spectre: An R package to estimate spatially-explicit community composition using sparse data
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...
Gespeichert in:
Hauptverfasser: | , , , , , |
---|---|
Format: | Dataset |
Sprache: | eng |
Schlagworte: | |
Online-Zugang: | Volltext bestellen |
Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
Zusammenfassung: | 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 presence and absence 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. |
---|---|
DOI: | 10.5061/dryad.fbg79cnz7 |