Identification of a spatially efficient portfolio of priority conservation sites in marine and estuarine areas of Florida.
код для вставкиСкачатьAQUATIC CONSERVATION: MARINE AND FRESHWATER ECOSYSTEMS Aquatic Conserv: Mar. Freshw. Ecosyst. 19: 408–420 (2009) Published online 25 November 2008 in Wiley InterScience (www.interscience.wiley.com). DOI: 10.1002/aqc.992 Identification of a spatially efficient portfolio of priority conservation sites in marine and estuarine areas of Florida LAURA GESELBRACHTa,, ROBERTO TORRESa, GRAEME S. CUMMINGb,y, DANIEL DORFMANc,z MICHAEL BECKc and DOUGLAS SHAWa a The Nature Conservancy, 222 S. Westmonte Drive, Suite 300, Altamonte Springs, Florida 32714 USA b Department of Wildlife Ecology and Conservation, University of Florida, Gainesville, Florida 32611 c The Nature Conservancy, University of California Santa Cruz, Center for Ocean Health, Santa Cruz, California 95060 USA ABSTRACT 1. A systematic conservation planning approach using benthic habitat and imperilled species data along with the site prioritization algorithm, MARXAN, was used to identify a spatially efficient portfolio of marine and estuarine sites around Florida with high biodiversity value. 2. Ensuring the persistence of an adequate geographic representation of conservation targets in a particular area is a key goal of conservation. In this context, development and testing of different approaches to spatiallyexplicit marine conservation planning remains an important priority. 3. This detailed case study serves as a test of existing approaches while also demonstrating some novel ways in which current methods can be tailored to fit the complexities of marine planning. 4. The paper reports on investigations of the influence of varying several algorithm inputs on resulting portfolio scenarios including the conservation targets (species observations, habitat distribution, etc.) included, conservation target goals, and socio-economic factors. 5. This study concluded that engaging stakeholders in the development of a site prioritization framework is a valuable strategy for identifying broadly accepted selection criteria; universal target representation approaches are more expedient to use as algorithm inputs, but may fall short in capturing the impact of historic exploitation patterns for some conservation targets; socio-economic factors are best considered subsequent to the identification of priority conservation sites when biodiversity value is the primary driver of site selection; and the influence of surrogate targets on portfolio selection should be thoroughly investigated to ensure unintended effects are avoided. 6. The priority sites identified in this analysis can be used to guide allocation of limited conservation and management resources. Copyright r 2008 John Wiley & Sons, Ltd. Received 28 November 2007; Revised 16 April 2008; Accepted 26 May 2008 KEY WORDS: systematic conservation planning; geographic representation; resilience; marine reserve INTRODUCTION Conservation efforts to protect biodiversity and maintain the integrity of ecological systems are limited by time, funding and staff resources. Often, conservation need is urgent. In the past, decisions regarding the selection of priority marine conservation areas have frequently been made on an ad hoc, as needed, or opportunity driven basis (Roberts et al., 2003a). While the designation of many managed areas has been valuable for conservation goals, some of the most ecologically important areas in a particular planning region may have been missed and some resources may be under-represented. A range *Correspondence to: Laura Geselbracht, The Nature Conservancy, 222 S. Westmonte Drive, Suite 300, Altamonte Springs, Florida 32714 USA. E-mail: lgeselbracht@tnc.org y Present Address: Percy FitzPatrick Institute, DST/NRF Center of Excellence, University of Cape Town, Cape Town, South Africa. z Present Address: Intelligent Marine Planning, St Petersburg, Florida 33704 USA. Copyright r 2008 John Wiley & Sons, Ltd. IDENTIFICATION OF PRIORITY CONSERVATION SITES IN MARINE AREAS OF FLORIDA of more systematic approaches, which may include the use of site selection algorithms, have been pioneered in recent times to facilitate the identification of priority marine and estuarine sites (Pressey et al., 1994; Ball and Possingham, 2000; Possingham et al., 2000; Salm et al., 2000; Beck, 2003; Groves, 2003; Leslie, 2005). Despite their relevance, many of these tools have had little application in real-world marine conservation planning. Consequently there is a clear need for further case studies that explore the strengths and weaknesses of different systematic approaches and develop novel ways of applying these methods in cases where conventional applications are difficult or inappropriate. This article seeks to contribute to the further development of systematic marine conservation planning efforts through a detailed exploration of their application in the state of Florida, USA. Systematic conservation approaches generally require identifying the best sites for concentrating conservation and management activities in a particular planning region. Site selection may be influenced by how much of the conservation targets are represented at the sites, the redundancy of the occurrences (to spread the risk in the face of stress), viability of the resource occurrences represented, and connectivity between occurrences to ensure replenishment and genetic diversity (Shaffer and Stein, 2000; Groves, 2003). Economic and social criteria may also be applied to either select or avoid areas of high importance (Hockey and Branch, 1997; Roberts et al., 2003b). Which selection criteria to use and how their use is applied will be driven by the specific goals of each planning effort. Site selection algorithms, such as MARXAN, have been developed to accept multiple selection criteria as inputs and have been used to identify priority conservation areas (e.g. marine reserve networks) for numerous seascapes over the last several years (Beck and Odaya, 2001; Floberg et al., 2004; DeBlieu et al., 2005). The basic aim of these optimization algorithms is to achieve conservation target goals in a spatially efficient manner. Such algorithms allow for the evaluation of dozens of conservation targets and hundreds of possible conservation sites in a practically limitless arrangement. In the past, such efforts have relied solely on expert opinion to identify priority sites, but the vast number of configurations to evaluate makes this a daunting task (Leslie et al., 2003). The use of site selection algorithms to identify priority conservation sites does not replace the need to engage resource experts, but rather simplifies the task and provides a tool for explicit identification of selection criteria and trade-offs. In areas such as Florida where an extensive system of existing marine and estuarine managed sites already exists, systematic conservation planning using site selection algorithms can be used to investigate the adequacy of the existing management regime and provide a means for prioritizing the allocation of conservation and management resources (funding and staff) around the planning area. This study explored the influence of a number of selection criteria on the development of a portfolio of priority marine and estuarine conservation sites based on spatially efficient representation of conservation features. It utilized existing biotic resources and socio-economic datasets along with the site selection algorithm, MARXAN, to