Supplementary information for Altermatt et al. Methods in Ecology and Evolution. DOI: 10.1111/2041-210X.12312

“Big answers from small worlds: a user's guide for protist microcosms as a model system in ecology and evolution”

Altermatt F, Fronhofer EA, Garnier A, Giometto A, Hammes F, Klecka J, Legrand D, Mächler E, Massie TM, Pennekamp F, Plebani M, Pontarp M, Schtickzelle N, Thuillier V & Petchey OL

2.12 Interaction strengths

Introduction

Measuring the strength of competition, predation and host-parasite interactions is often needed. Direct observations can be done in some cases but measuring carrying capacities in individual species and in two-species combinations is usually required to estimate the strength of interspecific competition. Interactions between predators and prey can be quantified via functional response experiments and by fitting a suitable dynamical model to time series of predator and prey population sizes. While this is relatively complex for many systems, protist microcosm are actually a feasible study system to look at predator prey dynamics. In order to fit a suitable predator-prey model to time series in order to estimate the parameters of the functional response, we refer to more specialised literature (e.g., Harrison 1995).

Materials

Equipment

Only standard equipment is required (e.g., that described in sections 1.1, 1.2, 1.3, 1.4, 1.5, and perhaps 2.1, 2.2, 2.3, 2.4)

Reagents

  • Lugol's solution can be used to preserve samples

Procedure

Competition

This is a simple procedure to estimate the strength of interspecific competition in a pairwise setting. For a detailed discussion and methodological guidelines on how to measure and calculate competitive interaction in protist communities, see Carrara et al. (Carrara et al. 2014a; Carrara et al. 2014b). These methods depend on measuring growth rate and carrying capacity of individual species in isolation first. Then species are mixed at half-carrying capacity to measure changes in population density caused by competition.

  1. Prepare a bottle of a suitable medium.

  2. Set up cultures of individual species at low density to measure growth curves to estimate growth rate (r) and carrying capacity (K). You can skip this step if you already have reliable measurements of these parameters.

  3. Take a sample of the two cultures at carrying capacity and estimate population density in these particular cultures.

  4. Take 5 ml of the culture of one species and put it to a suitable bottle (volume at least 20 ml).

  5. Add 5 ml of the second species.

  6. Create several replicates (at least four, preferably six to eight).

  7. Note the time of the beginning of the experiment and the density of the starting cultures (see point 3. above).

  8. Keep the mixed culture in a climate chamber with controlled temperature and suitable illumination for at least 10 days.

  9. Measure population density of both species at the end of the experiment. You can also do repeated measurements to get a two-species time series (this is not necessary but can decrease uncertainty).

  10. Fit a Lotka-Volterra model to the experimental measurements. You need to know r, K, initial density of both species and final density of both species to estimate competition coefficients. See also (Carrara et al. 2014a; Carrara et al. 2014b).

Predation

The procedures described below apply to predators feeding strictly on other protists and not on bacteria (e.g., Didinium). Some species feed on both bacteria and other protists. In such cases, predation rate (as a single parameter) can be estimated by fitting a Lotka-Volterra model described in the section for competition. In such case, one species will have a negative value and the other a positive value of the interaction coefficient. This approach can be also used when screening for potential predators among species whose diet is not well known. On the other hand, in predators feeding only on protists and not on bacteria, conducting functional response measurements is desirable.

Direct measurement of a functional response:

Detailed settings need to be adjusted according to the species used. Here we provide two examples of protocols used previously.

a. An example based on Hammill et al. (2010) using Paramecium as a prey and a small flatworm, Stenostomum, as a predator.

  1. Add a known number of prey individuals from the range of 1 to 60 (can be increased further to make sure that the functional response converges to an asymptote) to 500 microL of protist medium in a well plate. Instead of counting and transferring prey individuals one by one, you can prepare a series of cultures diluted to a varying degree and take a drop from the culture, count the number of prey individuals and use this drop as a source of prey for the experiment.

  2. Add one predator individual.

  3. Let the predator feed for 4 hours (the duration must be short enough so that prey reproduction can be neglected).

  4. Count the surviving prey individuals, or preserve the sample in Lugols’ solution (see section 1.8) and count the protists later.

