scholarly journals Generation and application of river network analogues for use in ecology and evolution

2020 ◽  
Author(s):  
Luca Carraro ◽  
Enrico Bertuzzo ◽  
Emanuel A. Fronhofer ◽  
Reinhard Furrer ◽  
Isabelle Gounand ◽  
...  
Keyword(s):  
Spatial Ecology ◽  
Spanning Trees ◽  
Random Search ◽  
Three Dimensional ◽  
R Package ◽  
River Network ◽  
River Networks ◽  
List Type ◽  
Freshwater Ecology ◽  
Scaling Features

AbstractSeveral key processes in freshwater ecology and evolution are governed by the connectivity inherent to dendritic river networks. These networks have extensively been analyzed from a geomorphological and hydrological viewpoint, yet network structures classically used in modelling have only been partially representative of the structure of real river basins, and have often failed to capture well known scaling features of real river networks. Pioneering work has identified optimal channel networks (OCNs) as spanning trees that reproduce all scaling features characteristic of real, natural stream networks worldwide. While these networks have been used to generate landscapes for studies on metapopulations, biodiversity and epidemiology, their generation has not been generally accessible.Given the increasing interest in dendritic riverine networks by ecologists and evolutionary biologists, we here present a method to generate OCNs and, to facilitate its application, we also provide the R-package OCNet. Owing to the random search process that generates OCNs, multiple network replicas spanning the same surface can be built, allowing one to perform computational experiments whose results do not depend on the particular shape of a single river network. The OCN construct also enables the generation of elevational gradients derived from the optimal network configuration, which can constitute three-dimensional landscapes for spatial studies in both terrestrial and freshwater realms. Moreover, the OCNet package provides functions that aggregate the OCN into an arbitrary number of nodes, calculate several metrics and descriptors of river networks, and draw relevant features of the network.We describe the main functionalities of the package and present how it can be integrated into other R-packages commonly used in spatial ecology. Moreover, we exemplify the generation of OCNs and discuss an application to a metapopulation model for an invasive riverine species.In conclusion, OCNet provides a powerful tool to generate and use realistic river network analogues for various applications. It thereby allows the design of spatially realistic studies in increasingly impacted ecosystems, and enhances our knowledge on spatial processes in freshwater ecology in general.

scholarly journals ShapeRotator: an R tool for standardised rigid rotations of articulated Three-Dimensional structures with application for geometric morphometrics

2017 ◽  
Author(s):  
Marta Vidal-García ◽  
Lashi Bandara ◽  
J. Scott Keogh
Keyword(s):  
Geometric Morphometrics ◽  
Phenotypic Diversity ◽  
Three Dimensional ◽  
R Package ◽  
Morphological Data ◽  
List Type ◽  
Shape Variation ◽  
Data Set ◽  
Articulated Structures ◽  
Shape And Size

SummaryThe quantification of complex morphological patterns typically involves comprehensive shape and size analyses, usually obtained by gathering morphological data from all the structures that capture the phenotypic diversity of an organism or object. Articulated structures are a critical component of overall phenotypic diversity, but data gathered from these structures are difficult to incorporate in to modern analyses because of the complexities associated with jointly quantifying 3D shape in multiple structures.While there are existing methods for analysing shape variation in articulated structures in Two-Dimensional (2D) space, these methods do not work in 3D, a rapidly growing area of capability and research.Here we describe a simple geometric rigid rotation approach that removes the effect of random translation and rotation, enabling the morphological analysis of 3D articulated structures. Our method is based on Cartesian coordinates in 3D space so it can be applied to any morphometric problem that also uses 3D coordinates (e.g. spherical harmonics). We demonstrate the method by applying it to a landmark-based data set for analysing shape variation using geometric morphometrics.We have developed an R tool (ShapeRotator) so that the method can be easily implemented in the commonly used R package geomorph and MorphoJ software. This method will be a valuable tool for 3D morphological analyses in articulated structures by allowing an exhaustive examination of shape and size diversity.

