Ecology

AbstractBacterial growth dynamics, typically represented by growth curves, are fundamental yet complex features of living populations. Traditional analyses focusing on specific parameters often overlook the full temporal patterns of growth. Here, we systematically investigated how genomic and environmental factors shape bacterial growth dynamics by analyzing 870 growth curves from five Escherichia coli strains with varying genome sizes cultured in 29 chemically defined media. Using dynamic time warping, clustering, and gradient boosting decision trees, we found that environmental components, especially glucose, primarily determine overall growth curve patterns, while genome size governs detailed growth parameters such as lag time, growth rate, and carrying capacity. Notably, finer clustering revealed increased genomic influence and decreased environmental impact, suggesting a hierarchical interaction where the environment modulates broad growth behavior and the genome fine-tunes specific growth responses. These findings provide insights into the coordinated roles of genome and environment in bacterial population dynamics, advancing our understanding of microbial growth regulation.

Similar content being viewed by others

Population Dynamics of Escherichia coli Growing under Chemically Defined Media

Article
Open access
11 June 2025

Data-driven discovery of the interplay between genetic and environmental factors in bacterial growth

Article
Open access
24 December 2024

Experimental mapping of bacterial fitness landscapes reveals eco-evolutionary fingerprints

Article
Open access
23 September 2025

Data availability

All data generated or analyzed in this study are available within the paper and its Supplementary Information.
Code availability

