Valle Denis, Shimizu Gilson, Izbicki Rafael, Maracahipes Leandro, Silverio Divino Vicente, Paolucci Lucas N, Jameel Yusuf, Brando Paulo
School of Forest, Fisheries, and Geomatics Sciences University of Florida Gainesville Florida USA.
Department of Statistics Federal University of Sao Carlos Sao Paulo Brazil.
Ecol Evol. 2021 May 5;11(12):7970-7979. doi: 10.1002/ece3.7626. eCollection 2021 Jun.
Understanding and predicting the effect of global change phenomena on biodiversity is challenging given that biodiversity data are highly multivariate, containing information from tens to hundreds of species in any given location and time. The Latent Dirichlet Allocation (LDA) model has been recently proposed to decompose biodiversity data into latent communities. While LDA is a very useful exploratory tool and overcomes several limitations of earlier methods, it has limited inferential and predictive skill given that covariates cannot be included in the model. We introduce a modified LDA model (called LDAcov) which allows the incorporation of covariates, enabling inference on the drivers of change of latent communities, spatial interpolation of results, and prediction based on future environmental change scenarios. We show with simulated data that our approach to fitting LDAcov is able to estimate well the number of groups and all model parameters. We illustrate LDAcov using data from two experimental studies on the long-term effects of fire on southeastern Amazonian forests in Brazil. Our results reveal that repeated fires can have a strong impact on plant assemblages, particularly if fuel is allowed to build up between consecutive fires. The effect of fire is exacerbated as distance to the edge of the forest decreases, with small-sized species and species with thin bark being impacted the most. These results highlight the compounding impacts of multiple fire events and fragmentation, a scenario commonly found across the southern edge of Amazon. We believe that LDAcov will be of wide interest to scientists studying the effect of global change phenomena on biodiversity using high-dimensional datasets. Thus, we developed the R package LDAcov to enable the straightforward use of this model.
鉴于生物多样性数据具有高度的多变量性，在任何给定的地点和时间都包含来自数十到数百个物种的信息，理解和预测全球变化现象对生物多样性的影响具有挑战性。最近有人提出使用潜在狄利克雷分配（LDA）模型将生物多样性数据分解为潜在群落。虽然LDA是一个非常有用的探索性工具，克服了早期方法的几个局限性，但由于模型中不能包含协变量，其推理和预测能力有限。我们引入了一种改进的LDA模型（称为LDAcov），该模型允许纳入协变量，从而能够推断潜在群落变化的驱动因素、对结果进行空间插值，并基于未来环境变化情景进行预测。我们通过模拟数据表明，我们拟合LDAcov的方法能够很好地估计组的数量和所有模型参数。我们使用来自巴西东南部亚马逊森林火灾长期影响的两项实验研究的数据来说明LDAcov。我们的结果表明，反复火灾会对植物群落产生强烈影响，特别是如果在连续火灾之间允许燃料积累的话。随着距离森林边缘的距离减小，火灾的影响会加剧，小型物种和树皮薄的物种受到的影响最大。这些结果突出了多次火灾事件和森林破碎化的复合影响，这是亚马逊南部边缘常见的情况。我们相信，LDAcov将引起使用高维数据集研究全球变化现象对生物多样性影响的科学家的广泛兴趣。因此，我们开发了R包LDAcov，以便能够直接使用该模型。
