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\begin{document}

\title{Automated image processing for quantitative characterization of
grassland vegetation structure: microhabitat selection in threatened
meadow and steppe vipers}



\author[1]{Edvard Mizsei}%
\author[2]{Mátyás Buday}%
\author[2]{Gergő Rák}%
\author[3]{Barnabás Bancsik}%
\author[4]{Dávid Radovics}%
\author[4]{Márton Szabolcs}%
\author[5]{Attila Móré}%
\author[6]{Csaba Vadász}%
\author[7]{György Dudás}%
\author[4]{Szabolcs Lengyel}%
\affil[1]{ELKH}%
\affil[2]{Eötvös Loránd University}%
\affil[3]{University of Veterinary Medicine}%
\affil[4]{Centre for Ecological Research}%
\affil[5]{University of Debrecen Faculty of Science and Technology}%
\affil[6]{Kiskunság National Park Directorate}%
\affil[7]{Bükk National Park Directorate}%


\vspace{-1em}



  
  \date{May 22, 2024}


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\selectlanguage{english}
\begin{abstract}
1. Understanding animals' selection of microhabitats is important in
both ecology and biodiversity conservation. However, there is no
generally accepted methodology for the characterisation of
microhabitats, especially for vegetation structure. 2. Here we present a
method that objectively characterises vegetation structure by using
automated processing of images taken of the vegetation against a
whiteboard under standardised conditions. We developed an R script for
automatic calculation of four vegetation structure variables derived
from raster data stored in the images: leaf area (LA), height of closed
vegetation (HCV), maximum height of vegetation (MHC), and foliage height
diversity (FHD). 3. We demonstrate the applicability of this method by
testing the influence of vegetation structure on the occurrence of three
viperid snakes in three grassland ecosystems: Vipera graeca in mountain
meadows in Albania, V. renardi in loess steppes in Ukraine and V.
ursinii in sand grasslands in Hungary. 4. We found that the variables
followed normal distribution and there was minimal correlation between
those. Generalized linear mixed models revealed that snake occurrence
was positively related to HCV in V. graeca, to LA in V. renardi and to
LA and MHC in V. ursinii, and negatively to FHD in V. renardi, and to
HCV in V. ursinii. 5. Our results demonstrate that biologically
meaningful vegetation structure variables can be derived from automated
image processing. Our method minimises the risk of subjectivity in
measuring vegetation structure, allows upscaling if neighbouring pixels
are combined, and is suitable for comparison of or extrapolation across
different grasslands, vegetation types or ecosystems.%
\end{abstract}%



\sloppy


TITLE

Automated image processing for quantitative characterization of
grassland vegetation structure: microhabitat selection in threatened
meadow and steppe vipers

AUTHORS

Edv\selectlanguage{ngerman}árd Mizsei\textsuperscript{1,2,3*}, Mátyás Budai\textsuperscript{4},
Gerg\selectlanguage{polish}ő \selectlanguage{english}R\selectlanguage{ngerman}ák\textsuperscript{4}, Barnabás Bancsik\textsuperscript{5}, Dávid
Radovics\textsuperscript{1,3}, Márton Szabolcs\textsuperscript{1},
Attila Móré\textsuperscript{2,3}, Csaba Vadász\textsuperscript{2},
György Dudás\textsuperscript{6}, Szabolcs Lengyel\textsuperscript{1}

AFFILIATIONS

\textsuperscript{1}Conservation Ecology Research Group, Department of
Tisza Research, Danube Research Institute, Centre for Ecological
Research, Hungary

\textsuperscript{2}Kiskunság National Park Directorate, Hungary

\textsuperscript{3}Department of Ecology, University of Debrecen,
Hungary

\textsuperscript{4}Department of Systematic Zoology an Ecology, Eötvös
Loránd University, Budapest, Hungary

\textsuperscript{5}Department of Ecology, University of Veterinary
Medicine, Budapest, Hungary

