Ecology

AbstractTuta absoluta is a significant invasive pest, severely impacting the global tomato industry. Prolonged application of chemical insecticides has led to varying degrees of resistance in T. absoluta populations. Additionally, chemical insecticides are causing serious threats to the environment. Aiming to develop a novel bioinsecticide based on Origanum vulgare essential oil (OVE) against T. absoluta, we carried out its nanostructured lipid carrier formulation (OVE-NLC). The obtained OVE-NLC had spherical particles approximately 94.26 nm in size with a uniform size distribution of less than 0.3 and a zeta potential of − 18.75 mV. The formulated NLC also had encapsulation efficiency up to 96% and was stable at 25 °C for 3 months. The FTIR results indicated no significant chemical interaction between EO and NLC components. OVE-NLC demonstrated significant toxicity towards T. absoluta larvae and a remarkable oviposition deterrence for females. The nanoformulation also negatively affected the population growth parameters of T. absoluta, significantly reducing its fecundity by approximately 70% and 42% in contact and topical assays, respectively. Additionally, OVE-NLC had no lethal effects on the generalist predator Macrolophus pygmaeus and pollinator bee Bombus terrestris as non-target organisms. Results suggested that OVE-NLC could be successfully used as a potential tool for tomato integrated pest management programs.

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Data availability

All data supporting this study’s findings are included in the article.
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Download referencesAcknowledgementsFinancial support from the Deputy of Research and Technology of Urmia University, Urmia, Iran (Number: 10/1352) is acknowledged.FundingThis study was funded by Urmia University, Iran (Number: 10/1352).Author informationAuthors and AffiliationsDepartment of Plant Protection, Faculty of Agriculture, Urmia University, Urmia, IranAsmar Soleymanzadeh & Orouj ValizadeganDrug Applied Research Center, Tabriz University of Medical Sciences, Tabriz, IranHamed HamishehkarResearch Center of New Material and Green Chemistry, Khazar University, Baku, AzerbaijanHamed HamishehkarAuthorsAsmar SoleymanzadehView author publicationsSearch author on:PubMed Google ScholarOrouj ValizadeganView author publicationsSearch author on:PubMed Google ScholarHamed HamishehkarView author publicationsSearch author on:PubMed Google ScholarContributionsAS, OV, and HH conceived and designed the experiments. AS collected data and carried out the bioassays. AS and OV analyzed the data. AS wrote the first draft of the manuscript, and OV and HH revised and improved it. All authors read and approved the manuscript.Corresponding authorCorrespondence to
Orouj Valizadegan.Ethics declarations

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Reprints and permissionsAbout this articleCite this articleSoleymanzadeh, A., Valizadegan, O. & Hamishehkar, H. Nanostructured lipid carrier of oregano essential oil for controlling Tuta absoluta with minimal impact on beneficial organisms.
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Tuta absoluta
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AbstractPlant-pollinator interactions are essential for plant productivity but face growing threats from climate change, including vegetation loss and mismatches in flowering. Yet, the consequences for bee food resources remain poorly understood at continental scales. Here, we analyse 2 500 samples collected by honey bees (Apis mellifera) between May and August 2023 from 310 locations across Europe using ITS2 metabarcoding. We derive climatic response curves of floral resources and assess exceedance risks of interaction loss under projected climate scenarios. Our findings reveal that rising temperatures and reduced precipitation decrease the diversity of foraging resources across Europe, pushing many plants beyond critical limits. When both warming and drying coincide, the potential for resilience through temporal or spatial buffering is strongly constrained. These declines pose serious risks to bee nutrition, ecosystem functioning, and food security. Our study underscores the urgency of mitigating climate change to preserve vital plant-pollinator systems and the services they sustain.

