Remote Sensing in Ecology and Conservation最新文献

{"title":"Remote Sensing‐Based Identification of Habitat Loss for Herbivorous Waterbirds Driven by Wetland Inundation Alterations","authors":"Dongshuo Lu,Haobo Zhang,Peng Zhang,Xiaoning Liu,Liwei Chang,Lei Cheng,Jianbo Chang","doi":"10.1002/rse2.70107","DOIUrl":"https://doi.org/10.1002/rse2.70107","url":null,"abstract":"ABSTRACT Altered inundation regimes are reshaping floodplain wetlands and degrading wintering habitats for herbivorous waterbirds, yet long‐term, spatially explicit assessments that link hydrological change to habitat dynamics remain limited. Here, we developed a remote sensing‐based deep learning framework to reconstruct wintering habitat dynamics of herbivorous waterbirds in Dongting Lake from 2000 to 2021. The framework integrates multi‐source environmental predictors, receptive‐field optimization for spatial–context recognition, and Shapley Additive Explanations (SHAP) analysis for ecological interpretation. Results revealed marked habitat decline over the study period, with total habitat area decreasing by nearly half. Habitat loss was highly uneven, with the most severe declines occurring in South (−77.3%) and West (−89.2%) Dongting Lake, while East Dongting Lake remained comparatively stable. Landscape metrics further indicated progressive habitat contraction, increased isolation, and structural simplification, reflecting weakened spatial connectivity and reduced ecological resilience. Among all predictors, water recession timing emerged as the most influential predictor of habitat suitability, with recession in mid‐to‐late October associated with the highest habitat probability. A comparison with the NDVI time series showed that this recession window coincided with higher vegetation productivity. These findings demonstrate that wintering habitat dynamics are strongly associated with hydrological timing, particularly water recession phenology, rather than static water extent alone. The proposed framework provides a transferable approach for identifying hydrological controls on waterbird habitats and offers a scientific basis for adaptive wetland management along migratory flyways.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"16 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148895359","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Synthetic Imagery Improves Ecological Classification When Real Data Are Scarce, Not Direct Substitutes","authors":"Thomas A. Lake,Brennen T. Farrell,Ross K. Meentemeyer,Chris M. Jones","doi":"10.1002/rse2.70105","DOIUrl":"https://doi.org/10.1002/rse2.70105","url":null,"abstract":"ABSTRACT The growing availability of ecological imagery creates new opportunities to monitor biodiversity and ecosystem change at broad scales. Yet image classification models remain constrained by uneven sampling, limited labeled data, and differences between training and deployment imagery. Synthetic images generated with generative methods may help address these limitations, but their value across image domains remains poorly tested. We evaluated whether synthetic imagery improves genus‐level classification for 20 North American tree genera using iNaturalist community‐science photographs and Auto Arborist street‐view images. We generated domain‐specific synthetic images with Stable Diffusion v1.5 and trained ResNet‐50 classifiers on controlled mixtures of real and synthetic data. We tested synthetic replacement, supplementation under data scarcity, conventional augmentation alternatives, and transfer between image domains. Replacing real images with synthetic images reduced performance on held‐out real test sets, although declines differed between domains. Adding synthetic images improved classification when real classifier‐training data were scarce, but gains diminished as real data increased. In cross‐domain tests, synthetic supplementation improved some scarce‐data baselines but did not close the transfer gap or replace real‐image training sets. Synthetic images were therefore most useful as supplements rather than direct substitutes. For ecological applications, generated images may support model development for poorly sampled taxa, but representative real images remain essential for robust model generalization.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"35 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-09-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148895357","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving Demographics and Conservation of Fishes Through Multi‐Frame Individual Distinctions by Size ( MIDS )","authors":"Hayden Swift, David Harasti, Megan Huggett, Michael Stat, Troy Gaston","doi":"10.1002/rse2.70104","DOIUrl":"https://doi.org/10.1002/rse2.70104","url":null,"abstract":"Reliable estimation of fish abundance and demographic structure is critical for assessing ecosystem condition and informing conservation management. Non‐invasive surveys, particularly remote video, are increasingly used for ecological monitoring; however, existing analytical approaches cannot distinguish individuals across video sequences unless