Accepted for publication in Societ y & Natural Resourcespending minor revisions, September 2006 

## Harmful Algal Blooms and Coastal Business: Economic Consequences in Florida

SHERRY L. LARKIN
CHARLES M. ADAMS Department of Food and Resource Economics University of Florida Gainesville, Florida, USA

This research was supported by the Florid a Agricultural Experiment Station (FAES), Florida Sea Grant, and a grant from the Flor ida Harmful Algal Bloom Task Force, Florida Marine Research Institute, Florida Fish a nd Wildlife Conservation Commission. The authors would also like to thank thr ee anonymous reviewers for helpfu l comments received during the review process. Address correspondence to Sherry Larkin, Department of Food and Resource Economics, P.O. Box 110240, University of Florida, Gainesville, FL 32611-0240, USA. E-mail: SLarkin@ufl.eduAccepted for publication in Societ y & Natural Resourcespending minor revisions, September 2006 

## Harmful Algal Blooms and Coastal Business: Economic Consequences in Florida

The impacts of harmful algal blooms on beachfront tourism-de pendent businesses in the Ft. Walton Beach and Destin areas of Northwest Fl orida were estimated for the 1995-1999 period using monthly gross taxable sales data. Separat e time series models for the restaurant and lodging sectors revealed that harmful algal bloo ms (HABs) were found to reduce restaurant and lodging revenues in the localized study ar ea by $2.8 million and $3.7 million per month, respectively, which represents a 29% to 35% decline in average monthly revenues for each sector during months of red tid e incidence. By comparison, a tr opical storm was found to reduce monthly restaurant revenues by $0.5 million, and each inch of rainfall reduced revenues an additional $41,000. Adverse weather was not found to affect the lodging sector. While the estimates are conservative given the resolution of data, the magnitude of effects indicate that tourism-dependent communities suffer significant revenue losses due to HABs, which were higher than previous studies indicated and larger than caused by other exogenous events. This underscores the need for e ffective resource management.

Keywords business losses, economic, Florida, harmful algae blooms, Karenia brevis , red tide, taxable sales

Running Head Harmful Algal Blooms and Coastal BusinessAccepted for publication in Societ y & Natural Resourcespending minor revisions, September 2006 1 The coastal waters of Florida host many indigen ous species of marine algae whose populations are greatly influenced by water quality (e.g., le vels of salinity, disso lved oxygen, nutrients, and minerals). Changing water quality can trigger ra pid growth in certain algal populations. These algal “blooms” (commonly referred to as harm ful algal blooms or HAB s) occur naturally along the southwest coast of Florida, as well as othe r regions in the Gulf of Mexico. During a highdensity bloom, some species of algae produce toxins that are transmitted within the water column and can become airborne. One particular red-pigmented species of marine algae, the dinoflagellate Karenia brevis, produces a suite of as many as nine toxins (Backer et al. 2003). These toxins can kill marine life and contamin ate shellfish beds. Aside from the environmental damage, the toxins from K. brevis are unique in that they can po se public health concerns from decomposing marine life on beaches and the possi bility of neurotoxic sh ellfish poisoning (NSP) in humans if contaminated shellfish are ingest ed (Magana et al. 2003). During periods of highdensity K. brevis blooms near beaches, humans can also experience eye, nose, and respiratory irritation, especially if there are onshore winds with break ing surf (Magana et al. 2003). In combination with the unpleasant smells, thes e conditions can disrupt local beach use and waterfront business patronage. The blooms of K

