Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
43
datasets available to search
ShareScore release 0.9.0
Dataset results
43 results for “land invasion”
Impacts of Land Use on Japanese Barberry Invasion in Central Massachusetts 2005
Despite the recognized importance of historical factors in controlling many native species distributions, few studies have incorporated historical landscape changes into models of invasive species distribution and abundance. We explore the possibility that the current distribution of invasive species may reflect legacies of historical land use despite nearly a century of forest succession and subsequent disturbances. We evaluated the modern distribution and abundance of Berberis thunbergii DC. (Japanese barberry), a problematic non-native shrub in forests of the northeastern U.S., relative to two distinct periods of historical land use, modern forest harvesting activity, and environmental and edaphic characteristics. Species questions addressed in this study include: (1) Do patterns of historical land use influence modern barberry distribution and abundance? (2) What is the influence of disturbance type and timing relative to the timing of introduction on current barberry distribution and abundance? (3) Which disturbance, environmental and edaphic variables best predict modern barberry distribution and abundance? Japanese barberry occurred more frequently and was more abundant in sites historically cleared for agriculture than in historically wooded sites. This relationship was strongest for areas in agriculture in the early 20th century after barberry was introduced to the region. The strong relationship between modern distribution patterns and prior land use suggests historical colonization of abandoned agricultural lands and persistence through subsequent reforestation. Contrary to our expectations, recent forest harvesting did not influence the occurrence or abundance of barberry. Our results indicate that interpretations of both native community composition and modern plant invasions must consider the importance of historical landscape changes and the timing of species introduction along with current environmental and edaphic conditions.
The InvL Dataset of Invasive Species from drones in a Land Environment
<p><strong>Summary</strong></p> <p>An original dataset for semantic segmentation, InvL, is introduced, which to the best of the authors' knowledge, is the first dataset of its kind with pixel-level annotations pertaining to invasive species (Himalayan balsam) in a land environment from drones with height 10-30 m (GSD 3-13 mm) using optical imaging and having diverse outdoor blur, noise and contrast.</p> <p><strong>Objectives</strong></p> <p>Automated detection and quantification of invasive species from drones would enable more efficient mapping using simple low-cost technologies. This would help to <em>remove invasive and later protect species. </em>However, it is difficult to distinguish invasive species from common ones from higher altitudes using drone images. Training a machine learning algorithm to accurately detect a given invasive species from images taken in the field requires a massive amount of human-generated training data.</p> <p>Objective is to encourage people to open share images that can be used to develop technology. We are interested expand database and add author for every 1 GB added to the new version InvL or every 200 hours spent to improve annotation quality or annotation type.</p> <p>Objective is to expand establish a reference dataset for automatic extraction using gold standard training sets to gain Intersection over Union (IoU) over 0.8 from drone height over 30 meters with least GSD using low cost drones.</p> <p><strong>Data description</strong></p> <p>This data set contains images of Himalayan balsam (Impatiens glandulifera) taken mostly with the DJI Mavic 2 PRO in Finland from heights 10, 15, 20 and 30 meters, low speed 1- 2 m/s, no filters, 90 degrees angle, still images, GSD varying from 3-13mm, image size ~15 MB, between July (usually no flowers) and August (flowers).