Philippa Kaur

Fire conditions and particulate and bioaerosol emissionsFire radiative power values estimated from satellite imagery ranged from 6 to 259 MW over three days of burning [19]. Smoke sampled above combusting vegetation contained high concentrations of PM10 (mean ± s.e. 928.4 ± 140.6 µg m−3; Fig. 1). Microbial cells are a component of total bioaerosols, and their abundance can correlate with PM in ambient conditions [24] as well as in wildland fire smoke [6]. However, we observed that only the concentration of viable cells (and not total cells) correlated with PM2.5 and PM10 values (r2 = 0.80, and 0.81, respectively; p  More

Citizen science program “Hanamaru-maruhana national census”We asked citizens to take bee photographs and send them by e-mails in citizen science program “Hanamaru-Maruhana national census (Bumble bee national census in English)” (http://hanamaruproject.s1009.xrea.com/hanamaru_project/index_E.html)8. We gave citizens previous notice that their photographs were going to be used for scientific studies, and for other non-profit activities on our homepage and flyers. From 2013 to 2016, we collected roughly 5000 photographs taken by citizens. Citizens sent photographs of various bee species, but most of them were bumble bees and honey bees. They have interspecific similarity and intraspecific variation, making it difficult for non-experts to identify species. Since species identification was not a requirement for participants, most citizens sent bee photographs without species identification. These bees were identified by one of the authors, J. Yokoyama. These bees are relatively easy for experts to identify because only two honey bee species and 16 bumble bee species inhabit the Japanese archipelago excluding the Kurile Islands. The consistency of species identification by J. Yokoyama was 95% for 15 bumble bee species, and 97.7% for major six bumble bee species in our test using 100 bumble bee photographs8.Bee photographs used for deep learningFrom bee species observed in citizen science program “Hanamaru-maruhana national census (Bumble bee national census in English)”, we selected two honey bee species and 10 bumble bee species having interspecific similarity and intraspecific variation. Two honey bee species consisted of Apis cerana Fabricius, and A. mellifera Linnaeus. 10 bumble bee species consisted of Bombus consobrinus Dahlbom, B. diversus Smith, B. ussurensis Radoszkowski, B. pseudobaicalensis Vogt, B. honshuensis Tkalcu, B. ardens Smith, B. beaticola Tkalcu, B. hypocrita Perez, B. ignitus Smith, and B. terrestris Linnaeus. To increase training data of B. pseudobaicalensis, we added photographs of B. deuteronymus Schulz to photographs of B. pseudobaicalensis because they can rarely be distinguished using only photographic images (see http://hanamaruproject.s1009.xrea.com/hanamaru_project/identification_E.html for the details of their color patterns). We primarily used photographs taken by citizens from 2013 to 2015 in the citizen science program, but also used photographs taken by citizens in 2016 if the number of photographs for a certain class was small.We cropped a bee part as a rectangle image from a photograph to reduce background effects. We increased the number of photographs by data augmentation (Fig. S1 in Appendix S1 in Supplementary information). Please see Appendix S1 in Supplementary information for the details of “Data augmentation.” We assigned 70, 10, and 20% of the total data of the training dataset, validation dataset, and test dataset, respectively. Please see Appendix S1 in Supplementary information for the details of “Data split and training parameters”.Deep convolutional neural network (DCNN)In this study, we chose a deep convolutional neural network Xception, as it provides a good balance between the accuracy of the model on one hand and a smaller network size on the other. We adopted transfer learning21,22 and data augmentation23 to solve the issue of a shortage of photographs. The Xception network has a depth of 126 layers (including activation layers, normalization layers etc.) out of which 36 are convolution layers. In this study, we employed the pretrained Xception V1 model provided on the Keras homepage. Please see Appendix S1 in Supplementary information for the details of “Xception”, and “Transfer learning.” For the training, we chose a learning rate of 0.0001 and a momentum of 0.9.Species identification by biologistsWe asked 50 biologists to identify the species present in nine photographs selected randomly from the photograph dataset using a questionnaire form. Their professions were forth undergraduate student (16%), Master’s student (14%), Ph.D. student (12%), Postdoctoral fellow (26%), Assistant professor (6%), Associate professor (12%), Professors (6%), and others (8%). Their research organisms were honey bees (6%), bumble bees (14%), bees (6%), insects (12%), plants and insects (12%), plants (22%), and others such as fishes, reptiles, and mammals (28%). 14% of the biologists were studying bumble bees, but they did not need to identify all bumble bee species in their researches because only several species inhabit their study areas. We allowed the biologists to see field guide books, illustrated books, and websites. We did not limit the method or time to identify the species of photographs to simulate the species identification of actual citizen science programs as much as possible, except for asking experts. The experiment was approved by the Ethics Committee in Tohoku University, and carried out in accordance with its regulations. Informed consent was obtained from the biologists.Species identification in species class experiment by XceptionWe conducted species class experiment by categorizing photographs into different classes according to species. A total of 3779 original photographs were used in species class experiment (Table S1 in Appendix S1 in Supplementary information). These photographs were classified into 12 classes according to species. We inputted test dataset to Xception, and recorded their predicted classes.Species identification in color class experiment by XceptionWe conducted color class experiment by categorizing photographs into different classes according to intraspecific color differences. Photographs of B. ardens were classified into the following four classes: female B. ardens ardens, B. ardens sakagamii, B. ardens tsushimanus, and male B. ardens (Table S1 in Appendix S1 in Supplementary information). Photographs of B. honshuensis, B. beaticola, B. hypocrita, and B. ignitus were classified into female and male classes. In trial experiments, we had found that the Xception cannot learn images in minor classes if the number of original photographs in the classes was less than 40. No photographs in the class were predicted correctly, and no photographs in the other classes were predicted as the class. Therefore, in color class experiment, we did not use the photographs of minor classes (B. ardens subspecies: B. ardens sakagamii and B. ardens tsushimanus, male B. honshuensis, and male B. beaticola). Therefore, a total of 3681 original photographs were used in color class experiment (Table S1 in Appendix S1 in Supplementary information). They were classified into 15 classes according to intraspecific color differences in addition to species classes. We inputted test dataset to Xception, and recorded their predicted classes. To compare the total accuracy of color class experiment by Xception with those of other experiments, it was normalized using the number of test data including those of the minor classes, assuming that all test data of the minor classes were misidentified.The accuracy of species identificationWe calculated total accuracy, precision, recall, and F-score in each class. Total accuracy is the number of total correct predictions divided by the number of all test datasets. Note that the total accuracy of color class experiment by Xception was normalized using the number of test data including those of the minor classes. It reduces the total accuracy of color class experiment by Xception, and enables to compare with those by biologists and species class experiment by Xception directly. Precision is the number of correct predictions as a certain class divided by the number of all predictions as the class returned by biologists or Xception. Recall, which is equivalent to sensitivity, is the number of correct predictions as a certain class divided by the number of test datasets as the class. F-score is the harmonic average of the precision and recall, (2 × precision × recall)/(precision + recall).To show the effect of interspecific similarity on the accuracy of species identification, we used confusion matrix. The confusion matrix represents the relationship between true and predicted classes. Each row indicates the proportion of predicted classes in a true class. All correct predictions are located in the diagonal of the matrix, wrong predictions are located out of the diagonal. In species identification by biologists, “Others” class represents cases that they wrote no species name or a species name other than two honey bee species and 10 bumble bee species in the answer column. More

1.United Nations. Transforming our World: The 2030 Agenda for Sustainable Development (United Nations General Assembly, 2015).
Google Scholar 
2.Godfray, H. C. J. et al. Food security: The challenge of feeding 9 billion people. Science (80-) 327, 812–818 (2010).ADS 
CAS 

Google Scholar 
3.FAO, IFAD, UNICEF, WFP & WHO. The State of Food Security and Nutrition in the World 2021. Transforming Food Systems for Food Security, Improved Nutrition and Affordable Healthy Diets for All (FAO, 2021). https://doi.org/10.4060/cb4474en.Book 

Google Scholar 
4.FAO. The Second Report on the State of the World’s Animal Genetic Resources for Food and Agriculture (FAO, 2010). https://doi.org/10.4060/i4787e.Book 

Google Scholar 
5.Gepts, P. Plant genetic resources conservation and utilization: The accomplishments and future of a societal insurance policy. Crop Sci. 46, 2278–2292 (2006).
Google Scholar 
6.McCouch, S. et al. Feeding the future. Nature 499, 23–24 (2013).ADS 
CAS 
PubMed 

Google Scholar 
7.Castañeda-Álvarez, N. P. et al. Global conservation priorities for crop wild relatives. Nat. Plants 2, 16022 (2016).PubMed 

Google Scholar 
8.Esquinas-Alcázar, J. Protecting crop genetic diversity for food security: Political, ethical and technical challenges. Nat. Rev. Genet. 6, 946–953 (2005).PubMed 

Google Scholar 
9.Fernández-Llamazares, Á. et al. Scientists’ warning to humanity on threats to indigenous and local knowledge systems. J. Ethnobiol. 41, 144–169 (2021).
Google Scholar 
10.FAOSTAT. Food and Agriculture Data. (2019). http://www.fao.org/faostat/en/#data/QC. (Accessed: 15th July 2021)11.Lebot, V. Tropical Root and Tuber Crops: Cassava, Sweet Potato, Yams and Aroids. Tropical Root and Tuber Crops: Cassava, Sweet Potato, Yams and Aroids (CABI, 2009). https://doi.org/10.5822/978-1-61091-225-9_2.Book 

Google Scholar 
12.Gade, D. W. Names for Manihot esculenta: Geographical variations and lexical clarification. J. Lat. Am. Geogr. 1, 55–74 (2002).
Google Scholar 
13.McKey, D. & Delêtre, M. The emergence of cassava as a global crop. in Achievng Sustainable Cultivation of Cassava, Vol. 1 (ed. Hershey, C. H.) 3–32 (Burleigh Dodds Science Publishing, 2017). https://doi.org/10.19103/as.2016.0014.04.14.Howeler, R., Lutaladio, N. & Thomas, G. Save and Grow: Cassava. A Guide to Sustainable Production Intensification (Food and Agriculture Organization of the United Nations, 2013).
Google Scholar 
15.Allem, A. C. The origin of Manihot esculenta Crantz (Euphorbiaceae). Genet. Resour. Crop Evol. 41, 133–150 (1994).
Google Scholar 
16.Olsen, K. M. & Schaal, B. A. Evidence on the origin of cassava: Phylogeography of Manihot esculenta. Proc. Natl. Acad. Sci. USA 96, 5586–5591 (1999).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
17.Olsen, K. M. & Schaal, B. A. Microsatellite variation in cassava (Manihot esculenta, Euphorbiaceae) and its wild relatives: Further evidence for a southern Amazonian origin of domestication. Am. J. Bot. 88, 131–142 (2001).CAS 
PubMed 

Google Scholar 
18.Olsen, K. M. SNPs, SSRs and inferences on cassava’s origin. Plant Mol. Biol. 56, 517–526 (2004).CAS 
PubMed 

Google Scholar 
19.Léotard, G. et al. Phylogeography and the origin of cassava: New insights from the northern rim of the Amazonian basin. Mol. Phylogenet. Evol. 53, 329–334 (2009).PubMed 

Google Scholar 
20.Mühlen, G. S. et al. Genetic diversity and population structure show different patterns of diffusion for bitter and sweet manioc in Brazil. Genet. Resour. Crop Evol. 66, 1773–1790 (2019).
Google Scholar 
21.Ménard, L., McKey, D., Mühlen, G. S., Clair, B. & Rowe, N. P. The evolutionary fate of phenotypic plasticity and functional traits under domestication in manioc: changes in stem biomechanics and the appearance of stem brittleness. PLoS ONE 8, e74727 (2013).ADS 
PubMed 
PubMed Central 

