Guidelines for the conservation and restoration of seagrasses in the United States and adjacent waters

Seagrass ecosystems are protected under the federal "no-net-loss" policy for wetlands and form one of the most productive plant communities on the planet, performing important ecological functions. Seagrass beds have been recognized as a valuable resource critical to the health and function of coastal waters. Greater awareness and public education, however, is essential for conservation of this resource. Tremendous losses of this habitat have occurred as a result of development within the coastal zone. Disturbances usually kill seagrasses rapidly, and recovery is often comparatively slow. Mitigation to compensate for destruction of existing habitat usually follows when the agent of loss and responsible party are known. Compensation assumes that ecosystems can be made to order and, in essence, trades existing functional habitat for the promise of replacement habitat. While ~lant ingse agrass is not technically complex, there is no easy way to meet the goal of maintaining or increasing seagrass acreage. Rather, the entire process of planning, planting and monitoring requires attention to detail and does not lend itself to oversimplification.

Ecological condition of coastal ocean waters of the U.S. continental shelf off South Florida: 2007

A study was initiated with field work in May 2007 to assess the status of ecological condition and stressor impacts throughout the U.S. continental shelf off South Florida, focusing on soft-bottom habitats, and to provide this information as a baseline for evaluating future changes due to natural or human-induced disturbances. The boundaries of the study region extended from Anclote Key on the western coast of Florida to West Palm Beach on the eastern coast of Florida, inclusive of the Florida Keys National Marine Sanctuary (FKNMS), and from navigable depths along the shoreline seaward to the shelf break (~100m). The study incorporated standard methods and indicators applied in previous national coastal monitoring programs — U.S. Environmental Protection Agency’s (EPA) Environmental Monitoring and Assessment Program (EMAP) and National Coastal Assessment (NCA) — including multiple measures of water quality, sediment quality, and biological condition. Synoptic sampling of the various indicators provided an integrative weight-of-evidence approach to assessing condition at each station and a basis for examining potential associations between presence of stressors and biological responses. A probabilistic sampling design, which included 50 stations distributed randomly throughout the region, was used to provide a basis for estimating the spatial extent of condition relative to the various measured indicators and corresponding assessment endpoints (where available). The study was conducted through a large cooperative effort by National Oceanic and Atmospheric Administration (NOAA)/National Centers for Coastal Ocean Science (NCCOS), EPA, U.S. Geological Survey (USGS), NOAA/Oceanic and Atmospheric Research (OAR)/Atlantic Oceanographic and Meteorological Laboratory in Miami, FKNMS, and the Florida Fish and Wildlife Conservation Commission (FWC). The majority of the South Florida shelf had high levels of dissolved oxygen (DO) in near-bottom water (> 5 mg L-1) indicative of “good” water quality.. DO levels in bottom waters exceeded this upper threshold at 98.8% throughout the coastal-ocean survey area. Only 1.2% of the region had moderate DO levels (2-5 mg/L) and no part of the survey area had DO <2.0 mg/L. In addition, offshore waters throughout the region had relatively low levels of total suspended solids (TSS), nutrients, and chlorophyll a indicative of oligotrophic conditions. Results suggested good sediment quality as well. Sediments throughout the region, which ranged from sands to intermediate muddy sands, had low levels of total organic carbon (TOC) below bioeffect guidelines for benthic organisms. Chemical contaminants in sediments were also mostly at low, background levels. For example, none of the stations had chemicals in excess of corresponding Effects-Range Median (ERM) probable bioeffect values or more than one chemical in excess of lower-threshold Effects-Range Low (ERL) values. Cadmium was the only chemical that occurred at moderate concentrations between corresponding ERL and ERM values. Sixty fish samples from 28 stations were collected and analyzed for chemical contaminants. Eleven of these samples (39% of sites) had moderate levels of contaminants, between lower and upper non-cancer human-health thresholds, and ten (36% of sites) had high levels of contaminants above the upper threshold.

Prediction of mesophotic coral distributions in the Au‘au Channel, Hawaii

The primary objective of this study was to predict the distribution of mesophotic hard corals in the Au‘au Channel in the Main Hawaiian Islands (MHI). Mesophotic hard corals are light-dependent corals adapted to the low light conditions at approximately 30 to 150 m in depth. Several physical factors potentially influence their spatial distribution, including aragonite saturation, alkalinity, pH, currents, water temperature, hard substrate availability and the availability of light at depth. Mesophotic corals and mesophotic coral ecosystems (MCEs) have increasingly been the subject of scientific study because they are being threatened by a growing number of anthropogenic stressors. They are the focus of this spatial modeling effort because the Hawaiian Islands Humpback Whale National Marine Sanctuary (HIHWNMS) is exploring the expansion of its scope—beyond the protection of the North Pacific Humpback Whale (Megaptera novaeangliae)—to include the conservation and management of these ecosystem components. The present study helps to address this need by examining the distribution of mesophotic corals in the Au‘au Channel region. This area is located between the islands of Maui, Lanai, Molokai and Kahoolawe, and includes parts of the Kealaikahiki, Alalākeiki and Kalohi Channels. It is unique, not only in terms of its geology, but also in terms of its physical oceanography and local weather patterns. Several physical conditions make it an ideal place for mesophotic hard corals, including consistently good water quality and clarity because it is flushed by tidal currents semi-diurnally; it has low amounts of rainfall and sediment run-off from the nearby land; and it is largely protected from seasonally strong wind and wave energy. Combined, these oceanographic and weather conditions create patches of comparatively warm, calm, clear waters that remain relatively stable through time. Freely available Maximum Entropy modeling software (MaxEnt 3.3.3e) was used to create four separate maps of predicted habitat suitability for: (1) all mesophotic hard corals combined, (2) Leptoseris, (3) Montipora and (4) Porites genera. MaxEnt works by analyzing the distribution of environmental variables where species are present, so it can find other areas that meet all of the same environmental constraints. Several steps (Figure 0.1) were required to produce and validate four ensemble predictive models (i.e., models with 10 replicates each). Approximately 2,000 georeferenced records containing information about mesophotic coral occurrence and 34 environmental predictors describing the seafloor’s depth, vertical structure, available light, surface temperature, currents and distance from shoreline at three spatial scales were used to train MaxEnt. Fifty percent of the 1,989 records were randomly chosen and set aside to assess each model replicate’s performance using Receiver Operating Characteristic (ROC), Area Under the Curve (AUC) values. An additional 1,646 records were also randomly chosen and set aside to independently assess the predictive accuracy of the four ensemble models. Suitability thresholds for these models (denoting where corals were predicted to be present/absent) were chosen by finding where the maximum number of correctly predicted presence and absence records intersected on each ROC curve. Permutation importance and jackknife analysis were used to quantify the contribution of each environmental variable to the four ensemble models.