Philippa Kaur

1.Nyakeri, E. M., Mwirichia, R. & Boga, H. Isolation and characterization of enzyme-producing bacteria from Lake Magadi, an extreme soda lake in Kenya. J. Microbiol. Exp. 6(2), 57–68 (2018).
Google Scholar 
2.Grant, W. D. Alkaline environments and biodiversity. In Extremophiles (eds Gerday, E. C. & Glansdorff, N.) (UNESCO, Eolss Publishers, 2006).
Google Scholar 
3.Jones, B. E. & Grant, W. D. Microbial diversity and ecology of alkaline environments. In Adaptation to Exotic Environments (ed. Seckbach, J.) 177–190 (Kluwer Academic Publishers, 2000).
Google Scholar 
4.Antony, C. P. et al. Microbiology of Lonar Lake and other soda lakes. J. Int. Soc. Microb. Ecol. 7(3), 468–476 (2013).
Google Scholar 
5.Boros, E. & Kolpakova, M. A review of the defining chemical properties of soda lakes and pans: An assessment on a large geographic scale of Eurasian inland saline surface waters. PLoS ONE 13(8), e0202205 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
6.Grant, W. D. & Jones, B. E. Bacteria, archaea and viruses of soda lakes. In Soda lakes of East Africa (ed. Schagerl, M.) 97–148 (Springer p, 2016).
Google Scholar 
7.Lanzén, A. et al. Surprising prokaryotic and eukaryotic diversity, community structure and biogeography of Ethiopian soda lakes. PLoS ONE 8(8), e72577 (2013).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
8.Asao, M., Pinkart, H. C. & Madigan, M. T. Diversity of extremophilic purple phototrophic bacteria in Soap Lake, a Central Washington (USA) Soda Lake. Environ. Microbiol. 13(8), 2146–2157 (2011).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
9.Kulp, T. R. et al. Dissimilatory arsenate and sulfate reduction in sediments of two hypersaline, arsenic-rich soda lakes: Mono and Searles Lakes, California. Appl. Environ. Microbiol. 72(10), 6514–6526 (2006).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
10.Sorokin, D. Y. & Kuenen, J. G. Chemolithotrophic haloalkaliphiles from soda lakes. FEMS Microbiol. Ecol. 52(3), 287–295 (2005).CAS 
PubMed 
Article 

Google Scholar 
11.Groth, I. et al. Bogoriella caseilytica gen. nov., sp. Nov., a new alkaliphilic actinomycete from a soda lake in Africa. Int. J. Syst. Evol. Microbiol. 47(3), 788–794 (1997).CAS 

Google Scholar 
12.Glombitza, C. et al. Sulfate reduction controlled by organic matter availability in deep sediment cores from the saline, alkaline Lake Van (Eastern Anatolia, Turkey). Front. Microbiol. 4, 1–11 (2013).Article 

Google Scholar 
13.Bilgili, A. et al. Van Gölü’nden avlanan inci kefali örneklerinde arsenik düzeyleri. Turk. J. Vet. Anim. Sci. 23(2), 367–371 (1999).MathSciNet 

Google Scholar 
14.Kremer, B., Kaźmierczak, J. & Kempe, S. Authigenic replacement of cyanobacterially precipitated calcium carbonate by aluminium-silicates in giant microbialites of Lake Van (Turkey). Sedimentology 66(1), 285–304 (2019).CAS 
Article 

Google Scholar 
15.Reimer, A., Landmann, G. & Kempe, S. Lake Van, Eastern Anatolia, hydrochemistry and history. Aquat. Geochem. 15(1–2), 195–222 (2009).CAS 
Article 

Google Scholar 
16.Tomonaga, Y. et al. Porewater salinity reveals past lake-level changes in Lake Van, the Earth’s largest soda lake. Sci. Rep. 7(1), 1–10 (2017).CAS 
Article 

Google Scholar 
17.Pecoraino, G., Dlessandro, W. & Inguaggiato, S. The other side of the coin: Geochemistry of alkaline lakes in volcanic areas. In Volcanic Lakes (eds Rouwet, D. et al.) 219–237 (Springer, 2015).Chapter 

Google Scholar 
18.Kaden, H. et al. Impact of lake level change on deep-water renewal and oxic conditions in deep saline Lake Van. Turkey. Water Resour. Res. https://doi.org/10.1029/2009WR008555 (2010).ADS 
Article 

Google Scholar 
19.Landmann, G. & Kempe, S. Annual deposition signal versus lake dynamics: Microprobe analysis of Lake Van (Turkey) sediments reveals missing varves in the period 11.2–10.2 ka BP. Facies 51(1–4), 135–145 (2005).Article 

Google Scholar 
20.Degens, E. T. et al. A geological study of Lake Van, eastern Turkey. Geol. Rundsch. 73(2), 701–734 (1984).ADS 
CAS 
Article 

Google Scholar 
21.Duckworth, A. W. et al. Phylogenetic diversity of soda lake alkaliphiles. FEMS Microbiol. Ecol. 19(3), 181–191 (1996).CAS 
Article 

Google Scholar 
22.Sorokin, D. Y. et al. Microbial diversity and biogeochemical cycling in soda lakes. Extremophiles 18(5), 791–809 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
23.Zargar, K. et al. Identification of a novel oxidase gene, arxA, in the haloalkaliphilic, arsenite-oxidizing bacterium Alkalilimnicola echrlichii strain MLHE-1. J. Bacteriol. 192, 3755–3762 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
24.Zargar, K. et al. ArxA, a new clade of arsenite oxidase within the DMSO reductase family of molybdenum oxidoreductases. Environ. Microbiol. 14(7), 1635–1645 (2012).CAS 
PubMed 
Article 

Google Scholar 
25.Zorz, J. K. et al. A shared core microbiome in soda lakes separated by large distances. Nat. Commun. 10(1), 1–10 (2019).CAS 
Article 

Google Scholar 
26.Matyugina, E. & Belkova, N. Distribution and diversity of microbial communities in meromictic soda Lake Doroninskoe (Transbaikalia, Russia) during winter. Chin. J. Oceanol. Limn. 33(6), 1378 (2015).ADS 
CAS 
Article 

Google Scholar 
27.Liu, D. et al. Use of PCR primers derived from a putative transcriptional regulator gene for species-specific determination of Listeria monocytogenes. Int. J. Food Microbiol. 91, 297–304 (2004).CAS 
PubMed 
Article 

Google Scholar 
28.Camacho, C. et al. BLAST+: Architecture and applications. BMC Bioinform. 10, 421 (2009).Article 
CAS 

Google Scholar 
29.Ionescu, D. et al. Microbial and chemical characterization of underwater fresh water springs in the Dead Sea. PLoS ONE 7, e38319 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Klindworth, A. et al. Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-generation sequencing-based diversity studies. Nucleic Acids Res. 41, e1 (2013).CAS 
PubMed 
Article 

Google Scholar 
31.Ondov, B. et al. Interactive metagenomic visualization in a web browser. BMC Bioinform. 12, 385 (2011).Article 

Google Scholar 
32.Pruesse, E. et al. SINA: Accurate high throughput multiple sequence alignment of ribosomal RNA genes. Bioinformatics 28(14), 1823–1829 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
33.Quast, C. et al. The silva ribosomal RNA gene database project: Improved data processing and webbased tools. Nucleic Acids Res. 41, D590–D596 (2013).CAS 
PubMed 
Article 

Google Scholar 
34.Rognes, T. et al. Vsearch: A versatile open source tool for metagenomics. Peer J. 4, e2584 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
35.Hammer, Ø., Harper, D. A. & Ryan, P. D. Past: Paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4(1), 1–9 (2001).
Google Scholar 
36.Duckworth, A. W. et al. Halomonas magadii sp. Nov., a new member of the genus Halomonas, isolated from a soda lake of the East African Rift Valley. Extremophiles 4(1), 53–60 (2000).MathSciNet 
CAS 
PubMed 
PubMed Central 

Google Scholar 
37.Xin, H. et al. Natronobacterium nitratireducens sp. nov., a aloalkaliphilic archaeon isolated from a soda lake in China. Int. J. Syst. Evol. Microbiol. 51(5), 1825–1829 (2001).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
38.Joshi, A. et al. Nitrincola tapanii sp. nov., a novel alkaliphilic bacterium from An Indian Soda Lake. Int. J. Syst. Evol. Microbiol. 70(2), 1106–1111 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
39.Menes, R. J. et al. Bacillus natronophilus sp. nov., an alkaliphilic bacterium isolated from a soda lake. Int. J. Syst. Evol. Microbiol. 70(1), 562–568 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
40.Vavourakis, C. D. et al. A metagenomics roadmap to the uncultured genome diversity in hypersaline soda lake sediments. Microbiome. 6(1), 1–18 (2018).Article 

Google Scholar 
41.Yigit, A. et al. Determination of water quality by ion characterization of Van Lake Water. Iğdır Univ. J. Inst. Sci. Tech. 7(4), 169–179. https://doi.org/10.21597/jist.2017.210 (2017).Article 

Google Scholar 
42.Bilgili, A. et al. The natural quality of Van Lake and the levels of some heavy metals in grey mullet (Chalcalburus tariehi, Pallas 1811) samples taken from this lake. Ankara Üniv Vet Fak Dergisi 42, 445–450 (1995).
Google Scholar 
43.Demir Yetis, A. & Ozguven, A. Investigation of heavy metal pollution in surface waters of the Van Lake Edremit coast. Uludağ Univ. J. Fac. Eng. 25(2), 831–847. https://doi.org/10.17482/uumfd.752460 (2020).Article 

Google Scholar 
44.Ersoy Omeroglu, E. & Karaboz, I. Characterization and arsenic-tolerance potential of Halomonas sp. from Van Lake, Turkey. VI Congress of Macedonian Microbiologists With International Participation, 30 May–6 June, Abstract Book, pp. 200–201 (2018).45.Ersoy Omeroglu, E. Evaluation of arsenic pollution and the effect of arsenic on Branchybacterium paraconglomeratum in Van Lake. 1st World Conference On Sustaninable Life Sciences WOCOLS 2019, 30 June–7 July, Abstract Book, p. 17 (2019).46.Reimer, A. Hydrochemie und Geochemie der Sedimente und Porenwa¨sser des hochalkalinen Van Sees in der Osttu¨rkei. Dissertation, Facult Geosci Univ Hamburg, 136 pp, unpublished, (1995).47.Kempe, S. et al. Largest known microbialites discovered in Lake Van, Turkey. Nature 349, 605–608 (1991).ADS 
Article 

Google Scholar 
48.Kazmierczak, J. & Kempe, S. Modern terrestrial analogues for the carbonate globules in Martian meteorite ALH84001. Naturwissenschaften 90, 167–172 (2003).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
49.Lopez-Garcia, P. et al. Bacterial diversity and carbonate precipitation in the microbialites of the highly alkaline Lake Van, Turkey. Extremophiles 9, 263–274 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
50.Poyraz, N. & Mutlu, M. B. Characterization of microbial populations of Lake Van by 16S metagenomics study. ESTUJST-A. 9(1), 80–88 (2020).
Google Scholar 
51.Poyraz, N. & Mutlu, B. M. Alkaliphilic bacterial diversity of Lake Van/Turkey. Biological Biodivers. Conserv. 10(1), 92–103 (2017).
Google Scholar 
52.Sen, F. et al. Endemic fish species of Van Lake basin. YYU J. Agr. Sci. 28, 63–70 (2018).
Google Scholar 
53.Danulat, E. & Kempe, S. Nitrogenous waste excretion at extremely alkaline pH: The story of Chalcalburnus tarichi (Cyprinidae), endemic to Lake Van, Eastern Turkey. Fish Physiol. Biochem. 9, 377–386 (1992).CAS 
PubMed 
Article 

Google Scholar 
54.Bostanci, D. & Polat, N. Age and growth of Alburnus tarichi (Güldenstädt, 1814): An endemic fish species of Lake Van (Turkey). J. Appl. Ichthyol. 27, 1346–1349 (2011).Article 

Google Scholar 
55.Burger, J. et al. Armenian Gull (Larus armenicus). Handbook of the Birds of the World Alive, Lynx Edicions, Barcelona (2015).56.Oremland, R. S. et al. Methanogenesis in Big Soda Lake, Nevada: An alkaline, moderately hypersaline desert lake. Appl. Environ. Microbiol. 43, 462–468 (1982).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
57.Iversen, N. et al. Big Soda Lake (Nevada): 3: Pelagic methanogenesis and anaerobic methane oxidation. Limnol. Oceanogr. 32, 804–814 (1987).ADS 
CAS 
Article 

Google Scholar 
58.Oremland, R. S. et al. The microbial arsenic cycle in Mono Lake, California. FEMS Microb. Ecol. 48, 15–27 (2004).CAS 
Article 

