Impacts of Habitat Degradation on Tropical Montane Biodiversity and Ecosystem Services: A Systematic Map for Identifying Future Research Priorities

Tropical montane forests (TMFs) are major centers of evolutionary change and harbor many endemic species with small geographic ranges. In this systematic map, we focus on the impacts of anthropogenic habitat degradation on TMFs globally. We first determine how TMF research is distributed across geographic regions, degradation type (i.e., deforestation, land-use conversion, habitat fragmentation, ecological level (i.e., ecosystem, community, population, genetic) and taxonomic group. Secondly, we summarize the impacts of habitat degradation on biodiversity and ecosystem services, and identify deficiencies in current knowledge. We show that habitat degradation in TMFs impacts biodiversity at all ecological levels and will be compounded by climate change. However, despite montane species being perceived as more extinction-prone due to their restricted geographic ranges, there are some indications of biotic resilience if the impacts to TMFs are less severe. Species richness and key species interactions can be maintained in mildly degraded sites, and gene flow can persist between TMF fragments. As such, minimally degraded areas such as secondary forests and restored areas could play a crucial role in maintaining the meta-community and ecosystem services of TMFs—either via resource provision or by linking patches of pristine forest. Research deficiencies highlighted include poor research representation in Asian and African TMFs, few assessments of population and genetic-level responses to fragmentation, and little assessment of the impacts of habitat fragmentation at all ecological levels. To address these concerns, we present a list of the top research priorities to urgently address the growing threat of habitat degradation in TMF

Impacts of Habitat Degradation on Tropical Montane Biodiversity and Ecosystem Services: A Systematic Map for Identifying Future Research Priorities

Tropical montane forests (TMFs) are major centers of evolutionary change and harbor many endemic species with small geographic ranges. In this systematic map, we focus on the impacts of anthropogenic habitat degradation on TMFs globally. We first determine how TMF research is distributed across geographic regions, degradation type (i.e., deforestation, land-use conversion, habitat fragmentation, ecological level (i.e., ecosystem, community, population, genetic) and taxonomic group. Secondly, we summarize the impacts of habitat degradation on biodiversity and ecosystem services, and identify deficiencies in current knowledge. We show that habitat degradation in TMFs impacts biodiversity at all ecological levels and will be compounded by climate change. However, despite montane species being perceived as more extinction-prone due to their restricted geographic ranges, there are some indications of biotic resilience if the impacts to TMFs are less severe. Species richness and key species interactions can be maintained in mildly degraded sites, and gene flow can persist between TMF fragments. As such, minimally degraded areas such as secondary forests and restored areas could play a crucial role in maintaining the meta-community and ecosystem services of TMFs—either via resource provision or by linking patches of pristine forest. Research deficiencies highlighted include poor research representation in Asian and African TMFs, few assessments of population and genetic-level responses to fragmentation, and little assessment of the impacts of habitat fragmentation at all ecological levels. To address these concerns, we present a list of the top research priorities to urgently address the growing threat of habitat degradation in TMF

Cross-sectional examination of 24-hour movement behaviours among 3-and 4-year-old children in urban and rural settings in low-income, middle-income and high-income countries : the SUNRISE study protocol

Introduction 24-hour movement behaviours (physical activity, sedentary behaviour and sleep) during the early years are associated with health and developmental outcomes, prompting the WHO to develop Global guidelines for physical activity, sedentary behaviour and sleep for children under 5 years of age. Prevalence data on 24-hour movement behaviours is lacking, particularly in low-income and middle-income countries (LMICs). This paper describes the development of the SUNRISE International Study of Movement Behaviours in the Early Years protocol, designed to address this gap. Methods and analysis SUNRISE is the first international cross-sectional study that aims to determine the proportion of 3- and 4-year-old children who meet the WHO Global guidelines. The study will assess if proportions differ by gender, urban/rural location and/or socioeconomic status. Executive function, motor skills and adiposity will be assessed and potential correlates of 24-hour movement behaviours examined. Pilot research from 24 countries (14 LMICs) informed the study design and protocol. Data are collected locally by research staff from partnering institutions who are trained throughout the research process. Piloting of all measures to determine protocol acceptability and feasibility was interrupted by COVID-19 but is nearing completion. At the time of publication 41 countries are participating in the SUNRISE study. Ethics and dissemination The SUNRISE protocol has received ethics approved from the University of Wollongong, Australia, and in each country by the applicable ethics committees. Approval is also sought from any relevant government departments or organisations. The results will inform global efforts to prevent childhood obesity and ensure young children reach their health and developmental potential. Findings on the correlates of movement behaviours can guide future interventions to improve the movement behaviours in culturally specific ways. Study findings will be disseminated via publications, conference presentations and may contribute to the development of local guidelines and public health interventions.Peer reviewe

Evolution of communities of software: using tensor decompositions to compare software ecosystems

