Ecological Informatics最新文献

{"title":"Spatiotemporal changes of landslide susceptibility in response to rainfall and its future prediction — A case study of Sichuan Province, China","authors":"Hao Zheng,&nbsp;Mingtao Ding","doi":"10.1016/j.ecoinf.2024.102862","DOIUrl":"10.1016/j.ecoinf.2024.102862","url":null,"abstract":"<div><div>In recent decades, global warming has significantly altered both the spatial and temporal distribution of rainfall patterns. This change has heightened the risk of rainfall-induced landslides, which are the most prevalent natural disasters in the mountainous regions of southwestern China. These events pose unpredictable and severe threats to the region, making it essential to forecast future rainfall trends and assess how landslide susceptibility will respond to these changes. Understanding these dynamics is crucial for developing effective strategies to mitigate and adapt to the changing rainfall patterns that influence landslides. This study focuses on Sichuan Province, China, and uses annual cumulative rainfall (ACR) as a key dynamic variable to create landslide susceptibility maps (LSMs). The goal is to explore the evolving relationship between rainfall and landslide susceptibility and use future rainfall projections to predict these risks. To achieve this, a historical landslide geospatial database was compiled across five temporal categories: 2000, 2001–2005, 2006–2010, 2011–2015, and 2016–2020. The extreme learning machine (ELM) was applied to generate LSMs for the years 2000 to2020, while an elasticity framework was used to assess how sensitive landslide susceptibility is to rainfall variations. To project future scenarios, a long short-term memory (LSTM) model was employed to project the ACR for 2030, using monthly rainfall data from 2000 to 2020. This projected ACR was then used to estimate future landslide susceptibility. Results showed a marked increase in high landslide susceptibility areas: 5.6 % by 2005, 0.3 % by 2010, 0.2 % by 2015, and 12.9 % by 2020, all relative to the year 2000. The elasticity analysis revealed that from 2000 to 2020, a 1 % change in rainfall would cause an average 1.35 % change in landslide susceptibility. Looking forward to 2030, the projected rise in ACR is expected to lead to a 2.44 % increase in areas of high landslide susceptibility. Multiple validation techniques were applied to ensure reliability and robustness of these findings.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102862"},"PeriodicalIF":5.8,"publicationDate":"2024-10-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Leveraging social media and community science data for environmental niche models: A case study with native Australian bees","authors":"Robert A. Moore ,&nbsp;Matthew R.E. Symonds ,&nbsp;Scarlett R. Howard","doi":"10.1016/j.ecoinf.2024.102857","DOIUrl":"10.1016/j.ecoinf.2024.102857","url":null,"abstract":"<div><div>Museum occurrence records are popular sources of information for creating Environmental Niche Models (ENMs), which allow the mapping of the potential niche ranges of species. Occurrence data is often downloaded <em>en masse</em> from established databases. However, the use of non-traditional data sources, such as occurrence records from community/citizen science outreach and social media, is increasing in use and abundance. Data from non-traditional data sources are potentially valuable records of information, particularly for species where museum occurrence records may be comparatively scarce. In the current study, we aimed to determine the impact of adding occurrence data from non-traditional databases to ENMs that were originally created using traditional databases with a group of comparatively understudied species, native Australian bees. We used the Maxent algorithm to model the potential environmental niches of eight species. We created three models for each species: 1) one consisting of only location data from museum specimen collection records from the Atlas of Living Australia (ALA) (a traditional database), 2) one combining ALA and geo-tagged social media (Flickr) data, and 3) a model combining ALA and geo-tagged community science data from iNaturalist. This resulted in 24 different models. By comparing the models produced from each of the augmented data sets with the traditional species data set (ALA vs. ALA &amp; Flickr; ALA vs. ALA &amp; iNaturalist) we showed that there were significant differences, not only in predicted ranges, but also in the weighting of environmental variables used by the models to predict the environmental niche. Differences were more greatly influenced by the geographic location of the extra occurrences rather than the number of additional occurrence points. We demonstrate the potential value and risks of including social media and community science geo-tagged image data in supplementing knowledge of species distributions, particularly for relatively under-sampled species such as native bees.