Understanding and accurately modeling species distributions lies at the heart of many problems in ecology, evolution, and conservation. Multiple sources of data are increasingly available for ...modeling species distributions, such as data from citizen science programs, atlases, museums, and planned surveys. Yet reliably combining data sources can be challenging because data sources can vary considerably in their design, gradients covered, and potential sampling biases. We review, synthesize, and illustrate recent developments in combining multiple sources of data for species distribution modeling. We identify five ways in which multiple sources of data are typically combined for modeling species distributions. These approaches vary in their ability to accommodate sampling design, bias, and uncertainty when quantifying environmental relationships in species distribution models. Many of the challenges for combining data are solved through the prudent use of integrated species distribution models: models that simultaneously combine different data sources on species locations to quantify environmental relationships for explaining species distribution. We illustrate these approaches using planned survey data on 24 species of birds coupled with opportunistically collected eBird data in the southeastern United States. This example illustrates some of the benefits of data integration, such as increased precision in environmental relationships, greater predictive accuracy, and accounting for sample bias. Yet it also illustrates challenges of combining data sources with vastly different sampling methodologies and amounts of data. We provide one solution to this challenge through the use of weighted joint likelihoods. Weighted joint likelihoods provide a means to emphasize data sources based on different criteria (e.g., sample size), and we find that weighting improves predictions for all species considered. We conclude by providing practical guidance on combining multiple sources of data for modeling species distributions.
The increasing rate of antibiotic resistance and slowing discovery of novel antibiotic treatments presents a growing threat to public health. Here, we consider a simple model of evolution in ...asexually reproducing populations which considers adaptation as a biased random walk on a fitness landscape. This model associates the global properties of the fitness landscape with the algebraic properties of a Markov chain transition matrix and allows us to derive general results on the non-commutativity and irreversibility of natural selection as well as antibiotic cycling strategies. Using this formalism, we analyze 15 empirical fitness landscapes of E. coli under selection by different β-lactam antibiotics and demonstrate that the emergence of resistance to a given antibiotic can be either hindered or promoted by different sequences of drug application. Specifically, we demonstrate that the majority, approximately 70%, of sequential drug treatments with 2-4 drugs promote resistance to the final antibiotic. Further, we derive optimal drug application sequences with which we can probabilistically 'steer' the population through genotype space to avoid the emergence of resistance. This suggests a new strategy in the war against antibiotic-resistant organisms: drug sequencing to shepherd evolution through genotype space to states from which resistance cannot emerge and by which to maximize the chance of successful therapy.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
The mechanism of resistance to favipiravir in influenza Goldhill, Daniel H.; te Velthuis, Aartjan J. W.; Fletcher, Robert A. ...
Proceedings of the National Academy of Sciences - PNAS,
11/2018, Letnik:
115, Številka:
45
Journal Article
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Favipiravir is a broad-spectrum antiviral that has shown promise in treatment of influenza virus infections. While emergence of resistance has been observed for many antiinfluenza drugs, to date, ...clinical trials and laboratory studies of favipiravir have not yielded resistant viruses. Here we show evolution of resistance to favipiravir in the pandemic H1N1 influenza A virus in a laboratory setting. We found that two mutations were required for robust resistance to favipiravir. We demonstrate that a K229R mutation in motif F of the PB1 subunit of the influenza virus RNA-dependent RNA polymerase (RdRP) confers resistance to favipiravir in vitro and in cell culture. This mutation has a cost to viral fitness, but fitness can be restored by a P653L mutation in the PA subunit of the polymerase. K229R also conferred favipiravir resistance to RNA polymerases of other influenza A virus strains, and its location within a highly conserved structural feature of the RdRP suggests that other RNA virusesmight also acquire resistance through mutations in motif F. The mutations identified here could be used to screen influenza virus-infected patients treated with favipiravir for the emergence of resistance.
