Philippa Kaur

Effects of water and nitrogen treatments on the yield of Isatis indigotica
As shown in Table 3, in the two-year experiment, both the water input and the nitrogen application rate had significant effects on the yield of Isatis indigotica.Table 3 Variance analysis of traits on the yield of Isatis indigotica.Full size tableAs shown in Fig. 4, with increasing water and nitrogen, the yield first increased and then decreased. The interaction between the water input and the nitrogen application rate reached a significant level (P  N1. At the levels of W1, W2, and W3, the yield of the N2 treatment increased by 5.3–7.9%, 6.5–6.9%, and 5.0–9.0% compared with those of the N3 treatment, respectively, and the yield of the N3 treatment increased by 1.4–1.9%, 1.5%-4.5%, and 1.7–3.5% compared with those of the N1 treatment, respectively. At the same nitrogen application level, the yield performance was W2  > W1  > W3. At the levels of N1, N2, and N3, the yield of the W2 treatment increased by 6.9–12.4%, 8.3–11.3%, and 6.8–13.5% compared with those of the W3 treatment, respectively, and the yield of the W3 treatment decreased by 1.6–3.9%, 1.5–1.6%, and 1.3–2.4% compared with those of the W1 treatment, respectively.Effects of the water and nitrogen treatments on the quality of Isatis indigotica
As shown in Table 4, in the two-year experiment, the water input and nitrogen application rate had significant impacts on the contents of indigo, indirubin, (R,S)-epigoitrin and polysaccharide in Isatis indigotica.Table 4 Variance analysis of traits the quality of Isatis indigotica.Full size tableAs shown in Fig. 5, The impacts decreased with increasing irrigation amount and nitrogen application rate. Compared with those in the W3N3 treatment, the contents of indigo, indirubin, (R,S)-epigoitrin and polysaccharide in the W2N2 treatment increased by 4.5–5.9%, 2.7–3.1%, 5.2–6.0% and 1.8–2.1%, respectively. At the same irrigation level, the contents of indigo, indirubin, (R,S)-epigoitrin and polysaccharides all decreased in the order N1  > N2  > N3. At the W2 level, the contents of indigo, indirubin, (R,S)-epigoitrin and polysaccharide in the N1 treatment increased by 0.5–1.7%, 0.8–0.9%, 0.8–1.1% and 0.1–0.4%, respectively, compared with those in the N2 treatment. Compared with those in the N3 treatment, the contents of indigo, indirubin, (R,S)-epigoitrin and polysaccharide in the N2 treatment increased by 1.9–2.1%, 1.5–2.2%, 2.1–2.2% and 0.6–1.1%, respectively.Figure 5The effects of the different treatments on the quality index of Isatis indigotica. The values shown are the mean ± SD, n = 3. Asterisks indicate a significant difference at the P ≤ 0.05 level.Full size imageAt the same nitrogen level, the contents of indigo, indirubin, (R,S)-epigoitrin and polysaccharides all decreased in the order W1  > W2  > W3. At the N2 level, the contents of indigo, indirubin, (R,S)-epigoitrin and polysaccharides in the W1 treatment increased by 1.5–2.0%, 1.8–2.1%, 3.0–3.1% and 0.4–0.9% compared with those in the W2 treatment, respectively. Compared with those in the W3 treatment, the contents of indigo, indirubin, (R,S)-epigoitrin and polysaccharides of the W2 treatment increased by 2.3–3.5%, 1.8–2.3%, 2.0–4.0% and 1.0–1.4%, respectively.Effects of the water and nitrogen treatments on the WUE of Isatis indigotica
As shown in Table 5, in the two-year experiment, the water input and nitrogen application rate had significant impacts on the WUE of Isatis indigotica (P  N3  > N1. At the W1, W2, and W3 levels, the WUE of the N2 treatment increased by 6.5–8.6%, 7.8–8.1%, and 7.4–10.4% compared with that of the N3 treatment, respectively, and the WUE of the N3 treatment increased by 2.9–3.1%, 3.9–6.0%, and 4.5–5.3% compared with that of the N1 treatment, respectively. Under the same nitrogen application rate level, the WUE performance was W1  > W2  > W3. At the N1, N2, and N3 levels, the WUE of the W1 treatment increased by 5.0–11.7%, 2.8–9.2%, and 2.0–10.9% compared with that of the W2 treatment, respectively, and the WUE of the W2 treatment increased by 24.2–29.5%, 24.3 -27.2%, and 23.5–30.3% compared with that of the W3 treatment, respectively.Effects of water and nitrogen treatments on NUE of Isatis indigotica
As shown in Table 6, in the two-year experiment, the water input and nitrogen application rate had significant impacts on the nitrogen fertilizer use efficiency (NUE) of Isatis indigotica (P  N2  > N3. At the levels of W1, W2, and W3, the NUE of the N1 treatment increased by 9.9–11.8%, 9.6–13.0%, and 6.3–11.6% compared with that of the N2 treatment, respectively, and the NUE of the N2 treatment increased by 31.0–37.6%, 28.8–29.2%, and 28.3–28.6% compared with that of the N3 treatment, respectively. At the same nitrogen application level, the NUE performance was W2  > W3  > W1. At the N1, N2, and N3 levels, the NUE of the W2 treatment increased by 5.7–6.1%, 2.5–4.8%, and 2.3–4.1% compared with that of the W3 treatment, respectively, and the NUE of the W3 treatment decreased by 3.4–8.0%, 6.9–8.2%, and 10.5–14.5% compared with that of the W1 treatment, respectively.Ethical guidelineThe authors confirm that relevant ethical guidelines were followed for plant usage.Land permit statementThe experimental land belongs to the Yimin Irrigation Experimental Station, in Minle County, Gansu Province, China. More

