TICS-1419; No. of Pages 3
Forum
The evolution of cognition in natural populations Julie Morand-Ferron1 and John L. Quinn2 1 2
Department of Biology, University of Ottawa, 30 Marie Curie, Gendron Hall, Room 160, Ottawa, ON K1N 6N5, Canada School of Biological, Earth and Environmental Sciences, University College Cork, Distillery Fields, North Mall, Cork, Ireland
Individual differences in cognitive abilities have been described in a range of species, but do they impact survival or reproduction? Several recent studies report links between putative cognitive and reproductive traits in avian systems. Whether or when selection should occur in the wild is becoming an exciting avenue of research.
Individual differences in cognitive performance: impact on fitness Evolutionary changes in physical and reproductive traits can sometimes be detected in natural populations over very short timescales. The existence of such changes depends on whether the traits are heritable and whether they influence Darwinian fitness. Human intelligence, as measured by g, is heritable and is associated with several indicators of fitness, including increased health and lower mortality rate (see references in [1]). In other words, genes have a role in g, it pays to be smart and, thus, g may be evolving in real time. However, does it also pay to be smart for nonhuman animals? Can we detect evolution of cognitive traits in the wild? An evolutionary ecology approach examining individual differences in cognitive performance and fitness within living natural populations has the potential to answer these emerging questions. Studies on captive animals demonstrate that cognitive performance has the potential to evolve in real time. For instance, learning contributes to reproductive success in male fruit flies by reducing time wasted in courting nonreceptive females, and learning ability is known to be heritable in several insect species (reviewed in [2]). Laboratory studies show that, under some environmental conditions, selection favors the smart because they reproduce better, leading to evolutionary change across generations in these artificial populations (e.g., [3]). However, the extent to which these processes also occur in the wild, a central question in evolutionary biology, is largely unknown. Some even argue that these microevolutionary processes are unlikely to ever be seen in cognitive traits [4]. The first evidence for links between variation in cognitive performance and fitness in a wild population was published only recently (reviewed in [5]; Figure 1). One study reported that male bowerbirds that could solve a novel, technical problem faster than their peers received Corresponding author: Morand-Ferron, J. ([email protected]). Keywords: reproductive success; evolutionary ecology; individual variation; life history; innovative problem-solving; learning. 1364-6613/ ß 2015 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.tics.2015.03.005
relatively more copulations ([6], but see [7]), a key determinant of reproductive success in this system. Innovative problem-solving success and speed (traits for which the cognitive basis remains unclear) have been tied to reproductive success in two independent studies of great tits [8,9], a well-studied avian model species in ecology. However, only one of these directly tested whether this led to a selective benefit. The authors found that problem-solvers produced more offspring but were no more likely to have their offspring survive to join the following breeding generation than were nonsolvers [8]. However, this mismatch is not necessarily surprising, because >90% of chicks that successfully leave the nest die within a couple of months. Parallel patterns observed in laboratory and field studies offer a likely explanation for why, thus far, selection has never been detected acting on cognitive performance in the wild. Both experimental evolution on laboratory fruit fly populations [10] and research on wild great tits [8], point to fitness trade-offs. Individuals exhibiting higher cognitive performance were found to be less able to compete for food [10,11]. High performers in cognitive tests were also more likely to desert their offspring before they could leave the nest [8], it is thought because of greater sensitivity to the perceived risk of predation at nests associated with being trapped by researchers for tagging (Figure 1). More generally, trade-offs with high performance are likely to be pervasive due to costs of collecting, remembering, and using information. Links between cognition and fitness could be masked because of confounding, correlated behavioral traits, such as personality. Ultimately, these trade-offs and complex relations between multiple traits may explain why individuals vary in their cognitive ability, and why natural selection might be expected to improve (or reduce) cognitive ability only under specific environmental conditions [5]. The trick will be to look for selection where it is likely to be seen. For instance, if good learners tend to be poor competitors [10,11], then they should be favored in novel foraging environments with few competitors. Benefits of an individual-based evolutionary ecology approach Experimental manipulations, comparisons between populations and species, and theoretical models all provide complementary approaches to the study of cognitive evolution and generate testable hypotheses for the individualfocused approach highlighted here. For instance, several species of parids (tits and chickadees) and corvids (crows and jays) rely on hundreds or even thousands of food caches to survive through the winter. Some of these species (Figure 1) will also readily hoard food in captivity. In these Trends in Cognitive Sciences xx (2015) 1–3
1
TICS-1419; No. of Pages 3
Forum
(A)
Trends in Cognitive Sciences xxx xxxx, Vol. xxx, No. x
(B)
Spaal memory
(+)
Responsiveness to predators?
