1. Location Choice and Expected Catch: Determining Causal Structures in Fisherman Travel Behavior Michael Robinson Department of Geography University of California, Santa Barbara

2. Research questions What variables influence when and where a fisherman goes fishing? Can we predict how a fleet will distribute its effort in space and time?

3. Introduction This research addresses two causal structures for modeling fisherman travel behavior: –Expected catch affects location choice –Location choice affects expected catch Useful to know, for a particular fishing fleet, whether catch or location is the predominant motivator in determining fishing effort.

4. Introduction If “catch determines location” is the dominant structure… –location choice is a secondary consideration that is itself a function of expected catch and other variables (ex. weather) –we may want to consider management controls such as quotas or trip limits before spatial management.

5. Introduction If “location determines catch” is the dominant structure… –fishermen in a fleet tend to choose a fishing location first and catch what they can based on stock size and fishing ability –we may want to consider spatial management controls (ex. closed areas) before quotas or trip limits.

6. Data California DF&G logbook data –Red sea urchin (Strongylocentrotus franciscanus) –California spiny lobster (Panulirus interruptus) NOAA National Data Buoy Center (NDBC) –Average daily wind speed, wave height, atmospheric pressure, air temperature, water temperature Santa Barbara County Flood Control District –Daily precipitation

7. Location choice and fleet catch at the Santa Barbara Channel Islands – Red sea urchin fishermen Average yearly effort Average yearly catch

8. Location choice and fleet catch at the Santa Barbara Channel Islands – Spiny lobster fishermen Average yearly effort Average yearly catch

9. Causal Structure Models Identify variables responsible for determining when and where someone goes fishing Produce models that predict fishing location choice based on these governing variables –Linear regression models determine the expected catch (a continuous variable) for red sea urchin and spiny lobster fishermen. –Multinomial logit (MNL) models determine expected fishing location (a discrete variable).

10. 1. Catch  Location 2. Location  Catch The models include S, seasonal effects (time of year) E, environmental effects (wind speed, wave height, barometric pressure, air temperature, water temperature, and precipitation) F, observed fisherman effects (hours diving, number of divers, number of traps, etc) unobserved (i.e. fixed and/or random) fisherman effects (total experience, boat size, boat speed, level of education, marital status, etc.) Causal Structure Models

11. Results – Spiny lobster fleet ModeldfR2R2 Log-likelihoodRestricted (b=0) No location, stratified by total events (without group dummy variables) 31252.3698848-154108.0-161332.3 No location, stratified by total events (with group dummy variables) 31187.4416157-152217.6-161332.3 Observed location, stratified by total events (without group dummy variables) 31247.3798906-153857.6-161332.3 Observed location, stratified by total events (with group dummy variables) 31182.4451895-152117.2-161332.3 Predicted location, stratified by total events (without group dummy variables Predicted location, stratified by total events (with group dummy variables) Probability of location, stratified by total events (without group dummy variables Probability of location, stratified by total events (with group dummy variables

12. Results – Red sea urchin fleet Linear Regression ModeldfLog likelihoodRestricted L-LR2R2 Expected catch, no location 27530-208620.2-226897.5.7317052 Expected catch, observed location 27524-208374.1-226897.5.7364168 Expected catch, predicted location 27524-208372.7-226897.5.7364431 Expected catch, probability of location 27524-208338.9-226897.5.7370824 Multinomial logit ModeldfLog likelihoodRestricted L-L Pseudo R 2 Expected location, no catch36-34007.89-35107.41.03132 Expected location, observed catch42-33387.20-35107.41.04900 Expected location, predicted catch39-33849.09-35107.41.03584

13. Conclusions This research improves our ability to model fishing fleets. –methodology for using currently available data to predict how a fishing fleet distributes its effort in space and time It informs management by helping understand the influences, impacts, and implications of various spatial and temporal management options. Future research will expand the models to include additional causal relationships and test the application of the models across a variety of fishing fleets.

14. The End …thank you! Special thanks to Dr. Kostas Goulias, UCSB Department of Geography Questions? Concerns? Get in line…