Ecological Informatics最新文献

{"title":"Capturing plant functional traits in coastal dunes using close-range remote sensing","authors":"Giacomo Trotta ,&nbsp;Marco Vuerich ,&nbsp;Elisa Petrussa ,&nbsp;Edoardo Asquini ,&nbsp;Paolo Cingano ,&nbsp;Francesco Boscutti","doi":"10.1016/j.ecoinf.2025.103159","DOIUrl":"10.1016/j.ecoinf.2025.103159","url":null,"abstract":"<div><div>Coastal dunes are dynamic ecosystems characterized by steep environmental gradients that impose significant stress on plant communities. These stressors, such as salinity, drought, and nutrient-poor soils, create a mosaic of plant communities with strong functional trait identity. Several studies have focused on plant functional responses to environmental conditions, but a gap remains in connecting plant functional traits to large-scale ecological processes through remote sensing. We studied a dune plant community (a total of 17 species) and the ecosystem key species <em>Cakile maritima</em> Scop. to explore how remote sensing-derived vegetation indices correlate with plant growth and specific physiological and morphological leaf traits, including specific leaf area, leaf dry matter content, and flavonoid concentration. We introduced a close-range approach using multispectral imaging to capture high-resolution (1.3 mm/px) data on plant functional traits in coastal dune ecosystems overcoming the limitations of broader-scale remote sensing methods which often suffer from lower spatial resolution and interference from non-vegetated areas. By semi-automatically identifying regions of interest for each species and eliminating background noise, we acquired accurate multispectral signatures that represent plant responses and highlight ecological processes of the key species and the broader community. We observed traits to be stronger than plant growth in explaining the variance of multispectral indices, with leaf flavonoids showing the highest contribution to plant spectral signature.</div><div>We demonstrated the effectiveness of close-range multispectral imaging in linking plant traits to ecological processes, with significant implications for upscaling plant responses to environmental variable across larger spatial scales. Furthermore, the research outlines practical guidelines for collecting and processing close-range multispectral data, offering a valuable new tool for and accurate field monitoring of ecosystem processes and plant functions.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"88 ","pages":"Article 103159"},"PeriodicalIF":5.8,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860501","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":"Time series modelling spatiotemporal changes in Biogeoclimatic ecosystem classification (BEC) zones between 1997 and 2019 in West-Central British Columbia, Canada","authors":"Ilythia D. Morley ,&nbsp;Kevin Hanna ,&nbsp;Chris T. Darimont ,&nbsp;Mathieu L. Bourbonnais ,&nbsp;Ilythia D. Morley","doi":"10.1016/j.ecoinf.2025.103155","DOIUrl":"10.1016/j.ecoinf.2025.103155","url":null,"abstract":"<div><div>Understanding the spatial extent and temporal variability of ecosystem processes is essential for contextualizing land use and land cover change due to disturbance. In this study, we apply an advanced time series modelling method to assess and map ecosystem change and characterize ecosystem cover in west-central British Columbia, Canada. We couple Biogeoclimatic Ecosystem Classification (BEC) zone data with metrics derived from Landsat imagery to model how biogeoclimatic ecosystem cover, interpreted as an indicator of shifting vegetation seasonality, varies over a broad spatiotemporal scale. To do so, we apply the Time-Weighted Dynamic Time Warping (TWDTW) time series modelling approach by relating the spectral characteristics of Landsat data and derived indices from 1997 to 2019. Results highlight important transitions between biogeoclimatic ecosystem classes, with a transition of the interior Douglas-fir Dry to the montane-spruce Dry and the Sub-Boreal Pine to the Spruce zone Dry zones in response to large wildfires in 2003 and 2009. The assessment of ecosystem change across broad spatial and temporal scales is important for assessing the cumulative impacts of changes across highly variable landscapes on essential landscape services.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"88 ","pages":"Article 103155"},"PeriodicalIF":5.8,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860502","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":"Using fishery-related data, scientific expertise, and machine learning to improve marine habitat mapping in northeastern Mediterranean waters","authors":"Loukas Katikas ,&nbsp;Sofia Reizopoulou ,&nbsp;Paraskevi Drakopoulou ,&nbsp;Vassiliki Vassilopoulou","doi":"10.1016/j.ecoinf.2025.103154","DOIUrl":"10.1016/j.ecoinf.2025.103154","url":null,"abstract":"<div><div>Marine habitat mapping is an essential tool for planning conservation efforts and sustainable management of marine activities. High spatial resolution in marine habitat maps is of utmost importance, as it may encompass more detail in imagery and reveal important biotopes. This level of detail supports directing monitoring and analysis efforts for effective implementation of European Union (EU) environmental policies and provides more relevant advice for robust decision-making under sectorial policies (e.g., the Common Fisheries Policy) and more integrated policies (e.g., marine spatial planning). In this study, sea bottom type data recorded during national monitoring of commercial fishing vessel operations and fishery surveys in the Greek Seas were used. These data were then assigned to the EU EMODnet seabed habitats using local ecological knowledge. Two machine-learning algorithms, i.e., random forest classifier (RFC) and gradient boosting classifier, were trained on the entire national-scale dataset and subsequently applied to assess their performance in predicting habitat types in the Saronikos Gulf (regional scale) using various environmental factors as predictors. The borderline synthetic minority oversampling technique was applied to manage inherent data class imbalances. A validation dataset and georeferenced data from previous studies were used to compare the accuracy and predictive performance of the models. Using this approach, the Saronikos Gulf was enriched with five more habitat types than visualised in the EMODnet portal, which also filled habitat gaps in areas where no data existed. Results from application of the RFC-Borderline Smote (BS) model (62 % accuracy, 0.51 kappa score) were then used to address conservation planning commitments recently made by the Greek government. The vast majority of marine seabed priority habitats in the study area appeared to fall outside the borders of the current Natura 2000 sites, which served as the baseline for the declared trawl bans in Greek waters, following the provisions of the EU Marine Action Plan.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"88 ","pages":"Article 103154"},"PeriodicalIF":5.8,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143855330","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":"Exploring new mangrove horizons: A scalable remote sensing approach with Planet-NICFI and Sentinel-2 images","authors":"Adam Irwansyah Fauzi ,&nbsp;Markus Immitzer ,&nbsp;Clement Atzberger","doi":"10.1016/j.ecoinf.2025.103152","DOIUrl":"10.1016/j.ecoinf.2025.103152","url":null,"abstract":"<div><div>Mangroves offer massive ecosystem services ranging from coastal protection, and wildlife habitat to carbon sequestration. This makes them an integral part of tropical developing countries' strategies to pursue climate neutrality targets. In this respect, the advanced development of big data, machine learning, and cloud computing in remote sensing provides a huge opportunity to explore this ecosystem and to provide scalable monitoring solutions. This study aims to discover new potential mangrove areas, focusing on far-off and under-monitored locations along the coasts and rivers of Indonesia, using a precise, practical, and scalable remote sensing approach via Google Earth Engine. To demonstrate the potential of our approach, we selected Lampung province, Indonesia as the study area, which has varied taxonomic, topographical, bathymetrical, and oceanographical characteristics. The methodology includes defining mapping zones using coastline, river, and elevation data. The satellite image processing is based on integrating Planet-NICFI and Sentinel-2 images using the Simple Non-Iterative Clustering (SNIC) segmentation and Random Forest (RF) classifier. Our classification with F1 score of 0.95 successfully mapped 10,290 ha of mangroves, with coastal mangroves contributing 6058 ha and riverine mangroves another 4250 ha. Importantly, this study discovered 1714 ha of previously unknown mangroves, equivalent to 18.55 % of the official area. These new areas are dominated by nypa palm, a native species that contributes to bioeconomy. This study contributes to refining carbon sequestration baselines and highlights the scalability of a national-level implementation to support progress towards net-zero emissions goals. The method can readily be deployed to other mangrove areas.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"88 ","pages":"Article 103152"},"PeriodicalIF":5.8,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143855331","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":"Automated near real-time monitoring in ecology: Status quo and ways forward","authors":"Anna Marie Davison ,&nbsp;Koen de Koning ,&nbsp;Franziska Taubert ,&nbsp;Jan-Kees Schakel","doi":"10.1016/j.ecoinf.2025.103157","DOIUrl":"10.1016/j.ecoinf.2025.103157","url":null,"abstract":"<div><div>In the current epoch of rapid biodiversity decline, monitoring of ecosystems and the species which inhabit them has become increasingly important. A near real-time approach to ecological monitoring facilitates decision making and timely interventions within rapidly changing systems. Despite fast-paced technological advancements making the automated workflows required for near real-time ecological monitoring possible, their use is highly limited and there is yet to be a review of the current capacity for their creation. This paper summarises the current methods and technologies which could be used to create such workflows and the considerations for establishing them in decision-making systems. We identify key barriers to the adoption of a NRT approach across geographies and different fields of study in ecology. We also highlight the need to work collaboratively with technologists and stakeholders to establish efficient and long-lasting NRT workflows which can inform evidence-based decision making.