Exploitation impacts and management options for 15 coral reef fish species central to the commercial and recreational fisheries of the southern Florida USA coral reef ecosystem were evaluated using a length-based risk analysis (LBRA) framework. Population abundance-at-length composition data were obtained from several regional federal-state sampling programs. These and updated life history demographic data were integrated into a length-based numerical cohort model to generate LBRA fishery sustainability metrics from a probabilistic perspective. Three of five groupers, eight of eight snappers, and two of two grunts were below the 40% spawning potential ratio (SPR) stock sustainability minimum; ten of these stocks are at < 20% of their historical spawning biomass, some as low as 5%. Therefore, to ameliorate overfishing for the 13 stocks with sustainability risks & nbsp;>=& nbsp;98%, fisheries management requires increased minimum sizes of first capture (L-c) and significant reductions in fishing mortality (F). To achieve sustainability and reduce sustainability risks area-time protections are also needed. While lack of data often limits the evaluation of management options, this paper establishes benchmarks from which data-limited approaches can move forward. In addition, the approach can be used to cross-check other data-rich analyses. A goal of this work is to effectively balance sustainability risks with fishery production to mitigate overfishing likelihoods and to increase the probability of sustainable fisheries.

We contend that harvest is a misleading term when referring to wild organisms removed from natural systems and suggest that fishery professionals reserve the word for aquaculture products. When referring to wild stock extraction, replacing the word harvest with catch removes the implication of extractor entitlement, better indicates the fishery product source, and does not distract from the issue of bycatch.