(+) Starvaon avoidance?
Dominance
Overwinter survival
Innovave problem-solving speed
Innovave problem-solving success
Food caches retrieval accuracy
–
General cognive ability?
(C)
Operant learning?
(+)
+
–
Number of fledglings
Nest success
+
Nest aenveness?
n.s. Recruitment to the pool of breeders
+
+
Bower quality?
Mang success
?
Reproducve success
+
TRENDS in Cognitive Sciences
Figure 1. Three study systems that have examined within-population variation in cognitive performance and fitness components. (A) Mountain chickadees, Poecile gambeli, from high elevations exhibited more accurate spatial memory but were socially subordinate to individuals of the same population living at lower elevation ([15] and references therein). (B) Individual great tits, Parus major, that successfully solved a novel foraging problem in captivity subsequently produced more offspring compared with nonsolvers in the wild [8]; this was not linked to food provisioning but could have been due to other unexamined differences in parental care. Solvers also had higher nest failure rates, although their own viability and that of their offspring were similar otherwise, generating the testable hypothesis that solvers were more responsive to predation risk. These contrasting effects may explain why problem-solving performance did not predict the number of offspring that were recruited into the next breeding generation [8]. (C) Male satin bowerbirds, Ptilonorhynchus violaceus, that solved novel problems relatively quickly in the wild received relatively more copulations [6]. Fast problem-solving was assumed to represent better general cognitive abilities, which were hypothesized to be sexually selected (but see [7]). Key: +, positive, or –, negative relations; n.s., not significant;?, need for additional empirical work; (), link assumed. Potential mechanisms shown in yellow.
species, individuals tend to look for food in the specific places where they cached them, even when the food has been removed, suggesting that they rely at least partly on spatial memory for food retrieval (reviewed in [12]). This body of research raises the expectation that, all other things being equal, individuals with better spatial memories should be more likely to survive harsh winters. An individual-based approach would enable one to test this long-standing, yet unexamined, prediction. In fact, we know of no studies that have directly evidenced a survival advantage or disadvantage to any better-performing cognitive phenotypes in the wild. Comparisons between species and populations suggest that social competition, food availability, and environmental variation generally drive selection and evolution on cognition. One common garden study on black-capped chickadees found that young birds taken from nests in Alaska had more numerous hippocampal neurons, more accurate spatial memory, faster habituation, and more rapid innovative problem-solving compared with their counterparts from Kansas, where winter conditions are milder and hoarded food is less critical for survival (reviewed in [12]). Here, the intensity of seasonal variation seems to select for a suite of cognitive traits within each of these populations, including habituation speed and spatial memory for food locations. A nonmutually exclusive possibility is that selection acts on some underlying trait, resulting in the expression of higher or lower performance on all of these cognitive traits. The individual-based approach could reveal which of these traits shows the strongest link with different fitness components and how this may vary across populations. Evolutionary models are also generating interesting testable hypotheses. For instance, models predict that living in environments with predictable events over an individual’s lifetime should lead to selection for increased 2
