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Estimating the size of small populations of large mammals can be achieved via censuses, or complete counts, of recognizable individuals detected over a time period: minimum detected (population) size (MDS). However, as a population grows larger and its spatial distribution expands, the risk of under-estimating population size using MDS rapidly increases because the assumption of perfect detection of all individuals in the population is violated. The need to report uncertainty around population size estimates consequently becomes crucial. We explored these biases using the monitoring framework of the critically endangered Pyrenean brown bear that was close to extinction in the mid-1990s, with only five individuals remaining, but was subsequently bolstered by the introduction of 11 bears from Slovenia. Each year since 1996, the abundance of the population has been assessed using MDS and minimum retained (population) size (MRS), which corresponded to a reassessment of the MDS in the light of the new information collected in subsequent years (e.g., adding bears which were not detected the previous years but detected the current year). We used Pollock’s closed robust design (PCRD) capture-recapture models applied to the cross-border non-invasive sampling data from France, Spain and Andorra to provide the first published annual abundance and temporal trend estimates of the Pyrenean brown bear population since 2008. Annual population size increased fivefold between 2008 and 2020, going from 13 to 66 individuals. PCRD estimates were globally close to MRS counts and had reasonably narrow associated 95% Credibility Intervals. Even in cases where sampling effort is large compared to population size, the PCRD estimates of population size can diverge from the MDS counts. We report individual heterogeneity in detection that might stem from intraspecific home range size variation that result in individuals that move the most being most likely to be detected. We also found that cubs had a higher mortality rate than adults and subadults, because of infanticide by males, predation, maternal death, or abandonment. Overall, the PCRD capture-recapture modelling approach provides estimates of abundance and demographic rates of the Pyrenean brown bear population, together with associated uncertainty, while minimizing bias due to inter-individual heterogeneity in detection probabilities. We strongly encourage wildlife ecologists and managers to use robust approaches when researching large mammal populations. Such information is vital for informing management decision-making and assessing population conservation status.

The ongoing recovery of terrestrial large carnivores in North America and Europe is accompanied by intense controversy. On the one hand, reestablishment of large carnivores entails a recovery of their most important ecological role, predation. On the other hand, societies are struggling to relearn how to live with apex predators that kill livestock, compete for game species, and occasionally injure or kill people. Those responsible for managing these species and mitigating conflict often lack fundamental information due to a long-standing challenge in ecology: How do we draw robust population-level inferences for elusive animals spread over immense areas? Here we showcase the application of an effective tool for spatially explicit tracking and forecasting of wildlife population dynamics at scales that are relevant to management and conservation. We analyzed the worlds largest dataset on carnivores comprising more than 35,000 noninvasively obtained DNA samples from over 6,000 individual brown bears (Ursus arctos), gray wolves (Canis lupus), and wolverines (Gulo gulo). Our analyses took into account that not all individuals are detected and, even if detected, their fates are not always known. We show unequivocal quantitative evidence of large carnivore recovery in northern Europe, juxtaposed with the finding that humans are the single-most important factor driving the dynamics of these apex predators. We present maps and forecasts of the spatiotemporal dynamics of large carnivore populations, transcending national boundaries and management regimes.

The efficiency of the management of predations on livestock by gray wolves (Canis lupus) through culling is under debate. Evaluating wolf culling efficiency requires to simultaneously analyze the effects of culling on the wolf population and the repercussions of these population changes on livestock predation. This protocol is technically difficult to implement in the field. To properly assess culling efficiency, we provided an integrated and flexible individual‐based model that simulated interactions between wolf population dynamics, predation behavior and culling management. We considered many social processes in wolves. We calibrated the model to match the Western Alps as a case study. By considering the prey community in this area and the opportunistic nature of wolf predation, we assumed that predation on livestock at the wolf territory level increased with pack's food needs. Under this assumption and considering livestock availability as high and livestock vulnerability as uniform in space and time, culling maintained wolf population size and predation risks at low levels. Contrary to what was expected, culling decreased the mean annual proportions of dispersing wolves in our simulations, by speeding settlement. This population‐level mechanism compensated for the high mortality and the pack instability caused by culling. Compensation was however dependent on the selectivity and the timing of culling. When executed before the natural mortality module in our model, the selective culling could undermine replacement of lost breeders and therefore decrease wolf population resilience to culling. Our model gives insights about culling effects in one specific simulated context, but we do not expect that our assumption about predation behavior necessarily holds in other ecological contexts and we therefore encourage further explorations of the model. [ABSTRACT FROM AUTHOR]

