Download Kratina Food Web Persistence 2015 Accepted

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Food web persistence is enhanced by non-trophic interactions
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Edd Hammill1, Pavel Kratina2, Matthijs Vos3,4, Owen L. Petchey5, and Bradley R. Anholt6,7
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School of the Environment, University of Technology Sydney, Australia.
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School of Biological and Chemical Sciences, Queen Mary University of London, UK.
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Gebäude ND05, D-44780 Bochum, Germany
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Royal Netherlands Academy of Arts and Sciences (NIOO-KNAW), Wageningen, the
Ruhr-Universität Bochum, Department of Zoological Biodiversity, Universitätsstr. 150 /
Department of Aquatic Ecology, Netherlands Institute of Ecology,
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Netherlands.
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Switzerland.
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Bamfield Marine Science Centre, 100 Pachena Road, Bamfield, BC, Canada.
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Department of Biology, University of Victoria, Victoria, BC, Canada.
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Abstract – 250
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Word Count – 4003
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Tables - 1
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Figures – 3
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References – 43
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Contact for correspondence – [email protected]
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EH, PK, MV and BRA originally formulated the idea, EH and OLP carried out the laboratory
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work and along with BRA analysed the data. All authors contributed to the writing and
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editing of the MS. The experiment complies with all national laws.
Institute for Evolutionary Biology and Environmental Studies, University of Zurich, Zurich,
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Abstract
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The strength of interspecific interactions is often proposed to affect food web stability, with
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weaker interactions increasing the persistence of species, and food webs as a whole.
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However, the mechanisms that modify interaction strengths, and their effects on food web
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persistence are not fully understood. Using food webs containing different combinations of
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predator, prey, and nonprey species, we investigated how predation risk of susceptible prey is
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affected by the presence of species not directly trophically linked to either predators or prey.
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We predicted that indirect alterations to the strength of trophic interactions translate to
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changes in persistence time of extinction-prone species. We assembled interaction webs of
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protist consumers and turbellarian predators with eight different combinations of prey,
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predators and nonprey species, and recorded abundances for over 130 prey generations.
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Persistence of predation-susceptible species was increased by the presence of nonprey.
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Furthermore, multiple nonprey species acted synergistically to increase prey persistence, such
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that persistence was greater than would be predicted from the dynamics of simpler food
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webs. We also found evidence suggesting increased food web complexity may weaken
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interspecific competition, increasing persistence of poorer competitors. Our results
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demonstrate that persistence times in complex food webs cannot be predicted from the
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dynamics of simplified systems, and that species not directly involved in consumptive
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interactions likely play key roles in maintaining persistence. Global species diversity is
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currently declining at an unprecedented rate and our findings reveal that concurrent loss of
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species that modify trophic interactions may have unpredictable consequences for food web
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stability.
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Key words: community persistence, interaction modifications, microcosms, nonprey species,
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predation, trophic interactions.
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Introduction
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A major argument for the conservation of intact ecosystems is that high levels of food web
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complexity maintain community persistence (Duffy 2009; Loreau and de Mazancourt 2013;
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Thompson and Starzomski 2007). But why should more complex ecosystems be more stable,
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with species persisting for long periods of time? Natural food webs are often characterised by
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many weak interactions among species, as opposed to fewer, stronger interactions in simpler
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food webs (Edwards et al. 2010; Hillebrand and Cardinale 2004). Theoretical analyses show
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that a relatively high abundance of weak interactions reduces the chances of extinctions
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caused by high levels of predation and competition (McCann et al. 1998; McCann 2000).
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Further evidence for the stabilizing effect of more complex food webs comes from the other
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end of the spectrum - very simple assemblages. Maintaining predator-prey pairs under
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simplified laboratory conditions is often difficult as predators tend to over-exploit prey
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(Bonsall et al. 2002).
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Homogeneous environments lacking structural heterogeneity and temporal or spatial
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refuges for prey ought to be intrinsically unstable, allowing predators to over-exploit their
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prey (Holyoak et al. 2005; Hutchinson 1961). Increasing the structural complexity of an
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ecosystem is associated with reductions in predator efficiency (Srivastava 2006), that can
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allow co-existence of predators and susceptible prey (Crowder and Cooper 1982). Similar to
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physical complexity, increasing the complexity of food webs by including ‘nonprey’ species,
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resistant to predation can reduce the foraging efficiency of predators when predators spend
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time encountering and handling nonprey (Kratina et al. 2007; Vos et al. 2001). Nonprey
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species may also form a cryptic background against which prey items are difficult to identify
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(Wootton 1992). Modifying the ability to detect prey can alter the handling time and attack
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rate of a predator’s functional response, reducing the per capita prey consumption rates
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(Kratina et al. 2007). Despite the demonstrated effects of nonprey species on predators’
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functional responses, it is currently unknown whether and how these short-term reductions in
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consumption rates translate into long-term persistence in ways similar to changes in structural
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or spatial complexity.