Google Scholar 
59.Sorokin, D. Y. et al. Microbial thiocyanate utilization under highly alkaline conditions. Appl. Environ. Microbiol. 67, 528–538 (2001).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
60.Sorokin, D. Y. et al. Thioalkalimicrobium aerophilum gen. nov., sp. nov. and Thioalkalimicrobium sibiricum sp. nov., and Thioalkalivibrio versutus gen. nov., sp. nov., Thioalkalivibrio nitratis sp. nov. and Thioalkalivibrio denitrificans sp. nov., novel obligately alkaliphilic and obligately chemolithoautotrophic sulfur-oxidizing bacteria from soda lakes. Int. J. Syst. Evol. Microbiol. 51, 565–580 (2001).CAS 
PubMed 
Article 

Google Scholar 
61.Sorokin, D. Y. et al. Thioalkalivibrio thiocyanooxidans sp. nov. and Thioalkalivibrio paradoxus sp. nov., novel alkaliphilic, obligately autotrophic, sulfur-oxidizing bacteria from the soda lakes able to grow with thiocyanate. Int. J. Syst. Evol. Microbiol. 52, 657–664 (2002).CAS 
PubMed 
Article 

Google Scholar 
62.Gorlenko, V. M. et al. Ectothiorhodospira variabilis sp. nov., an alkaliphilic and halophilic purple sulfur bacterium from soda lakes. Int. J. Syst. Evol. Microbiol. 69, 558–564 (2009).
Google Scholar 
63.Mwirichia, R. et al. Bacterial diversity in the haloalkaline Lake Elmenteita, Kenya. Curr. Microbiol. 62, 209–221 (2011).CAS 
PubMed 
Article 

Google Scholar 
64.Mesbah, N. M. et al. Novel and unexpected prokaryotic diversity in water and sediments of the alkaline, hypersaline lakes of the Wadi an Natrun, Egypt. Microbial Ecol. 54, 598–616 (2007).CAS 
Article 

Google Scholar 
65.Flandroy, L. et al. The impact of human activities and lifestyles on the interlinked microbiota and health of humans and of ecosystems. Sci. Total Environ. 627, 1018–1038 (2018).ADS 
CAS 
PubMed 
Article 

Google Scholar 
66.Deshmukh, K. B. et al. Bacterial diversity of Lonar soda lake of India. Indian J. Microbiol. 51, 107–111 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
67.Zhao, D. et al. Abundant taxa and favorable pathways in the microbiome of soda-saline lakes in Inner Mongolia. Front. Microbiol. 11, 1740 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
68.Lavrentyeva, E. V. et al. Prokaryotic diversity in the biotopes of the Gudzhirganskoe saline lake (Barguzin Valley, Russia). Mikrobiologiya 89, 359–368 (2020).CAS 

Google Scholar 
69.Glaring, M. A. et al. Microbial diversity in a permanently cold and alkaline environment in Greenland. PLoS ONE 10, e0124863 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
70.Tavormina, P. L. et al. Methyloprofundus sedimenti gen. nov., sp. nov., an obligate methanotroph from ocean sediment belonging to the ‘deep sea-1’clade of marine methanotrophs. Int. J. Syst. Evol. Microbiol. 65(1), 251–259 (2015).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
71.Sorokin, D. Y. et al. Dethiobacter alkaliphilus gen. nov. sp. nov., and Desulfurivibrio alkaliphilus gen. nov. sp. nov.: Two novel representatives of reductive sulfur cycle from soda lakes. Extremophiles 12, 431–439 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
72.Holmes, D. E. et al. Potential role of a novel psychrotolerant member of the family Geobacteraceae, Geopsychrobacter electrodiphilus gen. nov., sp. nov., in electricity production by a marine sediment fuel cell. Appl. Environ. Microbiol. 70, 6023–6030 (2005).ADS 
Article 
CAS 

Google Scholar 
73.Pikuta, E. V. et al. Proteocatella sphenisci gen. nov., sp. nov., a psychrotolerant, spore-forming anaerobe isolated from penguin guano. Int. J. Syst. Evol. Microbiol. 59, 2302–2307 (2009).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
74.Stams, A. J. M. & Hansen, T. A. Fermentation of glutamate and other compounds by Acidaminobacter hydrogenoformans gen. nov. sp. nov., an obligate anaerobe isolated from black mud: Studies with pure cultures and mixed cultures with sulfate-reducing and methanogenic bacteria. Arch. Microbiol. 137, 329–337 (1984).CAS 
Article 

Google Scholar 
75.Finegold, S. M. et al. Anaerofustis stercorihominis gen. nov., sp. nov., from human feces. Anaerobe 10, 41–45 (2004).CAS 
PubMed 
Article 

Google Scholar 
76.Matthies, C. et al. Anaerovorax odorimutans gen. nov., sp. nov., a putrescine-fermenting, strictly anaerobic bacterium. Int. J. Syst. Evol. Microbiol. 50, 1591–1594 (2000).CAS 
PubMed 
Article 

Google Scholar 
77.Higashiguchi, D. T. et al. Pilibacter termitis gen. nov., sp. nov., a lactic acid bacterium from the hindgut of the Formosan subterranean termite (Coptotermes formosanus). Int. J. Syst. Evol. Microbiol. 56, 15–20 (2006).CAS 
PubMed 
Article 

Google Scholar 
78.Labrenz, M. et al. Roseibaca ekhonensis gen. nov., sp. nov., an alkalitolerant and aerobic bacteriochlorophyll a-producing alphaproteobacterium from hypersaline Ekho Lake. Int. J. Syst. Evol. Microbiol. 59, 1935–1940 (2009).CAS 
PubMed 
Article 

Google Scholar 
79.Sorokin, D. Y. et al. Nitriliruptor alkaliphilus gen. nov., sp. nov., a deep-lineage haloalkaliphilic actinobacterium from soda lakes capable of growth on aliphatic nitriles, and proposal of Nitriliruptoraceae fam. Nov. and Nitriliruptorales ord. nov. Int. J. Syst. Evol. Microbiol. 59, 248–253 (2009).CAS 
PubMed 
Article 

Google Scholar 
80.Shahinpei, A. et al. Salinispirillum marinum gen. nov., sp. nov., a haloalkaliphilic bacterium in the family “Saccharospirillaceae”. Int. J. Syst. Evol. Microbiol. 64, 3610–3615 (2014).PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
81.Munson, M. A. et al. Buchnera gen. nov. and Buchnera aphidicola sp. nov., a taxon consisting of the mycetocyte-associated, primary endosymbionts of aphids. Int. J. Syst. Bacteriol. 41, 566–568 (1991).Article 

Google Scholar  More

1.United Nations Environment Programme. Making Peace With Nature (Tech. Rep, United Nations Environment Programme, 2021).2.Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50. https://doi.org/10.1038/nature14324 (2015).ADS 
CAS 
Article 
PubMed 

Google Scholar 
3.Urban, M. C. Accelerating extinction risk from climate change. Science. (80-. ) 348, 571–573. https://doi.org/10.1126/science.aaa4984 (2015).ADS 
CAS 
Article 

Google Scholar 
4.Rosenberg, K. V. et al. Decline of the North American avifauna. Science. (80-. ) 366, 120–124. https://doi.org/10.1126/science.aaw1313 (2019).ADS 
CAS 
Article 

Google Scholar 
5.Jetz, W., Wilcove, D. S. & Dobson, A. P. Projected impacts of climate and land-use change on the global diversity of birds. PLoS Biol. 5, 1211–1219. https://doi.org/10.1371/journal.pbio.0050157 (2007).CAS 
Article 

Google Scholar 
6.Abrahamczyk, S. & Renner, S. S. The temporal build-up of hummingbird/plant mutualisms in North America and temperate South America. BMC Evol. Biol.https://doi.org/10.1186/s12862-015-0388-z (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
7.Grant, V. & Grant, K. A. A Hummingbird-Pollinated Species of Boraginaceae in the Arizona Flora. Proc. Natl. Acad. Sci. 66, 917–919. https://doi.org/10.1073/pnas.66.3.917 (1970).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
8.Ratto, F. et al. Global importance of vertebrate pollinators for plant reproductive success: A meta-analysis. Front. Ecol. Environ. 16, 82–90. https://doi.org/10.1002/fee.1763 (2018).Article 

Google Scholar 
9.McGuire, J. A. et al. Molecular phylogenetics and the diversification of hummingbirds. Curr. Biol. 24, 910–916. https://doi.org/10.1016/j.cub.2014.03.016 (2014).CAS 
Article 
PubMed 

Google Scholar 
10.Sauer, J. R., Link, W. A., Fallon, J. E., Pardieck, K. L. & Ziolkowski, D. J. The North American breeding bird survey 1966–2011: Summary analysis and species accounts. N. Am. Fauna 79, 1–32. https://doi.org/10.3996/nafa.79.0001 (2013).Article 

Google Scholar 
11.Bairlein, F. Migratory birds under threat. Science (80-. ). 354, 547–548. https://doi.org/10.1126/science.aah6647 (2016).ADS 
CAS 
Article 

Google Scholar 
12.Battey, C. J. Ecological release of the Anna’s Hummingbird during a Northern range expansion. Am. Nat. 194, 306–315. https://doi.org/10.1086/704249 (2019).CAS 
Article 
PubMed 

Google Scholar 
13.Clark, C. J. EBird records show substantial growth of the Allen’s Hummingbird (Selasphorus sasin sedentarius) population in urban Southern California. Condor 119, 122–130. https://doi.org/10.1650/CONDOR-16-153.1 (2017).Article 

Google Scholar 
14.Sleeter, B. M. et al. Land-cover change in the conterminous United States from 1973 to 2000. Glob. Environ. Change 23, 733–748. https://doi.org/10.1016/j.gloenvcha.2013.03.006 (2013).Article 

Google Scholar 
15.Gallant, A. L., Loveland, T. R., Sohl, T. L. & Napton, D. E. Using an ecoregion framework to analyze land-cover and land-use dynamics. Environ. Manag.https://doi.org/10.1007/s00267-003-0145-3 (2004).Article 

Google Scholar 
16.Williamson, S. L. A Field Guide to Hummingbirds of North America (Peterson Field Guide Series) (Houghton Mifflin Company, 2002).
Google Scholar 
17.Panjabi, A. O. et al. Avian Conservation Assessment Database Handbook Version 2021. Tech. Rep. (Partners in Flight Technical Series, Bird Conservancy of the Rockies, 2021).
Google Scholar 
18.Gillespie, C., Contreras-Martinez, S., Bishop, C. & Alexander, J. Rufous Hummingbird: State of the Science and Conservation : simplebooklet.com. Tech. Rep., (Western Hummingbird Partnership, 2020).19.International Union for Conservation of Nature. IUCN Red List Categories and Criteria: Version 3.1. Tech. Rep. (IUCN Species Survival Commission, 2001).
Google Scholar 
20.Lehikoinen, A. Climate change, phenology and species detectability in a monitoring scheme. Popul. Ecol. 55, 315–323. https://doi.org/10.1007/s10144-012-0359-9 (2013).Article 

Google Scholar 
21.Massimino, D., Harris, S. J. & Gillings, S. Phenological mismatch between breeding birds and their surveyors and implications for estimating population trends. J. Ornithol. 162, 143–154. https://doi.org/10.1007/s10336-020-01821-5 (2021).Article 

Google Scholar 
22.McGrath, L. J., van Riper III, C. & Fontaine, J. J. Flower power: Tree flowering phenology as a settlement cue for migrating birds. J. Anim. Ecol. 78, 22–30. https://doi.org/10.1111/j.1365-2656.2008.01464.x (2009).Article 
PubMed 

Google Scholar 
23.Jones, T. & Cresswell, W. The phenology mismatch hypothesis: Are declines of migrant birds linked to uneven global climate change?. J. Anim. Ecol. 79, 98–108. https://doi.org/10.1111/j.1365-2656.2009.01610.x (2010).Article 
PubMed 

Google Scholar 
24.Courter, J. R. Changes in spring arrival dates of rufous hummingbirds (Selasphorus rufus) In Western North America in the past century. Wilson J. Ornithol. 129, 535–544. https://doi.org/10.1676/16-133.1 (2017).Article 

Google Scholar 
25.Rooney, T. Deer impacts on forest ecosystems: A North American perspective. Forestry 74, 201–208. https://doi.org/10.1093/forestry/74.3.201 (2001).Article 

Google Scholar 
26.Côté, S. D., Rooney, T. P., Tremblay, J.-P., Dussault, C. & Waller, D. M. Ecological impacts of deer overabundance. Annu. Rev. Ecol. Evol. Syst. 35, 113–147. https://doi.org/10.2307/annurev.ecolsys.35.021103.30000006 (2004).Article 

