Ecology

1.Eming, S. A., Martin, P. & Tomic-Canic, M. Wound repair and regeneration: Mechanisms, signaling, and translation. Sci. Transl. Med. 6, 265sr6–265sr6 (2014).2.Wilkinson, H. N. & Hardman, M. J. Wound healing: Cellular mechanisms and pathological outcomes. Open Biol. 10, 20023 (2020).
Google Scholar 
3.Dvorak, H. F. Tumors: Wounds that do not heal. Similarities between tumor stroma generation and wound healing. N. Engl. J. Med. 315, 1650–1659 (1986).CAS 
PubMed 

Google Scholar 
4.Dvorak, H. F. Tumors: Wounds that do not heal–Redux. Cancer Immunol. Res. 3, 1–11 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
5.Schäfer, M. & Werner, S. Cancer as an overhealing wound: An old hypothesis revisited. Nat. Rev. Mol. Cell Biol. 9, 628–638 (2008).PubMed 

Google Scholar 
6.MacCarthy-Morrogh, L. & Martin, P. The hallmarks of cancer are also the hallmarks of wound healing. Sci. Signal. 13, eaay8690 (2020).7.Trigos, A. S., Pearson, R. B., Papenfuss, A. T. & Goode, D. L. How the evolution of multicellularity set the stage for cancer. Br. J. Cancer 118, 145–152 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
8.Bely, A. E. & Nyberg, K. G. Evolution of animal regeneration: Re-emergence of a field. Trends Ecol. Evol. 25, 161–170 (2010).PubMed 

Google Scholar 
9.Bosch, T. C. G. Why polyps regenerate and we don’t: Towards a cellular and molecular framework for Hydra regeneration. Dev. Biol. 303, 421–433 (2007).CAS 
PubMed 

Google Scholar 
10.Gurtner, G. C., Werner, S., Barrandon, Y. & Longaker, M. T. Wound repair and regeneration. Nature 453, 314–321 (2008).ADS 
CAS 
PubMed 

Google Scholar 
11.Slack, J. M. Animal regeneration: Ancestral character or evolutionary novelty?. EMBO Rep. 18, 1497–1508 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
12.Wenger, Y., Buzgariu, W., Reiter, S. & Galliot, B. Injury-induced immune responses in Hydra. Semin. Immunol. 26, 277–294 (2014).CAS 
PubMed 

Google Scholar 
13.Poss, K. D., Wilson, L. G. & Keating, M. T. Heart regeneration in zebrafish. Science (80-. ). 298, 2188–2190 (2002).14.Kao, D., Felix, D. & Aboobaker, A. The planarian regeneration transcriptome reveals a shared but temporally shifted regulatory program between opposing head and tail scenarios. BMC Genomics 14, 1–17 (2013).
Google Scholar 
15.Gehrke, A. R. et al. Acoel genome reveals the regulatory landscape of whole-body regeneration. Science (80-. ). 363 (2019).16.DuBuc, T. Q., Traylor-Knowles, N. & Martindale, M. Q. Initiating a regenerative response; cellular and molecular features of wound healing in the cnidarian Nematostella vectensis. BMC Biol. 12, 24 (2014).PubMed 
PubMed Central 

Google Scholar 
17.Cary, G. A., Wolff, A., Zueva, O., Pattinato, J. & Hinman, V. F. Analysis of sea star larval regeneration reveals conserved processes of whole-body regeneration across the metazoa. BMC Biol. 17, 16 (2019).PubMed 
PubMed Central 

Google Scholar 
18.Owlarn, S. et al. Generic wound signals initiate regeneration in missing-tissue contexts. Nat. Commun. 8, 1–13 (2017).CAS 

Google Scholar 
19.Ramon-Mateu, J., Ellison, S. T., Angelini, T. E. & Martindale, M. Q. Regeneration in the ctenophore Mnemiopsis leidyi occurs in the absence of a blastema, requires cell division, and is temporally separable from wound healing. BMC Biol. 17, 80 (2019).PubMed 
PubMed Central 

Google Scholar 
20.Pawlik, J. R. & Deignan, L. K. Cowries graze Verongid sponges on Caribbean reefs. Coral Reefs 34, 663 (2015).ADS 

