The effects of inducible defenses on population stability in Paramecium aurelia

doi:10.21203/rs.3.rs-3959514/v1

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The effects of inducible defenses on population stability in Paramecium aurelia

https://doi.org/10.21203/rs.3.rs-3959514/v1

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Predator-Prey dynamics have been studied across many different systems over the past 80 years. The outcomes of this past research have yielded useful theoretical and empirical models of predator-prey systems. However, what stabilizes predator-prey dynamics is often debated and not well understood. One proposed stabilizing mechanism is that the inducible defenses of prey decrease predation risk by creating a prey population that is invulnerable to predation, leading to a reduction in trophic interaction strength. In this study, we investigated the potential stabilizing effects of inducible morphological defenses in the protozoan, Paramecium aurelia, across a range of nutrient concentrations to better understand a potential stabilizing mechanism of systems under nutrient enrichment (Paradox of Enrichment). Using P. aurelia clones that differ in their ability to induce defenses, we found that the most susceptible clone that does not express any known inducible defense showed reduced survival along a gradient of increasing nutrient concentrations. Clones expressing either inducible or permanent morphological defenses (increasing body width in response to predation threat) were not significantly affected by increasing nutrients demonstrating a potential benefit of these defenses. However, when evaluating population stability (coefficient of variation) rather than survival, we found a stabilizing effect of increasing nutrients on all P.aurelia populations. Our results demonstrate varied effects of increasing nutrients on population stability depending on the level of defense expression and stability metric used. Our results reinforce that choice of stability metric can alter conclusions about population stability and persistence, highlighting the need to adopt multiple metrics and approaches.

Predatory-Prey interactions

population ecology

Paradox of Enrichment

phenotypic plasticity

behavioral ecology

Research on species interactions and specifically predator-prey interactions has yielded many important theoretical and empirical models of simple bi-trophic predator-prey systems. These studies investigating predator-prey interactions include a wide range of organisms and systems including algae-Daphnia interactions in aquatic environments (McCauley & Murdoch, 1987), snowshoe hare-lynx interactions in boreal systems (Elton & Nicholson, 1942), and Paramecium-Didinium interactions in lab studies (Luckinbill, 1973; Veilleux, 1979). Most famously, the Lotka-Volterra models provide useful though simplistic models describing predator-prey interactions (Abrams, 2000; Lotka, 1920; Volterra, 1927). Using similar models, Rosenzweig (1971) proposed the destabilization of these simple predator-prey systems via increasing a limiting energy source in the system, known as the Paradox of Enrichment (POE). The POE argues that one or both species in a simple predator-prey system will go extinct due to increased oscillations from nutrient enrichment of the system (Rosenzweig, 1971). Consequently, the collapse of one species’ population may impact the broader community by disrupting interactions among other species. Several lab experiments have demonstrated this instability from resource enrichment using Paramecium-Didinium, mite, and rotifer-algae systems (Fussmann et al., 2000; Huffaker et al., 1963; Luckinbill, 1973; Veilleux, 1979).

Field and microcosm studies have produced varied results that at times do not support the conclusions drawn from theoretical investigations or simple lab experiments (McCauley & Murdoch, 1987, 1990; Persson et al., 2001; Roy & Chattopadhyay, 2007b). For example, in one study the predator released an auto-toxin as a form of density dependence that mediated the growth rate of the predator population and stabilized the system under enrichment (Kirk, 1998). These discrepancies between lab, field, and theoretical works demonstrate the need to further evaluate the underlying mechanisms driving instability from nutrient enrichment in predator-prey systems. A better understanding of the mechanisms driving these divergent results would improve our ability to predict when we can expect the POE to hold and when it will not in both natural and lab settings.

Several explanations have been proposed to potentially parse out these discrepancies between lab studies and those conducted in the field. Specifically, differences in prey behavior or prey qualities such as use of refugia (invulnerable prey; Abrams & Walters, 1996), having a nutritional value below a certain threshold (unpalatable prey; Genkai-Kato & Yamamura, 1999), or production of toxins (toxic prey; Roy & Chattopadhyay, 2007a) may provide stabilizing effects in situations where enrichment acts to destabilize a predator-prey interaction (Roy & Chattopadhyay, 2007b). Moreover, certain predator-induced morphological and behavioral variations within prey species have been posited as a stabilizing force when a predator-prey system is experiencing nutrient or energy enrichment. The inducible defense mechanisms of prey (e.g., changes in body size or shape, reduction in movement, production of spines or helmets, etc.) have been shown to help maintain stable oscillations within a simple predator-prey system by altering attack rates, handling times, and/or conversion efficiencies between defended and undefended prey (Verschoor et al., 2004a; Vos et al., 2004).

