- Email: [email protected]
Bird Migration: Life on the High Seas
Current Biology
Dispatches Ideally, we want to know the nature of genomic changes underlying behavioral evolution: How many loci contribute to behavioral changes and what is the distribution of effects? Do behavioral differences tend to involve coding or regulatory mutations? Do similar behaviors across lineages evolve via convergent mutations in the same genes or pathways? Mutations influencing behavior may act to differently organize neural circuits during development or may alter the function or sensitivity of structurally similar circuits. Determining how mutations lead to behavioral changes will require functional developmental studies. For organisms with even modestly long developmental times such as mice or other interesting cases of behavioral evolution (Figure 1), it is prohibitively time intensive and expensive to sample all of neuronal development. Carefully conducted studies of the development of genetically controlled behaviors such as the one reported by Metz and colleagues [4] have the potential to dig away at the genetic basis of adaptive behavioral evolution by allowing for more targeted functional studies. Importantly, functional genomic studies of developing brains will not
only provide evidence of how QTL are influencing neural development, but may also help pinpoint the causative mutations within QTL peaks in conjunction with population and comparative genomic data.
REFERENCES 1. Boake, C.R., Arnold, S.J., Breden, F., Meffert, L.M., Ritchie, M.G., Taylor, B.J., Wolf, J.B., and Moore, A.J. (2002). Genetic tools for studying adaptation and the evolution of behavior. Am. Nat. 160, S143–S159.
6. Weber, J.N., and Hoekstra, H.E. (2009). The evolution of burrowing behaviour in deer mice (genus Peromyscus). Anim. Behav. 77, 603–609. 7. Dawson, W.D., Lake, C.E., and Schumpert, S.S. (1988). Inheritance of burrow building in Peromyscus. Behav. Genet. 18, 371–382. 8. Moore, T.Y., Cooper, K.L., Biewener, A.A., and Vasudevan, R. (2017). Unpredictability of escape trajectory explains predator evasion ability and microhabitat preference of desert rodents. Nat. Commun. 8, 440. 9. Sheehan, M.J., and Tibbetts, E.A. (2011). Specialized face learning is associated with individual recognition in paper wasps. Science 334, 1272–1275.
2. Greenwood, A.K., Wark, A.R., Yoshida, K., and Peichel, C.L. (2013). Genetic and neural modularity underlie the evolution of schooling behavior in threespine sticklebacks. Curr. Biol. 23, 1884–1888.
10. Sheehan, M.J., and Tibbetts, E.A. (2010). Selection for individual recognition and the evolution of polymorphic identity signals in Polistes paper wasps. J. Evol. Biol. 23, 570–577.
3. Ding, Y., Berrocal, A., Morita, T., Longden, K.D., and Stern, D.L. (2016). Natural courtship song variation caused by an intronic retroelement in an ion channel gene. Nature 536, 329–332.
11. Shaw, K.L., and Lesnick, S.C. (2009). Genomic linkage of male song and female acoustic preference QTL underlying a rapid species radiation. Proc. Natl. Acad. Sci. USA 106, 9737–9742.
4. Metz, H.C., Bedford, N.L., Pan, Y.L., and Hoekstra, H.E. (2017). Evolution and genetics of precocious burrowing behavior in Peromyscus mice. Curr. Biol. 27, 3837– 3845.e3.
12. Fan, S., Elmer, K.R., and Meyer, A. (2012). Genomics of adaptation and speciation in cichlid fishes: recent advances and analyses in African and Neotropical lineages. Philos. Trans. R. Soc. Lond. B Biol. Sci. 367, 385–394.
5. Weber, J.N., Peterson, B.K., and Hoekstra, H.E. (2013). Discrete genetic modules are responsible for complex burrow evolution in Peromyscus mice. Nature 493, 402.
13. Boul, K.E., Funk, W.C., Darst, C.R., Cannatella, D.C., and Ryan, M.J. (2007). Sexual selection drives speciation in an Amazonian frog. Proc. R. Soc. Lond. B Biol. Sci. 274, 399–406.
