Abstract
The risk of consumption is a pervasive aspect of ecology and recent work has focused on synthesis of consumer–resource interactions (e.g., enemy–victim ecology). Despite this, theories pertaining to the timing and magnitude of defenses in animals and plants have largely developed independently. However, both animals and plants share the common dilemma of uncertainty of attack, can gather information from the environment to predict future attacks and alter their defensive investment accordingly. Here, we present a novel, unifying framework based on the way an organism’s ability to defend itself during an attack can shape their pre-attack investment in defense. This framework provides a useful perspective on the nature of information use and variation in defensive investment across the sequence of attack-related events, both within and among species. It predicts that organisms with greater proportional fitness loss if attacked will gather and respond to risk information earlier in the attack sequence, while those that have lower proportional fitness loss may wait until attack is underway. This framework offers a common platform to compare and discuss consumer effects and provides novel insights into the way risk information can propagate through populations, communities, and ecosystems.
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References
Agrawal AA, Laforsch C, Tollrian R (1999) Transgenerational induction of defenses in animals and plants. Nature 401:60–63
Aoki S, Kurosu U (2004) How many soldiers are optimal for an aphid colony? J Theoret Biol 230:313–317
Bateman AW, Vos M, Anholt BR (2014) When to defend: antipredator defenses and the predation sequence. Am Nat 183:847–855
Bednekoff PA (1997) Mutualism among safe, selfish sentinels: a dynamic game. Am Nat 150:373–390
Bednekoff PA, Lima SL (1998a) Randomness, chaos and confusion in the study of antipredator vigilance. Trends Ecol Evol 13:284–287
Bednekoff PA, Lima SL (1998b) Re-examining safety in numbers: interactions between risk dilution and collective detection depend upon predator targeting behaviour. Proc R Soc Lond B 265:2021–2026
Brown JS, Kotler BP (2004) Hazardous duty pay and the foraging cost of predation. Ecol Lett 10:999–1014
Brown JS, Laundré JW, Gurung M (1999) The ecology of fear: optimal foraging, game theory, and trophic interactions. J Mammal 80:385–399
Caro T (2005) Antipredator defenses in birds and mammals. University of Chicago Press, Chicago
Chase JM (2003) Experimental evidence for alternative stable equilibria in a benthic pond food web. Ecol Lett 6:733–741
Chittka L, Skorupski P, Raine NE (2009) Speed-accuracy tradeoffs in animal decision making. Trends Ecol Evol 24:400–407
Clinchy M, Sheriff MJ, Zanette L (2013) Predator-induced stress and the ecology of fear. Funct Ecol 27:56–65
Coley PD, Bryant JP, Chapin FS III (1985) Resource availability and plant antiherbivore defense. Science 230:895–899
Danchin E, Giraldeau LA, Valone TJ, Wagner RH (2004) Public information: from nosy neighbors to cultural evolution. Science 305:487–491
Donelan SC, Hellmann JK, Bell AM, Luttbeg B, Orrock JL, Sheriff MJ, Sih A (2020) Transgenerational plasticity in human-altered environments. Trends Ecol Evo 35:115–124
Falk KL, Kästner J, Bodenhausen N, Schramm K, Paetx C, Vassão DG, Reichelt M, von Knorre D, Bergelson J, Erb M, Gershenzon J, Meldau S (2014) The role of glucosinolates and the jasmonic acid pathway in resistance of Arabidopsis thaliana against molluskan herbivores. Mol Ecol 23:1188–1203
Fornoni J (2011) Ecological and evolutionary implications of plant tolerance to herbivory. Funct Ecol 25:399–407
