Abstract
The strength of trophic and non-trophic interactions in predator–prey dynamics may be modified by predator density/diversity and prey size characteristics. In aquatic environments, multi-dimensional scaling of search areas (water depth and surface area) may mediate interactions among predators, with implications for prey risk. Here, we used a comparative functional response (FR) approach to investigate the effects of search area, predator composition and prey size on the strength of trophic and non-trophic interactions in freshwater habitats. A model system comprising two predatory notonectids (Anisops breddini and Anisops sardeus) was examined consuming four larval instar prey of Culex quinquefasciatus mosquitoes at nine different arena sizes, consisting of three crossed levels of surface area and water depth. Type II FRs were most common among predator groups, with 2nd instar prey consumed the most overall. Here, A. sardeus exhibited significantly higher feeding rates as compared to A. breddini, particularly in shallow waters. Non-trophic interactions in conspecific A. breddini and heterospecific pairs were mostly negative, indicating reduced prey risk. Further, predator–predator antagonisms were most pronounced in the heterospecific pairs. Strength of trophic and non-trophic interactions is thus dependent on complex interplays between the characteristics of predator–prey participants in combination with environmental heterogeneities.
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References
Adrian, R., C. M. O’Reilly, H. Zagarese, S. B. Baines, D. O. Hessen, W. Keller, et al., 2009. Lakes as sentinels of climate change. Limnology and Oceanography 54: 2283–2297. https://doi.org/10.4319/lo.2009.54.6_part_2.2283.
Amundrud, S. L. & D. S. Srivastava, 2019. Disentangling how climate change can affect an aquatic food web by combining multiple experimental approaches. Global Change Biology 25: 3528–3538. https://doi.org/10.1111/gcb.14717.
Amundrud, S. L., S. A. Clay-Smith, B. L. Flynn, K. E. Higgins, M. S. Reich, D. R. Wiens & D. S. Srivastava, 2019. Drought alters the trophic role of an opportunistic generalist in an aquatic ecosystem. Oecologia 189: 733–744. https://doi.org/10.1007/s00442-019-04343-x.
Barrios-O’Neill, D., J. T. Dick, M. C. Emmerson, A. Ricciardi, H. J. MacIsaac, M. E. Alexander & H. C. Bovy, 2014. Fortune favours the bold: a higher predator reduces the impact of a native but not an invasive intermediate predator. Journal of Animal Ecology 83: 693–701. https://doi.org/10.1111/1365-2656.12155.
Batzer, D. P. & S. A. Wissinger, 1996. Ecology of insect communities in nontidal wetlands. Annual Review of Entomology 41: 75–100. https://doi.org/10.1146/annurev.en.41.010196.000451.
Berlow, E. L., S. A. Navarrete, C. J. Briggs, M. E. Power & B. A. Menge, 1999. Quantifying variation in the strengths of species interactions. Ecology 80: 2206–2224. https://doi.org/10.1890/0012-9658(1999)080[2206:QVITSO]2.0.CO;2.
Blaustein, L., 1998. Influence of the predatory backswimmer, Notonecta maculata, on invertebrate community structure. Ecological Entomology 23: 246–252. https://doi.org/10.1046/j.1365-2311.1998.00138.x.
Bolker, B. M., 2010. bbmle: Tools for General Maximum Likelihood Estimation. R Package [Online]. Available: www.cran.rproject.org [accessed 5 March 2019].
Bollache, L., J. T. A. Dick, K. D. Farnsworth & W. I. Montgomery, 2008. Comparison of the functional responses of invasive and native amphipods. Biology Letter 4: 166–169. https://doi.org/10.1098/rsbl.2007.0554.
Brose, U., 2010. Body-mass constraints on foraging behaviour determine population and food-web dynamics. Functional Ecology 24: 28–34. https://doi.org/10.1111/j.1365-2435.2009.01618.x.
Brose, U., T. Jonsson, E. L. Berlow, P. Warren, C. Banasek-Richter, L. F. Bersier, et al., 2006. Consumer–resource body-size relationships in natural food webs. Ecology 87: 2411–2417. https://doi.org/10.1890/0012-9658(2006)87[2411:CBRINF]2.0.CO;2.
Buxton, M., R. N. Cuthbert, T. Dalu, C. Nyamukondiwa & R. J. Wasserman, 2020. Predator density modifies mosquito regulation in increasingly complex environments. Pest Management Science 76: 2079–2086. https://doi.org/10.1002/ps.5746.
