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709 results for “Non-native”
Occurrence cubes for non-native taxa in Belgium and Europe
<p>This package contains aggregated occurrence data ("occurrence cubes") for non-native taxa in Belgium and Europe. These occurrence cubes were generated by grouping species occurrence data from the <a href="https://www.gbif.org/">Global Biodiversity Information Facility (GBIF)</a> by year (year), 1x1km spatial <a href="https://www.eea.europa.eu/en/datahub/datahubitem-view/3c362237-daa4-45e2-8c16-aaadfb1a003b">EEA reference grid</a> cell (eea_cell_code) and taxon (taxonKey or classKey). For each grouping, the number of occurrences found in GBIF (n) and the minimum <a href="http://rs.tdwg.org/dwc/terms/coordinateUncertaintyInMeters">coordinateUncertaintyInMeters</a> (min_coord_uncertainty) are provided. The provided coordinateUncertaintyInMeters of an occurrence is taken into account when assigning it to a grid cell (see <a href="https://github.com/trias-project/occ-cube-alien/blob/20201201/src/europe/2_assign_grid.Rmd#L463-L481">this code</a>). The occurrence cubes have been used as input data for indicators and risk modelling/mapping for the <a href="http://trias-project.be/">Tracking Invasive Alien Species (TrIAS)</a> project and are now used for monitoring the effectiveness of the early detection and rapid eradication of emerging Invasive Alien Species (IAS) for the <a href="https://www.riparias.be/">LIFE RIPARIAS</a> project.</p> <p>The occurrence cubes are built on open science principles and intended to be completely reproducible:</p> <ul> <li>The input data are publicly available on GBIF, with the download DOIs listed in the related identifiers of this package.</li> <li>The code to process the data to cubes is publicly available on GitHub at <a href="https://github.com/trias-project/occ-cube-alien">https://github.com/trias-project/occ-cube-alien</a> (version <a href="https://github.com/trias-project/occ-cube-alien/releases/tag/20240118">20240118</a>).</li> </ul> <h2>Files</h2> <ul> <li><strong>be_alientaxa_cube.csv</strong>: occurrence cube of alien taxa listed by the Global Register of Introduced and Invasive Species - Belgium (Desmet et al. 2019) (GRIIS) and limited to occurrences in Belgium (country=BE).</li> <li><strong>be_alientaxa_info.csv</strong>: taxonomic information for taxa in be_alientaxa_cube.csv.</li> <li><strong>be_classes_cube.csv</strong>: occurrence cube of all <a href="http://rs.tdwg.org/dwc/terms/class">classes</a> found in Belgium (country=BE), used to assess sampling effort bias in be_alientaxa_cube.csv.</li> <li><strong>eu_modellingtaxa_cube.csv</strong>: occurrence cube of <a href="https://github.com/trias-project/occ-cube-alien/blob/2ada0ded33c034946380b02a28cb9a8d2884d54a/references/modelling_species.tsv">selected modelling species</a> in Europe (bounding box).</li> <li><strong>eu_modellingtaxa_info.csv</strong>: taxonomic information for taxa in eu_modellingtaxa_cube.csv.</li> </ul> <h2>Acknowledgements</h2> <p>This work has been funded under the Belgian Science Policies Brain program (BelSPO BR/165/A1/TrIAS), the European Union's LIFE program (LIFE19 NAT/BE/000953 - LIFE RIPARIAS) and the European Union's Horizon Europe Research and Innovation Programme (ID No 101059592 - Biodiversity Building Blocks for Policy).</p>
International Non-native Insect Establishment Data
<p><span>These data list individual non-native insect species established in nine regions around the globe (New Zealand, South Korea, Japan, Okinawa, Ogasawara, Europe, Great Britain, North America (north of Mexico), Hawaii, Galapagos, Chile and South Africa). Taxonomy, attributes and occurrences for each taxa are included, as well as a source table which can be used to find more details on occurrences. </span></p> <p><span>This dataset was assembled from various sources by an interdisciplinary scientific working group funded by the National Socio-Environmental Synthesis Center. See the main source references in the References metadata section for this publication.