Ecological Informatics最新文献

{"title":"Distinguishing the main climatic drivers of terrestrial vegetation carbon dynamics in pan-Arctic ecosystems","authors":"Chengfeng Meng ,&nbsp;Hao Zhou ,&nbsp;Xichuan Liu ,&nbsp;Jiao Zheng ,&nbsp;Jun Wang ,&nbsp;Qingwei Zeng ,&nbsp;Shuai Hu","doi":"10.1016/j.ecoinf.2025.103267","DOIUrl":"10.1016/j.ecoinf.2025.103267","url":null,"abstract":"<div><div>Pan-Arctic terrestrial ecosystems have experienced widespread greening over the past few decades due to climate warming. However, the dynamic variations and climatic causes of such greening in the pan-Arctic are still unclear due to limitations in high-latitude ground-based measurements. The study first evaluated the applicability of three satellite-based vegetation indices (VIs) for pan-Arctic carbon dynamics in previous decades compared with multisource observations. Three VIs presented significant increasing trends up to 0.0005–0.0017 yr⁻¹ for pan-Arctic ecosystems in the past 23 years, and near-infrared reflectance of vegetation (NIRv) could better capture pan-Arctic terrestrial carbon dynamic variations (e.g., increasing trends) than other VIs among most vegetation types, such as grasslands. The separate contributions of climatic factors to satellite-based NIRv variability were further quantified using partial correlation and ridge regression analysis over pan-Arctic ecosystems from 2001 to 2023. Carbon dioxide (CO₂) and air temperature (AT) presented positive partial correlations with the satellite-based NIRv over entire pan-Arctic ecosystems, due to fertilization and warming effects. However, the vapor pressure deficit (VPD) showed positive (negative) partial correlations with NIRv variations in high (low) pan-Arctic ecosystems because of diverse stomatal responses to air dryness. Therefore, increases in VPD contributed 11.2 % of the pan-Arctic NIRv variability, although positive (negative) effects were detected for high (low) pan-Arctic ecosystems. These findings highlight the advantages of the NIRv for representing terrestrial carbon dynamics in fragile ecosystems (e.g., the pan-Arctic) and reveal the diverse effects of the regional climate (e.g., air dryness) on pan-Arctic ecosystems under climate warming.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103267"},"PeriodicalIF":5.8,"publicationDate":"2025-06-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144307996","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Adaptive weight optimization with large pretraining for pest detection","authors":"Kejian Yu ,&nbsp;Wenwen Xu ,&nbsp;Fuqin Geng ,&nbsp;Yunzhi Wu","doi":"10.1016/j.ecoinf.2025.103224","DOIUrl":"10.1016/j.ecoinf.2025.103224","url":null,"abstract":"<div><div>Frequent infestations by agricultural pests reduce crop production and significantly affect economic efficiency. Therefore, timely and effective pest control is critical to improving productivity and facilitate environmental protection. Herein, we propose an adaptive weight optimization method based on transfer learning for multimodal pest detection. This approach utilizes pretrained model parameters from public datasets to extract features and enhance cross-modal feature from text and images. Accurate pest recognition and localization are achieved through an adaptive loss function, which optimizes the model’s performance across multiple tasks. In tests conducted on IP102 (36 species) and Pest24 (24 species), which are major agricultural pest datasets, the proposed model achieves average precisions of 65.8% and 76.3% at 50% Intersection over Union (IoU) threshold, respectively. By doing so, our model outperforms existing state-of-the-art methods despite using only 30 training cycles. These results highlight the significant practical value of multimodal pest detection methods in enhancing the efficiency and accuracy of agricultural pest identification. The code and dataset are available at <span><span>https://github.com/Healer-ML/Pest-Detection</span><svg><path></path></svg></span>.