Environmental determinants of leaf litter ant community composition along an elevational gradient

Ant communities are extremely diverse and provide a wide variety of ecological functions in tropical forests. Here, we investigated the abiotic factors driving ant composition turnover across an elevational gradient at Mont Itoupé, French Guiana. Mont Itoupé is an isolated mountain whose top is covered by cloud forests, a biogeographical rarity that is likely to be threatened according to climate change scenarios in the region. We examined the influence of six soil, climatic, and LiDAR‐derived vegetation structural variables on leaf litter ant assembly (267 species) across nine 0.12‐ha plots disposed at three elevations (ca. 400, 600, and 800m asl). We tested (a) whether species cooccurring within a same plot or a same elevation were more similar in terms of taxonomic, functional, and phylogenetic composition, than species from different plots/elevations, and (b) which environmental variables significantly explained compositional turnover among plots. We found that the distribution of species and traits of ant communities along the elevational gradient was significantly explained by a turnover of environmental conditions, particularly in soil phosphorus and sand content, canopy height, and mean annual relative humidity of soil. Our results shed light on the role exerted by environmental filtering in shaping ant community assembly in tropical forests. Identifying the environmental determinants of ant species distribution along tropical elevational gradients could help predicting the future impacts of global warming on biodiversity organization in vulnerable environments such as cloud forests.     

Identification of protein-coding regions in DNA sequences using a time-frequency filtering approach

Accurate identification of protein-coding regions (exons) in DNA sequences has been a challenging task in bioinformatics. Particularly the coding regions have a 3-base periodicity, which forms the basis of all exon identification methods. Many signal processing tools and techniques have been applied successfully for the identification task but still improvement in this direction is needed. In this paper, we have introduced a new promising model-independent time-frequency filtering technique based on S-transform for accurate identification of the coding regions. The S-transform is a powerful linear time-frequency representation useful for filtering in time-frequency domain. The potential of the proposed technique has been assessed through simulation study and the results obtained have been compared with the existing methods using standard datasets. The comparative study demonstrates that the proposed method outperforms its counterparts in identifying the coding regions.     

Assessing metacommunity processes through signatures in spatiotemporal turnover of community composition

Although metacommunity ecology has been a major field of research in the last decades, with both conceptual and empirical outputs, the analysis of the temporal dynamics of metacommunities has only emerged recently and consists mostly of repeated static analyses. Here we propose a novel analytical framework to assess metacommunity processes using path analyses of spatial and temporal diversity turnovers. We detail the principles and practical aspects of this framework and apply it to simulated datasets to illustrate its ability to decipher the respective contributions of entangled drivers of metacommunity dynamics. We then apply it to four empirical datasets. Empirical results support the view that metacommunity dynamics may be generally shaped by multiple ecological processes acting in concert, with environmental filtering being variable across both space and time. These results reinforce our call to go beyond static analyses of metacommunities that are blind to the temporal part of environmental variability.     

pime: A package for discovery of novel differences among microbial communities

The data used for profiling microbial communities is usually sparse with some microbes having high abundance in a few samples and being nearly absent in others. However, current bioinformatics tools able to deal with this sparsity are lacking. pime (Prevalence Interval for Microbiome Evaluation) was designed to remove those taxa that may be high in relative abundance in just a few samples but have a low prevalence overall. The reliability and robustness of pime were compared against existing methods and tested using 16S rRNA independent data sets. pime filters microbial taxa not shared in a per treatment prevalence interval started at 5% prevalence with increasing increments of 5% at each filtering step. For each prevalence interval, hundreds of decision trees were calculated to predict the likelihood of detecting differences in treatments. The best prevalence‐filtered data set was user‐selected by choosing the prevalence interval that kept a large portion of the 16S rRNA sequences in the data set while also showing the lowest error rate. To obtain the likelihood of introducing type I error while building prevalence‐filtered data sets, an error detection step based was also included. A pime reanalysis of published data sets uncovered other expected microbial associations than previously reported, which may be masked when only relative abundance was considered.     

Negative relationship between interspecies spatial association and trait dissimilarity

Species’ response to environmental site conditions and neighborhood interactions are among the important drivers of species’ spatial distributions and the resultant interspecies spatial association. The importance of competition to interspecies spatial association can be inferred from a high degree of trait dissimilarity of the associated species, and vice versa for environmental filtering. However, because the importance of environmental filtering and competition in structuring plant communities often vary with spatial scale and with plant life stage, the species’ spatial association–trait dissimilarity relationship should vary accordingly. We tested these assumptions in a fully mapped 50‐ha subtropical evergreen forest of China, where we assessed the degrees of interspecies spatial associations between adult trees and between saplings at two different spatial scales (10 m versus 40 m) and measured the degrees of trait dissimilarity of the associated species using six traits (leaf area, specific leaf area, leaf dry‐matter content, wood density, wood dry‐matter content and maximum height). Consistent across spatial scales and plant life stages, the degree of interspecies spatial association and the degree of overall trait dissimilarity (i.e. all six traits together) were negatively correlated, suggesting that environmental filtering might help assemble functionally similar species in the forest under study. However, when we looked into the spatial association–trait dissimilarity relationship for individual traits, we found that the relationships between interspecies spatial associations and the dissimilarity of wood density and dry‐matter content were significant for adults but not for saplings, suggesting the importance of wood traits in species’ survival during ontogeny. We conclude that processes shaping interspecies spatial association are spatial scale and plant life stage dependent, and that the distributions of functional traits offer useful insights into the processes underlying community spatial structure.