Ecology

A multi-trait analysis of a migratory songbird species shows that extreme winter storms have, beyond direct mortality, long-lasting effects on the phenology, genetics and demography of survivors.

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Fig. 1: Phenological, genetic and demographic consequences of extreme winter storms.

ReferencesJenouvrier, S. Glob. Change Biol. 19, 2036–2057 (2013).Article 

Google Scholar 
Bomberger Brown, M. & Brown, C. R. Auk 128, 69–77 (2011).Article 

Google Scholar 
Stager, M. et al. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-026-03005-5 (2026).Fink, D. et al. eBird Status and Trends, data version 2023 (Cornell Lab of Ornithology, 2024).Brown, C. R. & Brown, M. B. Evolution 52, 1461–1475 (1998).Article 
PubMed 

Google Scholar 
Chapman, B. B. et al. Oikos 120, 1764–1775 (2011).Article 

Google Scholar 
Brown, C. R. & Bomberger Brown, M. Behav. Ecol. Sociobiol. 47, 339–345 (2000).Article 

Google Scholar 
Fraser, K. C. et al. Front. Ecol. Evol. 7, 324 (2019).Article 

Google Scholar 
Morton, E. S. & Derrickson, K. C. Condor 92, 1040 (1990).Article 

Google Scholar 
Brown, C. R. SENA 23, 597–603 (1978).
Google Scholar 
Cohen, J. et al. Nat. Geosci. 7, 627–637 (2014).Article 
CAS 

Google Scholar 
Flesher, J. & Stengle, J. Bats, birds among wildlife pummeled during southern freeze. Assoc. Press https://apnews.com/article/bats-birds-wildlife-southern-freeze-9070466a70d54ee6c84d060c9ea05005 (2021).Cooper, C. B., Bailey, R. L. & Leech, D. I. in Nests, Eggs, and Incubation (eds Deeming, D. C. & Reynolds, S. J.) 208–220 (Oxford Univ. Press, 2015).McQueen, A., Barnaby, R., Symonds, M. R. E. & Tattersall, G. J. Biol. Lett. 19, 20230373 (2023).Article 
PubMed 
PubMed Central 

Google Scholar 
Download referencesAuthor informationAuthors and AffiliationsDepartment of Biological Sciences, Arkansas State University, Jonesboro, AR, USAVirginie RollandAuthorsVirginie RollandView author publicationsSearch author on:PubMed Google ScholarCorresponding authorCorrespondence to
Virginie Rolland.Ethics declarations

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Rights and permissionsReprints and permissionsAbout this articleCite this articleRolland, V. Severe winter storms bring severe population changes.
Nat Ecol Evol (2026). https://doi.org/10.1038/s41559-026-02977-8Download citationPublished: 06 March 2026Version of record: 06 March 2026DOI: https://doi.org/10.1038/s41559-026-02977-8Share 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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Abstract

Climate change and increasing food demand pose major challenges to agricultural productivity, particularly for temperate crops cultivated under tropical conditions. This study investigated whether indigenous bacterial isolates obtained from cooled tropical soils exhibit putative diazotrophic traits and promote the growth of lettuce, a representative temperate crop, under tropical greenhouse conditions without soil cooling. Seven bacterial isolates were initially screened using preliminary assays indicative of putative diazotrophic traits, including nitrogen-free semisolid medium, bromothymol blue assays, and PCR detection of nif-related genes. Two strains, Agromyces sp. C10 and Bacillus sp. C21, were selected for further evaluation. Inoculation with these strains enhanced lettuce biomass in the presence and absence of fertilizer and was associated with increased total soil nitrogen after harvest. Although these results do not provide direct functional evidence of biological nitrogen fixation, they indicate that indigenous bacteria from cooled soils may serve as promising plant growth-promoting candidates for temperate crop cultivation in tropical environments.

