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Examples of prey predator interactions where density of predator is very low (has a threat of extinction) and prey form groups when the predator attacks the prey for food.
I have searched this in net but not get examples. Please help me to supply some examples with a brief interaction of type discussed above.
Specialty of predator:
Predator population has a threat of extinction due to their low population. They have the difficulties in locating acceptable mating partners for sexual reproduction during their receptive period due to low population densities.
A low predator density is not synonym with "threat of extinction". Of course large predators are more prone to extinction because of lower numbers, but they usually exist in lower densities than prey (look, for instance, Lotka-Volterra equations and graphics, and also biomass and food pyramids). The risk of extinction is usually caused by fragmentation of habitat, that reduces population and gene flow.
Many preys form groups to avoid predation. That happens with schools of fish, caterpillars that walk as a multi-organism mass, grasshoppers that change behavior into locusts, bovids and related mammals, that make a circle with the horns to the outside to defend against predators.
So, it's not clear what you're asking. Is there any special case of low density of predator/modified prey behavior?
EDIT: If you want a more concrete answer, it will depend of the geographic region. But everywhere the largest predators are usually the most endangered. They usually prey on large mammals (but not only, since a jaguar can eat an anaconda or an alligator, etc.).
Here is an example from Alaska:
If there is one predator-a lone wolf for example-the defense strategy is to form a line. If a wolf pack surrounds the group, the muskoxen will form a tight circle, all facing outward, forming a phalanx of heads and horns.
Although IUCN has no data about the subspecies Canis lupus arctos, they say:
Originally, the Grey Wolf [Canis lupus] was the world's most widely distributed mammal. It has become extinct in much of Western Europe, in Mexico and much of the USA, and their present distribution is more restricted; wolves occur primarily but not exclusively in wilderness and remote areas. Their original worldwide range has been reduced by about one-third by deliberate persecution due to depredation on livestock and fear of attacks on humans.
It may not be the case in Alaska, though.
Another example from Africa are zebras. There are many species from two different subgenera, but if Equus zebra is endangered, what we may think about their predators! Lions, for instance. They flock in groups so the stripes may confuse the predators about where one individual ends and the next begins - making a successful attack more difficult.
Many species of animals use flocking/ schooling/ shoaling/ etc as a mechanism to confuse predators. A few examples are tuna, starlings, goldband fusiliers. tuna and goldband fusiliers use their reflective scales to blind and confuse the predator, whereas starlings use sheer numbers to overpower and throw off their predator. The large groups tend to scare away far away predators.
The act of grouping is not dependent on whether or not the predator is endangered or not, but there would be cases where both the predator is endangered and the prey uses grouping. For example, several species of owl are endangered, which prey on starlings.
Browse Full Outline
- 2.1 Predator-Prey Relationships
- 2.2 Sexual Reproduction
- 2.3 Asexual Reproduction
- 2.4 The Selfish Gene Theory and Altruism
- 2.5 Eusociality
- 2.6 Animal Intelligence and Learning
- 2.7 Unique Animal Skills
- 2.8 Migration
- 2.9 Camouflage and Mimicry
- 2.10 Ecological Specialization
- 2.11 Tool Usage
- 2.12 Parenting Behavior
- 2.13 Defense Mechanisms
- 2.14 Unique Environmental Adaptations
- 3.1 Invertebrates
- 3.2 Vertebrates
- 3.3 Entomology
- 3.4 Ornithology
- 3.5 Icthyology
- 3.6 Mammalogy
- 3.7 Primatology
- 3.8 Marine Mammals
- 3.9 Primitive Animals
- 3.10 The Rarest Animals
- 3.11 Surprisingly Dangerous Animals
- 3.12 Poisonous and Venomous Animals
These strategies and adaptations can take many forms including camouflage, mimicry, defensive mechanisms, agility, speed, behaviors and even tool usage that make their job easier.
In nature a balance tends to exist between the predators and prey within an environment. There are a number of factors that can affect it but part of it is the birth and death rates of the predators and prey species.
Sagebrush (Artemisia tridentata) ecosystems are declining due to biological invasions and changes in fire regimes. Understanding how ecosystem changes influence functionally important animals such as ecosystem engineers is essential to conserve ecological functions. American badgers (Taxidea taxus) are an apex predator and ecosystem engineer in that they redistribute large amounts of soil within sagebrush ecosystems. Piute ground squirrels (Urocitellus mollis) are also an ecosystem engineer as well as an essential prey resource for many predators, including badgers. Our research objective was to evaluate the relative importance of biological invasions and fire, abiotic factors, and biotic factors on badgers and ground squirrels. We sampled 163 1-ha plots during April-June across a gradient of burn histories within a 1962 km 2 study area in southern Idaho, USA. At each plot, we characterized occupancy of ground squirrels and badgers and relative abundance of ground squirrels. Additionally, we characterized soil texture, climate, connectivity and dispersal potential, fire frequency, grazing, and cover of many plant species including a highly invasive exotic annual grass (cheatgrass Bromus tectorum). We used an integrated approach to evaluate competing hypotheses concerning factors influencing occupancy and abundance. Results suggested that occupancy of ground squirrels was positively associated with long-term precipitation, dispersal potential, and fine-grained soil. Abundance of ground squirrels was positively associated with fine-grained soil, but negatively associated with cheatgrass, fire frequency, agriculture, and shrub cover. Badger occupancy was positively associated with ground squirrel occupancy and agriculture, which indicated affinity to prey. Our results provide insight into the relative influence of abiotic and biotic factors on predator and prey, and highlight how effects change across different population parameters. Our research suggests that widespread environmental change within sagebrush ecosystems, especially the fire-cheatgrass cycle (e.g., invasion of cheatgrass and increased fire frequency) and human land disturbances, are directly and indirectly influencing ground squirrels and badgers. However, we also found evidence that efforts to mitigate these stressors, for example, establishing bunchgrasses postfire, may provide targeted conservation strategies that promote these species and thus preserve the burrowing and trophic functions they provide.
Review of field studies
A search of the literature using Web of Science (keywords used: apex predator, carnivore, interspecific killing, mesopredator, mesopredator release, predator interaction, trophic cascade) between the years of 1972 and 2009, as well as cross-citations and in press manuscripts from colleagues yielded an initial total of 94 studies of the effects of vertebrate apex predators on mesopredators and prey communities in terrestrial and marine ecosystems. These studies represent a variety of approaches, including phenomenological studies of mesopredator abundance comparing places or times with different abundance of apex predators, experimental removals of apex predators, or field studies of behavioural effects of apex predators on mesopredators. Of these, 73 (78%) reported primary data, all from the years of 1988–2009. These studies are divided between and summarized in Tables 1 and 2. Of all studies, 38% were of aquatic systems (all marine except one freshwater study) and 62% of terrestrial systems. Studies were geographically biased to North America and taxonomically biased towards mammals, especially canids (wolves, coyotes and foxes), but reports of systems in which apex predators affect mesopredator populations came from all continents (with the apparent exception of South America).