Google Scholar 
27.Decalesta, D. S. Effect of white-tailed deer on songbirds within managed forests in Pennsylvania. J. Wildl. Manag. 58, 711–718 (1994).Article 

Google Scholar 
28.English, S. G. et al. Neonicotinoid pesticides exert metabolic effects on avian pollinators. Sci. Rep. 11, 2914. https://doi.org/10.1038/s41598-021-82470-3 (2021).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
29.Bishop, C. A. et al. Determination of neonicotinoids and butenolide residues in avian and insect pollinators and their ambient environment in Western Canada (2017, 2018). Sci. Total Environ. 737, 139386. https://doi.org/10.1016/j.scitotenv.2020.139386 (2020).ADS 
CAS 
Article 
PubMed 

Google Scholar 
30.Graves, E. E. et al. Analysis of insecticide exposure in California hummingbirds using liquid chromatography-mass spectrometry. Environ. Sci. Pollut. Res. 26, 15458–15466. https://doi.org/10.1007/s11356-019-04903-x (2019).CAS 
Article 

Google Scholar 
31.Hill, G. E., Sargent, R. R. & Sargent, M. B. Recent change in the winter distribution of Rufous Hummingbirds. Auk 115, 240–245. https://doi.org/10.2307/4089135 (1998).Article 

Google Scholar 
32.Smith, A. C. & Edwards, B. P. M. North American Breeding Bird Survey status and trend estimates to inform a wide range of conservation needs, using a flexible Bayesian hierarchical generalized additive model. Condor 123, 1–16. https://doi.org/10.1093/ornithapp/duaa065 (2021).Article 

Google Scholar 
33.Wilson, S. et al. Prioritize diversity or declining species? Trade-offs and synergies in spatial planning for the conservation of migratory birds in the face of land cover change. Biol. Conserv. 239, 108285. https://doi.org/10.1016/j.biocon.2019.108285 (2019).Article 

Google Scholar 
34.Toledo-Aceves, T., Meave, J. A., González-Espinosa, M. & Ramírez-Marcial, N. Tropical montane cloud forests: Current threats and opportunities for their conservation and sustainable management in Mexico. J. Environ. Manag. 92, 974–981. https://doi.org/10.1016/j.jenvman.2010.11.007 (2011).Article 

Google Scholar 
35.Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science (80-. ). 342, 850–853. https://doi.org/10.1126/SCIENCE.1244693 (2013).ADS 
CAS 
Article 

Google Scholar 
36.Westerling, A. L. Increasing western US forest wildfire activity: Sensitivity to changes in the timing of spring. Philos. Trans. R. Soc. B Biol. Sci.https://doi.org/10.1098/RSTB.2015.0178 (2016).Article 

Google Scholar 
37.Neeraja, U. V., Rajendrakumar, S., Saneesh, C. S., Dyda, V. & Knight, T. M. Fire alters diversity, composition, and structure of dry tropical forests in the Eastern Ghats. Ecol. Evol. 11, 6593–6603. https://doi.org/10.1002/ECE3.7514 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
38.Courter, J. R., Johnson, R. J., Bridges, W. C. & Hubbard, K. G. Assessing migration of Ruby-throated Hummingbirds (Archilochus colubris) at broad spatial and temporal scales at broad spatial and temporal scales. Auk 130, 107–117. https://doi.org/10.1525/auk.2012.12058 (2013).Article 

Google Scholar 
39.Greig, E. I., Wood, E. M. & Bonter, D. N. Winter range expansion of a hummingbird is associated with urbanization and supplementary feeding. Proc. R. Soc. B Biol. Sci.https://doi.org/10.1098/rspb.2017.0256 (2017).Article 

Google Scholar 
40.Jepson, W. L. & Hickman, J. C. The Jepson manual: Higher plants of California (University of California Press, 1993).
Google Scholar 
41.Scarfe, A. & Finlay, J. C. Rapid second nesting by Anna’s Hummingbird near its Northern breeding limit. West. Birds 32, 131–133 (2001).
Google Scholar 
42.Bibby, C. J., Burgess, N. D. & Hill, D. A. Bird Census Techniques (Academic Press, 1992).
Google Scholar 
43.Thogmartin, W. E. et al. A review of the population estimation approach of the North American landbird conservation plan. Auk 123, 892–904. https://doi.org/10.1093/auk/123.3.892 (2006).Article 

Google Scholar 
44.Carter, M. F., Hunter, W. C., Pashley, D. N. & Rosenberg, K. V. Setting conservation priorities for landbirds in the United States: The partners in flight approach. Auk 117, 541–548. https://doi.org/10.1093/auk/117.2.541 (2000).Article 

Google Scholar 
45.Sauer, J. R. & Link, W. A. Analysis of the North American breeding bird survey using hierarchical models. Auk 128, 87–98. https://doi.org/10.1525/auk.2010.09220 (2011).Article 

Google Scholar 
46.Sauer, J. R., Niven, D. K., Pardieck, K. L., Ziolkowski, D. J. & Link, W. A. Expanding the North American Breeding Bird Survey analysis to include additional species and regions. J. Fish Wildl. Manag. 8, 154–172. https://doi.org/10.3996/102015-JFWM-109 (2017).Article 

Google Scholar 
47.Stanton, J. C., Blancher, P., Rosenberg, K. V., Panjabi, A. O. & Thogmartin, W. E. Estimating uncertainty of North American landbird population sizes. Avian Conserv. Ecol.https://doi.org/10.5751/ACE-01331-140104 (2019).Article 

Google Scholar 
48.Schuster, R. et al. Optimizing the conservation of migratory species over their full annual cycle. Nat. Commun.https://doi.org/10.1038/s41467-019-09723-8 (2019).Article 
PubMed 
PubMed Central 

Google Scholar 
49.Johnston, A. et al. Abundance models improve spatial and temporal prioritization of conservation resources. Ecol. Appl. 25, 1749–1756. https://doi.org/10.1890/14-1826.1 (2015).Article 
PubMed 

Google Scholar 
50.Robbins, C., Bystrak, D. & Geissler, P. The Breeding Bird Survey: Its First Fifteen Years, 1965–1979. Tech. Rep. (U.S. Fish and Wildlife Service, 1986).51.R Core Team. R: A language and environment for statistical computing (Version 4.0.3) [Computer software] (2020).52.Smith, A. C., Hudson, M.-A., Aponte, V. & Francis, C. North American Breeding Bird Survey—Canadian Trends Website. Data-version 2017 (2019).53.Edwards, B. P. M. & Smith, A. C. bbsBayes: An R package for hierarchical Bayesian analysis of North American breeding bird survey data. J. Open Res. Softw.https://doi.org/10.5334/JORS.329 (2021).Article 

Google Scholar 
54.North American Bird Conservation Initiative. Bird Conservation Region Descriptions. Tech. Rep. (U. S. Fish and Wildlife Service, 2000).
Google Scholar  More

Ethical statementThe present study was discussed and approved by the ethics and animals’ welfare committee of the University of Veterinary Medicine, Vienna, Austria, in accordance with good scientific practice and national legislation (GZ: BMWFW-68.205/0151-WF/V/3b/2016 and GZ: BMWFW-68.205/0224-WF/V/3b/2016). All methods were carried out in accordance with relevant guidelines and regulations. We confirm that the study was carried out in compliance with the ARRIVE guidelines. No plants or plant parts were used in this study.Animals and study areaThe study animals were kept in an outdoor enclosure (~ 55 ha, for details see “Supplementary Material”). The study enclosure was covered with a deciduous forest, mainly Turkey oak (Quercus cerris) and pubescent oak (Quercus pubescens) and included only few meadow patches. For the present study ten adult females, were used. We concentrated on females only because the live capture and handling of males are hampered by the large size and ferocity of boars. Also, due to competition and high levels of aggression between males during rut, the stocking of the enclosure was strongly female biased. During the study period (12/2016–01/2019), the animal density was ~ 1 adult female/ha plus up to 20 males (total) of different ages. Due to this relatively high density, animals were supplemented with 1–1.5 kg corn/individual once a day (at 2:00–14:00 h) at two feeding areas, each ~ 40 × 20 m. The enclosure was part of a game reserve, which was enclosed by 2.5 m high, solid, non-transparent fencing and was closed for the public. Thus, the study site provided an environment without disturbances due to hikers, bikers or straying dogs. There were no battue hunts or other disturbances due to hunting or forest management activities during the study period in the enclosure.Animals were trapped once a year in autumn within the feeding sites to collect data on reproductive success and body condition of females and to separate some of them for implantation/explantation of loggers. While feeding, we closed the access gates and released the boars one by one trough a wooden corridor back into the enclosure. While in the wooden corridor we recorded the body mass of each individual (Gallagher SmartScale® 500, Groningen, Netherlands). Due to management reasons the juveniles (born in spring) were removed from the enclosure during this procedure.Implantation of temperature and heart rate loggersWe implanted a heart rate logger (DST centi-HRT, Star-Oddi, Gardabaer, Iceland) and two custom-built temperature loggers in each of ten female wild boars in October/November 2016 and 2017 (age 5 and 6 years). All details about surgery techniques and anaesthesia protocols are provided in the “Supplementary Material”. Explantations were carried out approximately one year after implantations. The last explanation was carried out in January 2019. One female was implanted in two consecutive years. Mean body mass at date of implantation for all females was 71.8 ± 15.5 kg.The heart rate logger was adjusted to record data at a time interval of 12 min to cover one year of data recording. To remove outliers, all initial data from these recorders were subjected to a running median over five consecutive values. The HR recorder was positioned subcutaneously, in proximity to the heart on the lateral rib cage, behind the moving area of the elbow, to avoid rubbing, or inserted and tethered into the ventral subperitoneal space caudal of the xiphoid process of the sternum.The self-built temperature loggers were covered with inert surgical wax and had a weight of ~ 8 g. Time interval of recording was 4 min, the accuracy 0.01 °C. One of the two temperature loggers had an especially flat shape (3.4 × 1.9 × 0.5 cm) to fit smoothly into the subcutaneous neck region. The second temperature logger was placed into the intraperitoneal cavity, tethered at the Linea alba (diameter = 2.1 cm, height = 1.2 cm). For details on surgery, see “Supplement”.We collected and evaluated a mean of 227.45 ± 160.69 days of heart rate recording per individual (SD, n = 11: 33 days, 58 days, 79 days, 89 days, 143 days, 189 days, 272 days, 345 days, 412 days, 421 days, 461 days), and a mean of 382.00 ± 100.17 days (SD), of subcutaneous logger recording per individual (n = 8: 143 days, 363 days, 411 days, 414 days, 419 days, 421 days, 424 days, 461 days). From the loggers implanted in the abdominal cavity we collected 338.71 ± 117.01 days (SD) per individual (n = 10: 140 days, 143 days, 363 days, 364 days, 411 days, 419 days, 421 days, 421 days, 424 days, 461 days). The hourly means of monitored heart rates of each animal over the course of the year are shown in Supplementary Fig. S1.Activity dataTo record the activity of animals, a telemetry system (Smartbow System, Zoetis, New Jersey, USA) was installed around the two neighbouring feeding areas and two close water ponds in the enclosure. The system consisted of a central solar power and computing station and ten receivers located at the height of 2–3 m. Part of the system were ear-tags (34 g; 52 mm × 36 mm × 17 mm, for details see “Supplementary Material”). The accelerometer (located inside ear-tags) measured triaxial acceleration (x, y, z). As an estimate of locomotor activity (ACT), we computed the total acceleration vector from sqrt (x2 + y2 + z2).Climate and mastThe study site in Eastern Austria (altitude 130 m) is generally characterised by a Pannonian climate. According to long-term climate records, the mean annual temperature is 10 °C in combination with a mean precipitation of 600–700 mm and 1898 h of sunshine per year (ZAMG, 1971–2000).We recorded ambient temperature (Ta) and black bulb temperature (Tab) at 2 m height directly at the study site (Vantage Pro 2 with black bulb extension, Davis Instruments, Hayward, USA).To assess the extent of the acorn mast, each autumn seven nets, 4 × 4 m, were set up to collect acorns at random locations. The nets were regularly emptied between Sept. and Nov. each year, and the collected acorns were dried and weighed. In the autumns prior to the study (2016) and during both full study years (2017/2018) there was seeding of at least part of the oaks. Over ~ 90 days in each autumn we collected 52.4 g/m2, 134.8 g/m2, and 37.5 g/m2 acorn in 2016, 2017, and 2018, respectively. Thus, 2017 was a full mast year but there were acorns available in autumn throughout the study period.Data analysisTo facilitate handling of data and to reduce autocorrelation we compiled and evaluated hourly means for all data, i.e., heart rates (HR; see Suppl. Fig. S1), intraperitoneal and subcutaneous body temperature (Tbip and Tbsc, respectively) and activity (ACT), as well as ambient air temperature (Ta) and black-bulb temperature (Tab). We further tested for effects of day of year (DOY) and hour of day (HOUR). We did not assess the influence of environmental conditions in different years, because due to logger-failures and thus scarcity of heart rates, all data were pooled for different years (with similarly warm conditions and food available year-round). Also, we did not further evaluate daily rhythms, because animals were always fed in the early afternoon, which may have influenced their timing.We investigated the effects of season (DOY), hour of day (HOUR), and Ta on the response variables HR, Tbip, Tbsc, and ACT. We additionally used Tbip, Tbsc, and ACT as predictors for HR. As many of the relationships between these were non-linear, we used general additive mixed models (GAMMs), as implemented in package mgcv60 in R61. This function fits non-linear splines to the data, which are penalized for their “wiggliness”, i.e., the number of turning points in the fit. Because the data were repeated measurements, we calculated for all response variables mixed models with an intercept for each animal ID as a random factor (using s (ID, bs = ”re”)). Hence, these mixed models allowed for differences in the mean level of heart rates, temperatures and activities, between individuals. All residuals of models were approximately normally distributed, as inspected by normal quantile–quantile plots. Hourly means of the response variables contained various degrees of autocorrelation. This was corrected by including autoregressive order 1 (AR1) error models in GAMM-functions, which successfully reduced the autocorrelation at lag 1 to nonsignificant levels. This was confirmed by comparing the autocorrelation function of model residuals (ACF) before and after their correction. To illustrate the effects of independent variables, we show population-level predictions from GAMMs. These graphs contain rug plots to illustrate the distribution of independent variables. Because these plots were too dense for all original data (resulting in black bars), we show uniform random samples (n = 1000) from each independent predictor variable.Because hourly mean data consisted of ~ 117,000 observations we used the mgcv function “bam”, which uses numerical methods designed for large datasets. To fit non-linear functions to predictors, we used the default thin plate splines. Only the cyclic variables DOY and HOUR were modelled using cubic cyclic splines, which are guaranteed to have identical start- and endpoints (e.g., at Jan 1 and Dec 31). GAMMs were always fitted using method REML. As Tbip and Tbsc were only moderately correlated (r = 0.30), both were entered simultaneously as independent variables in the model on heart rate.We did not use partial regression plots from multiple regressions that included activity. This is because activity could only be recorded partly, in the vicinity of telemetry receivers. Thus, models that include ACT as well as all other predictors simultaneously, were restricted to ~ 7% of the data. However, we still used a full multiple regression model HR for the purpose of assessing relative variable importance (of DOY, HOUR, Ta, Tbip, Tbsc, and ACT). F-values from this model provide an indication of the importance of different predictors.To model a possible role of solar radiation and basking we computed the difference between Tab and Ta, called Tdiff, which represents an index of radiation. We used again GAMMs to test if Tdiff would affect Tbip, Tbsc and HR after adjusting for effects of Ta, hour of day, and the random factor animal ID.For a comparison of species we also computed monthly means and SEMs of HR in wild boars, and created a graph of seasonal time courses in other ungulates as published in Arnold2 that were kindly provided by the author. If not stated otherwise we provide means ± SEM. More