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102857"},"PeriodicalIF":5.8,"publicationDate":"2024-10-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529248","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Landscape and climatic factors shaping mosquito abundance and species composition in southern Spain: A machine learning approach to the study of vector ecology","authors":"Martina Ferraguti ,&nbsp;Sergio Magallanes ,&nbsp;Carlos Mora-Rubio ,&nbsp;Daniel Bravo-Barriga ,&nbsp;Florentino de Lope ,&nbsp;Alfonso Marzal","doi":"10.1016/j.ecoinf.2024.102860","DOIUrl":"10.1016/j.ecoinf.2024.102860","url":null,"abstract":"<div><div>Vector-borne diseases pose significant challenges to public health, with mosquitoes acting as crucial vectors for pathogens globally. This study explores the interaction between environmental and climate factors, investigating their influence on the abundance and species composition of mosquitoes in southwestern Spain, a region endemic to several mosquito-borne diseases.</div><div>Using comprehensive field data from 2020, we analysed mosquito abundance and species richness alongside remote sensing variables and modeling techniques, including the machine learning Random Forest. We collected 5859 female mosquitoes representing 13 species. Non-linear correlations were observed between mosquito abundance and climatic variables, notably temperature and rainfall. Extremely high temperatures correlated with a decrease in mosquito abundance, while accumulated rainfall in the three weeks preceding sampling positively impacted mosquito abundance by providing breeding habitats. A positive correlation between Normalized Difference Vegetation Index (NDVI) and mosquito metrics was also found, aligning with prior studies highlighting vegetation's role shaping mosquito habitats. Interestingly, a negative relationship was observed between mosquito species richness and autumn NDVI. Additionally, wind speed negatively affected mosquito species richness.</div><div>This research provides valuable insights into the ecological determinants of mosquito abundance and species composition in a Mediterranean climate. These findings are crucial for understanding disease transmission dynamics and improving vector control strategies. By integrating climatic characteristics into public health interventions, management measures can become more targeted and efficient, especially during periods of heightened temperature.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102860"},"PeriodicalIF":5.8,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Assessing water quality environmental grades using hyperspectral images and a deep learning model: A case study in Jiangsu, China","authors":"Hongran Li ,&nbsp;Hui Zhao ,&nbsp;Chao Wei ,&nbsp;Min Cao ,&nbsp;Jian Zhang ,&nbsp;Heng Zhang ,&nbsp;Dongqing Yuan","doi":"10.1016/j.ecoinf.2024.102854","DOIUrl":"10.1016/j.ecoinf.2024.102854","url":null,"abstract":"<div><div>Water quality assessment is essential for effective environmental management, yet traditional methods such as chemical sampling are often labor-intensive and inefficient for large-scale, continual monitoring. This study addresses these limitations by leveraging hyperspectral images (HSIs) analysis and introducing a capsule network (CapsNet) model enhanced with a multidimensional integration attention (MDIA) mechanism. The model is specifically designed to integrate both channel and spatial information, enabling precise water quality grade assessment by detecting subtle features within HSIs data. To validate the performance of the model, spectral data from 5 water quality regions are collected and processed via a UAV-carried spectrometer, with 4503 water quality data samples. Rigorous classification experiments demonstrated that the model achieves 98.73 % accuracy, with an average improvement of 4.89 % compared with the other models. This approach significantly improves decision support systems for water resource management, facilitating the sustainable use of water resources.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102854"},"PeriodicalIF":5.8,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529308","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Enhanced Yolov8 network with Extended Kalman Filter for wildlife detection and tracking in complex environments","authors":"Langkun Jiang,&nbsp;Li Wu","doi":"10.1016/j.ecoinf.2024.102856","DOIUrl":"10.1016/j.ecoinf.2024.102856","url":null,"abstract":"<div><div>Amid a growing global focus on ecological conservation and biodiversity monitoring, the efficient identification and tracking of wildlife are essential for environmental research, wildlife protection, and habitat management. Nevertheless, intricate landscapes, varied animal sizes, and obstructions obstruct wildlife detection and tracking. This study introduces the wilDT-YOLOv8n model, specifically engineered for the effective identification and tracking of animals. Initially, the Stable Diffusion model augments the dataset, establishing a basis for