Ecological Informatics最新文献

{"title":"A multi-source approach to mapping habitat diversity: Comparison and combination of single-date hyperspectral and multi-date multispectral satellite imagery in a Mediterranean Natural Reserve","authors":"Chiara Zabeo ,&nbsp;Gaia Vaglio Laurin ,&nbsp;Birhane Gebrehiwot Tesfamariam ,&nbsp;Diego Giuliarelli ,&nbsp;Riccardo Valentini ,&nbsp;Anna Barbati","doi":"10.1016/j.ecoinf.2024.102867","DOIUrl":"10.1016/j.ecoinf.2024.102867","url":null,"abstract":"<div><div>The increasing availability of spaceborne hyperspectral satellite imagery opens new opportunities for forest habitat mapping and monitoring, but the limitation of its generally low temporal resolution must be considered. In this study, we compare the ability of single-date PRISMA (PRecursore IperSpettrale della Missione Applicativa), the hyperspectral satellite from the Italian Space Agency, with that of both single-date and multi-date Sentinel-2 (S2) and PlanetScope (PS) to detect and correctly classify various EUNIS habitat types distributed over a relatively small spatial extent (6000 ha) in a natural reserve in Central Italy. The case study deals with multiple levels of spectral similarity, as the dominant canopy species of the target forest habitat classes belong to the same genus (<em>Quercus</em> spp., both deciduous and evergreen species) as well as of different taxa (<em>Pinus</em> and <em>Fraxinus</em> spp.). We performed a pixel-based classification with the Random Forest algorithm using a set of 28 spectral indices computed on PRISMA bands, 22 on S2, and 12 on PS. A Canopy Height Model (CHM) was also used as an input variable for the classification. Our results showed that PRISMA considerably outperforms the two multispectral satellites in single-date classifications, with an overall accuracy of 84 % compared to PlanetScope's 69 % and Sentinel-2's 72 %. Regarding the comparison between multi-date multispectral and single-date hyperspectral, 10-fold cross-validation results revealed that S2 achieves an out-of-bag error rate of approximately 16 %, while PRISMA achieves 17 % and PS 19 %. This demonstrates that a combination of spectral indices calculated during the growing season can capture phenological or physiological differences among the target species, which consequently results in a significant improvement in the classification accuracy of the multispectral sensors. Ultimately, classification results from all three sensors were combined to create probability maps for each forest class, identifying areas classified with a higher degree of certainty by each satellite tested and potentially contributing to forest management by defining areas with varying conservation levels.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552292","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":"Bayesian feedback in the framework of ecological sciences","authors":"Mario Figueira ,&nbsp;Xavier Barber ,&nbsp;David Conesa ,&nbsp;Antonio López-Quílez ,&nbsp;Joaquín Martínez-Minaya ,&nbsp;Iosu Paradinas ,&nbsp;Maria Grazia Pennino","doi":"10.1016/j.ecoinf.2024.102858","DOIUrl":"10.1016/j.ecoinf.2024.102858","url":null,"abstract":"<div><div>In ecological studies, it is not uncommon to encounter scenarios where the same phenomenon (e.g., species occurrence, species abundance) is observed using two different types of samplers. For example, species data can be collected from scientific sampling with a completely random sample pattern, but also from opportunistic sampling (e.g., whale watching from commercial fishing vessels or bird watching from citizen science), where observers tend to look for particular species in areas where they expect to find them.</div><div>Species Distribution Models (SDMs) are widely used tools for analysing this type of ecological data. In particular, two models are available for the aforementioned data: a geostatistical model (GM) for data collected where the sampling design is not directly related to the observations, and a preferential model (PM) for data obtained from opportunistic sampling.</div><div>The integration of information from disparate sources can be addressed through the use of expert elicitation and integrated models. This paper focuses on a sequential Bayesian procedure for linking two models by updating prior distributions. The Bayesian paradigm is implemented together with the integrated nested Laplace approximation (INLA) methodology, which is an effective approach for making inference and predictions in spatial models with high performance and low computational cost. This sequential approach has been evaluated through the simulation of various scenarios and the subsequent comparison of the results from sharing information between models using a variety of criteria. The procedure has also been exemplified on a real dataset.