Ecological Informatics最新文献

{"title":"Stochastic network to model the global spreading of respiratory diseases: From SARS-CoV-2 to pathogen X pandemic","authors":"Leonardo López ,&nbsp;Xavier Rodó","doi":"10.1016/j.ecoinf.2024.102827","DOIUrl":"10.1016/j.ecoinf.2024.102827","url":null,"abstract":"<div><div>The recent COVID-19 pandemic has underscored the vulnerability of global health systems. Emerging in November 2019 in Hubei, China, COVID-19 has had far-reaching consequences, affecting every corner of the globe. The impact has been particularly severe, causing widespread collapse of public health systems and contraction of the world economy. The imposition of stringent sanitary restrictions by the majority of countries, in response to SARS-CoV-2, disrupted various economic sectors on a massive scale. The existing gap between developed and underdeveloped countries further complicates the global scenario, raising uncertainties. This concern is amplified when considering the potential threat of other infectious diseases with dynamics akin to SARS-CoV-2, such as a new recombining H5N1 flu strain. Such a strain, if easily transmissible among humans, could lead to another pandemic. In this study, we introduce a stochastic network model designed to assess control strategies on a global scale. This model enables us to project how new variants, evading immunity, might respond to either a coordinated global response from governments or a complete lack of coordination. Our connectivity model between countries is based on a network of contacts derived from actual commercial air connectivity data. The disease dynamics within each country are simulated using a population-based approach with differential equations. The epidemiological model is fine-tuned using real SARS-CoV-2 data reported by various countries from 2019 to 2023.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"83 ","pages":"Article 102827"},"PeriodicalIF":5.8,"publicationDate":"2024-09-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142326678","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":"Quantitative spatiotemporal evolution of large urban agglomeration expansion based on 1995–2020 nighttime light and spectral data","authors":"Yuanmao Zheng ,&nbsp;Yaling Cai ,&nbsp;Kexin Yang ,&nbsp;Menglin Fan ,&nbsp;Mingzhe Fu ,&nbsp;Chenyan Wei","doi":"10.1016/j.ecoinf.2024.102824","DOIUrl":"10.1016/j.ecoinf.2024.102824","url":null,"abstract":"<div><div>The spatial distribution of urban agglomerations is an essential component of urban agglomeration development planning. To obtain information regarding the expansion of urban agglomerations over large spatiotemporal scales and long periods, this research quantitatively assess the spatiotemporal evolution of the large urban agglomerations in the Guangdong-Hong Kong-Macao Greater Bay Area (GBA) from 1995 to 2020 based on the multisource nighttime light and various spectral data. The results showed that, from 1995 to 2020, (i) the GBA expanded in a \"northwest-southeast\" pattern and showed a trend of slow expansion and then rapid expansion; (ii) the longest migration of the centroids of the cities in the GBA occurred in Foshan City (9965.22 m), which migrated at an angle of 37.88°to the west by north; the shortest migration distance of the centroid occurred in Macao (779.65 m), where it migrated at an angle of 33.96°to the south by the east; (iii) the GBA expanded in a \"circle radiation\" pattern, and the subcentre cities have more significant development potentia; (iv) the distribution of \"hot and cold spots\" of urban expansion in GBA remained stable; and (v) the aggregated autocorrelation of expansion in the GBA was not statistically significant but underwent continuous \"decentralisation\". Compared with previous studies, our work rapidly and accurately extracted the spatiotemporal evolution of GBA urban expansion from 1995 to 2020 at a spatial resolution of 30 m, which can effectively supplemented current socioeconomic statistics data lacking geospatial information, and detailedly discussed the geospatial displacements of the geographic elements for all cities to assess the differentiated information and agglomeration effects in the inner areas of large urban agglomeration. The results can provide valuable datasets, vital technical support and decision-making references for constructing sustainable development strategies in GBA and other large-scale urban agglomerations.