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Data availability

The raw sequencing data generated in this study have been deposited in the NCBI SRA database under accession code PRJNA1198597. The processed sequencing data (for process see code availability), climate data, and metadata of samples are available at Zenodo 10.5281/zenodo.17578272 [https://doi.org/10.5281/zenodo.17578272]77 and GitHub chiras/HoneyBee-ResistanceResilience [https://github.com/chiras/HoneyBee-ResistanceResilience].
Code availability

The pipeline for processing raw sequencing data for metabarcoding is publicly available at GitHub chiras/metabarcoding_pipeline [https://github.com/chiras/metabarcoding_pipeline]. Code for all downstream analyses is publicly available at Zenodo 10.5281/zenodo.17578272 [https://doi.org/10.5281/zenodo.17578272]77 and GitHub chiras/HoneyBee-ResistanceResilience [https://github.com/chiras/HoneyBee-ResistanceResilience]. All display items presented in the main manuscript and supplementary information can be reproduced from this public data and code.
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Keller, A., Quaresma, A. & Pinto, A. M. Data and code for “Honey bee food resources under threat from climate change”. Zenodo, https://doi.org/10.5281/zenodo.17578272 (2025).Download referencesAcknowledgementsWe thank all Citizen Scientists and National Coordinators from the 27 EU countries for their invaluable contributions to pollen sampling. We also acknowledge Maíra Costa, Maria Celenza, and Maria João Caldeira for their assistance with pollen homogenisation and DNA extractions. The Portuguese Foundation for Science and Technology (FCT) supported A.Q.‘s PhD scholarship (2020.05155.BD; DOI: 10.54499/2020.05155.BD). FCT provided financial support through national funds to CIMO and SusTEC via FCT/MCTES (PIDDAC): CIMO under UIDB/00690/2020 (DOI: 10.54499/UIDB/00690/2020) and UIDP/00690/2020 (DOI: 10.54499/UIDP/00690/2020); and SusTEC, under LA/P/0007/2020 (DOI: 10.54499/LA/P/0007/2020). This work was conducted within the framework of INSIGNIA-EU: Preparatory action for monitoring of environmental pollution using honey bees (European Union service contract 09.200200/2021/864096/SER/ ENV.D.2).FundingOpen Access funding enabled and organized by Projekt DEAL.Author informationAuthor notesThese authors jointly supervised this work: M. Alice Pinto, Alexander Keller.Authors and AffiliationsCIMO, LA SusTEC, Instituto Politécnico de Bragança, Campus de Santa Apolónia, Bragança, PortugalAndreia Quaresma & M. Alice PintoDepartamento de Biologia, Faculdade de Ciências da Universidade do Porto, Porto, PortugalAndreia QuaresmaCIBIO, Centro de Investigação em Biodiversidade e Recursos Genéticos, InBIO Laboratório Associado, Campus de Vairão, Universidade do Porto, Vila do Conde, PortugalAndreia QuaresmaBIOPOLIS Program in Genomics, Biodiversity and Land Planning, CIBIO, Campus de Vairão, Vila do Conde, PortugalAndreia QuaresmaWageningen Environmental Research, Wageningen, the NetherlandsJohannes M. Baveco & Willem Bastiaan BuddendorfDepartment of Biology, University of Graz, Universitätsplatz 2, Graz, AustriaRobert Brodschneider, Kristina Gratzer & Ivo RoessinkCarreck Consultancy Ltd, Shipley, West Sussex, UKNorman L. CarreckDepartment of Apiculture, Institute of Animal Science ELGO ‘DIMITRA’, Nea Moudania, GreeceFani HatjinaDanish Beekeepers Association (DBF), Sorø, DenmarkOle Kilpinen & Flemming VejsnaesAlveus AB Consultancy, Oisterwijk, NetherlandsJozef van der SteenCellular and Organismic Networks, Faculty of Biology, Ludwig-Maximilians-Universität München, Planegg-Martinsried, GermanyAlexander KellerAuthorsAndreia QuaresmaView author publicationsSearch author on:PubMed Google ScholarJohannes M. BavecoView author publicationsSearch author on:PubMed Google ScholarRobert BrodschneiderView author publicationsSearch author on:PubMed Google ScholarWillem Bastiaan BuddendorfView author publicationsSearch author on:PubMed Google ScholarNorman L. CarreckView author publicationsSearch author on:PubMed Google ScholarKristina GratzerView author publicationsSearch author on:PubMed Google ScholarFani HatjinaView author publicationsSearch author on:PubMed Google ScholarOle KilpinenView author publicationsSearch author on:PubMed Google ScholarIvo RoessinkView author publicationsSearch author on:PubMed Google ScholarFlemming VejsnaesView author publicationsSearch author on:PubMed Google ScholarJozef van der SteenView author publicationsSearch author on:PubMed Google ScholarM. Alice PintoView author publicationsSearch author on:PubMed Google ScholarAlexander KellerView author publicationsSearch author on:PubMed Google ScholarContributionsA.Q., A.K., and M.A.P. conceived the ideas and designed the methodology. A.K. and A.Q. conducted the analysis. A.Q., A.K., and M.A.P. drafted the manuscript. Particularly A.Q., M.A.P., A.K., and N.C., but all authors contributed to improving the manuscript. J.B., W.B.B., and I.R. modelled the apiaries’ location. R.B., K.G., F.H., O.K., M.A.P., I.R., F.V., N.C. and JvdS designed the pollen sampling methodology, prepared all the materials and manuals for pollen collection by Citizen Scientists, and obtained the INSIGNIA-EU funding. All the authors critically reviewed the manuscript for important intellectual content.Corresponding authorsCorrespondence to
M. Alice Pinto or Alexander Keller.Ethics declarations

