Challenges and opportunities for estimating abundance of a low-density moose population

Monitoring large herbivores across their core range has been readily accomplished using aerial surveys and traditional distance sampling. But for peripheral populations, where individuals may occur in patchy, low-density populations, precise estimation of population size and trend remains logistically and statistically challenging. For moose (Alces alces) along their southern range margin in northern New York, USA, we sought robust estimates of moose distribution, abundance, and population trend (2016–2019) using a combination of aerial surveys (line transect distance-sampling), repeated surveys in areas where moose were known to occur to boost the number of detections, and density surface modeling (DSM) with spatial covariates. We achieved a precise estimate of density (95% CI = 0.00–0.29 moose/km²) for this small population (656 moose, 95% CI = 501–859), which was patchily distributed across a large and heavily forested region (the 24,280-km² Adirondack Park). Local moose abundance was positively related to active timber management, elevation, and snow cover, and negatively related to large bodies of water. As expected, moose abundance in this peripheral population was low relative to its core range in other northern forest states. Yet, in areas where abundance was greatest, moose densities in New York approached those where epizootics of winter tick (Dermacentor albipictus) have been reported, underscoring the need for effective and efficient monitoring. By incorporating autocorrelation in observations and landscape covariates, DSM provided spatially explicit estimates of moose density with greater precision and no additional field effort over traditional distance sampling. Combined with repeated surveys of areas with known moose occurrence to achieve viable sample sizes, DSM is a useful tool for effectively monitoring low density and patchy populations.     

Managing for Elevated Yield of Moose in Interior Alaska

ABSTRACT Given recent actions to increase sustained yield of moose (Alces alces) in Alaska, USA, we examined factors affecting yield and moose demographics and discussed related management. Prior studies concluded that yield and density of moose remain low in much of Interior Alaska and Yukon, Canada, despite high moose reproductive rates, because of predation from lightly harvested grizzly (Ursus arctos) and black bear (U. americanus) and wolf (Canis lupus) populations. Our study area, Game Management Unit (GMU) 20A, was also in Interior Alaska, but we describe elevated yield and density of moose. Prior to our study, a wolf control program (1976–1982) helped reverse a decline in the moose population. Subsequent to 1975, moose numbers continued a 28-year, 7-fold increase through the initial 8 years of our study (λ_B1 = 1.05 during 1996–2004, peak density = 1,299 moose/1,000 km²). During these initial 8 hunting seasons, reported harvest was composed primarily of males ( = 88%). Total harvest averaged 5% of the prehunt population and 57 moose/1,000 km², the highest sustained harvest-density recorded in Interior Alaska for similar-sized areas. In contrast, sustained total harvests of <10 moose/1,000 km² existed among low-density, predator-limited moose populations in Interior Alaska (≤417 moose/1,000 km²). During the final 3 years of our study (2004–2006), moose numbers declined (λ_B2 = 0.96) as intended using liberal harvests of female and male moose ( = 47%) that averaged 7% of the prehunt population and 97 moose/1,000 km². We intentionally reduced high densities in the central half of GMU 20A (up to 1,741 moose/1,000 km² in Nov) because moose were reproducing at the lowest rate measured among wild, noninsular North American populations. Calf survival was uniquely high in GMU 20A compared with 7 similar radiocollaring studies in Alaska and Yukon. Low predation was the proximate factor that allowed moose in GMU 20A to increase in density and sustain elevated yields. Bears killed only 9% of the modeled postcalving moose population annually in GMU 20A during 1996–2004, in contrast to 18–27% in 3 studies of low-density moose populations. Thus, outside GMU 20A, higher bear predation rates can create challenges for those desiring rapid increases in sustained yield of moose. Wolves killed 8–15% of the 4 postcalving moose populations annually (10% in GMU 20A), hunters killed 2–6%, and other factors killed 1–6%. Annually during the increase phase in GMU 20A, calf moose constituted 75% of the predator-killed moose and predators killed 4 times more moose than hunters killed. Wolf predation on calves remained largely additive at the high moose densities studied in GMU 20A. Sustainable harvest-densities of moose can be increased several-fold in most areas of Interior Alaska where moose density and moose: predator ratios are lower than in GMU 20A and nutritional status is higher. Steps include 1) reducing predation sufficient to allow the moose population to grow, and 2) initiating harvest of female moose to halt population growth and maximize harvest after density-dependent moose nutritional indices reach or approach the thresholds we previously published.     