Gil MA, Hein AM, Spiegel O, Baskett ML, Sih A (2018) Social information-mediated behavioral correlations drive population and community dynamics. Trends Ecol Evol 33:535–548
Guiden PW, Bartel SL, Byer NW, Shipley AA, Orrock JL (2019) Predator-prey interactions in the anthropocene: reconciling multiple aspects of novelty. Trends Ecol Evol 34:616–627
Hairston NG, Smith FE, Slobodkin LB (1960) Community structure, population control, and competition. Am Nat 94:421–425
Hamilton WD (1971) Geometry for the selfish herd. J Theor Biol 31:295–311
Harvell CD (1990) The ecology and evolution of inducible defenses. Q Rev Biol 65:323–340
Heil M (2014) Herbivore-induced plant volatiles: targets, perception, and unanswered questions. New Phytol 204:297–306
Heil M, Karban R (2010) Explaining evolution of plant communication by airborne signals. Trends Ecol Evol 25:137–144
Helms AM, DeMoraes CM, Tröger A, Alborn HT, Francke W, Tooker JF, Mescher MC (2017) Identification of an insect-produced olfactory cue that primes plant defenses. Nat Comm 8:337
Helms AM, Ray S, Matulis NL, Kuzemchak MC, Grisales W, Tooker JF, Ali JG (2019) Chemical cues linked to risk: cues from below-ground natural enemies enhance plant defences and influence herbivore behavior and performance. Funct Ecol. https://doi.org/10.1111/1365-2435.13297
Hermann SL, Thaler JS (2014) Prey perception of predation risk: volatile chemical cues mediate non-consumptive effects of a predator on a herbivorous insect. Oecologia 176:669–676
Hilker M, Meiner T (2006) Early herbivore alert: insect eggs induce plant defense. J Chem Ecol 32:1379–1397
Holeski L, Jander G, Agrawal AA (2012) Transgenerational defense induction and epigenetic inheritance in plants. Trends Ecol Evol 27:618–626
Hunter MD (2016) The phytochemical landscape; linking trophic interactions and nutrient dynamics. Princeton University Press, USA, p 376
Huntzinger M, Karban R, Young TP, Palmer TM (2004) Relaxation of induced indirect defenses of acacuas following exclusion of mammalian herbivores. Ecology 85:609–614
Jensen EL, Dill LM, Cahill JF Jr (2011) Applying behavioural-ecological theory to plant defense: light-dependent movement in Mimosa pudica suggests a trade-off between predation risk and energetic reward. Am Nat 177:37–381
Karban R, Baldwin IT (1997) Induced responses to herbivory. University of Chicago Press, Chicago
Karban R, Agrawal AA, Thaler JS, Adler LS (1999) Induced plant responses and information content about risk of herbivory. Trends Ecol Evol 14:443–447
Karban R, Orrock JL, Preisser EL, Sih A (2016) A comparison of plants and animals in their responses to risk of consumption. Curr Opin Plant Biol 32:1–8
Kim J, Quaghebeur H, Felton GW (2011) Reiterative and interruptive signaling in induced plant resistance to chewing insects. Phytochemistry 72:1624–1634
Lafferty KD, DeLeo G, Briggs CJ, Dobson AP, Gross T, Kuris AM (2015) A general consumer–resource population model. Science 349:854–857
Lafferty KD, Kuris AM (2002) Trophic strategies, animal diversity and body size. Trends Ecol Evol 17:507–513
Laundré JW, Hernández L, Altendorf KB (2001) Wolves, elk, and bison: reestablishing the “landscape of fear” in Yellowstone National Park, USA. Can J Zool 79:1401–1409
Lima SL, Dill LM (1990) Behavioral decisions made under the risk of predation: a review and prospectus. Can J Zool 68:619–640
Lima SL, Bednekoff PA (1999) Temporal variation in danger drives antipredator behavior: the predation risk allocation hypothesis. Am Nat 153:649–659
Luttbeg B, Sih A (2010) Risk, resources and state-dependent adaptive behavioural syndromes. Philos Trans R Soc Lond B Biol Sci 365:3977–3990
Matassa CM, Donelan SC, Luttbeg B, Trussel GC (2016) Resource levels and prey state influence antipredator behavior and the strength of nonconsumptive predator effects. Oikos 125:1478–1488
