Download Relative importance of resource quantity, isolation and habitat

ECOGRAPHY 28: 465 /474, 2005
Relative importance of resource quantity, isolation and habitat quality
for landscape distribution of a monophagous butterfly
Jochen Krauss, Ingolf Steffan-Dewenter, Christine B. Müller and Teja Tscharntke
Krauss, J., Steffan-Dewenter, I., Müller, C. B. and Tscharntke, T. 2005. Relative
importance of resource quantity, isolation and habitat quality for landscape
distribution of a monophagous butterfly. / Ecography 28: 465 /474.
Fragmentation of food resources is a major cause of species extinction. We tested the
effects of habitat area, isolation and quality for the occurrence and population density
of the endangered butterfly Polyommatus coridon. Polyommatus coridon larvae are
monophagous on the plant Hippocrepis comosa, and both species are specialised on
calcareous grassland, which is an endangered and highly fragmented habitat type in
Germany.
In 2001 we surveyed all known calcareous grasslands (n/298) around the city of
Göttingen (Germany) to map the population size of H. comosa in these habitats.
Further, habitat isolation (between-patch distance: 70 /7220 m) and habitat quality
(cover of flowering plants, height of herb layer, percent bare ground, cover of shrub
layer, wind protection, inclination) were quantified. Hippocrepis comosa occurred
on only 124 fragments, which were then surveyed by 20 min transect counts for adult
P. coridon in 2001 and 2002.
Occurrence and population density of P. coridon were largely determined by the
population size of its larval food plant, which was correlated with grassland area.
Effects of habitat isolation and habitat quality on P. coridon populations contributed
only little to the explanation.
In conclusion, this monophagous habitat specialist depended on large habitats with
large food plant populations to exist in viable populations. Habitat isolation and
quality appear to contribute to occurrence and density patterns only in landscapes
where these factors shift towards extremes, therefore general recommendations for
conservation programs are difficult as they depend on regional distinctions.
J. Krauss ([email protected]), I. Steffan-Dewenter and T. Tscharntke,
Agroecology, Univ. of Göttingen, Waldweg 26, D-37073 Göttingen, Germany. / C. B.
Müller, Inst. of Environmental Sciences, Univ. of Zürich, Winterthurerstrasse 190, CH8057 Zürich, Switzerland.
Local species extinctions and reduced recolonization
rates are caused by habitat fragmentation and deteriorations of habitat quality (Thomas et al. 2001, Tscharntke
and Brandl 2004). The occurrence and population
density patterns in relation to area and isolation
of fragments are in the focus of metapopulation studies
(Thomas et al. 1992, Hanski 1994), and most studies
also include effects of habitat quality (Hanski et al. 1994,
Eber and Brandl 1996, Hjermann and Ims 1996, Dennis
and Eales 1997, Thomas et al. 2001, Wahlberg et al.
2002). However, the relative importance of the area,
quality and isolation of habitats for extinction and
recolonization remain controversial (Dennis and Eales
1997, Hanski and Singer 2001, Thomas et al. 2001,
Dennis et al. 2003). Habitat quality and isolation
are predicted to be the main factors driving species
occurrence and population density of species (Thomas
et al. 2001). Contrary to this Moilanen and Hanski
(1998) could not find an improvement of metapopulation models after inclusion of habitat quality, whereas
Accepted 1 February 2005
Copyright # ECOGRAPHY 2005
ISSN 0906-7590
ECOGRAPHY 28:4 (2005)
465
Dennis and Eales (1997) claim that habitat quality is as
important as patch size and isolation.
