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Supplementary material 6: Bombus spp trapped in Palmer Alaska, 2010 from: Bumble Bees (Hymenoptera: Apidae: Bombus spp.) of Interior Alaska: Species Composition, Distribution, Seasonal Biology, and Parasites - Biodiversity Data Journal 3: e5085 (08 May 2015) https://doi.org/10.3897/BDJ.3.e5085
764 specimens of fourteen species trapped using Blue Vane pollinator traps with counts of queens, workers, and males by date.
Data for article: Time-Resolved Spectroscopic Investigation of Charge Trapping in Carbon Nitrides Photocatalysts for Hydrogen Generation
<p>This is the data presented in the article titled 'Time-Resolved Spectroscopic Investigation of Charge Trapping in Carbon Nitrides Photocatalysts for Hydrogen Generation', published in the Journal of the American Chemical Society. DOI:10.1021/jacs.7b01547</p> <p>http://pubs.acs.org/doi/abs/10.1021/jacs.7b01547</p> <p> </p>
Comparing photoelectrochemical water oxidation, recombination kinetics and charge trapping in the three polymorphs of TiO2
<p>Please find the TA data sets and the model calculation of rutile attached. Note that the data is normalised to the amplitude at 100 us.</p>
The FAIR-Device - a non-lethal and generalist semi-automatic Malaise trap for insect biodiversity monitoring: Proof of concept - Supplementary Material
<h3>Abstract</h3> <p>Field monitoring plays a crucial role in understanding insect dynamics within ecosystems. It facilitates pest distribution assessment, control measure evaluation, and prediction of pest outbreaks. Additionally, it provides important information on bioindicators with which the state of biodiversity and ecological integrity in specific habitats and ecosystems can be accurately assessed. However, traditional monitoring systems can present various difficulties, leading to a limited temporal and spatial resolution of the obtained information. Despite recent advancements in automatic insect monitoring traps, also called e-traps, most of these systems focus exclusively on studying agricultural pests, rendering them unsuitable for monitoring diverse insect populations. To address this issue, we introduce the Field Automatic Insect Recognition (FAIR)-Device, a novel non-lethal field tool that relies on semi-automatic image capture and species identification using artificial intelligence via the iNaturalist platform. Our objective was to develop an automatic, cost-effective, and non-specific monitoring solution capable of providing high-resolution data for assessing insect diversity. During a 26-day proof-of-concept evaluation, the FAIR-Device recorded 24.8 GB of video, identifying 431 individuals from 9 orders, 50 families, and 69 genera. While improvements are possible, our device demonstrated potential as a cost-effective, non-lethal tool for monitoring insect biodiversity. Looking ahead, we envision new monitoring systems such as e-traps as valuable tools for real-time insect monitoring, offering unprecedented insights for ecological research and agricultural practices.</p> <h3>Description of the data and file structure</h3> <p>This repository complements the publication <a href="https://www.biorxiv.org/content/10.1101/2024.03.22.586299v2" target="_blank" rel="noopener">"The FAIR-Device - a non-lethal and generalist semi-automatic Malaise trap for insect biodiversity monitoring: Proof of concept".</a> It contains the result data from the proof of concept field test of V1.0 of the FAIR-Device, conducted between July and August 2021 at the Thünen Institute of Agricultural Technology in Braunschweig. The repository comprises three compressed files (.zip):</p> <ul> <li><strong>FAIR-D_captures.zip</strong>: <ul> <li>Video captures from the field tests organized by recording day.</li> <li>Filenames indicating recording time (hh-mm-ss).</li> </ul> </li> </ul> <ul> <li><strong>FAIR-D_Tables&Code.zip</strong>: <ul> <li>Processed results from the obtained image captures, organized into: <ul> <li><strong>Monitoring2021_TotalPeriod.xlsx: </strong>Result table with taxonomic classifications, data analysis, and charts.</li> <li><strong>iNat_observations.xlsx: </strong>iNaturalist reviews analysis table.</li> <li><strong>R: </strong>code for generating article graphics.</li> </ul> </li> </ul> </li> </ul> <ul> <li><strong>FAIR-D_V1.0_3D_Models.zip</strong>: <ul> <li>Complete 3D design of the FAIR-D V1.0 in .stl format, organized into: <ul> <li><strong>3D_print_parts</strong>: 3D-printable parts with spatial coordinates for correct positioning in CAD software.</li> <li><strong>other_3Dparts</strong>: Non-printable parts, also in .stl format with spatial coordinates.</li> </ul> </li> </ul> </li> </ul> <p> </p> <p>NOTE: For visualizing the videos, we recommend <strong>VLC media player</strong> - <a href="https://www.videolan.org/vlc/">https://www.videolan.org/vlc/</a> </p>
