Humpback whales feed on hatchery-released juvenile salmon

Thank you to staff and managers at NSRAA, Armstrong Keta Inc. and NOAA for collecting data daily during their release seasons. Bart Watson collaborated in study design. Thank you to Elena McCauley, R. Katy Pendell and Margaret Schoenfeld for data entry.Humpback whales are remarkable for the behavioural plasticity of their feeding tactics and the diversity of their diets. Within the last decade at hatchery release sites in Southeast Alaska, humpback whales have begun exploiting juvenile salmon, a previously undocumented prey. The anthropogenic source of these salmon and their important contribution to local fisheries makes the emergence of humpback whale predation a concern for the Southeast Alaska economy. Here, we describe the frequency of observing humpback whales, examine the role of temporal and spatial variables affecting the probability of sighting humpback whales and describe prey capture behaviours at five hatchery release sites. We coordinated twice daily 15 min observations during the spring release seasons 2010–2015. Using logistic regression, we determined that the probability of occurrence of humpback whales increased after releases began and decreased after releases concluded. The probability of whale occurrence varied among release sites but did not increase significantly over the 6 year study period. Whales were reported to be feeding on juvenile chum, Chinook and coho salmon, with photographic and video records of whales feeding on coho salmon. The ability to adapt to new prey sources may be key to sustaining their population in a changing ocean.Ye

Arctic Energy Technology Development Laboratory

The Arctic Energy Technology Development Laboratory was created by the University of Alaska Fairbanks in response to a congressionally mandated funding opportunity through the U.S. Department of Energy (DOE), specifically to encourage research partnerships between the university, the Alaskan energy industry, and the DOE. The enabling legislation permitted research in a broad variety of topics particularly of interest to Alaska, including providing more efficient and economical electrical power generation in rural villages, as well as research in coal, oil, and gas. The contract was managed as a cooperative research agreement, with active project monitoring and management from the DOE. In the eight years of this partnership, approximately 30 projects were funded and completed. These projects, which were selected using an industry panel of Alaskan energy industry engineers and managers, cover a wide range of topics, such as diesel engine efficiency, fuel cells, coal combustion, methane gas hydrates, heavy oil recovery, and water issues associated with ice road construction in the oil fields of the North Slope. Each project was managed as a separate DOE contract, and the final technical report for each completed project is included with this final report. The intent of this process was to address the energy research needs of Alaska and to develop research capability at the university. As such, the intent from the beginning of this process was to encourage development of partnerships and skills that would permit a transition to direct competitive funding opportunities managed from funding sources. This project has succeeded at both the individual project level and at the institutional development level, as many of the researchers at the university are currently submitting proposals to funding agencies, with some success

HatcheryObs

This dataframe provides observations from all five hatchery release sites over six years. The raw data has been filtered as described in the methods section of the manuscript

BehaveNewData2

This dataframe includes the combinations of predictors necessary for creation of Figure 3. The models created by Chenoweth_et_al_2017_Roy_Soc_Open_Sci.R provides the predicted probabilities of humpback whale sighting based on these predictors

Releases.Sum

This dataframe provides the Julian Day of the first and last releases from all five hatchery release site in all six years. This dataframe is required by Chenoweth et al 2017. Roy_Soc_Open_Science.

Chenoweth et al 2017. Roy_Soc_Open_Science.R

R Code for the calculation of most in-text results, Tables 1 and 2 and the production of Figure 3

Humpback whale feeding at Hidden Falls release site 2008 from Humpback whales feed on hatchery-released juvenile salmon

2008 Video by Northern Southeast Regional Aquaculture Association staff showing a humpback whale feeding at the Hidden Falls release site in Kasnyku Bay

Subsurface video of a humpback whale feeding on juvenile coho salmon from Humpback whales feed on hatchery-released juvenile salmon

Video of a humpback whale lunge feeding subsurface on juvenile coho salmon at Hidden Falls release site in May 201

When are estimates of spawning stock biomass for small pelagic fishes improved by taking spatial structure into account?

Consideration should be taken of including spatial structure in the models on which stock assessments are based. Abstract A simulation-estimation approach is used to evaluate the efficacy of stock assessment methods that incorporate various levels of spatial complexity. The evaluated methods estimate historical and future biomass for a situation that roughly mimics Pacific herring Clupea pallasii at Haida Gwaii, British Columbia, Canada. The baseline operating model theorizes ten areas arranged such that there is post-recruitment dispersal among all areas. Simulated data (catches, catch age-composition, estimates of spawning stock biomass and its associated age structure) generated for each area are analyzed using estimation methods that range in complexity from ignoring spatial structure to explicitly modelling ten areas. Estimation methods that matched the operating model in terms of spatial structure performed best for hindcast performance and short-term forecasting, i.e., adding spatial structure to assessments improved estimation performance. Even with similar time trajectories among sub-stocks, accounting for spatial structure when conducting the assessment leads to improved estimates of spawning stock biomass. In contrast, assuming spatial variation in productivity when conducting assessments did not appreciably improve estimation performance, even when productivity actually varied spatially. Estimates of forecast biomass and of spawning stock biomass relative to the unfished level were poorer than estimates of biomass for years with data, i.e., hindcasts. Overall, the results of this study further support efforts to base stock assessments for small pelagic fishes on spatially-structured population dynamics models when there is a reasonable likelihood of identifying the sub-stocks that should form the basis for the assessment