Effects of habitat isolation depend on 1) the dispersal
ability of a species, 2) the distance to the nearest
colonized habitat, and 3) the landscape matrix between
habitats including barriers, corridors and stepping stones
(Murphy and Lovett-Doust 2004, Tscharntke and
Brandl 2004). Therefore the relative importance of
habitat isolation on species occurrence is difficult to
measure and many different calculations of isolation
indices are suggested (see review in Moilanen and
Nieminen 2002). However the different isolation indices
are often closely correlated (Krauss et al. 2003a, b). The
a priori expectation that habitat isolation is of great
importance in conservation issues and the use of several
isolation measurements may contribute to an overestimation of this parameter. The dispersal abilities of
butterflies seem to be often underestimated, as shown
for the small blue Cupido minimus (Krauss et al. 2004a).
Most butterfly studies focusing on effects of habitat
fragmentation were conducted in Britain (Thomas et al.
1992, Dennis and Eales 1997, Gutierrez et al. 1999,
Thomas et al. 2001). Many regions in central Europe
appear to be less fragmented (Krauss et al. 2003a, b), so
isolation effects are less obvious as shown for plant
and butterfly community studies in Germany (SteffanDewenter and Tscharntke 2000, Krauss et al. 2003a, b,
2004b). As community studies lack the sensitivity to
detect effects on single species, we chose a monophagous
habitat specialist, the butterfly Polyommatus coridon on
calcareous grasslands, as a model organism. This species
is known to show reduced heterozygosity with decreasing population size (Schmitt and Seitz 2002) and with
increasing distance to the next conspecific population
(Krauss et al. 2004c). Polyommatus coridon also reaches
its northern distribution range in our study region
(Kudrna 2002), where effects of habitat fragmentation
should be particularly important (Bourn and Thomas
2002, Krauss et al. 2004c). Polyommatus coridon should
therefore be a suitable study organism to quantify the
relative effects of habitat fragmentation and habitat
quality. As ecological processes depend on large spatial
scales (Murphy and Lovett-Doust 2004), we focused our
study on all possible habitats of P. coridon and its food
plant in the study region (ca 2000 km2) and conducted
the study in two consecutive study years.
We addressed the question, how area, isolation and
quality of habitat fragments affect the occurrence and
population density of the butterfly P. coridon.
Materials and methods
Study species
The chalkhill blue Polyommatus coridon, (Lycaenidae,
Lepidoptera) and its only larval food plant in the
466
study region, the horseshoe vetch Hippocrepis comosa
(Fabaceae), occur only on calcareous grasslands (Asher
et al. 2001, Brunken 2002, Van Swaay 2002). Both
species are endangered in Lower Saxony (Jedicke 1997)
and reach their northern distribution range in the study
region (Kudrna 2002, Hennenberg and Bruelheide 2003,
Krauss et al. 2004c). The hardly distinguishable and
similarly specialized butterfly Polyommatus bellargus
does not occur in the study region, at least since 1976
(Brunken 2002).
Only one recent P. coridon population is known to
occur north of our study area (50 km from the most
northern population of our study region). This is
also the northern distribution limit of H. comosa
(Hennenberg and Bruelheide 2003). There is a gap of
at least 30 km with no populations of P. coridon to the
east and west of the study region, while it seems to be
well connected to populations further south (unpubl.
following the ‘‘Niedersächsisches Landesamt für Ökologie’’ and Krauss unpubl.).
Polyommatus coridon is strictly univoltine and population densities of adults are generally high and often
reach levels of 500 /1000 individuals ha1 (Bink 1992,
Weidemann 1995). In the study region P. coridon is after
Maniola jurtina the most abundant butterfly species on
calcareous grasslands (Krauss et al. 2003a, b). The
dispersal ability of adult P. coridon is expected to be
moderate, compared to other butterflies (Bink 1992,
Asher et al. 2001, Cowley et al. 2001). Mark-releaserecapture experiments in the UK suggest that adults
disperse between colonies 1 /2 km apart with 1 /2%
of the populations while single adults have been seen 10 /
20 km from known colonies (Asher et al. 2001).