scholarly journals Setting the Flow Accumulation Threshold Based on Environmental and Morphologic Features to Extract River Networks from Digital Elevation Models

2021 ◽  
Vol 10 (3) ◽  
pp. 186
Author(s):  
HuiHui Zhang ◽  
Hugo A. Loáiciga ◽  
LuWei Feng ◽  
Jing He ◽  
QingYun Du
Keyword(s):  
Spatial Scales ◽  
Digital Elevation Models ◽  
Regional Scale ◽  
Drainage Density ◽  
River Network ◽  
Landscape Patterns ◽  
Estimation Methods ◽  
River Networks ◽  
Flow Accumulation ◽  
Digital Elevation

Determining the flow accumulation threshold (FAT) is a key task in the extraction of river networks from digital elevation models (DEMs). Several methods have been developed to extract river networks from Digital Elevation Models. However, few studies have considered the geomorphologic complexity in the FAT estimation and river network extraction. Recent studies estimated influencing factors’ impacts on the river length or drainage density without considering anthropogenic impacts and landscape patterns. This study contributes two FAT estimation methods. The first method explores the statistical association between FAT and 47 tentative explanatory factors. Specifically, multi-source data, including meteorologic, vegetation, anthropogenic, landscape, lithology, and topologic characteristics are incorporated into a drainage density-FAT model in basins with complex topographic and environmental characteristics. Non-negative matrix factorization (NMF) was employed to evaluate the factors’ predictive performance. The second method exploits fractal geometry theory to estimate the FAT at the regional scale, that is, in basins whose large areal extent precludes the use of basin-wide representative regression predictors. This paper’s methodology is applied to data acquired for Hubei and Qinghai Provinces, China, from 2001 through 2018 and systematically tested with visual and statistical criteria. Our results reveal key local features useful for river network extraction within the context of complex geomorphologic characteristics at relatively small spatial scales and establish the importance of properly choosing explanatory geomorphologic characteristics in river network extraction. The multifractal method exhibits more accurate extracting results than the box-counting method at the regional scale.

scholarly journals A new vector-based global river network dataset accounting for variable drainage density

2021 ◽  
Vol 8 (1) ◽  
Author(s):  
Peirong Lin ◽  
Ming Pan ◽  
Eric F. Wood ◽  
Dai Yamazaki ◽  
George H. Allen
Keyword(s):  
Spatial Variability ◽  
Flow Direction ◽  
River System ◽  
Drainage Density ◽  
River Network ◽  
River Networks ◽  
Digital Elevation ◽  
Machine Learning Approach ◽  
Elevation Model ◽  
National Hydrography Dataset

AbstractSpatial variability of river network drainage density (Dd) is a key feature of river systems, yet few existing global hydrography datasets have properly accounted for it. Here, we present a new vector-based global hydrography that reasonably estimates the spatial variability of Dd worldwide. It is built by delineating channels from the latest 90-m Multi-Error-Removed Improved Terrain (MERIT) digital elevation model and flow direction/accumulation. A machine learning approach is developed to estimate Dd based on the global watershed-level climatic, topographic, hydrologic, and geologic conditions, where relationships between hydroclimate factors and Dd are trained using the high-quality National Hydrography Dataset Plus (NHDPlusV2) data. By benchmarking our dataset against HydroSHEDS and several regional hydrography datasets, we show the new river flowlines are in much better agreement with Landsat-derived centerlines, and improved Dd patterns of river networks (totaling ~75 million kilometers in length) are obtained. Basins and estimates of intermittent stream fraction are also delineated to support water resources management. This new dataset (MERIT Hydro–Vector) should enable full global modeling of river system processes at fine spatial resolutions.

scholarly journals An Innovative Framework for Real Time Monitoring of Pollutant Point Sources in River Networks

2021 ◽  
Author(s):  
Mehdi Mazaheri ◽  
J. M. V. Samani ◽  
Fulvio Boano
Keyword(s):  
Real Time ◽  
Transport Model ◽  
River Network ◽  
Point Sources ◽  
Release Time ◽  
River Networks ◽  
Observation Data ◽  
Geostatistical Approach ◽  
Pollutant Sources ◽  
Simultaneous Identification

