How Storms Affect Fishers' Decisions About Going To

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ICES Journal of Marine Science (2020), doi:10.1093/icesjms/fsaa145

How storms affect ﬁshers’ decisions about going to sea

Lisa Pfeiffer * Fisheries Resource Analysis and Monitoring Division, Northwest Fisheries Science Center, National Marine Fisheries Service, National Oceanic and Atmospheric Administration, Seattle, Washington, USA *Corresponding author: tel: þ1 206 861 8229; e-mail: [email protected]. Pfeiffer, L. How storms affect ﬁshers’ decisions about going to sea. – ICES Journal of Marine Science, doi:10.1093/icesjms/fsaa145. Received 3 January 2020; revised 10 July 2020; accepted 15 July 2020.

Fishermen are known to try to avoid fishing in stormy weather, as storms pose a physical threat to fishers, their vessels, and their gear. In this article, a dataset and methods are developed to investigate the degree to which fishers avoid storms, estimate storm aversion parameters, and explore how this response varies across vessel characteristics and across regions of the United States. The data consist of vessel-level trip-taking decisions from six federal fisheries across the United States combined with marine storm warning data from the National Weather Service. The estimates of storm aversion can be used to parameterize predictive models. Fishers’ aversion to storms decreases with increasing vessel size and increases with the severity of the storm warning. This information contributes to our understanding of the risk-to-revenue trade-off that fishers evaluate every time they consider going to sea, and of the propensity of fishers to take adaptive actions to avoid facing additional physical risk. Keywords: fisher behaviour, risk, safety, storms, weather

Introduction information is essential to parameterize temporally predictive Storms are a universal concern to fishermen. Most critically, models. In addition, there is an increasing amount of research storms increase the danger associated with fishing. Storms pose a about how climate change may drive changes in storminess physical threat to fishers, their vessels, and their gear. High winds, (Knutson et al., 2010; Kossin et al., 2016; Mo¨lter et al., 2016; large waves, cold temperatures, low visibility, and unstable condi- Teich et al., 2018; Ornes, 2018; Finnis et al., 2019). To be able to tions can increase the risk of falls overboard, on-board accidents, assess the impacts of such changes, it is crucial that we under- vessel sinkings, and lost or damaged gear (Schilling, 1971; Jensen, stand more about fishers’ behavioural response to storms, as well 1997; Lincoln and Conway, 1999; Lucas and Lincoln, 2007; Reid as how they may adapt to systematic changes in the storminess of and Finnis, 2019). Storms can increase the probability of an acci- the regions in which they fish. dent, as well as the severity of an accident, and can turn a me- It has recently been proposed that an interdisciplinary research chanical issue into a potentially fatal event (Norrish and Cryer, effort be undertaken to understand the climatic, social, and eco- 1990; Wu et al., 2009). logical interconnectedness of climate change-driven changes in It is commonly recognized that fishermen try to avoid storms storminess. Sainsbury et al. (2018) advocate “a roadmap that and may delay a trip or choose an alternate fishing location where draws on climate science, environmental social science, psychol- conditions are expected to be less severe (Christensen and Raakj, ogy, economics and ecology, and is based on four interlinked re- 2006; McDonald and Kucera, 2007). However, we lack parametric search areas: (1) developing climate modelling to better estimates based on actual fisher behaviour of how fishers respond understand changing storm hazards; (2) understanding fishers’ to storms and other meteorological conditions, and how this re- behavioral response to storms; (3) examining the effects of storms sponse may vary across a variety of vessel and human capital on coastal marine ecosystems and socio-economic linkages; and characteristics, fisheries management regimes, and regions. This (4) assessing fisheries vulnerability and adaptation strategies for