identify several alternative portfolios of priority sites. The results were vetted through a series of expert workshops and utilized the collective advice of reviewers to develop a preferred portfolio. The determination of the most appropriate conservation actions to take at any particular site is left to more detailed site planning processes. Copyright r 2008 John Wiley & Sons, Ltd. 409 METHODS MARXAN and existing geospatial datasets of marine and estuarine resources were utilized to develop several potential conservation portfolios for the marine and estuarine areas surrounding Florida. To accomplish this, planning area boundaries and subregions, conservation targets, appropriate target distribution and socio-economic use datasets, and alternative approaches for setting representation goals were identified. An index for spatially representing socio-economic activities likely to have an ‘irreversible’ adverse impact on biodiversity and/or resource viability was also created. Evaluation of the efficiency of the portfolio scenarios in terms of spatial representation and attainment of conservation target goals was carried out. Expert review workshops were held throughout the priority site identification process, and the comments from these were utilized, including those on a number of alternative portfolios developed to help shape selection of a preferred alternative. Planning area and geographic stratification This study encompassed marine and estuarine habitats surrounding the state of Florida in the USA. The 1350 mile Florida coastline supports a diverse and productive assemblage of marine and estuarine systems. Owing to its position (i.e. jutting from the North American continent south towards the Caribbean Sea), Florida’s marine systems range from temperate in the north to tropical in the south. Extensive salt marsh and mangrove systems grace Florida’s low energy coastlines. Beach and barrier island complexes are found in higher energy areas. Vast, globally significant seagrass meadows are present along portions of the Gulf Coast and in Florida Bay. Large expanses of mangrove forests dominate coastal areas of South Florida, which is also home to the third largest barrier reef tract in the world. Large and small estuaries and coastal rivers occur all along the Florida coast. These highly productive systems support key life stages of many commercially and recreationally important species such as pink shrimp, stone crab, lobster, scallops, clams, groupers, snappers, mullet and numerous other species of game fish. The Florida planning area for the project was broadly defined by setting the offshore boundary at the 500 m isobath which comfortably encompasses the continental margin. The inland boundary was defined by the zone of saltwater influence as identified by the National Wetlands Inventory marine and estuarine classifications (USFWS, 1979). The total area described by these boundaries is approximately 28.4 million hectares. To capture biogeophysical differences over this large planning area, it was divided into eight subregions based primarily on coastal geomorphology and faunal assemblages (Davis, 1997; Randazzo and Halley, 1997; Lodge, 2005) (Figure 1). Conservation targets and data sources For the priority areas analysis, benthic habitats and vulnerable species were selected as conservation targets to represent biodiversity. A hierarchical marine and estuarine habitat classification scheme developed by the State of Florida (System for Classification of Habitats in Estuarine and Marine Environments) was used to categorize the habitat types used in this analysis (Madley et al., 2002). The most Aquatic Conserv: Mar. Freshw. Ecosyst. 19: 408–420 (2009) DOI: 10.1002/aqc 410 L. GESELBRACHT ET AL. Figure 1. Planning area and subregions designated for this study. Outer planning area boundary is the 500 m isobath. Inland boundary is the zone of saltwater influence as identified by the National Wetlands Inventory (USFWS, 1979). Subregions are named as follows: 1 — Northeast Florida, 2 — East-Central Florida, 3 — Southeast Florida, 4 — Florida Keys/Florida Bay, 5 — Southwest Florida/Ten Thousand Islands, 6 — West-Central Florida, 7 — Big Bend, and 8 — Northwest Florida/Panhandle. recent geospatial datasets available were assembled for each broad marine and estuarine benthic habitat category. Selected datasets were limited to those that covered at minimum an entire subregion. Datasets were primarily obtained from state, federal and local government agencies and mostly covered only areas within state waters. In three cases datasets were derived. The coastal tidal river or stream dataset was derived from a data set representing the inland limit of tide available through the State of Florida and the National Hydrography Dataset. The fish spawning aggregation dataset was derived from interviewing fishermen and comparing with similar data collected by Environmental Defense (K. Lindeman, personal communication, 2005). The third derived dataset, benthic complexity, was created from bathymetry data. It was utilized to provide some means for predicting ecological importance where little or no geospatial data were available on benthic habitats in the planning area. Benthic complexity was the only dataset used in the study that covered almost the entire study area. Benthic complexity was estimated using an ArcInfo GIS model developed by Duke University Marine Geospatial Ecology Laboratory (2005). The model used bathymetry data (90 m grid-scale resolution) and four geophysical features: depth, topographic variety, amplitude of topographic change and substrate type. The model is based on the strong correlation between benthic complexity and Copyright r 2008 John Wiley & Sons, Ltd. species richness (Ardron, 2002). Topographic variety was classified as flat, slope, ridge and canyon. Sediment classes were extrapolated from data in the Atlantic and Gulf States Marine Fisheries Commissions’ Southeast Area Monitoring and Assessment Program (SEAMAP) and the USGS usSEABED Project. Application of the resulting model identified areas of relatively more complex bottom topography. Benthic complexity could not be calculated for a few areas where bathymetry data were unavailable, including some shallow areas (o50 m) of the Big Bend Subregion and the Florida Bay/Ten Thousand Islands area. Ecologically vulnerable species targets and/or aggregations were also included in the site selection process to ensure the inclusion of ecologically important sites that might not otherwise be identified from benthic habitat information alone. The number of species targets included was limited to ensure balance with the habitat targets, and to avoid a situation in which species targets alone drove algorithm results. The number of species targets included in this analysis was limited by using the objective selection criteria identified below: Globally, regionally or state imperilled species (G1-G2/ G3), S1–S3, State Species of Special Concern — SSC), Aquatic Conserv: Mar. Freshw. Ecosyst. 