A Unimodal Species Response Model Relating Traits to Environment with Application to Phytoplankton Communities.

In this paper we attempt to explain observed niche differences among species (i.e. differences in their distribution along environmental gradients) by differences in trait values (e.g. volume) in phytoplankton communities. For this, we propose the trait-modulated Gaussian logistic model in which the niche parameters (optimum, tolerance and maximum) are made linearly dependent on species traits. The model is fitted to data in the Bayesian framework using OpenBUGS (Bayesian inference Using Gibbs Sampling) to identify according to which environmental variables there is niche differentiation among species and traits. We illustrate the method with phytoplankton community data of 203 lakes located within four climate zones and associated measurements on 11 environmental variables and six morphological species traits of 60 species. Temperature and chlorophyll-a (with opposite signs) described well the niche structure of all species. Results showed that about 25% of the variance in the niche centres with respect to chlorophyll-a were accounted for by traits, whereas niche width and maximum could not be predicted by traits. Volume, mucilage, flagella and siliceous exoskeleton are found to be the most important traits to explain the niche centres. Species were clustered in two groups with different niches structures, group 1 high temperature-low chlorophyll-a species and group 2 low temperature-high chlorophyll-a species. Compared to group 2, species in group 1 had larger volume but lower surface area, had more often flagella but neither mucilage nor siliceous exoskeleton. These results might help in understanding the effect of environmental changes on phytoplankton community. The proposed method, therefore, can also apply to other aquatic or terrestrial communities for which individual traits and environmental conditioning factors are available

The effects of nickel on the structure and functioning of a freshwater plankton community under high dissolved organic carbon conditions : a microcosm experiment

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Predicting changes in ecosystem functioning from shifts in plant traits

Contains fulltext : 135500.pdf (publisher's version ) (Open Access

High dissimilarity within a multiyear annual record of pollen assemblages from a North American tallgrass prairie

Citation: Commerford, J. L., McLauchlan, K. K., & Minckley, T. A. (2016). High dissimilarity within a multiyear annual record of pollen assemblages from a North American tallgrass prairie. Ecology and Evolution, 6(15), 5273-5289. doi:10.1002/ece3.2259Grassland vegetation varies in composition across North America and has been historically influenced by multiple biotic and abiotic drivers, including fire, herbivory, and topography. Yet, the amount of temporal and spatial variability exhibited among grassland pollen assemblages, and the influence of these biotic and abiotic drivers on pollen assemblage composition and diversity has been relatively understudied. Here, we examine 4 years of modern pollen assemblages collected from a series of 28 traps at the Konza Prairie Long-Term Ecological Research Area in the Flint Hills of Kansas, with the aim of evaluating the influence of these drivers, as well as quantifying the amount of spatial and temporal variability in the pollen signatures of the tallgrass prairie biome. We include all terrestrial pollen taxa in our analyses while calculating four summative metrics of pollen diversity and composition -beta-diversity, Shannon index, nonarboreal pollen percentage, and Ambrosia: Artemisia -and find different roles of fire, herbivory, and topography variables in relation to these pollen metrics. In addition, we find significant annual differences in the means of three of these metrics, particularly the year 2013 which experienced high precipitation relative to the other 3 years of data. To quantify spatial and temporal dissimilarity among the samples over the 4-year study, we calculate pairwise squared-chord distances (SCD). The SCD values indicate higher compositional dissimilarity across the traps (0.38 mean) among all years than within a single trap from year to year (0.31 mean), suggesting that grassland vegetation can have different pollen signatures across finely sampled space and time, and emphasizing the need for additional long-term annual monitoring of grassland pollen