Google Scholar 
21.Rice, M. M., Ezzat, L. & Burkepile, D. E. Corallivory in the anthropocene: Interactive effects of anthropogenic stressors and corallivory on coral reefs. Front. Mar. Sci. 5, 1–14 (2019).
Google Scholar 
22.Pawlik, J. R., Loh, T.-L., McMurray, S. E. & Finelli, C. M. Sponge communities on Caribbean coral reefs are structured by factors that are top-down, not bottom-up. PLoS One 8, e62573 (2013).23.Mortimer, C., Dunn, M., Haris, A., Jompa, J. & Bell, J. Estimates of sponge consumption rates on an Indo-Pacific reef. Mar. Ecol. Prog. Ser. 672, 123–140 (2021).ADS 
CAS 

Google Scholar 
24.de Goeij, J. M. et al. Surviving in a marine desert: the sponge loop retains resources within coral reefs. Science (80-. ). 342, 108–10 (2013).25.Rix, L. et al. Differential recycling of coral and algal dissolved organic matter via the sponge loop. Funct. Ecol. 31, 778–789 (2016).
Google Scholar 
26.Maldonado, M. et al. Sponge grounds as key marine habitats: A synthetic review of types, structure, functional roles and conservation concerns. Mar. Animal Forests https://doi.org/10.1007/978-3-319-17001-5 (2015).Article 

Google Scholar 
27.Soubigou, A., Ross, E. G., Touhami, Y., Chrismas, N. & Modepalli, V. Regeneration in sponge Sycon ciliatum partly mimics postlarval development. Development https://doi.org/10.1242/dev.193714 (2020).Article 
PubMed 

Google Scholar 
28.Telford, M. J., Moroz, L. L. & Halanych, K. M. A sisterly dispute. Nature 529, 286–287 (2016).ADS 
CAS 
PubMed 

Google Scholar 
29.Feuda, R. et al. Improved modeling of compositional heterogeneity supports sponges as sister to all other animals. Curr. Biol. https://doi.org/10.1016/j.cub.2017.11.008 (2017).Article 
PubMed 

Google Scholar 
30.Dunn, C. W., Leys, S. P. & Haddock, S. H. D. The hidden biology of sponges and ctenophores. Trends Ecol. Evol. 30, 282–291 (2015).PubMed 

Google Scholar 
31.Borisenko, I. E., Adamska, M., Tokina, D. B. & Ereskovsky, A. V. Transdifferentiation is a driving force of regeneration in Halisarca dujardini (Demospongiae, Porifera). PeerJ 3, e1211 (2015).32.Lavrov, A. I., Bolshakov, F. V., Tokina, D. B. & Ereskovsky, A. V. Sewing up the wounds: The epithelial morphogenesis as a central mechanism of calcaronean sponge regeneration. J. Exp. Zool. Part B Mol. Dev. Evol. 330, 351–371 (2018).33.Ereskovsky, A. V. et al. Transdifferentiation and mesenchymal‐to‐epithelial transition during regeneration in Demospongiae (Porifera). J. Exp. Zool. Part B Mol. Dev. Evol. 334, 37–58 (2020).34.Alexander, B. E. et al. Cell kinetics during regeneration in the sponge Halisarca caerulea: how local is the response to tissue damage? PeerJ 3, e820 (2015).35.Pozzolini, M. et al. Insights into the evolution of metazoan regenerative mechanisms: TGF superfamily member roles in tissue regeneration of the marine sponge Chondrosia reniformis Nardo, 1847. J. Exp. Biol. 222, jeb207894 (2019).36.Kenny, N. J. et al. Towards the identification of ancestrally shared regenerative mechanisms across the Metazoa: A transcriptomic case study in the demosponge Halisarca caerulea. Mar. Genomics 37, 135–147 (2018).PubMed 

Google Scholar 
37.Pawlik, J. R. Handbook of marine natural products. in Handbook of Marine Natural Products (eds. Fattorusso, E., Gerwick, W. H. & Taglialatela-Scafati, O.) 677–705 (Springer, New York, 2012). https://doi.org/10.1007/978-90-481-3834-038.Walters, K. D. & Pawlik, J. R. Is there a trade-off between wound-healing and chemical defenses among Caribbean reef sponges?. Integr. Comp. Biol. 45, 352–358 (2005).PubMed 

Google Scholar 
39.Becerro, M. A., Turon, X., Uriz, M. J. & Templado, J. Can a sponge feeder be a herbivore? Tylodina perversa (Gastropoda) feeding on Aplysina aerophoba (Demospongiae). Biol. J. Linn. Soc. 78, 429–438 (2003).
Google Scholar 
40.Wu, Y.-C. et al. Opisthobranch grazing results in mobilisation of spherulous cells and re-allocation of secondary metabolites in the sponge Aplysina aerophoba. Sci. Rep. 10, 21934 (2020).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
41.Pita, L., Hoeppner, M. P., Ribes, M. & Hentschel, U. Differential expression of immune receptors in two marine sponges upon exposure to microbial-associated molecular patterns. Sci. Rep. 8, 16081 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
42.Stewart, F. J., Ottesen, E. A. & Delong, E. F. Development and quantitative analyses of a universal rRNA-subtraction protocol for microbial metatranscriptomics. ISME J. 4, 896–907 (2010).CAS 
PubMed 

