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Landuse/Landcover predictors for invasive species distribution modelling in Europe.
<p><strong>Description</strong></p> <p>This data set contains a set of predictors characterizing land use/land cover derived from the CORINE dataset, anthropogenic pressure from the global terrestrial human footprint dataset, and the distance to the nearest waterbody, for continental Europe. All have been aligned with the 1 km<sup>2</sup> EEA Reference Grid. The climate variables based on historical (1976-2005) and future (2040-2070) scenarios are available from De Troch et al., 2020 also via Zenodo. These rasters represent the habitat and anthropogenic predictors needed in the Tracking Invasive Alien Species (TrIAS) workflow for invasive species distribution modelling (wiSDM).</p> <p><strong>Geographic coverage</strong></p> <p>Europe</p> <p><strong>Methods</strong></p> <p>Land use classes were extracted from the CORINE06 100 m GeoTiff downloaded from Copernicus. The percentage of each 1 km<sup>2</sup> EEA Reference Grid cell occupied by coniferous forest, deciduous forest, wetlands, grasslands and agriculture was calculated. Multiple land use sub-classes were aggregated for the following categories: agriculture, wetlands, grasslands (Table 1). These data layers have been processed in R to replace all NAs that are within the European landmass, with zeros to distinguish them from the ocean, which remain NA, as in the CORINE dataset. In this context, a zero reflects the absence of a given land cover attribute. </p> <p>The mean anthropogenic pressure per 1km<sup>2 </sup>EEA Reference Grid cell was extracted from the global terrestrial human footprint dataset (Venter et al, 2016). Distance to the nearest waterbody within each 1km<sup>2</sup> EEA Reference Grid cell was calculated using the 2016 Surface Water Bodies shapefile available from the EEA (https://www.eea.europa.eu/data-and-maps/data/wise-wfd-spatial/surface-water-body). </p> <p> </p> <table> <tbody> <tr> <td>Land Use Class</td> <td>CORINE LABEL</td> </tr> <tr> <td>Agriculture</td> <td>Non-irrigated arable land (211), Rice fields (213),Vineyards (221),Fruit trees and berry plantations (222),Olive groves (223),Pastures (231),Annual crops associated with permanent crops (241),Complex cultivation patterns (242),Land principally occupied by agriculture, with significant areas of natural vegetation (243)</td> </tr> <tr> <td> </td> </tr> <tr> <td> </td> </tr> <tr> <td>Coniferous forest</td> <td>Coniferous forest (312)</td> </tr> <tr> <td>Deciduous forest</td> <td>Broad-leaved forest (311)</td> </tr> <tr> <td>Grassland</td> <td>Natural grasslands (321), Moors and heathland, (322) Sclerophyllous vegetation (323)</td> </tr> <tr> <td>Wetland</td> <td>Inland marshes (411), Peat bogs (412)</td> </tr> </tbody> </table> <p>Table 1. How the the original land use/land cover types as labelled in CORINE were combined (or not).</p> <p><strong>Files</strong></p> <p>distance2water_EEA_1km.tif (distance to nearest waterbody)</p> <p>ESM1000m.tif (mean anthropogenic pressure)</p> <p>corine_perAgriculture.tif</p> <p>corine_perWetland.tif</p> <p>corine_pergrass.tif</p> <p>corine_perdeciduous.tif</p> <p>corine_perConiferous.tif</p> <p> </p>
A lack of population structure characterizes the invasive Lonicera japonica in West Virginia and across eastern North America
<p>Figure S1. Mig-seq primers used in the current study.</p> <p>Dataset S1. SNP data in .vcf format for Lonicera japonica.</p> <p>Figure S2: STRUCTURE analyses. Left: Delta K plot showing the optional number of ancestral population clusters (based on Evanno et al. 2015 method). Right: Ancestry plots from analysis with ParallelStructure for k = 3 (above) and k = 5 (below). Colors correspond to each ancestral cluster.</p>
The Invasion of Ukraine Viewed through TikTok: A Dataset
<p>This is a dataset of videos and comments related to the invasion of Ukraine, published on TikTok by a number of users over the year of 2022. It was compiled by Benjamin Steel, Sara Parker and Derek Ruths at the Network Dynamics Lab, McGill University. We created this dataset to facilitate the study of TikTok, and the nature of social interaction on the platform relevant to a major political event.