Bird Migration: Life on the High Seas Stephen Votier Environment and Sustainability Institute, and Centre for Ecology and Conservation, University of Exeter, Penryn Campus, Cornwall TR10 9EZ, UK Correspondence: [email protected] https://doi.org/10.1016/j.cub.2017.11.032
Migratory animals show great diversity of movement within populations, but the causes and consequences of this variability are poorly understood. Tracking a migratory seabird across its range reveals how environmental, latitudinal and demographic conditions shape migratory journeys and fitness. Migration is one of the greatest spectacles in the natural world. Found across a huge diversity of animals from butterflies to birds and wildebeest to whales it involves the transfer of huge amounts of biomass in space and time [1], often over vast distances [2]. As well as variation among species, there is also great variation within the same
species [3]. Understanding the drivers of this variation and how they impact on the demographic process is central to the study of animal migration, but studies shedding light on this are about as common as the proverbial hen’s teeth. In a new paper in Current Biology, Annette Fayet, Tim Guilford and colleagues [4] study in detail the migration patterns of
Atlantic puffins (Fratercula arctica) in several populations across this seabird’s range. Tracking individual animals with tags, known as ‘bio-logging’ [5], has revolutionised our understanding of animal movement [6], and has the potential to address fundamental questions about variation in migratory
Current Biology 28, R17–R36, January 8, 2018 ª 2017 Published by Elsevier Ltd. R21
Current Biology
Dispatches
Figure 1. The Atlantic puffin Fratercula arctica — a model marine migrant? Atlantic puffins are highly migratory, travelling widely at sea from their terrestrial breeding grounds. Fayet et al. [4] reveal that puffin migrations are dispersive and vary from a few hundred to 1700 kilometres. This variation can be explained by colony size (birds from larger colonies travel further) and winter condition (birds migrate further when conditions are poor). This influences their wintering behaviour — long distance migrants have long flight periods, higher energy expenditure and visit less productive waters. Migration strategy also impacts reproductive success — puffins from large colonies that wintered at high latitude, had long migrations and experienced low ocean productivity all showed poor breeding performance back at the colony. (Photo: Tycho Anker-Nilssen.)
behaviour. This individual-based approach means it is now not only possible to precisely reconstruct specific migration events, but it may also allow us to collect important information about a focal animal such as its age, sex, reproductive status or where it comes from, which are critical to better understand the drivers of different migratory strategies and their fitness consequences. Moreover, as bio-logging devices have become cheaper and smaller it is now possible to safely track sufficiently large numbers of individuals to encompass an entire species’ range and importantly to move away from descriptive studies to perform robust statistical analysis that reveal the causes and consequences of variation in migratory behaviour [7]. In a rare study of avian migration across a species’ range Fayet and colleagues [4] used small leg-mounted geolocators (equipped with an accurate clock, a light sensor and a saltwater switch these loggers can estimate location twice per
day and also provide activity based on patterns of immersion in seawater) to study the migration of a high-latitude seabird to provide novel insights into the diversity of avian migration strategies, what factors shape this diversity and ultimately what this means for their population dynamics. Atlantic puffins (Figure 1) are probably best known for their colourful bills and charismatic behaviour when coming to land to breed, but beneath this crowdpleasing persona lies a hard-core, long-distant pelagic migrant. Puffins spend more than half of their year at sea, often wintering far from land [8]. However, there is much variation in where puffins migrate to [8,9] and it is this variability that Fayet, Guilford and colleagues [4] have sought to understand. Their study is impressive because of its scale — 270 birds from 13 populations in Canada, Iceland, Ireland, Norway, the UK and the USA, covering the species’ entire breeding range — and because it was able to make use of this variation, as well as detailed life-history information, to
R22 Current Biology 28, R17–R36, January 8, 2018
