Southern Florida coral reefs generated an estimated 71,000 jobs and US$6 billion in economic activity in 2001. These ecosystem goods and services, however, are threatened by increased exploitation and environmental changes from a rapidly growing regional human population. To address these threats, we adopted an ecosystem-based perspective and developed a systems science analysis framework over the last decade to better assess and improve sustainable multispecies reef fisheries in the Florida Keys. Here we describe our progress and provide three example applications. We first built upon traditional catch and effort stock assessment methodologies by collecting spatially-explicit, fishery-independent data covering all reef fishes and reef habitats in the Keys. An optimized sampling strategy and a new length-based assessment framework provided synoptic spatial estimates of species abundance and size structures. Models were developed that encompassed the complex biological dynamics of fish stocks and a broad range of environmental and human impacts, including fisheries, non-target species, predator-prey interactions, species movements, ontogenetic changes in habitat associations, and physical processes. We show that the snapper-grouper fishery in the Florida Keys is experiencing overfishing and that stocks are overfished relative to established benchmarks for resource sustainability. Spatially explicit models demonstrated the potential effectiveness of no-take marine reserves to support sustainable fisheries, and were employed to objectively evaluate marine reserve boundary options in the Dry Tortugas. We show the importance of considering physical coupling and regional water quality changes resulting from Everglades restoration. A fishery systems science framework improves understanding of impacts from fishery extraction, ecosystem alterations, and natural oceanographic variability on the dynamics of exploited fish stocks. Coral reefs in southeastern Florida and the Florida Keys provide the ecological foundation for vital fisheries and a tourism-based economy that generated an estimated 71,000 jobs and US$6 billion of economic activity in 2001 (Johns et al., 2001). They also contributed to the designation of Florida as the “fishing capital of the world” by the state legislature (FWC, 2003). Coral reef ecosystem goods and services, however, extend beyond fishing to include a range of educational, scientific, aesthetic, and other recreational uses, such as snorkeling, SCUBA diving, and tourism. The Florida Keys reef ecosystem is considered one of the nation’s most significant, yet most stressed marine resources (U.S. Department of Commerce, 1996) and is managed by Florida, the National Oceanic and Atmospheric Administration (NOAA), and the National Park Service. Reef fisheries target the “snapper-grouper complex,” which consists of 73 species of mostly groupers and snappers, but also grunts, jacks, porgies, and hogfish. The fishery has been intensively exploited over the past 75 yrs, during which the local human population has grown exponentially and generated concerns over sustainable fishery productivity. Many reef species are extremely sensitive to exploitation (Coleman et al., 2000; Musick et al., 2000), and coastal development subjects coral reefs to a suite of other stressors that can cumulatively impact reef fish populations by degrading water quality and damaging nursery BULLETIN OF MARINE SCIENCE, VOL. 76, NO. 2, 2005 596 and adult habitats (Bohnsack and Ault, 1996; Lindeman et al., 2000; Jackson et al., 2001; Porter and Porter, 2001). Traditional single-species assessment methods have proven conceptually and analytically inadequate for developing reliable multispecies assessments and models to address complex coral reef issues (Bohnsack and Ault, 1996; Bohnsack, 1998). For these reasons, we embarked on a research program in the early-1990s to begin to address the full range of issues for managing coral reef fisheries in the Florida Keys. The goal was to use an ecosystem-based perspective that integrates biological, oceanographic, habitat, and human dynamic factors to better manage sustainable multispecies reef fisheries. Here we describe our progress in developing and applying a fishery systems science framework to address assessment and modeling needs for managing the coral reef fishery in the Florida Keys. We provide three example applications of our research approach to explore inter-related management issues involving fishery exploitation, marine reserve design and utility, and potential impacts of the Everglades restoration on reef fish dynamics and productivity. FISHERY SYSTEMS SCIENCE Fishery systems science (FSS), a topic of theoretical and practical interest (cf., Rothschild, 1971, 1973; Walters, 1986; Ault, 1996; Rothschild et al., 1996), defines an analytical framework with two principal goals: (1) to improve the understanding of the impacts of fishery extractions, ecosystem alterations, and natural oceanographic variability on the dynamics of exploited fish stocks; and (2) to use this knowledge to recommend modifications of human activities to ensure long-term sustainable use of fishery resources. In principle, sustainable exploited populations need sufficient reproductive capacity in terms of the biomass of spawning adults to replenish themselves into the indefinite future. The FSS framework (Fig. 1) has two “primary” elements, the coral reef ecosystem and the interacting human-fishery