Ecological Informatics最新文献

{"title":"Marine protected area and climate change: A mapping review","authors":"Chandra Segaran Thirukanthan ,&nbsp;Parashuram Kallem ,&nbsp;Idham Sumarto Pratama ,&nbsp;Fathurrahman Lananan ,&nbsp;Lee Seong Wei ,&nbsp;Zulhisyam Abdul Kari ,&nbsp;Huan Gao ,&nbsp;Mohamad Nor Azra ,&nbsp;Wan Izatul Asma Wan Talaat","doi":"10.1016/j.ecoinf.2025.103042","DOIUrl":"10.1016/j.ecoinf.2025.103042","url":null,"abstract":"<div><div>This comprehensive scientometric analysis, utilizing CiteSpace and data from the Web of Science Core Collection, examines the trajectory of research on Marine Protected Areas (MPAs) in the context of climate change. Analysing 2782 articles and 117,904 cited references, the study observes a significant surge in publications between 2019 and 2023, with Australia, England and Canada as leading contributors. Our findings reveal key conceptual pillars such as ‘marine protected areas’, ‘climate change’, ‘conservation’, ‘management’, and ‘biodiversity’. The research domain is characterized by 10 major co-citation clusters, with a notable focus on “coral reefs”, “temperature-driven coral decline”, and “large MPAs”. The increasing citation frequency during 2020–2023, particularly in clusters related to coral reefs and regional studies, signals a heightened global awareness of MPAs' role in mitigating climate change impacts. This review provides essential insights, informing future directions for both academic research and policymaking in marine conservation amid ongoing climatic changes.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"86 ","pages":"Article 103042"},"PeriodicalIF":5.8,"publicationDate":"2025-01-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143102007","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":"Application of differential privacy to sensor data in water quality monitoring task","authors":"Audris Arzovs ,&nbsp;Sergei Parshutin ,&nbsp;Valts Urbanovics ,&nbsp;Janis Rubulis ,&nbsp;Sandis Dejus","doi":"10.1016/j.ecoinf.2025.103019","DOIUrl":"10.1016/j.ecoinf.2025.103019","url":null,"abstract":"<div><div>Although differential privacy (DP) is used to obfuscate local information and avoid data leakage, very little research exists on the neural network model performance with applied DP for datasets from online drinking water sensor monitoring. This study aims to examine the accuracy of four different neural network model architectures with DP applications. To compare the performance of the neural network model performance in total 2 215 906 augmented and experimentally obtained sensor readings were obtained from a drinking-water pilot system. Three types of contaminations at three different concentrations were applied as scenarios for anomalies in drinking water monitoring. The results achieved similar accuracy with all model architectures, with the best result showing only a 0.3% reduction in model accuracy compared with a nonprivate neural network model with 94% and 94.7% accuracy, respectively. Thus, differential privacy can be applied in the field of water quality monitoring with a reasonable decrease in the model performance.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"86 ","pages":"Article 103019"},"PeriodicalIF":5.8,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143102488","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Modeling and regulation of water exchange between the oxbow lake and the middle Yangtze River","authors":"Xiaoguang Liu ,&nbsp;Shiming Yao ,&nbsp;Zhongwu Jin ,&nbsp;Bing Ding ,&nbsp;Lican Ge ,&nbsp;Shuo Guan ,&nbsp;Weijie Wang","doi":"10.1016/j.ecoinf.2025.103018","DOIUrl":"10.1016/j.ecoinf.2025.103018","url":null,"abstract":"<div><div>Oxbow lakes in the middle Yangtze River are critical habitats for protected species such as the Yangtze finless porpoise and play a vital role in biodiversity conservation. The impacts of the Three Gorges Dam (TGD) on hydrological processes and water exchange dynamics between these lakes and the Yangtze River were analyzed. Since the TGD began operation in 2003, significant changes in water level fluctuations and their rates of change have reshaped water exchange intensity and ecological balance in the oxbow lakes. A statistical model characterized the probability density distribution of daily water-level change rates, identifying distinct operation-dependent shifts, with the most dynamic changes near the 30 m threshold. An empirical threshold regression model incorporating the Langmuir adsorption formula effectively described the nonlinear relationships among water level, water-level change rate, and water exchange flow, providing a reliable predictive tool. Seasonal and interannual variations in water exchange intensity were quantified across three critical intervals: flood preparation (Interval I), peak fish migration (Interval II), and post-flood recession (Interval III). Findings revealed reduced water exchange during Interval II negatively impacted small fish populations, challenging species such as the Yangtze finless porpoise. Increased water exchange during Interval III improved water quality by reducing nutrient concentrations and enhancing dissolved oxygen levels. Regulation strategies using an exponential function demonstrated the potential to optimize water exchange intensity by controlling water level variation rates. The proposed ecological hydrological regulation framework offers a scientific basis for improving water exchange during key biological periods, ensuring habitat quality and supporting biodiversity. These findings highlight the critical role of hydrological regulation in maintaining the ecological health and functions of oxbow lakes.