Will The TMDL Result in Increased Benefits from Recreational Fishing? Doug Lipton Department of Agricultural & Resource Economics University of Maryland College Park EPA Workshop October 31, 2011

Motivation Recreational demand modeling was sophisticated in developing values for quality (i.e., catch rate) changes, but naïve in linking these changes to environmental factors (e.g., including nitrogen directly) Spatial ecological modeling, particularly the work of Brandt et al. utilizing bioenergetic approaches for striped bass – Spatial temperature-oxygen squeeze in Bay – Differential impacts of temp/oxygen on predator and prey locations One could take the Chesapeake Bay model run outputs and assign a probability of catching striped bass based on the predicted DO, temperature, and location of prey species to every output cell

Ecosystem States & Recreational Fishing Expected Catch Rate Change Random Utility Model Water Quality Change Benefit Change

Basic Premise Fishermen (correcting for skill – avidity, age, etc.) would expect (historical catch rate) to catch striped bass at a certain location during a certain period. Through communication among recreational fishermen (fishing reports, radio communication, tackle shops, online forums, etc.) they modify those expectations and travel to different sites. Those modifications to historical expectations are underlain by unobserved water quality that change observed catch rates Thus fishermen choose to travel to alternative sites where catch rate expectations are higher (better water quality)

Expected Catch Rate is a Function of Ecosystem State Variable Coefficient t-test Constant * Historic catch rate * LN(Hours) * Years Fished * Days Fished (12) Surface Temp * Bottom Temp * Surface Oxygen * Bottom Oxygen * Oxygen *

Asset Values From Striped Bass RUM – 5% Discount Rate Total (Access Value) Current$1.071 Billion Increased Catch Rate$81.4 Million Lower Water Quality (DO) – <= 5 mg/l-$ 98.5 Million – <= 4 mg/l-$122.9 Million – <= 3 mg/l-$145.3 Million

Other Issues No a priori expectations that water quality would result in decreased welfare. Could have concentrated fish in areas closer to where fishermen were located For a given population of fish, so didn’t capture stock dynamics as dealt with in Massey and Newbold for flounder. Focused on striped bass based on Breitburg trawl survey data linked to oxygen – other species not as sensitive What about Breitburg work suggesting decreasing fisheries productivity with reduced nutrient loads?

Data Issues Assigning anglers from intercept sites to fishing location Using water quality station data or interpolated data (see Mason M.S. thesis 2008)

Challenges Still don’t know where people actually fish adds error DO and temperature move fish around directly and indirectly – i.e., availability of prey. Wanted to capture, but lacked spatial distribution of prey species. Tried using Chlorophyll a as indicator of abundance of prey species (e.g., menhaden, bay anchovy, etc.)

Additional Work with Bricker (NOAA) on Human Use Indicators of Eutrophication Gulf of Maine – Exploration of approach to different species, estuaries Barnegat Bay – Summer flounder Mason Thesis – Interpolated versus point data for water quality – No difference

Statistical Results of Developing Eutrophication Indicators (Bricker et al.)