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.

Harvest of Transboundary Gray Wolves from Yellowstone National Park is Largely Additive

https://doi.org/10.5061/dryad.x3ffbg7tc

These data are the files needed to reproduce results in Cassidy et al. (2024), including survival modeling and cumulative incidence function results.

Description of the data and file structure

Biological Year: biological year starting on September 1 and ending on August 31 of the following year

Wolf Count: the number of Yellowstone National Park resident wolves on December 31

Wolf ID: unique wolf ID number

Color: coat color of wolf, only black or gray

Date: date of capture, GPS location, VHF visual location, or mortality

Mortality Cause: cause of mortality (details of mortality causes can be found in the manuscript)

Entry Type: type of entry (Capture = capture of wolf, GPS = location from GPS collar [one random per day], Mortality = death of wolf, VHF = location from VHF signal with visual confirmation ...

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.

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: Montana Wolf Harvest Dashboard (arcgis.com). 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

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 conditions to model raven abundance during our study period and predict raven abundance on the Northern Range both before and after the wolf reintroduction. 4. In relatively severe winters with greater snowpack, raven abundance increased in areas of human use and decreased in wolf habitat. When wolves were able to acquire more carrion, however, ravens increased in wolf habitat and decreased in areas with anthropogenic resources. Raven populations prior to the wolf reintroduction were likely more variable and heavily dependent on ungulate winter-kill and hunter-provided carcasses. 5. The wolf recovery in Yellowstone helped stabilize raven populations by providing a regular food supply, regardless of winter severity. This stabilization has important implications for effective land management as wolves recolonize the west and global climate patterns change.

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.

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 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.

Recovering populations of carnivores suffering Allee effects risk extinction because positive population growth requires a minimum number of cooperating individuals. Conservationists seldom consider these issues in planning for carnivore recovery because of data limitations, but ignoring Allee effects could lead to overly optimistic predictions for growth and underestimates of extinction risk. We used Bayesian splines to document a demographic Allee effect in the time series of gray wolf (Canis lupus) population counts (1980–2011) in the southern Lake Superior region (SLS, Wisconsin and the upper peninsula of Michigan, USA) in each of four measures of population growth. We estimated that the population crossed the Allee threshold at roughly 20 wolves in four to five packs. Maximum per-capita population growth occurred in the mid-1990s when there were approximately 135 wolves in the SLS population. To infer mechanisms behind the demographic Allee effect, we evaluated a potential component Allee effect using an individual-based spatially explicit model for gray wolves in the SLS region. Our simulations varied the perception neighborhoods for mate-finding and the mean dispersal distances of wolves. Simulation of wolves with long-distance dispersals and reduced perception neighborhoods were most likely to go extinct or experience Allee effects. These phenomena likely restricted population growth in early years of SLS wolf population recovery.

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. ...

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

Admixture resulting from natural dispersal processes can potentially generate novel phenotypic variation that may facilitate persistence in changing environments or result in the loss of population-specific adaptations. Yet, under the US Endangered Species Act, policy is limited for management of individuals whose ancestry includes a protected taxon; therefore, they are generally not protected under the Act. This issue is exemplified by the recently re-established grey wolves of the Pacific Northwest states of Washington and Oregon, USA. This population was likely founded by two phenotypically and genetically distinct wolf ecotypes: Northern Rocky Mountain (NRM) forest and coastal rainforest. The latter is considered potentially threatened in southeast Alaska and thus the source of migrants may affect plans for their protection. Genetic analysis revealed that the Washington wolves share ancestry with both wolf ecotypes, whereas the Oregon population shares ancestry with NRM forest wolves only. Using ecological niche modelling, we found that the Pacific Northwest states contain environments suitable for each ecotype, with wolf packs established in both environmental types. Continued migration from coastal rainforest and NRM forest source populations may increase the genetic diversity of the Pacific Northwest population. However, this admixed population challenges traditional management regimes given that admixture occurs between an adaptively distinct ecotype and a more abundant reintroduced interior form. Our results emphasize the need for a more precise US policy to address the general problem of admixture in the management of endangered species, subspecies, and distinct population segments.

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).

Fecal prevalence (number of detected infections/number of samples) and apparent maximum prevalence (number of infected wolves/number of unique wolves) in northern Yellowstone wolves years 2018–2020.

In 2023/24, there were ** wolf couples counted in Germany. This was the highest figure since 2020/21.

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.

Percentage of female wolves excluding pups and yearlings that bred in various populations.