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43 results for “Insect trapping”
Aquatic and terrestrial insect activity phenology with trap collections at the Andrews Experimental Forest, 2009-2014
This study was designed to evaluate the influence of microclimatic heterogeneity, associated with complex terrain, on phenology and to evaluate potential trophic responses to scenarios of climate change, disturbance and land use. We focus on a simplified model trophic system involving vascular plants, terrestrial and aquatic insects, and migratory neotropical and resident birds. The model trophic system is interesting because the phenologies of different components in the model system are independent (cued by various abiotic drivers) and dependent (due to trophic interactions), potentially leading to complex system behaviors. Plant and poiklothermic animal (ex. invertebrates) phenologies are highly temperature dependent. Phenologies of terrestrial plants and invertebrates would therefore likely exhibit wide spatial and temporal variation across the landscape in response to temperature variation associated with elevational differences, cold air drainages patterns, and temperature inversions. Aquatic invertebrate phenologies are also tied to temperature, but stream temperatures are influenced by different factors than those driving air temperatures and they may be less sensitive to complex terrain. For the invertebrate part of this study, we are examining spring-time (April through June) flying (terrestrial and adult aquatic) insect activity and adult aquatic insect emergence across a range of sites in the HJ Andrews Experimental Forest. Flying insect activity will be assessed using malaise traps deployed at 16 sites ranging from 450m to over 1300m in elevation, and with a variety of forest stand ages and slope aspects. Emerging aquatic insects will be collected with emergence traps in six 1st to 2nd order streams ranging from 450m to 1000m in elevation, and differing in water source (spring vs run-off) and surrounding forest age. Insects from malaise traps will be identified to varying levels from order to genus, depending on the group and available keys. Adult aquat
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>
Catches of numerous insect species in Rothamsted 160W light trap at Devonport, Tasmania, 1992-2019
<p>These data derive from decades of near-continuous (1992 - 2019, apart from 2008 and 2009) operation of a 160W Rothamsted-design light trap at Stony Rise Centre, Devonport, Tasmania, Australia. Stony Rise was the last of several long term, continuous trapping sites operated over this period by the Tasmanian state agricultural agency, currently known as the Department of Natural Resources and Environment Tasmania.</p> <p>The light was normally operated every night, with all individuals of selected taxa counted. There were 5433 trapping events covering 7897 nights including 4167 single-night events, 502 two-night events, 516 three-night events and 165 four-night events. The remaining 83 events were variously 5-14 nights duration. There were 194 sporadic nights when the trap malfunctioned, which is about 2.5% of 7897 nights in the main trapping periods. The trap did not operate for extended periods (3-6 months) in early 1996, early 1998, all of 2007, all of 2008, all of 2009, early 2010 and late 2015. Enumeration of catches ceased on 6 February 2019. A total of 222,146 specimens were identified and enumerated for the data set.</p> <p><strong>Dataset</strong></p> <p>The initial focus of the trapping was on Noctuidae and insect species of economic importance for Tasmanian agriculture. The taxa selected for identification and counts grew from 104 taxa in 1992 to 273 taxa in 2019. Consequently, absence of some species from early samples should not be considered to indicate absence of these species. During periods in which any species was included in counts, a record is always included for the species in question, with a count of zero if no individuals were detected. During periods in which the species was not included in counts, no record is included for the species and period in question. Hence zero counts can always be considered to represent true absence within the sample of identified insects.