Ecological Informatics最新文献

{"title":"Decadal variations in the driving factors of increasing water-use efficiency in China's terrestrial ecosystems from 2000 to 2022","authors":"Zhongen Niu ,&nbsp;Honglin He ,&nbsp;Ying Zhao ,&nbsp;Bin Wang ,&nbsp;Lili Feng ,&nbsp;Yan Lv ,&nbsp;Mengyu Zhang ,&nbsp;Jiayi Fan ,&nbsp;Zhihao Li","doi":"10.1016/j.ecoinf.2024.102895","DOIUrl":"10.1016/j.ecoinf.2024.102895","url":null,"abstract":"<div><div>Ecosystem water-use efficiency (WUE) is a crucial indicator for evaluating carbon and water cycles. Although greening and climate change have significantly altered the WUE in Chinese terrestrial ecosystems, the roles of physiological and ecological processes are not fully understood. To address this, WUE is broken down into two key ratios: gross primary productivity to transpiration (GPP/T), which mainly reflects the effect of plant physiological processes, and transpiration to evapotranspiration (T/ET), which primarily indicates the impact of vegetation changes. Both ratios are influenced by climate change. This study employed a newly developed satellite-based ecosystem service process model Carbon and Exchange between Vegetation, Soil, and Atmosphere-ecosystem service (CEVSA-ES) to examine the impact of GPP/T and T/ET on WUE in China's terrestrial ecosystems from 2000 to 2022, alongside an analysis of the environmental variables affecting these ratios. This study revealed a general increase in WUE during the study period with significant interdecadal differences. Between 2000 and 2010, WUE was relatively stable (slope = 0.0023 g C kg⁻¹ H₂O a⁻¹, <em>p</em> &gt; 0.05), primarily because the decrease in GPP/T (<em>p</em> &lt; 0.05) offset the increase in T/ET (<em>p</em> &lt; 0.01). In contrast, from 2010 to 2022, a notable increase in WUE was observed (slope = 0.0145 g C kg⁻¹ H₂O a⁻¹, <em>p</em> &lt; 0.01), driven primarily by an increase in GPP/T (<em>p</em> &lt; 0.01), whereas T/ET remained relatively unchanged (<em>p</em> &gt; 0.05). Factors affecting GPP/T and T/ET showed considerable variability. Precipitation had the main influence on GPP/T, accounting for 70 % of its variation. The initial decade of the 21st century experienced an overall precipitation deficiency, followed by a sustained surplus in the subsequent years, resulting in interdecadal fluctuations in GPP/T. In contrast, T/ET was affected by a combination of factors, including the leaf area index, temperature, and precipitation, contributing 39 %, 29 %, and 32 %, respectively. The present study advances our understanding of the interaction of terrestrial ecosystem with the atmosphere amid global changes, offering crucial insights for forecasting the future dynamics of carbon and water cycles.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102895"},"PeriodicalIF":5.8,"publicationDate":"2024-11-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657796","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":"Automated identification of soil functional components based on NanoSIMS data","authors":"Yahan Hu ,&nbsp;Johann Maximilian Zollner ,&nbsp;Carmen Höschen ,&nbsp;Martin Werner ,&nbsp;Steffen A. Schweizer","doi":"10.1016/j.ecoinf.2024.102891","DOIUrl":"10.1016/j.ecoinf.2024.102891","url":null,"abstract":"<div><div>NanoSIMS technique allows to investigate the micro-spatial organization in complex structures in multiple scientific fields such as material science, cosmochemistry, and biogeochemistry. In soil biogeochemistry applications, NanoSIMS-based approaches aim to disentangle the interactions of organic matter (OM) and mineral phases in the heterogeneous soil microstructure. Investigating the spatial arrangement of distinct organic and mineral functional components is necessary to understand how these components interact and contribute to biogeochemical processes in soil systems. Identifying soil functional components within NanoSIMS measurements necessitates advanced and efficient data processing tools capable of accessibility and automation. We have developed a pre-processing tool to streamline NanoSIMS data preparation and handling. The tool is provided as an open-source software toolbox (NanoT). In addition, a two-step unsupervised segmentation method was developed to identify soil functional components based on NanoSIMS analyses. To illustrate the segmentation method, here we describe its application to two exemplary NanoSIMS measurements. This allows to distinguish mineral- and OM-dominated regions, as well as different mineral phases. To improve the detection of iron oxides and aluminosilicates, the ⁵⁶Fe¹⁶O⁻ channel was separately processed. The presented NanoSIMS-based processing workflow helps to disentangle functional components within a biogeochemically-diverse microstructure in soils and further warrants applications to a wide range of complex environmental samples.