Cuthbert, R. N., T. Dalu, R. J. Wasserman, A. Callaghan, O. L. F. Weyl & J. T. A. Dick, 2019. Using functional responses to quantify notonectid predatory impacts across increasingly complex environments. Acta Oecologica 95: 116–119. https://doi.org/10.1016/j.actao.2018.11.004.
Cuthbert, R. N., A. Callaghan, A. Sentis, A. Dalal & J. T. Dick, 2020a. Additive multiple predator effects can reduce mosquito populations. Ecological Entomology 45: 243–250. https://doi.org/10.1111/een.12791.
Cuthbert, R. N., R. J. Wasserman, T. Dalu, H. Kaiser, O. L. F. Weyl, J. T. A. Dick, A. Sentis, M. W. McCoy & M. E. Alexander, 2020b. Influence of intra- and interspecific variations in predator–prey body size ratio on trophic interaction strengths. Ecology and Evolution 10: 5946–5962. https://doi.org/10.1002/ece3.6332.
Cuthbert, R. N., T. Dalu, R. J. Wasserman, A. Sentis, O. L. Weyl, P. W. Froneman, et al., 2021. Prey and predator density-dependent interactions under different water volumes. Ecology and Evolution 11: 6504–6512. https://doi.org/10.1002/ece3.7503.
Dalal, A. & S. Gupta, 2016. A comparative study of the aquatic insect diversity of two ponds located in Cachar District, Assam, India. Turkish Journal of Zoology 40: 392–401. https://doi.org/10.3906/zoo-1505-18.
Dalal, A. & S. Gupta, 2018. Aquatic insects as pollution indicator – a study in Cachar, Assam, Northeast India. In Singh, V., S. Yadav & R. Yadava (eds), Environmental Pollution, in Water Science and Technology Library 77. Springer, Singapore: 103–124.
Dalal, A., R. N. Cuthbert, J. T. Dick & S. Gupta, 2019. Water depth-dependent notonectid predatory impacts across larval mosquito ontogeny. Pest Management Science 75: 2610–2617. https://doi.org/10.1002/ps.5368.
Dalal, A., R. N. Cuthbert, J. T. Dick & S. Gupta, 2020a. Prey preferences of notonectids towards larval mosquitoes across prey ontogeny and search area. Pest Management Science 76: 609–616. https://doi.org/10.1002/ps.5556.
Dalal, A., R. N. Cuthbert, J. T. Dick, A. Sentis, C. Laverty, D. Barrios-O’Neill, N. O. Perea, A. Callaghan & S. Gupta, 2020b. Prey size and predator density modify impacts by natural enemies towards mosquitoes. Ecological Entomology 45: 423–433. https://doi.org/10.1111/een.12807.
Denny, M. & L. Benedetti-Cecchi, 2012. Scaling up in ecology: mechanistic approaches. Annual Review of Ecology, Evolution, and Systematics 43: 1–22. https://doi.org/10.1146/annurev-ecolsys-102710-145103.
Dick, J. T., M. E. Alexander, J. M. Jeschke, A. Ricciardi, H. J. MacIsaac, T. B. Robinson, et al., 2014. Advancing impact prediction and hypothesis testing in invasion ecology using a comparative functional response approach. Biological Invasions 16: 735–753. https://doi.org/10.1007/s10530-013-0550-8.
Duffy, J. E., B. J. Cardinale, K. E. France, P. B. McIntyre, E. Thébault & M. Loreau, 2007. The functional role of biodiversity in ecosystems: incorporating trophic complexity. Ecology Letters 10: 522–538. https://doi.org/10.1111/j.1461-0248.2007.01037.x.
Emmerson, M. C. & D. Raffaelli, 2004. Predator–prey body size, interaction strength and the stability of a real food web. Journal of Animal Ecology 73: 399–409. https://doi.org/10.1111/j.0021-8790.2004.00818.x.
Eubanks, M. D. & R. F. Denno, 2001. Health food versus fast food: the effects of prey quality and mobility on prey selection by a generalist predator and indirect interactions among prey species. Ecological Entomology 25: 140–146. https://doi.org/10.1046/j.1365-2311.2000.00243.x.