The codes are available at https://github.com/g2kajun-dev/growth-curve-analysis.
ReferencesKlumpp, S. & Hwa, T. Bacterial growth: Global effects on gene expression, growth feedback and proteome partition. Curr. Opin. Biotechnol. 28, 96–102. https://doi.org/10.1016/j.copbio.2014.01.001 (2014).
Google Scholar 
Scott, M. & Hwa, T. Bacterial growth laws and their applications. Curr. Opin. Biotechnol. 22(4), 559–565. https://doi.org/10.1016/j.copbio.2011.04.014 (2011).
Google Scholar 
Ferenci, T. Growth of bacterial cultures’ 50 years on: Towards an uncertainty principle instead of constants in bacterial growth kinetics. Res. Microbiol. 150(7), 431–438. https://doi.org/10.1016/S0923-2508(99)00114-X (1999).
Google Scholar 
Maier, R. M. Bacterial growth. In: Maier RM, Pepper IL, Gerba CP, editors. Environmental Microbiology 37–54 (2009).Ughy, B. et al. Reconsidering dogmas about the growth of bacterial populations. Cells 12(10), 1430. https://doi.org/10.3390/cells12101430 (2023).
Google Scholar 
Winsor, C. P. The Gompertz curve as a growth curve. Proc. Natl. Acad. Sci. U.S.A. 18(1), 1–8. https://doi.org/10.1073/pnas.18.1.1 (1932).
Google Scholar 
Zwietering, M. H., Jongenburger, I., Rombouts, F. M. & van ‘t Riet, K. Modeling of the bacterial growth curve. Appl. Environ. Microbiol. 56(6), 1875–1881. https://doi.org/10.1128/aem.56.6.1875-1881.1990 (1990).
Google Scholar 
Tsuchiya, K. et al. A decay effect of the growth rate associated with genome reduction in Escherichia coli. BMC Microbiol. 18(1), 101. https://doi.org/10.1186/s12866-018-1242-4 (2018).
Google Scholar 
Verissimo, A., Paixao, L., Neves, A. R. & Vinga, S. BGFit: Management and automated fitting of biological growth curves. BMC Bioinform. 14, 283. https://doi.org/10.1186/1471-2105-14-283 (2013).
Google Scholar 
Navarro-Perez, M. L., Fernandez-Calderon, M. C. & Vadillo-Rodriguez, V. Decomposition of growth curves into growth rate and acceleration: A novel procedure to monitor bacterial growth and the time-dependent effect of antimicrobials. Appl. Environ. Microbiol. 88(3), e0184921. https://doi.org/10.1128/AEM.01849-21 (2022).
Google Scholar 
Sprouffske, K. & Wagner, A. Growthcurver: An R package for obtaining interpretable metrics from microbial growth curves. BMC Bioinform. 17, 172. https://doi.org/10.1186/s12859-016-1016-7 (2016).
Google Scholar 
Towbin, B. D. et al. Optimality and sub-optimality in a bacterial growth law. Nat. Commun. 8, 14123. https://doi.org/10.1038/ncomms14123 (2017).
Google Scholar 
Vadia, S. & Levin, P. A. Growth rate and cell size: A re-examination of the growth law. Curr. Opin. Microbiol. 24, 96–103. https://doi.org/10.1016/j.mib.2015.01.011 (2015).
Google Scholar 
Kurokawa, M., Seno, S., Matsuda, H. & Ying, B. W. Correlation between genome reduction and bacterial growth. DNA Res. Int. J. Rapid Publ. Rep. Genes Genomes. 23(6), 517–525. https://doi.org/10.1093/dnares/dsw035 (2016).
Google Scholar 
Aida, H., Hashizume, T., Ashino, K. & Ying, B. W. Machine learning-assisted discovery of growth decision elements by relating bacterial population dynamics to environmental diversity. Elife 11, e76846. https://doi.org/10.7554/eLife.76846 (2022).Peleg, M. & Corradini, M. G. Microbial growth curves: What the models tell us and what they cannot. Crit. Rev. Food Sci. Nutr. 51(10), 917–945. https://doi.org/10.1080/10408398.2011.570463 (2011).
Google Scholar 
Huang, L. Optimization of a new mathematical model for bacterial growth. Food Control. 32, 283–288. https://doi.org/10.1016/j.foodcont.2012.11.019 (2013).
Google Scholar 
Sant’Ana, A. S., Franco, B. D. & Schaffner, D. W. Modeling the growth rate and lag time of different strains of Salmonella enterica and Listeria monocytogenes in ready-to-eat lettuce. Food Microbiol. 30(1), 267–273. https://doi.org/10.1016/j.fm.2011.11.003 (2012).
Google Scholar 
Koseki, S. & Nonaka, J. Alternative approach to modeling bacterial lag time, using logistic regression as a function of time, temperature, pH, and sodium chloride concentration. Appl. Environ. Microbiol. 78(17), 6103–6112. https://doi.org/10.1128/AEM.01245-12 (2012).
Google Scholar 
Kremling, A., Geiselmann, J., Ropers, D. & de Jong, H. An ensemble of mathematical models showing diauxic growth behaviour. BMC Syst. Biol. 12(1), 82. https://doi.org/10.1186/s12918-018-0604-8 (2018).
Google Scholar 
Tonner, P. D. et al. A bayesian non-parametric mixed-effects model of microbial growth curves. PLoS Comput. Biol. 16(10), e1008366. https://doi.org/10.1371/journal.pcbi.1008366 (2020).
Google Scholar 
Loomis, W. F. & Magasanik, B. Glucose-Lactose Diauxie in Escherichia coli. J. Bacteriol. 93(4), 1397–1401. https://doi.org/10.1128/jb.93.4.1397-1401.1967 (1967).
Google Scholar 
Wong, P., Gladney, S. & Keasling, J. D. Mathematical model of thelacoperon: inducer exclusion, catabolite repression, and diauxic growth on glucose and lactose. Biotechnol. Prog. 13, 132–143. https://doi.org/10.1021/bp970003 (1997).
Google Scholar 
Russell, J. B. & Cook, G. M. Energetics of bacterial growth: Balance of anabolic and catabolic reactions. Microbiol. Rev. 59(1), 48–62 (1995).
Google Scholar 
Chu, D. & Barnes, D. J. The lag-phase during diauxic growth is a trade-off between fast adaptation and high growth rate. Sci. Rep. 6(1), 25191. https://doi.org/10.1038/srep25191 (2016).
Google Scholar 
Salvy, P. & Hatzimanikatis, V. Emergence of diauxie as an optimal growth strategy under resource allocation constraints in cellular metabolism. Proc. Natl. Acad. Sci. 118(8), e2013836118. https://doi.org/10.1073/pnas.2013836118 (2021).
Google Scholar 
