Ecological Informatics最新文献

{"title":"Predicting potential biomass production by geospatial modelling: The case study of citrus in a Mediterranean area","authors":"G.A. Catalano,&nbsp;P.R. D'Urso,&nbsp;C. Arcidiacono","doi":"10.1016/j.ecoinf.2024.102848","DOIUrl":"10.1016/j.ecoinf.2024.102848","url":null,"abstract":"<div><div>Residual biomass from agricultural production and processing, such as citrus pulp and olive pomace, is an important resource for energy production. In particular, this is the case in regions where transformation industries are concentrated.</div><div>Current biomass estimates often focus on actual production data, that may not fully capture the biomass potential across all suitable cultivation areas. To bridge this gap, the study predicts the overall potential biomass available for energy production, taking into account the total area suitable for citrus cultivation.</div><div>The research is focused on the study of citrus species in the province of Syracuse, Sicily, Italy. The methodology combines Geographic Information System (GIS) tools, for data interpolation and map overlays, with Software for Assisted Habitat Modelling (SAHM) for local level simulations.</div><div>The results of the different models showed accurate and spatially coherent predictions, with AUC values ranging from 0.85 to 0.90, and highest potentialities in the northern and eastern regions of the study area. The results highlighted potential citrus cultivation on 47,706 ha and estimated 184,340 t of biomass, 16,461,520.82 Nm³ of biogas, and 8110 t of digestate. The results of the study identified potential areas for both increasing biomass production and optimising the distribution of digestate, thus demonstrating the utility of these thematic maps as a decision support tool for land management. The simulations and the methodology applied in this study indicated potential economic and environmental benefits to be gained from sustainable by-product management. This approach facilitates the optimisation of decision-making processes for land planning, thereby contributing to the broader objective of improving resource efficiency and sustainability in the agricultural production and processing sectors.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"83 ","pages":"Article 102848"},"PeriodicalIF":5.8,"publicationDate":"2024-10-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142440947","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":"Functional data analysis to describe and classify southern resident killer whale calls","authors":"Paul Nguyen Hong Duc ,&nbsp;David A. Campbell ,&nbsp;Michael Dowd ,&nbsp;Ruth Joy","doi":"10.1016/j.ecoinf.2024.102841","DOIUrl":"10.1016/j.ecoinf.2024.102841","url":null,"abstract":"<div><div>The Southern Resident Killer Whale (SRKW) is an endangered population of whales found in the northeast Pacific. They have a vocal dialect unique from other killer whales, having a repertoire of distinct stereotyped calls. A framework for distinguishing SRKW call types using the frequency traces of the amplitude ridges from their spectrograms (termed frequency ridges) is proposed. The first step is the extraction of these ridges of SRKW calls using an Sequential Monte Carlo approach. Next, they are converted into functional data using B-spline functions. They are analyzed with a functional principal component (FPC) analysis to characterise the intrinsic variability of frequency ridges within a call type. The FPCs are able to capture the general patterns in the frequency ridges of the different SRKW call types. The FPCs are also used as the basis for call classification. Using a cross-validation procedure to assess the robustness of the classification, this framework proves to be successful for classification with some call types having an F1-score <span><math><mo>≥</mo><mn>80</mn><mo>%</mo></math></span>, but other calls less well discriminated. On balance, this approach showed reasonable performance given the small sample size available, and provides a useful contribution towards the development of a universal tool for call classification.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"83 ","pages":"Article 102841"},"PeriodicalIF":5.8,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419650","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":"Navigating uncertainty in carbon efficiency: A global assessment across income groups","authors":"Ziyao Li ,&nbsp;Sangmok Kang","doi":"10.1016/j.ecoinf.2024.102837","DOIUrl":"10.1016/j.ecoinf.2024.102837","url":null,"abstract":"<div><div>This study evaluates the carbon efficiency of 163 countries between 1992 and 2019, focusing on the relationship between economic growth and emission reductions. By using a novel approach that integrates Stochastic Metafrontier Analysis with Bayesian inference, the study robustly analyzes data variability and uncertainty. The results highlight significant differences in carbon efficiency across income groups. High-income countries (G1) show a technology gap uncertainty of 0.118, while low-income countries (G4) have a slightly higher uncertainty at 0.133, indicating challenges in technology transfer for both groups. Middle-income countries (G2), with the lowest uncertainty at 0.045, demonstrate a strong capacity to adopt advanced technologies and improve carbon efficiency. The study also identifies critical factors influencing carbon efficiency uncertainty, such as urbanization, forest area, and foreign direct investment. Urbanization affects these groups differently: it raises uncertainty in G4 by 0.0107 but reduces it in G1 by −0.0069, reflecting varying stages of urban development. These findings suggest the need for targeted policies to improve technology transfer, optimize urbanization, and enhance sustainable resource use, thereby facilitating a more effective shift to a low-carbon economy and reducing carbon efficiency uncertainties.