Data from: Multiresponse algorithms for community-level modeling: review of theory, applications, and comparison to species distribution models

1.Community-level models (CLMs) consider multiple, co-occurring species in
model fitting and are lesser known alternatives to species distribution
models (SDMs) for analyzing and predicting biodiversity patterns. CLMs
simultaneously model multiple species, including rare species, while
reducing overfitting and implicitly considering drivers of co-occurrence.
Many CLMs are direct extensions of well-known SDMs and therefore should be
familiar to ecologists. However, CLMs remain underutilized, and there have
been few tests of their potential benefits and no systematic reviews of
their assumptions and implementations. Here we review this emerging field
and provide examples in R to fit common CLMs. Our goal is to introduce
CLMs to a broader audience, and discuss their attributes, benefits, and
limitations relative to SDMs. 2.We review i) statistical implementations
and applications of CLMs, ii) their advantages and limitations, and iii)
comparative analyses of CLMs and SDMs. We also suggest directions for
future research. 3.We identify seven CLM algorithms with similar data
structures and predictive outputs as SDMs that should be most accessible
to ecologists familiar with species-level modeling, including five methods
that predict assemblage composition and individual species distributions
and two methods that model compositional turnover along environmental
gradients. CLMs have been applied to numerous taxa, regions, and spatial
scales, and a variety of topics (e.g., studying drivers of community
structure or assessing relationships between community composition and
functional traits). Studies suggest that the relative benefits of CLMs and
SDMs may be case specific, especially in terms of predicting species
distributions and community composition. However, CLMs may offer
advantages in terms of computational efficiency, modeling rare species,
and projecting to no-analog climates. A major shortcoming of CLMs is their
reliance on presence-absence community composition data. 4.Studies are
needed to assess the relative merits of SDMs and CLMs, and different CLM
algorithms, with a focus on three key areas: i) under which circumstances
CLMs improve predictions for rare species, ii) how CLMs perform under
different community compositions (e.g. relative abundance of rare vs.
common species), including the extent to which co-occurrence patterns are
structured by biotic interactions, and iii) ability to project across
time/space.