Update on EBFM in the Chesapeake Bay

Species Team Updates:  The Alosines Species Team will meet for their next team call on November 6^th to review biological background and issue brief outlines that team members have been collaborating on. The Blue Crab Species Team is currently submitting briefs and MD Sea Grant is editing the briefs as they are submitted. Once completed, they will be distributed to the QETs for use in their work. The Menhaden Species Team Biological Background and Issues Briefs continue to be reviewed by the QETs for use in identifying performance measures and development of reference points. The Striped Bass Editorial Oversight Committee is moving forward with a prospectus for pursuing formal publication of the Striped Bass Biological Background and Issues Briefs.  

Perspective: 

This month’s perspective, submitted by Kyle Hartman provides an overview of habitat issues and reference points in an EBFM context.  Kyle Hartman is an Associate Professor in the Wildlife and Fisheries Resources Program at West Virginia University and also a member of the Foodwebs QET.   

Habitat Issues and Reference Points in an EBFM context: Fish Growth

K.J. Hartman, Ph.D., West Virginia University, Wildlife and Fisheries Program

The major habitat issues facing the Chesapeake Bay clearly center on anthropogenic impact and how the ecosystem will respond to these impacts.  Increasing human populations in the watershed lead to ever increasing stress on water quality which leads to diminished habitat for most key bay resources.  Climate change offers additional stressors, likely working synergistically with watershed stressors to reduce the volume of the bay that can support growth of fishes.  For some habitats such as SAV (submerged aquatic vegetation), historical reference points and annual monitoring already exist.  For others such as those related to water quality we either already have the tools or may be developing them to establish reference points. 

Growth Rate Potential

Striped bass Morone saxatilis are clearly the poster child for such habitat issues.  Coutant (1985, 1990) theorized a temperature-oxygen squeeze for striped bass in Chesapeake Bay and Brandt and colleagues (1992, 1993) went so far as to characterize the bay from the perspective of what they called “growth rate potential.”  Growth rate potential (GRP) could represent a simple means of characterizing the potential volume of the bay that would support growth.  This volume could be used as a benchmark to compare gains or losses in habitat (Fig. 1).  GRP is a bioenergetics based spatial model that uses information on the distribution of prey (in Brandt et al.’s case this was obtained from hydroacoustic surveys) as well as seasonal characterization of the 3-D patterns in temperature and dissolved oxygen to arrive at fish growth.  Given the rich history of environmental data collection in the bay, it may be possible to back-cast GRP using those data and assumptions about prey density and distributions.  Similar benchmarks could be established for other taxa for which bioenergetics models exist (e.g. blue crab, white perch, summer flounder, weakfish, bluefish, Atlantic menhaden, Atlantic croaker, spot, and bay anchovy). 

Growth of Fishes

Growth of fishes is believed to be an integrated measure of well-being that is linked to reproductive success, survival, habitat quality and competition (Amara et al. 2009; Brandt et al. 1992; Roy et al. 2004; Vehanen et al. 2009).  However, a common problem with measuring growth in fishes is that typically we rely on total weight of the fish.  Because most fish are 60-90% water and fish compensate for loss of fat by replacing it with water, using total weight to measure growth is problematic (Breck 2008; Hartman and Margraf 2008; Shearer 1994).  To fully evaluate growth of fishes requires knowledge of the dry mass or dry proportion of fish.  Dry mass can be measured on an individual by oven-drying or by freeze-drying, but in addition to being lethal, this process can be cumbersome for large individuals, or impossible for rare taxa.

Fig. 1.  An example of fish growth rate potential showing how prey density (panel A), water temperature (B) are processed through a fish bioenergetics model to arrive at fish GRP (growth rate potential) .  {From Horn et al. http://www.ncgia.ucsb.edu/conf/SANTA_FE_CD-ROM/sf_papers/horne_john/hornpap.html}.

In the Chesapeake Bay Ecosystem Based Fisheries Management context, it would be ideal to use some measure of growth or condition of key piscivores to assess community balance.  If such a measure could be identified, and benchmarks established then it would be possible to establish threshold levels whereby management actions could be initiated.  Below I describe a BIA (Bioelectrical Impedance Analysis) model recently developed for bluefish Pomatomus saltatrix as an example of this tool. (Some data for striped bass have been collected, but further observations are needed for that model).

We used a tetra polar Quantum II BIA Analyzer (RJL Systems, Clinton Township, Michigan) to measure electrical properties of bluefish.  A range of bluefish body conditions were obtained by feeding and fasting age-0 and older fish in tanks at the NOAA Howard Marine Laboratory.  Needle electrodes (5 mm insertion length) were inserted into the fish at consistent locations: dorsally (posterior to the opercula and anterior of the caudal fin with both positioned midway between the lateral line and dorsal midline) and ventrally (posterior of the pectoral fin and anterior of the anal fin near the ventral mid line), (Fig. 2). Resistance, reactance and detector length were recorded on each fish at two temperatures to permit temperature-correction for field applications of the model.  Fish were dried to a constant weight and the resulting model to predict dry weight as a percentage of total weight was tested with an independent data set.

Fig. 2.  Diagram of a bluefish showing the location of needle electrodes for lateral and ventral measurement of resistance and reactance.

