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317 results for “R code”
Codes in R for spatial statistics analysis, ecological response models and spatial distribution models
<p>In the last decade, a plethora of algorithms have been developed for spatial ecology studies. In our case, we use some of these codes for underwater research work in applied ecology analysis of threatened endemic fishes and their natural habitat. For this, we developed codes in Rstudio® script environment to run spatial and statistical analyses for ecological response and spatial distribution models (e.g., Hijmans & Elith, 2017; Den Burg <em>et al.</em>, 2020). The employed R packages are as follows: caret (Kuhn et al., 2020), corrplot (Wei & Simko, 2017), devtools (Wickham, 2015), dismo (Hijmans & Elith, 2017), gbm (Freund & Schapire, 1997; Friedman, 2002), ggplot2 (Wickham et al., 2019), lattice (Sarkar, 2008), lattice (Musa & Mansor, 2021), maptools (Hijmans & Elith, 2017), modelmetrics (Hvitfeldt & Silge, 2021), pander (Wickham, 2015), plyr (Wickham & Wickham, 2015), pROC (Robin et al., 2011), raster (Hijmans & Elith, 2017), RColorBrewer (Neuwirth, 2014), Rcpp (Eddelbeuttel & Balamura, 2018), rgdal (Verzani, 2011), sdm (Naimi & Araujo, 2016), sf (e.g., Zainuddin, 2023), sp (Pebesma, 2020) and usethis (Gladstone, 2022).</p> <p>It is important to follow all the codes in order to obtain results from the ecological response and spatial distribution models. In particular, for the ecological scenario, we selected the Generalized Linear Model (GLM) and for the geographic scenario we selected DOMAIN, also known as Gower's metric (Carpenter <em>et al.</em>, 1993). We selected this regression method and this distance similarity metric because of its adequacy and robustness for studies with endemic or threatened species (<em>e.g.</em>, Naoki <em>et al.</em>, 2006). Next, we explain the statistical parameterization for the codes immersed in the GLM and DOMAIN running:</p> <p>In the first instance, we generated the background points and extracted the values of the variables (<a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code2_Extract_values_DWp_SC.R?versionId=c1ea0c61-53fe-4f95-ab88-0c1cb28399cb">Code2_Extract_values_DWp_SC.R</a>). Barbet-Massin <em>et al. </em>(2012) recommend the use of 10,000 background points when using regression methods (<em>e.g.</em>, Generalized Linear Model) or distance-based models (<em>e.g.</em>, DOMAIN). However, we considered important some factors such as the extent of the area and the type of study species for the correct selection of the number of points (Pers. Obs.). Then, we extracted the values of predictor variables (<em>e.g.</em>, bioclimatic, topographic, demographic, habitat) in function of presence and background points (<em>e.g.</em>, Hijmans and Elith, 2017).</p> <p>Subsequently, we subdivide both the presence and background point groups into 75% training data and 25% test data, each group, following the method of Soberón & Nakamura (2009) and Hijmans & Elith (2017). For a training control, the 10-fold (cross-validation) method is selected, where the response variable presence is assigned as a factor. In case that some other variable would be important for the study species, it should also be assigned as a factor (Kim, 2009).</p> <p>After that, we ran the code for the GBM method (Gradient Boost Machine; <a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code3_GBM_Relative_contribution.R?versionId=1656bbae-66aa-409e-bb91-d8007dee8f95">Code3_GBM_Relative_contribution.R</a> and <a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code4_Relative_contribution.R?versionId=0e1d9352-e6b2-43da-984b-d6853a914258">Code4_Relative_contribution.R</a>), where we obtained the relative contribution of the variables used in the model. We parameterized the code with a Gaussian distribution and cross iteration of 5,000 repetitions (<em>e.g.</em>, Friedman, 2002; kim, 2009; Hijmans and Elith, 2017). In addition, we considered selecting a validation interval of 4 random training points (Personal test). The obtained plots were the partial dependence blocks, in function of each predictor variable.</p> <p>Subsequently, the correlation of the variables is run by Pearson's method (<a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code5_Pearson_Correlation.R?versionId=275f8dd4-b056-44d2-bfe5-f6264bc3298b">Code5_Pearson_Correlation.R</a>) to evaluate multicollinearity between variables (Guisan & Hofer, 2003). It is recommended to consider a bivariate correlation ± 0.70 to discard highly correlated variables (<em>e.g.</em>, Awan <em>et al.</em>, 2021).</p> <p>Once the above codes were run, we uploaded the same subgroups (<em>i.e.