Habitat quality may influence the occurrence and
population density of butterflies. Low height of herb
layer (2 /10 cm) and bare ground are preferred habitat
conditions for P. coridon (Ebert and Rennwald 1991,
Asher et al. 2001) as well as low cover of shrub layer
(Brunken 2002). Wind protection and inclination
of habitats may affect the habitat quality of butterflies,
too. Cover of flowering plants is known to affect
population densities and species richness of adult
butterflies, because they are the main food resource for
adult butterflies (Feber et al. 1996, Krauss et al. 2003a).
Study sites and data collection
Region
The study region is located in the Leine-Weser mountains (Fig. 1). Calcareous grasslands in the vicinity of the
city of Göttingen (southern Lower Saxony, Germany)
were selected as study sites (n /298; following unpubl. of
the ‘‘Niedersächsisches Landesamt für Ökologie’’ and
Krauss unpubl.). The region covers ca 2000 km2 with a
structurally rich landscape mosaic of diverse habitat
ECOGRAPHY 28:4 (2005)
Fig. 1. Locations of the P. coridon and H. comosa populations in the study region around the city of Göttingen.
types. Calcareous grasslands are sharply delimited from
the surrounding landscape and cover only 0.26% of the
surface of the study region (Krauss et al. 2003a) and are
located at 200 /400 m a.s.l.
Habitat area
All calcareous grasslands were drawn in a Geographic
Information System (GIS) to calculate the habitat area
of each of the grasslands. To update these data, all
calcareous grasslands were visited in 2001 to estimate the
proportion of non-grassland area (e.g. area totally
overgrown with shrubs and trees, or destroyed habitats).
This area was subtracted from the original GIS area.
Hippocrepis comosa
The occurrence and population size of H. comosa per
habitat (in m2) were mapped together with student field
workers from 13 May /20 June 2001 (plus three patches
on 29 June) by walking throughout the whole habitats
of all known calcareous grasslands in the study region
(n /298). These walks were conducted in the main
flowering period of H. comosa, thus we assume to have
mapped all existing populations in the study region.
ECOGRAPHY 28:4 (2005)
Polyommatus coridon
The occurrence and population density of P. coridon
were established by counting adult individuals during
20 min transect walks in 2001 (13 July /2 August, one
patch 14 August) and again in 2002 (23 July / 3 August,
one patch 16 August) on all habitats with the larval food
plant H. comosa (n /124). The transect walks were
conducted from 10.00 /17.00 h when weather conditions
were suitable for butterfly activity (temperature: /178C,
wind: B/3 Beaufort scale) (Pollard 1977). From all
transect walks 33 were conducted between 17.00 and
19.00 h when it was sunny and temperature was above
208C, thus the butterflies have not started to roost. We
randomly selected the sequence of habitat visits, but to
reduce phenology effects we started with the habitats in
the southern part of the study region. To reduce daytime effects of sampling, we visited the sites in a different
time-sequence in the two study years. Butterfly individuals were counted within an area of 2.5 m to each side
of the randomised transect route and the transect
distance was measured with a step counter in 2002,
thereby allowing a calculation of population density
per square meter. We assume similar transect distance
in both study years, because transect time was equal
467
(20 min). For all habitats where P. coridon individuals were
found in at least one of the two study years we calculated
the mean population density of P. coridon. We further
reanalysed our data for each study year separately, but
show the results only when different patterns emerged.
Habitat isolation
Habitat isolation was defined as the shortest distance to
the nearest P. coridon population. Only patches with at
least ten individuals found in one of the two study years
were considered as potential source populations. Further
habitat isolation was calculated as 1) the shortest
distance to the next habitat with H. comosa, independent
of occurrence of P. coridon, and as 2) the connectivity
measurement according to Hanski et al. (1994) with
different indices (for more details see Krauss et al.
2004a). However, none of these additional eight indices
and isolation calculations increased the explanatory
power that we got from the simple nearest-neighbour
calculation. Therefore these results are not shown.
