Location Choice and Expected Catch: Determining Causal Structures in Fisherman Travel Behavior

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. Causal Structure Models • 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.)

11. Results – Spiny lobster fleet

12. Results – Red sea urchin fleet

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