Generating species assemblages for restoration and experimentation: A new method that can simultaneously converge on average trait values and maximize functional diversity

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dc.contributor.authorLaughlin, Daniel C.en_NZ
dc.contributor.authorChalmandrier, Loïcen_NZ
dc.contributor.authorJoshi, Chaitanyaen_NZ
dc.contributor.authorRenton, Michaelen_NZ
dc.contributor.authorDwyer, John M.en_NZ
dc.contributor.authorFunk, Jennifer L.en_NZ
dc.date.accessioned2018-11-08T23:11:06Z
dc.date.available2018-07-01en_NZ
dc.date.available2018-11-08T23:11:06Z
dc.date.issued2018en_NZ
dc.description.abstract1. Restoring resilient ecosystems in an era of rapid environmental change requires a flexible framework for selecting assemblages of species based on functional traits. However, current trait‐based models have been limited to algorithms that select species assemblages that only converge on specified average trait values, and could not accommodate the common desire among restoration ecologists to generate functionally diverse assemblages. 2. We have solved this problem by applying a nonlinear optimization algorithm to solve for the species relative abundances that maximize Rao's quadratic entropy (Q) subject to other linear constraints. Rao's Q is a closed‐form algebraic expression of functional diversity that is maximized when the most abundant species are functionally dissimilar. 3. Previous models have maximized species evenness subject to the linear constraints by maximizing the entropy function (H’). Maximizing Q alone produces an undesirable species abundance distribution because species that exhibit extreme trait values have the highest abundances. We demonstrate that the maximization of an objective function that additively combines Q and H’ produces a more even relative abundance distribution across the trait dimension. 4. Some ecological restoration projects aim to restore communities that converge on one set of traits while diverging across another. The selectSpecies r function can derive assemblages for any size species pool that maximizes the diversity of any set of traits, while simultaneously converging on average values of any other set of traits. We demonstrate how the function works through examples using uniformly spaced trait distributions and data with a known structure. We also demonstrate the utility of the function using real trait data collected on dozens of species from three separate ecosystems: serpentine grasslands, ponderosa pine forests, and subtropical rainforests. 5. The quantitative selection of species based on their functional traits for ecological restoration and experimentation must be both rigorous and accessible to practitioners. The selectSpecies function provides ecologists with an easy‐to‐use open‐source solution to objectively derive species assemblages based on their functional traits.
dc.format.mimetypeapplication/pdf
dc.identifier.citationLaughlin, D. C., Chalmandrier, L., Joshi, C., Renton, M., Dwyer, J. M., & Funk, J. L. (2018). Generating species assemblages for restoration and experimentation: A new method that can simultaneously converge on average trait values and maximize functional diversity. Methods in Ecology and Evolution, 9(7), 1764–1771. https://doi.org/10.1111/2041-210X.13023en
dc.identifier.doi10.1111/2041-210X.13023en_NZ
dc.identifier.eissn2041-2096en_NZ
dc.identifier.issn2041-210Xen_NZ
dc.identifier.urihttps://hdl.handle.net/10289/12154
dc.language.isoenen_NZ
dc.publisherWileyen_NZ
dc.relation.isPartOfMethods in Ecology and Evolutionen_NZ
dc.rights© 2018 The Authors. Methods in Ecology and Evolution © 2018 British Ecological Society
dc.subjectScience & Technologyen_NZ
dc.subjectLife Sciences & Biomedicineen_NZ
dc.subjectEcologyen_NZ
dc.subjectEnvironmental Sciences & Ecologyen_NZ
dc.subjectcomplementarityen_NZ
dc.subjectcommunity assemblyen_NZ
dc.subjectecological restorationen_NZ
dc.subjectfunctional diversityen_NZ
dc.subjectR packageen_NZ
dc.subjectselectSpeciesen_NZ
dc.subjectPLANT TRAITSen_NZ
dc.subjectECOLOGICAL RESTORATIONen_NZ
dc.subjectSERPENTINE GRASSLANDen_NZ
dc.subjectBIODIVERSITYen_NZ
dc.subjectFRAMEWORKen_NZ
dc.subjectCONSENSUSen_NZ
dc.titleGenerating species assemblages for restoration and experimentation: A new method that can simultaneously converge on average trait values and maximize functional diversityen_NZ
dc.typeJournal Article
pubs.begin-page1764
pubs.elements-id224922
pubs.end-page1771
pubs.issue7en_NZ
pubs.organisational-group/Waikato
pubs.organisational-group/Waikato/2018 PBRF
pubs.organisational-group/Waikato/FCMS
pubs.organisational-group/Waikato/FCMS/2018 PBRF - FCMS
pubs.organisational-group/Waikato/FCMS/Mathematics and Statistics
pubs.publication-statusPublisheden_NZ
pubs.user.infoJoshi, Chaitanya (cjoshi@waikato.ac.nz)
pubs.volume9en_NZ
uow.verification.statusverified
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