Philippa Kaur

The whiskers of seals are known to function as vibration receptors. Earlier experiments with blindfolded harbour seals in captivity have for example revealed that the animals can detect small water movements, and follow the hydrodynamic trails created by passing objects. But it is unclear if seals in the wild actively use this ability to find prey.
This is a preview of subscription content More

The Sargasso Sea was identified as the spawning area of the European eel (Anguilla anguilla) 100 years ago, and numerous subsequent surveys have verified that eel larvae just a week old are regularly recorded there. However, no adult eels or eel eggs have ever been found, leaving room for alternative hypotheses on the reproduction biology of this enigmatic species. Chang et al.1 theorize about an area along the Mid-Atlantic Ridge as a potential spawning ground. The main argument for this hypothesis was that the chemical signature found in eel otoliths would indicate that early stage larvae had been exposed to a volcanic environment, such as the one present along the Mid-Atlantic Ridge. Since this correlation was solely based on a mis-interpretation of cited literature data, no new, conclusive information to pinpoint the Mid-Atlantic Ridge as an additional or even alternative spawning area was presented by Chang et al.For more than 100 years, the life history of Atlantic eels remains a matter of scientific debate. In a recent paper by Chang and colleagues, published in Scientific Reports (Sci Rep 10, 15981 (2020)), it is hypothesized that the spawning areas of the European eel (Anguilla anguilla) and the American eel (A. rostrata) are located along the Mid-Atlantic Ridge at longitudes between 50° W and 40° W1. This area lies outside the Sargasso Sea, which has so far been widely assumed to be the spawning region of both species since the beginning of the twentieth century2. The Danish researcher Johannes Schmidt collected eel leptocephali 30 mm long or less, some as short as 9 mm, all south of 30° N and west of 50° W3,4. Since then, Schmidt’s assumption was supported by a number of investigations that found recently hatched European eel larvae ( More

Shultz, S. & Dunbar, R. Encephalization is not a universal macroevolutionary phenomenon in mammals but is associated with sociality. Proc. Natl Acad. Sci. USA 107, 21582–21586 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Jerison, H. J. Animal intelligence as encephalization. Phil. Trans. R. Soc. Lond. B 308, 21–35 (1985).CAS 
Article 

Google Scholar 
Roth, G. & Dicke, U. Evolution of the brain and intelligence. Trends Cogn. Sci. 9, 250–257 (2005).PubMed 
Article 

Google Scholar 
Lefebvre, L., Whitle, P., Lascaris, E. & Finkelstein, A. Feeding innovations and forebrain size in birds. Anim. Behav. 53, 549–560 (1997).Article 

Google Scholar 
Overington, S. E., Morand-Ferron, J., Boogert, N. J. & Lefebvre, L. Technical innovations drive the relationship between innovativeness and residual brain size in birds. Anim. Behav. 78, 1001–1010 (2009).Article 

Google Scholar 
Reader, S. M., Hager, Y. & Laland, K. N. The evolution of primate general and cultural intelligence. Phil. Trans. R. Soc. B 366, 1017–1027 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
Benson-Amram, S., Dantzer, B., Stricker, G., Swanson, E. M. & Holekamp, K. E. Brain size predicts problem-solving ability in mammalian carnivores. Proc Natl Acad. Sci. USA 113, 2532–2537 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Reader, S. M. & Laland, K. N. Social intelligence, innovation, and enhanced brain size in primates. Proc. Natl Acad. Sci. USA 99, 4436–4441 (2002).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Fristoe, T. S., Iwaniuk, A. N. & Botero, C. A. Big brains stabilize populations and facilitate colonization of variable habitats in birds. Nat. Ecol. Evol. 1, 1706–1715 (2017).PubMed 
Article 

Google Scholar 
van Woerden, J. T., van Schaik, C. P. & Isler, K. Effects of seasonality on brain size evolution: evidence from Strepsirrhine primates. Am. Nat. 176, 758–767 (2010).PubMed 
Article 

Google Scholar 
Ducatez, S., Sol, D., Sayol, F. & Lefebvre, L. Behavioural plasticity is associated with reduced extinction risk in birds. Nat. Ecol. Evol. 4, 788–793 (2020).PubMed 
Article 