investigate the role of geographical, environmental and demographic conditions on the puffins’ migration strategies. There are a number of important findings from this study. First, it revealed the great diversity of migratory strategies used by puffins. There was considerable overlap in winter ranges, and birds from some colonies had very different migration distances compared with those from other colonies. The average distance from the colony ranged from less than 250 km to 1,700 km, revealing strategies from short, to long-distance migration. Importantly, these movements were dispersive, with no clear patterns. This diversity of strategies is at odds with the orthodoxy about ‘chain’ (where latitudinal differences between populations are retained throughout the year) or ‘leapfrog’ (where high latitude populations overfly low latitude populations) migration, for example, revealed from other multi-population studies of birds [10], but instead suggests a more dispersive strategy [8]. Second, the authors were able to explain this variation by a combination of population size — birds from large colonies migrate further than those from small colonies — and local winter conditions — birds migrate further when local conditions are poor. Together, these observations suggest that density-dependent competition and environmental conditions around the colony are important determinants of migration distance, as indicated by previous studies [11,12]. Importantly, Fayet and colleagues [4] find that this effect influenced subsequent wintering behaviour and energy expenditure: birds migrating longer distances also spent more time in flight, had higher daily energy expenditure and visited less productive waters (as estimated by chlorophyll-a concentration). Latitude also influenced migratory behaviour; puffins wintering at higher latitudes spend more time foraging, occupy colder waters and have a higher daily energy expenditure compared with those wintering further south – although latitude had no effect on migration distance. Third, the authors were able to link migratory strategies to subsequent reproductive performance. Puffins from large colonies that wintered at high latitude, had long migration distances and (to a lesser extent)
Current Biology
Dispatches experienced low ocean productivity were all independently associated with poor reproductive performance at the breeding grounds. This is likely a consequence of the combined effects of prey availability during the breeding season (linked with density-dependent competition) and adult body condition related to conditions during the previous winter. Such interseasonal interactions are well known [13], but what is unusual here is the level of detail and the opportunity to explore different mechanisms. As well as providing unique insight into the diversity of migratory behaviours in birds, the study of Fayet and colleagues [4] provides important information relevant to conservation. Puffin numbers have declined sharply at some breeding colonies, and for this reason the species was recently reclassified from ‘Least Concern’ to ‘Vulnerable’. Protecting breeding colonies is the first and most straightforward step in the process, but it is clear that conditions in the wintering areas also shape the demographic fortunes of puffins. As the new study reveals, puffins are truly pelagic travellers, occupying huge tracts of the open ocean, crossing many international borders and jurisdictions. Developing an effective conservation strategy for vulnerable migratory species like this one is therefore extremely challenging, but some wintering hotspots were evident and it might therefore be desirable to consider these as priorities for protection [14].
A final heartening message from the study of Fayet and colleagues [4] was that, much like puffins, the authors had little regard for international boundaries — the research team comprised 22 authors from 8 countries. Adopting this outward-looking and collaborative ethos, at a time when some would create a world with more borders, is surely essential if we are to have any hope of maintaining a favourable future for the diversity of migratory animals.
golden age of bio-logging: how animal-borne sensors are advancing the frontiers of ecology. Ecology 96, 1741–1753. 7. Sherley, R.B., Ludynia, K., Dyer, B.M., Lamont, T., Makhado, A.B., Roux, J.-P., Scales, K.L., Underhill, L.G., and Votier, S.C. (2017). Metapopulation tracking juvenile penguins reveals an ecosystem-wide ecological trap. Curr. Biol. 27, 563–568. 8. Guilford, T., Freeman, R., Boyle, D., Dean, B., Kirk, H., Phillips, R., and Perrins, C. (2011). A dispersive migration in the Atlantic puffin and its implications for migratory navigation. PloS One 6, e21336.