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To test the hypothesis that weakening trophic interactions by nonprey can increase
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persistence in a simple four species food web, we manipulated complexity and recorded
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species’ long-term persistence. Using a system of protistan consumers and a turbellarian
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predator, we used eight different combinations of predators, prey, and two nonprey species to
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assemble aquatic microcosms that varied in richness from one to four species. We
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specifically assess whether: i) the presence of nonprey species reduces predation pressure on
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prey and consequently increases prey persistence time; ii) multiple nonprey synergistically or
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additively alter prey persistence.
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We here define a food web as persistent if all initially present species remain present
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for many generations (e.g. > 100). If a species is lost from a closed food web then there is no
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possibility for it to return (Staddon et al. 2010). The functions within the food web
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exclusively performed by that species are therefore lost (Petchey et al. 2004) until another
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species evolves to fill the vacant niche. Should a species be driven to very low population
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densities, it may be able to continue to be ecologically relevant by performing an ecosystem
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function (Lyons and Schwartz 2001), and the species’ continued presence ensures the
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potential for the population to rise in the future. Hence, although we track population
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dynamics of individual species, we focus on the persistence of species as a measure of food
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web stability. We adopt this approach as a species’ continued persistence within a food web
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maintains the potential for its density to rise to an ecological-important level, regardless of its
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current density.
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Materials and Methods
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To understand how food web stability is affected by the addition of extra species we used
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experimental 200 mL microcosms varying in number of interacting species. We assembled
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food webs based on trophic and competitive relationships elucidated from previous
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experiments (Fenchel 1980; Kratina et al. 2009; Hammill et al. 2010) We cultured a
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predatory turbellarian flatworm (Stenostomum virginianum), its ciliate prey Paramecium
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aurelia and two nonprey species, the ciliate Euplotes patella (hereafter referred to as nonprey
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1 or Euplotes) and the bdelloid rotifer Philodina roseola (hereafter nonprey 2 or Philodina).
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Previous short-term experiments have shown that neither of the two non-prey species are
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consumed by Stenostomum, likely due to their large body size (Kratina et al. 2007). The
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body width of Euplotes in the presence of Stenostomum predators is 93.7 ± 4.3 µm (mean ±
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SE, n = 3 cultures), and predation by gape-limited Stenostomum on individuals larger than 80
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µm is negligible (Altwegg et al. 2006). The body width of Philodina ranges between 75 µm
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and 150 µm, making the majority of individuals too large to be consumed by Stenostomum.
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In contrast, the maximum body width of Paramecium is 42.25 ± 1.64 µm (mean ± SE, n = 4
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cultures) making it highly susceptible to predation by Stenostomum. Previous studies have
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demonstrated that Stenostomum is a voracious predator of Paramecium, with a single
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predator able to consume 15 prey individuals in four hours (Kratina et al. 2007). In addition,
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the presence of cues from Stenostomum causes many ciliates including Paramecium to
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induce morphological and behavioural defences (Hammill et al. 2009; Hammill et al. 2010;
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Kusch and Kuhlmann 1994). We have also directly observed Stenostomum consuming
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Paramecium, in agreement with earlier observations that Stenostomum readily consumes
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ciliated protozoans (Archbold and Berger 1985).
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The experimental communities were sustained on a basal resource of bacteria,
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microflagellates and other small protozoa (1µm-5µm length). Paramecium (prey) and
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Euplotes (nonprey 1) both consume particles between 0.2µm – 1.5µm in diameter (Fenchel
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1980). However, within this size range interspecific differences in preferred food size exist,
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with Paramecium able to more efficiently consume particles <1.2µm in diameter, but
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Euplotes better able to consume particles at the higher end of the distribution spectrum
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(Fenchel 1980). Conversely, Philodina (nonprey 2) prefer food items at the upper end of the
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size spectrum preferred by Euplotes (and larger still), and with their complex feeding
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apparatus are able to efficiently harvest food from the ecosystem (Ricci 1984). The prey and
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both nonprey species therefore likely compete with each other over the basal resource
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(Lawler and Morin 1993; Worsfold et al. 2009). Predatory Stenostomum possess muscular
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mouthparts, geared towards the capture of relatively large, fast, motile prey (Nuttycombe and
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Waters 1935), as opposed to the filtering apparatus associated with capture of bacteria.