Google Scholar 
22.Brown, C. H., Clement, C. R., Epps, P., Luedeling, E. & Wichmann, S. The Paleobiolinguistics of domesticated manioc (Manihot esculenta). Ethnobiol. Lett. 4, 61–70 (2013).
Google Scholar 
23.Isendahl, C. The domestication and early spread of manioc (Manihot esculenta Crantz): A brief synthesis. Lat. Am. Antiq. 22, 452–468 (2011).
Google Scholar 
24.McKey, D., Elias, M., Pujol, B. & Duputié, A. Ecological approaches to crop domestication. in Biodiversity in Agriculture: Domestication, Evolution, and Sustainability (eds. Gepts, P. et al.) 377–406 (Cambridge University Press, 2012). https://doi.org/10.1017/CBO9781139019514.023.25.McKey, D. & Beckerman, S. Chemical ecology, plant evolution and traditional manioc cultivation systems. In Tropical forests, people and food. Biocultural interactions and applications to development (eds Hladik, C. M. et al.) 83–112 (Parthenon Carnforth and UNESCO, 1993).
Google Scholar 
26.Elias, M. & McKey, D. The unmanaged reproductive ecology of domesticated plants in traditional agroecosystems: An example involving cassava and a call for data. Acta Oecol. 21, 223–230 (2000).ADS 

Google Scholar 
27.Duputié, A., Massol, F., David, P., Haxaire, C. & McKey, D. Traditional Amerindian cultivators combine directional and ideotypic selection for sustainable management of cassava genetic diversity. J. Evol. Biol. 22, 1317–1325 (2009).PubMed 

Google Scholar 
28.Peroni, N., Kageyama, P. Y. & Begossi, A. Molecular differentiation, diversity, and folk classification of ‘sweet’ and ‘bitter’ cassava (Manihot esculenta) in Caiçara and Caboclo management systems (Brazil). Genet. Resour. Crop Evol. 54, 1333–1349 (2007).
Google Scholar 
29.Elias, M. et al. Unmanaged sexual reproduction and the dynamics of genetic diversity of a vegetatively propagated crop plant, cassava (Manihot esculenta Crantz), in a traditional farming system. Mol. Ecol. 10, 1895–1907 (2001).CAS 
PubMed 

Google Scholar 
30.Martins, P. S. Dinâmica evolutiva em roças de caboclos amazônicos. in Scientific Papers of Paulo Sodero Martins 1941–1997: A tribute (eds. Veasey, E. A., Oliveira, G. C. X. & Pinheiro, J. B.) 217–228 (SBG, 2007).https://doi.org/10.1590/s0103-40142005000100013.31.Coomes, O. T. Of stakes, stems, and cuttings: The importance of local seed systems in traditional Amazonian societies. Prof. Geogr. 62, 323–334 (2010).
Google Scholar 
32.Dyer, G. A., González, C. & Lopera, D. C. Informal ‘seed’ systems and the management of gene flow in traditional agroecosystems: The case of cassava in Cauca, Colombia. PLoS ONE 6, e29067 (2011).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
33.Salick, J., Cellinese, N. & Knapp, S. Indigenous diversity of cassava: Generation, maintenance, use and loss among the Amuesha, peruvian upper amazon. Econ. Bot. 51, 6–19 (1997).
Google Scholar 
34.Sambatti, J. B. M., Martins, P. S. & Ando, A. Folk taxonomy and evolutionary dynamics of cassava: A case study in Ubatuba, Brazil. Econ. Bot. 55, 93–105 (2001).
Google Scholar 
35.Heckler, S. & Zent, S. Piaroa manioc varietals: Hyperdiversity or social currency?. Hum. Ecol. 36, 679–697 (2008).
Google Scholar 
36.Delêtre, M., McKey, D. & Hodkinson, T. R. Marriage exchanges, seed exchanges, and the dynamics of manioc diversity. Proc. Natl. Acad. Sci. USA 108, 18249–18254 (2011).ADS 
PubMed 
PubMed Central 

Google Scholar 
37.Sardos, J. et al. Evolution of cassava (Manihot esculenta Crantz) after recent introduction into a South Pacific Island system: The contribution of sex to the diversification of a clonally propagated crop. Genome 51, 912–921 (2008).CAS 
PubMed 

Google Scholar 
38.Ellen, R. & Soselisa, H. L. A comparative study of the socio-ecological concomitants of cassava (Manihot esculenta Crantz) diversity, local knowledge and management in Eastern Indonesia. Ethnobot. Res. Appl. 10, 15–35 (2012).
Google Scholar 
39.Burns, A. E., Gleadow, R., Cliff, J., Zacarias, A. & Cavagnaro, T. Cassava: The drought, war and famine crop in a changing world. Sustainability 2, 3572–3607 (2010).
Google Scholar 
40.Pujol, B., David, P. & McKey, D. Microevolution in agricultural environments: How a traditional Amerindian farming practice favours heterozygosity in cassava (Manihot esculenta Crantz, Euphorbiaceae). Ecol. Lett. 8, 138–147 (2005).
Google Scholar 
41.Mba, R. E. C. et al. Simple sequence repeat (SSR) markers survey of the cassava (Manihot esculenta Crantz) genome: Towards an SSR-based molecular genetic map of cassava. Theor. Appl. Genet. 102, 21–31 (2001).CAS 

Google Scholar 
42.de Oliveira, E. J. et al. Genome-wide selection in cassava. Euphytica 187, 263–276 (2012).CAS 

Google Scholar 
43.Ferguson, M. E., Shah, T., Kulakow, P. & Ceballos, H. A global overview of cassava genetic diversity. PLoS ONE 14, e0224763 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
44.Wolfe, M. D. et al. Historical introgressions from a wild relative of modern cassava improved important traits and may be under balancing selection. Genetics 213, 1237–1253 (2019).PubMed 
PubMed Central 

Google Scholar 
45.Bredeson, J. V. et al. Sequencing wild and cultivated cassava and related species reveals extensive interspecific hybridization and genetic diversity. Nat. Biotechnol. 34, 562–570 (2016).CAS 
PubMed 

Google Scholar 
46.Kuon, J. E. et al. Haplotype-resolved genomes of geminivirus-resistant and geminivirus-susceptible African cassava cultivars. BMC Biol. 17, 1–15 (2019).CAS 

Google Scholar 
47.Prochnik, S. et al. The cassava genome: Current progress, future directions. Trop. Plant Biol. 5, 88–94 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
48.Rabbi, I. Y. et al. Tracking crop varieties using genotyping-by-sequencing markers: A case study using cassava (Manihot esculenta Crantz). BMC Genet. 16, 115 (2015).PubMed 
PubMed Central 

Google Scholar 
49.Albuquerque, H. Y. G., do Carmo, C. D., Brito, A. C. & de Oliveira, E. J. Genetic diversity of Manihot esculenta Crantz germplasm based on single-nucleotide polymorphism markers. Ann. Appl. Biol. 173, 271–284 (2018).
Google Scholar 
50.Ogbonna, A. C. et al. Large-scale genome-wide association study, using historical data, identifies conserved genetic architecture of cyanogenic glucoside content in cassava (Manihot esculenta Crantz) root. Plant J. 105, 754–770 (2021).CAS 
PubMed 

Google Scholar 
51.Allendorf, F. W. Genetics and the conservation of natural populations: Allozymes to genomes. Mol. Ecol. 26, 420–430 (2017).CAS 
PubMed 

Google Scholar 
52.Morrell, P. L., Buckler, E. S. & Ross-Ibarra, J. Crop genomics: Advances and applications. Nat. Rev. Genet. 13, 85–96 (2012).CAS 

Google Scholar 
53.Ahrens, C. W. et al. The search for loci under selection: Trends, biases and progress. Mol. Ecol. 27, 1342–1356 (2018).PubMed 

Google Scholar 
54.Lotterhos, K. E. & Whitlock, M. C. The relative power of genome scans to detect local adaptation depends on sampling design and statistical method. Mol. Ecol. 24, 1031–1046 (2015).PubMed 

Google Scholar 
55.Lotterhos, K. E. & Whitlock, M. C. Evaluation of demographic history and neutral parameterization on the performance of FST outlier tests. Mol. Ecol. 23, 2178–2192 (2014).PubMed 
PubMed Central 

Google Scholar 
56.Hoban, S. et al. Finding the genomic basis of local adaptation: Pitfalls, practical solutions, and future directions. Am. Nat. 188, 379–397 (2016).PubMed 
PubMed Central 

Google Scholar 
57.Pankin, A., Altmüller, J., Becker, C. & von Korff, M. Targeted resequencing reveals genomic signatures of barley domestication. New Phytol. 218, 1247–1259 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
58.Maccaferri, M. et al. Durum wheat genome highlights past domestication signatures and future improvement targets. Nat. Genet. 51, 885–895 (2019).CAS 
PubMed 

Google Scholar 
59.Allaby, R. G., Ware, R. L. & Kistler, L. A re-evaluation of the domestication bottleneck from archaeogenomic evidence. Evol. Appl. 12, 29–37 (2019).PubMed 

Google Scholar 
60.Brown, T. A. Is the domestication bottleneck a myth?. Nat. Plants 5, 337–338 (2019).PubMed 

Google Scholar 
61.Gaillard, M. D. P., Glauser, G., Robert, C. A. M. & Turlings, T. C. J. Fine-tuning the ‘plant domestication-reduced defense’ hypothesis: Specialist vs generalist herbivores. New Phytol. 217, 355–366 (2018).CAS 
PubMed 

Google Scholar 
62.Hillocks, R. J. & Wydra, K. Bacterial, fungal and nematode diseases. In Cassava: Biology, Production and Utilization (eds Hillocks, R. J. et al.) 261–280 (CABI, 2002).
Google Scholar 
63.Jarvis, A., Ramirez-Villegas, J., Campo, B. V. H. & Navarro-Racines, C. Is cassava the answer to African climate change adaptation?. Trop. Plant Biol. 5, 9–29 (2012).
Google Scholar 
64.Hanks, S. K. Genomic analysis of the eukaryotic protein kinase superfamily: A perspective. Genome Biol. 4, 111 (2003).PubMed 
PubMed Central 

Google Scholar 
65.Meng, X. & Zhang, S. MAPK cascades in plant disease resistance signaling. Annu. Rev. Phytopathol. 51, 245–266 (2013).CAS 
PubMed 

Google Scholar 
66.Champion, A., Kreis, M., Mockaitis, K., Picaud, A. & Henry, Y. Arabidopsis kinome: After the casting. Funct. Integr. Genomics 4, 163–187 (2004).CAS 
PubMed 

Google Scholar 
67.Lenser, T. & Theißen, G. Molecular mechanisms involved in convergent crop domestication. Trends Plant Sci. 18, 704–714 (2013).CAS 
PubMed 

Google Scholar 
68.Gepts, P. The contribution of genetic and genomic approaches to plant domestication studies. Curr. Opin. Plant Biol. 18, 51–59 (2014).PubMed 

Google Scholar 
69.Ceballos, H. et al. Discovery of an amylose-free starch mutant in cassava (Manihot esculenta Crantz). J. Agric. Food Chem. 55, 7469–7476 (2007).CAS 
PubMed 

Google Scholar 
70.Jennings, D. L. & Iglesias, C. Breeding for crop improvement. in Cassava: Biology, Production and Utilization (eds. Hillocks, R. J., Thresh, J. M. & Bellotti, A.) 149–166 (CABI, 2002). https://doi.org/10.18520/cs/v114/i02/256-257.71.Meyer, R. S. & Purugganan, M. D. Evolution of crop species: Genetics of domestication and diversification. Nat. Rev. Genet. 14, 840–852 (2013).CAS 
PubMed 

Google Scholar 
72.Meyer, R. S., DuVal, A. E. & Jensen, H. R. Patterns and processes in crop domestication: An historical review and quantitative analysis of 203 global food crops. New Phytol. 196, 29–48 (2012).PubMed 

Google Scholar 
73.Elias, M., Lenoir, H. & McKey, D. Propagule quantity and quality in traditional Makushi farming of cassava (Manihot esculenta): A case study for understanding domestication and evolution of vegetatively propagated crops. Genet. Resour. Crop Evol. 54, 99–115 (2007).
Google Scholar 
74.Zohary, D. Unconscious selection and the evolution of domesticated plants. Econ. Bot. 58, 5–10 (2004).
Google Scholar 
75.Lamberti, G., Gügel, I. L., Meurer, J., Soll, J. & Schwenkert, S. The cytosolic kinases STY8, STY17, and STY46 are involved in chloroplast differentiation in Arabidopsis. Plant Physiol. 157, 70–85 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
76.Pujol, B. et al. Evolution under domestication: Contrasting functional morphology of seedlings in domesticated cassava and its closest wild relatives. New Phytol. 166, 305–318 (2005).PubMed 

