A unified model of species abundance, genetic diversity, and functional diversity reveals the mechanisms structuring ecological communities

Overcast, Isaac; Ruffley, Megan; Rosindell, James; Harmon, Luke J.; Borges, Paulo A. V.; Emerson, Brent C.; Etienne, Rampal S.; Gillespie, Rosemary G.; Krehenwinkel, Henrik; Mahler, D. Luke; Massol, François; Parent, Christine; Patiño, Jairo; Peter, B.; Week, Bob; Wagner, Catherine E.; Hickerson, Michael J.; Rominger, Andrew J.

doi:10.1111/1755-0998.13514

Cited by 25 publications

(59 citation statements)

References 103 publications

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“…Following the MESS model of Overcast et al (2021), we simulate a fixed number of individuals in the local community. Each individual i is characterized by its trait value z i .…”

Section: The Mess Modelmentioning

confidence: 99%

“…In Overcast et al (2021), competition is modelled by a mean-field approximation: the trait value of an individual is compared to the mean trait value of the local community. The death rate q MF of any given individual i is then computed as…”

Section: The Mess Modelmentioning

confidence: 99%

“…A community is considered at equilibrium, and Λ=1, when the initial conditions are no longer detectable in the system, and this advancement toward equilibrium is measured as the proportion of individuals in the community descending from a lineage that colonized during the simulation. We visually inspected the resulting simulations by collapsing simulated summary statistics into principal components using the built-in PCA function of MESS (Overcast et al 2021) (Fig. 2).…”

Section: Exploration Of In Silico Experimentsmentioning

confidence: 99%

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Detecting the ecological footprint of selection

Luiselli

Overcast

Rominger

et al. 2021

Preprint

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The structure of communities is influenced by many processes, both ecological and evolutionary, but these processes are hard to distinguish from available data. The aim of this work is to distinguish the ecological footprint of selection from that of neutral processes that are invariant to species identity. To do this, we build on existing theory to produce a new mechanistic model of community structure incorporating ecology and evolution. We base our work on "massive eco-evolutionary synthesis simulations" (or MESS), which uses information from three biodiversity axes - species richness and abundance; population genetic diversity; and trait variation - to distinguish between processes with a mechanistic model. We added a new form of competition to MESS that explicitly compares the traits of each pair of individuals and allows us to distinguish between inter- and intra-specific competition. We find that this addition is essential to properly detect and characterise selection and it yields different results to the existing simpler model that only compares species' traits to the community mean. Neutral forces receive much less support from systems where trait data is incorporated into the inference mechanism.

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“…Following the MESS model of Overcast et al (2021), we simulate a fixed number of individuals in the local community. Each individual i is characterized by its trait value z i .…”

Section: The Mess Modelmentioning

confidence: 99%

Section: The Mess Modelmentioning

confidence: 99%

Section: Exploration Of In Silico Experimentsmentioning

confidence: 99%

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Detecting the ecological footprint of selection

Luiselli

Overcast

Rominger

et al. 2021

Preprint

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“…Integrative studies of species and genetic diversity, and the ecological factors underlying their association or lack thereof using the same type of index, would contribute to a better understanding of eco-evolutionary dynamics. Although not focused on spatial scenarios, some recent studies (Gaggiotti et al, 2018;Luiselli et al, 2021;Overcast et al, 2019Overcast et al, , 2021 have developed a community assembly model that makes predictions of genetic, species and functional diversity in terms of Hill numbers.…”

Section: Introductionmentioning

confidence: 99%

Information‐based summary statistics for spatial genetic structure inference

Qin

Gaggiotti

2022

Molecular Ecology Resources

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The measurement of biodiversity at all levels of organization is an essential first step to understand the ecological and evolutionary processes that drive spatial patterns of biodiversity. Ecologists have explored the use of a large range of different summary statistics and have come to the view that information-based summary statistics, and in particular so-called Hill numbers, are a useful tool to measure biodiversity.Population geneticists, on the other hand, have focused largely on summary statistics based on heterozygosity and measures of allelic richness. However, recent studies proposed the adoption of information-based summary statistics in population genetics studies. Here, we performed a comprehensive assessment of the power of this family of summary statistics to inform regarding spatial patterns of genetic diversity and we compared it with that of traditional population genetics approaches, namely measures based on allelic richness and heterozygosity. To give an unbiased evaluation, we used three machine learning methods to test the performance of different sets of summary statistics to discriminate between spatial scenarios. We defined three distinct sets, (i) one based on allelic richness measures which included the Jaccard index, (ii) a set based on heterozygosity that included F ST and (iii) a set based on Hill numbers derived from Shannon entropy, which included the recently proposed Shannon differentiation, ΔD. The results showed that the last of these performed as well or, under some specific spatial scenarios, even better than the traditional population genetics measures. Interestingly, we found that a rarely or never used genetic differentiation measure based on allelic richness, Jaccard dissimilarity (J), showed the highest discriminatory power to discriminate among spatial scenarios, followed by Shannon differentiation ΔD. We concluded, therefore, that information-based measures as well as Jaccard dissimilarity represent excellent additions to the population genetics toolkit.

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“…The method also garnered new insights into the empirical data and identified plausible feature interactions that are difficult to capture with other methods. Moreover, MrIML can be used in other ecological subdisciplines to, for example, predict microbiome composition, explore molecular epidemiological patterns(Fountain-Jones et al, 2017, 2018 and untangle ecological community structure.Lastly in this section,Overcast et al, (2021) demonstrate how population genetic data can be utilized to understand how ecological communities are organized. The authors introduce the Massive Eco-evolutionary Synthesis Simulations (MESS), which uses concepts in island biogeography to offer a unified model for community assembly.…”

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confidence: 99%