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709 results for “Non-native”
Fig. 1 in Growing, losing or introducing? Cage aquaculture as a vector for the introduction of non-native fish in Furnas Reservoir, Minas Gerais, Brazil
Fig. 1. Furnas Reservoir, Minas Gerais, Brazil. The circle indicates the study area (Carmo do Rio Claro town).
Figs 1–5 in A New Finding Of The Non-Native Copepod Sinodiaptomus Sarsi (Copepoda, Calanoida, Diaptomidae) In Ukraine
Figs 1–5. Sinodiaptomus sarsi: 1 — habitus of female (A) and male (B); 2 — last prosomal somite and urosome of female; 3 — leg 5, female; 4 — right antennule, male; 5 — leg 5, male.
Moths and butterflies on alien shores – global biogeography of non-native Lepidoptera
<p class="MsoNormal"><span>Lepidoptera is a highly diverse, predominantly herbivorous insect order, with species transported to outside their native range largely facilitated by the global trade of plants and plant-based goods. Analogous to island disharmony, we examine invasion disharmony, where species filtering during invasions increases systematic compositional differences between native and non-native species assemblages, and test whether some families are more successful at establishing in non-native regions than others. We compared numbers of non-native, unintentionally introduced Lepidoptera species with the land area of 11 regions worldwide (Hawaii, North America, Galapagos, Europe, South Africa, South Korea, Japan, Nansei Islands, Ogasawara Islands, Australia, New Zealand). Differences among native and non-native assemblages in the distribution of species among families were investigated using ordination analysis. We tested whether invasion disharmony is explained by propagule pressure (proxied by species richness in border interceptions) and if families were associated with specific trade commodities. In total, 741 non-native Lepidoptera species, accounting for 0.47% of the global diversity of lepidopterans, are established in at least one of the 11 regions. Crambidae, Pyralidae, Tineidae and Gracillariidae were particularly successful invaders, whereas the two most species-rich families, Erebidae and Geometridae, were under-represented among non-native Lepidoptera. Much of the variation in species numbers in the native, and less so in the non-native assemblages could be attributed to land area. Although native assemblages were similar among nearby regions, non-native assemblages were not, suggesting geography had little effect on invasion disharmony. Comparison of established with intercepted species revealed that macromoth families were generally under-represented in establishments, whereas several micromoth families were under-represented in interceptions. This discrepancy may relate to greater detectability of larger species or high propagule pressure via associations with specific invasion pathways. Invasion disharmony in Lepidoptera appears to be driven by processes unrelated to the success of native assemblages. While native assemblages developed through long-term evolutionary radiation, the composition of non-native assemblages is driven by differential invasion pathways and traits affecting the establishment of founder populations that vary among families.</span></p>
Data from: Non-native grazers affect physiological and demographic responses of Greater Sage-grouse
<p>1. Non-native ungulate grazing has negatively impacted native species across the globe, leading to massive loss of biodiversity and ecosystem services. Despite their pervasiveness, interactions between non-native grazers and native species are not fully understood. We often observe declines in demography or survival of these native species, but lack understanding about the mechanisms underlying these declines. Physiological stress represents one mechanism of (mal)adaptation but data are sparse.</p> <p>2. We investigated glucocorticoid levels in a native avian herbivore exposed to different intensities of non-native grazing in the cold desert Great Basin ecosystem, USA. We measured corticosterone, a glucocorticoid in feathers for a large sample (n = 280) of female Greater Sage-grouse (Centrocercus urophasianus) from three study areas in Northern Nevada and Southern Oregon with different grazing regimes of livestock and feral horses.</p> <p>3. We found greater feral horse density was associated with higher corticosterone levels, and this effect was exacerbated by drought conditions. Livestock grazing produced similar results; however there was more model uncertainty about the livestock effect. Subsequent nesting success was lower with increased feather corticosterone, but corticosterone levels were not predictive of other vital rates.</p> <p>4. Our results indicate a physiological response by sage-grouse to grazing pressure from non-native grazers. We found substantial among-individual variation in the strength of the response. These adverse effects were intensified during unfavorable weather events, highlighting the need to reevaluate management strategies in the face of climate change.</p>
