Philippa Kaur

Satellite tracking has yielded key information about the movements and behaviour of marine vertebrates in ways that were previously logistically impossible34. In the current study, we tagged the first 18 angler-caught ABFT in Skagerrak, and tracked their movements for up to one year. Despite the majority of tags detaching prematurely, our data provides new insights regarding the migration behaviour and habitat use of this species, both locally within the Nordic region and more widely throughout the northeast Atlantic and western Mediterranean Sea. Most fish (N = 9) left Skagerrak via the Norwegian Trench, heading north before exiting into the Atlantic. In addition, the two tags which remained deployed for approximately one full year showed a return migration into the Skagerrak from the northern North Sea and southern Norwegian Sea regions, re-entering north of the British Isles and through the Norwegian Trench. No fish exited or re-entered through the English Channel or the southern North Sea. These observations of entry/exit from the Skagerrak are similar to migration behaviour inferred from historical commercial fishery data in the region during the 1950s–1960s16,19. These historical records also demonstrated that some individuals migrated from the southern Norwegian Sea into the Skagerrak, Kattegat and Øresund, before leaving the area several weeks later, potentially indicating exploratory feeding on herring and mackerel, abundant in the area during this time of year. Our new tagging results confirm this behaviour among at least some of the ABFT migrating to these areas.The migration patterns revealed by our tagging study exposes tuna entering and exiting the Skagerrak, Kattegat and Øresund to targeted exploitation by regional commercial fishing vessels. Presently, these vessels catch ABFT under a Norwegian quota (315 tonnes in 2021) but additional countries in the region may acquire a quota in the future. Moreover, the relatively narrow size distribution of tunas caught indicates that this migratory behaviour may only be performed by a limited number of year classes35, meaning that the continued long-term migration of ABFT to these waters is highly dependent on recruitment and survival of younger year classes. These younger year classes, perhaps once they reach a certain size, could then also undertake a migration to Skagerrak–Kattegat–Øresund. However, the combination of local exploitation pressures, and the presently limited number of year classes found in Skagerrak could result in ABFT migrating into Skagerrak–Kattegat–Øresund being a short-lived phenomenon if those year classes are subject to a large yearly fishing mortality (both regionally within the Nordic region, and more generally throughout the population range) and no younger year classes appear. Additionally, currently there is no scientifically-derived estimates of ABFT abundance for this region. We suggest to monitor the size distribution and abundance of ABFT in Scandinavian waters in the coming years to (1) confirm that visiting ABFT consist of only a few year classes, and clarify if younger year classes begin to appear, (2) evaluate how the numbers migrating to the region annually may change over time (e.g., under different levels of exploitation, or in relation to environmental factors).While most of our tagged ABFT went north after exiting the Skagerrak, one individual turned south into the south-central North Sea before eventually leaving through the northern part of the North Sea. The region to which it migrated in the North Sea is congruent with earlier commercial catches and sightings in this region, including the Dogger Bank vicinity15,16. Although the exact routes that tagged individuals followed were not identical, no individuals used the shortest route to reach the Atlantic: from the Skagerrak through the North Sea to the English Channel, and further south to the Bay of Biscay and other southern regions. Migration along a northerly route probably reflects a trade-off between the potential for higher energetic gain from more abundant food and higher energy resources, and the longer migration distance. This could suggest that ABFT either follow the food, or simply follow the same route by which they came through learned behaviour.Three tags remained attached long enough to explore long-term migration patterns and showed widely different behaviours. One fish crossed the Atlantic and utilized areas near the Grand Banks, crossing the ICCAT management boundary between the Western and Eastern stocks of ABFT (the 45° meridian), while the other two fish remained in the eastern Atlantic. The area west of Ireland, the Bay of Biscay and the area west of Portugal appear to be important feeding areas when the fish are not in Skagerrak or the Norwegian Sea. These results reflect interconnected seascapes for foraging through the NE Atlantic. Connecting foraging grounds off Ireland and the Bay of Biscay, which was previously suggested by Ref.24 is further corroborated by one of the fish tagged in this study, which passed over the Irish continental shelf when returning to Skagerrak in 2018.Depth and temperature useWithin ICES Area 3a, ABFT were predominantly roaming the upper water column, with most observations in the upper 100 m. However, some ABFT did dive to much deeper depths, with the maximum depth recorded being 520 m, showing that they can use the majority of the depth range available in the area (max. depth in the Norwegian Trench is app. 725 m, but represents a relatively small area). The behaviour likely reflects foraging, as ABFT were also observed by both the scientific tagging crews and the anglers to actively chase prey fish, like garfish and mackerel, at the surface during the tagging operations. The temperature ranges recorded varied between 7 and 17 °C. Both the depths and temperatures recorded are well within the thermal and depth limits reported in the literature for ABFT36.SpawningABFT have been shown to successfully spawn at temperatures above 20 °C at night30,31, and to display a distinct dive pattern thought to represent courtship and spawning behaviour29. When matching this described behaviour with the data from fish 34859 in the Mediterranean Sea, almost identical behavioural patterns were detected on specific days (Fig. 4). In total, seven days aligned with temperatures above 20 °C and oscillatory movement past the thermocline. All detected spawning events occurred west of Sardinia, where fishing for mature ABFT has been conducted for centuries37.In light of the recently proposed third spawning area in the Slope Sea of northeast United States38 and other proposed areas outside the Mediterranean19, it is relevant to look for similar temperature and behavioural patterns for fish 34840, which did not enter the Mediterranean Sea, and instead stayed in the eastern Atlantic. We found that this fish did not display a similar oscillatory behaviour, and the temperature experienced during the alleged spawning period (June–July) was above 20 °C only once (20.4 °C on 11 July). In this period, the fish was on the continental shelf west of Ireland, likely feeding and not spawning. Due to the size of the fish (247 cm CFL), reflecting a likely age of 14–16 years (matching the strong 2003 cohort), and the assumption that all eastern ABFT above five years and western ABFT above eight years are mature, we find it unlikely that this fish was immature. As such, these observations may suggest that this fish skipped spawning in 2018. Fish 34861 surfaced on 25 April and the tag was not recovered. The transmitted data does not allow for a detailed analysis of potential spawning behaviour for this fish. It did however, display 6 days where maximum temperatures from the transmitted dataset reached 20 °C (observations from 15. March to 20 April, with temperatures ranging from 20 to 20.6 °C). Given the lack of detailed behaviour and the fact that this time is well outside the normal spawning time for Mediterranean ABFT, we propose that this ABFT did not spawn in that period. However, the documentation of spawning depends on the general applicability of the temperature limits and nightly spawning behaviour30,31. More studies documenting spawning behaviour will be needed to corroborate if this pattern is consistent among locations and stocks. We also suggest more studies with longer lasting tags to elucidate if skipped spawning is a common behaviour and if fish skip one or more consecutive spawning seasons. Skipped spawning has been demonstrated in many fish species, including both freshwater and marine fish39, and likely reflects physiological condition40. If a considerable proportion of the adult population skips spawning every season, current population models, which assume annual spawning by all adult fish, should be modified to more accurately reflect population egg production and reproductive output. Current population modelling may be even further challenged if the proportion of adults that skip spawning varies over time, perhaps depending on environmental conditions. However, we acknowledge that only one of two fish followed through the spawning season appeared to skip spawning, and therefore caution against broad general interpretations. More studies are needed to verify that skipped spawning is a common behaviour, and if so, to estimate just how common that behaviour is.
Return migrationIn exploited fish populations, large adults are hypothesized to be important components of the spawning population because they contribute more to recruitment than smaller individuals due to a variety of maternal effects including higher fecundity, better quality of eggs and differences in spawning behaviour (e.g. time, location)41. Although such effects remain to be documented for ABFT, it may be prudent to conserve these large individuals as a precautionary measure, to maximize their potential contributions to reproduction and recruitment.In order to protect these fish, new knowledge about their movements and distribution is required. Data from ABFT deployed with long-term electronic tags suggests that after spawning in the Gulf of Mexico, the fish return to the feeding grounds where they were initially tagged, indicating a return feeding migration7. The same has been observed more recently from ABFT tagged in Ireland24, and other large highly migratory fish species (e.g., swordfish, Xiphias gladius42). In the current study, both ABFT that retained the tag for one year also returned to the same area, suggesting a similar seasonal return feeding migration. We also note that ABFT appeared to perform recurrent visits to the Norwegian Sea, Ireland and the Bay of Biscay on their way from Nordic waters and upon their return to the latter. Hence, we hypothesize that large ABFT in Nordic waters generally return to the same feeding area the following year, given suitable habitat features (e.g., food and temperature conditions), and follow a similar migration route as they do so. More studies are nonetheless needed to confirm this hypothesis, given few long-term deployments in the current study. For a deeper understanding of behavioural repeatability, and if/when shifts in the behaviour occur, it will be necessary to follow the same fish over multiple years. Such studies would also act as a highly valuable indicator of survival, independent of stock assessment-derived mortality estimates, and could be used to estimate the local abundance of larger ABFT43. Thus, a promising avenue for future research would be to deploy long-lasting ( > 5 to 10 years) acoustic tags and use existing infrastructure from networks such as the European Tracking Network to track these large fish over the next decade44. Given that ABFT appear to return to the area annually, we suggest that Skagerrak is a promising area for the future deployment and retrieval of PSATs and other long-lasting tags, because of the relatively easy access to locate and recover detached floating tags, given that the area is reachable from land within a few hours by boat. Retrieving PSATs that have detached from animals enables scientists to access full datasets (in the present case with 5 s resolution, rather than the much coarser and variable resolution typically transmitted). This much higher resolution enables much more detailed analysis, as shown in our analysis of spawning behaviour. Additionally, floating Pop-off Data Storage Tags (PDST) tags may also be a prominent and less costly avenue forward as the geographical region is densely populated and contains many sandy beaches and highly visited coastal areas, giving ample opportunity for tag recovery. Previous studies with floating DSTs in this area have shown remarkably high return rates45.The evidence that ABFT have returned to Nordic waters following many years of rarity or absence, and our findings that at least some individuals return to the same site for feeding in consecutive years, raises new questions about the mechanisms that underlie habitat discovery—or the return to previously used habitats—by highly migratory fish species. How individuals or entire schools have discovered this region again as a suitable feeding area after an absence of more than 50 years is unclear. In light of the positive stock development in the last 1–2 decades22 and modelling studies showing suitable habitat in the area46, density-dependent foraging and exploratory behaviour for new feeding areas may be a prominent hypothesis for their return, potentially accompanied by complex social learning interactions among individuals within the population47,48. New tagging data which documents the use of new or formerly occupied habitats will be essential for understanding these processes and how they might be affected by human pressures (e.g., exploitation, climate change). Such data can help to parameterize and validate advanced conceptual models of group movement behaviour, collective memory and habitat use49,50,51, as well as to inform modern stock assessment models used for management.
Tag deploymentFollowing recommendations from experienced taggers previously operating in the Mediterranean, most fish were tagged in the water alongside the boat. All these tags surfaced prematurely, while two (out of three) tags deployed on tunas brought on board the tagging boat surfaced after approximately one year. Depending on the conditions at sea, tagging along the side of the boat may not be as precise as on-board tagging, and the quality of the tag anchoring cannot be properly assessed. We therefore suggest that tagging on-board a boat is superior to tagging in the water alongside the boat for the deployment of long-lasting tags. This was also suggested in Ref.24. Furthermore, on-board tagging makes biological sampling fast and feasible, as opposed to tagging in the water alongside the boat. However, our advice is limited by a small sample size, making it difficult to draw formal conclusions; more studies are necessary to assess the best method to tag large ABFT. More