REFERENCES 1. Hu, G., Lim, K.S., Horvitz, N., Clark, S.J., Reynolds, D.R., Sapir, N., and Chapman, J.W. (2016). Mass seasonal bioflows of high-flying insect migrants. Science 354, 1584–1587. 2. Egevang, C., Stenhouse, I.J., Phillips, R.A., Petersen, A., Fox, J.W., and Silk, J.R.D. (2010). Tracking of Arctic terns Sterna paradisaea reveals longest animal migration. Proc. Natl. Acad. Sci. USA 107, 2078–2081. 3. Weimerskirch, H., Delord, K., Guitteaud, A., Phillips, R.A., and Pinet, P. (2015). Extreme variation in migration strategies between and within wandering albatross populations during their sabbatical year, and their fitness consequences. Sci. Rep. 5, 8853. 4. Fayet, A.L., Freeman, R., Anker-Nilssen, T., Diamond, A., Erikstad, K.E., Fifield, D., Fitzsimmons, M.G., Hansen, E.S., Harris, M.P., Jessopp, M., et al. (2017). Ocean-wide drivers of migration strategies and their influence on population breeding performance in a declining seabird. Curr. Biol. 27, 3871– 3878.e3. 5. Fehlmann, G., and King, A.J. (2016). Bio-logging. Curr Biol. 26, R830–R831. 6. Wilmers, C.C., Nickel, B., Bryce, C.M., Smith, J.A., Wheat, R.E., and Yovovich, V. (2015). The
9. Fayet, A.L., Freeman, R., Shoji, A., Boyle, D., Kirk, H.L., Dean, B.J., Perrins, C.M., and Guilford, T.G. (2016). Drivers and fitness consequences of dispersive migration in a pelagic seabird. Behav. Ecol. 27, 1061–1072. 10. Newton, I. (2008). The Migration Ecology of Birds (London: Elsevier). 11. Taylor, C.M., and Norris, D.R. (2007). Predicting conditions for migration: effects of density dependence and habitat quality. Biol. Lett. 22, 280–284. , Y.G., and Jodice, P.G.R. 12. Lamb, J.S., Satge (2017). Influence of density-dependent competition on foraging and migratory behavior of a subtropical colonial seabird. Ecol. Evol. 7, 6469–6481. 13. Harrison, X.A., Blount, J.D., Inger, R., Norris, D.R., and Bearhop, S. (2011). Carry-over effects as drivers of fitness differences in animals. J. Anim. Ecol. 80, 4–18. 14. Grecian, W.J., Witt, M.J., Attrill, M.J., Bearhop, S., Becker, P.H., Egevang, C., Furness, R.W., millet, D., Godley, B.J., Gonza´lez-Solı´s, J., Gre et al. (2016). Seabird diversity hotspot linked to ocean productivity in the Canary Current Large Marine Ecosystem. Biol. Lett. 12, 20160024.
Learning: Plasticity without Stabilization in Olfactory Cortex Shivathmihai Nagappan and Kevin M. Franks* Department of Neurobiology, Duke University, Durham, NC, 27710, USA *Correspondence: [email protected] https://doi.org/10.1016/j.cub.2017.11.010
A new study reports unsupervised, experience-dependent reorganization, but not stabilization, of neural odor representations in the zebrafish olfactory system. A central goal in neuroscience is to understand learning, and this has largely been studied by investigating the
mechanisms and expression of neural plasticity. Plasticity allows the nervous system to generate flexible outcomes to a
given stimulus [1–3]. This flexibility is essential for organisms to learn and adapt to varying environmental conditions.
Current Biology 28, R17–R36, January 8, 2018 ª 2017 Elsevier Ltd. R23
Dispatches Ideally, we want to know the nature of genomic changes underlying behavioral evolution: How many loci contribute to behavioral changes and what is the distribution of effects? Do behavioral differences tend to involve coding or regulatory mutations? Do similar behaviors across lineages evolve via convergent mutations in the same genes or pathways? Mutations influencing behavior may act to differently organize neural circuits during development or may alter the function or sensitivity of structurally similar circuits. Determining how mutations lead to behavioral changes will require functional developmental studies. For organisms with even modestly long developmental times such as mice or other interesting cases of behavioral evolution (Figure 1), it is prohibitively time intensive and expensive to sample all of neuronal development. Carefully conducted studies of the development of genetically controlled behaviors such as the one reported by Metz and colleagues [4] have the potential to dig away at the genetic basis of adaptive behavioral evolution by allowing for more targeted functional studies. Importantly, functional genomic studies of developing brains will not
only provide evidence of how QTL are influencing neural development, but may also help pinpoint the causative mutations within QTL peaks in conjunction with population and comparative genomic data.
REFERENCES 1. Boake, C.R., Arnold, S.J., Breden, F., Meffert, L.M., Ritchie, M.G., Taylor, B.J., Wolf, J.B., and Moore, A.J. (2002). Genetic tools for studying adaptation and the evolution of behavior. Am. Nat. 160, S143–S159.
6. Weber, J.N., and Hoekstra, H.E. (2009). The evolution of burrowing behaviour in deer mice (genus Peromyscus). Anim. Behav. 77, 603–609. 7. Dawson, W.D., Lake, C.E., and Schumpert, S.S. (1988). Inheritance of burrow building in Peromyscus. Behav. Genet. 18, 371–382. 8. Moore, T.Y., Cooper, K.L., Biewener, A.A., and Vasudevan, R. (2017). Unpredictability of escape trajectory explains predator evasion ability and microhabitat preference of desert rodents. Nat. Commun. 8, 440. 9. Sheehan, M.J., and Tibbetts, E.A. (2011). Specialized face learning is associated with individual recognition in paper wasps. Science 334, 1272–1275.
