Competing roles for landscape, vegetation, topography and climate in predictive models of bird distribution

Abstract

Predictive habitat models rely on the relationship between a response variable (either occurrence or abundance of a species) and a set of environmental predictors. Vegetation is generally preferred as a source of potential predictors because of having a more direct link with reproductive necessities of species than topography and climate. However, vegetation maps are costly to produce and update and most land-use/land-cover maps are usually made with a general purpose, focused on land management, and not thinking on animal distribution. On the contrary, basic topographic and climatic maps are easier to obtain or to derive, are not so sensitive to legend design, and do not need of such frequent updates. In this study, we compare the predictive ability of different sets of environmental predictors when modelling the distribution of breeding birds. Models were generated for 79 bird species in South-western Spain using Generalised Additive Models (GAMs) with binomial errors and logit link. For each species, several models were created that differed in the set of candidate predictors initially tested (either derived from vegetation or from topo-climatic maps) or in the conditional order in which those predictor sets were tested. Within vegetation predictors, a similar strategy was used to ascertain the relative relevance of vegetation landscape (variables describing the surrounding habitat matrix) compared to vegetation cover (variables describing the type of vegetation found on sampling sites).Vegetation models were significantly more accurate than topo-climatic models, but the difference was due to the higher number of potential predictors in the set of vegetation variables. Vegetation landscape models were significantly more accurate than vegetation cover models, even when controlling for the number of candidate predictors. Models that included both sets of predictors (vegetation and topo-climatic variables) had a slightly superior predictive ability. Our results indicate that, when building predictive models of bird distribution, the best results are obtained using both vegetation and topo-climatic variables as potential predictors. If time or budget constrains compel to concentrate on a single set of predictors, selection should be done on grounds of data availability, because model accuracy is likely to be similar for models derived from vegetation compared to topographic and climatic predictors. In relation to vegetation predictors, vegetation landscape reflects important information not revealed by vegetation cover measures at the sampling site; thus, regional modelling programmes would certainly gain predictive ability by including landscape patterns that are currently disregarded.

The impact of environmental factors on distribution of Scops Owl Otus scops in the wider area of Kras (SW Slovenia)

“…At the regional scale, we aimed to asses the effect of land-use on spatial distribution of Scops Owl, since landscape has been found to affect patterns of abundance and distribution of birds (Seoane et al 2004). We compared the occupied land-use types (number of locations of males in each land-use type) with the available land-use types (relative area of each type).…”

Using estuarine landscape structure to model distribution patterns in nekton communities and in juveniles of fishery species

“…We described this broad-scale spatial structure using measurements of relative distance and the area of continuous seagrass habitat around the sites (using a 1600 m radius). For species that often move among multiple habitat types, measuring habitats at a broad scale around sampling sites may be a better indicator of the habitats available to species than measuring only those habitats within the sampling site (see review by Pittman & McAlpine 2003; for examples see McGarigal & McComb 1995, Pittman et al 2004, Seoane et al 2004). In addition, using habitat structure around sampling sites and spatial location is advantageous in modeling and mapping species distribution because, unlike variables measured only at sample point locations, coastal wetlands or seagrass beds have been comprehensively mapped for many estuaries and coastal areas.…”

Modelling bird species richness with neural networks for forest

“…At the scale of our study we assumed that climate may be acting at least partially as a surrogate (González-Taboada et al, 2007) for one or more factors that relate to space and co-vary with climate and are thought to directly influence species richness. On the contrary, forest features are likely to have an effect on breeding bird distribution closer to causality than climate by providing breeding substrates and foraging habitats (Seoane et al, 2004).…”