</p> <p> </p> <p> </p> <p><strong><em>Table 1. Drone images of invasive species</em></strong></p> <table> <tbody> <tr> <td> <p><strong><em>Label</em></strong></p> </td> <td> <p><strong><em>Name of data file/set</em></strong></p> <p> </p> </td> <td> <p><strong>Dataset size (GB)</strong></p> </td> <td> <p><strong>File type (extension)</strong></p> <p> </p> </td> <td> <p><strong>Data repository and identifier</strong></p> </td> <td> <p><strong>IoU estimate </strong><strong>- model</strong></p> </td> <td> <p><strong>Comment</strong></p> </td> </tr> <tr> <td> <p><em>Himalayan_balsam</em></p> </td> <td> <p><em>InvL/Himalayan_balsam</em></p> <p><em>../ann – contains annotations</em></p> <p><em>…/ann/H10m_Lahti_Peitsikatu_H10m_10072020</em></p> <ul> <li><em>H10 = 10 m UAV heigth</em></li> <li><em>Lahti = city</em></li> <li><em>10072020 (last) = date of fligth</em></li> </ul> <p><em>…/original – orginal UAV images, folder structure same as for “ann”</em></p> </td> <td> <p>2</p> </td> <td> <p>InvL.zip (folder structure with annotations and original images)</p> <p> </p> </td> <td> <p> </p> </td> <td> <p><em>Himalayan balsam </em></p> <p><em>IoU = 0.53, FCN, 15 m with augmentation IoU=0.37, FCN, 30 m</em></p> </td> <td> <p><em>Himalayan balsam, green</em></p> <p><em>Elevation varies in area +- 5m, so actual UAV flight height varies</em></p> <p><em>Each image around 15 MB</em></p> </td> </tr> </tbody> </table> <p><strong>Limitations</strong></p> <p>Annotations do not indicate width or margins.</p> <ul> <li>There is no indication of confidence of annotations.</li> <li>Blur, noise or contrast values are not calculated.</li> <li>Mostly species are easily visible and images with bright sunlight have been removed in selection of images.</li> <li>Annotator needs good and tested instruction to annotate images. Annotator needs to do work for tens of hours with developed and tested instructions to guarantee good quality. After image annotator each image should be checked by an “expert” and reannotated if necessary (silver standard, if this is done twice it is gold standard). If expert is used each image should be annotated once (silver standard) and twice (gold standard).</li> <li>Annotation time for each 15 MB image should be around one hour.</li> <li>Image GSD should be around 5 mm and less for smaller species.</li> <li>In different field areas species color and size varies and mixed vegetation may cause problems.</li> <li>In images there are flowers and non-flowers.</li> </ul> <p><strong>Abbreviations</strong> FCN: a fully convolutional neural network.</p> <p> </p>
The effects of agricultural land-use history on non-native plant invasion in Bent Creek Experimental Forest in 2006
The researchers considered the effects of agricultural land-use legacies on the distribution of non-native invasive plants a century after abandonment in a watershed in western North Carolina, USA. The study was conducted at the Bent Creek Experimental Forest (BCEF) 15 km southwest of Asheville, North Carolina, USA, in the Pisgah National Forest. Forest sites that were previously in cultivation and abandoned ca. 1905 were compared with nearby reference sites that were never cultivated. The most common invasive plants were Celastrus orbiculatus Thunb., Microstegium vimineum Trin., and Lonicera japonica Thunb. (Kuhman, Pearson, and Turner 2011). Disentangling the cause–effect relationships between land-use history, the biotic community, and the abiotic template presents a challenge, but understanding the role of land-use legacies may provide important insights regarding the mechanisms underlying the establishment and spread of invasive plants in forest ecosystems (Kuhman, Pearson, and Turner 2011). A total of 86 plots were established at Bent Creek Experimental Forest during the summer of 2006. Specifically, the study was conducted between June and August 2006. Half of these were established in historic agricultural plots and half in reference plots that were not formerly used for agriculture (pasture or rowcrops) based on the 1941 Forest Service Report by William Nesbitt and the appended land-use history map (History of early settlement and land use on the Bent Creek Experimental Forest Buncombe County, NC. 1941). Historic agriculture and reference plots were paired based on similarities in topography and bedrock geology (typically in relatively close proximity to one another). Within sites, two plots were established, one adjacent to the road and one 50 m away from the road (labeled as "A" and "B", respectively, in the "Plot #").
