Philippa Kaur

Goergen, G., Kumar, P. L., Sankung, S. B., Togola, A. & Tamo, M. First Report of Outbreaks of the Fall Armyworm Spodoptera frugiperda (J E Smith) (Lepidoptera, Noctuidae), a New Alien Invasive Pest in West and Central Africa. PLoS ONE 11, e0165632 (2016).PubMed 
PubMed Central 

Google Scholar 
Cock, M. J. W., Beseh, P. K., Buddie, A. G., Cafa, G. & Crozier, J. Molecular methods to detect Spodoptera frugiperda in Ghana, and implications for monitoring the spread of invasive species in developing countries. Sci. Rep. 7, 4103 (2017).PubMed 
PubMed Central 

Google Scholar 
Nagoshi, R. N. et al. Comparative molecular analyses of invasive fall armyworm in Togo reveal strong similarities to populations from the eastern United States and the Greater Antilles. PLoS ONE 12, e0181982 (2017).PubMed 
PubMed Central 

Google Scholar 
Jacobs, A., van Vuuren, A. & Rong, I. H. Characterisation of the fall armyworm (Spodoptera frugiperda JE Smith) (Lepidoptera: Noctuidae) from South Africa. Afr. Entomol. 26, 45–49 (2018).
Google Scholar 
Otim, M. H. et al. Detection of sister-species in invasive populations of the fall armyworm Spodoptera frugiperda (Lepidoptera: Noctuidae) from Uganda. PLoS ONE 13, e0194571 (2018).PubMed 
PubMed Central 

Google Scholar 
FAO. Briefing note on FAO actions on fall armyworm in Africa, (2018).FAO. Briefing note on FAO actions on fall armyworm, (2019).Ganiger, P. C. et al. Occurrence of the new invasive pest, fall armyworm, Spodoptera frugiperda (JE Smith) (Lepidoptera: Noctuidae), in the maize fields of Karnataka, India. Curr. Sci. India 115, 621–623 (2018).CAS 

Google Scholar 
Sharanabasappa, D. et al. First report of the fall Armyworm, Spodoptera frugiperda (J E Smith) (Lepidoptera, Noctuidae) an Alien invasive pest on Maize in India. Pest Manag. Horticultural Ecosyst. 24, 23–29 (2018).
Google Scholar 
FAO. in FAO Regional Conference for Asia and the Pacific, 35th Session 7 (Thimphu, Bhutan, 2019).EPPO. First report of Spodoptera frugiperda in Thailand. (2019).Tay, W. T. & Gordon, K. H. J. Going global – genomic insights into insect invasions. Curr. Opin. Insect Sci. 31, 123–130 (2019).PubMed 

Google Scholar 
Zhang, L. et al. Molecular identification of invasive fall armyworm Spodoptera frugiperda in Yunnan Province. Plant Prot. 45, 19–24 (2019).
Google Scholar 
Wu, Q., Jian, Y. & K, W. Analysis of migration routes of the fall armyworm Spodoptera frugiperda (J. E. Smith) form Myanmar to China. Plant Prot. 45, 1–6 (2019).CAS 

Google Scholar 
USDA. Fall armyworm damages corn and threatens other crops in Vietnam. United States Department of Agriculture, Foreign Agricultural Service, Report Number: VM2019-0017 (2019).FAO. Report of first detection of fall armyworm (FAW) in the Republic of the Philippines. Report No. PHL-02/1, (Food and Agriculture Organization of the United Nations, International Plant Protection Convention, 2019).Navasero, M. V. et al. Detection of the fall armyworm, Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae) using larval mrophological characters, and observations on its current local distribution in the Philippines. Philipp. Ent 33, 171–184 (2019).
Google Scholar 
Vennila, S. et al. in International Workshop on Facilitating International Research Collaboration on Transboundary Plant Pests. (Ministry of Agriculture, Forestry and Fisheries, Tsukuba, Ibaraki, Japan, 2019).FAO. First detection of fall armyworm in China. (Food and Agriculture Organization of the United Nations, International Plant Protection Convention, 2019).Silver, A. Caterpillar’s devastating march across China spurs hunt for native predator. Nature 570, 286–287 (2019).CAS 
PubMed 

Google Scholar 
Song, X. P. et al. Intrusion of Fall Armyworm (Spodoptera frugiperda) in Sugarcane and Its Control by Drone in China. Sugar Tech. 22, 734–737 (2020).
Google Scholar 
Czepak, C. et al. Especial Spodoptera: Migração acelerada. Cultivar Gd. Culturas 244, 26–29 (2019).
Google Scholar 
FAO. First detection of Fall armyworm in Torres Strait of Australia. (Food and Agriculture Organization of the United Nations, International Plant Protection Convention, 2020).Queensland Government, D. o. A. a. F. First mainland detection of fall armyworm, accessed 13 March 2020 (2020).Wild, S. Invasive pest hits Africa. Nature 543, 13–14 (2017).CAS 
PubMed 

Google Scholar 
Porter, J. E. & Hughes, J. H. Insect eggs transported on the outer surface of airplanes. J. Economic Entomol. 43, 555–557 (1950).
Google Scholar 
Jeger, M. et al. Pest categorisation of Spodoptera frugiperda. Efsa J. https://doi.org/10.2903/j.efsa.2017.4927 (2017).Early, R., Gonzalez-Moreno, P., Murphy, S. T. & Day, R. Forecasting the global extent of invasion of the cereal pest Spodoptera frugiperda, the fall armyworm. Neobiota https://doi.org/10.3897/neobiota.40.28165 (2018).FAO. Fall armyworm likely to spread from India to other parts of Asia with South East Asia and South China most at risk. (Food and Agriculture Organization of the United Nation, 2018).Gouin, A. et al. Two genomes of highly polyphagous lepidopteran pests (Spodoptera frugiperda, Noctuidae) with different host-plant ranges. Sci. Rep. 7, 11816 (2017).PubMed 
PubMed Central 

Google Scholar 
Zhang, L. et al. Genetic structure and insecticide resistance characteristics of fall armyworm populations invading China. Mol. Ecol. Resour. https://doi.org/10.1111/1755-0998.13219 (2020).Westbrook, J., Fleischer, S., Jairam, S., Meagher, R. & Nagoshi, R. Multigenerational migration of fall armyworm, a pest insect. Ecosphere 10, e02919 (2019).
Google Scholar 
du Plessis, H., van den Berg, J., Ota, N. & Kriticos, D. J. Spodoptera frugiperda (Fall Armyworm). in CSIRO-InSTePP Pest Geography. June, 2018 (2018).FAO. First detection report of the fall armyworm Spodoptera frugiperda (Lepdioptera: Noctuidae) on maize in Myanmar. (Food and Agriculture Organization of the United Nations, International Plant Protection Convention, 2019).Sun, X.-X. et al. Case study on the first immigration of fall armyworm Spodoptera frugiperda invading into China. J. Integr. Agriculture 18, 2–10 (2019).
Google Scholar 
Day, R. et al. Fall armyworm: impacts and implications for Africa. Outlooks Pest Manag. 28, 196–201 (2017).
Google Scholar 
Assefa, F. & Ayalew, D. Status and control measures of fall armyworm (Spodoptera frugiperda) infestations in maize fields in Ethiopia: a review. Cogent Food Agr. 5, 1641902 (2019).
Google Scholar 
Hurska, A. J. Fall armyworm (Spodoptera frugiperda) management by smallholders. CAB Rev. 14, 11 (2019).
Google Scholar 
Firake, D. M. & Behere, G. T. Natural mortality of invasive fall armyworm, Spodoptera frugiperda (J. E. Smith) (Lepidoptera: Noctuidae) in maize agroecosystems of northeast India. Biol. Control 148, 104303 (2020).CAS 

Google Scholar 
Guan, F. et al. Whole-genome sequencing to detect mutations associated with resistance to insecticides and Bt proteins in Spodoptera frugiperda. Insect Sci. https://doi.org/10.1111/1744-7917.12838 (2020).Dumas, P. et al. Phylogenetic molecular species delimitations unravel potential new species in the pest genus Spodoptera Guenee, 1852 (Lepidoptera, Noctuidae). PLoS ONE 10, e0122407 (2015).PubMed 
PubMed Central 

Google Scholar 
Dumas, P. et al. Spodoptera frugiperda (Lepidoptera: Noctuidae) host-plant variants: two host strains or two distinct species? Genetica 143, 305–316 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
Nagoshi, R. N. et al. Genetic characterization of fall armyworm (Spodoptera frugiperda) in Ecuador and comparisons with regional populations identify likely migratory relationships. PLoS ONE 14, e0222332 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Jing, D. P. et al. Initial detections and spread of invasive Spodoptera frugiperda in China and comparisons with other noctuid larvae in cornfields using molecular techniques. Insect Sci. 27, 780–790 (2020).CAS 
PubMed 

Google Scholar 
Nagoshi, R. N. et al. Southeastern Asia fall armyworms are closely related to populations in Africa and India, consistent with common origin and recent migration. Sci. Rep. 10, 1421 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Mahadeva, S. H. M. et al. Prevalence of “R” strain and molecular diversity of fall army worm Spodoptera frugiperda (J.E. Smith) (Lepidoptera: Noctuidae) in India. Indian J. Entomol. 80, 544–553 (2018).
Google Scholar 
Murua, M. G. et al. Demonstration using field collections that Argentina fall armyworm populations exhibit strain-specific host plant preferences. J. Econ. Entomol. 108, 2305–2315 (2015).PubMed 

Google Scholar 
Nagoshi, R. N. The fall armyworm triose phosphate isomerase (Tpi) gene as a marker of strain identity and interstrain mating. Ann. Entomol. Soc. Am. 103, 283–292 (2010).CAS 

Google Scholar 
Nagoshi, R. N., Goergen, G., Plessis, H. D., van den Berg, J. & Meagher, R. Jr. Genetic comparisons of fall armyworm populations from 11 countries spanning sub-Saharan Africa provide insights into strain composition and migratory behaviors. Sci. Rep. 9, 8311 (2019).PubMed 
PubMed Central 

Google Scholar 
Czepak, C., Albernaz, C., Vivan, L. M., Guimarães, H. O. & Carvalhais, T. First reported occurrence of Helicoverpa armigera (Hübner) (Lepidoptera: Noctuidae) in Brazil. Pesq. Agropec. Trop., Goia.̂nia 43, 110–113 (2013).
Google Scholar 
Arnemann, J. A. et al. Multiple incursion pathways for Helicoverpa armigera in Brazil show its genetic diversity spreading in a connected world. Sci. Rep. 9, 19380 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Tay, W. T. et al. A brave new world for an old world pest: Helicoverpa armigera (Lepidoptera: Noctuidae) in Brazil. PLoS ONE 8, e80134 (2013).Tay, W. T. et al. Mitochondrial DNA and trade data support multiple origins of Helicoverpa armigera (Lepidoptera, Noctuidae) in Brazil. Sci. Rep. 7, 45302 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Behere, G. T. et al. Mitochondrial DNA analysis of field populations of Helicoverpa armigera (Lepidoptera: Noctuidae) and of its relationship to H. zea. BMC Evol. Biol. 7, 117 (2007).PubMed 
PubMed Central 

Google Scholar 
Pearce, S. L. et al. Erratum to: Genomic innovations, transcriptional plasticity and gene loss underlying the evolution and divergence of two highly polyphagous and invasive Helicoverpa pest species. BMC Biol. 15, 69 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Pearce, S. L. et al. Genomic innovations, transcriptional plasticity and gene loss underlying the evolution and divergence of two highly polyphagous and invasive Helicoverpa pest species. BMC Biol. 15, 63 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Guillemaud, T., Ciosi, M., Lombaert, E. & Estoup, A. Biological invasions in agricultural settings: insights from evolutionary biology and population genetics. Cr Biol. 334, 237–246 (2011).
Google Scholar 
Elfekih, S. et al. Genome-wide analyses of the Bemisia tabaci species complex reveal contrasting patterns of admixture and complex demographic histories. PLoS ONE 13, e0190555 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Anderson, C. J., Tay, W. T., McGaughran, A., Gordon, K. & Walsh, T. K. Population structure and gene flow in the global pest, Helicoverpa armigera. Mol. Ecol. 25, 5296–5311 (2016).CAS 
PubMed 

Google Scholar 
Anderson, C. J. et al. Hybridization and gene flow in the mega-pest lineage of moth, Helicoverpa. Proc. Natl Acad. Sci. USA 115, 5034–5039 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Nagoshi, R. N., Meagher, R. L. & Hay-Roe, M. Inferring the annual migration patterns of fall armyworm (Lepidoptera: Noctuidae) in the United States from mitochondrial haplotypes. Ecol. Evol. 2, 1458–1467 (2012).PubMed 
PubMed Central 

