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SPECIAL ISSUE: BEHAVIOURAL PLASTICITY AND EVOLUTION Animal Behaviour 85 (2013) 1077e1088

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Understanding variation in behavioural responses to human-induced rapid environmental change: a conceptual overview Andrew Sih* Department of Environmental Science and Policy, University of California, Davis, CA, U.S.A.

a r t i c l e i n f o Article history: Received 2 September 2012 Initial acceptance 2 October 2012 Final acceptance 22 January 2013 Available online 26 March 2013 MS. number: ASI-12-00667 Keywords: adaptive plasticity behavioural plasticity cueeresponse system ecological trap environmental change evolution exotic species imperfect information learning maladaptive behaviour

A key issue in animal behaviour is the need to understand variation in behavioural responses to humaninduced rapid environmental change (HIREC) such as habitat loss, exotic species, pollution, human harvesting and climate change. Why do some individuals show maladaptive behaviours, while others show adaptive responses to evolutionarily novel situations? At present, we lack a unified conceptual framework for generating predictions and guiding empirical and theoretical work on this critical question. Drawing from the concept of ecological traps, I suggest that a conceptual framework for explaining this variation should include four main points: (1) behavioural responses (adaptive or not) are the result of cueeresponse systems, or behavioural ‘rules of thumb’; (2) limited or imprecise, unreliable information often underlies suboptimal behaviour; (3) the organism’s behavioural flexibility affects its response to novel situations; and (4) evolution (and development) in past environments has shaped cueeresponse systems, responses to imperfect information and degree of behavioural flexibility to be adaptive in past environments, but not necessarily in novel environments. The degree of match/ mismatch between past environments and novel environments altered by HIREC is thus a key to explaining adaptive versus maladaptive behaviours. I suggest several existing frameworks that address these four points, and are thus potentially useful for explaining behavioural responses to HIREC: signal detection theory, adaptive plasticity theory, extended reaction norms and costebenefit theory on variation in learning. I further discuss more complex aspects of reality that would be useful to add to these existing frameworks. Ó 2013 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved.

Humans are having large, often negative impacts on earth’s biota (Vitousek et al. 1997; Sanderson et al. 2002; Pereira et al. 2010). Five major types of human-induced change that have imperiled many species are: habitat change (Fahrig 2003; Watling & Donnelly 2006; Ferraz et al. 2007; Chaine & Clobert 2012), exotic species (Blackburn et al. 2004; McKenzie et al. 2007; Sih et al. 2010), pollution (Verhoeven et al. 2006; Butchart et al. 2010), human harvesting (Jackson et al. 2001; Worm et al. 2009) and climate change (Parmesan 2006; Deutsch et al. 2008). While many of these are natural forms of environmental variation, humans often drive changes on a larger spatial scale, and, in particular, at a faster rate of change. I thus refer to these collectively as human-induced rapid environmental change, or HIREC. While HIREC is having negative impacts on many taxa, many other species are doing well or better than ever (e.g. invasive pests and urbanized species). From a traitbased approach, a key question is what organismal traits explain this variation in ability to cope with HIREC? Behavioural plasticity

* Correspondence: A. Sih, Department of Environmental Science and Policy, University of California, Davis, CA 95616, U.S.A. E-mail address: [email protected].

clearly plays an important role. A meta-analysis of more than 3000 rates of recent phenotypic change (largely in response to HIREC) suggested that most of the change involved phenotypic plasticity rather than immediate genetic evolution (Hendry et al. 2008). Detailed analyses of specific taxa often confirm this assessment (Charmantier et al. 2008; Van Buskirk 2012). Furthermore, behaviour appears to be important in explaining variation in species’ abilities to cope well with HIREC. Maladaptive behaviours, such as consumption of novel toxic prey or failure to avoid novel predators, have been implicated in species declines (Buchholz 2007; Schlaepfer et al. 2010; Sih et al. 2010) while more appropriate behavioural responses facilitate species invasions (Holway & Suarez 1999; Sih et al. 2010). Recent papers (Sih et al. 2011; Tuomainen & Candolin 2011) have reviewed the literature on behavioural responses to HIREC. Many involve responses to novel options (e.g. novel habitats, foods, predators or parasites). While in some cases, animals show adaptive responses to novel options (e.g. urbanized pests), in other cases, organisms show maladaptive responses (e.g. ecological traps, described in the next section). Other responses involve adjustments in the use of existing options; e.g. adjustments required when HIREC alters the spatiotemporal pattern of key biotic or

0003-3472/$38.00 Ó 2013 The Association for the Study of Animal Behaviour. Published by Elsevier Ltd. All rights reserved. http://dx.doi.org/10.1016/j.anbehav.2013.02.017

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abiotic fitness factors. In particular, many studies have examined adjustments in timing of key seasonal life history events (e.g. onset of reproduction or migration, onset of or emergence from dormancy) in response to climate change. While some organisms have adjusted appropriately, others have not (Both et al. 2009; Knudsen et al. 2011). While a rapidly growing number of case studies describe behavioural responses to HIREC in specific systems (Candolin & Wong 2012), we currently lack a general conceptual framework for explaining variation in these responses. Why do some organisms respond poorly to HIREC (e.g. ignore novel predators, consume novel toxic prey), sometimes apparently oblivious to the obvious (to us) negative consequences? And, how and why do other organisms respond adaptively to novel situations that they have never seen before? The focus of this paper is on developing a conceptual framework for understanding this striking variation in behavioural response to HIREC. TOWARDS A THEORY OF BEHAVIOURAL RESPONSES TO HIREC Some animals are doing well despite HIREC because they are not experiencing the particular change. For example, some animals on isolated islands have not yet been exposed to dangerous novel predators. For animals that are indeed encountering HIREC, we can split their responses into three stages: (1) initial plastic responses to first encounters with a particular aspect of HIREC; (2) learning to better cope with HIREC; and (3) evolutionary responses to HIREC over multiple generations. Here, my main focus is on variation in initial behavioural responses to HIREC, with also a brief consideration of factors that might influence variation in learning to better respond to HIREC. Evolutionary responses to HIREC have been discussed elsewhere (Hendry et al. 2011; Lankau et al. 2011; Sih et al. 2011). Adaptive responses to HIREC should fit the predictions of behavioural ecology’s classic, costebenefit approach. An optimistic view is thus that behavioural ecology’s standard paradigms (e.g. optimal foraging theory, ideal free distribution theory, kin selection theory) can provide ecologists and conservation managers with useful predictions on how animals will respond to HIREC (Blumstein 2012; Brown 2012; Ydenberg & Prins 2012). Note that due to trade-offs, in some cases, the optimal response might be to show little or no apparent response to HIREC; for example, a shift in the best height in vegetation for singing (to attract mates) in response to climate change can be offset by enhanced predation risk (Møller 2012). Indeed, even optimal organisms will do badly in the face of HIREC if their best remaining options are now poor (i.e. the best adaptive response can still yield reduced individual and population fitness). In many cases, however, it appears that organisms are doing poorly with HIREC not simply because they have no good options remaining, but because they show maladaptive behavioural responses, such as those associated with ecological or evolutionary traps (Schlaepfer et al. 2002, 2005, 2010; Robertson & Hutto 2006). These traps often involve animals using (or accepting) low-quality novel options that should be rejected (e.g. toxic novel foods, lowquality novel habitats), or ignoring novel risks that should be avoided (e.g. novel predators, parasites, diseases or dangerous humans). The trap idea suggests that although animals have evolved (or developed) cueeresponse systems that produced adaptive outcomes under past conditions, after HIREC, these same cueeresponse systems now result in maladaptive behaviours. In the past, cue A might have indicated good habitat for settlement, cue B, good food that should be eaten, and cue C, danger that should be avoided. But, if the novel conditions after HIREC include an evolutionary mismatch where these cues no longer correctly match

