Leveraging Motivations, Personality, and Sensory Cues for Vertebrate Pest Management
Authors: Patrick M. Garvey, Peter B. Banks, Justin P. Suraci, Thomas W. Bodey, Alistair S. Glen, Chris J. Jones, Clare McArthur, Grant L. Norbury, Catherine J. Price, James C. Russell, Andrew Sih
Journal: Trends in Ecology & Evolution (2020)

The explicit consideration of individual traits is central to enhancing effective vertebrate pest management.We provide a heuristic framework for understanding animal motivations and cues across spatial scales.Focussing on individual motivations can improve population-level control measures and reduce the impacts of problematic individuals that cause the most damage.Incorporating principles of behavioural ecology will increase the effectiveness of any intervention while providing an opportunity to test behavioural theory in a natural context. Managing vertebrate pests is a global conservation challenge given their undesirable socio-ecological impacts. Pest management often focuses on the 'average' individual, neglecting individual-level behavioural variation ('personalities') and differences in life histories. These differences affect pest impacts and modify attraction to, or avoidance of, sensory cues. Strategies targeting the average individual may fail to mitigate damage by 'rogues' (individuals causing disproportionate impact) or to target 'recalcitrants' (individuals avoiding standard control measures). Effective management leverages animal behaviours that relate primarily to four core motivations: feeding, fleeing, fighting, and fornication. Management success could be greatly increased by identifying and exploiting individual variation in motivations. We provide explicit suggestions for cue-based tools to manipulate these four motivators, thereby improving pest management outcomes. Managing vertebrate pests is a global conservation challenge given their undesirable socio-ecological impacts. Pest management often focuses on the 'average' individual, neglecting individual-level behavioural variation ('personalities') and differences in life histories. These differences affect pest impacts and modify attraction to, or avoidance of, sensory cues. Strategies targeting the average individual may fail to mitigate damage by 'rogues' (individuals causing disproportionate impact) or to target 'recalcitrants' (individuals avoiding standard control measures). Effective management leverages animal behaviours that relate primarily to four core motivations: feeding, fleeing, fighting, and fornication. Management success could be greatly increased by identifying and exploiting individual variation in motivations. We provide explicit suggestions for cue-based tools to manipulate these four motivators, thereby improving pest management outcomes. Vertebrate pests, including invasive or overabundant predators and herbivores, frequently come into conflict with economic, social, and biodiversity values. Mammalian predators are responsible for some of the most devastating losses to native biodiversity [1.Doherty T.S. et al.Invasive predators and global biodiversity loss.Proc. Natl. Acad. Sci. 2016; 113: 11261-11265Crossref PubMed Scopus (498) Google Scholar] and frequently harm humans, their livestock, and pets, while herbivores can cause agricultural damage, vehicle collisions, and ecosystem-level impacts including overbrowsing [2.Côté S.D. et al.Ecological impacts of deer overabundance.Annu. Rev. Ecol. Evol. Syst. 2004; 35: 113-147Crossref Scopus (1357) Google Scholar,3.Honda T. et al.A review of urban wildlife management from the animal personality perspective: the case of urban deer.Sci. Total Environ. 