</p> <p>An explanation of the survey work leading to this dataset and an overview of species included was published in Hill, L., 2013b, Long-term light trap data from Tasmania, Australia, Plant Protection Quarterly Vol.28(1) (<a href="https://www.researchgate.net/publication/274700470_Long-term_light_trap_data_from_Tasmania_Australia">https://www.researchgate.net/publication/274700470_Long-term_light_trap_data_from_Tasmania_Australia</a>).</p> <p>The bibliography lists publications derived from analysis of these data.</p> <p><strong>Purpose</strong></p> <p>The focus was on prognosis of Persectania ewingii Westwood, southern armyworm and several other noctuid pests such as Helicoverpa punctigera (Wallengren), native budworm and Agrotis species, true cutworms, which were all subsequently shown to undertake substantial annual immigration from mainland Australia to Tasmania across Bass Strait (Drake et al. 1981, Hill 1993, Hill 2007a). The reliability of using light trap catches to forecast larval outbreaks of southern armyworm was determined empirically since 1953 (Hill, L. 2013c). A history of forecasting outbreaks of the southern armyworm, Persectania ewingii (Lepidoptera: Noctuidae) in Tasmania. Plant Protection Quarterly Vol.28(1), 15-21. Many frequent or infrequent vagrant Lepidoptera and other taxa were detected and the status of some of these was asserted in scientific publications (Hill 2011a, 2012a, 2013d, 2014, 2015, 2016a, 2016b, 2017). Sex ratio data for 38 species of Noctuidae was collected but is not provided in this dataset. Data from similar traps at other Tasmanian sites, back to 1953 for a few species, as described by Hill (2013c) is held variously in hardcopy format by the Department of Natural Resources and Environment Tasmania. It is available on request.</p> <p><strong>Temporal scope</strong></p> <p>January 1, 1992 - February 6, 2019</p> <p><strong>Geographic scope</strong></p> <p>Stony Rise Centre, Devonport, Tasmania, Australia </p> <p><strong>Taxonomic scope</strong></p> <p>227 species or higher taxa of Lepidoptera, representing about 30 families. 7 taxa of Coleoptera, representing 2 families. 6 taxa of Diptera representing 6 families. 12 taxa of Hemiptera representing 6 families. 3 taxa of Hymenoptera representing 1 family. 16 taxa of Neuroptera representing 5 families. 1 taxon of Blattodea. 1 taxon of Orthoptera.</p> <p><strong>Methodology</strong></p> <p><em>Study extent</em></p> <p>The light trap was installed at Stony Rise Centre (government offices), 1 Rundle Street, Devonport, Tasmania (146.32 E, 41.18 S).</p> <p><em>Sampling</em></p> <p>The trap was similar to the Rothamsted-design traps operated in the United Kingdom, consisting of a clear glass or Perspex, truncated pyramid of 52 cm square base, 22 cm height and 12 cm top aperture surrounding a square, glass funnel of slightly lesser height with 20 cm top aperture and 4 cm bottom aperture. This was mounted on a wooden base-board about 1.3 m above ground under a ridged, steel roof. A 160 W mercury vapour bulb was suspended within the funnel from the ceiling of the roof cavity, in which a clock switch was fitted. Clearance between the top aperture of the funnel and the ceiling of the roof was about 4 cm. The catch was collected into a single 10 cm square glass jar with a plaster of Paris floor bearing tetrachlorethane killing fluid and with a 9 cm orifice screwed to the underside of the baseboard. This jar contained a piece of crumpled paper towel to reduce rubbing of specimens. In December 2015 the trap was rebuilt in stainless steel to the same dimensions and using the original collection pyramid and funnel. The clock switch was replaced by a light sensitive switch.</p> <p><em>Quality control</em></p> <p>Only selected insect species were sorted and identified, counted and written into a data file. Some insects were only sorted and counted using supraspecific ranks. The range of included species grew over the period. Records for each interval exclude taxa which were not sorted or identified during the period in question. Hence zero counts indicate absence of the insects concerned during a trapping period.