Google Scholar 
77.Halkier, B. A. & Gershenzon, J. Biology and biochemistry of glucosinolates. Annu. Rev. Plant Biol. 57, 303–333 (2006).CAS 
PubMed 

Google Scholar 
78.Doebley, J. F., Gaut, B. S. & Smith, B. D. The molecular genetics of crop domestication. Cell 127, 1309–1321 (2006).CAS 
PubMed 

Google Scholar 
79.Purugganan, M. D. & Fuller, D. Q. The nature of selection during plant domestication. Nature 457, 843–848 (2009).ADS 
CAS 
PubMed 

Google Scholar 
80.An, F. et al. Domestication syndrome is investigated by proteomic analysis between cultivated cassava (Manihot esculenta Crantz) and its wild relatives. PLoS ONE 11, e0152154 (2016).PubMed 
PubMed Central 

Google Scholar 
81.Alves, A. A. C. Cassava botany and physiology. in Cassava: Biology, Production and Utilization (eds. Hillocks, R. J., Thresh, J. M. & Bellotti, A.) 67–89 (CABI, 2002). https://doi.org/10.1079/9780851995243.0067.82.Alves, A. A. C. & Setter, T. L. Response of cassava leaf area expansion to water deficit: Cell proliferation, cell expansion and delayed development. Ann. Bot. 94, 605–613 (2004).PubMed 
PubMed Central 

Google Scholar 
83.Nielsen, R. & Signorovitch, J. Correcting for ascertainment biases when analyzing SNP data: Applications to the estimation of linkage disequilibrium. Theor. Popul. Biol. 63, 245–255 (2003).PubMed 
MATH 

Google Scholar 
84.Arnold, B., Corbett-Detig, R. B., Hartl, D. & Bomblies, K. RADseq underestimates diversity and introduces genealogical biases due to nonrandom haplotype sampling. Mol. Ecol. 22, 3179–3190 (2013).CAS 
PubMed 

Google Scholar 
85.Alves-Pereira, A. et al. A population genomics appraisal suggests independent dispersals for bitter and sweet manioc in Brazilian Amazonia. Evol. Appl. 13, 342–361 (2020).PubMed 

Google Scholar 
86.Bradbury, E. J. et al. Geographic differences in patterns of genetic differentiation among bitter and sweet manioc (Manihot esculenta subsp. esculenta; Euphorbiaceae). Am. J. Bot. 100, 857–866 (2013).PubMed 

Google Scholar 
87.Kates, H. R. et al. Targeted sequencing suggests wild-crop gene flow is central to different genetic consequences of two independent pumpkin domestications. Front. Ecol. Evol. 9, 618380 (2021).
Google Scholar 
88.Talavera, A., Soorni, A., Bombarely, A., Matas, A. J. & Hormaza, J. I. Genome-wide SNP discovery and genomic characterization in avocado (Persea americana Mill.). Sci. Rep. 9, 20137 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
89.Barrett, R. D. H. & Hoekstra, H. E. Molecular spandrels: Tests of adaptation at the genetic level. Nat. Rev. Genet. 12, 767–780 (2011).CAS 
PubMed 

Google Scholar 
90.Ross-Ibarra, J., Morrell, P. L. & Gaut, B. S. Plant domestication, a unique opportunity to identify the genetic basis of adaptation. Proc. Natl. Acad. Sci. USA 104, 8641–8648 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
91.Ogbonna, A. C., Braatz de Andrade, L. R., Mueller, L. A., de Oliveira, E. J. & Bauchet, G. J. Comprehensive genotyping of a Brazilian cassava (Manihot esculenta Crantz) germplasm bank: insights into diversification and domestication. Theor. Appl. Genet. https://doi.org/10.1007/s00122-021-03775-5 (2021).Article 
PubMed 
PubMed Central 

Google Scholar 
92.McKey, D., Cavagnaro, T. R., Cliff, J. & Gleadow, R. Chemical ecology in coupled human and natural systems: People, manioc, multitrophic interactions and global change. Chemoecology 20, 109–133 (2010).CAS 

Google Scholar 
93.Clement, C. R., de Cristo-Araújo, M., Coppens d’Eeckenbrugge, G., Alves Pereira, A. & Picanço-Rodrigues, D. Origin and domestication of native Amazonian crops. Diversity 2, 72–106 (2010).
Google Scholar 
94.Peña-Venegas, C. P., Stomph, T. J., Verschoor, G., Lopez-Lavalle, L. A. B. & Struik, P. C. Differences in manioc diversity among five ethnic groups of the Colombian Amazon. Diversity 6, 792–826 (2014).
Google Scholar 
95.Moreira, P. A. et al. Diversity of treegourd (Crescentia cujete) suggests introduction and prehistoric dispersal routes into Amazonia. Front. Ecol. Evol. 5, 150 (2017).
Google Scholar 
96.Clement, C. R. et al. Origin and dispersal of domesticated peach palm. Front. Ecol. Evol. 5, 148 (2017).
Google Scholar 
97.Mutegi, E. et al. Genetic structure and relationships within and between cultivated and wild sorghum (Sorghum bicolor (L.) Moench) in Kenya as revealed by microsatellite markers. Theor. Appl. Genet. 122, 989–1004 (2011).CAS 
PubMed 

Google Scholar 
98.Roullier, C., Rossel, G., Tay, D., McKey, D. & Lebot, V. Combining chloroplast and nuclear microsatellites to investigate origin and dispersal of New World sweet potato landraces. Mol. Ecol. 20, 3963–3977 (2011).CAS 
PubMed 

Google Scholar 
99.Alves-Pereira, A. et al. Patterns of nuclear and chloroplast genetic diversity and structure of manioc along major Brazilian Amazonian rivers. Ann. Bot. 121, 625–639 (2018).PubMed 
PubMed Central 

Google Scholar 
100.Siqueira, M. V. B. M. et al. Genetic characterization of cassava (Manihot esculenta) landraces in Brazil assessed with simple sequence repeats. Genet. Mol. Biol. 32, 104–110 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
101.Allem, A. C. The origins and taxonomy of cassava. in Cassava: Biology, Production and Utilization (eds. Hillocks, R. J., Thresh, J. M. & Bellotti, A.) 1–16 (CABI, 2002). https://doi.org/10.1079/9780851995243.0001.102.Barbieri, R. L., Gomes, J. C. C., Alercia, A. & Padulosi, S. Agricultural biodiversity in southern Brazil: Integrating efforts for conservation and use of neglected and underutilized species. Sustainability 6, 741–757 (2014).
Google Scholar 
103.Khoury, C. K. et al. Increasing homogeneity in global food supplies and the implications for food security. Proc. Natl. Acad. Sci. USA 111, 4001–4006 (2014).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
104.Peroni, N. & Hanazaki, N. Current and lost diversity of cultivated varieties, especially cassava, under swidden cultivation systems in the Brazilian Atlantic Forest. Agric. Ecosyst. Environ. 92, 171–183 (2002).
Google Scholar 
105.Peroni, N. & Martins, P. S. Influência da dinâmica agrícola itinerante na geração de diversidade de etnovariedades cultivadas vegetativamente. Interciencia 25, 22–29 (2000).
Google Scholar 
106.Doyle, J. J. & Doyle, J. L. A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochem. Bull 19, 11–15 (1987).
Google Scholar 
107.Poland, J. A., Brown, P. J., Sorrells, M. E. & Jannink, J. L. Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach. PLoS ONE 7, e32253 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
108.Andrews, A. FastQC: A Quality Control Tool for High Throughput Sequence Data. http://www.bioinformatics.babraham.ac.uk/projects/fastqc/ (2010).109.Catchen, J., Hohenlohe, P. A., Bassham, S., Amores, A. & Cresko, W. A. Stacks: An analysis tool set for population genomics. Mol. Ecol. 22, 3124–3140 (2013).PubMed 
PubMed Central 

Google Scholar 
110.Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
111.Li, H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27, 2987–2993 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
112.Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).PubMed 
PubMed Central 

Google Scholar 
113.Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
114.Luu, K., Bazin, E. & Blum, M. G. B. pcadapt: An R package to perform genome scans for selection based on principal component analysis. Mol. Ecol. Resour. 17, 67–77 (2017).CAS 
PubMed 

Google Scholar 
115.R Core Team. A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2018). https://www.r-project.org/. (Accessed: 15th January 2018).116.Fariello, M. I., Boitard, S., Naya, H., SanCristobal, M. & Servin, B. Detecting signatures of selection through haplotype differentiation among hierarchically structured populations. Genetics 193, 929–941 (2013).PubMed 
PubMed Central 

Google Scholar 
117.Weir, B. S. & Cockerham, C. C. Estimating F-statistics for the analysis of population structure. Evolution (N. Y. ) 38, 1358–1370 (1984).CAS 

Google Scholar 
118.Keenan, K., McGinnity, P., Cross, T. F., Crozier, W. W. & Prodöhl, P. A. DiveRsity: An R package for the estimation and exploration of population genetics parameters and their associated errors. Methods Ecol. Evol. 4, 782–788 (2013).
Google Scholar 
119.Bonhomme, M. et al. Detecting selection in population trees: The Lewontin and Krakauer test extended. Genetics 186, 241–262 (2010).PubMed 
PubMed Central 

Google Scholar 
120.Reynolds, J., Weir, B. S. & Cockerham, C. C. Estimation of the coancestry coefficient: Basis for a short-term genetic distance. Genetics 105, 767–779 (1983).CAS 
PubMed 
PubMed Central 

Google Scholar 
121.Sabeti, P. C. et al. Genome-wide detection and characterization of positive selection in human populations. Nature 449, 913–918 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
122.Scheet, P. & Stephens, M. A fast and flexible statistical model for large-scale population genotype data: Applications to inferring missing genotypes and haplotypic phase. Am. J. Hum. Genet. 78, 629–644 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
123.Gautier, M., Klassmann, A. & Vitalis, R. rehh 2.0: A reimplementation of the R package rehh to detect positive selection from haplotype structure. Mol. Ecol. Resour. 17, 78–90 (2017).CAS 
PubMed 

Google Scholar 
124.Cingolani, P. et al. A program for annotating and predicting the effects of single nucleotide polyorphisms, SnpEff: SNPs in the genome of Drosophila melanogaster strain w1118; iso-2; iso-3. Fly (Austin). 6, 1–13 (2012).
Google Scholar 
125.Ten Blake, J. A. quick tips for using the Gene Ontology. PLoS Comput. Biol. 9, e1003343 (2013).ADS 
PubMed 
PubMed Central 

Google Scholar 
126.Quinlan, A. R. & Hall, I. M. BEDTools: A flexible suite of utilities for comparing genomic features. Bioinformatics 26, 841–842 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar 
127.Alexa, A. & Rahnenführer, J. TopGO: Enrichment analysis for Gene Ontology. R package version 2.44.0. (2021).128.Osuna-Cruz, C. M. et al. PRGdb 3.0: A comprehensive platform for prediction and analysis of plant disease resistance genes. Nucleic Acids Res. 46, D1197–D1201 (2018).CAS 
PubMed 

Google Scholar 
129.Camacho, C. et al. BLAST+: Architecture and applications. BMC Bioinformatics 10, 421 (2009).PubMed 
PubMed Central 

Google Scholar 
130.Paquette, S. R. Useful Functions for (Batch) File Conversion and Data Resampling in Microsatellite Datasets. https://cran.r-project.org/package=PopGenKit (2012).131.Excoffier, L. & Lischer, H. E. L. Arlequin suite ver 3.5: A new series of programs to perform population genetics analyses under Linux and Windows. Mol. Ecol. Resour. 10, 564–567 (2010).PubMed 

Google Scholar 
132.Frichot, E., Mathieu, F., Trouillon, T., Bouchard, G. & François, O. Fast and efficient estimation of individual ancestry coefficients. Genetics 196, 973–983 (2014).PubMed 
PubMed Central 

Google Scholar 
133.Jombart, T., Devillard, S. & Balloux, F. Discriminant analysis of principal components: A new method for the analysis of genetically structured populations. BMC Genet. 11, 94 (2010).PubMed 
PubMed Central 

Google Scholar 
134.Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar 
135.Frichot, E. & François, O. LEA: An R package for landscape and ecological association studies. Methods Ecol. Evol. 6, 925–929 (2015).
Google Scholar 
136.Jombart, T. & Ahmed, I. Genetics and population analysis. Adegenet 1.3-1: New tools for the analysis of genome-wide SNP data. Bioinformatics 27, 3070–3071 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar  More

Teasing apart the factors that influence prey choice and foraging tactics in the wild poses formidable logistical challenges because of multiple confounding features. For example, a particular type of prey may be rarely consumed not because of predator aversion, but because that prey type is more difficult to find or to capture than some other kind of prey22. Similarly, predators may key in on specific types of prey based on dietary preferences, prey size, or abundance23,24,25. The method of bait deployment that we adopted circumvents many of those problems, by standardising prey abundance, observability, and ease of capture by the predator. Under these conditions, free-ranging crocodiles from toad-sympatric versus toad-naïve populations showed substantial differences in foraging tactics and bait choice. In toad-naïve populations, crocodiles took equal numbers of treatment (toad) baits and control (chicken) baits, and frequently took baits located on land as well as over water. In contrast, crocodiles in toad-sympatric populations generally avoided toad baits in all locations and foraged primarily in the water rather than on land. Both of these shifts—in prey types and foraging locations—conceivably reduce the vulnerability of crocodiles to fatal ingestion of highly toxic cane toads.The relatively rapid ( More

BOOK REVIEW
24 January 2022

A call for governments to save soil

To ensure food security, the world must stop letting fertile soil wash and blow away.

