When less is more: positive population-level effects of mortality
Authors: Arne Schröder, Anieke van Leeuwen, Tom C. Cameron
Journal: Trends in Ecology & Evolution (2014)

•We review the evidence for higher population density under mortality.•The positive population-level effects of mortality are life-stage specific and may not be observed in total density.•Only theory predicting the life stage specific positive mortality effects accounts for fundamental aspects of individuals.•Mortality-induced density increases that are specific to life-history stage are common in nature. Experimental and theoretical studies show that mortality imposed on a population can counter-intuitively increase the density of a specific life-history stage or total population density. Understanding positive population-level effects of mortality is advancing, illuminating implications for population, community, and applied ecology. Reconciling theory and data, we found that the mathematical models used to study mortality effects vary in the effects predicted and mechanisms proposed. Experiments predominantly demonstrate stage-specific density increases in response to mortality. We argue that the empirical evidence supports theory based on stage-structured population models but not on unstructured models. We conclude that stage-specific positive mortality effects are likely to be common in nature and that accounting for within-population individual variation is essential for developing ecological theory. Experimental and theoretical studies show that mortality imposed on a population can counter-intuitively increase the density of a specific life-history stage or total population density. Understanding positive population-level effects of mortality is advancing, illuminating implications for population, community, and applied ecology. Reconciling theory and data, we found that the mathematical models used to study mortality effects vary in the effects predicted and mechanisms proposed. Experiments predominantly demonstrate stage-specific density increases in response to mortality. We argue that the empirical evidence supports theory based on stage-structured population models but not on unstructured models. We conclude that stage-specific positive mortality effects are likely to be common in nature and that accounting for within-population individual variation is essential for developing ecological theory. Natural populations experience externally imposed mortality arising from predation, parasitism, and disease, or through exploitation, pest control, and eradication programmes. The intuitive expectation is that increasing mortality should result in decreasing population densities, a principle on which population and ecosystem management is generally based. Nevertheless, positive effects of mortality at the population level are increasingly found in many different species. This is true for experiments in the laboratory (e.g., [1Cameron T.C. Benton T.G. Stage-structured harvesting and its effects: an empirical investigation using soil mites.J. Anim. 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The mechanisms behind positive population-level effects of mortality (see Glossary) and the circumstances under which these can be observed have recently been studied in several mathematical models (e.g., [5de Roos A.M. et al.Food-dependent growth leads to overcompensation in stage-specific biomass when mortality increases: The influence of maturation versus reproduction regulation.Am. Nat. 2007; 170: E59-E76Crossref PubMed Scopus (111) Google Scholar, 6Abrams P.A. When does greater mortality increase population size? The long history and diverse mechanisms underlying the hydra effect.Ecol. Lett. 2009; 12: 462-474Crossref PubMed Scopus (189) Google Scholar, 7Sieber M. Hilker F.M. The hydra effect in predator–prey models.J. Math. Biol. 2012; 64: 341-360Crossref PubMed Scopus (43) Google Scholar]). From these studies it has become clear that positive mortality effects have not only wide ranging implications for communities [6Abrams P.A. When does greater mortality increase population size? The long history and diverse mechanisms underlying the hydra effect.Ecol. Lett. 2009; 12: 462-474Crossref PubMed Scopus (189) Google Scholar, 8Miller T.E.X. Rudolf V.H.W. Thinking inside the box: community-level effects of stage-structure.Trend Ecol. Evol. 2011; 26: 457-466Abstract Full Text Full Text PDF PubMed Scopus (151) Google Scholar, 9de Roos A.M. Persson L. Population and Community Ecology of Ontogenetic Development. Princeton University Press, 2013Google Scholar] and ecosystems [10Jansson M. et al.Terrestrial carbon and intraspecific size-variation shape lake ecosystems.Trends Ecol. Evol. 2007; 22: 316-322Abstract Full Text Full Text PDF PubMed Scopus (240) Google Scholar] but also for the sustainable exploitation of natural resources, such as fish