Published by International Council for the Exploration of the Sea 2020. This work is written by a US Government employee and is in the public domain in the US. 2 L. Pfeiffer changing storminess”. By doing so, they propose that we can bet- in which fishermen are compelled to apply an excessive level of ter assess the vulnerability of fisheries that are an important operating inputs (e.g., labour, fuel, time) and capital inputs (e.g., source of protein, micronutrients, income, employment, and cul- vessel and gear improvements) as they compete with each other Downloaded from https://academic.oup.com/icesjms/advance-article/doi/10.1093/icesjms/fsaa145/5941847 by NOAA Seattle Regional Library user on 29 October 2020 tural identity. for catch before the season for the target species is closed In this article, I investigate the second area: understanding fish- (Anderson, 1977). In practice, these management institutions are ers’ behavioural response to storms. Data from six federal com- often referred to as “catch shares”, “individual fishing quota” mercial fisheries in the contiguous United States, from four (IFQ), or “individual tradeable quota”. In rationalized fisheries, regions, are combined with marine storm warning data from the fishermen are assumed to make welfare-maximizing choices re- National Weather Service (NWS). Characterizing a “storm” or garding expected profits and expected risks, given constraints. the severity of a storm requires combining and interpreting many For several fisheries included, rationalization occurred during this different types of data: wind speed, barometric pressure, wave time period (2009–2017) (NMFS, 2018), and the shift in manage- height, and many other factors interact in complicated ways to ment likely affected the incentives surrounding fishers’ respon- produce dangerous seafaring conditions (Rezaee et al., 2016; siveness to storms. For those fisheries, data were restricted to the Teich et al., 2018). Using post hoc data over an extended time pe- time period after rationalization. For several fisheries (New riod and spatial extent to profile conditions is difficult, and England and the Southeast), data availability is lagged by several researchers often resort to using one or two variables as proxies years, and as many years after rationalization as available were infor overall conditions (Jin et al., 2002; Smith and Wilen, 2005; cluded. Thus, for all fisheries included, there were no fundamen- Emery et al., 2014; Pfeiffer and Gratz, 2016; Finnis et al., 2019). tal changes to the incentive structure governing during the time The NWS marine storm warning data, in contrast, contain an ag- period studies. Of course, other factors such as market shocks, gregation and interpretation of real-time weather conditions by annual management adjustments, input controls, or spatial experts (professional weather forecasters). Moreover, NWS ma- restrictions affect fishers’ choices, and we do not control for rine storm warnings are highly correlated with near-term storm those. The effects of changes in management have been the sub- severity, are easily accessible and interpretable, and are often part ject of previous research (Smith and Wilen, 2005; Windle et al., of the information considered by fishers when deciding whether 2008; Pfeiffer and Gratz, 2016; Marvasti and Dakhlia, 2017; to take a fishing trip. Using these data, the effect of marine storm Petesch and Pfeiffer, 2019). These studies have used single- warnings on fishers’ decisions to go to sea is evaluated, taking variable proxies for weather conditions such as wind speed or into consideration differences in socio-economic factors that also wave height. affect the decision. The heterogeneity of fisher decisions across Table 1 shows the fisheries included, the region of the United predicted weather conditions, vessel size classes, vessel types, and States in which the fishery operates, and years of data used for regions is explored. By doing so, we can better understand the each fishery. Each trip record contains catch and/or landings by risk-to-profit trade-off that fishers are considering every time weight of each species caught, revenue, the port from which the they consider going to sea, what factors affect this trade-off, and vessel departed and returned to, trip date, and vessel identifica- understand more about the ability of fishers to make adaptive tion number. In several fisheries, only the landing port is identi- actions to avoid taking on additional risk if climate change fied. For these, we estimate the degree of port fidelity using increases the intensity, frequency, spatial scale, or other character- observer data and assume that the landing port is the same as the istics of storms. While this study is restricted to data from federal departure port in fisheries with high port fidelity (West Coast sa- commercial fisheries in the United States, the methodology can blefish and the Gulf reef fish fisheries). Vessel-level characteristics serve as a model for other countries, regions, or sectors. Future including vessel length and horsepower are available for all vessels efforts by researchers with access to different types of data for dif- (Table 2). ferent scales of fisheries will be necessary to more fully understand how storms interact with fishery social-ecological systems. NWS marine weather events The mission of the NWS is to “provide weather, water, and cli- Data and methods mate data, forecasts and warnings for the protection of life and Fishery data property and enhancement of the national economy”. This Trip-level data from six federal fisheries spanning four regions of includes issuance of watches and warnings for coastal and marine the contiguous United States were compiled, for the years 2009– zones. The NWS defines a marine weather event as a meteorologi- 2017 as appropriate. Data are routinely collected by the National cal phenomenon that impacts public safety, transportation, and/ Oceanic Administration National Marine Fisheries Service in the or commerce (NWS, 2018, PD 10-3). Marine weather events may form of vessel trip reports, vessel logbooks, vessel delivery reports consist of watches, warnings, and/or advisories and have a begin- (fish tickets), and fisheries observer reports. The data vary by fish- ning time (when the issuance criteria are forecast to be initially ery in terms of what is collected, the degree of quality assurance, exceeded, or when public safety, transportation and/or commerce and observer coverage; the best available data for each fishery are are adversely affected as a direct result of the expected meteoro- used and were obtained from each regional fisheries science cen- logical conditions) and an ending time (when the issuance criteria tre (Table 1). This includes nearly 1000 vessels. All prices were de- are forecast to be no longer met). Archived marine weather events flated to 2015 dollars using the GDP Implicit Price Deflator (watches and warnings) were obtained from Iowa State (https://fred.stlouisfed.org). Only rationalized fisheries are in- University’s Iowa Environmental Mesonet, which partners with cluded; “rationalization” generally refers to fisheries management NWS to store data and make them available to researchers institutions designed to result in an allocation of labour and capi- (https://mesonet.agron.iastate.edu/). These data were filtered to tal between fishing and other industries that maximize the net include only marine-specific watches and warnings, which in- value of production; i.e. institutions that reduce the “race to fish” clude: gale, hurricane, marine, dense fog, small craft-rough bar, Storms affect fishers’ decisions about going to sea 3

Table 1. Fisheries and time frames included in the analysis. Region of United States Fishery Years Type Data source

New England Scallop General Category fishery 2011–2015 Single species Vessel trip reports Downloaded from https://academic.oup.com/icesjms/advance-article/doi/10.1093/icesjms/fsaa145/5941847 by NOAA Seattle Regional Library user on 29 October 2020 Mid-Atlantic Surf clam and ocean quahog fishery 2009–2017 Single species Vessel trip reports Southeast (Gulf of Mexico) Reef fish (red snapper and grouper/tilefish) fishery 2010–2015 Multi-species Logbooks West Coast Sablefish “primary” fishery 2009–2017 Single species Fish tickets and observer records West Coast Shoreside Pacific whiting fishery 2011–2017 Single species Fish tickets and observer records West Coast Groundfish trawl fishery 2011–2017 Multi-species Fish tickets and observer records