</p> <p><em>Method steps</em></p> <ol> <li> <p>In the study, all individual records of selected target insect species were collected, identified to species level and counted yielding qualitative (species) and quantitative (number of individuals within each species) data for the entire study period. The recorded taxa are listed in taxon.csv in this dataset, along with summary information on the first and last events during which the taxon was monitored, the number of events in which the taxon could have been detected, the actual number in which it was detected, the total number of individuals detected, and the number of individuals detected in each year from 1992 to 2019 and in each month of the year.</p> </li> <li> <p>All handling and identification of material was carried out consistently throughout the entire period by the same researcher.</p> </li> <li> <p>Over 9000 specimens in several hundred taxa from this light trap are preserved in the Tasmanian Agricultural Insect Collection, Hobart, Tasmania (<a href="https://collections.ala.org.au/public/show/co131">https://collections.ala.org.au/public/show/co131</a>). Note specimen records are not included in this dataset. Images of representative specimens of most recorded taxa are included in the image subfolder and listed in image.csv. For insects identified only to genus or higher, it should not be assumed that all records over the period matched the species illustrated.</p> </li> <li> <p>The data were prepared for publication as a Darwin Core sampling event dataset via a series of transformations within Excel and comprises the following CSV files: event.csv (Darwin Core sampling event records) and occurrence.csv (Darwin Core occurrence records linked to event.csv by eventID). Two other CSV files are included but not mapped through the Darwin Core Archive meta.xml. First, image.csv lists images of example specimens of many of the taxa recorded in the dataset. These images are included in the image folder. Secondly, taxon.csv summarises the taxa referenced within occurrence.csv, cross-references the images as associatedMedia and provides summary counts for the number of individuals of each taxon recorded in each year of the study and in each calendar month through the period.</p> </li> </ol> <p><strong>Bibliography</strong></p> <ol> <li> <p>Drake et al. 1981. Insect migration across Bass Strait during spring: a radar study. Bulletin of Entomological Research 71, 449-66. https://doi.org/10.1017/S0007485300008476</p> </li> <li> <p>Hill 1993. Colour in adult Helicoverpa punctigera Wallengren (Lepidoptera: Noctuidae) as an indicator of migratory origin. Journal of the Australian Entomological Society 32, 145-51. https://doi.org/10.1111/j.1440-6055.1993.tb00563.x</p> </li> <li> <p>Hill 2007a. Agrotis (Lepidoptera: Noctuidae) species in Tasmania including montane, summer aestivation of the bogong moth, Agrotis infusa (Boisduval, 1832). Victorian Entomologist 37(1), 3-9. https://www.researchgate.net/publication/274700557</p> </li> <li> <p>Hill 2007b. The chevron cutworm, Diarsia intermixta in Tasmania. Victorian Entomologist 37(5), 68-76. https://www.researchgate.net/publication/274700503</p> </li> <li> <p>Hill 2011a. The Pacific damsel bug, Nabis kinbergii in Tasmania. Victorian Entomologist 41(5), 99-107. https://www.researchgate.net/publication/274700561</p> </li> <li> <p>Hill 2011b. The heliotrope moth, Utetheisa pulchelloides in Tasmania. Victoria Entomologist 41(4), 69-73. https://www.researchgate.net/publication/274700401</p> </li> <li> <p>Hill 2011c. Continual migration across Bass Strait? Victorian Entomologist 41(6), 117-22. https://www.researchgate.net/publication/274700626.</p> </li> <li> <p>Hill 2012a. Cabbage-centre grub Hellula hydralis, not resident in Tasmania. Plant Protection Quarterly 27(3), 91-100. https://www.researchgate.net/publication/274700462</p> </li> <li> <p>Hill 2012b. The brown lacewing, Micromus tasmaniae in Tasmania: Part 1. Victorian Entomologist 42(5), 94-101. https://www.researchgate.net/publication/274700563.