1. Chesapeake Bay Fishery- Independent Multispecies Survey (CHESFIMS) T. J. Miller 1, M. C. Christman 3, E. D. Houde 1, A. F. Sharov 2, J. H. Volstad 4, K. Curti 1, D. Loewensteiner 1, B. Muffley 2, and D. Sam 3 1. CBL UMCES Solomons, MD 20668 3. Biometry Program UMCP College Park, MD 20742 2. Fisheries Service MDNR Annapolis, MD 21401 4. Versar Corp Columbia, MD 21405

2. CHESFIMS accomplishments q Extended 9-year fishery- independent time series of bentho-pelagic fish, with a particular focus on forage fish q Quantified trophic interactions of key species in the fishery ecosystem q Developed model-based estimates of abundance, distribution and diversity q Developed statistical estimators for complemented survey designs

3. Toward multispecies management q “There is no substitute for good monitoring programs of fished species and of key interacting species. Modeling evolves from and depends on monitoring results, and management depends upon an understanding of the status and trends of stocks. Fishery-independent surveys to monitor resources and obtain biological data, if instituted and coordinated throughout the bay, would help improve management.” Executive summary of Multispecies Management workshop report. Houde et al. 1998

4. Characterizing a fishery ecosystem q In the CB fishery ecosystem 1Pelagic and pre-recruit stages of fish are important 1Individual species change “trophic roles” and habitat preferences during ontogeny q Multispecies efforts must assess the importance of spatial and temporal variability q Multispecies efforts should be sufficiently flexible to address questions at a range of scales of resolution

5. CHESFIMS Objectives  Conduct a baywide survey of the bentho-pelagic fish community, focusing on young (juveniles, and yearling) fishes in the mainstem of Chesapeake Bay.  Design and implement a complementary survey of the bentho-pelagic fish community in the extensive shoal habitats (< 5 m depth) in the mainstem of Chesapeake Bay.  Conduct pilot surveys of the pelagic fish community in key tributaries and in the mainstem to generate sampling statistics that will of use in subsequent design improvements.  Determine trophic interactions among key components of the pelagic fish community, and examine the implication of the relationships uncovered in empirical studies using bioenergetic modeling.  Conduct statistical analyses of existing and new data to optimize the complemented pelagic survey with respect to consistency and accuracy of key parameters.

6. CHESFIMS q 3 components 1Baywide, broadscale midwater trawl survey. 1Complex design involving fixed transects and random stations within three strata (upper, mid and lower Bay) 1Samples depths > 5m, using an 18 m 2 -midwater trawl (6 mm cod end) fished in 10 equal depth bins from surface to bottom. 1Builds on existing 1995 – 2000 NSF-sponsored survey (TIES). 1Regional, shoal survey. 1Stratified random sample currently involving 9 strata. 1Samples depths < 5m, using a 16’ otter trawl towed for 6 min. 1Complements and extends existing MDNR and VIMS surveys. 1Statistical evaluation. 1Analysis of alternative survey designs to optimize final survey design. 1Application of spatial statistical models to develop Baywide abundances.

7. Broadscale catch summaries q Spring survey catches were higher than our spring 2001 catch, but lower than 2002 catches. q Summer and autumn survey catches were lower than in either of the two previous years. q Notable, is that at some stations we recorded null catches for the first time. Summer time survey catches were particularly low in 2003 owing to the widespread hypoxic conditions encountered in mid-Bay tows.

8. Inter-annual comparisons 200120022003

9. Recruitment of alosids 2001 2002 2003

10. Biomass time series Bay anchovy White perch Croaker Spr Sum Aut

11. Shoal survey q Survey conducted three times during 2003, involving 9 strata q Sampling conducted with a 16’ otter trawl deployed < 5 m depth q 22,909 fish sampled 1Almost 3 fold 2002 catch

12. Shoal catch summaries

13. Biomass distributions Spring Summer Autumn

14. Shoal catches

15. Trophic interactions q Characterizing the diets 1By species 1By size 1By season q Spatial variation evident 1Feeding incidence 1Prey types 1Prey importance

16. Croaker diet

17. Seasonal variation in diet Spring SummerAutumn

18. Size-dependencies in diet

19. Evaluation of Survey Efficiency q Use the ’design effect’ and ’effective sample size’ to measure the efficiency of a specific survey design; q Estimates under simple random sampling are used as benchmarks for comparison; q Applied to mean CPUE as an example

20. Effective sample size q Broad scale survey has stratified random and transect-based components q Transect component surveys were not efficient 1Effective sample size was ~ number of transects q The effective sizes of clustered samples depends on the number of elements, but the number of clusters may be more important

21. Composite estimators q Estimates across surveys can be efficiently combined using a composite estimator. q Estimates of means and variances from complex surveys that ignores D eff can be biased because populations are not randomly distributed

22. Spatial modeling q Model-based estimators are empirical approaches to estimating parameters of interest, e.g., abundance, diversity q The scale of inference is not constrained by the design 1Post-hoc analyses are supported q Geostatistical approaches have been used to analyze in coastal fisheries

23. Comparison of alternative spatial models q Compared design-based, and four alternative spatial models 1Spatial models varied according to removal of trend q Predictions of CPE were compared

24. Simulation of model performance q To explore the performance of the different models with respect to uncertainty in the variogram, simulation studies were conducted q The technique is robust to some extent to misspecification of the model

25. Which method is best? q The design-based approach is best if one wishes minimal assumptions to be made and wishes the procedure to be data independent (i.e. methodology is NOT data-driven). q If spatial autocorrelation is present, and it is correctly modeled,then kriging-based estimates will be better q Spatial modeling provides additional insights including inferences regarding distribution patterns of species not available in design- based methods.

26. Conclusions 1Baywide, broadscale midwater trawl and regional shoal surveys 1Provide a basis for estimating time series of abundances (mean  SE) of individual species, of species guilds and of the overall fish community 1Provide data on the biological characteristics of the survey catch 1Provide inferences regarding the distribution of individual species, guilds and of the fish community 1Dietary analyses quantify trophic relationships within the fish community 1Revealing spatially and temporally variable patterns 1Statistical evaluation. 1Compares alternative survey designs to optimize final survey design. –Single survey design will not be optimal for all species 1Applies spatially-explicit statistical models to estimate baywide abundances and distributions for species for which the design is not optimal

27. 2004 q Full field season (broadscale and shoal survey) q Continued effort on dietary analyses (broadscale and shoal) q Statistical analysis of 1Multispecies patterns 1Abundance 1Distribution 1 Biological characteristics 1Efficiency and adequacy of alternative sampling designs 1Integration of multiple surveys 1Correlations with commercial landings CHESFIMS on the web at hjort.cbl.umces.edu/chesfims.html