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103224"},"PeriodicalIF":5.8,"publicationDate":"2025-06-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel scheme for seamless global mapping of daily mean air temperature (SGM_DMAT) at 1-km spatial resolution using satellite and auxiliary data","authors":"Ran Huang ,&nbsp;Shengcheng Li ,&nbsp;Xin Zhu ,&nbsp;Jianing Li ,&nbsp;Yuanjun Xiao ,&nbsp;Wei Weng ,&nbsp;Qi Shao ,&nbsp;Dengfeng Chai ,&nbsp;Jingcheng Zhang ,&nbsp;Yao Zhang ,&nbsp;Lingbo Yang ,&nbsp;Kaihua Wu ,&nbsp;Zhihao Hu ,&nbsp;Li Liu ,&nbsp;Weiwei Sun ,&nbsp;Weiwei Liu ,&nbsp;Jingfeng Huang","doi":"10.1016/j.ecoinf.2025.103266","DOIUrl":"10.1016/j.ecoinf.2025.103266","url":null,"abstract":"<div><div>The daily mean air temperature (DMAT) is an essential descriptor of climate change. Seamless global DMAT maps will significantly improve our knowledge of terrestrial meteorological and climatic conditions. This study proposes a novel scheme, Seamless Global Mapping of Daily Mean Air Temperatures (SGM_DMAT). The SGM_DMAT scheme comprises two key phases: Estimating DMAT under clear-sky conditions, and reconstructing missing values under cloudy conditions using data from 2020 to 2022 as the calibration dataset and data in 2023 as the validation dataset. The results demonstrate that combining all valid Moderate Resolution Imaging Spectroradiometer (MODIS) TERRA/AQUA daytime and nighttime land surface temperature (LST) observations under clear-sky conditions, and applying spatial temporal analysis techniques with reference images for cloudy days, ensures robust and seamless DMAT estimation. Specifically, the Extreme Gradient Boosting (XGBoost) was selected as the optimal model of DMAT estimation. The optimal feature dataset includes satellite-derived LSTs, latitude, longitude, elevation above sea level, month, and day of year. The optimal calibration dataset comprises all valid calibration data (AVCD). Additionally, the priority order of DMAT clear-sky estimation models was established using different LST combinations. Finally, robust and seamless global maps of DMAT were generated for the period 2020–2023. For globally seamless mapping products, the R² was 0.956, with an RMSE of 2.825 °C and a MAE of 1.985 °C. The proposed SGM_DMAT scheme may aid DMAT estimation in regions that lack sufficient meteorological stations. The seamless global DMAT products have broad applicability including in trend analysis, urban heat island research, and assessment of crop stress due to temperature extremes.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103266"},"PeriodicalIF":5.8,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144271530","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Quantifying the impact of drought on net primary productivity of different vegetations in a typical large lake floodplain wetland, China","authors":"Canyu Yuan ,&nbsp;Xianghu Li ,&nbsp;Dan Zhang ,&nbsp;Xuchun Ye ,&nbsp;Tong Sun ,&nbsp;Yaling Lin ,&nbsp;Zhiqiang Tan","doi":"10.1016/j.ecoinf.2025.103268","DOIUrl":"10.1016/j.ecoinf.2025.103268","url":null,"abstract":"<div><div>Wetland ecosystems are essential components of the global carbon and water cycles, but frequent and severe droughts pose significant disruptions to these ecosystems. As one of the most important Ramsar Wetlands globally, the Poyang Lake floodplain wetland ecosystem is facing severe drought challenges due to the combined impacts of climate change and human activities. This study investigated the spatio-temporal dynamics of net primary productivity (NPP) and its responses to drought in the Poyang Lake wetland from 1986 to 2020, using a process-based ecological model integrated with refined wetland vegetation classification datasets. The results indicated that the intra-annual NPP for most vegetation types exhibited two peaks, and the annual NPP ranged from 222.7 to 736.3 gC/m²/yr. Spatially, the high annual NPP values occurred in the southern and western regions of the wetland, while low values were found in the northern and eastern regions. Over the past 35 years, annual NPP decreased with the slope − 10.4 gC/m²/yr² across the entire wetland, with the most significant decline observed in the eastern region. Drought significantly altered NPP variability in the Poyang Lake wetland. Seasonal NPP slightly increased in spring and winter but decreased in summer and autumn during dry years. Specifically, the NPP declined by 57.2 % in the summer of dry years compared to that of normal years, causing annual NPP to decrease from 467.4 gC/m²/yr in normal years to 389.6 gC/m²/yr in dry years. Notably, the seasonal NPP of dominant communities, i.e., <em>Phragmites australis-Triarrhena lutarioriparia,</em> and <em>Carex cinerascens</em>, decreased by 77.8 and 114.5 gC/m²/yr in the summer of extreme drought and mild drought years, respectively, compared to normal years. The findings of this study contribute to understanding the response mechanisms of floodplain wetland vegetation to intensified droughts and accurately assessing regional carbon budgets.