</p> </li> <li> <p>Hill 2012c. The brown lacewing, Micromus tasmaniae in Tasmania: Part 2. Victorian Entomologist 42(6), 115-20. https://www.researchgate.net/publication/274700504</p> </li> <li> <p>Hill 2013a. A history of forecasting outbreaks of the southern armyworm, Persectania ewingii (Lepidoptera: Noctuidae) in Tasmania. Plant Protection Quarterly Vol.28(1), 15-21. https://www.researchgate.net/publication/274700550</p> </li> <li> <p>Hill 2013b. Long-term light trap data from Tasmania, Australia. Plant Protection Quarterly Vol.28(1), 22-27. https://www.researchgate.net/publication/274700470.</p> </li> <li> <p>Hill 2013c. The common armyworm, Mythimna convecta (Walker) (Noctuidae:Lepidoptera), a seasonal resident in Tasmania. Plant Protection Quarterly Vol.28(4), 114-119. https://www.researchgate.net/publication/274700391</p> </li> <li> <p>Hill 2013d. Earias moths, rare vagrants in Tasmania. Victorian Entomologist 43(2), 40-43. https://www.researchgate.net/publication/274700577.</p> </li> <li> <p>Hill 2013e. Australia painted lady butterflies light-trapped in Tasmania. Victorian Entomologist 43(4), 76-81. https://www.researchgate.net/publication/274700637</p> </li> <li> <p>Hill 2013f. The satin moth, Thalaina selenaea in Tasmania. Victorian Entomologist 43(5), 106-111. https://www.researchgate.net/publication/274700628</p> </li> <li> <p>Hill 2014. Lesser armyworm, Spodoptera exigua (Hübner) (Lepidoptera: Noctuidae), a vagrant moth in Tasmania. Plant Protection Quarterly Vol.29(4), 131-142. https://www.researchgate.net/publication/274700387</p> </li> <li> <p>Hill 2015. Eggfruit caterpillar, Sceliodes cordalis (Doubleday) (Lepidoptera: Pyralidae), a vagrant moth and indicator for likelihood of Queensland fruit fly establishment in Tasmania? Plant Protection Quarterly Vol.30(1), 27-39. https://www.researchgate.net/publication/311950878.</p> </li> <li> <p>Hill 2016a. An extreme rain event brings two vagrant moths to Tasmania. Victorian Entomologist 46(4), 88-89. https://www.researchgate.net/publication/311951473.</p> </li> <li> <p>Hill 2016b. Meyrickella ruptellus (Noctuidae: Hypeninae), a rare vagrant in Tasmania. Victorian Entomologist 46(3), 60-66. https://www.researchgate.net/publication/311950973</p> </li> <li> <p>Hill 2017. Migration of green mirid, Creontiades dilutus (Stål) and residence of potato bug, Closterotomus norwegicus (Gmelin) in Tasmania (Hemiptera: Miridae: Mirinae: Mirini). Crop Protection 96(2017), 211-220. https://doi.org/10.1016/j.cropro.2017.02.006</p> </li> </ol>
Fig. 3 in Glass Buildings As Bird Feeders: Urban Birds Exploit Insects Trapped By Polarized Light Pollution
Fig. 3. Timing of foraging visits of European magpie (Pica pica) to the northern building of the Eötvös University as detected by a web camera from 17:00 h on 16 May to 20:00 h on 23 May in 2007. Arrow lengths represent the proportion of all visits made during a particular hour over the
Fig. 2 in Glass Buildings As Bird Feeders: Urban Birds Exploit Insects Trapped By Polarized Light Pollution
Fig. 2. (A) Hovering white wagtail (Motacilla alba) catching caddis flies from a window. (B) House sparrow (Passer domesticus) capturing caddis flies from a vertical glass surface. (C) Great tit (Parus major) standing on a window's edge and catching caddis flies. (D) European magpie (Pica pica) on
Fig. 1 in Glass Buildings As Bird Feeders: Urban Birds Exploit Insects Trapped By Polarized Light Pollution
Fig. 1. (A) The southern (left arrow) and northern (right arrow) building of the Faculty of Natural Sciences of the Eötvös University in Budapest seen from the river Danube. (B) Mass-swarming caddis flies (Hydropsyche pellucidula, white dots) at the vertical glass surfaces of the northern building. (C) "Well-laid table" for urban birds: caddis fly imagoes (black dots) landed on white (untinted) and black (tinted) vertical glass surfaces. (D) An adult caddis fly landed on the outside surface of a window photographed from outside. (E) A copulating caddis fly pair on the outside surface of a window
Figure 3. The 95 in Effects of agroecosystems on insect and insectivorous bat activity: a preliminary finding based on light trap and mist net captures
Figure 3. The 95% family-wise confidence level for multiple comparisons test based on insectivorous bat species analyses. Left: H. aff. ruber; right: H. jonesi.
