### Combining Ecological and Economic Analysis: An Application to Valuation of Power Plant Impacts on Great Lakes Recreational Fishing

Elena Besedin, Ph.D.

Abt Associates Inc.

Marisa Mazzotta, Ph.D. M.J. Mazzotta, Ltd.
Dave Cacela Stratus Consulting Inc. Lynne Tudor, Ph.D. U.S. EPA/ Office of Water

Corresponding Author: Dr. Elena Besedin Abt Associates Inc 55 Wheeler St Cambridge, MA 02138 (617)-349-2770 Elena_Besedin@abtassoc.com

Paper Presented at American Fisheries Society Meeting Symposium: “Socio-economics and Extension: Empowering People in Fisheries Conservation” August, 2004
This research was supported by U.S. EPA Contr act No. 68-C-99-239. Opinions belong solely to the authors and do not necessarily reflect the views or policies of U.S. EPA, or imply endorsement by the funding agency.1 This study was part of EPA's regulatory analysis for Phase II of the regulation, which regul ated existing power generating fa cilities. Phase I developed regulations for new power gene rating and manufacturing facilities. Phase III will address facilities that employ a co oling water intake structure and are designed to withdraw two million gallons per day or more from waters of the United States, including: (1) exi sting manufacturing facilities, (2) existing electric power producing facilities with a design intake flow (DIF) of less than 50 million gallons pe r day (MGD), and (3) new offshore oil and gas extraction facilities.
2 Introduction This study evaluates recreational fishing losses in the Great Lakes hydrological region caused by impingement and entrainment of fish by power pl ant cooling water intake structures (CWIS). The study was completed as part of the Environmental Protection Agency’s (EPA’s) regulatory analysis for regulations of power plant CWIS under section 316(b) of the Clean Water Act. 1 Impingement and entrainment (I&E) kills or injures large numbers of aquatic organisms. By reducing the levels of impingement and entrainment, regulations would increase the number of fish, shellfish, and other aquatic life in local aquatic ecosystems. This, in turn, will directly and indirectly improve direct use benefits such as those associated with recreational and commercial fisheries. Other types of benefits, includi ng ecological and non-use values, would also be enhanced. Estimating economic benefits from reduced I&E can be challenging because of the many steps needed to first link reduced I&E to changes in aquatic ecosystems, and to then link these ecosystem changes to changes in quantities and values of environmental goods and services that ultimately affect human welfare. Our analysis thus included several steps: (1) extrapolation of available facility-specific I&E estimates to estimate regional losses; (2) conversion of estimated I&E to metrics suitable for economic benefits analysis; (3) estimation of a RUM model of recreational fishing for the state of Michigan; and (4) estimation of the value of fishery losses to recreational anglers in Michigan and other Great Lakes states.
Estimating Regional I&E Losses The Great Lakes hydrological region includes all pow er generating facilities that withdraw water from Lakes Ontario, Erie, Michigan, Huron, and Superior or are located on a waterway with open fish passage to a Great Lake and within 30 miles of the lake. Figure 1 shows both the entire Great Lakes region and the geographic area included in the RUM analysis.3 Figure 1: Great Lakes Study Area

There are 56 power plants in the Great Lakes region. Three of these facilities provided monitoring data, upon which estimates of total I&E were based. To obtain regional I&E estimates, we extrapolated losses from these three facilities to all other facilities within the region, based on operational flow, in millions of gallons per day (MGD), where MGD is the average operational flow over the period 1996-1998. Operational flow at each facility was scaled using factors reflecting the relative eff ectiveness of currently in-place technologies for reducing impingement and entrainment. We used available life history data to construct representative regional life histories for groups of closely related species. Because sampling methods, equipment, and procedures vary across facilities, we converted sampling counts into standardized estimates of the annual numbers of fish impinged or entrained and then expressed these estimates in terms of age 1 equivalent4 losses. The number of individuals of different ages impinged and entrained by facility intakes were expressed as age 1 equivalents using species-specific mortality schedules. Age 1 equivalent losses of harvested species were translated into estimates of foregone harvest using a simple yield per recruit model. Estimates of foregone harvest were then used in the economic benefits analysis to evaluate losses to recreational anglers. For the Great Lakes region, 182.75 million age 1 equivalent fish were impinged, and 41.66 m illion age 1 equivalent fish were entrained. Estimating benefits from reduced impingement and entrainment of forage species is more challenging because these species are not targeted directly by commercial or recreational anglers and have no direct use values that can be observed in markets or inferred from revealed actions of anglers. To estimate indirect use benefits from reducing impingement and entrainment losses to forage species, we used a simple trophic transfer model that translates changes in impingement and entrainment losses of forage fish into changes in the harvest of commercial and recreational species that are subject to impinge ment and entrainment (i.e., not the whole food web). Random Utility Model for Michigan To evaluate losses to recreational anglers, we developed a recreational anglers’ behavior model in a random utility framework (RUM) for the state of Michigan, and transferred benefits to other Great Lakes states. This section describes the data and models. Site Choice Models We used a standard RUM model to estimate anglers’ site choices. The RUM assumes that anglers maximize their utility by choosing the fish ing site, mode of fishing (i.e., from shore, private or rental boat, or charter boat), and species that give the greatest level of satisfaction, compared with all available substitutes. Angler k chooses site jif the utility from that site is greater than utility from all substitute sites:
(1)
where:

