Spatial Processes and Management of Marine Populations

Home , Cyclone Justin, Placopecten magellanicus

17th Lowell Wakefield Symposium

Gordon H. Kruse, Nicolas Bez, Anthony Booth, Martin W. Dorn, Sue Hills, Romuald N. Lipcius, Dominique Pelletier, Claude Roy, Stephen J. Smith, and David Witherell, Editors

Proceedings of the Symposium on Spatial Processes and Management of Marine Populations, October 27-30, 1999, Anchorage, Alaska

University of Alaska Sea Grant College Program Report No. AK-SG-01-02 2001

Price $40.00 Elmer E. Rasmuson Library Cataloging-in-Publication Data

International Symposium on Spatial processes and management of marine populations (1999 : Anchorage, Alaska.) Spatial processes and management of marine populations : proceedings of the Sympo- sium on spatial processes and management of marine populations, October 27-30, 1999, An- chorage, Alaska / Gordon H. Kruse, [et al.] editors. – Fairbanks, Alaska : University of Alaska Sea Grant College Program, [2001].

730 p. : ill. ; cm. – (University of Alaska Sea Grant College Program ; AK-SG-01-02)

Includes bibliographical references and index.

1. Aquatic animals—Habitat—Congresses. 2. Aquatic animals—Dispersal—Congresses. 3. Fishes—Habitat—Congresses. 4. Fishes—Dispersal—Congresses. 5. Aquatic animals—Spawn- ing—Congresses. 6. Spatial ecology—Congresses. I. Title. II. Kruse, Gordon H. III. Lowell Wakefield Fisheries Symposium (17th : 1999 : Anchorage, Alaska). IV. Series: Alaska Sea Grant College Program report ; AK-SG-01-02. SH3.I59 1999

ISBN 1-56612-068-3

Citation for this volume is: G.H. Kruse, N. Bez, A. Booth, M.W. Dorn, S. Hills, R.N. Lipcius, D. Pelletier, C. Roy, S.J. Smith, and D. Witherell (eds.). 2001. Spatial processes and management of marine populations. University of Alaska Sea Grant, AK-SG-01-02, Fairbanks.

Credits This book is published by the University of Alaska Sea Grant College Program, which is coop- eratively supported by the U.S. Department of Commerce, NOAA National Sea Grant Office, grant no. NA86RG-0050, project A/161-01; and by the University of Alaska Fairbanks with state funds. The University of Alaska is an affirmative action/equal opportunity institution. Sea Grant is a unique partnership with public and private sectors combining research, education, and technology transfer for public service. This national network of universities meets changing environmental and economic needs of people in our coastal, ocean, and Great Lakes regions.

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U E S (907) 474-6707 Fax (907) 474-6285 C D R E E P M A M RT O http://www.uaf.alaska.edu/seagrant/ MENT OF C Contents

About the Symposium ...... ix

About This Proceedings ...... ix

The Lowell Wakefield Symposium Series ...... x

Tools for Analysis Spatial Modeling of Fish Habitat Suitability in Florida Estuaries Peter J. Rubec, Steven G. Smith, Michael S. Coyne, Mary White, Andrew Sullivan, Timothy C. MacDonald, Robert H. McMichael Jr., Douglas T. Wilder, Mark E. Monaco, and Jerald S. Ault ...... 1

Recent Approaches Using GIS in the Spatial Analysis of Fish Populations Tom Nishida and Anthony J. Booth ...... 19

Using GIS to Analyze Animal Movements in the Marine Environment Philip N. Hooge, William M. Eichenlaub, and Elizabeth K. Solomon ...... 37

Modeling Approaches A Conceptual Model for Evaluating the Impact of Spatial Management Measures on the Dynamics of a Mixed Fishery Dominique Pelletier, Stéphanie Mahévas, Benjamin Poussin, Joël Bayon, Pascal André, and Jean-Claude Royer...... 53

Evaluating the Scientific Benefits of Spatially Explicit Experimental Manipulations of Common Coral Trout (Plectropomus leopardus) Populations on the Great Barrier Reef, Australia Andre E. Punt, Anthony D.M. Smith, Adam J. Davidson, Bruce D. Mapstone, and Campbell R. Davies ...... 67

Spatial Modeling of Atlantic Yellowfin Tuna Population Dynamics: Application of a Habitat-Based Advection- Diffusion-Reaction Model to the Study of Local Overfishing Olivier Maury, Didier Gascuel, and Alain Fonteneau ...... 105

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Integrated Tagging and Catch-at-Age Analysis (ITCAAN): Model Development and Simulation Testing Mark N. Maunder ...... 123

