How can overlooking social interactions, space familiarity or other invisible landscapes shaping animal movement bias habitat selection estimations and species distribution predictions?

Abstract

Species' 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 and non-physical landscapes, for example related to individual experiences such as space familiarity or previous encounters with conspecifics, 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 overestimating the strength of habitat selection. Consequently, projections of future population distributions were also biased, with animals following changes in preferred habitat 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 modeling integrating cognitive processes underlying movement should be developed.