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38 results for “insect monitoring”
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>
Fig. 1 in Potential of unmanned aerial sampling for monitoring insect populations in rice fields
Fig. 1. Rotary-wing unmanned aerial vehicle equipped with remote-controlled insect net openings (a). Layout of double-layered insect net designed to prevent loss of insect samples during aerial sampling (b). Representative flight path of unmanned aerial vehicle for aerial sampling over rice field (c).
Datasets for time-lapse camera monitoring of insects and their floral environments
<p>Contains the dataset for training and validation of models to estimate flower cover and identify taxa of arthropods in time-lapse camera recordings described in the paper:</p> <p>Kim Bjerge, Henrik Karstoft, Hjalte M. R. Mann, Toke T. Høye, A deep learning pipeline for time-lapse camera monitoring of insects and their floral environments, 2024, bioRxiv, <a href="https://doi.org/10.1101/2024.04.12.589205" rel="noopener">https://doi.org/10.1101/2024.04.12.589205</a></p> <p>The zip files contain the needed files and directory structure to train the models in Python code published at: <a href="https://github.com/kimbjerge/insectsFlowers">https://github.com/kimbjerge/insectsFlowers</a></p> <p>Content of zip files:<br>===============</p> <p>insects.zip: Contains images and labels in YOLO format: <a href="https://github.com/ultralytics/yolov5/issues/2293">https://github.com/ultralytics/yolov5/issues/2293</a></p> <p>trainI21m contains the images and labels to train the insect detector with YOLOv5. Contains only the motion-informed enhanced images (MIE).<br>testI21m contains the images and labels to test the insect detector trained with YOLOv5. Contains only the motion-informed enhanced images (MIE).</p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>Flowers.zip: contains the images of plants and flowers with black and white masks to train the DeepLabv3 flower semantic segmentation model.</p> <p>----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------</p> <p>NI2-19cls.zip: contains images for training and validation of the arthropod classifiers </p> <p>Image crops of arthropods are organized in 19 subdirectories one for each class.</p> <p>A1-Coccinellidae<br>B2-Coleoptera<br>C3-Background<br>D4-Bombus<br>E5-Syrphidae<br>F6-Lepidoptera<br>G7-Aranaeae<br>H8-Formidicidae<br>I9-Diptera<br>J10-Hemiptera<br>K11-Isopoda<br>L12-Uspecificerede<br>N13-Hymenoptera<br>O14-Orthoptera<br>P15-Rhagonycha_fulva<br>Q16-Satyrinae<br>R17-Aglais_urticea<br>S18-Odonata<br>T19-Apis_mellifera</p>
Linked collectors and determiners for: Insects in monitoring oaks in Norway – a pilot study.
Natural history specimen data linked to collectors and determiners held within, "Insects in monitoring oaks in Norway – a pilot study". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/0d9bfb0c-5b22-42aa-a7b4-1c96a47c7c86">https://bionomia.net/dataset/0d9bfb0c-5b22-42aa-a7b4-1c96a47c7c86</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/0d9bfb0c-5b22-42aa-a7b4-1c96a47c7c86">https://gbif.org/dataset/0d9bfb0c-5b22-42aa-a7b4-1c96a47c7c86</a>. Formatted as a Frictionless Data package.
Figure 3. A in A Low-Cost Trap to Monitor Parasitism of Macadamia Felted Coccid (Hemiptera: Eriococcidae) and Other Scale Insects
Figure 3. A. Encarsia lounsburyi parasitizing an MFC adult. B. A circular parasitoid emergence hole on an adult male MFC host, indicating parasitism.
Figure 2 in A Low-Cost Trap to Monitor Parasitism of Macadamia Felted Coccid (Hemiptera: Eriococcidae) and Other Scale Insects
Figure 2. The parasitoid emergence trap installed on a macadamia tree branch infested with macadamia felted coccid.