</p> <p>The dataset has been released here on Zenodo: <a href="https://doi.org/10.5281/zenodo.7534952">https://doi.org/10.5281/zenodo.7926959</a> as well as on Github: <a href="https://github.com/networkdynamics/data-and-code/tree/master/ukraine_tiktok">https://github.com/networkdynamics/data-and-code/tree/master/ukraine_tiktok</a></p> <p>To create the dataset, we identified hashtags and keywords explicitly related to the conflict to collect a core set of videos (or ”TikToks”). We then compiled comments associated with these videos. All of the data captured is publically available information, and contains personally identifiable information. In total we collected approximately 16 thousand videos and 12 million comments, from approximately 6 million users. There are approximately 1.9 comments on average per user captured, and 1.5 videos per user who posted a video. The author personally collected this data using the web scraping PyTok library, developed by the author: <a href="https://github.com/networkdynamics/pytok">https://github.com/networkdynamics/pytok</a>.</p> <p>Due to scraping duration, this is just a sample of the publically available discourse concerning the invasion of Ukraine on TikTok. Due to the fuzzy search functionality of the TikTok, the dataset contains videos with a range of relatedness to the invasion.</p> <p>We release here the unique video IDs of the dataset in a CSV format. The data was collected without the specific consent of the content creators, so we have released only the data required to re-create it, to allow users to delete content from TikTok and be removed from the dataset if they wish. Contained in this repository are scripts that will automatically pull the full dataset, which will take the form of JSON files organised into a folder for each video. The JSON files are the entirety of the data returned by the TikTok API. We include a script to parse the JSON files into CSV files with the most commonly used data. We plan to further expand this dataset as collection processes progress and the war continues. We will version the dataset to ensure reproducibility.</p> <p>To build this dataset from the IDs here:</p> <ol> <li>Go to <a href="https://github.com/networkdynamics/pytok">https://github.com/networkdynamics/pytok</a> and clone the repo locally</li> <li>Run <code>pip install -e .</code> in the pytok directory</li> <li>Run <code>pip install pandas tqdm</code> to install these libraries if not already installed</li> <li>Run <code>get_videos.py</code> to get the video data</li> <li>Run <code>video_comments.py</code> to get the comment data</li> <li>Run <code>user_tiktoks.py</code> to get the video history of the users</li> <li>Run <code>hashtag_tiktoks.py</code> or <code>search_tiktoks.py</code> to get more videos from other hashtags and search terms</li> <li>Run <code>load_json_to_csv.py</code> to compile the JSON files into two CSV files, <code>comments.csv</code> and <code>videos.csv</code></li> </ol> <p>If you get an error about the wrong chrome version, use the command line argument <code>get_videos.py --chrome-version YOUR_CHROME_VERSION</code> Please note pulling data from TikTok takes a while! We recommend leaving the scripts running on a server for a while for them to finish downloading everything. Feel free to play around with the delay constants to either speed up the process or avoid TikTok rate limiting.</p> <p>Please do not hesitate to make an issue in this repo to get our help with this!</p> <p> </p> <p>The <code>videos.csv</code> will contain the following columns:</p> <p><code>video_id</code>: Unique video ID</p> <p><code>createtime</code>: UTC datetime of video creation time in YYYY-MM-DD HH:MM:SS format</p> <p><code>author_name</code>: Unique author name</p> <p><code>author_id</code>: Unique author ID</p> <p><code>desc</code>: The full video description from the author</p> <p><code>hashtags</code>: A list of hashtags used in the video description</p> <p><code>share_video_id</code>: If the video is sharing another video, this is the video ID of that original video, else empty</p> <p><code>share_video_user_id</code>: If the video is sharing another video, this the user ID of the author of that video, else empty</p> <p><code>share_video_user_name</code>: If the video is sharing another video, this is the user name of the author of that video, else empty</p> <p><code>share_type</code>: If the video is sharing another video, this is the type of the share, stitch, duet etc.</p> <p><code>mentions</code>: A list of users mentioned in the video description, if any</p> <p> </p> <p>The <code>comments.csv</code> will contain the following columns:</p> <p><code>comment_id</code>: Unique comment ID</p> <p><code>createtime</code>: UTC datetime of comment creation time in YYYY-MM-DD HH:MM:SS format</p> <p><code>author_name</code>: Unique author name</p> <p><code>author_id</code>: Unique author ID</p> <p><code>text</code>: Text of the comment</p> <p><code>mentions</code>: A list of users that are tagged in the comment</p> <p><code>video_id</code>: The ID of the video the comment is on</p> <p><code>comment_language</code>: The language of the comment, as predicted by the TikTok API</p> <p><code>reply_comment_id</code>: If the comment is replying to another comment, this is the ID of that comment</p> <p>The date can be compiled into a user interaction network to facilitate study of interaction dynamics. There is code to help with that here: <a href="https://github.com/networkdynamics/polar-seeds">https://github.com/networkdynamics/polar-seeds</a>. Additional scripts for further preprocessing of this data can be found there too.</p>