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AbstractThe EU Nature Restoration Regulation suggests the implementation of green space as nature-based solutions to enhance urban resilience toward increasing climate risks such as extreme precipitation events and floods in Europe. Scenario based approaches enable the evaluation of the potential of specific nature-based solutions to mitigate flood risk in high flood hazard and vulnerable areas. In this study, we explore the flood risks from heavy rainfall and the potential of nature-based solutions implementations in the city of Hannover, Germany. Using the InVEST urban flood risk mitigation model, we modelled the surface runoff from heavy rainfall and assessed the social vulnerability using population and infrastructure data resulting in a flood risk evaluation. To test flood mitigation under nature-based solutions implementations including grass grid pavers and green roofs, we estimated the runoff improvement under three nature-based solutions scenarios following recommendations from the EU Nature Restoration Regulation. Our analysis revealed that Hannover’s inner-city area is particularly flood-prone and socially vulnerable, while peripheral districts are less affected. The combined risk and vulnerability arise from the surface sealing in built-up areas and their higher population density and associated infrastructure. The scenario results demonstrate flood risk reduction potential when combining different nature-based solutions, though on a limited level calling for more explicit and ambitious regulations at EU, regional and local levels.

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Spatial data are all available open access. Detailed data on modelling results are provided in the Supplementary Material.
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Nadja Kabisch.Ethics declarations

Competing interests
The authors declare no competing interests.

