Integrating animal tracking and trait data to facilitate global ecological discoveries

crossref(2024)

引用 0|浏览1
暂无评分
摘要
Understanding animal movement is at the core of ecology, evolution, and conservation science. Big data approaches for animal tracking have facilitated impactful synthesis research on spatial biology and behavior in ecologically important and human-impacted regions. Similarly, databases of animal traits (e.g., body size, limb length, locomotion method, lifespan) have been used for a wide range of comparative questions, with emerging data being shared at the levels of individuals and populations. Here, we argue that the proliferation of both types of publicly available data creates exciting opportunities to unlock new avenues of research, such as spatial planning and ecological forecasting, across a diverse range of species. We assessed the feasibility of combining animal tracking and trait databases to develop and test hypotheses across geographic, temporal, and biological allometric scales. We identified multiple research questions addressing performance and distribution constraints that could be answered by integrating trait and tracking data. For example, how do physiological (e.g., metabolic rates) and biomechanical traits (e.g., limb length, locomotion form) influence migration distances? How does habitat type influence movement metrics such as speed and energetic cost? We illustrate the potential of our framework with three case studies that effectively integrate trait and tracking data for comparative research. An important challenge ahead is the lack of taxonomic and spatial overlap in trait and tracking databases. We identify critical next steps for future integration of tracking and trait databases, with the most impactful being open and interlinked individual-level data. Coordinated efforts to combine trait and tracking databases will accelerate global ecological and evolutionary insights and inform conservation and management decisions in our changing world.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要