relationship between recreational trails and a forest insect
assemblage.
96
CHAPTER2: Human-Disturbance and Caterpillars in Managed Forest Fragments
Peter J.T. White1, Brian J. McGill2 and Martin J. Lechowicz1
1
McGill University, Department of Biology
1205 Dr. Penfield Ave., Montreal, Quebec
H3A 1B1, Canada
2
University of Maine, School of Biology & Ecology
Deering Hall 303, Orono, ME
04469, U.S.A.
*This chapter was first published by the above authors in Biodiversity and
Conservation (2011)
97
Abstract
The impact of forest-edge habitat on caterpillar assemblages has been wellstudied, but the impact of trailside habitat has rarely been considered. We
surveyed caterpillar populations in relation to recreational trails in forest
fragments in southeastern Quebec, Canada. We found a consistent negative
relationship between trails in the forest and both the abundance and species
richness of caterpillars within and among forest fragments. Conversely,
caterpillar presence was not related to the nearby presence of favourable host
trees.We suggest that the negative effect of trails may be due to increased
predation pressure in trailside habitat and to conditions that make trailside
habitat less preferable for oviposition. These results underscore the importance
of managing trails to limit the amount of intra-forest disturbance inforest
fragment remnants.
98
Introduction
The temperate deciduous forests of eastern North American have been
subjected to widespread habitat destruction over the past two hundred years
(Drushka 2003, Hannah et al. 1995). The forest fragments that remain have high
levels of edge-related effects that can have a significant impact on many forestdwelling organisms(Alverson et al. 1988, Murica 1995, Wilcove 1985). Habitat
destruction has been particularly widespread in the St. Lawrence Valley of
southeastern Quebec, Canada where most of the historic mixedwood forests
have been cleared in favour of agricultural development. The few forest
fragments that remain are critical habitat for many forest-dwelling species
(Warman et al. 2004).Since many of these remnant forest fragments are in close
proximity to urban areas they experience high volumes of human traffic on both
designated and unofficial hiking trails.
Trailside Habitat in Forests
Much has been made of the necessity to protect forest fragments from external
threats (such as cutting and isolation), but there has been comparatively little
focus on internal threats to forest fragments such as recreational hiking or
walking trails.Trailside habitat is distinct from forest-edge habitat because the
latter marks a transition to a different habitat type (e.g. agriculture, open field,
urban area, etc.) whereas the former often does not. For many taxa, trailside
communities differ significantly in species composition from forest interior
99
communities, the formeroften characterized by a high proportion of earlysuccession, disturbance-tolerant and invasive species (Dickens et al. 2005, Hall
and Kuss 1989). Trails can facilitate soil-compaction via trampling that adversely
affects root development and the growth of trailside woody plants (Bhuju and
Ohsawa 1998). Trails can also lead to increased soil erosion, muddiness and
vegetation trampling in trailside habitat (Bhuju and Ohsawa 1998, Dale and
Weaver 1994, Farrell and Marion 2001).
The few studies that have examined the impact of trailside habitat on
forest-dwelling species often have uncovered a pattern similar to what is found
associated with edge habitat. Recreational trails generally have a negative effect
on small-mammal populations (Boyle and Samson 1985, Meaney et al. 2002,
Sauvajot et al. 1998) – a relationship that is typically mirrored by small mammal
populations in forest-edge habitat (Bayne and Hobson 1998, Miller and Hobbs
2000, Wolf and Batzli 2004); but see also (Anderson et al. 2003). Certain bat
species are well known to prefer both forest-edge and trailside habitat over
forest-interior habitat (Krusic et al. 1996, Patriquin and Barclay 2003). Many
carabid beetles often favour heavily-trampled trailside habitat over forestinterior habitat (Grandchamp et al. 2000, Raymond et al. 2002), a similar pattern
occurring in forest-edge habitat (Magura 2002, Magura and Tothmeresz 1997,
Molnár et al. 2001; but see alsoDavies and Margules 1998). Among birds, many
disturbance-tolerant species tend to favour trailside habitat over forest-interior
habitat (Miller et al. 1998) in contrast to forest-edge habitat where the overall
100
species richness and abundance of the bird assemblage can be significantly
higher than the forest-interior (Best et al. 1990, Strelke and Dickson 1980).
An Analogy of Forest Edge Habitat
The impact of forest-edge habitat on forest-dwelling moth populations has also
been well-examined, but little research exists on the effects of trailside habitat.
Insight from edge-effect literature on forest-dwelling moths can help inform an a
priori hypothesis about the impact of trails. For example, in boreal forest habitat,
Mönkkönen and Mutanen (2003) found no difference in moth species richness
or abundance in edge versus non-edge (interior) habitat, which may suggest that
trails could have little or no impact. Conversely, in tropical areas, Arctiidae moths
have been found to be significantly more species rich in recovering secondary
forests (edge-like habitat) compared to mature forest (non edge-like
habitat)(Fiedler et al. 2007, Noske et al. 2009). Noske et al (2009) argue that this
pattern may be because recovering forest stands tend to have more habitat
niches than mature forest stands – a feature that could certainly be applicable to
trailside habitat. In temperate regions, Summerville and Crist (2004) show that
small forest fragments can have higher than expected moth richness in cases
where there is a high host plant richness to offset the loss of forest area. A
similar pattern could be predicted for trailside habitat especially if there is an
increase in host plant richness driven by an increase in disturbance-tolerant
plants. This higher-than-expected richness typically results from a greater
101
proportion of matrix-dwelling moths, sometimes at the expense of the forestdwellers (Summerville 2004). Fortin and Maufette (2001) found better caterpillar
performance (bigger pupae, higher larval and pupal survivorship) and larger egg
masses in edge habitat and connected their findings to an increased nutritional
quality of edge trees. Roland (1993) uncovered higher caterpillar abundance at
forest edges but suggested that the pattern may be due to an edge-associated
decrease in top-down parasitoid or pathogen pressure. Although there are a
plethora of proposed mechanisms (niche availability, host plant richness, host
plant nutritional quality, parasitoid/pathogen pressure), the general trend shows
either a neutral effect or a positive effect of edge or edge-like habitat on
caterpillar species richness and abundance. Although many of these mechanisms
could act as described in trailside habitat, no studies to our knowledge have
examined the pattern of caterpillar richness or abundance associated with forest
trails.
Hypotheses
In this study we therefore test two hypotheses about the drivers of caterpillar
assemblages in relation to trailsides within forest fragments:
1. Trailside habitat is beneficial to caterpillar assemblages.
We pose this hypothesis based on the set of forest-edge literature showing that
there is a positive relationship between forest-edge habitat and moth species
(richness or abundance). Presuming that trailside habitat is analogous to forest-
102
edge habitat, we predict caterpillar species richness and abundance to be higher
in trailside habitat than in forest-interior habitat.
2. Host plant availability determines caterpillar species richness and
abundance.
Because the forest fragments we examine in this study are intensively managed
we decided to put the impact of trails in a habitat context, to control for the
impact of habitat suitability. We therefore examine whether caterpillar
occurrence is related to host plant availability (i.e. the quantity of host plants at
a given quadrat) and predict that quadrats with higher host plant availability will
have more diverse and abundant caterpillar assemblages.
Methods
Study Area
We studied caterpillar assemblages associated with the Monteregian Hills
(Feininger and Goodacre, 1995) in the St. Lawrence River valley in southeastern
Quebec, Canada (45°30'N, 73°30'W to 45°24'N, 72°35'W; Figure 1).During
presettlement times all these Monteregian Hills would have had broadly similar
forests embedded in a more or less continuously forested landscape (Richard
and Grondin 2009), but now they exist as a series of large remnant forest
fragments isolated in the developed landscape.We established eighteen 400 m2
(20 m x 20 m) quadrats in the forests at each of four sampling sites on
Monteregian Hills.
103
Our first site was at Parc Mont Royal - an urban park in middle on
Montreal, a city of 3.5 million inhabitants. A much-disturbed secondary
broadleaf deciduous forest comprises about 100 ha of the 190 ha park on this
Monteregian Hill in city centre. Many trees were cut in the 1950s and early
1960s (Brunel et al. 2005) and more than 70,000 trees were subsequently
replanted up to the early 1990s in an attempt to restore the forest. Currently,
the park has more than 110,000 trees and more than 80 species of woody plants
(Brunel et al. 2005).
Our second site was at Parc National du Mont St. Bruno – a protected
provincial park in the eastern suburbs of greater Montreal. The forest at Mont
St. Bruno is a broadleaf deciduous forest covering more than 500 ha of the 790
ha park. Aerial photography records indicate that 60 or more ha of forest were
cut prior to the 1940s in the northern part of the park, and subsequently
replanted or allowed to regrow (National Air Photo Library 1950). The forest has
more than 85 species of woody plants.
Our third site was at the Gault Nature Reserve on Mont St. Hilaire – a
protected park and UNESCO Biosphere Reserve 38 km east of Montreal. The
forest at Mont St. Hilaire is an old-growth broadleaf deciduous forest covering
most of the 1000 ha reserve. This site has a long history of protection dating
back to the 1600s (Arii 2004, Maycock 1961), and is recognized as a biodiversity
hotspot not only for vascular plants (Holland 1980, Karst and Lechowicz 2007),
but also moths and butterflies (Handfield 1999), mammals (Grant 1976,
104
Humphries et al. 2003), birds (Harris and Lemon 1974, Quellet 1967), and
herpetofauna(Denman and Lapper 1964).
Our fourth site was at Mont Shefford – one of the easternmost
Monteregian Hills and well outside greater Montreal. At the time of this study
different parts of the site nonetheless were in varying states of disturbance and
urban development. Numerous small houses and some maple sugar bushes
occur along several paved two-lane roads that run through the centre of Mont
Shefford and there is a ski-hill on the northern slope as well. We conducted
caterpillar sampling at three sub-sites around the hill. Six quadrats were in a 100
ha semi-disturbed patch of forest on the west side of Mont Shefford and six
quadrats in a 25 ha patch of forest on the east side set aside as a forested
community park. A quarter of the former site was a sugar bush and there were
numerous trails used by deer-hunters throughout the area. The community park
had several trails spread out within the park, but off-trail use was discouraged.
Six quadrats were in a third sub-site, Parc de la Yamaska, a provincial park just
north of Mont Shefford. Our quadrats were in the east-portion of the 450 ha
semi-disturbed park that has several trails and a high volume of pedestrian
traffic (ParcsQuebec 2010).
105
Trail Index Calculation
We created a geospatial variable called trail index to measure the impact of trails
at every 1 m2 pixel p across an entire site:
[1]
n
trail index p = ∑
t =1
wt
d pt
where the width w (in meters) of trail tis divided by distance dpt , the distance
between a given pixel p (at a 30 meter resolution) and trail t in meters. Trail
index was calculated only for pixels that had at least one dpt less than 50 meters,
the distance at which a given pixel p is considered to be forest interior habitat
(Matlack 1993). Thus, for each pixel p, trail index is calculated as the cumulative
impact of all ntrails within 50 meters of that pixel.Pixels where all dptdistances
were greater than 50 meters were given a trail index value of zero. In our data
trail index values scale from 0.02 (a pixel 50 meters away from a 1 meter trail) to
6.0 (a pixel 1 meter away from a 6 meter trail) for each trail (Figure2).
We created a map of trail index for each of the four study sites based on
documented trail data (Bossé 2005, CantonShefford 2010,
CentredelaNatureMontSaintHilaire 2007, LesAmisDeLaMontagne 2008,
ParcsQuebec 2010, ParcsQuebec 2010).There were no existing trail data for our
first sub-site at Mont Shefford, so we georeferenced the trails that we observed
adjacent to our six study quadrats in this area. Finally, we also calculated an
index of overall trail impact for each 400 m2 quadratby averaging the trail index
values of the pixels within the given 20 m x 20 m quadrat boundary.
106
Caterpillar Surveying and Identification
Most studies examining moths in ecological settings use light traps to capture
adult morphs, which yields large sample sizes from a wide area that can
encompass multiple habitats. Beck and Linsenmair(2006)calculate that the
attraction radius of light trapping is typically around 15 meters, although this
may vary between an open and dense forest understory (but see alsoBaker and
Sadovy 1978). For the purposes of surveying moth populations adjacent to
recreational trails, caterpillar sampling via tree-beating(Futuyma and Gould
1979)is a more direct measure of the association between individuals and the
trailside trees.We therefore focus our sampling on the caterpillar life-stage to
ensure that the species we document are forest-dwelling rather than migrant
from any adjacent matrix-habitat
Caterpillars were collected at 18 quadrats at each of the four study sites.
At each of the 72 quadrats, ten sugar maple (Acer saccharum) trees between 3
and 10 cm dbh were sampled by striking the bole and lower branches of each
tree ten times with a 20 oz, 30" aluminum baseball bat and catching dislodged
caterpillars on a sheet. Caterpillar collections were made three times at each
quadrat (between June 1 and June 6, July 4 and July 9 and August 3 and August 6
in 2009), yielding a total of 21600 tree-strikes (10 strikes/tree x 10 trees/plot x
18 quadrats/site x 4 sites/study region x 3 survey windows).
Macrolepidopteran moth caterpillars were identified to species with a
dissecting microscope using Wagner’s Caterpillars of Eastern North America Field
107
Guide(2005). Microlepidopteran moths were counted and identified only to
morphospecies for lack of an accurate identification guide. Macrolepidopteran
moths collected in early instars were reared so that positive identifications could
be made. Flightless moth species (with wingless or flight-limited adult female
morphs) were retained in our dataset because of their ability to select host
plants by tree-to-tree movement of late instars and aerial ballooning of first
instars (Barbosa et al. 1989, Bell et al. 2005). In our data these included
Lymantria dispar, Alsophila pometaria, Operophtera bruceata, Phigalia titea,
Orgyia definita, and Orgyia leucostigma.
Site Host Plant Availability
All trees greater than 1 cm diameter at breast height (dbh) were counted and
identified to species at each quadrat. Host plant preferences for macroLepidoptera were taken from Handfield’s Le Guide des Papillons du
Quebec(Handfield 1999) and cross-referenced with Wagner’s account (2005).
Host plant availability was calculated for each quadrat using the basal area
measurement of each quadrat tree as an index of foliage biomass (Tucker et al.
1993). Specifically, the host plant availability of each quadrat was calculated as:
m
[2]
hostplant availability =
108
n
∑∑ b
i =1 j =i
m
ij
where bij is the basal area in meters-squared for the jth of ni host plant species at
a quadrat for the ith among m macromoth caterpillar species observed we
identified in our study region.
Statistical Analyses
We used a general linear model (SPSS Inc. 2000) to examine how host plant
availability and trail index relate to caterpillar richness and abundance across
quadrats:
[3]
caterpillar species = trail index + hostplant availability
Trail index, caterpillar species richness and caterpillar abundance were 1+ log10
transformed to improve normality.
We also tested the probability of random distribution of caterpillars
across our study quadrats for any caterpillar species found at 20% or more of the
quadrats. For each of these caterpillar species we calculated the species-specific
host plant availability at each quadrat. We then categorized quadrat host plant
availability into one of five equal-interval categories: Very Low: 0-0.250 m2, Low:
0.251-0.500 m2, Medium: 0.501-0.750 m2, High: 0.751-1.00 m2 and Very High: >
1.00 m2. The expected occupancy of each species in each category was
calculated as the total number of quadrats a given species was observed to
occupy multiplied by the proportion of quadrats that fell into each of the above
host plant availability categories.Because the distribution of a given species
could incorporate as few as 15 quadrats (20% of 72), we used a randomization
109
test for goodness-of-fit (McDonald 2009) to see whether abundant caterpillar
species were non-randomly distributed with respect to site host plant
availability. P-values were calculated based on 10,000 replicates of distribution
for each caterpillar across the five categories.
Results
We collected 75 species across our four sites, 36 macrolepidopteran moths (420
individuals, Appendix A1) and 39 microlepidopteran moths (253 individuals,
Appendix A2). Of these, 21 species were collected on Mont Royal (11 macro, 10
micro), 46 on Mont St. Bruno (24 macro, 22 micro), 37 species on Mont St.
Hilaire (20 macro, 17 micro) and 43 species on Mont Shefford (21 macro, 22
micro).
Trail Index and Host Plant Availability
Trails were most prevalent across Mont Royal where 95% of the forested area
had non-zero trail index values (Figure 3); the average trail index was 0.164
across the entire site and 0.188 when averaged among quadrats. Trails were
least prevalent at the Mont St. Hilaire site where only 29% of forested area had
non-zero trail index values; the average trail index was 0.021 across the entire
site and 0.015 when averaged among quadrats. The Mont St. Bruno and Mont
Shefford sites were intermediate in terms of trail presence with 56% and 36% of
forested areas having non-zero trail index values, respectively.Trail indices were
0.047 averaged across St. Bruno and 0.042 averaged across Shefford; trail index
110
values when averaged among quadrats were 0.028 for St. Bruno and 0.038 for
Shefford.
