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172 results for “R script”

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zenodo40/100

R script to simulate the phenology of the pine processionary moth, Thaumetopoea pityocampa, from the egg to the last larval instar (L5).

This folder contains : - ReadMe file with the following explanation - the R script to simulate the phenology of the pine processionary moth (PPM_phenological_model.R), - the flight curve of the pine processionary moth in Orleans (France) in 2019 (Flight_Orleans_2019.csv ; col 1 = date, col 2 = number of adult catches, col 3 = cumulated number of catches)  - temperature dataset from June 2019 to December 2020 (Temperature.csv; col 1 = mean daily temperature, col 2 = Date, col 3 = Julian day continuously counted from one year to another). These meteorological data were recorded by the agroclimatic station at Orleans (number 45234, north latitude 47.827 °, east longitude 1.909 °), part of the INRAE national agroclimatic network managed by the service unit AgroClim (Avignon, France). The R script was used in R version 3.5.1 (2018-07-02) The R script will call automatically the flight curve and the temperature dataset given that you have placed them in your working directory, it requires the following R packages : lubridate, hydroGOF, ggplot2 (to be installed beforehand). The function coded to simulate the phenology of the pine processionary moth is called « param ». Input parameters are : - Rm (Maximal development rate in days^(-1) ), - Tm (Optimum temperature in °C) - To (Spread of curve in °C) for each life stage from the egg to L5. This phenology model accumulates the daily development rate given by the Taylor equation and provides for each life stage (resegg, resL1,… resL5) : "min First day" (first day with an individual entering at this stage), "max First day" (last day with an individual entering at this stage), "min Last day" (first day with an individual achieving this stage development), "max Last day" (last day with an individual achieving this stage development), "mean duration" (mean duration of this life stage across all individuals). This is a summary of the phenology but “tabnb” provides the number of individuals present in each stage according to the date. To download the software R, please visit: <a href="https://www.r-project.org/">https://www.r-project.org/</a> This work was supported by: the national project called PHENEC (grant from the French Research Agency – ANR – number19-CE32-0007; <a href="https://www6.inrae.fr/phenec/Project-description">https://www6.inrae.fr/phenec/Project-description</a>), the SOERE called TEMPO (<a href="https://tempo.pheno.fr/soere-tempo_eng/">https://tempo.pheno.fr/soere-tempo_eng/</a>), and the French region Val de Loire. Associated scientific article: Poitou L, Laparie M, Pincebourde S, Rousselet J, Suppo C, Robinet C (2022) Warming causes atypical phenology in a univoltine moth with differentially sensitive larval stages. Frontiers in Ecology and Evolution, DOI: 10.3389/fevo.2022.825875.

opencc-by-4.0Oct 2021View details →

dryad40/100

Effects of exogenous elevation of corticosterone on immunity and the skin microbiome of Eastern Newts (Notophthalmus viridescens): Data & R scripts

The amphibian chytrid fungus, Batrachochytrium salamandrivorans (Bsal) threatens salamander biodiversity. The factors underlying Bsal susceptibility may include glucocorticoid hormones (GCs). The effects of GCs on immunity and disease susceptibility are well studied in mammals, but less is known in other groups, including salamanders. We used Notophthalmus viridescens (Eastern Newts) to test the hypothesis that GCs modulate salamander immunity. We first determined the dose required to elevate corticosterone (CORT; primary GC in amphibians) to physiologically relevant levels. We then measured immunity (neutrophil-lymphocyte ratios, plasma bacterial killing ability [BKA], skin microbiome, splenocytes, melanomacrophage centers [MMCs]) and overall health in newts following treatment with CORT or an oil vehicle control. Treatments were repeated for a short (2 treatments over 5 days) or long (18 treatments over 26 days) time period. Contrary to our predictions, most immune and health parameters were similar for CORT and oil-treated newts. Surprisingly, differences in BKA, skin microbiome, and MMCs were observed between newts subjected to short and long-term treatments, regardless of treatment type (CORT, oil vehicle). Taken together, CORT does not appear to be a major factor contributing to immunity in Eastern Newts, although more studies examining additional immune factors are necessary.

opencc-zeroNov 2022View details →

zenodo40/100

Data and R scripts for KCC plots in Douville (2023)

Data and R scripts for plotting Fig. 3 and Fig.4 in Douville (2023) Douville H. (2023) Robust and perfectible constraints on human-induced Arctic climate change. Communications Earth & Environment (revised)

opencc-by-4.0Jun 2023View details →

zenodo40/100

R SCRIPT: INFLUENCE OF ENVIRONMENTAL FACTORS ON THE POWER PRODUCED BY PHOTOVOLTAIC PANELS ARTIFICIALLY WEATHERED

The files describe the r-script code and data base used in the article titled:  "INFLUENCE OF ENVIRONMENTAL FACTORS ON THE POWER PRODUCED BY PHOTOVOLTAIC PANELS ARTIFICIALLY WEATHERED"

opencc-by-4.0Jun 2023View details →

zenodo40/100

Dataset and R script: a longitudinal hospital study of febrile children in Ghana

This is the dataset and R script accompanying the manuscript "Fever in focus: symptoms, diagnoses, and treatment of febrile children in Ghana, a longitudinal hospital study."