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103268"},"PeriodicalIF":5.8,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144270691","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A novel approach to spectral moisture interference correction for nitrogen and soil organic matter inversion in native black soils: Bayesian-optimized dynamic moisture mitigation","authors":"Jiaze Tang ,&nbsp;Qisong Wang ,&nbsp;Dan Liu ,&nbsp;Junbao Li ,&nbsp;Ruifeng Zhang ,&nbsp;Meiyan Zhang ,&nbsp;Jinwei Sun","doi":"10.1016/j.ecoinf.2025.103240","DOIUrl":"10.1016/j.ecoinf.2025.103240","url":null,"abstract":"<div><div>In recent years, portable near-infrared spectrometers have emerged as viable alternatives to conventional chemical methods for measuring total nitrogen (TN) and soil organic matter (SOM). Advances in unmanned aerial vehicle technology have enabled low-altitude aerial surveys, facilitating the quantification of TN and SOM in agricultural soils—an approach beneficial for applications such as fertilizer management. However, most studies rely on laboratory-based analyses using high-precision and nonimaging spectrometers that test dried and processed soil samples. This preference stems from the significant impact of moisture on soil reflectance spectra, particularly in moisture-rich black soils. To address this challenge, this study investigated the in situ quantitative inversion of TN and SOM contents in moist black soil using a high-throughput hyperspectral imaging system. We introduced the Bayesian-optimized dynamic moisture mitigation (BO-DMM) method—an approach that effectively corrected moisture-induced spectral distortions. The BO-DMM method reduced moisture interference, calibrating the spectral angle of moist soil spectra to shrink by 50 % toward that of dry soil spectra. To further assess the effectiveness of the BO-DMM method, we integrated it with different machine learning models to test soil properties and predict the TN and SOM contents. The results indicated that BO-DMM significantly enhanced the prediction accuracy of different soil properties across different models, providing a robust strategy to mitigate environmental interference in soil spectroscopy. This advancement paves the way for additional accurate field-based soil assessments.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103240"},"PeriodicalIF":5.8,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144297310","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Associations between forest vertical structure and habitat preferences of black-and-white snub-nosed monkeys (Rhinopithecus bieti) in high-elevation environments","authors":"Zhipang Huang ,&nbsp;Yuling Chen ,&nbsp;Haitao Yang ,&nbsp;Yihao Fang ,&nbsp;Kai Cheng ,&nbsp;Hongcan Guan ,&nbsp;Cyril C. Grueter ,&nbsp;Wen Xiao ,&nbsp;Qinghua Guo","doi":"10.1016/j.ecoinf.2025.103269","DOIUrl":"10.1016/j.ecoinf.2025.103269","url":null,"abstract":"<div><div>Understanding how wildlife behavior relates to habitat characteristics is essential for ecology and conservation, particularly in remote, structurally complex landscapes. In this study, we examine seasonal and behavior-specific habitat preferences in black-and-white snub-nosed monkeys (<em>Rhinopithecus bieti</em>), an endangered primate endemic to high-elevation forests in Yunnan, China. We combined long-term behavioral observations (2008–2018) with UAV-based LiDAR data on forest structure (collected in 2022–2023), and camera trap data on human and wildlife activity (2017–2018), to assess how these factors are associated with space use across different behaviors and seasons. Using machine learning models, we identified structural and disturbance-related attributes that were consistently associated with <em>R. bieti</em> behavior at the home-range scale. Vertical forest structure, particularly canopy height and vegetation layering, showed strong associations with foraging and sleeping locations across both wet and dry seasons. These patterns varied depending on behavioral context, supporting the idea that <em>R. bieti</em> adjusts its space use in response to seasonal and structural variation. Human and livestock presence were also negatively associated with habitat use during feeding and movement. While our findings align with established ecological expectations for semi-arboreal primates, they provide one of the first fine-scale, spatially explicit analyses of habitat preferences in <em>R. bieti</em>. We acknowledge the temporal mismatch between datasets as a limitation, and interpret our results conservatively as correlational. Nonetheless, this study highlights the value of combining behavioral, structural, and disturbance data to inform habitat conservation in montane forest ecosystems.