Figure 1. A. A low-cost 100 in A Low-Cost Trap to Monitor Parasitism of Macadamia Felted Coccid (Hemiptera: Eriococcidae) and Other Scale Insects
Figure 1. A. A low-cost 100 ml PET bottle can be used to construct a parasitoid emer- gence trap to study parasitism of MFC in the field. B. Basic components of the parasitoid emergence trap showing the PET bottle with modifications allowing for attachment to the tree branch and insertion of a glass collection vial. C. A fully assembled trap.
Fig. 2 in Data On Protected And Insufficiently Known Insect Species Obtained From The Invertebrate Monitoring In Latvia (2015 - 2016)
Fig. 2. The spatial arrangement scheme of the monitoring activities in one of the squares.
Fig. 1 in Data On Protected And Insufficiently Known Insect Species Obtained From The Invertebrate Monitoring In Latvia (2015 - 2016)
Fig. 1. The layout of invertebrate monitoring sites in Latvia.
Data for: Spatial monitoring of flying insects over a Swedish lake using a CW lidar system
<p>Data for a field experiment on remote sensing of flying insects are supplied. A bistatic CW lidar system of the Scheimpflug type was employed, and echoes from flying insects over and close to a Swedish lake were recorded as read-out files from an array detector making observations along the emitted laser beam. The activated pixels of the detector could be converted into range data by triangulation. With a high detector read-out frequency, data on numerous insects could be obtained. Special emphasis was put on distinguishing between large and small insects (from the intensities of the recorded echoes) and the insect positions with regard to the shores of the lake. </p>
Data for: Spatial monitoring of flying insects over a Swedish lake using a CW lidar system
Open the record for dataset details and reuse information.
From buzzes to bytes: A systematic review of automated bioacoustics models used to detect, classify, and monitor insects
Open the record for dataset details and reuse information.
Data from: Designing monitoring protocols to measure population trends of threatened insects: a case study of the cryptic, flightless grasshopper Brachaspis robustus
<p>Statistically robust monitoring of threatened populations is essential for effective conservation management because the population trend data that monitoring generates is often used to make decisions about when and how to take action. Despite representing the highest proportion of threatened animals globally, the development of best practice methods for monitoring populations of threatened insects is relatively uncommon. Traditionally, population trend data for the Nationally Endangered New Zealand grasshopper <em>Brachaspis robustus</em> has been determined by counting all adults and nymphs seen on a single ~1.5 km transect searched once annually. This method lacks spatial and temporal replication, both of which are essential to overcome detection errors in highly cryptic species like <em>B</em>. <em>robustus</em>. It also provides no information about changes in the grasshopper's distribution throughout its range. Here, we design and test new population density and site occupancy monitoring protocols by comparing a) comprehensive plot and transect searches at one site and b) transect searches at two sites representing two different habitats (gravel road and natural riverbed) occupied by the species across its remaining range. Using power analyses, we determined a) the number of transects, b) the number of repeated visits and c) the grasshopper demographic to count to accurately detect long term change in relative population density. To inform a monitoring protocol design to track trends in grasshopper distribution, we estimated the probability of detecting an individual with respect to a) search area, b) weather and c) the grasshopper demographic counted at each of the two sites. Density estimates from plots and transects did not differ significantly. Population density monitoring was found to be most informative when large adult females present in early summer were used to index population size. To detect a significant change in relative density with power > 0.8 at the gravel road habitat, at least seventeen spatial replicates (transects) and four temporal replicates (visits) were required. Density estimates at the natural braided river site performed poorly and likely require a much higher survey effort. Detection of grasshopper presence was highest (<em>p</em><sub><em>g</em></sub> > 0.6) using a 100 m x 1 m transect at both sites in February under optimal (no cloud) conditions. At least three visits to a transect should be conducted per season for distribution monitoring. Monitoring protocols that inform the management of threatened species are crucial for better understanding and mitigation of the current global trends of insect decline. This study provides an exemplar of how appropriate monitoring protocols can be developed for threatened insect species.</p>
