Reconstructing shifts in vital rates driven by long-term environmental change: a new demographic method based on readily available data
Article first published online: 7 JUN 2013
© 2013 The Authors. Ecology and Evolution published by John Wiley & Sons Ltd.
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Ecology and Evolution
Volume 3, Issue 7, pages 2273–2284, July 2013
How to Cite
Ecology and Evolution 2013; 3(7): 2273–2284
- Issue published online: 10 JUL 2013
- Article first published online: 7 JUN 2013
- Manuscript Accepted: 9 MAR 2013
- Manuscript Revised: 1 MAR 2013
- Manuscript Received: 15 JAN 2013
- PAPIIT project. Grant Number: IN225511
- Environmental drivers;
- human impacts;
- integral projection models;
- population biology;
- population structure;
- time series
Frequently, vital rates are driven by directional, long-term environmental changes. Many of these are of great importance, such as land degradation, climate change, and succession. Traditional demographic methods assume a constant or stationary environment, and thus are inappropriate to analyze populations subject to these changes. They also require repeat surveys of the individuals as change unfolds. Methods for reconstructing such lengthy processes are needed. We present a model that, based on a time series of population size structures and densities, reconstructs the impact of directional environmental changes on vital rates. The model uses integral projection models and maximum likelihood to identify the rates that best reconstructs the time series. The procedure was validated with artificial and real data. The former involved simulated species with widely different demographic behaviors. The latter used a chronosequence of populations of an endangered cactus subject to increasing anthropogenic disturbance. In our simulations, the vital rates and their change were always reconstructed accurately. Nevertheless, the model frequently produced alternative results. The use of coarse knowledge of the species' biology (whether vital rates increase or decrease with size or their plausible values) allowed the correct rates to be identified with a 90% success rate. With real data, the model correctly reconstructed the effects of disturbance on vital rates. These effects were previously known from two populations for which demographic data were available. Our procedure seems robust, as the data violated several of the model's assumptions. Thus, time series of size structures and densities contain the necessary information to reconstruct changing vital rates. However, additional biological knowledge may be required to provide reliable results. Because time series of size structures and densities are available for many species or can be rapidly generated, our model can contribute to understand populations that face highly pressing environmental problems.