Occupancy models were originally developed to better understand species distribution while accounting for imperfect detection. Because species distribution is not only shaped by habitat quality but also by the ability of individuals to reach suitable habitats, spatial dynamic occupancy models have been proposed to extend the original framework by defining that site colonisation was a function of the Euclidean distance to occupied sites. However, not all sites in the landscape are equally accessible due to the presence of barriers, that of corridors, etc. To account for connectivity between sites, the Euclidean distance has recently been replaced by a least‐cost path distance, which explicitly accounts for landscape resistance, but assumes that individuals will follow the optimal route.To relax this assumption, we first developed a new spatial occupancy model that incorporates commute‐time distance derived from circuit theory to model accessibility across sites. This distance has the advantage of modelling movement as a random walk and accounting for the fact that colonisation could be achieved from multiple paths. Our approach allows for the explicit estimation of landscape connectivity from detection/non‐detection data and a direct measure of connectivity uncertainty.We implemented the model in the Bayesian framework using the nimble R package, which allows useful R connectivity functions to be called from within the model. Second, we carried out a simulation study to assess the performance of our model by considering four scenarios depicting an increasing level of landscape resistance. Third, to illustrate our new approach, we studied the recolonisation of two carnivores in France.We quantified the degree to which rivers facilitate Eurasian otter (Lutra lutra) colonisation and highways impede Eurasian lynx (Lynx lynx) colonisation. Overall, spatial occupancy models provide a flexible framework to accommodate any distance metric designed to align with species dispersal ecology. [ABSTRACT FROM AUTHOR]

Understanding how populations respond to variability in environmental conditions and interspecific interactions is one of the biggest challenges of population ecology, particularly in the context of global change. Although many studies have investigated population responses to climate change, very few have explicitly integrated interspecific relationships when studying these responses. In this study, we aimed to understand the combined effects of interspecific interactions and environmental conditions on the demographic parameters of a prey–predator system of three sympatric seabird populations breeding in Antarctica: the south polar skua (Catharacta maccormicki) and its two main preys during the breeding season, the Adélie penguin (Pygoscelis adeliae) and the emperor penguin (Aptenodytes forsteri). We built a two‐species integrated population model (IPM) with 31 years of capture–recapture and count data and provided a framework that made it possible to estimate the demographic parameters and abundance of a predator–prey system in a context where capture–recapture data were not available for one species. Our results showed that predator–prey interactions and local environmental conditions differentially affected south polar skuas depending on their breeding state of the previous year. Concerning prey–predator relationships, the number of Adélie penguin breeding pairs showed a positive effect on south polar skua survival and breeding probability, and the number of emperor penguin dead chicks showed a positive effect on the breeding success of south polar skuas. In contrast, there was no evidence for an effect of the number of south polar skuas on the demography of Adélie penguins. We also found an important impact of sea ice conditions on both the dynamics of south polar skuas and Adélie penguins. Our results suggest that this prey–predator system is mostly driven by bottom‐up processes and local environmental conditions. [ABSTRACT FROM AUTHOR]

The desire to predict the consequences of global environmental change has been the driver towards more realistic models embracing the variability and uncertainties inherent in ecology. Statistical ecology has gelled over the past decade as a discipline that moves away from describing patterns towards modelling the ecological processes that generate these patterns. Following the fourth International Statistical Ecology Conference (1-4 July 2014) in Montpellier, France, we analyse current trends in statistical ecology. Important advances in the analysis of individual movement, and in the modelling of population dynamics and species distributions, are made possible by the increasing use of hierarchical and hidden process models. Exciting research perspectives include the development of methods to interpret citizen science data and of efficient, flexible computational algorithms for model fitting. Statistical ecology has come of age: it now provides a general and mathematically rigorous framework linking ecological theory and empirical data.

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