A Abdel-Kader N S . . . . . . . . . . B36 . . 951 Abdou S . . . . . . . . . . . . . . . . . D24 . 1783 Abutalebi H R . . . . . . . . . . . . . D26 . 1855 Acero A . . . . . . . . . . . . . . . . . D26 . 1903 Acero A . . . . . . . . . . . . . . . . . A41 . . 217 Acero A . . . . . . . . . . . . . . . . . B35 . . 901 Achariyakulporn V . . . . . . . . . B26 . . 775 Adank P . . . . . . . . . . . . . . . . . B13 . . 481 Adda G . . . . . . . . . . . . . . . . . . A42 . . 255 Adda-Decker M . . . . . . . . . . . A42 . . 255 Afify M . . . . . . . . . . . . . . . . . . C16 . 1323 Afify M . . . . . . . . . . . . . . . . . . E14 . 2355 Afify M . . . . . . . . . . . . . . . . . . B14 . . 495 Afify M . . . . . . . . . . . . . . . . . . B21 . . 633 Afify M . . . . . . . . . . . . . . . . . . B34 . . 851 Ahkuputra V . . . . . . . . . . . . . . E35 . 2753 Ahn D-H . . . . . . . . . . . . . . . . . D25 . 1821 Ahn S . . . . . . . . . . . . . . . . . . . A35 . . 107 Ainsworth W . . . . . . . . . . . . . A46 . . 371 Ainsworth W A . . . . . . . . . . . . B13 . . 477 Akagi M . . . . . . . . . . . . . . . . . E16 . 2439 Akahane-Yamada R . . . . . . . . A36a . 145 Akiba T . . . . . . . . . . . . . . . . . . B25 . . 705 Albino N . . . . . . . . . . . . . . . . . E32 . 2679 Alexander G . . . . . . . . . . . . . . D12 . 1559 Alku P . . . . . . . . . . . . . . . . . . . B36 . . 919 Allen J . . . . . . . . . . . . . . . . . . C16 . 1323 Alm N . . . . . . . . . . . . . . . . . . . E15 . 2401 Alm N . . . . . . . . . . . . . . . . . . . E15 . 2405 Alonso L . . . . . . . . . . . . . . . . . D23 . 1753 Altosaar T . . . . . . . . . . . . . . . . D11 . 1537 Álvarez Salgado J F . . . . . . . . E15 . 2393 Álvarez-Marquina A . . . . . . . . E26 . 2615 Álvarez-Marquina A . . . . . . . . E26 . 2619 Alwan A . . . . . . . . . . . . . . . . . A36b . 175 Alwan A . . . . . . . . . . . . . . . . . A36b . 179 Alwan A . . . . . . . . . . . . . . . . . A41 . . 185 Alwan A . . . . . . . . . . . . . . . . . E34 . 2703 Amador-Hernandez M . . . . . . B15 . . 513 Amaral R . . . . . . . . . . . . . . . . E33 . 2689 Ambikairajah E . . . . . . . . . . . A46 . . 411 Amir N . . . . . . . . . . . . . . . . . . A35 . . 127 Ammicht E . . . . . . . . . . . . . . . D45 . 2217 Andersen E . . . . . . . . . . . . . . . E32 . 2675 Andersen O . . . . . . . . . . . . . . . E11 . 2289 Andersen O . . . . . . . . . . . . . . . B42 . . 975 Ando A . . . . . . . . . . . . . . . . . . B14 . . 495 Ando A . . . . . . . . . . . . . . . . . . B25 . . 709 Ando A . . . . . . . . . . . . . . . . . . B34 . . 859 Andorno M . . . . . . . . . . . . . . . E13 . 2325 Andrassy B . . . . . . . . . . . . . . . A41 . . 193 André-Obrecht R . . . . . . . . . . C11 . 1145 Andreasen P N . . . . . . . . . . . . E15 . 2401 Andrews W D . . . . . . . . . . . . . E23 . 2517 Angkititrakul P . . . . . . . . . . . . D13 . 1573 Angkititrakul P . . . . . . . . . . . . D36 . 2023 Antonio B . . . . . . . . . . . . . . . . E32 . 2679 Applebaum T H . . . . . . . . . . . B15 . . 537 Arai T . . . . . . . . . . . . . . . . . . . A36a . 149 Arai T . . . . . . . . . . . . . . . . . . . E26 . 2639 Arai T . . . . . . . . . . . . . . . . . . . E36a 2791 Arai T . . . . . . . . . . . . . . . . . . . A46 . . 391 Arai T . . . . . . . . . . . . . . . . . . . B13 . . 473 Araki K . . . . . . . . . . . . . . . . . . A46 . . 375 Araki K . . . . . . . . . . . . . . . . . . B36 . . 935 Araki M . . . . . . . . . . . . . . . . . C16 . 1283 Araki M . . . . . . . . . . . . . . . . . D22 . 1743 Araki M . . . . . . . . . . . . . . . . . D45 . 2185 Araki S . . . . . . . . . . . . . . . . . . E26 . 2595 Araki S . . . . . . . . . . . . . . . . . . E26 . 2599 Arcienega M . . . . . . . . . . . . . . E36b 2821 Ariki Y . . . . . . . . . . . . . . . . . . C23 . 1381 Ariki Y . . . . . . . . . . . . . . . . . . D26 . 1879 Ariki Y . . . . . . . . . . . . . . . . . . E25 . 2577 Ariyaeeinia A M . . . . . . . . . . . B26 . . 795 Arnott J L . . . . . . . . . . . . . . . . E15 . 2405 Arunachalam S . . . . . . . . . . . . E32 . 2675 Asano F . . . . . . . . . . . . . . . . . B43 . 1013 Ashby S . . . . . . . . . . . . . . . . . A45 . . 321 Asoh H . . . . . . . . . . . . . . . . . . B43 . 1013 Asunción M . . . . . . . . . . . . . . E32 . 2679 Attias H . . . . . . . . . . . . . . . . . D26 . 1903 Attwater D . . . . . . . . . . . . . . . C16 . 1323 Aubert X . . . . . . . . . . . . . . . . . B14 . . 499 Auer E . . . . . . . . . . . . . . . . . . A36b . 179 Avanzini F . . . . . . . . . . . . . . . A33 . . . 51 Avesani C . . . . . . . . . . . . . . . . B15 . . 509 Aylett M P . . . . . . . . . . . . . . . C26 . 1491 Azzini I . . . . . . . . . . . . . . . . . . C16 . 1327

This study extended a data-limited length-based stock assessment approach to a risk analysis context. The estimation-simulation method used length frequencies as the principal data in lieu of catch and effort. Key developments were to: (i) incorporate probabilistic mortality and growth dynamics into a numerical cohort model; (ii) employ a precautionary approach for setting sustainability reference points for fishing mortality (FREF) and stock reproductive biomass (BREF); (iii) define sustainability risks in terms of probability distributions; and, (iv) evaluate exploitation status in terms of expected length frequencies, the main observable population metric. This refined length-based approach was applied to six principal exploited reef fish species in the Florida Keys region, consisting of three groupers (black grouper, red grouper, and coney), two snappers (mutton snapper and yellowtail snapper), and one wrasse (hogfish). The estimated sustainability risks for coney were low (<35%) in terms of benchmarks for fishing mortality rate and stock reproductive biomass. The other five species had estimated sustainability risks of greater than 95% for both benchmarks. The data-limited risk analysis methodology allowed for a fairly comprehensive probabilistic evaluation of sustainability status from species and community perspectives, and also a frame of reference for exploring management options balancing sustainability risks and fishery production.