reliance on learning, especially when there is variability in the environment between generations (see references in [3]). For example, invasion by a novel prey species into a new area can create a situation in which the individual resident predators that express a steeper learning curve for new prey cues could suddenly see their fitness increase relative to the rest of the population. Similarly, species undergoing range expansions or being introduced to new habitats may experience drastic changes in ecological conditions and be subject to novel selection pressures for a range of behavioral and cognitive traits [13]. Functional links between cognition and fitness The precise relation between cognition and fitness can also generate predictions for the behavioral mechanisms linking the two. For example, innovative problem-solving performance assayed among wild great tits temporarily taken into captivity during winter was associated with increased clutch size and number of fledglings, but not with offspring body condition, when they later reproduced in the wild in Wytham Woods (UK) [8]. Similar effects were seen in a Swedish population [9]. There was also a combined effect of male and female problem-solving success on clutch size in Wytham, but not on the number of fledglings or fledgling body mass [8]. These two results suggest that the processes underlying innovative problem-solving performance are especially important in determining foraging efficiency during egg laying, when food is scarcer and when males feed their mates. This idea was supported by the finding that the size of the area around the nest used to search for food (the home range) was half as large for birds who were good problem-solvers compared with poor problem-solvers [8]. Understanding how specific cognitive processes influence the way selection acts on natural behavior will remain contingent on our knowledge of detailed cognitive
TICS-1419; No. of Pages 3
Forum mechanisms. Consider the hypothetical example of positive selection for associative learning performance in honeybees when flower colors are predictive cues of nectar rewards (cf. [14]). What specifically could be the target of selection? Is it increased attention towards color, higher weighting of learned information when foraging, more efficient neural connectivity, or a combination of all of the above? Experimental manipulations, such as blocking a given cognitive process via pharmacological means, or comparative work, such as studying multiple populations under different selective pressures, will be necessary to deconstruct individual performance into its constituents and identify which of these drive differences in functional behavior associated with fitness. Concluding remarks A range of approaches can be used to examine the evolution of cognitive processes. An individual-based approach can reveal the role that short-term evolutionary processes have in driving cognitive variation within extant populations. Significant challenges remain in the field (see [5] and references therein). For example, many if not all measured cognitive traits in studies to date are likely a combination of cognitive and noncognitive components because some factors, such as motivation, are difficult to standardize across individuals. However, rather than removing ‘noncognitive’ components, studying these traits together may be essential if it reflects more accurately the natural expression of cognitive variation upon which selection acts. Fortunately, the intersection between ecology, psychology, and neurosciences has already produced exciting results that will continue to stimulate research into whether the evolution of cognition in real time can be demonstrated in nature. Acknowledgments We would like to thank Louis Lefebvre for comments on a previous version of the manuscript, and Ella Cole, Maxime Cauchoix, William O’Shea, Neeltje Boogert, Simon Reader, and Luc-Alain Giraldeau for discussion on the evolution of cognition. J.M-F. would also like to thank speakers of the symposium ‘Individual variation in cognition and consequences for life-history and fitness in natural populations’ held at
Trends in Cognitive Sciences xxx xxxx, Vol. xxx, No. x the 2014 International Society of Behavioral Ecology Conference: Vladimir Pravosudov, Nigel Raine, Lisa Evans, Simon Ducatez, and Jason Keagy. H. Doug Pratt and Chris Pendleton kindly provided vignette drawings of birds; copyright remains with the artists. J.M-F. was funded by Natural Sciences and Engineering Research Council of Canada (435596-2013), and J.L.Q. was funded by grants from the Royal Society, the Natural Environment Research Council (NE/I017208/1), and the Leverhulme Trust (RPG-265).