19: 408–420 (2009) DOI: 10.1002/aqc IDENTIFICATION OF PRIORITY CONSERVATION SITES IN MARINE AREAS OF FLORIDA IUCN red-listed, federally listed/candidate species and American Fisheries Society threatened or endangered distinct population segments; and Vulnerable breeding aggregations subject to harvest or other forms of direct human disturbance (e.g. marine dependent bird nesting aggregations and known fish spawning aggregations). In all, 12 benthic habitat and 35 species/aggregation targets were included in the study. Species targets were only included in subregions where they are known to occur, and where data on their distribution were uniformly collected for most of the subregion, or data had been collected over a long enough period that the discovery of a significant number of additional occurrence sites is not anticipated. The set of conservation targets used in the analysis is listed in Table 1. Most of the conservation targets that were used in this study only covered areas within state waters (3 nautical miles on the Atlantic Coast and 9 nautical miles on the Gulf of Mexico coast) with the exception of benthic complexity, marine hardbottom, inwater sea turtles and the well-documented deep Oculina banks (Reed, 2004). Selection of priority sites The optimization algorithm, MARXAN was used to aid in identification of priority sites. MARXAN was developed by Hugh Possingham and Ian Ball of the University of Queensland, Australia, and along with related site selection 411 algorithms has been used for a variety of marine applications (Ball and Possingham, 2000; Possingham et al., 2000). MARXAN enabled the specified conservation target goals to be achieved while minimizing the total area of all sites selected. While biodiversity and ecosystem conservation objectives may direct us towards maximizing the size and number of conservation areas, practical considerations of funding and staffing levels lead towards the identification of focal areas in which to concentrate efforts. MARXAN seeks to minimize total cost of the selected sites using the following objective function: X X Total Cost ¼ Cost site i þ Penalty cost for element j i þ wb X j boundary length Cost may be represented as total area of the sites, but may also include socio-economic costs. Socio-economic costs may be thought of in terms of existing human activities or structures that may render conservation more expensive. Penalty cost, also known as the species penalty factor, represents the penalty given for not adequately representing a conservation feature and is summed for all conservation features. Boundary length is the total perimeter of all the sites. Its inclusion in the objective function controls fragmentation. Fragmentation of the solution can be reduced (i.e. clumpiness of the solution increased) by setting the constant Wb to a value greater than 0. If the constant is given a value of 0, then boundary length has no effect on the solution. Three optimization methods are used for the objective function: the iterative improvement Table 1. Conservation targets represented in site prioritization framework Benthic habitat Species and vulnerable aggregations Coral reef (incl. deep Oculina Banks) Mangrove forest Beach/surf zone Salt marsh Submerged aquatic vegetation Coastal tidal river or stream Tide flats Marine hardbottom Bivalve reef (Oyster) Annelid worm reef (Sabellariidae) Ocean inlets and passes Benthic complexity Florida manatee, Trichechus manatus latirostris Northern right whale, Eubalaena glacialis American oystercatcher, Haematopus palliatus Black skimmer, Rynchops niger Brown pelican, Pelecanus occidentalis Least tern, Sterna antillarum Piping plover, Charadrius melodus Reddish egret, Egretta rufescens Roseate spoonbill, Ajaia ajaia Roseate tern, Sterna dougallii Snowy egret, Egretta thula Snowy plover, Charadrius alexandrinus tenuirostris Waterbird nesting sites American crocodile, Crocodylus acutus Sea turtle nesting sites Sea turtles, in-water surveys Diamondback terrapin (Ornate, Mississippi and Carolina), Malaclemys terrapin macrospilota, M.t. pileata and M.t. centrata Smalltooth sawfish, Pristis pectinata Slashcheek goby, Ctenogobius pseudofasciatus River goby, Awaous banana Bigmouth sleeper, Gobiomorus dormitor Mangrove Rivulus, Rivulus marmoratus Opossum pipefish, Microphis brachyurus lineatus Striped croaker, Bairdiella sanctaeluciae Gulf sturgeon, Acipenser oxyrhynchus desotoi Atlantic sturgeon, Acipenser oxyrhynchus oxyrhynchus Shortnose sturgeon, Acipenser brevirostrum Saltmarsh topminnow, Fundulus jenkinsi Alabama shad, Alosa alabamae Key silverside, Menidia conchorum Elkhorn coral, Acropora palmata Johnson’s seagrass, Halophila johnsonii Spawning aggregations, harvested species Copyright r 2008 John Wiley & Sons, Ltd. Aquatic Conserv: Mar. Freshw. Ecosyst. 19: 408–420 (2009) DOI: 10.1002/aqc 412 L. GESELBRACHT ET AL. algorithm, simulated annealing algorithm and greedy heuristic. MARXAN starts with an initial random solution, compares sequential random changes to the original solution and selects the better of the two for the next iteration. As this iterative process continues, MARXAN becomes increasingly selective as it considers improved alternative solutions. A full description of MARXAN can be found at the University of Queensland, Ecology Centre website (www.ecology.uq.edu.au/ marxan.htm). The parameter values selected for this analysis and the rationale for doing so are described below: Planning units The planning area was divided into 1500 ha hexagonal planning units resulting in a total of 18 943 units. The 1500 ha planning unit size was selected to provide fine enough detail to resolve habitat within coastal features (especially within bays and estuaries) for statewide analysis while not overwhelming processing capabilities or exceeding the resolution of the habitat data. Conservation target goals The amount of each target required to meet conservation goals is a key planning parameter and input to MARXAN. In priority conservation site identification, the purpose may be to identify the minimum amount of each habitat type required to maintain fully viable populations and communities into the future. So far, there is no universal agreement on what this representation should be or how representation should be derived. Several publications on the topic suggest that target representation for such purposes be set between 20% and 40% of the habitat’s historic distribution (Roberts and Hawkins, 1999; Ward et al., 1999; Turpie et al., 2000; Beck, 2003; Groves, 2003). This study aimed to identify a set of priority sites on which to focus scarce management resources or conservation dollars. While it was recognized that all areas are potentially important for natural resource conservation, the goal in this study was to identify areas to direct limited financial resources, staff and time with the hope that conservation successes in these areas could be used as leverage to achieve conservation in adjacent areas or at a larger scale. As a means of identifying an appropriate target representation approach, several representation scenarios were explored, including two universal representation scenarios (20% and 40% representation for all conservation targets) and a variable representation approach developed by The Nature Conservancy (2003). In the variable target representation approach, individual target representation was based on four attributes: degree of rarity, vulnerability to human activities, current status compared with historic and whether the target represents a breeding site such as nesting colony or spawning aggregation, etc. Three of the attributes (degree of rarity, vulnerability to human activities, and current status compared with historic) were rated on a scale of 1 to 3, with 1 being less rare, vulnerable or compromised and 3 being more rare, vulnerable or compromised. The breeding site attribute was rated as either a ‘1’ for not a breeding site or a ‘3’ for breeding site. All scores were based