Toward an Environmental Analysis System to Forecast Spawning Probability in the Gulf of California Sardine S.E. Lluch-Cota, D. Kiefer, A. Parés-Sierra, D.B. Lluch-Cota, J. Berwald, and D. Lluch-Belda ...... 147

Patterns in Life History and Population Parameters Ocean Current Patterns and Aspects of Life History of Some Northwestern Pacific Scorpaenids Alexei M. Orlov ...... 161

Comparative Spawning Habitats of Anchovy (Engraulis capensis) and Sardine (Sardinops sagax) in the Southern Benguela Upwelling Ecosystem C.D. van der Lingen, L. Hutchings, D. Merkle, J.J. van der Westhuizen, and J. Nelson ...... 185

Spatially Specific Growth Rates for Sea Scallops (Placopecten magellanicus) Stephen J. Smith, Ellen L. Kenchington, Mark J. Lundy, Ginette Robert, and Dale Roddick ...... 211

Spatial Distribution and Recruitment Patterns of Snow Crabs in the Eastern Bering Sea Jie Zheng, Gordon H. Kruse, and David R. Ackley ...... 233

Yelloweye Rockfish (Sebastes ruberrimus) Life History Parameters Assessed from Areas with Contrasting Fishing Histories Allen Robert Kronlund and Kae Lynne Yamanaka ...... 257

Spatial Distributions of Populations Distribution Patterns and Survey Design Considerations of Pacific Ocean Perch (Sebastes alutus) in the Gulf of Alaska Chris Lunsford, Lewis Haldorson, Jeffrey T. Fujioka, and Terrance J. Quinn II ...... 281

Spatial Inferences from Adaptive Cluster Sampling of Gulf of Alaska Rockfish Dana H. Hanselman, Terrance J. Quinn II, Jonathan Heifetz, David Clausen, and Chris Lunsford ...... 303

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Evaluating Changes in Spatial Distribution of Blue King Crab near St. Matthew Island Ivan Vining, S. Forrest Blau, and Doug Pengilly ...... 327

Density-Dependent Ocean Growth of Some Bristol Bay Sockeye Salmon Stocks Ole A. Mathisen and Norma Jean Sands ...... 349

Evidence of Biophysical Coupling from Shifts in Abundance of Natural Stable Carbon and Nitrogen Isotopes in Prince William Sound, Alaska Thomas C. Kline Jr...... 363

Relationships with the Physical Environment Classification of Marine Habitats Using Submersible and Acoustic Seabed Techniques John T. Anderson ...... 377

Environmental Factors, Spatial Density, and Size Distributions of 0-Group Fish Boonchai K. Stensholt and Odd Nakken ...... 395

Spatial Distribution of Atlantic Salmon Postsmolts: Association between Genetic Differences in Trypsin Isozymes and Environmental Variables Krisna Rungruangsak-Torrissen and Boonchai K. Stensholt ...... 415

Critical Habitat for Ovigerous Dungeness Crabs Karen Scheding, Thomas Shirley, Charles E. O’Clair, and S. James Taggart ...... 431

Large-Scale Long-Term Variability of Small Pelagic Fish in the California Current System Rubén Rodríguez-Sánchez, Daniel Lluch-Belda, Héctor Villalobos-Ortiz, and Sofia Ortega-García ...... 447

Spatial Distribution and Selected Habitat Preferences of Weathervane Scallops (Patinopecten caurinus) in Alaska Teresa A. Turk ...... 463

Species Interactions Spatial Dynamics of Cod-Capelin Associations off Newfoundland Richard L. O’Driscoll and George A. Rose ...... 479

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Spatial Patterns of Pacific Hake (Merluccius productus) Shoals and Euphausiid Patches in the California Current Ecosystem Gordon Swartzman ...... 495

Spatial Patterns in Species Composition in the Northeast United States Continental Shelf Fish Community during 1966-1999 Lance P. Garrison ...... 513

Patterns in Fisheries An Empirical Analysis of Fishing Strategies Derived from Trawl Logbooks David B. Sampson ...... 539

Distributing Fishing Mortality in Time and Space to Prevent Overfishing Ross Claytor and Allen Clay ...... 543

In-Season Spatial Modeling of the Chesapeake Bay Blue Crab Fishery Douglas Lipton and Nancy Bockstael ...... 559

Territorial Use Rights: A Rights Based Approach to Spatial Management Keith R. Criddle, Mark Herrmann, and Joshua A. Greenberg ...... 573

Marine Protected Areas and Experimental Management Sanctuary Roles in Population and Reproductive Dynamics of Caribbean Spiny Lobster Rodney D. Bertelsen and Carrollyn Cox ...... 591

