North America is currently home to a number of grey wolf (Canis lupus) and wolf-like canid populations, including the coyote (Canis latrans) and the taxonomically controversial red, Eastern timber and Great Lakes wolves. We explored their population structure and regional gene flow using a dataset of 40 full genome sequences that represent the extant diversity of North American wolves and wolf-like canid populations. This included 15 new genomes (13 North American grey wolves, 1 red wolf and 1 Eastern timber/Great Lakes wolf), ranging from 0.4 to 15x coverage. In addition to providing full genome support for the previously proposed coyote-wolf admixture origin for the taxonomically controversial red, Eastern timber and Great Lakes wolves, the discriminatory power offered by our dataset suggests all North American grey wolves, including the Mexican form, are monophyletic, and thus share a common ancestor to the exclusion of all other wolves. Furthermore, we identify three distinct populations in the high arctic, one being a previously unidentified “Polar wolf” population endemic to Ellesmere Island and Greenland. Genetic diversity analyses reveal particularly high inbreeding and low heterozygosity in these Polar wolves, consistent with long-term isolation from the other North American wolves.

Large carnivores are globally threatened due to habitat fragmentation and loss, prey depletion, and human exploitation. Human exploitation includes both legal and illegal hunting and trapping. Protected areas can create refugia from hunting and trapping, however, hunting can still threaten wide-ranging large carnivores when they leave these areas. Large carnivore reintroductions to protected areas are often motivated to restore ecological processes, including wolf reintroduction to Yellowstone National Park (YNP). Determining if harvest is compensatory or additive is essential for informed conservation strategies, as it influences the overall impact on wolf populations and their ecosystems. If the harvest was compensatory, then increasing harvest pressure outside YNP should not decrease overall survival for transboundary wolves. Alternatively, if increasing harvest was additive, then increasing harvest pressure outside YNP should decrease overall survival for transboundary wolves. We tested the effects of variable harvest pressure following delisting on the survival of YNP gray wolves (Canis lupus) from 1995 to 2022. We defined three harvest levels: no harvest, harvest with limited quotas, and unlimited harvest. We used Cox-proportional hazards models and cumulative incidence functions to estimate survival rates, factors affecting survival, and cause-specific mortality between these three harvest periods to test predictions of the additive mortality hypothesis. Most wolves that primarily lived in YNP were harvested adjacent to the park border. Cox-proportional hazards models revealed that mortality was highest during years of unlimited harvest during winter outside YNP. Cause-specific mortality analyses showed that natural mortality from other wolves and harvest were the two leading causes of death, but that harvest mortality had additive effects on wolf mortality. Wolf survival decreased with increased harvest mortality, while natural mortality remained relatively unchanged. High rates of additive harvest mortality of wolves could negatively impact wolf survival in YNP. Harvest mortality of transboundary wolves is additive possibly due to source-sink dynamics of uneven spatial susceptibility of wolves to harvest mortality across protected area borders, as well as effects of harvest on complex social dynamics of wolves in YNP. Transboundary management of large carnivores is challenging, yet cooperation between agencies is vital for wolf management in and around Yellowstone National Park. Our results support the use of small quota zones surrounding protected areas, that minimize transboundary mortality impacts on large carnivores living primarily inside protected areas.

Since 1986, surveys in spring and fall each year count the number of wolves found in Denali National Park and Preserve, north of the Alaska Range.

Wolf harvest numbers and quota numbers by FWP's trapping districts and wolf management unit (WMU) for the current hunting/trapping season in Montana. For display in the Montana Wolf Harvest Dashboard: https://www.arcgis.com/apps/dashboards/e6fb069d45b74034ad85569e5f96ae7a . Data are from the Montana Fish, Wildlife and Parks' mandatory reporting records provided by hunters and trappers, wolf regulations and FWP Commission. Harvest numbers are updated multiple times per day during the hunting/trapping season. This data is also displayed on the wolf harvest status web page: https://myfwp.mt.gov/fwpPub/speciesHuntingGuide?wmrSpeciesCd=GW. More information about wolf hunting and trapping in Montana is available at: https://fwp.mt.gov/hunt/regulations/wolf

