evolution & development · 2019. 9. 3. · uller, moczek, watson, brakefield, & laland,...

Evolution & Development. 2019;e12311. wileyonlinelibrary.com/journal/ede © 2019 Wiley Periodicals, Inc. | 1 of 17https://doi.org/10.1111/ede.12311

DOI: 10.1111/ede.12311

RE S EARCH

Animal learning as a source of developmental bias

Kevin N. Laland1 | Wataru Toyokawa1,2 | Thomas Oudman1,3

1School of Biology, University of St.Andrews, St. Andrews, UK2Department of Evolutionary Studies ofBiosystems, SOKENDAI (The GraduateUniversity for Advanced Studies),Hayama, Kanagawa, Japan3Department of Coastal Systems, NIOZRoyal Netherlands Institute for SeaResearch, Utrecht University, Utrecht,The Netherlands

CorrespondenceKevin N. Laland, School of Biology,University of St. Andrews,St. Andrews, UK.Email: [email protected]

Present addressWataru Toyokawa, Department ofPsychology, University of Konstanz,Germany.

Funding informationJohn Templeton Foundation, Grant/Award Number: 60501

Abstract

As a form of adaptive plasticity that allows organisms to shift their phenotype

toward the optimum, learning is inherently a source of developmental bias.

Learning may be of particular significance to the evolutionary biology

community because it allows animals to generate adaptively biased novel

behavior tuned to the environment and, through social learning, to propagate

behavioral traits to other individuals, also in an adaptively biased manner. We

describe several types of developmental bias manifest in learning, including an

adaptive bias, historical bias, origination bias, and transmission bias, stressing

that these can influence evolutionary dynamics through generating nonrandom

phenotypic variation and/or nonrandom environmental states. Theoretical

models and empirical data have established that learning can impose direction

on adaptive evolution, affect evolutionary rates (both speeding up and slowing

down responses to selection under different conditions) and outcomes,

influence the probability of populations reaching global optimum, and affect

evolvability. Learning is characterized by highly specific, path‐dependentinteractions with the (social and physical) environment, often resulting in

new phenotypic outcomes. Consequently, learning regularly introduces novelty

into phenotype space. These considerations imply that learning may commonly

generate plasticity first evolution.

KEYWORD S

developmental bias, evolvability, learning, plasticity, plasticity first

1 | INTRODUCTION

A central, largely unresolved, issue in the field ofevolutionary biology is whether and how developmentalprocesses contribute to evolutionary change (Love,2015), and one much‐discussed means by whichdevelopment might do so is through generating a biaseddistribution of phenotypic variation (i.e., “developmen-tal bias”; Brakefield, 2006; Maynard‐Smith et al., 1985;Uller, Moczek, Watson, Brakefield, & Laland, 2018).Perhaps some developmental biologists will not think oflearning as a “developmental” process, as it is typicallystudied without consideration of gastrulation, celldivision, or hox genes. However, if development is

viewed broadly to comprise all of the changes in size,shape, and function that take place during the life of anorganism, learning is unquestionably a developmentalprocess. Viewed in this manner, any biases in behavioralphenotypes that are generated through learning are aform of developmental bias.

This point takes on new significance once some well‐documented properties of learning are recognized. Whatis immediately apparent to most students of animallearning, but perhaps less obvious to many otherbiologists, is that learning typically is a highly flexibleform of adaptive plasticity that shifts many aspects of thephenotype toward the optimum. Also self‐apparent is thefact that animals commonly learn knowledge and skills

http://orcid.org/0000-0002-2457-0900

mailto:[email protected]

through observing and interacting with other animals,including other species, again with a strong tendency toacquire adaptive knowledge.

In this article we describe how phenotypes generatedthrough animal learning are generally not just biased, butadaptively biased. Moreover, such phenotypes are notjust manifest in a single individual but commonly rapidlypropagated to multiple individuals and, through socialtransmission, passed on to descendants. We also reviewthe evidence that novel phenotypes propagated throughlearning can impact genetic evolution. Here we set out topresent the evidence, both experimental and theoretical,that leads us to the view that learning should be regardedas an important source of developmental bias in animals,and a profound influence on adaptive evolution.

2 | DEFINITIONS

2.1 | What is developmental bias?

The term “developmental bias,” as commonly deployed,is somewhat ambiguous because it is manifestly subjectto two distinct readings: is it the products of developmentthat are biased (i.e., are a biased set of phenotypicvariants generated)? Or are developmental processesbiasing something else—most obviously, the course ofevolution? While the most evident way in whichdevelopmental processes can bias the course of evolutionis through biasing phenotypic variation, in principle,these two readings can be disassociated. Biased pheno-typic variation need not affect the course of evolution(e.g., if dynamics are dominated by selection), whiledevelopmental processes could affect the course ofevolution even if phenotypic variation were isotropic(e.g., through nonrandom modification of selectiveenvironments). We suspect that usage of the term slipsbetween these two meanings, with users perhaps some-times unaware of the inherent ambiguity, and frequentlycommitted to both interpretations. This slippage maypartly account for the diversity of usage of this term.

One way of resolving the ambiguity is to distinguishbetween the two usages explicitly: that is, distinguishingbetween a biased product of development (henceforth“developmentally biased phenotypic variation”), and abiasing process of development (“developmentally biasedevolutionary process”). This approach has proven usefulin helping to clarify a similar ambiguity in the use of theterm “innovation” (Reader & Laland, 2003).

To characterize the process or the product ofdevelopment as “biased” we must have some notion ofwhat an unbiased product/process would look like.Hence, ideally a definition of developmental bias wouldstate relative to what baseline the bias arises. Here,

following Uller et al. (2018, p. 949), we define devel-opmentally biased phenotypic variation as “the biasimposed on the distribution of phenotypic variation,arising from the structure, character, composition, ordynamics of the developmental system, relative to theassumption of isotropic variation.” We also define adevelopmentally biased evolutionary process as “any biasin the rate, dynamics and pattern of evolution arisingfrom the production of nonrandom phenotypic variation(broadly construed), relative to the expected rate,dynamics and pattern of evolution arising from randomgenetic variation.”

We introduce the qualification “broadly construed” asour use of this term will encompass cases in whichorganisms express their nonrandom (extended) pheno-typic variation in the external environment, for instancethrough building nests, burrows, mounds, webs, andpupal cases, or modifying local soils, hydrology, chem-istry, nutrients, or flows (i.e., niche construction), andwhich thereby bias the sources of selection that they, andother ecologically interacting populations, experience(Laland, Odling‐Smee, & Feldman, In Press; Odling‐Smee, Laland, & Feldman, 2003).

In this article, we are interested in both the specificform of developmentally biased phenotypic variation thatarises from animal learning, and in its evolutionaryconsequences. We suggest that the forms of bias thatresult from learning are of relevance to the evolutionarybiology community because, (a) through learning,animals tune their behavior to environments, includingnovel environments, by selectively generating, retaining,and refining adaptive behaviors more readily thannonadaptive ones. Moreover, (b) these adaptive novelphenotypic variants are commonly propagated to otherindividuals, including nonrelatives, and across genera-tions, through social learning. This social transmission ofbehavior is a second source of bias toward the propaga-tion of adaptive variants—evidence from diverse speciesof animals demonstrates that the likelihood of socialtransmission of novel learned knowledge and skills issignificantly higher for adaptive than for nonadaptivebehavioral variants. In addition, (c) learned and sociallytransmitted behavior can bring about consistent changesin the social and ecological environments, therebybiasing the selective environment of both the learnerand of other species that experience modified conditions(i.e., niche construction). We submit that by generatingand propagating novel phenotypes that are adaptive (i.e.,nonrandom with respect to fitness), and modifyingselective environments in reliable ways, animal learningbiases the course of evolution. We go on to make furtherdistinctions between different types of developmentalbias manifest in, and resulting from, learning (Table 1).

2 of 17 | LALAND ET AL.

TABLE 1 Categories of developmental bias generated by learning, with examples (see text for details)

Category Examples

Adaptive bias • Reinforcement learning (Staddon, 2007)

Developmental variation biased toward adaptive outcomes • Adaptive filtering and biased cultural transmission (Enquist &Ghirlanda, 2007; Rendell et al., 2010)

• Teaching behavior in ants, bees, meerkats, and pied babblers(Hoppitt et al., 2008; Thornton & Raihani, 2010)

Historical bias • Rats avoid taste or smell of poisoned food, but are slow to learnthat a sound or light predicts illness (Garcia & Koelling, 1966).

Developmental variation biased by historical legacy (e.g.,ancestral selection, or tradition)

• Lab‐reared rhesus monkeys learn a fear of snakes morereadily than a fear of arbitrary objects, after watchingconspecifics behaving fearfully (Mineka & Cook, 1988; but seeStephenson, 1967).

• Raccoons could not be conditioned to pick up coins andplace them in a money box for a food reward (Breland &Breland, 1961).

Origination bias • Juveniles of some migratory birds exhibit more route variationthan older individuals (Mueller et al., 2013; Oppel et al., 2015;Vansteelant et al., 2017)

Developmental variation biased in its origination • The inventors of novel behavior are usually more likely to beexperienced individuals than youngsters in nonhuman primates(Kendal et al., 2005; Reader & Laland, 2001), and more commonlylow‐ranking than dominants (Reader & Laland, 2001).

• Innovativeness of monkey species was predicted by their relianceon extractive foraging (Kendal et al., 2005)

Transmission bias • Red‐winged blackbirds copy feeding conspecifics exceptwhen they show an aversive reaction to food (Mason &Reidinger, 1982).

Biased transmission of developmental variation, with some formsbeing propagated more readily than others

• Bats unsuccessful at locating food alone follow successful bats tofeeding sites, using cues indicative of feeding, for example,defecation (Wilkinson, 1992).

• Insects and birds copy the nest‐site decisions of successfulconspecifics and heterospecifics (Forsman & Seppänen, 2011;Pasqualone & Davis, 2011; Sarin & Dukas, 2009; Seppänenet al., 2011).

Variational bias • Culturally transmitted dietary traditions in killer whale havefavored population‐specific genes influencing morphology anddigestion (Foote et al., 2016; Hoelzel & Moura, 2016).

Developmental processes bias evolutionary processes throughgenerating some phenotypic forms more readily than others

• Mate choice copying influences sexual selection of maletraits (Gibson et al., 1991; Kirkpatrick & Dugatkin, 1994;Nöbel et al., 2018).

• Reed warbler learning drives the evolution of plumagepatterns in cuckoos (Davies & Welbergen, 2009; Thorogood &Davies, 2012).