adaptive fitness outcomes, then organisms might now follow inappropriate information resulting in responses that are sometimes strikingly maladaptive. For example, numerous insects seeking aquatic oviposition sites lay their eggs on glass (even vertical windows) and asphalt apparently because these surfaces reflect polarized light in a way that has, in the past, been a good indicator of water (Kriska et al. 2008). A converse potential error occurs when animals ignore or even avoid high-quality options that should be utilized (e.g. novel resources or habitats). Gilroy & Sutherland (2007) referred to these as undervalued resources. Examples include lack of use of available crops by most insects that could consume those crops (I. S. Pearse, D. J. Harris, R. Karban & A. Sih, unpublished data), and overavoidance of good-quality, human-inhabited habitats that are not actually dangerous. Drawing from the concept of ecological and evolutionary traps, I suggest four starting points for a general framework for understanding variation in behavioural responses to HIREC: (1) behavioural responses (adaptive or not) are the outcome of cueeresponse systems, or behavioural ‘rules of thumb’; (2) limited or imprecise, unreliable information often underlies suboptimal behaviour; (3) the organism’s behavioural flexibility, whether it is a generalist versus a specialist, has important effects on its response to novel situations, and (4) cueeresponse systems, responses to poor information and behavioural flexibility have all been shaped by evolution (and development) to be adaptive in past environments. Figure 1 summarizes how these points can be integrated. In brief, variation in responses to current HIREC-altered conditions depends on variation in cueeresponse systems (the cues used and the response thresholds) that were shaped by past selection pressures and the reliability of past cues to be adaptive in past conditions. Whether organisms show adaptive responses to HIREC or not depends on the match versus mismatch between past and HIRECaltered conditions. Below, I discuss each of the above four points in some detail. Point 4, regarding the overarching role of past environments, follows the adaptationist view that extant traits, including behaviours, are shaped by past selection pressures to be adaptive. Assuming that with trade-offs, traits tend to work well only under a limited range of conditions, a key is the similarity of the past conditions that shaped the organism’s traits to the current, modern HIREC-altered conditions. Organisms tend to respond poorly to HIREC if the modern world is truly novel (i.e. if it differs fundamentally from the conditions that shaped the organism’s traits). In

Evolutionary match/mismatch

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Learning tendencies Figure 1. The conceptual overview. Selection pressures and cue reliabilities in the past shape cueeresponse systems that were adaptive in the past. Responses to HIREC and performance (short-term success and long-term fitness) depend on how well the cuee response systems function in current, HIREC-altered conditions. If initial success is adequate, animals can learn to better cope with HIREC via learning tendencies that were also shaped by past conditions. A key is thus the match or mismatch between past and HIREC-altered conditions.

SPECIAL ISSUE: BEHAVIOURAL PLASTICITY AND EVOLUTION A. Sih / Animal Behaviour 85 (2013) 1077e1088

contrast, organisms are more likely to respond well to HIREC if the ‘novel’ conditions are similar to the past environments that shaped the organism’s traits. For example, prey will likely recognize an introduced predatory fish as dangerous if the prey have evolved with other predatory fish, particularly if the familiar predators are similar (in the eyes, ears, nose, etc., of the focal prey) to the introduced predator. In contrast, prey that have never seen predatory fish in their evolutionary past are less likely to recognize exotic fish as dangerous, and thus are likely to suffer heavy predation (Sih et al. 2010). Along similar lines, animals are more likely to adopt a novel resource if the animal has an evolutionary history of using similar resources. Non-native plants whose major chemical defences (and attractants/deterrents) were also reported from local native plants experienced more herbivory than non-native plants with unfamiliar defences (Cappuccino & Arnason 2006). To understand responses to HIREC, a key is the evolutionary match versus mismatch between past and present, human-altered conditions. If the animal’s behavioural response to HIREC is shaped heavily by its early experiences, then the individual’s earlier experiences also matter. In that case, a key is the developmental match versus mismatch between an animal’s earlier experiences and current conditions. Earlier exposure to similar stressors should prepare an animal to cope better with a ‘novel’ stressor, whereas lack of exposure to similar stressors should make the animal more likely to respond inappropriately to the novel stressor (Frankenhuis & Del Giudice 2012; Sih 2011). Evolutionary and developmental history can interact. Evolution can shape how early experiences govern the development of later behavioural responses (e.g. via learning). Points 1 and 2 posit that, at a mechanistic level, to understand variation in behaviour, it can be critical to understand how environmental cues are detected and processed to produce behavioural responses guided by relatively simple behavioural ‘rules of thumb’ (McNamara & Houston 2009). As suggested by the ecological trap concept, the cueeresponse view can be particularly important for understanding suboptimal responses to novel cues or novel environments where simple optimality theory that ignores mechanisms does not obviously apply. Optimality theory with information constraints (i.e. that incorporates imprecise information), however, should be useful, particularly if it accounts for the possibility that past information constraints have shaped cue use that might be nonadaptive after HIREC. For example, if a cue has always been an accurate indicator of a good resource, then an evolutionary mismatch occurs if a new cue is similar to the old reliable cue but associated with a poor option (particularly if the new cue is a supernormal stimulus). If a cue has always been reliable in the past, we have a ‘selective hole’ in the sense that there has been no past selection requiring more precise cue evaluation (Kriska et al. 2008). Finally, although it is not explicitly part of the trap concept, conventional wisdom suggests that generalists or more flexible organisms should adjust better to change than specialists or less flexible organisms (Sol et al. 2002, 2005; Wright et al. 2010). Indeed, animals with larger brains that might often be more behaviourally flexible or even innovative appear to adjust better to HIREC in the sense of being more likely to become invasive or urbanized (Sol et al. 2005, 2013). Being flexible, however, is not the same as being correct. Generalists (particularly neophilic ones) can, for example, be too catholic and inappropriately ready to accept (be trapped by) poor, novel options. If novel items have typically been beneficial in the past, this can explain a tendency to be neophilic even if, after HIREC, this makes the animal more susceptible to being trapped by poor novel options. If, however, novel items have often been poor options in the past, then animals should be neophobic and not easily trapped. The general point is that responses to HIREC should depend, of course, not just on the cues, but also on the animal’s evolved behavioural tendencies.