Does land-use history facilitate non-native plant invasion? A field experiment with Celastrus orbiculatus in the Bent Creek Experimental Forest in the southern Appalachians from 2008 to 2009
Although historic land use is often implicated in non-native plant invasion of forests, little is known about how land-use legacies might actually facilitate invasion. The researchers conducted a 2-year field seeding experiment in western North Carolina, USA, to compare germination and first-year seedling survival of Celastrus orbiculatus Thunb. in stands that had been cultivated and abandoned a century earlier and were dominated by tulip poplar (Liriodendron tulipifera L.), and in paired stands that had never been cultivated and were dominated by oaks (Quercus spp.). Experiments were conducted at five sites with paired tulip poplar and oak stands by varying litter mass (none, low, or high) and litter type (tulip poplar or oak).
Figure 4 in Predation on invasive land gastropods by a Neotropical land planarian
Figure 4. Obama ladislavii capturing Deroceras laeve: (A) planarian encountering the slug; (B) planarian attaching its anterior end to the slug; (C, D) planarian moving towards the slug's head to prevent escape; (E–H) slug surrounded by the planarian and pressed against the substrate.
Figure 1 in Predation on invasive land gastropods by a Neotropical land planarian
Figure 1. Experimental design for determining prey tracking behaviour. Solid line indicates outline of the slime trail. Dashed line indicates a possible path taken by the planarian, and X the planarian's initial position.
Data and Reproducible Analysis For: "Fine-Scale Associations Between Land Cover Composition and the Oviposition Activity of Native and Invasive Aedes Vectors of La Crosse Virus"
<h1><strong>Data and Reproducible Analysis For: "Fine-Scale Associations Between Land Cover Composition and the Oviposition Activity of Native and Invasive Aedes Vectors of La Crosse Virus"</strong></h1> <p>This repository contains pre-processed data sets and code scripts to reproduce the data processing and analyses that are presented in the corresponding manuscript. Some minor pre-processing was completed before presenting this -- namely, the land cover raster was clipped to the study area of Knox County, Tennessee, USA, prior to placing in the repository to reduce the file size. </p> <h2><strong>How to use this repository to reproduce results </strong></h2> <p>This repository is designed to support the reproduction of analyses in the associated manuscript. The entire project can be downloaded and stored anywhere on your computer, as long as the file structure is not altered. The project contains folders with all data sets and code scripts necessary for analysis.</p> <p><strong>What you will need: </strong><br> - Installed R and RStudio for purely spatial cluster and global model analyses<br> - Basic understanding of how to open R and run code </p> <p><strong> You do NOT need:</strong><br> - To download or install R packages on your own; that is taken care of within this environment<br> - To write any code <br> - To set up any working directories in R </p> <h3><strong>Important: Using `renv`</strong></h3> <p>Short Version: When you open the R project, run `renv::restore()` and follow the prompts to install the necessary R packages. </p> <p>The R package `renv` was used to create a <strong>project library</strong>, which contains all R packages that are used by the project. The packages in the project library are <strong>the versions used during the original analysis</strong>. This means that if any packages are updated by developers in ways that would change the results of the analysis, this project can still produce the original results because of `renv`. When you open this project for the first time, `renv` will automatically download and install itself and ask you to run `renv::restore()`. <strong>You should run `renv::restore()` to automatically download and install all of the packages within this reproducible environment</strong>. </p> <h2><strong>## Basic step-by-step guide:</strong></h2> <p>- 1. Download the entire repository by clicking "Code -> Download ZIP" on GitHub or by downloading the ZIP file in Zenodo<br>- 2. Extract the ZIP file anywhere on your computer (do not change the structure of the files once extracted)<br>- 3. In RStudio, click *File -> Open Project* and browse to the location where you extracted the repository; in the repository file, open the knoxaedeslandcover R Project file <br>- 4. Open any of the R scripts in the `analysis/` folder<br>- 5. Run the code `renv::restore()` in the script or in the console and follow the prompt to install the packages <br> - Now you can run the R Scripts; start from the top with loading the packages and data, then work your way down line-by-line</p> <h3><strong># `analysis/` Folder</strong></h3> <p>The `analysis/` folder