Avian diversity across guilds in North America versus vegetation structure as measured by the Global Ecosystem Dynamics Investigation (GEDI)

IF 11.1 1区 地球科学 Q1 ENVIRONMENTAL SCIENCES Remote Sensing of Environment Pub Date : 2024-10-10 DOI:10.1016/j.rse.2024.114446
Jin Xu , Laura Farwell , Volker C. Radeloff , David Luther , Melissa Songer , William Justin Cooper , Qiongyu Huang
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Abstract

Avian diversity, a key indicator of ecosystem health, is closely related to canopy structure. Most avian diversity models are based on either optical remote sensing or airborne lidar data, but the latter is limited to small study areas. The launch of the Global Ecosystem Dynamics Investigation (GEDI) instrument in 2018 has opened new avenues for exploring the influence of vegetation structure on avian diversity. To examine how direct measurements of canopy structural characteristics explain bird diversity across North America, we analyzed 18 GEDI metrics from 2019 to 2022, along with corresponding Breeding Bird Survey (BBS) counts and AVONET morphological data, analyzing effects across broad regions and at varying spatial extents. We grouped 440 bird species into 20 ecological guilds under six guild categories and employed random forest algorithms to model avian diversity across eight spatial extents (1, 2, 3, 4, 5, 10, 20, and 39.2 km). The models predicted six diversity indices, including species richness (sRich), functional richness (fRich), evenness (fEve), dispersion (fDis), divergence (fDiv), and redundancy (fRed) across eight spatial extents. The best-predicted guilds varied for each diversity index. The most accurate models were sRich (pseudo-R2 = 0.71, RMSE = 4.28) and fRed (pseudo-R2 = 0.60, RMSE = 0.13) for forest specialists guilds; fRich (pseudo-R2 = 0.55, RMSE = 0.18) for urban guilds; fEve (pseudo-R2 = 0.28, RMSE = 0.08) for insectivore guilds; and fDiv (pseudo-R2 = 0.38, RMSE = 0.12) and fDis (pseudo-R2 = 0.53, RMSE = 0.87) for short distance migrants guilds. Our results highlight the critical role of canopy structure, including its horizontal and vertical distribution and variation, in predicting avian diversity, as measured by the mean number of detected modes (num_detectedmodes), the standard deviation of foliage height diversity (FHD), num_detectedmodes, canopy cover, and plant area index (PAI) across the spatial extents centered on BBS routes. Therefore, we recommend incorporating the GEDI metrics into avian diversity modeling and mapping across North America, thereby potentially enhancing bird habitat management and conservation efforts.
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北美洲各行业的鸟类多样性与全球生态系统动态调查(GEDI)所测植被结构的关系
鸟类多样性是生态系统健康的一个关键指标,与树冠结构密切相关。大多数鸟类多样性模型都基于光学遥感或机载激光雷达数据,但后者仅限于小型研究区域。2018年全球生态系统动态调查(GEDI)仪器的启动为探索植被结构对鸟类多样性的影响开辟了新途径。为了研究冠层结构特征的直接测量如何解释北美地区的鸟类多样性,我们分析了2019年至2022年的18个GEDI指标,以及相应的繁殖鸟类调查(BBS)计数和AVONET形态数据,分析了在广泛区域和不同空间范围内的影响。我们将 440 种鸟类分为六大类 20 个生态区,并采用随机森林算法建立了八个空间范围(1、2、3、4、5、10、20 和 39.2 公里)的鸟类多样性模型。这些模型预测了八个空间范围内的六个多样性指数,包括物种丰富度(sRich)、功能丰富度(fRich)、均匀度(fEve)、分散度(fDis)、分化度(fDiv)和冗余度(fRed)。每个多样性指数的最佳预测行会各不相同。对森林专家行会而言,最准确的模型是 sRich(假 R2 = 0.71,RMSE = 4.28)和 fRed(假 R2 = 0.60,RMSE = 0.13);对城市行会而言,是 fRich(假 R2 = 0.55,RMSE = 0.18);食虫动物群落的 fEve(pseudo-R2 = 0.28,RMSE = 0.08);以及短距离迁徙动物群落的 fDiv(pseudo-R2 = 0.38,RMSE = 0.12)和 fDis(pseudo-R2 = 0.53,RMSE = 0.87)。我们的结果突显了树冠结构(包括其水平和垂直分布及变化)在预测鸟类多样性方面的关键作用,其衡量标准包括以 BBS 路线为中心的空间范围内的检测模式平均数量(num_detectedmodes)、叶高多样性标准偏差(FHD)、检测模式数量(num_detectedmodes)、树冠覆盖率和植物面积指数(PAI)。因此,我们建议将 GEDI 指标纳入北美地区的鸟类多样性建模和绘图中,从而有可能加强鸟类栖息地的管理和保护工作。
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来源期刊
Remote Sensing of Environment
Remote Sensing of Environment 环境科学-成像科学与照相技术
CiteScore
25.10
自引率
8.90%
发文量
455
审稿时长
53 days
期刊介绍: Remote Sensing of Environment (RSE) serves the Earth observation community by disseminating results on the theory, science, applications, and technology that contribute to advancing the field of remote sensing. With a thoroughly interdisciplinary approach, RSE encompasses terrestrial, oceanic, and atmospheric sensing. The journal emphasizes biophysical and quantitative approaches to remote sensing at local to global scales, covering a diverse range of applications and techniques. RSE serves as a vital platform for the exchange of knowledge and advancements in the dynamic field of remote sensing.
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