Ecological Constraint Mapping: Understanding Outcome-Limiting Bottlenecks for Improved Environmental Decision-Making in Marine and Coastal Environments

Despite genuine attempts, the history of marine and coastal ecosystem management is littered with examples of poor environmental, social and financial outcomes. Marine ecosystems are largely populated by species with open populations, and feature ecological processes that are driven by multiple, interwoven, dynamic causes and effects. This complexity limits the acquisition of relevant knowledge of habitat characteristics, species utilisation and ecosystem dynamics. The consequence of this lack of knowledge is uncertainty about the link between action taken and outcome achieved. Such uncertainty risks misdirected human and financial investment, and sometimes may even lead to perverse outcomes. Technological advances offer new data acquisition opportunities, but the diversity and complexity of the biological and ecological information needed to reduce uncertainty means the increase in knowledge will be slow unless it is undertaken in a structured and focussed way. We introduce “Ecological Constraint Mapping” – an approach that takes a “supply chain” point of view and focusses on identifying the principal factors that constrain life-history outcomes (success/productivity/resilience/fitness) for marine and coastal species, and ultimately the quality and resilience of the ecosystems they are components of, and the life-history supporting processes and values ecosystems provide. By providing a framework for the efficient development of actionable knowledge, Ecological Constraint Mapping can facilitate a move from paradigm-based to knowledge-informed decision-making on ecological issues. It is suitable for developing optimal solutions to a wide range of conservation and management problems, providing an organised framework that aligns with current perspectives on the complex nature of marine and coastal systems.

Scalable uncertainty estimation for nonlinear inverse problems using parameter reduction, constraint mapping, and geometric sampling: Marine controlled-source electromagnetic examples

We have developed a new uncertainty estimation method that accounts for nonlinearity inherent in most geophysical problems, allows for the explicit search of model posterior space, is scalable, and maintains computational efficiencies on the order of deterministic inverse solutions. We accomplish this by combining an efficient parameter reduction technique, a parameter constraint mapping routine, a sparse geometric sampling scheme, and an efficient forward solver. In order to reduce our model domain and determine an independent basis, we implement both a typical principal component analysis, which factorizes the model covariance matrix, and an alternative compression method, based on singular-value decomposition, which acts on training models, directly, and is storage efficient. Once we have a reduced base, we map parameter constraints, from our original model domain, to this reduced domain to define a bounded geometric region of feasible model space. We utilize an optimal scheme to sample this reduced model space that uses Smolyak sparse grids and minimizes the number of forward solves by finding the sparsest sampling required to produce convergent uncertainty measures. The result is an ensemble of equivalent models, consistent with our inverse solution structure, which is used to infer inverse uncertainty. We tested our method with a 1D synthetic example, a comparison with a published Metropolis-Hastings sampling example, and an extension to 2D problems using a field data inversion.