A mapping of keywords from published papers on alien squirrels to biological invasion research themes
<p><strong>Context</strong></p> <p>This dataset was used to produce the worldl and the graphs in the editorial to the research topic <a href="https://www.frontiersin.org/research-topics/29270/ecology-impact-and-management-of-squirrel-invasions"><em>Ecology, impact and management of squirrel invasions</em></a> (La Morgia et al. 2023).</p> <p><strong>Contents of the dataset</strong></p> <p>The dataset contains the keywords of papers since 2000 harvested with a Web of Science search (performed on 29/05/2023) using the advanced search string TS=(invasive squirrel) OR TI=(invasive squirrel) OR AB=(invasive squirrel). We screened the search results, excluding papers irrelevant to alien squirrels, for example, papers on computer science or physiology, medical or other aspects without any bearing to conservation science. To do this, we checked the abstract and keywords of the papers. Out of the 401 initial papers, after this first screening, we kept 217 in this dataset. The keywords of these papers were manually assigned to alien squirrel research topics by the authors of this dataset (using an own categorisation) and then mapped to the seven broad themes of invasive alien species research of <a href="https://doi.org/10.1007/s10530-023-03067-7">Stevenson et al. (2023)</a>: </p> <ol> <li>Ecosystems: topics which discuss a specific region, or biome, or focused on a particular species strongly associated with one ecosystem type;</li> <li>Monitoring: topics regarding all aspects of monitoring, including detection, identification, and distributional mapping;</li> <li>Management and decision-making: topics discussing the management and socio-political aspects of invasion science, such as prevention, control, and policy;</li> <li>Interactions: topics discussing the interactions with native species, or the effects of those interactions</li> <li>Assessing change: topics focused on studying and analysing temporal and ecological change;</li> <li>Traits: topics that explored the characteristics of alien squirrels;</li> <li>Invasion mechanisms: topics discussing dispersal pathways and drivers of spread.</li> </ol> <p><strong>Dataset description</strong></p> <p>Every row (N = 1275) in the comma-separated .csv represents one original keyword with reference to the paper in which that keyword appears and mapped to the research topics on invasive squirrels and the broad themes in invasion biology research. The .csv contains the following fields:</p> <ul> <li>ID: a unique ID assigned to the combination of an original keyword and the corresponding paper harvested from the WoS search</li> <li>original_keyword: the original keywords associated with the paper (WoS search)</li> <li>keyword_topic: categorization of original keywords into topics related to invasive squirrel research by La Morgia et al. (2023)</li> <li>mapped_category: mapping to one of the seven broad themes of invasive alien species research of <a href="https://doi.org/10.1007/s10530-023-03067-7">Stevenson et al. (2023)</a> as listed and described above</li> <li>authors: author(s) of the paper (WoS search)</li> <li>year: publication year of paper (WoS search)</li> <li>title: title of the paper (WoS search)</li> <li>journal: full journal name (WoS search)</li> <li>doi: full doi of the paper (WoS search)</li> </ul> <p><strong>Potential applications of the dataset</strong></p> <p>This dataset can be used to reproduce the graphs in La Morgia et al. (2023) or to perform more in-depth review or analysis of the literature on alien squirrel invasions. For more information and graph code, we refer to <a href="https://github.com/Vale-LaMo/squirrels">this GitHub repository</a>.</p>
Different facets of the same niche: integrating citizen science and scientific survey data to predict biological invasion risk under multiple global change drivers
<p>Raw data (occurrences and environmental predictors) used in the manuscript "Different facets of the same niche: integrating citizen science and scientific survey data to predict biological invasion risk under multiple global change drivers"</p>
Distribution of invasive alien species of Union concern (Regulation (EU) 1143/2014) in Belgium for the reporting period 2015-2018