u j (k) = utility of visiting site j for angler k, uh(k) = utility of visiting a substitute site h for angler k, and J = the total number of feasible sites in the angler’s choice set. The RUM travel cost model includes the effects of substitute sites on site values. For any particular site, assuming that it is not totally unique in nature, the availability of substitutes makes the value for that site lower than it would be without available substitutes. An angler choosing to fish on a particular day c hooses a site based on site attributes. The angler weighs the attributes for various “choice set” sites against the travel costs to each site. These5 travel costs include both the cost of operating a vehicle and the opportunity costs of time spent traveling. The angler then weighs the value given to the site’s attributes against the cost of getting to the site when making a site selection. The RUM therefore assumes that the probability of selecting a particular site is a function of the site attributes, including catch rates, and travel costs to the site:
(2) The RUM assumes that there is a non-random component (v j ) and a random component ( 

### , j ) to each angler’s utility. The random component is not observable by the researcher (Maddala, 1983; and McFadden, 1981). The model therefore assumes that the utility function has a fixed component and a random component, so that:
(3)
where:

u j (k) = utility of visiting site jfor angler k ; v

j (k) = the observable component of utility; and 

### , j = the random, or unobservable component. The conditional logit model, most often used to estimate the RUM, is based on the assumption that the random error terms 

### , j , have independently and identically distributed extreme value distributions, and are additive with the observable part of utility (McFadden, 1981; Ben-Akiva and Lerman, 1985). The logit model therefore becomes:

(4)

where: Prob(site j ) = the probability that angler k will select site j ; exp[v j (k)] = the angler’s utility from visiting site j ; Σ

j exp [v j (k)] = the sum of the angler’s utility for each site, summed over all sites in the opportunity set for a given region. This is estimated as: (5)2 The data required to calculate a RUM model fo r other Great Lakes states were not available.

3 Bass includes largemouth bass, rock ba ss, smallmouth bass, and white bass; perch includes white perch and yellow perch; salmon-trout includes Atlantic trout, brook trout, br own trout, lake trout, rainbow trout, chinook salmon, coho salmon, pink salmon, siscowet, splake, and other salmon and trout; walle ye-pike includes muskellunge, tiger muskellunge, northern pike, and walleye; and the general cate gory includes all of these species, plus all other species (including catfish, crappie, he rring, whitefish, and pumpkinseed).

4 MDNR also surveys charter boat anglers. We did not in clude charter anglers in the model, because the charter data are not exactly comparable to the data from surveys of boat, shore and ice anglers.

5 The MDNR data did not distinguish between single-day and multiple-day trips. Anglers who traveled more than 120 miles one way were excluded fro m the model, based on the assumption th at these longer trips are most likely multiple-day trips.

