Joint Species Distribution Models are essential for understanding how ecological covariates shape species communities. However, most existing approaches are limited by rigid parametric distributions for count data and the inability to model how interspecific associations change with those covariates. We introduce joint count transformation models, a novel framework designed to overcome these limitations. Our approach combines distribution-free marginal count transformation models for multiple species with a covariate-dependent latent Gaussian copula to model interspecific correlations, interpretable as Spearman's rank correlation on the observed count scale. All model parameters are estimated efficiently via joint maximum likelihood estimation, implemented in the R package tram. We apply this framework to model the joint abundance of three fish-eating bird species, using seasonality as the primary covariate. Our model successfully captured the complex, species-specific seasonal abundance patterns, including periods of high zero-counts and seasonal shifts in variance. Furthermore, the model revealed strong, seasonally-varying correlations between the species. These findings are consistent with an empirical approach and similar to those from the computationally expensive parametric Bayesian Hierarchical Modelling of Species Communities (HMSC) framework. Consistency, accuracy and feasibility of our approach are demonstrated in a simulation study for up to 10 species.

翻译：联合物种分布模型对于理解生态协变量如何塑造物种群落至关重要。然而，现有方法大多受限于计数数据的刚性参数分布，且无法刻画种间关联随协变量变化的过程。我们提出联合计数变换模型——一种旨在突破这些局限的新框架。该方法将多个物种的无分布边际计数变换模型与协变量相关的潜高斯Copula相结合，以建模种间相关性，该相关性可解释为观测计数尺度上的斯皮尔曼秩相关系数。所有模型参数均通过联合最大似然估计高效求解，并已在R包tram中实现。我们应用该框架对三种食鱼鸟类的联合丰度进行建模，以季节性作为主要协变量。该模型成功捕捉了复杂且具物种特异性的季节性丰度模式，包括高零计数期和方差的季节性波动。此外，模型揭示了物种间随季节变化的强相关性。这些发现与实证方法结果一致，且与计算成本高昂的参数化贝叶斯层级群落建模框架（HMSC）结论相似。我们通过模拟研究（最多10个物种）证明了该方法的一致性、准确性和可行性。