Identifying models of trait‐mediated community assembly using random forests and approximate Bayesian computation

Ruffley, Megan; Peterson, Katie; Week, Bob; Tank, David C.; Harmon, Luke J.

doi:10.1002/ece3.5773

Cited by 16 publications

(30 citation statements)

References 85 publications

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“…Analysis of the community phylogenies provides a deeptime lens on community structure which can be used to estimate speciation and extinction rates (Manceau et al 2015) and make inferences about diversification processes (Emerson and Gillespie 2008;Morlon 2014;Pearse et al 2014). Recent methods have also been developed to simultaneously model trait evolution and species diversification (Weber et al 2017) to investigate the importance of competition in shaping evolutionary radiations (Aristide and Morlon 2019), and the joint contribution of competition and environmental filtering in structuring ecological communities (Ruffley et al 2019). Community-scale trait data can also be analyzed along with metabarcoding data in a hierarchical modeling framework to further account for feedbacks among processes happening at disparate timescales (Overcast et al n.d.).…”

Section: Linking Theoretical Biology and Dna Barcodingmentioning

confidence: 99%

High-throughput sequencing for community analysis: the promise of DNA barcoding to uncover diversity, relatedness, abundances and interactions in spider communities

et al. 2020

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Large-scale studies on community ecology are highly desirable but often difficult to accomplish due to the considerable investment of time, labor and, money required to characterize richness, abundance, relatedness, and interactions. Nonetheless, such large-scale perspectives are necessary for understanding the composition, dynamics, and resilience of biological communities. Small invertebrates play a central role in ecosystems, occupying critical positions in the food web and performing a broad variety of ecological functions. However, it has been particularly difficult to adequately characterize communities of these animals because of their exceptionally high diversity and abundance. Spiders in particular fulfill key roles as both predator and prey in terrestrial food webs and are hence an important focus of ecological studies. In recent years, large-scale community analyses have benefitted tremendously from advances in DNA barcoding technology. High-throughput sequencing (HTS), particularly DNA metabarcoding, enables community-wide analyses of diversity and interactions at unprecedented scales and at a fraction of the cost that was previously possible. Here, we review the current state of the application of these technologies to the analysis of spider communities. We discuss amplicon-based DNA barcoding and metabarcoding for the analysis of community diversity and molecular gut content analysis for assessing predator-prey relationships. We also highlight applications of the third generation sequencing technology for long read and portable DNA barcoding. We then address the development of theoretical frameworks for community-level studies, and finally highlight critical gaps and future directions for DNA analysis of spider communities.

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Section: Linking Theoretical Biology and Dna Barcodingmentioning

confidence: 99%

High-throughput sequencing for community analysis: the promise of DNA barcoding to uncover diversity, relatedness, abundances and interactions in spider communities

et al. 2020

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“…Notably, the available process‐based models either do not yet accommodate multiple traits (Botta‐Dukát & Czúcz, 2016; Münkemüller & Gallien, 2015; Ruffley et al., 2019) or their use has not yet been rigorously tested to a point that allows their application here (Munoz et al., 2018). In practice, however, at least one continuous trait is typically available (e.g.…”

Section: Methodsmentioning

confidence: 99%

Pitfalls of ignoring trait resolution when drawing conclusions about ecological processes

Kohli

Jarzyna

2021

Global Ecol. Biogeogr.

