Statistical analyses
We analysed whether and how vegetation structure and other
characteristics of grassland habitats affect the fine-scale occurrence
of snakes by building Generalized Linear Mixed Models (GLMM) separately
for each study species. In GLMMs, presence/absence of vipers was
incorporated as a binary dependent variable, while the four variables
characterising vegetation structure were applied as fixed explanatory
variables, with grass/rock/shrub surface cover in V. graeca GLMMs
and number of burrows in V. renardi GLMMs as additional fixed
variables. The sampling site was incorporated in the GLMMs as a random
factor to control for the spatial non-independence of the observations.
We fitted the GLMMs specifying binomial error distribution using the
‘lme4’ package (Bates et al., 2014). We then used an
information-theoretic framework and a model selection approach (Burnham
& Anderson, 2002) to run all possible combinations of fixed effects to
identify models with substantial empirical support based on Akaike
differences (Δi = AICi- AICmin < 2.0) and to perform model
averaging based on the relative importance of explanatory variables
using the ‘MuMIn’ package in R (Bartoń, 2018).
RESULTS
The number of snakes found (presence locations) varied from 32 to 73
across the three species. V. graeca and V. ursinii were
much rarer locally than V. renardi , and the search
effort-corrected density was an order of magnitude higher in V.
renardi than in the other two species (Table 1). Vegetation structure
was recorded by white-board photography in a total of 141 presence
locations and 726 random locations. Almost half of the pre-randomised
locations in the alpine habitats of V. graeca were in
inaccessible cliffs and 17% of pre-randomised locations fell on roads
or water bodies in V. renardi habitats (Table 1).
Most of the vegetation structure variables followed a normal
distribution, except for MHV in V. ursinii habitats, where
vegetation was at some sampling points taller than 1 m, i.e., the height
of the whiteboard (Fig. 1, 3). The correlations between vegetation
structure variables were usually not significant, except between LA and
HCV in V. graeca and in V. ursinii habitats and when data
were pooled across species, and also between LA and FHD in V.
graeca habitats (Fig. 3).
In V. graeca , the full GLMM returned no significant main effect,
whereas HCV was included in all and shrub cover was included in five of
the six best models (ΔAICc < 2). The averaged parameter
estimate was significant and positive only for HCV (Table 2), indicating
a higher chance of occurrence of V. graeca in taller and closed
vegetation.
In V. renardi , LA and the number of burrows had significant
explanatory power in the full model and LA, FHD and number of burrows
were included in both best models (ΔAICc < 2). The effects of
LA and number of burrows were positive, whereas that of FHD was negative
(Table 2), indicating higher chances of V. renardi occurrence in
microhabitats with higher, more homogeneous cover and more burrows.
In V. ursinii , the full model had the lowest AICc value, and in
the two best models (ΔAICc < 2), LA and MHV had significant
positive effects, whereas HCV had a significant negative effect (Table
2), indicating higher chances of V. ursinii occurrence in tall,
high-cover but more open vegetation.
DISCUSSION
Our study provided key results in the development of field data
collection and data processing methodology for quantifying the role of
vegetation structure in studies of animal microhabitat selection and in
understanding how vegetation affects the occurrence of snakes in
grasslands. Our results demonstrate that variables relevant in
describing vegetation structure can be derived from the automated
processing of images taken by standardised whiteboard photographs. In
addition, at least one of the variables so derived influenced the
occurrence of snakes in three species in three widely differing
grassland ecosystems.
Our method decreases subjectivity in quantifying vegetation structure as
it returns exact cover values along with the vertical range in pixel
rows and does not rely on estimates by eye, and minimises observer bias
and measurement error. Moreover, it does not require arbitrarily
delimited measurement classes to characterise vertical variation in
structure. Our method considers image pixels as the unit of analysis,
however, specification of larger units, e.g. 4, 9 or 16 image pixels
combined is also possible, which allows decreasing the resolution
(upscaling) and computing time. Repeating the analyses at different unit
sizes can provide further insight as it offers the possibility of
studying scale-dependence in habitat selection, i.e., the identification
of the environmental grain size at which animal-vegetation relationships
are the strongest (Gunton et al., 2014; Lengyel et al., 2014). The
objectivity of the method also allows comparisons made in two or more
species or ecosystems and also allows local measurements to be
extrapolated to larger areas, habitat types or ecosystems.
