Integrating experimental and distribution data to predict future species patterns

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dc.contributor.author Kotta, Jonne
dc.contributor.author Vanhatalo, Jarno Petteri
dc.contributor.author Jänes, Holger
dc.contributor.author Orav-Kotta, Helen
dc.contributor.author Rugiu, Luca
dc.contributor.author Jormalainen, Veijo
dc.contributor.author Bobsien, Ivo
dc.contributor.author Viitasalo, Markku
dc.contributor.author Virtanen, Elina
dc.contributor.author Nyström Sandman, Antonia
dc.contributor.author Isaeus, Martin
dc.contributor.author Leidenberger, Sonja
dc.contributor.author Jonsson, Per R.
dc.contributor.author Johannesson, Kerstin
dc.date.accessioned 2019-03-04T13:21:02Z
dc.date.available 2019-03-04T13:21:02Z
dc.date.issued 2019-02-12
dc.identifier.citation Kotta , J , Vanhatalo , J P , Jänes , H , Orav-Kotta , H , Rugiu , L , Jormalainen , V , Bobsien , I , Viitasalo , M , Virtanen , E , Nyström Sandman , A , Isaeus , M , Leidenberger , S , Jonsson , P R & Johannesson , K 2019 , ' Integrating experimental and distribution data to predict future species patterns ' , Scientific Reports , vol. 9 , 1821 . https://doi.org/10.1038/s41598-018-38416-3
dc.identifier.other PURE: 120569170
dc.identifier.other PURE UUID: b33d65fb-ac6d-424a-84a2-7f1f7d65cc91
dc.identifier.other WOS: 000458401500024
dc.identifier.other Scopus: 85061499547
dc.identifier.uri http://hdl.handle.net/10138/299845
dc.description.abstract Predictive species distribution models are mostly based on statistical dependence between environmental and distributional data and therefore may fail to account for physiological limits and biological interactions that are fundamental when modelling species distributions under future climate conditions. Here, we developed a state-of-the-art method integrating biological theory with survey and experimental data in a way that allows us to explicitly model both physical tolerance limits of species and inherent natural variability in regional conditions and thereby improve the reliability of species distribution predictions under future climate conditions. By using a macroalga-herbivore association (Fucus vesiculosus - Idotea balthica) as a case study, we illustrated how salinity reduction and temperature increase under future climate conditions may significantly reduce the occurrence and biomass of these important coastal species. Moreover, we showed that the reduction of herbivore occurrence is linked to reduction of their host macroalgae. Spatial predictive modelling and experimental biology have been traditionally seen as separate fields but stronger interlinkages between these disciplines can improve species distribution projections under climate change. Experiments enable qualitative prior knowledge to be defined and identify cause-effect relationships, and thereby better foresee alterations in ecosystem structure and functioning under future climate conditions that are not necessarily seen in projections based on non-causal statistical relationships alone. en
dc.format.extent 14
dc.language.iso eng
dc.relation.ispartof Scientific Reports
dc.rights cc_by
dc.rights.uri info:eu-repo/semantics/openAccess
dc.subject 1181 Ecology, evolutionary biology
dc.subject CLIMATE-CHANGE IMPACTS
dc.subject NO-ANALOG COMMUNITIES
dc.subject BALTIC SEA
dc.subject DISTRIBUTION MODELS
dc.subject PHENOTYPIC PLASTICITY
dc.subject SPATIAL-DISTRIBUTION
dc.subject FUCUS-VESICULOSUS
dc.subject LOCAL ADAPTATION
dc.subject IDOTEA-BALTICA
dc.subject SHIFTS
dc.title Integrating experimental and distribution data to predict future species patterns en
dc.type Article
dc.contributor.organization Department of Mathematics and Statistics
dc.contributor.organization Organismal and Evolutionary Biology Research Programme
dc.contributor.organization Research Centre for Ecological Change
dc.contributor.organization Helsinki Institute of Sustainability Science (HELSUS)
dc.contributor.organization Environmental and Ecological Statistics Group
dc.contributor.organization Biostatistics Helsinki
dc.description.reviewstatus Peer reviewed
dc.relation.doi https://doi.org/10.1038/s41598-018-38416-3
dc.relation.issn 2045-2322
dc.rights.accesslevel openAccess
dc.type.version publishedVersion
dc.relation.funder SUOMEN AKATEMIA
dc.relation.grantnumber

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