Remote Sensing in Ecology and Conservation最新文献

{"title":"Rhyming in the cold: first evidence of soniferous fishes in the Southern Ocean","authors":"Fannie W. Shabangu, Grant van der Heever, Charles von der Meden, Hannah Truter, Stephen J. Lamberth, Ofer Gon","doi":"10.1002/rse2.70065","DOIUrl":"https://doi.org/10.1002/rse2.70065","url":null,"abstract":"Acoustic ecology of Southern Ocean fishes is currently unknown due to lack of dedicated fish acoustic research from those remote/inaccessible areas. The objective of this study was to investigate the monthly and diel acoustic occurrence pattern of benthic fishes relative to environmental conditions at the sub‐Antarctic Prince Edward Islands (PEIs) in the Southern Ocean. To collect our passive acoustic data, we used an autonomous recorder deployed at ~167 m water depth on an oceanographic mooring over 21 months (April 2021 to December 2022). Benthic Ski‐Monkey III towed camera was deployed around the PEIs to identify potential sources of recorded underwater fish sounds. Three types of sounds (pops, grunts and drum sounds) were detected and validated using random forest models based on their characteristics. Pops and grunts were produced in series and as singlets. Pops were the most frequently detected sounds and were detected in December 2021 through May 2022, whereas grunts were detected in January through March 2022. Drum sounds were rare and were detected as singlets on a few occasions in December 2021 through March 2022. These monthly fish occurrences correspond to the breeding season of fishes in the Southern Ocean, suggesting the use of acoustic cues during breeding. From camera footage, <jats:italic>Nototheniops larseni</jats:italic> (painted notothen) was the only fish species found around the acoustic recorder location, and pops were putatively attributed to this abundant species, whereas other sounds were attributed to other observed species. Fish sound occurrence increased around sunrise and sunset. Sea surface temperatures between 5.2°C and 8°C were the primary predictor of fish acoustic occurrence, underscoring the potential vulnerability of these fish to environmental change. This study provides the first evidence of monthly and diel acoustic occurrence of soniferous fishes and demonstrates that bioacoustics can monitor fish biodiversity and breeding phenology in the Southern Ocean.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"412 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-03-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147447947","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":"Identification of initial vegetation and habitat changes in small temperate fens using remote sensing","authors":"Lubomír Tichý, Patricia Singh, Petra Hájková, Anna Müllerová, Tomáš Peterka, Zuzana Plesková, Karel Prach, Adéla Široká, Kamila Vítovcová, Michal Hájek","doi":"10.1002/rse2.70067","DOIUrl":"https://doi.org/10.1002/rse2.70067","url":null,"abstract":"Small temperate fens rank among the most endangered habitats in temperate Europe. In agricultural landscapes, they are highly vulnerable to eutrophication and desiccation, which accelerate biodiversity loss and shifts in the carbon balance due to peat mineralization. The initial signs of habitat change are commonly manifested by shifts in vegetation structure and dominance, accompanied by increasing productivity, which precede major qualitative changes in species composition. The in‐time monitoring of vegetation productivity and site wetness at large areas is essential for guiding conservation management strategies for fens to slow down or reverse undesired changes. Here, we evaluated the ability of satellite (Sentinel‐2) and high‐resolution aerial imagery to detect early, structure‐ and productivity‐related signals of fen deterioration. We compared multispectral and optical imagery with ground‐based data, including both direct measurements and indicators derived from the species composition of the vegetation plots. At the landscape scale where both the acidic poor fens and the base‐rich fens occurred, MSAVI and NGRDI indices performed best, indicating primarily the vascular plant cover, species richness and representation of nutrient‐demanding species. At the within‐site scale, where the differences among plots were largely driven by habitat deterioration, NDVI, NDWI and RENDVI well captured differences in vascular plant productivity estimates and moss biomass measurements. Our results indicate that remote sensing is applicable for the identification of individual fen habitats and their nutrient status at the landscape scale and is even effective in detecting incipient habitat deterioration associated with increasing productivity. We demonstrate that remote sensing also performs well for small, island‐like fen patches. Its wider integration into the mire research would improve monitoring and enhance the amount of available ecological data.