Despite the proposed stabilizing effect of inducible defenses, there remains a lack of consensus on when we can expect the POE to prevail as well as why discrepancies exist between lab and field experiments. Here we explore the potential stabilizing effects of inducible defense mechanisms on the POE as it pertains to the predator-prey system of Paramecium aurelia and Stenostotum flatworms in a lab setting. By using different P.aurelia clones that either do or do not display inducible defense strategies, we investigate the potential to observe the POE in this system, as well as research the effects of inducible defense strategies on both protist population stability and survival under nutrient enrichment. Specifically, we expected the clonal lines of P.aurelia that do induce (i.e., grow wider in response to predation threat) or are in a permanently defended state (i.e., increased body width) to have increased population stability and longer population persistence. Conversely, those clones that do not respond to predation threat by increasing body width will have decreased survival rates and population stability.

The objectives of this research were to 1) test whether the POE can be observed in a protist/flatworm predator-prey system, and 2) explore the potential for inducible defense mechanisms to prevent the effects of the POE. Ultimately, this research will help increase the understanding of the potential stabilizing effects of inducible defense traits on population dynamics in a laboratory-grown predator-prey system, and provide further empirical data to the debate surrounding the POE.

Study Species

To evaluate the POE in a protist-flatworm system, we used the protozoa species, P. aurelia as the prey and the flatworm species, S. virginianum as the predator. Paramecium aurelia were used as the prey species due to the ability to rapidly raise large populations and easily quantify inducible defenses. Additionally, populations can be grown and maintained for use in experiments due to short generation times and the ability of P. aurelia to persist in lab environments for many generations (Gause, 1937). Further, P. aurelia reproduce asexually, meaning that a population grown from a single individual will be genetically identical. Paramecium aurelia used in this study were collected from multiple sites along the Logan River in Logan, Utah, USA as well as from commercially available populations (Carolina Biological Supply Company, Burlington, NC) in order to cover a range in inducible defense responses. For use in the experiment, three clonal populations of P. aurelia were chosen to represent three different levels of an inducible defense. One clone (“FD4”) isolated from First Dam in the Logan River (41°44'31.7"N, 111°47'24.5"W) is “permanently induced” (i.e., expresses increased width with or without predators present), one clone (“EV2”) that does not induce (i.e., no significant increase in width with predators present), and one clone (“AUR”) shows induced changes in width in the presence of predators (Fig. 1). Stenostomum virginianim populations, a known predator of P. aurelia (Hammill et al., 2010, 2015; Kratina et al., 2007), were grown from a single individual collected in the Logan River (41°44'31.7"N, 111°47'24.5"W).

Study Design

All experimental populations were maintained in new petri dishes that had been soaked in DI water for at least 24 hours before the start of the experiment. In each dish 30mL of media at a pre-determined nutrient concentration was added along with 100 protists and 10 flatworms. These initial populations of predator and prey were determined based on preliminary analyses that demonstrated that these populations are below the carrying capacity of both species but are high enough to minimize stochastic extinction events early in the experiment. Protozoa pellets (© Carolina Biological supply) dissolved in Arrowhead^™ mineral water and filtered through Melitta Super Premium Unbleached brown filters (Melitta Group, Germany; were used to generate a gradient of six different nutrient levels (0.03, 0.07, 0.15, 0.30, 0.60, and 0.80 g.\({L}^{-1}\)). Every third day, both a sample of the protists as well as the total population of flatworms were counted. A 3 mL sample of each population was collected, preserved, and stained using 5% Lugol’s Solution, and then counted using a Bogorov counting chamber. If no P.aurelia were observed in the sample, the whole petri dish was then scanned to ascertain if there were still P. aurelia present in the whole population. If a population (either predator or prey) was recorded as zero in two consecutive sampling events, the population was deemed extinct, and the petri dish was removed from the experiment. At each sampling event, 3 mL of the appropriate media was then added to each petri dish to account for the removed sample and to maintain a base level of fresh nutrients. Additionally, Arrowhead Mountain Spring Mineral Water (USA) was added to each population every other week to account for any potential evaporation throughout the experiment. The experiment ran for 40 days (~ 40–80 generations of P. aurelia), after which Lugol’s Solution was added to the petri dishes for a final count of flatworms and P. aurelia. In addition to the design above, trials were run under the same nutrient levels but without predators present to better understand the underlying growth rate and population patterns of P. aurelia. The study design was fully replicated six times with each of the three clonal P.aurelia populations.

