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 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.

Studies identifying interspecific competition require the investigation of negative long‐term effects between sympatric species showing overlap in resource use. A potential for competition exists between red deer Cervus elaphus and chamois Rupicapra spp., as revealed by the high dietary overlap observed throughout the range where the species co‐occur. Furthermore, some studies have recently reported negative demographic consequences on chamois populations living in sympatry with red deer. Using time series of counts spanning 35 years between 1984 and 2018 in the Stelvio National Park (Central Italian Alps), we tested for density dependence using state‐space models and explored the evidence of competitive interaction through Ricker‐like models on the growth rate of both species. We contrasted alternative hypotheses for the processes explaining the trends of (decreasing) chamois and (increasing) red deer populations. We expected chamois dynamics to be negatively affected by increasing deer abundance, while deer dynamics should be primarily affected by climate forcing and density dependence. We found evidence that resources were limiting for both species. In particular, growth rates were negatively affected by the synergistic effect of winter weather conditions and density dependence. The most important variable limiting the chamois population, however, was the increase in red deer numbers. The dynamics of this species was unaffected by chamois numbers. While causality cannot be inferred from the data, these results are consistent with the hypothesis of a negative effect of red deer on chamois dynamics, thus supporting the occurrence of interference or exploitation competition between sympatric mountain‐dwelling ungulates. Understanding the processes underlying temporal dynamics is pivotal for informed management of wildlife. Consequences of interspecific competition should be carefully evaluated where the populations of the weaker competitor are of conservation concern, especially in the light of the negative effects of anthropogenic environmental change on animal populations.

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.