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102824"},"PeriodicalIF":5.8,"publicationDate":"2024-09-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657842","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":"Monitoring native, non-native, and restored tropical dry forest with Landsat: A case study from the Hawaiian Islands","authors":"Monica Dimson ,&nbsp;Kyle C. Cavanaugh ,&nbsp;Erica von Allmen ,&nbsp;David A. Burney ,&nbsp;Kapua Kawelo ,&nbsp;Jane Beachy ,&nbsp;Thomas W. Gillespie","doi":"10.1016/j.ecoinf.2024.102821","DOIUrl":"10.1016/j.ecoinf.2024.102821","url":null,"abstract":"<div><p>Tropical dry forests are highly threatened at a global scale. Long-term monitoring of remaining stands is needed to assess forest health, efficacy of management practices, and potential impacts of climate change. Using a multi-seasonal Landsat time series, we examined Normalized Difference Vegetation Index (NDVI) patterns in native dry forest, non-native vegetation types, and dry forest restoration sites from 1999 to 2022 in the Hawaiian Islands. We calculated trends in median NDVI and robust coefficient of variation of NDVI for dry and wet seasons, and used Breaks for Additive Seasonal and Trend analysis to detect trend departures. To assess the impact of regional drying trends, NDVI trends were compared to the seasonal long-term precipitation anomaly and cumulative precipitation anomaly. We found that native dry forest was less green than non-native forest, particularly during the dry season, and that median NDVI increased in both native and non-native dry forests over the study period despite negative precipitation anomaly trends. This result differs from coarser-scale studies in Hawaii, but is supported by trends in other dry forest regions. Greening was also observed in restoration study sites, especially larger sites where native species establishment and recruitment has been reported. Non-native grassland NDVI exhibited a strong positive link to precipitation anomalies, suggesting that drier climate scenarios may exacerbate the invasive grass-wildfire cycle that threatens native dry forest. These results demonstrate that Landsat time series may be used to detect seasonal variation in dry forest plots and to support restoration site monitoring in a highly fragmented ecosystem.</p></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"83 ","pages":"Article 102821"},"PeriodicalIF":5.8,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1574954124003637/pdfft?md5=27e562428781b1279ae61aeb6096c8bc&pid=1-s2.0-S1574954124003637-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142230331","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":"MoMFormer: Mixture of modality transformer model for vegetation extraction under shadow conditions","authors":"Yingxuan He ,&nbsp;Wei Chen ,&nbsp;Zhou Huang ,&nbsp;Qingpeng Wang","doi":"10.1016/j.ecoinf.2024.102818","DOIUrl":"10.1016/j.ecoinf.2024.102818","url":null,"abstract":"<div><p>Accurate estimation of fractional vegetation coverage (FVC) is essential for assessing the ecological environment and acquiring ecological information. However, under natural lighting conditions, shadows in vegetation scenes can easily lead to confusion between shadowed vegetation and shadowed soil, leading to misclassification and omission errors. This issue limits the precision of both vegetation extraction and FVC estimation. To address this challenge, this study introduces a novel deep learning model, the Mixture of Modality Transformer (MoMFormer), which is specifically designed to mitigate shadow interference in vegetation extraction. Our model uses the Swin-transformer V2 as a feature extractor, effectively capturing vegetation features from a dual-modality (regular-exposure RGB and high dynamic range HDR) dataset. A dynamic aggregation module (DAM) is integrated to adaptively blend the most relevant vegetation features. We selected several state-of-the-art (SOTA) methods and conducted extensive experiments using a self-annotated dataset featuring diverse vegetation–soil scenes and compare our model with several state-of-the-art methods. The results demonstrate that MoMFormer achieves an accuracy of 89.43 % on the HDR-RGB dual-modality dataset, with an FVC accuracy of 87.57 %, outperforming other algorithms and demonstrating high vegetation extraction accuracy and adaptability under natural lighting conditions. This research offers new insights into accurate vegetation information extraction in naturally lit environments with shadows, providing robust technical support for high-precision validation of vegetation coverage products and algorithms based on multimodal data. The code and datasets used in this study are publicly available at <span><span>https://github.com/hhhxiaohe/MoMFormer</span><svg><path></path></svg></span>.