description: 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.; abstract: 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.

Data and R code for "Competition, prey, and mortalities influence gray wolf group size" by Sells et al. (2022, Journal of Wildlife Management). The datasets can be used with the included R code to re-create analyses and figures from Sells et al. (2022). The metadata file describes each column in the datasets.

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.

Wolves 2

## Overview

Wolves 2 is a dataset for object detection tasks - it contains Wolves annotations for 381 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).

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.

Data file containing spatial variables of wolf GPS-positions and random points for step selection functions that is used in the article “Wolves at the door? Factors influencing the individual behavior of wolves in relation to anthropogenic features”. Abstract: The recovery of large carnivores in human-dominated landscapes comes with challenges. In general, large carnivores avoid humans and their activities, and human avoidance favors coexistence, but individual variation in large carnivore behavior may occur. The detection of individuals close to human settlements or roads can trigger fear in local communities and in turn demand management actions. Understanding the sources of individual variation in carnivore behavior towards human features is relevant and timely for ecology and conservation. We studied the movement behavior of 52 adult established wolves (44 wolf pairs) with GPS-collars over two decades in Scandinavia in relation to settlements, buildings, and roads. We fit fine-scale movement data to individual step selection functions to depict the movement decisions of wolves while travelling, and then used weighted linear mixed models to identify factors associated with potential individual pair deviations from the general behavioral patterns. Wolves consistently avoided human settlements and main roads, with little individual variation. Indeed, after correcting for season, time of the day, and latitude, there was little variability in habitat selection among wolf pairs, demonstrating that all wolf pairs had similar movement pattern and generally avoided human features of the landscape. Wolf avoidance of human features was lower at higher latitudes particularly in winter, likely due to seasonal prey migration. Although occasional sightings of carnivores or their tracks near human features do occur, they do not necessarily require management intervention. Communication of scientific findings on carnivore behavior to the public should suffice in most cases.

This dataset includes information on deer-vehicle collisions, vehicle miles traveled, weather, and deer, wolf, and human populations, for counties in Wisconsin from 1981-2016.

Methods These data are described in the README.txt file. A full replication package for the paper associated with this data is available at: https://github.com/jennifer-raynor/wolvs_and_DVCs.

The continuous growth of the global human population results in increased use and change of landscapes, with infrastructures like transportation or energy facilities, being a particular risk to large carnivores. Environmental Impact Assessments were established to identify the probable environmental consequences of any new proposed project, find ways to reduce impacts, and provide evidence to inform decision making and mitigation. Portugal has a wolf population of around 300 individuals, designated as an endangered species with full legal protection. They occupy the northern mountainous areas of the country which has also been the focus of new human infrastructures over the last 20 years. Consequently, dozens of wolf monitoring programs have been established to evaluate wolf population status, to identify impacts, and to inform appropriate mitigation or compensation measures. We reviewed Portuguese wolf monitoring programs to answer four key questions: do wolf programs examine adequate ..., We reviewed all major wolf monitoring programs developed for environmental impact assessments in Portugal since 2002 (Table S1, Supplementary material). Given that the focus here is on the adequacy of targeted wolf monitoring for delivering conclusions about the effects of infrastructure development, we reviewed only monitoring programs that were specifically designed for wolves and not those concerned with general mammalian assessment. The starting point was a compilation from the 2019-2021 National Wolf Census (Pimenta et al., 2023), where every wolf monitoring program that occurred between 2014 and 2019 in Portugal was identified. The list was completed with projects that started before 2014 or after 2019 based on personal knowledge, inquires to principal scientific teams, governmental agencies, and EIA consultants. Depending on duration, wolf monitoring programs can produce several, usually annual, reports that are not peer-reviewed and do not appear on standard search engines (e.g...., , # Environmental impact assessment and large carnivores: a methodological review of the wolf (Canis lupus) monitoring in Portugal.

This is a compilation of 30 wolf monitoring programs for Environmental Impact Assessment that occurred in Portugal between 2002 and 2022.

We characterized and quantified four components of each wolf monitoring program, namely: (1) biological parameters, i.e., what wolf variables were meant to be studied to assess impacts; (2) study design, i.e., what schemes were followed to collect data in the field; (3) data collection, i.e., which methodologies and effort was used to collect data from the field; (4) data analysis, i.e., how data from the field were used to estimate relevant parameters and assess impacts on the species.

• The Tab "Projects_Review" corresponds to a descriptive breakdown of the reviewed projects.
• The Tab "Data" corresponds to a numerical breakdown of the reviewed projects, used in descriptive statistics and graphs. In the "...

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.

In 2023/24, there were 209 wolf packs counted in Germany. The numbers have been constantly increasing since 2013 and 2023/24, saw the highest number of packs.