Cole, C. T., Anderson, J. E., Lindroth, R. L. & Waller, D. M. Rising concentrations of atmospheric CO2 have increased growth in natural stands of quaking aspen (Populus tremuloides). Glob. Change Biol. 16, 2186–2197 (2010).Article 

Google Scholar 
Urrutia-Jalabert, R. et al. Increased water use efficiency but contrasting tree growth patterns in Fitzroya cupressoides forests of southern Chile during recent decades. J. Geophys. Res. Biogeosci. 120, 2505–2524 (2015).Article 

Google Scholar 
Cienciala, E. et al. Increased spruce tree growth in Central Europe since 1960s. Sci. Total Environ. 619–620, 1637–1647 (2018).PubMed 
Article 

Google Scholar 
Mathias, J. M. & Thomas, R. B. Disentangling the effects of acidic air pollution, atmospheric CO2, and climate change on recent growth of red spruce trees in the Central Appalachian Mountains. Glob. Change Biol. 24, 3938–3953 (2018).Article 

Google Scholar 
Körner, C. et al. Carbon flux and growth in mature deciduous forest trees exposed to elevated CO2. Science 309, 1360–1362 (2005).PubMed 
Article 

Google Scholar 
Klein, T. et al. Growth and carbon relations of mature Picea abies trees under 5 years of free-air CO2 enrichment. J. Ecol. 104, 1720–1733 (2016).CAS 
Article 

Google Scholar 
Norby, R. J. & Zak, D. R. Ecological lessons from free-air CO2 enrichment (FACE) experiments. Annu. Rev. Ecol. Evol. Syst. 42, 181–203 (2011).Article 

Google Scholar 
Peñuelas, J., Canadell, J. G. & Ogaya, R. Increased water-use efficiency during the 20th century did not translate into enhanced tree growth. Glob. Ecol. Biogeogr. 20, 597–608 (2011).Article 

Google Scholar 
IPCC. Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).Dong, N. et al. Rising CO2 and warming reduce global canopy demand for nitrogen. New Phytol. https://doi.org/10.1111/nph.18076 (2022).Finzi, A. C., Allen, A. S., DeLucia, E. H., Ellsworth, D. S. & Schlesinger, W. H. Forest litter production, chemistry, and decomposition following two years of free-air CO2 enrichment. Ecology 82, 470–484 (2001).
Google Scholar 
Liberloo, M. et al. Elevated CO2 concentration, fertilization and their interaction: growth stimulation in a short-rotation poplar coppice (EUROFACE). Tree Physiol. 25, 179–189 (2005).PubMed 
Article 

Google Scholar 
Hungate, B. A. et al. Nitrogen cycling during seven years of atmospheric CO2 enrichment in a scrub oak woodland. Ecology 87, 26–40 (2006).PubMed 
Article 

Google Scholar 
Ainsworth, E. A. & Long, S. P. What have we learned from 15 years of free-air CO2 enrichment (FACE)? A meta-analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO2. New Phytol. 165, 351–372 (2005).PubMed 
Article 

Google Scholar 
Liberloo, M. et al. Coppicing shifts CO2 stimulation of poplar productivity to above-ground pools: a synthesis of leaf to stand level results from the POP/EUROFACE experiment. New Phytol. 182, 331–346 (2009).CAS 
PubMed 
Article 

Google Scholar 
McCarthy, H. R. et al. Re-assessment of plant carbon dynamics at the Duke free-air CO2 enrichment site: interactions of atmospheric [CO2] with nitrogen and water availability over stand development. New Phytol. 185, 514–528 (2010).CAS 
PubMed 
Article 

Google Scholar 
Dawes, M. A. et al. Species-specific tree growth responses to 9 years of CO2 enrichment at the alpine treeline. J. Ecol. 99, 383–394 (2011).
Google Scholar 
Luo, Y. Q. et al. Progressive nitrogen limitation of ecosystem responses to rising atmospheric carbon dioxide. Bioscience 54, 731–739 (2004).Article 

Google Scholar 
Keenan, T. F. et al. Increase in forest water-use efficiency as atmospheric carbon dioxide concentrations rise. Nature 499, 324–327 (2013).CAS 
PubMed 
Article 

Google Scholar 
Luo, Y. Q., Hui, D. F. & Zhang, D. Q. Elevated CO2 stimulates net accumulations of carbon and nitrogen in land ecosystems: a meta-analysis. Ecology 87, 53–63 (2006).PubMed 
Article 

Google Scholar 
Kikuzawa, K. A cost-benefit analysis of leaf habit and leaf longevity of trees and their geographical pattern. Am. Nat. 138, 1250–1263 (1991).Article 

Google Scholar 
Wright, I. J. et al. The worldwide leaf economics spectrum. Nature 428, 821–827 (2004).CAS 
PubMed 
Article 

Google Scholar 
Luo, T. X. et al. Summer solstice marks a seasonal shift in temperature sensitivity of stem growth and nitrogen-use efficiency in cold-limited forests. Agric. For. Meteorol. 248, 469–478 (2018).Article 

Google Scholar 
Rossi, S. et al. Conifers in cold environments synchronize maximum growth rate of tree-ring formation with day length. New Phytol. 170, 301–310 (2006).PubMed 
Article 