Data from: Historic deforestation and non-native plant invasions determine vegetation trajectories across an oceanic archipelago
<p>This archive contains data produced in a study of the vegetation trajectories of Ogasawara Islands in 77 years related to following article:</p> <p>Ohashi, H., Kato, H., Murao, M., Kato, H., Kawakami, K., Kurokawa, H., Oguro, M., Kimura, F., Niiyama, K., Matsui, T., and Shibata, M. (2024) Historic deforestation and non-native plant invasions determine vegetation trajectories across an oceanic archipelago. <em>Applied Vegetation Science</em>, 27 (1), e12767. <a href="https://doi.org/10.1111/avsc.12767">https://doi.org/10.1111/avsc.12767</a></p> <p> </p> <p><strong>Archive contents</strong><br>The archive contents are organized into five parts, each stored as a .zip compressed file.</p> <p><strong>X1_tif_original_vegmap_scan_georeference</strong></p> <p>Scanned and georeferenced original vegetation maps in GeoTiff format, which was drawn in 1935, scanned at 300 dpi. Coordinate reference system was set at WGS84 (ESPG: 4326).</p> <p>This directory includes:</p> <p><em>kitanoshima_isl_WGS84.tif<br>mukojima_isl_WGS84.tif<br>yomejima_isl_WGS84.tif<br>ototojima_isl_WGS84.tif<br>anijima_isl_WGS84.tif<br>nishijima_isl_WGS84.tif<br>chichijima_isl_WGS84.tif<br>hahajima_isl_WGS84.tif<br>mukohjima_isl_WGS84.tif<br>kitaiwoto_isl_WGS84.tif<br>iwoto_isl_WGS84.tif</em></p> <p> </p> <p><strong>X2_shp_vegmap</strong></p> <p>Shapefile of the geospatial polygon data of vegetation map of Ogasawara Islands surveyed in 1935, and stored as a .zip compressed file. Coordinate reference system was set at WGS84 (ESPG: 4326).</p> <p>This directory includes:</p> <p><em>VegetationMap_OgasaawraIsl_1935_en_UTF8_v0.dbf<br>VegetationMap_OgasaawraIsl_1935_en_UTF8_v0.prj<br>VegetationMap_OgasaawraIsl_1935_en_UTF8_v0.shp<br>VegetationMap_OgasaawraIsl_1935_en_UTF8_v0.shx<br>attribute_ForSect_code_en.csv<br>attribute_Veg_name_en.csv<br>metadata_vegmap_shp_ogasawara1935_en.csv</em></p> <p>Following files includes Japanese character (which may corrupt in non-Japanese environment):</p> <p><em>attribute_ForSect_jp.csv<br>attribute_Veg_name_jp.csv<br>metadata_vegmap_shp_ogasawara1935_jp.csv</em></p> <p> </p> <p><strong>X3_tif_vegmap_converted_from_shp</strong></p> <p>Rasterized data of polygon data of vegetation map for analysis. Coordinate reference system was set at JGD2000 / Japan Plane Rectangular CS XIV (EPSG: 2456)</p> <p>This directory includes:</p> <p><em>vegmap_1935.zip (compressed “vegmap_1935.tif (0.7GB)”)<br>vegnap_1979.zip (compressed “vegmap_1979.tif (1.5GB)”)<br>vegmap_2011.zip (compressed “vegmap_2011.tif (1.5GB)”)<br>islcode_raster.zip (compressed “vegmap_2011.tif (1.5GB)”)<br>attribute_integratedveg_ecoltype.csv<br>attribute_vegid_1935.csv<br>attribute_vegid_1979.csv<br>attribute_vegid_2011.csv</em></p> <p> </p> <p><strong>X4_scanned_image_vegdata</strong></p> <p>Scanned images of original vegetation data in 1935.</p> <p>The directory includes:<br><em>vegetation_survey_sheet_1.pdf<br>vegetation_survey_sheet_2.pdf</em><br><em>vegetation_survey_sheet_3.pdf</em></p> <p> </p> <p><strong>X5_digitized_vegdata</strong></p> <p>Digitized vegetation data.</p> <p>The directory includes:<br><em>plot_species_abundance_matrix_v0.csv<br>plotinfo_v0.csv<br>attribute_Species_en_v0.csv</em></p> <p>Following file includes Japanese character (which may corrupt in non-Japanese environment)<br><em>attribute_Species_jp_v0.csv</em><br> </p> <p><strong>X6_code_for_analysis</strong></p> <p>Tentative.</p> <p> </p> <p>このアーカイブには、小笠原諸島の77年間の植生の変遷(1935年、1979年、2012年)に関するデータが含まれています。</p> <p> </p>
Data from: Grazing by non-native ungulates negatively impacts vegetation important to a native species of concern
<p>Non-native grazers compete with native species across the globe. In the northwestern Great Basin of the western United States competition among livestock, feral horses, and Greater Sage-grouse has been the subject of numerous legal actions and management policies, yet spatially explicit temporal data documenting the details of this competition are lacking. We present a novel approach to studying the composition of the herbaceous understory across three study areas within the Great Basin with different historic and contemporary grazing regimes. We surveyed the landscape using distance sampling for livestock and horse feces as an index of use. In addition, we surveyed the herbaceous understory of random sites as well as sites chosen by female Greater Sage-grouse to nest and brood their chicks. We used a novel Bayesian hierarchical modeling framework to link vegetation metrics with the spatial-temporal distribution of horses and livestock while accounting for observation error. When livestock and feral horses