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102891"},"PeriodicalIF":5.8,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700599","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":"A deep learning approach to detect diseases in pomegranate fruits via hybrid optimal attention capsule network","authors":"P. Sajitha ,&nbsp;A. Diana Andrushia ,&nbsp;N. Anand ,&nbsp;M.Z. Naser ,&nbsp;Eva Lubloy","doi":"10.1016/j.ecoinf.2024.102859","DOIUrl":"10.1016/j.ecoinf.2024.102859","url":null,"abstract":"<div><div>In 2022, the production rate of pomegranate is estimated at approximately 4.8 million metric tons. Unfortunately, these fruits are susceptible to many different kinds of diseases caused by bacterial, viral, and fungal infections. Such diseases can have a major negative impact on fruit quality, production, and the profitability of pomegranate cultivation. Nowadays, several machine learning and deep learning methods are used to identify pomegranate fruit diseases automatically and effectively. In post-harvest pomegranate fruit disease detection, deep learning has great potential to extract complex patterns and features from large datasets. This can improve disease identification accuracy, enabling more efficient disease control, lower crop losses, and better resource management. The proposed work introduces an intelligent deep learning-based approach for accurately detecting pomegranate diseases, begins with Improved Guided Image Filtering (Improved GIF) and resizing to pre-process fruit images, followed by feature extraction (shape, color, texture) using GLCM and GLRLM to streamline classification. Extracted features are then fed into a novel Hybrid Optimal Attention Capsule Network (Hybrid OACapsNet), which classifies the images as normal or diseased, conditions such as bacterial blight, heart rot, and scab. Our analysis indicates that the proposed classifier has a classification accuracy of 99.19 %, precision of 98.45 %, recall of 98.41 %, F1-score of 98.43 %, and specificity of 99.45 % compared to other techniques. So this approach offers a framework, which is a feasible solution for automated detection of diseases in fruits, thereby benefiting farmers and supporting their farming operations.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102859"},"PeriodicalIF":5.8,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142700605","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 a knowledge representation logic to estimate the availability of Imbrasia epimethea (Lepidoptera: Saturniidae), an important edible insect in Subsaharan Africa","authors":"Komi M. Agboka ,&nbsp;José T.C. Ouaba ,&nbsp;Felix Meutchieye ,&nbsp;Timoléon Tchuinkam ,&nbsp;Tobias Landmann ,&nbsp;Elfatih M. Abdel-Rahman ,&nbsp;Saliou Niassy ,&nbsp;Henri E.Z. Tonnang","doi":"10.1016/j.ecoinf.2024.102890","DOIUrl":"10.1016/j.ecoinf.2024.102890","url":null,"abstract":"<div><div>Based on monthly abundance patterns, we model the density of <em>Imbrasia epimethea</em> (Drury, 1773), an important edible insect in Africa. Categorical data was collected from various regions in Cameroon, and data analysis techniques were used to infer relationships between environmental variables and the level of insect abundance. Through fuzzy logic modeling, we identified the key environmental factors and rules that influence the density of the insect. To visualize the distribution of <em>I. epimethea</em> across African landscapes, interpolation techniques were used on the study area matrix of geographical coordinates based on the corresponding monthly predictor variables for the most recent available year (2022). The results suggested a clear dynamic across Africa through the different months of the year with potentially overlapping generations with relatively high accuracy (&gt;90%). A clear relationship between regional climatic conditions and the density of <em>I. epimethea</em> could be established across Africa. The models provide insights into the complex dynamics of insect populations and sheds light on the stability and transferability of our results across different African regions (during stability analysis). This research offers a foundation for further investigations on sustainable food production and the promotion of edible insects as a viable protein source.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102890"},"PeriodicalIF":5.8,"publicationDate":"2024-11-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657792","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":"Analysis of vegetation dynamics from 2001 to 2020 in China's Ganzhou rare earth mining area using time series remote sensing and SHAP-enhanced machine learning","authors":"Lei Ming ,&nbsp;Yuandong Wang ,&nbsp;Guangxu Liu ,&nbsp;Lihong Meng ,&nbsp;Xiaojie Chen","doi":"10.1016/j.ecoinf.2024.102887","DOIUrl":"10.1016/j.ecoinf.2024.102887","url":null,"abstract":"<div><div>Rare earth mining, essential for modern industries and economic growth, often leads to severe environmental degradation. Previous research has explored the ecological impacts of rare earth mining but has not fully investigated the intricate interplay and subdivisions of environmental and anthropogenic factors driving vegetation changes over extended periods. This study addresses this gap by employing time series remote sensing and SHAP-enhanced machine learning to analyze vegetation dynamics in China's Ganzhou rare earth mining area from 2001 to 2020. Using the kNDVI derived from Landsat data, we identified three distinct vegetation trajectory types: pro-environment, des-environment, and res-environment. An ensemble machine learning model combined with SHAP analysis revealed the cropland area proportion, PM10 levels, and shrubland area proportion as the most influential factors affecting vegetation across all mining types. Additionally, after 2012, the palmer drought severity index and downward surface shortwave radiation emerged as positive contributors to vegetation health, while population pressure had a more substantial negative influence in des-environment areas. Our findings highlight spatial heterogeneity in vegetation recovery patterns and highlight the complex interactions among land cover changes, air quality, climate factors, and human activities in shaping vegetation dynamics. This study provides valuable insights for developing targeted, context-specific restoration strategies in rare earth mining areas, contributing to more sustainable mining practices and global environmental management.