DOI: 10.5061/dryad.rv15dv45w
title: Disentangling drivers of spatial autocorrelation in species distribution models

description: 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.
description: The dataset contains 563 observations of the corncrake
 <em>Crex Crex</em> 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.
description: 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.