Migration of northern Yellowstone elk: implications of spatial structuring

White, P. J.; Proffitt, Kelly M.; Mech, L. David; Evans, Shaney B.; Cunningham, Julie A.; Hamlin, Kenneth L.

doi:10.1644/08-mamm-a-252.1

Cited by 82 publications

(95 citation statements)

References 45 publications

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“…Because 1st-year juveniles are not found away from adults during the winter, over-winter juvenile mortality probably does not explain the decline in detection rates of adult female + juvenile groups that we observed. Prior studies have shown that elk movement to lower elevations as the snow increases at high elevations [31], but we suggest that this movement is stronger for females than males. This could be due to higher tolerance of deep snow by males, perhaps because their larger body size facilitates digging for sub-nivean forage, they lack the energetic reserves, or they have a higher probability of avoiding encounters with wolves.…”

Section: Discussioncontrasting

confidence: 68%

Trophic interactions and dynamic herbivore responses to snowpack

Brodie

Post

Berger

et al. 2014

Clim Chang Responses

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Background: Trophic interactions and population structure can shape how climate change influences ecosystems by modifying herbivore responses to environmental conditions. Predation can influence herbivore behaviour and demography, but how changes in predation and population structure affect herbivore distribution across abiotic gradients remains little known. We assessed whether predators altered the response of different age and sex classes of a dominant ungulate herbivore to changing abiotic conditions.

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Section: Discussioncontrasting

confidence: 68%

Trophic interactions and dynamic herbivore responses to snowpack

Brodie

Post

Berger

et al. 2014

Clim Chang Responses

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“…The acquisition of continuous data on timing of migration or other life-history events under field conditions is challenging and sometimes impossible to achieve (Garrott et al 1987, Pulido 2007, Meunier et al 2008, unless animal location data are obtained from collars with global positioning system technology (White et al 2010). Because of logistical constraints, we were unable to monitor presence or absence of mule Notes: Extrinsic factors in interval-censored models for autumn migration with individual covariates included: daily snow depth (PC1), daily temperature (PC3), daily snowfall and precipitation (PC4), daily metric of change in temperature (PC5), quadratic time trend (T and TT), and year (Year).…”

Section: Discussionmentioning

confidence: 99%

“…Although autumn migration is typically rapid with little delay following severe weather (Kucera 1992), females summering on the west side of the Sierra crest may take advantage of comparatively milder conditions after crossing the crest for foraging and resting before proceeding to winter range (Sawyer et al 2009). Likewise, Mysterud (1999) and White et al (2010) reported little correlation between the timing of autumn migration and distance migrated in roe deer (Capreolus capreolus) and North American elk, respectively. The absence of a relationship between location of summer residency and the phenology of spring migration (Table 4) implies that individuals respond to their local environment.…”

Section: Effects Of Life-history Characteristicsmentioning

confidence: 99%

“…The parameter estimate for the relationship between change in snow depth (PC2) and daily migration for mule deer, however, was not significant, which indicated that the absolute depth of snow was more important in affecting long-distance movement by mule deer than was the rate of snow accumulation or disappearance (Table 2). Likewise, delayed spring migration following winters with heavy snow pack, and early migration in years with low snow pack and early vegetation green-up is common among large herbivores (Garrott et al 1987, Nelson 1995, Brinkman et al 2005, Grovenburg et al 2009, White et al 2010). The effect of snow pack on large herbivores can severely restrict mobility and exhaust energy reserves (Parker et al 1984).…”

Section: Spring Migrationmentioning

confidence: 99%

“…Although effects of climatic patterns and plant phenology on the timing of migratory events for large herbivores have been documented (Albon and Langvatn 1992, Kucera 1992, Nelson 1995, Sabine et al 2002, the influence of intrinsic factors, such as age, location of summer residency (which may differ for populations using the same winter range), and reproductive and nutritional state, rarely have been considered (White et al 2010). Failure to recognize other important factors related to individual life-history characteristics may lead to spurious correlations between indices of climate and the timing of migration.…”

