Diatom Cooccurrence Shows Less Segregation than Predicted from Niche Modeling

Abstract

Species cooccurrence patterns give significant insights into the processes shaping communities. While biotic interactions have been widely studied using cooccurrence analyses in animals and larger plants, studies about cooccurrences among micro-organisms are still relatively rare. We examined stream diatom cooccurrences in France through a national database of samples. In order to test the relative influence of environmental, biotic and spatial constraints on species' incidence distribution, cooccurrence and nestedness patterns of real communities were compared with the patterns generated from a set of standard and environmentally constrained null models. Real communities showed a higher level of segregation than the most conservative standard null models, but a general aggregation of cooccurrences when compared to environmentally constrained null models. We did not find any evidence of limiting similarity between cooccurring species. Aggregations of species cooccurrences were associated with the high levels of nestedness. Altogether, these results suggested that biotic interactions were not structuring cooccurrences of diatom species at our study scale. Instead, the patterns were more likely to be related with colonization patterns, mass effect, and local temporal dynamics of diatom biofilms. We further highlight that the association of standard and environmentally constrained null models may give realistic insight into the cooccurrence patterns of microbial communities.

Fig 1. Location of sampling sites in France.

Fig 2. Triplot of the db-RDA.

(Alti = Altitude, Dist = Distance from the source, Slop = Slope, Oxyg = concentration of dissolved oxygen, InoN = total inorganic nitrogen, PO4 = phosphate concentration, Calc = calcium concentration, HCO3 = hydrogen carbonate concentration, Temp = temperature, SusM = suspended solids concentration, NO3 = nitrate concentration)

Fig 3. C-scores of pseudo-community matrices, in comparison to the C-score of the real matrix, in the different datasets.

Bold segments represent 95% of the distributions. Perpendicular marks represent the median of distributions. The arrow indicates the value of the real community matrix. The models with names beginning by “NullMod” correspond to standard null models, those with names beginning by “Logit” correspond to environmentally constrained null models based on logistic regression predictions and those beginning by “RandFor” correspond to constrained null models based on Random Forest predictions. At the end of the name “FE” stands for occurrences Fixed and Equiprobable site richnesses, “FF” stands for occurrences and richnesses Fixed, “PF” stands for proportional occurrences (depending on pseudo-probabilities) and fixed richnesses, and “PP” stands for proportional occurrences and richnesses.

Fig 4. Relationship between C-scores and NODF nestedness indices of real and pseudo-communities.

Lines crossing the symbols represent standard deviations of the pseudo-community indices. The dashed grey lines represent linear models fitted on the 6000 pseudo-communities. The dotted grey lines represent the predictive confidence interval of these models. For model names, see Table 1.