Statistical Methods

To evaluate the potential beneficial properties of inducible defense mechanisms in P.aurelia under nutrient enrichment, we conducted a survival analysis utilizing Cox Proportional Hazards Models to evaluate time to extinction within P. aurelia and S. virginianum populations. Cox Proportional Hazard Models are a semi-parametric method that are useful when investigating if and how time-to-event data (in this research, extinction of populations) are affected by one or more covariates (Landes et al., 2020). For this research, we conducted Cox Proportional Hazard Models evaluating whether we found evidence for the POE within this protist-flatworm lab system. Reduced time to extinction under high nutrient conditions would support the POE, while increased time to extinction in high nutrient conditions would provide evidence in opposition to the POE in this system. Additionally, we were interested in the stabilizing effects of inducible defenses under nutrient enrichment, and utilized the coefficient of variation and linear mixed effects modeling to evaluate stability over time within protist populations.

We performed a Cox Proportional Hazards Model on time to extinction data for both protists and flatworm extinction events. These extinction events included both protist and flatworm extinctions as a predator-prey population was deemed extinct when one or both populations were no longer present in the petri dish (zero counts in two consecutive sampling events). The fixed effects were nutrient level and clonal line. Additionally, we stratified the model (i.e., random intercept) allowing the baseline hazard functions to vary by time block, allowing us to account for any effects that could be attributed to differences among temporal blocks. We found no significant interactions, therefore we only incorporated the two fixed effects and the single random effect in the model. The model met the proportional hazards assumption, which was evaluated using both a formal score test and visual assessment of Schoenfeld residuals.

To evaluate the potential stabilizing properties of inducible defense mechanisms in P.aurelia across a moving time window, we calculated the coefficient of variation (CV) across two time periods for each replicate throughout the 40-day experiment. To reduce the effects from initial population growth and transient dynamics, the first two sampling periods were not included in the model (days 0–6). The final three sampling periods of the experiment (days 33–40) were also removed to reduce the bias of decreasing data as populations went extinct. Removing the initial and final sampling days left us with two time steps: sampling days 9–18 and sampling days 21–30. The CV was calculated by dividing the standard deviation of the population during the time periods designated above by the mean of the population during the same time period. This calculation was repeated for each level of treatment (i.e., nutrient and predator presence), across all three P.aurelia clonal lines, and for each replicate. Linear mixed effects models using the R statistical package, NMLE (J. Pinheiro et al., 2023; J. C. Pinheiro & Bates, 2000) were run with nutrient, predator presence, clone, and time step as fixed effects, and replicate as a random intercept. Due to significant interactions with all fixed effects and because we were mostly interested in results when predators were present (to better understand effects from the expression of inducible defenses), the dataset was divided by predator treatment (i.e., populations in which predators were present or absent) and models were only run for populations in which predators were present. Finally, due to a significant three-way interaction and because we were interested in clone-specific results, three separate models were run for each clonal population. A linear mixed effects model using the R statistical package, NMLE (J. Pinheiro et al., 2023; J. C. Pinheiro & Bates, 2000) with nutrient and time step as fixed effects, and replicate as a random intercept, was run for each clonal population. All analyses were run using the R Statistical Software (V4.3.2; R Core Team, 2023) .

Descriptive Stats

The minimum number of days that a population persisted was 18 days and the average was 33.5 days. AUR had 20 populations persist through the 40-day experiment with a mean of 36.8 days and minimum of 21 days (Figure S1). FD4 had 13 populations persist through the 40-day experiment with a mean of 32.1 days and minimum of 18 days (Figure S1). Finally, EV2 had 11 populations persist through the 40-day experiment with a mean of 32 days and a minimum of 18 days (Figure S1).

Paradox of enrichment

We found mixed evidence of the POE within a protist-flatworm system, represented in terms of both time to extinction and population stability. Increasing nutrients resulted in a significantly faster time to extinction for all populations of P.aurelia collectively (Hazard Ratio (HR) = 2.56 [1.00, 6.57]; p = 0.051; Fig. 2). When looking at how nutrient affected the hazard risk of each clonal line individually, we saw a significant increase in the hazard risk for EV2 populations along an increasing nutrient concentration gradient (HR = 5.5 [1.13, 26.9]; p = 0.035; Fig. 3), but did not observe any significant effect of nutrient on the time to extinction for both FD4 and AUR (p > 0.05; Fig. 3).