contains scripts for processing data and conducting analyses. Each file is an R script that should be opened in R studio. The first shows how to process and aggregate the various raw data files; if you are only interested in reproducing analyses from the manuscript, you can skip to the second file and work from there. </p> <p><strong><em>## Files within the `analysis/` folder</em></strong></p> <p>The files are numbered in the order that they were run for the original analysis. In this case, none of the analyses are dependent on the others, so they can technically be used in any order. The numbers associated with each file describe the order that the analyses would normally be run. </p> <p> - `(1)dataprep.R` contains the code for cleaning and combining the land cover, climate, and mosquito data -- this includes calculating the land cover percentages at different scales and calculating weekly and timelagged climate values<br> - `(2)summary_analysis.R` contains code for reproducing summary data and creating graphs from the manuscript<br> - `(3)variable_selection.R` contains code for asssessing collinearity and fitting models to identify the best fitting variables for each speceis<br> - `(4)finalmodels.R` contains code for fitting the final models using the selected variables for each species </p> <h3><strong># `data/` Folder</strong></h3> <p>This folder contains several datasets, including one that compiles them all for analyses (`knox_joined`). The raw data are included to show how the data was processed and aggregated, but the individual raw data files are not needed for analyses. See `data dictionary.txt` for a description of all attributes contained within each file. </p> <p><strong><em>## Files within the `data/` folder</em></strong></p> <p> - `knox22_joined.RDS` contains a cleaned and joined version of land cover, climate, and mosquito data in R Data Serialization format, which maintains predefined factor and numeric designations for columns. <br> - `knox22_joined.csv` contains a cleaned and joined version of land cover, climate, and mosquito data in CSV format -- identical to 'knox22_joined.RDS'<br> - `sites22.csv` contains the names, site codes, and coordinates of the study sites<br> - `aedes22_clean.csv` contains the raw mosquito collection data for the study without any climate or land cover information <br> - `NLCD_2019_landcover_clippedtoKnox.tif` contains the NLCD land cover data, already clipped to Knox County, TN, USA<br> - `knox22_temperature.csv` contains raw daily temperatures for the city of Knoxville in 2022<br> - `knox22_rainfall.csv` contains raw daily precipitation for the city of Knoxville watersheds in 2022<br> - `rainfall_stations.csv` contains the descriptions, approximate street addresses, and geographic coordinates for rainfall monitoring sites <br> - `data dictionary.txt` file that defines column names and other data attributes for every dataset </p> <h3><strong># `renv/` Folder</strong></h3> <p>The `renv/` folder contains bits and pieces needed for the `renv` package. Nothing should be altered in this folder. </p> <p> </p> <h2><strong>References for source data </strong></h2> <p> - Some of the data in this repository were originally obtained from open access sources. </p> <p> - Land cover data was obtained from the National Land Cover Database (NLCD) 2019 data product, specifically the "NLCD 2019 Land Cover (CONUS)" product. The original, unclipped raster can be freely downloaded here: https://www.mrlc.gov/data/nlcd-2019-land-cover-conus</p> <p> - Temperature data was downloaded from the United States National Oceanic and Atmospheric Administration (NOAA) weather station for Knoxville, Tennessee. The source data can be downloaded from this site: https://www.weather.gov/mrx/tysclimate</p> <p> - Rainfall data was obtained from the City of Knoxville rainfall data website, located here: https://www.knoxvilletn.gov/government/city_departments_offices/engineering/stormwater_engineering_division/rainfall_data</p> <p> - All mosquito collection data was collected directly by the manuscript authors</p>
РИС. 4. ROC-кривые и плоЩадь под ROC-кривой (AUC) для раЗных моделей. А. РеЗультаты для Xeropicta derbentina. B. РеЗультаты для Brephulopsis cylindrica. Красная кривая иллюстрирует покаЗатели модели для обучаюЩего набора, бледно-голубые линии — для тестовых наборов и отдельных моделей, толстая синяя линия — обЩие покаЗатели всех моделей. in Land snails Brephulopsis cylindrica and Xeropicta derbentina (Gastropoda: Stylommatophora): case study of invasive species distribution modelling
РИС. 4. ROC-кривые и плоЩадь под ROC-кривой (AUC) для раЗных моделей. А. РеЗультаты для Xeropicta derbentina. B. РеЗультаты для Brephulopsis cylindrica. Красная кривая иллюстрирует покаЗатели модели для обучаюЩего набора, бледно-голубые линии — для тестовых наборов и отдельных моделей, толстая синяя линия — обЩие покаЗатели всех моделей.
