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J. Plankton Res. (2015) 37(1): 75– 89. First published online October 23, 2014 doi:10.1093/plankt/fbu094
Environmental filtering of crustacean
zooplankton communities in fishless
boreal lakes: expectations and exceptions
M. U. MOHAMED ANAS1*, KENNETH A. SCOTT2 AND BJÖRN WISSEL1
1
DEPARTMENT OF BIOLOGY, UNIVERSITY OF REGINA, REGINA, SK, CANADA S4S
SK, CANADA S4S
0A2 AND 2SASKATCHEWAN MINISTRY OF ENVIRONMENT, REGINA,
5W6
*CORRESPONDING AUTHOR: [email protected] and [email protected]
Received May 1, 2014; accepted October 1, 2014
Corresponding editor: Beatrix E. Beisner
To test the paradigm that large-bodied zooplankton species dominate in fishless lakes, we evaluated crustacean zooplankton composition of 53 fishless boreal lakes across a broad geographic scale (north-west Saskatchewan, Canada)
in relation to contemporary environmental factors. Lake productivity, acid – base status and intensity of invertebrate
predation were the three major environmental filters of crustacean zooplankton composition of the survey lakes, while
lake morphometry and drainage basin characteristics likely had indirect effects by influencing the variations in above
environmental factors. The traditionally expected dominance of large-bodied zooplankton species in fishless conditions only occurred in well-buffered shallow lakes with higher productivity and intense invertebrate predation. Daphnia
pulex was dominant in more eutrophic lakes, while higher abundances of Holopedium gibberum were favored by relatively
mesotrophic conditions. In exception to the traditional view, small-bodied species dominated in 57% of survey lakes
as influenced by interactions of biotic and abiotic factors. Bosmina longirostris and cyclopoid copepods dominated less
productive, deeper lakes with low invertebrate predation. Predominance of Leptodiaptomus minutus was supported under
unfavorable environmental conditions for other species, i.e. poor buffering capacity and/or low calcium concentration. The present study demonstrates that exceptions to the generally expected species composition in fishless lakes
can be caused by interactions among abiotic and biotic factors.
KEYWORDS: fishless lakes; crustacean zooplankton; environmental filters; classification and ordination analysis
available online at www.plankt.oxfordjournals.org
# The Author 2014. Published by Oxford University Press. All rights reserved. For permissions, please email: [email protected]
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preferentially prey on small-bodied zooplankton species,
there is evidence that they may preferentially prey on
large-bodied species (Wissel and Benndorf, 1998; Pagano
et al., 2003). This is be particularly true when larger chaoborids (e.g. C. trivittatus) are the dominant predators
(Young and Riessen, 2005). Probably, small body size is
actually a competitive advantage under such conditions
by reducing the encounter frequency with Chaoborus predators (Riessen and Young, 2005). It is also feasible that
other anti-predator strategies, such as morphological and
behavioral adaptations, are more important than the size
refuge to withstand invertebrate predation in these habitats (O’Brien and Schmidt, 1979; Riessen et al., 1988;
Kats and Dill, 1998). Further, several field, laboratory and
model-based studies indicate contrasting competitive outcomes between large- and small-bodied zooplankton taxa
under fishless conditions depending on quantity and
quality of algal resources. Small-bodied species are likely
favored and numerically dominant when resource abundance is limited (Lynch, 1978; Smith and Cooper, 1982;
Romanovsky and Feniova, 1985; Tessier and Goulden,
1985) and resource quality (in terms of algal composition
and algal stoichiometry, i.e. C : N : P ratio) is sub-optimal
for large-bodied species (Gliwicz and Lampert, 1990;
Steiner, 2003; Steiner and Roy, 2003; Weidman et al.,
2014). Finally, abiotic factors (e.g. pH, temperature, light
intensity) can also influence the zooplankton community
structure of fishless aquatic habitats directly by sorting
species based on differential physiological tolerances, or
indirectly by interacting with biotic conditions such as
primary production and invertebrate predation (Arnott
and Vanni, 1993; Steiner, 2004; Weidman et al., 2014).
In our previous work, we analyzed a broadscale zooplankton survey which included both fish-bearing and
fishless boreal lakes in NW Saskatchewan to evaluate indicator properties of zooplankton communities in relation to potential regional environmental changes (Anas
et al., 2013, 2014). Broadly, these studies indicated that
large-bodied species were more dominant in fishless
lakes, with top-down control determining the size structure of crustacean zooplankton communities. The goal of
the present study was to focus solely on environment –
species relationships in fishless lakes of this region to test
if the size structure of zooplankton communities of these
lakes is in fact variable and dependent on several biotic
and abiotic factors. A better understanding of these relationships in fishless lakes is important for several reasons:
From an ecological and conservation perspective, the importance of fishless lakes is beginning to be more recognized due to their unique biotic communities (Stoks and
Mcpeek, 2003; Knapp et al., 2007a, b; Drouin et al., 2009;
Schilling et al., 2009), and a considerable number of such
habitats exist across the boreal Shield ecozone (Kurek
The high taxonomic diversity in zooplankton communities
is only partially expressed in individual freshwater habitats,
and the differences in zooplankton community structures
among systems is largely associated with specific environmental conditions (Havens and Hanazato, 1993; Wellborn
et al., 1996; Gyllström et al., 2005). In some cases, zooplankton communities respond strongly to a single environmental factor (Sternberger and Lazorchak, 1994), such
as predation (Brooks and Dodson, 1965; McQueen et al.,
1986; Jeppesen et al., 1997), chemical (acids, pesticides or
trace metals) stress (Havens and Hanazato, 1993; Xu,
1999; Anas et al., 2013) and nutrient enrichment (Gannon
and Stemberger, 1978; Carpenter et al., 1985). Yet often,
zooplankton assemblages are influenced by multiple
factors, which may confound clear zooplankton–environment associations (Wellborn et al., 1996).