1.Pradhan, S., Saha, G. K. & Khan, J. A. Food habits of the red panda, Ailurus fulgens, in the Singalila National Park, Darjeeling, India. J. Bombay Nat. Hist. Soc. 98, 224–230 (2001).
Google Scholar 
2.Bista, D. et al. Distribution and habitat use of red panda in the Chitwan–Annapurna Landscape of Nepal. PLoS ONE 12, e0178797 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
3.Martin, P. S. The discovery of America. Science 179, 969–974 (1973).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
4.Miller, G. H. et al. Pleistocene extinction of Genyornis newtoni: human impact on Australian megafauna. Science 283, 205–208 (1999).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
5.Grayson, D. K. & Meltzer, D. J. A requiem for North America overkill. J. Archaeol. Sci. 30, 585–593 (2003).Article 

Google Scholar 
6.van der Kaars, S. et al. Humans rather than climate the primary cause of Pleistocene megafaunal extinction in Australia. Nat. Commun. https://doi.org/10.1038/ncomms14142 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
7.Louys, J. & Roberts, P. Environmental drivers of megafaunal and hominin extinction in Southeast Asia. Nature 586, 402–406 (2020).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
8.Ripple, W. J. et al. Tertiary fossil fungi from Kiandra, New South Wales. Proc. Linn. Soc. NSW. 97, 141–149 (1975).
Google Scholar 
9.Schipper, J. et al. The status of the world’s land and marine mammals: diversity, threat, and knowledge. Science 322, 225–230 (2008).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
10.Brook, S. M. et al. Lessons learned from the loss of a flagship: the extinction of the Javan rhinoceros Rhinoceros sondaicus annamiticus from Vietnam. Biol. Conserv. 174, 21–29 (2014).Article 

Google Scholar 
11.Prasad, V., Stromberg, C. A. E., Alimohammadian, H. & Sahni, A. Dinosaur coprolites and the early evolution of grasses and grazers. Science 310, 1177–1180 (2005).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Shillito, L. M., Blong, J. C., Green, E. J. & VanAsperen, E. N. The what, how and why of archaeological human coprolite analysis. Earth Sci. Rev. 207, 103196 (2020).CAS 
Article 

Google Scholar 
13.van Geel, B. et al. The ecological implications of a Yakutian mammoth’s last meal. Quat. Res. 69, 361–376 (2008).Article 
CAS 

Google Scholar 
14.Rawlence, N. J., Wood, J. R., Bocherens, H. & Rogers, K. M. Dietary interpretations for extinct megafauna using coprolites, intestinal contents and stable isotopes: Complimentary or contradictory?. Quat. Sci. Rev. 142, 173–178 (2016).ADS 
Article 

Google Scholar 
15.Carrion, J. S. Pleistocene landscape in central Iberia inferred from pollen analysis of hyena coprolite. J. Quat. Sci. 22(2), 191–202 (2007).Article 

Google Scholar 
16.Wood, J. R. et al. Coprolite deposits reveal the diet and ecology of the extinct New Zealand megaherbivore moa (Aves, Dinornithiformes). Quat. Sci. Rev. 27, 2593–2602 (2008).ADS 
Article 

Google Scholar 
17.Gravendeel, B. et al. Multiproxy study of the last meal of a mid-Holocene Oyogos Yar horse, Sakha Republic, Russia. The Holocene 24(10), 1288–1296 (2014).ADS 
Article 

Google Scholar 
18.Akeret, O., Haas, J. N., Leuzinger, U. & Jacomet, S. Plant macrofossils and pollen in goat/sheep faeces from the Neolithic lake-shore settlement Arbon Bleiche 3, Switzerland. The Holocene 9(2), 175–182 (1999).ADS 
Article 

Google Scholar 
19.Birks, H. H. et al. Evidence for the diet and habitat of two late Pleistocene mastodons from the Midwest, USA. Quat. Res. 79, 1–21 (2018).ADS 

Google Scholar 
20.van der Waal, C. et al. Large herbivores may alter vegetation structure of semi-arid savannas through soil nutrient mediation. Oecologia 165, 1095–1107 (2011).ADS 
PubMed 
PubMed Central 
Article 

Google Scholar 
21.Velazquez, N. J. & Burry, L. S. Palynological analysis of Lama guanicoe modern feces and its importance for the study of coprolites from Patagonia, Argentina. Rev. Palaeob. Palynol. 184, 14–23 (2012).Article 

Google Scholar 
22.Basumatary, S. K., McDonald, H. G. & Gogoi, R. Pollen and non-pollen palynomorph preservation in the dung of the Greater one –horned rhino (Rhinoceros unicornis), and its implication to palaeoecology and palaeodietary analysis: a case study from India. Rev. Palaeo. Palynol. 244, 153–162 (2017).Article 

Google Scholar 
23.Basumatary, S. K., Singh, H., McDonald, H. G., Tripathi, S. & Pokharia, A. K. Modern botanical analogue of endangered Yak (Bos mutus) dung from India: Plausible linkage with living and extinct megaherbivores. PLoS ONE 14(3), e0202723 (2019).24.Roberts, M. S. & Gittleman, J. L. Ailurus fulgens. Mammalian species. Am. Soc. Mammal. 222, 1–8 (1984).
Google Scholar 
25.Johnson, K. G., Schaller, G. B. & Hu, J. C. Comparative behavior of red and giant pandas in the Wolong Reserve, China. J. Mammal. 69, 552–564 (1988).Article 

Google Scholar 
26.Yonzon, P. B. & Hunter, M. L. Ecological study of the red panda in Nepal-Himalaya. red panda Biology 1, 7 (1989).
Google Scholar 
27.Wei, F. W., Wang, W., Zhou, A., Hu, J. & Wei, Y. Preliminary study on food selection and feeding strategy of red pandas. Acta Theriol. Sin. 15, 259–266 (1995).
Google Scholar 
28.Zhang, Z. J., Hu, J. C., Yang, J. D., Li, M. & Wei, F. W. Food habits and space-use of red panda, Ailurus fulgens in the Fengtongzhai Nature Reserve, China: Food effects and behavioural response. Acta Theriol. 54, 225–234 (2009).Article 

Google Scholar 
29.Dorji, S., Vernes, K. & Rajaratnam, R. Habitat correlates of the red panda in the temperate forests of Bhutan. PLoS ONE 6, e26483 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
30.Panthi, S., Aryal, A., Raubenheimer, D., Lord, J. & Adhikari, B. Summer diet and distribution of the Red Panda (Ailurus fulgens fulgens) in Dhorpatan Hunting Reserve, Nepal. Zool. Stud. 51(5), 701–709 (2012).
Google Scholar 
31.Sharma, H. P., Swenson, J. E. & Belant, J. L. Seasonal food habits of the red panda (Ailurus fulgens) in Rara National Park, Nepal. Hystrix 25(1), 47–50 (2014).
Google Scholar 
32.Panthi, S., Coogan, S. C. P., Aryal, A. & Raubenheimer, D. Diet and nutrient balance of red panda in Nepal. Sci. Nat. 102, 54 (2015).Article 
CAS 

Google Scholar 
33.Thapa, A. & Basnet, K. Seasonal diet of wild red panda (Ailurus fulgens) in Langtang national park, Nepal Himalaya. Inter. J. Conser. Sci. 6(2), 261–270 (2015).CAS 

Google Scholar 
34.Thapa, A. et al. The endangered red panda in Himalayas: potential distribution and ecological habitat associates. Glob. Ecol. Conser. 21, e00890 (2020).35.Hu, Y. et al. Genomic evidence for two phylogenetic species and long-term population bottlenecks in red pandas. Sci. Adv. 6, eaax5751 (2020).36.IUCN. IUCN red list of threatened species. Version 2018.1. [Online] Available: www.iucnredlist.org (August 14, 2018).37.Salesa, M. J., Peigne, S., Antón, M. & Morales, J. Evolution of the Family Ailuridae: Origins and Old- World Fossil Record. In Red Panda: Biology and Conservation of the First Panda (ed. Glatston, A. R.) 27–41 (Elsevier, 2011).Chapter 

Google Scholar 
38.Thapa, A. et al. Predicting the potential distribution of the endangered red panda across its entire range using MaxEnt modeling. Ecol. Evol. 8, 10542–10554 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
39.Chaudhury, A. An overview of the status and conservation of the red panda (Ailurus fulgens) in India, with reference to its global status. Oryx 35(3), 250–259 (2001).Article 

Google Scholar 
40.Eizirik, E. et al. Pattern and timing of diversification of the mammalian order carnivora inferred from multiple nuclear gene sequences. Mol. Phylogenet. Evol. 56(1), 49–63 (2015).Article 
CAS 

Google Scholar 
41.Hu, Y. et al. Comparative genomics reveals convergent evolution between bamboo-eating giant and red pandas. Proc. Natl. Acad. Sci. 114(5), 1081–1086 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Jha, A. K. Release and reintroduction of captive-bred red pandas into Singalila National Park, Darjeeling, India. In Red panda: biology and conservation of the first panda (ed. Glatson, A. R.) 435–446 (Academic Press, 2011).Chapter 

Google Scholar 
43.Wikramanayake, E., E. Terrestrial Ecoregions of the Indo-Pacific: A Conservation Assessment. Washington, D.C.: Island Press. ISBN 1-55963-923-7 (2002).44.Janzen, D. H. Why bamboos wait so long to flower. Ann. Rev. Eco. Syst. 7, 347–391 (1976).Article 

Google Scholar 
45.van Geel, B. et al. Giant deer (Megaloceros giganteus) diet from Mid-Weichselian deposits under the present North Sea inferred from molar-embedded botanical remains. J. Quat. Sci. 33, 924–933 (2018).Article 

