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Beavers (Castor canadensis) can be a significant prey item for wolves (Canis lupus) in boreal ecosystems due to their abundance and vulnerability on land. How wolves hunt beavers in these systems is largely unknown, however, because observing predation is challenging. We inferred how wolves hunt beavers by identifying kill sites using clusters of locations from GPS-collared wolves in Voyageurs National Park, Minnesota. We identified 22 sites where wolves from 4 different packs killed beavers. We classified these kill sites into 8 categories based on the beaver-habitat type near which each kill occurred. Seasonal variation existed in types of kill sites as 7 of 12 (58%) kills in the spring occurred at sites below dams and on shorelines, and 8 of 10 (80%) kills in the fall occurred near feeding trails and canals. From these kill sites we deduced that the typical hunting strategy has 3 components: 1) waiting near areas of high beaver use (e.g., feeding trails) until a beaver comes near shore or ashore, 2) using vegetation, the dam, or other habitat features for concealment, and 3) immediately attacking the beaver, or ambushing the beaver by cutting off access to water. By identifying kill sites and inferring hunting behavior we have provided the most complete description available of how and where wolves hunt and kill beavers.

Given recent and abrupt declines in the abundance of moose ( Alces alces ) throughout parts of Minnesota and elsewhere in North America, accurately estimating statewide population trends and demographic parameters is a high priority for their continued management and conservation. Statistical population reconstruction using integrated population models provides a flexible framework for combining information from multiple studies to produce robust estimates of population abundance, recruitment, and survival. We used this framework to combine aerial survey data and survival data from telemetry studies to recreate trends and demographics of moose in northeastern Minnesota, USA, from 2005 to 2020. Statistical population reconstruction confirmed the sharp decline in abundance from an estimated 7,841 (90% CI = 6,702–8,933) in 2009 to 3,386 (90% CI = 2,681–4,243) animals in 2013, but also indicated that abundance has remained relatively stable since then, except for a slight decline to 3,163 (90% CI = 2,403–3,718) in 2020. Subsequent stochastic projection of the population from 2021 to 2030 suggests that this modest decline will continue for the next 10 years. Both annual adult survival and per-capita recruitment (number of calves that survived to 1 year per adult female alive during the previous year) decreased substantially in years 2005 and 2019, from 0.902 (SE = 0.043) to 0.689 (SE = 0.061) and from 0.386 (SE = 0.030) to 0.303 (SE = 0.051), respectively. Sensitivity analysis revealed that moose abundance was more sensitive to fluctuations in adult survival than recruitment; thus, we conclude that the steep decline in 2013 was driven primarily by decreasing adult survival. Our analysis demonstrates the potential utility of using statistical population reconstruction to monitor moose population trends and to identify population declines more quickly. Future studies should focus on providing better estimates of per-capita recruitment, using pregnancy rates and calf survival, which can then be incorporated into reconstruction models to help improve estimates of population change through time.

Wolves (Canis lupus) are opportunistic predators and will capitalize on available abundant food sources. However, wolf diet has primarily been examined at monthly, seasonal, or annual scales, which can obscure short-term responses to available food. We examined weekly wolf diet from late June to early October by collecting scats from a single wolf pack in northeastern Minnesota. During our 15 wk study, nonungulate food types constituted 58% of diet biomass. Deer (Odocoileus virginianus) fawns were a major food item until mid-July after which berries (primarily Vaccinium and Rubus spp.) composed 56–83% of weekly diet biomass until mid-August. After mid-August, snowshoe hares (Lepus americanus) and adult deer were the primary prey. Weekly diet diversity approximately doubled from June to October as wolves began using several food types in similar proportions as the summer transitioned into fall. Recreational hunting of black bears (Ursus americanus) contributed to weekly wolf diet in the fall as wolves consumed foods from bear bait piles and from gut piles/carcasses of successfully harvested or fatally wounded bears. To our knowledge, we are the first to examine wolf diet via scat analysis at weekly intervals, which enabled us to provide a detailed description of diet plasticity of this wolf pack, as well as the rapidity with which wolves can respond to new available food sources.

Intraspecific competition plays an important role for territory acquisition and occupancy, in turn affecting individual fitness. Thus, understanding the drivers of intraspecific aggression can increase our understanding of population dynamics. Here, we investigated intraspecific aggression in Eurasian (Castor fiber) and North American (Castor canadensis) beavers that are both monogamous, territorial mammals. Combined, we examined tail scars from >1,000 beavers (>2,000 capture events) as part of two long‐term studies in Norway and the USA. We investigated the influence of landscape structure, population density, sex, age, and (for Eurasian beavers only) social status and group size on the number of tail scars caused by conspecifics. The number of tail scars was affected by population density in well‐connected landscape types (large lakes and rivers), but not in more isolated areas (ponds), where individuals generally had fewer tail scars. Further, the relationship of population density was not linear. In the North American beaver population occurring in large lakes, intraspecific aggression increased with population density. Conversely, in the saturated Eurasian beaver population, intraspecific aggression was in a negative relationship with population density (except at the highest densities), likely due to inverse density‐dependent intruder pressure via dispersers. Our findings emphasize that population density can affect intraspecific aggression depending on landscape structure, which might have important consequences for local patterns of dispersal, mate change, and territory occupancy, all of which can affect population dynamics. We investigated intraspecific aggression in beavers using tail scars from >1,000 individuals. The number of tail scars was affected by population density in well‐connected landscape types (large lakes and rivers), but not in more isolated areas (ponds), where individuals generally had fewer tail scars. Our findings emphasize that population density can affect intraspecific aggression depending on landscape structure, which might have important consequences for local patterns of dispersal, mate change, and territory occupancy, all of which can affect population dynamics.

