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Yvonne C. Collingham

Researcher at Durham University

Publications -  38
Citations -  11272

Yvonne C. Collingham is an academic researcher from Durham University. The author has contributed to research in topics: Climate change & Range (biology). The author has an hindex of 27, co-authored 38 publications receiving 10469 citations. Previous affiliations of Yvonne C. Collingham include University of York.

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Evaluating the effectiveness of conservation site networks under climate change: accounting for uncertainty.

TL;DR: Overall, while the IBA network will continue to sustain bird conservation, climate change will modify which species each site will be suitable for, and adaptive management of the network is critical to ensure effective future conservation.
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Potential impacts of climatic change on the breeding and non‐breeding ranges and migration distance of European Sylvia warblers

TL;DR: In this article, the authors explore the potential impacts of climatic change on species with different migratory strategies using Sylvia warblers breeding in Europe as a ‘model’ species group.
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The migration of sessile organisms: a simulation model with measurable parameters

TL;DR: In this paper, the authors developed a spatially-explicit mechanistic model (MIGRATE) able to simulate the migration of a single species across a realistically heterogeneous landscape.
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Using distribution models to test alternative hypotheses about a species’ environmental limits and recovery prospects

TL;DR: A series of models are developed to test hypotheses about the factors influencing the distribution of a species of conservation importance – the hen harrier Circus cyaneus, with results consistent with historical data on the species’ distribution, its habitat use in other parts of its range and with the climate-based model.
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Dynamic distribution modelling : predicting the present from the past

TL;DR: This paper used a dynamic model framework to demonstrate that recently-observed changes at the expanding northern boundaries of three British butterfly species can be predicted with good accuracy, using a combination of individual species traits, species-specific habitat associations and distance-dependent dispersal.