stocks [11van Leeuwen A. et al.How cod shapes its world.J. Sea Res. 2008; 60: 89-104Crossref Scopus (74) Google Scholar], and for the successful implementation of pest control and eradication programmes [12Zipkin E.F. et al.When can efforts to control nuisance and invasive species backfire?.Ecol. Appl. 2009; 19: 1585-1595Crossref PubMed Scopus (112) Google Scholar, 13Pardini E.A. et al.Complex population dynamics and control of the invasive biennial, Alliaria petiolata (garlic mustard).Ecol. Appl. 2009; 19: 387-397Crossref PubMed Scopus (75) Google Scholar]. Hence, the occurrence of positive mortality effects at the population level (simply denoted positive mortality effects hereafter) might indeed be a common phenomenon in natural populations, and understanding these effects is imperative for basic and applied ecology. To facilitate this understanding we reconcile findings from theoretical approaches with evidence from empirical studies. We first review the different mathematical models predicting positive mortality effects and the proposed mechanisms for these effects. Second, we compile results from published mortality experiments and show what types of positive mortality effects occur in natural populations. Finally, theoretical predictions are compared to empirical patterns, leading to an evaluation of the level of support for different hypotheses and mechanisms invoked to explain the occurrence of positive mortality effects. Positive mortality effects were first proposed for a discrete-time single-population model that does not explicitly account for resource dynamics [14Ricker W.E. Stock and recruitment.J. Fish. Res. Board Can. 1954; 11: 559-623Crossref Google Scholar]. Single-population models can demonstrate positive mortality effects given one or several a priori assumed processes, such as overcompensating density dependence, mortality preceding density dependence, scramble competition, or the relative timing of mortality and population census [6Abrams P.A. When does greater mortality increase population size? The long history and diverse mechanisms underlying the hydra effect.Ecol. Lett. 2009; 12: 462-474Crossref PubMed Scopus (189) Google Scholar, 15Jonzén N. Lundberg P. Temporally structured density-dependence and population management.Ann. Zool. Fenn. 1999; 36: 39-44Google Scholar, 16Seno H. A paradox in discrete single species population dynamics with harvesting/thinning.Math. Biosci. 2008; 214: 63-69Crossref PubMed Scopus (44) Google Scholar, 17Hilker F.M. Liz E. Harvesting, census timing and "hidden" hydra effects.Ecol. Complex. 2013; 14: 95-107Crossref Scopus (27) Google Scholar, 18Liz E. Pilarczyk P. Global dynamics in a stage-structured discrete-time population model with harvesting.J. Theoret. Biol. 2012; 297: 148-165Crossref PubMed Scopus (43) Google Scholar]. In single-population models, however, population growth, resource use, and density dependence are defined in a phenomenological way, meaning that biological processes are modelled as predetermined functions of consumer density. The objective of this paper is to review the processes and mechanisms resulting in positive mortality effects in theoretical studies and to summarise the empirical support for the different mechanisms by comparing the predicted positive mortality effects to patterns found in empirical settings. In light of this objective, the explicit link between resource availability and consumer population dynamics is essential, because an increase in consumer population density must be based on the transformation of energy from the resource to the consumer. In order to draw such an explicit link, two-trophic consumer-resource models explicitly account for dynamics of the resource density alongside the dynamics of the consumer population. Density dependence in consumer population growth then arises without a priori assuming a population-level functional form for that process. The consumer population response to mortality emerges from changes in the resource availability for individual consumers, which in turn is determined by the feedback from consumption itself. We will therefore mainly focus on consumer-resource models and only briefly discuss single-species models. One prominent class of mathematical models predicting positive mortality effects consists of unstructured models of continuous-time consumer-resource systems that are based on the well-known Lotka–Volterra equations [19Rosenzweig M.L. McArthur R.H. Graphical representation and stability conditions of predator-prey interactions.Am. Nat. 1963; 97: 209-223Crossref Google Scholar, 20Rosenzweig M.L. Paradox of enrichment destabilization of exploitation ecosystems in ecological time.Science. 