Determining alternative models for vegetation response analysis: a non-parametric approach

Abstract. Vegetation models based on multiple logistic regression are of growing interest in environmental studies and decision making. The relatively simple sigmoid Gaussian optimum curves are most common in current vegetation models, although several different other response shapes are known. However, improvements in the technical means for handling statistical data now facilitate fast and interactive calculation of alternative complex, more data-related, non-parametric models. The aim in this study was to determine whether, and if so how often, a complex response shape could be more adequate than a linear or quadratic one. Using the framework of Generalized Additive Models, both parametric (linear and quadratic) and non-parametric (smoothed) stepwise multiple logistic regression techniques were applied to a large data set on wetlands and water plants and to six environmental variables: pH, chloride, orthophosphate, inorganic nitrogen, thickness of the sapropelium layer and depth of the water-body. All models were tested for their goodness-of-fit and significance. Of all 156 generalized additive models calculated, 77 % were found to contain at least one smoothed predictor variable, i.e. an environmental variable with a response better fitted by a complex, non-parametric, than by a linear or quadratic parametric curve. Chloride was the variable with the highest incidence of smoothed responses (48 %). Generally, a smoothed curve was preferable in 23 % of all species-variable correlations calculated, compared to 25 % and 18 % for sigmoid and Gaussian shaped curves, respectively. Regression models of two plant species are presented in detail to illustrate the potential of smoothers to produce good fitting and biologically sound response models in comparison to linear and polynomial regression models. We found Generalized Additive Modelling a useful and practical technique for improving current regression-based vegetation models by allowing for alternative, complex response shapes.     

Genome sequencing reveals fine scale diversification and reticulation history during speciation in Sus

Background

Elucidating the process of speciation requires an in-depth understanding of the evolutionary history of the species in question. Studies that rely upon a limited number of genetic loci do not always reveal actual evolutionary history, and often confuse inferences related to phylogeny and speciation. Whole-genome data, however, can overcome this issue by providing a nearly unbiased window into the patterns and processes of speciation. In order to reveal the complexity of the speciation process, we sequenced and analyzed the genomes of 10 wild pigs, representing morphologically or geographically well-defined species and subspecies of the genus Sus from insular and mainland Southeast Asia, and one African common warthog.

Results

Our data highlight the importance of past cyclical climatic fluctuations in facilitating the dispersal and isolation of populations, thus leading to the diversification of suids in one of the most species-rich regions of the world. Moreover, admixture analyses revealed extensive, intra- and inter-specific gene-flow that explains previous conflicting results obtained from a limited number of loci. We show that these multiple episodes of gene-flow resulted from both natural and human-mediated dispersal.

Conclusions

Our results demonstrate the importance of past climatic fluctuations and human mediated translocations in driving and complicating the process of speciation in island Southeast Asia. This case study demonstrates that genomics is a powerful tool to decipher the evolutionary history of a genus, and reveals the complexity of the process of speciation.     

Local Knowledge and Conservation of Seagrasses in the Tamil Nadu State of India

Local knowledge systems are not considered in the conservation of fragile seagrass marine ecosystems. In fact, little is known about the utility of seagrasses in local coastal communities. This is intriguing given that some local communities rely on seagrasses to sustain their livelihoods and have relocated their villages to areas with a rich diversity and abundance of seagrasses. The purpose of this study is to assist in conservation efforts regarding seagrasses through identifying Traditional Ecological Knowledge (TEK) from local knowledge systems of seagrasses from 40 coastal communities along the eastern coast of India. We explore the assemblage of scientific and local traditional knowledge concerning the 1. classification of seagrasses (comparing scientific and traditional classification systems), 2. utility of seagrasses, 3. Traditional Ecological Knowledge (TEK) of seagrasses, and 4. current conservation efforts for seagrass ecosystems. Our results indicate that local knowledge systems consist of a complex classification of seagrass diversity that considers the role of seagrasses in the marine ecosystem. This fine-scaled ethno-classification gives rise to five times the number of taxa (10 species = 50 local ethnotaxa), each with a unique role in the ecosystem and utility within coastal communities, including the use of seagrasses for medicine (e.g., treatment of heart conditions, seasickness, etc.), food (nutritious seeds), fertilizer (nutrient rich biomass) and livestock feed (goats and sheep). Local communities are concerned about the loss of seagrass diversity and have considerable local knowledge that is valuable for conservation and restoration plans. This study serves as a case study example of the depth and breadth of local knowledge systems for a particular ecosystem that is in peril.     