References 1 Plomin, J. and Deary, I.J. (2015) Genetics and intelligence differences: five special findings. Mol. Psychiatry 20, 98–108 2 Dukas, R. (2008) Evolutionary biology of insect learning. Annu. Rev. Entomol. 53, 145–160 3 Dunlap, A.S. and Stephens, D.W. (2009) Components of change in the evolution of learning and unlearned preference. Proc. R. Soc. B 276, 3201–3208 4 Rowe, C. and Healy, S.D. (2014) Measuring variation in cognition. Behav. Ecol. 25, 1287–1292 5 Morand-Ferron, J. et al. (2015) Studying the evolutionary ecology of cognition in the wild: a review of practical and conceptual challenges. Biol. Rev. Published January 28, 2015. http://dx.doi.org/10.1111/brv. 12174 6 Keagy, J. et al. (2009) Male satin bowerbird problem-solving ability predicts mating success. Anim. Behav. 78, 809–817 7 Isden, J. et al. (2013) Performance in cognitive and problem-solving tasks in male spotted bowerbirds does not correlate with mating success. Anim. Behav. 86, 829–838 8 Cole, E.F. et al. (2012) Cognitive ability influences reproductive life history variation in the wild. Curr. Biol. 22, 1808–1812 9 Cauchard, L. et al. (2013) Problem-solving performance correlates with reproductive success in a wild bird population. Anim. Behav. 85, 19–26 10 Mery, F. and Kawecki, T.J. (2003) A fitness cost of learning ability in Drosophila melanogaster. Proc. R. Soc. B 270, 2465–2469 11 Cole, E.F. and Quinn, J.L. (2012) Personality and problem-solving explain competitive ability in the wild. Proc. R. Soc. B 279, 1168–1175 12 Pravosudov, V.V. and Roth, T.C. (2013) Cognitive ecology of food hoarding: the evolution of spatial memory and the hippocampus. Annu. Rev. Ecol. Ecol. Syst. 44, 173–193 13 Sol, D. et al. (2005) Big brains, enhanced cognition, and response of birds to novel environments. Proc. Natl. Acad. Sci. U.S.A. 102, 5460–5465 14 Raine, N.E. and Chittka, L. (2008) The correlation of learning speed and natural foraging success in bumble-bees. Proc. R. Soc. B 275, 803–808 15 Kozlovsky, D. et al. (2014) Elevation-related differences in novel environment exploration and social dominance in food-caching mountain chickadees. Behav. Ecol. Sociobiol. 68, 1871–1881
3
Forum
The evolution of cognition in natural populations Julie Morand-Ferron1 and John L. Quinn2 1 2
Department of Biology, University of Ottawa, 30 Marie Curie, Gendron Hall, Room 160, Ottawa, ON K1N 6N5, Canada School of Biological, Earth and Environmental Sciences, University College Cork, Distillery Fields, North Mall, Cork, Ireland
Individual differences in cognitive abilities have been described in a range of species, but do they impact survival or reproduction? Several recent studies report links between putative cognitive and reproductive traits in avian systems. Whether or when selection should occur in the wild is becoming an exciting avenue of research.
Individual differences in cognitive performance: impact on fitness Evolutionary changes in physical and reproductive traits can sometimes be detected in natural populations over very short timescales. The existence of such changes depends on whether the traits are heritable and whether they influence Darwinian fitness. Human intelligence, as measured by g, is heritable and is associated with several indicators of fitness, including increased health and lower mortality rate (see references in [1]). In other words, genes have a role in g, it pays to be smart and, thus, g may be evolving in real time. However, does it also pay to be smart for nonhuman animals? Can we detect evolution of cognitive traits in the wild? An evolutionary ecology approach examining individual differences in cognitive performance and fitness within living natural populations has the potential to answer these emerging questions. Studies on captive animals demonstrate that cognitive performance has the potential to evolve in real time. For instance, learning contributes to reproductive success in male fruit flies by reducing time wasted in courting nonreceptive females, and learning ability is known to be heritable in several insect species (reviewed in [2]). Laboratory studies show that, under some environmental conditions, selection favors the smart because they reproduce better, leading to evolutionary change across generations in these artificial populations (e.g., [3]). However, the extent to which these processes also occur in the wild, a central question in evolutionary biology, is largely unknown. Some even argue that these microevolutionary processes are unlikely to ever be seen in cognitive traits [4]. The first evidence for links between variation in cognitive performance and fitness in a wild population was published only recently (reviewed in [5]; Figure 1). One study reported that male bowerbirds that could solve a novel, technical problem faster than their peers received Corresponding author: Morand-Ferron, J. ([email protected]). Keywords: reproductive success; evolutionary ecology; individual variation; life history; innovative problem-solving; learning. 1364-6613/ ß 2015 Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.tics.2015.03.005