Selective bias • Dairy farming created selection alleles for adult lactasepersistence (Gerbault et al., 2011)

Developmental processes bias evolutionary processes throughgenerating some environmental states more readily than others(niche construction)

• Agricultural practices (e.g., cultivating yams) haveinadvertently promoted the spread of malaria in somepopulations, leading to selection of the HbS allele whichconfers resistance (Durham, 1991).

• The farming and consumption of starchy foods has favored highcopy number of AMY1, which facilitates the breakdown of theexcess starch in agricultural diets (Perry et al., 2007).

LALAND ET AL. | 3 of 17

2.2 | What is animal learning?

Learning is generally defined as a relatively permanentchange in behavior (or potential behavior) that resultsfrom experience (Dickinson, 1980; Kirkpatrick & Hall,2004; Rescorla, 1988). Central to definitions of learningis the requirement that there must be a memory traceof what has been learned, allowing the animal onsubsequent occasions to recall or do what they havelearned previously. Transient changes, such as reflexes,do not qualify. Learning is usually inferred fromchanges in observable behavior, but is not alwaysimmediately manifest, and can be stored to influencefuture performance.

There are many forms of learning (Kirkpatrick &Hall, 2004; Staddon, 2016). Animals may learn about asingle event through habituation or sensitization, orform associations between events, which includes“classical” (a.k.a. “Pavlovian”) and “operant” (a.k.a.“instrumental”) conditioning. The latter is of mostinterest here, as it is thought to be the primary meansby which animals acquire behaviour (Staddon, 2016).This kind of learning occurs through reinforcement,in which positive or negative experiences provideanimals with a mechanism for the selection ofappropriate behavior. Actions that bring about posi-tive consequences (e.g., finding food, avoiding danger)are more likely to be performed in the future, whilethose with negative consequences (causing pain,missing out on reward) become less likely. Suchactions occur in a particular context, or in response toa particular cue, and the learning that takes place isdescribed as “associative” because the animal learnsto produce the action in response to the cue (i.e.,stimulus–response learning) or else learns that theaction has a particular consequence (i.e., response–r-einforcement learning).

That animals learn through operant conditioninghas been known for a long time (Thorndike, 1898) andhas been demonstrated in so many species, includingcountless invertebrates, that it is commonly regardedas virtually universal across animals. Skinner (1938)stressed three general features of animal learning:(a) animals are frequently active, and hence arecontinuously emitting behavior; (b) these emittedbehavior patterns frequently have consequences thatinfluence the frequency with which the behavior isrepeated in the future; and (c) the effects of theconsequences are influenced by the animal’s motiva-tional state, as well as by the physical and socialenvironment. The first of these is rarely emphasized,but important. Learned behavior is often the result ofan exploratory search conducted over multiple trials,

through which individuals hone their behavior toexploit their environment. This exploratory compo-nent to learning is significant, because it generatesbehavioral flexibility and variability. There is see-mingly no end to the associations that many animalscould form.

In their natural environment, animals frequentlylearn from other individuals. In this manner, animalsrapidly acquire skills and functional informationconcerning what to eat, where to find it, how toprocess it, pathways to take through their environ-ment, what a predator looks like, how to escape thepredator, calls, songs, and more (Hoppitt & Laland,2013). Social learning is defined as learning that isfacilitated by observation of, or interaction with,another individual or its products (Heyes, 1994;Hoppitt & Laland, 2013). It is also widespread amonganimals, including invertebrates, and even in speciesdescribed as “solitary” (Wilkinson, Kuenstner, Muel-ler, & Huber, 2010). Theoretical analyses and experi-mental studies both strongly suggest that sociallearning is more efficient than individuals learningalone, for instance, through trial‐and‐error (Hoppitt &Laland, 2013; Rendell et al., 2010). That is becausewhen animals learn socially, the aforementioned“exploratory search” is effectively expanded to en-compass the trials and associated experiences ofmultiple individuals. Social learning is particularlyvaluable in helping animals to solve difficult problemswhere the optimal action is one of many possibleactions, or when a long sequence of actions is requiredto be performed in the correct order to elicit thereward (Whalen, Cownden, & Laland, 2015). Iffinding the optimal behavior is like searching for aneedle in a haystack, then learning is as if someonetells you when you are getting close (Hinton &Nowlan, 1987; Smith, 1987); social learning is whensomeone actually shows you where to look.

Social learning occurs throughout the lifespan, andfrom many different individuals, and thereby allows forthe propagation of phenotypic variants among unrelatedindividuals, often within timespans significantly shorterthan a generation (e.g., social transmission of predatorrecognition in minnows, Pimephales promelas; Chivers &Smith, 1995; socially learned mating preferences ingrouse, Centrocercus urophasianus; Gibson, Bradbury, &Vehrencamp, 1991). Socially transmitted activities (e.g.,foraging) can modify ecological circumstances, some-times in ways that feed back to impact natural selection(niche construction). Social learning generates an addi-tional mechanism of inheritance operating parallel togenes “cultural inheritance”) (Hoppitt & Laland, 2013;Whiten, Ayala, Feldman, & Laland, 2017).


3 | BIASES IN PHENOTYPICVARIATION ARISING FROMANIMAL LEARNING

This section focuses on developmentally biased pheno-typic variation resulting from animal learning.

3.1 | Biases arising from individuallearning

Experimental research in a very large number of speciesof animals provides compelling evidence that learningtypically increases the rate of reinforcement, andreduces the rate of punishment, experienced by animals(Dickinson, 1980; Kamil, 1983; Kirkpatrick & Hall, 2004;Staddon, 2007, 2016). Faced with multiple potential foodsites, an animal will typically sample the alternatives andchoose the option yielding the highest return. Thedecision made, the amount of sampling, the number oftimes the animal performs the action (e.g., if on a “fixed‐reinforcement schedule”), the timing of the performance(e.g., when on an “interval schedule”), and otherparameters, have all been reliably shown to improveover trials with relevant experience (Dickinson, 1980;Kamil, 1983; Kirkpatrick & Hall, 2004; Krebs, Kacelnik,& Taylor, 1978; Staddon, 2016). Similar findings apply tolearning to avoid threats.

This kind of associative learning is widespread andhas general properties that allow animals to learn aboutthe causal relationships among a wide variety of events(Dickinson, 1980; Mackintosh, 1974). Learning canoccur through quite simple rules. One example is thetheory known as the Rescorla–Wagner rule, whichdescribes updates in knowledge as a linear combinationof current knowledge and new information. It hasproved useful in explaining the results of experiments asdiverse as foraging in honeybees, avoidance condition-ing in goldfish, and inferential reasoning in humans(Rescorla & Wagner, 1972).

Thirty years ago, inspired by optimal foraging theory(Stephens & Krebs, 1986), there was extensive theoreticalinterest in the idea that learned behavior might maximizethe rate of reinforcement, and thereby generate optimalbehavior (Kamil, 1983; Staddon, 2007). That particularresearch agenda met only partial success (Staddon, 2007):animals were found to learn optimally only underrestricted conditions, with their behavior often followingmechanical rules that seemingly evolved to produceclose‐to‐optimal behavior only under natural conditionsencountered by the species during its evolutionaryhistory. Yet this does not undermine the generalconclusion that the learned behavior of animals istypically adaptive, and that reinforcement learning

generally shifts behavior toward the optimum. Learningis adaptive precisely because animals have been fash-ioned by evolution actively to seek out high‐fitnessbehavioral outcomes, and to forestall activities thatmight negatively impact their survival and reproduction(Pulliam & Dunford, 1980; Staddon, 2016).

Skinner’s (1953) bold claim that “reinforcementshapes behavior as a sculptor shapes a lump of clay”somewhat exaggerates the power of reward andpunishment to influence behavior. In practice, evolvedpredispositions bias what learning takes place (a.k.a.“constraints on learning”; Hinde & Stephenson‐Hinde,1973). Garcia and Koelling (1966) famously gave rats foodand then radiation that made them sick. The rats tendedsubsequently to avoid food with that taste or smell,but struggled to learn an association between othercharacteristics of the food and sickness, and wereextremely slow to learn that a sound or light predictsillness. From an evolutionary perspective, this makessense, as sickness generally results from eating ratherthan from noises or lights and taste is a reliable indicatorof a food’s nature. Likewise, Mineka and Cook (1988)demonstrated that laboratory‐reared, juvenile rhesusmonkeys exhibit fear responses to snakes only afterwatching adults respond fearfully to snakes. The mon-keys failed to acquire fear responses to either a toyrabbit or flowers after watching video sequences ofconspecifics appearing to behave fearfully toward them,but such video presentations were sufficient to inducefear of snake‐like stimuli. Interestingly, an earlierstudy had found that these monkeys could be sociallyconditioned to fear other arbitrary stimuli (kitchenutensils) (Stephenson, 1967). A similar study of black-birds, which learn to recognize predators throughattending to the mobbing behavior of conspecifics, alsofound that they could be conditioned to acquire a fear ofarbitrary objects, in this case, plastic bottles, in thismanner (Curio, 1988; Vieth, Curio, & Ernst, 1980).Seemingly, the monkeys have evolved a perceptual biasthat enhances the salience of snake‐shaped stimuli,making it easier for them to learn about snakes thanflowerpots, but not precluding their acquiring fears ofnovel stimuli through observational conditioning. Incomparison, the blackbirds do not yet seem to haveevolved this kind of perceptual bias. The adaptive valueof an observational‐conditioning mechanism, wherebyanimals learn to be fearful of objects or events thatconspecifics fear, is easy to envisage. Sensitivity topersistent threats (e.g., snakes, in the case of monkeys)can be heightened through natural selection upregulatingthe salience of such stimuli, yet the general observa-tional‐conditioning mechanism allows the animal tolearn about novel threats with rapidity and flexibility.


These data suggest that learned behavior is neitheralways optimal, nor infinitely malleable through reinfor-cement. Animal learning typically begins with anexploratory search or sampling procedure, and the initialtrials may not be biased toward adaptive decisions (e.g.,in which direction to search of food, or which food patchto try first). However, after a small number of trials theanimal’s behavior will adjust to the patterns of reinforce-ment encountered. The final form of the behavior, afterrepeated trials, is generally highly adaptive (foragingefficiency is enhanced, the best food patch is selected,dangers are avoided, and so forth) (Staddon, 2016). Thisgeneric shift toward adaptive or optimal behavior as aconsequence of reinforcement learning we label here anadaptive bias (see Table 1).