Google Scholar 
Bauerle, W. L. et al. Photoperiodic regulation of the seasonal pattern of photosynthetic capacity and the implications for carbon cycling. Proc. Natl Acad. Sci. USA 109, 8612–8617 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jarvis, P. & Linder, S. Constraints to growth of boreal forests. Nature 405, 904–905 (2000).CAS 
PubMed 
Article 

Google Scholar 
Sullivan, P. F., Ellison, S. B., McNown, R. W., Brownlee, A. H. & Sveinbjörnsson, B. Evidence of soil nutrient availability as the proximate constraint on growth of treeline trees in northwest Alaska. Ecology 96, 716–727 (2015).PubMed 
Article 

Google Scholar 
Dodd, A. N. et al. Plant circadian clocks increase photosynthesis, growth, survival, and competitive advantage. Science 309, 630–633 (2005).CAS 
PubMed 
Article 

Google Scholar 
Jackson, S. D. Plant responses to photoperiod. New Phytol. 181, 517–531 (2009).CAS 
PubMed 
Article 

Google Scholar 
Chapin III, F. S., Matson, P. A. & Mooney, H. A. Principles of Terrestrial Ecosystem Ecology (Springer-Verlag, 2002).Hikosaka, K. Leaf canopy as a dynamic system: ecophysiology and optimality in leaf turnover. Ann. Bot. 95, 521–533 (2005).CAS 
PubMed 
Article 

Google Scholar 
Jiang, M. K. et al. The fate of carbon in a mature forest under carbon dioxide enrichment. Nature 580, 227–231 (2020).CAS 
PubMed 
Article 

Google Scholar 
Guerrieri, R. et al. Disentangling the role of photosynthesis and stomatal conductance on rising forest water-use efficiency. Proc. Natl Acad. Sci. USA 116, 16909–16914 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mathias, J. M. & Thomas, R. B. Global tree intrinsic water use efficiency is enhanced by increased atmospheric CO2 and modulated by climate and plant functional types. Proc. Natl Acad. Sci. USA 118, e2014286118 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Finzi, A. C. et al. Increases in nitrogen uptake rather than nitrogen-use efficiency support higher rates of temperate forest productivity under elevated CO2. Proc. Natl Acad. Sci. USA 104, 14014–14019 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Soulé, P. T. & Knapp, P. A. Radial growth rate increases in naturally occurring ponderosa pine trees: a late-20th century CO2 fertilization effect? New Phytol. 171, 379–390 (2006).PubMed 
Article 

Google Scholar 
Linares, J. C. & Camarero, J. J. From pattern to process: linking intrinsic water-use efficiency to drought-induced forest decline. Glob. Change Biol. 18, 1000–1015 (2012).Article 

Google Scholar 
Kagawa, A., Sugimoto, A. & Maximov, T. C. 13CO2 pulse-labelling of photoassimilates reveals carbon allocation within and between tree rings. Plant Cell Environ. 29, 1571–1584 (2006).CAS 
PubMed 
Article 

Google Scholar 
Epron, D. et al. Pulse-labelling trees to study carbon allocation dynamics: a review of methods, current knowledge and future prospects. Tree Physiol. 32, 776–798 (2012).CAS 
PubMed 
Article 

Google Scholar 
Wiley, E. & Helliker, B. A re-evaluation of carbon storage in trees lends greater support for carbon limitation to growth. New Phytol. 195, 285–289 (2012).CAS 
PubMed 
Article 

Google Scholar 
Rocha, A. V., Goulden, M. L., Dunn, A. L. & Wofsy, S. C. On linking interannual tree ring variability with observations of whole-forest CO2 flux. Glob. Change Biol. 12, 1378–1389 (2006).Article 

Google Scholar 
Zweifel, R. et al. Link between continuous stem radius changes and net ecosystem productivity of a subalpine Norway spruce forest in the Swiss Alps. New Phytol. 187, 819–830 (2010).CAS 
PubMed 
Article 

Google Scholar 
Kong, G. Q., Luo, T. X., Liu, X. S., Zhang, L. & Liang, E. Y. Annual ring widths are good predictors of changes in net primary productivity of alpine Rhododendron shrubs in the Sergyemla Mountains, southeast Tibet. Plant Ecol. 213, 1843–1855 (2012).Article 

Google Scholar 
Teets, A. et al. Linking annual tree growth with eddy-flux measures of net ecosystem productivity across twenty years of observation in a mixed conifer forest. Agric. For. Meteorol. 249, 479–487 (2018).Article 

Google Scholar 
Luo, T. X., Li, M. C. & Luo, J. Seasonal variations in leaf δ13C and nitrogen associated with foliage turnover and carbon gain for a wet subalpine fir forest in the Gongga Mountains, eastern Tibetan Plateau. Ecol. Res. 26, 253–263 (2011).CAS 
Article 

Google Scholar 
Kobe, R. K., Lepczyk, C. A. & Iyer, M. Resorption efficiency decreases with increasing green leaf nutrients in a global data set. Ecology 86, 2780–2792 (2005).Article 

Google Scholar 
Vergutz, L., Manzoni, S., Porporato, A., Novais, R. F. & Jackson, R. B. Global resorption efficiencies and concentrations of carbon and nutrients in leaves of terrestrial plants. Ecol. Monogr. 82, 205–220 (2012).Article 

Google Scholar 
Holmes, R. L. Computer-assisted quality control in tree-ring dating and measurement. Tree-Ring Bull. 43, 69–78 (1983).
Google Scholar 
Cook, E. R. & Kairiukstis, L. A. Methods of Dendrochronology: Applications in the Environmental Sciences (Kluwer Academic Publishers, 1990).Editorial Board of Vegetation Map of China, Chinese Academy of Sciences. Vegetation Atlas of China (Science Press, 2001). More

Center for Microbial Communities, Department of Chemistry and Bioscience, Aalborg University, Aalborg, DenmarkMorten Kam Dahl Dueholm, Marta Nierychlo, Kasper Skytte Andersen, Vibeke Rudkjøbing, Simon Knutsson, Per H. Nielsen, Mads Albertsen & Per Halkjær NielsenEnvironmental Science Department, The Institute for Scientific and Technological Research of San Luis Potosi (IPICYT), San Luis Potosí, MexicoSonia ArriagaDepartment of Process, Energy and Environmental Technology, University College of Southeast Norway, Porsgrunn, NorwayRune BakkeCenter for Microbial Ecology and Technology, Ghent University, Ghent, BelgiumNico BoonInstitute for Water and Wastewater Technology, Durban University of Technology, Durban, South AfricaFaizal Bux & Sheena KumariVeolia Water Technologies AB, AnoxKaldnes, Lund, SwedenMagnus ChristenssonDepartment Of Chemical Engineering, Faculty of Engineering, University of Malaya, Kuala Lumpur, MalaysiaAdeline Seak May ChuaEnvironmental Engineering, Newcastle University, Newcastle, EnglandThomas P. CurtisThe Cytryn Lab, Microbial Agroecology, Volcani Center, Agricultural Research Organization, Rishon Lezion, IsraelEddie CytrynINGEBI-CONICET, University of Buenos Aires, Buenos Aires, ArgentinaLeonardo ErijmanDepartment of Biochemistry and Microbial Genetics, Biological Research Institute “Clemente Estable”, Montevideo, UruguayClaudia EtchebehereNIREAS-International Water Research Center, University of Cyprus, Nicosia, CyprusDespo Fatta-KassinosEnvironmental Engineering, McGill University, Montreal, QC, CanadaDominic FrigonSchool of Microbiology, Universidad de Antioquia, Medellín, ColombiaMaria Carolina Garcia-ChavesSchool of Civil and Environmental Engineering, Cornell University, Ithaca, NY, USAApril Z. GuWater Chemistry and Water Technology and DVGW Research Laboratories, Karlsruhe Institute of Technology (KIT), Karlsruhe, GermanyHarald HornDavid Jenkins & Associates, Inc, Kensington, CA, USADavid JenkinsInstitute for Water Quality and Resource Management, TU Wien, Vienna, AustriaNorbert KreuzingerWater Innovation and Research Centre, University of Bath, Bath, EnglandAna LanhamSingapore Centre of Environmental Life Sciences Engineering (SCELSE) Nanyang Technological University, Singapore, SingaporeYingyu LawWater Desalination and Reuse Center, King Abdullah University of Science and Technology (KAUST), Thuwal, Saudi ArabiaTorOve LeiknesProcess Engineering in Urban Water Management, ETH Zürich, Zürich, SwitzerlandEberhard MorgenrothDepartment of Biology, Warsaw University of Technology, Warsaw, PolandAdam MuszyńskiEnvironmental Microbial Genetics Lab, La Trobe University, Melbourne, VIC, AustraliaSteve PetrovskiTechnologies and Evaluation Area, Catalan Institute for Water Research, ICRA, Girona, SpainMaite PijuanVA Tech Wabag Ltd, Chennai, IndiaSuraj Babu PillaiBiochemical Engineering Group, Universidade Nova de Lisboa, Lisboa, PortugalMaria A. M. ReisState Key Laboratory of Environmental Aquatic Chemistry, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, ChinaQi RongWater Research Institute IRSA – National Research Council (CNR), Rome, ItalySimona RossettiLa Trobe University, Melbourne, VIC, AustraliaRobert SeviourDepartment of Civil and Environmental Engineering, University of Massachusetts Amherst, Amherst, MA, USANick TookerKemira Oyj, Espoo R&D Center, Espo, FinlandPirjo VainioEnvironmental Biotechnology, TU Delft, Delft, The NetherlandsMark van LoosdrechtVA Tech Wabag, Philippines Inc., Makati City, PhilippinesR. VikramanDepartment of Water Technology and Environmental Engineering, University of Chemistry and Technology, Prague, Czech RepublicJiří WannerEnvironmental Life Science Engineering, TU Delft, Delft, The NetherlandsDavid WeissbrodtSchool of Environment, Tsinghua University, Beijing, ChinaXianghua WenEnvironmental Biotechnology Lab, Department of Civil Engineering, The University of Hong Kong, Hong Kong, Hong KongTong Zhang More