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While the above points provide some qualitative guidelines for a theory on behavioural responses to HIREC, a key need is to convert these intuitive ideas into quantitative theory that generates explicit predictions. I suggest a hybrid approach that starts by using traditional costebenefit models (but with information constraints) to understand cueeresponse relationships that were adaptive in past conditions. The new step is to provide explicit frameworks for predicting, based on previously adaptive behavioural tendencies, how animals will respond, and how well they will perform when faced with specific novel cues and conditions. This approach blends evolutionary history, development, cognitive/sensory ecology and classic costebenefit behavioural ecology; that is, it integrates Tinbergen’s four approaches to studying behaviour (Tinbergen 1952). Using a metaphor from ecology (Connell 1980), we seek explicit theory to understand the ‘ghosts of adaptation past’: how previously adaptive behavioural responses help to explain both appropriate and suboptimal responses to HIREC. Below, I suggest and describe two existing theoretical frameworks that address the four main points discussed above. To examine the decision to accept versus reject or avoid a novel option (e.g. a novel habitat, food item or predator), a potentially useful framework is signal detection theory (Macmillan & Creelman 2005). To understand adjustments to novel environmental gradients that can be either within a ‘familiar’ range (but where HIREC requires behavioural adjustments; e.g. shifts in optimal timing of life history events with climate change), or outside of a familiar range (e.g. responses to drastically higher carbon dioxide levels, or to captivity), a relevant framework is adaptive plasticity theory (Via et al. 1995; Tufto 2000; Gabriel et al. 2005), along with the concept of ‘extended reaction norms’ (Ghalambor et al. 2007; Sih et al. 2011). SIGNAL DETECTION THEORY AND RESPONSES TO NOVEL OPTIONS: THE ‘GHOST OF SIGNALS PAST’ Signal detection theory (SDT) is a well-developed, quantitative framework with explicit theory and statistical methods for analysing cueeresponse relationships in the presence of uncertainty (Macmillan & Creelman 2005). SDT is often represented as a choice between two options or classes of options each with an associated distribution of cue strengths (Fig. 2). For example, the two distributions might represent the strengths of cues (attractants or deterrents) associated with dangerous versus nondangerous conditions, or high- versus low-quality resources (foods, mates or habitat). Or, the distributions might reflect cues that predict good versus poor conditions for a life history decision (e.g. reproducing). Using an animals’ avoidance of potential danger as a focal example, if uncertainty is low, the cue distributions for safe versus dangerous situations are narrow and might overlap very little (Fig. 2a). In that case, using cue strength as an indicator, prey can easily discriminate between dangerous and safe situations. In contrast, if uncertainty is high, the cue distributions are broad, and if their peaks are similar, then prey cannot easily discriminate between the two (Fig. 2b). However, even if distributions are broad, if they are far apart and do not overlap much, they can be easily discriminated (Fig. 2c). This basic framework has been applied to prey assessing danger (Ings & Chittka 2008; Trimmer et al. 2008), foragers assessing foods (Getty & Krebs 1985), including toxic models and palatable mimics (Getty 1985), and mate choice (Wiley 2006). The animal’s behavioural response (e.g. avoid or not, accept or not) depends not only on cue discrimination, but also on the animal’s response threshold: the cue level that must be exceeded to elicit a response. If the prey organism sets a very high response threshold (Fig. 2b, the dark vertical line to the right), it ignores all nonpredators, but also errs in ignoring many predators. With a



Response thresholds

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Figure 2. The basic idea of signal detection theory. (aec ) Probability distributions for cue strengths for safe versus dangerous options. (a) Narrow cues distributions that are easily discriminated despite being similar in mean cue value. (b) Broad cue distributions that are too similar in mean cue value to be easily discriminated. Behavioural responses depend on response thresholds: here, the animal responds (e.g. avoids the option) if cue strengths are above the threshold. See text for discussion of implications of different response thresholds. (c) Broad cue distributions that are different enough in mean value to be easily discriminated. (d) ROC curves. Curve 1 represents the case where safe and dangerous options are completely indiscriminable. Curve 2 is where the two can be somewhat discriminated whereas curve 3 is where the two are easily discriminated. For curve 3, Y is where the animal has a high response threshold whereas X is for a low response threshold.

lower threshold (the left vertical line), it correctly avoids a higher proportion of dangerous predators, but at the cost of avoiding some nonpredators. The magnitude of the trade-off between increasing both correct responses and erroneous incorrect ones depends on the animal’s ability to discriminate between cues. If the cues of predators and nonpredators are easy to discriminate (Fig. 2a, c), then the animal can make numerous correct decisions (avoiding predators) while making few erroneous ones (unnecessarily avoiding nonpredators). In contrast, if the cues are difficult to discriminate, the uncertainty (broad cue distributions) or cue similarity between dangerous and nondangerous forces the animal to make many errors (avoid numerous nonpredators) to correctly avoid predators. The trade-off associated with overlapping cue distributions is embodied in receiver operator characteristic (ROC) curves (Fig. 1d) where the X axis is the error response rate (e.g. avoidance of nonpredators) and the Y axis is the correct response rate (e.g. avoidance of predators). A very high response threshold (i.e. an animal that is nonresponsive) puts the animal near the origin in the ROC space where it shows little response to either predators or nonpredators. Reducing the threshold (increasing responsiveness) moves the animal along its ROC curve away from the origin, increasing the rate of both correct and incorrect responses. The animal’s optimal response threshold depends on the relative benefits of increasing the number of correct responses to predators versus the associated cost of overresponding to nonpredators. Perhaps obviously, if the benefit of avoiding predators is much higher than the cost of also avoiding nonpredators, animals should have a low threshold (and be responsive, point X in Fig. 2d) despite the cost of erroneously avoiding nonpredators. That is, prey should have a low optimal response threshold (be highly responsive to predation risk) if predators are very dangerous (i.e. if prey escape success is low) and the opportunity cost of hiding is low (e.g. prey are not starving and it is not the mating season). Similarly, animals should have low response thresholds for food if they are hungry (high benefit of feeding) and if accepting lower-quality food is not too costly (e.g.

low-quality food is not toxic and not too abundant). In signal detection theory (SDT) terminology, an organism’s optimal bias is the point where the marginal benefit from increasing sensitivity in terms of increased correct responses (reduced type II error) exactly balances the marginal cost from increased false alarms (increased type I error). With regard to the four basic points of a theory of responses to HIREC outlined above, SDT clearly fits the suggested need for a framework that addresses cues and responses under uncertainty (points 1 and 2). It also incorporates point 3, the animal’s tendency to be a generalist versus specialist; generalists have lower response thresholds than specialists. And, for point 4, the organism’s past history of selection is embodied in its optimal response threshold that reflects both its ability to discriminate familiar high- versus low-quality options and the fitness benefits versus costs of different levels of responsiveness. These fitness effects should depend on both the relative value of different options and their history of availability. To emphasize, real animals do not typically have the omniscience to know the current or future values for all these variables, but they might estimate them based on past experiences or on long-term averages represented in evolved behavioural tendencies (i.e. in their genes or epigenetics). To predict initial responses to a novel option, we need to account for the cues associated with that option, and the animal’s response threshold, which we assume is a previously optimal response threshold shaped by its past. An evolutionary (or developmental) mismatch occurs if the previously optimal response threshold differs substantially from the new optimum. I next outline several main insights from SDT about when we might expect suboptimal initial behavioural responses to HIREC, again, using prey responses to novel predators as a focal example. The response to a novel item should clearly depend on the similarity of the novel item’s cues to familiar cues. Imagine a novel item with a cue distribution that has a lower mean cue value than a familiar option. If the mean cue values for the novel and familiar options are very similar, the two should be difficult to discriminate.