Data availability

The datasets generated and/or analyzed during the current study are available in the NCBI GenBank repository, under BioProject accession number PRJNA825673 (BioSamples: SAMN27553938 and SAMN27553940; GenBank assemblies: JALPSX000000000 and JALPSZ000000000). All other relevant data are included in the article and Supplementary Information.
ReferencesIPCC (Intergovernmental Panel on Climate Change). Climate Change 2014: Impacts, Adaptation, and Vulnerability. Working Group II Contribution to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge University Press, 2014). https://www.ipcc.ch/report/ar5/wg2/Arora, S. & Jha, P. N. Springer,. Impact of plant-associated microbial communities on host plants under abiotic stresses. In: Microbial Interventions in Agriculture and Environment eds. Singh, D., Gupta, V. & Prabha, R. 303–340 https://doi.org/10.1007/978-981-13-8383-0_10 (2019).Malhi, G. S. et al. Arbuscular mycorrhiza in combating abiotic stresses in vegetables: An eco-friendly approach. Saudi J. Biol. Sci. 28, 1465–1476. https://doi.org/10.1016/j.sjbs.2020.12.001 (2021).
Google Scholar 
Kaminsky, L. M., Trexler, R. V., Malik, R. J., Hockett, K. L. & Bell, T. H. The inherent conflicts in developing soil microbial inoculants. Trends Biotechnol. 37, 140–151. https://doi.org/10.1016/j.tibtech.2018.11.011 (2019).
Google Scholar 
Mitchell, C. A., Chun, C., Brandt, W. E. & Nielsen, S. S. Environmental modification of yield and nutrient composition of ‘Waldmann’s Green’ leaf lettuce. J. Food Qual. 20, 73–80. https://doi.org/10.1111/j.1745-4557.1997.tb00453.x (1997).
Google Scholar 
Wahid, A., Gelani, S., Ashraf, M. & Foolad, M. R. Heat tolerance in plants: an overview. Environ. Exp. Bot. 61, 199–223. https://doi.org/10.1016/j.envexpbot.2007.05.011 (2007).
Google Scholar 
Batista, B. D. & Singh, B. K. Realities and hopes in the application of microbial tools in agriculture. Microb. Biotechnol. 14, 1258–1268. https://doi.org/10.1111/1751-7915.13866 (2021).
Google Scholar 
Naamala, J. & Smith, D. L. Relevance of plant growth promoting microorganisms and their derived compounds, in the face of climate change. Agronomy 10, 1179. https://doi.org/10.3390/agronomy10081179 (2020).
Google Scholar 
Wagner, S. C. Biological nitrogen fixation. Nat. Educ. Knowl. 3, 15 (2011).
Google Scholar 
Rees, D. C. & Howard, J. B. Nitrogenase: standing at the crossroads. Curr. Opin. Chem. Biol. 4, 559–566. https://doi.org/10.1016/S1367-5931(00)00132-0 (2000).
Google Scholar 
Izquierdo, J. A. & Nüsslein, K. Distribution of extensive nifH gene diversity across physical soil microenvironments. Microb. Ecol. 51, 441–452. https://doi.org/10.1007/s00248-006-9044-x (2006).
Google Scholar 
Penton, C. R. et al. NifH-harboring bacterial community composition across an Alaskan permafrost thaw gradient. Front. Microbiol. 7, 1894. https://doi.org/10.3389/fmicb.2016.01894 (2016).
Google Scholar 
You, M. et al. Expression of the nifH gene of a Herbaspirillum endophyte in wild rice species: daily rhythm during the light-dark cycle. Appl. Environ. Microbiol. 71, 8183–8190. https://doi.org/10.1128/AEM.71.12.8183-8190.2005 (2005).
Google Scholar 
Allison, S. D. & Treseder, K. K. Warming and drying suppress microbial activity and carbon cycling in boreal forest soils. Glob Change Biol. 14, 2898–2909. https://doi.org/10.1111/j.1365-2486.2008.01716.x (2008).
Google Scholar 
Bradford, M. A. Thermal adaptation of decomposer communities in warming soils. Front. Microbiol. 4, 333. https://doi.org/10.3389/fmicb.2013.00333 (2013).
Google Scholar 
Karhu, K. et al. Temperature sensitivity of soil respiration rates enhanced by microbial Community response. Nature 513, 81–84. https://doi.org/10.1038/nature13604 (2014).
Google Scholar 
Shaárani, S. et al. A Soil-cooling Approach Supporting the Growth of Temperate Root Crops under a Tropical Climate. Trop. Agric. Dev. 65, 146–152. https://doi.org/10.11248/jsta.65.146 (2021).
Google Scholar 
Wu, S. C., Cao, Z. H., Li, Z. G., Cheung, K. C. & Wong, M. H. Effects of biofertilizer containing N-fixer, P and K solubilizers and AM fungi on maize growth: a greenhouse trial. Geoderma 125, 155–166. https://doi.org/10.1016/j.geoderma.2004.07.003 (2005).
Google Scholar 
Shaárani, S. et al. Draft Genome Sequences of Three Nitrogen-Fixing Strains Isolated from Soil Cooled for Growing Temperate Root Crops in a Tropical Climate. Microbiol. Resour. Announc. 11, e00501–e00522. https://doi.org/10.1128/mra.00501-22 (2022).
Google Scholar 
Chen, D. S., Li, Y. G. & Zhou, J. C. The symbiosis phenotype and expression patterns of five nodule-specific genes of Astragalus sinicus under ammonium and salt stress conditions. Plant Cell Rep. 26, 1421–1430. https://doi.org/10.1007/s00299-007-0346-3 (2007).
Google Scholar 
Peck, M. C., Fisher, R. F. & Long, S. R. Diverse flavonoids stimulate NodD1 binding to nod gene promoters in Sinorhizobium meliloti. J. Bacteriol. 188, 5417–5427. https://doi.org/10.1128/jb.00376-06 (2006).
Google Scholar 
Sabri, N. S. A., Zakaria, Z., Mohamad, S. E., Jaafar, A. B. & Hara, H. Importance of soil temperature for the growth of temperate crops under a tropical climate and functional role of soil microbial diversity. Microbes Environ. 33, 144–150. https://doi.org/10.1264/jsme2.ME17181 (2018).
Google Scholar 
Sabri, N. S. A., Zakaria, Z., Mohamad, S. E., Jaafar, A. B. & Hara, H. The use of soil cooling for growing temperate crops under tropical climate. Int. J. Environ. Sci. Technol. 16, 1449–1456. https://doi.org/10.1007/s13762-018-1787-7 (2019).
Google Scholar 
Kifle, M. H. & Laing, M. D. Isolation and screening of bacteria for their diazotrophic potential and their influence on growth promotion of maize seedlings in greenhouses. Front. Plant. Sci. 6, 1225. https://doi.org/10.3389/fpls.2015.01225 (2016).
Google Scholar 
Scopel, E. et al. Conservation agriculture cropping systems in temperate and tropical conditions, performances and impacts. A review. Agron. Sustain. Dev. 33, 113–130. https://doi.org/10.1007/s13593-012-0106-9 (2013).
Google Scholar 
Hertel, T., Elouafi, I., Tanticharoen, M. & Ewert, F. Diversification for enhanced food systems resilience in Science and Innovations for Food Systems Transformation 207–215 (Springer, 2023).An, N., Turp, M. T., Orgen, B., Bilgin, B. & Kurnaz, M. L. Analysis of the impact of climate change on grapevines in Turkey using heat unit accumulation–based indices. Int. J. Biometeorol. 66, 2325–2338. https://doi.org/10.1007/s00484-022-02360-9 (2022).
Google Scholar 
Balci, G. & Demirsoy, H. Effect of organic and conventional growing systems with different mulching on yield and fruit quality in strawberry cvs. Sweet Charlie and Camarosa. Biol. Agric. Hortic. 26, 121–129. https://doi.org/10.1080/01448765.2008.9755075 (2008).
Google Scholar 
Cunningham, M. M. et al. Honey bees as biomonitors of environmental contaminants, pathogens, and climate change. Ecol. Indic. 134, 108457. https://doi.org/10.1016/j.ecolind.2021.108457 (2022).
Google Scholar 
Khuluq, A. D., Widaryanto, E., Ariffin, A. & Nihayati, E. Adaptive strategy of Stevia rebaudiana to environmental change in tropical climate based on anatomy and physiology characteristics. Biodiversitas J. Biol. Divers. 23, 5710–5717. https://doi.org/10.13057/biodiv/d231122 (2022).
Google Scholar 
Fernandes, G. D. C., Trarbach, L. J., de Campos, S. B., Beneduzi, A. & Passaglia, L. M. Alternative nitrogenase and pseudogenes: unique features of the Paenibacillus riograndensis nitrogen fixation system. Res. Microbiol. 165, 571–580. https://doi.org/10.1016/j.resmic.2014.06.002 (2014).
Google Scholar 
Mulley, G. et al. Pyruvate is synthesized by two pathways in pea bacteroids with different efficiencies for nitrogen fixation. J. Bacteriol. 192, 4944–4953. https://doi.org/10.1128/jb.00294-10 (2010).
Google Scholar 
Presta, L., Fondi, M., Emiliani, G. & Fani, R. Nitrogen fixation, a molybdenum-requiring process. In Molybdenum Cofactors and Their Role in the Evolution of Metabolic Pathways. 53–66. https://doi.org/10.1007/978-94-017-9972-0_5 (Springer, (2015).Hodkinson, B. P. et al. Lichen-symbiotic cyanobacteria associated with Peltigera have an alternative vanadium-dependent nitrogen fixation system. Eur. J. Phycol. 49, 11–19. https://doi.org/10.1080/09670262.2013.873143 (2014).
Google Scholar 
Sanz-Luque, E., Bhaya, D. & Grossman, A. R. Polyphosphate: a multifunctional metabolite in cyanobacteria and algae. Front. Plant. Sci. 11, 938. https://doi.org/10.3389/fpls.2020.00938 (2020).
Google Scholar 
Masalkar, P. D. & Roberts, D. M. Glutamine synthetase isoforms in nitrogen-fixing soybean nodules: distinct oligomeric structures and thiol-based regulation. FEBS Lett. 589, 215–221. https://doi.org/10.1016/j.febslet.2014.11.048 (2015).
Google Scholar 
Schreier, H. J., Caruso, S. M. & Maier, K. C. Control of Bacillus subtilis glutamine synthetase expression by glnR from Staphylococcus aureus. Curr. Microbiol. 41, 425–429. https://doi.org/10.1007/s002840010162 (2000).
Google Scholar 
Wang, T. et al. Positive and negative regulation of transferred nif genes mediated by indigenous GlnR in Gram-positive Paenibacillus polymyxa. PLoS Genet. 14, e1007629. https://doi.org/10.1371/journal.pgen.1007629 (2018).
Google Scholar 
Fei, H. & Vessey, J. K. Stimulation of nodulation in Medicago truncatula by low concentrations of ammonium: quantitative reverse transcription PCR analysis of selected genes. Physiol. Plant. 135, 317–330. https://doi.org/10.1111/j.1399-3054.2008.01192.x (2009).
Google Scholar 
Hungria, M., Rondina, A. B. L., Nunes, A. L. P., Araujo, R. S. & Nogueira, M. A. Seed and leaf-spray inoculation of PGPR in brachiarias (Urochloa spp.) as an economic and environmental opportunity to improve plant growth, forage yield and nutrient status. Plant. Soil. 463, 171–186. https://doi.org/10.1007/s11104-021-04908-x (2021).
Google Scholar 
Souza, L. A. & Tavares, R. Nitrogen and stem development: a puzzle still to be solved. Front. Plant. Sci. 12, 630587. https://doi.org/10.3389/fpls.2021.630587 (2021).
Google Scholar 
de Leon, V., Orr, K., Stelinski, L. L., Mandadi, K. & Ibanez-Carrasco, F. Inoculation of Tomato with Plant Growth Promoting Rhizobacteria Affects the Tomato—Potato Psyllid—Candidatus Liberibacter Solanacearum Interactions. J. Econ. Entomol. 116, 379–388. https://doi.org/10.1093/jee/toad006 (2023).
Google Scholar 
Hyder, S. et al. Characterization of native plant growth promoting rhizobacteria and their anti-oomycete potential against Phytophthora capsici affecting chilli pepper (Capsicum annuum L). Sci. Rep. 10, 13859. https://doi.org/10.1038/s41598-020-69410-3 (2020).
Google Scholar 
Nadeem, S. M. et al. Synergistic use of biochar, compost and plant growth-promoting rhizobacteria for enhancing cucumber growth under water deficit conditions. J. Sci. Food Agric. 97, 5139–5145. https://doi.org/10.1002/jsfa.8393 (2017).
Google Scholar 
Shabaan, M., Asghar, H. N., Akhtar, M. J., Ali, Q. & Ejaz, M. Role of plant growth promoting rhizobacteria in the alleviation of lead toxicity to Pisum sativum L. Int. J. Phytorem. 23, 837–845. https://doi.org/10.1080/15226514.2020.1859988 (2021).
Google Scholar 
Zia, M. A. et al. Glucanolytic rhizobacteria associated with wheat-maize cropping system suppress the Fusarium wilt of tomato (Lycopersicum esculentum L). Sci. Hortic. 287, 110275. https://doi.org/10.1016/j.scienta.2021.110275 (2021).
Google Scholar 
Gtari, M., Ghodhbane-Gtari, F., Nouioui, I., Beauchemin, N. & Tisa, L. S. Phylogenetic perspectives of nitrogen-fixing actinobacteria. Arch. Microbiol. 194, 3–11. https://doi.org/10.1007/s00203-011-0733-6 (2012).
Google Scholar 
Sellstedt, A. & Richau, K. H. Aspects of nitrogen-fixing Actinobacteria, in particular free-living and symbiotic Frankia. FEMS Microbiol. Lett. 342, 179–186. https://doi.org/10.1111/1574-6968.12116 (2013).
Google Scholar 
Abdel Wahab, A. M. Nitrogen fixation by Bacillus strains isolated from the rhizosphere of Ammophila arenaria. Plant. Soil. 42, 703–708. https://doi.org/10.1007/BF00009953 (1975).
Google Scholar 
Ding, Y., Wang, J., Liu, Y. & Chen, S. Isolation and identification of nitrogen-fixing bacilli from plant rhizospheres in Beijing region. J. Appl. Microbiol. 99, 1271–1281. https://doi.org/10.1111/j.1365-2672.2005.02738.x (2005).
Google Scholar 
Jain, S., Varma, A. & Choudhary, D. K. Perspectives on nitrogen-fixing Bacillus species. In Soil Nitrogen Ecology (eds Cruz, C., Vishwakarma, K., Choudhary, D. K. & Varma, A.) 359–369. https://doi.org/10.1007/978-3-030-71206-8_18 (Springer, (2021).Yousuf, J. et al. Nitrogen fixing potential of various heterotrophic Bacillus strains from a tropical estuary and adjacent coastal regions. J. Basic. Microbiol. 57, 922–932. https://doi.org/10.1002/jobm.201700072 (2017).
Google Scholar 
Syed-Ab-Rahman, S. F. et al. Identification of soil bacterial isolates suppressing different Phytophthora spp. and promoting plant growth. Front. Plant. Sci. 9, 1502. https://doi.org/10.3389/fpls.2018.01502 (2018).
Google Scholar 
Khan, M. S. et al. Isolation and characterization of plant growth-promoting endophytic bacteria Bacillus stratosphericus LW-03 from Lilium wardii. 3 Biotech. 10, 1–15 https://doi.org/10.1007/s13205-020-02294-2 (2020).Shen, F. T., Yen, J. H., Liao, C. S., Chen, W. C. & Chao, Y. T. Screening of rice endophytic biofertilizers with fungicide tolerance and plant growth-promoting characteristics. Sustainability 11, 1133. https://doi.org/10.3390/su11041133 (2019).
Google Scholar 
Wang, J. et al. Co-inoculation of antagonistic Bacillus velezensis FH-1 and Brevundimonas diminuta NYM3 promotes rice growth by regulating the structure and nitrification function of rhizosphere microbiome. Front. Microbiol. 14, 1101773. https://doi.org/10.3389/fmicb.2023.1101773 (2023).
Google Scholar 
Cui, L. et al. Potential of an endophytic bacteria Bacillus amyloliquefaciens 3–5 as biocontrol agent against potato scab. Microb. Pathog. 163, 105382. https://doi.org/10.1016/j.micpath.2021.105382 (2022).
Google Scholar 
Kuan, K. B., Othman, R., Abdul Rahim, K. & Shamsuddin, Z. H. Plant growth-promoting rhizobacteria inoculation to enhance vegetative growth, nitrogen fixation and nitrogen remobilisation of maize under greenhouse conditions. PLoS One. 11, e0152478. https://doi.org/10.1371/journal.pone.0152478 (2016).
Google Scholar 
Akinrinlola, R. J., Yuen, G. Y., Drijber, R. A. & Adesemoye, A. O. Evaluation of Bacillus strains for plant growth promotion and predictability of efficacy by in vitro physiological traits. Int. J. Microbiol. 2018, 1–11. https://doi.org/10.1155/2018/5686874 (2018).
Google Scholar 
Borriss, R. Springer,. Use of plant-associated Bacillus strains as biofertilizers and biocontrol agents in agriculture in Bacteria in agrobiology: Plant growth responses ed. Maheshwari, D 41–76. (2011). https://doi.org/10.1007/978-3-642-20332-9_3Fan, B. et al. Bacillus velezensis FZB42 in 2018: The gram-positive model strain for plant growth promotion and biocontrol. Front. Microbiol. 9, 2491. https://doi.org/10.3389/fmicb.2018.02491 (2018).
Google Scholar 
Döbereiner, J. Isolation and identification of root associated diazotrophs. Plant. Soil. 110, 207–212. https://doi.org/10.1007/BF02226800 (1988).
Google Scholar 
Poly, F., Monrozier, L. J. & Bally, R. Improvement in the RFLP procedure for studying the diversity of nifH genes in communities of nitrogen fixers in soil. Res. Microbiol. 152, 95–103. https://doi.org/10.1016/S0923-2508(00)01172-4 (2001).
Google Scholar 
Potrich, D. P., Passaglia, L. M. P. & Schrank, I. S. Partial characterization of nif genes from the bacterium Azospirillum amazonense. Braz J. Med. Biol. Res. 34, 1105–1113. https://doi.org/10.1590/s0100-879×2001000900002 (2001).
Google Scholar 
Dedysh, S. N., Ricke, P. & Liesack, W. NifH and NifD phylogenies: an evolutionary basis for understanding nitrogen fixation capabilities of methanotrophic bacteria. Microbiology 150, 1301–1313. https://doi.org/10.1099/mic.0.26585-0 (2004).
Google Scholar 
Wick, R. R., Judd, L. M., Gorrie, C. L. & Holt, K. E. Unicycler: resolving bacterial genome assemblies from short and long sequencing reads. PLoS Comput. Biol. 13, e1005595. https://doi.org/10.1371/journal.pcbi.1005595 (2017).
Google Scholar 
Gurevich, A., Saveliev, V., Vyahhi, N. & Tesler, G. QUAST: quality assessment tool for genome assemblies. Bioinformatics 29, 1072–1075. https://doi.org/10.1093/bioinformatics/btt086 (2013).
Google Scholar 
Tatusova, T. et al. NCBI prokaryotic genome annotation pipeline. Nucleic Acids Res. 44, 6614–6624. https://doi.org/10.1093/nar/gkw569 (2016).
Google Scholar 
Aziz, R. K. et al. The RAST server: Rapid Anno-tations using Subsystems Technology. BMC Genom. 9, 75. https://doi.org/10.1186/1471-2164-9-75 (2008).
Google Scholar 
Download referencesFundingFunding support for this research was provided by Japan Society for the Promotion of Science KAKENHI, Fostering Joint International Research (B) (Grant Number 20KK0128) and the Junior Visiting Researcher (JVR) Scheme (Vote no. Q.K130000. 21A6.00P66).Author informationAuthors and AffiliationsDepartment of Chemical and Environmental Engineering, Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur, 54100, MalaysiaShazwana Shaárani, Nurul Syazwani Ahmad Sabri, Fatimah Azizah Riyadi, Siti Noor Fitriah Azizan, Fazrena Nadia Md Akhir & Hirofumi HaraUniversiti Utara Malaysia Kampus Kuala Lumpur, Jalan Raja Muda Abdul Aziz, Kampung Baru, Wilayah Persekuatuan Kuala Lumpur, 50300, MalaysiaNurul Syazwani Ahmad SabriDepartment of Chemical Engineering, Faculty of Engineering, Universitas Indonesia, Depok, 16424, West Java, IndonesiaFatimah Azizah RiyadiSchool of Agriculture, Meiji University, 1-1-1 Higashi-Mita, Tama-ku, Kawasaki, 214-8571, JapanSiti Noor Fitriah AzizanDepartment of Mechanical Precision Engineering, Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Jalan Sultan Yahya Petra, Kuala Lumpur, 54100, MalaysiaNor’azizi OthmanDepartment of Biotechnology, Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1, Yayoi, Bunkyo-ku, Tokyo, 113-8657, JapanHirofumi HaraAuthorsShazwana ShaáraniView author publicationsSearch author on:PubMed Google ScholarNurul Syazwani Ahmad SabriView author publicationsSearch author on:PubMed Google ScholarFatimah Azizah RiyadiView author publicationsSearch author on:PubMed Google ScholarSiti Noor Fitriah AzizanView author publicationsSearch author on:PubMed Google ScholarFazrena Nadia Md AkhirView author publicationsSearch author on:PubMed Google ScholarNor’azizi OthmanView author publicationsSearch author on:PubMed Google ScholarHirofumi HaraView author publicationsSearch author on:PubMed Google ScholarContributionsH. H. designed the study and approved the final manuscript, S. S. performed the experiment, and wrote the manuscript, N. S. A. S. and F. A. R. contributed to analysis and interpretation of data, S. N. F. A., F. N. M. A., and N. O. critically reviewed the manuscript. All authors have read, checked and approved the final version of the manuscript.Corresponding authorCorrespondence to
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Reprints and permissionsAbout this articleCite this articleShaárani, S., Sabri, N.S.A., Riyadi, F.A. et al. Eco-functional deployment of indigenous nitrogen-fixing microbes to enable temperate crop cultivation in tropical climates.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-43406-xDownload citationReceived: 30 September 2025Accepted: 04 March 2026Published: 06 March 2026DOI: https://doi.org/10.1038/s41598-026-43406-xShare 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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KeywordsClimate changePlant growth-promoting rhizobacteriaPutative diazotrophic traitsSoil coolingTropical agriculture More

AbstractDiseases are threatening forests worldwide. In North America, white pine blister rust (WPBR) is one of the most damaging tree epidemics. To understand patterns in the current and future risk for high-elevation five-needle white pine species (High-5), we compiled data from independent studies across the western U.S. to estimate WPBR risk. Contrary to previous predictions, the future climate is not expected to reduce the prevalence of WPBR risk on High-5 species in the western U.S. The prevalence of WPBR is predicted to increase, with most distributions of the High-5 species projected to experience elevated WPBR prevalence over the next century. Temperature and moisture conditions ensure that while some newly invaded areas are projected to experience regular periods of elevated risk, others can expect intermittent episodes of high risk. Restoration in impacted areas and proactive management in regions at increased risk are warranted to ensure High-5 population sustainability and ecosystem function.

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Data availability

Malone, S.L., A.W. Schoettle, K.S. Burns, H.S. Kearns, J.E. Stewart, M. Newcomb, and C.M. Cleaver. (2025). White Pine Blister Rust (WPBR) Plot Data from the Western United States ver 2. Environmental Data Initiative. https://doi.org/10.6073/pasta/70d7cd56799fc79668343e48e87bb9ef65. Malone, S.L., A.W. Schoettle, K.S. Burns, H.S. Kearns, J.E. Stewart, M. Newcomb, and C.M. Cleaver (2026) RustMapper: White Pine Blister Rust Risk in the Western United States 1980-2023. Environmental Data Initiative. https://doi.org/10.6073/pasta/677bc02adfe51760f752494cac74d11e94. Malone, S.L., A.W. Schoettle, K.S. Burns, H.S. Kearns, J.E. Stewart, M. Newcomb, and C.M. Cleaver. (2026). RustMapper: White Pine Blister Rust Risk in the Western United States, 2030-2099 ver 1. Environmental Data Initiative. https://doi.org/10.6073/pasta/0a5a5880cdba8761c3f2de32f457580489.
Code availability