Learning may generally be adaptive but it is notuniversally optimal. Evolved predispositions themselvesbias the learning process, a phenomenon that we refer toas an historical bias (see Table 1). Seemingly, animalshave been tuned by their evolutionary history to formsome associations more readily than others, or to performparticular actions in particular contexts. Apparently,ancestral natural selection has fashioned dedicatedlearning predispositions or motivational prioritiestailored to the particular ecological circumstances ofeach species. However, the generality of the observedmechanisms of learning imply that the observed species‐specificity is better regarded as selection tinkeringwith a general system than as selection constructing anindependent set of species‐specific learning processes(Bolhuis & MacPhail, 2001). What an animal learns mayvary adaptively between species, but how animals learnappears to be broadly similar across diverse taxa(Shettleworth, 2000).

Nonetheless, the aforementioned literature also makesit clear that historical biases exert a probabilistic influenceon learning and are rarely hard or prohibitive con-straints. This is because learning by definition resultsfrom historically contingent experiences, which oftendepend on complex interactions between many environ-mental factors. For example, why an animal learns toforage on one specific food type instead of another maybe because of the idiosyncratic sequence of food items itencountered during earlier decision‐making episodes.Such contingencies could not have been predicted at theoutset of its development because of chance eventsexperienced, the recursive interactions between learningon many environmental factors, and decision‐makingbased upon the knowledge of other individuals in thelocal environment. This contrasts with many othersources of phenotypic plasticity that can more satisfacto-rily be characterized as reaction norms to an environ-mental factor (but see Sultan, 2019). On the one hand,

this means that the existence of evolutionary biases onlearning cannot always prevent the coincidental devel-opment of nonadaptive behavior, even under normalenvironmental conditions. On the other hand, it meansthat evolutionary biases do not preclude the acquisitionof entirely novel and yet adaptively biased behavior, oneconsequence of which is that learning is a major source ofbehavioral innovation.

Through learning, for instance, how to discover andexploit new foods, or devising novel means to escape oravoid a threat, animals can introduce new behavior into thepopulation’s repertoire. Such instances are labeled beha-vioral innovations, defined by Reader and Laland, (2003, p.14) as new or modified learned behavior not previously foundin the population. Innovations are novel functional solutionstailored to new challenges or hitherto unexploited oppor-tunities. Not all cases of learning result in innovation, sinceanimals frequently learn associations that other members oftheir population have learned previously. However, beha-vioral innovation, as currently generally understood,requires learning, since otherwise innovation could not bedistinguished from exploration, or from any idiosyncratic oraccidental behavior (Reader & Laland, 2003). Learnedbehavioral innovation is now extensively documented inanimals (Lefebvre, Whittle, Lascaris, & Finkelstein, 1997;Reader & Laland, 2003; Reader, Flynn, Morand‐Ferron, &Laland, 2016). Examples include killdeer feeding on livefrogs (a novel food item; Schardien & Jackson, 1982),Northwestern crows fishing for sand eels by digging in thesand at low tide (i.e., inventing novel foraging technique;Robinette & Ha, 1997), and New Caledonian crows craftinghooked tools for foraging (e.g., devising novel tools; St Clairet al., 2018). Learned behavioral innovations are thus novelphenotypes—traits not previously observed in the lineage.However, while analogous to genetic mutation in therespect that they introduce novel variation, as the aboveexamples highlight, behavioral innovations are usually notrandom but exhibit an adaptive bias (Snell‐Rood, Kobiela,Sikkink, & Shepherd, 2018).

Innovations may also exhibit historical biases. Perhapsthe most famous example of an animal innovation is theinvention of the habit of washing sweet potatoes inwater by Japanese macaques (Kawai, 1965). Subsequentresearch established that food washing is common inseveral species of macaques, which means that thisparticular innovation involved the application of anestablished behavior to a novel food (Reader & Laland,2003). In fact, many animal innovations fall into thiscategory of established behavior applied in a novelcontext, or to a novel stimulus (Reader & Laland, 2003;Reader et al., 2016). Behavioural innovation throughlearning commonly allows the generalization or newapplication of a behavioral phenotype to novel


environments or contexts, as well as the de novoinvention of novel solutions. There is a lot of interestcurrently in whether organisms adapt to the rapidlychanging world, and the role that plasticity plays in this(Fox, Donelson, Schunter, Ravasi, & Gaitán‐Espitia, 2019;Snell‐Rood et al., 2018), but few articles in a recentspecial edition on this topic (Fox et al., 2019) evenmention learning. Nonetheless, this community havestressed how the processes of plasticity and adaptation,traditionally considered independently of each other,need to be viewed synergistically (Fox et al., 2019).Greater attention to how animals adjust to novelenvironments through learning is surely merited.

Additionally, unlike genetic mutations, which gener-ally occur equally likely across all members of thepopulation, behavioral innovations may be significantlymore likely to arise among particular classes of indivi-duals in the population. For instance, inexperiencedindividuals may be more likely to try novel behaviors, asin several large migratory bird species where juvenilesexhibit more variation in migratory routes than olderindividuals (Mueller, O’Hara, Converse, Urbanek, &Fagan, 2013; Oppel et al., 2015; Vansteelant, Kekkonen,& Byholm, 2017), particularly when there are fewexperienced adults in the population (Mueller et al.,2013; Oppel et al., 2015). Although juveniles suffer fromhigher mortality, they are thought to be more likely todiscover novel adaptive routes. Conversely, amongnonhuman primates, the inventors of novel behaviorare usually more likely to be older, experiencedindividuals than youngsters (Kendal, Coe, & Laland,2005; Reader & Laland, 2001), and more commonly low‐ranking than dominants (Reader & Laland, 2001). Wecharacterize these patterns as manifestations of anothertype of developmental bias, which we call an originationbias, since they exhibit a bias in where the innovationwill originate (see Table 1).

3.2 | Biases arising from social learning

Animals frequently acquire knowledge and skills fromconspecifics and heterospecifics, including adoptinginnovations devised by others. Here, the mechanism oftransmission can influence the frequency of phenotypicvariants in the next generation. This contrasts with thegenetic inheritance system that follows the Hardy–Wein-berg principle, implying that the mechanism of genetictransmission does not by itself cause changes in allelefrequencies and, by implication, the frequency distribu-tion of phenotypes. There are many mechanisms thatcause exceptions to this principle, such as interspecieshybridization and the presence of standing geneticvariation. This is important for evolution, because these

processes can bias novel phenotypic variation towardadaptive variants (e.g., Lai et al., 2019; Rieseberg et al.,2003; Seehausen, 2004). Social learning is rarely con-sidered in this context, perhaps because it is not expectedto cause differences between alleles in the probabilitythat they are being passed on to the offspring; itnonetheless clearly does affect the frequency distributionof phenotypes, and typically will do so in adaptive ways.Moreover, it also provides a mechanism for the inheri-tance of these phenotypes across generations.

Typically, animals do not copy at random, nor at allconceivable opportunities, nor simply copy the firstindividual that they see (Coussi‐Korbel & Fragaszy,1995; Laland, 2004). Rather, animals copy highlyselectively, deploying social learning strategies, whichare transmission biases in copying (Boyd & Richerson,1985; Henrich & McElreath, 2003; see Table 1). There arewell‐documented tendencies of animals to copy success-ful individuals and high‐payoff behaviour preferentially,to conform to the majority behavior (known to beadaptive in spatially variable environments; Boyd &Richerson, 1985), and also to copy more when uncertainor when learning asocially would be costly or difficult(Kendal et al., 2018; Laland, 2004; Rendell et al., 2011).Strategic copying is a general feature of animal sociallearning, while random copying is a comparatively rarespecial case (Hoppitt & Laland, 2013). Almost all animal(including human) social learning is subject to atransmission bias.

Extensive experimental evidence has accumulatedshowing that animals exhibit a range of nonrandomcopying strategies, often causing a transmission biastoward the more adaptive trait variants (Kendal et al.,2018; Rendell et al., 2011). A prime example is success‐biased copying. For instance, red knots are more likelyto join groups of foraging conspecifics that are success-ful at obtaining food (Bijleveld, van Gils, Jouta, &Piersma, 2015), while red‐winged blackbirds copyfeeding conspecifics except when they exhibit anaversive reaction to the food (Mason & Reidinger,1982). Animals may also have a copying bias towarddemonstrators that are more likely to be successful. Forexample, young female guppies have a preference forolder over younger female models during mate‐choicecopying (Amlacher & Dugatkin, 2005). Similarly, nine‐spined sticklebacks can monitor the foraging success ofother fish through observation and subsequently selectthe richer of the alternative food patches, a mechanismthat allows them to converge on the optimum (Coolen,Bergen, Day, & Laland, 2003; Kendal, Rendell, Pike, &Laland, 2009). Whether animals copy, and whom theycopy, can depend on their relative state of knowledge.For example, less‐experienced pigeons are more likely to


follow more‐experienced pigeons than vice versa (Flack,Pettit, Freeman, Guilford, & Biro, 2012). Bats that areunsuccessful at locating food alone follow previouslysuccessful bats to feeding sites, using cues indicative ofrecent feeding, such as defecation (Wilkinson, 1992).Insects and birds too are known to copy the nest‐sitedecisions of successful conspecifics and heterospecifics(Forsman & Seppänen, 2011; Pasqualone & Davis, 2011;Sarin & Dukas, 2009; Seppänen, Forsman, Mönkkönen,Krams, & Salmi, 2011). One consequence of theexistence of these transmission biases is that learnedinformation does not spread randomly, but alongspecified (i.e., biased) pathways.

A second source of transmission bias in social learningstems from the fact that the learned traits an individualexhibits (unlike the individual’s genes) are modifiedduring the course of its development through experience.The net consequence is that the set of behavior patternsperformed, and hence available to other individuals tocopy, is not a random set but rather a biased set of high‐performance, high‐payoff behaviors. This mechanismleads to “adaptive filtering” (Enquist & Ghirlanda,2007), and a transmission bias to propagate adaptiveknowledge. This adaptive bias associated with sociallylearned information was demonstrated by Rendell et al.(2010), who found that social learning was highlyadaptive under normal circumstances, where demonstra-tors chose to perform the highest‐payoff option in theirrepertoire. But when the adaptive filtering of demonstra-tors was artificially switched off, such that demonstratorsdrew from their repertoire at random, social learning wasno longer advantageous.