Contrary to the claim by Nelson et al. (2016; Endang Species Res 30:187-190), no court has rejected the biological framework we proposed for interpreting the 'significant portion of its range' (SPOIR) language in the US Endangered Species Act. The relative importance placed on current vs. historical range during implementation will be important in determining the success of the new ESA SPOIR policy.

The US Endangered Species Act (ESA) allows protection of any species that is at risk in all or ‘a significant portion of its range’ (SPOIR). Because this provision is open to many possible interpretations, the agencies responsible for implementing the ESA recently published a SPOIR policy. The policy is based on a framework we developed that asks a simple question: ‘If the portions of the range that are currently at risk were lost, would the entire species, at that point, be threatened or endangered?’ If so, the portion of the range is significant. Some commentators have argued that the policy departs from goals the ESA was originally intended to accomplish. We disagree; biologists and managers struggling to implement provisions of the ESA in complex, real-world situations need practical guidance, and we believe our framework provides that. In particular, it avoids as much as possible normative considerations in evaluating ‘significance’ in terms of human values; instead, we focus on significance to the species, which is consistent with the ESA focus on preventing extinctions, as well as with the mandate that listing determinations be based ‘solely’ on scientific information. However, we agree with some critics that a crucial factor in implementation of the policy will be how historical versus current concepts of range are reconciled. We believe that historical distribution and abundance are important, not as specific restoration goals, but as reference points that characterize conditions under which we are confident the species was viable.

Commercial and recreational fisheries target hundreds of fish and shellfish species across the seascape of southern Florida including inshore coastal bays, the flats of barrier islands, coral reefs and offshore pelagic waters. The ecological dynamics and economic sustainability of these valuable fishery resources are key conservation concerns. This study examined two ecological indicators of fishing impacts on exploited populations: (1) the more traditional metric catch per unit of fishing effort (CPUE); and (2) the non-traditional metric average length (L¯) in the exploited life stage of a population. We show that both indicators were closely related to stock productivity via fisheries population dynamics theory, and that either indicator could be used to estimate fishing mortality rates (F). Data requirements are much less stringent for estimating F from the L¯ indicator than CPUE, making it more practical for data-poor situations common to tropical marine fisheries. Using indicator-based estimates of Fˆ within a population dynamic modeling framework enabled an evaluation of fishing impacts on sustainability at both the species and community levels, an important step toward ecosystem-based fisheries assessment and management. A comparison of these approaches applied to the assessment of southern Florida coral reef fisheries suggested that fishing has fundamentally altered the ecological structure of the fish community by depleting the biomass of higher-trophic level carnivores to the extent that the stocks are unsustainable.

The efficacy of no-take marine reserves (NTMRs) to enhance and sustain regional coral reef fisheries was assessed in Dry Tortugas, Florida, through 9 annual fishery-independent research surveys spanning 2 years before and 10 years after NTMR implementation. A probabilistic sampling design produced precise estimates of population metrics of more than 250 exploited and non-target reef fishes. During the survey period more than 8100 research dives utilizing SCUBA Nitrox were optimally allocated using stratified random sampling. The survey domain covered 326 km2, comprised of eight reef habitats in four management areas that offered different levels of resource protection: the Tortugas North Ecological Reserve (a NTMR), Dry Tortugas National Park (recreational angling only), Dry Tortugas National Park Research Natural Area (a NTMR), and southern Tortugas Bank (open to all types of fishing). Surveys detected significant changes in population occupancy, density, and abundance within management zones for a suite of exploited and non-target species. Increases in size, adult abundance, and occupancy rates were detected for many principal exploited species in protected areas, which harbored a disproportionately greater number of adult spawning fishes. In contrast, density and occupancy rates for aquaria and non-target reef fishes fluctuated above and below baseline levels in each management zone. Observed decreases in density of exploited species below baseline levels only occurred at the Tortugas Bank area open to all fishing. Our findings indicate that these NTMRs, in conjunction with traditional fishery management control strategies, are helping to build sustainable fisheries while protecting the fundamental ecological dynamics of the Florida Keys coral-reef ecosystem.