© 2019 The Authors. Published by Springer. This is an open access article available under a Creative Commons licence. The published version can be accessed at the following link on the publisher’s website: https://doi.org/10.1007/s41109-019-0193-5Modern software development is often a collaborative effort involving many authors through the re-use and sharing of code through software libraries. Modern software “ecosystems” are complex socio-technical systems which can be represented as a multilayer dynamic network. Many of these libraries and software packages are open-source and developed in the open on sites such as , so there is a large amount of data available about these networks. Studying these networks could be of interest to anyone choosing or designing a programming language. In this work, we use tensor factorisation to explore the dynamics of communities of software, and then compare these dynamics between languages on a dataset of approximately 1 million software projects. We hope to be able to inform the debate on software dependencies that has been recently re-ignited by the malicious takeover of the npm package and other incidents through giving a clearer picture of the structure of software dependency networks, and by exploring how the choices of language designers—for example, in the size of standard libraries, or the standards to which packages are held before admission to a language ecosystem is granted—may have shaped their language ecosystems. We establish that adjusted mutual information is a valid metric by which to assess the number of communities in a tensor decomposition and find that there are striking differences between the communities found across different software ecosystems and that communities do experience large and interpretable changes in activity over time. The differences between the elm and R software ecosystems, which see some communities decline over time, and the more conventional software ecosystems of Python, Java and JavaScript, which do not see many declining communities, are particularly marked.OAB’s work was supported as part of an Engineering and Physical Sciences Research Council (EPSRC) grant, project reference EP/I028099/1.Published versio

Sequencing three crocodilian genomes to illuminate the evolution of archosaurs and amniotes

The International Crocodilian Genomes Working Group (ICGWG) will sequence and assemble the American alligator (Alligator mississippiensis), saltwater crocodile (Crocodylus porosus) and Indian gharial (Gavialis gangeticus) genomes. The status of these projects and our planned analyses are described

Multiple novel prostate cancer susceptibility signals identified by fine-mapping of known risk loci among Europeans

Genome-wide association studies (GWAS) have identified numerous common prostate cancer (PrCa) susceptibility loci. We have fine-mapped 64 GWAS regions known at the conclusion of the iCOGS study using large-scale genotyping and imputation in 25 723 PrCa cases and 26 274 controls of European ancestry. We detected evidence for multiple independent signals at 16 regions, 12 of which contained additional newly identified significant associations. A single signal comprising a spectrum of correlated variation was observed at 39 regions; 35 of which are now described by a novel more significantly associated lead SNP, while the originally reported variant remained as the lead SNP only in 4 regions. We also confirmed two association signals in Europeans that had been previously reported only in East-Asian GWAS. Based on statistical evidence and linkage disequilibrium (LD) structure, we have curated and narrowed down the list of the most likely candidate causal variants for each region. Functional annotation using data from ENCODE filtered for PrCa cell lines and eQTL analysis demonstrated significant enrichment for overlap with bio-features within this set. By incorporating the novel risk variants identified here alongside the refined data for existing association signals, we estimate that these loci now explain ∼38.9% of the familial relative risk of PrCa, an 8.9% improvement over the previously reported GWAS tag SNPs. This suggests that a significant fraction of the heritability of PrCa may have been hidden during the discovery phase of GWAS, in particular due to the presence of multiple independent signals within the same regio

Gene expression imputation across multiple brain regions provides insights into schizophrenia risk

Transcriptomic imputation approaches combine eQTL reference panels with large-scale genotype data in order to test associations between disease and gene expression. These genic associations could elucidate signals in complex genome-wide association study (GWAS) loci and may disentangle the role of different tissues in disease development. We used the largest eQTL reference panel for the dorso-lateral prefrontal cortex (DLPFC) to create a set of gene expression predictors and demonstrate their utility. We applied DLPFC and 12 GTEx-brain predictors to 40,299 schizophrenia cases and 65,264 matched controls for a large transcriptomic imputation study of schizophrenia. We identified 413 genic associations across 13 brain regions. Stepwise conditioning identified 67 non-MHC genes, of which 14 did not fall within previous GWAS loci. We identified 36 significantly enriched pathways, including hexosaminidase-A deficiency, and multiple porphyric disorder pathways. We investigated developmental expression patterns among the 67 non-MHC genes and identified specific groups of pre- and postnatal expression

Telomerecat: A ploidy-agnostic method for estimating telomere length from whole genome sequencing data.

