Abstract

Piñon-juniper is one of the most common vegetation types in the Four Corners states of the western United States (Arizona, Colorado, New Mexico, and Utah). Because of its high degree of community heterogeneity across the landscape, development of a more detailed and statistically supported classification system for piñon-juniper has been requested by regional land managers. We used a USDA Forest Service Forest Inventory and Analysis (FIA) data set from the Four Corners states to develop a statistics-based classification system for piñon-juniper vegetation. Cluster analysis was used to group piñon-juniper FIA data into community classes. Classification and regression tree analysis was then used to develop a model for predicting piñon-juniper community types. To determine which variables contributed most to classifying piñon-juniper FIA data, a random forest analysis was conducted. Results from these analyses support a six-class piñon-juniper community-type model within the Four Corners states. Using the classification tree, membership of FIA piñon-juniper communities can be accurately predicted (r² = 0.81) using only relative overstory species abundance. Our dominance-based classification system was useful in classifying piñon-juniper community types and could be used in the field to identify broad community types and complement more refined tools available for stand-scale decisionmaking. Study Implications: Piñon-juniper vegetation communities commonly occur in the Four Corners region of the United States. We used a regional data set to develop a statistically based classification system for piñon-juniper communities. We found support for a dominance-based approach supporting initial classification into six community classes. Classes were based on different overstory species dominance patterns, stand structural characteristics (stand density index, basal area [square meters per hectare], trees per hectare, and stand age), and precipitation patterns (mean annual precipitation and monsoonal index) (Table S2Table S2). Community type can be predicted using relative overstory abundance to help managers prioritize regional areas (~6,000 acres [2,428 hectares]) for management and predict responses based on precipitation patterns, current understory tree regeneration, and plant community abundance. This system could lead to better planning documents and management decisions on a regional scale to complement more refined tools available for stand-scale management such as plant associations and detailed soil maps.

中文翻译：

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美国四个角落地区的松嫩杜松植被群落分类

摘要

Piñon-juniper是美国西部四角州（亚利桑那州，科罗拉多州，新墨西哥州和犹他州）最常见的植被类型之一。由于其在整个景观中高度的社区异质性，区域土地经理要求开发一种更加详细的和统计支持的松柏分类系统。我们使用了来自“四个角落”州的USDA森林服务森林清单和分析（FIA）数据集，以开发基于统计的松柏植被分类系统。聚类分析用于将松树FIA数据分组为社区类。然后，使用分类和回归树分析来开发一个预测松树-杜松群落类型的模型。为了确定哪些变量最有助于对Pi-on-Juniper FIA数据进行分类，进行了随机森林分析。这些分析的结果支持在“四角州”内的六类pi-on-juniper社区类型模型。使用分类树，可以准确地预测FIA pi-on-Juniper社区的成员身份（[R ²= 0.81）仅使用相对过剩的物种丰度。我们基于优势的分类系统可用于对pi-on-juniper群落类型进行分类，并且可以在现场用于识别广泛的群落类型，并补充用于标准规模决策的更完善的工具。研究意义：皮涅-杜松植被群落通常发生在美国的四个角落地区。我们使用了区域数据集来为松树杜松群落开发基于统计的分类系统。我们发现支持基于优势的方法，该方法支持将初始分类分为六个社区类。分类基于不同的上层树种优势模式，林分结构特征（林分密度指数，基础面积[每公顷平方米]，每公顷树木和林分年龄），和降水模式（平均年降水量和季风指数）（表S2表S2）。可以使用相对过量的林木丰度来预测社区类型，以帮助管理者优先管理区域区域（约6,000英亩[2,428公顷]），并根据降水模式，当前林下树木的再生和植物群落的丰度来预测响应。该系统可能会导致在区域范围内更好的计划文件和管理决策，以补充可用于林分规模管理的更完善的工具，例如植物协会和详细的土壤图。428公顷]]，以便根据降水模式，当前林下树木的再生和植物群落的丰度来管理和预测响应。该系统可能会导致在区域范围内更好的计划文件和管理决策，以补充可用于林分规模管理的更完善的工具，例如植物协会和详细的土壤图。428公顷]]，以便根据降水模式，当前林下树木的再生和植物群落的丰度来管理和预测响应。该系统可能会导致在区域范围内更好的计划文件和管理决策，以补充可用于林分规模管理的更完善的工具，例如植物协会和详细的土壤图。