The code for RustMapper can be found here: Sparkle Malone. (2026). Malone-Disturbance-Ecology-Lab/RUSTMapper: Future climate will not save high-elevation white pines. Zenodo. https://doi.org/10.5281/zenodo.18234106
ReferencesSturrock, R. N. et al. Climate change and forest diseases. Plant Pathol 60, 133–149 (2011).
Google Scholar 
Pathak, R., Singh, S. K., Tak, A. & Gehlot, P. Impact of climate change on host, pathogen and plant disease adaptation regime: a review. Biosci. Biotechnol. Res. Asia 15, 529–540 (2018).
Google Scholar 
Bouzid & Maha. Examining the Role of Environmental Change on Emerging Infectious Diseases and Pandemics. (IGI Global, 2016).Ghelardini, L., Pepori, A. L., Luchi, N., Capretti, P. & Santini, A. Drivers of emerging fungal diseases of forest trees. For. Ecol. Manage. 381, 235–246 (2016).
Google Scholar 
Chaloner, T. M., Gurr, S. J. & Bebber, D. P. Geometry and evolution of the ecological niche in plant-associated microbes. Nat. Commun. 11, 2955 (2020).
Google Scholar 
Donald, F., Green, S., Searle, K., Cunniffe, N. J. & Purse, B. V. Small scale variability in soil moisture drives infection of vulnerable juniper populations by invasive forest pathogen. For. Ecol. Manage. 473, 118324 (2020).
Google Scholar 
Dudney, J. et al. Nonlinear shifts in infectious rust disease due to climate change. Nat. Commun. 12, 5102 (2021).
Google Scholar 
Bebber, D. P. Range-expanding pests and pathogens in a warming world. Annu. Rev. Phytopathol. 53, 335–356 (2015).
Google Scholar 
Bebber, D. P., Ramotowski, M. A. T. & Gurr, S. J. Crop pests and pathogens move polewards in a warming world. Nat. Clim. Chang. 3, 985–988 (2013).
Google Scholar 
Rohr, J. R. et al. Frontiers in climate change–disease research. Trends Ecol. Evol. 26, 270–277 (2011).
Google Scholar 
Peterson, A. T. Shifting suitability for malaria vectors across Africa with warming climates. BMC Infect. Dis. 9, 59 (2009).
Google Scholar 
Garamszegi, L. Z. Climate change increases the risk of malaria in birds. Glob. Chang. Biol. 17, 1751–1759 (2011).
Google Scholar 
Gougherty, A. V. Emerging tree diseases are accumulating rapidly in the native and non-native ranges of Holarctic trees. NeoBiota 87, 143–160 (2023).
Google Scholar 
Tomback, D. F. & Achuff, P. Blister rust and western forest biodiversity: ecology, values and outlook for white pines. For. Pathol. 40, 186–225 (2010).
Google Scholar 
Goeking, S. A. & Windmuller-Campione, M. A. Comparative species assessments of five-needle pines throughout the western United States. For. Ecol. Manage. 496, 119438 (2021).
Google Scholar 
Schoettle, A. W. et al. Integrating forest health conditions and species adaptive capacities to infer future trajectories of the high elevation five-needle white pines. For. Ecol. Manage. 521, 120389 (2022).
Google Scholar 
Campbell, E. M. & Antos, J. A. Distribution and severity of white pine blister rust and mountain pine beetle on whitebark pine in British Columbia. Can. J. For. Res. 30, 1051–1059 (2000).
Google Scholar 
Geils, B. W., Hummer, K. E. & Hunt, R. S. White pines, Ribes, and blister rust: a review and synthesis: Review and synthesis. For. Pathol. 40, 147–185 (2010).
Google Scholar 
Mielke, J. L. White pine blister rust in western North America. vol. Bulletin 52 (1943).Akiba, M. Diversity of Pathogenicity and Virulence in the Pinewood Nematode, Bursaphelenchus xylophilus. J. Jpn. For. Soc. 88, 383–391 (2006).McDonald, G. I., Richardson, B. A., Zambino, P. J., Klopfenstein, N. B. & Kim, M.-S. Pedicularis and Castilleja are natural hosts of Cronartium ribicola in North America: a first report. For. Pathol. 36, 73–82 (2006).
Google Scholar 
Endangered and Threatened Wildlife and Plants; Threatened Species Status with Section 4(d) Rule for Whitebark Pine (Pinus albicaulis). 87 FR 76882 (2022).Kolb, T. E. et al. Observed and anticipated impacts of drought on forest insects and diseases in the United States. For. Ecol. Manage. 380, 321–334 (2016).
Google Scholar 
Hennon, P. E. et al. A framework to evaluate climate effects on forest tree diseases. For. Pathol. 50, p.e12469 (2020).Bega, R. The effect of environment on germination of sporidia in Cronartium ribicola. Phytopathology 50, 61–69 (1960).Hirt, R. R. The relation of certain meteorological factors to the infection of Eastern White Pine by the blister-rust fungus. Bull N.Y. Coll. For., 15, 65p (1942).Van Arsdel, E. P., Riker, A. J. & Patton, R. The effects of temperature and moisture on the spread of White Pine blister rust. Phytopathology 46, 307–318 (1956).
Google Scholar 
Lachmund, H. G. Mode of entrance and periods in the life cycle of Cronartium ribicola on Finns montícola. J. Agric. Res. 47, 791–805 (1933).
Google Scholar 
Jacobi, W. R., Kearns, H. S. J., Cleaver, C. M., Goodrich, B. A. & Burns, K. S. Epidemiology of white pine blister rust on limber pine in Colorado and Wyoming. For. Pathol. 48, e12465 (2018).
Google Scholar 
Woods, A. J., Heppner, D., Kope, H. H., Burleigh, J. & Maclauchlan, L. Forest health and climate change: A British Columbia perspective. For. Chron. 86, 412–422 (2010).
Google Scholar 
Thoma, D. P., Shanahan, E. K. & Irvine, K. M. Climatic correlates of white pine blister rust infection in whitebark pine in the greater Yellowstone ecosystem. Forests 10, 666 (2019).
Google Scholar 
Burns, K. S. et al. Interactions between white pine blister rust, bark beetles, and climate over time indicate vulnerabilities to limber pine health. Front. For. Glob. Change. 6, 114956 (2023).Kearns, H. S. J., Jacobi, W. R. & Geils, B. W. A method for estimating white pine blister rust canker age on limber pine in the central Rocky Mountains. For. Pathol. 39, 177–191 (2009).
Google Scholar 
Hunt, R. S. & Jensen, G. D. Long infection period for white pine blister rust in coastal British Columbia. Horttechnology 10, 530–532 (2000). American Society for Horticultural Science.
Google Scholar 
Kinloch, B. B. White pine blister rust in North America: past and prognosis. Phytopathology 93, 1044–1047 (2003).
Google Scholar 
Kearns, H. S. J. et al. Risk of white pine blister rust to limber pine in Colorado and Wyoming, USA. For. Pathol. 44, 21–38 (2014).
Google Scholar 
Frank, K. L., Geils, B. W., Kalkstein, L. S. & Thistle, H. W. Jr Synoptic climatology of the long-distance dispersal of white pine blister rust II. Combination of surface and upper-level conditions. Int. J. Biometeorol. 52, 653–666 (2008).
Google Scholar 
Blodgett, J. T. & Sullivan, K. F. First report of white pine blister rust on Rocky Mountain bristlecone pine. Plant Dis 88, 311 (2004).
Google Scholar 
Vogler, D. R., Geils, B. W. & Coats, K. First report of the white pine blister rust fungus, Cronartium ribicola, infecting Ribes inerme in north-central Utah. Plant Dis 101, 386–386 (2017).
Google Scholar 
Hawksworth, F. G. White Pine blister rust in southern New Mexico. Plant Dis 74, 938 (1990).
Google Scholar 
Sniezko, R. A. & Liu, J.-J. Genetic resistance to white pine blister rust, restoration options, and potential use of biotechnology. For. Ecol. Manage. 520, 120168 (2022).
Google Scholar 
Mulvey, R. L. & Hansen, E. M. Castilleja and Pedicularis confirmed as telial hosts for Cronartium ribicola in whitebark pine ecosystems of Oregon and Washington. For. Pathol. 41, 453–463 (2011).
Google Scholar 
Zambino, P. J. Biology and pathology of Ribes and their implications for management of white pine blister rust. For. Pathol. 40, 264–291 (2010).
Google Scholar 
Van Arsdel, E. P. Environment in relation to white pine blister rust infection. in Biology of rust resistance in forest trees: Proceedings of a NATO-IUFRO Advanced Study Institute: Biology of rust resistance in forest trees (eds. Bingham, R. T., Hoffand, R. & McDonald, G. I.) Publication 112; 479–493 (USDA Forest Service, Washington, D.C., USA, 1972).Malone, S. L. et al. RustMapper: White Pine blister rust risk across high elevation forests in the western United States. Sci. Data 12, 1–17 (2025).
Google Scholar 
Harvey, A. E., Byler, J. W., McDonald, G. I., Neuenschwander, L. F. & Tonn, J. R. Death of an Ecosystem: Perspectives on Western White Pine https://www.fs.usda.gov/rm/pubs/rmrs_gtr208.pdf (2008).Maloy, O. C. White pine blister rust control in North America: a case history. Annu. Rev. Phytopathol. 35, 87–109 (1997).
Google Scholar 
Geils, B. W., Conklin, D. A. & Van Arsdel, E. P. A Preliminary Hazard Model of White Pine Blister Rust for the Sacramento Ranger District, Lincoln National Forest. USDA Forest Service. https://www.fs.usda.gov/rm/pubs/rmrs_rn006.pdf (1999).Howell, B. E., Burns, K. S. & Kearns, H. S. J. Biological Evaluation of a Model for Predicting Presence of White Pine Blister Rust in Colorado Based on Climatic Variables and Susceptible White Pine. (2006).Chabot, B. F. & Mooney, H. A. Physiological Ecology of North American Plant Communities. vol. 39 351 (Chapman and Hall, New York, 1985).Angert, A. L., Bradshaw, H. D. Jr. & Schemske, D. W. Using experimental evolution to investigate geographic range limits in monkeyflowers. Evolution 62, 2660–2675 (2008).
Google Scholar 
Aitken, S. N., Yeaman, S., Holliday, J. A., Wang, T. & Curtis-McLane, S. Adaptation, migration or extirpation: climate change outcomes for tree populations. Evol. Appl. 1, 95–111 (2008).
Google Scholar 
Shirk, A. J. et al. Southwestern white pine (Pinus strobiformis) species distribution models project a large range shift and contraction due to regional climatic changes. For. Ecol. Manage. 411, 176–186 (2018).
Google Scholar 
Malone, S. L., Schoettle, A. W. & Coop, J. D. The future of subalpine forests in the Southern Rocky Mountains: Trajectories for Pinus aristata genetic lineages. PLoS One 13, e0193481 (2018).
Google Scholar 
Millar, C. I. et al. Do low-elevation ravines provide climate refugia for subalpine limber pine (Pinus flexilis) in the Great Basin, USA? Can. J. For. Res. 48, 663–671 (2018).
Google Scholar 
Charlet, D. A. Nevada Mountains: Landforms, Trees, and Vegetation. (University of Utah Press, 2019). https://doi.org/10.1353/book74205.Jenkins, M. B. et al. Restoring a forest keystone species: A plan for the restoration of whitebark pine (Pinus albicaulis Engelm.) in the Crown of the Continent ecosystem. For. Ecol. Manage. 522, 120282 (2022).
Google Scholar 
Schoettle, A. W., Jacobi, W. R., Waring, K. M. & Burns, K. S. Regeneration for resilience framework to support regeneration decisions for species with populations at risk of extirpation by white pine blister rust. New Forests 50, 89–114 (2019).
Google Scholar 
Schoettle, A. W. & Sniezko, R. A. Proactive intervention to sustain high-elevation pine ecosystems threatened by white pine blister rust. J. Forest Res. 12, 327–336 (2007).
Google Scholar 
Tomback, D. F. et al. Tamm review: Current and recommended management practices for the restoration of whitebark pine (Pinus albicaulis Engelm.), an imperiled high-elevation western North American forest tree. For. Ecol. Manage. 522, 119929 (2022).
Google Scholar 
Schoettle, A. Taking the long view and acting now – prioritizing management of high elevation five-needle pines. Mountain Views (CIRMOUNT) 13, 2–7 (2019).
Google Scholar 
Keane, R. E., Schoettle, A. W. & Tomback, D. F. Effective actions for managing resilient high elevation five-needle white pine forests in western North America at multiple scales under changing climates. For. Ecol. Manage. 505, 119939 (2022).
Google Scholar 
Keane, R. E. & Parsons, R. A. Restoring Whitebark Pine Forests of the Northern Rocky Mountains, USA. Ecological Restoration 28, 56–70 (2010).
Google Scholar 
Logan, J. A., MacFarlane, W. W. & Willcox, L. Whitebark pine vulnerability to climate-driven mountain pine beetle disturbance in the Greater Yellowstone Ecosystem. Ecol. Appl. 20, 895–902 (2010).
Google Scholar 
Malone, S. L. et al. White Pine Blister Rust (WPBR) Plot Data from the Western United States ver 2. Environmental Data Initiative. https://doi.org/10.6073/pasta/70d7cd56799fc79668343e48e87bb9ef (2025).U.S. Geological Survey. National Hydrography Dataset. (2019).Van Arsdel, E. P. & Geils, B. W. The Ribes of Colorado and New Mexico and Their Rust Fungi. vols 4-13 https://research.fs.usda.gov/treesearch/download/66502.pdf (2004).Hollister, J. W. elevatr: access elevation data from various APIs. R package version 0.4.2. (2022).De Reu, J. et al. Application of the topographic position index to heterogeneous landscapes. Geomorphology 186, 39–49 (2013).
Google Scholar 
Riley, S. J., DeGloria, S. D. & Elliot, S. D. A Terrain Ruggedness Index that Quantifies Topographic Heterogeneity. Intermt. J. Sci. 5, 23–27 (1999).
Google Scholar 
Thornton, M. M., Thornton, P. E., Wei, Y., Vose, R. S. & Boyer, A. G. Daymet: Station-level inputs and model predicted values for North America, Version 3. ORNL DAAC (2017).Liaw, A. & Wiener, M. Classification and regression by randomForest. R news 2, 18–22 (2002).
Google Scholar 
Genuer, R., Poggi, J.-M. & Tuleau-Malot, C. VSURF: An R Package for Variable Selection Using Random Forests. (2015).Speiser, J. L., Miller, M. E., Tooze, J. & Ip, E. A comparison of randomForest variable selection methods for classification prediction modeling. Expert Syst. Appl. 134, 93–101 (2019).
Google Scholar 
Kuhn, M. et al. Package ‘caret’. R J. 223, (2020).Abatzoglou, J. T. Development of gridded surface meteorological data for ecological applications and modelling. Int. J. Climatol. 33, 121–131 (2013).
Google Scholar 
Ji, D. et al. Description and basic evaluation of Beijing Normal University Earth System Model (BNU-ESM) version 1. Geosci. Model Dev. 7, 2039–2064 (2014).
Google Scholar 
You, C., Hou, M. & Duan, W. Why does there occur spring predictability barrier for eastern Pacific El Niño but summer predictability barrier for central Pacific El Niño? Clim. Dyn. (2024) https://doi.org/10.1007/s00382-024-07429-2.Vinod, D. & Agilan, V. Ranking of CMIP 6 climate models in simulating precipitation over India. Acta Geophys 72, 3703–3717 (2024).
Google Scholar 
Osipov, A. & Gushchina, D. The heat budget of the tropical Pacific mixed layer during two types of El Niño based on reanalysis and global climate model data. Atmosphere (Basel) (2023) https://doi.org/10.3390/atmos15010047.Chylek, P., Li, J., Dubey, M. K., Wang, M. & Lesins, G. Observed and model simulated 20th century Arctic temperature variability: Canadian Earth System Model CanESM2. Atmos. Chem. Phys. Discuss. 11, 22893–22907 (2011).
Google Scholar 
Lawrence, D. M. et al. The CCSM4 land simulation, 1850–2005: Assessment of surface climate and new capabilities. J. Clim. 25, 2240–2260 (2012).
Google Scholar 
da Silva, V. et al. Modeling future carbon stock in melon cultivation agroecosystems under different climate scenarios. Revista Brasileira de Ciências Ambientais (RBCIAMB) 59, e1729–e1729 (2024).
Google Scholar 
Crookston, N. L. Plant Species and Climate Profile Predictions. http://charcoal.cnre.vt.edu/climate/species/ (2012).Monahan, W. B., Cook, T., Melton, F., Connor, J. & Bobowski, B. Forecasting distributional responses of limber pine to climate change at management-relevant scales in Rocky Mountain National Park. PLoS One 8, e83163 (2013).
Google Scholar 
Fryer, J. L. Tree species distribution maps from Little’s‘ Atlas of United States trees’ series. USDA Forest Service (2018).Keane, R. E., Holsinger, L. M., Mahalovich, M. F. & Tomback, D. F. Restoring Whitebark Pine Ecosystems in the Face of Climate Change. https://research.fs.usda.gov/treesearch/download/54577.pdf (2017) https://doi.org/10.2737/rmrs-gtr-361.Whitebark Pine Ecosystem Foundation. Whitebark pine and limber pine range maps. (2014).Malone, S. L. et al. RustMapper: White Pine Blister Rust Risk in the Western United States, 2030-2099 ver 1. Environmental Data Initiative (2026).Ruaro, R., Gubiani, ÉA. & Hughes, R. M. Omernik’s ecoregion framework: A legacy for understanding regional patterns in attainable resource quality. Environ. Manage. 73, 354–364 (2024).
Google Scholar 
EPA. Ecoregions of the continental United States. Ecoregions of the continental United States U.S. EPA (2013).Hijmans, R. J. Spatial Data Analysis: R Package Terra Version 1.7. (Comprehensive R Archive Network (CRAN), 2023).Wickham, H. ggplot2: Elegant Graphics for Data Analysis. (Springer-Verlag New York, 2016).Malone, S. L. et al. RustMapper: White Pine Blister Rust Risk in the Western United States. Environmental Data Initiative. https://doi.org/10.6073/pasta/677bc02adfe51760f752494cac74d11e (2026).Download referencesAcknowledgementsThis work was supported by funding from the U.S. Forest Service through the Western Wildland Environmental Threat Assessment Center (WWETAC) grant under agreement 19-JV-11221633-129. The contributions of U.S. Government employees to this research were partially supported by their respective agencies. The Yale School for the Environment and the Yale Center for Natural Carbon Capture provided additional support. The findings and conclusions in this paper are those of the authors and should not be construed to represent any official USDA or U.S. Government determination or policy. The authors extend their gratitude to Anthony Culpepper of the Mountain Studies Institute for his assistance with GIS analysis and to Angel Chen for her efforts in publishing the final data products.Author informationAuthors and AffiliationsYale School of the Environment, Yale University, New Haven, CT, USASparkle L. MaloneUSDA Forest Service, Rocky Mountain Research Station, Fort Collins, CO, USAAnna W. SchoettleUSDA Forest Service, Forest Health Protection, Golden, CO, USAKelly S. BurnsUSDA Forest Service, Forest Health Protection, Sandy, OR, USAHolly S. J. KearnsDepartment of Agricultural Biology, Colorado State University, Fort Collins, CO, USAJane E. StewartUSDA Forest Service, Forest Health Protection, Missoula, MT, USAMaria NewcombUSDA Forest Service, Forest Health Protection, Coeur d’Alene, ID, USAChristy M. CleaverAuthorsSparkle L. MaloneView author publicationsSearch author on:PubMed Google ScholarAnna W. SchoettleView author publicationsSearch author on:PubMed Google ScholarKelly S. BurnsView author publicationsSearch author on:PubMed Google ScholarHolly S. J. KearnsView author publicationsSearch author on:PubMed Google ScholarJane E. StewartView author publicationsSearch author on:PubMed Google ScholarMaria NewcombView author publicationsSearch author on:PubMed Google ScholarChristy M. CleaverView author publicationsSearch author on:PubMed Google ScholarContributionsSLM and AWS conceived the project idea. AWS and KSB harmonized data from various independent studies, while SLM collected auxiliary data, developed models, and maintained the code. AWS also reviewed analyses and collaborated with SLM on data visualization. Expert reviews were provided by KSB, HSJK, JES, MN, and CMC. The manuscript was written, reviewed, and edited collectively by SLM, AWS, KSB, HSJK, JES, MN, and CMC.Corresponding authorCorrespondence to
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AbstractWith the intensification of global climate change and human activities, grassland ecological degradation has become a prominent issue constraining ecological security on the Qinghai–Tibet Plateau and the sustainable development of pastoral areas. Existing research on pastoral grasslands has largely focused on surface-level variables such as policy responses and the acceptance of ecological compensation, with limited systematic exploration of the psychological and emotional factors underlying herders’ ecological behavioral intention. In particular, empirical analyses based on well-established theoretical frameworks remain scarce. In Ruoergai County, where grassland degradation is severe, the pathways and synergistic mechanisms through which key factors such as place attachment and risk perception influence behavioral intention have yet to be systematically verified, which to some extent restricts the precision of ecological policy interventions and the sustainability of governance outcomes. Therefore, this study aims to systematically reveal the formation mechanism of herders’ intention to engage in grassland ecological restoration. Drawing on the Value–Belief–Norm (VBN) theory and incorporating two extended variables—risk perception and place attachment—this study constructs a theoretical model and employs both structural equation modeling (SEM) and fuzzy-set qualitative comparative analysis (fsQCA) for dual validation. To enhance accessibility and sample representativeness in pastoral areas, a mixed data collection strategy combining online and offline questionnaire surveys was adopted. Based on this research design, 620 valid questionnaire responses were collected from pastoral grassland areas of Ruoergai County, Sichuan Province. The study employed scale measurements to ensure both reliability and cultural adaptability. The SEM results indicate that value cognition, risk perception, and place attachment all exert significant positive effects on ecological restoration belief, which in turn significantly promotes the formation of personal norm. Personal norm further enhances herders’ intention to engage in ecological restoration. In addition, both risk perception and place attachment have direct positive impacts on herders’ ecological restoration intention. The fsQCA results further reveal that the synergy of high levels of value cognition, risk perception, place attachment, ecological restoration belief, and personal norm constitutes a key configuration for fostering strong ecological restoration intention among herders. This study extends the applicability of the VBN theory to the context of herders’ grassland ecological restoration behavior and emphasizes that integrated strategies—such as shaping value cognition, strengthening risk perception, and cultivating emotional bonds of place attachment—should be adopted to enhance herders’ participation in grassland ecological restoration. The findings provide both theoretical and practical references for optimizing ecological governance policies and designing behavioral interventions in pastoral areas.