FIG. 3 in Land snails Brephulopsis cylindrica and Xeropicta derbentina (Gastropoda: Stylommatophora): case study of invasive species distribution modelling
FIG. 3. Principal component analysis (PCA). The dimensionality reduction technique represents multivariate data on the 2D plane thus showing their structure. Datapoint colors are the same as in Fig. 2.
РИС. 1. Точки находок видов на исследуемой территории. БаЗовая карта: береговая линия, речная сеть – Natural Earth @ naturalearthdata.com; рельеф – Terrain Tiles @ registry.opendata.aws/terrain-tiles; Экорегионы по Dinerstein E. et al. [2017]. in Land snails Brephulopsis cylindrica and Xeropicta derbentina (Gastropoda: Stylommatophora): case study of invasive species distribution modelling
РИС. 1. Точки находок видов на исследуемой территории. БаЗовая карта: береговая линия, речная сеть – Natural Earth @ naturalearthdata.com; рельеф – Terrain Tiles @ registry.opendata.aws/terrain-tiles; Экорегионы по Dinerstein E. et al. [2017].
The effects of land-use history and the contemporary landscape on non-native plant invasion at local and regional scales in the French Broad Watersheds, 2007
Determining what factors explain the distribution of non-native invasive plants that can spread in forest-dominated landscapes could advance understanding of the invasion process and identify forest areas most susceptible to invasion. The researchers conducted roadside surveys to determine the presence and abundance of 15 non-native plant species known to invade forests in western North Carolina, USA. Prior to sampling, the researchers identified 15 non-native invasive plant species that were of concern in the study region. Generalized linear models were used to examine how contemporary and historic land use, landscape context, and topography influenced presence and abundance of the species at local and regional scales.
Data from: Land-use intensity and relatedness to native plants promote exotic plant invasion in a tropical biodiversity hotspot
<p>Exotic plant invasions threaten biodiversity and are costly to farmers. Land use is a major pathway promoting the spread of exotic plant species; however, little is known about the processes underlying the success of exotic plants in tropical agricultural landscapes. Focussing on the heterogeneous smallholder landscapes of north-eastern Madagascar, we studied exotic plants of understorey communities across a land-use intensity gradient from unburned lands (old-growth forests, forest fragments, and forest-derived vanilla agroforests) to burned ones (fallow-derived vanilla agroforests, woody fallows, and herbaceous fallows). </p> <p>We quantified the absolute species richness, abundance, and cover of exotic plants across land-use types and their proportional contribution to community richness, abundance, and cover as indicators of exotic plant invasion. We tested for the effects of land-use parameters, namely land-use history, canopy closure, and landscape-level forest cover, on exotic plants. Additionally, we tested whether the phylogenetic relatedness between exotic and native species in the same plot affected invasion success, testing Darwin's naturalization and pre-adaptation hypotheses. </p> <p>All indicators of exotic plant invasion were lowest in old-growth forests and forest fragments and highest in fallow-derived vanilla agroforests, woody fallows, and herbaceous fallows. Absolute and proportional exotic richness was negatively affected by canopy closure, and landscapes with high forest cover had lower proportions of exotic plant richness. High phylogenetic relatedness between exotics and natives was associated with lower proportional richness but higher proportions of exotics in abundance and cover. However, individual exotic species showed contrasting responses to land-use parameters and relatedness to natives.</p> <p>Synthesis and applications: Our results indicate that maintaining unburned lands, land-use types with dense canopies, and landscapes with high forest cover prevents the spread of exotic plants within agricultural landscapes of north-eastern Madagascar. Supporting Darwin's pre-adaptation hypothesis, exotic plants phylogenetically closely related to native plants are more likely to become successful invaders in terms of abundance and cover. Nevertheless, individual species show different responses to land-use changes and phylogenetic relatedness. Therefore, land-use decisions and management choices can be tailored to limit the spread of exotic species and to preserve native plants in this global biodiversity hotspot.</p>
Fig. 1 in Invasive Land Snail Oxychilus Translucidus (Stylommatophora, Zonitidae) In The Catacombs Of Odesa (Ukraine)
Fig. 1. Oxychilus translucidus in Odesa Catacombs (photo by O. Kovtun).
