Ecological Informatics最新文献

{"title":"Contrasting the efficiency of imaging systems for mesozooplankton indicators across Pacific and Atlantic coastal ecosystems","authors":"Anaïs Lacoursière-Roussel ,&nbsp;Luke McLean ,&nbsp;Cyril Aubry ,&nbsp;Frédéric Maps ,&nbsp;Stephen Finnis ,&nbsp;Julie Arseneau ,&nbsp;Rebecca Milne ,&nbsp;Tara Macdonald ,&nbsp;Thomas Guyondet","doi":"10.1016/j.ecoinf.2025.103372","DOIUrl":"10.1016/j.ecoinf.2025.103372","url":null,"abstract":"<div><div>Mesozooplankton have a pivotal role in marine food webs, linking primary producers to higher trophic levels. Their abundance and traits serve as key indicators of ecosystem structure and function, making them essential components of long-term ocean monitoring. However, the need to monitor biodiversity and functional traits, combined with their pronounced spatial and temporal variability, requires extensive sampling and presents significant laboratory bottlenecks and cost-related challenges. Imaging instruments, combined with automated image classifiers such as Ecotaxa, offer a promising solution by enabling high-throughput, cost-effective processing of large numbers of samples, while also providing highly precise trait measurements previously unattainable with traditional methods. In this study, we compare the performance of human-sorted microscopy, human-sorted images and computer-sorted images across three contrasting coastal ecosystems on Canada's Pacific and Atlantic coasts. First, we demonstrated that upfront investment in identifying a larger number of images contributed to the development of robust regional image libraries, which significantly enhanced the performance of automated classifiers (e.g., mean F1 score = 0.54 with up to 200 images per taxon and 0.68 with up to 5000 images per taxon). Results showed that automated image classification performance varies with specimen characteristics such as symmetry, geodesic thickness, and taxa richness. We then assessed how each method captures local mesozooplankton diversity and altered key ecological indicators. Based on observed ecosystem-specific differences, we provide recommendations for optimizing classification workflows in relation to local diversity patterns. This study provides large-scale empirical evidence that investing in the development of regional image libraries enhances the scalability and accuracy of coastal ecological assessments. These emerging digital assets have the potential to significantly advance ecosystem monitoring and management.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"91 ","pages":"Article 103372"},"PeriodicalIF":7.3,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144842686","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":"An adaptive method for individual tree segmentation synthesizing canopy cover and competitive mechanism using UAV data","authors":"Qiyu Guo,&nbsp;Kangning Li,&nbsp;Xiaojun Qiao,&nbsp;Jinbao Jiang,&nbsp;Yinpeng Zhao","doi":"10.1016/j.ecoinf.2025.103360","DOIUrl":"10.1016/j.ecoinf.2025.103360","url":null,"abstract":"<div><div>Accurate individual tree segmentation (ITS) is crucial for precision forestry and small-scale carbon sink accounting; however, canopy overlap in complex forest stands—particularly in northern plantations, presents substantial challenges for conducting ITS using LiDAR point cloud. This study introduces an adaptive ITS method that incorporates canopy cover as the primary constraint in marker-controlled watershed segmentation. This addresses two typical segmentation biases: low canopy cover areas that are prone to under-segmentation are refined using the DBSCAN spatial clustering to recover missed tree boundaries, whereas high canopy cover regions that were prone to over-segmentation were optimized using Hegyi index-enhanced improved K-means clustering method of raw point cloud data for context-aware region merging. By fusing the canopy height model (CHM) efficiency for rapid canopy contour extraction with point cloud-derived 3D structural details, this “cover-degree-driven, scene-adaptive” method balances computational speed and segmentation precision. The method was validated across 28 plots, the method achieving F1 scores of 0.89–0.95 for four tree species and outperforming traditional ITS methods in mixed forests with F1 improvements of 0.12–0.24. This method enhances the ITS accuracy of individual tree aboveground biomass estimation, thereby directly facilitating efficient small-scale carbon accounting, streamlined forest inventories, and sustainable precision management in complex ecosystems.