Average host plant availability for macromoths across all quadrats at each
site ranged from 0.11 m2 to 1.34 m2 basal area (mean = 0.63 m2, standard
deviation = 0.24) (see Appendix A3 for complete list of trees surveyed). Average
host plant availability did not vary by site (ANOVA among sites, p = 0.80). Across
all quadrats, trail index was a significant (negative) predictor of caterpillar
richness and abundance; site host plant availability was a near-significant
(positive) predictor of caterpillar richness and abundance (Table 1). Performing
regression analyses on a site-by-site basis, trail index remained significant at all
four sites whereas host plant availability was significant only at the Mont
Shefford (Table 1).
Random Versus Non-Random Distribution of Caterpillar Species
Seven of the observed 36 macrolepidopteran moth species were present at
more than 20% of the study quadrats: Cyclophora pendulinaria, Itame pustularia,
Lambdina fiscellaria, Lithophane antennata, Lymantria dispar, Melanolophia
canadaria and Morrisonia latex. Of these, only I. pustularia had a distribution
across quadrats significantly different from random expectation (p = 0.0094,
Appendix A4).I. pustularia occurred at high host plant availability quadrats more
frequently than expected and low host plant availability quadrats less frequently
than expected.
111
Discussion
We show a consistent negative relationship between trail presence and
caterpillar abundance and richness. This relationship was present both within
and among the four forest fragments we examined. This relationship is contrary
to our hypothesis that trailside habitat would be associated with an increase in
caterpillar abundance and/or richness. We also did not find evidence that host
plant availability was limiting caterpillar abundance or species richness across
our sites. There was no consistent link between host plant availability and
caterpillar abundance or richness across quadrats, and only one of the seven
most abundant caterpillar species was distributed across quadrats nonrandomly
in relation to host plant availability.
Possible Mechanisms of Negative Relationship
A few mechanisms may interact to explain the significant negative relationship
between trails and caterpillars. First, changes in trailside microsite conditions
may make potential host plants less attractive to gravid female moths (resulting
in fewer caterpillars). Often, host selection by moths occurs via chemodetection
of volatile organic compounds (VOC) emanating from candidate host plants
(Mphosi 2007, Pophof et al. 2005, Shields and Hildebrand 2001). Changes in
habitat temperature and moisture can result in changes to tree VOC emission
levels (Tollsten and Müller 1996) potentially making them more or less attractive
for oviposition. The assortment of unique habitat conditions at trailside habitat
112
(e.g. higher understory light levels, muddier terrain, more compact soil and more
understory herbaceous vegetation) could therefore result in trailside trees
having different VOC levels that attract fewer moths than interior-forest trees.
This mechanism could also apply to species whose females are flightless or flightlimited as adults, resulting in redispersal by ballooning in early instars or intertree migration in later instars in Alsophila pometaria, Operophtera bruceata,
Phigalia titea, Orgyia definita, Orgyia leucostigma and Lymantria dispar.
Additionally, increased light levels in trailside habitat may lead to higher leaf
toughness levels which can decrease host plant suitability for caterpillars
(Choong 1996, Feeny 1970).
Secondly, the aversion to trailside host trees may in part be driven by
top-down predation or parasitism. In our system, insectivorous birds, bats,
omnivorous rodents, coleopteran beetles and parasitic hymenoptera are major
caterpillar predators that have been observed to play a role in regulating
caterpillar populations (Barbosa et al. 2001, Grushecky et al. 1998, Lill et al.
2002, Medina and Barbosa 2002, Sanz 2001). While trailside habitat does not
always benefit small mammal populations, caterpillar populations could
experience high predation and parasitism rates from disturbance-tolerant
(insectivorous) coleoptera, birds, bats and (parasitic) hymenoptera (Grandchamp
et al. 2000, Holzschuh et al. 2009, Krusic et al. 1996, Miller et al. 1998).
Thirdly, it is possible that overwintering egg and larval mortality is higher
in trailside habitat due to lethal microclimatic conditions. High winter winds,
113
trampling and tracking for cross-country skiing all can remove insulating snow
cover along trails (Leonard 1972) and the resulting exposure to extreme freezing
conditions can lead to high rates of egg and larva death (Brokerhof et al. 1993,
Cooke and Roland 2003, Layne and Peffer 2006, Waggoner 1985). This may seem
like an unlikely explanation, particularly as edge habitat (which would be
exposed to similar conditions) has been found to have high egg masses, larval
and pupal densities in some caterpillar species (Bellinger et al. 1989, Fortin and
Mauffette 2001, Roland 1993) – but survivorship in leeward versus windward
edges has not been tested. If certain trailside habitats experience significantly
harsher wintertime conditions then springtime caterpillar populations may be
affected.
Trails Versus Edges
Many of the potential mechanisms driving reduced caterpillar presence in
trailside habitat could arguably be applied to forest-edge habitat, yet forest-edge
habitat often shows a positive rather than negative relationship with caterpillar
abundance and richness. There are, however, two fundamental differences
between forest-edge habitat and trailside habitat that could, in this case,
account for the difference. In forest-edges moth populations transition from
forest-interior species to edge-tolerant species as the habitat type transitions
from undisturbed forest interior to disturbed successional edge-habitat (Fiedler
et al. 2007, Noske et al. 2009). In many cases the abundance and richness of the
114
edge-dwelling moth assemblage is augmented by species from an adjacent soft
matrix (e.g.Ricketts et al. 2001). For moths, a soft matrix would be agricultural
habitat with sparse tree and shrub cover (Fischer et al. 2005). This type of
habitat is not necessarily hostile to forest-dwelling moths, but generally lacks the
host plants required to support stable populations. Similarly, for open-field moth
species, a soft matrix is a forest-edge habitat that has a low abundance of
suitable host- or food-plant species (Summerville and Crist 2003). Therefore,
because forest-edge habitat is flanked by two distinct moth assemblages (forestdwelling and field-dwelling) that often occupy different habitat niches,
abundance and richness tend to be higher. In contrast, trails that cut through
forest interior habitat do not mark a transition to open field soft-matrix
conditions. This was documented in a tropical forest system by Fiedler et al.
(2007) who showed that Geometrid and Pyraloid moth richness decreased in
edge habitat whereas Arctiid or Sphingid moth richness often increased.
A second consideration is that thesurvey methods we used in this study are not
the standard light-trapping methods most often adopted for Lepidoptera
population studies in forest-edge habitat. Our tree beating methodology gives
the advantage of surveying caterpillar populations at very specific locations in
proximity to trails and allows us to control for microhabitat (host plant)
characteristics. Gaston (1988) summarizes the many limitations of light trapping,
including the inability to associate individual moths to specific microhabitats.
This makes it very difficult to sample edge populations independent of matrix-
115
populations. Furthermore, the sampling effectiveness of light-traps deployed
across a large region can be quite variable due to fluctuations in wind, cloud
cover, moonlight and temperature from one site to another (McGeachie 1989,
Yela and Holyoak 1997).
Conclusion
The strong negative association of trails with forest-dwelling moth richness and
abundance suggests that management of forest fragments should focus on
limiting proliferation of recreational-trails as a primary conservation activity. In
our dataset, the host plant availability of a given site was rarely important in
predicting moth richness or abundance, suggesting that tree abundance and
richness maintenance could be designated a second-tier priority in Monteregian
forest fragments. We don’t suggest that a healthy tree assemblage is not
important for forest communities but rather that limiting intra-forest
disturbance due to trail networks may be more important. Ours is not the first
study to document the negative impact of recreational trails on diverse
organisms in forest communities – significant and negative impacts have been
widely reported (e.g.Bhuju and Ohsawa 1998, Dale and Weaver 1974, Dickens et
al. 2005, Farrell and Marion 2001, Hall and Kuss 1989, Leung and Marion 1999,
Miller et al. 1998, Wilson and Seney 1994). Ours is however one of the first to
document a negative relationship specifically associated with moths, a popular
indicator species of forest disturbance (New 1997, Summerville et al. 2004).
116
Leung and Marion (1999) note that closemonitoring of trail networks is needed
in places where human disturbance is present and that pedestrian traffic should
be limited to few well-defined trails. Both boardwalks and physical barriers can
be effective for directing and minimizing the impact of pedestrian traffic on
natural habitats (Doucette and Kimball 1990, Zhou and Tachibana 2004) but they
can be costly to construct and maintain. However, forest fragments that have a
high degree of human visitation – particularly those fragments that are managed
with a mandate to protect wildlife and biodiversity for future generations – may
require these types of extra measures to ensure that trails have a minimum
impact on the surrounding habitat.
Acknowledgements
We would like to thank R. Feldman, J. Messier and S. Estrada for their helpful
reviews and suggestions. M. VonButtlar and R. MacKenzie helped with the data
collection. This research was made possible by funds provided by the Natural
Sciences and Engineering Research Council of Canada. We would also like to
thank D. Rodrigue and A. Mochon from Parcs Québec for their help at Parc
National du Mont-Saint-Bruno and Parc National de la Yamaska.
117
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Figure 1We sampled at four sites in the St. Lawrence River valley of southern Quebec, Canada (Figure adapted from Atlas of Canada
2010), each progressively farther from the center of Montreal. Urban development is highest in the centre of the Montreal
Metropolitan Area decreasing towards the edges of the dashed boundary that marks the geographical limits of greater-Montreal.
The urban development gives way to farmlands eastward in the St-Lawrence Lowland, and then extensive forests in the Appalachian
Highlands.
131
Figure 2Trail index calculation for a given pixel p at distance d from trail t that has a width w. Index shown untransformed (a) and
log-transformed (b).
132
Figure 3Trail index across our four study sites: (A) Mont Royal, (B) Mont St. Bruno, (C) Mont St. Hilaire and (D1, D2, D3) Mont
Shefford. The geospatial arrangement in this Figure does not reflect the regional geolocations of the sites (see Figure 1).
133
Table 1Trail Index is a consistent negative predictor of caterpillar abundance and
caterpillar species richness both across and within study sites.Hostplant availability
is rarely significantly linked to caterpillar species abundance or richness, only
explaining a large degree of caterpillar variance at Mont Shefford. Near-significant
(*) and significant (**) p-values are marked.
134
Dependent Variable
Independent Variable
Caterpillar
Abundance
Caterpillar Species
Richness
Trail Index
Site Host Plant Availability
Trail Index
Site Host Plant Availability
Trail Index
Caterpillar
Abundance
Site Host Plant Availability
Trail Index
Caterpillar Species
Richness
Site Host Plant Availability
Site
All
All
St. Bruno
St. Hilaire
Royal
Shefford
St. Bruno
St. Hilaire
Royal
Shefford
St. Bruno
St. Hilaire
Royal
Shefford
St. Bruno
St. Hilaire
Royal
Shefford
135
Standardized
Coefficient
-0.66
0.17
-0.64
0.17
-0.592
-0.697
-0.473
-0.450
0.214
-0.356
-0.005
0.570
-0.498
-0.520
-0.479
-0.368
0.189
-0.222
0.059
0.453
Partial R2
0.434
0.030
0.416
0.030
0.315
0.465
0.223
0.196
0.042
0.122
0.001
0.322
0.223
0.259
0.228
0.131
0.032
0.047
0.004
0.199
p-value
1.8 x 10-10**
0.054*
5.7 x 10-10**
0.055*
0.019**
0.002**
0.054*
0.038**
0.357
0.073*
0.982
0.011**
0.056*
0.035**
0.052*
0.120*
0.442
0.340
0.798
0.060*
Linking Statement 2
In Chapter 2 I showed that human disturbance associated with trails is negatively related
to caterpillar richness and abundance in forest fragments. I developed a simple and
effective index to measure the impact of trails. This indexcomplements the NSDVI-based
remote-sensing ice storm impact framework developed in Chapter 1 to provide a
management toolkit for holistically monitoring disturbances in remnant forest
fragments.The next question that needs to be addressed relates to the biotic forces that
affect species richness. Specifically, bottom-up effects from host plants. Both ice storms
and recreational trails impact habitat quality, but for phytophagous insects “habitat
quality” is highly dependent on the availability of suitable host plants. The methods used
in Chapter 2 did not identify acceptable host plant abundance as a consistent
determinant of caterpillar richness or abundance. In Chapter 3 I examine this in greater
detail and expand my sampling regime to include a diversity of host plants at each
quadrat (rather than just Acer saccharum). This will allow me to directly test the impact
of different host plant species and overall host plant richness on caterpillar richness and
abundance.
136
CHAPTER 3: Testing Two Methods that Relate Herbivorous Insects to Host Plants
Peter J.T. White1
1
McGill University, Department of Biology
1205 Dr. Penfield Ave., Montreal, Quebec
H3A 1B1, Canada
137
Abstract
Insect herbivores are integral to terrestrial ecosystems. They provide essential food for
higher trophic levels and aid in nutrient cycling. In general, research tends to relate
individual insect herbivore species to host plant identity where a species will show
preference one host over another. In contrast, insect herbivore assemblages are often
related to host plant richness where an area with a higher richness of hosts will also have a
higher richness of herbivores. In this study I test the ability of these two approaches (host
plant identity/abundance vs. host plant richness) to describe the diversity, richness and
abundance of an herbivorous Lepidoptera assemblage in temperate forest fragments in
southern Canada. Many of these fragments are intensively managed in an effort to
maximize the protection and preservation of biodiversity while simultaneously allowing for
recreational use. Analyses indicated that caterpillar diversity, richness and abundance were
better described by quadrat-scale host plant identity and abundance than by host plant
richness. Most host plant-herbivore studies to date have only considered investigating host
plant preferences at a species level; this type of assemblage level preference I show has
been rarely considered. In addition, host plant replacement simulations indicate that
increasing the abundance of preferred host plants could increase Lepidopterarichness and
abundance by as much as 30% and 40% (respectively) in disturbed remnant forest
fragments. This differs from traditional thinking that suggests higher levels of insect
richness can be best obtained by maximizing plant richness. Host plant species that are
highly preferred by the forest-dwelling caterpillar assemblage should be given special
138
management and conservation considerations to maximize biodiversity in forest
communities.
139
Introduction
Lepidoptera are very important in forest ecosystems. They are an intricate link between
forest foliage and higher trophic levels. As larva (caterpillars) and as pupa, they are
components of forest food webs providing an essential food source for birds, small
mammals, snakes, amphibians and other insects(Hamilton and Pollack 1956, Lill et al. 2002,
Moore and Strickland 1955, Murakami and Nakano 2000, Vasconcelos et al. 1996,
Whitaker 1966). As adult moths, they are food for bats and birds (Goiti et al. 2009, Murray
et al. 1980) and can be important flower pollinators (Pellmyr et al. 1996).Being herbivores
in their larval life stage,Lepidoptera play a critical role in forest nutrient cycling, converting
nutrient-rich leaves into nutrient-rich feces (either their own or those of a predator) that
are easily digestible by soil organisms (Schowalter et al. 1986).
Overlooked in Conservation Planning
Even though forest-dwelling Lepidoptera play a central role in forest processes they
are often overlooked in conservation planning (New 2004). This is in part because the
natural history of most forest-dwelling Lepidoptera is very poorly known. In temperate
regions research has focused on species capable of outbreak conditions like the spruce
budworm (Choristoneura occidentalis), the gypsy moth (Lymantria dispar), the forest tent
caterpillar (Malacosome dispar) or the fall webworm (Hyphantria cunea). More cryptic
species and virtually all microlepidopteran species have been largely ignored.
Acknowledging that individual species-targeted conservation management of Lepidoptera
is often not possible, New (2004) suggests that an assemblage level approach could make it
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easier to make conservation-oriented management decisions leading to the protection of
entire lepidopteran assemblages and their natural habitats.
The Relationship between Hosts and Lepidoptera
The goal of this study is to determine how host plants richness, abundance and
identity determine Lepidoptera assemblage richness in temperate deciduous forests.
Across terrestrial ecosystems, theory has often focused on the richness of host plants
driving the richness of Lepidoptera. This builds off the theory that richness at one trophic
level determines the richness of the trophic level above (Andow 1991, Rosenzweig 1995).
This means that in terrestrial ecosystems host plant richness would be a logical driver of
insect herbivore richness. Indeed, this is what is often reported. In fields, plots with more
forb, grass, legume and woody shrub species have been found to support higher insect
herbivore richness and abundance than plots with less plant richness (Haddad et al. 2001,
Siemann et al. 1998). A similar pattern has been observed in forest ecosystems where
forest fragments with many tree species sustain higher insect herbivore richness than
those with few (Summerville and Crist 2004). Along a successional gradient, insect
herbivore richness can be tightly linked to plant species richness in young communities but
more tightly linked to structural diversity in old communities (Southwood et al. 1979). The
proposed mechanism to explain thesetypes of relationships is that a richer or more diverse
host plant community provides more diverse foliar resources and more diverse structural
resourcesthan a less diverse host plant community, allowing it to meet the physiological
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and niche demands of more insect herbivore species (Lawton 1983, Murdoch 1972,
Siemann 1998).
