Predicting time-specific changes in demographic processes using remote-sensing data

Authors

  • HENRIK B. RASMUSSEN,

    1. Department of Zoology, University of Oxford, South Parks Road, Oxford OX1 3PS, UK;
    2. Save the Elephants, PO Box 54667, Nairobi, Kenya; and
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    • §

      Joint authorship/equal contribution.

  • GEORGE WITTEMYER,

    1. Save the Elephants, PO Box 54667, Nairobi, Kenya; and
    2. Department of Environmental Science, Policy and Management, University of California at Berkeley, 201 Wellman Hall, Berkeley, CA 94720, USA
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    • §

      Joint authorship/equal contribution.

  • IAIN DOUGLAS-HAMILTON

    1. Save the Elephants, PO Box 54667, Nairobi, Kenya; and
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Henrik B. Rasmussen, University of Oxford Department of Zoology, South Parks Road, Oxford OX1 3PS, UK (e-mail henrik.rasmussen@zoo.ox.ac.uk).

Summary

  • 1Models of wildlife population dynamics are crucial for sustainable utilization and management strategies. Fluctuating ecological conditions are often key factors influencing both carrying capacity, mortality and reproductive rates in ungulates. To be reliable, demographic models should preferably rely on easily obtainable variables that are directly linked to the ecological processes regulating a population.
  • 2We compared the explanatory power of rainfall, a commonly used proxy for variability in ecological conditions, with normalized differential vegetation index (NDVI), a remote-sensing index value that is a more direct measure of vegetation productivity, to predict time-specific conception rates of an elephant population in northern Kenya. Season-specific conception rates were correlated with both quality measures. However, generalized linear logistic models compared using Akaike's information criteria showed that a model based on the NDVI measure outperformed models based on rainfall measures.
  • 3A predictive model based on coarse demographic data and the maximum seasonal NDVI value was able to trace the large variation in observed season-specific conception rates (Range 0–0·4), with a low median deviation from observed values of 0·07.
  • 4By combining the model of season-specific conception rates with the average seasonal distribution of conception dates, the monthly number of conceptions (range 0–22) could be predicted within ±3 with 80% confidence.
  • 5Synthesis and applications. The strong predictive power of the normalized differential vegetation index on time-specific variation in a demographic variable is likely to be generally applicable to resource-limited ungulate species occurring in ecologically variable ecosystems, and could potentially be a powerful factor in demographic population modelling.

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