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ReferencesChina Government Network. Ecological Environment. Available online: (2005). https://www.gov.cn/test/2005-07/28/content_17792.htm (Accessed 9 Aug 2025).Ministry of Agriculture and Rural Affairs of the People’s Republic of China. Reply to Proposal No. 3893 of the Second Session of the 14th National People’s Congress. Available online: (2024). https://www.moa.gov.cn/govpublic/CWS/202407/t20240711_6458804.htm (Accessed 9 August 2025).China Nonferrous Metals Network. Hulunbuir Gradually Degrades: The Game between Environmental Protection and Mining. Available online: (2010). https://www.cnmn.com.cn/ShowNews1.aspx?id=182070&page=2 (Accessed 9 Aug 2025).Li, Z., Su, B. & Liu, M. Research progress on the theory and practice of grassland eco-compensation. China Agriculture. 12 (5), 721 (2022).
Google Scholar 
Akiyama, T. & Kawamura, K. Grassland degradation in china: methods of monitoring, management and restoration. Grassl. Sci. 53 (1), 1–17 (2007).
Google Scholar 
Li, T. et al. Characteristics and trends of grassland degradation research. J. Soils Sediments. 22 (7), 1901–1912 (2022).
Google Scholar 
Dong, S., Zhang, Y., Shen, H., Li, S. & Xu, Y. Grassland degradation and restoration. In: Grasslands on the Third Pole of the World: Structure, Function, Process, and Resilience of Social-Ecological Systems, 269–310. (2023).Shengqiang, Z. & Kai, Z. Evaluation of the effects of implementing degraded grassland ecosystem restoration technology: A case study on technology for returning grazing land to grassland. J. Resour. Ecol. 8 (4), 359–368 (2017).
Google Scholar 
Li, J. et al. Benefits, potential and risks of china’s grassland ecosystem conservation and restoration. Sci. Total Environ. 905, 167413 (2023).
Google Scholar 
Lü, Y., Fu, B., Feng, X., Zeng, Y., Liu, Y., Chang, R., Wu, B. (2012). A policy-driven large scale ecological restoration: quantifying ecosystem services changes in the Loess Plateau of China.PloS one, 7 (2), e31782.Niamir-Fuller, M. Policy drivers for rangelands in developing countries. Multifunctional Grasslands Chang. World. 2, 887–892 (2008).
Google Scholar 
Yang, Q., Nan, Z. & Tang, Z. Influencing factors of the grassland ecological compensation policy to herdsmen’s behavioral response: an empirical study in Hexi corridor. Acta Ecol. Sin. 42 (2), 74–79 (2022).
Google Scholar 
Zhang, Z., Lingshu, Z. & Jina, C. A study of the impact of risk perception on the pro-environmental behaviour of herders in the Sanjiangyuan region. Sci. Rep. 14 (1), 6788 (2024).
Google Scholar 
Molnár, Z. et al. Common and conflicting objectives and practices of herders and conservation managers: the need for a conservation herder. Ecosyst. Health Sustain., 2 (4), e01215. (2016).Shi, Y., Li, C. & Zhao, M. Behavioral mechanism of herders to maintain forage-livestock balance: an explanatory framework and empirical test incorporating emotions and desires. Curr. Psychol. 43 (6), 4839–4855 (2024).
Google Scholar 
Hu, J., Liu, Y., Zhang, X. & Zhang, J. Analysis of selectional preference for grassland ecological compensation methods under the perspective of herders differentiation. Front. Sustainable Food Syst. 9, 1512088 (2025).
Google Scholar 
Tang, H., Liu, Z. & Long, X. Analyzing the farmers’ pro-environmental behavior intention and their rural tourism livelihood in tourist village where its ecological environment is polluted. Plos One, 16(3), e0247407. (2021).Deng, J. et al. Analysis of the ecological conservation behavior of farmers in payment for ecosystem service programs in eco-environmentally fragile areas using social psychology models. Sci. Total Environ. 550, 382–390 (2016).
Google Scholar 
Deng, J. et al. Exploring farmers’ pro-ecological intentions after ecological rehabilitation in a fragile environment area: A structural equation modeling approach. Sustainability 10 (1), 29 (2017).
Google Scholar 
Wu, C. et al. Chinese residents’ perceived ecosystem services and disservices impacts behavioral intention for urban community garden: an extension of the theory of planned behavior. Agronomy 12 (1), 193 (2022).
Google Scholar 
Wei, J., Chen, H. & Long, R. Is ecological personality always consistent with low-carbon behavioral intention of urban residents? Energy Policy. 98, 343–352 (2016).
Google Scholar 
Sarpong, K. A. & Amankwaa, G. Household behavioral intention, environmental habit and attitude related to efficient water management: an empirical analysis on pro-environmental behavior among urban residents. H2Open J. 5 (3), 438–455 (2022).
Google Scholar 
Cao, H., Li, F., Zhao, K., Qian, C. & Xiang, T. From value perception to behavioural intention: study of Chinese smallholders’ pro-environmental agricultural practices. J. Environ. Manage. 315, 115179 (2022).
Google Scholar 
Wynveen, C. J., Wynveen, B. J. & Sutton, S. G. Applying the value-belief-norm theory to marine contexts: implications for encouraging pro-environmental behavior. Coastal. Manage. 43 (1), 84–103 (2015).
Google Scholar 
Gifford, R. & Nilsson, A. Personal and social factors that influence pro-environmental concern and behaviour: A review. Int. J. Psychol. 49 (3), 141–157 (2014).
Google Scholar 
Saifulina, N., Carballo-Penela, A. & Ruzo-Sanmartín, E. Effects of personal environmental awareness and environmental concern on employees’ voluntary pro-environmental behavior: a mediation analysis in emerging countries. Baltic J. Manage. 18 (1), 1–18 (2023).
Google Scholar 
Niu, N., Fan, W., Ren, M., Li, M. & Zhong, Y. The role of social norms and personal costs on pro-environmental behavior: the mediating role of personal norms. Psychol. Res. Behav. Manage., 2059–2069. (2023).Rajala, K. & Sorice, M. G. Sense of place on the range: landowner place meanings, place attachment, and well-being in the Southern great plains. Rangelands 44 (5), 353–367 (2022).
Google Scholar 
Li, H. et al. Evaluation of ecosystem services in ruoergai National park, China. Sustainability 16 (8), 3241 (2024).
Google Scholar 
Baidu Baike. Ruoergai County. Available online: (2024). https://baike.baidu.com/item/%E8%8B%A5%E5%B0%94%E7%9B%96%E5%8E%BF/8746187 (Accessed 11 August 2025).Stern, P. C. New environmental theories: toward a coherent theory of environmentally significant behavior. J. Soc. Issues. 56 (3), 407–424 (2000).
Google Scholar 
Lebrument, N., Zumbo-Lebrument, C. & Rochette, C. Application of the VBN theory to understand residents’ participation in the smart city: the case of French metropolises. Urban Stud. 62 (5), 954–975 (2025).
Google Scholar 
Momenpour, Y. et al. Towards predicting the pro-environmental behaviour of wheat farmers by using the application of value-belief-norm theory. Environ. Dev. Sustain., 1–31. (2024).Sharma, R. & Gupta, A. Pro-environmental behaviour among tourists visiting National parks: application of value-belief-norm theory in an emerging economy context. Asia Pac. J. Tourism Res. 25 (8), 829–840 (2020).
Google Scholar 
Gatersleben, B., Murtagh, N. & Abrahamse, W. Values, identity and pro-environmental behaviour. Contemp. Social Sci. 9 (4), 374–392 (2014).
Google Scholar 
Batool, N., Wani, M. D., Shah, S. A. & Dada, Z. A. Theory of planned behavior and value-belief norm theory as antecedents of pro-environmental behaviour: evidence from the local community. J. Hum. Behav. Social Environ. 34 (5), 693–709 (2024).
Google Scholar 
Landon, A. C., Woosnam, K. M. & Boley, B. B. Modeling the psychological antecedents to tourists’ pro-sustainable behaviors: an application of the value-belief-norm model. J. Sustainable Tourism. 26 (6), 957–972 (2018).
Google Scholar 
Sokkar, M. A Research of Millennials and Pro-Sustainable Tourism Behaviors: A Perspective of Value-Belief-Norm (VBN) Theory. Vedoucí Javed, Mohsin. Zlín: Univerzita Tomáše Bati Ve Zlíně (Fakulta managementu a ekonomiky, Ústav podnikové ekonomiky, 2024).Al Mamun, A. et al. Modelling the significance of value-belief-norm theory in predicting solid waste management intention and behavior. Front. Environ. Sci. 10, 906002 (2022).
Google Scholar 
De Groot,. Judith, I. M. & Krista Bondy Geertje Schuitema.(2021). Listen to others or yourself? The role of personal norms on the effectiveness of social norm interventions to change pro-environmental behavior. J. Environ. Psychol. 78, 101688 .Pradhananga, A. K., Mae, A. & Davenport Predicting farmer adoption of water conservation practices using a norm-based moral obligation model. Environ. Manage. 64 (4), 483–496 (2019).
Google Scholar 
Lapinski, M. K. et al. Culture and social norms: behavioral decisions about grassland conservation among ethnically Tibetan pastoralists. J. Int. Intercultural Communication. 15 (3), 333–354 (2022).
Google Scholar 
Mancha, R. M. & Yoder, C. Y. Cultural antecedents of green behavioral intent: an environmental theory of planned behavior. J. Environ. Psychol. 43, 145–154 (2015).
Google Scholar 
Malhotra, N. K. & Daniel, J. M. A cross-cultural comparison of behavioral intention models‐Theoretical consideration and an empirical investigation. Int. Mark. Rev. 18 (3), 235–269 (2001).
Google Scholar 
Gifford, R. & Andreas, N. Personal and social factors that influence pro-environmental concern and behaviour: A review. Int. J. Psychol. 49 (3), 141–157 (2014).
Google Scholar 
Abbasi, I. S. & Abbasi, M. S. People’s perceptions of environmental risks influence their pro-environmental behaviors and mental well-being: A qualitative study. Sarhad J. Manage. Sci. 10 (1), 197–208 (2024).
Google Scholar 
Yoon, A., Jeong, D. & Chon, J. The impact of the risk perception of ocean microplastics on tourists’ pro-environmental behavior intention. Sci. Total Environ. 774, 144782 (2021).
Google Scholar 
Raymond, C. M., Brown, G. & Weber, D. The measurement of place attachment: Personal, community, and environmental connections. J. Environ. Psychol. 30 (4), 422–434 (2010).
Google Scholar 
DeVellis, R. Scale Development: Theory and Applications (Sage, 1991).Bian, R., Che, H. S. & Yang, H. Application of project combination in structural equation modeling. Adv. Psychol. Sci. 15 (3), 10 (2007).
Google Scholar 
Ragin, C. C. Analytic Induction for Social Research (University of California Press, 2023).Pappas, I. O. & Woodside, A. G. Fuzzy-set qualitative comparative analysis (fsQCA): guidelines for research practice in information systems and marketing. Int. J. Inf. Manag. 58, 102310 (2021).
Google Scholar 
Duşa, A. QCA with R: A Comprehensive Resource (Springer, 2018).Download referencesFundingThis research was funded by the Scientific Research Funds for High-Level Talent Introduction at Kashi University.Author informationAuthors and AffiliationsSchool of Design, Kashi University, Kashi, 844000, ChinaChuhao Shen & Zhonghui ChenSchool of Design, Jiangnan University, Wuxi, 214122, ChinaKun WangDepartment of Performance, Film and Animation, Graduate School, Sejong University, Seoul, 05006, KoreaLinyu Huang, Haibo Yuan & Limin ZhengAuthorsChuhao ShenView author publicationsSearch author on:PubMed Google ScholarKun WangView author publicationsSearch author on:PubMed Google ScholarLinyu HuangView author publicationsSearch author on:PubMed Google ScholarZhonghui ChenView author publicationsSearch author on:PubMed Google ScholarHaibo YuanView author publicationsSearch author on:PubMed Google ScholarLimin ZhengView author publicationsSearch author on:PubMed Google ScholarContributionsConceptualization: Chuhao Shen and Kun Wang; methodology: Chuhao Shen and Linyu Huang; formal analysis: Chuhao Shen and Zhonghui Chen; investigation: Chuhao Shen, Haibo Yuan and Limin Zheng; data curation: Chuhao Shen and Linyu Huang; writing—original draft preparation: Chuhao Shen and Zhonghui Chen; writing—review and editing: Chuhao Shen, Linyu Huang and Kun Wang; visualization: Chuhao Shen; supervision: Linyu Huang; project administration: Chuhao Shen; funding acquisition: Chuhao Shen. All authors have read and agreed to the published version of the manuscript.Corresponding authorCorrespondence to
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Reprints and permissionsAbout this articleCite this articleShen, C., Wang, K., Huang, L. et al. Factors influencing herders’ willingness to engage in grassland ecological restoration in Ruoergai County.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-39449-9Download citationReceived: 03 September 2025Accepted: 05 February 2026Published: 06 March 2026DOI: https://doi.org/10.1038/s41598-026-39449-9Share 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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KeywordsGrassland ecological restorationHerders’ behavioral intentionValue–Belief–Norm theoryRisk perceptionPlace attachment More

