Non-independent interactions among predators can have important consequences for the structure and dynamics of ecological communities by enhancing or reducing prey mortality rate through, e.g., predator facilitation or interference. The multiplicative risk model, traditionally used to detect these emergent multiple predator effects (MPEs), is biased because it assumes linear functional response (FR) and no prey depletion. To rectify these biases, two approaches based on FR modelling have recently been proposed: the direct FR approach and the population-dynamic approach. Here we compare the strengths, limitations and predictions of the three approaches using simulated data sets. We found that the predictions of the direct FR and the multiplicative risk models are very similar and underestimate predation rates when prey density is high or prey depletion is substantial. As a consequence, these two approaches often fail in detecting risk reduction. Finally, parameters estimated with the direct FR approach lack mechanistic interpretation, which limits the understanding of the mechanisms driving multiple predator interactions and potential extension of this approach to more complex food webs. We thus strongly recommend using the population-dynamic approach because it is robust, precise, and provides a scalable mechanistic framework to detect and quantify MPEs.

Czech Academy of Sciences Biology Centre Institute of Entomology Branišovská 31 37005 České Budějovice Czech Republic

Ecological Networks and Global Change Group Experimental and Theoretical Ecology Station UMR5321 CNRS University Paul Sabatier Moulis France

Unité Mixte de Recherche 5174 Evolution et Diversité Biologique Centre National de la Recherche Scientifique Université de Toulouse 3 Institut de Recherche pour le Développement 31062 Toulouse France

University of South Bohemia Faculty of Science Department of Ecosystem Biology Branišovská 31a 37005 České Budějovice Czech Republic

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McCann KS, Hastings A, Huxel GR. Weak trophic interactions and the balance of nature. Nature. 1998;395:794–798. doi: 10.1038/27427. DOI

Berlow EL. Strong effects of weak interactions in ecological communities. Nature. 1999;398:330–334. doi: 10.1038/18672. DOI

Tang S, Pawar S, Allesina S. Correlation between interaction strengths drives stability in large ecological networks. Ecol. Lett. 2014;17:1094–1100. doi: 10.1111/ele.12312. PubMed DOI

Vázquez DP, Ramos-Jiliberto R, Urbani P, Valdovinos FS. A conceptual framework for studying the strength of plant–animal mutualistic interactions. Ecol. Lett. 2015;18:385–400. doi: 10.1111/ele.12411. PubMed DOI

Bascompte J, Jordano P, Olesen JM. Asymmetric coevolutionary networks facilitate biodiversity maintenance. Science. 2006;312:431–433. doi: 10.1126/science.1123412. PubMed DOI

Schmitz OJ. Predator diversity and trophic interactions. Ecology. 2007;88:2415–2426. doi: 10.1890/06-0937.1. PubMed DOI

Werner EE, Peacor SD. A review of trait-mediated indirect interactions in ecological communities. Ecology. 2003;84:1083–1100. doi: 10.1890/0012-9658(2003)084[1083:AROTII]2.0.CO;2. DOI

Bolker B, Holyoak M, Křivan V, Rowe L, Schmitz O. Connecting theoretical and empirical studies of trait-mediated interactions. Ecology. 2003;84:1101–1114. doi: 10.1890/0012-9658(2003)084[1101:CTAESO]2.0.CO;2. DOI

Sih A, Englund G, Wooster D. Emergent impacts of multiple predators on prey. Trends Ecol. Evol. 1998;13:350–355. doi: 10.1016/S0169-5347(98)01437-2. PubMed DOI

Sentis A, Gémard C, Jaugeon B, Boukal DS. Predator diversity and environmental change modify the strengths of trophic and non-trophic interactions. Global Change Biol. 2017;23:2629–2640. doi: 10.1111/gcb.13560. PubMed DOI

Vance-Chalcraft HD, Rosenheim JA, Vonesh JR, Osenberg CW, Sih A. The influence of intraguild predation on prey suppression and prey release: a meta-analysis. Ecology. 2007;88:2689–2696. doi: 10.1890/06-1869.1. PubMed DOI

Okuyama, T. & Bolker, B. M. In

McCoy MW, Stier AC, Osenberg CW. Emergent effects of multiple predators on prey survival: the importance of depletion and the functional response. Ecol. Lett. 2012;15:1449–1456. doi: 10.1111/ele.12005. PubMed DOI

