Data and Management Strategies for Recreational Fisheries with Annual Catch Limits (2021) / Chapter Skim
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4 Optimizing Use of MRIP Data and Complementary Data for In-Season Management
4 Optimizing Use of MRIP Data and Complementary Data for In-Season Management
Pages 73-130
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From page 73... ...
and generate estimates of recreational fisheries catch and effort that are better suited for use in assessment and management, as indicated in Chapter 3, MRIP surveys were not intended or designed to support in-season quota monitoring. The main products of the MRIP general survey are bi-monthly catch estimates that are relatively precise at the annual and regional (i.e., multistate)
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From page 74... ...
For example, MRIP could use a weighted allocation for the APAIS intercept sample to improve monitoring of catch during high-intensity fishing periods, similar to what is done in support of improved sampling during Florida's Red Snapper recreational fishing season. Increasing the Speed of Existing MRIP Data Collection, Processing, and Release A focus on the timeliness with which fishery managers can access and use MRIP data is by no means new.
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From page 75... ...
This in turn will impact the timeliness with which final estimates can be produced, thereby potentially offsetting some of the time savings realized with electronic data capture. Shorter Time Period Between MRIP Data Collection and Release of Primary Estimates MRIP could retain the current bi-monthly wave and annual reporting timing but through staffing increases or process changes, might shorten the elapsed time between the end of each wave and the release of preliminary estimates.
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From page 76... ...
Another point in this discussion of shortening the period of time before MRIP data can be used to inform in-season management relates to the release of raw data before MRIP estimates have been produced. In presentations to the committee, a number of fishery scientists and managers at the regional and state levels expressed strong interest in having timely access to data that would enable them to monitor recreational fishing effort and catch rates more continuously.
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From page 77... ...
Frequency Another strategy for improving the timeliness of MRIP data estimates for use in in-season management would be to transition from bi-monthly to monthly waves for data collection and reporting of catch estimates. This strategy would have clear advantages for in-season management of species populations for which fishing intensity is not highly variable over time or species access is not limited by such natural factors as migratory patterns or seasonal barriers (e.g., weather)
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From page 78... ...
to improve annual and in-season catch forecasts. This section looks at methods for integrating supplemental and ancillary data with MRIP catch estimates to improve catch forecasts.
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Chapter 5 also considers supplemental surveys in the context of alternative management strategies. Supplemental Data on Recreational Fisheries Effort and Catch State-Specific Supplemental Survey Data As reviewed in Chapter 3, data from state-specific recreational fishery survey programs may be used to supplement data collected by MRIP.
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of catch forecasts by combining location-specific supplemental data with traditional MRIP-produced effort and catch estimates using multiple-frame survey methods (described below) , for example.
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From page 81... ...
, water depth, ocean conditions (e.g., seawater temperature and currents) , fuel prices, unemployment rate, fishing access infrastructure, boat ownership, social media search terms, and electronic device use and location, could be combined with MRIP recreational catch estimates in projection models to improve both annual and in-season catch forecasts.
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From page 82... ...
Commercial Fishery Landings and Effort State fisheries agencies collect commercial fisheries data through "trip ticket" programs. To the extent that recreational fishing effort and catch are correlated with commercial fishing effort and catch, it may be possible to use commercial fishery data to improve annual and in-season recreational catch and effort forecasts made with recreational fishery projection models.
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From page 83... ...
with recreational fishing effort or catch, it may be possible to use weather data to improve annual and in-season recreational fishery catch and effort forecasts made with recreational fishery projection models. In addition to the degree of correlation between the weather data and the recreational fishery data, the usefulness of weather data would depend on the accuracy and precision of the data, the frequency with which the data are collected, and the timeliness with which they are made available.
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From page 84... ...
NOAA-PSL also provides an online tool for extracting monthly or seasonal time series of precipitation and temperature variables.14 The National Centers for Environmental Prediction's North American Regional Reanalysis provides eight times daily data on temperature, winds, and precipitation for 1979 to the present for a spatial grid of 0.3 degrees longitude by 0.3 degrees latitude.15 NOAA's National Centers for Environmental Information Climate Data Online Data Tools provide daily and sometimes hourly weather data by weather station.16 Oceanographic variables, such as winds at sea, wave height, seawater temperature, tide, and current direction and strength may affect fishing effort or catch (Powers and Anson, 2016, 2019)
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From page 85... ...
