Spatial patterns of breeding success of grizzly bears derived from hierarchical multistate models

Spatial patterns of reproduction suggest marginal habitat limits continued range expansion of black bears at a forest-desert ecotone

Investigating spatial patterns of animal occupancy and reproduction in peripheral populations can provide insight into factors that form species range boundaries. Following historical extirpation, American black bears (Ursus americanus) recolonized the western Great Basin in Nevada from the Sierra Nevada during the late 1900s. This range expansion, however, has not continued further into the Great Basin despite the presence of additional habitat. We aimed to quantify whether reduced reproduction toward the range edge contributes to this range boundary. We analyzed black bear detections from 100 camera traps deployed across black bear distribution in western Nevada using a multistate occupancy model that quantified the probability of occupancy and reproduction (i.e., female bears with cubs occupancy) in relation to changes in habitat type and habitat amount toward the range boundary. We detected a strong effect of habitat amount and habitat type on the probability of black bear occupancy and reproduction. At similar levels of landscape-scale habitat amount (e.g., 50%), estimated probability of occupancy for adult bears in piñon-juniper woodlands near the range boundary was 0.39, compared to ~1.0 in Sierra Nevada mixed-conifer forest (i.e., core habitat). Furthermore, estimated probability of cub occupancy, conditional on adult bear occupancy, in landscapes with 50% habitat was 0.32 in Great Basin piñon-juniper woodlands, compared to 0.92 in Sierra Nevada mixed-conifer forest. Black bear range in the western Great Basin conforms to the center-periphery hypothesis, with piñon-juniper woodland at the range edge supporting ecologically marginal habitat for the species compared to habitat in the Sierra Nevada. Further geographic expansion of black bears in the Great Basin may be limited by lower occupancy of reproducing females in piñon-juniper woodland. Center-periphery range dynamics may be common in large carnivore species, as their dispersal ability allows them to colonize low-quality habitat near range edges.

Spatial structure of reproductive success infers mechanisms of ungulate invasion in Nearctic boreal landscapes

Landscape change is a key driver of biodiversity declines due to habitat loss and fragmentation, but spatially shifting resources can also facilitate range expansion and invasion. Invasive populations are reproductively successful, and landscape change may buoy this success.We show how modeling the spatial structure of reproductive success can elucidate the mechanisms of range shifts and sustained invasions for mammalian species with attendant young. We use an example of white-tailed deer (deer; Odocoileus virginianus) expansion in the Nearctic boreal forest, a North American phenomenon implicated in severe declines of threatened woodland caribou (Rangifer tarandus).We hypothesized that deer reproductive success is linked to forage subsidies provided by extensive landscape change via resource extraction. We measured deer occurrence using data from 62 camera traps in northern Alberta, Canada, over three years. We weighed support for multiple competing hypotheses about deer reproductive success using multistate occupancy models and generalized linear models in an AIC-based model selection framework.Spatial patterns of reproductive success were best explained by features associated with petroleum exploration and extraction, which offer early-seral vegetation resource subsidies. Effect sizes of anthropogenic features eclipsed natural heterogeneity by two orders of magnitude. We conclude that anthropogenic early-seral forage subsidies support high springtime reproductive success, mitigating or exceeding winter losses, maintaining populations. Synthesis and Applications. Modeling spatial structuring in reproductive success can become a key goal of remote camera-based global networks, yielding ecological insights into mechanisms of invasion and range shifts to inform effective decision-making for global biodiversity conservation.

Factors influencing territorial occupancy and reproductive success in a Eurasian Eagle-owl (Bubo bubo) population