The claim that host plant richness drives insect herbivore richness is problematic for
two reasons. First, there are many exceptions to this relationship - especially when other
factors are tested alongside host plant richness as competing explanatory variables. These
factors include site-specific soil nutrient conditions (Hartley and Jones 2003), primary
productivity and topography (Hawkins and Porter 2003), and habitat disturbance (Kruess
and Tscharntke 2002). In addition, significant differences between insect herbivore
richness have been observed among co-occurring host plants. For example,when the black
willow tree (Salix nigra)and the box elder tree (Acer negundo) co-occur, the former tends
to host a richer and more abundant Lepidoptera assemblage than the latter (Barbosa et al.
2000). Similarly, when the Norway maple (Acer platanoides) and sugar maple (Acer
saccharum) co-occur, the former experiences significantly less insect herbivory than the
latter (Cincotta et al 2009).These types of results seem to indicate that higher abundances
of certain host plants in forest stands may be more important in facilitating a diverse and
abundant insect herbivore assemblage. To my knowledge this assertion not been formally
tested.
Second, the claim that host plant richness drives insect herbivore richness is
actuallydisconnected from the mechanisms that drive individual insect herbivore species
abundance and distribution. For individual insect herbivore species the relationship with
host plants is typically described in terms of host plant identity and host plant abundance
rather than host plant richness (Thompson and Pellmyr 1991). For example, both the gypsy
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moth (Lepidoptera: Lymantria dispar) and the winter moth (Lepidoptera: Operophtera
brumata) are broad generalists but they tend to have faster developmental rates and
higher population abundances when they feed on a select set of preferred host plants
(Barbosa 1978, Liebhold et al. 1995, Maufette et al. 1983, Tikkanen et al 1999, Wint 1983).
Many other similar examples exist (e.g. Busching and Turpin 1977, Capinera 1978, Deslile
and Hardy 1997, Karban and English-Loeb 1997, Wiklund 1981). These types of preferences
are usually driven by host plant-specific foliar nutrient qualities or natural enemy densities,
which both have significant impacts on insect herbivore performance and survival (Hunter
and Price 1992, Scriber and Slansky 1981, Thompson and Pellmyr 1991). Insect herbivore
assemblage richness in a given locale is the culmination of these kinds of host plant choices
made by individual species based on the host plants that are present. Since the individual
choices are usually made based on host plant identity, insect herbivore richness may be
best modeled by taking host plant identity into account.
In this study I perform two tests to determine how host plants drive insect
herbivore assemblage richness in temperate forests.First,I test whether caterpillar diversity
and richness are related to host plant richness. Based on the aforementioned studies (and
rationale), an increase in host plant species diversity or richnessis expected to be
proportional to an increase in insect species diversity or richness. Second, I test whether
caterpillar richness, diversity and abundance are more accurately described by the
abundance of specific host plants. Both positive and negative host plant associations may
be expected, synonymous to host plant choices made by individual insect herbivore
species and indicative of the presence of preferred and non-preferredhosts. Given that
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relationships between host plant identity and individual insect herbivore species are often
evident, it is reasonable to test whether the cumulative selections made across all species
results in detectable preference patterns at the assemblage level. I test these relationships
with a Lepidoptera assemblage in forest fragments of the mixed wood plains in the St.
Lawrence River valley of southeastern Canada. This area has historically experienced
widespread forest habitat destruction; 85% of the original landscape has been cleared of
old-growth forest in favour of agricultural, industrial and urban development (Allen 2001,
Drushka 2003). This type of widespread habitat loss and associated habitat fragmentation
can have significant detrimental effects on both generalist and specialist species (Bender et
al. 1998). With this in mind, one of the biggest challenges is to manage forest fragments in
a way that benefits forest-dwelling species assemblages, maximizing and preserving
species richness in intensively developed landscapes.
Methods
Study Area
The forest fragments I studied were associated with the Monteregian Hills (Feininger and
Goodacre, 1995), in the St. Lawrence River valley in southeastern Quebec, Canada
(45°30'N, 73°30'W to 45°24'N, 72°35'W; Figure 1). During pre-settlement times all these
Monteregian Hills would have had broadly similar forests embedded in a more or less
continuously forested landscape (Richard and Grondin 2009), but now exist as a series of
large forest remnants isolated in the developed landscape. I established eighteen 400 m2
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(20 m x 20 m) quadrats in the remnant forests at each of four sampling sites on
Monteregian Hills.
The first site was at Parc Mont Royal - an urban park in the middle of Montreal, a
city of 3.5 million inhabitants. The forest at the park was cut in the 1950s and early 1960s
(Brunel et al. 2005) and was subsequently reforested up until the early 1990s. The land
area of the park is 190 ha, ~100 ha of which is forested.
The second site was at Parc National du Mont St. Bruno – a protected provincial
park in the eastern suburbs of greater Montreal. The forest at Mont St. Bruno is a
broadleaf deciduous forest covering more than 500 ha of the 790 ha park. Aerial
photography records indicate that 60 or more hectares of forest were cut prior to the
1940s in the northern part of the park, and subsequently replanted or allowed to regrow
(National Air Photo Library 1950). The forest has more than 85 species of woody plants.
The third site was at the Gault Nature Reserve on Mont St. Hilaire – a protected
park and UNESCO Biosphere Reserve, 38 km east of Montreal. The forest at Mont St.
Hilaire is an old-growth broadleaf deciduous forest covering most of the 1000 ha reserve.
This site has a long history of protection dating back to the 1600s (Arii 2004, Maycock
1961).
The fourth site was at Mont Shefford – one of the easternmost Monteregian Hills,
70 km east of Montreal. At the time of this study different parts of the site were in varying
states of disturbance and urban development. I conducted caterpillar sampling in three
sub-sites around the hill. Six quadrats were in a 100 ha semi-disturbed patch of forest on
the west side of the Mont Shefford and six quadrats in a 25 ha patch of forest on the east
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side of the hill set aside as a forested community park. A quarter of the former site was a
sugar bush and there were numerous trails used by deer-hunters throughout the area. The
community park had several recreational trails, but off-trail use was discouraged. Six
quadrats were in a third sub-site, Parc de la Yamaska, a provincial park just north of Mont
Shefford.
Caterpillar Survey and Identification
Caterpillars were collected at 18 quadrats at each of the four study sites. Prior to caterpillar
surveying a vegetation analysis indicated that sugar maple (Acer saccharum) was the most
abundant host plant across sites and was the only host plant that was present at every
quadrat. Therefore, at each of the 72 quadrats, ten sugar maples and up to 10 of all other
tree species between 3 and 10 cm dbh were sampled for caterpillars (Appendix B1). This
sampling method was chosen to survey a representative proportion of host plants among
quadrats. When a given host plant species was abundant in excess of 10 individuals,
sample trees were chosen at random. Each sample tree was surveyed by striking the bole
and lower branches ten times with a 20 oz, 30" aluminum baseball bat and catching
dislodged caterpillars on a 1 m2 sheet. Caterpillar collections were made three times at
each quadrat (between June 1 and June 6, July 4 and July 9, August 3 and August 6 in
2009). This resulted in a total of 2090 sampled trees and 62700 total tree-strikes (2090
trees x 10 tree-strikes per tree x three caterpillar collection periods).
Macrolepidopteran moth caterpillars were identified to species with a dissecting
microscope using Wagner’s Caterpillars of Eastern North America Field Guide(2005).
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Microlepidopteran moths were counted and identified only to morphospecies for lack of
an accurate identification guide. Macrolepidopteran moths collected in early instars were
reared so that positive identifications could be made.
Controlling for Habitat Disturbance
Recent investigations have shown that forest-dwelling caterpillar assemblages are
sensitive to intra-habitat disturbances. Any investigation of herbivore-host plant
relationships should therefore take this into account. White et al.(2011) showed that there
is a consistent negative relationship between recreational trail presence and caterpillar
richness in forest fragments in southeastern Quebec, Canada. They suggested that this
relationship may be due to increases in caterpillar parasitism/predation and/or changes in
trail-side conditions that make trail-side habitat less suitable for caterpillars.Non-native
tree species introductions are sometimes correlated to management and can have a
negative impact on caterpillar species richness and abundance. To control for these effects
I used a variable called Trail Index to measure the impact of trails at each quadrat. Trail
index values for the quadrats in this study were described and calculated in White et al.
(2011).
Analyses
I performed a two-run stepwise (forwards) multiple regression to determine the
impacts of trail index and host plant frequencies on caterpillar abundance in the sampled
quadrats. This type of analysis is very useful when a large number of independent variables
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are used and the goal is to eliminate variables of marginal (or non-) significance. While it
lacks the sophistication of other multivariate statistical methods (e.g. ordination or
regression trees), one advantage it provides is that the final model it computes is
independent of insignificant variables. It assumes a Gaussian distribution of the model
residual values; collinearity in independent variables can be tested with interaction terms.
The decision threshold to include a given independent variable in each step of each
regression was based on p < 0.05. In the first run-through of the stepwise regression, I used
25 candidate independent variables to explain the variance in (log10 transformed)
caterpillar abundances in the 72 study quadrats. This suite of 25 independent variable
consisted of the host plant frequencies (24 species, Appendix A1) and the trail-index value
of each quadrat. I only included host plants that were present at more than two quadrats,
resulting in the exclusion of 13 of the original 38 host plant species. Acer saccharum was
also excluded from the analysis because it is ubiquitous throughout the study region and I
wanted to focus on the impact that additional host plant species had on quadrat caterpillar
abundance. After the first run-through of the stepwise regression, I conducted a second
run-through using the independent variables selected in the first run-through and the
interactions between each of these variables and Trail Index. The standard coefficients and
partial R2 values of the remaining independent variables were then calculated.I conducted
two identical analyses using caterpillar species richness and caterpillar Shannon’s diversity
(Shannon and Weaver 1949) as the dependent variables in place of caterpillar abundance.
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Host Plant and Caterpillar Relationships
I used two simple multiple regressions to test significance and strength of the relationship
between host plants and caterpillars among quadrats. The first compared caterpillar
richness to host plant richness and Trail Index (log10 + 1 transformed); the second
compared caterpillar Shannon’s diversity to host plant Shannon’s diversity and Trail Index
(log10 + 1 transformed). Interaction terms were included in both models to determine
whether there was a relationship between independent variables.
Testing Host Plant-Specific Preferences
The preference of each tree species by the caterpillar assemblage was calculated as a
Caterpillar Assemblage Preference Index (CAPIr) which measures the observed caterpillar
richness in j trees of host plant species i minus the average caterpillar species richness in j
trees drawn at random from the entire host plant-caterpillar dataset. It is calculated as:
1000
[1] CAPIri = Ri −
∑ {r}
n =1
j
1000
where a given tree species i has a cumulative caterpillar species richness R summed across
j trees that were surveyed. In this calculation, r is the caterpillar species richness in a
subset of j individuals selected at random (with replacement) from the entire dataset of all
2090 host plant samples from all host plant species; 1000 subsets of rwere selected and
averaged. CAPIr is essentially the actual caterpillar species richness in host plant species i
with j individuals minus the average (i.e. expected) caterpillar species richness found in j
trees. Thus, if a host plant species with j trees has a CAPIr value of x, it would be said to
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support x more (or less if x is negative) caterpillar species than would be found if a random
sample of j trees was sampled from the set of all trees.
Similarly, the Caterpillar Assemblage Preference Index in terms of caterpillar
abundance (CAPIa) computes the observed caterpillar abundance in j trees of host plant
species i minus the average caterpillar abundance in j trees drawn at random from the
entire host plant dataset. It is calculated as:
1000
[2] CAPIai = Ai −
∑{a}
n =1
j
1000
where A and a are the abundances of host plant species i and of a random tree subset
with j individuals respectively.
For the purposes of this study, the acceptabilityof a host plant species is defined as
the number of caterpillar species in an assemblage that are documented to use it. Host
plant acceptability measures were taken from Wagner (2005) and Handfield (1999). Host
plant acceptability was correlated to CAPIr and CAPIa to test whether caterpillars are
distributed relative to the occurrence of acceptable host plants.
Results
Caterpillar Sampling
I collected 1896 caterpillars including 53 macrolepidoptera species (1305 individuals) and
56 microlepidopteran morphospecies (591 individuals) (Appendix B2; Appendix A3 for
botanical authorities) from 38 different host plant tree species (2090 total trees). The five
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most common trees among quadrats (Acer saccharum, Fagus grandifolia, Fraxinus
americana, Acer pensylvanicum and Ostrya virginiana) yielded 81% of caterpillar catches
and high levels of caterpillar richness (Appendix B1).
Host Plant and Caterpillar Relationships
There was a significant positive relationship between host plant Shannon’s diversity and
caterpillar Shannon’s diversity (Table 1a). Trail Index was also a significant descriptor; the
total model 45% of the variance in caterpillar diversity. Host plant richness however was
not significantly related to caterpillar richness (Table 1b). Trail Index remained a significant
descriptor with the total model explaining 27% of the variance in caterpillar richness.
CAPIr and CAPIa
CAPIr values ranged between 7.9 (Prunus serotina) and -11.6 (Rhamnus cathartica); CAPIa
values ranged between 121.5 (Fagus grandifolia) and -99.7 (Fraxinus americana).
Increasingly positive CAPI scores indicate that a host plant is used by caterpillars more than
average; increasingly negative CAPI scores indicate that a host plant is used by caterpillars
less than average (Table 2). Host plant acceptability documented by Wagner (2005) and
Handfield (1999) did a very poor job explaining the variance in CAPIr and CAPIa (Figure
2a,b). This indicates that there is no detectable connection between acceptable host plants
and preferred host plants in my study system.
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Host Plant Importance Relative to Trail Index
For caterpillar abundance, Acer pensylvanicum, Ostrya virginiana and Fagus grandifolia
had the highest partial R2 values in the step-wise regression. Host plant frequencies
accounted for 21.0% of the variance in caterpillar abundance independent of Trail Index
which accounted for 19.8% of the variance; this includes a significant interaction between
Trail Index and F. americana frequency (Table 2a). The combination of independent
variables accounted for an additional 20.5% of the variance (total R2 of model = 0.61).
For caterpillar species richness, host plant frequencies of Acer pensylvanicum and
Ostrya virginiana accounted for a combined 24.9% of the variance in caterpillar species
richness among quadrats (Table 2b). Trail Index accounted for an additional 22.3% of the
variance; this includes a significant interaction between Trail Index and F. americana
frequency. The combination of independent variables accounted for an additional 11.5% of
the variance (total R2 of model = 0.59).
For caterpillar Shannon’s diversity, host plant frequencies of Acer pensylvanicum,
Ostrya virginiana and Ulmus americana accounted for 27.9% of the variance in caterpillar
diversity (Table 2c). Trail Index accounted for an additional 18.7% of the variance (this
again includes a significant interaction between Trail Index and F. americana frequency);
the combination of independent variables accounted for an additional 9.4% of the variance
(total R2 of model = 0.56).
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Discussion
Host plant identity and abundance were statistically significant and strong predictors of
caterpillar richness. This was contrasted by host plant diversity that had a statistically
significant – but very weak – effect on caterpillar diversity. Tree richness and caterpillar
richness were unrelated. These results are in sharp contrast with the idea that host plant
richness drives insect herbivore richness (Lawton 1983, Southwood et al. 1979). Instead
myresults indicate that host plant identity and abundance are more appropriate measures
for explaining insect herbivore assemblage diversity, richness and abundance. This is a
novel result that has not yet been described in analyses examining the relationship
between host plants and insect herbivores.
Biodiversity and Conservation
My results suggest that reserve management should adopt an approach that identifies and
promotes high biodiversity host plants. This is in contrast with other popular approaches
such as maximizing stand structural complexity, maximizing floral biodiversity and using
natural disturbance regimes (Battles et al 2001, Lindenmayer et al. 2006). Niemela and
Neuvonen (1981, Neuvonen and Nimela 1983) suggested that the most important host
plants in temperate forests for Lepidoptera biodiversity are those with the highest
abundance. This is true in a static sense - in most northeastern broadleaved forests the
sugar maple (Acer saccharum) is the most important for insect herbivores. By virtue of
being the most abundant tree it hosts the highest insect herbivore species richness. But
this is a narrow view that does not take into account low abundance host plants. In
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situations where reserve management has a mandate to manage tree relative abundances
to maximize overall forest health and biodiversity, a more nuanced approach is warranted.
For the range of host plants in northeastern deciduous forest, this suggests that
restoration and replanting efforts should include black cherry (Prunus serotina),
serviceberry (Amelanchier spp.), mountain maple (Acer spicatum), striped maple (Acer
pensylvanicum) and yellow birch (Betula alleghaniensis). In regions where different forest
types persist, a system-specific analysis of tree hosting abilities should be conducted to
identify high and low biodiversity host plants. Pair-wise host plant comparisons can be
useful for this purpose and have been conducted for many common plants (Barbosa et al
2000, Cincotta et al 2009). That said, there is evidence to suggest that herbivore-hosting
capabilities of trees can be conserved across large geographic scales. Moran and
Southwood (1982) found that the relative species richness of insect herbivores and insect
predators were very similar on five tree taxa present in both the United Kingdom and
South Africa. This might suggest that the preference indices I calculated for broadleaved
deciduous forests in southern Canada may be broadly applicable to maple-dominated
broadleaved and mixedwood forests across northeastern North America.
