Department of Biology, Pennsylvania State University, University Park, PA, USA.
U.S. Geological Survey, Colorado Cooperative Fish and Wildlife Research Unit, Colorado State University, Fort Collins, CO, USA.
Proc Biol Sci. 2020 Dec 9;287(1940):20202219. doi: 10.1098/rspb.2020.2219.
An urgent challenge facing biologists is predicting the regional-scale population dynamics of species facing environmental change. Biologists suggest that we must move beyond predictions based on phenomenological models and instead base predictions on underlying processes. For example, population biologists, evolutionary biologists, community ecologists and ecophysiologists all argue that the respective processes they study are essential. Must our models include processes from all of these fields? We argue that answering this critical question is ultimately an empirical exercise requiring a substantial amount of data that have not been integrated for any system to date. To motivate and facilitate the necessary data collection and integration, we first review the potential importance of each mechanism for skilful prediction. We then develop a conceptual framework based on reaction norms, and propose a hierarchical Bayesian statistical framework to integrate processes affecting reaction norms at different scales. The ambitious research programme we advocate is rapidly becoming feasible due to novel collaborations, datasets and analytical tools.
生物学家面临的一个紧迫挑战是预测面临环境变化的物种的区域尺度种群动态。生物学家认为,我们必须超越基于现象学模型的预测,而是基于潜在过程进行预测。例如,种群生物学家、进化生物学家、群落生态学家和生理生态学家都认为他们所研究的过程是必不可少的。我们的模型是否必须包含来自所有这些领域的过程?我们认为,回答这个关键问题最终是一个需要大量数据的经验性练习,而迄今为止,还没有为任何系统整合这些数据。为了激发和促进必要的数据收集和整合,我们首先回顾了每种机制在熟练预测方面的潜在重要性。然后,我们基于反应规范建立了一个概念框架,并提出了一个层次贝叶斯统计框架,以整合不同尺度上影响反应规范的过程。由于新的合作、数据集和分析工具,我们所倡导的雄心勃勃的研究计划正在迅速成为可能。