on information available in the scientific literature. Representation for each target was then assigned, ranging from 20% to 100%, based on the total attribute score (Figure 2). Boundary length Boundary length or perimeter of the MARXAN solution (boundary of all solution sites added together) can be modified using the boundary length modifier (Wb), a constraint entered into the MARXAN algorithm. Adjustment of this variable affects the spatial arrangement and efficiency of the results. MARXAN allows Wb to be set at values between 0 and 1. A Wb of 0 returns the most spatially efficient result with no consideration given to arrangement of the selected sites which are typically small (single planning unit) and scattered throughout the planning area. As the Wb value is increased, overall site number is reduced and sites become more aggregated (multiple planning unit sites). Adjustment of this variable allows for the development of solutions that make more sense from a practical conservation or resource management perspective as attempting to manage thousands of small sites scattered throughout the planning area would be difficult and expensive. A Wb value of 0.05 was used in the analyses presented here. Planning unit cost MARXAN requires that each planning unit be assigned a base cost as the algorithm seeks to minimize cost in its optimum solution. The cost function in the algorithm may also be used to vary the relative value of each planning unit depending on their attributes. The effect of varying planning unit cost is that those with a lower cost have a greater likelihood of being selected in the MARXAN output all other factors being equal. Two approaches for assigning values to planning units were Figure 2. Assignment of variable representation factors. Representation of each target in each subregion was scored based on four attributes: rarity vulnerability, current status as compared to historic condition, and whether the distribution coverage represents a breeding aggregation. Representation factors ranging from 20% to 100% were assigned based on the frequency distribution of target scores. Copyright r 2008 John Wiley & Sons, Ltd. Aquatic Conserv: Mar. Freshw. Ecosyst. 19: 408–420 (2009) DOI: 10.1002/aqc 413 IDENTIFICATION OF PRIORITY CONSERVATION SITES IN MARINE AREAS OF FLORIDA Table 2. Socio-economic use index - factors and scoring for each planning unit Socio-economic factors Most Concentrated Impacts: Major NPDES discharges Superfund sites (range, 1–2) Offshore dredge disposal sites (range, 1–2) More Dispersed Impacts: Major shipping lanes (uptonnageX272,155 metric tons) Most Dispersed Impacts: Hardened shoreline (range, 0.003–115 km) Dredged shipping channels (range, 1–15) Port facilities (range, 1–31 facilities) Population density (range, 0.000218/km2 –18998/km2) Road density (range, 0.004–246 km) Marine facilities/boat ramps (range: 1–33) Unit Index Points Data source Presence in planning unit For each site For each site 500 500 500 NOAA-OPIS NOAA-OPIS NOAA-OPIS Presence in planning unit 250 USACE - Navigation Data Center (NDC) For every 2.5 kilometres For each dredging project For each facility For each 38.6 people per square kilometre 100 25 20 10 FWC/FWRI USACE- NDC USACE- NDC U.S. Census Census 2000 For each kilometre 10 For each facility/boat ramp 10 U.S. Census Census 2000 FWC/FWRI Note: Socio-economic index points were added to the planning unit base cost of 85 to derive the total planning unit cost. NPDES: National Pollutant Discharge Elimination System (http://cfpub.epa.gov/npdes/). examined. In the first approach, all planning units were valued equally, and assigned a base cost of 85 points. In this type of approach, where all planning units are valued equally, the value of the planning unit cost selected is not important as long as it is greater than 0 and is in balance with the species penalty factor (discussed below). The second approach to assigning costs to planning units, involved the development of a spatial index of socio-economic factors. The purpose of this approach was to better inform marine resource managers and conservation practitioners where priority attention could be focused while minimizing conflicts with existing resource users and avoiding more impacted locations. The spatial socio-economic index was composed of activities not likely to be reversible or reversible only at a high to very high cost. Only socio-economic uses that have a demonstrated adverse impact on native habitats and species (e.g. development, high intensity use and pollution) and may be described as structures, facilities or activities were considered for inclusion. Eleven such socio-economic uses impacting marine and estuarine habitats in Florida where geospatial information was available were identified. The 10 socio-economic factors selected and the scoring used to describe the inlevel of impact are listed in Table 2 and include population and road density, port facilities, major shipping lanes, hardened shorelines, Superfund sites, major National Pollutant Discharge Elimination System permitted point source (NPDES) discharges, marine facilities and boat ramps, offshore dredged disposal sites, and dredged shipping channels. Relative scoring of the socio-economic factors was developed and refined through an iterative expert review process that included marine resource managers, scientists and conservationists working in Florida. Point locations of the most concentrated adverse impacts (e.g. major municipal discharges and Superfund sites) were assigned the highest score of 500 points. More dispersed, but still intensive impacts (i.e. shipping lanes with greater than 272 155 metric tons annual uptonnage) were assigned 250 points along the entire shipping lane length. The most dispersed socio-economic factors (e.g. population, roads, hardened shoreline) were assigned scores ranging from 10 to 100 points per unit of measure. Scores for Copyright r 2008 John Wiley & Sons, Ltd. these most dispersed socio-economic factors are lower because multiple factor units may occur within a single planning unit (see Table 2 for the range of units for each socio-economic factor). For example, of the planning units with the highest socio-economic index scores (total score ranging from 4000 to 6212), none contain a Superfund site, major wastewater discharge or offshore dredged disposal site. The high socioeconomic scores for these planning units are derived primarily from population density, road density, and hardened shoreline. A map depicting the socio-economic index is shown in Figure 3. There are other threat factors that would have been advantageous to include in this analysis if the time and resources had been available, including trawling intensity, observed seagrass propeller scarring and coastal watershed impacts, etc. Where the described socio-economic uses were present, the cost points outlined in the socio-economic index were added to the base cost of 85 points for each planning unit. Species penalty factor A species penalty factor is set for each conservation target and represents the cost added to the total portfolio cost if the conservation target goal is not met. Setting a high species penalty helps to ensure that MARXAN will meet conservation target goals. A species penalty factor of 500 was applied to all conservation targets for all scenarios presented here. Algorithm application For each MARXAN run, the number of iterations was set at 10 million to ensure a high degree of confidence in optimizing the conservation goals. Each run was repeated 100 times and MARXAN selected the best of these configurations as the optimal solution. RESULTS Two aspects of MARXAN output were evaluated, total portfolio area selected and efficiency at meeting target goals, Aquatic Conserv: Mar. Freshw. Ecosyst. 