Do ecological differences between taxonomic groups influence the relationship between species’ distributions and climate? A global meta-analysis using species distribution models

Understanding whether and how ecological traits affect species’ geographic distributions is a fundamental issue that bridges ecology and biogeography. While climate is thought to be the major determinant of species’ distributions, there is considerable variation in the strength of species’ climate–distribution relationships. One potential explanation is that species with relatively low dispersal ability cannot reach all geographic areas where climatic conditions are suitable. We tested the hypothesis that species from different taxonomic groups varied in their climate–distribution relationships because of differences in life history strategies, in particular dispersal ability. We conducted a meta-analysis by combining the discrimination ability (AUC values) from 4317 species distribution models (SDMs) using fit as an indication of the strength of the species’ climate–distribution relationship. We found significant differences in the strength of species’ climate–distribution relationships across taxonomic groups, however we did not find support for the dispersal hypothesis. Our results suggest that relevant ecological trait variation among broad taxonomic groups may be related to differences in species’ climate–distribution relationships, however which ecological traits are important remains unclear

Biases in Reanalysis Snowfall Found by Comparing the JULES Land Surface Model to GlobSnow

Snow exerts a strong influence on weather and climate. Accurate representation of snow processes within models is needed to ensure accurate predictions. Snow processes are known to be a weakness of land surface models (LSMs), and studies suggest that more complex snow physics is needed to avoid early melt. In this study the European Space Agency (ESA)'s Global Snow Monitoring for Climate Research (GlobSnow) snow water equivalent and NASA's MOD10C1 snow cover products are used to assess the accuracy of snow processes within the Joint U.K. Land Environment Simulator (JULES). JULES is run offline from a general circulation model and so is driven by meteorological reanalysis datasets: Princeton, Water and Global Change-Global Precipitation Climatology Centre (WATCH-GPCC), and WATCH-Climatic Research Unit (CRU). This reveals that when the model achieves the correct peak accumulation, snow does not melt early. However, generally snow does melt early because peak accumulation is too low. Examination of the meteorological reanalysis data shows that not enough snow falls to achieve observed peak accumulations. Thus, the earlier studies' conclusions may be as a result of weaknesses in the driving data, rather than in model snow processes. These reanalysis products bias correct precipitation using observed gauge data with an undercatch correction, overriding the benefit of any other datasets used in their creation. This paper argues that using gauge data to bias-correct reanalysis data is not appropriate for snow-affected regions during winter and can lead to confusion when evaluating model processes

Seasonal controls on net branch CO2 assimilation in sub-Arctic Mountain Birch (Betula pubescens ssp. czerepanovii (Orlova) Hamet-Ahti)

Forests at northern high latitudes are experiencing climate-induced changes in growth and productivity, but our knowledge on the underlying mechanisms driving seasonal CO2 fluxes in northern boreal trees comes almost exclusively from ecosystem-level studies on evergreen conifers. In this study, we measured growing season whole-branch CO2 exchange in a deciduous tree species of the tundra-taiga ecotone, Mountain Birch (Betula pubescens ssp. czerepanovii (Orlova) Hamet-Ahti), at two locations in northern Fennoscandia: Abisko (Sweden) and Kevo (Finland). We identified strong seasonal and environmental controls on both photosynthesis and respiration by analysing the parameters of light response curves. Branch-level photosynthetic parameters showed a delayed response to temperature, and, at Kevo, they were well described by sigmoid functions of the state of acclimation (S). Temperature acclimation was slower (time constant, τ = 7 days) for maximum photosynthesis (βbr) than for quantum efficiency (αbr) (τ = 5 days). High temperature-independent values of the respiration parameter (γbr) during leaf and shoot expansion were consistent with associated higher growth respiration rates. The ratio γbr/βbr was positively related to temperature, a result consistent with substrate-induced variations in leaf respiration rates at the branch level. Differences in stand structure and within-site variation in the active period of C uptake determined the spatiotemporal patterns in net assimilation amongst branches. Growing season CO2 uptake of individual branches on a leaf area basis did not show a significant relationship with total incident photosynthetically active radiation, and did not differ across sites, averaging ca. 640 g CO2 m−2

Fast and flexible Bayesian species distribution modelling using Gaussian processes

