Cooperative Research to Evaluate an Incidental Catch Distribution Forecast

Concern over incidental catches in commercial fisheries has been increasing, and while simple mitigation strategies have been effective, few effective mitigation strategies have been established for more complex species interactions. Incidental catches of alewife (Alosa pseudoharengus) and blueback herring (A. aestivalis) in the commercial Atlantic herring (Clupea harengus) fishery have received substantial attention on the Northeast U.S. continental shelf, despite an existing bycatch avoidance program. This study evaluates the utility of existing species distribution forecasts to predict river herring catches in the southern New England small mesh bottom trawl Atlantic herring fishery, with the ultimate goal of incorporating incidental catch forecasts into the bycatch avoidance program. Commercial Atlantic herring bottom trawl vessels assisted with field-based evaluation of alewife, blueback herring, and Atlantic herring species distribution forecast models. Vessels were equipped with conductivity, temperature, and depth probes, and sampling occurred throughout the fishery season (January – March). Locations of expected low and high forecasted incidental catches were sampled, as well as locations the captain expected to find low and high incidental catches. This allowed us to sample within the spatial area the fishery occurs, and to evaluate the forecasted conditions, and predictions, at the spatial scale of the fishery. Catch differences between high and low probability stations were small and variable, as were differences in modeled probability of species presence. No differences were observed between observations at model-predicted stations and captain-selected stations. The sampling provided a better understanding of the potential effectiveness of distribution forecasts for further reducing incidental catches. Existing models have limited use at the spatial scale of this fishery, but could be improved by developing models with fishery-dependent data. Collaborations between researchers, managers, and the Atlantic herring commercial fleet have improved relationships between the groups, and continued collaboration in the development and evaluation of incidental catch reduction tools is key for further reducing incidental catches.

[1]  Alistair J. Hobday,et al.  Near real‐time spatial management based on habitat predictions for a longline bycatch species , 2006 .

[2]  Changsheng Chen,et al.  An Unstructured Grid, Finite-Volume Coastal Ocean Model (FVCOM) System , 2006 .

[3]  M. L. Scott,et al.  Essential fish habitat source document. Atlantic herring, Clupea harengus, life history and habitat characteristics , 2005 .

[4]  J. Kritzer,et al.  Spatial and temporal patterns of anadromous alosine bycatch in the US Atlantic herring fishery , 2013 .

[5]  Patrick N Halpin,et al.  Dynamic ocean management increases the efficiency and efficacy of fisheries management , 2016, Proceedings of the National Academy of Sciences.

[6]  R. Neves,et al.  Species Profiles. Life Histories and Environmental Requirements of Coastal Fishes and Invertebrates (Mid-Atlantic). STRIPED BASS, , 1983 .

[7]  James D. Scott,et al.  Projected ocean warming creates a conservation challenge for river herring populations , 2015 .

[8]  David E. Richardson,et al.  Evaluation of species distribution forecasts: a potential predictive tool for reducing incidental catch in pelagic fisheries , 2017 .

[9]  Alistair J. Hobday,et al.  Dynamic Ocean Management: Identifying the Critical Ingredients of Dynamic Approaches to Ocean Resource Management , 2015 .

[10]  R Core Team,et al.  R: A language and environment for statistical computing. , 2014 .

[11]  Alistair J. Hobday,et al.  Seasonal forecasting of tuna habitat in the Great Australian Bight , 2015 .

[12]  K. Mann,et al.  Physical oceanography, food chains, and fish stocks: a review , 1993 .

[13]  E. Gilman,et al.  Fleet communication to abate fisheries bycatch , 2006 .

[14]  J. Galbraith,et al.  Northeast Fisheries Science Center bottom trawl survey protocols for the NOAA Ship Henry B. Bigelow , 2014 .

[15]  J. Kohut,et al.  Ocean observatory data are useful for regional ­habitat modeling of species with different vertical habitat preferences , 2011 .

[16]  L. Crowder,et al.  Comparing Effectiveness of Experimental and Implemented Bycatch Reduction Measures: the Ideal and the Real , 2007, Conservation biology : the journal of the Society for Conservation Biology.

[17]  Daniel C. Dunn,et al.  Empirical move-on rules to inform fishing strategies: a New England case study , 2014 .

[18]  David E. Richardson,et al.  Using habitat association models to predict Alewife and Blueback Herring marine distributions and overlap with Atlantic Herring and Atlantic Mackerel: can incidental catches be reduced? , 2016 .

[19]  James D. Scott,et al.  Forecasting the dynamics of a coastal fishery species using a coupled climate--population model. , 2010, Ecological applications : a publication of the Ecological Society of America.

[20]  Alistair J. Hobday,et al.  Habitat overlap between southern bluefin tuna and yellowfin tuna in the east coast longline fishery - implications for present and future spatial management , 2011 .

[21]  A. Hirzel,et al.  Habitat suitability modelling and niche theory , 2008 .

[22]  Coby L. Needle,et al.  Real‐time spatial management approaches to reduce bycatch and discards: experiences from Europe and the United States , 2015 .

[23]  Kevin D. E. Stokesbury,et al.  Developing a fine scale system to address river herring (Alosa pseudoharengus, A. aestivalis) and American shad (A. sapidissima) bycatch in the U.S. Northwest Atlantic mid-water trawl fishery , 2013 .

[24]  Teresa R. Johnson,et al.  Benefits and organization of cooperative research for fisheries management , 2007 .

[25]  Michael Collins Palmer An evaluation of the northeast region's study fleet pilot program and electronic logbook system phases I and II , 2007 .

[26]  Troy W. Hartley,et al.  Emergence of multi-stakeholder-driven cooperative research in the Northwest Atlantic: The case of the Northeast Consortium , 2006, Marine Policy.

[27]  R. Tibshirani,et al.  Generalized Additive Models , 1991 .

[28]  S. Cadrin,et al.  Evaluating effectiveness of time/area closures, quotas/caps, and fleet communications to reduce fisheries bycatch , 2014 .

[29]  Gregory D. Williams,et al.  Cloudy with a chance of sardines: forecasting sardine distributions using regional climate models , 2016 .