Relationships between quality of life and family function in caregiver

<p>Abstract</p> <p>Background</p> <p>There are caregivers who see their quality of life (QoL) impaired due to the demands of their caregiving tasks, while others manage to adapt and overcome the crises successfully. The influence of the family function in the main caregiver's situation has not been the subject of much evaluation. The aim of this study is to analyse the relationship between the functionality of the family and the QoL of caregivers of dependent relatives.</p> <p>Methods</p> <p>We conducted a cross-sectional study including 153 caregivers. Setting: Two health centers in the city of Salamanca(Spain). Caregiver variables analysed: demographic characteristics, care recipient features; family functionality (Family APGAR-Q) and QoL (Ruiz-Baca-Q) perceived by the caregiver. Five multiple regressions are performed considering global QoL and each of the four QoL dimensions as dependent variables. The Canonical Correspondence Analysis (CCA) was used to study the influence of the family function questionnaire on QoL.</p> <p>Results</p> <p>Family function is the only one of the variables evaluated that presented an association both with global QoL and with each of the four individual dimensions (p < 0.05). Using the CCA, we found that the physical and mental well-being dimensions are the ones which present a closer relationship with family functionality, while social support is the quality dimension that is least influenced by the Family APGAR-Q.</p> <p>Conclusion</p> <p>We find an association between family functionality and the caregiver's QoL. This relation holds for both the global measure of QoL and each of its four individual dimensions.</p

Mammary gland tumor promotion by chronic administration of IGF1 and the insulin analogue AspB10 in the p53R270H/⁺WAPCre mouse model

Insulin analogues are structurally modified molecules with altered pharmaco-kinetic and -dynamic properties compared to regular human insulin used by diabetic patients. While these compounds are tested for undesired mitogenic effects, an epidemiological discussion is ongoing regarding an association between insulin analogue therapy and increased cancer incidence, including breast cancer. Standard in vivo rodent carcinogenesis assays do not pick up this possible increased carcinogenic potential. Here we studied the role of insulin analogues in breast cancer development. For this we used the human relevant mammary gland specific p53R270H/⁺WAPCre mouse model. Animals received life long repeated treatment with four different insulin (-like) molecules: normal insulin, insulin glargine, insulin X10 (AspB10) or insulin-like growth factor 1 (IGF1). Insulin-like molecules with strong mitogenic signaling, insulin X10 and IGF1, significantly decreased the time for tumor development. Yet, insulin glargine and normal insulin, did not significantly decrease the latency time for (mammary gland) tumor development. The majority of tumors had an epithelial to mesenchymal transition phenotype (EMT), irrespective of treatment condition. Enhanced extracellular signaling related kinase (Erk) or serine/threonine kinase (Akt) mitogenic signaling was in particular present in tumors from the insulin X10 and IGF1 treatment groups. These data indicate that insulin-like molecules with enhanced mitogenic signaling increase the risk of breast cancer development. Moreover, the use of a tissue specific cancer model, like the p53R270H/⁺WAPCre mouse model, is relevant to assess the intrinsic pro-carcinogenic potential of mitogenic and non-mitogenic biologicals such as insulin analogues. INTRODUCTION METHODS RESULTS CONCLUSION

Globally sparse PLS regression

Volume 56 ; Print ISBN : 978-1-4614-8282-6Partial least squares (PLS) regression combines dimensionality reduction and prediction using a latent variable model. It provides better predictive ability than principle component analysis by taking into account both the independent and re- sponse variables in the dimension reduction procedure. However, PLS suffers from over-fitting problems for few samples but many variables. We formulate a new criterion for sparse PLS by adding a structured sparsity constraint to the global SIMPLS optimization. The constraint is a sparsity-inducing norm, which is useful for selecting the important variables shared among all the components. The optimization is solved by an augmented Lagrangian method to obtain the PLS components and to perform variable selection simultaneously. We propose a novel greedy algorithm to overcome the computation difficulties. Experiments demonstrate that our approach to PLS regression attains better performance with fewer selected predictor

Photosynthetic traits of Sphagnum and feather moss species in undrained, drained and rewetted boreal spruce swamp forests

Peer reviewe