A predictive model for respiratory syncytial virus (RSV) hospitalisation of premature infants born at 33–35 weeks of gestational age,based on data from the Spanish FLIP study

Background

The aim of this study, conducted in Europe, was to develop a validated risk factor based model to predict RSV-related hospitalisation in premature infants born 33–35 weeks'' gestational age (GA).

Methods

The predictive model was developed using risk factors captured in the Spanish FLIP dataset, a case-control study of 183 premature infants born between 33–35 weeks'' GA who were hospitalised with RSV, and 371 age-matched controls. The model was validated internally by 100-fold bootstrapping. Discriminant function analysis was used to analyse combinations of risk factors to predict RSV hospitalisation. Successive models were chosen that had the highest probability for discriminating between hospitalised and non-hospitalised infants. Receiver operating characteristic (ROC) curves were plotted.

Results

An initial 15 variable model was produced with a discriminant function of 72% and an area under the ROC curve of 0.795. A step-wise reduction exercise, alongside recalculations of some variables, produced a final model consisting of 7 variables: birth ± 10 weeks of start of season, birth weight, breast feeding for ≤ 2 months, siblings ≥ 2 years, family members with atopy, family members with wheeze, and gender. The discrimination of this model was 71% and the area under the ROC curve was 0.791. At the 0.75 sensitivity intercept, the false positive fraction was 0.33. The 100-fold bootstrapping resulted in a mean discriminant function of 72% (standard deviation: 2.18) and a median area under the ROC curve of 0.785 (range: 0.768–0.790), indicating a good internal validation. The calculated NNT for intervention to treat all at risk patients with a 75% level of protection was 11.7 (95% confidence interval: 9.5–13.6).

Conclusion

A robust model based on seven risk factors was developed, which is able to predict which premature infants born between 33–35 weeks'' GA are at highest risk of hospitalisation from RSV. The model could be used to optimise prophylaxis with palivizumab across Europe.     

Diacylglycerols interact with the L2 lipidation site in TRPC3 to induce a sensitized channel state

Coordination of lipids within transient receptor potential canonical channels (TRPCs) is essential for their Ca²⁺ signaling function. Single particle cryo‐EM studies identified two lipid interaction sites, designated L1 and L2, which are proposed to accommodate diacylglycerols (DAGs). To explore the role of L1 and L2 in TRPC3 function, we combined structure‐guided mutagenesis and electrophysiological recording with molecular dynamics (MD) simulations. MD simulations indicate rapid DAG accumulation within both L1 and L2 upon its availability within the plasma membrane. Electrophysiological experiments using a photoswitchable DAG‐probe reveal potentiation of TRPC3 currents during repetitive activation by DAG. Importantly, initial DAG exposure generates a subsequently sensitized channel state that is associated with significantly faster activation kinetics. TRPC3 sensitization is specifically promoted by mutations within L2, with G652A exhibiting sensitization at very low levels of active DAG. We demonstrate the ability of TRPC3 to adopt a closed state conformation that features partial lipidation of L2 sites by DAG and enables fast activation of the channel by the phospholipase C‐DAG pathway.     

Identification of novel Y chromosome encoded transcripts by testis transcriptome analysis of mice with deletions of the Y chromosome long arm

Background  

The male-specific region of the mouse Y chromosome long arm (MSYq) is comprised largely of repeated DNA, including multiple copies of the spermatid-expressed Ssty gene family. Large deletions of MSYq are associated with sperm head defects for which Ssty deficiency has been presumed to be responsible.