Habitat quality
We estimated six parameters of habitat quality, which
may have affected occurrence and population density of
P. coridon. The cover of plant species in flower (%), the
height of herb layer (cm), the percent bare ground (%),
the cover of shrub layer (%), and inclination (%) were
estimated in the field after the butterfly surveys. Wind
protection (%) was estimated as the proportion of shrub
or tree layer adjacent to the edges of the study site. Wind
protection and cover of shrub layer were estimated for
the study site as a whole (entire calcareous grassland),
while the other habitat factors were estimated within the
corridors covered by the transect walks.
matrix of probability of occupancy, in the figures
showing logistic regressions. Arithmetic means9/one
standard error of back-transformed data are given in
the text.
All independent non-transformed habitat factors for
the 124 habitats where H. comosa occurred are shown in
Table 1, while Pearson correlations between the transformed habitat factors are given in Table 2. Spatial
autocorreleation was tested for the nine habitat factors
(n /124), the occurrence data of P. coridon (n /124)
and the population density data of P. coridon (n/88).
We used the statistic program R (Anon. 2004) to
calculate Mantel statistics based on Spearman’s rank
correlation with 1000 permutations and euclidian distances as similarity indices (Legendre and Legendre
1998). No significant spatial autocorrelations were found
(all p /0.3), except for the habitat factor ‘‘isolation’’ (r/
0.113, pB/0.001).
Results
Hippocrepis comosa
Plants of H. comosa were found in 42% of the surveyed
298 calcareous grasslands. The percentage of deviance
explained by habitat area was 5% (Fig. 2A).
In the 124 habitats where H. comosa occurred, habitat
area was the only remaining significant factor in multiple
regressions of population size, explaining 21% (Fig. 2B),
after taking into account all independent habitat factors
(see Table 1). The distance to the nearest H. comosa
population was also taken into account, but did not
contribute to the explanation.
Statistical analyses
Occurrence of Polyommatus coridon
The statistical analyses of data were performed using the
software ‘‘Statgraphics Plus for Windows 5.1’’ (Anon.
2001). All data were tested for normality (skewness and
kurtosis), and were transformed where necessary. The
independent variables habitat area, cover of H. comosa
and habitat isolation were log10 transformed, while
percent bare ground, cover of shrub layer and inclination
were arcsine-square root transformed; the factors height
of herb layer, cover of plant species in flower and wind
protection were not transformed.
Polyommatus coridon population density was the
dependent variable and log10 transformed. We calculated
sign-tests, Pearson correlations, single and multiple regressions and single and multiple logistic regressions. The
statistic tests are mentioned with the result throughout the
text. Multiple regressions were calculated with stepwise
backward elimination (Sokal and Rohlf 1995). We
used the average of 10 data points, in the 0/1 data
Individuals of the butterfly P. coridon were found on
71% of the calcareous grasslands harbouring the larval
food plant H. comosa. The probability of P. coridon
occupancy was not significantly different between the
study years (2001: 0.6539/0.044; 2002: 0.6109/0.045;
sign-test: n/118, p/0.359). Even though 19 fragments
with B/10 individuals in one year and no butterflies in
the other year occurred.
The probability of occurrence of P. coridon individuals
was best explained by the size of H. comosa population
explaining 23% of the deviance (Fig. 3A). Distance to
the nearest P. coridon population explained a further
7%, but even the most isolated habitats in our study
region had a 50% probability of occupancy (Fig. 3B).
The most isolated P. coridon population was even found
7.2 km from the nearest known population (Table 1).
Habitat area and wind protection appeared to play
a minor role for patch occupancy, explaining less than
468
ECOGRAPHY 28:4 (2005)
Table 1. Mean9/standard error, median, minimal and maximal values of habitat factors (n/124) with occurrence of H. comosa in
the study region.
