Effect of limited antlered harvest on mule deer sex and age ratios

Bishop, Chad J.; White, Gary C.; Freddy, David J.; Watkins, Bruce E.

doi:10.2193/0091-7648(2005)33[662:eolaho]2.0.co;2

Cited by 34 publications

(24 citation statements)

References 8 publications

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“…The ecotype‐specific effects of summer‐fall forage and winter snow on mule deer overwinter survival varied between ecotypes in ways that corresponded to important differences between PMUs. This is similar to many previous studies of mule deer, and other ungulates, that used a variety of different measures of forage and winter severity (Bishop et al 2005b, 2009; Hurley et al ). Our approach evaluated the same consistent set of predictors across a wide geographical gradient and revealed important between‐ecotype differences.…”

Section: Discussionsupporting

confidence: 86%

Regional‐scale models for predicting overwinter survival of juvenile ungulates

et al. 2017

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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. © 2016 The Wildlife Society.

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Section: Discussionsupporting

confidence: 86%

Regional‐scale models for predicting overwinter survival of juvenile ungulates

et al. 2017

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“…Adult sex‐ratio objectives can also be set to ensure productivity is not adversely affected by maintaining enough adult males to breed adult females. For mule deer, however, sex‐ratio objectives are primarily used to achieve hunting expectations related to the quality of the male hunting opportunity (Hammitt et al , White et al , Bishop et al , Freeman et al ).…”

Section: Discussionmentioning

confidence: 99%

An integrated modeling approach for assessing management objectives for mule deer in central British Columbia

2017

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We used an integrated Bayesian state‐space population model to assess whether management objectives were met before (1995–2003), during (2004–2010), and after (2011–2013) antlerless permits to harvest mule deer (Odocoileus hemionus) were increased in response to stakeholder concerns in central British Columbia, Canada. Data inputs included 19 years of harvest data, 7 years of autumn age–sex composition data, 17 years of spring age–sex composition data, and 15 years of a population index. Management objectives were to maintain a spring population of 7,000–9,000 deer and a posthunt adult sex ratio of 20–30 males:100 females. An 8.5‐fold increase in antlerless permits raised the antlerless harvest rates from 1.0% (1995–2003) to 4.8% (2004–2010). Antlerless harvest rates decreased to 2.8% following a 53% decrease in permits from 2011 to 2013. Population projections from 2014 to 2018 fell within the bounds of the management objectives, but 95% credibility intervals revealed great uncertainty in population size and composition. We recommend a structured, adaptive approach to mule deer management that includes annual adjustment of harvests, monitoring, and modeling, with an open‐ended stakeholder engagement process to ensure objectives remain relevant, measurable, and achievable. © 2017 The Wildlife Society.

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“…Although managers have some control over harvest rates, changes in hunter attitude and hunting styles cause additional problems with SAK estimation given its sensitivity to male harvest rate. Several states have instituted changes to deer hunting regulations that affect male harvest rates (Bishop et al 2005). For example, in certain management units the state of Wisconsin has implemented an ''earn-a-buck'' program that requires that hunters harvest an antlerless deer before being authorized to harvest an antlered deer.…”

Section: Discussionmentioning

confidence: 99%

An Evaluation of Sex‐Age‐Kill (SAK) Model Performance

Millspaugh¹,

Skalski²,

Townsend³

et al. 2009

J Wildl Manag

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The sex‐age‐kill (SAK) model is widely used to estimate abundance of harvested large mammals, including white‐tailed deer (Odocoileus virginianus). Despite a long history of use, few formal evaluations of SAK performance exist. We investigated how violations of the stable age distribution and stationary population assumption, changes to male or female harvest, stochastic effects (i.e., random fluctuations in recruitment and survival), and sampling efforts influenced SAK estimation. When the simulated population had a stable age distribution and λ > 1, the SAK model underestimated abundance. Conversely, when λ < 1, the SAK overestimated abundance. When changes to male harvest were introduced, SAK estimates were opposite the true population trend. In contrast, SAK estimates were robust to changes in female harvest rates. Stochastic effects caused SAK estimates to fluctuate about their equilibrium abundance, but the effect dampened as the size of the surveyed population increased. When we considered both stochastic effects and sampling error at a deer management unit scale the resultant abundance estimates were within ±121.9% of the true population level 95% of the time. These combined results demonstrate extreme sensitivity to model violations and scale of analysis. Without changes to model formulation, the SAK model will be biased when λ ≠ 1. Furthermore, any factor that alters the male harvest rate, such as changes to regulations or changes in hunter attitudes, will bias population estimates. Sex‐age‐kill estimates may be precise at large spatial scales, such as the state level, but less so at the individual management unit level. Alternative models, such as statistical age‐at‐harvest models, which require similar data types, might allow for more robust, broad‐scale demographic assessments.

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Effect of limited antlered harvest on mule deer sex and age ratios

Cited by 34 publications

References 8 publications

Regional‐scale models for predicting overwinter survival of juvenile ungulates

Regional‐scale models for predicting overwinter survival of juvenile ungulates

An integrated modeling approach for assessing management objectives for mule deer in central British Columbia

An Evaluation of Sex‐Age‐Kill (SAK) Model Performance

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