Philippa Kaur

1.Griffin, J. N. et al. Spatial heterogeneity increases the importance of species richness for an ecosystem process. Oikos 118, 1335–1342 (2009).
Google Scholar 
2.Bulling, M. T. et al. Species effects on ecosystem processes are modified by faunal responses to habitat composition. Oecologia 158, 511–520 (2008).ADS 
PubMed 

Google Scholar 
3.Godbold, J. A., Bulling, M. T. & Solan, M. Habitat structure mediates biodiversity effects on ecosystem properties. Proc. R. Soc. B Biol. Sci. 278, 2510–2518 (2011).CAS 

Google Scholar 
4.Carlucci, R. et al. Exploring spatio-temporal changes in the demersal and benthopelagic assemblages of the north-western Ionian Sea (central Mediterranean Sea). Mar. Ecol. Prog. Ser. 598, 1–19 (2018).ADS 

Google Scholar 
5.Garrison, L. P. & Link, J. S. Fishing effects on spatial distribution and trophic guild structure of the fish community in the Georges Bank region. ICES J. Mar. Sci. 57, 723–730 (2000).
Google Scholar 
6.Worm, B. & Myers, R. A. Meta-analysis of COD–shrimp interactions reveals top-down control in oceanic food webs. Ecology 84, 162–173 (2003).
Google Scholar 
7.Savenkoff, C. et al. Changes in the northern Gulf of St. Lawrence ecosystem estimated by inverse modelling: evidence of a fishery-induced regime shift?. Estuar. Coast. Shelf Sci. 73, 711–724 (2007).ADS 

Google Scholar 
8.Ellingsen, K. E. et al. The rise of a marine generalist predator and the fall of beta diversity. Glob. Chang. Biol. 6, 1–11. https://doi.org/10.1111/gcb.15027 (2020).Article 

Google Scholar 
9.Casellato, S. & Stefanon, A. Coralligenous habitat in the northern Adriatic Sea: an overview. Mar. Ecol. 29, 321–341 (2008).ADS 

Google Scholar 
10.Guidetti, P., Lorenti, M., Buia, M. C. & Mazzella, L. Temporal dynamics and biomass partitioning in three Adriatic seagrass species: Posidonia oceanica, Cymodocea nodosa, Zostera marina. Mar. Ecol. 23, 51–67 (2002).ADS 

