A Bayesian Belief Network learning tool integrates multi-scale effects of riparian buffers on stream invertebrates.

Riparian forest buffers have multiple benefits for biodiversity and ecosystem services in both freshwater and terrestrial habitats but are rarely implemented in water ecosystem management, partly reflecting the lack of information on the effectiveness of this measure. In this context, social learning is valuable to inform stakeholders of the efficacy of riparian vegetation in mitigating stream degradation. We aim to develop a Bayesian belief network (BBN) model for application as a learning tool to simulate and assess the reach- and segment-scale effects of riparian vegetation properties and land use on instream invertebrates. We surveyed reach-scale riparian conditions, extracted segment-scale riparian and subcatchment land use information from geographic information system data, and collected macroinvertebrate samples from four catchments in Europe (Belgium, Norway, Romania, and Sweden). We modelled the ecological condition based on the Average Score Per Taxon (ASPT) index, a macroinvertebrate-based index widely used in European bioassessment, as a function of different riparian variables using the BBN modelling approach. The results of the model simulations provided insights into the usefulness of riparian vegetation attributes in enhancing the ecological condition, with reach-scale riparian vegetation quality associated with the strongest improvements in ecological status. Specifically, reach-scale buffer vegetation of score 3 (i.e. moderate quality) generally results in the highest probability of a good ASPT score (99-100%). In contrast, a site with a narrow width of riparian trees and a small area of trees with reach-scale buffer vegetation of score 1 (i.e. low quality) predicts a high probability of a bad ASPT score (74%). The strengths of the BBN model are the ease of interpretation, fast simulation, ability to explicitly indicate uncertainty in model outcomes, and interactivity. These merits point to the potential use of the BBN model in workshop activities to stimulate key learning processes that help inform the management of riparian zones.

Wolf diet and prey selection in the South-Eastern Carpathian Mountains, Romania.

The Romanian wolf population, one of the largest in Europe, occupies a total home-range of 154500 km2 and is spread across a variety of landscapes-from anthropized hills and plateaus to remote, densely forested mountains. However, this population is markedly understudied, and even basic knowledge of the species' feeding habits is deficient. Wolf diet was assessed based on 236 scat samples collected between November 2013 and October 2014, by following pre-established transects (total length = 774 km). The study area (600 km2) is a multi-prey ecosystem in the southern sector of the Eastern Romanian Carpathians. Our results emphasize that more than 80% of the wolf diet is based on wild ungulates. The wild boar is clearly selected (D = 0.74) and is the most common species in the diet (Bio = 72%), while roe deer (Bio = 10%) and red deer (Bio = 5%) have a smaller contribution. Domestic species represented the second-largest prey category in both seasons. Among them, dog is a particularly important source of food (Bio 3.5-10.9%). Other domestic species (goat, sheep, horse) have marginal importance in the wolf diet and seasonal occurrence. Standardized niche breadths are low in both seasons (BAw = 0.07, BAs = 0.12), and a high degree of overlap in the resources used has been observed (Ôws = 0.99). Our study represents the first step towards understanding the wolf foraging behaviour in the Romanian Carpathians and is valuable to address the complex issues of wolf and wild ungulate population management and conservation.

Data summarizing monitoring and evaluation for three European environmental policies in 9 cases across Europe.

The data presented in this DiB article provide an overview of Monitoring and Evaluation (M&E) carried out for 3 European environmental policies (the Water Framework Directive, the Natura 2000 network of protected areas, and Agri-Environment Schemes implemented under the Common Agricultural Policy), as implemented in 9 cases (Catalonia (Spain), Estonia, Finland, Flanders (Belgium), Hungary, Romania, Slovakia, Scotland (UK), Sweden). These data are derived from reports and documents about monitoring programs that were publicly-available online in 2017. The literature on M&E to support adaptive management structured the issues that have been extracted and summarized. The data is related to the research article entitled "Policy-driven monitoring and evaluation: does it support adaptive management of socio-ecological systems?" [Stem et al., 2005]. The information provides a first overview of monitoring and evaluation that has been implemented in response to key European environmental policies. It provides a structured overview that permits a comparison of cases and policies and can assist other scholars and practitioners working on monitoring and evaluation.

Policy-driven monitoring and evaluation: Does it support adaptive management of socio-ecological systems?

Inadequate Monitoring and Evaluation (M&E) is often thought to hinder adaptive management of socio-ecological systems. A key influence on environmental management practices are environmental policies: however, their consequences for M&E practices have not been well-examined. We examine three policy areas - the Water Framework Directive, the Natura 2000 Directives, and the Agri-Environment Schemes of the Common Agricultural Policy - whose statutory requirements influence how the environment is managed and monitored across Europe. We use a comparative approach to examine what is monitored, how monitoring is carried out, and how results are used to update management, based on publicly available documentation across nine regional and national cases. The requirements and guidelines of these policies have provided significant impetus for monitoring: however, we find this policy-driven M&E usually does not match the ideals of what is needed to inform adaptive management. There is a tendency to focus on understanding state and trends rather than tracking the effect of interventions; a focus on specific biotic and abiotic indicators at the expense of understanding system functions and processes, especially social components; and limited attention to how context affects systems, though this is sometimes considered via secondary data. The resulting data are sometimes publicly-accessible, but it is rarely clear if and how these influence decisions at any level, whether this be in the original policy itself or at the level of measures such as site management plans. Adjustments to policy-driven M&E could better enable learning for adaptive management, by reconsidering what supports a balanced understanding of socio-ecological systems and decision-making. Useful strategies include making more use of secondary data, and more transparency in data-sharing and decision-making. Several countries and policy areas already offer useful examples. Such changes are essential given the influence of policy, and the urgency of enabling adaptive management to safeguard socio-ecological systems.