Low Caterpillar Richness and Abundance in Invasive Trees
My results also indicate that invasive trees may be problematic in deciduous broadleaved
forests. The impoverished caterpillar assemblages found on Acer platanoides (Norway
maple) and Rhamnus cathartica (European Buckthorn) add to a growing body of evidence
showing that non-native host plants are a detriment to forest insect assemblages. A.
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platanoides was introduced in the late 1700s (Spongberg 1990) and has periodically been
planted for forest restoration (Larson 1996, Webb and Kaunzinger 1993). However, it often
outcompetes native tree species and is able to invade intact woodlands (Bertin et al. 2005,
Wyckoff and Webb 1996). My results reinforce the trend identified by Cincotta et al (2009)
showing that A. platanoides is not a favoured host plant of forest insect herbivores. Their
results compared A. platanoides to A. saccharum, whereas I show it in relation to other
common sub-dominant host plant trees where it ranked 21st among 24 tree species for
both CAPIr and CAPIa (Table 3). Similarly, Rhamus cathartica ranked 24th and 22nd in CAPIr
and CAPIa, respectively. Research has shown that Rhamnus cathartica is a detriment to
forest communities as it modifies soil nitrogen conditions, reduces leaf litter levels,
propagates the spread of invasive species, is not consumed by many native herbivores and
has allopathic effects on native trees (Heneghan et al. 2004, Knight et al. 2007). While R.
cathartica can be beneficial for sustaining insect populations in disturbed and urban
settings (VanVeldhuizen et al. 2005) its negative association with forest-dwelling moth
populations give further reason for its control in North American forest fragments
(Gassmann 2005, Moriarty 2005). Curiously, Fraxinus americana also had markedly low
CAPIr and CAPIa scores, ranking 22nd and 24th (respectively) among 24 tree species. Species
in the genusFraxinustend to be higher than average in terms of leaf toughness (Ricklefs and
Matthew 1982), support high caterpillar parasitoid loads(Lill et al. 2002) and can have
prohibitively toxic phenolic compounds (i.e. in the case of the closely related Fraxinus
pennsylvanica;Markovic et al. 1996). Despite these, they are however documented as
widely used host plants by caterpillar species (Handfield 1999, Karban and Ricklefs 1984,
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Wagner 2005). The step-wise regression analysis indicated that F.americana has a strong
association with trails at my study sites which when combined with these other deterrents
may have resulted in the low CAPI scores.
From the point of view of forest management for biodiversity conservation, the full
impact of A. platanoides and R. cathartica on forest dwelling moth assemblages can be
enumerated with a host plant replacement simulation. At Mont Royal, caterpillar surveys
included a total of 629 sampled host plants, the most abundant being A. saccharum (180
trees surveyed), F. americana (102 trees surveyed), R. cathartica (48 trees surveyed), T.
americana (48 trees surveyed) and A. platanoides (39 trees surveyed); the remaining 212
surveyed trees were made up of 26 different host plant species. Caterpillar species
richness and abundance collector curves can be created (using the second half of
equations [1] and [2], respectively), first using all trees at Mont Royal and second by
replacing the data collected from A. platanoides and R. cathartica on Mont Royal with data
collected from O. virginiana and A. pensylvanicum from other locations (Figure 3). This
results in an increase of 30% in caterpillar species richness and 40% in caterpillar
abundance. In this exercise, O. virginiana was chosen as a replacement for A. platanoides
because it has a similar abundance (averaged per site) and average height (5.6 meters
versus 5.5 meters). A. pensylvanicum was chosen as a replacement for R. cathartica
because these species are both often subdominant trees associated with disturbed areas
(sun-loving) and are relatively similar in average height (5.4 meters versus 3.8 meters).A
similar replacement simulation can be run for F. americana. While native, it has a CAPIr
and CAPIa scores were amongst the lowest in the host plant data set. When the caterpillar
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data collected from 102 F. americana surveyed at Mont Royal are replaced with data from
F. grandifolia (both are common canopy-contributing species and have similar average
heights; 7.4 meters versus 6.9 meters) caterpillar species richness increases 18%,
abundance 37% (Figure 3).
Mechanisms
Given the important implications of these results, it is worthwhile considering the
ecological mechanisms that may be driving the observed relationships. Since host plant
choices are made by individual Lepidoptera species (and scale up to the assemblage level),
any driving mechanism explaining the assemblage-wide pattern would need to be
operational at the individual species level. The most well established factors used to
explain host plant choices amongst individual species are host plant foliar quality (Feeny
1970, Matteson 1980, Buse et al. 1998), and the presence of natural enemies (Hunter and
Price 1992, Siemann et al. 1998, Lill 2001).
Nitrogen has been identified as one of the preeminent foliar nutrients associated
with insect herbivore host plant selection and performance (Matteson 1980). This makes it
a prime foliar candidate to explain the host plant preferences I observed. One of the most
preferred host plant species in my study, Ostrya virginiana,is documented as having
moderate to high levels of foliar nitrogen compared to other broad-leaved species
(Mertzger 1990). However, the widely preferred host plant Acer pensylvanicum has a
moderate to low level of foliar nitrogen (Zehnder et al. 2009) similar to that of the avoided
host plant Fraxinus americana (Abrams and Mostoller 1995, Côté et al. 2002). One of the
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most nitrogen-rich host plant species Betula papyrifera(Abrams 1998) was also has lower
than average CAPIr and CAPIa values (Table 3). This would be consistent with the findings
of Karban and Ricklefs (1984) who found no relationship between foliar nutritional quality
and caterpillar species richness and abundance in broad-leaved deciduous caterpillar
communities. Although a simple relationship between CAPI values and foliar nitrogen
content does not seem apparent in this case, other foliar variables such as water content
(Scriber and Slansky 1981) and foliar toxins (Gatehouse 2002) have also been commonly
cited as determinants of insect herbivore performance and distribution. In an in-depth
analysis of host plant-insect interactions, Futuyma and Gould (1979) concluded that the
variation in insect populations among hosts is likely due to a multiplicity of plant leaf
chemical variables.
Top-down pressure from parasitoids can also have a significant impact on
oviposition host choices of adult Lepidoptera (Karban and English-Loeb 1997, Thompson
and Pellmyr 1991). Lill et al. (2002) documented parasitoid-host plant-caterpillar
interactions in common host plant genera and showed that the genera Fraxinus and Acer
were associated with higher than expected caterpillar parasitoid loads while the genus
Ulmus with lower than expected caterpillar parasitoid loads. They did not include Ostrya or
Fagus in their analyses. The positive association between Fraxinus and caterpillar
parasitoids could explain the exceptionally low Fraxinus americana CAPIr and CAPIa values.
However, Ulmus americana alsohad lower-than average CAPIr and CAPIa values, even
though this genus is a documented predator-reduced space. This discrepancy could
indicate that parasitism is not the primary driving mechanism of caterpillar assemblage
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richness in Ulmus hosts in my study region. The wide range in CAPIr and CAPIa values for
different Acer species in my data further suggest that a genus-level parasitoid-control of
caterpillar assemblages may not be the dominant driver impacting caterpillar host-plant
preferences. For example, A. pensylvanicum and A. spicatum had significantly positive
CAPIr values (6.1 and 6.4 respectively) but A. rubrum and A. platanoideshad significantly
negative CAPIr values (-6.1 and -9.7 respectively). Similarly, A. pensylvanicum and A.
spicatum had significantly positive CAPIa values (44.5 and 14.6) but A. rubrum and A.
platanoides had significantly negative CAPIa values (-10.3 and -22.3 respectively). If
parasitoid regulation of caterpillars was occurring at the host plant genus level, then
species within a given genera would be expected to have roughly similar CAPIr and CAPIa
values. While it is still possible that host plant caterpillar parasitoid loads may play a role in
driving caterpillar assemblage richness among the other host plant genera, it seems
implausible that they are the sole driving mechanism determining caterpillar species
richness and abundance among Acer or Ulmus host plants in my study.
Conclusion
Understanding the important determinants of insect assemblage richness and abundance
in remnant forest fragments can improve management and conservation efforts. In a
landscape where pristine forest habitat is rare, conservation-based management should
attempt to maximize and maintain the richness in the forest fragments that remain.In this
study, I have shown that the richness and abundance of an insect herbivore assemblage
can be more effectively described in terms of host plant identity and host plant abundance.
159
Describing the insect herbivore assemblage in these terms is more consistent with singlespecies studies that show insect herbivore host choice is often a function of host plant
identity rather than quadrat- or stand-scale host plant richness. In the system I examined,
it does not appear as though top-down parasitoid control is the driving force behind host
choice at the herbivore assemblage level (Lill et al. 2002). It also seems unlikely that host
plant foliar nitrogen content is driving assemblage level host choice. Given that neither of
these two mechanisms seem dominant, it is possible that the assemblage-level host plant
selections are driven by a complex interaction of multiple foliar nutrient properties and
top-down pressure (Mayhew 1997). It is also possible that there is a third factor (e.g.
historical disturbance events) driving both the relative abundance of community host plant
species and Lepidoptera assemblage host plant occupancy. In a direct conservation
application of my results, host plant replacement simulations indicate that planting
preferred host plants in the place of non-preferred host plants could result in a profound
impact on insect herbivore assemblage richness and abundance. At a broader level, these
results call for a shift in conservation management principles where some emphasis should
be placed on identifying and protecting high value host plants that are synonymous with
high levels of insect herbivore richness and abundance.
160
Acknowledgements
I would like to thank P. Peres-Neto, C. Buddle, K. Summerville, J. Donoghue, R. Feldman
and J. Messier for their important reviews. BothM.J. Lechowicz and B. McGill played
integral roles commenting on drafts of this MS throughout its creation. I am grateful to M.
VonButtlar and R. MacKenzie for help with data collection andam indebted to both D.
Rodrigue (Supervisor, Conservation and Education Service at Parc du Mont St. Bruno) and
A. Mochon (Supervisor, Conservation and Education Service at Parc de la Yamaska) for
their help in selecting research locations within their respective parks. This research was
made possible by funds provided by the Natural Sciences and Engineering Research Council
of Canada.
161
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171
Mont RoyalMont St. Bruno
Downtown
Montreal
Mont St. Hilaire
Mont Shefford
CANADA
10 km
U.S.A.
U.S.A.
Figure 1 Caterpillars were collected from four sites in the St. Lawrence River valley of southern Quebec, Canada (Figure adapted
from Atlas of Canada 2010). The matrix surrounding each site isdominated by agricultural lands and urban development with the
exception of Mont Royal, which is a forest fragment in an exclusively urban setting. The dashed boundary represents the Montreal
Metropolitan Community (MMC). Urban development is very dense in downtown Montreal decreasing towards the MMC boundary.
172
Figure 2There was no relationship between the number of caterpillars reported
to use a given host plant and either (a) CAPIr or (b) CAPIa scores. These
relationships are expected to be positive as a host plant’s acceptability should be
indicative of the caterpillar assemblage preference of that host plant relative to
other host plants in the community.
173
174
Figure 3A host plant replacement simulation for (a) caterpillar species richness
and (b) caterpillar richness in the Mont Royal forest fragment. In these
simulations F. grandifolia and A. pensylvanicum were substituted for A.
platanoides and R. cathartica (dashed line) and O. virginiana was substituted for
F. americana (dotted line). The substituted species were chosen because they
had high CAPIr and/or CAPIa scores and commonly share the same general
canopy position as the species they replace. Replacement of invasive species
with O. virginiana and A. pensylvanicum resulted in an increase of 30% in
caterpillar species richness and 40% in caterpillar abundance. Replacement of F.
americana with F. grandifolia resulted in an increase of 18% in caterpillar species
richness and 37% in caterpillar abundance.
175
176
Table 1 The relationship between host plants and caterpillars shows that (a) host
plant (Shannon’s) diversity is a significant descriptor of caterpillar (Shannon’s)
diversity when Trail disturbance is accounted for (total model adjusted R2 =
0.45F3,68 = 9.9) but (b)host plant richness is a non-significant descriptor of
caterpillar richness when Trail disturbance is accounted for (total model adjusted
R2 = 0.27, F3,68 = 20.1).
a) Caterpillar Shannon’s diversity = host Shannon’s Diversity + trail index + interactions
Independent Variable
Standardized
t-value
p-value
Coefficient
Host Shannon’s Diversity
0.32
2.2
0.035
Trail Index
-2.7
-3.0
0.0037
Host x Trail Interaction
0.29
1.1
0.30
b) Caterpillar Richness = host plant richness + trail index + interactions
Independent Variable
Standardized
t-value
p-value
Coefficient
Host Richness
0.11
0.34
0.51
Trail Index
-1.9
-2.2
0.032
Host x Trail Interaction
0.010
0.034
0.97
177
Table 2CAPIa and CAPIr values for host plant trees (sorted in order of decreasing
CAPIr values) are calculated as the difference between the observed and the
average caterpillar abundances and richness in host plant tree species (see
equations 1 and 2). Greater CAPI values indicate that a host plant is more
preferred by the caterpillar assemblage.
178
Host Plant Species
# of Trees
Surveyed
Observed
AB
Average
AB*
CAPIa
Observed
SR
Average
SR*
Prunus serotina
19
38
16.5
21.5†
17
9.1
†
Amelanchier arborea
35
58
30.0
28.0
21
14.6
†
Acer spicatum
20
32
17.4
14.6
16
9.6
†
Acer pensylvanicum
206
221
176.4
44.6
52
45.9
†
Betula alleghaniensis
10
17
8.5
8.5
11
5.1
Cratageus spp
19
15
16.5
-1.5
11
9.1
Carya cordiformis
15
9
12.9
-3.9
9
7.4
†
Ostrya virginiana
145
239
124.0
115.0
39
38.0
Carpinus caroliniana
26
21
19.1
1.9
10
10.3
Malus pumila
11
4
9.4
-5.4
4
5.6
†
Fagus grandifolia
229
317
195.5
121.5
46
48.4
Cornus alternifolia
13
3
11.2
-8.2
3
6.5
Ulmus americana
35
19
30.0
-11.0
11
14.6
Prunus nigra
11
3
9.4
-6.4
2
5.6
‡
Betula papyrifera
18
5
15.6
-10.6
5
8.7
Tsuga canadensis
33
25
28.3
-3.3
10
14.0
‡
Acer rubrum
12
0
10.3
-10.3
0
6.0
‡
Rhus typhina
15
4
12.9
-8.9
1
7.4
‡
Tilia americana
66
24
56.7
-32.7
16
23.1
‡
Quercus rubra
34
11
29.1
-18.1
6
14.3
‡
Acer platanoides
39
11
33.3
-22.3
6
15.7
‡
Fraxinus americana
228
95
194.7
-99.7
38
48.3
‡
Prunus virginiana
55
26
47.4
-21.4
10
20.5
‡
Rhamnus cathartica
48
11
41.1
-30.1
7
18.6
* Average SR and AB values are based on the sample size (# of trees surveyed) for a
given host plant.
**Mostly composed of a mixture of Cratageus punctata Jacq. and Cratageus mollis
Scheele and their hybrids.
†
CAPIa or CAPIr greater than 1 standard deviation above mean expected value.
‡
CAPIa or CAPIr greater than 1 standard deviation below mean expected value.
