Science-based restoration monitoring of coastal habitats, Volume One: A framework for monitoring plans under the Estuaries and Clean Waters Act of 2000 (Public Law 160-457)

Executive Summary: The Estuary Restoration Act of 2000 (ERA), Title I of the Estuaries and Clean Waters Act of 2000, was created to promote the restoration of habitats along the coast of the United States (including the US protectorates and the Great Lakes). The NOAA National Centers for Coastal Ocean Science was charged with the development of a guidance manual for monitoring plans under this Act. This guidance manual, titled Science-Based Restoration Monitoring of Coastal Habitats, is written in two volumes. It provides technical assistance, outlines necessary steps, and provides useful tools for the development and implementation of sound scientific monitoring of coastal restoration efforts. In addition, this manual offers a means to detect early warnings that the restoration is on track or not, to gauge how well a restoration site is functioning, to coordinate projects and efforts for consistent and successful restoration, and to evaluate the ecological health of specific coastal habitats both before and after project completion (Galatowitsch et al. 1998). The following habitats have been selected for discussion in this manual: water column, rock bottom, coral reefs, oyster reefs, soft bottom, kelp and other macroalgae, rocky shoreline, soft shoreline, submerged aquatic vegetation, marshes, mangrove swamps, deepwater swamps, and riverine forests. The classification of habitats used in this document is generally based on that of Cowardin et al. (1979) in their Classification of Wetlands and Deepwater Habitats of the United States, as called for in the ERA Estuary Habitat Restoration Strategy. This manual is not intended to be a restoration monitoring “cookbook” that provides templates of monitoring plans for specific habitats. The interdependence of a large number of site-specific factors causes habitat types to vary in physical and biological structure within and between regions and geographic locations (Kusler and Kentula 1990). Monitoring approaches used should be tailored to these differences. However, even with the diversity of habitats that may need to be restored and the extreme geographic range across which these habitats occur, there are consistent principles and approaches that form a common basis for effective monitoring. Volume One, titled A Framework for Monitoring Plans under the Estuaries and Clean Waters Act of 2000, begins with definitions and background information. Topics such as restoration, restoration monitoring, estuaries, and the role of socioeconomics in restoration are discussed. In addition, the habitats selected for discussion in this manual are briefly described. (PDF contains 116 pages)

Using climate extension to assist coastal decision-makers with climate adaptation

Coastal managers need accessible, trusted, tailored resources to help them interpret climate information, identify vulnerabilities, and apply climate information to decisions about adaptation on regional and local levels. For decades, climate scientists have studied the impacts that short term natural climate variability and long term climate change will have on coastal systems. For example, recent estimates based on Intergovernmental Panel on Climate Change (IPCC) warming scenarios suggest that global sea levels may rise 0.5 to 1.4 meters above 1990 levels by 2100 (Rahmstorf 2007; Grinsted, Moore, and Jevrejeva 2009). Many low-lying coastal ecosystems and communities will experience more frequent salt water intrusion events, more frequent coastal flooding, and accelerated erosion rates before they experience significant inundation. These changes will affect the ways coastal managers make decisions, such as timing surface and groundwater withdrawals, replacing infrastructure, and planning for changing land use on local and regional levels. Despite the advantages, managers’ use of scientific information about climate variability and change remains limited in environmental decision-making (Dow and Carbone 2007). Traditional methods scientists use to disseminate climate information, like peer-reviewed journal articles and presentations at conferences, are inappropriate to fill decision-makers’ needs for applying accessible, relevant climate information to decision-making. General guides that help managers scope out vulnerabilities and risks are becoming more common; for example, Snover et al. (2007) outlines a basic process for local and state governments to assess climate change vulnerability and preparedness. However, there are few tools available to support more specific decision-making needs. A recent survey of coastal managers in California suggests that boundary institutions can help to fill the gaps between climate science and coastal decision-making community (Tribbia and Moser 2008). The National Sea Grant College Program, the National Oceanic and Atmospheric Administration's (NOAA) university-based program for supporting research and outreach on coastal resource use and conservation, is one such institution working to bridge these gaps through outreach. Over 80% of Sea Grant’s 32 programs are addressing climate issues, and over 60% of programs increased their climate outreach programming between 2006 and 2008 (National Sea Grant Office 2008). One way that Sea Grant is working to assist coastal decision-makers with using climate information is by developing effective methods for coastal climate extension. The purpose of this paper is to discuss climate extension methodologies on regional scales, using the Carolinas Coastal Climate Outreach Initiative (CCCOI) as an example of Sea Grant’s growing capacities for climate outreach and extension. (PDF contains 3 pages)

