Ecological Informatics最新文献

{"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}

{"title":"The fractal characteristics and risk indicators of urban ecological environment vulnerability evolution","authors":"Xuefeng Wu ,&nbsp;Xing Huang ,&nbsp;Sidai Guo","doi":"10.1016/j.ecoinf.2025.103033","DOIUrl":"10.1016/j.ecoinf.2025.103033","url":null,"abstract":"<div><div>Exploring whether the evolution system of urban ecological environment vulnerability had fractal characteristics is an important basis for objectively evaluating the risk of urban ecological environment vulnerability and improving the reliability of risk warning. Existing research focused on explaining the performance characteristics of complex systems through single scale and multi-dimensional features, and could not dynamically depict the infinite fine approximation characteristics between local development and overall situation of complex systems, resulting in weak reliability of urban ecological environment vulnerability risk warning. Introducing fractal theory into the study of the characteristics of urban ecological environment vulnerability systems, a systematic framework for judging the fractal characteristics of urban ecological environment vulnerability evolution was established around the nonlinear, long-range correlation, and scale-free aspects of fractal theory. Based on the 20 years panel data of 35 cities in China, the fractal characteristics of urban ecological environment vulnerability systems were verified, and the relationship between Hurst index and fractal dimension in continuous time windows was further established. The complexity of urban ecological environment vulnerability systems in continuous time windows was accurately characterized by fractal dimension, providing effective indicators for risk assessment. The results showed that the vulnerability evolution system of urban ecological environment had complexed nonlinear characteristics, and the random Hurst indexes calculated by rescaled range analysis (R/S) was close to 0.5, indicating that the vulnerability evolution system had long-term memory. The vulnerability evolution system described by the random walk model follows a power-law relationship, indicating that the vulnerability evolution system had scale-free self-similarity characteristics. The risk level of the vulnerability evolution system described by the fractal dimension is consistent with reality, indicating that the fractal dimension had strong indicative effect on the risk judgment of the vulnerability evolution system.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"86 ","pages":"Article 103033"},"PeriodicalIF":5.8,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143102123","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":"The construction of shelterbelts along the desert highway has increased the carbon sequestration capacity of the Taklimakan Desert, China","authors":"Ali Mamtimin ,&nbsp;Kun Zhang ,&nbsp;Hajigul Sayit ,&nbsp;Yu Wang ,&nbsp;JiaCheng Gao ,&nbsp;Ailiyaer Aihaiti ,&nbsp;Meiqi Song ,&nbsp;Junjian Liu ,&nbsp;Fan Yang ,&nbsp;Chenglong Zhou ,&nbsp;Wen Huo ,&nbsp;Siqi Wang ,&nbsp;Yangyao Xu ,&nbsp;Gulinur Amar ,&nbsp;Wei Liu","doi":"10.1016/j.ecoinf.2025.103027","DOIUrl":"10.1016/j.ecoinf.2025.103027","url":null,"abstract":"<div><div>Human endeavors exert profound influences on the storage of carbon and the net primary productivity (NPP) of land, particularly in the environmentally sensitive arid territories. The Taklimakan Desert, known as the second largest migratory desert on Earth, necessitates an examination of the effects of the desert thoroughfare and its adjoining ecological windbreaks on carbon sequestration. This inquiry employed a myriad of data sources and harnessed the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST), Carnegie-Ames-Stanford Approach (CASA), and Patch-level Land Use Simulation Model (PLUS) methodologies to delve into the spatial and temporal metamorphosis and future outlook of the Taklimakan Desert in China over the past three decades. The findings reveal that grasslands serve as the preeminent carbon reservoir in the Taklimakan Desert, witnessing a surge of 16.31 tons over the previous 30 years. Of particular note, the ecological windbreaks encircling the desert highway have bolstered carbon storage by an added 0.15 tons from the completion of the road in 1996 to 2020. Moreover, following the establishment of the ecological windbreak in 2005, there has been a notable upsurge in the values of net primary productivity (NPP) within this locality. Looking towards the future, various prospective scenarios, especially those centered on ecological conservation, underscore an escalating carbon sequestration effect in the Taklimakan Desert. Concurrently, there is an augmentation in carbon retention linked to the desert thoroughfare. The prognostications of maximum, minimum, and mean NPP values from 2030 to 2100 exhibit substantial oscillations, delineating the intricate interplay between climatic shifts and human endeavors in shaping regional NPP. In sum, these revelations intimate that well-designed human undertakings have engendered an expansion of verdant domains within the desert, ultimately benefiting carbon capture in these parched terrains.