Philippa Kaur

AbstractClimate change is expected to alter the extent and severity of forest pest damage, with substantial economic and ecological consequences, but predicting future pest impacts is challenging because of complex feedbacks among climate, pests and host trees. Here we use 20 years of data from the conterminous USA to assess how bioclimatic and biotic factors have influenced forest damage by 30 high-impact pest species and to identify ecological signals in those relationships. We found consistency in pest damage responses to maximum temperature in the warmest month, including recent average conditions and shifts from a historical baseline. Mean damage from focal pest species tends to be higher in regions with moderate maximum temperatures and in regions with faster rates of warming. In certain cases, the direction and magnitude of relationships between climate and forest damage vary by pest guild, native status and region of occurrence. Our findings provide empirical support for expectations of climate-induced stress to host trees and temperature-boosted pest performance, leading to increased pest damage in future forests.

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Fig. 1: Maps of 2000–2019 IDS data for the conterminous USA, showing the extent and amount of tree damage from two focal pest species.The alternative text for this image may have been generated using AI.Fig. 2: Heat maps showing slope coefficient values associated with 14 predictor variables and their interactions for the two response metrics.The alternative text for this image may have been generated using AI.Fig. 3: Plots of marginal effects for each predictor variable from damage footprint models for two focal pest species.The alternative text for this image may have been generated using AI.Fig. 4: Diagram of significance and direction of each climate variable for all damage footprint models and for specific groups of pest species.The alternative text for this image may have been generated using AI.Fig. 5: Quadrant plots of slope coefficient values corresponding to the five bioclimatic metrics from the damage footprint models.The alternative text for this image may have been generated using AI.

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Data availability

All data used in this study are freely and publicly available at the cited sources. IDS data were obtained from the USDA Forest Service (https://www.fs.usda.gov/science-technology/data-tools-products/fhp-mapping-reporting/detection-surveys; accessed July 2023). Climate data were obtained from PRISM (https://prism.oregonstate.edu/recent/; accessed August 2023). Tree species basal area maps were obtained from ref. 85 (https://doi.org/10.2737/RDS-2013-0013; accessed October 2023). Ecoregion shapefiles were obtained from the USDA Forest Service (https://data.fs.usda.gov/geodata/edw/datasets.php; accessed December 2023). Processed data and all model outputs (including coefficients for all temporal windows) associated with the study are available via figshare at https://doi.org/10.6084/m9.figshare.26426608 (ref. 95).
Code availability

The R code associated with the study is available via figshare at https://doi.org/10.6084/m9.figshare.26426608 (ref. 95).
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Andrew V. Gougherty.Ethics declarations

