Separating components of variation in survival of mule deer in Colorado

Paul M. Lukacs, Gary C. White, Bruce E. Watkins, Richard H. Kahn, Bradley A. Banulis, Darby J. Finley, A. Andrew Holland, Justin A. Martens, Jack Vayhinger

Research output: Contribution to journalArticlepeer-review

25 Scopus citations


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.

Original languageEnglish
Pages (from-to)817-826
Number of pages10
JournalJournal of Wildlife Management
Issue number6
StatePublished - Aug 2009


  • Bayesian estimators
  • Markov chain Monte Carlo (MCMC)
  • Mule deer
  • Odocoileus hemionus
  • Population modeling
  • Process variance
  • Program MARK
  • Radiotelemetry
  • Survival
  • Variance components


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