1. image: www.montecitofire.com Michael Robinson Geography Department UC Santa Barbara Fisherman Behavior and Fishery Management: A Cooperative Investigation

2. Overview Original mapping projects –Socio-economic interviews Fishermen mapping –“Fishermen’s Ecological Knowledge” Scale issues –Fishing effort –Regulatory effort Fishermen travel behavior

3. Original mapping efforts Socioeconomic Profile information

4. Original mapping efforts Where did data come from? –CINMS socioeconomic monitoring –MRWG process –Fishermen interviews  Barilotti & Pomeroy samples What types of data? –Economic –Ethnographic

5. Original mapping efforts 14 fisheries: –Market squid –Kelp –Urchins –Spiny lobster –Prawn –Rockfishes –Flatfishes –Sea cucumbers –Wetfish –Crabs –California sheephead –Sculpin & Bass –Tuna –Shark The 13 fish species accounted for over 99% of ex-vessel value of the 1999 CINMS commercial catch. 19% of the fishermen in the CINMS accounted for 82% of the value of catch.

6. Data: CA DFG, NOAA Bathymetry in fathoms

7. Data: CA DFG, NOAA Bathymetry in fathoms

8. Data: CA DFG, NOAA Bathymetry in fathoms

9. Data: CA DFG, NOAA Bathymetry in fathoms

10. Fishermen mapping efforts Fishermen’s ecological knowledge & effect of scale

11. Effect of scale Small scale map –covers large area –view relationships within entire region –scale of analysis & regulation Large scale map –covers small area –see detail & differences –identify “hot spots” –scale of fishing effort & extraction

12. Data: CA DFG, NOAA Bathymetry in fathoms

13. Data: CA DFG, NOAA Bathymetry in fathoms

14. Data: CA DFG, NOAA Bathymetry in fathoms

15. Misalignment of data A noticeable portion of the economic data isn’t in the correct place. Why? –Difficulty identifying exact locations –Intentional misrepresentation Improved economic data will improve the accuracy of biocomplexity models and reserve analysis

16. Data: CA DFG, NOAA, C.Miller Bathymetry in fathoms

17. Data: CA DFG, NOAA, C.Miller Bathymetry in fathoms

18. Data: CA DFG, NOAA, C.Miller Bathymetry in fathoms

19. Regulatory Overlap Existing fishery management & “regulatory redundancy” Concentrated fishing efforts outside reserves

20. Data: CA DFG, NOAA Bathymetry in fathoms

21. Data: CA DFG, NOAA Bathymetry in fathoms

22. Regulatory Overlap Existing fishery management & “regulatory redundancy” Concentrated fishing efforts outside reserves

23. Data: CA DFG, NOAA Bathymetry in fathoms

24. Fishermen travel behavior

25. Modeling fisherman travel behavior What factors determine when and where a fisherman goes to work? –Smith & Wilen, 2003 How do these factors vary across: –Fleets –Ports –Fisheries How does this affect the spatial distribution of fish stocks?

26. Time & Distance What factors determine when and where a fisher- man goes to work? Fishing location Physical Variables Water temperature Wind conditions Other “NOAA parameters” Mechanical Variables Market value vs. cost to fish Season closures Equipment “restrictions” (boat/ trap maintenance, etc) WHEN Mechanical Variables Gear restrictions Size restrictions Marine reserves Other closures Physical Variables Bathymetry Substrate Kelp presence Fish presence WHERE Decision “paradigm” Will I fish today? If so, where? Realistic range? Optimizer or satisficer? Wave height

27. Modeling fisherman travel behavior Determining behavior variables: –Location –Duration –Range –Quantity & value Levels of interest: –Boat (individuals interacting on a particular boat) –Port (boats interacting at a certain port) –Fishery (ports interacting/impacting a certain fishery)

28. Conclusions…so far

29. Conclusions Everything is preliminary Scale of extraction seems small Scale of regulation is comparatively large This disparity has significant implications for fish stock, economic, and reserve models Need more detailed and realistic modeling of fishermen: –Decision-making –Travel behavior

30. Prospects Improved communication –Among fishermen –Between fishermen, regulators, and scientists Fishermen involvement in mapping and reserve process –Fox guarding the henhouse vs. farmer in charge of the farm (Hilborn et al, 2005) Expanded data collection efforts More efficient and realistic management practices

31. image: Wm. B. Dewey, www.islandpackers.com