Ecological Informatics最新文献

{"title":"Forest aboveground biomass retrieval integrating ICESat-2, Landsat-8, and environmental factors","authors":"Sunjie Ma ,&nbsp;Jisheng Xia ,&nbsp;Chun Wang ,&nbsp;Zhifang Zhao ,&nbsp;Fuyan Zou ,&nbsp;Maolin Zhang ,&nbsp;Guize Luan ,&nbsp;Ci Li ,&nbsp;Xi Tu ,&nbsp;Letian Li","doi":"10.1016/j.ecoinf.2025.103194","DOIUrl":"10.1016/j.ecoinf.2025.103194","url":null,"abstract":"<div><div>The synergistic integration of optical imSagery and LiDAR data provides a comprehensive spatial framework for the precise estimation of aboveground biomass (AGB). However, the technical pathway for AGB estimation in complex mountainous regions using multi-source heterogeneous data, including active and passive remote sensing and environmental data, requires further validation. This study proposes a novel framework for high-resolution AGB retrieval by integrating ICESat-2 LiDAR and Landsat-8 data, along with meteorological and topographic factors. AGB estimates were derived from ICESat-2 footprints using second-class forest survey data from the Jinsha River Basin, China. Relationships between canopy metrics and AGB were analyzed across beam types using LASSO and random forest (RF) models. The optimized RF model was then used to generate wall-to-wall AGB maps incorporating Landsat-8, meteorological, and topographic variables. The Nighttime-Strong beam achieved the highest AGB retrieval accuracy (R² = 0.71), followed by the Nighttime-Weak beam (R² = 0.69), all beams combined (R² = 0.68), the Daytime-Strong beam (R² = 0.68), and the Daytime-Weak beam (R² = 0.55); the LASSO model outperformed the RF model. In the AGB retrieval model using canopy metrics, mean canopy height, relative canopy height, canopy coverage, and canopy quadratic mean were strong predictors (correlation coefficients of 0.67, 0.65, 0.63, and 0.62, respectively). Adding meteorological and topographic data substantially improved wall-to-wall AGB mapping, with topography having a greater impact than meteorology. In conclusion, AGB retrieval accuracy can be markedly improved by using ICESat-2 Nighttime-Strong beams combined with meteorological and topographic datasets. This study proposes a more precise and effective methodology for forest monitoring in complex environments.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103194"},"PeriodicalIF":5.8,"publicationDate":"2025-05-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143932086","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":"Exploiting facial side similarities to improve AI-driven sea turtle photo-identification systems","authors":"Lukáš Adam ,&nbsp;Kostas Papafitsoros ,&nbsp;Claire Jean ,&nbsp;ALan F. Rees ,&nbsp;Vojtěch Čermák","doi":"10.1016/j.ecoinf.2025.103158","DOIUrl":"10.1016/j.ecoinf.2025.103158","url":null,"abstract":"<div><div>Animal photo-identification (photo-ID), the process of identifying individual animals from images, has proven to be a valuable tool for various studies on sea turtles, increasing the knowledge of their ecology and informing conservation efforts. Photo-ID in sea turtles is predominantly based on the geometric patterns of the scales of their two head sides, which are unique to every individual and different from side to side. As such, both manual and automated photo-ID techniques are traditionally performed under a side-specific setting. There, an image showing a single profile of an unknown individual is compared only to images showing the same side of previously identified individuals. In this paper, we show for the first time an inherent visual similarity between left and right facial profiles of the same individuals in three sea turtle species. We do so by employing two state-of-the-art automated neural network-based photo-ID methods, one local feature-based and one deep embedding-based, designed to rank profiles based on their similarities. Both methods rank the similarity of the left and right profiles of the same individual higher than those of different individuals. These similarities are detectable even when images are taken years apart under diverse conditions. We further show that the exploitation of this similarity results in improved accuracies when compared to the traditional side-specific photo-ID setting. Our results indicate two concrete guidelines for improving automated sea turtle photo-ID workflows. When trying to match a photo of a given profile, searches should not be restricted only to photos of the same profile. As the first method of choice, a deep embedding model finely-trained using a photo-database of the focal sea turtle population should be used. In the absence of such training database, a neural network-based local feature method is preferable, but in that case searches should be performed with both the original query image and its horizontally flipped version.