Space Use and Habitat Selection by Bobcats in the Fragmented Landscape of South-Central Iowa

Abstract: Historically, bobcats (Lynx rufus) were found throughout the Corn Belt region, but they nearly disappeared from this area due to habitat loss and unregulated harvest that occurred during the century after European settlement. Reports of bobcat occurrences have been increasing in Iowa, USA, and biologists would like to understand the mechanisms enabling bobcats to recolonize this fragmented agricultural landscape. We determined space use and habitat selection of bobcats by radiocollaring 68 bobcats in south-central Iowa during 2003–2006. We triangulated 12,966 locations and recovered an additional 1,399 3-dimensional locations from Global Positioning System collars. We used a fixed kernel estimator to calculate 95% utilization distributions (UDs) for home ranges and 50% UDs for cores. Annual home range area of males (x̄ = 58.6 km², 95% CI = 49.2–69.9) was nearly 3 times that of females (x̄=19.9 km², 95% CI = 17.0–23.3). Females used smaller home ranges during April-September when they were suspected to have kittens with them (x̄ = 16.8 km², 95% CI = 13.7–20.7), as compared to October-March (x̄ = 24.1 km², 95% CI = 19.0–30.7), whereas home ranges of males did not differ between seasons. Similarly, core area of males (x̄ = 7.7 km², 95% CI = 6.2–9.6) was larger than that of females (x̄ = 2.3 km², 95% CI = 1.9–2.7). Females used significantly smaller cores in April-September (x̄ = 1.8 km², 95% CI = 1.4–2.3) as compared to October-March (x̄ = 2.8 km², 95% CI = 2.2–3.7), whereas males did not. For both sexes, compositional analysis indicated that forest habitat was ranked higher than all other habitat classes at both the landscape and local scale. Standardized habitat selection ratios illustrate that female and male bobcats selected forest habitat about twice as frequently as any other habitat class, including grassland and Conservation Reserve Program land. Predictive models indicated that home range and core area was smaller in landscapes where perennial forest and grassland habitats were less fragmented. Predictive models indicated home ranges were more irregular in shape in landscapes where row crop patches were less aggregated within home ranges. Our results have practical implications for wildlife managers regarding expected bobcat habitat use and distribution as the species becomes more abundant in the agricultural landscape of the Midwest.     

Leopard density in post-conflict landscape,Cambodia: Evidence from spatially explicit capture–recapture

Effective conservation of large carnivores requires reliable estimates of population density, often obtained through capture–recapture analysis, in order to prioritize investments and assess conservation intervention effectiveness. Recent statistical advances and development of user-friendly software for spatially explicit capture–recapture (SECR) circumvent the difficulties in estimating effective survey area, and hence density, from capture–recapture data. We conducted a camera-trapping study on leopards (Panthera pardus) in Mondulkiri Protected Forest, Cambodia. We compared density estimates using SECR with those obtained from conventional approaches in which the effective survey area is estimated using a boundary strip width based on observed animal movements. Density estimates from Chao heterogeneity models (3.8 ± SE 1.9 individuals/100 km²) and Pledger heterogeneity models and models accounting for gender-specific capture and recapture rates (model-averaged density 3.9 ± SE 2.9 individuals/100 km²) were similar to those from SECR in program DENSITY (3.6 ± SE 1.0/100 km²) but higher than estimates from Jack-knife heterogeneity models (2.9 ± SE 0.9 individuals/100 km²). Capture probabilities differed between male and female leopards probably resulting from differences in the use of human-made trails between sexes. Given that there are a number of biologically plausible reasons to expect gender-specific variation in capture probabilities of large carnivores, we recommend exploratory analysis of data using models in which gender can be included as a covariate affecting capture probabilities particularly given the demographic importance of breeding females for population recovery of threatened carnivores. © 2011 The Wildlife Society.     