</em>, presence and background groups with 75% training and 25% testing) (<a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code6_Presence&backgrounds.R?versionId=d797b528-782f-4a19-bd61-cfb197f38513">Code6_Presence&backgrounds.R</a>) for the GLM method code (<a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code7_GLM_model.R?versionId=e4aca276-d601-49ec-a62c-a9223b05a7ed">Code7_GLM_model.R</a>). Here, we first ran the GLM models per variable to obtain the <em>p</em>-significance value of each variable (alpha ≤ 0.05); we selected the value one (<em>i.e.</em>, presence) as the likelihood factor. The generated models are of polynomial degree to obtain linear and quadratic response (<em>e.g.</em>, Fielding and Bell, 1997; Allouche <em>et al.</em>, 2006). From these results, we ran ecological response curve models, where the resulting plots included the probability of occurrence and values for continuous variables or categories for discrete variables. The points of the presence and background training group are also included.</p> <p>On the other hand, a global GLM was also run, from which the generalized model is evaluated by means of a 2 x 2 contingency matrix, including both observed and predicted records. A representation of this is shown in Table 1 (adapted from Allouche et al., 2006). In this process we select an arbitrary boundary of 0.5 to obtain better modeling performance and avoid high percentage of bias in type I (omission) or II (commission) errors (e.g., Carpenter et al., 1993; Fielding and Bell, 1997; Allouche et al., 2006; Kim, 2009; Hijmans and Elith, 2017).</p> <p>Table 1. Example of 2 x 2 contingency matrix for calculating performance metrics for GLM models. A represents true presence records (true positives), B represents false presence records (false positives - error of commission), C represents true background points (true negatives) and D represents false backgrounds (false negatives - errors of omission).</p> <table align="center"> <tbody> <tr> <td> <p> </p> </td> <td> <p>Validation set</p> </td> </tr> <tr> <td> <p>Model</p> </td> <td> <p>True</p> </td> <td> <p>False</p> </td> </tr> <tr> <td> <p>Presence</p> </td> <td> <p>A</p> </td> <td> <p>B</p> </td> </tr> <tr> <td> <p>Background</p> </td> <td> <p>C</p> </td> <td> <p>D</p> </td> </tr> </tbody> </table> <p>We then calculated the Overall and True Skill Statistics (TSS) metrics. The first is used to assess the proportion of correctly predicted cases, while the second metric assesses the prevalence of correctly predicted cases (Olden and Jackson, 2002). This metric also gives equal importance to the prevalence of presence prediction as to the random performance correction (Fielding and Bell, 1997; Allouche <em>et al.</em>, 2006).</p> <p>The last code (<em>i.e.</em>, <a href="https://zenodo.org/api/files/fdd5446b-dee9-4b52-ad4f-cf556443d3dd/Code8_DOMAIN_SuitHab_model.R?versionId=d951a8f2-d3a4-4804-b862-1b2762061876">Code8_DOMAIN_SuitHab_model.R</a>) is for species distribution modelling using the DOMAIN algorithm (Carpenter <em>et al.</em>, 1993). Here, we loaded the variable stack and the presence and background group subdivided into 75% training and 25% test, each. We only included the presence training subset and the predictor variables stack in the calculation of the DOMAIN metric, as well as in the evaluation and validation of the model.</p> <p>Regarding the model evaluation and estimation, we selected the following estimators:</p> <p>1) partial ROC, which evaluates the approach between the curves of positive (<em>i.e.</em>, correctly predicted presence) and negative (i.e., correctly predicted absence) cases. As farther apart these curves are, the model has a better prediction performance for the correct spatial distribution of the species (Manzanilla-Quiñones, 2020).</p> <p>2) ROC/AUC curve for model validation, where an optimal performance threshold is estimated to have an expected confidence of 75% to 99% probability (De Long <em>et al.</em>, 1988).</p>
Stowaways Supplement S6. Relative Size Index (RSI) Comparative Dataset & R Code for Mediterranean Ship Rats
<p>Relative size index (RSI) comparative dataset and R code used to compare the RSI <em>sensu </em>Lomolino (1985) of the ship rats from the Ma'agan Mikhael B (MMB) shipwreck with other Mediterranean island populations. </p> <p>Worksheet 1: RSI_Dataframe - data frame used in R to produce RSI bar chart.</p> <p>Worksheet 2: RSI Data - Mainland and island samples' mean condylobasal lengths (CBL) and further calculations used to derive the RSI values per island population.</p> <p>Worksheet 3: CBL values for each Provençal and Corso-Sardinian Islet that was combined in the RSI analysis, taken directly from Granjon & Cheylan (1990).</p> <p>Data sources:</p> <p>MMB - Southern Levant (this study)</p> <p>Galite & Zembra Islands - Tunisia (Ibrahim et al., 2017)</p> <p>Congreso Island - Morocco (Ventura & Lopez Fuster, 2000)</p> <p>São Miguel & Terceira Island (Azores) - Portugal (Ramalhinho et al., 1996)</p> <p>Corsica & Provençal Islets - Provence (Granjon & Cheylan, 1990)</p> <p>Sardinia & Corso-Sardinian Islets - Tuscany (Granjon & Cheylan, 1990)</p>