sector, and three “derived” elements: data acquisition, model building, and resource risk assessment. Data acquired from biotic and abiotic components of the coral reef ecosystem and the human-fishery sector are used to construct mathematical and statistical models that reflect the complexity and uncertainty of real ecosystem processes and their interactions with the human-fishery sector. Models of perceived reality are then employed to assess the risks to fish stock sustainability under current and anticipated future conditions of fishing intensity, water management practices, and other environmental conditions. Knowledge and insights gained are provided to managers and policymakers, who in turn implement regulations to modulate human impacts on the ecosystem to ensure fishery sustainability. The key difference between our FSS approach and simpler decision theory approaches is our assumption that more complex models are necessary to adequately describe and manage complex natural ecosystems. The elements of the analysis framework (data acquisition, modeling, and risk assessment) are ubiquitous to most real-life fishery science applications (Fig. 1). The centerpiece of the analysis process is an integrated suite of mathematical models that couple ecosystem dynamics and human impacts (Ault et al., 1998, 1999b, 2003a; Wang et al., 2003). This suite of models evolved from cohort-structured models of fish population dynamics impacted by fishing and is rooted in fundamental fishery science concepts pioneered by Baranov (1918), Russell (1931), Thompson and Bell AULT ET AL.: SUSTAINABLE FISHERIES IN THE FLORIDA CORAL REEF ECOSYSTEM 597 (1934), and Beverton and Holt (1957). Age-structured stock production models integrate a series of mathematical functions that explicitly represent population processes of recruitment, growth, reproduction, and mortality from natural causes and fishing (Table 1) to describe the dynamics of abundance or biomass of a cohort (i.e., a group of individuals born at the same time) over its lifespan, or the entire population of cohorts over time. Age-structured models are currently used to assess dynamic fishing impacts on a target population (e.g., Quinn and Deriso, 1999; Haddon, 2001). The demographic modeling framework is analogous to an engineering “control system” (Wagner, 1975; Sterman, 2000), in which a fish population is viewed as a biomass production facility and population dynamic processes are the machinery that governs the capacity to produce biomass. Resource managers achieve sustainability goals by manipulating two control variables: size-age of capture and fishing mortality rate. Traditionally, size-age of capture is controlled by minimum size limits or gear restrictions (e.g., mesh size, trap escape vents, hook size, seasonal and spatial closures), while mortality rates are controlled by fishing effort restrictions through quotas, bag limits, limited entry, and time and spatial closures. Fishing impacts are normally evaluated as tradeoffs between yields (in biomass) extracted by the fishery relative to the biomass of spawners remaining in the sea that are required to ensure sustained production. This concept is illustrated in Figure 2 using two widely used fishery management benchmarks: yield-per-recruit (YPR) and spawning potential ratio (SPR). YPR is the expected lifetime yield of a cohort scaled to annual recruitment of newborns for a given combination of fishing mortality rate and minimum capture age or size. SPR is the expected lifetime spawning biomass of a cohort for a given combination of fishing mortality and age of capture scaled to the unexploited lifetime spawning biomass. In the U.S. south Atlantic, the federal miniFigure 1. Overview of the components in a systems science approach to multispecies fishery management in coral reef ecosystems. BULLETIN OF MARINE SCIENCE, VOL. 76, NO. 2, 2005 598 mum standard is 40% SPR for Goliath grouper, Epinephelus itajara (Lichtenstein, 1822), and 30% SPR for other reef fish stocks (NOAA Fisheries, 2002). These values are derived from density-dependent stock-recruitment theory where the number of recruits to a population is expected to be approximately the same at or above the minimum SPR threshold. As shown for hogfish, Lachnolaimus maximus (Walbaum, 1792), the maximum YPR value at which SPR is at or above the 30% threshold denotes the level of exploitation expected to produce “maximum sustainable yield” (MSY) (Fig. 2). Our program
[1]
Steven G. Smith,et al.
An integrated simulation modeling and operations research approach to spatial management decision making
,
2001
.
[2]
J. Ault,et al.
Correction to the Beverton and Holt Z-estimator for truncated catch length-frequency distributions
,
1991
.
[3]
John D. Wang,et al.
Finite Element Characteristic Advection Model
,
1988
.
[4]
E. S. Russell,et al.
Some theoretical Considerations on the “Overfishing” Problem
,
1931
.
[5]
J. J..
Fisheries Biology
,
2022,
Nature.
[6]
Michael H. Prager,et al.
A suite of extensions to a nonequilibrium surplus-production model
,
1994
.
[7]
Joseph E. Powers,et al.
Precautionary control rules in US fisheries management: specification and performance
,
1999
.
[8]
J. M. Elliott,et al.
Fish Stock Assessment. A Manual of Basic Methods
,
1984
.
[9]
M. Clarke,et al.
Evolution of the Tortugas Gyre and its influence on recruitment in the Florida Keys
,
1994
.
[10]
A. Grant,et al.
Long-Term Region-Wide Declines in Caribbean Corals
,
2003,
Science.