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103157"},"PeriodicalIF":5.8,"publicationDate":"2025-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873952","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":"Robust real-time detection of right whale upcalls using neural networks on the edge","authors":"Matthew D. Hyer ,&nbsp;Austin T. Anderson ,&nbsp;David A. Mann ,&nbsp;T. Aran Mooney ,&nbsp;Nadège Aoki ,&nbsp;Frants H. Jensen","doi":"10.1016/j.ecoinf.2025.103130","DOIUrl":"10.1016/j.ecoinf.2025.103130","url":null,"abstract":"<div><div>Animals worldwide are facing ecological pressures from global climate change and increasing anthropogenic activities. To transition to a renewable energy future, extensive offshore wind development is planned globally. In the North Atlantic, future development sites overlap with the migratory range of critically endangered North Atlantic right whales (NARW) and will lead to increased risk of ship strikes, pile driving impacts, and other population risks. New methods to accurately detect cetaceans and provide real-time feedback for mitigation will be increasingly important to enact sustainable management actions to facilitate the recovery of the NARW. Recent developments in acoustic event detection made possible by deep learning have shown significantly improved detection performance across many different taxa, but such models tend to be too computationally expensive to run on existing wildlife monitoring platforms. Here, we use model compression techniques combined with an autonomous acoustic recording platform integrating an ESP32 microcontroller to bring real-time detection with deep learning to the edge. We test if edge-based inference using a compressed network running on a microprocessor entails significant performance loss and find that this loss is negligible. We leverage large, open-source datasets of noise from the NOAA SanctSound project for generating semi-synthetic training datasets that encourage model generalization to novel noise conditions. Our compressed model achieves improved performance across all tested recording sites in the Western North Atlantic Ocean, demonstrating that deep learning powered wildlife monitoring solutions can provide reliable real-time data for mitigation of human impacts and help ensure a sustainable green energy transition.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103130"},"PeriodicalIF":5.8,"publicationDate":"2025-04-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143873953","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":"Forest variables from LiDAR: Drone flight parameters impact the detection of tree stems and diameter estimates","authors":"Paul M. Eisenschink,&nbsp;Wolfgang A. Obermeier,&nbsp;Vinzenz H.D. Zerres,&nbsp;Annika M. Suerbaum,&nbsp;Lukas W. Lehnert","doi":"10.1016/j.ecoinf.2025.103127","DOIUrl":"10.1016/j.ecoinf.2025.103127","url":null,"abstract":"<div><div>Ecosystem services provided by central European forests, often dominated by Norway spruce or Scots pine, are increasingly threatened by climate change. Monitoring, while labour intensive, is key to ensure continuing forest health. Consequently, UAV-based LiDAR remote sensing has become a valuable tool. However, the impact of drone flight parameters on LiDAR data quality has not yet been extensively studied. To address this, we first present a methodology for delineating tree stems, estimating their diameter at breast height (DBH), and separating understory vegetation from stems and old-grown trees to subsequently compare the approach to other existing methods. Second, we analyse how drone flight parameters influence the accuracy of forest parameter detection. Our methodology outperformed existing approaches in stem detection and DBH estimation. Understory detection enabled the identification of forest paths, roads, and areas without understory vegetation. Differences in flight parameters had a large effect on the accuracy of the approach. Optimal data usability was achieved by flying the drone at low flight height above the trees, at relatively high speeds, and with high LiDAR stripe overlap, balancing detailed data collection with efficient area coverage. We conclude that the new approach can provide foresters with detailed insights into forest structure and dynamics, reducing the need for extensive fieldwork.