This can result in over-response to novel options that should be ignored (e.g. attraction to food traps, habitat traps) or unnecessary avoidance of nondangerous options (e.g. human-altered habitat, tourists, or road noises that are not dangerous). Conversely, if the novel cues are very different from familiar cues, this can result in nonresponse to novel options that should be responded to (e.g. underuse of (undervalued) novel resources, lack of avoidance of novel predators). Note, however, that cue overlap between novel and familiar items depends not just on the similarity of their distribution peaks, but also on the narrowness (reliability) of their cue distributions. If the familiar item’s cues have been unreliable and imprecise, it is more likely that the novel item will be mistaken for the familiar one. For example, if an animal has evolved (or developed) in an environment with unreliable cues for predators, the animal will need to set a lower response threshold to ensure that it avoids most (say, 90%) of potential predators. Unreliable predator cues could occur either when dangerous predators hide their cues, or when predators that only forage occasionally appear dangerous even during times they are not actually dangerous. With a lower threshold, prey will be more likely to respond to a novel predator. However, with unreliable past cues, animals should also be more susceptible to avoiding novel options that are not actually dangerous, and if the cues indicate resources, unreliable cues should make animals more susceptible to accepting novel items (foods, habitats, etc.) that are suboptimal traps. Conversely, if the familiar item’s cues have been highly reliable and precise, it is less likely that the novel option will be mistaken for the familiar one (Fig. 2b). In that case, it is more likely that the novel option will be ignored, which is clearly a mistake if the novel option is a danger that should be avoided or an undervalued resource that should be adopted. Cue distributions as perceived by the receiver depend not just on the cues themselves, but also on the receiver and the environment. HIREC can also interfere with adaptive decision making by interfering with the animal’s ability to discriminate cues. Pollutants can both mask cues and reduce the receiver’s cue sensitivity (Rosenthal & Stuart-Fox 2012). For example, urban or road noise can mask acoustic cues (Slabbekoorn & Ripmeester 2008; Barber et al. 2010; Slabbekoorn et al. 2010; Møller 2011), human-caused increases in water turbidity can mask visual cues (Wong et al. 2007), and changes in water chemistry can mask chemical cues (Fisher et al. 2006; Herbert-Read et al. 2010). HIREC can involve new or altered physical structures (e.g. buildings, vegetation) that alter cue transmission. Or, HIREC can have indirect effects on cues by changing the biotic community and thus affect signals produced by other animals. Alternatively, contaminants can negatively impact the sensory systems required to detect and recognize cues (Herbert-Read et al. 2010). Because the response to any item depends on response thresholds, previous selection pressures that affect these thresholds influence an organism’s response to novel items. As noted above, organisms with a low response threshold are generalists that are more likely to get trapped into using an inappropriate new item, but also less likely to ignore new dangers. Also as noted above, organisms are more likely to have low response thresholds (respond readily to cues) if, in the past, the cost of responding inappropriately to a ‘poor option’ was not too bad relative to the lost opportunity cost of missing out on a ‘good option’ that deserves a response. Individuals within species likely differ in their response thresholds (i.e. traps should often be age-, sex- or condition-specific in predictable ways). For example, if younger, low-ranking or lowcondition animals tend to have less access to food, they should be more susceptible to resource traps (even under the same current food regime). Individual differences in response thresholds might

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also often reflect individual differences in personality (Sih et al. 2004; Sih & Bell 2008; Sih & Del Giudice 2012). Animals that are more voracious or aggressive have relatively low response thresholds to attack (prey or conspecifics) and should thus be more susceptible to overutilizing inappropriate novel options. Bolder animals have higher response thresholds for avoiding danger and should thus be more likely to exploit novel opportunities (e.g. invade urban habitats: Møller 2008, 2010; McCleery 2009; Evans et al. 2010) than fearful, reactive individuals, but also more likely to ignore novel predators. Neophobic individuals should, by definition, be more likely than neophilic, exploratory individuals to avoid novel dangers, but also less likely to adopt novel resources. While these starting points are perhaps obvious, the suggestion is that more detailed analyses connecting SDT, behavioural syndromes and HIREC should yield new insights. An example of the above ideas is the thrifty phenotype hypothesis for explaining human obesity where past selection favouring a high responsiveness to fatty or sugary foods potentially explains the modern overattraction to these cues. Note, however, that rather than explain obesity per se, the focus here on explaining variation in responses to HIREC emphasizes the need to explain both why many people overeat, and also why many others do not. For example, if some age classes (e.g. younger individuals), social classes (e.g. subordinate classes in poorer energetic condition) or parts of the world (e.g. based on climate) have experienced either sustained food limitation or greater fluctuations in food supply, this should have resulted in stronger selection favouring high responsiveness to these foods and thus a greater susceptibility to obesity (Higginson et al. 2012). Overall, the SDT framework predicts that organismal responses to novel items should depend on cue similarity, cue reliability, and the relative costs and benefits of type I versus type II errors. For example, naïve prey should be more likely to inappropriately ignore novel predators if (1) novel and familiar predators put out very different cues, (2) familiar predators have been characterized by highly reliable cues, (3) familiar predators have not been abundant or particularly dangerous and (4) the prey have a history of food scarcity. While separately, each of the insights is not new, the SDT framework organizes them into one cohesive framework that can be studied by adapting existing theoretical and empirical methods. REACTION NORMS AND ADJUSTMENTS TO ENVIRONMENTAL CHANGE: ‘GHOSTS OF ADAPTIVE PLASTICITY PAST’ A second existing framework that addresses changes in organismal traits in response to variation in environments and their associated cues is the theory of adaptive plasticity and optimal reaction norms (Gomulkiewicz & Kirkpatrick 1992; Tufto 2000; McNamara et al. 2011). While this framework does not address cues mechanistically the way SDT does, it provides theoretical and empirical methods for looking at how cue reliability, genetics and selection influence the optimal reaction norm. For simplicity, the field often depicts a world with two environments, each with an optimal trait level (Fig. 3). The optimal reaction norm, characterized by the genotype’s average trait level and its degree of plasticity, depends on the strength of selection in each environment, the frequency of exposure to the different environments and the reliability of environmental cues. If the organism is usually found in environment 1, or if selection is stronger in environment 1, its optimal reaction norm is pushed closer to the optimum for that environment. Importantly, the optimal degree of plasticity depends heavily on the reliability of environmental cues. If cues are totally reliable, then organisms should evolve an optimal reaction norm that hits the optimal trait in both (all) environments. That is, with