[11]
Warren P. Kriesel,et al.
Economic contribution of recreating visitors to the Florida Keys/Key West
,
1996
.
[12]
J. Ault,et al.
An Efficient Sampling Survey Design to Estimate Pink Shrimp Population Abundance in Biscayne Bay, Florida
,
1999
.
[13]
C. J. Neumann.
The national hurricane center risk analysis program (HURISK)
,
1987
.
[14]
Benjamin D. Cowie-Haskell,et al.
Integrating science into the design of the Tortugas Ecological Reserve
,
2003
.
[15]
Jerald S. Ault,et al.
A spatial dynamic multistock production model
,
1999
.
[16]
E. Sala,et al.
Marine, Estuarine, and Diadromous Fish Stocks at Risk of Extinction in North America (Exclusive of Pacific Salmonids)
,
2000
.
[17]
K. Bjorndal,et al.
Historical Overfishing and the Recent Collapse of Coastal Ecosystems
,
2001,
Science.
[18]
D. Olson.
Biophysical dynamics of western transition zones: a preliminary synthesis
,
2001
.
[19]
H. M. Wagner,et al.
Principles of Operations Research: With Applications to Managerial Decisions.
,
1971
.
[20]
J. Ault,et al.
Analysis of two length-based mortality models applied to bounded catch length frequencies
,
1992
.
[21]
Andrew A. Rosenberg,et al.
Managing to the margins: the overexploitation of fisheries
,
2003
.
[22]
J. Ault,et al.
A Spatial Ecosystem Model to Assess Spotted Seatrout Population Risks from Exploitation and Environmental Changes
,
2002
.
[23]
J. Bohnsack,et al.
Fisheries trends from Monroe County, Florida
,
1994
.
[24]
Benjamin S. Halpern,et al.
Marine reserves have rapid and lasting effects
,
2002
.
[25]
B. Rothschild.
Questions of Strategy in Fishery Management and Development
,
1973
.
[26]
Jerald S. Ault,et al.
Designing Marine Reserves for Fishery Management
,
2004,
Manag. Sci..
[27]
D. DeAngelis,et al.
Effects of spatial grouping on the functional response of predators.
,
1999,
Theoretical population biology.
[28]
Terrance J. Quinn,et al.
Quantitative Fish Dynamics
,
1999
.
[29]
C. Roberts,et al.
Effects of marine reserves on adjacent fisheries.
,
2001,
Science.
[30]
Bernard A. Megrey,et al.
Mathematical analysis of fish stock dynamics
,
2004,
Reviews in Fish Biology and Fisheries.
[31]
J. Ault,et al.
Why Have No-Take Marine Protected Areas?
,
2004
.
[32]
P. Hastings.
ECOLOGY OF THE MARINE FISHES OF CUBA
,
2003,
Copeia.
[33]
F. Coleman,et al.
Long-lived Reef Fishes: The Grouper-Snapper Complex
,
2000
.
[34]
D. Olson,et al.
A Multicohort Stock Production Model
,
1996
.
[35]
Steven L. Miller,et al.
Baseline multispecies coral reef fish stock assessment for the Dry Tortugas
,
2002
.
[36]
J. Ault,et al.
Management Strategies to Conserve Marine Biodiversity
,
1996
.
[37]
G. Meester.
A mathematical programming and simulation-based approach to determining critical factors in the design of effective marine reserve plans for coral reef fishes
,
2000
.
[38]
R. Beverton,et al.
On the dynamics of exploited fish populations
,
1993,
Reviews in Fish Biology and Fisheries.
[39]
J. Ault,et al.
An extensive deep reef terrace on the Tortugas Bank, Florida Keys National Marine Sanctuary
,
2001,
Coral Reefs.
[40]
B. Lockwood,et al.
Recreational Fisheries in Biscayne National Park, Florida, 1976–19
,
2000
.
[41]
C. Walters,et al.
Quantitative fisheries stock assessment: Choice, dynamics and uncertainty
,
2004,
Reviews in Fish Biology and Fisheries.
[42]
M. Domeier,et al.
Tropical reef fish spawning aggregations : Defined and reviewed
,
1997
.
[43]
Jerald S. Ault,et al.
Benthic Habitat Mapping in the Tortugas Region, Florida
,
2003
.
[44]
J. Ault,et al.
Evaluation of average length as an estimator of exploitation status for the Florida coral-reef fish community
,
2005
.
[45]
J. Bohnsack.
Application of marine reserves to reef fisheries management
,
1998
.
[46]
Malcolm Haddon,et al.
Modelling and quantitative methods in fisheries
,
2001
.
[47]
J. Bohnsack,et al.
A stationary visual census technique for quantitatively assessing community structure of coral reef fishes
,
1986
.