Genetic mark–recapture population estimation in black bears and issues of scale

Abundance estimates for black bears (Ursus americanus) are important for effective management. Recently, DNA technology has resulted in widespread use of noninvasive, genetic capture–mark–recapture (CMR) approaches to estimate populations. Few studies have compared the genetic CMR methods to other estimation methods. We used genetic CMR to estimate the bear population at 2 study sites in northern New Hampshire (Pittsburg and Milan) in 2 consecutive years. We compared these estimates to those derived from traditional methods used by the New Hampshire Fish and Game Department (NHFG) using hunter harvest and mortality data. Density estimates produced with genetic CMR methods were similar both years and were comparable to those derived from traditional methods. In 2006, the estimated number of bears in Pittsburg was 79 (95% CI = 60–98) corresponding to a density of 15–24 (95% CI) bears/100 km²; the 2007 estimate was 83 (95% CI = 67–99; density = 16–24 bears/100 km²). In 2006, the estimated number of bears in Milan was 95 (95% CI = 74–117; density = 16–25 bears/100 km²); the 2007 estimate was 96 (95% CI = 77–114; density = 17–25 bears/100 km²). We found that genetic CMR methods were able to identify demographic variation at a local scale, including a strongly skewed sex ratio (2 M:1 F) in the Milan population. Genetic CMR is a useful tool for wildlife managers to monitor populations of local concern, where abundance or demographic characteristics may deviate from regional estimates. Future monitoring of the Milan population with genetic CMR is recommended to determine if the sex ratio bias continues, possibly warranting a change in local harvest regimes. © 2011 The Wildlife Society.     

Effects of predator treatments,individual traits,and environment on moose survival in Alaska

We studied moose (Alces alces) survival, physical condition, and abundance in a 3-predator system in western Interior Alaska, USA, during 2001–2007. Our objective was to quantify the effects of predator treatments on moose population dynamics by investigating changes in survival while evaluating the contribution of potentially confounding covariates. In May 2003 and 2004, we reduced black bear (Ursus americanus) and brown bear (U. arctos) numbers by translocating bears ≥240 km from the study area. Aircraft-assisted take reduced wolf (Canis lupus) numbers markedly in the study area during 2004–2007. We estimated black bears were reduced by approximately 96% by June 2004 and recovered to within 27% of untreated numbers by May 2007. Brown bears were reduced approximately 50% by June 2004. Late-winter wolf numbers were reduced by 75% by 2005 and likely remained at these levels through 2007. In addition to predator treatments, moose hunting closures during 2004–2007 reduced harvests of male moose by 60% in the study area. Predator treatments resulted in increased calf survival rates during summer (primarily from reduced black bear predation) and autumn (primarily from reduced wolf predation). Predator treatments had little influence on survival of moose calves during winter; instead, calf survival was influenced by snow depth and possibly temperature. Increased survival of moose calves during summer and autumn combined with relatively constant winter survival in most years led to a corresponding increase in annual survival of calves following predator treatments. Nonpredation mortalities of calves increased following predator treatments; however, this increase provided little compensation to the decrease in predation mortalities resulting from treatments. Thus, predator-induced calf mortality was primarily additive. Summer survival of moose calves was positively related to calf mass (β > 0.07, SE = 0.073) during treated years and lower (β = −0.82, SE = 0.247) for twins than singletons during all years. Following predator treatments, survival of yearling moose increased 8.7% for females and 21.4% for males during summer and 2.2% for females and 15.6% for males during autumn. Annual survival of adult (≥2 yr old) female moose also increased in treated years and was negatively (β = −0.21, SE = 0.078) related to age. Moose density increased 45%, from 0.38 moose/km² in 2001 to 0.55 moose/km² in 2007, which resulted from annual increases in overall survival of moose, not increases in reproductive rates. Indices of nutritional status remained constant throughout our study despite increased moose density. This information can be used by wildlife managers and policymakers to better understand the outcomes of predator treatments in Alaska and similar environments. © 2011 The Wildlife Society.     

