DOI: 10.5061/dryad.4j0zpc8dk
title: Habitats as predictors in species distribution models: Shall we use continuous or binary data?

description: The representation of a land cover type (i.e., habitat) within an area is
 often used as an explanatory variable in species distribution models.
 However, it is possible that a simple binary presence/absence of the
 suitable habitat might be the most important determinant of the
 presence/absence of some species and, thus, be a better predictor of
 species occurrence than the continuous parameter (area). We hypothesize
 that the binary predictor is more suitable for relatively rare habitats
 (e.g., wetlands) while for common habitats (e.g., forests) the amount of
 the focal habitat is a better predictor. We used the Third Atlas of
 Breeding Birds in the Czech Republic as the source of species distribution
 data and CORINE Land Cover inventory as the source of the landcover
 information. To test our hypothesis, we fitted generalized linear models
 of 32 water and 32 forest bird species. Our results show that for water
 bird species, models using binary predictors (presence/absence of the
 habitat) performed better than models with continuous predictors (i.e.,
 the amount of the habitat); for forest species, however, we observed the
 opposite. Thus, future studies using habitats as predictors of species
 occurrences should consider the prevalence of the habitat in the
 landscape, and the biological role of the habitat type in the particular
 species’ life history. In addition, performing a preliminary comparison of
 the performance of the binary and continuous versions of habitat
 predictors (e.g., using information criteria) prior to modelling, during
 variable selection, can be beneficial. These are simple steps that will
 improve explanatory and predictive performance of models of species
 distributions in biogeography, community ecology, macroecology, and
 ecological conservation.
description: <strong>Study area and
 bird distribution data</strong>
 The study area was the
 territory of the Czech Republic, a central European country covering
 almost 79,000 km<sup>2</sup> (see Figure 2a). Data on bird
 species were obtained from the Third Atlas of Breeding Bird Distribution
 in the Czech Republic (Šťastný et al. 2006). The study area is divided
 into 628 grid squares of approx. 134 km<sup>2</sup> (10’ east
 longitude × 6’ north latitude; hereafter referred to as mapping squares)
 to which bird occurrences and environmental predictors are referred. The
 fieldwork for the atlas was conducted by volunteers between 2001 and 2003
 where the breeding status of all species was recorded in each mapping
 square. Field observations of the bird species occurring in each mapping
 square were recorded using 17 numerical breeding codes (Hagemeijer and
 Blair 1997). Breeding occurrence of each bird species within a given
 mapping square was classified into one of the following categories: 0 –
 Non-breeding (where no observations of the species were made, or where the
 species was observed but no breeding evidence was found), A – Possible
 breeding, B – Probable breeding or C – Confirmed breeding. For the purpose
 of our study, all breeding categories (A, B and C) were used as presences
 whereas category 0 was used as absences. We prepared data for 85 bird species, 36 of them
 nesting in wetlands and surrounding habitats (e.g., standing water,
 littoral zones of ponds, swamps), and 49 species nesting in forests,
 following classification of Reif et al. (2006). Nevertheless, we had to
 remove 21 species with relatively small (less than 30 presence cells out
 of 628 cells), and relatively high occupancy (more than 598 presence cells
 out of 628 cells). Therefore, 32 water and 32 forest bird species (see
 Table A1) were included in the study.
 <strong>Habitat
 variables</strong> We derived four habitat predictors from the
 CORINE Land Cover database at 100 m resolution (Feranec et al., 2010).
 Specifically, within mapping squares, we derived the area of
 <em>agricultural</em> <em>areas</em> (CORINE class
 2), <em>artificial surfaces</em> divided into four classes (0,
 0–20, 20–40, &gt; 40 km<sup>2</sup>; CORINE class 1),
 continuous area of <em>water bodies</em> (CORINE class 5.1.2)
 and area of forest (CORINE class 3.1). In addition, binary factors
 representing presence or absence of water bodies and forests,
 respectively, were calculated. In order to generate binary habitat maps,
 it is necessary to determine an area threshold that defines the
 presence-absence of the habitat. An appropriate threshold should consider
 the prevalence of the habitat across the region of interest, the grain
 size at which the variable is being considered (i.e., the size of the grid
 cells at which the species are recorded) and the original grain size that
 the habitat variable is being aggregated from (i.e., the size of the grid
 cells of the original land-cover data, which is then aggregated to the
 larger modelling grain size). Due to the uncommonness of water habitats as
 well as due to the coarse resolution of CORINE Land Cover, we considered
 any amount of the water habitat in a cell as presence (i.e., the
 proportion of the cell occupied by one hectare set to &gt; 0%). Forest
 pixels are, on the other hand, present in all mapping squares across the
 study region and, for this reason, we tested several thresholds (10%, 20%,
 30%, 40%, and 50%) to derive the binary predictor.
 <strong>Other
 environmental variables</strong>
 Although the habitat
 predictors were our main focus, other predictors, such as climate, may
 also be important in determining the distributions of species. As climatic
 predictors, we used current climatic data from WorldClim
 (Hijmans et al.,
 2005). Following previous
 studies, we used two predictors: mean temperature and mean precipitation
 during the breeding season, i.e. in April–June (e.g.,
 Moudrý and Šímová 2013,
 Venne and Currie 2021). We
 downloaded these at a resolution of 30 arc seconds (~ 1
 km<sup>2</sup>) and averaged them inside each mapping square
 to match the grid resolution of the species distribution data (~ 100
 km<sup>2</sup>). We also considered usage of elevation
 predictors such as maximum, minimum, and range of elevation derived from
 Shuttle radar topography mission (SRTM, Farr et al. 2007;
 Moudrý et al. 2018) as
 they might be ecologically important to birds (e.g., Kosicky 2017).
 However, as these variables were highly correlated with the mean
 temperature in April–June, we eventually decided not to include them. The
 data were processed in ArcGIS 10.7.1 (ESRI, CA, USA) and R (R Development
 Core Team) software.
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