Emma Marris

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Emma Marris

Emma Marris is an environmental writer who lives in Oregon.

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Rock becomes visible as topsoil is eroded away.Credit: Martin Harvey/Getty

A World Without Soil: The Past, Present, and Precarious Future of the Earth Beneath Our Feet Jo Handelsman Yale Univ. Press (2021)Soil creates life from death. The production of more than 95% of the food we eat relies on soil, a heady mix of rock particles, decaying organic matter, roots, fungi and microorganisms. Yet this precious resource is eroding at a global average of 13.5 tonnes per hectare per year. Instead of nourishing crops, fertile topsoil is ending up in inconvenient places such as ditches, reservoirs and the ocean.Microbiologist Jo Handelsman takes on the challenge of making readers care in A World Without Soil, aided by environmental researcher Kayla Cohen. Their prologue takes the form of a letter about soil erosion that Handelsman wishes she had sent to US president Barack Obama while working in the White House’s Office of Science and Technology Policy in the mid-2010s. Alas, she did not understand the true gravity of the problem until the waning days of the administration. Her biggest regret? That she wasn’t able to make soil management the federal priority she thinks it should be.Soil can be created over time, as dead things break down and contribute energy and nutrients to an ecosystem based on the underlying rock. But it erodes 10–30 times faster than it is produced. Globally, erosion reduces annual crop yields by 0.3%. At that rate, 10% of production could be lost by 2050. In erosion hotspots such as Nigeria, 80% of the land has been degraded. In Iowa, up to 17% of land is almost devoid of topsoil. Almost more convincing than the many facts and figures is a colour photograph of a field in Iowa with so little topsoil that the pale, lifeless sandy rubble beneath pokes through.Age-old solutionsA sense of dread builds in the chapters that cover the basic science of soil as well as the causes and consequences of its erosion. The last part of the book brings a burst of enthusiasm, as the authors turn to possible solutions — many of them simple, and some millennia old. These involve improving holding capacity through planting diverse crops in rotation; increasing organic content with additions such as compost and biochar; reducing the erosional effects of water and wind by reshaping the land with contouring, terraces, windbreaks and the like; and ploughing as little as possible.In a chapter on traditional soil-management techniques around the world, Handelsman and Cohen describe deep black “plaggen” soils on Scottish islands, made rich with cattle manure; rice terraces managed for 2,000 years by the Ifugao people in the Philippines; the milpa farming system of the Maya in Latin America, with its 25-year rotation of crops including trees; and compost made of seaweed, shells and plant material by the Māori in New Zealand. Each system yields rich agricultural productivity while maintaining deep banks of carbon-rich, fertile soil. “We know how to do this,” write Handelsman and Cohen.

Cactus farming in Mexico, where the traditional system of crop rotation helps to replenish the soil.Credit: Omar Torres/AFP/Getty

Why, then, is fertile soil being allowed to wash and blow away? The answer, not surprisingly, rests in the shackles of global capitalism. Farming’s profit margins are razor-thin, forcing producers to plant the highest-yielding variety of the highest-profit crop from field edge to field edge every season. Terracing, rotating crops and forgoing tilling enrich soil in the long run, but nibble into profits this year. And farmers can’t pay their mortgages or lease equipment with the aroma of deep black topsoil.
Food systems: seven priorities to end hunger and protect the planet
Handelsman and Cohen urge the world to demand real change in how mainstream agricultural production is managed. “The burden of protecting soil cannot be relegated to indigenous people and environmental activists,” they note. But their specific suggestions are a little underwhelming. They join the calls for international soil treaties, but given how poorly climate treaties have worked, I am cynical about the potential of such agreements. Countries seem likely to both under-promise and under-deliver unless there are costly penalties for failure. The same goes for the consumer-facing labels that the authors propose for food produced on farms that are working to improve their soil. Similar labels have not put a meaningful dent in climate change or other environmental problems — and many customers cannot afford to spend more on “soil-friendly” food.Top-down changeWhat farming needs is a top-down overhaul. Handelsman and Cohen gesture at this with proposed discounts on crop-insurance premiums for farmers who increase the carbon in their soil. More is needed. Governments must pay farmers to build soil. In the United States, farmers can apply for funding for anti-erosion improvements through the Environmental Quality Incentives Program, run by the Department of Agriculture. Funding announced this month will increase the amount of land planted with cover crops to 12 million hectares by 2030 — but even that would represent only some 7% of US cropland. It is not enough.We need to change how we think of farming. We have already begun to move towards a model in which farmers are less independent businesspeople growing and selling food, and more government-supported land stewards managing a complex mix of food production, soil fertility, wildlife habitat and more. Around the world, many farmers depend on subsidies, drought relief and payments from piecemeal schemes to conserve soil and nature. Such programmes — currently small-scale, ad hoc fixes for a broken system — should be the core of the agricultural sector.Our land, our fresh water, our biodiversity and our soil are too precious to be destroyed by the market price of commodity grains and other foodstuffs. We must invest deeply and thoughtfully in our farmers so that they can invest deeply and thoughtfully in the land, becoming holistic landscape-management professionals. This is the future of farming.

Nature 601, 503-504 (2022)
doi: https://doi.org/10.1038/d41586-022-00158-8

Competing Interests
The author declares no competing interests.

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1.Simpson, G. G. History of the fauna of Latin America. Am. Sci. 38, 361–389 (1950).
Google Scholar 
2.Patterson, B. D. & Costa, L. P. Bones, Clones, and Biomes: The History and Geography of Recent Neotropical Mammals (The University of Chicago Press, 2012).3.Cidade, G. M., Fortier, D. & Hsiou, A. S. The crocodylomorph fauna of the Cenozoic of South America and its evolutionary history: a review. J. South Am. Earth Sci. 90, 392–411 (2019).ADS 

Google Scholar 
4.Tambussi, C. P. & Degrange, F. J. South American and Antarctic Continental Cenozoic Birds. (Springer Netherlands, 2013). https://doi.org/10.1007/978-94-007-5467-6.5.Marshall, L. G. Evolution of the carnivorous adaptive zone in South America. In Major Patterns in Vertebrate Evolution (eds. Hecht, M. K., Goody, P. C. & Hecht, B. M.) 709–721 (Springer US, 1977). https://doi.org/10.1007/978-1-4684-8851-7_24.6.Marshall, L. G. & Cifelli, R. L. Analysis of changing diversity patterns in Cenozoic land mammal age faunas, South America. Palaeovertebrata 19, 169–210 (1990).
Google Scholar 
7.Prevosti, F. J., Forasiepi, A. & Zimicz, N. The evolution of the cenozoic terrestrial mammalian predator guild in South America: competition or replacement?. J. Mamm. Evol. 20, 3–21 (2013).
Google Scholar 
8.Prevosti, F. J. & Forasiepi, A. M. Evolution of South American mammalian predators during the Cenozoic: paleobiogeographic and paleoenvironmental contingencies. (Springer International Publishing, 2018). https://doi.org/10.1007/978-3-319-03701-1.9.Croft, D. A. Do marsupials make good predators? Insights from predator-prey diversity ratios. Evol. Ecol. Res. 8, 1193–1214 (2006).
Google Scholar 
10.Albino, A. M. Snakes from the Paleocene and Eocene of Patagonia (Argentina): Paleoecology and coevolution with mammals. Hist. Biol. 7, 51–69 (1993).
Google Scholar 
11.Head, J. J. et al. Giant boid snake from the Palaeocene neotropics reveals hotter past equatorial temperatures. Nature 457, 715–717 (2009).ADS 
CAS 
PubMed 

Google Scholar 
12.de Muizon, C., Ladevèze, S., Selva, C., Vignaud, R. & Goussard, F. Allqokirus australis (Sparassodonta, Metatheria) from the early Palaeocene of Tiupampa (Bolivia) and the rise of the metatherian carnivorous radiation in South America. Geodiversitas 40, 363 (2018).
Google Scholar 
13.Forasiepi, A. M. Osteology of Arctodictis sinclairi (Mammalia, Metatheria, Sparassodonta) and phylogeny of Cenozoic metatherian carnivores from South America. Monogr. Mus. Argentino Cienc. Nat., n.s. 6, 1–174 (2009).14.Prevosti, F. J. et al. New radiometric 40Ar–39Ar dates and faunistic analyses refine evolutionary dynamics of Neogene vertebrate assemblages in southern South America. Sci. Rep. 11, 9830 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
15.Pérez, L. F. et al. Oceanographic and climatic consequences of the tectonic evolution of the southern scotia sea basins, Antarctica. Earth-Science Rev. 198, 102922 (2019).
Google Scholar 
16.Williams, S. E., Whittaker, J. M., Halpin, J. A. & Müller, R. D. Australian-Antarctic breakup and seafloor spreading: Balancing geological and geophysical constraints. Earth-Science Rev. 188, 41–58 (2019).ADS 

Google Scholar 
17.Gelfo, J. N. Considerations about the evolutionary stasis of Notiolofos arquinotiensis (Mammalia: Sparnotheriodontidae), Eocene of Seymour Island, Antartica. Ameghiniana 53, 316–332 (2016).
Google Scholar 
18.Goin, F. J. et al. New metatherian mammal from the Early Eocene of Antarctica. J. Mamm. Evol. https://doi.org/10.1007/s10914-018-9449-6 (2018).Article 

Google Scholar 
19.Reguero, M. A. et al. Final Gondwana breakup: The Paleogene South American native ungulates and the demise of the South America-Antarctica land connection. Glob. Planet. Change 123, 400–413 (2014).ADS 

Google Scholar 
20.Ramos, V. A. & Aleman, A. Tectonic evolution of the Andes. in Tectonic evolution of South America (eds. Cordani, U. G., Milani, E. J., Thomaz Filho, A. & Campos, D. A.) 635–685 (2000).21.Boschman, L. M. Andean mountain building since the Late Cretaceous: A paleoelevation reconstruction. Earth-Science Rev. 220, 103640 (2021).
Google Scholar 
22.Fosdick, J. C., Reat, E. J., Carrapa, B., Ortiz, G. & Alvarado, P. M. Retroarc basin reorganization and aridification during Paleogene uplift of the southern central Andes. Tectonics 36, 493–514 (2017).ADS 

Google Scholar 
23.Leier, A., McQuarrie, N., Garzione, C. & Eiler, J. Stable isotope evidence for multiple pulses of rapid surface uplift in the Central Andes, Bolivia. Earth Planet. Sci. Lett. 371–372, 49–58 (2013).ADS 

Google Scholar 
24.Cione, A. L., Gasparini, G. M., Soibelzon, E., Soibelzon, L. H. & Tonni, E. P. The Great American Biotic Interchange: a South American perspective. (Springer, 2015).25.Coates, A. G. & Stallard, R. F. How old is the isthmus of Panama?. Bull. Mar. Sci. 89, 801–813 (2013).
Google Scholar 
26.Montes, C. et al. Middle Miocene closure of the Central American Seaway. Science (80-) 348, 226–229 (2015).ADS 
CAS 

