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Models of upland species’ distributions are improved by accounting for geodiversity
Landscape Ecology ( IF 5.2 ) Pub Date : 2018-10-28 , DOI: 10.1007/s10980-018-0723-z
Joseph J Bailey 1, 2 , Doreen S Boyd 1 , Richard Field 1
Affiliation  

ContextRecent research suggests that novel geodiversity data on landforms, hydrology and surface materials can improve biodiversity models at the landscape scale by quantifying abiotic variability more effectively than commonly used measures of spatial heterogeneity. However, few studies consider whether these variables can account for, and improve our understanding of, species’ distributions.ObjectivesAssess the role of geodiversity components as macro-scale controls of plant species’ distributions in a montane landscape.MethodsWe used an innovative approach to quantifying a landscape, creating an ecologically meaningful geodiversity dataset that accounted for hydrology, morphometry (landforms derived from geomorphometric techniques), and soil parent material (data from expert sources). We compared models with geodiversity to those just using topographic metrics (e.g. slope and elevation) and climate data. Species distribution models (SDMs) were produced for ‘rare’ (N = 76) and ‘common’ (N = 505) plant species at 1 km2 resolution for the Cairngorms National Park, Scotland.ResultsThe addition of automatically produced landform geodiversity data and hydrological features to a basic SDM (climate, elevation, and slope) resulted in a significant improvement in model fit across all common species’ distribution models. Adding further geodiversity data on surface materials resulted in a less consistent statistical improvement, but added considerable conceptual value to many individual rare and common SDMs.ConclusionsThe geodiversity data used here helped us capture the abiotic environment’s heterogeneity and allowed for explicit links between the geophysical landscape and species’ ecology. It is encouraging that relatively simple and easily produced geodiversity data have the potential to improve SDMs. Our findings have important implications for applied conservation and support the need to consider geodiversity in management.

中文翻译:

通过考虑地理多样性改进了高地物种分布模型

背景最近的研究表明,关于地貌、水文学和地表物质的新型地球多样性数据可以通过比常用的空间异质性测量更有效地量化非生物变异性来改善景观尺度的生物多样性模型。然而,很少有研究考虑这些变量是否可以解释物种的分布,并提高我们对物种分布的理解。目标评估地理多样性成分作为山地景观中植物物种分布的宏观控制的作用。方法我们使用了一种创新的方法来量化景观,创建具有生态意义的地球多样性数据集,该数据集考虑了水文、形态测量(源自地貌测量技术的地貌)和土壤母质(来自专家来源的数据)。我们将具有地理多样性的模型与仅使用地形指标(例如坡度和海拔)和气候数据的模型进行了比较。为苏格兰凯恩戈姆国家公园的“稀有”(N = 76)和“常见”(N = 505)植物物种生成了 1 平方公里分辨率的物种分布模型 (SDM)。结果添加了自动生成的地形地理多样性数据和水文数据基本 SDM(气候、海拔和坡度)的特征导致所有常见物种分布模型的模型拟合度显着提高。添加更多关于表面材料的地球多样性数据导致统计改进不太一致,但为许多单独的稀有和常见的 SDM 增加了相当大的概念价值。结论这里使用的地球多样性数据帮助我们捕获非生物环境的异质性,并允许地球物理景观和地球物理景观之间的明确联系。物种的生态。令人鼓舞的是,相对简单且易于生成的地球多样性数据有潜力改进 SDM。我们的研究结果对应用保护具有重要意义,并支持在管理中考虑地理多样性的必要性。
更新日期:2018-10-28
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