Google Scholar 
Wright, S. The interpretation of population-structure by F-statistics with special regard to systems of mating. Evolution 19, 395–420 (1965).
Google Scholar 
Luikart, G. & Cornuet, J. M. Empirical evaluation of a test for identifying recently bottlenecked populations from allele frequency data. Conserv. Biol. 12, 228–237 (1998).
Google Scholar 
Nagoshi, R. N. et al. Genetic characterization of fall armyworm infesting South Africa and India indicate recent introduction from a common source population. PLoS ONE 14, e0217755 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Nagoshi, R. N. et al. Analysis of strain distribution, migratory potential, and invasion history of fall armyworm populations in northern Sub-Saharan Africa. Sci. Rep.-Uk 8, 3710 (2018).
Google Scholar 
Arias, O. et al. Population genetic structure and demographic history of Spodoptera frugiperda (Lepidoptera: Noctuidae): implications for insect resistance management programs. Pest Manag. Sci. 75, 2948–2957 (2019).CAS 
PubMed 

Google Scholar 
Nguyen, T. K. O. & Vu, T. P. Checklist of turfgrass insect pests, morphology, biology and population fluctuation of Herpetograma phaeopteralis (Guenee) (Lepidopera: Pyralidae) in Ha Noi, in Spring-Summer 2008. in The 3rd National Conference of Ecology and Natural Resources, Ha Noi. 1490–1498.Pham, V. L. On time to recognise first potential Spodoptera frugiperda (Smith) (Lepidoptera: Noctuidae) in Vietnam and its Vietnamese name. in Plant Protection Magazine No. 4/2019 (Plant Protection Research Institute of Vietnam, July, 2019).Vu, T. P. Insect pests of turf grass, biology, ecology and the control of Herpetogramma phaeoptralis (Guenée) in Hà Nội in Spring Summer 2008 MSc. Thesis, Hà Nội Agriculture University, Vietnam (2008).Nguyen, V. D., Ha, Q. H. & Nguyen, T. T. C. in Vietnam Insects and Pests. (ed. V. L. Pham) (2012).Gilligan, T. M. & Passoa, S. C. LepIntercept, An identification resource for intercepted Lepidoptera larvae. Identification Technology Program (ITP), (2014).Gui, F. R. et al. Genomic and transcriptomic analysis unveils population evolution and development of pesticide resistance in fall armyworm Spodoptera frugiperda. Protein Cell https://doi.org/10.1007/s13238-020-00795-7 (2020).Stokstad, E. FOOD SECURITY New crop pest takes Africa at lightning speed. Science 356, 473–474 (2017).CAS 
PubMed 

Google Scholar 
Baloch, M. N., Fan, J. Y., Haseeb, M. & Zhang, R. Z. Mapping potential distribution of Spodoptera frugiperda (Lepidoptera: Noctuidae) in Central Asia. Insects 11, 172 (2020).PubMed Central 

Google Scholar 
Juarez, M. L. et al. Population structure of Spodoptera frugiperda maize and rice host forms in South America: are they host strains? Entomol. Exp. Appl. 152, 182–199 (2014).CAS 

Google Scholar 
Groot, A. T. et al. Evolution of reproductive isolation of Spodoptera frugiperda. Pheromone Communication in Moths: Evolution, Behavior, and Application, 291–300 (2016).Nagoshi, R. N., Meagher, R. L., Nuessly, G. & Hall, D. G. Effects of fall armyworm (Lepidoptera: Noctuidae) interstrain mating in wild populations. Environ. Entomol. 35, 561–568 (2006).
Google Scholar 
Haenniger, S. et al. Sexual communication of Spodoptera frugiperda from West Africa: adaptation of an invasive species and implications for pest management. Sci. Rep. 10, 2892 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Orsucci, M. et al. Transcriptional plasticity evolution in two strains of Spodoptera frugiperda (Lepidoptera: Noctuidae) feeding on alternative host-plants. Preprint at bioRxiv https://doi.org/10.1101/263186 (2018).Lopes-da-Silva, M., Sanches, M. M., Stancioli, A. R., Alves, G. & Sugayama, R. The role of natural and human-mediated pathways for invasive agricultural pests: a historical analysis of cases from Brazil. Agric. Sci. 5, 634–646 (2014).
Google Scholar 
Nagoshi, R. N. et al. Haplotype profile comparisons between Spodoptera frugiperda (Lepidoptera: Noctuidae) populations from Mexico with those from Puerto Rico, South America, and the United States and their implications to migratory behavior. J. Economic Entomol. 108, 135–144 (2015).CAS 

Google Scholar 
Tembrock, L. R., Timm, A. E., Zink, F. A. & Gilligan, T. M. Phylogeography of the recent expansion of Helicoverpa armigera (Lepidoptera: Noctuidae) in South America and the Caribbean basin. Ann. Entomol. Soc. Am. 112, 388–401 (2019).CAS 

Google Scholar 
Lombaert, E. et al. Bridgehead effect in the worldwide invasion of the biocontrol harlequin ladybird. PLoS ONE 5, e9743 (2010).PubMed 
PubMed Central 

Google Scholar 
Desneux, N., Luna, M. G., Guillemaud, T. & Urbaneja, A. The invasive South American tomato pinworm, Tuta absoluta, continues to spread in Afro-Eurasia and beyond: the new threat to tomato world production. J. Pest Sci. 84, 403–408 (2011).
Google Scholar 
Valencia-Montoya, W. A. et al. Adaptive introgression across semipermeable species boundaries between local Helicoverpa zea and invasive Helicoverpa armigera moths. Mol. Biol. Evol. https://doi.org/10.1093/molbev/msaa108 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
Walsh, T. K. et al. Multiple recombination events between two cytochrome P450 loci contribute to global pyrethroid resistance in Helicoverpa armigera. PLoS ONE 13, e0197760 (2018).PubMed 
PubMed Central 

Google Scholar 
Liu, X. et al. Risks of biological invasion on the belt and road. Curr. Biol. 29, 499–505.e494 (2019).CAS 
PubMed 

Google Scholar 
Gimenez, S. et al. Adaptation by copy number variation increases insecticide resistance in the fall armyworm. Preprint at Commun Biol. 664, https://doi.org/10.1038/s42003-020-01382-6 (2020).Yainna, S. et al. Genomic balancing selection is key to the invasive success of the fall armyworm. Preprint at bioRxiv https://doi.org/10.1101/2020.06.17.154880 (2020).Tay, W. T. et al. Novel molecular approach to define pest species status and tritrophic interactions from historical Bemisia specimens. Sci. Rep.-Uk 7, ARTN 429 (2017).
Google Scholar 
Walsh, T. K. et al. Mitochondrial DNA genomes of five major Helicoverpa pest species from the Old and New Worlds (Lepidoptera: Noctuidae). Ecol. Evol. 9, 2933–2944 (2019).PubMed 
PubMed Central 

Google Scholar 
Bernt, M. et al. MITOS: improved de novo metazoan mitochondrial genome annotation. Mol. Phylogenet. Evol. 69, 313–319 (2013).PubMed 

Google Scholar 
Villesen, P. FaBox: an online toolbox for FASTA sequences. Mol. Ecol. Notes 7, 965–968 (2007).CAS 

Google Scholar 
Katoh, K., Misawa, K., Kuma, K. & Miyata, T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 30, 3059–3066 (2002).CAS 
PubMed 
PubMed Central 

Google Scholar 
Katoh, K. & Standley, D. M. MAFFT multiple sequence alignment software version 7: improvements in performance and usability. Mol. Biol. Evol. 30, 772–780 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Nam, K. et al. Divergent selection causes whole genome differentiation without physical linkage among the targets in Spodoptera frugiperda (Noctuidae). Preprint at bioRxiv https://doi.org/10.1101/452870 (2018).Liu, H. et al. Chromosome level draft genomes of the fall armyworm, Spodoptera frugiperda (Lepidoptera: Noctuidae), an alien invasive pest in China. Preprint at bioRxiv https://doi.org/10.1101/671560 (2019).Xiao, H. et al. The genetic adaptations of fall armyworm Spodoptera frugiperda facilitated its rapid global dispersal and invasion. Mol. Ecol. Resour. 20, 1050–1068 (2020).CAS 
PubMed 

Google Scholar 
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. Preprint at https://arxiv.org/abs/1303.3997 (2013).Bushnell, B. BBMap: A Fast, Accurate, Splice-Aware Aligner. (Lawrence Berkeley National Laboratory. 2014).Li, H. et al. The Sequence Alignment/Map format and SAMtools. Bioinformatics 25, 2078–2079 (2009).PubMed 
PubMed Central 

Google Scholar 
Purcell, S. et al. PLINK: a tool set for whole-genome association and population-based linkage analyses. Am. J. Hum. Genet. 81, 559–575 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
Trifinopoulos, J., Nguyen, L. T., von Haeseler, A. & Minh, B. Q. W-IQ-TREE: a fast online phylogenetic tool for maximum likelihood analysis. Nucleic Acids Res. 44, W232–W235 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Lewis, P. O. A likelihood approach to estimating phylogeny from discrete morphological character data. Syst. Biol. 50, 913–925 (2001).CAS 
PubMed 

Google Scholar 
Minh, B. Q., Nguyen, M. A. & von Haeseler, A. Ultrafast approximation for phylogenetic bootstrap. Mol. Biol. Evol. 30, 1188–1195 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Huson, D. H. & Scornavacca, C. Dendroscope 3: an interactive tool for rooted phylogenetic trees and networks. Syst. Biol. 61, 1061–1067 (2012).PubMed 

Google Scholar 
Hellenthal, G. et al. A genetic atlas of human admixture history. Science 343, 747–751 (2014).CAS 
PubMed 
PubMed Central 

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

Google Scholar 
Jombart, T. adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405 (2008).CAS 
PubMed 
PubMed Central 

Google Scholar 
Jombart, T. & Ahmed, I. adegenet 1.3-1: new tools for the analysis of genome-wide SNP data. Bioinformatics 27, 3070–3071 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Chang, C. C. et al. Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience 4, 7 (2015).PubMed 
PubMed Central 

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

Google Scholar 
Tajima, F. Statistical method for testing the neutral mutation hypothesis by DNA polymorphism. Genetics 123, 585–595 (1989).CAS 
PubMed 
PubMed Central 

Google Scholar 
Fu, Y. X. & Li, W. H. Statistical tests of neutrality of mutations. Genetics 133, 693–709 (1993).CAS 
PubMed 
PubMed Central 

Google Scholar 
Pfeifer, B., Wittelsburger, U., Ramos-Onsins, S. E. & Lercher, M. J. PopGenome: an efficient Swiss army knife for population genomic analyses in R. Mol. Biol. Evol. 31, 1929–1936 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Wright, S. The genetical structure of populations. Ann. Eugen. 15, 323–354 (1951).CAS 
PubMed 

Google Scholar 
Raymond, M. & Rousset, F. Genepop (Version-1.2) – population-genetics software for exact tests and ecumenicism. J. Hered. 86, 248–249 (1995).
Google Scholar 
Alexander, D. H., Novembre, J. & Lange, K. Fast model-based estimation of ancestry in unrelated individuals. Genome Res. 19, 1655–1664 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
Neuditschko, M., Khatkar, M. S. & Raadsma, H. W. NetView: a high-definition network-visualization approach to detect fine-scale population structures from genome-wide patterns of variation. PLoS ONE 7, e48375 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
Steinig, E. J., Neuditschko, M., Khatkar, M. S., Raadsma, H. W. & Zenger, K. R. netview p: a network visualization tool to unravel complex population structure using genome-wide SNPs. Mol. Ecol. Resour. 16, 216–227 (2016).CAS 
PubMed 

Google Scholar 
Paradis, E. & Schliep, K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics 35, 526–528 (2019).CAS 
PubMed 

Google Scholar 
Keenan, K., McGinnity, P., Cross, T. F., Crozier, W. W. & Prodohl, P. A. diveRsity: An R package for the estimation and exploration of population genetics parameters and their associated errors. Methods Ecol. Evol. 4, 782–788 (2013).
Google Scholar 
Sundqvist, L., Keenan, K., Zackrisson, M., Prodohl, P. & Kleinhans, D. Directional genetic differentiation and relative migration. Ecol. Evol. 6, 3461–3475 (2016).PubMed 
PubMed Central 

Google Scholar 
Bastian, M., Heymann, S. & Jacomy, M. in International AAAI Conference on Weblogs and Social Media (2009).Tay, T. et al. Global FAW population genomic signature supports complex introduction events across the Old World. v1. CSIRO. Data Collection. https://doi.org/10.25919/y3nd-2903 (2021).Nei, M. Analysis of gene diversity in subdivided populations. Proc. Natl Acad. Sci. USA 70, 3321–3323 (1973).CAS 
PubMed 
PubMed Central 

Google Scholar 
Nei, M. & Chesser, R. K. Estimation of fixation indices and gene diversities. Ann. Hum. Genet. 47, 253–259 (1983).CAS 
PubMed 

Google Scholar  More

Protected Planet: The World Database on Protected Areas (UNEP-WCMC and IUCN, accessed 2021); www.protectedplanet.netVenter, O. et al. Bias in protected-area location and its effects on long-term aspirations of biodiversity conventions. Conserv. Biol. 32, 127–134 (2018).Article 