1971; 171: 385-387Crossref PubMed Scopus (1363) Google Scholar, 21Turchin P. Complex Population Dynamics: A Theoretical/Empirical Synthesis. Princeton University Press, 2003Google Scholar]. These models are unstructured with respect to any within-population variation among individuals; for example, in body size, life-history stage, and ecology. The processes contributing to changes in population density in these unstructured models are therefore identical for all individuals in the population, reflecting the assumption that individuals are represented by the same average body size, life-history stage, and are ecologically equivalent. Unstructured consumer-resource models predict higher total consumer density with increasing consumer mortality [6Abrams P.A. When does greater mortality increase population size? The long history and diverse mechanisms underlying the hydra effect.Ecol. Lett. 2009; 12: 462-474Crossref PubMed Scopus (189) Google Scholar, 7Sieber M. Hilker F.M. The hydra effect in predator–prey models.J. Math. Biol. 2012; 64: 341-360Crossref PubMed Scopus (43) Google Scholar, 22Abrams P.A. Will declining population sizes warn us of impending extinctions?.Am. Nat. 2002; 140: 293-305Crossref Scopus (66) Google Scholar]. This phenomenon has been called the 'hydra effect' [23Abrams P.A. Matsuda H. The effect of adaptive change in the prey on the dynamics of an exploited predator population.Can. J. Fish. Aquat. Sci. 2005; 62: 758-766Crossref Scopus (72) Google Scholar] and it occurs when consumer-resource models exhibit cyclic dynamics. These consumer-resource cycles are caused by the combination of logistic resource growth and saturating consumption rate of individual consumers [19Rosenzweig M.L. McArthur R.H. Graphical representation and stability conditions of predator-prey interactions.Am. Nat. 1963; 97: 209-223Crossref Google Scholar, 20Rosenzweig M.L. Paradox of enrichment destabilization of exploitation ecosystems in ecological time.Science. 1971; 171: 385-387Crossref PubMed Scopus (1363) Google Scholar] (Box 1). Under these conditions, hydra effects can appear as an increase in the time-averaged density of consumers if mortality leads to a shorter period and lower amplitude of the consumer-resource cycles. The occurrence and strength of hydra effects caused by this mechanism of 'altered variation in population abundance' [6Abrams P.A. When does greater mortality increase population size? The long history and diverse mechanisms underlying the hydra effect.Ecol. Lett. 2009; 12: 462-474Crossref PubMed Scopus (189) Google Scholar] (Table 1) then strongly depends on the way the cycle characteristics are affected by mortality [6Abrams P.A. When does greater mortality increase population size? The long history and diverse mechanisms underlying the hydra effect.Ecol. Lett. 2009; 12: 462-474Crossref PubMed Scopus (189) Google Scholar, 7Sieber M. Hilker F.M. The hydra effect in predator–prey models.J. Math. Biol. 2012; 64: 341-360Crossref PubMed Scopus (43) Google Scholar]. In any case, at high mortality rates consumer density declines (and cycles disappear) when the direct negative effect of mortality outweighs the indirect positive effect [6Abrams P.A. When does greater mortality increase population size? The long history and diverse mechanisms underlying the hydra effect.Ecol. Lett. 2009; 12: 462-474Crossref PubMed Scopus (189) Google Scholar, 7Sieber M. Hilker F.M. The hydra effect in predator–prey models.J. Math. Biol. 2012; 64: 341-360Crossref PubMed Scopus (43) Google Scholar].Box 1Resource dynamics and positive mortality effectsOne way of illustrating the effects of different resource dynamics on a modelled consumer populations' response to increased mortality is by means of a phase space: the zero growth isoclines of consumer and resource densities (i.e., the solutions of the individual differential equations are zero and resource and consumer densities do not change) are plotted as function of one another (Figure I). The intersections of these isoclines are the steady states (equilibria) of the consumer-resource system described by the two ordinary differential equations for resource R (Equation I) and consumer C (Equation II):dRdt=g(R)R−f(R)RC[I] dCdt=ef(R)RC−mC[II] where g(R) is the per-capita resource growth rate without consumers and f(R)=a1+ahR when we define the functional response with a saturating resource consumption rate for individual consumers. The parameter e is the efficiency with which consumers turn ingested resource into new consumers and m is the consumer mortality rate. Straightforward analyses and classical studies show that the consumer isocline dcdt=0 in this model is defined by a single resource value (e.g., [19Rosenzweig M.L. McArthur R.H. Graphical representation and stability conditions of predator-prey interactions.Am. Nat. 1963; 97: 209-223Crossref Google Scholar, 20Rosenzweig M.L. Paradox of enrichment destabilization of exploitation ecosystems in ecological time.Science. 