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Camera traps are an effective tool for monitoring insect–plant interactions
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What this work says
Outgoing claims: the papers this work cites, builds on, or argues with.
inferredcites →https://doi.org/10.1002/ece3.8962openalex-snapshot
Camera traps are an effective tool for monitoring insect–plant interactions
inferredcites →https://doi.org/10.1002/ecy.3482openalex-snapshot
Nocturnal ambush predators and their potential impact on flower‐visiting moths
inferredcites →https://doi.org/10.1002/fee.1436openalex-snapshot
Assessing data quality in citizen science
inferredcites →https://doi.org/10.1006/anbo.2000.1241openalex-snapshot
Nectar Amount, Pollinator Visit Duration and Pollination Success in the Mediterranean Shrub Cistus creticus
inferredcites →https://doi.org/10.1007/s11252-017-0674-1openalex-snapshot
Soil lead contamination decreases bee visit duration at sunflowers
inferredcites →https://doi.org/10.1016/j.ecolecon.2008.06.014openalex-snapshot
Economic valuation of the vulnerability of world agriculture confronted with pollinator decline
inferredcites →https://doi.org/10.1016/j.tree.2005.09.004openalex-snapshot
Pollinator diversity and crop pollination services are at risk
inferredcites →https://doi.org/10.1016/j.tree.2010.01.007openalex-snapshot
Global pollinator declines: trends, impacts and drivers
inferredcites →https://doi.org/10.1042/etls20190074openalex-snapshot
Automated video monitoring of insect pollinators in the field
inferredcites →https://doi.org/10.1093/ae/58.3.172openalex-snapshot
Big Brother is Watching: Studying Insect Predation in the Age of Digital Surveillance
inferredcites →https://doi.org/10.1111/1365-2664.13755openalex-snapshot
Pollinator monitoring more than pays for itself
inferredcites →https://doi.org/10.1111/1365-2664.13884openalex-snapshot
Roads pose a significant barrier to bee movement, mediated by road size, traffic and bee identity
inferredcites →https://doi.org/10.1111/2041-210x.12654openalex-snapshot
Diel activity, frequency and visit duration of pollinators in focal plants: in situ automatic camera monitoring and data processing
inferredcites →https://doi.org/10.1111/2041-210x.13011openalex-snapshot
Scene‐specific convolutional neural networks for video‐based biodiversity detection
inferredcites →https://doi.org/10.1111/2041-210x.13618openalex-snapshot
PICT: A low‐cost, modular, open‐source camera trap system to study plant–insect interactions
inferredcites →https://doi.org/10.1111/een.12394openalex-snapshot
The use of digital video recorders in pollination biology
inferredcites →https://doi.org/10.1111/een.12827openalex-snapshot
From dusk till dawn: camera traps reveal the diel patterns of flower feeding by hawkmoths
inferredcites →https://doi.org/10.1111/j.0030-1299.2005.13903.xopenalex-snapshot
Crab spiders deter insect visitations to slickspot peppergrass flowers
inferredcites →https://doi.org/10.1111/j.1523-1739.2011.01657.xopenalex-snapshot
Evaluating the Quality of Citizen‐Scientist Data on Pollinator Communities
inferredcites →https://doi.org/10.1111/j.1600-0706.2010.18644.xopenalex-snapshot
How many flowering plants are pollinated by animals?