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103269"},"PeriodicalIF":5.8,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144262355","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Testing life-cycle assessment data quality with Benford’s law reveals geographic variation","authors":"Bogdan Šinik ,&nbsp;Aleksandar Tošić","doi":"10.1016/j.ecoinf.2025.103227","DOIUrl":"10.1016/j.ecoinf.2025.103227","url":null,"abstract":"<div><div>Life-Cycle Assessment (LCA) is a methodology that is used extensively for evaluating the environmental impacts of products and processes throughout their lifetime. The method is highly dependent on the quality and accuracy of the underlying data. Moreover, the data acquisition process can be subjective, raising concerns about potential inconsistencies. In this study, we perform Benford’s law conformity tests (first digit) on all numerical data in ecoinvent, focusing on individual compartments (air, water, soil, and natural resources) and environmental elementary flows (carbon, toxic substances, greenhouse gases, and heavy metals), and discrepancies across continents are examined.</div><div>Life Cycle Inventory data met the requirements of Benford’s law and generally exhibited high conformity. Substantial differences in conformity were observed between Africa and Europe. Individual processes and measurements were inspected to further isolate potential sources of the non-conformity. The statistical significance of the results was increased using open-source databases available on OpenLCA Nexus, including WorldSteel, OzLCI2019, ELCD, NEEDS, BioenergieDat, and Exiobase. Finally, the Environmental Performance Index (EPI) was used, and a strong correlation between continental Benford conformity results and corresponding EPI scores was observed.</div><div>The findings suggest that discrepancies in conformity across continents reflect differences in data transparency and reporting practices. European datasets generally show higher conformity, likely owing to the use of more standardized methodologies. In contrast, data from regions with limited infrastructure or less established LCA practices tend to show lower conformity. Benford’s law offers a simple and computationally efficient alternative to conventional data quality assessments without requiring additional metadata or probabilistic modeling. Its application can support the detection of systemic biases and improve the reliability of LCA-based indicators such as environmental product declarations.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103227"},"PeriodicalIF":5.8,"publicationDate":"2025-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144291193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Optimizing cocoa biomass density through integrated irrigation and drainage management under water stress: A linear programming approach","authors":"Leonardo H. Talero-Sarmiento ,&nbsp;Henry Lamos-Diaz ,&nbsp;Juan D. Marquez-Gonzalez","doi":"10.1016/j.ecoinf.2025.103262","DOIUrl":"10.1016/j.ecoinf.2025.103262","url":null,"abstract":"<div><div>This study presents a novel linear programming model that combines crop growth and hydrological dynamics to optimize irrigation and drainage schedules, enhancing cocoa biomass density under variable water stress conditions. Using data from the NASA POWER database and incorporating detailed climate, soil, and crop management variables specific to San Vicente del Chucurí, Colombia, the model evaluated 19,980 environmental and policy scenarios to test its effectiveness. The results indicate that targeted management of irrigation and drainage frequencies can substantially improve cocoa yield by adapting to fluctuating environmental conditions. This model provides key insights into the optimal timing and frequency of water applications, critical for sustaining crop health and productivity in the face of climate variability. These findings hold significant implications for sustainable agricultural practices, offering a practical tool for farmers and policymakers to increase yield while conserving water resources. Future research will focus on enhancing this model through real-time climate data integration and exploring its potential application to diverse crops, further advancing sustainable resource management in agriculture.