Twenty six wolves were captured and radio collared in 1984 and 1985 on the Arctic National Wildlife Refuge. These wolves included members of 8 packs and 11 lone wolves. Average weights were 43.1 kg for males and 36.7 kg for females with the average age being 2-3 years old. Only 5 wolves were 4 years old and older. Activity areas were delinieated for all packs as some packs had insufficient data to accurately define territories. These activity areas were non-overlaping. Only 1 wolf pack had a large scale seasonal shift in areas used. Formation of new packs and long-distance movements were common. One wolf had a documented movement of 770 km, the longest recorded movement in Alaksa. Wolf densities were 1/726 km2 in 1984 and 1/686 km2 in 1985 for an area of 24,700 km2. Litter sizes averaged 3.0 and 4.2-4.75 in 1984 and 1985 respectively. Over-summer pup survival was related to pack size; more pups survived in larger packs. This was in contrast to other studies where pup survival and pack size were unrelated. After wolves had left, den sites were visited, scats were collected, and dens were mapped. Mortality (natural and human induced) was 35% of the fall population. Rabies was documented in the wolf population in the spring on 1985. It is believed that rabies in the wolf population in the arctic is more common than previously thought and may be cyclic in conjunction with outbreaks of rabies in the Arctic fox (Alopex lagopus) population.

We used two census methods to estimate N2020 and N2021 and reproductive parameter R (mean, lower and upper bounds of the 95% CI from [53] for 256 wolf packs. D is estimated as (N2021-N2020) divided by (0.5 * R2020 + N2020) following Eq 3. We assumed the mean value for N2021 because the state did so for setting policy.

1. Although local variation in territorial predator density is often correlated with habitat quality, the causal mechanism underlying this frequently observed association is poorly understood and could stem from facultative adjustment in either group size or territory size. 2. To test between these alternative hypotheses, we used a novel statistical framework to construct a winter population-level utilization distribution for wolves (Canis lupus) in northern Ontario, which we then linked to a suite of environmental variables to determine factors influencing wolf space use. Next, we compared habitat quality metrics emerging from this analysis as well as an independent measure of prey abundance, with pack size and territory size to investigate which hypothesis was most supported by the data. 3. We show that wolf space use patterns were concentrated near deciduous, mixed deciduous/coniferous and disturbed forest stands favoured by moose (Alces alces), the predominant prey species in the diet of wolves in northern Ontario, and in proximity to linear corridors, including shorelines and road networks remaining from commercial forestry activities. 4. We then demonstrate that landscape metrics of wolf habitat quality – projected wolf use, probability of moose occupancy and proportion of preferred land cover classes – were inversely related to territory size but unrelated to pack size. 5. These results suggest that wolves in boreal ecosystems alter territory size, but not pack size, in response to local variation in habitat quality. This could be an adaptive strategy to balance trade-offs between territorial defence costs and energetic gains due to resource acquisition. That pack size was not responsive to habitat quality suggests that variation in group size is influenced by other factors such as intraspecific competition between wolf packs.

1. Top predators have cascading effects throughout the food web but their impacts on scavenger abundance are largely unknown. Gray wolves (Canis lupus) provide carrion to a suite of scavenger species, including the common raven (Corvus corax). Ravens are wide-ranging and intelligent omnivores that commonly take advantage of anthropogenic food resources. In areas where they overlap with wolves, however, ravens are numerous and ubiquitous scavengers of wolf-acquired carrion. 2. We aimed to determine whether subsidies provided through wolves are a limiting factor for raven populations in general and how the wolf reintroduction to Yellowstone National Park in 1995-1997 affected raven population abundance and distribution on the Yellowstone’s Northern Range specifically. 3. We counted ravens throughout Yellowstone’s Northern Range in March from 2009 to 2017 in both human-use areas and wolf habitat. We then used statistics related to the local wolf population and the winter weather condit...

