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Record W3005916987 · doi:10.1016/j.tree.2020.01.004

The Role of Evolution in Shaping Ecological Networks

2020· review· en· W3005916987 on OpenAlexaff
Simon T. Segar, Tom M. Fayle, Diane S. Srivastava, Thomas M. Lewinsohn, Owen T. Lewis, Vojtêch Novotný, R. L. Kitching, Sarah C. Maunsell

Bibliographic record

VenueTrends in Ecology & Evolution · 2020
Typereview
Languageen
FieldAgricultural and Biological Sciences
TopicPlant and animal studies
Canadian institutionsUniversity of British Columbia
FundersH2020 European Research CouncilWissenschaftskolleg zu BerlinNatural Environment Research CouncilFundação de Amparo à Pesquisa do Estado de São PauloGrantová Agentura České RepublikySight Research UKConselho Nacional de Desenvolvimento Científico e TecnológicoHarper Adams University
KeywordsBlueprintEvolutionary dynamicsEcologyEvolutionary ecologyVariation (astronomy)Process (computing)Temporal scalesEvolutionary biologyComputer scienceBiologySociologyEngineering

Abstract

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Networks of ecological interactions define the way that ecosystems function. Network assembly and temporal persistence can be thought of as contemporary ecological functions but shaped by historical evolutionary processes. Increasingly, researchers study networks within a phylogenetic comparative context, acknowledging that networks are sensitive to evolutionary constraints operating at regional or local scales. Methodological progress in population genomics and molecular detection, combined with theoretical developments in modelling, now permit investigation of ecoevolutionary feedback loops within networks. Although understanding of isolated parts of network assembly and persistence is developing, a unifying framework for making connections and predictions across evolutionary scales is lacking. Approaches are being developed on multiple fronts from which such a framework may well emerge. The structure of ecological networks reflects the evolutionary history of their biotic components, and their dynamics are strongly driven by ecoevolutionary processes. Here, we present an appraisal of recent relevant research, in which the pervasive role of evolution within ecological networks is manifest. Although evolutionary processes are most evident at macroevolutionary scales, they are also important drivers of local network structure and dynamics. We propose components of a blueprint for further research, emphasising process-based models, experimental evolution, and phenotypic variation, across a range of distinct spatial and temporal scales. Evolutionary dimensions are required to advance our understanding of foundational properties of community assembly and to enhance our capability of predicting how networks will respond to impending changes. The structure of ecological networks reflects the evolutionary history of their biotic components, and their dynamics are strongly driven by ecoevolutionary processes. Here, we present an appraisal of recent relevant research, in which the pervasive role of evolution within ecological networks is manifest. Although evolutionary processes are most evident at macroevolutionary scales, they are also important drivers of local network structure and dynamics. We propose components of a blueprint for further research, emphasising process-based models, experimental evolution, and phenotypic variation, across a range of distinct spatial and temporal scales. Evolutionary dimensions are required to advance our understanding of foundational properties of community assembly and to enhance our capability of predicting how networks will respond to impending changes. ‘A network whose links change adaptively with respect to its states, resulting in a dynamical interplay between the state and the topology of the network’ [46.Gross T. Sayama H. Adaptive networks.in: Adaptive Networks. Springer, 2009: 1-8Crossref Scopus (42) Google Scholar]. networks in which the links represent interactions with negative impacts on the fitness of one level of interacting species. evolution of a continuous trait across a phylogeny, modelled as a random walk for comparison with other processes [20.Revell L. J. et al.Phylogenetic signal, evolutionary process, and rate.Syst. Biol. 2008; 57: 591-601Crossref PubMed Scopus (571) Google Scholar]. simultaneous diversification (speciation) of two interacting lineages. mutual and concurrent evolutionary adaptation of traits in a population of one species to individuals from another [34.Janzen D. H. When is it coevolution?.Evolution. 1980; 34: 611-612Crossref PubMed Google Scholar]. phylogenies pruned to include only cooccurring species rather than all species within a taxon or clade. ‘cyclical interaction between ecology and evolution such that changes in ecological interactions drive evolutionary change in organismal traits that, in turn, alter the form of ecological interactions, and so forth’ [87.Post D. M. Palkovacs E. P. Eco-evolutionary feedbacks in community and ecosystem ecology: interactions between the ecological theatre and the evolutionary play.Philos. Trans. R. Soc. B. 2009; 364: 1629-1640Crossref PubMed Scopus (397) Google Scholar]. ‘process whereby organisms colonise and persist in novel environments, use novel resources or form novel associations with other species as a result of the suites of traits that they carry at the time they encounter the novel condition’ [88.Agosta S. J. On ecological fitting, plant–insect associations, herbivore host shifts, and host plant selection.Oikos. 2006; 114: 556-565Crossref Scopus (218) Google Scholar]. any depiction of a set of interindividual or interspecies interactions in nature, usually comprising nodes (the species themselves) and edges (the functional links among species). field of study focused on exploring patterns and process in ecology through the combination of ecological data with phylogenetic and biogeographic data. frequency and/ or fidelity of a connection between two nodes in a network when sampled at multiple points (across time and/or space). evolution on a scale at or above the level of species. relates specifically to the turnover of allele frequencies within a population through inheritance, selection and drift. networks in which the links represent interactions with positive impacts on the fitness of both sets of interacting species. statistical nonindependence, and phylogenetic clustering, among interactions in a network due to the phylogenetic relatedness of nodes (modified from [20.Revell L. J. et al.Phylogenetic signal, evolutionary process, and rate.Syst. Biol. 2008; 57: 591-601Crossref PubMed Scopus (571) Google Scholar]). multidimensional component (metrics include persistence, robustness, resistance, resilience and variability) that quantifies the ability of a network to resist restructuring or collapse following perturbation. tendency of species to retain ancestral traits. extent to which a trait in one species exceeds or overcomes a corresponding trait in another ( e. g., animal gape must exceed fruit diameter in seed dispersal mutualisms) [35.Nuismer S. L. et al.Coevolution and the architecture of mutualistic networks: coevolving networks.Evolution. 2013; 67: 338-354Crossref PubMed Scopus (94) Google Scholar]. phenotypic resource traits that match those of consumers, ( e. g., phenological cooccurrence of plants and pollinators). ‘ability of individual genotypes to produce different phenotypes when exposed to different environmental conditions’ [89.Pigliucci M. Phenotypic plasticity and evolution by genetic assimilation.J. Exp. Biol. 2006; 209: 2362-2367Crossref PubMed Scopus (693) Google Scholar]. ‘phylogenetic tracking occurs if there is strong asymmetry in the interaction between two species, implying one species is much more dependent on the other. This leads to parallel phylogenetic trees’ [31.Russo L. et al.Quantitative evolutionary patterns in bipartite networks: Vicariance, phylogenetic tracking or diffuse co-evolution ?.Methods Ecol. Evol. 2018; 9: 761-772Crossref Scopus (13) Google Scholar]. morphological, behavioural, ecological, or chemical features of a species reflecting both its evolutionary history and its local phenotypic adaptation.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.976
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.067
GPT teacher head0.274
Teacher spread0.207 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations105
Published2020
Admission routes1
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