Google Scholar 
43.Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: A flexible read trimming tool for Illumina NGS data. Bioinformatics btu170 (2014).44.Menzel, P. & Krogh, A. Fast and sensitive taxonomic classification for metagenomics with Kaiju. Nat. Commun. 7, 11257 (2015).ADS 

Google Scholar 
45.Haas, B. J. et al. De novo transcript sequence reconstruction from RNA-Seq: reference generation and analysis with Trinity. Nat. Protoc. 8, 1494 (2013).CAS 
PubMed 

Google Scholar 
46.Smith-Unna, R., Boursnell, C., Patro, R., Hibberd, J. M. & Kelly, S. TransRate: Reference free quality assessment of de-novo transcriptome assemblies. Genome Res. 26, 1134–1144 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
47.Simao, F. A., Waterhouse, R. M., Ioannidis, P., Kriventseva, E. V. & Zdobnov, E. M. Genome analysis BUSCO: Assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31, 3210–3212 (2015).CAS 
PubMed 

Google Scholar 
48.Bryant, D. M. et al. A tissue-mapped axolotl de novo transcriptome enables identification of limb regeneration factors. Cell Rep. 18, 762–776 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
49.Kanehisa, M. KEGG: Kyoto encyclopedia of genes and genomes. Nucleic Acids Res. 28, 27–30 (2000).CAS 
PubMed 
PubMed Central 

Google Scholar 
50.Conesa, A. et al. Blast2GO: A universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 21, 3674–3676 (2005).CAS 
PubMed 

Google Scholar 
51.Supek, F., Bošnjak, M., Škunca, N. & Šmuc, T. REVIGO summarizes and visualizes long lists of gene ontology terms. PLoS One 6, e21800 (2011).52.Wickham, H. ggplot2: Elegant graphics for data analysis. (Springer, Berlin, 2016).53.Team, R. C. R: A language and environment for statistical computing. (2019).54.Team, Rs. RStudio: Integrated Development for R. (2015).55.Szklarczyk, D. et al. STRING v11: Protein–protein association networks with increased coverage, supporting functional discovery in genome-wide experimental datasets. Nucleic Acids Res. 47, D607–D613 (2019).CAS 
PubMed 

Google Scholar 
56.Pritchard, L., Jones, S. & Cock, P. IBioIC Introd. Bioinform. Train. Course https://doi.org/10.5281/zenodo.1184095 (2018).57.Forbes, S. A. et al. The catalogue of somatic mutations in cancer (COSMIC). Curr. Protoc. Hum. Genet. 57 (2008).58.Trigos, A. S., Pearson, R. B., Papenfuss, A. T. & Goode, D. L. Somatic mutations in early metazoan genes disrupt regulatory links between unicellular and multicellular genes in cancer. Elife 8, 1–28 (2019).
Google Scholar 
59.Cerenius, L. & Söderhäll, K. Coagulation in invertebrates. J. Innate Immun. 3, 3–8 (2011).PubMed 

Google Scholar 
60.Davie, E. W., Fujikawa, K. & Kisiel, W. The coagulation cascade: Initiation, maintenance, and regulation. Biochemistry 30, 10363–10370 (1991).CAS 
PubMed 

Google Scholar 
61.Richardson, V. R., Cordell, P., Standeven, K. F. & Carter, A. M. Substrates of factor XIII-A: Roles in thrombosis and wound healing. Clin. Sci. 124, 123–137 (2013).CAS 

Google Scholar 
62.Domazet-Lošo, T. & Tautz, D. Phylostratigraphic tracking of cancer genes suggests a link to the emergence of multicellularity in metazoa. BMC Biol. 8, 1–10 (2010).
Google Scholar 
63.Trigos, A. S., Pearson, R. B., Papenfuss, A. T. & Goode, D. L. Altered interactions between unicellular and multicellular genes drive hallmarks of transformation in a diverse range of solid tumors. Proc. Natl. Acad. Sci. USA 114, 6406–6411 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
64.Rohani, M. G. & Parks, W. C. Matrix remodeling by MMPs during wound repair. Matrix Biol. 44–46, 113–121 (2015).PubMed 