</p></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"83 ","pages":"Article 102818"},"PeriodicalIF":5.8,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1574954124003601/pdfft?md5=f86e3b9567567c1cac9fdc7b86af1f24&pid=1-s2.0-S1574954124003601-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142173880","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":"Multi-decadal temporal reconstruction of Sentinel-3 OLCI-based vegetation products with multi-output Gaussian process regression","authors":"Dávid D.Kovács ,&nbsp;Pablo Reyes-Muñoz ,&nbsp;Katja Berger ,&nbsp;Viktor Ixion Mészáros ,&nbsp;Gabriel Caballero ,&nbsp;Jochem Verrelst","doi":"10.1016/j.ecoinf.2024.102816","DOIUrl":"10.1016/j.ecoinf.2024.102816","url":null,"abstract":"&lt;div&gt;&lt;p&gt;Operational Earth observation missions, like the Sentinel-3 (S3) satellites, aim to provide imagery for long-term environmental assessment to monitor and analyze vegetation changes and dynamics. However, the S3 archive is limited in temporal availability to the year 2016. Although S3 provides continuity of previous missions, key vegetation products (VPs) including leaf area index (LAI), fraction of photosynthetically active radiation (FAPAR), fractional vegetation cover (FVC), and leaf chlorophyll content (LCC), can be reliably produced from Ocean and Land Colour Instrument (OLCI) data only since the sensors' launch. To overcome this limitation, our study proposes a reconstruction workflow that extends the data record beyond its data acquisition. By using multi-output Gaussian process regression (MOGPR) fusion, we explored guiding predictor VPs from the Moderate Resolution Imaging Spectroradiometer (MODIS) sensor for the reconstruction of multi-decadal (spanning two decades, 2002–2022) temporal profiles of four OLCI-derived VPs (S3-MOGPR), moving past S3's launch. We first evaluated three MODIS-derived inputs as predictor variables: LAI, FAPAR, and the Normalised Difference Vegetation Index (NDVI) over nine sites with distinct land covers from the Ground-Based Observations for Validation (GBOV) service. Each predictor produced a distinct time series for the four reconstructed S3 VPs. To determine which predictor variable most accurately reconstructs data streams of the targeted variable, all S3-MOGPR VPs were compared to satellite-based products from the Copernicus Global Land Service (CGLS). MOGPR models were trained for 2019 and compared to reference data. Since MODIS LAI demonstrated the best reconstruction performance of all predictors, S3-MOGPR VPs were fully reconstructed from 2022 back to 2002 using guiding MODIS LAI and evaluated with in-situ data. The most consistent reconstructed product was FVC (&lt;span&gt;&lt;math&gt;&lt;mi&gt;R&lt;/mi&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;mn&gt;0.96&lt;/mn&gt;&lt;/math&gt;&lt;/span&gt;, NRMSE = 0.17) over mixed forests compared to CGLS estimates. FVC also yielded the highest validation statistics (&lt;span&gt;&lt;math&gt;&lt;mi&gt;R&lt;/mi&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;mn&gt;0.93&lt;/mn&gt;&lt;/math&gt;&lt;/span&gt;, &lt;span&gt;&lt;math&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;mo&gt;=&lt;/mo&gt;&lt;mn&gt;0.92&lt;/mn&gt;&lt;/math&gt;&lt;/span&gt;, NRMSE = 0.14) over croplands. The highest correlation coefficients were achieved by the predictor variable LAI reconstructing FVC with mean &lt;span&gt;&lt;math&gt;&lt;mi&gt;R&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt;, &lt;span&gt;&lt;math&gt;&lt;mi&gt;ρ&lt;/mi&gt;&lt;/math&gt;&lt;/span&gt; and NRMSE = 0.11 among all sites of 0.91 and 0.88, respectively. In the absence of both satellite and ground-based LCC reference measurements, the reconstructed LCC profiles were compared to the OLCI and MERIS Terrestrial Chlorophyll Index (OTCI, MTCI). The correlation metrics provided strong evidence of the reconstructed LCC product's integrity, with the highest correlation over deciduous broadleaf, mixed forests and croplands (&lt;span&gt;&lt;math&gt;&lt;mi&gt;R&lt;/mi&gt;&lt;mo&gt;&gt;&lt;/mo&gt;&lt;mn&gt;0.9&lt;/mn&gt;&lt;/math&gt;&lt;/span&gt;). The lowest correlations for all reconstructe","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"83 ","pages":"Article 102816"},"PeriodicalIF":5.8,"publicationDate":"2024-09-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1574954124003583/pdfft?md5=e9b712c255026d945be9ad65c09438f4&pid=1-s2.0-S1574954124003583-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142242524","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":"Process-based forecasts of lake water temperature and dissolved oxygen outperform null models, with variability over time and depth","authors":"Whitney M. Woelmer ,&nbsp;R. Quinn Thomas ,&nbsp;Freya Olsson ,&nbsp;Bethel G. Steele ,&nbsp;Kathleen C. Weathers ,&nbsp;Cayelan C. Carey","doi":"10.1016/j.ecoinf.2024.102825","DOIUrl":"10.1016/j.ecoinf.2024.102825","url":null,"abstract":"<div><p>Near-term iterative ecological forecasting has great potential for providing new insights into our ability to predict multiple ecological variables. However, true, out-of-sample probabilistic forecasts remain rare, and variability in forecast performance has largely been unexamined in process-based forecasts which predict multiple ecosystem variables. To explore how forecast performance varies for water temperature and dissolved oxygen, two freshwater variables important for lake ecosystem functioning, we produced probabilistic forecasts at multiple depths over two open-water seasons in Lake Sunapee, NH, USA. Our forecasting system, FLARE (Forecasting Lake And Reservoir Ecosystems), uses a 1-D coupled hydrodynamic-biogeochemical process model, which we assessed relative to both climatology and persistence null models to quantify how much information process-based FLARE forecasts provide over null models across varying environmental conditions. We found that FLARE water temperature forecasts were always more skillful than FLARE oxygen forecasts. Specifically, temperature forecasts outperformed both null models up to 11 days into the future, as compared to only two days for oxygen. Across different years, we observed variable forecast skill, with performance generally decreasing with depth for both variables. Overall, all temperature forecasts and surface oxygen, but not deep oxygen, forecasts were more skillful than at least one null model &gt;80 % of the forecasted period, indicating that our process-based model was able to reproduce the dynamics of these two variables with greater reliability than the null models. However, process-based oxygen forecasts from deeper waters were less skillful than both null models during a majority of the forecasted period, which suggests that deep-water oxygen dynamics are dominated by autocorrelation and seasonal change, which are inherently captured by the null forecasts. Our results highlight that forecast performance varies among lake water quality metrics and that process-based forecasts can provide important information in conjunction with null models in varying environmental conditions. Altogether, these process-based forecasts can be used to develop quantitative tools which inform our understanding of future ecosystem change.</p></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"83 ","pages":"Article 102825"},"PeriodicalIF":5.8,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1574954124003674/pdfft?md5=9a53fafcb216d3f908b82767ac100cd5&pid=1-s2.0-S1574954124003674-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142241916","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":"Some limitations of the concordance correlation coefficient to characterise model accuracy","authors":"Alexandre M.J.-C. Wadoux ,&nbsp;Budiman Minasny","doi":"10.1016/j.ecoinf.2024.102820","DOIUrl":"10.1016/j.ecoinf.2024.102820","url":null,"abstract":"<div><p>Perusal of the environmental modelling literature reveals that the Lin's concordance correlation coefficient is a popular validation statistic to characterise model or map quality. In this communication, we illustrate with synthetic examples three undesirable statistical properties of this coefficient. We argue that ignorance of these properties have led to a frequent misuse of this coefficient in modelling and mapping studies. The stand-alone use of the concordance correlation coefficient is insufficient because i) it does not inform on the relative contribution of bias and correlation, ii) the values cannot be compared across different datasets or studies and iii) it is prone to the same problems as other linear correlation statistics. The concordance coefficient was, in fact, thought initially for evaluating reproducibility studies over repeated trials of the same variable, not for characterising model accuracy. For the validation of models and maps, we recommend calculating statistics that, combined with the concordance correlation coefficient, represent various aspects of the model or map quality, which can be visualised together in a single figure with a Taylor or solar diagram.