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Figure 3. (a) Optimal reaction norms for two environments, E1 and E2, where the stars represent the optimal trait in each environment. If cues are reliable, the optimal reaction norm comes close to the optima for the two environments (line 1), but if cues are only somewhat reliable, the optimal reaction norm is line 2, and if cues are completely unreliable, the optimum is a fixed trait (line 3) that best balances selection pressures from the two environments. (b) Optimal reaction norms (T(c)) depending on past selection pressures and cue reliabilities. The stars represent the optimal trait in a past environment and in a novel environment. See text for discussion of conditions when organisms might adjust well (or not) to a novel environment.

perfect information, organisms should be highly flexible (Fig. 3a, line 1). If, however, cues are unreliable, then this reduces the expected degree of plasticity (Fig. 3a). With unreliable cues, organisms might express trait z1 based on cues that suggest that the organism will be in environment E1, only to find itself in environment E2 where trait z1 is an inappropriate trait. To account for this possibility, the organism should shade its trait to be intermediate between the optima for environments E1 and E2 (Fig. 3a, line 2). Indeed, if cues are totally unreliable, then, of course, the organism should pay no attention to them. Instead, it should express a fixed trait (Fig. 3a, line 3) that represents the best balance of selection pressures across environments that it might experience. While reaction norms are usually defined as traits expressed by a genotype, the concept can also be applied to individual ‘behavioural reaction norms’ (Dingemanse et al. 2010). Simple models of adaptive plasticity assume that the organism shows irreversible developmental plasticity (Tufto 2000; McNamara et al. 2011). An earlier environmental cue determines the development of a phenotype that the organism is stuck with later on when selection hits. For example, predator cues early in life can induce prey to build a spine that subsequently influences their ability to cope with predation risk (Tollrian & Dodson 1999). In this two-stage view, the accuracy of the cue depends largely on whether the environment changes over ontogeny, particularly if the change is unpredictable. However, even with reversible plasticity, as with most behaviours, if cues are unreliable, this reduces the optimal plasticity (Padilla & Adolph 1996; Gabriel et al. 2005). For example, if prey have unreliable information about the presence/absence of predators, this can cause prey to show antipredator behaviour (e.g. stay in or near refuge, or live in groups for safety in numbers, or remain vigilant) even when, in fact, predators are only occasionally present (Sih 1992). This overall framework clearly embodies the cueeresponse relationships, uncertainty and focus on flexibility that we seek in a theory of behavioural response to HIREC. Evolutionary history enters in the simple sense that the relative strength of selection and the frequency of the different environments in the past (as a predictor of the future) affect the optimal reaction norm, and in that the optimal reaction norm assumes that animals have evolved to ‘know’ both the optimal trait in each environment and the reliability of available cues. To extend standard adaptive plasticity theory to address initial responses to HIREC, we ask: if the environment changes, but

organisms initially do not change their reaction norm, when might organisms show adaptive versus suboptimal responses to the environmental change? McNamara et al. (2011) recently provided the first model, to my knowledge, using adaptive plasticity theory to examine responses (adaptive or not) to environmental change, assuming that organisms initially retain their previously optimal reaction norm. They couched their model in terms of adaptive timing of a key life history event (e.g. migration or onset of reproduction) where the timing has large fitness effects, but to gain fitness benefits the organism must begin preparing (e.g. by building a nest or energy reserves) well in advance of the future event. As with previous adaptive plasticity models, they show that in the absence of informative cues, the organism’s best strategy is to choose a fixed, average best timing, T*, averaged across years. However, with a reliable cue, the best timing, T(c) is plastic depending on the cue, where the magnitude of the effect of the cue value on T(c) is proportional to the reliability of the cue. If we assume that, after an environmental change, the cue and/or the best time changes, but the organism retains its previously optimal reaction norm, then whether the animal adjusts adaptively depends on whether the change matches the shift in timing induced by the organism’s reaction norm (Fig. 3b). For example, animals facing environmental change should often adjust their optimal seasonal timing of reproduction. A potential problem arises if the cue they use to adjust their timing either has not changed (e.g. photoperiod) or has not changed much relative to the change in the optimal timing (both measured in units of standard deviation). If their cue has not been reliable in the past, and the slope of their previously optimal reaction norm is shallow, then they will not adjust adequately (Fig. 3b, line 3), whereas if their cues have been highly reliable in the past, they should adjust better (Fig. 3b, line 1). In contrast, if cues have changed substantially, but the best time to begin reproducing has not changed, then organisms that had previously unreliable cues will ignore the changed cues and behave more adaptively than those that evolved with highly reliable cues. McNamara et al. (2011) couched their model in terms of the timing of key activities; however, the adaptive plasticity framework (indeed McNamara’s specific model) can be applied to any plastic trait. For example, the trait could be an animal’s call frequency for signalling to conspecifics. To avoid interference from the background, animals should adjust their frequency to be different from their environment (Patricelli & Blickley 2006; Slabbekoorn & den Boer-Visser 2006; Parris et al. 2009; Potvin et al. 2011). Humangenerated (e.g. urban or road) noise is often low frequency. Birds from forested habitats with flowing streams often experience lowfrequency background noise and thus have evolved higherfrequency calls (Slabbekoorn 2013); these species do not need to adjust their call frequencies to avoid urban noise. In contrast, birds from open habitats have evolved lower-frequency calls, and thus a greater need to adjust. Adaptive plasticity theory has the potential to predict which species with low-frequency calls will adjust their call frequency, as opposed to adjusting their signalling in other ways (e.g. by calling louder), or simply avoiding urban or road habitats. Even more broadly, the trait of interest could be the preference for a novel resource or habitat, or avoidance of a novel predator, where adaptive plasticity models are used to investigate predicted changes in the cueepreference relationships that could result in a trap. A particularly interesting issue involves organismal responses to environmental conditions that lie outside of the range that the organism has seen in its evolutionary (or developmental) past. This could include responses to novel chemicals (or other contaminants), including much higher carbon dioxide levels, ocean acidification, and responses to novel, supernormal stimuli or responses to guns or cars (a much faster ‘predator’), or simply to being in