Our results support the role of vegetation structure in microhabitat
selection of snakes. While compositional habitat diversity (plant
species composition) and estimated cover of vegetation have been
reported to influence the occurrence of reptiles (Nemes et al., 2006;
Stumpel & van der Werf, 2012), our study confirmed that vertical
aspects of vegetation structure can also be important in the habitat
selection of reptiles (Mizsei et al., 2020b). For all three viper
species studied, HCV and LA were the most important variables,
indicating that vipers chose microhabitats where the vertical cover of
the grass was higher than average, as measured in random locations. In
the case of V. ursinii , a previous study (Máté & Vidéki, 2007)
did not find a relationship between snake occurrence and plant species
composition of the same study grassland. Our study thus also exemplifies
that considering the structural aspects of vegetation can provide
additional explanatory power in predicting the occurrence of snakes in
microhabitats.
The role of vegetation structure in the microhabitat selection of snakes
is probably determined by a trade-off between the need to hide from
predators, for which the chances are better in higher or more dense
vegetation (Wilgers & Home, 2007; Hansen et al., 2018), and the need to
thermoregulate, for which the chances are better in lower or sparser
vegetation (Muri et al., 2015). This trade-off is probably the reason
why HCV had opposite effects on the occurrence of V. graeca(positive) and V. ursinii (negative) because different species
may find different optimum values along the continuum of vegetation
height. Considering the latter negative relationship, it has to be noted
that tall wetland plants were common in V. ursinii habitats,
representing more dense cover and shading, which probably reduced the
possibility of sunbathing for the vipers (Muri et al., 2015), which can
explain why vipers appeared to avoid microhabitats with tall and dense
vegetation. Further studies of other species with different needs for
hiding vs. thermoregulation will certainly shed more light on this
trade-off in habitat selection in snakes. Such knowledge will be
fundamental for habitat restoration and conservation management actions
for snakes.
Two limitations of this study need to be mentioned for the correct
interpretation of our results. First, our study was limited by the small
number of presence locations for V. ursinii and the high spatial
scatter of V. graeca presence locations in the study area.
Unfortunately, these rare endangered species have low detectability due
to their low abundance, hidden lifestyle and camouflaged body pattern.
Data collection requires huge sampling effort that represents
significant challenges in logistics and person-power. A potential source
of error common in such studies is the assumption of absence in random
locations where the species is not found because absence cannot be
deduced without uncertainty as the individuals of the study species may
actually live at particular random locations (Olivier & Wotherspoon,
2006; Phillips et al., 2009). In our study, the possibility of this
error was high in V. renardi , which species showed extreme
abundance in the study habitat and several new presence locations were
found during sampling “random” locations. Another practical limitation
of our method is that placing the whiteboard on uneven ground or very
dense vegetation may result in changes in the vegetation next to the
whiteboard, which may distort the value of vegetation structure
variables. To avoid this problem, we recommend a careful trimming of the
vegetation in the plane and on the backside of the board to make sure
that the board is standing firmly on the ground.
In conclusion, the supplementation of standardised whiteboard
photography with automated image processing allows the calculation of
simple measures of vegetation structure that can provide additional
insight into animal-vegetation relationships beyond the role of plant
species composition. The combined use of field recording and image
processing and analysis offers several options that broaden the range of
cost-effective ecological survey methods and can make a substantial
contribution to the design and implementation of evidence-based
conservation, including the conservation of endangered, grassland
specialist vipers.
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ACKNOWLEDGEMENTS
We thank Galyna Mykytynets, Csaba Vadász, the volunteers of the Greek
Meadow Viper Working Group, the Conservation Herpetology Lab and the
LIFE HUNVIPHAB project for their assistance in the field. EM and GR were
supported by the ÚNKP-19-3-II-DE-46 and ÚNKP-20-1-I-ÁTE-1 grants
respectively within the frame of the New National Excellence Program of
the Ministry for Innovation and Technology (Hungary). EM, MS, and SL
were funded by a grant from the National Research, Development and
Innovation Office of Hungary (OTKA K 134391). Collecting data onVipera ursinii were financially supported by the European
Commission (LIFE HUNVIPHAB LIFE18 NAT/HU/000779), by the Hungarian
Ministry of Agriculture and the Kiskunság National Park Directorate.
CONFLICT OF INTEREST
The authors declare no confilct of interest.
AUTHORS’ CONTRIBUTION
E.M., M.B., G.R., G.D. and S.L. conceived the ideas and designed
methodology; E.M., M.B., G.R., B.B., D.R., M.S. and A.M. collected data;
E.M. programmed image processing and analysed the data, E.M. and S.L.
led the writing of the manuscript. All authors contributed critically to
the drafts and gave final approval for publication.
DATA AVAILABILITY STATEMENT
This study was conducted via the implementation of an R script
consisting automated image processing to measure leaf area (LA), height
of closed vegetation (HCV), maximum height of vegetation (MHC), and
foliage height diversity (FHD). The R script, example images and example
data are available at Zenodo (url).
TABLES
Table 1. Number of snakes found (presence locations), search
effort, density of snakes and number of random locations studied in the
three species.