The Sargasso Sea has long been considered as the spawning area for Atlantic eels, despite the absence of direct observations after more than a hundred years of the survey. We proposed a new insight on the location of Atlantic eels spawning areas eastward of the Sargasso Sea at the intersection between the Mid-Atlantic Ridge and the oceanic fronts1. Our hypothesis is based on a body of corroborating cues from literature. We suggested that European silver eels converge towards the Azores whatever their departure point from Europe and Northern Africa, then they follow the Mid-Atlantic Ridge south westerly until they reach oceanographic fronts where temperature and depths are favourable for reproduction. These orientation behaviours are potentially based on magnetic fields and odours that might be generated by the Mid-Atlantic Ridge volcanic activity and detected by eels during their diel vertical movements. The first favourable meeting point is then located at the crossing between the Mid-Atlantic Ridge and the oceanic thermic isotherms located around 45° W and 26° N. Our hypothesis is supported by (i) microchemical differences between the core of otoliths extracted from leptocephali collected in the Sargasso Sea and from glass eels collected across Europe suggesting that glass eels hatch in different chemical environments than leptocephali (ii) an asymmetric genetic introgression between American and European eels2 suggesting that the overlapping spawning areas favour transport of hybrids towards northern Europe rather than to America and to southern Europe. This supports the possible existence of several distinct spawning areas, where currents favour transport either westward (American eel), north eastward (hybrids and European eels) or eastward (European eels). To test this hypothesis, we developed a transport model and compared the dispersion dynamics of virtual leptocephali released from the Sargasso Sea and from above the Mid-Atlantic Ridge. The transport models showed that virtual eels released from the Mid-Atlantic Ridge reached Europe and America following similar patterns than those released from the Sargasso Sea thus supporting the Mid-Atlantic Ridge spawning hypothesis.Hanel et al.3 have raised several concerns, one of which being that “microchemical evidence was the only was the major argument supporting the Mid-Atlantic Ridge hypothesis”. This was their start point of a critical rebuttal of our findings to question our hypothesis. Instead, we consider that our regrettable error does not fundamentally contradict the possibility that eels do indeed successfully spawn outside the so-called Sargasso Sea.(Comment 1) The importance of seamounts as orientation and navigation cues towards a spawning area was hypothesized, no clear mechanism is proposed for how the migrating eels can detect the ridge.(Response 1) Our Hypothesis does not state that eels find a kind of shallow seamount where they spawn. Instead, we propose that orientation of silver eels during their spawning migration could be based on a combination of behavioural mechanisms including geomagnetism, odours, temperature and salinity gradients4,5,6,7,8. These environmental cues and related gradients are strongly controlled or influenced by the topography of the oceanic floor. The Mid-Atlantic Ridge and the Mariana areas have similarities with ridges and seamount chains oriented perpendicularly to temperature and salinity fronts surrounded by deep abyssal plains. Our Mid-Atlantic Ridge hypothesis proposed that Atlantic eels could use similar signposts as Japanese eel, which hatch near the Mariana Ridge9. Indeed, as for the Japanese eels, the orientation mechanism that lead Atlantic eels from the growth areas to the ridge are not understood, but the empirical observations from Righton et al.10 suggest that eels converge towards the Azores whatever their release point across Europe and that their diel vertical migration takes them down to 500–1000 m every day. The reasons for this behaviour are not elucidated, but since they cost energy, they are likely compensated by advantages such as orientation together with predator avoidance and sexual maturation11,12,13. Following our hypothesis, eels search for orientation cues during DVM. The geomagnetic fields are suggested to provide detectable information for silver eels on their oceanic spawning migration14. However, whether magnetic characteristics of the Mid-Atlantic ridge may provide detectable orientation cues still needs to be documented. Similarly, the existence of detectable odours that might be generated by the tectonic activity and hydrodynamics of the Mid-Atlantic ridge and serve as orientation cues for eels is still unknown. Hydrodynamic mesoscale turbulence and vertical flows have been shown to be generated along the Mid-Atlantic Ridge15, which we propose eels might be able to detect. There are no well supported spawning areas of freshwater eels other than A. japonica and one north Pacific population of A. marmorata. The spawning areas of the other species remain unknown. In the south west Indian Ocean, spawning areas of 3 species (A. mossambica, A. marmorata and A. bicolor) were proposed on the east of the Mascarene Ridge with a similar topography (although shallower) than along the Mid-Atlantic Ridge and the Mid-Pacific ridge and seamounts16,17. Inaccurate spawning areas were also proposed for the South Pacific A. diffenbachii between Fiji, New Caledonia and New Zealand; in the vicinity of a number of oceanic ridges and trenches18 that may also serve as landmarks. Because all eel species studied on their spawning migration show similar diel vertical migration behaviours, it is likely that common orientation mechanisms could lead to detection of oceanographic variability related to the topography of the sea floor and related geomagnetism, local hydrodynamic turbulence and odour caused by vertical currents. This kind of oceanic landscape (chains of seamounts) occurs on narrow areas which strongly increase the meeting probability of spawners searching for partners and favourable spawning places.(Comment 2) Drift simulation with departures from the Mid-Atlantic Ridge and from the Sargasso Sea showed similar results. This is not surprising since the modelling of larval drift seems essentially just to reflect the slow westward drift prevailing both in the Sargasso Sea and Mid-Atlantic Ridge areas. The assumption of using the intersection of the Mid-Atlantic Ridge by the two thermal fronts as presumed spawning places seems to have little basis. There is no indication neither of one nor two temperature fronts at depths where leptocephali are found along a 45  W latitudinal section in the middle of the Mid-Atlantic Ridge area.(Response 2) We agree with the comments that the similar distributions between the departure from the Sargasso Sea and the Mid-Atlantic Ridge are expected, as they mainly reflect the ocean circulation. This is also what we wanted to address, if different departures could lead to similar distributions, either Sargasso Sea or Mid-Atlantic Ridge could be candidates for the spawning area. We also agree that many eel larvae were collected at the two fronts in the Sargasso Sea, but not near the Mid-Atlantic Ridge. However, if the departure from the Sargasso Sea and the Mid-Atlantic Ridge led to similar distributions after 720 days, they were not the result of westward current, but the cause of a relatively quiet ocean in the Sargasso Sea and its surrounding area (i.e. Fig. 1). Without prevailing current, small larvae were mainly transported by ocean dispersion, and would later be transported by the major currents that lie in the north (Azores Current), south (North Equatorial Current), and west (Gulf Stream) of the Sargasso Sea. So, we compared departures at 100 km from west and east of the Mid-Atlantic Ridge. Subtle differences occurred (figure below). V-larvae departing from the east of the ridge dispersed relatively less northward compared to larvae released 100 km at the west of the ridge (this figure and original paper). Secondly v-larvae released at the south east of the study area (red dots on the figure, right panel) disperse relatively less towards the Caribbean Sea than when released at the west (red dots of the figure, left panel). This suggests that the dispersion of European eel larvae is optimum in an area comprised between the Mid-Atlantic Ridge and the Sargasso Sea (our previous simulation in the original paper), and declines eastward of the Mid-Atlantic Ridge (present simulation below).Figure 1Distribution of v-larvae released departure at the west (left) and east (right) of Mid-Atlantic Ridge. The tracking method is the same as described in the paper, v-larvae were release within 100 km west and east of the ridge.Full size imageHanel et al. also indicate that the convergence front weakens from West, in the Sargasso Sea, to East above the ridge. We consider that this constitutes an additional argument that the Mid-Atlantic Ridge is indeed at the edge of the convergence zone at the first area of the Atlantic Ocean where currents and temperatures are favourable for reproduction of eels.(Comment 3) Elevated manganese (Mn) concentrations in the otolith cores of glass eels as a hint for successful spawning only in areas with volcanic activity based on observations of Martin et al.18. However, the results from Martin et al.19 were entirely misread, resulting in a mis-interpretation of the data.(Response 3) Based on Martin et al.19, we stated that higher concentrations of Mn were found in glass eels’ otoliths collected across European estuaries than in otoliths of leptocephali larvae sampled in the Sargasso Sea. We suggested that this was the indication that glass eels were born in areas where volcanic activity produces high loads of Mn and other metals. This formed one of the arguments supporting our hypothesis that Atlantic eels could spawn in the proximity to the Mid-Atlantic Ridge. Thanks to Reinhold Hanel and colleagues, we realized that Martin et al.19 in fact showed that concentrations of Mn were higher in the center of otoliths of leptocephali larvae than in those of glass eels collected along the European coasts. Consequently, this argument is no longer valid. Nonetheless, otolith microchemical fingerprints significantly differ between young leptocephali sampled in the Sargasso Sea in 2008 and glass eels collected in Europe, hence suggesting that they have distinct spawning areas19. These authors indicated that the incorporation of elements from the environment to the otoliths needed to be better understood, namely as stated by Hanel et al., because of physiological and environmental control such as temperature and salinity. In addition, they outline that the dynamics of elements from the sea floor to the subsurface is not well understood and could be slow. We totally share these conclusions that are well known facts, and that simply confirm that environmental characteristics (trace element concentrations, salinity and temperature) are responsible for the elemental signature of the central part of otoliths. Hanel et al. also state that the composition of otoliths are also controlled by elemental maternal transfer from the egg to the otoliths. We are aware of this fact that has been shown is other fish species. However, the laser ablations were performed after the first feed check where maternal influence is reduced and is overruled by environment18. This supports the idea that glass eels collected in Europe do not originate from the same environments as leptocephali captured in the Sargasso Sea.(Comment 4) Insufficient sampling efforts and a limited area coverage of recent surveys as a possible reason for “false negative” observations along the Mid-Atlantic Ridge. This statement does not recognize the investigations by Johannes Schmidt as well as earlier and later surveys in the Mid-Atlantic Ridge area. The ICES “Eggs and Larvae database” records a total of 48 A anguilla leptocephali caught within the area 15–29 N and 43–48 W, at 10 stations between 1913 and 1970.Thanks for pointing out that larvae have been caught near the Mid-Atlantic Ridge, in which larvae were not newly hatched because of their relatively large size (23–45 mm). Ocean currents were weak and could flow either eastward or westward in this region, indicating that the spawning could occur from west to east of the ridge, without considering swimming. Note that ocean currents could change directions, so that it was also possible to spawn near the ridge after been transported eastward and westward.The observed distribution of small larvae  More

