John Fieberg

This paper introduces a learning-explicit step-selection analysis to model how animals integrate environmental information during movement, showing it improves habitat selection predictions compared to traditional methods.

The paper examines perceived and observed biases in movement ecology through surveys and author affiliation analysis, identifying geographic and demographic disparities in research participation.

The paper shows that imperfect knowledge can lead to sustainable resource exploitation in competitive scenarios without cooperation, resulting in higher long-term yields than omniscient or cooperative strategies.

The paper tracks migration strategies in re-established trumpeter swans, finding 59% engage in long-distance migration, with movement patterns linked to breeding status and latitude.

The paper reviews methods to incorporate memory into movement models using wildlife tracking data and provides code examples for implementation.

This paper extends step selection analysis by incorporating non-linear and random effects using penalised smooths in the mgcv R package, enhancing flexibility and biological realism in modeling animal movement and habitat selection.

The paper evaluates goodness-of-fit for animal movement models using the lineup protocol, demonstrating its application to fisher data with integrated step-selection and hidden Markov models.

The paper evaluates methods for handling incomplete data in integrated step-selection analyses by comparing alternative approaches to a baseline method that ignores temporal irregularity.

The paper develops methods to model individual variability in habitat selection and movement using integrated step-selection analyses with random effects, applied to red snapper data.

This paper introduces an R package to simulate animal space use from fitted integrated Step-Selection Functions (iSSFs) to better understand movement ecology.