Mean9/standard error
2
Size of H. comosa population (m )
Habitat area (m2)
Cover of plant species in flower (%)
Distance to next P. coridon population (m)
Distance to next H. comosa population (m)
Height herb layer (cm)
Cover bare ground (%)
Cover shrub layer (%)
Inclination (%)
Wind protection (%)
2309/60
95909/1670
219/1.1
12109/100
5109/48
209/0.5
109/1.0
239/1.6
99/0.5
639/2.4
a further 5% each (Table 3). Habitat area and size of
H. comosa population were correlated (Table 2). Multiple logistic regressions for each of the two study years
separately confirmed the dominant role of H. comosa
population size for occurrence of P. coridon individuals
(results not shown), but in 2002, cover of plant species in
flower, cover of shrub layer and height of herb layer were
also significant factors, although explaining less than a
further 5% each.
Population density of Polyommatus coridon
Population densities of P. coridon were not significantly
different between the two study years (2001: 0.00909/
0.0016 m 2; 2002: 0.00999/0.0018 m 2; sign-test: n/
84, p/0.822). The population density was mainly
related to H. comosa population size in multiple regression analysis, explaining 42% of variance (Table 4).
Habitat area, isolation and height of herb layer had also
a significant effect on population density in multiple
regression analyses (Table 4). In simple regressions
population size of H. comosa, area and height of herb
layer were significant, but not habitat isolation (Fig. 4
A /D). Further, cover of plant species in flower in 2001
and percent bare ground in 2002 showed significant
effects (results not shown), but explained less than a
further 5% each.
Discussion
Habitat area and food plant population size
The main focus of this study was on the relative
importance of habitat fragmentation and habitat quality
for occurrence and population density of the butterfly
Polyommatus coridon. The results show the importance
of larval food plant availability (which is correlated with
habitat area) for this monophagous habitat specialist.
Host plant density is also the most important predictor
for the population size of another monophagous, habitat
specialised butterfly, the small blue Cupido minimus
(Leon-Cortes et al. 2003, Krauss et al. 2004a). Further
ECOGRAPHY 28:4 (2005)
Median
Minimum
Maximum
28
3700
20
980
340
20
5
20
8
65
0.1
50
0.1
70
60
5
1
1
1
0
5000
152330
60
7220
2880
35
80
80
25
100
studies confirm the importance of resource quantity. For
example, extinction and colonisation rates of Euphydryas
aurinia were mainly affected by host plant abundance
(Wahlberg et al. 2002, Hula et al. 2004) and Thomas
(1983) found that one third of the extinction of Polyommatus bellargus resulted from the loss of its larval
food plant. In contrast to C. minimus and P. bellargus
(Thomas 1983, Rusterholz and Erhardt 2000, Harper et
al. 2003), adults of P. coridon do not feed on their larval
food plant, due to a missing phenological concurrence
(Ebert and Rennwald 1991). Nevertheless P. coridon and
its host plant are specialised on calcareous grasslands
(Hennenberg and Bruelheide 2003, Krauss et al. 2004c),
and adult densities were driven by the amount of larval
food plants.
Habitat quality
Larval food plants may differ in food quality for their
hosts (Goverde and Erhardt 2003) and hence, cause
differences in habitat suitability (Thomas et al. 2001,
Wahlberg et al. 2002, Konvicka et al. 2003, Leon-Cortes
et al. 2003). The food plant quality for larvae was not
directly measured in this study, but indirectly by habitat
characteristics that may have affected food plant growth
and quality due to changed environmental conditions
(e.g. microclimate). However, the measured habitat
quality did not substantially contribute to the explanation of food-plant or butterfly density in our study.
Habitat quality factors were sometimes significant,
especially in single study years, but they never explained
/5% of the variance. This pattern suggests that habitat
quality may play a role under certain circumstances,
especially when habitat area and food plant populations
are small.