The non-impact of hunting on moose <Emphasis Type="Italic">Alces alces</Emphasis> movement,diurnal activity,and activity range

Previous studies on moose Alces alces have suggested that interactions with humans may trigger anti-predator behaviors and generate a demographical cost. Therefore, we hypothesized that disturbances from small and big game hunting may have negative effects on moose movements, diurnal activity, and activity range. Using location data from 64 moose equipped with GPS collars from three populations (Low Alpine, Inland, Coastal) with different temporal human presence and spatial accessibility, we evaluated the impact of hunting on moose activity rhythms. On average, female moose in the low human population density (Low Alpine) area (<0.5/km²) had significantly lower movement rates during moose hunting season, but variation in movement rates among individuals were higher compared with female moose in regions with denser human populations (6–24/km²). We found no evidence that reproductive status influenced female moose sensitivity to disturbance. As expected, females used smaller activity ranges and were less active nocturnally than males. The high within-group variation suggests that current hunting disturbance levels do not alter moose population behavior in general. Our data indicate that alterations in movement were related to rutting activity, not human disturbance induced by hunting. In line with behavioral theory, our study suggests that some individuals were more sensitive to hunting disturbance than the general population. Our work suggests that individual moose may perceive human predation risk to be similar to other predation risks.     

Estimating abundance of mountain lions from unstructured spatial sampling

Mountain lions (Puma concolor) are often difficult to monitor because of their low capture probabilities, extensive movements, and large territories. Methods for estimating the abundance of this species are needed to assess population status, determine harvest levels, evaluate the impacts of management actions on populations, and derive conservation and management strategies. Traditional mark–recapture methods do not explicitly account for differences in individual capture probabilities due to the spatial distribution of individuals in relation to survey effort (or trap locations). However, recent advances in the analysis of capture–recapture data have produced methods estimating abundance and density of animals from spatially explicit capture–recapture data that account for heterogeneity in capture probabilities due to the spatial organization of individuals and traps. We adapt recently developed spatial capture–recapture models to estimate density and abundance of mountain lions in western Montana. Volunteers and state agency personnel collected mountain lion DNA samples in portions of the Blackfoot drainage (7,908 km²) in west-central Montana using 2 methods: snow back-tracking mountain lion tracks to collect hair samples and biopsy darting treed mountain lions to obtain tissue samples. Overall, we recorded 72 individual capture events, including captures both with and without tissue sample collection and hair samples resulting in the identification of 50 individual mountain lions (30 females, 19 males, and 1 unknown sex individual). We estimated lion densities from 8 models containing effects of distance, sex, and survey effort on detection probability. Our population density estimates ranged from a minimum of 3.7 mountain lions/100 km² (95% CI 2.3–5.7) under the distance only model (including only an effect of distance on detection probability) to 6.7 (95% CI 3.1–11.0) under the full model (including effects of distance, sex, survey effort, and distance × sex on detection probability). These numbers translate to a total estimate of 293 mountain lions (95% CI 182–451) to 529 (95% CI 245–870) within the Blackfoot drainage. Results from the distance model are similar to previous estimates of 3.6 mountain lions/100 km² for the study area; however, results from all other models indicated greater numbers of mountain lions. Our results indicate that unstructured spatial sampling combined with spatial capture–recapture analysis can be an effective method for estimating large carnivore densities. Published 2012. This article is a U.S. Government work and is in the public domain in the USA.     

Sex-specific population dynamics and demography of capercaillie (Tetrao urogallus L.) in a patchy environment

Modeling the population dynamics of patchily distributed species is a challenge, particularly when inference must be based on incomplete and small data sets such as those from most species of conservation concern. Here, we develop an open population spatial capture–recapture (SCR) model with sex-specific detection and population dynamics parameters to investigate population trend and sex-specific population dynamics of a capercaillie (Tetrao urogallus) population in Switzerland living in eight distinct forest patches totaling 22 km² within a region of 908 km² and sampled via scat collection. Our model accounts for the patchy distribution of habitat and the uncertainty introduced by collecting data only every third year, while producing sex by patch population trajectories. The estimated population trajectory was a decline of 2% per year; however, the sex specificity of the model revealed a decline in the male population only, with no evidence of decline in the female population. The decline observed in males was explained by the demography of just two of the eight patches. Our study highlights the flexibility of open population SCR models for assessing population trajectories through time and across space and emphasizes the desirability of estimating sex-stratified population trends especially in species of conservation concern.     

Distribution and population density of badgers Meles meles in Luxembourg

1. The distribution and density of Eurasian badgers Meles meles in Luxembourg was estimated by gathering information about the location of badger setts with a questionnaire survey, by visiting 708 setts in order to classify them as ‘main setts’ or ‘outliers’, and by estimating social group size by directly counting emerging badgers. 2. Badgers were found to be widely distributed in Luxembourg, with a minimum main sett density of 0.17 setts/km². Setts were sited preferentially in forest habitat. The mean minimum group size was 4.6 badgers. 3. The Luxembourg badger population was conservatively estimated to contain at least 2010 adult and young badgers (95% CI 1674–2347) in spring 2002, equivalent to a density of 0.78 adult and young badgers/km² (95% CI 0.65–0.91). This is moderate compared to most of continental Europe.     