Google Scholar 
Ward, M. et al. Just ten percent of the global terrestrial protected area network is structurally connected via intact land. Nat. Commun. 11, 4563 (2020).CAS 
Article 

Google Scholar 
Adams, W. M. Against Extinction: The Story of Conservation (Earthscan, 2004).Watson, J. E. M. Dudley, Segan, N. & Hockings, D. B. The performance and potential of protected areas. Nature 515, 67–73 (2014).CAS 
Article 

Google Scholar 
Butchart, S. H. M. et al. Shortfalls and solutions for meeting national and global conservation area targets. Conserv. Lett. 8, 329–337 (2015).Article 

Google Scholar 
Stolton, S. et al. The Futures of Privately Protected Areas (IUCN, 2014).Protected Planet: The World Database on Protected Areas (UNEP-WCMC and IUCN, accessed November 2018); www.protectedplanet.netBingham, H. et al. Privately protected areas: advances and challenges in guidance, policy and documentation. Parks 23, 13–28 (2017).Article 

Google Scholar 
Gallo, J., Pasquini, L., Reyers, B. & Cowling, R. M. The role of private conservation areas in biodiversity representation and target achievement within the Little Karoo region, South Africa. Biol. Conserv. 142, 446–454 (2009).Article 

Google Scholar 
Schutz, J. Creating an integrated protected area network in Chile: a GIS assessment of ecoregion representation and the role of private protected areas. Environ. Conserv. 45, 269–277 (2018).Article 

Google Scholar 
Ielyzaveta, I. M. & Cook, C. N. The role of privately protected areas in achieving biodiversity representation within a national protected area network. Conserv. Sci. Pract. 2, e307 (2020).
Google Scholar 
Graves, R. A., Williamson, M. A., Belote, R. T. & Brandt, J. S. Quantifying the contribution of conservation easements to large‐landscape conservation. Biol. Conserv. 232, 83–96 (2019).Article 

Google Scholar 
De Vos, A. & Cumming, G. S. The contribution of land tenure diversity to the spatial resilience of protected area networks. People Nat. 1, 331–346 (2019).Article 

Google Scholar 
Olson, D. M. et al. Terrestrial ecoregions of the world: a new map of life on Earth: a new global map of terrestrial ecoregions provides an innovative tool for conserving biodiversity. BioScience 51, 933–938 (2001).Article 

Google Scholar 
Myers, N., Mittermeier, R. A., Mittermeier, C. G., Da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).CAS 
Article 

Google Scholar 
Borrini-Feyerabend, G. et al. Governance of Protected Areas: From Understanding to Action (IUCN, 2013).Lee, A. & Schultz, K. A. Comparing British and French colonial legacies: a discontinuity analysis of Cameroon. Q. J. Polit. Sci. 7, 365–410 (2012).Article 

Google Scholar 
Acemoglu, D., Johnson, S. & Robinson, J. A. The colonial origins of comparative development: an empirical investigation. Am. Econ. Rev. 91, 1369–1401 (2001).Article 

Google Scholar 
De Vos, A., Clements, H. S., Biggs, D. & Cumming, G. S. The dynamics of proclaimed privately protected areas in South Africa over 83 years. Conserv. Lett. 12, e12644 (2019).
Google Scholar 
Conservation Programs (USDA, accessed 21 October 2021); https://www.ers.usda.gov/topics/natural-resources-environment/conservation-programs/Zimmer, H. C., Mavromihalis, J., Turner, V. B., Moxham, C. & Liu, C. Native grasslands in the PlainsTender incentive scheme: conservation value, management and monitoring. Rangel. J. 32, 205–214 (2010).Article 

Google Scholar 
A Global Standard for the Identification of Key Biodiversity Area (IUCN, 2021); https://portals.iucn.org/library/sites/library/files/documents/Rep-2016-005.pdfVenter, O. et al. Last of the Wild Project, Version 3 (LWP-3): 2009 Human Footprint, 2018 Release (SEDAC, 2021); https://doi.org/10.7927/H46T0JQ4Hoekstra, J. M., Boucher, T. M., Ricketts, T. H. & Roberts, C. Confronting a biome crisis: global disparities of habitat loss and protection. Ecol. Lett. 8, 23–29 (2005).Article 

Google Scholar 
Newbold, T. et al. Has land use pushed terrestrial biodiversity beyond the planetary boundary? A global assessment. Science 353, 288–291 (2016).CAS 
Article 

Google Scholar 
Bengtsson, J. et al. Grasslands—more important for ecosystem services than you might think. Ecosphere 10, e02582 (2019).Article 

Google Scholar 
Working Together for Grasslands. How Ranchers and the WWF Help Protect the Northern Great Plains (WWF, 2021); https://www.worldwildlife.org/stories/working-together-for-grasslandsHenderson, K. A. et al. Landowner perceptions of the value of natural forest and natural grassland in a mosaic ecosystem in southern Brazil. Sustain. Sci. 11, 321–330 (2016).Article 

Google Scholar 
Kamal, S., Grodzinska-Jurczak, M. & Brown, G. Conservation on private land: a review of global strategies with a proposed classification system. J. Environ. Plan. Manag. 58, 576–597 (2015).Article 

Google Scholar 
Williamson, M. A., Schwartz, M. W. & Lubell, M. N. Spatially explicit analytical models for social–ecological systems. BioScience 68, 885–895 (2018).
Google Scholar 
Watson, J. E. M. et al. Persistent disparities between recent rates of habitat conversion and protection and implications for future global conservation targets. Conserv. Lett. 9, 413–421 (2016).Article 

Google Scholar 
Di Marco, M. et al. Quantifying the relative irreplaceability of important bird and biodiversity areas. Conserv. Biol. 30, 392–402 (2015).Article 

Google Scholar 
Jones, K. R. et al. One-third of global protected land is under intense human pressure. Science 360, 788–791 (2018).CAS 
Article 

Google Scholar 
Sanderson, E. W. et al. The human footprint and the last of the wild: the human footprint is a global map of human influence on the land surface, which suggests that human beings are stewards of nature, whether we like it or not. BioScience 52, 891–904 (2002).Article 

Google Scholar 
Clements, H. S., Kerley, G. I. H., Cumming, G. S., De Vos, A. & Cook, C. N. Privately protected areas provide key opportunities for the regional persistence of large‐ and medium‐sized mammals. J. Appl. Ecol. 56, 537–546 (2018).Article 

Google Scholar 
Song, P., Kim, G., Mayer, A., He, R. & Tian, G. Assessing the ecosystem services of various types of urban green spaces based on i-Tree Eco. Sustainability 12, 1630 (2020).CAS 
Article 

Google Scholar 
Trzyna, T. Urban Protected Areas: Profiles and Best Practice Guidelines (IUCN, 2014).Li, E. et al. (2019) An urban biodiversity assessment framework that combines an urban habitat classification scheme and citizen science data. Front. Ecol. Evol. https://doi.org/10.3389/fevo.2019.00277 (2019).Venter, O. et al. Global terrestrial Human Footprint maps for 1993 and 2009. Sci. Data 3, 160067 (2016).Article 

Google Scholar 
Rissman, A. R. & Merenlender, A. M. The conservation contributions of conservation easements: analysis of the San Francisco Bay Area protected lands spatial database. Ecol. Soc. 13, 25 (2008).Article 

Google Scholar 
Strategic Plan for Biodiversity 2011–2020, Including Aichi Biodiversity Targets (CBD, 2011); https://www.cbd.int/sp/Saura, S., Bastin, L., Battistella, L., Mandrici, A. & Dubois, G. Protected areas in the world’s ecoregions: how well connected are they? Ecol. Indic. 76, 144–158 (2017).Article 

Google Scholar 
World Database of Key Biodiversity Areas (BirdLife International, accessed September 2020); http://www.keybiodiversityareas.org/site/requestgisSaura, S. & Torné, J. Conefor Sensinode 2.2: a software package for quantifying the importance of habitat patches for landscape connectivity. Environ. Model. Softw. 24, 135–139 (2009).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2014). http://www.R-Project.org/Milam, A. et al. in Protected Areas: Are They Safeguarding Biodiversity? (eds Joppa, L. et al.) 81–101 (Wiley-Blackwell, 2016).Mason, C. et al. Telemetry reveals existing marine protected areas are worse than random for protecting the foraging habitat of threatened shy albatross. Divers. Distrib. 24, 1744–1755 (2018).Article 

Google Scholar 
Lewis, E. et al. Dynamics in the global protected-area estate since 2004. Conserv. Biol. 33, 570–579 (2017).Article 

Google Scholar 
Venter, O. et al. Sixteen years of change in the global terrestrial human footprint and implications for biodiversity conservation. Nat. Commun. 7, 12558 (2016).CAS 
Article 

Google Scholar 
Schleicher, J., Peres, C. A., Amano, T., Llactayo, W. & Leader-Williams, N. Conservation performance of different conservation governance regimes in the Peruvian Amazon. Nature 7, 113–118 (2017).
Google Scholar 
Shumba, T. et al. Effectiveness of private land conservation areas in maintaining natural land cover and biodiversity intactness. Glob. Ecol. Conserv. 22, e00935 (2020).Article 

Google Scholar  More

Zhang, N. et al. Members of the Fusarium solani species complex that cause infections in both humans and plants are common in the environment. J. Clin. Microbiol. 44, 2186–2190 (2006).CAS 
PubMed 
PubMed Central 

Google Scholar 
O’Donnell, K. et al. Molecular Phylogenetic Diversity, Multilocus Haplotype Nomenclature, and In Vitro antifungal resistance within the Fusarium solani species complex. J. Clin. Microbiol. 46, 2477–2490 (2008).PubMed 
PubMed Central 

Google Scholar 
Schroers, H. J. et al. Epitypification of Fusisporium (Fusarium) solani and its assignment to a common phylogenetic species in the Fusarium solani species complex. Mycologia 108, 806–819 (2016).CAS 
PubMed 

Google Scholar 
O’Donnell, K. Molecular phylogeny of the Nectria haematococca-Fusarium solani species complex. Mycologia 92, 919–938 (2000).
Google Scholar 
Gleason, F., Allerstorfer, M. & Lilje, O. Newly emerging diseases of marine turtles, especially sea turtle egg fusariosis (SEFT), caused by species in the Fusarium solani complex (FSSC). Mycology 11, 184–194 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Fernando, N. et al. Fatal Fusarium solani species complex infections in elasmobranchs: the first case report for black spotted stingray (Taeniura melanopsila) and a literature review. Mycoses 58, 422–431 (2015).PubMed 

Google Scholar 
Sarmiento-Ramírez, J. M. et al. Global distribution of two fungal pathogens threatening endangered Sea Turtles. PLoS ONE 9, e85853 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Mayayo, E., Pujol, I. & Guarro, J. Experimental pathogenicity of four opportunist Fusarium species in a murine model. J. Med. Microbiol. 48, 363–366 (1999).CAS 
PubMed 

Google Scholar 
Muhvich, A. G., Reimschuessel, R., Lipsky, M. M. & Bennett, R. O. Fusarium solani isolated from newborn bonnethead sharks, Sphyrna tiburo (L.). J. Fish Dis. 12, 57–62 (1989).
Google Scholar 
Crow, G. L., Brock, J. A. & Kaiser, S. Fusarium solani fungal infection of the lateral line canal system in captive scalloped hammerhead sharks (Sphyrna lewini) in Hawaii. J. Wildl. Dis. 31, 562–565 (1995).CAS 
PubMed 

Google Scholar 
Cabañes, F. J. et al. Cutaneous hyalohyphomycosis caused by Fusarium solani in a loggerhead sea turtle (Caretta caretta L.). J. Clin. Microbiol. 35, 3343–3345 (1997).PubMed 
PubMed Central 

Google Scholar 
Cafarchia, C. et al. Fusarium spp. in Loggerhead Sea Turtles (Caretta caretta): From Colonization to Infection. Vet. Pathol. 57, 139–146 (2019).PubMed 

Google Scholar 
Garcia-Hartmann, M., Hennequin, C., Catteau, S., Béatini, C. & Blanc, V. Cas groupés d’infection à Fusarium solani chez de jeunes tortues marines Caretta caretta nées en captivité. J. Mycol. Med. 28, 113–118 (2017).
Google Scholar 
Orós, J., Delgado, C., Fernández, L. & Jensen, H. E. Pulmonary hyalohyphomycosis caused by Fusarium spp in a Kemp’s ridley sea turtle (Lepidochelys kempi): An immunohistochemical study. N. Z. Vet. J. 52, 150–152 (2004).PubMed 

Google Scholar 
Candan, A. Y., Katılmış, Y. & Ergin, Ç. First report of Fusarium species occurrence in loggerhead sea turtle (Caretta caretta) nests and hatchling success in Iztuzu Beach, Turkey. Biologia (Bratisl). https://doi.org/10.2478/s11756-020-00553-4 (2020).Article 