Efficacy of Blue Crab Spawning Sanctuaries in Chesapeake Bay Rochelle D. Seitz, Romuald N. Lipcius, William T. Stockhausen, and Marcel M. Montane ...... 607

Simulation of the Effects of Marine Protected Areas on Yield and Diversity Using a Multispecies, Spatially Explicit, Individual-Based Model Yunne-Jai Shin and Philippe Cury ...... 627

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A Deepwater Dispersal Corridor for Adult Female Blue Crabs in Chesapeake Bay Romuald N. Lipcius, Rochelle D. Seitz, William J. Goldsborough, Marcel M. Montane, and William T. Stockhausen ...... 643

Managing with Reserves: Modeling Uncertainty in Larval Dispersal for a Sea Urchin Fishery Lance E. Morgan and Louis W. Botsford ...... 667

Reflections on the Symposium “Spatial Processes and Management of Marine Populations” Dominique Pelletier ...... 685

Participants ...... 695

Index ...... 703

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Contents

About the Symposium The International Symposium on Spatial Processes and Management of Fish Populations is the seventeenth Lowell Wakefield symposium. The program concept was suggested by Gordon Kruse of the Alaska Department of Fish and Game. The meeting was held October 27-30, 1999, in Anchorage, Alaska. Eighty presentations were made. The symposium was organized and coordinated by Brenda Baxter, Alaska Sea Grant College Program, with the assistance of the organizing and program committees. Organizing committee members are: Martin Dorn, U.S. National Marine Fisheries Service, Alaska Fisheries Science Center; Su- san Hills, University of Alaska Fairbanks, Institute of Marine Science; Gor- don Kruse, Alaska Department of Fish and Game; and David Witherell, North Pacific Fishery Management Council. Program planning committee members are: David Ackley, U.S. National Marine Fisheries Service; Bill Ballantine, University of Auckland, New Zealand; Nicolas Bez, École des Mines de Paris, France; Tony Booth, Rhodes University, South Africa; John Caddy, Food and Agriculture Organization, Italy; Nick Caputi, Western Australian Marine Research Laboratories, Australia; Jeremy Collie, University of Rhode Island; Rom Lipcius, Virginia Institute of Marine Science; Jeff Polovina, U.S. Na- tional Marine Fisheries Service, Hawaii; Claude Roy, ORSTOM, Sea Fisheries Research Institute, South Africa; Stephen J. Smith, Department of Fisheries and Oceans, Canada; Gordon Swartzman, University of Washington; and Sigurd Tjelmeland, Institute of Marine Research, Norway Symposium sponsors are: Alaska Department of Fish and Game; North Pacific Fishery Management Council; U.S. National Marine Fisheries Ser- vice, Alaska Fisheries Science Center; and Alaska Sea Grant College Pro- gram, University of Alaska Fairbanks.

About This Proceedings This publication has 35 symposium papers. Each paper has been reviewed by two peer reviewers. Peer reviewers are: David Ackley, Milo Adkison, Jeff Arnold, Andrew Bakun, Rodney Bertelsen, Philippe Borsa, Jean Boucher, Alan Boyd, Evelyn Brown, Lorenzo Ciannelli, Espérance Cillaurren, Kevern Cochrane, Catherine Coon, Keith Criddle, Philippe Cury, Doug Eggers, Alain Fonteneau, Charles Fowler, Kevin Friedland, Rob Fryer, Lance Garrison, Stratis Gavaris, François Gerlotto, Henrik Gislason, John Gunn, Lew Haldorson, Jonathan Heifetz, Sarah Hinckley, Dan Holland, Philip Hooge, Astrid Jarre, Tom Kline, K Koski, Rob Kronlund, Gordon Kruse, Han-lin Lai, Bruce Leaman, Salvador Lluch- Cota, Nancy Lo, Elizabeth Logerwell, Alec MacCall, Brian MacKenzie, Stephanie Mahevas, Jacques Masse, Olivier Maury, Murdoch McAllister, Geoff Meaden, Rick Methot, Mark Monaco, Lance Morgan, Nathaniel Newlands, Tom Nishida, Brenda Norcross, Charles O’Clair, William Overholtz, Wayne Palsson, Daniel Pauly, Ian Perry, Randall Peterman, Tony Pitcher, Jeffrey Polovina, Terry Quinn, Steve Railsback, Dave Reid, Jake Rice, Ruben Roa,

ix Contents

Ruben Rodríguez-Sánchez, Peter Rubec, David Sampson, Jake Schweigert, Rochelle Seitz, L.J. Shannon, Tom Shirley, Jeff Short, Paul Smith, Steven Smith, Stephen Smith, William Stockhausen, Patrick Sullivan, Gordon Swartzman, Jack Tagart, Christopher Taggart, Teresa Turk, Dan Urban, Carl van der Lingen, Hans van Oostenbrugge, Ivan Vining, Dave Witherell, Bruce Wright, Kate Wynne, Lynne Yamanaka, and Jie Zheng. Copy editing is by Kitty Mecklenburg of Pt. Stephens Research Associ- ates, Auke Bay, Alaska. Layout, format, and proofing are by Brenda Baxter and Sue Keller of University of Alaska Sea Grant. Cover design is by David Brenner and Tatiana Piatanova.

