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317 results for “R code”
Data and R code for The Finer Points of Urban Adaptation: Intraspecific Variation in Lizard Claw Morphology (2020; Biological Journal of the Linnean Society)
<p>This zip file contains all data (tps files, Rdata, csv) and annotated R script to conduct all analyses presented in Falvey et al. (2020, BJLS), which examines claw morphology in 5 species of anole lizards using geometric morphometrics.</p>
R code for Snyder, Ellner, and Hooker, "Time and chance: using age partitioning to understand how luck drives variation in reproductive success"
<p>Over the course of individual lifetimes, luck usually explains a large fraction of the between- individual variation in lifespan or lifetime reproductive output (LRO) within a population, while variation in individual traits or "quality" explains much less. To understand how, where in the life cycle, and through which demographic processes luck trumps trait variation, we show how to partition by age the contributions of luck and trait variation to LRO variance, and how to quantify three distinct components of luck. We apply these tools to several empirical case studies.</p> <p>We find that luck swamps effects of trait variation at all ages, primarily due to randomness in individual state dynamics ("state trajectory luck"). Luck early in life is most important. Very early state trajectory luck generally determines whether or not an individual ever breeds, likely by ensuring that they are not dead or doomed quickly. Less early luck drives variation in success among those breeding at least once. Consequently, the importance of luck often has a sharp peak early in life, or two peaks. We suggest that ages/stages where the importance luck peaks are potential targets for interventions to benefit a population of concern, different from those identified by eigenvalue elasticity analysis.</p>
Supplementary files for Crane et al., "Lots of movement, little progress": R Code, data and figures
<p>Datasets, R code and figures pertaining to the manuscript: Crane M, Silva I, Marshall BM, & Strine CT.<em> Lots of movement, little progress: A review of reptile home range literature.</em></p>
Data and R code from: Modelling the evolution of cognitive styles
Background <p>Individuals consistently differ in behaviour, exhibiting so-called personalities. In many species, individuals differ also in their cognitive abilities. When personalities and cognitive abilities occur in distinct combinations, they can be described as 'cognitive styles'. Both empirical and theoretical investigations produced contradicting or mixed results regarding the complex interplay between cognitive styles and environmental conditions.</p> Results <p>Here we use individual-based simulations to show that, under just slightly different environmental conditions, different cognitive styles exist and under a variety of conditions, can also co-exist. Co-existences are based on individual specialization on different resources, or, more generally speaking, on individuals adopting different niches or microhabitats.</p> Conclusions <p>The results presented here suggest that in many species, individuals of the same population may adopt different cognitive styles. Thereby the present study may help to explain the variety of styles described in previous studies and why different, sometimes contradicting, results have been found under similar conditions.</p>
Replication Code in R for "Do Insurers Compete on the Federal Heath Insurance Exchange
<p>R code for univariate and multivariate analyses to model premiums based on the number of insurers in geographic rating areas.</p>
Data and R code used in Alonso-Crespo et al (2024) Exploring priority and year effects on plant diversity, productivity and vertical root distribution: first insights from a grassland field experiment
<p>This release contains the raw data, R code, and RootPainter model supporting the results described in Alonso-Crespo et al (2024) Exploring priority and year effects on plant diversity, productivity and vertical root distribution: first insights from a grassland field experiment.</p>
Data and R analysis code: Asian elephants distinguish sexual status and identity of unfamiliar elephants using urinary odours
<p class="MsoNormal"><span>Despite the ubiquity of odours in mammals, few studies have documented the natural olfactory abilities of many "non-model" species such as the Asian elephant. As Asian elephants are endangered, we may apply odours to more effectively manage threatened populations. We implemented a habituation–discrimination paradigm for the first time in Asian elephants to test the ability of elephants to discriminate between unfamiliar male elephant urine, hypothesizing that elephants would successfully distinguish non-musth from musth urine and also distinguish identity between two closely related individuals. We conducted two bioassay series, exposing three female and three male zoo-housed elephants to the same urine sample (non-musth urine in the first series, and urine from an unfamiliar individual in the second) over five days. On the sixth day, we simultaneously presented each elephant with a novel sample (either musth urine or urine from a second unfamiliar individual) alongside the habituated urine sample, comparing rates of chemosensory response to each sample to indicate discrimination. All elephants successfully discriminated non-musth from musth urine, and also urine from two unfamiliar half-brothers. Our results further demonstrate the remarkable olfactory abilities of elephants with promising implications for conservation and management.</span></p>