Modelling territorial occupancy and reproductive success is a key issue for better understanding the population dynamics of territorial species. This study aimed to investigate these ecological processes in a Eurasian Eagle-owl (Bubo bubo) population in south-eastern Spain during a seven-year period. A multi-season, multi-state modelling approach was followed to estimate the probabilities of occupancy and reproductive success in relation to previous state, time and habitat covariates, and accounting for imperfect detection. The best estimated models showed past breeding success in the territories to be the most important factor determining a high probability of reoccupation and reproductive success in the following year. In addition, alternative occupancy models suggested the positive influence of crops on the probability of territory occupation. By contrast, the best reproductive model revealed strong interannual variations in the rates of breeding success, which may be related to changes in the abundance of the European Rabbit, the main prey of the Eurasian Eagle-owl. Our models also estimated the probabilities of detecting the presence of owls in a given territory and the probability of detecting evidence of successful reproduction. Estimated detection probabilities were high throughout the breeding season, decreasing in time for unsuccessful breeders but increasing for successful breeders. The probability of detecting reproductive success increased with time, being close to one in the last survey. These results suggest that reproduction failure in the early stages of the breeding season is a determinant factor in the probability of detecting occupancy and reproductive success.

Grizzly Bear Noninvasive Genetic Tagging Surveys: Estimating the Magnitude of Missed Detections

Sound wildlife conservation decisions require sound information, and scientists increasingly rely on remotely collected data over large spatial scales, such as noninvasive genetic tagging (NGT). Grizzly bears (Ursus arctos), for example, are difficult to study at population scales except with noninvasive data, and NGT via hair trapping informs management over much of grizzly bears’ range. Considerable statistical effort has gone into estimating sources of heterogeneity, but detection error–arising when a visiting bear fails to leave a hair sample–has not been independently estimated. We used camera traps to survey grizzly bear occurrence at fixed hair traps and multi-method hierarchical occupancy models to estimate the probability that a visiting bear actually leaves a hair sample with viable DNA. We surveyed grizzly bears via hair trapping and camera trapping for 8 monthly surveys at 50 (2012) and 76 (2013) sites in the Rocky Mountains of Alberta, Canada. We used multi-method occupancy models to estimate site occupancy, probability of detection, and conditional occupancy at a hair trap. We tested the prediction that detection error in NGT studies could be induced by temporal variability within season, leading to underestimation of occupancy. NGT via hair trapping consistently underestimated grizzly bear occupancy at a site when compared to camera trapping. At best occupancy was underestimated by 50%; at worst, by 95%. Probability of false absence was reduced through successive surveys, but this mainly accounts for error imparted by movement among repeated surveys, not necessarily missed detections by extant bears. The implications of missed detections and biased occupancy estimates for density estimation–which form the crux of management plans–require consideration. We suggest hair-trap NGT studies should estimate and correct detection error using independent survey methods such as cameras, to ensure the reliability of the data upon which species management and conservation actions are based.

Multimethod, multistate Bayesian hierarchical modeling approach for use in regional monitoring of wolves

In many cases, the first step in large-carnivore management is to obtain objective, reliable, and cost-effective estimates of population parameters through procedures that are reproducible over time. However, monitoring predators over large areas is difficult, and the data have a high level of uncertainty. We devised a practical multimethod and multistate modeling approach based on Bayesian hierarchical-site-occupancy models that combined multiple survey methods to estimate different population states for use in monitoring large predators at a regional scale. We used wolves (Canis lupus) as our model species and generated reliable estimates of the number of sites with wolf reproduction (presence of pups). We used 2 wolf data sets from Spain (Western Galicia in 2013 and Asturias in 2004) to test the approach. Based on howling surveys, the naïve estimation (i.e., estimate based only on observations) of the number of sites with reproduction was 9 and 25 sites in Western Galicia and Asturias, respectively. Our model showed 33.4 (SD 9.6) and 34.4 (3.9) sites with wolf reproduction, respectively. The number of occupied sites with wolf reproduction was 0.67 (SD 0.19) and 0.76 (0.11), respectively. This approach can be used to design more cost-effective monitoring programs (i.e., to define the sampling effort needed per site). Our approach should inspire well-coordinated surveys across multiple administrative borders and populations and lead to improved decision making for management of large carnivores on a landscape level. The use of this Bayesian framework provides a simple way to visualize the degree of uncertainty around population-parameter estimates and thus provides managers and stakeholders an intuitive approach to interpreting monitoring results. Our approach can be widely applied to large spatial scales in wildlife monitoring where detection probabilities differ between population states and where several methods are being used to estimate different population parameters.