1. Species distribution modelling (SDM) is widely used in ecology, and predictions of species distributions inform both policy and ecological debates. Therefore, methods with high predictive accuracy and those that enable biological interpretation are preferable. Gaussian processes (GPs) are a highly flexible approach to statistical modelling and have recently been proposed for SDM. GP models fit smooth, but potentially complex response functions that can account for high-dimensional interactions between predictors. We propose fitting GP SDMs using deterministic numerical approximations, rather than Markov chain Monte Carlo methods in order to make GPs more computationally efficient and easy to use. 2. We introduce GP models and their application to SDM, illustrate how ecological knowledge can be incorporated into GP SDMs via Bayesian priors and formulate a simple GP SDM that can be fitted efficiently. This model can be fitted either by learning the hyperparameters or by using a fixed approximation to them. Using a subset of the North American Breeding Bird Survey data set, we compare the out-of-sample predictive accuracy of these models with several commonly used SDM approaches for both presence/absence and presence-only data. 3. Predictive accuracy of GP SDMs fitted by Laplace approximation was greater than boosted regression trees, generalized additive models (GAMs) and logistic regression when trained on presence/absence data and greater than all of these models plus MaxEnt when trained on presence-only data. GP SDMs fitted using a fixed approximation to hyperparameters were no less accurate than those with MAP estimation and on average 70 times faster, equivalent in speed to GAMs. 4. As well as having strong predictive power for this data set, GP SDMs offer a convenient method for incorporating prior knowledge of the species' ecology. By fitting these methods using efficient numerical approximations, they may easily be applied to large data sets and automatically for many species. An r package, GRaF, is provided to enable SDM users to fit GP models

Analysis of SARS-CoV-2 infection associated cell entry proteins ACE2, CD147, PPIA, and PPIB in datasets from non SARS-CoV-2 infected neuroblastoma patients, as potential prognostic and infection biomarkers in neuroblastoma

SARS-CoV-2 viral contagion has given rise to a worldwide pandemic. Although most children experience minor symptoms from SARS-CoV-2 infection, some have severe complications including Multisystem Inflammatory Syndrome in Children. Neuroblastoma patients may be at higher risk of severe infection as treatment requires immunocompromising chemotherapy and SARS-CoV-2 has demonstrated tropism for nervous cells. To date, there is no sufficient epidemiological data on neuroblastoma patients with SARS-CoV-2. Therefore, we evaluated datasets of non-SARS-CoV-2 infected neuroblastoma patients to assess for key genes involved with SARS-CoV-2 infection as possible neuroblastoma prognostic and infection biomarkers. We hypothesized that ACE2, CD147, PPIA and PPIB, which are associated with viral-cell entry, are potential biomarkers for poor prognosis neuroblastoma and SARS-CoV-2 infection. We have analysed three publicly available neuroblastoma gene expression datasets to understand the specific molecular susceptibilities that high-risk neuroblastoma patients have to the virus. Gene Expression Omnibus (GEO) GSE49711 and GEO GSE62564 are the microarray and RNA-Seq data, respectively, from 498 neuroblastoma samples published as part of the Sequencing Quality Control initiative. TARGET, contains microarray data from 249 samples and is part of the Therapeutically Applicable Research to Generate Effective Treatments (TARGET) initiative. ACE2, CD147, PPIA and PPIB were identified through their involvement in both SARS-CoV-2 infection and cancer pathogenesis. In-depth statistical analysis using Kaplan-Meier, differential gene expression, and Cox multivariate regression analysis, demonstrated that overexpression of ACE2, CD147, PPIA and PPIB is significantly associated with poor-prognosis neuroblastoma samples. These results were seen in the presence of amplified MYCN, unfavourable tumour histology and in patients older than 18 months of age. Previously, we have shown that high levels of the nerve growth factor receptor NTRK1 together with low levels of the phosphatase PTPN6 and TP53 are associated with increased relapse-free survival of neuroblastoma patients. Interestingly, low levels of expression of ACE2, CD147, PPIA and PPIB are associated with this NTRK1-PTPN6-TP53 module, suggesting that low expression levels of these genes are associated with good prognosis. These findings have implications for clinical care and therapeutic treatment. The upregulation of ACE2, CD147, PPIA and PPIB in poor-prognosis neuroblastoma samples suggests that these patients may be at higher risk of severe SARS-CoV-2 infection. Importantly, our findings reveal ACE2, CD147, PPIA and PPIB as potential biomarkers and therapeutic targets for neuroblastoma

Climatic Disequilibrium Threatens Conservation Priority Forests

AcceptedArticle in PressThis is the final version of the article. Available from Wiley Open Access via the DOI in this record.© 2017 Wiley Periodicals, Inc.We test the hypothesis that climatic changes since 1800 have resulted in unrealized potential vegetation changes that represent a "climatic debt" for many ecosystems. Caledonian pinewoods, an EU priority forest type, are used as a model system to explore potential impacts of two centuries of climatic change upon sites of conservation importance and surrounding landscapes. Using methods that estimate topographic microclimate, current and preindustrial climates were estimated for 50 m grid cells and simulations made using a dynamic vegetation model. Core Caledonian pinewood areas are now less suitable for growth of pine and more favorable for oak than in 1800, whereas landscapes as a whole are on average more favorable for both. The most favorable areas for pine are now mainly outside areas designated to conserve historical pinewoods. A paradigm shift is needed in formulating conservation strategies to avoid catastrophic losses of this habitat, and of many others globally with trees or other long-lived perennials as keystone species.Natural Environment Research Council. Grant Number: NE/I011234/