<p><strong>Aims and scope</strong></p> <p>Member State authorities are required to report on the distribution in their territory of each of the invasive alien species (IAS) of Union concern. These are species with documented biodiversity impacts sensu the European Union Regulation on the prevention and management of the introduction and spread of Invasive Alien Species in Europe (IAS Regulation No 1143/2014) (European Union 2014). This distribution represents the official reporting under Article 24(1) of R.1143/2014 on invasive alien species for the period 2015–2018. Baseline distribution of these species has previously been reported and published (Adriaens et al. 2018, ).</p> <p>Data were compiled from various datasets holding invasive species observations such as data from research institutes and research projects (9%), citizen science observatories (68%) and a range of other sources (23%) such as governmental agencies, water managers etc. More specifically the dataset includes:</p> <ul> <li>The citizen science recording portals www.waarnemingen.be and www.observation.be which has a specific alert system for IAS where nature volunteers can report their observations (Adriaens et al. 2018);</li> <li>Data from the Research Institute for Nature and Forest (INBO), the Flemish government institute that coordinates N2000, WFD and BIrd Directive and IAS monitoring in the terrestrial, estuarine and freshwater environment;</li> <li>Data from the Flemish Environment Agency which performs management of muskrat and invasive water plants in Flanders, gathered with a dedicated smartphone app since 2015;</li> <li>Data from the Flemish provinces and Rato vzw that manage water plants, muskrat, giant hogweed etc.;</li> <li>Some smaller datasets from cities;</li> <li>Data from the Brussels Capital Region from the Brussels Environment data portal;</li> <li>Plant inventories of the ‘contrats de rivière’ along watercourses in Wallonia, making use of a dedicated application to collect data directly from the field (fulcrum);</li> <li>The government reporting portals for IAS of the ‘Observatoire wallon de la flore, de la faune et des habitats (Service Public de Wallonie)’;</li> <li>Some validated data from specific datasets on gbif (iNaturalist, Natusfera, Naturgucker).</li> </ul> <p>Data were normalized using a custom mapping of the original data files to Darwin Core (Wieczorek et al. 2012) where possible. Species names were mapped to the GBIF Backbone Taxonomy (GBIF 2016) using the species API (http://www.gbif.org/developer/species). The mapping was assisted by dedicated software (SMARTIE) which was specifically written for the purpose of aggregating IAS data from various sources. Appropriate selection of records was performed based on the cut-off dates (see data range) and record content validation (see validation procedure). Data were then joined with GRID10k layer Belgium based on GRID10k cellcodes (ETRS_1989_LAEA). The technical format is in line with the <a href="http://cdr.eionet.europa.eu/help/ias_regulation/material/IAS-species-distribution-user-manual">guidelines</a> provided to the member states for the compilation of reports on Species Distribution (SD) of Invasive Alien Species of Union concern.</p> <p><strong>File description</strong></p> <p>The dataset contains a shapefiles (<em>T1_Belgium_Union_List_Species.shp</em>) with the distribution of the species of Union Concern at 10km<sup>2</sup> (European Terrestrial Reference System projection - 1989 ETRS_1989_LAEA) level. The attributes table contains <em>Cellcode </em>(ETRS<sup> </sup>grid cell code) and <em>Species </em>(scientific name + authority).</p> <p><strong>Date range</strong></p> <p>The data reflects the distribution of the IAS of Union concern in Belgium in the first reporting period for the EU Regulation hence comprises observations of Union List invasive species between January 2015 (2015-01-01) and December 2018 (2018-12-31). </p> <p><strong>Validation procedure</strong></p> <p>Record validation was performed to exclude dubious records, wrong identifications etc. This was done based on the IdentificationVerificationStatus field (to which validation information from original data were mapped) if available. In general, non-validated data were not considered. Data were validated in the original datasets based on evidence (e.g. pictures), on the observer’s experience, or based on a set of predefined rules (e.g. automated validation based on geographic filtering). Data from research institutes were generally considered validated. A few casual records of EU list species that were clearly planted were discarded manually. When the original dataset did not mention any validation status, records were not considered validated and therefore not taken into account unless for Chinese mitten crab <em>Eriocheir