With the advent of WhaleWatch, we knew that monthly predictions and 0.25° were too coarse to inform fine scale management decisions yet that was the best we could do with the environmental data we had available. In two short years, we have now updated the model to an ensemble of multiple modeling approaches, and moved from predictions using satellite data to higher resolution ocean model output (daily and 0.10 °). Briana also used a thorough dataset of independent data to check the model’s accuracy and found very good performance, particularly during the peak of whale migration. Improving and assessing the accuracy of operational tools is a necessity, but one that we would not have been able to accomplish without support from the Benioff Ocean Initiative.
B. Abrahms, H. Welch, S. Brodie, M.G. Jacox, E.A. Becker, S.J. Bograd, L.M. Irvine, D.M. Palacios, B.R. Mate, and E.L. Hazen, 2019. Dynamic ensemble models to predict distributions and anthropogenic risk exposure for highly mobile species. Diversity and Distributions. doi.org/10.1111/ddi.12940 PDF
The question of habitat use varies significantly when separating individuals based on sex and size. Blue sharks use significantly different habitat, particularly in the fall based on their sex and size highlighting the importance of considering multiple life history stages, or at least the most vulnerable, in management.
S.M. Maxwell, K.L. Scales, S.J. Bograd, D.K. Briscoe, H. Dewar, E.L. Hazen, R.L. Lewison, H. Welch, and L.B. Crowder, 2019. Oceanographic drivers of spatial segregation in blue sharks by sex and size class. Diversity and Distributions. doi.org/10.1111/ddi.12941. PDF
By combining species distribution models from Hazen et al. 2013 with Global Fishing Watch data from Kroodsma et al. 2018, White et al. assesses overlap between tunas and sharks and Pacific fishing vessels. In addition, the manuscript assesses which species occur within North American Exclusive Economic Zones versus the open ocean requiring different approaches towards management.
There has been a good discussion on how scale effects overlap calculations for GFW data as well by Amaroso et al. and in the Kroodsma et al. response finding that “fished area” could be between 4% and 55% depending on the scale of calculation. Both articles provide a valid rationale for why their scale was chosen. The work here was conducted on a coarse spatial scale, so it is highly likely that overlap would decrease if finer resolution data were available, yet this scale is appropriate for the ecosystem footprint of much of the gear and the top predator models as well.
Many species of sharks and tunas are threatened by overexploitation, yet the degree of overlap between industrial fisheries and pelagic fishes remains poorly understood. Using satellite tracks from 1,007 industrial fishing vessels in conjunction with predictive habitat models built using 2,406 electronic tags deployed on seven pelagic shark and tuna species, we developed fishing effort maps by gear type across the Northeast Pacific Ocean and assessed overlap with core habitats of pelagic fishes. We found that up to 35% of species’ core habitats overlapped with industrial fishing effort and identified overlap hotspots along the North American continental shelf, the equatorial Pacific, and Mexico’s Exclusive Economic Zone. Our results indicate which species require international, high seas conservation efforts for effective management (e.g., 90% of blue shark overlap and 48% of albacore tuna overlap occurs in international waters) and which may be effectively managed by single nations (e.g., 75% of salmon shark overlap occurs in U.S. waters). Vessels flagged to just 5 nations (Mexico, China, Taiwan, Japan, and the U.S.) account for the vast majority (> 95%) of overlap with core habitats of our focal sharks and tunas on the high seas. These results may inform ongoing, global negotiations over national fishing rights and conservation priorities to achieve sustainability on the high seas.
T.D. White, F. Ferretti, D.A. Kroodsma, E.L. Hazen, A.B. Carlisle, K.L. Scales, S.J. Bograd, B.A. Block, 2018. Predicted hotspots of overlap between highly migratory fishes and industrial fishing fleets in the Northeast Pacific. Science Advances. PDF
Blue whales are the largest animals to every exist on earth but feed on some of the smallest animals on earth, so they need to eat a huge amount of krill to meet their energy needs. Blue whales are estimated to eat 8,000 pounds of krill per day! So it’s important they’re able to find enough food as they’re migrating up the coast of North America. Rather than surfing the contemporaneous “green wave,” the whales can hedge their bets by going with the average timing they’ve experienced in the past. This suggests memory or social communication over basin scales may be at play. From a commentary by William Fagan, “The ultimate analysis and results underpinning conclusions about memory-driven movement in whales are deceptively simple, but the data-intensive process to get there underscores just how much integration is necessary to make progress in cognitive movement ecology.”
B. Abrahms, E.L. Hazen, E.O. Aikens, M.S. Savoca, J.A. Goldbogen, S.J. Bograd, M. Jacox, L. M. Irvine, D.M. Palacios, B.R. Mate, 2019. Memory and resource tracking drive blue
whale migrations. Proceedings of the National Academy of Sciences, 10.1073/pnas.1819031116. PDF
Not all fisheries dependent data tell the same story. Runcie et al. 2018 in Fisheries Oceanography shows how different fisheries and different gears show different preferences when fit with habitat models. It highlights an inherent difficulty in understanding the underlying habitat of a highly migratory predator from spatially restricted datasets.
Just because data on rare species bycatch is sparse and from multiple sources does not mean it is useless for fisheries management. Welch et al. show that correlations can be used to assess and improve the enactment of a seasonal closure.
Figure shows SST anomalies were correlated with sightings and increased interactions with loggerhead turtles. Historical closures are shown with black rectangles. Continue reading
With the Bakun upwelling index almost 30 years old, a new publication by Mike Jacox explores two new indices derived from modeled vertical velocity: CUTI (coastal upwelling and transport index) and BEUTI (biologically effective upwelling and transport index). Mike has also provided a history of the upwelling indices focusing on where the indices agree and diverge. The publication is available in early view at JGR. Continue reading
Elucidating connections between ocean climate variability and change, and recruitment of juvenile fishes to adult populations, is critical for understanding variability in stock-recruit dynamics. Recruitment to adult rockfish populations in the California Current Ecosystem (CCE) is highly variable, leading to short and long-term changes in abundance, productivity, forage availability and potential fisheries yield. We used regional ocean model output, oceanographic data, and a 34-year time series of pelagic juvenile rockfish, to investigate the interaction between changes in CCE source waters as reflected by physical water mass properties and recruitment variability. Specifically, variability of spiciness on upper water isopycnals explains a substantial fraction of the variation in pelagic juvenile rockfish abundance. High rockfish abundances correspond to cooler, fresher waters with higher dissolved oxygen (i.e., minty) conditions, indicative of Pacific Subarctic Water. By contrast, years of low rockfish abundance are associated with warmer, more saline, and more oxygen deficient (i.e., spicy) conditions, reflecting waters of subtropical or equatorial origin. Transport and source waters in the CCE are key factors determining density-independent processes and subsequent recruitment to adult populations.
Future Seas is a project exploring potential impacts of climate change on the swordfish, albacore, and Pacific sardine fisheries in the California Current System. A suite of dynamical, statistical, and conceptual models is being applied to explore future scenarios in an “end-to-end” framework spanning physical changes to socio-economic consequences, and to evaluate uncertainty associated with individual elements of the modeling framework.