New workflow helps ecologists measure how individual animals use their habitat

A researcher has developed a new, reproducible workflow that enables ecologists to measure, in a standardized manner, how individual animals within a species differ in their habitat use based on GPS tracking data. Dr. Elina Takola from the UFZ—Helmholtz Centre for Environmental Research's Department of Computational Landscape Ecology created this tool.

The ecological niche concept, which outlines the set of environmental conditions a species needs to survive and reproduce, has historically been applied at the species or population level. However, individual animals within the same species often exhibit varying behavior, diet, and habitat preferences. Dr. Takola explains that these individual differences, once dismissed as statistical noise, now have significant implications for species survival.

The new workflow, published in the journal Individual-based Ecology, aims to address this gap by treating each animal's range of habitat conditions as its own individualized niche. This approach distinguishes between the conditions an individual actually uses (its realized niche) and the conditions available but not necessarily used (its potential niche).

The workflow combines mixed-effects resource selection functions, statistical models estimating average habitat preferences and individual deviations, with hypervolume exploration methods, which map all environmental conditions an individual could occupy as a multidimensional space. Implemented in R, the workflow consists of three stages: data preparation, analysis, and output generation.

It measures niche breadth, overlap, and repeatability, distinguishing between environmental conditions available and used by an individual. The workflow's application is illustrated through a case study on northern lapwings using publicly available GPS tracking data. The study demonstrates how the workflow integrates various environmental layers such as earthworm abundance, human presence, pesticides, management, soil variables, and vegetation.

This individual-based approach allows researchers to quantify niche breadth, overlap, and repeatability, providing a more accurate and mechanistic understanding of wild population dynamics and improving conservation predictions. The data and code for the workflow are available on GitHub, enabling ecologists to incorporate individual variation into habitat and conservation models across taxa and data sets.