The file is relatively small, less than 100 kB, and is updated approximately every 5 minutes. Historical data files are available by station.21 Some stations are equipped with "BuoyCAM" cameras that provide periodic online photos during daylight hours.22 The National Hurricane Center's "Blue Water Mariners" program provides a new, experimental, online graphical ocean conditions forecast for mariners that travel the open ocean (NOAA NHC, 2021)
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From page 86... ...
To the extent that the El Niño and La Niña cycle is correlated with recreational fishing effort or catch, it may be possible to use ENSO data to improve annual and in-season recreational fishery catch and effort forecasts made with recreational fishery projection models. ENSO data may be correlated with recreational fishing catch and effort for two, interrelated reasons: first, ENSO effects 25See https://gdo-dcp.ucllnl.org/downscaled_cmip_projections.
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From page 87... ...
with recreational fishing effort or catch, it may be possible to use such economic data to improve annual and in-season recreational fishery catch and effort forecasts made with recreational fishery projection models. For example, Farmer et al.
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From page 88... ...
Fishing access infrastructure may increase recreational fishing effort and catch by lowering the cost to anglers of accessing fishing locations along the coast and in the open ocean. To the extent that recreational fishing effort and catch are correlated with fishing access infrastructure, it may be possible to use infrastructure data to improve annual recreational catch and effort forecasts made with recreational fishery projection models.
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From page 89... ...
State recreational fishing vessel ownership registries could be combined with saltwater fishing license registries to determine the proportion of saltwater anglers that own boats, as well as how this proportion varies over time and by geographic region. Social, Cultural, and Demographic Factors Fishing effort is influenced by a wide variety of social and cultural factors, some of which may be useful as ancillary variables in effort forecasting models.
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From page 90... ...
To the extent that these hurricane strikes are correlated (either positively or negatively) with recreational fishing effort or catch, it may be possible to use data on hurricane strikes to improve annual and in-season recreational fishery catch and effort forecasts made with recreational fishery projection models.
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From page 91... ...
Of the trips that did not occur in the North Gulf or west Florida study regions, approximately 39 percent still occurred but were relocated to the coastal areas of Texas and the east coasts of Florida and Georgia. Results from such studies give some indication of the duration and magnitude of the impacts of disasters on recreational fishing effort, including spatial relocation of fishing effort outside the region of immediate impact.
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From page 92... ...
This methodology is used by the Global Fishing Watch to produce a Daily Fishing Hours dataset, which provides estimates of fishing effort measured in hours of inferred fishing activity. These data are available on the Google Earth Engine and can provide valuable information for local fishery management.
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From page 93... ...
that draw on MRIP data streams, supplementary data, and auxiliary data to improve timely forecasting and tracking of both point-in-time and cumulative statistics on recreational catch. This section presents several lines of potential development related to catch forecast modeling using MRIP data and other available data sources.
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. The forecasts precede the publication of end-of-season state estimates, similar to the situation in which in-season forecasts of fish catch are needed for in-season management before the end-of-season final catch estimates are available.
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From page 95... ...
OPTIMIZING USE OF MRIP DATA AND COMPLEMENTARY DATA 95 estimating a set of desired statistics (e.g., a numerator, a denominator, and their ratio) is a difficult problem because the final triplet estimates need to satisfy identity constraints (ratio of numerator to denominator)
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Given the typically large variance in forecasts of fish catch, fishery managers may be willing to accept a little bias in the catch estimates if the variance (PSE) can be reduced substantially.
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of catch forecasts and lower the forecasts' overall MSE. Small-Area Models Small-area models are small-area estimation methods that account for differences in variation among areas (domains)
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that can occur when MRIP data and self-reported data from smartphone apps are used to estimate catch using capture-recapture methods. The researchers estimate the bias in catch estimates from each source of nonsampling error in an application to recreational fisheries in the Gulf of Mexico in 2017.