Spatial organisation and dynamics of the pine marten Martes martes population in Białowieza Forest (E Poland) compared with other European woodlands

We studied factors affecting density and spacing patterns in the pine marten Martes martes population inhabiting temperate forests of Bia?owieza National Park, eastern Poland. From 1985/1986 to 1995/1996 marten densities ranged from 3.63 to 7.57 individuals 10 km^?2 (mean 5.4) and were positively correlated with abundance of forest rodents in the previous year. The rate of marten population growth was inversely density‐dependent and positively related to rodent density. Annual mortality rate averaged 0.384 and tended to be negatively related to marten densities. Mean annual home range of males (2.58 km², SE=0.24) was larger than that of females (1.41 km², SE=0.20). Seasonal home ranges also differed significantly between males and females. Both sexes held the smallest ranges in December–January. Female ranges increased in April–May, whereas those of males increased in June–September when they were mating. Fidelity of pine martens to their home ranges was very high. The mean shift between arithmetic centres of seasonal ranges was 0.25 km, and the ranges recorded in two consecutive seasons overlapped, on average, by 87–90%. We observed very little home range overlap between neighbouring male (mean 4–6%) or female (mean 6%) marten. Year round the neighbouring individuals of the same sex neither avoided nor attracted each other. Females attracted males only during the spring‐summer mating season. A review of other studies has documented that winter severity and seasonal variation in ecosystem productivity were essential factors shaping the biogeographic variation in pine marten densities between 41^o and 68^oN. The density of marten populations increased in areas with mild winters and lower seasonality. Maximum population densities (indicative of habitat carrying capacity) were correlated with mean winter temperature. In Europe, male home ranges increased with decreasing forest cover in a study area, whereas female ranges varied positively with rodent abundance.     

Estimating Carrying Capacity for Sea Otters in British Columbia

ABSTRACT We estimated carrying capacity for sea otters (Enhydra lutris) in the coastal waters of British Columbia, Canada, by characterizing habitat according to the complexity of nearshore intertidal and sub-tidal contours. We modeled the total area of complex habitat on the west coast of Vancouver Island by first calculating the complexity of the Checleset Bay-Kyuquot Sound (CB-KS) region, where sea otters have been at equilibrium since the mid-1990s. We then identified similarly complex areas on the west coast of Vancouver Island (WCVI model), and adapted the model to identify areas of similar complexity along the entire British Columbia coast (BC model). Using survey data from the CB-KS region, we calculated otter densities for the habitat predicted by the 2 models. The density estimates for CB-KS were 3.93 otters/km² and 2.53 otters/km² for the WCVI and BC models, respectively, and the resulting 2 estimates of west coast of Vancouver Island complex habitat carrying capacity were not significantly different (WCVI model: 5,123, 95% CI = 3,337–7,104; BC model: 4,883, 95% CI = 3,223–6,832). The BC model identified the region presently occupied by otters on the central British Columbia coast, but the amount of coast-wide habitat it predicted (5,862 km²) was relatively small, and the associated carrying capacity estimate (14,831, 95% CI = 9,790–20,751) was low compared to historical accounts. We suggest that our model captured a type of high-quality or optimum habitat prevalent on the west coast of Vancouver Island, typified by the CB-KS region, and that suitable sea otter habitat elsewhere on the coast must include other habitat characteristics. We therefore calculated a linear, coast-wide carrying capacity of 52,459 sea otters (95% CI = 34,264–73,489)—a more realistic upper limit to sea otters in British Columbia. Our carrying capacity estimates are helping set population recovery targets for sea otters in Canada, and our habitat predictions represent a first step in Critical Habitat identification. This habitat-based approach to estimating carrying capacity is likely suitable for other nonmigratory, density-dependent species.     

