dynamAedes: a unified modelling framework for invasive Aedes mosquitoes

Mosquito species belonging to the genus Aedes have attracted the interest of scientists and public health officers for their invasive species traits and efficient capacity of transmitting viruses affecting humans. Some of these species were brought outside their native range by human activities such as trade and tourism, and colonised new regions thanks to a unique combination of eco-physiological traits. Considering mosquito physiological and behavioural traits to understand and predict the spatial and temporal population dynamics is thus a crucial step to develop strategies to mitigate the local densities of invasive Aedes populations. Here, we synthesised the life cycle of four invasive Aedes species (Ae. aegypti, Ae. albopictus, Ae. japonicus and Ae. koreicus) in a single multi-scale stochastic modelling framework which we coded in the R package dynamAedes. We designed a stage-based and time-discrete stochastic model driven by temperature, photo-period and inter-specific larval competition that can be applied to three different spatial scales: punctual, local and regional. These spatial scales consider different degrees of spatial complexity and data availability, by accounting for both active and passive dispersal of mosquito species as well as for the heterogeneity of the input temperature data. Our overarching aim was to provide a flexible, open-source and user-friendly tool rooted in the most updated knowledge on species biology which could be applied to the management of invasive Aedes populations as well as for more theoretical ecological inquiries.

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Stochastic modelling of hydropower generation from small hydropower plants under limited data availability: from post-assessment to forecasting

10.5194/egusphere-egu2020-8129 ◽

2020 ◽

Author(s):

Georgia Konstantina Sakki ◽

Vassiliki Maria Papalamprou ◽

Ioannis Tsoukalas ◽

Nikos Mamassis ◽

Andreas Efstratiadis

Keyword(s):

Hydroelectric Plant ◽

Stochastic Modelling ◽

Small Time ◽

Data Availability ◽

Small Hydropower ◽

Modelling Framework ◽

Time Horizons ◽

Hydropower Plants ◽

Hydropower Production ◽

Stochastic Forecasting

Due to their negligible storage capacity, small hydroelectric plants cannot offer regulation of flows, thus making the prediction of energy production a very difficult task, even for small time horizons. Further uncertainties arise due to the limited hydrological information, in terms of upstream inflow data, since usually the sole available measurements refer to the power production, which is a nonlinear transformation of the river discharge. In this context, we develop a stochastic modelling framework comprising two steps. Initially, we extract past inflows on the basis of energy data, which may be referred to as the inverse problem of hydropower. Key issue of this approach is that the model error is expressed in stochastic terms, which allows for embedding uncertainties within calculations. Next, we generate stochastic forecasting ensembles of future inflows and associated hydropower production, spanning from small (daily to weekly) to meso-scale (monthly to seasonal) time horizons. The methodology is tested in the oldest (est. 1926) small hydroelectric plant of Greece, located at Glafkos river, in Northern Peloponnese. Among other complexities, this comprises a mixing of Pelton and Francis turbines, which makes the overall modelling procedure even more challenging.

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siland: an R package for estimating the spatial influence of landscape

10.1101/692566 ◽

2019 ◽

Author(s):

Florence Carpentier ◽

Olivier Martin

Keyword(s):

Scale Effects ◽

Spatial Scales ◽

R Package ◽

Landscape Features ◽

Spatial Influence ◽

Scale Of Effect ◽

Distance Weighted ◽

Models Comparison ◽

Practical Tool ◽

User Friendly

AbstractContextThe spatial distributions of species and populations are both influenced by local variables and by characteristics of surrounding landscapes. Understanding how landscape features spatially structure the frequency of a trait in a population, the abundance of a species or the species’ richness remains difficult specially because the spatial scale effects of the landscape variables are often unknown.ObjectivesHere, we present “siland”, an R package for analyzing the effect of landscape features on georeferenced point observations (described in a Geographic Information System shapefile format).Methods & Results“siland” simultaneously estimates the spatial scales and intensities of landscape variable effects. It does not require any information about the scale of effect. Two methods are available: one is based on focal sample site (Bsiland method, b for buffer) and one is distance weighted using Spatial Influence Function (Fsiland method, f for function). ‘siland’ allows for effects tests, effects maps and models comparison.ConclusionsAdaptable and user-friendly, the “siland” package is a very practical tool to perform landscape analysis.