Teaching (i.e., behavior that functions to facilitatelearning in another individual; Hoppitt et al., 2008;Thornton & Raihani, 2010) is a third source of transmis-sion bias. While social learning is widespread amonganimals, teaching is rarer. Nonetheless, there is evidencefor teaching in a small number of species, including someants, bees, birds, and meerkats, with other plausible butnot yet experimentally demonstrated cases (Hoppitt et al.,2008). Theory shows that it is only worth investing inteaching (by definition, a costly means of informationdonation) if the adaptive advantage of the trait is large(Fogarty, Strimling, & Laland, 2011). This conclusion issupported by experimental data. For example, honeybeesactively communicate potential nesting sites to thecolony in autumn, but only after they have decided thatthe nesting site is of appropriate quality (Seeley, 1977;Seeley & Buhrman, 2001). As a consequence, the moreadaptive behavioral choice is more readily transmittedthrough the colony.

Transmission biases on the part of the receiver,adaptive filtering by the information transmitter, and

teaching combine to reinforce the adaptive bias generatedthrough asocial learning. They ensure that “goodinformation” (supporting fitness‐enhancing behavior) isfar more likely to be propagated than “bad information.”Social learning is applied flexibly, encompassing learningfrom both conspecifics and heterospecifics, which meansthat animals are not restricted to learning solely aboutthose environmental features previously encountered bytheir lineage (e.g., established predators or foods).Animals can also learn about entirely novel stimuli orevents, and devise appropriate responses to them (e.g.,birds learn to evade a novel predator; Davies &Welbergen, 2009; Thorogood & Davies, 2012). In addi-tion, learning can also generate opportunities forphenotypic change in the absence of any immediateenvironmental change or stressor (such as when orangu-tans, Pongo pygmaeus, proactively devise new food‐processing techniques, social learning allows others toaccess hitherto‐unexploited foods, in this case palmheart; Russon, 2003). Thus, through learning, animalscan generate adaptive responses to conditions withoutthe prior evolution of dedicated traits with suitablereaction norms.

Various biases in the distribution of phenotypicvariation that result from animal learning are summar-ized in Table 1, together with illustrative examples. Theseterms are neither mutually exclusive (for instance,historical, origin, and transmission biases will also oftenbe adaptive) nor is our classification designed to beexhaustive (plausibly, biases may exist that do not fit anyof the categories in Table 1).

4 | THE EVOLUTIONARYCONSEQUENCES OF BIASEDVARIATION ARISING THROUGHLEARNING

Thus far, our focus has been on the learning processesresponsible for the generation of biased phenotypicvariation. In this section, we move on to consider theevolutionary consequences of biased variation arisingthrough learning. We show that the production ofnonrandom phenotypic through animal learning causesbiases in the rate, dynamics, and pattern of evolution(i.e., triggers developmentally biased evolutionary pro-cesses). In fact, learning can influence evolutionaryprocesses in at least two separate ways: either throughgenerating some phenotypic forms more readily thanothers (a variational bias) or through generating someenvironmental states more readily than others(a selective bias, a.k.a. “niche construction”).


4.1 | Learning affects evolutionary rates

Theoretical work has established that learning canboth speed up and slow down genetic evolution (Ancel,2000; Borenstein, Meilijson, & Ruppin, 2006; Boyd &Richerson, 1985; Cavalli‐Sforza & Feldman, 1981),consistent with the role of phenotypic plasticity in bothdriving and inhibiting genetic evolution (e.g., Chevin,Lande, & Mace, 2010; Edelaar, Jovani, & Gomez‐Mestre,2017; Ghalambor, McKay, Carroll, & Reznick, 2007;Price, Qvarnström, & Irwin, 2003). Learning has anadvantageous effect on adaptation in relatively quicklychanging environments, allowing individuals to accli-mate to changes that cannot be tracked by selection ofgenes (Aoki & Feldman, 2014; Boyd & Richerson, 1985;Cavalli‐Sforza & Feldman, 1981; Todd, 1991). Thebenefits of learning in stationary or slowly changingenvironments are more complex. Hinton and Nowlan(1987) suggested that learning could accelerate evolutionin a static environment by helping genotypes to locateotherwise difficult‐to‐find fitness peaks. However, learn-ing can also weaken selection by reducing phenotypicdifferences between genotypes (Ancel, 2000; Anderson,1995; Frank, 2011). These seemingly conflicting resultsfollow from different theoretical assumptions (Borensteinet al., 2006; Frank, 2011; Paenke, Sendhoff, & Kawecki,2007). The emerging consensus is that individual learn-ing typically slows evolution in static unimodal fitnesslandscapes, but typically accelerates evolution indynamic or static multimodal fitness landscapes. In thelatter case, the existence of multiple optima usually slowsdown evolution as populations get trapped on suboptimalfitness peaks. By generating adaptive variation andthereby smoothing the fitness landscape, learningincreases the likelihood of a directly increasing path offitness to the global optimum (Borenstein et al., 2006;Frank, 2011; Mills & Watson, 2006).

4.2 | Learning can generate “plasticityfirst” evolution

The “plasticity first hypothesis” is “a mechanism ofadaptive evolution in which environmental perturbationleads, via phenotypic plasticity, to … a novel develop-mental variant (i.e., trait) that … is subsequently refinedthrough “genetic accommodation”” (Levis & Pfennig,2016, p. 564). Here, “genetic accommodation” means therefinement or stabilization of the trait through selectionof underlying genetic variation. Where learningaccelerates evolution, phenotypic change (a.k.a. “pheno-typic accommodation”) precedes, and then facilitates,genetic adaptation by modifying selection on geneticvariation (Levis & Pfennig, 2016; West‐Eberhard, 2003).

Conversely, when sources of learning are reliably present(e.g., the availability of a parent when a chick hatches),social learning can buffer selection on genetic variationthat would otherwise lead to genetic adaptation, asselection cannot favor a trait that compensates for theloss of developmental input that is reliably present(Griffiths, 2002). However, in many cases this bufferingwill not be perfect, and hence will not preclude selectionof alleles that increase the probability of producing, or theperformance of, the learned phenotype, a form of geneticaccommodation known as “genetic assimilation”(West‐Eberhard, 2003). Indeed, (particularly stochastic)learning can facilitate genetic adaptation through produ-cing adaptively biased phenotypic variation that changesthe selection on genotypes (Borenstein et al., 2006).

There is empirical evidence that learning can generateplasticity first evolution (Whitehead, Laland, Rendell,Thorogood, & Whiten, 2019). For instance, killer whale(Orcinus orca) populations exhibit culturally transmittedspecializations on particular prey resources (e.g., fish,dolphins, pinnipeds). These dietary traditions havefavored population‐specific genes influencing morphol-ogy and digestion, giving rise to different killer whale“ecotypes” (Foote et al., 2016; Hoelzel & Moura, 2016).Population‐genomic studies confirm that these lineageshave diverged genetically, and that functional genesassociated with digestion differ between ecotypes, sug-gesting that this is an instance of genetic assimilation(Foote et al., 2016; Hoelzel & Moura, 2016). Seemingly,the ecotypes arose through culturally mediated speciali-zation in matrilineal groups, which later developedreproductive barriers (Riesch, Barrett‐Lennard, Ellis,Ford, & Deecke, 2012). Here, learning and socialtransmission appears to have triggered the evolution ofmultiple lineages considered to be undergoing speciation,influencing the direction of adaptive evolution.

Social learning can also trigger genetic adaptation inother traits (Whitehead et al., 2019). For instance, insome fruit flies, fishes, birds, and mammals, the choice ofmating partner is influenced by the mate‐choice decisionsof other individuals (mate‐choice copying). This propa-gates mating preferences over short periods, such as aseason, yet experimental data and population‐geneticmodels demonstrate that it can strongly influence thesexual selection of male traits (Gibson et al., 1991;Kirkpatrick & Dugatkin, 1994; Nöbel, Allain, Isabel, &Danchin, 2018). Birdsong provides another illustration ofhow animal culture can be consequential for geneticevolution, affecting patterns of migration and assortativemating, and facilitating speciation (Beltman, Haccou, &Ten Cate, 2003; Lachlan & Slater, 1999). There isevidence for reed warbler learning driving the evolutionof plumage patterns in cuckoos (Davies & Welbergen,


2009; Thorogood & Davies, 2012), and for social learningreducing genetic diversity in socially structured whalepopulations (Whitehead, 1998). Finally, extensive em-pirical evidence now implicates learning in mate choice,sexual selection, and reproductive isolation, whereexperimental studies clearly show that learning imposesbiases on signal evolution (ten Cate & Rowe, 2007;Verziijden et al., 2012). Hence, the theoretical expectationof genetic accommodation and genetic assimilation inresponse to animal learning is supported by a small andgrowing number of studies.

In these examples, learning is not just changing therate of evolution but codirecting the outcome. It was theculturally transmitted dietary tradition of each killerwhale population that caused the natural selection ofgenes for a morphology and digestive physiology thatmatches their learned dietary habits. Similar points canbe made with respect to the mate choice, bird song, andplumage evolution examples.

Learning can also modify natural selection and triggerplasticity first evolution through bringing about physicalchanges in environments, or through learned habitatchoice. Over the past 50,000 years, humans have spreadfrom Africa around the globe, begun to exploit agricul-ture, witnessed rapid increases in densities as a directconsequence, domesticated hundreds of species of plantsand animals and, by keeping animals, experienced a newproximity to animal pathogens. Each of these eventsrepresents a major transformation in human selectionpressures, recognized though substantive genetic changein human populations, and each is a self‐induced changein environmental conditions (Laland, Odling‐Smee, &Myles, 2010). Humans have modified selection throughtheir learning and culture, for instance by dispersing intonew environments with different climatic regimes, bydevising agricultural practices or domesticating livestock,and causing extinctions and dramatic shifts in commu-nity structure (Boivin et al., 2016). It is now well‐established that dairy farming created the selectionpressure that led to the spread of alleles for adult lactasepersistence (Gerbault et al., 2011). Similarly, agriculturalpractices, such as cultivating yams, appear to haveinadvertently promoted the spread of malaria in someWest African populations, leading to the selection of theHbS allele which confers some resistance to malaria inthe heterozygote form, but leads to sickle‐cell anaemia inhomozygotes (Durham, 1991). The same practices appearalso to have favored high copy number of the salivaryamylase (AMY1) gene that facilitate the breakdown ofthe excess starch in agricultural diets (Perry et al., 2007).Again, such examples illustrate how learning can affectevolutionary outcomes, and not just rates. Producing andconsuming milk and alcohol has selected for alleles for

adult lactose absorption and alcohol dehydrogenase,while the agricultural practices that led to greaterconsumption of starch, protein, lipids, and phosphatehave selected for alleles that metabolize these foods(Laland et al., 2010). Given that both niche constructionand learning are ubiquitous in animals, it would seemhighly likely that the learned environmental modificationof other animals has similar consequences, althoughwell‐researched examples are rare.

