Entangled effects of allelic and clonal (genotypic) richness in the resistance and resilience of experimental populations of the seagrass Zostera noltii to diatom invasion

Abstract

Background: The relationship between species diversity and components of ecosystem stability has been extensively studied, whilst the influence of the genetic component of biodiversity remains poorly understood. Here we manipulated both genotypic and allelic richness of the seagrass Zostera noltii, in order to explore their respective influences on the resistance of the experimental population to stress. Thus far intra-specific diversity was seldom taken into account in management plans, and restoration actions showed very low success. Information is therefore needed to understand the factors affecting resistance and resilience of populations.

Results: Our results show a positive influence of both allelic and genotypic richness on the resistance of meadows to environmental perturbations. They also show that at the low genotypic (i.e. clonal) richness levels used in prior experimental approaches, the effects of genotypic and allelic richness could not be disentangled and allelic richness was a likely hidden treatment explaining at least part of the effects hitherto attributed to genotypic richness.

Conclusions: Altogether, these results emphasize the need to acknowledge and take into account the interdependency of both genotypic and allelic richness in experimental designs attempting to estimate their importance alone or in combination. A positive influence of allelic richness on resistance to perturbations, and of allelic richness combined with genotypic richness on the recovery (resilience) of the experimental populations is supported by differential mortality. These results, on the key species structuring of one of the most threatened coastal ecosystem worldwide, seagrass meadows, support the need to better take into account the distinct compartments of clonal and genetic diversity in management strategies, and in possible restoration plans in the future.

Figure 1

Ideal vs. possible experimental designs. 1a) Ideal experimental design for each of the 4 plots containing 9 subplots of 27 shoots, with crossed levels of genotypic and allelic richness designed to disentangle their respective effects. b) Frequency distribution of allelic richness (in total number of alleles) across the three levels of genotypic richness (3 MLGs in light grey, 6 MLGs in medium grey and 9 MLGs in dark grey). c) Evolution of allelic richness for a broader range of levels of genotypic richness. d) Best possible design at the levels of genotypic richness manipulated in the experiments, illustrating the composition of each of the 4 plots made of 9 subplots of 27 shoots each, in terms of allelic richness with increasing levels of genotypic richness. The schemes of plot are designed with nested and increasing orders of richness for illustrative proposes, but the positions of the 36 sub-plots in the aquaculture tank where the experiment took place were randomized.

Figure 2

Evolution of allelic and genotypic richness with time. Boxplots illustrating the relationships between both allelic (left) and genotypic (right) richness and the number of surviving shoots, after the diatom bloom (top, resistance) and after 10 months survey (bottom, resilience). These graphs illustrate the tendency that could be misleadingly attributed to each parameter alone if ignoring the parallel increase of the other (“hidden effect” illustrated in the upper rectangles with arrows). In regression analysis associated to those graphs, a correlation would be detected between each estimator of richness and the resistance of subplots (upper part of the graphs; p = 0.002 for allelic richness and p = 0.015 for genotypic richness), and only the “genotypic richness” analysis would show a positive relationship with resilience (bottom part of the graphs; p = 0.171 for allelic richness, p = 0.025 for genotypic richness).

Figure 3

Combined effect of allelic and genotypic richness on survival. Mean shoot density for the five levels of allelic richness (16, 25, 31, 41 and 47) in the three genotypic richness levels (3 MLGs in light gray, 6 MLGs in medium-dark gray and 9 MLGs in dark gray). Top: for the first count. Bottom: for the last count. All values are represented by 25th and 75th percentile and minimum and maximum values.

Figure 4

Path analysis of allelic and genotypic richness. Path analysis showing the direct effects, equivalent to correlation coefficients, of allelic richness (A) and genotypic richness (G) on the resistance to perturbation (as the number of shoots having survived the perturbation). The coefficient linking A and G is the correlation coefficient between these two components of genetic diversity. Path coefficients calculated after Alvin and Hauser (1975), and all are supported by p-values < 0.05.