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Siland a R package for estimating the spatial influence of landscape

Scientific Reports ◽

10.1038/s41598-021-86900-0 ◽

2021 ◽

Vol 11 (1) ◽

Author(s):

Florence Carpentier ◽

Olivier Martin

Keyword(s):

Scale Effects ◽

Spatial Scales ◽

Likelihood Estimation ◽

Codling Moth ◽

R Package ◽

Landscape Features ◽

Spatial Influence ◽

Distance Weighted ◽

Models Comparison ◽

User Friendly

AbstractThe spatial distributions of populations are both influenced by local variables and by characteristics of surrounding landscapes. Understanding how landscape features spatially structure the frequency of a trait in a population, the abundance of a species or the species’ richness remains difficult specially because the spatial scale effects of the landscape variables are unknown. Various methods have been proposed but their results are not easily comparable. Here, we introduce “siland”, a general method for analyzing the effect of landscape features. Based on a sequential procedure of maximum likelihood estimation, it simultaneously estimates the spatial scales and intensities of landscape variable effects. It does not require any information about the scale of effect. It integrates two landscape effects models: one is based on focal sample site (Bsiland, b for buffer) and one is distance weighted using Spatial Influence Function (Fsiland, f for function). We implemented “siland” in the adaptable and user-friendly R eponym package. It performs landscape analysis on georeferenced point observations (described in a Geographic Information System shapefile format) and allows for effects tests, effects maps and models comparison. We illustrated its use on a real dataset by the study of a crop pest (codling moth densities).

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The role of habitat configuration in shaping animal population processes: a framework to generate quantitative predictions

Oecologia ◽

10.1007/s00442-021-04967-y ◽

2021 ◽

Author(s):

Peng He ◽

Pierre-Olivier Montiglio ◽

Marius Somveille ◽

Mauricio Cantor ◽

Damien R. Farine

Keyword(s):

Population Level ◽

R Package ◽

Habitat Configuration ◽

Modelling Framework ◽

Alternative Habitat ◽

Animal Populations ◽

Research Questions ◽

Habitat Networks ◽

Animal Habitat

AbstractBy shaping where individuals move, habitat configuration can fundamentally structure animal populations. Yet, we currently lack a framework for generating quantitative predictions about the role of habitat configuration in modulating population outcomes. To address this gap, we propose a modelling framework inspired by studies using networks to characterize habitat connectivity. We first define animal habitat networks, explain how they can integrate information about the different configurational features of animal habitats, and highlight the need for a bottom–up generative model that can depict realistic variations in habitat potential connectivity. Second, we describe a model for simulating animal habitat networks (available in the R package AnimalHabitatNetwork), and demonstrate its ability to generate alternative habitat configurations based on empirical data, which forms the basis for exploring the consequences of alternative habitat structures. Finally, we lay out three key research questions and demonstrate how our framework can address them. By simulating the spread of a pathogen within a population, we show how transmission properties can be impacted by both local potential connectivity and landscape-level characteristics of habitats. Our study highlights the importance of considering the underlying habitat configuration in studies linking social structure with population-level outcomes.

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DIscBIO: A User-Friendly Pipeline for Biomarker Discovery in Single-Cell Transcriptomics

International Journal of Molecular Sciences ◽

10.3390/ijms22031399 ◽

2021 ◽

Vol 22 (3) ◽

pp. 1399

Author(s):

Salim Ghannoum ◽

Waldir Leoncio Netto ◽

Damiano Fantini ◽

Benjamin Ragan-Kelley ◽

Amirabbas Parizadeh ◽

...