Denny M, Benedetti-Cecchi L. Scaling up in ecology: mechanistic approaches. Annu. Rev. Ecol., Evol. Syst. 2012;43:1–22. doi: 10.1146/annurev-ecolsys-102710-145103. DOI

Snyder WE, Snyder GB, Finke DL, Straub CS. Predator biodiversity strengthens herbivore suppression. Ecol. Lett. 2006;9:789–796. doi: 10.1111/j.1461-0248.2006.00922.x. PubMed DOI

Losey JE, Denno RF. Positive predator-predator interactions: enhanced predation rates and synergistic suppression of aphid populations. Ecology. 1998;79:2143–2152.

Sentis A, Hemptinne JL, Brodeur J. Towards a mechanistic understanding of temperature and enrichment effects on species interaction strength, omnivory and food-web structure. Ecol. Lett. 2014;17:785–793. doi: 10.1111/ele.12281. PubMed DOI

Soluk DA. Multiple predator effects: predicting combined functional response of stream fish and invertebrate predators. Ecology. 1993;74:219–225. doi: 10.2307/1939516. DOI

Griffen B. Detecting emergent effects of multiple predator species. Oecologia. 2006;148:702–709. doi: 10.1007/s00442-006-0414-3. PubMed DOI

Wasserman RJ, et al. Using functional responses to quantify interaction effects among predators. Funct. Ecol. 2016;30:1988–1998. doi: 10.1111/1365-2435.12682. DOI

Vance-Chalcraft HD, Soluk DA. Multiple predator effects result in risk reduction for prey across multiple prey densities. Oecologia. 2005;144:472–480. doi: 10.1007/s00442-005-0077-5. PubMed DOI

Vonesh JR, Osenberg CW. Multi‐predator effects across life‐history stages: non‐additivity of egg‐and larval‐stage predation in an African treefrog. Ecol. Lett. 2003;6:503–508. doi: 10.1046/j.1461-0248.2003.00470.x. DOI

Collins, M. D., Ward, S. A. & Dixon, A. F. G. Handling time and the functional response of

Sentis A, Hemptinne JL, Brodeur J. How functional response and productivity modulate intraguild predation. Ecosphere. 2013;4:1–14. doi: 10.1890/ES12-00379.1. DOI

Juliano, S. A. In

Jeschke JM, Kopp M, Tollrian R. Consumer‐food systems: why Type I functional responses are exclusive to filter feeders. Biological Reviews. 2004;79:337–349. doi: 10.1017/S1464793103006286. PubMed DOI

Rall BC, Guill C, Brose U. Food web connectance and predator interference dampen the paradox of enrichment. Oikos. 2008;117:202–213. doi: 10.1111/j.2007.0030-1299.15491.x. DOI

R Development Core Team.

Li, Y., Rall, B. C. & Kalinkat, G. Experimental duration and predator satiation levels systematically affect functional response parameters.

Koen-Alonso, M. In

Bolker, B. M.

Rogers D. Random search and insect population models. J. Anim. Ecol. 1972;41:369–383. doi: 10.2307/3474. DOI

Pritchard DW, Paterson RA, Bovy HC, Barrios-O’Neill D. frair: an R package for fitting and comparing consumer functional responses. Methods in Ecology and Evolution. 2017;8:1528–1534. doi: 10.1111/2041-210X.12784. DOI

Soetaert K, Petzoldt T. Inverse modelling, sensitivity and Monte Carlo analysis in R using package FME. Journal of Statistical Software. 2010;33:1–28.

Englund G, Ohlund G, Hein CL, Diehl S. Temperature dependence of the functional response. Ecol. Lett. 2011;14:914–921. doi: 10.1111/j.1461-0248.2011.01661.x. PubMed DOI

Sentis A, Hemptinne JL, Brodeur J. Using functional response modeling to investigate the effect of temperature on predator feeding rate and energetic efficiency. Oecologia. 2012;169:1117–1125. doi: 10.1007/s00442-012-2255-6. PubMed DOI

Soetaert K, Petzoldt T, Setzer RW. Solving differential equations in R: package deSolve. Journal of Statistical Software. 2010;33:1–25.

The presence of multiple parasitoids decreases host survival under warming, but parasitoid performance also decreases