Aida, H. & Ying, B-W. Population dynamics of Escherichia coli growing under chemically defined media. Sci. Data 12, 984 https://doi.org/10.1038/s41597-025-05356-3 (2025).Hilau, S., Katz, S., Wasserman, T., Hershberg, R. & Savir, Y. Density-dependent effects are the main determinants of variation in growth dynamics between closely related bacterial strains. PLoS Comput. Biol. 18(10), e1010565. https://doi.org/10.1371/journal.pcbi.1010565 (2022).
Google Scholar 
Ashino, K., Sugano, K., Amagasa, T. & Ying, B. W. Predicting the decision making chemicals used for bacterial growth. Sci. Rep. 9(1), 7251. https://doi.org/10.1038/s41598-019-43587-8 (2019).
Google Scholar 
Senin, P. Dynamic time warping algorithm review. Inf. Comput. Sci. Dep. Univ. Hawaii Manoa Honolulu, USA 855(1–23), 40 (2008).Salvador, S. & Chan, P. Toward accurate dynamic time warping in linear time and space. Intell. Data Anal. 11, 561–580. https://doi.org/10.3233/IDA-2007-11508 (2007).
Google Scholar 
Sakoe, H. & Chiba, S. Dynamic programming algorithm optimization for spoken word recognition. IEEE Trans. Acoust. Speech Signal Process. 26(1), 43–49. https://doi.org/10.1109/TASSP.1978.1163055 (1978).
Google Scholar 
Cao, Y. Y., Yomo, T. & Ying, B. W. Clustering of bacterial growth dynamics in response to growth media by dynamic time warping. Microorganisms 8(3), 331. https://doi.org/10.3390/microorganisms8030331 (2020).
Google Scholar 
Aida, H. & Ying, B-W. Data-driven discovery of the interplay between genetic and environmental factors in bacterial growth. Commun. Biology. 7(1), 1691. https://doi.org/10.1038/s42003-024-07347-3 (2024).Matsui, Y., Nagai, M. & Ying, B. W. Growth rate-associated transcriptome reorganization in response to genomic, environmental, and evolutionary interruptions. Front. Microbiol. 14, 1145673. https://doi.org/10.3389/fmicb.2023.1145673 (2023).
Google Scholar 
Lundberg, S. M. & Lee, S-I. A unified approach to interpreting model predictions. https://doi.org/10.48550/arXiv.1705.07874 (2017).Fraebel, D. T. et al. Environment determines evolutionary trajectory in a constrained phenotypic space. Elife 6, e24669. https://doi.org/10.7554/eLife.24669 (2017).Nagai, M., Kurokawa, M. & Ying, B. W. The highly conserved chromosomal periodicity of transcriptomes and the correlation of its amplitude with the growth rate in Escherichia coli. DNA Research: Int. J. Rapid Publication Rep. Genes Genomes. 27(3), dsaa018. https://doi.org/10.1093/dnares/dsaa018 (2020).Liu, L., Kurokawa, M., Nagai, M., Seno, S. & Ying, B. W. Correlated chromosomal periodicities according to the growth rate and gene expression. Sci. Rep. 10(1), 15531. https://doi.org/10.1038/s41598-020-72389-6 (2020).
Google Scholar 
Long, A. M., Hou, S., Ignacio-Espinoza, J. C. & Fuhrman, J. A. Benchmarking microbial growth rate predictions from metagenomes. ISME J. 15(1), 183–195. https://doi.org/10.1038/s41396-020-00773-1 (2021).
Google Scholar 
Wytock, T. P. & Motter, A. E. Predicting growth rate from gene expression. Proc. Natl. Acad. Sci. U.S.A. 116(2), 367–372. https://doi.org/10.1073/pnas.1808080116 (2019).
Google Scholar 
Wytock, T. P. et al. Extreme antagonism arising from gene-environment interactions. Biophys. J. 119(10), 2074–2086. https://doi.org/10.1016/j.bpj.2020.09.038 (2020).
Google Scholar 
Scheuerl, T. et al. Bacterial adaptation is constrained in complex communities. Nat. Commun. 11(1), 754. https://doi.org/10.1038/s41467-020-14570-z (2020).Bank, C., Epistasis and adaptation on fitness landscapes. Annu. Rev. Ecol. Evol. Syst. 53, 457–479. https://doi.org/10.1146/annurev-ecolsys-102320-112153 (2022).Cvijovic, I., Nguyen Ba, A. N. & Desai, M. M. Experimental studies of evolutionary dynamics in microbes. Trends Genet. 34(9), 693–703. https://doi.org/10.1016/j.tig.2018.06.004 (2018).
Google Scholar 
DelaFuente, J., Diaz-Colunga, J., Sanchez, A. & San Millan, A. Global epistasis in plasmid-mediated antimicrobial resistance. Mol. Syst. Biol. 20(4), 311–320. https://doi.org/10.1038/s44320-024-00012-1 (2024).
Google Scholar 
Ying, B. W., Seno, S., Kaneko, F., Matsuda, H. & Yomo, T. Multilevel comparative analysis of the contributions of genome reduction and heat shock to the Escherichia coli transcriptome. BMC Genom. 14, 25. https://doi.org/10.1186/1471-2164-14-25 (2013).
Google Scholar 
Fink, J. W. & Manhart, M. How do microbes grow in nature? The role of population dynamics in microbial ecology and evolution. Curr. Opin. Syst. Biol. 36, 100470. https://doi.org/10.1016/j.coisb.2023.100470 (2023).Borer, B. & Or, D. Spatiotemporal metabolic modeling of bacterial life in complex habitats. Curr. Opin. Biotechnol. 67, 65–71. https://doi.org/10.1016/j.copbio.2021.01.004 (2021).
Google Scholar 
Pentney, W. & Meilă, M. Spectral clustering of biological sequence data. In: Proceedings of the AAAI Conference on Artificial Intelligence, 20 845–850 (2005).Löffler, M., Zhang, A. Y. & Zhou, H. H. Optimality of spectral clustering in the Gaussian mixture model. Ann. Stat. 49(5), 2506–2530. https://doi.org/10.1214/20-aos2044 (2021).Mahmood, H., Mehmood, T. & Al-Essa, L. A. Optimizing clustering algorithms for anti-microbial evaluation data: A majority score-based evaluation of K-means, Gaussian mixture model, and multivariate T-distribution mixtures. IEEE Access. ;11, 79793–79800. https://doi.org/10.1109/access.2023.3288344 (2023).Sharma, A. et al. Hierarchical maximum likelihood clustering approach. IEEE Trans. Biomed. Eng. 64(1), 112–122. https://doi.org/10.1109/TBME.2016.2542212 (2017).
Google Scholar 
Warren Liao, T. Clustering of time series data: A survey. Pattern Recogn. 38, 1857–1874. https://doi.org/10.1016/j.patcog.2005.01.025 (2005).
