Ecological Informatics最新文献

{"title":"ResHAN-GAM: A novel model for the inversion and prediction of soil organic matter content","authors":"Liying Cao,&nbsp;Dongjie Yin,&nbsp;Miao Sun,&nbsp;Yuzhu Yang,&nbsp;Musharaf Hassan,&nbsp;Yunpeng Duan","doi":"10.1016/j.ecoinf.2025.103192","DOIUrl":"10.1016/j.ecoinf.2025.103192","url":null,"abstract":"<div><div>Soil organic matter (SOM) is crucial in determining soil health, improving crop production, and enabling sustainability in agriculture. Precise determination of SOM content is thus crucial for land management as well as for maintaining ecological equilibrium. This research introduces a new hierarchical attention mechanism that unifies residual networks with GAM attention. Through data smoothing and discretization in terms of fractions, the model is equipped to effectively repress noise as it enhances primary spectral features related to SOM, thus enhancing the robustness as well as explainability of the model. Hyperspectral reflectance data were recorded in the visible to near-infrared (Vis-NIR) range (350–2500 nm) with a high spatial-resolution sensor. The dataset is made available with samples from lands under cultivation for soybean as well as corn in the fertile black soil region. Experimental results indicate that the proposed method achieves an <span><math><msup><mrow><mi>R</mi></mrow><mrow><mn>2</mn></mrow></msup></math></span> value of 0.945, an RMSE of 0.117% and RPD of 4.26 on the validation set. Furthermore, the model’s generalization ability was validated using the Land Use/Cover Area Frame Survey (LUCAS) dataset, a large-scale European soil database, where similarly high performance was achieved. These results highlight the effectiveness and transferability of the proposed method in estimating SOM content, offering a reliable, non-destructive tool for large-scale soil monitoring and environmental protection applications.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103192"},"PeriodicalIF":5.8,"publicationDate":"2025-05-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144138396","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":"Multi-modal, interrelated navigation in migratory birds: A data mining study","authors":"Ali Moayedi ,&nbsp;Jed A. Long ,&nbsp;Andrea Kölzsch ,&nbsp;Helmut Kruckenberg ,&nbsp;Fernando Benitez-Paez ,&nbsp;Urška Demšar","doi":"10.1016/j.ecoinf.2025.103218","DOIUrl":"10.1016/j.ecoinf.2025.103218","url":null,"abstract":"<div><div>Understanding how long-distance migratory birds navigate remains challenging, particularly in how they integrate multiple environmental cues. Traditional studies, primarily based on laboratory experiments and displacement studies, may not capture the complexity of navigation in the wild. In this study, we applied a data mining approach to investigate the navigational strategies of greater white-fronted geese (<em>Anser albifrons</em>) during their annual migrations between the Arctic and central Europe. We integrated a decade of tracking data from 117 individuals with high-resolution geomagnetic data (including solar-wind–induced variations), wind conditions, and a potential visual cue. Hierarchical cluster analysis revealed multi-modal and interrelated navigation strategies that flexibly adapted to environmental conditions such as wind, diurnal cycles, and flock-specific dynamics. Under favourable tailwinds, geese maintained stable headings with minimal changes in geomagnetic heading and apparent angle of geomagnetic inclination, consistent with both geomagnetic loxodrome and magnetoclinic routes, enabling efficient flights towards stopovers or simultaneously towards stopovers and final destinations. Geese also appeared to combine visual landmarks with geomagnetic information, adjusting their reliance on landmarks between day and night. Our findings highlight the complexity and adaptability of avian navigation and emphasise the role of multi-modal sensory integration and environmental context in shaping migratory decisions.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103218"},"PeriodicalIF":5.8,"publicationDate":"2025-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144166880","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":"Simplifying the calibration of ecological models by using the parameter estimation tool (PEST): The Curonian Lagoon case","authors":"Burak Kaynaroglu ,&nbsp;Mindaugas Zilius ,&nbsp;Rasa Idzelytė ,&nbsp;Artūras Razinkovas-Baziukas ,&nbsp;Georg Umgiesser","doi":"10.1016/j.ecoinf.2025.103213","DOIUrl":"10.1016/j.ecoinf.2025.103213","url":null,"abstract":"<div><div>In this study, we implemented an automated calibration procedure for an ecological model of the Curonian Lagoon, supported by a comprehensive two-year field observation dataset. Data from the second-year were used for model calibration, while first-year observations served for the validation of the model's performance in simulating nutrient dynamics. Calibration is essential for improving the accuracy and reliability of process-based ecological models. However, subjective and time-consuming manual (trial-and-error) calibration methods cannot ensure optimal parameter match.