2018; 644: 576-582Crossref PubMed Scopus (28) Google Scholar]. Mitigating the impacts of vertebrate pests thus presents one of the major challenges currently facing wildlife managers. Managers require effective strategies to: (i) reduce pest populations (e.g., by attracting individuals to traps or toxic baits), and (ii) deter individuals from sensitive areas or valuable species (e.g., threatened prey or plant species, livestock, agricultural, and forestry sites). Yet, pest control measures are often only partially effective [4.Doherty T.S. Ritchie E.G. Stop jumping the gun: a call for evidence-based invasive predator management.Conserv. Lett. 2017; 10: 15-22Crossref Scopus (88) Google Scholar,5.Nugent G. et al.Effect of prefeeding, sowing rate and sowing pattern on efficacy of aerial 1080 poisoning of small-mammal pests in New Zealand.Wildl. Res. 2011; 38: 249-259Crossref Scopus (37) Google Scholar], with some individuals avoiding lethal control or ignoring deterrents. Attractants and deterrents typically target the 'average' individual in a population, with the goal of maximising the number of animals responding to stimuli. However, the most intractable challenges of vertebrate pest management may occur precisely because some individuals do not behave like the average, and therefore, are not effectively targeted. Within a pest population, individuals exhibit a range of responses to management actions. Deviations from the average response may be transient (e.g., dependent on internal state, body condition, current perceived risk, or density of conspecifics) [6.Bisi F. et al.The strong and the hungry: bias in capture methods for mountain hares Lepus timidus.Wildl. Biol. 2011; 17: 311-317Crossref Scopus (19) Google Scholar], or may represent persistent, individual-level behavioural differences ('personalities') [7.Réale D. et al.Integrating animal temperament within ecology and evolution.Biol. Rev. 2007; 82: 291-318Crossref PubMed Scopus (2112) Google Scholar,8.Merrick M.J. Koprowski J.L. Should we consider individual behavior differences in applied wildlife conservation studies?.Biol. Conserv. 2017; 209: 34-44Crossref Scopus (97) Google Scholar]. By understanding the drivers of individual-level differences in behaviour, management can be optimized to target not just the average individual, but the full range of behavioural types within a population. Such insights may be particularly valuable in managing rogue and recalcitrant individuals (see Glossary), two non-exclusive behavioural types that occur in many pest populations and often have disproportionate economic and conservation impacts. Rogues (also known as 'problem individuals') cause inordinate damage [3.Honda T. et al.A review of urban wildlife management from the animal personality perspective: the case of urban deer.Sci. Total Environ. 2018; 644: 576-582Crossref PubMed Scopus (28) Google Scholar,9.Swan G.J. et al.Ecology of problem individuals and the efficacy of selective wildlife management.Trends Ecol. Evol. 2017; 32: 518-530Abstract Full Text Full Text PDF PubMed Scopus (54) Google Scholar], often due to low responsiveness to deterrents aimed at excluding or deflecting them from valued species or areas. Recalcitrants are particularly difficult to attract to management devices. Their survival allows populations to re-establish quickly after the majority of the pest population has been removed, and, in the case of invasive predators, recalcitrants can maintain predation pressure on native species [10.Banks P.B. et al.Reinvasion is not invasion again.Bioscience. 2018; 68: 792-804Crossref Scopus (13) Google Scholar]. Anticipating and targeting the full range of behavioural variation within a pest population could substantially enhance the effectiveness of control efforts and reduce the risk of inadvertently favouring management-resistant behavioural types [8.Merrick M.J. Koprowski J.L. Should we consider individual behavior differences in applied wildlife conservation studies?.Biol. Conserv. 2017; 209: 34-44Crossref Scopus (97) Google Scholar,11.Tuyttens F. et al.Differences in trappability of European badgers Meles meles in three populations in England.J. Appl. Ecol. 1999; 36: 1051-1062Crossref Scopus (77) Google Scholar]. Here we present a framework to explicitly incorporate individual behavioural variability into vertebrate pest management. This framework is based on the fact that, like all behavioural decision-making, an individual's decision to move towards an attractant or avoid a deterrent is largely driven by a set of fitness-based motivations characterized by the 4Fs of animal behaviour [12.Dill L.M. Behavioural ecology and marine conservation: a bridge over troubled water?.ICES J. Mar. Sci. 2017; 74: 1514-1521Crossref Scopus (5) Google Scholar]: feeding (energy demands), fleeing (predator avoidance), fighting (competition), and fornicating (mating). While behaviour can be governed by other specific cues, the underlying motivations ultimately involve these 4Fs (e.g., attraction to conspecifics usually reflects feeding, safety, agonistic, or mating needs). We explore how differences in these motivations underlie individual-level differences in pest responses to management actions, and how these motivations can be exploited to increase the effectiveness of attractants and deterrents. We build on previous work (e.g., [3.Honda T. et al.A review of urban wildlife management from the animal personality perspective: the case of urban deer.Sci. Total Environ. 