Study population and song recordingsAll animal procedures were carefully reviewed by the Williams College IACUC (WH-D), the Bowdoin College Research and Oversight Committee (2009–18), and the University of Guelph Animal Care Committee (08R601), and were carried out as specified by the Canadian Wildlife Service (banding permit 10789D).We studied Savannah sparrows (Passerculus sandwichensis) at the Bowdoin Scientific Station on Kent Island, New Brunswick, Canada (44.5818°N, 66.7547°W). Since 1988, individuals nesting within a 10 ha study area in the middle of the island (30–70 pairs each year; part of a larger population of 350–500 males breeding on Kent Island and two adjacent islands) have been colour-banded to facilitate visual identification, and complete demographic information is available for birds on the study site (though not for the entire population) for the years 1989–2004 and 2009–2013. Because of strong natal and breeding philopatry51, birds hatched on the study site itself represent 40–80% of adult breeders in that area, and because of the systematic banding program, ages are known. Each year adds a new generation to the population, with yearlings making up approximately half of the adult breeding males. The birds banded and recorded on the study site are estimated to make up 10–20% of the Savannah sparrow population on Kent Island and two nearby islands.Details of the recording methods used in this study (covering the years 1980, 1982, 1988-9, 1993-8, and 2003–13) can be found elsewhere36,49. Using digitally generated sound spectrograms (using SoundEdit Pro and Audacity), birds were scored as having either a) high note cluster=a final introductory segment interval including at least two different note types, or b) a click train=one or more introductory segment intervals including at least two clicks and no other note types, or c) both features36 (see Supplementary Fig. 1 for a full description of note types). Although a small proportion of birds (mean = 8.3%) did not include either feature in their songs (such birds either had no feature in the introductory segment intervals or one non-click note type in the final interval), we did not include this option in the model and omitted these birds from summaries of the data. We did not include data after the breeding year 2013 because of we began an experimental field tutoring study in the summer of 201364.ModellingWe used a dynamic, discrete time model which allowed us to focus our analysis to specific time points within the year that are related to song learning (the beginning and end of the breeding season). These were: (1) the return of older birds between breeding seasons, (2) the recruitment of young birds singing newly crystallized songs in the spring, and (3) reproduction, resulting in the addition of juveniles during the summer breeding season.Because survival data were not available for every year during the time span we studied, we captured the variation in survival rates observed in the field57 by using a binomial distribution centered on the average historical survival rate for each age class (addressing the possibility that cultural drift resulting from random differences in survival rates was responsible for the shift in song features). The model incorporates stochasticity to capture the variation in population dynamics and return rates by assigning parameter values for survival and return rates from empirically generated probability distributions.We did not include spatial distribution of song variants in the model; although spatial patterns can be important for the dynamics of language loss58, territories with birds singing click trains and high note clusters were intermixed and no spatial structure was apparent (Fig. 3).The model assumes that males choose which features to incorporate into the introductory sections of their songs during song development. Individuals fall into one of six mutually exclusive classes of male Savannah sparrows. The classes are defined by (1) the bird’s developmental stage in the song learning process: juvenile (J, the first year, when the song is plastic) or adult (A, after the first spring, when the song is crystallized), and (2) the variant or variants sung as part of the bird’s introduction (high note clusters, click trains, or both). Denoting note high note clusters with X and click trains with C, the adult classes are therefore AX, AC, and AXC, and the juvenile classes are JX, JC, and JXC. The sum of the individuals in these classes is the total male population.We used two times during each year – late spring and late summer – to correspond to stages in song development (Fig. 5). At a given time t, when breeding is underway in the late spring, the male population consists entirely of adults singing crystallized song, and therefore each juvenile class is empty. At the end of the summer, the population of males has been augmented by juveniles, which are initially assigned to the same variant class as their fathers. To capture these dynamics, we define an intermediate time step, denoted ti. Time t + 1 then corresponds to the following breeding season (late spring), when juvenile males hatched the previous year have completed song development, crystallized their songs, and joined the adult class.Fig. 5: Model of song development.We used two age classes (J = juvenile and A = adult) and three classes of introductions (C = click trains, X = high note clusters, and  XC = both). In the late spring of a given year (time = t), only adult males are present. In late summer, those adults have bred and both they and juvenile males are present; at this intermediate time (ti) each male is initially allocated the same introduction type as his father (solid lines). Then, as song development progresses and juvenile males can be influenced by other tutors, they may retain their initial introduction type or switch to either of the other two types (dashed lines) before they crystallize their songs late in the following spring (time = t+1), and join the breeding cohort, which also includes adult males from the previous year who returned to breed again.Full size imageIn the late summer the male population increases with the addition of juveniles hatched that year, some of which will return to join the singing population the following year; survivors will return to breed within a few hundred meters of where they hatched51. To fit the observed historical decline in the Kent Island population57, the total number of returning juveniles, r (including both those hatched on site and those immigrating from nearby populations at time), follows a Poisson distribution where m = 33.6 – .182x and x is the number of years since 1980 (this function results in a decline of 5 males per decade; the initial number on the study site used in the model, 70, was extrapolated from historical data). The size of each returning juvenile class at time ti then takes the form:$${{{{{{rm{JY}}}}}}}_{{{{{{{rm{t}}}}}}}^{{{{{{rm{i}}}}}}}} sim {{{{{rm{Poisson}}}}}}left(mright)frac{{{{{{rm{A}}}}}}{{{{{{rm{Y}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}}}{{{{{{rm{A}}}}}}{{{{{{rm{X}}}}}}}_{{{{{{rm{t}}}}}}}+{{{{{rm{A}}}}}}{{{{{{rm{C}}}}}}}_{{{{{{rm{t}}}}}}}+{{{{{rm{AX}}}}}}{{{{{{rm{C}}}}}}}_{{{{{{rm{t}}}}}}}}$$
(1)
for each Y ∈ {X, C, XC}.After the following winter, the proportion of surviving adults at time t + 1 follows a binomial distribution where the mean survival rate s = 0.48 is derived from historical data. Therefore, each adult class takes the form:$${{{{{rm{A}}}}}}{{{{{{rm{Y}}}}}}}_{{{{{{rm{t}}}}}}+1} sim {{{{{rm{Binomial}}}}}}left({{{{{rm{AY}}}}}},{{{{{rm{s}}}}}}right)* {{{{{rm{A}}}}}}{{{{{{rm{Y}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}}$$
(2)
At the beginning of the next breeding season, juveniles complete song learning64, choosing which variant to crystallize as part of the song, and enter an adult song class; thus all of the juvenile classes disappear at t + 1. Which adult class juveniles join depends on separate learning functions for each of the two variants, ϕX for the high note cluster and ϕC for the click train. The ϕ function takes values between 0 and 1 and gives the probability of crystallizing a song form during the transition from natal year to breeding, depending upon the frequency-dependent bias and selection parameters (see below). These functions define the proportion of features that appear in the next generation as compared to that of the previous generation. Therefore we have:$${{{{{rm{A}}}}}}{{{{{{rm{X}}}}}}}_{{{{{{rm{t}}}}}}+1}={left({{{upphi }}}_{{{{{{rm{X}}}}}}}right)}^{2}{{{{{rm{J}}}}}}{{{{{{rm{X}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}}+{left(1-{{{upphi }}}_{{{{{{rm{C}}}}}}}right)}^{2}{{{{{rm{J}}}}}}{{{{{{rm{C}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}}+{{{upphi }}}_{{{{{{rm{X}}}}}}}left(1-{{{upphi }}}_{{{{{{rm{C}}}}}}}right){{{{{rm{JX}}}}}}{{{{{{rm{C}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}}+{{{{{rm{A}}}}}}{{{{{{rm{X}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}}$$
(3)
$${{{{{rm{A}}}}}}{{{{{{rm{C}}}}}}}_{{{{{{rm{t}}}}}}+1}={left(1-{{{upphi }}}_{{{{{{rm{X}}}}}}}right)}^{2}{{{{{rm{J}}}}}}{{{{{{rm{X}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}}+{left({{{upphi }}}_{{{{{{rm{C}}}}}}}right)}^{2}{{{{{rm{J}}}}}}{{{{{{rm{C}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}}+left(1-{{{upphi }}}_{{{{{{rm{X}}}}}}}right){{{upphi }}}_{{{{{{rm{C}}}}}}}{{{{{rm{JX}}}}}}{{{{{{rm{C}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}}+{{{{{rm{A}}}}}}{{{{{{rm{C}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}}$$
(4)
$${{{{{rm{A}}}}}}{{{{{{rm{XC}}}}}}}_{{{{{{rm{t}}}}}}+1}=2{{{upphi }}}_{{{{{{rm{X}}}}}}}left(1-{{{upphi }}}_{{{{{{rm{X}}}}}}}right){{{{{rm{J}}}}}}{{{{{{rm{X}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}}+2{{{upphi }}}_{{{{{{rm{C}}}}}}}left(1-{{{upphi }}}_{{{{{{rm{C}}}}}}}right){{{{{rm{J}}}}}}{{{{{{rm{C}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}}+({{{upphi }}}_{{{{{{rm{X}}}}}}}{{{upphi }}}_{{{{{{rm{C}}}}}}}left(1-{{{upphi }}}_{{{{{{rm{X}}}}}}}right)left(1-{{{upphi }}}_{{{{{{rm{C}}}}}}}right){{{{{rm{JX}}}}}}{{{{{{rm{C}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}})+{{{{{rm{A}}}}}}{{{{{{rm{XC}}}}}}}_{{{{{{{rm{t}}}}}}}_{{{{{{rm{i}}}}}}}}$$
(5)
The sum of probabilities defining all of song crystallization outcomes for the songs of fathers with song type X is:$${left({{{upphi }}}_{{{{{{rm{X}}}}}}}right)}^{2}+{left(1-{{{upphi }}}_{{{{{{rm{X}}}}}}}right)}^{2}+2{{{upphi }}}_{{{{{{rm{X}}}}}}}left(1-{{{upphi }}}_{{{{{{rm{X}}}}}}}right)=1$$
(6)
Learning curvesTo define how young males’ song learning is influenced by the songs they hear, we used learning curves based on type III Holling response curves59 which provide a means to numerically capture functional responses. In our model, the type III curve models the response of juvenile to the song form of adults in the population based on two variables: (1) frequency-dependent bias that favors one form based on its prevalence within the adult population, and (2) selection that favors a particular form of the song.The learning curves, ϕx for the high note cluster and ϕc for the click train, are modified forms of the type III Holling response curve):$${{{upphi }}}_{{{{{{rm{x}}}}}}}=frac{{x}^{{{{{{rm{beta }}}}}}}/{{{{{rm{sigma }}}}}}}{{(1-x)}^{{{{{{rm{beta }}}}}}}+({x}^{{{{{{rm{beta }}}}}}}/{{{{{rm{sigma }}}}}})}$$
(7)
and$${{{upphi }}}_{{{{{{rm{c}}}}}}}=frac{{{{{{rm{sigma }}}}}},{c}^{{{{{{rm{beta }}}}}}}}{{(1-c)}^{{{{{{rm{beta }}}}}}}+{{{{{rm{sigma }}}}}}{{c}}^{{{{{{rm{beta }}}}}}}}$$
(8)
where x is the proportion of the high note cluster within the population, c is the proportion of the click train within the population, β is frequency-dependent bias (favoring learning the novel or retaining the common variant), and σ is selection on the novel variant (a preference for learning the variant that is not dependent on frequency of the variant and includes factors such as prestige bias, success bias, status, and content bias). Note that the two learning curves do not have identical equations, because selection is not frequency-dependent. In these equations, β  > 1 corresponds to conformist selection, and when β  1 correspond to selection for a novel variant and values of σ  More