Increasing nutrient concentrations resulted in a significant increase in population stability (i.e., decrease in CV) for the population of P.aurelia that does not induce (EV2) and the population that remains in a defended state (FD4; Table 1; Panels B and C of Figs. 4 and S2). For the population that displays an inducible defense (AUR) the effect of increasing nutrients was not significant, however the trend in the data suggested that increases in nutrient concentration were resulting in a stabilizing effect on the AUR populations (Table 1; Panel A of Figs. 4 and S2). The stabilization of populations occurred in the first time window (i.e, days 9–18) for the AUR and EV2 populations (Panels A and C of Figs. 4 and S2), and the stabilization of the FD4 population occurred in the second time step (i.e., days 21–30; Panel B of Figs. 4 and S2).

Table 1

Results from mixed effects models investigating the effects of increasing nutrient concentration and time (i.e., either days 9–18 or days 21–30) on the population stability (i.e., coefficient of variation) of the protozoa, *Paramecium aurelia*, over a 40-day experiment. The model accounts for effects from time block. Significant results (p ≤ 0.05) are in bold.
Clone	Factor	DF	F-value	p-value
AUR	Intercept	1	54.0	< 0.0001
	Nutrient	1	2.92	0.09
	Time Window	1	4.62	0.04
	Nutrient* Time Window	1	2.67	0.11
EV2	Intercept	1	56.0	< 0.0001
	Nutrient	1	11.0	0.002
	Time Window	1	2.10	0.15
	Nutrient* Time Window	1	1.62	0.21
FD4	Intercept	1	52.6	< 0.0001
	Nutrient	1	4.70	0.04
	Time Window	1	0.40	0.53
	Nutrient* Time Window	1	2.40	0.13

We found mixed support of the Paradox of Enrichment (POE) when evaluating both time to extinction and population stability. In fact, we found evidence in support of the POE when looking at time to extinction, yet found evidence against the POE when evaluating the coefficient of variation. Only the most vulnerable P.aurelia population (EV2) experienced a faster time to extinction along an increasing nutrient gradient, while for the other two populations increasing nutrients had no significant effect on time to extinction. These findings suggest that defense expression, whether inducible or permanent, resulted in increased survival at high levels of nutrient concentration, potentially providing evidence of a stabilizing mechanism of the POE. Conversely, increasing nutrient concentrations resulted in an increase in population stability for all three clonal populations when utilizing the coefficient of variation. These results demonstrate that, within this protist-flatworm system, we were only able to illustrate the instability proposed by Rosenzwieg (1973) when using one of two stability metrics. These results suggest that evaluating population stability is highly affected by the stability metric chosen to analyze the data, and that there may be other factors not evaluated in this study that may be resulting in this discrepancy between stability metrics. In all, this research demonstrates the mixed results that can occur when different stability metrics are chosen, and that additional research is needed to fully evaluate the stabilizing potential of inducible defenses within this system.

Research on the POE spanning nearly 50 years has shown a perplexing mix of results that both support and reject the POE in various systems (Fussmann et al., 2000; Luckinbill, 1973; Persson et al., 2001; Roy & Chattopadhyay, 2007b). Our results add to the conflicting conclusions of past work. Past research shows that there may be complexities that prevent observing the POE within systems such as ours. For example, Jensen & Ginsburg (2005) argue that most systems are not sufficiently simple enough to meet the assumptions of the paradox, and therefore result in data that reject the POE. For example, we assumed that we were testing a bitrophic system, yet within our system, protozoa do not directly consume the resource, but instead the bacteria that themselves feed on the resource. This potential for three trophic levels within our experiment does not however exclude the possibility for the POE to operate as there are several studies that demonstrate the POE in tritrophic systems (Meyer et al., 2012; Verschoor et al., 2004b). Other complexities that may violate the assumptions of the POE and have been shown to counter the effects of the POE include inedible or invulnerable prey (Abrams & Walters, 1996), unpalatable prey (Genkai-Kato & Yamamura, 1999), density dependence of predator death rates (Kirk, 1998), and spatial heterogeneity (Jensen & Ginzburg, 2005; Scheffer & De Boer, 1995). In our system, there may be unknown inducible defenses of the paramecium not accounted for within our study design that may have added to these mixed results. The inducible defenses of P.aurelia have only recently been investigated (Hammill et al., 2010), and therefore there may be other undocumented defense strategies of P.aurelia affecting our results in this study. Another poorly documented phenomenon that may have affected our results is the density dependent autotoxicity of predator death rates. Kirk (1998) found that a predatory rotifer (Synchaeta pectinate) released a toxin at high densities resulting in a toxin-mediated density dependent death rate. While there is no documentation of autotoxicity within S. virginianim, this may be a future area of research to investigate within this system to evaluate its potential role in countering the effects of the POE. Other predator-specific considerations that have been found to be stabilizing in other systems are predator interference (Rall et al., 2008), cannibalism (Chakraborty & Chattopadhyay, 2011), and the production of resting dormant eggs (Nakazawa et al., 2011). All of these ecological complexities may provide context to explain our mixed results in this study.