they are observed simultaneously within the same video frame. Consequently, the number of individuals and their life‐stage compositions may be underestimated relative to the footage captured. Here, we present MIDS (Multi‐frame Individual Distinctions by Size), a novel analytical software that uses length measurements, precision error and statistical inference to distinguish individuals across multiple frames within a video sequence. We applied MIDS to 48 stereo‐video deployments across two estuaries (Wallis Lake and Lake Macquarie, New South Wales, Australia) and two habitat types (rocky reefs and sandy beaches), and compared abundance and life‐stage estimates to the single‐frame MaxN metric. MIDS significantly increased abundance estimates across most stereo‐camera deployments despite differences among habitats and estuaries. The largest increases occurred within juvenile cohorts of Yellowfin Bream ( <jats:styled-content style=\"fixed-case\"> <jats:italic>Acanthopagrus australis</jats:italic> </jats:styled-content> ), Sand Whiting ( <jats:styled-content style=\"fixed-case\"> <jats:italic>Sillago ciliata</jats:italic> </jats:styled-content> ) and Luderick ( <jats:styled-content style=\"fixed-case\"> <jats:italic>Girella tricuspidata</jats:italic> </jats:styled-content> ), where the median difference between MIDS and MaxN ranged from one to six additional juveniles per camera deployment across most estuaries and habitats (all effect sizes <jats:italic>r</jats:italic> &gt; 0.70). Increases among adult and harvestable life stages were generally smaller and based on fewer observations, reducing statistical power to detect consistent differences between methods. While MIDS provides an improved analytical framework for extracting additional abundance and demographic information from stereo‐video surveys, further validation is required before its broader applications to conservation and management can be fully realised. Nevertheless, MIDS opens opportunities for improved data extraction from spatially separated camera arrays, longer sampling durations and emerging AI‐driven length and identification workflows, by enabling the removal of repeat observations across time. Collectively, these advances position MIDS as a scalable tool for strengthening ecological inference in aquatic systems.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"43 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148860795","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Paradigms and Principles for Integrating Nature Technology Data: Challenges, Opportunities, and Next Steps in Real‐World Biodiversity Monitoring","authors":"Seamus Lombardo, Amy Rosenthal, Christopher Anderson, Ken Bagstad, Carly Batist, Christopher Beirne, Paul Bunje, Marconi Campos‐Cerqueira, Jeremy Cusack, Elsa Carla De Grandi, Rachel King, Trond Larsen, David Luther, Jennifer McGowan, Adrien Pajot, Xavier Raick, Vijay Ramesh, Chelsea Smith, Ali Swanson, Tom Walker","doi":"10.1002/rse2.70100","DOIUrl":"https://doi.org/10.1002/rse2.70100","url":null,"abstract":"Employing a combination of nature technology data (NTD) from satellites, eDNA, camera traps, acoustic monitors, and GPS can augment traditional biodiversity measurements to help understand conservation status and outcomes. While previous works have integrated these data, there remains a significant gap between individual exploratory cases and replicable, scalable, and standardized approaches. Notably, there is no clear guidance or consensus on how NTD are best integrated in practice. To address this gap, we propose a set of paradigms (survey design, tip and cue, validation, consilience, interpolation and extrapolation, and data fusion) and prioritize a set of principles (metadata standards, use rights and licensing, scientific rigor, and utility) as the foundations of an actionable framework for NTD integration. We then analyze seven diverse, real‐world case studies across biomes, use cases, and user types to understand what paradigms are leveraged in practice, how prioritized principles have been implemented, and identify challenges, opportunities, and next steps. Distilling NTD integration into paradigms helped map the relationships between workflows, such as how low‐barrier‐to‐entry methods (like consilience) can lead to deeper integration (extrapolation or data fusion), and revealed that there are different purposes and tools needed even when sensor use is similar. The paradigms also helped elucidate key challenges (such as mismatches in scale and lack of data, reference libraries, and methodological guidance) and opportunities (increasing data resolution, systematizing guidance, and producing new repeatable tools). For prioritized principles, metadata standards and vehicles for sharing permissions and use rights could increase impact and reuse. We conclude that next steps, including new training resources, use of high‐resolution satellite data, increased multimodal field data collection, use of standardized repositories, and APIs that support integration, are needed to operationalize these paradigms and principles. These steps will provide a foundation for realizing the potential of integrated biodiversity monitoring to help guide conservation action.