Fossil remains from the Family Bovidae, such as antelopes and buffalo, are frequently used to reconstruct past environments1,2,3. Bovids reflect distinct ecological adaptations in terms of diet, habitat, water dependence, and seasonal migrations that vary according to their respective ecological niches. Widespread cooling in the late Miocene led to a major adaptive radiation of the bovids, and increasingly they began to exploit more open environments4,5,6. Thus, by approximately 4 Ma, bovids came to dominate the African fauna, replacing the previously abundant suids7,8,9. The current distribution of bovids extends across the African continent in myriad environments that differ significantly in proportions of wood and grass cover.The importance of bovid remains to paleoanthropological research was established initially by Broom10,11 and Wells and Cooke12. This dependence has been expanded and now ranges from paleodietary studies and evolutionary trends to hominin behavioral patterns13,14,15. In addition, several studies have demonstrated that changes in the relative abundance of bovid taxa reflected in fossil assemblages are indicative of fluctuations in environmental conditions, as bovids appear to be particularly responsive to environmental changes16,17,18.Bovid teeth, in particular isolated teeth, make up a majority of the southern African fossil record. Thus, bovid teeth, coupled with their ecological tendencies, are important sources of information for reconstructing the paleoenvironments associated with the fossil hominins. Taxonomic identification of fossil bovid teeth, however, is often problematic; biasing factors such as age and degree of wear complicate identifications and often result in considerable overlap in the shape and size of teeth. Traditionally, researchers rely upon modern and fossil comparative collections to identify isolated bovid teeth. However, researchers are somewhat limited by travel and the specific type and number of bovids housed at each institution. Here, we present B.O.V.I.D. (Bovidae Occlusal Visual IDentification) which is a repository of images of the occlusal surface of bovid teeth (~3900). The purpose of the database is to allow researchers to visualize a large sample of teeth from different tribes, genera, and species. The sample includes the three upper and three lower molars in multiple states of wear from the seven most common tribes in the southern African fossil record and the twenty most common species from those tribes. This design will help researchers see the natural variation that exists within a specific tooth type of a taxon and, with the current sample, help taxonomically identify extant and fossil teeth with modern counterparts. More