Google Scholar 
De Souto, M. C., Costa, I. G., De Araujo, D. S., Ludermir, T. B. & Schliep, A. Clustering cancer gene expression data: A comparative study. BMC Bioinform. 9(1), 497. https://doi.org/10.1186/1471-2105-9-497 (2008).
Google Scholar 
Ashari, I. F., Dwi Nugroho, E., Baraku, R., Novri Yanda, I. & Liwardana, R. Analysis of elbow, silhouette, Davies-Bouldin, Calinski-Harabasz, and rand-index evaluation on K-means algorithm for classifying flood-affected areas in Jakarta. J. Appl. Inf. Comput. 7(1), 89–97. https://doi.org/10.30871/jaic.v7i1.4947 (2023).
Google Scholar 
Li, J., Hassan, D., Brewer, S. & Sitzenfrei, R. Is clustering time-series water depth useful? An exploratory study for flooding detection in urban drainage systems. Water 12(9), 2433. https://doi.org/10.3390/w12092433 (2020).
Google Scholar 
Suarez-Alvarez, M. M., Pham, D-T., Prostov, M. Y. & Prostov, Y. I. Statistical approach to normalization of feature vectors and clustering of mixed datasets. Proc. R. Soc. A Math. Phys. Eng. Sci. 468(2145), 2630–2651. https://doi.org/10.1098/rspa.2011.0704 (2012).Wongoutong, C. The impact of neglecting feature scaling in k-means clustering. PLoS One. 19(12), e0310839. https://doi.org/10.1371/journal.pone.0310839 (2024).
Google Scholar 
Baba, T. et al. Construction of Escherichia coli K-12 in-frame, single-gene knockout mutants: the Keio collection. Mol. Syst. Biol. 2, 20060008. https://doi.org/10.1038/msb4100050 (2006).
Google Scholar 
Kato, J. & Hashimoto, M. Construction of consecutive deletions of the Escherichia coli chromosome. Mol. Syst. Biol. 3, 132. https://doi.org/10.1038/msb4100174 (2007).
Google Scholar 
Jagdish, T. & Nguyen Ba, A. N. Microbial experimental evolution in a massively multiplexed and high-throughput era. Curr. Opin. Genet. Dev. 75, 101943. https://doi.org/10.1016/j.gde.2022.101943 (2022).
Google Scholar 
Smith, T. P. et al. High-throughput characterization of bacterial responses to complex mixtures of chemical pollutants. Nat. Microbiol. https://doi.org/10.1038/s41564-024-01626-9 (2024).
Google Scholar 
Zhang, S. & Ying, B-W. Experimental mapping of bacterial fitness landscapes reveals eco-evolutionary fingerprints. Sci. Rep. 15(1), 32634. https://doi.org/10.1038/s41598-025-17103-0 (2025).Keogh, E. J. & Pazzani, M. J. Derivative dynamic time warping. In: Proceedings of the 2001 SIAM International Conference on Data Mining 1–11 (SIAM, 2001).Jeong, Y. S., Jeong, M. K. & Omitaomu, O. A. Weighted dynamic time warping for time series classification. Pattern Recogn. 44(9), 2231–2240. https://doi.org/10.1016/j.patcog.2010.09.022 (2011).
Google Scholar 
Zhao, J. P. & Itti, L. shapeDTW: Shape dynamic time warping. Pattern Recogn. 74, 171–184. https://doi.org/10.1016/j.patcog.2017.09.020 (2018).
Google Scholar 
Mizoguchi, H., Sawano, Y., Kato, J. & Mori, H. Superpositioning of deletions promotes growth of Escherichia coli with a reduced genome. DNA Res. Int. J. Rapid Publ. Rep. Genes Genomes. 15(5), 277–284. https://doi.org/10.1093/dnares/dsn019 (2008).
Google Scholar 
Kurokawa, M., Nishimura, I. & Ying, B. W. Experimental evolution expands the breadth of adaptation to an environmental gradient correlated with genome reduction. Front. Microbiol. 13, 826894. https://doi.org/10.3389/fmicb.2022.826894 (2022).
Google Scholar 
Kurokawa, M. & Ying, B. W. Precise, high-throughput analysis of bacterial growth. J. Vis. Exp. 127, 56197. https://doi.org/10.3791/56197 (2017).Gupta, Y. M., Kirana, S. N. & Homchan, S. Representing DNA for machine learning algorithms: A primer on one-hot, binary, and integer encodings. Biochem. Mol. Biol. Educ. 53(2), 142–146. https://doi.org/10.1002/bmb.21870 (2025).
Google Scholar 
Download referencesAcknowledgementsWe thank the National BioResource Project, National Institute of Genetics (Shizuoka, Japan), for providing the E. coli strains and Issei Nishimura for his experimental assistance in acquiring the growth curves.FundingThis work was supported by the JSPS KAKENHI grant number 25K02259 (to BWY).Author informationAuthors and AffiliationsSchool of Life and Environmental Sciences, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8572, JapanJiarun Gong & Bei-Wen YingMiCS, University of Tsukuba, 1-1-1 Tennodai, Tsukuba, Ibaraki, 305-8572, JapanBei-Wen YingAuthorsJiarun GongView author publicationsSearch author on:PubMed Google ScholarBei-Wen YingView author publicationsSearch author on:PubMed Google ScholarContributionsJG conducted data mining, validation, and drafted the manuscript. BWY conceived the research, experiments, and rewrote the manuscript. All authors approved the final manuscript.Corresponding authorCorrespondence to
Bei-Wen Ying.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 1Supplementary Material 2Supplementary Material 3Supplementary Material 4Supplementary Material 5Supplementary Material 6Supplementary Material 7Supplementary Material 8Rights 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 articleGong, J., Ying, BW. Data-driven analysis reveals distinct genomic and environmental contributions to bacterial growth curves.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-32144-1Download citationReceived: 06 November 2025Accepted: 08 December 2025Published: 12 December 2025DOI: https://doi.org/10.1038/s41598-025-32144-1Share 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
Provided by the Springer Nature SharedIt content-sharing initiative
KeywordsBacterial growth dynamicsTime series data miningGenome-environment interactionDynamic time warpingMachine learning in biology More

