TITLE
Automated image processing for quantitative characterization of
grassland vegetation structure: microhabitat selection in threatened
meadow and steppe vipers
AUTHORS
Edvárd Mizsei1,2,3*, Mátyás Budai4,
Gergő Rák4, Barnabás Bancsik5, Dávid
Radovics1,3, Márton Szabolcs1,
Attila Móré2,3, Csaba Vadász2,
György Dudás6, Szabolcs Lengyel1
AFFILIATIONS
1Conservation Ecology Research Group, Department of
Tisza Research, Danube Research Institute, Centre for Ecological
Research, Hungary
2Kiskunság National Park Directorate, Hungary
3Department of Ecology, University of Debrecen,
Hungary
4Department of Systematic Zoology an Ecology, Eötvös
Loránd University, Budapest, Hungary
5Department of Ecology, University of Veterinary
Medicine, Budapest, Hungary
6Bükk National Park Directorate, Eger, Hungary
*Corresponding author; e-mail: edvardmizsei@gmail.com
ABSTRACT
- 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.
- 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).
- 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 andV. ursinii in sand grasslands in Hungary.
- 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 inV. graeca , to LA in V. renardi and to LA and MHC inV. ursinii , and negatively to FHD in V. renardi , and to
HCV in V. ursinii .
- 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.
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 (Vipera ursinii complex) are among the
most threatened reptiles. Lowland populations of this complex (V.
renardi , V. u. rakosiensis , 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 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