relatively more copulations ([6], but see [7]), a key determinant of reproductive success in this system. Innovative problem-solving success and speed (traits for which the cognitive basis remains unclear) have been tied to reproductive success in two independent studies of great tits [8,9], a well-studied avian model species in ecology. However, only one of these directly tested whether this led to a selective benefit. The authors found that problem-solvers produced more offspring but were no more likely to have their offspring survive to join the following breeding generation than were nonsolvers [8]. However, this mismatch is not necessarily surprising, because >90% of chicks that successfully leave the nest die within a couple of months. Parallel patterns observed in laboratory and field studies offer a likely explanation for why, thus far, selection has never been detected acting on cognitive performance in the wild. Both experimental evolution on laboratory fruit fly populations [10] and research on wild great tits [8], point to fitness trade-offs. Individuals exhibiting higher cognitive performance were found to be less able to compete for food [10,11]. High performers in cognitive tests were also more likely to desert their offspring before they could leave the nest [8], it is thought because of greater sensitivity to the perceived risk of predation at nests associated with being trapped by researchers for tagging (Figure 1). More generally, trade-offs with high performance are likely to be pervasive due to costs of collecting, remembering, and using information. Links between cognition and fitness could be masked because of confounding, correlated behavioral traits, such as personality. Ultimately, these trade-offs and complex relations between multiple traits may explain why individuals vary in their cognitive ability, and why natural selection might be expected to improve (or reduce) cognitive ability only under specific environmental conditions [5]. The trick will be to look for selection where it is likely to be seen. For instance, if good learners tend to be poor competitors [10,11], then they should be favored in novel foraging environments with few competitors. Benefits of an individual-based evolutionary ecology approach Experimental manipulations, comparisons between populations and species, and theoretical models all provide complementary approaches to the study of cognitive evolution and generate testable hypotheses for the individualfocused approach highlighted here. For instance, several species of parids (tits and chickadees) and corvids (crows and jays) rely on hundreds or even thousands of food caches to survive through the winter. Some of these species (Figure 1) will also readily hoard food in captivity. In these Trends in Cognitive Sciences xx (2015) 1–3
1
TICS-1419; No. of Pages 3
Forum
(A)
Trends in Cognitive Sciences xxx xxxx, Vol. xxx, No. x
(B)
Spaal memory
(+)
Responsiveness to predators?
(+) Starvaon avoidance?
Dominance
Overwinter survival
Innovave problem-solving speed
Innovave problem-solving success
Food caches retrieval accuracy
–
General cognive ability?
(C)
Operant learning?
(+)
+
–
Number of fledglings
Nest success
+
Nest aenveness?
n.s. Recruitment to the pool of breeders
+
+
Bower quality?
Mang success
?
Reproducve success
+
TRENDS in Cognitive Sciences
Figure 1. Three study systems that have examined within-population variation in cognitive performance and fitness components. (A) Mountain chickadees, Poecile gambeli, from high elevations exhibited more accurate spatial memory but were socially subordinate to individuals of the same population living at lower elevation ([15] and references therein). (B) Individual great tits, Parus major, that successfully solved a novel foraging problem in captivity subsequently produced more offspring compared with nonsolvers in the wild [8]; this was not linked to food provisioning but could have been due to other unexamined differences in parental care. Solvers also had higher nest failure rates, although their own viability and that of their offspring were similar otherwise, generating the testable hypothesis that solvers were more responsive to predation risk. These contrasting effects may explain why problem-solving performance did not predict the number of offspring that were recruited into the next breeding generation [8]. (C) Male satin bowerbirds, Ptilonorhynchus violaceus, that solved novel problems relatively quickly in the wild received relatively more copulations [6]. Fast problem-solving was assumed to represent better general cognitive abilities, which were hypothesized to be sexually selected (but see [7]). Key: +, positive, or –, negative relations; n.s., not significant;?, need for additional empirical work; (), link assumed. Potential mechanisms shown in yellow.