Abstract

Many species are composed of two or more genetically distinct clades, indicating ongoing or past evolutionary divergence. Often however, there are no obvious morphological differences between clades, making it difficult to accurately assess specific aspects of biodiversity or to enact targeted conservation efforts. New advancements in artificial intelligence tools can be used to categorise individuals into their respective genetic clades and to highlight their distinguishing morphological characters that would otherwise be hidden from human observers. Here, we applied computer vision and explainable artificial intelligence techniques to four limpet species that display well-defined phylogeographic breaks along the Baja California and California coasts. A fine-tuned convolutional network, trained and evaluated over 100 resampling iterations, classified individuals into their genetic clades with median F1-scores of up to 0.96. F1-score performance was markedly higher for true clade groups than the controlled mixed-groups, confirming the presence of features specific to the clades. Saliency maps consistently emphasised structures such as the keyhole in Fissurella volcano and the ridge tips in Lottia conus as distinguishing features, and subsequent shape analyses confirmed significant divergence between clades. These results demonstrate the power of computer vision and explainable artificial intelligence to expose otherwise cryptic morphological diversity and provide a scalable, reproducible workflow that can broaden the biodiversity toolkit and refine eco-evolutionary research across taxa.

Data availability

Data for this project is available at:https://github.com/JackDanHollister/chapter_3-_genes_shells_and_AI_data.
ReferencesGovaert, L. et al. Integrating fundamental processes to understand eco-evolutionary community dynamics and patterns. Funct. Ecol. 35, 2138–2155 (2021).
Google Scholar 
Lu, S. et al. Cryptic divergence in and evolutionary dynamics of endangered hybrid Picea brachytyla sensu stricto in the Qinghai-Tibet Plateau. BMC Plant Biol. 24, 1202 (2024).
Google Scholar 
Pfenninger, M. & Schwenk, K. Cryptic animal species are homogeneously distributed among taxa and biogeographical regions. BMC Evolutionary Biol. 7, 1–6 (2007).
Google Scholar 
Riddle, B. R., Hafner, D. J., Alexander, L. F. & Jaeger, J. R. Cryptic vicariance in the historical assembly of a Baja California Peninsular Desert biota. Proc. of the National Academy of Sciences, 97, 14438-14443 (2000)Hebert, P. D., Penton, E. H., Burns, J. M., Janzen, D. H. & Hallwachs, W. Ten species in one: DNA barcoding reveals cryptic species in the neotropical skipper butterfly Astraptes fulgerator. Proc. of the National Academy of Sciences, 101, 14812-14817 (2004)Aysel, H. I., Cai, X. & Prugel-Bennett, A. Multilevel explainable artificial intelligence: visual and linguistic bonded explanations. IEEE Trans. Artific. Intell. 5, 2055–2066 (2023).
Google Scholar 
Pinho, C., Kaliontzopoulou, A., Ferreira, C. A. & Gama, J. Identification of morphologically cryptic species with computer vision models: wall lizards (Squamata: Lacertidae: Podarcis) as a case study. Zool. J. Linnean Soc. 198, 184–201 (2023).
Google Scholar 
Aysel, H. I., Cai, X. & Prugel-Bennett, A. Explainable artificial intelligence: advancements and limitations. Appl. Sci. 15, 7261 (2025).
Google Scholar 
AlqaraawI, A., Schuessler, M., Weiß, P., Costanza, E. & Berthouze, N. Evaluating saliency map explanations for convolutional neural networks: a user study. Proc. of the 25th international conference on intelligent user interfaces 275-285. (2020)Selvaraju, R. R. et al. Grad-CAM: visual explanations from deep networks via gradient-based localization. Int. J. Comput. Vision 128, 336–359 (2020).
Google Scholar 
Jiang, P.-T. et al. Layercam: exploring hierarchical class activation maps for localization. IEEE Trans. Image Process. 30, 5875–5888 (2021).
Google Scholar 
Hollister, J. D. et al. A computer vision method for finding mislabelled specimens within natural history collections. Ecol. Evolution 15(7), e71648 (2025).
Google Scholar 
Hollister, J. D. et al. Using computer vision to identify limpets from their shells: a case study using four species from the Baja California peninsula. Front. Marine Sci. 10, 1167818 (2023).
Google Scholar 
Nielsen, E. S. et al. Pushed waves, trailing edges, and extreme events: eco-evolutionary dynamics of a geographic range shift in the owl limpet Lottia gigantea. Global Change Biol. 30, e17414 (2024).
Google Scholar 
Zarzyczny, K. M. et al. Opposing genetic patterns of range shifting temperate and tropical gastropods in an area undergoing tropicalisation. J. Biogeogr. 51, 246–262 (2024).
Google Scholar 
Reeve, L. Monograph of the genus Fissurella. Conchologia iconica: or, Illustrations of the Shells of Molluscous Animals, 6 1-16 (1849.)Test, A. Description of new species of Acmaea. Nautilus 58(92), 96 (1945).
Google Scholar 
Sowerby, G.B., The genera of recent and fossil shells, for the use of students, in conchology and geology. Vol. 2. London: G.B. Sowerby. (1834)Carpenter, P.P., Diagnoses of new forms of Mollusca collected at Cape St. Lucas, Lower California, by Mr. Xantus. Annals and Magazine of Natural History 3 13 311–315 & 474–479. (1864)Fenberg, P. B., Hellberg, M. E., Mullen, L. & Roy, K. Genetic diversity and population structure of the size-selectively harvested owl limpet, Lottia gigantea. Marine Ecol. 31, 574–583 (2010).
Google Scholar 
Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556. (2014)Deng, J., Dong, W., Socher, R., li, L.-J., li, K. & Fei-fei, L. Imagenet: A large-scale hierarchical image database. 2009 IEEE Conference on computer vision and pattern recognition Ieee, 248-255. (2009)Shorten, C. & Khoshgoftaar, T. M. A survey on image data augmentation for deep learning. J. Big Data 6, 1–48 (2019).
Google Scholar 
Xu, M., Yoon, S., Fuentes, A. & Park, D. S. A comprehensive survey of image augmentation techniques for deep learning. Pattern Recognit. 137, 109347 (2023).
Google Scholar 
Zhang, C., Bengio, S., Hardt, M., Recht, B. & Vinyals, O. Understanding deep learning (still) requires rethinking generalization. Commun. ACM 64(3), 107–115 (2021).
Google Scholar 
Arpit, D., Jastrzębski, S., Ballas, N., Krueger, D., Bengio, E., Kanwal, M. S., Maharaj, T., Fischer, A., Courville, A. & Bengio, Y. A closer look at memorization in deep networks. International conference on machine learning, PMLR, 233-242. (2017)Cawley, G. C. & Talbot, N. L. On over-fitting in model selection and subsequent selection bias in performance evaluation. J. Mach. Learn. Res. 11, 2079–2107 (2010).
Google Scholar 
Ying, X. An overview of overfitting and its solutions. Journal of Physics: Conference series IOP Publishing, 022022. (2019)Oyedotun, O. K., Olaniyi, E. O. & Khashman, A. A simple and practical review of over-fitting in neural network learning. Int. J. Appl. Pattern Recognit. 4, 307–328 (2017).
Google Scholar 
Advani, M. S., Saxe, A. M. & Sompolinsky, H. High-dimensional dynamics of generalization error in neural networks. Neural Netw. 132, 428–446 (2020).
Google Scholar 
Oróstica, M. H. et al. Shell growth and age determined from annual lines in the southern warm-water limpet Patella depressa at its poleward geographic boundaries. J. Marine Biol. Assoc. United Kingdom 101, 707–716 (2021).
Google Scholar 
Vasconcelos, J. et al. The shell phenotypic variability of the keyhole limpet Fissurella latimarginata: insights from an experimental approach using a water flow flume. J. Molluscan Stud. 87, eyab043 (2021).
Google Scholar 
Henriques, P., Delgado, J. & Sousa, R. Patellid limpets: an overview of the biology and conservation of keystone species of the rocky shores. Organismal and molecular malacology. (2017)Wright, W. G. Sex change in the Mollusca. Trends Ecol. Evolution 3(137), 140 (1988).
Google Scholar 
Wright, W. G. & Lindberg, D. R. Direct observation of sex change in the patellacean limpet Lottia gigantea. J. Marine Biol. Assoc. United Kingdom 62, 737–738 (1982).
Google Scholar 