|Study||Number||Region||System||Apex predator(s)||Mesopredator(s)||Summary research results|
|Creel & Creel 1996 Creel 2001||1, 2||Africa||T||Hyena, lion||Wild dog||Wild dogs may be kept at low densities or driven to extinction by lions and hyenas, especially in open habitats.|
|Durant 1998, 2000||3, 4||Africa||T||Hyena, lion||Cheetah||Cheetahs survive with larger predators by seeking areas with low predator densities.|
|Ainley et al. 2006||5||Antarctica||T, M||Adelie penguin, minke and killer whales||Antarctic silverfish, krill||High seasonal increased abundance of apex predators led to a decrease in silverfish and krill.|
|Blanchard et al. 2003 Daan et al. 2005||6, 7||Atlantic Ocean||M||Large fish||Small fish||Large fish overexploited, lead to increase in smaller size-classed fish.|
|Carscadden et al. 2001||8||Atlantic Ocean||M||Cod, benthic fishes, harp seals||Capelin||Exploitation of apex predators resulted in increased capelin biomass.|
|Estes et al. 1998 Springer et al. 2003||9, 10||Atlantic Ocean||M||Killer whale||Sea otter||Killer whale predation of otters resulted in release of urchins and substantial overgrazing of kelp.|
|Fogarty & Murawski 1998 Shackell & Frank 2007||11, 12||Atlantic Ocean||M||Cod and groundfish||Silver hake, redfish, yellow tail and winter flounder||Exploitation of cod and groundfish resulted in release of mesoconsumers.|
|Frank et al. 2006||13||Atlantic Ocean||M||Cod, benthic fishes||Small pelagic fish||Exploitation of cod and benthic fish led to increases in small pelagic fish, but only in northern regions southern regions were under apparent bottom-up control.|
|Myers et al. 2007||14||Atlantic Ocean||M||Large elasmobranchs||Medium-sized elasmobranchs||As large shark abundance fell over 35 years, their prey species (elasombranch mesopredators) increased four to 10-fold.|
|Shepherd & Myers 2005||15||Atlantic Ocean||M||Large elasmobranchs||Smaller elasmobranchs||Exploitation of large elasmobranchs lead to increased deepwater small elasmobranchs.|
|Burrows et al. 2003||16||Australia||T||Dingo||Domestic cat||An index of cat abundance doubled following dingo removal.|
|Johnson & VanDerWal in press||17||Australia||T||Dingo||Red fox||When dingoes are abundant foxes are rare. Dingoes set an upper limit to fox abundance.|
|Heithaus & Dill 2002, 2006||18, 19||Australia||M||Tiger shark||Bottlenose dolphin||Dolphin foraging indicated a trade-off between predation risk and food availability|
|Mitchell & Banks 2005||20||Australia||T||Dingo/wild dogs||Red fox||Evidence of dietary competition and fine-scale exclusion of foxes by larger canids, but no support for landscape-scale exclusion.|
|Moreno et al. 2006||21||Central America||T||Jaguar||Puma and ocelot||Evidence of competitive release for puma and ocelot, following the decline of jaguars.|
|Boveng et al. 1998||22||Europe||M||Leopard seal||Antarctic fur seal||Leopard seals limit fur seal population growth.|
|Elmhagen & Rushton 2007||23||Europe||T||Wolf, Eurasian lynx||Red fox||Red fox increase most rapid where decline in top predators most rapid. However, productivity had greater impact (∼10 times) than mesopredator release on population growth.|
|Fedriani et al. 1999||24||Europe||T||Iberian lynx, Eurasian badger||Red fox||Foxes avoided habitats frequented by lynxes. The particular foraging mode of badgers may aid their coexistence with other carnivores.|
|Helldin et al. 2006||25||Europe||T||Eurasian lynx||Red fox||Annual number of fox litters declined after lynx re-established.|
|McDonald et al. 2007||26||Europe||T||Eurasian otter||American mink||Between 1991 and 2002, where found, mink signs decreased by 60% and otter signs increased by 62%, providing evidence of the reversal of mesopredator release|
|O’Gorman et al. 2008||27||Europe||M||Benthic fish||Small fish||Increased top predator diversity coincided with increased secondary production. Manipulating top predators suppressed mesopredator density, releasing benthic invertebrates from heavy predation. Without top predators a cascade occurred.|
|Palomares et al. 1996||28||Europe||T||Iberian lynx, European badger||Common genet, Egyptian Mongoose, Red fox||Mongooses and genets avoided areas used by lynx but not badgers. The relationship between foxes and lynx is unclear.|
|Salo et al. 2008||29||Europe||T, M||White-tailed sea eagle||American mink||Mink modified behaviour according to predation risk, which may lower population growth and have cascading effects on lower trophic levels.|
|Scheinin et al. 2006||30||Europe||T||Golden jackal||Red fox||Experiments show that foxes fear jackals.|
|Trewby et al. 2008||31||Europe||T||European badger||Red fox||Culling badgers was associated with an increase in red fox densities of > 40% per km 2 .|
|Mukherjee et al. 2009||32||Middle East||T||Striped hyena||Red fox||Foxes are more active when hyena activity is low.|
|Tompkins & Veltman 2006||33||New Zealand||T||Stoat, brushtail possum||House mouse, Ship rat||Reduction in either rat and stoat numbers or rats only released mice. Reduction in stoats led to increases in rats. Possums can regulate rats. Complex negative indirect effects can occur during pest control.|
|Barr & Babbitt 2007||34||North America||F||Brook trout||Two-lined salamanders||Salamander density and daytime activity decreased following trout addition to streams.|
|Barton & Roth 2008||35||North America||T, M||Raccoon||Ghost crab||Predation by raccoons limits ghost crabs.|
|Berger & Conner 2008 Berger et al. 2008||36, 37||North America||T||Wolf||Coyote||Wolves limit coyote habitat use and density. In particular, transient rather than resident coyotes are most vulnerable to wolf attack.|
|Burkepile & Hay 2007||38||North America||M||Predatory fish, invertebrates||Gastropod||Eight times more coral damage by gastropods where predators excluded|
|Crooks & Soulé 1999||39||North America||T||Coyote||Domestic cat, gray fox, opossum, raccoon, skunk||Coyotes suppressed cats and other mesopredators.|
|Ellis et al. 2007||40||North America||M||Herring and black-backed gulls||Crab||Gulls reduced abundance of one dominant crab, increasing the abundance of two other predators (a gastropod and another crab).|
|Fedriani et al. 2000||41||North America||T||Coyote||Gray fox, bobcat||Coyotes kill both gray fox and bobcats, but exert greater pressure on foxes.|
|Frid et al. 2008||42||North America||M||Pacific sleeper shark||Harbour seal||Reduced fear of sharks by seals allowed change to habitat use and foraging.|
|Harrison et al. 1989||43||North America||T||Coyote||Red fox||Coyotes presence appears to limit red fox habitat use|
|Henke & Bryant 1999||44||North America||T||Coyote||American badger, bobcat, gray fox||Following coyote removal mesopredator abundance increased.|
|Gehrt & Prange 2007||45||North America||T||Coyote||Raccoon||No evidence that coyotes reduce or limit raccoon abundance.|
|Kamler et al. 2003||46||North America||T||Coyote||Swift fox||Coyotes suppress swift foxes and reducing coyotes can assist increases in swift fox populations.|
|Karki et al. 2007||47||North America||T||Coyote||Swift fox||Swift fox density similar between areas with varying coyote abundance. Despite coyote predation being additive for juvenile foxes, it appeared compensatory with dispersal.|
|Mezquida et al. 2006||48||North America||T||Coyote||American badger, common raven, red fox||Control of coyotes may cause mesopredator release and decrease the survival of sage grouse.|
|Moehrenschlager et al. 2007||49||North America||T||Coyote, golden eagle||Mexican kit fox||Fox survival regionally dependent on prey availability and predator (coyote) abundance.|
|Prange & Gehrt 2007||50||North America||T||Coyote||Skunk||No evidence that coyotes adversely affect skunks.|
|Ralls & White 1995||51||North America||T||Coyote, red fox||Kit fox||Large canids caused 78% of all verified fox deaths.|
|Switalski 2003||52||North America||T||Wolf||Coyote||There is an apparent trade-off, where wolf kills provide a low-energy cost food source to coyotes but, coyotes must increase vigilance, decrease rest, and may be killed when co-occurring with wolves|
|Thompson & Gese 2007||53||North America||T||Coyote||Swift fox||Fox density negatively related to coyote abundance. Fox exposure to predation moderated by shrub density.|
|Dulvy et al. 2004||54||Pacific Ocean||M||Predatory fish||Starfish||Predatory fish declined by 61% due to fishing, starfish densities increased three orders of magnitude.|
|Essington 2006||55||Pacific Ocean||M||Sperm whales, swordfish, blue shark||Large squid||When apex predators were reduced through exploitation, squid became the dominant predator guild.|
|Kitchell et al. 2006||56||Pacific Ocean||M||Billfish, sharks and yellowfin tuna||Mahi-mahi, smaller tuna and other pelagic fishes||Decrease in large predatory fish resulted in release of mesopredators. Following cessation of exploitation of large pelagic fishes, marlin increased eightfold, and tuna and sharks two- to four-fold, with a subsequent decline in some pelagic (mesoconsumers) fishes.|
|Oguz & Gilbert 2007 Daskalov et al. 2007 Daskalov 2002||57, 58, 59||Pacific Ocean||M||Bonito, mackerel, bluefish||Horse mackerel, sprat, anchovy, jellyfish||When large pelagic fishes decreased, there was an increase in smaller fish species and a jellyfish invasion.|
|Parrish 2009||60||Pacific Ocean||M||Hawaiian monk seal||Subphotic fishes||Changes in fish biomass density correlated with spatial variation in distance to seal colonies and their size.|
|Ward & Myers 2005||61||Pacific Ocean||M||Tunas, billfishes, elasmobranchs||Various mesoconsumers||Ten fold declines in large pelagic predators coincided with 10–100-fold increases in small-bodied mesoconsumers.|
|Study||Number||Region||Apex predator(s)||Mesopredator(s)||Summary research results|
|Lloyd 2007||1||Africa||Large carnivores||Mongoose species||No evidence that removing apex predators reduces bird nesting success through mesopredator release.|
|Myers et al. 2007||2 (14)||Atlantic Ocean||Large elasmobranchs||Medium-sized elasmobranchs||Overexploitation of large elasmobranchs led to increases in cownose rays (eightfold) and a crash in bay scallops.|
|Johnson et al. 2007||3||Australia||Dingo||Domestic cat, red fox||Mammal persistence strongly positively associated with persistence of dingoes. Dingoes are thought to suppress cat and fox populations.|
|Letnic et al. in press||4||Australia||Dingo||Domestic cat, red fox||Abundance of a threatened rodent species was positively associated with dingo activity|
|Lundie-Jenkins et al. 1993||5||Australia||Dingo||Red fox||A solitary red fox may have caused the extinction of the last remaining mainland rufous hare-wallaby population, following dingo removal.|
|Wallach et al. 2009||6||Australia||Dingo||Domestic cat, red fox||Positive association found between the occurrence of dingoes and two threatened species (rock wallaby and malleefowl).|
|Palomares et al. 1995||7||Europe||Iberian lynx||Egyptian mongoose||4.8–9.5 times more rabbits eaten by mesopredators when lynx absent. Rabbit population growth 12–22% lower when lynx absent. Rabbit densities in areas used by lynx were two to four times higher than in areas not used by lynx.|
