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为宏观生态学创建多主题生态区域:测试一种灵活、可重复且易于使用的聚类方法。

Creating multithemed ecological regions for macroscale ecology: Testing a flexible, repeatable, and accessible clustering method.

作者信息

Cheruvelil Kendra Spence, Yuan Shuai, Webster Katherine E, Tan Pang-Ning, Lapierre Jean-François, Collins Sarah M, Fergus C Emi, Scott Caren E, Henry Emily Norton, Soranno Patricia A, Filstrup Christopher T, Wagner Tyler

机构信息

Department of Fisheries and Wildlife & Lyman Briggs College Michigan State University East Lansing MI USA.

Department of Computer Science & Engineering Michigan State University East Lansing MI USA.

出版信息

Ecol Evol. 2017 Mar 26;7(9):3046-3058. doi: 10.1002/ece3.2884. eCollection 2017 May.

Abstract

Understanding broad-scale ecological patterns and processes often involves accounting for regional-scale heterogeneity. A common way to do so is to include ecological regions in sampling schemes and empirical models. However, most existing ecological regions were developed for specific purposes, using a limited set of geospatial features and irreproducible methods. Our study purpose was to: (1) describe a method that takes advantage of recent computational advances and increased availability of regional and global data sets to create customizable and reproducible ecological regions, (2) make this algorithm available for use and modification by others studying different ecosystems, variables of interest, study extents, and macroscale ecology research questions, and (3) demonstrate the power of this approach for the research question-How well do these regions capture regional-scale variation in lake water quality? To achieve our purpose we: (1) used a spatially constrained spectral clustering algorithm that balances geospatial homogeneity and region contiguity to create ecological regions using multiple terrestrial, climatic, and freshwater geospatial data for 17 northeastern U.S. states (~1,800,000 km); (2) identified which of the 52 geospatial features were most influential in creating the resulting 100 regions; and (3) tested the ability of these ecological regions to capture regional variation in water nutrients and clarity for ~6,000 lakes. We found that: (1) a combination of terrestrial, climatic, and freshwater geospatial features influenced region creation, suggesting that the oft-ignored freshwater landscape provides novel information on landscape variability not captured by traditionally used climate and terrestrial metrics; and (2) the delineated regions captured macroscale heterogeneity in ecosystem properties not included in region delineation-approximately 40% of the variation in total phosphorus and water clarity among lakes was at the regional scale. Our results demonstrate the usefulness of this method for creating customizable and reproducible regions for research and management applications.

摘要

理解大规模生态模式和过程通常需要考虑区域尺度的异质性。一种常见的方法是在采样方案和实证模型中纳入生态区域。然而,大多数现有的生态区域是为特定目的而开发的,使用的地理空间特征有限且方法不可重复。我们的研究目的是:(1)描述一种利用近期计算进展以及区域和全球数据集可用性增加的方法来创建可定制和可重复的生态区域;(2)使该算法可供研究不同生态系统、感兴趣变量、研究范围和宏观尺度生态研究问题的其他人使用和修改;(3)展示这种方法对于研究问题——这些区域能多好地捕捉湖泊水质的区域尺度变化——的有效性。为实现我们的目的,我们:(1)使用空间受限的光谱聚类算法,该算法平衡地理空间同质性和区域连续性,利用美国东北部17个州(约180万平方公里)的多种陆地、气候和淡水地理空间数据创建生态区域;(2)确定在创建最终的100个区域时,52个地理空间特征中哪些最具影响力;(3)测试这些生态区域捕捉约6000个湖泊中水体养分和透明度区域变化的能力。我们发现:(1)陆地、气候和淡水地理空间特征的组合影响区域创建,这表明常被忽视的淡水景观提供了关于景观变异性的新信息,而这些信息未被传统使用的气候和陆地指标所捕捉;(2)划定的区域捕捉了区域划分中未包括的生态系统属性的宏观尺度异质性——湖泊中总磷和水体透明度约40%的变化处于区域尺度。我们的结果证明了这种方法对于为研究和管理应用创建可定制和可重复区域的有用性。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cde6/5415510/b7399ff855e7/ECE3-7-3046-g001.jpg

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