Esociformes: Esocidae, Pikes, and Umbridae (Mudminnows)

The order Esociformes (Pikes and Mudminnows) comprises two families, Esocidae (Pikes) and Umbridae (Mudminnows). The Pikes are a small Holarctic (Northern Hemisphere) family, that includes large, elongate predators with duckbill-like snouts full of sharp teeth. Popular with sport fishers, the largest Pikes fight fiercely on hook and line. As piscivorous, voracious, ambush predators, the Pikes play an important functional role in the trophic ecology and fish assemblage structure of many aquatic systems, especially in northern lakes. Other esocids, such as the Olympic Mudminnow, Novumbra hubbsi, and Blackfishes, genus Dallia, are interesting because of their tolerance of low dissolved oxygen and pH. The Alaska Blackfish, Dallia pectoralis, and the Northern Pike, Esox lucius, can also withstand the extremely cold conditions of the Arctic and subarctic waters of Canada, Alaska, and Siberia. The name Esocidae is derived from Linnaeus’s (1758) generic name for Pike, Esox, from the Latin word esox meaning Pike, which came originally from the Greek isox or possibly the Gaelic eog, ehawe (salmon) (Boschung & Mayden 2004)

River ecosystem conceptual models and non‐perennial rivers: A critical review

Conceptual models underpin river ecosystem research. However, current models focus on continuously flowing rivers and few explicitly address characteristics such as flow cessation and drying. The applicability of existing conceptual models to nonperennial rivers that cease to flow (intermittent rivers and ephemeral streams, IRES) has not been evaluated. We reviewed 18 models, finding that they collectively describe main drivers of biogeochemical and ecological patterns and processes longitudinally (upstream-downstream), laterally (channel-riparian-floodplain), vertically (surface water-groundwater), and temporally across local and landscape scales. However, perennial rivers are longitudinally continuous while IRES are longitudinally discontinuous. Whereas perennial rivers have bidirectional lateral connections between aquatic and terrestrial ecosystems, in IRES, this connection is unidirectional for much of the time, from terrestrial-to-aquatic only. Vertical connectivity between surface and subsurface water occurs bidirectionally and is temporally consistent in perennial rivers. However, in IRES, this exchange is temporally variable, and can become unidirectional during drying or rewetting phases. Finally, drying adds another dimension of flow variation to be considered across temporal and spatial scales in IRES, much as flooding is considered as a temporally and spatially dynamic process in perennial rivers. Here, we focus on ways in which existing models could be modified to accommodate drying as a fundamental process that can alter these patterns and processes across spatial and temporal dimensions in streams. This perspective is needed to support river science and management in our era of rapid global change, including increasing duration, frequency, and occurrence of drying.info:eu-repo/semantics/publishedVersio

Should I Stay or Should I Go? A Habitat-Dependent Dispersal Kernel Improves Prediction of Movement

The analysis of animal movement within different landscapes may increase our understanding of how landscape features affect the perceptual range of animals. Perceptual range is linked to movement probability of an animal via a dispersal kernel, the latter being generally considered as spatially invariant but could be spatially affected. We hypothesize that spatial plasticity of an animal's dispersal kernel could greatly modify its distribution in time and space. After radio tracking the movements of walking insects (Cosmopolites sordidus) in banana plantations, we considered the movements of individuals as states of a Markov chain whose transition probabilities depended on the habitat characteristics of current and target locations. Combining a likelihood procedure and pattern-oriented modelling, we tested the hypothesis that dispersal kernel depended on habitat features. Our results were consistent with the concept that animal dispersal kernel depends on habitat features. Recognizing the plasticity of animal movement probabilities will provide insight into landscape-level ecological processes

A systems biology approach to understand gut microbiota and host metabolism in morbid obesity: design of the BARIA Longitudinal Cohort Study

Introduction: Prevalence of obesity and associated diseases, including type 2 diabetes mellitus, dyslipidaemia and non-alcoholic fatty liver disease (NAFLD), are increasing. Underlying mechanisms, especially in humans, are unclear. Bariatric surgery provides the unique opportunity to obtain biopsies and portal vein blood-samples. Methods: The BARIA Study aims to assess how microbiota and their metabolites affect transcription in key tissues and clinical outcome in obese subjects and how baseline anthropometric and metabolic characteristics determine weight loss and glucose homeostasis after bariatric surgery. We phenotype patients undergoing bariatric surgery (predominantly laparoscopic Roux-en-Y gastric bypass), before weight loss, with biometrics, dietary and psychological questionnaires, mixed meal test (MMT) and collect fecal-samples and intra-operative biopsies from liver, adipose tissues and jejunum. We aim to include 1500 patients. A subset (approximately 25%) will undergo intra-operative portal vein blood-sampling. Fecal-samples are analyzed with shotgun metagenomics and targeted metabolomics, fasted and postprandial plasma-samples are subjected to metabolomics, and RNA is extracted from the tissues for RNAseq-analyses. Data will be integrated using state-of-the-art neuronal networks and metabolic modeling. Patient follow-up will be ten years. Results: Preoperative MMT of 170 patients were analysed and clear differences were observed in glucose homeostasis between individuals. Repeated MMT in 10 patients showed satisfactory intra-individual reproducibility, with differences in plasma glucose, insulin and triglycerides within 20% of the mean difference. Conclusion: The BARIA study can add more understanding in how gut-microbiota affect metabolism, especially with regard to obesity, glucose metabolism and NAFLD. Identification of key factors may provide diagnostic and therapeutic leads to control the obesity-associated disease epidemic