Since the classic work by Hrbáček et al. (1961) and
Brooks and Dodson (1965), numerous studies have shown
that predation by planktivorous fishes and invertebrates
can be a major factor structuring zooplankton community composition (Hall et al., 1976; Zaret, 1980; Carpenter,
1988). Based on this paradigm, zooplankton communities subjected to fish predation are dominated by smallbodied species as visually oriented, planktivorous fishes
preferentially prey on large-bodied species (Zaret, 1980;
Kerfoot and Sih, 1987). In the absences of planktivorous
fishes, large-bodied zooplankton species commonly dominate, which is a result of (i) gape-limited, invertebrate
planktivores preying on small-bodied species and/or
(ii) competitive exclusion of small-bodied zooplankton
species by superior, large-bodied species (Hall et al.,
1976). This expected pattern of zooplankton communities in fishless lakes has been supported by a large body
of evidence from broadscale zooplankton surveys that
included both fishless and fish-bearing lakes (e.g. Patalas,
1971; Keller and Conlon, 1994; Donald et al., 2001;
Knapp et al., 2007b; Anas et al., 2014). Yet, exceptions to
this pattern, i.e. dominance of small-bodied species in
the absence of planktivorous fish have been detected in a
limited number of lake surveys (O’Brien et al., 1979; Malkin
et al., 2006; Drouin et al., 2009). Hence, determinants of
size structure of zooplankton communities in fishless lakes
can be more complex than the commonly assumed topdown control framework.
In fact, experimental studies suggest that a number of
factors can potentially contribute to variations in size
structure of zooplankton communities during the absence
of fish. First, invertebrate predators such as Chaoborus spp.
can constrain the size structure of zooplankton communities in the absence of planktivorous fish. In contrast to
the well-established pattern that gape-limited predators
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et al., 2010). In addition, the lakes of this region are of
particular interest, as they may be exposed to large-scale
environmental stressors, notably acidifying/eutrophying
emissions from the Athabasca oil sands region and
climate change (Jeffries et al., 2010; Scott et al., 2010;
Anas et al., 2014).
The climate is subarctic with mean annual, July and
January temperatures of 2.3, 16 and 2248C, respectively. Mean annual precipitation is between 450 and
530 mm, with 288 and 318 mm falling as rain between
May and September. The prevailing wind is from the
W-NW (Shewchuck, 1986; Acton et al., 1998).
Lake selection
METHOD
The present study lakes are a subset from a survey of
244 headwater lakes in northwest Saskatchewan conducted from 2007 to 2009 in order to evaluate the
status of lakes in relation to potential effects of atmospheric emissions from Athabasca oil sands operations.
A detailed description of lake selection criteria for this
survey is given in Scott et al. (2010) and Anas et al.
(2014). Of the 244 survey lakes, 53 lakes were classified as “fishless” based on the presence of larvae of
the phantom midge Chaoborus americanus and used for
the subsequent data analyses. Chaoborus americanus is a
robust indicator of fishless conditions, owing to its
larger size, strong pigmentation and lack of diel vertical migration (Von Ende, 1979; Wissel et al., 2003;
Sweetman and Smol, 2006; Garcia and Mittelbach,
2008). Of the 53 lakes, 16 were sampled in all 3
years, 17 were sampled twice and 20 were sampled
once.
Study area
The sampling area intersected three ecoregions, namely,
Athabasca Plain, Churchill River Upland and MidBoreal Upland (Fig. 1). Of these, Athabasca Plain and
Churchill River Upland are physiographic divisions of
the Boreal Shield ecozone, while the Mid-Boreal Upland
is part of the Boreal Plain ecozone. The landscape
changes from flat to strongly rolling, with distinctive
upland areas. Localized relief can be as much as 90 m
but is generally ,60 m. The elevation of the study lakes
ranges from 210to 554 m a.s.l. In respect to vegetation,
Jackpine (Pinus banksiana)/lichen forest with variable
canopy closure predominates on well-drained sand plains
and till ridges throughout the area. A more complete description of the survey domain, together with the derivation and summary of catchment-specific environmental
factors is given in Scott et al. (2010).