opencc-by-4.0Aug 2023View details →

zenodo40/100

Dataset and R script used in "Local knowledge reconstructs historical resource use" published in Frontiers in Ecology and the Environment

Data and R code used in: L. Castello, E.G. Martins, M. Sorice, E. Smith, M. Almeida, G.C.C. Bastos, L.G. Cardoso, M. Clauzet, A.P. Dopona, B. Ferreira, M. Haimovici, M. Jorge, J. Mendonça, A.O. Ávila-da-Silva, A.P.O. Roman, M. Ramires, L.M. Villwock, P.F.M. Lopes. (Year) Local knowledge reconstructs historical resource use. Frontiers in Ecology and the Environment.

openother-openSep 2023View details →

zenodo40/100

Data and R scripts associated with Clark, Moles, Fazlioglu, Brandenburger & Hartley, "Rapid loss of phenotypic plasticity in the introduced range of the beach daisy, Arctotheca populifolia."

Data to accompany article accepted for publication in Journal of Ecology. "Rapid loss of phenotypic plasticity in the introduced range of the beach daisy, Arctotheca populifolia" By Charlie D. Clark, Angela T. Moles, Fatih Fazlioglu, Claire R. Brandenburger, & Stephen Hartley 1 zip file that contains the following:       2 datasets (xlsx format)       9 R scripts to run the analyses and produce figures

opencc-by-4.0Sep 2023View details →

zenodo40/100

Data and R-scripts for estimating carbon dioxide emissions from drained peatland forest soils for the greenhouse gas inventory of Finland