Table 2. Summary statistics for the fisheries included in the analysis. Fishery characteristic Scallop Surf clam/ocean quahog Reef fish Sablefish vessels Pacific whiting Groundfish Number of vesselsa 146 60 465 167 27 96 Year rationalized 2010 1990 2010b 2001 2011 2011 Trip length (mean, days) 1.3 1.5 4.4 2.9 1.5 2.6 Vessel length (mean, m) 16.6 26.0 11.7 14.3 27.2 20.3 Main gear type Dredge Dredge Vertical line and longline Pot and longline Mid-water trawl Bottom trawl aVessels with <10 trips over the entire time period were dropped from the analysis, so the number of vessels noted here may be less than the number of partic- ipating vessels from other sources. bA portion of the reef fish fishery (red snapper) was rationalized in 2007, and the other major portion (grouper-tilefish complex) was rationalized in 2010. small craft, hazardous seas, small craft-wind, storm, high surf, this defined surface circulation but have sustained winds of at small craft-hazardous seas, tropical storm, tsunami, and heavy least 64 knots (74 miles per hour). freezing spray. A full accounting of phenomena is included in the Marine warnings are issued for a specific geographical polygon current NWS directive (NWS, 2018, SI 10-17). A large portion of defined in the text of the warning. They can be any shape and can these marine phenomena were added to the directive in mid-2008, include off-shore areas. All the polygons representing marine which restricts this analysis to 2009 onwards. “Marine Weather warnings were joined with the location of all ports used by each Messages” existed prior to 2008, but it is not possible to extract the of the fisheries described above (Supplementary material). Data same information from them to construct a longer time series. were cleaned to eliminate a number of warnings that were can- NWS forecasters use a variety of numerical models, statistical celled before they began. They were then collapsed and combined and conceptual models, and local experience to forecast how con- to a daily level to join with the daily fishing activity data. While ditions will change over time. Forecasters will often determine weather conditions can change rapidly and may increase and then which models perform best for a given situation or blend multiple decrease in significance over hours or minutes in a particular lo- models based on their experience with past phenomena and vali- cation, fishing decisions are more likely to be at a slightly longer dation assessments (Bruick and Karstens, 2017). For this reason, time scale. The weather warnings in effect on a particular day there is some degree of discretion on the part of local forecasters (measured midnight to midnight) are assumed to be the most rel- to evaluate the conditions and any other compounding circum- evant for the decision to start a fishing trip on that day. Thus, all stances or uncertainties and decide whether or when to issue a of the NWS warnings that were under issuance on each day are warning. In addition, there is not a definitive ordinal ranking of combined. For example, a day could have had small craft, gale, severity for most of the marine phenomena issued. The details of and hazardous seas warnings at various times throughout the a particular event are highly dynamic, are described in detail in day, while the next day might have just a small craft warning in the text of an event message, and are designed for contemporane- place as the storm subsided. The majority of days with marine ous interpretation and use. Looking backward, over large warnings had multiple phenomena identified. Four distinct cate- amounts of data, it is difficult to systematically interpret the level gories were defined: “hurricane” if any of the marine warnings of threat beyond the categorical phenomenon. For example, one under issuance on a particular day included a hurricane or tropi- cannot say that an event characterized by the “dense fog” phe- cal storm warning; “gale” if any of the marine warnings under is- nomenon is better or worse than an event characterized by the suance included a gale warning (but not hurricane or tropical “high surf” phenomenon. The exceptions, with qualification, are storm); and “small craft” if any of the marine warnings included for small craft warnings, gale warnings, tropical storm, and hurri- the codes for small craft, small craft-winds, small craft-hazardous cane warnings. Small craft warnings are generally issued if winds seas, small craft-rough bar, or any combination thereof, but not are forecasted to reach 22–33 knots (25–38 miles per hour), and hurricane or gale. The fourth category includes all other codes gale warnings are generally issued if winds are forecasted to reach that are not ordinally comparable (marine, dense fog, hazardous 34–50 knots (39–57 miles per hour). Forecasters integrate other seas, storm, high surf, heavy freezing spray). Table 3 shows the conditions and local expectations into their decisions, so wind mean and standard deviation of the number of days in each speed ranges are not perfect predictors of the type of warning is- month with small craft, gale, and hurricane (or tropical storm) sued in practice. Tropical storm warnings are also issued at maxi- warnings in each of six representative ports in 2009–2016. Small mum sustained winds between 34 and 64 knots but are craft warnings are relatively common, while hurricane warnings characterized by a defined surface circulation. Hurricanes share are relatively rare. 4 L. Pfeiffer

Table 3. The mean number of days in each month with small craft, gale, and hurricane warnings in each of six representative ports in 2009– 2016.