Fig. 1. Sampling domain and locations of the 53 fishless survey lakes (based on presence of C. americanus) in north-west Saskatchewan, showing
their distribution across three ecoregions [AP, Athabasca Plain; CRU, Churchill River Upland (AP & CRU ¼ Boreal Shield); MBU, Mid-Boreal
Upland (Boreal Plain); AOSR, Athabasca Oil Sands Region—portion of approximate active mining area is shown].
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We performed hierarchal cluster analysis to identify
clusters of lakes based on species composition. First, we
preformed hierarchal cluster analysis on species composition using four different methods, i.e. single linkage
clustering, complete linkage agglomerative clustering,
average agglomerative clustering (unweighted pair-group
method using arithmetic averages) and Ward’s minimum
variance clustering and subsequently evaluated for the
best method/model by calculating cophenetic correlations for distance matrices produced by each method. We
selected the outcome of Ward’s minimum variance clustering as the best clustering model as cophenetic correlation was highest for this particular model. Finally, in
order to determine the optimum number of clusters for
objective interpretation, both average Silhouette widths
and Mantel correlation coefficients were calculated for
each clustering level from 1 (i.e. all lakes belong to a
single cluster) to 53 (i.e. each lake is a separate cluster)
(Borcard et al., 2011).
To quantify statistically significant relationships between
environmental factors and crustacean zooplankton species
abundances, redundancy analysis (RDA) was conducted.
RDA is a direct gradient analysis technique that performs
well with linear species–environmental relationships (Ter
Braak and Prentice, 2004). Several lake parameters that
could be important in structuring zooplankton communities were incorporated into this analysis as predictors, in2
cluding [TP], [TN], [chlorophyll a], [NHþ
4 -N], [NO3 -N],
22
[DOC], color, pH, [Ca],[ SO4 ], [ANC], C. americanus
density, maximum depth, surface area, drainage basin
area, % forest area and % bog area in catchment. Prior to
the subsequent analyses, all these variables except pH
were either log10 (x þ 0.01) or log10 transformed to
improve homoscedasticity and normality while average
species abundances were Hellinger transformed, which
preserves the asymmetrical distance among sites, allowing
the use of a Euclidean-based ordination methods such as
RDA (Legendre and Gallagher, 2001). Species occurring
in ,5% of survey lakes were removed from the ordination
analysis (Leps and Smilauer, 2003). After testing for the
significance of a global model with the complete set of predictors (P , 0.05), significant predictors were forwardselected by Monte Carlo permutation tests at P , 0.05
with 999 iterations. As forward-selection procedure is too
liberal, we added a double stopping criterion (Blanchet
et al., 2008) to select the most parsimonious set of predictors from each explanatory variable set. Further, we evaluated for any collinearity among selected predictors
(variance inflation factor .3). Subsequently, significance
of individual canonical axes of the selected model (with
only significant predictors) was tested using permutation
tests (1000) and only the significant (P , 0.05) canonical
axes were interpreted (Borcard et al., 2011).
Selected lakes were sampled by helicopter annually from
2007 to 2009 in late September during the turnover
period. After locating the helicopter in the approximate
center of the lake, a water sample was collected at 1 m
depth for physiochemical factors (generally representing
integrated vertical state based on temperature), and zooplankton was sampled by vertical net tows (near bottom
to surface) using a conical plankton net (mesh size ¼ 68
mm, diameter ¼ 0.3 m) that was equipped with a flow
meter.
Laboratory analysis
Zooplankton of each lake was enumerated and identified
to species except cyclopoid copepods. Whole samples
were used for species identification while samples were
split for enumeration when necessary. Standard references were used for taxonomic identifications (Hebert,
1995; Sandercock and Scudder, 1996; Aliberti et al.,
2009). Subsequently zooplankton densities were calculated by using filtered volumes (based on impeller rotations and diameter of plankton net). The analytical
procedures for environmental (chemical) factors are
described in Scott et al. (2010).
Data analysis
We used averages of all environmental factors and species
abundances over the 3 survey years for subsequent data
analyses as there were no major variations in either environmental factors or species composition of the lakes
among survey years (Anas et al., 2014).
We performed a principal component analysis (PCA)
on standardized (scaled to unit variance) water chemistry
variables, i.e. total nitrogen (TN), total phosphorus (TP),
chlorophyll a, dissolved organic carbon (DOC), nitrate
þ
2–
(NO2
3 -N), ammonium (NH4 -N), sulfate (SO4 ), pH,
calcium (Ca), charge-balanced acid-neutralizing capacity
(ANC) and color. PCA compresses the variance in the
data matrix by identifying the correlation structure
within the data matrix. In addition, we included two
morphometric variables, i.e. maximum water depth and
surface area and three catchment variables, i.e. drainage
basin area, % forest area in catchment and % bog area in
catchment, and C. americanus density as supplementary
variables. These supplementary variables had no influence
on the principal components of the analysis, yet showed
how these variables were correlated to water chemistry.
Subsequently, the broken-stick model was used to determine the number of meaningful principal components that
represented interesting variation of the data (Borcard et al.,
2011).