Fischer, S., D. Pereyra & L. Fernández, 2012. Predation ability and non-consumptive effects of Notonecta sellata (Heteroptera: Notonectidae) on immature stages of Culex pipiens (Diptera: Culicidae). Journal of Vector Ecology 37: 245–251. https://doi.org/10.1111/j.1948-7134.2012.00223.x.
Fox, J., & S. Weisberg, 2018. An R Companion to Applied Regression, 3rd edn. Sage, London.
Griffen, B. D. & T. Williamson, 2008. Influence of predator density on nonindependent effects of multiple predator species. Oecologia 155: 151–159. https://doi.org/10.1007/s00442-007-0889-6.
Griffiths, D., 1980. Foraging costs and relative prey size. The American Naturalist 116: 743–752. https://doi.org/10.1086/283666.
Gunderson, A. R., E. E. King, K. Boyer, B. Tsukimura & J. H. Stillman, 2017. Species as stressors: heterospecific interactions and the cellular stress response under global change. Integrative and Comparative Biology 57: 90–102. https://doi.org/10.1093/icb/icx019.
Hammill, E., P. Kratina, M. Vos, O. L. Petchey & B. R. Anholt, 2015. Food web persistence is enhanced by non-trophic interactions. Oecologia 178: 549–556. https://doi.org/10.1007/s00442-015-3244-3.
Holling, C. S., 1959. Some characteristics of simple types of predation and parasitism. The Canadian Entomologist 91: 385–398. https://doi.org/10.4039/Ent91385-7.
Hughes, A. R. & J. H. Grabowski, 2006. Habitat context influences predator interference interactions and the strength of resource partitioning. Oecologia 149: 256–264. https://doi.org/10.1007/s00442-006-0439-7.
Jacob, B. G., J. Shililu, E. J. Muturi, J. M. Mwangangi, S. M. Muriu, J. Funes, et al., 2006. Spatially targeting Culex quinquefasciatus aquatic habitats on modified land cover for implementing an Integrated Vector Management (IVM) program in three villages within the Mwea Rice Scheme, Kenya. International Journal of Health Geographics 5: 18. https://doi.org/10.1186/1476-072X-5-18.
Jeschke, J. M., M. Kopp & R. Tollrian, 2002. Predator functional responses: discriminating between handling and digesting prey. Ecological Monographs 72: 95–112. https://doi.org/10.1890/0012-9615(2002)072[0095:PFRDBH]2.0.CO;2.
Jeschke, J. M., M. Kopp & R. Tollrian, 2004. Consumer-food systems: why type I functional responses are exclusive to filter feeders. Biological Reviews 79: 337–349. https://doi.org/10.1017/S1464793103006286.
Juliano, S. A., 2001. Nonlinear curve fitting: predation and functional response curves. In Scheiner, S. M. & J. Gurevitch (eds), Design and Analysis of Ecological Experiments Oxford University Press, Oxford: 178–196.
Kéfi, S., E. L. Berlow, E. A. Wieters, S. A. Navarrete, O. L. Petchey, S. A. Wood, et al., 2012. More than a meal… integrating non-feeding interactions into food webs. Ecology Letters 15: 291–300. https://doi.org/10.1111/j.1461-0248.2011.01732.x.
Kolar, V., D. S. Boukal & A. Sentis, 2019. Predation risk and habitat complexity modify intermediate predator feeding rates and energetic efficiencies in a tri-trophic system. Freshwater Biology 64: 1480–1491. https://doi.org/10.1111/fwb.13320.
Kreuzinger-Janik, B., H. Brüchner-Hüttemann & W. Traunspurger, 2019. Effect of prey size and structural complexity on the functional response in a nematode-nematode system. Scientific Reports 9: 5696. https://doi.org/10.1038/s41598-019-42213-x.
Laverty, C., J. T. A. Dick, M. E. Alexander & F. E. Lucy, 2015. Differential ecological impacts of invader and native predatory freshwater amphipods under environmental change are revealed by comparative functional responses. Biological Invasions 17: 1761–1770. https://doi.org/10.1007/s10530-014-0832-9.
Laverty, C., K. D. Green, J. T. Dick, D. Barrios-O’Neill, P. J. Mensink, V. Médoc, et al., 2017. Assessing the ecological impacts of invasive species based on their functional responses and abundances. Biological Invasions 19: 1653–1665. https://doi.org/10.1007/s10530-017-1378-4.