Google Scholar 
65.Grose, R. et al. A crucial role of beta 1 integrins for keratinocyte migration in vitro and during cutaneous wound repair. Development 129, 2303–2315 (2002).CAS 
PubMed 

Google Scholar 
66.Sebé-Pedrós, A. et al. Early metazoan cell type diversity and the evolution of multicellular gene regulation. Nat. Ecol. Evol. 2, 1176–1188 (2018).PubMed 
PubMed Central 

Google Scholar 
67.Paps, J. & Holland, P. W. H. Reconstruction of the ancestral metazoan genome reveals an increase in genomic novelty. Nat. Commun. 9, 1730 (2018).ADS 
PubMed 
PubMed Central 

Google Scholar 
68.Sharrocks, A. D. The ETS-domain transcription factor family. Nat. Rev. Mol. Cell Biol. 2, 827–837 (2001).CAS 
PubMed 

Google Scholar 
69.Larroux, C. et al. Developmental expression of transcription factor genes in a demosponge: Insights into the origin of metazoan multicellularity. Evol. Dev. 8, 150–173 (2006).CAS 
PubMed 

Google Scholar 
70.Petersen, H. O. et al. A comprehensive transcriptomic and proteomic analysis of Hydra head regeneration. Mol. Biol. Evol. 32, 1928–1947 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
71.Cardozo, M. J., Mysiak, K. S., Becker, T. & Becker, C. G. Reduce, reuse, recycle—Developmental signals in spinal cord regeneration. Dev. Biol. 432, 53–62 (2017).CAS 
PubMed 

Google Scholar 
72.Adamska, M. et al. Wnt and TGF-β expression in the sponge Amphimedon queenslandica and the origin of metazoan embryonic patterning. PLoS One 2, e1031 (2007).73.Stewart, Z. K. et al. Transcriptomic investigation of wound healing and regeneration in the cnidarian Calliactis polypus. Sci. Rep. 7, 41458 (2017).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
74.Chablais, F. & Jazwinska, A. The regenerative capacity of the zebrafish heart is dependent on TGF signaling. Development 139, 1921–1930 (2012).CAS 
PubMed 

Google Scholar 
75.Chen, H., Lin, F., Xing, K. & He, X. The reverse evolution from multicellularity to unicellularity during carcinogenesis. Nat. Commun. 6, 1–10 (2015).ADS 

Google Scholar 
76.Srivastava, M. et al. The Amphimedon queenslandica genome and the evolution of animal complexity. Nature 466, 720–726 (2010).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
77.Ćetković, H., Halasz, M. & Herak Bosnar, M. Sponges: A reservoir of genes implicated in human cancer. Mar. Drugs 16, 20 (2018).PubMed Central 

Google Scholar  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

1.Clobert, J., Danchin, E., Dhondt, A. A. & Nichols, J. D. Dispersal (Oxford University Press, 2001).
Google Scholar 
2.Ronce, O. How does it feel to be like a rolling stone? Ten questions about dispersal evolution. Annu. Rev. Ecol. Evol. Syst. 38, 231–253 (2007).
Google Scholar 
3.Clobert, J., Baguette, M., Benton, T. G. & Bullock, J. M. Dispersal Ecology and Evolution (Oxford University Press, 2012).
Google Scholar 
4.Cote, J., Fogarty, S., Brodin, T., Weinersmith, K. & Sih, A. Personality-dependent dispersal in the invasive mosquitofish: Group composition matters. Proc. R. Soc. B Biol. Sci. 278, 1670–1678 (2011).
Google Scholar 
5.Quinn, J. L., Cole, E. F., Patrick, S. C. & Sheldon, B. C. Scale and state dependence of the relationship between personality and dispersal in a great tit population. J. Anim. Ecol. 80, 918–928 (2011).PubMed 

Google Scholar 
6.Brodin, T., Lind, M. I., Wiberg, M. K. & Johansson, F. Personality trait differences between mainland and island populations in the common frog (Rana temporaria). Behav. Ecol. Sociobiol. 67, 135–143 (2013).
Google Scholar 
7.Wilson, D. S. Adaptive individual differences within single populations. Philos. Trans. R. Soc. London. Ser. B Biol. Sci. 353, 199–205 (1998).
Google Scholar 
8.Sih, A., Bell, A. & Johnson, J. C. Behavioral syndromes: An ecological and evolutionary overview. Trends Ecol. Evol. 19, 372–378 (2004).PubMed 