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103158"},"PeriodicalIF":5.8,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143935028","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":"Wild ActionFormer: Enhancing wildlife action recognition for 11 endangered species in Wolong","authors":"Dan Liu ,&nbsp;Qianqian Sun ,&nbsp;Jin Hou ,&nbsp;Bochuan Zheng ,&nbsp;Jindong Zhang ,&nbsp;Desheng Li ,&nbsp;Tomas Norton ,&nbsp;Jifeng Ning","doi":"10.1016/j.ecoinf.2025.103148","DOIUrl":"10.1016/j.ecoinf.2025.103148","url":null,"abstract":"<div><div>The video of wild animals captured by trap cameras provides conservationists with intuitive information on animal action, holding significant potential in ethology and ecology. This study focuses on 11 endangered wild animal species videos in the Wolong Nature Reserve and develops a video self-supervised learning-based animal action recognition network—Wild ActionFormer, to achieve automated analysis of wild animal action classes. We utilize UniformerV2 as the base backbone network, integrating self-supervised learning methods to enhance feature extraction capabilities. We constructed a differential dispersion regularization loss function to maintain the alignment of self-supervised learning features and improve the network’s robustness against interference. The introduction of the Focal Loss reweighting strategy optimizes the loss for long-tail classes, mitigating the bias towards head data. Experimental results on our released LoTE-Animal open-source dataset show that the proposed action recognition network achieves a Top-1 accuracy of 95.09%, approximately 4 percentage points higher than the baseline. The LoTE-Animal dataset comprises 10k videos, including endangered wild animals from the Wolong Nature Reserve in Sichuan, China, such as the giant panda, sambar, and Sichuan snub-nosed monkey.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103148"},"PeriodicalIF":5.8,"publicationDate":"2025-05-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143932088","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":"Bias in transect counts of forest birds: An agent-based simulation model and an empirical assessment","authors":"Asko Lõhmus,&nbsp;Ants Kaasik","doi":"10.1016/j.ecoinf.2025.103181","DOIUrl":"10.1016/j.ecoinf.2025.103181","url":null,"abstract":"<div><div>Transect counts are often used to estimate broad-scale densities of conspicuous organisms, notably birds. However, these counts are prone to numerous biases, which are difficult to disentangle in purely empirical studies due to observer-related and contextual uncertainty. To measure how different biases combine, we constructed a model that simulates observer movement across a theoretical landscape that is inhabited by birds moving within their circular territories. The model was parameterized based on data from Estonian forests where, as an additional field test, we conducted actual transect counts of bird assemblages that had been territory-mapped based on multiple visits. The simulations revealed that biases vary significantly among bird species. In dense populations, accurately locating detections can be a key issue that can produce either over- or underestimation when combined with observer speed. Counts of sparsely distributed, poorly or only seasonally detectable species appeared most challenging. Compared with these field errors, record interpretation had smaller effect on the density estimates. The test counts confirmed variable underestimation of the territory-mapped bird densities and a resulting underestimation of local species richness. We conclude that biases of single-visit transect counts cannot be easily corrected to reveal true densities of birds and should be considered as abundance indices. The capacity to detect trends in repeated counts is profoundly affected by changes in observer persona and may be sufficient in common species only. We encourage using agent-based models to analyze the behavior of researchers who collect ecological data as a tool to inform methodological standardization and researcher training.