Google Scholar 
Sarmiento-Ramirez, J. M., van der Voort, M., Raaijmakers, J. M. & Diéguez-Uribeondo, J. Unravelling the Microbiome of eggs of the endangered Sea Turtle Eretmochelys imbricata identifies bacteria with activity against the emerging pathogen Fusarium falciforme. PLoS ONE 9, e95206 (2014).ADS 
PubMed 
PubMed Central 

Google Scholar 
Sarmiento-Ramírez, J. M. et al. Fusarium solani is responsible for mass mortalities in nests of loggerhead sea turtle, Caretta caretta, in Boavista, Cape Verde. FEMS Microbiol. Lett. 312, 192–200 (2010).PubMed 

Google Scholar 
Sarmiento-Ramirez, J. M., Sim, J., Van West, P. & Dieguez-Uribeondo, J. Isolation of fungal pathogens from eggs of the endangered sea turtle species Chelonia mydas in Ascension Island. J. Mar. Biol. Assoc. United Kingdom 97, 661–667 (2017).CAS 

Google Scholar 
Hoh, D., Lin, Y., Liu, W., Sidique, S. & Tsai, I. Nest microbiota and pathogen abundance in sea turtle hatcheries. Fungal Ecol. 47, 100964 (2020).
Google Scholar 
Güçlü, Ö., Bıyık, H. & Şahiner, A. Mycoflora identified from loggerhead turtle (Caretta caretta) egg shells and nest sand at Fethiye beach, Turkey. Afr. J. Microbiol. Res. 4, 408–413 (2010).
Google Scholar 
Gambino, D. et al. First data on microflora of loggerhead sea turtle (Caretta caretta) nests from the coastlines of Sicily. Biol. Open 9, bio045252 (2020).PubMed 
PubMed Central 

Google Scholar 
Bailey, J. B., Lamb, M., Walker, M., Weed, C. & Craven, K. S. Detection of potential fungal pathogens Fusarium falciforme and F. keratoplasticum in unhatched loggerhead turtle eggs using a molecular approach. Endanger. Species Res. 36, 111–119 (2018).
Google Scholar 
Summerbell, R. C. & Schroers, H.-J. Analysis of Phylogenetic Relationship of Cylindrocarpon lichenicola and Acremonium falciforme to the Fusarium solani Species Complex and a Review of similarities in the spectrum of opportunistic infections caused by these fungi. J. Clin. Microbiol. 40, 2866–2875 (2002).CAS 
PubMed 
PubMed Central 

Google Scholar 
Nel, R., Punt, A. E. & Hughes, G. R. Are coastal protected areas always effective in achieving population recovery for nesting sea turtles?. PLoS ONE 8, e63525 (2013).ADS 
CAS 
PubMed 
PubMed Central 

Google Scholar 
Branch, G. & Branch, M. Living Shores. (Pippa Parker, 2018).Fuller, M. S., Fowles, B. E. & Mclaughlin, D. J. Isolation and pure culture study of marine phycomycetes. Mycologia 56, 745–756 (1964).
Google Scholar 
Greeff, M. R., Christison, K. W. & Macey, B. M. Development and preliminary evaluation of a real-time PCR assay for Halioticida noduliformans in abalone tissues. Dis. Aquat. Organ. 99, 103–117 (2012).CAS 
PubMed 

Google Scholar 
Sandoval-Denis, M., Lombard, L. & Crous, P. W. Back to the roots: a reappraisal of Neocosmospora. Persoonia Mol. Phylogeny Evol. Fungi 43, 90–185 (2019).CAS 

Google Scholar 
O’Donnell, K., Cigelnik, E. & Nirenberg, H. I. Molecular systematics and phylogeography of the Gibberella fujikuroi species complex. Mycologia 90, 465–493 (1998).
Google Scholar 
Geiser, D. M. et al. FUSARIUM-ID v. 1. 0: A DNA sequence database for identifying Fusarium. Eur. J. Plant Pathol. 110, 473–479 (2004).ADS 
CAS 

Google Scholar 
O’Donnell, K. et al. Phylogenetic diversity of insecticolous fusaria inferred from multilocus DNA sequence data and their molecular identification via FUSARIUM-ID and FUSARIUM MLST. Mycologia 104, 427–445 (2012).PubMed 

Google Scholar 
Chehri, K., Salleh, B. & Zakaria, L. Morphological and phylogenetic analysis of Fusarium solani species complex in Malaysia. Microb. Ecol. 69, 457–471 (2015).PubMed 

Google Scholar 
Lanfear, R., Frandsen, P., Wright, A., Senfeld, T. & Calcott, B. PartionFinder 2: new methods for selecting partioned models of evolution for molecular and morphological phylogenetic analyses. Mol. Biol. https://doi.org/10.1093/molbev/msw260 (2016).Article 

Google Scholar 
Ronquist, F. et al. Efficient Bayesian phylogenetic inference and model selection across a large model space. Syst. Biol. 61, 539–542 (2012).PubMed 
PubMed Central 

Google Scholar 
Leslie, J. F. & Summerell, B. A. The Fusarium Laboratory manual (Blackwell Publishing, Hoboken, 2006).
Google Scholar 
Fisher, N. L., Burgess, L. W., Toussoun, T. A. & Nelson, P. E. Carnation leaves as a substrate and for preserving cultures of Fusarium species. Phytopathology 72, 151 (1982).
Google Scholar 
Smyth, C. W. et al. Unraveling the ecology and epidemiology of an emerging fungal disease, sea turtle egg fusariosis (STEF). PLOS Pathog. 15, e1007682 (2019).CAS 
PubMed 
PubMed Central 

Google Scholar 
Rachowicz, L. J. et al. The novel and endemic pathogen hypotheses: Competing explanations for the origin of emerging infectious diseases of wildlife. Conserv. Biol. 19, 1441–1448 (2005).
Google Scholar 
Lombard, L., Sandoval-Denis, M., Cai, L. & Crous, P. W. Changing the game: resolving systematic issues in key Fusarium species complexes. Persoonia Mol. Phylogeny Evol. Fungi 43, i–ii (2019).CAS 

Google Scholar 
Short, D. P. G., Donnell, K. O., Zhang, N., Juba, J. H. & Geiser, D. M. Widespread occurrence of diverse human pathogenic types of the fungus Fusarium detected in plumbing drains. J. Clin. Microbiol. 49, 4264–4272 (2011).PubMed 
PubMed Central 

Google Scholar 
White, T. J., Burns, T., Lee, S. & Taylor, J. Amplification and direct identification of fungal ribosomal RNA genes for phylogenetics. In PCR Protocols: a guide to methods and applications (eds Innis, M. A. et al.) 315–322 (Academic Press, San Diego, 1990).
Google Scholar 
Sekimoto, S., Hatai, K. & Honda, D. Molecular phylogeny of an unidentified Haliphthoros-like marine oomycete and Haliphthoros milfordensis inferred from nuclear-encoded small- and large-subunit rRNA genes and mitochondrial-encoded cox2 gene. Mycoscience 48, 212–221 (2007).CAS 

Google Scholar 
Petersen, A. B. & Rosendahl, S. Ø. Phylogeny of the Peronosporomycetes (Oomycota) based on partial sequences of the large ribosomal subunit (LSU rDNA). Mycol. Res. 104, 1295–1303 (2000).CAS 

Google Scholar 
O’Donnell, K. et al. Phylogenetic diversity and microsphere array-based genotyping of human pathogenic fusaria, including isolates from the multistate contact lens-associated U.S. keratitis outbreaks of 2005 and 2006. J. Clin. Microbiol. 45, 2235–2248 (2007).PubMed 
PubMed Central 

Google Scholar 
Migheli, Q. et al. Molecular Phylogenetic diversity of dermatologic and other human pathogenic fusarial isolates from hospitals in Northern and Central Italy. J. Clin. Microbiol. 48, 1076–1084 (2010).CAS 
PubMed 
PubMed Central 

Google Scholar  More

ColoniesColony setup occurred prior to initiation of the study, between March and May 2017, in Mississippi, USA. Using established methods, queenless colony divisions, obtained from a large commercial beekeeping operation, were equalised to an average calculated population size of ~ 7000 workers112, and housed in 10-frame Langstroth hives (Table S1). After acclimatisation for 24–48 h, they each received an imminently emerging queen cell, containing a queen from one of two stocks, added to the same worker baseline. The stocks used consisted of an Italian ‘Commercial’ stock, propagated from collaborator established breeder queens, and thus representative of the industry standard, and the Varroa-resistant ‘Pol-line’ stock54. To ensure consistency, all queens were reared in the same ‘cell builder’ colonies, based at the USDA Honey Bee Breeding, Genetics and Physiology Laboratory, in Baton Rouge, Louisiana, USA. Colonies from each stock were held in independent apiaries, 80 km apart to maintain physical isolation; and to control genetic fidelity, virgin queens were open mated to drones of the same stock via drone saturation. Fourteen days after queen emergence, colonies were inspected, and mated queens were marked with paint on the thorax, to assist with identification, with white corresponding to Commercial, and blue to Pol-line. Colonies were allowed to acclimatise for six weeks before sampling began, and those that failed to achieve mating success, or had unacceptably high [≥ 3.0 ‘mites per hundred bees’ (MPHB)] Varroa levels, were removed, normalising the average between-stock Varroa difference to  More

Butzer, K. W. Early Hydraulic Civilization in Egypt: a Study in Cultural Ecology (University of Chicago Press, Chicago, 1976).Said, R. The River Nile: Geology, Hydrology and Utilization (Pergamon Press, Oxford, 1993).Zeder, M. A. Domestication and early agriculture in the Mediterranean Basin: origins, diffusion, and impact. Proc. Natl Acad. Sci. USA 105, 11597–11604 (2008).CAS 
Article 

Google Scholar 
Shirai, N. The Archaeology of the First Farmer-Herders in Egypt: New Insights into the Fayum Epipalaeolithic and Neolithic (Uni. Leiden Press, the Netherlands, 2010).Garcea, E. A. A. Multi-stage dispersal of Southwest Asian domestic livestock and the path of pastoralism in the Middle Nile Valley. Quat. Int. 412, 54–64 (2016).Article 

Google Scholar 
Wilson, P. Prehistoric settlement in the western Delta: a regional and local view from Sais (Sa el-Hagar). J. Egypt. Archaeol. 92, 75–126 (2006).Article 

Google Scholar 
Van Geel, B. Non-Pollen Palynomorphs. Smol J. P., Birks H. J. B., Last W. M., Bradley R. S., Alverson K. (eds) Tracking Environmental Change Using Lake Sediments. Developments in Paleoenvironmental Research, 3 (Springer, Dordrecht, 2002).Van Geel, B., Hallewas, J. P. & Pals, J. P. A Late Holocene deposit under the Westfriese Zeedijk near Nkhuizen (Prov. of N-Holland, The Netherlands): palaeoecological and archaeological aspects. Rev. Palaeobot. Palyno 38, 269–335 (1983).Article 

Google Scholar 
Van Geel, B. A paleoecological study of Holocene peat bog sections in Germany and the Netherlands. Rev. Palaeobot. Palyno 25, 1–120 (1978).Article 

Google Scholar 
Marinova, E. & Atanassova, J. Anthropogenic impact on vegetation and environment during the bronze age in the area of Lake Durankulak, NE Bulgaria: pollen, microscopic charcoal, non-pollen palynomorphs and plant macrofossils. Rev. Palaeobot. Palyno. 141, 165–178 (2006).Article 

Google Scholar 
Van Geel, B. et al. Diversity and ecology of tropical African fungal spores from a 25,000-year palaeoenvironmental record in southeastern Kenya. Rev. Palaeobot. Palynol. 164, 174–190 (2011).Article 

Google Scholar 
Gelorini, V., Verbeken, A., van Geel, B. B., Cocquyt, C. & Verschuren, D. Modern non-pollen palynomorphs from East African lake sediments. Rev. Palaeobot. Palyno 164, 143–173 (2011).Article 

Google Scholar 
Hillbrand, M., Geel, B. V., Hasenfratz, A., Hadorn, P. & Haas, J. N. Non-pollen palynomorphs show human-and livestock-induced eutrophication of Lake Nussbaumersee (Thurgau, Switzerland) since Neolithic times (3840 BC). Holocene 24, 559–568 (2014).Article 

Google Scholar 
Stanley, J. D. & Warne, A. G. Sea level and initiation of Predynastic culture in the Nile delta. Nature 363, 435–438 (1993).Article 

Google Scholar 
Pennington, B. T., Sturt, F., Wilson, P., Rowland, J. & Brown, A. G. The fluvial evolution of the Holocene Nile Delta. Quarter. Sci. Rev 170, 212–231 (2017).Article 

Google Scholar 
Negm, A. M., Saavedra O., & El-Adawy A. In The Handbook of Environmental Chemistry, 55 (Springer, 2017).Viste, E. & Sorteberg, A. The effect of moisture transport variability on Ethiopian summer precipitation. Int. J. Climatol. 33, 3106–3123 (2013).Article 