19: 408–420 (2009) DOI: 10.1002/aqc 414 L. GESELBRACHT ET AL. Figure 3. Spatial Index of socio-economic factors. The spatial index is a relative scoring of the following socio-economic factors not likely to be reversible or reversible only at a high to very high cost: population and road density, port facilities, major shipping lanes, hardened shorelines, Superfund sites, major permitted point source discharges, marine facilities and boat ramps, offshore dredged material disposal sites, and dredged shipping channels. The darkest areas represent the highest concentration of these socio-economic factors. Table 2 provides the scoring methodology. to assess the influence of the algorithm parameters that were varied (conservation targets, their representation and planning unit cost). The efficiency of meeting target goals for each scenario was calculated by averaging the percentage of target goals met in each subregion, then averaged across all subregions. Table 3 illustrates how this was done for MARXAN scenario 2. The conservation targets listed in Table 1 were included in all of the MARXAN runs presented in this section with the exception of benthic complexity, which was included in only one of the scenarios to illustrate its influence on the MARXAN output. Likewise, the socioeconomic index was only included in one scenario, scenario 5, to illustrate its influence on results. A preferred alternative was selected from among the scenarios based on analysis of the MARXAN results and expert review comments, before conducting a spatial comparison of the preferred alternative with existing managed areas. Target goals Under the universal target goal approach, the total amount of priority area selected in the optimal solution increased as target goals were increased. When conservation targets were universally represented at 20% and 40%, 2.9% versus 5.6% of the total planning area was selected (Table 4). The portfolio resulting from using a 20% target goal is for the most part a subset of the 40% goal portfolio. The only exception to this is Copyright r 2008 John Wiley & Sons, Ltd. the southern tip of the Florida peninsula. The 20% target goal scenario selected all of Biscayne Bay whereas the 40% goal scenario selected northern and southern areas of Biscayne Bay and a large area of Florida Bay. The variable target goal approach delivered an intermediate result of 5.0% of the total planning area selected. Differences between areas selected in all three scenarios are modest (Figure 4(a)–(c)). When benthic complexity is not included as a conservation target, most target distribution included in this study are nearshore and coastal. If the planning area was limited to state waters (3 nautical miles off the Atlantic Coast and 9 nm off the Gulf of Mexico coastline) where target data density is high, the total amount of planning area selected for the 20%, 40% and variable target representation scenarios is as follows: 18.6%, 36.3% and 31.6%, respectively. Regarding efficiency of meeting the target goals, goals were achieved for all of the conservation targets in all of the scenarios presented in this study. Efficiency of meeting target goals, however, differed among the scenarios. Thirty percent over target goals was selected as an arbitrary limit for acceptable representation and anything over this 130% value was considered as excessive over-representation. Under the variable target goal approach, 51% of the targets exceeded their goals by 30% or more. Under the universal goal approaches of 20% and 40%, the portion of targets exceeding 30% of their goals was 62% and 46%, respectively (Table 4). Therefore, the 40% universal target goal approach Aquatic Conserv: Mar. Freshw. Ecosyst. 19: 408–420 (2009) DOI: 10.1002/aqc 415 IDENTIFICATION OF PRIORITY CONSERVATION SITES IN MARINE AREAS OF FLORIDA Table 3. Calculation of efficiency at meeting target goals for marxan scenario 2 (40% universal target goal; benthic complexity excluded) Percentage target goals captured by subregion: Conservation targets 1 2 3 4 5 6 7 8 Patch coral reef na na 250% 250% 102% 102% 102% 127% 113% 114% 116% 250% 225% 112% 160% 213% 113% 100% 179% 138% 156% na 188% 167% na 115% na 167% na 113% 103% 158% na na 106% na na na 125% 200% 188% 150% 109% 132% 250% na 250% na na na na 110% na 54% na 114% 103% 103% 117% 123% 142% 216% 108% 245% na 155% 122% 116% 137% na na 250% na 125% 250% 250% 250% na 167% na 250% 129% 131% 177% 214% na na 129% na na na 250% 250% na 250% 125% 250% na na na na na na 167% 126% 250% 63% 103% 117% 100% 103% 128% 203% 174% 100% 119% 101% na na 131% 125% 120% na na 250% 105% 125% 125% 160% 201% 100% 155% 250% 104% 154% 135% 100% na 250% na 100% 100% na na 109% na na na 167% na na na na na na 120% 237% na 103% 38% na na na na 100% 138% 131% 120% 165% 103% 208% na na na 141% na 208% 200% 125% 208% 125% na na na 125% 250% 167% 262% na 113% na na na na 108% na na 118% na na na na na na na na na na na na na na 55% na na na na 121% 100% 126% 101% 112% 100% 173% na na 101% 188% na 100% 104% 110% 125% 103% 117% 107% 250% 128% 113% 104% 120% 239% 102% na na na na 119% na na 103% na na na 167% 250% na 125% na na na na na na na 21% na na na na 115% 133% 105% 100% 115% 101% 134% na na 104% 250% na 116% 200% 125% 208% 125% 250% na na 167% 114% 167% na 166% 100% 154% na na na 122% na na 111% na na na na na na 250% na na 161% na na na na 48% na na na na na 103% 106% 145% 103% 101% na na na 107% na na 250% 133% 250% 117% 150% 250% na na 150% 145% na na 100% 108% na na na na 123% 116% na na na 250% na na na na 125% na 107% 231% na na na na 45% Shallow bank coral reef Deep bank coral reef Deep reef resources Mangrove swamp Beach/Surf zone Salt marsh Submerged aquatic vegetation Coastal tidal river or stream Tide flat Bivalve (oyster) reef Annelid (worm) reef Hardbottom Inlets and passes Florida manatee Right whale calving grounds American oystercatcher Black skimmer Brown pelican Least tern Piping plover Reddish egret Roseate spoonbill Roseate tern Snowy egret Snowy plover Wading bird colony American crocodile Green turtle nesting beaches Loggerhead turtle nesting beaches Leatherback turtle nesting beaches Hawksbill turtle nesting beaches Kemp’s Ridley nesting beaches Turtles in-water Ornate diamondback terrapin Mississippi diamondback terrapin Carolina diamondback terrapin Smalltooth sawfish Slashsheek goby River goby Bigmouth sleeper Mangrove Rivulus Opossum pipefish Striped croaker Gulf sturgeon Atlantic sturgeon Saltmarsh topminnow Alabama shad Key silverside Acropora palmata Johnson’s seagrass Spawning aggregations Percentage targets with goals 4130% in subregion: Percentage targets with goals 4130% statewide: na na 239% 100% 104% 148% 128% 100% 157% 159% na na 190% 202% 100% 159% 208% 103% 111% 100% 125% 150% na 125% na 125% na 188% 166% 101% na 129% na na na 242% 125% na na na na 125% na na 250% na na na na na na 46% 46% The coral reef conservation target was split into the reef types listed above. na 5 not applicable. Target was not present in subregion. delivered the most efficient results in terms of meeting target goals among the three approaches examined. The variable target goal approach was viewed less favourably by the expert review panel owing to the necessity to employ some subjectivity in rating criteria for some conservation targets. For several targets, quantitative information on pre-settlement extent or population size is limited or unavailable (Geselbracht et al., 2005). Copyright r 2008 John Wiley & Sons, Ltd. Influence of including benthic complexity The effect of including the surrogate target, benthic complexity was examined by comparing scenarios that were identical except for their inclusion/omission of benthic complexity, using the scenario with a 40% universal target goal. When benthic complexity was included, results for the offshore portions of the planning area (here defined as greater than Aquatic Conserv: Mar. Freshw. Ecosyst. 