Personality differences in the selection of dynamic refugia have demographic consequences for a winter-adapted bird

Shipley, Amy A.; Cruz, Jennyffer; Zuckerberg, Benjamin

doi:10.1098/rspb.2020.0609

Cited by 16 publications

(49 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Moreover, it provides cover from avian predators (Heinrich, 2017;Marjakangas, 1990). Therefore, snow roosting has fitness consequences for ruffed grouse because it influences overwinter survival (Shipley et al, 2020). Understanding the effects of snow roosting habitat on ruffed grouse survival would require identification of candidate snow properties with the potential to mediate this relationship.…”

Section: Defining Dataset Specificationsmentioning

confidence: 99%

“…A simple way to produce snow property distribution maps is to spatially interpolate field observations (Figure 5a) of depth, SWE, or other snow variables of interest (Gilbert et al, 2017;Shipley et al, 2020). A widely used interpolation scheme is the inverse distance weighting (IDW) method (Bartier & Keller, 1996;Erxleben et al, 2002;Shepard, 1968).…”

Section: Inverse Distance Weighted Interpolationmentioning

confidence: 99%

“…For wildlife, snow properties can impact individuals by affecting movements and behaviors (Balkenhol et al, 2020; Berman et al, 2019; Boelman et al, 2017; Chimienti et al, 2020; Coady, 1974; Droghini & Boutin, 2018; Mahoney et al, 2018; Oliver et al, 2018; Oliver et al, 2020; Pedersen et al, 2021); predator–prey interactions (Horne et al, 2019; Nelson & Mech, 1986; Peers et al, 2020; Sirén et al, 2021); energetics related to foraging (Dumont et al, 2005; Fancy & White, 1985), locomotion (Fancy & White, 1987; Gurarie et al, 2019; Lundmark & Ball, 2008; Parker et al, 1984), and thermoregulation (Karniski, 2014; Pruitt Jr., 1957; Thompson III & Fritzell, 1988); forage accessibility (Hupp & Braun, 1989; Takatsuki et al, 1995; Visscher et al, 2006; White et al, 2009); as well as ground (Boelman et al, 2016) and subnivean habitat use (Bilodeau et al, 2013; Glass et al, 2021; Petty et al, 2015). Additionally, the effects of snow on individual survival (Hurley et al, 2017; Reinking et al, 2018; Shipley et al, 2020) and reproduction (Apollonio et al, 2013; Barnowe‐Meyer et al, 2011; Liston et al, 2016; Schmidt et al, 2019) can ultimately alter population‐level demographics (Apollonio et al, 2013; Berteaux et al, 2017; Boelman et al, 2019; Cosgrove et al, 2021; Desforges et al, 2021; Van de Kerk et al, 2018; Van de Kerk et al, 2020). Effective evaluation of such wildlife–snow relationships requires identification, acquisition, and incorporation of appropriate and relevant snow property information.…”

Section: Motivationmentioning

confidence: 99%

See 2 more Smart Citations

Collaborative wildlife–snow science: Integrating wildlife and snow expertise to improve research and management

et al. 2022

View full text Add to dashboard Cite

For wildlife inhabiting snowy environments, snow properties such as onset date, depth, strength, and distribution can influence many aspects of ecology, including movement, community dynamics, energy expenditure, and forage accessibility. As a result, snow plays a considerable role in individual fitness and ultimately population dynamics, and its evaluation is, therefore, important for comprehensive understanding of ecosystem processes in regions experiencing snow. Such understanding, and particularly study of how wildlife–snow relationships may be changing, grows more urgent as winter processes become less predictable and often more extreme under global climate change. However, studying and monitoring wildlife–snow relationships continue to be challenging because characterizing snow, an inherently complex and constantly changing environmental feature, and identifying, accessing, and applying relevant snow information at appropriate spatial and temporal scales, often require a detailed understanding of physical snow science and technologies that typically lie outside the expertise of wildlife researchers and managers. We argue that thoroughly assessing the role of snow in wildlife ecology requires substantive collaboration between researchers with expertise in each of these two fields, leveraging the discipline‐specific knowledge brought by both wildlife and snow professionals. To facilitate this collaboration and encourage more effective exploration of wildlife–snow questions, we provide a five‐step protocol: (1) identify relevant snow property information; (2) specify spatial, temporal, and informational requirements; (3) build the necessary datasets; (4) implement quality control procedures; and (5) incorporate snow information into wildlife analyses. Additionally, we explore the types of snow information that can be used within this collaborative framework. We illustrate, in the context of two examples, field observations, remote‐sensing datasets, and four example modeling tools that simulate spatiotemporal snow property distributions and, in some cases, evolutions. For each type of snow data, we highlight the collaborative opportunities for wildlife and snow professionals when designing snow data collection efforts, processing snow remote sensing products, producing tailored snow datasets, and applying the resulting snow information in wildlife analyses. We seek to provide a clear path for wildlife professionals to address wildlife–snow questions and improve ecological inference by integrating the best available snow science through collaboration with snow professionals.

