Titlebook: Machine Learning for Ecology and Sustainable Natural Resource Management; Grant Humphries,Dawn R. Magness,Falk Huettmann Book 2018 Springe

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‘Batteries’ in Machine Learning: A First Experimental Assessment of Inference for Siberian Crane Brec, and two subpopulations are known. Here we present for the first time a machine learning-based summer habitat analysis using nesting data for the eastern population in the breeding grounds employing predictive modeling with 74 GIS predictors. There is a typical desire for parsimony to help increas

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Landscape Applications of Machine Learning: Comparing Random Forests and Logistic Regression in Multmentation. Our goal was to compoare logistic regression and random forest in multi-scale optimized predictive model of occurrence of the American marten (.) in northern Idaho USA. There have been relatively few formal comparisons of the performance of multi-scale modeling between logistic regression

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Using Interactions among Species, Landscapes, and Climate to Inform Ecological Niche Models: A Case s. Machine-learning based ecological niche models that account for landscape characteristics and changes in climate have been effective tools for deciphering patterns in messy, presence-only datasets, and predicting shifts in wildlife distributions over time. Bioclimatic niche models are sometimes c

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Advanced Data Mining (Cloning) of Predicted Climate-Scapes and Their Variances Assessed with Machinend temporal scale these ‘climate-scapes’ are often less studied, are poorly understood and assessments are lacking. The accuracy of climate-scapes is often affected by local topography and wider couplings. The science of local climate-scapes is still in its infancy, so are the methods of inquiry and

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Using TreeNet, a Machine Learning Approach to Better Understand Factors that Influence Elevated Blooiated with exposure are often complex and difficult to assess. Machine learning models are suitable for prediction and for gaining biologically meaningful insight into the potential impacts of Pb on wildlife populations. However, despite their potential, they are often under-utilized in the field of

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Image Recognition in Wildlife Applications the images delivered to our inboxes, are widely available (O’Connell AF, Nichols JD, Ullas Karanth K Camera traps in animal ecology: methods and analyses. Book, Whole. Springer Science & Business Media, 2010). Ecologists and wildlife biologists are also deploying camera and videography equipment as

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Machine Learning Techniques for Quantifying Geographic Variation in Leach’s Storm-Petrel (,) Vocaliznd North Pacific. Although some mixing occurs during the non-breeding season, genetic evidence demonstrates that these populations are diverging. However, genetic information for the study of phylogenetics can be costly and time-consuming to obtain. Vocalizations could offer a more cost-effective wa

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https://doi.org/10.1007/978-3-319-96978-7Quantitative ecology; artificial intelligence; Statistics; data mining; machine learning; Wildlife biolog

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