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Previous studies have shown that when presented with a wide variety of prey choices,
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Stenostomum guts contained a high percentage of animal and protist matter, with very little
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bacteria or algae (Nandini et al. 2011). Although we have observed Stenostomum surviving
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on the basal resource we used in our experiment, the morphological descriptions of
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Stenostomum mouthparts show that the basal resource is at the low end of the diet breadth of
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Stenostomum, and likely not their preferred food when Paramecium are available. The
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consumptive and competitive interactions present in the experimental food web are depicted
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in Fig. 1.
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To assess how over-exploitation of prey is altered by different levels of nonprey, we
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established the following treatments: i) Prey (Paramecium) alone (i.e., control to ensure that
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prey persist over the experimental duration); ii) Prey and nonprey 1 (Euplotes); iii) Prey and
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nonprey 2 (Philodina); iv) Prey and both nonprey; v) Prey and predators; vi) Prey, predators
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and nonprey 1; vii) Prey, predators and nonprey 2; viii) all four species present. We incubated
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five replicates of each treatment for 78 days (approximately 130 prey generations). The
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experiment was performed in media consisting of 0.4 g/L crushed protozoa pellets (no. 13-
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2360, Carolina Biological Supply, Burlington, NC) dissolved in PurelifeTM mineral water
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(Nestle, Canada). Prior to species inoculations, media was filtered through standard coffee
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filters (Thrifty Foods, Canada) and sterilized by autoclaving (Hammill et al. 2009).
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In each of the 200 mL microcosms, 100 individuals of the appropriate prey and/or
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nonprey species were introduced after the cultures had been inoculated with 0.5 mL of mixed
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bacterial culture and left to stand for 24 hours. Within two hours of adding prey and nonprey,
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20 Stenostomum predators were introduced into the predator treatments. We sampled 10 mL
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(5%) from each vessel every 3 days and replaced with 10 mL of fresh media. During the
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course of the experiment, the same amount of abiotic nutrients were added to each of the
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microcosms. Replacing 5% every 3 days made some nutrients present for the duration of the
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experiment. However, different combinations of prey, non-prey and predators presumably
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reduced the resources to different levels. We recorded the abundance of each consumer
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species using a Leica MZ8 dissecting microscope and then calculated the total abundance of
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each consumer species in the microcosm, from the abundance within the sample. Microcosms
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were swirled prior to sampling to ensure homogenous mixtures. At the end of the experiment
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we searched all media remaining in the microcosms using the dissecting microscope and
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recorded the abundance of each consumer species. During this final sorting, we never “re-
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discovered” species in replicates in which they had earlier dropped below detection limits.
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To compare differences in persistence times among individual treatments we used
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parametric survival analyses, a branch of generalised linear models. Analysing time-to-event
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data (such as species extinctions) is difficult, as the variance tends to increase with the mean,
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confounding the constant variance assumption of linear model analyses. In addition to the
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increasing variance, time-to-event data may also contain censored data if an event, such as
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extinction, does not occur over the experimental duration. Parametric survival analyses were
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carried out by calculating a survival object using the function surv(), and analyzing
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differences using population stochastic modeling (function psm()) in the R programming
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language (R Development Core Team 2012). These functions were specifically designed for
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use with time-dependent and potentially censored data (Crawley 2007; Harrell 2001). Each of
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our survival analysis models incorporated a time-specific hazard function and lognormal
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error distribution as this generated the best fit to the data. Because certain treatments were
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used in two analyses, we applied a Bonferroni correction to avoid inflating the chance of
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finding spurious significant results.
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In order to understand the competitive interactions among prey, predators, and both
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nonprey in treatments where a species of interest persisted to the end of the experiment, we
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analysed its densities between 20 days and the termination of the experiment. By 20 days, the
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dynamics of populations tended to have plateaued, suggesting they reached a carrying
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capacity (Fig 2). Densities were analysed using mixed effects ANOVA with the presence of
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other species and date being included as fixed factors, and “microcosm” included as a
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random factor to account for multiple samples being taken from the same microcosm
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(Pinheiro and Bates 2000).
