Accounting for preferential sampling in species distribution models

cient 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 occur‐ rence) 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 compu‐ tational 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 ac‐ count for it in a model when a survey is based on non‐randomized and/or non‐sys‐ tematic sampling.

D. C., A. L. Q. and F. M. would like to thank the Ministerio de Educación y Ciencia (Spain) for financial support (jointly financed by the European Regional Development Fund) via Research Grants MTM2013-42323-P and MTM2016-77501-P, and ACOMP/2015/202 from Generalitat Valenciana (Spain). The authors are very grateful to all the observers, shipowners and crews who have contributed to the data collection of fishery data and MEDITSs. Fishery data collection was cofunded by the EU through the European Maritime and Fisheries Fund (EMFF) within the National Programme of collection, management and use of data in the fisheries sector and support for scientific advice regarding the Common Fisheries Policy. The authors have no conflict of interests to declare.

Peer reviewed