Disentangling drivers of spatial autocorrelation in species distribution models
Mielke, Konrad P. et al. (2020), Disentangling drivers of spatial autocorrelation in species distribution models, Dryad, Dataset, https://doi.org/10.5061/dryad.rv15dv45w
Species distribution models (SDMs) are frequently used to understand the influence of site properties on species occurrence. For robust model inference, SDMs need to account for the spatial autocorrelation of virtually all species occurrence data. Current methods do not routinely distinguish between extrinsic and intrinsic drivers of spatial autocorrelation, although these may have different implications for conservation. Here, we present and test a method that disentangles extrinsic and intrinsic drivers of spatial autocorrelation using repeated observations of a species. We focus on unknown habitat characteristics and conspecific interactions as extrinsic and intrinsic drivers, respectively. We model the former with spatially correlated random effects and the latter with an autocovariate, such that the spatially correlated random effects are constant across the repeated observations whereas the autocovariate may change. We tested the performance of our model on virtual species data and applied it to observations of the corncrake Crex crex in the Netherlands. Applying our model to virtual species data revealed that it was well able to distinguish between the two different drivers of spatial autocorrelation, outperforming models with no or a single component for spatial autocorrelation. This finding was independent of the direction of the conspecific interactions (i.e., conspecific attraction versus competitive exclusion). The simulations confirmed that the ability of our model to disentangle both drivers of autocorrelation depends on repeated observations. In the case study, we discovered that the corncrake has a stronger response to habitat characteristics compared to a model that did not include spatially correlated random effects, whereas conspecific interactions appeared to be less important. This implies that future conservation efforts should primarily focus on maximizing habitat availability. Our study shows how to systematically disentangle extrinsic and intrinsic drivers of spatial autocorrelation. The method we propose can help to correctly identify the main drivers of species distributions.
The dataset contains 563 observations of the corncrake Crex Crex in the floodplains of the Rhine River in the Netherlands which provide the species with an important breeding habitat. Sovon Dutch Centre for Field Ornithology has been conducting systematic simultaneous surveys in the floodplain areas twice per breeding season since 2001. In these surveys, the entire study area is scrutinized for the presence of corncrakes. Presence records refer to singing males, which are indicative of breeding sites, and are obtained at night when the singing activity is highest. Males sing more or less continuously between 11:00 pm and 3:00 am at stable singing sites, and their songs can be heard over considerable distances (500–1000 m), ensuring a very high probability of detection.
The dataset is stored as a shapefile. For each observation, the date of observation (variable DATUM), the location (variable geometry) and information on whether it was observed as part of one of the two systematic simultaneous surveys (variable SIMULTAAN) are given.
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