</span></p> <p><span>Data have been cleaned of most typographic and taxonomic errors using the code in the R package insectcleanr: Initial release (DOI: 10.5281/zenodo.4555787), which is based on the Global Biodiversity Information Facility (GBIF) taxonomic backbone (GBIF Secretariat (2021). GBIF Backbone Taxonomy. Checklist dataset https://doi.org/10.15468/39omei accessed via GBIF.org on 2022-02-09, i.e. the </span><a href="https://doi.org/10.15468/43g7-9874"><span>https://doi.org/10.15468/43g7-9874</span></a><span> backbone).</span></p> <p><em>DISCLAIMER: This dataset is provisional. Although these data have been subjected to review and the dataset is substantially complete, the authors reserve the right to revise the data pursuant to further analysis and review. There may be remaining errors, and additions and removals of data in future updates may occur. Neither the University of Maryland, U.S. Government, Scion, nor any of their employees, contractors, or subcontractors, make any warranty, express or implied, nor assume any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, nor represent that its use would not infringe on privately owned rights.</em></p>
The Sierra Lakes Inventory Project: Non-Native fish and community composition of lakes and ponds in the Sierra Nevada, California
The Sierra Lakes Inventory Project (SLIP) was a research endeavor that ran from 1995-2002 and has supported research and management of Sierra Nevada aquatic ecosystems and their terrestrial interfaces. We described the physical characteristics of and surveyed aquatic communities for > 8,000 lentic water bodies in the southern Sierra Nevada, including lakes, ponds, marshes, and meadows. We also created digital map layers for these water bodies when such layers did not exist. The original objective of SLIP was to describe impacts of non-native fish on lake communities, but SLIP data has subsequently enabled study of additional ecological issues, including regional amphibian declines and their impacts on communities, and impacts of non-native fish on terrestrial species. In addition, these data are being used to develop fish removal efforts to restore aquatic ecosystems and recover endangered amphibians. The SLIP data is stored in a relational database that collectively describes water bodies (e.g., depth, elevation, location), surveys (conditions, effort), and communities (including approximately 170 fish, amphibian, reptile, benthic macroinvertebrate, and zooplankton taxa).
Bird Communities in Fragmented, Non-Native Pine Plantations in the Oak Openings Region of Northwest Ohio
Comprehensive surveys, while preferred, are not always feasible due to time, logistical, and funding constraints. However, limited surveys of focal taxa, such as birds, coupled with vegetation surveys, can provide critical information to guide land management. In the 1930s non-native conifers were planted in the Oak Openings Region of northwestern Ohio, a biodiversity hotspot. The stands are declining, and management is needed, but restoration to native habitat is time consuming and expensive. Our research utilized an avian perspective of ecological function of introduced pine plantations versus native remnants to guide management. We surveyed bird activity May through July 2020 with point-counts in nine sites (1.3-2.3 ha) with three each of white pine, red pine, and oak forest sites. At each site, we estimated bird richness, abundance, and diversity, as well as structural characteristics (e.g., canopy cover), composition (e.g., vegetation types), and landscape context (e.g., landcover). Superficially, the pine sites appear to be beneficial as pine habitat for breeding birds, with high Simpson’s indices (up to 0.89) and high species richness compared to oak sites. However, our results reveal that the pines are not truly functioning as pine habitat for birds based on the limited occurrence of pine specialist species, proportion of generalists to pine specialists, and landscape context. Simple measures of diversity with no consideration as to species identity and without the environmental context fail to provide reliable measures of ecological value. Instead, we recommend selective sampling and consideration of landscape context, vegetation structure, and species classification to guide management.