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Reprints and permissionsAbout this articleCite this articleSchröder, P.T., Wübbelmann, T. & Kabisch, N. A scenario-based modelling approach to implementing nature-based solutions for flood risk mitigation in Hannover.
npj Urban Sustain (2025). https://doi.org/10.1038/s42949-025-00326-5Download citationReceived: 25 June 2025Accepted: 12 December 2025Published: 30 December 2025DOI: https://doi.org/10.1038/s42949-025-00326-5Share 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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AbstractMalaria remains a leading cause of morbidity and mortality in the developing world, particularly in sub-Saharan Africa. Spatial variability significantly influences efforts to control malaria and its incidence, which remains a serious public health concern in Ethiopia. Using geostatistical methods, this study investigates the environmental factors and spatial distribution of malaria incidence in the Hadiya Zone across woredas in 2022 and 2023. Descriptive analyses revealed consistent spatial heterogeneity, with high incidence rates in Shashogo, Soro, and Misrak Badawacho. Global spatial autocorrelation measures Moran’s I (0.558 in 2022 and 0.483 in 2023; p < 0.01) and Geary’s C (0.63 and 0.69, respectively) confirmed statistically significant clustering of malaria cases. Local Moran’s I analysis identified hot spots in Shashogo, Soro, and Misrak Badawacho, and cold spots in Misha, Duna, and Gombora, indicating localized spatial dependence. Spatial regression analysis, comparing Ordinary Least Squares (OLS) and Spatial Autoregressive (SAR) models, highlighted average maximum temperature (β = 0.945, p = 0.017) and proportion of highland terrain (β = 0.543, p = 0.040) as key predictors of malaria incidence. The SAR model showed superior fit, evidenced by lower AIC and higher log-likelihood values, confirming the influence of spatial dependence. These findings support geographically targeted malaria interventions in high-risk woredas. Limitations include the short study period (2022–2023) and the absence of socioeconomic variables due to lack of household survey and secondary data.

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Data availability

The datasets generated and/or analyzed during the current study are not publicly available and must be obtained from the corresponding author upon reasonable request. The data were sourced from the Hadiyya Zone Health Bureau, the SNNPR Meteorological Center, and the Hadiyya Zone Agricultural and Finance Bureaus, and access requires official permission from the respective agencies.
AbbreviationsSAR:
Spatial autoregressive model
OLS:
Ordinary least squares
AIC:
Akaike information criterion
LM:
Lagrange multiplier
SNNPR:
Southern nations, nationalities, and peoples’ region
WHO:
World health organization
GIS:
Geographic information system
ARC GIS:
A specific GIS software platform
ITN:
Insecticide-treated nets
IRS:
Indoor residual spraying
RF:
Rainfall
MIT:
Minimum temperature
MAT:
Maximum temperature
LL:
Log-likelihood
GMI:
Global Moran’s I
LCI:
Local Moran’s I
Geary’s C:
Geary’s contiguity ratio
API:
Annual parasite incidence
MoH:
Ministry of health
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Shambel Selman Abdo.Ethics declarations

Competing interests
The authors declare no competing interests.

Ethical approval
Ethical clearance for this study was obtained from the Institutional Review Board (IRB) of Wachemo University (WCU 17.2023). The study was based on secondary data without personal identifiers. All methods were performed in accordance with the relevant guidelines and regulations of Wachemo University and national research ethics standards. All necessary permissions were secured from relevant health and administrative offices in the Hadiyya Zone to ensure the ethical use of the data.

Informed consent
As the study utilized secondary, de-identified data and did not involve direct interaction with human participants, informed consent was not applicable. However, permission to use the data was formally obtained from the appropriate authorities.

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Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by-nc-nd/4.0/.
Reprints and permissionsAbout this articleCite this articleAbdo, S.S. Spatial analysis of malaria incidence and environmental determinants in Hadiya Zone, Ethiopia.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-33236-8Download citationReceived: 09 July 2025Accepted: 17 December 2025Published: 30 December 2025DOI: https://doi.org/10.1038/s41598-025-33236-8Share 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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KeywordsMalariaIncidenceGeostatistics and spatial autocorrelation More

Abstract

The abundance-biomass comparison curves (ABC curves) method was adopted to analyze the temporal stability of the fish and nekton community structure in Laoshan Bay, China, during the spring and summer seasons from 2013 to 2015. This study aimed to explore the feasibility of using stable species in the nekton structure as stock enhancement candidates and to enrich the guidelines for responsible stock enhancement. Results showed that the W-statistic values of ABC curves for nekton were generally higher than those for fish across the three years’ spring and summer seasons. For fish, the biomass curves were often located below their abundance curves; in contrast, the biomass curves of nekton were generally positioned above their abundance curves or intersected with them. These findings indicated that the nekton community structure was more complex and stable than that of the fish community. Portunus trituberculatus played an important role in maintaining the stability of nekton community structure during spring and summer. Therefore, stock enhancement of P. trituberculatus in Laoshan Bay every spring could improve the stability of nekton community structure in the subsequent summer through trophic relationships. Given that ABC curves have a solid ecological theoretical basis, the stability of nekton structure could be one of joint ecological elements for selecting stock enhancement species.

