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Mutualistic interactions drive ecological niche convergence in a diverse butterfly community.

ABSTRACT: Ecological communities are structured in part by evolutionary interactions among their members. A number of recent studies incorporating phylogenetics into community ecology have upheld the paradigm that competition drives ecological divergence among species of the same guild. However, the role of other interspecific interactions, in particular positive interactions such as mutualism, remains poorly explored. We characterized the ecological niche and inferred phylogenetic relationships among members of a diverse community of neotropical Müllerian mimetic butterflies. Müllerian mimicry is one of the best studied examples of mutualism, in which unpalatable species converge in wing pattern locally to advertize their toxicity to predators. We provide evidence that mutualistic interactions can drive convergence along multiple ecological axes, outweighing both phylogeny and competition in shaping community structure. Our findings imply that ecological communities are adaptively assembled to a much greater degree than commonly suspected. In addition, our results show that phenotype and ecology are strongly linked and support the idea that mimicry can cause ecological speciation through multiple cascading effects on species' biology.

SUBMITTER: Elias M

PROVIDER: S-EPMC2592358 | biostudies-literature | 2008 Dec

REPOSITORIES: biostudies-literature

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Mutualistic interactions drive ecological niche convergence in a diverse butterfly community.

Elias Marianne M Gompert Zachariah Z Jiggins Chris C Willmott Keith K

PLoS biology 20081201 12

Ecological communities are structured in part by evolutionary interactions among their members. A number of recent studies incorporating phylogenetics into community ecology have upheld the paradigm that competition drives ecological divergence among species of the same guild. However, the role of other interspecific interactions, in particular positive interactions such as mutualism, remains poorly explored. We characterized the ecological niche and inferred phylogenetic relationships among mem ...[more]

PMID: 19055316

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Project description:AimsTo identify putative mutualistic interactions driving community composition in polymicrobial chronic wound infections using metabolic modelling.Methods and resultsWe developed a 12 species metabolic model that covered 74% of 16S rDNA pyrosequencing reads of dominant genera from 2963 chronic wound patients. The community model was used to predict species abundances averaged across this large patient population. We found that substantially improved predictions were obtained when the model was constrained with genera prevalence data and predicted abundances were averaged over 5000 ensemble simulations with community participants randomly determined according to the experimentally determined prevalences. Staphylococcus and Pseudomonas were predicted to exhibit a strong mutualistic relationship that resulted in community growth rate and diversity simultaneously increasing, suggesting that these two common chronic wound pathogens establish dominance by cooperating with less harmful commensal species. In communities lacking one or both dominant pathogens, other mutualistic relationship including Staphylococcus/Acinetobacter, Pseudomonas/Serratia and Streptococcus/Enterococcus were predicted consistent with published experimental data.ConclusionsMutualistic interactions were predicted to be driven by crossfeeding of organic acids, alcohols and amino acids that could potentially be disrupted to slow chronic wound disease progression.Significance and impact of the studyApproximately 2% of the US population suffers from nonhealing chronic wounds infected by a combination of commensal and pathogenic bacteria. These polymicrobial infections are often resilient to antibiotic treatment due to the nutrient-rich wound environment and species interactions that promote community stability and robustness. The simulation results from this study were used to identify putative mutualistic interactions between bacteria that could be targeted to enhance treatment efficacy.

Project description:Biotic interactions are crucial for determining the structure and dynamics of communities; however, direct measurement of these interactions can be challenging in terms of time and resources, especially when numerous species are involved. Inferring species interactions from species co-occurrence patterns is increasingly being used; however, recent studies have highlighted some limitations. To our knowledge, no attempt has been made to test the accuracy of the existing methods for detecting mutualistic interactions in terrestrial ecosystems. In this study, we compiled two literature-based, long-term datasets of interactions between butterflies and herbaceous plant species in two regions of Germany and compared them with observational abundance and presence/absence data collected within a year in the same regions. We tested how well the species associations generated by three different co-occurrence analysis methods matched those of empirically measured mutualistic associations using sensitivity and specificity analyses and compared the strength of associations. We also checked whether flower abundance data (instead of plant abundance data) increased the accuracy of the co-occurrence models and validated our results using empirical flower visitation data. The results revealed that, although all methods exhibited low sensitivity, our implementation of the Relative Interaction Intensity index with pairwise null models performed the best, followed by the probabilistic method and Spearman's rank correlation method. However, empirical data showed a significant number of interactions that were not detected using co-occurrence methods. Incorporating flower abundance data did not improve sensitivity but enhanced specificity in one region. Further analysis demonstrated incongruence between the predicted co-occurrence associations and actual interaction strengths, with many pairs exhibiting high interaction strength but low co-occurrence or vice versa. These findings underscore the complexity of ecological dynamics and highlight the limitations of current co-occurrence methods for accurately capturing species interactions.

Project description:Bacterial interactions play significant roles in ecological functions in responding to anthropogenic interference and soil structure changes. However, it remains largely unknown how fertilizer regimes and soil particle sizes drive bacterial interactions. To evaluate bacterial interaction patterns in soil aggregates under long-term fertilizer treatments, we sampled nine bacterial co-occurrence communities and compared the difference between interspecies resource consumption patterns and network structure. Despite the differences between fertilizer treatments, the negative correlation ratios of interaction networks in soil aggregates were macroaggregates > microaggregates > silt + clays. Likewise, NPK-supplement (chemical fertilizer) had also decreased the number of positive correlations of the interaction network than M-supplement (organic fertilizer), regardless of the size of soil aggregates. Linear model analysis revealed that interspecies trophic patterns, including niche overlap and nestedness, drove bacterial competition in the interaction networks. Most importantly, interspecies niche overlap may be the intrinsic factor in the effects of fertilizer treatments and soil aggregates on bacterial interactions. This study enhances our understanding of the potential for changes in species trophic patterns and might guide the promotion of land management. IMPORTANCE Despite that the influence of soil structure and fertilizer treatments on the bacterial community has been widely studied, how they drive interspecies interactions has not been largely explored. Connectance and nestedness were significantly correlated with bacterial interactions, but no differences were found in different soil aggregates and fertilizer treatments. However, interspecies niche overlap could respond to soil aggregates and fertilizer treatments and ultimately drive the bacterial interactions. This study enhances our understanding of the mechanism of microbial interactions and highlights the importance of trophic patterns in the bacterial community. Our findings extend knowledge for nutrient availability on interspecific interactions.

Dataset Information

Mutualistic interactions drive ecological niche convergence in a diverse butterfly community.

Publications

Mutualistic interactions drive ecological niche convergence in a diverse butterfly community.

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