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Complementary to RDA, we tested how individual environmental factors varied among the species composition-based lake clusters. The aim of this analysis was to
further emphasize the environment – species relationships
established using RDA by providing a more simplified
and structured view of these relationships. Kruskal –
Wallis tests determined if median environmental factors
were significantly different among lake clusters based on
species composition followed by multiple comparisons of
median values (Conover, 1999). The a level (0.05) for
multiple comparisons was adjusted using Holm’s correction (Holm, 1979).
In addition, we tested if species diversity varied among
the lake clusters based on variable dominance by different
taxa. First, we calculated the species richness and
Shannon’s diversity index for each lake. Subsequently,
Kruskal–Wallis tests were performed to determine if these
diversity indices were significantly different among lake clusters, followed by multiple comparisons of median values
(Conover, 1999). The a level (0.05) for multiple comparisons was adjusted using Holm’s correction (Holm, 1979).
Finally, a spatial analysis was performed to identify any
spatial structures in species composition caused either by
dispersal-related processes or by spatially structured environmental controls. Spatial structuring of communities
independent of the influence of environmental factors
represents a coarse measure of dispersal processes (Cottenie
et al., 2003; Beisner et al., 2006; Gray et al., 2012). First, presence of any linear spatial trends in species data was tested
by performing a trend-surface analysis (Borcard and
Legendre, 2002). As no significant broadscale linear trends
were detected (P . 0.05), subsequently an eigenvectorbased spatial modeling approach was used to identify more
complex and non-linear spatial structures (Borcard and
Legendre, 2002; Dray et al., 2006). For this, a set of spatial
variables (i.e. eigenvectors) was generated using a distancebased Moran’s Eigenvector Mapping (dbMEM) method
(Dray et al., 2006) based on X–Y coordinates of each survey
lake. This method produces a spectral decomposition of
spatial relationships among sampling sites based on the
diagonalization of a spatial weighting matrix. The generated spatial variables can be directly linked to the spatial
patterns of species and environmental factors (Bellier et al.,
2007; Borcard et al., 2011). A truncation distance of 64 km
was used (i.e. shortest distance for all survey lakes to remain
connected by links smaller than or equal to this distance).
This yielded 15 positive dbMEM variables. We used a
forward-selection criterion in a RDA to select the
minimum subset of significant variables from them, by following the same procedure described above for RDA
between environmental factors and species composition.
Finally, lake clusters based on species composition were
plotted on a map of the study area to provide a visual presentation of existing spatial structures in species composition.
All statistical analysis were performed in R version
2.15.3(R Core Team, 2012) using the following packages;
(i) “FactoMineR” (Husson et al., 2013) for PCA, (ii)
“vegan” (Oksanen et al., 2012) for RDA, associated permutation tests, (iii) “packfor” (Dray et al., 2011) for the forward
selection with double stopping criterion, (iv) ‘cluster’
(Maechler et al., 2012) for cluster analysis, (v) “agricolae”
(de Mendiburu, 2014) for Kruskal–Wallis test and multiple comparisons of median values and (vi) “PCNM”
(Legendre et al., 2012) for generating dbMEM variables.
R E S U LT S
Limnological characteristics
Limnological characteristics varied considerably among
the study lakes (Table I). Lake morphometry ranged from
shallow lakes with variable size to small and deep
“kettle”-type lakes. Trophic state of study lakes ranged
from oligotrophic (5th percentile [TP], [TN] and
Table I: Limnological characteristics of the 53 survey lakes located in north-west Saskatchewan
Variable
Mean
Median
5th percentile
95th percentile
Maximum depth (m)
Surface area (ha)
Drainage basin area (ha)
TN (mg L21)
TP (mg L21)
21
)
NHþ
4 -N (mg L
21
)
NO2
3 -N (mg L
Chlorophyll a (mg L21)
21
DOC (mg L )
Color (mg L21 Pt)
Ca (mg L21)
ANC (meq L21)
pH
21
)
SO22
4 (mg L
5.89
75.12
670.00
585.54
19.37
52.23
13.38
6.42
8.84
47.58
1.70
179.01
6.77
0.80
3.07
47.48
401.00
514.67
14.00
23.00
2.33
5.07
7.75
24.95
1.46
154.97
6.88
0.73
1.32
8.64
56.20
259.10
4.30
12.60
1.00
1.98
3.76
6.81
0.59
53.48
5.90
0.32
18.46
218.15
2311.20
1328.80
51.40
135.40
84.60
15.39
14.90
127.43
3.55
393.83
7.31
1.59
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[chlorophyll a] 4.3, 259.1 and 1.98mg L21, respectively)
to relatively eutrophic conditions (95th percentile
[TP], [TN] and [chlorophyll a] 51.4, 1328.8 and
15.39 mg L21, respectively) (Forsberg and Riding, 1980;
Carlson and Simpson, 1996). The contribution of inorganic nitrogen species to TN was generally low (median
21
2
, respective[NHþ
4 -N] and [NO3 -N] 23 and 2.33 mg L
ly) indicating most of the TN of these lakes was organic.