Learned human activities are also driving evolutionaryresponses in other animals. Recent studies have demon-strated strong phenotypic changes in organisms inresponse to urban and other anthropogenic environ-ments, ranging from supplemental feeding affecting beakshape in garden birds, to earthworms and insectsevolving tolerance of pollutants (Alberti, 2015; Albertiet al., 2017; Palkovacs, Kinnison, Correa, Dalton, &Hendry, 2012; Sullivan, Bird, & Perry, 2017). Anthro-pogenic change studies suggest plasticity is important toevolutionary responses (Fox et al., 2019; Snell‐Rood et al.,2018), and the field could benefit from greater considera-tion of the role played by animal learning in theseadaptive responses.

4.3 | Learning can generate“adaptation” without natural selection

It is often claimed that natural selection is the onlyprocess that can systematically lead to increments infitness across generations, but adaptive evolution can alsoresult from phenotypic plasticity, habitat choice, andniche construction (Edelaar & Bolnick, 2019). This is animportant point, since it is widely, and mistakenly,believed that incremental improvements in a trait overgenerations, with corresponding enhancements in fit-ness, can only arise through the natural selection ofgenetic variation.

Experimental evidence reveals that social learning(a form of adaptive phenotypic plasticity) can causeoffspring fitness to be higher than that of their parents,even in novel habitats. This is, at least to some extent,recognized for humans where, for example, agriculturaladvances (irrigation methods, fertilizers, breeding pro-grams, insecticides, etc.) have repeatedly underpinnedpopulation growth (i.e., increased absolute fitness).However, evidence is starting to emerge for similarprocesses operating in other animals. For instance,Jesmer et al. (2018) showed that bighorn sheep andmoose adjust to novel environments in the course ofseveral generations, through a process of learning andcultural transmission. These animals generally migratethrough exploiting the high‐quality forage manifest in“green‐wave surfing” (van der Graaf, Stahl, Klimkowska,


Bakker, & Drent, 2006), which requires possessing therequisite knowledge of where and when to find high‐quality food; a typical needle‐in‐a‐haystack problem. Thestudy shows that after translocation to a novel environ-ment, the animals do not show their typical migratorybehavior. Apparently, past natural selection did notenable individuals to find the optimal foraging strategyin a single lifetime. Rather, the study shows an iterativeincrease over generations in the fraction of translocatedpopulations that migrate, and that—due to these move-ments—their whereabouts increasingly overlaps with thephenology of suitable habitat. The bighorn sheep acquireand accumulate this “adaptation” to a novel habitat notthrough natural selection of genes, but through learning.Seemingly, individual learning allows each generation toexploit high‐quality forage more effectively than theirparental generation, and social learning allows theseiterative improvements to be transmitted to the rest of thegroup, including the next generation. Here improvementsin the adaptive fit between individuals and theirenvironment accumulate over generations.

The seminatural experiment of translocated sheep isunique in its temporal and spatial scale, but similarprocesses likely operate widely. Sasaki and Biro (2017)demonstrate the process of incremental increase inmovement efficiency (which in the wild should correlatewith fitness) among gps‐tagged homing pigeons, whicharises through “cumulative culture.” Similarly, stickle-backs have been found to exhibit a hill‐climbing learningstrategy of selectively adopting the food‐patch choices offish more successful than them (Coolen et al., 2003;Kendal et al., 2009). While these studies do notdemonstrate increases in fitness across generations, theydo show that adaptive phenotypic plasticity mechanismsexist that mimic “adaptation” without the naturalselection of genetic variation.

The evolutionary relevance of such mechanisms willnot go unnoticed to those familiar with the Priceequation. In addition to the selective term, the Priceequation contains a term that captures whether the nextoffspring systematically differ from their parents, whichcan be rendered positive by adaptive phenotypic plasticity(Edelaar & Bolnick, 2019). Many open questions remainas to the extent of such phenomena among different taxaand in the wild, but there can be little doubt that theyhave far‐reaching consequences for evolution (Berdahlet al., 2018; Brakes et al., 2019; Edelaar & Bolnick, 2019).

4.4 | Learning can help explain theexistence of maladaptation

Without gainsaying the general conclusion that learningtypically generates adaptive phenotypic variation, there

are restricted and reasonably well‐understood circum-stances in which learning can generate, propagate, andmaintain maladaptive behavior. It is commonly assumedthat natural selection will shape organisms to reflectenvironmental conditions, but cultural transmission canallow animal behavior to become partially disconnectedfrom their environments. For instance, Bluehead wrasseThalassoma bifasciatum mating sites cannot be predictedfrom knowledge of environmental resource distributions(Warner, 1988, 1990). Rather, removal and replacementexperiments demonstrate that mating sites are main-tained as traditions, with young fish and newcomersadopting the mating sites of residents (similar findingsare observed in French grunts; Helfman & Schultz, 1984).Under restricted circumstances arbitrary and evenmaladaptive information can spread, or initially‐but‐no‐longer‐adaptive traits can be preserved (Richerson &Boyd, 2005). Traditions are maintained as Nash equili-bria, in which it rarely pays any individual to abandonthe tradition unilaterally; each is forced to do what othersare doing, leaving populations locked into conventionsthat track changing environments only slowly. In animalsthat aggregate for protection, like shoaling fishes, takingthe same route as others to a resource, such as a food site,offers fitness benefits even when the route is suboptimal,since going it alone is dangerous (Laland & Williams,1998). This behavior, and other conformist tendencies(Day, MacDonald, Brown, Laland, & Reader, 2001), helpexplain the traditions observed in natural fish popula-tions. In the case of the wrasse, initially adaptivepathways were rendered suboptimal by environmentalchange, but the population remained locked into adifficult‐to‐change convention. Another case is informa-tional cascades, where individuals base behavioraldecisions on prior decisions of others (Giraldeau, Valone,& Templeton, 2002). For instance, among lekking sagegrouse C. urophasianus, the decisions of females usingsocial information to decide with whom to mate were lessclosely correlated with male traits indicating quality thanwere the decisions of females making their ownjudgments about males (Gibson et al., 1991). Theseinstances are a form of historical bias, although distinctfrom genetically evolved biases.

Theoretical studies suggest further ways by whichlearning can generate maladaptation. As describedabove, reinforcement learning typically comprises anexploratory search (information gathering) followed bydecision‐making (information exploitation). However,the two stages are not mutually exclusive: ratherreinforcement learning often entails both processesoperating simultaneously, or in repeated sequence,allowing animals to gain information from a decision‐making experience and to refine their decision in light


of updated knowledge, in an iterative manner (Sutton &Barto, 1998). As a consequence, biases in decision‐making can bias the acquisition of knowledge, and vice‐versa (Hertwig & Erev, 2009; March, 1996). Thissequential, path‐dependent nature of animal learningcan be a crucial determinant of the behavior manifest inthe population. For instance, March (1996) demon-strated formally how animal learning could lead to risk‐averse behavior when its expected reward was lowerthan a risky alternative. Animals must strike a balancebetween exploration and exploitation, which typicallyleads them to reduce the rate of sampling of apparentlyinferior options. As a result, risky alternatives, whichusually give a poor reward but occasionally give a verygood reward, are interpreted as worse than they actuallyare, leading individuals to over‐exploit safe alternatives(Denrell, 2007; March, 1996; empirical examples oflearning‐induced risk aversion are reviewed in Weber,Shafir, & Blais, 2004, while similar “peak shift”phenomena are described by ten Cate & Rowe, 2007).Outside of humans, cases of the cultural transmission ormaintenance of maladaptive behavior appear rare: morecommonly, social learning strategies allow individualsto revisit superior options, even despite repeatedpersonal failures, circumventing potentially maladap-tive risk aversion (Arbilly, Motro, Feldman, & Lotem,2011; Rendell et al., 2010).

5 | CONCLUSIONS

Developmental biases remain contentious in evolution-ary biology, in part because of the claim that develop-mental processes may impose direction on adaptiveevolution and/or account for adaptation—a claim thatostensibly challenges the widespread belief that naturalselection does all of the explanatory work in accountingfor adaptive evolution (Arthur, 2004; Brakefield, 2006;Laland et al., 2015; Smith et al., 1985; Uller et al., 2018).Given that developmental processes themselves evolve,it is perhaps tempting to respond to such claims byregarding the bias as itself a product of natural selection,a stance that might (at least in some researcher’s eyes)restore natural selection’s privileged status. However, atleast in the case of developmental biases that result fromlearning, such a response appears inadequate.

While the general capacity to learn has clearly evolvedthrough natural selection, the above literature leaves nodoubt that the content of learning (the precise associa-tions formed, and the behavioral phenotypes that result)is rarely, if ever, specified by ancestral selection. Asdocumented above, animals are able to learn to exploitfoods, or evade threats, even when they are novel and

have not been encountered by the lineage, oftenacquiring them from other species. Moreover, suchlearning is frequently evolutionarily consequential. Insuch instances, the traditional line that ancestral naturalselection favored genes or genotypes with reaction normsthat allow animals to adjust their phenotypes toenvironmental inputs appears overly simplistic. Inlearning, ancestral selection has conferred on animalsan unusually rich form of plasticity that appears topossess some level of autonomy to generate “adaptive fit”within an individual as a result of its experience, throughan ontogenetic selective process that in many respectsresembles natural selection (Plotkin, 1994; Snell‐Roodet al., 2018).

Above we present clear evidence that (a) individuallearning commonly allows animals to generate noveland adaptively biased behavior tuned to the localenvironment, (b) social learning further biases thepropagation of these phenotypic improvements to otherindividuals, (c) this learning can modify selection andaffect evolutionary dynamics, and (d) culture some-times allows animals to improve mean fitness itera-tively across generations in a process that resembles“adaptation.” While these observations are well‐recog-nized within the animal learning literature, theyremain poorly appreciated within the evolutionarybiology community. For instance, in an otherwiseadmirable book, Bonduriansky and Day (2018) claimthat “only cognitively sophisticated animals” couldlearn adaptive solutions to novel circumstances, andsuggest that maladaptive behavior would spread just asreadily as accessing a novel food—claims that are badlyout of touch with the literature. There is now extensivedata showing that, through learning, a very broadrange of species of animals regularly invent andpropagate adaptive behavior that introduces noveltyinto phenotype space.