Google Scholar 
Revel, M., Colin, C., Bernasconi, S., Combourieu-Nebout, N. & Mascle, J. 21,000 years of Ethiopian African monsoon variability recorded in sediments of the western Nile deep-sea fan. Reg. Environ. Change 14, 1685–1696 (2014).Article 

Google Scholar 
Wijmstra, T. A., Smit, A., Van der Hammen, T. & Van Geel, B. Vegetational succession, fungal spores and short-term cycles in pollen diagrams from the Wietmarscher Moor. Acta Botanica Neerlandica 20, 401–410 (1971).Article 

Google Scholar 
Wilson, P. In The Nile Delta as a centre of cultural interactions between Upper Egypt and the Southern Levant in the 4th millennium BC, 299–318 (Poznań Archaeological Museum, Poznan, 2014).Zong, Y. Q. et al. Fire and flood management of coastal swamp enabled first rice paddy cultivation in east China. Nature 449, 459–462 (2007).CAS 
Article 

Google Scholar 
Yang, S. et al. Modern pollen assemblages from cultivated rice fields and rice pollen morphology: application to a study of ancient land use and agriculture in the Pearl River delta, China. The Holocene 22, 1393–1404 (2012).Article 

Google Scholar 
He, K. et al. Middle-Holocene sea-level fluctuations interrupted the developing Hemudu Culture in the lower Yangtze River. China. Quarter. Sci. Rev. 188, 90–103 (2018).Article 

Google Scholar 
Edwards, K. J., Whittington, G., Robinson, M. & Richter, D. Palaeoenvironments, the archaeological record and cereal pollen detection at Clickimin, Shetland, Scotland. J. Archaeo. Sci. 32, 1741–1756 (2005).Article 

Google Scholar 
Andersen, S. T. Identification of Wild Grass and Cereal Pollen [fossil Pollen, Annulus Diameter, Surface Sculpturing], Aarbog, 69–92 (Danmarks Geologiske Undersoegelse, 1979).Tweddle, J. C., Edwards, K. J. & Fieller, N. R. Multivariate statistical and other approaches for the separation of cereal from wild Poaceae pollen using a large Holocene dataset. Veg. Hist. Archaeobot. 14, 15–30 (2005).Article 

Google Scholar 
Joly, C., Barille, L., Barreau, M., Mancheron, A. & Visset, L. Grain and annulus diameter as criteria for distinguishing pollen grains of cereals from wild grasses. Rev. Palaeobot. Palynol. 146, 221–233 (2007).Article 

Google Scholar 
Salgado-Labouriau, M. L. & Rinaldi, M. Palynology of Gramineae of the Venezuelan mountains. Grana Palynologica 29, 119–128 (1990).Article 

Google Scholar 
Josefsson, T., Ramqvist, P. H. & Rnberg, G. The history of early cereal cultivation in northernmost Fennoscandia as indicated by palynological research. Veg. Hist. Archaeobot. 23, 821–840 (2014).Article 

Google Scholar 
Zhao, X. S. et al. Climate-driven early agricultural origins and development in the Nile Delta. Egypt. J. Archaeo. Sci. 136, 105498 (2021).Article 

Google Scholar 
Willcox, G. The distribution, natural habitats and availability of wild cereals in relation to their domestication in the near east: multiple events, multiple centres. Veg. Hist. Archaeobot. 14, 534–541 (2005).Article 

Google Scholar 
Riemer, H. Barbara e. barich. People, water and grain: the beginnings of domestication in the Sahara and the Nile Valley, Roma 1998. Archol. Inf. 24, 117–119 (2014).
Google Scholar 
Arranz-Otaegui, A., Colledge, S., Zapata, L., Teira-Mayolini, L. C. & Juan, J. Regional diversity on the timing for the initial appearance of cereal cultivation and domestication in Southwest Asia. Proc. Natl Acad. Sci. USA 113, 201612797 (2016).Article 

Google Scholar 
Zohary, D., Hopf, M. & Weiss, E. Domestication of plants in the Old World (Oxford University Press, Oxford, 2012).Kvavadze, E. & Bitadze, N. L. Special issue: fresh insights into the palaeoecological and palaeoclimatological value of quaternary non-pollen palynomorphs || Fibres of Linum (flax), Gossypium (cotton) and animal wool as non-pollen palynomorphs in the Late Bronze Age burials of Saphar-Kharaba, southern Georgia. Veg. Hist. Archaeobot. 19, 479–494 (2010).Article 

Google Scholar 
Karg, S. New research on the cultural history of the useful plant Linum usitatissimum L. (flax), a resource for food and textiles for 8,000 years. Veg. Hist. Archaeobot. 20, 507–508 (2011).Article 

Google Scholar 
Zhao, X. S. et al. Holocene climate change and its influence on early agriculture in the Nile Delta, Egypt. Palaeogeogr. Palaeoclimatol. Palaeoecol. 547, 109702 (2020).Article 

Google Scholar 
Reimer, P. et al. The IntCal20 northern hemisphere radiocarbon age calibration curve (0–55 cal kBP). Radiocarbon 62, 725–757 (2020).CAS 
Article 

Google Scholar 
Blaauw, M. & Christen, J. A. Flexible paleoclimate age-depth models using an autoregressive gamma process. Bayesian Analysis. 6, 457–474 (2011).Article 

Google Scholar 
Moore, P. D., Webb, J. A. & Collison, M. E. Pollen analysis (Blackwell Scientific Publications, Oxford, UK, 1991).Kholeif, S. E. A. & Mudie, P. J. Palynological records of climate and oceanic conditions in the Late Pleistocene and Holocene of the Nile Cone, Southeastern Mediterranean, Egypt. Palynology 33, 1–24 (2009).Article 

Google Scholar 
Leroy, S. A. G. Palynological evidence of Azolla nilotica Dec. in recent Holocene of the eastern Nile Delta and palaeoenvironment. Veg. Hist. Archaeobot. 1, 43–52 (1992).Article 

Google Scholar 
Kholeif, S. E. A. Holocene paleoenvironmental change in inner continental shelf sediments, Southeastern Mediterranean, Egypt. J. Afr. Earth. Sci. 57, 143–153 (2010).CAS 
Article 

Google Scholar  More

IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).Sindelar, A. J. et al. Winter oilseed production for biofuel in the US Corn Belt: opportunities and limitations. GCB Bioenergy 9, 508–524 (2017).CAS 

Google Scholar 
Stöckle, C. O. et al. Evaluating opportunities for an increased role of winter crops as adaptation to climate change in dryland cropping systems of the U.S. Inland Pacific Northwest. Clim. Change 146, 247–261 (2018).
Google Scholar 
Williams, C. M., Henry, H. A. L. & Sinclair, B. J. Cold truths: how winter drives responses of terrestrial organisms to climate change. Biol. Rev. 90, 214–235 (2015).
Google Scholar 
Seifert, C. A., Azzari, G. & Lobell, D. B. Satellite detection of cover crops and their effects on crop yield in the Midwestern United States. Environ. Res. Lett. 13, 064033 (2018).
Google Scholar 
Marcillo, G. S. & Miguez, F. E. Corn yield response to winter cover crops: an updated meta-analysis. J. Soil Water Conserv. 72, 226–239 (2017).
Google Scholar 
Zhu, L., Ives, A. R., Zhang, C., Guo, Y. & Radeloff, V. C. Climate change causes functionally colder winters for snow cover-dependent organisms. Nat. Clim. Change 9, 886–893 (2019).
Google Scholar 
Mankin, J. S. & Diffenbaugh, N. S. Influence of temperature and precipitation variability on near-term snow trends. Clim. Dynam. 45, 1099–1116 (2015).
Google Scholar 
Zhu, L., Radeloff, V. C. & Ives, A. R. Characterizing global patterns of frozen ground with and without snow cover using microwave and MODIS satellite data products. Remote Sens. Environ. 191, 168–178 (2017).
Google Scholar 
Huning, L. S. & AghaKouchak, A. Global snow drought hot spots and characteristics. Proc. Natl Acad. Sci. USA 117, 19753–19759 (2020).CAS 

Google Scholar 
Qin, Y. et al. Agricultural risks from changing snowmelt. Nat. Clim. Change 10, 459–465 (2020).
Google Scholar 
Trnka, M. et al. Adverse weather conditions for European wheat production will become more frequent with climate change. Nat. Clim. Change 4, 637–643 (2014).
Google Scholar 
Li, D., Wrzesien, M. L., Durand, M., Adam, J. & Lettenmaier, D. P. How much runoff originates as snow in the western United States, and how will that change in the future? Geophys. Res. Lett. 44, 6163–6172 (2017).
Google Scholar 
Biemans, H. et al. Importance of snow and glacier meltwater for agriculture on the Indo-Gangetic Plain. Nat. Sustain. 2, 594–601 (2019).
Google Scholar 
Acevedo, E., Silva, P. & Silva, H. in Bread Wheat: Improvement and Production (eds Curtis, B. C. et al.) 39–70 (FAO Plant Production and Protection, 2002).Baker, J. T., Pinter, P. J., Reginato, R. J. & Kanemasu, E. T. Effects of temperature on leaf appearance in spring and winter wheat cultivars. Agron. J. 78, 605–613 (1986).
Google Scholar 
Tack, J., Barkley, A. & Nalley, L. L. Effect of warming temperatures on US wheat yields. Proc. Natl Acad. Sci. USA 112, 6931–6936 (2015).CAS 

Google Scholar 
Müller, C. et al. Global gridded crop model evaluation: benchmarking, skills, deficiencies and implications. Geosci. Model Dev. 10, 1403–1422 (2017).
Google Scholar 
Talukder, A. S. M. H. M., McDonald, G. K. & Gill, G. S. Effect of short-term heat stress prior to flowering and early grain set on the grain yield of wheat. Field Crops Res. 160, 54–63 (2014).
Google Scholar 
Farooq, M., Bramley, H., Palta, J. A. & Siddique, K. H. M. Heat stress in wheat during reproductive and grain-filling phases. Crit. Rev. Plant Sci. 30, 491–507 (2011).Cuadra, S. V., Kimball, B. A., Boote, K. J., Suyker, A. E. & Pickering, N. Energy balance in the DSSAT-CSM-CROPGRO model. Agric. For. Meteorol. 297, 108241 (2021).
Google Scholar 
Harder, P., Helgason, W. D. & Pomeroy, J. W. Modeling the snowpack energy balance during melt under exposed crop stubble. J. Hydrometeorol. 19, 1191–1214 (2018).
Google Scholar 
Barlow, K. M., Christy, B. P., O’Leary, G. J., Riffkin, P. A. & Nuttall, J. G. Simulating the impact of extreme heat and frost events on wheat crop production: a review. Field Crops Res. 171, 109–119 (2015).
Google Scholar 
Wang, W. et al. Evaluation of air–soil temperature relationships simulated by land surface models during winter across the permafrost region. Cryosphere 10, 1721–1737 (2016).
Google Scholar 
Seifert, C. A. & Lobell, D. B. Response of double cropping suitability to climate change in the United States. Environ. Res. Lett. 10, 024002 (2015).
Google Scholar 
Pullens, J. W. M. et al. Risk factors for European winter oilseed rape production under climate change. Agric. For. Meteorol. 272–273, 30–39 (2019).
Google Scholar 
Chopra, R. et al. Identification and stacking of crucial traits required for the domestication of pennycress. Nat. Food 1, 84–91 (2020).
Google Scholar 
Crews, T. E., Carton, W. & Olsson, L. Is the future of agriculture perennial? Imperatives and opportunities to reinvent agriculture by shifting from annual monocultures to perennial polycultures. Glob. Sustain. 1, e11 (2018).Harkness, C. et al. Adverse weather conditions for UK wheat production under climate change. Agric. Meteorol. 282–283, 107862 (2020).
Google Scholar 
Schierhorn, F., Hofmann, M., Gagalyuk, T., Ostapchuk, I. & Müller, D. Machine learning reveals complex effects of climatic means and weather extremes on wheat yields during different plant developmental stages. Clim. Change 169, 39 (2021).Michel, S. et al. Improving and maintaining winter hardiness and frost tolerance in bread wheat by genomic selection. Front. Plant Sci. 10, 1195 (2019).
Google Scholar 
Mahfoozi, S., Limin, A. E. & Fowler, D. B. Influence of vernalization and photoperiod responses on cold hardiness in winter cereals. Crop Sci. 41, 1006–1011 (2001).
Google Scholar 
Dutra, E. et al. An improved snow scheme for the ECMWF land surface model: description and offline validation. J. Hydrometeorol. 11, 899–916 (2010).
Google Scholar 
Ge, Y. & Gong, G. Land surface insulation response to snow depth variability. J. Geophys. Res. Atmos. 115, 8107 (2010).
Google Scholar 
Hunt, J. R. et al. Early sowing systems can boost Australian wheat yields despite recent climate change. Nat. Clim. Change 9, 244–247 (2019).
Google Scholar 
Sloat, L. L. et al. Climate adaptation by crop migration. Nat. Commun. 11, 1243 (2020) .Ainsworth, E. A. & Long, S. P. 30 years of free-air carbon dioxide enrichment (FACE): what have we learned about future crop productivity and its potential for adaptation? Glob. Change Biol. 27, 27–49 (2021).
Google Scholar 
Shimoda, S. et al. Effects of snow compaction ‘yuki-fumi’ on soil frost depth and volunteer potato control in potato–wheat rotation system in Hokkaido. Plant Prod. Sci. 24, 186–197 (2021).CAS 