. brevis are sometimes so dense that they impart a reddish color to the water, thereby giving rise to the term “red tide”. Several studies have calculated the economic impacts associated with HAB monitoring and management (e.g., water sampling and beach cleanup) , reduced fishing (due to closures and NSP warnings), public health effect s (costs of medical visits), a nd reduced tourism for individual HAB events, particularly for specific sectors of the economy (Hoagland and Scatasta 2006; Adams et al. 2002; Hoagland et al. 2002; Andersen et al. 2000; Evans and Jones 2001; Tomerlin and Adams 1999). The fishing-related losses calc ulated for events from 1997 to 2005 rangedAccepted for publication in Societ y & Natural Resourcespending minor revisions, September 2006 2 from $10 million for closure of a recreational fishery in Washington State to $50 million for closure of a recreational and a commercial fish ery in Maryland (Hoaglan d and Scatasta 2006 and Hoagland et al. 2002, respectively). Direct losses to the fishing a nd tourism sectors, including the cost to cleanup, was estimated at $10 million fo llowing an event in Texas in 2000. Including subsequent impacts on the statewide economy incr eased the impacts to $18 million (Evans and Jones 2001). HAB induced losses due to impacts on public health within the United States were estimated at $22 million per year, on average, fr om 1987-2002 (Hoagland et al. 2002). Overall, a recent report by the National Oceanic and Atmo spheric Administration (NOAA) summarized the majority of this recent literat ure to conclude that the United States suffers annual losses of $75 million in economic impacts due to HABs (Weiher and Sen 2006). Despite the recent and varied attempts to quantify the economic impacts caused by HAB events, examinations of tourism-dependent busin esses are limited in general (Hoagland et al. 2002). By only covering the immediate impacts fo llowing single events, such estimates can be subject to recall bias from the us e of personal interviews or the estimates were not compared to losses from other exogenous e nvironmental events. For a tour ism-dependent state that is particularly dependent on marine -based recreational activities such as Florida, this information need is critical. A recent time-series study in Florida attempte d to measure recreational and tourism effects resulting from red tide events near a series of barrier islands in the Sarasota and Manatee Counties region (Adams et al. 2000). Although th e study did show a negative impact on beach attendance during a red tide, the analysis did no t find any statistically significant economic effects on the local business community resulting fr om red tides. The resulting inability to isolate a change in business activity due to red tide events may be due to (1) the ab sence of an effect ofAccepted for publication in Societ y & Natural Resourcespending minor revisions, September 2006 3 red tides on local business communities, (2) the data being collected on a spatial and temporal resolution that is too coarse to allow detection of red tide effects (i.e ., lost revenues may have been recovered during the same month the red tide event occurred), and or expenditure effects of a red tide event were dissipated because busine ss patrons had alternatives within the study region on which to spend money during a red tide event. The purpose of this study is to generate in formation that would provide for a better understanding of the economic conse quences to coastal businesses of red tide events in Florida. This is accomplished by consid ering the results of Tomerlin and Adams (1999) and applying the methodology of Adams et al. (2000) to a localized economy that is less diverse in general and heavily dependent on beachfront business activit y, namely the Ft. Walton Beach and Destin areas of Okaloosa County, Florida. The focus on business revenues derived from restaurant and lodging establishments allows for the use of consiste ntly reported gross taxa ble sales data that is correlated with tax revenues accru ing to the local community and reflective of beach and tourism activity. The focus on a smaller geographical regi on than in the Adams et al. (2000) study (i.e., 10 miles versus 30 miles of beachfront) allows for an estimation of the effects of red tides that have not been mearsured for areas that include larger coastal a nd inland areas. Using data from 1995 through 1999 allows for the capture of both red tide events and tropi cal storms (including hurricanes), which are hypothesized to have th e same negative impact on business revenues. Red tides have a long history in Florida, withthe earliest recorded event dating to the 1800s (Kusek et al. 1999), and red tides are likely to continue, with re creation and tourism expenditures declining (at least in the short run) due to the inhospitable beach environment and potential sickness from red tide toxins. As a result, credib le estimates of business losses obtained from a study period that covers multiple environmental events is needed to help guide policy andAccepted for publication in Societ y & Natural Resourcespending minor revisions, September 2006 4 research funding decisions. This information need is especially important given that state funds for red tide events in Florida that are used for research, monitoring, and cleanup are typically from non-recurring sources. Funding levels and allocation decisions are often ad hoc and reactive in nature. To that end, this study will estimate th e absolute and relative e ffect of a red tide event on the restaurant and lodging sectors of a small, localized economy that can potentially be used in benefit transfer analyses. Such information is expected to affect decisions regarding HAB awareness campaigns, research (including mon itoring and forecasting), and management such that they may be more efficacious by pr oviding a better understanding of the economic consequences for the local business community.