training data. Subsequently, enhancements to the Yolov8n model are implemented through the incorporation of the deformable convolutional network DCNv3 and the utilization of the C2f_DCNV3 layer to augment feature extraction efficacy, while addressing detection challenges associated with small targets and intricate backgrounds by integrating the EMGA attention mechanism and the ASPFC feature fusion module. Enhancing the Extended Kalman Filter algorithm guarantees reliable and precise tracking. The research findings reveal that the wilDT-YOLOv8n model attained an average detection accuracy (mAP50) of 88.54 % on the custom dataset, reflecting a 4.57 % enhancement over the original YOLOv8n model; the refined Extended Kalman Filter realizes a Multi-Object Tracking Accuracy (MOTA) of 40.35 %, representing a 3.923 % advancement over the original Kalman Filter. The results indicate the feasibility of accurately detecting and monitoring wildlife in intricate environments, offering significant insights for ecological research and biodiversity conservation, and aiding in the protection of endangered species.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102856"},"PeriodicalIF":5.8,"publicationDate":"2024-10-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529309","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving acoustic species identification using data augmentation within a deep learning framework","authors":"Jennifer MacIsaac ,&nbsp;Stuart Newson ,&nbsp;Adham Ashton-Butt ,&nbsp;Huma Pearce ,&nbsp;Ben Milner","doi":"10.1016/j.ecoinf.2024.102851","DOIUrl":"10.1016/j.ecoinf.2024.102851","url":null,"abstract":"<div><div>Convolutional neural networks (CNNs) are effective tools for acoustic classification tasks such as species identification. Large datasets of labelled recordings are required to develop CNN classifiers which can be difficult to obtain, particularly if species are rare or vocalise infrequently. Additionally, data often requires manual labelling which can be time consuming requiring expert analysis. Artificially generating data using augmentation can address these challenges, however the impact of data augmentation on CNN performance is poorly understood and often omitted in bioacoustic studies. Here, we empirically test the impact of CNN architecture and 20 data augmentation methods on classifier performance. We use acoustic identification of 18 small mammal species as a case study of a species group that can be effectively surveyed by acoustic monitoring, but recordings for training data are scarce and difficult to collect. Networks that achieved the highest accuracy across all sample sizes was a 10-layer CNN (96.43 %) and a pre-trained ResNet50 model (96.37 %). Overall, all augmentation effects improved ResNet50 model performance and 17 effects improved Conv10 performance, increasing relative change in accuracy (RCA) by 0.021–0.641. Three augmentation effects negatively impacted Conv10 RCA by −0.042 to −0.182. We also show that adding augmented data when the number of original samples is low has the greatest positive impact on accuracy and this effect was larger with ResNet50 models. Our work demonstrates that using data augmentation where few original samples are available can considerably improve model performance and highlights the potential of augmentation in developing acoustic classifiers for species where data are limited or difficult to obtain.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"83 ","pages":"Article 102851"},"PeriodicalIF":5.8,"publicationDate":"2024-10-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441573","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Autonomous data sampling for high-resolution spatiotemporal fish biomass estimates","authors":"Astrid A. Carlsen ,&nbsp;Michele Casini ,&nbsp;Francesco Masnadi ,&nbsp;Olof Olsson ,&nbsp;Aron Hejdström ,&nbsp;Jonas Hentati-Sundberg","doi":"10.1016/j.ecoinf.2024.102852","DOIUrl":"10.1016/j.ecoinf.2024.102852","url":null,"abstract":"<div><div>Many key ecological dynamics such as biomass distributions are only detectable on a fine spatiotemporal scale. Autonomous data collection with Unmanned Surface Vehicles (USV) creates new possibilities for cost efficient and high-resolution aquatic data sampling. However, the spatial coverage and sampling resolution remain uncertain due to the novelty of the technology. Further, there is no established method for analysing such fine-scale autocorrelated data without aggregation, potentially compromising data resolution. We here used a USV with an echosounder, a conductivity-temperature sensor and a flourometer to collect data from April–July 2019–2023 in a 60x80km area in the central Baltic Sea. The USV covered a total distance of 8000 nmi, over 42–81 days per year, with an average speed of 0.5 m/s. We combined the hydroacoustic data with publicly available oceanographic data from Copernicus Marine Service Information (CMSI) to describe seasonal distribution dynamics of a small pelagic fish