|Sergio et al. 2007||8||Europe||Eagle owl||Tawny owl||Tawny owl avoidance of eagle owls allows increased diversity and abundance of other owls.|
|Rayner et al. 2007||9||New Zealand||Domestic cat||Pacific rat||Breeding success of Cooks Petrel with cats and rats was ∼3.5 times higher than with rats only.|
|Barton & Roth 2008||10 (35)||North America||Raccoon||Ghost crab||Mesopredator release of ghost crabs can increase sea turtle egg mortality.|
|Berger & Conner 2008 Berger et al. 2008||11 (36), 12 (37)||North America||Wolf||Coyote||Pronghorn fawn mortality 34% lower in presence of wolves, which suppress coyote populations and habitat use.|
|Crooks & Soulé 1999||13 (39)||North America||Coyote||Domestic cat, gray fox, opossum, raccoon, skunk||Coyotes protect songbirds through mesopredator suppression.|
|Ellis et al. 2007||14 (40)||North America||Herring and black-backed gulls||Crab||Reduced abundance of one mesopredator led to an increase in the abundance of two other mesopredators.|
|Frid et al. 2008||15 (42)||North America||Pacific sleeper shark||Harbour seal||Change in habitat use by seals put pressure on fish prey.|
|Henke & Bryant 1999||16 (44)||North America||Coyote||American badger, bobcat, gray fox||Mesopredator increases after coyote removal led to reduced rodent diversity and increased dominance of one species.|
|Rogers & Caro 1998||17||North America||Coyote||Raccoon||Lower nest success of song sparrows when coyote absent due to probable raccoon predation.|
|Souléet al. 1988||18||North America||Coyote||Gray fox, domestic cat||Evidence that coyotes suppress foxes and cats and may benefit bird populations.|
|Sovada et al. 1995||19||North America||Coyote||Red fox||Duck nest success 88% higher where coyotes were most abundant than where red foxes were most abundant, due to presumed negative relationship between coyotes and foxes.|
|Dulvy et al. 2004||20 (54)||Pacific Ocean||Predatory fish||Starfish||Starfish release from predatory fish resulted in subsequent coral and coralline algae decreases of 35% and were replaced by non-reef building taxa.|
Table 1 summarizes 61 studies that reported on interactions between apex- and mesopredators. More than 95% of studies found evidence consistent with mesopredator release and/or the suppression of mesopredators by apex predators. Two studies found no evidence of mesopredator control by apex predators (studies 45 and 50, Table 1). These exceptions help to identify conditions under which the intensity of competitive and antagonistic interactions between predators is reduced. These include mesopredators having specialized defences, such as the repellent chemicals sprayed by skunks (Mephitis mephitis) that are effective against large attackers (study 50, Table 1). In other cases, resource availability appears to have been very high so that competitive interactions between predators were reduced (studies 45 and 50, Table 1), or mesopredators used very different structural niches from apex predators, such as by being arboreal and thus avoiding ground-dwelling apex predators (study 45, Table 1). One study (study 40, Table 1) found that the presence of apex predators (gulls) facilitated higher abundance of some mesopredators (a crab and gastropod), by reducing the abundance of a dominant member of the mesopredator guild (another crab).
These exceptions notwithstanding, and allowing for the strong possibility of publication bias in favour of findings of mesopredator suppression/release, it seems that the control of community organization through effects of apex predators on mesopredators may be common and widespread in both marine and terrestrial ecosystems. In some cases predators may even be involved in complex trophic linkages across ecosystems. Estes et al. (1998) showed how changes to predator - prey relationships in oceanic environments (caused by declines in fish and seals) had a direct impact on the functioning of near-shore coastal environments, through killer whale (Orcinus orca) prey switching from Stellar sea lions and harbour seals (Eumetopias jubat and Phoca vituline) to sea otters (Enhydra lutris).
Our experiments showed that prey that were exposed to adult predators when juvenile, developed into adults that killed more juvenile predators per unit of time than non-exposed prey. This was most likely caused by changes in the behaviour of the individual prey, not by selective predation during the exposure to predators. This shows that prey can tune adult antipredator behaviour based on juvenile experience after an ecological role reversal. We furthermore showed that the increased attack of juvenile predators deterred adult predators and consequently reduced predation risk of juvenile prey.
We furthermore showed that selective predation during the exposure of juvenile prey to adult predators is an unlikely explanation for the increased attack rate. We mimicked selective predation by removing the prey that killed the fewest juvenile predators from the control group. Assuming a syndrome of correlated behaviours 27 , the only explanation for our results would have been that bold and aggressive juvenile prey would run a lower predation risk, whereas bold, aggressive adult prey would kill more juvenile predators. Hence, selective predation would then favour bold, aggressive juvenile prey. However, the opposite could also be argued, i.e. that bold, aggressive juvenile prey would run a higher predation risk. However, individuals that survived exposure to adult predators were more ferocious than individuals that had not been exposed, showing that this latter scenario is unlikely to have occurred.
Commonly used methods to avoid the problem of selective predation in the study of antipredator behaviour are to expose the prey to cues of predators or to predators that are in some way restrained so as not to be able to kill prey, for example by being placed in cages or by their mouthparts being manipulated to prevent them from killing prey 34 . Although these methods have often been used 4,22,35,36 , they may not be fully adequate for long-term experiments. It is common knowledge that scarecrows are not very efficient in keeping crows at bay because the birds rapidly learn that they have nothing to fear from these straw men. Such “scarecrow effects” are likely to occur in prey that are exposed to restrained predators or predator cues for prolonged periods, without there being actual predation risk. Experience has been shown to reinforce antipredator behaviour 18,19,20,21,22 and the behaviour of prey exposed to predator cues without experiencing predation risk would not be reinforced. Hence, prey will gradually ignore restrained predators or cues of predators because they are no longer associated with predation risk. Another argument against prolonged exposure of prey to restrained predators is that predators that are not capable of killing prey are likely to change their foraging behaviour as a consequence. The predators' hunger level increases during long-term experiments and this may result in them (unsuccessfully) attacking many more prey than unrestrained predators would normally attack, because the latter would become satiated after the consumption of some prey. Alternatively, restrained predators may perceive that their attacks are futile or they may be weakened because of lack of food, leading to fewer and less severe attacks. Taken together, it is highly unlikely that restrained predators or predator cues would have the same effect on prey behaviour as real predation risk. We therefore suggest that mimicking selective predation as done here is better than exposing prey to constrained predators or cues associated with predators, particularly in experiments that last long enough for the prey to adapt to such cues (the scarecrow effect).
Our results show evidence for recognition of species by the prey: adult prey killed more juvenile predators only when predator juveniles were of the same species as to which the prey had been exposed in the juvenile phase (Fig. 1). This could be caused by a preference of adult prey for juveniles of the predator species to which they had been exposed. However, adult prey from the control group killed equal amounts of larvae of both N. cucumeris and A. swirskii, showing that they had no preference for either species of juvenile predators when pollen was present (cf. survival of juveniles of both predator species in the presence of adult prey of the control group, Fig. 1A,B: X 2 = 2.9. d.f. = 1, P = 0.089). Notice that a preference for larvae of N. cucumeris would not explain the main results, i.e. the increased predation rate of prey that were previously exposed to predators relative to the control group (Figure 1A).
Our results thus imply that adult female prey recognize the juveniles of their childhood foe, whereas they were only exposed to adult females and eggs of this predator when young. We attempted to further test this by exposing juvenile prey to another predator, i.e. A. swirskii, but none of the juvenile prey survived exposure to this voracious predator, hence, the test could not be completed. Meanwhile, we do not know how the prey recognized the juveniles of the predator species to which they had been exposed. In general, prey are known to perceive predation risk via excretion and secretion products of predators 37,38 and by alarm pheromones produced by conspecific prey 5,39,40 . However, the behavioural changes observed in our experiments cannot have been a response to such cues because these were absent when the predation by adult prey was tested. Possibly, adults and juveniles of the predator carry the same cues (perhaps cuticular hydrocarbons), which would then facilitate species recognition. Yet, the response to these cues would then be dependent on the developmental stage of the prey: juvenile prey that perceive cues of adult predators should try to escape, whereas adults that perceive cues of juvenile predators counterattack. Perhaps they learn the association between predator-specific cues and the risk of predation when exposed to the adult predators and this learning experience carries over to later in life 41 , when they recognize the juveniles of their childhood enemies by the same cues. The fact that adult prey that had experienced adult predators, but no predation, did not kill more juvenile predators (Fig. 2) suggests that the association of predator cues with predation risk is indeed essential to induce changes of behaviour in adult prey. It is still an open question why the prey do not simply always kill juvenile predators at the same rate, but instead need experience to increase their killing rate. We suspect that this is related to the costs associated with this antipredator behaviour. Perhaps the adult prey risk being injured when attacking juveniles of other predatory mite species 42,43 and they should therefore only attack juveniles of the species that pose a serious threat to their offspring.