Telomere length is a risk factor in disease and the dynamics of telomere length are crucial to our understanding of cell replication and vitality. The proliferation of whole genome sequencing represents an unprecedented opportunity to glean new insights into telomere biology on a previously unimaginable scale. To this end, a number of approaches for estimating telomere length from whole-genome sequencing data have been proposed. Here we present Telomerecat, a novel approach to the estimation of telomere length. Previous methods have been dependent on the number of telomeres present in a cell being known, which may be problematic when analysing aneuploid cancer data and non-human samples. Telomerecat is designed to be agnostic to the number of telomeres present, making it suited for the purpose of estimating telomere length in cancer studies. Telomerecat also accounts for interstitial telomeric reads and presents a novel approach to dealing with sequencing errors. We show that Telomerecat performs well at telomere length estimation when compared to leading experimental and computational methods. Furthermore, we show that it detects expected patterns in longitudinal data, repeated measurements, and cross-species comparisons. We also apply the method to a cancer cell data, uncovering an interesting relationship with the underlying telomerase genotype

Mapping genomic loci implicates genes and synaptic biology in schizophrenia

Schizophrenia has a heritability of 60-80%1, much of which is attributable to common risk alleles. Here, in a two-stage genome-wide association study of up to 76,755 individuals with schizophrenia and 243,649 control individuals, we report common variant associations at 287 distinct genomic loci. Associations were concentrated in genes that are expressed in excitatory and inhibitory neurons of the central nervous system, but not in other tissues or cell types. Using fine-mapping and functional genomic data, we identify 120 genes (106 protein-coding) that are likely to underpin associations at some of these loci, including 16 genes with credible causal non-synonymous or untranslated region variation. We also implicate fundamental processes related to neuronal function, including synaptic organization, differentiation and transmission. Fine-mapped candidates were enriched for genes associated with rare disruptive coding variants in people with schizophrenia, including the glutamate receptor subunit GRIN2A and transcription factor SP4, and were also enriched for genes implicated by such variants in neurodevelopmental disorders. We identify biological processes relevant to schizophrenia pathophysiology; show convergence of common and rare variant associations in schizophrenia and neurodevelopmental disorders; and provide a resource of prioritized genes and variants to advance mechanistic studies

Analysis of shared heritability in common disorders of the brain

ience, this issue p. eaap8757 Structured Abstract INTRODUCTION Brain disorders may exhibit shared symptoms and substantial epidemiological comorbidity, inciting debate about their etiologic overlap. However, detailed study of phenotypes with different ages of onset, severity, and presentation poses a considerable challenge. Recently developed heritability methods allow us to accurately measure correlation of genome-wide common variant risk between two phenotypes from pools of different individuals and assess how connected they, or at least their genetic risks, are on the genomic level. We used genome-wide association data for 265,218 patients and 784,643 control participants, as well as 17 phenotypes from a total of 1,191,588 individuals, to quantify the degree of overlap for genetic risk factors of 25 common brain disorders. RATIONALE Over the past century, the classification of brain disorders has evolved to reflect the medical and scientific communities' assessments of the presumed root causes of clinical phenomena such as behavioral change, loss of motor function, or alterations of consciousness. Directly observable phenomena (such as the presence of emboli, protein tangles, or unusual electrical activity patterns) generally define and separate neurological disorders from psychiatric disorders. Understanding the genetic underpinnings and categorical distinctions for brain disorders and related phenotypes may inform the search for their biological mechanisms. RESULTS Common variant risk for psychiatric disorders was shown to correlate significantly, especially among attention deficit hyperactivity disorder (ADHD), bipolar disorder, major depressive disorder (MDD), and schizophrenia. By contrast, neurological disorders appear more distinct from one another and from the psychiatric disorders, except for migraine, which was significantly correlated to ADHD, MDD, and Tourette syndrome. We demonstrate that, in the general population, the personality trait neuroticism is significantly correlated with almost every psychiatric disorder and migraine. We also identify significant genetic sharing between disorders and early life cognitive measures (e.g., years of education and college attainment) in the general population, demonstrating positive correlation with several psychiatric disorders (e.g., anorexia nervosa and bipolar disorder) and negative correlation with several neurological phenotypes (e.g., Alzheimer's disease and ischemic stroke), even though the latter are considered to result from specific processes that occur later in life. Extensive simulations were also performed to inform how statistical power, diagnostic misclassification, and phenotypic heterogeneity influence genetic correlations. CONCLUSION The high degree of genetic correlation among many of the psychiatric disorders adds further evidence that their current clinical boundaries do not reflect distinct underlying pathogenic processes, at least on the genetic level. This suggests a deeply interconnected nature for psychiatric disorders, in contrast to neurological disorders, and underscores the need to refine psychiatric diagnostics. Genetically informed analyses may provide important "scaffolding" to support such restructuring of psychiatric nosology, which likely requires incorporating many levels of information. By contrast, we find limited evidence for widespread common genetic risk sharing among neurological disorders or across neurological and psychiatric disorders. We show that both psychiatric and neurological disorders have robust correlations with cognitive and personality measures. Further study is needed to evaluate whether overlapping genetic contributions to psychiatric pathology may influence treatment choices. Ultimately, such developments may pave the way toward reduced heterogeneity and improved diagnosis and treatment of psychiatric disorders