were not present, we found that Greater Sage-grouse chose sites with higher percentages of perennial grasses and forbs to build their nests and brood their chicks compared to what was available to them. As livestock increased, we found evidence for decreases in the percentage of perennial grasses, forbs, cheatgrass (<em>Bromus tectorum</em>), and increases in the amount of bare ground. These effects were consistent at available sites and brood sites, however, we found less evidence for an impact of livestock at nest sites. As feral horses increased, we observed similar results at available sites, but at sites chosen by females to nest and brood their chicks, we observed increases in the amount of invasive cheatgrass as feral horses increased, which could reflect attempts by Greater Sage-grouse to compensate for reductions in protective cover. We present a noninvasive approach to assess space use that can be applied to other species. More importantly, we document that grazing by non-native ungulates impacts components of the plant community important to Greater Sage-grouse reproduction. We provide spatial-temporal maps of livestock and feral horse use to aid managers attempting to balance the needs of livestock producers, feral horses, Greater Sage-grouse, and ecosystem function.</p>
Supplementary material from: Aslan CE, Sikes BA, Gedan KB (2015) Research on mutualisms between native and non-native partners can contribute critical ecological insights. NeoBiota 26: 39-54. https://doi.org/10.3897/neobiota.26.8837
Supplementary material from: Aslan CE, Sikes BA, Gedan KB (2015) Research on mutualisms between native and non-native partners can contribute critical ecological insights. NeoBiota 26: 39-54. https://doi.org/10.3897/neobiota.26.8837
Supplementary material 1 from: Hirsch H, Wypior C, von Wehrden H, Wesche K, Renison D, Hensen I (2012) Germination performance of native and non-native Ulmus pumila populations. NeoBiota 15: 53-68. https://doi.org/10.3897/neobiota.15.4057
Location and climate information of the sampled Ulmus pumila populations in China and the U.S. Maximum (max.) temperatures for the months May, June and July are provided to show the temperature range during the main germination period (lowest and highest values are italicized). Climatic information was extracted from the WORLDCLIM database (Hijmans et al. 2005).
Supplementary material 2 from: Hirsch H, Wypior C, von Wehrden H, Wesche K, Renison D, Hensen I (2012) Germination performance of native and non-native Ulmus pumila populations. NeoBiota 15: 53-68. https://doi.org/10.3897/neobiota.15.4057
Comparison of climatic conditions (a: mean annual temperature; b: annual precipitation) between the Chinese and North American locations of Ulmus pumila. Wilcoxon rank sum tests were used to test for differences between both ranges. Mean annual temperatures are significantly higher for locations from the U.S. (W = 7, p < 0.05). Annual precipitation is marginal higher in the invasive populations compared to the native populations (W = 9, p = 0.05). Significant differences are symbolized by different lowercases above the boxes.
Native plant diversity creates microbial legacies that either promote or suppress non-natives, depending on drought history
<p>High-diverse native plant communities resist non-native plants more strongly than low-diverse communities, in part through resource competition. Yet, the role of soil biota is largely unknown, although non-native plants interact with soil biota. Here, we tested the responses of non-native plants to soil conditioned by different native plant diversities. We applied well-watered and dry treatments in the conditioning and response phases to explore the effects of historical and contemporary environmental stresses. Historical water conditions determined the effects of native diversity via soil biota on responding non-natives grown in well-watered environments. Non-native growth decreased with native species richness for well-watered soil inocula but increased for dry soil inocula. However, non-native growth in dry environments did not depend on conditioning native species richness of soil inocula. We provide a new understanding of mechanisms behind diversity-invasibility relationships and demonstrate that temporal variation in environmental stress shapes relationships among native plant diversity, soil biota, and non-native plants.</p>
Figure 1 in Non-native Chelonians in the National Zoological Collections of Zoological Survey of India
Figure 1. Representative of exotic Chelonians (the common snapping turtle, Chelydra serpentina) preserved in Zoological Survey of India, Kolkata, species tag and morphometric measurements. HL = Head Length, SCL = Straight Carapace Length, SCW = Straight Carapace Width, CCL= Curved Carapace Length, CCW- Curved Carapace Width, SPL = Straight Plastron Length, SPW= Straight Plastron Width, BD = Body Depth.
