How Storms Affect Fishers' Decisions About Going To
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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 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-tak- ing 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 in- for 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