Abstract
Species distribution models (SDMs) are now being widely used in ecology for management and conservation purposes across terrestrial, freshwater, and marine realms. The increasing interest in SDMs has drawn the attention of ecologists to spatial models and, in particular, to geostatistical models, which are used to associate observations of species occurrence or abundance with environmental covariates in a finite number of locations in order to predict where (and how much of) a species is likely to be present in unsampled locations. Standard geostatistical methodology assumes that the choice of sampling locations is independent of the values of the variable of interest. However, in natural environments, due to practical limitations related to time and financial constraints, this theoretical assumption is often violated. In fact, data commonly derive from opportunistic sampling (e.g., whale or bird watching), in which observers tend to look for a specific species in areas where they expect to find it. These are examples of what is referred to as preferential sampling, which can lead to biased predictions of the distribution of the species. The aim of this study is to discuss a SDM that addresses this problem and that it is more computationally efficient than existing MCMC methods. From a statistical point of view, we interpret the data as a marked point pattern, where the sampling locations form a point pattern and the measurements taken in those locations (i.e., species abundance or occurrence) are the associated marks. Inference and prediction of species distribution is performed using a Bayesian approach, and integrated nested Laplace approximation (INLA) methodology and software are used for model fitting to minimize the computational burden. We show that abundance is highly overestimated at low abundance locations when preferential sampling effects not accounted for, in both a simulated example and a practical application using fishery data. This highlights that ecologists should be aware of the potential bias resulting from preferential sampling and account for it in a model when a survey is based on non‐randomized and/or non‐systematic sampling.
Highlights
An increasing interest in Species distribution models (SDMs) for management and conservation purposes has drawn the attention of ecologists to spatial models (Dormann et al, 2007)
As shown by our results, using a non‐accurate approach could culminate in misidentification of a species habitat and uncertain predictions and so in inappropriate management measures which can sometimes be irreversible
The results of the practical application on blue and red shrimp, included here as a real world scenario, show that predictive maps significantly improve the prediction of the target species when the model accounts for preferential sampling
Summary
An increasing interest in Species distribution models (SDMs) for management and conservation purposes has drawn the attention of ecologists to spatial models (Dormann et al, 2007). With the excep‐ tion of a few studies (Thogmartin, Knutson, & Sauer, 2006), SDMs frequently rely on opportunistic data collection due to the high cost and time consuming nature of sampling data in the field, especially on a large spatial scale (Kery et al, 2010). It is often infea‐ sible to collect data based on a well‐designed, randomized, and/or systematic sampling scheme to estimate the distribution of a specific species over the entire area of interest (Brotons, Herrando, & Pla, 2007). The fishing boats naturally tend to fish in locations where they expect a high concen‐ tration of their target species (Vasconcellos & Cochrane, 2005)
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