In:
Methods in Ecology and Evolution, Wiley, Vol. 9, No. 4 ( 2018-04), p. 834-848
Abstract:
Community‐level models (CLMs) consider multiple, co‐occurring species in model fitting and are lesser known alternatives to species distribution models (SDMs) for analysing and predicting biodiversity patterns. Community‐level models 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. We review (1) statistical implementations and applications of CLMs, (2) their advantages and limitations, and (3) comparative analyses of CLMs and SDMs. We also suggest directions for future research. 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 modelling, including five methods that predict assemblage composition and individual species distributions and two methods that model compositional turnover along environmental gradients. Community‐level models 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, modelling rare species, and projecting to no‐analog climates. A major shortcoming of CLMs is their reliance on presence–absence community composition data. Studies are needed to assess the relative merits of SDMs and CLMs, and different CLM algorithms, with a focus on three key areas: (1) under which circumstances CLMs improve predictions for rare species, (2) 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 (3) ability to project across time/space.
Type of Medium:
Online Resource
ISSN:
2041-210X
,
2041-210X
DOI:
10.1111/mee3.2018.9.issue-4
DOI:
10.1111/2041-210X.12936
Language:
English
Publisher:
Wiley
Publication Date:
2018
detail.hit.zdb_id:
2528492-7
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