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frame. MRIP combines information from the two frames to produce total catch estimates.
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The discussion focuses on methods that could be used to improve the accuracy and precision of catch forecasts based on MRIP catch estimate data, perhaps integrated with additional data from supplemental surveys and
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From page 101... ...
Although there are several directions for potential model improvement (allowing anglers to reallocate or shift trips across waves, incorporating information on weather and general economic conditions, considering contemporaneous correlation, etc.) , this model is a good example of how a forecasting model using MRIP data can contribute to fishery management under ACLs.
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on fishing effort (trips per angler) targeting Red Snapper for reef fish anglers in the Gulf of Mexico in 1991.
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The fourth section points out the important role of covariances when fishery managers choose to aggregate or disaggregate MRIP catch estimates across domains after receiving the catch estimates from MRIP, with a subsequent note on why covariances among catches in a multispecies fishery constrained by a binding ACL will likely be negative. The final section of Appendix B describes how covariances could be used together with the methodology for using control variates to reduce the variance of catch forecasts.
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From page 104... ...
For example, because of the accuracy of the federal for-hire forecasts for Red Snapper in the Gulf, the Gulf Council recently reset the component ACT buffer for the federal for-hire component of the Red Snapper fishery from 20 percent to 9 percent below the federal for-hire component ACL, allowing a greater harvest while meeting the ACL (GMFMC, 2019)
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If the variance is biased downward, then the PSE of the catch forecast is underestimated. MRIP catch estimates are derived from APAIS estimates of catch per trip and FES estimates of trips.
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. Furthermore, looking at MRIP catch estimates over time will almost certainly reveal seasonal patterns, which implies autocorrelation between seasons as well as across years.
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From page 107... ...
The investigators identified "the best-fitting model with meaningful covariates for each state and component combination, evaluated the retrospective performance of the forecasting method, and applied our forecasts to predict the 2017 federal season." Importantly, MRIP estimates of mean catch and the variance of catch were used to identify the best model and to make catch forecasts using the model.54 This provides a workable example of how MRIP estimates can be incorporated into a catch forecasting model. The investigators note that "improvements upon this approach may explicitly incorporate the behavioral response of anglers into landings forecasts" (Lee et al., 2017)
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of catch forecasts made by fishery managers using MRIP output estimates of mean catch, variance (PSE) of catch, and covariance of catches across domains.
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to update catch estimates for the purpose of in-season management of recreational fisheries with ACLs. For example, a Bayesian model that uses MRIP mean catch and PSE estimates to parameterize prior probability distributions and then uses MRIP mean catch and PSE estimates by wave to update priors could provide a method for optimally updating catch estimates and forecasts, setting season lengths, and determining dates of season closures.
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for in-season fishery management under ACLs. The Bayesian approach has several advantages, including the ability to make use of either MRIP data alone, supplementary (e.g., state survey)
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Similarly, when stock abundance decreases, fishery managers may want to know how related decreases in fishing effort and catch are partitioned between anglers who remain in the fishery and anglers who drop out of the fishery altogether. 55In many cases, the net improvements in management outcomes from AAM relative to PAM have been found to be modest.
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(2000) compared the Poisson distribution with two versions of the negative binomial distribution for modeling recreational fishing effort targeting Red Snapper in the Gulf of Mexico in the early 1990s.
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Appendix F presents the Handane method with applications to the management of rare fish species. Uninformative Priors, Catch Proportional to Abundance, and Bayes' Rule Under the assumption that the catch of various fish species is in proportion to their prevalence in the overall fish population, so-called "uninformative prior" probability distributions in combination with Bayes' rule may offer a method of modeling rare-event fish species.
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The influence of so-called "outlier" MRIP estimates (estimates that are unusually large or small relative to other MRIP estimates from other time periods or locations) on mean catch or catch forecasts is an important in-season fishery management issue.