Bailey, K. M. & Houde, E. D. Predation on eggs and larvae of marine fishes and the recruitment problem. Adv. Mar. Biol. 25, 1–83. https://doi.org/10.1016/S0065-2881(08)60187-X (1989).Article 

Google Scholar 
Houde, E. D. Fish early life dynamics and recruitment variability. Am. Fish. Soc. Symp. 2, 17–29 (1987).ADS 

Google Scholar 
Anderson, J. T. A review of size dependent survival during pre-recruit stages of fishes in relation to recruitment. J. Northw. Atl. Fish. Sci. 8, 55–66. https://doi.org/10.2960/J.v8.a6 (1988).Article 

Google Scholar 
McCarthy, I. D. Temporal repeatability of relative standard metabolic rate in juvenile Atlantic salmon and its relation to life history variation. J. Fish Biol. 57, 224–238. https://doi.org/10.1111/j.1095-8649.2000.tb00788.x (2000).Article 

Google Scholar 
Biro, P. A. & Stamps, J. A. Do consistent individual differences in metabolic rate promote consistent individual differences in behavior?. Trends Ecol. Evol. 25, 653–659. https://doi.org/10.1016/j.tree.2010.08.003,Pubmed:20832898 (2010).Article 
PubMed 

Google Scholar 
Endler, J. A. Natural Selection in the Wild (Princeton Univ. Pr., 1986).Meekan, M. G. & Fortier, L. Selection for fast growth during the larval life of Atlantic cod Gadus morhua on the Scotian Shelf. Mar. Ecol. Prog. Ser. 137, 25–37. https://doi.org/10.3354/meps137025 (1996).ADS 
Article 

Google Scholar 
Gilly, W. F. et al. Vertical and horizontal migrations by the jumbo squid Dosidicus gigas revealed by electronic tagging. Mar. Ecol. Prog. Ser. 326, 1–17 (2006).ADS 
Article 

Google Scholar 
Watanabe, H., Kubodera, T., Moku, M. & Kawaguchi, K. Diel vertical migration of squid in the warm core ring and cold water masses in the transition region of the western North Pacific. Mar. Ecol. Prog. Ser. 315, 187–197. https://doi.org/10.3354/meps315187 (2006).ADS 
Article 

Google Scholar 
Phillips, K. L., Jackson, G. D. & Nichols, P. D. Predation on myctophids by the squid Moroteuthis ingens around Macquarie and Heard Islands: stomach contents and fatty acid analyses. Mar. Ecol. Prog. Ser. 215, 179–189. https://doi.org/10.3354/meps215179 (2001).ADS 
CAS 
Article 

Google Scholar 
Field, J. C., Baltz, K., Phillips, A. J. & Walker, W. A. Range expansion and trophic interactions of the jumbo squid, Dosidicus gigas, in the California Current. CalCOFI Rep. 48, 131–146 (2007).
Google Scholar 
Ellis, T. & Gibson, R. N. Size-selective predation of 0-group flatfishes on a Scottish coastal nursery ground. Mar. Ecol. Prog. Ser. 127, 27–37. https://doi.org/10.3354/meps127027 (1995).ADS 
Article 

Google Scholar 
Takasuka, A., Aoki, I. & Oozeki, Y. Predator-specific growth-selective predation on larval Japanese anchovy Engraulis japonicus. Mar. Ecol. Prog. Ser. 350, 99–107. https://doi.org/10.3354/meps07158 (2007).ADS 
Article 

Google Scholar 
Tucker, S., Hipfner, J. M. & Trudel, M. Size- and condition-dependent predation: A seabird disproportionately targets substandard individual juvenile salmon. Ecology 97, 461–471. https://doi.org/10.1890/15-0564.1,Pubmed:27145620 (2016).Article 
PubMed 

Google Scholar 
Rodhouse, P. G. & Nigmatullin, C. M. Role as consumers. Phil. Trans. R. Soc. Lond. B 351, 1003–1022. https://doi.org/10.1098/rstb.1996.0090 (1996).ADS 
Article 

Google Scholar 
Hunsicker, M. E. & Essington, T. E. Size-structured patterns of piscivory of the longfin inshore squid (Loligo pealeii) in the mid-Atlantic continental shelf ecosystem. Can. J. Fish. Aquat. Sci. 63, 754–765. https://doi.org/10.1139/f05-258 (2006).Article 

Google Scholar 
Hunsicker, M. E. & Essington, T. E. Evaluating the potential for trophodynamic control of fish by the longfin inshore squid (Loligo pealeii) in the northwest Atlantic Ocean. Can. J. Fish. Aquat. Sci. 65, 2524–2535. https://doi.org/10.1139/F08-154 (2008).Article 

Google Scholar 
Wang, K. Y., Liao, C. H. & Lee, K. T. Population and maturation dynamics of the swordtip squid (Photololigo edulis) in the southern East China Sea. Fish. Res. 90, 178–186. https://doi.org/10.1016/j.fishres.2007.10.015 (2008).Article 

Google Scholar 
Sassa, C., Yamamoto, K., Tsukamoto, Y., Konishi, Y. & Tokimura, M. Distribution and migration of age-0 jack mackerel (Trachurus japonicus) in the East China and Yellow Seas, based on seasonal bottom trawl surveys. Fish. Oceanogr. 18, 255–267. https://doi.org/10.1111/j.1365-2419.2009.00510.x (2009).Article 

Google Scholar 
Tokai, T., Shiode, D., Sakai, T. & Yoda, M. Codend selectivity in the East China Sea of a trawl net with the legal minimum mesh size. Fish. Sci. 85, 19–32. https://doi.org/10.1007/s12562-018-1270-x (2019).CAS 
Article 

Google Scholar 
Sassa, C. & Konishi, Y. Vertical distribution of jack mackerel Trachurus japonicus larvae in the southern part of the East China Sea. Fish. Sci. 72, 612–619. https://doi.org/10.1111/j.1444-2906.2006.01191.x (2006).CAS 
Article 

Google Scholar 
Takahashi, M., Sassa, C. & Tsukamoto, Y. Growth-selective survival of young jack mackerel Trachurus japonicus during transition from pelagic to demersal habitats in the East China Sea. Mar. Biol. 159, 2675–2685. https://doi.org/10.1007/s00227-012-2025-3 (2012).Article 

Google Scholar 
Ishida, K. Feeding ecology of swordtip squid (Loligo edulis). Rep. Shimane Pref. Fish. Exp. Stan. 3, 31–35 (1981) (in Japanese).
Google Scholar 
Tashiro, M., Tokunaga, T., Machida, S. & Takata, J. Distribution of a squidfish, Loliogo edulis HOYLE, in the East China Sea. Bull. Nagasaki Pref. Inst. Fish. 7, 21–30 (1981) (in Japanese).
Google Scholar 
Jennings, S. & Warr, K. J. Smaller predator-prey body size ratios in longer food chains. Proc. Biol. Sci. 270, 1413–1417. https://doi.org/10.1098/rspb.2003.2392 (2003).Article 
PubMed 
PubMed Central 

Google Scholar 
Barnes, C., Maxwell, D., Reuman, D. C. & Jennings, S. Global patterns in predator-prey size relationships reveal size dependency of trophic transfer efficiency. Ecology 91, 222–232. https://doi.org/10.1890/08-2061.1 (2010).Article 
PubMed 

Google Scholar 
Cabana, G. & Rasmussen, J. B. Modelling food chain structure and contaminant bioaccumulation using stable nitrogen isotopes. Nature 372, 255–257. https://doi.org/10.1038/372255a0 (1994).ADS 
CAS 
Article 