Keyword(s):

Single Cell ◽

Biomarker Discovery ◽

Enrichment Analysis ◽

Myxoid Liposarcoma ◽

R Package ◽

Differential Analysis ◽

A Cell ◽

Reproducible Analysis ◽

Transcriptomic Level ◽

User Friendly

The growing attention toward the benefits of single-cell RNA sequencing (scRNA-seq) is leading to a myriad of computational packages for the analysis of different aspects of scRNA-seq data. For researchers without advanced programing skills, it is very challenging to combine several packages in order to perform the desired analysis in a simple and reproducible way. Here we present DIscBIO, an open-source, multi-algorithmic pipeline for easy, efficient and reproducible analysis of cellular sub-populations at the transcriptomic level. The pipeline integrates multiple scRNA-seq packages and allows biomarker discovery with decision trees and gene enrichment analysis in a network context using single-cell sequencing read counts through clustering and differential analysis. DIscBIO is freely available as an R package. It can be run either in command-line mode or through a user-friendly computational pipeline using Jupyter notebooks. We showcase all pipeline features using two scRNA-seq datasets. The first dataset consists of circulating tumor cells from patients with breast cancer. The second one is a cell cycle regulation dataset in myxoid liposarcoma. All analyses are available as notebooks that integrate in a sequential narrative R code with explanatory text and output data and images. R users can use the notebooks to understand the different steps of the pipeline and will guide them to explore their scRNA-seq data. We also provide a cloud version using Binder that allows the execution of the pipeline without the need of downloading R, Jupyter or any of the packages used by the pipeline. The cloud version can serve as a tutorial for training purposes, especially for those that are not R users or have limited programing skills. However, in order to do meaningful scRNA-seq analyses, all users will need to understand the implemented methods and their possible options and limitations.

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An Interactive WebGIS Framework for Coastal Erosion Risk Management

Journal of Marine Science and Engineering ◽

10.3390/jmse9060567 ◽

2021 ◽

Vol 9 (6) ◽

pp. 567

Author(s):

Alessandra Capolupo ◽

Cristina Monterisi ◽

Alessandra Saponieri ◽

Fabio Addona ◽

Leonardo Damiani ◽

...

Keyword(s):

Open Source ◽

3D Visualization ◽

Erosion Risk ◽

Innovative Strategies ◽

Web Mapping ◽

Natural Processes ◽

Multi Scale ◽

Interactive Interface ◽

Multi Temporal ◽

User Friendly

The Italian coastline stretches over about 8350 km, with 3600 km of beaches, representing a significant resource for the country. Natural processes and anthropic interventions keep threatening its morphology, moulding its shape and triggering soil erosion phenomena. Thus, many scholars have been focusing their work on investigating and monitoring shoreline instability. Outcomes of such activities can be largely widespread and shared with expert and non-expert users through Web mapping. This paper describes the performances of a WebGIS prototype designed to disseminate the results of the Italian project Innovative Strategies for the Monitoring and Analysis of Erosion Risk, known as the STIMARE project. While aiming to include the entire national coastline, three study areas along the regional coasts of Puglia and Emilia Romagna have already been implemented as pilot cases. This WebGIS was generated using Free and Open-Source Software for Geographic information systems (FOSS4G). The platform was designed by combining Apache http server, Geoserver, as open-source server and PostgreSQL (with PostGIS extension) as database. Pure javascript libraries OpenLayers and Cesium were implemented to obtain a hybrid 2D and 3D visualization. A user-friendly interactive interface was programmed to help users visualize and download geospatial data in several formats (pdf, kml and shp), in accordance with the European INSPIRE directives, satisfying both multi-temporal and multi-scale perspectives.