Datasets and R source code of manuscript "No evidence for an effect of chronic boat noise on the fitness of reared water fleas"
<p>Datasets and R source code of manuscript "No evidence for an effect of chronic boat noise on the fitness of reared water fleas"</p> <p>Experiments : exposition of Daphnia magna to boatnoise or silence along all their life. Measure of survival and clonal reproduction.</p>
Armed to the teeth: Supplementary material, R code, Rdata, mesh, landmarks
<p>The structure, composition, and shape of teeth have been related to dietary specialization in many vertebrate species, but comparative studies on snakes' teeth are lacking. Yet, snakes have diverse dietary habits that may impact the shape of their teeth. We hypothesize that prey properties, such as hardness and shape, as well as feeding behavior, such as aquatic or arboreal predation, or holding vigorous prey, impose constraints on the evolution of tooth shape in snakes. We compared the morphology of the dentary teeth of 63 species that cover the phylogenetic and dietary diversity of snakes, using 3D geometric morphometrics and linear measurements. Our results show that prey hardness, foraging substrate, and the main foraging mechanical challenge are important drivers of tooth shape, size, and curvature. Overall, long, slender, curved teeth with a thin layer of hard tissue are observed in species that need to maintain a grip on their prey. Short, stout, less curved teeth are associated with species that undergo high or repeated loads. Our study demonstrates the diversity of tooth morphology in snakes and the need to investigate its underlying functional implications to better understand the evolution of teeth in vertebrates.</p>
Datasets and R source code of manuscript "From behaviour to complex communities: Resilience to anthropogenic noise in a fish-induced trophic cascade" by Emilie Rojas et al.
<p>Datasets and R source code of manuscript "From behaviour to complex communities: Resilience to anthropogenic noise in a fish-induced trophic cascade" by Emilie Rojas et al.</p>
Red deer growth data and R code for analysis
<p>Dataset and R code (Rmd-file) for analysis of seasonal growth of body weight in red deer. </p> <p>Supplementary material for the paper "Shifting seasonality of annual growth through ontogeny for red deer at northern latitudes".</p> <p>This study was part of the AgriDeer project (318575), funded by the Research Council of Norway.</p>
R codes from: Breakdown of the growth–mortality trade-off along a soil phosphorus gradient in diverse neotropical forest
<p><span>An ecological paradigm predicts that plant species adapted to low resource availability grow slower and live longer than those adapted to high resource availability when growing together. We tested this by using hierarchical Bayesian analysis to quantify variations in growth and mortality of ~ 40,000 individual trees from > 400 species in response to limiting resources in the tropical forests of Panama. In contrast to theoretical expectations of the growth–mortality paradigm, we find that tropical tree species restricted to low-phosphorus soils simultaneously achieve faster growth rates and lower mortality rates than species restricted to high-phosphorus soils. This result demonstrates that adaptation to phosphorus limitation in diverse plant communities modifies the growth–mortality trade-off, with important implications for understanding long-term ecosystem dynamics.</span></p>
2023_ Datasets and R source code of "Allometry Bird Mitochondrial Bioenergetics"
<p>METHODOLOGICAL INFORMATION</p> <p>This dataset contains mitochondrial bioenergetic data and enzymatic activities of our studies from two tissues: skeletal and cardiac muscles, of 13 bird species ranging from 15 g to 160 kg.<br> Methodology: mitochondrial isolation, respiration, enzyme assays, measurement of body mass<br> All analyses were performed in R version 4.2.1 (R Core Team 2022), using phylogenetic comparative analyses.</p> <p><strong>## Description of the Data and file structure "2023_Data_Allometry Bird mitochondrial bioenergetics"</strong></p> <p>The file contains two sheets: one for the skeletal muscle data and the second for the cardiac muscle. <br> For each section you will find: the name of the species studied, the number of individuals, their body mass (in grams), mitochondrial flux measurements (oxygen consumption, ATP synthesis, ROS generation), ratios (RCR, Slope, Mitochondrial efficiency ATP/O...) and enzymatic activity measurements. </p> <p>Missing data correspond to individuals for whom we were unable to collect data (e.g. no heart samples, not enough tissue for analysis...)</p> <p><strong>## Phylogenetic tree " BirdTree_MCMCglmm "</strong></p> <p>The phylogenetic tree combining the 13 species studied was obtained from the BirdTree.org website (Rubolini et al., 2015).The tree source used was Hackett Sequenced Species: a set of 10 000 trees with 6670 OTUs each (Hackett et al., 2008). We performed 1000 simulations to create the most parsimonious tree. The avian tree was summarized using BEAST (v1.10.4, 2002-2018) to create a target tree usable in nexus format in R version 4.2.1 (R Core Team 2022). The parameters used were: burnin as a number of trees (100), maximum clade credibility tree as target tree type, and common ancestor heights.</p>