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"1143 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-03-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147447951","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":"Historical remote sensing highlights long‐term persistence of Emperor Penguin ( Aptenodytes forsteri ) colonies","authors":"Martynas Bielinis, Michelle LaRue, Benjamin M. Kraemer, Catalina Munteanu","doi":"10.1002/rse2.70064","DOIUrl":"https://doi.org/10.1002/rse2.70064","url":null,"abstract":"Satellite imagery extending as far back as the 1960's has the potential to inform Antarctic conservation by providing insights into habitat and population dynamics that are otherwise difficult to observe. Here we demonstrate the detection of Emperor Penguin ( <jats:italic>Aptenodytes forsteri</jats:italic> ) guano stains on sea ice using Keyhole, Landsat, and Sentinel‐2 imagery from the 1960s to 2024. For 18 of the 66 known emperor penguin colonies, we confirmed colony presence in images that predate their earliest published records. Beyond presence detection, we examined the colony with the densest available imagery (Cape Washington) to quantify change in guano area over time. The guano area detected with satellites was correlated with observed chick counts from ground surveys (Spearman's ρ = 0.59, <jats:italic>P</jats:italic> ‐value = 0.017), and showed no strong evidence for a long‐term trend ( <jats:italic>P</jats:italic> = 0.61). Taken together, our results indicate substantial interannual and intra‐annual variability in colony size, but no evidence for a consistent long‐term directional trend and highlight that the use of remote sensing imagery across the Antarctic could inform conservation efforts and benefit the ongoing historical studies of penguin colony dynamics.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"12 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-03-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147447954","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":"Semi‐automated seal detection on the Western Antarctic Peninsula: an unsupervised machine learning approach for detecting ice seals in aerial survey data","authors":"Claire McGinnity, Connor C.G. Bamford, Nathan Fenney, Andrew Fleming, Jaume Forcada, Michael S. Tift, Luis A. Hückstädt, Daniel P. Costa, Peter T. Fretwell","doi":"10.1002/rse2.70060","DOIUrl":"https://doi.org/10.1002/rse2.70060","url":null,"abstract":"Over the past 25 years, the Western Antarctic Peninsula (WAP) has experienced dramatic shifts in sea ice extent. This change has coincided with rapid alterations in ice‐dependent ecosystems, including those supporting crabeater seals—the most abundant Antarctic seal and one of the largest mammalian consumers of krill. Despite their ecological importance, population estimates for ice seals remain scarce due to the difficulty of surveying large‐scale, remote, ice‐covered habitats. In 2023, during an abnormally low sea ice year, we conducted aerial surveys over Crystal Sound and Marguerite Bay during the end of the breeding season, flying over 1000 km of transects. Seals were extremely sparse in the resulting imagery—occupying less than 1% of the surveyed area. This posed a significant challenge for both manual annotation and automated detection. Here, we present a semi‐automated, rule‐based image analysis pipeline to substantially reduce human annotation time. Our method leverages hierarchical clustering with just two tuneable parameters, avoiding the computational burden and opacity of deep learning models. Using this method, we identified 758 seals within an ~350 km <jats:sup>2</jats:sup> survey subset, achieving a test recall of 79% ± 9.1%. In the absence of concurrent tagging data to estimate haul‐out corrections, we refrain from extrapolating to a population estimate. However, the low observed densities highlight the urgent need for continued monitoring. Our improved data processing pipeline is a key step in facilitating the large‐scale analysis required to inform conservation strategies for this key species.