Google Scholar 
Luojus, K. et al. GlobSnow v3.0 Northern Hemisphere snow water equivalent dataset. Sci. Data 8, 163 (2021)..IMS Daily Northern Hemisphere Snow and Ice Analysis at 1 km, 4 km, and 24 km Resolutions Version 1 (NSIDC, 2008).Jing, Q. et al. Assessing the options to improve regional wheat yield in Eastern Canada using the CSM–CERES–wheat model. Agron. J. 109, 510–523 (2017).
Google Scholar 
Vogel, F. A. & Bange, G. A. Understanding USDA Crop Forecasts (USDA, 1999).Daly, C. et al. Physiographically sensitive mapping of climatological temperature and precipitation across the conterminous United States. Int. J. Climatol. 28, 2031–2064 (2008).
Google Scholar 
Brown, R. D. & Brasnett, B. Daily Snow Depth Analysis Data Version 1 (Canadian Meteorological Centre, 2010).Brasnett, B. A global analysis of snow depth for numerical weather prediction. J. Appl. Meteorol. Climatol. 38, 726–740 (1999).
Google Scholar 
Toure, A. M., Reichle, R. H., Forman, B. A., Getirana, A. & De Lannoy, G. J. M. Assimilation of MODIS snow cover fraction observations into the NASA catchment land surface model. Remote Sens. 10, 316 (2018).
Google Scholar 
Snauffer, A. M., Hsieh, W. W. & Cannon, A. J. Comparison of gridded snow water equivalent products with in situ measurements in British Columbia, Canada. J. Hydrol. 541, 714–726 (2016).
Google Scholar 
Census of Agriculture (USDA National Agricultural Statistics Service, 2017).Skinner, D. Z. & Mackey, B. Freezing tolerance of winter wheat plants frozen in saturated soil. Field Crops Res. 113, 335–341 (2009).
Google Scholar 
Lollato, R. P. et al. Climate-risk assessment for winter wheat using long-term weather data. Agron. J. 112, 2132–2151 (2020).
Google Scholar 
Siebers, M. H. et al. Heat waves imposed during early pod development in soybean (Glycine max) cause significant yield loss despite a rapid recovery from oxidative stress. Glob. Change Biol. 21, 3114–3125 (2015).
Google Scholar 
Çakir, R. Effect of water stress at different development stages on vegetative and reproductive growth of corn. Field Crops Res. 89, 1–16 (2004).
Google Scholar 
Lobell, D. B. et al. The critical role of extreme heat for maize production in the United States. Nat. Clim. Change 3, 497–501 (2013).
Google Scholar 
Chen, M., Griffis, T. J., Baker, J., Wood, J. D. & Xiao, K. Simulating crop phenology in the Community Land Model and its impact on energy and carbon fluxes. J. Geophys. Res. Biogeosci. 120, 310–325 (2015).CAS 

Google Scholar 
Larson, K. M. & Small, E. E. Daily Snow Depth and SWE from GPS Signal-to-Noise Ratios Version 1 (NSIDC, 2017).Sturm, M. et al. Estimating snow water equivalent using snow depth data and climate classes. J. Hydrometeorol. 11, 1380–1394 (2010).
Google Scholar 
McCabe, G. J. & Wolock, D. M. Recent declines in western U.S. snowpack in the context of twentieth-century climate variability. Earth Interact. 13, 1–15 (2009).
Google Scholar 
Wu, X. et al. Uneven winter snow influence on tree growth across temperate China. Glob. Change Biol. 25, 144–154 (2019).
Google Scholar 
Qiao, S. et al. Robust negative impacts of climate change on African agriculture. Environ. Res. Lett. 5, 014010 (2010).
Google Scholar 
Lobell, D. B., Schlenker, W. & Costa-Roberts, J. Climate trends and global crop production since 1980. Science 333, 616–620 (2011).CAS 

Google Scholar 
Xie, Y., Gibbs, H. K. & Lark, T. J. Landsat-based Irrigation Dataset (LANID): 30 m resolution maps of irrigation distribution, frequency, and change for the US, 1997–2017. Earth Syst. Sci. Data 13, 5689–5710 (2021).
Google Scholar 
Mueller, N. D. et al. Closing yield gaps through nutrient and water management. Nature 490, 254–257 (2012).CAS 

Google Scholar 
Friedman, J., Hastie, T. & Tibshirani, R. Regularization paths for generalized linear models via coordinate descent. J. Stat. Softw. 33, 1–22 (2010).
Google Scholar 
Elliott, J. et al. The global gridded crop model intercomparison: data and modeling protocols for phase 1 (v1.0). Geosci. Model Dev. 8, 261–277 (2015).
Google Scholar 
Li, X., Shen, Z., Harri, A. & Coble, K. H. Comparing survey-based and programme-based yield data: implications for the U.S. Agricultural Risk Coverage-County programme. Geneva Pap. Risk Insur. Issues Pract. 45, 184–202 (2020).
Google Scholar 
Hawkins, E., Osborne, T. M., Ho, C. K. & Challinor, A. J. Calibration and bias correction of climate projections for crop modelling: an idealised case study over Europe. Agric. Meteorol. 170, 19–31 (2013).
Google Scholar 
Ho, C. K., Stephenson, D. B., Collins, M., Ferro, C. A. T. & Brown, S. J. Calibration strategies: a source of additional uncertainty in climate change projections. Bull. Am. Meteorol. Soc. 93, 21–26 (2012).
Google Scholar  More

Ellis, E. C., Beusen, A. H. W. & Goldewijk, K. K. Anthropogenic Biomes: 10,000 BCE to 2015 CE. Land. 9(5), 129 (2020).Article 

Google Scholar 
Seto, K. C., Guneralp, B. & Hutyra, L. R. Global forecasts of urban expansion to 2030 and direct impacts on biodiversity and carbon pools. PNAS. 109, 16083–8 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Doherty, T. S., Hays, G. C. & Driscoll, D. A. Human disturbance causes widespread disruption of animal movement. Nat. Ecol. Evol. 5, 513–519 (2021).PubMed 
Article 

Google Scholar 
Frid, A. & Dill, L. Human-caused disturbance stimuli as a form of predation risk. Conserv. Ecol. 6, 11 (2002).
Google Scholar 
Rodgers, J. A. & Schwikert, S. T. Buffer-zone distances to protect foraging and loafing waterbirds from disturbance by personal watercraft and outboard-powered boats. Conserv. Bio. 16, 216–224 (2002).Article 

Google Scholar 
Constantine, R., Brunton, D. H. & Dennis, T. Dolphin-watching tour boats change bottlenose dolphin (Tursiops truncates) behaviour. Biol. Conserv. 117, 299–307 (2004).Article 

Google Scholar 
Gill, J. A., Sutherland, W. J. & Watkinson, A. R. A method to quantify the effects of human disturbance on animal populations. J. Appl. Ecol. 33, 786–792 (1996).Article 

Google Scholar 
King, J. M. & Heinen, J. T. An assessment of the behaviors of overwintering manatees as influenced by interactions with tourists at two sites in central Florida. Biol. Conserv 117, 227–234 (2004).Article 

Google Scholar 
Stockwell, C. A., Bateman, G. C. & Berger, J. Conflicts in national parks: A case study of helicopters and bighorn sheep time budgets at the Grand Canyon. Biol. Conserv 56, 317–328 (1991).Article 

Google Scholar 
Diamond, J. M. The design of a nature reserve system for Indone-Asian New Guinea. In Conservation Biology: The Science of Scarcity and Cliversity (ed. Soule, M.) 485–503 (Sinauer, Sunderland, Massachusetts, 1986).Ceballos, G., García, A. & Ehrlich, P. R. The sixth extinction crisis loss of animal populations and species. J. Cosmol. 8, 1821–1831 (2010).
Google Scholar 
Kerr, J. T. & Deguise, I. Habitat loss and the limits to endangered species recovery. Ecol. Lett. 7, 1163–1169 (2004).Article 

Google Scholar 
Mbora, D. N. M. & McPeek, M. A. Host density and human activities mediate increased parasite prevalence and richness in primates threatened by habitat loss and fragmentation. J. Anim. Ecol. 78, 210–218 (2009).PubMed 
Article 

Google Scholar 
Low, T. The New Nature (Penguin Books Limited, 2003).
Google Scholar 
Baxter-Gilbert, J., Riley, J. L. & Measey, J. Fortune favors the bold toad: Urban-derived behavioral traits may provide advantages for invasive amphibian populations. Behav. Ecol. Sociobiol. 75, 130 (2021).Article 

Google Scholar 
Coleman, J. L. & Barclay, R. M. R. Prey availability and foraging activity of grassland bats in relation to urbanization. J. Mammal. 94, 1111–1122 (2013).Article 

Google Scholar 
Castellano, M. J. & Valone, T. J. Effects of livestock removal and perennial grass recovery on the lizards of a desertified arid grassland. J. Arid Environ. 66, 87e95 (2006).Article 

Google Scholar 
Huey, R. B. Temperature, physiology, and the ecology of reptiles. In Biology of the Reptilia (eds. Gans, C., & Pough, F.H.) Vol. 12. (Academic Press, London, 1982).White, D. et al. Assessing risks to biodiversity from future landscape change. Conserv. Biol. 11, 349360 (1997).Article 

Google Scholar 
Carpio, A. J., Oteros, J., Tortosa, F. S. & Guerrero-Casado, J. Land use and biodiversity patterns of the herpetofauna: The role of olive groves. Acta Oecol. 70, 103–111 (2016).Article 

Google Scholar 
Geyle, H. M., Tingley, R., Amey, A. P. & Chapple, D. G. Reptiles on the brink: Identifying the Australian terrestrial snake and lizard species most at risk of extinction. Pac. Conserv. Biol. 27, 3–12 (2021).Article 

Google Scholar 
Doherty, T. S. et al. Reptile responses to anthropogenic habitat modification: A global meta-analysis. Glob. Ecol. Biogeogr. 29(7), 1265–1279 (2020).Article 

Google Scholar 
Hu, Y., Doherty, T. S. & Jessop, T. S. How influential are squamate reptile traits in explaining population responses to environmental disturbances?. Wildl. Res. 47(3), 249–259 (2020).Article 

Google Scholar 
Poole, G. & Berman, C. An ecological perspective on in-stream temperature: natural heat dynamics and mechanisms of human-caused thermal degradation. Environ. Manag. 27, 787–802 (2001).CAS 
Article 

Google Scholar 
Tang, X. et al. Human activities enhance radiation forcing through surface albedo associated with vegetation in beijing. Remote Sens. 12(5), 837 (2020).Article 

Google Scholar 
Barna, A., Masum, A. K. M., Hossain, M. E., Bahadur, E.H. & Alam, M. S. A study on human activity recognition using gyroscope, accelerometer, temperature and humidity data. In 2019 International Conference on Electrical, Computer and Communication Engineering (ECCE), pp. 1–6 (2019).Moore, M. & Seigel, R. A. No place to nest or bask: Effects of human disturbance on the nesting and basking habits of yellow-blotched map turtles (Graptemys flavimaculata). J. Biol. Conserv. 130(3), 386–393 (2006).Article 

Google Scholar 
Bonnet, X., Naulleau, G. & Shine, R. The dangers of leaving home: Dispersal and mortality in snakes. Biol. Conserv. 89(1), 39–50 (1999).Article 

Google Scholar 
Haxton, T. Road mortality of Snapping Turtles, Chelydra serpentina, in central Ontario during their nesting period. Can. Field-Nat. 114(1), 106–110 (2000).
Google Scholar 
Koenig, J., Shine, R. & Shea, G. L. The ecology of an Australian reptile icon: How do blue-tongued lizards (Tiliqua scincoides) survive in suburbia?. Wildl. Res. 28(3), 214–227 (2001).Article 

Google Scholar 
Uetz, P. How many Reptile species?. Herpetol. Rev. 31, 13–15 (2000).
Google Scholar 
Todd, R. L., Steven, P., Rowland, G. & Oldham, G. Herpetological observations from field expeditions to North Karnataka and Southwest Maharashtra, India. Herpetol. Bull. 112, 17–37 (2010).
Google Scholar 
Sathish Kumar, V. M. The conservation of Indian Reptiles: An approach with molecular aspects. Reptile Rap. 14, 2–8 (2012).
Google Scholar 
Berryman, A. A. & Hawkins, B. A. The refuge as an integrating concept in ecology and evolution. Oikos. 115, 92–196 (2006).Article 