2018; 644: 576-582Crossref PubMed Scopus (28) Google Scholar,8.Merrick M.J. Koprowski J.L. Should we consider individual behavior differences in applied wildlife conservation studies?.Biol. Conserv. 2017; 209: 34-44Crossref Scopus (97) Google Scholar,9.Swan G.J. et al.Ecology of problem individuals and the efficacy of selective wildlife management.Trends Ecol. Evol. 2017; 32: 518-530Abstract Full Text Full Text PDF PubMed Scopus (54) Google Scholar]) by providing recommendations for enhancing management effectiveness across all pest behavioural types involving manipulation of exposure to sensory cues (Box 1).Box 1A Behavioural Framework for Pest Attraction and DeterrenceEffective pest management often depends on animals being motivated to interact with a management action. Sometimes the goal is attraction (e.g., attraction to traps, monitoring devices, or less sensitive areas), and sometimes deterrence (e.g., deterring pests from valuable resources or vulnerable species). We present a simple heuristic model that provides a framework for understanding the various motivators acting on an animal's decision to approach and interact with a management device or avoid a sensitive area. Our model is based on Brown's classic equation describing patch-quitting decisions while foraging under the risk of predation [66.Brown J.S. Patch use as an indicator of habitat preference, predation risk, and competition.Behav. Ecol. Sociobiol. 1988; 22: 37-47Crossref Scopus (996) Google Scholar].We consider that the probability, P, of interacting with a stimulus (e.g., a trap, bait station, or other resource like a crop or threatened prey species), once it is detected, is shaped by three motivational elements: (i) the value of the attractive stimulus (VA, be it a food cue, a social cue, or other information), (ii) the perceived danger (PD) of interaction, and (iii) the missed opportunity costs (MOC) (other food or mating opportunities the animal misses out on by interacting).P is proportional to VA–PD–MOCA wildlife manager wishing to change interaction probability has three 'levers' to pull: the attractiveness of the stimulus, the perceived danger of the stimulus, or the animal's perception of missed opportunity costs. Adjusting any one of these levers changes the probability the animal will interact with the stimulus.Wildlife managers intuitively tinker with these elements when trying to manage pest species, but we argue that practitioners have overlooked the role of an animal's state and traits (i.e., its personality, experience, physiology, and reproductive condition) in shaping its motivations and responses.Attempts to increase rates of interaction with devices have largely tried to increase VA by creating better food-based attractants in the search for a so-called super lure [24.Linklater W. et al.Pied-pipers wanted: the search for super-lures of New Zealand mammal pests.N. Z. Sci. Rev. 2013; 70: 31-36Google Scholar]. However, food-based lures are less effective when there is abundant alternative food available (higher MOC) or the device is perceived to be too risky or threatening (higher PD). There is also a large body of work attempting to decrease the interactions of animals with important assets (such as seedlings or crops) by increasing PD at the site through the use of predator cues [57.Parsons M.H. et al.Biologically meaningful scents: a framework for understanding predator–prey research across disciplines.Biol. Rev. 2018; 93: 98-114Crossref PubMed Scopus (68) Google Scholar]. These attempts routinely fail if the assets are too valuable for animals to ignore (high VA), too easy to find relative to other foraging opportunities (low MOC), or if the animals habituate in the absence of real risk [67.Banks P.B. et al.Olfaction and