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"86 ","pages":"Article 103018"},"PeriodicalIF":5.8,"publicationDate":"2025-01-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143102484","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":"Mangrove aboveground biomass estimation using UAV imagery and a constructed height model in Budeng–Perancak, Bali, Indonesia","authors":"Mohammad Basyuni ,&nbsp;Alfian Mubaraq ,&nbsp;Rizka Amelia ,&nbsp;Anindya Wirasatriya ,&nbsp;Sigit Bayhu Iryanthony ,&nbsp;Bejo Slamet ,&nbsp;Shofiyah S. Al Mustaniroh ,&nbsp;Novia Arinda Pradisty ,&nbsp;Frida Sidik ,&nbsp;Rizki Hanintyo ,&nbsp;Elham Sumarga ,&nbsp;Siti H. Larekeng ,&nbsp;Severino G. Salmo III ,&nbsp;Tadashi Kajita ,&nbsp;Hayssam M. Ali ,&nbsp;Anjar Dimara Sakti ,&nbsp;Virni B. Arifanti","doi":"10.1016/j.ecoinf.2025.103037","DOIUrl":"10.1016/j.ecoinf.2025.103037","url":null,"abstract":"<div><div>Mangrove forests store higher amounts of organic carbon than other forest types. Despite advancements in remote sensing, accurate mapping of mangrove biomass remains a challenge due to ecosystem complexity and varying forest structures. Although traditional in-situ methodologies have been widely used for carbon stock assessments, numerous studies have demonstrated the effectiveness of remote sensing techniques, including unmanned aerial vehicles (UAVs) for mapping mangrove biomass over larger areas. These techniques are combined with allometric equations and UAV photogrammetry to improve accuracy. This study aimed to spatially estimate aboveground biomass (AGB) in various ecosystems by integrating high-resolution digital surface models and digital terrain models (DTMs) with Lorey's height measurements. Moreover, this study utilized UAV imagery and in-situ measurements to enhance the accuracy of the carbon assessments. The integration of Lorey height into our methodology is essential, as Lorey height, which is the average height of unevenly aged forest stands, is a valuable parameter for mangrove ecosystem management. Accordingly, the correlation between UAV-derived canopy height and field measurements will be improved, resulting in more reliable AGB data in mangrove ecosystems. This study has been conducted in Budeng–Perancak, Bali, Indonesia, including restored mangroves, undisturbed mangroves, Nypa, and ponds. The UAV imagery acquisition is supported by a series of in-situ measurements to obtain field data on the forest structure (for canopy surface model [CSM] cross-validation). The AGB across the mangrove land cover types in Budeng–Perancak ranged from 2 Mg ha⁻¹ to 480 Mg ha⁻¹ (mean: 240 Mg ha⁻¹), with the highest average total AGB in natural mangroves (239 Mg ha⁻¹), followed by restored mangroves (232 Mg ha⁻¹), indicating a successful restoration effort at Budeng–Perancak. UAVs enable detailed data collection at small spatial scales to map mangroves and obtain precise spatial information on mangrove ecosystems. This finding can improve the accuracy of greenhouse gas inventory and carbon storage estimates.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"86 ","pages":"Article 103037"},"PeriodicalIF":5.8,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143101512","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":"Identification and spatio-temporal analysis of Ulva prolifera in a typical coastal area using SAR imageries","authors":"Yanxia Wang ,&nbsp;Xiaoyu Ni ,&nbsp;Xiaoshuang Ma","doi":"10.1016/j.ecoinf.2025.103039","DOIUrl":"10.1016/j.ecoinf.2025.103039","url":null,"abstract":"<div><div>The occurrence of <em>Ulva prolifera</em> (<em>U. prolifera</em>) can cause significant environmental damage in coastal areas, making its monitoring crucial. Remote sensing technology provides an effective tool for large-scale monitoring of <em>U. prolifera</em>. Most studies rely on optical images to monitor <em>U. prolifera</em>, which are highly dependent on weather conditions. Synthetic Aperture Radar (SAR) can penetrate clouds, rain, and fog, providing clear observations of ocean surfaces in a large scale regardless of time of day. However, current research on SAR data for <em>U. prolifera</em> detection primarily focuses on SAR intensity or amplitude information, while its rich polarimetric data remains underutilized. This paper presents <em>U. prolifera</em> Detection Network (UDNet), an intelligent detection framework based on the DeepLabV3+ deep learning model, leveraging amplitude and polarimetric information from Sentinel-1 dual-polarimetric imageries. To construct the proposed model, 2283 