Data availability

The datasets used and analysed during the current study available from the corresponding author on reasonable request.
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Download referencesAcknowledgementsThe authors are grateful to the staff in the Key Laboratory of Sustainable Development of Marine Fisheries at the Yellow Sea Fisheries Research Institute for their cooperation during the sampling operation. FundingThis work was supported by the National Key Research and Development Program of China (Grant No.: 2023YFD2401101).Author informationAuthors and AffiliationsYellow Sea Fisheries Research Institute, Key Laboratory of Sustainable Development of Marine Fisheries, Ministry of Agriculture and Rural Affairs, Chinese Academy of Fishery Science, 106 Nanjing Road, 266071, Qingdao, ChinaZhong Yi Li & Qun LinShandong Provincial Key Laboratory for Fishery Resources and Eco-environment, Yellow Sea Fisheries Research Institute, Chinese Academy of Fishery Sciences, Qingdao, 266071, ChinaZhong Yi Li & Qun LinAuthorsZhong Yi LiView author publicationsSearch author on:PubMed Google ScholarQun LinView author publicationsSearch author on:PubMed Google ScholarContributionsZhong Yi Li wrote the main manuscript text. Zhong Yi Li prepared Figs. 1, 2 and 3. Qun Lin performed data analysis.Both authors reviewed the manuscript.Corresponding authorCorrespondence to
Zhong Yi Li.Ethics declarations

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KeywordsLaoshan bayCommunity structureABC curvesStock enhancementSpecies selection More

AbstractExtreme temperature events are becoming widespread with global warming, impacting phytoplankton, the foundation of the marine ecosystem. In the Southern Ocean, these impacts are not well understood, despite the key role of phytoplankton in global carbon cycling and climate. Here, we use 26 years of satellite observations and confirm previously identified impacts of marine heatwaves (MHWs) on phytoplankton in the Southern Ocean, while systematically comparing the opposite impacts of marine cold spells (MCSs). MHWs decrease phytoplankton chlorophyll-a (Chl-a) in subtropical regions (−21.11%) but less so in polar regions, with Chl-a even increasing in the Sub-Antarctic Zone ( + 22.26%). MCSs exhibit opposite patterns, enhancing Chl-a in subtropical regions ( + 32.37%) while inhibiting it in southern regions (−21.19%). These regional differences in Chl-a anomalies are mediated by distinct responses in phytoplankton size composition to MHWs and MCSs. As extreme events intensify with global warming, Southern Ocean’s phytoplankton will be disrupted, with implications for global biogeochemical cycles. These findings highlight the importance of simultaneously considering both MHWs and MCSs when assessing the ecological impacts of climate extremes.

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Data availability

The daily SST-CCI (version 3.0) and SIC data are provided by ESA from their website: https://doi.org/10.5285/4a9654136a7148e39b7feb56f8bb02d2. The daily Rrs-CCI and kd (version 6.0) data are provided by ESA from their website: https://rsg.pml.ac.uk/thredds/catalog-cci.html. The PAR data are provided by GlobColour from their website: https://hermes.acri.fr/index.php?class=archive. The nitrate data provided by CMEMS from their website: https://doi.org/10.48670/moi-00019. The MLD (GLORYS12V1) data provided by CMEMS from their website: https://doi.org/10.48670/moi-00021. The HPLC data provided by PANGAEA from their website: https://doi.org/10.1594/PANGAEA.938703. The HPLC data provided by ADON from their website: https://portal.aodn.org.au/search. The data used to generate the figures presented in this study are available via figshare at https://doi.org/10.6084/m9.figshare.3021702187.
Code availability