Since its emergence in late 2019, the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has caused a pandemic with more than 55 million reported cases and 1.3 million estimated deaths ...worldwide. While epidemiological and clinical characteristics of COVID-19 have been reported, risk factors underlying the transition from mild to severe disease among patients remain poorly understood. In this retrospective study, we analysed data of 879 confirmed SARS-CoV-2 positive patients admitted to a two-site NHS Trust hospital in London, England, between January 1st and May 26th, 2020, with a majority of cases occurring in March and April. We extracted anonymised demographic data, physiological clinical variables and laboratory results from electronic healthcare records (EHR) and applied multivariate logistic regression, random forest and extreme gradient boosted trees. To evaluate the potential for early risk assessment, we used data available during patients' initial presentation at the emergency department (ED) to predict deterioration to one of three clinical endpoints in the remainder of the hospital stay: admission to intensive care, need for invasive mechanical ventilation and in-hospital mortality. Based on the trained models, we extracted the most informative clinical features in determining these patient trajectories. Considering our inclusion criteria, we have identified 129 of 879 (15%) patients that required intensive care, 62 of 878 (7%) patients needing mechanical ventilation, and 193 of 619 (31%) cases of in-hospital mortality. Our models learned successfully from early clinical data and predicted clinical endpoints with high accuracy, the best model achieving area under the receiver operating characteristic (AUC-ROC) scores of 0.76 to 0.87 (F1 scores of 0.42-0.60). Younger patient age was associated with an increased risk of receiving intensive care and ventilation, but lower risk of mortality. Clinical indicators of a patient's oxygen supply and selected laboratory results, such as blood lactate and creatinine levels, were most predictive of COVID-19 patient trajectories. Among COVID-19 patients machine learning can aid in the early identification of those with a poor prognosis, using EHR data collected during a patient's first presentation at ED. Patient age and measures of oxygenation status during ED stay are primary indicators of poor patient outcomes.
Across the planet, high-intensity farming has transformed native vegetation into monocultures, decreasing biodiversity on a landscape scale. Yet landscape-scale changes to biodiversity and community ...structure often emerge from processes operating at local scales. One common process that can explain changes in biodiversity and community structure is the creation of abrupt habitat edges, which, in turn, generate edge effects. Such effects, while incredibly common, can be highly variable across space and time; however, we currently lack a general analytical framework that can adequately capture such spatio-temporal variability. We extend previous approaches for estimating edge effects to a non-linear mixed modeling framework that captures such spatio-temporal heterogeneity and apply it to understand how agricultural land-uses alter wildlife communities. We trapped small mammals along a conservation-agriculture land-use interface extending 375 m into sugarcane plantations and conservation land-uses at three sites during dry and wet seasons in Swaziland, Africa. Sugarcane plantations had significant reductions in species richness and heterogeneity, and showed an increase in community similarity, suggesting a more homogenized small mammal community. Furthermore, our modeling framework identified strong variation in edge effects on communities across sites and seasons. Using small mammals as an indicator, intensive agricultural practices appear to create high-density communities of generalist species while isolating interior species in less than 225 m. These results illustrate how agricultural land-use can reduce diversity across the landscape and that effects can be masked or magnified, depending on local conditions. Taken together, our results emphasize the need to create or retain natural habitat features in agricultural mosaics.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Endoplasmic reticulum (ER) stress is a condition in which the protein folding capacity of the ER becomes overwhelmed by an increased demand for secretion or by exposure to compounds that disrupt ER ...homeostasis. In yeast and other fungi, the accumulation of unfolded proteins is detected by the ER-transmembrane sensor IreA/Ire1, which responds by cleaving an intron from the downstream cytoplasmic mRNA HacA/Hac1, allowing for the translation of a transcription factor that coordinates a series of adaptive responses that are collectively known as the unfolded protein response (UPR). Here, we examined the contribution of IreA to growth and virulence in the human fungal pathogen Aspergillus fumigatus. Gene expression profiling revealed that A. fumigatus IreA signals predominantly through the canonical IreA-HacA pathway under conditions of severe ER stress. However, in the absence of ER stress IreA controls dual signaling circuits that are both HacA-dependent and HacA-independent. We found that a ΔireA mutant was avirulent in a mouse model of invasive aspergillosis, which contrasts the partial virulence of a ΔhacA mutant, suggesting that IreA contributes to pathogenesis independently of HacA. In support of this conclusion, we found that the ΔireA mutant had more severe defects in the expression of multiple virulence-related traits relative to ΔhacA, including reduced thermotolerance, decreased nutritional versatility, impaired growth under hypoxia, altered cell wall and membrane composition, and increased susceptibility to azole antifungals. In addition, full or partial virulence could be restored to the ΔireA mutant by complementation with either the induced form of the hacA mRNA, hacA(i), or an ireA deletion mutant that was incapable of processing the hacA mRNA, ireA(Δ10). Together, these findings demonstrate that IreA has both HacA-dependent and HacA-independent functions that contribute to the expression of traits that are essential for virulence in A. fumigatus.