Lenth, R., 2018. emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.3.0 [Online]. Available: https://CRAN.Rproject.org/package=emmeans [accessed 5 March 2019].
Losey, J. E. & R. F. Denno, 1998. Positive predator–predator interactions: enhanced predation rates and synergistic suppression of aphid populations. Ecology 79: 2143–2152. https://doi.org/10.1890/0012-9658(1998)079[2143:PPPIEP]2.0.CO;2.
Matthews, P. G. & R. S. Seymour, 2008. Haemoglobin as a buoyancy regulator and oxygen supply in the backswimmer (Notonectidae, Anisops). Journal of Experimental Biology 211: 3790–3799. https://doi.org/10.1242/jeb.018721.
McCoy, M. W., A. C. Stier & C. W. Osenberg, 2012. Emergent effects of multiple predators on prey survival: the importance of depletion and the functional response. Ecology Letters 15: 1449–1456. https://doi.org/10.1111/ele.12005.
McHugh, P. A., R. M. Thompson, H. S. Greig, H. J. Warburton & A. R. McIntosh, 2015. Habitat size influences food web structure in drying streams. Ecography 38: 700–712. https://doi.org/10.1111/ecog.01193.
Miyagi, I., T. Toma & M. Mogi, 1992. Biological control of container-breeding mosquitoes, Aedes albopictus and Culex quinquefasciatus, in a Japanese Island by release of Toxorhynchites splendens adults. Medical and Veterinary Entomology 6: 290–300. https://doi.org/10.1111/j.1365-2915.1992.tb00620.x.
Mondol, R. P., G. Chandra, S. Bandyopadhyay & A. Ghosh, 2017. Effect of temperature and search area on the functional response of Anisops sardea (Hemiptera: Notonectidae) against Anopheles stephensi in laboratory bioassay. Acta Tropica 166: 262–267. https://doi.org/10.1016/j.actatropica.2016.11.034.
Murdoch, W. W. & A. Oaten, 1975. Predation and population stability. Advances in Ecological Research 9: 1–131. https://doi.org/10.1016/S0065-2504(08)60288-3.
Okiwelu, S. N. & M. A. E. Noutcha, 2012. Breeding sites of Culex quinquefasciatus (Say) during the rainy season in rural lowland rainforest, Rivers State, Nigeria. Public Health Research 2: 64–68. https://doi.org/10.5923/j.phr.20120204.01.
Okuyama, T. & B. M. Bolker, 2012. Model-based, response-surface approaches to quantifying indirect interactions. In Ohgushi, T., O. Schmitz & R. D. Holt (eds), Trait-Mediated Indirect Interactions: Ecological and Evolutionary Perspectives Cambridge University Press, New York: 186–204.
Paine, R. T., 1980. Food webs: linkage, interaction strength and community infrastructure. Journal of Animal Ecology 49: 667–685.
Palmer, M. A., H. L. Menninger & E. Bernhardt, 2010. River restoration, habitat heterogeneity and biodiversity: a failure of theory or practice? Freshwater Biology 55: 205–222. https://doi.org/10.1111/j.1365-2427.2009.02372.x.
Pastorok, R. A., 1981. Prey vulnerability and size selection by Chaoborus larvae. Ecology 62: 1311–1324. https://doi.org/10.2307/1937295.
Pawar, S., A. I. Dell & V. M. Savage, 2012. Dimensionality of consumer search space drives trophic interaction strengths. Nature 486: 485. https://doi.org/10.1038/nature11131.
Peacor, S. D. & E. E. Werner, 2001. The contribution of trait-mediated indirect effects to the net effects of a predator. Proceedings of the National Academy of Sciences of the United States of America 98: 3904–3908. https://doi.org/10.1073/pnas.071061998.
Peckarsky, B. L., C. A. Cowan, M. A. Penton & C. Anderson, 1993. Sublethal consequences of stream-dwelling predatory stoneflies on mayfly growth and fecundity. Ecology 74: 1836–1846. https://doi.org/10.2307/1939941.
Polis, G. A. & R. D. Holt, 1992. Intraguild predation: the dynamics of complex trophic interactions. Trends in Ecology & Evolution 7: 151–154. https://doi.org/10.1016/0169-5347(92)90208-S.
