Elizabeth A. Leipold, Claire N. Gower, Lance McNew
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The Integrated Monitoring in Bird Conservation Regions program provided a multi-year dataset of Dusky Grouse observations, which we reduced to detected (n=132) and pseudo-absent (n=5960) locations, using geospatial datasets to obtain topographic and vegetation characteristics for each location. We evaluated the predictability of the two models using receiver operating characteristics and area under the curve (ROC/AUC) with k-fold cross validation and classification accuracy of an independent dataset of incidental Dusky Grouse locations. We found both models to be highly predictive and multiple habitat characteristics were found to help predict relative probability of use such as proportion of trees with a height of 16–20m and conifer forest vegetation types. We converted both models to binary values and used an ensemble (frequency histogram) approach to combine the models into a final predictive map. Consensus between the resource selection function and random forest models was high (93%) and the ensemble map had higher predictive accuracy when classifying the independent dataset than the other two models. Our results show that our ensembled model approach was able to accurately predict potential Dusky Grouse habitat and therefore can be used to delineate areas for future population monitoring of Dusky Grouse in Montana.</p>\n<p>The post Using an ensemble approach to predict habitat of Dusky Grouse (<em>Dendragapus obscurus</em>) in Montana, USA first appeared on Avian Conservation and Ecology.</p>","PeriodicalId":49233,"journal":{"name":"Avian Conservation and Ecology","volume":"24 1","pages":""},"PeriodicalIF":1.4000,"publicationDate":"2024-08-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Using an ensemble approach to predict habitat of Dusky Grouse (Dendragapus obscurus) in Montana, USA\",\"authors\":\"Elizabeth A. Leipold, Claire N. 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The Integrated Monitoring in Bird Conservation Regions program provided a multi-year dataset of Dusky Grouse observations, which we reduced to detected (n=132) and pseudo-absent (n=5960) locations, using geospatial datasets to obtain topographic and vegetation characteristics for each location. We evaluated the predictability of the two models using receiver operating characteristics and area under the curve (ROC/AUC) with k-fold cross validation and classification accuracy of an independent dataset of incidental Dusky Grouse locations. We found both models to be highly predictive and multiple habitat characteristics were found to help predict relative probability of use such as proportion of trees with a height of 16–20m and conifer forest vegetation types. We converted both models to binary values and used an ensemble (frequency histogram) approach to combine the models into a final predictive map. Consensus between the resource selection function and random forest models was high (93%) and the ensemble map had higher predictive accuracy when classifying the independent dataset than the other two models. 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引用次数: 0
摘要
灰松鸡(Dendragapus obscurus)是蒙大拿州及其他分布区监测不足的一种野味。如果没有种群监测,就很难制定适当的采伐规定,也很难了解环境干扰(如木材采伐、气候变化)对种群的影响。作为制定无偏见种群监测方法的第一步,我们必须确定适当的采样地点,这就需要了解灰松鸡的栖息地。我们的目标是探索松鸡的使用与栖息地特征之间的关系,然后利用资源选择函数和随机森林分类器这两种方法生成一张预测蒙大拿州松鸡栖息地的全州地图。鸟类保护区域综合监测计划提供了一个多年的松鸡观测数据集,我们利用地理空间数据集获得了每个地点的地形和植被特征,并将其还原为探测到的地点(n=132)和假缺失的地点(n=5960)。我们使用接收器操作特征和曲线下面积(ROC/AUC)评估了这两个模型的预测能力,并对一个独立的偶发松鸡地点数据集进行了 k 倍交叉验证和分类准确性验证。我们发现这两个模型都具有很高的预测性,而且发现多个栖息地特征有助于预测使用的相对概率,如高度在 16-20 米的树木比例和针叶林植被类型。我们将两个模型转换为二进制值,并使用集合(频率直方图)方法将模型组合成最终的预测地图。资源选择函数和随机森林模型之间的一致性很高(93%),在对独立数据集进行分类时,集合图的预测准确率高于其他两个模型。我们的结果表明,我们的集合模型方法能够准确预测潜在的灰松鸡栖息地,因此可用于划定蒙大拿州未来灰松鸡种群监测的区域。
Using an ensemble approach to predict habitat of Dusky Grouse (Dendragapus obscurus) in Montana, USA
Dusky Grouse (Dendragapus obscurus) are an under-monitored game species in Montana and elsewhere across their distribution. Without population monitoring it is difficult to establish appropriate harvest regulations or understand the impact of environmental disturbances (e.g., timber harvest, climate change) on populations. As a first step toward developing methods for unbiased population monitoring, we must identify appropriate sampling sites, which requires knowledge of Dusky Grouse habitat. Our goal was to explore relationships between Dusky Grouse use and habitat characteristics, and then generate a state-wide map predicting Dusky Grouse habitat in Montana using two methods: resource selection functions and random forest classifiers. The Integrated Monitoring in Bird Conservation Regions program provided a multi-year dataset of Dusky Grouse observations, which we reduced to detected (n=132) and pseudo-absent (n=5960) locations, using geospatial datasets to obtain topographic and vegetation characteristics for each location. We evaluated the predictability of the two models using receiver operating characteristics and area under the curve (ROC/AUC) with k-fold cross validation and classification accuracy of an independent dataset of incidental Dusky Grouse locations. We found both models to be highly predictive and multiple habitat characteristics were found to help predict relative probability of use such as proportion of trees with a height of 16–20m and conifer forest vegetation types. We converted both models to binary values and used an ensemble (frequency histogram) approach to combine the models into a final predictive map. Consensus between the resource selection function and random forest models was high (93%) and the ensemble map had higher predictive accuracy when classifying the independent dataset than the other two models. Our results show that our ensembled model approach was able to accurately predict potential Dusky Grouse habitat and therefore can be used to delineate areas for future population monitoring of Dusky Grouse in Montana.
The post Using an ensemble approach to predict habitat of Dusky Grouse (Dendragapus obscurus) in Montana, USA first appeared on Avian Conservation and Ecology.
期刊介绍:
Avian Conservation and Ecology is an open-access, fully electronic scientific journal, sponsored by the Society of Canadian Ornithologists and Birds Canada. We publish papers that are scientifically rigorous and relevant to the bird conservation community in a cost-effective electronic approach that makes them freely available to scientists and the public in real-time. ACE is a fully indexed ISSN journal that welcomes contributions from scientists all over the world.
While the name of the journal implies a publication niche of conservation AND ecology, we think the theme of conservation THROUGH ecology provides a better sense of our purpose. As such, we are particularly interested in contributions that use a scientifically sound and rigorous approach to the achievement of avian conservation as revealed through insights into ecological principles and processes. Papers are expected to fall along a continuum of pure conservation and management at one end to more pure ecology at the other but our emphasis will be on those contributions with direct relevance to conservation objectives.