The Lowell Wakefield Symposium Series The University of Alaska Sea Grant College Program has been sponsoring and coordinating the Lowell Wakefield Fisheries Symposium series since 1982. These meetings are a forum for information exchange in biology, management, economics, and processing of various fish species and com- plexes as well as an opportunity for scientists from high latitude countries to meet informally and discuss their work. Lowell Wakefield was the founder of the Alaska king crab industry. He recognized two major ingredients necessary for the king crab fishery to survive—ensuring that a quality product be made available to the con- sumer, and that a viable fishery can be maintained only through sound management practices based on the best scientific data available. Lowell Wakefield and Wakefield Seafoods played important roles in the development and implementation of quality control legislation, in the preparation of fishing regulations for Alaska waters, and in drafting international agree- ments for the high seas. Toward the end of his life, Lowell Wakefield joined the faculty of the University of Alaska as an adjunct professor of fisheries where he influenced the early directions of the university’s Sea Grant Pro- gram. This symposium series is named in honor of Lowell Wakefield and his many contributions to Alaska’s fisheries. Three Wakefield symposia are planned for 2002-2004.

x Spatial Processes and Management of Marine Populations 1 Alaska Sea Grant College Program • AK-SG-01-02, 2001

Spatial Modeling of Fish Habitat Suitability in Florida Estuaries

Peter J. Rubec Florida Fish and Wildlife Conservation Commission, Florida Marine Research Institute, St. Petersburg, Florida

Steven G. Smith University of Miami, Rosenstiel School of Marine and Atmospheric Science, Miami, Florida

Michael S. Coyne National Oceanic and Atmospheric Administration, National Ocean Service, Center for Coastal Monitoring and Assessment, Silver Spring, Maryland

Mary White, Andrew Sullivan, Timothy C. MacDonald, Robert H. McMichael Jr., and Douglas T. Wilder Florida Fish and Wildlife Conservation Commission, Florida Marine Research Institute, St. Petersburg, Florida

Mark E. Monaco National Oceanic and Atmospheric Administration, National Ocean Service, Center for Coastal Monitoring and Assessment, Silver Spring, Maryland

Jerald S. Ault University of Miami, Rosenstiel School of Marine and Atmospheric Science, Miami, Florida

Abstract Spatial habitat suitability index (HSI) models were developed by a group of collaborating scientists to predict species relative abundance distributions by life stage and season in Tampa Bay and Charlotte Harbor, Florida.

Habitat layers and abundance-based suitability index (Si) values were derived from fishery-independent survey data and used with HSI models 2 Rubec et al. — Spatial Modeling of Fish Habitat Suitability in Florida that employed geographic information systems. These analyses produced habitat suitability maps by life stage and season in the two estuaries for spotted seatrout (Cynoscion nebulosus), bay anchovy (Anchoa mitchilli), and pinfish (Lagodon rhomboides). To verify the reliability of the HSI models, mean catch rates (CPUEs) were plotted across four HSI zones. Analyses showed that fish densities increased from low to optimum zones for the majority of species life stages and seasons examined, particularly for Char- lotte Harbor. A reciprocal transfer of Si values between estuaries was conducted to test whether HSI modeling can be used to predict species distributions in estuaries lacking fisheries-independent monitoring. The

similarity of Si functions used with the HSI models accounts for the high similarity of predicted seasonal maps for juvenile pinfish and juvenile bay anchovy in each estuary. The dissimilarity of Si functions input into HSI models can account for why other species life stages had dissimilar predicted maps.