Markovic D, Colzi I, Taiti C, Scalone R, Ali JG, Mancuso S, Ninkovic V (2019) Airborne signals synchronize the defenses of neighboring plants in response to touch. J Exp Bot 70:691–700
McKey D (1974) Adaptive patterns in alkaloid physiology. Am Nat 108:305–320
McNamara JM, Houston AI (1986) The common currency for behavioral decisions. Am Nat 127:358–378
McNamara JM, Houston AI (1992) Risk-sensitive foraging: a review of the theory. Bull Math Biol 54:355–378
Middleton AD, Kauffman MJ, McWhirter DE, Jimenez MD, Cook RC, Cook JG, Albeke SE, Sawyer H, White PJ (2013) Linking anti-predator behaviour to prey demography reveals limited risk effects of an actively hunting large carnivore. Ecol Lett 16:1023–1030
Minorsky PV (2019) The functions of foliar nyctinasty: a review and hypothesis. Biol Rev 94:216–229
Niu Y, Sun H, Stevens M (2018) Plant camouflage: ecology, evolution, and implications. Trends Ecol Evol 33:608–618
Ohgushi T (2005) Indirect interaction webs: herbivore-induced effects through trait change in plants. Ann Rev Ecol Evol Syst 36:81–105
Orrock JL (2013) Exposure of unwounded plants to chemical cues associated with herbivores leads to exposure-dependent changes in subsequent herbivore attack. PLoS ONE 8:e79900
Orrock JL, Fletcher RJ Jr (2014) An island-wide predator manipulation reveals immediate and long-lasting matching of risk by prey. Proc R Soc B 281:20140391
Orrock JL, Sih A, Ferrari MCO, Karban R, Preisser EL, Sheriff MJ, Thaler JS (2015) Error management in plant allocation to herbivore defense. Trends Ecol Evol 30:441–445
Orrock JL, Connolly BM, Choi WG, Guiden PW, Swanson SJ, Gilroy S (2018) Plants eavesdrop on cues produced by snails and induce costly defenses that affect insect herbivores. Oecologia 186:703–710
Parsons MH, Apfulback R, Banks PB, Cameron EZ, Dickman CR, Frank ASK, Jones ME, McGregor IA, McLean S, Müller-Schwarze D, Sparrow EE, Blumstein DT (2018) Biologically meaningful scents: a framework for understanding predator–prey research across disciplines. Biol Rev 93:98–114
Preisser EL, Orrock JL (2012) The allometry of fear: interspecific relationships between body size and response to predation risk. Ecosphere 3:77
Peiffer M, Tooker JF, Luthe DS, Felton GW (2009) Plants on early alert: glandular trichomes as sensors for insect herbivores. New Phytol 184:644–656
Prugh LR, Golden CD (2014) Does moonlight increase predation risk? Meta-analysis reveals divergent responses of nocturnal mammals to lunar cycles. J Anim Ecol 83:504–514
Raffel TR, Martin LB, Rohr JR (2008) Parasites as predators: unifying natural enemy ecology. Trends Ecol Evol 23:610–618
Reznick DA, Endler JA (1982) The impact of predation on life history evolution in Trinidadian guppies (Poecilia reticulata). Evolution 36:160–177
Reznick DA, Bryga H, Endler JA (1990) Experimentally induced life-history evolution in a natural population. Nature 346:357–359
Rossiter MC (1996) Incidence and consequences of inherited environmental effects. Ann Rev Ecol System 27:451–476
Schmitz OJ, Miller JRB, Trainor AM, Abrahms B (2017) Toward a community ecology of landscapes: predicting multiple predator–prey interactions across geographic space. Ecology 98:2281–2292
Schultz JC, Appel HM, Ferrieri AP, Arnold TM (2013) Flexible resource allocation during plant defense responses. Front Plant Sci 4:1–11
Sheriff MJ, Krebs CJ, Boonstra R (2010) The ghosts of predators past: population cycles and the role of maternal programming under fluctuating predation risk. Ecology 91:2983–2994
Sheriff MJ, Bell A, Boonstra R, Dantzer B, Lavergne SG, McGhee KE, MacLeod KJ, Winandy L, Zimmer C, Love OP (2017) Integrating ecological and evolutionary context in the study of maternal stress. Int Comp Biol 57:437–449