Google Scholar 
46.Basumatary, S. K. & McDonald, H. G. Coprophilous fungi from dung of the greater one-horned Rhino in Kaziranga National Park, India and its implication to palaeoherbivory and palaeoecology. Quat. Res. 88, 14–22 (2017).Article 

Google Scholar 
47.Swati, T. et al. Multiproxy studies on dung of endangered sangai (Rucervus eldii eldii) and Hog deer (Axis porcinus) from Manipur, India: Implication for paleoherbivory and paleoecology. Rev. Palaeob. Palyn. 263, 85–103 (2019).Article 

Google Scholar 
48.Goh, T. K., Ho, W. H., Hyde, K. D., Whitton, S. R. & Umali, T. E. New records and species of Canalisporium (Hyphomycetes), with a revision of the genus. Canadian J. Bot. 76, 142–152 (1998).
Google Scholar 
49.Heudre, D., Wetzel, C. E., Moreau, L. & Ector, L. Sellaphora davoutiana sp. Nov.: a new freshwater diatom species (Sellaphoraceae, Bacillariophyta) in lakes of Northeastern France. Phytotaxa 346(3), 269–279 (2018).Article 

Google Scholar 
50.Biswas, O. et al. Can grass phytoliths and indices be relied on during vegetation and climate interpretations in the eastern Himalayas? Studies from Darjeeling and Arunachal Pradesh, India. Quat. Sci. Rev. 134, 114–132 (2016).ADS 
Article 

Google Scholar 
51.Biswas, O. et al. A comprehensive calibrated phytolith based climatic index from the Himalaya and its application in palaeotemperature reconstruction. Sci. Total Environ. 750, 142 (2021).Article 
CAS 

Google Scholar 
52.Chaudhuri, A. B. Common grasses and sedges of Kurseong, Kalimpong and Darjeeling forest divisions, West Bengal. Indian For. 86(6), 336–348 (1960).
Google Scholar 
53.Hajra, P. K. & Verma, D. M. Flora of Sikkim, Vol. II. Botanical Survey of India, (1996).54.Neto, M. A. M. & Guerra, M. P. A new method for determination of the photosynthetic pathway in grasses. Photosyn. Res. 142, 51–56 (2019).CAS 
Article 

Google Scholar 
55.Frank, K., Bruckner, A., Hilpert, A., Heethoft, M. & Bluthgen, N. Nutrient quality of vertebrate dung as a diet for dung beetles. Sci. Rep. 17, 12141 (2017).ADS 
Article 
CAS 

Google Scholar 
56.Tieszen, L. L. Natural variations in the carbon isotope values of plants: implications for archaeology, ecology, and palaeoecology. J. Archaeol. Sci. 78, 227–248 (1991).Article 

Google Scholar 
57.Heaton, T. Spatial, species, and yemporal variations in the 13C/12C ratios of C3 plants: Implications for palaeodiet studies. J. Archaeol. Sci. 26, 637–649 (1999).Article 

Google Scholar 
58.Arens, N. C., Jahren, A. H. & Amundson, R. Can C3 plants faithfully record the carbon isotopic composition of atmospheric carbon dioxide?. Paleobiology 26(1), 137–164 (2000).Article 

Google Scholar 
59.Cerling, T. E., Harris, J. M. & Leakey, M. G. Browsing and grazing in modern and fossil proboscideans. Oecologia 120, 364–374 (1999).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
60.Mac Fadden, B. J., Cerling, T. E., Harries, J. M. & Prado, J. L. Ancient latitudinal gradients of C3/C4 grasses interpreted from stable isotopes of New World Pleistocene horse (Equus) teeth. Global Ecol. Biog. 8, 137–149 (1999).
Google Scholar 
61.Burney, D. A., Robinson, G. S. & Burney, L. P. Sporormiella and the late Holocene extinctions in Madagascar. Proc. Natl Acad. Sci. U.S.A. 100(19), 10800–10805 (2003).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
62.Davis, O. K. & Shafer, D. S. Sporormiella fungal spores, a palynological means of detecting herbivore density. Palaeog. Palaeoclim. Palaeo. 237, 40–50 (2006).ADS 
Article 

Google Scholar 
63.Raper, D. & Bush, M. A test of Sporormiella representation as a predictor of megaherbivore presence and abundance. Quat. Res. 71, 490–496 (2009).Article 

Google Scholar 
64.Perrotti, A. G. & Van Asperen, E. N. 2019: Dung fungi as a proxy for megaherbivores: opportunities and limitations for archaeological applications. Veget. Hist. Archaeobot. 28, 93–104 (2019).Article 

Google Scholar 
65.Ingold, C. T. Ballistics in certain ascomycetes. New Phytol. 60, 143–149 (1961).Article 

Google Scholar 
66.Trail, F. Fungal cannons: explosive spore discharge in the Ascomycota. FEMS Microbio. Letters 276, 12–18 (2007).CAS 
Article 

Google Scholar 
67.Yafetto, L. The fastest flights in nature: high-speed spore discharge mechanisms among fungi. PLoS ONE 3, e3237 (2008).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
68.Erdtman, G. An introduction to Pollen Analysis (Waltham, 1953).
Google Scholar 
69.Gupta, H.P. & Sharma, C. Pollen flora of North-west Himalaya. Indian Association of Palynostratigraphers, Lucknow, India, (1986).70.Van Geel, B. Environmental reconstruction of a Roman Period settlement site in Uitgeest (The Netherlands), with special reference to coprophilous fungi. J. Archaeo. Sci. 30, 873–883 (2003).Article 

Google Scholar 
71.Van Asperen, E. N., Kirby, J. R. & Hunt, C. O. The effect of preparation methods on dung fungal spores: Implications for recognition of megafaunal populations. Rev. Palaeobot. Palynol. 229, 1–8 (2016).Article 

Google Scholar 
72.Neumann, K. International code for phytolith nomenclature ICPN 2.0. Ann. Bot. 124, 189–199 (2019).Article 

Google Scholar 
73.Hill, M. O. & Gauch, H. G. Detrended correspondence analysis, an improved ordination technique. Vegetatio 42(1), 47–58 (1980).Article 

Google Scholar 
74.Ter Braak, C. J. F. Canonical correspondence analysis: a new eigenvector technique for multivariate direct gradient analysis. Ecology 67, 1167–1179 (1986).Article 

Google Scholar 
75.Ter Braak, C. J. F. Canoco-a FORTRAN program for canonical community ordination by (partial) (detrended) (canonical) correspondence analysis, principal components analysis and redundancy analysis (version 2.1).Technical Rep. LWA-88-02. GLW, Wageningen, 95 pp. (1988).76.Ter Braak, C. J. F. & Smilauer, P. CANOCO 4.5. Biometris. Wageningen University and Research Center, Wageningen, 500 pp. (2002).77.Agnihotri, R. et al. Radiocarbon measurements using new automated graphite preparation laboratory coupled with stable isotope mass-spectrometry at Birbal Sahni Institute of Palaeosciences, Lucknow (India). J. Environ. Radioact. 213, 106156 (2020).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar  More

NEWS AND VIEWS
15 September 2021

A spotlight on seafood for global human nutrition

What role might seafood have in boosting human health in diets of the future? A modelling study that assesses how a rise in seafood intake by 2030 might affect human populations worldwide offers a way to begin to answer this.

Lotte Lauritzen

 ORCID: http://orcid.org/0000-0001-7184-5949

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Lotte Lauritzen

Lotte Lauritzen is in the Department of Nutrition, Exercise and Sports, University of Copenhagen, 1958 Frederiksberg C, Denmark.

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An adequate and sustainable supply and intake of nutritious food is essential to tackle major global health issues such as dietary deficiencies. Seafood, which in this context includes fish, shellfish and marine mammals, is rich in micronutrients (such as vitamin A, iron, vitamin B12 and calcium) needed to combat the most common such deficiencies. Seafood is also the dominant source of marine omega-3 fatty acids, which have many health-promoting effects. Writing in Nature, Golden et al.1 present ambitious research that puts seafood centre stage.

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doi: https://doi.org/10.1038/d41586-021-02436-3

References1.Golden, C. et al. Nature https://doi.org/10.1038/s41586-021-03917-1 (2021).Article 

Google Scholar 
2.Food and Agriculture Organization of the United Nations. The State of World Fisheries and Aquaculture 2020. Sustainability in Action (FAO, 2020).3.FAO, IFAD, UNICEF, WFP & WHO. The State of Food Security and Nutrition in the World 2021. Transforming Food Systems for Food Security, Improved Nutrition and Affordable Healthy Diets for All (FAO, 2021).4.Kumssa, D. B. et al. Sci. Rep. 5, 10974 (2015).PubMed 
Article 

Google Scholar 
5.Mithal, A. et al. Osteoporosis Int. 20, 1807–1820 (2009).Article 

Google Scholar 
6.Vuholm, S. et al. Eur. J. Nutr. 59, 1205–1218 (2020).PubMed 
Article 

Google Scholar 
7.Gebauer, S. K., Psota, T. L., Harris, W. S. & Kris-Etherton, P. M. Am. J. Clin. Nutr. 83, 1526S–1535S (2006).PubMed 
Article 

Google Scholar 
8.Djuricic, I. & Calder, P. C. Nutrients 13, 2421 (2021).PubMed 
Article 