In general, the study lakes were slightly colored (median
color 24.95 mg L21 Pt). Accordingly, [DOC] were high
(median [DOC] 7.75 mg L21) relative to those recorded
for boreal Shield lakes of eastern Canada (Jeffries et al.,
2005). For most lakes, pH was in the circum-neutral
range and only 7% of the lakes had a pH below 6. Yet,
the wide range in ANC indicated that acid sensitivity of
the lakes varied from highly sensitive (,50 meq L21) to
insensitive (.200 meq L21) (Sullivan, 2002), reflecting
the underlying geological acid sensitivity gradient (Scott
et al., 2010). Variation in [Ca] (which was the major base
cation) was closely related to that of ANC.
Based on the broken-stick model, the first two principal components of the PCA (which collectively explained
54% of the variation) were selected to represent the variation in water chemistry (Fig. 2). The first principal component (PC-1) corresponded to nutrient status (TN, TP,
2
NHþ
4 -N and NO3 -N) and acid – base status (ANC and
Ca) of the study lakes. Meanwhile, the density of C. americanus and surface area of lakes [supplementary variables
(Supplementary data)] were positively correlated to
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higher nutrient loadings along PC-1. On the other hand,
PC-2 mostly represented the variation in DOC and correlated factors. The positive correlation of color with
DOC along PC-2 indicated the greater importance of
allocthonous DOC in these lakes (Wetzel, 1983). This
was further emphasized by the positive correlation of %
peatland in the catchment (supplementary variables) to
PC-2. In the meantime, pH was negatively associated
with PC-2 mostly representing the organic acidity (Kahl
et al., 1989), while negative association of maximum
depth (supplementary variables) indicated higher [DOC]
in shallow lakes. The positive relationship of [chlorophyll
a] to PC-2, but not PC-1 (nutrient status) was somewhat
unexpected. This relationship may reflect autochthonous
DOC production within lakes via primary production
(Wetzel, 2001), yet autochthonous DOC is only of a
minor importance in these systems given the greater importance of colored DOC of allochthonous origin. In
addition, two supplementary variables (Supplementary
data), i.e. drainage basin area and % forest area in catchment were not related to either PC.
Variations in species composition
Hierarchal cluster analysis identified five relatively different clusters based on the species composition. Five clusters were the optimum number for interpretation as both
average Silhouette width and Mantel correlation coefficient were highest at this particular level (Supplementary
data, Fig. S1). Individual clusters were mostly separated
based on a single dominant species/taxon in each cluster
(Fig. 3). Clusters 1 and 2 were each dominated by one
large-bodied species, i.e. Daphnia pulex and Holopedium gibberum, respectively. In contrast, Clusters 3, 4 and 5 were
each dominated by one small-bodied species/taxa, i.e.
Bosmina longirostris, cyclopoid spp., and Leptodiaptomus
minutus, respectively.
Environmental predictors of species
composition
RDA between species assemblages and environmental
factors identified significant (P , 0.05) environment –
species relationships along the first three axes, which collectively explained 30% of the total variation in species
composition (Fig. 4). Kruskal – Wallis tests and subsequent multiple comparisons detected significant differences for several individual environmental factors among
the five clusters (based on species compositions), further
emphasizing the strong environment– species relationships identified by RDA (Supplementary data, Fig. S2).
Only the relatively stronger environment – species relationships are described below.
Fig. 2. Principal components analysis (PCA) on limnological variables
of 53 fishless lakes. Variables indicated by dash-lined arrows and
italicized labels were supplementary variables of the analysis.
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Fig. 3. Heat map and dendrogram of hierarchical cluster analysis based on relative abundances of crustacean zooplankton taxa/species of 53
fishless lakes of north-west Saskatchewan; Ward’s minimum variance clustering method was used for the analysis. Color intensities of the heat map
are proportional to relative abundances of species/taxa.
Daphnia pulex (Cluster 1) dominated more productive
lakes (Fig. 4a and b; along RDA-2 and Supplementary
data, Fig. S2c, d and e) with relatively higher concentrations of inorganic nitrogen species, i.e. NO2
3 -N (Fig. 4c and
d; along RDA-3 and Supplementary data, Fig. S2a) and
NHþ
4 -N (Supplementary data, Fig. S2b). Furthermore,
these lakes were well buffered (Fig. 4a and b; along RDA-1
and Supplementary data, Fig. S2f), somewhat shallow
(Fig. 4a and b; along RDA-2 and Supplementary data,
Fig. S2l) and had relatively small drainage basins (Fig. 4a
and b; along RDA-2 and Supplementary data, Fig. S2m).
In addition, these lakes also had relatively higher densities
of C. americanus (Fig. 4a and b; along RDA-1 and
Supplementary data, Fig. S2i). However, while there were
significant variations in C. americanus density per unit
volume among lake clusters based on species composition,
such variations were not evident for C. americanus density
per unit area (Supplementary data, Fig. S2j). Rationale for
this discrepancy is discussed below.
