Biological Assessment of Coral Reefs in Southern Puerto Rico: A Technical Approach for Coral Reef Protection Under the U.S. Clean Water Act.

States and other jurisdictions may protect coral reefs using biological water quality standards outlined by the United States Clean Water Act (CWA). Such protection will require long-term, regional monitoring of the resource using biological indicators and a probability-based sampling design. A 60-station survey targeting nearshore linear coral reef was conducted across southern Puerto Rico in December 2011 to document the status of reef inhabitants using a probabilistic, regional sampling design. The quantity, type and condition of stony corals, fish, gorgonians and sponges were documented from each station, providing a robust representation of linear reef status and composition across the region. Fish represented 106 unique taxa and stony corals 32 unique taxa. Benthic organisms (stony corals, sponges and gorgonians) averaged nearly 12 colonies per square meter, more than half of which were gorgonians. Assessment results can be used as a baseline to compare with future regional surveys to quantify change in reef condition over time (trend). Both temporal and spatial changes can be expected after large-scale disturbances like hurricanes Maria and Irma in 2017. The indicators and probabilistic sampling design support the long-term regional monitoring envisioned by the Environmental Protection Agency to implement CWA protections in Puerto Rico and elsewhere.

An introduction to Bayesian networks as assessment and decision support tools for managing coral reef ecosystem services.

Quantifying ecosystem goods and services can help evaluate policies aimed at protecting present and future generations from losing ecosystem benefits. Explicating and quantifying the relationships among risk factors, ecological structure and function, and delivery of ecosystem goods and services requires analytical methodologies that propagate uncertainties. The capabilities of Bayesian networks in generating predictions and accounting for uncertainty are explored with a focus on coral reef ecosystem service assessments. The qualitative aspects of Bayesian networks can be applied to conceptual frameworks developed for coral reef ecosystem service assessments. This is demonstrated using qualitative graphs that describe the relationships between coral reef condition endpoints and benefits from ecosystem services including property protection, recreational opportunities, fish for fisheries, and biochemical metabolites for commercial products developed from reef organisms. Bayesian networks help weigh uncertainties between management decision impacts on stressors and the corresponding delivery of ecosystem services. Quantitative capabilities for inferences are examined in hypothetical scenarios evaluating how decisions affect coral reef ecosystem services and economic benefits and resilience to episodic stress. The described methods facilitate visualizing the potential impacts on ecosystem services from alternative scenarios.

Regional status assessment of stony corals in the US Virgin Islands.

States may protect coral reefs using biological water quality standards outlined by the Clean Water Act. This requires biological assessments with indicators sensitive to human disturbance and regional, probability-based survey designs. Stony coral condition was characterized on a regional scale for the first time in the nearshore waters of the US Virgin Islands (USVI). Coral composition, abundance, size, and health were assessed at 66 stations in the St. Croix region in fall 2007 and at 63 stations in the St. Thomas and St. John region in winter 2009. Indicators were chosen for their sensitivity to human disturbance. Both surveys were probability-based (random) designs with station locations preselected from areas covered by hardbottom and coral reef substrate. Taxa richness was as high as 21 species but more than half the area of both regions exhibited taxa richness of <10 species in the 25 m(2) transect area. Coral density was as high as 5 colonies m(-2) but more than half the area of both regions had <2 colonies m(-2). Both regions showed similar dominant species based on frequency of occurrence and relative abundance. Because of large colony sizes, Montastrea annularis provided more total surface area and live surface area than more abundant species. The surveys establish baseline regional conditions and provide a foundation for long-term regional monitoring envisioned by the USVI Department of Planning and Natural Resources. The probabilistic sampling design assures the data can be used in Clean Water Act reporting.

Integrating human health and environmental health into the DPSIR framework: a tool to identify research opportunities for sustainable and healthy communities.

The U.S. Environmental Protection Agency has recently realigned its research enterprise around the concept of sustainability. Scientists from across multiple disciplines have a role to play in contributing the information, methods, and tools needed to more fully understand the long-term impacts of decisions on the social and economic sustainability of communities. Success will depend on a shift in thinking to integrate, organize, and prioritize research within a systems context. We used the Driving forces-Pressures-State-Impact-Response (DPSIR) framework as a basis for integrating social, cultural, and economic aspects of environmental and human health into a single framework. To make the framework broadly applicable to sustainability research planning, we provide a hierarchical system of DPSIR keywords and guidelines for use as a communication tool. The applicability of the integrated framework was first tested on a public health issue (asthma disparities) for purposes of discussion. We then applied the framework at a science planning meeting to identify opportunities for sustainable and healthy communities research. We conclude that an integrated systems framework has many potential roles in science planning, including identifying key issues, visualizing interactions within the system, identifying research gaps, organizing information, developing computational models, and identifying indicators.