One question that remains is how our results translate to predator-prey systems in the real world. In our experiments, prey and predators were confined to small arenas in order to study their behaviour. As a consequence, juvenile predators and prey could not escape from attacks. Notably, the mortality of juvenile predators of all species was high after 24 h of exposure to adult prey (see legend to Figure 1). We expect that some of these juveniles would escape from the attacking adult prey under more natural conditions. Likewise, adult prey may prefer to settle on patches where adult predators do not kill juvenile prey. Hence, the behaviour we describe here will probably give rise to distributions of predators and prey that depend on the stage of the individuals of the species present on any given patch as well as on the experience of these individuals 44 . This will result in spatial separation of predators and prey: prey can drive away predators by killing their young, but at the same time will avoid settling in patches occupied by predators 5,45,46 . Such spatial separation will reduce the interaction strength between predators and prey 47 .
The species studied here are engaged in intraguild predation, an interaction in which two species compete for resources and one of the species (the intraguild predator) attacks and feeds on the other (the intraguild prey) 13 . Classic theory, derived from simple Lotka–Volterra models on well-mixed populations, predicts limited possibilities for coexistence of such species 13,48,49 . Usually, intraguild prey either exclude intraguild predators through competition for resources or intraguild predators exclude intraguild prey through predation 13,48,49 . Indeed, several experiments have shown extinction of populations of one of the two species engaged in intraguild predation 24,50,51 . Persistence of intraguild prey and intraguild predators is, however, possible at larger spatial scales, where populations of each species can occupy patches of the shared resource 52 . However, patches occupied by the intraguild prey will then still be vulnerable to invasion by the intraguild predator. By attacking juvenile intraguild predators, intraguild prey decrease the success of such invasions 25 , hence, the order of invasion of patches will determine which species will persist 53 , thus increasing persistence of populations at a metapopulation scale. We expect that the increased counterattacks of juvenile predators by adult prey that have experienced predation risk will further reduce the invasion success of intraguild predators, thus ensuring increased persistence of local populations of the intraguild prey through antipredator behaviour 54 .
Another question that begs and answer is why adult invulnerable prey would kill harmless juvenile predators. Other experiments have shown that such killing and consumption of juvenile predators did not directly increase the survival or oviposition of the adult prey individuals 24 . However, the behaviour may serve as a form of maternal care 11 : we found here that survival of juvenile prey was increased in the presence of killed juvenile predators. Hence, adult prey can deter predators by killing predator offspring 15 , thus creating a place with lower predation risk for their own offspring. Although interspecific infanticide has been reported for several species 12,13,14 , its function is often unclear (but see Saito 11 ). Palomares and Caro 14 , for example, reported that many mammalian carnivores kill young of other species, sometimes without feeding on them. There is also at least one example of adult African buffalo killing lion cubs 55 , but an explanation for this behaviour, which is risky for the buffaloes, was, to our knowledge, never proposed. We suggest that such behaviour serves to expel predators, thus increasing the survival probability of the vulnerable offspring of the adult prey. We furthermore suggest that experience at a vulnerable stage serves to fine-tune such risky antipredator behaviour when prey may potentially interact with various species of predators. We expect that the killing of juvenile predators by adult prey occurs more frequently than thought thus far, particularly in prey species that are vulnerable when young and invulnerable when adult and when these prey have several potential predators, to which they have to fine-tune antipredator behaviour.
Examples of prey predator interactions where density of predator is very low and prey form groups when the predator attacks the prey for food - Biology
Perhaps the classical example of species interaction is predation: the consumption of prey by its predator. Nature shows on television highlight the drama of one living organism killing another. Populations of predators and prey in a community are not constant over time: in most cases, they vary in cycles that appear to be related. The most often cited example of predator-prey dynamics is seen in the cycling of the lynx (predator) and the snowshoe hare (prey), using nearly 200 year-old trapping data from North American forests (Figure 1). This cycle of predator and prey lasts approximately 10 years, with the predator population lagging 1–2 years behind that of the prey population. As the hare numbers increase, there is more food available for the lynx, allowing the lynx population to increase as well. When the lynx population grows to a threshold level, however, they kill so many hares that hare population begins to decline, followed by a decline in the lynx population because of scarcity of food. When the lynx population is low, the hare population size begins to increase due, at least in part, to low predation pressure, starting the cycle anew.
Figure 1. The cycling of lynx and snowshoe hare populations in Northern Ontario is an example of predator-prey dynamics.
Some researchers question the idea that predation models entirely control the population cycling of the two species. More recent studies have pointed to undefined density-dependent factors as being important in the cycling, in addition to predation. One possibility is that the cycling is inherent in the hare population due to density-dependent effects such as lower fecundity (maternal stress) caused by crowding when the hare population gets too dense. The hare cycling would then induce the cycling of the lynx because it is the lynxes’ major food source. The more we study communities, the more complexities we find, allowing ecologists to derive more accurate and sophisticated models of population dynamics.
Herbivory describes the consumption of plants by insects and other animals, and it is another interspecific relationship that affects populations. Unlike animals, most plants cannot outrun predators or use mimicry to hide from hungry animals. Some plants have developed mechanisms to defend against herbivory. Other species have developed mutualistic relationships for example, herbivory provides a mechanism of seed distribution that aids in plant reproduction.
Defense Mechanisms against Predation and Herbivory
The study of communities must consider evolutionary forces that act on the members of the various populations contained within it. Species are not static, but slowly changing and adapting to their environment by natural selection and other evolutionary forces. Species have evolved numerous mechanisms to escape predation and herbivory. These defenses may be mechanical, chemical, physical, or behavioral.
Mechanical defenses, such as the presence of thorns on plants or the hard shell on turtles, discourage animal predation and herbivory by causing physical pain to the predator or by physically preventing the predator from being able to eat the prey. Chemical defenses are produced by many animals as well as plants, such as the foxglove which is extremely toxic when eaten. Figure 2 shows some organisms’ defenses against predation and herbivory.
Figure 2. The (a) honey locust tree (Gleditsia triacanthos) uses thorns, a mechanical defense, against herbivores, while the (b) Florida red-bellied turtle (Pseudemys nelsoni) uses its shell as a mechanical defense against predators. (c) Foxglove (Digitalis sp.) uses a chemical defense: toxins produced by the plant can cause nausea, vomiting, hallucinations, convulsions, or death when consumed. (d) The North American millipede (Narceus americanus) uses both mechanical and chemical defenses: when threatened, the millipede curls into a defensive ball and produces a noxious substance that irritates eyes and skin. (credit a: modification of work by Huw Williams credit b: modification of work by “JamieS93”/Flickr credit c: modification of work by Philip Jägenstedt credit d: modification of work by Cory Zanker)
Many species use physical appearance, such as body shape and coloration, to avoid being detected by predators. The tropical walking stick is an insect with the coloration and body shape of a twig which makes it very hard to see when stationary against a background of real twigs (Figure 3a). In another example, the chameleon can, within limitations, change its color to match its surroundings (Figure 3b). Both of these are examples of camouflage , or avoiding detection by blending in with the background. There are many behavioral adaptations to avoid or confuse predators. Playing dead and traveling in large groups, like schools of fish or flocks of birds, are both behaviors that reduce the risk of being eaten.
Figure 3. (a) The tropical walking stick and (b) the chameleon use body shape and/or coloration to prevent detection by predators. (credit a: modification of work by Linda Tanner credit b: modification of work by Frank Vassen)
Some species use coloration as a way of warning predators that they are not good to eat. For example, the cinnabar moth caterpillar, the fire-bellied toad, and many species of beetle have bright colors that warn of a foul taste, the presence of toxic chemical, and/or the ability to sting or bite, respectively. Predators that ignore this coloration and eat the organisms will experience their unpleasant taste or presence of toxic chemicals and learn not to eat them in the future. This type of defensive mechanism is called aposematic coloration, or warning coloration.
Figure 4. (a) The strawberry poison dart frog (Oophaga pumilio) uses aposematic coloration to warn predators that it is toxic, while the (b) striped skunk (Mephitis mephitis) uses aposematic coloration to warn predators of the unpleasant odor it produces. (credit a: modification of work by Jay Iwasaki credit b: modification of work by Dan Dzurisin)
While some predators learn to avoid eating certain potential prey because of their coloration, other species have evolved mechanisms to mimic this coloration to avoid being eaten, even though they themselves may not be unpleasant to eat or contain toxic chemicals. In Batesian mimicry, a harmless species imitates the warning coloration of a harmful one. Assuming they share the same predators, this coloration then protects the harmless ones, even though they do not have the same level of physical or chemical defenses against predation as the organism they mimic. Many insect species mimic the coloration of wasps or bees, which are stinging, venomous insects, thereby discouraging predation (Figure 5).
Figure 5. Batesian mimicry occurs when a harmless species mimics the coloration of a harmful species, as is seen with the (a) bumblebee and (b) bee-like robber fly. (credit a, b: modification of work by Cory Zanker)
Figure 6. Several unpleasant-tasting Heliconius butterfly species share a similar color pattern with better-tasting varieties. (credit: Joron M, Papa R, Beltrán M, Chamberlain N, Mavárez J, et al.)
