APA
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Dornelas, M., Magurran, A., Buckland, S. T., Chao, A., Chazdon, R., Colwell, R. K., … Vellend, M. (2013). Quantifying temporal change in biodiversity: challenges and opportunities. Proceedings of the Royal Society B: Biological Sciences.
Chicago/Turabian
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Dornelas, M., A. Magurran, S. T. Buckland, A. Chao, R. Chazdon, R. K. Colwell, T. Curtis, et al. “Quantifying Temporal Change in Biodiversity: Challenges and Opportunities.” Proceedings of the Royal Society B: Biological Sciences (2013).
MLA
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Dornelas, M., et al. “Quantifying Temporal Change in Biodiversity: Challenges and Opportunities.” Proceedings of the Royal Society B: Biological Sciences, 2013.
BibTeX Click to copy
@article{m2013a,
title = {Quantifying temporal change in biodiversity: challenges and opportunities},
year = {2013},
journal = {Proceedings of the Royal Society B: Biological Sciences},
author = {Dornelas, M. and Magurran, A. and Buckland, S. T. and Chao, A. and Chazdon, R. and Colwell, R. K. and Curtis, T. and Gaston, K. and Gotelli, N. and Kosnik, M. and McGill, B. and McCune, J. L. and Morlon, H. and Mumby, P. and Øvreås, L. and Studeny, A. and Vellend, M.}
}
Growing concern about biodiversity loss underscores the need to quantify and understand temporal change. Here, we review the opportunities presented by biodiversity time series, and address three related issues: (i) recognizing the characteristics of temporal data; (ii) selecting appropriate statistical procedures for analysing temporal data; and (iii) inferring and forecasting biodiversity change. With regard to the first issue, we draw attention to defining characteristics of biodiversity time series—lack of physical boundaries, uni-dimensionality, autocorrelation and directionality—that inform the choice of analytic methods. Second, we explore methods of quantifying change in biodiversity at different timescales, noting that autocorrelation can be viewed as a feature that sheds light on the underlying structure of temporal change. Finally, we address the transition from inferring to forecasting biodiversity change, highlighting potential pitfalls associated with phase-shifts and novel conditions.