New challenges in lake and river monitoring

Freshwater ecosystems are highly dynamic and change on time-scales that range from a few hours to several months. The development of models that simulate these processes is often hampered by the lack of sufficient data to parameterize the processes and validate the models. In this article, I review some of the challenges posed by this lack of information and suggest ways in which they can be met by using automatic monitoring systems. One of these studies is the project tempQsim (EVK1-CT2002-00112) funded by the European Commission. In this project, detailed field and model analyses have been performed at eight catchment study sites in south and south-east Europe. A number of perceptual models for the study sites have been established, and results are being used to improve selected catchment models and provide a more adequate description of pollution dynamics. Results from the extensive field studies and model tests are now being used to derive recommendations for more tailored monitoring concepts in highly dynamic, but ‘data scarce’ environments, such as are frequently found in Mediterranean river basins. The author includes implications of the EU Water Framework Directive on monitoring methods.

Newport's mooring regulations: legal and policy analysis

The legal and policy issues facing Newport as it revises and implements its ordinances are numerous. Most of the issues have not been squarely resolved for Rhode Island. While Newport may take guidance from other states, it will be Rhode Island's task going forward to define the reach of its PTD as applied to some novel issues raised by mooring administration. The benefit of the flexibility of the PTD is allowing smaller units of government like Newport to define their regulatory goals based on a locally-tailored balancing test of competing interests facing scarce ocean resources. This report was designed to facilitate decision-maker discussion of how to strike that delicate balance.

Invasionsoft: a web-enabled tool for invasive species colonization predictions

Predicting and averting the spread of invasive species is a core focus of resource managers in all ecosystems. Patterns of invasion are difficult to forecast, compounded by a lack of user-friendly species distribution model (SDM) tools to help managers focus control efforts. This paper presents a web-based cellular automata hybrid modeling tool developed to study the invasion pattern of lionfish (Pterois volitans/miles) in the western Atlantic and is a natural extension our previous lionfish study. Our goal is to make publically available this hybrid SDM tool and demonstrate both a test case (P. volitans/miles) and a use case (Caulerpa taxifolia). The software derived from the model, titled Invasionsoft, is unique in its ability to examine multiple default or user-defined parameters, their relation to invasion patterns, and is presented in a rich web browser-based GUI with integrated results viewer. The beta version is not species-specific and includes a default parameter set that is tailored to the marine habitat. Invasionsoft is provided as copyright protected freeware at http://www.invasionsoft.com.

Evaluating the efficacy of managing West Coast groundfish resources through simulations

Management of West Coast groundfish resources by the Pacific Fishery Management Council involves Federal government and academic scientists conducting stock assessments, generally using the stock synthesis framework, applying the 40-10 rule to determine harvest guidelines for resources that are not overfished and conducting rebuilding analyses to determine harvest guidelines for resources that have been designated as overfished. However, this management system has not been evaluated in terms of its ability to satisfy the National Standard 1 goals of the Sustainable Fisheries Act. A Monte Carlo simulation framework is therefore outlined that can be used to make such evaluations. Based on simulations tailored to a situation similar to that of managing the widow rockfish (Sebastes entomelas) resource, it is shown that catches during recovery and thereafter are likely to be highly variable (up to ±30% from one year to the next). Such variability is far greater than has been presented to the decision makers to date. Reductions in interannual variability in catches through additional data collection are, however, unlikely. Rather, improved performance will probably arise from better methods for predicting future recruitment. Rebuilding analyses include quantities such as the year to which the desired probability of recovery applies. The estimates of such quantities are, however, very poorly determined.