Contact

Robert K. Colwell


Museum Curator Adjoint in Entomology


robertkcolwell [at] gmail.com


Museum of Natural History


University of Colorado


Boulder, CO 80309, USA




robertkcolwell [at] gmail.com


Museum of Natural History


University of Colorado


Boulder, CO 80309, USA



Quantifying temporal change in biodiversity: challenges and opportunities


Journal article


M. Dornelas, A. Magurran, S. T. Buckland, A. Chao, R. Chazdon, R. K. Colwell, T. Curtis, K. Gaston, N. Gotelli, M. Kosnik, B. McGill, J. L. McCune, H. Morlon, P. Mumby, L. Øvreås, A. Studeny, M. Vellend
Proceedings of the Royal Society B: Biological Sciences, 2013

Semantic Scholar DOI PubMedCentral PubMed
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APA
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
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
Dornelas, M., et al. “Quantifying Temporal Change in Biodiversity: Challenges and Opportunities.” Proceedings of the Royal Society B: Biological Sciences, 2013.


Abstract

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.