Size-dependent fitness trade-offs of foraging in the presence of predators for prey with different growth patterns
Tóm tắt
Prey species make choices about whether to employ costly predator avoidance behaviors throughout their growth and lifecycle. Here, we explore the effects of prey size at a given age (ontogenetic size) and prey growth on optimal behavior using a dynamic optimization model. Under the assumption that prey experience greatest predation risk at intermediate or large sizes, and that growth is fastest at intermediate or large sizes, we find that prey should generally forage when they are small in size and hide when they are larger due to a critical strategy switching size threshold. But this is dependent both on the mortality risks and on the rate of growth. Higher background mortality rates or lower predator-induced detection costs of foraging reduce the size at which prey switches from foraging to hiding. Rapid initial growth leads to decreased overall survival and a wider range of conditions under which the prey hides from the predator. As a test case, the model is parametrized with data and applied to understand differing risk-reducing behaviors between cannibal and non-cannibal Leptinotarsa decemlineata, Colorado potato beetle, larvae. The model predicts that a wide range of parameter values lead to differing behaviors of cannibals and non-cannibals of the same age due to differences in ontogenetic size. We also see that individuals with swifter early growth switch to hiding at larger sizes but will often have earlier strategy switching times. This increases survival of cannibals to the critical pupation size with the largest increases occurring when the baseline death rate is high. Our findings suggest that ecological factors that affect the rate of growth during development, even if final size is not affected, may have an important role in prey responses to predators.
Tài liệu tham khảo
Abrams PA, Leimar O, Nylin S, Wiklund C (1996) The effect of flexible growth rates on optimal sizes and development times in a seasonal environment. Am Nat 147(3):381–395
Barnett C, Bateson M, Rowe C (2007) State-dependent decision making: educated predators strategically trade off the costs and benefits of consuming aposematic prey. Behav Ecol 18(4):645–651
Berger D, Walters R, Gotthard K (2006) What keeps insects small?–size dependent predation on two species of butterfly larvae. Evol Ecol 20(6):575
Bernays E (1997) Feeding by lepidopteran larvae is dangerous. Ecol Entomol 22(1):121–123
Bollens SM, Stearns DE (1992) Predator-induced changes in the diel feeding cycle of a planktonic copepod. J Exp Mar Biol Ecol 156(2):179–186
Brown JS, Kotler BP (2004) Hazardous duty pay and the foraging cost of predation. Ecol Lett 7(10):999–1014
Collie K, Kim SJ, Baker MB (2013) Fitness consequences of sibling egg cannibalism by neonates of the Colorado potato beetle Leptinotarsa decemlineata. Anim Behav 85(2):329–338. https://doi.org/10.1016/j.anbehav.2012.11.013
Davidowitz G, D’Amico LJ, Nijhout HF (2003) Critical weight in the development of insect body size. Evol Dev 5(2):188–197
Elvidge CK, Ramnarine I, Brown GE (2014) Compensatory foraging in trinidadian guppies: effects of acute and chronic predation threats. Current Zoology 60(3):323–332
Ferrari MC, Chivers DP (2009) Sophisticated early life lessons: threat-sensitive generalization of predator recognition by embryonic amphibians. Behav Ecol 20(6):1295–1298. Retrieved from http://doi.org/10.1093/beheco/arp135. https://academic.oup.com/beheco/article-pdf/20/6/1295/17278740/arp135.pdf
Hare JD (1990) Ecology and management of the Colorado potato beetle. Annu Rev Entomol 35(1):81–100. https://doi.org/10.1146/annurev.en.35.010190.000501
Heithaus MR, Frid A, Wirsing AJ, Dill LM, Fourqurean JW, Burkholder D, Thomson J, Bejder L (2007) State-dependent risk-taking by green sea turtles mediates top-down effects of tiger shark intimidation in a marine ecosystem. J Anim Ecol 76(5):837–844. Retrieved from https://besjournals.onlinelibrary.wiley.com/doi/pdf/10.1111/j.1365-2656.2007.01260.x. https://doi.org/10.1111/j.1365-2656.2007.01260.x
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(3):669–676. https://doi.org/10.1007/s00442-014-3069-5
Houston AI (2010) Evolutionary models of metabolism, behaviour and personality. Philos Trans R Soc B 365(1560):3969–3975
Karpestam E, Merilaita S, Forsman A (2014) Body size influences differently the detectabilities of colour morphs of cryptic prey. Biol J Lin Soc 113(1):112–122
Kerkhoff A (2012) Modeling metazoan growthand ontogeny. Metabolic Ecology: A Scaling Approach p 48
Lee L, Atkinson D, Hirst AG, Cornell SJ (2020) A new framework for growth curve fitting based on the von bertalanffy growth function. Sci Rep 10(1):1–12
