1. Quantifying Fisher Behavior Under Restrictions Hans van Oostenbrugge, Jeff Powell and Jos Smit

2. Introduction Indirectly Incorporate Fisher Behavior Nonlinear catch - input relationship Accepted theory in economics (decreasing returns to inputs) Seldom implemented in models of fishery management EU fishery management: TAC ----> Effort regulation Single species -----> Multi species Integration of economic activities into models

3. Fisher behaviour under TAC limitation F Effort

4. Fisher behavior under effort limitation F

5. Relationship Effort - F

6. Effort limit TAC limit

7. Objective of the study A simple and practical algorithm for short-run, Catch-Effort relationship Behavior based on economic motivation Appropriate for full feedback biological models Based on variation in results by vessel and by trip Applicable for other EU fleet segments

8. Fisher behaviour under effort limitation Fishers optimize Revenue / Effort by means of re-allocation of effort Spatial, where trips are taken Timing of trips Transfer between vessels Work through Q Nonlinear short-run relationship Effort and F Qt=q0*(Et/E0)^c

9. Data and methodology NL flatfish fishery Mixed fishery – 2 main target species (plaice/sole)‏ 1 gear (large BTs), 1 fishing ground Transferability of effort between vessels Data Landings(value) by vessel by trip by rectangle 2001 - 2006 Cross sectional analysis ANOVA Estimating exponential relationship E and F

10. Results: fishing patterns Cumulative effortCumulative value landings

11. Modeling effects on forecasts IMARES- model Full feedback model Developed for evaluation flatfish management plan Dutch flatfish fishery (Plus UK and “other”)‏ 2 species Sensitivity analysis to non-linearity in relationship f and F

12. Simulating effects: catchability +20%

13. Simulating effects: landings +3% +1%

14. Simulating effects: economics +1% +3%

15. Simulating effects: biology -20% -13%

16. Discussion Limitations of the approach Applies to effort reduction scenarios Short run – no investments Applicability to other fisheries: Theoretically to all management systems: fishermen: optimize Revenue / limiting input In practice only in specific fleet segments: flexible transfer of fishing rights logbook data available

17. Conclusions Nonlinearity in the relationship effort F is important Especially in transition from TAC to effort management Integration biological and economic models is essential for proper management

18. Future work Models for other types of management Further simplification: Can non-linear relationship between E and F be derived from variance of trip results?

19. Questions © Wageningen UR

20. R code to calculate effort beta. load("C:\\Documents and Settings\\J Powell\\Bureaublad\\EFIMAS.RData") ‏ ###Main idea: Sort Revenues per Unit Effort, Rank trips in decreasing order of value ###Order of trips will impact distribution of catch dat <- list(); dat <- visYears[[6]]; length(dat[[1]]); names(dat) ‏ ranker <- dat$Total.Value.of.all.Species/dat$Effort df1 <- data.frame(TR = dat$Total.Value.of.all.Species, Plaice.Catch = dat$Plaice.Catch, Sole.Catch = dat$Sole.Catch, Effort = dat$Effort, ranker) ‏ ordDf1 <- df1[order(ranker, decreasing = TRUE), ] ordDf1[1:20,] ###Plot Data Effort <- cumsum(ordDf1$Effort) ‏ Catch.Plaice <- cumsum(ordDf1$Plaice.Catch) ‏ Catch.Sole <- cumsum(ordDf1$Sole.Catch) ‏ library(lattice) ‏ xyplot(Catch.Plaice ~ Effort, type = "l", col = "green") ‏ xyplot(Catch.Sole ~ Effort, type = "l", col = "red") ‏