BIA was able to predict the percent dry weight (PDW) of bluefish (Fig. 3).  Models including both ventral and lateral BIA measures provided the best models with R² values of 0.78-0.86 (p < 0.001) and R²_pred values of 73-81% suggest strong future predictive power of these models for bluefish.  Strong relationships between PDW and percent body fat would enable managers to estimate fat content.  Assuming similar relationships hold for other species—and work with centrarchids, salmonids, and so far with striped bass suggest they will--  incorporation of BIA measures into our traditional fisheries measurements would yield two additional measures of growth and condition (PDW and fat%) that can be used to develop benchmarks and management thresholds for Chesapeake Bay fishes.

Fig 3.  Comparison of predicted and observed PDW for an independent data set of bluefish using a BIA model.

Next Steps in Developing Reference Points

Most of the pieces of the puzzle to develop GRP models for Chesapeake Bay fishes are already in place and need only the efforts to put the pieces together.  For example, water quality models exist upon which other GRP models can be developed with assumptions regarding prey responses to hypoxia.  Johnson et al. (1993) presented a validation of a 3-D hydrodynamic model of Chesapeake Bay that can predict temperature and dissolved oxygen levels in time and space.  Roman et al. (1993) determined experimentally that waters < 1.0 mg/l of oxygen resulted in reduced survival and recruitment of copepods.  Such information could be linked with the bay anchovy or Atlantic menhaden bioenergetics models (Luo et al. 1993, 2001) to 3-D water quality models to assess GRP for planktivorous species like bay anchovy and young menhaden. 

Developing reference points for fish growth as a measure of habitat quality is a bit more complex.  Growth is sensitive to density-dependent factors so parameters like year-class strength influence this metric.  Due to this, there would need to be some agreement amongst the stake-holders as to what level of population size would reflect the desired state for a species.   This could be a difficult negotiation for species like menhaden and striped bass.  Further, while there is a large volume of data on the length and weight of Chesapeake Bay fishes from a variety of monitoring and research efforts, differences in the timing of collections as well as aging nomenclature (e.g. age-I is sometimes what we would now call age-0 or YOY) in historical collections often makes comparisons of age-growth across time scales difficult.  Since reference points for fish growth may not be as easily arrived at as those for water quality and resulting GRP, it may be necessary to establish protocols for assessing fish growth, perhaps utilizing BIA, and relating measures of PDW and fat levels to subsequent survival and recruitment for key species.  Further BIA model development is needed before this approach can be used for Chesapeake Bay species.

References:

Amara, R., J. Selleslagh, G. Billon, and C. Minier.  2009.  Growth and condition of 0-group European flounder, Platichthys flesus as indicator of estuarine habitat quality.  Hydrobiologia 627:87-98.

Brandt, S.B., D.M. Mason, and E.V. Patrick.  1992.  Spatially-explicit models of fish growth rate.  Fisheries 17(2):23-31.

Brandt, S.B. and J. Kirsch. 1993.  Spatially explicit models of striped bass growth potential in Chesapeake Bay.  Transactions of the American Fisheries Society 122:845-869.

Breck, J. S. (2008). Enhancing bioenergetics models to account for dynamic changes in fish body composition and energy densityModels for fish body composition and energy density. Transactions of the American Fisheries Society 137, 340-356.

Coutant, C.C. 1985.  Striped bass, temperature, and dissolved oxygen: a speculative hypothesis for environmental risk.  Transactions of the American Fisheries Society 114:31-61.

Coutant, C.C.  1990.  Temperature-oxygen habitat for freshwater and coastal striped bass in a changing climate.  Transactions of the American Fisheries Society 119:240-253.

Hartman, K.J.  and F.J. Margraf.  2008. Common relationships among proximate composition components in fishes.  Journal of Fish Biology 73:2352-2360.

Johnson, B.H., K.W. Kim, R.E. Heath, B.B, Hsieh, and H.L. Butler.  1993.  Validation of three-dimensional hydrodynamic model of Chesapeake Bay.  Journal of Hydrological Engineering 119(1): 2-20.

Luo, J. and S.B. Brandt.  1993.  Bay anchovy Anchoa mitchilli production and consumption in mid-Chesapeake Bay based on a bioenergetics model and acoustic measures of fish abundance.   Marine Ecology Progress Series 98:223-236.

Luo, J., K. J. Hartman, S. B. Brandt, T. A. Rippetoe, and C. F. Cerco.  2001.  A spatially-explicit approach for estimating carrying capacity: An application for the Atlantic menhaden (Brevoortia tyrannus) in Chesapeake Bay.   Estuaries 24(4):545-555.

Roman, M.R., A.L. Gauzens, W.K. Rhinehart, and J.R. White. 1993.  Effects of low oxygen waters on Chesapeake Bay zooplankton.  Limnology and Oceanography 38(8):1603-1614.

Roy, D., Haffner, G.D., and S.B. Brandt.  2004.  Estimating fish production potentials using a temporally explicit model.  Ecological Modelling 173(no. 2-3):241-257.

Shearer, K.D.  1994.  Factors affecting the proximate composition of cultured fishes with emphasis on salmonids.  Aquaculture 119:63-88.

Vehanen, T., A. Huusko, and R. Hokki.  2009.  Competition between hatchery-raised and wild brown trout Salmo trutta in enclosures – do hatchery releases have negative effects on wild populations?  Ecology of Freshwater Fish 18(2):261-268.