Figure 2. The 95 in Effects of agroecosystems on insect and insectivorous bat activity: a preliminary finding based on light trap and mist net captures
Figure 2. The 95% family-wise confidence level for multiple comparisons test based on insect order analyses. Left: Lepidoptera; right: Diptera.
Linked collectors and determiners for: Insect Specimens from Trap Nests deployed in Vermont, USA.
Natural history specimen data linked to collectors and determiners held within, "Insect Specimens from Trap Nests deployed in Vermont, USA". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/b34cd86f-6b46-48cb-9cc1-de4a2c5718e9">https://bionomia.net/dataset/b34cd86f-6b46-48cb-9cc1-de4a2c5718e9</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/b34cd86f-6b46-48cb-9cc1-de4a2c5718e9">https://gbif.org/dataset/b34cd86f-6b46-48cb-9cc1-de4a2c5718e9</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Catches of numerous insect species in Rothamsted 160W light trap at Devonport, Tasmania, 1992-2019.
Natural history specimen data linked to collectors and determiners held within, "Catches of numerous insect species in Rothamsted 160W light trap at Devonport, Tasmania, 1992-2019". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/044f96bc-3bf2-4a38-9f7c-8808ab48dbf1">https://bionomia.net/dataset/044f96bc-3bf2-4a38-9f7c-8808ab48dbf1</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/044f96bc-3bf2-4a38-9f7c-8808ab48dbf1">https://gbif.org/dataset/044f96bc-3bf2-4a38-9f7c-8808ab48dbf1</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Select Insect Specimens from Malaise Traps in Orleans County, Vermont, USA.
Natural history specimen data linked to collectors and determiners held within, "Select Insect Specimens from Malaise Traps in Orleans County, Vermont, USA". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/30968d94-12c8-48d6-9cc7-1a407f4c5e1b">https://bionomia.net/dataset/30968d94-12c8-48d6-9cc7-1a407f4c5e1b</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/30968d94-12c8-48d6-9cc7-1a407f4c5e1b">https://gbif.org/dataset/30968d94-12c8-48d6-9cc7-1a407f4c5e1b</a>. Formatted as a Frictionless Data package.
Benchmark datasets for detection and identification of insects from camera trap images with deep learning
<p><strong>Insect benchmark datasets for training, validation and test (train1201.zip, val1201.zip and test1201.zip) with time-lapse images as described in paper:</strong></p> <p><a href="https://www.biorxiv.org/content/10.1101/2022.10.25.513484v1">Bjerge K, Alison J, Dyrmann M, Frigaard C.E., Mann H. M. R., Høye T.T., Accurate detection and identification of insects from camera trap images with deep learning, bioRxiv:10.1101/2022.10.25.513484v1</a></p> <p>Labels in <strong>YOLO format: <a href="https://github.com/ultralytics/yolov5/issues/2293">ultralytics/yolov5: label format</a></strong></p> <p>The annotated training and validation datasets contains insects of nine different species as listed below:</p> <table> <tbody> <tr> <td>0 <em>Coccinellidae septempunctata</em></td> </tr> <tr> <td>1 <em>Apis mellifera</em></td> </tr> <tr> <td>2 <em>Bombus lapidarius</em></td> </tr> <tr> <td>3 <em>Bombus terrestris</em></td> </tr> <tr> <td>4 <em>Eupeodes corolla</em></td> </tr> <tr> <td>5 <em>Episyrphus balteatus</em></td> </tr> <tr> <td>6 <em>Aglais urticae</em></td> </tr> <tr> <td>7 <em>Vespula vulgaris</em></td> </tr> <tr> <td>8 <em>Eristalis tenax</em></td> </tr> </tbody> </table> <p>The test dataset contains additional classes of insects.