179
CAPIr
7.9†
6.4†
6.4†
6.1†
5.9†
1.9
1.6
1.0
-0.3
-1.6
-2.4
-3.5
-3.6
-3.6‡
-3.7
-4.0
-6.0‡
-6.4‡
-7.1‡
-8.3‡
-9.7‡
-10.3‡
-10.5‡
-11.6‡
Table 3Host plant frequencies (combined) explained (a) 21.0% of caterpillar
abundance, (b) 24.9% of caterpillar richness and (c) 27.9% of caterpillar
Shannon’s diversity among quadrats. This was independent of trail index which,
when combined with a F. americana interaction term, explained (a) 19.8%, (b)
22.3% and (c) 18.7% of the variances.
a) Caterpillar Abundance = host plant frequencies + trail index + interactions
Independent Variable
Standardized Coefficient
Partial R2
Acer pensylvanicum
0.369
0.108
Ostrya virginiana
0.304
0.083
Fagus grandifolia
0.168
0.019
Log 10 (Trail Index)
-0.610
0.198
Log10 (Trail Index) * F. americana
0.497
Total Model
0.613
b) Caterpillar Richness = host plant frequencies + trail index + interactions
Independent Variable
Standardized Coefficient
Partial R2
Acer pensylvanicum
0.415
0.150
Ostrya virginiana
0.331
0.099
Log 10 (Trail Index)
-0.636
0.223
Log10 (Trail Index) * F. americana
0.556
Total Model
0.590
c) Caterpillar Shannon’s Div = host plant frequencies + trail index + interactions
Independent Variable
Standardized Coefficient
Partial R2
Acer pensylvanicum
0.453
0.175
Ostrya virginiana
0.321
0.094
Ulmus americana
0.189
0.010
Log 10 (Trail Index)
-0.574
0.187
Log10 (Trail Index) * F. americana
0.527
Total Model
0.560
180
Linking Statement 3
In Chapter 3 I showed that there is a strong effect of bottom-up forces on
insect herbivore richness. This mechanism was strongest when described as
assemblage-level preference for specific host plants. Host plant species
richness was a poor predictor of insect herbivore species richness. In addition I
showed that the replacement of non-native host plants could have significant
positive impacts on caterpillar richness and abundance. While this pattern is
described in an exclusively “bottom-up” perspective, it is possible that both
bottom-up and top-down forces are at work. Bottom-up forces can be
described as the quality of foliage – often expressed as the concentration of
foliar nutrients. Top-down forces can be described as the relative occupancy of
enemies in prospective host plants (these enemies more often tend to be
parasitoids in the case of Lepidoptera). Both of these forces may be highly
seasonal in their nature. Foliar quality is usually highest in the beginning of the
growing season when parasitoid abundance is low. Parasitoid abundance is
often high in the latter half of the growing season when foliar quality is low.
The question of how foliar quality impacts caterpillar assemblages thus has an
intra-seasonal temporal component. In Chapter 4 I relate the intra-seasonal
changes in quadrat-scale foliar quality (biotic bottom-up driver) to caterpillar
richness and abundance, andI take parasitoids (biotic top-down driver) into
consideration late in the growing season (August) to examine their impact.
181
CHAPTER4: Intra-Seasonal Relationships between Insect Herbivores and their
Hosts
Peter J.T. White1
1
McGill University, Department of Biology
1205 Dr. Penfield Ave., Montreal, Quebec
H3A 1B1, Canada
182
Abstract
Research has often linked host plant foliar qualities to insect herbivore
distribution and performance but rarely have these factors been related to the
richness or abundance of an entire caterpillar assemblage or examined in a
seasonal context. This “bottom-up” effect can often be complicated by a “topdown” effect driven by the presence of natural enemies. The purpose of this
research is to examine how a caterpillar assemblage in temperate broadleaved
forest fragments is related to quadrat-scale foliar properties in three sampling
windows within a growing season (early June, early July and early August). In
addition, I test whether top-down parasitoid pressure supersedes foliar quality
as the prime predictor of caterpillar assemblage richness and abundance in the
third (August) sampling window. A regression tree analysis suggests that
quadrat-scale foliar polyphenol content was important for caterpillar richness
and abundance in June and August as a negative control. Foliar phosphorus was
the primary predictor for the month of July. Parasitoid pressure superseded
polyphenol content as the primary predictor of caterpillar richness in August.
Since the foliar quality data, the parasitoid data and the caterpillar assemblage
data I use come from different sources, my results should be interpreted with
caution. However, there is good evidence to suggest that foliar quality plays an
important role in affecting caterpillar assemblages early in the growing season,
but that the nature of this effect differs throughout the growing season and can
depend on parasitoid presence.
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Introduction
Determining the factors that affect insect herbivore distribution and
performance has been an important topic in ecological research over the past
four decades (Scriber 1978, Scriber 2010, Strong et al 1984, Wint 1983). These
factors mostly fall into two categories: (i) bottom-up effects that pertain to the
nutritional or structural quality of foliar resources and (ii) top-down effects that
pertain to natural enemies finding and killing (or parasitizing) insect herbivores
(Hunter and Price 1992, Mayhew 1997). In recent years, research has aimed at
better understanding how these two forces shape insect herbivore communities
given their high diversity (Wilson 1987) and the essential role they play in food
web structure (Murakami and Nakano 2000, Vasconselos et al. 1996, Goiti et al.
2009) and ecosystem nutrient cycling (Schowalter et al. 1986). In this study, I
hope to address two important gaps in the understanding of these tri-trophic
relationships – namely (i) the effect of top-down and bottom-up forces on entire
insect herbivore assemblages and (ii) the intra-seasonal variation in the nature
and magnitude of top-down and bottom-up forces.
Bottom-Up Effects: Foliar Quality
A bottom-up effect occurs when the distribution or performance of insect
herbivores is controlled or limited by aspects of foliar quality (Power 1992) –
often defined by the concentration of specific nutrients present in edible foliage
(Amwack and Leather 2000, Forkner and Hunter 2000). While different insect
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herbivore species have different nutrient needs, foliar properties such as high
nitrogen content and high water content are generally identified as positive
controls of insect herbivore performance and distribution; high toughness and
polyphenol (toxin) content are generally identified as negative controls (Amwack
and Leather 2002, Choong 1996, Feeny 1970, Matteson 1980, Schowalter et al.
1986, Scriber 1977, Scriber 1878, Scriber and Slansky 1981). Feeny (1970) was
among the first to describe this relationship for Lepidoptera larva (caterpillars)
when he showed that Winter Moth (Operophtera brumata) early season
herbivory was limited by available foliar nitrogen content but late season
herbivory was limited by foliar tannin (toxin) content. Subsequent experiments
also identified that foliar water content is an important driver of caterpillar
performance (development times, weights, etc.) and caterpillar distribution (host
plant choices) (Amwack and Leather 2002, Scriber and Slansky 1981). A third
foliar quality, phosphorus, has more recently been identified as important to
insect herbivore development (Perkins et al. 2004, Woods et al. 2002). While
phosphorus is not generally considered of primary importance (like nitrogen or
water content) its impact has a strong mechanistic basis for being a positive
control as it is used in rRNA formation, which may be very important for fastgrowing organisms like caterpillars (Elser et al 1996). Of the negative control
foliar nutrient controls, high foliar “toughness” can cause insect herbivores to
eat foliage at a slower rate and interfere with gut processes (Clissold et al. 2009).
Slower development times may also make them vulnerable to predators and
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parasitoids for longer. Toughness is often described as a function of foliar lignin
content and foliar fiber content; when these are present in high concentrations,
they can result in host plant avoidance by gravid females (Rausher 1981 Slansky
and Scriber 1981, Schowalter et al. 1986).
Bottom-Up Effects: Foliar Toxins
Another class of foliar nutrients, toxins (polyphenols, alkaloids and terpenes)
have been both positively and negatively linked to host plant selection
depending on the scenario. Feeny (1970) identified three mechanisms by which
these types of toxic compounds can negatively affect caterpillars: (1) by acting as
a repellent to discourage oviposition, (2) by interfering with protein synthesis
and functioning and (3) by acting as a direct toxic compound. A fourth
mechanism of impact has since been observed in cases where toxic compounds
act as attractants to herbivore enemies, particularly parasitic wasps (DeMoraes
et al 1998, Hoballah and Turlings 2001). Empirical tests have often shown
diminished larval growth rates, lower larval weight, higher larval mortality and
lower overall fitness associated with feeding on foliage high in toxin content (for
reviews see: Amwack and Leather 2002, Pasteels et al. 1983, Schowalter et al
1986, Scriber and Slansky 1981). The ability of some insect herbivores to
sequester plant toxins as a defense against predators and parasitoids is often
juxtaposed to this (Blum 1983, Duffey 1980, Nishida 2002). For example, the
wooly bear caterpillar (Grammia geneura) can increase its toxicity to natural
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enemies by feeding on a diet highin pyrrolizidine alkaloids (Singer 2004a). This
can be at the expense of feeding on host plants that result in faster
development, but can be an advantageous behaviour where enemies are
common. The eastern tent caterpillar (Malacosoma americanum) can sequester
foliar hydrocyanic acid found in Prunus host plants (e.g. black cherry, P. serotina
and choke cherry, P. virginiana), making their active defense against predators
more effective (i.e. a more toxic regurgitation when attacked) (Peterson et al.
1987). There are many other such examples in the literature (e.g. Boppré 1990,
Bowers 1992, del Campo et al. 2005, Marsh and Rothschild 1974, Moore et al.
1990) but Pasteels et al. (1983) contend that this phenomenon occurs far more
frequently in monophagic and oligophagic insect herbivores than in polyphagic
herbivores. To my knowledge this assertion has not been verified and since the
ecology and life history of many insect herbivore species is poorly known (with
the exception of well-documented defoliators), it is difficult to formally test.
Top-Down Effects
A top-down effect occurs when the distribution or performance of insect
herbivores is controlled or limited by a natural enemy – often a predator or a
parasitoid (Hunter and Price 1992). Top-down effects can play a direct role in
regulating insect herbivore population size (Holmes et al. 1979, Hooks et al.
2003, Mols and Visser 2002, Sanz 2001) and their presence may force insect
herbivores to make sub-optimal host plant choices in search of “enemy free”
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space (Jeffries and Lawton 1984, Lill et al. 2002). This type of relationship has
also been framed in the context of density-dependence – a high density of
caterpillars (presumably on a high quality host plant) can become a magnet for
would-be predators and parasitoids (Mayhew 1997). Because of this, some
caterpillar species change their choice behaviour in favour of lower quality hosts.
There is strong evidence to support this indirect negative effect of natural
enemies on caterpillars. In northeastern North American forests tree genera with
high parasitoid loads tend to have fewer caterpillar species than genera with low
parasitoid loads (Lill et al. 2002). The pine beauty moth (Panolis flammea) has a
higher survival rate on low quality host plants because of the prominence of
parasitoids associated with high quality host plants (Leather and Walsh 1993).In
field ecosystems, insect herbivore diversity is initially determined by host plant
diversity (i.e. at initial community assembly), but it is regulated predator and
parasitoid density thereafter (Siemann et al. 1998).
Current Gaps in Foliar Quality Research
There are two gaps in host plant host plant-caterpillar research that need to be
addressed. First, the vast literature on foliar quality-caterpillar relationships has
focused primarily on how foliar quality impacts single or only a few caterpillar
species (e.g.Camara 1997, Clancy and King 1993, Feeny 1970, Hagen and Chabot
1986, Hough and Pimentel 1978, Hunter and Lechowicz 1992, Karban and
English-Loeb 1997, Lill and Marquis 2001, Rausher 1981, Singer et al. 2004a,
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Woods et al. 2002). These types of studies are often of limited utility in
management or conservation contexts because they are difficult to apply to
larger species groups. Important pests such as Alsophila pometaria, Lymantria
dispar, Malacosoma disstria, Hyphantria cunea or H. textorare often studied but
overall caterpillar assemblages are rarely considered (but see Karban and
Ricklefs 1984). In other contexts, assemblage-based approaches have been very
successful in describing factors that affect richness and abundance.For example,
in a landscape-disturbance context, assemblage based approaches have
demonstrated that species richness in moth assemblages is significantly higher in
agricultural fields adjacent to forest habitat compared to those that were distant
(Ricketts et al. 2001). In a forest fragmentation context, an assemblage-based
approach was used to show how the host plant composition of a given fragment
offset the species-loss expected due to small fragment size (Summerville 2004,
Summerville and Crist 2003). Still other contexts exist (e.g. host plant
preferences - White, Chapter 3), but an assemblage-based approach has not yet
been used to examine bottom-up and top-down effects on Lepidoptera. An
assemblage-based approach also has the advantage of including
microlepidopteran species that are rarely studied because their taxonomy is very
poorly known, even in well-studied locales.
A second gap in our knowledge is that the impact of foliar quality on
caterpillars is not often examined in a seasonal context. This is in part a function
of the first gap (studies with one or few species); most species are very short
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lived and it is only possible to study them in their limited seasonal timeframe.
Yet, at the Lepidoptera assemblage level it is well known that significant
composition changes occur over the course of the growing season - to such a
degree that experimental designs have to take this factor into account. Because
of these intra-seasonal compositional changes, it is possible that the balance
between top-down and bottom-up regulators may change as well. It is not wellknown how early season versus late season caterpillars distribute themselves
with respect to foliar qualities. In one rare example, Niemela and Haukioja
(1982) showed that host plant use patterns among Finnish Lepidoptera change
from the beginning to the end of the growing season. They attributed these
changes to the timing of shoot growth patterns throughout the season, but did
not have the data to implicate specific changes in foliar qualities. There are some
examples of single insect herbivore species changing their feeding habits from
early to late in their flight seasons – often tracking changes in host plant quality
(e.g. Feeny 1970, Rausher 1981) – but this falls under the purview of the “first
gap” above (i.e. most studies focus on one or few caterpillar species).
Many tree species in temperate broadleaved forests have very significant
seasonal trends in foliar qualities. Ricklefs and Matthew (1982) documented
foliar qualities of 34 broad-leaved deciduous tree species in southern Canada
and showed that average foliar nitrogen dropped by 19% and average leaf water
content dropped by 14% from early June to August (Table 1). Since nitrogen and
water content levels are often related to faster caterpillar development times
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and larger population sizes (Matteson 1980, Scriber 1977, Scriber and Slansky
1981)a loss of nitrogen- or water-rich host plants could have significant
consequences. Phosphorus, linked to caterpillar development, is 47% lower in
early July than in early June and also 41% lower in early July than in early August
(Table 1). Toughness, lignin content and fiber content also have strong intraseasonal variation. Toughness peaks late in the growing season whereas lignin
content and fiber content peak mid-season (on average) (Table 1); all three are
lowest early in the growing season.
Like foliar quality, parasitoid (or predator) pressure can be highly
seasonal, peaking multiple times or in the latter half of the growing season
(Correa-Ferreira and Moscardi 1995, Damman 1987, Kato 1994, Liu et al. 2000,
McAuslane et al. 1993, Okada 1989, Peña et al. 1996, Wong et al. 1984). It thus
becomes quite difficult to analyze something like herbivorous caterpillar richness
in a system where both foliar quality and parasitoid pressure are seasonally
variable. For this type of analysis seasonally sensitive tri-trophic models may be
warranted.
Tri-Trophic Relationships
Tri-trophic relationships occur when both bottom-up pressure (effects of foliar
quality) and top-down pressure (effects of natural enemies) impact the
performance or distribution of an herbivorous species (Leibold 1989). While the
strength and significance of top-down and bottom-up forces vary depending on
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the system in question (as discussed in the sections above), many authors argue
for the primacy of bottom-up forces pointing out that all levels of the tri-trophic
system will cease to exist in the absence of the lowest trophic level (i.e. no host
plants = no herbivores = no natural enemies) butnot in the absence of the
highest trophic level as herbivores can continue to graze on plants in the
absence of natural enemies (Hunter and Price 1992, Power 1992, Siemann et al.
1998). Primacy aside, the seasonal nature of bottom-up foliar quality and topdown natural enemy abundance suggests that tri-trophic relationships should be
studied in seasonal contexts. This is particularly appropriate when the unit of
examination is a species assemblage and the period of examination is an entire
foliar growth-season. In one of the few studies examining tri-trophic
relationships in a seasonal context, Kato (1994) showed that a bi-voltine insect
herbivore was distributed with respect to foliar quality in the first generation
when parasitoids are absent, but distributed with respect to top-down forces
(i.e. parasitoid presence) in the second generation when parasitoids are
abundant. Further investigation is required to test if this type of pattern applies
to an entire herbivorous assemblage.
Objective
The objective of this study is to use an assemblage-based approach to examine
how caterpillar richness and abundance are related to foliar quality over the
course of a growing season and, in addition, to determine if top-down parasitoid
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pressure impacts this relationship. The base expectations are that caterpillar
richness and abundance will be (i) positively related to foliar nitrogen content,
water content and phosphorus content, (ii) negatively related to foliar toxin
content, toughness, lignin content and fiber content, (iii) positively related to
toxin content in the scenario where assemblage-level toxin sequestration occurs,
and (iv) negatively related to parasitoid pressure. I hypothesize that the relative
effects of these factors will change over the course of the growing season.
Specifically, positive foliar properties (nitrogen, water content and phosphorus)
should play a stronger role determining caterpillar distribution amongst trees
early in the growing season when quality is high; negative foliar properties
(toughness, lignin content, fiber content) should play a stronger role late in the
growing season. Parasitoid pressure should also have a higher impact on
caterpillar richness and abundance late in the growing season when parasitoids
tend to be more common. Given that the impact of foliar polyphenol content
could be either positive or negative it is difficult to predict how the magnitude of
its impact may change over the growing season.
Methods
Study Area
I surveyed caterpillars in four forest fragments located on the Monteregian Hills
located in the St. Lawrence River valley in southeastern Quebec, Canada
(45°30'N, 73°30'W to 45°24'N, 72°35'W; Figure 1). During pre-settlement times
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all these Monteregian Hills would have had broadly similar forests embedded in
a more or less continuously forested landscape (Richard and Grondin 2009).
They now exist as a series of remnant forest fragments isolated in a developed
landscape. Eighteen 400 m2 (20 m x 20 m) quadrats were placed randomly within
each of the four sampling sites on Monteregian Hills.
