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1,074 results for “invasive species”
Figure 2 from: Marescaux J, Van Doninck K (2013) Using DNA barcoding to differentiate invasive Dreissena species (Mollusca, Bivalvia). ZooKeys 365: 235-244. https://doi.org/10.3897/zookeys.365.5905
Figure 2 - Haplotype networks based on a fragment of 654 base pairs of the COI gene. Our seven haplotypes are labelled: Q1 and Q2 for haplotypes 1 and 2 (belonging to Dreissena rostriformis bugensis) / Z1 to Z5 for the 5 other haplotypes (belonging to Dreissena polymorpha).
Figure 1 from: Marhold K, Šlenker M, Kudoh H, Zozomová-Lihová J (2016) Cardamine occulta, the correct species name for invasive Asian plants previously classified as C. flexuosa, and its occurrence in Europe. PhytoKeys 62: 57-72. https://doi.org/10.3897/phytokeys.62.7865
Figure 1 - Localities of the first occurrences of Cardamine occulta Hornem. for European countries and their administrative divisions. The year of the first occurrence at each locality is given. The inset shows Tenerife and Gran Canaria of the Canary Islands.
Supplementary material 1 from: Watermann LY, Rotert J, Erfmeier A (2022) Coming home: Back-introduced invasive genotypes might pose an underestimated risk in the species´ native range. NeoBiota 78: 159-183. https://doi.org/10.3897/neobiota.78.91394
Supplementary information
Supplementary material 1 from: Balzani P, Cuthbert RN, Briski E, Galil B, Castellanos-Galindo GA, Kouba A, Kourantidou M, Leung B, Soto I, Haubrock PJ (2022) Knowledge needs in economic costs of invasive species facilitated by canalisation. NeoBiota 78: 207-223. https://doi.org/10.3897/neobiota.78.95050
Knowledge needs in economic costs of invasive species facilitated by canalization
Supplementary material 4 from: Petrosyan V, Osipov F, Feniova I, Dergunova N, Warshavsky A, Khlyap L, Dzialowski A (2023) The TOP-100 most dangerous invasive alien species in Northern Eurasia: invasion trends and species distribution modelling. NeoBiota 82: 23-56. https://doi.org/10.3897/neobiota.82.96282
Geographic partitioning of the SOR
Supplementary material 1 from: Petrosyan V, Osipov F, Feniova I, Dergunova N, Warshavsky A, Khlyap L, Dzialowski A (2023) The TOP-100 most dangerous invasive alien species in Northern Eurasia: invasion trends and species distribution modelling. NeoBiota 82: 23-56. https://doi.org/10.3897/neobiota.82.96282
Criterion selection of the TOP-100 IAS
Supplementary material 7 from: Petrosyan V, Osipov F, Feniova I, Dergunova N, Warshavsky A, Khlyap L, Dzialowski A (2023) The TOP-100 most dangerous invasive alien species in Northern Eurasia: invasion trends and species distribution modelling. NeoBiota 82: 23-56. https://doi.org/10.3897/neobiota.82.96282
The short description of invasive range of IAS in Russia
Supplementary material 3 from: Petrosyan V, Osipov F, Feniova I, Dergunova N, Warshavsky A, Khlyap L, Dzialowski A (2023) The TOP-100 most dangerous invasive alien species in Northern Eurasia: invasion trends and species distribution modelling. NeoBiota 82: 23-56. https://doi.org/10.3897/neobiota.82.96282
Species native range, introduction year, occurrence records
Supplementary material 2 from: Petrosyan V, Osipov F, Feniova I, Dergunova N, Warshavsky A, Khlyap L, Dzialowski A (2023) The TOP-100 most dangerous invasive alien species in Northern Eurasia: invasion trends and species distribution modelling. NeoBiota 82: 23-56. https://doi.org/10.3897/neobiota.82.96282
General description and conceptual structure of the database (FDB)
Supplementary material 8 from: Petrosyan V, Osipov F, Feniova I, Dergunova N, Warshavsky A, Khlyap L, Dzialowski A (2023) The TOP-100 most dangerous invasive alien species in Northern Eurasia: invasion trends and species distribution modelling. NeoBiota 82: 23-56. https://doi.org/10.3897/neobiota.82.96282
Species richness of IAS in Northern Eurasia
Supplementary material 5 from: Petrosyan V, Osipov F, Feniova I, Dergunova N, Warshavsky A, Khlyap L, Dzialowski A (2023) The TOP-100 most dangerous invasive alien species in Northern Eurasia: invasion trends and species distribution modelling. NeoBiota 82: 23-56. https://doi.org/10.3897/neobiota.82.96282
Moran's I indexes of residual spatial autocorrelation for MaxEnt models
Supplementary material 6 from: Petrosyan V, Osipov F, Feniova I, Dergunova N, Warshavsky A, Khlyap L, Dzialowski A (2023) The TOP-100 most dangerous invasive alien species in Northern Eurasia: invasion trends and species distribution modelling. NeoBiota 82: 23-56. https://doi.org/10.3897/neobiota.82.96282
Moran's I correlograms of residual spatial autocorrelation for MaxEnt models
Supplementary material 4 from: Molofsky J, Thom D, Keller SR, Milbrath LR (2023) Closely related invasive species may be controlled by the same demographic life stages. NeoBiota 82: 189-207. https://doi.org/10.3897/neobiota.82.95127
Elasticities by species by plot by site by year
Supplementary material 3 from: Molofsky J, Thom D, Keller SR, Milbrath LR (2023) Closely related invasive species may be controlled by the same demographic life stages. NeoBiota 82: 189-207. https://doi.org/10.3897/neobiota.82.95127
Vital rates for each knapweed species by each site and by each year
Supplementary material 2 from: Molofsky J, Thom D, Keller SR, Milbrath LR (2023) Closely related invasive species may be controlled by the same demographic life stages. NeoBiota 82: 189-207. https://doi.org/10.3897/neobiota.82.95127
Lower-level vital rates for the knapweed matrix population model
Supplementary material 1 from: Molofsky J, Thom D, Keller SR, Milbrath LR (2023) Closely related invasive species may be controlled by the same demographic life stages. NeoBiota 82: 189-207. https://doi.org/10.3897/neobiota.82.95127
Knapweed locations in New York State. FLNF = Finger Lakes National Forest
Datasets and R source code of manuscript "Adding insult to injury: anthropogenic noise intensifies predation risk by an invasive freshwater fish species" by Fernandez Declerck et al.