Google Scholar 
11.Sanfilippo, R. et al. Serpula aggregates and their role in deep-sea coral communities in the southern Adriatic Sea. Facies 59, 663–677 (2013).
Google Scholar 
12.FAO. The state of the Mediterranean and Black Sea fisheries 2020. (2020).13.Mannini, P. & Massa, F. Brief overview of Adriatic fisheries landing trends (1972–1997). Support paper prepared for the first Adriamed Coordination Committee Meeting. General Fisheries Commission for the Mediterranean (FAO). Annex G. 3, 1–19 (2000).
Google Scholar 
14.Adriamed. Priority Topics Related to Small Pelagic Fishery Resources of the Adriatic Sea. Report of the First Meeting of the Adriamed Working Group on Shared Demersal Resources. FAO-MiPAF Scientific Cooperation to Support Responsible Fisheries in the Adriatic Sea. AdriaMed Tech. Doc. 1–21 (2000).15.Mannini, P., Massa, F. & Milone, N. Priority topics related to small pelagic fishery resources of the Adriatic Sea. Report of the first meeting of the adriamed working group on small pelagic resources. FAO-MiPAF scientific cooperation to support responsible fisheries in the Adriatic Sea. Adriamed Tech. Doc. 6, 1–92 (2001).
Google Scholar 
16.Vrgoč, N. et al. Review of current knowledge on shared demersal stocks of the Adriatic Sea. (Food and agriculture organization of the United nations (FAO), 2004).17.Cerrano, C. et al. Adriatic Sea: Description of the ecology and identification of the areas that may deserve to be protected. (2015).18.Arneri, E. & Morales-Nin, B. Aspects of the early life history of European hake from the central Adriatic. J. Fish Biol. 56, 1368–1380 (2000).
Google Scholar 
19.Zupanovic, S. & Jardas, I. A contribution to the study of biology and population dynamics of the Adriatic hake, M. merluccius (L). Acta Adriat. 27, 97–146 (1986).
Google Scholar 
20.Colloca, F. et al. Mapping of nursery and spawning grounds of demersal fish. Mediterr. Sensitive Habitats Final Report, DG MARE Specif. Contract SI2 600741, (2013).21.Sion, L. et al. Spatial distribution pattern of European hake, M. merluccius (Pisces: Merlucciidae), in the Mediterranean Sea. Sci. Mar. 83, 21–32 (2020).
Google Scholar 
22.GFCM. FAO: The state of the Mediterranean and Black Sea fisheries 2016. General Fisheries Commission for the Mediterranean (2016). https://doi.org/10.1163/156853010X510807.23.NGOs. Urgent call for a Fisheries Restricted Area in the Jabuka/Pomo Pit closed to demersal fisheries. (2017).24.Fisher, W., Bauchot, W. M. & Schneider, M. Fiches FAO d’identification pour les besoins de la pêche (rev. 1). Méditerranée et mer Noire. Zone de pêche 37 2, 761–1530 (1987).
Google Scholar 
25.Carpentieri, P., Colloca, F. & Ardizzone, G. Daily ration and feeding activity of juvenile hake in the central Mediterranean Sea. J. Mar. Biol. Assoc. UK 88, 1493–1501 (2008).
Google Scholar 
26.Cartes, J. E., Hidalgo, M., Papiol, V., Massutí, E. & Moranta, J. Changes in the diet and feeding of the hake M. merluccius at the shelf-break of the Balearic Islands: influence of the mesopelagic-boundary community. Deep Sea Res. Part I Oceanogr. Res. Pap. 56, 344–365 (2009).ADS 

Google Scholar 
27.Modica, L., Cartes, J. E., Velasco, F. & Bozzano, A. Juvenile hake predation on Myctophidae and Sternoptychidae: quantifying an energy transfer between mesopelagic and neritic communities. J. Sea Res. 95, 217–225 (2015).ADS 

Google Scholar 
28.Druon, J.-N. et al. Modelling of European hake nurseries in the Mediterranean Sea: an ecological niche approach. Prog. Oceanogr. 130, 188–204 (2015).ADS 

Google Scholar 
29.Mellon-Duval, C. et al. Trophic ecology of the European hake in the Gulf of Lions, northwestern Mediterranean Sea. Sci. Mar. 81, 7–18 (2017).
Google Scholar 
30.Stagioni, M., Montanini, S. & Vallisneri, M. Feeding habits of European hake, M. merluccius (Actinopterygii: Gadiformes: Merlucciidae), from the Northeastern Mediterranean Sea. Acta Ichthyol. Piscat. 41, 109 (2011).
Google Scholar 
31.Albaina, A., Aguirre, M., Abad, D., Santos, M. & Estonba, A. 18S rRNA V9 metabarcoding for diet characterization: a critical evaluation with two sympatric zooplanktivorous fish species. Ecol. Evol. 6, 1809–1824 (2016).PubMed 
PubMed Central 

Google Scholar 
32.Berry, O. et al. Comparison of morphological and DNA metabarcoding analyses of diets in exploited marine fishes. Mar. Ecol. Prog. Ser. 540, 167–181 (2015).ADS 
CAS 

Google Scholar 
33.Leray, M. et al. A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: application for characterizing coral reef fish gut contents. Front. Zool. 10, 34 (2013).PubMed 
PubMed Central 

Google Scholar 
34.Siegenthaler, A. et al. Metabarcoding of shrimp stomach content: Harnessing a natural sampler for fish biodiversity monitoring. Mol. Ecol. Resour. 19, 206–220 (2019).CAS 
PubMed 

Google Scholar 
35.Riccioni, G., Stagioni, M., Piccinetti, C. & Libralato, S. A metabarcoding approach for the feeding habits of European hake in the Adriatic Sea. Ecol. Evol. 8, 10435–10447 (2018).PubMed 
PubMed Central 