Holopedium gibberum-dominated lakes (Cluster 2) were
somewhat similar to D. pulex-dominated lakes in terms
of productivity (Supplementary data, Fig. S2c, d and e),
lake morphometry (Fig. 4c and d; along RDA-1 and
Supplementary data, Fig. S2k and l), acid sensitivity
(Fig. 4c and d; along RDA-1 and Supplementary data,
Fig. S2f ) and C. americanus density (Fig. 4c and d; along
RDA-1 and Supplementary data, Fig. S2i). Yet, these
lakes were different from D. pulex-dominated lakes mostly
based on relatively lower concentrations of inorganic nitrogen species, i.e. NO2
3 -N (Fig. 4c and d; along RDA-3
and Supplementary data, Fig. S2a) and NHþ
4 -N
(Supplementary data, Fig. S2b).
Lakes dominated by B. longirostris (Cluster 3) and cyclopoid spp. (Cluster 4) were relatively deep kettle-type lakes
(Fig. 4a and b; along RDA-2, Fig. 4c and d; along RDA-3
and Supplementary data, Fig. S2l). In addition, these
lakes were more oligotrophic compared with lakes in
Clusters 1 and 2 (Fig. 4a and b; along RDA-2 and
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Fig. 4. Crustacean zooplankton–environment relationship for 53 fishless lakes as identified by redundancy analysis (RDA). Average species
abundances were Hellinger transformed and environmental factors except pH were log10-transformed. Only significant (P , 0.05) and
non-collinear environmental factors (variance inflation factor ,3) were retained in the analysis. (a) Biplot between environmental factors and
species (Axis 1 vs. Axis 2); (b) biplot between environmental factors and sites (Axis 1 vs. Axis 2); (c) biplot between environmental factors and species
(Axis 1 vs. Axis 3); (d) biplot between environmental factors and sites (Axis 1 vs. Axis 3); site symbols of (b) and (d) refer to lake clusters based on
species composition. Axes 1, 2 and 3 explained 15.9, 8.8 and 5.3% of total variation in species composition, respectively.
Supplementary data, Fig. S2c, d and e), and had significantly lower C. americanus densities (Fig. 4a and b; along
RDA-1, Fig. 4c and d; along RDA-3 and Supplementary
data, Fig. S2i). Although not revealed by RDA as a significant relationship, [DOC] and consequently color were significantly lower in B. longirostris-dominant lakes relative to
those of cyclopoid-dominant lakes (Supplementary data,
Fig. S2n and o).
Highly acid-sensitive survey lakes (Fig. 4a and b; along
RDA-1 and Supplementary data, Fig. S2f ) with relatively
lower pH values (Supplementary data, Fig. S2g) were
dominated by L. minutus (Cluster 5). In the meantime,
these lakes also had the lowest [Ca] relative to other lake
clusters (Supplementary data, Fig. S2h). Further, these
lakes were relatively small (Fig. 4a and b; along RDA-2
and Supplementary data, Fig. S2k), shallow (Fig. 4c and
82
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ZOOPLANKTON COMMUNITIES OF FISHLESS BOREAL LAKES
Fig. 5. Comparison of (a) species richness (b) Shannon’s diversity index of 53 fishless lakes among lake clusters based on species composition.
Median values of the clusters indicated by same letters are not significantly different according to Kruskal–Wallis test followed by multiple
comparisons (adj. P , 0.05).
(P , 0.05) linear or non-linear spatial structures in species
composition. This was further emphasized by the nonexistence of any spatial patterns in the distribution of lake
clusters based on species composition in the study area
(Fig. 6).
DISCUSSION
This study identified diverse patterns in crustacean zooplankton community composition in fishless lakes, which
were significantly related to variations in both abiotic and
biotic factors. In contrast to the existing paradigm, the
expected dominance of large-bodied species, such as
D. pulex or H. gibberum occurred in only 43% of the study
lakes, while small-bodied species including B. longirostris,
cyclopoids and L. minutus were dominant in 57% of the
lakes. The observed variation in species composition
among lakes was due to interactive effects of lake productivity, acid sensitivity, lake morphometry, catchment
characteristics and invertebrate predation.
Fig. 6. Spatial distribution of the lake clusters based on species
composition in the sampling domain.
d; along RDA-3 and Supplementary data, Fig. S2l) and
had smaller drainage basins (Fig. 4a and b; RDA-axis 2
and Supplementary data, Fig. S2m).
In addition, species diversity was lowest in L. minutusdominated lakes (Cluster 5) relative to all other lake clusters. Although species richness did not vary much among
clusters (Fig. 5a), the Shannon’s diversity index was significantly lower in L. minutus-dominated lakes (Fig. 5b),
which indicates greater dominance by a single species
(i.e. L. minutus) in lakes of cluster 5.