Google Scholar 
Webb, J. K., Pringle, R. M. & Shine, R. How do nocturnal snakes select diurnal retreat sites?. Copeia 2004, 919–925 (2004).Article 

Google Scholar 
Skinner, M. & Miller, N. Aggregation and social interaction in garter snakes (Thamnophis sirtalis sirtalis). Behav. Ecol. Sociobiol. 74, 51 (2020).Article 

Google Scholar 
Aubret, F. & Shine, R. Causes and consequences of aggregation by neonatal tiger snakes (Notechis scutatus, Elapidae). Austral Ecol. 34(2), 210–217 (2009).Article 

Google Scholar 
Myres, B. & Eells, M. Thermal aggregation in Boa constrictor. Herpetologica 24(1), 61–66 (1968).
Google Scholar 
Parrish, J. K. & Edelstein-keshet, L. Coinplexity, pattern, and evolutionary trade-offs in animal aggregation. Science 284, 99–101 (1999).CAS 
PubMed 
Article 

Google Scholar 
Trevesa, A. Theory and method in studies of vigilance and aggregation. Anim. Behav. 60, 711–722 (2000).Article 

Google Scholar 
Greene, H. W. Snakes (University of California Press, 1997).Book 

Google Scholar 
Huey, R. B., Peterson, C. R., Arnold, S. J. & Porter, W. P. Hot rocks and not-so-hot rocks: Retreat-site selection by garter snakes and its thermal consequences. Ecology 70, 931–944 (1989).Article 

Google Scholar 
Christian, K. & Weavers, B. Analysis of activity and energetics of the lizard Varanus rosenbergi. Copeia 1994, 289–295 (1994).Article 

Google Scholar 
Autumn, K. & de Nardo, D. F. Behavioural thermoregulation increases growth rate in nocturnal lizard. J. Herpetol. 29, 157–162 (1995).Article 

Google Scholar 
Milne, T., Bull, C. M. & Hutchinson, M. N. Use of burrows by the endangered pygmy blue-tongue lizard, Tiliqua adelaidensis (Scincidae). Wildl. Res. 30, 523–528 (2003).Article 

Google Scholar 
Sunday, J. M. et al. Thermal-safety margins and the necessity of thermoregulatory behavior across latitude and elevation. PNAS. 111, 5610–5615 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kearney, M., Shine, R. & Porter, W. P. The potential for behavioral thermoregulation to buffer ‘“cold-blooded”’ animals against climate warming. PNAS 106, 3835–3840 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Stevenson, D. J., Dyer, K. J. & Willis-Stevenson, B. A. Survey and monitoring of the eastern indigo snake in georgia. Southeast. Nat. 2(3), 393–408 (2003).Article 

Google Scholar 
Zappalorti, R. T. & Reinert, H. K. Artificial refugia as a habitat-improvement strategy for snake conservation. Contrib. Herpetol. 11, 369–375 (1994).
Google Scholar 
Griffith, B., Scott, J. M., Carpenter, J. W. & Reed, C. Translocation as a species conservation tool: Status and strategy. Science 245, 477–480 (1989).CAS 
PubMed 
Article 

Google Scholar 
Mullin, S. J. Snakes Ecology and Conservation (eds. Stephen, J. M. & Richard, A. S.). (Cornell University Press, 2011).Lei, J., Booth, D. T. & Dwyer, R. G. Spatial ecology of yellow-spotted goannas adjacent to a sea turtle nesting beach. Aust. J. Zool. 65, 77–86 (2017).Article 

Google Scholar 
Ermi, Z. Snakes of China. (Anhui Science and Technology Press, 2006).Schulz, K. D. A Monograph of the Colubrid Snakes of the Genus Elaphe Fitzinger (Czech Republic, Koeltz Scientific Books, 1996).
Google Scholar 
Pallas, P. S. Reise durch verschiedene Provinzen des Russischen Reiches, Vol. 2. 744 (Kaiserl. Akad. Wiss., St. Petersburg, 1773).Auffenberg, W., Arian, Q. N. & Kurshid, N. Preferred habitat, home range and movement patterns of Varanus bengalensis in southern Pakistan. Mertensiella 2, 7–28 (1991).
Google Scholar 
McDiarmid, R. W. Reptile Biodiversity: Standard Methods for Inventory and Monitoring. (University of California Press, 2002).Riley, J. L., Baxter-gilbert, J. H. & Litzgus, J. D. A comparison of three external transmitter attachment methods for snakes. Wildl. Soc. Bull. 41(1), 132–139 (2017).Article 

Google Scholar 
Meine, C., & Archibald, G. The Cranes: Status Survey and Conservation Action Plan (IUCN, 1996).Mori, A. & Toda, M. Body temperature of subtropical snakes at night: How cold is their blood?. Curr. Herpetol. 37(2), 151–157 (2018).Article 

Google Scholar 
Crane, M., Silva, I., Marshall, B. M. & Strine, C. T. Lots of movement, little progress: A review of reptile home range literature. PeerJ 9, e11742 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Calabrese, J. M., Fleming, C. H. & Gurarie, E. ctmm: An R package for analyzing animal relocation data as a continuous-time stochastic process. Methods Ecol. Evol. 7, 1124–1132 (2016).Article 

Google Scholar 
Fleming, C. H. & Calabrese, J. M. A new kernel density estimator for accurate home-range and species-range area estimation. Methods Ecol. Evol. 8, 571–579 (2017).Article 

Google Scholar 
Fleming, C. H. et al. From fine-scale foraging to home ranges: A semivariance approach to identifying movement modes across spatiotemporal scales. Am. Nat. 183, 154–167 (2014).Article 

Google Scholar 
Fleming, C. H., Noonan, M. J., Medici, E. P. & Calabrese, J. M. Overcoming the challenge of small effective sample sizes in home-range estimation. Methods Ecol. Evol. 10, 1679–1689 (2019).Article 

Google Scholar 
Uhlenbeck, G. E. & Ornstein, L. S. On the theory of the Brownian motion. Phys. Rev. 36, 823 (1930).CAS 
MATH 
Article 

Google Scholar 
Bürkner, P. C. brms: An R package for Bayesian multilevel models using Stan. J. Stat. Softw. 80, 1–28 (2017).Article 

Google Scholar 
Bhattacharyya, A. On a measure of divergence between two statistical populations defined by their probability distributions. News Bull. Calcutta Math. Soc. 35, 99–109 (1943).MathSciNet 
MATH 

Google Scholar 
Winner, K. et al. Statistical inference for home range overlap. Methods Ecol. Evol. 9, 1679–1691 (2018).Article 

Google Scholar 
R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna (2016). http://www.R-project.org. Accessed September 2022.Calenge, A. The package ‘“adehabitat”’ for the R software: Tool for the analysis of space and habitat use by animals. Ecol. Modell. 197, 516–519 (2006).Article 

Google Scholar 
Manley, B. F. J., McDonald, L. L. & Thomas, D. L. Resource Selection by Animals: Statistical Design and Analysis for Field Studies (Chapman and Hall, 1993).Book 

Google Scholar  More

Hyman, L. H. The Invertebrates: Protozoa Through Ctenophora Vol. 1 (McGraw-Hill, 1940).Taylor, M. W., Radax, R., Steger, D. & Wagner, M. Sponge-associated microorganisms: evolution, ecology, and biotechnological potential. Microbiol. Mol. Biol. Rev. 71, 295–347 (2007).CAS 
PubMed 
PubMed Central 

Google Scholar 
Giles, E. C. et al. Bacterial community profiles in low microbial abundance sponges. FEMS Microbiol. Ecol. 83, 232–241 (2013).CAS 
PubMed 

Google Scholar 
Gloeckner, V. et al. The HMA–LMA dichotomy revisited: an electron microscopical survey of 56 sponge species. Biol. Bull. 227, 78–88 (2014).PubMed 

Google Scholar 
Moitinho-Silva, L. et al. Predicting the HMA–LMA status in marine sponges by machine learning. Front. Microbiol. 8, 752 (2017).PubMed 
PubMed Central 

Google Scholar 
Cárdenas, C. A. et al. High similarity in the microbiota of cold-water sponges of the genus Mycale from two different geographical areas. PeerJ 6, e4935 (2018).PubMed 
PubMed Central 

Google Scholar 
Webster, N. S. & Taylor, M. W. Marine sponges and their microbial symbionts: love and other relationships. Environ. Microbiol. 14, 335–346 (2012).CAS 
PubMed 

Google Scholar 
Freeman, C. J. et al. Microbial symbionts and ecological divergence of Caribbean sponges: a new perspective on an ancient association. ISME J. 14, 1571–1583 (2020).PubMed 
PubMed Central 

Google Scholar 
Bell, J. J. et al. Climate change alterations to ecosystem dominance: how might sponge-dominated reefs function? Ecology 99, 1920–1931 (2018).PubMed 

Google Scholar 
Gardner, T. A., Côté, I. M., Gill, J. A., Grant, A. & Watkinson, A. R. Long-term region-wide declines in Caribbean corals. Science 301, 958–960 (2003).CAS 
PubMed 

Google Scholar 
Lesser, M. P. Benthic–pelagic coupling on coral reefs: feeding and growth of Caribbean sponges. J. Exp. Mar. Biol. Ecol. 328, 277–288 (2006).
Google Scholar 
de Goeij, J. M., Lesser, M. P. & Pawlik, J. R. in Climate Change, Ocean Acidification and Sponges (eds Carballo, J. L. & Bell, J. J.) 373–410 (Springer, 2017); https://doi.org/10.1007/978-3-319-59008-0_8Pita, L., Rix, L., Slaby, B. M., Franke, A. & Hentschel, U. The sponge holobiont in a changing ocean: from microbes to ecosystems. Microbiome 6, 46 (2018).CAS 
PubMed 
PubMed Central 

Google Scholar 
Slaby, B. M., Hackl, T., Horn, H., Bayer, K. & Hentschel, U. Metagenomic binning of a marine sponge microbiome reveals unity in defense but metabolic specialization. ISME J. 11, 2465–2478 (2017).PubMed 
PubMed Central 

Google Scholar 
Moitinho-Silva, L. et al. Revealing microbial functional activities in the Red Sea sponge Stylissa carteri by metatranscriptomics. Environ. Microbiol. 16, 3683–3698 (2014).CAS 
PubMed 

Google Scholar 
Weisz, J. B., Lindquist, N. & Martens, C. S. Do associated microbial abundances impact marine demosponge pumping rates and tissue densities? Oecologia 155, 367–376 (2008).PubMed 

Google Scholar 
Poppell, E. et al. Sponge heterotrophic capacity and bacterial community structure in high- and low-microbial abundance sponges. Mar. Ecol. 35, 414–424 (2014).
Google Scholar 
McFall-Ngai, M. J. et al. Animals in a bacterial world, a new imperative for the life sciences. Proc. Natl Acad. Sci. USA 110, 3229–3236 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Douglas, A. E. Symbiosis as a general principle in eukaryotic evolution. Cold Spring Harb. Perspect. Biol. 6, a016113 (2014).PubMed 
PubMed Central 

Google Scholar 
Moran, N. A. & Sloan, D. B. The hologenome concept: helpful or hollow? PLoS Biol. 13, e1002311 (2015).PubMed 
PubMed Central 

Google Scholar 
Brooks, A. W., Kohl, K. D., Brucker, R. M., van Opstal, E. J. & Bordenstein, S. R. Phylosymbiosis: relationships and functional effects of microbial communities across host evolutionary history. PLoS Biol. 14, e2000225–e2000229 (2016).PubMed 
PubMed Central 

Google Scholar 
O’Brien, P. A. et al. Diverse coral reef invertebrates exhibit patterns of phylosymbiosis. ISME J. 14, 2211–2222 (2020).PubMed 
PubMed Central 

Google Scholar 
Houwenhuyse, S., Stoks, R., Mukherjee, S. & Decaestecker, E. Locally adapted gut microbiomes mediate host stress tolerance. ISME J. 15, 2401–2414 (2021).CAS 
PubMed 
PubMed Central 

Google Scholar 
Moeller, A. H. et al. Experimental evidence for adaptation to species-specific gut microbiota in house mice. mSphere 4, e00387-19 (2019).PubMed 
PubMed Central 

Google Scholar 
van Opstal, E. J. & Bordenstein, S. R. Phylosymbiosis impacts adaptive traits in Nasonia wasps. mBio https://doi.org/10.1128/mBio.00887-19 (2019).Lim, S. J. & Bordenstein, S. R. An introduction to phylosymbiosis. Proc. R. Soc. B https://doi.org/10.1098/rspb.2019.2900 (2020).Pollock, F. J. et al. Coral-associated bacteria demonstrate phylosymbiosis and cophylogeny. Nat. Commun. https://doi.org/10.1038/s41467-018-07275-x (2018).Douglas, A. E. & Werren, J. H. Holes in the hologenome: why host–microbe symbioses are not holobionts. mBio 7, e02099 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Hadfield, J. D., Krasnov, B. R., Poulin, R. & Nakagawa, S. A tale of two phylogenies: comparative analyses of ecological interactions. Am. Nat. 183, 174–187 (2014).PubMed 

