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Separating Components of Variation in Survival of Mule Deer in Colorado
Author(s) -
LUKACS PAUL M.,
WHITE GARY C.,
WATKINS BRUCE E.,
KAHN RICHARD H.,
BANULIS BRADLEY A.,
FINLEY DARBY J.,
HOLLAND A. ANDREW,
MARTENS JUSTIN A.,
VAYHINGER JACK
Publication year - 2009
Publication title -
the journal of wildlife management
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.94
H-Index - 111
eISSN - 1937-2817
pISSN - 0022-541X
DOI - 10.2193/2008-480
Subject(s) - odocoileus , population , survival analysis , biology , markov chain monte carlo , mark and recapture , demography , statistics , bayesian probability , ecology , mathematics , sociology
ABSTRACT Survival is an important parameter for understanding population dynamics of mule deer ( Odocoileus hemionus ) and other large herbivores. To understand long‐term dynamics it is important to separate sampling and biological process variation in survival. Moreover, knowledge of correlations in survival across space and between young and adults can provide more informed predictions of survival in unsampled areas. We estimated survival of fawn, yearling, and adult mule deer from 4 spatially separated regions of Colorado, USA, from 1997 to 2008. We also estimated process variance in survival across time for each age and site using Markov chain Monte Carlo (MCMC) methods. Finally, we estimated correlations in survival among sites and ages with MCMC methods. Average winter fawn survival was 0.721 (SD = 0.024) for the 4 regions. Average winter adult female survival was 0.935 (SD = 0.007). Annual adult female survival ranged from 0.803 (SD = 0.017) to 0.900 (SD = 0.028) for the 4 regions, excluding hunting mortality. The correlation between fawn and adult female survival was high, 0.563 (SD = 0.253). Correlations in winter fawn survival were higher between populations at the same latitude than they were for populations to the north and south. We used survival estimates from our analysis to inform prior distributions for a Bayesian population dynamics model from one population in Colorado and compared that model to one with noninformative prior distributions. Population models including informative prior distributions based on our results performed better than those noninformative prior distributions on survival, providing more biologically defensible results when data were sparse. Knowledge of process distributions of survival can help wildlife managers better predict future population status and understand the likely range of survival rates.