Dominance of large-bodied species
Large-bodied zooplankton species were dominant in
43% of lakes representing the generally expected pattern
for fishless lakes (Brooks and Dodson, 1965; Hall et al.,
1976). The proposed mechanisms responsible for the
reduced abundance of small-bodied species in fishless
lakes are still disputed. One argument is that smallbodied species are competitively suppressed by superior
large-bodied species monopolizing food resources
(Brooks and Dodson, 1965; Hall et al., 1976; DeMott and
Kerfoot, 1982; Vanni, 1986). Alternatively, several studies
suggest that selective predation by invertebrates alone
can account for reduced abundances of small-bodied
species, while resource reduction by large-bodied species
Spatial structures in species composition
Trend-surface analysis and subsequent eigenvector-based
spatial modeling did not reveal any statistically significant
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densities (Von Ende and Dempsey, 1981). On the other
hand, successful colonization of large-bodied species may
be hampered by low productivity of these lakes, favoring
small-bodied species at reduced competition interactions
(Lynch, 1978; Romanovsky and Feniova, 1985; Vanni,
1986). For instance, it has been suggested that D. pulex can
become more vulnerable to predation by C. americanus with
decline in productivity, as reduced growth rates may not
allow them to achieve a size refuge from Chaoborus predation (Riessen and Young, 2005). The distinction between
B. longirostris- versus cyclopoid-dominated lakes was poorly
explained by environmental factors. Although cyclopoiddominated lakes had higher [DOC] compared with B.
longirostris-dominated lakes, any underlying mechanism for
this difference is uncertain. Potentially, predation by cyclopoids on B. longirostris may reduce the relative abundances
of B. longirostris (Ha and Hanazato, 2009).
Dominance of L. minutus (Cluster 5) is probably the
result of suppression of other species due to unfavorable
environmental conditions. Relatively low ANC/pH
conditions of these lakes were likely unfavorable for acidsensitive zooplankton species while L. minutus, a highly
acid-tolerant generalist, can dominate zooplankton communities under acid-stressed conditions (Brett, 1989;
Marmorek and Korman, 1993; Havens and Hanazato,
1993). It should be noted however that dominance of
L. minutus has been mostly recorded in culturally acidified
systems elsewhere, while low ANC/pH of the current set
of survey lakes were largely due to low concentrations of
base cations. Combined carbonate-poor geology and
short hydraulic retention times of these small headwater
lakes are likely contributing to the loss of ANC in these
systems (Gubala and Driscoll, 1991; Sullivan, 2002). It is
also likely that low ambient [Ca] is acting as a barrier for
successful colonization of zooplankton species with high
physiological demand for Ca such as daphnids (Cairns
and Yan, 2009). Further, large-bodied species like daphnids are likely more vulnerable to Chaoborus predation
under such unfavorable environmental conditions
(Pagano et al., 2003; Riessen et al., 2012). Consequently,
the small-bodied generalist L. minutus may be favored in
these lakes due to release from the competition from superior species (Brett, 1989). Declined overall zooplankton
species diversity is hence a likely result of this predominance by a single tolerant species under unfavorable conditions (Brett, 1989; Marmorek and Korman, 1993;
Stenson et al., 1993).
is not sufficient to cause the scarcity of small-bodied
species (Dodson, 1974; Hall et al., 1976; Zaret, 1980). The
scarcity of small-bodied species in many lakes of our survey
may invoke either or both of these two alternatives.
According to the predation hypothesis, relatively higher
C. americanus densities would exert a greater selective predation pressure on small-bodied species in these lakes. If
competition is crucial, higher population densities of largebodied zooplankton supported by the relatively eutrophic
conditions would competitively suppress small-bodied
species by altering the resource base (Romanovsky and
Feniova, 1985; Vanni, 1986; Kerfoot et al., 1988). However,
the latter option should be less likely for H. gibberumdominated lakes of our survey (discussed below).
Dominance of either D. pulex or H. gibberum is likely
determined by differences in lake productivity. Typically
higher densities of D. pulex are supported by more eutrophic conditions (Romanovsky and Feniova, 1985; Vanni,
1986; Steiner and Roy, 2003; Riessen and Young, 2005),
while H. gibberum is largely confined to oligo to mesotrophic lakes (Hamilton, 1958; Pejler, 1965; Hessen et al.,
1995). Although D. pulex- and H. gibberum-dominated lakes
had comparable [TN] and [TP], it is likely that D. pulexdominated lakes had greater contributions of bioavailable
(inorganic) forms of nitrogen as indicated by relatively
2
higher [NHþ
4 -N] and [NO3 -N] (discussed below). These
superior food environments may stimulate fast growth
rates of D. pulex, allowing them to achieve a size refuge
from C. americanus predation (Allan, 1973; Riessen and
Young, 2005) or enhance their reproduction leading to
increased survivorship from invertebrate predation (Neill
and Peacock, 1980). Meanwhile, H. gibberum may be competitively suppressed by increased densities of D. pulex
under such conditions (Hessen et al., 1995). On the other
hand, D. pulex can be more vulnerable to C. americanus predation in poor food conditions (Allan, 1973; Riessen and
Young, 2005). In contrast, H. gibberum is generally highly
resistant to Chaoborus predation due to protection provided
by its gelatinous capsule and thus likely to thrive in these
lakes due to reduced competition from D. pulex (Allan,
1973; Vinyard and Menger, 1980). However, given the
close resemblance of these lakes to D. pulex-dominated
lakes in terms of most limnological characteristics, it is
likely that these lakes would shift to D. pulex dominance
upon equivalent nutrient enrichment.