Google Scholar 
36.Carpentieri, P., Colloca, F., Cardinale, M., Belluscio, A. & Ardizzone, G. Feeding habits of European hake (M. merluccius) in the central Mediterranean Sea. Fish. Bull. 103, 411–416 (2005).
Google Scholar 
37.Carrozzi, V. et al. Prey preferences and ontogenetic diet shift of European hake M. merluccius (Linnaeus, 1758) in the central Mediterranean. Reg. Stud. Mar. Sci. 25, 100440 (2019).
Google Scholar 
38.Bozzano, A., Sardà, F. & Ríos, J. Vertical distribution and feeding patterns of the juvenile European hake, M. merluccius in the NW Mediterranean. Fish. Res. 73, 29–36 (2005).
Google Scholar 
39.Cartes, J. E., Rey, J., Lloris, D. & De Sola, L. G. Influence of environmental variables on the feeding and diet of European hake (M. merluccius) on the Mediterranean Iberian coasts. J. Mar. Biol. Assoc. UK 84, 831–835 (2004).
Google Scholar 
40.Papaconstantinou, C. & Caragitsou, E. The food of hake (M. merluccius) in Greek Seas. Vie milieu 37, 77–83 (1987).
Google Scholar 
41.Sartor, P., Carlini, F. & De Ranieri, S. Diet of young European hake (M. merluccius) in the Northern Tyrrhenian Sea. (Società italiana di biologia marina, 2003).42.Ungaro, N., Mannini, P. & Vrgoč, N. The biology and stock assessment of M. merluccius in the Adriatic Sea: an historical review by geographical subareas. Acta Adriat. 44, 9–20 (2003).
Google Scholar 
43.Froglia, C. & Gramitto, M. E. Summary of biological parameters on Micromesistius poutassou (Risso) in the Adriatic. FAO Fish. Report= FAO Rapp. sur les pêches (1981).44.Krstulovic, S. S. et al. Composition and distribution of the cephalopod fauna in the eastern Adriatic and eastern Ionian Sea. Isr. J. Zool. 51, 315–330 (2005).
Google Scholar 
45.Nožina, I. Biogenic deep scattering layers in the Adriatic mesopelagial. (1979).46.Sobrino, I., Silva, C., Sbrana, M. & Kapiris, K. A review of the biology and fisheries of the deep water rose shrimp, parapenaeus longirostris, in European atlantic and Mediterranean Waters (Decapoda, Dendrobranchiata, Penaeidae). Crustaceana 78, 1153–1184 (2005).
Google Scholar 
47.Ciavaglia, E. & Manfredi, C. Distribution and some biological aspects of cephalopods in the North and Central Adriatic. Boll. Malacol 45, 61–69 (2009).
Google Scholar 
48.Stagioni, M., Montanini, S. & Vallisneri, M. Feeding habits of European hake, M. merluccius (Actinopterygii: Gadiformes: Merlucciidae), from the Northeastern Mediterranean Sea. Acta Ichthyol. Piscat. 41, 277–284 (2011).
Google Scholar 
49.Cartes, J. E., Sorbe, J. C. & Sardà, F. Spatial distribution of deep-sea decapods and euphausiids near the bottom in the northwestern Mediterranean. J. Exp. Mar. Bio. Ecol. 179, 131–144 (1994).
Google Scholar 
50.Despalatovic, M., Grubelic, I. & Simunovic, A. Distribution and abundance of the Atlantic mud shrimp, Solenocera membranacea (Risso, 1816)(Decapoda, Solenoceridae) in the northern and central Adriatic Sea. Crustac. J. Crustac. Res. 79, 1025 (2006).
Google Scholar 
51.Koulouri, P., Dounas, C. & Eleftheriou, A. Hyperbenthic community structure over oligotrophic continental shelves and upper slopes: crete (South Aegean Sea, NE Mediterranean). Estuar. Coast. Shelf Sci. 117, 188–198 (2013).ADS 

