Species abundance distributions and richness estimations in fungal metagenomics--lessons learned from community ecology

Results of diversity and community ecology studies strongly depend on sampling depth. Completely surveyed communities follow log-normal distribution, whereas power law functions best describe incompletely censused communities. It is arguable whether the statistics behind those theories can be applied to voluminous next generation sequencing data in microbiology by treating individual DNA sequences as counts of molecular taxonomic units (MOTUs). This study addresses the suitability of species abundance models in three groups of plant-associated fungal communities - phyllosphere, ectomycorrhizal and arbuscular mycorrhizal fungi. We tested the impact of differential treatment of molecular singletons on observed and estimated species richness and species abundance distribution models. The arbuscular mycorrhizal community of 48 MOTUs was exhaustively sampled and followed log-normal distribution. The ectomycorrhizal (153 MOTUs) and phyllosphere (327 MOTUs) communities significantly differed from log-normal distribution. The fungal phyllosphere community in particular was clearly undersampled. This undersampling bias resulted in strong sensitivity to the exclusion of molecular singletons and other rare MOTUs that may represent technical artefacts. The analysis of abundant (core) and rare (satellite) MOTUs clearly identified two species abundance distributions in the phyllosphere data - a log-normal model for the core group and a log-series model for the satellite group. The prominent log-series distribution of satellite phyllosphere fungi highlighted the ecological significance of an infrequent fungal component in the phyllosphere community.     

Chemotactic cell movement during Dictyostelium development and gastrulation

Many developmental processes involve chemotactic cell movement up or down dynamic chemical gradients. Studies of the molecular mechanisms of chemotactic movement of Dictyostelium amoebae up cAMP gradients highlight the importance of PIP3 signaling in the control of cAMP-dependent actin polymerization, which drives the protrusion of lamellipodia and filopodia at the leading edge of the cell, but also emphasize the need for myosin thick filament assembly and motor activation for the contraction of the back of the cell. These process become even more important during the multicellular stages of development, when propagating waves of cAMP coordinate the chemotactic movement of tens of thousands of cells, resulting in multicellular morphogenesis. Recent experiments show that chemotaxis, especially in response to members of the FGF, PDGF and VEGF families of growth factors, plays a key role in the guidance of mesoderm cells during gastrulation in chick, mouse and frog embryos. The molecular mechanisms of signal detection and signaling to the actin-myosin cytoskeleton remain to be elucidated.     

Hes1 Increases the Invasion Ability of Colorectal Cancer Cells via the STAT3-MMP14 Pathway

The Notch pathway contributes to self-renewal of tumor-initiating cell and inhibition of normal colonic epithelial cell differentiation. Deregulated expression of Notch1 and Jagged1 is observed in colorectal cancer. Hairy/enhancer of split (HES) family, the most characterized targets of Notch, involved in the development of many cancers. In this study, we explored the role of Hes1 in the tumorigenesis of colorectal cancer. Knocking down Hes1 induced CRC cell senescence and decreased the invasion ability, whereas over-expression of Hes1 increased STAT3 phosphorylation activity and up-regulated MMP14 protein level. We further explored the expression of Hes1 in human colorectal cancer and found high Hes1 mRNA expression is associated with poor prognosis in CRC patients. These findings suggest that Hes1 regulates the invasion ability through the STAT3-MMP14 pathway in CRC cells and high Hes1 expression is a predictor of poor prognosis of CRC.     

Improved species‐occurrence predictions in data‐poor regions: using large‐scale data and bias correction with down‐weighted Poisson regression and Maxent

Species distribution modelling (SDM) has become an essential method in ecology and conservation. In the absence of survey data, the majority of SDMs are calibrated with opportunistic presence‐only data, incurring substantial sampling bias. We address the challenge of correcting for sampling bias in the data‐sparse situations. We modelled the relative intensity of bat records in their entire range using three modelling algorithms under the point‐process modelling framework (GLMs with subset selection, GLMs fitted with an elastic‐net penalty, and Maxent). To correct for sampling bias, we applied model‐based bias correction by incorporating spatial information on site accessibility or sampling efforts. We evaluated the effect of bias correction on the models’ predictive performance (AUC and TSS), calculated on spatial‐block cross‐validation and a holdout data set. When evaluated with independent, but also sampling‐biased test data, correction for sampling bias led to improved predictions. The predictive performance of the three modelling algorithms was very similar. Elastic‐net models have intermediate performance, with slight advantage for GLMs on cross‐validation and Maxent on hold‐out evaluation. Model‐based bias correction is very useful in data‐sparse situations, where detailed data are not available to apply other bias correction methods. However, bias correction success depends on how well the selected bias variables describe the sources of bias. In this study, accessibility covariates described bias in our data better than the effort covariate, and their use led to larger changes in predictive performance. Objectively evaluating bias correction requires bias‐free presence–absence test data, and without them the real improvement for describing a species’ environmental niche cannot be assessed.     

Experimental evidence rejects pairwise modelling approach to coexistence in plant communities

Competition is often invoked as the cause of plant species loss with increasing system productivity. Experimental results for multispecies assemblages are virtually absent and mathematical models are thus used to explore the relationship between competition and coexistence. Modelling approaches to coexistence and diversity in competitive communities commonly employ Lotka-Volterra-type (LV) models with additive pairwise competitive effects.Using pairwise plant competition experiments, we calibrate the LV system and use it to predict plant biomass and coexistence in six three-species and one seven-species experimental mixture. Our results show that five out of the six three-species sets and the seven-species set deviate significantly from LV model predictions. Fitting an additional non-additive competition coefficient resulted in predictions that more closely matched the experimental results, with stable coexistence suggested in all but one case. These results are discussed with particular reference to the possible underlying mechanisms of coexistence in our experimental community. Modelling the effect of competition intensity on stability indicates that if non-additive effects occur, they will be relevant over a wide range of community sizes. Our findings caution against relying on coexistence predictions based on LV models.     

