More stories

  • in

    Molecular phylogeny and historical biogeography of marine palaemonid shrimps (Palaemonidae: Palaemonella–Cuapetes group)

    Phylogenetic relationships inside the family Palaemonidae remain unresolved, despite being frequently discussed in recent publications9,10. Nevertheless, the last published study5 presented the main lineages of the family as well supported. Among those, the studied Pon-I group of predominantly free-living taxa is basal-positioned to the remaining genera of the former subfamily Pontoniinae, usually more specialised and associated with a wide range of hosts. The basal separation of the symbiotic genera led some authors to consider the assemblage, following Bruce22, to be a primitive group, or descendants of such7,23. Additionally, Gan et al.8 suggested that the taxa of the Pon-I group might be direct descendants of the ancestors of the former subfamily Pontoniinae, sharing the main plesiomorphies appearing frequently in former palaemonine taxa, e.g., the genera Brachycarpus, Leptocarpus, Macrobrachium, or Palaemon. The median process on the fourth thoracic sternite can be considered a plesiomorphic feature; indeed, it is a common symplesiomorphy of all Pon-I taxa, including Ischnopontonia and Anapontonia, for which the process was formerly reported as missing24 (its presence was confirmed in present examined specimens). In addition to that, the mandibular palp occurring in the genera Exoclimenella, Eupontonia, Palaemonella, and Vir25, or the presence of two arthrobranchs on the third maxilliped in Exoclimenella26, can also be considered plesiomorphic features.The Pon-I group’s internal relations have been unclear until now due to lower generic and species coverage in previous studies4,5,8. The present analysis based on a six-marker molecular dataset allows a deeper insight into the phylogenetic relationships of the study group involving all 11 currently recognised genera, and represented by 52 species, i.e. about 60% of the overall known species diversity of the group. The results provide a strong support for the monophyly and/or taxonomic validity of the current genera Exoclimenella, Anapontonia, Ischnopontonia, and suggest the monophyly of genera Harpilius and Philarius. Moreover, the results reveal non-monophyly of the most speciose genera Palaemonella and Cuapetes, as well as the species-poor Eupontonia. The genus Palaemonella was found to be paraphyletic owing to the nested species of the genera Eupontonia and Vir, which all share a common synapomorphy, the presence of the mandibular palp (mentioned above). Such conclusion was expressed also in the study of Chow et al.5.The present phylogenetic analysis confirmed that the genus Cuapetes is not monophyletic, as found to a lesser extent, in a few previous molecular studies 4,5,23. In this study, the genus Cuapetes was recovered in four separate genetic lineages. The type species C. nilandensis is nested in the Clade 1 along with C. johnsoni and C. seychellensis. This phylogenetic finding is in line with the study of Marin and Sinelnikov27, who indicated morphological differences between two of the above-mentioned species and most of the remaining species of the genus (respective of the present Clade 5, also covering C. grandis, the type species of the ex-genus Kemponia), and questioned the validity of the two latter generic names. The further genetic lineage is shown by the position of C. americanus nested in the eastern Pacific—Atlantic branch of the genus Palaemonella (Clade 3). This result is also supported by recent phylogenetic studies suggesting the different systematic positions of this species4,5,10. Due to the lack of the mandibular palp, the species had been properly, but evidently incorrectly, assigned to the genus Cuapetes. The fourth genetic lineage is shown by a separate position of C. darwiniensis in the Clade 4 as the sister species of Madangella altirostris.The remaining majority of the Cuapetes species (Clade 5) are heterogeneous due to comprising also representatives of the genus Periclimenella. Ďuriš and Bruce26 hypothesised, based on morphological traits (mainly the unique shape of the first pereiopod chelae and the distinctly asymmetrical and specific second pereiopods), that the genera Exoclimenella and Periclimenella are closely related. Nevertheless, the present study revealed Periclimenella as a part of the genus Cuapetes. This result was previously supported in the molecular study by Horká et al.4 and weakly supported by Kou et al.23.Fossil records of palaemonid shrimps are rare due to their aquatic habit and poorly calcified exoskeletons. Only a few palaemonid representatives are known compared to many extant taxa; the oldest fossil records contain only genera from the previous subfamily Palaemoninae from the Lower Cretaceous (middle Albian, 100 Myr)28. For this reason, we used the known mutation rate of mitochondrial gene (16S rRNA) for dating rather than fossil records.The present inferred phylogeny and ancestral analysis indicate multiple formations of primary symbioses within the clades dominated by free-living relatives, as shown by previous molecular analyses4,5. Our results revealed eight independent lineages within the Pon-I group that evolved from free-living ancestors (Fig. 3). Free-living palaemonids (Exoclimenella, Palaemonella, Cuapetes; Fig. 2) are characterised by an elongate body shape with a dentate rostrum, slender, long, a/symmetrical chelipeds and slender ambulatory pereiopods with simple dactyli. Their carapace might bear the full complement of teeth (i.e., supraorbital, antennal, hepatic, epigastric)25. Primary symbiotic forms do not fundamentally differ morphologically from free-living ancestors. Their adaptations to the host affiliation have mainly manifested by changes in body shape, colouration, and the reduction of carapace ornamentation. Their hosts belong to different invertebrate phyla, including Cnidaria (mainly Scleractinia and Antipatharia22) and Echinodermata (Crinoidea29) in ectosymbiotic forms, but also to spoon worms (Echiura), burrowing Crustacea (alpheid shrimps), and/or gobiid fishes15, in inquilinistic forms.While scleractinian corals were hypothesised as the primary hosts of palaemonid shrimp commensalism7, our results revealed the antipatharian association as possibly the earlier one among the Pon-I shrimps. That association was established via a single speciation act at approximately 43 Myr (Eocene), specifically with the ancestor of the recent Cuapetes nilandensis (Clade 1). Except a small body size, this species does not show specific morphological adaptations to antipatharian association. The possibly oldest lineage associated with the scleractinian corals forms a common multigeneric composition of Anapontonia, Ischnopontonia, Harpilius and Philarius (Clade 4), which was established at approximately 38.2 Myr (Eocene). The genera share some homoplasic adaptations with ectosymbioses, such as strongly hooked dactyli of the ambulatory pereiopods adapted to climbing on coral colonies. An extremely compressed body and similar tail fan structure of the genera Ischnopontonia (Fig. 1H) and Anapontonia (Fig. 1D) are adaptations to life in narrow spaces amongst corallites of the oculinid coral Galaxea24,30; the intercorallite channels might be temporarily fully covered by tentacles of exposed polyps. This lifestyle was thus termed ‘semi-endosymbiosis’ by Horká et al.4, as potential evolutionary precursors of the true endosymbioses. In contrast, the genera Philarius and Harpilius have depressed bodies and associate exclusively as regular ectosymbionts with scleractinian corals, mainly of the genera Acropora and Pocillopora22.A further multispecies symbiotic lineage is represented by the genus Vir (Clade 3), whose origin is dated to approximately 21.1 Myr (Miocene). All species of this genus live in associations mainly with the acroporid, pocilloporid and euphylliid genera of scleractinian corals31,32. The adaptation to their symbiotic lifestyle is expressed in the loss of the hepatic tooth, partial or full reduction of ambulatory propodal spines, and cryptic colouration, including transparency of the body and appendages31,33 (Fig. 1J). Subsequent scleractinian-associated lineages are represented by separate species that appeared in the Miocene (21.9–10.1 Myr), namely: Eupontonia oahu, Cuapetes amymone, and C. kororensis, which live in association with Pocillopora, Acropora, and Heliofungia, and show only minor adaptations to their symbiotic habits, e.g. loss of the hepatic tooth, dense distal setae on the walking propodi, or extremely slender chelae and a specific cryptic colouration, respectively22,34,35.A single crinoid-associated species, Palaemonella pottsi (Clade 3), represents the only case of the switch from a free-living lifestyle to the association with echinoderms in the present study group; it originated at approximately 10.4 Myr (Miocene). Retaining the body shape typical for Palaemonella12, the species also does not show any noticeable morphological adaptation to such a host; its affiliation with the symbiotic life is, however, clearly observed in the deep-red to black cryptic colouration36.In Palaemonella aliska (Fig. 1E) and Eupontonia nudirostris (Clade 3), a pair of sister-positioned species in the present analyses (Figs. 2, 3), the ability to co-habit with burrowing animals (e.g., alpheids, gobiid fish, or echiurids) had developed. Their type of symbiosis, inquilinism, formed at approximately 14.8 Myr (Miocene). The reduction of the rostrum length, depressed body, stout main chelae in both, and full lack of the epigastric and hepatic teeth in the latter species15,25, were evidently due to that mode of life. Inquilinism is best known in the family Alpheidae, in which multiple genera associate with a variety of burrowing animals37. In the family Palaemonidae, inquilinism developed only in the Pon-I group, including Palaemonella shirakawai (not analysed here)14.As evident from the present and previously published reports4,5,7,8,10, the life history of the Pon-I group was largely shaped by coevolution with coral reefs. The coral reefs were deeply impacted by the K–T mass extinction at the end of the Cretaceous, which was one of the most destructive events in the Phanerozoic38. However, coral reefs recovered and became increasingly abundant in the Eocene39. This also matches the time of either the origin of host associations, or a wider species radiation of the Pon-I group. The first fossil records of the main coral hosts of the present shrimps are dated after the K-T extinction during the Paleogene (e.g., Euphyllia 66.0–61.6 Myr, Acropora 59.2–56.0 Myr, Galaxea and Pocillopora 56–33.9 Myr40).The biogeographic history suggested by S-DIVA analysis points to some dispersal and vicariant events shaping the current pattern of the Pon-I group’s distribution. This reconstruction (Fig. 4) estimates the present-day IWP region within the former Paleo-Tethys Ocean as the most likely ancestral area of the present study group, which originated ~ 91.6 Myr (Late to Early Cretaceous). The present shrimp group had radiated across the entire IWP region and subsequently expanded into the Atlantic Ocean. We assume that the spread of the group took place in the following sequence of events: (1) dispersal of Palaemonella spp. from the IWP into the eastern Pacific in the Paleocene (∼ 55.2 Myr; P. asymmetrica and P. holmesi); (2) dispersal into the western Atlantic (2 spp., complex of “Cuapetes” americanus) via the eastern Pacific and vicariance event separating the IWP at Eocene (∼ 46.2 Myr). It was the time after the formation of the Eastern Pacific Barrier (EPB), which was considered the largest extension of the open ocean (ca. 5000 km), that separated the IWP area from the eastern Pacific17; (3) the another vicariance event, separating the western Atlantic populations from those of the eastern Pacific in the Oligocene (∼ 30.9 Myr), i.e., before the closure of the Isthmus of Panama, followed by a dispersion of P. atlantica into the eastern Atlantic in the Miocene (∼ 21.6 Myr). The exact time of the formation of the Isthmus of Panama, which separated the Atlantic from the eastern Pacific and remained isolated from the central Pacific by the EPB, still remains questionable. Bacon et al.18 assume that the initial land bridge formed at approximately 23 Myr, and the final closure of the Isthmus of Panama formed between 10 and 6 Myr. Montes et al.19 presupposed the earlier formation of the barrier at ∼ 14 Myr, whereas O’Dea et al.20 concluded that the potential gene flow continued between the Pacific and Atlantic subpopulations of marine organisms until at least ∼ 2.8 Myr.The eastern Pacific Cuapetes canariensis closely related to IWP Cuapetes spp., has been recently described by Fransen et al.41, from the Canary Islands. This could indicate alternative dispersal pathways into the Atlantic, as suggested by recent studies17,42. The Tethys seaway allowed natural dispersion between the Atlantic and Indian Oceans across the region of the Mediterranean Sea. The closure of this interoceanic seaway at approximately 14 Myr (18–12 Myr) was caused by intense tectonic activity in the Near East17. Since the closure of that seaway, remaining possible dispersal to the Atlantic has been limited to the warm-water corridor around the southern tip of Africa, however curtailed by the cold Benguela Current upwelling from the Late Pliocene43. More