Adult P. coridon individuals are known to feed mainly
on Lotus corniculatus, Scabiosa columbaria, Centaurea
jacea and C. scabiosa (Ebert and Rennwald 1991). Lotus
corniculatus was ubiquitous in the study sites, while
the combined abundance of Centaurea sp. and Scabiosa
columbaria was closely related to total cover of plant
species in flower (Pearson: n/124, r/0.839, pB/
0.0001). Adult food plant availability seems to play a
469
Probability of occupancy in %
75
50
25
0
10
100
10000
(B)
1000
100
10
1
0.1
1000
10000
100000
Habitat area in m²
/
/
/
/
/
/
/
/
/
/
/
/
/
100
Size of H. comosa population
Habitat area
Cover of plant species in
flower
Distance to next P. coridon
population
Height herb layer
Cover bare ground
Cover shrub layer
Inclination
1000 10000 100000
Habitat area in m²
H. comosa population size
in m²
n.s.
n.s.
**
n.s.
0.002
0.004
0.276
0.052
/0.106 n.s.
0.275 **
0.316 ***
/
0.153 (*)
/0.003 n.s.
/
/
/0.527 ***
/
/
/
/
/
/
/
0.102 n.s.
/0.105 n.s.
/0.095 n.s.
/0.170 (*)
/
/
/0.023 n.s.
0.092 n.s.
0.041 n.s.
/0.069 n.s.
0.051 n.s.
0.159 (*)
/0.040 n.s.
/0.062 n.s.(1)
/0.114 n.s.
0.036 n.s.
0.252 **
0.270 **
/
0.458 ***
/
/
/0.111 n.s.
0.003 n.s.
0.135 n.s.
0.066 n.s.
0.034 n.s.
/0.153 (*)
/0.161 (*)
/0.0434 n.s.
/0.117 n.s.
Wind protection
Inclination
Cover shrub
layer
Cover bare
ground
Height herb
layer
Distance to next
P. coridon population
Cover of plant
species in flower
Habitat area
Table 2. Pearson correlation matrix between the transformed habitat factors (n/124). Transformations see Statistical analyses in the Material and methods section. Significance levels
are: *** pB/0.001, ** p B/0.01, * pB/0.05, (*) pB/0.1, n.s./not significant. (1) Correlation: size of H. comosa population with distance to next H. comosa population r /0.121 n.s.
470
Hippocrepis comosa
100 (A)
Fig. 2. (A) Probability of occupancy of H. comosa populations
in relation to habitat area of calcareous grasslands (n/298).
Logistic regression: y/exp(eta)/(1/exp(eta)), eta/ /2.59/
0.70 log10(x), x2 /21.01, percentage of deviance/5.14%, p B/
0.0001. Note: only the factor habitat area was tested. Note:
we used the average of 10 data points, in the 0/1 data matrix of
probability of occupancy to reduce data points. (B) Hippocrepis
comosa population size in relation to habitat area of calcareous
grasslands (n /124). Simple regression: log10(y)/ /0.73/
0.64log10(x), F/32.44; r/0.458; p B/0.0001. Note: habitat area
is the only remaining significant factor in multiple regressions.
Habitat factors see Table 1, Pearson correlations see Table 2.
less important role, as food resources are usually not
limited on these flower-rich calcareous grasslands.
Roosts for adult butterflies are assumed to be of
importance for several species (Dennis et al. 2003). At
night time most P. coridon seem to roost on the
calcareous grassland sites (Krauss unpubl.). This is in
agreement with observations for the closely related
P. bellargus in Britain (Thomas 1983).
Habitat isolation
Contrary to expectations by many lepidopterists (Cowley
et al. 2001), even the butterfly C. minimus which is
ECOGRAPHY 28:4 (2005)
Probability of occupancy in %
Polyommatus coridon
100
(A)
75
50
25
0
0.1
1
10
100
1000 10000
Probability of occupancy in %
Hippocrepis comosa population size in m²
100
Polyommatus coridon
(B)
isolation did not appear to be a major factor limiting
occurrence or population density, even though the
genetic diversity of P. coridon decreased with increasing
isolation (Krauss et al. 2004c). Distance to the nearest
P. coridon population explained in the current study only
a further 7% of probability of occupancy, but even for the
largest distances in our study region the probability was
ca 50%. The most isolated P. coridon population was
found 7.2 km from the next known populations. For the
closely related butterfly P. bellargus an isolation effect
seemed unlikely in a study /20 yr ago (Thomas 1983),
but in a recent study isolation affected its occurrence
negatively (Thomas et al. 2001). Probably the most
important reason, why occurrence (and density) of
species are affected by isolation, is the dispersal ability
of a species in relation to the extent of the tested
isolation. This is obviously contingent on the study
region (Krauss et al. 2004a).