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Results
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The prey survived to the end of the experiment in all treatments without predators, but
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rapidly dropped below detection limits when housed only with predators (Figs 2, 3). Prey
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persistence times with predators significantly increased in the presence of nonprey 1 (Figs 2d,
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3a, P < 0.001, z = 18.44). We observed no increase in prey persistence when housed with
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predators and nonprey 2 compared to the predator-only treatment (Figs 2f, 3a, z = 0.833, P =
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0.41). The presence of both nonprey species increased prey persistence in the presence of
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predators to a greater extent than would be predicted from the effects of each nonprey in
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isolation (Figs 2h, 3a, interaction term between nonprey 1 and nonprey 2, z = 2.53, P = 0.02).
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Nonprey 1 populations in the presence of predators and prey were significantly lower
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than when housed with prey alone (Table 1, Fig 2c and 2d), although populations of nonprey
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1 remained stable in the presence of predators after prey had been driven to extinction. When
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housed with prey and nonprey 2, nonprey 1 dropped below the detection limit in all replicates
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by day 9 (Figs 2g and 3b, z = 13.27, P < 0.001, parametric survival analysis), suggesting they
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were unable to survive in the combined presence of prey and nonprey 2. In contrast, when all
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four species were present, nonprey 1 populations dropped below the detection limit in only 2
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out of 5 replicates on days 48 and 75 (Figs 2i and 3b, z = 31.77, P < 0.001), although nonprey
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1 densities were significantly lower than when housed with either prey alone or predators and
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prey (Table 1, Figs 2c and 2d).
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Nonprey 2 populations were not affected by the presence of nonprey 1, but were
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significantly reduced in the presence of predators (Table 1, Fig 2e, 2f, 2g, and 2h). Nonprey 2
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populations remained stable while co-existing with predators isolation for ~70 days following
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extinction of prey (Fig 2g).
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Predators populations grew to carrying capacity in all replicates where they were
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present. Nonprey 1 had no effect on the carrying capacity of predators (Table 1, Fig 2d),
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however nonprey 2 reduced predator carrying capacity (Table 1, Fig 2e and 2f). Predators
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could persist in the absence of prey (Fig. 2b), suggesting an intraguild predation system (see
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also Kratina et al (2010)). An intraguild system would also explain why predators were able
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to persist after they had driven prey to extinction or low levels.
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Discussion
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Our results show that predation-susceptible prey may persist for longer in more complex food
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webs. This effect was likely driven by the weakening of trophic interactions in more complex
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assemblages (Vos et al. 2001, Kratina et al. 2007). Specifically, we show that: i) the presence
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of an inedible nonprey species increases the persistence time of predation-susceptible prey,
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although the effect of single nonprey was species specific; ii) multiple nonprey act
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synergistically to increase prey persistence to a greater extent than would be predicted from
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additive nonprey effects. These findings suggest that non-trophic interaction modification is
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an important driver of long-term persistence in multi-trophic systems. We also found that the
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stabilising effects of species in combination cannot be accurately predicted from the effects
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of those species in simpler assemblages, suggesting that any species within a community can
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potentially be important for maintaining community dynamics. The loss of any species may
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therefore have unpredictable secondary effects leading to subsequent loss of other species.
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In the absence of nonprey, predatory Stenostomum rapidly over-exploited populations
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of their prey (Paramecium), driving them below detection limits in all replicates within nine
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days (~18 prey generations). This rapid reduction of prey agrees with short-term experiments
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showing Stenostomum are voracious predators of Paramecium (Kratina et al. 2007; Kratina et
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al. 2009). The predation rates observed in these short-term experiments would be sufficient
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for Stenostomum to drive Paramecium (prey) to extinction within the time frame we observed
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here (for quantitative comparison see Supplementary Materials). This leads us to conclude
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that predation, rather than competition was the primary cause of prey extinction.