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Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Extended dataExtended Data Fig. 1 Map of tree species richness across the conterminous United States of America.This map was created by stacking the 324 tree species basal area maps modeled at a 250-m resolution by Wilson et al85. and converting basal area to occurrence, resulting in a single raster containing the number of tree species present in each cell.Extended Data Fig. 2 Map of ecoregions within the conterminous United States of America.These ecoregions (N = 11, excluding water) correspond to ecosystem divisions delineated in the USDA Forest Service EcoMap33,90.Extended Data Fig. 3 Conceptual diagram of the Insect and Disease Survey (IDS) data processing.IDS polygons (box 1) represent areas with discrete boundaries that contain tree mortality or defoliation damage caused by insect and disease activity. For each IDS polygon, we calculated a damage footprint metric (Response 1) as the proportion of forest cover within the polygon using National Land Cover Database75 (NLCD) data from the year closest to the survey year (box 2). For the subset of polygons with information on percent damage (the percent area showing damage within the polygon) provided by the surveyor, we additionally calculated a refined damage metric (Response 2) as the product of Response 1 and the midpoint of the percent range (box 3) indicated by either a percent affected code (1 = 1–3%, 2 = 4–10%, 3 = 11–29%, 4 = 30–50%, 5 = > 50%) or legacy pattern code (1 | 2 = > 50%, 3 | 4 = < 50%), the latter of which links contemporary data standards to the legacy standards.Extended Data Fig. 4 Results of cross-validation and spatial autocorrelation analyses.Models were cross-validated by splitting observations by state-year and then assigning each record to one of four folds. Using this spatial-temporal structure ensured state-years were contributing to each fold in proportion to their total data. Models were then retrained, but with one fold withheld for testing. The retrained ‘reduced’ models were compared to the ‘full’ model (with no data withheld) by assessing (a) percent difference in root mean square error (RMSE), (b) the average number of climate predictors with different signs compared to the full model, and (c) the correlation between climate predictors in the full and reduced models. Each of these plots indicate that models trained on the full dataset were generally robust to data being withheld. (d) Spatial autocorrelation was calculated on the residuals of the full models. Generally, lower values (near or <-1) indicate residuals were dispersed, while higher values (near or >1) indicate clustering. This plot indicates that most models had relatively low residual spatial autocorrelation. In each histogram, counts are the number of models.Extended Data Fig. 5 Diagram of significance and direction of each climate variable for all refined damage models and for specific groups of pest species.Columns correspond to the climate window (‘CW’) and climate anomaly (‘CA’) metrics of the five bioclimatic metrics. Rows correspond to different groupings of the focal pest species, including all refined damage models, bark beetles vs. defoliators, native vs. non-native species, and western vs. nationwide vs. eastern species. Pie charts indicate the proportion of significant positive relationships (gold), significant negative relationships (gray), and non-significant relationships (white).Extended Data Fig. 6 Quadrant plots of slope coefficient values corresponding to the five bioclimatic metrics from the refined damage models.Within each plot, coordinates of the points reflect mean slope coefficient values for each bioclimatic metric (mean temperature range, maximum temperature in the warmest month, minimum temperature in the coldest month, precipitation in the wettest month, and precipitation in the driest month), calculated within a climate window (x-axis) and as a climate anomaly (y-axis), from the 28 refined damage models. Horizontal and vertical lines represent 95% confidence intervals of the slope coefficients for the climate window variable and climate anomaly variable, respectively. If the 95% confidence interval overlaps 0, then that slope coefficient value is not considered statistically significant (as reflected by the color and fill of the point). Pest type is denoted by the shape of the point, and pest distribution within the USA is delineated by columns and color themes. Axes labels describe the climate conditions associated with each quadrant, and shading of a single quadrant (‘Quadrant Emphasis’) indicates that quadrant contains the greatest number of significant points.Extended Data Table 1 List of 30 focal pest species in our studyFull size tableExtended Data Table 2 List of model information corresponding to the 30 focal pest species in our studyFull size tableExtended Data Table 3 Effect sizes of the slope coefficients for the climate window and anomaly metrics of the five bioclimatic variablesFull size tableExtended Data Table 4 Standardized means of the slope coefficients for the climate window and anomaly metrics of the five bioclimatic variablesFull size tableSupplementary informationReporting Summary (download PDF )Rights and permissionsReprints and permissionsAbout this articleCite this articleClipp, H.L., Potter, K.M., Peters, M.P. et al. Warming temperatures and shifting precipitation patterns may exacerbate pest damage in North American forests.
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AbstractSince 2012, tetrodotoxin (TTX) has been found in seafoods such as bivalve mollusks in temperate European waters. TTX contamination leads to food safety risks and economic losses, making early prediction of TTX contamination vital to the food industry and competent authorities. Recent studies have pointed to shallow habitats and water temperature as main drivers to TTX contamination in bivalve mollusks. However, the temporal relationships between abiotic factors, biotic factors, and TTX contamination remain unexplored. We have developed an explainable, deep learning-based model to predict TTX contamination in the Dutch Zeeland estuary. Inputs for the model were meteorological and hydrological features; output was the presence or absence of TTX contamination. Results showed that the time of sunrise, time of sunset, global radiation, water temperature, and chloride concentration contributed most to TTX contamination detection. Thus, the photoperiod (time of sunset/sunrise) and solar irradiance (global radiation), were identified as potential drivers for tetrodotoxin contamination in bivalve mollusks. To conclude, our explainable deep learning model identified environmental factors to be associated with TTX contamination in bivalve mollusks, supporting its suspected exogenous origin. These findings make our approach a valuable tool to mitigate marine toxin risks for food industry and competent authorities.

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17 April 2026

Data availability

The underlying data for this study is available on Zenodo and can be accessed via this DOI: doi.org/10.5281/zenodo.18958221. The resulting model weights of this study are available on Hugging Face and can be accessed via the following link: huggingface.co/DataScienceWFSR/XAI4TTX.
Code availability

The underlying code for this study is available on GitHub in the XAI4TTX repository and can be accessed via this link: github.com/WFSRDataScience/XAI4TTX.
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M. C. Schoppema.Ethics declarations

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The authors declare no competing interests.