Abstract The simultaneous identification of location and source release history in complex river networks is a very complicated ill-posed problem, particularly in a case of multiple unknown pollutant sources with time-varying release pattern. This study presents an innovative method for simultaneous identification of the number, locations and release histories of multiple pollutant point sources in a river network using minimum observation data. Considering two different type of monitoring stations with an adaptive arrangement as well as real-time data collection at those stations and using a reliable numerical flow and transport model, at first the number and suspected reach of presence of pollutant sources are determined. Then the source location and its intensity function is calculated by solving inverse source problem using a geostatistical approach. A case study with three different scenarios in terms of the number, release time and location of pollutant sources are discussed, concerning a river network with unsteady and non-uniform flow. Results showed the capability of the proposed method in identifying of sought source characteristics even in complicated cases with simultaneous activity of multiple pollutant sources.

scholarly journals On the propagation of diel signals in river networks using analytic solutions of flow equations

2015 ◽  
Vol 12 (8) ◽  
pp. 8175-8220 ◽  
Author(s):  
M. Fonley ◽  
R. Mantilla ◽  
S. J. Small ◽  
R. Curtu
Keyword(s):  
Analytic Solutions ◽  
River Network ◽  
Low Flow ◽  
River Networks ◽  
Signal Delay ◽  
Flow Conditions ◽  
Flow Equations ◽  
Linear Transport Equation ◽  
Daily Evapotranspiration ◽  
Self Similar

Abstract. Two hypotheses have been put forth to explain the magnitude and timing of diel streamflow oscillations during low flow conditions. The first suggests that delays between the peaks and troughs of streamflow and daily evapotranspiration are due to processes occurring in the soil as water moves toward the channels in the river network. The second posits that they are due to the propagation of the signal through the channels as water makes its way to the outlet of the basin. In this paper, we design and implement a theoretical experiment to test these hypotheses. We impose a baseflow signal entering the river network and use a linear transport equation to represent flow along the network. We develop analytic streamflow solutions for two cases: uniform and nonuniform velocities in space over all river links. We then use our analytic solutions to simulate streamflows along a self-similar river network for different flow velocities. Our results show that the amplitude and time delay of the streamflow solution are heavily influenced by transport in the river network. Moreover, our equations show that the geomorphology and topology of the river network play important roles in determining how amplitude and signal delay are reflected in streamflow signals. Finally, our results are consistent with empirical observations that delays are more significant as low flow decreases.

scholarly journals FIREcaller: Detecting Frequently Interacting Regions from Hi-C Data

2019 ◽  
Author(s):  
Cheynna Crowley ◽  
Yuchen Yang ◽  
Yunjiang Qiu ◽  
Benxia Hu ◽  
Armen Abnousi ◽  
...  
Keyword(s):  
Gene Regulation ◽  
Spatial Organization ◽  
R Package ◽  
Specific Gene ◽  
List Type ◽  
Cell Type ◽  
R Software ◽  
Computational Tools ◽  
Cell Type Specific ◽  
User Friendly

AbstractHi-C experiments have been widely adopted to study chromatin spatial organization, which plays an essential role in genome function. We have recently identified frequently interacting regions (FIREs) and found that they are closely associated with cell-type-specific gene regulation. However, computational tools for detecting FIREs from Hi-C data are still lacking. In this work, we present FIREcaller, a stand-alone, user-friendly R package for detecting FIREs from Hi-C data. FIREcaller takes raw Hi-C contact matrices as input, performs within-sample and cross-sample normalization, and outputs continuous FIRE scores, dichotomous FIREs, and super-FIREs. Applying FIREcaller to Hi-C data from various human tissues, we demonstrate that FIREs and super-FIREs identified, in a tissue-specific manner, are closely related to gene regulation, are enriched for enhancer-promoter (E-P) interactions, tend to overlap with regions exhibiting epigenomic signatures of cis-regulatory roles, and aid the interpretation or GWAS variants. The FIREcaller package is implemented in R and freely available at https://yunliweb.its.unc.edu/FIREcaller.Highlights– Frequently Interacting Regions (FIREs) can be used to identify tissue and cell-type-specific cis-regulatory regions.– An R software, FIREcaller, has been developed to identify FIREs and clustered FIREs into super-FIREs.