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"253 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147373924","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":"Monitoring feral pigs ( Sus scrofa ): Complementarity between autonomous sensing methods increases detection probability","authors":"Marina D. A. Scarpelli, Stewart Macdonald, Maryam Golchin, Simon Linke, Jens G. Froese","doi":"10.1002/rse2.70062","DOIUrl":"https://doi.org/10.1002/rse2.70062","url":null,"abstract":"Invasive alien species are a major threat to biodiversity, with significant impacts on threatened species and priority sites. Monitoring is essential to inform appropriate management strategies, and autonomous sensors are increasingly used to address data collection at large spatio‐temporal scales. Feral pigs ( <jats:italic>Sus scrofa</jats:italic> ) are a major threat to native fauna in Australia. Here, the utility of passive acoustic monitoring for detecting feral pigs and its complementarity to camera trap detection was tested. A custom‐built deep‐learning BirdNET recogniser was used to automatically scan sound for pig presence; image data was manually scanned. Detection probabilities and effects of covariates were compared for detections of each method, separately and combined, using multi‐season occupancy models. There was little spatio‐temporal overlap between image and sound detections. Modelled detection probability was the highest when sound and image detections were combined, followed by sound and, lastly, images. Seasonality affected detectability: camera traps were most successful in the Late Wet, when sound detection was poor. Sound detection was more successful in all other seasons, with the highest detection probability in the Late Dry. The intrinsic variation across survey methods along with the effects of environmental factors in species behaviour can be accounted for by combining methods, improving overall detections and providing complementary information on the same species. Autonomous sensors can provide comprehensive data to inform land management decisions, including population control and impact mitigation of invasive species. However, the utility of different sensors is context‐dependent. Combining multiple technologies can harness the strengths of each and mitigate against weaknesses. Increasing technology accessibility and decreasing costs is key to facilitate uptake by land managers.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"51 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-03-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"147373925","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":"Knee height is often right: evaluating device height effects on camera trapping rate","authors":"Jorge Sereno‐Cadierno, Tim R. Hofmeester, Marcus Becker, Alice Bernard, Lizette Moolman, Hervé Fritz, Pelayo Acevedo","doi":"10.1002/rse2.70053","DOIUrl":"https://doi.org/10.1002/rse2.70053","url":null,"abstract":"Camera traps (CTs) are widely used in wildlife monitoring, but sampling design choices can introduce significant biases in trapping rates (TR) that, depending on the evaluated parameter, can be propagated to dependent estimates (e.g., density). This study evaluates the effect of camera height placement on TR across five experiments encompassing 172 paired sampling points (i.e., with a low and a high camera per point) in four biomes across Europe, North America and Africa. We analysed data of 49 vertebrate species, ranging from small mammals and birds to large ungulates and carnivores (0.013–461 kg), using generalised linear and multinomial models to assess how TR varies with body mass and camera height. Our results show that lower camera placements significantly increase TR for small (0–10 kg) and medium‐sized species (11–50 kg), while the opposite is found in larger animals. Simultaneous detections by both high‐ and low‐placed cameras increased with body mass, but small species were often missed by high cameras alone. Camera height introduces systematic biases in TR, affecting its comparability across time and space. For multispecies monitoring, lower cameras (30–50 cm above ground) offer better overall performance, though higher placements may be more suitable for large‐bodied focal species. We recommend consistent, standardised height measurements in long‐term monitoring to ensure reliable TR‐based inferences and validate the recommendation of using target species' shoulder height when monitoring single species. This study provides the most comprehensive cross‐continental evaluation of camera height effects to date and offers empirically grounded guidance for optimising sampling design in wildlife monitoring.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"41 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-02-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146215636","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":"Lacking data? No worries! How synthetic images can alleviate image scarcity in wildlife surveys: A case study with muskox ( Ovibos moschatus )","authors":"Simon Durand, Samuel Foucher, Alexandre Delplanque, Joëlle Taillon, Jérôme Théau","doi":"10.1002/rse2.70056","DOIUrl":"https://doi.org/10.1002/rse2.70056","url":null,"abstract":"Accurate population estimates are essential for wildlife management, providing critical insights into species abundance and distribution. Traditional survey methods, including visual aerial counts and GNSS telemetry tracking, are widely used to monitor muskox ( <jats:italic>Ovibos moschatus</jats:italic> ) populations in Arctic regions. These