Can overlooking ‘invisible landscapes’ bias habitat selection estimation and population distribution projections?

Abstract

ABSTRACTSpecies’ future distributions are commonly predicted using models that link the likelihood of occurrence of individuals to the environment. Although animals’ movements are influenced by physical landscapes and individual experiences (for example space familiarity), species distribution models developed from observations of unknown individuals cannot integrate these latter variables, turning them into ‘invisible landscapes’. In this theoretical study, we address how overlooking ‘invisible landscapes’ impacts the estimation of habitat selection and thereby the projection of future distributions. Overlooking the attraction towards some ‘invisible’ variable consistently led to over-estimating the strength of habitat selection. Consequently, projections of future population distributions were also biased, with animals tracking habitat changes less than predicted. Our results reveal an overlooked challenge faced by correlative species distribution models based on the observation of unknown individuals, whose past experience of the environment is by definition not known. Mechanistic distribution modelling integrating cognitive processes underlying movement should be developed.