Identifying global hotspots of plastic waste accumulation along river networks

2021 ◽  
Author(s):  
Jesus Gomez-Velez ◽  
Stefan Krause
Keyword(s):  
Multiple Scales ◽  
Transport Model ◽  
Freshwater Ecosystems ◽  
River Network ◽  
Plastic Waste ◽  
Trapping Efficiency ◽  
River Networks ◽  
Clear Understanding ◽  
Relative Location ◽  
Plastic Pollution

<p>Global plastic pollution is affecting ecosystems and human health globally. Proposing solutions and coping strategies for this threat requires a clear understanding of the processes controlling the fate and transport of mismanaged plastics at multiple scales, going from watersheds to regions and even continents. River corridors are the primary conveyor and trap for mismanaged plastic produced within the landscape and eventually released to the ocean. New approaches that apply technological sensing innovations for monitoring plastic waste in aquatic environments can improve observations and plastic waste datasets globally. However, our understanding of when, where, and how to target monitoring is limited, reducing the benefit gained. There is therefore a critical demand for predictions of hotspots (as well as hot moments) of plastic accumulation along river networks globally, in order to optimize observational capacity.     </p><p>Here, we present a new global flow and transport model for plastic waste in riverine environments. Our model predicts that only a small fraction (roughly 2.5%) of the global mismanaged plastic that entered rivers since the 1950s has been delivered to the ocean by 2020, with an overwhelming majority sequestered in freshwater ecosystems. Furthermore, we predict the patterns of mismanaged plastic accumulation and its residence time depend on (i) the topology and geometry of the river network, (ii) the relative location of plastic sources, and (ii) the relative location and trapping efficiency of flow regulation structures, primarily large dams. Our results highlight the role of rivers as major sinks for plastic waste and the need for targeted remedial strategies that consider the structure of the river network and anthropogenic regulation when proposing intervention measures and sampling efforts.</p>

scholarly journals multiHiCcompare: joint normalization and comparative analysis of complex Hi-C experiments

2019 ◽  
Vol 35 (17) ◽  
pp. 2916-2923 ◽  
Author(s):  
John C Stansfield ◽  
Kellen G Cresswell ◽  
Mikhail G Dozmorov
Keyword(s):  
Comparative Analysis ◽  
A Priori ◽  
Three Dimensional ◽  
R Package ◽  
Supplementary Information ◽  
Chromatin Interaction ◽  
Model Framework ◽  
Chromatin Interactions ◽  
Loess Regression ◽  
Sequencing Studies

Abstract Motivation With the development of chromatin conformation capture technology and its high-throughput derivative Hi-C sequencing, studies of the three-dimensional interactome of the genome that involve multiple Hi-C datasets are becoming available. To account for the technology-driven biases unique to each dataset, there is a distinct need for methods to jointly normalize multiple Hi-C datasets. Previous attempts at removing biases from Hi-C data have made use of techniques which normalize individual Hi-C datasets, or, at best, jointly normalize two datasets. Results Here, we present multiHiCcompare, a cyclic loess regression-based joint normalization technique for removing biases across multiple Hi-C datasets. In contrast to other normalization techniques, it properly handles the Hi-C-specific decay of chromatin interaction frequencies with the increasing distance between interacting regions. multiHiCcompare uses the general linear model framework for comparative analysis of multiple Hi-C datasets, adapted for the Hi-C-specific decay of chromatin interaction frequencies. multiHiCcompare outperforms other methods when detecting a priori known chromatin interaction differences from jointly normalized datasets. Applied to the analysis of auxin-treated versus untreated experiments, and CTCF depletion experiments, multiHiCcompare was able to recover the expected epigenetic and gene expression signatures of loss of chromatin interactions and reveal novel insights. Availability and implementation multiHiCcompare is freely available on GitHub and as a Bioconductor R package https://bioconductor.org/packages/multiHiCcompare. Supplementary information Supplementary data are available at Bioinformatics online.