1971; 171: 385-387Crossref PubMed Scopus (1363) Google Scholar]) given by R*=ma(e−mah) and hence shows up as a straight line in the phase space. The resource isocline dRdt=0 is determined by both the resource and consumer density, and so is the consumer equilibrium density C*=g(R*)f(R*) as defined by this isocline. As the saturating consumption rate allows the resource to escape control by consumers it can realise its growth potential.With logistic resource dynamics g(R)R=rR(1−RRmax), where resource productivity is positively related to resource density, at least at low resource densities, higher consumer mortality can result in higher resource productivity, benefiting consumer reproduction. The consumer equilibrium density increases with increasing mortality in this case (Figure I). With semi-chemostat resource dynamics g(R)R = b(Rmax − R), resource productivity is independent of resource density and the resource isocline is a monotonously decreasing curve in the phase-space. While higher consumer mortality leads to higher resource density, resource productivity does not increase and the consumer density only declines with increasing consumer mortality (Figure I).Table 1Mechanisms and predictions regarding positive mortality effects in different mathematical modelsMechanismModel characteristicsIncrease inDecline inType of effectasee Glossary.Refs1Altered variation in population size1.1Unstructured, discrete time, single-populationTotal time-averaged density-Hydra effect6Abrams P.A. When does greater mortality increase population size? The long history and diverse mechanisms underlying the hydra effect.Ecol. Lett. 2009; 12: 462-474Crossref PubMed Scopus (189) Google Scholar1.2Unstructured, continuous time, consumer-resourceTotal time-averaged density-Hydra effect6Abrams P.A. When does greater mortality increase population size? The long history and diverse mechanisms underlying the hydra effect.Ecol. Lett. 2009; 12: 462-474Crossref PubMed Scopus (189) Google Scholar2Prudent resource exploitation2.1Unstructured, continuous time, consumer-resourceTotal equilibrium density-Hydra effect6Abrams P.A. When does greater mortality increase population size? The long history and diverse mechanisms underlying the hydra effect.Ecol. Lett. 2009; 12: 462-474Crossref PubMed Scopus (189) Google Scholar3Temporal separationof density dependence and mortality3.1Unstructured, discrete time, single-populationTotal equilibrium density-Hydra effect15Jonzén N. Lundberg P. Temporally structured density-dependence and population management.Ann. Zool. Fenn. 1999; 36: 39-44Google Scholar3.2Stage-structured, discrete time, single-populationDensity in the targeted stage (if non-regulatory) and total numbers driven by higher juvenile numbersDensity in bottleneck stageStage-specific hydra effects12Zipkin E.F. et al.When can efforts to control nuisance and invasive species backfire?.Ecol. Appl. 2009; 19: 1585-1595Crossref PubMed Scopus (112) Google Scholar3.3Stage-structured, continuous time, single-populationDensity in the targeted stage (if non-regulatory) and total numbers driven by higher juvenile numbersDensity in bottleneck stageStage-specific hydra effects6Abrams P.A. When does greater mortality increase population size? The long history and diverse mechanisms underlying the hydra effect.Ecol. Lett. 2009; 12: 462-474Crossref PubMed Scopus (189) Google Scholar4Relaxation of density-dependent bottleneck4.1Stage-structured, discrete time, single-populationDensity in the non-regulatory stage and total numbersDensity in bottleneck stageStage-specific overcompensation12Zipkin E.F. et al.When can efforts to control nuisance and invasive species backfire?.Ecol. Appl. 2009; 19: 1585-1595Crossref PubMed Scopus (112) Google Scholar4.2Stage-structured, continuous time, consumer-resourceDensity (number and biomass) in the non-regulatory stageDensity (numbers and biomass) in bottleneck stage and total densityStage-specific overcompensation5de Roos A.M. et al.Food-dependent growth leads to overcompensation in stage-specific biomass when mortality increases: The influence of maturation versus reproduction regulation.Am. Nat. 2007; 170: E59-E76Crossref PubMed Scopus (111) Google Scholara see Glossary. Open table in a new tab One way of illustrating the effects of different resource dynamics on a modelled consumer populations' response to increased mortality is by means of a phase space: the zero growth isoclines of consumer and resource densities (i.e., the solutions of the individual differential equations are zero and resource and consumer densities do not change) are plotted as function of one another (Figure I). The intersections of these isoclines are the steady states (equilibria) of the consumer-resource system described by the two ordinary differential equations for resource R (Equation I) and consumer C (Equation II):dRdt=g(R)R−f(R)RC[I] dCdt=ef(R)RC−mC[II] where g(R) is the per-capita resource growth rate without consumers and f(R)=a1+ahR when we define the functional response with a saturating resource consumption rate for individual consumers. The parameter e is the efficiency with which consumers turn ingested resource into new consumers and m is the consumer mortality rate. Straightforward analyses and classical studies show that the consumer isocline dcdt=0 in this model is defined by a single resource value (e.g., [19Rosenzweig M.L. McArthur R.H. Graphical representation and stability conditions of predator-prey interactions.Am. Nat. 1963; 97: 209-223Crossref Google Scholar, 20Rosenzweig M.L. Paradox of enrichment destabilization of exploitation ecosystems in ecological time.Science. 