  1. Joint species distribution models (JSDMs) have gained considerable
traction among ecologists over the past decade, due to their capacity to answer
a wide range of questions at both the species- and the community-level. The
family of generalized linear latent variable models in particular has proven
popular for building JSDMs, being able to handle many response types including
presence-absence data, biomass, overdispersed and/or zero-inflated counts.
  2. We extend latent variable models to handle percent cover data, with
vegetation, sessile invertebrate, and macroalgal cover data representing the
prime examples of such data arising in community ecology.
  3. Sparsity is a commonly encountered challenge with percent cover data.
Responses are typically recorded as percentages covered per plot, though some
species may be completely absent or present, i.e., have 0% or 100% cover
respectively, rendering the use of beta distribution inadequate.
  4. We propose two JSDMs suitable for percent cover data, namely a hurdle beta
model and an ordered beta model. We compare the two proposed approaches to a
beta distribution for shifted responses, transformed presence-absence data, and
an ordinal model for percent cover classes. Results demonstrate the hurdle beta
JSDM was generally the most accurate at retrieving the latent variables and
predicting ecological percent cover data.