Below-ground pitfall traps for standardised monitoring of soil mesofauna: Design and comparison to Berlese/Tullgren funnels
<p>The attached CSV file supports the article <em>Below-ground pitfall traps for standardised monitoring of soil mesofauna: Design and comparison to Berlese/Tullgren funnels </em>(Pedobiologia, Volume 101, 2023, 150911, ISSN 0031-4056, <a href="https://doi.org/10.1016/j.pedobi.2023.150911)">https://doi.org/10.1016/j.pedobi.2023.150911)</a> relative to its comparison part. It presents the specimen count of the deployed pitfall traps and Berlese/Tullgren extractions across five environments at different positions along transects. </p>
Figs 1–2 in Another new trap-jaw ant (Hymenoptera, Formicidae, Odontomachus LATREILLE, 1804) from the Philippines
Figs 1–2. Odontomachus pangantihoni nov.sp., paratype: (1) head, frontal view; (2) mesosoma and petiole, dorsal view.
Combining camera trap surveys and IUCN range maps to improve knowledge of species distributions
<p><span>Reliable maps of species distributions are fundamental for biodiversity research and conservation. Range maps created by the International Union for Conservation of Nature (IUCN) Red List are often considered authoritative but may not match species occurrence data. We tested concordance between occurrences from camera trap surveys and predicted occurrence from IUCN maps for 510 medium- to large-bodied mammalian species in 80 camera-trap sampling areas. Across all areas, cameras detected 39% of the species that were expected to occur based on IUCN ranges. The probability of mismatches between camera traps and IUCN range maps was significantly higher for smaller-bodied mammals and habitat specialists in the Neotropics and Indomalaya, and in areas with shorter canopy forests. Our results indicate that in many areas within their range map distributions species may be rare or absent. We suggest that combining range map data with accumulating data from ground-based biodiversity sensors, such as camera traps, acoustic recorders, and eDNA surveys, provides a richer knowledge base for conservation mapping and planning.</span></p>
Black Aphids Glue Paper Traps Dataset
<p>This dataset focuses on black aphid detection. It was created in a greenhouse cucumber cultivation as part of the H2020 PestNu project (No. 101037128). Images were captured over 44 days at UTH facilities in Volos, Greece, using mobile phone cameras positioned 30-40 cm from five pheromone-based glue-paper traps. One trap replacement occurred due to insect accumulation, with images collected mainly on weekdays, totaling 220 images.</p> <p>Expert agronomists annotated 13,357 black aphids using Roboflow, averaging 60.71 annotations per image. The dataset was split into training and validation subsets with an 80–20% ratio, leading to 175 images for training and 45 for validation. The dataset is organized into two main folders: “<em>0_captured_dataset</em>" contains the original 220 .jpg images. "<em>1_annotated_dataset</em>" includes the images and the annotated data, split into separate subfolders for training and validation. </p> <p>The Black Aphids count in each subset can be seen in the following table:</p> <table> <tbody> <tr> <td><strong>Set</strong></td> <td><strong>Images</strong></td> <td><strong>Black Aphids Instances</strong></td> </tr> <tr> <td>Training</td> <td>175</td> <td>10873</td> </tr> <tr> <td>Validation</td> <td>45</td> <td>2484</td> </tr> <tr> <td>Total</td> <td>220</td> <td>13357</td> </tr> </tbody> </table> <p> </p> <p>Acknowledgments: This work is supported by the Green Deal PestNu project, funded by European Union’s Horizon 2020 research and innovation programme under the grant agreement No. 101037128, and the E-SPFdigit project, funded by European Union’s Horizon Europe research and innovation programme under the grant agreement No. 101157922.</p>
Figures 6–9 in The potential of Malaise traps as an important tool in butterfly (Lepidoptera, Papilionoidea) inventories, based on studies conducted in Republic of Congo
Figures 6–9 – Examples showing the condition of larger butterflies sampled in Malaise traps in Parc National de Nouabalé-Ndoki, Republic of Congo. 6 – Papilio (Princeps) hesperus hesperus Westwood. 7 – Papilio (Princeps) chrapkowskoides nurettini Koçak. 8 – Laodice mycerina nausicaa (Staudinger). 9 – Charaxes nobilis nobilis Druce.