AbstractAgroecosystems in arid and semi-arid regions face growing risks of climate extremes and soil degradation. The addition of exogenous carbon can restore degraded soils by adding soil organic carbon, but its effects on greenhouse gas (GHG) emissions and global warming mitigation remain elusive. This study evaluated emissions of three major GHGs–nitrous oxide (N2O), carbon dioxide (CO2), and methane (CH4)–following soil amendment with biochar, compost, and a biochar + compost (BC) mixture. Biochar application reduced cumulative N2O–N and CH4–C emissions by 52% and 16%, respectively. Soil CH4–C emissions were generally negative, being lowest with biochar and highest with compost. During the crop season, average CO2–C and N2O–C emissions were 75% and 45% greater, respectively, while CH4–C was 66% less compared to the no-crop season. Increasing soil moisture content increased N2O–N emissions (R2 = 0.39), while soil temperature influenced CH4–C emissions (R2 = 0.37). Among amendments, biochar-treated soil had the lowest cumulative N2O–N and CH4–C emissions, reducing net global warming potential (GWP) by 43% and 30%, respectively, compared to compost-treated soil and control (CTRL). Biochar amendment can be a climate-smart strategy for semi-arid regions as it improves soil health and mitigates GWP by reducing N2O and CH4 emissions.

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ReferencesUN. The Sustainable Development Goals Report 2023 | Department of Economic and Social Affairs. (2023). https://sdgs.un.org/documents/sustainable-development-goals-report-2023-53220US EPA. O. Global Greenhouse Gas Overview. (2016). https://www.epa.gov/ghgemissions/global-greenhouse-gas-overviewAydinalp, C. & Cresser, M. S. The effects of global climate change on agriculture. J. Agric. Env Sci. 3, 672–676 (2008).
Google Scholar 
Singh, D. et al. Drivers of greenhouse gas emissions in agricultural soils: The effect of residue management and soil type. Front Environ. Sci 12, (2024).US EPA. O. Agriculture Sector Emissions. (2025). https://www.epa.gov/ghgemissions/agriculture-sector-emissionsUS EPA. Inventory of U.S. Greenhouse Gas Emissions and Sinks (1990–2022). (2024).Ray, R. L. et al. Soil CO2 emission in response to organic amendments, temperature, and rainfall. Sci. Rep. 10, 5849 (2020).
Google Scholar 
Malyan, S. K., Maithani, D. & Kumar, V. Nitrous oxide production and mitigation through nitrification inhibitors in agricultural soils: A mechanistic understanding and comprehensive evaluation of influencing factors. Nitrogen 6, 14 (2025).
Google Scholar 
Zhu, X., Burger, M., Doane, T. A. & Horwath, W. R. Ammonia oxidation pathways and nitrifier denitrification are significant sources of N2O and NO under low oxygen availability. Proc. Natl. Acad. Sci. 110, 6328–6333 (2013).Themistokleous, G., Savvides, A. M., Philippou, K., Ioannides, I. M. & Omirou, M. A high-frequency greenhouse gas flux analysis tool: Insights from automated non‐steady‐state transparent soil chambers. Eur J. Soil. Sci 75, (2024).USGS. Dryland Ecosystems | U.S. Geological Survey. (2016). https://www.usgs.gov/special-topics/drought/science/dryland-ecosystemsGhimire, R. et al. Soil health assessment and management framework for water-limited environments: Examples from the great plains of the USA. Soil. Syst. 7, 22 (2023).
Google Scholar 
Martins, C. S. C., Macdonald, C. A., Anderson, I. C. & Singh, B. K. Feedback responses of soil greenhouse gas emissions to climate change are modulated by soil characteristics in dryland ecosystems. Soil. Biol. Biochem. 100, 21–32 (2016).
Google Scholar 
Savage, K., Phillips, R. & Davidson, E. High temporal frequency measurements of greenhouse gas emissions from soils. Biogeosciences 11, 2709–2720 (2014).
Google Scholar 
Veettil, A. V. et al. Transforming soil: Climate-smart amendments boost soil physical and hydrological properties. Soil. Syst. 8, 134 (2024).
Google Scholar 
Davis, J. G. & Whiting, D. Choosing a soil amendment – 7.235. Extension (2025). https://extension.colostate.edu/topic-areas/yard-garden/choosing-a-soil-amendment/Bhattacharyya, P. N. et al. Biochar as soil amendment in climate-smart agriculture: Opportunities, future prospects, and challenges. J. Soil. Sci. Plant. Nutr. 24, 135–158 (2024).
Google Scholar 
Lentz, R. D., Ippolito, J. A. & Spokas, K. A. Biochar and manure effects on net nitrogen mineralization and greenhouse gas emissions from calcareous soil under corn. Soil. Sci. Soc. Am. J. 78, 1641–1655 (2014).
Google Scholar 
Agbohessou, Y. et al. Modelling CO2 and N2O emissions from soils in silvopastoral systems of the West African Sahelian band. Biogeosciences 21, 2811–2837 (2024).
Google Scholar 
Jahangir, M. M. R. et al. Carbon footprint and greenhouse gas emissions of different rice-based cropping systems using LCA. Sci. Rep. 15, 10214 (2025).
Google Scholar 
Rubin, R. et al. Climate mitigation through soil amendments: Quantification, evidence, and uncertainty. Carbon Manag. 14, 2217785 (2023).
Google Scholar 
Ghimire, R., Norton, U., Bista, P., Obour, A. K. & Norton, J. B. Soil organic matter, greenhouse gases and net global warming potential of irrigated conventional, reduced-tillage and organic cropping systems. Nutr. Cycl. Agroecosyst. 107, 49–62 (2017).
Google Scholar 
Sainju, U. M. Agricultural management impact on greenhouse gas emissions. In Rao, C. S., Shanker, A. K. & Shanker, C. (eds) InTech, (2018). https://doi.org/10.5772/intechopen.72368Görres, C. M., Kammann, C. & Ceulemans, R. Automation of soil flux chamber measurements: Potentials and pitfalls. Biogeosciences 13, 1949–1966 (2016).
Google Scholar 
van der Weerden, T., Clough, T. & Styles, T. Using near-continuous measurements of N2O emission from urine-affected soil to guide manual gas sampling regimes. N. Z. J. Agric. Res. 56, 60–76 (2013).
Google Scholar 
Koschorreck, M. et al. Diurnal versus spatial variability of greenhouse gas emissions from an anthropogenically modified lowland river in Germany. Biogeosciences 21, 1613–1628 (2024).
Google Scholar 
Courtois, E. A. et al. Automatic high-frequency measurements of full soil greenhouse gas fluxes in a tropical forest. Biogeosciences 16, 785–796 (2019).
Google Scholar 
Fiedler, J. et al. Best Practice Guideline Measurement of carbon dioxide, methane and nitrous oxide fluxes between soil-vegetation-systems and the atmosphere using non-steady state chambers. (2022).IPCC. AR6 Synthesis Report. Climate Change (2023). https://www.ipcc.ch/report/ar6/syr/ (2023).Cayuela, M. L. et al. Biochar’s role in mitigating soil nitrous oxide emissions: A review and meta-analysis. Agric. Ecosyst. Environ. 191, 5–16 (2014).
Google Scholar 
Trost, B. et al. Irrigation, soil organic carbon and N2O emissions. A review. Agron. Sustain. Dev. 33, 733–749 (2013).
Google Scholar 
Ji, Q., Babbin, A. R., Jayakumar, A., Oleynik, S. & Ward, B. B. Nitrous oxide production by nitrification and denitrification in the Eastern Tropical South Pacific oxygen minimum zone. Geophys. Res. Lett. 42, (2015). 10,755 – 10,764.Chapuis-Lardy, L., Wrage, N., Metay, A., Chotte, J. L. & Bernoux, M. Soils, a sink for N2O? A review. Glob. Chang. Biol. 13, 1–17 (2006).
Google Scholar 
Wu, D. et al. N2O consumption by low-nitrogen soil and its regulation by water and oxygen. Soil. Biol. Biochem. 60, 165–172 (2013).
Google Scholar 
Atarashi-Andoh, M., Koarashi, J., Ishizuka, S. & Hirai, K. Seasonal patterns and control factors of CO2 effluxes from surface litter, soil organic carbon, and root-derived carbon estimated using radiocarbon signatures. Agric. Meteorol. 152, 149–158 (2012).
Google Scholar 
Reth, S., Reichstein, M. & Falge, E. The effect of soil water content, soil temperature, soil pH-value and the root mass on soil CO2 efflux – A modified model. Plant. Soil. 268, 21–33 (2005).
Google Scholar 
Yerli, C., Cakmakci, T. & Sahin, U. CO2 emissions and their changes with H2O emissions, soil moisture, and temperature during the wetting–drying process of the soil mixed with different biochar materials. J. Water Clim. Chang. 13, 4273–4282 (2022).
Google Scholar 
Olaniyan, J. O. et al. An investigation of the effect of biochar application rates on CO2 emissions in soils under upland rice production in southern Guinea Savannah of Nigeria. Heliyon 6, e05578 (2020).Tang, Y. et al. Effects of biochar amendment on soil carbon dioxide emission and carbon budget in the karst region of southwest China. Geoderma 385, 114895 (2021).
Google Scholar 
Zhu, X. et al. Short-term effects of biochar on soil CO2 efflux in boreal Scots pine forests. Ann. Sci. 77, 1–15 (2020).
Google Scholar 
Hansen, L. V. et al. Methane uptake rates across different soil types and agricultural management practices in Denmark. Agric. Ecosyst. Environ. 363, 108878 (2024).
Google Scholar 
Wu, X. et al. Effects of soil moisture and temperature on CO2 and CH4 soil–atmosphere exchange of various land use/cover types in a semi-arid grassland in Inner Mongolia, China. Soil. Biol. Biochem. 42, 773–787 (2010).
Google Scholar 
Hou, L. Y. et al. Growing season in situ uptake of atmospheric methane by desert soils in a semiarid region of northern China. Geoderma 189–190, 415–422 (2012).
Google Scholar 
van den Pol-van Dasselaar, A., van Beusichem, M. L. & Oenema, O. Effects of soil moisture content and temperature on methane uptake by grasslands on sandy soils. Plant. Soil. 204, 213–222 (1998).
Google Scholar 
Chen, X. et al. Warming promoted CH4 absorption compared with precipitation addition in typical steppe in Inner Mongolia. Front Ecol. Evol 11, (2023).He, Y. et al. Nitrous oxide emissions from aerated composting of organic waste. Environ. Sci. Technol. 35, 2347–2351 (2001).
Google Scholar 
Kim, D. J., Kim, H. K. & Kim, Y. R. Effect of nitrogen compounds and organic carbon concentrations on N2O emission during denitrification. In Clean Technology vol. 17, 134–141 (2011).He, Y. et al. Effects of biochar application on soil greenhouse gas fluxes: A meta-analysis. GCB Bioenergy. 9, 743–755 (2017).
Google Scholar 
Abban-Baidoo, E., Manka’abusi, D., Apuri, L., Marschner, B. & Frimpong, K. A. Biochar addition influences C and N dynamics during biochar co-composting and the nutrient content of the biochar co-compost. Sci. Rep. 14, 23781 (2024).
Google Scholar 
Jeffery, S., Verheijen, F. G. A., Kammann, C. & Abalos, D. Biochar effects on methane emissions from soils: A meta-analysis. Soil. Biol. Biochem. 101, 251–258 (2016).
Google Scholar 
Yu, L., Tang, J., Zhang, R., Wu, Q. & Gong, M. Effects of biochar application on soil methane emission at different soil moisture levels. Biol. Fertil. Soils. 49, 119–128 (2013).
Google Scholar 
Johnson, M. S., Webster, C., Jassal, R. S., Hawthorne, I. & Black, T. A. Biochar influences on soil CO2 and CH4 fluxes in response to wetting and drying cycles for a forest soil. Sci. Rep. 7, 6780 (2017).
Google Scholar 
Yeboah, S., Lamptey, S., Cai, L. & Song, M. Short-term effects of biochar amendment on greenhouse gas emissions from rainfed agricultural soils of the semi–arid loess plateau region. Agronomy 8, 74 (2018).
Google Scholar 
Yu, L., Tang, J., Zhang, R., Wu, Q. & Gong, M. Effects of biochar application on soil methane emission at different soil moisture levels. Biol. Fertil. Soils. 49, 119–128 (2013).
Google Scholar 
Nilahyane, A., Ghimire, R., Thapa, V. R. & Sainju, U. M. Cover crop effects on soil carbon dioxide emissions in a semiarid cropping system. Agrosyst. Geosci. Environ. 3, e20012 (2020).
Google Scholar 
Akinremi, O. O., MxGinn, S. M. & McLean, H. D. J. Effects of soil temperature and moisture on soil respiration in barley and fallow plots. Can. J. Soil. Sci. 79, 1–241 (1999).
Google Scholar 