Smilkov, D., Thorat, N., Kim, B., Viégas, F. & Wattenberg, M. Smoothgrad: removing noise by adding noise. arXiv preprint arXiv:1706.03825 (2017)Sohan, M., Sai ram, T., Reddy, R. & Venkata, C. A review on yolov8 and its advancements. International Conference on Data Intelligence and Cognitive Informatics Springer 529-545. (2024)Kirillov, A., Mintun, E., Ravi, N., Mao, H., Rolland, C., Gustafson, L., Wright, R., Ganguli, D., Yastrow, A., Spilsbury, C., Zitnick, C.L., Dollár, P. and Girshick, R., Segment Anything. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), Paris, France, 1–6 4015–4026 (2023)Grupstra, C. G. et al. Integrating cryptic diversity into coral evolution, symbiosis and conservation. Nat. Ecol. Evolution 8, 622–636 (2024).
Google Scholar 
Sauer, F. G. et al. A convolutional neural network to identify mosquito species (Diptera: Culicidae) of the genus Aedes by wing images. Sci. Rep. 14, 3094 (2024).
Google Scholar 
Hollister, J., Vega, R. & Azhar, M. A. H. B. Automatic identification of non-biting midges (chironomidae) using object detection and deep learning techniques. Proc. of the 11th International Conference on Pattern Recognition Applications and Methods. (2022)Younis, S. et al. Detection and annotation of plant organs from digitised herbarium scans using deep learning. Biodivers. Data J. 8, e57090 (2020).
Google Scholar 
Piva, A., Raimondi, L., Rasca, E., Kozmanyan, A. & de Matteis, M. A machine learning application for the automatic recognition of planktonic foraminifera in thin sections. Marine Petrol. Geol. 166, 106911 (2024).
Google Scholar 
Blagoderov, V. et al. No specimen left behind: industrial scale digitization of natural history collections. ZooKeys 209, 133 (2012).
Google Scholar 
Allan, E. L., Livermore, L., Price, B. W., Shchedrina, O. & Smith, V. S. A novel automated mass digitisation workflow for natural history microscope slides. Biodivers. Data J. 7, e32342 (2019).
Google Scholar 
Khalid, S., Khalil, T. & Nasreen, S. A survey of feature selection and feature extraction techniques in machine learning. 2014 science and information conference 2014 IEEE 372-378.Dhal, P. & Azad, C. A comprehensive survey on feature selection in the various fields of machine learning. Appl. Intell. 52, 4543–4581 (2022).
Google Scholar 
Avise, J. C. Phylogeography: the history and formation of species, Harvard university press. (2000)Toews, D. P. & Brelsford, A. The biogeography of mitochondrial and nuclear discordance in animals. Mol. Ecol. 21, 3907–3930 (2012).
Google Scholar 
Kido, J. S. & Murray, S. N. Variation in owl limpet Lottia gigantea population structures, growth rates, and gonadal production on southern California rocky shores. Marine Ecol. Progress Series 257, 111–124 (2003).
Google Scholar 
Mann, K., Edsinger-Gonzales, E. & Mann, M. In-depth proteomic analysis of a mollusc shell: acid-soluble and acid-insoluble matrix of the limpet Lottia gigantea. Proteome Sci. 10, 1–18 (2012).
Google Scholar 
Gong, Y., Liu, G., Xue, Y., Li, R. & Meng, L. A survey on dataset quality in machine learning. Inf. Softw. Technol. 162, 107268 (2023).
Google Scholar 
Picard, S., Chapdelaine, C., Cappi, C., Gardes, L., Jenn, E., Lefèvre, B. & Soumarmon, T. Ensuring dataset quality for machine learning certification. 2020 IEEE international symposium on software reliability engineering workshops (ISSREW), IEEE 275-282. (2020)Folmsbee, J., Johnson, S., Liu, X., Brandwein-weber, M. & Doyle, S. 2019 Fragile neural networks: the importance of image standardization for deep learning in digital pathology. Medical Imaging 2019: Digital Pathology SPIE, 222-228.Glučina, M., Lorencin, A., Anđelić, N. & Lorencin, I. Cervical cancer diagnostics using machine learning algorithms and class balancing techniques. Appl. Sci. 13(2), 1061 (2023).
Google Scholar 
Zhang, S., Wang, Y. & Wu, G. Earthquake-induced landslide susceptibility assessment using a novel model based on gradient boosting machine learning and class balancing methods. Remote Sens, 14, 5945 (2022).
Google Scholar 
Boaventura, D., da Fonseca, L. S. C. & Hawkins, S. J. Analysis of competitive interactions between the limpets Patella depressa Pennant and Patella vulgata L. on the northern coast of Portugal. J. Exp. Marine Biol. Ecol. 271, 171–188 (2002).
Google Scholar 
Vermeij, G. J. Morphological patterns in high-intertidal gastropods: adaptive strategies and their limitations. Marine Biol. 20(319), 346 (1973).
Google Scholar 
Trussell, G. C. Phenotypic plasticity in an intertidal snail: the role of a common crab predator. Evolution 448-454. (1996)Mclean, J. H. Systematics of Fissurella in the Peruvian and Magellanic faunal provinces (Gastropoda: Prosobranchia), Natural History Museum of Los Angeles County. (1984)Sempere-Valverde, J., Ostalé-valriberas, E., Espinosa, F. & Márquez, F. Morphometric variations of two patellid limpets between artificial breakwaters and natural reefs Estuarine. Coastal Shelf Sci. 297, 108617 (2024).
Google Scholar 
Paulo Cabral, J. Shape and growth in European Atlantic Patella limpets (Gastropoda, Mollusca). Ecol. Implic. Surviv. Web Ecol. 7(1), 11–21 (2007).
Google Scholar 
Williams, S. T. Molluscan shell colour. Biol. Rev. 92, 1039–1058 (2017).
Google Scholar 
He, Y. et al. Opportunities and challenges in applying AI to evolutionary morphology. Integr. Organismal Biol. 6, 036 (2024).
Google Scholar 
María C., Yuvero J., & Giménez. Morphological shell characterization of Fissurellidea and Fissurella (Vetigastropoda: Fissurellidae) along the Argentinean coast from temperate to subantarctic waters Polar Biology. 44(9), 1903-1909.https://doi.org/10.1007/s00300-021-02927-z.Download referencesAcknowledgementsWe would like to give special thanks to Shreya Banerjee for her help in the field and Doug Eernisse for helpful discussions about the project and limpet taxonomy. We are grateful for the help of Lindsey Groves at the LACM for facilitating access the collections and for many helpful conversations about the project.FundingThis project was funded by NERC grant NE/X011518/1 to PBF and a Malacology Society of London grant to JDH. Nerc also funded the PhD programme for JDH. Author informationAuthors and AffiliationsSchool of Ocean and Earth Science, National Oceanography Centre, University of Southampton, Waterfront Campus, European Way, Southampton, SO14 3ZH, UKJack D. Hollister & Phillip B. FenbergNatural History Museum, London, Cromwell Road, South Kensington, London, SW7 5BD, UKJack D. Hollister & Phillip B. FenbergCentro de Investigaciones Biológicas del Noroeste (CIBNOR), Conservation Genetics Laboratory, Playa Palo de Santa Rita Sur, IPN Street 195, 23096, La Paz, Baja California Sur, MexicoDavid A. Paz-GarcíaFaculty of Marine Sciences, Autonomous University of Baja California, Ensenada, Baja California, MexicoDavid A. Paz-García & Rodrigo Beas-LunaNational Laboratory of Climate Change Biology, SECIHTI, Mexico City, MexicoRodrigo Beas-LunaNational Oceanography Centre, European Way, Southampton, SO14 3ZH, UKTammy HortonSchool of Electronics and Computer Science, University of Southampton, University Road, Southampton, SO17 1BJ, UKXiaohao CaiAuthorsJack D. HollisterView author publicationsSearch author on:PubMed Google ScholarDavid A. Paz-GarcíaView author publicationsSearch author on:PubMed Google ScholarRodrigo Beas-LunaView author publicationsSearch author on:PubMed Google ScholarTammy HortonView author publicationsSearch author on:PubMed Google ScholarXiaohao CaiView author publicationsSearch author on:PubMed Google ScholarPhillip B. FenbergView author publicationsSearch author on:PubMed Google ScholarContributionsJ.H. planned the original project, collected specimens in field, took photos, created dataset, created code, ran the code, created figures, wrote most of the paper. P.F. advised on project, helped collect specimens, advised on figures, advised on writing, helped edit final drafts of paper. D.P-G helped with locations for specimen collections. advised on and edited paper. R.B-L helped with locations for specimen collections. advised on and edited paper. T.H. and X.C. advised on and edited paper.Corresponding authorCorrespondence to
Jack D. Hollister.Ethics declarations

