A GRIP fellow from our group, Dr. Steph Dodson has published her work with us in Ecological modeling today, titled “Disentangling the biotic and abiotic drivers of emergent migratory behavior using individual-based models.” Using published info on blue whale movement and fine scale models of krill distribution, Steph was able to test whether an IBM could recreate observed movement timing and extent. Interested in more, please read below!
Fig. 2. (a) Model algorithm in pictorial form. The acronym ARS stands for area restricted search. (b) Behavioral states for each model. Arrows indicate possible transitions. (c) Step length and turning angle distributions for all transiting and foraging states. These distributions have been scaled from Bailey et al. (2009) to account for the 6 h time step used in the IBM. A turning angle of 0∘ corresponds to straight in all behavioral states except of the north-south model, where instead 0∘ is due south.
Dodson, B. Abrahms, S.J. Bograd, J. Fiechter, and E.L. Hazen. 2020. Disentangling the biotic and abiotic drivers of emergent migratory behavior using individual-based models. Ecological Modeling. DOI: 10.1016/j.ecolmodel.2020.109225.
Megan’s recent paper uses NOAA’s RREAS cruise data to look at combined krill biomass from net-tows and differences in habitat use between T. Spinifera and E. Pacifica in the California Current. The paper highlights the issues that arise when modeling a species complex rather than individual species habitat preferences. Getting species identification will help partition net tow data in addition to fisheries acoustic measurements of krill biomass moving forward. Krill also had broad scale response to oceanic warming from El Niño events Also not surprising, predictions of high krill biomass corresponded with top predator sightings as well.
Fig. 4: Climatology (temporal mean) of predicted ln(CPUE + 1) for krill from the Full model along the central California coast (top panel) and along the U.S. West Coast (bottom panel) from 2002 to 2018 for (a, d) TSPIN, (b, e) EPAC, and (c, f) total krill. Points in (a–c) are mean observations from the mid‐water trawl sampling stations. Bathymetry line contours (contour interval of 500 m) are shown in black. The red box in (d–f) represents the region shown in (a–c).
M.A. Cimino, J.A. Santora, I. Schroeder, W. Sydeman, M.G. Jacox, E.L. Hazen, S.J. Bograd. 2020. Essential krill species habitat resolved by seasonal upwelling and ocean circulation models within the large marine ecosystem of the California Current System. Ecography.
DOI: 10.1111/ecog.05204. PDF
Forecasting aids in the management of marine resources and communities. New paper led by Mike Jacox reviews forecasting methods, mechanisms of predictability, and priority developments for coastal marine ecosystems.M.G. Jacox, M. Alexander, D. Barrie, S.J. Bograd, S. Brodie, A. Capotondi, K. Chen, W. Cheng, E. Di Lorenzo, C. Edwards, J. Fiechter, P. Fratantoni, R. Griffis, E.L. Hazen, A. Hermann, H. Kim, A. Kumar, Y. Kwon, M. Merrifield, A. Miller, I. Ortiz, D. Pirhalla, M. Pozo Buil, S. Ray, S. Sheridan, S. Siedlecki, A. Subramanian, P. Thompson, L. Thorne, D. Tommasi, M. Widlansky, 2020. Seasonal-to-interannual prediction of U.S. coastal marine ecosystems: Forecast methods, mechanisms of predictability, and priority developments. Progress in Oceanography. DOI: 10.1016/j.pocean.2020.102307.
A recent review from our group at ERD highlights the ability for highly mobile predators to serve a role as ecosystem sentinels, by integrating the ocean processes around them, and telling us something we would not otherwise know about the oceanic ecosystems. In an ideal world, we’d have fine scale measurements of the ecosystem components and thresholds that result in change, but top predators respond in multiple scales, from changes in breeding success, movement patterns, to diet analyses we can understand more about ocean ecosystems and when changes are likely to occur. We hope this manuscript will both further the discussion of the roles of top predators in the global ocean observing system, but also as sentinels for rapid response, when ecosystem changes are likely to occur, and when adaptive management will be most needed.
E.L. Hazen, B. Abrahms, S. Brodie, G. Carroll, M. Jacox, M.S. Savoca, K.L. Scales, W.J. Sydeman, and S.J. Bograd, 2019. Marine Top Predators as Climate and Ecosystem Sentinels. Frontiers in Ecology and the Environment. DOI: 10.1002/fee.2125 PDF
Climate variability and change can result in ecosystem response via trophic pathways. Trophic linkages (gray and colored arrows) are represented in a generic pelagic food web. Solid colored lines represent a direct relationship between a sentinel via the metric measured and an ecosystem component; dashed colored lines represent the capacity of an organism to function as a leading sentinel, which can be used to predict a future ecosystem response; and dotted colored arrows represent the ecosystem link that is heralded by a leading sentinel.
Papers often use the metrics most familiar to the authors without a systematic assessment of which may be the best for the given purpose. A more holistic assessment of how metrics may assess abundance, range overlap, or even temporal components of how this may change are presented by Gemma using both a case study from Alaska and simulated data. Hopefully this manuscript can serve as a guide for researchers to decide which metric may be best for their purpose when testing predator-prey overlap and how it may change over time.
G. Carroll, K.K. Holsman, S. Brodie, J.T. Thorson, E.L. Hazen, S.J. Bograd, M.A. Haltuch, S. Kotwicki, J. Samhouri, P. Spencer, E. Willis-Norton, and R.L. Selden, 2019. A review of methods for quantifying spatial predator-prey overlap. Global Ecology and Biogeography, DOI: 10.1111/geb.12984 PDF
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.