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"91 ","pages":"Article 103360"},"PeriodicalIF":7.3,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144829777","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":"Changes in the relationship between climate variables and vegetation carbon uptake in East Asia","authors":"Min-Seok Shin ,&nbsp;Sang-Wook Yeh ,&nbsp;Hak-Jun Lee ,&nbsp;Chang-Eui Park","doi":"10.1016/j.ecoinf.2025.103375","DOIUrl":"10.1016/j.ecoinf.2025.103375","url":null,"abstract":"<div><div>To understand the carbon cycle in East Asia in the context of rising CO₂, we analyzed a land carbon cycle dataset (TRENDY) from 1982 to 2020, examining the relationship between vegetation carbon uptake and two climate variables (i.e. precipitation and surface temperature) during the vegetation growing season (March to September). Our results show that, since the early 2000s, the relationship between gross primary production (GPP) and surface temperature has strengthened, while the relationship between GPP and precipitation has weakened in East Asia. Further analyses suggest that this strengthening of the GPP-surface temperature relationship is primarily due to a combination of CO₂ fertilization effects and significant increases in surface temperature, which lead to reduced soil moisture and increased water use efficiency in vegetation. This appears to result in an increase in GPP in the long term along with the absence of significant changes in precipitation. As a result, vegetation carbon uptake is less dependent on precipitation and more correlated with surface temperature in the recent decades. This result indicates that the relationship between vegetation carbon uptake and climate variables is non-stationary, and therefore requires careful attention to properly develop carbon mitigation plans through afforestation.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"91 ","pages":"Article 103375"},"PeriodicalIF":7.3,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144861319","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":"Reconstructing coastal ponds functional classification: Integration of multi-feature remote sensing","authors":"Yijun Tong ,&nbsp;Chen Lin ,&nbsp;Ke Song ,&nbsp;Tingchen Jiang ,&nbsp;Ronghua Ma ,&nbsp;Wenzhuo Cui ,&nbsp;Danhua Ma ,&nbsp;Jianchun Chen ,&nbsp;Zhenxing Wang ,&nbsp;Xiaofen Bai","doi":"10.1016/j.ecoinf.2025.103370","DOIUrl":"10.1016/j.ecoinf.2025.103370","url":null,"abstract":"<div><div>Pond water surfaces (PWS) play a crucial role in the ecological sustainability and development of coastal zones. However, in these areas, different types of PWS have significant differences, and the absence of a universal pond classification system complicates the analysis of PWS characteristics. To address this bottleneck, this study introduces a refined PWS classification system for coastal zones, including landside clustering aquaculture ponds (LCAP), marine aquaculture ponds (MAS), salt pans (SP), landscape ponds (LP), and natural ponds (NP). A multifeatured fusion object-oriented (MFFO) method for PWS was established using Sentinel-2 images, based on the Google Earth Engine. Consequently, 10-m resolution PWS classification data were formed in the coastal zone of Jiangsu, China. Results showed that: (1) The total surface area of PWS was 904.01 km², which accounted for 7.08 % of the study area, reaching 83.18 % overall accuracy. The functional types of PWS can be categorized as aquaculture, landscaping, water storage, and salt drying. (2) Regarding different PWS functional types, significant differences were demonstrated in terms of remote sensing features and geographical patterns. Remote sensing features revealed that LCAP, MAS, and SP differ greatly across various spectral bands, whereas NP varied substantially in shape characteristics, and LP exhibited distinct spatial distribution. Geographically, LCAP and SP were mostly distributed in coastal mudflats, LP were mainly situated in cities, NP were largely distributed in rural and mountainous areas, and MAS were situated on the ocean surface. Above all, the PWS classification system and data products developed in this study reveal the diverse relationships among “functional types-remote sensing features-geographical patterns” regarding PWS, implicating a crucial foundation for clarifying the ecological functional value of PWS and appropriate planning in the coastal zone.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"91 ","pages":"Article 103370"},"PeriodicalIF":7.3,"publicationDate":"2025-08-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144865177","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":"Towards automated and real-time multi-object detection of anguilliform fishes from sonar data using YOLOv8 deep learning algorithm","authors":"Tao Huang ,&nbsp;Xiaoqin Zang ,&nbsp;Grigoriy Kondyukov ,&nbsp;Zhangshuan Hou ,&nbsp;Guanze Peng ,&nbsp;Joachim Pander ,&nbsp;Josef Knott ,&nbsp;Juergen Geist ,&nbsp;Meklit Berihun Melesse ,&nbsp;Paul Jacobson ,&nbsp;Zhiqun Daniel Deng","doi":"10.1016/j.ecoinf.2025.103381","DOIUrl":"10.1016/j.ecoinf.2025.103381","url":null,"abstract":"<div><div>Freshwater eels (<em>Anguilla</em> spp.), including American eels (<em>Anguilla rostrata</em>), European eels (<em>Anguilla anguilla</em>), and Japanese eels (<em>Anguilla japonica</em>), are target species for conservation and of regulatory concern due to their vulnerability to various stressors during obligatory migrations from freshwater into oceanic spawning grounds. Accurate and efficient detection of migrating eels can improve our understanding of fish behaviors