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Probabilistic Seasonal Prediction of the Distribution of Fish and Marine Mammals in the Northeast US

Distributional shifts related to climate variability and oceanographic conditions have been observed across a broad range of marine taxa, and these shifts have both negative and positive socioeconomic consequences. The Northeast United States Large Marine Ecosystem (NEUS LME), a highly productive marine ecosystem that supports important commercial and recreational fisheries, has experienced some of the highest rates of warming in the last few decades. Climate-driven shifts in fish distribution have been observed in this region for most fish species and for whole fish communities. In addition to distributional changes, climate-driven environmental change can influence the phenology of marine processes such as the timing of breeding or spawning, seasonal movements and migrations. Marine organisms show species-specific responses to changing thermal regimes, and thus distributional overlap between species can be strongly impacted by climate-driven change. Therefore, quantifying the biophysical links driving species distributions and understanding how seasonality and climate-driven change together impact living marine resources is imperative to predicting the impacts of future change.

Bycatch, the incidental capture of non-target species in fisheries, is an important source of mortality for several marine mammal and fish species in the NEUS LME, and is strongly impacted by both species overlap and overlap between fishermen and non-target species. In addition to impacts on non-target species, bycatch is a concern for commercial fishermen as it can increase costs and decrease yield. Bycatch could be reduced by incorporating dynamic environmental variables to more precisely estimate spatio-temporal limits on the distribution of marine mammals and commercially important fish. Accurately predicting the distribution of living marine resources on seasonal timescales would be particularly beneficial since it would allow fishing and management approaches to be adjusted based on environmental conditions. Recently developed state-of-the-art seasonal climate models (e.g NMME, S2S) now provide the unprecedented opportunity to make significant strides in the seasonal predictions of living marine resources. However, despite the advantage of the hybrid model in seasonal prediction, this method has been applied only to limited properties of climate phenomena such as hurricane activity. Here we propose to use output from these climate models to generate probabilistic predictions of forage fish and marine mammal distribution in order to inform dynamic management of protected species in the NEUS LME. Specifically, this work will develop bycatch reduction tools to inform decision making for fishermen and managers by highlighting regions that fishermen could avoid in order to decrease the likelihood of bycatch.

The proposed research directly addresses priorities of the MAPP program. A major goal of the MAPP program is to increase the resilience and intelligence of coastal communities through improved products and services relevant to NOAA. The proposed work will contribute to this goal by elucidating the impacts of climate-driven environmental variability on fish and protected marine mammal species, and by generating probabilistic predictions regarding the biological impacts of forecasted changes in the NEUS LME. These products can then be used to inform the management of commercially and ecologically important species, and to provide information required for dynamic management. The proposed studies are thus consistent with the mission of NOAA  MAPP “to enhance the Nation’s capability to predict variability and changes in the Earth’s climate system” and directly contribute to NOAA’s long-term goals, especially for “(1) improved scientific understanding of the changing climate system and its impacts” and “(3) mitigation and adaptation choices supported by sustained, reliable, and timely climate services”.

Climate Risk Area: Marine Ecosystems

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