\textsuperscript{6}Bükk National Park Directorate, Eger, Hungary

*Corresponding author; e-mail: edvardmizsei@gmail.com

ABSTRACT

\begin{enumerate}
\tightlist
\item
  Understanding animals' selection of microhabitats is important in both
  ecology and biodiversity conservation. However, there is no generally
  accepted methodology for the characterisation of microhabitats,
  especially for vegetation structure.
\item
  Here we present a method that objectively characterises vegetation
  structure by using automated processing of images taken of the
  vegetation against a whiteboard under standardised conditions. We
  developed an R script for automatic calculation of four vegetation
  structure variables derived from raster data stored in the images:
  leaf area (LA), height of closed vegetation (HCV), maximum height of
  vegetation (MHC), and foliage height diversity (FHD).
\item
  We demonstrate the applicability of this method by testing the
  influence of vegetation structure on the occurrence of three viperid
  snakes in three grassland ecosystems: \emph{Vipera graeca} in mountain
  meadows in Albania, \emph{V. renardi} in loess steppes in Ukraine
  and\emph{V. ursinii} in sand grasslands in Hungary.
\item
  We found that the variables followed normal distribution and there was
  minimal correlation between those. Generalized linear mixed models
  revealed that snake occurrence was positively related to HCV
  in\emph{V. graeca} , to LA in \emph{V. renardi} and to LA and MHC
  in\emph{V. ursinii} , and negatively to FHD in \emph{V. renardi} , and
  to HCV in \emph{V. ursinii} .
\item
  Our results demonstrate that biologically meaningful vegetation
  structure variables can be derived from automated image processing.
  Our method minimises the risk of subjectivity in measuring vegetation
  structure, allows upscaling if neighbouring pixels are combined, and
  is suitable for comparison of or extrapolation across different
  grasslands, vegetation types or ecosystems.
\end{enumerate}

KEYWORDS

biodiversity monitoring, ecological complexity, habitat diversity,
habitat selection, reptile, Viperidae, visual obstruction reading

INTRODUCTION

Predicting the occurrence or abundance of animals hiding in the
vegetation has been one of the earliest challenges for mankind and
remains so for many ecologists. Understanding how animals choose
microhabitats is a central aim in ecology and is fundamental for
evidence-based conservation (Johnson et al., 2014). Habitat selection is
a key evolutionary strategy because it both depends on and is influenced
by resource availability and interactions with conspecifics and other
species. Thus it has inevitable influence on individual fitness, and
accordingly the evolution of life-history traits is associated with
habitat properties (Morris, 2003). Habitat selection can thus be
interpreted to reflect an adaptive strategy on evolutionary time scale
optimalization (MacArthur et al., 1962; Pianka, 1973), although
intraspecific competition and population density may also influence the
choice of the individuals (Fretwell \& Lucas, 1970; Lawlor \& Smith,
1976), and these costs of a particular choice are rarely considered
(Rosenzweig, 1981).

The characterisation of microhabitats, however, has proven to be
difficult and there is no generally accepted methodology applicable
across ecosystems, habitat types and animal groups (Stein et al., 2014).
More complex habitats, i.e., those characterized by higher microhabitat
diversity, are supposed to sustain a higher number of ecological niches
and species occupying them compared to habitats with decreased
structural diversity (MacArthur \& MacArthur, 1961; Loke et al., 2015).
Habitat or microhabitat diversity is often divided into two components:
compositional diversity arises from the identity of different elements,
whereas structural diversity arises from the two- or three-dimensional
physical arrangement of the elements (Tews et al., 2004; Lengyel et al.,
2016). Both aspects can be further subdivided into abiotic components
(e.g., composition: soil types, hydrology; structure: elevation,
topography) and biotic components (e.g., composition: plant species
identity; structure: vegetation complexity). The quantification of
abiotic elements and biotic compositional elements is usually
straightforward via objective measurements (e.g., for soil types,
hydrology: qualitative list of soil types, maps, measurement of
groundwater table; for elevation, topography: GPS readings, landform
diversity; plant species/association identity: list of species or plant
associations). In contrast, a plethora of context-dependent methods have
been used to measure vegetation structure (Mushinsky \& McCoy, 2016).
Several terms have been used for vegetation structure, such as
structural complexity/diversity, canopy/foliage height/diversity,
vegetation complexity/heterogeneity, architectural complexity (Tews et
al., 2004). In studies of animal habitat selection, vegetation structure
is often quantified by estimates of phytomass or by cover estimates.
Additional methods include quantifying the presence or cover of
structures formed by plants, e.g. tussocks, shrubs, dead phytomass such
as leaves, height of shoots, leaf area, cover at various heights
(Benkobi et al., 2000; Vermeire \& Gillen, 2001; Pringle et al., 2003;
Garden et al., 2007; Faria \& Silva, 2010; Stumpel \& van der Werf,
2012; Mizsei et al., 2020a). Many of these methods depend on subjective
eyeball estimates made in the field confounded by observer bias and
measurement error (Milber et al., 2008; Bergstedt et al., 2009), e.g. on
plant cover, or return one value, e.g. vegetation height or phytomass,
which, at most, is a proxy for the 3-D physical arrangement or
distribution of vegetation elements. All these drawbacks prevent
generalisations of animal-vegetation structure relationships across
habitats, ecosystems and spatial scales. There is thus a clear need for
objective methods that provide balanced measurements on multiple
variables including both the horizontal and vertical distribution of
vegetation elements and the one-value characteristics that succinctly
summarise important aspects of vegetation structure.