  • in

    Landscape genetics of a sub-alpine toad: climate change predicted to induce upward range shifts via asymmetrical migration corridors

    Alexander MA, Eischeid JK (2001) Climate variability in regions of amphibian declines. Conserv Biol 15:930–942Article 

    Google Scholar 
    Bates D, Mächler M, Bolker BM, Walker SC (2015) Fitting linear mixed-effects models using lme4. J Stat Softw 67:1–48Article 

    Google Scholar 
    Baur B (1986) Patterns of dispersion, density and dispersal in alpine populations of the land snail Arianta arbustorum (L.) (Helicidae). Holarct Ecol 9:117–125
    Google Scholar 
    Beier P, Majka DR, Spencer WD (2008) Forks in the road: choices in procedures for designing wildland linkages. Conserv Biol 22:836–851PubMed 
    Article 

    Google Scholar 
    Berlow EL, Knapp R, Ostoja SM, Williams RJ, McKenny H, Matchett JR et al. (2013) A network extension of species occupancy models in a patchy environment applied to the Yosemite toad (Anaxyrus canorus). PLoS ONE 8:e72200CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Bingaman JW (1968) Pathways: a story of trails and men. End-Kian Publishing Company, Lodi, CABozzuto C, Biebach I, Muff S, Ives AR, Keller LF (2019) Inbreeding reduces long-term growth of Alpine ibex populations. Nat Ecol Evol 3:1359–1364PubMed 
    Article 

    Google Scholar 
    Bradford D, Gordon M (1994) Acidic deposition as an unlikely cause for amphibian population declines in the Sierra Nevada, California. Biol Conserv 69:155–161Article 

    Google Scholar 
    Brattstrom BH (1962) Thermal control of aggregation behavior in tadpoles. Herpetologica 18:38–46
    Google Scholar 
    Breiman L (2001) Random forests. Mach Learn 45:5–32Article 

    Google Scholar 
    Brown C, Hayes MP, Green GA, Macfarlane DC, Lind AJ (2015) Yosemite toad conservation assessment. USDA Forest Service report. Sonora, CABrown C, Olsen AR (2013) Bioregional monitoring design and occupancy estimation for two Sierra Nevadan amphibian taxa. Freshw Sci 32:675–691Article 

    Google Scholar 
    Cal Fire (2022) Fire perimeters. FRAP Mapp. https://frap.fire.ca.gov/mapping/gis-data/Catchen JM, Amores A, Hohenlohe P, Cresko W, Postlethwait JH (2011) Stacks: building and genotyping loci de novo from short-read sequences. G3 Genes Genomes Genet 1:171–182CAS 

    Google Scholar 
    Catchen J, Hohenlohe PA, Bassham S, Amores A, Cresko WA (2013) Stacks: an analysis tool set for population genomics. Mol Ecol 22:3124–3140PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Chetkiewicz C-LB, St. Clair CC, Boyce MS (2006) Corridors for conservation: integrating pattern and process. Annu Rev Ecol Evol Syst 37:317–342Article 

    Google Scholar 
    Corn PS (2003) Amphibian breeding and climate change importance of snow in the mountains. Conserv Biol 17:622–625Article 

    Google Scholar 
    Csárdi G, Nepusz T (2006) The igraph software package for complex network research. Int J Complex Syst 1695:1–9
    Google Scholar 
    Davidson C (2004) Declining downwind: amphibian population declines in California and historical pesticide use. Ecol Appl 14:1892–1902Article 

    Google Scholar 
    Dileo MF, Siu JC, Rhodes MK, Lõpez-Villalobos A, Redwine A, Ksiazek K et al. (2014) The gravity of pollination: integrating at-site features into spatial analysis of contemporary pollen movement. Mol Ecol 23:3973–3982PubMed 
    Article 

    Google Scholar 
    Dodge C, Cheng T, Vredenburg V (2012) Exploring the evidence of a historical chytrid epidemic in the Yosemite toad by PCR analysis of museum specimensDouglas DH (1994) Least-cost path in GIS using an accumulated cost surface and slopelines. Cartographica 31:37–51Article 

    Google Scholar 
    Dozier J, Frew J (2009) Computational provenance in hydrologic science: a snow mapping example. Philos Trans R Soc A Math Phys Eng Sci 367:1021–1033Article 

    Google Scholar 
    Dozier J, Painter TH, Rittger K, Frew JE (2008) Time-space continuity of daily maps of fractional snow cover and albedo from MODIS. Adv Water Resour 31:1515–1526Article 

    Google Scholar 
    Drost C, Fellers G (1994) Decline of frog species in the Yosemite section of the Sierra Nevada. National Park Service report. Davis, CADrost C, Fellers G (1996) Collapse of a regional frog fauna in the Yosemite area of the California Sierra Nevada, USA. Conserv Biol 10:414–425Article 

    Google Scholar 
    Dyer RJ, Nason JD (2004) Population graphs: the graph theoretic shape of genetic structure. Mol Ecol 13:1713–1727PubMed 
    Article 

    Google Scholar 
    Dyer RJ, Nason JD, Garrick RC (2010) Landscape modelling of gene flow: improved power using conditional genetic distance derived from the topology of population networks. Mol Ecol 19:3746–3759PubMed 
    Article 

    Google Scholar 
    Epps CW, Wehausen JD, Bleich VC, Torres SG, Brashares JS (2007) Optimizing dispersal and corridor models using landscape genetics. J Appl Ecol 44:714–724Article 

    Google Scholar 
    van Etten J (2017) R Package gdistance: distances and routes on geographical grids. J Stat Softw 76:1–21
    Google Scholar 
    Evans J, Oakleaf J, Cushman S, Theobald D (2014) An ArcGIS toolbox for surface gradient and geomorphometric modelingExcoffier L, Smouse PE, Quattro JM (1992) Analysis of molecular variance inferred from metric distances among DNA haplotypes: application to human mitochondrial DNA restriction data. Genetics 131:479–491CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Fitzpatrick MC, Keller SR (2015) Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation. Ecol Lett 18:1–16PubMed 
    Article 

    Google Scholar 
    Flint LE, Flint AL, Thorne JH, Boynton R (2013) Fine-scale hydrologic modeling for regional landscape applications: the California Basin Characterization Model development and performance. Ecol Process 2:1–21Article 

    Google Scholar 
    Gaggiotti OE (2003) Genetic threats to population persistence. Ann Zool Fennici 40:155–168
    Google Scholar 
    Garroway CJ, Bowman J, Carr D, Wilson PJ (2008) Applications of graph theory to landscape genetics. Evol Appl 1:620–630PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Gotelli NJ (1991) Metapopulation models: the rescue effect, the propagule rain, and the core-satellite hypothesis. Am Nat 138:768–776Article 

    Google Scholar 
    Grasso RL, Coleman RM, Davidson C (2010) Palatability and antipredator response of Yosemite toads (Anaxyrus canorus) to nonnative brook trout (Salvelinus fontinalis) in the Sierra Nevada Mountains of California. Copeia 2010:457–462Article 

    Google Scholar 
    Graves TA, Beier P, Royle JA (2013) Current approaches using genetic distances produce poor estimates of landscape resistance to interindividual dispersal. Mol Ecol 22:3888–3903PubMed 
    Article 

    Google Scholar 
    Gregorutti B, Michel B, Saint-Pierre P (2017) Correlation and variable importance in random forests. Stat Comput 27:659–678Article 

    Google Scholar 
    Grinnell J, Storer TI (1924) Animal life in the Yosemite: an account of the mammals, birds, reptiles, and amphibians in a cross-section of the Sierra Nevada. University of California Press, Berkeley, CAHall DK, Riggs GA, Salomonson VV, Digirolamo NE, Bayr KJ (2002) MODIS snow-cover products. Remote Sens Environ 83:181–194Article 

    Google Scholar 
    Hansson L (1991) Dispersal and connectivity in metapopulations. Biol J Linn Soc 42:89–103Article 

    Google Scholar 
    Heenkenda MK, Joyce KE, Maier SW, de Bruin S (2015) Quantifying mangrove chlorophyll from high spatial resolution imagery. ISPRS J Photogramm Remote Sens 108:234–244Article 

    Google Scholar 
    Hether TD, Hoffman EA (2012) Machine learning identifies specific habitats associated with genetic connectivity in Hyla squirella. J Evol Biol 25:1039–1052CAS 
    PubMed 
    Article 

    Google Scholar 
    Houborg R, McCabe MF (2018) A hybrid training approach for leaf area index estimation via Cubist and random forests machine-learning. ISPRS J Photogramm Remote Sens 135:173–188Article 

    Google Scholar 
    Huber N, Bateman P, Wahrhaftig C (2003) Geologic map of Yosemite National Park and Vicinity, California: a digital database. Menlo Park, CAIPCC (2014) Climate change 2014: synthesis report. Contribution of working groups I, II and III to the fifth assessment report of the Intergovernmental Panel on Climate Change. Geneva, SwitzerlandJennings M, Hayes M (1994) Amphibian and reptile species of special concern in California. California Department of Fish & Game report. Rancho Cordova, CAKarlstrom EL (1962) The toad genus Bufo in the Sierra Nevada of California: ecological and systematic relationships. Unviersity Calif Publ Zool 62:1–104
    Google Scholar 
    Keeler-Wolf T, Reyes ET, Menke JM, Johnson DN, Karavidas. DL (2012) Yosemite National Park vegetation classification and mapping project report. National Park Service report. Fort Collins, COKittlein MJ, Mora MS, Mapelli FJ, Austrich A, Gaggiotti OE (2022) Deep learning and satellite imagery predict genetic diversity and differentiation. Methods Ecol Evol 13:711–721Article 

    Google Scholar 
    Kleinberg JM (1999) Authoritative sources in a hyperlinked environment. J ACM 46:604–632Article 

    Google Scholar 
    Knapp RA, Fellers GM, Kleeman PM, Miller DAW, Vredenburg VT, Rosenblum EB et al. (2016) Large-scale recovery of an endangered amphibian despite ongoing exposure to multiple stressors. Proc Natl Acad Sci 113:11889–11894CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Knapp RA, Matthews KR (2000) Non-native fish introductions and the decline of the mountain yellow-legged frog from within protected areas. Conserv Biol 14:428–438Article 

    Google Scholar 
    Kuhn M (2008) Building predictive models in R using the caret package. J Stat Softw 28:1–26Article 

    Google Scholar 
    Lee SR, Ostoja SM, Maier PA, Matchett JR, McKenny HC, Brooks ML et al. Distribution and spatio-temporal variation of Yosemite toad populations in Sierra Nevada national parks (in preparation)Liang CT (2010) Habitat modeling and movements of the Yosemite toad (Anaxyrus (=Bufo) canorus) in the Sierra Nevada, California. Ph.D. Dissertation. University of California, DavisLiang CT, Grasso RL, Nelson-Paul JJ, Vincent KE, Lind AJ (2017) Fine-scale habitat characteristics related to occupancy of the Yosemite toad, Anaxyrus canorus. Copeia 105:120–127Article 

    Google Scholar 
    Liang CT, Stohlgren TJ (2011) Habitat suitability of patch types: a case study of the Yosemite toad. Front Earth Sci 5:217–228CAS 
    Article 

    Google Scholar 
    Lindauer AL, Maier PA, Voyles J (2020) Daily fluctuating temperatures decrease growth and reproduction rate of a lethal amphibian fungal pathogen in culture. BMC Ecol 20:1–9Article 
    CAS 

    Google Scholar 
    Lindauer AL, Voyles J (2019) Out of the frying pan, into the fire? Yosemite toad (Anaxyrus canorus) susceptibility to Batrachochytrium dendrobatidis after development under drying conditions. Herpetol Conserv Biol 14:185–198
    Google Scholar 
    Littlefield CE, Krosby M, Michalak JL, Lawler JJ (2019) Connectivity for species on the move: supporting climate-driven range shifts. Front Ecol Environ 17:270–278Article 