Figures 2–5 in The potential of Malaise traps as an important tool in butterfly (Lepidoptera, Papilionoidea) inventories, based on studies conducted in Republic of Congo
Figures 2–5 – Malaise traps deployed in various localities and habitat types in Parc National de Nouabalé-Ndoki, Republic of Congo. 2 – Bomassa Forest (Gressitt & Gressitt-type trap over streambed). 3 – Makao Forest (Townes-type trap over streambed). 4 – Mondika Camp (Gressitt & Gressitt-type trap across forest path). 5 – Mombongo Camp (Gressitt & Gressitttype trap across disused forest road). Photographs: Violette Dérozier.
Figure 1 – Parc National d in The potential of Malaise traps as an important tool in butterfly (Lepidoptera, Papilionoidea) inventories, based on studies conducted in Republic of Congo
Figure 1 – Parc National d'Nouabalé-Ndoki (PNNN) and the surrounding Unité Forestière d'Aménagement. Sampling localities: 1. Mombongo Camp; 2. Bomassa Forest; 3. Wali Forest; 4. Mondika Camp; 5. Mbeli Camp; 6. Ndoki Formation; 7. Makao Forest.
Data from: Rhode Island wildlife camera trap survey 2018 to 2023
<p>Camera trap detections from a statewide survey of Rhode Island wildlife conducted between 2018 and 2023. </p> <p>This dataset contains two .CSV files. "RI_CameraSurvey_Deployments.csv" contains the camera operation dates (start and end dates), and coordinates for all cameras during each survey season. "RI_CameraSurvey_Detections.csv" contains all independent detections of animals at a camera location (Site and camera identifiers, species identification, data and time of detection). The station and camera identifications are consistent between the deployment table and the detection table. </p> <p>Version 2 includes additional fields in "RI_CameraSurvey_Detections.csv" to specify taxonomic Order, Class, and Family.</p> <p>Version 3 "RI_CameraSurvey_Detections.csv" contains all detections of animals (i.e. a row of data for each image captured) at a camera location. Both files include identifiers for the primary survey location and the specific camera location. An additional field for YearSeason is included in both files.</p>
3D model of a box-type structure under a small cairn near the Bear Trap in Northwest Greenland
<p>This dataset consists of files that can be used to view a high-resolution 3D model of a box-type structure under a small cairn in the vicinity of ‘The Bear Trap’. The interior of the stone box appears to have been completely empty. Similar small stone box structures have been identified and discussed by Schedermann (e.g. 1990: 159) for Arctic Small Tool tradition (ASTt) sites on Skraeling Island and by McGee (1979) for Port Refuge in the Canadian High Arctic. Similar and equally enigmatic features have also been described by Knuth (1966/67: 203) for far north and northeast Greenland. They have been alternatively attributed to numerous PalaeoEskimo cultural complexes, but without dated materials from the site current attribution of the feature’s function, significance or date are not possible.</p> <p>The 3D model was created from 280 digital photographs that were processed usingAgisoft Metashape Pro v1.7. More information is provided in processing report and the readme file that accompanies this dataset. </p> <p>The image survey was conducted as part of the Vaigat Iceberg-Microbial Oil Degradation and Archaeological Heritage Investigation (VIMOA) project, which was funded by the Danish Centre for Marine Research and supported by the Arctic Research Centre at Aarhus University, the National Museum of Denmark, the Greenland Institute of Natural Resources, and The Greenland National Museum and Archives in Nuuk. Proper permits for the survey were obtained in advance from the Greenland National Museum and Archives in Nuuk. Walsh et al. (2020) provides an overview of the archaeological surveys conducted during the VIMOA project and Walsh et al. (in prep) provides further details specific to The Bear Trap and surrounding archaeological contexts. </p> <p>Knuth, Egil. (1966/67) The ruins of the Musk Ox Highway. <em>Folk</em> 8-9: 191-219.</p> <p>McGee, Robert. (1979) <em>The Palaeoeskimo occupations at Port Refuge, High Arctic Canada</em>. National Museum of Man Mercury Series. Archaeological Survey of Canada Paper No. 92. Ottawa: National Museums of Canada.</p> <p>Schledermann, Peter. (1990) <em>Crossroads to Greenland: 3000 years of prehistory in the Eastern High Arctic</em>.Calgary: The Arctic Institute of North America of the University of Calgary.</p> <p>Walsh et al. (2020) The VIMOA project and archaeological heritage in the Nuussuaq Peninsula of north-west Greenland. <em>Antiquity</em> 94:e6 doi:10.15184/aqy.2019.230</p> <p>Walsh, Matthew J., Daniel F. Carlson, Pelle Tejsner, and Steffen Thomsen. The Bear Trap: Reinvestigating a unique stone structure on the northwest tip of the Nuussuaq Peninsula, Greenland. Submitted to <em>Arctic Anthropology</em></p>