species, individuals tend to look for food in the specific places where they cached them, even when the food has been removed, suggesting that they rely at least partly on spatial memory for food retrieval (reviewed in [12]). This body of research raises the expectation that, all other things being equal, individuals with better spatial memories should be more likely to survive harsh winters. An individual-based approach would enable one to test this long-standing, yet unexamined, prediction. In fact, we know of no studies that have directly evidenced a survival advantage or disadvantage to any better-performing cognitive phenotypes in the wild. Comparisons between species and populations suggest that social competition, food availability, and environmental variation generally drive selection and evolution on cognition. One common garden study on black-capped chickadees found that young birds taken from nests in Alaska had more numerous hippocampal neurons, more accurate spatial memory, faster habituation, and more rapid innovative problem-solving compared with their counterparts from Kansas, where winter conditions are milder and hoarded food is less critical for survival (reviewed in [12]). Here, the intensity of seasonal variation seems to select for a suite of cognitive traits within each of these populations, including habituation speed and spatial memory for food locations. A nonmutually exclusive possibility is that selection acts on some underlying trait, resulting in the expression of higher or lower performance on all of these cognitive traits. The individual-based approach could reveal which of these traits shows the strongest link with different fitness components and how this may vary across populations. Evolutionary models are also generating interesting testable hypotheses. For instance, models predict that living in environments with predictable events over an individual’s lifetime should lead to selection for increased 2
reliance on learning, especially when there is variability in the environment between generations (see references in [3]). For example, invasion by a novel prey species into a new area can create a situation in which the individual resident predators that express a steeper learning curve for new prey cues could suddenly see their fitness increase relative to the rest of the population. Similarly, species undergoing range expansions or being introduced to new habitats may experience drastic changes in ecological conditions and be subject to novel selection pressures for a range of behavioral and cognitive traits [13]. Functional links between cognition and fitness The precise relation between cognition and fitness can also generate predictions for the behavioral mechanisms linking the two. For example, innovative problem-solving performance assayed among wild great tits temporarily taken into captivity during winter was associated with increased clutch size and number of fledglings, but not with offspring body condition, when they later reproduced in the wild in Wytham Woods (UK) [8]. Similar effects were seen in a Swedish population [9]. There was also a combined effect of male and female problem-solving success on clutch size in Wytham, but not on the number of fledglings or fledgling body mass [8]. These two results suggest that the processes underlying innovative problem-solving performance are especially important in determining foraging efficiency during egg laying, when food is scarcer and when males feed their mates. This idea was supported by the finding that the size of the area around the nest used to search for food (the home range) was half as large for birds who were good problem-solvers compared with poor problem-solvers [8]. Understanding how specific cognitive processes influence the way selection acts on natural behavior will remain contingent on our knowledge of detailed cognitive
TICS-1419; No. of Pages 3
