Dataset Information

A national-scale model of linear features improves predictions of farmland biodiversity.

ABSTRACT: Modelling species distribution and abundance is important for many conservation applications, but it is typically performed using relatively coarse-scale environmental variables such as the area of broad land-cover types. Fine-scale environmental data capturing the most biologically relevant variables have the potential to improve these models. For example, field studies have demonstrated the importance of linear features, such as hedgerows, for multiple taxa, but the absence of large-scale datasets of their extent prevents their inclusion in large-scale modelling studies.We assessed whether a novel spatial dataset mapping linear and woody-linear features across the UK improves the performance of abundance models of 18 bird and 24 butterfly species across 3723 and 1547 UK monitoring sites, respectively.Although improvements in explanatory power were small, the inclusion of linear features data significantly improved model predictive performance for many species. For some species, the importance of linear features depended on landscape context, with greater importance in agricultural areas. Synthesis and applications. This study demonstrates that a national-scale model of the extent and distribution of linear features improves predictions of farmland biodiversity. The ability to model spatial variability in the role of linear features such as hedgerows will be important in targeting agri-environment schemes to maximally deliver biodiversity benefits. Although this study focuses on farmland, data on the extent of different linear features are likely to improve species distribution and abundance models in a wide range of systems and also can potentially be used to assess habitat connectivity.

SUBMITTER: Sullivan MJP

PROVIDER: S-EPMC5697618 | biostudies-literature | 2017 Dec

REPOSITORIES: biostudies-literature

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A national-scale model of linear features improves predictions of farmland biodiversity.

Sullivan Martin J P MJP Pearce-Higgins James W JW Newson Stuart E SE Scholefield Paul P Brereton Tom T Oliver Tom H TH

The Journal of applied ecology 20170507 6

Modelling species distribution and abundance is important for many conservation applications, but it is typically performed using relatively coarse-scale environmental variables such as the area of broad land-cover types. Fine-scale environmental data capturing the most biologically relevant variables have the potential to improve these models. For example, field studies have demonstrated the importance of linear features, such as hedgerows, for multiple taxa, but the absence of large-scale data ...[more]

PMID: 29200496

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Project description:BackgroundGenome-scale metabolic model (GSMM) is a powerful tool for the study of cellular metabolic characteristics. With the development of multi-omics measurement techniques in recent years, new methods that integrating multi-omics data into the GSMM show promising effects on the predicted results. It does not only improve the accuracy of phenotype prediction but also enhances the reliability of the model for simulating complex biochemical phenomena, which can promote theoretical breakthroughs for specific gene target identification or better understanding the cell metabolism on the system level.ResultsBased on the basic GSMM model iHL1210 of Aspergillus niger, we integrated large-scale enzyme kinetics and proteomics data to establish a GSMM based on enzyme constraints, termed a GEM with Enzymatic Constraints using Kinetic and Omics data (GECKO). The results show that enzyme constraints effectively improve the model's phenotype prediction ability, and extended the model's potential to guide target gene identification through predicting metabolic phenotype changes of A. niger by simulating gene knockout. In addition, enzyme constraints significantly reduced the solution space of the model, i.e., flux variability over 40.10% metabolic reactions were significantly reduced. The new model showed also versatility in other aspects, like estimating large-scale [Formula: see text] values, predicting the differential expression of enzymes under different growth conditions.ConclusionsThis study shows that incorporating enzymes' abundance information into GSMM is very effective for improving model performance with A. niger. Enzyme-constrained model can be used as a powerful tool for predicting the metabolic phenotype of A. niger by incorporating proteome data. In the foreseeable future, with the fast development of measurement techniques, and more precise and rich proteomics quantitative data being obtained for A. niger, the enzyme-constrained GSMM model will show greater application space on the system level.

Project description:Drainage ditches, either seasonally flooded or permanent, are commonly found on intensively managed lowland farmland in the UK. They are potentially important for wetland biodiversity but, despite their ubiquity, information on their biodiversity and management in the wider countryside is scarce. We surveyed 175 ditches for their physical and chemical characteristics, spatial connectivity, plant communities and aquatic invertebrates in an area of intensively managed farmland in Oxfordshire, UK and collected information on ditch management from farmer interviews. Water depth and shade had a small impact on the diversity of plant and invertebrate communities in ditches. Increased shade over the ditch channel resulted in reduced taxonomic richness of both channel vegetation and aquatic invertebrates and channel vegetation cover was lower at shaded sites. Invertebrate taxonomic richness was higher when water was deeper. Spatial connectivity had no detectable impact on the aquatic invertebrate or plant communities found in ditches. The number of families within the orders Ephemeroptera, Plecoptera and Trichoptera (EPT), which contain many pollution-sensitive species, declined with decreasing pH of ditch water. As time since dredging increased, the number of EPT families increased in permanent ditches but decreased in temporary ditches. Whether or not a ditch was in an agri-environment scheme had little impact on the reported management regime or biodiversity value of the ditch. Measures for increasing the amount of water in ditches, by increasing the water depth or promoting retention of water in ditches, could increase the biodiversity value of ditches in agricultural land. Some temporary ditches for specialised species should be retained. Reducing the amount of shade over narrow ditches by managing adjacent hedgerows is also likely to increase the species diversity of plant and invertebrate communities within the ditch. We recommend that to preserve or enhance the biodiversity value of ditches, and improve their ecosystem service delivery, management prescriptions for hedgerows adjacent to ditches should differ from those aimed at hedgerows only.

Project description:BackgroundCancer genomic studies often include data collected from several omics platforms. Each omics data source contributes to the understanding of the underlying biological process via source specific ("individual") patterns of variability. At the same time, statistical associations and potential interactions among the different data sources can reveal signals from common biological processes that might not be identified by single source analyses. These common patterns of variability are referred to as "shared" or "joint". In this work, we show how the use of joint and individual components can lead to better predictive models, and to a deeper understanding of the biological process at hand. We identify joint and individual contributions of DNA methylation, miRNA and mRNA expression collected from blood samples in a lung cancer case-control study nested within the Norwegian Women and Cancer (NOWAC) cohort study, and we use such components to build prediction models for case-control and metastatic status. To assess the quality of predictions, we compare models based on simultaneous, integrative analysis of multi-source omics data to a standard non-integrative analysis of each single omics dataset, and to penalized regression models. Additionally, we apply the proposed approach to a breast cancer dataset from The Cancer Genome Atlas.ResultsOur results show how an integrative analysis that preserves both components of variation is more appropriate than standard multi-omics analyses that are not based on such a distinction. Both joint and individual components are shown to contribute to a better quality of model predictions, and facilitate the interpretation of the underlying biological processes in lung cancer development.ConclusionsIn the presence of multiple omics data sources, we recommend the use of data integration techniques that preserve the joint and individual components across the omics sources. We show how the inclusion of such components increases the quality of model predictions of clinical outcomes.

Dataset Information

A national-scale model of linear features improves predictions of farmland biodiversity.

Publications

A national-scale model of linear features improves predictions of farmland biodiversity.

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