Stenostomum has a relatively high consumption rate, up to ca. 10 Paramecium within four hours (Hammill, Petchey & Anholt 2010), which facilitates the measurements.

b. An example protocol based on Delong and Vasseur (2013) using Paramecium as a prey and Didinium as a predator.

  1. Prepare a series of cultures diluted to a varying degree and place a 50 μl drop from the culture into a Petri dish, count the number of prey individuals (a reasonable range of prey numbers would be ca. 1-20) and use this drop as a source of prey for the experiment.

  2. Add one predator individual in a known amount of medium (e.g. 20 μl) so that the total volume of the drop is known (in this case 70 μl).

  3. Close the Petri dish to minimise evaporation.

  4. Let the predator feed for 4 hours (the duration must be short enough so that prey reproduction can be neglected).

  5. Count the surviving prey individuals.

Delong and Vasseur (2013) measured maximum consumption rate by Didinium using this setup to be around 5 Paramecium consumed during two hours. Based on this, using a slightly longer duration (e.g. 4 hours) of the experiment would be preferable.

Estimating the parameters of a functional response from two-species time series:

Measuring interaction strength this way is more uncertain than measuring the functional response in short-term experiments described above. However it can be used in predators with very low predation rates. As long as one is interested in fitting predator-prey models (e.g., Lotka-Volterra), this method is more precise, because it allows fitting the interaction strength. Thus, the two methods differ in the quantities that they allow to measure.

  1. Prepare a bottle of suitable medium (see section 1.2 for details).

  2. Set up cultures of the prey species at low density to measure growth curves to estimate growth rate (r) and carrying capacity (K; see section 2.2 for details). You can skip this step if you already have reliable measurements of these parameters.

  3. Take a sample of the prey culture at carrying capacity and estimate population density in this particular culture (see section 2.2 or 2.3 for details).

  4. Take 10 ml of culture of the prey species and put it to a suitable bottle (volume at least 20 ml). Use larger volume if the predator occurs at low density in cultures. For example, for Didinium-Paramecium species combination, using 100 ml of medium would be preferable to decrease the effect of demographic stochasticity.

  5. Add a known number of predator individuals (within the range observed in stock cultures) and close the bottle (do not close the lid firmly to allow exchange of gases between the bottle and the surrounding air).

  6. Create several replicates (at least four, preferably six to eight).

  7. Note the exact time of the beginning of the experiment and the density of the starting cultures (see points 3. and 5. above).

  8. Keep the mixed culture in a climate chamber with controlled temperature and suitable illumination for at least 10 days.

  9. Measure population density of both species at regular intervals during the experiment to obtain a two-species time series (see section 2.11 for details).

  10. The suitable frequency depends on the generation time of your predator; measuring population density every 24 hours would be suitable for Didinium.

  11. Fit a suitable predator-prey model to your time series to estimate the parameters of the functional response. As this goes beyond the focus of our work, we recommend looking up the details for doing so in the relevant literature (Jost & Arditi 2001).

References

Carrara, F., Giometto, A., Seymour, M., Rinaldo, A. & Altermatt, F. (2014a) Experimental evidence for strong stabilizing forces at high functional diversity in aquatic microbial communities. Ecology, in press.

Carrara, F., Giometto, A., Seymour, M., Rinaldo, A. & Altermatt, F. (2014b) Inferring species interactions in ecological communities: a comparison of methods at different levels of complexity. Methods in Ecology and Evolution, in review.

Delong, J.P. & Vasseur, D.A. (2013) Linked exploitation and interference competition drives the variable behavior of a classic predator–prey system. Oikos, 122, 1393-1400.

Hammill, E., Petchey, O.L. & Anholt, B.R. (2010) Predator Functional Response Changed by Induced Defenses in Prey. American Naturalist, 176, 723-731.

Harrison, G.W. (1995) Comparing Predator-Prey Models to Luckinbill's Experiment with Didinium and Paramecium. Ecology, 76, 357-374.

Jost, C. & Arditi, R. (2001) From pattern to process: identifying predator–prey models from time-series data. Population Ecology, 43, 229-243.