approaches are resource‐intensive and constrained by logistical challenges. Advances in remote sensing, artificial intelligence, and high‐resolution aerial imagery offer promising alternatives for wildlife detection. Yet, the effectiveness of deep learning object detection models (ODMs) is often limited by small datasets, making it challenging to train robust ODMs for sparsely distributed species like muskoxen. This study investigates the integration of synthetic imagery, created with diffusion‐based models, to supplement limited training data and improve muskox detection in zero‐shot and few‐shot settings. We compared a baseline model trained solely on real imagery with five zero‐shot (ZS1–ZS5) and five few‐shot (FS1–FS5) models that incorporated progressively more synthetic imagery in the training set. For the zero‐shot models, where no real images were included in the training set, adding synthetic imagery improved detection performance. As more synthetic images were added, performance in precision, recall, and F1 score increased, but eventually plateaued, suggesting diminishing returns when synthetic images exceeded 100% of the baseline model training dataset. For few‐shot models, combining real and synthetic images led to better recall and slightly higher overall accuracy compared with using real images alone, though these improvements were not statistically significant. Our findings demonstrate the potential of synthetic images to train accurate ODMs when data are scarce, offering important perspectives for wildlife monitoring by enabling rare or inaccessible species to be monitored and to increase monitoring frequency. This approach could be used to initiate ODMs without real data and refine it as real images are acquired over time.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"29 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-02-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146169709","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":"Hyperspectral species maps and LiDAR‐based structured population models show future forest fire frequency may compromise forest resilience","authors":"Jessica McLean, Tommaso Jucker, Alice Rosen, Sean M. McMahon, Roberto Salguero‐Gómez","doi":"10.1002/rse2.70057","DOIUrl":"https://doi.org/10.1002/rse2.70057","url":null,"abstract":"Forest disturbances are accelerating biodiversity loss and altering tree productivity worldwide. Post‐disturbance recovery time, a component of resilience, is critical for identifying vulnerable areas and targeting conservation but varies with environmental conditions. Monitoring recovery at scale requires tracking tree dynamics, yet traditional ground‐based approaches are resource‐intensive. We present a pipeline to parameterize integral projection models (IPMs) using LiDAR data and hyperspectral‐based species maps to assess post‐fire recovery across large, forested areas. Focusing on the fire‐adapted <jats:italic>Picea mariana</jats:italic> , we model passage times to reproductive heights and life expectancy under different fire regimes as indicators of recovery time. To do this, we combined hyperspectral‐based species maps and LiDAR‐based crown heights to track individual tree survival and growth at the Caribou‐Poker Creek Research Watershed (BONA) from 2017 to 2023. We incorporated fire history, aspect, slope, elevation and surrounding canopy height into our models and found partial support for their expected effects on survival and growth. Once accounting for topography and competition, we estimated passage times to reproductive maturity (11–22 years). Life expectancy in the absence of fire is shortest on North‐facing slopes with recent fire (581 years). Sensitivity analyses highlight fire history and aspect as key modulators of population resilience, with elevation exerting strong influence on life expectancy across all conditions. Our results demonstrate that remotely sensed IPMs can effectively quantify forest recovery at scale, revealing that in some contexts, stands of <jats:italic>P. mariana</jats:italic> may not recover between fire disturbances. We discuss the implications of these findings for advancing modelling of resilience and highlight both the challenges and opportunities of using LiDAR and hyperspectral data to build demographic models for forecasting forest dynamics.