Dataset of Proportion of non-native plants in urban parks correlates with climate, socioeconomic factors and plant traits
<p>Full datasets for the research entitled 'Proportion of non-native plants in urban parks correlates with climate, socioeconomic factors and plant traits'.</p>
Harnessing the power of digitized natural history collections to visualize spatiotemporal patterns in native and non-native bee flight phenology
<p>What time of year are bees flying, where are they flying, and how do biogeographical factors, sex, and native status affect flight phenology? Consistent monitoring along with creating spatially and temporally explicit visualizations using large openly available data sets enhance our understanding of trends in flight time phenology and shape our understanding of bee-plant interactions, including shifts in the phenology of bee pollinators.</p> <p>Species occurrence data from digitized collection networks (iNaturalist, Global Biodiversity Information Faculty (GBIF), Integrated Digitized Biocollections (iDigBio), Symbiota Collections of Arthropods Network (SCAN), and UC Santa Barbara Collection Network) are part of an effort to improve our understanding of bees in coastal Santa Barbara County, including the California Channel Islands. New inventory collections combined with historical data from over 11 natural history museums and 2 observation networks are used in an effort to examine patterns and changes in phenology of native and non-native bee species, and create updated species inventories.</p> <p>Synthesizing species observation data from digitized natural history collections makes use of a wealth of existing data and multiplies the analytical power of isolated observations, but it is not without limitations and challenges. By exploring novel techniques to generate clear and accurate visualizations to communicate bee flight time, we present our key initial findings and identify geographic, temporal, and taxonomic gaps, which will lead to further focused inventory projects of coastal Santa Barbara County, improved data quality for phenological analyses, and reusable methods for visualizing insect phenology data across taxa or geography.</p> <p><strong>The attached files include the R code and some of the .csv files used to produce the figures in my poster that was available on demand at the Entomology Society of America 2020 virtual meeting. </strong></p>
How do native and non-native speakers recognize emotions in the instructor's voice in educational videos? Exploring the first step of the cognitive-affective model of e-learning for international learners [dataset]
<p>Dataset for the journal article <em>How do native and non-native speakers recognize emotions in the instructor’s voice in educational videos? Exploring the first step of the cognitive-affective model of e-learning for international learners.</em></p>
Data for Stabilization of non-native folds and programmable protein gelation in compositionally designed deep eutectic solvents
<div> <p>Full set of data related to the publication "Stabilization of non-native folds and programmable protein gelation in compositionally designed deep eutectic solvents", published in ACS Nano with DOI:<a title="https://doi.org/10.1021/acsnano.4c01950" href="https://doi.org/10.1021/acsnano.4c01950">10.1021/acsnano.4c01950</a></p> <p> Full details on data treatment and logging are included in the file "DataLogging.pdf". All data use ASCII encoding in delimited .txt files.</p> <p> </p> </div>
Data from "Resource pulses drive spatio-temporal dynamics of non-native bark beetles and wood borers"
<p>This is a compilation of datasets that were used for the publication entitled "Resource pulses drive spatio-temporal dynamics of non-native bark beetles and wood borers" by Eckehard G. BROCKERHOFF, Stephanie L. SOPOW, and Martin K.-F. BADER, published in the Journal of Applied Ecology, 'in press' in October 2024.</p> <p>Note: The date format is either (i) season (spring/summer/autumn/winter) plus a two-figure short form for the year (e.g., "autumn08" stands for autumn 2008), or (ii) just the year for an annual total in either four- or two-figure form in the file name (e.g., "reg2010sums.csv" or "reg10sums.csv" for the year 2010).</p> <p>1. File "mean_trap_catches.csv" = Data used for Fig. 1 - Mean trap catch data of Hylastes ater, Hylurgus ligniperda and Arhopalus ferus over time in Kaingaroa forest stands 378 ("F2006"), 377 ("F2009"), and 383 ("F2010"). For further explanations see methods of Brockerhoff et al. (2024).</p> <p>2. File "reg2010sums.csv" = Data used for Fig. 2 - Year 2010, annual trap catches of Hylastes ater, Hylurgus ligniperda and Arhopalus ferus indicating approximate dispersal distances between Pinus radiata stands. For details see caption of Fig. 2 in Brockerhoff et al. (2024).