R code and datasets for flower-visitor interactions (pollinators, robbers, thieves) and plant traits from Mount Cameroon
<p>Raw data and R code for: <strong>Cheaters among pollinators: Nectar robbing and thieving vary spatiotemporally with floral traits in Afrotropical forests. </strong><i>Ecosphere, 2023</i>. doi: 10.1002/ecs2.4696<br> </p><p>When using the dataset for anything, cite the Sakhalkar et al. <i>Ecosphere </i>paper.</p><p><br>All related information can be found in the cited paper. For additional information, refer to the paper or write to either robert.tropek@gmail.com or sailee.sakha@gmail.com.</p>
Datasets and R code associated with: Acute Health Effects of Wildfire Smoke Exposure During a Compound Event: A Case-Crossover Study of the 2016 Great Smoky Mountain Wildfires
<p>The attached code and csv files accompany the manuscript titled: Acute Health Effects of Wildfire Smoke Exposure During a Compound Event: A Case-Crossover Study of the 2016 Great Smoky Mountain Wildfires by Duncan et al. accepted for publication in the journal GeoHealth in September 2023. </p> <p><a href="https://zenodo.org/api/files/8e030710-b091-48a9-aec4-f9b3de6a0325/Project%20wildfire%20data.csv">Project wildfire data.csv</a> contains a subset wildfire data obtained from:</p> <p>Short, Karen C. 2022. Spatial wildfire occurrence data for the United States, 1992-2020 [FPA_FOD_20221014]. 6th Edition. Fort Collins, CO: Forest Service Research Data Archive. https://doi.org/10.2737/RDS-2013-0009.6</p> <p><a href="https://zenodo.org/api/files/8e030710-b091-48a9-aec4-f9b3de6a0325/Modeled%20PM2.5%20data_clean.csv">Modeled PM2.5 data_clean.csv</a> includes modeled PM<sub>2.5</sub> concentrations used to make Figure 2. Model results can be obtained from <a href="https://www.epa.gov/hesc/rsig-related-downloadable-data-files">EPA's Fused Air Quality Surfaces Using Downscaling Tool</a>. <a href="https://zenodo.org/api/files/8e030710-b091-48a9-aec4-f9b3de6a0325/plot_Modeled.R">plot_Modeled.R</a> contains the R code to generate this figure. </p> <p><a href="https://zenodo.org/api/files/8e030710-b091-48a9-aec4-f9b3de6a0325/ORs%20all%20Counties.csv">ORs all Counties.csv</a> contains the odds ratios, confidence intervals, and p-values used to make Figures 3, 4, and 5.<a href="https://zenodo.org/api/files/8e030710-b091-48a9-aec4-f9b3de6a0325/PM2.5%2035ug-m3%20ORs.csv">PM2.5 35ug-m3 ORs.csv</a> contains the odds ratios, confidence intervals, and p-values used to make Figure S1. <a href="https://zenodo.org/api/files/8e030710-b091-48a9-aec4-f9b3de6a0325/Wildfire_Forest.R">Wildfire_Forest.R</a> contains the R code to generate these figures. </p>
R code for differential gene expression and enrichment analyses
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Data and R code used for the GLMM and NBDA analyses in 'Captive Asian short-clawed otters (Aonyx cinereus) learn to exploit unfamiliar natural prey'
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R code for: Following regulation, imidacloprid persists and flupyradifurone increases in non-target wildlife
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Data and R code used in: Plant geographic distribution influences chemical defenses in native and introduced Plantago lanceolata populations
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Source code for R tutorials and dataset for empirical case study on Malurus elegans (red-winged fairy wren)
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Datasets and R code for: Brood as booty: The effect of colony size and resource value in social insect contests
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Code and example images from: recolorize: An R package for flexible color segmentation of biological images
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Data and R code from: Relics of beavers past: time and population density drive scale-dependent patterns of ecosystem engineering
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Data and R code from: Fin whale song evolution in the North Atlantic
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Data and R code from: Pollination interactions reveal direct costs and indirect benefits of plant–plant facilitation for ecosystem engineers
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ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.