Trophic interactions and dynamic herbivore responses to snowpack

Brodie, Jedediah F.; Post, Eric; Berger, Joël; Watson, Fred

doi:10.1186/s40665-014-0004-2

Cited by 7 publications

(5 citation statements)

References 39 publications

(46 reference statements)

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“…Understanding how adverse weather conditions may affect elk movements can help anticipate changes in elk use of conflict zones and the level of management actions needed. Snowpack may influence wildlife distribution through locomotion restrictions, energetic demands, and decreased access to forage resources (Reed et al 2009, Brodie et al 2014, Beumer et al 2017, Honda and Kozakai 2020). Our results indicate that increasing snowpack, as measured by SWE, reduced the number of elk using the conflict zone in Madison despite its lower elevation than surrounding tolerance zones.…”

Section: Discussionmentioning

confidence: 99%

Elk Responses to Management Hunting and Hazing

Jones¹,

Proffitt

Paterson

et al. 2021

J Wildl Manag

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Human‐wildlife conflicts are widespread around the world and result in property damage, disease spillover, financial loss, and decreased tolerance of wildlife. Increasing elk (Cervus canadensis) populations and land‐use changes in the western United States are challenging resource managers tasked with managing conflict. Lethal and non‐lethal management actions are commonly used to remove elk from conflict zones where they are not desired. We used radio‐collar location data collected from female elk in 2 study areas in Montana, USA, from 2017–2020 to evaluate population‐ and individual‐level responses to management actions (i.e., hunting, hazing) and environmental factors (i.e., weather, season, time of day). First, we used a generalized linear model with a logit link to evaluate the effects of hunting, hazing, time period, seasonality, and weather on the proportion of collared elk that used a conflict zone. Second, we used an ordinary linear model to assess the influence of hunting, hazing, seasonality, and weather on the duration of time that individual elk remained away from conflict zones. The proportion of elk using conflict zones was reduced by hunting, modestly reduced by hazing and increasing snowpack for 1 study area, increased at night, and decreased by a seasonal trend across months. The time individual elk remained away from conflict zones increased with the number of hazing events that occurred during an event and showed a modest seasonal trend increasing across months. For 1 study area, time away increased with the number of hunting days during an event and increasing snowpack, but the increase was biologically trivial. Our results indicate mixed responses of elk to hunting and hazing actions and provide evidence that management actions can influence elk use of conflict areas. Agencies trying to reduce conflicts may want to consider a combination of hunting and hazing, while accounting for site‐specific characteristics to keep elk away from conflict zones. © 2021 The Wildlife Society.

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Section: Discussionmentioning

confidence: 99%

Elk Responses to Management Hunting and Hazing

Jones¹,

Proffitt

Paterson

et al. 2021

J Wildl Manag

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“…For example, cameras helped to assess the impacts of climate change and trophic interactions on elk ( Cervus canadensis ; Brodie et al . ), to measure plant phenology and climate (Morisette et al . ), and to determine how large‐mammal food webs respond to forest fragmentation (Brodie et al .…”

Section: Current Applications Of Remote Cameras To Biodiversity Consementioning

confidence: 99%

Scaling‐up camera traps: monitoring the planet's biodiversity with networks of remote sensors

Steenweg

Hebblewhite

Kays

et al. 2016

Frontiers in Ecol & Environ

343

323

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Countries committed to implementing the Convention on Biological Diversity's 2011–2020 strategic plan need effective tools to monitor global trends in biodiversity. Remote cameras are a rapidly growing technology that has great potential to transform global monitoring for terrestrial biodiversity and can be an important contributor to the call for measuring Essential Biodiversity Variables. Recent advances in camera technology and methods enable researchers to estimate changes in abundance and distribution for entire communities of animals and to identify global drivers of biodiversity trends. We suggest that interconnected networks of remote cameras will soon monitor biodiversity at a global scale, help answer pressing ecological questions, and guide conservation policy. This global network will require greater collaboration among remote‐camera studies and citizen scientists, including standardized metadata, shared protocols, and security measures to protect records about sensitive species. With modest investment in infrastructure, and continued innovation, synthesis, and collaboration, we envision a global network of remote cameras that not only provides real‐time biodiversity data but also serves to connect people with nature.