6 We decreased the size of the data set to accommodate software and computer resource limitations.
6 The RUM model used data from the Michigan Department of Natural Resources (MDNR) recreational angler survey, conducted in 2001, whic h surveyed anglers at fishing sites on Lakes Michigan, Huron, Superior, and Erie, along with historical catch rate data for these lakes (MDNR, 2002). 2 We aggregated species into five groups: bass, perch, walleye-pike, salmontrout, and general. 3 The "general" category includes all species in the three other categories, plus additional species. We used the general catch rate to represent site quality for no-target anglers, and for anglers who target species that are not included in one of the modeled species groups. The model of recreational fishing behavior relies on a subset of the 2001 MDNR data for boat, shore and ice-fishing anglers, which included 9,758 anglers. 4 First, anglers who live outside of Michigan, and anglers who travel more than 120 miles one way to the fishing site were excluded. 5 We then randomly selected 10,000 anglers from the resulting data set. 6 After additional data cleaning, we estimated the RUM model using data for 9,758 anglers. Summary of Anglers’ Characteristics
Table 1 presents summary statistics on fishing mode and targeted species for the RUM sample of anglers. Around 65 percent of anglers in the sample fished from boats; 22 percent fished from piers, docks, or shore; and 12 percent fished from open ice or shanty. Over 59 percent of anglers target either salmon or trout species; 17.5 percent target perch; 14 percent target walleye or pike; 1.5 percent target bass; and 7.4 percent do not target a particular species.

Table 1: Mode Choice and Targeted Species Fishing Mode Number of Anglers Percent of Sample7 Boat 6,356 65.14% Pier/Dock 878 9.00% Shore 1,311 13.44% Open Ice 950 9.74% Shanty 263 2.70% Targeted Species Number of Anglers Bass 143 1.47% Perch 1,712 17.54% Walleye-Pike 1,393 14.28% Salmon-Trout 5,788 59.32% No Target 722 7.40% Source: MDNR, 2002.
Table 2 presents a summary of angler characteristics, for anglers in the RUM sample. On average, anglers in the case study area traveled 30.6 miles, one way, to the visited fishing site. The average round trip travel cost, excluding opportunity cost of time, was $21; and the average travel cost, including opportunity cost of time, was $31.46. The average angler in the Michigan survey fished for 3.8 hours on the intercepted trip.
Table 2: Data Summary for Anglers in the RUM Sample
Variable Mean Value Std Dev Minimum Maximum One Way Distance to Visited Site (Miles) 30.57 30.82 0.4 119.9
Trip Cost a,c $21.09 $21.27 $0.28 $82.73 Travel Cost a,c $31.46 $32.65 $0.43 $155.22
Household Income c $39,151 $8,800 $11,667 $112,809 Average Hours Fished (n=44933) d 3.79 2.46 0 19

Sources: MDNR, 2002 and U.S. Census Bureau, 2002.

aTrip cost is the round trip cost to the visited site, excluding opportunity cost of time. b Travel cost is the round trip cost to the visited site, including opportunity cost of time.

c Calculation of these values is described below. d Calculated for entire Michigan sample. The MDNR study did not collect socio-economic da ta. Therefore, we used median household income data by zip code, from the 2000 U.S. Census, to approximate income data for survey7 Census data for median income by zip code are from Census Summary File 3 (U.S. Census Bureau, 2002).
8 respondents. 7 The average annual census data income for the respondent anglers was $39,151. Recreational Fishing Choice Sets Figure 1 shows both the entire Great Lakes region and the geographic area included in the RUM analysis. To analyze welfare effects from I& E in the Great Lakes region, we first modeled recreational anglers’ behavior in the state of Mi chigan. This analysis includes only Great Lakes sites, including sites on tributaries up to 30 miles from the lakes in the state of Michigan. We did not include other river and lake sites in the model, because catch rate data were not available for these sites, and because the large size of the resulting data set led to estimation problems. MDNR provided data on site locations, catch rates, and other site characteristics, such as fish stocking and presence of boat ramps. Each angl er’s choice set was drawn from 105 Great Lakes sites in Michigan for which catch rate data were available over a five-year period (1997-2001). We initially included all 331 Great Lakes fishing sites in Michigan that were included in the MDNR database, interpolating catch rate values for sites without catch data. Inclusion of the interpolated catch rates did not produce satisfactory model results for no-target anglers. The results for target anglers were similar with and without the interpolated catch rates, so we included only sites with measured catch rates in the model reported here. We selected each angler’s choice set by, first, eliminating all sites farther than 120 miles from the angler’s home zip code, and then randomly selecting up to 74 sites per angler. Each angler’s choice set, by definition, includes the site actually visited. The average number of sites in each angler’s choice set for the RUM was 22, and ranged from 6 to 36 sites. Site Attributes This analysis assumes that the angler chooses among site alternatives based on catch rates at each site, and whether fish are stocked at the site. Catch rate is the most important attribute of a fishing site from the anglers’ perspective (McConnell and Strand, 1994; Haab et al., 2000). This attribute is also a policy variable of concern because catch rate is a function of fish abundance, which is affected by fish mortality due to I&E. The catch rate variable in the RUM therefore provides the means to measure losses to recreational anglers due to I&E. To specify the fishing quality of the case study sites, we calculated historic catch rate based on MDNR creel surveys for the years 1997 to 2007 for the species groups described above. The catch rates represent the number of fish caught on a fishing trip divided by the number of hours spent fishing (i.e., the number of fish caught per hour per angler). The estimated catch rates are averages across all anglers in a given year over the five-year period. The catch rate variables include total catch, in cluding fish caught and kept and fish released. Some studies use the catch-and-keep measure as the relevant catch rate. Although a greater error may be associated with measured number of fish not kept, the total catch measure is most appropriate because a large number of anglers catch and release fish. For anglers who don’t9 target any species, we used the “general” catch rate to characterize fishing quality. Table 3 summarizes average catch rates by species for all sites with data in the study area. Anglers who target perch catch the most fishper hour, followed by anglers who bass, anglers who target salmon or trout, and anglers who target walleye or pike.