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"83 ","pages":"Article 102837"},"PeriodicalIF":5.8,"publicationDate":"2024-10-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142432080","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":"SooSim, a landscape model for assessing mire habitat degradation and restoration","authors":"Asko Lõhmus ,&nbsp;Raido Kont ,&nbsp;Triin Kaasiku ,&nbsp;Marko Kohv ,&nbsp;Tauri Arumäe ,&nbsp;Ants Kaasik","doi":"10.1016/j.ecoinf.2024.102844","DOIUrl":"10.1016/j.ecoinf.2024.102844","url":null,"abstract":"<div><div>Open mires constitute a characteristic part of boreal natural landscapes, which is under various cumulative anthropogenic pressures. As a response, remaining mires are increasingly protected, and their degradation is addressed by ecological restoration (mostly drainage closure). To evaluate alternative environmental policies, there is a necessity for high-resolution landscape simulation models to assess future dynamics of mires under different management scenarios. We present such a model, <em>SooSim</em>, its R-script, and derivation and validation of its key parameters. <em>SooSim</em> iterates mire types and woody encroachment dynamics within 25 × 25 m grid at 1-year intervals. Management interventions (restoration; ditch renovation) are sequentially introduced based on priority rules in locations delineated prior to simulation. We applied <em>SooSim</em> to three management scenarios, compared with natural succession, until 2050 in Estonia. The ‘current’ (2022) database comprised &gt;3.8 M mire pixels and &gt; 7 M peatland-forest pixels (sparse-cover ones considered for mire restoration). The model parameterization, based on Lidar data, revealed rapid ongoing woody encroachment across all mire types, with significant positive feedback. The simulations revealed that, even in scenarios with intensive restoration (2500 ha annually), open mire conditions are reduced by &gt;10 % until 2050, while few mire types lose &gt;1 % in area. Ditch renovations mostly reduced restoration perspectives in currently forested peatlands. Thus, <em>SooSim</em> explicitly depicts a decision-making dilemma where mire restoration is time-sensitive but also uncertain. To address this and related land-use dilemmas in the environmental policy, landscape models such as <em>SooSim</em> have further importance as visualization tools to explain complex processes to a wide range of stakeholders.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"83 ","pages":"Article 102844"},"PeriodicalIF":5.8,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419500","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":"Automated long-term monitoring of stereotypical movement in polar bears under human care using machine learning","authors":"Matthias Zuerl ,&nbsp;Philip Stoll ,&nbsp;Ingrid Brehm ,&nbsp;Jonas Sueskind ,&nbsp;René Raab ,&nbsp;Jan Petermann ,&nbsp;Dario Zanca ,&nbsp;Ralph Simon ,&nbsp;Lorenzo von Fersen ,&nbsp;Bjoern Eskofier","doi":"10.1016/j.ecoinf.2024.102840","DOIUrl":"10.1016/j.ecoinf.2024.102840","url":null,"abstract":"<div><div>The welfare of animals under human care is often assessed by observing behaviours indicative of stress or discomfort, such as stereotypical behaviour (SB), which often shows as repetitive, invariant pacing. Traditional behaviour monitoring methods, however, are labour-intensive and subject to observer bias. Our study presents an innovative automated approach utilising computer vision and machine learning to non-invasively detect and analyse SB in managed populations, exemplified by a longitudinal study of two polar bears. We designed an animal tracking framework to localise and identify individual animals in the enclosure. After determining their position on the enclosure map via homographic transformation, we refined the resulting trajectories using a particle filter. Finally, we classified the trajectory patterns as SB or normal behaviour using a lightweight random forest approach with an accuracy of 94.9 %. The system not only allows for continuous, objective monitoring of animal behaviours but also provides insights into seasonal variations in SB, illustrating its potential for improving animal welfare in zoological settings. Ultimately, we analysed 607 days for the occurrence of SB, allowing us to discuss seasonal patterns of SB in both the male and female polar bear monitored. This work advances the field of animal welfare research by introducing a scalable, efficient method for the long-term, automated detection and monitoring of stereotypical behaviour, paving the way for its application across various settings and species that can be continuously monitored with cameras. We made the code publicly available at <span><span><span>https://github.com/team-vera/stereotypy-detector</span></span><svg><path></path></svg></span>.