Lima SL (1998) Stress and decision-making under the risk of predation: recent developments from behavioral, reproductive, and ecological perspectives. Adv Study Behav 27(8):215–290
Lima SL, Dill LM (1990) Behavioral decisions made under the risk of predation: a review and prospectus. Can J Zool 68(4):619–640. Retrieved from http://doi.org/10.1139/z90-092
Losey JE, Denno RF (1998) The escape response of pea aphids to foliar-foraging predators: factors affecting dropping behaviour. Ecol Entomol 23(1):53–61
Ludwig D, Rowe L (1990) Life-history strategies for energy gain and predator avoidance under time constraints. Am Nat 135(5):686–707
Maino JL, Kearney MR (2015) Testing mechanistic models of growth in insects. Proc R Soc B Biol Sci 282(1819):20151973
Mänd T, Tammaru T, Mappes J (2007) Size dependent predation risk in cryptic and conspicuous insects. Evol Ecol 21(4):485
McKay MD (1992) Latin hypercube sampling as a tool in uncertainty analysis of computer models. In: Proceedings of the 24th conference on Winter simulation, pp 557–564
Moschilla JA, Tomkins JL, Simmons LW (2018) State-dependent changes in risk-taking behaviour as a result of age and residual reproductive value. Anim Behav 142:95–100. Retrieved from https://www.sciencedirect.com/science/article/pii/S0003347218301933730. https://doi.org/10.1016/j.anbehav.2018.06.011
Nelson EH, Matthews CE, Rosenheim JA (2004) Predators reduce prey population growth by inducing changes in prey behavior. Ecology 85(7):1853–1858
Nowlin WH, Drenner RW, Guckenberger KR, Lauden MA, Alonso GT, Fennell JE, Smith JL (2006) Gape limitation, prey size refuges and the top-down impacts of piscivorous largemouth bass in shallow pond ecosystems. Hydrobiologia 563(1):357–369
Palmer M, Fieberg J, Swanson A, Kosmala M, Packer C (2017) A ‘dynamic’landscape of fear: prey responses to spatiotemporal variations in predation risk across the lunar cycle. Ecol Lett 20(11):1364–1373
Pettersson LB, Brönmark C (1993) Trading off safety against food: state dependent habitat choice and foraging in crucian carp. Oecologia 95(3):353–357
R Core Computing Team (2017) R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria
Renner-Martin K, Brunner N, Kühleitner M, Nowak WG, Scheicher K (2018) On the exponent in the von bertalanffy growth model. PeerJ 6:e4205
Rowe L, Ludwig D (1991) Size and timing of metamorphosis in complex life cycles: time constraints and variation. Ecology 72(2):413–427
Rudolf VH (2008) The impact of cannibalism in the prey on predator-prey systems. Ecology 89(11):3116–3127
Stoks R, Swillen I, De Block M (2012) Behaviour and physiology shape the growth accelerations associated with predation risk, high temperatures and southern latitudes in ischnura damselfly larvae. J Anim Ecol 81(5):1034–1040
Tammaru T, Esperk T (2007) Growth allometry of immature insects: larvae do not grow exponentially. Funct Ecol 21(6):1099–1105
Thaler JS, Griffin CA (2008) Relative importance of consumptive and non-consumptive effects of predators on prey and plant damage: the influence of herbivore ontogeny. Entomol Exp Appl 128(1):34–40
Tigreros N, Norris RH, Wang EH, Thaler JS (2017) Maternally induced intraclutch cannibalism: an adaptive response to predation risk? Ecol Lett 20(4):487–494. Retrieved from https://onlinelibrary.wiley.com/doi/abs/10.1111/ele.12752. https://doi.org/10.1111/ele.12752
Tigreros N, Wang EH, Thaler JS (2018) Prey nutritional state drives divergent behavioural and physiological responses to predation risk. Funct Ecol 32(4):982–989. Retrieved from https://onlinelibrary.wiley.com/doi/abs/10.1111/1365-2435.13046. https://doi.org/10.1111/1365-2435.13046
Tigreros N, Norris RH, Thaler JS (2019) Maternal effects across life stages: larvae experiencing predation risk increase offspring provisioning. Ecol Entomol 44(6):738–744
Urban MC (2007) The growth-predation risk trade-off under a growing gape-limited predation threat. Ecology 88(10):2587–2597. https://doi.org/10.1890/06-1946.1
Verdolin JL (2006) Meta-analysis of foraging and predation risk trade-offs in terrestrial systems. Behav Ecol Sociobiol 60(4):457–464
von Bertalanffy L (1951) Metabolic types and growth types. Am Nat 85(821):111–117
Von Bertalanffy L (1957) Quantitative laws in metabolism and growth. Q Rev Biol 32(3):217–231
Wagenmakers EJ, Farrell S (2004) Aic model selection using akaike weights. Psychon Bull Review 11(1):192–196
Werner EE, Gilliam JF (1984) The ontogenetic niche and species interactions in size-structured populations. Annu Rev Ecol Syst 15:393–425
West GB, Brown JH, Enquist BJ (2001) A general model for ontogenetic growth. Nature 413(6856):628–631
Winnie J Jr, Creel S (2017) The many effects of carnivores on their prey and their implications for trophic cascades, and ecosystem structure and function. Food Webs 12:88–94