Data for: Trap type affects dung beetle taxonomic and functional diversity in Bornean tropical forests
<p>Dung beetle community composition data. Data was collected using either dung-baited pitfall traps or flight interception traps. Each row represents one trap, with the author/study information, name of study site, sampling period, trap type and habitat type. Dung beetle species and their abundances are listed. See "metadata" tab for more details.</p> <p>Paper abstract: Baited pitfall traps (BPTs) and flight intercept traps (FITs) are the most common methods employed for sampling dung beetle communities. These methods vary in their efficacy and are affected by factors such as the bait types used and the dispersal abilities of different dung beetle species. We present the first quantitative comparison of the taxonomic and functional diversity, and community composition of dung beetles caught in BPTs and FITs in Bornean tropical forests. We show that BPTs and FITs captured complementary communities with different functional traits, and that BPTs captured more functionally diverse communities. We therefore recommend using a combination of both baited BPTs and FITs for studies assessing the composition of dung beetles across habitat types. Our results also highlight that it is important to consider how trap type affects the trait composition of communities when relating dung beetle communities and functional traits to ecological functioning. We suggest modifications to FITs based on the design of harp traps to increase their effectiveness in capturing larger-bodied beetles.</p>
Assessing the Impact of Pest Monitoring Traps on Bombus griseocollis (Hymenoptera: Apidae) Colony Growth and Development
<p>Insect traps use visual and olfactory cues to attract target pests; however, they vary in their specificity and often unintentionally capture non-target beneficial insects (bycatch), including <em>Bombus</em>. Concerns have been expressed that bycatch may contribute to <em>Bombus</em> mortality and the consequential loss of pollination services. Here, we quantified the impact of trap captures on <em>Bombus griseocollis</em> colony growth and development by evaluating the following four treatments: colonies paired with traps, colonies paired with traps and pheromone lures, traps and pheromone lures (but no colonies), and colonies with no trap and no lure. Trap contents were collected biweekly to determine <em>B. griseocollis </em>capture rates. Colony growth and development data were collected weekly by weighing colonies and recording foraging activity. Based on microsatellite polymerase chain reaction (PCR) amplification, three <em>B. griseocollis </em>were collected from released colonies, while the remaining five were residents within the environment. Given the low number of <em>B. griseocollis </em>workers collected, any differences in colony weight change and active foraging were likely not a result of pest monitoring trap captures. However, trap captures could have a greater impact by interfering with functional diversity, colony establishment, and pollination services, emphasizing the need for additional research. Building on this research will provide a more comprehensive view of the impact of pest monitoring traps on <em>Bombus </em>populations, which could minimize risk to pollinator populations and pollination services.</p>
Fig. 6. Amphibian fences with bucket-traps along T1425 in Mortality Of Amphibians On The Roads Of Lviv Region (Ukraine): Trend For The Last Decade
Fig. 6. Amphibian fences with bucket-traps along T1425 road section in Roztochia Nature Reserve (April, 2018).