</div><div>To address this, we automated the calibration of a newly developed ecological model to improve the simulation of nutrient dynamics as ammonia, nitrate, and phosphate in the estuarine system (Curonian Lagoon). Calibration was carried out using Parameter Estimation (PEST) and PEST++ tools, focusing on three aforementioned limiting nutrient forms. We applied the method of Morris for global sensitivity analysis to determine the key parameters influencing model behavior. As biogeochemical models are highly nonlinear and multimodal, global methods are often assumed to provide a better fit. However, we challenged this assumption by initiating the inverse problem at different locations in the parameter space using a robust variant of a gradient-based method, which ultimately resulted in a better fit than global methods.</div><div>We tested four different optimization algorithms available in the PEST and PEST++ suites. The results demonstrated that PEST significantly improved model calibration performance followed the nutrient dynamics more effectively than more complex biogeochemical models for the Curonian Lagoon, and outperformed manual calibration methods. Furthermore, we employed an ensemble-based method within the PEST++ suite for parameter estimation and uncertainty quantification, significantly reducing the computational burden of these analyses.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103213"},"PeriodicalIF":5.8,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144138399","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 driving force analysis method for drought-flood abrupt alternation events","authors":"Li Wang ,&nbsp;Zhe Yuan ,&nbsp;Xiaoliang Shi ,&nbsp;Jun Yin ,&nbsp;Tianling Qin ,&nbsp;Jie Zhang","doi":"10.1016/j.ecoinf.2025.103214","DOIUrl":"10.1016/j.ecoinf.2025.103214","url":null,"abstract":"<div><div>Drought-Flood Abrupt Alternation (DFAA) events are complex hydrometeorological disasters that have become increasingly frequent under global warming, posing significant threats to socio-economic systems and the ecological stability. This study applies the Standardized Weighted Average Precipitation (SWAP) index to identify DFAA events at a daily scale in the middle and lower reaches of the Yangtze River Basin (MLRYRB) from 2001 to 2022. Additionally, a method is proposed to analyze the driving factors of DFAA and quantify the spatial distribution of dominant contributors. The results indicate that: (1) in the MLRYRB, DFAA events lasted up to 50 days and occurred 7 to 10 times in total, with the highest frequency in southern Shaanxi, central Hubei, central Anhui, and southern Jiangxi; (2) the areas affected by DTF and FTD events decreased at rates of 0.43/a and 0.62/a, respectively. DTF events were predominantly moderate in intensity, while FTD events were mostly slight; (3) the key factors driving drought-flood alternation included convective precipitation (CP), total cloud cover (TCC), top net solar radiation (TNSR), 2-m temperature (TEM), and relative humidity (RH). Regions dominated by RH and TEM accounted for 44.62 % and 30.22 % of the study area, respectively, mainly in Hubei, Hunan, Jiangxi, and Anhui. These findings offer a scientific basis for developing disaster prevention and mitigation strategies in the region.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103214"},"PeriodicalIF":5.8,"publicationDate":"2025-05-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134883","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 multitemporal vegetation indices from Zhuhai-1 hyperspectral images for detecting a rapidly spreading invasive species - Spartina alterniflora","authors":"Yuanhui Zhu ,&nbsp;Soe W. Myint ,&nbsp;Jingjing Cao ,&nbsp;Kai Liu ,&nbsp;Mei Zeng ,&nbsp;Chenxi Diao","doi":"10.1016/j.ecoinf.2025.103208","DOIUrl":"10.1016/j.ecoinf.2025.103208","url":null,"abstract":"<div><div>Monitoring the spatiotemporal changes of <em>Spartina alterniflora</em> (SA) is essential in effectively managing coastal ecology since it is one of the most harmful invasive weeds worldwide. However, it remains challenging to accurately identify SA invasion, especially in regions subject to periodic tidal flooding. Recent studies have shown that utilizing traditional