Accounting for preferential sampling in species distribution models

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.

Toward reliable habitat suitability and accessibility models in an era of multiple environmental stressors

“…To overcome imperfect detection , informative field surveys should be combined with a modeling approach accounting for the detection process. This could be realized through cropping the environmental input maps to the regions that were surveyed prior to extrapolation of the environment–occurrence probability relations, or through including model parameters describing the detection conditions (Acevedo, Jiménez‐Valverde, Lobo, & Real, 2012; Guillera‐Arroita, 2017; Pennino et al., 2019). The choice of absence data could furthermore have strong implications on model outcomes (Hattab et al., 2017) (see also Box 1) and are ideally categorized into: (a) environmental absence data; reflecting unsuitable habitat; (b) dispersal‐limited absences, reflecting inaccessible but suitable habitat; and where tight species’ interactions occur, (c) community‐limited absences, reflecting accessible and suitable habitat but lacking the species upon which the focal species obligatory depends.…”

Ecological responses to blue water MPAs

“…The study assessed a subset of possible meaningful ecological responses to the MPAs. The study's assessment of ecological responses was limited by using only fishery-dependent data for species susceptible to capture in pelagic longline gear, using non-randomized and non-systematic preferential sampling, a research approach commonly employed in many disciplines (e.g., species distribution modeling, [106]). Evaluating conservation interventions using fishery-dependent data is challenging and can lead to ambiguous conclusions.…”

Incorporating Biotic Information in Species Distribution Models: A Coregionalized Approach

“…It is worth also noting that all this can be done thanks to the integrated nested Laplace approximation methodology and software that, jointly with the SPDE approach can help to minimize the computational burden while constituting a flexible tool in order to fit complex geostatistical models [44].…”