Port January February March April May June July August September October November December Downloaded from https://academic.oup.com/icesjms/advance-article/doi/10.1093/icesjms/fsaa145/5941847 by NOAA Seattle Regional Library user on 29 October 2020 Small craft warnings Westport, WA 18.9 19.4 19.3 19.6 16.9 15.1 15.4 11.4 16.4 17.8 15.5 18.6 (3.8) (2.7) (1.7) (3.5) (4.2) (5.0) (4.3) (3.4) (4.1) (4.0) (2.3) (3.7) Newport, OR 16.8 19.9 16.8 18.5 13.4 14.4 17.6 13.5 14.4 16.5 16.1 16.6 (4.6) (3.8) (3.8) (4.7) (4.0) (4.5) (3.7) (4.2) (2.9) (4.6) (3.4) (1.8) New Bedford, MA 12.3 11.8 13.1 12.5 13.9 7.8 9.6 6.1 11.3 13.3 12.0 11.3 (5.8) (5.7) (6.6) (5.6) (5.8) (6.0) (5.3) (2.6) (5.1) (6.0) (5.1) (5.4) Point Pleasant, NJ 12.8 9.6 13.3 13.5 13.3 8.6 6.5 6.4 12.3 13.3 12.9 11.5 (5.9) (5.4) (6.0) (6.4) (6.0) (5.5) (5.9) (3.4) (5.4) (6.3) (6.6) (6.1) Monroe county, FL 13.5 10.4 10.3 7.5 6.3 3.5 1.1 2.4 1.4 10.4 12.6 11.8 (4.1) (6.9) (3.2) (5.0) (4.0) (3.0) (1.8) (1.9) (1.3) (7.0) (5.3) (3.6) Plaquemines Parish, LA 12.4 11.5 8.9 8.5 3.6 2.4 1.5 1.6 2.5 7.0 10.6 10.6 (1.8) (4.7) (3.5) (2.7) (1.7) (1.9) (3.1) (2.5) (1.9) (2.9) (4.4) (3.7) Gale warnings Westport, WA 7.5 6.8 8.1 3.4 1.4 0.5 0.1 0.1 1.1 5.9 11.9 9.3 (4.7) (3.0) (3.7) (2.0) (1.8) (0.9) (0.4) (0.4) (1.1) (3.5) (3.3) (4.6) Newport, OR 6.3 3.9 7.6 2.9 2.0 0.5 0.1 0.1 1.5 4.8 8.9 9.0 (4.6) (1.9) (3.4) (2.0) (2.3) (0.9) (0.4) (0.4) (0.8) (3.6) (3.3) (3.7) New Bedford, MA 6.8 7.0 4.5 3.4 0.8 0.9 0.1 0.3 0.5 4.9 6.3 6.5 (3.2) (4.3) (3.2) (2.3) (0.9) (1.1) (0.4) (0.7) (0.8) (3.7) (3.9) (4.7) Point Pleasant, NJ 6.9 7.9 5.8 3.8 0.5 0.1 0.0 0.0 1.0 3.9 5.9 7.3 (3.2) (4.3) (4.1) (2.0) (0.9) (0.4) (0.9) (2.5) (4.0) (4.1) Monroe county, FL 0.8 0.3 0.3 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.4 0.6 (1.2) (0.7) (0.7) (0.7) (1.8) Plaquemines Parish, LA 2.0 0.9 0.9 0.5 0.3 0.0 0.0 0.1 0.3 0.8 1.0 1.4 (1.4) (1.4) (1.0) (0.5) (0.7) (0.4) (0.7) (0.9) (1.1) (1.9) Hurricane or tropical storm warnings New Bedford, MA 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.8 0.4 0.0 0.0 0.0 (0.7) (1.5) (1.1) Point Pleasant, NJ 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.5 0.4 0.0 0.0 0.0 (1.4) (1.1) Monroe county, FL 0.0 0.0 0.0 0.0 0.0 0.0 0.3 0.4 0.3 0.9 0.0 0.0 (0.7) (1.1) (0.7) (1.6) Plaquemines Parish, LA 0.0 0.0 0.0 0.0 0.0 0.4 0.3 0.9 0.5 0.5 0.3 0.0 (1.1) (0.7) (1.8) (1.4) (1.4) (0.7) Standard deviations are presented in parentheses.

Methods where Fit ¼1 indicates vessel i took a trip on day t. Weather, w,is Fishers make near-daily decisions about whether to go fishing. This defined as a vector of binary variables equal to 1 if there was a repeated decision-making results in high-frequency, observable marine weather warning of each type (small craft, gale, hurricane, data (whether a fisher started a fishing trip on each day). These or others) in effect on day t in vessel i’s port of potential depar- data are used to estimate models useful for examining the contri- ture. The vector xt contains day of week and other indicator vari- bution of expected revenue and the physical risk associated with ables that affect the probability of fishing but are uncorrelated weather events of varying severities on the decision to start a trip. with revenue or weather, such as holidays (including Easter, The models are based on the random utility model framework Thanksgiving, Christmas, and New Years Day). If a vessel is al- used by Smith and Wilen (2005), Emery et al. (2014),andPfeiffer ready out on a multi-day trip, that day is removed from the and Gratz (2016). Fishers are assumed to manage risk by respond- choice set until the vessel is back at port. The regression coeffi- ing to temporally-varying factors (Smith and Wilen, 2005). cients d are not comparable across fleets without considering the First, a simplistic model of fishers’ daily fishing decision that can systematic differences in the average probability of starting a trip. be applied to all six fisheries is used. A trip is assumed to be a func- Therefore, the semi-elasticities, or the percentage change in the tion of the type of marine weather warning in place on each day, ves- average probability of starting a trip resulting from a change in sel fixed effects to account for unobserved vessel-level heterogeneity the marine weather warning, are calculated and plotted. It is hy- in the propensity to fish, and day of week and holiday controls. pothesized that vessels will most strongly avoid the most severe types of warnings, which is equivalent to expecting that the d co- ln½PFð it ¼ 1xt ; wit ; aiÞ ln ½1 PFð it ¼ 1xt ; wit ; aiÞ efficient will be negative, and largest in absolute value for hurri- 0 0 canes, smaller for gale warnings, and smallest in absolute value ¼ xt c þ wit d þ ai ; (1) for small craft warnings. Vessels are expected to avoid the Storms affect fishers’ decisions about going to sea 5 conditions associated with the fourth category (other types of Table 4. Number of vessels by length category cut-points at 12.2, marine warnings) as well, but there is no a priori expectation 18.3, and 27.4 m (40, 60, and 90 ft). about where the estimated coefficient would fall on this ordinal Vessels Vessels 12.2 Vessels 18.3 Vessels Downloaded from https://academic.oup.com/icesjms/advance-article/doi/10.1093/icesjms/fsaa145/5941847 by NOAA Seattle Regional Library user on 29 October 2020 scale. In fact, interpretation of this parameter is difficult because Fishery 12.2 m to 18.3 m to 27.4 >27.4 m it represents the combined effect of several types of marine Reef fish 288 163 14a weather warnings that do not fit into the hurricane, gale, or small Scallops 15a 84 43 4a craft categories. The discussion is focused on the definable Surfclam/ocean quahog 5a 33 22 categories. Groundfish trawl fishery 2a 32 59 3a However, this specification ignores that commercial vessels are Sablefish 52 90 24 1a also driven by the amount of income they expect to make on a Whiting 18 9 trip. All else equal, a vessel is more likely to take a trip when their aThe category was combined with the larger or smaller category because expected revenue is higher. Expected revenue is added to the to there was an insufficient number of vessels to estimate the model given the the fixed effects logit specification: number of days in the season. À ln ½PFit ¼ 1xt ; rit ; wit ; aiÞ ln ½1 PFð it ¼ 1xt ; rit ; 0 0 wit ; aiÞ ¼ xt c þ b rit þ wit d þ ai: (2) hypothesizing that the absolute value of the MRS will be largest for hurricanes, smaller for gale warnings, and smallest for small craft warnings. Expected revenue r is estimated using observed prices and it The model is estimated separately for each fleet to investigate catches. Expected revenue is the product of expected prices differences in response by fleet, and because the scale of expected (E(P )) and expected catch per trip (E(C )), which are modelled it it revenue differs greatly by fleet. For example, mean expected reve- separately. Expected prices are estimated using a 15-day moving nue on a scallop trip is about $7000, while for a Pacific whiting average of catch-weighted average prices received by all vessels in vessel, it is over $24,000. After model estimation, the estimate of each fleet, for the targeted species. If there were price differences MRS is scaled by the fleet’s mean expected revenue to compare over space (by state, for example, or groups of states), the moving across fisheries. This model implicitly assumes that all fishers in averages were calculated for each area. Individual harvesters are each fleet have the same risk preferences. assumed to take prices as given. Expected catches were modelled The distribution of vessel lengths by fleet and the length cate- as follows: gories is shown in Table 4. Models 1 and 2 are estimated for each

