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

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.     

Accounting for multiple testing in the analysis of spatio-temporal environmental data

The statistical analysis of environmental data from remote sensing and Earth system simulations often entails the analysis of gridded spatio-temporal data, with a hypothesis test being performed for each grid cell. When the whole image or a set of grid cells are analyzed for a global effect, the problem of multiple testing arises. When no global effect is present, we expect $$ \alpha $$% of all grid cells to be false positives, and spatially autocorrelated data can give rise to clustered spurious rejections that can be misleading in an analysis of spatial patterns. In this work, we review standard solutions for the multiple testing problem and apply them to spatio-temporal environmental data. These solutions are independent of the test statistic, and any test statistic can be used (e.g., tests for trends or change points in time series). Additionally, we introduce permutation methods and show that they have more statistical power. Real-world data are used to provide examples of the analysis, and the performance of each method is assessed in a simulation study. Unlike other simulation studies, our study compares the statistical power of the presented methods in a comprehensive simulation study. In conclusion, we present several statistically rigorous methods for analyzing spatio-temporal environmental data and controlling the false positives. These methods allow the use of any test statistic in a wide range of applications in environmental sciences and remote sensing.