This dataset contains gray wolf (Canis lupus) survival and cause-specific mortality data from radiocollared wolves (n=756 collared-wolf tenures) from 1968-2018 in the USGS Wolf Project study area (2,060 km2) of the Superior National Forest, USA, an area that also includes the Boundary Waters Canoe Area Wilderness. Also, included are the annual resident winter wolf counts for the study area.

The removal or addition of a predator in an ecosystem can trigger a trophic cascade, whereby the predator indirectly influences plants and/or abiotic processes via direct effects on its herbivore prey. A trophic cascade can operate through a density-mediated indirect effect (DMIE), where the predator reduces herbivore density via predation, and/or through a trait-mediated indirect effect (TMIE), where the predator induces an herbivore trait response that modifies the herbivore’s effect on plants. Manipulative experiments suggest that TMIEs are an equivalent or more important driver of trophic cascades than are DMIEs. Whether this applies generally in nature is uncertain because few studies have directly compared the magnitudes of trait- and density-mediated indirect effects on natural unmanipulated field patterns. A TMIE is often invoked to explain the textbook trophic cascade involving wolves (Canis lupus), elk (Cervus canadensis), and aspen (Populus tremuloides) in northern Yellowstone National Park. This hypothesis posits that wolves indirectly increase recruitment of young aspen into the overstory primarily through reduced elk browsing in response to spatial variation in wolf predation risk rather than through reduced elk population density. To test this hypothesis, we compared the effects of spatiotemporal variation in wolf predation risk and temporal variation in elk population density on unmanipulated patterns of browsing and recruitment of young aspen across 113 aspen stands over a 21-year period (1999-2019) in northern Yellowstone National Park. Only two of ten indices of wolf predation risk had statistically meaningful effects on browsing and recruitment of young aspen, and these effects were 8-20 times weaker than the effect of elk density. To the extent that temporal variation in elk density was attributable to wolf predation, our results suggest that the wolf-elk-aspen trophic cascade was primarily density-mediated rather than trait-mediated. This aligns with the alternative hypothesis that wolves and other actively hunting predators with broad habitat domains cause DMIEs to dominate whenever prey, such as elk, also have a broad habitat domain. For at least this type of predator-prey community, our study suggests that risk-induced trait responses can be abstracted or ignored while still achieving an accurate understanding of trophic cascades. Methods Aspen data

Beginning in 1999, we measured young aspen height and browsing at 113 stands selected in a stratified random sample reflecting high and low wolf use areas (see Brice, Larsen, and MacNulty 2022 for details). All stands were selected from aerial photographs taken after the 1988 fires; as such, selected aspen stands were those whose overstory at least partially survived the 1988 fires. Each stand contained a 20-x-1 m belt transect, and we surveyed all young aspen (≤ 600 cm tall, “stems”) within the transect (“plot”) at the end of the growing season (late July – September) each year. For each stem, we measured height of the leader (i.e., tallest stem), and whether the leader was browsed the previous winter. The number of stands sampled each year varied from 61 – 113 (μ = 97.3, σ = 18.3), and the number of plots with stems each year varied from 55 – 108 (μ = 84.9, σ = 17.2). Sampling occurred annually from 1999-2019, excluding 2000 and 2015, resulting in 26,012 stem-level observations over 19 years.

Elk data

Aerial winter surveys of elk were conducted annually using 3-4 fixed wing aircraft simultaneously flying non-overlapping areas between Dec – Mar (see Lemke, Mack, and Houston 1998). In years with no survey (i.e., 2006, 2014), elk counts were interpolated with a state-space model and corrected for the effects of elk group size on sightability (Tallian et al. 2017; B. J. Smith and MacNulty 2023). We divided annual counts of elk within the Park by the study area (995 km2) to calculate annual elk density (number of individuals per km2).

