Remote Sensing in Ecology and Conservation最新文献

{"title":"Programmed unmanned aerial vehicles show great potential for monitoring marine megafauna in specific areas of interest","authors":"Dinah Hartmann, Valdemar Palmqvist, Johanna Stedt","doi":"10.1002/rse2.70043","DOIUrl":"https://doi.org/10.1002/rse2.70043","url":null,"abstract":"Targeted conservation measures are contingent on robust knowledge of spatio‐temporal animal distribution in areas of interest. We explore unmanned aerial vehicle (UAV) transect monitoring as a novel method for standardized digital aerial surveys of marine megafauna by investigating the fine‐resolution spatio‐temporal distribution of harbour porpoises ( <jats:italic>Phocoena phocoena</jats:italic> ) in a Swedish nature reserve along with drivers of this distribution and potential biases. Biweekly UAV video data were collected along pre‐programmed strip transects over 17 weeks from June to September 2023, totalling a survey area of 3.37 km <jats:sup>2</jats:sup> , thereby providing porpoise monitoring data covering 89% of a special area of conservation for the species. All UAV video data were manually reviewed by a primary observer, and 25% of the UAV footage was also reviewed by a second, unexperienced observer to identify observer bias and learning effects. No significant observer bias or learning effect was found, but increased sea state affected porpoise density negatively. From the monitoring data, we were able to calculate relative density estimates, identify small‐scale spatio‐temporal differences and detect negative effects of recreational boat activity on porpoise presence. We further demonstrate that within this restricted area, porpoises are found in higher relative densities outside a designated conservation area, compared to within the conservation area, providing important knowledge to guide fine‐scale local conservation actions. We highlight advantages and areas of improvement of UAV transect monitoring as an accessible, versatile and adaptable method to survey marine megafauna in spatially restricted specific areas of interest. We conclude that this method constitutes a promising and valuable tool for wildlife monitoring, especially as it can be easily adapted and modified for specific contexts and species.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"121 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-11-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145593542","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 forest age estimation to understand subtropical forest regrowth dynamics using deep learning image segmentation of time‐series historical aerial photographs","authors":"Ying Ki Law, Yi‐Fei Gu, Shuwen Liu, Guangqin Song, Aland H. Y. Chan, Cham Man Tse, Zhonghua Liu, Martha J. Ledger, Billy C. H. Hau, Sawaid Abbas, Jin Wu","doi":"10.1002/rse2.70042","DOIUrl":"https://doi.org/10.1002/rse2.70042","url":null,"abstract":"Accurate forest age estimation is essential for understanding forest recovery trajectories and evaluating the efficacy of restoration strategies. While field‐based methods for forest age estimation offer high accuracy, they are spatially constrained and challenging to apply retrospectively. In contrast, satellite‐based approaches provide extensive regional coverage but may lack precision at the local landscape level. Historical aerial photographs can bridge this gap by delivering fine‐scale land cover information. However, challenges such as limited spectral bands and topographic shadows in hilly terrains introduce uncertainty in land cover segmentation and temporal dynamics, complicating accurate forest age determination. To address these challenges, we developed a two‐step deep learning approach for image segmentation using historical aerial photographs. The method involves using a pre‐trained deep learning model with open‐source forest labels, followed by fine‐tuning based on localized forest data. This approach achieved accurate forest segmentation, with our highest accuracy model (mean IoU of 0.859) utilizing a combined U‐Net and ResNet50 architecture. Our forest age estimates demonstrated superior agreement, significantly outperforming existing national forest age products for China in terms of both temporal coverage and accuracy. By overlaying our age product with LiDAR structural metrics, we uncovered strong yet distinct recovery trajectories across forest structure attributes. Collectively, our study demonstrates the effectiveness of deep learning algorithms for forest age monitoring using greyscale historical aerial photographs, while pinpointing the limitations of existing national‐scale forest age products for local monitoring. Enhanced fine‐scale forest age mapping provides an essential technique and dataset to advance our understanding of forest regrowth and structural dynamics, and this improved knowledge of forest dynamics will aid in assessing carbon sequestration potential and informing targeted forest management and restoration strategies.