Shang, Z. et al. Global cropland nitrous oxide emissions in fallow period are comparable to growing-season emissions. Glob. Chang. Biol. 30, (2024).Chen, W. et al. Annual methane uptake by typical semiarid steppe in Inner Mongolia. J. Geophys. Res. Atmos. 115, (2010).Acharya, P., Ghimire, R., Paye, W. S., Ganguli, A. C. & DelGrosso, S. J. Net greenhouse gas balance with cover crops in semi-arid irrigated cropping systems. Sci. Rep. 12, 12386 (2022).
Google Scholar 
Gehl, K., Sharma, S., Tomlinson, P. J., Warren, J. G. & Haag, L. A. Quantifying Nitrous Oxide Emissions from Grain Sorghum Cropping Systems in the Southern Great Plains (in (ASA-CSSA-SSSA, 2019).Dong, Y. & Ouyang, Z. Effects of organic manures on CO2 and CH4 fluxes of farmland. Chin. J. Appl. Ecol. 16, 1303–1307 (2005).
Google Scholar 
Duval, B. D., Martin, J. & Marsalis, M. A. The effect of variable fertilizer and irrigation treatments on greenhouse gas fluxes from aridland sorghum. Agronomy 12, (2022).Zhang, S. H., Wang, J., Fang, Z. W. & Fu, X. Effect of winter cover cropping on soil greenhouse gas emissions in a dryland spring maize field on the loess plateau of China. Huan Jing Ke Xue Huanjing Kexue. 43, 4848–4857 (2022).
Google Scholar 
Leonardo Interactive Pty Ltd. Image Creation – Leonardo.Ai. Leonardo.Ai (2025). https://app.leonardo.ai/image-generationSainju, U. M. Net global warming potential and greenhouse gas intensity. Soil. Sci. Soc. Am. J. 84, 1393–1404 (2020).
Google Scholar 
Ernest, B., Yanda, P. Z., Hansson, A. & Fridahl, M. Long-term effects of adding biochar to soils on organic matter content, persistent carbon storage, and moisture content in Karagwe, Tanzania. Sci. Rep. 14, 30565 (2024).
Google Scholar 
Yun, H. B. et al. Effect of crude carbohydrate content in livestock manure compost on organic matter decomposition rate in upland soil. Korean J. Soil. Sci. Fertil. 40, 364–368 (2007).
Google Scholar 
USDA NRCS. Web Soil Survey – Home. (2019). https://websoilsurvey.nrcs.usda.gov/app/Sapkota, S., Ghimire, R., Schutte, B. J., Idowu, O. J. & Angadi, S. Soil aggregates and associated carbon and nitrogen storage in circular grass buffer integrated cropping systems. J. Soils Sediments. 24, 1665–1679 (2024).
Google Scholar 
US Department of Commerce, N. National Weather Service. (2025). https://www.weather.gov/wrh/Climate?wfo=abqSapkota, S., Ghimire, R., Brewer, C. E. & Fernando, S. Contrasting effects of plant and animal residue biochars on soil health, carbon stability, and crop yield. J. Soils Sediments. 25, 703–717 (2025).
Google Scholar 
Barba, J., Poyatos, R. & Vargas, R. Automated measurements of greenhouse gases fluxes from tree stems and soils: Magnitudes, patterns and drivers. Sci. Rep. 9, 4005 (2019).
Google Scholar 
Collins, L. et al. Understorey productivity in temperate grassy woodland responds to soil water availability but not to elevated [CO2]. Glob. Chang. Biol. 24, 2366–2376 (2018).
Google Scholar 
West, T. O. & Marland, G. A synthesis of carbon sequestration, carbon emissions, and net carbon flux in agriculture: Comparing tillage practices in the United States. Agric. Ecosyst. Environ. 91, 217–232 (2002).
Google Scholar 
Borsato, E., Martello, M., Marinello, F. & Bortolini, L. Environmental and economic sustainability assessment for two different sprinkler and a drip irrigation systems: A case study on maize cropping. Agriculture 9, 187 (2019).
Google Scholar 
Pattey, E., Trzcinski, M. K. & Desjardins, R. L. Quantifying the reduction of greenhouse gas emissions as a result of composting dairy and beef cattle manure. Nutr. Cycl. Agroecosyst. 72, 173–187 (2005).
Google Scholar 
Desjardins, S. M., Ter-Mikaelian, M. T. & Chen, J. Cradle-to-gate life cycle analysis of slow pyrolysis biochar from forest harvest residues in Ontario. Can. Biochar. 6, 58 (2024).
Google Scholar 
Fu, S., Cheng, W. & Susfalk, R. Rhizosphere respiration varies with plant species and phenology: A greenhouse pot experiment. Plant. Soil. 239, 133–140 (2002).
Google Scholar 
Johnson, J. M. F., Allmaras, R. R. & Reicosky, D. C. Estimating source carbon from crop residues, roots and rhizodeposits using the national grain-yield database. Agron. J. 98, 622–636 (2006).
Google Scholar 
Morra, L., Cerrato, D., Bilotto, M. & Baiano, S. Introduction of Sorghum [Sorghum Bicolor (L.) Moench] green manure in rotations of head salads and baby leaf crops under greenhouse. Ital. J. Agron. 12, 753 (2017).
Google Scholar 
Ngidi, A., Shimelis, H., Abady, S., Figlan, S. & Chaplot, V. Response of Sorghum bicolor genotypes for yield and yield components and organic carbon storage in the shoot and root systems. Sci. Rep. 14, 9499 (2024).
Google Scholar 
Nour, A. E. M. & Weibel, D. E. Evaluation of root characteristics in grain Sorghum1. Agron. J. 70, 217–218 (1978).
Google Scholar 
Maestrini, B. et al. Carbon losses from pyrolysed and original wood in a forest soil under natural and increased N deposition. Biogeosciences 11, 5199–5213 (2014).
Google Scholar 
Worrall, J.D.. Industrial biochar characterization: Properties and potential applications. 37pp., Undergraduate Thesis, Lakehead University (2021).Download referencesAcknowledgementsThis work was supported by the United States Department of Agriculture (USDA) Natural Resources Conservation Service (GR0007378) and the National Science Foundation (2316278). We thank the ASC Clovis field staff for their help in maintaining plots and with routine farm operations.Author informationAuthors and AffiliationsDepartment of Plant and Environmental Sciences, New Mexico State University, Las Cruces, NM, USAPiumi Madhuwanthi, Rajan Ghimire & April UleryAgricultural Science Center, New Mexico State University, 2346 State Road 288, Clovis, NM, 88101, USARajan Ghimire & Sundar SapkotaDepartment of Agricultural and Extension Education, New Mexico State University, Las Cruces, NM, USAShannon Norris-ParishAuthorsPiumi MadhuwanthiView author publicationsSearch author on:PubMed Google ScholarRajan GhimireView author publicationsSearch author on:PubMed Google ScholarSundar SapkotaView author publicationsSearch author on:PubMed Google ScholarShannon Norris-ParishView author publicationsSearch author on:PubMed Google ScholarApril UleryView author publicationsSearch author on:PubMed Google ScholarContributions**PM: ** Writing – original draft, Investigation, Visualization, Formal analysis, Data curation, Methodology; **RG: ** Writing – original draft, Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Conceptualization; **SS: ** Writing – review & editing, Investigation, Formal analysis, Data curation, Validation; **SNP: ** Writing – review & editing, Resources, Funding acquisition, Supervision; **AU: ** Writing – review & editing, Validation, Supervision.Corresponding authorCorrespondence to
Rajan Ghimire.Ethics declarations

Competing interests
The authors declare no competing interests.

Statement for standard protocol
All methods were carried out in accordance with the relevant guidelines and regulations of the United States Department of Agriculture and New Mexico State University. The study was conducted following the standard GHG emissions and soil and plant sampling protocol.

Additional informationPublisher’s noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary InformationBelow is the link to the electronic supplementary material.Supplementary Material 1Rights and permissions
Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, 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 changes were made. 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/4.0/.
Reprints and permissionsAbout this articleCite this articleMadhuwanthi, P., Ghimire, R., Sapkota, S. et al. Contrasting effects of biochar and compost on greenhouse gas emissions and the global warming potential of semi-arid cropping systems.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-42554-4Download citationReceived: 31 October 2025Accepted: 26 February 2026Published: 06 March 2026DOI: https://doi.org/10.1038/s41598-026-42554-4Share 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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KeywordsSoil amendmentsNitrous oxide emissionsClimate changeDry environmentsHigh-frequency monitoring More

An analysis of hundreds of fish biomass surveys shows that warmer years combined with marine heatwaves can enhance regional population abundances in the cold edges of species’ biogeographical distributions, but contribute to population declines at warmer latitudes.

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Fig. 1: Opposite range-edge effects of marine heatwaves on the biomass of regional fish populations.

ReferencesPecl, G. T. et al. Science 355, eaai9214 (2017).Article 
PubMed 

Google Scholar 
Lenoir, J. et al. Nat. Ecol. Evol. 4, 1044–1059 (2020).Article 
PubMed 

Google Scholar 
Chaikin, S., González-Trujillo, J. D. & Araújo, M. B. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-026-03013-5 (2026).Article 
PubMed 

Google Scholar 
Maureaud, A. A. et al. Sci. Data 11, 24 (2024).Article 
PubMed 
PubMed Central 

Google Scholar 
European Commission, Joint Research Centre (JRC). EU Mediterranean and Black Sea fisheries independent survey data up to 2023. europa.eu http://data.europa.eu/89h/f25092c4-3f0f-449f-ba60-5fbfe385defc (2025).van Denderen, D. et al. Glob. Ecol. Biogeogr. 32, 1846–1857 (2023).Article 

Google Scholar 
Ceglar, A. & Toreti, A. npj Clim. Atmos. Sci. 4, 42 (2021).Article 

Google Scholar 
Curd, A. et al. Glob. Change Biol. 29, 631–647 (2023).Article 
CAS 

Google Scholar 
McLean, M. et al. Curr. Biol. 31, 4817–4823 (2021).Article 
CAS 
PubMed 

Google Scholar 
Marzloff, M. P. et al. Nat. Clim. Change 8, 873–878 (2018).Article 

Google Scholar 
Oliver, E. C. J. et al. Front. Mar. Sci. 6, 734 (2019).Article 

Google Scholar 
Download referencesAuthor informationAuthors and AffiliationsIfremer, DYNECO, Plouzané, FranceMartin P. MarzloffAuthorsMartin P. MarzloffView author publicationsSearch author on:PubMed Google ScholarCorresponding authorCorrespondence to
Martin P. Marzloff.Ethics declarations

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The author declares no competing interests.

Rights and permissionsReprints and permissionsAbout this articleCite this articleMarzloff, M.P. Range-edge effects of marine heatwaves.
Nat Ecol Evol (2026). https://doi.org/10.1038/s41559-026-03023-3Download citationPublished: 06 March 2026Version of record: 06 March 2026DOI: https://doi.org/10.1038/s41559-026-03023-3Share 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

Population status of the Greater Caribbean manatee (Trichechus manatus manatus) is unknown across most of its geographic range, including the western part of the Caribbean in Central America. No statistically derived estimates of abundance are available for this region, but the population appears to be low and possibly stable or declining. To assess abundance and distribution of manatees in Belize, we conducted aerial surveys and estimated abundance by accounting for detection using a framework designed for marine mammals. Using a generalized additive model (GAM), we predicted abundance of manatees in 2014 and 2022 and how it varied across space as a function of spatial habitat covariates. We estimated that in the sampled area (within 500 m of flight path) there were 479 (95% CI: 275–857) manatees in 2014 and 555 (95% CI: 316–998) in 2022. Manatee abundance was higher closer to the coast, freshwater, and shallow waters. These estimates are the first for Greater Caribbean manatees in the western Caribbean Sea that account for sources of error such as observer perception bias, availability bias and spatial variation. Monitoring and assessing the manatee population is important for managers tasked with developing effective strategies to balance the use of marine habitats with biodiversity conservation.