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"73 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-08-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148809808","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Discovery and Fine‐Scale Mapping of Hadal Xenophyophore Habitat in the Nova Canton Trough, Central Pacific Ocean","authors":"Denise J. B. Swanborn, Joana Gafeira, Megan Cundy, Daniel C. Bates, Heather A. Stewart, Alan J. Jamieson","doi":"10.1002/rse2.70101","DOIUrl":"https://doi.org/10.1002/rse2.70101","url":null,"abstract":"The occurrence and heterogeneity of biogenic structures at hadal depths remain poorly mapped and quantified at ecologically relevant scales. Xenophyophores can be conspicuous biogenic components of deep‐sea sedimentary environments, but records of their abundance, spatial configuration, structural complexity and ecological role are scarce at hadal depths. Here, we describe the discovery of a dense epifaunal xenophyophore population at ~8000 m depth in the Nova Canton Trough. We integrate high‐resolution photogrammetry with semi‐automated delineation approaches to map and quantify their abundance and distribution and, relate this to the occurrence of benthic metazoan taxa. Spatial point pattern analysis and terrain metrics were used to characterise horizontal aggregation, vertical relief and surface complexity across 20 transects, with a total length of ~200 m. Xenophyophores were highly abundant with a mean density of 35 ind/m <jats:sup>2</jats:sup> and an estimated footprint of 4.1% of the seafloor. Principal Component Analysis revealed partially decoupled gradients in their horizontal aggregation and vertical structure, indicating that habitat heterogeneity reflects both variation in spatial configuration and structural complexity. Distance‐based linear models indicated that spatial patterning was significantly associated with metazoan community composition. Negative binomial generalised linear models showed taxon‐specific responses, with several taxa positively associated with elevated xenophyophore density, and xenophyophore height driving amphipod abundance. Results demonstrate that xenophyophores are prevalent on the sedimentary hadal basin floor of the Nova Canton Trough and are consistent with a role as biogenic habitat formers, generating structurally complex habitats that host associated assemblages. Methodologically, this study illustrates the value of photogrammetry to enable the use of semi‐automated mapping tools to characterise deep‐sea biogenic habitats.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"23 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-08-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148809809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"How Passive Acoustic and Traditional Monitoring Estimate Bird Diversity Across Habitats","authors":"Jarek Scanferla, Julia Seeber, Giordano Brambilla, Mattia Brambilla, Andreas Hilpold, Ulrike Tappeiner, Emmanuel Requena, Matteo Anderle","doi":"10.1002/rse2.70098","DOIUrl":"https://doi.org/10.1002/rse2.70098","url":null,"abstract":"Bird monitoring techniques have evolved substantially in recent years. While the most common protocols remain the point count and transect survey, new technologies such as passive acoustic monitoring (PAM) are becoming increasingly popular. As more monitoring programs adopt PAM, it is crucial to understand how comparable it is with classical monitoring derived data. We compared diversity indices (i.e., species richness, Shannon diversity and Jaccard index) derived from both classical (by means of observer‐based point counts) and passive acoustic (by means of recording devices) monitoring and developed a novel estimated Shannon index based on PAM, using species‐specific confidence thresholds and detection rates. We applied the protocols at 126 sites in a heterogeneous landscape within the Italian Alps, enabling us to investigate for the first time whether the effectiveness of different monitoring schemes depends on the habitat type. We show that PAM captured approximately twice as many species as the classical point count method. The species richness estimated by both protocols was strongly correlated (r <jats:sub>s</jats:sub> = 0.71), but with major differences between habitat types. The difference was greater in diversity hotspots such as wetlands and meadows and lower in alpine habitats. The novel PAM‐based Shannon index was very reliable and yielded similar results to the observer‐based Shannon index, illustrating its effectiveness. Species composition differed significantly between the two protocols (average Jaccard index = 0.38). Although this study highlights the additional information that PAM can provide, bird counts remain essential as they offer further insights (e.g., breeding status) and not all species can be captured by PAM (e.g., acoustically indistinguishable). As point counts are time‐ and cost‐efficient, they will continue to play an important role in bird monitoring. Our results highlight the strengths and weaknesses of each approach and provide recommendations for future monitoring programs.