Google Scholar 
27.Auderset, A. et al. Gulf Stream intensification after the early Pliocene shoaling of the Central American Seaway. Earth Planet. Sci. Lett. 520, 268–278 (2019).CAS 

Google Scholar 
28.Scher, H. D. et al. Onset of Antarctic Circumpolar Current 30 million years ago as Tasmanian Gateway aligned with westerlies. Nature 523, 580–583 (2015).ADS 
CAS 
PubMed 

Google Scholar 
29.Benton, M. J. The Red Queen and the Court Jester: species diversity and the role of biotic and abiotic factors through time. Science (80-) 323, 728–732 (2009).ADS 
CAS 

Google Scholar 
30.Van Valen, L. New evolutionary law. Evol. Theory 1, 1–30 (1973).
Google Scholar 
31.Cione, A. L., Tonni, E. P. & Soibelzon, L. The Broken Zig-Zag: Late Cenozoic large mammal and tortoise extinction in South America. Rev. del Mus. Argentino Ciencias Nat. n.s. 5, 1–19 (2003).32.Leigh, E. G., O’Dea, A. & Vermeij, G. J. Historical biogeography of the isthmus of Panama. Biol. Rev. 89, 148–172 (2014).PubMed 

Google Scholar 
33.Werdelin, L. Jaw geometry and molar morphology in Marsupial carnivores: Analysis of a constraint and its macroevolutionary consequences. Paleobiology 13, 342–350 (1987).
Google Scholar 
34.Goin, F. J. & Montalvo, C. Revisión sistemática y reconocimiento de una nueva especie del género Thylatheridium Reig (Marsupialia, Didelphidae). Ameghiniana 25, 161–167 (1988).
Google Scholar 
35.Goin, F. J. & Pardiñas, U. F. J. Revision de las especies del genero Hyperdidelphys Ameghino, 1904 (Mammalia, Marsupialia, Didelphidae). Su significación filogenética, estratigráfica y adaptativa en el Neogeno del Cono Sur Sudamericano. Estud. Geológicos 52, 327–359 (1996).36.Beck, R. M. D. & Taglioretti, M. L. A nearly complete juvenile skull of the marsupial Sparassocynus derivatus from the Pliocene of Argentina, the affinities of “sparassocynids”, and the diversification of opossums (Marsupialia; Didelphimorphia; Didelphidae). J. Mamm. Evol. 27, 385–417 (2020).
Google Scholar 
37.López-Aguirre, C., Archer, M., Hand, S. J. & Laffan, S. W. Extinction of South American sparassodontans (Metatheria): Environmental fluctuations or complex ecological processes?. Palaeontology 60, 91–115 (2017).
Google Scholar 
38.Forasiepi, A. M., Martinelli, A. G. & Goin, F. J. Revisión taxonómica de Parahyaenodon argentinus Ameghino y sus implicancias en el conocimiento de los grandes mamíferos carnívoros del Mio-Plioceno de América de Sur. Ameghiniana 44, 143–159 (2007).
Google Scholar 
39.Zimicz, N. Avoiding competition: the ecological history of late Cenozoic metatherian carnivores in South America. J. Mamm. Evol. 21, 383–393 (2014).
Google Scholar 
40.Echarri, S., Ercoli, M. D., Chemisquy, M. A., Turazzini, G. & Prevosti, F. J. Mandible morphology and diet of the South American extinct metatherian predators (Mammalia, Metatheria, Sparassodonta). Earth Environ. Sci. Trans. R. Soc. Edinburgh 106, 277–288 (2017).41.Croft, D. A., Engelman, R. K., Dolgushina, T. & Wesley, G. Diversity and disparity of sparassodonts (Metatheria) reveal non-analogue nature of ancient South American mammalian carnivore guilds. Proc. R. Soc. B Biol. Sci. 285, 20172012 (2018).
Google Scholar 
42.Engelman, R. K. & Croft, D. A. A new species of small-bodied sparassodont (Mammalia, Metatheria) from the middle Miocene locality of Quebrada Honda, Bolivia. J. Vertebr. Paleontol. 34, 672–688 (2014).
Google Scholar 
43.Engelman, R. K. & Croft, D. A. Strangers in a strange land: ecological dissimilarity to metatherian carnivores may partly explain early colonization of South America by Cyonasua- group procyonids. Paleobiology 45, 598–611 (2019).
Google Scholar 
44.Engelman, R. K., Anaya, F. & Croft, D. A. Australogale leptognathus, gen. et sp. nov., a Second Species of Small Sparassodont (Mammalia: Metatheria) from the Middle Miocene Locality of Quebrada Honda, Bolivia. J. Mamm. Evol. 27, 37–54 (2020).45.Silvestro, D., Schnitzler, J., Liow, L. H., Antonelli, A. & Salamin, N. Bayesian estimation of speciation and extinction from incomplete fossil occurrence data. Syst. Biol. 63, 349–367 (2014).PubMed 

Google Scholar 
46.Lehtonen, S. et al. Environmentally driven extinction and opportunistic origination explain fern diversification patterns. Sci. Rep. 7, 4831 (2017).ADS 
PubMed 
PubMed Central 

Google Scholar 
47.Rangel, C. et al. Diversity, affinities and adaptations of the basal sparassodont Patene Simpson, 1935 (Mammalia, Metatheria). Ameghiniana 56, 263–289 (2019).
Google Scholar 
48.Alroy, J. et al. Effects of sampling standardization on estimates of Phanerozoic marine diversification. Proc. Natl. Acad. Sci. 98, 6261–6266 (2001).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
49.Badgley, C. The multiple scales of biodiversity. Paleobiology 29, 11–13 (2003).
Google Scholar 
50.Behrensmeyer, A. K., Kidwell, S. M. & Gastaldo, R. A. Taphonomy and paleobiology. Paleobiology 26, 103–147 (2000).
Google Scholar 
51.Butler, R. J., Barrett, P. M., Nowbath, S. & Upchurch, P. Estimating the effects of sampling biases on pterosaur diversity patterns: implications for hypotheses of bird/pterosaur competitive replacement. Paleobiology 35, 432–446 (2009).
Google Scholar 
52.Crampton, J. S. et al. Estimating the rock volume bias in paleobiodiversity studies. Science (80-) 301, 358–360 (2003).ADS 
CAS 

Google Scholar 
53.Kalmar, A. & Currie, D. J. The completeness of the continental fossil record and its impact on patterns of diversification. Paleobiology 36, 51–60 (2010).
Google Scholar 
54.Newham, E., Benson, R., Upchurch, P. & Goswami, A. Mesozoic mammaliaform diversity: the effect of sampling corrections on reconstructions of evolutionary dynamics. Palaeogeogr. Palaeoclimatol. Palaeoecol. 412, 32–44 (2014).
Google Scholar 
55.Prevosti, F. J. & Soibelzon, L. H. Evolution of the South American carnivores (Mammalia, Carnivora): a paleontological perspective. In Bones, Clones, and Biomes: The History and Geography of Recent Neotropical Mammals (eds. Patterson, B. D. & Costa, L. P.) 102–122 (University of Chicago Press, 2012).56.Carrillo, J. D., Forasiepi, A., Jaramillo, C. & Sánchez-Villagra, M. R. Neotropical mammal diversity and the Great American Biotic Interchange: spatial and temporal variation in South America’s fossil record. Front. Genet. 5, 451 (2015).PubMed 
PubMed Central 

Google Scholar 
57.Silvestro, D., Salamin, N., Antonelli, A. & Meyer, X. Improved estimation of macroevolutionary rates from fossil data using a Bayesian framework. Paleobiology https://doi.org/10.1017/pab.2019.23 (2019).Article 

Google Scholar 
58.Condamine, F. L., Guinot, G., Benton, M. J. & Currie, P. J. Dinosaur biodiversity declined well before the asteroid impact, influenced by ecological and environmental pressures. Nat. Commun. 12, 3833 (2021).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
59.Silvestro, D., Antonelli, A., Salamin, N. & Quental, T. B. The role of clade competition in the diversification of North American canids. Proc. Natl. Acad. Sci. 112, 8684–8689 (2015).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
60.Alroy, J. Constant extinction, constrained diversification, and uncoordinated stasis in North American mammals. Palaeogeogr. Palaeoclimatol. Palaeoecol. 127, 285–311 (1996).
Google Scholar 
61.Moen, D. & Morlon, H. Why does diversification slow down?. Trends Ecol. Evol. 29, 190–197 (2014).PubMed 

Google Scholar 
62.Bromham, L. The genome as a life-history character: why rate of molecular evolution varies between mammal species. Philos. Trans. R. Soc. B Biol. Sci. 366, 2503–2513 (2011).
Google Scholar 
63.Feng, P. & Zhou, Q. Absence of relationship between mitochondrial DNA evolutionary rate and longevity in mammals except for CYTB. J. Mamm. Evol. 26, 1–7 (2019).
Google Scholar 
64.Gillooly, J. F., Allen, A. P., West, G. B. & Brown, J. H. The rate of DNA evolution: Effects of body size and temperature on the molecular clock. Proc. Natl. Acad. Sci. 102, 140–145 (2005).ADS 
CAS 
PubMed 

Google Scholar 
65.Martin, A. P. & Palumbi, S. R. Body size, metabolic rate, generation time, and the molecular clock. Proc. Natl. Acad. Sci. 90, 4087–4091 (1993).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
66.Barraclough, T. G., Vogler, A. P. & Harvey, P. H. Revealing the factors that promote speciation. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 353, 241–249 (1998).67.Barraclough, T. G. & Savolainen, V. Evolutionary rates and species diversity in flowering plants. Evolution (N. Y) 55, 677–683 (2001).CAS 

Google Scholar 
68.Raia, P., Passaro, F., Fulgione, D. & Carotenuto, F. Habitat tracking, stasis and survival in Neogene large mammals. Biol. Lett. 8, 64–66 (2012).CAS 
PubMed 

Google Scholar 
69.Viranta, S. Geographic and temporal ranges of middle and late Miocene Carnivores. J. Mammal. 84, 1267–1278 (2003).
Google Scholar 
70.Flynn, L. J. et al. Neogene Siwalik mammalian lineages: Species longevities, rates of change, and modes of speciation. Palaeogeogr. Palaeoclimatol. Palaeoecol. 115, 249–264 (1995).
Google Scholar 
71.Van Valen, L. Group selection, sex, and fossils. Evolution (N. Y) 29, 87 (1975).
Google Scholar 
72.Cardillo, M. Biological determinants of extinction risk: why are smaller species less vulnerable?. Anim. Conserv. 6, 63–69 (2003).
Google Scholar 
73.Liow, L. H. et al. Higher origination and extinction rates in larger mammals. Proc. Natl. Acad. Sci. 105, 6097–6102 (2008).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
74.McLain, D. K. Cope’s rules, sexual selection, and the loss of ecological plasticity. Oikos 68, 490–500 (1993).
Google Scholar 
75.Hautmann, M. What is macroevolution?. Palaeontology 63, 1–11 (2020).
Google Scholar 
76.Stanley, S. M. The general correlation between rate of speciation and rate of extinction: fortuitous causal linkages. In Causes of evolution: A paleontological perspective (eds. Ross, R. M. & Allmon, W. D.) 103–127 (University of Chicago Press, 1990).77.Marshall, C. R. Five palaeobiological laws needed to understand the evolution of the living biota. Nat. Ecol. Evol. 1, 0165 (2017).
Google Scholar 
78.Ercoli, M. D., Prevosti, F. J. & Forasiepi, A. M. The structure of the mammalian predator guild in the Santa Cruz Formation (late Early Miocene). J. Mamm. Evol. 21, 369–381 (2014).
Google Scholar 
79.Marshall, L. G. Evolution of the Borhyaenidae, extinct south american predaceous marsupials. Univ. Calif. Publ. Geol. Sci. 17, 1–89 (1978).
Google Scholar 
80.Prevosti, F. J., Forasiepi, A. M., Ercoli, M. D. & Turazzini, G. F. Paleoecology of the mammalian carnivores (Metatheria, Sparassodonta) of the Santa Cruz Formation (late Early Miocene). In Early Miocene paleobiology in Patagonia (eds. Vizcaino, S. F., Kay, R. F. & Bargo, M. S.) 173–193 (Cambridge University Press, 2012). https://doi.org/10.1017/CBO9780511667381.012.81.Carbone, C., Teacher, A. & Rowcliffe, J. M. The costs of carnivory. PLoS Biol. 5, e22 (2007).PubMed 
PubMed Central 