Table 3: Average Catch Rates (1997-2001) for MI Great Lakes Sites with Measured Catch Rates (Number of Fish per Hour) Species Mean Value Std Dev Minimum Maximum Bass 0.15 0.46 0 3.89 Perch 0.758 0.95 0 4.15 Walleye-Pike 0.081 0.13 0 0.91 Salmon-Trout 0.129 0.08 0 0.42
General 0.337 0.28 0.098 1.54 Source: MDNR, 2002. In addition to catch rates, anglers may view boat launching facilities, fish stocking, and other site characteristics as important factors that may affect their site choice. We therefore included dummy variables in the model to indicate whether a site had boat launch facilities, whether there is a park at the site, and whether stocking occurs at the site. The boat launch dummy was included for boat anglers only. We included three stocking dummies in the model: for trout, salmon, and walleye. Each of these was included only for anglers targeting that species. Each stocking site was linked to the closest survey site within one kilometer. Of the 105 sites with measured catch rates, 56 (53.3 percent) had stocking sites within 1 kilometer. Travel Cost We used ArcView 3.2a software to estimate distances from each angler’s zip code to each fishing site. We obtained fishing site locati ons from a database supplied by the MDNR. The distance estimation program measured the distance in miles for the shortest route, using state and U.S. highways, from the household zip code to each fishing site, then added the distances from the zip code location to the closest highway and from the site location to the closest highway. The average one way distance to the visited site for all modes is 30.6 miles. We estimated trip “price” as the sum of travel costs plus the opportunity cost of time following the procedure described in Haab et al. (2000) . Based on Parsons and Kealy (1992), this study assumed that time spent “on-site” is constant across sites and can be ignored in the price calculation. To estimate anglers’ travel costs, we multiplied round trip distance by average8 We used the 2001 government rate ($0.345) for travel reimburseme nt to estimate travel costs per mile traveled. This estimate includes vehicle operating cost only.
10 (6) motor vehicle cost per mile ($0.345, 2001 dollars). 8 To estimate the opportunity cost of travel time, we first divided round trip distance by 40 miles per hour to estimate trip time, and next used one third of the household’s wage to yield the opportunity cost of time. We estimated household wage by dividing household income by 2,080 (i.e., the number of full time hours potentially worked). We calculated visit price as:

The model assumes that the individual angler makes a choice among mutually exclusive site alternatives based on the attributes of those alternatives. We identified each angler’s choice set based on a travel distance constraint. All fishing sites within a 120 mile distance from the angler’s hometown are eligible for inclusion in the angler’s choice set. To prevent the model from becoming overly complex, we estimated the site choice model using the site actually visited and up to 35 randomly drawn sites from the choice set for each angler. An angler’s choice of sites relies on utility maximization. An angler will choose site jif the utility ( u j ) from visiting site jis greater than that from vising other sites ( h ), such that: (7) Recreational fishing models generally assume that anglers first choose a fishing mode and species, and then a site. Instead of incorporating the angler’s decision regarding the mode of fishing and target species in the model, we assumed that the mode/species choice is exogenous to the model and the angler simply chooses the site. We used the following general model to specify the deterministic part of the utility function:
(8)
where:

v j = the expected utility for site j( j=1,...105); TC j = travel cost to site j; TARGET s = dummy variable indicating whether species s is targeted or not;11 STOCK s j = fish stocking for species s at site j ; BOAT = boat mode; PARK = park at site; and CATCH sj = catch rate for species s at site j. Table 4 gives the parameter estimates for this model.