and fish-hydraulic structure interactions from both ecological and economic perspectives. However, a real-time and automated framework for detecting migrating eels in real-world applications is currently lacking. Leveraging imaging sonar as a reliable technology for fish passage monitoring in dark, turbid and high-flow environments, field data are acquired using imaging sonar and then converted to single sonar frames/images for subsequent analysis. In this study, a framework based on the “You Only Look Once” Version 8 (YOLOv8)-based convolutional neural network is proposed for multi-object detection of eels and non-eel fish using the sonar images after image subtraction and additional wavelet denoising. The results from both training and testing phases demonstrate that the framework's ability can successfully detect both eels and non-eel fish in preprocessed sonar images, achieving <em>F</em>1-scores and [email protected] exceeding 0.80. Additionally, the incorporation of wavelet denoising during preprocessing slightly improves detection performance. Furthermore, the transferability of this framework from eel to lamprey detection is demonstrated to be feasible given the similar morphological characteristics of these two species. Overall, the proposed framework achieves accurate and efficient detection of migrating eels, providing reliable and real-time information that can help conserve vulnerable eel and eel-like species and facilitate the design, operation, and optimization of more effective downstream passage facilities.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"91 ","pages":"Article 103381"},"PeriodicalIF":7.3,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144809700","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":"Data quality issues in data used in species distribution models: A systematic literature review","authors":"Wesley Lourenco Barbosa,&nbsp;Solange Nice Alves-Souza","doi":"10.1016/j.ecoinf.2025.103378","DOIUrl":"10.1016/j.ecoinf.2025.103378","url":null,"abstract":"<div><div>Species distribution models (SDM) are important tools for decision-making in several application areas, being essential for managing biodiversity resources in the world. The ability of these models to represent the reality is strongly dependent on the fitness of the data from which they are generated. Although scientific literature recognizes the occurrence of several data quality (DQ) problems, little work has focused on conducting a comprehensive survey to identify and quantify these challenges. Thus, this paper conducts a systematic review of the literature to examine the DQ problems observed in species occurrence and environmental data applied to the SDM context. It also identifies and discusses solutions that have been proposed to address these problems. A total of 212 articles were selected and analyzed to identify 14 recurring DQ problems. Misidentification errors and spatial or geographical bias were the most prevalent. Data gathered through Citizen Science initiatives continue to be a subject of scrutiny, with observer skill identified as the third most frequent challenge. Resolving data quality issues remains a significant research challenge due to the specific characteristics of the data types involved. Our findings highlight the need for a more detailed examination of the impact of data quality on SDMs and call for the development of robust methodologies for data quality assessment and improvement. The paper emphasizes the importance of context-specific knowledge for the effective management of data quality, which is essential for enhancing the reliability of SDMs and supporting more accurate ecological forecasting and conservation planning. Consequently, a substantial body of research remains to be conducted, particularly at the intersection of computational methodologies and the specialized domain of biogeography.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"91 ","pages":"Article 103378"},"PeriodicalIF":7.3,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144842687","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Improving oil slick trajectory simulations with Bayesian optimization","authors":"Gabriele Accarino ,&nbsp;Marco M. De Carlo ,&nbsp;Igor Ruiz Atake ,&nbsp;Donatello Elia ,&nbsp;Anusha L. Dissanayake ,&nbsp;Antonio Augusto Sepp Neves ,&nbsp;Juan Peña Ibañez ,&nbsp;Italo Epicoco ,&nbsp;Paola Nassisi ,&nbsp;Sandro Fiore ,&nbsp;Giovanni Coppini","doi":"10.1016/j.ecoinf.2025.103368","DOIUrl":"10.1016/j.ecoinf.2025.103368","url":null,"abstract":"<div><div>Accurate simulations of oil spill trajectories are essential for supporting practitioners' response and mitigating environmental and socioeconomic impacts. Numerical models, such as MEDSLIK-II, simulate advection, dispersion, and transformation processes of oil particles, but their accuracy depends strongly on the correct tuning of physical parameters, often relying on manual calibration and expert knowledge. This approach is suboptimal, especially in dynamic and uncertain environmental conditions. To overcome these limitations, we couple