19: 408–420 (2009) DOI: 10.1002/aqc 416 L. GESELBRACHT ET AL. 50 m) differed substantially, as anticipated, because the benthic complexity dataset was the only dataset used in this study that covered the entire offshore portions of the planning area (Figures 4(b) and (d)). Many of the offshore areas gained, as a result of including benthic complexity, coincide with known deep reef features of high biodiversity value including Pulley Ridge, Pourtales Terrace, Miami Terrace, Steamboat Lumps and DeSoto Canyon (Koenig et al., 2000; Scanlon, 2000; Scanlon et al., 2001; Sheridan and Caldwell, 2002; Gardner et al., 2003; Jarrett et al., 2004; Reed, 2004). Differences in results between the nearshore and coastal portions of these two scenarios were modest with four exceptions. The scenario excluding benthic complexity (4b) includes a large site in Charlotte Harbor. The scenario with benthic complexity (4d) includes two large sites along the Florida Panhandle and one large site in Tampa Bay. In addition, the scenario with benthic complexity favoured Biscayne Bay over Florida Bay and split the large Panhandle sites into several small sites. Closer examination of why including benthic complexity may have influenced some of the results in this way revealed that in the shallower portions of the planning area, benthic complexity highlighted man-made features such as shipping lanes and other dredged areas. The scenario that included benthic complexity (4d) selected more than twice the area of the scenario without benthic complexity, reflecting greater spatial representation in the offshore area. In terms of efficiency of meeting conservation target goals, all goals were met with 54% of targets exceeding the target goal by 30% or more compared with 46% for the scenario without benthic complexity (Table 4). Therefore, the scenario with benthic complexity was less efficient at meeting target goals than the identical scenario without benthic complexity. Effect of including socio-economic use index as a cost surface The effect of including a spatial index of socio-economic activities or threats was explored to examine its influence on selection of priority areas. A comparison of the optimal portfolio of sites, both with and without the socio-economic index included, shows many similarities (Figures 4(b) and (e)). A large site off extreme north-east Florida was selected by both approaches as were sites in the Mosquito, Banana and Indian River lagoons (subregions 1 and 2). The scenario including the socio-economic index (4e) differs from the scenario excluding it (4b) in the following ways: the Florida Panhandle sites are more fragmented, there is a gap in the selected areas at Crystal River in the southern Big Bend Subregion, and smaller sites in Florida Bay and the St Lucie Estuary were selected. In the portfolio with the socio-economic index included, results appear to avoid more developed or higher cost areas. In terms of efficiency, the scenario including the socioeconomic index exhibits improved efficiency both spatially and at meeting target goals. This scenario selected 4.8% of the total planning area, all conservation target goals were met and 38% (versus 46%) of targets exceeded the 40% target goal by 30% or more (Table 4). While the expert review panel recognized the value of a socio-economic index for informing conservation and resource management activities, they expressed a distinct preference for selecting priority sites based solely on biodiversity importance rather than giving preference to sites less subject to human use/disturbance. Their rational was that important biodiversity sites should not be deCopyright r 2008 John Wiley & Sons, Ltd. Table 4. Summary of MARXAN output statistics MARXAN scenario Percentage of planning area selected 1. 20% universal target goal, benthic complexity excluded, BLM 5 0.05 2. 40% universal target goal, benthic complexity excluded, BLM 5 0.05 3. Variable target goal, benthic complexity excluded, BLM 5 0.05 4. 40% universal target goal, benthic complexity included, BLM 5 0.05 5. 40% universal target goal, benthic complexity excluded, BLM 5 0.05, socio-economic index included. Preferred alternative. 40% universal target goal, benthic complexity only in areas 450 m, BLM 5 0.05; socio-economic index excluded Percentage of targets overrepresented by 430% of goal 2.9% 62% 5.6% 46% 5.0% 51% 13.4% 54% 4.8% 38% 9.7% 48% emphasized if they were also important for socio-economic uses. Rather the expert review panel suggested that sites important for both biodiversity values and socio-economic uses may warrant more concentrated management attention rather than less. They recommended using the socio-economic index to guide resource management and conservation activities following identification of priority sites. Identification of a preferred portfolio and comparison with existing managed areas Based on the analysis of the MARXAN output and the general advice of our expert review panel, a preferred portfolio was selected that represented all targets at the 40% level, included benthic complexity as a target only in deeper reaches of the planning area (450 m depth) and did not include the socioeconomic index in site selection. The results of this scenario are presented in Figure 4(f). The preferred scenario selected 9.7% of the planning area, more than the scenarios that completely excluded benthic complexity, but less than the scenario that included it throughout the planning area. Efficiency at meeting target goals was intermediate (48%) compared with the other scenarios presented in this study. A spatial comparison of the preferred alternative with existing managed areas was undertaken to evaluate the extent to which preferred alternative sites fall within some type of management regime (Figure 5). Approximately 39% of the preferred alternative falls into some form of existing managed area. The overlapping managed areas range from seasonal fishing closure areas to no take marine reserves. DISCUSSION The site selection algorithm, MARXAN, greatly simplifies identification of a spatially efficient portfolio of priority areas Aquatic Conserv: Mar. Freshw. Ecosyst. 