Table 4: Estimated Coefficients for the Conditional Site Choice Model
Variable Estimated Coefficient t-statistic TRAVEL COST -0.0530 -91.88 Boat*SQRT(BASS) 2.9575 4.29 Shore*SQRT(BASS) 3.8969 7.44 Boat*SQRT(PERCH) 1.6052 16.18 Shore*SQRT(PERCH) 1.0871 7.44 Ice*SQRT(PERCH) 1.2578 12.74 Boat*SQRT(WALLEYE-PIKE) 4.8011 21.70 Shore*SQRT(WALLEYE-PIKE) 2.6388 4.20 Ice*SQRT(WALLEYE-PIKE) 4.2422 7.44 Boat*SQRT(SALMON-TROUT) 5.1282 28.17Shore*SQRT(SALMONTROUT) 5.5799 18.03
Boat*SQRT(GENERAL) 0.5290 2.38 Shore*SQRT(GENERAL) 1.0342 4.37 BOAT_DUM 0.2022 6.78 STOCK-TROUT 0.6660 14.83 STOCK-SALMON 0.7829 14.23 STOCK-WALLEYE 0.2459 3.66 PARK -0.3987 -12.32

Table 4 shows that all coefficients have the e xpected signs and are statistically significant. Travel cost has a negative effect on the probability of selecting a site, indicating that anglers prefer to visit sites closer to their homes (other things being equal). A positive sign on the stock variables indicate that anglers who target trout, salmon and walleye are more likely to choose9 Originally, following Lupi and Hoehn (1997), we attempted to es timate a nested logit model, with separate nests for warm-wate r species/sites and cold-water species/sites. However, th e model results were not as good as those from a single logit model, most likely due to a large overlap in warm- and cold-water species fishing sites.
12 sites where those fish are stocked. The positive sign for the boat ramp variable indicates that boat anglers are more likely to choose sites with launching ramps. The negative sign for the park variable indicates that sites with parks are less valued by anglers in general than sites without parks. We estimated a number of model specifications, including a nested model that distinguished between cold- and warm-water species. 9 We found the model presented here provided the best fit for the data.

Welfare Estimates This section presents estimates of welfare losses to recreational anglers from fish mortality due to I&E. Estimating Changes in the Quality of Fishing Sites To estimate changes in the quality of fishing sites under different policy scenarios, we used estimates of recreational losses from I&E, combined with recreational fishery landings data by state. I&E affects recreational species in two ways: by directly killing recreational species, and by killing forage species, thus indirectly aff ecting recreational species through the food chain. The indirect effects on recreational species were cal culated in two steps. First, we estimated the total number of fish lost due to forage fish losses. Second, we allocated this total number of fish among recreational species according to each species’ percent of total recreational landings. We obtained recreational landings data from eachstate in the Great Lakes region: New York, Pennsylvania, Ohio, Michigan, Illinois, Indi ana, Wisconsin, and Minnesota. Some states reported both the number of fish harvested and the total number of fish caught, which includes fish caught and released. We used the total number of fish caught to measure total landings. For states that only reported fish harvested, we ad justed harvest figures upward, using adjustment factors based on the average proportion of catch to harvest in Indiana, Pennsylvania, and Michigan, the three states that reported both valu es. The adjustment factors ranged from 1.09 for walleye to 9.28 for bass. We estimated changes in the quality of recreational fishing sites under different policy scenarios in terms of the percentage change in the historic catch rate. We assumed that catch rates will10 This assumption may not hold across lakes, as some lakes (e.g ., Lakes Michigan and Erie) have more facilities, and therefore are likely to have greater benefits from reduced I&E. However, data were not sufficient to estimate welfare changes by lake.

