Limnological layers improve species distribution modeling of aquatic macrophytes at fine-spatial resolution

ABSTRACT Species distribution modeling (SDM) studies of aquatic macrophytes are still attached to methodological paradigms focused on terrestrial plants, such as the use of bioclimatic layers. Our goal was to evaluate the applicability of this paradigm based on a SDM study of Egeria densa, Pontederia crassipes, and Salvinia auriculata in the São Francisco river, Brazil. We compared performances of optimizations of computed models using AUC and TSS with distribution records of these species and bioclimatic layers, or limnological layers generated from the interpolation of data obtained in the field. We calculated models using six algorithms. The models calculated using layers of limnological variables had higher performances than did those calculated using layers of bioclimatic variables, except when the Maximum Entropy Default algorithm was used. We attribute these results to the specificities of the data obtained to develop the limnological layers, such as observations obtained in different habitats of the river and during different hydrologic periods. We conclude that the use of bioclimatic layers, a methodological paradigm traditionally used for SDM of aquatic macrophytes, can be questionable for some situations, such as in investigations at local and regional scales.

摘要 针对水生大型植物（aquatic macrophytes）的物种分布模型（Species Distribution Modeling, SDM）研究，目前仍依附于针对陆生植物构建的方法论范式，例如生物气候层的应用。本研究以巴西圣弗朗西斯科河中的伊乐藻（Egeria densa）、凤眼蓝（Pontederia crassipes）和槐叶苹（Salvinia auriculata）为研究对象，通过开展相关物种分布模型研究，旨在评估该范式的适用性。研究中，我们结合上述物种的分布记录，分别采用生物气候层与由野外实测数据插值生成的湖沼学层构建模型，并利用AUC与TSS指标对比各优化模型的性能。本次研究共使用六种算法构建模型。结果显示，除采用最大熵默认（Maximum Entropy Default）算法的场景外，基于湖沼学变量层构建的模型性能均优于基于生物气候变量层的模型。我们将该结果归因于湖沼学层构建所用数据的特异性，例如在河流不同生境及不同水文周期下获取的观测数据。本研究最终得出结论：作为水生大型植物物种分布模型研究中传统沿用的方法论范式，生物气候层的应用在部分场景下（如局域与区域尺度的调查研究中）或存在合理性争议。