Regional-scale models for predicting overwinter survival of juvenile ungulates

Mark A. Hurley, Mark Hebblewhite, Paul M. Lukacs, J. Joshua Nowak, Jean Michel Gaillard, Christophe Bonenfant

Research output: Contribution to journalArticlepeer-review

22 Scopus citations

Abstract

Juvenile survival is highly variable in ungulate populations and often influences their dynamics. However, this vital rate is difficult to estimate with common wildlife management methods. Yet managers would benefit from being able to predict juvenile survival to reliably assess population dynamics for harvest management. In the case of mule deer (Odocoileus hemionus), previous studies reported that overwinter survival is the demographic parameter that influences population dynamics. We predicted winter survival of mule deer fawns under a range of habitat quality, weather, and predation regimes. We modeled overwinter survival of 2,529 fawns within 11 Population Management Units (PMU) in Idaho, 2003–2013. We used remotely sensed Normalized Difference Vegetation Index (NDVI) as a measure of summer plant productivity and both Moderate Resolution Infrared Spectroscopy Snow Data (MODIS SNOW) and the modeled Snow Data Assimilation System (SNODAS) as measures of winter snow conditions to capture spatiotemporal variation in winter survival. We used Bayesian hierarchical models to estimate survival, including covariates at the appropriate spatial and temporal resolution for each level: individual, capture site, PMU, and ecotype scales. We evaluated the predictive capacity of models using internal validation and external (out-of-sample) validation procedures comparing non-parametric Kaplan-Meier (KM) survival estimates with estimates from Bayesian hierarchical models. Statewide survival of fawns from 16 December to 1 June ranged from 0.32 to 0.71 during 2003 to 2013, with relatively low survival in 2006, 2008, and 2011 in most Game Management Units. Survival for individual PMUs ranged from 0.09 in the Weiser-McCall PMU in 2011 to 0.95 in the same PMU in 2005. Internal validation revealed models predicted KM survival well, over a range of R2 from 0.78 to 0.82, with the most complex model explaining the most variance as expected. However, because our goal was to predict winter survival of mule deer in the future, we evaluated our candidate models by withholding 2 years of data and then predicted those years with each model. The best-supported predictive model was our simplest model with 3 covariates, accounting for 71% of the variance in withheld years. Forage quality in late summer-fall increased winter mule deer survival, whereas early and late winter snow cover decreased survival. At finer-spatial scales within ecotypes, our internal validation was slightly better in aspen (0.86), similar in conifer (0.80), and poorer in shrub-steppe (0.60) ecotypes than our best statewide overall survival models, which accounted for 82% of the variance. Our analyses demonstrate the generality versus precision tradeoff across ecotypes and spatial scales to understand the extent that our survival models may be applied to different landscapes with varied predator communities, climate, and plant nutrition.

Original languageEnglish
Pages (from-to)364-378
Number of pages15
JournalJournal of Wildlife Management
Volume81
Issue number3
DOIs
StatePublished - Apr 1 2017

Keywords

  • Bayesian model
  • MODIS
  • Odocoileus hemionus
  • SNODAS
  • hierarchical model
  • mule deer
  • prediction
  • survival
  • weather

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