    Google Scholar 
    Lowe WH, Allendorf FW (2010) What can genetics tell us about population connectivity? Mol Ecol 19:3038–3051PubMed 
    Article 

    Google Scholar 
    Maher SP, Morelli TL, Hershey M, Flint AL, Flint LE, Moritz C et al. (2017) Erosion of refugia in the Sierra Nevada meadows network with climate change. Ecosphere 8:1–17Article 

    Google Scholar 
    Maier PA (2018) Evolutionary past, present, and future of the Yosemite toad (Anaxyrus canorus): a total evidence approach to delineating conservation units. Ph.D. Dissertation. University of California RiversideMaier PA, Vandergast AG, Ostoja SM, Aguilar A, Bohonak AJ (2019) Pleistocene glacial cycles drove lineage diversification and fusion in the Yosemite toad (Anaxyrus canorus). Evolution 73:2476–2496PubMed 
    Article 

    Google Scholar 
    Maier PA, Vandergast AG, Ostoja SM, Aguilar A, Bohonak AJ (2022) Gene pool boundaries for the Yosemite toad (Anaxyrus canorus) reveal asymmetrical migration within meadow neighborhoods. Front Conserv Sci 3:1–14Article 

    Google Scholar 
    Manel S, Holderegger R (2013) Ten years of landscape genetics. Trends Ecol Evol 28:614–621PubMed 
    Article 

    Google Scholar 
    Manel S, Schwartz MK, Luikart G, Taberlet P (2003) Landscape genetics: combining landscape ecology and population genetics. Trends Ecol Evol 18:189–197Article 

    Google Scholar 
    Martin DL (2008) Decline, movement and habitat utilization of the Yosemite toad (Bufo canorus): an endangered anuran endemic to the Sierra Nevada of California. Ph.D. Dissertation. University of California, Santa BarbaraMasek JG, Vermote EF, Saleous NE, Wolfe R, Hall FG, Huemmrich KF et al. (2006) A landsat surface reflectance dataset, 1990-2000. IEEE Geosci Remote Sens Lett 3:68–72Article 

    Google Scholar 
    Matchett JR, Stark PB, Ostoja SM, Knapp RA, McKenny HC, Brooks ML et al. (2015) Detecting the influence of rare stressors on rare species in Yosemite National Park using a novel stratified permutation test. Sci Rep 5:1–12Article 
    CAS 

    Google Scholar 
    Mathieu J, Barot S, Blouin M, Caro G, Decaëns T, Dubs F et al. (2010) Habitat quality, conspecific density, and habitat pre-use affect the dispersal behaviour of two earthworm species, Aporrectodea icterica and Dendrobaena veneta, in a mesocosm experiment. Soil Biol Biochem 42:203–209CAS 
    Article 

    Google Scholar 
    Matthysen E (2005) Density-dependent dispersal in birds and mammals. Ecography 28:403–416Article 

    Google Scholar 
    McRae B (2006) Isolation by resistance. Evolution 60:1551–1561PubMed 
    Article 

    Google Scholar 
    McRae BH, Beier P (2007) Circuit theory predicts gene flow in plant and animal populations. Proc Natl Acad Sci USA 104:19885–19890CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Meyer H, Pebesma E (2021) Predicting into unknown space? estimating the area of applicability of spatial prediction models. Methods Ecol Evol 12:1620–1633Article 

    Google Scholar 
    Morelli TL, Maher SP, Lim MCW, Kastely C, Eastman LM, Flint LE et al. (2017) Climate change refugia and habitat connectivity promote species persistence. Clim Chang Responses 4:8Article 

    Google Scholar 
    Morton M (1981) Seasonal changes in total body lipid and liver weight in the Yosemite toad. Copeia 1981:234–238Article 

    Google Scholar 
    Morton M, Pereyra M (2010) Habitat use by Yosemite toads: life history traits and implications for conservation. Herpetol Conserv Biol 5:388–394
    Google Scholar 
    Mullally D (1953) Observations on the ecology of the toad Bufo canorus. Copeia 1953:182–183Article 

    Google Scholar 
    Mullally D, Cunningham J (1956) Aspects of the thermal ecology of the Yosemite toad. Herpetologica 12:57–67
    Google Scholar 
    Murphy MA, Dezzani R, Pilliod D, Storfer A (2010a) Landscape genetics of high mountain frog metapopulations. Mol Ecol 19:3634–3649PubMed 
    Article 

    Google Scholar 
    Murphy MA, Evans JS, Storfer A (2010b) Quantifying Bufo boreas connectivity in Yellowstone National Park with landscape genetics. Ecology 91:252–261PubMed 
    Article 

    Google Scholar 
    National Park Service (2022) National park service visitor use statisticsNei M, Chesser RK (1983) Estimation of fixation indices and gene diversities. Ann Hum Genet 47:253–259CAS 
    PubMed 
    Article 

    Google Scholar 
    Nunney L, Campbell KA (1993) Assessing minimum viable population size: demography meets population genetics. Trends Ecol Evol 8:234–239CAS 
    PubMed 
    Article 

    Google Scholar 
    Painter TH, Rittger K, McKenzie C, Slaughter P, Davis RE, Dozier J (2009) Retrieval of subpixel snow covered area, grain size, and albedo from MODIS. Remote Sens Environ 113:868–879Article 

    Google Scholar 
    Parmesan C (2006) Ecological and evolutionary responses to recent climate change. Annu Rev Ecol Evol Syst 37:637–669Article 

    Google Scholar 
    Peterman WE (2018) Surfaces using genetic algorithms ResistanceGA: an R package for the optimization of resistance. Methods Ecol Evol 9:1638–1647Article 

    Google Scholar 
    Peterman WE, Pope NS (2021) The use and misuse of regression models in landscape genetic analyses. Mol Ecol 30:37–47PubMed 
    Article 

    Google Scholar 
    Peterson MA (1997) Host plant phenology and butterfly dispersal: causes and consequences of uphill movement. Ecology 78:167–180Article 

    Google Scholar 
    Pflüger FJ, Balkenhol N (2014) A plea for simultaneously considering matrix quality and local environmental conditions when analysing landscape impacts on effective dispersal. Mol Ecol 23:2146–56PubMed 
    Article 

    Google Scholar 
    Pless E, Saarman NP, Powell JR, Caccone A, Amatulli G (2021) A machine-learning approach to map landscape connectivity in Aedes aegypti with genetic and environmental data. Proc Natl Acad Sci USA 118:1–8Article 
    CAS 

    Google Scholar 
    Pounds J (2001) Climate and amphibian declines. Nature 410:639–640CAS 
    PubMed 
    Article 

    Google Scholar 
    Pounds JA, Bustamante MR, Coloma LA, Consuegra JA, Fogden MPL, Foster PN et al. (2006) Widespread amphibian extinctions from epidemic disease driven by global warming. Nature 439:161–167CAS 
    PubMed 
    Article 

    Google Scholar 
    Quinlan JR (1992) Learning with continuous classes. Aust Jt Conf Artif Intell 92:343–348
    Google Scholar 
    Quinlan JR (1993) Combining instance-based and model-based learning. Mach Learn Proc 1993 93:236–243Article 

    Google Scholar 
    Rabus B, Eineder M, Roth A, Bamler R (2003) The shuttle radar topography mission—a new class of digital elevation models acquired by spaceborne radar. ISPRS J Photogramm Remote Sens 57:241–262Article 

    Google Scholar 
    Ratliff RD (1985) Meadows in the Sierra Nevada of California: state of knowledge. U.S. Forest Service report. Berkeley, CAReich KD, Berg N, Walton DB, Schwartz M, Sun F, Huang X et al. (2018) Climate change in the Sierra Nevada: California’s water future. UCLA Center for Climate Science report. Los Angeles, CAReynolds SJ, Christian KA (2009) Environmental moisture availability and body fluid osmolality in introduced toads. J Herpetol 43:326–331Article 

    Google Scholar 
    Riahi K, Rao S, Krey V, Cho C, Chirkov V, Fischer G et al. (2011) RCP 8.5—a scenario of comparatively high greenhouse gas emissions. Clim Change 109:33–57CAS 
    Article 

    Google Scholar 
    Roche LM, Allen-Diaz B, Eastburn DJ, Tate KW (2012a) Cattle grazing and Yosemite toad (Bufo canorus, Camp) breeding habitat in Sierra Nevada meadows. Rangel Ecol Manag 65:56–65Article 

    Google Scholar 
    Roche LM, Latimer AM, Eastburn DJ, Tate KW (2012b) Cattle grazing and conservation of a meadow-dependent amphibian species in the Sierra Nevada. PLoS ONE 7:e35734CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Sacchei I, Kuussaari M, Kankare M, Vikman P, Fortelius W, Hanski I (1998) Inbreeding and extinction in a butterfly metapopulation. Nature 392:491–494Article 
    CAS 

    Google Scholar 
    Sadinski W (2004) Amphibian declines: causes. U.S. Geological Survey report. La Crosse, WisconsinSadinski W, Gallant AL, Cleaver JE (2020) Climate’s cascading effects on disease, predation, and hatching success in Anaxyrus canorus, the threatened Yosemite toad. Glob Ecol Conserv 23:e01173Article 

    Google Scholar 
    Sawyer SC, Epps CW, Brashares JS (2011) Placing linkages among fragmented habitats: do least-cost models reflect how animals use landscapes? J Appl Ecol 48:668–678Article 

    Google Scholar 
    Schlaepfer DR, Braschler B, Rusterholz HP, Baur B (2018) Genetic effects of anthropogenic habitat fragmentation on remnant animal and plant populations: a meta-analysis. Ecosphere 9 e02488Schmidt G, Jenkerson C, Masek J, Vermote E, Gao F (2013) Landsat ecosystem disturbance adaptive processing system (LEDAPS) algorithm description. U.S. Geological Survey report. Reston, VAShaffer H, Fellers G, Magee A, Voss S (2000) The genetics of amphibian declines: population substructure and molecular differentiation in the Yosemite toad, Bufo canorus (Anura, Bufonidae) based on single-strand conformation polymorphism analysis (SSCP) and mitochondrial DNA sequence data. Mol Ecol 9:245–257CAS 
    PubMed 
    Article 

    Google Scholar 
    Sherman CK (1980) A comparison of the natural history and mating system of two anurans: Yosemite toads (Bufo canorus) and Black toads (Bufo exsul). Ph.D. Dissertation. University of MichiganSherman CK, Morton ML (1984) The toad that stays on its toes. Nat Hist 93:72–78
    Google Scholar 
    Sherman CK, Morton ML (1993) Population declines of Yosemite toads in the eastern Sierra Nevada of California. J Herpetol 27:186–198Article 

    Google Scholar 
    Shirk AJ, Wallin DO, Cushman SA, Rice CG, Warheit KI (2010) Inferring landscape effects on gene flow: a new model selection framework. Mol Ecol 19:3603–3619CAS 
    PubMed 
    Article 

    Google Scholar 
    Smith JB, Tirpak DA (1988) The potential effects of global climate change on the United States: draft: report to Congress. U.S. Environmental Protection Agency, Office of Policy, Planning and Evaluation, Office of Research and DevelSork VL, Davis FW, Westfall R, Flint A, Ikegami M, Wang H et al. (2010) Gene movement and genetic association with regional climate gradients in California valley oak (Quercus lobata Née) in the face of climate change. Mol Ecol 19:3806–3823PubMed 
    Article 

    Google Scholar 
    Spear SF, Balkenhol N, Fortin M-J, McRae BH, Scribner K (2010) Use of resistance surfaces for landscape genetic studies: considerations for parameterization and analysis. Mol Ecol 19:3576–3591PubMed 
    Article 