2. Greenwood, A.K., Wark, A.R., Yoshida, K., and Peichel, C.L. (2013). Genetic and neural modularity underlie the evolution of schooling behavior in threespine sticklebacks. Curr. Biol. 23, 1884–1888.
10. Sheehan, M.J., and Tibbetts, E.A. (2010). Selection for individual recognition and the evolution of polymorphic identity signals in Polistes paper wasps. J. Evol. Biol. 23, 570–577.
3. Ding, Y., Berrocal, A., Morita, T., Longden, K.D., and Stern, D.L. (2016). Natural courtship song variation caused by an intronic retroelement in an ion channel gene. Nature 536, 329–332.
11. Shaw, K.L., and Lesnick, S.C. (2009). Genomic linkage of male song and female acoustic preference QTL underlying a rapid species radiation. Proc. Natl. Acad. Sci. USA 106, 9737–9742.
4. Metz, H.C., Bedford, N.L., Pan, Y.L., and Hoekstra, H.E. (2017). Evolution and genetics of precocious burrowing behavior in Peromyscus mice. Curr. Biol. 27, 3837– 3845.e3.
12. Fan, S., Elmer, K.R., and Meyer, A. (2012). Genomics of adaptation and speciation in cichlid fishes: recent advances and analyses in African and Neotropical lineages. Philos. Trans. R. Soc. Lond. B Biol. Sci. 367, 385–394.
5. Weber, J.N., Peterson, B.K., and Hoekstra, H.E. (2013). Discrete genetic modules are responsible for complex burrow evolution in Peromyscus mice. Nature 493, 402.
13. Boul, K.E., Funk, W.C., Darst, C.R., Cannatella, D.C., and Ryan, M.J. (2007). Sexual selection drives speciation in an Amazonian frog. Proc. R. Soc. Lond. B Biol. Sci. 274, 399–406.
Bird Migration: Life on the High Seas Stephen Votier Environment and Sustainability Institute, and Centre for Ecology and Conservation, University of Exeter, Penryn Campus, Cornwall TR10 9EZ, UK Correspondence: [email protected] https://doi.org/10.1016/j.cub.2017.11.032
Migratory animals show great diversity of movement within populations, but the causes and consequences of this variability are poorly understood. Tracking a migratory seabird across its range reveals how environmental, latitudinal and demographic conditions shape migratory journeys and fitness. Migration is one of the greatest spectacles in the natural world. Found across a huge diversity of animals from butterflies to birds and wildebeest to whales it involves the transfer of huge amounts of biomass in space and time [1], often over vast distances [2]. As well as variation among species, there is also great variation within the same
species [3]. Understanding the drivers of this variation and how they impact on the demographic process is central to the study of animal migration, but studies shedding light on this are about as common as the proverbial hen’s teeth. In a new paper in Current Biology, Annette Fayet, Tim Guilford and colleagues [4] study in detail the migration patterns of
Atlantic puffins (Fratercula arctica) in several populations across this seabird’s range. Tracking individual animals with tags, known as ‘bio-logging’ [5], has revolutionised our understanding of animal movement [6], and has the potential to address fundamental questions about variation in migratory
Current Biology 28, R17–R36, January 8, 2018 ª 2017 Published by Elsevier Ltd. R21
Current Biology
Dispatches
Figure 1. The Atlantic puffin Fratercula arctica — a model marine migrant? Atlantic puffins are highly migratory, travelling widely at sea from their terrestrial breeding grounds. Fayet et al. [4] reveal that puffin migrations are dispersive and vary from a few hundred to 1700 kilometres. This variation can be explained by colony size (birds from larger colonies travel further) and winter condition (birds migrate further when conditions are poor). This influences their wintering behaviour — long distance migrants have long flight periods, higher energy expenditure and visit less productive waters. Migration strategy also impacts reproductive success — puffins from large colonies that wintered at high latitude, had long migrations and experienced low ocean productivity all showed poor breeding performance back at the colony. (Photo: Tycho Anker-Nilssen.)