Figure 2 in Risk screening of non-native freshwater fishes in Yunnan Province, China
Figure 2. Risk screening scores for the non-native fish species screened with the AS-ISK: (A) basic risk assessment (BRA) for Yunnan Province; (B) BRA plus climate-change assessment (BRA+CCA) for Yunnan Province; dashed lines indicate thresholds for different intrusion risk levels (see thresholds in Table 2)
Figure 1 in Developing biosecurity plans for non-native species in marine dependent areas: the role of legislation, risk management and stakeholder engagement
Figure 1. Five-stage approach for risk assessment management of NNS in Shetland, adapted from the ecosystem-based risk management framework (Cormier et al. 2013).
Figure 1 in The value of regular monitoring and diverse sampling techniques to assess aquatic non-native species: a case study from Orkney
Figure 1. Locations of the monitoring sites. A: North of Orkney Mainland and northern isles, B: Scapa Flow and southern isles. For corresponding site names refer to Supplementary material Table S1.
Figure 3 in The value of regular monitoring and diverse sampling techniques to assess aquatic non-native species: a case study from Orkney
Figure 3. The total number of sites for which each non-native or cryptogenic species has been recorded for each sampling method for 2016 and 2017. NB: Only species recorded in these two years are reported in this figure. Abbreviations: Cm: Caprella mutica, As: Ascidiella scabra, Bh: Bonnemaisonia hamifera, Sj: Schizoporella japonica, Ce: Corella eumyota, Aa: Ascidiella aspersa, Cp: Colpomenia peregrina, Cf: Codium fragile ssp. fragile, Ti: Tricellaria inopinata, Ah: Asterocarpa humilis, Bs: Botryllus schlosseri, Bl: Botrylloides leachii, Mh: Melanothamnus harveyi, Bf: Bugulina fulva, Dj: Dasysiphonia japonica, Jm: Jassa marmorata, Mi: Monocorophium insidiosum, Ma: Monocorophium acherusicum, Cs: Ctenodrilus serratus, Tj: Telmatogeton japonicus, Pa: Potamopyrgus antipodarum, Cb: Crassicorophium bonellii, Dl: Diplosoma listerianum.
Figure 2 in The value of regular monitoring and diverse sampling techniques to assess aquatic non-native species: a case study from Orkney
Figure 2. The total number of non-native and cryptogenic species recorded at each monitoring location (2012–2017).
Figure 1 in Expert bioblitzes facilitate non-native fish tracking and interagency partnerships
Figure 1. Sampling locations for ten Fish Slam events (2012–2019). Counties shaded in green were sampled from 2012–2019; blue in 2017; purple and orange in 2019. Sampling in counties shaded in yellow is being planned for 2020.
Fig. 2 in Trichodinid Ectoparasites (Ciliophora: Peritrichia) of Non-native Pumpkinseed (Lepomis gibbosus) in Europe
Fig. 2. Trichodina cf. heterodentata Dunkan, 1977. A – silver impregnated photomicrograph; B – dentical diagram. Scale: 20 μm.
Fig 1. Trichodina acuta Lom, 1961. A in Trichodinid Ectoparasites (Ciliophora: Peritrichia) of Non-native Pumpkinseed (Lepomis gibbosus) in Europe
Fig 1. Trichodina acuta Lom, 1961. A – silver impregnated photomicrograph; B – dentical diagram. Scale: 20 μm.
Fig. 3 in A newly established non-native praying mantis species, Liturgusa maya (Mantodea: Liturgusidae) in Florida, USA, and a key to Florida mantis genera
Fig. 3. Praying mantis genera in Florida: (A) Mantoida maya female (photograph by Cheryl Harleston (www.inaturalist.org, CC BY-NC-SA); (B) Brunneria borealis female (photograph by Gary L. Dearman); (C) Oligonicella scudderi female (photograph by Jennifer Thompson); (D) Thesprotia graminis female (photograph by Sturgis McKeever, Georgia Southern University (www.Bugwood.org, CC-BY-NC); (E) Gonatista grisea female (photograph by Scott D. Nelson); (F) Liturgusa maya female (photograph by Brian Fridie Jr.); (G) Stagmomantis carolina female (photograph by Wendy Garfinkel-Gold); (H) Stagmomantis floridensis female (photograph by Andrew Nisip).
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.