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103181"},"PeriodicalIF":5.8,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143924165","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":"Adversarial domain adaptation for deforestation detection in remote sensing imagery","authors":"José Matheus Fonseca dos Santos ,&nbsp;Pedro Juan Soto Vega ,&nbsp;Guilherme Lucio Abelha Mota ,&nbsp;Gilson Alexandre Ostwald Pedro da Costa","doi":"10.1016/j.ecoinf.2025.103124","DOIUrl":"10.1016/j.ecoinf.2025.103124","url":null,"abstract":"<div><div>Semantic segmentation models aim at classifying images at the pixel level. In general terms, training such models with the traditional supervised approach requires sufficient amount of images and corresponding class label maps. While state-of-the-art deep semantic segmentation networks offer high classification performance, producing the references for supervised training often proves to be quite laborious and costly. Additionally, the accuracy delivered by those networks is directly impacted by the quality and volume of training data. Moreover, the resulting classifiers are, in general, domain specific, what means that after being trained with specific domain data, a significant performance drop is expected when evaluating them on data from another domain, even when dealing with the exact same classification task. In the context of remote sensing applications, a domain is represented by images from different sites, related to different landscapes and/or captured at different dates, likely with different acquisitions conditions. Alike other remote sensing applications, deforestation detection tends to present a poor accuracy when evaluated in a cross-domain scenario. As solution to mitigate such a problem, this work investigates the use of unsupervised domain adaptation techniques combined in a novel method. Despite requiring source domain data alongside the respective class labels, the devised method needs no references for the target domain data during training. Our solution, specialized for deforestation detection, combines two domain adaptation strategies, namely, appearance adaptation and representation matching. In the experimental analysis, we assess the performance of different variants of the proposed method, and compare their outcomes with those delivered by state-of-the-art domain adaptation methods for deforestation detection, over forest areas in the Brazilian Amazon and Cerrado biomes.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103124"},"PeriodicalIF":5.8,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143924695","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":"Assessment of land surface vulnerability using time-series geospatial datasets","authors":"Bo Yuan ,&nbsp;Shanchuan Guo ,&nbsp;Haowei Mu ,&nbsp;Xiaoquan Pan ,&nbsp;Chunqiang Li ,&nbsp;Zilong Xia ,&nbsp;Xingang Zhang ,&nbsp;Peijun Du","doi":"10.1016/j.ecoinf.2025.103178","DOIUrl":"10.1016/j.ecoinf.2025.103178","url":null,"abstract":"<div><div>Assessing land surface vulnerability is important for understanding ecosystem responses to environmental changes. However, quantitative studies are still lacking, particularly in capturing temporal dynamics. This study proposes a framework for quantitatively assessing land surface vulnerability by integrating time-series geospatial datasets from the “water-soil-climate-plant” system, which reflects the dynamics of surface water, soil erosion, drought, and vegetation. Based on dynamic data from these four subsystems during 1990−2022, the spatial heterogeneity of land surface vulnerability and its relationship with both natural and anthropogenic factors were analyzed in the Hohhot-Baotou-Ordos-Yulin urban agglomeration. The results indicate that a significant spatial overlap between areas of high land surface vulnerability and ecological management zones. Specifically, 6.1 % of severely vulnerable regions are concentrated in the Maowusu sandy land, the Kubuqi desert, and the Loess hilly-gully region. Severe vulnerability is also evident in the central part of the urban agglomeration, largely influenced by the compound effects of multiple subsystems. Among these subsystems, the proportion of regions with high and severe vulnerability is highest in drought (33.4 %), followed by soil (16.7 %), vegetation (9.9 %), and surface water (9.3 %). Human activities have facilitated ecosystem recovery in the Yinshan Daqing Mountains and parts of the Kubuqi desert, whereas restoration efforts in the Maowusu sandy land remains limited. In the Loess hilly-gully region, vulnerability intensifies with increasing human activity but is relatively less affected by aridity intensity. By integrating annual fluctuations from key land surface subsystems, this study offers a dynamic vulnerability assessment framework, providing valuable insights for enhancing land surface system resilience in response to ongoing climatic and anthropogenic challenges.