multitemporal vegetation indices (VIs), such as NDVI and EVI derived from multispectral image features, can improve the accuracy of identifying SA. Still, the application potential of multitemporal hyperspectral images with rich derived VIs has not yet been explored. The Zhuhai-1 hyperspectral satellite offers high spectral, spatial, and temporal resolution, providing crucial multitemporal features for accurately identifying SA. This study examined multitemporal VIs from nine months using hyperspectral images and common machine learning methods (i.e., K-nearest neighbor, support vector machine, random forest) to compare a variety of VIs' performance in identifying SA invasion in the Guangxi Zhuang Autonomous Region. Results showed that multitemporal VIs are more effective in identifying SA in periodic tidal flooding areas than individual hyperspectral parameters (spectral features, VIs, and spatial texture features). Significantly, the unique multitemporal VIs derived from red-edge bands of hyperspectral images constantly demonstrated higher accuracies (exceeding 91.6 %) than traditional NDVI (91.47 %) and EVI (84.78 %). Our results consistently identified June, February, and November as the most critical months for identifying SA invasion, as observed across all three algorithms and VIs. These months are connected to SA phenology's greening, yellowing, and withering. Results and findings from this study provided insight into the overwhelming potential of multitemporal hyperspectral image analyses to improve the monitoring and management of invasive species for sustainable coastal ecosystems. The same procedure, algorithms, indices, and features can be employed to effectively identify any other specific species or detailed land cover types.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103208"},"PeriodicalIF":5.8,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134882","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":"Safe AI for coral reefs: Benchmarking out-of-distribution detection algorithms for coral reef image surveys","authors":"Mathew Wyatt ,&nbsp;Sharyn Hickey ,&nbsp;Ben Radford ,&nbsp;Manuel Gonzalez-Rivero ,&nbsp;Nader Boutros ,&nbsp;Nikolaus Callow ,&nbsp;Nicole Ryan ,&nbsp;Arjun Chennu ,&nbsp;Mohammed Bennamoun ,&nbsp;James Gilmour","doi":"10.1016/j.ecoinf.2025.103207","DOIUrl":"10.1016/j.ecoinf.2025.103207","url":null,"abstract":"<div><div>Although deep learning has demonstrated significant advances in qualitative domains, deep learning algorithms remain poor at quantifying the uncertainty of their predictions. This is especially true when applied in domains where there is data shift outside of which an algorithm has been trained. This has major implications for the use of deep learning in accurately estimating change in environmental monitoring applications. In the case of image classification for coral reef habitats, time series imagery is rarely consistent due to changing environmental conditions, differing sensors and inconsistencies in data capture, which means traditional machine learning metrics simply do not work when applied to new out of distribution datasets.<ul><li><span>1.</span><span><div>For this reason, we benchmark the latest state-of-the-art OOD (Out Of Distribution) detection algorithms on publicly available coral reef image datasets, and evaluate histogram intersection of outlier scores as an indicator for human intervention.</div></span></li><li><span>2.</span><span><div>We show with a comparative analysis that the performance of OOD detection algorithms is variable, and highly dependent on in-distribution and out-of-distribution data composition. We show that KNN (K-Nearest Neighbour) distance was the most consistent across datasets, followed by Virtual-logit matching (ViM).</div></span></li><li><span>3.</span><span><div>This research shows a compelling example of how a handful of openly available algorithms can easily be used as a complimentary indicator alongside confidence (Softmax probability), in turn providing more efficient and safe deployment of deep learning for rapid reporting of coral reef habitats.