Data availability

The datasets generated as part of this study are available from the corresponding author Holly H. Edwards, [email protected].
ReferencesMignucci-Giannoni, A. et al. What’s in a name? Standardization of vernacular names for Trichechus manatus. Caribb. Nat. 98, 1–17 (2024).
Google Scholar 
Self-Sullivan, C. & Mignucci-Giannoni. A. A. Trichechus manatus ssp. manatus. IUCN Red List of Threatened Species. Version 2012.1. http://www.iucnredlist.org (2008).Morales-Vela, B., Quintana-Rizzo, E. & Mignucci-Giannoni, A. Trichechus manatus ssp. manatus. The IUCN Red List of Threatened Species 2024: e.T22105A43793924. (2024). https://www.iucnredlist.org/species/22105/43793924#assessment-information (2024).Endangered and Threatened Wildlife and Plants; Threatened Status for the Florida Manatee and Endangered Status for the Antillean Manatee, 90 F.R. 3131. (50 CFR part 17). proposed January 14, (2025). https://www.federalregister.gov/documents/2025/01/14/2025-00467/endangered-and-threatened-wildlife-and-plants-threatened-status-for-the-florida-manatee-and (2025).UNEP. Regional management plan for the West Indian manatee (Trichechus manatus). CEP Technical Report No. 51. UNEP Caribbean Environment Programme, Kingston https://wedocs.unep.org/bitstream/handle/20.500.11822/39756/CEP_TR_51-en.pdf?sequence=1&isAllowed=y (2010).Galves, C. G. et al. Increasing mortality of endangered Antillean manatees Trichechus manatus manatus due to watercraft collisions in Belize. Endanger. Species Res. 51, 103–113. https://doi.org/10.3354/esr01247 (2023).
Google Scholar 
Alvarez-Alemán, A. et al. Causes of mortality for endangered Antillean manatees in Cuba. Front. Mar. Sci. 8, 646021. https://doi.org/10.3389/fmars.2021.646021 (2021).
Google Scholar 
Domínguez Tejo, H. M. History and conservation status of the Antillean manatee Trichechus manatus manatus in Hispaniola. Oryx 55, 284–293. https://doi.org/10.1017/S0030605319000140 (2021).
Google Scholar 
Mignucci-Giannoni, A. et al. Manatee mortality in Puerto Rico. J. Environ Manage. 25, 189–198. https://doi.org/10.1007/s002679910015 (2000).
Google Scholar 
Galves, J. et al. Analysis of a long-term dataset of Antillean manatee strandings in Belize: implications for conservation. Oryx 57, 80–88. https://doi.org/10.1017/S0030605321000983 (2023).
Google Scholar 
Olivera-Gómez, L. D. & Mellink, E. Distribution of the Antillean manatee (Trichechus manatus manatus) as a function of habitat characteristics, in Bahı́a de Chetumal, Mexico. Biol. Conserv. 121, 127–133 (2005).
Google Scholar 
O’Shea, T. J. & Salisbury, C. A. Belize—a last stronghold for manatees in the Caribbean. Oryx 25, 156–164 (1991).
Google Scholar 
Edwards, H. H., Stone, S. B., Hines, E. M., Gomez, A. & Winning, B. E. Documenting manatee (Trichechus manatus manatus) presence at Turneffe Atoll, Belize, Central America and its conservation significance. Caribb. J. Sci. 48, 71–75 (2014).
Google Scholar 
González-Socoloske, D., Taylor, C. R. & Rendon Thompson, O. R. Distribution and conservation status of Antillean manatees (Trichechus manatus manatus) in Honduras. LAJAM 9, 123–131. https://doi.org/10.5597/lajam00176 (2011).Auil, N. E. Abundance and distribution trends of the West Indian manatee in the coastal zone of Belize: Implications for conservation (Master Thesis, Texas A&M University) (2004).Morales-Vela, B., Saldivar, J. A. P. & Mignucci-Giannoni, A. A. Status of the manatee (Trichechus manatus) along the Northern and Western Coasts of the Yucatan Peninsula, Mexico. Caribb. J. Sci. 39, 42–49 (2003).
Google Scholar 
Morales-Vela, B., Olivera-Gómez, D., Reynolds III, J. E. & Rathbun, G. B. Distribution and habitat use by manatees (Trichechus manatus manatus) in Belize and Chetumal Bay, Mexico. Biol. Conserv. 95, 67–75 (2000).
Google Scholar 
Marsh, H. & Sinclair, D. F. Correcting for visibility bias in strip transect aerial surveys of aquatic fauna. J. Wildl. Manage. 53 (4), 1017–1024 (1989).
Google Scholar 
Lefebvre, L. W., Ackerman, B. B., Portier, K. M. & Pollock, K. H. Aerial survey as a technique for estimating trends in manatee population size—problems and prospects in Population biology of the Florida manatee. (eds. T. J. O’Shea, B. B. Ackerman, and H. F. Percival) 63–74 National Biological Service Information and Technology Report No. 1, Washington, D.C., USA (1995).Pollock, K. H. et al. Separating components of detection probability in abundance estimation: an overview with diverse examples in Sampling rare and elusive species: concepts, designs and techniques for estimating population parameters (ed. Thompson, W. L.) 43–58 (Island, Washington, D.C., USA. (2004).
Google Scholar 
Pollock, K. H., Marsh, H., Lawler, I. & Alldredge, M. W. Estimating animal abundance in heterogeneous environments: an application to aerial surveys of dugong. J. Wildl. Manage. 70, 255–262 (2006).
Google Scholar 
Edwards, H. H., Pollock, K. H., Ackerman, B. B., Reynolds III, J. E.  & Powell, J. A. Estimation of detection probability in manatee aerial surveys at a winter aggregation site. J. Wildl. Manage. 71, 2052–2060. https://doi.org/10.2193/2005-645 (2007).
Google Scholar 
Martin, J. et al. Combining information for monitoring at large spatial scales: first statewide abundance estimate of the Florida manatee. Biol. Conserv. 186, 44–51 (2015).
Google Scholar 
Williams, B. K., Nichols, J. D. & Conroy, M. J. Analysis and management of animal populations (Academic, 2002).Hammond, P. S. et al. Cetacean abundance and distribution in European Atlantic shelf waters to inform conservation and management. Biol. Conserv. 164, 107–122. https://doi.org/10.1016/j.biocon.2013.04.010 (2013).
Google Scholar 
Kellner, K. F. & Swihart, R. K. Accounting for imperfect detection in ecology: A quantitative review. PLoS ONE. 9 (10), e111436. https://doi.org/10.1371/journal.pone.0111436 (2014).
Google Scholar 
Gowan, T. A., Moore, J. F., Edwards, H. H., Goode, A. B. C. & Martin, J. An approach to modeling abundance of marine wildlife over space and time using unstructured aerial surveys. J. Wildl. Manage. https://doi.org/10.1002/jwmg.70123 (2026).
Google Scholar 
Gowan, T. A., Edwards, H. H., Krzystan, A. M., Martin, J. & Hostetler, J. A. 2021–2022 Statewide abundance estimates for the Florida manatee. Florida Fish and Wildlife Conservation Commission, Fish and Wildlife Research Institute Technical Report No. 27 (2023).Collazo, J. A. et al. Population estimates of Antillean manatees in Puerto Rico: an analytical framework for aerial surveys using multi-pass removal sampling. J. Mammal. 100, 1340–1349 (2019).
Google Scholar 
Hostetler, J. A., Edwards, H. H., Martin, J. & Schueller, P. Updated statewide abundance estimates for the Florida manatee. Florida Fish and Wildlife Conservation Commission, Fish and Wildlife Research Institute. Technical Report No. 23 (2018).ESRI 2021. ArcGIS Pro: Release 2.9. Redlands, CA: Environmental Systems Research Institute (2021).Rycyk, A. et al. Manatee behavioral response to boats. Mar. Mamm. Sci. 34, 924–962. https://doi.org/10.1111/mms.12491 (2018).
Google Scholar 
Edwards, H. H., Koslovsky, S. M. & Reinert, T. R. Measuring Availability Bias in Florida Manatee (Trichechus manatus latirostris) Aerial Surveys Using Controlled Experiments and a Life-Sized Manatee Replica. Mar. Mamm. Sci. 42 (2), e70141. (2026). https://doi.org/10.1111/mms.70141
Google Scholar 
Royle, J. A. & Dorazio, R. M. Hierarchical modeling and inference in ecology. https://doi.org/10.1016/B978-0-12-374097-7.X0001-4 (2009).Fiske, I. & Chandler, R. Unmarked: an R package for fitting hierarchical models of wildlife occurrence and abundance. J. Stat. Softw. 43, 1–23. https://doi.org/10.18637/jss.v043.i10 (2011).
Google Scholar 
Miller, D. L., Burt, M. L., Rexstad, E. A. & Thomas. L. Spatial models for distance sampling data: recent developments and future directions. Methods Ecol. Evol. 4, 1001–1010 (2013).
Google Scholar 
Wood, S. N. Generalized Additive Models: An Introduction with R, Second Edition. CRC Press (2017).Hartig, F. _DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models_. R package version 0.4.7, (2024). https://CRAN.R-project.org/package=DHARMaCastelblanco-Martínez, D. N. et al. Movement patterns of Antillean manatees in Chetumal Bay (Mexico) and coastal Belize: A challenge for regional conservation. Mar. Mamm. Sci. 29, 166–182. https://doi.org/10.1111/j.1748-7692.2012.00602.x (2013).
Google Scholar 
Hunter, M. E., Auil-Gomez, N. E., Tucker, K. P., Bonde, R. K. & Powell, J. Mcguire. Low genetic variation and evidence of limited dispersal in the regionally important Belize manatee. Anim. Conserv. 13, 592–602 (2010).
Google Scholar 
Hostetler, J. A. et al. Reconstructing population dynamics of a threatened marine mammal using multiple data sets. Sci. Rep. 11, 2702. https://doi.org/10.1038/s41598-021-81478-z (2021).
Google Scholar 
Gulland, F. M. et al. A review of climate change effects on marine mammals in United States waters: past predictions, observed impacts, current research and conservation imperatives. Clim. Chang. Ecol. 3, 100054 (2022).
Google Scholar 
Edwards, H. H. Potential impacts of climate change on warmwater megafauna: the Florida manatee example (Trichechus manatus latirostris). Clim. Change. 121, 727–738 (2013).
Google Scholar 
Deeks, E., Kratina, P., Normande, I. & Da Silva Cerqueira, A. Dawson. Proximity to freshwater and seagrass availability mediate the impacts of climate change on the distribution of the West Indian manatee. LAJAM 19, 15–31. https://doi.org/10.5597/lajam00321 (2024).
Google Scholar 
Download referencesAcknowledgementsWe would like to thank the following: Funding was provided by Belize Marine Fund, Harvest Caye Conservation Foundation, Light Hawk, Inc., Columbus Zoo and Aquarium through the Wider Caribbean Manatee Alliance and the Clearwater Marine Aquarium Research Institute Belize (CMARI). The 2022 research was conducted under a Memorandum of Understanding (MOU) and research permits issued by the Fisheries Department and the Ministry of Blue Economy & Civil Aviation, Government of Belize. The 2014 surveys were conducted under research permits issued by the Belize Forestry Department, and Coastal Zone Management Authority & Institute. Logistic and aerial support in 2014 was provided by Lighthawk and Bill Rush (volunteer pilot for Lighthawk, Inc.) and in 2022 by Technology Service Corporation, Chris Kluckhuhn, ASI Division Manager and pilot. We also thank observer Samir Rosado for his help in the 2014 survey.Author informationAuthors and AffiliationsIndependent Researcher, 7023 Mango Ave. South, St Petersburg, FL, 33707, USAHolly H. EdwardsMoore Ecological Analysis and Management, LLC, 7901 4th St N. Ste 300, St. Petersburg, FL, 33702, USAJennifer F. MooreWildlife Conservation Society, 1755 Coney Drive, Belize City, BelizeNicole Auil GomezClearwater Marine Aquarium Research Institute, Belmopan City, FL, USAJamal A. Galves & Celeshia Guy GalvesIndependent Researcher, Belize City, BelizeAngeline ValentineUniversity of Florida, Florida Sea Grant College Program, Green Bldg. 6700 Clark Road, Sarasota, FL, 34241, USAArmando J. UbedaClearwater Marine Aquarium Research Institute, 249 Windward Passage, Clearwater, FL, 33767, USAAnmari Álvarez-AlemánAuthorsHolly H. EdwardsView author publicationsSearch author on:PubMed Google ScholarJennifer F. MooreView author publicationsSearch author on:PubMed Google ScholarNicole Auil GomezView author publicationsSearch author on:PubMed Google ScholarJamal A. GalvesView author publicationsSearch author on:PubMed Google ScholarAngeline ValentineView author publicationsSearch author on:PubMed Google ScholarCeleshia Guy GalvesView author publicationsSearch author on:PubMed Google ScholarArmando J. UbedaView author publicationsSearch author on:PubMed Google ScholarAnmari Álvarez-AlemánView author publicationsSearch author on:PubMed Google ScholarContributionsHHE developed and implemented the field methods, acted as the primary aerial observer (2014, 2022), entered and compiled data and drafted the manuscript. JFM analysed data and contributed to writing and editing the manuscript, NAG and AV assisted with survey logistics, acted as aerial observers and contributed to writing and editing the manuscript. JAG and CGG assisted with survey logistics and contributed to writing and editing the manuscript. AJU procured funding for the project (2014) through Lighthawk, LLC, coordinated survey logistics, acted as an aerial observer and contributed to writing and editing the manuscript. AAA procured funding for the project (2022) through Clearwater Marine Aquarium, coordinated survey logistics and contributed to writing and editing the manuscript.Corresponding authorCorrespondence to
Holly H. Edwards.Ethics declarations

Competing interests
The authors declare no competing interests.

Additional informationPublisher’s noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary InformationBelow is the link to the electronic supplementary material.Supplementary Material 1Rights and permissions
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 articleEdwards, H.H., Moore, J.F., Gomez, N.A. et al. First abundance estimate for greater Caribbean manatees (Trichechus Manatus Manatus) in Belize.
Sci Rep (2026). https://doi.org/10.1038/s41598-026-38453-3Download citationReceived: 02 April 2025Accepted: 29 January 2026Published: 06 March 2026DOI: https://doi.org/10.1038/s41598-026-38453-3Share 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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KeywordsAntillean manateeAbundanceDistributionAerial surveyGAMCaribbean conservation More

AbstractWetlands face threats from climate change and human activities worldwide, yet the status of African wetlands remains unknown. This study mapped African wetlands and assessed area loss, drivers, and future trends under climate change using 270,000 sampling points, 810,000 Landsat images, and soil moisture data from 14 CMIP6 models. The results reveal no large-scale loss of wetlands in Africa from 1984 to 2021 (0.51% net loss), with the loss concentrated in coastal areas (9.64% net loss), while inland wetlands show a slight increase in area (0.50% net increase). A comparison of the time series of wetland area and related drivers showed that the change of inland wetland area is closely related to climate change, and human activities have exacerbated the loss of coastal wetlands. TOPMODEL projections suggest an upward trend in inland wetland area by 2100, but uncertainty persists and inland wetlands remain at risk of loss in the future.