The significance of these findings may be profound.Recently, the suggestion (associated with the extendedevolutionary synthesis) that “novel phenotypic variantswill frequently be directional and functional” (Lalandet al., 2015, p. 10) excited considerable contention (e.g.,Charlesworth, Barton, & Charlesworth, 2017). Yet wesuspect to readers familiar with the above referencedliterature on animal learning, the assertion would notappear unreasonable. Almost all animal innovation, andalmost all socially transmitted knowledge and skills, arelikely to be adaptive, and those cases that are not can bepredicted a priori. Animal learning is an importantaddition to a range of phenomena that are now broadlyaccepted to undermine the classic view that adaptationarises solely from natural selection acting on randomgenetic variation.


The reason that learning evolved to become anunusually rich form of adaptive plasticity is preciselybecause of the benefits to animals of being able toresponse appropriately to unanticipated eventualities(Plotkin, 1994; Staddon, 2016). The term “evolvability”attempts to capture the capacity of a system for adaptiveevolution. There are diverse definitions of evolvability,many inherently assuming that adaptive evolutionrequires genetic change. However, we embrace thebroader definition provided by Kirschner and Gerhardt(1998)—“an organism’s capacity to generate heritablephenotypic variation”—in the context of which it ispossible to recognize how learning (a source of novel,frequently heritable, phenotypic variation) contributes toevolvability. Through behavioral innovation and sociallearning, animals can adjust to environments phenotypi-cally, sometimes buffering genetic responses, but perhapsmore commonly triggering genetic accommodation. Theimpact of learning on evolvability is further suggested byrecent studies showing a robust relationship betweeninnovativeness and speciosity in birds (Lefebvre, Ducatez,& Audet, 2016; Nicolakakis, Sol, & Lefebvre, 2003; Sol,Stirling, & Lefebvre, 2005). These data reinforce theaforementioned evidence that learning can imposedirection on adaptive evolution, affecting evolutionaryrates, and influencing the probability of populationsreaching global optimum.

There are many open questions ripe for investigation.For instance, do biases that arise through learning differfrom other developmental biases, for instance, in thelevel of integration or diversity of phenotypes generated?Are there different patterns of bias associated withindividual and social learning? Will success‐based copy-ing generate more rapid convergence through geneticaccommodation on fitness peaks than conformist sociallearning, which is prone to historical lags? Furtherinvestigation is required, but there is already sufficientdata to suggest that phenotypic accommodation throughlearning may be common, rapid and powerful, particu-larly in vertebrates, and that developmentally biasedevolutionary processes resulting from learning may be atruly fundamental feature of animal evolution.

ACKNOWLEDGEMENTS

Research supported in part by a grant from theJohn Templeton Foundation to K. N. L. (“Putting theextended evolutionary synthesis to the test”, ref 60501),by Japan Society for the Promotion of Science KAKEN-HI to W. T. (ref 17J01559), and a grant from theNetherlands Organization for Scientific Research to T.O. (ref 019.172EN.011).

CONFLICT OF INTERESTS

The authors declare that there are no conflict of interests.

ORCID

Kevin N. Laland http://orcid.org/0000-0002-2457-0900

REFERENCES

Alberti, M. (2015). Eco‐evolutionary dynamics in an urbanizingplanet. Trends in Ecology and Evolution, 30, 114–126.

Alberti, M., Correa, C., Marzluff, J. M., Hendry, A. P., Palkovacs, E.P., Gotanda, K. M., … Zhou, Y. (2017). Global urban signaturesof phenotypic change in animal and plant populations.Proceedings of the National Academy of Sciences of the UnitedStates of America, 114, 8951–8956.

Amlacher, J., & Dugatkin, L. A. (2005). Preference for older overyounger models during mate‐choice copying in young guppies.Ethology Ecology and Evolution, 17, 161–169.

Ancel, L. W. (2000). Undermining the Baldwin expediting effect:Does phenotypic plasticity accelerate evolution? TheoreticalPopulation Biology, 58, 307–319.

Anderson, R. W. (1995). Learning and evolution: A quantitativegenetics approach. Journal of Theoretical Biology, 175,89–101.

Aoki, K., & Feldman, M. W. (2014). Evolution of learning strategiesin temporally and spatially variable environments: A review oftheory. Theoretical Population Biology, 91, 3–19.

Arbilly, M., Motro, U., Feldman, M. W., & Lotem, A. (2011).Evolution of social learning when high expected payoffs areassociated with high risk of failure. Journal of the Royal Society,Interface, 8, 1604–1615.

Arthur, W. (2004). The effect of development on the direction ofevolution: Toward a twenty‐first century consensus. Evolutionand Development, 6, 282–288.

Beltman, J., Haccou, P., & Ten Cate, C. (2003). The impact oflearning foster species’ song on the evolution of specialist avianbrood parasitism. Behavioral Ecology, 14, 917–923.

Berdahl, A. M., Kao, A. B., Flack, A., Westley, P. A. H., Codling, E.A., Couzin, I. D., … Biro, D. (2018). Collective animal navigationand migratory culture: From theoretical models to empiricalevidence. Philosophical Transactions of the Royal Society, B:Biological Sciences, 373, 20170009.

Bijleveld, A. I., van Gils, J. A., Jouta, J., & Piersma, T. (2015).Benefits of foraging in small groups: An experimental study onpublic information use in red knots Calidris canutus. Beha-vioural Processes, 117, 74–81.

Boivin, N. L., Zeder, M. A., Fuller, D. Q., Crowther, A., Larson, G.,Erlandson, J. M., … Petraglia, M. D. (2016). Ecologicalconsequences of human niche construction: Examining long‐term anthropogenic shaping of global species distributions.Proceedings of the National Academy of Sciences of the UnitedStates of America, 113(23), 6388–6396.

Bolhuis, J. J., & MacPhail, E. M. (2001). A critique of theneuroecology of learning and memory. Trends in CognitiveSciences, 5, 426–433.

Bonduriansky, R., & Day, T. (2018). Extended heredity. Princeton:Princeton University Press.


http://orcid.org/0000-0002-2457-0900

Borenstein, E., Meilijson, I., & Ruppin, E. (2006). The effect ofphenotypic plasticity on evolution in multipeaked fitnesslandscapes. Journal of Evolutionary Biology, 19, 1555–1570.

Boyd, R., & Richerson, P. J. (1985). Culture and the evolutionaryprocess. Chicago: Chicago University Press.

Brakefield, P. M. (2006). Evo‐devo and constraints on selection.Trends in Ecology and Evolution (Personal Edition), 21, 362–368.

Brakes, P., Dall, S. R. X., Aplin, L. M., Bearhop, S., Carroll, E. L.,Ciucci, P., … Rutz, C. (2019). Animal cultures matter forconservation. Science, 363(6431), 1032–1034.

Breland, K., & Breland, M. (1961). The misbehavior of organisms.American Psychologist, 16, 681–684.

Cavalli‐Sforza, L. L., & Feldman, M. W. (1981). Cultural transmis-sion and evolution. Princeton: Princeton University Press.

Charlesworth, D., Barton, N. H., & Charlesworth, B. (2017). Thesources of adaptive variation. Proceedings of the Royal Society B:Biological Sciences, 284, 20162864.

Chevin, L. M., Lande, R., & Mace, G. M. (2010). Adaptation,plasticity, and extinction in a changing environment: Towards apredictive theory. PLOS Biology, 8(4):e1000357.

Chivers, D. P., & Smith, R. J. F. (1995). Chemical recognition of riskyhabitats is culturally transmitted among fathead minnows, Pime-phales promelas (Osteichthyes, Cyprinidae). Ethology, 99, 286–296.

Coolen, I., Bergen, Y. V., Day, R. L., & Laland, K. N. (2003). Speciesdifference in adaptive use of public information in sticklebacks.Proceedings of the Royal Society of London. Series B: BiologicalSciences, 270, 2413–2419.

Coussi‐Korbel, S., & Fragaszy, D. M. (1995). On the relationbetween social dynamics and social learning. Animal Behaviour,50(6), 1441–1453.

Curio, E. (1988). Cultural transmission of enemy recognition bybirds. In B. G. Galef, & T. R. Zentall (Eds.), Social Learning:Psychological and Biological Perspectives (pp. 75–97). Hillsdale,NJ: Erlbaum.

Davies, N. B., & Welbergen, J. A. (2009). Social transmissionof a host defense against cuckoo parasitism. Science, 324,1318–1320.

Day, R. L., MacDonald, T., Brown, C., Laland, K. N., & Reader, S.M. (2001). Interactions between shoal size and conformity inguppy social foraging. Animal Behaviour, 62, 917–925.

Denrell, J. (2007). Adaptive learning and risk taking. PsychologicalReview, 114, 177–187.

Dickinson, A. (1980). Contemporary animal learning theory. Cam-bridge: Cambridge University Press.

Durham, W. H. (1991). Coevolution. genes, culture and humandiversity. Stanford: Stanford University Press.

Edelaar, P., & Bolnick, D. I. (2019). Appreciating the multipleprocesses increasing individual or population fitness. Trends inEcology and Evolution, 34, 435–446. https://doi.org/10.1016/j.tree.2019.02.001

Edelaar, P., Jovani, R., & Gomez‐Mestre, I. (2017). Should I changeor should I go? Phenotypic plasticity and matching habitatchoice in the adaptation to environmental heterogeneity. TheAmerican Naturalist, 190(4), 506–520.

Enquist, M., & Ghirlanda, S. (2007). Evolution of social learningdoes not explain the origin of human cumulative culture.Journal of Theoretical Biology, 246, 129–135.

Flack, A., Pettit, B., Freeman, R., Guilford, T., & Biro, D. (2012).What are leaders made of? The role of individual experience in

determining leader–follower relations in homing pigeons.Animal Behaviour, 83, 703–709.

Fogarty, L., Strimling, P., & Laland, K. N. (2011). The evolution ofteaching. Evolution, 65, 2760–2770.

Foote, A. D., Vijay, N., Ávila‐Arcos, M. C., Baird, R. W., Durban, J.W., Fumagalli, M., … Wolf, J. B. W. (2016). Genome‐culturecoevolution promotes rapid divergence of killer whale ecotypes.Nature Communications, 7, 11693.

Forsman, J. T., & Seppänen, J. T. (2011). Learning what (not) to do:Testing rejection and copying of simulated heterospecificbehavioural traits. Animal Behaviour, 81, 879–883.

Fox, R. J., Donelson, J. M., Schunter, C., Ravasi, T., &Gaitán‐Espitia, J. D. (2019). Beyond buying time: The role ofplasticity in phenotypic adaptation to rapid environmentalchange. Philosophical Transactions of the Royal Society B, 374,20180174. https://doi.org/10.1098/rstb.2018.0174

Frank, S. A. (2011). Natural selection II. Developmental variabilityand evolutionary rate. Journal of Evolutionary Biology, 24,2310–2320.