1971; 171: 385-387Crossref PubMed Scopus (1363) Google Scholar]) given by R*=ma(e−mah) and hence shows up as a straight line in the phase space. The resource isocline dRdt=0 is determined by both the resource and consumer density, and so is the consumer equilibrium density C*=g(R*)f(R*) as defined by this isocline. As the saturating consumption rate allows the resource to escape control by consumers it can realise its growth potential. With logistic resource dynamics g(R)R=rR(1−RRmax), where resource productivity is positively related to resource density, at least at low resource densities, higher consumer mortality can result in higher resource productivity, benefiting consumer reproduction. The consumer equilibrium density increases with increasing mortality in this case (Figure I). With semi-chemostat resource dynamics g(R)R = b(Rmax − R), resource productivity is independent of resource density and the resource isocline is a monotonously decreasing curve in the phase-space. While higher consumer mortality leads to higher resource density, resource productivity does not increase and the consumer density only declines with increasing consumer mortality (Figure I). When these hydra effects occur, the consumer equilibrium density increases with increasing mortality over the same range of mortality rates for which the system displays consumer-resource cycles [6Abrams P.A. When does greater mortality increase population size? The long history and diverse mechanisms underlying the hydra effect.Ecol. Lett. 2009; 12: 462-474Crossref PubMed Scopus (189) Google Scholar, 7Sieber M. Hilker F.M. The hydra effect in predator–prey models.J. Math. Biol. 2012; 64: 341-360Crossref PubMed Scopus (43) Google Scholar, 22Abrams P.A. Will declining population sizes warn us of impending extinctions?.Am. Nat. 2002; 140: 293-305Crossref Scopus (66) Google Scholar] (Box 1). Because hydra effects based on higher consumer equilibrium densities also arise via modification of individual traits that alter the trophic interaction between consumer and resource and stabilise dynamics [22Abrams P.A. Will declining population sizes warn us of impending extinctions?.Am. Nat. 2002; 140: 293-305Crossref Scopus (66) Google Scholar, 23Abrams P.A. Matsuda H. The effect of adaptive change in the prey on the dynamics of an exploited predator population.Can. J. Fish. Aquat. Sci. 2005; 62: 758-766Crossref Scopus (72) Google Scholar, 24Abrams P.A. Vos M. Adaptation, density dependence, and the abundance of trophic levels.Evol. Ecol. Res. 2003; 5: 1113-1132Google Scholar, 25Matsuda H. Abrams P.A. Effects of predator-prey interactions and adaptive change on sustainable yield.Can. J. Fish. Aquat. Sci. 2004; 61: 175-184Crossref Scopus (47) Google Scholar] (Box 2) this second mechanism underlying positive mortality effects in unstructured models has been termed 'prudent resource exploitation' [6Abrams P.A. When does greater mortality increase population size? The long history and diverse mechanisms underlying the hydra effect.Ecol. Lett. 2009; 12: 462-474Crossref PubMed Scopus (189) Google Scholar] (Table 1).Box 2Positive mortality effects with complex interactions or life historiesThe mathematical models reviewed here represent the simplest one consumer–one resource systems with interactions between consumer and resource that are independent of consumer or resource traits (in unstructured models) or with life histories distinguishing a juvenile and an adult stage (in stage-structured models). Positive mortality effects have, however, also been reported in systems with larger ecological complexity.(i)Unstructured models: The mortality of conspecifics can have strong non-lethal effects on survivors' plastic behavioural or morphological traits. Individuals may switch to different resources or habitats, reduce their activity, or develop anti-predator morphs when predation or exploitation pressure increases [57Agrawal A.A. Phenotypic plasticity in the interaction and evolution of species.Science. 