</p> <p>3. File "reg2010sums.csv" = Data used for Fig. 2 - Year 2011, annual trap catches of Hylastes ater, Hylurgus ligniperda and Arhopalus ferus indicating approximate dispersal distances between Pinus radiata stands. For details see caption of Fig. 2 in Brockerhoff et al. (2024).</p> <p>4. File "reg2010sums.csv" = Data used for Fig. 2 - Year 2012, annual trap catches of Hylastes ater, Hylurgus ligniperda and Arhopalus ferus indicating approximate dispersal distances between Pinus radiata stands. For details see caption of Fig. 2 in Brockerhoff et al. (2024).</p> <p>5. File "reg10sums.csv" = Data used for Fig. 3 - Year 2010, annual trap catches of Hylastes ater, Hylurgus ligniperda and Arhopalus ferus indicating approximate dispersal distances between Pinus radiata stands. For details see caption of Fig. 3 in Brockerhoff et al. (2024).</p> <p>6. File "reg11sums.csv" = Data used for Fig. 3 - Year 2011, annual trap catches of Hylastes ater, Hylurgus ligniperda and Arhopalus ferus indicating approximate dispersal distances between Pinus radiata stands. For details see caption of Fig. 3 in Brockerhoff et al. (2024).</p> <p>7. File "reg12sums.csv" = Data used for Fig. 3 - Year 2012, annual trap catches of Hylastes ater, Hylurgus ligniperda and Arhopalus ferus indicating approximate dispersal distances between Pinus radiata stands. For details see caption of Fig. 3 in Brockerhoff et al. (2024).</p> <p>8. File "hylu2010-fitted_dispersal_to_5km-Version_23May2024.csv" = Data shown in Fig. 4 - Extension of the prediction range to 5 km of Hylurgus ligniperda dispersal data, using a generalised additive mixed model (GAMM) with beta distributed errors and the default logarithmic link. For details see caption of Fig. 4 and methods in Brockerhoff et al. (2024).</p> <p> </p>
Data from: Efficacy of labile carbon addition to reduce fast-growing, invasive non-native plants: A review and meta-analysis
<p>Data and analysis in R for the publication "Efficacy of labile carbon addition to reduce fast-growing, invasive non-native plants: A review and meta-analysis" by Ossanna & Gornish (2023), <em>Journal of Applied Ecology</em>, <em>60</em>(2), 218-228. <a href="http://doi.org/10.1111/1365-2664.14324">https://doi.org/10.1111/1365-2664.14324</a>.</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).
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.
Dataset of Invasion risks and social interest of non-native woody plants in urban parks of Spain
<p>Full datasets for the research entitled "Invasion risks and social interest of non-native woody plants in urban parks of Spain"</p>
F I G U R E 1 in Evidence of successful recruitment of non-native pink salmon Oncorhynchus gorbuscha in Iceland
F I G U R E 1 Distribution of pink salmon Oncorhynchus gorbuscha in Iceland. (a) Location of rivers in Iceland with reported catches of adult O. gorbuscha in 2000, 2005, and annually from 2015 according to Bárðarson et al. (2022), and (b) locations of fishing surveys in 2022 to catch smolts of O. gorbuscha in three rivers of southwest Iceland
Fig. 3 in Effects Of Leaf-Litter Addition On Carabid Beetles In A Non-Native Norway Spruce Plantation
Fig. 3. Seasonal dynamics of the average number of individuals per trap for the two species (± S. E.)
Fig. 1 in Effects Of Leaf-Litter Addition On Carabid Beetles In A Non-Native Norway Spruce Plantation
Fig. 1. Ordination (NMDS) of the pitfall catches based on the Bray-Curtis similarity index. ¡: Traps of the control plots and l: Traps of the leaf-litter plots
Fig. 4 in Growing, losing or introducing? Cage aquaculture as a vector for the introduction of non-native fish in Furnas Reservoir, Minas Gerais, Brazil
Fig. 4. Main events along the production system (i.e. juvenile stocking, length classification and fish capture) and the moments in which escapes occur (solid arrows: AC = accidental; IN = intentional). S = small-sized fish; M = mediumsized; L = large-sized.
Fig. 2 in Growing, losing or introducing? Cage aquaculture as a vector for the introduction of non-native fish in Furnas Reservoir, Minas Gerais, Brazil
Fig. 2. Frequency of fish farmers (%) operating different number of cages in Furnas Reservoir (n = 19).
Fig. 3 in Growing, losing or introducing? Cage aquaculture as a vector for the introduction of non-native fish in Furnas Reservoir, Minas Gerais, Brazil
Fig. 3. Frequency of fish farmers (%) reporting the occurrence of fish escapes during different events of the production chain (n = 19). Accidental: length classification (LC); fish removal (FR); juvenile stocking (JS); cage damage (CD). Deliberate: intentional releases (IR).
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
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DANDI Archive for NWB datasets
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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.