Garcia, J., & Koelling, R. A. (1966). Relation of cue to consequencein avoidance learning. Psychonomic Science, 4, 123–124.

Gerbault, P., Liebert, A., Itan, Y., Powell, A., Currat, M.,Burger, J., … Thomas, M. G. (2011). Evolution of lactasepersistence: An example of human niche construction.Philosophical Transactions of the Royal Society, B: BiologicalSciences, 366, 863–877.

Ghalambor, C. K., McKay, J. K., Carroll, S. P., & Reznick, D. N.(2007). Adaptive versus non‐adaptive phenotypic plasticity andthe potential for contemporary adaptation in new environ-ments. Functional Ecology, 21(3), 394–407.

Gibson, R. M., Bradbury, J. W., & Vehrencamp, S. L. (1991). Matechoice in lekking sage grouse revisited: The roles of vocaldisplay, female site fidelity, and copying. Behavioral Ecology, 2,165–180.

Giraldeau, L. A., Valone, T. J., & Templeton, J. J. (2002).Potential disadvantages of using socially acquired informa-tion. Philosophical Transactions of the Royal Society B, 357,1559–1566.

van der Graaf, A. J., Stahl, J., Klimkowska, A., Bakker, J. P., &Drent, R. H. (2006). Surfing on a green wave: How plant growthdrives spring migration in the barnacle goose. Ardea, 94,567–577.

Griffiths, P. E. (2002). What Is Innateness? The Monist, 85, 70–85.Helfman, G. S., & Schultz, E. T. (1984). Social transmission of

behavioural traditions in a coral reef fish. Animal Behaviour, 32,379–384.

Henrich, J., & McElreath, R. (2003). The evolution of culturalevolution. Evolutionary Anthropology, 12, 123–135.

Hertwig, R., & Erev, I. (2009). The description‐experience gap inrisky choice. Trends in Cognitive Sciences, 13, 517–523.

Heyes, C. M. (1994). Social learning in animals: Categories andmechanisms. Biological Reviews, 69, 207–231.

Hinde, R. A., & Stephenson‐Hinde, J. (1973). Constraints on learning.Limitations and predispositions. London: Academic Press.

Hinton, G. E., & Nowlan, S. J. (1987). How learning can guideevolution. Complex Systems, 1, 495–502.

Hoelzel, A. R., & Moura, A. E. (2016). Killer whales differentiatingin geographic sympatry facilitated by divergent behaviouraltraditions. Heredity, 117, 481–482.


https://doi.org/10.1016/j.tree.2019.02.001

https://doi.org/10.1016/j.tree.2019.02.001

https://doi.org/10.1098/rstb.2018.0174

Hoppitt, W., & Laland, K. N. (2013). Social learning: Anintroduction to mechanisms, methods and models. Princeton,NJ: Princeton University Press.

Hoppitt, W. J. E., Brown, G. R., Kendal, R., Rendell, L., Thornton,A., Webster, M. M., & Laland, K. N. (2008). Lessons fromanimal teaching. Trends in Ecology and Evolution, 23, 486–493.

Jesmer, B. R., Merkle, J. A., Goheen, J. R., Aikens, E. O., Beck, J. L.,Courtemanch, A. B., … Kauffman, M. J. (2018). Is ungulatemigration culturally transmitted? Evidence of social learningfrom translocated animals. Science, 361, 1023–1025.

Kamil, A. C. (1983). Optimal foraging theory and the psychology oflearning. American Zoologist, 23, 291–302.

Kawai, M. (1965). Newly‐acquired pre‐cultural behavior of the naturaltroop of Japanese monkeys on Koshima islet. Primates, 6, 1–30.

Kendal, J. R., Rendell, L., Pike, T. W., & Laland, K. N. (2009). Nine‐spined sticklebacks deploy a hill‐climbing social learningstrategy. Behavioural Ecology, 20, 238–244.

Kendal, R. L., Boogert, N. J., Rendell, L., Laland, K. N., Webster, M.,& Jones, P. L. (2018). Social learning strategies: Bridge‐buildingbetween fields. Trends in Cognitive Sciences, 22(7), 651–665.

Kendal, R. L., Coe, R. L., & Laland, K. N. (2005). Age differences inneophilia, exploration and innovation in family groups ofCallitrichid monkeys. American Journal of Primatology, 66, 167–188.

Kirkpatrick, K., & Hall, G. (2004). Learning and memory. InBolhuis, J. J., & Giraldeau, L. A. (Eds.), The Behavior of Animals:Mechanisms, Function, and Evolution. Malden, MA: Blackwell.

Kirkpatrick, M., & Dugatkin, L. A. (1994). Sexual selection and theevolutionary effects of copying mate choice. Behavioral Ecologyand Sociobiology, 34, 443–449.

Kirschner, M., & Gerhart, J. (1998). Evolvability. Proceedings of theNational Academy of Sciences of the United States of America,95(15), 8420–8427.

Krebs, J. R., Kacelnik, A., & Taylor, P. (1978). Test of optimalsampling by foraging great tits. Nature, 275, 27–31.

Lachlan, R. F., & Slater, P. J. B. (1999). The maintenance of vocallearning by gene‐culture interaction: The cultural trap hypoth-esis. Proceedings of the Royal Society, 266, 701–706.

Lai, Y.‐T., Yeung, C. K. L., Omland, K. E., Pang, E.‐L., Hao, Y., Liao,B.‐Y., … Li, S.‐H. (2019). Standing genetic variation as thepredominant source for adaptation of a songbird. Proceedings ofthe National Academy of Sciences of Sciences of the United Statesof America, 116, 2152–2157.

Laland, K.N. (2004). Social learning strategies. Animal Learningand Behavior, 32, 4–14.

Laland, KN, Odling‐Smee, J, & Feldman, MW. (in press) Under-standing niche construction as an evolutionary process. In:Uller T & Laland KN (eds.) Evolutionary Causation. Biologicaland Philosophical Reflections. Cambridge, MA: MIT Press.

Laland, K. N., Odling‐Smee, J., & Myles, S. (2010). How cultureshaped the human genome: Bringing genetics and the humansciences together. Nature Reviews Genetics, 11, 137–148.

Laland, K. N., Uller, T., Feldman, M. W., Sterelny, K., Müller, G.B., Moczek, A., … Odling‐Smee, J. (2015). The extendedevolutionary synthesis: Its structure, assumptions andpredictions. Proceedings of the Royal Society B: BiologicalSciences, 282, 20151019.

Laland, K. N., & Williams, K. (1998). Social transmission ofmaladaptive information in the guppy. Behavioral Ecology, 9,493–499.

Lefebvre, L., Whittle, P., Lascaris, E., & Finkelstein, A. (1997).Feeding innovations and forebrain size in birds. AnimalBehaviour, 53, 549–560.

Lefebvre, L., Ducatez, S., & Audet, J. N. (2016). Feeding innovationsin a nested phylogeny of Neotropical passerines. PhilosophicalTransactions of the Royal Society, B: Biological Sciences, 371,20150188.

Levis, N. A., & Pfennig, D. W. (2016). Evaluating ‘plasticity‐first’evolution in nature: Key criteria and empirical approaches.Trends in Ecology and Evolution, 31, 563–574.

Love, A. C. (Ed.), 2015). Conceptual Change in Biology. Dordrecht:Springer.

Mackintosh, N. (1974). The psychology of animal learning. London:Academic Press.

March, J. G. (1996). Learning to be risk averse. Psychological Review,103, 309–319.

Mason, J. R., & Reidinger, R. F. (1982). Observational learning offood aversions in red‐winged blackbirds (Agelaius phoeniceus).Auk, 99, 548–554.

Maynard‐Smith, J., Burian, R., Kauffman, S., Alberch, P., Campbell,J., Goodwin, B. …Wolpert, L. (1985). Developmental constraintsand evolution. The Quarterly Review of Biology, 60, 265–287.

Mills, R., & Watson, R. A. (2006) On crossing fitness valleys withthe Baldwin effect. Proceedings of the Tenth InternationalConference on the Simulation and Synthesis of Living Systems.493–499. MIT Press, Cambridge, MA.

Mineka, S., & Cook, M. (1988). Social learning and the acquisitionof snake fear in monkeys. In B. G. Galef, & T. R. Zentall (Eds.),Social Learning: Psychological and biological perspectives (pp.51–73). Hillsdale, NJ: Lawrence Erlbaum.

Mueller, T., O’Hara, R. B., Converse, S. J., Urbanek, R. P., &Fagan, W. F. (2013). Social learning of migratory perfor-mance. Science, 341, 999–1002.

Nicolakakis, N., Sol, D., & Lefebvre, L. (2003). Behaviouralflexibility predicts species richness in birds, but not extinctionrisk. Animal Behaviour, 65, 445–452.

Nöbel, S., Allain, M., Isabel, G., & Danchin, E. (2018). Mate copyingin Drosophila melanogaster males. Animal Behaviour, 141, 9–15.

Odling‐Smee, F. J., Laland, K. N., & Feldman, M. W. (2003). NicheConstruction. The Neglected Process in Evolution. Princeton:Princeton University Press.

Oppel, S., Dobrev, V., Arkumarev, V., Saravia, V., Bounas, A., Kret,E., … Nikolov, S. C. (2015). High juvenile mortality duringmigration in a declining population of a long‐distance migratoryraptor. Ibis, 157, 545–557.

Paenke, I., Sendhoff, B., & Kawecki, T. J. (2007). Influence ofplasticity and learning on evolution under directional selection.The American Naturalist, 170, E47–E58.

Palkovacs, E. P., Kinnison, M. T., Correa, C., Dalton, C. M., &Hendry, A. P. (2012). Fates beyond traits: Ecologicalconsequences of human‐induced trait change. EvolutionaryApplications, 5, 183–191.

Pasqualone, A. A., & Davis, J. M. (2011). The use of conspecificphenotypic states as information during reproductive decisions.Animal Behaviour, 82, 281–284.

Perry, G. H., Dominy, N. J., Claw, K. G., Lee, A. S., Fiegler, H.,Redon, R., … Stone, A. C. (2007). Diet and the evolution ofhuman amylase gene copy number variation. Nature Genetics,39, 1256–1260.


Plotkin, H. C. (1994). Darwin machines and the nature of knowledge.London: Penguin.

Price, T. D., Qvarnström, A., & Irwin, D. E. (2003). The role ofphenotypic plasticity in driving genetic evolution. Proceedings ofthe Royal Society of London. Series B: Biological Sciences,270(1523), 1433–1440.

Pulliam, H., & Dunford, C. (1980). Programmed to learn. New York:Columbia University Press.