</p> <table> <tbody> <tr> <td>9 Non-Bombus Anthophila</td> </tr> <tr> <td>10 Bombus spp.</td> </tr> <tr> <td>11 Syrphidae</td> </tr> <tr> <td>12 Fly spp.</td> </tr> <tr> <td>13 Unclear insect</td> </tr> <tr> <td>14 Mixed animals:<br> ——————————<br> Rhopalocera<br> Non-Anthophila Hymenoptera<br> Non-Syrphidae Diptera<br> Non-Conccinalidae Coleoptera<br> Concinellidae<br> Other animals</td> </tr> </tbody> </table> <p><strong>There are two naming conventions for image (.jpg) and label (.txt) files.</strong></p> <p><em>Background images without insects are named</em>:<br> “<strong>X_Seq-YYYYMMDDHHMMSS</strong>-snapshot”.<br> E.g.:<br> Background image: 12_13-20190704172200-snapshot.jpg<br> Empty label file: 12_13-20190704172200-snapshot.txt</p> <p><em>Images annotated with insects are named:</em><br> “<strong>SZ_IP-MonthDate_C_Seq-YYYYMMDDHHMMSS</strong>”.<br> E.g.:<br> Image file: S1_146-Aug23_1_156-20190822133230.jpg<br> Label file: S1_146-Aug23_1_156-20190822133230.txt</p> <p><strong>Abbreviations</strong>:</p> <p><strong>YYYYMMDDHHMMSS </strong>– Capture timestamp with year, month, date, hour, minutes, and second<br> <strong>Seq</strong> – Sequence number created by the motion program to separate images<br> <strong>C</strong> – Identification of two cameras with Id=0 or Id=1 in system identified by <strong>SZ_IP</strong><br> <strong>MonthDate </strong>– Folder name for where the original image were stored in the system<br> <strong>SZ_IP</strong> – Identification of five camera systems: S1_123, S2_146, S3_194, S4_199, S5_187 (Two cameras in each system)<br> <strong>X</strong> – An index number related to a specific camera and folder ensuring unique file names of background images from different camera systems.<br> <br> The important information in a filename is system (<strong>SZ_IP</strong>), camera Id (<strong>C</strong>) and timestamp (<strong>YYYYMMDDHHMMSS</strong>).</p> <p><strong>The three best YOLOv5 models (YOLOv5models.zip) from the paper are available in pytorch format.</strong></p> <p>All models are tested with YOLOv5 release v7.0 (22-11-2022): <a href="https://github.com/ultralytics/yolov5">ultralytics/yolov5: YOLOv5 in PyTorch</a></p> <p><strong>insect1201-bestF1-640v5m.pt</strong>: Model no. 6 in Table 2 (F1=0.912)<br> <strong>insect1201-bestF1-1280v5m6.pt</strong>: Model no. 8 in Table 2 (F1=0.925)<br> <strong>insect1201-bestF1-1280v5m6.pt</strong>: Model no. 10 in Table 2 (F1=0.932)</p> <p><strong>insects-1201val.yaml</strong>: YAML file with label names to train YOLOv5</p> <p><strong>trainInsects-1201m.sh</strong>: Linux bash shell script with parameters to train YOLOv5m6<br> <strong>valInsectsF1-1201.sh</strong>: Linux bash shell script with parameters to validated models</p> <p> </p>
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.