Forum mechanisms. Consider the hypothetical example of positive selection for associative learning performance in honeybees when flower colors are predictive cues of nectar rewards (cf. [14]). What specifically could be the target of selection? Is it increased attention towards color, higher weighting of learned information when foraging, more efficient neural connectivity, or a combination of all of the above? Experimental manipulations, such as blocking a given cognitive process via pharmacological means, or comparative work, such as studying multiple populations under different selective pressures, will be necessary to deconstruct individual performance into its constituents and identify which of these drive differences in functional behavior associated with fitness. Concluding remarks A range of approaches can be used to examine the evolution of cognitive processes. An individual-based approach can reveal the role that short-term evolutionary processes have in driving cognitive variation within extant populations. Significant challenges remain in the field (see [5] and references therein). For example, many if not all measured cognitive traits in studies to date are likely a combination of cognitive and noncognitive components because some factors, such as motivation, are difficult to standardize across individuals. However, rather than removing ‘noncognitive’ components, studying these traits together may be essential if it reflects more accurately the natural expression of cognitive variation upon which selection acts. Fortunately, the intersection between ecology, psychology, and neurosciences has already produced exciting results that will continue to stimulate research into whether the evolution of cognition in real time can be demonstrated in nature. Acknowledgments We would like to thank Louis Lefebvre for comments on a previous version of the manuscript, and Ella Cole, Maxime Cauchoix, William O’Shea, Neeltje Boogert, Simon Reader, and Luc-Alain Giraldeau for discussion on the evolution of cognition. J.M-F. would also like to thank speakers of the symposium ‘Individual variation in cognition and consequences for life-history and fitness in natural populations’ held at
Trends in Cognitive Sciences xxx xxxx, Vol. xxx, No. x the 2014 International Society of Behavioral Ecology Conference: Vladimir Pravosudov, Nigel Raine, Lisa Evans, Simon Ducatez, and Jason Keagy. H. Doug Pratt and Chris Pendleton kindly provided vignette drawings of birds; copyright remains with the artists. J.M-F. was funded by Natural Sciences and Engineering Research Council of Canada (435596-2013), and J.L.Q. was funded by grants from the Royal Society, the Natural Environment Research Council (NE/I017208/1), and the Leverhulme Trust (RPG-265).
References 1 Plomin, J. and Deary, I.J. (2015) Genetics and intelligence differences: five special findings. Mol. Psychiatry 20, 98–108 2 Dukas, R. (2008) Evolutionary biology of insect learning. Annu. Rev. Entomol. 53, 145–160 3 Dunlap, A.S. and Stephens, D.W. (2009) Components of change in the evolution of learning and unlearned preference. Proc. R. Soc. B 276, 3201–3208 4 Rowe, C. and Healy, S.D. (2014) Measuring variation in cognition. Behav. Ecol. 25, 1287–1292 5 Morand-Ferron, J. et al. (2015) Studying the evolutionary ecology of cognition in the wild: a review of practical and conceptual challenges. Biol. Rev. Published January 28, 2015. http://dx.doi.org/10.1111/brv. 12174 6 Keagy, J. et al. (2009) Male satin bowerbird problem-solving ability predicts mating success. Anim. Behav. 78, 809–817 7 Isden, J. et al. (2013) Performance in cognitive and problem-solving tasks in male spotted bowerbirds does not correlate with mating success. Anim. Behav. 86, 829–838 8 Cole, E.F. et al. (2012) Cognitive ability influences reproductive life history variation in the wild. Curr. Biol. 22, 1808–1812 9 Cauchard, L. et al. (2013) Problem-solving performance correlates with reproductive success in a wild bird population. Anim. Behav. 85, 19–26 10 Mery, F. and Kawecki, T.J. (2003) A fitness cost of learning ability in Drosophila melanogaster. Proc. R. Soc. B 270, 2465–2469 11 Cole, E.F. and Quinn, J.L. (2012) Personality and problem-solving explain competitive ability in the wild. Proc. R. Soc. B 279, 1168–1175 12 Pravosudov, V.V. and Roth, T.C. (2013) Cognitive ecology of food hoarding: the evolution of spatial memory and the hippocampus. Annu. Rev. Ecol. Ecol. Syst. 44, 173–193 13 Sol, D. et al. (2005) Big brains, enhanced cognition, and response of birds to novel environments. Proc. Natl. Acad. Sci. U.S.A. 102, 5460–5465 14 Raine, N.E. and Chittka, L. (2008) The correlation of learning speed and natural foraging success in bumble-bees. Proc. R. Soc. B 275, 803–808 15 Kozlovsky, D. et al. (2014) Elevation-related differences in novel environment exploration and social dominance in food-caching mountain chickadees. Behav. Ecol. Sociobiol. 68, 1871–1881
3