Reptiles are among the most threatened vertebrates and decline globally
due to habitat loss and degradation, introduced invasive species,
environmental pollution, diseases, unsustainable use of
natural/seminatural habitats and climate change (Gibbons et al., 2000).
By now, one out of five reptile species has become threatened by global
extinction, and local extinctions are becoming common. To cope with
this, conservation priorities should be determined and actions should be
implemented to reduce this rate (Böhm et al., 2013). In Europe, the
meadow and steppe vipers (\emph{Vipera ursinii} complex) are among the
most threatened reptiles. Lowland populations of this complex (\emph{V.
renardi} , \emph{V. u. rakosiensis} , \emph{V. u. moldavica} ) lost
almost all their habitats due to transformation of grasslands to
croplands, and populations in Austria, Bulgaria and Moldova have gone
completely extinct (Krecsák et al., 2003; Tupikov \& Zinenko, 2015;
Mizsei et al., 2018a). Alpine populations are threatened by overgrazing
and climate change (Mizsei et al., 2020b). Although habitat restoration
is increasingly used in reptile conservation (e.g. Péchy et al., 2015;
Triska et al., 2016; Michael et al., 2018), little is known on the
efficiency of these actions due to lack of knowledge on vegetation
characteristics preferred by target reptiles or due to lack of proper
monitoring (Block et al., 2001; Ruiz-Jaen \& Aide, 2005; Jellinek et
al., 2014; Mizsei et al., 2020b).

Here we present an approach to explore animal-vegetation relationships
by objectively characterising vegetation structure by using
photography-based standardized field data recording followed by
computer-based automated quantification of particular structural
attributes of the vegetation. We demonstrate the applicability of this
approach in a case study using data collected in grassland habitats of
three populations of the threatened \emph{V. ursinii} complex of meadow
vipers. While the applied field photography, based on images taken from
vegetation against a whiteboard under standard conditions, has been used
before, the algorithm-based quantification of vegetation structure, to
our knowledge, is novel in the literature. We show that the variables
derived from this methodology explain a significant part of the
variation in snake occurrence and that the approach can thus be an
important part of the repertoire of methods to characterise vegetation
structure in studies of animal habitat selection.

MATERIAL AND METHODS

\textbf{Study species and questions}

We studied snake-vegetation relationships in habitats of three species
representing the three main phylogenetic lineages of the \emph{V.
ursinii} complex (Freitas et al., 2020). \emph{V. graeca} (Nilson \&
Andrén, 1988) is an endangered species endemic to the Pindos mountains
in Albania and Greece, inhabiting dry sub-alpine grasslands above the
treeline between 1600 and 2200 m above sea level (Mizsei et al.,
2018b).\emph{V. renardi} (Christoph, 1861) is widely distributed in the
steppe biome from Ukraine to China, from lowlands to high mountains, and
shows high intraspecific genetic diversity (Zinenko et al., 2015). The
nominal subspecies \emph{V. r. renardi} , sometimes referred to as
``western'' or ``lowland'' \emph{V. renardi} (Nilson \& Andrén, 2001;
Zinenko et al., 2015) was involved in our study. \emph{V. ursinii}
(Bonaparte, 1835) is a vulnerable species endemic to Europe, consisting
of three phylogenetically divergent lineages in sub-alpine meadows and
two on lowland grasslands (Ferchaud et al., 2012; Mizsei et al.,
2017).\emph{V. ursinii rakosiensis} (Méhely, 1893), also known as the
Hungarian meadow viper, an endangered lowland subspecies (Péchy et al.,
2015) was involved in our study. In each study system, we addressed the
same three questions: (1) Which variables describing vegetation
structure explain the occurrence of particular snake species? (2) Are
differences between influential vegetation structure variables related
to differences in habitat selection between species? (3) Which variables
can be recommended for use in studies of snake habitat selection in
grasslands?

