Joint species distribution models with species correlations and imperfect detection.
Joint species distribution models with species correlations and imperfect detection.
复制标题
具有物种相关性和不完善检测的联合物种分布模型。
作者：
M. Tobler;M. Kéry;Francis K. C. Hui;G. Guillera‐Arroita;Peter Knaus;T. Sattler
生物群落的时空格局通常是由环境因素和物种相互作用驱动的。来自群落的空间数据自然地被描述为群落中所有物种的堆叠模型。在这种多物种或联合物种分布模型(JSDM)中，两个重要的考虑因素是测量误差和物种之间的相关性。到目前为止，几乎所有的JSDM都包括了一个或另一个特征，但不是同时包括两个特征，即使测量误差和物种相关性对于实现关于群落分布和物种共生模式的无偏见推断可能是必不可少的。我们开发了两个在场-缺席JSDM来建模两个物种之间的关联，同时适应不完全的检测：一个使用潜在变量，另一个使用多变量概率位方法。我们进行了三项模拟研究，以评估我们的新模型的性能，并将它们与以前没有考虑到不完美检测的潜在变量JSDM进行比较。我们用瑞士62种雀形目鸟类的大型Atlas数据集来说明我们的模型。在广泛的条件下，我们的新的潜在变量JSDM具有不完全的检测和物种关联，得到的估计对占有率、占有率回归系数和物种关联矩阵几乎没有偏差。相比之下，在多变量Probit模型中，我们看到了大数据集(许多物种和站点)的收敛问题，导致运行时间非常长，误差也更大。忽略不完美检测的潜变量模型产生的相关性估计始终是负偏的，即低估。我们发现，充分表示物种关联矩阵所需的潜在变量的数量可能比之前建议的要多得多，即大约n/2，其中n是群落大小。对瑞士雀形目数据集的分析表明，不考虑不完美的检测将导致入住率估计中的负偏差，并导致联合统计模型中估计的协变量系数的衰减。此外，如果不考虑，检测中的空间异质性可能会导致估计的物种相关矩阵中的虚假模式。我们新的JSDM代表了当前群落建模方法的一个重要扩展，即不能确定地检测到物种的存在和缺失的常见情况。
Spatiotemporal patterns in biological communities are typically driven by environmental factors and species interactions. Spatial data from communities are naturally described by stacking models for all species in the community. Two important considerations in such multispecies or joint species distribution models (JSDMs) are measurement errors and correlations between species. Up to now, virtually all JSDMs have included either one or the other, but not both features simultaneously, even though both measurement errors and species correlations may be essential for achieving unbiased inferences about the distribution of communities and species co-occurrence patterns. We developed two presence-absence JSDMs for modeling pairwise species correlations while accommodating imperfect detection: one using a latent variable and the other using a multivariate probit approach. We conducted three simulation studies to assess the performance of our new models and to compare them to earlier latent variable JSDMs that did not consider imperfect detection. We illustrate our models with a large Atlas data set of 62 passerine bird species in Switzerland. Under a wide range of conditions, our new latent variable JSDM with imperfect detection and species correlations yielded estimates with little or no bias for occupancy, occupancy regression coefficients, and the species correlation matrix. In contrast, with the multivariate probit model we saw convergence issues with large data sets (many species and sites) resulting in very long run times and larger errors. A latent variable model that ignores imperfect detection produced correlation estimates that were consistently negatively biased, that is, underestimated. We found that the number of latent variables required to represent the species correlation matrix adequately may be much greater than previously suggested, namely around n/2, where n is community size. The analysis of the Swiss passerine data set exemplifies how not accounting for imperfect detection will lead to negative bias in occupancy estimates and to attenuation in the estimated covariate coefficients in a JSDM. Furthermore, spatial heterogeneity in detection may cause spurious patterns in the estimated species correlation matrix if not accounted for. Our new JSDMs represent an important extension of current approaches to community modeling to the common case where species presence-absence cannot be detected with certainty.