Google Scholar 
82.Carbone, C., Mace, G. M., Roberts, S. C. & Macdonald, D. W. Energetic constraints on the diet of terrestrial carnivores. Nature 402, 286–288 (1999).ADS 
CAS 
PubMed 

Google Scholar 
83.Rovinsky, D. S., Evans, A. R., Martin, D. G. & Adams, J. W. Did the thylacine violate the costs of carnivory? Body mass and sexual dimorphism of an iconic Australian marsupial. Proc. R. Soc. B Biol. Sci. 287, 20201537 (2020).
Google Scholar 
84.Van Valkenburgh, B., Wang, X. & Damuth, J. Cope’s Rule, hypercarnivory, and extinction in North American canids. Science (80-) 306, 101–104 (2004).ADS 

Google Scholar 
85.Finarelli, J. A. Mechanisms behind active trends in body size evolution of the Canidae (Carnivora: Mammalia). Am. Nat. 170, 876–885 (2007).PubMed 

Google Scholar 
86.Cione, A. L. & Tonni, E. P. Chronostratigraphy and “Land-Mammal Ages” in the Cenozoic of southern South America: principles, practices, and the “Uquian” problem. J. Paleontol. 69, 135–159 (1995).
Google Scholar 
87.Soibelzon, L. H. & Prevosti, F. J. Los carnívoros (Carnivora, Mammalia) terrestres del Cuaternario de América del Sur. in Geomorfología Litoral i Quaternari. Homenatge a Joan Cuerda Barceló. Mon. Soc. Hist. Nat. (eds. Pons, G. X. & Vicens, D.) vol. 14 49–68 (2007).88.Argot, C. Evolution of South American mammalian predators (Borhyaenoidea): Anatomical and palaeobiological implications. Zool. J. Linn. Soc. 140, 487–521 (2004).
Google Scholar 
89.Degrange, F. J. Hind limb morphometry of terror birds (Aves, Cariamiformes, Phorusrhacidae): functional implications for substrate preferences and locomotor lifestyle. Earth Environ. Sci. Trans. R. Soc. Edinburgh 106, 257–276 (2015).90.Angst, D., Buffetaut, E., Lecuyer, C. & Amiot, R. A new method for estimating locomotion type in large ground birds. Palaeontology 59, 217–223 (2016).
Google Scholar 
91.Gurevitch, J. & Padilla, D. K. Are invasive species a major cause of extinctions?. Trends Ecol. Evol. 19, 470–474 (2004).PubMed 

Google Scholar 
92.Davis, M. A. Biotic globalization: Does competition from introduced species threaten biodiversity?. Bioscience 53, 481–489 (2003).
Google Scholar 
93.Prowse, T. A. A., Johnson, C. N., Bradshaw, C. J. A. & Brook, B. W. An ecological regime shift resulting from disrupted predator–prey interactions in Holocene Australia. Ecology 95, 693–702 (2014).PubMed 

Google Scholar 
94.Marshall, L. G. A new species of Lycopsis (Borhyaenidae: Marsupialia) from the La Venta Fauna (Late Miocene) of Colombia, South America. J. Paleontol. 51, 633–642 (1977).
Google Scholar 
95.Tomassini, R. L., Montalvo, C. I., Bargo, M. S., Vizcaíno, S. F. & Cuitiño, J. I. Sparassodonta (Metatheria) coprolites from the early-mid Miocene (Santacrucian age) of Patagonia (Argentina) with evidence of exploitation by coprophagous insects. Palaios 34, 639–651 (2019).ADS 

Google Scholar 
96.Bond, M., Cerdeño, E. & López, G. Los ungulados nativos de América del Sur. in Evolución biológica y climática de la región Pampeana durante los últimos cinco millones de años (eds. Alberdi, M. T., Leone, G. & Tonni, E. P.) 258–275 (Museo Nacional de Ciencias Naturales, 1995).97.Croft, D. A., Gelfo, J. N. & López, G. M. Splendid innovation: The extinct South American native ungulates. Annu. Rev. Earth Planet. Sci. 48, 259–290 (2020).ADS 
CAS 

Google Scholar 
98.Pascual, R. & Jaureguizar, E. O. Evolving climates and mammal faunas in cenozoic South America. J. Hum. Evol. 19, 23–60 (1990).
Google Scholar 
99.Patterson, B. & Pascual, R. The fossil mammal fauna of South America. Q. Rev. Biol. 43, 409–451 (1968).
Google Scholar 
100.Miller, K. G. et al. Cenozoic sea-level and cryospheric evolution from deep-sea geochemical and continental margin records. Sci. Adv. 6, eaaz1346 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
101.Rae, J. W. B. et al. Atmospheric CO2 over the past 66 million years from marine archives. Annu. Rev. Earth Planet. Sci. 49, 609–641 (2021).ADS 
CAS 

Google Scholar 
102.Insel, N., Poulsen, C. J. & Ehlers, T. A. Influence of the Andes mountains on South American moisture transport, convection, and precipitation. Clim. Dyn. 35, 1477–1492 (2010).
Google Scholar 
103.Garzione, C. N. et al. Tectonic evolution of the Central Andean Plateau and implications for the growth of plateaus. Annu. Rev. Earth Planet. Sci. 45, 529–559 (2017).ADS 
CAS 

Google Scholar 
104.Lossada, A. C. et al. Cenozoic uplift and exhumation of the Frontal Cordillera between 30° and 35° S and the influence of the subduction dynamics in the flat slab subduction context, South Central Andes. In The Evolution of the Chilean-Argentinean Andes (eds. Folguera, A. et al.) 387–409 (Springer Earth System Sciences, 2018). https://doi.org/10.1007/978-3-319-67774-3_16.105.Mora, A. et al. Tectonic history of the Andes and sub-Andean zones: implications for the development of the Amazon drainage basin. In Amazonia: Landscape and Species Evolution (eds. Hoorn, C. & Wesselingh, F. P.) 38–60 (Wiley-Blackwell Publishing Ltd., 2011). https://doi.org/10.1002/9781444306408.ch4.106.Pingel, H. et al. Late Cenozoic topographic evolution of the Eastern Cordillera and Puna Plateau margin in the southern Central Andes (NW Argentina). Earth Planet. Sci. Lett. 535, 116112 (2020).CAS 

Google Scholar 
107.Takahashi, K. & Battisti, D. S. Processes controlling the mean tropical Pacific precipitation pattern. Part I: The Andes and the Eastern Pacific ITCZ. J. Clim. 20, 3434–3451 (2007).ADS 

Google Scholar 
108.Amidon, W. H. et al. Mio-Pliocene aridity in the south-central Andes associated with Southern Hemisphere cold periods. Proc. Natl. Acad. Sci. 114, 6474–6479 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
109.Carrapa, B., Clementz, M. & Feng, R. Ecological and hydroclimate responses to strengthening of the Hadley circulation in South America during the Late Miocene cooling. Proc. Natl. Acad. Sci. 116, 9747–9752 (2019).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
110.Hartley, A. J. Andean uplift and climate change. J. Geol. Soc. Lond. 160, 7–10 (2003).
Google Scholar 
111.Barreda, V., Guler, V. & Palazzesi, L. Late Miocene continental and marine palynological assemblages from Patagonia. Dev. Quat. Sci. 11, 343–350 (2008).
Google Scholar 
112.Barreda, V. & Palazzesi, L. Patagonian vegetation turnovers during the Paleogene-early Neogene: Origin of arid-adapted floras. Bot. Rev. 73, 31–50 (2007).
Google Scholar 
113.Ortiz-Jaureguizar, E. & Cladera, G. A. Paleoenvironmental evolution of southern South America during the Cenozoic. J. Arid Environ. 66, 498–532 (2006).ADS 

Google Scholar 
114.Krockenberger, A. Lactation. In Marsupials (eds. Armati, P. J., Dickman, C. R. & Hume, I. D.) 108–136 (Cambridge University Press, 2006). https://doi.org/10.1017/CBO9780511541889.005.115.Morton, S. R., Recher, H. F., Thompson, S. D. & Braithwaite, R. W. Comments on the relative advantages of marsupial and eutherian reproduction. Am. Nat. 120, 128–134 (1982).
Google Scholar 
116.Thompson, S. D. Body size, duration of parental care, and the intrinsic rate of natural increase in eutherian and metatherian mammals. Oecologia 71, 201–209 (1987).ADS 
CAS 
PubMed 

Google Scholar 
117.Holloway, J. C. & Geiser, F. Seasonal changes in the thermoenergetics of the marsupial sugar glider Petaurus breviceps. J. Comp. Physiol. B Biochem. Syst. Environ. Physiol. 171, 643–650 (2001).CAS 

Google Scholar 
118.Sánchez-Villagra, M. R. Why are there fewer marsupials than placentals? On the relevance of geography and physiology to evolutionary patterns of mammalian diversity and disparity. J. Mamm. Evol. 20, 279–290 (2013).
Google Scholar 
119.Bennett, C. V., Upchurch, P., Goin, F. J. & Goswami, A. Deep time diversity of metatherian mammals: Implications for evolutionary history and fossil-record quality. Paleobiology 44, 171–198 (2018).
Google Scholar 
120.Goin, F. J., Woodburne, M. O., Zimicz, A. N., Martin, G. M. & Chornogubsky, L. A Brief History of South American Metatherians: Evolutionary Contexts and Intercontinental Dispersals (Springer, 2016).121.Jablonski, D. Biotic interactions and macroevolution: Extensions and mismatches across scales and levels. Evolution (N. Y) 62, 715–739 (2008).
Google Scholar 
122.Mills, B. J. W., Belcher, C. M., Lenton, T. M. & Newton, R. J. A modeling case for high atmospheric oxygen concentrations during the Mesozoic and Cenozoic. Geology 44, 1023–1026 (2016).ADS 
CAS 

Google Scholar 
123.Westerhold, T. et al. An astronomically dated record of Earth’s climate and its predictability over the last 66 million years. Science (80-) 369, 1383–1387 (2020).ADS 
CAS 

Google Scholar 
124.Silvestro, D., Salamin, N. & Schnitzler, J. PyRate: A new program to estimate speciation and extinction rates from incomplete fossil data. Methods Ecol. Evol. 5, 1126–1131 (2014).
Google Scholar 
125.Green, P. J. Reversible jump Markov chain Monte Carlo computation and Bayesian model determination. Biometrika 82, 711–732 (1995).MathSciNet 
MATH 

Google Scholar 
126.Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarization in Bayesian phylogenetics using Tracer 1.7. Syst. Biol. 67, 901–904 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar  More

1.Mesoudi, A. & Thornton, A. What is cumulative cultural evolution? Proc. R. Soc. B 285, 20180712 (2018).PubMed 
PubMed Central 

Google Scholar 
2.Schofield, D. P., McGrew, W. C., Takahashi, A. & Hirata, S. Cumulative culture in nonhumans: overlooked findings from Japanese monkeys? Primates 59, 113–122 (2018).PubMed 

Google Scholar 
3.Boesch, C. & Tomasello, M. Chimpanzee and human cultures. Curr. Anthropol. 39, 591–614 (1998).
Google Scholar 
4.Tennie, C., Call, J. & Tomasello, M. Ratcheting up the ratchet: on the evolution of cumulative culture. Philos. Trans. R. Soc. B 364, 2405–2415 (2009).
Google Scholar 
5.Dean, L. G., Vale, G. L., Laland, K. N., Flynn, E. & Kendal, R. L. Human cumulative culture: a comparative perspective. Biol. Rev. Camb. Philos. Soc. 89, 284–301 (2014).PubMed 

Google Scholar 
6.Whiten, A., McGuigan, N., Marshall-Pescini, S. & Hopper, L. M. Emulation, imitation, over-imitation and the scope of culture for child and chimpanzee. Philos. Trans. R. Soc. B 364, 2417–2428 (2009).
Google Scholar 
7.Tennie, C., Bandini, E., van Schaik, C. P. & Hopper, L. M. The zone of latent solutions and its relevance to understanding ape cultures. Biol. Philos. 35, 55 (2020).PubMed 
PubMed Central 