Google Scholar 
9.Sih, A., Bell, A. M., Johnson, J. C. & Ziemba, R. E. Behavioral syndromes: An integrative overview. Q. Rev. Biol. 79, 241–277 (2004).PubMed 

Google Scholar 
10.Réale, D., Reader, S. M., Sol, D., McDougall, P. T. & Dingemanse, N. J. Integrating animal temperament within ecology and evolution. Biol. Rev. 82, 291–318 (2007).PubMed 

Google Scholar 
11.Wolf, M. & Weissing, F. J. Animal personalities: Consequences for ecology and evolution. Trends Ecol. Evol. 27, 452–461 (2012).PubMed 

Google Scholar 
12.Juette, T., Cucherousset, J. & Cote, J. Animal personality and the ecological impacts of freshwater non-native species. Curr. Zool. 60, 417–427 (2014).
Google Scholar 
13.Duckworth, R. A. & Badyaev, A. V. Coupling of dispersal and aggression facilitates the rapid range expansion of a passerine bird. Proc. Natl. Acad. Sci. 104, 15017–15022 (2007).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
14.Conrad, J. L., Weinersmith, K. L., Brodin, T., Saltz, J. B. & Sih, A. Behavioural syndromes in fishes: A review with implications for ecology and fisheries management. J. Fish Biol. 78, 395–435 (2011).CAS 
PubMed 

Google Scholar 
15.Cote, J., Fogarty, S., Weinersmith, K., Brodin, T. & Sih, A. Personality traits and dispersal tendency in the invasive mosquitofish (Gambusia affinis). Proc. R. Soc. B Biol. Sci. 277, 1571–1579 (2010).
Google Scholar 
16.Malange, J., Izar, P. & Japyassú, H. Personality and behavioural syndrome in Necromys lasiurus (Rodentia: Cricetidae): Notes on dispersal and invasion processes. Acta Ethol. 19, 189–195 (2016).
Google Scholar 
17.Rees, E. M. A. et al. Socio-economic drivers of specialist anglers targeting the non-native European catfish (Silurus glanis) in the UK. PLoS ONE 12, e0178805 (2017).PubMed 
PubMed Central 

Google Scholar 
18.Bowler, D. E. & Benton, T. G. Causes and consequences of animal dispersal strategies: Relating individual behaviour to spatial dynamics. Biol. Rev. 80, 205–225 (2005).PubMed 

Google Scholar 
19.Clobert, J., Le Galliard, J.-F., Cote, J., Meylan, S. & Massot, M. Informed dispersal, heterogeneity in animal dispersal syndromes and the dynamics of spatially structured populations. Ecol. Lett. 12, 197–209 (2009).PubMed 

Google Scholar 
20.Dukes, J. S. & Mooney, H. A. Does global change increase the success of biological invaders?. Trends Ecol. Evol. 14, 135–139 (1999).CAS 
PubMed 

Google Scholar 
21.Gozlan, R. E., Britton, J. R., Cowx, I. & Copp, G. H. Current knowledge on non-native freshwater fish introductions. J. Fish Biol. 76, 751–786 (2010).
Google Scholar 
22.Pimentel, D. et al. Economic and environmental threats of alien plant, animal, and microbe invasions. Agric. Ecosyst. Environ. 84, 1–20 (2001).
Google Scholar 
23.Dingemanse, N. J., Kazem, A. J. N., Réale, D. & Wright, J. Behavioural reaction norms: Animal personality meets individual plasticity. Trends Ecol. Evol. 25, 81–89 (2010).PubMed 

Google Scholar 
24.Dochtermann, N. A., Schwab, T. & Sih, A. The contribution of additive genetic variation to personality variation: Heritability of personality. Proc. R. Soc. B Biol. Sci. 282, 20142201 (2015).
Google Scholar 
25.Duckworth, R. A. Evolution of personality: Developmental constraints on behavioral flexibility. Auk 127, 752–758 (2010).
Google Scholar 
26.Trillmich, F., Müller, T. & Müller, C. Understanding the evolution of personality requires the study of mechanisms behind the development and life history of personality traits. Biol. Lett. 14, 20170740 (2018).PubMed 
PubMed Central 

Google Scholar 
27.Dingemanse, N. J. & Réale, D. Natural selection and animal personality. Behaviour 142, 1159–1184 (2005).
Google Scholar 
28.Sih, A., Cote, J., Evans, M., Fogarty, S. & Pruitt, J. Ecological implications of behavioural syndromes. Ecol. Lett. 15, 278–289 (2012).PubMed 