Google Scholar 
52.Panzeri, D. et al. Developing spatial distribution models for demersal species by the integration of trawl surveys data and relevant ocean variables. Copernicus Mar. Serv. Ocean State Rep. J. Oper Oceanogr. 14, 114–124 (2021).
Google Scholar 
53.Albo-Puigserver, M. et al. Year-round energy dynamics of sardine and anchovy in the north-western Mediterranean Sea. Mar. Environ. Res. 159, 105021 (2020).CAS 
PubMed 

Google Scholar 
54.Harmelin-Vivien, M., Bӑnaru, D., Dromard, C. R., Ourgaud, M. & Carlotti, F. Biochemical composition and energy content of size-fractionated zooplankton east of the Kerguelen Islands. Polar Biol. 42, 603–617 (2019).
Google Scholar 
55.McClatchie, S. et al. Food limitation of sea lion pups and the decline of forage off central and southern California. R. Soc. Open Sci. 3, 150628 (2020).
Google Scholar 
56.Schaafsma, F. L. et al. Review: the energetic value of zooplankton and nekton species of the Southern Ocean. Mar. Biol. 165, 129 (2018).PubMed 
PubMed Central 

Google Scholar 
57.Cai, L. et al. Interrelationships between feeding, food deprivation and swimming performance in juvenile grass carp. Aquat. Biol. 20, 69–76 (2014).
Google Scholar 
58.Nunn, A. D., Tewson, L. H. & Cowx, I. G. The foraging ecology of larval and juvenile fishes. Rev. Fish Biol. Fish. 22, 377–408 (2012).
Google Scholar 
59.Ferraton, F., Harmelin-Vivien, M. & Mellon-Duval, C. Spatio-temporal variation in diet may affect condition and abundance of juvenile European hake in the Gulf of Lions (NW Mediterranean). Mar. Ecol. Prog. Ser. 337, 197–208 (2007).ADS 

Google Scholar 
60.Bascompte, J., Jordano, P., Melián, C. J. & Olesen, J. M. The nested assembly of plant–animal mutualistic networks. Proc. Natl. Acad. Sci. 100, 9383–9387 (2003).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
61.Almeida-Neto, M., Guimaraes, P., Guimaraes, P. R. Jr., Loyola, R. D. & Ulrich, W. A consistent metric for nestedness analysis in ecological systems: reconciling concept and measurement. Oikos 117, 1227–1239 (2008).
Google Scholar 
62.Baumgartner, M. T. Connectance and nestedness as stabilizing factors in response to pulse disturbances in adaptive antagonistic networks. J. Theor. Biol. 486, 110073 (2020).PubMed 
MATH 

Google Scholar 
63.Libralato, S. et al. Food-web traits of protected and exploited areas of the Adriatic Sea. Biol. Conserv. 143, 2182–2194 (2010).
Google Scholar 
64.van Denderen, P. D., van Kooten, T. & Rijnsdorp, A. D. When does fishing lead to more fish? Community consequences of bottom trawl fisheries in demersal food webs. Proc. R. Soc. B Biol. Sci. 280, 20131883 (2013).
Google Scholar 
65.Agnetta, D. et al. Benthic-pelagic coupling mediates interactions in Mediterranean mixed fisheries: an ecosystem modeling approach. PLoS One 14, e0210659 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
66.Walters, C. J., Christensen, V., Martell, S. J. & Kitchell, J. F. Possible ecosystem impacts of applying MSY policies from single-species assessment. ICES J. Mar. Sci. 62, 558–568 (2005).
Google Scholar 
67.GFCM. Report of the nineteenth session of the Scientific Advisory Committee on Fisheries. Working copy vol. 1209 (2017).68.NOAA. Essential fish habitat and consultation. NOAA Fish. Pacific Isl. Reg. Off. (2004) https://doi.org/10.17128/9781589483651_11.1.69.Rohland, N. & Reich, D. Cost-effective, high-throughput DNA sequencing libraries for multiplexed target capture. Genome Res. 22, 939–946 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
70.Boyer, F. et al. obitools: a unix-inspired software package for DNA metabarcoding. Mol. Ecol. Resour. 16, 176–182 (2016).CAS 
PubMed 