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“…For many animals, however, snow signals a period of caloric stress brought on by limited access to high-quality forage and increased metabolic demands associated with thermoregulation and impaired mobility (Parker et al 1984, 2009, Robinson and Merrill 2012, Gilbert et al 2017. Conditions such as deep snow or icing caused by rain-on-snow events (Stien et al 2010) can severely limit the ability of herbivores to access ground forage, decrease efficiency of movement (Parker et al 1984), and increase predation risk (Hebblewhite et al 2005, Sand et al 2006, Brodie et al 2014, Lendrum et al 2017. Thus, the snow-covered period is often limiting for terrestrial mammals living in temperate and Arctic regions (Parker et al 2009), with strong selective forces linking environmental conditions, animal behavior, and fitness (Boutin and Lane 2014 Snowscapes may have especially strong effects on wildlife in Arctic and boreal ecosystems where snow cover persists for much of the year (Callaghan et al 2011a, Pozzanghera et al 2016).…”

Section: Introductionmentioning

confidence: 99%

Navigating snowscapes: scale‐dependent responses of mountain sheep to snowpack properties

Mahoney

Liston

LaPoint

et al. 2018

Ecological Applications

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Winters are limiting for many terrestrial animals due to energy deficits brought on by resource scarcity and the increased metabolic costs of thermoregulation and traveling through snow. A better understanding of how animals respond to snow conditions is needed to predict the impacts of climate change on wildlife. We compared the performance of remotely sensed and modeled snow products as predictors of winter movements at multiple spatial and temporal scales using a data set of 20,544 locations from 30 GPS-collared Dall sheep (Ovis dalli dalli) in Lake Clark National Park and Preserve, Alaska, USA from 2005 to 2008. We used daily 500-m MODIS normalized difference snow index (NDSI), and multi-resolution snow depth and density outputs from a snowpack evolution model (SnowModel), as covariates in step selection functions. We predicted that modeled snow depth would perform best across all scales of selection due to more informative spatiotemporal variation and relevance to animal movement. Our results indicated that adding any of the evaluated snow metrics substantially improved model performance and helped characterize winter Dall sheep movements. As expected, SnowModel-simulated snow depth outperformed NDSI at fine-to-moderate scales of selection (step scales < 112 h). At the finest scale, Dall sheep selected for snow depths below mean chest height (<54 cm) when in low-density snows (100 kg/m ), which may have facilitated access to ground forage and reduced energy expenditure while traveling. However, sheep selected for higher snow densities (>300 kg/m ) at snow depths above chest height, which likely further reduced energy expenditure by limiting hoof penetration in deeper snows. At moderate-to-coarse scales (112-896 h step scales), however, NDSI was the best-performing snow covariate. Thus, the use of publicly available, remotely sensed, snow cover products can substantially improve models of animal movement, particularly in cases where movement distances exceed the MODIS 500-m grid threshold. However, remote sensing products may require substantial data thinning due to cloud cover, potentially limiting its power in cases where complex models are necessary. Snowpack evolution models such as SnowModel offer users increased flexibility at the expense of added complexity, but can provide critical insights into fine-scale responses to rapidly changing snow properties.

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Trophic interactions and dynamic herbivore responses to snowpack

Cited by 7 publications

References 39 publications

Elk Responses to Management Hunting and Hazing

Elk Responses to Management Hunting and Hazing

Scaling‐up camera traps: monitoring the planet's biodiversity with networks of remote sensors

Navigating snowscapes: scale‐dependent responses of mountain sheep to snowpack properties

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