Restoring shellfish reefs: Global guidelines for practitioners and scientists

Widespread global declines in shellfish reefs (ecosystem-forming bivalves such as oysters and mussels) have led to growing interest in their restoration and protection. With restoration projects now occurring on four continents and in at least seven countries, global restoration guidelines for these ecosystems have been developed based on experience over the past two decades. The following key elements of the guidelines are outlined: (a) the case for shellfish reef resto- ration and securing financial resources; (b) planning, feasibility, and goal set- ting; (c) biosecurity and permitting; (d) restoration in practice; (e) scaling up from pilot to larger scale restoration, (f) monitoring, (g) restoration beyond oyster reefs (specifically mussels), and (h) successful communication for shell- fish reef restoration projects

Dynamics of sea level rise and coastal flooding on a changing landscape

Standard approaches to determining the impacts of sea level rise (SLR) on storm surge flooding employ numerical models reflecting present conditions with modified sea states for a given SLR scenario. In this study, we advance this paradigm by adjusting the model framework so that it reflects not only a change in sea state but also variations to the landscape (morphologic changes and urbanization of coastal cities). We utilize a numerical model of the Mississippi and Alabama coast to simulate the response of hurricane storm surge to changes in sea level, land use/land cover, and land surface elevation for past (1960), present (2005), and future (2050) conditions. The results show that the storm surge response to SLR is dynamic and sensitive to changes in the landscape. We introduce a new modeling framework that includes modification of the landscape when producing storm surge models for future conditions. Key Points --Storm surge response to climate change impacts is dynamic. --A framework for constructing dynamic assessments of SLR is develope

Mapping reef fish and the seascape: using acoustics and spatial modeling to guide coastal management

Reef fish distributions are patchy in time and space with some coral reef habitats supporting higher densities (i.e., aggregations) of fish than others. Identifying and quantifying fish aggregations (particularly during spawning events) are often top priorities for coastal managers. However, the rapid mapping of these aggregations using conventional survey methods (e.g., non-technical SCUBA diving and remotely operated cameras) are limited by depth, visibility and time. Acoustic sensors (i.e., splitbeam and multibeam echosounders) are not constrained by these same limitations, and were used to concurrently map and quantify the location, density and size of reef fish along with seafloor structure in two, separate locations in the U.S. Virgin Islands. Reef fish aggregations were documented along the shelf edge, an ecologically important ecotone in the region. Fish were grouped into three classes according to body size, and relationships with the benthic seascape were modeled in one area using Boosted Regression Trees. These models were validated in a second area to test their predictive performance in locations where fish have not been mapped. Models predicting the density of large fish (≥29 cm) performed well (i.e., AUC = 0.77). Water depth and standard deviation of depth were the most influential predictors at two spatial scales (100 and 300 m). Models of small (≤11 cm) and medium (12–28 cm) fish performed poorly (i.e., AUC = 0.49 to 0.68) due to the high prevalence (45–79%) of smaller fish in both locations, and the unequal prevalence of smaller fish in the training and validation areas. Integrating acoustic sensors with spatial modeling offers a new and reliable approach to rapidly identify fish aggregations and to predict the density large fish in un-surveyed locations. This integrative approach will help coastal managers to prioritize sites, and focus their limited resources on areas that may be of higher conservation value

Estuary-Specific and Adaptive Habitat Suitability Index Model for the Eastern Oyster <i>Crassostrea Virginica</i> in the Pensacola Bay System, Florida, USA

Oyster reef, a key ecosystem component in many temperate and subtropical estuarine systems, has been greatly diminished by human influences and ongoing climatic changes. Changes in precipitation, oyster adult and larval distribution, benthic composition, hydrology, currents and/or inputs to the system such as pollutants or sediments complicate the identification of locations that may be suitable for oyster reef restoration. In such cases, development of a habitat suitability index model (HSI) can provide useful guidance on where restoration activities might be successful, and once complete, help to focus the area for restoration project siting consideration. HSI models can be constructed from a variety of data categories including biological requirements of the species to be restored, physical characteristics of the system, regulatory constraints, and socio-economic concerns. Here we describe the development of a geographic information system based HSI model to inform restoration of oyster reef in the Pensacola Bay System, Florida, USA that can be used as a template for oyster reef restoration in other Florida estuaries and beyond. We demonstrate that an oyster habitat HSI model can be relatively simple to construct, useful even with limited environmental data, improved with community stakeholder and resource user input, and easily adaptable.</p

Modeled Sea Level Rise Impacts on Coastal Ecosystems at Six Major Estuaries on Florida’s Gulf Coast: Implications for Adaptation Planning

<div><p>The Sea Level Affecting Marshes Model (SLAMM) was applied at six major estuaries along Florida’s Gulf Coast (Pensacola Bay, St. Andrews/Choctawhatchee Bays, Apalachicola Bay, Southern Big Bend, Tampa Bay and Charlotte Harbor) to provide quantitative and spatial information on how coastal ecosystems may change with sea level rise (SLR) and to identify how this information can be used to inform adaption planning. High resolution LiDAR-derived elevation data was utilized under three SLR scenarios: 0.7 m, 1 m and 2 m through the year 2100 and uncertainty analyses were conducted on selected input parameters at three sites. Results indicate that the extent, spatial orientation and relative composition of coastal ecosystems at the study areas may substantially change with SLR. Under the 1 m SLR scenario, total predicted impacts for all study areas indicate that coastal forest (-69,308 ha; -18%), undeveloped dry land (-28,444 ha; -2%) and tidal flat (-25,556 ha; -47%) will likely face the greatest loss in cover by the year 2100. The largest potential gains in cover were predicted for saltmarsh (+32,922 ha; +88%), transitional saltmarsh (+23,645 ha; na) and mangrove forest (+12,583 ha; +40%). The Charlotte Harbor and Tampa Bay study areas were predicted to experience the greatest net loss in coastal wetlands The uncertainty analyses revealed low to moderate changes in results when some numerical SLAMM input parameters were varied highlighting the value of collecting long-term sedimentation, accretion and erosion data to improve SLAMM precision. The changes predicted by SLAMM will affect exposure of adjacent human communities to coastal hazards and ecosystem functions potentially resulting in impacts to property values, infrastructure investment and insurance rates. The results and process presented here can be used as a guide for communities vulnerable to SLR to identify and prioritize adaptation strategies that slow and/or accommodate the changes underway.</p></div

IONiC: A cyber-enabled community of practice for improving inorganic chemicalEducation

IONiC’s purpose is to enhance the inorganicChemistry classroom and laboratory experience for students and faculty members through the development of a vibrant virtual “community of practice”

Digital Elevation Model (DEM) inputs and NOAA tide stations utilized for all study areas.

<p>Tidal parameters and location of stations informed the creation of subsites for each study area as illustrated in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0132079#pone.0132079.g001" target="_blank">Fig 1</a>.</p

Quantitative SLAMM Results–coastal ecosystem change under a 1 meter SLR scenario through the year 2100, developed dry land protected from changing.

<p>¹Results exclude tidal flats with no elevation data (approximately 10,000 ha).</p><p>Quantitative SLAMM Results–coastal ecosystem change under a 1 meter SLR scenario through the year 2100, developed dry land protected from changing.</p

Parameters and their statistical distribution input into the SLAMM uncertainty analysis module.

<p>U = Uniform distribution (minimum, maximum); T = Triangular distribution (minimum, most likely, maximum).</p