Posterior estimates of spatial scale parameter achieved using structured (SCR) data alone or integrated with unstructured (telemetry and opportunistic; tel, opp) information available for the brown bear population in the Italian Alps.

<p>Parameters are denoted as follows (m = male, f = female): sex-specific spatial scale parameter shared among different data types, <i>σ</i>_sex; sex-specific spatial scale parameter for structured and unstructured data, <i>σ</i>_st,sex and <i>σ</i>_un,sex, respectively.</p

Spatial distribution of the three types of data available for the brown bear population in the central Alps.

<p>(a) Distance from the point were founders were released (in km) and location of bear captures from systematic sampling with hair traps and rub trees (SCR), telemetry and opportunistic records. (b-c) Location of the records for the two collared individuals from which telemetry information was derived. Grey dots indicate the location of all observed individuals.</p

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

<div><p>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.</p></div

Posterior estimates of spatial scale parameter achieved using structured (SCR) data alone or integrated with unstructured (telemetry and opportunistic; tel, opp) information available for the brown bear population in the Italian Alps.

<p>Parameters are denoted as follows (m = male, f = female): sex-specific spatial scale parameter shared among different data types, <i>σ</i>_sex; sex-specific spatial scale parameter for structured and unstructured data, <i>σ</i>_st,sex and <i>σ</i>_un,sex, respectively.</p

Timeline of data collection.

<p>The diagram shows the period when SCR data were systematically collected (i) using an array of hair traps checked on five occasions of variable length (black blocks), and (ii) from rub trees checked for hairs in two period (grey blocks). Telemetry data were thinned by randomly selecting one record per day, and opportunistic recovery of biological samples was performed in 23 days.</p

Comparison of posterior estimates for population size (<i>N</i>) and spatial scale parameters of the gaussian kernel (<i>σ</i>) from the different models.

<p>Mean and 95% Bayesian Credible Interval achieved using structured (SCR) data only, or integrating them with unstructured data, i.e. telemetry (‘tel’) and opportunistic (‘opp’) data, available for the brown bear population in the Italian Alps. Filled points correspond to models with implied data consistency, empty points refer to the fully integrated model that account for data inconsistency.</p

Posterior estimates of population size achieved using structured (SCR) data alone or integrated with unstructured (telemetry and opportunistic; tel, opp) information available for the brown bear population in the Italian Alps.

<p>Parameters estimates are reported for males (<i>N</i>_m) and females (<i>N</i>_f).</p

Summary of contributions that provide an integrated framework for spatially-referenced individual data.

<p>Systematic data are collected under specific study designs: spatial capture-recapture (SCR), telemetry, and counts or binary detections (survey). Parameter shared: <i>ψ</i>, Data Augmentation parameter; <i>σ</i>, scale parameter of the observation model; <i>ϕ</i>, survival probability; <i>α</i>, effect of a landscape covariate on the relative probability of use; <i>δ</i>, individual-level recruitment probability.</p

bear_damage_europe-Data

Number of compensated claims for brown bear damage in Europe obtained from national and regional wildlife agencies and published literature and reports, as well as from researchers and practitioners. The file also contains environmental and socioeconomic information of each of the study sites

Archive of map shape files

Shape files of current and historical distribution maps of large carnivore species in Europ