Data Secondary sales data for two busin ess activities represen ting the restaurant a nd lodging sectors of Ft. Walton Beach and Destin were obtained from the Florida Department of Revenue (FDOR). Each business in Florida is required to report sales data during the prio r month to FDOR, thus these data are only available on a monthly ba sis. The Retail Sales and Use Tax Business Classifications for these two business sectors ar e 08 (restaurants) and 39 (hotels and motels). These business sectors were selected because they are the largest and are re presentative of beach and tourism activities that are likely to be impacted during a re d tide event. In a ddition, since lost daily restaurant revenues cannot be entirely recap tured and since lodging reservations are often accompanied with a deposit and employee requests for time off (i.e., vacation time can be difficult and costly to reschedule) , the potential for delayed and or recaptured sales is limited. In an effort to isolate the effect of red tide events on business activities located closest to the water, the data were requested for two zip codes, 32541 (Destin) and 32548 (Ft. Myers Beach, along the barrier island beaches, the waterfront on the “n arrows” just north of Okaloosa Island, and theAccepted for publication in Societ y & Natural Resourcespending minor revisions, September 2006 5 western shore of Choctawhatchee Bay south of Gap Creek). These monthly data were obtained for a five-year period beginning January 1995. The real gross taxable sales revenue in Ft. Wa lton Beach and Destin were corrected for the general inflation using the CPI such that fi gures are based on January 1995. The revenues generated by the two business t ypes are characterized by strong s easonal variations that reflect intrayear variation in tourist activity. The peak revenue generating months are generally those in the early to late summer (April through August) (Fig. 1). In term s of monthly revenue (sales), each sector is nearly identical in size over the study period; rest aurant and lodging sales averaged $9.6 million and $9.4 million, respectively. The lodgi ng sector is, however, more variable. Monthly revenues ranged from $1.8 million to $ 32.1 million for the lodging sector during the study period compared to a range of $4.8 million to $21.1 million for the restaurant sector. Both sectors have shown an increase in revenues during the tourist season from 1995 to 1999, which likely reflects an increase in the number of repor ting establishments and not just an increase in sales for a given number of businesses. The data on tropical storm conditions, includi ng hurricanes, and precipitation were obtained from the National Climatic Data Center (NCD C). The closest NCDC precipitation reporting station to the Ft. Walton Beach a nd Destin areas is Pensacola. These data were used as a proxy for rainfall in the Ft. Walton Beach and Destin communities. Only three tropical storms or hurricanes were recorded during the study period, or 5% of the 60-month study period (Table 1). Rainfall ranged from zero to approximately 20 inches per month, but averaged 5.8 inches from January 1995 through December 1999 (Fig. 2). The data on red tide occurrences in the areas of interest were obtained from Red Tide Status Sheets compiled by the Florida Marine Research Institute, Florida Fish and WildlifeAccepted for publication in Societ y & Natural Resourcespending minor revisions, September 2006 6 Conservation Commission. Following the results of Tomerlin and Adams (1999) and subsequent examination by Adams et al. (2002; 2000), a binary variable was used to indicate the presence of a red tide event. This variable took the value of 1 for months when a red tide was at least within the low intensity level range (i .e., at least 10,000 cells per lite r) on a four-level scale, and 0 otherwise. In the data set used for the current study, four red tid e events covering eight (13%) of the 60-month study period were recorded (Table 1).
Empirical model A multiple regression time series model was used to measure the impact of a red tide event on the business activity in the combined Ft. Walt on Beach and Destin areas of Florida. The dependent variable measures th e level of business activity ( Y ), which is assumed a linear function of variables that account for both time and environmental conditions:

