Quantifying temporal change in biodiversity : challenges and opportunities
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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.
Dornelas , M , Magurran , A , Buckland , S T , Chao , A , Chazdon , R L , Colwell , R K , Curtis , T , Gaston , K J , Gotelli , N J , Kosnik , M A , McGill , B , McCune , J L , Morlon , H , Mumby , P J , Øvreås , L , Studeny , A & Vellend , M 2013 , ' Quantifying temporal change in biodiversity : challenges and opportunities ' Proceedings of the Royal Society B: Biological Sciences , vol. 280 , no. 1750 , 20121931 . https://doi.org/10.1098/rspb.2012.1931
Proceedings of the Royal Society B: Biological Sciences
© 2012 The Authors. Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/3.0/, which permits unrestricted use, provided the original author and source are credited.
- Centre for Biological Diversity (CBD) Research
- University of St Andrews Research
- Biology Research
- Mathematics & Statistics Research
- Centre for Research into Ecological & Environmental Modelling (CREEM) Research
- Institute of Behavioural and Neural Sciences Research
- St Andrews Sustainability Institute Research
- Scottish Oceans Institute Research
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