LiDAR and Photogrammetry Point Clouds and R code
<p>Some examples of point clouds from lidar and photogrammetry, from drone and from helicopter surveys.</p> <p>These data are used in the course "Remote Sensing" to forestry students at University of Padova, TESAF Department</p> <p> </p>
Raw data and R code for statistical analyses from: Sensory trap leads to reliable communication without a shift in nonsexual responses to the model cue
<p>The sensory trap model of signal evolution suggests that males manipulate females into mating using traits that mimic cues used in a nonsexual context. Despite much empirical support for sensory traps, little is known about how females evolve in response to these deceptive signals. Female sea lamprey (<em>Petromyzon marinus</em>) evolved to discriminate a male sex pheromone from the larval odor it mimics and orient only towards males during mate search. Larvae and males release the attractant 3-keto petromyzonol sulfate (3kPZS), but spawning females avoid larval odor using the pheromone antagonist, petromyzonol sulfate (PZS), which larvae but not males, release at higher rates than 3kPZS. We tested the hypothesis that migratory females also discriminate between larval odor and the male pheromone and orient only to larval odor during anadromous migration, when they navigate within spawning streams using larval odor before they begin mate search. In-stream behavioral assays revealed that, unlike spawning females, migratory females do not discriminate between mixtures of 3kPZS and PZS applied at ratios typical of larval versus male odorants. Our results indicate females discriminate between the sexual and nonsexual sources of 3kPZS during but not outside of mating and show sensory traps can lead to reliable sexual communication without females shifting their responses in the original context.</p>
Dataset and R-codes for Publication: "Best performances of visible-near infrared models in soils with little carbonate - a field study in Switzerland" (accepted version)
<p><span>In this upload you can find the R-codes and dataset for the Publication: </span></p> <p><span>"Best performances of visible-near infrared models in soils with little carbonate - a field study in Switzerland" by Simon Oberholzer Laura Summerauer, Markus Steffens and Chinwe Ifejika Speranza accepted in SOIL (https://doi.org/10.5194/egusphere-2023-1087)</span></p> <p><span>To reproduce the results of the manuscript, start with the R-file “ResampleandPreprocess.R” to prepare spectral data and then continue with the R-file “PLSRmodelling_GroupedCV.R” for the modelling.</span></p> <p><span>The R-files “control_train.R” and “rep_grouped_kfold_CV.R” are helper-functions for the grouped cross-validation. The file metadata.csv explains the column names in the spectral data (spcdata.RDS).</span></p>
Harmonized data and R code for "Coherent response of zoo- and phytoplankton assemblages to global warming since the Last Glacial Maximum"
<p>Harmonized data and R code for "<em>Coherent response of zoo- and phytoplankton assemblages to global warming since the Last Glacial Maximum</em>"<br>by Tonke Strack, Lukas Jonkers, Marina C. Rillo, Karl-Heinz Baumann, Helmut Hillebrand and Michal Kucera (submitted to <em>Global Ecology and Biogeography</em>, 2024).</p> <p><strong>STRUCTURED ABSTRACT</strong><br><em>Aim</em>: We use the fossil record of different marine plankton groups to determine how their biodiversity changed during past climate warming comparable to projected future warming.<br><em>Location</em>: North Atlantic Ocean and adjacent seas. Time series cover a latitudinal range of 75°N to 6°S.<br>Time period: Past 24,000 years, i.e., from the Last Glacial Maximum (LGM) to the current warm period covering the last deglaciation.<br><em>Major taxa studied</em>: Planktonic foraminifera, dinoflagellates and coccolithophores.<br><em>Methods</em>: We analyse time series of fossil plankton communities using principal component analysis and generalised additive models to estimate the overall trend of temporal compositional change in each plankton group and identify periods of significant change. We further analyse local biodiversity change by analysing species richness, species gains and losses, and the effective number of species in each sample and compare alpha diversity to the LGM mean.