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Aim Understanding how ecological communities are assembled remains a grand challenge in ecology with direct implications for charting the future of biodiversity. Trait‐based methods have emerged as the leading approach for quantifying functional community structure (convergence, divergence) but their potential for inferring assembly processes rests on accurately measuring functional dissimilarity among community members. Here, we argue that trait resolution (from finest‐resolution continuous measurements to coarsest‐resolution binary categories) remains a critically overlooked methodological variable, even though categorical classification is known to mask functional variability and inflate functional redundancy among species or individuals. Innovation We present the first detailed predictions of trait resolution biases and demonstrate, with simulations, how the distortion of signal strength by increasingly coarse‐resolution traits can fundamentally alter functional structure patterns and the interpretation of causative ecological processes (e.g. abiotic filters, biotic interactions). We show that coarser trait data impart different impacts on the signals of divergence and convergence, implying that the role of biotic interactions may be underestimated when using coarser traits. Furthermore, in some systems, coarser traits may overestimate the strength of trait convergence, leading to erroneous support for abiotic processes as the primary drivers of community assembly or change. Main conclusions Inferences of assembly processes must account for trait resolution to ensure robust conclusions, especially for broad‐scale studies of comparative community assembly and biodiversity change. Despite recent improvements in the collection and availability of trait data, great disparities continue to exist among taxa in the number and availability of continuous traits, which are more difficult to acquire for large numbers of species than coarse categorical assignments. Based on our simulations, we urge the consideration of trait resolution in the design and interpretation of community assembly studies and suggest a suite of practical solutions to address the pitfalls of trait resolution biases.

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“…Simulation studies are becoming increasingly influential in ecology because of their ability to disentangle tightly correlated variables in a way that observational studies cannot (Furness et al, 2021;Pontarp et al, 2019;Pontarp & Wiens, 2016;Saupe et al, 2019;Zurell et al, 2010). However, unlike many previous simulation studies (Gotelli et al, 2009;Münkemüller & Gallien, 2015;van der Plas et al, 2015;Rangel et al, 2018;Ruffley et al, 2019), REvoSim works at the level of the individual, which has the benefit of removing otherwise necessary assumptions about species-level processes (Pontarp & Wiens, 2016). REvoSim models processes such as mutation, reproduction, and dispersal within a controlled environment and in the absence of ecological interactions more complex than exploitation-competition.…”

Section: Introductionmentioning

confidence: 99%

Productivity, niche availability, species richness, and extinction risk: Untangling relationships using individual‐based simulations

Furness

Garwood

Mannion

et al. 2021

Ecology and Evolution

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It has often been suggested that the productivity of an ecosystem affects the number of species that it can support. Despite decades of study, the nature, extent, and underlying mechanisms of this relationship are unclear. One suggested mechanism is the “more individuals” hypothesis (MIH). This proposes that productivity controls the number of individuals in the ecosystem, and that more individuals can be divided into a greater number of species before their population size is sufficiently small for each to be at substantial risk of extinction. Here, we test this hypothesis using REvoSim: an individual‐based eco‐evolutionary system that simulates the evolution and speciation of populations over geological time, allowing phenomena occurring over timescales that cannot be easily observed in the real world to be evaluated. The individual‐based nature of this system allows us to remove assumptions about the nature of speciation and extinction that previous models have had to make. Many of the predictions of the MIH are supported in our simulations: Rare species are more likely to undergo extinction than common species, and species richness scales with productivity. However, we also find support for relationships that contradict the predictions of the strict MIH: species population size scales with productivity, and species extinction risk is better predicted by relative than absolute species population size, apparently due to increased competition when total community abundance is higher. Furthermore, we show that the scaling of species richness with productivity depends upon the ability of species to partition niche space. Consequently, we suggest that the MIH is applicable only to ecosystems in which niche partitioning has not been halted by species saturation. Some hypotheses regarding patterns of biodiversity implicitly or explicitly overlook niche theory in favor of neutral explanations, as has historically been the case with the MIH. Our simulations demonstrate that niche theory exerts a control on the applicability of the MIH and thus needs to be accounted for in macroecology.

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Identifying models of trait‐mediated community assembly using random forests and approximate Bayesian computation

Cited by 16 publications

References 85 publications

High-throughput sequencing for community analysis: the promise of DNA barcoding to uncover diversity, relatedness, abundances and interactions in spider communities

High-throughput sequencing for community analysis: the promise of DNA barcoding to uncover diversity, relatedness, abundances and interactions in spider communities

Pitfalls of ignoring trait resolution when drawing conclusions about ecological processes

Productivity, niche availability, species richness, and extinction risk: Untangling relationships using individual‐based simulations

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