samples were annotated using SAR images of the Yellow Sea, of which 1737 samples were used for training and 546 samples were used for validation and testing. The well-trained model was used to detect <em>U. prolifera</em> in a typical coastal area from 2018 to 2021. The experimental results demonstrate that the proposed UDNet achieves superior performance with an overall accuracy of 0.9859, a mean intersection over union of 0.9198, and an F1 score of 0.9239. Spatio-temporal distribution analyses indicate that the most severe outbreak of <em>U. prolifera</em> in the study area occurred in 2019, with intensive occurrences in June of each year. The outbreak was more severe in the southwest region of the study area than in the northeast. Besides, it was observed that the outbreak area of <em>U. prolifera</em> was larger at night than that during the day, mainly driven by changes in summer temperature. In addition, a larger diurnal temperature difference generally promoted the growth of <em>U. prolifera</em>. These findings are instrumental in formulating management policies and taking actions to control the outbreak of <em>U. prolifera</em>.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"86 ","pages":"Article 103039"},"PeriodicalIF":5.8,"publicationDate":"2025-01-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143102009","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":"Comparing the performance of vegetation indices for improving urban vegetation GPP estimation via eddy covariance flux data and Landsat 5/7 data","authors":"Qianghao Zeng ,&nbsp;Xuehe Lu ,&nbsp;Suwan Chen ,&nbsp;Xuan Cui ,&nbsp;Haidong Zhang ,&nbsp;Qian Zhang","doi":"10.1016/j.ecoinf.2025.103023","DOIUrl":"10.1016/j.ecoinf.2025.103023","url":null,"abstract":"<div><div>Urban vegetation is pivotal in enhancing regional ecological balance and sequestering significant amounts of carbon dioxide (CO₂) through photosynthesis, thereby contributing substantially to regional carbon budgets. However, the gross primary productivity (GPP) of urban vegetation remains underexplored due to the absence of robust estimation methodologies, often leading to its exclusion from global and regional carbon budgets. Advances in vegetation indices (VIs) offer promising solutions for improving the accuracy and spatial resolution of urban GPP estimation. In this study, we compared the performance of the enhanced vegetation index (EVI), normalized difference vegetation index (NDVI), near-infrared reflectance of vegetation (NIRv), and kernel normalized difference vegetation index (kNDVI) calculated from Landsat 5/7 images in estimating flux-site-level GPP and incorporated meteorological factors to construct a high-performance VI-GPP model for urban GPP estimation. Our findings demonstrated that the EVI, NIRv, and kNDVI exhibited stronger correlations with GPP dynamics and higher R² values than did the NDVI in linear VI-GPP relationships across most plant functional types (PFTs). Exceptions were observed in evergreen broadleaf forest (EBF), evergreen needle-leaf forest (ENF), and savanna (SAV), where GPP variations were strongly influenced by temperature, shortwave radiation, and vapor pressure. Incorporating these meteorological factors significantly enhanced GPP estimation accuracy for these PFTs. Among the indices, the NIRv achieved the highest overall model performance, with an R² of 0.60 and a root-mean-square error (RMSE) of 2.05 g C m⁻² d⁻¹ across PFTs. The kNDVI demonstrated unique advantages for specific PFTs, such as deciduous broadleaf forest (DBF) and ENF. Compared with existing VI-GPP relationships created with coarse-spatial-resolution remote sensing data, our model was more suitable for high-spatial-resolution GPP estimation in urban areas. Our results highlight the performance of the NIRv and kNDVI in urban vegetation GPP estimation and provide a solution for estimating fine-resolution GPP to reveal the importance of urban vegetation to regional carbon budgets.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"86 ","pages":"Article 103023"},"PeriodicalIF":5.8,"publicationDate":"2025-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143101511","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":"Using PLS-SEM models to explore the interactions of meteorology and landscape pattern changes on waterbird diversity: A case of the Liaohe Estuary","authors":"Xiuzhong Li ,&nbsp;Baocun Ji ,&nbsp;Na Li ,&nbsp;Christopher J. Anderson ,&nbsp;Qiuying Chen","doi":"10.1016/j.ecoinf.2025.103022","DOIUrl":"10.1016/j.ecoinf.2025.103022","url":null,"abstract":"<div><div>Waterbirds are highly sensitive to environmental quality, with climate and landscape patterns being the two most important factors for influencing waterbird diversity. Understanding the effects of climate and landscape may lead to more effective policies and management strategies. This study focused on theinteractions of meteorological factors and landscape patterns on waterbird diversity in the Liaohe Estuary, an internationally important wetland system in northeast China and an important habitat for waterbirds on the East-Asian