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In the presence of predators, nonprey 1 substantially increased prey persistence times
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in all replicates, with prey still being present after 48 days in one replicate. This increase in
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prey persistence in the presence of nonprey 1 provides further evidence that predation, not
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competition, is the mechanism causing prey to go extinct when housed with predators. If
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competition were driving prey extinctions, we would expect the addition of nonprey 1 to
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increase the speed of prey extinction, due to the combined competitive pressure of nonprey 1
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and predators. Conversely, the reverse is true; nonprey 1 increased the ability of prey to co-
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exist with predators. The presence of nonprey 2 (Philodina), however, did not alter prey
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persistence, and no prey were detected in any replicate after day nine. This lack of an effect
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due to nonprey 2 may be because nonprey 2 were easily distinguishable from prey in
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isolation, and in simpler food webs predators were easily able to avoid unpalatable species
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and focus on prey (Ihalainen et al. 2012). As the presence of nonprey 2 did not affect the
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persistence time of prey when incubated with predators, we would expect adding nonprey 2
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to the three species food web (predator, prey, non-prey 1) to have no effect on prey
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persistence time. However, in the four species community, prey persisted approximately 33%
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longer than predicted from simple additive effects of the two nonprey. This suggests that
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nonprey 2 enhanced the effect of nonprey 1 on the strength of trophic interactions, with
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consequences for prey population dynamics. This synergistic effect of multiple nonprey may
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be because in more complex food webs, predators have more difficulty avoiding multiple
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different inedible species that have different movement patterns and occupy different
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microhabitats (bottom and water column). Although not all nonprey species may be able to
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modulate the persistence of predator-prey interactions, our findings highlight the importance
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of understanding both, trophic and non-trophic interactions in the context of the wider overall
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food web.
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For predators with an asymptotic functional response, rates of prey consumption are
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described by two parameters, attack rate and handling time (Holling 1959). Attack rates
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dictate the rate at which predators encounter and successfully capture prey, while handling
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times reflects the amount of time required to process a single prey before recommencing
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search. Handling time may include time required to physically overcome a prey, ingest, and
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digest before a predator is able to deal with the next prey item (Jeschke et al. 2002). Nonprey
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may mask or disrupt cues that predators use to detect prey, and therefore reduce the
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predator’s attack rate on focal prey. Short-term experiments investigating predator functional
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responses in the presence of nonprey demonstrate how nonprey reduce per capita predation
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rates (Kratina et al. 2007). Using the same species as our study, the previous experiment of
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Kratina et al (2007) showed that multiple nonprey reduced predation rates to a greater extent
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than would be predicted from the results of single nonprey trials. This synergistic effect of
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multiple nonprey indicates how increased species number can reduce the strength of trophic
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interactions. The results of the present study show that this weakening of trophic interactions
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translates into long-term persistence of predator and prey in more complex assemblages.
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Nonprey 1 persisted in all microcosms where they were housed with only one other
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species (prey alone, or predators alone following extinction of prey), although their
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populations were lower in the presence of predators, suggesting a dietary overlap. As
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population sizes of nonprey 1 were similar in the presence and absence of predators, our
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results agree with previous work showing nonprey 1 are not consumed by predators (Kratina
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et al. 2007). When nonprey 1 were incubated with both prey and nonprey 2 but in the absence
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of predators, their populations dropped below detection limits in all replicates by day nine,
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suggesting that may have been excluded by the combined competitive pressure of prey and
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nonprey 2. Although we cannot be certain competitive exclusion was the mechanism leading
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to the extirpation of nonprey 1, competitive exclusion is the simplest explanation given the
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diet breadths of prey (Paramecium), nonprey 1 (Euplotes), and nonprey 2 (Philodina) overlap
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considerably (Fenchel 1980; Ricci 1984).
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When prey, predators and both nonprey were combined, nonprey 1 dropped below the
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detection limit in only 2 out of 5 microcosms, on days 48 and 75 (~90 and ~125 prey
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generations). This increase in nonprey 1 persistence may be mediated by predators reducing
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densities of prey. Although some prey individuals remained to compete with nonprey 1, prey
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populations were substantially reduced by predation. While we cannot categorically conclude
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that the predator-driven reduction in prey made resources available for nonprey 1 (preventing
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extinction), this explanation would appear the most parsimonious given the results of the
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current and previous experiments (Fenchel 1980). As prey are able to out-compete nonprey 1
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at the lower end of nonprey 1’s diet breadth (Fenchel 1980), a reduction in prey by predators
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may increase nonprey 1’s access to small food items and increase their persistence.