Sheriff MJ, Peacor S, Hawlena D, Thaker M (2020) Non-consumptive predator effects on prey population size: a dearth of evidence. J Anim Ecol 89:1302–1316. https://doi.org/10.1111/1365-2656.13213
Sih A (1992) Prey uncertainty and the balancing of antipredator behavior and feeding needs. Am Nat 139:1052–1069
Sih A (2005) Predator-prey space use as an emergent outcome of a behavioral response race. Ecology of predator–prey interactions. Oxford University Press, USA, pp 240–255
Smith JA, Donadio E, Pauli JN, Sheriff MJ, Middleton AD (2019) Integrating temporal refugia into landscapes of fear: prey exploit predator downtimes to forage in risky places. Oecologia. https://doi.org/10.1007/s00442-019-04381-5
Song YY, Ye M, Li C, He X, Zhu-Salzman K, Wang RL, Su YJ, Luo SM, Zeng RS (2014) Hijacking common mycorrhizal networks for herbivore-induced defence signal transfer between tomato plants. Sci Rep. https://doi.org/10.1038/srep03915
Stamp N (2003) Out of the quagmire of plant defense hypotheses. Q Rev Biol 78:23–55
Stankowich T, Blumstein DT (2005) Fear in animals: a meta-analysis and review of risk assessment. Proc R Soc B 272:2627–2634
Stowe KA, Marquis RJ, Hochwender CG, Simms EL (2000) The evolutionary ecology of tolerance to consumer damage. Annu Rev Ecol Evol Syst 31:565–595
Strauss SY, Agrawal AA (1999) The ecology and evolution of plant tolerance to herbivory. Trends Ecol Evol 14:179–185
Strong DR (1992) Are trophic cascades all wet? Differentiation and donor-control in speciose ecosystems. Ecology 73:747–754
Tambling CJ, Minnie L, Meyer J, Freeman EW, Santymire RM, Adendorff J, Kerley GI (2015) Temporal shifts in activity of prey following large predator reintroductions. Behav Ecol Sociobiol 69:1153–1161
Tigreros N, Norris R, Wang E, Thaler JS (2017) Maternally induced intraclutch cannibalism: an adaptive response to predation risk? Ecol Lett 20:487–494
Valeix M, Loveridge AJ, Chamaillé-Jammes S, Davidson Z, Murindagomo F, Fritz H, Macdonald DW (2009) Behavioral adjustments of African herbivores to predation risk by lions: spatiotemporal variations influence habitat use. Ecology 90:23–30
Valone TJ, Templeton JJ (2002) Public information for the assessment of quality: a widespread social phenomenon. Phil Trans R Soc Lond B 357:1549–1557
Weissburg MJ, Smee DL, Ferner MC (2014) The sensory ecology of nonconsumptive predator effects. Am Nat 184:141–157
Werner JR, Krebs CJ, Donker SA, Sheriff MJ (2015) Forest or meadow: the consequences of habitat for the condition of female arctic ground squirrels (Urocitellus parryii plesius). Can J Zool 93:791–797
Wishingrad V, Ferrari MCO, Chivers DP (2014) Behavioural and morphological defences in a fish with a complex antipredator phenotype. Anim Behav 95:137–143
Ydenberg RC, Dill LM (1986) The economics of fleeing from predators. Adv Study Behav 16:229–249
Zangerl AR, Rutledge CE (1996) The probability of attack and patterns of constitutive and induced defense: a test of optimal defense theory. Am Nat 147:599–608
Acknowledgements
We would like to thank Ken Schmidt for insightful comments on draft versions. JO gratefully acknowledges support by the Wisconsin Alumni Research Foundation and the Vilas Mid-Career Fellowship for a portion of this work. MJS was supported by a Fellowship from the Institute of Advanced Studies, Hebrew University for a portion of this work. NSF IOS 1456724 to AS. USDA NIFA 2018-67013-28068to JT. RK was supported by a fellowship from the Center for Ecological Research, Kyoto University.
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All authors conceived of the concepts and ideas. MJS led the writing with significant contributions from all others.