    Google Scholar 
    Spear SF, Peterson CR, Matocq MD, Storfer A (2005) Landscape genetics of the blotched tiger salamander (Ambystoma tigrinum melanostictum). Mol Ecol 14:2553–2564CAS 
    PubMed 
    Article 

    Google Scholar 
    Spielman D, Brook BW, Briscoe DA, Frankham R (2004) Does inbreeding and loss of genetic diversity decrease disease resistance? Conserv Genet 5:439–448Article 

    Google Scholar 
    Stewart IT (2009) Changes in snowpack and snowmelt runoff for key mountain regions. Hydrol Process 23:78–94Article 

    Google Scholar 
    Storfer A, Murphy M, Evans J, Goldberg C, Robinson S, Spear S et al. (2007) Putting the ‘landscape’ in landscape genetics. Heredity 98:128–142CAS 
    PubMed 
    Article 

    Google Scholar 
    van Strien M (2013) Advances in landscape genetic methods and theory: lessons leart from insects in agricultural landscapes. Ph.D. Dissertation. ETH Zürichvan Strien MJ, Keller D, Holderegger R (2012) A new analytical approach to landscape genetic modelling: least-cost transect analysis and linear mixed models. Mol Ecol 21:4010–23Article 

    Google Scholar 
    Strobl C, Boulesteix AL, Zeileis A, Hothorn T (2007) Bias in random forest variable importance measures: illustrations, sources and a solution. BMC Bioinform 8 25Sundqvist L, Keenan K, Zackrisson M, Prodöhl P, Kleinhans D (2016) Directional genetic differentiation and relative migration. Ecol Evol 6:3461–3475PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Sylvester EVA, Beiko RG, Bentzen P, Paterson I, Horne JB, Watson B et al. (2018) Environmental extremes drive population structure at the northern range limit of Atlantic salmon in North America. Mol Ecol 27:4026–4040PubMed 
    Article 

    Google Scholar 
    Toloşi L, Lengauer T (2011) Classification with correlated features: Unreliability of feature ranking and solutions. Bioinformatics 27:1986–1994PubMed 
    Article 
    CAS 

    Google Scholar 
    Travis JMJ, Murrell DJ, Dytham C (1999) The evolution of density–dependent dispersal. Proc R Soc Lond Ser B Biol Sci 266:1837–1842Article 

    Google Scholar 
    Trexler KA (1975) The Tioga road: a history, 1883-1961. Yosemite Natural History Association, El Portal, CAU.S. Fish & Wildlife Service (2014) Endangered and threatened wildlife and plants; endangered status for the Sierra Nevada yellow-legged frog and the northern distinct population segment of the mountain yellow-legged frog, and threatened status for the Yosemite toad: final rule. Fed Regist 79:1–56. https://www.federalregister.gov/documents/2014/04/29/2014-09488/endangered-and-threatened-wildlife-andplants-endangered-species-status-for-sierra-nevadaVandergast AG, Bohonak AJ, Hathaway SA, Boys J, Fisher RN (2008) Are hotspots of evolutionary potential adequately protected in southern California? Biol Conserv 141:1648–1664Article 

    Google Scholar 
    Viers JH, Purdy SE, Peek RA, Fryjoff-Hung A, Santos NR, Katz JV et al (2013) Montane meadows in the Sierra Nevada: changing hydroclimatic conditions and concepts for vulnerability assessment. Centre for Watershed Sciences report. Davis, CAVredenburg VT, Knapp RA, Tunstall TS, Briggs CJ (2010) Dynamics of an emerging disease drive large-scale amphibian population extinctions. Proc Natl Acad Sci USA 107:9689–9694CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Wang IJ (2012) Environmental and topographic variables shape genetic structure and effective population sizes in the endangered Yosemite toad. Divers Distrib 18:1033–1041Article 

    Google Scholar 
    Weir BS (1996) Genetic data analysis II: methods for discrete population genetic data. Sinauer Associates, Inc., Sunderland, MAWhitlock MC, Ingvarsson PK, Hatfield T (2000) Local drift load and the heterosis of interconnected populations. Heredity 84:452–457PubMed 
    Article 

    Google Scholar 
    Wood SH (1975) Holocene stratigraphy and chronology of mountain meadows, Sierra Nevada, California. Ph.D. Dissertation. California Institute of TechnologyWright S (1931) Evolution in Mendelian populations. Genetics 16:97–159CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Zeller KA, McGarigal K, Whiteley AR (2012) Estimating landscape resistance to movement: a review. Landsc Ecol 27:777–797Article 

    Google Scholar  More

  • in

    Variations in limited resources allocation towards friends and strangers in children and adolescents from seven economically and culturally diverse societies

    Tomasello, M. Why we cooperate (MIT Press, 2009).Book 

    Google Scholar 
    Turchin, P. The puzzle of human ultrasociality: How did large-scale complex societies evolve? In Cultural Evolution, Strüngmann Forum Report Vol. 12 (eds Richerson, P. J. & Christiansen, M. H.) 61–73 (MIT Press, 2013).
    Google Scholar 
    Kramer, K. L. How there got to be so many of us: The evolutionary story of population growth and a life history of cooperation. J. Anthropol. Res. 75, 472–497 (2019).Article 

    Google Scholar 
    Wrangham, R. W. The Goodness Paradox: The Strange Relationship Between Virtue and Violence in Human Evolution (Alfred A. Knopf, 2019).
    Google Scholar 
    Fruth, B. & Hohmann, G. Food sharing across borders. Hum. Nat. 29, 91–103 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Garfield, Z. H., Hubbard, R. L. & Hagen, E. H. Evolutionary models of leadership. Hum. Nat. 30, 23–58 (2019).PubMed 
    Article 

    Google Scholar 
    Rodrigues, J. & Hewig, J. Let´ s call it altruism! A psychological perspective and hierarchical framework of altruism and prosocial behavior. Preprint at https://psyarxiv.com/pj7eu/ (2021).Davies, A. Food sharing. In Routledge Handbook of Sustainable and Regenerative Food Systems (eds Duncan, J. et al.) 204–217 (Routledge, 2020).Chapter 

    Google Scholar 
    Ember, C. R., Skoggard, I., Ringen, E. J. & Farrer, M. Our better nature: Does resource stress predict beyond-household sharing?. Evol. Hum. Behav. 39, 380–391 (2018).Article 

    Google Scholar 
    Crittenden, A. N. & Schnorr, S. L. Current views on hunter-gatherer nutrition and the evolution of the human diet. Am. J. Phys. Anthropol. 162, 84–109 (2017).PubMed 
    Article 

    Google Scholar 
    Ferguson, M. et al. Traditional food availability and consumption in remote Aboriginal communities in the Northern Territory, Australia. Aust. NZ. J. Publ. Heal. 41, 294–298 (2017).Article 

    Google Scholar 
    Poulain, J. P. The Sociology of Food: Eating and the Place of Food in Society (Bloomsbury Publishing, 2017).
    Google Scholar 
    Ready, E. & Power, E. A. Why wage earners hunt: food sharing, social structure, and influence in an Arctic mixed economy. Curr. Anthropol. 59, 74–97 (2018).Article 

    Google Scholar 
    Gould, R. A. To have and have not: The ecology of sharing among hunter-gatherers. In Resource Managers: North American and Australian Hunter-Gatherers (eds Williams, N. M. & Hunn, E. S.) 69–91 (Routledge, 2019).Chapter 

    Google Scholar 
    Allen-Arave, W., Gurven, M. & Hill, K. Reciprocal altruism, rather than kin selection, maintains nepotistic food transfers on an Ache reservation. Evol. Hum. Behav. 29, 305–318 (2008).Article 

    Google Scholar 
    Crittenden, A. N. & Zes, D. A. Food sharing among hadza hunter-gatherer children. PLoS One 10, e0131996. https://doi.org/10.1371/journal.pone.0131996 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Rochat, P. et al. Fairness in distributive justice by 3-and 5-year-olds across seven cultures. J. Cross. Cult. Psychol. 40, 416–442 (2009).Article 

    Google Scholar 
    Cashdan, E. A. Coping with risk: Reciprocity among the Basarwa of Northern Botswana. Man 20, 454 (1985).Article 

    Google Scholar 
    Fehr, E., Glätzle-Rützler, D. & Sutter, M. The development of egalitarianism, altruism, spite and parochialism in childhood and adolescence. Eur. Econ. Rev. 64, 369–383 (2013).Article 

    Google Scholar 
    Almås, I., Cappelen, A. W., Sørensen, E. Ø. & Tungodden, B. Fairness and the development of inequality acceptance. Science 328, 1176–1178 (2010).ADS 
    PubMed 
    Article 
    CAS 

    Google Scholar 
    Malti, T. et al. “Who is worthy of my generosity?” Recipient characteristics and the development of children’s sharing. Int. J. Behav. Dev. 40, 31–40 (2016).Article 

    Google Scholar 
    Olson, K. R. & Spelke, E. S. Foundations of cooperation in young children. Cognition 108, 222–231 (2008).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Renno, M. P. & Shutts, K. Children’s social category-based giving and its correlates: expectations and preferences. Dev. Psychol. 51, 533 (2015).PubMed 
    Article 

    Google Scholar 
    Samek, A. et al. The development of social comparisons and sharing behavior across 12 countries. J. Exp. Child Psychol. 192, 104778. https://doi.org/10.1016/j.jecp.2019.104778 (2020).Article 
    PubMed 

    Google Scholar 
    Henrich, J., Heine, S. J. & Norenzayan, A. Most people are not WEIRD. Nature 466, 29–29 (2010).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    House, B. R. et al. Ontogeny of prosocial behavior across diverse societies. P. Natl. Acad. Sci. USA 110, 14586–14591 (2013).ADS 
    CAS 
    Article 

    Google Scholar 
    Schäfer, M., Haun, D. B. & Tomasello, M. Fair is not fair everywhere. Psychol. Sci. 26, 1252–1260 (2015).PubMed 
    Article 

    Google Scholar 
    Callaghan, T. & Corbit, J. Early prosocial development across cultures. Curr. Opin. Psychol. 20, 102–106 (2018).PubMed 
    Article 

    Google Scholar 
    Rodriguez, L. M., Martí-Vilar, M., Esparza Reig, J. & Mesurado, B. Empathy as a predictor of prosocial behavior and the perceived seriousness of delinquent acts: A cross-cultural comparison of Argentina and Spain. Ethics Behav. 31, 91–101 (2021).Article 

    Google Scholar 
    Fehr, E., Bernhard, H. & Rockenbach, B. Egalitarianism in young children. Nature 454, 1079–1083 (2008).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Henrich, J. & Muthukrishna, M. The origins and psychology of human cooperation. Ann. Rev. Psychol. 72, 207–240 (2021).Article 

    Google Scholar 
    Thomas, M. G. et al. Kinship underlies costly cooperation in Mosuo villages. Roy. Soc. Open Sci. 5(2), 171535. https://doi.org/10.1098/rsos.171535 (2018).ADS 
    Article 

    Google Scholar 
    O’Gorman, R., Sheldon, K. M. & Wilson, D. S. For the good of the group? Exploring group-level evolutionary adaptations using multilevel selection theory. Group. Dyn. Theor. Res. 12, 17 (2008).Article 

    Google Scholar 
    Boyd, R. & Richerson, P. J. Culture and the evolution of human cooperation. Philos. Trans. R. Soc. B 364, 3281–3288 (2009).Article 

    Google Scholar 
    Handley, C. & Mathew, S. Human large-scale cooperation as a product of competition between cultural groups. Nat. Commun. 11, 1–9 (2020).Article 
    CAS 

    Google Scholar 
    Gintis, H., van Schaik, C. & Boehm, C. Zoon politikon: The evolutionary origins of human socio-political systems. Behav. Process. 161, 17–30 (2019).Article 