Google Scholar 
8.Sasaki, T. & Biro, D. Cumulative culture can emerge from collective intelligence in animal groups. Nat. Commun. 8, 15049 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
9.Musgrave, S. et al. Teaching varies with task complexity in wild chimpanzees. Proc. Natl Acad. Sci. USA 117, 969–976 (2020).CAS 
PubMed 

Google Scholar 
10.Boesch, C. et al. Chimpanzee ethnography reveals unexpected cultural diversity. Nat. Hum. Behav. 4, 910–916 (2020).PubMed 

Google Scholar 
11.Whiten, A. The burgeoning reach of animal culture. Science 372, eabe6514 (2021).CAS 
PubMed 

Google Scholar 
12.Whiten, A. et al. Cultures in chimpanzees. Nature 399, 682–685 (1999).CAS 

Google Scholar 
13.Sanz, C. & Morgan, D. Chimpanzee tool technology in the Goualougo Triangle, Republic of Congo. J. Hum. Evol. 52, 420–433 (2007).PubMed 

Google Scholar 
14.Whiten, A. & van Schaik, C. P. The evolution of animal ‘cultures’ and social intelligence. Philos. Trans. R. Soc. B 362, 603–620 (2007).
Google Scholar 
15.McGrew, W. C. The Cultured Chimpanzee: Reflections on Cultural Primatology (Cambridge Univ. Press, 2004).16.McGrew, W. C. Chimpanzee Material Culture: Implications for Human Evolution (Cambridge Univ. Press, 1992).17.Sanz, C., Schöning, C. & Morgan, D. Chimpanzees prey on army ants with specialized tool set. Am. J. Primatol. 72, 17–24 (2010).PubMed 

Google Scholar 
18.Reindl, E., Apperly, I. A., Beck, S. R. & Tennie, C. Young children copy cumulative technological design in the absence of action information. Sci. Rep. 7, 1788 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
19.Neadle, D., Allritz, M. & Tennie, C. Food cleaning in gorillas: social learning is a possibility but not a necessity. PLoS ONE 12, e0188866 (2017).PubMed 
PubMed Central 

Google Scholar 
20.Tennie, C., Hopper, L. M. & van Schaik, C. P. in Chimpazees in Context (eds Hopper, L. M. & Ross, S. R.) 428–453 (Univ. of Chicago Press, 2020).21.Bandini, E. & Tennie, C. Spontaneous reoccurrence of “scooping”, a wild tool-use behaviour, in naive chimpanzees. PeerJ 5, e3814 (2017).PubMed 
PubMed Central 

Google Scholar 
22.Bandini, E. & Tennie, C. Individual acquisition of “stick pounding” behavior by naive chimpanzees. Am. J. Primatol. 81, e22987 (2019).PubMed 

Google Scholar 
23.Menzel, C., Fowler, A., Tennie, C. & Call, J. Leaf surface roughness elicits leaf swallowing behavior in captive chimpanzees (Pan troglodytes) and bonobos (P. paniscus), but not in gorillas (Gorilla gorilla) or orangutans (Pongo abelii). Int. J. Primatol. 34, 533–553 (2013).
Google Scholar 
24.Tonooka, R., Tomonaga, M. & Matsuzawa, T. Acquisition and transmission of tool making and use for drinking juice in a group of captive chimpanzees (Pan troglodytes). Jpn. Psychol. Res. 39, 253–265 (1997).
Google Scholar 
25.Celli, M. L., Hirata, S. & Tomonaga, M. Socioecological influences on tool use in captive chimpanzees. Int. J. Primatol. 25, 1267–1281 (2004).
Google Scholar 
26.Bernstein-Kurtycz, L. M., Hopper, L. M., Ross, S. R. & Tennie, C. Zoo-housed chimpanzees can spontaneously use tool sets but perseverate on previously successful tool-use methods. Anim. Behav. Cogn. 7, 288–309 (2020).
Google Scholar 
27.Boesch, C. in Evolution of Social Behaviour Patterns in Primates and Man (eds Runciman, W. G. et al.) 251–268 (Oxford Univ. Press, 1996).28.Neadle, D., Bandini, E. & Tennie, C. Testing the individual and social learning abilities of task-naïve captive chimpanzees (Pan troglodytes sp.) in a nut-cracking task. PeerJ 8, e8734 (2020).PubMed 
PubMed Central 

Google Scholar 
29.Biro, D. et al. Cultural innovation and transmission of tool use in wild chimpanzees: evidence from field experiments. Anim. Cogn. 6, 213–223 (2003).PubMed 

Google Scholar 
30.Boesch, C., Bombjaková, D., Meier, A. & Mundry, R. Learning curves and teaching when acquiring nut-cracking in humans and chimpanzees. Sci. Rep. 9, 1515 (2019).PubMed 
PubMed Central 

Google Scholar 
31.Matsuzawa, T. in Chimpanzee Cultures (eds Wrangham, R. W. et al.) 351–370 (Harvard Univ. Press, 1994).32.Matsuzawa, T. in Oxford Research Encyclopedia of Psychology 1–42 (Oxford Univ. Press, 2021).33.Boesch, C., Marchesi, P., Marchesi, N., Fruth, B. & Joulian, F. Is nut cracking in wild chimpanzees a cultural behaviour? J. Hum. Evol. 26, 325–338 (1994).
Google Scholar 
34.Carvalho, S., Matsuzawa, T. & McGrew, W. C. in Tool Use in Animals: Cognition and Ecology (eds Sanz, C. et al.) 225–241 (Cambridge Univ. Press, 2013).35.Morgan, B. & Abwe, E. Chimpanzees use stone hammers in Cameroon. Curr. Biol. 16, 632–633 (2006).
Google Scholar 
36.Sumita, K., Kitahara-Frisch, J. & Norikoshi, K. The aquisition of stone-tool use in captive chimpanzees. Primates 26, 168–181 (1985).
Google Scholar 
37.Marshall-Pescini, S. & Whiten, A. Social learning of nut-cracking behaviour in East African sanctuary-living chimpanzees (Pan troglodytes schweinfurthii). J. Comp. Psychol. 122, 186–194 (2008).PubMed 

Google Scholar 
38.Inoue-Nakamura, N. & Matsuzawa, T. Development of stone tool use by wild chimpanzees (Pan troglodytes). J. Comp. Psychol. 111, 159–173 (1997).CAS 
PubMed 

Google Scholar 
39.van Schaik, C. P., Deaner, R. O. & Merrill, M. The conditions for tool use in primates: implications for the evolution of material culture. J. Hum. Evol. 36, 719–741 (1999).PubMed 

Google Scholar 
40.Humle, T. & Matsuzawa, T. Oil palm use by adjacent communities of chimpanzees at Bossou and Nimba Mountains, West Africa. Int. J. Primatol. 25, 551–581 (2004).
Google Scholar 
41.Koops, K., McGrew, W. C. & Matsuzawa, T. Ecology of culture: do environmental factors influence foraging tool use in wild chimpanzees (Pan troglodytes verus)? Anim. Behav. 85, 175–185 (2013).
Google Scholar 
42.Koops, K., Visalberghi, E. & van Schaik, C. P. The ecology of primate material culture. Biol. Lett. 10, 20140508 (2014).PubMed 
PubMed Central 

Google Scholar 
43.Matsuzawa, T., Humle, T. & Sugiyama, Y. The Chimpanzees of Bossou and Nimba (eds Matsuzawa, T. & Yamagiwa, J.) (Springer, 2011).44.Matsuzawa, T. et al. in Primate Origins of Human Cognition and Behavior (ed. Matsuzawa, T.) 557–574 (Springer, 2001).45.Bandini, E., Motes-Rodrigo, A., Steele, M. P., Rutz, C. & Tennie, C. Examining the mechanisms underlying the acquisition of animal tool behaviour. Biol. Lett. 16, 20200122 (2020).PubMed 
PubMed Central 

Google Scholar 
46.Koops, K., McGrew, W. C., Matsuzawa, T. & Knapp, L. A. Terrestrial nest-building by wild chimpanzees (Pan troglodytes): implications for the tree-to-ground sleep transition in early hominins. Am. J. Phys. Anthropol. 148, 351–361 (2012).PubMed 

Google Scholar 
47.Schuppli, C. et al. The effects of sociability on exploratory tendency and innovation repertoires in wild Sumatran and Bornean orangutans. Sci. Rep. 7, 15464 (2017).PubMed 
PubMed Central 

Google Scholar 
48.Roberts, G. Why individual vigilance declines as group size increases. Anim. Behav. 51, 1077–1086 (1996).
Google Scholar 
49.Visalberghi, E. & Addessi, E. Seeing group members eating a familiar food enhances the acceptance of novel foods in capuchin monkeys. Anim. Behav. 60, 69–76 (2000).CAS 
PubMed 

Google Scholar 
50.Kalan, A. K. et al. Novelty response of wild African apes to camera traps. Curr. Biol. 29, 1211–1217 (2019).CAS 
PubMed 

Google Scholar 
51.Gruber, T., Zuberbühler, K. & Neumann, C. Travel fosters tool use in wild chimpanzees. eLife 5, e16371 (2016).PubMed 
PubMed Central 

Google Scholar 
52.Grund, C., Neumann, C., Zuberbühler, K. & Gruber, T. Necessity creates opportunities for chimpanzee tool use. Behav. Ecol. 30, 1136–1144 (2019).
Google Scholar 
53.Kühl, H. S. et al. Human impact erodes chimpanzee behavioral diversity. Science 363, 1453–1455 (2019).PubMed 

Google Scholar 
54.Wrangham, R. W. Chimpanzees: the culture-zone concept becomes untidy. Curr. Biol. 16, R634–R635 (2006).CAS 
PubMed 

Google Scholar 
55.McGrew, W. C., Ham, R. M., White, L. J. T., Tutin, C. E. G. & Fernandez, M. Why don’t chimpanzees in Gabon crack nuts? Int. J. Primatol. 18, 353–374 (1997).
Google Scholar 
56.Whiten, A. Experimental studies illuminate the cultural transmission of percussive technologies in Homo and Pan. Philos. Trans. R. Soc. B 370, 20140359 (2015).
Google Scholar 
57.Hirata, S., Morimura, N. & Houki, C. How to crack nuts: acquisition process in captive chimpanzees (Pan troglodytes) observing a model. Anim. Cogn. 12, 87–101 (2009).PubMed 

Google Scholar 
58.Hayashi, M., Mizuno, Y. & Matsuzawa, T. How does stone-tool use emerge? Introduction of stones and nuts to naïve chimpanzees in captivity. Primates 46, 91–102 (2005).PubMed 

Google Scholar 
59.Funk, M. Werkzeuggebrauch Beim öffnen von Niessen: Unterschiedliche Bewaltigungen des Problems bei Schimpansen und Orang-Utans (Univ. of Zurich, 1985).60.Visalberghi, E. Acquisition of nut‐cracking behavior by two capuchin monkeys (Cebus apella). Folia Primatol. 49, 168–181 (1987).
Google Scholar 
61.Resende, B. D., Ottoni, E. B. & Fragaszy, D. M. Ontogeny of manipulative behavior and nut-cracking in young tufted capuchin monkeys (Cebus apella): a perception–action perspective. Dev. Sci. 11, 828–840 (2008).PubMed 

Google Scholar 
62.Bandini, E., Grossmann, J., Funk, M., Albiach-Serrano, A. & Tennie, C. Naïve orangutans (Pongo abelii and Pongo pygmaeus) individually acquire nut-cracking using hammer tools. Am. J. Primatol. 83, e23304 (2021).PubMed 

Google Scholar 
63.Damerius, L. A. et al. Orientation toward humans predicts cognitive performance in orang-utans. Sci. Rep. 7, 40052 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
64.Reindl, E., Beck, S. R., Apperly, A. & Tennie, C. Young children spontaneously invent wild great apes’ tool-use behaviours. Proc. R. Soc. B 283, 20152402 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
65.Neldner, K. et al. A cross-cultural investigation of young children’s spontaneous invention of tool use behaviours. R. Soc. Open Sci. 7, 192240 (2020).PubMed 
PubMed Central 

Google Scholar 
66.Lucas, A. J. et al. The value of teaching increases with tool complexity in cumulative cultural evolution. Proc. R. Soc. B 287, 20201885 (2020).PubMed 
PubMed Central 