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BREC: an R package/Shiny app for automatically identifying heterochromatin boundaries and estimating local recombination rates along chromosomes

BMC Bioinformatics ◽

10.1186/s12859-021-04233-1 ◽

2021 ◽

Vol 22 (S6) ◽

Author(s):

Yasmine Mansour ◽

Annie Chateau ◽

Anna-Sophie Fiston-Lavier

Keyword(s):

Data Quality ◽

Data Science ◽

Fruit Fly ◽

R Package ◽

Model Organisms ◽

Data Quality Control ◽

Recombination Rates ◽

Functional Dynamics ◽

Shiny App ◽

User Friendly

Abstract Background Meiotic recombination is a vital biological process playing an essential role in genome's structural and functional dynamics. Genomes exhibit highly various recombination profiles along chromosomes associated with several chromatin states. However, eu-heterochromatin boundaries are not available nor easily provided for non-model organisms, especially for newly sequenced ones. Hence, we miss accurate local recombination rates necessary to address evolutionary questions. Results Here, we propose an automated computational tool, based on the Marey maps method, allowing to identify heterochromatin boundaries along chromosomes and estimating local recombination rates. Our method, called BREC (heterochromatin Boundaries and RECombination rate estimates) is non-genome-specific, running even on non-model genomes as long as genetic and physical maps are available. BREC is based on pure statistics and is data-driven, implying that good input data quality remains a strong requirement. Therefore, a data pre-processing module (data quality control and cleaning) is provided. Experiments show that BREC handles different markers' density and distribution issues. Conclusions BREC's heterochromatin boundaries have been validated with cytological equivalents experimentally generated on the fruit fly Drosophila melanogaster genome, for which BREC returns congruent corresponding values. Also, BREC's recombination rates have been compared with previously reported estimates. Based on the promising results, we believe our tool has the potential to help bring data science into the service of genome biology and evolution. We introduce BREC within an R-package and a Shiny web-based user-friendly application yielding a fast, easy-to-use, and broadly accessible resource. The BREC R-package is available at the GitHub repository https://github.com/GenomeStructureOrganization.

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Complex community responses underpin biodiversity change following invasion

Biological Invasions ◽

10.1007/s10530-021-02559-8 ◽

2021 ◽

Author(s):

Alessandra R. Kortz ◽

Anne E. Magurran

Keyword(s):

Invasive Species ◽

Species Richness ◽

Spatial Scales ◽

Species Abundance ◽

Ecological Systems ◽

Multi Scale ◽

Mechanistic Approach ◽

Α Diversity ◽

Biodiversity Change ◽

Insight Into

AbstractHow do invasive species change native biodiversity? One reason why this long-standing question remains challenging to answer could be because the main focus of the invasion literature has been on shifts in species richness (a measure of α-diversity). As the underlying components of community structure—intraspecific aggregation, interspecific density and the species abundance distribution (SAD)—are potentially impacted in different ways during invasion, trends in species richness provide only limited insight into the mechanisms leading to biodiversity change. In addition, these impacts can be manifested in distinct ways at different spatial scales. Here we take advantage of the new Measurement of Biodiversity (MoB) framework to reanalyse data collected in an invasion front in the Brazilian Cerrado biodiversity hotspot. We show that, by using the MoB multi-scale approach, we are able to link reductions in species richness in invaded sites to restructuring in the SAD. This restructuring takes the form of lower evenness in sites invaded by pines relative to sites without pines. Shifts in aggregation also occur. There is a clear signature of spatial scale in biodiversity change linked to the presence of an invasive species. These results demonstrate how the MoB approach can play an important role in helping invasion ecologists, field biologists and conservation managers move towards a more mechanistic approach to detecting and interpreting changes in ecological systems following invasion.