Locating wolf ( Canis lupus ) homesites is valuable for understanding the foraging behavior, population dynamics, and reproductive ecology of wolves during summer. During this period wolf pack members (adults and pups) readily respond to simulated wolf howls (i.e., howl surveys), which allows researchers to estimate the location of the homesite via triangulation. Confirming the actual locations of homesites via ground truthing is labor intensive because of the error surrounding estimated locations. Our objectives were (1) to quantify observer error during howl surveys and compare amongst experience levels, (2) provide a simple method for locating homesites in the field by incorporating observer error, and (3) further document the value of this method for monitoring wolf packs throughout the summer. We located 17 homesites by howl surveys during 2015–2017 in the Greater Voyageurs Ecosystem, Minnesota, USA. Of 62 bearings taken by observers during howl surveys, bearings erred by an average of 7.6° ± 6.3° (SD). There was no difference in observer error between novice and experienced observers. A simple way to increase efficiency when searching for homesites is to search concentric areas (bands) based on estimated observer error, specifically by: (1) adding ±10° error bands around howl survey bearings when ≥3 bearings can be obtained, (2) ±10° and ±20° error bands when 2 bearings are obtained, and (3) ±10° and ±26° error bands when 1 bearing is obtained. By incorporating observer error and understanding how frequently and how far wolves move homesites, it is possible to monitor wolf packs and confirm most, if not all, homesites used by a pack from at least June until August without having a collared individual in a pack.

Wolves (Canis lupus) can be primary predators of beavers (Castor canadensis), but little is known about wolf-beaver dynamics. We identified kills from 1 wolf (V009) of the Ash River Pack in Voyageurs National Park from 1 April to 5 November 2015 to provide direct estimates of wolf pack kill and predation rates of beavers. We documented 12 beaver kills by V009 during the 2015 ice-free season and estimated V009 killed 22 beavers during this period. Based on the number of beavers killed by V009, we estimated the Ash River Pack removed 80–88 beavers (kill rate of 0.085–0.095 beavers/wolf/day), which was 38–42% of the beaver population in their home range during the ice-free season. Even with this substantial level of predation in 2015, the beaver population in the Ash River Pack home range increased by an estimated 43% in 2016, which suggested dispersal from more densely populated adjacent areas likely compensated for the effects of wolf predation. We have presented the first direct estimate of wolf kill and predation rates on beavers, but more research is necessary to understand how wolf predation affects beaver populations under a variety of conditions.

ContextInterspecific competition can limit species distributions unless competitors partition niche space to enable coexistence. Landscape heterogeneity can facilitate niche partitioning and enable coexistence, but land-use change is restructuring terrestrial ecosystems globally with unknown consequences for species interactions.ObjectivesWe tested the relationship between landscape heterogeneity and carnivore co-occurrence in natural and human-dominated ecosystems to assess the landscape-mediated impacts of anthropogenic change on coexistence.MethodsWe used boosted regression trees to model the distributions and co-occurrence of two competing forest carnivores, American martens and fishers, at two contrasting sites in the Great Lakes region, USA: a “natural” site largely devoid of human impacts and a “human-dominated” site with substantial development and a history of land-use change. We assessed the importance of climate and habitat variables for each species, measured spatial niche overlap, and quantified co-occurrence as a function of compositional (patch richness), configurational (landscape shape), and topographical (elevation range) heterogeneity per site.ResultsWe observed significant differences in the effect of heterogeneity on co-occurrence between sites. The natural landscape exhibited little niche overlap and co-occurrence had a significant, positive relationship with heterogeneity. Conversely, the human-dominated site exhibited high niche overlap with variable effects of heterogeneity on co-occurrence. Elevation, snowpack, and development had strong, contrasting effects on marten and fisher distributions, suggesting that differential use of habitat and anthropogenic features facilitates coexistence.ConclusionsHeterogeneity can facilitate coexistence, but too much may undermine carnivore coexistence in human-dominated landscapes where habitat and space are limited. Moreover, future climate change will likely erode niche partitioning among martens and fishers, with particularly strong consequences for coexistence in human-dominated landscapes and at range boundaries.