0 fleet and length group combination. Using model 1, comparisons ECðÞ¼ a þ X c þ e; (3) can be made within fisheries. Using model 2, the estimated MRS is scaled by the expected revenue in each group to compare across where the variables in X include month and year dummies, vessel fleet and length groups. characteristics such as size, and interaction variables. This model is estimated only for single-species fisheries where the price- Results taking assumption reasonably holds: the Atlantic Scallop General The estimates from model 1 indicate that vessels avoid starting Category fishery, West Coast sablefish, and West Coast shoreside trips during all types of marine weather warnings, but that the re- Pacific whiting fisheries. The price-taking assumption is reason- sponse varies by the severity of the warning and by fleet able for these fisheries because: (i) the General Category scallop (Table 5). The model estimates in Table 5 are converted to semi- fleet is allocated only 5% of the total New England scallop allow- elasticities (percent change in the probability of a fishing trip able catch, (ii) West Coast sablefish is mainly exported as a frozen given a change in marine warning type) and plotted in Figure 1. product that completes with Alaska and Canadian sablefish which For all fleets, vessels avoid starting trips during gale warnings is produced at much higher volumes, and (iii) Pacific whiting more strongly than small craft warnings. In the Northeast and competes with Alaska pollock (about nine times the volume) in Southeast regions, vessels avoid fishing during hurricane warn- the global whitefish market. For multi-species fisheries, the com- ings more strongly than gale warnings. There were no hurricane plexity involved with modelling the trade-offs of targeting deci- warnings on the West Coast in the time periods included. The sions in multi-species is beyond the scope of this paper. For the surf clam and ocean quahog fishery, which has the largest vessels surfclam/ocean quahog fishery, prices are not assumed to be ex- but also the widest distribution of vessel sizes in our sample, had ogenous (the quantity delivered affects the price received), so it the smallest percentage decrease in the probability of a trip result- cannot be included as an exogenous regressor (Northern ing from small craft (21%) and gale warnings (61%). Economics, Inc., 2019). Day of week and holiday indicators are included in the models This specification allows the estimation of the trade-off be- to account for systematic differences in trip timing; for these tween risk (beginning a fishing trip in a storm) and reward commercial fleets, starting a trip is less likely on Fridays, week- (expected revenue). The ratio of the coefficients delta and beta is ends, and holidays (the results are suppressed for conciseness and an estimate of the marginal rate of substitution (MRS) between because they are not of primary interest). risk and financial gain from a fishing trip (Smith and Wilen, To investigate differences in risk preferences and response to 2005). The MRS can be interpreted as the additional compensa- each type of warning by vessel size, model 1 is estimated using tion, in dollars of expected revenue, that would be required for a indicators for vessel size categories with cut-points at 12.2, 18.3, fisher to fish on a day where a marine warning of each type was and 27.4 m (40, 60, and 90 ft) (Table 4). Some fleets do not have in effect. Again, it is expected that vessels will most strongly avoid very large or very small vessels so those categories are empty. If the most severe types of warnings, which is equivalent to there were insufficient observations in a category to estimate the 6 L. Pfeiffer

Table 5. Model results for daily fishing decision. Variable Groundfish trawl fishery Whiting Sablefish Reef fish Surf clam/ocean quahog Scallops