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Scale-dependent intrinsic entropies of complex time series

Philosophical Transactions of The Royal Society A Mathematical Physical and Engineering Sciences ◽

10.1098/rsta.2015.0204 ◽

2016 ◽

Vol 374 (2065) ◽

pp. 20150204 ◽

Cited By ~ 2

Author(s):

Jia-Rong Yeh ◽

Chung-Kang Peng ◽

Norden E. Huang

Keyword(s):

Time Series ◽

Time Scales ◽

Spatial Scales ◽

Multiple Time Scales ◽

Multiple Time ◽

Complex Time ◽

Multi Scale ◽

Mode Decomposition ◽

Simulated Time ◽

Simulated Time Series

Multi-scale entropy (MSE) was developed as a measure of complexity for complex time series, and it has been applied widely in recent years. The MSE algorithm is based on the assumption that biological systems possess the ability to adapt and function in an ever-changing environment, and these systems need to operate across multiple temporal and spatial scales, such that their complexity is also multi-scale and hierarchical. Here, we present a systematic approach to apply the empirical mode decomposition algorithm, which can detrend time series on various time scales, prior to analysing a signal’s complexity by measuring the irregularity of its dynamics on multiple time scales. Simulated time series of fractal Gaussian noise and human heartbeat time series were used to study the performance of this new approach. We show that our method can successfully quantify the fractal properties of the simulated time series and can accurately distinguish modulations in human heartbeat time series in health and disease.

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Global scale hydrological modelling at 100 m, 1 h resolution, in Python

10.5194/egusphere-egu21-7154 ◽

2021 ◽

Author(s):

Kor de Jong ◽

Marc van Kreveld ◽

Debabrata Panja ◽

Oliver Schmitz ◽

Derek Karssenberg

Keyword(s):

Large Scale ◽

Model Building ◽

Flow Direction ◽

Building Blocks ◽

Global Scale ◽

Data Availability ◽

Hydrological Models ◽

Continental Scale ◽

Modelling Framework ◽

Scale Models

Data availability at global scale is increasing exponentially. Although considerable challenges remain regarding the identification of model structure and parameters of continental scale hydrological models, we will soon reach the situation that global scale models could be defined at very high resolutions close to 100 m or less. One of the key challenges is how to make simulations of these ultra-high resolution models tractable ([1]).Our research contributes by the development of a model building framework that is specifically designed to distribute calculations over multiple cluster nodes. This framework enables domain experts like hydrologists to develop their own large scale models, using a scripting language like Python, without the need to acquire the skills to develop low-level computer code for parallel and distributed computing.We present the design and implementation of this software framework and illustrate its use with a prototype 100 m, 1 h continental scale hydrological model. Our modelling framework ensures that any model built with it is parallelized. This is made possible by providing the model builder with a set of building blocks of models, which are coded in such a manner that parallelization of calculations occurs within and across these building blocks, for any combination of building blocks. There is thus full flexibility on the side of the modeller, without losing performance.This breakthrough is made possible by applying a novel approach to the implementation of the model building framework, called asynchronous many-tasks, provided by the HPX C++ software library ([3]). The code in the model building framework expresses spatial operations as large collections of interdependent tasks that can be executed efficiently on individual laptops as well as computer clusters ([2]). Our framework currently includes the most essential operations for building large scale hydrological models, including those for simulating transport of material through a flow direction network. By combining these operations, we rebuilt an existing 100 m, 1 h resolution model, thus far used for simulations of small catchments, requiring limited coding as we only had to replace the computational back end of the existing model. Runs at continental scale on a computer cluster show acceptable strong and weak scaling providing a strong indication that global simulations at this resolution will soon be possible, technically speaking.Future work will focus on extending the set of modelling operations and adding scalable I/O, after which existing models that are currently limited in their ability to use the computational resources available to them can be ported to this new environment.More information about our modelling framework is at https://lue.computationalgeography.org.References[1] M. Bierkens. Global hydrology 2015: State, trends, and directions. Water Resources Research, 51(7):4923&#8211;4947, 2015. [2] K. de Jong, et al. An environmental modelling framework based on asynchronous many-tasks: scalability and usability. Submitted. [3] H. Kaiser, et al. HPX - The C++ standard library for parallelism and concurrency. Journal of Open Source Software, 5(53):2352, 2020.

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