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103262"},"PeriodicalIF":5.8,"publicationDate":"2025-06-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144253947","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Parallelization of forest landscape model to improve computational efficiency and simulation realism","authors":"Xianghua Zou ,&nbsp;Hang Sun ,&nbsp;Kai Liu ,&nbsp;Mia M. Wu ,&nbsp;Hong S. He","doi":"10.1016/j.ecoinf.2025.103264","DOIUrl":"10.1016/j.ecoinf.2025.103264","url":null,"abstract":"<div><div>Forest landscape models (FLMs) are computationally intensive because of complex spatial interactions simulated. The current FLMs use sequential processing that simulates from the upper left pixel of the landscape to the lower right pixel. Sequential processing has series shortcomings among which simulation time and realism are the bottlenecks. In this study, we present a parallel processing design embedded in the LANDIS forest landscape model. Specifically, we apply a spatial domain decomposition approach that assigns pixel subsets to individual cores, enabling parallel execution of species- and stand-level processes on each core, while dynamically reallocating subsets across cores to execute landscape-level processes, i.e., seed dispersal. We compare the simulation results between parallel and sequential processing to evaluate the effectiveness and performance of the new design. Our result showed that when the number of pixels reaches millions parallel processing will save about 32.0–64.6 % of the time than sequential processing, for a 200-year simulation at 10-year time step. When the simulation time step is 1 year for a 200-year simulation, parallel processing can save 64.6–76.2 % of the time compared with sequential processing. Parallel processing improves simulation realism because it simulates multiple blocks simultaneously and performs multiple tasks, which is closer to the reality of species-level, stand-level, and seed dispersal processes. This study highlights the potential of parallel processing in improving the computational efficiency and simulation realism of FLMs.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103264"},"PeriodicalIF":5.8,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144279857","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Sampling effort for community composition higher by a magnitude compared to species richness in passive acoustic monitoring","authors":"Kim Lindner ,&nbsp;Sascha Rösner ,&nbsp;Dana G. Schabo ,&nbsp;Hicham Bellafkir ,&nbsp;Markus Vogelbacher ,&nbsp;Markus Mühling ,&nbsp;Daniel Schneider ,&nbsp;Nicolas Friess ,&nbsp;Stephan Wöllauer ,&nbsp;Bernd Freisleben ,&nbsp;Nina Farwig","doi":"10.1016/j.ecoinf.2025.103265","DOIUrl":"10.1016/j.ecoinf.2025.103265","url":null,"abstract":"<div><div>1. Comprehensive estimations of relative abundances and changes therein are one of the most important measures of conservation monitoring. The use of passive acoustic monitoring (PAM) has the potential to greatly enhance information availability, yet has mainly focused on species diversity or coverage, yielding results comparable to those from expert observers. Comparisons with conventional monitoring data have been made mainly for point counts, even though area-wide territory mapping is a widely used standard in many monitoring programs.</div><div>2. We compare data derived from the combination of PAM and automated identification via machine learning with an area-wide conventional breeding bird survey conducted by expert observers across the Marburg Open Forest in Hesse, Germany. By varying the number of survey intervals, recording duration and locations, we then determined the sampling effort needed to adequately reflect both species coverage and community composition of the conventional survey.</div><div>3. Only small subsamples of PAM data were required to reach maximum species richness of the conventional survey; minimum requirements were as low as 1) one survey interval or 2) 30 min duration or 3) three recording locations if other factors were allowed to vary accordingly. Revealing the community composition of the conventional survey, however, required sampling over three to six survey intervals with recording durations between 10 and 100 h. While communities were similar between methods in terms of species activity and relative abundance, PAM also partially reflected the composition of territories in the conventional survey.</div><div>4. Our study demonstrates the relative importance of greater sampling effort especially for monitoring community composition, requiring more recording locations, duration and intervals in comparison to species richness. We provide insight into the current applicability of PAM in monitoring practice and present best-use scenarios on how to make the most of high spatio-temporal resolution acoustic monitoring data.</div></div>","PeriodicalId":51024,"journal":{"name":"Ecological Informatics","volume":"90 ","pages":"Article 103265"},"PeriodicalIF":5.8,"publicationDate":"2025-06-07","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144290987","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"环境科学与生态学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}