Dominance of small-bodied species
Predominance of small-bodied B. longirostris and cyclopoids
in relatively deep lakes may be attributed to low predation
pressure from C. americanus and reduced competition
from large-bodied species. The predation pressure from
C. americanus is likely low in these lakes owing to their lower
Nutrient and productivity dynamics of the
survey lakes
It is likely that the variation in productivity between
large-bodied species (D. pulex and H. gibberum)-dominated
84
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ZOOPLANKTON COMMUNITIES OF FISHLESS BOREAL LAKES
lakes and small-bodied species-dominated lakes (B. longirostris and cyclopoids) is related to the differences in lake
morphometry. Greater productivity in the former group
should have been supported by the uniform internal nutrient distribution throughout the growing season in these
shallow polymictic lakes (Wetzel, 2001). On the other hand,
poor productivity of the latter group may be associated
with the resistance to nutrient mixing due to thermal stratification of these deeper lakes (Richardson, 1975; Wetzel,
2001; Stefanidis and Papastergiadou, 2012) and/or smaller
nutrient pulses relative to the volume of water (lower perimeter to volume ratio) (Leibold and Norberg, 2004).
Despite comparable [TN] in D. pulex and H. gibberumdominated lakes, it is likely that D. pulex-dominated lakes
were relatively more productive as reflected by their relatively higher dissolved inorganic nitrogen (DIN) concentrations. Several indications suggest that relatively higher
[DIN] can be symptomatic of more productive conditions (Park and Marshall, 2000; Quirós, 2003; Bergström,
2010). On the other hand, low absolute [DIN]
2
([NHþ
4 -N] þ [NO3 ]) can be indicative of either high nutrient turnover under high demand, even though nutrient
supply is high or limited nutrient supply and hence, low
system productivity (Dodds, 2003). The former is likely
in the case of D. pulex-dominated lakes of the present
study, given that greater abundances of D. pulex are typically associated with more eutrophic conditions
(Romanovsky and Feniova, 1985; Vanni, 1986; Steiner
and Roy, 2003; Riessen and Young, 2005). Because
many of the factors driving the variability in terrestrial
export may operate more strongly in small drainage
basins, greater terrestrial export of inorganic nitrogen
(mostly NO2
3 -N) may have lead to higher [DIN] in
these lakes (Caraco et al., 2003; Inamdar and Mitchell,
2006). On the other hand, our assumption is further
supported by the typical association of H. gibberum with
relatively oligo to mesotrophic conditions (Hamilton,
1958; Pejler, 1965; Hessen et al., 1995).
Variations in lake productivity among survey lakes (discussed above) were not reflected by [chlorophyll a] for
several reasons. Primary productivity of lakes enhances
in response to increase in nutrients, which will in turn
enhance the biomass of grazers. Subsequently, this
increased grazer density can exert a reverse cascading
effect on phytoplankton (Vanni, 1987; Carpenter et al.,
2001). Another possibility is that the increase in nutrients
may be causing a shift in food quality (in terms of alterations in phytoplankton composition) rather than a
change in food quantity (Vanni, 1987; Hessen et al.,
2009). Lastly, the lakes were sampled during fall circulation, during which algal biomass is negatively influenced
by the light limitation and declining water temperatures.
Therefore, general measures of phytoplankton such as
[chlorophyll a] may fail to reflect the effects of nutrient
variations on primary production.
Variations in Chaoborus americanus
densities
There are two alternative explanations for the differences
in C. americanus densities among lakes. Similar to the
present study, several others have shown that relatively
higher C. americanus densities are supported in highly productive freshwater habitats (Yan et al., 1982; Wissel et al.,
2003; Riessen and Young, 2005; Labaj et al., 2013). In fact,
this can be a bottom-up effect, i.e. increased predator survivorship due to enhanced prey (grazer) abundance stimulated by greater productivity of these lakes (Neill and
Peacock, 1980). On the other hand, although there were
significant variations in C. americanus density per unit
volume among lake clusters based on species composition,
such variations were not evident for C. americanus density
per unit area. This suggests that C. americanus might have
equally used all lakes during egg deposition. Therefore,
the total number of C. americanus larvae in a lake should
only depend on its surface area. Consequently, larval
density per unit volume should be high in relatively
shallow lakes (given that surface areas of most lakes are
more or less similar), as larvae are more concentrated in a
smaller volume relative to that of deeper lakes, while exerting a greater predation pressure on zooplankton.
Spatial structuring of zooplankton
communities
The non-existence of any spatial structures in species
composition of fishless lakes across the region emphasizes
that variations in species composition among fishless
lakes was not a result of dispersal limitation, but rather
due to localized variations in environmental controls
among study lakes. This agrees with the findings of our
previous comprehensive spatial analysis which included
both fish-bearing and fishless lakes of this region, where
we revealed that dispersal of crustacean zooplankton was
not strongly limiting across the geographic region and
spatial structures of zooplankton distribution were
induced by the spatial patterns in environmental controls
(Anas et al., 2014).
Distribution of fishless lakes in the boreal
shield ecozone
The present study provides strong evidence that naturally
occurring fishless lakes represent a sizeable portion of
Canadian boreal Shield lakes. Based on the occurrences
of C. americanus, 22% (53 of 244) of the survey lakes were
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