In Müllerian mimicry, multiple species share the same warning coloration, but all of them actually have defenses. Figure 6 shows a variety of foul-tasting butterflies with similar coloration.
In Emsleyan/Mertensian mimicry, a deadly prey mimics a less dangerous one, such as the venomous coral snake mimicking the nonvenomous milk snake. This type of mimicry is extremely rare and more difficult to understand than the previous two types. For this type of mimicry to work, it is essential that eating the milk snake has unpleasant but not fatal consequences. Then, these predators learn not to eat snakes with this coloration, protecting the coral snake as well. If the snake were fatal to the predator, there would be no opportunity for the predator to learn not to eat it, and the benefit for the less toxic species would disappear.
The methods of data collection for our study are fully explained in . We summarise this information here to set the context for our statistical analysis, and then go on to present the statistical approach used to estimate the functional response parameters.
We consider populations of hen harriers that breed on UK grouse moors. UK moorland is upland habitat, generally characterised by wet acidic soils and heather (Calluna vulgaris). Female hen harriers hunt over these areas throughout the year, although males often winter elsewhere. Harriers return to their breeding sites in spring and generally make their nests in tall heather . Adult birds can consume a wide variety of prey but during the breeding season prey items delivered to nest for hen-harrier chicks tend to be dominated by meadow pipits, voles and red grouse chicks (adult grouse are rarely consumed during summer).
The data for this study were collected between 1992 to 1996 on 6 study moors in Scotland, and with additional data from a subset of sites in 1988. In total, 11 separate estimates of predation rates at different combinations of prey density were obtained. The methods used to obtain estimates of both predation rates and prey density are described briefly below. Details on the data collection are given in , .
Harrier diet was recorded by observers watching from hides set close to hen harrier nests over thousands of hours. The number and type of prey brought to the nest by parent birds was noted. Nests were observed during weeks 1 to 4 of the breeding season during which time it is estimated that 89% of prey items could be correctly identified . Unidentified prey tended to be small and quickly consumed.
Prey density for the three major prey species of the hen harriers was estimated in each year. Red grouse chick density was estimated by transect sampling using pointing dogs with brood size and nest density estimated based on counts in June and July. Meadow pipits were counted by visual observation using line transect surveys. An index of field vole abundance was obtained from the numbers of voles caught per 100 snap-trap nights.
To provide a baseline for evaluating the implications of an MSFR, we first fitted a generalised single species functional response  to our data (equation (3)). Consumption of grouse by harriers is treated as a function of grouse density alone, and the presence of other prey in the system is ignored. F is the number of grouse chicks brought to a harrier nest per hour by individual parents, N is the density of grouse chicks in the area based on grouse nest counts and brood sizes.
Eq. (3) was fitted to the data using computer-intensive Bayesian methods (Monte Carlo Markov Chain –MCMC). We adopted this approach for three reasons. First, it allowed us to use a plausible sampling distribution for our response data, avoiding the need to assume normality or use transformations. Because the data were counts of predation events over fixed units of time and space, consumption was initially modelled by a Poisson sampling distribution around the fitted function. However, as is often the case with data of this kind , the residuals of the Poisson model were overdispersed. We therefore used a negative binomial sampling distribution, which includes an additional parameter for the degree of over-dispersion in the data . Second, the Bayesian approach enabled us to incorporate independent information about the values for t, a and m in the form of prior distributions (more details of these distributions are given below). Third, the joint posterior distribution of the parameters could be directly approximated from the MCMC draws. This flexible approach to uncertainty has several advantages . For example, we avoided the need for the kind of 2-stage fitting process that has previously been used to decide whether a functional response should be considered sigmoidal , because we represent uncertainty about the form of the functional response explicitly by the posterior distribution of the parameter m.
We then fitted a multispecies extension of eq. (4) in which consumption of any one prey type is a function of the availability of all types of prey. As in the single-species case, we assumed no observation error in the Nj and individual negative binomial sampling distributions for the consumption rates Fj. The correlation structure of the Fj follows from eq. (2).
The MSFR is a non-linear function that employs as many response variables (the consumption of each prey species) as explanatory variables (the availability of each prey species). Not only does this impose apparently severe demands for data on prey availability and consumption, but there are few standard statistical techniques for fitting this kind of relationship. Those that are available cannot satisfactorily account for parameter and model uncertainty. However, Bayesian, computer-intensive methods place few restrictions on model structure, allowing us to apply a negative binomial sampling distribution to estimate the over-dispersion in the data and thus quantify parameter uncertainty rigorously and comprehensively for the multi-species case.
We used additional data and biological first principles to provide prior distributions for model parameters in both the single- and multi-species FR. For the ti we used a gamma prior with prey-specific mean and variance derived from published data. In our formulation of the FR, the attack rate on prey species i is given by . We used observational data on the attack rate for grouse  to derive a joint prior for agrouse and mgrouse. Negative values of m are meaningless, and values of m between 0 and 1 imply that at unchanged abundance of other prey species, attack rate on one prey species can decrease with increasing density of this prey, which is implausible. We therefore chose a shifted gamma prior for m with minimum 1. The prior mean and variance of all mi were set to 2 and 0.9 respectively, giving a 95th percentile of 3.9. No prior knowledge was available for apipit and avole, so various relatively uninformative priors were used to check for robustness in the choice of prior. Results are shown for a gamma prior with mean 1 and variance 0.99.
MCMC was implemented with a Random Walk Metropolis-Hastings algorithm . Variances of the proposal distributions were adjusted to achieve acceptance rates between 15–30% for each parameter. Plots of cumulative parameter means indicated that 4,000,000 iterations were satisfactory for convergence. We preceded these with a burn-in phase of 10,000 draws that did not contribute to the posterior. The validity of assuming a negative binomial sampling distribution for the consumption data was checked by comparing cumulative left probabilities for each datum, computed from its corresponding predictive distribution, with the negative binomial distribution (QQ plot).
Population dynamics [ edit | edit source ]
It is fairly clear that predators tend to lower the survival and fecundity of their prey, but on a higher level of organization, populations of predator and prey species also interact. It is obvious that predators depend on prey for survival, and this is reflected in predator populations being affected by changes in prey populations. It is not so obvious, however, that predators affect prey populations. ⎝] Eating a prey organism may simply make room for another if the prey population is approaching its carrying capacity.
The population dynamics of predator-prey interactions can be modelled using the Lotka–Volterra equations. These provide a mathematical model for the cycling of predator and prey populations. Predators tend to select young, weak, and ill individuals. ⎞]
Global determinants of prey naiveté to exotic predators
Prey naiveté—the failure of prey to recognize novel predators as threats—is thought to exacerbate the impact that exotic predators exert on prey populations. Prey naiveté varies under the influence of eco-evolutionary mediating factors, such as biogeographic isolation and prey adaptation, although an overall quantification of their influence is lacking. We conducted a global meta-analysis to test the effects of several hypothesized mediating factors on the expression of prey naiveté. Prey were overall naive towards exotic predators in marine and freshwater systems but not in terrestrial systems. Prey naiveté was most pronounced towards exotic predators that did not have native congeneric relatives in the recipient community. Time since introduction was relevant, as prey naiveté declined with the number of generations since introduction on average, around 200 generations may be required to erode naiveté sufficiently for prey to display antipredator behaviour towards exotic predators. Given that exotic predators are a major cause of extinction, the global predictors and trends of prey naiveté presented here can inform efforts to meet conservation targets.
The introduction of exotic species can cause substantial impacts on biodiversity and ecosystems [1–3], and such impacts are likely to exacerbate as rates of species introduction continue to increase [4,5]. Exotic predators are arguably the most disruptive group of introduced species [2,6], as they often exert impacts on native species far greater than those attributed to their native counterparts [7–9] and they are implicated in the extinction of hundreds of native species [1,2]. For instance, the accidental introduction of the brown tree snake (Boiga irregularis) onto the island of Guam, where there are no native arboreal vertebrate predators, caused the extinction of numerous species of birds, mammals, and reptiles . The disproportionate impact of exotic predators on native communities is often attributed to prey naiveté—the failure of prey to recognize (or respond appropriately) to a novel predator species and/or the lack of an appropriate defence (sensu ). Such prey naiveté towards exotic predators likely derives from insufficient eco-evolutionary exposure [10–16]. For example, rats introduced to oceanic islands worldwide are implicated in numerous extinctions of mammals, birds, and reptiles that have no evolutionary experience with generalist mammalian nest predators . However, rat impacts are reduced on islands that possess native rats or functionally similar land crabs, presumably because fauna on those islands are less naive to the effects of introduced omnivores .
