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Predicting Landscape-Scale Habitat Distribution for Ruffed Grouse Bonasa umbellus Using Presence-Only Data

机译:仅使用状态数据预测松鸡uff鱼的景观尺度生境分布

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Ruffed grouse Bonasa umbellus populations in North America have declined as forests have matured and the extent of early successional forest habitat required by the species has diminished. When wildlife species decline because of habitat loss, determining where to focus habitat management efforts is difficult because both the wildlife population and the required habitat(s) are usually limited in distribution. We adopted a relatively new ecological modeling method, partitioned Mahalanobis D2, which allowed us to predict the distribution of potential ruffed grouse habitat across a landscape of management concern where high quality habitat was uncommon. We used presence data derived from radio-telemetry locations, and GIS habitat data from publicly available sources to create competing partitioned Mahalanobis D2 models. The competing models identified important habitat variables and predicted ruffed grouse habitat distribution at 1-ha and 25-ha scales in southwestern Rhode Island, USA. The 1- and 25-ha models produced comparable overall classification accuracy (83.1% and 81.4%, respectively) but differed substantially in the area of predicted habitat (4,475.5 ha and 10,133.8 ha, respectively). We selected the more conservative 1-ha model as the ‘best’ model, and expanded it to a larger landscape extent. Once expanded, the model predicted 11,463 ha (15.5% of total land area) of potential ruffed grouse habitat for a 735-km2 landscape in southwestern Rhode Island. This model identified those areas with varying proximities to the following features as likely to contain ruffed grouse habitat: early successional forests, river and stream corridors, mixed conifer forests, conifer forests, shrub wetlands and deciduous forests. Early successional forests were the most consistent component of habitat used by grouse, despite the fact that this habitat type was uncommon in our study area (< 1% of total land area). Our model can be used to identify areas of existing ruffed grouse habitat for management focus.
机译:随着森林的成熟以及该物种所需的早期演替森林生境的减少,北美的皱褶松鸡Bonasa umbellus种群数量有所减少。当野生生物物种由于生境丧失而减少时,很难确定将重点放在生境管理工作上的原因,因为野生生物种群和所需生境通常都受到分布的限制。我们采用了一种相对较新的生态建模方法,即分区的Mahalanobis D2,这使我们能够在高品质栖息地不常见的管理环境中预测潜在的皱纹松鸡栖息地的分布。我们使用了来自无线电遥测位置的状态数据以及来自公开来源的GIS栖息地数据来创建竞争性分区Mahalanobis D2模型。竞争模型确定了重要的生境变量,并预测了美国西南部罗德岛州1公顷和25公顷尺度上的皱纹松鸡栖息地分布。 1公顷和25公顷的模型产生了可比的总体分类准确度(分别为83.1%和81.4%),但在预测栖息地面积方面分别存在较大差异(分别为4,475.5公顷和10,133.8公顷)。我们选择了较为保守的1公顷模型作为“最佳”模型,并将其扩展到更大的景观范围。模型一旦扩展,就可以预测罗得岛西南部735平方公里景观的11463公顷(占总土地面积的15.5%)潜在的皱纹松鸡栖息地。该模型确定了具有以下特征的地区,这些地区可能具有松散的松鸡栖息地:早期演替森林,河流和溪流走廊,针叶树混交林,针叶林,灌木湿地和落叶林。早期演替森林是松鸡使用的栖息地中最一致的组成部分,尽管在我们的研究区域中这种栖息地类型并不常见(<土地总面积的1%)。我们的模型可用于识别现有的皱纹松鸡栖息地区域,以进行管理。

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