sinensis</em>, ruddy duck <em>Oxyura jamaicensis</em>, raccoon <em>Procyon lotor</em>, Siberian ground squirrel <em>Tamias sibiricus</em>, sacred ibis <em>Threskiornis aethiopicus</em>, Egyptian goose <em>Alopochen aegyptiaca, </em>Himalayan balsam <em>Impatiens glandulifera</em>, giant hogweed <em>Heracleum mantegazzianum, </em>muskrat <em>Ondatra zibethicus </em>and red-eared slider <em>Trachemys spp</em>. For these species, it was assumed all records were correct as they originate from dedicated sampling (<em>E. sinensis</em>) within research projects, were gathered by public bodies (e.g. muskrat), or represent species that are readily recognizable by people in the field. Data provided by EASIN in the care package and GBIF data were carefully checked.</p> <p>A visual check was performed on the resulting distribution maps by representatives of the Belgian national scientific council on invasive alien species, an official consultative structure coordinating scientific input and data aggregation between Belgian regions and institutions with regards to technical implementation of the Regulation No 1143/2014 on invasive alien species.</p> <p><strong>Data providers</strong></p> <p>The providers of the invasive species data for this exercise (individuals and their respective organizations) are listed in the "data providers" section of the dataset metadata. Much of the primary occurrence data that formed the basis for this aggregated dataset will be published as open data on the Global Biodiversity Information Facility (GBIF).</p>
Invasive species and thermal squeeze: Distribution of two invasive predators and drivers of ship rat (Rattus rattus) invasion in mid-elevation Fuscospora forest
This data package is from a trapping network set up in Craigieburn Forest Park, New Zealand, in 2013. These are records of the stoats and rats caught in the traps each time the traps were checked by volunteers since 2013. Associated long term air temperature and seedfall data from the Craigieburn area is also provided. If the original trapping records (containing more mammalian catch information such as weasels and cats) are required please contact the data providers.
The dual invasion of Amur honeysuckle and Emerald Ash Borer Shifts Fungal Driven Decomposition in Midwestern Forests
Midwestern forests are currently impacted by two prominent invaders, the Emerald Ash Borer (EAB), Agrilus planipennis and Amur honeysuckle, Lonicera maackii. The loss of ash (Fraxinus spp.) trees due to EAB invasion can further facilitate honeysuckle invasion, driving changes in the composition of forest leaf litter. To evaluate the extent to which these changes alter ecosystem function, we conducted litter bag and culture-based decomposition experiments using leaf litter from sugar maple (Acer saccharum), oak (Quercus spp.), black ash (Fraxinus nigra), green ash (Fraxinus pennsylvanica), spicebush (Lindera benzoin), and Amur honeysuckle (Lonicera maackii). To further understand the mechanism driving differences in decay rates, we inoculated six species of decomposing fungi separately onto both single species and multispecies (half honeysuckle and half native species) leaf litter and measured decomposition rate, fungal growth and enzymatic activity in laboratory-based cultures. Honeysuckle leaf litter decomposed faster, had increased fungal growth, and had higher activity for carbon degrading enzymes compared to native species leaf litter. Furthermore, multispecies mixtures followed the same patterns as honeysuckle, suggesting that the addition of honeysuckle to leaf litter will accelerate ecosystem functions related to carbon breakdown. Consequently, forests that experience the invasion of honeysuckle and EAB induced loss of ash are likely to have faster decomposition, potentially resulting in an influx of available nutrients.
Invasive rat eradication effects on seedling counts on Palmyra Atoll, 2004-2016
In order to investigate the impacts of rat eradication on plant communities on the Palmyra Atoll, Line Islands, Central Pacific Ocean ecosystem, a study was conducted surveying the native and non-native tree and palm seedling abundance before and after an eradication event which took place in June 2011. The common plant species examined were P. grandis and C. nucifera, while the less-common species were B. asiatica, C. subcordata, H. sonora, G. speciosa, N. oppositifolium, and C. inophyllum. Data for common plant species were collected in 2007, 2011, 2012, 2014, and 2016, and for less-common species in 2004, 2011, 2012, 2014, and 2016. Data were collected via survey methods; 55 transect sites were examined for the common species, and between 49 to 55 sites were examined depending on the year, for the less-common species.