Population densities
75
50
25
0
100
1000
10000
Distance to next P. coridon population in m
Fig. 3. (A) Probability of occupancy of P. coridon populations in relation to its host plant population size (n/124).
Logistic regression: y/exp(eta)/(1/exp(eta)), eta/ /1.30/
1.68log10(x), x2 /34.34, percentage of deviance /22.98%, pB/
0.0001. (B) Probability of occupancy of P. coridon populations
in relation to the distance to the next P. coridon population
(n/124). Logistic regression: y /exp(eta)/(1/exp(eta)), eta/
5.07/1.42log10(x), x2 /9.91, percentage of deviance /6.63%,
p/0.002. Note: H. comosa population size and distance to the
next P. coridon population were the best predictors for the
occupancy of P. coridon in multiple logistic regressions, see
Table 3. Note: We used the average of 10 data points, in the 0/1
data matrix of probability of occupancy to reduce data points.
categorized as a most sedentary species was not affected
by isolation (Krauss et al. 2004a). Polyommatus coridon
is expected to be a better disperser, and similarly, habitat
Population density increased with increasing food plant
abundance and habitat area. This is in coincidence
with most findings for insects (Connor et al. 2000).
For butterflies, densities of selected species have been
shown to increase (Thomas et al. 1992), to decrease
(Hanski et al. 1994) or to show no effect (Thomas et al.
2001) with increasing habitat area. These inconsistent
results might depend on the number of replicates and
the range of habitat area (Bowers and Matter 1997)
and temporal variation (Matter 2003). In our study,
population density did not significantly decrease with
increasing habitat isolation with simple regressions,
while it did so in multiple regressions, explaining
additionally 4% to the model (Table 4). Hence, isolation
effects are masked by other habitat factors in our study
region. In a study in Finland on Melitaea cinxia
population density does decrease with increasing habitat
isolation (Hanski et al. 1994), whereas Thomas et al.
(2001) found no isolation effect, but a habitat quality
effect on the density of all studied butterfly species.
Population density patterns of herbivores are not yet
fully understood and may be related not only to food
plant quantity but also to area-dependent changes in
predation or parasitism rates (Steffan-Dewenter and
Tscharntke 2002); thus further research is urgently
required (Matter 2003).
Table 3. Multiple logistic regression of the transformed habitat factors and the probability of occupancy of P. coridon . Occupancy
of a habitat was assumed, when P. coridon was found either in the year 2001 or 2002. Only significant factors are shown.
n/124; Model: p B/0.0001
2
Size of H. comosa population (m )
Distance to next P. coridon population (m)
Habitat area (m2)
Wind protection (%)
ECOGRAPHY 28:4 (2005)
x2
p
Percentage of deviance%
delta
22.66
8.13
6.49
5.05
B/0.0001
0.004
0.011
0.025
22.98
29.58
33.98
37.36
22.98
/6.60
/4.40
/3.38
471
Table 4. Simple regressions and multiple regression models between P. coridon population density and the transformed habitat
factors. Transformations see Statistical analyses in the Material and methods section.