Neighbour identity modifies effects of elevated temperature on plant performance in the High Arctic

Competition among plants in extreme environments such as the High Arctic has often been described as unimportant, or even nonexistent; environmental factors are thought to overrule any negative plant–plant interactions. However, few studies have actually addressed this question experimentally in the Arctic, and those that did found only little evidence for competition. Such species interactions will presumably become more important in the future, as Global Climate Change takes effect on terrestrial ecosystems. We investigated plant–plant interactions in the High Arctic, following the growth of Luzula confusa and Salix polaris in pure and mixed stands, and under elevated‐temperature treatment over 2 years. To understand the mechanisms of competition, a parallel experiment was undertaken in phytotrons, manipulating competition, temperature and nutrient availability. Our findings indicate that competition is acting in the natural vegetation, and that climatic warming will alter the balance of interactions in favour of the dwarf shrub S. polaris. The phytotron experiment suggested that the mechanism is a higher responsiveness of Salix to nutrient availability, which increased under warming in the field. While Luzula showed a positive response to higher temperature in the lab, its performance in mixed stands in the field was actually reduced by warming, indicating a competitive repression of growth by Salix. The growth of Salix was also reduced by the presence of Luzula, but it was still able to profit from warming. Our findings suggest that climatic warming will result in greater shrub dominance of High Arctic tundra, but we also conjecture that grazing could reverse the situation to a graminoid‐dominated tundra. These two divergent scenarios would have different implications for ecosystem feedbacks to climatic change.     

Towards novel approaches to modelling biotic interactions in multispecies assemblages at large spatial extents

Aim Biotic interactions – within guilds or across trophic levels – have widely been ignored in species distribution models (SDMs). This synthesis outlines the development of ‘species interaction distribution models’ (SIDMs), which aim to incorporate multispecies interactions at large spatial extents using interaction matrices. Location Local to global. Methods We review recent approaches for extending classical SDMs to incorporate biotic interactions, and identify some methodological and conceptual limitations. To illustrate possible directions for conceptual advancement we explore three principal ways of modelling multispecies interactions using interaction matrices: simple qualitative linkages between species, quantitative interaction coefficients reflecting interaction strengths, and interactions mediated by interaction currencies. We explain methodological advancements for static interaction data and multispecies time series, and outline methods to reduce complexity when modelling multispecies interactions. Results Classical SDMs ignore biotic interactions and recent SDM extensions only include the unidirectional influence of one or a few species. However, novel methods using error matrices in multivariate regression models allow interactions between multiple species to be modelled explicitly with spatial co‐occurrence data. If time series are available, multivariate versions of population dynamic models can be applied that account for the effects and relative importance of species interactions and environmental drivers. These methods need to be extended by incorporating the non‐stationarity in interaction coefficients across space and time, and are challenged by the limited empirical knowledge on spatio‐temporal variation in the existence and strength of species interactions. Model complexity may be reduced by: (1) using prior ecological knowledge to set a subset of interaction coefficients to zero, (2) modelling guilds and functional groups rather than individual species, and (3) modelling interaction currencies and species’ effect and response traits. Main conclusions There is great potential for developing novel approaches that incorporate multispecies interactions into the projection of species distributions and community structure at large spatial extents. Progress can be made by: (1) developing statistical models with interaction matrices for multispecies co‐occurrence datasets across large‐scale environmental gradients, (2) testing the potential and limitations of methods for complexity reduction, and (3) sampling and monitoring comprehensive spatio‐temporal data on biotic interactions in multispecies communities.     

The responses of grassland plants to experimentally simulated climate change depend on land use and region

Macroclimatic niche properties derived from species distribution ranges are fundamental for projections of climate change impacts on biodiversity. However, it has been recognized that changes in regional or local distribution patterns also depend on interactions with land use. The reliability and transferability of large scale geographic predictions to small scale plant performance need to be tested experimentally. Thus, we asked how grassland plant species pairs with different macroclimatic niche properties respond to increased spring temperature and decrease summer precipitation in three different land‐use types. An experiment was carried out in the framework of the German Biodiversity Exploratories simulating climate change in 45 experimental plots in three geographical regions (Schorfheide‐Chorin, Hainich‐Dün, Schwäbische Alb) and three grassland management types (meadow, pasture, mown pasture). We planted six plant species as phytometers, each two of them representing congeneric species with contrasting macroclimatic niches and recorded plant survival and growth over 1 year. To quantify the species macroclimatic niches with respect to drought tolerance, the species’ distribution ranges were mapped and combined with global climate data. The simulated climate change had a general negative effect on plant survival and plant growth, irrespective of the macroclimatic niche characteristics of the species. Against expectation, species with ranges extending into drier regions did not generally perform better under drier conditions. Growth performance and survival was best in mown pastures, representing a quite intensive type of land use in all study regions. Species with higher macroclimatic drought tolerance were generally characterized by lower growth rates and higher survival rates in land‐use types with regular mowing regimes, probably because of reduced competition in the growing season. In conclusion, plant species with similar climatic niche characteristics cannot be expected to respond consistently over different regions owing to complex interactions of climate change with land use practices.