Dataset for: Common carp (Cyprinus carpio) invasion alters greenhouse gas emissions in shallow lakes.
Climate change and invasive species are among the most important environmental problems of this century. Freshwaters are important regulators of the global carbon cycle and a key source of atmospheric greenhouse gases. However, freshwater environments may be particularly susceptible to species invasion and adverse effects, and the consequences of altered species assemblages on greenhouse gas emissions remain poorly understood. In this study, we analyzed the impact of one of the world's most damaging invasive species, the common carp, on freshwater greenhouse gas emissions. We show that lakes with invasive carp had lower methane emissions despite increased eutrophication, contradicting the well-established assumption that methane emissions from lakes increase with nutrient levels and productivity. This is likely due to substantial depletion of the benthic environment. As invasive species spread continues, new species assemblages may therefore disrupt ecosystem functioning and diverse global cycles in unexpected ways.
Dataset for: Invasion of Common Carp (Cyprinus carpio) Reduces the Quality of Bottom Sediments in Shallow Lakes
Species invasions are one of the main anthropogenic forces reshaping ecological structure and function in lakes during the 21st century. Common carp (Cyprinus carpio) are among the most globally widespread and damaging aquatic invasive species, with the capacity to significantly alter lake ecosystems. While it is well documented that carp feeding activity can disturb sediments, decrease water quality, and reduce macrophyte and fish diversity, less is known about how carp influence sediment chemistry and nutrient cycling. Here, we examined the effect of carp invasion on sediment phosphorus (P) dynamics and organic matter quality in shallow lakes. We compared P fractions in sediments of lakes with established carp populations and those from carp-free reference lakes. We found that lakes with carp had depleted surficial sediments, with significantly lower organic P (0.16 vs. 0.41 mg g-1) and higher C/P ratios (972 vs. 639) than lakes without carp. Carp lakes had higher concentrations of water-column total P (370 vs. 160 μg L-1), though a mass balance between sediment labile P and water-column P was similar for lakes with and without carp, indicating sediment P loss due to carp is largely kept in the water column. Sediments are a crucial component of lake ecosystems, and a reduction in sediment organic matter quality by invasive carp can alter food web dynamics and geochemical processes in invaded lakes.
Effects of experimental manipulation of light and nutrients on establishment of seedlings of native and invasive woody species in Long Island, NY, USA forests 2000 - 2003
While several studies on the process of invasion often focused on single factors or on the general explanation of ‘disturbance,' recent work has attempted to move towards a more mechanistic understanding of the factors that promote plant community invasion. Manipulative experiments provide a means for discerning causal relationships and interactive effects of environmental factors in promoting invasion. This dataset contains the results of multifactor manipulative experiments in forest communities, which compared factors influencing early seedling establishment for native and invasive woody plants. In an earlier study, we found that in Long Island, NY, invasion patterns are correlated with forest community type (pine barrens or hardwood), light availability, and soil N and Ca. Therefore, we conducted manipulative field experiments in two different years to determine the relative importance and interaction of experimental gaps and N and Ca addition in pine barrens and hardwood forests in promoting invasion. We used seedlings of seven common native and invasive species in the first experiment, and 16 native and invasive species paired phylogenetically in the second experiment. This was done in the years 2000 and 2003 respectively.
The influence of environmental factors on the distribution and density of invasive Centaurea stoebe across Northeastern USA, 2013 - 2018
Centaurea stoebe (Asteraceae; spotted knapweed) is an emerging invader in northeast US, and is a major invasive plant in the northern Midwest and western USA. Although it has been present in New York State (NYS) for over 100 years, its apparent recent population increases and spread provide a rare opportunity to study a plant in the early stages of invasion. Therefore, a study was carried out understand how distinct environmental factors influence the distribution, density and change in density C. stoebe at different spatial scales within its novel range in the northeastern USA. First, we collected field data on the occurrence, density and change in density of this species in North Eastern United States, from 2013 to 2014. Then, using species distribution models, we assessed the potential influence of environmental factors on the invasion of spotted knapweed in northeast US. Within different parts of C. stoebe‘s range, different factors explained its occurrence, density and change in density over 2 years. Across northeast US, climate and soil factors were the most influential predictors explaining C. stoebe‘s distribution, while within Long Island in southeastern NYS and the Adirondack Mountains in northern NYS, precipitation and disturbance respectively were the most important. These results are published in the paper titled The influence of environmental factors on the distribution and density of invasive Centaurea stoebe across Northeastern USA (Akin-Fajiye and Gurevitch, 2018).