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"86 ","pages":"Article 103027"},"PeriodicalIF":5.8,"publicationDate":"2025-01-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143101995","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":"Computational methods for detecting insect vibrational signals in field vibroscape recordings","authors":"Matija Marolt ,&nbsp;Matevž Pesek ,&nbsp;Rok Šturm ,&nbsp;Juan José López Díez ,&nbsp;Behare Rexhepi ,&nbsp;Meta Virant-Doberlet","doi":"10.1016/j.ecoinf.2025.103003","DOIUrl":"10.1016/j.ecoinf.2025.103003","url":null,"abstract":"<div><div>The ecological significance of vibroscape has been largely overlooked, excluding an important part of the available information from ecosystem assessment. Insects rely primarily on substrate-borne vibrational signalling in their communication, which is why the majority of terrestrial insects are excluded from passive acoustic monitoring. The ability to monitor the biological component of the natural vibroscape has been limited due to a lack of data and methods to analyse the data. In this paper, we evaluate the use of deep learning models to automatically detect and classify vibrational signals from field recordings obtained with laser vibrometry. We created a dataset of annotated vibroscape recordings of meadow habitats, containing vibrational signals categorized as pulses, harmonic signals, pulse trains, and complex signals. We compared different deep neural network architectures for the detection and classification of vibrational signals, including convolutional and transformer models. The PaSST transformer architecture, which was fine-tuned from a pre-trained checkpoint demonstrated the highest performance on all tasks, achieving an average precision of 0.79 in signal detection. For signals with more than one hour of annotated data, the classification models achieved instance-based F1-scores above 0.8, enabling automatic analysis of activity patterns. In our case study, where 24-hour field recordings were analysed, the trained models (even those with lower precision) revealed interesting activity patterns of different species. The presented study, together with the dataset we publish with this paper, lays the foundation for further analysis of the vibroscape and the development of automated methods for ecotremological monitoring that complement passive acoustic monitoring and provide a comprehensive approach to ecosystem assessment.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"86 ","pages":"Article 103003"},"PeriodicalIF":5.8,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143097677","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":"Multiscale feature fusion and enhancement in a transformer for the fine-grained visual classification of tree species","authors":"Yanqi Dong ,&nbsp;Zhibin Ma ,&nbsp;Jiali Zi ,&nbsp;Fu Xu ,&nbsp;Feixiang Chen","doi":"10.1016/j.ecoinf.2025.103029","DOIUrl":"10.1016/j.ecoinf.2025.103029","url":null,"abstract":"<div><div>Accurate and rapid fine-grained visual classification (FGVC) of tree species within the same family can provide technical support for tree surveys, research, and conservation. However, FGVC faces challenges such as large intraclass differences and small interclass differences. Recognizing tree species within the same family requires focusing on and correlating overall and multiorgan features of the trees while mitigating the influence of complex natural backgrounds, occlusion effects and other factors. To address these challenges, we propose multiscale feature fusion (MFF) and enhancement in transformers to improve recognition performance. The method consists of a Swin transformer backbone, an MFF module, a discriminative feature enhancement (DFE) module, and a texture feature enhancement (TFE) module. The MFF module aims to strike a balance between global and local feature extraction. The DFE module is employed to mitigate the impact of background noise, whereas the TFE module is used to enhance the feature extraction associated with complex textures and spatial patterns. We conducted experiments on a constructed dataset of tree species from the same family, achieving a top-1 accuracy of 90.3 % and a top-3 accuracy of 96.8 %. In addition, the method performed well on three popular FGVC datasets, namely, the Flavia, Oxford Flowers, and PlantCLEF 2015 datasets, with top-1 accuracies of 100 %, 99.2 %, and 81.4 %, respectively. The ablation experiments and module visualizations also yielded satisfactory results. Thus, this work provides a solution to enhance the FGVC task.