Supplementary material 4 from: Romiti F, Redolfi De Zan L, Rossi de Gasperis S, Tini M, Scaccini D, Anaclerio M, Carpaneto G (2017) Latitudinal cline in weapon allometry and phenology of the European stag beetle. In: Campanaro A, Hardersen S, Sabbatini Peverieri G, Carpaneto GM (Eds) Monitoring of saproxylic beetles and other insects protected in the European Union. Nature Conservation 19: 57-80. https://doi.org/10.3897/natureconservation.19.12681
Allometric relationship between mandible (LnML) and elytron (LnEL) length of each population : Data type: statistical data
Supplementary material 3 from: Romiti F, Redolfi De Zan L, Rossi de Gasperis S, Tini M, Scaccini D, Anaclerio M, Carpaneto G (2017) Latitudinal cline in weapon allometry and phenology of the European stag beetle. In: Campanaro A, Hardersen S, Sabbatini Peverieri G, Carpaneto GM (Eds) Monitoring of saproxylic beetles and other insects protected in the European Union. Nature Conservation 19: 57-80. https://doi.org/10.3897/natureconservation.19.12681
Allometric relationship between mandible (LnML) and elytron (LnEL) length for minor and major morph : Data type: statistical data
Supplementary material 1 from: Romiti F, Redolfi De Zan L, Rossi de Gasperis S, Tini M, Scaccini D, Anaclerio M, Carpaneto G (2017) Latitudinal cline in weapon allometry and phenology of the European stag beetle. In: Campanaro A, Hardersen S, Sabbatini Peverieri G, Carpaneto GM (Eds) Monitoring of saproxylic beetles and other insects protected in the European Union. Nature Conservation 19: 57-80. https://doi.org/10.3897/natureconservation.19.12681
Shapiro-Wilk normality test on biometric, phenological and climatic variable : Data type: statistical data
Supplementary material 1 from: Zapponi L, Mazza G, Farina A, Fedrigoli L, Mazzocchi F, Roversi PF, Sabbatini Peverieri G, Mason F (2017) The role of monumental trees for the preservation of saproxylic biodiversity: re-thinking their management in cultural landscapes. In: Campanaro A, Hardersen S, Sabbatini Peverieri G, Carpaneto GM (Eds) Monitoring of saproxylic beetles and other insects protected in the European Union. Nature Conservation 19: 231-243. https://doi.org/10.3897/natureconservation.19.12464
List of recorded species : Explanation note: The supplementary material contains the list of species recorded during the 1982 and 2017 inventories, showing for each species: number of individuals, average circunference and average height.
Supplementary material 1 from: Kadej M, Zając K, Smolis A, Tarnawski D, Tyszecka K, Malkiewicz A, Pietraszko M, Warchałowski M, Gil R (2017) The great capricorn beetle Cerambyx cerdo L. in south-western Poland – the current state and perspectives of conservation in one of the recent distribution centres in Central Europe. In: Campanaro A, Hardersen S, Sabbatini Peverieri G, Carpaneto GM (Eds) Monitoring of saproxylic beetles and other insects protected in the European Union. Nature Conservation 19: 111-134. https://doi.org/10.3897/natureconservation.19.11838
Table S1. Correlation matrix of all initial environmental layers selected for modelling in MaxEnt : Data type: statistical data
Fig. 2 in A Solar-Powered UV Light Trap for Long-Term Monitoring of Insects in Remote Habitats
Fig. 2. The solar-powered UV light trap in A) stabilized-vegetated sands, B) open sand dunes, and C) agricultural crop margin. Note that in addition to the components in Fig. 1, 6–8 ft (1.8–2.4 m) pieces of steel rebar and wire are used to position the solar panel.
Fig. 1 in A Solar-Powered UV Light Trap for Long-Term Monitoring of Insects in Remote Habitats
Fig. 1. Exploded view of solar-powered UV light trap, with component labels corresponding to the items in Table 1. The light trap components battery (n), ballast box (a), photoelectric switch (g), and battery clamps (l) were put inside a second bucket to protect them from corrosion and overheating.
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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.