2025-04-16 スウォンジー大学
スウォンジー大学の研究チームは、気候変動や土地利用の変化など、急速に変化する環境下での動物の移動予測に関する新たな枠組みを提案しました。従来の研究は過去のデータに基づく記述的な分析が中心でしたが、これでは将来の動物の移動を正確に予測することが困難です。研究チームは、生物学的メカニズムを組み込んだ動的モデルの開発を提唱し、都市化や気候変動などの人為的要因が動物の移動に与える影響を考慮する必要性を強調しています。また、より多様な種や人間活動の影響を受ける環境でのデータ収集を推進し、再野生化や移送などの保全活動と連携した予測モデルの検証を提案しています。この研究は、動物の移動に関する科学を記述から予測へと進化させ、生態系の保全や政策立案に貢献することを目指しています。
<関連情報>
- https://www.swansea.ac.uk/press-office/news-events/news/2025/04/predicting-animal-movements-under-global-change–why-science-fails-in-this-vital-task-and-how-it-can-improve.php
- https://besjournals.onlinelibrary.wiley.com/doi/10.1111/1365-2656.70040
人新世における動物の移動と分布の理解と予測 Understanding and predicting animal movements and distributions in the Anthropocene
Sara Gomez, Holly M. English, Vanesa Bejarano Alegre, Paul G. Blackwell, Anna M. Bracken, Eloise Bray, Luke C. Evans, Jelaine L. Gan, W. James Grecian, Catherine Gutmann Roberts …
Journal of Animal Ecology Published: 04 April 2025
DOI:https://doi.org/10.1111/1365-2656.70040
Abstract
- Predicting animal movements and spatial distributions is crucial for our comprehension of ecological processes and provides key evidence for conserving and managing populations, species and ecosystems. Notwithstanding considerable progress in movement ecology in recent decades, developing robust predictions for rapidly changing environments remains challenging.
- To accurately predict the effects of anthropogenic change, it is important to first identify the defining features of human-modified environments and their consequences on the drivers of animal movement. We review and discuss these features within the movement ecology framework, describing relationships between external environment, internal state, navigation and motion capacity.
- Developing robust predictions under novel situations requires models moving beyond purely correlative approaches to a dynamical systems perspective. This requires increased mechanistic modelling, using functional parameters derived from first principles of animal movement and decision-making. Theory and empirical observations should be better integrated by using experimental approaches. Models should be fitted to new and historic data gathered across a wide range of contrasting environmental conditions. We need therefore a targeted and supervised approach to data collection, increasing the range of studied taxa and carefully considering issues of scale and bias, and mechanistic modelling. Thus, we caution against the indiscriminate non-supervised use of citizen science data, AI and machine learning models.
- We highlight the challenges and opportunities of incorporating movement predictions into management actions and policy. Rewilding and translocation schemes offer exciting opportunities to collect data from novel environments, enabling tests of model predictions across varied contexts and scales. Adaptive management frameworks in particular, based on a stepwise iterative process, including predictions and refinements, provide exciting opportunities of mutual benefit to movement ecology and conservation.
- In conclusion, movement ecology is on the verge of transforming from a descriptive to a predictive science. This is a timely progression, given that robust predictions under rapidly changing environmental conditions are now more urgently needed than ever for evidence-based management and policy decisions. Our key aim now is not to describe the existing data as well as possible, but rather to understand the underlying mechanisms and develop models with reliable predictive ability in novel situations.
