热带鸟类物种丰富度与动态栖息地指数捕获的初级生产力模式密切相关,Remote Sensing of Environment

当前位置： X-MOL 学术 › Remote Sens. Environ. › 论文详情

Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)

热带鸟类物种丰富度与动态栖息地指数捕获的初级生产力模式密切相关
Remote Sensing of Environment ( IF 11.1 ) Pub Date : 2019-10-01 , DOI: 10.1016/j.rse.2019.111306
Naparat Suttidate , Martina L. Hobi , Anna M. Pidgeon , Philip D. Round , Nicholas C. Coops , David P. Helmers , Nicholas S. Keuler , Maxim Dubinin , Brooke L. Bateman , Volker C. Radeloff

摘要生物多样性科学和保护都需要环境指标来了解物种丰富度和预测物种分布模式。动态栖息地指数 (DHI) 是一组三个指数，它们总结了生物多样性应用卫星数据中的年度生产力措施，包括： a) 累积年生产力；b) 最低年生产力；c) 年生产力的变化。在全球范围内和温带地区，DHI 可以很好地预测物种多样性模式，但 DHI 尚未在热带地区进行测试，那里的生产力水平较高导致许多遥感植被指数饱和。我们的目标是根据热带地区的 DHI 解释鸟类物种丰富度模式。我们基于 a) 217 个物种的物种分布模型 (SDM) 图和 b) 564 个物种的范围图，将 DHI 与 5 个行会（森林、灌木丛、草地、多面手和所有留鸟）的留鸟物种丰富度相关联整个泰国。我们还在多元回归模型中量化了 DHI 的相对重要性，该模型包括使用多元回归、最佳子集和分层分区分析的两种地形测量和两种气候指标。我们发现仅三个 DHI 就可以最好地解释森林鸟类的丰富度（对于基于 SDM 和范围图的丰富度，R2adj 为 0.61；对于其他公会，R2adj 为 0.15-0.54）。当将 DHI 与地形和气候相结合时，森林鸟类和所有留鸟物种的丰富程度同样得到了很好的解释（R2adj 0.85 和 0.67 对 0.81 和 0.68）。在三个 DHI 中，基于 SDM 丰富度图 (R2adj 0.54–0.61)，累积年生产力对所有公会具有最大的解释力。DHI 与泰国鸟类物种丰富度之间的密切关系表明，DHI 可以很好地捕获能源可用性，并且可用于热带地区的生物多样性评估和潜在的鸟类保护。

"点击查看英文标题和摘要"

Tropical bird species richness is strongly associated with patterns of primary productivity captured by the Dynamic Habitat Indices

Abstract Biodiversity science and conservation alike require environmental indicators to understand species richness and predict species distribution patterns. The Dynamic Habitat Indices (DHIs) are a set of three indices that summarize annual productivity measures from satellite data for biodiversity applications, and include: a) cumulative annual productivity; b) minimum annual productivity; and c) variation in annual productivity. At global scales and in temperate regions the DHIs predict species diversity patterns well, but the DHIs have not been tested in the tropics, where higher levels of productivity lead to the saturation of many remotely sensed vegetation indices. Our goal was to explain bird species richness patterns based on the DHIs in tropical areas. We related the DHIs to species richness of resident landbirds for five guilds (forest, scrub, grassland, generalist, and all resident birds) based on a) species distribution model (SDM) maps for 217 species, and b) range map for 564 species across Thailand. We also quantified the relative importance of the DHIs in multiple regression models that included two measures of topography, and two climate metrics using multiple regression, best-subsets, and hierarchical partitioning analyses. We found that the three DHIs alone explained forest bird richness best (R2adj 0.61 for both SDM- and rangemap based richness; 0.15–0.54 for the other guilds). When combining the DHIs with topography and climate, the richness of both forest birds and all resident bird species was equally well explained (R2adj 0.85 and 0.67 versus 0.81 and 0.68). Among the three DHIs, cumulative annual productivity had the greatest explanatory power for all guilds based on SDM richness maps (R2adj 0.54–0.61). The strong relationship between the DHIs and bird species richness in Thailand suggests that the DHIs capture energy availability well and are useful in biodiversity assessments and potentially bird conservation in tropical areas.

更新日期：2019-10-01

点击分享查看原文

点击收藏

阅读更多本刊新发论文本刊介绍/投稿指南