Understanding how animals move through heterogeneous landscapes is central to ecology and conservation. In this context, step selection functions (SSFs) have emerged as the main statistical framework to analyze how biotic and abiotic predictors influence movement paths observed by radio tracking, GPS tags, or similar sensors. A traditional SSF consists of a generalized linear model (GLM) that infers the animal's habitat preferences (selection coefficients) by comparing each observed movement step to random steps. Such GLM-SSFs, however, cannot flexibly consider non-linear or interacting effects, unless those have been specified a priori. To address this problem, generalized additive models have been integrated in the SSF framework, but those GAM-SSFs are still limited in their ability to represent complex habitat preferences and inter-individual variability. Here we explore the utility of deep neural networks (DNNs) to overcome these limitations. We find that DNN-SSFs, coupled with explainable AI to extract selection coefficients, offer many advantages for analyzing movement data. In the case of linear effects, they effectively retrieve the same effect sizes and p-values as conventional GLMs. At the same time, however, they can automatically detect complex interaction effects, nonlinear responses, and inter-individual variability if those are present in the data. We conclude that DNN-SSFs are a promising extension of traditional SSF. Our analysis extends previous research on DNN-SSF by exploring differences and similarities of GLM, GAM and DNN-based SSF models in more depth, in particular regarding the validity of statistical indicators that are derived from the DNN. We also propose new DNN structures to capture inter-individual effects that can be viewed as a nonlinear random effect. All methods used in this paper are available via the 'citoMove' R package.

翻译：理解动物如何穿越异质景观是生态学与保护生物学的核心议题。在此背景下，步态选择函数（SSFs）已成为分析生物与非生物预测因子如何影响通过无线电追踪、GPS标签或类似传感器观测到的运动路径的主要统计框架。传统SSF由广义线性模型（GLM）构成，通过将每个观测运动步长与随机步长进行比较，推断动物的栖息地偏好（选择系数）。然而，此类GLM-SSF无法灵活处理非线性或交互效应，除非这些效应已先验指定。为解决该问题，广义加性模型被整合至SSF框架中，但GAM-SSF在表征复杂栖息地偏好及个体间变异性方面仍存在局限性。本文探索了深度神经网络（DNN）在克服这些局限中的效用。研究发现，结合可解释人工智能提取选择系数的DNN-SSF，在分析运动数据方面具有多重优势。在线性效应场景下，其能有效还原与传统GLM相同的效应量与p值；同时，若数据中存在复杂交互效应、非线性响应及个体间变异性，则可自动检测这些模式。我们认为DNN-SSF是传统SSF的一种有前景的扩展。本研究通过更深入地探索GLM、GAM与基于DNN的SSF模型间的异同（尤其关注从DNN导出的统计指标的有效性），拓展了先前关于DNN-SSF的研究。此外，我们提出了新型DNN结构以捕获可视为非线性随机效应的个体间影响。本文使用的所有方法均通过citoMove R包实现。