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Reprints and permissionsAbout this articleCite this articleSchoppema, M.C., van der Velden, B.H.M., Hürriyetoğlu, A. et al. Identifying environmental factors associated with tetrodotoxin contamination in bivalve mollusks using eXplainable AI.
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AbstractUrban wetlands serve a variety of healthful roles in cities, including as ‘sponges’ that absorb potential flood waters, leisure spaces for people and habitat for many species. However, urban wetlands also receive contaminated surface runoff and may become reservoirs of harmful contaminants, including related to our earlier efforts to safeguard health. Antibiotics are a public health mainstay, but overuse has promoted the spread among bacteria of genes enabling them to survive treatment. Here, by collecting and analyzing samples from 17 urban wetlands across China and comparing them with global datasets from natural lakes and urban raw sewage, we find these urban wetlands to be hotspots of antibiotic resistance genes (ARGs), with average abundances about nine times higher than in natural lakes and comparable to that in raw urban sewage. We further discover both human bacterial pathogens and indicators of the potential transfer of ARGs among bacteria (‘horizontal transfer’), suggesting viruses in urban wetlands carrying ARGs might facilitate their spread within bacterial communities there. We also find higher levels of economic development associated with lower ARG abundances, suggesting socioeconomic factors could also shape the geographical distribution of ARGs in urban wetlands, perhaps through associated improvements in sewer systems. These findings emphasize the importance of collecting and treating stormwater before its release into urban wetlands to safeguard wildlife and human health.

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Fig. 1: Sampling locations and ARG distribution.The alternative text for this image may have been generated using AI.Fig. 2: HBPs in urban wetlands.The alternative text for this image may have been generated using AI.Fig. 3: Characterization of the interaction between MAGs and viruses.The alternative text for this image may have been generated using AI.Fig. 4: Predictors influencing ARGs in urban wetland.The alternative text for this image may have been generated using AI.

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Data availability

All sequence data are available in the NCBI database under SRA accession number PRJNA1172644. Source data are provided with this paper.
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Download referencesAcknowledgementsThis work was financially supported by the National Key Research and Development Program of China (grant no. 2024YFE0106300 to Y.-G.Z.), National Natural Science Foundation of China (grant nos. 22193062 to D.Z. and 42207260 to X.L.), Fujian Provincial Natural Science Foundation of China (grant no. 2023J02031 to D.Z.), Taishan Scholars Program of Shandong Province (grant no. tsqn202312094 to X.L.) and Shandong Provincial Higher Education Institution Youth Innovation Teams (grant no. 2023KJ034 to X.L.).Author informationAuthor notesThese authors contributed equally: Da Lin, Ying Liu.Authors and AffiliationsKey Laboratory of Marine Environment and Ecology, Ministry of Education and College of Environmental Science and Engineering, Ocean University of China, Qingdao, ChinaDa Lin & Xiaohui LiuState Key Laboratory of Regional and Urban Ecology, Ningbo Observation and Research Station, Institute of Urban Environment, Chinese Academy of Sciences, Xiamen, ChinaDa Lin, Shuai Du, Yong-Guan Zhu & Dong ZhuUniversity of Chinese Academy of Sciences, Beijing, ChinaDa Lin, Yong-Guan Zhu & Dong ZhuSchool of Environmental Science and Engineering, Qingdao University, Qingdao, ChinaYing LiuZhejiang Key Laboratory of Pollution Control for Port-Petrochemical Industry, CAS Haixi Industrial Technology Innovation Center in Beilun, Ningbo, ChinaShuai Du, Yong-Guan Zhu & Dong ZhuState Key Laboratory of Marine Pollution, Department of Chemistry, and School of Energy and Environment, City University of Hong Kong, Hong Kong, ChinaKenneth Mei Yee LeungState Key Laboratory of Regional and Urban Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, ChinaYong-Guan ZhuState Key Laboratory of Environmental Criteria and Risk Assessment, Chinese Research Academy of Environmental Sciences, Beijing, ChinaFengchang WuAuthorsDa LinView author publicationsSearch author on:PubMed Google ScholarYing LiuView author publicationsSearch author on:PubMed Google ScholarXiaohui LiuView author publicationsSearch author on:PubMed Google ScholarShuai DuView author publicationsSearch author on:PubMed Google ScholarKenneth Mei Yee LeungView author publicationsSearch author on:PubMed Google ScholarYong-Guan ZhuView author publicationsSearch author on:PubMed Google ScholarFengchang WuView author publicationsSearch author on:PubMed Google ScholarDong ZhuView author publicationsSearch author on:PubMed Google ScholarContributionsD.L., X.L., D.Z. and F.W. conceived of and designed the research. D.L., Y.L. and X.L. performed the experiments. D.L., S.D. and D.Z. analyzed the data and prepared the figures. D.L., X.L., D.Z., K.M.Y.L, Y.-G.Z. and F.W. wrote and revised the paper. All authors read and approved the paper.Corresponding authorsCorrespondence to
Xiaohui Liu, Fengchang Wu or Dong Zhu.Ethics declarations