Google Scholar 
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We first identify the location of organic crop fields in Kern County and then estimate whether status as organic versus conventional fields determines pesticide use (Fig. 5).Fig. 5: Methodology overview.Figure outlines the main method steps from identifying organic fields to creating the analysis data to performing the statistical analyses. All images shown are simplified, visual representations of the datasets. CDFA refers to the California Department of Food and Agriculture, while APN is the Assessor’s Parcel Number and TRS is the Township-Range-Section. Identifying organic fields combines the created CDFA organic APN, CDFA organic TRS, and organic pesticides data layers together to create the final organic versus conventional fields layer used in the analysis data section. All analysis data layers are then inputted into the various statistical analyses.Full size imageIdentifying organic fieldsWe identified organic fields using a combination of California Department of Food and Agriculture (CDFA) records and Kern County Agricultural Commissioner’s Office spatial data (“fields shapefiles”) and pesticide use records. No single source was complete, and as such, we evaluated several different approaches to identifying organic fields.California Department of Food and Agriculture (CDFA) recordsData on the location of organic fields, per the California State Organic Program, for 2013–2019 was obtained by request from the California Department of Food and Agriculture (CDFA). The CDFA, through the State Organic Program, requires annual registration of certified organic producers who have an expected gross sale of over $5000. We were specifically interested in the pesticide aspects of organic production, which is governed in our study region by the USDA’s National List of Allowed and Prohibited Substances. The National List of Allowed and Prohibited Substances delineates which synthetic substances can be used and which natural substances cannot be used for pest control in US organic production. Besides substances specifically (dis)allowed on the National List, allowed substances include non-synthetic biological, botanical, and mineral inputs. Field location data were in the form of either Assessor’s Parcel Number (APN) or PLS System Township-Range-Section (TRS) values, though data were reported without systematic formatting. We harmonized the CDFA APN values to merge with the Kern County Assessor’s parcel shapefile (2017), which we then spatially joined with the Kern fields shapefiles. We followed a similar process with PLSS TRS values, which were then merged with the Kern County PLS Section shapefile, and joined to Kern field shapefiles. We refer to our final organic designation as “CDFA Organic”. Details of the data cleaning process are described in the Ancillary Data Processing Methods section below.Using pesticide use reports to refine organic field identificationAfter spot-checking pesticide use on CDFA Organic fields, it became clear we had not entirely eliminated conventional fields. This was likely due to variation in polygon geometries between PLSS Sections, Kern County Assessor parcels, and Kern agricultural fields data. To further refine our classification, we used field-level pesticide use, again from the Kern County Agricultural Commissioner’s Office. As thousands of pesticide products (active ingredients + adjuvants) are in use in Kern County, we took an iterative approach to eliminate fields using conventional pesticides. We first limited the universe of pesticides to those applied to fields that were CDFA Organic. We then identified the 50 most commonly used pesticide products by a number of applications, and manually classified each as organic or conventional. Having identified these products as described below, we matched them back in, eliminating fields that used conventional products and identifying as “PUR Organic” those that used only organic products. We repeated this process, hand identifying the most commonly used products and eliminating fields using conventional products until we had isolated fields using only organic products.To classify a product as organic or conventional, we first searched for each product’s U.S. EPA-registered product label, using the exact product name and EPA product registration number. If there was any indication on the label that the product was certified as organic by the Organic Materials Review Institute (OMRI), or said “for use in organic production” or “organic”, then the pesticide was identified as organic (n = 132). If there was no organic indication on the product label, we searched the OMRI certification database for products with identical names and manufacturers, and identified products as organic if such certifications existed (n = 39). If all ingredients were defined (i.e., no inert or undefined ingredients) and were known organic active ingredients, then the pesticide was identified as organic (n = 1) (Supplementary Data 1). We failed to find EPA-registered labels for three products and confirmed on the California Department of Pesticide Regulation website that they are either inactive or out of production (EPA registration numbers: 52467-50008-AA-5905, 36208-50020-AA, 2935-48-AA-120). Each of the three was rarely used (n  0) to be the same as the mechanisms determining the amount sprayed when some pesticides are used (pesticides when pesticides  > 0). Double-hurdle models64 are an alternative to the Tobit model that allows for the separation of these two decisions.The mechanisms underlying the two decisions (to spray, how much to spray if spraying) can differ such that different covariates can describe each process, and the same covariates are allowed to influence the two processes in different ways (i.e., sign and magnitude can differ). The first, binary decision is usually modeled with a probit model.$${{{{{rm{P}}}}}}left(y=0|{{{{{bf{x}}}}}}right)=1-Phi left({{{{{bf{x}}}}}}gammaright)$$
(1)
Then, the second decision is modeled as a linear model with pesticide use following a lognormal distribution, conditional on positive pesticide use64$$log (y)|{{{{{bf{x}}}}}},y , > , 0 sim {{{{{rm{Normal}}}}}}({{{{{bf{x}}}}}}{{{{{mathbf{upbeta }}}}}},{sigma }^{2})$$
(2)
where Φ is the standard normal cdf, x is a vector of explanatory variables including organic status, y is pesticide use, and ({{{{{mathbf{upbeta }}}}}}) is a vector of coefficients. We use a lognormal hurdle model rather than a truncated normal hurdle model since pesticide use is highly non-normal, and Q-Q plots suggested substantial model improvement using a lognormal rather than normal distribution. In contrast to the panel data models described in the Ancillary Statistical Methods below, our estimation equation used natural log-transformed variables for pesticides (and field and farm size) rather than inverse hyperbolic sine (IHS) transformation since only positive observations are included in the second hurdle model. Following insights derived from our panel data models (Supplementary Notes), we build on the basic hurdle model concept using the farm-by-crop family interaction as a random intercept in both the first and second hurdle. We chose the farm-by-crop family interaction rather than a crossed random effect due to computational feasibility with thousands of permits and hundreds of crops, due to similarity of results to the within estimator model (i.e., fixed effects in causal inference terminology; Supplementary Notes, Supplementary Table 2), and due to AIC/BIC (Supplementary Table 3). Further, we find evidence of heteroskedasticity from both visual inspection and Levine’s test, which adds additional complications to computing crossed random effects. Thus, we proceed with the farm-by-crop family interaction in a random intercept model with cluster robust standard errors clustered at the same grouping. In doing so, observations, where the taxonomic family of the crop was unclear, were dropped. Of the 7367 fields that were dropped due to missing crop families, 6684 were uncultivated agriculture.Our data are effectively repeated cross-sections rather than a true panel since fields are defined by the farm-site-year combination and thus generally change year-to-year or when crops rotate. We model it as such. This implies we do not require observations to have no spray in all time periods, as would be the case in a double hurdle panel model. Linking field IDs over time through spatial processing is complicated by crop rotations of different size areas. Since farmers may farm multiple fields under different management systems, as we illustrate here, and have different contractual obligations at a sub-farm level, requiring farms to never use pesticides on all fields is not reflective of on-the-ground decisions.We repeated the lognormal hurdle models individually for carrots, grapes, oranges, potatoes, and onions, which were the only widely-grown crops with more than 100 organic fields. This allowed for a different slope and intercept by crop type.We conduct several robustness checks. First, we do not have data on crop yields. However, to assess the potential implications of a yield gap on our results, we modify our per hectare rates following Ponisio et al.15 as a robustness check. We group commodities into cereals, roots and tubers, oilseeds, legumes/pulses, fruits, and vegetables and assign them the Ponisio et al.15 yield gap estimates for that group. Crops that did not fall into any of the above groups (e.g., cannabis) were provided the all-crop average from Ponisio et al.15. Second, we analyze how conventional and organic differ with respect to soil quality using a within estimator approach to account for crop-specific differences in soil quality. Third, binary toxicity metrics, while valuable given the number of chemicals and endpoints of interest here, nevertheless fail to distinguish gradations of toxicity for chemicals above (or below) the regulatory threshold. As mentioned above, the data needed to calculate many aggregate indices (e.g., Pesticide Load57 and Environmental Impact Quotient58) are not readily available for all of the chemicals in our study. For completeness, we attempted to calculate the Pesticide Toxicity Index for one well-studied endpoint, fish. We supplemented data provided in Nowell et al.41 with data from Standartox42. However, only about 70% of the chemicals used in our study matched, and pesticide products used on organic fields were more likely to lack toxicity information for one or more chemicals. We briefly discuss the highly preliminary investigation, given the non-random missing toxicity data.All spatial analyses were performed in R Statistical Software v 3.5.3 and all statistical analyses were performed Stata 16 MP. For all tests, statistical significance was based on two-tailed tests with (alpha =0.05.)Ancillary data processing methodsCleaning parcel dataTo spatially locate organic fields, we needed to match the Assessor’s parcel numbers (APNs) provided in the CDFA tabular data to APNs in the Kern County Parcel shapefile (from 2017). Over 90% of the APN entries in the CDFA data were in the format [xxx-xxx-xx], though multiple APNs were often provided in the same cell separated by line breaks, semi-colons, commas, and/or spaces. We made initial edits separating values into individual cells in Microsoft Excel since formatting was highly inconsistent. Observations whose APNs were not in the [xxx-xxx-xx] were modified so that their format matched. In the R environment, dashes were inserted after the third, sixth, and eighth characters (1234567895 became 123-456-78-95) for APNs that did not already contain them. Occasionally, APN numbers were provided with dashes, but with segments of incorrect length (e.g., 12-34-567). In these instances, APN segments were either trimmed from the right or padded with a zero on the left so they matched the [xxx-xxx-xx] format. This approach yielded the greatest number of matches and was checked for accuracy as described below. Additional segments (from APNs with more than two dashes and eight numeric characters) were dropped. A handful of APNs with fewer than eight numeric characters and no dashes were dropped entirely.The edited CDFA APNs were then joined with the Kern County Assessor’s parcel shapefile, creating the “CDFA organic shapefile”. In total, 1637 of 1829 individual CDFA records joined successfully. To evaluate the accuracy of joins between CDFA tabular data, Kern County parcel, and Kern County agricultural spatial data, we spot-checked ownership information using “Company” (CDFA) and “PERMITTEE” (Kern County agricultural data) values.To then identify the crop fields within the organic parcels, we performed a spatial join between the CDFA organic shapefile and the Kern County fields shapefiles. Prior to performing the join, the CDFA parcels’ dimensions were reduced with a 50-m buffer to eliminate spatial joins between CDFA parcels and crop fields that were only touching the parcel margins. Of five different buffer widths evaluated, 50 m reduced the number of false positives and negatives, as determined by comparing the “Company” and “PERMITTEE” values. We refer to the fields that match as “APN Organic”.Cleaning PLSS Township-Range-Section valuesEach year several producers reported Township, Section, and Range (TRS) values, consistent with the PLS System (PLSS), rather than APN values. We used these TRS values to identify PLSS Sections that contained organic fields.We separated any cell containing multiple TRS values and removed any prefixes such as “S”, “Section”, “Sec.”, “T”, and “R” that would prevent joining to Kern County PLSS spatial data in Excel. In the R environment, we padded the left side of the “S” value with a 0 if it was a single digit, then concatenated the three columns into a “TRS” column. We joined TRS from the CDFA tabular data to PLSS spatial data, which identified 563 Sections as containing organic fields, from 2013 to 2019, out of a total of 664 unique TRS codes in the CDFA dataset. We then performed a spatial join between PLSS Sections that contain organic fields and Kern County fields shapefiles, to identify all agriculture fields that overlap with those Sections. Additional processing using the Pesticide Use Reports is described above.Ancillary statistical methodsWe began with a pooled ordinary least squares (OLS) model that, as the name suggests, pools observations over farms, years, and crop types. However, there may be attributes of crops or farms that may be systematically different between organic and conventional, and this systematic difference could bias our pooled OLS results. To address this, we first considered propensity score approaches but were unable to find a sufficient balance of our covariate distribution between organic and conventional fields. As an alternative, we limited our sample to fields with overlapping farmers and crop types. In other words, we focused on the subset of fields that are grown by farmers producing both organic and conventional fields and to crops that are produced both conventionally and organically. However, this shrunk our dataset by two-thirds.To leverage more of our data, we next considered panel data models as a means to address unobserved variables. We consider both within-estimator models (also known as “fixed effects” in causal inference terminology, but different from the biostatistical use of the term) and random effects models (with random intercepts), seeking to capture characteristics of the crop, grower, and year. The advantage of a within-estimator approach is that the omitted variables are removed (through differencing) and thus, they can be correlated with covariates without biasing the estimation. In other words, pesticide use and all covariates are differenced from their crop-specific mean (or crop family, farmer, etc. specific mean, depending on the model). In doing so, the propensity for certain crops (crop family, farmer) to be grown organic or to be fast or slow adopters of new technologies is removed. The disadvantage is that characteristics shared by all fields of a crop (e.g., value) are lost in the differencing, and more importantly, that the differencing is not easily translated to nonlinear models that we employ later in the analysis. Random effects are more easily translated to nonlinear models. The disadvantage of random effects is the strong assumption that the unobserved variables are uncorrelated with the covariates18,65, which is required for random effects coefficient estimates to be unbiased. Here, we see the difference in coefficient estimates between the within-estimator and random effects models are quite small (Supplementary Table 2).Random effects particularly crossed random effects with thousands of permits and hundreds of crops, introduce computational challenges due to large, sparse matrices. Further, we find evidence of heteroskedasticity from both visual inspection and Levine’s test, which adds additional complications to computing crossed random effects. We proceed using the farm-by-crop family interaction in a random intercept model with cluster robust standard errors clustered at the same grouping based on AIC/BIC (Supplementary Table 3), computational feasibility, and similarity to the within-estimator results (Supplementary Table 2). Observations, where the taxonomic family of the crop was unclear, were dropped in any models including family in either the random effects or the cluster robust standard errors. Of the 7367 fields that were dropped due to missing crop families, 6684 were uncultivated agriculture.In the panel data models, we used IHS transformations to accommodate highly non-normal pesticide (and field and farm size) data. IHS is very similar to natural log transformation66 but is defined at zero, which is important given a sizable fraction of our observations have zero pesticide use. As with log–log transformations, IHS–IHS transformation can be interpreted as elasticities. We pre-multiply pesticide use by 100 to improve estimation66, though this does not affect interpretation. As described above, we leverage insights on model specification from the panel data models, but rely on the double hurdle models to parse apart the decision to spray from the decision of how much to spray.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