    Google Scholar 
    Markovits, H., Benenson, J. F. & Kramer, D. L. Children and adolescents’ internal models of food-sharing behavior include complex evaluations of contextual factors. Child Dev. 74, 1697–1708 (2003).PubMed 
    Article 

    Google Scholar 
    Kaplan, H., Gurven, M., Hill, K. & Hurtado, A. M. The natural history of human food sharing and cooperation: a review and a new multi-individual approach to the negotiation of norms. Moral Sentim. Mater. Interests Found. Coop. Econ. Life 6, 75–113 (2005).
    Google Scholar 
    Crittenden, A. N. To share or not to share? Social processes of learning to share food among Hadza hunter-gatherer children. In Social Learning and Innovation in Contemporary Hunter-Gatherers (eds Hewlett, B. S. & Terashima, H.) 61–70 (Springer, 2016).Chapter 

    Google Scholar 
    Barragan, R. C., Brooks, R. & Meltzoff, A. N. Altruistic food sharing behavior by human infants after a hunger manipulation. Sci. Rep. 10, 1–9 (2020).Article 
    CAS 

    Google Scholar 
    Singh, M., Wrangham, R. & Glowacki, L. Self-interest and the design of rules. Hum. Nat. 28, 457–480 (2017).PubMed 
    Article 

    Google Scholar 
    Richerson, P. J., Gavrilets, S. & de Waal, F. B. Modern theories of human evolution foreshadowed by Darwin’s Descent of Man. Science 372, eaba3776. https://doi.org/10.1126/science.aba3776 (2021).CAS 
    Article 
    PubMed 

    Google Scholar 
    Jordan, F. M. et al. Cultural evolution of the structure of human groups. In Cultural Evolution: Society, Technology, Language, and Religion (eds Richerson, P. J. & Christiansen, M. H.) 87–116 (MIT Press, 2013).Chapter 

    Google Scholar 
    Henrich, J. & Broesch, J. On the nature of cultural transmission networks: Evidence from Fijian villages for adaptive learning biases. Philos. T. Roy. Soc. B 366, 1139–1148 (2011).Article 

    Google Scholar 
    Hawley, P. H. The ontogenesis of social dominance: A strategy-based evolutionary perspective. Dev. Rev. 19, 97–132 (1999).Article 

    Google Scholar 
    Hawley, P. H., Little, T. D. & Card, N. A. The allure of a mean friend: Relationship quality and processes of aggressive adolescents with prosocial skills. Int. J. Behav. Dev. 31, 170–180 (2007).Article 

    Google Scholar 
    Marlowe, F. The Hadza: Hunter-Gatherers of Tanzania Vol. 3 (University of California Press, 2010).
    Google Scholar 
    Jones, N. B. Demography and Evolutionary Ecology of Hadza Hunter-Gatherers Vol. 71 (Cambridge University Press, 2016).
    Google Scholar 
    Butovskaya, M. L. Aggression and conflict resolution among the nomadic Hadza of Tanzania as compared with their pastoralist neighbors. In War, Peace, and Human Nature: the Convergence of Evolutionary and Cultural Views (ed. Fry, D. P.) 278–296 (Oxford University Press, 2013).Chapter 

    Google Scholar 
    Apicella, C. L., Marlowe, F. W., Fowler, J. H. & Christakis, N. A. Social networks and cooperation in hunter-gatherers. Nature 481, 497–501 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Sands, B., Maddieson, J. & Ladefoged, P. The phonetic structures of Hadza. Stud. Afr. Linguist. 25, 171–204 (1996).Article 

    Google Scholar 
    Butovskaya, M. et al. Approach to resource management and physical strength predict differences in helping: evidence from two small-scale societies. Front. Psychol. 11, 373 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Mous, M. A Grammar of Iraqw (University of Leiden, 1992).
    Google Scholar 
    Rekdal, O. B. The invention by tradition: Creativity and change among the Iraqw of northern Tanzania. PhD thesis, Department of Social Anthropology, University of Bergen, Bergen (1999).Snyder, K. A. The Iraqw of Tanzania: Negotiating Rural Development (Routledge, 2018).Book 

    Google Scholar 
    Butovskaya, M., Burkova, V. & Mabulla, A. Sex differences in 2D: 4D ratio, aggression and conflict resolution in African children and adolescents: a cross-cultural study. J. Aggress. Confl. Peace Res. 2, 17–31 (2010).Article 

    Google Scholar 
    Butovskaya, M. L., Burkova, V. N. & Karelin, D. V. The Wameru of Tanzania: Historical origin and their role in the process of National Integration. Soc. Evol. Hist. 15, 141–163 (2016).
    Google Scholar 
    Lerner, G. The Creation of Patriarchy Vol. 1 (Oxford University Press, 1986).
    Google Scholar 
    Maruo, S. Differentiation of subsistence farming patterns among the Haya banana growers in northwestern Tanzania. Afr. Study Monog. 23, 147–175 (2002).
    Google Scholar 
    Ishengoma, J. M. African oral traditions: Riddles among the Haya of Northwestern Tanzania. Int. Rev. Educ. 51, 139–153 (2005).Article 

    Google Scholar 
    Stevens, L. Religious change in a Haya village, Tanzania. J. Relig. Afr. 21, 2–25 (1991).Article 

    Google Scholar 
    Kradin, N. N. The transformation of pastoralism in Buryatia: the Aginsky Steppe example. Inner Asia 6, 95–109 (2004).Article 

    Google Scholar 
    Rostovtseva, V. V., Weissing, F. J., Mezentseva, A. A. & Butovskaya, M. L. Sex differences in cooperativeness—an experiment with Buryats in Southern Siberia. PLoS One 15, e0239129. https://doi.org/10.1371/journal.pone.0239129 (2020).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Krader, L. Buryat religion and society. Southwest. J. Anthropol. 10, 322–351 (1954).Article 

    Google Scholar 
    Hooper, P. L. Quantitative description of the pastoral economy of Western Tuvan nomads. New Res. Tuva 4, 19–27 (2020).
    Google Scholar 
    Lindquist, G. Loyalty and command: Shamans, lamas, and spirits in a Siberian ritual. Soc. Anal. 52, 111–126 (2008).
    Google Scholar 
    Walters, P. Religion in Tuva: Restoration or innovation?. Relig. State Soc. 29, 23–38 (2001).Article 

    Google Scholar 
    Dyrtyk-ool, A. O., & Orgezhik, C. M. Kollektsiya vostochnih tuvintsev-olenevodov v natsionalnom muzee Respubliki Tyva: istoriya komplektovaniya i obshaya harakteristika [Collection of the Eastern Tuvans – deer herders in the National Museum of the Republic of Tuva: Background and general description of the acquisition]. Scientific notes of the museum-reserve “Tomskaya Pisanitsa”. 3, 4–9 (2016).Alexandrov, V. A., Vlasova, I. V. & Polischuk, N. S. The Russians (Nauka, 1997).
    Google Scholar 
    Fehr, E. & Schmidt, K. M. A theory of fairness, competition, and cooperation. Q. J. Econ. 114, 817–868 (1999).MATH 
    Article 

    Google Scholar 
    Charness, G. & Rabin, M. Understanding social preferences with simple tests. J. Q. Econ. 117, 817–869 (2002).MATH 
    Article 

    Google Scholar  More

  • in

    The conditional defector strategies can violate the most crucial supporting mechanisms of cooperation

    We used two agent-based simulation models to investigate the concepts of “cooperate for the spread” and “pay for the escape,” both were net logo models created by Dr. Susan Hanisch.Afterward, we modified the first model to represent the concept of sharing the dispersal costs. We used the second model without modifications. Instead, we assigned definite values of some parameters that highlight the pay for the escape strategy.First modelThe original model was entitled “Evolution and patchy resource”18. She first developed it for educational purposes. It illustrates the concepts of cooperator-cheater competition, natural selection, spatial structure mechanisms, multilevel selection, and founder effects.Changeable variables

    Distance-resource-areas: the distance between the centers of the resource areas.

    Size-resource areas: the size of resource areas as a radius in the number of patches.

    Living costs: the costs that each agent has to deduct from energy per iteration for basic survival.

    Mutation rate: The probability that offspring agents have different traits than their parents.

    Evolution: the ability of agents to produce offspring.

    Constant variables

    The number of patches is 112 × 112 patches.

    Carrying capacity per patch: Resource = 10, Agents = 1

    The growth rate of the resource = 0.2

    The resources on a patch regrow by a logistic growth function up to the carrying capacity: New resource level = current resource level + (Growth-Rate × current resource level) × (1 – (Current resource level/carrying capacity)).

    The cost for producing offspring is ten subtracted units of energy.

    The initial level of energy of agents is set at living costs.

    Role of randomness

    Agents are distributed randomly in resource areas at the beginning of a simulation.

    Sustainable behavior is distributed randomly with a probability of percent sustainables among the initial agent population.

    The order in which agents move and harvest within one iteration is random.

    Agents move to a randomly selected patch if several patches fulfill the objectives.

    The order in which agents produce offspring within one iteration is random.

    Agents reproduce offspring with a probability of (0.0005 × Energy).

    Agents place offspring on a randomly selected unoccupied neighboring patch.

    Offspring mutate with a potential mutation rate.

    Model processesIn each iteration, each agent moves around in random order. There are three likelihoods:

    If there are no unoccupied patches in a two-patch radius, they stay on the current patch.

    If there are unoccupied patches with resources amounting to more than living costs, the agents move to them.

    If the resource amount is less than the living costs, the agents move randomly to other unoccupied patches.

    The agents harvest the resources from separated patches to gain energy for metabolism and proliferation. If the energy level of any agent falls to zero, it dies. The cooperator type harvests half of the resource, while the greedy type consumes 99%.The living costs are deducted from the energy amount of the agent constantly everywhere all the time. This process occurs whether an agent moves within the patch, between the patches, or even not. Therefore, the model does not consider dispersal cost explicitly.If there is an unoccupied neighbor patch, the agent can reproduce with a probability of 0.0005 of his energy, place the offspring on the unoccupied neighbor patch, and then transfer ten units of the energy to his offspring.Resources regrow only on resource patches. When the resource amount is more than or equal to 0.1, then it regrows. When the resource is less than 0.1, its value is set to 0.1.Output diagrams and monitors

    The average energy of agents: average energy levels of sustainable and greedy agents, resulting from resource harvest minus living costs and reproduction.

    Trait frequencies: the relative frequencies of sustainable and greedy agents in the total population, resulting from mutations, different reproduction rates, and death.

    Agent population: the absolute number of the total population size resulting from reproduction and death.

    ModificationsIn the first modification, we added a different type of cost that agents only incur when they disperse from one patch to another (in-between the patches). It is the slider entitled “dispersal costs”.In the second modification, we added another sharing dispersal costs tool to reduce them by dividing their value by the number of included agents (flock-mates) in the identified range from the same type. It is the slider entitled “group-dispersal-range.” which is the flock mate’s areas as a radius in the number of patches. Therefore, changing the value of the group dispersal range will change the area around every agent. Accordingly, the number of its flock mates who share the dispersal costs also adjusts.The group dispersal range is not confined to greedy agents but applies to all agents. Therefore, it represents the case of the wild-type cooperators who can also cooperate for the spread. The group dispersal range also does not only target the agents in between patches. However, it counts the agents inside and outside the patches. For example, once an agent starts its dispersion with a determined range containing ten agents, four from another type, three non-dispersal agents from the same type that existed inside a patch, and three dispersal agents from the same type outside the patches. The dispersal costs for this agent will be divided by 6.Our assumption that non-dispersal agents at the pre-departure stage share dispersion costs with dispersal agents; seems justified because they reap mutual benefits by reducing kin competition inside patches if they promote the migrators. However, can agents remotely pay the dispersion costs? Yes. For instance, some bacterial species can trigger the migration of other species if located in their vicinity, even if the two bacterial colonies are separated by a barrier19,20 or if they are non-motile21. On the other hand, dispersion is an extended process with many factors, including escape from predators, suppression of host defense mechanisms, and production of biosurfactants to reduce surface tension to facilitate motility. Therefore, the agent’s contribution (inside/outside the patches) to support such factors is considered a shared dispersal cost.Finally, cheaters can arise within cooperator patches by mutation or immigration. Therefore, to investigate the efficacy of migration, the mutation rate value should be 0 to cancel its effect in the meta-population dynamics.Second modelThe model is entitled “Evolution, resources, monitoring, and punishment.”22 is a simulation of a population with four types of agents competing for the same resource. It demonstrates many concepts, such as kin selection, cooperation, selfishness, public good, monitoring, punishment, sharing the costs, positive/negative frequency-dependent selection, and multilevel selection. The four agent colors and types: (1) Red: greedy, non-punishing. (2) Orange: greedy, punishing. (3) Turquoise: sustainable, non-punishing. (4) Green: sustainable, punishing.Punishing agents can perceive other agents in their environment to some degree (perception accuracy) and react to their behavior. There are three kinds of punishment: Punishers can kill agents with greedy harvesting behavior, stop them from harvesting in the next iteration, or have them pay a penalty fee to their neighbors.Agents have a cost (energy) to pay for, both detection and punishment, so this behavior is altruistic. Punisher agents of one type share punishment costs equally.Changeable variables

    Death rate: the probability that agents die independent of their energy level.

