Spatial Modeling Of Fish Habitat Suitability In Florida Estuaries

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

1

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 Charlotte 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 evaluation 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 University of Miami have been collaborating on suitability model development and implementation using geographic information systems (GIS). A primary 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 described 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 technologies 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 Management District (SWFWMD), Florida Department of Environmental Protection, Shellfish Environmental Assessment Section (SEAS), and the Hillsborough County Environmental Protection Commission (EPC). Submerged 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 Spatial 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): HSI = ( ∏si )l/n

(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 adjusted 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 (