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"1 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-08-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148729070","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A Tracking‐By‐Detection Framework to Automatically Count Rare and Elusive Megafauna in Aerial Video Surveys","authors":"Laura Mannocci, Auguste Verdier, Louis Bézard, Valentine Fleuré, Gerard Subsol, Marc Chaumont, David Mouillot","doi":"10.1002/rse2.70097","DOIUrl":"https://doi.org/10.1002/rse2.70097","url":null,"abstract":"Video surveys from drones or planes equipped with cameras have become invaluable tools for monitoring populations of rare and elusive megafauna over increasingly large spatial scales. To analyze hours of such video surveys, automated species detection approaches based on deep learning are now widely used, but simply tallying detections on the successive frames of videos leads to double counting of individuals, significantly biasing population size estimates. Here, we leverage a multiple object tracking approach that links detections across video frames to derive counts of unique individuals. Our tracking‐by‐detection framework integrates the full pipeline from aerial video surveys to automated individual count and estimation of minimum population abundance. Applied to aerial video surveys of dugongs ( <jats:styled-content style=\"fixed-case\"> <jats:italic>Dugong dugon</jats:italic> </jats:styled-content> ) on the west coast of New Caledonia in winter, our framework automatically counted individuals with a slight negative bias of −5.7% (−15 individuals over a total of 265 ground‐truth individuals) compared to a baseline method relying on the sum of detections that led to a 48 times overestimation. Our tracking‐by‐detection framework directly estimated the number of unique individuals per flight, leading to a mean seasonal index of abundance of 42 individuals (±28 SD) in the surveyed area. Overall, 35 individuals were missed across flights, representing 13.2% of all ground‐truth individuals. Among missed individuals, two‐thirds were calves superposed to their mothers, stressing the challenge of occlusion when counting group‐living animals. Despite being applied to entire videos with very low prevalence of dugongs, our framework generated just 8% of false positives (20 instances) across all flights, mostly due to misidentifications with other species. Illustrated through the case of dugong, our tracking‐by‐detection framework more generally provides a scalable method to support digital population surveys, benefitting long‐term monitoring and conservation programs of vulnerable megafauna.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"166 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148717651","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Widespread Bomb Fishing in Indo‐Pacific Archipelago Revealed Through Machine Learning Accelerated Passive Acoustic Monitoring","authors":"Ben Williams,Mars Coral Restoration Project Monitoring Team,Aimee du Luart,Sonny Burniston,Tries B. Razak,Timothy A. C. Lamont,David J. Smith,Jamaluddin Jompa,David Curnick,Kate E. Jones","doi":"10.1002/rse2.70093","DOIUrl":"https://doi.org/10.1002/rse2.70093","url":null,"abstract":"ABSTRACT Bomb fishing is recognised as the most destructive fishing practice that can be performed in our oceans. This practice involves the use of improvised explosives to stun or kill fish for harvest. Bomb fishing is primarily undertaken on highly threatened tropical coral reef habitats where it accelerates ecological degradation that destabilises communities dependent on these habitats. However, the true extent of bomb fishing in impacted regions is unknown, making it difficult to identify hotspots and activity patterns required to inform management. Here, we present the first quantitative estimate of bomb fishing intensity and temporal patterns in the Spermonde Archipelago, South Sulawesi, Indonesia—a global marine biodiversity hotspot. We developed a machine learning‐based acoustic monitoring system (BombDetect), with an estimated detection distance of at least 16.95 km, and deployed this over a 16‐month period to measure bomb fishing incidents. From the 3698.88 h of audio gathered, we detected and verified 3,567 blasts, equivalent to 23.14 per day, equating to 8446.1 blasts and up to 16.72 ha of reef degradation per year. Bomb fishing occurred year‐round, mostly during daylight hours, with significant reductions on Fridays (local prayer day) and during a period of stormy weather. These findings quantify patterns of occurrence of a dominant driver of long‐term reef decline in the region. We demonstrate the efficacy of a low‐cost approach for quantifying this underreported destructive fishing pressure at scale, which can be readily adapted to other impacted regions to support urgent conservation action.