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103178"},"PeriodicalIF":5.8,"publicationDate":"2025-05-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143934852","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":"A novel hybrid machine learning approach for δ13C spatial prediction in polish hard-water lakes","authors":"Himan Shahabi ,&nbsp;Ataollah Shirzadi ,&nbsp;Alicja Ustrzycka ,&nbsp;Natalia Piotrowska ,&nbsp;Janusz Filipiak ,&nbsp;Marzieh Hajizadeh Tahan","doi":"10.1016/j.ecoinf.2025.103187","DOIUrl":"10.1016/j.ecoinf.2025.103187","url":null,"abstract":"<div><div>Comprehension of carbon cycling, climate change, paleoecology, environmental reconstruction, and aquatic ecosystem health is essential for the environmental sciences, and one of the invaluable tools is the δ¹³C record of lake deposits. In this study, we propose a novel hybrid machine learning (ML) algorithm known as the ARAMT model, which combines two key components: a meta-classifier of additive regression (AR) and a base classifier of alternating model trees (AMT). The AR-AMT hybrid model significantly enhances the prediction of carbon isotopes (δ¹³C) by addressing limitations in existing methodologies. For the first time, this model is used to predict the spatial prediction of a stable isotope in Polish lakes. The δ¹³C for 30 Polish lakes is determined using the calcite (CaCO₃) precipitated in the near-surface layers of the lakes (epilimnion). The chemical composition (Ca²⁺, HCO₃⁻, Na, K, sulfates, fluorides, Cl, Mg, and P) and temperature of the surface water at a depth of 1 m is analyzed seasonally. The current approaches for predicting δ¹³C have demonstrated shortcomings in accuracy and precision. In this study, a random forest (RF), M5P, AMT, and Gaussian process (GP) are the four cutting-edge ML algorithms that are compared with the proposed hybrid model (ARAMT). In accordance with the results, the ARAMT hybrid model performed more effectively in predicting δ¹³C than the other benchmark ML methods (<em>R</em>² = 0.9882, MAE = 0.456, and RMSE = 0.527), the others giving: AMT (<em>R</em>² = 0.982, MAE = 0.558, and RMSE = 0.347), RF (<em>R</em>² = 0.8014, MAE = 0.612, and RMSE = 0.550), M5P (<em>R</em>² = 0.7508, MAE = 0.813, and RMSE = 0.701), and GP (<em>R</em>² = 0.7315, MAE = 0.768, and RMSE = 0.683). Although the ARAMT hybrid doesn't directly preserve lake ecosystems, the enhanced accuracy of its δ¹³C predictions by providing a more detailed understanding of carbon cycling dynamics can indirectly inform and improve lake ecosystem health and management.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103187"},"PeriodicalIF":5.8,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143934744","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":"Identification of glass eel capture equipment in the Yangtze River estuary based on high-spatial -resolution imagery and an improved YOLOv8 model","authors":"Pengfei Zhu ,&nbsp;Weifeng Zhou","doi":"10.1016/j.ecoinf.2025.103188","DOIUrl":"10.1016/j.ecoinf.2025.103188","url":null,"abstract":"<div><div>Although eel farming has become an industry, people still cannot achieve large-scale artificial reproduction of eels. Hence, the recruitment of eel for aquaculture can only rely on the capture of natural eel fry, i.e. glass eels. The capture intensity of glass eels is crucial for the sustainable development of natural eel resources, especially for wild eel stocks. The continental shelf of China is an important habitat in the life history of Japanese eel. China's fisheries authorities have adopted a special permit regulation for glass eel capture to control the scale and intensity of these activities. However, the scale of glass eel capture in the Yangtze River Estuary and along the Chinese coast is not fully grasped at the macro level, because of the possibility of poaching by illegal and unreported fishing. To address this problem of monitoring glass eel capture along the coast of China, especially in the Yangtze River Estuary, this study explored a method for identifying and monitoring glass eel capture activities from high spatial resolution satellite image of Jilin-1 by improving YOLOv8 model. The sample dataset was created by data labelling, and split into training, validation, and test sets. To avoid the false detection of small targets, we introduce the asymptotic feature pyramid network to replace the original detection head, and add a detection layer for small targets, which improves the accuracy but increases the parameters and computation volume. Then, C2f module was improved with dual convolutional kernels, and the pooling process was improved by introducing the spatial pyramid pooling fast module with enhanced local attention network. Thereupon the detection speed and accuracy are both improved. So the experiment was carried out based on the sample dataset using the improved YOLOv8 model, which showed that the average precision (mAP@50 %) is 94.8 %, 4.5 % higher than that of the original YOLOv8. The improved model proposed in this article improved localization ability and detection accuracy of tiny targets of capture equipment for glass eel from high-spatial-resolution images, and hence the method can be used to monitor glass eel capture activities and evaluate the intensity of glass eel capture.