k j i k j l k j l l k j j j j o k j i P W T D Y

, , , , , 2

1 , 12

2 , , ε φ α γ β β + + + + + = 

## ∑ ∑

=

= where i identifies two business types ( i = restaurants, lodging); j and k index months ( j = 1, 2, ... 12) and years ( k = 1995, 1996, ... 2000), respectively; and l indexes two types of environmental events ( l = 1 for storms, 2 for red tides). The dependent variable ( Y ) is measured by real gross revenues for each business type in Ft. Walton Beach and Destin. Although the use of real figures will adjust the series for inflation, strong seas onal variations are expected due to monthly variation in tourist activity. The monthly dummy variables, M , identify months February ( j = 2) through December ( j = 12). January ( j = 1) is used as the base month. The co efficients of the monthly dummy variables will reflect the seasonal nature of business activi ties in the geographical areas of interest. Since the revenues generated by the two business types are characterized by stro ng seasonal variationsAccepted for publication in Societ y & Natural Resourcespending minor revisions, September 2006 7 as evidenced in Figure 1, the signs of the coeffi cients for these seasonal dummy variables are expected to vary by each business type. For exam ple, the peak revenue generating months of April through August are expected to have positive signs and la rger coefficients than the remaining months. The variables representing mo nths when environmental events such as tropical storms or hurricanes are more likely (i .e., June and August through December in this data set; Table 1) may capture a share of the e ffects of economic conditio ns. If so, the monthly and environmental variables will be biased upward and downward, respectively, resulting in conservative estimates of the environmental effects. Time series data in business and economic applications are typical ly characterized by a deterministic time trend that cannot be captured with the available data . In this analysis, information on the number of reporting establishm ents is unavailable but hypothesized to have increased overtime. 1 This assertion is supported by the recent ranking that the Ft. Walton BeachCrestview-Destin area received base d on 5-year estimates of job gr owth, salary growth, and local GDP (Devol et al. 2006). The Milk en Institute rated this area as the Best Performing City in the Smallest Metros category during 2005, which cap ped a steady rise in the ranking over time for this area. In this study, an increase in the number of businesses over time would be highly correlated with time and with the total taxable gross sales of the tour ism-dependent businesses examined in this study. T hus, a general time trend ( T ) is included that begins with a value of 1 in January 1995. The a priori expected sign for the trend term, T , is positive. This reflects the hypothesis that, over the long term, increases in business activities lead to some gradual growth in reported real gross taxable sales in the local economy.
W

l represents two binary variable s included to capture the presen ce of any storm that reached at least a tropical storm categorization (inc luding hurricanes) and all red tide events. W l= 1 equalsAccepted for publication in Societ y & Natural Resourcespending minor revisions, September 2006 8 1 for months when a tropical st orm or hurricane occurred and W l= 2 equals 1 for months when a red tide event of at least low intens ity occurred, and 0 otherwise for each. P is precipitation measured in inches. The revenues of restaurant s and lodging establishments (hotels and motels) are expected to be indirectly related to inclement environmen tal conditions with respect to human use of the beachfront areas. Since red tides from K. brevis in particular are associated with fish kills, eye irritations, and nasal and othe r respiratory distresses, the occurrences of red tides in Florida are hypothesized to reduce b each and water-related act ivity resulting in a negative impact on business revenu es (especially after such even ts have been reported by the local media despite their intensity). The same hy pothesis applies to precipitation associated with storms. Consequently, negative sign s are expected for all of the e nvironmental coefficients (i.e.,
W