Section: Introductionmentioning

confidence: 99%

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Timing of seasonal migration in mule deer: effects of climate, plant phenology, and life-history characteristics

et al. 2011

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Abstract. Phenological events of plants and animals are sensitive to climatic processes. Migration is a life-history event exhibited by most large herbivores living in seasonal environments, and is thought to occur in response to dynamics of forage and weather. Decisions regarding when to migrate, however, may be affected by differences in life-history characteristics of individuals. Long-term and intensive study of a population of mule deer (Odocoileus hemionus) in the Sierra Nevada, California, USA, allowed us to document patterns of migration during 11 years that encompassed a wide array of environmental conditions. We used two new techniques to properly account for interval-censored data and disentangle effects of broad-scale climate, local weather patterns, and plant phenology on seasonal patterns of migration, while incorporating effects of individual life-history characteristics. Timing of autumn migration varied substantially among individual deer, but was associated with the severity of winter weather, and in particular, snow depth and cold temperatures. Migratory responses to winter weather, however, were affected by age, nutritional condition, and summer residency of individual females. Old females and those in good nutritional condition risked encountering severe weather by delaying autumn migration, and were thus risk-prone with respect to the potential loss of foraging opportunities in deep snow compared with young females and those in poor nutritional condition. Females that summered on the west side of the crest of the Sierra Nevada delayed autumn migration relative to east-side females, which supports the influence of the local environment on timing of migration. In contrast, timing of spring migration was unrelated to individual life-history characteristics, was nearly twice as synchronous as autumn migration, differed among years, was related to the southern oscillation index, and was influenced by absolute snow depth and advancing phenology of plants. Plasticity in timing of migration in response to climatic conditions and plant phenology may be an adaptive behavioral strategy, which should reduce the detrimental effects of trophic mismatches between resources and other life-history events of large herbivores. Failure to consider effects of nutrition and other life-history traits may cloud interpretation of phenological patterns of mammals and conceal relationships associated with climate change.

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Assessing the importance of demographic parameters for population dynamics using Bayesian integrated population modeling

Eacker

Lukacs

Proffitt

et al. 2017

Ecological Applications

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To successfully respond to changing habitat, climate or harvest, managers need to identify the most effective strategies to reverse population trends of declining species and/or manage harvest of game species. A classic approach in conservation biology for the last two decades has been the use of matrix population models to determine the most important vital rates affecting population growth rate (λ), that is, sensitivity. Ecologists quickly realized the critical role of environmental variability in vital rates affecting λ by developing approaches such as life-stage simulation analysis (LSA) that account for both sensitivity and variability of a vital rate. These LSA methods used matrix-population modeling and Monte Carlo simulation methods, but faced challenges in integrating data from different sources, disentangling process and sampling variation, and in their flexibility. Here, we developed a Bayesian integrated population model (IPM) for two populations of a large herbivore, elk (Cervus canadensis) in Montana, USA. We then extended the IPM to evaluate sensitivity in a Bayesian framework. We integrated known-fate survival data from radio-marked adults and juveniles, fecundity data, and population counts in a hierarchical population model that explicitly accounted for process and sampling variance. Next, we tested the prevailing paradigm in large herbivore population ecology that juvenile survival of neonates <90 d old drives λ using our Bayesian LSA approach. In contrast to the prevailing paradigm in large herbivore ecology, we found that adult female survival explained more of the variation in λ than elk calf survival, and that summer and winter elk calf survival periods were nearly equivalent in importance for λ. Our Bayesian IPM improved precision of our vital rate estimates and highlighted discrepancies between count and vital rate data that could refine population monitoring, demonstrating that combining sensitivity analysis with population modeling in a Bayesian framework can provide multiple advantages. Our Bayesian LSA framework will provide a useful approach to addressing conservation challenges across a variety of species and data types.

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Migration of northern Yellowstone elk: implications of spatial structuring

Cited by 82 publications

References 45 publications

Trophic interactions and dynamic herbivore responses to snowpack

Trophic interactions and dynamic herbivore responses to snowpack

Timing of seasonal migration in mule deer: effects of climate, plant phenology, and life-history characteristics

Assessing the importance of demographic parameters for population dynamics using Bayesian integrated population modeling

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