Celebrating wildlife population recovery through education

Large mammal populations are rapidly recovering across Europe, yet people have not readapted to living with wild animals, resulting in human–wildlife conflict. We believe that society should unite to make the most of the instances of nature recovery, and propose science and education as the key to succes

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

Carabid beetle (Coleoptera: Carabidae) richness and functional traits in relation to differently managed grasslands in the Alps

<div><p>Summary</p><p>Species richness, composition, and functional traits of carabid beetle assemblages (Coleoptera: Carabidae) were studied in relation to different grassland management. Carabid beetles were sampled during the summers 2008 and 2009 by 165 traps located in 11 sites in the central-eastern Italian Alps. Using mixed effect models to account for potential spatial bias, we found that mown grasslands had significantly more species, a lower proportion of wingless species and a lower proportion of species with long larval development than grazed and natural grasslands. Within grazed and mown grasslands, neither cattle density nor number of cuts had any significant effect neither on species richness nor on any of the traits. The influence of grassland management can be summarised as follows: (1) grazing does not change community structure and functional traits compared to natural grasslands; (2) mowing negatively affects the carabid beetle assemblages; (3) the intensity of grazing and of cutting may not affect the structure of species assemblages of ground beetles. Our results support the hypothesis that agroecosystem practices in alpine grasslands influence carabid beetle communities. Specifically, the species with traits typical of undisturbed habitats (low dispersal abilities and long larval development) are more sensitive to perturbations (e.g. cutting). Our suggestion for agricultural and environmental planning and for conservation schemes is that the preservation of natural grasslands (e.g. forest gaps) and the implementation of grazing should be promoted during the planning of agroecosystem mosaics.</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

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

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 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