captivity (Mason et al. 2013). What should we expect for ‘extended reaction norms’ when organisms are found in these extreme environments? Whether organisms respond well to extreme environments would depend on whether these extended reaction norms happen to match the new optima. One possibility is extrapolation of the linear (or nonlinear) optimal reaction norm shaped by past environments (Chevin et al. 2010). If organisms show extrapolated reaction norms (Fig. 4, region A), the points discussed above on factors influencing the mean and slope (plasticity) of optimal reaction norms in past environments should be useful for explaining or even predicting responses to novel extreme environments. Another possibility, however, is that extended reaction norms might exhibit threshold effects where a trait abruptly changes when the environment exceeds some threshold (Fig. 4, the dashed vertical line). This appears to apply for temperature tolerances and seems likely to occur for other extreme environments. A better understanding of mechanisms underlying threshold effects should be highly insightful. Most interestingly, Ghalambor et al. (2007) noted that, while a reaction norm could be ‘held taut like a string’ by natural selection across the range of current and relevant past environments, due to lack of selection, reaction norms might be ‘floppy’ outside of their familiar range of environments (Fig. 4, region B). Thus extended reaction norms might harbour substantial cryptic genetic variation that could be valuable if the new optimum does not lie close to a simple extrapolation of evolved reaction norms. This idea is associated with the possibility of genetic assimilation or genetic accommodation, both of which have been suggested to be key parts of how plasticity influences evolution (West-Eberhard 2003; Pigliucci et al. 2006), including evolution in response to HIREC (Ghalambor et al. 2007). Although they have not been couched in these terms, many of our laboratory experiments and studies on animals in captivity are studies of extended reaction norms. While laboratory studies, typically try to create situations that are plausibly within a natural range (as opposed to an extreme environment), due to logistical constraints, in many cases, some environmental variables extend into extreme ranges (e.g. arenas that are much smaller than natural home ranges). Some animals are good laboratory subjects in that they seem to adjust well behaviourally to the non-natural conditions in the laboratory, while others do not adjust so well. Even good laboratory animals adjust well to some conditions but not others. While we all develop a sense of the details of husbandry for our subjects (that we often view as somewhat idiosyncratic), little effort has been put into incorporating this information into a

B

New optimum

Trait

A

Past environments

Novel environment

Figure 4. Extended reaction norms. Within a range of normal past environments, animals might show optimal reaction norms that match environmental optima reasonably well. In region A, just outside of the range of past environments, organisms might simply extend their reaction norms; however, beyond some threshold (region B), lack of past selection might allow the maintenance of genetic variation in reaction norms, some of which might come close to matching even a very different optimum in a novel environment that is well outside the range of past environments.

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conceptual framework. In some fields (e.g. cognitive psychology), animals are explicitly placed into novel environments (e.g. water mazes, Skinner boxes) to study their responses. Some insights on the relative ability of animals to learn particular tasks in these extreme environments have come through referring to their likely evolutionary history (e.g. Garcia et al. 1974; Shettleworth 2010). Perhaps the best developed example of using evolutionary history to understand animal behaviour in extreme environments involves the study of normal versus stereotypical behaviour in captive animals (Mason et al. 2013). The suggestion here is that further quantitative analyses of the behaviour of laboratory and captive animals within a comprehensive, conceptual framework (e.g. using SDT or adaptive plasticity) could prove insightful for understanding behavioural responses to HIREC. ADDING REALISTIC COMPLEXITIES The conceptual and theoretical approaches outlined above potentially yield two main benefits: (1) they generate general, testable predictions; and (2) they provide an organizing framework for guiding more specific modelling and empirical study. However, as is usually the case, general theory leaves out many aspects of reality. Explaining behavioural variation in specific systems will require taking on the nontrivial challenge of identifying and incorporating key complexities that are important for each specific system and issue. I next discuss some important aspects of complex reality that are not considered explicitly in simple SDT or adaptive plasticity models, but that represent important directions for further theoretical and empirical work: multiple cues, multiple simultaneous options, sampling behaviour that underlies cue reliability, multiple responses, multiple stressors, correlated responses (e.g. behavioural syndromes) to multiple stressors and social influences on responses to HIREC. First, whereas most models of signal detection and adaptive plasticity address a scenario with one cue axis, in many cases, organisms use multiple cues. In particular, organisms differ in whether they rely primarily on one versus multiple cues. Some insect herbivores are stimulated to feed by a single plant metabolite whereas others require a specific blend of metabolites (Metcalf et al. 1980; Webster et al. 2010). Some prey require only chemical cues from damaged conspecifics to drive them into hiding, while others require those chemical cues as well as chemical cues from specific predators, or perhaps also visual cues (Kats & Dill 1998). Some organisms rely primarily on photoperiod to govern the timing of key life history events, while others also use information about food, temperature or other behavioural stimuli (Wingfield et al. 1992; Bradshaw & Holzapfel 2007; Bronson 2009). In some cases, the sexes differ in their use of multiple cues; for example, in some bird species, reproductive development in males relies on photoperiod alone, while in females it is more tightly tied to food and temperature (Wingfield et al. 1996, 1997; Caro & Visser 2009; Visser et al. 2009). In mammals, in larger species, reproduction is cued more by photoperiod, whereas smaller species rely also on temperature or rainfall to time their reproduction (Bronson 2009). When organisms use multiple cues, a key issue is how they integrate the information from multiple cues. If the multiple cues have additive effects, then they can be combined into a single metric, with the effect of each weighted appropriately. A more complex scenario arises if the multiple cues have nonadditive effects: where animals respond only if they receive both cue A and cue B, or where the presence of a single ‘stop’ deterrent cue can override the presence of all attractants. Integration of multiple cues can be suboptimal. For example, humans are known to exhibit a ‘halo effect’ where trait A (e.g. physical attractiveness) influences the perception of trait B (intelligence or competence) in the same



individual even when the two traits are uncorrelated. Animals that require both cue A and cue B to elicit a response should be less likely to respond to novel item (i.e. less likely to be trapped by a poor novel item, but also more likely to ignore a novel danger). Animals that require a ‘stop signal’ to not respond are more likely to be trapped by a novel poor item, but also more likely to respond to a novel danger. Second, while simple SDT considers the situation where animals choose between two options (or two categories of options), real organisms are often faced with multiple simultaneous options. In theory, SDT can apply to numerous cue distributions where the optimal response threshold appropriately accounts for the discriminability and costs and benefits of all options. As with multiple cues, multiple options pose no special problem if they have additive effects. However, animals can exhibit cognitive biases where a third option alters the animal’s choice between two options in a nonadditive, sometimes irrational way. For example, the decoy effect occurs when animals alter their assessment of the relative value of two options depending on the presence of a third option. In humans, this is a common marketing ploy where a salesperson offers a poor option C to induce the consumer to show a stronger preference for and thus purchase A over B. The presence of too many simultaneous options, however, tends to confuse animals (including humans), presumably making them less likely to act (i.e. less likely to adopt a novel resource, good or bad). The predictions of SDT and adaptive plasticity theory depend on cue reliability; however, the accuracy of the cues is not a set property, but instead depends on the organism’s sampling behaviour, which can be an adaptation shaped by past selection pressures. This is yet another way that the ‘ghost of signals past’ can shape current responses to HIREC. A key issue is the speede accuracy trade-off (Trimmer et al. 2008; Chittka et al. 2009; Sih & Del Giudice 2012). Getting better information is beneficial in that it increases cue reliability and discrimination among options, but it is also costly in terms of time, energy, lost opportunity costs and/or exposure to risk (Sih 1992). Thus, above and beyond the effects of risk and lost opportunities on response thresholds, these same risks and lost opportunities can affect the sampling behaviour that determines information availability, which, in turn, influences responses. For examples, risky situations favour speed over accuracy. This should result in less accurate information about novel options, which could then make animals more likely to avoid a novel potential danger, but could also make them more likely to be trapped by a novel resource (or habitat) even when a more careful assessment would conclude that it is a poor option. More models integrating the speedeaccuracy trade-off and optimal sampling into models of decision making under uncertainty should prove useful. The overall response to a novel situation often involves multiple responses in a series of steps. The above discusses why animals might show errors in assessment that result in errors in whether to accept or avoid a novel option. To be successful, however, animals must also handle (adopt or avoid) the option adaptively. In some cases, prey respond to cues from a novel predator, but show inappropriate responses that fit familiar predators but are ineffective against a novel predator. For example, New Zealand mudsnails, an invasive species in North America responds to chemical cues from predatory crayfish even though the snails have no apparent evolutionary history with crayfish. However, they respond by going under rocks or by burrowing into the substrate, which can be adaptive against predatory fish from their native habitat but is ineffective against crayfish (J. Stapley, B.C. Ajie & A. Sih, unpublished data). Although these snails apparently use generalized cues to assess risk, they show a specialized response that is ineffective because the novel predator uses a different attack mode than familiar native predators. Along similar lines, to successively invade

