Forecasting productivity and stress across an ecosystem is complicated by the multiple interactions between competing species, the unknown levels of intra- and interspecific trait plasticity, and the dependencies between those traits within individuals. Integrating these features into a trait-based quantitative framework requires a conceptual and quantitative synthesis of how multiple species and their functional traits interact and respond to changing environments, a challenge known in community ecology as the “fourth-corner problem.” We propose such a novel synthesis, implemented as an integrated Bayesian hierarchical model. This allows us to (1) simultaneously model trait–trait and trait–environment relationships by explicitly accounting for both intra- and interspecific trait variabilities in a single analysis using all available data types, (2) quantify the strength of the trait–environment relationships, (3) identify trade-offs between multiple traits in multiple species, and (4) faithfully propagate our modeling uncertainties when making species-specific and community-wide trait predictions, reducing false confidence in our spatial prediction results. We apply this integrated approach to the world’s largest mangrove forest, the Sundarbans, a sentinel ecosystem impacted simultaneously by both climate change and multiple types of human exploitation. The Sundarbans presents extensive variability in environmental variables, such as salinity and siltation, driven by changing seawater levels from the south and freshwater damming from the north. We find that tree species growing under stress have a typical functional response to the environmental drivers with inter-specific variability around this average, and the amount of variability is further contingent upon the nature and magnitude of the environmental drivers. Our model captures the retreat in traits related to resource acquisition and a plastic enhancement of traits related to resource conservation, both clear indications of stress. We predict that, if historical increases in salinity and siltation are maintained, one-third of whole-ecosystem productivity will be lost by 2050. Our integrated modeling approach bridges community and ecosystem ecology through simultaneously modeling trait–environment correlations and trait–trait trade-offs at organismal, community, and ecosystem levels; provides a generalizable foundation for powerful modeling of trait-environment linkages under changing environments to predict their consequences on ecosystem functioning and services; and is readily applicable across the Earth’s ecosystems.

中文翻译：

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解决第四个角问题：从基于空间多物种特征的模型预测生态系统初级生产

由于竞争物种之间的多重相互作用、种内和种间性状可塑性的未知水平以及个体内部这些性状之间的依赖性，预测整个生态系统的生产力和压力变得复杂。将这些特征整合到基于性状的定量框架中，需要对多个物种及其功能性状如何相互作用以及如何应对不断变化的环境进行概念和定量综合，这一挑战在社区生态学中被称为“第四角问题”。我们提出了这样一种新颖的综合，实现为一个集成的贝叶斯分层模型。这使我们能够 (1) 通过在使用所有可用数据类型的单一分析中明确考虑种内和种间特征变异性，同时对特征-特征和特征-环境关系进行建模，(2) 量化性状-环境关系的强度，(3) 确定多个物种的多个性状之间的权衡，以及 (4) 在进行物种特异性和社区范围的性状预测时忠实地传播我们的建模不确定性，减少错误对我们的空间预测结果的信心。我们将这种综合方法应用于世界上最大的红树林 Sundarbans，这是一个同时受到气候变化和多种人类开发类型影响的哨兵生态系统。孙德尔本斯在环境变量方面呈现出广泛的变化，例如盐度和淤积，这是由于南部海水水位变化和北部淡水筑坝造成的。我们发现，在压力下生长的树种对环境驱动因素具有典型的功能响应，在该平均值附近具有种间变异性，并且变异性的量进一步取决于环境驱动因素的性质和大小。我们的模型捕捉了与资源获取相关的特征的消退和与资源保护相关的特征的可塑性增强，这两者都是压力的明确迹象。我们预测，如果盐度和淤泥的历史增加保持不变，到 2050 年，整个生态系统的生产力将损失三分之一。生物体、群落和生态系统层面的关闭；为不断变化的环境下特征-环境联系的强大建模提供可推广的基础，以预测其对生态系统功能和服务的影响；并且很容易适用于整个地球的生态系统。