<p>Datasets and R source code of manuscript "Adding insult to injury: anthropogenic noise intensifies predation risk by an invasive freshwater fish species" by Fernandez Declerck et al. (submitted)</p> <p> Project<br> ├── README<br> ├── data_functional_response.txt<br> ├── data_prey_behaviour.txt<br> ├── R_script.R<br> └── BINV-D-22-00447_Playback.wav</p> <p>Main dataset: 'data_functional_response.txt'. Dataset for the functional response of the predator. Fish behaviour was recorded under two noise conditions (either boat noise or ambient noise). The dataset corresponds to data frame "d" in the script. Variables description:<br> - id: identity of the fish<br> - condition: noise condition, either "boat noise" or "ambient noise"<br> - prey_number: number of chironomid larvae introduced in the tank<br> - prey_captured : number of chironomid larvae consumed<br> - fish_mass : fish body mass (g)<br> - swim_distance: swim distance (m)</p> <p>Secondary dataset: 'data_prey_behaviour.txt'. Dataset for control experiment on prey behaviour. Prey behaviour was recorded under two noise conditions (either boat noise or ambient noise). The dataset corresponds to data frame "f" in the script. We used 20 replicates with 10 replicates for ambient noise condition, and 10 replicates for boat noise condition. Two focal prey larva were observed per replicates. Each prey larva was observed during two time periods (corresponding to two noise sequences). Variables description:<br> - condition: noise condition, either "boat noise" or "ambient noise"<br> - replicate: number of the replicate<br> - unique_id: identity of each focal larva<br> - noise_sequence: number of the noise sequence (either second or third)<br> - prop_inactive: proportion of time spent inactive by the focal larva<br> - prop_active: proportion of time spent active by the focal larva</p> <p>R source code 'code R_script.R'. Complete analysis as one single R script. See comments for additional information.</p> <p>The last file `BINV-D-22-00447_Playback.wav` is an audio file (Waveform Audio File Format). It is the soundtrack used in the playback experiments.</p>
Supplementary material 1 from: Cocos D, Klapwijk MJ, Schroeder M (2023) Tree species preference and impact on native species community by the bark beetle Ips amitinus in a recently invaded region. In: Jactel H, Orazio C, Robinet C, Douma JC, Santini A, Battisti A, Branco M, Seehausen L, Kenis M (Eds) Conceptual and technical innovations to better manage invasions of alien pests and pathogens in forests. NeoBiota 84: 349-367. https://doi.org/10.3897/neobiota.84.86586
Ips amitinus description, table S1
Supplementary material 1 from: Roques A, Ren L, Rassati D, Shi J, Akulov E, Audsley N, Auger-Rozenberg M-A, Avtzis D, Battisti A, Bellanger R, Bernard A, Bernadinelli I, Branco M, Cavaletto G, Cocquempot C, Contarini M, Courtial B, Courtin C, Denux O, Dvořák M, Fan J-t, Feddern N, Francese J, Franzen EKL, Garcia A, Georgiev G, Georgieva M, Giarruzzo F, Gossner M, Gross L, Guarneri D, Hoch G, Hölling D, Jonsell M, Kirichenko N, Loomans A, Luo Y-q, McCullough D, Maddox C, Magnoux E, Marchioro M, Martinek P, Mas H, Mériguet B, Pan Y-z, Phélut R, Pineau P, Ray AM, Roques O, Ruiz M-C, Sarto i Monteys V, Speranza S, Sun J-h, Sweeney JD, Touroult J, Valladares L, Veillat L, Yuan Y, Zalucki MP, Zou Y, Žunič-Kosi A, Hanks LM, Millar JG (2023) Worldwide tests of generic attractants, a promising tool for early detection of non-native cerambycid species. In: Jactel H, Orazio C, Robinet C, Douma JC, Santini A, Battisti A, Branco M, Seehausen L, Kenis M (Eds) Conceptual and technical innovations to better manage invasions of alien pests and pathogens in forests. NeoBiota 84: 169-209. https://doi.org/10.3897/neobiota.84.91096
Total trapping network
Supplementary material 1 from: Arianoutsou M, Adamopoulou C, Andriopoulos P, Bazos I, Christopoulou A, Galanidis A, Kalogianni E, Karachle PK, Kokkoris Y, Martinou AF, Zenetos A, Zikos A (2023) HELLAS-ALIENS. The invasive alien species of Greece: time trends, origin and pathways. NeoBiota 86: 45-79. https://doi.org/10.3897/neobiota.86.101778
List of HELLAS-ALIENS species
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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.