Light and malaise traps tell different stories about the spatial variations in arthropod biomass and method-specific insect abundance
<p><span>1. Conclusions reached in meta-analyses of changes in insect communities may be influenced by method-specific sampling biases, which may lead to inappropriate conservation measures.</span></p> <p><span>2. </span><span>We argue that the contradictory conclusions regarding terrestrial insect biomass, abundance and richness patterns are, at least partly, due to methodological limitations that reflect taxon-specific responses to environmental changes.</span></p> <p><span>3. </span><span>In this study, light and Malaise traps were simultaneously deployed to sample insects at 52 plots in a temperate forest in Germany along gradients of elevation (> 1000 m) and canopy openness (3 - 100 %). These gradients were used as predictors in models of total arthropod biomass according to the two trapping methods, and in models of abundance and richness of three commonly targeted groups: nocturnal moths, sampled using light traps, and hoverflies and bees, collected with Malaise traps.</span></p> <p><span>4. </span><span>A comparison of the total arthropod biomass obtained with the two methods revealed contrary results along the canopy openness gradient. Biomass in light traps showed a decreasing trend with increasing canopy openness while biomass in Malaise traps increased. The same opposing pattern was found for the abundance of selected taxa.</span></p> <p><span>5. </span><span>The different patterns describing spatial variation of arthropod communities obtained using light and Malaise traps can be explained by differences in the taxa predominantly collected. Regarding the ongoing debate on insect decline, our results demonstrate that comparing different taxa from different taxon-specific traps is inappropriate. Thus, we recommend that future meta-analyses take into account the sampling methods and taxon-specific responses to environmental changes.</span></p>
2-class Grapevine Pest Dataset of Scaphoideus titanus and Orientus ishidae on yellow Sticky traps for Insect Detection
<p>This dataset consists of 615 images of <em>Scaphoideus titanus</em> (ST) and <em>Orientus ishidae</em> (OI) from yellow sticky traps (YST). Among these, 150 photos, which lack target insects, have been repurposed as background images. Insect annotations comprise 1329 ST and 1506 for OI, ensuring an almost class-balanced dataset. The images were acquired through four distinct methods:</p> <ul> <li>Photos from the field;</li> <li>Images of stored YST (T = 5±1°C) and reared insects within a controlled greenhouse environment;</li> <li>Digital scans of YST collected during regular monitoring activities in the fields;</li> <li>Photos from a smart trap prototype installed in our experimental vineyard.</li> </ul> Structure of the dataset, showing the number of images from each data source and the corresponding class annotations. <table><tbody> <tr> <td>Image source</td> <td>Number of images</td> <td>ST annotations</td> <td>OI annotations</td> <td>Number of background images</td> </tr> <tr> <td> <p>Field</p> </td> <td>18</td> <td>3</td> <td>101</td> <td>8</td> </tr> <tr> <td>Laboratory</td> <td>157</td> <td>473</td> <td>863</td> <td>8</td> </tr> <tr> <td>scanned</td> <td>390</td> <td>853</td> <td>542</td> <td>84</td> </tr> <tr> <td>smart-trap</td> <td>50</td> <td>0</td> <td>0</td> <td>50</td> </tr> </tbody> </table> <p>We provide the yellow sticky trap images already cropped in the pre-processing stage, the corresponding enhanced datasets focusing on <em>brightness & contrast</em>, <em>sharpness</em>, and a combination of both. Finally the annotations exported in YOLO format.</p> <h3>Dataset structure</h3> <ol> <li><em>crop/</em></li> <li><em>bright/</em></li> <li><em>sharp/</em></li> <li><em>bright_and_sharp/</em> </li> <li><em>labels/</em></li> </ol> <p>At the time of publication, this dataset is the largest publicly available resource in the control of FD vectors. Detailed documentation, along with model benchmarking and performance results is given in an accompanying journal paper: (paper under submission).</p> <h3>Deployment</h3> <p>You can use this dataset as starting point to train your own insect detection models. Open source Python scripts to deploy the trained models can be found in our <a href="https://github.com/checolag/insect-detection-scripts/tree/main">Github</a> repository.</p>
Figure 1 in Effects of agroecosystems on insect and insectivorous bat activity: a preliminary finding based on light trap and mist net captures
Figure 1. Map of Sekyere Central District showing study area (Kwamang) in Ghana.
