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. Sams 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. Toward ecosystem-based 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

3. 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.

4. 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 as to develop Baywide abundances.

5. Broadscale catch summaries

6. 2002 Catch summary 1 10 50 100 500 1000 5000 10000 15000 #/tow (#/tow) SpringSummer Autumn

7. 2001 2002 SpringSummer Autumn Interannual comparisons

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

9. Shoal survey q Survey conducted three times during 2002, involving 9 strata q Sampling conducted with a 16’ otter trawl deployed < 5 m depth q 7,365 fish sampled 1Less than 50% of 2001 catch, despite increased effort

10. Shoal catch summaries

11. Shoal catches 61.02

12. CHESFIMS as a single species monitoring tool q Multispecies surveys can also provide single species indices 1Calibration with existing single species surveys is important CHESFIMS

13. Diet characterization q Sample size adequacy determined for key species by region q Diets of random samples of preserved fish quantified q Biological characteristics of fish and prey items determined q Evidence in diet data of substantial variation spatially and temporally in key species

14. Croaker diets q Spatial variation evident 1Feeding incidence 1Prey types 1Prey importance q In mid-Bay during summer ~60% by weight of croaker diets is comprised of fish, principally anchovy

15. White perch diets q Temporal variation apparent 1Some prey items consistently represented throughout year 1Some prey appear dominant only in certain periods of the year q Quantifying allometric influences

16. 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

17. The ’design effect’ q The ’design effect’ is the ratio of variances from complex and simple random sampling:  N eff is the number of stations that would be required under simple random sampling to achieve the same precision obtained with the complex design q The effective sample size is estimated as

18. CHESFIMS 2002 Design effects for mean CPUE,

19. Design comparison q Estimates of mean CPUE, precision and design effects suggest that the stratified random survey is more effective than the transect sampling for most species; 1This suggest that CPUE from stations within transects on average are more similar than hauls from different transects. q The relative abundances of weakfish were more effectively estimated from the transect survey during all seasons (deff < 1); 1This indicates that the CPUE by station within transects exhibit minimal intra-cluster correlation for this species The designated strata did not substantially reduce variability in CPUE between stations; q The designated strata did not substantially reduce variability in CPUE between stations; 1Alternative stratification should be explored in future surveys, possibly based on post-stratification analysis of exisiting survey data

20. Design recommendations q The design efficiency 1Depends on the underlying spatial distribution of the target species; 1May vary with the season, and over time; 1Depends on survey cost (e.g., # stations that can be sampled per day); 1Model-based estimators that incorporate spatial auto- correlation could potentially improve the effective sample size

21. Spatial modeling q Abundane estimation: comparison of techniques 1Design-based using the actual sampling design (correct design- based approach) 1Design-based assuming the stations had been selected according to simple random sampling (a very typical but incorrect approach) 1Model-based using a geostatistical spatial autocorrelation model (a model-based method)

22. 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 Iif 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.

23. 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 as to estimate baywide abundances and distributions for species for which the design is not optimal

24. 2003 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