Prey naiveté was originally conceived as a simplistic phenomenon where native animals become ‘easy prey’ to exotic predators owing to naive behaviour . However, prey naiveté is now recognized as a more complex phenomenon and four levels of prey naiveté have been proposed [15,16,19]. Level-1 naive prey do not recognize the exotic predator as a threat, which precludes any antipredator behavioural responses . Native animals experience level-2 naiveté if they recognize the exotic predator but show inappropriate antipredator behaviour . Level-3 naive prey display an appropriate but ineffective behavioural response towards an exotic predator . Lastly, level-4 naive prey over-respond to the exotic predator after experiencing excessive sublethal costs of predation . In addition to exhibiting inadequate antipredator behaviour, prey species that lack evolutionary experience to exotic predation may also possess other morphological or physiological traits that make them susceptible to exotic predators such as insufficient armature, flightlessness, conspicuous scent, or inadequate camouflage . Although prey naiveté is a well-accepted phenomenon , it varies under the influence of eco-evolutionary factors [14,15,21] whose relative importance and generality have yet to be quantified.
We hypothesize that the occurrence and strength of prey naiveté stems from several, non-exclusive factors that can be clustered into four themes (table 1). First, prey naiveté can be promoted by persistent biogeographic (hence evolutionary) isolation between predator and prey . The pronounced isolation of freshwater biota has been hypothesized to render prey more sensitive to introduced predators compared with terrestrial or marine biota [10,22] (Hypothesis 1 in table 1). Prey naiveté is also presumed to be more prevalent on islands than on mainlands [23–25], owing to lack of eco-evolutionary experience with exotic predators—or even native ones on predator-free islands (Hypothesis 2 in table 1). Likewise, predators introduced to geographically isolated or species-poor biotas are more likely to represent a novel archetype—that is, prey will not display antipredator responses towards exotic predators that are unfamiliar, where a practical proxy for ‘archetype’ distinction has been proposed at the taxonomic level of genus or family [10,16,26,27] (Hypothesis 3 in table 1). The introduction of a predator from a different biogeographic realm enhances the probability that the predator will be distinct from those of the recipient biota and thus unfamiliar  (Hypothesis 4 in table 1). The second theme is related to the way animals acquire antipredator responses (and lose prey naiveté) over time through adaptation, which could be a function of the number of prey generations since the introduction of a predator [28–30] (Hypothesis 5 in table 1). The third theme is related to the mediating role of latitude on prey naiveté, as novel predator recognition could be higher in low latitude communities, which generally experience greater and more diverse predation pressure [31–33] and thus whose prey may display antipredator behaviours to a broader variety of predator archetypes (Hypothesis 6 in table 1). Finally, the fourth theme is related to taxonomic specificity, as the recognition of introduced predators might vary across taxa , such that certain predators are more recognizable than others and certain prey are better adapted to recognize certain predators or entire suites of predatory taxa (Hypotheses 7 and 8, respectively, in table 1).
Table 1. Determinants of prey naiveté and the eight hypotheses tested in this study.
Many case studies suggest that these hypotheses are important predictors of prey naiveté [10,12,26,30,35,36], but no synthesis of global trends has been conducted. Here, we tested the generality of these eight hypothesized drivers of prey naiveté, with the goal of revealing which of these drivers can be used to effectively predict prey naiveté and conservation outcomes.
2. Materials and methods
We performed a search on 1 May 2019 following the guidelines of PRISMA (preferred reporting items for meta-analyses  electronic supplementary material, table S1). We entered the following terms in the Web of Science using the Advanced Search option: TS = (prey naiveté OR prey naivety OR naive prey OR lack of predator recognition OR antipredator behavio*) AND TS = (exotic OR invasive OR alien OR non-native), which produced 199 publications (electronic supplementary material, table S1). We also added 12 additional studies by examining the references of papers focused on prey naiveté (electronic supplementary material, table S1).
Studies were included if they met the following criteria. First, each study empirically compared—in field or laboratory experiments—the behavioural response of prey to an exotic and a native predator. In this study, we only evaluated evidence of predator recognition (Level-1 prey naiveté ), which has been proposed as the most fundamental form of prey naiveté . Second, the studies quantified behavioural responses and reported the mean, some form of variance (standard deviation, standard error, or confidence interval) and the sample size. Third, experiments within published articles were included as individual observations (i.e. number of rows on the database) if (1) investigators used different species of prey, native predator, and/or exotic predator, (2) experiments were performed with individuals from different locations, and (3) studies provided measurements for distinct behavioural responses to the same set of species of predators and prey because antipredator responses can be contrasting (e.g. prey might reduce activity in the presence of an exotic predator but not alter refuge use). Finally, to avoid temporal pseudoreplication, if a study measured a behavioural response through time (e.g. longitudinal studies), then the mean response over time was calculated. This criterion was adopted to better represent the generality of the behavioural responses.
The effect size g was calculated as follows :
One can think of the effect size g as the difference in prey behaviour when in the presence of an exotic versus a native predator. Careful consideration was given when obtaining data from different metrics of predator avoidance, because the direction of the response variable depends on the specific behavioural response quantified. For instance, prey activity is a common metric of predator avoidance and decreases with increasing perception of risk, because prey are usually less active in the presence of a predator. On the other hand, refuge use—another common metric of antipredator behaviour—increases with increasing perception of risk. In order to standardize the direction of our metrics on antipredator behaviour, the data obtained from metrics that increased with increasing perception of risk were not changed and data obtained from metrics that decreased with increasing perception of risk were transformed to negative numbers (a negative symbol was added to the raw values for XE and XC). Therefore, g values near zero indicated predator recognition (e.g. prey respond similarly to an exotic and native predator), whereas values less than zero suggested prey naiveté (e.g. less perception of risk of the exotic predator than to the native predator), and positive values indicated prey perceiving an exotic predator to be more risky than a native predator. We obtained data from the text or tables of the studies or extracted measurements from figures in digital PDFs using ImageJ.
The pooled standard deviation (SDpooled) was calculated as [40,41]
There were four studies (see electronic supplementary material, table S2) that compared the behavioural response of an exotic prey to native and exotic predators and the native predators were considered to be the novel consumers of the exotic prey, therefore qualifying as tests of prey naiveté. Three papers that investigated antipredator responses to the exotic green crab Carcinus maenas in the North-Western Atlantic [42–44] were not included in the meta-analysis because the native prey Nucella lapillus is sympatric with C. maenas in the North-Eastern Atlantic (the native range of the green crab), and, hence, did not meet our criteria.
In addition to the effect size, we recorded from each study the following factors: (1) ecosystem type (whether the exotic predator was introduced in a terrestrial, freshwater, or marine system), (2) insularity (whether the introduction was on an island or on a continental mainland including only data from terrestrial systems Australia was considered a continental mainland, following ), (3) biogeographic realm difference (whether or not the location of introduction and the native range of the exotic species occupy the same biogeographic realm terrestrial and freshwater systems were assigned to one of 11 biogeographic realms and marine ecosystems to 1 of 12—see electronic supplementary material, table S3), (4) taxonomic distinctiveness of the exotic predator (presence/absence of native predators in the introduced biogeographic region that belong to the same genus as the exotic predator), (5) the exotic predator taxonomic group (a posteriori categorized as six levels: fish, mammal, crustacean, herpetofauna, insect, and echinoderm), (6) the taxonomic group of the native prey (a posteriori categorized in six levels: fish, mammal, crustacean, herpetofauna, insect, and mollusc), (7) the number of prey generations since introduction (calculated by dividing the time passed since the exotic species was first recorded in the exotic region by the generation time of the prey species), and (8) the absolute latitude of the introduction of the novel predator measured in decimal degrees. We used the point of introduction instead of other potential spatial proxies—for instance, the midpoint of the full range of predator introduction—because the distributions of exotic predators and their native prey are usually patchy and often times unknown, and, more importantly, because many researchers used the patchiness of predator distributions to define the area of study (based on the presence or absence of the exotic predator).
(a) Statistical meta-analyses
Meta-analyses were performed using the metafor package for R . Treatments with less than or equal to 10 observations were dropped from the analyses to improve statistical robustness, which only included the removal of exotic echinoderms and insects (3 and 4 observations, respectively) from the analysis (see electronic supplementary material, table S4). We ran six independent mixed-effect models with different fixed predictors and in which ‘study ID’ and ‘experiment ID’ were always included as nested random factors, to account for multiple observations attained from the same study and experiment. In addition, the number of generations since introduction was added in each of the six models as a covariate to account for the potential effects of adaptation on prey naiveté through time. These six independent models included the following fixed, categorical predictors: (1) the system type of the introduction (with three levels: terrestrial, freshwater, or marine), (2) insularity (with two levels: mainland or island), (3) taxonomic distinctiveness of the introduced predator (with two levels: yes or no), (4) difference in biogeographic realm (with two levels: same or different if the biogeographic realm of the exotic predator was the same or different than the biogeographic realm of the introduction), (5) the taxonomic group of the exotic predator (with four levels: fish, mammal, crustacean, and herpetofauna insect and echinoderm were excluded because they had ≤10 replicates), and (6) the taxonomic group of the native prey (with six levels: fish, mammal, crustacean, herpetofauna, insect, and mollusc some important taxa (e.g. birds) were not included as no publications were found that met our criteria for comparing the response of these groups towards native and exotic predators). Effect sizes were considered significant if the 95% CIs did not overlap with zero. We also ran two further independent mixed-effects models with the nested random factors described above and a continuous fixed factor: the number of prey generations since the introduction of the novel predator, and the absolute latitude of introduction. For these models with continuous predictors, their significance was determined by the p-value of the moderator .