    Carrying capacity: the maximum amount of resource units on a patch from 1 to 100.

    Growth rate: the rate at which resources on patches regrow. The maximum sustainable yield is calculated based on the carrying capacity and growth rate.

    Harvest-sustainable: the number of resource units harvested by sustainable agents.

    Harvest-greedy: the number of resource units harvested by sustainable agents.

    Perception accuracy: the probability with which punishing agents notice greedy agents.

    Costs-perception: the costs in units of energy, punishing agents have to pay for perceiving other agents.

    Costs-punishment: the costs as units of energy that punishing agents have to pay in each iteration to punish other agents. All punishing agents of an agent divide the costs of punishment.

    Punishment: the kinds of punishing behavior that punishing agents perform.

    Fine: if the kind of punishment is “pay fine”, the fine in energy units that punished agents have to pay (shared between all their neighbors).

    Living costs and mutation rate: see the first model.

    Constant variables

    The number of patches: There are 60 × 60 patches in the world.

    The initial energy level of agents is set at living costs + 1.

    The initial number of resource units on a patch is set to the carrying capacity.

    The resources on a patch regrow: see the first model.

    Role of randomness* In addition to items in the first model.

    Agents take on their traits (harvest preference and ability to notice and punish) randomly based on the probability of percent-sustainable and percent-punishers.

    The order in which punishing agents notice greedy agents within one iteration is random.

    Greedy agents are noticed by punishing agents with a probability of perception accuracy.

    The order in which detected greedy agents are punished within one iteration is random.

    Agents produce offspring with a probability of (0.001 × Energy).

    Agents die with a probability of (death-rate).

    Model processesIn each iteration, each agent attempts to harvest resources from the patches it is on and the eight neighboring patches until the harvest preference level is reached, except for the punished agent with the sanction (suspend harvest once), its harvest amount = 0 in the current iteration. If the amount of resources available is lower than the amount that the unpunished agent attempts to harvest. Then, the agent moves to a neighboring unoccupied patch with the most resources after losing one energy unit as a move cost.Punishers pay the costs of perceiving the greedy agents. The greedy neighbors have been noticed with the probability of perception accuracy. The agent lost an amount of energy as living costs. The agent dies with the likelihood of death rate or if the energy level falls to zero.If there is an unoccupied neighbor patch, the agent can reproduce with a probability of 0.001 of its energy, place the offspring on the unoccupied neighbor patch, and then transfer half of its energy to its offspring that mutate according to the probability of the mutation rate.Resources regrow on all patches. When the resource amount is more than or equal to 0.1, then it regrows. When the resource is less than 0.1, its value is set to 0.1.Output diagrams and monitors

    Populations (% of carrying capacity): the state of the resource and the agent population in the world as a percentage of total carrying capacity resulting from resource harvesting behavior and resource regrowth, agent reproduction, and death.

    Average harvest per iteration: the average harvested amounts of agents per iteration by trait, resulting from harvested resource units, minus costs for monitoring and punishing (for punishing agents), minus fines (for punished agents in case of punishment “Pay fine”)

    The average energy of agents and trait frequencies: see the first model.

    How does the model represent a conditional defector strategy?The model aims to highlight the role of kin selection and punishment mechanisms in supporting cooperation evolution against cheats. We did not need to modify the model but just thought about what the conditional defector should do to upside down the game. The answer was to pay for the escape.For instance, if the standard Harvest-greedy of a cheater (greedy, non-punishing) was 13 and the Perception-accuracy of its actual punishers was 75%. Now suppose this cheater faces troubles, and it cannot dominate. However, if it gives up some of its profit to become 12, to escape punishment, and to reduce the perception accuracy to 60%, it could dominate and take over the population.The conditional cheater can pay something and reduce its profit to escape punishment by reducing perception accuracy if there is a positive correlation between these two variables. Therefore, this model is appropriate if it can support/deny such a correlation. More

  • in

    Marine predators aggregate in anticyclonic ocean eddies

    RESEARCH BRIEFINGS
    07 September 2022

    A diverse range of marine predators — including tunas, billfishes and sharks — in the North Pacific Ocean cluster together in clockwise-rotating eddies, seemingly to hunt deep-ocean prey, which are unusually abundant there. This suggests that there is a relationship between the foraging opportunities of predators and the energetics of this marine biome. More

  • in

    Register animal-tracking tags to boost conservation

    In early 2020, my colleagues and I realized that animal-tracking data collected before, during and after the pandemic lockdowns could provide invaluable insights into human–wildlife interactions and conservation benefits on a global scale. We launched a research consortium — the COVID-19 Bio-Logging Initiative — to investigate how animals behaved while much of the world’s human population sheltered at home.But we had no way to establish how many, and which, animals were wearing tags. Miniature tracking devices are routinely attached to a vast range of species — from songbirds to whales — to collect detailed data on their movements, behaviour and physiology. Yet, of the thousands of ‘bio-loggers’ deployed every year, many generate data sets that remain effectively undiscoverable — they are saved on personal hard drives or institutional servers, inaccessible to the wider community. This problem can be solved by setting up a global registry for all tags on wild animals.Although individual tracking studies make important contributions to our understanding of the ecological needs of animal species, pooling data (across taxa, longer time periods or multiple locations) can reveal general patterns, aiding the design of particularly effective conservation strategies. For example, integrating the tracks of 4,060 animals across 17 marine species (including albatrosses, penguins, seals and whales) has helped to identify conservation priority areas in the Southern Ocean (M. A. Hindell et al. Nature 580, 87–92; 2020).In an ideal world, all animal-tracking data would be archived — with either open or restricted access — in public repositories, such as Movebank. Excellent progress has been made towards this goal, but universal uptake is hindered by time constraints, governmental or institutional restrictions and concerns over inappropriate data use.To encourage as many data owners as possible to join the COVID-19 Bio-Logging Initiative, we launched a recruitment campaign through Movebank, social media, mailing lists, newsletters, personal contacts and a published call to action (C. Rutz et al. Nature Ecol. Evol. 4, 1156–1159; 2020). Our consortium has grown to more than 600 international collaborators, accumulating a staggering one billion location records for some 200 animal species. Despite this impressive community response, we know that this is only the tip of the iceberg.The global tag registry that I suggest would contain metadata for tags (including tag type and settings, information on the animal, and date and location of deployment), as well as researchers’ contact details — but not the actual tracking data. This decoupling of information would unlock the field’s full conservation potential in the short term and would build the trust required to allow raw data to be archived routinely in public repositories in the longer term. Over time, the tag registry is likely to evolve naturally into a ‘meta-repository’, linking to raw data sets hosted across a multitude of repositories.The registry would enable researchers to check data availability at the push of a button — for example, for a particular taxonomic group, such as terrestrial carnivores, or a specific region, such as the Pacific Ocean — and to get in touch with the relevant data owners. Registry management must comply with international best practices, so robust processes would need to be set up to vet queries, pass on collaboration proposals to data owners and minimize overlap between studies.For the registry to fulfil its intended purpose, it must be used by the entire animal-tracking community. How can this be achieved? I see an opportunity to integrate tag registration into existing ethical-review processes. Governmental authorities, research institutions, funders, publishers and fieldworkers agree that permits must be in place before animals can be tagged. Building on this international consensus, ethical review boards could make tag registration a condition of study approval.To complement this bottom-up approach, well established initiatives — such as those associated with the United Nations Environment Programme or the International Union for Conservation of Nature — could help to build an international policy mandate and provide independent oversight. The International Bio-Logging Society, which has been working to unite animal-tracking efforts on land and at sea, could provide crucial support.This vision is no doubt ambitious, but it is achievable. Every civil aircraft on the planet must be registered — so I am convinced that, with effective coordination, we can accomplish the same for tagged animals. Furthermore, the basic principle of hosting metadata, but not raw data, is being used productively by other databases, such as AviSample — a registry for biological samples collected from wild birds.Many researchers, myself included, feel a moral obligation to the animals carrying our tags. A global tag registry would help to realize the full conservation potential of all tracking data, minimize duplication of tagging efforts and facilitate sharing of welfare-related expertise. The conservation cost of missing data in large-scale collaborative projects cannot be easily measured, but is probably substantial. We simply cannot afford this, and must ensure that all animal-tracking data are immediately discoverable.

    Competing Interests
    This article is a contribution of the COVID-19 Bio-Logging Initiative, which is funded in part by the Gordon and Betty Moore Foundation (GBMF9881) and the National Geographic Society (NGS-82515R-20) (both grants to C.R.), and endorsed by the United Nations Decade of Ocean Science for Sustainable Development. More

  • in

    The micronutrient content in underutilized crops: the Lupinus mutabilis sweet case

    Taco-Taype, N. & Zúñiga-Dávila, D. Efecto de la inoculación de plantas de Tarwi con cepas de Bradyrhizobium spp. aisladas de un lupino silvestre, en condiciones de invernadero. Revista peruana de biología. 27, 35–42 (2022).Article 

    Google Scholar 
    Atchison, G. W. et al. Lost crops of the Incas: Origins of domestication of the Andean pulse crop tarwi Lupinus mutabilis. Am. J. Bot. 103, 1592–1606 (2016).CAS 
    Article 

    Google Scholar 
    Peru Origins. Tarwi (Lupinus Mutabilis). https://peruorigins.com/tarwi/ (2022).Guilengue, N., Alves, S., Talhinhas, P. & Neves-Martins, J. Genetic and genomic diversity in a tarwi (Lupinus mutabilis Sweet) germplasm collection and adaptability to Mediterranean climate conditions. Agronomy 10, 21 (2020).Article 

    Google Scholar 
    Repo-Carrasco-Valencia, R., Basilio-Atencio, J., Luna-Mercado, G. I., Pilco-Quesada, S. & VidaurreRuiz, J. Andean ancient grains: Nutritional value and novel uses. Biol. Life Sci. Forum. https://doi.org/10.3390/blsf2021008015 (2022).Article 

    Google Scholar 
    Gulisano, A., Alves, S., Martins, J. N. & Trindade, L. M. Genetics and breeding of Lupinus mutabilis: An emerging protein crop. Front. Plant Sci. https://doi.org/10.3389/fpls.2019.01385 (2019).Article 

    Google Scholar 
    Chen, Y., She, Y., Zhang, R., Wang, J. & Zhang, X. Use of starch-based fat replacers in foods as a strategy to reduce dietary intake of fat and risk of metabolic diseases. Food Sci. Nutr. 8, 16–22 (2020).CAS 
    Article 