l and P variables).
Estimation procedure and results The models were initially estimated separately by ordinary least squares using the Time Series Processor (TSP) computing software. The Durbin-W atson (DW) statistics revealed first order autocorrelation of the error terms. 2 A maximum likelihood procedure, which imposed stationarity on the data and utilized a grid search for the serial correlation parameter, was used to transform the data and thereby co rrect for autocorrelation. The mode ls were then estimated with the generalized least squares (GLS) estimator. Table 2 shows the GLS estimation results including model statistics. The DW statistics reveal that autocorrelation was corrected and suggest that time is properl y accounted for in the model. The models appear to fit the data well as reflecte d in relatively high coeffi cients of determination (i.e., adjusted R 2 of 88.5% and 90.0% for restaurants and ho tels/motels, respectively) and high Fstatistics. The signs of the coe fficients for the time trend ( T ), hurricanes/tropical storms ( W l=1 ),Accepted for publication in Societ y & Natural Resourcespending minor revisions, September 2006 9 red tide ( W l=2 ), and precipitation (P ) meet a priori expectations. The monthly dummies ( D ) also show the expected pattern of relative importance for seasonal tourism. The coefficients for the monthly dummy variab les were statistically significant and positive in March through September in both the restaurant and lodging models. For the restaurant sector, with average sales of $5.9 million in January (the base month), the estimated increase in sales ranged from $3.0 million in March to nearly $8.9 m illion in July, which represents increases of 52% and 153%, respectively. For the lodging sector, with lower average sales of $3.7 million in January (the base month), the estimated increase in sales ranged from $3.0 million in March (the same as the restaurant sector) to nearly $19.9 m illion in July, which represents increases of 82% and 537%, respectively. Clearly, th e lodging sector exhibits great er seasonal variability in taxable revenues. The coefficient for the time trend variable wa s also statistically si gnificant in both models and nearly identical for each sect or. The trend variable coefficien t indicates that sales increased $119,000 and $116,000 on a monthly basis for rest aurants and lodging establishments, respectively. Given average monthly sales of $9.6 million and $9.4 million for restaurants and lodging establishments, respectively, the monthly trend in sales (per haps reflecting an increase in the size of the sector in terms of number of establishments) increased just 1.2%. The presence of a hurricane or tropical st orm was found to reduce restaurant sales by $532,000 for every month affected. Gi ven that average monthly rest aurant sales in August and September were $12.9 million and $9.4 million, respectively, tropical storms reduced restaurant revenues during the study period by 4.1% to 5.7%. This relatively sm all effect could reflect the short duration of such events (i.e., storms rarely last more than a few days) compared to the temporal resolution of the data that allows fo r such effects to be recovered. The negativeAccepted for publication in Societ y & Natural Resourcespending minor revisions, September 2006 10 coefficient for the lodging sector , while larger, was not statistical ly significant, suggesting that the lodging sector was unaffected by tropical storms or hurricanes. This result could indicate that lodging reservations often cannot be canceled without penalties near the reservation date. The presence of a red tide event resulted in a statistically significant decline in taxable sales for both restaurants and lodging establishments during the 1995 through 19 99 period in the Fort Walton Beach and Destin areas of Florida. Specif ically, the presence of a low intensity red tide event or stronger reduced m onthly restaurant and lodging revenues by approximately $2.8 million and $3.7 million, respectively. When compar ing these effects with the average monthly sales across months during which the red tide events occurred, which were $9.4 million for restaurants and $10.7 million for lodging establis hments, red tide events reduced average monthly revenues by 29.3% and 34.6%, respectivel y. In total, the tourism-dependent sectors included in this study “lost” approximately $6.5 m illion (32.3%) in each month that a red tide event of at least low intensity occurred. The precipitation coefficient indicated that every one inch of rain redu ced restaurant sales by an additional $41,000 per month, but did not affect lodging revenues (while the coefficient was much larger at $505,000, it was not statistically significant). For co mparison, average rainfall is 5.8 inches, which translates to a reduction in sales of $237,800, which is 2.5% of monthly average sales for the restaurant sector. In summary, while the lodging sector is more va riable on an annual basis, it was not affected by either tropical storm or precip itation levels but did suffer larger relative losses in sales due to red tide events. Thus, the exogenous envir onmental events modeled in this study had disproportionate effects on differe nt sectors of the tourism-based business community in the Ft. Walton Beach and Destin areas of Florida during the late 1990s. In addition, the relatively largeAccepted for publication in Societ y & Natural Resourcespending minor revisions, September 2006 11 effects associated with red tide events resu lted from conservative modeling assumptions, including the identificati on of red tide events down to 10,000 cells per liter, which captures low, medium, and high intensity red tide levels.
Summary Prior to this study, estimates of business losses due to HAB events have been derived from case studies involving post-even t interviews and other anecdotal information. This study employed a more rigorous empirical methodology for estima ting impacts from multiple HAB events and from other exogenous environmental events (i.e., tropical storms), the latter of which were hypothesized to similarly impact sales. This ap proach uses a time seri es of reported taxable income from business sectors that are locate d near the waterfront and are dependent on beachfront conditions (i.e., restaurants and lodgi ng). Results indicated st atistically significant declines in business activity associated with th e presence of red tide of at least low intensity; average monthly revenue was estimated to fall 32 .3% ($6.5 million) for the combined restaurant and lodging sectors for months with reported re d tide events (and fall 68.4% compared to the average annual revenue). For a three-month red tid e, the total direct impacts would be a loss of $19.5 million to the restaurant and lodging sectors, which exceeds previous estimates of single events that include additional sectors as reported by Weiher a nd Sen (2006). Hence, the most recent national estimates of losses due to red tides may be significantly underestimated. Coastal counties in Florida have municipal and or county staff that are tasked with decisionmaking (such as allocation of disaster relief f unding) regarding waterfront planning, beach use, storm preparedness and other activit ies that relate to the coastal environment. For some counties, a history of red tides has necessitated the develo pment of protocols that apply during a red tide event. These protocols provide guidance to local officials regarding red tide monitoring, publicAccepted for publication in Societ y & Natural Resourcespending minor revisions, September 2006 12 awareness, health advisories, beach cleanup and media contact. E ach of these items requires the use of scarce municipal and county funding. Maki ng certain these funds are used efficaciously with respect to red tide events is important, particularly in regi ons where red tide events occur on a much more frequent basis. Such decisions associated with beach clean-up, educational activities, and other HAB-related expenditures ca n be better made with a more complete understanding of the magnitude of revenue losse s resulting from red tides. Moreover, costly efforts at improving monitoring and prediction of re d tide events might be better justified with this information, especially th e finding that red tide can reduce local revenues by 29.3 and 34.6% for the restaurant and lodging sectors, while the passage of a tropical storm (or hurricane) could only result in the loss of 4.8% fo r the restaurant sector only. While this study was able to contribute to the growing literature on the economic impacts of environmental events by using a time-series an alysis of reported business revenues, including multiple business sectors and capturing red tide and tropical storm events, certain limitations remain. Since revenues are only available on a monthl y basis, factors such as red tide intensity and duration and wind speed and direction that can exacerbate or ameliorate the effects on a daily basis cannot be captured. Thus , proprietary data from establ ishments are needed to more precisely measure the short and long run impacts of a red tide event. Inclus ion of individual firmlevel information associated with each type of event, such as cancellations, sales losses, cleanup costs, and red-tide rela ted promotion, would improve the precision of red tide impact estimates.

