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Free, publicly-accessible full text available June 1, 2024
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Abstract Matrix population models are frequently built and used by ecologists to analyse demography and elucidate the processes driving population growth or decline. Life Table Response Experiments (LTREs) are comparative analyses that decompose the realized difference or variance in population growth rate () into contributions from the differences or variances in the vital rates (i.e. the matrix elements). Since their introduction, LTREs have been based on approximations and have not included biologically relevant interaction terms.
We used the functional analysis of variance framework to derive an exact LTRE method, which calculates the exact response of to the difference or variance in a given vital rate, for all interactions among vital rates—including higher‐order interactions neglected by the classical methods. We used the publicly available COMADRE and COMPADRE databases to perform a meta‐analysis comparing the results of exact and classical LTRE methods. We analysed 186 and 1487 LTREs for animal and plant matrix population models, respectively.
We found that the classical methods often had small errors, but that very high errors were possible. Overall error was related to the difference or variance in the matrices being analysed, consistent with the Taylor series basis of the classical method. Neglected interaction terms accounted for most of the errors in fixed design LTRE, highlighting the importance of two‐way interaction terms. For random design LTRE, errors in the contribution terms present in both classical and exact methods were comparable to errors due to neglected interaction terms. In most examples we analysed, evaluating exact contributions up to three‐way interaction terms was sufficient for interpreting 90% or more of the difference or variance in .
Relative error, previously used to evaluate the accuracy of classical LTREs, is not a reliable metric of how closely the classical and exact methods agree. Error compensation between estimated contribution terms and neglected contribution terms can lead to low relative error despite faulty biological interpretation. Trade‐offs or negative covariances among matrix elements can lead to high relative error despite accurate biological interpretation. Exact LTRE provides reliable and accurate biological interpretation, and the R package
exactLTRE makes the exact method accessible to ecologists. -
Abstract Causal effects of biodiversity on ecosystem functions can be estimated using experimental or observational designs — designs that pose a tradeoff between drawing credible causal inferences from correlations and drawing generalizable inferences. Here, we develop a design that reduces this tradeoff and revisits the question of how plant species diversity affects productivity. Our design leverages longitudinal data from 43 grasslands in 11 countries and approaches borrowed from fields outside of ecology to draw causal inferences from observational data. Contrary to many prior studies, we estimate that increases in plot-level species richness caused productivity to decline: a 10% increase in richness decreased productivity by 2.4%, 95% CI [−4.1, −0.74]. This contradiction stems from two sources. First, prior observational studies incompletely control for confounding factors. Second, most experiments plant fewer rare and non-native species than exist in nature. Although increases in native, dominant species increased productivity, increases in rare and non-native species decreased productivity, making the average effect negative in our study. By reducing the tradeoff between experimental and observational designs, our study demonstrates how observational studies can complement prior ecological experiments and inform future ones.more » « lessFree, publicly-accessible full text available December 1, 2024
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Abstract Community assembly is often treated as deterministic, converging on one or at most a few possible stable endpoints. However, in nature, we typically observe continuous change in community composition, which is often ascribed to environmental change. But continuous changes in community composition can also arise in deterministic, time‐invariant community models, especially food web models. Our goal was to determine why some models produce continuous assembly and others do not. We investigated a simple two‐trophic‐level community model to show that continuous assembly is driven by the relative niche width of the trophic levels. If predators have a larger niche width than prey, community assembly converges to a stable equilibrium. Conversely, if predators have a smaller niche width than prey, then community composition never stabilizes. Evidence that food webs need not reach a stable equilibrium has important implications, as many ecological theories of community ecology based on equilibria may be difficult to apply to such food webs.
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Abstract Interactions between plants and soil microbes can influence plant population dynamics and diversity in plant communities. Traditional theoretical paradigms view the microbial community as a black box with net effects described by phenomenological models.
This approach struggles to quantify the importance of plant–microbe interactions relative to other competition and coexistence mechanisms and to explain context dependence in microbe effects.
We argue that a mechanistic framework focused on microbial functional groups will lead to conceptual and empirical advances, as demonstrated by extending resource ratio theory to plant–microbe interactions. We review the diverse pathways by which different microbial functional groups can influence plant resource competition. Finally, we suggest approaches to link theory with observations to measure the key parameters of our framework.
Synthesis : Our review highlights recent experimental advancements for uncovering microbial mechanisms that alter plant host resource competition and coexistence. We synthesize these mechanisms into a conceptual model that provides a framework for future experiments to investigate the importance of plant–microbe interactions in structuring plant populations and communities.