Estimating the population size of the Sanje mangabey (Cercocebus sanjei) using acoustic distance sampling

The Sanje mangabey (Cercocebus sanjei) is endemic to the Udzungwa Mountains, Tanzania, and is classified as Endangered due to its putatively declining population size, habitat degradation and fragmentation. Previous population size estimates have ranged from 1,350 to 3,500 individuals, with the last direct survey being conducted 15 years before the present study. Previous estimates are now thought to have underestimated the population due to a limited knowledge of group and habitat size, nonsystematic approaches and the use of visual methods that are not suitable for surveying the Sanje mangabey with its semi-terrestrial and elusive behaviors. We used an acoustic survey method with observers recording the distinctive “whoop-gobble” vocalization produced by mangabeys and point transect distance sampling to model a detection function and estimate abundance. Twenty-eight surveys were conducted throughout the two forests where Sanje mangabeys are found: Mwanihana forest in the Udzungwa Mountains National Park (n = 13), and the Uzungwa Scarp Nature Reserve (n = 15). Group density was found to be significantly lower in the relatively unprotected Uzungwa Scarp forest (0.15 groups/km²; 95% CI: 0.08–0.27) compared to the well-protected Mwanihana forest (0.29 groups/km²; 95% CI: 0.19–0.43; p = .03). We estimate that there are 1,712 (95% CI: 1,141–2,567) individuals in Mwanihana and 1,455 (95% CI: 783–2,702) in the Uzungwa Scarp, resulting in a total population size of 3,167 (95% CI: 2,181–4,596) individuals. The difference in group density between sites is likely a result of the differing protection status and levels of enforcement between the forests, suggesting that protection of the Uzungwa Scarp should be increased to encourage recovery of the population, and reduce the threat of degradation and hunting. Our results contribute to the reassessment of the species' IUCN Red List status and informing management and conservation action planning.     

Estimating minke whale (Balaenoptera acutorostrata) boing sound density using passive acoustic sensors

Density estimation for marine mammal species is performed primarily using visual distance sampling or capture‐recapture. Minke whales in Hawaiian waters are very difficult to sight; however, they produce a distinctive “boing” call, making them ideal candidates for passive acoustic density estimation. We used an array of 14 bottom‐mounted hydrophones, distributed over a 60 × 30 km area off Kauai, Hawaii, to estimate density during 12 d of recordings in early 2006. We converted the number of acoustic cues (i.e., boings) detected using signal processing software into a cue density by accounting for the false positive rate and probability of detection. The former was estimated by manual validation, the latter by applying spatially explicit capture‐recapture (SECR) methods to a subset of data where we had determined which hydrophones detected each call. Estimated boing density was 130 boings per hour per 10,000 km² (95% CI 104–163). Little is known about the population's acoustic behavior, so conversion from boing to animal density is difficult. As a demonstration of the method, we used a tentative boing rate of 6.04 boings per hour, from a single animal tracked in 2009, to give an estimate of 21.5 boing‐calling minke whales per 10,000 km².     

Density-dependent habitat selection and partitioning between two sympatric ungulates

Theory on density-dependent habitat selection predicts that as population density of a species increases, use of higher quality (primary) habitat by individuals declines while use of lower quality (secondary) habitat rises. Habitat partitioning is often considered the primary mechanism for coexistence between similar species, but how this process evolves with changes in population density remains to be empirically tested for free-ranging ungulates. We used resource-selection functions to quantify density effects on landscape-scale habitat selection of two sympatric species of ungulates [moose (Alces alces) and elk (Cervus canadensis manitobensis)] in Riding Mountain National Park, Manitoba, Canada (2000–2011). The density of elk was actively reduced from 1.2 to 0.4 elk km^?2 through increased hunting effort during the period of study, while moose density decreased without additional human influence from 1.6–0.7 moose km^?2. Patterns of habitat selection during winter by both species changed in accordance to expectations from density-dependent habitat-selection theory. At low intraspecific density, moose and elk did not partition habitat, as both species selected strongly for mixed forest (primary habitat providing both food and cover), but did so in different areas segregated across an elevational gradient. As intraspecific density increased, selection for primary habitat by both species decreased, while selection for secondary, lower quality habitat such as agricultural fields (for elk) and built-up areas (for moose) increased. We show that habitat-selection strategies during winter for moose and elk, and subsequent effects on habitat partitioning, depend heavily on the position in state space (density) of both species.     