Introduction Understanding and predicting relationships of the dynamics between fish stocks and important habitats is fundamental for the effective assessment and management of marine fish populations. Managers of commercial and recreational fisheries now recognize the importance of habitat to the productivity of fish stocks (Rubec et al. 1998a, Friel 2000), and accurate maps of habitats and fish populations are becoming important tools for the management and protection of essential habitats and for building sustainable fisheries (Rubec and McMichael 1996; Rubec et al. 1998b, 1999; Ault et al. 1999a, 1999b). However, mathematical models that describe spatial relationships between habitats and fish abundance are not available for most species, generally because it is not clear what constitutes “habitat” or how it relates to the spatial and temporal variations in abundance of fish stocks. Rather than a simple relationship, areas of higher or lower population abundance are typically complex functions of several environmental and biological factors. Some early attempts to quantify linkages between fish stocks and habitat were developed by the U.S. Fish and Wildlife Service (FWS) habitat evalua- tion program. The most visible product of those efforts was the Habitat Suitability Index (HSI) (FWS 1980a, 1980b, 1981; Terrell and Carpenter 1997). The central premise of the HSI approach derives from ecological theory, which states that the “value” of an area of “habitat” to the productivity of a given species is determined by habitat carrying capacity as it relates to density-dependent population regulation (FWS 1981). Empirical suitability index (Si) functions were derived by relating population abundance to the quantity and quality of given habitats (Terrell 1984, Bovee 1986, Bovee and Zuboy 1988). Suitability indices are generally continuous functions of environmental gradients, but they can be scaled to a fixed range or made dimensionless. Higher suitability index values de facto mean that areas Spatial Processes and Management of Marine Populations 3 with higher relative abundance in terms of numbers or biomass are “more suitable habitat.” Suitability indices have been multiplied against the amount of area constituting the index score to create habitat units that quantify the extent of suitable habitats (FWS 1980b, 1981; Bovee 1986). Historically, spatial calculations were limited by computational capabilities. Today, geographic information systems operating on powerful desktop computers make such spatially intensive analyses tractable. Florida is undergoing rapid human population growth and development in the coastal margins, and this explosive growth is believed to be detrimental to the sustainability and conservation of coastal fisheries resources. Intensive fishery-independent monitoring programs have been established in 5 of 18 major estuaries spread throughout Florida (McMichael 1991). In the management of the state’s extensive and valuable marine fishery resources, one of the principal questions that has arisen is: Is it possible to use the empirical functions developed for one estuary and transfer them to another where abundance data are not available, but environmental regimes are known, to predict the likelihood of species occurrences, relative abundance, and spatial distributions? To address these issues, scientists from the Florida Fish and Wildlife Conservation Commission, the National Oceanic and Atmospheric Administration (NOAA), and the Univer- sity of Miami have been collaborating on suitability model development and implementation using geographic information systems (GIS). A pri- mary research goal is to predict the spatial distributions of given fish species by estuary, life stage, and season from empirical functions derived from similar aquatic systems. In this paper, we show how the dependent variable “relative abundance” can be related to a suite of independent environmental variables to examine two main hypotheses: (1) that relative abundance increases with habitat suitability, and (2) that predicted species spatial distributions produced from Si functions and habitat layers in one estuary will be similar to the predicted maps derived from Si functions transferred from another estuary.

Methods In the present study, we adopted an analytical approach previously de- scribed in Rubec et al. (1998b, 1999), that follows methods published by FWS and NOAA (FWS 1980a, 1980b, 1981; Christensen et al. 1997, Brown et al. 2000). This methodology links HSI modeling to GIS visualization tech- nologies to produce spatial predictions of relative abundance of selected fish species by life stages and seasons.

CPUE Data and Standardization Since 1989, the Florida Marine Research Institute (FMRI) has conducted fishery-independent monitoring (FIM) in principal Florida estuaries (Nelson et al. 1997). In this study, we used FIM random and fixed-station data collected from 1989 to mid-1997 in Tampa Bay (6,286 samples) and Charlotte 4 Rubec et al. — Spatial Modeling of Fish Habitat Suitability in Florida

Harbor (3,716 samples). Data were collected using a variety of gear types and mesh sizes during the survey’s history. To use all survey data in a comprehensive analysis, we standardized sample CPUEs across gears for each species’ life stage using a modification of Robson’s (1966) “fishing power” estimation method (Ault and Smith 1998). Gear-standardized data sets for Tampa Bay and Charlotte Harbor were created for the following species and species life stages: early-juvenile (10-119 mm SL ), late-juvenile (120-199 mm SL), and adult (≥200 mm SL) spotted seatrout (Cynoscion nebulosus); juvenile (10-99 mm SL) and adult (≥100 mm SL) pinfish (Lagodon rhomboides); and juvenile (15-29 mm SL) and adult (≥30 mm SL) bay anchovy (Anchoa mitchilli).

