The outcomes of biological control programs can be highly variable, with natural enemies often failing to establish or spread in pest populations. This variability has posed a major obstacle in use of the bacterial parasite Pasteuria penetrans for biological control of Meloidogyne species, economically devastating plant-parasitic nematodes for which there are limited management options. A leading hypothesis for this variability in control is that infection is successful only for specific combinations of bacterial and nematode genotypes.  Under this hypothesis, failure of biological control results from the use of P. penetrans genotypes that cannot infect local Meloidogyne genotypes. We tested this hypothesis using isofemale lines of M. arenaria derived from a single field population and multiple sources of P. penetrans from the same and nearby fields. In strong support of the hypothesis, susceptibility to infection depended on the specific combination of host line and parasite source, with lines of M. arenaria varying substantially in which P. penetrans source could infect them. In light of this result, we tested whether using a diverse pool of P. penetrans could increase infection and thereby control. We found that increasing the diversity of the P. penetrans inoculum from one to eight sources more than doubled the fraction of M. arenaria individuals susceptible to infection and reduced variation in susceptibility across host lines. Together, our results highlight genotype-by-genotype specificity as an important cause of variation in biological control and call for the maintenance of genetic diversity in natural enemy populations.

Methods to collect the Data

Experiment 1: evaluating the significance of GxG interactions

The objective of this first experiment was to determine if attachment varies with the interaction of host line and parasite source. To do so, we measured attachment rate and load of J2s of four of our P. penetrans sources to our 13 M. arenaria isofemale lines. Attachment rate is the percentage of nematodes with one or more endospores attached. This measurement captures variation between host-parasite combinations in the percentage of hosts susceptible to infection. To measure attachment rate, we counted the number of nematodes with and without endospores for an average of 17.45 ± 0.70 (standard error) nematodes per flask. A second measurement, attachment load, is the number of endospores attached per nematode. Attachment load is estimated only for hosts with one or more endospores attached. Thus, attachment load can capture variation between host-parasite combinations in the number of parasites that hosts acquire, given that a host line is susceptible to infection by a parasite source (i.e., attachment rate > 0). To measure attachment load, we counted the number of endospores attached to the cuticle of up to 15 nematodes with endospores per flask.

Experiment 2: evaluating the effect of parasite diversity on infection probability  

The objective of this experiment was to determine if increasing parasite diversity increases attachment rate. We used all eight P. penetrans sources (Supplement 2.3) and four host lines (H04, H08, H10, H13) that varied from low to high mean attachment rates in Experiment 1. We used the eight individual sources of P. penetrans as our low-diversity treatment (P01 – P08). We created a high-diversity parasite source (MIX) by combining equal volumes of soil from each of the eight individual sources. This approach created parasite treatments that varied qualitatively in diversity; we did not quantify the diversity of the high vs. low diversity treatments, because we did not perform genetic analyses of field-sampled parasite sources. We tested each host line against the eight low-diversity parasite sources and the high-diversity parasite source, inoculating with equal volumes of soil across diversity treatments. Each host-parasite combination was replicated in four flasks. We measured attachment rate and load as above. 

Please find more details of the methods in the main manuscript and the supplemental material. 

Data 

The raw data collected from Experiment 1 is in the file FM&AG_2022_Exp1.csv

The raw data collected from Experiment 2 is in the file FM&AG2022_Exp2.csv

The other file is the Rscript for the whole analyses we did that are presented in the results of the manuscript. (Rscript_FM&AG_2022EAFinal)

The data files are saved in CSV. We used Excel to build the data set and R to analyse the data.