The analyses were performed using MATLAB and Python; the main code used in this study is available at https://doi.org/10.5281/zenodo.1722329288, with BGC-Argo data processing based on code from https://github.com/NOAA-PMEL/OneArgo-Mat and MHWs/MCSs detection using code from https://github.com/ZijieZhaoMMHW/m_m hw1.0.
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Bai, Z. et al. Extreme temperature events reshuffle the ecological landscape of the Southern Ocean. figshare https://doi.org/10.6084/m9.figshare.30217021 (2025).Bai, Z. et al. Model code and output for: Extreme temperature events reshuffle the ecological landscape of the Southern Ocean. Zenodo https://doi.org/10.5281/zenodo.17223292 (2025).Download referencesAcknowledgementsThis work was supported by the National Natural Science Foundation of China (NSFC). Specifically, Z.J. received support from NSFC Grants #42176173 and 42476168, while D.L. was supported by NSFC Grant #42406172. Additional funding was provided by the Guangdong Basic and Applied Basic Research Foundation (Grant # 2023A1515110837) awarded to D.L., and by the Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (Grants #SML2024SP023 and SML2024SP029), and the Innovation Group Project of Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai) (Grant #311020004), awarded to Z.J. R.J.W.B. is supported by a UKRI Future Leader Fellowship (MR/V022792/1) and the European Space Agency’s project Tipping points and abrupt changes In the Marine Ecosystem (TIME). The author acknowledges the European Space Agency (ESA) for providing daily Sea Surface Temperature (SST), Sea Ice Concentration (SIC), Diffuse Attenuation Coefficient (Kd), and Remote Sensing Reflectance (Rrs) data products through the Ocean Colour Climate Change Initiative (OC-CCI). The author also thanks the Copernicus Marine Environment Monitoring Service (CMEMS) for providing daily model data of Nitrate (NO₃), and Mixed-Layer Depth (MLD). Appreciation is extended to GlobColour for providing Level-3 (L3) daily Photosynthetically Available Radiation (PAR) data. Additionally, the author acknowledges the PANGAEA Data Publisher for Earth & Environmental Science and the Australian Waters Bio-optical Database (AWBD) for providing High-Performance Liquid Chromatography (HPLC) datasets. We further acknowledge the Argo Program, which is part of the Global Ocean Observing System (https://www.seanoe.org/data/00311/42182/), as well as the Southern Ocean Observing System (SOOS) and the Southern Ocean Carbon and Climate Observations and Modeling (SOCCOM) Project, funded by the National Science Foundation, Division of Polar Programs (NSF PLR-1425989 and OPP-1936222), and supplemented by NASA. The BGC-Argo data were collected and made freely available by the International Argo Program and the national programs that contribute to it (http://www.argo.ucsd.edu, http://argo.jcommops.org).Author informationAuthor notesThese authors contributed equally: Zhimin Bai, Lin Deng.Authors and AffiliationsSchool of Marine Sciences, Sun Yat-sen University, Zhuhai, Guangdong, ChinaZhimin Bai 
(摆智敏), Lin Deng 
(邓霖), Wenbo He 
(何文博), Qilin Chunpi 
(七林春批) & Jun Zhao 
(赵俊)Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai, Guangdong, ChinaLin Deng 
(邓霖) & Jun Zhao 
(赵俊)Pearl River Estuary Marine Ecosystem Research Station, Ministry of Education, Zhuhai, Guangdong, ChinaLin Deng 
(邓霖) & Jun Zhao 
(赵俊)Guangdong Provincial Key Laboratory of Marine Resources and Coastal Engineering, Guangzhou, Guangdong, ChinaLin Deng 
(邓霖) & Jun Zhao 
(赵俊)Centre for Geography and Environmental Science, Department of Earth and Environmental Sciences, Faculty of Environment, Science and Economy, University of Exeter, Penryn, Cornwall, UKRobert J. W. BrewinAuthorsZhimin Bai 
(摆智敏)View author publicationsSearch author on:PubMed Google ScholarLin Deng 
(邓霖)View author publicationsSearch author on:PubMed Google ScholarRobert J. W. BrewinView author publicationsSearch author on:PubMed Google ScholarWenbo He 
(何文博)View author publicationsSearch author on:PubMed Google ScholarQilin Chunpi 
(七林春批)View author publicationsSearch author on:PubMed Google ScholarJun Zhao 
(赵俊)View author publicationsSearch author on:PubMed Google ScholarContributionsB.Z.M. and D.L. conducted data processing and analysis under Z.J.’s instruction. H.W.B. provided the conceptual framework. H.W.B. and Q. C. contributed data and algorithms. B.Z.M. drafted the initial manuscript, and Z.J. and R.J.W.B. reviewed the paper.Corresponding authorCorrespondence to
Jun Zhao 
(赵俊).Ethics declarations

