Proportional fitness loss and the timing of defensive investment: a cohesive framework across animals and plants

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.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2

References

  1. Agrawal AA, Laforsch C, Tollrian R (1999) Transgenerational induction of defenses in animals and plants. Nature 401:60–63

    CAS  Article  Google Scholar 

  2. Aoki S, Kurosu U (2004) How many soldiers are optimal for an aphid colony? J Theoret Biol 230:313–317

    Google Scholar 

  3. Bateman AW, Vos M, Anholt BR (2014) When to defend: antipredator defenses and the predation sequence. Am Nat 183:847–855

    Google Scholar 

  4. Bednekoff PA (1997) Mutualism among safe, selfish sentinels: a dynamic game. Am Nat 150:373–390

    CAS  Google Scholar 

  5. Bednekoff PA, Lima SL (1998a) Randomness, chaos and confusion in the study of antipredator vigilance. Trends Ecol Evol 13:284–287

    CAS  Google Scholar 

  6. 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

    Google Scholar 

  7. Brown JS, Kotler BP (2004) Hazardous duty pay and the foraging cost of predation. Ecol Lett 10:999–1014

    Google Scholar 

  8. Brown JS, Laundré JW, Gurung M (1999) The ecology of fear: optimal foraging, game theory, and trophic interactions. J Mammal 80:385–399

    Google Scholar 

  9. Caro T (2005) Antipredator defenses in birds and mammals. University of Chicago Press, Chicago

    Google Scholar 

  10. Chase JM (2003) Experimental evidence for alternative stable equilibria in a benthic pond food web. Ecol Lett 6:733–741

    Google Scholar 

  11. Chittka L, Skorupski P, Raine NE (2009) Speed-accuracy tradeoffs in animal decision making. Trends Ecol Evol 24:400–407

    Google Scholar 

  12. Clinchy M, Sheriff MJ, Zanette L (2013) Predator-induced stress and the ecology of fear. Funct Ecol 27:56–65

    Google Scholar 

  13. Coley PD, Bryant JP, Chapin FS III (1985) Resource availability and plant antiherbivore defense. Science 230:895–899

    CAS  Google Scholar 

  14. Danchin E, Giraldeau LA, Valone TJ, Wagner RH (2004) Public information: from nosy neighbors to cultural evolution. Science 305:487–491

    CAS  Google Scholar 

  15. 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

    Google Scholar 

  16. 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

    CAS  PubMed  PubMed Central  Google Scholar 

  17. Fornoni J (2011) Ecological and evolutionary implications of plant tolerance to herbivory. Funct Ecol 25:399–407

    Google Scholar 

  18. 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

    Google Scholar 

  19. 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

    Google Scholar 

  20. Hairston NG, Smith FE, Slobodkin LB (1960) Community structure, population control, and competition. Am Nat 94:421–425

    Google Scholar 

  21. Hamilton WD (1971) Geometry for the selfish herd. J Theor Biol 31:295–311

    CAS  Google Scholar 

  22. Harvell CD (1990) The ecology and evolution of inducible defenses. Q Rev Biol 65:323–340

    CAS  Google Scholar 

  23. Heil M (2014) Herbivore-induced plant volatiles: targets, perception, and unanswered questions. New Phytol 204:297–306

    CAS  Google Scholar 

  24. Heil M, Karban R (2010) Explaining evolution of plant communication by airborne signals. Trends Ecol Evol 25:137–144

    Google Scholar 

  25. 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

    Google Scholar 

  26. 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

    Article  Google Scholar 

  27. 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

    Google Scholar 

  28. Hilker M, Meiner T (2006) Early herbivore alert: insect eggs induce plant defense. J Chem Ecol 32:1379–1397

    CAS  Google Scholar 

  29. Holeski L, Jander G, Agrawal AA (2012) Transgenerational defense induction and epigenetic inheritance in plants. Trends Ecol Evol 27:618–626

    Google Scholar 

  30. Hunter MD (2016) The phytochemical landscape; linking trophic interactions and nutrient dynamics. Princeton University Press, USA, p 376

    Google Scholar 

  31. 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

    Google Scholar 

  32. 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

    Google Scholar 

  33. Karban R, Baldwin IT (1997) Induced responses to herbivory. University of Chicago Press, Chicago

    Google Scholar 

  34. 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

    CAS  Google Scholar 

  35. 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

    Google Scholar 

  36. Kim J, Quaghebeur H, Felton GW (2011) Reiterative and interruptive signaling in induced plant resistance to chewing insects. Phytochemistry 72:1624–1634

    CAS  Google Scholar 

  37. Lafferty KD, DeLeo G, Briggs CJ, Dobson AP, Gross T, Kuris AM (2015) A general consumer–resource population model. Science 349:854–857

    CAS  Google Scholar 

  38. Lafferty KD, Kuris AM (2002) Trophic strategies, animal diversity and body size. Trends Ecol Evol 17:507–513

    Google Scholar 

  39. 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

    Google Scholar 

  40. Lima SL, Dill LM (1990) Behavioral decisions made under the risk of predation: a review and prospectus. Can J Zool 68:619–640

    Google Scholar 

  41. Lima SL, Bednekoff PA (1999) Temporal variation in danger drives antipredator behavior: the predation risk allocation hypothesis. Am Nat 153:649–659