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"133 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-02-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146146105","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":"From snapshots to continuous estimates: Augmenting citizen science with computer vision for fish monitoring","authors":"Zhongqi Chen, Timm Haucke, Sara Beery, Keven Bennett, Austin Powell, Lydia Zuehsow, Robert Vincent, Linda Deegan","doi":"10.1002/rse2.70055","DOIUrl":"https://doi.org/10.1002/rse2.70055","url":null,"abstract":"Monitoring fish movement is essential for understanding population dynamics, informing conservation efforts and supporting fisheries management. Traditional methods, such as visual observations by volunteers, are constrained by time limitations, environmental conditions and labour intensity. Recent advancements in computer vision (CV) and deep learning offer promising solutions for automating fish counting from underwater videos, improving efficiency and data resolution. In this study, we developed and applied a deep learning‐based CV system to monitor river herring ( <jats:italic>Alosa</jats:italic> spp.) migration, covering all essential steps from field camera deployment, video annotation to model training and in‐season population counting. We assessed the labelling and training efforts required to achieve good model performance and explored the use of importance sampling to correct biases in CV‐based fish counts. Our results demonstrated that CV models trained on a single site and year showed limited generalization to sites or years unseen during training, while models trained on more diverse labelled data generalized better. We also found that the amount of annotations required is related to dataset complexity. When applied for in‐season fish counting, CV efficiently processed season‐long datasets and produced counts consistent with human review, with some moderate differences under migration pulses that can be adjusted by importance sampling. By providing continuous, high‐resolution monitoring throughout the entire migration season, CV counts offer more reliable run size estimates and greater insight into the spawning migration of river herring. This study demonstrates a scalable, cost‐effective and efficient approach with significant potential for addressing complex ecological questions and supporting conservation strategies and resource management.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"51 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146122092","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":"Incorporating environmental DNA metabarcoding for improved benthic biodiversity and habitat mapping","authors":"Rylan J. Command, Shreya Nemani, Benjamin Misiuk, Mehrdad Hajibabaei, Nicole Fahner, Emily Porter, Greg Singer, Beverly McClenaghan, Katleen Robert","doi":"10.1002/rse2.70048","DOIUrl":"https://doi.org/10.1002/rse2.70048","url":null,"abstract":"Complex coastal seascapes harbor high marine biodiversity from which humans derive numerous ecosystem services. Maps of benthic habitats are important tools used to inform coastal development and conservation efforts. Seafloor imagery is commonly used to collect information about the distribution of benthic organisms, but these data are often limited to low taxonomic resolutions and may systematically underrepresent local biodiversity. Recent advances in genomics enable rapid and accurate detection of taxa with high taxonomic resolution from environmental DNA (eDNA) extracted from water samples, but there are few examples of broad‐spectrum eDNA biodiversity data in nearshore benthic habitat mapping. We combined an eDNA‐based biodiversity assessment with concurrently collected high‐resolution video ground‐truth data to assess the benefit of metabarcoding data for improving benthic habitat mapping in the sub‐Arctic coastal embayment of Mortier Bay, Newfoundland and Labrador, Canada. Features derived from acoustic bathymetry and backscatter data were used to develop full‐coverage habitat and biodiversity maps using a joint species distribution‐modeling framework. The predicted taxonomic richness spatial patterns were similar between video‐only, eDNA‐only and combined datasets, suggesting diversity patterns were accurately represented by both methods. However, 226 additional taxa (72 species, 109 genera) were identified using eDNA compared to the 46 detected by video ground‐truthing. Averaged over all taxa, the video‐only model performed best in terms of discriminating presences from absences; however, we found that most sessile taxa were better predicted by the combined dataset compared to video data alone. These results highlight the limitations of imagery‐only datasets for biodiversity surveys and demonstrate the utility and limitations of metabarcoding data to improve benthic habitat and diversity maps in complex coastal habitats. This study highlights opportunities to fill gaps that could improve spatial modeling of seafloor assemblages derived from metabarcoding data, including sources and sinks of DNA in the environment and water column properties that control its dispersal.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"28 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2026-02-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"146122094","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}