</p></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"83 ","pages":"Article 102820"},"PeriodicalIF":5.8,"publicationDate":"2024-09-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1574954124003625/pdfft?md5=598076128189827bbb1d60591fdbe37f&pid=1-s2.0-S1574954124003625-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142232943","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":"Modelling soil prokaryotic traits across environments with the trait sequence database ampliconTraits and the R package MicEnvMod","authors":"Jonathan Donhauser ,&nbsp;Anna Doménech-Pascual ,&nbsp;Xingguo Han ,&nbsp;Karen Jordaan ,&nbsp;Jean-Baptiste Ramond ,&nbsp;Aline Frossard ,&nbsp;Anna M. Romaní ,&nbsp;Anders Priemé","doi":"10.1016/j.ecoinf.2024.102817","DOIUrl":"10.1016/j.ecoinf.2024.102817","url":null,"abstract":"<div><p>We present a comprehensive, customizable workflow for inferring prokaryotic phenotypic traits from marker gene sequences and modelling the relationships between these traits and environmental factors, thus overcoming the limited ecological interpretability of marker gene sequencing data. We created the trait sequence database <em>ampliconTraits</em>, constructed by cross-mapping species from a phenotypic trait database to the SILVA sequence database and formatted to enable seamless classification of environmental sequences using the SINAPS algorithm. The R package <em>MicEnvMod</em> enables modelling of trait – environment relationships, combining the strengths of different model types and integrating an approach to evaluate the models' predictive performance in a single framework. Traits could be accurately predicted even for sequences with low sequence identity (80 %) with the reference sequences, indicating that our approach is suitable to classify a wide range of environmental sequences. Validating our approach in a large trans-continental soil dataset, we showed that trait distributions were robust to classification settings such as the bootstrap cutoff for classification and the number of discrete intervals for continuous traits. Using functions from <em>MicEnvMod,</em> we revealed precipitation seasonality and land cover as the most important predictors of genome size. We found Pearson correlation coefficients between observed and predicted values up to 0.70 using repeated split sampling cross validation, corroborating the predictive ability of our models beyond the training data. Predicting genome size across the Iberian Peninsula, we found the largest genomes in the northern part. Potential limitations of our trait inference approach include dependence on the phylogenetic conservation of traits and limited database coverage of environmental prokaryotes. Overall, our approach enables robust inference of ecologically interpretable traits combined with environmental modelling allowing to harness traits as bioindicators of soil ecosystem functioning.</p></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"83 ","pages":"Article 102817"},"PeriodicalIF":5.8,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1574954124003595/pdfft?md5=a975351ee65c86e764ade9d9b4d869ae&pid=1-s2.0-S1574954124003595-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142173774","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":"Spatiotemporal evolution and driving mechanism of Dongting Lake based on 2005–2020 multi-source remote sensing data","authors":"Mingzhe Fu ,&nbsp;Yuanmao Zheng ,&nbsp;Changzhao Qian ,&nbsp;Qiuhua He ,&nbsp;Yuanrong He ,&nbsp;Chenyan Wei ,&nbsp;Kexin Yang ,&nbsp;Wei Zhao","doi":"10.1016/j.ecoinf.2024.102822","DOIUrl":"10.1016/j.ecoinf.2024.102822","url":null,"abstract":"<div><div>As one of the largest inland lakes in China, Dongting Lake has attracted widespread attention owing to its rich natural resources, unique geographical