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Therefore, while the addition of predators may increase the number of species competing
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with nonprey 1, the indirect effect of predators (through consuming prey) may increase
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nonprey 1’s access to a portion of the basal resource. Our results therefore agree with earlier
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research demonstrating that the presence of predators can alter the strength of interactions
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between competitors (Morin 1986; van der Stap et al. 2008), and improve community
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persistence (Borrvall and Ebenman 2006).
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The persistence times of both prey and nonprey 1 in the four species system could not
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be predicted from the results of any of the three species treatments (prey + single nonprey +
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predator, or prey + both nonprey treatments). In all three species treatments, one species on
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average went extinct. In the four species food web, we may therefore predict nonprey 1 to
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decline as was observed when they were incubated only with prey and nonprey 2, potentially
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due to high levels of competition. This loss of nonprey 1 could then lead to exploitation of
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prey by predators, as we observed when prey were incubated with only nonprey 2 and
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predators. However, we detected significantly fewer extinctions in the four species system,
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suggesting that the increased level of complexity enhanced species persistence. The two
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nonprey species appeared to synergistically reduce predation pressure on prey, preventing
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their overexploitation. This weakening of multiple interspecific links is potentially the
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mechanism leading to all four species persisting for longer in the more complex food webs.
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These findings highlight the need to consider non-trophic interactions in predicting food web
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dynamics (Loreau and de Mazancourt 2013). However, this effect of nonprey interaction
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modifications on species persistence may prevail predominantly in ecosystems that are
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controlled by top-down forces.
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Conclusions
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We showed that by weakening predation and potentially competition, additional consumers
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can increase persistence times of species susceptible to extinction. Species are currently being
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lost from ecosystems at rates far greater than historically observed (Cardinale et al. 2012).
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We found that the loss of species that initially appear irrelevant may have far-reaching
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consequences for food web persistence due to increasing the strength of trophic interactions
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among other species. These changes to interaction strengths could result in secondary
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extinctions and reorganization of entire ecosystems (Dunne and Williams 2009; Golubski and
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Abrams 2011).
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Acknowledgements
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We would like to thank Anita Narwani, Trisha Atwood and Finn Hamilton for their insightful
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comments and discussions. Earlier versions of this work were substantially improved buy the
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efforts of Scott Peacor, Joel Trexler and two anonymous reviewers. Stephanie Lingard
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provided invaluable laboratory support. This work was funded by the Canada Research
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Chairs program and a NSERC Discovery Grant awarded to B. R. A.
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The authors have no conflicts of interest to declare.
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All applicable institutional and/or national guidelines for the care and use of animals were
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followed.
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Table legends
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Table 1. Results of mixed effects ANOVAs of species densities after day 20. All interactions
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were tested, and non-significant interactions removed through sequential stepwise removal.
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Figure Legends
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Fig. 1. Consumptive and potential competitive interactions among predators (Stenostomum),
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prey (Paramecium) and two nonprey species (Euplotes and Philodina) making up the
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experimental food web. Consumptive links and lack thereof are confirmed from a previous
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short-term experiment (Kratina et al. 2007), competitive links are suggested by (Fenchel
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1980) and the results of this study. Arrow widths are indicative of interaction strength
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Fig. 2. Population dynamics of predator, prey and two nonprey species in assemblages with
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different composition, N=5 for each different treatment. a) prey (Paramecium) alone, b) prey
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and predators (Stenostomum), c) prey and nonprey 1 (Euplotes), d) prey, nonprey 1 and
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predators, e) prey and nonprey 2 (Philodina), f) prey, nonprey 2 and predators, g) prey and
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nonprey 1 + 2, h) prey, nonprey 2 and predators in microcosms containing all species, i)
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nonprey 1 in microcosms containing all species, illustrated separately from other species to
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improve clarity. For all species in all treatments, central thick lines represent means of five
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replicates within a treatment, thinner lines represent standard errors. The end of the line
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denotes the last time a species was detected in all replicates of the treatment
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Figure 3. Number of days above detection limit and modelled survival probabilities of
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extinction prone species: a) prey (Paramecium) persistence time (days above detection limit),
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b) nonprey 1 (Euplotes) persistence time (days above detection limit). Labels under bars
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denote the other species present in the experimental treatment, each treatment contained five
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replicates. Data are means ± standard errors, dotted line denotes the end of the experiment, no
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error bars in last columns as species survived till the end of the experiment in all replicates.
20
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Persistence times of predators and nonprey 2 are not shown as both species survived to the
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end of the experiment in all treatments in which they were present.
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21