11 Fish lost to I&E are most often very small fish, which are t oo small to catch. By the time these fish have grown to catchabl e size, they may have traveled some distance from the facilitywhere I&E occurs. Without collecting extensive data on migratory patterns of all affected fish, it is not possible to evaluate whether catch rates will change uniformly or in some other pattern. Thus, we assumed that catch rates wil l change uniformly across each lake.
13 change uniformly across all fishing sites in the region where each species is found. 10,11 For each species included in the model, we used five-year recreational landings data (1997-2001) to calculate average landings per year. We then divided losses to the recreational fishery from I&E by the total recreational landings to calculate the percent change in historic catch rate from eliminating I&E completely. Table 5 presents results of this analysis.
Table 5: Estimated Changes in Catch Rates from Eliminating all I&E of Affected Species in the Great Lakes Region

Species Total Recreational Losses from I&E (number of fish) Total Recreational Landings (fish per year) Percent Increase in Recreational Catch from Elimination of I&E Bass 822,049 3,048,444 26.97% Perch 644,405 10,808,297 5.96% Walleye-Pike 94,289 1,693,872 5.57% Salmon-Trout 120,661 1,905,185 6.33% No Target/General a 3,061,981 28,885,829 6.61% Sources: States of New York, Pennsylvania, Oh io, Michigan, Illinois, Indiana, Wisconsin, and Minnesota, total catch data for Great Lakes sites.

aTotal landings for the no target/general category incl ude all fish reported in catch or harvest data by each state. Total recreational losses for this category are the sum of losses over all species.
Estimating Losses from I&E in the Great Lakes The recreational behavior model described in the preceding sections provides a means for estimating the economic effects of recreational fishery losses from I&E in the Great Lakes region. This estimate represents economic damages to recreational anglers from I&E of recreational fish species in the Great Lakes region under the baseline scenario. We estimated anglers’ willingness-to-pay for improvements in the quality of recreational fishing by first calculating an average per-day welfare gain to Michigan anglers, based on the expected changes in catch rates from eliminating I&E. Table 6 presents the compensating variation per trip (averaged over all anglers in the sample) associated with reduced fish mortality from eliminating I&E for each fish species group of concern, and the per-trip welfare gain attributable12 A compensating variation equates the expected value of r ealized utility under the baseline and post-compliance conditions. F or more detail see Chapter A11 of this report.
14 to reduced I&E resulting from the preferred technology option. 12 Table 6 also shows the per-trip welfare gain for a one fish increase in catch rates.

Table 6: Per-Trip Welfare Gain from Eliminating I&E and I&E Reductions with the Preferred Technology in the Great Lakes Region

Targeted Species Group Per-Trip Welfare Gain (2002$) WTP for an Additional Fish per Trip (2002$)
Boat Shor e Ice Boat Shore Ice
Bass $2.75 $4.07 N/A $12.86 $16.76 N/A Perch $1.12 $0.71 $0.63 $2.47 $1.75 $2.89 Walleye-Pike $0.99 $0. 42 $0.74 $18.43 $9.96 $20.89 Salmon-Trout $1.22 $1.34 N/A $20.13 $22.87 N/A General - No Target $0.17 $0.37 N/A $1.55 $3.27 N/A

Table 6 shows that anglers targeting bass have the largest per-trip gain from eliminating I&E; followed by anglers targeting walleye or pike, anglers targeting perch anglers targeting salmon or trout, and no-target anglers. Table 6 al so reports the willingness-to-pay for a one-unit increase in historic catch rate by species. For anglers who target a particular species, salmon and trout are the most highly valued fish, followed by walleye and pike, bass, and perch. The values for a one fish increase in catch are generally consistent with values estimated in other studies (Whitehead and Aiken, 2000; Lupi and Hoehn, 1997). However, the values for a one-fish increase in bass are higher than expected, perhaps because of the small number of bass anglers in the sample. We calculated the total economic value of eliminating I&E in the Great Lakes region by multiplying the estimated per-trip welfare gain by the total number of fishing days in the region. Each of the Great Lakes has a different number of power plants, and therefore will have different levels of losses caused by I&E. For this study, we averaged I&E losses over all lakes, and assumed that catch rates would change uniformly across lakes with elimination of I&E. While this is not a completely realistic assumption, the data were not sufficient to estimate separate welfare changes for each lake. Therefore, the total welfare could be either overstated or understated.13 Some anglers surveyed by FWS reported targeting more than one species. Therefore, We adjusted the total number of days per s pecies to add up to the total number of reported fishing days for all sp ecies. We multiplied the total reported days for each species bythat species’ portion of total days for all species.