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"83 ","pages":"Article 102840"},"PeriodicalIF":5.8,"publicationDate":"2024-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419502","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":"Collectively advancing deep learning for animal detection in drone imagery: Successes, challenges, and research gaps","authors":"Daniel Axford ,&nbsp;Ferdous Sohel ,&nbsp;Mathew A Vanderklift ,&nbsp;Amanda J Hodgson","doi":"10.1016/j.ecoinf.2024.102842","DOIUrl":"10.1016/j.ecoinf.2024.102842","url":null,"abstract":"<div><div>Drones have emerged as a powerful tool in animal detection, significantly advancing wildlife monitoring, conservation, and management by capturing high-resolution, real-time imagery over areas often inaccessible or challenging for human observers to reach. However, manual analysis of drone imagery for animal detection is labour-intensive and time-consuming. The application of deep learning methods, particularly convolutional neural networks, in automating animal detection from drone imagery has the potential to revolutionise wildlife monitoring, conservation, and management protocols.</div><div>This review provides a comprehensive overview of the increasing use and prospects of deep learning in animal detection using drone imagery. It explores successful applications of deep learning for animal detection, localisation, recognition, and their combinations. The paper also discusses the challenges, limitations, and future research directions of this field. A key message from this review is the need for representative training data covering the various scenarios in which target animals may appear, image annotation difficulties, and the comparability of DL model results across studies. Many studies have focused on single species, locations, or images with a high density of common target species. Assessments of models are potentially biased from using a single test set; many studies lack metrics to evaluate model efficiency, feasibility, and generalizability, and there are uncertainties regarding the optimal number of training images and required ground sample distance (GSD) for different animal detection tasks in drone imagery.</div><div>The potential applications of deep learning in wildlife monitoring, conservation, and ecological research using drone imagery are substantial. By enhancing the accuracy and efficiency of animal detection in imagery, this technology could contribute to the understanding and protecting animal populations. To expand the applicability of deep learning to diverse species, environments, and spatial scales, researchers should create standardised benchmark datasets and prioritise open collaboration and data sharing, which would aid in addressing the current challenges.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"83 ","pages":"Article 102842"},"PeriodicalIF":5.8,"publicationDate":"2024-10-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419504","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":"Urban expansion in highly populous East Asian megacities during 1990–2020: Tokyo, Seoul, Beijing, and Shanghai","authors":"Rui Xiao ,&nbsp;Yuji Murayama ,&nbsp;Kun Qin ,&nbsp;Jingling Su ,&nbsp;Zhi Gao ,&nbsp;Liu Liu ,&nbsp;Gang Xu ,&nbsp;Limin Jiao","doi":"10.1016/j.ecoinf.2024.102843","DOIUrl":"10.1016/j.ecoinf.2024.102843","url":null,"abstract":"<div><div>The world is experiencing unprecedented urbanization, particularly through the continuous growth of megacities, leading to significant urban land expansion. The spatial layout and growth patterns of urban areas play a critical role in determining cities' sustainable development. However, the optimal path for cities undergoing rapid urbanization remains uncertain. This study examines four megacities—Tokyo, Seoul, Beijing, and Shanghai—and, from the perspective of urban land density, compares their patterns and processes of land expansion in 1990, 2000, 2010, and 2020. Findings reveal that the urban land area of China's megacities has expanded nearly fourfold over the past three decades. Meanwhile, Tokyo and Seoul have adopted a polycentric urban structure, becoming increasingly compact. In contrast, Beijing and Shanghai are only beginning to show signs of polycentric development. The study concludes that cities experiencing rapid growth should not impose excessive limitations on urban expansion. Polycentric and compact development has become a critical strategy for megacities. These insights offer valuable guidance for urban planning and sustainable development in both established and emerging megacities across East Asia.