The first site was at Parc Mont Royal - an urban park in the middle of
Montreal, a city of 3.5 million inhabitants. The park has a secondary-growth
forest that covers approximately 100 of 190 total hectares. The second site was
at Parc National du Mont St. Bruno – a protected provincial park in the eastern
suburbs of greater Montreal. The forest at Mont St. Bruno is a secondary-growth
forest that covers more than 500 ha of the 790 ha park. The forests are only
minimally disturbed by a series of trails designated for public use. The third site
was at the Gault Nature Reserve on Mont St. Hilaire – a protected park and
UNESCO Biosphere Reserve, 38 km east of Montreal. The forest at Mont St.
Hilaire is primarily an old-growth broadleaf deciduous forest covering most of
the 1000 ha reserve. This site has a long history of protection dating back to the
17600s (Arii 2004, Maycock 1961). The fourth site was at Mont Shefford – one of
the easternmost Monteregian Hills, 70 km east of Montreal. I conducted
caterpillar sampling in three sub-sites around the hill. Six quadrats were in a 100
ha semi-disturbed patch of forest on the west side of the Mont Shefford and six
quadrats in a 25 ha patch of forest on the east side of the hill set aside as a
forested community park. A quarter of the former site was a sugar bush and
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there were numerous trails used by deer-hunters throughout the area. The
community park had several recreational trails, but off-trail use was discouraged.
Six quadrats were in a third sub-site, Parc de la Yamaska, a provincial park just
north of Mont Shefford.
Caterpillar Surveys and Identification
Caterpillars were surveyed at each quadrat in three time periods in the growing
season of 2009: (i) June 1 to 6, (ii) July 4 to 9 and (iii) August 3 to 6. At each
quadrat, ten sugar maples (Acer saccharum) and up to 10 of every other tree
species between 3 and 10 cm dbh were sampled for caterpillars (Table 2). Each
target tree was sampled by striking the bole and lower branches ten times with a
20 oz, 30" aluminum baseball bat; dislodged caterpillars were caught on a 1 m2
sheet held under the point of striking. All macrolepidopteran caterpillars
collected were identified to species with a dissecting microscope using Wagner’s
Caterpillars of Eastern North America Field Guide(2005). Microlepidopteran
caterpillars were counted and identified to morphospecies for lack of an
accurate identification guide. Any caterpillar that was collected in an early instar
was reared to a late instar so that positive identifications could be made.
Measuring the Bottom-Up Effect
I computed the foliar quality of the trees surveyed at each quadrat using
seasonal leaf properties reported by Ricklefs and Matthew (1982) for the study
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species in similar forests in southern Canada. From their data, the foliar qualities
I calculated for each quadrat were: percent water content, toughness (measured
in grams), percent polyphenol content, percent nitrogen content, percent
phosphorus content, percent lignin content and percent fiber content. In
quadrats where more than one tree species was present (i.e. all but two
quadrats), quadrat foliar qualities were averaged across host plant species
weighted by their relative basal areas in a given quadrat. In other words, the
caterpillar assemblage is related to an aggregated estimate of foliar quality in
each quadrat; this is an approach routinely used in assessing the relationship
between foliar traits and ecosystem functions such as decomposition (Kakazou
et al 2006). Quadrats that contained host plants undocumented by Ricklefs and
Matthew (1982) were included in the analysis only if three or fewer trees were
undocumented. This resulted in foliar quality profiles being constructed for 52 of
72 possible quadrats. The tree species undocumented by Ricklefs and Matthews
which resulted in the exclusion of quadrats were: Abies balsamea, Acer
platanoides, Acer saccharinum, Acer spicatum, Amelanchier arborea, Crataegus
spp., Dirca palustris, Picea glauca, Rhamnus cathartica, Prunus nigra, Robinia
pseudoacacia and Tsuga canadensis.
Measuring the Top-Down Effect
An index of parasitoid pressure was created using the Forest Insect
Survey data compiled by Natural Resources Canada. These data record the
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occurrence of several common species of caterpillars sampled from coniferous
and mixed wood forests across Ontario and Quebec between 1937 and 1949.
The focal caterpillar species that Natural Resources Canada provided records for
were: Acleris variana, Archips cerasivorana, Caripeta divisata, Hyphantria cunea,
Lambdina fiscellaria, Nepytia canosaria and Semiothisa granitata (now Macaria
granitata). Each of these species has a set of collection records documenting
caterpillars collected on specific dates from specific host plants at specific
locations. Each record also documented the number of parasitoids (and
sometimes parasitoid taxa or species) that were observed emerging from
collected caterpillars. The parasitoid emergence data associated with these
records were divided into three subsets to match the three sampling periods
that I used for my caterpillar collecting; a two week buffer was given around
each sampling window for matching parasitoid emergence dates. Parasitoid
emergence data were then pooled across the seven focal caterpillar species so
that a parasitism likelihood score for each host plant could be created, simply
calculated as the number of parasitoids observed divided by the number of
caterpillars observed (both specific to a given host plant) multiplied by 100 to
give the percentage of samples that were parasitized. There were not enough
caterpillar collection records from June and July to construct a comprehensive
set of host plant specific parasitoid pressure scores, so these months were
omitted from the analysis; only 7 of my host plants were sampled by Natural
Resources in the June sampling window and 11 in the July sampling window (see
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Table 2 for a complete list). Finally, quadrat-specific parasitoid pressure values
were calculated as the average parasitoid pressure of all trees present in each
given quadrat.
Analysis
Quadrat-scale foliar qualities were used in a regression tree analysis
(De’Ath and Fabricus 2000) to describe caterpillar richness and abundance across
quadrats in June, July and August. Regression trees are good instruments to use
when the dependent variable may not be linearly related to one or more
independent variables homogenously throughout its numeric range. For
example, when caterpillar richness is low it may have a strong relationship with
nitrogen content and water content, but when it is high it may be limited by
toughness and lignin. One of the advantages of regression trees is that they
require no foreknowledge of the nature of the relationships between dependent
and independent variables prior to analysis. In my case, a regression tree analysis
is an appropriate choice given that the relationship between caterpillar richness
and foliar nutrients is likely complex and non-linear (Futuyama et al 1984, Karban
and Ricklefs 1984). Prior to regression tree computation, caterpillar abundance
was log10 transformed to reduce the impact that outlier data points had on
dataset variance. A second set of regression trees were calculated for August,
including August parasitoid pressure values at each quadrat. Regression trees
were pruned to omit nodes that had a complexity parameter of less than 0.05.
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Methodological Assumptions
There are two assumptions inherent to the methodology of calculating
parasitoid pressure that should be taken into consideration before examining the
regression tree results. First, I made the assumption that parasitoid host plant
pressures in 2009 are similar to those observed between 1937 and 1949. In some
areas, forests may have undergone significant compositional changes in the past
six decades that may have caused certain parasitoids to adjust their preferred
host plant targets for finding caterpillar hosts. For example, the caterpillar
species Archips cerasivorana was often found on Prunus (cherry) host plants. If
cherry trees have been significantly reduced in forest fragments over the past 60
years Archips may have adjusted its feeding habits to incorporate a much larger
proportion of other host plant species. It is not known, were this the scenario,
whether parasitoids that typically parasitize Archips and other Prunus feeders
would be associated with Prunus in the same way they were from 1937 to 1949
or if they would become more closely associated with other host plant species.
Even though most of the caterpillar species I analyze in this study are widely
polyphagous, changes to caterpillar feeding preferences over the past 60 years
could bring the parasitoid-association data into question because it is derived
from parasitized caterpillar capture records.
Second, the caterpillar assemblage I draw parasitoid data from consists of
only seven species. I am assuming that the parasitoid-host plant associations
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observed amongst these seven species are representative of the parasitoid-host
plant associations present throughout the entire caterpillar assemblage. The
accuracy of both these assumptions is not immediately testable, but is an
important consideration. Extensive searches failed to find additional data with
which to test the questions presented in this chapter. Collecting parasitoid data
myself was not feasible given the timeframe and funding available for this
project. As such, the choices that remained were to conduct the analyses as
described with the understanding that any results may require further testing
before definitive conclusions are drawn, or to leave parasitoids out of the
analysis. I chose the former.
Results
In the June sampling period, surveys yielded 845 caterpillars from 26
macrolepidoptera species and 18 microlepidoptera morphospecies. In the July
sampling period, surveys yielded 489 caterpillars from 25 macrolepidoptera
species and 25 microlepidoptera morphospecies. In the August sampling period,
surveys yielded 562 caterpillars from 32 macrolepidoptera species and 35
microlepidoptera morphospecies. May and June shared 22 of a combined 72
species; July and August shared 26 of 91 combined species; June and August
shared 16 of 95 combined species.
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Bottom-Up Foliar Quality
Quadrat water content, nitrogen content and phosphorus content is highest in
June (Figure 2a, b, c). Both water content and nitrogen content are lowest in
August; phosphorus content is lowest in July. Quadrat toughness, fiber content
and lignin content are lowest in June (Figure 2d, e, f). Both lignin and fiber
content peak in July while toughness peaks in August. Average quadrat
polyphenol content is lowest in August and highest in July (Figure 2g).
Top-Down Pressure
Parasitoid pressure is highest in Acer saccharum (13.6%), Ulmus americanum
(13.3%) and Tsuga canadensis (11.9%). It is lowest in Acer pensylvanicum (0%),
Cratagus spp (0%) and Fagus grandifolia (0%). These three host plant species
also had a very low sample size (of caterpillars) within the Natural Resources
Canada data (Table 3), suggesting that the results may be unreliable. Further
testing shows no evidence a significant correlation between the number of
caterpillars collected and parasitoid pressure across host plants (i.e. correlating
column 2 and column 4 in Table 3: p = 0.19, R2 = 0.09, F1,19 = 1.85).
Seasonality of Bottom-Up Effects
Regression tree analyses of the June sampling period showed that foliar quality
explained 0.41 of the variance in caterpillar species richness and 0.44 of the
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variance in caterpillar abundance among quadrats (Figure 3a, b). The most
important foliar quality in both regression trees was polyphenol content, which
explained 0.33 of the variance in caterpillar richness and 0.44 of the variance in
caterpillar abundance. Quadrats where the polyphenol content was greater than
10.9% (left hand fork of the regression tree 3a, b) had significantly fewer species
and total caterpillars compared to quadrats where polyphenol content was
lower. Phosphorus content explained 0.10 of the variance in the caterpillar
richness regression tree (positive relationship, regression tree 3a).
In the July sampling period, regression tree analyses showed that foliar
quality explained a 0.42 of the variance in caterpillar species richness and 0.44 of
the variance in caterpillar abundance among quadrats (Figure 3c, d). In both
cases, phosphorus content was the most important foliar quality; sites with high
phosphorus content (greater than 0.19%) had lower caterpillar richness and
abundance than sites where phosphorus content was lower. The similarity
between these two regression trees can be attributed to a strong correlation
between caterpillar richness and abundance among quadrats in July (R = 0.82,
F1,50 = 2.27, p< 0.0001). Lignin content was a significant variable in both trees,
positively linked to caterpillar richness and abundance, but explaining only 0.06
and 0.10 of the variance respectively. Polyphenol content explained an
additional 0.06 of the variance in the caterpillar richness tree (Figure 3c).
In the August sampling period, regression tree analyses showed that
foliar quality explained 0.30 of the variance in caterpillar species richness and
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0.20 of the variance in caterpillar abundance among quadrats (Figure 3c, d). The
most important foliar quality in the caterpillar richness tree was fiber content.
Quadrats where fiber content was less than 28.5% (left hand fork of regression
tree 3e) contained fewer caterpillar species than quadrats where fiber content
was higher. For caterpillar abundance, quadrats that had high polyphenol
content (greater than 10.3%) had significantly fewer species than quadrats
where polyphenol content was lower. Polyphenol was additionally important in
the caterpillar richness regression tree and provided a second node in the
caterpillar abundance regression tree. Lignin content explained a small amount
of variance in the caterpillar richness regression tree.
Tri-Trophic Relationship
When parasitoid pressure was added to the August analyses, there were notable
changes to both regression trees. Quadrats where parasitoid pressure was
greater than 6 % had significantly lower caterpillar richness and abundance than
quadrats where parasitoid pressure was lower. In the caterpillar richness
regression tree parasitoid pressure explained 0.28 of caterpillar variance (Figure
4a), superseding all of the foliar quality variables that were significant in Figure
3e. In the caterpillar abundance tree parasitoid pressure superseded polyphenol
content for the primary regression tree node explaining 0.18 of the caterpillar
variance (Figure 4b). Water content and phosphorus content explained smaller
proportions of caterpillar abundance (0.10 and 0.06 respectively).
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Discussion
There appears to be a transition in the nature of bottom-up effects over the
course of the growing season. Early in the season, low polyphenol content is the
most important variable in regression tree models. In the middle of the summer,
foliar phosphorus contentbecomes the most important variable, with low levels
corresponding to high levels of caterpillar richness and abundance. By late
summer, polyphenol and fiber content are both important, but when a tritrophic relationship is considered top-down parasitoid pressure supersedes both
foliar variables to explain a higher proportion of the variance in caterpillar
richness and abundance. There was no evidence to support the hypothesis that
polyphenols are being selected for at the assemblage level, which would be
expected if toxin sequestration for defense was a primary driver of
caterpillarrichness or abundance among quadrats. Instead, the opposite was
prevalent (a negative relationship between assemblage richness/abundance and
polyphenol content). This set of results should however be interpreted with
caution and as a preliminary finding rather than as a definitive test of these
relationships across seasons.
Bottom-Up: Foliar Quality
Traditionally, nitrogen and water content have been identified as the more
important foliar drivers of caterpillar distribution and performance; the negative
effect of phosphorus I identified has received little attention. The insignificance
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of nitrogen and water content in most intra-seasonal models could suggest that
nitrogen and water content are not limiting foliar traits for caterpillars in
northeastern temperate forests. These variables are often associated with
feeding preferences in feeding trials, but the cost associated with redispersal in
search of more nitrogen-rich and water rich foliage may outweigh the benefits.
This non-effect of nitrogen and water content was also reported by Karban and
Ricklefs (1984) who examined Lepidoptera assemblages in a similar forest type
using the same foliar nutrient data. They concluded that top-down forces (i.e.
parasitoids) or historical factors (i.e. that limit geographical distribution) might
instead be responsible for Lepidoptera distribution amongst trees. My analysis
agrees with this suggestion for the month of August (i.e. late in the growing
season) but identified polyphenol content and phosphorus content as the driving
nutrient foliar properties for June and July regression trees.
The negative relationship between foliar phosphorus and caterpillars has
a few possible explanations. The expectation was for a positive relationship,
reflecting increased growth and shorter instar durations observed in some
species (Apple et al. 2009, Perkins et al. 2004). One possibility is that foliar
phosphorous is linked to other foliar qualities and the negative link described in
the regression trees is merely coincidental. To test this, I ran a multiple
regression analysis of quadrat foliar qualities (in July) where phosphorus content
is a function of lignin content, fiber content and toughness. The results of this
regression identify lignin content and toughness as significant positive predictors
205
of phosphorus content (p < 0.005, adj R2 = 0.24, F3,48 = 6.22) while fiber content
is a negative predictor. Both toughness and lignin content make leaves less
palatable to caterpillars (Choong 1996, Scriber 1977). Therefore, it is possible
that quadrat-selection by Lepidoptera is driven by preference for low toughness
and low lignin content and the negative relationship between caterpillars and
phosphorus is merely a byproduct. This is exemplified by the foliar qualities of
three very common host plants: striped maple (Acer pensylvanicum)
hophornbeam (Ostrya virginiana) and white ash (Fraxinus americana). Striped
maple and hophornbeam are highly preferred host plants for caterpillars
whereas white ash is often avoided (White - Chapter 3); these three host plant
species make up nearly 30% of the host plants I sampled. According to Ricklefs
and Matthew (1982) the former two species have low toughness (453 g and 539
g respectively) and low phosphorus content (0.12 % and 0.16 % respectively) and
the latter species has high toughness (672 g) and high phosphorus content (0.25
%) (all are July values). A similar pattern is evident with lignin content.
Another possible explanation of the observed negative relationship
between phosphorus and caterpillars is that the impact of dietary phosphorus is
being mediated by another dietary element. Clancy and King (1993) suggest that
this may be the case with the spruce budworm and identify dietary magnesium
as the likely mediating element. In their research they showed that there was a
negative relationship between dietary phosphorus intake and pupal mass and
survival among caterpillars at high levels of dietary magnesium intake. At my
206
study quadrats the ratio of P to Mg in June is 1.64; in July it is 0.81 reflecting a
38% increase in magnesium and a 32% decrease in phosphorus from June to July
(data not shown). It is not known whether or not (or to what extent) Clancy and
King’s (1993) research on a conifer-feeding caterpillar species would be relevant
to a deciduous-feeding caterpillar assemblage, but the dramatic change in
relative foliar magnesium and potassium I observe among quadrats is consistent
with a magnesium-mediating-phosphorus scenario.