Image dataset for training of an insect detection model for the Insect Detect DIY camera trap
<p>This dataset contains images of an artifical flower platform with different insects sitting on it or flying above it. All images were automatically recorded with the <a href="https://maxsitt.github.io/insect-detect-docs/">Insect Detect DIY camera trap</a>, a hardware combination of the Luxonis OAK-1, Raspberry Pi Zero 2 W and PiJuice Zero pHAT for automated insect monitoring (<a href="https://doi.org/10.1101/2023.12.05.570242">bioRxiv preprint</a>).</p><h2>Classes</h2><p>The following object classes were annotated in this dataset:</p><ul><li><strong>wasp</strong> (mostly <i>Vespula</i> sp.)</li><li><strong>hbee</strong> (<i>Apis mellifera</i>)</li><li><strong>fly</strong> (mostly Brachycera)</li><li><strong>hovfly</strong> (various Syrphidae, e.g. <i>Episyrphus balteatus</i>)</li><li><strong>other</strong> (all Arthropods with insufficient occurences, e.g. various Hymenoptera, true bugs, beetles)</li><li><strong>shadow</strong> (shadows of the recorded insects)</li></ul><p>View the <a href="https://universe.roboflow.com/maximilian-sittinger/insect_detect_detection/health">Health Check</a> for more info on class balance.</p><h2>Versions</h2><ul><li><a href="https://universe.roboflow.com/maximilian-sittinger/insect_detect_detection/dataset/4">v4 insect_detect_416_1class</a><ul><li>squashed to square (aspect ratio 1:1)</li><li>downscaled to 416x416 pixel</li><li>all classes merged into one class ("insect")</li></ul></li><li><a href="https://universe.roboflow.com/maximilian-sittinger/insect_detect_detection/dataset/5">v5 insect_detect_raw_4K</a><ul><li>original images in 4K resolution (3840x2160 pixel)</li></ul></li><li><a href="https://universe.roboflow.com/maximilian-sittinger/insect_detect_detection/dataset/7">v7 insect_detect_320_1class</a><ul><li>squashed to square (aspect ratio 1:1)</li><li>downscaled to 320x320 pixel</li><li>all classes merged into one class ("insect")</li></ul></li></ul><h2>Deployment</h2><p>You can use this dataset as starting point to train your own insect detection models. Check the <a href="https://maxsitt.github.io/insect-detect-docs/modeltraining/train_detection/">model training instructions</a> for more information.</p><p>Open source Python scripts to deploy the trained models can be found at the <a href="https://github.com/maxsitt/insect-detect">insect-detect GitHub repo</a>.</p>
Pan trap and plant-flower visitor observation data for: Multi-species crop mixtures increase insect biodiversity in an intercropping experiment
<ol> <li><span>Recent biodiversity declines require action across sectors such as agriculture. The situation is particularly acute for arthropods, a species-rich taxon providing important ecosystem services. To counteract negative consequences of agricultural intensification, creating a less hostile agricultural "matrix" through growing crop mixtures can reduce harm for arthropods without yield losses. </span></li> <li><span>While grassland biodiversity experiments showed positive plant biodiversity effects on arthropods, experiments manipulating crop diversity and agrochemical input use to study arthropods are lacking. </span></li> <li><span>Here, we experimentally manipulated crop diversity (1–3 species, fallows), crop species (wheat, faba bean, linseed, oilseed rape) and agrochemical input (high vs. low) and studied responses of arthropod biodiversity. We tested if arthropod responses were affected by crop diversity, mixtures and management. Additionally, we measured crop biomass.</span></li> <li><span>Crop biomass increased with crop diversity under high-input mangement, while under low management intensity, biomass was highest in two-species mixtures.</span></li> <li><span>Increasing crop diversity positively affected arthropod abundance and diversity, both under low- and high-input management. Crop mixtures containing faba bean, linseed or oilseed rape had particularly high arthropod diversity.</span></li> <li><span>Mass-flowering crops attracted more arthropods than legumes or cereals. Integrating intercropping into agricultural systems could increase flower visits by insects up to 15 million per hectare, thus likely also supporting pollination and pest-control ecosystem services.</span></li> <li><span>Flower-visitor network complexity increased in mixtures containing linseed and faba bean, and under low-input management.</span></li> <li><span>Intercropping can counteract insect declines in farmland by creating beneficial matrix habitat without compromising crop yield.</span></li> </ol>
ScienceDex guides
Understand access before you commit
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.