    Google Scholar 

  42. Luttbeg B, Sih A (2010) Risk, resources and state-dependent adaptive behavioural syndromes. Philos Trans R Soc Lond B Biol Sci 365:3977–3990

    PubMed  PubMed Central  Google Scholar 

  43. 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

    Google Scholar 

  44. 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

    CAS  Google Scholar 

  45. McKey D (1974) Adaptive patterns in alkaloid physiology. Am Nat 108:305–320

    Google Scholar 

  46. McNamara JM, Houston AI (1986) The common currency for behavioral decisions. Am Nat 127:358–378

    Google Scholar 

  47. McNamara JM, Houston AI (1992) Risk-sensitive foraging: a review of the theory. Bull Math Biol 54:355–378

    Google Scholar 

  48. 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

    Google Scholar 

  49. Minorsky PV (2019) The functions of foliar nyctinasty: a review and hypothesis. Biol Rev 94:216–229

    Google Scholar 

  50. Niu Y, Sun H, Stevens M (2018) Plant camouflage: ecology, evolution, and implications. Trends Ecol Evol 33:608–618

    Google Scholar 

  51. Ohgushi T (2005) Indirect interaction webs: herbivore-induced effects through trait change in plants. Ann Rev Ecol Evol Syst 36:81–105

    Google Scholar 

  52. 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

    PubMed  PubMed Central  Google Scholar 

  53. 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

    Google Scholar 

  54. 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

    Google Scholar 

  55. 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

    Google Scholar 

  56. 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

    Google Scholar 

  57. Preisser EL, Orrock JL (2012) The allometry of fear: interspecific relationships between body size and response to predation risk. Ecosphere 3:77

    Google Scholar 

  58. 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

    CAS  Google Scholar 

  59. 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

    Google Scholar 

  60. Raffel TR, Martin LB, Rohr JR (2008) Parasites as predators: unifying natural enemy ecology. Trends Ecol Evol 23:610–618

    Google Scholar 

  61. Reznick DA, Endler JA (1982) The impact of predation on life history evolution in Trinidadian guppies (Poecilia reticulata). Evolution 36:160–177

    Google Scholar 

  62. Reznick DA, Bryga H, Endler JA (1990) Experimentally induced life-history evolution in a natural population. Nature 346:357–359

    Google Scholar 

  63. Rossiter MC (1996) Incidence and consequences of inherited environmental effects. Ann Rev Ecol System 27:451–476

    Google Scholar 

  64. 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

    Google Scholar 

  65. Schultz JC, Appel HM, Ferrieri AP, Arnold TM (2013) Flexible resource allocation during plant defense responses. Front Plant Sci 4:1–11

    Google Scholar 

  66. 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

    Google Scholar 

  67. 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

    Google Scholar 

  68. 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

    Article  Google Scholar 

  69. Sih A (1992) Prey uncertainty and the balancing of antipredator behavior and feeding needs. Am Nat 139:1052–1069

    Google Scholar 

  70. 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

    Google Scholar 

  71. 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

    Article  Google Scholar 

  72. 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

    Article  PubMed  PubMed Central  Google Scholar 

  73. Stamp N (2003) Out of the quagmire of plant defense hypotheses. Q Rev Biol 78:23–55

    Google Scholar 

  74. Stankowich T, Blumstein DT (2005) Fear in animals: a meta-analysis and review of risk assessment. Proc R Soc B 272:2627–2634

    Google Scholar 

  75. 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

    Google Scholar 

  76. Strauss SY, Agrawal AA (1999) The ecology and evolution of plant tolerance to herbivory. Trends Ecol Evol 14:179–185

    CAS  Google Scholar 

  77. Strong DR (1992) Are trophic cascades all wet? Differentiation and donor-control in speciose ecosystems. Ecology 73:747–754

    Google Scholar 

  78. 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

    Google Scholar 

  79. Tigreros N, Norris R, Wang E, Thaler JS (2017) Maternally induced intraclutch cannibalism: an adaptive response to predation risk? Ecol Lett 20:487–494

    Google Scholar 

  80. 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

    CAS  Google Scholar 

  81. 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

    Google Scholar 

  82. Weissburg MJ, Smee DL, Ferner MC (2014) The sensory ecology of nonconsumptive predator effects. Am Nat 184:141–157

    Google Scholar 

  83. 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

    Google Scholar 

  84. Wishingrad V, Ferrari MCO, Chivers DP (2014) Behavioural and morphological defences in a fish with a complex antipredator phenotype. Anim Behav 95:137–143

    Google Scholar 

  85. Ydenberg RC, Dill LM (1986) The economics of fleeing from predators. Adv Study Behav 16:229–249

    Google Scholar 

  86. 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

    Google Scholar 

Download references

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.

Author information

Affiliations

Authors

Contributions

All authors conceived of the concepts and ideas. MJS led the writing with significant contributions from all others.

Corresponding author

Correspondence to Michael J. Sheriff.

Additional information

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.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

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

Download citation

Keywords

  • Predation risk
  • Herbivory
  • Induced defense
  • Anti-predator response
  • Information
  • Non-consumptive effects
  • Trait-mediated effects
  • Vulnerability