Meta-GWAS Reveals Novel Genetic Variants Associated with Urinary Excretion of Uromodulin

Background Uromodulin, the most abundant protein excreted in normal urine, plays major roles in kidney physiology and disease. The mechanisms regulating the urinary excretion of uromodulin remain essentially unknown. Methods We conducted a meta-analysis of genome-wide association studies for raw (uUMOD) and indexed to creatinine (uUCR) urinary levels of uromodulin in 29,315 individuals of European ancestry from 13 cohorts. We tested the distribution of candidate genes in kidney segments and investigated the effects of keratin-40 (KRT40) on uromodulin processing. Results Two genome-wide significant signals were identified for uUMOD: a novel locus (P 1.24E-08) over the KRT40 gene coding for KRT40, a type 1 keratin expressed in the kidney, and the UMOD-PDILT locus (P 2.17E-88), with two independent sets of single nucleotide polymorphisms spread over UMOD and PDILT. Two genome-wide significant signals for uUCR were identified at the UMOD-PDILT locus and at the novel WDR72 locus previously associated with kidney function. The effect sizes for rs8067385, the index single nucleotide polymorphism in the KRT40 locus, were similar for both uUMOD and uUCR. KRT40 colocalized with uromodulin and modulating its expression in thick ascending limb (TAL) cells affected uromodulin processing and excretion. Conclusions Common variants in KRT40,WDR72, UMOD, and PDILT associate with the levels of uromodulin in urine. The expression of KRT40 affects uromodulin processing in TAL cells. These results, although limited by lack of replication, provide insights into the biology of uromodulin, the role of keratins in the kidney, and the influence of the UMOD-PDILT locus on kidney function

Simple large wood structures promote hydromorphological heterogeneity and benthic macroinvertebrate diversity in low-gradient rivers

This work has been carried out within the SMART Joint Doctorate Programme ‘Science for the MAnagement of Rivers and their Tidal systems’ funded by the Erasmus Mundus programme of the European Union

Regression with Empirical Variable Selection: Description of a New Method and Application to Ecological Datasets

Despite recent papers on problems associated with full-model and stepwise regression, their use is still common throughout ecological and environmental disciplines. Alternative approaches, including generating multiple models and comparing them post-hoc using techniques such as Akaike's Information Criterion (AIC), are becoming more popular. However, these are problematic when there are numerous independent variables and interpretation is often difficult when competing models contain many different variables and combinations of variables. Here, we detail a new approach, REVS (Regression with Empirical Variable Selection), which uses all-subsets regression to quantify empirical support for every independent variable. A series of models is created; the first containing the variable with most empirical support, the second containing the first variable and the next most-supported, and so on. The comparatively small number of resultant models (n = the number of predictor variables) means that post-hoc comparison is comparatively quick and easy. When tested on a real dataset – habitat and offspring quality in the great tit (Parus major) – the optimal REVS model explained more variance (higher R2), was more parsimonious (lower AIC), and had greater significance (lower P values), than full, stepwise or all-subsets models; it also had higher predictive accuracy based on split-sample validation. Testing REVS on ten further datasets suggested that this is typical, with R2 values being higher than full or stepwise models (mean improvement = 31% and 7%, respectively). Results are ecologically intuitive as even when there are several competing models, they share a set of “core” variables and differ only in presence/absence of one or two additional variables. We conclude that REVS is useful for analysing complex datasets, including those in ecology and environmental disciplines

Application of Probabilistic Neural Networks to microhabitat suitability modelling for adult brown trout (Salmo trutta L.) in Iberian rivers

Probabilistic Neural Networks (PNN) have been tested for the first time in microhabitat suitability modelling for adult brown trout (Salmo trutta L.). The impact of data prevalence on PNN was studied. The PNN were evaluated in an independent river and the applicability of PNN to assess the environmental flow was analysed. Prevalence did not affect significantly the results. However PNN presented some limitations regarding the output range. Our results agreed previous studies because trout preferred deep microhabitats with medium-to-coarse substrate whereas velocity showed a wider suitable range. The 0.5 prevalence PNN showed similar classificatory capability than the 0.06 prevalence counterpart and the outputs covered the whole feasible range (from 0 to 1), but the 0.06 prevalence PNN showed higher generalisation because it performed better in the evaluation and it allowed a better modulation of the environmental flow. PNN has demonstrated to be a tool to be into consideration.The authors would like to thank the Spanish Ministry of Economy and Competitiveness for its financial support through the SCARCE project (Consolider-Ingenio 2010 CSD2009-00065). We are grateful to the colleagues who worked in the field and in the preliminary data analyses, especially Marta Bargay, Aina Hernandez and David Argibay. The works were partially funded by the Confederacion Hidrografica del Jucar (Spanish Ministry of Agriculture, Food and Environment), that also provided hydrological and environmental information about the study sites. The authors also thank the Direccion General del Agua and INFRAECO for the cession of the microhabitat data. Finally, we also thank Javier Ferrer, Teodoro Estrela and Onofre Gabaldo (Confederacion Hidrografica del Jucar) for their help and the data provided. Thanks to Grieg Davies for the academic review of English.Muñoz Mas, R.; Martinez-Capel, F.; Garófano-Gómez, V.; Mouton, A. (2014). Application of Probabilistic Neural Networks to microhabitat suitability modelling for adult brown trout (Salmo trutta L.) in Iberian rivers. Environmental Modelling and Software. 59:30-43. https://doi.org/10.1016/j.envsoft.2014.05.003S30435