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Predicting extinction risks under climate change: coupling stochastic population models with dynamic bioclimatic habitat models.

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TLDR
A novel mechanistic approach allows more complete and direct appraisal of future biotic responses than do static bioclimatic habitat modelling approaches, and will ultimately support development of more effective conservation strategies to mitigate biodiversity losses due to climate change.
Abstract
Species responses to climate change may be influenced by changes in available habitat, as well as population processes, species interactions and interactions between demographic and landscape dynamics. Current methods for assessing these responses fail to provide an integrated view of these influences because they deal with habitat change or population dynamics, but rarely both. In this study, we linked a time series of habitat suitability models with spatially explicit stochastic population models to explore factors that influence the viability of plant species populations under stable and changing climate scenarios in South African fynbos, a global biodiversity hot spot. Results indicate that complex interactions between life history, disturbance regime and distribution pattern mediate species extinction risks under climate change. Our novel mechanistic approach allows more complete and direct appraisal of future biotic responses than do static bioclimatic habitat modelling approaches, and will ultimately support development of more effective conservation strategies to mitigate biodiversity losses due to climate change.

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Journal ArticleDOI

Species Distribution Models: Ecological Explanation and Prediction Across Space and Time

TL;DR: Species distribution models (SDMs) as mentioned in this paper are numerical tools that combine observations of species occurrence or abundance with environmental estimates, and are used to gain ecological and evolutionary insights and to predict distributions across landscapes, sometimes requiring extrapolation in space and time.
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Impacts of climate change on the future of biodiversity.

TL;DR: Overall, this review shows that current estimates of future biodiversity are very variable, depending on the method, taxonomic group, biodiversity loss metrics, spatial scales and time periods considered.
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Mechanistic niche modelling: combining physiological and spatial data to predict species' ranges.

TL;DR: Here, the principles of biophysical ecology can be used to link spatial data to the physiological responses and constraints of organisms, which provides a mechanistic view of the fundamental niche which can then be mapped to the landscape to infer range constraints.
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Beyond Predictions: Biodiversity Conservation in a Changing Climate

TL;DR: This work introduces a framework that uses information from different sources to identify vulnerability and to support the design of conservation responses, and reviews the insights that different approaches bring to anticipating and managing the biodiversity consequences of climate change.
References
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Book

Climate Change 1995: The Science of Climate Change

TL;DR: The most comprehensive and up-to-date assessment available for scientific understanding of human influences on the past present and future climate is "Climate Change 1995: The Science of Climate Change" as mentioned in this paper.
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Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful?

TL;DR: In this paper, a hierarchical modeling framework is proposed through which some of these limitations can be addressed within a broader, scale-dependent framework, and it is proposed that, although the complexity of the natural system presents fundamental limits to predictive modelling, the bioclimate envelope approach can provide a useful first approximation as to the potentially dramatic impact of climate change on biodiversity.
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Ensemble forecasting of species distributions

TL;DR: It is argued that, although improved accuracy can be delivered through the traditional tasks of trying to build better models with improved data, more robust forecasts can also be achieved if ensemble forecasts are produced and analysed appropriately.
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Predicting global change impacts on plant species' distributions: Future challenges

TL;DR: This review proposes two main avenues to progress the understanding and prediction of the different processes occurring on the leading and trailing edge of the species' distribution in response to any global change phenomena and concludes with clear guidelines on how such modelling improvements will benefit conservation strategies in a changing world.
Journal ArticleDOI

The importance of biotic interactions for modelling species distributions under climate change

TL;DR: This paper examined whether biotic interactions exert a dominant role in governing species distributions at macro-ecological scales, and provided tests for two null hypotheses: (H 0 1) "Biotic interactions do not exert a significant role in explaining current distributions of a particular species of butterfly (clouded Apollo, Parnassius mnemosyne ) in Europe; and ( H 0 2) ''Biotic interaction does not influence the prediction of altered species' ranges under climate change''.
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