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.

翻译：1. 联合物种分布模型（JSDMs）因其能够回答物种层面和群落层面的广泛问题，在过去十年间受到生态学家的广泛关注。其中，广义线性潜变量模型特别适用于构建JSDMs，可处理多种响应数据类型，包括存在-缺失数据、生物量、过度离散和/或零膨胀计数数据。2. 本研究将潜变量模型扩展至百分比覆盖数据的处理，植被、固着无脊椎动物和大型藻类覆盖数据是群落生态学中此类数据的典型代表。3. 稀疏性是百分比覆盖数据面临的常见挑战。响应变量通常记录为每个样方的覆盖百分比，但有些物种可能完全缺失或完全覆盖（即覆盖率为0%或100%），这使得贝塔分布的应用存在局限性。4. 我们提出了两种适用于百分比覆盖数据的JSDMs：障碍贝塔模型和有序贝塔模型。将这两种模型与移位响应的贝塔分布、转换后的存在-缺失数据以及百分比覆盖等级的序数模型进行对比。结果表明，障碍贝塔JSDM在恢复潜变量和预测生态百分比覆盖数据方面总体表现最优。