Principles of statistical sampling design were used to guide refinement of a 30-year multispecies fishery-independent diver visual survey of population abundance and size structure of more than 250 exploited and non-target fishes in the Florida coral reef ecosystem. Reef habitat features and no-take marine reserves (NTMRs) were used to partition the 885km2 sampling domain into sub-areas (or strata) to control the variation of fish density. For the period 1999–2008, survey precision of population density and abundance (CV, coefficient of variation, ratio of standard error to mean) ranged from 7% to 20% for the majority of 13 primary exploited species in the Florida Keys and Dry Tortugas regions. Population sustainability metrics like species average length in the exploited life stage were comparable between our fishery-independent survey and fishery-dependent catch-sampling. The survey design also performed well for non-target fishes, yielding CVs between 6% and 15% for population density for the majority of 36 species. Sampling efficiency was improved over time via an iterative learning process by which past survey data was used to refine the stratification and allocation schemes of future surveys. We show how survey data are used to support multispecies stock assessments, evaluate the effectiveness of NTMRs, and assess ecosystem condition for the reef fish community.

The present study examines the influence of coastal marine protected areas (mpas) and statewide fishing regulations on recreational trophy fisheries for four important estuarine game fishes in florida, where -59% of the mainland coast consists of mpas. The distribution of International Game fish association (IGfa) recreational world records achieved over 70 years (1939-2009) were correlated with the strength and duration of fishery restrictions in mpas. no difference in record density was detected between coastal areas inside and outside of mpas where fishing was managed by statewide regulations. However, 74% (n = 143) of all records for three species were concentrated near the two mpas that had additional fishery restrictions. The highest concentration was along -11% of the mainland coast at cape canaveral (can) near mpas closed to all fishing since 1962. It included 42% of spotted seatrout [Cynoscion nebulosus (cuvier in cuvier and Valenciennes, 1830)], 55% of red drum [Sciaenops ocellatus (linnaeus, 1766)], and 69% of black drum [Pogonias cromis (linnaeus, 1766)] florida records. everglades national park (enp) had the second highest concentration with 7% of spotted seatrout, 32% of red drum, and 24% of black drum records caught along -9% of the mainland coast. enp partially limited fishing starting in 1980 by establishing a closed area, daily bag limits, and eliminating commercial fishing. common snook [Centropomus undecimalis (Bloch, 1792)] records did not increase significantly at can or enp. recreational fishery statistics corroborated IGfa record patterns. total recreational catch and catch per trip (cpue) increased significantly for spotted seatrout, red drum, and black drum in northeast and southwest florida, the two regions with the most protective mpas, and either declined or were unchanged in the northeast and southeast, which did not have mpas with fishing restrictions. Both datasets supported predictions of marine reserve theory that mpas can benefit fisheries by increasing the abundance and size of exploited species. data did not support other alternative hypotheses proposed to explain record patterns. In conclusion, evidence indicates that florida coastal estuarine mpas with fishery restrictions allowed recreational anglers to increase their total catch and cpue, and achieve more game fish world records than would have occurred if all coastal areas had been regulated by existing statewide fishing regulations.

In response to coral reef decline, the U.S. Coral Reef Task Force adopted a goal of protecting a minimum of 20% by area of all representative coral reefs and associated habitats as no-take reserves by 2010. Here we provide a rationale for using 20-30% minimum no-take protection to conserve coral reef ecosystems. Support comes from reproductive theory, knowledge about the vulnerability of reef species to exploitation, analysis of fishery failures, and empirical and modeling studies of reserves. Other support comes from applying principles of precautionary management and a need for having minimally disturbed reference sites. Reserves alone will not protect all species and must be used in addition to other fishery and resource management measures to obtain high sustainable fishery production. Ultimately, human activities must be within sustainable limits of coral reef ecosystems. 1 National Marine Fisheries Service, 75 Virginia Beach Dr., Miami, FL 33149 USA Jim.Bohnsack@noaa.gov 2 Florida Keys National Marine Sanctuary, P.O. Box 500368, Marathon, FL 33050 USA 3 NOAA and USAID, Rm. 3.08, Reagan Bldg. 1300 Pennsylvania Ave., NW, Washington, DC 20523-3800 USA 4 NOAA, USDOC, HCHB RM: 6117, 14th & Constitution Ave, NW, Washington, D.C. 20230-0001 USA 5 Department of Zoology, Oregon State University, Corvallis, OR 97331-2914 USA 6 Nat. Marine Fishery Service, SSMC3, RM 13806, 1315 East-West Hwy, Silver Spring, MD 20910-3282 USA 7 US Dept. of Interior, 1849 C. St., N.W., Washington, D.C. 20240 USA 8 US Fish and Wildlife Serv., 300 Ala Moana Blvd., Rm 5-231, Honolulu, HI 96850 USA 9 National Park Service, 1201 Oak Ridge Dr., Fort Collins, CO 80525 USA