Wolf data

Since 1995, the Yellowstone Wolf Project has studied wolves for two 30-day periods each winter: (1) mid-Nov to mid-Dec (early winter) and (2) the month of March (late winter). Each winter, 20-30 wolves (~35-40% of population) were captured and fitted with VHF and GPS collars (D. W. Smith et al. 2004). All wolves were captured and handled following protocols in accordance with guidelines from the American Society of Mammalogists (Sikes 2016) and approved by the National Park Service Institutional Animal Care and Use Committee (IACUC permit IMR_YELL_Smith_wolves_2012). All wolf packs in northern YNP had at least one collared wolf each year. Locations from both VHF and GPS collars were recorded approximately daily during early and late winter periods, and weekly outside of these periods. GPS collars also recorded hourly locations during each 30-day winter study, and at variable times otherwise. During winter study, ground and aerial crews searched for wolf kills by tracking collared wolves and investigating clusters of locations.

Weather data

We obtained data on SWE at each aspen stand from Daymet, which produced daily gridded estimates of weather parameters from meteorological observations at a 1-km2 resolution (Thornton et al. 2020). We calculated total winter SWE (tons/m2) by summing daily estimates from Nov 1st – Apr 30th at each stand each year. We also obtained data on spring precipitation, which we estimated as the sum of daily precipitation (cm; sum of all forms converted to water-equivalent) from Apr 1st – July 31st, again obtained from Daymet for each stand each year.

Spatiotemporal variation in wolf predation risk

Winter wolf spatial density

We used VHF and GPS locations of wolves in the study area to calculate winter (Nov 1 – Apr 30) wolf density each year and across years. We restricted the data to wolves with at least 30 days of observations, which proved to be highly correlated with the full 6-months of locations (Pearson’s r = 0.99). Additionally, we only used wolves with at least 10 locations per winter, the minimum number of locations needed for the models to converge. After restricting the data, there were 142,087 total locations and 777 unique wolf-year combinations (“wolf-years”) from 1999 – 2019, with wolf-years spanning 30 – 181 days (median = 152 days) and containing 10 – 4,194 locations (median = 42).

To estimate the spatial densities of wolves, we used the locations to fit individual continuous time movement models (CTMM) to each wolf-year using the ctmm package (Calabrese, Fleming, and Gurarie 2016) in R (V1.2.5019, R Core Team 2018). We used the Ornstein-Uhlenbeck Foraging (OUF) anisotropic process for each wolf, which accounts for correlated velocities and restricted space use (C. H. Fleming et al. 2014). Once each wolf had its own CTMM, we calculated an autocorrelated kernel density estimate (AKDE) at a 30-m2 resolution for each wolf-year. If there were multiple collared wolves within a pack, we averaged their AKDEs and divided by the sum of all values to ensure that the AKDE summed to one and could be interpreted as a probability density.

Once we had a single AKDE for each pack each winter, we weighted each pack-specific AKDE by the corresponding number of wolves in each pack (lone wolves unweighted), and then summed the densities of all packs and lone wolves each winter, resulting in a single wolf AKDE each year. Finally, we created a long-term average measure of wolf density by taking the mean of all annual AKDEs. We intersected all spatial layers of risk with the aspen stand locations to derive stand-specific estimates of risk.

Kill Spatial Density

We used positional data of wolf-killed elk to calculate a kernel density estimate (KDE) of elk kills each winter using the sp.kde function from the spatialEco package in R (Evans, Murphy, and Ram 2021). We used a bandwidth of 3 km per the methods of Kohl et al. (2018) and Fortin et al. (2005), and a resolution of 30-m2 (Kauffman et al. 2007; Kohl et al. 2018). We distinguished kills by sex, creating annual KDEs with all kills (N = 2448, Annual range = 61-193), adult male elk and male yearlings (N = 729, Range = 17-69), and adult female elk and calves (N = 1430, Range = 28-125) for each winter. As with wolf density, we also calculated long-term averages of kill density using kills across all years for the three categories.

Topography and vegetation openness

We extracted land-cover type using the Rangeland Analysis Platform (Allred et al. 2021), and calculated openness for each year as the proportion of each 30-m2 cell that was not tree cover. To calculate smoothness, we used a 30-m2 digital elevation model (DEM) and the terrain function from the raster package in R (Hijmans and van Etten 2014), which produced a map of roughness. Roughness was defined as the difference between the maximum and minimum elevation of a cell and its surrounding 8 cells. We converted roughness to smoothness by scaling it from 0-1 and subtracting from 1.