Effects of Exploitation on Black Bear Populations at White River National Wildlife Refuge

Abstract: We live-trapped American black bears (Ursus americanus) and sampled DNA from hair at White River National Wildlife Refuge, Arkansas, USA, to estimate annual population size (N), growth (γ), and density. We estimated N and γ with open population models, based on live-trapping data collected from 1998 through 2006, and robust design models for genotyped hair samples collected from 2004 through 2007. Population growth was weakly negative (i.e., 95% CI included 1.0) for males (0.901, 95% CI = 0.645–1.156) and strongly negative (i.e., 95% CI excluded 1.0) for females (0.846, 95% CI = 0.711–0.981), based on live-trapping data, with N from 1999 to 2006 ranging from 94.1 (95% CI = 70.3–137.1) to 45.2 (95% CI = 27.1–109.3), respectively, for males and from 151.4 (95% CI = 127.6–185.8) to 47.1 (95% CI = 24.4–140.4), respectively, for females. Likewise, mean annual γ based on hair-sampling data was weakly negative for males (0.742, 95% CI = 0.043–1.441) and strongly negative for females (0.782, 95% CI = 0.661–0.903), with abundance estimates from 2004 to 2007 ranging from 29.1 (95% CI = 21.2–65.8) to 11.9 (95% CI = 11.0–26.9), respectively, for males and from 54.4 (95% CI = 44.3–77.1) to 27.4 (95% CI =24.9–36.6), respectively, for females. We attribute the decline in the number of females in this isolated population to a decrease in survival caused by a past translocation program and by hunting adjacent to the refuge. We suggest that managers restructure the quota-based harvest limits until these growth rates recover.     

Demography and Genetic Structure of a Recovering Grizzly Bear Population

ABSTRACT Grizzly bears (brown bears; Ursus arctos) are imperiled in the southern extent of their range worldwide. The threatened population in northwestern Montana, USA, has been managed for recovery since 1975; yet, no rigorous data were available to monitor program success. We used data from a large noninvasive genetic sampling effort conducted in 2004 and 33 years of physical captures to assess abundance, distribution, and genetic health of this population. We combined data from our 3 sampling methods (hair trap, bear rub, and physical capture) to construct individual bear encounter histories for use in Huggins—Pledger closed mark—recapture models. Our population estimate, Ň = 765 (95% CI = 715–831) was more than double the existing estimate derived from sightings of females with young. Based on our results, the estimated known, human-caused mortality rate in 2004 was 4.6% (95% CI = 4.2–4.9%), slightly above the 4% considered sustainable; however, the high proportion of female mortalities raises concern. We used location data from telemetry, confirmed sightings, and genetic sampling to estimate occupied habitat. We found that grizzly bears occupied 33,480 km² in the Northern Continental Divide Ecosystem (NCDE) during 1994–2007, including 10,340 km² beyond the Recovery Zone. We used factorial correspondence analysis to identify potential barriers to gene flow within this population. Our results suggested that genetic interchange recently increased in areas with low gene flow in the past; however, we also detected evidence of incipient fragmentation across the major transportation corridor in this ecosystem. Our results suggest that the NCDE population is faring better than previously thought, and they highlight the need for a more rigorous monitoring program.     

The Abundance and Distribution of Wolverines in British Columbia,Canada

ABSTRACT The abundance and distribution of carnivores and their habitat are key information needed for status assessment, conservation planning, population management, and assessment of the effects of human development on their habitat and populations. We developed a habitat quality rating system, using existing wolverine (Gulo gulo) distribution, wolverine food, ecosystem mapping, and human development data. We used this and empirically derived estimates of wolverine density to predict wolverine distribution and abundance at a provincial scale. Density estimates for wolverines in high-quality habitat averaged 6.2 wolverines/1,000 km² (95% CI = 4.2–9.5). We predicted mean densities ranging from 0.3/1,000 km² in rare-quality habitat to 4.1/1,000 km² in moderate-quality habitat. Our predicted population estimate for wolverines in British Columbia was 3,530 (95% CI = 2,700-4,760). We predicted highest densities of wolverines in interior mountainous regions, moderate densities in interior plateau and boreal forest regions, and low densities in mainland coastal regions and drier interior plateaus. We predicted that wolverines would be rare on Vancouver Island, along the outer mainland coast, and in the dry interior forests, and absent from the Queen Charlotte Islands, interior grassland environments, and areas of intensive urban development.     