Small craft 0.497*** 0.853*** 0.755*** 1.005*** 0.436*** 0.873*** Downloaded from https://academic.oup.com/icesjms/advance-article/doi/10.1093/icesjms/fsaa145/5941847 by NOAA Seattle Regional Library user on 29 October 2020 (0.068) (0.073) (0.067) (0.038) (0.045) (0.052) Other type 1.286*** 2.832*** 1.309*** 0.198*** 0.302*** 0.019 (0.142) (0.330) (0.204) (0.021) (0.054) (0.062) Gale 1.389*** 2.834*** 1.622*** 1.293*** 1.421*** 2.376*** (0.109) (0.148) (0.134) (0.091) (0.076) (0.111) Hurricane NAa NAa NAa 1.943*** 2.741*** 2.386*** (0.123) (0.220) (0.270) Holiday 2.573*** 1.026*** NAb 1.056*** 1.662*** 0.914*** (0.462) (0.143) (0.095) (0.147) (0.063) Constant 2.733*** 0.443*** 1.831*** 2.925*** 0.068* 2.464*** (0.045) (0.059) (0.044) (0.030) (0.033) (0.037) Day of week effects Included Included Included Included Included Included Vessel ﬁxed effects Included Included Included Included Included Included Pseudo R2 0.06 0.12 0.10 0.10 0.08 0.14 N 88 033 17 555 95 695 727 209 76 344 165 666 Standard errors are presented in parentheses. aThere were no hurricane warnings on the West Coast. bSeason was closed during the major holidays considered. *p < 0.05, and ***p < 0.001.

avoid starting trips during hurricane warnings more strongly than they avoid starting trips during gale warnings. The reef fish fleet falls in the smallest two size categories, the majority of the scallop fleet falls in the second smallest size category, while the surf clam and ocean quahog vessels tend to be larger. For the largest size category of sablefish vessels (18.3–27.4 m), a small craft warning is associated with an increase in the probability of fishing, and a gale warning is not significantly associated with a change in trip probability. Model 2 includes expected revenue as a predictive variable of trip-taking. For the three fisheries that can be used for model 2, all fleets are more likely to take trips if the expected revenue is higher (Table 6). Expected revenue may be seasonally or spatially variable and may vary with expected catches. Expected catch may vary annually due to factors such as variable biomass, or across vessels due to capacity. Inclusion of expected revenue allows the Figure 1. Percentage change in probability of taking a trip resulting calculation of the MRS between risk and expected revenues. The from each type of marine weather warning, by fishery. Bars show marginal rates of substitution for each fleet and size category 95% confidence intervals. “Other types” of warnings are not included within each fleet are in Table 7. They can be interpreted in mone- in the figure because they combine multiple types of warnings that tary terms; for example, a vessel of 12.2–18.3 m in length fishing do not have an ordinal ranking. in the Northeast scallop IFQ fishery would require an additional $6140 of expected revenue to fish on day with a small craft warn- model, they were combined with the next larger or smaller cate- ing in effect (Table 7, column 3). A larger vessel (18.3–27.4 m) in gory. Combining did not significantly affect the estimates, nor the same fishery would require only $1010 of additional expected did dropping them. The semi-elasticity estimates for each size cat- revenue to fish on day with a small craft warning in effect egory within each fleet are calculated and plotted in Figure 2.A (Table 7, column 4). Among fleets, the MRS decreases with vessel table of regression results is available as Supplementary Figure S1. size and increases with storm severity in most cases. With the exception of whiting, the percent decrease in the proba- The marginal rates of substitution are scaled by the relative bility of a trip is smaller in absolute value for larger vessels for gross revenues of each fleet to make meaningful comparisons each type of warning. As the storm severity increases, the percent across fleets (Figure 3). The Y-axis of Figure 3 is the revenue- decrease in the probability of a trip increases. Hurricane warnings scaled MRS and can be interpreted as the multiplier of expected are relatively rare, but in the reef fish (red snapper and grouper/ revenue that a vessel of a given size would require to start a fish- tilefish) fleet and the scallop fleet, vessels avoid starting trips during trip on a day with given weather phenomenon warning. The ing gale warnings with about the same intensity as they avoid X-axis shows the mean length of vessels in each size category in hurricanes. In the surf clam and ocean quahog fishery, vessels each fleet. Both across fleets and among them, the multiplier Storms affect fishers’ decisions about going to sea 7 Downloaded from https://academic.oup.com/icesjms/advance-article/doi/10.1093/icesjms/fsaa145/5941847 by NOAA Seattle Regional Library user on 29 October 2020

Figure 2. Percentage change in probability resulting from each type of marine weather warning, by ﬁshery and mean vessel length in each length category. Bars show 95% conﬁdence intervals.

