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Understanding the links between causes and consequences of biodiversity is key to achieving conservation of both biodiversity and its functions, yet previous studies have mostly studied them independently. Ecologists have developed bipartite frameworks of high-level processes to understand mechanisms of species coexistence (niche and fitness differences) and biodiversity effects on ecosystem functioning (complementarity and selection effects) and stability (species stability and asynchrony). These high-level processes represent combined effects of low-level processes, which mediate either positive or negative associations between coexistence and biodiversity effects on functioning or stability. Our synthesis, based on high-level processes, provides a first step towards an integrative framework of biodiversity science. Future research needs to address low-level processes using more mechanistic approaches across broader contexts and scales. The global biodiversity crisis has stimulated decades of research on three themes: species coexistence, biodiversity–ecosystem functioning relationships (BEF), and biodiversity–ecosystem functional stability relationships (BEFS). However, studies on these themes are largely independent, creating barriers to an integrative understanding of the causes and consequences of biodiversity. Here we review recent progress towards mechanistic integration of coexistence, BEF, and BEFS. Mechanisms underlying the three themes can be linked in various ways, potentially creating either positive or negative relationships between them. That said, we generally expect positive associations between coexistence and BEF, and between BEF and BEFS. Our synthesis represents an initial step towards integrating causes and consequences of biodiversity; future developments should include more mechanistic approaches and broader ecological contexts. [ABSTRACT FROM AUTHOR]

Models of natural processes necessarily sacrifice some realism for the sake of tractability. Detailed, parameter‐rich models often provide accurate estimates of system behaviour but can be data‐hungry and difficult to operationalize. Moreover, complexity increases the danger of 'over‐fitting', which leads to poor performance when models are applied to novel conditions. This challenge is typically described in terms of a trade‐off between bias and variance (i.e. low accuracy vs. low precision).In studies of ecological communities, this trade‐off often leads to an argument about the level of detail needed to describe interactions among species. Here, we used data from a grassland biodiversity experiment containing nine locally abundant plant species (the Jena 'dominance experiment') to parameterize models representing six increasingly complex hypotheses about interactions. For each model, we calculated goodness‐of‐fit across different subsets of the data based on sown species richness levels, and tested how performance changed depending on whether or not the same data were used to parameterize and test the model (i.e. within vs. out‐of‐sample), and whether the range of diversity treatments being predicted fell inside or outside of the range used for parameterization.As expected, goodness‐of‐fit improved as a function of model complexity for all within‐sample tests. In contrast, the best out‐of‐sample performance generally resulted from models of intermediate complexity (i.e. with only two interaction coefficients per species—an intraspecific effect and a single pooled interspecific effect), especially for predictions that fell outside the range of diversity treatments used for parameterization. In accordance with other studies, our results also demonstrate that commonly used selection methods based on AIC of models fitted to the full dataset correspond more closely to within‐sample than out‐of‐sample performance.Synthesis. Our results demonstrate that models which include only general intra and interspecific interaction coefficients can be sufficient for estimating species‐level abundances across a wide range of contexts and may provide better out‐of‐sample performance than do more complex models. These findings serve as a reminder that simpler models may often provide a better trade‐off between bias and variance in ecological systems, particularly when applying models beyond the conditions used to parameterize them. [ABSTRACT FROM AUTHOR]