Commission 49: The Interplanetary Plasma and the Heliosphere (Plasma Intsrplanetaire Et L’heliosphere)

1988 ◽  
Vol 20 (1) ◽  
pp. 677-681
Author(s):  
S. Grzedzielski ◽  
L.F. Burlaga
Keyword(s):  
Solar Wind ◽  
Three Dimensional ◽  
List Type ◽  
Type Number ◽  
Local Interstellar Medium ◽  
Solar Origin ◽  
Area Of Interest ◽  
Recent Developments ◽  
Interplanetary Plasma ◽  
Solar Wind Composition

The area of interest to the Commission includes: 1.Solar wind composition and dynamics;2.Solar Interaction of solar wind with extended interplanetary sources of plasma and gases of non-solar origin;3.SolarStructure and dynamics of the three-dimensional heliosphere;4.SolarInteraction of heliosphere with the local interstellar medium.The following reports summarize recent developments in the aforementioned fields.

On-Farm Testing of a Zeolite Filter to Capture Ammonia and Odors from a Dairy Manure Flushing System

2020 ◽  
Vol 63 (3) ◽  
pp. 597-607
Author(s):  
Mario Emanuel de Haro Martí ◽  
William Howard Neibling ◽  
Lide Chen ◽  
Mireille Chahine
Keyword(s):  
Residence Time ◽  
Production Systems ◽  
Three Dimensional ◽  
Ammonia Concentration ◽  
Dairy Manure ◽  
Operating Conditions ◽  
List Type ◽  
Air Filtration ◽  
Odor Concentration ◽  
On Farm

Highlights A zeolite filter achieved 92% reduction in ammonia emissions from a dairy flushed manure pit. The reduction in odor concentration was 45% at a minimum filter residence time of 0.9 seconds. The on-farm filter and air collection structure over a manure pit demonstrated the applicability of the project. Abstract. The concentration of large numbers of animals in relatively small areas, high production output per animal unit, and concentration of animal excretions and air emissions are some of the characteristics of modern animal agriculture. Ammonia (NH3) and odors are among the most noticeable as well as locally and regionally problematic emissions generated by concentrated animal feeding operation (CAFO) dairy production systems. Zeolites are defined as aluminosilicates with open three-dimensional framework structures composed of corner-sharing TO4 tetrahedra, where T is Al or Si. Zeolites are able to lose or gain water reversibly and to exchange and retain cations, including NH3 and ammonium. This study demonstrated the design and performance of a zeolite filter treating the airstream from a flushed manure pit installed on-farm at a commercial dairy. Clinoptilolite zeolite mined in Idaho was used as the filter medium. The capacity of the filter to reduce NH3 emissions was tested at three, six, and 59 days of filter operation. Reduction of odor emissions was tested at six days of operation. NH3 emissions were reduced by 92% (p < 0.001) at three days of operation and by 42% (p = 0.13) at 59 days of operation. The ammonia concentration in the pre-treatment airstream from a dairy manure collection pit was relatively high at 5.287 ±0.04 mg NH3-N m-3 (p < 0.001). The odor concentration reduction was 45% (p = 0.001) at six days of operation with the minimum empty bed residence time of 0.9 s. Total trial running time was 59 days. The roof-like pit cover structure used for air collection and the zeolite filter were proven to be capable of operating in the harsh on-farm environment and to be adaptable to changing operating conditions within the dairy. Keywords: Air filtration, Ammonia, Clinoptilolite, Dairy, Manure, Odor, Zeolite

Sign in / Sign up

Export Citation Format

Share Document