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Nature Cities thanks Andrea di Cesare, Kazuaki Matsui and Lisa Paruch for their contribution to the peer review of this work.

Additional informationPublisher’s note Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Extended dataExtended Data Fig. 1 The abundances of phage ARGs in urban wetlands, including intracellular and extracellular ARGs in Beijing.Pie chart represents the proportion of different antibiotic resistance mechanisms.Source dataExtended Data Fig. 2 Comparison of the abundances of risk indices for ARGs in this study with those from natural lakes, sewage effluents, and urban wetlands worldwide.For box plots, the tops of the boxes represent the 75th percentile, the bottoms indicate the 25th percentile and the centre lines denote the median. The whiskers extend to the maximum and minimum non-outlier values. As shown in Supplementary Table 13, the data (n = 347) did not follow a normal distribution, so P values were calculated using the two-sided Kruskal-Wallis H test as detailed in Supplementary Table 14. Different lowercase letters indicate significant differences among the treatments at P < 0.05.Source dataExtended Data Fig. 3 The relationship between phage ARG abundances and total ARG abundances.Linear regression model with a two-sided test adjusted using the Benjamini-Hochberg was employed for statistical analysis. The shaded areas represent the 95% confidence interval, while centre lines indicate the line of best fit.Source dataExtended Data Fig. 4 The relationship between the number of ARGs carried by MAGs, the number of phages hosted by them and the number of ARGs present in those phages.Linear regression model with a two-sided test adjusted using the Benjamini-Hochberg was employed for statistical analysis. The shaded areas represent the 95% confidence interval, while centre lines indicate the line of best fit.Source dataExtended Data Fig. 5 Host status of Pseudomonas by phages.Heatmap showing the infection profiles of phages across the Pseudomonas genus.Source dataExtended Data Fig. 6 Interaction network between phages and their key hosts (Pseudomonas genus) in urban wetlands.Green nodes represent bacterial hosts belonging to the Pseudomonas genus, while pink nodes represent phages. Node size indicates the degree of connectivity.Source dataExtended Data Fig. 7 The ARGs carry by Pseudomonas.The asterisk and red mark indicating that these ARGs are also carried by phages.Source dataExtended Data Fig. 8 Traits of Pseudomonas and their associated phages.(a) Comparison of genome sizes between Pseudomonas and other bacteria (n = 274); (b) Comparison of genome sizes between viruses hosted by the Pseudomonas and other phages (n = 1690). For box plots, the tops of the boxes represent the 75th percentile, the bottoms indicate the 25th percentile and the centre lines denote the median. The whiskers extend to the maximum and minimum non-outlier values. Since the data did not follow a normal distribution as shown in Supplementary Table 15, P values were calculated using two-sided Mann-Whitney U test adjusted with the false discovery rate (FDR).Source dataSupplementary informationReporting Summary (download PDF )Supplementary Tables (download XLSX )Supplementary Tables 1–15.Source dataSource Data Figs. 1–4 and Extended Data Figs. 1–8 (download XLSX )Statistical source data.Rights and permissionsSpringer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.Reprints and permissionsAbout this articleCite this articleLin, D., Liu, Y., Liu, X. et al. Urban wetlands as hotspots of antibiotic resistomes and their potential viral transmission.
Nat Cities (2026). https://doi.org/10.1038/s44284-026-00433-zDownload citationReceived: 19 January 2025Accepted: 19 March 2026Published: 17 April 2026Version of record: 17 April 2026DOI: https://doi.org/10.1038/s44284-026-00433-zShare this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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AbstractPermafrost carbon vulnerability, particularly concerning temperature thresholds and old carbon mobilization, remains a critical uncertainty in climate projections. Through a five-year, multi-level warming experiment on the Tibetan Plateau, we investigate these dynamics using >40,000 hourly flux measurements combined with vertical CO2 concentration and δ13C-CO2 profiling. Here we demonstrate under low-to-moderate warming (<2 °C), respiratory carbon loss (Reco) increments exceed photosynthetic carbon uptake (GPP) gains by 1–16 fold, driving a quantitative shift toward ecosystem carbon source. Extreme warming (2−4 °C) triggers a surge in growing-season deep carbon loss to 59% Reco, while GPP declines precipitously. The decoupling between Reco and GPP drives a qualitative transition to strong carbon source, implying the existence of a tipping point within 2−4 °C. Projected to end-of-century warming levels (2.69 °C) across Tibetan permafrost regions, this could release 24−47 g CO2 m−2 yr−1 old carbon. These findings establish quantitative thresholds for permafrost carbon vulnerability and inform carbon-climate feedback projections in global cold regions.