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"9 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148896054","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Efficient and Accurate Spatio‐Temporal Population Monitoring of Mobile Animals Using Drones and Computer Vision","authors":"Benjamin Koger, Albert B. Kao, Curry J. Cunningham, Thomas P. Quinn, Caroline Kittle, Andrew M. Berdahl","doi":"10.1002/rse2.70096","DOIUrl":"https://doi.org/10.1002/rse2.70096","url":null,"abstract":"Estimating population sizes is critical for fish and wildlife management but often time‐intensive and sometimes unreliable. Aerial imagery coupled with automated computer vision tools has emerged as an efficient way to obtain robust abundance estimates in certain contexts. However, to accurately estimate abundance from survey imagery without double counting, current methods typically rely on human observers or image stitching that requires motionless animals, thus limiting scalability and the range of suitable survey conditions. Here, using simulations and a field case study, we demonstrate two computer vision methods for surveying animal populations from aerial imagery that, when animals are correctly detected, generate unbiased counts without human intervention, even when animals move during surveys. One approach partitions sections of the raw survey imagery and associated detections into a continuous non‐overlapping mosaic of the survey area using calculated 3D landscape information. The second approach combines all counts from all images and weights detections inversely by the number of overlapping images at each survey location. In our case study of spawning salmon in Alaska, both automated approaches yield counts with mean absolute percentage error (MAPE) of &lt; 12%, with the best achieving 5.5% MAPE. This is similar to on‐the‐ground counts made by human observers as part of a long‐term field monitoring effort (MAPE: 9.4%) and far outperforms existing orthomosaic counting methods (MAPE: 44.4%). Our method also generates fine‐scale locations of each individual, providing novel ecological descriptions of the system. Our approach to surveying highly mobile animals is directly applicable to aerial surveys across many populations and species. Its robustness to animal movement and the ability to easily incorporate detections from artifact‐free raw survey imagery versus orthomosaics will allow researchers and resource managers to monitor a wider range of animal populations while collecting more detailed ecological information at higher frequency than current methods allow.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"47 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-07-31","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148633760","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Development and Evaluation of a Low‐Cost, Semi‐Automated Camera Trap for Surveying Bumble Bee Communities","authors":"Michael P. Getz, Lincoln R. Best, Oksana Ostroverkhova, Andony P. Melathopoulos, Timothy L. Warren","doi":"10.1002/rse2.70091","DOIUrl":"https://doi.org/10.1002/rse2.70091","url":null,"abstract":"Widespread declines in insect diversity and abundance underscore an urgent need for standardized, nonlethal monitoring methods for important pollinators such as bumble bees ( <jats:italic>Bombus</jats:italic> spp.). Camera traps are widely used for nonintrusive, continuous surveys of large animals but have not yet been extensively adopted for monitoring diurnal insects. Automated camera traps could improve current insect survey methods, which often rely on lethal traps or in‐person observation. Here we developed an open‐source, low‐cost camera trap for monitoring wild insects and evaluated its performance relative to established sampling approaches. We used a low‐power microcomputer to collect time‐lapse images on colored platforms that served as non‐rewarding visual lures. We compared the performance of whole‐image and tiled deep‐learning models for automated detection of small insects in images captured by the traps. We found that tiled inference—which performs detection inside subframes of the original image—significantly improved accuracy, even outperforming human reviewers. Bumble bee visitation was highest on platforms with a repeating fluorescent bullseye pattern. Via continuous monitoring, the camera traps recorded bumble bee visits during all daylight hours over 18 days. From the recorded images, expert human taxonomists identified six <jats:italic>Bombus</jats:italic> species, yielding community composition and diversity estimates comparable to those obtained by hand netting. We simulated the effect of deploying variable numbers of cameras at sites with two levels of diversity. These simulations suggested that adding cameras substantially increased sampling completeness, particularly in species‐rich communities. By pairing low‐cost, semi‐automated camera traps with deep‐learning image analysis, our work provides a foundation for scalable, nonlethal studies of bumble bee diversity and activity patterns.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"61 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148633761","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}