A trait-based approach for predicting species responses to environmental change from sparse data : how well might terrestrial mammals track climate change?

Funded by COST (European Cooperation in Science and Technology) CEH projects. Grant Numbers: NEC05264, NEC05100 Natural Environment Research Council UK. Grant Number: NE/J008001/1 © 2016 The Authors. Global Change Biology Published by John Wiley & Sons Ltd. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Predicting and understanding spatio-temporal dynamics of species recovery : implications for Asian crested ibis Nipponia nippon conservation in China

Acknowledgements This work was supported by the National Natural Science Foundation of China (No. 31372218) and cofunded by the China Scholarship Council (CSC) and the ITC Research Fund, Enschede, the Netherlands. We thank Shaanxi Hanzhong Crested Ibis National Nature Reserve for sharing the data of nest site locations. We are grateful to Brendan Wintle, Justin Travis and two anonymous reviewers for helpful comments on a previous version of the manuscript.

Predicting residual stiffness of cracked composite laminates subjected to multi-axial in-plane loading

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Looking to the future : Predicting renal replacement outcomes in a large community cohort with chronic kidney disease

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Predicting residual stiffness of cracked composite laminates subjected to multi-axial in-plane loading

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Looking to the future : Predicting renal replacement outcomes in a large community cohort with chronic kidney disease

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Predicting functional disability : One year results from the Scottish Early Rheumatoid Arthritis Inception Cohort

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Important factors in predicting mortality outcome from stroke : findings from the Anglia Stroke Clinical Network Evaluation Study

The Anglia Stroke Clinical Network Evaluation Study (ASCNES) is funded by the National Institute for Health Research (NIHR) Research for Patient Benefit Programme (PB-PG-1208-18240). This paper presents independent research funded by the NIHR under its Research for Patient Benefit (RfPB) programme (Grant Reference Number PB-PG-1208-18240). The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR or the Department of Health. EAW receives funding support from the NIHR Biomedical Research Centre award to Cambridge

Looking to the future : Predicting renal replacement outcomes in a large community cohort with chronic kidney disease

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