Google Scholar 
Herculano-Houzel, S. Brains matter, bodies maybe not: the case for examining neuron numbers irrespective of body size. Ann. NY Acad. Sci. 1225, 191–199 (2011).PubMed 
Article 

Google Scholar 
Logan, C. J. et al. Beyond brain size: uncovering the neural correlates of behavioral and cognitive specialization. Comp. Cogn. Behav. Rev. 13, 55–89 (2018).Article 

Google Scholar 
Jerison, H. Evolution of the Brain and Intelligence (Academic Press, 1973).Herculano-Houzel, S. Numbers of neurons as biological correlates of cognitive capability. Curr. Opin. Behav. Sci. 16, 1–7 (2017).Article 

Google Scholar 
Van Schaik, C. P., Triki, Z., Bshary, R. & Heldstab, S. A. A farewell to the encephalization quotient: a new brain size measure for comparative primate cognition. Brain Behav. Evol. 96, 1–12 (2021).PubMed 
Article 

Google Scholar 
Striedter, G. F. Principles of Brain Evolution (Sinauer Associates, 2005).
Google Scholar 
MacLean, E. L. et al. The evolution of self-control. Proc. Natl Acad. Sci. USA 111, E2140–E2148 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Matějů, J. et al. Absolute, not relative brain size correlates with sociality in ground squirrels. Proc. R. Soc. B 283, 20152725 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Deaner, R. O., Isler, K., Burkart, J. & Van Schaik, C. Overall brain size, and not encephalization quotient, best predicts cognitive ability across non-human primates. Brain Behav. Evol. 70, 115–124 (2007).PubMed 
Article 

Google Scholar 
Smaers, J. B., Dechmann, D. K. N., Goswami, A., Soligo, C. & Safi, K. Comparative analyses of evolutionary rates reveal different pathways to encephalization in bats, carnivorans, and primates. Proc. Natl Acad. Sci. USA 109, 18006–18011 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Smaers, J. B. et al. The evolution of mammalian brain size. Sci. Adv. 7, eabe2101 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Němec, P. & Osten, P. The evolution of brain structure captured in stereotyped cell count and cell type distributions. Curr. Opin. Neurobiol. 60, 176–183 (2020).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Olkowicz, S. et al. Birds have primate-like numbers of neurons in the forebrain. Proc. Natl Acad. Sci. USA 113, 7255–7260 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kverková, K. et al. The evolution of brain neuron numbers in amniotes. Proc. Natl Acad. Sci. USA 119, e2121624119 (2022).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Iwaniuk, A. N. & Hurd, P. L. The evolution of cerebrotypes in birds. Brain Behav. Evol. 65, 215–230 (2005).PubMed 
Article 

Google Scholar 
Timmermans, S., Lefebvre, L., Boire, D. & Basu, P. Relative size of the hyperstriatum ventrale is the best predictor of feeding innovation rate in birds. Brain Behav. Evol. 56, 196–203 (2000).CAS 
PubMed 
Article 

Google Scholar 
Sayol, F., Lefebvre, L. & Sol, D. Relative brain size and its relation with the associative pallium in birds. Brain Behav. Evol. 87, 69–77 (2016).PubMed 
Article 

Google Scholar 
Healy, K. et al. Ecology and mode-of-life explain lifespan variation in birds and mammals. Proc. R. Soc. B 281, 20140298 (2014).Deaner, R. O., Barton, R. A. & van Schaik, C. P. in Primate Life Histories and Socioecology (eds Kappeler, P. M. & Pereira, M. E.) 233–265 (Univ. of Chicago Press, 2003).Sol, D., Sayol, F., Ducatez, S. & Lefebvre, L. The life-history basis of behavioural innovations. Phil. Trans. R. Soc. B 371, 20150187 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Dukas, R. Evolutionary biology of animal cognition. Ann. Rev. Ecol. Evol. Syst. 35, 347–374 (2004).Article 

Google Scholar 
Ricklefs, R. E. The cognitive face of life histories. Wilson Bull. 116, 119–133 (2004).Article 