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Data availability

The main flux datasets generated during this study are fully presented in the figures and supplementary materials of this paper. Auxiliary data are not publicly available due to ongoing research utilizing these data for related studies, but are available from the corresponding author upon request. No external data repositories were used.
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Download referencesAcknowledgementsThis study was supported by the Second Tibetan Plateau Scientific Expedition and Research Program (2022QZKK0101, J.D.), National Natural Science Foundation of China (42471159, J.D.; 42588201, S.P.), and Chinese Academy of Sciences (CAS) Project for Young Scientists in Basic Research (YSBR-037, J.D.).Author informationAuthor notesThese authors contributed equally: Yuxi Wei, Juan Li.Authors and AffiliationsState Key Laboratory of Tibetan Plateau Earth System, Environment and Resources (TPESER), Institute of Tibetan Plateau Research, Chinese Academy of Sciences, Beijing, ChinaYuxi Wei, Juan Li, Xiling Gu, Huangyu Huo, Tao Wang, Shiping Wang, Baosheng An, Tandong Yao, Shilong Piao & Jinzhi DingCollege of Ecology, Lanzhou University, Lanzhou, ChinaYuxi WeiCollege of Urban and Environmental Sciences, Peking University, Beijing, ChinaShilong PiaoAuthorsYuxi WeiView author publicationsSearch author on:PubMed Google ScholarJuan LiView author publicationsSearch author on:PubMed Google ScholarXiling GuView author publicationsSearch author on:PubMed Google ScholarHuangyu HuoView author publicationsSearch author on:PubMed Google ScholarTao WangView author publicationsSearch author on:PubMed Google ScholarShiping WangView author publicationsSearch author on:PubMed Google ScholarBaosheng AnView author publicationsSearch author on:PubMed Google ScholarTandong YaoView author publicationsSearch author on:PubMed Google ScholarShilong PiaoView author publicationsSearch author on:PubMed Google ScholarJinzhi DingView author publicationsSearch author on:PubMed Google ScholarContributionsJ.D. conceived the study. Y.W. carried out field monitoring and data collection, processed the data, and prepared the results. J.L. provided assistance with the field experiment. J.D. and Y.W. drafted the manuscript. All authors (J.D., Y.W., J.L., X.G., H.H., T.W., S.W., B.A., T.Y., and S.P.) contributed to the interpretation of the results.Corresponding authorCorrespondence to
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Reprints and permissionsAbout this articleCite this articleWei, Y., Li, J., Gu, X. et al. Permafrost tipping point triggered by warming-driven loss of old carbon.
Nat Commun (2026). https://doi.org/10.1038/s41467-026-72122-3Download citationReceived: 03 July 2025Accepted: 08 April 2026Published: 17 April 2026DOI: https://doi.org/10.1038/s41467-026-72122-3Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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Abstract