Bottom-Up: Toxins
The negative relationship between the caterpillar assemblage and foliar
polyphenol content is an example of where processes applicable to singlespecies are not scalable to entire assemblages. Foliar polyphenol content levels
were derived from data published by Ricklefs and Matthew (1982),who state
that the method they used to measure polyphenol content in leaves was “not
specific for polyphenols but may also measure flavonoids and other easily
oxidized compounds…”Thus, high polyphenol content levels in any two given
host plant species may be representative of very different toxic compounds that
may be tolerated (or sequestered) by different caterpillar species. While many
examples of species-specific toxin sequestration exist (e.g. Barbehenn et al.
2005, Nishida 2002, Marsh and Rothschild 1974, Peterson et al. 1987, Singer
2004),the relationship between an entire insect herbivore assemblage has not
yet formally been explored. Most of the positive relationships between
207
caterpillars and toxins are very species specific and unrelated Lepidoptera rarely
tolerant to the same toxin (Nishida 2002). These types of species- and generaspecifictoxic compound tolerances may explainthe nature of the negative
relationship between caterpillars and quadrat-scale polyphenols; caterpillartoxin relationships do not scale up to entire assemblages. An increase in quadratscale “polyphenols” would thus only truly be beneficial for caterpillars if the
group of compounds in question could be sequestered by most of the
caterpillars in the assemblage, which is not often the case (Pasteels et al. 1983).
Top-Down: Parasitoids
My analyses suggest that late-season top-down control of caterpillar
assemblages may be occurring. It was not possible to examine parasitoid data in
a seasonal context but the August analyses (Figure 4a, b) showed that top-down
parasitoid pressure was negatively associated with caterpillar richnessand
abundance late in the growing season. A negative impact of parasitoids or
predatorson caterpillars is well established,(Gripenberg and Roslin 2007, Hunter
et al. 1997, Preszler and Boecklen 1996, Scheirs and Bruyn 2002) but it has rarely
been examined in relation to changes in bottom-up variables throughout a
growing season. This raises questions about my June and July analyses– would
the regression trees in Figure 3a-d look different if parasitoid data were taken
into consideration? The reason that August parasitoids were the only ones
included in my analyses was because of the limited parasitoid data within June
208
and July caterpillar records. I decided to try to address this question further with
a simple post-hoc analysis examining an additional parasitoid dataset provided
by Natural Resources Canada that detailed parasitoid records (without host plant
associations) from the same period (1937 to 1949). Out of 487 emergence
records in this dataset from mid-May to mid-August pooled across 13 parasitoid
species, 293 were from the third sampling period (mid-July to mid-August - this
includes a sampling window buffer as described in the methods), 173 were from
the second sampling period (mid-June to mid-July) and only 21 were from the
first sampling period (mid-May to mid-June) (Table 4). Based on the scarcity of
parasitoids documented early in the growing season it seems unlikely that
parasitoids would play a large role driving caterpillar assemblage richness or
abundance in early June. The large increase in parasitoid records from June into
July may be synonymous with an increasing significance of their impact on
caterpillar assemblages. Gratton and Denno (2003) showed this type of trend
with Prokelisia planthoppers that were distributed with respect to quadrat
quality early in the season but with respect to predator (spider) densities late in
the season. On an assemblage level, Morais et al (1999) documented a strong
seasonal trend in tropical caterpillar abundance and postulated that the
significant decline they observed towards the end of the season was likely due to
an increase in predator and parasitoid activity. Both of these examples provide
interesting parallels to my results and show that there is an empirical basis for an
increased importance of late-season top-down effects.
209
Conclusion
An assemblage-based approach has become more popular in studies of forest
Lepidoptera and has been applied to landscape fragmentation questions
(Ricketts et al. 2001, Summerville and Crist 2003), human-disturbance problems
(White et al. 2011) and conservation problems (White - Chapter 3). In this study,
I documented a change in the nature of the bottom-up relationship between
host plant foliar quality and an herbivorous caterpillar assemblage over the
course of a growing season. My results also suggested that late-season top-down
effects might be greater than early-season top-down effects, at least in the case
of caterpillar parasitoids. These results should be treated as preliminary, and a
more direct measurement of bottom-up and top-down variables needs to be
conducted before definitive conclusions can be made. That said, there is good
reason to believe that many of the findings reported in this study are accurate.
First, early season foliage tends to be more nutritious than late season foliage
(Feeny 1970, Ricklefs and Matthew 1980). Host plant rejection and reselection
(by larval ballooning or crawling)is more likely to happen when the likelihood of
finding a high quality host plant is great (Mayhew 1997). Late in the season when
average host quality is lower, caterpillars would be more likely to settle for a
poor quality host because the chances of finding a better host are small. These
observations support a hypothesis that bottom-up control is the predominant
driver in early season tri-trophic relationships. Second, in my study system
parasitoid abundance is very low in June and very high in August. While this in
210
itself is not evidence for the primacy of top-down effects in August, the scarcity
of parasitoids in June supports a non-top-down (parasitoid) control scenario
early in the growing season. While other top-down enemies may factor into the
tri-trophic relationship, demand for prey can be considerably higher after
predator young become independent, later in the growing season (Adams et al.
1991, Drent and Daan 1980). At the most basic level this study has shown that
the nature of top-down and bottom-effects are probably variable across the
length of a growing season and that tri-trophic analyses should take this intraseasonal variance into account.
Acknowledgements
I am very grateful to C. Buddle, P. Peres-Neto, K. Summerville, R. Feldman, J.
Messier, S. Estrada B. McGill and M.J. Lechowicz for comments an early draft;B.
McGill was also instrumental in helping to revise and refine the final draft. The
vegetation and Lepidoptera field surveys were conducted with the help of R.
MacKenzie and M. VonButtlar. The parasitoid data was assembled with the help
of Isabelle Ochoa at the Canada Forest Service’s Great Lakes Forestry Centre. I
would also like to thank A. Mochon (SEPAQ), D. Rodrigue (SEPAQ) and J. Lapalme
(Les Amis de la Montagne) who were consultants for field work planning at Parc
de la Yamaska, Parc Mont St Bruno and Parc Mont Royal (respectively). This
research was funded by NSERC.
211
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222
Mont Royal
Mont St. Bruno
Mont St. Hilaire
Mont Shefford
City of
Montreal
CANADA
U.S.A
Figure 1Caterpillars were collected from four sites in the St. Lawrence River valley of southern Quebec, Canada (Figure adapted
from Atlas of Canada 2010) at the northern edge of the deciduous forest biome in eastern North America. The matrix surrounding
each site isdominated by agricultural lands and urban development with the exception of Mont Royal, which is a forest fragment in
an exclusively urban setting. Forest patches across the region are shown in dark gray, urban areas in light gray (including the City of
Montreal at the left side of the pane).
223
b
a
%Water
%Nitrogen
%Phosphorus
c
Toughness (g)
e
d
%Fiber
%Lignin
f
g
%Polyphenol
Figure 2Average foliar qualities at quadrats in the months of June (Je), July (Jy) and August (Au) for (a) % water content , (b) %
nitrogen content, (c) % phosphorus content, (d) toughness (grams), (e) % fiber content, (f) % lignin content and (g) % polyphenol
content. Bars represent standard error. ANOVAs between months are significant for all foliar qualities at p < 0.001. Average foliar
qualities for each quadrat were weighted based on the proportion of the total basal area occupied by each sampled host plant
species in the quadrat. Host plant-specific foliar qualities were taken from Ricklefs and Matthew (1982).
224
Figure 3Regression tree analyses of the determinants of caterpillar richness
(left hand panels) and abundance (right panels) at the quadrat level.
Analyses are shown for June (a, b), July (c, d) and August (e, f). The variables
shown are % Phosphorus content (P), % Polyphenol content (Phenol),%
Fiber content (Fiber) and % Lignin content (Lig). Water content, Nitrogen
content and Toughness were included, but were not significant. The clause
presented at each node is the condition corresponding to the left hand fork
(the right hand fork would be the opposite condition). Each clause is paired
with an R2 value associated with that node in brackets; this value is equal to
the complexity parameter of the node. At each terminus the average
caterpillar richness or abundance is given (depending on the tree) along
with the number of quadrats that satisfy the conditions of the fork (in
brackets). Trees are pruned and show splits corresponding to R2> 0.05.
225
Caterpillar Richness
b
Phenol > 10.9
% (0.31)
|
Caterpillar Abundance (log10)
Phenol > 10.9
% (0.44)
|
June
a
P < 0.24% (0.10)
2.1(9)
4.6 (10)
P > 0.19| % (0.30)
d
1.13 (43)
P > 0.19| % (0.34)
July
c
0.54 (9)
7.0 (33)
Lig < 10.5 % (0.06)
0.4 (10) 2.4 (11)
3.8(22)
5.7 (9)
Fiber < 28.4
% (0.14)
|
0.12 (10) 0.54 (11)
f
Phenol > 9.8 % (0.08)
Lig > 9.2 % (0.08)
0.83 (31)
5.7 (22)
Phenol > 10.3
% (0.13)
|
August
e
Lig < 10.5 % (0.10)
Phenol > 8.2 % (0.06)
Phenol < 6.2 % (0.07)
0.54 (10)
2.5 (10)
0.69 (19)
2.7 (9) 5.5 (11)
226
0.92 (36)
Figure 4Regression tree analyses for the determinants of caterpillar richness (a)
and abundance (b) for the month of August. These trees were created using the
same data as for Figure 3e and 3f (respectively) but with the addition of
parasitoid pressure data for each quadrat. In (a) parasitoid pressure supersedes
% fiber content as the most important determinant of caterpillar richness. In (b)
parasitoid pressure is less important and supersedes % polyphenol content as
the primary determinant of caterpillar abundance.
b
a
Parasitoids| > 6 % (0.18)
Parasitoids >| 6 % (0.28)
H2O > 51 % (0.10)
1.0 (19)
P < 0.18 % (0.06)
0.93 (1259)
3.5 (35)
6.6 (17)
0.50 (9)
227
0.74 (15)
Table 1Foliar nutrient properties in broadleaf trees in southern Quebec and
Ontario in early June, early July and early August. Foliar nutrient data are
averaged across 34 different common deciduous tree species. This table is
adapted from Ricklefs and Matthew’s (1982), Table 3.
Foliar Property
June
July
August
Water %
Nitrogen %
Phosphorus %
Polyphenols (%)
Lignin %
Crude fiber %
Toughness (g)
62.8
2.33
0.23
5.53
10.73
26.52
484
57.3
2.08
0.12
5.68
10.48
26.24
598
54.9
1.89
0.20
6.43
10.24
25.21
621
228
Relationship with
Caterpillars
+
+
+
+/-
Table 2A list of trees sampled (where n > 10) across the 72 study sites, and their
associated caterpillar richness and abundance.
Host Plant Tree Species
Acer saccharum
Fagus grandifolia
Fraxinus americana
Acer pensylvanicum
Ostrya virginiana
Tilia americana
Prunus virginiana
Rhamnus cathartica
Acer platanoides
Amelanchier arborea
Ulmus rubra
Quercus rubra
Tsuga canadensis
Carpinus caroliniana
Acer spicatum
Prunus serotina
Cratageus spp
Betula papyrifera
Carya cordiformis
Rhus typhina
Cornus alternifolia
Acer rubrum
Malus pumila
Prunus nigra
Betula alleghaniensis
# of Trees
Surveyed
720
229
228
206
145
66
55
48
39
35
35
34
33
26
20
19
19
18
15
15
13
12
11
11
10
229
Total
Caterpillar
Richness
75
46
38
52
39
16
10
7
6
21
11
6
10
10
16
17
11
5
9
1
3
0
4
2
11
Total
Caterpillar
Abundance
673
317
95
221
239
24
26
11
11
58
19
11
25
21
32
38
15
5
9
4
3
0
4
3
17
Table 3 Parasitoid pressure on host plants in August.
Host Plant
Acer saccharum
Ulmus americana
Tsuga canadensis
Carya cordiformis
Prunus virginiana
Fraxinus americana
Salix nigra
Quercus rubra
Prunus serotina
Malus pumila
Acer negundo
Alnus incana
Acer rubra
Ostrya virginiana
Betula alleghaniensis
Betula papyrifera
Amelanchier arborea
Tilia americana
Acer pensylvanicum
Cratagus spp.
Fagus grandifolia
# of Caterpillars Collected
127
919
590
225
1333
339
644
200
673
240
271
1137
96
68
81
1223
335
199
22
38
21
230
# of Parasitoids
20
141
80
22
106
20
35
9
27
8
9
34
2
1
1
15
4
1
0
0
0
Parasitoid Pressure
13.6
13.3
11.9
8.9
7.4
5.6
5.2
4.3
3.9
3.2
3.2
2.9
2.0
1.4
1.2
1.2
1.2
0.5
0
0
0
Table 4Emergence records for parasitoids in Ontario and Quebec documented by
Natural Resources Canada, 1937-1949.
Parasitoid
Apanteleshyphantriae
Apantelespolychrosidis
Apecthisontario
Campoplexvalidus
Casinariaeupitheciae
Casinariasemiothisae
Hyposoterfugitivus
Itoplectisconquisitor
Meteorusbakeri
Meteorushyphantriae
Phobocampegeometrae
Pimplapedalis
Total
mid-May to
mid-June
0
16
0
0
0
0
0
2
0
1
0
2
21
231
mid-June to
mid-Jul
1
21
41
1
0
1
18
77
0
7
3
3
173
mid-July to
mid-August
11
9
63
0
3
2
22
168
3
9
0
3
293
General Conclusion
One of the central aims of conservation biology is to better understand the
forces that drive species richness, particularly across fragmented and disturbed
landscapes (Andrén 1997, Fahrig 2003). Forest habitat represents one of the
most modifiedhabitat types in North America (Ricketts and Imhoff 2003) and in
Canada there is no ecozone more imperiled than the Mixedwood Plains in
southern Ontario and Quebec (Gibbs et al. 2009). Because such a small
proportion of the remnant forest fragments are protected,(Ministère des
Ressources Naturelles 2002) there is little preventing the situation from getting
worse in the coming decades. Understanding how biotic and abiotic forces affect
species richness is important for the current and future conservation of species
across this landscape. In this thesis I have focused on four facets of abiotic and
biotic forces: (i) the impacts of natural disturbances, (ii) the impacts of human
disturbances (iii) the effects of bottom-up forces, and (iv) the effects of topdown forces.
Natural ice storm disturbances can have both positive and negative
impacts on the richness in forest communities. There have been remote-sensing
based tools used similar to the one I developed in Chapter 1 (e.g. King et al.
2005, Millward and Kraft 2004), but they have seldom been transformed into
meaningful indices of habitat change relevant to different assemblages of coarse
woody debris dependent guilds. While these previously-developed tools are
useful on many levels, they are insufficient for making species-specific
232
predictions about ice storm impacts. Biomonitoring of rare or threatened species
requires special knowledge about the geographical range of suitable habitat
within a reserve. The ability to map the influx of specific types of coarse woody
debris accomplishes this for the guilds in question. In addition, I have provided
an important empirical example showing that NDVI outperforms other popular
vegetation indices for making geospatial predictions of coarse woody debris
influx following a major canopy-damaging disturbance. Beyond Mont St. Hilaire,
there is now an empirical basis to use pre- and post-storm NDVI imagery to
create a base map of damage in other protected forested areas using freely
available archived Landsat 5 imagery (USGS 2007).
There has been much research on the impacts of habitat destruction and
fragmentation on forest communities (Fahrig 2003) while the impact of intraforest disturbances like trails are often overlooked. Biodiversity and abundance
gains associated with trails are often due to the mixing of forest-dwelling and
open-habitat species. This can result in an apparent positive impact of trailside
habitat on overall biodiversity (somewhat analogous to edge-habitat) even
though biodiversity may be declining amongst forest-dwelling species. Strong
research documenting a negative relationship between trails and forest
assemblages (Chapter 2) has been sorely needed to help make empiricallysupported management decisions to limit trails in forest reserves. Many forest
reserve areas have a joint mandate to protect natural habitat while allowing
access (through trails) for the enjoyment of the general public. In light of the
233
relationships I uncovered in Chapter 2, these two goals seem to be in direct
conflict. One solution to this conflict is to manage trails in such a way so as to
minimize impacts on trailside habitat. Recommendations to accomplish this goal
are (i) construct boardwalks or physical barriers (i.e. rock walls or fences) to
discourage pedestrians from walking off-trail (Doucette and Kimball 1990, Zhou
and Tachibana 2004), (ii) limit the width of trails thereby increasing cross-trail
canopy cover which will keep wind and temperature conditions in trailside
habitat similar to that of forest-interior habitat, (iii) limit the prevalence of trails
to reduce overall impact. In reserves that have some control over the amount
public access (e.g. Mont St. Hilaire, or Mont St. Bruno and Parc de la Yamaska),
trail traffic could be reduced by setting maximums for daily visitors.