Table 6. Model results for daily fishing decision in the each fishery. correspond with expectations about how vessels adjust trip timing to reduce physical risks. Variable Scallops Whiting Sablefish However, the estimates derived from model 1 presented in Expected revenue, $1 000 0.018*** 0.045*** 0.022*** Table 5 assume homogeneous risk preferences. In reality, fishers’ (0.001) (0.002) (0.002) Small craft 0.849*** 0.699*** 0.762*** risk preferences may differ (Eggert and Martinsson, 2004; (0.015) (0.039) (0.027) Christensen and Raakj, 2006; Brick et al., 2012). Differences in Gale 2.307*** 2.717*** 1.643*** risk preferences could result from a variety of characteristics, (0.036) (0.136) (0.092) many of which are unobservable to researchers or would require Hurricane 2.342*** NA NA additional data collection. More experienced captains, for exam- (0.250) ple, may be less averse to certain types of storms because they *** *** Other type 0.009 2.512 1.323 have experience-based confidence in their ability to safely navi- (0.032) (0.302) (0.147) gate their vessel through particular conditions (Real, 2008). Holiday 0.889*** 0.934*** NAa (0.059) (0.146) Fishing business practices, such as quota leasing or delivery con- Day of week effects Included Included Included tracts with processors, may reduce a fisher’s ability to adjust trip Vessel fixed effects Included Included Included timing and minimize risk (Christensen and Raakj, 2006; Emery Observations 9 751 14 264 95 176 et al., 2014; Petesch and Pfeiffer, 2019) Although uniform infor- Standard errors are presented in parentheses. mation on business practices or captain experience does not exist aSeason was closed during the major holidays considered. for this set of fisheries, it is an area of potential future research for *p < 0.05, fisheries with additional in-depth captain information or data **p < 0.01, and that could be collected through interviews. ***p < 0.001. However, vessel size is a characteristic that is potentially important to both risk preferences and the ability to withstand weather conditions. Larger vessels are generally more stable and able to withstand larger waves and higher winds (Biran and generally declines with vessel size over all the fleets included and, Lopez-Pulido, 2014). “Small craft advisories” are so named as again, generally increases with the severity of the weather they are meant to be applicable mainly to smaller vessels, al- warning. though the NWS and Coast Guard do not define a “small craft”. They are meant to apply to “any vessel that may be adversely affected by Small Craft Advisory criteria” (https://w1.weather.gov/ Discussion glossary/). Vessel length is observable for all fleets in this dataset. The results generally indicate that a fisher’s aversion to starting a In the fleet-level results, the fleet with the largest vessels (surf trip increases with an increase in storm severity. These results clam and ocean quahog) had the smallest response to small craft 8 L. Pfeiffer

Table 7. Marginal rate of substitution (thousands of 2015$) between expected revenue and starting a ﬁshing trip on a day with each type of marine warning in each IFQ ﬁshery.

(1) (2) (3) (4) (5) Downloaded from https://academic.oup.com/icesjms/advance-article/doi/10.1093/icesjms/fsaa145/5941847 by NOAA Seattle Regional Library user on 29 October 2020 Fishery and warning severity All vessels Vessels <12.2 m Vessels 12.2–18.3 m Vessels 18.3–27.4 m Vessels >27.4 m Scallop MRS small craft 2.98*** 6.14*** 1.01*** (0.1) (0.6) (0.1) MRS gale 8.36*** 16.31*** 3.71*** (0.4) (1.5) (0.2) MRS hurricane 8.08*** 14.97*** 3.65*** (1.0) (2.3) (0.8) MRS other types 0.06 0.06 0.10 (0.1) (0.2) (0.1) Mean expected revenue, $1 000 5.89 5.70 6.23 Whiting MRS small craft 15.651*** 14.813*** 16.966*** (1.246) (1.520) (2.259) MRS gale 60.813*** 62.114*** 53.338*** (4.449) (5.822) (6.831) MRS other types 56.219*** 52.924*** 63.342*** (7.461) (8.236) (17.753) Mean expected revenue, $1 000 24.247 21.320 31.697 Sableﬁsh MRS small craft 35.30*** 28.19*** 15.46*** 0.48 (3.0) (5.1) (1.5) (5.9) MRS gale 76.15*** 54.45*** 34.32*** 51.10** (7.2) (10.6) (4.0) (19.6) MRS other types 61.31*** 37.36*** 23.88*** 129.19* (8.3) (8.4) (5.2) (55.9) Mean expected revenue, $1 000 13.71 5.81 11.19 28.03 Standard errors are presented in parentheses. ***p < 0.001.

10 risk and income. It also allows the scaling of risk aversion through calculation of the MRS. The restricted set of ﬁsheries for which 8 the MRS can be calculated provides additional evidence that, for 6 all size categories of vessels, vessels would need a larger compen- sation (in terms of expected revenue) for gale warnings than for 4 small craft warnings. In most cases, it is approximately twice as 2 large or more. Again, this reﬂects the higher danger that a gale

Revenue-scaled MRS 0 warning is meant to convey. The estimates for “other types” of 0 5 10 15 20 25 30 35 40 marine weather warnings generally fall between the estimates for -2 Mean length within size categories (m) small craft and gale warnings and, for scallops, are insigniﬁcant.