Publication bias can distort the results in a meta-analysis  by, for instance, overestimating prey naiveté towards exotic species. The functions regtest and trimfill are not implemented in the metafor package for mixed-effects models. Therefore, potential publication bias was evaluated using Egger's regression test  by running models that included the standard error of the effect sizes (included as the square root of the variance) as a moderator  bias was determined when the intercept of the model was different from zero at p-values ≤ 0.05. In addition, we examined the data for potential outliers by looking at the effect sizes with standardized residual values exceeding the absolute value of three  using the rstandard function in R. Adjusting for publication bias did not change the outcome of the analyses (by comparing fitted random-effects models with and without the influence of the potential outliers electronic supplementary material, table S4), indicating minimal influence of potential outliers.
We found 40 studies that met our criteria to be included in the final dataset (electronic supplementary material, table S2), which comprised a total of 214 observations. The studies were published between 1993 and 2018 (electronic supplementary material, table S2) and included 47 unique study locations of introduction (electronic supplementary material, figure S1). Overall, we included reports assessing prey naiveté in 61 species of prey, with 38 species of exotic predators and 57 species of native predators (electronic supplementary material, table S2). The majority of species of introduced predators in our study were from freshwater systems (54.6% 117 observations out of 214) when compared with terrestrial (33.6% 72 observations) and marine systems (11.7% 25 observations). The models that included ‘number of prey generations’ had a lower Akaike's Information Criterion (AIC) than those that excluded this variable, so the variable was kept in the models, regardless of its significance.
Naiveté was found to be significantly pronounced in animals from marine and freshwater systems (mean Hedge's g ± 95%CI = −0.79 ± 0.38 and −0.32 ± 0.25, p < 0.001, and p = 0.013, respectively figure 1a) but not significant in terrestrial systems (g = −0.35 ± 0.42, p = 0.107 figure 1a). Likewise, significant levels of prey naiveté were exhibited by prey on islands (g = −0.31 ± 0.18, p = 0.001 figure 1b), but not by animals in terrestrial continents (g = −0.02 ± 0.15, p = 0.789 figure 1b). Prey naiveté was significant only when the original biogeographic realm of the exotic predator differed from the realm in which it was introduced (g = −0.47 ± 0.20, p < 0.001 figure 1c), but not if the introduction occurred within the same biogeographic realm (g = −0.30 ± 0.42, p = 0.165 figure 1c). Similarly, the taxonomic distinctiveness of the exotic predator in the introduced realm also predicted prey naiveté, as native prey were significantly naive to distinct exotic genera (g = −0.47 ± 0.21 p < 0.001 figure 1d), but not towards introduced species with a sympatric species in the same genus (g = −0.35 ± 0.37, p = 0.087 figure 1d).
Figure 1. Determinants of prey naiveté. Influence of (a) system type, (b) insularity on terrestrial systems, (c) distinctiveness of the biogeographic realm, (d) taxonomic distinctiveness of the exotic predator (i.e. a congeneric species of the exotic predator does not exist within the recipient community), (e) exotic predator taxa, and (f) native prey taxa, which was assessed by comparing the behavioural response of native prey towards native and novel predators. Points indicate the mean effect sizes bracketed by 95% CIs estimated using mixed-effects models. Effect sizes less than zero indicate less antipredator response to a novel predator than to a native predator, and the opposite for effect sizes higher than zero. Effect sizes are considered significant if their 95% CIs do not overlap with zero. Number of observations used to calculate the effect sizes are indicated in parentheses.
We found significant evidence that two taxa of exotic predators (fish and herpetofauna—e.g. amphibians and reptiles) were not recognized by the native prey (g = −0.57 ± 0.23, and −0.53 ± 0.39, p = less than 0.001, and p = 0.007, respectively figure 1e), whereas exotic mammals and crustaceans were recognized similar to native predators (g = −0.25 ± 0.37, and 0.008 ± 0.35, p = 0.193, and 0.962, respectively figure 1e). We found significant evidence supporting that two taxa of native prey, herpetofauna and fish (g = −0.37 ± 0.34 and −0.60 ± 0.36, p = 0.036, and less than 0.001, respectively figure 1f) were prone to be naive towards exotic predators, whereas species from four taxa (insects, molluscs, crustaceans, and mammals) did not exhibit overall prey naiveté (g = −0.42 ± 0.92, −0.32 ± 0.49, −0.41 ± 0.51, and −0.44 ± 0.46, p = 0.370, 0.202, 0.108, and 0.06, respectively figure 1f).
The probability of individuals expressing prey naiveté significantly decreased with the number of prey generations since introduction ((Q-test of the moderator (QM) = 4.332, p = 0.037 figure 2a). Prey species recognized novel predators as threatening as native predators after existing with the novel predators for an average of 215 prey generations, which coincided with the predicted 95% CI of the effect size overlapping with zero (figure 2a). The latitude of the introduction did not influence predator avoidance behaviour of prey to novel predators (QM = 0.287, p = 0.592 figure 2b).
Figure 2. Determinants of prey naiveté. Influence of two continuous predictors: (a) number of prey generations and (b) absolute latitude of the introduction on prey naiveté. Solid line indicates the mean predicted effect sizes bracketed by 95% CIs (dashed lines) estimated using mixed-effects models.
Our meta-analysis supports the generality of some, but not all of our hypotheses concerning invasions, and yields some novel insights (table 1). As postulated, we found that prey naiveté was pronounced in freshwater systems but not in terrestrial systems. In concordance with the aquatic-terrestrial dichotomy hypothesis, terrestrial animals were rarely naive towards exotic predators. This phenomenon in terrestrial systems has been attributed to the homogenizing effects of historical biotic interchanges across land masses, whereas the persistent isolation of freshwater systems might have rendered them less experienced to a broader suite of predatory archetypes . Unexpectedly, marine environments appeared to be the most susceptible to introduced predators, contrary to the expectation that they are similar to terrestrial continents in terms of biotic connectivity . Reports of prey naiveté in marine systems are rare and we gathered information from seven publications that compared antipredator responses with exotic and native marine predators. Four of these studies investigated fish naiveté to the exotic lionfish Pterois volitans in the Caribbean, reporting consistent naive fish behaviour. Two other studies [50,51] investigated the exotic marine green crab C. maenas, which found support for predator recognition by native crabs and gastropods. The other marine study  reported a pronounced degree of prey naiveté towards the exotic seastar Asterias amurensis by native scallops in Australia. Therefore, although exotic lionfish might have skewed our overall findings on marine prey naiveté, results from the exotic A. amurensis support this trend and suggest that exotic predation threats in marine systems might have been underestimated by conventional wisdom as opposed to actual data.
Evidence from this study supports the hypothesis that terrestrial animals on islands are generally naive towards exotic predators, representing the first global quantification of prey naiveté on islands. When isolated from predators, prey on islands can experience a rapid loss of antipredator behaviour through relaxed selection [53–56]. Indeed, some prey species lack predators in the isolated Galapagos Islands, which are often described as being naive to predatory risk . Similar examples exist on less remote islands, such as snake-free Balearic Islands in the Mediterranean, where wall-lizards show a lack of antipredator behaviours such as tail-waving or slow-motion movement when exposed to introduced snakes . Prey naiveté is a primary explanation for the more devastating impacts of introduced predators on oceanic islands compared with continental terrestrial systems . However, only three studies in our database addressed prey naiveté on islands and two of those were coastal islands (the exception was New Zealand). We hypothesize that the degree of prey naiveté on remote oceanic islands likely exceeds that reported in this meta-analysis. Australia was included as a continental mainland in our study owing to its large size we performed an additional test by including Australia in the island category, which did not change our findings (g = −0.16. ± 0.15. p = 0.039 and g = −0.04 ± 0.15 p = 0.853 for island and mainland, respectively), suggesting that our results robustly support the hypothesis that terrestrial species on islands display pronounced levels of prey naiveté.
Prey species adapt to predators by accumulating eco-evolutionary experience  that familiarizes them with a particular species or archetype—a set of predatory species that have similar morphological and/or behavioural adaptations to obtain prey . Recognition of a novel predation threat by a native prey species depends in part on the degree of similarity between the exotic predator and native predators present in the invaded community [14,16]. Hence, differences in predator archetypes between the area of origin and the area of introduction of an exotic species can profoundly influence the degree of prey naiveté. In the present study, we tested two proxies of distinctive predator archetype (allopatric origin and generic distinctiveness of the exotic predator ) and both were related to prey naiveté. The response towards exotic predators was limited when the introduced predator belonged to a novel genus in the invaded community or originated in a different biogeographic realm. Our results support the hypothesis that predator archetypes might be limited to congeneric species, as suggested previously [27,35], but phylogenetic analyses assessing evolutionary distance between predator species would be warranted to test this hypothesis. These results also substantiate a statistical synthesis  showing that high-impact invaders, including predators, are likely to belong to genera not present in the invaded community, which expectedly occurs more frequently if the predator is native to a foreign biogeographic realm.