Fig. 3 in Observation Of Eurasian Otter'S Diel Activity Using Camera Trapping In Central-Eastern Romania
Fig. 3. The activity pattern of the otter in the three protected areas, based on the number of otter recordings at the observation sites during March 2011–April 2016.
Fig. 6 in Observation Of Eurasian Otter'S Diel Activity Using Camera Trapping In Central-Eastern Romania
Fig. 6. Seasonal activity patterns of Lutra lutra in the study area during the study period based on the number of otter crossings through the observation sites.
Camera trap data suggest uneven predation risk across vegetation types in a mixed farmland landscape
<p>Ground-nesting farmland birds such as the grey partridge (<em>Perdix perdix</em>) have been rapidly declining due to a combination of habitat loss, food shortage and predation. Predator activity is the least understood factor, especially its modulation by landscape composition and complexity. An important question is whether agri-environment schemes such as flower strips are potentially useful for reducing predation risk, e.g., from red fox (<em>Vulpes vulpes</em>). We employed 120 camera traps for two summers in an agricultural landscape in Central Germany to record predator activity (i.e., the number of predator captures) as a proxy for predation risk and used generalized linear mixed models (GLMMs) to investigate how the surrounding landscape affects predator activity in different vegetation types (flower strips, hedges, field margins, winter cereal and rapeseed fields). Additionally, we used 48 cameras to study the distribution of predator captures within flower strips. Vegetation type was the most important factor determining the number of predator captures and captures rates in flower strips were lower than in hedges or field margins. Red fox capture rates were the highest of all predators in every vegetation type, confirming their importance as a predator for ground-nesting birds. The number of fox captures increased with woodland area and decreased with structural richness and distance to settlements. In flower strips, capture rates in the centre were approximately 9 times lower than at the edge. We conclude that the optimal landscape for ground-nesting farmland birds seems to be open farmland with broad extensive vegetation elements and a high structural richness. Broad flower blocks provide valuable, comparatively safe nesting habitats and the predation risk can further be minimized by placing them away from woods and settlements. Our results suggest that adequate landscape management may reduce predation pressure. </p>
Behavioral "bycatch" from camera trap surveys yields insights on prey responses to human-mediated predation risk
<p>Human disturbance directly affects animal populations but indirect effects of disturbance on species behaviors are less well understood. Camera traps provide an opportunity to investigate variation in animal behaviors across gradients of disturbance. We used camera trap data to test predictions about predator-sensitive behavior in three ungulate species (caribou Rangifer tarandus; white-tailed deer, Odocoileus virginianus; moose, Alces alces) across two boreal forest landscapes varying in disturbance. We quantified behavior as the number of camera trap photos per detection event and tested its relationship to predation risk between a landscape with greater industrial disturbance and predator abundance (Algar) and a "control" landscape with lower human and predator activity (Richardson). We also assessed the influence of predation risk and habitat on behavior across camera sites within the disturbed Algar landscape. We predicted that animals in areas with greater predation risk (more wolf activity, less cover) would travel faster and generate fewer photos per event, while animals in areas with less predation risk would linger (rest, forage), generating more photos per event. Consistent with predictions, caribou and moose had more photos per event in the landscape where predation risk was reduced. Within the disturbed landscape, no prey species showed a significant behavioral response to wolf activity, but the number of photos per event decreased for white-tailed deer with increasing line of sight (m) along seismic lines (i.e. decreasing visual cover), consistent with a predator-sensitive response. The presence of juveniles was associated with shorter behavioral events for caribou and moose, suggesting greater predator sensitivity for females with calves. Only moose demonstrated a positive association with vegetation productivity (NDVI), suggesting that for other species influences of forage availability were generally weaker than those from predation risk. Behavioral insights can be gleaned from camera trap surveys and provide information about animal responses to predation risk and the indirect impacts of human disturbances.</p>
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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.