2001; 294: 321-326Crossref PubMed Scopus (1124) Google Scholar, 58Mittelbach G. et al.Fish behavioural types and their ecological consequences.Can. J. Fish. Aquat. Sci. 2014; 71: 927-944Crossref Scopus (152) Google Scholar]. When such mortality-induced trait modifications alter consumer–resource interactions, hydra effects are no longer restricted to unstable systems displaying consumer–resource cycles. Trait-mediated decreased consumption rates raise resource density and higher consumer equilibrium density can be attained [6Abrams P.A. When does greater mortality increase population size? The long history and diverse mechanisms underlying the hydra effect.Ecol. Lett. 2009; 12: 462-474Crossref PubMed Scopus (189) Google Scholar, 7Sieber M. Hilker F.M. The hydra effect in predator–prey models.J. Math. Biol. 2012; 64: 341-360Crossref PubMed Scopus (43) Google Scholar, 22Abrams P.A. Will declining population sizes warn us of impending extinctions?.Am. Nat. 2002; 140: 293-305Crossref Scopus (66) Google Scholar, 23Abrams P.A. Matsuda H. The effect of adaptive change in the prey on the dynamics of an exploited predator population.Can. J. Fish. Aquat. Sci. 2005; 62: 758-766Crossref Scopus (72) Google Scholar, 24Abrams P.A. Vos M. Adaptation, density dependence, and the abundance of trophic levels.Evol. Ecol. Res. 2003; 5: 1113-1132Google Scholar, 25Matsuda H. Abrams P.A. Effects of predator-prey interactions and adaptive change on sustainable yield.Can. J. Fish. Aquat. Sci. 2004; 61: 175-184Crossref Scopus (47) Google Scholar].(ii)Stage-structured models: Life histories often include more than two stages and hence more possibilities for density-dependent regulatory processes. Nonetheless, increasing mortality will always relax the bottleneck and increase the biomass in the non-regulatory stages not limiting population growth, while total biomass and biomass in the bottleneck stage decline [9de Roos A.M. Persson L. Population and Community Ecology of Ontogenetic Development. Princeton University Press, 2013Google Scholar]. Overcompensation in total biomass is possible when different life-history stages feed on different resources or exploit different habitats varying in productivity (consumers exhibit an ontogenetic niche shift). Total biomass overcompensation with increased mortality then occurs as a result of a more efficient use of the resource in the bottleneck stage. For example, when juveniles specialise on a resource that provides an unlimited supply, growth and development is rapid and maturation rate is high. The inflow of biomass to the adult stage is then also high, leading to strong competition among adults and regulation through reproduction. Increasing mortality reduces the competition among adults, leading to a higher density of juveniles that still grow and mature rapidly. As a result, population maturation rate increases and hence also adult biomass. In this case, the total population biomass can increase because the biomass produced in the juvenile stage is not lost in the adult stage, but rather transformed more efficiently into new juvenile biomass [9de Roos A.M. Persson L. Population and Community Ecology of Ontogenetic Development. Princeton University Press, 2013Google Scholar, 48Schreiber S. Rudolf V.H.W. Crossing habitat boundaries: coupling dynamics of ecosystems through complex life cycles.Ecol. Lett. 2008; 11: 576-587Crossref PubMed Scopus (117) Google Scholar]. Note that this increase in total biomass and the underlying mechanism are not equivalent to hydra effects in unstructured models.None of these predictions have been explicitly tested yet. Behavioural and morphological trait modifications are common in natural systems [57Agrawal A.A. Phenotypic plasticity in the interaction and evolution of species.Science. 2001; 294: 321-326Crossref PubMed Scopus (1124) Google Scholar, 58Mittelbach G. et al.Fish behavioural types and their ecological consequences.Can. J. Fish. Aquat. Sci. 2014; 71: 927-944Crossref Scopus (152) Google Scholar], but they have not been quantified in the studies summarised in Table 2. Many of the species studied in Table 2 have multiple juvenile stages (e.g., blow flies, soil mites) or display ontogenetic niche shifts (e.g., Eurasian perch). However, juvenile stages have usually been pooled and ontogenetic niche shifts were unaccounted for when only one resource was provided. The mathematical models reviewed here represent the simplest one consumer–one resource systems with interactions between consumer and resource that are independent of consumer or resource traits (in unstructured models) or with life histories distinguishing a juvenile and an adult stage (in stage-structured models). Positive mortality effects have, however, also been reported in systems with larger ecological complexity.(i)Unstructured models: The mortality of conspecifics can have strong non-lethal effects on survivors' plastic behavioural or morphological traits. Individuals may switch to different resources or habitats, reduce their activity, or develop anti-predator morphs when predation or exploi