    Google Scholar 
    Frick, K. M., Kamphuis, L. G., Siddique, K. H. M., Singh, K. B. & Foley, R. C. Quinolizidine alkaloid biosynthesis in lupins and prospects for grain quality improvement. Front. Plant Sci. https://doi.org/10.3389/fpls.2017.00087 (2017).Article 

    Google Scholar 
    Chirinos-Arias, M. C. Andean Lupin (Lupinus mutabilis Sweet) a plant with nutraceutical and medicinal potential. Revista Bio. Ciencias. 3, 163–172 (2015).
    Google Scholar 
    Wink, M. Chemical defense of leguminosae. Are quinolizidine alkaloids part of the antimicrobial defense system of lupins?. Zeitschrift für Naturforschung C. 39, 548–552 (1984).Article 

    Google Scholar 
    Hidalgo, M. et al. Evaluation of in vitro suceptibility to spartein in four strain of Mycobacterium tuberculosis. Rev. Peru Med Exp Salud Publica. 39, 77–82 (2022).Article 

    Google Scholar 
    Muñoz, E. B., Luna-Vital, D. A., Fornasini, M., Baldeón, M. E. & Gonzalez de Mejia, E. Gamma-conglutin peptides from Andean lupin legume (Lupinus mutabilis Sweet) enhanced glucose uptake and reduced gluconeogenesis in vitro. J. Funct. 45, 339–347 (2018).Article 

    Google Scholar 
    Bryant, L., Rangan, A. & Grafenauer, S. Lupins and health outcomes: A systematic literature review. Nutrients 14, 327 (2022).CAS 
    Article 

    Google Scholar 
    Jacobsen, S. & Mujica, A. Geographical distribution of the Andean lupin (Lupinus mutabilis Sweet). Plant Genet. Resour. Newslett. 155, 1–8 (2008).
    Google Scholar 
    Antunez de Mayolo, S. Nutricion en el antiguo Peru. Banco Central de la Republica. Lima, Peru. 127 (1981).FAO. Perfiles nutricionales por paises: Peru. (ed. FAO) 36 p. (2000).UNICEF. Estado Mundial de la Infancia 2019 incluye a Perú entre las experiencias exitosas de lucha contra la desnutrición crónica infantile. https://www.unicef.org/peru/nota-de-prensa/estado-mundial-infancia-nutricion-alimentos-derechos-peru-experiencias-exitosas-desnutricion-cronica-infantil-reporte (2022).MINSA (Ministry of health – Peru). Situacion actual de la anemia. https://anemia.ins.gob.pe/situacion-actual-de-la-anemia-c1 (2022).WHO. Anemia. https://www.who.int/es/health-topics/anaemia#tab=tab_1 (2022).Galani, Y. J. H., Orfila, C. & Gong, Y. Y. A review of micronutrient deficiencies and analysis of maize contribution to nutrient requirements of women and children in Eastern and Southern Africa. Crit. Rev. Food Sci. Nutr. 62, 1568–1591 (2022).CAS 
    Article 

    Google Scholar 
    White, P. J. & Martin, R. B. Biofortifying crops with essential mineral elements. Trends Plant Sci. 10, 586–593 (2005).Article 

    Google Scholar 
    White, P. J. & Martin, R. B. Biofortification of crops with seven mineral elements often lacking in human diets-iron, zinc, copper, calcium, magnesium, selenium and iodine. New Phytol. 182, 49–84 (2009).CAS 
    Article 

    Google Scholar 
    Waters, B. M. & Sankaran, R. P. Moving micronutrients from the soil to the seeds: genes and physiological processes from a biofortification perspective. Plant Sciences. 180, 562–574 (2011).CAS 
    Article 

    Google Scholar 
    Brooker, R. W. et al. Improving intercropping: A synthesis of research in agronomy, plant physiology and ecology. New Phytol. 206, 107–117 (2015).Article 

    Google Scholar 
    Ducsay, L. et al. Possibility of selenium biofortification of winter wheat grain. Plant Soil Environ. 62, 379–383 (2016).CAS 
    Article 

    Google Scholar 
    Kumar, S. & Pandey, G. Biofortification of pulses and legumes to enhance nutrition. Heliyon. https://doi.org/10.1016/j.heliyon.2020.e03682 (2020).Article 

    Google Scholar 
    Diehn, T. A. et al. Boron demanding tissues of Brassica napus express specific sets of functional Nodulin26-like Intrinsic Proteins and BOR 1 transporters. Plant J. 100, 68–82 (2019).CAS 
    Article 

    Google Scholar 
    Jayalakshmi, V. A., Reddy, T. & Nagamadhuri, K. V. Genetic diversity and variability for protein and micro nutrients in advance breeding lines and chickpea varieties grown in Andhra Pradesh.”. Legume Res. Int. J. 42, 768–772 (2019).
    Google Scholar 
    Bouis, H. & Saltzman, A. Improving nutrition through biofortification: A review of evidence from HarvestPlus, 2003 through 2016. Glob Food Sec. 12, 49–58 (2017).Article 

    Google Scholar 
    Sanca, D. Composición nutricional de diez genotipos de lupino (L. mutabilis y L. albus) desamargados por proceso acuoso. Thesis. Universidad Nacional Agraria La Molina. (2015).Rodríguez, A. Evaluación “in vitro” de la actividad antibacteriana de los alcaloides del agua de desamargado del chocho (Lupinus mutrabilis Sweet). Thesis. Escuela Superior Politécnica de Chimborazo, Ecuador (2009).Villacres, E. et al. Germination, an effective process to in-crease the nutritional value and reduce non-nutritive factors of lupine grain (Lupinus mutabilis Sweet). Int. J. Food Sci. Nutr. Eng. 5, 163–168 (2015).
    Google Scholar 
    Villacres, E., Rubio, A., Egas, L., Segovia, G. Usos alternativos del chocho: Chocho (Lupinus mutabilis Sweet) alimento andino redescubierto. IOP publishing: repositorio. https://repositorio.iniap.gob.ec/handle/41000/298 (2006).Ortega-David, E. A., Rodríguez, A. D. & Burbano, A. Z. Caracterización de semillas de lupino (Lupinus mutabilis) sembrado en los Andes de Colombia. Acta Agronómica. 59, 111–118 (2010).
    Google Scholar 
    White, P. J. & Broadley, M. R. Physiological limits to zinc biofortification of edible crops. Front Plant Sci. 80, 1–11 (2011).
    Google Scholar 
    Zhao, F., Su, Y. H., Dunham, S. J. & Rakszegiet, M. Variation in mineral micronutrient concentrations in grain of wheat lines of diverse origin. J. Cereal Sci. 49, 290–295 (2009).CAS 
    Article 

    Google Scholar 
    Uauy, C., Distelfeld, A., Fahima, T., Blechl, A. & Dubcovsky, J. A NAC Gene regulating senescence improves grain protein, zinc, and iron content in wheat. Science 24, 1298–1301 (2006).ADS 
    Article 

    Google Scholar 
    Shorrocks, V. M. The occurrence and correction of boron deficiency. Plant Soil 193, 121–148 (1997).CAS 
    Article 

    Google Scholar 
    D’Imperio, M. et al. Boron biofortification of Portulaca oleracea L. through soilless cultivation for a new tailored crop. Agronomy. 10, 999–1013 (2020).Article 

    Google Scholar 
    Boyacioglu, O., Orenay-Boyacioglu, S., Yildirim, H. & Korkmaz, M. Boron intake, osteocalcin polymorphism and serum level in postmenopausal osteoporosis. J. Trace Elem. Med. Biol. 48, 52–56 (2018).CAS 
    Article 

    Google Scholar 
    Oliveira Araújo, E., Ferreira Dos Santos, E. & Camacho Oliveira, M. A. Boron-zinc interaction in the absorption of micronutrients by cotton. Agronomía Colombiana. 36, 51–57 (2018).Article 

    Google Scholar 
    Squitti, R., Siotto, M. & Polimanti, R. Low-copper diet as a preventive strategy for Alzheimer’s disease. Neurobiol. Aging 2, 40–50 (2014).Article 

    Google Scholar 
    Schilsky, M.L. Management of Wilson Disease (A Pocket Guide), 1st ed.; Publisher: Humana Press, Farmington, CT, USA. 154–196 (2018).Martins, A. C. et al. Manganese in the diet: Bioaccessibility, adequate intake, and neurotoxicological effects. J. Agric. Food Chem. 46, 12893–12903 (2020).Article 

    Google Scholar 
    Falah, S. A. & Saja, N. M. Essential trace elements and their vital roles in human body. Indian J. Adv. Chem. Sci. 3, 127–136 (2017).
    Google Scholar 
    National institutes of health. Manganese. Fact Sheet for Health Professionals. IOP Publishing ods.od.nih.gov. https://ods.od.nih.gov/factsheets/Manganese-HealthProfessional/. (2021).Savadi, S. Molecular regulation of seed development and strategies for engineering seed size in crop plants. Plant Growth Regul. 84, 401–422 (2018).CAS 
    Article 

    Google Scholar 
    Ge, L. et al. (2016) Increasing seed size and quality by manipulating BIG SEEDS1 in legume species. Proc Natl Acad Sci. 113, 12414–12419 (2016).CAS 
    Article 

    Google Scholar 
    Zou, L. Effects of gradual and sudden heat stress on seed quality of Andean lupin, Lupinus mutabilis. Thesis. University of Helsinki. https://helda.helsinki.fi/handle/10138/16501 (2009).Buircell, B.J., Cowling, A.W. Genetic Resources in Lupins (eds. Gladstones, J.S., Atkins, C.A., Hamblin, J.) (United Kingdom: CAB International, 1998).Aguilar-Angulo, L. A. Evaluación del rendimiento de grano y capacidad simbiótica de once accesiones de tarwi (Lupinus mutabilis Sweet), bajo condiciones de Otuzco-La Libertad (Universidad Nacional Agraria La Molina, 2015).
    Google Scholar 
    De La Cruz, N. Caracterización fenotípica y de rendimiento preliminar de ecotipos de tarwi (Lupinus mutabilis sweet), bajo condiciones del Callejón de Huaylas – Ancash (Universidad Nacional Agraria la Molina, 2018).
    Google Scholar 
    Huisa, J. Evaluación del comportamiento agronómico de catorce accesiones del ensayo nacional de tarwi (Lupinus mutabilis sweet.) en el CIP Camacani Puno – Perú”. Thesis. Universidad Nacional Agraria la Moina (2018).Cayo, B. Evaluación del comportamiento agronómico de ocho genotipos selectos de tarwi (Lupinus mutabilis sweet) bajo condiciones del CIP. CAMACANI – UNA – PUNO. Thesis. Universidad Nacional del Altiplano (2020).Buircell, B.J., Cowling, A.W. Lupin. Lupinus spp. Promoting the conservation and use of underutilized and ne-glected crops (eds. Gladstones, J.S., Atkins, C.A., Hamblin, J.) (United Kingdom: CAB International, 1998).Plata, J. Comportamiento Agronómico de dos Variedades de tarwi (Lupinus mutabilis Sweet), bajo tres densidades de siembra en la comunidad Marka Hilata Carabuco (Universidad San Andres, 2016).
    Google Scholar 
    Mendoza, C. Rendimiento de ecotipos regionales y variedades de tarwi (Lupínus mutabilis Sweet.) en el valle del Mantaro, Jauja, Junín. Thesis. Universidad Nacional Agraria la Moina (2020).Aguilar, S. Sistemas de producción de Lupinus mutabilis Sweet ‘chocho’ en terrazas y laderas con fertilización fosfatada en Cajamarca. Dissertation. La Molina National Agrarian University (2011).Aquino, S. Sustentabilidad del cultivo de tarwi (Lupinus mutabilis sweet) en la zona altoandina del Valle del Mantaro (Universidad Nacional Agraria la Molina, 2018).
    Google Scholar 
    Barda, M. S., Chatzigeorgiou, T., Papadopoulos, G. K. & Bebeli, P. J. Agro-morphological evaluation of Lupinus mutabilis in two locations in greece and association with insect pollinators. Agriculture https://doi.org/10.3390/agriculture11030236 (2021).Article 