predator-prey interactions amongst mammals in Australia.in: Glen A.S. Dickman C.R. Carnivores of Australia: Past, Present and Future. CSIRO, 2014: 389-403Google Scholar]. Effective pest management often depends on animals being motivated to interact with a management action. Sometimes the goal is attraction (e.g., attraction to traps, monitoring devices, or less sensitive areas), and sometimes deterrence (e.g., deterring pests from valuable resources or vulnerable species). We present a simple heuristic model that provides a framework for understanding the various motivators acting on an animal's decision to approach and interact with a management device or avoid a sensitive area. Our model is based on Brown's classic equation describing patch-quitting decisions while foraging under the risk of predation [66.Brown J.S. Patch use as an indicator of habitat preference, predation risk, and competition.Behav. Ecol. Sociobiol. 1988; 22: 37-47Crossref Scopus (996) Google Scholar]. We consider that the probability, P, of interacting with a stimulus (e.g., a trap, bait station, or other resource like a crop or threatened prey species), once it is detected, is shaped by three motivational elements: (i) the value of the attractive stimulus (VA, be it a food cue, a social cue, or other information), (ii) the perceived danger (PD) of interaction, and (iii) the missed opportunity costs (MOC) (other food or mating opportunities the animal misses out on by interacting). P is proportional to VA–PD–MOC A wildlife manager wishing to change interaction probability has three 'levers' to pull: the attractiveness of the stimulus, the perceived danger of the stimulus, or the animal's perception of missed opportunity costs. Adjusting any one of these levers changes the probability the animal will interact with the stimulus. Wildlife managers intuitively tinker with these elements when trying to manage pest species, but we argue that practitioners have overlooked the role of an animal's state and traits (i.e., its personality, experience, physiology, and reproductive condition) in shaping its motivations and responses. Attempts to increase rates of interaction with devices have largely tried to increase VA by creating better food-based attractants in the search for a so-called super lure [24.Linklater W. et al.Pied-pipers wanted: the search for super-lures of New Zealand mammal pests.N. Z. Sci. Rev. 2013; 70: 31-36Google Scholar]. However, food-based lures are less effective when there is abundant alternative food available (higher MOC) or the device is perceived to be too risky or threatening (higher PD). There is also a large body of work attempting to decrease the interactions of animals with important assets (such as seedlings or crops) by increasing PD at the site through the use of predator cues [57.Parsons M.H. et al.Biologically meaningful scents: a framework for understanding predator–prey research across disciplines.Biol. Rev. 2018; 93: 98-114Crossref PubMed Scopus (68) Google Scholar]. These attempts routinely fail if the assets are too valuable for animals to ignore (high VA), too easy to find relative to other foraging opportunities (low MOC), or if the animals habituate in the absence of real risk [67.Banks P.B. et al.Olfaction and predator-prey interactions amongst mammals in Australia.in: Glen A.S. Dickman C.R. Carnivores of Australia: Past, Present and Future. CSIRO, 2014: 389-403Google Scholar]. Animal decision making depends on sensory cues (sight, sound, smell) that convey information about resources [13.Carthey A.J. et al.Negotiating a noisy, information-rich environment in search of cryptic prey: olfactory predators need patchiness in prey cues.J. Anim. Ecol. 2011; 80: 742-752Crossref PubMed Scopus (25) Google Scholar,14.Goerlitz H. Siemers B.M. Sensory ecology of prey rustling sounds: acoustical features and their classification by wild grey mouse lemurs.Funct. Ecol. 2007; 21: 143-153Crossref Scopus (34) Google Scholar], predation risk [15.Suraci J.P. et al.Fear of large carnivores causes a trophic cascade.Nat. Commun. 2016; 7: 10698Crossref PubMed Scopus (227) Google Scholar,16.Garvey P.M. et al.Dominant predator odour triggers caution and eavesdropping behaviour in a mammalian mesopredator.Behav. Ecol. Sociobiol. 