Habitat Mapping The FMRI-FIM program in Tampa Bay and Charlotte Harbor provided the bulk of the data used in these analyses. At each sampling site, environmental information on water temperature, salinity, depth, and bottom type, and biological data on species presence, size, and abundance were collected (Rubec et al. 1999). Surface and bottom temperature and salinity data from the FIM database were supplemented with temperature and salinity data from other agencies, including the Southwest Florida Water Man- agement District (SWFWMD), Florida Department of Environmental Protection, Shellfish Environmental Assessment Section (SEAS), and the Hillsborough County Environmental Protection Commission (EPC). Sub- merged aquatic vegetation (SAV) coverages were created using the Arc/Info GIS from SWFWMD 1996 aerial photographs of both estuaries. Areas with rooted aquatic plants (e.g., seagrass) or marine macro-algae were coded as SAV, while remaining areas were coded as bare bottom. Bathymetry data for both estuaries were obtained from the National Ocean Service, National Geophysical Data Center (NGDC) database. To deal with temporal and spatial biases, the combined datasets derived from 8.5 years of sampling were then used to determine mean temperatures and mean salinities by month within cells associated with the 1-square-nautical-mile sampling grid. The mean values were associated with the latitude and longitude at the center of each cell. Universal linear kriging associated with the ArcView GIS Spa- tial Analyst was used to interpolate monthly mean temperatures and mean salinities across the cells using a variable radius with 12 neighboring points (ESRI 1996). Shoreline barriers were imposed to prevent interpolation between neighboring data points across land features, such as an island or peninsula. Rasterized surface and bottom temperature or salinity habitat layers (each composed of 18.5-m2 cells) were created for each estuary (24 monthly layers for each environmental factor). Monthly layers for each estuary were then averaged to produce seasonal habitat layers for spring (March-May), summer (June-August), fall (September-November), and winter (December-February). Depth layers for each estuary were derived by interpolation of NGDC bathymetry data using inverse distance weighting with 8 neighboring points and a power of 2 (ESRI 1996). Spatial Processes and Management of Marine Populations 5

HSI Models and Parameter Estimation HSI modeling for each species life stage has two main steps. First, a function was derived that relates a suitability index Si to a habitat variable Xi for each i-th environmental factor,

Si = f(Xi) (1)

Suitability functions are expressed in terms of species relative density (CPUE) related to a particular environmental factor (i.e., temperature, salinity, depth, or bottom type). Second, HSI values for each map cell were computed as the geometric mean of the Si scores for n environmental factors within each cell (Lahyer and Maughan 1985):

∏ l/n HSI = ( si) (2)

A smooth-mean method was used for deriving equation 1 for continuous environmental “habitat” variables (Rubec et al. 1999). For each species life stage, mean annual CPUEs (number/m2) were determined at predefined intervals for temperature (1°C), salinity (1 g/L), and depth (1 m). These data were then fit with single independent-variable polynomial regressions (JMP software, SAS 1995). Anomalies in the tails of the Si functions for two species life stages (juvenile pinfish, early-juvenile spotted seatrout) were ad- justed based on expert opinion. Mean CPUEs for each bottom type were calculated (bare and SAV). Values from predicted Si functions were divided by their respective maxima and then scaled from 0 to 10. Each computed HSI value (equation 2) used all four environmental factors. Suitability indices for each species life stage were assigned to the habitat layers in ArcView Spatial Analyst (ESRI 1996), and used in the model to create predicted HSI maps (Fig. 1) for each season of the year. Bay anchovy, a pelagic species, was modeled using surface-habitat layers for temperature and salinity, whereas bottom temperature and salinity layers were used for spotted seatrout and pinfish. The final predicted HSI values were further classified into quartile ranges to create four HSI zones: low (0-2.49), moderate (2.50-4.99), high (5.00-7.49), and optimum (7.50-10.00).

Model Performance The models presented above are heuristic and qualitative in nature, thereby precluding any formal statistical testing of model efficacy. We therefore developed two simple measures of model performance. The first evaluates the within-estuary correspondence between predicted seasonal HSI zones and the means of actual CPUE values that fall within the predicted zones. If histograms of mean CPUE values increased across “low” to “optimum” HSI zones, then model performance was judged to be adequate, and we scored the result with a YES. Performance was also scored a YES if the differences between sequential mean CPUEs were small (<10% difference) but an overall 6 Rubec et al. — Spatial Modeling of Fish Habitat Suitability in Florida

Figure 1. Raster-based GIS modeling process used to produce seasonal Habitat Suit- ability Index (HSI) maps of fish species life-stages.

increasing trend was apparent across zones. Performance was scored as NO when an increasing trend in CPUEs was not apparent across the zones. Another test of performance evaluated the efficacy of using predictor relationships developed for one estuary, in a blind transfer of the model to a set of environmental conditions in a second estuary. This test had two attributes: (1) that CPUEs increase across HSI zones as defined above, and