Medium- and small-sized carnivores are important indicator species of forest ecosystems, their circadian activity rhythms play a crucial role in regulating resource use, mediating interspecific competition, and responding to environmental change, yet quantitative studies of these rhythms remain relatively scarce. To investigate how leopard cats (Prionailurus bengalensis) balance predation and competition in southern Anhui Province, from May 2023 to April 2024, we conducted systematic infrared camera monitoring in Anhui’s Lingnan Provincial Nature Reserve, Zhejiang’s Qianjiangyuan National Forest Park, and adjacent areas. Using extensive diel activity data of leopard cats and co-occurring potential prey and competitors obtained from 37 validly recovered cameras (40 deployed), we adopted temporal overlap analysis, kernel density estimation, and other methods to test two hypotheses: (1) leopard cats show strong temporal overlap with their primary prey (rats), thereby increasing prey access while reducing direct competition; and (2) seasonal environmental variation drives shifts in temporal overlap, with greater overlap expected during seasons characterized by constrained resources or climatic stress. Leopard cats exhibited a clear bimodal activity pattern with pre-dawn and late-evening peaks. They showed strong temporal correspondence with primary prey such as rats, as well as with nocturnal or crepuscular competitors including masked palm civets (Paguma larvata) and Chinese ferret-badgers (Melogale moschata), while overlap with strictly diurnal birds remained minimal. Seasonal comparisons indicated that temporal overlap between leopard cats and sympatric mammals increased from spring through winter, with more pronounced overlap in summer and winter, suggesting that resource dynamics and climatic pressures modulate activity timing across seasons. These findings demonstrate that leopard cats rely on temporal plasticity to balance hunting efficiency and interspecific interactions under varying environmental conditions. Understanding this behavioral flexibility provides valuable insight into coexistence mechanisms among small carnivores and offers a scientific basis for seasonally informed conservation and management strategies in southern Anhui.

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Data availability

The datasets used and analyzed are available from the corresponding author on reasonable request.
AbbreviationsSO:
Site occupancy
RAI:
Relative abundance index
IP:
Independent valid photos
MRAI:
Monthly relative abundance index
NRAI:
Nocturnal relative abundance index
KDE:
Kernel density estimation
PDF:
Probability density function
95% CI:
95% confidence interval
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Download referencesAcknowledgementsWe gratefully acknowledge Lanrong Wang, Shilong Yu, and Mengru Li for their assistance in this study. Additionally, we extend our sincere thanks to Dr. Shunqing Lyu and Dr. Chao Yu from Huangshan University, as well as students Xuanshuo Qi and Jinjie Wang, for their dedicated support during fieldwork. Finally, we appreci-ate the logistical support and permissions provided by the staff of Lingnan Provincial Nature Reserve and Qianjiangyuan National Park.FundingThis work was supported by Anhui Province Wildlife Epidemic Source Disease Traceability, Monitoring, and Emergency Prevention and Control Project (2022BFAFN02323).Author informationAuthor notesThese authors contributed equally: Yongshen Wang and Xinyi Zhai.Authors and AffiliationsSchool of Life Sciences and Medical Engineering, Anhui University, Hefei, 230601, Anhui, ChinaYongshen Wang, Xinyi Zhai, Lanrong Wang, Shilong Yu, Mengru Li, Dapeng Pang & Baowei ZhangXiuning County State-owned Forest Farm, Huangshan, 245499, Anhui, ChinaZhonghui WangLingnan Sub-farm, Xiuning County State-owned Forest Farm, Huangshan, 245421, Anhui, ChinaGuohua HuQianjiangyuan National Park Administration, Quzhou, 324399, Zhejiang, ChinaWenchao LanAuthorsYongshen WangView author publicationsSearch author on:PubMed Google ScholarXinyi ZhaiView author publicationsSearch author on:PubMed Google ScholarLanrong WangView author publicationsSearch author on:PubMed Google ScholarShilong YuView author publicationsSearch author on:PubMed Google ScholarMengru LiView author publicationsSearch author on:PubMed Google ScholarZhonghui WangView author publicationsSearch author on:PubMed Google ScholarGuohua HuView author publicationsSearch author on:PubMed Google ScholarWenchao LanView author publicationsSearch author on:PubMed Google ScholarDapeng PangView author publicationsSearch author on:PubMed Google ScholarBaowei ZhangView author publicationsSearch author on:PubMed Google ScholarContributions**Yongshen Wang** : Methodology, software, writing—original draft, validation, resources. **Xinyi Zhai** : Conceptualization, investigation, data curation, formal analysis, visualization. **Lanrong Wang** : Investigation, methodology. **Shilong Yu** : Investigation, methodology. **Mengru Li** : Validation, investigation. **Zhonghui Wang** and **Guohua Hu** : Investigation, supervision. **Wenchao Lan** : Supervision. **Dapeng Pang** and **Baowei Zhang** : Writing review, editing, project administration, funding acquisition. All authors have read and agreed to the published version of the manuscript.Corresponding authorsCorrespondence to
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Reprints and permissionsAbout this articleCite this articleWang, Y., Zhai, X., Wang, L. et al. Seasonal variation in activity rhythms of leopard cats (Prionailurus bengalensis), their prey and competitors in southern Anhui.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-43879-wDownload citationReceived: 27 April 2025Accepted: 06 March 2026Published: 17 April 2026DOI: https://doi.org/10.1038/s41598-026-43879-wShare this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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KeywordsLeopard cat (Prionailurus bengalensis)Infrared camera monitoringPreyCompetitorsDaily activity rhythmTemporal niche More

