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Journal ArticleDOI

Applications of species distribution modeling to paleobiology

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TLDR
The SDM approach offers important opportunities for advances in paleobiology by providing a quantitative ecological perspective, and also offers the potential for an enhanced contribution of paleo- and neobiology to ecology and conservation biology, e.g., for estimating climate change impacts and for informing ecological restoration.
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This article is published in Quaternary Science Reviews.The article was published on 2011-10-01. It has received 262 citations till now.

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Is my species distribution model fit for purpose? Matching data and models to applications

TL;DR: In this paper, the authors synthesize current knowledge and provide a simple framework that summarizes how interactions between data type and the sampling process determine the quantity that is estimated by a species distribution model.
Book

Habitat Suitability and Distribution Models

TL;DR: In this article, the authors introduce the key stages of niche-based habitat suitability model building, evaluation and prediction required for understanding and predicting future patterns of species and biodiversity, including the main theory behind ecological niches and species distributions.
Journal ArticleDOI

ENVIREM: an expanded set of bioclimatic and topographic variables increases flexibility and improves performance of ecological niche modeling

TL;DR: The ENVIREM dataset as discussed by the authors is a set of 16 climatic and two topographic variables in the literature, which are likely to have direct relevance to ecological or physiological processes determining species distributions.
References
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Journal ArticleDOI

Maximum entropy modeling of species geographic distributions

TL;DR: In this paper, the use of the maximum entropy method (Maxent) for modeling species geographic distributions with presence-only data was introduced, which is a general-purpose machine learning method with a simple and precise mathematical formulation.
BookDOI

Regression modeling strategies : with applications to linear models, logistic regression, and survival analysis

TL;DR: In this article, the authors present a case study in least squares fitting and interpretation of a linear model, where they use nonparametric transformations of X and Y to fit a linear regression model.
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