Accounting for forest condition in Europe based on an international statistical standard

Panel-based assessment of ecosystem condition as a platform for adaptive and knowledge driven management

Ecosystems are subjected to increasing exposure to multiple anthropogenic drivers. This has led to the development of national and international accounting systems describing the condition of ecosystems, often based on few, highly aggregated indicators. Such accounting systems would benefit from a stronger theoretical and empirical underpinning of ecosystem dynamics. Operational tools for ecosystem management require understanding of natural ecosystem dynamics, consideration of uncertainty at all levels, means for quantifying driver-response relationships behind observed and anticipated future trajectories of change, and an efficient and transparent synthesis to inform knowledge-driven decision processes. There is hence a gap between highly aggregated indicator-based accounting tools and the need for explicit understanding and assessment of the links between multiple drivers and ecosystem condition as a foundation for informed and adaptive ecosystem management. We describe here an approach termed PAEC (Panel-based Assessment of Ecosystem Condition) for combining quantitative and qualitative elements of evidence and uncertainties into an integrated assessment of ecosystem condition at spatial scales relevant to management and monitoring. The PAEC protocol is founded on explicit predictions, termed phenomena, of how components of ecosystem structure and functions are changing as a result of acting drivers. The protocol tests these predictions with observations and combines these tests to assess the change in the condition of the ecosystem as a whole. PAEC includes explicit, quantitative or qualitative, assessments of uncertainty at different levels and integrates these in the final assessment. As proofs-of-concept we summarize the application of the PAEC protocol to a marine and a terrestrial ecosystem in Norway.

Humans versus models: a comparative assessment of ecosystem services models and stakeholders' perceptions

Mapping the production of Ecosystem Services (ES) is imperative for sustainable ecosystem management. Likewise, incorporating expert knowledge enhances ES research. Here, we calculate eight multi-temporal ES indicators for mainland Portugal using a spatial modelling approach. These indicators are then integrated into the novel ASEBIO index-Assessment of Ecosystem Services and Biodiversity-which depicts a combined ES potential based on CORINE Land Cover, using a multi-criteria evaluation method with weights defined by stakeholders through an Analytical Hierarchy Process (AHP). Outputs from the modelling show how ES have changed in Portugal in relation to land use changes, including trade-offs between 1990 and 2018. The composed ASEBIO index is compared against the stakeholders' valuation of ES potential for the year 2018. The results reveal a significant mismatch between the ES potential perceived by stakeholders and the models, with stakeholder estimates being 32.8% higher on average. All the selected ES were overestimated by the stakeholders. Drought regulation and erosion prevention have the highest contrasts, while water purification, food production and recreation are the most closely aligned among both approaches. Providing the first national overview about the status of multiple ES over a 28 year-period, our findings highlight potential disparities between data-driven and stakeholder-based evaluations. Therefore, we suggest the need for integrative strategies that consider scientific models with expert knowledge for more effective ES assessments and land-use planning. This approach could help bridge the gap between data-driven models and human perspectives, resulting in more balanced and inclusive decision-making.

A decision methodology for site-level ecosystem accounting

The United Nations System of Environmental-Economic Accounting Ecosystem Accounting (SEEA EA) framework is the international standard for ecosystem accounting. To date, application of SEEA EA has been predominantly at large scales, usually at landscape and national levels. However, many environmental management decisions are taken locally, in site-specific contexts. While the use of SEEA EA continues to develop at all scales, there is currently no widely endorsed methodology for employing SEEA EA at local scales, such as the site level. We present a methodology for developing site-level ecosystem accounts, describing the important decisions at each step of the process. We also provide two case studies that demonstrate the context-dependent nature of the decision-making process of ecosystem accounting at small scales. The two major challenges for site-level accounting are stakeholder engagement and data availability. As the use of SEEA EA continues to increase in policy and decision-making processes worldwide, there is a need for local-scale case studies that adapt this methodology across a broad range of contexts. Our case studies provide some of the first published examples of the application of SEEA EA at the site level and are intended to promote consistent implementation of ecosystem accounting across scales.

Effectiveness of cork and pine bark powders as biosorbents for potentially toxic elements present in aqueous solution

Cork oak and pine bark, two of the most prolific byproducts of the European forestry sector, were assessed as biosorbents for eliminating potentially toxic elements (PTEs) from water-based solutions. Our research suggests that bioadsorption stands out as a viable and environmental eco-friendly technology, presenting a sustainable method for the extraction of PTEs from polluted water sources. This study aimed to evaluate and compare the efficiency of cork powder and pine bark powder as biosorbents. Specifically, the adsorption of Fe, Cu, Zn, Cd, Ni, Pb and Sn at equilibrium were studied through batch experiments by varying PTEs concentrations, pH, and ionic strength. Results from adsorption-desorption experiments demonstrate the remarkable capacity of both materials to retain the studied PTE. Cork powder and pine bark powder exhibited the maximum retention capacity for Fe and Cd, while they performed poorly for Pb and Sn, respectively. Nevertheless, pine bark showed a slightly lower retention capacity than cork. Increasing the pH resulted in cork showing the highest adsorption for Zn and the lowest for Sn, while for pine bark, Cd was the most adsorbed, and Sn was the least adsorbed, respectively. The highest adsorption of both materials occurred at pH 3.5-5, depending on the PTE tested. The ionic strength also influenced the adsorption of the various PTEs for both materials, with decreased adsorption as ionic strength increased. The findings suggest that both materials could be effective for capturing and eliminating the examined PTEs, albeit with different efficiencies. Remarkably, pine bark demonstrated superior adsorption capabilities, which were observed to vary based on the specific element and the experimental conditions. These findings contribute to elucidating the bio-adsorption potential of these natural materials, specifically their suitability in mitigating PTEs pollution, and favoring the recycling and revalorization of byproducts that might otherwise be considered residue.

Revisiting the Landscape Mosaic model

The landscape mosaic model quantifies and maps the spatial juxtaposition of different land uses. It provides a landscape perspective of anthropic threats posed by agriculture and urban development, and the spatial-temporal shifting of the landscape mosaic indicates landscapes where anthropic intensity has changed. We use the U.S. Geological Survey provided National Land Cover Database (NLCD) for the years 2001 and 2021 to derive the landscape mosaic at five analysis scales. To improve earlier implementations of the model, we introduce the heatmap, a flexible scheme providing more thematic reporting opportunities and allowing for better quantitative summary reporting across analysis scales as well as for temporal trends. The results are exemplified at regional scale for the Atlanta metropolitan area. We use the improved model to investigate the land cover context over time and at different analysis scales and show how custom color tables detail different thematic features of the landscape mosaic, including the degree and change of anthropic intensity. We conclude with a discussion of potential applications in ecology, landscape planning, and restoration to illustrate the benefits of the revised landscape mosaic model. All assessment tools are now available in open-source software packages.