The absence of the first principles for biological systems in general, and in particular for vegetation populations where phenomena are interconnected makes their mathematical modelling complex. The theory of vegetation pattern formation rests on the self-organisation hypothesis and symmetry-breaking instability that provoke the fragmentation of the uniform cover. The symmetry-breaking instability takes place even if the environment is isotropic31,33,35. This instability may be an advection-induced transition that requires the pre-existence of the environment anisotropy due to the topography of the landscape34,39,40. Generally speaking, this transition requires at least two feedback mechanisms having a short-range activation and a long-range inhibition. In this respect, we consider three different vegetation models that are experimentally relevant systems: (i) the generic interaction redistribution model describing vegetation pattern formation which incorporates explicitly the facilitation, competition and seed dispersion nonlocal interactions (ii) the local nonvariational partial differential model described by a nonvariational Swift–Hohenberg type of model equation, and (iii) the reaction–diffusion system that incorporate explicetely water transport.The interaction-redistribution approachThe integrodifferential modelThis approach consists of considering a well-known logistic equation with nonlocal plant-to-plant interactions. Three types of interactions are considered: the facilitative (M_{f}(mathbf {r},t)), the competitive (M_{c}(mathbf {r},t)), and the seed dispersion (M_{d}(mathbf {r},t)) nonlocal interactions. To simplify further the mathematical modelling, we consider that the seed dispersion obeys a diffusive process (M_{d}(mathbf {r},t)approx nabla ^{2}b(mathbf {r},t)), with D the diffusion coefficient, b the biomass density, and (nabla ^{2}=partial ^2/partial x^2+partial ^2/partial y^2) is the Laplace operator acting in the (x,y) plane. The interaction-redistribution reads$$begin{aligned} M_{i}=expleft{ frac{xi _{i}}{N_{i}}int b(mathbf {r}+mathbf {r}’,t)phi _i(r,t)dmathbf {r}’right} , { text{ with } } phi _i(r,t)= exp(-r/L_{i}) end{aligned}$$
(1)
where (i=f,c). (xi _i) represents the strength of the interaction, (N_i) is a normalisation constant. We assume that their Kernels (phi _i(r,t)) are exponential functions with (L_i) the range of their interactions. The facilitative interaction (M_{f}(mathbf {r},t)) favouring vegetation development. They involve the accumulation of nutrients in the neighbourhood of plants, the reciprocal sheltering of neighbouring plants against climatic harshness which improves the water budget in the soil. The range of the facilitative interaction (L_f) operates on the crown size. The competitive interaction operates over a length (L_c) and involves the below-ground structures, i.e., the rhizosphere. In nutrient-poor or/and in water-limited territories, lateral spreading may extend beyond the radius of the crown. This extension of roots relative to their crown size is necessary for the survival and the development of the plant in order to extract enough nutrients and/or water from the soil. When incorporating these nonlocal interactions in the paradigmatic logistic equation, the spatiotemporal evolution of the normalised biomass density (b(mathbf {r}, t)) in isotropic environmental conditions reads14$$begin{aligned} partial _{t} b(mathbf {r},t)=b(mathbf {r},t)[1-b(mathbf {r},t)]M_{f}(mathbf {r},t)- mu b(mathbf {r},t)M_{c}(mathbf {r},t)+Dnabla ^{2}b(mathbf {r},t). end{aligned}$$
(2)
The normalisation is performed with respect to the total amount of biomass supported by the system. The first two terms in the logistic equation with nonlocal interaction Eq. (2) describe the biomass gains and losses, respectively. The third term models seed dispersion. The aridity parameter (mu) accounts for the biomass loss and gain ratio, which depends on water availability and nutrients soil distribution, topography, etc. The homogeneous cover solutions of Eq. (2) are: (b_{o}=0) which corresponds to the state totally devoid of vegetation, and the homogeneous cover solutions satisfy the equation$$begin{aligned} mu =(1-b)exp (Delta b), end{aligned}$$
(3)
with (Delta =xi _{f}-xi _{c}) measures the community cooperativity if (Delta >0) or anti-cooperativity when (Delta 0). The solution (u_{-}) is always unstable even in the presence of small spatial fluctuations. The linear stability analysis of vegetated cover ((u_{+})) with respect to small spatial fluctuations, yields the dispersion relation$$begin{aligned} sigma (k)=u_{+}(kappa -2u_{+})-(nu -gamma u_{+})k^{2}-alpha u_{+}k^{4}. end{aligned}$$
(8)
Imposing (partial sigma /partial k|_{k_{c}}=0) and (sigma (k_{c})=0), the critical mode can be determined$$begin{aligned} k_{c}=sqrt{frac{gamma -nu /u_{c}}{2alpha }}, end{aligned}$$
(9)
where (u_{c}) satisfies (4alpha u_{c}^2(2u_{c}-kappa )=(2gamma u_{c}-nu )^2). The corresponding aridity parameter (eta _{c}) can be calculated from Eq. (7).The reaction–diffusion approachThe second approach explicitly adds the water transport by below ground diffusion. The coupling between the water dynamics and the plant biomass involves positive feedbacks that tend to enhance water availability. Negative feedbacks allow for an increase in water consumption caused by vegetation growth, which inhibits further biomass growth.The modelling considers the coupled evolution of biomass density (b(mathbf {r},t)) and groundwater density (w(mathbf {r},t)). In its dimensionless form, this model reads33$$begin{aligned} frac{partial b}{partial t}= & {} frac{gamma w}{1+omega w}b-b^{2}-theta b+nabla ^{2}b, end{aligned}$$
(10)
$$begin{aligned} frac{partial w}{partial t}= & {} p-(1-rho b)w-w^{2}b+delta nabla ^{2}(w-beta b). end{aligned}$$
(11)
The first term in the first equation describes plant growth at a constant rate ((gamma /omega)) that grows linearly with w for dry soil. The quadratic nonlinearity (-b^{2}) accounts for saturation imposed by poor nutrients soil. The term proportional to (theta) accounts for mortality, grazing or herbivores. The mechanisms of dispersion are modelled by a simple diffusion process. The groundwater evolves due to a precipitation input p. The term ((1-rho b)w) in the second equation accounts for the evaporation and drainage, that decreases with the presence of vegetation. The term (w^{2}b) models the water uptake by the plants due to the transpiration process. The groundwater movement follows the Darcy’s law in unsaturated conditions; that is, the water flux is proportional to the gradient of the water matric potential41. The matric potential is equal to w, under the assumption that the hydraulic diffusivity is constant41. To model the suction of water by the roots, a correction to the matric potential is included; (-beta b), where (beta) is the strength of the suction. More

1.Frankham, R., Briscoe, D. A. & Ballou, J. D. Introduction to conservation genetics (Cambridge university press, 2002).2.Nadachowska-Brzyska, K., Burri, R., Smeds, L. & Ellegren, H. PSMC analysis of effective population sizes in molecular ecology and its application to black-and-white Ficedula flycatchers. Mol. Ecol. 25, 1058–1072 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
3.Martínez-Freiría, F., Velo-Antón, G. & Brito, J. C. Trapped by climate: Interglacial refuge and recent population expansion in the endemic Iberian adder Vipera seoanei. Divers. Distrib. 21, 331–344 (2015).Article 

Google Scholar 
4.Martínez-Freiría, F. et al. Integrative phylogeographical and ecological analysis reveals multiple pleistocene refugia for Mediterranean Daboia vipers in north-west Africa. Biol. J. Linn. Soc. 122, 366–384 (2017).Article 

Google Scholar 
5.Veríssimo, J. et al. Pleistocene diversification in Morocco and recent demographic expansion in the Mediterranean pond turtle Mauremys leprosa. Biol. J. Linn. Soc. 119, 943–959 (2016).Article 

Google Scholar 
6.Chattopadhyay, B., Garg, K. M., Gwee, C. Y., Edwards, S. V. & Rheindt, F. E. Gene flow during glacial habitat shifts facilitates character displacement in a Neotropical flycatcher radiation. BMC Evol. Biol. 17, 1–15 (2017).Article 

Google Scholar 
7.Garg, K. M., Chattopadhyay, B., Koane, B., Sam, K. & Rheindt, F. E. Last Glacial Maximum led to community-wide population expansion in a montane songbird radiation in highland Papua New Guinea. BMC Evol. Biol. 20, 82 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
8.Vences, M., Wollenberg, K. C., Vieites, D. R. & Lees, D. C. Madagascar as a model region of species diversification. Trends Ecol. Evol. 24, 456–465 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
9.Goodman, S. M., Raherilalao, M. J. & Wohlhauser, S. The Terrestrial Protected Areas of Madagascar: Their History, Description and Biota (Association Vahatra in Antananarivo, The University of Chicago Press, 2018).10.Douglass, K. The diversity of late holocene shellfish exploitation in Velondriake, Southwest Madagascar. J. Island Coast. Archaeol. 12, 333–359 (2016).11.Yoder, A. D., Campbell, C. R., Blanco, M. B., Ganzhorn, J. U. & Goodman, S. M. Geogenetic patterns in mouse lemurs (genus Microcebus) reveal the ghosts of Madagascar’s forests past. PNAS 113, 8049–8056 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.Salmona, J., Heller, R., Quéméré, E. & Chikhi, L., Climate change. and human colonization triggered habitat loss and fragmentation in Madagascar. Mol. Ecol. 26, 5203–5222 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
13.Townsend, T. M., Vieites, D. R., Glaw, F. & Vences, M. Testing species-level diversification hypotheses in Madagascar: the case of microendemic Brookesia leaf Chameleons. Syst. Biol. 58, 641–656 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
14.Brown, J. L., Cameron, A., Yoder, A. D. & Vences, M. A necessarily complex model to explain the biogeography of the amphibians and reptiles of Madagascar. Nat. Commun. 5, 5046 (2014).15.Schüßler, D. et al. Ecology and morphology of mouse lemurs (Microcebus spp.) in a hotspot of microendemism in northeastern Madagascar, with the description of a new species. Am. J. Primatol. 82, e23180 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
16.Chikhi, L. & Bruford, M. Mammalian population genetics and genomics. Mamm. Genome https://doi.org/10.1079/9780851999104.0539 (2005).17.Olivieri, G. L., Sousa, V., Chikhi, L. & Radespiel, U. From genetic diversity and structure to conservation: Genetic signature of recent population declines in three mouse lemur species (Microcebus spp.). Biol. Conserv. 141, 1257–1271 (2008).Article 

Google Scholar 
18.Gutenkunst, R. N., Hernandez, R. D., Williamson, S. H. & Bustamante, C. D. Inferring the joint demographic history of multiple populations from multidimensional SNP frequency data. PLoS Genet. 5, e1000695 (2009).19.Li, H. & Durbin, R. Inference of human population history from individual whole-genome sequences. Nature 475, 493–496 (2011).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Excoffier, L., Dupanloup, I., Huerta-Sánchez, E., Sousa, V. C. & Foll, M. Robust demographic inference from genomic and SNP data. PLoS Genet. 9, e1003905 (2013).21.Liu, X. & Fu, Y.-X. Exploring population size changes using SNP frequency spectra. Nat Genet. 47, 555–559 (2015).22.Salmona, J., Heller, R., Lascoux, M. & Shafer, A. Inferring demographic history using genomic data. in Population Genomics 511–537 (Springer, 2017).23.Beichman, A. C., Huerta-Sanchez, E. & Lohmueller, K. E. Using genomic data to infer historic population dynamics of nonmodel organisms. Annu. Rev. Ecol. Evol. Syst. 49, 433–456 (2018).Article 

Google Scholar 
24.Sgarlata, G. M. et al. Genetic and morphological diversity of mouse lemurs (Microcebus spp.) in northern Madagascar: The discovery of a putative new species? Am. J. Primatol. 81, e23070 (2019).25.Demenocal, P. et al. Abrupt onset and termination of the African humid period:: rapid climate responses to gradual insolation forcing. Quat. Sci. Rev. 19, 347–361 (2000).Article 

Google Scholar 
26.Tierney, J. E. & DeMenocal, P. B. Abrupt shifts in Horn of Africa hydroclimate since the last glacial maximum. Science 342, 843–846 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Los, S. O. et al. Sensitivity of a tropical montane cloud forest to climate change, present, past and future: Mt. Marsabit, N. Kenya. Quat. Sci. Rev. 218, 34–48 (2019).Article 

Google Scholar 
28.Ivory, S. J. & Russell, J. Climate, herbivory, and fire controls on tropical African forest for the last 60ka. Quat. Sci. Rev. 148, 101–114 (2016).Article 

Google Scholar 
29.Conroy, J. L., Overpeck, J. T., Cole, J. E., Shanahan, T. M. & Steinitz-Kannan, M. Holocene changes in eastern tropical Pacific climate inferred from a Galápagos lake sediment record. Quat. Sci. Rev. 27, 1166–1180 (2008).Article 

Google Scholar 
30.Martin-Puertas, C., Tjallingii, R., Bloemsma, M. & Brauer, A. Varved sediment responses to early Holocene climate and environmental changes in Lake Meerfelder Maar (Germany) obtained from multivariate analyses of micro X-ray fluorescence core scanning data. J. Quat. Sci. 32, 427–436 (2017).Article 

Google Scholar 
31.Flenley, J. R. Tropical forests under the climates of the last 30,000 years. in Potential Impacts of Climate Change on Tropical Forest Ecosystems, 37–57 (Springer, 1998).32.Burrough, S. L. & Thomas, D. S. G. Central southern Africa at the time of the African humid period: a new analysis of Holocene palaeoenvironmental and palaeoclimate data. Quat. Sci. Rev. 80, 29–46 (2013).Article 

Google Scholar 
33.Ivory, S. J. & Russell, J. Lowland forest collapse and early human impacts at the end of the African humid period at Lake Edward, equatorial East. Afr. Quat. Res. 89, 7–20 (2018).Article 

Google Scholar 
34.Anderson, A. et al. New evidence of megafaunal bone damage indicates late colonization of Madagascar. PLoS ONE 13, 1–14 (2018).
Google Scholar 
35.Hansford, J. et al. Early Holocene human presence in Madagascar evidenced by exploitation of avian megafauna. Sci. Adv. 4, eaat6925 (2018).36.Burney, D. A., Robinson, G. S. & Burney, L. P. Sporormiella and the late holocene extinctions in Madagascar. Proc. Natl Acad. Sci. USA 100, 10800–10805 (2003).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
37.Railsback, L. B. et al. Relationships between climate change, human environmental impact, and megafaunal extinction inferred from a 4000-year multi-proxy record from a stalagmite from northwestern Madagascar. Quat. Sci. Rev. 234, 106244 (2020).Article 