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Communicated by Mathew Samuel Crowther.
Box. 1: Proportional fitness loss of an individual and its timing of defense
Box. 1: Proportional fitness loss of an individual and its timing of defense
We suggest the proportional fitness loss (PFL) if an individual does not initiate defense until attacked is a critical component of understanding its defensive investment. PFL relies on an individual’s fitness potential if it initiates defense prior to an attack compared to its fitness potential if it initiates defense during an attack. For example, individual A is 50% likely to survive if it defends during the detection stage and only 20% likely to survive if it defends during an attack, its PFL is 60% ((50−20)/50). Individual B has a 95% chance of surviving if it defends during the detection stage and a 30% chance of survival, if it defends during an attack, its PFL is 68%. From this scenario, it becomes clear that the PFL of an individual depends on both its ability to survive an attack and, also, the effectiveness of its early defense. In such a scenario, individual B has a higher PFL and should initiate defense earlier than individual A, even though it has a higher probability of surviving an attack. Our concept helps to clarify why individuals with low expected fitness, regardless of whether they initiate defense early or late (thus a low PFL), would be expected to wait and initiate defense late (if at all) given the ineffectiveness of their (early) defense.
The fact that consumer-resource encounters progress through time along a common interaction sequence of events (Lima and Dill 1990; Karban and Baldwin 1997, Caro 2005; Fig. 1) allows us to build relative PFL curves across the interaction sequence to better understand the timing of defensive investment. As individuals delay their defensive investment, their fitness potential will approach that which they would have if they did not invest in defense until attacked. This also allows us to visualize inflection points where fitness potential will greatly decrease if defense is not initiated. In the first two examples above, the fitness potential difference between early and late defensive investment is relatively large and, if their PFL curve was relatively linear, both individuals may greatly increase their survival for incremental advances in the timing of their defense. Alternatively, if, for example, we extend the above scenario such that individual A had a 95% chance of survival if it defended during the encounter stage (thus a PFL of 79% between encounter and attack, but a 47% PFL between encounter and detection), while individual B had a 99.9% chance of survival if it defended during the encounter stage (thus a PFL of 70%, but only 4% PFL between encounter and detection), our curve would predict that individual A would most benefit from defending during the encounter stage, while individual B may benefit from delaying defensive investment until the detection stage.
Additionally, if individuals invest too early or respond to unreliable information they will pay a cost of unnecessary defense (e.g., cost of defense itself, missed opportunity costs, reductions in growth and reproduction). The willingness of individuals to pay a cost of unnecessary defense will also depend on their PFL. Individuals with a high PFL can pay a relatively high cost of unnecessary defense and still benefit significantly from early defensive investment. Alternatively, individuals with a low PFL if attacked may not be willing to pay as high a cost of unnecessary defense and should defend relatively later.
While we discuss the fitness aspect of PFL as a loss of survival, individual fitness could also be measured as a loss of reproduction (number of babies born or weaned, loss of litters, loss of seed set or flowers, etc.) or a loss of growth or tissue (in many species growth is directly related to reproductive potential and in the case of plants or other organisms that can be partially consumed a loss of tissue may be a better metric) if attacked. It is important to appreciate that in these latter two fitness measures, with respect to PFL, the loss of fitness is due to attack not the initiation of defense (as is often the case). Because of this, however, these latter two fitness measures may be particularly insightful given (i) they can be used to estimate PFL if attacked, but also the cost of unnecessary defense (defending too early) and (ii) that they can be used to estimate the loss of relative fitness at any point along the interaction sequence when defense is initiated. Understanding an individual’s PFL across the interaction sequence will provide valuable insights into when it should initiate defense and has significant implications for understanding how prey and plants will respond to the risk of consumption.
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Sheriff, M.J., Orrock, J.L., Ferrari, M.C.O. et al. Proportional fitness loss and the timing of defensive investment: a cohesive framework across animals and plants. Oecologia 193, 273–283 (2020). https://doi.org/10.1007/s00442-020-04681-1
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DOI: https://doi.org/10.1007/s00442-020-04681-1