Native seedlings recorded on field plots with and without invasive buffel grass during the monsoon season of 2013 near Tucson, Arizona, USA
Although buffel grass (Cenchrus ciliaris) invasions on several continents have significant ecological impacts, little information is available on its effect on seedling emergence and establishment of native vegetation. In highly impacted areas of the Sonoran Desert of North America, perennial plants are particularly vulnerable during their seedling stage. We studied the impact of buffel grass on the emergence and early survival of native seedlings in a field experiment. We marked out 2m x 2m field plots at three locations near Tucson, Arizona, with and without buffel grass. We removed the buffel grass from half of those with the invasion, and censused and marked native perennial seedlings that emerged in each plot for ten weeks during July-September (monsoon season) of 2013. Emergence and survival of native perennials in the field were both significantly higher where mature buffel grass was removed or had never invaded than where it remained. Our results highlight the need for more manipulative studies of density to improve mechanistic understanding of population dynamics, and to forecast how populations and communities will respond in the long term to perturbations such as invasion.
Emerging fungal pathogen of an invasive grass: Implications for competition with native plant species
This data package includes data and code from an experiment testing the effects of a leaf spot fungal infection and competition from the invasive (to the U.S.) grass Microstegium vimineum on the performance of three native grass species: Dichanthelium clandestinum, Elymus virginicus, and Eragrostis spectabilis. The experiment was performed between June and September of 2019 in a greenhouse on the University of Florida campus in Gainesville, FL, USA. The leaf spot infection is caused by the fungal pathogen Bipolaris gigantea, which has recently emerged on populations of M. vimineum in the U.S. We tested the hypothesis that infection of B. gigantea would both directly and indirectly affect the native grass species by measuring the change in biomass of each species with and without pathogen inoculation (direct effects) and by measuring the effect of pathogen inoculation on M. vimineum competition through changes in native grass biomass across a density gradient of M. vimneum (indirect effects). The code includes statistical analyses and figures. The code was run using R (version 4.0.1).
Final seedling counts for invasive plants seeded at CPCRW on a variety of substrate types.
This dataset contains final seedling counts for invasive plants seeded at the CPCRW research (seeding and measurements taken summer 2012) site on a variety of substrate types.
Dalton and Nenana study site data including: invasive plant density estimates, invasive plant density, soil data, seedling estimates for dominant tree species and ground cover estimates for sites
This dataset contains invasive plant and stand level data for study sites along the Dalton and Parks highways in interior Alaska in the summer of 2012. Study sites were situated in burned and mature black spruce forests to compare invasive plant colonization patterns. Invasive plant density estimates along the road adjacent to each site are included, as well as invasive plant density within study sites. Other data includes ground cover estimates for dominant ground cover types, estimates of seedling abundance for dominant tree species, soil paramters (mineral soil pH and mineral soil moisture, residual organic layer/ organic layer depths, and active layer depths).
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).
Throw trap and electrofishing data collected during 1996–2022 from the Everglades, Florida, United States for the publication "Contrasting invasion histories and effects of three non-native fishes observed with long-term monitoring data"
This dataset was used to analyze the effects of three non-native fishes in the Florida Everglades for a publication in the journal Biological Invasions. The dataset incorporates plot-level mean densities (# of individuals per square meter) of common aquatic animals collected during 1996–2022 from 17 sites across three regions of the Everglades: Taylor Slough, Shark River Slough, and Water Conservation Area 3A. Prey species included are nine common small fishes and three common decapod species (two crayfish species and grass shrimp). The dataset includes throw trap data on three predator taxa: African Jewelfish (Hemichromis letourneuxi), Mayan Cichlids (Mayaheros uruphthalmus), and sunfishes (Lepomis spp.). Annual indices of mean wet season electrofishing catch-per-unit-effort of Asian Swamp Eels (Monopterus albus/javanesis), Mayan Cichlids, sunfishes, and the three other large 'top predator' fishes (Amia calva, Lepisosteus platyrhincus, Micropterus salmoides) are included for plots where electrofishing was performed from 1997-2021. Hydrologic measures used in analyses and R code used to conduct analyses are also included.
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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.