DOI: 10.5061/dryad.fbg79cnz7
title: spectre: An R package to estimate spatially-explicit community composition using sparse data

description: 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.
description: The simulated community datasets
 were built using the virtualspecies V1.5.1 R package (Leroy et al., 2016), which
 generates spatially-explicit presence/absence matrices from habitat
 suitability maps. We simulated these suitability maps using Gaussian
 fields neutral landscapes produced using the NLMR V1.0 R package (Sciaini
 et al., 2018). To allow for some level of overlap between species
 suitability maps, we divided the γ-diversity (i.e., the total number of
 simulated species) by an adjustable correlation value to create several
 species groups that share suitability maps. Using a full factorial design,
 we developed 81 presence/absence maps varying across four axes (see
 Supplemental Table 1 and Supplemental Figure 1): 1) landscape size,
 representing the number of sites in the simulated landscape; 2)
 γ-diversity; 3) the level of correlation among species suitability maps,
 with greater correlations resulting in fewer shared species groups among
 suitability maps; and 4) the habitat suitability threshold of the virtual
 species distribution function. The latter corresponds to the level to
 which a species is a generalist or a specialist represented by the degree
 a species distribution can be outside its preferred habitat type from a
 suitability map. Every variable set in the factorial design was replicated
 three times. Species richness, pairwise dissimilarity and γ-diversity
 measures (used as the inputs for the spectre algorithm) were taken directly from the simulated
 community composition maps, thus avoiding any errors produced in the
 process of estimating these values. 
description: The data is stored here in .RDS format and as such can be
 directly loaded into any R environment using the `readRDS()` function.
 Additionally, the data can be recreated and fully re-analysed by accessing
 it via the spectre usecase GitHub repo https://github.com/r-spatialecology/spectre_usecase.