\textbf{Field data collection}

Sampling sites were selected in typical viper habitats in Albania
(\emph{V. graeca} : Kulmak and Tomorr mountains), Hungary (\emph{V.
ursinii} : Peszéradacs meadows) and Ukraine (\emph{V. renardi} :
Davydivka steppe) (Fig. 1). We searched for vipers by walking slowly in
the designated habitat patches in weather conditions suitable for viper
activity. When observing a viper, we recorded the coordinates of the
location with standard GPS precision, the time of observation, and the
age and sex of the individual. Spatial data on fine-scale location of
recorded vipers (hereinafter: points of known viper presence) were
collected for several days at each site (\emph{V. graeca} : July 31 to
August 12, 2019; \emph{V. renardi} : September 25 to October 2,
2019;\emph{V. ursinii} : September 10 to 15, 2019, and April 19 to May
18, 2020). The number of days spent with field data collection was
adjusted to the density of snakes estimated in previous site visits. At
the end of each search period, we recorded vegetation structure at all
the points of known viper presence. To draw a distinction between the
parts of habitats actually used by vipers and the surrounding parts not
used by vipers, a minimum convex polygon (MCP) covering the points of
known viper presence with a 10 m buffer was drawn. Outside the MCP
covering the parts of habitats actually used by vipers randomly selected
points were placed (i.e., at least 10 m far from the closest point of
known viper presence (hereinafter: random points). The randomly selected
points were not in the analyses in case of being placed in areas
inaccessible to the surveyor or apparently unsuitable for snakes such as
rocky cliffs, water bodies or arable fields. The number of random points
was set at five times the number of presence locations.

\textbf{Quantifying vegetation structure}

Vegetation structure was recorded in the field by taking photographs of
the vegetation against a whiteboard applying standardised settings. The
whiteboard was made of plexiglass of size 0.25 m (width) ×1 m (height),
installed in a vertical position on its shorter edge at all viper
presence and random locations. The vegetation against the whiteboard was
photographed with a digital SLR camera (55 mm focal length and maximum
f/11 aperture) fixed at a height of 0.5 m in a distance of 4 m from the
whiteboard (Fig. 2) as in Volesky et al. (1999).

The resulting photographs were pre-processed (cropping, white
adjustment, retouching) with the GIMP 2.8.18. image editing software.
Next, we applied image processing using an automated \emph{for
loop}written in the R statistical environment (version 3.6.1., R Core
Team, 2019). The script is available in Supplementary Material (SM). The
script first retrieved the images by the `load.image' function of the
`imager' package (Barthelme, 2019), converted it to a black and white
image by the `grayscale' function, and then to a binary (0-1) image by
the `threshold' function of the `imager' package. The resulting image
was converted to a data frame using the `as.data.frame' function and the
coordinates of every image pixel covering the whiteboard were calculated
(0.25×1 m, average resolution: 1 to 1.5 megapixels
image\textsuperscript{-1}). The resulting data frame had three columns
for each image, the x and y pixel coordinates (in cm) and the pixel
value (0 = white, 1 = black).