Mean (2001, 2002), n/88
Simple regression
2
Size of H. comosa population (m )
Habitat area (m2)
Cover of plant species in flower (%)
Distance to next P. coridon population (m)
Height herb layer (cm)
Cover bare ground (%)
Cover shrub layer (%)
Inclination (%)
Wind protection (%)
F
r
p
T
%
Delta
p
62.29
22.33
3.81
0.77
7.86
2.81
2.18
0.55
1.46
0.648
0.454
0.206
/0.094
/0.289
0.178
/0.157
/0.080
0.129
B/0.0001
B/0.0001
0.054
0.384
0.006
0.097
0.143
0.460
0.230
6.18
3.16
/
/2.59
/3.35
/
/
/
/
42.00
51.04
/
54.71
46.23
/
/
/
/
42.00
/4.81
/
/3.67
/4.23
/
/
/
/
B/0.0001
0.002
n.s.
0.011
0.001
n.s.
n.s.
n.s.
n.s.
The relative importance of habitat area, isolation
and quality
Larval food plant resource is the main predictor for
adult butterfly occurrence and butterfly density in this
study. Habitat isolation and quality played a minor role
in our study region. Habitat definition is often based on
food plant availability, but other factors might contribute to describe a habitat (Dennis et al. 2003). The
definition of habitat quality is also crucial and researcher
might define larval food plant quantity as habitat
quality. Habitat quality might be as important as area
0.1
(A)
Polyommatus coridon
population density per m ²
Polyommatus coridon
population density per m²
0.1
0.03
0.01
0.003
0.001
0.0003
10
1
100
1000
0.03
0.01
0.003
0.001
0.0003
100
10000
0.1
Polyommatus coridon
population density per m²
(B)
0.03
0.01
0.003
0.001
0.0003
1000
10000
Distance to next P. coridon population in m
Hippocrepis comosa population size in m²
Polyommatus coridon
population density per m²
(C)
0.0001
0.0001
0.1
Multiple regression model Final model: 54.71%
(D)
0.03
0.01
0.003
0.001
0.0003
0.0001
0.0001
100
1000
10000
100000
Habitat area in m²
0
10
20
30
40
Height herb layer in cm
Fig. 4. (A) Population density of P. coridon in relation to the population size of its larval food plant H. comosa (n /88). Simple
regression: log10(y)/ /3.62/0.59log10(x). Statistics see Table 4. (B) Population density of P. coridon in relation to the habitat area
of calcareous grasslands (n/88). Simple regression: log10(y)/ /4.45/0.53log10(x). Statistics see Table 4. (C) Population density of
P. coridon in relation to the distance to the next P. coridon population (n/88). Simple regression: not significant. Statistics see Table
4. (D) Population density of P. coridon in relation to the height herb layer (n/88). Simple regression: log10(y)/ /1.82 / 0.04(x).
Statistics see Table 4.
472
ECOGRAPHY 28:4 (2005)
and isolation (Dennis and Eales 1997, Thomas et al.
2001), even though metapopulation ecologists did not
find an improvement of their model taking habitat
quality into account (Moilanen and Hanski 1998). The
possible reason for these findings is that habitat area
often strongly correlates with larval food plant quantity
(Krauss et al. 2004a, this study). Further habitat quality
factors, for example the number of roosts, might be
masked by habitat area. We conclude that the relative
importance of habitat area, isolation and quality highly
depend on the definition of the habitat and of habitat
quality and depend on possible correlations between
habitat area and quality.
Conclusions for conservation
In this study larval food plant quantity is the first and
most important habitat factor for host occurrence and
host density. As a good guideline for conservation
programs, food plant specialists can be protected by a
high quantity of the larval food plant, even independent
of habitat quality and isolation, if these factors do not
shift towards extremes. However, food plant and habitat
quality as well as resource isolation may become more
important in other regions greatly differing in distances
and overall landscape matrix. Thus management suggestions are difficult to generalize for other regions and for
species that are not monophagous habitat specialists.
Acknowledgements / We thank Marcel Goverde and Toomas
Tamaru for helpful comments on the manuscript, Stefanie
Spiller, Marina Tsaliki and Viola Vorwald for field assistance
and Doreen Gabriel for her advice in spatial autocorrelation
statistics. This work was financially supported by the German
Science Foundation (Deutsche Forschungsgemeinschaft).
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