(2) that zones defined by the independent models (transferred Si functions) appear similar to those defined by functions derived from within each estuary. In the latter tests, HSI zone values (1-4) associated with within-estuary HSI maps were compared on a cell-by-cell basis with zone values of the corresponding seasonal HSI map for the same estuary derived from trans-

ferred Si functions. Each pair of predicted seasonal HSI maps were consid- ered to be similar if ≥60% of the cells by zone were scored the same in the Spatial Processes and Management of Marine Populations 7 majority (2 out of 3 or 3 out of 4) of the HSI zones. To evaluate whether the maps minimally identified the most suitable habitats associated with higher species life stage abundances, we also computed differences between predictions of the “optimum” zones by the two models. Agreement (at the ≥60% no difference level) in the optimum zone indicated that the two inde- pendently derived maps qualitatively reflected areas of similar critical importance to stock productivity.

Results Suitability Functions

With pinfish, the Si functions for juveniles were similar between Tampa Bay ° and Charlotte Harbor (Fig. 2). The highest Si values occurred near 25 C, at

34-35 g/L salinities, less than 1 m depths, and over SAV. The highest Si values for adults occurred between 28 and 33°C, at salinities of 31 g/L in Tampa Bay and 37 g/L in Charlotte Harbor, at 1 m depth in both estuaries, and over SAV in both estuaries. The Si functions were quite broad across the temperature and salinity gradients for both juvenile and adult life stages. The depth curves from the two estuaries overlapped for juveniles but were more divergent (not closely overlapping) for adults across the entire depth range and were found to occur in deeper water (10 m) in Tampa Bay than in Charlotte Harbor (7 m).

With spotted seatrout, early-juvenile Si functions were similar between Tampa Bay and Charlotte Harbor, with the highest peaks at 30-35°C, 17-23 g/L salinity, over SAV, and in shallow water (<1 m). The early-juvenile seatrout occurred in deeper water in Tampa Bay (6 m) than in Charlotte Harbor (4 m). The temperature curves for late-juvenile seatrout were markedly dif- ferent. A broad peak occurred near 22°C in Tampa Bay, and a narrow peak ° near 28 C occurred in Charlotte Harbor. The late-juvenile Si functions for salinity were broad in both estuaries, peaking near 20 g/L in Charlotte

Harbor and 27 g/L in Tampa Bay. Both Si functions for depth peaked in the 1-2 m range, but the one for Tampa Bay diverged markedly and extended into deeper (8 m) water than the function for Charlotte Harbor (4 m) did.

The Si values for late-juvenile seatrout were highest over SAV in both estuaries but were also high over bare bottom. The highest Si values for adult spotted seatrout in both estuaries (Fig. 3) occurred near 25°C, at 26-34 g/L salinity, and within the first meter depth along the shoreline. The depth functions diverged markedly and extended into deeper water in Tampa

Bay (8 m) than in Charlotte Harbor (4 m). Adult seatrout Si values were high over SAV and low over bare bottom. Both juvenile and adult bay anchovy in Tampa Bay and Charlotte Har- bor were found over broad ranges of temperature and salinity and pre- dominated in shallow water. With juvenile bay anchovy, the Si functions peaked at 28°C in Tampa Bay and 33°C in Charlotte Harbor, at 5-10 g/L salinity, near 1 m depth, and over bare bottom in both estuaries. The functions overlapped closely for temperature, salinity, and bottom type but 8 Rubec et al. — Spatial Modeling of Fish Habitat Suitability in Florida

Charlotte Harbor Tampa Bay 10 8 6 4 2

Suitability Index 0 0 10203040 Temperature (oC)

10 8 6 4 2 0 Suitability Index 0 5 10 15 20 25 30 35 40 45 50 Salinity (ppt)( )

10 8 6 4 2 0 Suitability Index 024681012 Depth (m)

TB-Si CH-Si

5 Suitability Index 0 BARE SAV Bottom Type

Figure 2. Suitability index (Si ) functions for juvenile pinfish across gradients of temperature, salinity, depth, and bottom type. Spatial Processes and Management of Marine Populations 9

Charlotte Harbor Tampa Bay 10 8 6 4 2

Suitability Index 0 0 10203040 Temperature (oC)

10 8 6 4 2 0 Suitability Index 0 5 10 15 20 25 30 35 40 45 50

Salinity (ppt)

10 8 6 4 2

Suitability Index 0 024681012 Depth (m)

TB-Si CH-Si 15

Suitability Index 0 BARE SAV Bottom Type

Figure 3. Suitability index (Si) functions for adult spotted seatrout across gradients of temperature, salinity, depth, and bottom type. 10 Rubec et al. — Spatial Modeling of Fish Habitat Suitability in Florida

diverged to some degree for depth. The adult bay anchovy Si functions peaked at 28-33°C, and the peak salinity ranged from 6 to 8 g/L in Charlotte Harbor and 8 to 15 g/L in Tampa Bay. The highest Si values occurred over bare bottom (but were also high over SAV) and at <1 m depth in both estuaries.