Reader, S. M., Flynn, E., Morand‐Ferron, J., & Laland, K. N. (2016).Innovation in animals and humans: understanding the originsand development of novel and creative behaviour. PhilosophicalTransactions of the Royal Society of London. Series B, BiologicalSciences, 371, 1690.

Reader, S. M., & Laland, K. N. (2001). Primate innovation: Sex, ageand social rank differences. International Journal of Primatol-ogy, 22, 787–805.

Reader, S. M., & Laland, K. N. (2003). Animal innovation. Oxford,UK: Oxford University Press.

Rendell, L., Boyd, R., Cownden, D., Enquist, M., Eriksson, K.,Feldman, M. W., … Laland, K. N. (2010). Why copy others?Insights from the social learning strategies tournament. Science,328, 208–213.

Rendell, L., Fogarty, L., Hoppitt, W. J. E., Morgan, T. J. H., Webster,M. M., & Laland, K. N. (2011). Cognitive culture: Theoreticaland empirical insights into social learning strategies. Trends inCognitive Sciences, 15, 68–76.

Rescorla, R. A. (1988). Pavlovian conditioning. American Psychol-ogist, 43(3), 151–160.

Rescorla, R. A., & Wagner, A. R. (1972). A theory of Pavlovianconditioning: Variations in the effectiveness of reinforcementand nonreinforcement. In A. H. Black, & W. F. Prokasy (Eds.),Classical conditioning II: Current research and theory (pp.64–99). New York: Appleton.

Richerson, P. J., & Boyd, R. (2005). Not by genes alone: How culturetransformed human evolution. Chicago: Chicago University Press.

Riesch, R., Barrett‐Lennard, L. G., Ellis, G. M., Ford, J. K. B., &Deecke, V. B. (2012). Cultural traditions and the evolution ofreproductive isolation: Ecological speciation in killer whales?Biological Journal of the Linnean Society, 106, 1–17.

Rieseberg, L. H., Raymond, O., Rosenthal, D. M., Lai, Z.,Livingstone, K., Nakazato, T., … Lexer, C. (2003). Majorecological transitions in wild sunflowers facilitated by hybridi-zation. Science, 301, 1211–1216.

Robinette, R. L., & Ha, J. C. (1997). The significance of fishing bynorthwestern crows. Wilson Bulletin, 109, 748–749.

Russon, A. E. (2003). Innovation and creativity in forest‐livingrehabilitant orang‐utans. In S. M. Reader, & K. N. Laland (Eds.),Animal innovation (pp. 279–306). New York, NY: OxfordUniversity Press.

Sarin, S., & Dukas, R. (2009). Social learning about egg layingsubstrates in fruit flies. Proceedings of the Royal Society B:Biological Sciences, 276, 4323–4328.

Sasaki, T., & Biro, D. (2017). Cumulative culture can emerge fromcollective intelligence in animal groups. Nature Communica-tions, 8, 15049.

Schardien, B. J., & Jackson, J. A. (1982). Killdeers feeding on frogs.Wilson Bulletin, 94, 85–87.

Seehausen, O. (2004). Hybridization and adaptive radiation. Trendsin Ecology and Evolution, 19, 198–207.

Seeley, T. (1977). Measurement of nest cavity volume by the honeybee (Apis mellifera). Behavioral Ecology and Sociobiology,2, 201–227.

Seeley, T. D., & Buhrman, S. C. (2001). Nest‐site selection in honeybees: How well do swarms implement the “best‐of‐n” decisionrule? Behavioral Ecology and Sociobiology, 49, 416–427.

Seppänen, J. T., Forsman, J. T., Mönkkönen, M., Krams, I., & Salmi,T. (2011). New behavioural trait adopted or rejected byobserving heterospecific tutor fitness. Proceedings of the RoyalSociety B: Biological Sciences, 278, 1736–1741.

Shettleworth, S. (2000). Modularity and the evolution of cognition.In C. Heyes, & L. Huber (Eds.), The evolution of cognition(pp. 43–60). Cambridge, MA: MIT Press.

Skinner, B. F. (1938). The behavior of organisms. Appleton CenturyCrofts.

Skinner, B. F. (1953). Science and human behavior. New York, NY:The Free Press.

Smith, J. M. (1987). When learning guides evolution. Nature,329, 761–762.

Smith, J. M., Burian, R., Kauffman, S., Alberch, P., Campbell, J.,Goodwin, B., … Wolpert, L. (1985). Developmental constraintsand evolution. The Quarterly Review of Biology, 60, 265–287.

Snell‐Rood, E. C., Kobiela, M. E., Sikkink, K. L., & Shephard, A. M.(2018). Mechanisms of plastic rescue in novel environments.Annual Review of Ecology, Evolution, and Systematics.49, 331–354.

Sol, D., Stirling, D. G., & Lefebvre, L. (2005). Behavioral drive orbehavioral inhibition in evolution: subspecific diversification inholarctic passerines. Evolution, 59, 2669–2677.

St Clair, J. J. H., Klump, B. C., Sugasawa, S., Higgott, C. G.,Colegrave, N., & Rutz, C. (2018). Hook innovation boostsforaging efficiency in tool‐using crows. Nature Ecology andEvolution, 2, 441–444. https://doi.org/10.1038/s41559‐017‐0429‐7

Staddon, J. E. R. (2007). Is animal learning optimal? Constructaltheory of social dynamics. Boston, MA: Springer.

Staddon, J. E. R. (2016). Adaptive behavior and learning (2nd Eds).Cambridge University Press.

Stephens, D. W., & Krebs, J. R. (1986). Foraging theory. Princeton,NJ: Princeton University Press.

Stephenson, G. (1967). Cultural acquisition of a specific learnedresponse among rhesus monkeys. In D. Starck, R. Schneider, &H. Kuhn (Eds.), Progress in primatology. Stuttgart: GustavFisher Verlag.

Sullivan, A. P., Bird, D. W., & Perry, G. H. (2017). Humanbehaviour as a long‐term ecological driver of non‐humanevolution. Nature Ecology and Evolution, 1, 65.

Sultan, S. (2019). Genotype‐environment interaction and theunscripted reaction norm. In Uller, T., & Laland, K. N. (Eds.),Evolutionary causation. Biological and philosophical reflections(p. 109). Cambridge, MA: MIT Press.

Sutton, R. S., & Barto, A. G. (1998). Reinforcement learning: Anintroduction. Cambridge, MA: MIT Press.

ten Cate, C., & Rowe, C. (2007). Biases in signal evolution:learningLearning makes a difference. Trends in Ecology andEvolution, 22(7), 380–387.

Thorndike, E. L. (1898). Animal intelligence: An experimentalstudy of the associative processes in animals. PsychologicalReview Monographs, 2. (Whole No. 8).


https://doi.org/10.1038/s41559-017-0429-7

https://doi.org/10.1038/s41559-017-0429-7

Thornton, A., & Raihani, N. J. (2010). Identifying teaching in wildanimals. Learning and Behavior, 38(3), 297–309.

Thorogood, R., & Davies, N. B. (2012). Cuckoos combat sociallytransmitted defenses of reed warbler hosts with a plumagepolymorphism. Science, 337, 578–580.

Todd, P. M. G. (1991) Exploring adaptive agency II: simulating theevolution of associative learning. In J. M. S. Wilson (ed.).Animals to Animals: Proceedings of the First InternationalConference on Simulation of Adaptive Behavior (pp. 306–315).Cambridge, MA: MIT Press.

Uller, T., Moczek, A. P., Watson, R. A., Brakefield, P. M., & Laland,K. N. (2018). Developmental bias and evolution: A regulatorynetwork perspective. Genetics, 209(4), 949–966.

Vansteelant, W. M. G., Kekkonen, J., & Byholm, P. (2017). Windconditions and geography shape the first outbound migration ofjuvenile honey buzzards and their distribution across sub‐Saharan Africa. Proceedings of the Royal Society B: BiologicalSciences, 284, 20170387.

Verzijden, M. N., ten Cate, C., Servedio, M. R., Kozak, G. M.,Boughman, J. W., & Svensson, E. I. (2012). The impact oflearning on sexual selection and speciation. Trends in Ecologyand Evolution, 27(9), 511–519.

Vieth, W., Curio, E., & Ernst, U. (1980). The adaptive significance ofavian mobbing. III. Cultural transmission of enemy recognitionin blackbirds: Cross‐species tutoring and properties of learning.Animal Behaviour, 28, 1217–1229.

Warner, R. R. (1988). Traditionality of mating‐site preferences in acoral reef fish. Nature, 335, 719–721.

Warner, R. R. (1990). Male versus female influences on mating‐site determination in a coral‐reef fish. Animal Behaviour, 39,540–548.

Weber, E. U., Shafir, S., & Blais, A. R. (2004). Predicting risksensitivity in humans and lower animals: Risk as variance orcoefficient of variation. Psychological Review, 111, 430–445.https://doi.org/10.1037/0033‐295X.111.2.430

West‐Eberhard, M. J. (2003). Developmental plasticity and evolution.Oxford, UK: Oxford University Press.

Whalen, A., Cownden, D., & Laland, K. (2015). The learning ofaction sequences through social transmission. Animal Cogni-tion, 18, 1093–1103. https://doi.org/10.1007/s10071‐015‐0877‐x

Whitehead, H. (1998). Cultural selection and genetic diversity inmatrilineal whales. Science, 282, 1708–1711.

Whitehead, H., Laland, K. N., Rendell, L., Thorogood, R., &Whiten, A. (2019). The reach of gene‐culture coevolution inanimals. Nature Communications, 10, 2405.

Whiten, A., Ayala, F. J., Feldman, M. W., & Laland, K. N. (2017).The extension of biology through culture. Proceedings of theNational Academy of Sciences, 114, 7775–7781.

Wilkinson, A., Kuenstner, K., Mueller, J., & Huber, L. (2010). Sociallearning in a non‐social reptile (Geochelone carbonaria). BiologyLetters, 6(5), 614–616.

Wilkinson, G. S. (1992). Information transfer at evening batcolonies. Animal Behaviour, 44, 501–518.

How to cite this article: Laland KN, ToyokawaW, Oudman T. Animal learning as a source ofdevelopmental bias. Evolution & Development.2019;e12311. https://doi.org/10.1111/ede.12311


https://doi.org/10.1037/0033-295X.111.2.430

https://doi.org/10.1007/s10071-015-0877-x

https://doi.org/10.1111/ede.12311

evolution & development · 2019. 9. 3. · uller, moczek, watson, brakefield, & laland,...

Documents