Google Scholar 
38.Dewar, R. E. et al. Stone tools and foraging in northern Madagascar challenge Holocene extinction models. PNAS 110, 12583–12588 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
39.Radimilahy, C. Mahilaka: an Archaeological Investigation of an Early Town in Northwestern Madagascar. Acta Universitatis Upsaliensis (University of Uppsala, 1998).40.Liu, X. & Fu, Y.-X. Exploring population size changes using SNP frequency spectra. Nat. Genet 47, 555–559 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
41.Lapierre, M., Lambert, A. & Achaz, G. Accuracy of demographic inferences from the site frequency spectrum: the case of the yoruba population. Genetics 206, 139–449 (2017).Article 

Google Scholar 
42.Terhorst, J., Kamm, J. A. & Song, Y. S. Robust and scalable inference of population history from hundreds of unphased whole genomes. Nat. Genet. 49, 303–309 (2017).CAS 
Article 

Google Scholar 
43.Patton, A. H. et al. Contemporary demographic reconstruction methods are robust to genome assembly quality: a case study in Tasmanian devils. Mol. Biol. Evol. 36, 2906–2921 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
44.Mazet, O., Rodríguez, W., Grusea, S., Boitard, S. & Chikhi, L. On the importance of being structured: Instantaneous coalescence rates and human evolution-lessons for ancestral population size inference? Heredity 116, 362–371 (2016).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.Orozco-terWengel, P. The devil is in the details: the effect of population structure on demographic inference. Heredity 116, 349–350 (2016).46.Mazet, O., Rodríguez, W. & Chikhi, L. Demographic inference using genetic data from a single individual: separating population size variation from population structure. Theor. Popul. Biol. 104, 46–58 (2015).PubMed 
Article 
PubMed Central 

Google Scholar 
47.Chikhi, L. et al. The IICR (inverse instantaneous coalescence rate) as a summary of genomic diversity: Insights into demographic inference and model choice. Heredity 120, 13–24 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
48.Simons, E. L., Godfrey, L. R., Vuillaume-Randriamanantena, M., Chatrath, P. S. & Gagnon, M. Discovery of new giant subfossil lemurs in the Ankarana Mountains of Northern Madagascar. J. Hum. Evol. 19, 311–319 (1990).Article 

Google Scholar 
49.Jungers, W. L., Godfrey, L. R., Simons, E. L. & Chatrath, P. S. Subfossil Indri indri from the Ankarana Massif of northern Madagascar. Am. J. Phys. Anthropol. 97, 357–366 (1995).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
50.Wilson, J. M., Stewart, P. D. & Fowler, S. V. Ankarana — a rediscovered nature reserve in northern Madagascar. Oryx 22, 163–171 (1988).Article 

Google Scholar 
51.Everson, K. M., Jansa, S. A., Goodman, S. M. & Olson, L. E. Montane regions shape patterns of diversification in small mammals and reptiles from Madagascar’s moist evergreen forest. J. Biogeogr. 47, 2059–2072 (2020).Article 

Google Scholar 
52.Douglass, K., Hixon, S., Wright, H. T., Godfrey, L. R. & Crowley, B. E. A critical review of radiocarbon dates clarifies the human settlement of Madagascar. Quat. Sci. Rev. 221, 105878 (2019).53.Orozco-Terwengel, P., Andreone, F., Louis, E. & Vences, M. Mitochondrial introgressive hybridization following a demographic expansion in the tomato frogs of Madagascar, genus. Dyscophus. Mol. Ecol. 22, 6074–6090 (2013).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
54.Johnson, J. A. et al. Long-term survival despite low genetic diversity in the critically endangered Madagascar fish-eagle. Mol. Ecol. 18, 54–63 (2009).PubMed 
PubMed Central 

Google Scholar 
55.Sommer, S. Effects of habitat fragmentation and changes of dispersal behaviour after a recent population decline on the genetic variability of noncoding and coding DNA of a monogamous Malagasy rodent. Mol. Ecol. 12, 2845–2851 (2003).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
56.Craul, M. et al. Influence of forest fragmentation on an endangered large-bodied lemur in northwestern Madagascar. Biol. Conserv. 142, 2862–2871 (2009).Article 

Google Scholar 
57.Parga, J. A., Sauther, M. L., Cuozzo, F. P., Jacky, I. A. Y. & Lawler, R. R. Evaluating ring-tailed lemurs (Lemur catta) from southwestern Madagascar for a genetic population bottleneck. Am. J. Phys. Anthropol. 147, 21–29 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
58.Dewar, R. E. et al. Stone tools and foraging in northern Madagascar challenge Holocene extinction models. Proc. Natl Acad. Sci. USA 110, 12583–12588 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
59.Schüler, L. & Hemp, A. Atlas of pollen and spores and their parent taxa of Mt. Kilimanjaro and tropical East Africa. Quat. Int. 425, 301–386 (2016).Article 

Google Scholar 
60.Du Puy, D. J. & Moat, J. Vegetation mapping and classification in Madagascar (using GIS): implications and recommendations for the conservation of biodiversity. in Chorology, Taxonomy and Ecology of the floras of Africa and Madagascar, 97–117 (1998, in press).61.Guillaumet, J.-L., Betsch, J.-M. & Callmander, M. W. Renaud Paulian et le programme du CNRS sur les hautes montagnes à Madagascar: étage vs domaine. Zoosystema 30, 723 (2008).PubMed 
PubMed Central 

Google Scholar 
62.Weisrock, D. W. et al. Delimiting species without nuclear monophyly in Madagascar’s mouse lemurs. PLoS ONE 5, e9883 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
63.Croudace, I. W., Rindby, A. & Rothwell, R. G. ITRAX: description and evaluation of a new multi-function X-ray core scanner. Geol. Soc. Lond. Spec. Publ. 267, 51–63 (2006).CAS 
Article 

Google Scholar 
64.Blott, S. J. & Pye, K. GRADISTAT: a grain size distribution and statistics package for the analysis of unconsolidated sediments. Earth Surf. Process. Landforms 26, 1237–1248 (2001).Article 

Google Scholar 
65.Hogg, A. G. et al. SHCal13 Southern Hemisphere calibration, 0–50,000 years cal BP. Radiocarbon 55, 1889–1903 (2013).CAS 
Article 

Google Scholar 
66.Rina Evasoa, M. et al. Sources of variation in social tolerance in mouse lemurs (Microcebus spp.). BMC Ecol. 19, 1–16 (2019).CAS 
Article 

Google Scholar 
67.Aleixo-Pais, I. et al. The genetic structure of a mouse lemur living in a fragmented habitat in Northern Madagascar. Conserv. Genet. 20, 229–243 (2019).Article 

Google Scholar 
68.Radespiel, U., Jurić, M. & Zimmermann, E. Sociogenetic structures, dispersal and the risk of inbreeding in a small nocturnal lemur, the golden-brown mouse lemur (Microcebus ravelobensis). Behaviour 146, 607–628 (2009).Article 

Google Scholar 
69.Radespiel, U., Ehresmann, P. & Zimmermann, E. Species-specific usage of sleeping sites in two sympatric mouse lemur species (Microcebus murinus and M. ravelobensis) in northwestern Madagascar. Am. J. Primatol. 59, 139–151 (2003).PubMed 
Article 
PubMed Central 

Google Scholar 
70.Larsen, P. A. et al. Hybrid de novo genome assembly and centromere characterization of the gray mouse lemur (Microcebus murinus). BMC Biol. 15, 1–17 (2017).Article 
CAS 

Google Scholar 
71.Metzker, M. L. Sequencing technologies — the next generation. Nat. Rev. Genet. 11, 31 (2010).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
72.Skotte, L., Korneliussen, T. S. & Albrechtsen, A. Estimating individual admixture proportions from next generation sequencing. Data 195, 693–702 (2013).CAS 

Google Scholar 
73.Korneliussen, T. S., Albrechtsen, A. & Nielsen, R. ANGSD: analysis of next generation sequencing data. BMC Bioinforma. 15, 1–13 (2014).Article 

Google Scholar 
74.Korneliussen, T. S. & Moltke, I. Sequence analysis NgsRelate: a software tool for estimating pairwise relatedness from next-generation sequencing data. Bioinformatics 31, 4009–4011 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
75.Soraggi, S., Wiuf, C. & Albrechtsen, A. Powerful inference with the D-Statistic on low-coverage whole-genome data. G3 8, 551–566 (2017).PubMed Central 
Article 

Google Scholar 
76.Chikhi, L., Sousa, V. C., Luisi, P., Goossens, B. & Beaumont, M. A. The confounding effects of population structure, genetic diversity and the sampling scheme on the detection and quantification of population size changes. Genetics 186, 983–995 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
77.Kopelman, N. M., Mayzel, J., Jakobsson, M., Rosenberg, N. A. & Mayrose, I. Clumpak: a program for identifying clustering modes and packaging population structure inferences across K. Mol. Ecol. Resour. 15, 1179–1191 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
78.Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol. Ecol. 14, 2611–2620 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
79.Salmona, J., Heller, R., Quéméré, E. & Chikhi, L. Climate change and human colonization triggered habitat loss and fragmentation in Madagascar. Mol. Ecol. 26, 5203–5222 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
80.Schneider, N., Chikhi, L., Currat, M. & Radespiel, U. Signals of recent spatial expansions in the grey mouse lemur (Microcebus murinus). BMC Evol. Biol. 10, 105 (2010).81.Radespiel, U., Lutermann, H., Schmelting, B. & Zimmermann, E. An empirical estimate of the generation time of mouse lemurs. Am. J. Primatol. 81, 1–8 (2019).Article 

Google Scholar 
82.Hawkins, M. T. R. et al. Genome sequence and population declines in the critically endangered greater bamboo lemur (Prolemur simus) and implications for conservation. BMC Genomics 19, 1–15 (2018).Article 
CAS 

Google Scholar 
83.Poelstra, J. et al. Cryptic patterns of speciation in cryptic primates: microendemic mouse lemurs and the multispecies coalescent. Syst. Biol. https://doi.org/10.1093/sysbio/syaa053 (2020).84.Campbell, C. R. et al. Pedigree-based and phylogenetic methods support surprising patterns of mutation rate and spectrum in the gray mouse lemur. Heredity 127.2, 233–244 (2021).Article 

Google Scholar 
85.Hudson, R. R. Generating samples under a Wright–Fisher neutral model of genetic variation. Bioinformatics 18, 337–338 (2002).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
86.Fredsted, T., Pertoldi, C., Schierup, M. H. & Kappeler, P. M. Microsatellite analyses reveal fine-scale genetic structure in grey mouse lemurs (Microcebus murinus). Mol. Ecol. 14, 2363–2372 (2005).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
87.Radespiel, U., Schulte, J., Burke, R. J. & Lehman, S. M. Molecular edge effects in the endangered golden-brown mouse lemur Microcebus ravelobensis. Oryx 53, 716–726 (2019).Article 

Google Scholar 
88.Radespiel, U., Lutermann, H., Schmelting, B., Bruford, M. W. & Zimmermann, E. Patterns and dynamics of sex-biased dispersal in a nocturnal primate, the grey mouse lemur, Microcebus murinus. Anim. Behav. 65, 709–719 (2003).Article 

Google Scholar 
89.Radespiel, U., Rakotondravony, R. & Chikhi, L. Natural and anthropogenic determinants of genetic structure in the largest remaining population of the endangered golden-brown mouse lemur, Microcebus ravelobensis. Am. J. Primatol. 70, 860–870 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
90.Schliehe-Diecks, S., Eberle, M. & Kappeler, P. M. Walk the line-dispersal movements of gray mouse lemurs (Microcebus murinus). Behav. Ecol. Sociobiol. 66, 1175–1185 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
91.Excoffier, L., Dupanloup, I., Huerta-Sánchez, E., Sousa, V. C. & Foll, M. Robust demographic inference from genomic and SNP data. PLoS Genet. 9, e1003905 (2013).92.Beerli, P. Effect of unsampled populations on the estimation of population sizes and migration rates between sampled populations. Mol. Ecol. 13, 827–836 (2004).PubMed 
Article 
PubMed Central 

Google Scholar 
93.Akaike, H. A new look at the statistical model identification. IEEE Trans. Automat. Control 19, 716–723 (1974).Article 

Google Scholar 
94.Bagley, R. K., Sousa, V. C., Niemiller, M. L. & Linnen, C. R. History, geography and host use shape genomewide patterns of genetic variation in the redheaded pine sawfly (Neodiprion lecontei). Mol. Ecol. 26, 1022–1044 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar  More