<br><em>Results</em>: All plankton groups show remarkably similar trends in the rates and spatio-temporal dynamics of local biodiversity change and a pronounced non-linearity with climate change in the current warm period. Assemblages of planktonic foraminifera and dinoflagellates started to significantly change with the onset of global warming around 15,500 to 17,000 years ago and continued to change at the same pace during the current warm period until at least 5,000 years ago, while coccolithophores assemblages changed at a constant rate throughout the past 24,000 years seemingly irrespective of the prevailing temperature change.<br><em>Main conclusions</em>: The climate change during the transition from the LGM to the current warm period led to a long-lasting reshuffling of the zoo- and phytoplankton assemblages likely associated with the emergence of new ecological interactions and possibly a shift in the dominant drivers of plankton assemblage change from more abiotic-dominated causes during the last deglaciation to more biotic-dominated causes with the onset of the Holocene.</p> <p><strong>CONTENT</strong><br>This dataset includes the harmonized assemblage data of the three investigated plankton groups (planktonic foraminifera, dinoflagellates and coccolithophores) as well as all the R code needed to re-produce the results of this study and it's main figures.</p> <p>Scripts written by Tonke Strack</p> <p><br><strong>DATA SOURCES</strong><br>1) GMST: Osman, M. B. et al. Globally resolved surface temperatures since the Last Glacial Maximum. <br> <em>Nature</em> 599, 239-244, doi:10.1038/s41586-021-03984-4 (2021).<br>2) WOA18: Locarnini, R. A. et al. World Ocean Atlas 2018, Volume 1: Temperature. A. Mishonov, <em>Technical Editor. </em><br><em> NOAA Atlas NESDIS</em> 81, 52 (2019).<br>3) plankton assemblage data: individual data references provided in CoreList.csv</p> <p><br><strong>DATA</strong><br>1. Harmonized assemblage data<strong>*</strong>: <em>FullDataTable_PF_harmonized.txt</em><br>2. Core list of additional information on time series: <em>CoreList.csv</em><br>3. Reference lists for species names names: <em>ReferenceList_PlanktonicForaminifera.csv, ReferenceList_Dino.csv, ReferenceList_Cocco.csv</em></p> <p><br><strong>CODE</strong><br>1. <em>01_LoadData.R</em>: loads harmonized assemblage data from planktonic foraminifera, dinocyst and coccolithophores<br>2. <em>02_GMST_import.R</em>: loads loads the globally resolved surface temperature since the LGM from Osman et al. (2011)<br>3. <em>03_DataAnalysis_PCA_GAM.R</em>: PCA/GAM analysis on the plankton assemblage data (results shown in Figure 2 and 3), sensititvity analysis (results shown in Figure 4), and some summary statistics<br>4. <em>04_DataAnalysis_MH_GAM_AlternativeApproach.R</em>: alternative GAM approach using Morisita-Horn index (results shown in Figure S2, S3 and S4)<br>5. <em>05_DataAnalysis_BiodiversityChange.R</em>: local biodiversity change analysis of individual time series (results shown in Figure 5, 6 and S9)</p> <p><br>*Assemblage data of individual time series were manually downloaded, quality checked, taxonomically harmonized, and combined into one data file.<br>Planktonic foraminifera data were harmonized following Siccha and Kuchera (2017). We merged <em>Globigerinoides ruber ruber</em> and <em>Globigerinoides ruber </em><br><em>albus</em>, because some studies only reported them together as<em> Globigerinoides ruber</em>. Also, P/D intergrades (an informal category of morphological<br>intermediates between <em>Neogloboquadrina incompta</em> and <em>Neogloboquadrina dutertrei</em>) were merged with <em>Neogloboquadrina incompta</em>.<br>Dinocyst taxonomy was harmonized following de Vernal et al. (2020) with slight additions following Zonneveld et al. (2013). Names that could not be<br>resolved using synonym lists and assigned a harmonized name following de Vernal et al. (2020) and Zonneveld et al. (2013) were treated as unidentified<br>specimens and were excluded from the assemblage analyses. These specimens were present in 4 time series and were rare taxa (relative abundances < 3%).<br>The protoperidinoids were also excluded from further assemblage analyses as this category includes all unidentified brownish cysts (de Vernal et al., 2020).<br>Coccolithophore taxonomy follows Young et al. (2003) and coccolith countings were conducted on a scanning-electron microscope (SEM) to ensure that all<br>specimens are resolved to the species level. We merged <em>Coccolithus pelagicus</em> subspecies, because they were not distinguished in all studies. <br>Species not reported in the time series data were assumed to be absent (that is, zero abundance) which is in accordance with the completeness of the counts<br>reported in the original studies. The original data were either given in absolute or relative abundances, and after excluding unnecessary columns<br>(unidentified or rare taxa that could not be harmonised) the abundances were recalculated to 100 %. In total, 41 species of planktonic foraminifera,<br>30 species of coccolithophores and 53 species of organic-walled dinocysts were observed in our study.</p> <p><strong>REFERENCES</strong><br>de Vernal, A., Radi, T., Zaragosi, S., Van Nieuwenhove, N., Rochon, A., Allan, E., . . . Richerol, T. (2020). Distribution of common modern dinoflagellate cyst taxa in surface sediments of the Northern Hemisphere in relation to environmental parameters: The new n=1968 database. <em>Mar. Micropaleontol.