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"17 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145525203","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":"Wall‐to‐wall Amazon forest height mapping with planet NICFI , Aerial LiDAR , and a U‐Net regression model","authors":"Fabien H. Wagner, Ricardo Dalagnol, Griffin Carter, Mayumi C. M. Hirye, Shivraj Gill, Le Bienfaiteur Sagang Takougoum, Samuel Favrichon, Michael Keller, Jean P. H. B. Ometto, Lorena Alves, Cynthia Creze, Stephanie P. George‐Chacon, Shuang Li, Zhihua Liu, Adugna Mullissa, Yan Yang, Erone G. Santos, Sarah R. Worden, Martin Brandt, Philippe Ciais, Stephen C. Hagen, Sassan Saatchi","doi":"10.1002/rse2.70041","DOIUrl":"https://doi.org/10.1002/rse2.70041","url":null,"abstract":"Tree canopy height is a key indicator of forest biomass, productivity and structure, yet measuring it accurately at regional or larger scales, whether from the ground or remotely, remains challenging. The objective of this study is to generate the first complete canopy height map of the Amazon forest at ~4.78 m resolution using Planet NICFI imagery and deep learning. Specifically, we (i) trained a U‐Net regression model with canopy height models (CHMs) derived from tropical airborne LiDAR and their corresponding Planet NICFI images to estimate canopy height, (ii) evaluated the accuracy of our map against existing global products based on Sentinel‐2/1 and Maxar Vivid2 imagery and (iii) assessed its capacity to capture small‐scale canopy height changes. Tree height predictions on the validation sample had a mean absolute error of 3.68 m, with minimal systematic bias across the full range of tree heights in the Amazon forest. The main biases are a slight overestimation (up to 5 m) for heights of 5–15 m and an underestimation for most trees above 50 m. Outperforming existing global model‐based canopy height products in this region, the model accurately estimated canopy heights up to 40–50 m with minimal saturation. We determined that the Amazon forest has an average canopy height of ~22 m (standard deviation ~5.3 m) and exhibits large‐scale patterns, ranging from the tallest forests of the Guiana Shield to shorter forests along wetlands, rivers, rocky outcrops, savannas and high elevations. Events such as logging or deforestation could be detected from changes in tree height, and the results demonstrated a first success in monitoring the height of regenerating forests. Finally, the map of the Amazon forest canopy height is displayed.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"92 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145525202","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":"Comparing convolutional neural network and random forest for benthic habitat mapping in Apollo Marine Park","authors":"Henry Simmons, Dang Nguyen, Benjamin Misiuk, Daniel Ierodiaconou, Sunil Gupta, Oli Dalby, Mary Young","doi":"10.1002/rse2.70038","DOIUrl":"https://doi.org/10.1002/rse2.70038","url":null,"abstract":"Marine habitat maps are essential tools for marine spatial planning, providing information for decision‐making in conservation and resource management. Accurate classification of benthic habitats supports their sustainable use and identifies key areas for protection. Convolutional neural networks (CNNs) are powerful deep learning algorithms that have shown promise for advancing habitat classification tasks and mapping complex marine environments. This study compares the performance of a CNN and a Random Forest (RF) model in classifying benthic habitats within Apollo Marine Park, Victoria, Australia. Models were trained to classify three distinct habitat types using bathymetry, multibeam backscatter, wave height and positioning data; however, the RF model had access to 100 additional bathymetric derivatives, of which 10 were selected as predictors. The CNN achieved an overall accuracy of 67.32%, while the RF model achieved 62.57%. For individual habitats, the CNN obtained F1‐scores of 0.664 for <jats:italic>high energy circalittoral rock with seabed‐covering sponges</jats:italic> , 0.538 for <jats:italic>low complexity circalittoral rock with non‐crowded erect sponges</jats:italic> and 0.774 for <jats:italic>infralittoral sand and shell mixes</jats:italic> . The corresponding RF scores were 0.598, 0.506 and 0.739. Both models encountered challenges in classifying transitional habitat zones, where diffuse boundaries between habitat types led to overlaps and shared acoustic properties. However, the CNN demonstrated an advantage due to its ability to automatically analyse spatial patterns across multiple scales. In contrast, while the RF model incorporated terrain attributes that capture local variation, its ability to utilize spatial context was constrained to predefined scales of the derived features. The CNN's ability to leverage spatial relationships resulted in clearer and more coherent habitat maps, reducing the salt‐and‐pepper effect commonly observed in pixel‐based classifications. This study highlights the potential of CNNs for marine habitat mapping through their ability to classify data derived from multibeam bathymetry, while also identifying avenues for further refinement to enhance their utility in marine spatial planning tasks.