and Australasian flyway. Waterbird abundance and species richness (2010−2022) were related to meteorological factors represented by annual mean temperature and annual precipitation and various measures of landscape fragmentation caused by human land uses and natural landscapes. Structural equation models were constructed using four latent variables: waterbird abundance or richness, human activities, natural landscape, and meteorology, and the models were estimated through uncertainty and sensitivity analysis. The results showed that (1) landscape fragmentation of human activities (abundance model = 0.606, richness model = 0.719) was higher than the natural landscape (abundance model = 0.596, richness model = 0.703) with climate warming and precipitation decreasing, human activities were the strongest factors for natural landscape fragmentation (abundance model = 0.807, richness model = 0.803). (2) Meteorology (0.647) and human activities (0.679) showed nearly identical effects on waterbird abundance, while the natural landscape showed the largest effects (0.908) on waterbird richness, meteorology still showed similar effects (0.665), climate and landscape finally observed positive influences. (3) The combined effects of climate and landscape on abundance were higher than richness, the Charadriiformers and Lariformes groups showing a stronger response in both abundance and richness compared to the Podicipediformes, Pelecaniformes, and Anseriformes group. Based on these findings, we suggest that climate had more consistent total effects on waterbird abundance and richness than landscape. As long as landscape fragmentation remains below the waterbirds' tolerance threshold, it can benefit both waterbird abundance and richness by providing more ecotones and wider inches for the waterbirds adapting to climate changes. Moderate human activities leading to natural landscape fragmentation may also have direct and indirect benefits for waterbird abundance. However, this study doesn't clarify the different waterbird tolerance values or the mechanism through which climate and landscape changes affect different orders of waterbirds, and the spatial ecological corridor and the ecological flow among different sample points were ignored, both of which will be well worth exploring in the future.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"86 ","pages":"Article 103022"},"PeriodicalIF":5.8,"publicationDate":"2025-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143097679","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":"Flood-drought shifts monitoring on arid Xinjiang, China using a novel machine learning based algorithm","authors":"Sulei Naibi ,&nbsp;Anming Bao ,&nbsp;Ye Yuan ,&nbsp;Jiayu Bao ,&nbsp;Rafiq Hamdi ,&nbsp;Tao Yu ,&nbsp;Xiaoran Huang ,&nbsp;Ting Wang ,&nbsp;Tao Li ,&nbsp;Jingyu Jin ,&nbsp;Gang Long ,&nbsp;Piet Termonia","doi":"10.1016/j.ecoinf.2025.103030","DOIUrl":"10.1016/j.ecoinf.2025.103030","url":null,"abstract":"<div><div>This study addresses the growing challenges of climate extremes and their impact on flood-drought shifts in Xinjiang, China, a region highly sensitive to climate variations. While existing classification models such as logistic regression (LR), support vector machines (SVMs), and geographically weighted logistic regression (GWLR) have been applied to spatial data, they exhibit limitations in handling spatial nonstationarity and balancing accuracy with interpretability. To fill this gap, we propose a novel least squares SVM (LSSVM)-based spatially varying coefficient logistic regression (LSSVM-SVCLR) model, which combines the flexibility of LSSVM with the interpretability of logistic regression and the spatial adaptability of spatially varying coefficient models. Through simulations under varying data sizes and complexity, the model achieved high accuracy, with area under the curve (<em>AUC</em>) values approaching 1 in simpler cases and around 0.8 in more complex scenarios. A case study analyzing the relationship between climate extremes and flood-drought shifts in Xinjiang demonstrated the model's applicability, achieving training and testing accuracies of 0.994 and 0.831, respectively, outperforming state-of-the-art machine learning models. Furthermore, the model revealed specific spatial effects of climate extremes on flood-drought shifts, providing probabilistic predictions across the study area. The findings highlight the potential of the proposed model to improve predictions of extreme climate-related events, offering valuable insights for disaster management and climate risk evaluation. This study provides a robust framework for analyzing the complexities of spatial nonstationarity in climate risk analysis.