Google Scholar 
71.Zhang, Z., Schwartz, S., Wagner, L. & Miller, W. A greedy algorithm for aligning DNA sequences. J. Comput. Biol. 7, 203–214 (2000).CAS 
PubMed 

Google Scholar 
72.Oksanen, J. Vegan: an introduction to ordination. (2016).73.Legendre, P. & Gallagher, E. D. Ecologically meaningful transformations for ordination of species data. Oecologia 129, 271–280 (2001).ADS 
PubMed 
PubMed Central 

Google Scholar 
74.R Core Team. R: A Language and Environment for Statistical Computing. (2015).75.Dormann, C. F. How to be a specialist? Quantifying specialisation in pollination networks. Netw. Biol. 1, 1–20 (2011).
Google Scholar 
76.Almeida-Neto, M. & Ulrich, W. A straightforward computational approach for measuring nestedness using quantitative matrices. Environ. Model. Softw. 26, 173–178 (2011).
Google Scholar 
77.Jacobs, J. Quantitative measurement of food selection. Oecologia 14, 413–417 (1974).ADS 
PubMed 

Google Scholar  More

Effects of environmental fluctuations on co-culture composition and intermixingWe first tested the effects of fluctuations between anoxic (inducing a mutualistic interaction) and oxic (inducing a competitive interaction) conditions on co-culture composition (quantified as the ratio of consumer-to-producer at the expansion edge) and interspecific mixing (quantified as the number of interspecific boundaries divided by the colony circumference). We expected that, over a series of anoxic/oxic transitions, the ratio of consumer-to-producer at the expansion edge and the degree of intermixing would both decrease (Fig. 1d). To test this, we performed range expansions where we transitioned the environment between anoxic and oxic conditions. While we performed the experiments with defined anoxic and oxic incubation times, our main prediction (i.e., that repeated transitions between anoxic and oxic conditions can induce irreversible pattern transitions that alter co-culture composition and functioning) is independent of the time spent under either of those conditions as far as cells can adjust their metabolism to the new environment (Fig. 1d).As expected, the ratio of consumer-to-producer and the intermixing index both decreased over the series of anoxic/oxic transitions (Fig. 2a, b). The changes in these quantities appear to have two distinct dynamic phases; a first phase with a relatively steep decay and a second phase with a shallower decay. We therefore modeled their dynamics using a two-phase linear regression model [53,54,55]. During the first phase, the ratio of consumer-to-producer decreased significantly more rapidly at pH 7.5 (r2 = 0.90, p = 2 × 10−9, coeff = −0.0374, 95% CI = [−0.038, −0.0368]) than at 6.5 (r2 = 0.94, p = 1 × 10−7, coeff = −0.0103, 95% CI = [−0.0108, −0.0097]) (Fig. 2a). We observed consistent results for the intermixing index, where it also decreased significantly more rapidly at pH 7.5 (r2 = 0.90, p = 2 × 10−9, coeff = −0.0289, 95% CI = [−0.0295, −0.0284]) than at 6.5 (r2 = 0.93, p = 9 × 10−8, coeff = −0.01, 95% CI = [−0.0109, −0.0098]) (Fig. 2b). During the second phase, the change in the ratio of consumer-to-producer did not significantly differ between pH 7.5 (r2 = 0.90, p = 2 × 10−9, coeff = 0.0008, 95% CI = [0.0002, 0.0014]) and 6.5 (r2 = 0.94, p = 1 × 10−7, coeff = 0.0003, 95% CI = [−0.0002, 0.0008]) (Fig. 2a). However, we observed that the decrease in the intermixing index was significantly different between pH 7.5 (r2 = 0.94, p = 2 × 10−9, coeff = 0.0018, 95% CI = [0.0013, 0.0024]) and 6.5 (r2 = 0.94, p = 8 × 10−8, coeff = −0.0019, 95% CI = [−0.0025, −0.0013]). Overall, the final ratio of consumer-to-producer is lower at pH 7.5 (mean = 0.0163, SD = 0.01) than at 6.5 (mean = 0.052, SD = 0.02) (two-sample two-sided t-test; p = 0.03, n = 4) (Fig. 2). Consistently, the final intermixing index is also lower at pH 7.5 (mean = 0.0039, SD = 0.0032) than at 6.5 (mean = 0.0107, SD = 0.0049) (two-sample two-sided t-test; p = 0.05, n = 4) (Fig. 2b).Fig. 2: Dynamics of co-culture composition and intermixing during repeated anoxic/oxic transitions.a Co-culture composition measured as the ratio of consumer-to-producer. b Intermixing between the consumer and producer measured as the intermixing index, where N is the number of interspecific boundaries between the two strains. Experiments were performed at pH 6.5 (strong mutualistic interaction) (magenta data points) or pH 7.5 (weak mutualistic interaction) (cyan data points). Each data point is for an independent replicate (n = 4). The solid black lines are the two-phase linear regression models for pH 6.5, while the dashed black lines are the two-phase linear regression models for pH 7.5. Images of the final expansions after 350 h of incubation at c pH 6.5 and d pH 7.5. The scale bars are 1000 μm.Full size imageThe results described above yielded two important outcomes. First, the modeled two-phase linear regression of the ratio of consumer-to-producer and the intermixing index both depended on the strength of the mutualistic interaction, where the initial rate of decay was faster at pH 7.5 than at 6.5 (Fig. 2a, b). Thus, as the strength of the interdependency increases, the decay in the ratio and the intermixing index slows. Second, at pH 6.5 we never observed the complete loss of the consumer from the expansion edge (i.e., neither the ratio of consumer-to-producer nor the intermixing index reached zero) (Fig. 2a, b), which is counter to our initial expectation (Fig. 1d).We further performed controls under continuous oxic and continuous anoxic conditions (Supplementary Fig. S5). The ratio of consumer-to-producer and the intermixing indices both significantly differed between continuous oxic and continuous anoxic conditions regardless of the pH (two-sample two-sided t-tests; p  More