Sablefish Small craft Sablefish Gale It is difficult to interpret the results for “other types” because of Whiting Small craft Whiting Gale the diversity and overlap of warning types that the category Scallop Small craft Scallop Gale contains. Figure 3. Comparison of marginal rates of substitution scaled by Regional comparisons or comparisons across gear type are of average trip revenue over vessel size categories. interest, but with this dataset they are limited to the West Coast. Both the sablefish and whiting fleets operate off the West Coast. and gale warnings (Table 5 and Figure 1). In the models esti- Whiting vessels are significantly larger; however, whiting vessels mated by vessel length category, larger vessels responded less to respond more strongly to gale warnings than the smaller sablefish storms than smaller vessels do. This reflects the decrease in per- vessels. This could be due to gear type (sablefish uses fixed gear, ceived or actual danger of fishing during a small craft warning while whiting uses mid-water trawl gear), or trip length. Whiting that a larger vessel commands. Considering that “small craft trips average 1.5 days in length, while sablefish trips average warnings” are so named to discourage smaller crafts from being 2.9 days. The longer trip length may mean that sablefish vessels at sea, this result is expected. It holds for each fleet for all size are less willing to delay a trip, or that they have methods of reduc- categories. ing the risk (such as choosing safer locations to set their gear). The models that include expected revenue allow the explora- The largest size category of sablefish vessels shows an increase in tion of the trade-off that vessels are assumed to make between the probability of fishing when there is a small craft warning. This Storms affect fishers’ decisions about going to sea 9 result could reflect the small number of vessels in that category, response can be quantified and is relatively consistent across or differences in gear type, trip length, or vessel stability. regions of the United States. This framework could be built upon However, 18.3–27.4-m scallop vessels show only a very small re- to understand the use of weather information in decision-making Downloaded from https://academic.oup.com/icesjms/advance-article/doi/10.1093/icesjms/fsaa145/5941847 by NOAA Seattle Regional Library user on 29 October 2020 sponse to small craft and gale warnings, and they take mainly day in other contexts. trips (mean trip length is 1.3 days) and use dredge gear. Of Other factors could easily be added to this framework if the course, there could be regional differences that drive the different data were available: characteristics such as captain age or experi- responses, such as the dangerous bar crossings necessary in many ence, additional vessel ownership information (i.e., corporate vs. ports on the West Coast (33 CFR 165.1325(a)). Investigation of private ownership), the level of quota concentration, levels of in- these differences will require future research into these and other debtedness, or gear type in a fishery that uses multiple gears unobserved factors. would add valuable comparative margins. This type of analysis It has also been suggested that some gear types perform better, would provide additional context to quantitative results. The or that species of fish are easier to catch, in certain types of model could also be applied to subsistence or recreational fishers, stormy weather. Fishers respond to fishing conditions with a wide although estimates of expected catch and daily trip data would variety of adjustments—many more than can be captured in this likely be more challenging to acquire, and the values placed on simple model. Trawling or setting gear in a certain direction rela- fishing trips may need to be elicited through choice experiments. tive to wind direction, for example, can affect the performance of Estimates from the modelling framework proposed here can be the gear (Maynou and Sarda, 2001; Queirolo et al., 2012). Fishers develop expertise to cope with (or take advantage of) all types of used to build projection models of harvest patterns or fishery conditions (McDonald and Kucera, 2007; Finnis et al., 2019). timing, Management Strategy Evaluations, or other modelling This could be the case for the largest category of sablefish vessels. frameworks that benefit from the incorporation the socio- More detailed modelling that considers key within-trip adjust- economic drivers of fishers’ behaviour (Dichmont et al., 2008; ments could be explored in future research. Holland, 2010). As our understanding of the climate modelling of Finally, the set of fisheries included in this article is restricted storm hazards increases, it is important to also increase and in- to rationalized fisheries. The costs, benefits, and constraints sur- corporate our understanding of fishers’ behavioural responses rounding a decision to delay a fishing trip in fisheries with differ- and adaptation strategies. Finally, assessing the vulnerability of ent incentives at work are likely to be very different. Much of the fishery participants, communities, and industries to climate- empirical work on non-rationalized fisheries has focused on the driven changes in storminess is necessary for developing shift to IFQs from other forms of management and its implica- resiliency-enhancing policy. Fisheries management policy can be tions for safety (Lincoln et al., 2007; Windle et al., 2008; Emery designed to support fishermen and their ability to make the best et al., 2014; Pfeiffer and Gratz, 2016; Marvasti and Dakhlia, decisions for their health and safety. For example, policies that in- 2017). Another body of work has explored fishers’ perspectives of centivize competitive (race for fish) behaviour, incentivize capital risk and risk-taking and has discussed risk preferences, adoption accumulation, or restrict the flexibility of fishermen to making of safety-improving practices, social norms, and the contribution trip timing decisions will likely have adverse effects on safety. On of fisheries management to risk-taking (Poggie et al., 1996; the other hand, weather information networks that are designed Petursdottir et al., 2001; Davis, 2012; Holland et al., 2020). The to support accessibility, reliability, and ease-of-use for decision- findings in this article substantiate the results in Pfeiffer and making will likely have positive effects on safety. Gratz (2016) and Marvasti and Dakhlia (2017) for a larger number of US fisheries. Namely, that in the period after IFQs were Supplementary data implemented, fishers avoid poor weather conditions if they have Supplementary material is available at the ICESJMS online ver- the flexibility to do so. The NWS marine watches and warnings sion of the manuscript. provide a more comprehensive representation of “poor weather conditions”, which are proxied by wind speed (Pfeiffer and Gratz, Acknowledgements 2016; Marvasti and Dakhlia, 2017) or wave height (Smith and Allison Bailey (Sound GIS) developed the ArcGIS tool to join Wilen, 2005; Emery et al., 2014) in previous research. port locations with National Weather Service watches and warn- Conclusion ings. The author thanks John Walden, Tammy Murphy, Min- Understanding the public’s responsiveness to weather warnings Yang Lee, and Larry Peruso from the National Marine Fisheries and forecasts is a principle area of interest to the NWS and other Service for assistance acquiring the data from each region. weather forecasting organizations, but there has been little empirical research thus far (Lazo et al., 2010). Fishermen and other References types of boaters are some of the most sophisticated public users Anderson, L. 1977. The Economics of Fisheries Management. The of weather forecast information (Savelli and Joslyn, 2012; Finnis John Hopkins University Press, Baltimore. et al., 2019). In this article, generalizable methods are developed Biran, A., and Lopez-Pulido, R. 2014. Ship Hydrostatics and Stability, 2nd edn. Elsevier, Waltham, MA. for estimating parameters that describe the risk aversion of fish Brick, K., Visser, M., and Burns, J. 2012. 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