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The above rules of thumb can be used to classify potential outliers into four categories. The four categories are illustrated in Figure 4.2 in the context of the simple example linear catch forecasting model described above.
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. If an outlier were to occur, fishery managers would first check to ensure that the outlier was not due to an error in the data or in data processing.58 If the outlier was not due to an error, managers would need to decide whether (1)
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Order Statistics In some cases, especially data-limited cases in which there are too few data points to develop a formal projection/forecasting model, fishing regulations have been based on ad hoc measures that attempt to find a balance between the "average" and the "variation" in the available MRIP catch estimates. An example of such a method is basing a catch forecast on "the third-largest of the five most recent MRIP catch estimates." To find the threshold for identifying an outlier in such cases, one first needs to derive the probability distribution of such statistics, and here the concept of order statistics might be useful.
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It is possible that the raw MRIP data streams could be used to inform more timely catch estimates through such approaches as nowcasting or other in-season projection methods. Recommendation: The Marine Recreational Information Program (MRIP)
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However, the committee identified a number of supplementary data sources and analytical approaches likely to improve the precision, timeliness, and adaptability of MRIP data for the purpose of improving catch forecasts for recreational fisheries subject to ACLs. Conclusion: Further development of in-season management approaches utilizing novel statistical methods and additional data sources, such as state surveys, voluntary reporting, and analyses of social media posts, has the potential to improve incrementally the timeliness and precision of annual catch management.
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. Recommendation: The National Marine Fisheries Service Regional Offices, Science Cen ters, and state agencies should explore and identify ancillary variables that have high correlations with the Fishing Effort Survey and Access Point Angler Intercept Survey response propensities, catch per unit effort, and catch estimates and supplemental survey estimates for potential use in annual and in-season forecasting models.
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From page 121... ...
Conclusion: Bayesian modeling methodology may serve as a good overarching framework for regional federal and state fishery managers to use in integrating and updating MRIP catch estimates, supplemental survey data, and ancillary variables for the purpose of producing annual catch forecasts and in-season catch forecasts. Furthermore, many, if not all, of the other methodological approaches described in this report can be integrated within a Bayesian framework.
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From page 122... ...
, and the possible use of control variates, to reduce the PSE of catch forecasts; • Bayesian modeling methods that could provide a consistent framework for updating annual and in-season catch forecasts and projections utilizing data streams of differ ent precision and frequency, including MRIP estimates of given precision available by year and by 2-month wave, and estimates from other, supplemental sources that may have different precision and be available with different frequency; • the combination of uninformative priors, an assumption of catch proportional to abundance, and Bayesian updating for forecasting the catch of rare-event species and possibly estimating the population sizes of such species; • alternative statistical definitions of outlier catch estimates and the adoption of stan dard definitions to facilitate consistency in management actions; • change in detection methods in time-series data analysis to help answer the question of when an outlier should trigger management change; and • contemporaneous correlation in the errors across MRIP domains (the Seemingly Unrelated Regression method, its extension to situations with heteroskedasticity and autocorrelation, and its implementation within a Bayesian forecasting model could help reduce the variance and PSEs of catch forecasts)
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2018. Review of Options for Electronic Reporting in Survey Research Applied to Estimating Fishing Effort.
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1982. Estimating boat-based fishing effort in a marine recreational fishery.
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From page 125... ...
2019. Modification to the Recreational For-Hire Red Snapper Annual Catch Target Buffer: Framework Action to the Fishery Management Plan for the Reef Fish Resources of the Gulf of Mexico Including Draft Environmental Assessment, Regulatory Impact Review, and Regulatory Flexibility Act Analysis.
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2017. Estimation of a total from a population of unknown size and applica tion to estimating recreational Red Snapper catch in Texas.
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2014. 2014 Gulf of Mexico Red Snapper Recreational Season Length Estimates (Revised and Updated)
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National Marine Fisheries Service's Marine Recreational Information Program. Updated March 2021.
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2017. Survey methods for estimating Red Snapper landings in a high-effort recreational fishery managed with a small annual catch limit.
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2020. The importance of fishing opportunity to angler utility analysis in marine recreational fisheries.
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