Hanß, Sebastian

[["dc.bibliographiccitation.artnumber","e06272"],["dc.bibliographiccitation.issue","12"],["dc.bibliographiccitation.journal","Ecography"],["dc.bibliographiccitation.volume","2022"],["dc.contributor.author","Simpkins, Craig Eric"],["dc.contributor.author","Hanß, Sebastian"],["dc.contributor.author","Spangenberg, M. C."],["dc.contributor.author","Salecker, Jan"],["dc.contributor.author","Hesselbarth, Maximilian H. K."],["dc.contributor.author","Wiegand, Kerstin"],["dc.date.accessioned","2022-10-25T09:40:17Z"],["dc.date.available","2022-10-25T09:40:17Z"],["dc.date.issued","2022"],["dc.description.abstract","An understanding of how biodiversity is distributed across space is key to much of ecology and conservation. Many predictive modelling approaches have been developed to estimate the distribution of biodiversity over various spatial scales. Community modelling techniques may offer many benefits over single species modelling. However, techniques capable of estimating precise species makeups of communities are highly data intensive and thus often limited in their applicability. Here we present an R package, spectre, which can predict regional community composition at a fine spatial resolution using only sparsely sampled biological data. The package can predict the presences and absences of all species in an area, both known and unknown, at the sample site scale. Underlying the spectre package is a min-conflicts optimisation algorithm that predicts species' presences and absences throughout an area using estimates of α-, β- and γ-diversity. We demonstrate the utility of the spectre package using a spatially-explicit simulated ecosystem to assess the accuracy of the package's results. spectre offers a simple to use tool with which to accurately predict community compositions across varying scales, facilitating further research and knowledge acquisition into this fundamental aspect of ecology."],["dc.identifier.doi","10.1111/ecog.06272"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/116496"],["dc.language.iso","en"],["dc.relation.issn","0906-7590"],["dc.relation.issn","1600-0587"],["dc.relation.orgunit","Abteilung Ökosystemmodellierung"],["dc.relation.orgunit","Zentrum für Biodiversität und Nachhaltige Landnutzung"],["dc.rights","CC BY 3.0"],["dc.title","spectre: an R package to estimate spatially‐explicit community composition using sparse data"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.version","published_version"],["dspace.entity.type","Publication"]]

[["dc.bibliographiccitation.firstpage","842"],["dc.bibliographiccitation.issue","4"],["dc.bibliographiccitation.journal","Global Ecology and Biogeography"],["dc.bibliographiccitation.lastpage","851"],["dc.bibliographiccitation.volume","30"],["dc.contributor.affiliation","Udy, Kristy; 1Agroecology, Department of Crop Sciences University of Göttingen Göttingen Germany"],["dc.contributor.affiliation","Fritsch, Matthias; 2Department of Ecosystem Modelling University of Göttingen Göttingen Germany"],["dc.contributor.affiliation","Grass, Ingo; 3Ecology of Tropical Agricultural Systems University of Hohenheim Stuttgart Germany"],["dc.contributor.affiliation","Hanß, Sebastian; 2Department of Ecosystem Modelling University of Göttingen Göttingen Germany"],["dc.contributor.affiliation","Hartig, Florian; 4Theoretical Ecology University of Regensburg Regensburg Germany"],["dc.contributor.affiliation","Kneib, Thomas; 5Chair of Statistics and Econometrics University of Göttingen Göttingen Germany"],["dc.contributor.affiliation","Kreft, Holger; 6Biodiversity, Macroecology and Conservation Biogeography University of Göttingen Göttingen Germany"],["dc.contributor.affiliation","Kukunda, Collins B.; 7Forest Inventory and Remote Sensing, Faculty of Forest Sciences and Forest Ecology University of Göttingen Göttingen Germany"],["dc.contributor.affiliation","Pe’er, Guy; 9German Centre for Integrative Biodiversity Research (iDiv) Halle‐Jena‐Leipzig Leipzig Germany"],["dc.contributor.affiliation","Reininghaus, Hannah; 1Agroecology, Department of Crop Sciences University of Göttingen Göttingen Germany"],["dc.contributor.affiliation","Tietjen, Britta; 11Theoretical Ecology, Institute of Biology Freie Universität Berlin Berlin Germany"],["dc.contributor.affiliation","Tscharntke, Teja; 1Agroecology, Department of Crop Sciences University of Göttingen Göttingen Germany"],["dc.contributor.affiliation","van Waveren, Clara‐Sophie; 2Department of Ecosystem Modelling University of Göttingen Göttingen Germany"],["dc.contributor.affiliation","Wiegand, Kerstin; 2Department of Ecosystem Modelling University of Göttingen Göttingen Germany"],["dc.contributor.author","Udy, Kristy"],["dc.contributor.author","Fritsch, Matthias"],["dc.contributor.author","Meyer, Katrin Mareike"],["dc.contributor.author","Grass, Ingo"],["dc.contributor.author","Hanß, Sebastian"],["dc.contributor.author","Hartig, Florian"],["dc.contributor.author","Kneib, Thomas"],["dc.contributor.author","Kreft, Holger"],["dc.contributor.author","Kukunda, Collins B."],["dc.contributor.author","Pe’er, Guy"],["dc.contributor.author","Reininghaus, Hannah"],["dc.contributor.author","Tietjen, Britta"],["dc.contributor.author","Tscharntke, Teja"],["dc.contributor.author","Wiegand, Kerstin"],["dc.contributor.author","van Waveren, Clara‐Sophie"],["dc.contributor.editor","Keil, Petr"],["dc.date.accessioned","2021-03-16T11:08:51Z"],["dc.date.available","2021-03-16T11:08:51Z"],["dc.date.issued","2021"],["dc.date.updated","2022-02-09T13:21:24Z"],["dc.description.abstract","Abstract Aim It is widely accepted that biodiversity is influenced by both niche‐related and spatial processes from local to global scales. Their relative importance, however, is still disputed, and empirical tests are surprisingly scarce at the global scale. Here, we compare the importance of area (as a proxy for pure spatial processes) and environmental heterogeneity (as a proxy for niche‐related processes) for predicting native mammal species richness world‐wide and within biogeographical regions. Location Global. Time period We analyse a spatial snapshot of richness data collated by the International Union for Conservation of Nature. Major taxa studied All terrestrial mammal species, including possibly extinct species and species with uncertain presence. Methods We applied a spreading dye algorithm to analyse how native mammal species richness changes with area and environmental heterogeneity. As measures for environmental heterogeneity, we used elevation ranges and precipitation ranges, which are well‐known correlates of species richness. Results We found that environmental heterogeneity explained species richness relationships better than did area, suggesting that niche‐related processes are more prevalent than pure area effects at broad scales. Main conclusions Our results imply that niche‐related processes are essential to understand broad‐scale species–area relationships and that habitat diversity is more important than area alone for the protection of global biodiversity."],["dc.description.sponsorship","Deutsche Forschungsgemeinschaft http://dx.doi.org/10.13039/501100001659"],["dc.identifier.doi","10.1111/geb.13261"],["dc.identifier.uri","https://resolver.sub.uni-goettingen.de/purl?gro-2/80524"],["dc.language.iso","en"],["dc.notes.intern","DeepGreen Import"],["dc.relation.issn","1466-822X"],["dc.relation.issn","1466-8238"],["dc.relation.orgunit","Abteilung Ökosystemmodellierung"],["dc.relation.orgunit","Professuren für Statistik und Ökonometrie"],["dc.relation.orgunit","Abteilung Agrarökologie"],["dc.rights","This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited."],["dc.title","Environmental heterogeneity predicts global species richness patterns better than area"],["dc.type","journal_article"],["dc.type.internalPublication","yes"],["dc.type.subtype","original_ja"],["dspace.entity.type","Publication"]]