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Data availability

All data used in this study are freely available from public repositories. The Landsat images used in this study are available from the US Geological Survey (http://earthexplorer.usgs.gov) and Google Earth Engine (https:// earthengine.google.com). The data of African national boundaries and 6-meter water depth boundaries are available from https://developers.google.com/earth-engine/datasets/catalog/USDOS_LSIB_SIMPLE_2017 and https://developers.google.com/earth-engine/datasets/catalog/NOAA_NGDC_ETOPO1. Human Footprint dataset (HFP) can be accessed freely at the figshare repository (https://doi.org/10.6084/m9.figshare.16571064). Temperature data are available from https://developers.google.com/earth-engine/datasets/catalog/ECMWF_ERA5_MONTHLY. Precipitation data are available from https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_TERRACLIMATE, https://developers.google.com/earth-engine/datasets/catalog/NASA_FLDAS_NOAH01_C_GL_M_V001, and https://www.ncei.noaa.gov/data/global-precipitation-climatology-project-gpcp-monthly/access/. PDSI is available from https://developers.google.com/earth-engine/datasets/catalog/IDAHO_EPSCOR_TERRACLIMATE. Soil moisture is available from https://developers.google.com/earth-engine/datasets/catalog/NASA_FLDAS_NOAH01_C_GL_M_V001. CTI data is available from https://catalogue.ceh.ac.uk/documents/6b0c4358-2bf3-4924-aa8f-793d468b92be. Africa watershed vector data is available from https://developers.google.com/earth-engine/datasets/catalog/WWF_HydroSHEDS_v1_Basins_hybas_8. CMIP6 data is available from https://esgf-node.llnl.gov/search/cmip6/. The wetland maps for ten historical periods and the wetland simulation results for future periods produced in this study have been deposited in the Zenodo database and are provided as open data (https://doi.org/10.5281/zenodo.17865977).
ReferencesGardner, R. C. & Finlayson, C. M. Global Wetland Outlook: State of the World’s Wetlands and their Services to People (Ramsar Convention Secretariat, 2018).Fluet-Chouinard, E. et al. Extensive global wetland loss over the past three centuries. Nature 614, 281–286 (2023).
Google Scholar 
Matthews, G. V. T. The Ramsar Convention on Wetlands: Its History and Development. (Ramsar Convention Bureau, 1993).Juffe-Bignoli, D. et al. Achieving Aichi Biodiversity Target 11 to improve the performance of protected areas and conserve freshwater biodiversity. Aquat. Conserv. Mar. Freshwater Ecosyst. 26, 133–151 (2016).
Google Scholar 
Lee, B. X. et al. Transforming our world: implementing the 2030 agenda through sustainable development goal indicators. J. Public Health Policy 37, 13–31 (2016).
Google Scholar 
Rogelj, J. et al. Paris Agreement climate proposals need a boost to keep warming well below 2 C. Nature 534, 631–639 (2016).
Google Scholar 
Davidson, N. C. et al. Trends in the ecological character of the world’s wetlands. Mar. Freshwater Res. 71, 127–138 (2019).
Google Scholar 
Zedler, J. B. & Kercher, S. Wetland resources: status, trends, ecosystem services, and restorability. Annu. Rev. Environ. Resour. 30, 39–74 (2005).
Google Scholar 
Hu, S., Niu, Z., Chen, Y., Li, L. & Zhang, H. Global wetlands: potential distribution, wetland loss, and status. Sci. Total Environ. 586, 319–327 (2017).
Google Scholar 
Li, A. et al. Mapping African wetlands for 2020 using multiple spectral, geo-ecological features and Google Earth Engine. ISPRS J. Photogramm. Remote Sens. 193, 252–268 (2022).
Google Scholar 
Mahdavi, S. et al. Remote sensing for wetland classification: a comprehensive review. GISci. Remote Sens. 55, 623–658 (2017).
Google Scholar 
Thamaga, K. H., Dube, T. & Shoko, C. Advances in satellite remote sensing of the wetland ecosystems in Sub-Saharan Africa. Geocarto Int. 37, 5891–5913 (2022).
Google Scholar 
Murray, N. J. et al. The global distribution and trajectory of tidal flats. Nature 565, 222–225 (2019).
Google Scholar 
Murray, N. J. et al. High-resolution mapping of losses and gains of Earth’s tidal wetlands. Science 376, 744–749 (2022).
Google Scholar 
Abatzoglou, J. T., Dobrowski, S. Z., Parks, S. A. & Hegewisch, K. C. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 1–12 (2018).
Google Scholar 
Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorolog. Soc. 146, 1999–2049 (2020).
Google Scholar 
McNally, A. et al. A land data assimilation system for sub-Saharan Africa food and water security applications. Sci. Data 4, 1–19 (2017).
Google Scholar 
Masih, I., Maskey, S., Mussá, F. E. F. & Trambauer, P. A review of droughts on the African continent: a geospatial and long-term perspective. Hydrol. Earth Syst. Sci. 18, 3635–3649 (2014).
Google Scholar 
Venter, O. et al. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nat. Commun. 7, 12558 (2016).
Google Scholar 
Lu, M. et al. Anthropogenic disturbances caused declines in the wetland area and carbon pool in China during the last four decades. Global Change Biol 27, 3837–3845 (2021).
Google Scholar 
Zhou, L. et al. Widespread decline of Congo rainforest greenness in the past decade. Nature 509, 86–90 (2014).
Google Scholar 
O’Neill, B. C. et al. The scenario model intercomparison project (ScenarioMIP) for CMIP6. Geosci. Model Dev. 9, 3461–3482 (2016).
Google Scholar 
Almazroui, M. et al. Projected change in temperature and precipitation over Africa from CMIP6. Earth Syst. Environ. 4, 455–475 (2020).
Google Scholar 
Xi, Y., Peng, S., Ciais, P. & Chen, Y. Future impacts of climate change on inland Ramsar wetlands. Nat. Clim. Change 11, 45–51 (2021).
Google Scholar 
Tootchi, A., Jost, A. & Ducharne, A. Multi-source global wetland maps combining surface water imagery and groundwater constraints. Earth Syst. Sci. Data 11, 189–220 (2019).
Google Scholar 
Stocker, B., Spahni, R. & Joos, F. DYPTOP: a cost-efficient TOPMODEL implementation to simulate sub-grid spatio-temporal dynamics of global wetlands and peatlands. Geosci. Model Dev. 7, 3089–3110 (2014).
Google Scholar 
Zhang, Z., Zimmermann, N. E., Kaplan, J. O. & Poulter, B. Modeling spatiotemporal dynamics of global wetlands: comprehensive evaluation of a new sub-grid TOPMODEL parameterization and uncertainties. Biogeosciences 13, 1387–1408 (2016).
Google Scholar 
Davidson, N. C. How much wetland has the world lost? Long-term and recent trends in global wetland area. Mar. Freshwater Res. 65, 934–941 (2014).
Google Scholar 
Assessment, M. E. Ecosystems and Human Well-being: Wetlands and Water (World Resources Institute, 2005).Hu, S., Niu, Z. & Chen, Y. Global wetland datasets: a review. Wetlands 37, 807–817 (2017).
Google Scholar 
Gxokwe, S., Dube, T. & Mazvimavi, D. Multispectral remote sensing of wetlands in semi-arid and arid areas: a review on applications, challenges and possible future research directions. Remote Sens 12, 4190 (2020).
Google Scholar 
Rejou-Mechain, M. et al. Unveiling African rainforest composition and vulnerability to global change. Nature 593, 90–94 (2021).
Google Scholar 
Li, X., Bellerby, R., Craft, C. & Widney, S. E. Coastal wetland loss, consequences, and challenges for restoration. Anthropocene Coasts 1, 15 (2018).
Google Scholar 
Song, X.-P. et al. Global land change from 1982 to 2016. Nature 560, 639–643 (2018).
Google Scholar 
Venter, O. et al. Global terrestrial human footprint maps for 1993 and 2009. Sci. Data 3, 1–10 (2016).
Google Scholar 
Mao, D. et al. Remote observations in China’s Ramsar sites: wetland dynamics, anthropogenic threats, and implications for sustainable development goals. J. Rem. Sensing 2021, 1–13 (2021).
Google Scholar 
Lloréns, J. L. P. Impacts of climate change on wetland ecosystems. Water Supply 7, 8 (2008).
Google Scholar 
Day, J. W. et al. Consequences of Climate Change on the Ecogeomorphology of Coastal Wetlands. Estuaries Coasts 31, 477–491 (2008).
Google Scholar 
Schuerch, M. et al. Future response of global coastal wetlands to sea-level rise. Nature 561, 231–234 (2018).
Google Scholar 
Saintilan, N. et al. Thresholds of mangrove survival under rapid sea level rise. Science 368, 1118–1121 (2020).
Google Scholar 
Junk, W. J. et al. Current state of knowledge regarding the world’s wetlands and their future under global climate change: a synthesis. Aquat. Sci. 75, 151–167 (2013).
Google Scholar 
Erwin, K. L. Wetlands and global climate change: the role of wetland restoration in a changing world. Wetlands Ecol. Manag. 17, 71–84 (2008).
Google Scholar 
Maidment, R. I., Allan, R. P. & Black, E. Recent observed and simulated changes in precipitation over Africa. Geophys. Res. Lett. 42, 8155–8164 (2015).
Google Scholar 
Finlayson, C. M., Davis, J. A., Gell, P. A., Kingsford, R. T. & Parton, K. A. The status of wetlands and the predicted effects of global climate change: the situation in Australia. Aquat. Sci. 75, 73–93 (2011).
Google Scholar 
Sheffield, J., Wood, E. F. & Roderick, M. L. Little change in global drought over the past 60 years. Nature 491, 435–438 (2012).
Google Scholar 
Trenberth, K. E. et al. Global warming and changes in drought. Nat. Clim. Change 4, 17–22 (2013).
Google Scholar 
Berg, A. & McColl, K. A. No projected global drylands expansion under greenhouse warming. Nat. Clim. Change 11, 331–337 (2021).
Google Scholar 
Engelbrecht, F. et al. Projections of rapidly rising surface temperatures over Africa under low mitigation. Environ. Res. Lett. 10, 085004 (2015).
Google Scholar 
Dosio, A. et al. Projected future daily characteristics of African precipitation based on global (CMIP5, CMIP6) and regional (CORDEX, CORDEX-CORE) climate models. Clim. Dyn. 57, 3135–3158 (2021).
Google Scholar 
Dosio, A., Lennard, C. & Spinoni, J. Projections of indices of daily temperature and precipitation based on bias-adjusted CORDEX-Africa regional climate model simulations. Clim. Change 170, 13 (2022).
Google Scholar 
Bobde, V., Akinsanola, A., Folorunsho, A., Adebiyi, A. & Adeyeri, O. Projected regional changes in mean and extreme precipitation over Africa in CMIP6 models. Environ. Res. Lett. 19, 074009 (2024).Wood, A. P., Dixon, A. & McCartney, M. P. Wetland Management and Sustainable Livelihoods in Africa (Routledge/Earthscan from Routledge, 2013).Sayre, A. P. Africa Vol. 8 (Twenty-First Century Books, 1999).Rebelo, L.-M., McCartney, M. P. & Finlayson, C. M. Wetlands of Sub-Saharan Africa: distribution and contribution of agriculture to livelihoods. Wetlands Ecol. Manag. 18, 557–572 (2010).
Google Scholar 
Kabiri, S. et al. Detecting level of wetland encroachment for urban agriculture in Uganda using hyper-temporal remote sensing. AAS Open Res. 3, 18 (2020).
Google Scholar 
Finlayson, M., Davidson, N. C., Pritchard, D., Milton, G. R. & MacKay, H. The Ramsar Convention and ecosystem-based approaches to the wise use and sustainable development of wetlands. J. Int. Wildl. Law Policy 14, 176–198 (2011).
Google Scholar 
Mao, D. et al. National wetland mapping in China: a new product resulting from object-based and hierarchical classification of Landsat 8 OLI images. ISPRS J. Photogramm. Remote Sens. 164, 11–25 (2020).
Google Scholar 
Amante, C. & Eakins, B. W. ETOPO1 Arc-minute Global Relief Model: Procedures, Data Sources and Analysis (National Centers for Environmental Information, 2009).Breiman, L. Random forests. Machine learning 45, 5–32 (2001).
Google Scholar 
Belgiu, M. & Drăguţ, L. Random forest in remote sensing: a review of applications and future directions. ISPRS J. Photogramm. Remote Sens. 114, 24–31 (2016).
Google Scholar 
Mu, H. et al. A global record of annual terrestrial Human Footprint dataset from 2000 to 2018. Sci. Data 9, 176 (2022).
Google Scholar 
Download referencesAcknowledgementsThis research was jointly funded by the National Key Research and Development Program of China (2020YFA0714103), which supported S.C. and A.L.; the National Natural Science Foundation of China (42494821), which supported A.L. and D.M.; and the Science and Technology Development Program of Jilin Province (YDZJ202501ZYTS579), which supported A.L.Author informationAuthors and AffiliationsCollege of Geo-Exploration Science and Technology, Jilin University, Changchun, ChinaAnzhen Li & Shengbo ChenState Key Laboratory of Black Soils Conservation and Utilization, Northeast Institute of Geography and Agroecology, Chinese Academy of Sciences, Changchun, ChinaAnzhen Li, Kaishan Song, Zongming Wang & Dehua MaoSchool of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing, ChinaYuanzhi ZhangDepartment of Architecture and Civil Engineering, Faculty of Engineering, City University of Hong Kong, Hong Kong, ChinaYuanzhi ZhangAuthorsAnzhen LiView author publicationsSearch author on:PubMed Google ScholarShengbo ChenView author publicationsSearch author on:PubMed Google ScholarKaishan SongView author publicationsSearch author on:PubMed Google ScholarYuanzhi ZhangView author publicationsSearch author on:PubMed Google ScholarZongming WangView author publicationsSearch author on:PubMed Google ScholarDehua MaoView author publicationsSearch author on:PubMed Google ScholarContributionsA.L., S.C. and Y.Z. conceptualized the project, acquired funding. A.L. developed wetland classification, conducted the model simulations and analysis with support from D.M., K.S. and Z.W., and drafted the manuscript with contributions from all co-authors. S.C., K.S., Y.Z., Z.W. and D.M. participated in the discussion and analysis of the results and edited the manuscript. All authors reviewed the results, revised, and approved the manuscript.Corresponding authorsCorrespondence to
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Reprints and permissionsAbout this articleCite this articleLi, A., Chen, S., Song, K. et al. African inland wetland area on the rise during the 21st century.
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