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Google Scholar  More

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

BOOK REVIEW
24 January 2022

A call for governments to save soil

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

Emma Marris

0

Emma Marris

Emma Marris is an environmental writer who lives in Oregon.

View author publications

You can also search for this author in PubMed
 Google Scholar

Twitter

Facebook

Email

Download PDF

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.

Related Articles

Food systems: seven priorities to end hunger and protect the planet

Globe to gut: inside Big Food

The novelist who loved soil

The business case for soil

Subjects

Sustainability

Ecology

Environmental sciences

Latest on:

Sustainability

Portugal leads with Europe’s largest marine reserve
Correspondence 18 JAN 22

Sustainability at the crossroads
Editorial 21 DEC 21

The UN must get on with appointing its new science board
Editorial 08 DEC 21

Ecology

Biodiversity faces its make-or-break year, and research will be key
Editorial 19 JAN 22

Portugal leads with Europe’s largest marine reserve
Correspondence 18 JAN 22

Wind power versus wildlife: root mitigation in evidence
Correspondence 11 JAN 22

Environmental sciences

Air pollution takes a bite out of Asia’s grain crops
Research Highlight 21 JAN 22

Global fine-scale changes in ambient NO2 during COVID-19 lockdowns
Article 19 JAN 22

Message to mayors: cities need nature
World View 17 JAN 22

Jobs

Postdoc position to investigate composition and function of human hnRNP complexes

University of Bern
Bern, Switzerland

Doctoral Candidate and Postdoc Positions on Magnonic/Plasmonic Devices, Magnonic Neural Networks, and Plasmonic Condensates/Nanolasers

Aalto University
Espoo, Finland

211-0115/21-2N Tenure-track Assistant Professor in Plant Cell Biology and Biochemistry with emphasis on Glycobiology

University of Copenhagen (UCPH)
Copenhagen, Denmark

Postdoctoral Research Scientist – Cellular Degradation Systems Lab

Francis Crick Institute
London, United Kingdom More

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