</div><div>The primary findings indicate that, in general, it is preferable to transfer information from the independent (with a completely random sampling) model to the preferential model rather than in the alternative direction. However, this depends on several factors, including the spatial range and the spatial arrangement of the sampling locations.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552291","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":"Unveiling the dynamic flows and spatial inequalities arising from agricultural methane and nitrous oxide emissions","authors":"Fan Zhang ,&nbsp;Yuping Bai ,&nbsp;Xin Xuan ,&nbsp;Ying Cai","doi":"10.1016/j.ecoinf.2024.102863","DOIUrl":"10.1016/j.ecoinf.2024.102863","url":null,"abstract":"<div><div>Tracing the spatial transfer and heterogeneity of agricultural methane (CH₄) and nitrous oxide (N₂O) emissions in China is a prerequisite for the sustainable transformation of agricultural systems. In this study, we established a research framework for evaluating agricultural CH₄ and N₂O flows and convergence. Using this framework, we established an inventory of China's agricultural CH₄ and N₂O emissions calculated according to the IPCC inventory guidelines, built a food trade model to simulate the spatial transfer, and revealed the regional differences. Finally, we analyzed the influence mechanism by combining extended Kaya identity and the logarithmic mean divisia index (LMDI) model. We found that inter-regional transfer of agricultural CH₄ and N₂O emissions in China have intensified, increasing from 56.14 % of total transfers in 2000 to 67.28 % in 2019. The spatial inequalities of agricultural CH₄ and N₂O increased, and emission intensity varied more within regions than between regions, with per capita emissions showing a club convergence with “intragroup convergence and intergroup divergence”. Although the contribution of agricultural CH₄ and N₂O emissions varies across provinces, controlling emissions intensity and land use intensity while maintaining GDP per capita is the key to emission mitigation. Our study provides theoretical support for prioritizing policies to mitigate agricultural CH₄ and N₂O emissions.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-10-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142571645","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":"Modeling forest canopy structure and developing a stand health index using satellite remote sensing","authors":"Pulakesh Das ,&nbsp;Parinaz Rahimzadeh-Bajgiran ,&nbsp;William Livingston ,&nbsp;Cameron D. McIntire ,&nbsp;Aaron Bergdahl","doi":"10.1016/j.ecoinf.2024.102864","DOIUrl":"10.1016/j.ecoinf.2024.102864","url":null,"abstract":"<div><div>Biotic and abiotic disturbances modify tree structure and degrade stand health. Accurate geospatial data on stand structure is important for monitoring tree growth, forest health, progression and severity of diseases and pests, and estimating resilience to climate stress. The live crown ratio (LCR) of trees serves as a key health indicator but has been understudied at the landscape level using remote sensing data. This study generated the leaf area index (LAI) and a novel spatial layer of LCR at site and landscape scales using a combination of satellite data and ground observations. We conducted field surveys to collect plot-level (10 m × 10 m) data in four eastern white pine (EWP; <em>Pinus strobus L.</em>)-dominated sites in the state of Maine, USA. The plot-level data were used to develop regression models for LAI and LCR estimation using microwave (Sentinel-1) and optical (Sentinel-2) remote sensing data and applying the Random Forest (RF) and Support Vector Machine (SVM) machine learning algorithms. The RF model showed higher prediction accuracy than the SVM model at the site level. Moreover, the prediction accuracy at the site and landscape levels were comparable for LAI (R² &gt; 0.76) and LCR (R² &gt; 0.71) using the RF model. Furthermore, the predicted LAI and LCR were integrated with canopy height and stand density to develop a novel health index map for EWP. The resulting health index map successfully delineated patches representing various health categories. Forestry practitioners and decision-makers can use the derived health index map and intermediate spatial data layers (LAI and LCR) to guide stand management. The developed framework can potentially be applied to other coniferous and broadleaved species for remote sensing-based LCR estimation and forest health assessment upon further studies and verification.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":null,"pages":null},"PeriodicalIF":5.8,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142552233","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":"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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":null,"pages":null},"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}