Habitat and HSI Maps One bottom type, 1 bathymetry, 4 surface salinity, 4 bottom salinity, 4 surface temperature, and 4 bottom temperature seasonal habitat layers were created for each estuary for a total of 36 habitat maps for Tampa Bay and Charlotte Harbor. To test the reciprocal transfer of Si functions be-

tween the two estuaries, we produced 56 predicted HSI maps using Si functions derived from within each estuary (28 per estuary) and 56 maps with

Si functions transferred from the other estuary. Increasing CPUE Relationship by HSI Zones Many species’ life stages showed increasing mean CPUEs across HSI zones (Table 1). For Charlotte Harbor, mean CPUEs increased across low to optimum HSI zones for 78.6% of the 28 predicted HSI maps produced using

Charlotte Harbor habitat layers and Si functions. Similarly with Si functions transferred from Tampa Bay, 82.1% of 28 cases showed increasing mean CPUEs using Charlotte Harbor habitat layers. In Tampa Bay, increasing mean

CPUEs occurred in 42.9% of 28 cases using habitat layers and Si functions from within Tampa Bay. Increasing mean CPUEs occurred in 50% of 28 cases

for predicted HSI maps created from Tampa Bay habitat layers and Si functions transferred from Charlotte Harbor.

Similarity of Predicted HSI Maps In Tampa Bay, juvenile pinfish met the similarity criterion across zones for all four seasons (Table 2). None of the adult pinfish seasonal HSI maps in Tampa Bay were similar across zones. In Charlotte Harbor, the predicted HSI maps for juveniles were similar across zones for summer, fall, and winter (Table 3). Adult pinfish maps were only similar across zones during the spring and winter seasons. With the optimum zone analysis for pinfish, the criterion was met in Tampa Bay with juveniles for all seasons and not met for any season with adults (Table 2). In Charlotte Harbor, the optimum zone criterion was met for all four seasons for juveniles and for 2 out of 3 seasons for adult pinfish (Table 3). When we compared HSI maps by season for spotted seatrout for Tampa Bay only maps for early-juveniles in the winter exceeded the ≥60% no difference criterion across the zones (Table 4). This was also the case in Char- lotte Harbor (Table 5). None of the maps for late-juveniles and adults met the similarity criterion across zones for any of the four seasons in either estuary (Tables 4 and 5). For the optimum zone comparison with early- juvenile seatrout in Tampa Bay, the 60% criterion was exceeded for spring, summer, and fall (Table 4). No optimum zone occurred during the winter. Spatial Processes and Management of Marine Populations 11

Table 1. Number of cases across seasons where increasing mean CPUE versus mean HSI relationships were found.

Ratio (yes) scores across seasons Charlotte Harbor Tampa Bay

Species Life stage CH-Si TB-Si TB-Si CH-Si

Spotted seatrout Early-juvenile 3/4 4/4 2/4 1/4 Late-juvenile 3/4 1/4 1/4 0/4 Adult 2/4 3/4 1/4 3/4 Bay anchovy Juvenile 3/4 4/4 0/4 1/4 Adult 4/4 4/4 3/4 3/4 Pinfish Juvenile 3/4 3/4 2/4 2/4 Adult 4/4 4/4 3/4 4/4 Total 22/28 23/28 12/28 14/28 (78.6%) (82.1%) (42.9%) (50.0%)

TB-Si = Suitability indices from Tampa Bay. CH-Si = Suitability indices from Charlotte Harbor.

Table 2. Percent similarity by grid cells for pinfish between Tampa Bay predicted HSI maps.

Similarity scores If count “0” ≥ 60% Percent no difference In In the “0” cell counts by zone majority optimum Life stage Season Low Moderate High Optimum of zones zone

Juvenile Spring 100.0 66.8 99.9 97.2 Yes Yes Summer 99.6 69.6 94.5 100 Yes Yes Fall 99.4 72.2 99.9 98.7 Yes Yes Winter 100.0 61.5 69.1 68.7 Yes Yes Adult Spring 36.0 69.2 92.0 15.4 No No Summer 41.9 65.7 83.7 16.7 No No Fall 16.3 68.5 95.1 17.8 No No Winter 58.4 85.9 9.1 — No — 12 Rubec et al. — Spatial Modeling of Fish Habitat Suitability in Florida

Table 3. Percent similarity by grid cells for pinfish between Charlotte Harbor predicted HSI maps.