</div></span></li></ul></div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103207"},"PeriodicalIF":5.8,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124710","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":"Spatio-temporal patterns of holoplanktonic mollusc assemblages and indicator species of hydrodynamic conditions in the northwestern South China Sea","authors":"Junce Liang ,&nbsp;Yuan Liu ,&nbsp;Kaizhi Li ,&nbsp;Yehui Tan ,&nbsp;Jiaxing Liu ,&nbsp;Pengli Xiong ,&nbsp;Yu Zhong","doi":"10.1016/j.ecoinf.2025.103206","DOIUrl":"10.1016/j.ecoinf.2025.103206","url":null,"abstract":"<div><div>Holoplanktonic molluscs are a group of overlooked but ecologically important zooplankton in marine ecosystems that play a crucial role in the marine food web and carbon cycle. However, changes in the marine environment can lead to an increase in the abundance of pelagic molluscs, causing ecological disasters. To understand the environmental factors that structure holoplanktonic mollusc assemblages, their diversity and abundance were analyzed in the northwestern South China Sea (NWSCS). A total of 39 holoplanktonic molluscs were identified, including 24 pteropod and 15 heteropod species. Significant seasonal and regional differences were observed in species diversity and abundance. Species richness was higher in offshore waters than in nearshore waters during summer and winter, whereas species abundance was significantly higher in summer than in winter. High species abundance was mainly concentrated in waters influenced by cyclonic eddies and coastal upwelling during summer. Temperature, salinity and chlorophyll <em>a</em> are important factors for structuring holoplanktonic mollusc assemblages. <em>Creseis acicula</em> was an effective indicator species in nearshore waters in both seasons, whereas <em>Limacina bulimoides</em> and <em>Heliconoides inflatus</em> were the best indicators in offshore waters in summer and winter, respectively. These findings can form a baseline for understanding the distribution of holoplanktonic molluscs in the marginal sea of the northwestern Pacific Ocean and provide a solid foundation for monitoring zooplankton in a changing ocean and for sustainable ecosystem management.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103206"},"PeriodicalIF":5.8,"publicationDate":"2025-05-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144124805","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 method for monitoring Cladophora blooms in Qinghai Lake based on UAV imagery","authors":"Juan Zhang ,&nbsp;Xiaojun Yao ,&nbsp;Xinde Chu ,&nbsp;Hongyu Duan ,&nbsp;Yuxuan Zhang ,&nbsp;Hui Chang ,&nbsp;Ju Huang","doi":"10.1016/j.ecoinf.2025.103210","DOIUrl":"10.1016/j.ecoinf.2025.103210","url":null,"abstract":"<div><div>The outbreak of <em>Cladophora</em> in the nearshore region of Qinghai Lake has significantly affected the surrounding ecological landscape and tourism industry. However, existing satellite remote sensing methods for monitoring <em>Cladophora</em> blooms have several limitations owing to cloud cover in optical images and the small areas of <em>Cladophora</em> bloom patches, making it challenging to obtain detailed dynamic characteristics of these blooms. Unmanned aerial vehicles (UAVs) equipped with high-resolution cameras serve as valuable complements to traditional satellite remote sensing techniques. In this study, we propose a new spectral index called the visible band spectral slope index (VSSI) and integrate it with a triangular threshold segmentation algorithm to automatically extract features of <em>Cladophora</em> blooms from very-high-resolution UAV imagery. Comparative analysis with commonly used visible band indices such as the normalized green-red difference index (NGRDI), color index vegetation (CIVE), vegetation (VEG) index, visible-band difference vegetation index (VDVI), and red-green-blue floating algae index (RGB-FAI) demonstrated that the VSSI was most effective for detecting <em>Cladophora</em> blooms. The validation results revealed that the VSSI had the highest accuracy, achieving an F1 score of 0.89 and improving overall accuracy by 11.25 % to 36.92 % compared to other indices' accuracy levels. When compared with UAV images, both Sentinel-2 MSI and Landsat OLI images significantly overestimated the areas of <em>Cladophora</em> blooms by values of 110.65 % and 517.99 %, respectively. Moreover, UAV images captured at different times within the same day confirmed that wind speed and direction are crucial factors influencing dynamic changes in <em>Cladophora</em> blooms over short periods. This work provides a valuable reference for accurately mapping spatiotemporal dynamics of <em>Cladophora</em> blooms and effectively managing lake water environments.