behaviour. This individual-based approach means it is now not only possible to precisely reconstruct specific migration events, but it may also allow us to collect important information about a focal animal such as its age, sex, reproductive status or where it comes from, which are critical to better understand the drivers of different migratory strategies and their fitness consequences. Moreover, as bio-logging devices have become cheaper and smaller it is now possible to safely track sufficiently large numbers of individuals to encompass an entire species’ range and importantly to move away from descriptive studies to perform robust statistical analysis that reveal the causes and consequences of variation in migratory behaviour [7]. In a rare study of avian migration across a species’ range Fayet and colleagues [4] used small leg-mounted geolocators (equipped with an accurate clock, a light sensor and a saltwater switch these loggers can estimate location twice per
day and also provide activity based on patterns of immersion in seawater) to study the migration of a high-latitude seabird to provide novel insights into the diversity of avian migration strategies, what factors shape this diversity and ultimately what this means for their population dynamics. Atlantic puffins (Figure 1) are probably best known for their colourful bills and charismatic behaviour when coming to land to breed, but beneath this crowdpleasing persona lies a hard-core, long-distant pelagic migrant. Puffins spend more than half of their year at sea, often wintering far from land [8]. However, there is much variation in where puffins migrate to [8,9] and it is this variability that Fayet, Guilford and colleagues [4] have sought to understand. Their study is impressive because of its scale — 270 birds from 13 populations in Canada, Iceland, Ireland, Norway, the UK and the USA, covering the species’ entire breeding range — and because it was able to make use of this variation, as well as detailed life-history information, to
R22 Current Biology 28, R17–R36, January 8, 2018
investigate the role of geographical, environmental and demographic conditions on the puffins’ migration strategies. There are a number of important findings from this study. First, it revealed the great diversity of migratory strategies used by puffins. There was considerable overlap in winter ranges, and birds from some colonies had very different migration distances compared with those from other colonies. The average distance from the colony ranged from less than 250 km to 1,700 km, revealing strategies from short, to long-distance migration. Importantly, these movements were dispersive, with no clear patterns. This diversity of strategies is at odds with the orthodoxy about ‘chain’ (where latitudinal differences between populations are retained throughout the year) or ‘leapfrog’ (where high latitude populations overfly low latitude populations) migration, for example, revealed from other multi-population studies of birds [10], but instead suggests a more dispersive strategy [8]. Second, the authors were able to explain this variation by a combination of population size — birds from large colonies migrate further than those from small colonies — and local winter conditions — birds migrate further when local conditions are poor. Together, these observations suggest that density-dependent competition and environmental conditions around the colony are important determinants of migration distance, as indicated by previous studies [11,12]. Importantly, Fayet and colleagues [4] find that this effect influenced subsequent wintering behaviour and energy expenditure: birds migrating longer distances also spent more time in flight, had higher daily energy expenditure and visited less productive waters (as estimated by chlorophyll-a concentration). Latitude also influenced migratory behaviour; puffins wintering at higher latitudes spend more time foraging, occupy colder waters and have a higher daily energy expenditure compared with those wintering further south – although latitude had no effect on migration distance. Third, the authors were able to link migratory strategies to subsequent reproductive performance. Puffins from large colonies that wintered at high latitude, had long migration distances and (to a lesser extent)
Current Biology
Dispatches experienced low ocean productivity were all independently associated with poor reproductive performance at the breeding grounds. This is likely a consequence of the combined effects of prey availability during the breeding season (linked with density-dependent competition) and adult body condition related to conditions during the previous winter. Such interseasonal interactions are well known [13], but what is unusual here is the level of detail and the opportunity to explore different mechanisms. As well as providing unique insight into the diversity of migratory behaviours in birds, the study of Fayet and colleagues [4] provides important information relevant to conservation. Puffin numbers have declined sharply at some breeding colonies, and for this reason the species was recently reclassified from ‘Least Concern’ to ‘Vulnerable’. Protecting breeding colonies is the first and most straightforward step in the process, but it is clear that conditions in the wintering areas also shape the demographic fortunes of puffins. As the new study reveals, puffins are truly pelagic travellers, occupying huge tracts of the open ocean, crossing many international borders and jurisdictions. Developing an effective conservation strategy for vulnerable migratory species like this one is therefore extremely challenging, but some wintering hotspots were evident and it might therefore be desirable to consider these as priorities for protection [14].
