Mapping ecological indicators of human impact with statistical and machine learning methods: Tests on the California coast

Characterizing Exposure to and Sharing Knowledge of Drivers of Environmental Change in the St. Lawrence System in Canada

“…These results are consistent with observations elsewhere in the world, where cumulative exposure conspicuously arises from and markedly intensifies close to coastal cities and at the mouth of rivers draining highly populated areas (e.g., Halpern et al, 2015b;Feist and Levin, 2016;Mach et al, 2017;Stock et al, 2018). These are areas where human activities (e.g., coastal development and shipping) and footprints (e.g., pollution runoff) are most intense (Feist and Levin, 2016), and on which is overlaid a background of natural disturbances (Micheli et al, 2016).…”

“…Understanding how ecosystem state will be affected by global change requires a comprehensive understanding of how threats are distributed and interact in space and time, which in turn hinges on appropriate data tailored to multi-driver studies (Dafforn et al, 2016;Stock et al, 2018;Bowler et al, 2019). In the St. Lawrence, we found that few areas are free from cumulative exposure and that the whole Estuary, the Anticosti Gyre, and coastal southwestern Gulf are particularly exposed to cumulative drivers, especially close to urban areas.…”

“…However, equivalent platforms for drivers have largely focused on single drivers (e.g., Global Fishing Watch) and platforms collating data-based indicators and knowledge on multiple drivers in a comparable and interoperable way remain conspicuously missing (but see Halpern et al, 2015a). This is in spite of integrated management and assessment approaches requiring efficient data reporting, standardized data management practices, and tools tailored to the study of the effects of multiple drivers (Dafforn et al, 2016;Stock et al, 2018).…”

A Cluster-Stacking-Based Approach to Forecasting Seasonal Chlorophyll-a Concentration in Coastal Waters

“…A multitude of anthropic influences has significantly altered the environmental conditions and the diversity of marine biological communities [24]. However, understanding and predicting the combined impacts of single and multiple stressors are particularly challenging [21].…”

Accuracy of Empirical Satellite Algorithms for Mapping Phytoplankton Diagnostic Pigments in the Open Ocean: A Supervised Learning Perspective

“…Here, we build on supervised learning methods adjusted for spatially auto-correlated data to partially address the first of these challenges, i.e., the lack of in situ observations in some marine regions, and their possible non-independence. First, to estimate errors when extrapolating to regions without in situ data and to account for possible non-independence of in situ observations, we use spatial block cross-validation (Roberts et al, 2017), in a hierarchical variant designed for model selection and tuning among many possibilities when data are spatially clustered (Stock et al, 2018a). We also report prediction errors estimated with fivefold cross-validation, ignoring the data's spatial structure, for comparison.…”