Google Scholar 
67.Boesch, C. Teaching among wild chimpanzees. Int. J. Primatol. 41, 530–532 (1991).
Google Scholar 
68.Musgrave, S., Morgan, D., Lonsdorf, E., Mundry, R. & Sanz, C. Tool transfers are a form of teaching among chimpanzees. Sci. Rep. 6, 34783 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
69.Koops, K. in The Chimpanzees of Bossou and Nimba (eds Matsuzawa, T. et al.) 277–287 (Springer, 2011).70.van Schaik, C. P. & Pfannes, K. R. in Seasonality in Primates: Studies of Living and Extinct Human and Non-human Primates (eds Brockman, D. K. & van Schaik, C. P.) 23–54 (Cambridge Univ. Press, 2005).71.Matsuzawa, T. in The Chimpanzees of Bossou and Nimba (eds Matsuzawa, T. et al.) 157–164 (Springer, 2011).72.Carvalho, S., Cunha, E., Sousa, C. & Matsuzawa, T. Chaînes opératoires and resource exploitation strategies in chimpanzee nut-cracking (Pan troglodytes). J. Hum. Evol. 55, 148–163 (2008).PubMed 

Google Scholar 
73.Sanz, C., Morgan, D. & Gulick, S. New insights into chimpanzees, tools, and termites from the Congo basin. Am. Nat. 164, 67–581 (2004).
Google Scholar 
74.Hockings, K. J., Anderson, J. R. & Matsuzawa, T. Flexible feeding on cultivated underground storage organs by rainforest-dwelling chimpanzees at Bossou, West Africa. J. Hum. Evol. 58, 227–233 (2010).PubMed 

Google Scholar 
75.Takemoto, H. Seasonal change in terrestriality of chimpanzees in relation to microclimate in the tropical forest. Am. J. Phys. Anthropol. 124, 81–92 (2004).PubMed 

Google Scholar 
76.van Leeuwen, K. L., Matsuzawa, T., Sterck, E. H. M. & Koops, K. How to measure chimpanzee party size? A methodological comparison. Primates 61, 201–212 (2020).PubMed 

Google Scholar 
77.Field, A. P. Discovering Statistics Using IBM SPSS Statistics (SAGE, 2018).78.Humle, T. in The Chimpanzees of Bossou and Nimba (eds Matsuzawa, T. et al.) 61–72 (Springer, 2011).79.Humle, T. & Matsuzawa, T. Behavioural diversity among the wild chimpanzee populations of Bossou and neighbouring areas, Guinea and Côte d’Ivoire, West Africa. Folia Primatol. 72, 57–68 (2001).CAS 

Google Scholar 
80.Sugiyama, Y. & Koman, J. Tool-using and making behavior in wild chimpanzees at Bossou, Guinea. Primates 20, 513–524 (1979).
Google Scholar 
81.Sugiyama, Y. & Koman, J. A preliminary list of chimpanzees’ alimentation at Bossou, Guinea. Primates 28, 133–147 (1987).
Google Scholar 
82.Joulian, F. in Modelling the Early Human Mind (eds Mellars, P. & Gibson, K.) 173–189 (McDonald Institute Monographs, 1996).83.Matsuzawa, T. & Yamakoshi, G. in Reaching into Thought: The Minds of the Great Apes (eds Russon, A. E. et al.) 211–232 (Cambridge Univ. Press, 1996). More

Our results show that differences in environmental conditions between inland and coastal sinkholes, caused mainly by the inflow of seawater in the latter, influence the microbial community structure of their sediments. Furthermore, the microbial community structure also varied within the sinkholes and according to the sediment zone sampled, suggesting that a connection between the atmosphere in the outermost location of the sediments and sunlight creates an environment distinct from that found in deeper caves. Together with the different environmental factors that were measured (in situ physicochemical composition of water and sediment) these characteristics could drive niche-specific microbial community structures associated with the sediment zones. Additionally, beta-diversity analysis showed separate clustering of the sediment microbial communities from the coastal and inland sinkholes, and of the WM zone from cavern and cave zones at both sinkholes. Microbial community structure associated with karst environments have shown to be significantly influenced by environmental factors as seen in the Bahamian blue holes19, a coastal sinkhole13, a Floridan anchialine sinkhole20, and sediments from Chinese karst caves21.Microbial communities from karst sediments can be limited by nutrients such as carbon, phosphorus, and nitrogen21, therefore, influencing their structure. Differences in the microbial community composition associated with multiple environmental factors (moisture, type of niche, nitrogen) were also reported in karst cave sediments from China21. Previous studies had shown there was no effect on the alpha diversity of water column assemblages in the Yucatan groundwater associated with the type of sinkhole (inland or coastal)6. However, this observation may be limited due to the low sample number used in the study6. For other karst sinkholes, the microbial community dynamics differ between the water column and the sediments6,22,23,24.The karst caves and sinkholes of the underground river in Yucatan are characterized by low phosphorus concentrations and high levels of nitrate, mostly related to Anthropocentric activities (urban developments, farms and agriculture)3. The inland sinkhole at Noh Mozón showed the highest concentration of nitrate detected in the study and, not surprisingly, the area is surrounded by agricultural fields. The presence of organic matter in the sinkholes from the Yucatan peninsula are highly dependent on the connection between the cave systems, on the levels of exposure to light, and on their morphology3. High concentrations of organic carbon (661 ± 132 μM) and methane (6466 ± 659 nM) have been reported in the top layer of the water masses in coastal sinkholes before13. In this study, the highest concentration of organic carbon was observed in the sediments from the coastal sinkhole, likely originating from the surrounding vegetation and from seawater intrusion. We hypothesize that the differences in nutrients found at these two types of sinkholes influence the structure of their microbial communities.Other environmental factors such as pH and dissolved oxygen (DO), may also contribute significantly to the composition and structure of microbial communities, as seen in freshwater lake sediments22. Davis and Garey20 reported distinct microbial communities with unique functions for each water layer from an anchialine sinkhole from the Florida karst aquifer and suggested that this occurred as a result of the influence of the hydrochemistry, including differences in the concentration carbon and other nutrients from the environment20. Analyses of the sinkhole caves from the Yucatan underwater river support observations that physical and chemical parameters create distinct ecological niches which host unique microbes, as a high abundance of exclusive (not shared) ASVs were observed in the three sediment zones at both locations.The taxonomic diversity from the coastal and inland sinkholes included Chloroflexi, Crenarchaeota, Desulfobacterota, Proteobacteria, Nitrospirota, Bacteroidota, and Firmicutes as the most abundant phyla in the sediment samples, however, there were differences in the relative abundance associated with the type of sinkhole and sediment zone. Some of these phyla (Chloroflexi, Proteobacteria, and Bacteroidetes) have been reported in sediments from freshwater karst sinkholes from Lake Huron25 in water and sediments from other sinkholes in the Yucatan peninsula6, and in the karst caves bacteriome from southwest China21. A study that included coastal marine sediments from two sites in the Yucatan peninsula, showed high abundances of Spirochaeta, Desulfococcus, Clostridium, Psychrobacter26, four genera that were abundant in the coastal sinkhole. However, Desulfococcus, Synechococcus were also abundant in the inland sinkhole. Of the most abundant genera reported for sediments from different marine environments in the Yucatan coast23, Acinetobacter, Desulfotignum, Desulfovibrio, Pseudomonas, Sedimenticola, and Sulfurimonas were also present in the coastal sinkhole while only Pseudomonas and Sedimenticola were also present in the inland sinkhole23. The high number of families shared between the coastal sinkhole and marine sediments from the Yucatan coast, together with the salinity levels registered at the bottom layer of the water column in the coastal sinkhole, suggest an interconnection between these two environments which shapes the microbial communities present in the sediments of caverns and caves of this sinkhole. The genus Nitrospira was abundant in the WM from the coastal sinkhole and in all sediment zones from the inland sinkhole. This genus has been reported as one of the most abundant in the surface of speleothems from El Zapote coastal sinkhole2, and is considered a complete ammonia oxidizer (comammox), meaning it converts ammonia to nitrate through nitrite. A negative correlation between abundance of this genus and salinity has been reported before, which could explain the low concentration of Nitrospira in the cavern and cave from the coastal sinkhole, where the highest salinity was observed27. Connectivity between coastal sinkholes and the ocean, as well as the terrestrial input of soil organic matter (OM) has been reported for the underground karst aquifer in the Yucatan peninsula13. As in other sediments, degradation of OM is carried out by several MFGs including acetogenic bacteria, methanogens, and sulfate reducers13,20,28. When these MFGs were analyzed in coastal and inland sinkholes, differences in their relative abundances were clearly marked by the type of sinkhole and by the sediment zone analyzed, supporting the hypothesis that environmental differences drive microbial community distributions in these niches. The high abundance of sulfate-reducing bacteria (SRB) in the three sediment zones from the coastal sinkhole suggests that sulfate reduction is a predominant function. SRB degrade organic matter using sulfate with sulfide as waste or end-product19,30, originating hydrogen sulfide (H2S)29, which could explain the low concentration of sulfate the hydrogen sulfide (H2S) cloud observed and previously reported in the WM zone of El Zapote coastal sinkhole30. In this study, high levels of sulfate (SO−4) were measured in the water samples from the cavern and cave zones from El Zapote sinkhole, which could be associated with sulfate-rich deposits, such as gypsum beds, which have been reported in other sinkholes from the Yucatan peninsula (up to 2400 mg/L of sulfate concentration)3. However, we do not disregard other possible sources of sulfate, associated with seawater intrusion or as a product of sulfide or sulfur oxidation29,31 by sulfur-oxidizing bacteria detected in this study.The inland sinkhole had a low concentration of sulfate and low abundances of SRB. The high abundance of methanogenic bacteria in the WM zone from the coastal sinkhole detected in the MFG analysis supports the previous hypothesis of acetoclastic methanogenesis due to high inputs of organic matter13. Methylotrophic bacteria were most abundant at the inland sinkhole in the WM zone, suggesting the presence of methyl compounds, such as methane or methanol which can be used as a source of carbon and energy32. High methane concentrations have been quantified in shallow water masses from the Yucatan aquifer system13, consistent with observations from this study. ‘Candidatus Methylomirabilis’ was identified in the sediment of the WM zone from Noh-Mozón and has been previously described as being able to perform nitrite-dependent anaerobic methane oxidation, using methane as electron donor and nitrate and nitrite as electron acceptors33, which would be possible in these sediments considering the low levels of oxygen (average of 2.3 mg/L) detected in the water column above them and assuming this would lead to lower levels of oxygen in the sediments. We hypothesize that bacteria from this genus could be using the nitrite produced by ammonia oxidizing bacteria and archaea observed in this zone (Nitrosomonadaceae, Nitrospiraceae, and Nitrosococcaceae). The low abundance of methanotrophic microbes in the coastal sinkhole (mainly the cavern and cave zones) could be derived from the high concentrations of hydrogen sulfide previously reported at this location, which have been suggested to be toxic to methane-oxidizing microbes34. Therefore, a decrease in the anaerobic oxidation of methane, and a poor methane removal capacity is hypothesized in the sediments from this coastal sinkhole. Further research could focus on the influence of the saline intrusion on methanotrophic microbes and methane levels in El Zapote sinkhole sediments. As expected, photosynthetic bacterial abundances differed with the type of sinkhole and sediment zone. Both sinkholes are so the presence of daylight can start the photosynthetic process which would occur most in the WM zone. However, only the inland sinkhole showed a high abundance of photosynthetic bacteria within its WM zone. The coastal sinkhole water column and sediments would be deprived of photosynthetic bacteria since the water source is the underground aquifer, lacks photosynthesizers. Ammonia oxidizing bacteria (AOB) and archaea (AOA), and nitrite-oxidizing bacteria (NOB) were relatively abundant in the three sediment zones from the inland sinkhole and in the WM from the coastal sinkhole, these observation at the WM from El Zapote agree with previous observations2. Anaerobic ammonium oxidation (anammox) uses nitrite (as a product of nitrate reduction), as electron acceptor35. The high levels of nitrate concentration in the water column from the three sediment zones at the inland sinkhole and at the WM from the coastal sinkhole may influence the abundance of AOB, AOA and NOB in the sediments from these zones, while NH4+ and nitrite values were below detection limit ( More