2016; 70: 481-492Crossref Scopus (32) Google Scholar], heterospecifics [17.Hughes N.K. et al.The predation risks of interspecific eavesdropping: weasel-vole interactions.Oikos. 2010; 119: 1210-1216Crossref Scopus (32) Google Scholar], or conspecifics [18.Marneweck C. et al.Dung odours signal sex, age, territorial and oestrous state in white rhinos.Proc. R. Soc. B Biol. Sci. 2017; 284: 201-208Crossref Scopus (29) Google Scholar]. Cues provide the link between a pest management objective (attract or deter) and the behavioural responses of the targeted animals (Figure 1). Here, we review the types of sensory cues and how they can be utilized in a management context. We then draw on signal detection theory (SDT) for insights into maximizing cue effectiveness based on the range of motivations, states, and perceptual abilities in a pest population. An animal's decision to engage with a sensory stimulus can be divided into two stages: encounter and response. An encounter (i.e., cue detection) depends on the animal's perceptual abilities [19.Stevens M. Predator perception and the interrelation between different forms of protective coloration.Proc. R. Soc. 2007; 274: 1457-1464Crossref Scopus (201) Google Scholar] as well as the spatial range of the cue type(s) used (see below). Once encountered, the probability that an animal responds to a cue (i.e., approaches and interacts or moves away) depends on the animal's state (i.e., the relative importance of each of the 4F motivators) and its assessment of the environmental context in which the cue is embedded (e.g., missed opportunity costs, see Box 1) [18.Marneweck C. et al.Dung odours signal sex, age, territorial and oestrous state in white rhinos.Proc. R. Soc. B Biol. Sci. 2017; 284: 201-208Crossref Scopus (29) Google Scholar]. Managers can take advantage of the information offered by sensory cues, and the spatial scales over which they operate (Figure 1B) to exploit multiple behavioural motivators, ideally together (Figure 1A). Acoustic cues operate over the largest spatial scales, drawing or repelling animals from beyond the immediate vicinity of the cue source. Acoustic cues signalling danger (e.g., from predators or humans) can deter target animals from relatively large areas [15.Suraci J.P. et al.Fear of large carnivores causes a trophic cascade.Nat. Commun. 2016; 7: 10698Crossref PubMed Scopus (227) Google Scholar,20.Suraci J.P. et al.A new Automated Behavioural Response system to integrate playback experiments into camera trap studies.Methods Ecol. 2017; 8: 957-964Crossref Scopus (19) Google Scholar] or reduce foraging impacts where lethal control is not possible or desirable (e.g., with native 'rogues') [21.Wild L. et al.Field trials of an acoustic decoy to attract sperm whales away from commercial longline fishing vessels in western Gulf of Alaska.Fish. Res. 2017; 196: 141-150Crossref Scopus (7) Google Scholar,22.King L.E. et al.African elephants run from the sound of disturbed bees.Curr. Biol. 2007; 17: 832-833Abstract Full Text Full Text PDF PubMed Scopus (50) Google Scholar]. Alternatively, broadcasting vocalizations of prey or conspecifics can attract individuals over long distances [23.Wilson G.J. Delahay R.J. A review of methods to estimate the abundance of terrestrial carnivores using field signs and observation.Wildl. Res. 2001; 28: 151-164Crossref Scopus (238) Google Scholar]. Olfactory cues can also be effective over intermediate to large spatial scales and provide a range of information types. At high population densities, essential resources are limited, leading to strong conspecific competition. Olfactory cues that represent food, a mainstay of traditional pest management, may be highly effective in these situations. However, at lower population densities (e.g., following lethal control), food resources may not be limited and thus may not be a strong attractant. Olfactory social lures, such as conspecific or competitor scent, are then powerful alternatives to food lures, exploiting three of the four Fs that motivate behaviour (i.e., fighting, fear, and/or fornication) [24.Linklater W. et al.Pied-pipers wanted: the search for super-lures of New Zealand mammal pests.N. Z. Sci. Rev. 2013; 70: 31-36Google Scholar, 25.Long R.A. et al.Noninvasive survey methods for carnivores. Island Press, 2012Google Scholar, 26.Garvey P.M. et