the MEDSLIK-II oil spill model with a Bayesian optimization framework to iteratively estimate the optimal values of key parameters, such as the horizontal diffusivity, wind angle and wind drag, in order to obtain simulation closer to satellite observations of the slick. We adopt a stochastic parameterization strategy, which probabilistically explores the parameter space to enhance simulation skill. To this end, the Fraction Skill Score (FSS) is maximized to evaluate spatial-temporal overlap between simulated and observed oil distributions. The framework is validated for the Baniyas oil incident that occurred in Syria between August 23 and September 4, 2021, which released over 12,000 m<span><math><msup><mspace></mspace><mn>3</mn></msup></math></span> of oil. The approach improves FSS from 7. 97 % to 20. 66 %, on average, compared to control simulations initialized with default parameters. Results demonstrate consistent improvements across time steps, highlighting the method's robustness and suitability for operational oil spill modeling under uncertainty.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"91 ","pages":"Article 103368"},"PeriodicalIF":7.3,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"145003698","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 identification of wild raptors using a deep learning approach: A case study of the white-tailed eagle","authors":"Xi Guo ,&nbsp;Yufeng Chen ,&nbsp;Yu Guan ,&nbsp;Hongfang Wang ,&nbsp;Tianming Wang ,&nbsp;Jianping Ge ,&nbsp;Lei Bao","doi":"10.1016/j.ecoinf.2025.103379","DOIUrl":"10.1016/j.ecoinf.2025.103379","url":null,"abstract":"<div><div>Individual identification is essential for elucidating animal population structures, tracking population dynamics, and uncovering social networks. Advances in computational technology have enabled the application of deep learning-based methods for individual wildlife identification. However, accurately identifying individual animals in complex wild environments remains a significant challenge. Motivated by the need for accurate and efficient identification of individual animals in the wild, a deep learning-based individual identification framework, the object tracking–face extraction–sampling–recognition (OFSR) approach, is proposed. This framework uses deep learning to extract facial features and a multitask module with cross-task information sharing to integrate supplementary data, enhancing individual identification accuracy. By employing the OFSR framework, we identified individual white-tailed eagles in the Jingxin Wetland during the overwinter period. Our results demonstrated that the OFSR framework could accurately identify individual white-tailed eagles in wild environments, achieving an accuracy exceeding 93 %. In addition, in the multitask module of the OFSR framework, age recognition is used to increase the individual identification accuracy, successfully separating recurring and new individuals and increasing the accuracy by 2 % without adding extra costs. Our results demonstrate the potential of deep learning in identifying individual animals in complex wild environments, and the proposed OFSR framework is universally applicable to other raptors. The findings highlight that the added multitask module increases the accuracy of identifying individual animals. Our framework could improve the accuracy of identifying individuals in complex wild environments, offering a promising method for population detection and conservation research involving wild animals.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"91 ","pages":"Article 103379"},"PeriodicalIF":7.3,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144878477","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":"Advancing soil biology research: Empowering European databases with ontological frameworks for enhanced data integration of soil biodiversity data","authors":"José F. Aldana-Martín ,&nbsp;David J. Russell ,&nbsp;Carlos A. Martínez-Muñoz ,&nbsp;Christine Driller ,&nbsp;Stephan Lesch ,&nbsp;Ismael Navas-Delgado","doi":"10.1016/j.ecoinf.2025.103356","DOIUrl":"10.1016/j.ecoinf.2025.103356","url":null,"abstract":"<div><div>Recognizing soil biodiversity’s critical role in soil quality and health has gained prominence in environmental policy and research. There is a pressing need to integrate taxonomic data with functional traits to understand the functional significance of soil biodiversity and its distribution across various environmental contexts. This long-term goal can only be achieved after comprehensive taxonomy ontologies are in place.</div><div>Ontologies are a powerful tool to facilitate database interoperability, ensuring a seamless connection between diverse datasets. Adopting ontologies aligns with the FAIR principles, enhancing data discoverability, accessibility, and machine-readability. In biology, ontologies offer a robust framework for formalizing complex relationships between taxa, traits, and environments. Repositories like the OBO Foundry and NCBO BioPortal further promote the integration of controlled bioscientific vocabularies. However, careful selection of vocabulary is essential to ensure effective interoperability among ontologies, especially when dealing with closely related taxa.