community. Key oceanographic variables collected by the USV were correlated with CMSI estimates at daily/monthly resolution, respectively, to test for suitability to scale (Temperature 0.99/0.97; Salinity −0.77/−0.26; Chlorophyll-a 0.12/0.28). We investigated two approaches of Species Distribution Models (SDMs): generalized additive models (GAM) versus spatiotemporal generalized linear mixed effect models (GLMM). The GLMMs explained the observed data better than the GAMs (R² 0.31 and 0.20, respectively). The addition of environmental variables increased the explanatory capability of GAM and GLMM by 25 % and ∼ 3 %, respectively. Due to the high data resolution, we found significant amounts of positive autocorrelation (R: 0.05–0.30) across more than 50 sequential observations (&gt;6 hours). However, we found that diel patterns in fish detection strongly affected the abundance estimates due to vertically migrating species hiding in the ‘acoustic dead zone’ near the seabed. Such dynamics could only be estimated and corrected for in predictions on the high-resolution data, complicating the trade-off between autocorrelation and high-resolution for SDMs. We compared estimates and effect sizes/directions in identical SDMs on 2x2km/month aggregated (i.e non-autocorrelated) observations and non-aggregated (i.e. autocorrelated) observations, and found relatively little difference in spatiotemporal estimates (<em>r</em> = 0.80). For the first time, we predicted the distribution of a small pelagic fish community at a high spatial resolution, in an area essential to breeding top predators, opening up for new applications in ecological studies locally and globally.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102852"},"PeriodicalIF":5.8,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142529246","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A multidimensional machine learning framework for LST reconstruction and climate variable analysis in forest fire occurrence","authors":"Hatef Dastour,&nbsp;Quazi K. Hassan","doi":"10.1016/j.ecoinf.2024.102849","DOIUrl":"10.1016/j.ecoinf.2024.102849","url":null,"abstract":"<div><div>Land Surface Temperature (LST) datasets play a crucial role in understanding the complex interplay between forest fires, climate variables, and vegetation dynamics. This study is divided into two primary parts: the first part investigates the predictive performance of a machine learning framework based on CatBoost and XGBoost models in estimating LST across different land cover classes in Alberta, Canada. On the test set, for LST-Day data, CatBoost and XGBoost achieved Median Absolute Errors (MedAE) of approximately 1.434 °C and 1.425 °C, respectively. For LST-Night data, also on the test set, the MedAE values were approximately 1.186 °C for CatBoost and 1.176 °C for XGBoost. The second part explores the intricate relationships between climatic variables—LST, precipitation, and relative humidity—forest fire occurrences, and vegetation dynamics in various subregions. The findings revealed complex interactions, with high LST, reduced precipitation, and humidity associated with increased forest fire activity and subsequent changes in vegetation patterns, particularly in the Central Mixedwood, Dry Mixedwood, and Montane subregions. A notable potential association was identified between high LST, reduced precipitation and humidity, and increased forest fire activity in these areas. These climate change impacts and fire events were found to influence ecological processes, altering species composition, reducing biodiversity, and potentially disrupting ecosystem services such as carbon sequestration and nutrient cycling. These insights are crucial for informing adaptive forest management strategies aimed at understanding and mitigating the cascading effects of climate change on fire regimes and vegetation dynamics in Alberta's diverse landscapes.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"83 ","pages":"Article 102849"},"PeriodicalIF":5.8,"publicationDate":"2024-10-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142441572","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Post-fire vegetation dynamic patterns and drivers in Greater Hinggan Mountains: Insights from long-term remote sensing data analysis","authors":"Bohan Jiang ,&nbsp;Wei Chen ,&nbsp;Yuan Zou ,&nbsp;Chunying Wu ,&nbsp;Ziyi Wu ,&nbsp;Xuechun Kang ,&nbsp;Haiting Xiao ,&nbsp;Tetsuro Sakai","doi":"10.1016/j.ecoinf.2024.102850","DOIUrl":"10.1016/j.ecoinf.2024.102850","url":null,"abstract":"<div><div>Fire has become a major disturbing factor in boreal forests, and giant forest disturbances play a vital role in regulating the climate under global warming. Therefore, it is essential to investigate the spatiotemporal patterns and main drivers of post-fire vegetation recovery for forest ecological research and post-fire recovery management. However, previous studies have focused on the post-fire forest change within the entire fire perimeter, lacking