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"83 ","pages":"Article 102843"},"PeriodicalIF":5.8,"publicationDate":"2024-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419501","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":"Evaluation of machine learning algorithm capability for Bosten Lake Wetland classification based on multi-temporal Sentinel-2 data","authors":"Feiying Xia ,&nbsp;Guanghui Lv","doi":"10.1016/j.ecoinf.2024.102839","DOIUrl":"10.1016/j.ecoinf.2024.102839","url":null,"abstract":"<div><div>As crucial carbon sinks within terrestrial ecosystems, wetlands have been extensively studied in terms of spatio-temporal distributions. However, existing methods for classifying wetlands are of limited accuracy, and it is difficult to acquire consistent samples over time. Therefore, precise classification methods are required to facilitate wetland conservation and ecological restoration. In this study, multiple machine learning (ML) algorithms in combination with feature sets based on Sentinel-2 data were used to accurately classify the land-use types (LUTs) of the Bosten Lake Wetland (BLW) in Xinjiang, China. The enhanced water index (EWI), modified normalised difference water index (MNDWI), and normalised difference water index (NDWI) were selected to extract water information and distinguish water bodies from land surfaces in the BLW. Three classification plans based on vegetation indices, water indices, and textural features were developed using artificial neural network (ANN), support vector machine (SVM), random forest (RF) algorithms. Plan 9 combined vegetation water and texture with the highest overall accuracy (OA) 91.02 % and kappa coefficient (KC) 0.89. This plan obtained a producer accuracy of over 90 % for lake wetlands, river wetlands, grassland wetlands, mud flats, and farmland and &gt; 83 % for construction land and bareland. According to Plan 9, the wetland area during 2018–2023 showed noticeable seasonal fluctuations but stable interannual changes. Conversely, non-wetland areas demonstrated significant interannual fluctuations, particularly in bareland and farmland, which might have been influenced by urbanisation and ecological policies. This study provides insights into the data sources, feature selection, and methodological approaches for wetland information extraction in arid regions.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102839"},"PeriodicalIF":5.8,"publicationDate":"2024-09-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142701322","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":"Corrigendum to “The bioacoustic soundscape of a pandemic: Continuous annual monitoring using a deep learning system in Agmon Hula Lake Park”","authors":"Yizhar Lavner ,&nbsp;Ronen Melamed ,&nbsp;Moshe Bashan ,&nbsp;Yoni Vortman","doi":"10.1016/j.ecoinf.2024.102834","DOIUrl":"10.1016/j.ecoinf.2024.102834","url":null,"abstract":"","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"83 ","pages":"Article 102834"},"PeriodicalIF":5.8,"publicationDate":"2024-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142538109","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":"Estimating the dynamics and driving factors of gross primary productivity over the Chinese Loess Plateau by the modified vegetation photosynthesis model","authors":"Enjun Gong ,&nbsp;Jing Zhang ,&nbsp;Zhihui Wang ,&nbsp;Jun Wang","doi":"10.1016/j.ecoinf.2024.102838","DOIUrl":"10.1016/j.ecoinf.2024.102838","url":null,"abstract":"<div><div>Gross primary productivity (GPP) is a key parameter in research on the global carbon cycle and changes. Understanding the spatiotemporal dynamics and influencing factors of GPP on the Loess Plateau (LP) helps identify the health status of ecosystems, thereby enabling the implementation of effective conservation and restoration measures. In this study, we used a modified vegetation photosynthesis model (VPM) to simulate a long-term series of GPP in the LP from 2001 to 2022, and the impacts of different land-use patterns and meteorological factors on GPP were investigated using a transition matrix, linear regression, and partial correlation analyses. The findings suggested that the modified simulation yielded a more reliable performance (coefficient of determination (<em>R</em>²) = 0.89, root mean square error (RMSE) = 143.47 gC·m⁻²·yr⁻¹) and was suitable for further research endeavors. (1) The GPP on the LP significantly increased by 232.65 TgC from 2001 to 2022. The southeastern region contributed more than the northwestern region, and the GPP exhibited higher multi-year averages and growth rates below 1000 m elevation. (2) Forests in the southeastern region of the LP, characterized by a heightened growth rate, will influence future spatial variations in GPP increases across the LP. Despite the decline in grassland and cultivated land areas, substantial land coverage has significantly contributed to the overall GPP growth. Urbanization encroaching on cultivated land has emerged as a key contributor to the decline in GPP in low-altitude regions. (3) Air temperature was the main physical driving force for GPP change in the LP. Additionally, the GPP in forested regions exhibited a negative correlation with rainfall, whereas the GPP in areas undergoing the return of cropland to forest–grassland and cropland reclamation correlated negatively with solar radiation. (4) The attribution analysis indicated that the surge in vegetation GPP on the LP was collectively driven by human activities and meteorological changes, with human activities dominating these changes by 61.41 %. This study deepens the understanding of terrestrial ecology in semi-humid regions and provides scientific insights for implementing ecological governance strategies in the LP.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"83 ","pages":"Article 102838"},"PeriodicalIF":5.8,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142419649","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}