This map depicts IDFG wolf management zones, towns, roads, and hydrography.2013 Idaho Wolf Monitoring Progress Report

Land use and climate alter species distributions worldwide, and detecting and understanding how species ranges shift can facilitate conservation planning and action. Following extirpation from most of the contiguous USA, gray wolves (Canis lupus) have partially recolonized former range in the western Great Lakes region, but it is unknown how land use and climate change may alter amounts of wolf habitat. Using wolf observation data collected during winters 2017–2020 in Minnesota, Wisconsin, and Michigan, we created ensemble models to predict how land use and climate change may affect the amount of wolf habitat within these states. A projection model for the western Great Lakes region suggested three of four scenarios of land use and climate change will lead to 9–35% increases in wolf habitat, while a solely climate-based projection model supported our expectation that changes in climate, in isolation, will have limited effect on current wolf range. Our results support stable or increasing amounts of wolf habitat in the western Great Lakes region during the 21st century, suggesting limited or no adverse effects on the current distribution or further recolonization of wolves. Our findings can inform policy development regarding wolf conservation, and identify areas where recolonization is plausible, thus where promoting human-wolf co-existence is most pertinent. Methods The files '01_process_landuse_data.R' and '02_fit_landuse_model.R', along with supporting code in 'plot_functions.R', can be used to run the land use model in the paper. Data for this analysis includes wolf presence coordinates (at 0.25-degree resolution) for Michigan and Minnesota in the file 'wolf_presence_coordinates.csv'. Raw species presence data are not publicly available as the subject species (Gray wolf) is federally protected under the Endangered Species Act, and subject to poaching within the study area. Data for Wisconsin cannot be made publicly available due to legal restrictions, but data can be obtained by qualified researchers through the Wisconsin Department of Natural Resources (david.macfarland@wisconsin.gov). See the R files for information on how to download the LUH2 data. The file '03_fit_climate_model.R' fits the climate model and uses the historic wolf range shapefile found in the 'wolf_historic_range' folder. See the R file for information on how to download the corresponding climate data.

We assessed changes in the population size, density, and diet composition of wolves inhabiting the Romincka Forest (RF), an area of 480 km2 situated along the state border between Poland, Russian Federation (Kaliningrad), and Lithuania. We compared the results of our research in 2020-2021 with data from other projects conducted since 1999. We found that both packs living in RF had transboundary territories. The number of packs was stable over 21 years, the average pack size almost doubled (from 4-4.5 to 7.5-8 wolves per pack), the total wolf number increased 1.8 times, reaching 15-16 wolves, the density increased 1.5 times up to 3.1-3.3 wolves/100 km2 in winter 2020/2021. Our analyses of 165 scats revealed that beavers Castor fiber made up 45.6% of food biomass in the wolf diet in 2020, which was 3.4 times more than in 1999-2004 (n=84 scats, 13.4%). Wild ungulates constituted 44.8% of the wolf food biomass in 2020, 1.6 times less than before (71.1%). In our study, among wild ungulates, ..., Tracking. We tracked wolves by foot or by car, using the regular and dense network of dirt roads, routes, and other linear structures, and the plowed strip of soil along the borderline, across the whole Polish portion of RF, that wolves used for traveling and scent-marking. In snow-free seasons, we found tracks on mud or sand and followed them as far as were visible, usually at distances of 100-300 m, while in winter, snow cover allowed us to follow wolf tracks up to 10 km. Species identification was based on the shape and size of tracks and evidence of animal behavior during scent-marking. Additionally, track identification was verified with genetic analysis of scat and urine samples collected during tracking. In winter, we estimated the number of wolves in the tracked group on snow by counting the number of individual trails when wolves split, which usually happened on road junctions and was associated with intense scent-marking. We measured the length of the footprint of the front pa..., , # Dataset for paper: Wolves in the borderland – changes in population and wolf diet in Romincka Forest, along the Polish-Russian-Lithuanian state borders

The dataset provides data to assess the wolf numbers and diet in the Romincka Forest in northern Poland.

Description of the data and file structure

Data are grouped into three files:

Nowak_Repository_genotyping.txt. Results of genetic fingerprinting based on 13 DNA microsatellite markers for non-invasive samples found during the fieldwork in the Romincka Forest, along with reference samples from Baltic, Central European, and Carpathian wolf subpopulations. This is a TAB-separated file that contains the following columns:

(1) ID - identification number of the sample;

(2) sex - sex of the individual based on the analysis of DBX intron 6 and DBY intron 7;

Followed by columnes with numerical data for allele sizes of 13 polymorphic microsatellite loci: FH2001, FH2010, FH2017, FH2054, FH2087L, FH2088, FH2096, FH2137, FH2140,...

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).

The wolf population in Riding Mountain National Park is monitored through track counts that are conducted each winter according to methods established by Canadian Wildlife Service in the 1970’s. Wolves are the top predator in Riding Mountain National Park and monitoring their numbers assists in determining their long-term sustainability in the park.

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. ...

Deriving mean age of primiparity from Superior National Forest wolf breeder data in Table 3.

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.

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.