Measuring biodiversity vulnerability in French lakes - The IVCLA index

Assessing the vulnerability of ecosystems to biodiversity loss has become increasingly crucial in conservation and ecology research. This study proposed a methodology for measuring lake vulnerability to biodiversity loss employing an established framework that combines three components. For this, we measured the resilience (functional redundancy) and sensitivity (an index considering three characteristics of rarity) components for fish and phytoplankton communities. We also measured the exposure component of the main stressors in lakes. We then combined the three components and calculated the vulnerability index (IVCLA) using data from 255 French lakes. We found that all lakes exhibited low levels of resilience, elevated sensitivity regarding average values for fish and phytoplankton groups, and medium exposure to stressors associated with human activities. In addition, there were some discrepancies in resilience and sensitivity patterns between fish and phytoplankton groups, emphasizing the importance of considering information from multiple biological groups when assessing ecosystem vulnerability. Hydrological alterations and low water quality were key stressors related to higher lake vulnerability. Most French lakes have been classified as exhibiting moderate vulnerability. It is crucial to emphasize the potential increase in exposure risks, which could lead to even higher vulnerability levels and, subsequently, biodiversity loss in the future. The IVCLA index offers several advantages, including integrating multiple taxa groups and stressors. We recommend incorporating additional data, such as the resilience and sensitivity of the entire food web, and considering temporal responses to stressors to improve accuracy and predictive power. The IVCLA was developed with the purpose of serving as an effective tool for guiding environmental managers in designing conservation strategies and making informed decisions for lake ecosystems.

Implementing Bayesian networks for ISO 31000:2018-based maritime oil spill risk management: State-of-art, implementation benefits and challenges, and future research directions

The risk of a large-scale oil spill remains significant in marine environments as international maritime transport continues to grow. The environmental as well as the socio-economic impacts of a large-scale oil spill could be substantial. Oil spill models and modeling tools for Pollution Preparedness and Response (PPR) can support effective risk management. However, there is a lack of integrated approaches that consider oil spill risks comprehensively, learn from all information sources, and treat the system uncertainties in an explicit manner. Recently, the use of the international ISO 31000:2018 risk management framework has been suggested as a suitable basis for supporting oil spill PPR risk management. Bayesian networks (BNs) are graphical models that express uncertainty in a probabilistic form and can thus support decision-making processes when risks are complex and data are scarce. While BNs have increasingly been used for oil spill risk assessment (OSRA) for PPR, no link between the BNs literature and the ISO 31000:2018 framework has previously been made. This study explores how Bayesian risk models can be aligned with the ISO 31000:2018 framework by offering a flexible approach to integrate various sources of probabilistic knowledge. In order to gain insight in the current utilization of BNs for oil spill risk assessment and management (OSRA-BNs) for maritime oil spill preparedness and response, a literature review was performed. The review focused on articles presenting BN models that analyze the occurrence of oil spills, consequence mitigation in terms of offshore and shoreline oil spill response, and impacts of spills on the variables of interest. Based on the results, the study discusses the benefits of applying BNs to the ISO 31000:2018 framework as well as the challenges and further research needs.