19: 408–420 (2009) DOI: 10.1002/aqc IDENTIFICATION OF PRIORITY CONSERVATION SITES IN MARINE AREAS OF FLORIDA 417 Figure 4. Results of site prioritization analyses using MARXAN with several different scenarios. Expert review of the various scenario outputs resulted in the creation of the preferred scenario, which can be used to guide future investment in conservation and management activates. All runs used a boundary length modifier of 0.05 and a species penalty factor of 500. using spatial information on conservation targets and objective criteria on desired levels of representation. It is an excellent tool that informs a complex process and enables those interested in identifying priority sites to observe how changes in selection criteria influence portfolio design. A good knowledge of how input parameters influence portfolio design will greatly enhance the ability of MARXAN users to satisfactorily integrate the expert advice of scientists, resource managers and the sometimes competing concerns of other stakeholders. Identification of priority sites using only expert input has been described as less defensible and scientifically credible (Lourie and Vincent, 2004). However, development of priority sites using biogeographic data and a site selection algorithm without stakeholder input may be equally problematic. Employing quantitative information and decision-support tools such as MARXAN, while soliciting expert and other stakeholder input throughout the process, is likely the best approach to priority site identification. Broader support of the process and results are built concurrently with development of the priority site portfolio. Setting target goals can be a difficult exercise due to the varying opinions on how it should be done and the lack of well established guidance. Employing universal target goals is an expedient method. Scenario results can be compared on the basis of both spatial efficiency and efficiency at meeting the target goals. Although the effort to set goals individually for each conservation target was not popular among the expert Copyright r 2008 John Wiley & Sons, Ltd. reviewers, refining this approach has merit. Setting all target goals at the same level does not account for differences in historical exploitation levels or the shifting of baseline conditions. Refining an approach that sets representation goals individually for each target will require advancing our understanding of historical distribution of conservation targets. Pauly (1995) supports improving our understanding of historical distributions as a means for setting more appropriate conservation target goals. Inclusion of surrogate conservation targets in MARXAN analyses may return some unexpected results. In shallower portions of the planning area, benthic complexity highlighted areas where shipping channels and dredged sites were present. These high impact sites are not typically considered of high biodiversity value, so the benthic complexity target was excluded from areas with depths less than 50 m in the preferred alternative. This result in shallow portions of Florida marine and estuarine areas may also be a function of Florida’s predominantly low relief environment with the exception of coral reef areas. Benthic complexity does, however, appear to be a valuable surrogate target to include in the deeper portions of the planning area and may aid in the identification of areas of potential conservation interest that have not been subject to extensive scientific investigations. Use of benthic complexity as a target in site selection may best be confined to deeper portions of marine planning areas where conservation target Aquatic Conserv: Mar. Freshw. Ecosyst. 19: 408–420 (2009) DOI: 10.1002/aqc 418 L. GESELBRACHT ET AL. Figure 5. Preferred alternative is compared with existing managed areas in the study area. Dark shading indicates permanently designated managed areas. Light shading indicates areas only subject to temporary or seasonal fishing closures. distribution data are limited, or to areas where man-made features are not likely to influence the results. The purpose of conducting the site prioritization effort will drive the extent to which socio-economic information is incorporated. The emphasis of this effort was to identify a portfolio of sites representing high biodiversity value. Participants in the expert review sessions were heavily weighted towards marine scientists, government resource managers and members of conservation organizations. There was little representation from user groups. Representation by user groups under these conditions was not essential as the process did not result in any actual proposals for new marine reserve sites, which would have resulted in limiting the access of some user groups. The use of socio-economic data to identify sites of high use was much less an issue here than in other similar reserve design efforts. Subsequent efforts to identify and implement appropriate conservation strategies for priority sites will require the use of socio-economic information and the involvement of user groups, especially if any restrictions on site use are proposed. While it was found that approximately 39% of the preferred alternative falls within the boundaries of existing managed areas, this should not be construed as 39% successful conservation of the preferred portfolio. The existing managed areas overlapping the preferred alternative sites vary greatly in regulatory purview, staffing, enforcement, etc. Only the notake marine reserves, which represent less than 1% of preferred Copyright r 2008 John Wiley & Sons, Ltd. alternative area, provide protection from most of the direct human use impacts. The majority of the managed areas overlapping the preferred portfolio provide incomplete protection, which may include seasonal or permanent prohibitions on some but not all types of fishing and/or limited protection from habitat destruction. Establishing an appropriate managed areas framework from which to launch effective, long-term conservation strategies for the marine systems in Florida’s and surrounding waters will likely require expanding some existing managed areas, creating new managed areas and strengthening regulatory protection so that at the very least direct site impacts are minimized. Identifying priority areas will always be controversial, but avoiding this kind of analysis risks losing the systems we seek to protect (Lourie and Vincent, 2004). Marine conservation in Florida has reached a crossroads. While there is an extensive system of state, federal, local and private marine managed areas in state and adjacent marine waters, many of these sites operate independently of one another and most offer only limited protection to marine resources. The goals and strategies of these managed area programmes should be aligned, quantitatively articulated and their regulatory authorities strengthened to ensure the long-term health of marine systems in and around Florida’s waters for the use, enjoyment and benefit of future generations. More generally, quantitative explorations of the effectiveness of tools like MARXAN in specific case studies have represented a gap in Aquatic Conserv: Mar. Freshw. Ecosyst. 