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Reprints and permissionsAbout this articleCite this articleBai, Z., Deng, L., Brewin, R.J.W. et al. Extreme temperature events reshuffle the ecological landscape of the Southern Ocean.
Nat Commun (2025). https://doi.org/10.1038/s41467-025-68029-0Download citationReceived: 17 July 2024Accepted: 16 December 2025Published: 30 December 2025DOI: https://doi.org/10.1038/s41467-025-68029-0Share 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

Cordia africana and Terminalia brownii, indigenous Ethiopian multipurpose trees of high ecological and socioeconomic value, are increasingly threatened by overexploitation and climate change. Understanding how climatic and anthropogenic factors shape their distribution is critical for sustainable management and conservation. This study identified key environmental drivers, predicted suitable habitats under current and future climates, and assessed shifts in distribution, geographic range, and niche overlap. Future climate projections were assessed under two scenarios: the medium-emission scenario (SSP2-4.5) and the high-emission scenario (SSP5-8.5), for two time horizons: the 2050s and 2070s. An ensemble modeling framework was applied using seven algorithms: boosted regression trees, random forest, generalized linear model, generalized additive model, maximum entropy, support vector machine, and multivariate adaptive regression splines. Each model was run with ten sub-sampled replicates. We evaluated model performance using the area under the curve (AUC) for C. africana (0.84) and T. brownii (0.82), and the true skill statistic (TSS), which was 0.60 for both species. Predicted suitable habitat for C. africana (20.1%) was concentrated in the western, central, and southwestern regions, while T. brownii (19.9%) was mainly distributed in the northern, eastern, and southeastern lowlands. Under future climate scenarios, suitable areas are projected to decline sharply, shrinking to 8.68% and 2.07% of Ethiopia’s land area, respectively, by the 2070s. Suitable habitats for both species are expected to contract, with potential refugia for C. africana shifting toward highland areas. Geographic and environmental overlap between the two species was minimal. Given their multipurpose use, increasing vulnerability, and limited niche and geographic overlap, our results show that the projected habitat contraction, the marked decline of T. brownii within its natural range, and the upslope shift of C. africana refugia require species-specific conservation actions. C. africana needs refugia protection and climate-adapted management, while T. brownii requires targeted measures to support its long-term viability. Conservation efforts should focus on safeguarding existing habitats, regulating harvesting, strengthening community-based forest management, and maintaining ecological connectivity. Both species will also require stricter control of human use under future climate change.