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"86 ","pages":"Article 103029"},"PeriodicalIF":5.8,"publicationDate":"2025-01-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143101500","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 soil heat flux from MODIS products for arid regions","authors":"Fahime Arabi Aliabad ,&nbsp;Ebrahim Ghaderpour","doi":"10.1016/j.ecoinf.2025.103005","DOIUrl":"10.1016/j.ecoinf.2025.103005","url":null,"abstract":"<div><div>Soil heat flux (SHF) is the rate of heat transfer between the Earth’s surface and the underlying soil which affects important processes, such as evapotranspiration and climate changes. Accurate estimation of SHF is therefore very important. This research models SHF images with hourly sequence in Yazd–Ardakan plain, an arid region in central part of Iran, employing moderate resolution imaging spectroradiometer (MODIS) products from 2014 to 2021, such as land surface temperature, emissivity, albedo and normalized difference vegetation index. Two methods are utilized to estimate SHF hourly. The first method (M1) is based on thermal inertia, calculated through the albedo image and the temperature of the Earth’s surface. In M1, SHF is estimated using the range of land surface temperature, thermal inertia and applying the harmonic relationship. The second method (M2) is based on modeling the daily SHF cycle using four MODIS-SHF images, calculated by the energy balance equation. In M2, the average and range of SHF in each day are calculated using four available images of SHF for each day, considering day length, time of sunrise, sunset and local noon. The results show that the root mean square error in M1 is 12.48 W/m<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> while in M2 is 7.61 W/m<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>. The mean absolute deviation for M1 and M2 are estimated as 15.61 W/m<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> and 5.42 W/m<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>, respectively. Cross-validation results demonstrates that M2 has higher accuracy in modeling the daily cycle of SHF. The results also show that the pattern of changes in SHF during the day and night in one-year time series is completely opposite of each other. The SHF during the day has varied from 64 to 98 W/m<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span> and at night from 0 to 64 W/m<span><math><msup><mrow></mrow><mrow><mn>2</mn></mrow></msup></math></span>. Plain lands have shown the highest SHF during the day compared to other land covers throughout the year. In the summer season, residential, agriculture, sand dune, mountain, plain, and bare lands respectively have the lowest to the highest SHF. Examining the changes in SHF in different land covers in one-year time series at nighttime indicates that SHF is higher in mountain throughout the year and lower in residential areas.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"86 ","pages":"Article 103005"},"PeriodicalIF":5.8,"publicationDate":"2025-01-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143097676","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":"eDNA tech tracks lethal jellyfish with CRISPR precision","authors":"Maslin Osathanunkul","doi":"10.1016/j.ecoinf.2025.103008","DOIUrl":"10.1016/j.ecoinf.2025.103008","url":null,"abstract":"<div><div>Coastal public safety depends on detecting and monitoring dangerous box jellyfish, especially <em>Chiropsoides buitendijki</em>. CRISPR-Cas12a technology was used to create and validate a new eDNA-based detection technique for rapid, sensitive, and accurate identification of <em>C. buitendijki</em> eDNA in the Gulf of Thailand. After analysing 567 reactions across 63 sites, CRISPR-Cas12a successfully detected eDNA at 17 locations where earlier methods had been unsuccessful. Interestingly, a single water sample and one CRISPR-Cas12a replicate per site could achieve 95 % or higher detection rates, proving the method's efficacy. This method's accuracy was evident in both field sample collection and laboratory analysis stages, as it exhibited a low false positive rate and a high true positive rate. Although the CRISPR-Cas12a eDNA method does not require expensive scientific instruments, it cannot quantify eDNA in samples, simply reporting its presence or absence. Despite this limitation, the method achieved a detection limit (LOD) of 0.15 copies per reaction, with robust specificity validated through both in silico and in vitro testing. CRISPR-Cas12a's effectiveness, cost-efficiency, and ease to use make it particularly advantageous for developing countries. The method offers a powerful tool for early warning and preventive measures, enhancing beach safety protocols, and mitigating risks associated with jellyfish encounters. Its scalability and affordability support routine monitoring, promoting sustainable coastal management, and protecting tourism-dependent economies. The CRISPR-Cas12a eDNA system enhances marine biodiversity monitoring by integrating scientific rigour with practical utility, paving the way for advancements in public health and environmental management.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"86 ","pages":"Article 103008"},"PeriodicalIF":5.8,"publicationDate":"2025-01-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"143101992","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}