Comparing Hydrogeomorphic Approaches to Lake Classification
Comparing Hydrogeomorphic Approaches to Lake Classification
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DOI：
10.1007/s00267-011-9740-2
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发表时间：
2011-08
影响因子：
3.5
通讯作者：
S. L. Martin;P. Soranno;M. T. Bremigan;K. Cheruvelil
中科院分区：
文献类型：
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作者：
S. L. Martin;P. Soranno;M. T. Bremigan;K. Cheruvelil
分类系统通常用于减少政府机构负责监测和管理的不同生态系统类型的数量。我们比较了几种不同的基于水文地貌 (HGM) 的分类对湖泊进行水化学/澄清度分组的能力。我们问：（1）哪种湖泊分类方法对于类似水化学/透明度的湖泊分类最成功？ (2) 哪些 HGM 特征与湖泊类别的相关性最强？ (3) 单一分类能否针对所有检查的水化学/澄清度变量成功对湖泊进行分类？我们使用 HGM 特征的单变量和多变量分类和回归树（CART 和 MvCART）分析，对美国密歇根州 151 个受干扰程度最低的湖泊的碱度、水质、Secchi、总氮、总磷和叶绿素进行分类。我们总体上开发了两个 MvCART 模型，并为每个水化学/澄清度变量开发了两个 CART 模型，在每种情况下进行比较：单独的局部 HGM 特征和结合区域化和景观位置的局部 HGM 特征。对于除水彩和叶绿素之外的所有水化学/澄清度变量，组合的 CART 模型具有最高强度的证据（ωirange 0.92-1.00）和最大的类内同质性（ICC 范围 36-66%）。由于最成功的单一分类在对其他水化学/澄清度变量进行分类时平均成功率低 20%，因此我们发现没有单一分类能够捕获所有测试的湖泊响应的变异性。因此，我们建议最成功的分类 (1) 特定于个体响应变量，(2) 合并来自多个空间尺度（区域化和局部 HGM 变量）的信息。
A classification system is often used to reduce the number of different ecosystem types that governmental agencies are charged with monitoring and managing. We compare the ability of several different hydrogeomorphic (HGM)—based classifications to group lakes for water chemistry/clarity. We ask: (1) Which approach to lake classification is most successful at classifying lakes for similar water chemistry/clarity? (2) Which HGM features are most strongly related to the lake classes? and, (3) Can a single classification successfully classify lakes for all of the water chemistry/clarity variables examined? We use univariate and multivariate classification and regression tree (CART and MvCART) analysis of HGM features to classify alkalinity, water color, Secchi, total nitrogen, total phosphorus, and chlorophyllafrom 151 minimally disturbed lakes in Michigan USA. We developed two MvCART models overall and two CART models for each water chemistry/clarity variable, in each case comparing: local HGM characteristics alone and local HGM characteristics combined with regionalizations and landscape position. The combined CART models had the highest strength of evidence (ωirange 0.92–1.00) and maximized within class homogeneity (ICC range 36–66%) for all water chemistry/clarity variables except water color and chlorophylla. Because the most successful single classification was on average 20% less successful in classifying other water chemistry/clarity variables, we found that no single classification captures variability for all lake responses tested. Therefore, we suggest that the most successful classification (1) is specific to individual response variables, and (2) incorporates information from multiple spatial scales (regionalization and local HGM variables).