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"88 ","pages":"Article 103127"},"PeriodicalIF":5.8,"publicationDate":"2025-04-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143838154","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":"Explainable models for predicting crab weight based on genetic programming","authors":"Tao Shi ,&nbsp;Lingcheng Meng ,&nbsp;Limiao Deng ,&nbsp;Juan Li","doi":"10.1016/j.ecoinf.2025.103131","DOIUrl":"10.1016/j.ecoinf.2025.103131","url":null,"abstract":"<div><div>Reliable weight prediction plays an important role in commercial transactions and population management of crabs. Existing research usually used predefined models to explain the relationship between the weight and the length of crabs. In this paper, we propose an effective regression method using genetic programming (GP) to build explainable models, which include more features to explore potential relationships between the weight and the physical features of crabs. The GP-based method has been evaluated on a publicly available dataset of crabs. The experimental results were compared with several baseline methods for predicting two kinds of crab weights. GP shows the best performance among all the baseline methods on the test set, i.e., 90.8% for predicting the weight of crabs and 81.3% for predicting the shucked weight of crabs in terms of coefficient of determination. Thanks to the explicit ability of feature selection, GP can select more important features to improve the prediction performance. More importantly, the generated models can provide potential interpretability, which is particularly valuable for domain experts in fisheries management and ecological research.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"88 ","pages":"Article 103131"},"PeriodicalIF":5.8,"publicationDate":"2025-04-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143852207","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":"Individual-based modelling to fine-tune management measures for commercial demersal sharks","authors":"Carlo Zampieri ,&nbsp;Carlotta Mazzoldi ,&nbsp;Saša Raicevich ,&nbsp;Alberto Barausse","doi":"10.1016/j.ecoinf.2025.103147","DOIUrl":"10.1016/j.ecoinf.2025.103147","url":null,"abstract":"<div><div>Individual-Based Models (IBMs) can be used to study population dynamics by emphasising individual variability in life-history traits and behaviour. Here, we built a zero-dimensional IBM to describe the population dynamics of smooth-hound sharks (<em>Mustelus spp.</em>) living in the northern Adriatic Sea, a highly exploited basin of the Mediterranean Sea. We integrated the density-dependence of individual-level processes based on bioenergetic theory with the explicit consideration of the intra-specific variability of life cycle processes into the model. The individual-based approach allowed us to investigate population status, highlight and fill knowledge gaps in the biology of the species, and test the potential effectiveness of innovative conservation measures. Although IBMs are a complex and potentially data-intensive methodology, this work demonstrates their usefulness as screening tools to systematically identify and select, within a pool of options, promising fisheries management measures for elasmobranch conservation and sustainable exploitation.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"88 ","pages":"Article 103147"},"PeriodicalIF":5.8,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143860500","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":"Modeling the spread of forest fires through cellular automata by leveraging deep learning to derive transition rules","authors":"Zucheng Zhou ,&nbsp;Quanli Xu ,&nbsp;Junhua Yi ,&nbsp;Youyou Li ,&nbsp;Shiying Zhang ,&nbsp;Wenhui Li","doi":"10.1016/j.ecoinf.2025.103150","DOIUrl":"10.1016/j.ecoinf.2025.103150","url":null,"abstract":"<div><div>Predicting forest fire spread using simulation models is crucial for the effective management of forest fires. Cellular Automaton (CA) is a key model, and CA transition rules play a decisive role in the effectiveness of the simulation, highlighting the importance of accurately defining these rules. Traditional methods for extracting CA transition rules frequently neglect the intermediate stages of fire development, resulting in less effective outcomes. To overcome this limitation, our study introduces a deep-learning Transformer model to derive more accurate transition rules. The Transformer model excels in capturing fire-spread patterns owing to its robust feature extraction abilities and capacity to manage long-range dependencies, enabling the automatic generation of CA transition rules that more accurately reflect real fire behavior and ultimately improve the simulation of fire spread. Using forest fires in the back mountains of Wenbi Village, Dali City, Yunnan Province, and Sahai Village, Dongchuan District, Kunming City, Yunnan Province as case studies, we initially trained a Transformer model using historical fire data from these areas. We then extracted the CA transition rules from the training results and assessed the model performance using a least-squares support vector machine (LSSVM) model for comparison. The results revealed that the Transformer-CA model surpasses the LSSVM model for predicting fire spread, achieving simulation outcomes that closely align with real fire footprints and improving the overall accuracy, Kappa coefficient and IoU by 4.1 %,5.0 %, 5.5 %, and 3.8 %, 6.0 %,7.0 %, respectively, in the two study areas. This study demonstrated that the Transformer model is ideal for capturing the spatiotemporal evolution of forest fires and constitutes an effective technical approach for fire prevention and management.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"88 ","pages":"Article 103150"},"PeriodicalIF":5.8,"publicationDate":"2025-04-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143834475","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}