Google Scholar 
Castilla, A. C., Hernández-Urcera, J., Gouranguine, A., Guerra, Á. & Cabanellas-Reboredo, M. Predation behaviour of the European squid Loligo vulgaris. J. Ethol. 38, 311–322. https://doi.org/10.1007/s10164-020-00652-4 (2020).Article 

Google Scholar 
Fiorito, G. et al. Guidelines for the Care and Welfare of Cephalopods in Research–A consensus based on an initiative by CephRes, FELASA and the Boyd Group. Lab. Anim. 49, 1–90. https://doi.org/10.1177/0023677215580006la.sagepub.com (2015).Article 
PubMed 

Google Scholar 
Percie du Sert, N. et al. Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0. PLoS Biol. 18, e3000411 (2020). https://doi.org/10.1371/journal.pbio.3000411Campana, S. E. How reliable are growth back-calculations based on otoliths?. Can. J. Fish. Aquat. Sci. 47, 2219–2227. https://doi.org/10.1139/f90-246 (1990).Article 

Google Scholar 
Xie, S. et al. Growth and morphological development of sagittal otoliths of larval and early juvenile Trachurus japonicus. J. Fish Biol. 66, 1704–1719. https://doi.org/10.1111/j.0022-1112.2005.00717.x (2005).Article 

Google Scholar 
Yasui, T. & Sakurai, Y. Gastric evacuation rate of Todarodes pacificus. Rep. Annu. Meet. Squid Res 32, 55–57 (2005) (in Japanese).
Google Scholar 
Šifner, S. K. & Vrgoč, N. Population structure, maturation and reproduction of the European squid, Loligo vulgaris, in the Central Adriatic Sea. Fish. Res. 69, 239–249. https://doi.org/10.1016/j.fishres.2004.04.011 (2004).Article 

Google Scholar 
Kono, N., Tsukamoto, Y. & Zenitani, H. RNA:DNA ratio for diagnosis of the nutritional condition of Japanese anchovy larvae Engraulis japonicus during the first-feeding stage. Fish. Sci. 69, 1096–1102. https://doi.org/10.1111/j.0919-9268.2003.00733.x (2003).CAS 
Article 

Google Scholar 
Booman, C., Folkvord, A. & Hunter, J. R. Responsiveness of starved northern anchovy Engraulis mordax larvae to predation attacks by adult anchovy. Fish. Bull. 89, 707–711 (1991).
Google Scholar 
Chick, J. H. & Van Den Avyle, M. J. Effects of feeding ration on larval swimming speed and responsiveness to predator attacks: Implications for cohort survival. Can. J. Fish. Aquat. Sci. 57, 106–115. https://doi.org/10.1139/f99-185 (2000).Article 

Google Scholar 
Hunsicker, M. E. et al. Functional responses and scaling in predator-prey interactions of marine fishes: Contemporary issues and emerging concepts. Ecol. Lett. 14, 1288–1299. https://doi.org/10.1111/j.1461-0248.2011.01696.x (2011).Article 
PubMed 

Google Scholar 
Chambers, R. C. & Miller, T. J. Evaluating fish growth by means of otolith increment analysis: spectral properties of individual-level longitudinal data in in Recent Developments in Fish Otolith Research (ed. Secor, D. H., Dean, J. M. & Campana, S. E.) 155–175 (University of South Carolina Press, 1995).Mizutani, T. et al. Diel variability in the catch composition of bottom trawl survey in East China Sea. Nippon Suisan Gakkaishi 71, 44–53 (2005). (in Japanese with English abstract). https://doi.org/10.2331/suisan.71.44.Sassa, C., Takahashi, M., Konishi, Y. & Tsukamoto, Y. Interannual variations in distribution and abundance of Japanese jack mackerel Trachurus japonicus larvae in the East China Sea. ICES J. Mar. Sci. 73, 1170–1185. https://doi.org/10.1093/icesjms/fsv269 (2016).Article 

Google Scholar 
Takahashi, M., Sassa, C., Nishiuchi, K. & Tsukamoto, Y. Interannual variations in rates of larval growth and development of jack mackerel (Trachurus japonicus) in the East China Sea: Implications for juvenile survival. Can. J. Fish. Aquat. Sci. 73, 155–162. https://doi.org/10.1139/cjfas-2015-0077 (2016).Article 

Google Scholar 
Takahashi, M., Sassa, C., Nishiuchi, K. & Tsukamoto, Y. Variability in growth rates of Japanese jack mackerel Trachurus japonicus larvae and juveniles in the East China Sea—effects of temperature and prey abundance in in Kuroshio Current, Physical, Biogeochemical and Ecosystem Dynamics (ed. Nagai, T., Saito, H., Suzuki, K. & Takahashi, M.) 295–307 (Wiley, 2019).Anraku, M. & Azeta, M. The feeding habits of larvae and juveniles of the yellowtail, Seriola quinqueradiata Temminck et Schlegel, associated with floating seaweeds. Bull. Seikai Reg. Fish. Res. Lab 33, 13–45 (1965) (in Japanese with English abstract).
Google Scholar 
Villanueva, R., Perricone, V. & Fiorito, G. Cephalopods as predators: a short journey among behavioral flexibilities, adaptations, and feeding habits. Front. Physiol. 8, 598. https://doi.org/10.3389/fphys.2017.00598,Pubmed:28861006 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
Wang, R., Zuo, T. & Wang, K. The Yellow Sea Cold Bottom Water—an oversummering site for Calanus sinicus (Copepoda, Crustacea). J. Plankton Res. 25, 169–183. https://doi.org/10.1093/plankt/25.2.169 (2003).CAS 
Article 

Google Scholar 
Sassa, C., Kitajima, S., Nishiuchi, K. & Takahashi, M. Ontogenetic and inter-annual variation in the diet of Japanese jack mackerel (Trachurus japonicus) juveniles in the East China Sea. J. Mar. Biol. Assoc. U K 99, 525–538. https://doi.org/10.1017/S0025315418000206 (2019).Article 

Google Scholar 
Nakazawa, T., Ushio, M. & Kondoh, M. Scale dependence of predator–prey mass ratio: Determinants and applications. Adv. Ecol. Res. 45, 269–302. https://doi.org/10.1016/B978-0-12-386475-8.00007-1 (2011).Article 

Google Scholar 
Ohshimo, S., Tanaka, H., Nishiuchi, K. & Yasuda, T. Trophic positions and predator-prey mass ratio of the pelagic food web in the East China Sea and Sea of Japan. Mar. Freshw. Res. 67, 1692–1699. https://doi.org/10.1071/MF15115 (2016).Article 

Google Scholar 
Vidal, E. A. G. & Salvador, B. The tentacular strike behavior in squid: functional interdependency of morphology and predatory behaviors during ontogeny. Front. Physiol. 10, 1558. https://doi.org/10.3389/fphys.2019.01558 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
Doubleday, Z. A. et al. Global proliferation of cephalopods. Curr. Biol. 26, R406–R407. https://doi.org/10.1016/j.cub.2016.04.002 (2016).CAS 
Article 
PubMed 

Google Scholar 
Overholtz, W. J., Link, J. S. & Suslowicz, L. E. Consumption of important pelagic fish and squid by predatory fish in the northeastern USA shelf with some fishery comparisons. ICES J. Mar. Sci. 57, 1147–1159. https://doi.org/10.1006/jmsc.2000.0802 (2000).Article 

Google Scholar 
Montevecchi, W. A. & Myers, R. A. Prey harvests of seabirds reflect pelagic fish and squid abundance on multiple spatial and temporal scales. Mar. Ecol. Prog. Ser. 117, 1–9. https://doi.org/10.3354/meps117001 (1995).ADS 
Article 

Google Scholar  More

Dispersal is assumed to contribute to microbiome composition and function; however, it is difficult to measure. Walters et al. now set out a 6-month experiment looking at different dispersal routes of environmental microorganisms to the surface soil layer. They set up different ‘traps’, either glass slides or freshly cut grass, to determine the number, identity and function of incoming microorganisms. The traps ‘recorded’ dispersal through air, from plants and their litter, or from below through the decomposing litter and bulk soil. This was achieved by placing the traps either on a pedestal, closing them off at the bottom or leaving them open, respectively. The authors found that the overall dispersal rate was low, with little influence of the route, with only 0.5% incoming bacterial cells per day compared with the number of resident cells. However, the dispersal routes did influence microbiome composition, at least if from above and close to the surface. Finally, without dispersal, the initial decomposition of the cut grass was slower.
This is a preview of subscription content More