spectre: an R package to estimate spatially‐explicit community composition using sparse data

2022 | journal article. A publication with affiliation to the University of Göttingen.

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​spectre: an R package to estimate spatially‐explicit community composition using sparse data​
Simpkins, C. E. ; Hanß, S. ; Spangenberg, M. C.; Salecker, J. ; Hesselbarth, M. H. K.   & Wiegand, K. ​ (2022) 
Ecography2022(12) art. e06272​.​ DOI: https://doi.org/10.1111/ecog.06272 


Simpkins, Craig Eric ; Hanß, Sebastian ; Spangenberg, M. C.; Salecker, Jan ; Hesselbarth, Maximilian H. K. ; Wiegand, Kerstin 
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.
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Fakultät für Forstwissenschaften und Waldökologie ; Büsgen-Institut ; Abteilung Ökosystemmodellierung ; Zentrum für Biodiversität und Nachhaltige Landnutzung 
0906-7590; 1600-0587
Open-Access-Publikationsfonds 2022



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