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"29 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-11-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145525204","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":"Evaluating methods for high‐resolution, national‐scale seagrass mapping in Google Earth Engine","authors":"Matthew Floyd, Holly K. East, Andrew J. Suggitt","doi":"10.1002/rse2.70039","DOIUrl":"https://doi.org/10.1002/rse2.70039","url":null,"abstract":"National‐scale benthic marine habitat maps underpin monitoring and conservation of vulnerable marine and coastal ecosystems. Cloud‐based satellite remote sensing can streamline these processes over spatial scales that would otherwise be financially and logistically challenging. Here, we test the sensitivity of mapped outputs to three key methodological choices when generating open‐source cloud‐based satellite maps of seagrass meadows: (1) period of image retrieval (seasonality, tested at <jats:italic>n</jats:italic> = 7 sites over <jats:italic>n</jats:italic> = 5 years); (2) machine learning classification method (SVM, RF, CART) over a range of training pixel densities ( <jats:italic>n</jats:italic> = 12 points with 0.0004–0.8757 training points/km <jats:sup>2</jats:sup> ) and (3) input satellite data choice ( <jats:italic>n</jats:italic> = 3: Landsat 8, Planet NICFI and Sentinel‐2). We found that in the Maldives, when using best available cloud masking methods, monsoonal cloud patterns introduce noise into satellite images, with implications for mapping accuracy. Comparing methods at the classification phase, Overall Accuracy (OA) was similar between classification methods, though SVM performed best (OA = 84.6%). We also determined that workflows using data derived from Sentinel‐2 resulted in the most accurate binary thematic seagrass map (OA = 80.3%), compared to Landsat 8 and Planet NICFI (OA = 72.7 and 74.8%, respectively). These results indicate that data source has a larger effect on OA than classifier type, and therefore should be the primary consideration for map producers. We further recommend that, as studies increasingly work over larger extents (i.e. &gt;1,000 km <jats:sup>2</jats:sup> ), the minimum density of points used to train a binary classification of seagrass from Sentinel‐2 data ought to be 0.67/km <jats:sup>2</jats:sup> . We present an open‐source (for non‐commercial uses) workflow for generating high‐resolution national‐scale seagrass maps. Insights from this work can be applied in other settings globally to improve outcomes for marine planning and international targets on climate change and the conservation of biodiversity.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"56 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145382037","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":"Ground‐truthing of satellite imagery to assess seabird colony size: A test using Adélie penguins","authors":"Alexandra J. Strang, Dean P. Anderson, Esme Robinson, Grant Ballard, Annie E. Schmidt, David G. Ainley, Kerry Barton, Fiona Shanhun, Elissa Z. Cameron, Michelle A. LaRue","doi":"10.1002/rse2.70040","DOIUrl":"https://doi.org/10.1002/rse2.70040","url":null,"abstract":"Adélie penguin ( <jats:italic>Pygoscelis adeliae</jats:italic> ) colonies can be detected from space using very high‐resolution (VHR; 0.3–0.6 m resolution) satellite imagery, as the contrast between their guano and the surrounding terrain enables colony identification even when physical access is not possible. While VHR imagery has been used to estimate colony size, its potential to detect annual changes remains underexplored, yet is critical for linking population dynamics to oceanographic change. We investigated the utility of VHR imagery for indirect population assessments of this species, expanding on previous work with a decade of imagery and independent population counts. We studied VHR images from four well‐surveyed Ross Sea colonies, that together represent ~10% of the global population: capes Crozier, Bird and Royds, and Inexpressible Island, over the austral summers of 2009–2021. We used supervised object‐based support vector machine classifications to extract guano area from 30 VHR images. We related guano area (m <jats:sup>2</jats:sup> ) to colony size (aerial census counts), assessing for both spatial and temporal autocorrelation. In the process, we investigated various spatial parameters (the average slope steepness, aspect, and perimeter‐to‐area ratio of the guano). Guano area was highly correlated with concurrent counts of breeding pairs, indicating the ability to detect several orders of magnitude difference in colony size. However, large within‐colony variation meant that when using guano area alone the number of breeding pairs had to change by 44% to confidently detect a true change in colony size. Therefore, although VHR imagery can be used to detect significant differences in colony size, minimal sensitivity to interannual fluctuations was indicated, likely due to the difficulty in distinguishing the fresh, current‐year guano from guano of previous years, affected by the rate of weathering. This highlights an important limitation to advances in VHR imagery for some wildlife monitoring and enforces the criticality of ground validation.