If any of these population-regulating or defense mechanisms were at play in this system, this might explain the stabilizing effect of nutrient enrichment that we observed in all three P.aurelia populations using the coefficient of variation. This suggests that, if a density-dependent mechanism is acting on either the predator or prey population, or that a portion of the prey population is unavailable to the predator, then the paradox would not exist as the nutrient enrichment is a benefit rather than a destabilizing risk. Yet, we did observe one result that supported the POE when looking at time to extinction. In the population that never induces (EV2), we found that there was a significantly shorter time to extinction along an increasing nutrient gradient. We did not observe any significant effect of increasing nutrient concentration on time to extinction for either of the other two populations (FD4 and AUR). If we were to expect the POE to exist in this system (and assuming that inducible defenses act to stabilize systems under enrichment) we would expect to observe an increase in the instability of the most vulnerable population (EV2) coupled with a quickened time to extinction due to its inability to maintain an invulnerable population to predators. Interestingly, increasing nutrient concentration resulted in a significantly more stable EV2 population when utilizing the coefficient of variation, even if this population did not persist as long as the other two populations.

We found competing results based on the stability metric used suggesting that more research is needed on the most appropriate stability metric or that multiple stability metrics are needed to analyze the POE. For example, Mougi & Nishimura (2008) emphasize the various ways in which stability can be analyzed and interpreted. They advocate that while most studies utilize local stability analyses, non-equilibrium dynamics may be of better use in systems with empirical and multispecies interaction data. By using stability analyses that assume systems are constantly undergoing some level of change, we can better interpret the results from studies investigating the effects of nutrient enrichment on population stability. Specifically, nonequilibrium ecology assumes that there is a limited capacity within a system to withstand change, and that these systems are heavily influenced by disturbance (Briske et al., 2017; DeAngelis & Waterhouse, 1987). However, there is much debate surrounding nonequilibrium versus equilibrium dynamics in ecology and the potential implications of using one approach over the other (Briske et al., 2017). Our research highlights a clear example of when the type of analysis used heavily affects the interpretation of the results. Additionally, it demonstrates that there may be other factors not assessed in this study that are influencing these population responses, and that each analysis is picking up different stability signals. These results support the idea of multidimensional ecological stability which was proposed by Donohue et al. (2013), stating that stability cannot be researched in isolation, rather that the different components of stability are interrelated. Future research should take into consideration the choice and variety of stability metrics when investigating stability in populations.

In all, our study adds to the mixed results of research that has investigated the validity of the POE. While we are unable to determine if inducible defenses are in fact a stabilizing mechanism within this system, there are several potential explanations and a growing body of research that demonstrate many possible stabilizing mechanisms for systems under enrichment. We did show that the stability metric used can have a significant effect on the interpretation of results and that future research should heavily consider which stability metric is most appropriate for their study goals. Additional research into the costs and benefits of inducible defenses, either in terms of the POE or how they allow prey populations to persist in changing or variable environments, can help us better understand population and community dynamics in a rapidly changing world.

Acknowledgements

Funding for this project was provided by the National Science Foundation (award number 1916610) and the Utah State Ecology Center.

Funding: Funding for this project was provided by the National Science Foundation (award number 1916610) and the Utah State Ecology Center.

Conflicts of Interest:The authors declare no conflicts of interest.

Ethics Approval: Not applicable

Consent to Participate: Not applicable

Consent for publication: Not applicable

Availability of data and material: Upon acceptance all data will be uploaded to Dryad.

Code availability: Not applicable

Authors Contributions: Conceptualization: CM, EH; Methodology: CM, EH, KH; Formal; Analysis: CM, EH; Investigation: CM, KH; Resources: EH; Data Curation: CM, KH; Writing – Original Draft: CM, EH; Writing – Review & Editing: CM, EH, KH; Visualization: CM; Funding Acquisition: EH

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14 Feb, 2024

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The effects of inducible defenses on population stability in Paramecium aurelia

Status:

Version 1

Abstract

Figures

Introduction

Materials and Methods

Study Species

Study Design

Statistical Methods

Results

Descriptive Stats

Paradox of enrichment

Discussion

Declarations

References

Supplementary Files

Status:

Version 1