Notes 1. At the time of the study, the FDOR would only provide data for which there were at least 10 reporting establishments within each zip code and for each business code. These constraints didAccepted for publication in Societ y & Natural Resourcespending minor revisions, September 2006 13 not preclude the use of any data for this analys is, but only ensures that there were at least 20 reporting establishments in each month for each business type (10 fr om each zip code). 2. Separate estimations were maintained (ver sus interaction terms w ith each variable to distinguish between business type s) in order to improve the e fficiency of the correction for autocorrelation and to facilitate the analysis of results. Since the independent variables are identical for both sectors, a s eemingly unrelated regression pr ocedure would not improve the results. Preliminary analyses also estimated separate models for each area, however, F-tests revealed that the coefficient estimates were not st atistically different between areas (Adams et al. 2002). The analysis reported in this paper uses the combined data.

References Adams, C. M., D. Mulkey, S. Holland, A. Hodges, and J. W. Milon. 2000. Development of an economic impact assessment methodol ogy for occurrence of red tide . Gainesville, FL: Staff Paper SP 00-12, Food and Resource Economics De partment, University of Florida. Adams, C., S. Larkin, D. Mulkey, A. Hodges, Ballyram. 2002. Measuring the economic consequences and public awareness of red tide events in Florida . St. Petersburg, FL: Harmful Algal Task Force, Florid a Marine Research Institute. Anderson, D. M., P. Hoagland, Y. Kaoru, and A. W. White. 2000. Estimated annual economic impacts from harmful algal bl ooms (HABs) in the United States . Woods Hole, MA: Technical Report WHO-2000-11 Woods Hole Oceanographic Institution. Backer, L. C., L. E. Fleming, A. Rowan, Y.-S. Cheng, J. Benson, R. H. Pierce, J. Zaias, J. Bean, G. D. Bossart, D. Johnson, R. Quimbo, a nd D. G. Baden. 2003. Recreational exposure to aerosolized brevetoxins duri ng Florida red tide events. Harmful Algae 2:19-28.Accepted for publication in Societ y & Natural Resourcespending minor revisions, September 2006 14 Devol, R., L. Wallace, and A. Bedroussian. 2006. Best Performing Cities 2005: Where America's Jobs are Created and Sustained . Santa Monica, CA: The Milken Institute. Evans, G. and L. Jones. 2001. Economic impact of the 2000 re d tide on Galveston County, Texas: A case study . College Station, TX: Department of Agricultural Economics, Texas A&M University. Hoagland, P., D. M. Anderson, Y. Kaoru, and A. W. White. 2002. The economic effects of harmful algal blooms in the United States: Estimates, assessment issues, and information needs. Estuaries 25(4b):819-837. Hoagland, P. and S. Scatasta. 2006. The economic effects of harmful algal blooms. In Ecology of Harmful Algae , eds. E. Graneli and J. Turner, chp 29. Dordrecht, The Netherlands: SpringerVerlag. Kusek, K. M., G. Vargo, K. Steidinger. 1999. Gymnodinium breve in the field, in the lab, and in the newspaper – a scientific and journa listic analysis of Florida red tides. Contributions in
Marine Science 34:1-229. Magana, H. A., C. Contreras, T. A. Villareal. 2003. A historical assessment of Karenia brevis in the western Gulf of Mexico. Harmful Algae 2:163-171. Tomerlin, A. T., and C. M. Adams. 1999. The economics of harmful algal blooms (HABs): An annotated bibliography . Gainesville, FL: Technical Paper TP-98, Florida Sea Grant College Program. Weiher, R. and A. Sen. 2006. Economic statistics for NOAA , 5 th ed. Silver Spring, MD: U.S. Department of Commerce.Accepted for publication in Societ y & Natural Resourcespending minor revisions, September 2006 15 Table 1. Incidence of environmental events in Ft. Walton Beach and Destin, Florida, 1995-1999