AbstractRemediating historical mining waste creates a paradox when environmental safety conflicts with the preservation of the visual integrity of UNESCO World Heritage sites. This study characterizes the geochemical constraints on Zn-Pb-Ag tailings at the Tarnowskie Góry site (Poland), where strict conservation laws prohibit traditional capping methods. Using X-ray diffraction, SEM-EDS, and sequential extraction, we identified contrasting mobility patterns in potentially toxic elements. While Pb (up to 2.15 wt%) and Zn (up to 11.2 wt%) remain sequestered in stable phases, cadmium (up to 1020 mg kg− 1) exhibits lability, with up to 74% partitioned in exchangeable (up to 13%) and carbonate (up to 61%) fractions. Although aqueous leaching demonstrates negligible current mobilization, this partitioning poses a latent risk of release due to localized rhizosphere acidification or microenvironmental carbonate depletion. Furthermore, heritage status effectively restricts the potential extraction of an estimated 150 tons of Ag, 123,000 tons of Zn, and 19,800 tons of Pb. We propose a conceptual dual-zone sustainable management model: (1) Assisted phytostabilization using native calcicolous species for stable slopes; and (2) ‘invisible’ chemostabilization using Fe–modified biochar amendments for protected zones where vegetation would compromise historical industrial aesthetics. Engineered biochar effectively immobilizes labile Cd while preventing the secondary mobilization of background As, a risk typically associated with conventional biochar. Concurrently, it reduces wind erosion without altering the waste’s visual character. These findings provide a scalable conceptual framework for reconciling pollution control with the preservation of Outstanding Universal Value at carbonate-hosted mining legacies globally.

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Rafał Warchulski.Ethics declarations

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Reprints and permissionsAbout this articleCite this articleCabała, J., Warchulski, R., Kupczak, K. et al. Geochemical constraints and heritage conflicts during cadmium stabilization of Zn-Pb-Ag tailings at a UNESCO World Heritage site.
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KeywordsMining legacyUNESCO World HeritageBiochar chemostabilizationCadmium mobilityCarbonate-hosted depositsSustainable remediation More

Abstract

Parameter calibration of climate suitability models is often hindered by the lack of absence data for plant species, limiting their effectiveness for global scale applications. Here we propose a novel calibration approach based on the Generalized Likelihood Uncertainty Estimation (GLUE) framework that eliminates the needs for background samples. This method defines the likelihood statistics under the assumption that the distribution of the climate suitability index at the occurrence sites differs from that across all locations within a given region. To prioritize presence data, a weighted likelihood function was incorporated into the GLUE procedure. We demonstrated the utility of this approach through a case study on orange (Citrus sinensis), a crop whose climate suitability has rarely been evaluated at a global scale. Model performance improved when the parameter search spaces were defined with minimal ecological constraints, which resulted in a clear separation between producing and non-producing countries. These findings suggest that the proposed approach offers a robust and scalable alternative for climate suitability modeling in data-sparse contexts. This framework is broadly applicable to both cultivated and invasive species, enabling reliable projection of the potential distribution to inform land-use planning and climate adaptation strategies.