</em>, 159. doi:10.1016/j.marmicro.2019.101796<br>Siccha, M. & Kucera, M. ForCenS, a curated database of planktonic foraminifera census counts in marine surface sediment samples. S<em>ci. Data</em> 4, 170109, doi:10.1038/sdata.2017.109 (2017).<br>Young, J. R., Geisen, M., Cros, L., Kleijne, A., Sprengel, C., Probert, I., & Østergaard, J. B. (2003). A guide to extant coccolithophore taxonomy. <em>Journal of Nannoplankton Research Special Issue</em>, 1, 1-125. doi:10.58998/jnr2297<br>Zonneveld, K. A. F., Marret, F., Versteegh, G. J. M., Bogus, K., Bonnet, S., Bouimetarhan, I., . . . Young, M. (2013). Atlas of modern dinoflagellate cyst distribution based on 2405 data points. <em>Rev. Palaeobot. Palynol.</em>, 191, 1-197. doi:10.1016/j.revpalbo.2012.08.003</p>
Functioning of a canopy-dominated intertidal community during emersion: highly productive but heterotrophic at the annual scale - R code
<p>This repository contains the data and code for our paper:</p> <p>Claire Golléty, Jon Yearsley, Aline Migné, Dominique Davoult (2024). Functioning of a canopy-dominated intertidal community during emersion: highly productive but heterotrophic at the annual scale. Marine Biology<br>https://doi.org/10.1007/s00227-024-04395-5</p>
Data and R code for machine learning modelling of favourite places and routes of outdoor recreation
<p>This is a script showing the analysis used in a paper submitted for review in Landscape and Urban Planning, titled "Seeing through their eyes: Revealing recreationists’ landscape preferences through viewshed analysis and machine learning", by Carl Lehto, Marcus Hedblom, Anna Filyushkina and Thomas Ranius. </p> <p>The zip file contains an R script, data saved in .rds format and a R workspace. </p>
Data and R code for Reddin et al. 'Marine species and assemblage change foreshadowed by their thermal bias over Early Jurassic warming''
<p>This repository holds the raw and prepared datasets and R code to handle them for the manuscript Reddin et al. 'Marine species and assemblage change foreshadowed by their thermal bias over Early Jurassic warming'. Nature Communications</p>
Data and code to replicate: Diet analysis using generalized linear models derived from foraging processes using R package mvtweedie
<p>Diet analysis integrates a wide variety of visual, chemical and biological identification of prey. Samples are often treated as compositional data, where each prey is analyzed as a continuous percentage of the total. However, analyzing compositional data results in analytical challenges, e.g., highly parameterized models or prior transformation of data. Here, we present a novel approximation involving a Tweedie generalized linear model (GLM). We first review how this approximation emerges from considering predator foraging as a thinned and marked point process (with marks representing prey species and individual prey size). This derivation can motivate future theoretical and applied developments. We then provide a practical tutorial for the Tweedie GLM using new package <i>mvtweedie</i> that extends capabilities of widely used packages in R (<i>mgcv</i> and <i>ggplot2</i>) by transforming output to calculate prey compositions. We demonstrate this approach and software using two examples. Tufted puffins (<i>Fratercula cirrhata</i>) provisioning their chicks on a colony in the northern Gulf of Alaska show decadal prey switching among sand lance and prowfish (1980-2000) and then Pacific herring and capelin (2000-2020), while wolves (<i>Canis lupus ligoni</i>) in Southeast Alaska forage on mountain goats and marmots in northern uplands and marine mammals in seaward island coastlines. </p>
Data and R codes from: Exploring the effect of 195 years-old locks on species movement: Landscape genetics of painted turtles in the Rideau Canal, Canada
<p>Aquatic systems have been extensively altered by human structures (e.g., construction of dams/canals) and these have major impacts on the connectivity of wildlife populations through the loss and isolation of suitable habitats. Habitat loss and isolation affect gene flow and influence the persistence of populations in time and space by restricting movements. Isolation can result in higher inbreeding, lower genetic diversity, and greater genetic structure, which may render populations more vulnerable to environmental changes, and thus to extinction. Given the ubiquity and the persistence of dams and canals in space and time, it is crucial to understand their effects on the population genetics of aquatic species. Here, we documented the genetic diversity and structure of painted turtle (<em>Chrysemys picta</em>) populations in the Rideau Canal, Ontario, Canada. More specifically, we used 13 microsatellites to evaluate the influence of locks on genetic variation in 822 painted turtles from 22 sites evenly distributed along the 202-km canal. Overall, we found low, but significant, genetic differentiation suggesting that some dispersal is occurring throughout the canal. In addition, we showed that locks contribute to the genetic differentiation observed in the system. Clustering analysis revealed two distinct genetic groups whose boundary is associated with a series of six locks. Our results illustrate how artificial waterways, such as canal systems, can influence population genetic structure. We highlight the importance of adopting management plans that can mitigate the impacts of human infrastructure and preserve gene flow across the landscape to maintain viable populations.</p>