inferredcites →https://doi.org/10.1126/science.1127863openalex-snapshot
Parallel Declines in Pollinators and Insect-Pollinated Plants in Britain and the Netherlands
inferredcites →https://doi.org/10.1139/b08-068openalex-snapshot
Orchid–bird interactions: a case study from Angraecum (Vandeae, Angraecinae) and Zosterops (white-eyes, Zosteropidae) on Reunion Island
inferredcites →https://doi.org/10.1371/journal.pone.0150794openalex-snapshot
Focal Plant Observations as a Standardised Method for Pollinator Monitoring: Opportunities and Limitations for Mass Participation Citizen Science
inferredcites →https://doi.org/10.1371/journal.pone.0239504openalex-snapshot
Tracking individual honeybees among wildflower clusters with computer vision-facilitated pollinator monitoring
inferredcites →https://doi.org/10.1371/journal.pone.0251572openalex-snapshot
Towards precision apiculture: Traditional and technological insect monitoring methods in strawberry and raspberry crop polytunnels tell different pollination stories
inferredcites →https://doi.org/10.18637/jss.v067.i01openalex-snapshot
Fitting Linear Mixed-Effects Models Using lme4
inferredcites →https://doi.org/10.21105/joss.03139openalex-snapshot
performance: An R Package for Assessment, Comparison and Testing of Statistical Models
inferredcites →https://doi.org/10.2307/1940859openalex-snapshot
Sex and the Single Mustard: Population Density and Pollinator Behavior Effects on Seed‐Set
inferredcites →https://doi.org/10.2307/2265753openalex-snapshot
Analysis of Frequency Count Data Using the Negative Binomial Distribution
inferredcites →https://doi.org/10.2307/3565470openalex-snapshot
Predatory Risk to Insects Foraging at Flowers
inferredcites →https://doi.org/10.2307/jj.28833751openalex-snapshot
Camera Trapping for Wildlife Research
inferredcites →https://doi.org/10.26786/1920-7603(2011)20openalex-snapshot
From birds to bees: applying video observation techniques to invertebrate pollinators
inferredcites →https://doi.org/10.32614/r.manualsopenalex-snapshot
R: A Language and Environment for Statistical Computing
What points at this work
Incoming claims: where this work appears in other papers’ reference lists and arguments.
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Spatio-Temporal Variation in Arthropod-Plant Interactions Identified Using Edna and Digital Video Recordings
inferred← citeshttps://doi.org/10.1002/3070-5436.70005openalex-snapshot
Insect monitoring without pitfalls: Seven steps for robust insect sensing systems
inferred← citeshttps://doi.org/10.1002/ece3.11357openalex-snapshot
Shooting area of infrared camera traps affects recorded taxonomic richness and abundance of ground‐dwelling invertebrates
inferred← citeshttps://doi.org/10.1002/ece3.70032openalex-snapshot
Using motion‐detection cameras to monitor foraging behaviour of individual butterflies
inferred← citeshttps://doi.org/10.1002/ece3.70327openalex-snapshot
Multi‐method approach to assessing the floral‐visiting insect assemblage of rare, ambophilous plant Baccharis vanessae in Southern California
inferred← citeshttps://doi.org/10.1002/ece3.70947openalex-snapshot
Ecological Insights From Camera Trapping Span Biological Taxa, and the Globe
inferred← citeshttps://doi.org/10.1002/edn3.411openalex-snapshot
Environmental DNA metabarcoding from flowers reveals arthropod pollinators, plant pests, parasites, and potential predator–prey interactions while revealing more arthropod diversity than camera traps
inferred← citeshttps://doi.org/10.1002/oik.11552openalex-snapshot
Trait matching affects the probability of nectar robbing in plant–pollinator networks
inferred← citeshttps://doi.org/10.1002/rse2.70091openalex-snapshot
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inferred← citeshttps://doi.org/10.1007/s10841-026-00809-5openalex-snapshot
Alternate floral resources within apple orchards increase apple flower visitation
inferred← citeshttps://doi.org/10.1007/s11263-026-02803-5openalex-snapshot
A Motion-Based Compression and Tracking System for Video Camera Trap-Based Insect Behaviour Studies
inferred← citeshttps://doi.org/10.1007/s11829-024-10055-8openalex-snapshot
Ants vs crabs: competition over seeds on a tropical island ecosystem
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Intercropped rye strips constrain the within-field foraging behavior of wild and managed bees on watermelon flowers
inferred← citeshttps://doi.org/10.1016/b978-0-443-40490-0.00006-4openalex-snapshot
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inferred← citeshttps://doi.org/10.1016/j.agee.2025.109670openalex-snapshot