Google Scholar 
Hill, M. S. et al. Reconstruction of family-level phylogenetic relationships within Demospongiae (Porifera) using nuclear encoded housekeeping genes. PLoS ONE 8, e50437 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Redmond, N. E. et al. Phylogeny and systematics of Demospongiae in light of new small-subunit ribosomal DNA (18S) sequences. Int. Comp. Biol. 53, 388–415 (2013).CAS 

Google Scholar 
Worheide, G. et al. in Advances in Marine Biology: Advances in Sponge Science Vol. 61 (eds Becerro, M. A. et al.) 1–78 (Elsevier, 2012).Schuster, A. et al. Divergence times in demosponges (Porifera): first insights from new mitogenomes and the inclusion of fossils in a birth–death clock model. BMC Evol. Biol. 18, 114 (2018).PubMed 
PubMed Central 

Google Scholar 
Stanley, G. D. & Fautin, D. G. Paleontology and evolution. Orig. Mod. Corals Sci. 291, 1913–1914 (2001).CAS 

Google Scholar 
Brinkmann, C. M., Marker, A. & Kurtböke, D. I. An overview on marine sponge-symbiotic bacteria as unexhausted sources for natural product discovery. Diversity 9, 40 (2017).
Google Scholar 
Rust, M. et al. A multiproducer microbiome generates chemical diversity in the marine sponge Mycale hentscheli. Proc. Natl Acad. Sci. USA 117, 9508–9518 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
Faulkner, D. J., Harper, M. K., Haygood, M. G., Salomon, C. E. & Schmidt, E. W. in Drugs from the Sea (ed. Fusetani, N.) 107–119 (Karger, 2000).Loh, T.-L. & Pawlik, J. R. Chemical defenses and resource trade-offs structure sponge communities on Caribbean coral reefs. Proc. Natl Acad. Sci. USA 111, 4151–4156 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Pagel, M. Detecting correlated evolution on phylogenies—a general method for the comparative analysis of discrete characters. Proc. R. Soc. Lond. B 255, 37–45 (1994).
Google Scholar 
Easson, C. G. & Thacker, R. W. Phylogenetic signal in the community structure of host-specific microbiomes of tropical marine sponges. Front. Microbiol. 5, 532 (2014).PubMed 
PubMed Central 

Google Scholar 
Thomas, T. et al. Diversity, structure and convergent evolution of the global sponge microbiome. Nat. Commun. 7, 11870 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Schöttner, S. et al. Relationships between host phylogeny, host type and bacterial community diversity in cold-water coral reef sponges. PLoS ONE 8, e55505 (2013).PubMed 
PubMed Central 

Google Scholar 
Robinson, D. R. & Foulds, L. R. Comparison of phylogenetic trees. Math. Biosci. 53, 131–147 (1981).
Google Scholar 
Gloor, G. B., Macklaim, J. M., Pawlowsky-Glahn, V. & Egozcue, J. J. Microbiome datasets are compositional: and this is not optional. Front. Microbiol. 8, 2224 (2017).PubMed 
PubMed Central 

Google Scholar 
Apprill, A. The role of symbioses in the adaptation and stress responses of marine organisms. Annu. Rev. Mar. Sci. 12, 291–314 (2020).
Google Scholar 
Lesser, M. P., Slattery, M. & Mobley, C. Biodiversity and functional ecology of mesophotic coral reefs. Annu. Rev. Ecol. Evol. Syst. 49, 49–71 (2018).
Google Scholar 
Lipps, J. H. & Stanley, G. D. in Coral Reefs at the Crossroads (eds Hubbard, D. K. et al.) 175–196 (Springer, 2016); https://doi.org/10.1007/978-94-017-7567-0_8Macartney, K. J., Slattery, M. & Lesser, M. P. Trophic ecology of Caribbean sponges in the mesophotic zone. Limnol. Oceanogr. 66, 1113–1124 (2021).CAS 

Google Scholar 
McMurray, S. E., Stubler, A. D., Erwin, P. M., Finelli, C. M. & Pawlik, J. R. A test of the sponge-loop hypothesis for emergent Caribbean reef sponges. Mar. Ecol. Prog. Ser. 588, 1–14 (2018).CAS 

Google Scholar 
Olinger, L. K., Strangman, W. K., McMurray, S. E. & Pawlik, J. R. Sponges with microbial symbionts transform dissolved organic matter and take up organohalides. Front. Mar. Sci. 8, 665789 (2021).
Google Scholar 
Haas, A. F. et al. Effects of coral reef benthic primary producers on dissolved organic carbon and microbial activity. PLoS ONE 6, e27973 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Sánchez-Baracaldo, P. Origin of marine planktonic cyanobacteria. Sci. Rep. 5, 17418 (2015).PubMed 
PubMed Central 

Google Scholar 
Sanchez-Bracaldo, P., Ridgwell, A. & Raven, J. A. A neoproterozoic transition in the marine nitrogen cycle. Curr. Biol. 24, 652–657 (2014).
Google Scholar 
Falkowski, P. G. et al. The evolution of modern eukaryotic phytoplankton. Science 305, 354–360 (2004).CAS 
PubMed 

Google Scholar 
Wang, D. et al. Coupling of ocean redox and animal evolution during the Ediacaran–Cambrian transition. Nat. Commun. 9, 2575 (2018).PubMed 
PubMed Central 

Google Scholar 
Bellwood, D. R., Goatley, C. H. R. & Bellwood, O. The evolution of fishes and corals on reefs: form, function and interdependence. Biol. Rev. 92, 878–901 (2017).PubMed 

Google Scholar 
Ehrlich, P. R. & Raven, P. H. Butterflies and plants: a study in coevolution. Evolution 18, 586–608 (1964).
Google Scholar 
Després, L., David, J.-P. & Gallet, C. The evolutionary ecology of insect resistance to plant chemicals. Trends Ecol. Evol. 22, 298–307 (2007).PubMed 

Google Scholar 
Richardson, K. L., Gold-Bouchot, G. & Schlenk, D. The characterization of cytosolic glutathione transferase from four species of sea turtles: loggerhead (Caretta caretta), green (Chelonia mydas), olive ridley (Lepidochelys olivacea), and hawksbill (Eretmochelys imbricata). Comp. Biochem. Physiol. C 150, 279–284 (2009).
Google Scholar 
Bayer, K., Jahn, M. T., Slaby, B. M., Moitinho-Silva, L. & Hentschel, U. Marine sponges as Chloroflexi hot spots: genomic insights and high-resolution visualization of an abundant and diverse symbiotic clade. mSystems 3, e00150-18 (2018).PubMed 
PubMed Central 

Google Scholar 
Sachs, J. L., Skophammer, R. G., Bansal, N. & Stajich, J. E. Evolutionary origins and diversification of proteobacterial mutualists. Proc. R Soc. B https://doi.org/10.1098/rspb.2013.2146 (2014).Sachs, J. L., Skophammer, R. G. & Regus, J. U. Evolutionary transitions in bacterial symbiosis. Proc. Natl Acad. Sci. USA 108, 10800–10807 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Seutin, G., White, B. N. & Boag, P. T. Preservation of avian blood and tissue samples for DNA analyses. Can. J. Zool. https://doi.org/10.1139/z91-013 (2011).Sunagawa, S. et al. Generation and analysis of transcriptomic resources for a model system on the rise: the sea anemone Aiptasia pallida and its dinoflagellate endosymbiont. BMC Genomics 10, 258 (2009).PubMed 
PubMed Central 

Google Scholar 
Song, L. & Florea, L. Rcorrector: efficient and accurate error correction for Illumina RNA-seq reads. GigaScience 4, 48 (2015).PubMed 
PubMed Central 

Google Scholar 
Grabherr, M. G. et al. Full-length transcriptome assembly from RNA-seq data without a reference genome. Nat. Biotechnol. 29, 644–652 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
Chevreux, B., Wetter, T. & Suhai, S. Genome sequence assembly using trace signals and additional sequence information. Comput. Sci. Biol. 99, 45–56 (1999).
Google Scholar 
Li, W. & Godzik, A. CD-HIT: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).CAS 

Google Scholar 
Francis, W. R. et al. The genome of the contractile demosponge Tethya wilhelma and the evolution of metazoan neural signalling pathways. Preprint at bioRxiv https://doi.org/10.1101/120998 (2017).Altschul, S. F. A protein alignment scoring system sensitive at all evolutionary distances. J. Mol. Evol. 36, 290–300 (1993).CAS 
PubMed 

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

Google Scholar 
Simion, P. et al. A large and consistent phylogenomic dataset supports sponges as the sister group to all other animals. Curr. Biol. https://doi.org/10.1016/j.cub.2017.02.031 (2017).Katoh, K., Misawa, K., Kuma, K.-I. & Miyata, T. MAFFT: a novel method for rapid multiple sequence alignment based on fast Fourier transform. Nucleic Acids Res. 30, 3059–3066 (2002).CAS 
PubMed 
PubMed Central 

Google Scholar 
Castresana, J. Selection of conserved blocks from multiple alignments for their use in phylogenetic analysis. Mol. Biol. Evol. 17, 540–552 (2000).CAS 

Google Scholar 
Kalyaanamoorthy, S. et al. ModelFinder: fast model selection for accurate phylogenetic estimates. Nat. Methods 14, 587–589 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Stamatakis, A. RAxML-VI-HPC: maximum likelihood-based phylogenetic analyses with thousands of taxa and mixed models. Bioinformatics 22, 2688–2690 (2006).CAS 

Google Scholar 
Nguyen, L.-T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: a fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).CAS 

Google Scholar 
Dohrmann, M. & Wörheide, G. Dating early animal evolution using phylogenomic data. Sci. Rep. 7, 3599 (2017).PubMed 
PubMed Central 

Google Scholar 
Smith, S. A. & O’Meara, B. C. treePL: divergence time estimation using penalized likelihood for large phylogenies. Bioinformatics 28, 2689–2690 (2012).CAS 
PubMed 

Google Scholar 
Parada, A. E., Needham, D. M. & Fuhrman, J. A. Every base matters: assessing small subunit rRNA primers for marine microbiomes with mock communities, time series and global field samples. Environ. Microbiol. 18, 1403–1414 (2016).CAS 
PubMed 

Google Scholar 
Apprill, A., McNally, S., Parsons, R. & Weber, L. Minor revision to V4 region SSU rRNA 806R gene primer greatly increases detection of SAR11 bacterioplankton. Aquat. Microb. Ecol. 75, 129–137 (2015).
Google Scholar 
Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods 13, 581–583 (2016).CAS 
PubMed 
PubMed Central 

Google Scholar 
Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013).CAS 
PubMed 

Google Scholar 
McMurdie, P. J. & Holmes, S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE 8, e61217 (2013).CAS 
PubMed 
PubMed Central 

Google Scholar 
Oksanen, J. et al. vegan: Community Ecology Package. R package version 2.5-5 (2019).Lahti, L. et al. Tools for Microbiome Analysis in R. Microbiome package version 1.17.2 https://github.com/microbiome/microbiome (2017).Harmon, L. J., Weir, J. T., Brock, C. D., Glor, R. E. & Challenger, W. GEIGER: investigating evolutionary radiations. Bioinformatics 24, 129–131 (2008).CAS 
PubMed 

Google Scholar 
Schliep, K. P. phangorn: phylogenetic analysis in R. Bioinformatics 27, 592–593 (2011).CAS 

Google Scholar 
Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).
Google Scholar 
Kuhn, M. Building predictive models in R using the caret package. J. Stat. Softw. 28, 1–26 (2008).
Google Scholar 
Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120 (2014).CAS 
PubMed 
PubMed Central 

Google Scholar 
Westbrook, A. et al. PALADIN: protein alignment for functional profiling whole metagenome shotgun data. Bioinformatics 33, 1473–1478 (2017).CAS 
PubMed 
PubMed Central 

Google Scholar 
Waddell, B. & Pawlik, J. R. Defenses of Caribbean sponges against invertebrate predators. I. Assays with hermit crabs. Mar. Ecol. Prog. Ser. 195, 125–132 (2000).
Google Scholar 
Paradis, E., Claude, J. & Strimmer, K. APE: analyses of phylogenetics and evolution in R language. FEMS Microbiol. Ecol. 20, 289–290 (2004).CAS 

Google Scholar 
Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J. Stat. Softw. 33, 1–22 (2010).
Google Scholar 
Nakagawa, S., Johnson, P. C. D. & Schielzeth, H. The coefficient of determination R2 and intra-class correlation coefficient from generalized linear mixed-effects models revisited and expanded. J. R. Soc. Interface 14, 20170213 (2017).PubMed 
PubMed Central 

Google Scholar  More