Competing interest
The authors declare no competing interests.

Ethics
The animal study was reviewed and approved by Animal Welfare and Ethical Review Body – ERGO II 63575. The permit to collect the field samples was provided by the Secretaría de Agricultura, Ganadería, Desarrollo Rural, Pesca y Alimentación (SAGARPA, Permiso de Pesca de Fomento No. PPF/DGOPA-291/17 and PPF/DGOPA-010/19).

Additional informationPublisher’s noteSpringer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.Supplementary InformationSupplementary Information.Rights 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 articleHollister, J.D., Paz-García, D.A., Beas-Luna, R. et al. Genes, shells, and AI: using computer vision to detect cryptic morphological divergence between genetically distinct populations of limpets.
Sci Rep (2025). https://doi.org/10.1038/s41598-025-30613-1Download citationReceived: 29 August 2025Accepted: 26 November 2025Published: 12 December 2025DOI: https://doi.org/10.1038/s41598-025-30613-1Share 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
Provided by the Springer Nature SharedIt content-sharing initiative More

A tropical forest meets the Atlantic Ocean at the Amazon Delta.Credit: NASA/AlamyAs the world gathered in Belém, Brazil, for the COP30 United Nations climate conference, Brazilian President Luiz Inácio Lula da Silva reignited an old but urgent debate: whether the multilateral process that has sustained climate diplomacy for three decades is still fit for purpose. His proposal for a global climate council — a smaller, more agile entity to lead negotiations and ensure that climate commitments are implemented — reflects mounting frustration with the slow pace of outcomes from the annual Conference of the Parties (COP).How to fight climate change without the US: a guide to global actionLula’s proposal deserves serious consideration. The COP process has become sprawling, performative and, at times, politically paralysed. Yet, as someone who helped to draft the UN Framework Convention on Climate Change (UNFCCC) in 1991–92, I think that the answer is not to abandon this architecture, but to reform it.When we negotiated the UNFCCC, our goal was to create a durable legal scaffold that was flexible enough to evolve with scientific understanding and national capacities for climate action. In 1992, there was no precedent for a framework that addressed an issue that spanned every economy, ecosystem and generation of people. We had the science, but not yet the institutions or political will to act at scale. The framework-convention model allowed successive protocols, decisions and mechanisms to develop.That design has proven remarkably resilient. If someone had told me in 1992 that the world would still be negotiating climate action at COP30, I might have sighed. Yet, if they had told me that every nation would still be adhering to the same framework, guided by science and law, I would have been profoundly hopeful — as I am today.Climate diplomacy is slow because it is systemic. It forces nations to reconcile competing imperatives — development versus decarbonization, growth versus justice, and responsibility versus capability. The principle of common but differentiated responsibilities was born from these tensions: recognition that all countries share the problem but not the same level of blame or means to act.‘Almost utopian’: how protecting the environment is boosting the economy in BrazilProgress has been incremental but cumulative. The UNFCCC, Kyoto Protocol and Paris agreement have created a coherent legal architecture for climate governance. The International Court of Justice delivered an advisory opinion in July, reaffirming that states have binding obligations to protect the climate, an indication of how far we’ve come.Criticism of the COP process is understandable. Yearly gatherings of tens of thousands of people can seem detached from the crisis outside the conference halls. But dismissing COPs ignores their ability to provide universality, legitimacy and accountability. President Lula’s call for a leaner climate council acknowledges this frustration while maintaining the inclusivity of the COP process.

Enjoying our latest content?
Log in or create an account to continue

Access the most recent journalism from Nature’s award-winning team
Explore the latest features & opinion covering groundbreaking research

Access through your institution

or

Sign in or create an account

Continue with Google

Continue with ORCiD More

COP30 left many countries disappointed because no new road maps were created to help nations transition away from fossil fuels.Credit: Wagner Meier/GettyTen years after the Paris agreement was adopted, world leaders left the United Nations COP30 climate summit in Belém, Brazil, with an outcome that kept the process alive but does little to stave off the perils of global warming. Many scientists walked away dismayed and disappointed.Despite years of commitments and research that have laid the groundwork for action, the climate summit of achieved “essentially nothing”, says Johan Rockström, director of the Potsdam Institute for Climate Impact Research in Germany.But, there were some signs of hope that multilateralism can tackle climate change. Over the course of two weeks, representatives from nearly 200 governments worked through hot days, long nights, a fire in the venue and numerous protests — including by Indigenous groups and others fighting for the protection of the Amazon and other tropical forests.Heatwaves linked to emissions of individual fossil-fuel and cement producersIn the end, governments agreed to a package of measures that pushes forward discussions on financial aid and a new ‘just transition’ mechanism designed to ensure a fair and equitable shift from fossil fuels to clean energy. A glaring omission was language calling for the creation of road maps to phase out fossil fuels and halt deforestation, but Brazil has announced it will push those ideas forward independently of the COP process.Here, Nature takes a look at the results from COP30 and what comes next.Sidestepping fossil fuelsThe summit failed to deliver major new pledges to curb greenhouse-gas emissions. Out of the 194 entities and countries that sent representatives to COP30, roughly 80 didn’t submit new commitments for 2035, as required under the accord, and the rest submitted weak pledges that are unlikely to alter global trajectory. As a result, scientists with the Climate Action Tracker consortium still project that the world is on track for upwards of 2.6 ° C of warming by 2100.“The new commitments don’t even move the needle,” says Niklas Höhne, a climate-policy researcher at the NewClimate Institute, an environmental think tank based in Berlin. “It’s really a sign that countries have limited appetite to support something more ambitious on climate.”Fossil fuels took centre stage briefly. More than 80 countries joined Brazil’s President Luiz Inácio Lula da Silva in the call for the creation of a road map to phase out fossil fuels, but the proposal ultimately foundered after reported opposition from oil-producing nations including Saudi Arabia and others.But the idea isn’t dead yet. Brazil promised to push forwards with it independently of the COP process, while the governments of Colombia and the Netherlands announced that they would host the first global conference on the just transition away from fossil fuels in April next year.Protesters carrying signs that read “our forests are not for sale” broke through security lines of the COP30 climate talks on 12 November.Credit: Pablo Porciuncula/AFP via GettyFinancing climate actionOne of the largest disputes at the summit was who should pay for climate action and help developing nations to prepare for and adapt to the unavoidable impacts of global warming. Progress was made: wealthier nations committed to tripling the amount of money they provide to help low-income countries tackle global warming — to US$300 billion annually by 2035. The agreement also carries forward a broader goal of boosting the total to $1.3 trillion annually from all sources, including private investments.But questions remain about how this will be financed. Previous deals on climate action have been plagued by delays in achieving financial goals as well as by disagreements about how much money should come from publicly funded grants, as opposed to loans and private investments.China pledges to cut emissions by 2035: what does that mean for the climate?The final deal at COP30 lays out a process to clarify these issues over the next two years. It’s a step towards ensuring that wealthy countries meet their responsibilities to provide climate finance to those in need, says Jodi-Ann Wang, who researches equity and climate finance at the London School of Economics and Political Science.