    Google Scholar 
    Herniter, I. A., Jia, Z. & Kusi, F. Market preferences for cowpea (Vigna unguiculata [L.] Walp) dry grain in Ghana. African J Ag Res. 14, 928–934 (2019).Article 

    Google Scholar 
    Dordas, C. Foliar boron application affects lint and seed yield and improves seed quality of cotton grown on calcareous soils. Nutr. Cycl. Agroecosyst. 76, 19–28 (2006).CAS 
    Article 

    Google Scholar 
    Kristek, S. et al. Effect of various rates of boron on yield and quality of high-grade sugar beet varieties. Listy Cukrovarnické a Řepařské. 4, 146–150 (2018).
    Google Scholar 
    Thomas, C. L. et al. Root morphology and seed and leaf ionomic traits in a Brassica napus L. diversity panel show wide phenotypic variation and are characteristic of crop habit. BMC Plant Biol. 16, 214–232 (2016).CAS 
    Article 

    Google Scholar 
    Dursun, A. et al. Effects of boron fertilizer on tomato, pepper and cucumber yields and chemical composition. Commun Soil Sci Plant Anal. 1, 1576–1593 (2010).Article 

    Google Scholar 
    Sotiropoulos, T. E., Therios, T. N., Dimassi, K. N., Bosabalidis, A. & Kofidis, G. Nutritional status, growth, CO2 assimilation, and leaf anatomical responses in two kiwifruit species under boron toxicity. J Plant Nutr. 25, 1249–1261 (2002).CAS 
    Article 

    Google Scholar 
    Muccifora, S. & Bellani, L. Effects of copper on germination and reserve mobilization in Vicia sativa L. seeds. Environ. Pollut. 179, 68–74 (2013).CAS 
    Article 

    Google Scholar 
    Kobraee, S. Effect of foliar fertilization with zinc and manganese sulfate on yield, dry matter accumulation, and zinc and manganese contents in leaf and seed of chickpea (Cicer arietinum). J. Appl. Biol. Biotechnol. 7, 20–28 (2019).CAS 

    Google Scholar 
    IBPGR (1981) Lupin descriptors. https://www.bioversityinternational.org/fileadmin/bioversity/publications/Web_version/103/ (1981).Zasoski, R. J. & Burau, R. G. A rapid nitric-perchloric acid digestion method for multi-element tissue analysis. Commun. Soil Sci. Plant Anal. 8, 425–436 (1997).Article 

    Google Scholar 
    Pereira, T., Coelho, C. M. M., Bogo, A., Guidolin, A. F. & Miquelluti, D. J. Diversity in common bean landraces from south Brazil. Acta Bot. Croat. 1, 79–92 (2009).
    Google Scholar 
    Pujar, M., Govindaraj, M., Gangaprasad, S., Kanatti, A. & Shivade, H. Genetic variation and diversity for grain iron, zinc, protein and agronomic traits in advanced breeding lines of pearl millet [Pennisetum glaucum (L.) R Br] for biofortification breeding. Genet. Resour. Crop Evol. 67, 2009–2022 (2020).CAS 
    Article 

    Google Scholar 
    Lira, J. P. E. et al. Safflower genetic diversity based on agronomic characteristics in Mato Grosso state, Brazil, for a crop improvement program. Genet. Mol. Res. 1, 1–12 (2021).
    Google Scholar 
    de Sá, S. F. et al. Genetic diversity via REML-BLUP of ex situ conserved macauba [Acrocomia aculeata (Jacq.) Lodd. ex Mart.] ecotypes. Genet. Resour. Crop Evol. 68, 3193–3204 (2021).Article 

    Google Scholar 
    Kuru, R., Yilmaz, S., Tasli, P. N., Yarat, A. & Sahin, F. Boron content of some foods consumed in Istanbul, Turkey. Biol. Trace Elem. Res. 187, 1–8 (2019).CAS 
    Article 

    Google Scholar 
    Shokunbi, O., Adepoju, O., Mojapelo, P., Ramaite, I. & Akinyele, I. Copper, manganese, iron and zinc contents of Nigerian foods and estimates of adult dietary intakes. J. Food Compos. Anal. 82, 103–245 (2019).Article 

    Google Scholar 
    Norwegian scientific committee for food and environment. Assessment of dietary intake of manganese in rela-tion to tolerable upper intake. IOP Publishing wkm. www.vkm.no. (2018).Gil, V., Guzmán, L. & Quintero, E. Caracterización de la variabilidad morfológica de un “genotipo local” de maíz y dos de sus selecciones. Centro Agrícola. 4, 79–83 (2004).
    Google Scholar  More

  • in

    Vegetation cover and seasonality as indicators for selection of forage resources by local agro-pastoralists in the Brazilian semiarid region

    In line with the results of present study, we suggest that the exploitation of forage resources by agro-pastoralists occurs in a non-random manner. The use of forage resources is guided by a series of functional characters related to palatability and nutritional value, which determine preferential use due to the better quality of resource. At the same time, we understand that forage uses are complex and multifactorial in nature, and regulated in a substantial way by seasonality and ecological factors (Fig. 5), such as the availability of plant resources and local diversity.Figure 5Diagrammatic representation for the effects of vegetation cover and seasonality on forage resource selection in Dry Forests. Image created with Microsoft Office 2019 PowerPoint (www.office.com).Full size imageThe differences of plant species cited between areas reveal the positive effect of vegetation cover on the use and knowledge of plants by agro-pastoralists. Our findings reveal that the greater number of plant species mentioned by agro-pastoralists in Area II is directly associated with greater availability of resources in this area, as long as we consider vegetation cover as availability of resources, which allows different species to be used throughout the year. On the other hand, in regions with low vegetation cover (Area I), the low availability of resources limits the use and knowledge of plants by residents, which can lead to greater pressure on a small set of available species. Such findings reinforce the importance of vegetation cover for ecosystem provision of goods and services to human populations that depend directly or indirectly on these services.The most represented families found in the present study have also been reported in several other ethnobotanical studies6,16,17,29, with emphasis on Fabaceae and Poaceae, which are recognized for their high forage potential, which derives, above all, from high palatability and nutritional value30. Simultaneously, citations mostly for native species reflect the importance and potential of Caatinga resources as important components of the ruminant diet11, both for the woody and herbaceous strata, corroborating the estimate in the literature that 70% of vegetation has potential use as forage31.The characteristic seasonality of vegetation, on the other hand, represents a limiting factor for forage productivity, culminating in high fluctuations in quality and availability, as well as changes in the dominance of different strata and composition of forage species throughout the seasons11,32. The seasonal distribution of species explains the similarity of seasons between areas, with a higher similarity percentage for the dry seasons, since there is less availability of resources to be exploited compared to the rainy season. In this context, the potentially used species are commonly accessible woody species in both areas. However, during the rainy season, the high availability of herbaceous plants regulates different uses (Fig. 4), but even so, they also exhibit relatively similar patterns, mainly due to the woody component that denotes the common demand by ruminants at the beginning of this season.The effect of climatic variables on vegetation use patterns was documented by16,17, both of which showed greater richness in the use of herbaceous forage during the rainy season, a finding that reflects the seasonal distribution—restriction to that season—and decrease in the qualitative character of annual species33. At the same time, it also reflects the greater number of unique species for the rainy season. However, when compared to woody strata, significant differences in terms of richness are not found because although the diversity of herbaceous species in the Caatinga is greater24, it is much less known than that of the tree-shrub stratum11.Agro-pastoralists even characterize animal preferences for herbaceous stratum, but as its diversity is immense and ephemeral, they claim to have limited ability to identify the species. The high abundance of resources in the rainy season also reduces the concern with forage use, which implies less attention to the species that are consumed. In contrast, woody species, due to multiple uses and greater availability over time, tend to be better known10,34, with a different effect in the dry season making the optimal foraging pattern in this period inherent to the knowledge of agro-pastoralists35.In addition, according to the ecological appearance hypothesis, there is a general tendency for less apparent species to be neglected by populations36. Some studies have corroborated the hypothesis within the context of forage use, with woody species being cited more and having more uses6,15. In addition, people tend to focus on resources whose supply is given continuously10, which may explain why woody species are well represented in both seasons.Security in the provisioning of ecosystem services is an essential component for local populations, and thus woody species are highly valued because they reflect predictability of use15,35. This can be a particularly influential criterion because perennial or late leaf deciduous species, such as Cynophalla flexuosa and Myracrodruon urundeva, had significant amounts of citations and perceptions employing high valuation, as represented by some statements by some interviewees: “É um refrigero na seca” (it is savage in the dry season), “É uma ração boa na seca” (it is a good food in the dry season).In turn, differences in richness of the species cited by the two areas corroborate our first hypothesis that populations inserted in environments with greater vegetation cover tend to cite more species. In line with these findings, considerable floristic dissimilarity was also found between the two areas, given the exclusivity of species. Such dissimilarity may suggest particularities in the vegetation attributes of each area, such as greater floristic diversity7,37,38.Since anthropic processes are irregularly distributed in space, variation in the provisioning of ecosystem services by vegetation also occurs, and influences different collection profiles39. On the other hand, areas with greater species richness have been shown to have greater use patterns6,7. The larger number of species cited as woody and native for Area II is, therefore, associated with greater general richness, as well as herbaceous species present in the rainy season. In contrast, common species are reflected in trends of similar foraging patterns, as well as the presence of common species between areas38. In addition to different levels of disturbance, differences in floristic composition between areas may also be due to edaphic variation40.Our second hypothesis was refuted because the difference in the richness of exotic species between the areas. Plausible explanations for this finding are that, in general, exotic herbaceous species are commonly used for forage in the semi-arid region of Brazil41. Herbaceous species comprise the primary component of the ruminant diet. However, in the midst of their occurrence restricted to the short rainy period, exotic species, mainly of Fabaceae and Poaceae, have been introduced to increase the forage availability, which currently represents an important attribute of forage resources in the Caatinga41,42,43. At the same time, and to also increase the availability of forage resources, the cultivation of species by agro-pastoralists may be common in their properties44, mainly exotics, such as Prosopis juliflora, that have high adaptive potential and governmental incentives45.Regarding use patterns, according to the data presented here it is possible to state that agro-pastoralists ’ experiences with herding activities provide an accumulation of a vast knowledge about forage resources15. This knowledge allows forage resources to be characterized by their potential according to a variety of criteria associated with seasonal variation and qualitative attributes, as commonly found by other studies14,15,16,17,37. Such criteria are often revealed by qualitative approaches that define the valuation perception of resources. Thus, nutritional value and palatability can be implicitly associated with the definitions of “É uma ração boa” (it is a good food), “o bicho gosta muito” (the animals like it very much) and “Rico em proteínas” (rich in protein).It should be added that the establishment of intrinsic relationships with resources allows a particular understanding at a high level of detail15,35, such as changes in palatability throughout development with descriptions including chemical17 and structural changes. Studies confirm that some Caatinga species vary in their chemical composition during leaf maturation, which influences nutritional quality17,46.In addition to revealing the domain of information, this body of knowledge allows maximizing forage use based on nutritional properties weighted by availability14,37. Nunes37 confirmed that the forage species selected by informants and the criteria they adopted coincided with nutritional values measured by the literature, and that, as also found in the present study, younger plants were recognized as highly appreciated by animals. This appreciation is due to the greater palatability of plant organs at this stage47. This is a matter of concern for the sustainability of the Caatinga, since direct or indirect grazing has compromised the regeneration process12 since younger individuals are clearly more sensitive to damage48.Also, considering the potential of Caatinga, we suggest that investment through government actions encourage the cultivation of native species to ensure the production of forage and, consequently, guarantee the sustainability of livestock activity and the ecosystem in question. More