The presence of many pathogens varies in a predictable manner with latitude, with infections decreasing from the equator towards the poles. We investigated the geographic trends of pathogens infecting a widely distributed carnivore: the gray wolf (Canis lupus). We compiled a large serological dataset of nearly 2000 wolves from 17 study areas, spanning 80º longitude and 50º latitude. Generalized linear mixed models were constructed to predict the probability of seropositivity of four important viruses: canine adenovirus, herpesvirus, parvovirus, and distemper virus – and two parasites: Neospora caninum and Toxoplasma gondii.

Canine adenovirus and herpesvirus were the most widely distributed pathogens, whereas N. caninum was relatively uncommon. Canine parvovirus and distemper had high annual variation, with western populations experiencing more frequent outbreaks than eastern populations. Seroprevalence of all infections increased as wolves aged, and denser wolf populations had a greate...

This map depicts IDFG wolf management zones, towns, roads, and hydrography. This format is the most suitable for inclusion in reports or presentations. Please cite appropriately.2013 Idaho Wolf Monitoring Progress Report

Gray wolf suitable habitat for the U.S. and Mexico based on a compilation of habitat models and sources (listed below).Sources:Bennett, L.E. 1994. Colorado Gray Wolf Recovery: A biological feasibility study. Final Report. U.S. Fish and Wildlife Service and University of Wyoming Fish and Wildlife Cooperative research unit, Laramie, Wyoming, USA. Available at: https://babel.hathitrust.org/cgi/pt?id=umn.31951p00672031a;view=1up;seq=146California Department of Fish and Wildlife. 2016b. Potential Suitable Habitat in California. Pages 153-160 in Conservation Plan for Gray Wolves in California Part 2. Carroll, C., Phillips, M.K., Lopez-Gonzalez, C.A., and Schumaker, N.H. 2006. Defining Recovery Goals and Strategies for Endangered Species: The Wolf as a Case Study. BioScience 56(1): 25–37, https://doi.org/10.1641/0006-3568(2006)056[0025:DRGASF]2.0.CO;2Carroll, C. 2003. Impacts of Landscape Change on Wolf Viability in the Northeastern U.S. and Southeastern Canada. Wildlands Project Special Paper No. 5, available at https://www.klamathconservation.org/docs/wolfviabilitypaper.pdf.Carroll, C. 2007. Application of habitat models to wolf recovery planning in Washington. Unpublished report.Defendersof Wildlife. 2006. Places for Wolves: A Blueprint for Restoration and Recovery in the Lower 48 StatesDefenders of Wildlife. 2013. Places for WolvesHarrison, D. J., and T. G. Chapin. 1998. An assessment of potential habitat for eastern timber wolves in the northeastern United States and connectivity with occupied habitat in southeastern Canada. Wildlife Conservation Society, Working Paper Number 7.Harrison, D. J., and T. G. Chapin. 1998. Extent and connectivity of habitat for wolves in eastern North America. Wildlife Society Bulletin 26: 767-775, available at https://wolfology1.tripod.com/id207.htmHearne D., Lewis K., Martin M., Mitton E., and Rocklen C. 2003. Assessing the Landscape: Toward a Viable Gray Wolf Population in Michigan and Wisconsin. Hendricks, S.A., Schweizer, R.M., Harrigan, R.J., Pollinger, J.P., Paquet, P.C., Darimont, C.T., Adams, J.R., Waits, L.P., vonHoldt, B.M., Hohenlohe1, P.A. and R.K. Wayne. 