Introduction A new method for estimating carbon dioxide emissions from rained peatland forest soils was developed for the Greenhouse Gas Inventory of Finland (GHG inventory). The method is based on a set of models (Ojanen et al. 2014, Tuomi et al., 2009) that dynamically compile all relevant carbon inputs and outputs into a time series of soil CO2 emission. A complete description of the method is described in Alm et al. (2023). Here we present the input data and R-scripts (R Core Team, 2020) for computing the time series from year 1990 to 2022 of CO2 emission from soil in forest land on drained organic soil, like it was reported by the Finnish GHG inventory (Statistics Finland, 2023). Time series data The source of forest and area data is the Finnish National Forest Inventory (NFI) as a part of Luke Statutory Services. The NFI standing forest data in the data files includes annual country-wide estimates of mean basal area and standing biomass of Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) H. Karst) and all the broadleaved forest trees combined. The data concerns forest land on drained organic soil only (class FRA 1 according to the FAO forest land definition). The NFI data for each year has been averaged by different drained peatland forest site types (FTYPE) and by inventory regions of southern and northern Finland. The areas and proportions of FTYPEs of all drained peatland “forests remaining forests” (i.e., forests that have not undergone another change in land use in the past 20 years) in southern and northern Finland (Alm et al., 2023), derived from NFI12 (2014–2018). Annual litter input from harvest residues was estimated using statistics of harvested stem volumes by species, collected and published by Luke (Luke statistics). The stem volumes were converted to whole trees and further to litter fractions and further to The share of residues remaining in forest is estimated by subtracting the amount of the logging residues collected for energy use, the data obtained from Luke statistics/energy. The biomass of live trees, annual litterfall from live trees aboveground and root litter belowground are derived from the National Forest Inventory of Finland (inventory rounds NFI8 to NFI13). The R-code also includes calculation of annual litter production from the harvesting residues. The regression-based transfer models, implemented in the R-code, also need meteorological time series inputs: The soil organic matter decomposition model (Ojanen et al. 2014) uses May-October mean temperature. Decomposition model yasso07 (Tuomi et al., 2009), applied for estimating the CO2 release by decomposition of harvesting residues and above ground litter from natural mortality, is constrained by annual temperature, annual temperature amplitude and annual precipitation. Starting from the original country-wide grid produced by the Finnish Meteorological Institute (FMI) the weather time series were spatially averaged so that the FMI weather grid values were collected from those locations where peatlands representing each FTYPE in southern and northern Finland were observed by the NFI, respectively. The pre-prepared input data are given in files, see Table 1 for descriptions.     Table 1. Description of input data files. <table> <tbody> <tr> <td> File </td> <td> Description of data </td> </tr> <tr> <td> basal.areas.csv </td> <td> Time series of years 1990-2022 for annual average basal area (m2 ha-1) by year, by peatland forest site type (peat_type) and by tree species or group (tree_type).   Values of peat_type correspond to FTYPE: 1  Herb-rich type 2  Vaccinium myrtillus type 4  Vaccinium vitis-idaea type 6  Dwarf shrub type 7  Cladina type   Values of tree species or group correspond to: 1  Scots pine 2  Norway spruce 3  Broadleaved species </td> </tr> <tr> <td> biomass.csv </td> <td> Time series of years 1990-2022 for annual biomass (biomass, t ha-1 of dry mass) by year, by biomass component, by tree species and by peatland forest site type (tkg).   Values of peat_type correspond to FTYPE: 1  Herb-rich type 2  Vaccinium myrtillus type 4  Vaccinium vitis-idaea type 6  Dwarf shrub type 7  Cladina type   </td> </tr> <tr> <td> dead_litter.csv </td> <td> Time series of years 1990-2022 of annual aboveground litter from dead wood: Harvesting residues and natural mortality combined (C, t ha-1 of dry mass; lognat_litter).   Values of region correspond to GHG inventory region: south  South Finland north  North Finland </td> </tr> <tr> <td> ghgi_litter.csv </td> <td> Time series of years 1990-2022 for litter AWEN-fractions (A=acid soluble, W=water soluble, E=ethanol soluble, N=non-soluble; C, t ha-1) by different litter types: Above-ground coarse woody litter (coarse_woody_litter), fine woody litter (fine_woody_litter), non-woody litter (non_woody_litter) by litter source and deposition type by region. “org” denotes organic soil.   Values of region correspond to GHG inventory region: south  South Finland north  North Finland   Values of ground correspond to litter deposition environment: above  Above-ground litter below  Below-ground litter </td> </tr> <tr> <td> lognat_decomp.csv </td> <td> Time series of years 1990-2022 for C, t ha-1 of dry mass, decomposed from logging residues and natural mortality by region.   Values of variable “region” correspond to GHG inventory region: south  South Finland north  North Finland </td> </tr> <tr> <td> logyasso_weather_data.csv </td> <td> Time series of years 1990-2022 for regional (region) precipitation sum (mm, sum_P), average annual temperature (°C, mean_T) and amplitude of the annual temperature (°C , ampli_T).   Values of region correspond to GHG inventory region: south  South Finland north  North Finland   </td> </tr> <tr> <td> total_area.csv </td> <td> Areas (ha) of drained peatland forests remaining forest land by region and peat_type.   Values of variable “region” correspond to GHG inventory region: south  South Finland north  North Finland   Values of peat_type correspond to FTYPE: 1  Herb-rich type 2  Vaccinium myrtillus type 4  Vaccinium vitis-idaea type 6  Dwarf shrub type 7  Cladina type   </td> </tr> <tr> <td> weather_data.csv </td> <td> Time series of years 1990-2022 for 30-year rolling mean temperature for the May-October period (roll_T) used by the soil decomposition models. The values are calculated for each FTYPE (peat_type) using their spatial distributions (see details in Alm et al., 2023).   Values of variable “region” correspond to GHG inventory region: south  South Finland north  North Finland   Values of peat_type correspond to FTYPE: 1  Herb-rich type 2  Vaccinium myrtillus type 4  Vaccinium vitis-idaea type 6  Dwarf shrub type 7  Cladina type   </td> </tr> </tbody> </table>   The R-scripts The scripts are an excerpt from the Finnish greenhouse gas inventory code set, applying the necessary pre-processed input data and producing the soil CO2 emissions for each FTYPE separately. The necessary R-packages (R Core Team, 2020) are managed in the script LIBRARIES.R. Guidance for running the R-scripts is given in the README.txt. References Alm, J., Wall, A., Myllykangas, J-P., Ojanen, P., Heikkinen, J., Henttonen, H. M., Laiho, R., Minkkinen, K., Tuomainen, T. and Mikola, J. A new method for estimating carbon dioxide emissions from drained peatland forest soils for the greenhouse gas inventory of Finland. Biogeosciences https://doi.org/10.5194/bg-20-1-2023, 2023. LUKE Statistics <ul> <li>https://www.luke.fi/en/statistics/total-roundwood-removals-and-drain, last access 8.12.2022.</li> </ul> <ul> <li>https://www.luke.fi/en/statistics/commercial-fellings/commercial-fellings-72023. last access 8.12.2022.</li> </ul> Statistics Finland 2023. URL: https://unfccc.int/documents/627718 (last access 13.9.2023). Ojanen, P., Lehtonen, A., Heikkinen, J., Penttilä, T., and Minkkinen, K.: Soil CO2 balance and its uncertainty in forestry drained peatlands in Finland, Forest Ecol. Manage., 325, 60–73, 2014. R Core Team: R: A language and environment for statistical computing. R Foundation forStatistical Computing, Vienna, Austria, URL https://www.R-project.org, 2020. Tuomi, M., Thum, T., Järvinen, H., Fronzek, S., Berg, B., Harmon, M., Trofymow, J.A., Sevanto, S. and Liski, J.: Leaf litter decomposition - Estimates of global variability based on Yasso07 model, Ecol. Modell. 220 (23):3362-3371, 2009.

opencc-by-4.0Sep 2023View details →

dryad40/100