We used the data frame to calculate four variables to quantify
particular attributes of the vegetation structure. At first, leaf area,
referred to as LA hereafter, a frequently used quantity in vegetation
characterisation (Volesky, 1999), was calculated as the count of black
pixels rescaled to cm\textsuperscript{2} units. At second, we calculated
visual obstruction readings (VOR), developed primarily for prairie
vegetation based on the Robel pole method (Benkobi et al., 2000;
Vermeire \& Gillen, 2001). This method takes two readings by eye at a
height of 1 m from a distance of 4 m from the pole with height
tick-marks: (i) the height at which the pole is first visible, i.e., not
obstructed by vegetation (low reading) and (ii) the maximum height
reached by the vegetation (high reading). The average of the two
readings strongly correlates with prairie phytomass (Benkobi et al.,
2000; Vermeire \& Gillen, 2001). In our study, we modified the lower
reading by calculating the maximum height at which 95\% of the
whiteboard is mantled by the vegetation, and we refer to this as the
height of closed vegetation (HCV) (Fig. 2) to avoid confusion with the
VOR reading terminology. We chose 95\% as a threshold because glint on
some leaves in the image could return white cells and could thus reduce
the true coverage. At third, the high reading was calculated as the
maximum height of the vegetation (MHV) regardless of its width, cover,
or surface area (Fig. 2). Finally, to characterise the vertical
distribution of vegetation, we calculated foliage height diversity (FHD)
(Karr \& Roth, 1971) as the Shannon diversity of the number of black
cells in each pixel row using the `diversity' function of the `vegan'
package (Oksanen et al., 2019). Calculating FHD from values in each
pixel row circumvents the problem of arbitrarily choosing counting
intervals, e.g. ten 10-cm height intervals in each of which cover is
estimated or measured for the calculation of FHD (MacArthur et al.,
1962; Karr \& Roth, 1971).

\textbf{Other variables}

We applied further variables that may influence the occurrence of snakes
at each site. In \emph{V. graeca} habitats, we estimated the cover of
bare rock, grass and shrub surfaces in a circle of 1-m radius around
points of viper presence and random points. In \emph{V.
renardi}habitats, we recorded the number of rodent burrows in a circle
of 1-m radius around points of viper presence and random points as these
snakes often use rodent burrows for hiding. In \emph{V. ursinii}
habitats, there were no rocks or shrubs, and rodent burrows were rare
and not recorded.

\textbf{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 \emph{V. graeca} GLMMs
and number of burrows in \emph{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 (\selectlanguage{greek}Δ\selectlanguage{english}i = AICi- AICmin \textless{} 2.0) and to perform model
averaging based on the relative importance of explanatory variables
using the `MuMIn' package in R (Barto\selectlanguage{polish}ń, \selectlanguage{english}2018).

RESULTS

The number of snakes found (presence locations) varied from 32 to 73
across the three species. \emph{V. graeca} and \emph{V. ursinii} were
much rarer locally than \emph{V. renardi} , and the search
effort-corrected density was an order of magnitude higher in \emph{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 \emph{V. graeca} were in
inaccessible cliffs and 17\% of pre-randomised locations fell on roads
or water bodies in \emph{V. renardi} habitats (Table 1).

Most of the vegetation structure variables followed a normal
distribution, except for MHV in \emph{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 \emph{V. graeca} and in \emph{V. ursinii} habitats and when data
were pooled across species, and also between LA and FHD in \emph{V.
graeca} habitats (Fig. 3).

In \emph{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 (\selectlanguage{greek}Δ\selectlanguage{english}AICc \textless{} 2). The averaged parameter
estimate was significant and positive only for HCV (Table 2), indicating
a higher chance of occurrence of \emph{V. graeca} in taller and closed
vegetation.

In \emph{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 (\selectlanguage{greek}Δ\selectlanguage{english}AICc \textless{} 2). The effects of
LA and number of burrows were positive, whereas that of FHD was negative
(Table 2), indicating higher chances of \emph{V. renardi} occurrence in
microhabitats with higher, more homogeneous cover and more burrows.

In \emph{V. ursinii} , the full model had the lowest AICc value, and in
the two best models (\selectlanguage{greek}Δ\selectlanguage{english}AICc \textless{} 2), LA and MHV had significant
positive effects, whereas HCV had a significant negative effect (Table
2), indicating higher chances of \emph{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 \emph{V. ursinii} , a previous study (M\selectlanguage{ngerman}áté \& 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 \emph{V.
graeca}(positive) and \emph{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 \emph{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 \emph{V. ursinii} and the high spatial
scatter of \emph{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 \emph{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
on\emph{Vipera 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