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103188"},"PeriodicalIF":5.8,"publicationDate":"2025-05-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143918604","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":"Underwater object detection algorithm integrating image enhancement and deformable convolution","authors":"Lijia Guo ,&nbsp;Xiangchun Liu ,&nbsp;Dongsheng Ye ,&nbsp;Xuebao He ,&nbsp;Jianxin Xia ,&nbsp;Wei Song","doi":"10.1016/j.ecoinf.2025.103185","DOIUrl":"10.1016/j.ecoinf.2025.103185","url":null,"abstract":"<div><div>Underwater biological detection plays a crucial role in the conservation of biodiversity and the exploration of underwater mineral resources. However, traditional object detection algorithms often suffer considerable performance degradation when confronted with underwater-specific challenges, including image blurring, small-scale targets, aggregation-induced occlusion, and irregular object shapes. To address these limitations, this study proposes a novel underwater object detection algorithm, DeformableConvModule-You Only Look Once (DCM-YOLO), based on YOLOv8s. First, to alleviate the degradation in underwater image quality, we incorporate an integrated image enhancement module, that is, UnitModule, which generates optimal input images for the detector. Second, the backbone network is redesigned via Deformable Convolution v4 (DCNv4), with targeted optimizations applied to enhance the model's capacity for detecting irregularly shaped objects and small-scale targets. In addition, the separated and enhancement attention module (SEAM) is integrated to better capture features of occluded targets. Finally, a dedicated detection head is added to further improve the model's ability to detect small objects. Comparative experiments and ablation experiments conducted on the Detecting Underwater Objects dataset validate the effectiveness of the proposed method. Specifically, the model improves the value of average precision (AP) from 60.8 % (for the YOLOv8s baseline model) to 65.0 %, and the AP at an intersection-over-union threshold of 0.5 (<span><math><msub><mi>AP</mi><mn>50</mn></msub></math></span>) from 80.1 % (for the YOLOv8s baseline model) to 83.5 %. In addition, experimental evaluations on the URPC2020 and MS COCO2017 datasets confirm the advantages of the proposed DCM-YOLO algorithm.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103185"},"PeriodicalIF":5.8,"publicationDate":"2025-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143932085","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":"Evaluating the usability and utility of a spatial decision support system for pollinator ecology","authors":"Lily Houtman ,&nbsp;Anthony C. Robinson ,&nbsp;Dave McLaughlin ,&nbsp;Christina M. Grozinger","doi":"10.1016/j.ecoinf.2025.103182","DOIUrl":"10.1016/j.ecoinf.2025.103182","url":null,"abstract":"<div><div>Environmental spatial decision support systems (SDSS) can help users make ecological choices informed by geographic information system (GIS) data, often using interactive maps. However, these tools often serve diverse stakeholder groups, each with their own expectations and goals. Here, we present results from an iterative process of user-centered design and evaluation that shaped the full redesign and development of the map-based pollinator health decision support tool <em>Beescape NexGen</em>. This tool assists beekeepers, growers, researchers, and other groups assess the quality of their landscape to support pollinators such as bees. Building upon results from an earlier usability evaluation of a <em>Beescape</em> prototype and a follow-up needs assessment focus group with key stakeholders, we designed an all-new interface and implemented an improved version of the tool called <em>Beescape NexGen</em>. A usability study of <em>Beescape NexGen</em> conducted with hobbyist beekeepers found significant improvements in usability and utility compared to the previous version of this tool. This project serves as an example of an iterative, long-term user-centered design project in ecological informatics resulting in direct comparisons between two versions of a SDSS.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103182"},"PeriodicalIF":5.8,"publicationDate":"2025-05-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143902478","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}