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The data supporting the findings of this study are available in the supplementary material.
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Download referencesAcknowledgementsThe authors gratefully acknowledge the Department of Plant Biology and Biodiversity Management for providing research facilities and the herbarium expert for identifying the specimen and facilitating access to the herbarium specimens used in this study.FundingThere are no funding resources for this study.Author informationAuthors and AffiliationsDepartment of Plant Biology and Biodiversity Management, College of Natural and Computational Sciences, Addis Ababa University, Addis Ababa, EthiopiaDaniel MeleseDepartment of Biology, Mizan Tepi University, Tepi, EthiopiaDaniel Melese, Muluye Asnakew & Ashebir AwokeDessie Tissue Culture Center, Dessie, EthiopiaAtnafu TesfawMekdela Amba University, Tuluawulia, EthiopiaYibeltal MeslieDepartment of Biology, University of Gondar, Gondar, EthiopiaYibelu Yitayih HailieAuthorsDaniel MeleseView author publicationsSearch author on:PubMed Google ScholarMuluye AsnakewView author publicationsSearch author on:PubMed Google ScholarAshebir AwokeView author publicationsSearch author on:PubMed Google ScholarAtnafu TesfawView author publicationsSearch author on:PubMed Google ScholarYibeltal MeslieView author publicationsSearch author on:PubMed Google ScholarYibelu Yitayih HailieView author publicationsSearch author on:PubMed Google ScholarContributionsD.M. was involved in data collection, compilation, methodology, modeling, analyzing the output, and drafting the manuscript. M.A., A.A., A.T., Y.M., and Y.Y. contributed to data collection and manuscript writing. We would like to clarify that the work presented here is original research that has not previously been published and is not under consideration for publication elsewhere, in whole or in part. All authors participated in revising and approved the final manuscript.Corresponding authorCorrespondence to
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Reprints and permissionsAbout this articleCite this articleMelese, D., Asnakew, M., Awoke, A. et al. Climate-driven habitat shifts And niche overlap of overexploited trees Cordia africana Lam. and Terminalia brownii Fresen in Ethiopia.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-33743-8Download citationReceived: 19 September 2025Accepted: 22 December 2025Published: 30 December 2025DOI: https://doi.org/10.1038/s41598-025-33743-8Share 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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KeywordsAnthropogenicConservationEnsemble modelMultipurpose trees More

AbstractCrayfish play an important role in freshwater ecosystems, and sex classification is crucial for analyzing their demographic structures. This study performed binary classification using traditional machine learning and deep learning models on tabular and image datasets with an imbalanced class distribution. For tabular classification, features related to crayfish weight and size were used. Missing values were handled using different methods to create various datasets. Kolmogorov-Arnold networks demonstrated the best performance across all metrics, achieving accuracy rates between 95 and 100%. Image data were generated by combining at least five images of each crayfish. Autoencoders were employed to extract meaningful features. In experiments conducted on these extracted features, support vector machines achieved 84% accuracy, and multilayer perceptrons achieved 82% accuracy, outperforming other models. To enhance performance, a novel architecture based on stacked autoencoders was proposed. While some models experienced performance declines, Kolmogorov-Arnold networks showed an average improvement of 3.5% across all metrics, maintaining the highest accuracy. To statistically evaluate performance differences, McNemar’s and Wilcoxon tests were applied. The results confirmed significant differences between Kolmogorov-Arnold networks, support vector machines, multilayer perceptrons, and naive Bayes. In conclusion, this study highlights the effectiveness of deep learning and machine learning models in crayfish sex classification and provides a significant example of hybrid artificial intelligence models incorporating autoencoders.

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Data availability

The datasets generated and/or analysed during the current study are available in the Zenodo repository: https://doi.org/10.5281/zenodo.17516963. The source codes developed for the experiments are stored in a GitHub repository at https://github.com/yasinatilkan60/Crayfish-Sex-Identification.
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Reprints and permissionsAbout this articleCite this articleAtilkan, Y., Kirik, B., Acikbas, E.T. et al. Enhancing crayfish sex identification with Kolmogorov-Arnold networks and stacked autoencoders.
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KeywordsCrayfishSex identificationDeep learningMachine learningKolmogorov-Arnold networksStacked autoencoders More