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"110 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-10-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145382040","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":"Covariates influence optimal camera‐trap survey design for occupancy modelling","authors":"Owain Barton, Brian D. Gerber, Line S. Cordes, John R. Healey, Graeme Shannon","doi":"10.1002/rse2.70031","DOIUrl":"https://doi.org/10.1002/rse2.70031","url":null,"abstract":"Motion‐activated cameras (‘camera‐traps’) have become indispensable for wildlife monitoring. Data from camera‐trap surveys can be used to make inferences about animal behaviour, space use and population dynamics. Occupancy modelling is a statistical framework commonly used to analyse camera‐trap data, which estimates species occurrence while accounting for imperfect detection. Including covariates in models enables the investigation of relationships between occupancy and the environment. Survey design studies help practitioners decide the number of cameras to deploy, deployment duration and camera positioning. However, existing assessments have generally assumed constant occupancy and detectability (i.e. no covariates were considered), which is unrealistic for most real‐world scenarios. We investigated the effects of covariates on the relationship between survey effort and the combination of accuracy and precision (i.e. error) of occupancy models. Camera‐trap data for a ‘virtual’ species were simulated as a function of randomly generated, site‐ and survey‐specific covariates (e.g. habitat type/quality and temperature, respectively). We then assessed how varying survey design and total effort influenced estimation error with and without covariate information. Increasing the number of cameras consistently reduced error, while longer deployments were only beneficial when the covariate influenced occupancy. When both parameters were affected by covariates, omitting effects on detectability had limited impact on model performance. However, failing to account for effects on occupancy significantly increased error, and none of the predefined thresholds (root mean squared error = 0.15, 0.10 and 0.075) were achievable, even with the maximum survey effort of 9000 camera‐days. These results suggest that increasing survey effort is unlikely to improve model performance unless site‐level conditions are appropriately modelled. Thus, robust study design should consider total effort and the monitoring of covariates across sites to ensure efficient use of time and financial resources.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"64 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145305899","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":"Assessing group size and the demographic composition of a canopy‐dwelling primate, the northern muriqui (Brachyteles hypoxanthus), using arboreal camera trapping and genetic tagging","authors":"Mariane C. Kaizer, Naiara G. Sales, Thiago H. G. Alvim, Karen B. Strier, Fabiano R. de Melo, Jean P. Boubli, Robert J. Young, Allan D. McDevitt","doi":"10.1002/rse2.70035","DOIUrl":"https://doi.org/10.1002/rse2.70035","url":null,"abstract":"Obtaining accurate population measures of endangered species is critical for effective conservation and management actions and to evaluate their success over time. However, determining the population size and demographic composition of most canopy forest‐dwelling species has proven to be challenging. Here, we apply two non‐invasive biomonitoring methods, arboreal camera trap and genetic tagging of fecal samples, to estimate the population size of a critically endangered primate, the northern muriqui (<jats:italic>Brachyteles hypoxanthus</jats:italic>), in the Caparaó National Park, Brazil. When comparing group sizes between camera trapping and genetic tagging, the genetic tagging survey estimated fewer individuals for one of the muriqui groups studied but showed slightly higher population size estimates for the other group. In terms of the cost‐efficiency of both methods, arboreal camera trapping had high initial costs but was more cost‐effective in the long term. Genetic tagging, on the other hand, did not require expensive equipment for data collection but had higher associated expenses for laboratory consumables and data processing. We recommend the use of both methods for northern muriqui monitoring and provide suggestions for improving the implementation of these non‐invasive methods for future routine monitoring. Our findings also highlight the potential of arboreal camera trapping and genetic tagging for other arboreal mammals in tropical forests.