Enjoying our latest content?
Log in or create an account to continue

Access the most recent journalism from Nature’s award-winning team
Explore the latest features & opinion covering groundbreaking research

Access through your institution

or

Sign in or create an account

Continue with Google

Continue with ORCiD More

As Brazil hosts the United Nations climate summit COP30 in Belém, its government is taking steps to boost the economy without damaging the environment. The goal sounds almost utopian: by 2050, Brazil aims to double income per capita, slash emissions of every greenhouse gas to attain ‘net zero’, and distribute wealth more fairly to achieve a 20% reduction in the Gini index — a metric that reflects income inequality.There has already been substantial progress. For example, deforestation has substantially decreased since the start of President Luiz Inácio Lula da Silva’s latest term in 2023, and is now at its lowest rate for 11 years (see go.nature.com/442bqtp). As a member of the team in charge of the Ecological Transformation Plan at Brazil’s Ministry of Finance, I hope that our early successes will encourage other nations to proceed in a similar direction.For real climate action, empower womenTwo initiatives, in particular, could fundamentally reshape global climate finance — the funding systems that support climate mitigation and adaptation efforts. The first is the Tropical Forest Forever Facility (TFFF), a fund ran by participating nations, with support from the World Bank, to help low- and middle-income countries (LMICs) to preserve their tropical forests. The fund aims to reach US$125 billion in the coming years. An initial investment of $25 billion should come from nations and philanthropic organizations. Brazil has pledged to invest the first $1 billion, and other countries, including Norway, Indonesia, France and Germany, have announced substantial contributions. Another $100 billion is expected to come from capital markets, through the issuing of bonds that pay interest at the market rate.The fund is made up of investments rather than donations — the distinction matters. Experience shows that aid flows are volatile and are subject to political winds and fiscal constraints in donor countries. The TFFF breaks this cycle. Here’s how it works: sponsors invest in the fund and receive annual returns that are comparable with those from low-risk sovereign bonds, at, say, 4%. The fund deploys this capital in a diversified portfolio of fixed-income assets, mainly bonds from emerging markets, with an expected yield of 7–8% (historically, a representative portfolio of diversified emerging-market bonds has paid out at around that rate). This portfolio excludes investments that finance fossil fuels or activities linked to deforestation.When mature, this mechanism could generate nearly $4 billion annually for forest conservation — rewarding ecosystem services and paying for anti-deforestation enforcement and capacity-building to foster the bioeconomy, such as biotechnology for pharmaceutical production.The ‘implementation COP’: why the Belém summit must ratchet up climate action

Enjoying our latest content?
Log in or create an account to continue

Access the most recent journalism from Nature’s award-winning team
Explore the latest features & opinion covering groundbreaking research

Access through your institution

or

Sign in or create an account

Continue with Google

Continue with ORCiD More

Qiushi Chair Professor; Qiushi Distinguished Scholar; ZJU 100 Young Researcher; Distinguished researcher
No. 3, Qingchun East Road, Hangzhou, Zhejiang (CN)
Sir Run Run Shaw Hospital Affiliated with Zhejiang University School of Medicine More

In 1961, conservationist Peter Scott warned that the black rhinoceros (Diceros bicornis) could be extinct in 30 years. His prediction nearly came true: in Kenya, numbers fell from about 20,000 in the 1970s to fewer than 400 by 1989. At the start of this year, Kenya’s eastern black rhino (D. b. michaeli) population had surpassed 1,000. The strategy behind this recovery holds lessons for conservation efforts.
Competing Interests
The authors declare no competing interests. More

This week, as world leaders gather in Belém, Brazil, for the 30th United Nations Climate Change Conference (COP30), once again it seems that there will be more rhetoric than real solutions for the countries that are most vulnerable to the effects of a changing climate.My native Belize — with its tropical rainforests, coastal wetlands and coral reefs — is one such country. Warming seas are killing our reefs. Hurricanes and wildfires are increasing in frequency and intensity.‘Almost utopian’: how protecting the environment is boosting the economy in BrazilAs a biologist with two decades of experience in academia and conservation, I have learnt that, to protect natural resources, local communities must bey empowered to steward these efforts — and that women are often best placed to drive them, at least in Latin America and the Caribbean, but probably everywhere.In many cultures, women are the de facto main carers and custodians of community knowledge. They are often in positions, paid or otherwise, to make their communities healthier, more prosperous and more peaceful — making them well-suited to lead climate action and conservation work.I am the managing director of the Belize Maya Forest Trust, a non-profit organization dedicated to conserving nearly 1,000 square kilometres of the country’s tropical forest. I also chair the board of directors of the Belize Network of NGOs, an umbrella organization for many of the more than 100 non-governmental organizations (NGOs) in the country. In Belize, many prominent conservation organizations are led by women. Take the Community Baboon Sanctuary, a globally recognized conservation effort that protects the habitat of the Yucatán black-howler monkey (Alouatta pigra). Today, the sanctuary is managed by the Community Baboon Sanctuary Women’s Conservation Group, which is directed by women from seven Belizean villages.And over the past five years, working with the government of Belize and The Nature Conservancy, a global environmental organization headquartered in Arlington, Virginia, women in Belize have had key roles in the execution of game-changing sustainable-financing initiatives. One is Belize’s first large-scale project to reduce emissions by avoiding deforestation, which is helping to protect the Belize Maya Forest, an area that includes some of the world’s most biodiversity-rich lands. Another is Belize Blue Bonds — which, when the deal closed in November 2021, was the world’s largest debt-refinancing scheme for ocean conservation.The more I participate in advocacy and interact with civil-society organizations — both in Belize and internationally — the more I find myself in rooms full of women striving to protect the environment and defend human rights.But I have also noticed that women are not usually negotiating for funds, deciding whether to launch large initiatives or setting national priorities.Is it time to give up trying to save coral reefs? My research says noBelize is again a case in point. In any of the ten elections since Belize gained independence in 1981, at most four women have been elected to the 28–31-member House of Representatives, the most powerful arm of the country’s legislature. Other female parliamentarians have been appointed by the prime minister, but these positions tend to have much less authority than do those elected through popular vote. Also, female ministers are usually put in charge of human or rural-development issues, rather than the country’s economy, land, infrastructure or defence.For countries to build resilience and protect their biodiversity in the time needed, more women must be made equal partners in decision making.

Enjoying our latest content?
Log in or create an account to continue

Access the most recent journalism from Nature’s award-winning team
Explore the latest features & opinion covering groundbreaking research

Access through your institution

or

Sign in or create an account

Continue with Google

Continue with ORCiD More