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

Massa SI, Paulino CM, Serrão EA, Duarte CM, Arnaud-Haond S - BMC Ecol. (2013)

Bottom Line: 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.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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: IFREMER, Bd Jean Monnet, BP 171, Sète 34203, France. Sophie.Arnaud@ifremer.fr.

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.

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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).
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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).

Mentions: Genotyping of the 376 collected clones returned a total of 343 individuals, fully-genotyped at all loci, of which 164 were distinct MLGs. Allelic richness in one thousand possible combinations of 3, 6 and 9 genotypes ranged from 16 to 31 at G = 3, 24 to 42 at G = 6, and 31 to 48 at G = 9 (Figure 1d), with hardly any overlap between the minimum and the maximum levels (Figure 1b, c). A strong correlation between genotypic and allelic richness was observed at the lower levels of genotypic richness, between 1 and 20 (Figure 1b, c), representative of levels commonly manipulated in experiments (r = 0.904, p < 0.001), although it became marginal at higher levels of genotypic richness more typically observed in natural meadows (G > 20). Across 1000 combinations, Â was 23.93 ± 2.63 for G = 3, 33.44 ± 2.97 for G = 6 and 39.39 ± 2.96 for G = 9, and specific low, medium and high levels of allelic richness had to be defined independently for each MLG level. As a result, identical levels of allelic richness could not be standardized for the three genotypic richness levels, and a fractional factorial design was obtained with five levels corresponding to 16, 25, 31, 41 and 47 alleles to distribute among low, medium and high levels of genotypic richness as detailed in Table 2 (Figure 1d). These levels were therefore not equivalent among genotypic richness plots. As an example, the highest level of allelic richness in plots with three genotypes was the same as the intermediate level for 6 genotypes and the lowest level for 9 genotypes (Figure 1d). There was a clear increase in allelic richness levels parallel to the increase in genotypic richness (Figures 1, 2). As a consequence, combined effects of allelic and genotypic diversities could not be simply disentangled through a two-way ANOVA analysis.


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

Massa SI, Paulino CM, Serrão EA, Duarte CM, Arnaud-Haond S - BMC Ecol. (2013)

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).
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3818440&req=5

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).
Mentions: Genotyping of the 376 collected clones returned a total of 343 individuals, fully-genotyped at all loci, of which 164 were distinct MLGs. Allelic richness in one thousand possible combinations of 3, 6 and 9 genotypes ranged from 16 to 31 at G = 3, 24 to 42 at G = 6, and 31 to 48 at G = 9 (Figure 1d), with hardly any overlap between the minimum and the maximum levels (Figure 1b, c). A strong correlation between genotypic and allelic richness was observed at the lower levels of genotypic richness, between 1 and 20 (Figure 1b, c), representative of levels commonly manipulated in experiments (r = 0.904, p < 0.001), although it became marginal at higher levels of genotypic richness more typically observed in natural meadows (G > 20). Across 1000 combinations, Â was 23.93 ± 2.63 for G = 3, 33.44 ± 2.97 for G = 6 and 39.39 ± 2.96 for G = 9, and specific low, medium and high levels of allelic richness had to be defined independently for each MLG level. As a result, identical levels of allelic richness could not be standardized for the three genotypic richness levels, and a fractional factorial design was obtained with five levels corresponding to 16, 25, 31, 41 and 47 alleles to distribute among low, medium and high levels of genotypic richness as detailed in Table 2 (Figure 1d). These levels were therefore not equivalent among genotypic richness plots. As an example, the highest level of allelic richness in plots with three genotypes was the same as the intermediate level for 6 genotypes and the lowest level for 9 genotypes (Figure 1d). There was a clear increase in allelic richness levels parallel to the increase in genotypic richness (Figures 1, 2). As a consequence, combined effects of allelic and genotypic diversities could not be simply disentangled through a two-way ANOVA analysis.

Bottom Line: 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.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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: IFREMER, Bd Jean Monnet, BP 171, Sète 34203, France. Sophie.Arnaud@ifremer.fr.

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.

Show MeSH
Related in: MedlinePlus