urban habitats, animals must both accept the novel habitat (either because urban habitat resembles the animal’s natural habitat in some fundamental way or because the animal is bold and exploratory) and have the flexibility to adjust to novel stressors associated with the new habitat (Sol et al. 2013). Above, we examined aspects of HIREC one at a time. In reality, organisms usually face the substantially more complex challenge of coping simultaneously with multiple stressors associated with multiple aspects of HIREC. Species declines are often caused by the combined negative impacts of these multiple stressors (Blaustein & Kiesecker 2002; Blaustein & Bancroft 2007). In particular, novel aspects of HIREC can have negative synergistic effects with each other and with natural stressors. For example, while many tadpoles have adaptations to cope with invertebrate predators and can cope physiologically with low concentrations of pesticides, when they are exposed to both, they show particularly poor survival (Relyea & Mills 2001; Rohr et al. 2006). In addition, animals might often face conflicting demands where an adaptive response to one stressor can expose organisms to another stressor that then causes declines. For example, avoiding degradation of natural habitat by moving into suburban habitat can result in increased exposure to disease (Carrete et al. 2009). To explain why some organisms cope better than others with multiple stressors, we thus need a better understanding of multiple traits and responses to the different stressors and how these multiple responses interact. One common form of interaction among responses involves the existence of personalities or behavioural syndromes where, in this context, the response to one novel option might be correlated with the response to another rather different one. Bold, exploratory animals might be more likely than others to adopt a valuable new resource (or to move in with humans), but might also be susceptible to becoming trapped into using a lowquality novel food, or to ignoring a novel dangerous predator. Indeed, correlated suites of behavioural tendencies might often dispose animals to cope well with some aspects of HIREC, but not others. More broadly, if evolution has shaped alternative adaptive, integrated, multitrait responses to multiple natural stressors, when do we expect organisms to be able to co-opt their evolved integrated phenotype to cope well with a novel mix of old and novel stressors? While animals might have evolved an adaptive mix of responses to balance conflicting demands in their natural habitats, with HIREC, they need to recognize and evaluate cues from multiple stressors to produce an integrated, multitrait response that balances these multiple, often conflicting, demands (Ghalambor et al. 2007; Crozier et al. 2008). General theory on the relative robustness and adaptive flexibility of different system architectures might yield insights on the short- versus long-term effectiveness of multitrait responses to multiple aspects of HIREC. In social groups, individual responses are often influenced by the responses of others (Stamps 1991; Galef & Giraldeau 2001; Galef & Laland 2005). Individuals can get trapped by following others who are trapped (e.g. via conspecific attraction, copying or bandwagon effects: Nocera et al. 2006; Lopez-Sepulcre & Kokko 2012). Alternatively, individuals can be prevented from getting trapped by others who are trapped; for example, if high-quality, dominant individuals get trapped into low-quality habitat, they can exclude lower-quality subordinates, and ironically, push lowerquality subordinates into better habitat. Lopez-Sepulcre & Kokko (2012) noted that this can result in an Allee effect where at low density, individuals mistakenly use poor habitat (resulting in low mean fitness), but at higher density, competition drives individuals into better habitat (resulting in higher mean fitness). Social network theory can be useful for quantifying the group’s social structure (Wey et al. 2008; Sih et al. 2009) and how that might influence individual responses to HIREC. Aspects of social structure


that could be important include patterns of direct and indirect connectivity among leaders and followers, and group substructure (cliques). For cohesive groups that make group decisions, an interesting, relatively unexplored issue involves effects of social structure on group responses to HIREC. These could involve group foraging, antipredator behaviour or habitat use decisions in social systems ranging in complexity from fish schools with or without clear leaders, to social carnivores or primates with dominance hierarchies and coalitions, to integrated social insect societies, to human societies. Following our general framework for individual responses to HIREC, the hypothesis is that evolutionary history has potentially shaped social network structures that respond well to previous situations. The adaptations could include how the social structure influences information use and evaluation of cues, as well as group responses. A fascinating issue for group responses to HIREC for humans and other species would involve determining when more egalitarian societies with distributed information and control respond better to environmental change (e.g. climate change) than dictatorial societies, versus when less egalitarian societies respond better to novel situations that require change. For dictatorial societies, a key should be the behavioural tendencies, incentives and leadership style of the leaders. Drawing insights from network theory for parallel issues in other systems (e.g. evolvability of genetic systems, contrasting those with major genes versus many genes of small effect) could be exciting. VARIATION IN LEARNING TO COPE WITH HIREC If organisms survive initial rounds of exposure to HIREC, they can potentially learn to cope better with HIREC. Some studies have indeed documented learning to better respond to HIREC (e.g. learning to avoid eating toxic cane toads (O’Donnell et al. 2010), or to consume them without eating the toxic parts (Donato & Potts 2004)); however, others have noted an apparent lack of learning about novel options (e.g. Parmesan et al. 1995). Given a general expectation that animals should learn (assuming that they survive to learn), a key question is why might animals sometimes not learn and thus stay trapped? Again, following the basic framework outlined in Fig. 1, past selection pressures should explain current learning tendencies, which should in turn explain how well organisms learn to better deal with novel situations. Animal taxa, of course, vary in their cognitive abilities (Shettleworth 2010). We do not expect some ‘simple’ invertebrates to learn as much or as well as some more cognitively complex vertebrates. Beyond that, costebenefit considerations can explain finer-scale variation in learning, including apparently lack of learning. Given that learning has costs, the tendency to learn (adjust behaviour based on past experiences and information) should be lower if the benefits of learning have historically been low (Stephens 1991; Dukas 1998). For example, the incentive to learn is low if the value of different options is stable, particularly if the best options have not varied; in that case, stick with what always works well. Along similar lines, since the animal’s survival per se is a direct indicator that past choices (by the individual or its parent) were good ones, organisms often show site fidelity (or diet fidelity, or natal imprinting: Davis & Stamps 2004) that represents one-shot learning with little or no later adjustment. This strategy can work well if site quality (food quality, etc.) is indeed stable, but can clearly result in lack of adjustment to HIREC. Even if the value of different options or decisions is unstable and uncertain, learning is not favoured if ‘information’ has generally been unreliable. If cues have historically been inaccurate and/or if outcomes have been unpredictable, then animals should obviously not learn to use these useless cues. HIREC can further reduce

