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.

Large-scale integrated assessment of soil carbon and organic matter-related nitrogen fluxes in Saxony (Germany).

Changes in land-use, agricultural management and climate affect the turnover and storage of organic carbon in soils (SOC) as well as the nitrogen mobilization from soil organic matter (SOM), with potential side effects on nitrogen availability and leaching. When addressing the requests for increased carbon storage in soil as well as for the reduction of nitrogen losses, integrated approaches on regional scales are required that take into account the actual changes in agricultural management and climate. This study investigated the arable land (7345 km2) of Saxony (Germany) with regard to the following: (1) the trends of SOC storage and organic matter-related nitrogen fluxes, including their subregional and annual dynamics, (2) changes in the carbon input to arable soils and the turnover of organic matter, and (3) the contribution of different drivers (climate, crop production and fertilization, tillage system) to the simulated SOM changes for the period 1998-2014 on a 500 m grid. The model CANDY carbon balance (CCB) was specifically adapted for large-scale simulations of SOM turnover to link spatial data on soils and climate with regional statistics on agricultural management. This new 'regional mode' of CCB has been validated using data from 391 plots across different European locations. The initial SOC levels for Saxony assumed steady state conditions at the beginning of the simulation period and have been validated using data from 667 monitoring sites. The results showed an increase in the SOC stocks of the arable soils of Saxony of 785 × 103 t C (1.24‰ annually) during the simulation period. At the same time, the model simulated an average increase in organic nitrogen stored in SOM of approximately 7.5 kg N ha-1 a-1, with considerable differences between individual years and subregions. Both the increase in carbon inputs to soil (+8%) and the reduction of carbon turnover rates (-10%) had positive effects on SOC storage. While the increased use of conservation tillage was the most important driver for the overall increase in SOM storage in Saxony, climate variability and crop production and fertilization had the largest effect on its annual dynamics.