Density and population structure of the jaguar (<Emphasis Type="Italic">Panthera onca</Emphasis>) in a protected area of Los Llanos,Venezuela, from 1 year of camera trap monitoring

Density is crucial for understanding large carnivore ecology and conservation, but estimating it has proven methodologically difficult. We conducted 1 year of camera trapping to estimate jaguar (Panthera onca) density and population structure in the Los Llanos region of Venezuela on the Hato Piñero ranch, where hunting is prohibited and livestock are excluded from half of ranch lands. We identified 42 different jaguars and determined their sex, age class, and reproductive status. We estimated adult jaguar densities with spatial capture-recapture models, using sex/reproductive state and session as covariates. Models without temporal variation received more support than models that allowed variation between sessions. Males, reproductive females, and nonreproductive females differed in their density, baseline detectability, and movement. The best estimate of total adult jaguar population density was 4.44 individuals/100 km². Based on reproductive female density and mean number of offspring per female, we estimated cub density at 3.23 individuals/100 km² and an overall density of 7.67 jaguars/100 km². Estimated jaguar population structure was 21% males, 11% nonreproductive females, 26% reproductive females, and 42% cubs. We conclude that extending the sampling period to 1 year increases the detectability of females and cubs and makes density estimates more robust as compared to the more common short studies. Our results demonstrate that the Venezuelan Llanos represent important jaguar habitat, and further, they emphasize the importance of protected areas and hunting restrictions for carnivore conservation.     

Status,trends, and equilibrium abundance estimates of the translocated sea otter population in Washington State

Sea otters (Enhydra lutris kenyoni) historically occurred in Washington State, USA, until their local extinction in the early 1900s as a result of the maritime fur trade. Following their extirpation, 59 sea otters were translocated from Amchitka Island, Alaska, USA, to the coast of Washington, with 29 released at Point Grenville in 1969 and 30 released at La Push in 1970. The Washington Department of Fish and Wildlife has outlined 2 main objectives for sea otter recovery: a target population level and a target geographic distribution. Recovery criteria are based on estimates of population abundance, equilibrium abundance (K), and geographic distribution; therefore, estimates of these parameters have important management implications. We compiled available survey data for sea otters in Washington State since their translocation (1977–2019) and fit a Bayesian state-space model to estimate past and current abundance, and equilibrium abundance at multiple spatial scales. We then used forward projections of population dynamics to explore potential scenarios of range recolonization and as the basis of a sensitivity analysis to evaluate the relative influence of movement behavior, frontal wave speed, intrinsic growth, and equilibrium density on future population recovery potential. Our model improves upon previous analyses of sea otter population dynamics in Washington by partitioning and quantifying sources of estimation error to estimate population dynamics, by providing robust estimates of K, and by simulating long-term population growth and range expansion under a range of realistic parameter values. Our model resulted in predictions of population abundance that closely matched observed counts. At the range-wide scale, the population size in our model increased from an average of 21 independent sea otters (95% CI = 13–29) in 1977 to 2,336 independent sea otters (95% CI = 1,467–3,359) in 2019. The average estimated annual growth rate was 12.42% and varied at a sub-regional scale from 6.42–14.92%. The overall estimated mean K density of sea otters in Washington was 1.71 ± 0.90 (SD) independent sea otters/km² of habitat (1.96 ± 1.04 sea otters/km², including pups), and estimated densities within the current range correspond on average to 87% of mean sub-regional equilibrium values (range = 66–111%). The projected value of K for all of Washington was 5,287 independent sea otters (95% CI = 2,488–8,086) and 6,080 sea otters including pups (95% CI = 2,861–9,300), assuming a similar range of equilibrium densities in currently un-occupied habitats. Sensitivity analysis of simulations of sea otter population growth and range expansion suggested that mean K density estimates in currently occupied sub-regions had the largest impact on predicted future population growth (r² = 0.52), followed by the rate of southward range expansion (r² = 0.26) and the mean K density estimate of currently unoccupied sub-regions to the south of the current range (r² = 0.04). Our estimates of abundance and sensitivity analysis of simulations of future population abundance and geographic range help determine population status in relation to population recovery targets and identify the most influential parameters affecting future population growth and range expansion for sea otters in Washington State.