Reef fish populations are conspicuous and essential components of coral reef ecosystems, but monitoring strategies have historically varied across agencies in their objectives and designs. An unprecedented multi-agency reef fish monitoring effort involving NOAA Fisheries, the University of Miami, the National Park Service and the Florida Fish and Wildlife Conservation Commission was initiated across the Florida coral reef ecosystem in 2008. This collaboration builds directly upon a large-scale fisheries-independent survey that began in 1979. The methods are directed to address precipitous declines in fishery resources, understand variability in natural processes, and to evaluate new ecosystem-based management tools like MPAs. There is significant motivation to address the biological, political, economic, and legal issues of these objectives through a shared vision for monitoring and assessment. In this study, precise and cost-effective sampling was achieved by collaborative surveying across the heterogeneous reef landscape using a two-stage habitat-stratified random sampling design. The realized efficiencies and benefits of the multiagency collaboration significantly outweighed the costs and difficulties encountered along the way.

Conservation BiologyVolume 22, Issue 4 p. 1075-1077 Legal Viability, Societal Values, and SPOIR: Response to D'Elia et al. ROBIN S. WAPLES, ROBIN S. WAPLES Northwest Fisheries Science Center, 2725 Montlake Boulevard East, Seattle, WA 98112, U.S.A., email robin.waples@noaa.govSearch for more papers by this authorPETER B. ADAMS, PETER B. ADAMS Southwest Fisheries Science Center, 110 Shaffer Road, Santa Cruz, CA 95060, U.S.A.Search for more papers by this authorJAMES BOHNSACK, JAMES BOHNSACK Southeast Fisheries Science Center, 75 Virginia Beach Drive, Miami, FL 33149, U.S.A.Search for more papers by this authorBARBARA L. TAYLOR, BARBARA L. TAYLOR Southwest Fisheries Science Center, 8604 La Jolla Shores Drive, La Jolla, CA 92037, U.S.A.Search for more papers by this author ROBIN S. WAPLES, ROBIN S. WAPLES Northwest Fisheries Science Center, 2725 Montlake Boulevard East, Seattle, WA 98112, U.S.A., email robin.waples@noaa.govSearch for more papers by this authorPETER B. ADAMS, PETER B. ADAMS Southwest Fisheries Science Center, 110 Shaffer Road, Santa Cruz, CA 95060, U.S.A.Search for more papers by this authorJAMES BOHNSACK, JAMES BOHNSACK Southeast Fisheries Science Center, 75 Virginia Beach Drive, Miami, FL 33149, U.S.A.Search for more papers by this authorBARBARA L. TAYLOR, BARBARA L. TAYLOR Southwest Fisheries Science Center, 8604 La Jolla Shores Drive, La Jolla, CA 92037, U.S.A.Search for more papers by this author First published: 04 August 2008 https://doi.org/10.1111/j.1523-1739.2008.00980.xCitations: 4Read the full textAboutPDF ToolsRequest permissionExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinked InRedditWechat Citing Literature Volume22, Issue4August 2008Pages 1075-1077 RelatedInformation

Chantal Collier1, Rob Ruzicka1, Ken Banks2, Luiz Barbieri3, Jeff Beal3, David Bingham3, James Bohnsack4, Sandra Brooke5, Nancy Craig2, Richard Dodge6,7, Lou Fisher2, Nick Gadbois1, David Gilliam6,7, Lisa Gregg3, Todd Kellison4, Vladimir Kosmynin1, Brian Lapointe8, Erin McDevitt3, Janet Phipps9, Nikki Poulos1, John Proni10, Patrick Quinn2, Bernhard Riegl6,7, Richard Spieler6,7, Joanna Walczak1, Brian Walker6,7 and Denise Warrick3

1. NOAA, Florida Keys National Marine Sanctuary 2. Florida Fish and Wildlife Conservation Commission, Fish and Wildlife Research Institute 3. Reef Environmental Education Foundation 9. NOAA, Office of National Marine Sanctuaries 4. University of Miami, Rosenstiel School for Marine and Atmospheric Science 10. NOAA, National Ocean Service, Special Projects Office 5. NOAA, Southeast Fisheries Science Center 11. Scripps Institute of Oceanography 6. Florida International University, Southeast Environmental Research Center 12. University of North Carolina, Wilmington 7. Florida Department of Environmental Protection 13. Mote Marine Laboratory 8. Monroe County Division of Marine Resources 14. U.S. Environmental Protection Agency, Gulf Ecology Division9. The State of Coral Reef Ecosystems of the Florida Keys