Google Scholar 
29.Stamps, J. A. Growth-mortality tradeoffs and ‘personality traits’ in animals. Ecol. Lett. 10, 355–363 (2007).PubMed 

Google Scholar 
30.Chapple, D. G., Simmonds, S. M. & Wong, B. B. M. Can behavioral and personality traits influence the success of unintentional species introductions?. Trends Ecol. Evol. 27, 57–64 (2012).PubMed 

Google Scholar 
31.Hirsch, P. E., Thorlacius, M., Brodin, T. & Burkhardt-Holm, P. An approach to incorporate individual personality in modeling fish dispersal across in-stream barriers. Ecol. Evol. 7, 720–732 (2017).PubMed 

Google Scholar 
32.Groen, M. et al. Is there a role for aggression in round goby invasion fronts?. Behaviour 149, 685–703 (2012).
Google Scholar 
33.Urban, M. C., Phillips, B. L., Skelly, D. K. & Shine, R. A toad more traveled: The heterogeneous invasion dynamics of cane toads in Australia. Am. Nat. 171, E134–E148 (2008).PubMed 

Google Scholar 
34.Lopez, D. P., Jungman, A. A. & Rehage, J. S. Nonnative African jewelfish are more fit but not bolder at the invasion front: A trait comparison across an Everglades range expansion. Biol. Invasions 14, 2159–2174 (2012).
Google Scholar 
35.Dingemanse, N. J. & Wolf, M. Recent models for adaptive personality differences: A review. Philos. Trans. R. Soc. B Biol. Sci. 365, 3947–3958 (2010).
Google Scholar 
36.Dingemanse, N. J. & Réale, D. What is the evidence that natural selection maintains variation in animal personalities? In Animal Personalities: Behavior, Physiology, and Evolution (eds Carere, C. & Maestripieri, D.) 201–220 (University of Chicago Press, 2013).
Google Scholar 
37.Weiss, A. Personality traits: A view from the animal kingdom. J. Pers. 86, 12–22 (2018).PubMed 

Google Scholar 
38.Archard, G. A. & Braithwaite, V. A. The importance of wild populations in studies of animal temperament. J. Zool. 281, 149–160 (2010).
Google Scholar 
39.Holt, R. D., Keitt, T. H., Lewis, M. A., Maurer, B. A. & Taper, M. L. Theoretical models of species’ borders: Single species approaches. Oikos 108, 18–27 (2005).
Google Scholar 
40.Liedvogel, M., Chapman, B. B., Muheim, R. & Åkesson, S. The behavioural ecology of animal movement: Reflections upon potential synergies. Anim. Migr. 1, 39–46 (2013).
Google Scholar 
41.Campos-Candela, A., Palmer, M., Balle, S., Álvarez, A. & Alós, J. A mechanistic theory of personality-dependent movement behaviour based on dynamic energy budgets. Ecol. Lett. 22, 213–232 (2019).PubMed 

Google Scholar 
42.Bubb, D. H., Thom, T. J. & Lucas, M. C. Movement, dispersal and refuge use of co-occurring introduced and native crayfish. Freshw. Biol. 51, 1359–1368 (2006).
Google Scholar 
43.Luque, G. M. et al. The 100th of the world’s worst invasive alien species. Biol. Invasions 16, 981–985 (2014).
Google Scholar 
44.Galib, S. M., Findlay, J. S. & Lucas, M. C. Strong impacts of signal crayfish invasion on upland stream fish and invertebrate communities. Freshw. Biol. 66, 223–240 (2021).
Google Scholar 
45.Lindstrom, T., Brown, G. P., Sisson, S. A., Phillips, B. L. & Shine, R. Rapid shifts in dispersal behavior on an expanding range edge. Proc. Natl. Acad. Sci. 110, 13452–13456 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
46.Bubb, D. H., Thom, T. J. & Lucas, M. C. The within-catchment invasion of the non-indigenous signal crayfish Pacifastacus leniusculus (Dana), in upland rivers. Bull. Fr. Pêche Piscic. 376–377, 665–673 (2005).
Google Scholar 
47.Závorka, L., Lassus, R., Britton, J. R. & Cucherousset, J. Phenotypic responses of invasive species to removals affect ecosystem functioning and restoration. Glob. Chang. Biol. https://doi.org/10.1111/gcb.15271 (2020).Article 
PubMed 