14 Other species that are affected by I&E include crappi e, sunfish, catfish, whitefish, rainbow smelt, and bluegill.
15 The Great Lakes data did not indicate whether a trip was a single- or multiple-day trip. We assume that by limiting travel distance in selecting angler’s choice sets, we has eliminated most multiple-day trips from the data. Therefore, we assume that per-trip values as estimated in the model are equivalent to per-day values. We obtained data on the total number of fishing days from the U.S. Fish and Wildlife Services (FWS) annual survey of fishing, hunting, and wildlif e-related recreation (U.S. Department of the Interior, 2001). This total number of fishing days includes both single- and multiple-day trips. Table 7 presents the number of fishing days by species and mode. The number of days presented for each species in the table were adjusted downward from the FWS totals to avoid double counting of days per species. 13

Table 7: Recreational Fishing Participat ion in 2001, by Species, for the Great Lakes Region

Species Total Number of Fishing Days per Year Total Days by Mode a Boat Shore Ice Bass 3,944,365 1,737, 727 2,206,638 N/A Perch 4,094,568 1, 755,498 645,755 1,693,314 Walleye-Pike 4,295, 665 3,197,850 348,464 749,351 Salmon-Trout 8,467,817 6,115,320 1,995,526 356,971 No Target and Other Speci es 2,335,586 1,106,330 1,171,028 58,228 Source: U.S. Department of Interior, 2001. a. Calculated by multiplying sample propor tions for modes by total fishing days.
We assumed that the welfare gain per day of fishing is independent of the number of days fishedper trip and therefore equivalent for both single- and multiple-day trips. Each day of a multipleday trip is valued the same as a single-day trip. In the Great Lakes region, anglers who target salmon or trout fish the most days, followed by anglers who target walleye or pike, anglers who target perch, anglers who target bass, and anglers who target all other species or do not have a target species. When estimating total welfare, we used the no-target per-day welfare estimates to estimate welfare changes for anglers who target all other species. 14G4-16 Internal Draft - 12 September 2003: Draft - Deliberati ve, Predecisional - Do Not Quote, Cite, or Distribute 16 The estimated number of trips represents the baseline level of participation. Anglers may take more fishing trips as recreational fishing circ umstances change. However, we were unable to estimate a trip participation model for the Great Lakes, because the required data were not available. Therefore, the welfare estimates presented here do not account for likely increases in the number of trips due to elimination of I&E, and thus understate total welfare effects. Table 8 provides total annual welfare estimates. These values were discounted, to reflect the fact that fish must grow to a certain size before they will be caught by recreational anglers. We calculated discount factors separately for impingement and entrainment of each species. To estimate discounted total benefits, we calculated weighted averages of these discount factors for each species group, and applied them to estimated willingness-to-pay values. Discount factors were calculated for both a three percent discount rate and a seven percent discount rate. Table 8 presents annual losses to recreational anglers from baseline I&E effects in the Great Lakes region. Total recreational losses (2002$) to Great Lakes anglers, before discounting, from I&E of bass, perch, walleye, pike, and other species are $31.9 million per year. Total discounted baseline losses are $29.4 million, discounted using a three percent discount rate; and $26.6 million, discounted using a seven percent discount rate.

Table 8: Total Estimated Annual Base line Losses from I&E for Great Lakes Anglers (2002$)
Species Total Losses By Mode Total Losses all Modes boat shore ice Bass $4,780,140 $8,987, 857 N/A $13,767,997 Perch $1,961,593 $459, 649 $1,073,731 $3,494,973 Walleye-Pike $3,172, 587 $147,052 $552,422 $3,872,061 Salmon-Trout $7,438,675 $2,680,390 N/A $10,119,065 No Target or Other Targets $186,306 $427,894 N/A $614,200 Total Recreational Use Losses Before Discounting $17,539,301 $12,702,842 $1,626,153 $31,868,296
Total Losses with 3% Discounting $16,189,926 $11,716,309 $1,501,451 $29,407,686
Total Losses with 7% Discounting $14,677,628 $10,599,384 $1,362,684 $26,639,696

Discussion17 This study understates the total benefits eliminating impingement and entrainment, because estimates are limited to recreational use benefits. Many other forms of benefits, including commercial benefits, habitat values for a variety of species (in addition to recreational fish), and nonuse values, are also likely to be important. The estimates presented in this paper do not include estimates for policy options that would reduce, rather than eliminate, I&E. However, these and other policy cases could be evaluated using the model results.

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