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102887"},"PeriodicalIF":5.8,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657789","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":"Deep learning-enhanced remote sensing-integrated crop modeling for rice yield prediction","authors":"Seungtaek Jeong ,&nbsp;Jonghan Ko ,&nbsp;Jong-oh Ban ,&nbsp;Taehwan Shin ,&nbsp;Jong-min Yeom","doi":"10.1016/j.ecoinf.2024.102886","DOIUrl":"10.1016/j.ecoinf.2024.102886","url":null,"abstract":"<div><div>This study introduces a novel crop modeling approach based on cutting-edge computational tools to advance crop production monitoring methodologies, and, thereby, tackle global food security issues. Our approach pioneers integrating deep learning and remote sensing with process-based crop models to enhance rice yield predictions while leveraging the strengths and weaknesses of each model. We developed and evaluated four models based on distinct deep neural network architectures: feed-forward neural network, long short-term memory (LSTM), gated recurrent units, and bidirectional LSTM. All the models demonstrated high predictive accuracies, with percent biases of 0.74–2.62 and Nash–Sutcliffe model efficiencies of 0.954–0.996; however, the LSTM performed best among the four models. Notably, the models' performances varied when applied to regional datasets that were not included in the training phase; this highlighted the critical need for diverse training data to enhance model robustness. This research marks a significant advancement in agricultural modeling by combining state-of-the-art computational techniques with established methodologies, setting a new standard for crop yield prediction.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102886"},"PeriodicalIF":5.8,"publicationDate":"2024-11-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657794","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":"Integrating complexity in population modelling: From matrix to dynamic models","authors":"Adrián Flores-García ,&nbsp;John Y. Dobson ,&nbsp;Eva S. Fonfría ,&nbsp;David García-García ,&nbsp;César Bordehore","doi":"10.1016/j.ecoinf.2024.102884","DOIUrl":"10.1016/j.ecoinf.2024.102884","url":null,"abstract":"<div><div>Matrix models are widely used in population ecology studies and are valuable for analysing population dynamics, although they are limited in the use of time-varying parameters. This limitation can be overcome by dynamic models. In this study, we revisit a previously published study on a matrix model of a population of the box jellyfish <em>Carybdea marsupialis</em> (L. 1758) in the Western Mediterranean. A dynamic model integrating the transition matrix of the original model is developed in STELLA Architect with the following improvements: (1) Sensitivity study of the reliability of the methodology for calculating the transition matrix and estimation of the errors of the fitting parameters; (2) Closure of the jellyfish life cycle by adding the polyp stage. This will make it possible to simulate scenarios of ecological interest over several years such as a decline in food supply, jellyfish removal strategies, changes in drift currents and changes in substrate availability for planulae to settle. (3) The inclusion of more biological reality. In particular, a temporal pattern of strobilation is added, which improves the fit of the model to the field data.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102884"},"PeriodicalIF":5.8,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657795","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":"Socio-economic factors boosting the effectiveness of marine protected areas: A Bayesian network analysis","authors":"Antonio Di Cintio ,&nbsp;Jose Antonio Fernandes-Salvador ,&nbsp;Riikka Puntila-Dodd ,&nbsp;Igor Granado ,&nbsp;Federico Niccolini ,&nbsp;Fabio Bulleri","doi":"10.1016/j.ecoinf.2024.102879","DOIUrl":"10.1016/j.ecoinf.2024.102879","url":null,"abstract":"<div><div>Marine protected areas (MPAs) represent an example of nature-based solutions for the conservation and sustainable management of marine biodiversity. Despite the number of MPAs growing worldwide, many of them fail to achieve their goals, sometimes up to the point of becoming the so-called “paper parks”: protected areas without real protection or enforcement that are virtually non-existent in terms of their effectiveness in achieving the ecological and socioeconomic goals for which they have been set up. Following the Kunming–Montreal Biodiversity Agreement (COP 15), the EU Biodiversity Strategy for 2030, and the Biodiversity Beyond National Jurisdiction treaty, global MPA coverage should increase substantially in the coming years. Hence, identifying the factors that