Museum of Vertebrate Zoology, University of California-Berkeley, Berkeley, California, USA.
Department of Environmental Science, Policy, and Management, University of California-Berkeley, Berkeley, California, USA.
Glob Chang Biol. 2024 Jan;30(1):e17019. doi: 10.1111/gcb.17019. Epub 2023 Nov 21.
Correlative species distribution models are widely used to quantify past shifts in ranges or communities, and to predict future outcomes under ongoing global change. Practitioners confront a wide range of potentially plausible models for ecological dynamics, but most specific applications only consider a narrow set. Here, we clarify that certain model structures can embed restrictive assumptions about key sources of forecast uncertainty into an analysis. To evaluate forecast uncertainties and our ability to explain community change, we fit and compared 39 candidate multi- or joint species occupancy models to avian incidence data collected at 320 sites across California during the early 20th century and resurveyed a century later. We found massive (>20,000 LOOIC) differences in within-time information criterion across models. Poorer fitting models omitting multivariate random effects predicted less variation in species richness changes and smaller contemporary communities, with considerable variation in predicted spatial patterns in richness changes across models. The top models suggested avian environmental associations changed across time, contemporary avian occupancy was influenced by previous site-specific occupancy states, and that both latent site variables and species associations with these variables also varied over time. Collectively, our results recapitulate that simplified model assumptions not only impact predictive fit but may mask important sources of forecast uncertainty and mischaracterize the current state of system understanding when seeking to describe or project community responses to global change. We recommend that researchers seeking to make long-term forecasts prioritize characterizing forecast uncertainty over seeking to present a single best guess. To do so reliably, we urge practitioners to employ models capable of characterizing the key sources of forecast uncertainty, where predictors, parameters and random effects may vary over time or further interact with previous occurrence states.
关联物种分布模型被广泛用于量化过去的范围或群落变化，并预测在持续的全球变化下的未来结果。从业者面临着广泛的潜在合理的生态动态模型，但大多数具体应用只考虑了一个狭窄的范围。在这里，我们澄清了某些模型结构可以将对预测不确定性的关键来源的限制假设嵌入到分析中。为了评估预测不确定性和我们解释群落变化的能力，我们拟合并比较了 39 个候选多或联合物种占有模型，这些模型基于 20 世纪初在加利福尼亚州 320 个地点收集的鸟类发病率数据，并在一个世纪后进行了重新调查。我们发现，模型之间的信息准则的内部差异非常大（>20,000 LOOIC）。排除多元随机效应的拟合较差的模型预测物种丰富度变化和较小的当代群落的变化较少，而在模型之间，丰富度变化的预测空间模式有很大的差异。顶级模型表明，鸟类的环境关联随时间发生变化，当代鸟类占有率受到以前特定地点占有率状态的影响，而潜在的地点变量和物种与这些变量的关联也随时间而变化。总的来说，我们的结果表明，简化的模型假设不仅会影响预测拟合，还可能掩盖预测不确定性的重要来源，并在试图描述或预测群落对全球变化的反应时错误地描述当前系统理解的状态。我们建议，寻求进行长期预测的研究人员应优先考虑描述预测不确定性，而不是寻求呈现单一最佳猜测。为了可靠地做到这一点，我们敦促从业者使用能够描述预测不确定性的关键来源的模型，其中预测因子、参数和随机效应可能随时间变化或与以前的出现状态进一步相互作用。