2018. Natural recolonization and admixture of wolves (Canis lupus) in the US Pacific Northwest: challenges for the protection and management of rare and endangered taxa. The Genetics Society. Heredity. https://doi.org/10.1038/s41437-018-0094-x.Jimenez, M.D. et al. 2017. Wolf Dispersal in the Rocky Mountains, Western United States: 1993–2008. The Journal of Wildlife Management 81(4):581–592.Larson, T. and W.J. Ripple. 2006. Modeling Gray Wolf (Canis lupus) habitat in the Pacific Northwest, U.S.A. Journal of Conservation Planning 2:17-33.Maletzke, B.T. and R.B. Wielgus. 2011. Development of wolf population models for RAMAS© analysis by the Washington Department of Fish and Wildlife.Martinez-Meyer E., Gonzalez-Bernal A., Velasco J.A., Swetnam T.L., Gonzalez-Saucedo Z.Y., Servin J., Lopez-Gonzalez C.A., Oakleaf, J.A., Liley S., and Heffelfinger J.R. 2020. Rangewide habitat suitability analysis for the Mexican wolf (Canis lupus baileyi) to identify recovery areas in its historical distribution. Diversity and Distributions 00:1-13.McNab, W.H., Cleland, D.T., Freeouf, J.A., Keys, Jr., J.E., Nowacki, G.J., Carpenter, C.A., comps. 2007. Description of ecological subregions: sections of the conterminous United States [CD-ROM]. Gen. Tech. Report WO-76B. Washington, DC: U.S. Department of Agriculture, Forest Service. 80 p.McNab, W.H. and P.E. Avers. 1995. Ecological subregions of the United States. Washington, DC: U.S. Department of Agriculture, Forest Service, available at https://www.fs.fed.us/land/pubs/ecoregions/.Mladenoff, D.J., Sickley, T.A., Haight, R.G. and Wydeven, A.P. 1995. A Regional Landscape Analysis and Prediction of Favorable Gray Wolf Habitat in the Northern Great Lakes RegionMladenoff, D.J. and T.A. Sickley. 1998. Assessing Potential Gray Wolf Restoration in the Northeastern United States: A Spatial Source. Journal of Wildlife Management 62(1): 1-10.Minnesota Dept. of Natural Resources. 2001. Minnesota Wolf Management Plan. Minnesota Dept. Natural Resources. 2017a. Gray Wolf, available at https://www.dnr.state.mn.us/mammals/wolves/mgmt.html.Montana Fish Wildlife & Parks. 2004. Montana Gray Wolf Conservation and Management Plan.Montana Fish,Wildlife & Parks. 2018. Montana Annual Report 2018: Wolf Conservation and Management.Oakleaf J.K., Murray D.L., Oakleaf J.R., Bangs E.E., Mack C.M., Smith D.W., Fontaine J.A., Jimenez M.D., Meier T.J., and C.C. Niemeyer. 2006. Habitat Selection by Recolonizing Wolves in the Northern Rocky Mountains of the United States. Journal of Wildlife Management 70(2):554-563.Oregon Department of Fish and Wildlife. 2015. Updated mapping potential gray wolf range in Oregon.Potvin M.J., Drummer T.D., Vucetich J.A., Beyer E. Jr., and J.H. Hammill. 2005. Monitoring and Habitat Analysis for Wolves in Upper Michigan. Journal of Wildlife Management 69(4):1660-1669.Treves A., Martin K.A., Wiedenhoeft J.E., Wydeven A.P. (2009) Dispersal of Gray Wolves in the Great Lakes Region. In: Wydeven A.P., Van Deelen T.R., Heske E.J. (eds) Recovery of Gray Wolves in the Great Lakes Region of the United States. Springer, New York, NY. https://doi.org/10.1007/978-0-387-85952-1_12USGS Gap Analysis Project Species Range and Predicted Habitat: Gray wolf: https://gapanalysis.usgs.gov/apps/species-data-download/Washington Dept. of Fish and Wildlife (WDFW). 2017. Washington Gray Wolf Conservation and Management 2017 Annual Report.Wiles, G. J., H. L. Allen, and G. E. Hayes. 2011. Wolf conservation and management plan for Washington. Washington Department of Fish and Wildlife, Olympia, Washington. 297 pp.