Preisser, E. L., D. I. Bolnick & M. F. Benard, 2005. Scared to death? The effects of intimidation and consumption in predator–prey interactions. Ecology 86: 501–509. https://doi.org/10.1890/04-0719.
Pritchard, D. W., R. A. Paterson, H. C. Bovy & D. Barrios-O’Neill, 2017. Frair: an R package for fitting and comparing consumer functional responses. Methods in Ecology and Evolution 8: 1528–1534. https://doi.org/10.1111/2041-210X.12784.
R Core Team, 2020. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna. Available: https://www.R-project.org/.
Rall, B. C., U. Brose, M. Hartvig, G. Kalinkat, F. Schwarzmüller, O. Vucic-Pestic & O. L. Petchey, 2012. Universal temperature and body-mass scaling of feeding rates. Philosophical Transactions of the Royal Society B: Biological Sciences 367: 2923–2934. https://doi.org/10.1098/rstb.2012.0242.
Real, L. A., 1977. The kinetics of functional response. The American Naturalist 111: 289–300. https://doi.org/10.1086/283161.
Rogers, D., 1972. Random search and insect population models. The Journal of Animal Ecology 41: 369–383. https://doi.org/10.2307/3474.
Saha, N., G. Aditya, A. Bal & G. K. Saha, 2007. Comparative studies on functional response of common Hemipteran bugs of East Calcutta Wetlands, Kolkata, India. International Review of Hydrobiology 92: 242–257. https://doi.org/10.1002/iroh.200610939.
Schausberger, P. & A. Walzer, 2001. Combined versus single species release of predaceous mites: predator–predator interactions and pest suppression. Biological Control 20: 269–278. https://doi.org/10.1006/bcon.2000.0908.
Schmitz, O. J., 2009. Effects of predator functional diversity on grassland ecosystem function. Ecology 90: 2339–2345. https://doi.org/10.1890/08-1919.1.
Sentis, A. & D. S. Boukal, 2018. On the use of functional responses to quantify emergent multiple predator effects. Scientific Reports 8: 11787. https://doi.org/10.1038/s41598-018-30244-9.
Sentis, A., J. L. Hemptinne & J. Brodeur, 2012. Using functional response modeling to investigate the effect of temperature on predator feeding rate and energetic efficiency. Oecologia 169: 1117–1125. https://doi.org/10.1007/s00442-012-2255-6.
Sentis, A., J. L. Hemptinne, & J. Brodeur. (2014). Towards a mechanistic understanding of temperature and enrichment effects on species interaction strength, omnivory and food-web structure. Ecol Lett 17:785–793. https://doi.org/10.1111/ele.12281
Sentis, A., C. Gémard, B. Jaugeon & D. S. Boukal, 2017. Predator diversity and environmental change modify the strengths of trophic and nontrophic interactions. Global Change Biology 23: 2629–2640. https://doi.org/10.1111/gcb.13560.
Sih, A., G. Englund & D. Wooster, 1998. Emergent impacts of multiple predators on prey. Trends in Ecology & Evolution 13: 350–355. https://doi.org/10.1016/S0169-5347(98)01437-2.
Soetaert, K. & T. Petzoldt, 2010. Inverse modelling, sensitivity and Monte Carlo analysis in R using package FME. Journal of Statistical Software 33: 1–28. https://doi.org/10.18637/jss.v033.i03.
Suchman, C. & B. Sullivan, 1998. Vulnerability of the copepod Acartia tonsa to predation by the scyphomedusa Chrysaora quinquecirrha: effect of prey size and behavior. Marine Biology 132: 237–245. https://doi.org/10.1007/s002270050389.
Townroe, S., & A. Callaghan, 2014. British container breeding mosquitoes: the impact of urbanisation and climate change on community composition and phenology. PLoS ONE 9: e95325. https://doi.org/10.1371/journal.pone.0095325
Uiterwaal, S. F. & J. P. Delong, 2018. Multiple factors, including arena size, shape the functional responses of ladybird beetles. Journal of Applied Ecology 55: 2429–2438. https://doi.org/10.1111/1365-2664.13159.
Uiterwaal, S. F., C. Mares & J. P. DeLong, 2017. Body size, body size ratio, and prey type influence the functional response of damselfly nymphs. Oecologia 185: 339–346. https://doi.org/10.1007/s00442-017-3963-8.