Google Scholar 
Martin, T. E., Oteyza, J. C., Boyce, A. J., Lloyd, P. & Ton, R. Adult mortality probability and nest predation rates explain parental effort in warming eggs with consequences for embryonic development time. Am. Nat. 186, 223–236 (2015).PubMed 
Article 

Google Scholar 
Unzeta, M., Martin, T. E. & Sol, D. Daily nest predation rates decrease with body size in passerine birds. Am. Nat. 196, 743–754 (2020).PubMed 
Article 

Google Scholar 
Charvet, C. J. & Striedter, G. F. Developmental modes and developmental mechanisms can channel brain evolution. Front. Neuroanat. 5, 4 (2011).Finlay, B. L. & Darlington, R. B. Linked regularities in the development and evolution of mammalian brains. Science 268, 1578–1584 (1995).CAS 
PubMed 
Article 

Google Scholar 
Herculano-Houzel, S. Isotropic fractionator: a simple, rapid method for the quantification of total cell and neuron numbers in the brain. J. Neurosci. 25, 2518–2521 (2005).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Massen, J. J. M. et al. Brain size and neuron numbers drive differences in yawn duration across mammals and birds. Commun. Biol. 4, 1–10 (2021).Article 

Google Scholar 
Ramsey, G., Bastian, M. L. & Schaik, C. Van Animal innovation defined and operationalized. Behav. Brain Sci. 30, 393–437 (2007).PubMed 
Article 

Google Scholar 
Lefebvre, L. A global database of feeding innovations in birds. Wilson J. Ornithol. 132, 803–809 (2021).Article 

Google Scholar 
Barton, R. A. Embodied cognitive evolution and the cerebellum. Phil. Trans. R. Soc. B 367, 2097–2107 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
Gutiérrez-Ibáñez, C., Iwaniuk, A. N. & Wylie, D. R. Parrots have evolved a primate-like telencephalic–midbrain–cerebellar circuit. Sci. Rep. 8, 9960 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Brieuc, M. S. O. O., Waters, C. D., Drinan, D. P. & Naish, K. A. A practical introduction to random forest for genetic association studies in ecology and evolution. Mol. Ecol. Res. 18, 755–766 (2018).Article 

Google Scholar 
Hadfield, J. D. & Nakagawa, S. General quantitative genetic methods for comparative biology: phylogenies, taxonomies and multi-trait models for continuous and categorical characters. J. Evol. Biol. 23, 494–508 (2010).CAS 
PubMed 
Article 

Google Scholar 
Güntürkün, O., Ströckens, F., Scarf, D. & Colombo, M. Apes, feathered apes, and pigeons: differences and similarities. Curr. Opin. Behav. Sci. 16, 35–40 (2017).Article 

Google Scholar 
Ströckens, F. et al. High associative neuron numbers could drive cognitive performance in corvid species. J. Comp. Neurol. 530, 1588–1605 (2022).PubMed 
Article 

Google Scholar 
Shanahan, M., Bingman, V. P., Shimizu, T., Wild, M. & Güntürkün, O. Large-scale network organisation in the avian forebrain: a connectivity matrix and theoretical analysis. Front. Comput. Neurosci. 7, 89 (2013).Emery, N. J. Cognitive ornithology: the evolution of avian intelligence. Phil. Trans. R. Soc. B 361, 23–43 (2006).PubMed 
Article 

Google Scholar 
Lambert, M. L., Jacobs, I., Osvath, M. & von Bayern, A. M. P. Birds of a feather? Parrot and corvid cognition compared. Behaviour 156, 505–594 (2019).Article 

Google Scholar 
Ksepka, D. T. et al. Tempo and pattern of avian brain size evolution. Curr. Biol. 30, 2026–2036 (2020).CAS 
PubMed 
Article 

Google Scholar 
Herculano-Houzel, S., Manger, P. R. & Kaas, J. H. Brain scaling in mammalian evolution as a consequence of concerted and mosaic changes in numbers of neurons and average neuronal cell size. Front. Neuroanat. 8, 77 (2014).Smaers, J. B., Mongle, C. S., Safi, K. & Dechmann, D. K. N. Allometry, evolution and development of neocortex size in mammals. Prog. Brain Res. 250, 83–107 (2019).PubMed 
Article 