al.Exploiting interspecific olfactory communication to monitor predators.Ecol. Appl. 2017; 27: 389-402Crossref PubMed Scopus (24) Google Scholar] (Figure 1A,i,ii). Olfactory cues that signal danger, such as predator odour [27.Sullivan T.P. et al.Use of predator odors as repellents to reduce feeding damage by herbivores.J. Chem. Ecol. 1988; 14: 363-377Crossref PubMed Scopus (72) Google Scholar] or territorial scent marks [28.Welsh R.G. Muller-Schwarze D. Experimental habitat scenting inhibits colonization by beaver, Castor canadensis.J. Chem. Ecol. 1989; 15: 887-893Crossref PubMed Scopus (20) Google Scholar], can reduce the impacts of pests. Though examples are rare, applying odour cues can change pest behaviour, such as camouflaging native prey to reduce the impacts of invasive predators [29.Price C.J. Banks P.B. Exploiting olfactory learning in alien rats to protect birds' eggs.Proc. Natl. Acad. Sci. 2012; 109: 19304-19309Crossref PubMed Scopus (31) Google Scholar,30.Latham M.C. et al.Modeling habituation of introduced predators to unrewarding bird odors for conservation of ground-nesting shorebirds.Ecol. Appl. 2019; 29e01814Crossref PubMed Scopus (9) Google Scholar] (Figure 1A,iii). Visual cues typically operate at the smallest spatial scales and have been used successfully to lure or deter pests. For example, a toy mouse is an effective attractant for feral cats [31.Dickman C.R. Newsome T.M. Individual hunting behaviour and prey specialisation in the house cat Felis catus: implications for conservation and management.Appl. Anim. Behav. Sci. 2015; 173: 76-87Crossref Scopus (71) Google Scholar], fladry impedes canid access to livestock [32.Musiani M. et al.Wolf depredation trends and the use of fladry barriers to protect livestock in western North America.Conserv. Biol. 2003; 17: 1538-1547Crossref Scopus (90) Google Scholar], and scarecrows deter pests from crops [33.Beringer J. et al.Evaluation of an animal-activated scarecrow and a monofilament fence for reducing deer use of soybean fields.Wildl. Soc. Bull. 2003; : 492-498Google Scholar]. Simultaneous deployment of multiple cues may prove most effective at eliciting responses from an entire pest population given the different scales over which cues operate, the diversity in individual responses, and the greater realism that multiple cues provide [34.Piep M. et al.The sensory basis of prey detection in captive-born grey mouse lemurs, Microcebus murinus.Anim. Behav. 2008; 75: 871-878Crossref Scopus (31) Google Scholar]. Importantly, whether a cue attracts or deters an animal depends not just on the cue itself, but also on properties of the individual and the environmental context. These include: (i) the animal's state, (ii) how its past experience shapes its assessment of risk and reward, (iii) how well cues can be discriminated from each other (cue uncertainty) and from 'noisy' background environments, and (iv) the relative costs of over- or under-responding to the cue [35.Trimmer P.C. et al.Predicting behavioural responses to novel organisms: state-dependent detection theory.Proc. R. Soc. 2017; 284: 20162108Crossref Scopus (27) Google Scholar]. SDT integrates the animal's assessment of cues and their reliability with the costs and benefits. This includes the costs of uncertainty and unavoidable errors. SDT provides a useful framework linking behavioural variation to the effectiveness of different management options (Box 2).Box 2Signal Detection TheorySignal detection theory (SDT) provides insights into how cues, past experiences, and cost-benefit asymmetries influence animal attraction to a device or avoidance of deterrents [36.Ehlman S.M. et al.Prey responses to exotic predators: effects of old risks and new cues.Am. Nat. 2019; 193: 575-587Crossref PubMed Scopus (21) Google Scholar,68.Trimmer P.C. et al.The erroneous signals of detection theory.Proc. R. Soc. 2017; 284: 20171852Crossref Scopus (22) Google Scholar]. SDT typically simplifies situations into two types: good (food, safety) versus bad (risky). An animal has a prior assessment of good and bad based on experience. It uses a cue to adjust its assessment of the current situation and then responds, balancing the relative costs of two potential errors: the cost of being inappropriately attracted to a bad option versus the cost of unnecessarily