Google Scholar 
48.Sbragaglia, V. & Breithaupt, T. Daily activity rhythms, chronotypes, and risk-taking behavior in the signal crayfish. Curr. Zool. https://doi.org/10.1093/cz/zoab023 (2021).Article 

Google Scholar 
49.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.r-project.org/ (2020).50.Pintor, L. M., Sih, A. & Bauer, M. L. Differences in aggression, activity and boldness between native and introduced populations of an invasive crayfish. Oikos 117, 1629–1636 (2008).
Google Scholar 
51.Rupia, E. J., Binning, S. A., Roche, D. G. & Lu, W. Fight-flight or freeze-hide? Personality and metabolic phenotype mediate physiological defence responses in flatfish. J. Anim. Ecol. 85, 927–937 (2016).PubMed 

Google Scholar 
52.Karavanich, C. & Atema, J. Individual recognition and memory in lobster dominance. Anim. Behav. 56, 1553–1560 (1998).CAS 
PubMed 

Google Scholar 
53.Houlihan, D., Govind, C. & El Haj, A. Energetics of swimming in Callinectes sapidus and walking in Homarus americanus. Comp. Biochem. Physiol. Part A Physiol. 82, 267–279 (1985).
Google Scholar 
54.Vogt, G. Functional anatomy. In Biology of Freshwater Crayfish (ed. Holdich, D. M.) 53–151 (Blackwell Science Ltd., 2002).
Google Scholar 
55.Southwood, T. R. E. & Henderson, P. A. Ecological Methods (Blackwell Science Ltd., 2000).
Google Scholar 
56.Clark, J. & Kershner, M. Size-dependent effects of visible implant elastomer marking on crayfish (Orconectes obscurus) growth, mortality, and tag retention. Crustaceana 79, 275–284 (2006).
Google Scholar 
57.Streissl, F. & Hödl, W. Habitat and shelter requirements of the stone crayfish, Austropotamobius torrentium Schrank. Hydrobiologia 477, 195–199 (2002).
Google Scholar 
58.Chadwick, D. D. A. et al. A novel ‘triple drawdown’ method highlights deficiencies in invasive alien crayfish survey and control techniques. J. Appl. Ecol. 58, 316–326 (2021).
Google Scholar 
59.Stoffel, M. A., Nakagawa, S. & Schielzeth, H. rptR: repeatability estimation and variance decomposition by generalized linear mixed-effects models. Methods Ecol. Evol. 8, 1639–1644 (2017).
Google Scholar 
60.Holm, S. A simple sequentially rejective multiple test procedure. Scand. J. Stat. 6, 65–70 (1979).MathSciNet 
MATH 

Google Scholar 
61.Quinn, G. P. & Keough, M. J. Experimental Design and Data Analysis for Biologists (Cambridge University Press, 2002).
Google Scholar 
62.Jackson, D. A. Stopping rules in principal components analysis: A comparison of heuristical and statistical approaches. Ecology 74, 2204–2214 (1993).
Google Scholar 
63.Budaev, S. V. Using principal components and factor analysis in animal behaviour research: Caveats and guidelines. Ethology 116, 472–480 (2010).
Google Scholar 
64.Robinson, C. A., Thom, T. J. & Lucas, M. C. Ranging behaviour of a large freshwater invertebrate, the white-clawed crayfish Austropotamobius pallipes. Freshw. Biol. 44, 509–521 (2000).
Google Scholar 
65.Bubb, D. H., O’Malley, O. J., Gooderham, A. C. & Lucas, M. C. Relative impacts of native and non-native crayfish on shelter use by an indigenous benthic fish. Aquat. Conserv. Mar. Freshw. Ecosyst. 19, 448–455 (2009).
Google Scholar 
66.Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage, 2011).
Google Scholar 
67.Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inferences: A Practical Information-Theoretic Approach (Springer, 2002).MATH 

Google Scholar 
68.Bartoń, K. MuMIn: Multi-Model Inference. R Package version 1.43.6. (2019).69.Kleiber, C. & Zeileis, A. Applied Econometrics with R (Springer, 2008).MATH 

Google Scholar 
70.Edwards, D. D., Rapin, K. E. & Moore, P. A. Linking phenotypic correlations from a diverse set of laboratory tests to field behaviors in the crayfish, Orconectes virilis. Ethology 124, 311–330 (2018).
Google Scholar 
71.Teknomo, K. Similarity Measurements. https://people.revoledu.com/kardi/tutorial/Similarity (2015).72.Bell, A. M., Hankison, S. J. & Laskowski, K. L. The repeatability of behaviour: A meta-analysis. Anim. Behav. 77, 771–783 (2009).PubMed 
PubMed Central 