</div><div>While databases like Edaphobase provide comprehensive taxonomic information for soil invertebrate animals, they lack specific ontologies for the underlying taxonomic structure. This research addresses this gap by proposing the EUdaphobase Taxonomy Ontology (EUTaxO) tailored to soil biology taxonomy. As Edaphobase is continuously updated to accommodate changes in taxonomic classifications, the related EUTaxO will require maintenance. This work presents an automated pipeline to synchronize the proposed ontology with Edaphobase’s classification.</div><div>The integration of observational databases, such as Edaphobase, with domain-specific trait databases will enable the aggregation of species into functional or ecological groups based on traits. This integration, primarily reliant on taxonomic characteristics, will be critical in evaluating the spatio-temporal distribution of functional soil biodiversity across diverse habitats, soil types, climate zones, and land-use patterns.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"91 ","pages":"Article 103356"},"PeriodicalIF":7.3,"publicationDate":"2025-08-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144809752","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":"Factors influencing the consistency in crowdsourced interpretations of aerial photographs to measure tree canopy cover","authors":"Jill M. Derwin ,&nbsp;Valerie A. Thomas ,&nbsp;Randolph H. Wynne ,&nbsp;Karen G. Schleeweis ,&nbsp;John W. Coulston ,&nbsp;S. Seth Peery ,&nbsp;Kurt Luther ,&nbsp;Greg C. Liknes ,&nbsp;Stacie Bender ,&nbsp;Susmita Sen","doi":"10.1016/j.ecoinf.2025.103300","DOIUrl":"10.1016/j.ecoinf.2025.103300","url":null,"abstract":"&lt;div&gt;&lt;div&gt;Machine learning models are typically data-hungry algorithms that require large data inputs for training. When they produce wall-to-wall remote sensing products, model validation also requires large sets of temporally harmonized field observations. Crowdsourcing may offer a potential solution for the collection of photointerpretations for the training and validation of spatial models of tree canopy cover (TCC), as it harnesses the power of a large anonymous crowd in the completion of repetitive discrete analyses or human intelligence tasks (HITs). This study explores the factors that determine the consistency of TCC interpretations collected by an anonymous crowd to those collected by a control group. The crowd interpretations were obtained through an anonymous platform with a task-reward framework, while those collected by the control group were collected by known interpreters in a more traditional setting. Both groups carried out this task using an interface developed for Amazon’s Mechanical Turk platform. We collected multiple interpretations at sample plot locations from both crowd and control interpreters, and sampled these data in a Monte Carlo framework to estimate a classification model predicting the consistency of each crowd interpretation with control interpretations. Using this model, we identified the most important variables in estimating the relationship between a location’s characteristics and interpretation behaviors which affect consistency in interpretations between crowd workers our control group. Overall, we show low agreement between crowdsourced and control interpretations, as well as interpretations from individual control group members. This warrants caution in considering the crowdsourced photointerpretation of TCC as a data source for model training and validation without adequate interpreter training as well as significant quality control measures and consistency standards. We show that the number of plots interpreted was the strongest indicator of the reliability of an individual’s interpretations, further evidenced by apparent fatigue effects in crowd interpretations. The second most important variable related to the use of the false color display during interpretation followed by a variable related to the use of the natural color display during interpretation, reflecting the differences in interpretation methodologies used by crowd workers and control group interpreters and the impact display has on the interpretation of tree canopy cover. Finally, we discuss recommendations for further study and future implementations of crowdsourced photointerpretation. These include the enhanced use of existing mechanisms within Mechanical Turk such as worker qualifications to identify and reward more attentive workers, as well as enhanced attention to quality control measures throughout the data collection process and measures to increase intrinsic motivation. For our study we also recommend a minimum time on task or o","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"91 ","pages":"Article 103300"},"PeriodicalIF":7.3,"publicationDate":"2025-07-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144920125","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}