avoiding a good one.Animals are more likely to be trapped if the trap appears 'good'; for example, emanates cues associated with high attractant value (VA) and low perceived danger (PD). By contrast, they will be more difficult to trap if, in their past, situations resembling the trap were perceived as risky. The current environmental context also matters. Animals are more willing to enter a trap if they are very food-limited (since the cost of rejecting a trap baited with valuable food is high), and also more willing to enter a trap with moderate PD provided the outside environment appears even more dangerous.SDT also emphasizes cue reliability; that is, not just whether the trap appears good or bad, but also how well the animal was previously able to use cues to distinguish good versus bad. If, over generations, certain cues have consistently and accurately indicated high- versus low-quality options, animals may have evolved to rely heavily on those cues. This is what makes some animals susceptible to evolutionary traps, when previously reliable cues lead them astray [69.Robertson B.A. et al.Ecological novelty and the emergence of evolutionary traps.Trends Ecol. Evol. 2013; 28: 552-560Abstract Full Text Full Text PDF PubMed Scopus (270) Google Scholar]. Alternatively, animals can learn about cue reliability from their experiences. If an animal evolved or developed in an environment where it could reliably determine whether trap-like situations were safe or dangerous, it should be willing to enter a trap that is designed to appear safe and attractive. By contrast, if the animal evolved or developed in an environment where it was difficult for it to use cues to distinguish safe versus dangerous situations (i.e., low cue reliability), then even if a trap appears safe and rewarding, the animal will likely ignore those cues. That is, 'recalcitrant' animals might be cautious not just because their environment is particularly dangerous, but because of the uncertainty of their cues. For these wary animals, it is important to give them numerous experiences with 'traps' that provide safety and food rewards (i.e., prebaiting) to train them to trust cues that later lure them into traps. Signal detection theory (SDT) provides insights into how cues, past experiences, and cost-benefit asymmetries influence animal attraction to a device or avoidance of deterrents [36.Ehlman S.M. et al.Prey responses to exotic predators: effects of old risks and new cues.Am. Nat. 2019; 193: 575-587Crossref PubMed Scopus (21) Google Scholar,68.Trimmer P.C. et al.The erroneous signals of detection theory.Proc. R. Soc. 2017; 284: 20171852Crossref Scopus (22) Google Scholar]. SDT typically simplifies situations into two types: good (food, safety) versus bad (risky). An animal has a prior assessment of good and bad based on experience. It uses a cue to adjust its assessment of the current situation and then responds, balancing the relative costs of two potential errors: the cost of being inappropriately attracted to a bad option versus the cost of unnecessarily avoiding a good one. Animals are more likely to be trapped if the trap appears 'good'; for example, emanates cues associated with high attractant value (VA) and low perceived danger (PD). By contrast, they will be more difficult to trap if, in their past, situations resembling the trap were perceived as risky. The current environmental context also matters. Animals are more willing to enter a trap if they are very food-limited (since the cost of rejecting a trap baited with valuable food is high), and also more willing to enter a trap with moderate PD provided the outside environment appears even more dangerous. SDT also emphasizes cue reliability; that is, not just whether the trap appears good or bad, but also how well the animal was previously able to use cues to distinguish good versus bad. If, over generations, certain cues have consistently and accurately indicated high- versus low-quality options, animals may have evolved to rely heavily on those cues. This is what makes some animals susceptible to evolutionary traps, when previously reliable cues lead them astray [69.Robertson B.A. et al.Ecological novelty and the emergence of evolutionary traps.Trends Ecol. Evol. 2013; 28: 552-560Abstract Full Text Full Text PDF Pu