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Predicting time‐specific changes in demographic processes using remote‐sensing data
Author(s) -
RASMUSSEN HENRIK B.,
WITTEMYER GEORGE,
DOUGLASHAMILTON IAIN
Publication year - 2006
Publication title -
journal of applied ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.503
H-Index - 181
eISSN - 1365-2664
pISSN - 0021-8901
DOI - 10.1111/j.1365-2664.2006.01139.x
Subject(s) - normalized difference vegetation index , ungulate , akaike information criterion , ecology , population , environmental science , predictive power , vegetation (pathology) , proxy (statistics) , statistics , geography , mathematics , climate change , demography , biology , epistemology , pathology , medicine , philosophy , sociology
Summary1 Models 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. 2 We 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. 3 A 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. 4 By 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. 5 Synthesis 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.