A final heartening message from the study of Fayet and colleagues [4] was that, much like puffins, the authors had little regard for international boundaries — the research team comprised 22 authors from 8 countries. Adopting this outward-looking and collaborative ethos, at a time when some would create a world with more borders, is surely essential if we are to have any hope of maintaining a favourable future for the diversity of migratory animals.
golden age of bio-logging: how animal-borne sensors are advancing the frontiers of ecology. Ecology 96, 1741–1753. 7. Sherley, R.B., Ludynia, K., Dyer, B.M., Lamont, T., Makhado, A.B., Roux, J.-P., Scales, K.L., Underhill, L.G., and Votier, S.C. (2017). Metapopulation tracking juvenile penguins reveals an ecosystem-wide ecological trap. Curr. Biol. 27, 563–568. 8. Guilford, T., Freeman, R., Boyle, D., Dean, B., Kirk, H., Phillips, R., and Perrins, C. (2011). A dispersive migration in the Atlantic puffin and its implications for migratory navigation. PloS One 6, e21336.
REFERENCES 1. Hu, G., Lim, K.S., Horvitz, N., Clark, S.J., Reynolds, D.R., Sapir, N., and Chapman, J.W. (2016). Mass seasonal bioflows of high-flying insect migrants. Science 354, 1584–1587. 2. Egevang, C., Stenhouse, I.J., Phillips, R.A., Petersen, A., Fox, J.W., and Silk, J.R.D. (2010). Tracking of Arctic terns Sterna paradisaea reveals longest animal migration. Proc. Natl. Acad. Sci. USA 107, 2078–2081. 3. Weimerskirch, H., Delord, K., Guitteaud, A., Phillips, R.A., and Pinet, P. (2015). Extreme variation in migration strategies between and within wandering albatross populations during their sabbatical year, and their fitness consequences. Sci. Rep. 5, 8853. 4. Fayet, A.L., Freeman, R., Anker-Nilssen, T., Diamond, A., Erikstad, K.E., Fifield, D., Fitzsimmons, M.G., Hansen, E.S., Harris, M.P., Jessopp, M., et al. (2017). Ocean-wide drivers of migration strategies and their influence on population breeding performance in a declining seabird. Curr. Biol. 27, 3871– 3878.e3. 5. Fehlmann, G., and King, A.J. (2016). Bio-logging. Curr Biol. 26, R830–R831. 6. Wilmers, C.C., Nickel, B., Bryce, C.M., Smith, J.A., Wheat, R.E., and Yovovich, V. (2015). The
9. Fayet, A.L., Freeman, R., Shoji, A., Boyle, D., Kirk, H.L., Dean, B.J., Perrins, C.M., and Guilford, T.G. (2016). Drivers and fitness consequences of dispersive migration in a pelagic seabird. Behav. Ecol. 27, 1061–1072. 10. Newton, I. (2008). The Migration Ecology of Birds (London: Elsevier). 11. Taylor, C.M., and Norris, D.R. (2007). Predicting conditions for migration: effects of density dependence and habitat quality. Biol. Lett. 22, 280–284. , Y.G., and Jodice, P.G.R. 12. Lamb, J.S., Satge (2017). Influence of density-dependent competition on foraging and migratory behavior of a subtropical colonial seabird. Ecol. Evol. 7, 6469–6481. 13. Harrison, X.A., Blount, J.D., Inger, R., Norris, D.R., and Bearhop, S. (2011). Carry-over effects as drivers of fitness differences in animals. J. Anim. Ecol. 80, 4–18. 14. Grecian, W.J., Witt, M.J., Attrill, M.J., Bearhop, S., Becker, P.H., Egevang, C., Furness, R.W., millet, D., Godley, B.J., Gonza´lez-Solı´s, J., Gre et al. (2016). Seabird diversity hotspot linked to ocean productivity in the Canary Current Large Marine Ecosystem. Biol. Lett. 12, 20160024.
Learning: Plasticity without Stabilization in Olfactory Cortex Shivathmihai Nagappan and Kevin M. Franks* Department of Neurobiology, Duke University, Durham, NC, 27710, USA *Correspondence: [email protected] https://doi.org/10.1016/j.cub.2017.11.010
A new study reports unsupervised, experience-dependent reorganization, but not stabilization, of neural odor representations in the zebrafish olfactory system. A central goal in neuroscience is to understand learning, and this has largely been studied by investigating the
mechanisms and expression of neural plasticity. Plasticity allows the nervous system to generate flexible outcomes to a
given stimulus [1–3]. This flexibility is essential for organisms to learn and adapt to varying environmental conditions.
Current Biology 28, R17–R36, January 8, 2018 ª 2017 Elsevier Ltd. R23