Plant–mycorrhizal fungus co-occurrence network lacks substantial structure Francisco Encinas-Viso, David Alonso, John N. Klironomos, Rampal S. Etienne and Esther R. Chang
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- Niche-based factors
- Limitations and future directions
Spatial overlap Spatial overlap as a predictor of species interaction has been used to test competition among plants (Roxburgh and Chesson, 1998) and to describe plant-animal mutualistic networks (Vázquez et al. 2009). Vázquez et al. (2009) showed that spatial overlap, in combination with temporal variation and species abundance, is a good predictor of species interac- tions. Moreover, a simulation study by Morales and Vázquez (2008) indicated that spatial aggregation of individuals and limited dispersal strongly affects network statistics (e.g. connectance, nestedness), thus affecting the probability of species interactions. We obtained different predictions when incorporating spatial auto-correlation in our null models. More signifi- cant spatial co-occurrences were found when spatial auto- correlation was not considered. All metrics pointed out at the high aggregation between the plant species Helictotrichon
cal evidence from greenhouse experiments has indicated that these species interact positively (Klironomos 2003). In our analysis, the high aggregation of Bromus inermis and Glomus etunicatum seemed to be less strong than predicted by mutual dependence; however experimental evidence has 465 of how to identify those ‘real’ interactions and avoid false positives. Different methods exist for this purpose, such as Bayesian methods and Bonferroni corrections (Gotelli and Ulrich 2010), but even when using these methods, one has to be cautious. A priori biological knowledge and the use of specific predictor variables (abiotic or biotic) are important to avoid false-positives. Moreover, AMF sporulation changes depending on physiological and environmental conditions and our AMF data may be biased by cultivation method because we used trap cultures (Sýkorová et al. 2007). How- ever, the alternative, using molecular methods, has the dis- advantage of allowing only a relatively low number of plant individuals to be sampled per species, which could increase type II errors (false negatives). For example, the number of individuals sampled per plant species in the dataset used by Opik et al. (2009), Davison et al. (2011) and Chagnon et al. (2012) varied from 6 to 31 (and these ranged over different successional stages and seasons). In the Montesinos-Navarro et al. (2012) dataset, this number ranged from 1 (for almost a third of the plant species) to 18. In contrast, records for plant species in our data set ranged from 73 to 1292, which is one to three orders of magnitude higher in sampling effort. It must be noted that the species concept is still ambigu- ous for AMF (Redecker et al. 2003, van der Heijden and Scheublin 2007). Given the complicated population genet- ics of AMF species and the current lack of consensus in assigning taxa with molecular methods (Kruger et al. 2012, Redecker et al. 2013), identifying AMF species by spore morphology seems more consistent. Some AMF types can only be detected using spore morphology, although others are only detected using molecular tools (Clapp et al. 2002). Ideally, a combination of the two methods is needed to cover the whole spectrum of AMF in a community. However, there is no doubt that the development of next-generation sequencing tools has opened up many new possibilities for studying AMF communities and new studies of plant–AMF networks should take advantage of that, but without neglect- ing spatial structure. Finally, we only studied one snapshot of the community but, there is evidence for phenology and seasonal dynam- ics being crucial in the assembly of AMF communities (Dumbrell et al. 2011). As these factors have been shown to be important in shaping in plant–animal mutualistic webs as well (Vázquez et al. 2009), future studies should consider spatio-temporal variability when investigating plant-AMF networks (but see Bennett et al. 2013). We have shown that stochastic processes seem to be important for at least the first step in assembly of our plant– AMF community, the probability of encountering potential partners. Thus, plants and AMF species do not track each other with high efficacy over the space. This gives support to recent evidence that stochasticity is very important in struc- turing AMF communities (Dumbrell et al. 2010a, Lekberg et al. 2012).
Acknowledgements – R. S. Etienne and E. R. Chang share last authorship. We would like to thank Roger Guimerá for providing us with the simulated annealing algorithm to estimate network modularity, the Natural Sciences and Engineering Research Coun- cil of Canada (NSERC) for a post doctoral fellowship awarded to ERC, the Spanish ‘Ministerio de Economía y Competitividad’ Rosendahl 2008). We argue, however, that invoking niche related processes in plant–AMF networks to explain nested- ness must be done cautiously, as it is highly dependent on the null model. Niche-based factors The predictions of the ENV null model did not differ from the CSR null model, indicating that pH and organic matter (OM) cannot predict the distribution of plant–AMF co-occurrences in this community. Hence, we do not find any clear evidence of niche-related processes based upon abi- otic factors in shaping this web, contrary to other studies that have found that pH is an important factor structuring AMF communities (van Aarle et al. 2002, Dumbrell et al. 2010b). However, the regression analysis showed that plant and AMF species frequency in a spatial grid was positively associated with broad ranges of pH and OM. Thus, plants and AMF seem to be spatially distributed according to gra- dients of pH and OM, although these abiotic factors do not predict how often plant and AMF species co-occurr. Even though the spatial variation of pH and OM was very low in this homogeneous old field site (m
6.8, s pH 0.5;
m OM 6, s
OM 1.3), the frequency of both plant and AMF species still responded significantly to niche breadth. How- ever, species establishment at a specific site may be more influenced by the probability that it reaches that site before a competitor does than by its ability of establishing at that site. Thus, the positive relationship between niche breadth and species frequency can be partially explained by competitive interactions and dispersal limitation. Moreover, abundant species are more likely to encounter broader ranges of pH and OM. The partial Mantel test revealed a strong associa- tion between plant and AMF when conditioned by abiotic factors (pH and OM). However, a recent study has shown that high spatial autocorrelation in the variables can generate type I errors (Guillot and Rousset 2013); therefore, we need to be cautious with this result. We argue that a combination of dispersal limitation, competition and priority effects may be stronger than the effect of abiotic niche breadth in this co- occurrence network. However, both niche (pH) and stochas- tic processes might be shaping these communities (Dumbrell et al. 2010b).
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