separate analysis and comparison of the burned zone (BZ) and unburned zone (UNBZ). Here, we propose the utilization of Moderate Resolution Imaging Spectroradiometer land cover type and vegetation index data to monitor vegetation dynamics and explore its drivers after the most serious forest fire in the history of P.R. China in the Greater Hinggan Mountains (GHM). The temporal and spatial patterns of vegetation recovery in the BZ/UNBZ in the GHM were analyzed using the Sen &amp; Mann-Kendall method, Hurst index and coefficient of variation, and their driving mechanisms were explored using GeoDetector and geographically weighted regression. The results showed that there were significant differences in the spatial distribution and fluctuation of vegetation between the BZ and UNBZ, and that the BZ exhibited higher productivity and vigor. Vegetation recovery was influenced by different dominant factors and changed over time, in which land surface temperature and precipitation dominated all the time, whereas topographic relief and elevation had a more significant contribution to vegetation recovery in the BZ and UNBZ, respectively. This study provides a scientific basis for the protection and management of vegetation in disturbed forested areas, particularly after fires.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"83 ","pages":"Article 102850"},"PeriodicalIF":5.8,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419653","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Rapid diagnosis of the geospatial distribution of intertidal macroalgae using large-scale UAVs","authors":"Andrea Martínez-Movilla ,&nbsp;Juan Luis Rodríguez-Somoza ,&nbsp;Marta Román ,&nbsp;Celia Olabarria ,&nbsp;Joaquín Martínez-Sánchez","doi":"10.1016/j.ecoinf.2024.102845","DOIUrl":"10.1016/j.ecoinf.2024.102845","url":null,"abstract":"<div><div>Macroalgae have been used as indicators of the health of coastal ecosystems, they function as sinks of CO<span><math><msub><mrow></mrow><mn>2</mn></msub></math></span> and are essential contributors to primary production. With the increase in anthropogenic activities, it is crucial to assess the impact of such activities on these ecosystems. As traditional surveying techniques, although accurate, are time-consuming and their area coverage is limited, novel techniques are required to monitor the coverage and diversity of intertidal macroalgae. We propose a methodology using the free-source Semi-Automatic Classification Plugin from QGIS to use UAV and multispectral cameras for the spatiotemporal monitoring of intertidal macroalgae. We also compared the performance of six classifiers: Minimum Distance (MD), Maximum Likelihood (ML), Spectral Angle Mapping (SAM), Multi-Layer Perceptron (MLP), Random Forest (RF) and Support Vector Machine (SVM), for three types of macroalgae classification: general, taxonomical groups and species. As proof of concept, an intertidal rocky shore in a marine protected area (NW Spain) was studied for four months. RF and SVM achieved similar results, with both being recommended for the general (OA<span><math><msub><mrow></mrow><mi>SVM</mi></msub></math></span> = 97.4<span><math><mo>±</mo></math></span>1.7 and OA<span><math><msub><mrow></mrow><mi>RF</mi></msub></math></span> = 98.3<span><math><mo>±</mo></math></span>1.7) and taxonomical groups (OA<span><math><msub><mrow></mrow><mi>SVM</mi></msub></math></span> = 91.6<span><math><mo>±</mo></math></span>1.9 and OA<span><math><msub><mrow></mrow><mi>RF</mi></msub></math></span> = 89.2<span><math><mo>±</mo></math></span>4.5). SVM and ML were found to be more suitable for species classification (OA<span><math><msub><mrow></mrow><mi>SVM</mi></msub></math></span> = 77.4<span><math><mo>±</mo></math></span>11.4 and OA<span><math><msub><mrow></mrow><mi>ML</mi></msub></math></span> = 74.2<span><math><mo>±</mo></math></span>9.7). SAM and MLP provided the least performant species classifiers because of the overlap in the macroalgae spectral signatures. The plugin showed limitations when tuning the input parameters of the MLP classifier and did not let to add a validation dataset. Additionally, we present an open-access GIS web application, Alganat 2000 GIS web, to facilitate the monitoring and management of coastal areas. We conclude that the proposed methodology using the SVM or ML classifiers is an effective tool for assessing intertidal macroalgal assemblages. Its easy and rapid implementation is beneficial for researchers who are not very familiar with coding and machine learning frameworks and reduces the time and cost of fieldwork. As future work, we propose the combination of the multispectral bands with topographic and spectral indices and to research the application of deep learning models to the classification of intertidal macroalgae.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"83 ","pages":"Article 102845"},"PeriodicalIF":5.8,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419652","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}