Solitary bees reduce pollination and production deficits in apple cultivation
inferred← citeshttps://doi.org/10.1016/j.baae.2026.04.007openalex-snapshot
Prolonged reduction in grassland arthropod activity after mowing revealed by AI camera trap monitoring
inferred← citeshttps://doi.org/10.1016/j.biocon.2026.112007openalex-snapshot
3D-printed flowers for standardised pollinator monitoring
inferred← citeshttps://doi.org/10.1016/j.biocon.2026.112008openalex-snapshot
Closer to fine: Surviving interactions between Hawaiian honeycreepers and a critically endangered aster
inferred← citeshttps://doi.org/10.1016/j.compag.2026.112110openalex-snapshot
Automated insect behavioral phenotyping with computer vision and deep learning: Current knowledge and future directions
inferred← citeshttps://doi.org/10.1016/j.cropro.2026.107868openalex-snapshot
Citrus production in sub-Saharan Africa: status, gaps in pest and pollinator management, and opportunities for novel monitoring technologies
inferred← citeshttps://doi.org/10.1016/j.ecolind.2024.112827openalex-snapshot
Spatio-temporal variation in arthropod-plant interactions: A direct comparison of eDNA metabarcoding of tree crop flowers and digital video recordings
inferred← citeshttps://doi.org/10.1016/j.ecolind.2026.114856openalex-snapshot
Measuring what matters: operationalising biodiversity indicators for results-based CAP monitoring
inferred← citeshttps://doi.org/10.1016/j.molp.2024.06.011openalex-snapshot
Phenotyping floral attractiveness to pollinators using volatilomics, 3D imaging, and insect monitoring
inferred← citeshttps://doi.org/10.1016/j.sajb.2023.08.015openalex-snapshot
The importance of wild pollinators for indigenous crop pollination: The case of Cyclopia (honeybush)
inferred← citeshttps://doi.org/10.1038/s41598-023-43482-3openalex-snapshot
YOLO object detection models can locate and classify broad groups of flower-visiting arthropods in images
inferred← citeshttps://doi.org/10.1093/jisesa/ieag091openalex-snapshot
Machine learning workflows detect ground-dwelling arthropods against heterogenous backgrounds
inferred← citeshttps://doi.org/10.1098/rstb.2022.0232openalex-snapshot
Camera trapping expands the view into global biodiversity and its change
inferred← citeshttps://doi.org/10.1109/cvprw67362.2025.00487openalex-snapshot
EV-Flying: An Event-Based Dataset for In-the-Wild Recognition of Flying Objects
inferred← citeshttps://doi.org/10.1109/iotais67227.2025.11282090openalex-snapshot
PELC: Power-Efficient and Low-Cost IoT-Based Real Time Operating System Enabled Solution For Biodiversity Application
inferred← citeshttps://doi.org/10.1109/jsen.2025.3566171openalex-snapshot
Power- and Memory-Efficient Low-Cost IoT Sensing System for Biodiversity Surveillance Developed on Resource-Constrained Platform
inferred← citeshttps://doi.org/10.1109/wacv57701.2024.00700openalex-snapshot
Towards a Dynamic Vision Sensor-based Insect Camera Trap
inferred← citeshttps://doi.org/10.1111/1442-1984.70048openalex-snapshot
Effectiveness of infrared sensor camera monitoring for detecting pollinator visits to Cremastra appendiculata var. variabilis in a temperate forest in Central Japan
inferred← citeshttps://doi.org/10.1111/1755-0998.13814openalex-snapshot
eDNA metabarcoding of avocado flowers: ‘Hass’ it got potential to survey arthropods in food production systems?
inferred← citeshttps://doi.org/10.1111/2041-210x.14322openalex-snapshot
Monitoring fast‐moving animals—Building a customized camera system and evaluation toolset
inferred← citeshttps://doi.org/10.1111/2041-210x.70098openalex-snapshot
Towards scalable insect monitoring: Ultra‐lightweight CNNs as on‐device triggers for insect camera traps
inferred← citeshttps://doi.org/10.1111/2041-210x.70165openalex-snapshot
Comparative assessment of automated and manual monitoring in comprehensive plant–pollinator communities
inferred← citeshttps://doi.org/10.1111/afe.70081openalex-snapshot
Comparing time‐lapse photography and traditional human observation in collecting crop pollinator visitation data
inferred← citeshttps://doi.org/10.11160/bah.340openalex-snapshot
New perspectives on Iberian vipers monitoring: camera trapping on Vipera aspis and V. latastei populations, methodological insights and behavioural records
inferred← citeshttps://doi.org/10.1139/cjz-2024-0084openalex-snapshot
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inferred← citeshttps://doi.org/10.26786/1920-7603(2026)893openalex-snapshot
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