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103210"},"PeriodicalIF":5.8,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144106102","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 approach to mapping and monitoring land carbon sinks by combining remote sensing and biogeochemical modeling: A case study in Burkina Faso","authors":"Mohamed M.M. Ahmed ,&nbsp;Egor Prikaziuk ,&nbsp;Moritz Laub ,&nbsp;Annemarie L. Klaasse ,&nbsp;Lucas E. Ellerbroek","doi":"10.1016/j.ecoinf.2025.103174","DOIUrl":"10.1016/j.ecoinf.2025.103174","url":null,"abstract":"<div><div>Accurate and timely estimation of carbon sequestration in soil and forest biomass is crucial for applications such as carbon stock assessment, forest degradation monitoring, and climate change mitigation. Traditional methods such as field inventories, remote sensing, and biogeochemical models each have strengths and limitations, particularly in data-scarce regions. To address these challenges, this study integrates the light-use efficiency based ETLook model, which is driven by remotely sensed data, with the biogeochemical model DayCent, which is driven by management and weather data, to spatially model aboveground biomass and carbon sequestration. This novel approach aims to improve carbon sequestration estimates in a case study area in Burkina Faso, where ongoing political instability severely limits the availability of field data. In the absence of ground-truth data, we compare the outputs from DayCent and ETLook across time and space to build confidence in our estimates. Our findings indicate that, despite being driven by different input data, the DayCent model closely matches the aboveground biomass patterns observed in the ETLook model, with an r<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> value of 0.81, a Kling-Gupta efficiency (KGE) of 0.77, low bias, and consistent seasonal patterns. Since ETLook lacks a soil carbon module, combining its Net Primary Productivity (NPP) and growth estimates with DayCent’s soil organic carbon (SOC) outputs provides a more robust estimate of total carbon sequestration than either model alone. Future work will focus on applying this hybrid approach across different ecological and geographical regions to evaluate its broader applicability.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103174"},"PeriodicalIF":5.8,"publicationDate":"2025-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144134881","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":"Montrends: A Google Earth engine application for analysing species' habitat suitability over time","authors":"João Alírio ,&nbsp;Nuno Garcia ,&nbsp;João C. Campos ,&nbsp;Salvador Arenas-Castro ,&nbsp;Isabel Pôças ,&nbsp;Lia B. Duarte ,&nbsp;Ana Cláudia Teodoro ,&nbsp;Neftalí Sillero","doi":"10.1016/j.ecoinf.2025.103201","DOIUrl":"10.1016/j.ecoinf.2025.103201","url":null,"abstract":"<div><div>Human activities are impacting biodiversity worldwide. Biodiversity monitoring is essential to assess and support conservation status and trends. Remote sensing has played a crucial role in supporting biodiversity monitoring, but more intuitive and fast-processing tools are still required to improve biodiversity conservation. Herein, we present a Google Earth Engine (GEE) App called Montreds, which implements a biodiversity monitoring tool to measure trends in species habitat suitability over time by calculating ecological niche models (ENMs) with a time series of satellite products. The application is specific to Montesinho Natural Park/Nogueira Special Conservation Area, a protected area located in northeastern Portugal. The application calculates ENMs over time with MaxEnt for five taxa (vascular plants, amphibians, reptiles, birds, and mammals), using a time series of six Moderate-Resolution Imaging Spectroradiometer (MODIS) products between 2001 and 2023. Habitat suitability trends are estimated using the Mann-Kendall test. The Montrends' main output is a map for each modelled species with positive, negative, or null trends over time. If habitat suitability decreases monotonically over time, the trend is identified as negative. The application allows the users to select the species to be modelled, the temporal period, the number of model replicates, and the proportion of training and test records. The application runs the analyses intuitively in about a minute. Several results are displayed: the mean MaxEnt model over time and the Mann-Kendall trends for the whole study area, the species presences, the pixels with significant trends, and the species' occurrences in significant pixels. The application also provides the main MaxEnt outputs, including Area Under the Curve (AUC) values and variable contributions, plots of the global contributions of predictor variables over time, average trend values, and information on MaxEnt parameters. Decision-makers and conservation planners can use this application as a complementary tool for biodiversity monitoring and conservation.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"89 ","pages":"Article 103201"},"PeriodicalIF":5.8,"publicationDate":"2025-05-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144116283","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}