landscape, and important ecological functions. Recently, Dongting Lake has experienced phenomena such as an early dry season and backflow during the flood season. Multi-source remote sensing data and the normalised difference water index (NDWI) threshold method were used to systematically analyse the water area of the lake from 2005 to 2020. Additionally, it employed a centre of gravity migration model and a geographic detector model to investigate the lake's evolution patterns and driving mechanisms. The research identified notable fluctuations in Dongting Lake's water area during this period, with a particularly sharp decline in 2006—from 1509.74 km² to 815 km², marking a decrease of 694.74 km² and a shrinkage rate of 46.01 %. Spatial analysis indicated that the centre of gravity of these water areas changed primarily between Nandashan Town, the Dongting Lake Management Committee, Wanzihu Township, and Qingtan Township, underscoring their significant influence on lake dynamics, including runoff, surface water availability, sediment deposition, and precipitation, all of which displayed strong positive correlations (Pearson coefficients of 0.57, 0.68, and 0.63, respectively), whereas population density showed a negative correlation (Pearson coefficient of −0.56). Furthermore, the study highlighted the substantial impact of the Digital Elevation Model (DEM) and its interaction with slope and aspect on Dongting Lake's evolution, with Q values of 0.537 and 0.543, respectively, emphasising their critical roles in shaping lake area changes and providing a crucial scientific basis for enhancing the understanding and effective management of water resources in the Dongting Lake Basin through comprehensive analysis of its spatiotemporal evolution and driving mechanisms.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"83 ","pages":"Article 102822"},"PeriodicalIF":5.8,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1574954124003649/pdfft?md5=7c5aa5f56347f8489f910ec55f75d4d6&pid=1-s2.0-S1574954124003649-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142315036","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":"Harnessing artificial intelligence for efficient systematic reviews: A case study in ecosystem condition indicators","authors":"Isabel Nicholson Thomas ,&nbsp;Philip Roche ,&nbsp;Adrienne Grêt-Regamey","doi":"10.1016/j.ecoinf.2024.102819","DOIUrl":"10.1016/j.ecoinf.2024.102819","url":null,"abstract":"<div><p>Effective evidence synthesis is important for the integration of scientific research into decision-making. However, fully depicting the vast mosaic of concepts and applications in environmental sciences and ecology often entails a substantial workload. New Artificial Intelligence (AI) tools present an attractive option for addressing this challenge but require sufficient validation to match the vigorous standards of a systematic review. This article demonstrates the use of generative AI in the selection of relevant literature as part of a systematic review on indicators of ecosystem condition. We highlight, through the development of an optimal prompt to communicate inclusion and exclusion criteria, the need to describe ecosystem condition as a multidimensional concept whilst also maintaining clarity on what does not meet the criteria of comprehensiveness. We show that, although not completely infallible, the GPT-3.5 model significantly outperforms traditional literature screening processes in terms of speed and efficiency whilst correctly selecting 83 % of relevant literature for review. Our study highlights the importance of precision in prompt design and the setting of query parameters for the AI model and opens the perspective for future work using language models to contextualize complex concepts in the environmental sciences. Future development of this methodology in tandem with the continued evolution of the accessibility and capacity of AI tools presents a great potential to improve evidence synthesis through gains in efficiency and possible scope.</p></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"83 ","pages":"Article 102819"},"PeriodicalIF":5.8,"publicationDate":"2024-09-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S1574954124003613/pdfft?md5=a2a00c40d3636d32055ec22bbf0011ce&pid=1-s2.0-S1574954124003613-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142173240","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}