Google Scholar 
73.Vainikka, A., Rantala, M. J., Niemelä, P., Hirvonen, H. & Kortet, R. Boldness as a consistent personality trait in the noble crayfish, Astacus astacus. Acta Ethol. 14, 17–25 (2011).
Google Scholar 
74.Fraser, D. F., Gilliam, J. F., Daley, M. J., Le, A. N. & Skalski, G. T. Explaining leptokurtic movement distributions: Intrapopulation variation in boldness and exploration. Am. Nat. 158, 124–135 (2001).CAS 
PubMed 

Google Scholar 
75.Dingemanse, N. J., Both, C., van Noordwijk, A. J., Rutten, A. L. & Drent, P. J. Natal dispersal and personalities in great tits (Parus major). Proc. R. Soc. London. Ser. B Biol. Sci. 270, 741–747 (2003).
Google Scholar 
76.McMahon, T. E. & Tash, J. C. Experimental analysis of the role of emigration in population regulation of desert pupfish. Ecology 69, 1871–1883 (1988).
Google Scholar 
77.Porter, J. H. & Dooley, J. L. Animal dispersal patterns: A reassessment of simple mathematical models. Ecology 74, 2436–2443 (1993).
Google Scholar 
78.Einum, S., Sundt-Hansen, L. & Nislow, K. H. The partitioning of density-dependent dispersal, growth and survival throughout ontogeny in a highly fecund organism. Oikos 113, 489–496 (2006).
Google Scholar 
79.Lodge, D. M. & Hill, A. M. Factors governing species composition, population size and productivity of coolwater crayfishes. Nord. J. Freshw. Res. 69, 111–136 (1994).
Google Scholar 
80.Berthouly-Salazar, C., van Rensburg, B. J., Le Roux, J. J., van Vuuren, B. J. & Hui, C. Spatial sorting drives morphological variation in the invasive bird, Acridotheris tristis. PLoS ONE 7, e38145 (2012).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
81.Juanes, F. & Smith, L. D. The ecological consequences of limb damage and loss in decapod crustaceans: A review and prospectus. J. Exp. Mar. Biol. Ecol. 193, 197–223 (1995).
Google Scholar 
82.Wilshin, S. et al. Limping following limb loss increases locomotor stability. J. Exp. Biol. 221, jeb174268 (2018).PubMed 

Google Scholar 
83.Podgorniak, T., Blanchet, S., De Oliveira, E., Daverat, F. & Pierron, F. To boldly climb: Behavioural and cognitive differences in migrating European glass eels. R. Soc. Open Sci. 3, 150665 (2016).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
84.Bubb, D. H., Thom, T. J. & Lucas, M. C. Movement patterns of the invasive signal crayfish determined by PIT telemetry. Can. J. Zool. 84, 1202–1209 (2006).
Google Scholar 
85.Bilton, D. T., Freeland, J. R. & Okamura, B. Dispersal in freshwater invertebrates. Annu. Rev. Ecol. Syst. 32, 159–181 (2001).
Google Scholar 
86.Bubb, D. H., Thom, T. J. & Lucas, M. C. Movement and dispersal of the invasive signal crayfish Pacifastacus leniusculus in upland rivers. Freshw. Biol. 49, 357–368 (2004).
Google Scholar 
87.Hudina, S., Kutleša, P., Trgovčić, K. & Duplić, A. Dynamics of range expansion of the signal crayfish (Pacifastacus leniusculus) in a recently invaded region in Croatia. Aquat. Invasions 12, 67–75 (2017).
Google Scholar 
88.Wutz, S. & Geist, J. Sex- and size-specific migration patterns and habitat preferences of invasive signal crayfish (Pacifastacus leniusculus Dana). Limnologica 43, 59–66 (2013).
Google Scholar 
89.Fraser, H., Barnett, A., Parker, T. H. & Fidler, F. The role of replication studies in ecology. Ecol. Evol. 10, 5197–5207 (2020).PubMed 
PubMed Central 

Google Scholar 
90.Linzmaier, S. M., Goebel, L. S., Ruland, F. & Jeschke, J. M. Behavioral differences in an over-invasion scenario: marbled vs. spiny-cheek crayfish. Ecosphere 9, e02385 (2018).
Google Scholar 
91.Wang, X. et al. Anthropogenic habitat loss accelerates the range expansion of a global invader. Divers. Distrib. https://doi.org/10.1111/ddi.13359 (2021).Article 

Google Scholar  More