Tag: fuzzy logic

Opportunities for climate‐risk reduction through effective fisheries management

Posted on by d.burnside

Risk of impact of marine fishes to fishing and climate change (including ocean acidification) depend on the species’ ecological and biological characteristics, as well as their exposure to over‐exploitation and climate hazards. These human‐induced hazards should be considered concurrently in conservation risk assessment. In this study, we aim to examine the combined contributions of climate change and fishing to the risk of impacts of exploited fishes, and the scope for climate‐risk reduction from fisheries management. We combine fuzzy logic expert system with species distribution modeling to assess the extinction risks of climate and fishing impacts of 825 exploited marine fish species across the global ocean. We compare our calculated risk index with extinction risk of marine species assessed by the International Union for Conservation of Nature (IUCN). Our results show that 60% (499 species) of the assessed species are projected to experience very high risk from both overfishing and climate change under a “business‐as‐usual” scenario (RCP 8.5 with current status of fisheries) by 2050. The risk index is significantly and positively related to level of IUCN extinction risk (ordinal logistic regression, p < 0.0001). Furthermore, the regression model predicts species with very high risk index would have at least one in five (>20%) chance of having high extinction risk in the next few decades (equivalent to the IUCN categories of vulnerable, endangered or critically endangered). Areas with more at‐risk species to climate change are in tropical and subtropical oceans, while those that are at risk to fishing are distributed more broadly, with higher concentration of at‐risk species in North Atlantic and South Pacific Ocean. The number of species with high extinction risk would decrease by 63% under the sustainable fisheries‐low emission scenario relative to the “business‐as‐usual” scenario. This study highlights the substantial opportunities for climate‐risk reduction through effective fisheries management.

A fuzzy logic expert system for evaluating policy progress towards sustainability goals.

Posted on by d.burnside

Evaluating progress towards environmental sustainability goals can be difficult due to a lack of measurable benchmarks and insufficient or uncertain data. Marine settings are particularly challenging, as stakeholders and objectives tend to be less well defined and ecosystem components have high natural variability and are difficult to observe directly. Fuzzy logic expert systems are useful analytical frameworks to evaluate such systems, and we develop such a model here to formally evaluate progress towards sustainability targets based on diverse sets of indicators. Evaluation criteria include recent (since policy enactment) and historical (from earliest known state) change, type of indicators (state, benefit, pressure, response), time span and spatial scope, and the suitability of an indicator in reflecting progress toward a specific objective. A key aspect of the framework is that all assumptions are transparent and modifiable to fit different social and ecological contexts. We test the method by evaluating progress towards four Aichi Biodiversity Targets in Canadian oceans, including quantitative progress scores, information gaps, and the sensitivity of results to model and data assumptions. For Canadian marine systems, national protection plans and biodiversity awareness show good progress, but species and ecosystem states overall do not show strong improvement. Well-defined goals are vital for successful policy implementation, as ambiguity allows for conflicting potential indicators, which in natural systems increases uncertainty in progress evaluations. Importantly, our framework can be easily adapted to assess progress towards policy goals with different themes, globally or in specific regions.

Using fuzzy logic to determine the vulnerability of marine species to climate change.

Posted on by d.burnside

Marine species are being impacted by climate change and ocean acidification, although their level of vulnerability varies due to differences in species’ sensitivity, adaptive capacity and exposure to climate hazards. Due to limited data on the biological and ecological attributes of many marine species, as well as inherent uncertainties in the assessment process, climate change vulnerability assessments in the marine environment frequently focus on a limited number of taxa or geographic ranges. As climate change is already impacting marine biodiversity and fisheries, there is an urgent need to expand vulnerability assessment to cover a large number of species and areas. Here, we develop a modelling approach to synthesize data on species-specific estimates of exposure, and ecological and biological traits to undertake an assessment of vulnerability (sensitivity and adaptive capacity) and risk of impacts (combining exposure to hazards and vulnerability) of climate change (including ocean acidification) for global marine fishes and invertebrates.

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