The bottom-up effect of host plants on insect herbivores has been a
popular research area for pest-species capable of outbreak conditions
(e.g.Maufette et al. 1983, Stuart and Polavarapu 1998) but has not been wellexamined across an entire species assemblage. For an assemblage, this question
can be broken into two parts: (i) how are host plants related to insect herbivore
assemblages (Chapter 3), and (ii) what is the mechanism behind host selection
(Chapter 4). My analysis of caterpillars in the Monteregian showed that insect
herbivore assemblages are more tightly linked to host plant identity and
abundance; host plant richness was a poor predictor of caterpillars.
Furthermore, caterpillars largely avoided non-native plants. The implications of
these results are most applicable to Mont Royal, which has a high abundance of
234
non-native host plants (Acer platanoides and Rhamnus cathartica) and a low
abundance of highly preferred host plants (A. pensylvanicum and O. virginiana).
Although the eradication of A. platanoides would be impractical because they
are often very large trees, it could be feasible to remove the vast majority of R.
cathartica, replacing it with either A. pensylvanicum or O. virginiana.
The mechanism behind these host plant preferences may be based on
bottom-up foliar quality early in the season (June), but on top-down parasitoid
pressure late in the season (August). These two mechanisms (bottom-up foliar
nutrient pressure versus top-down parasitoid pressure) have been juxtaposed
for a long time in ecology (Lill 2001). Mine is one of the first studies to draw links
between these pressures and entire assemblages. The implication of this result is
both research-oriented and conservation oriented. It underscores the need to
examine tri-trophic relationships within assemblages in a seasonal context,
largely because of temporal changes in foliar quality and parasitoidpressure. It
also suggests that if there are significant declines in insect herbivore richness,
late season parasitoidpressure may be a good factor to investigate as causal.
Overall, the research presented in this thesis converges to single theme;
conservation of forest fragment biodiversity in Mixedwood Plains forest
fragments can be best achieved through the monitoring and understanding of
both abiotic and biotic forces. Two very important management
recommendations come from this. The first is to be aware of and manage
human-presence in conservation areas. The second is to be aware of and
235
manage habitat quality (when necessary) through control of host plant species
(planting or eradication) and coarse woody debris (protecting rare or important
coarse woody debris habitat).
236
References
Andrén, H. 1997. Habitat fragmentation and changes in biodiversity. - Ecological
Bulletins 46: 171-181.
Doucette, J. E. and Kimball, K. D. 1990. Passive trail management in northeastern
alpine zones: A case study. - In: Moore, T. A., Donnelly, M. P., Graefe, A.
R. and Vaske, J. J. (eds.), Proceedings of the 1990 Northeastern
Recreation Research Synposium. - United States Department of
Agriculture, Forest Service, pp. 195-201.
Fahrig, L. 2003. Effects of habitat fragmentation on biodiversity. - Annual Review
of Ecology and Systematics 34: 487-515.
Gibbs, K. E., Mackey, R. L. and Currie, D. J. 2009. Human land use, agriculture,
pesticides and losses of imperiled species. - Diversity and Distributions
15: 242-253.
King, D. J., Olthof, I., Pellikka, P. K. E., Seed, E. D. and Butson, C. 2005. Modelling
and mapping damage to forests from an ice storm using remote sensing
and environmental data. - Natural Hazards 35: 321-342.
Lill, J. T. 2001. Selection on herbivore life-history traits by the first and third
trophic levels: the devil and the deep blue sea revisited. - Evolution 55:
2236-2247.
Maufette, Y., Lechowicz, M. J. and Jobin, L. 1983. Host preferences of the gypsy
moth, Lymantria dispar(L.), in southern Quebec. – Canadian Journal of
Forest Research 13: 53-60.
237
Millward, A. A. and Kraft, C. E. 2004. Physical influences of landscape on a largeextent ecological disturbance: the northeastern North American ice
storm of 1998. - Landscape Ecology 19: 99-111.
Ministère des Ressources Naturelles. 2002. Rapport sur L'état des forêt
québécoises 1995-1999. Ressources Naturelles et Faune, Gouvernement
du Québec, Charlesbourg, Quebec.
Ricketts, T. H. and Imhoff, M. 2003. Biodiversity, urban areas and agriculture:
locating priority ecoregions for conservation. - Conservation Ecology 8: 115.
Stuart, R. J. and Polavarapu, S. 1998. Oviposition preferences of the polyphagous
moth Choristoneura parallela (Lepidoptera: Tortricidae): Effects of plant
species, leaf size, and experimental design. - Environmental Entomology
27: 102-109.
USGS. 2007. EarthExplorer. Earth Resources Observation and Science (EROS)
Center. U.S. Department of the Interior and the U.S. Geological Survey,
Sioux Falls, South Dakota.
Zhou, J. and Tachibana, H. 2004. Natural revegetation after elimination of
disturbance of human treading in the Tennyogahara Mire, the Taisetsu
Mountains, Japan. - Vegetation Science 21: 65-78.
238
Appendix A1We surveyed 36 macrolepidopteran moth species across the four
sites in our study region Mount Royal (R), Mont St. Bruno (B), Mont St. Hilaire
(H) and Mont Shefford (S). All species IDs were based on 5th or 6th instar larvae
identified using Wagner (2005).
239
Family
Geometridae
Lymantriidae
Geometridae
Noctuidae
Geometridae
Noctuidae
Geometridae
Geometridae
Notodontidae
Geometridae
Geometridae
Lymantriidae
Noctuidae
Noctuidae
Noctuidae
Noctuidae
Noctuidae
Arctiidae
Noctuidae
Notodontidae
Geometridae
Geometridae
Geometridae
Geometridae
Noctuidae
Noctuidae
Geometridae
Geometridae
Lasiocampidae
Lymantriidae
Noctuidae
Noctuidae
Noctuidae
Noctuidae
Nolidae
Notodontidae
Genus and species
Lambdina fervidaria
Lymantria dispar**
Melanolophia canadaria
Lithophane antennata
Itame pustularia
Morrisonia latex
Cyclophora pendulinaria
Operophtera bruceata
Symmerista leucitys
Plagodis alcoolaria
Hypagyrtis unipunctata
Orgyia definite
Crocigrapha normani
Lithophane baileyi
Zanclognatha cruralis
Lithophane patefacta
Morrisonia confuse
Halysidota tessellaris
Zale minerea
Clostera spp
Alsophila pometaria
Phigalia titea
Ennomos subsignaria
Tetracis cachexiata
Egira alternans
Elaphria versicolor
Lomographa vestaliata
Pero ancetaria
Malacosoma disstria
Orgyia leucostigma
Acronicta morula
Bomolocha baltimoralis
Eupsilia vinulenta
Orthosia hibisci
Baileya opthalmica
Heterocampa guttivitta
Distribution
Among Sites
BHS
RBH
RBHS
BHS
RBHS
RBHS
RBHS
RBHS
H
BHS
RBH
BHS
BHS
HS
BS
BHS
BH
RH
H
S
RB
RB
BS
S
BS
BS
B
B
B
S
B
H
S
R
H
Y
Number of
Host Plants*
26
45
42
11
9
12
14
24
9
19
22
11
32
11
3
12
30
40
19
7
35
36
29
37
5
13
19
4
30
90
12
6
18
43
7
38
Total # of
Individuals
70
59
52
52
44
20
15
14
11
7
6
6
6
6
6
5
5
4
4
4
3
3
2
2
2
2
1
1
1
1
1
1
1
1
1
1
* This is based on Handfield’s account (1999). He notes that some of these species have many more
recorded host plants than he has documented. This is non-problematic for our study as all of the treespecies at our study quadrats are counted.
** Non-native species
240
Appendix A2 Micromoth distribution.
Species ID
A302
A309
A313
A315
A401
A402
A405
A406
A411
A501
A502
A503
A601
A604
A605
A606
A607
J1001
J1004
J1005
J1007
J1008
J1101
J1103
J1106
J800
J804b
J805b
J811
J901
J903
J905
Y1401
Y1405
Y1406
Y702
Y703
Y707
Y708
Distribution
Among Sites
RBHY
R
R
H
B
BH
B
RBHY
B
HY
H
HY
Y
Y
Y
Y
Y
B
RBHY
B
BHY
RBHY
BHY
BY
Y
RBHY
B
BHY
BR
Y
B
BY
Y
Y
Y
BH
RBH
R
BH
Total # of
Individuals
18
1
1
2
1
3
1
46
1
4
1
5
1
1
1
1
1
2
37
1
11
9
12
4
1
29
1
11
2
1
6
2
1
2
1
3
24
1
3
241
Appendix A3 Complete list of host plant species documented in vegetation
surveys.
Host Plant Species
Abies balsamea (L.) Mill.
Acer negundo L.
Acer nigrum Michx. f.
Acer pensylvanicum L.
Acer platanoides L.
Acer rubrum L.
Acer saccharinum (L.) Small
Acer saccharum Marsh.
Acer spicatum Lam.
Alnus incana (L.) Moench ssp. rugosa (Du Roi) R.T. Clausen*
Amelanchier arborea (Michx. F.) Fernald
Betula alleghaniensis Britt.
Betula papyrifera Marsh
Carpinus caroliniana Walter
Carya cordiformis (Wangenh.) K. Koch
Cornus alternifolia L.f.
Crataegus spp**
Dirca palustris L.
Fagus grandifolia Ehrh.
Fraxinus americana L.
Malus pumila Mill.
Ostrya virginiana(Mill.) K. Koch
Picea glauca (Moench) Voss
Pinus resinosa Aiton
Pinus strobus L.
Populus deltoides Bartram ex Marsh.
Populus grandidentata Michx.
Populus tremuloides Michx.
Prunus nigra Aiton
Prunus serotina Ehrh.
Prunus virginiana L.
Quercus rubra L.
Rhamnus cathartica L.
Rhus typhina L.
Robinia pseudoacacia L.
Salix nigra Marsh.
Sorbus aucuparia L.
Tilia americana L.
242
Tsuga canadensis (L.) Carrière
Ulmus americana L.
Viburnum lantanoides Michx.
Viburnumopulus L. var. americanum Aiton †
* Referred to as Alnus rugosa in this thesis.
** Mostly composed of a mixture of Cratageus punctata Jacq. and Cratageus mollis Scheele and
theirhybrids.
† Referred to as Viburnum trilobum in this thesis.
243
Appendix A4 Randomness was tested using randomization goodness-of-fit tests. P-values were calculated using 10,000 replicates of
randomization.
Caterpillar Species
Cyclophora pendulinaria
Itame pustularia
Lambdina fervidaria
Lithophane antennata
Lymantria dispar
Melanolophia canadaria
Morrisonia latex
<0.25
11.7
4.9
1.5
18.3
2.5
0.0
0.9
Expected Occurrence
Actual Occurrence
0.26-0.50 0.51-0.75 0.76-1.0 >1.0
<0.25 0.26-0.50 0.51-0.75 0.76-1.0 >1.0
2.5
0.4
0.2
0.2
10
3
1
1
0
3.8
6.3
4.9
5.2
1
0
7
10
7
1.9
1.9
5.3
16.5
0
0
1
4
22
5.4
3.3
0.8
2.1
22
7
1
0
0
2.5
4.2
4.2
16.7
3
5
4
3
15
1.8
2.2
8.0
20.0
0
3
2
10
17
3.8
3.1
3.3
4.9
0
2
3
3
8
244
p-value
0.30
0.0094
0.19
0.20
0.55
0.63
0.43
Appendix B1Caterpillar collections were made from 38 host plant trees in 72
quadrats across the four study sites.
245
Host plant
Acer saccharum
Fagus grandifolia
Fraxinus americana
Acer pensylvanicum
Ostrya virginiana
Tilia americana
Prunus virginiana
Rhamnus cathartica
Acer platanoides
Amelanchier arborea
Ulmus rubra
Quercus rubra
Tsuga canadensis
Carpinus caroliniana
Acer spicatum
Cratageus spp
Prunus serotina
Betula papyrifera
Carya cordiformis
Rhus typhina
Cornus alternifolia
Acer rubrum
Malus pumila
Prunus nigra
Betula alleghaniensis
Viburnum lantanoides
Sorbus aucuparia
Alnus rugosa
Viburnum trilobum
Abies balsamea
Acer negundo
Acer saccharinum
Dirca palustris
Picea glauca
Populus deltoides
Robina pseudoacacia
Salix sp
Quadrat
Occurrence
of Host Plant
72
35
38
29
29
21
11
9
5
7
7
7
6
4
6
7
8
8
6
2
3
4
3
2
6
3
4
1
1
1
1
1
2
1
1
1
1
Total Trees
Sampled
Total Caterpillar
Abundance
Total Caterpillar
Species
720
229
228
206
145
66
55
48
39
35
35
34
33
26
20
19
19
18
15
15
13
12
11
11
10
5
4
3
3
2
2
2
2
1
1
1
1
673
317
95
221
239
24
26
11
11
58
19
11
25
21
32
15
38
5
9
4
3
0
4
3
17
1
0
2
1
6
0
0
1
0
0
3
1
77
46
38
52
39
16
10
7
6
21
11
6
10
10
16
11
17
5
9
1
3
0
4
2
11
1
0
2
1
4
0
0
1
0
0
2
1
246
Appendix B2A record of the macrolepidoptera and microlepidoptera
morphospecies that were surveyed. Microlepidoptera morphospecies were
given unique alphanumeric designations and subsequent individuals were
verified with digital images. All macrolepidoptera were identified using Wagner
(2005) and Handfield (1999).
247
Family
Genus
Arctiidae
Halysidota
Arctiidae
Lophocampa
Drepanidae
Euthyatira
Drepanidae
Pseudothyatira
Geometridae
Alsophila
Geometridae
Campaea
Geometridae
Cingilia
Geometridae
Cyclophora
Geometridae
Ectropis
Geometridae
Ennomos
Geometridae
Eufidonia
Geometridae
Eutrapela
Geometridae
Hypagyrtis
Geometridae
Itame
Geometridae
Lambdina
Geometridae
Lomographa
Geometridae
Melanolophia
Geometridae
Operophtera
Geometridae
Paleacrita
Geometridae
Pero
Geometridae
Phigalia
Geometridae
Phigalia
Geometridae
Plagodis
Geometridae
Prochoerodes
Geometridae
Tetracis
Geometridae
Xanthotype
Lasiocampidae Malacosoma
Lymantriidae
Dasychira
Lymantriidae
Lymantria
Lymantriidae
Orgyia
Lymantriidae
Orgyia
Noctuidae
Achatia
Noctuidae
Acronicta
Noctuidae
Amphipyra
Noctuidae
Bomolocha
Noctuidae
Catocala
Noctuidae
Crocigrapha
Noctuidae
Egira
Noctuidae
Elaphria
Noctuidae
Eupsilia
Noctuidae
Lithophane
Noctuidae
Lithophane
Species
tessellaris
caryae
pudens
cymatophoroides
pometaria
perlata
catenaria
pendulinaria
crepuscularia
subsignaria
notataria
clemataria
unipunctata
pustularia
fervidaria
vestaliata
canadaria
bruceata
vernata
ancetaria
strigataria
titea
alcoolaria
lineola
cachexiata
spp
disstria
plagiata
dispar
definita
leucostigma
distincta
morula
pyramidoides
baltimoralis
ultronia
normani
alternans
versicolor
vinulenta
antennata
patafacta
248
Total
Microlepidoptera
Total
Abundance Morphospecies Abundance
5
A302
41
7
A307
1
1
A308
2
7
A309
1
6
A312
1
5
A313
1
1
A315
2
45
A401
1
1
A402
6
8
A404
7
7
A405
1
3
A406
52
15
A410
4
71
A411
2
183
A501
5
21
A502
1
112
A503
9
65
A504
3
7
A505
3
3
A601
1
1
A602
1
9
A604
6
10
A605
1
1
A606
3
2
A607
2
2
J1001
6
6
J1004
60
1
J1005
1
206
J1007
25
15
J1008
29
2
J1101
19
3
J1103
11
6
J1104
1
2
J1106
1
13
J1501
2
2
J1502
1
26
J703
9
6
J800
51
8
J804
5
7
J804b
4
197
J805b
21
9
J811
6
Noctuidae
Noctuidae
Noctuidae
Noctuidae
Noctuidae
Noctuidae
Noctuidae
Noctuidae
Nolidae
Notodontidae
Notodontidae
Lithophane
Morrisonia
Morrisonia
Orthosia
Orthosia
Zale
Zale
Zanclognatha
Baileya
Heterocampa
Symmerista
viridipallens var
confusa
latex
hibisci
rubescens
minera
unilineata
cruralis
ophthalmica
guttivitta
leucitys
249
10
12
54
11
12
9
1
20
20
5
44
J901
J904
J905
Y1401
Y1404
Y1405
Y1406
Y1407
Y1409
Y702
Y703
Y707
Y708
Y710
3
12
14
8
1
3
1
1
6
13
97
10
11
2