Global potential invasion maps of traded birds under climate and land-cover change
<p>Biological invasions rank among the top five threatening factors affecting biodiversity, but ongoing changes in climate and land cover might exacerbate risks. We used species distribution models for 609 traded bird species on the CITES list to examine the combined effects of projected climate change and land-cover change worldwide on the potential range expansion of bird species with commercial value as pets. The maps of potential invasion (may be inferred as the invasion risk) have been provided in the main manuscript and here, the potential invasion dataset for the current and future times is provided including the species distribution maps, all as GeoTiff files. The maps for the future time are provided for different future years and over a range of climate scenarios (SSP245, SSP370, and SSP585).</p>
РИС. 10. Относительная ваЖность предикторов для моделей распространения Brephulopsis.cylindrica. in Land snails Brephulopsis cylindrica and Xeropicta derbentina (Gastropoda: Stylommatophora): case study of invasive species distribution modelling
РИС. 10. Относительная ваЖность предикторов для моделей распространения Brephulopsis.cylindrica.
РИС. 7. Пригодность местообитаний для Xeropicta derbentina согласно ансамблевой модели. in Land snails Brephulopsis cylindrica and Xeropicta derbentina (Gastropoda: Stylommatophora): case study of invasive species distribution modelling
РИС. 7. Пригодность местообитаний для Xeropicta derbentina согласно ансамблевой модели.
Island area and remoteness shape plant and soil bacterial diversity through land use and biological invasion
<p>Biodiversity is declining dramatically due to human-driven land use change and biological invasion, but our knowledge of how such drivers influence plant and heterotroph diversity on island ecosystems remains limited. Historically island biogeography theory has focused solely on the direct effects of island size and remoteness on biodiversity, but these factors can also indirectly affect species gain and/or loss by impacting land use change and biological invasion. We built the structural equation model to explore the direct effects of island size and remoteness, and indirect effects of these factors via land use intensity and pinewood nematode invasion, on the diversity of plants and soil bacteria across 37 continental shelf islands in the largest land-bridge archipelago in eastern China.</p> <p>As expected we found that increasing island area directly promoted plant diversity. However, land use intensity increased with island area which also promoted plant diversity, and loss of pine forest by the pinewood nematode invasion increased with island remoteness which reduced plant diversity. Island remoteness only indirectly reduced plant diversity through increasing pine forest loss. Soil bacterial diversity was directly negatively impacted by island remoteness, and indirectly negatively impacted by island remoteness through increased soil electrical conductivity likely caused by greater salinity from sea spray. Furthermore, soil bacterial diversity was indirectly promoted by island area through increased plant diversity and decreased soil electrical conductivity, and indirectly reduced by pine forest loss through decreased plant diversity. Our findings highlight that island biogeography theory has relevance to understanding human impacts in the Anthropocene, and that there is a need to more explicitly recognize how island size and remoteness affect biodiversity not only directly, but also indirectly via their effects on human-induced drivers of biodiversity, such as land use change and biological invasion.</p>
Island area and remoteness shape plant and soil bacterial diversity through land use and biological invasion
Open the record for dataset details and reuse information.
Data from: Land cover, individual’s age and spatial sorting shape landscape resistance in the invasive frog Xenopus laevis
Open the record for dataset details and reuse information.
Global potential invasion maps of traded birds under climate and land-cover change
Open the record for dataset details and reuse information.
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.