Rapid Evolution of Prehistoric Dogs from Wolves by Natural and Sexual Selection Emerges from an Agent-Based Model

Here, we provide the necessary .py files to recreate the results found in the above-entitled manuscript. If you desire to load the data provided, it's recommended you use pandas 2.0.3 and python 3.10.13.

Nearly all .py files will require you have evolutuion_system.py file in the base directory. This .py file enacts the ABM as described in the manuscript. All other .py files should be placed in the same base directory.

You should construct a data folder and a figures folder in the base directory. In the data folder create an efast, prcc, and a monotonicity subfolder. These exists so you do not have to re-run the efast, prcc, or monotonicity simulations. In the figures folder create subfolders for default_distributions, distributions, efast, monotonicity, prcc, validation, and verification. Any figures generated will be produced in the corresponding figures sub-folder. ...

Testing the correlation between abundance trends of wolves and (i) their prey and (ii) human-caused wolf mortality in the southern part of wolf range (packs OSM and RO+KON).

This dataset provides annual gray wolf (Canis lupus) denning spatial information and timing, associated climatic and phenologic metrics, and reproductive success (i.e., pup survival) in wolf populations across areas of western Canada and Alaska within the NASA ABoVE Core Domain. The study encompasses 18 years between the period 2000-2017. Wolves were captured from eight populations following standard animal care protocols and released with Global Positioning System (GPS) collars. Data from 388 wolves were used to estimate den initiation dates (n=227 dens of 106 packs) and reproductive success in the eight populations. Each population was monitored from 1 to 12 years between 2000 and 2017. Denning parturition phenology was measured each year as the number of calendar days from January 1st to the initiation date of each documented denning event. Reproductive success was determined as to whether pups survived through the end of August following a reproductive event. To evaluate the effect of climate factors on reproductive phenology, aggregated seasonal climate metrics for temperature, precipitation, and snow water equivalent based on three biological seasons for seasonal wolf home ranges were produced. Normalized Difference Vegetation Index (NDVI) time-series data were used to estimate phenological metrics such as the start of the growing season (SOS), length of the growing season (LOS), and time-integrated NDVI (tiNDVI), and were summarized for the populations' home range.

Wolves Finder

## Overview

Wolves Finder is a dataset for object detection tasks - it contains Wolves annotations for 551 images.

## Getting Started

You can download this dataset for use within your own projects, or fork it into a workspace on Roboflow to create your own model.

  ## License

  This dataset is available under the [CC BY 4.0 license](https://creativecommons.org/licenses/CC BY 4.0).

This map depicts the home ranges of documented, suspected, and terminated wolf packs located in and near Idaho. Wolf management zones, prominent cities, major roads, major lakes, national forest lands, and wilderness areas are also depicted.The lighter colored packs near the Idaho border are monitored by surrounding states.2015 Idaho Wolf Monitoring Progress Report Click here for more information about wolf management in Idaho.

Nonbreeding helpers can greatly improve the survival of young and reproductive fitness of breeders in many cooperatively breeding species. Breeder turnover, in turn, can have profound effects on dispersal decisions made by helpers. Despite its importance in explaining group size and predicting population demography of cooperative breeders, our current understanding of how individual traits influence animal behavior after disruptions to social structure is incomplete particularly for terrestrial mammals. We used 12 years of genetic sampling and group pedigrees of gray wolves (Canis lupus) in Idaho, USA, to ask questions about how breeder turnover affected the apparent decisions by mature helpers (>2-year-old) to stay or leave a group over a one-year time interval. We found that helpers showed plasticity in their responses to breeder turnover. Most notably, helpers varied by sex and appeared to base dispersal decisions on the sex of the breeder that was lost as well. Male and female helpers stayed in a group slightly more often when there was breeder turnover of the same sex, although males that stayed were often recent adoptees in the group. Males, however, appeared to remain in a group less often when there was breeding female turnover likely because such vacancies were typically filled by related females from the males’ natal group (i.e., inbreeding avoidance). We show that helpers exploit instability in the breeding pair to secure future breeding opportunities for themselves. The confluence of breeder turnover, helper sex, and dispersal and breeding strategies merge to influence group composition in gray wolves.