Like many ecological processes, natural disturbances exhibit scale‐dependent dynamics that are largely a function of the magnitude, frequency and scale at which they are assessed. Ecosystem engineers create patch‐scale disturbances that affect ecological processes, yet we know little about how these effects scale across space or vary through time. Here, we investigate how patch disturbances by beavers Castor canadensis, ecosystem engineers renowned for their pond‐creation behavior, affect ecological processes across space and time. We evaluated how beaver population recovery influenced surface water dynamics in relation to population density over 70 years across multiple spatial scales (pond, watershed and regional) in northern Minnesota. Surface water area was positively related to population density at the watershed scale; however, despite variation in beaver densities (and therefore surface water area) at the watershed scale, regional‐scale surface water area was stable through time. This stability appears to have been driven by asynchronous beaver density fluctuations among watersheds, combined with the increasing importance of abandoned ponds. Beavers initially created and occupied larger ponds with greater surface water area, but through time shifted towards occupying smaller ponds. As ponds accumulated on the landscape proportionally more surface water was stored within abandoned ponds, which offset the smaller size of occupied ponds. Beaver engineering – driven by density‐dependent mechanisms and the legacy effects from abandoned ponds – not only follows general patterns of patch disturbance dynamics by creating a spatial mosaic of patches, but the organism‐created mosaic also appears to generate ecological stability at greater spatial scales. We suggest restoring beavers to landscapes is a viable method for increasing surface water storage and will ultimately help advance numerous conservation and rewilding objectives. Our study demonstrates that ecosystem engineering effects can be scale‐dependent, indicating researchers should evaluate the ecological impact of engineers across diverse spatiotemporal scales to fully understand their functional roles in ecosystems.

Many reptile species are in decline and turtles are especially susceptible. In Massachusetts, eastern box turtle (Terrapene Carolina Carolina) population densities are critically low, and they are listed as a Species of Special Concern. To aid in the conservation of this species, we developed a statewide population monitoring program to track large-scale population trends. We used GENPRES3 to identify the most efficient sampling design a priori. Using this design, we performed visual surveys in 2010–2012 and used site occupancy models to evaluate baseline occupancy and abundance data. We surveyed 62 4-ha monitoring plots within early successional and forest edge habitat where box turtles congregate in the spring for foraging, mating, nesting, and thermoregulation. We also used radio-telemetry at 2 survey sites to evaluate assumptions and further assess occupancy rates, detection estimates, and population size. The best fit Royle–Nichols model predicted a probability of box turtle occupancy of 0.81 ± 0.10 (mean ± SE) and a mean probability of detection of 0.29 ± 0.18. Roads and vegetation density were important covariates affecting the probability of occurrence. Survey start time, humidity, and surveyor were important covariates affecting detection probability. A power analysis indicated that we could detect a 10% decline in occupancy between 5-year sampling rounds within 15 years. The proportion of radio-tagged turtles inside the survey plots during surveys was relatively constant at each site (0.44–0.63 and 0.36–0.43), mean detection rate was 0.35 ± 0.10, and the total estimated population size of the 2 survey plots (8 ha total) was 13.31 ± 1.53. Our results can be used to track the status of this rare species as well as guide conservation actions and evaluate the effectiveness of site-specific and statewide management plans. Our approach and design can serve as a model for other states developing monitoring programs for the eastern box turtle and other similar, rare and difficult to detect species. Published 2015. This article is a U.S. Government work and is in the public domain in the USA.

Over the past two decades, there have been numerous calls to make ecology a more predictive science through direct empirical assessments of ecological models and predictions. While the widespread use of model selection using information criteria has pushed ecology toward placing a higher emphasis on prediction, few attempts have been made to validate the ability of information criteria to correctly identify the most parsimonious model with the greatest predictive accuracy. Here, we used an ecological forecasting framework to test the ability of information criteria to accurately predict the relative contribution of density dependence and density-independent factors (forage availability, harvest, weather, wolf [Canis lupus] density) on inter-annual fluctuations in beaver (Castor canadensis) colony densities. We modeled changes in colony densities using a discrete-time Gompertz model, and assessed the performance of four models using information criteria values: density-independent models with (1) and without (2) environmental covariates; and density-dependent models with (3) and without (4) environmental covariates. We then evaluated the forecasting accuracy of each model by withholding the final one-third of observations from each population and compared observed vs. predicted densities. Information criteria and our forecasting accuracy metrics both provided strong evidence of compensatory density dependence in the annual dynamics of beaver colony densities. However, despite strong within-sample performance by the most complex model (density-dependent with covariates) as determined using information criteria, hindcasts of colony densities revealed that the much simpler density-dependent model without covariates performed nearly as well predicting out-of-sample colony densities. The hindcast results indicated that the complex model over-fit our data, suggesting that parameters identified by information criteria as important predictor variables are only marginally valuable for predicting landscape-scale beaver colony dynamics. Our study demonstrates the importance of evaluating ecological models and predictions with long-term data and revealed how a known limitation of information criteria (over-fitting of complex models) can affect our interpretation of ecological dynamics. While incorporating knowledge of the factors that influence animal population dynamics can improve population forecasts, we suggest that comparing forecast performance metrics can likewise improve our knowledge of the factors driving population dynamics.