Google Scholar 
Cárdenas, A. & Borrell, V. Molecular and cellular evolution of corticogenesis in amniotes. Cell Mol. Life Sci. 77, 435–1460 (2020).Article 
CAS 

Google Scholar 
García-Moreno, F. & Molnár, Z. Variations of telencephalic development that paved the way for neocortical evolution. Prog. Neurobiol. 194, 101865 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Charvet, C. J. & Striedter, G. F. Developmental basis for telencephalon expansion in waterfowl: enlargement prior to neurogenesis. Proc. R. Soc. B 276, 3421–3427 (2009).PubMed 
PubMed Central 
Article 

Google Scholar 
Striedter, G. F. & Charvet, C. J. Developmental origins of species differences in telencephalon and tectum size: morphometric comparisons between a parakeet (Melopsittacus undulatus) and a quail (Colinus virgianus). J. Comp. Neurol. 507, 1663–1675 (2008).PubMed 
Article 

Google Scholar 
Sibly, R. M. & Brown, J. H. Effects of body size and lifestyle on evolution of mammal life histories. Proc. Natl Acad. Sci. USA 104, 17707–17712 (2007).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Uomini, N., Fairlie, J., Gray, R. D. & Griesser, M. Extended parenting and the evolution of cognition. Phil. Trans. R. Soc. Lond. B 375, 20190495 (2020).Article 

Google Scholar 
Reiner, A. et al. Revised nomenclature for avian telencephalon and some related brainstem nuclei. J. Comp. Neurol. 473, 377–414 (2004).PubMed 
PubMed Central 
Article 

Google Scholar 
Mullen, R. J., Buck, C. R. & Smith, A. M. NeuN, a neuronal specific nuclear protein in vertebrates. Development 116, 201–211 (1992).CAS 
PubMed 
Article 

Google Scholar 
Mezey, S. et al. Postnatal changes in the distribution and density of neuronal nuclei and doublecortin antigens in domestic chicks (Gallus domesticus). J. Comp. Neurol. 520, 100–116 (2012).CAS 
PubMed 
Article 

Google Scholar 
Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).Article 

Google Scholar 
Ducatez, S. & Lefebvre, L. Patterns of research effort in birds. PLoS ONE 9, e89955 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Sheard, C. et al. Ecological drivers of global gradients in avian dispersal inferred from wing morphology. Nat. Commun. 11, 2463 (2020).Cooney, C. R. et al. Ecology and allometry predict the evolution of avian developmental durations. Nat. Commun. 11, 2383 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Botelho, J. F. & Faunes, M. The evolution of developmental modes in the new avian phylogenetic tree. Evol. Dev. 17, 221–223 (2015).PubMed 
Article 

Google Scholar 
Bürkner, P.-C. Brms: an R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).Article 

Google Scholar 
Pigot, A. L. et al. Macroevolutionary convergence connects morphological form to ecological function in birds. Nat. Ecol. Evol. 4, 230–239 (2020).PubMed 
Article 

Google Scholar 
Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).CAS 
PubMed 
Article 

Google Scholar 
Berk, R. A. Statistical Learning from a Regression Perspective (Springer International, 2017).Liaw, A. & Wiener, M. Classification and regression by randomForest. R News 2, 18–22 (2002).
Google Scholar 
Lleonart, J., Salat, J. & Torres, G. J. Removing allometric effects of body size in morphological analysis. J. Theor. Biol. 205, 85–93 (2000).CAS 
PubMed 
Article 

Google Scholar 
Sayol, F., Downing, P. A., Iwaniuk, A. N., Maspons, J. & Sol, D. Predictable evolution towards larger brains in birds colonizing oceanic islands. Nat. Commun. 9, 2820 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Torres, C. R., Norell, M. A. & Clarke, J. A. Bird neurocranial and body mass evolution across the end-Cretaceous mass extinction: the avian brain shape left other dinosaurs behind. Sci. Adv. 7, eabg7099 (2021). More

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

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

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

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

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