Animal movements occurring during COVID-19 lockdown were predicted by connectivity models.

Recent events related to the measures taken to control the spread of the Coronavirus (SARS-CoV-2) reduced human mobility (i.e. anthropause), potentially opening connectivity opportunities for wildlife populations. In the Italian Alps, brown bears have recovered after reintroduction within a complex anthropogenic matrix, but failed to establish a metapopulation due to reduced connectivity and human disturbance (i.e. infrastructure, land use, and human mobility). Previous work from Peters et al. (2015, Biol. Cons. 186, 123-133) predicted the main corridors and suitable hot spots for road network crossing for this population across all major roads and settlement zones, to link most suitable habitats. Bears used the identified hot spots for road network crossing over the years, but major barriers such as main motor roads were not overcome, possibly due to functional anthropogenic disturbance, specifically human mobility. By analyzing 404 bear occurrences reported to local authorities (as bear-related complaints) collected between 2016 and 2020 (March 9th - May 18th), hence including the COVID-19 related lockdown, we tested the effect of human presence on brown bears' use of space and hot spots for road network crossing. Animals occupied human-dominated spaces and approached hot spots for crossing at a higher rate during the lockdown than in previous years, suggesting that connectivity temporarily increased with reduced human mobility for this population. As a result of their increased use of hot spots, bears expanded their use of suitable areas beyond the population core area. Movement of animals across structural barriers such as roads and human settlements may therefore occur in absence of active disturbance. We also showed the value of predictive models to identify hot spots for animal barrier crossing, the knowledge of which is critical when implementing management solutions to enhance connectivity. Understanding the factors that influence immigration and emigration across metapopulations of large mammals, particularly carnivores that may compete indirectly with humans for space or directly as super-predators, is critical to ensure the long-term viability of conservation efforts for their persistence. We argue that dynamic factors such as human mobility may play a larger role than previously recognized.

Data integration for inference about spatial processes: A model-based approach to test and account for data inconsistency.

Recently-developed methods that integrate multiple data sources arising from the same ecological processes have typically utilized structured data from well-defined sampling protocols (e.g., capture-recapture and telemetry). Despite this new methodological focus, the value of opportunistic data for improving inference about spatial ecological processes is unclear and, perhaps more importantly, no procedures are available to formally test whether parameter estimates are consistent across data sources and whether they are suitable for integration. Using data collected on the reintroduced brown bear population in the Italian Alps, a population of conservation importance, we combined data from three sources: traditional spatial capture-recapture data, telemetry data, and opportunistic data. We developed a fully integrated spatial capture-recapture (SCR) model that included a model-based test for data consistency to first compare model estimates using different combinations of data, and then, by acknowledging data-type differences, evaluate parameter consistency. We demonstrate that opportunistic data lend itself naturally to integration within the SCR framework and highlight the value of opportunistic data for improving inference about space use and population size. This is particularly relevant in studies of rare or elusive species, where the number of spatial encounters is usually small and where additional observations are of high value. In addition, our results highlight the importance of testing and accounting for inconsistencies in spatial information from structured and unstructured data so as to avoid the risk of spurious or averaged estimates of space use and consequently, of population size. Our work supports the use of a single modeling framework to combine spatially-referenced data while also accounting for parameter consistency.

The role of weather and density dependence on population dynamics of Alpine-dwelling red deer.

The dynamics of red deer Cervus elaphus populations has been investigated across different environmental conditions, with the notable exception of the European Alps. Although the population dynamics of mountain-dwelling ungulates is typically influenced by the interaction between winter severity and density, the increase of temperatures and the reduction of snowpack occurring on the Alps since the 1980s may be expected to alter this pattern, especially in populations dwelling at medium - low elevations. Taking advantage of a 29-year time series of spring count data, we explored the role of weather stochasticity and density dependence on growth rate and vital rates (mortality and weaning success), and the density-dependent variation in body mass in a red deer population of the Italian Alps. The interaction between increasing values of density and snow depth exerted negative and positive effects on growth and mortality rates, respectively, while weaning success was negatively affected by increasing values of density, female-biased sex ratio and snow depth. Body mass of males and females of different age classes declined as population size increased. Our data support the role of winter severity and density dependence as key components of red deer population dynamics, and provide insight into the species' ecology on the European Alps. Despite the recent decline of snowpack on the Alpine Region, the negative impacts of winter severity and population abundance on growth rrate (possibly mediated by the density-dependent decline in body mass) confirms the importance of overwinter mortality in affecting the population dynamics of Alpine-dwelling red deer.

Evaluating mortality rates with a novel integrated framework for nonmonogamous species.

The conservation of wildlife requires management based on quantitative evidence, and especially for large carnivores, unraveling cause-specific mortalities and understanding their impact on population dynamics is crucial. Acquiring this knowledge is challenging because it is difficult to obtain robust long-term data sets on endangered populations and, usually, data are collected through diverse sampling strategies. Integrated population models (IPMs) offer a way to integrate data generated through different processes. However, IPMs are female-based models that cannot account for mate availability, and this feature limits their applicability to monogamous species only. We extended classical IPMs to a two-sex framework that allows investigation of population dynamics and quantification of cause-specific mortality rates in nonmonogamous species. We illustrated our approach by simultaneously modeling different types of data from a reintroduced, unhunted brown bear (Ursus arctos) population living in an area with a dense human population. In a population mainly driven by adult survival, we estimated that on average 11% of cubs and 61% of adults died from human-related causes. Although the population is currently not at risk, adult survival and thus population dynamics are driven by anthropogenic mortality. Given the recent increase of human-bear conflicts in the area, removal of individuals for management purposes and through poaching may increase, reversing the positive population growth rate. Our approach can be generalized to other species affected by cause-specific mortality and will be useful to inform conservation decisions for other nonmonogamous species, such as most large carnivores, for which data are scarce and diverse and thus data integration is highly desirable.