contribute to raising the effectiveness of MPAs is pivotal to informing their planning and management. Our study introduces a model based on the Bayesian network that allows testing how different socioeconomic factors (e.g., stakeholder involvement, increased communication and enforcement) can impact the effectiveness of MPAs. The system is a user-friendly decision-support tool to quantify the contribution of each factor in the creation of a successful MPA, thus narrowing the gap between science and decision-making. We modelled the evolution of the effectiveness of MPAs under three contrasting policy-relevant scenarios based on the Intergovernmental Panel on Climate Change frameworks. Our results indicate that the highest and lowest the effectiveness of MPAs is achieved under the “global sustainability” and “national enterprise” scenarios, respectively. Our work sheds light on the complexity of the interactions among the different factors underpinning the effectiveness of MPAs and supports the growth process of MPAs at the global level on the pathway towards the sustainable exploitation of marine living resources.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102879"},"PeriodicalIF":5.8,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657788","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":"Gaussian process regression as a powerful tool for analysing time series in environmental geochemistry","authors":"Teba Gil-Díaz ,&nbsp;Michael Trumm","doi":"10.1016/j.ecoinf.2024.102877","DOIUrl":"10.1016/j.ecoinf.2024.102877","url":null,"abstract":"<div><div>Monitoring programs require more advanced data management for the registered time series. Classical temporal series decomposition cannot fulfil current needs regarding adequate data representation, optimization of the spatial-temporal sampling resolution and predictive power. In the manuscript at hand, we will demonstrate that Gaussian process regression (GPR) models are a vital machine-learning tool to interpret temporal series, improving understanding of geochemical cycles, providing input data for geochemical models and acting as a guide for future decisions in environmental monitoring. Firstly, we explore the impacts of sampling frequency in the GPR performance for temporal series with variable lengths and sampled frequencies of water discharges. On a second approach, we present the strengths and weaknesses between classical decomposition of temporal series and GPR results for a case study: a 14-year record of water discharge, suspended particulate matter and antimony concentrations in the Garonne River. Our results suggest that (i) even short temporal series with low sampling resolution can be accurately characterized by GPR when presenting well defined seasonal patterns, and (ii) GPR provides more detailed and robust support than classical statistics to identify processes responsible for multi-scale geochemical signals. This work provides a reference for researchers, engineers, and stakeholders for more reliable monitoring, understanding, and managing aquatic ecosystems.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102877"},"PeriodicalIF":5.8,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657845","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":"Integrity-centered framework for determining protected areas boundary: An application in the China's national park","authors":"Xiang Kang ,&nbsp;Mingxi Du ,&nbsp;Li Zhao ,&nbsp;Qiuyu Liu ,&nbsp;Ziyan Liao ,&nbsp;Hao Su ,&nbsp;Ting Xiang ,&nbsp;Cong Gou ,&nbsp;Nan Liu","doi":"10.1016/j.ecoinf.2024.102885","DOIUrl":"10.1016/j.ecoinf.2024.102885","url":null,"abstract":"<div><div>National parks are critical components of protected areas and are drawing increased global attention. Clear and rational boundaries may provide a scientific foundation for the sustainable protection and management of these areas. However, criteria for feasible national park delimitation and techniques for resolving conflicts between socioeconomic development and environmental protection are still poorly defined, particularly regarding integrity during park establishment. Consequently, a systematic analytical framework is essential for advancing research and practice. This study developed a systematic framework to determine the spatial boundaries, using the Kunlun Mountain region in China as a case for empirical analysis. Key issues in national park development—ecology and management—were systematically integrated into this framework. Most importantly, spatial connectivity, a critical concern in national park planning, was thoroughly considered for improving integrity optimization, an aspect often overlooked in previous delimitation studies. The results demonstrated that our framework effectively identified potential national park boundaries, achieving a balance between development and conservation. Furthermore, core protected and general control areas were delineated to support differentiated management approaches. This framework offers a scientific foundation for policymakers and managers to pursue sustainable protection and management of national parks.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"84 ","pages":"Article 102885"},"PeriodicalIF":5.8,"publicationDate":"2024-11-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"142657793","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}