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Modeling species' distributions to improve conservation in semiurban landscapes: Koala case study

机译：模拟物种分布以改善半郊区景观的保护：考拉案例研究

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Models of species' distributions are commonly used to inform landscape and conservation planning. In urban and semiurban landscapes, the distributions of species are determined by a combination of natural habitat and anthropogenic impacts. Understanding the spatial influence of these two processes is crucial for making spatially explicit decisions about conservation actions. We present a logistic regression model for the distribution of koalas ( Phascolarctos cinereus) in a semiurban landscape in eastern Australia that explicitly separates the effect of natural habitat quality and anthropogenic impacts on koala distributions. We achieved this by comparing the predicted distributions from the model with what the predicted distributions would have been if anthropogenic variables were at their mean values. Similar approaches have relied on making predictions assuming anthropogenic variables are zero, which will be unreliable if the training data set does not include anthropogenic variables close to zero. Our approach is novel because it can be applied to landscapes where anthropogenic variables are never close to zero. Our model showed that, averaged across the study area, natural habitat was the main determinant of koala presence. At a local scale, however, anthropogenic impacts could be more important, with consequent implications for conservation planning. We demonstrated that this modeling approach, combined with the visual presentation of predictions as a map, provides important information for making decisions on how different conservation actions should be spatially allocated. This method is particularly useful for areas where wildlife and human populations exist in close proximity.
机译：物种分布模型通常用于为景观和保护规划提供信息。在城市和半城市景观中，物种分布是由自然栖息地和人为影响共同决定的。了解这两个过程的空间影响对于做出关于保护行动的空间明确决定至关重要。我们为澳大利亚东部半城市景观中的考拉（Phascolarctos cinereus）分布提供了逻辑回归模型，该模型明确区分了自然栖息地质量的影响和对考拉分布的人为影响。我们通过将模型的预测分布与人为变量处于平均值的预测分布进行比较来实现这一目标。类似的方法还依赖于假设，假设人为变量为零，如果训练数据集不包括接近零的人为变量，则这将是不可靠的。我们的方法很新颖，因为它可以应用于人为变量永远不会接近零的景观。我们的模型表明，在整个研究区域中，自然栖息地是考拉存在的主要决定因素。但是，在地方范围内，人为影响可能更为重要，从而对保护规划产生影响。我们证明了这种建模方法，结合可视化的预测预测图，为决策如何在空间上分配不同的保护措施提供了重要信息。这种方法对于野生动植物和人类种群非常靠近的地区特别有用。

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