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"86 ","pages":"Article 103030"},"PeriodicalIF":5.8,"publicationDate":"2025-01-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143102125","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":"In-field monitoring of ground-nesting insect aggregations using a scaleable multi-camera system","authors":"Daniela Calvus ,&nbsp;Karoline Wueppenhorst ,&nbsp;Ralf Schlösser ,&nbsp;Felix Klaus ,&nbsp;Ulrich Schwanecke ,&nbsp;Henri Greil","doi":"10.1016/j.ecoinf.2025.103004","DOIUrl":"10.1016/j.ecoinf.2025.103004","url":null,"abstract":"<div><div>Insects provide essential ecosystem services, but are threatened by multiple anthropogenic stressors. Observing insect populations and behaviour is crucial to gain a better understanding of species’ interactions, and their responses to different stressors and conservation measures. However, the observation of insects can be challenging, especially, when observing large scale aggregations of ground nesting insects. Here, many individuals of the same species nest close together and interact with each other making the simultaneous observation difficult.</div><div>Camera based motion detection and neural networks have recently emerged for insect observations. They have the potential to make insect monitoring continuous and more precise, as well as more cost-efficient, compared to more traditional methods, such as manual observation or trapping.</div><div>We are presenting an automated multi-camera observation system for aggregations of ground-nesting insects. The system has been tested and improved over two seasons observing an aggregation of the ground-nesting bee species <em>Andrena vaga</em> Panzer, 1799 and is to our knowledge the first system with which long-term observation of an aggregation of ground-nesting insects has been conducted. It offers the following main advantages over existing systems:</div><div>The system is adaptable to different observation projects and able to detect insects of different sizes and shapes (e.g. parasites of <em>Andrena vaga</em>, or bumblebees) scaling the monitored area through height adjustments. Images from multiple cameras are stitched into an overview image with minimal overlap. The system can be used under different weather and environmental conditions (winter and summer, outdoor and laboratory). By only storing imagery if the detected motion in front of the camera is likely originated from an insect, it reduces post-processing work and required data storage capacity. In observing the natural environment, no attraction mechanism is employed, allowing for the monitoring of the insects’ natural behaviour. Our tests confirmed the capability of the system with motion detection reducing manual observation time of the <em>Andrena vaga</em> aggregation by 92.2<!--> <!-->% providing new insights into their interactions and behaviour.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"86 ","pages":"Article 103004"},"PeriodicalIF":5.8,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143101513","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":"Hyperspectral sensing of aboveground biomass and species diversity in a long-running grassland experiment","authors":"Ramesh K. Ningthoujam ,&nbsp;Keith J. Bloomfield ,&nbsp;Michael J. Crawley ,&nbsp;Catalina Estrada ,&nbsp;I. Colin Prentice","doi":"10.1016/j.ecoinf.2025.103028","DOIUrl":"10.1016/j.ecoinf.2025.103028","url":null,"abstract":"<div><div>Vegetation properties can be assessed through analysis of canopy reflectance spectra. Early techniques relied on simple two-band vegetation indices (VIs) that exploit leaf reflectance properties at key wavelengths. As the technology matures it is now possible to gather and test hyperspectral data. Little evidence exists on how different management regimes, such as nutrient addition, might affect hyperspectral reflectance and thus influence derived estimates of plant diversity and productivity. At a grassland experiment in southern England, we used a portable spectroradiometer to sample 96 plots exposed to multifactorial treatments combining herbivory, plant competition, soil pH and fertility. Our objective was to compare the predictive performance of popular two-band VIs with a multivariate partial least square regression (PLSR) model that uses all available wavelengths. We found that the PLSR models showed higher predictive power than the best performing VIs – that was especially true for our measure of species diversity (<span><math><msubsup><mi>R</mi><mi>cv</mi><mn>2</mn></msubsup></math></span> = 0.36 compared with a Pearson correlation of 0.21). The predictive power for our PLSR model of biomass (<span><math><msubsup><mi>R</mi><mi>cv</mi><mn>2</mn></msubsup></math></span> = 0.54) compares favourably with values reported in earlier grassland studies. These results confirm that hyperspectral measurement combined with multivariate regression techniques is a promising approach for monitoring grassland properties. There is evidence of particular benefit in capturing narrow bands associated with the red edge region of the spectrum (700–750 nm). Remotely sensed hyperspectral images at a fine spatial scale offer the prospect for matching with sampling units as small as the 2 × 2 m nutrient subplots measured here.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"86 ","pages":"Article 103028"},"PeriodicalIF":5.8,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143102008","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}