Integrated community models: A framework combining multispecies data sources to estimate the status, trends and dynamics of biodiversity
Integrated community models: A framework combining multispecies data sources to estimate the status, trends and dynamics of biodiversity
复制标题
综合社区模型：结合多物种数据源来估计生物多样性状况、趋势和动态的框架
DOI：
10.1111/1365-2656.14012
复制
发表时间：
2023
影响因子：
4.8
通讯作者：
Davis, Kayla L.
中科院分区：
文献类型：
--
作者：
Zipkin, Elise F.;Doser, Jeffrey W.;Davis, Courtney L.;Leuenberger, Wendy;Ayebare, Samuel;Davis, Kayla L.
由于缺乏对稀有或隐种的数据，许多现有方法无法评估群落水平的过程，从而限制了我们对大量物种的趋势和压力源的理解。然而，评估整个群落的动态，而不仅仅是普通或有魅力的物种，对于理解生物多样性对持续环境压力的反应至关重要。最近，公共科学和政府资助的数据收集工作的激增导致了丰富的生物多样性数据。然而，这些数据收集计划使用了广泛的采样协议（从对野生动物的非结构化、机会性观察到结构良好、基于设计的计划），并在各种时空尺度上记录信息。因此，现有的生物多样性数据在数量和信息内容上差异很大，为了进行有意义的生态分析，必须仔细协调这些数据。分层模型，包括单物种综合模型和分层群落模型，提高了我们评估和预测生物多样性趋势和过程的能力。在这里，我们重点介绍了新兴的“综合群落建模”框架，该框架结合了数据集成和群落建模，以改进对物种和群落水平动态的推断。我们用一系列工作示例来说明该框架。我们的三个案例研究表明，在评估物种分布和群落水平丰富度模式时，如何使用综合群落模型来扩展地理范围；辨别人口和社区随时间变化的趋势；并估计同域物种群落的人口增长率和人口增长率。我们通过R平台使用多种软件方法，通过带有基于公式的接口的包，以及在JAGS、NIMBLE和Stan中开发自定义代码，实现了这些工作示例。综合群落模型提供了一种令人兴奋的方法，可以同时使用多种数据类型和来源来模拟多种物种的生物和观测过程，从而在统一的框架内考虑不确定性和抽样误差。通过利用数据整合和群落建模的综合优势，综合群落模型可以产生关于常见和稀有物种以及群落水平动态的有价值的信息，从而可以全面评估全球变化对生物多样性的影响。
Data deficiencies among rare or cryptic species preclude assessment of community‐level processes using many existing approaches, limiting our understanding of the trends and stressors for large numbers of species. Yet evaluating the dynamics of whole communities, not just common or charismatic species, is critical to understanding and the responses of biodiversity to ongoing environmental pressures.A recent surge in both public science and government‐funded data collection efforts has led to a wealth of biodiversity data. However, these data collection programmes use a wide range of sampling protocols (from unstructured, opportunistic observations of wildlife to well‐structured, design‐based programmes) and record information at a variety of spatiotemporal scales. As a result, available biodiversity data vary substantially in quantity and information content, which must be carefully reconciled for meaningful ecological analysis.Hierarchical modelling, including single‐species integrated models and hierarchical community models, has improved our ability to assess and predict biodiversity trends and processes. Here, we highlight the emerging ‘integrated community modelling’ framework that combines both data integration and community modelling to improve inferences on species‐ and community‐level dynamics.We illustrate the framework with a series of worked examples. Our three case studies demonstrate how integrated community models can be used to extend the geographic scope when evaluating species distributions and community‐level richness patterns; discern population and community trends over time; and estimate demographic rates and population growth for communities of sympatric species. We implemented these worked examples using multiple software methods through the R platform via packages with formula‐based interfaces and through development of custom code in JAGS, NIMBLE and Stan.Integrated community models provide an exciting approach to model biological and observational processes for multiple species using multiple data types and sources simultaneously, thus accounting for uncertainty and sampling error within a unified framework. By leveraging the combined benefits of both data integration and community modelling, integrated community models can produce valuable information about both common and rare species as well as community‐level dynamics, allowing for holistic evaluation of the effects of global change on biodiversity.