Hurricane or Tropical Storm Red Tide ( W l = 1 = 1) ( W l=2 = 1) Event 1 August 1995 June 1996 Event 2 September 1995 October 1996 Event 3 September 1998 September 1998 Event 4 None August – December 1999

Note : During these months, the corresponding variable ( W 1 or W 2 ) will have a value equal to one. In all other months the values of these variables is zero.Accepted for publication in Societ y & Natural Resourcespending minor revisions, September 2006 16 Table 2. Regression coefficients fo r restaurant and lodging (hotel s and motel) revenue models

Restaurants Hotels & Motels

Coefficients Standard Errors Coefficients Standard Errors Variable Constant 2.920* 1.277 1.171 1.337 February ( D 2 ) 0.477 0.293 0.853 0.691 March ( D 3 ) 3.018* 0.391 3.043* 0.887 April ( D 4 ) 3.711* 0.454 5.074* 1.003 May ( D 5 ) 4.805* 0.495 6.939* 1.072 June ( D 6 ) 7.134* 0.524 14.519* 1.122 July ( D 7 ) 8.878* 0.535 19.863* 1.139 August ( D 8 ) 6.826* 0.528 11.574* 1.127 September ( D 9 ) 3.372* 0.505 4.633* 1.089 October ( D 10 ) 1.509* 0.473 1.587 1.043 November ( D 11 ) 0.0793 0.411 -0.826 0.927 December ( D 12 ) 0.312 0.334 -0.828 0.777 Time Trend ( T ) 0.119* 0.034 0.116* 0.035 Storm ( W 1 ) -0.532* 0.262 -0.991 0.631 Red Tide ( W 2 ) -2.751* 0.739 -3.703* 1.476 Precipitation (P ) -0.041* 0.021 -0.505 0.496 Equation Statistics Observations 60 60 Adjusted R 2 0.885 0.900 DW-Statistic 1.759 1.811 F-Statistic 31.035 36.374 Rho 0.900* 14.158 0.700* 6.777
Note : Asterisk indicates coeffi cient is significant at p < 0.5.Accepted for publication in Societ y & Natural Resourcespending minor revisions, September 2006 17 Figure 1. Real monthly gross taxable sales revenues for hotels/motels and restaurants in the Ft. Walton Beach and Destin areas, Florida, 1995-1999. 0 5 10 15 20 25 30 35 40$ (millions)

Restaurants Hotels & Motels Jan-95 Jan-96 Jan-97 Jan-98 Jan-99Accepted for publication in Societ y & Natural Resourcespending minor revisions, September 2006 18 Figure 2. Monthly precipitation in the Ft. Walt on Beach and Destin areas, Florida, 1995-1999. 0 5 10 15 20 25Inches