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Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Connectivity has long played a central role in ecological and evolutionary theory and is increasingly emphasized for conserving biodiversity. Nonetheless, connectivity assessments often focus on ...individual species even though understanding and preserving connectivity for entire communities is urgently needed. Here we derive and test a framework that harnesses the well-known allometric scaling of animal movement to predict community-level connectivity across protected area networks. We used a field translocation experiment involving 39 species of southern African birds to quantify movement capacity, scaled this relationship to realized dispersal distances determined from ring-and-recovery banding data, and used allometric scaling equations to quantify community-level connectivity based on multilayer network theory. The translocation experiment explained observed dispersal distances from ring-recovery data and emphasized allometric scaling of dispersal based on morphology. Our community-level networks predicted that larger-bodied species had a relatively high potential for connectivity, while small-bodied species had lower connectivity. These community networks explained substantial variation in observed bird diversity across protected areas. Our results highlight that harnessing allometric scaling can be an effective way of determining large-scale community connectivity. We argue that this trait-based framework founded on allometric scaling provides a means to predict connectivity for entire communities, which can foster empirical tests of community theory and contribute to biodiversity conservation strategies aimed at mitigating the effects of environmental change.
Conservation needs a COVID-19 bailout McCleery, Robert A; Fletcher, Jr, Robert J; Kruger, Laurence M ...
Science (American Association for the Advancement of Science),
07/2020, Letnik:
369, Številka:
6503
Journal Article
Aim: Across the planet, grass-dominated biomes are experiencing shrub encroachment driven by atmospheric CO2 enrichment and land-use change. By altering resource structure and availability, shrub ...encroachment may have important impacts on vertebrate communities. We sought to determine the magnitude and variability of these effects across climatic gradients, continents, and taxa, and to learn whether shrub thinning restores the structure of vertebrate communities. Location: Worldwide. Time period: Contemporary. Major taxa studied: Terrestrial vertebrates. Methods: We estimated relationships between percentage shrub cover and the structure of terrestrial vertebrate communities (species richness, Shannon diversity and community abundance) in experimentally thinned and unmanipulated shrub-encroached grass-dominated biomes using systematic review and meta-analyses of 43 studies published from 1978 to 2016. We modelled the effects of continent, biome, mean annual precipitation, net primary productivity and the normalized difference vegetation index (NDVI) on the relationship between shrub cover and vertebrate community structure. Results: Species richness, Shannon diversity and total abundance had no consistent relationship with shrub encroachment and experimental thinning did not reverse encroachment effects on vertebrate communities. However, some effects of shrub encroachment on vertebrate communities differed with net primary productivity, amongst vertebrate groups, and across continents. Encroachment had negative effects on vertebrate diversity at low net primary productivity. Mammalian and herpetofaunal diversity decreased with shrub encroachment. Shrub encroachment also had negative effects on species richness and total abundance in Africa but positive effects in North America. Main conclusions: Biodiversity conservation and mitigation efforts responding to shrub encroachment should focus on low-productivity locations, on mammals and herpetofauna, and in Africa. However, targeted research in neglected regions such as central Asia and India will be needed to fill important gaps in our knowledge of shrub encroachment effects on vertebrates. Additionally, our findings provide an impetus for determining the mechanisms associated with changes in vertebrate diversity and abundance in shrub-encroached grass-dominated biomes.
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