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"35 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145311579","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 UAV‐based deep learning pipeline for intertidal macrobenthos monitoring: Behavioral and age classification in Tachypleus tridentatus","authors":"Xiaohai Chen, Yuyuan Bao, Ziwei Ying, Mujiao Xie, Ting Li, Jixing Zou, Jun Shi, Xiaoyong Xie","doi":"10.1002/rse2.70036","DOIUrl":"https://doi.org/10.1002/rse2.70036","url":null,"abstract":"Intertidal macrobenthos are vital bioindicators of coastal ecosystem health due to their ecological roles, limited mobility, and sensitivity to environmental disturbances. However, traditional field‐based monitoring methods are time‐consuming, spatially restricted, and unsuitable for large‐scale ecological surveillance. Integrating unmanned aerial vehicles (UAVs) with deep learning offers a promising alternative for high‐resolution, cost‐effective monitoring. Yet, species‐specific object detection frameworks for mobile macrobenthic fauna remain underdeveloped. <jats:italic>Tachypleus tridentatus</jats:italic>, an endangered “living fossil” with over 430 million years of evolutionary history, serves as a flagship species for intertidal conservation due to its ecological significance and biomedical value. This study develops a customized deep learning pipeline for monitoring <jats:italic>T. tridentatus</jats:italic>, combining UAV‐based image acquisition, automated detection, and ecological trait inference. We constructed the first UAV‐derived dataset of juvenile <jats:italic>T. tridentatus</jats:italic> (<jats:italic>n</jats:italic> = 761) and implemented a convolutional autoencoder for unsupervised behavioral classification, achieving 96% accuracy in distinguishing buried from exposed individuals. A YOLO‐based detection model was optimized using lightweight pruning and a high–low frequency fusion module (HLFM), improving detection accuracy (mAP@50 increased by 1.74%) and computational efficiency. Additionally, we established robust regression models linking crawling trace width to prosomal width (<jats:italic>R</jats:italic><jats:sup>2</jats:sup> = 0.99) and prosomal width to instar stage (<jats:italic>R</jats:italic><jats:sup>2</jats:sup> = 0.91). The inferred instar stages showed no significant deviation across datasets, validating their use as indicators of age structure. By bridging species‐level detection with population‐level ecological inference, this study provides a scalable, field‐deployable framework for monitoring <jats:italic>T. tridentatus</jats:italic> and other intertidal macrobenthic taxa. The approach supports data‐driven conservation strategies and enhances our capacity to assess the status of endangered coastal species in complex intertidal environments.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"117 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145277482","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":"Breaking down seagrass fragmentation in a marine heatwave impacted World Heritage Area","authors":"Michael D. Taylor, Simone Strydom, Matthew W. Fraser, Ana M. M. Sequeira, Gary A. Kendrick","doi":"10.1002/rse2.70032","DOIUrl":"https://doi.org/10.1002/rse2.70032","url":null,"abstract":"Marine heatwaves, and other extreme climatic events, are driving mass mortality of habitat‐forming species and substantial ecological change worldwide. However, habitat fragmentation is rarely considered despite its role in structuring seascapes and potential to exacerbate the negative impacts of habitat loss. Here, we quantify fragmentation of globally significant seagrass meadows within the Shark Bay World Heritage Area before and after an unprecedented marine heatwave impacting the Western Australian coastline over the austral summer of 2010/11. We use a spatial pattern index to quantify seagrass fragmentation from satellite‐derived habitat maps (2002, 2010, 2014 and 2016), assess potential predictors of fragmentation and investigate seascape dynamics defined by relationships between seagrass fragmentation and cover change. Our spatiotemporal analysis illustrates widespread fragmentation of seagrass following the marine heatwave, contributing to a dramatic alteration of seascape structure across the World Heritage Area. Fragmentation immediately following the marine heatwave coincided with widespread seagrass loss and was best explained by interactions between a heat stress metric (i.e. degree heating weeks) and depth. Based on the relationship between fragmentation and seagrass cover change, we revealed near‐ubiquitous fragmentation from 2014 to 2016 represents a mixture of long‐term seagrass degradation and evidence of early, patchy recovery. Fragmentation effects are expected to compound the ecological impacts of seagrass mortality following the marine heatwave and prolong recovery. As sea temperatures and the threat of marine heatwaves continue to rise globally, our results highlight the importance of considering fragmentation effects alongside the negative impacts of habitat loss. Our seascape dynamic framework provides a novel approach to define the response of habitat‐forming species to disturbances, including marine heatwaves, that integrates the processes of fragmentation and cover change. This framework provides the opportunity to consider these important processes across a range of threatened ecosystems and identify areas of vulnerability, stability and recovery.","PeriodicalId":21132,"journal":{"name":"Remote Sensing in Ecology and Conservation","volume":"157 1","pages":""},"PeriodicalIF":5.5,"publicationDate":"2025-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145226606","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}