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Download referencesFundingThis work was supported by Korea Environment Industry & Technology Institute (KEITI) through Climate Change R&D Project for New Climate Regime, funded by Korea Ministry of Environment (MOE) (RS-2022-KE002294), and Cooperative Research Program for Agriculture Science &Technology Development (Project No. RS-2025-02223519) Rural Development Administration, Republic of Korea.Author informationAuthors and AffiliationsResearch Institute of Agriculture and Life Sciences, Seoul National University, Seoul, Republic of KoreaShinwoo Hyun & Kwang Soo KimDepartment of Smart Systems, Seoul National University, Seoul, Republic of KoreaKwang Soo KimNew Zealand Institute for Bioeconomy Science Limited, Private Bag 92 169, Auckland, 1142, New ZealandRobert M. BeresfordAuthorsShinwoo HyunView author publicationsSearch author on:PubMed Google ScholarKwang Soo KimView author publicationsSearch author on:PubMed Google ScholarRobert M. BeresfordView author publicationsSearch author on:PubMed Google ScholarContributionsS.H. and K.S.K. conceptualized the approach. S.H. and K.S.K. wrote the main manuscript. S.H. prepared data sets. R.M.B. revised and edited the manuscript. All authors reviewed the manuscript.Corresponding authorCorrespondence to
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Reprints and permissionsAbout this articleCite this articleHyun, S., Kim, K.S. & Beresford, R.M. Calibration of a climate suitability model using a generalized likelihood uncertainty estimation (GLUE): a global case study of orange production.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-44664-5Download citationReceived: 10 September 2025Accepted: 12 March 2026Published: 17 April 2026DOI: https://doi.org/10.1038/s41598-026-44664-5Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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KeywordsSpecies distribution modelLikelihoodEcoCropPresence-only dataGLUE More

Abstract

Seed priming and organic nutrient management are widely recognized strategies for improving seed germination and early seedling establishment; however, their combined effects on seed physiological quality in common bean remain insufficiently understood, particularly under controlled production systems. This study evaluated the effects of five fertilizer regimes (Control, Chemical, Organo-mineral, Cattle Manure, and Vermicompost) and eight seed-priming treatments (No priming, Hydropriming, Vermipriming, Cow Urine Priming, and their accelerated aging combinations) on germination and emergence performance of the Sazova 1949 dwarf bean cultivar across two production years. Significant effects (p < 0.05) of both fertilizer and priming treatments were observed for most seed quality traits, including germination percentage, mean germination time, germination and emergence indices, and electrical conductivity. Fertilizer regimes significantly influenced seed electrical conductivity, with organo-mineral fertilizer showing the lowest values, indicating improved membrane integrity, although some effects varied across years. Priming treatments had a pronounced effect on seed performance, where hydropriming and vermipriming significantly reduced mean germination time and improved germination percentage and vigor indices compared with non-primed seeds, while cow urine priming generally resulted in lower performance. Accelerated aging significantly reduced seed vigor; however, some priming treatments partially mitigated these negative effects. Overall, the combined use of effective seed priming and organic-based fertilizer regimes significantly enhanced seed physiological quality under laboratory conditions. However, the study was limited to controlled experiments on a single cultivar, and therefore results should be interpreted as indicators of seed vigor rather than direct agronomic performance. Field validation and long-term assessments are required to confirm broader applicability.

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Data availability

The datasets generated and analyzed during the current study are available from the corresponding author on reasonable request.
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Download referencesFundingThe work was supported by the Van Yuzuncu Yil University University Scientific Research Projects Unit (project number: FDK- 2019–8156).Author informationAuthors and AffiliationsInstitute of Natural and Applied Sciences, Horticultural Sciences, Van Yuzuncu Yil University, Van, TürkiyeYekbun AlpFaculty of Agriculture, Department of Horticulture, Van Yuzuncu Yil University, Van, TürkiyeSuat SensoyAuthorsYekbun AlpView author publicationsSearch author on:PubMed Google ScholarSuat SensoyView author publicationsSearch author on:PubMed Google ScholarContributionsCRediT authorship contribution statement Yekbun Alp: Methodology, Formal analysis and investigation, Writing—original draft preparation Suat Sensoy: Methodology, Formal analysis and investigation, Writing—review and editing, Funding acquisition, Supervision:Corresponding authorCorrespondence to
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Reprints and permissionsAbout this articleCite this articleAlp, Y., Sensoy, S. Effects of seed priming and organic nutrient management on germination and emergence of common bean.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-48120-2Download citationReceived: 28 August 2025Accepted: 06 April 2026Published: 17 April 2026DOI: https://doi.org/10.1038/s41598-026-48120-2Share this articleAnyone you share the following link with will be able to read this content:Get shareable linkSorry, a shareable link is not currently available for this article.Copy shareable link to clipboard
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KeywordsFertilizer managementSeed vigorGermination performanceSeedling establishmentLegume crops More