Native prey appeared more likely to be naive towards reptile and fish predators. Indeed, many species from these groups have been implicated in extirpations and extinctions , although most attention has been given to iconic cases such as the Nile perch Lates niloticus  and the brown tree snake B. irregularis . Native amphibians appeared to be sensitive to the introduction of predatory herpetofauna—mainly freshwater turtles and frogs (92% of the 24 observations)—where the majority of prey species were frogs (83% of 24 observations). On the other hand, exotic predatory fishes were represented broadly (17 freshwater and one marine exotic fish species with 78% and 22% of the 78 total observations, respectively) and their prey belong to four taxonomic groups (insects, fishes, herpetofauna, crustaceans), suggesting that the identification of exotic fish as a predation threat might be generally elusive. We performed an additional analysis to ascertain whether the high probability of herpetofauna and fish to encounter naive prey was due to taxonomic affiliation and not simply driven by ecosystem type (freshwater, terrestrial, marine). We found similar results for these two taxonomic groups, regardless of the ecosystem type (g = −0.39 ± 0.27 p = 0.005 and g = −1.07 ± 0.44 p < 0.001 for freshwater and marine fishes, respectively, and g = −0.39 ± 0.41 p = 0.060 and g = −1.29 ± 1.11 p = 0.022 for freshwater and terrestrial herpetofauna, respectively), supporting our findings of likelihood of prey naiveté towards fish and herpetofauna. A surprising result was that prey recognize exotic carnivorous mammals as a predation threat, despite that their exacerbated impacts have been commonly attributed to prey naiveté . Similarly, a recent meta-analysis investigating prey naiveté towards exotic mammals in Australia found high-risk aversion towards canids: the European red fox Vulpes vulpes and the dingo/dog Canis lupus dingo/familiaris . The majority (62%) of observations in our dataset involving exotic mammalian predators were for canids. When we re-ran our analysis excluding canids, prey were marginally naive to carnivorous mammals (g = −0.42 ± 0.49 p = 0.09). Thus, we speculate that the canid family, which has long been present in most continents (including Australia, where Canis lupus dingo was introduced 4000 years ago ), represents a predator archetype that could be more broadly recognized than many other archetypes, perhaps because of extensive evolutionary exposure associated with human domestication.
Our findings suggest that fish, amphibians, and reptiles are generally more naive to exotic predators than mammals and invertebrates (crustaceans, insects, and molluscs) and thus likely more sensitive to the introduction of predators. Exotic species have been identified as the number one threat associated with the extinctions of herpetofauna worldwide according to the International Union for Conservation of Nature (IUCN) Red List , but the extent to which prey naiveté drove these extinctions remains to be determined in many cases. We did not find significant levels of naiveté for mammalian prey in general, although exotic species are also the most frequent threat recorded for their extinctions . Similar to our findings, a recent meta-analysis indicates that mammals in Australia identify exotic foxes and cats as a predation threat . The authors argue that despite this lack of prey naiveté (level 1 sensu ) the rampant decline of prey by exotic mammals in Australia  might still be driven by inappropriate or ineffective prey responses (levels 2 and 3 naiveté sensu ), which are rarely quantified. Remarkably, although prey naiveté is invoked as responsible for the strong ecological impacts of exotic species on birds [2,6,65,66], we did not find any papers that met our criteria for quantifying prey naiveté in birds, mainly because the few studies addressing prey naiveté in birds lack a comparative treatment with a native predator. Finally, fish do not appear to respond to the risk of predation by novel fish. Collectively, these findings suggest global patterns that could strengthen predictions concerning evolutionary exposure.
The antipredator response of native prey to novel predators can evolve through time, if predation selects for predator recognition and avoidance behaviour . Behaviours that determine the survival of individuals facing a novel predation threat can be subject to strong selection in the persistent presence of a predator . If extinction is averted, evolutionary adaptation can be achieved in a small number of generations. For instance, the fence lizard Sceloporus undulatus acquired the capacity to avoid exotic predatory red fire ants Solenopsis invicta in North America within 40 generations . Our meta-analysis shows that naiveté erodes with the number of prey generations following predator introduction, indicating a generalized pattern of adaptation [30,68]. Averaged across the various taxa in our study, approximately 200 generations are required for native prey to acquire an antipredator response towards exotic predators in the same manner as native predators. This phenomenon could explain, in part, the observed declines in negative ecological impacts of exotic predators over time. For example, the ecological impacts of the brown trout Salmo trutta—an exotic predator intentionally introduced globally—decreased linearly with time since introduction . This reduction in the negative ecological impacts occurs circa one century after the introduction of the brown trout and it was hypothesized to result from either rapid evolutionary adaptation or prompt local extinction of native prey . The capacity of prey to recognize exotic predators is conditional on the native prey averting extinction that often occurs before prey naiveté is assessed [1,2]. Our study might have underestimated the generation time required for predator recognition by omitting prey species that can never adapt or learn how to recognize exotic predators. We are aware of at least one extreme case in our dataset: several fishes exhibit limited antipredator behaviour in the presence of the exotic lionfish P. volitans in the Caribbean [35,70], where strong reductions [71,72] and even local extirpations  of fish populations have been reported.
We also predicted that prey at lower latitudes would be less naive towards novel predators owing to the large suite of predatory species and relative high intensity of predation in the tropics [32,33]. Although our results suggest that novel predator recognition is not influenced by latitude, data from the tropics were limited—perhaps reflecting actual low numbers of successful introduced predators  or historical low sampling effort of non-native species in the tropics .
There are several potential limitations to the data included in this meta-analysis. First, studies were excluded unless they met several criteria, with the disadvantage of not considering the totality of evidence generated globally on prey naiveté. We only considered experimental designs that included empirical comparisons between native and exotic predators, to ensure a direct and consistent way to quantify the perceived risk threat of an exotic predator. Consequently, we excluded studies with controls such as ‘absence of exotic predator’, as those comparisons often require cautious interpretation (e.g. does the behavioural response of prey towards the exotic predator as compared with an empty control indicate predator recognition or simply a response to the presence of an organism, regardless if it is perceived as a predatory threat?). Second, our study only included measurements of level-1 prey naiveté (sensu [16,19]), which interprets a lack of response to an exotic predator (when compared with a native predator) as a lack of recognition of the exotic predator as a threat. However, native animals experience additional levels of naiveté (level-2, -3, and -4), which relate to appropriate, effective, and/or commensurate responses to exotic predators, respectively. Therefore, wildlife might still experience heavy predation by exotic predators despite low level-1 naiveté. By focusing on level-1 naiveté, our study did not consider physiological responses to the presence of predators , which can be considered another important form of prey naiveté. Finally, our dataset did not include a random subset of all exotic predators, which might have biased our results towards the most notorious (and presumably detrimental) of exotic species.
Our meta-analysis identifies some global drivers of prey naiveté, paving the way for testing these drivers in different contexts. Assuming that prey naiveté results in increased mortality , our results point to several animal groups as being disproportionately sensitive to introduced predators. Some of these vulnerable cases were expected, such as insular terrestrial and freshwater fauna, whereas other cases were unpredicted, such as the high susceptibility of native prey to exotic predators in marine systems, or the vulnerability of specific prey taxa, including fishes and amphibians. The relationship between overall prey naiveté and the number of prey generations suggests that long-lived species could be particularly vulnerable to introduced predators. It remains to be determined how other eco-evolutionary factors influence the loss of prey naiveté through time—e.g. how does this rate differ across taxonomic groups and ecosystem types? Additionally, the most damaging groups of exotic predators were found to be animals that originate from a foreign biogeographic realm or that represent a new generic archetype. Particular attention should be given to the introduction of predatory fishes, reptiles, and mammals (perhaps with the exception of canids). This information could guide efforts to prioritize invasion threats to biodiversity and inform risk assessments of conservation schemes involving assisted colonization. Finally, we identified several areas in which the quantification of prey naiveté is notably scant (e.g. marine ecosystems, remote oceanic islands, and many common prey taxa) and these should be prioritized to clarify predictive patterns of prey naiveté.
Our results support the view that prey naiveté is shaped by multiple eco-evolutionary factors [16,19,21,38]. The phenomenon is of increasing relevance to conservation, given that species introductions to novel ecosystems are accelerating globally , along with other forms of global change that might promote ‘disturbed predator-prey interactions’ (sensu ). For example, the poleward migration of species driven by changing isotherms , including the imminent arrival of unique shell-breaking predators in Antarctica , will add novel predator-prey interactions even into historically isolated regions. Therefore, we recommend that factors influencing prey naiveté be given explicit consideration in biodiversity risk assessments.
This study has been supported by the Alberta Ingenuity Fund and the EU-FP7 project Epiwork. MS acknowledges partial travel support from the Universitätsgesellschaft Osnabrück. The authors thank members of the University of Alberta’s Centre for Mathematical Biology and Robert Holt as well as an anonymous reviewer for fruitful discussions and helpful comments.
Appendix S1. Details of the coordinate transformation.
Appendix S2. Basins of attraction in the case of multistability.
Appendix S3. An example of an IGP module transformed into a food chain.
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