Data and R code - ISRR11/Rooting2021 Meeting Report
<p>This repository contains the raw data and R code used to analyse the results of the online root phenotyping survey that was created and disseminated by ISRR Ambassadors during the ISRR11/Rooting2021 meeting.</p>
Coded data and R scripts for the article-Toward a dynamic behavioral profile of the Mandarin Chinese temperature term re
<p>These are the coded dataset and R scripts for the article "Towards a dynamic behavioral profile of Mandarin Chinese temperature term re: A diachronic semasiological approach".</p>
R code and example data for using genogeographic clustering approach
<p>While in recent years there have been considerable advances in discerning spatial genetic patterns within species, the task of identifying common patterns across species is still challenging. Approaches using new data from co-sampled species permit rigorous statistical analysis but are often limited to a small number of species; meta-analyses of published data can encompass a much broader range of species, but are usually restricted by uneven data properties. There is a need for new approaches that bring greater statistical rigour to meta-analyses, and are also able to discern more than a single spatial pattern among species.</p> <p>We propose a new approach for comparative multi-species meta-analyses of published population genetic data that addresses many existing limitations. This analysis takes a three-stage approach: (i) use common genetic metrics to measure location-specific diversity across the sampled range of each species, (ii) use an innovative graphing technique to describe spatial patterns within each species, and (iii) quantitatively cluster species by their similarity in pattern. We apply this technique to 21 species of intertidal invertebrate from the New Zealand coastline, to resolve common spatial patterns from disparate profiles of genetic diversity.</p> <p>The genogeographic curves are shown to successfully capture the known spatial patterns within each intertidal species, and readily permit statistical comparison of those patterns, regardless of sampling and marker inconsistencies. The species clustering technique is shown to discern groups of species that clearly share spatial patterns within groups but differ significantly among groups. The species groups defined were not identifiable a <em>priori</em> from their taxonomy or life history, but their spatial genetic patterns appear biologically relevant.</p> <p>Genogeographic species clustering provides a novel approach to discerning multiple common spatial patterns of diversity among a large number of species. It will permit more rigorous comparative studies from diverse published data, and can be easily extended to a wide variety of alternative measures of genetic diversity or divergence. We see the approach best used as an exploratory method, to uncover the patterns often hidden in multi-species communities, likely to be followed by more targeted model-testing analyses.</p>
Dataset and R code from: Positive and negative effects of land abandonment on butterfly communities revealed by a hierarchical sampling design across climatic regions
<p class="MsoNormal">Land abandonment may decrease biodiversity but also provides an opportunity for rewilding. It is therefore necessary to identify areas that may benefit from traditional land management practices and those that may benefit from a lack of human intervention. In this study, we conducted comparative field surveys of butterfly occurrence in abandoned and inhabited settlements in 18 regions of diverse climatic zones in Japan to test the hypotheses that species-specific responses to land abandonment correlate with climatic niches and habitat preferences. Hierarchical models that unified species occurrence and habitat preferences revealed that negative responses to land abandonment were associated with species that have cold climatic niches and utilize open habitats, suggesting that species negatively impacted by land abandonment will decline more due to future climate warming. Maps representing species gains and losses due to land abandonment, which were created from the model estimates, showed similar geographic patterns, but some areas exhibited high species losses relative to gains. Our hierarchical modelling approach was useful for scaling up local-scale effects of land abandonment to a macro-scale assessment, which is crucial to developing spatial conservation strategies in the era of depopulation.</p>
ScienceDex guides
Understand access before you commit
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.