show abstract

Section: Defining Dataset Specificationsmentioning

confidence: 99%

Section: Inverse Distance Weighted Interpolationmentioning

confidence: 99%

Section: Motivationmentioning

confidence: 99%

See 1 more Smart Citation

Collaborative wildlife–snow science: Integrating wildlife and snow expertise to improve research and management

et al. 2022

View full text Add to dashboard Cite

show abstract

“…Grouse were monitored through August 2018. Further details on ruffed grouse capture and monitoring can be found in Shipley et al (2019Shipley et al ( , 2020.…”

Section: Ruffed Grousementioning

confidence: 99%

“…Indications of population declines have occurred synchronously in porcupines and grouse, although the strength of indirect interactions between each of these species and hares may differ. While all three species experience predation from the terrestrial predators, grouse experience more predation from avian predators (Shipley et al, 2020), compared with hares or porcupines (Wilson et al, 2019). Moreover, grouse may experience higher direct costs from climate change, rather than predation, due to increased mortality in shallow snow (<15 cm; Shipley et al, 2020), compared with when snow is absent or sufficiently deep for burrowing (Shipley et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

Experimental repatriation of snowshoe hares along a southern range boundary reveals historical community interactions

Wilson

Zuckerberg

Peery

et al. 2022

Ecological Monographs

Self Cite

View full text Add to dashboard Cite

Climate change is altering interspecific interactions globally, yet community-level responses are difficult to predict due to both the direct and indirect effects of changing abiotic and biotic conditions. Snowshoe hares (Lepus americanus) are particularly vulnerable to decreasing snow cover and resultant camouflage mismatch. This species shares a suite of predators with alternative prey species including porcupines (Erethizon dorsatum) and ruffed grouse (Bonasa umbellus), and all three species historically exhibited synchronized population dynamics. Recently, the community has become partially disassembled, notably with the loss of snowshoe hares and associated enemy-mediated indirect interactions resulting from declining snow duration. Specifically, we hypothesized that the extirpation of hares in the early 1990s indirectly increased predation pressure on ruffed grouse and porcupines. To test our hypothesis, we experimentally translocated 96 snowshoe hares to a site within a regional ecotone between northern and

show abstract

Dynamic winter weather moderates movement and resource selection of wild turkeys at high‐latitude range limits

Gonnerman

Shea

Sullivan

et al. 2022

Ecological Applications

View full text Add to dashboard Cite

For wide‐ranging species in temperate environments, populations at high‐latitude range limits are subject to more extreme conditions, colder temperatures, and greater snow accumulation compared with their core range. As climate change progresses, these bounding pressures may become more moderate on average, while extreme weather occurs more frequently. Individuals can mitigate temporarily extreme conditions by changing daily activity budgets and exhibiting plasticity in resource selection, both of which facilitate existence at and expansion of high‐latitude range boundaries. However, relatively little work has explored how animals moderate movement and vary resource selection with changing weather, and a general framework for such investigations is lacking. We applied hidden Markov models and step selection functions to GPS data from wintering wild turkeys (Meleagris gallopavo) near their northern range limit to identify how weather influenced transition among discrete movement states, as well as state‐specific resource selection. We found that turkeys were more likely to spend time in a stationary state as wind chill temperatures decreased and snow depth increased. Both stationary and roosting turkeys selected conifer forests and avoided land covers associated with foraging, such as agriculture and residential areas, while shifting their strength of selection for these features during poor weather. In contrast, mobile turkeys showed relatively weak resource selection, with less response in selection coefficients during poor weather. Our findings illustrate that behavioral plasticity in response to weather was context dependent, but movement behaviors most associated with poor weather were also those in which resource selection was most plastic. Given our results, the potential for wild turkey range expansion will partly be determined by the availability of habitat that allows them to withstand periodic inclement weather. Combining hidden Markov models with step selection functions is broadly applicable for evaluating plasticity in animal behavior and dynamic resource selection in response to changing weather. We studied turkeys at northern range limits, but this approach is applicable for any system expected to experience significant changes in the coming decade, and may be particularly relevant to populations existing at range peripheries.

show abstract

Personality differences in the selection of dynamic refugia have demographic consequences for a winter-adapted bird

Cited by 16 publications

References 65 publications

Collaborative wildlife–snow science: Integrating wildlife and snow expertise to improve research and management

Collaborative wildlife–snow science: Integrating wildlife and snow expertise to improve research and management

Experimental repatriation of snowshoe hares along a southern range boundary reveals historical community interactions

Dynamic winter weather moderates movement and resource selection of wild turkeys at high‐latitude range limits

Contact Info

Product

Resources

About