19: 408–420 (2009) DOI: 10.1002/aqc IDENTIFICATION OF PRIORITY CONSERVATION SITES IN MARINE AREAS OF FLORIDA our current body of knowledge. The methodologies and results presented in this paper can be used by resource managers, conservation professionals and other stakeholders to help define how marine resource management can be strengthened and to identify where scarce conservation and management funds may best be focused in a particular planning area. ACKNOWLEDGEMENTS Funding for this project was provided by the US Department of Interior, State Wildlife Grants Program T-4 Grant administered by the State of Florida and by The Nature Conservancy. We are grateful for the contributions of the many participants and reviewers of this effort which are too numerous to list here. A portion of the funding for this project was provided by the US Fish and Wildlife Service State Wildlife Grants Program T-4 Grant administered by the Florida Fish and Wildlife Conservation Commission (FWC Contract No. 04122). REFERENCES Ardron J. 2002. A recipe for determining benthic complexity: an indicator of species richness. In Marine Geography: GIS for the Oceans and Seas, Breman J (ed.). ESRI Press: Redlands, CA; 169–175. Ball I, Possingham H. 2000. MARXAN (V1.8.2): Marine Reserve Design Using Spatially Explicit Annealing, a Manual. The Ecology Centre, University of Queensland: Brisbane. Beck M. 2003. The sea around: marine regional planning. In Drafting a Conservation Blueprint: a Practitioners’ Guide to Planning for Biodiversity, Groves CR (ed.). Island Press: Washington, DC; 319–344. Beck M, Odaya M. 2001. Ecoregional planning marine environments: identifying priority sites for conservation in the northern Gulf of Mexico. Aquatic Conservation: Marine and Freshwater Ecosystems 11: 235–242. Davis Jr R. 1997. Geology of the Florida Coast. In The Geology of Florida, Randazzo A, Jones D (eds). University of Florida Press: Gainesville; 155–168. DeBlieu J, Beck M, Dorfman D, Ertel P. 2005. Conservation in the Carolinian Ecoregion: An Ecoregional Assessment. The Nature Conservancy: Arlington, VA. Floberg J, Goering M, Wilhere G, MacDonald C, Chappell C, Rumsey C, Ferdana Z, Holt A, Skidmore P, Horsman T, et al. 2004. Willamette Valley-Puget Trough-Georgia Basin Ecoregional Assessment, Volume One: Report. Prepared by The Nature Conservancy with support from the Nature Conservancy of Canada, Washington Department of Fish and Wildlife, Washington Department of Natural Resources (Natural Heritage and Nearshore Habitat programs), Oregon State Natural Heritage Information Center and the British Columbia Conservation Data Centre. Gardner JV, Hughes Clarke J, Mayer L. 2003. Bathymetry and Acoustic Backscatter of the Mid and Outer Continental Shelf, Head of De Soto Canyon, Northeastern Gulf of Mexico — Data, Images, and GIS. US Geological Survey Open-File Report OF 02–396. Geselbracht L, Torres R, Cumming G, Dorfman D, Beck M. 2005. Marine/Estuarine Site Assessment for Florida: A Framework for Site Prioritization. The Nature Conservancy: Gainesville, FL. Copyright r 2008 John Wiley & Sons, Ltd. 419 Groves C. 2003. Drafting a Conservation Blueprint: a Practitioner’s Guide to Planning for Biodiversity. Island Press: Washington, DC. Hockey PAR, Branch GM. 1997. Criteria, objectives and methodology for evaluating marine protected areas in South Africa. South African Journal of Marine Science 18: 369–383. Jarrett BD, Hine AC, Halley RB, Naar DF, Locer SD, Neumann AC, Twichell D, Donahue CHBT, Jaap WC, Palandeo D, Cembronowicz . 2004. Strange bedfellows — a deep-water hermatypic coral reef superimposed on a drowned barrier island; southern Pulley Ridge SW Florida platform margin. Marine Geology 24: 295–307. Koenig CC, Coleman FC, Grimes CB, Fitzhugh GR, Scanlon KM, Gledhill CT, Grace M. 2000. Protection of fish spawning habitat for the conservation of warm temperate reef fish fisheries of shelf-edge reefs of Florida. Bulletin of Marine Science 66: 593–616. Leslie H. 2005. A synthesis of marine conservation planning approaches. Conservation Biology 19: 1701–1713. Leslie H, Ruckelshaus M, Ball I, Andelman S, Possingham H. 2003. Using siting algorithms in the design of marine reserve networks. Ecological Applications 13: S185–S198. Lodge T.2005. The Everglades Handbook– Understanding the Ecosystem. CRC Press: Washington, DC. Lourie S, Vincent A. 2004. Using biogeography to help set priorities in marine conservation. Conservation Biology 18: 1004–1020. Madley KA, Sargent B, Sargent FJ. 2002. Development of a System for Classification of Habitats in Estuarine and Marine Environments (SCHEME) for Florida. Report to the US Environmental Protection Agency, Gulf of Mexico Program (Grant Assistance Agreement MX-97408100). Florida Marine Research Institute, Florida Fish and Wildlife Conservation Commission, St. Petersburg. Pauly D. 1995. Anecdotes and the shifting baseline syndrome of fisheries. Trends in Ecology and Evolution 10: 430. Possingham HP, Ball IR, Andelman S. 2000. Mathematical methods for identifying representative reserve networks. In Quantitative Methods for Conservation Biology, Ferson S, Burgman M (eds). Springer-Verlag: New York; 291–305. Pressey RL, Johnson IR, Wilson PD. 1994. Shades of irreplaceability: towards a measure of the contribution of sites to a reservation goal. Biodiversity and Conservation 3: 242–262. Randazzo A, Halley R. 1997. Geology of the Florida Keys. In The Geology of Florida, Randazzo A, Jones D (eds). University of Florida Press: Gainesville; 251–260. Reed JK. 2004. General description of deep-water coral reefs of Florida, Georgia and South Carolina: A summary of current knowledge of the distribution, habitat, and associated fauna. Report to the South Atlantic Fishery Management Council, NOAA, NMFS. Roberts CM, Hawkins JP. 1999. Extinction risk in the sea. Trends in Ecology and Evolution. 15: 241–246. Roberts CM, Branch G, Bustamante RH, Castilla JC, Dugan J, Halpern BS, Lafferty KD, Leslie H, Lubcenco J, McArdle D, et al. 2003a. Application of ecological criteria in selecting marine reserves and developing reserve networks. Ecological Applications 13: S215–S228. Roberts CM, Andelman S, Branch G, Bustamante RH, Castilla JC, Dugan J, Halpern BS, Lafferty KD, Leslie H, Lubcenco J, McArdle D, et al. 2003b. Ecological criteria for evaluating candidate sites for marine reserves. Ecological Applications 13: S199–S214. Salm RV, Clark J, Siirila E. 2000. Marine and Coastal Protected Areas. A Guide for Planners and Managers, 3rd edn. IUCN: Washington, DC. Aquatic Conserv: Mar. Freshw. Ecosyst. 19: 408–420 (2009) DOI: 10.1002/aqc 420 L. GESELBRACHT ET AL. Scanlon KM. 2000. Surficial seafloor geology of a shelf-edge area off West Florida. In West Florida Shelf: Sidescan-sonar and Sediment Data from Shelf-edge Habitats in the NorthEastern Gulf of Mexico, Briere PR, Scanlon KM, Fitzhugh G, Gledhill CT, Koenig CC (eds) US. Geological Survey, Open-file Report 99–589. Scanlon KM, Koenig CC, Coleman FC, Rozycki JE. 2001. Paleoshorelines, drowned reefs, and grouper habitat in the northeastern Gulf of Mexico. Geology of Marine Habitat Session, Geological Association of Canada Annual Meeting, 2001, St. Johns, Vol. 26. Shaffer ML, Stein BL. 2000. Safeguarding our precious heritage. In Precious Heritage: The Status of Biodiversity in the United States, Stein BA, Kutner LS, Adams JS (eds). Oxford University Press: Oxford; 301–322. Sheridan P, Caldwell P. 2002. Compilation of data sets relevant to the identification of essential fish habitat on the Gulf of Mexico continental shelf and for Copyright r 2008 John Wiley & Sons, Ltd. the estimation of the effects of shrimp trawling gear on habitat. NOAA Technical Memorandum NMFSSEFSC-483 The Nature Conservancy, Greater Caribbean Ecoregional Plan Team. 2003. An Ecoregional Plan for Puerto Rico: Portfolio Design. Report to Bristol-Myers Squibb Company. Turpie JK, Beckley LE, Katua SM. 2000. Biogeography and the selection of priority areas for conservation of South African coastal fishes. Biological Conservation 92: 59–72. US Fish and Wildlife Service. 1979. Classification of wetlands and deepwater habitats of the United States. Biological Services Program, FWS/OBS-79-31. Ward TJ, Vanderklift MA, Nicholls AO, Kenchington RA. 1999. Selecting marine reserves using habitats and species assemblages as surrogates for biological diversity. Ecological Applications 9: 691–698. Aquatic Conserv: Mar. Freshw. Ecosyst. 19: 408–420 (2009) DOI: 10.1002/aqc
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