Uiterwaal, S. F., A. I. Dell & J. P. DeLong, 2018. Arena size modulates functional responses via behavioral mechanisms. Behavioral Ecology 30: 483–489. https://doi.org/10.1093/beheco/ary188.
Urban, M. C., 2004. Disturbance heterogeneity determines freshwater metacommunity structure. Ecology 85: 2971–2978. https://doi.org/10.1890/03-0631.
Vance-Chalcraft, H. D. & D. A. Soluk, 2005. Multiple predator effects result in risk reduction for prey across multiple prey densities. Oecologia 144: 472–480. https://doi.org/10.1007/s00442-005-0077-5.
Vázquez, D. P., R. Ramos-Jiliberto, P. Urbani & F. S. Valdovinos, 2015. A conceptual framework for studying the strength of plant–animal mutualistic interactions. Ecology Letters 18: 385–400. https://doi.org/10.1111/ele.12411.
Veselý, L., D. S. Boukal, M. Buřič, P. Kozák, A. Kouba & A. Sentis, 2017. Effects of prey density, temperature and predator diversity on nonconsumptive predator–driven mortality in a freshwater food web. Scientific Reports 7: 18075. https://doi.org/10.1038/s41598-017-17998-4.
Veselý, L., D. S. Boukal, M. Buřič, I. Kuklina, M. Fořt, B. Yazicioglu, et al., 2019. Temperature and prey density jointly influence trophic and non‐trophic interactions in multiple predator communities. Freshwater Biology 64: 1984–193.
Vucic-Pestic, O., B. C. Rall, G. Kalinkat & U. Brose, 2010. Allometric functional response model: body masses constrain interaction strengths. Journal of Animal Ecology 79: 249–256. https://doi.org/10.1111/j.1365-2656.2009.01622.x.
Wasserman, R. J., M. E. Alexander, T. Dalu, B. R. Ellender, H. Kaiser & O. L. Weyl, 2016a. Using functional responses to quantify interaction effects among predators. Functional Ecology 30: 1988–1998. https://doi.org/10.1111/1365-2435.12682.
Wasserman, R. J., M. E. Alexander, O. L. Weyl, D. Barrios‐O'Neill, P. W. Froneman & T. Dalu, 2016b. Emergent effects of structural complexity and temperature on predator–prey interactions. Ecosphere 7: e01239. https://doi.org/10.1002/ecs2.1239.
Wasserman, R. J., M. E. Alexander, D. Barrios-O’Neill, O. L. Weyl & T. Dalu, 2016c. Using functional responses to assess predator hatching phenology implications for pioneering prey in arid temporary pools. Journal of Plankton Research 38: 154–158. https://doi.org/10.1093/plankt/fbv114.
Williams, R. J. & N. D. Martinez, 2004. Stabilization of chaotic and non-permanent food-web dynamics. The European Physical Journal B 38: 297–303. https://doi.org/10.1140/epjb/e2004-00122-1.
Wilson, E. O., 1975. Sociobiology, Belknap Press of Harvard University Press, Cambridge:
Woodward, G. & P. Warren, 2007. Body size and predatory interactions in freshwaters: scaling from individuals to communities. In Hildrew, A. G., D. G. Raffaelli & R. Edmonds-Brown (eds), Body Size: The Structure and Function of Aquatic Ecosystems Cambridge University Press, New York: 98–117.
Yamaguchi, A. & O. Kishida, 2016. Antagonistic indirect interactions between large and small conspecific prey via a heterospecific predator. Oikos 125: 271–277. https://doi.org/10.1111/oik.02443.
Yodzis, P. & S. Innes, 1992. Body size and consumer-resource dynamics. The American Naturalist 139: 1151–1175. https://doi.org/10.1086/285380.
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We are grateful to the Department of Ecology and Environmental Science, Assam University, Silchar for providing laboratory facilities.
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This work is funded by the Department of Science and Technology, India through an INSPIRE Fellowship (INSPIRE Fellowship Code: IF130185) awarded to AD. RC. is funded by an Early Career Fellowship from the Leverhulme Trust (Grant No. ECF-2021-001).
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Dalal, A., Sentis, A., Cuthbert, R.N. et al. Multiple predator effects are modified by search area and prey size. Hydrobiologia 850, 1817–1835 (2023). https://doi.org/10.1007/s10750-023-05183-w
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DOI: https://doi.org/10.1007/s10750-023-05183-w