\textbf{Table 1.} Number of snakes found (presence locations), search
effort, density of snakes and number of random locations studied in the
three species.\selectlanguage{english}
\begin{longtable}[]{@{}llll@{}}
\toprule
\textbf{Variable} & V. graeca & V. renardi & V. ursinii\tabularnewline
\midrule
\endhead
Number of presence locations & 36 & 73 & 32\tabularnewline
Search effort (person-days) & 125 & 22 & 48\tabularnewline
Effort-corrected density (individuals/person-day) & 0.288 & 3.318 &
0.667\tabularnewline
Number of random locations & 157 & 249 & 320\tabularnewline
\% of random locations in inaccessible areas & 47\% & 17\% &
0\%\tabularnewline
\bottomrule
\end{longtable}

\textbf{Table 2.} Averaged parameter estimates of the best Generalized
Linear Mixed Models testing the effects of vegetation structure and
other relevant variables on presence-absence of vipers in grasslands.
Significant parameter estimates are highlighted in bold letters.\selectlanguage{english}
\begin{longtable}[]{@{}llllll@{}}
\toprule
\textbf{Species} & \textbf{Explanatory variable} & \textbf{Estimate} &
\textbf{SE} & \textbf{Z} & \textbf{P}\tabularnewline
\midrule
\endhead
\emph{Vipera graeca} & (Intercept) & -6.345 & 14.131 & 0.449 &
0.653\tabularnewline
& \textbf{Height of closed vegetation (HCV)} & \textbf{0.073} &
\textbf{0.029} & \textbf{2.444} & \textbf{0.014}\tabularnewline
& Shrub surface cover & 0.089 & 0.149 & 0.594 & 0.552\tabularnewline
& Grass surface cover & 0.081 & 0.200 & 0.409 & 0.682\tabularnewline
& Rock surface cover & 0.058 & 0.205 & 0.283 & 0.777\tabularnewline
& Maximum height of vegetation (MHV) & -0.004 & 0.011 & 0.401 &
0.688\tabularnewline
\emph{Vipera renardi} & (Intercept) & 0.315 & 1.360 & 0.232 &
0.816\tabularnewline
& \textbf{Burrow availability} & \textbf{0.184} & \textbf{0.078} &
\textbf{2.358} & \textbf{0.018}\tabularnewline
& Foliage height diversity (FHD) & -0.442 & 0.240 & 1.842 &
0.065\tabularnewline
& \textbf{Leaf area (LA)} & \textbf{0.002} & \textbf{0.001} &
\textbf{2.980} & \textbf{0.003}\tabularnewline
& Maximum height of vegetation (MHV) & -0.003 & 0.008 & 0.458 &
0.646\tabularnewline
\emph{Vipera ursinii} & (Intercept) & -1.261 & 2.893 & 0.436 &
0.663\tabularnewline
& Foliage height diversity (FHD) & -0.779 & 0.475 & 1.641 &
0.101\tabularnewline
& \textbf{Maximum height of vegetation (MHV)} & \textbf{0.016} &
\textbf{0.008} & \textbf{1.987} & \textbf{0.046}\tabularnewline
& \textbf{Leaf area (LA)} & \textbf{0.003} & \textbf{0.001} &
\textbf{2.266} & \textbf{0.023}\tabularnewline
& \textbf{Height of closed vegetation (HCV)} & \textbf{-0.074} &
\textbf{0.033} & \textbf{2.230} & \textbf{0.025}\tabularnewline
\bottomrule
\end{longtable}

FIGURES

\textbf{Figure 1.} (a) Distribution of the study species in Europe
according to Mizsei et al. (2018) and the location of study sites
(dots), (b) example insets for presence (dots) and absence (crosses)
points at the study sites, (c) grasslands at the study sites.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image1/image1}

\end{center}
\end{figure}

\textbf{Figure 2.} Main steps of processing the whiteboard images and
example of the vegetation structure variables.\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image2/image2}

\end{center}
\end{figure}

\textbf{Figure 3.} Vegetation structure variables: distributions
(histograms in diagonal), correlations (lower left panels), Pearson's
correlation coefficients (upper right panels), and boxplots (right-hand
column) of vegetation structure variables by species. Correlation
coefficients in black are for data pooled for the three species, and
coefficients in boldface type indicate significant correlations (p
\textless{} 0.05).\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/image3/image3}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/fig1/fig1}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/fig2/fig2}

\end{center}
\end{figure}\selectlanguage{english}
\begin{figure}[H]
\begin{center}
\includegraphics[width=0.70\columnwidth]{figures/fig3-01/fig3-01}

\end{center}
\end{figure}

\selectlanguage{english}
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