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learning by reducing cue reliability via environmental disruption of sensory systems or signal transmission (Ferrari et al. 2010, 2012; Rosenthal & Stuart-Fox 2012). The ease and value of learning also depends on the timescale of behaviour and fitness outcomes. Even if cues are reliable, learning can be difficult if there are long, delayed feedbacks between a behavioural choice and the fitness outcome. Also, even if learning yields benefits, the benefits are proportional to the opportunity to use the new information to better cope with a future challenge. Animals that have a short life span or a short remaining life span should be less likely to learn (Kokko & Sutherland 2001). Conversely, if a learning rule has worked well in the (evolutionary or developmental) past, then animals should continue to use it. For example, it seems reasonable that animals should learn to prefer options (e.g. habitats, diet items) that have yielded good results, and avoid options that yielded poor results. This has been termed a simple ‘winestay versus loseeswitch’ learning rule. Following this rule, a bird should renest in the same site as a previous year if that spot worked well last year, but move to a new site if the previous nest was destroyed by a predator (Schmidt 2004; Chalfoun & Martin 2010). While this rule seems reasonable, we should only expect animals to follow it if predation risk (or nesting success in general) has exhibited a clear positive spatiotemporal autocorrelation; that is, if past wins or losses are indeed good indicators of future wins or losses. Animals that use this rule should adjust well to novel nest predators if these new predators search in a way that preserves the positive correlation. Novel predators can thus be particularly dangerous if they represent a change in that correlation such that the old learning rule no longer applies. The tendency to learn should also depend on the costs of learning (e.g. in terms of energy required to build and maintain the sensory/cognitive machinery required to assess information and learn, and in terms of the opportunity costs, time, energy and exposure to danger while learning). As noted earlier, learning typically involves a speedeaccuracy trade-off where the time taken to learn can make it adaptive to not learn, depending of course on the benefit of learning (Sih 1992). Finally, recent work emphasizes the existence of individual differences in learning tendencies (Koolhaas et al. 2007; Sih & Del Giudice 2012). Proactive individuals (who are often also bold and aggressive) tend to show low environmental sensitivity. They follow set routines, paying relatively little attention to environmental changes. In contrast, reactive individuals (who tend to be unaggressive and fearful) are highly sensitive. Both of these behavioural types might have trouble learning to cope with HIREC but for different reasons. Reactive individuals will likely avoid exposure to HIREC, and should thus have little opportunity to learn to better cope with HIREC. Proactive individuals will more likely be exposed to, but ignore, HIREC and thus not learn well. A similar idea involves individual differences in a ‘dual process’ approach to human decision making where an initial, rapid assessment based on ‘intuitions’ is followed by a slower, information-gathering stage where intuitions can be refined or altered (Tversky & Kahneman 1974; Kahneman 2011). These two stages parallel priors and updating in a Bayesian statistical framework. Interestingly, ‘experts’ in a given field often apparently trust their initial intuitions, and pay less attention to additional, potentially useful information (Kahneman 2011). Rather than alter their views, they fit additional information into their established view. Thus, perhaps paradoxically, experts might be more susceptible to not learning after environmental change. The fact that reactive individuals might avoid novel situations is an example of the general notion of behavioural ‘niche construction’ (Saltz & Foley 2011) where situation choice (habitat choice, social situation choice) influences the range of options



encountered, and thus the opportunity for learning. The result can be a positive feedback loop where neophilic, highly exploratory individuals will be more likely to encounter and learn about novel options and thus be more ready to cope with yet more novel situations. In contrast, neophobic, low explorers avoid novel options, do not learn how to better cope with them, and thus continue to have reason to avoid them.

organized by the Animal Behavior Graduate Group at the University of California/Davis (UC/Davis). The ideas gradually took on a more organized form shaped by ongoing discussion with Caitlin McGaw, members of the Sih lab, and numerous others at UC/Davis. A NESCent workshop on evolutionary mismatch also provided an invaluable format for trading thoughts with a diverse array of scientists and philosophers. Finally, the manuscript was improved by comments from two anonymous referees.

CONCLUDING REMARKS: THE NEW BEHAVIOURAL ECOLOGY? Some have defined behavioural ecology, in essence, as the study of the adaptive significance of behaviours in the environmental conditions that they originally evolved in. Given that definition, since HIREC is putting many if not most organisms in novel conditions that do not match the conditions in which they originally evolved, HIREC is threatening not only our study subjects, but also the discipline itself (Caro & Sherman 2011). Caro & Sherman (2011) thus called for behavioural ecologists to commit increased personal efforts towards conservation activism. While I am certainly in favour of conservation, I suggest an alternative, more positive view of the role of the science of behavioural ecology in the modern world. In my view, we should broaden the domain of behavioural ecology to explicitly include, and indeed embrace, the study of behaviour in human-altered circumstances. Our exciting challenge is to develop and implement an integrative, interdisciplinary framework for understanding variation in behavioural response to HIREC, including both adaptive and nonadaptive responses. As outlined here, my view draws on traditional behavioural ecology by using costebenefit approaches to explain observed behavioural tendencies (including learning tendencies) under the assumption that evolutionary history (including evolved developmental programmes) has shaped current traits. My approach explicitly incorporates sensory/cognitive mechanisms, and the role of uncertainty in explaining adaptive and maladaptive behaviours. That is, to explain variation in behavioural responses to HIREC, it is important to understand mechanisms underlying how animals actually make decisions, rather than rely on mechanism-free optimality theory. Intriguingly, the framework I outline integrates all four of Tinbergen’s classic approaches to understanding behaviour (Tinbergen 1952). A key next step is to convert a verbal conceptual framework into a more explicit science, with models, predictions, tests of predictions and further refining of theory. Here, I suggest several existing theoretical frameworks (signal detection theory, adaptive plasticity, extended reaction norms, costebenefit theory on learning) that can be adapted to serve this goal. The ability to explain and ideally predict how individuals and species respond behaviourally to HIREC clearly has important implications for management of declining species, pests, and those that fall between these ends of the spectrum. Applications can include early warning systems for both vulnerable species and potential pests, and proactive manipulations to produce more favourable outcomes. Specific potential management actions have been discussed elsewhere (Schlaepfer et al. 2010; Lankau et al. 2011; Sih et al. 2011). Getting in on the ground floor to be part of the process of developing the science of understanding behaviour in response to HIREC is an important and exciting opportunity and a challenge for both empirical and theoretical animal behaviourists. Acknowledgments I join Susan Foster and the other authors of this Special Issue in thanking the Animal Behavior Society and the National Science Foundation for supporting the symposium that led to this set of papers. The ideas in this paper emerged originally from a workshop

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