1.Caddy, J. F. & Gulland, J. A. Historical patterns of fish stocks. Mar. Policy 7, 267–278 (1983).
Google Scholar 
2.Steele, J. H. & Henderson, E. W. Coupling between physical and biological scales. Philos. Trans. R. Soc. Lond. Ser. B-Biol. Sci. 343, 5–9 (1994).
Google Scholar 
3.Bjornstad, O. N., Fromentin, J. M., Stenseth, N. C. & Gjosaeter, J. Cycles and trends in cod populations. Proc. Natl Acad. Sci. USA 96, 5066–5071 (1999).CAS 
PubMed 
PubMed Central 

Google Scholar 
4.Piatt, J. F. et al. Extreme mortality and reproductive failure of common murres resulting from the northeast Pacific marine heatwave of 2014–2016. PLoS One 15, e0226087 (2020).CAS 
PubMed 
PubMed Central 

Google Scholar 
5.Oremus, K. L. Climate variability reduces employment in New England fisheries. Proc. Natl Acad. Sci. USA 16, 26444–26449 (2018).
Google Scholar 
6.Shelton, A. O. & Mangel, M. Fluctuations of fish populations and the magnifying effect of fishing. Proc. Natl Acad. Sci. USA 108, 7075–7080 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
7.Essington, T. E. et al. Fishing amplifies forage fish population collapses. Proc. Natl Acad. Sci. USA 112, 6648–6652 (2015).CAS 
PubMed 
PubMed Central 

Google Scholar 
8.Memarzadeha, M., Britten, G. L., Wormd, B. & Boettigere, C. Rebuilding global fisheries under uncertainty. Proc. Natl Acad. Sci. USA 116, 15985–15990 (2019).
Google Scholar 
9.Pauly, D. & Zeller, D. Sea Around Us Concepts, Design and Data (seaaroundus.org) (2015).10.Bjornstad, O. N., Nisbet, R. M. & Fromentin, J. M. Trends and cohort resonant effects in age-structured populations. J. Anim. Ecol. 73, 1157–1167 (2004).
Google Scholar 
11.Botsford, L. W., Holland, M. D., Field, J. C. & Hastings, A. Cohort resonance: a significant component of fluctuations in recruitment, egg production, and catch of fished populations. ICES J. Mar. Sci. 71, 2158–2170 (2014).
Google Scholar 
12.Di Lorenzo, E. & Ohman, M. D. A double-integration hypothesis to explain ocean ecosystem response to climate forcing. Proc. Natl Acad. Sci. USA 110, 2496–2499 (2013).PubMed 
PubMed Central 

Google Scholar 
13.Bjorkvoll, E. et al. Stochastic population dynamics and life-history variation in marine fish species. Am. Naturalist 180, 372–387 (2012).
Google Scholar 
14.Hsieh, C. H. et al. Fishing elevates variability in the abundance of exploited species. Nature 443, 859–862 (2006).CAS 
PubMed 

Google Scholar 
15.Beamish, R. J., McFarlane, G. A. & Benson, A. Longevity overfishing. Prog. Oceanogr. 68, 289–302 (2006).
Google Scholar 
16.Anderson, C. N. K. et al. Why fishing magnifies fluctuations in fish abundance. Nature 452, 835–839 (2008).CAS 
PubMed 

Google Scholar 
17.Hutchings, J. A. & Myers, R. A. Effect of age on the seasonality of maturation and spawning of Atlantic cod, Gadus morhua, in the northwest Atlantic. Can. J. Fish. Aquat. Sci. 50, 2468–2474 (1993).
Google Scholar 
18.Bobko, S. J. & Berkeley, S. A. Maturity, ovarian cycle, fecundity, and age-specific parturition of black rockfish (Sebastes melanops). Fish. Bull. 102, 418–429 (2004).
Google Scholar 
19.Berkeley, S. A., Chapman, C. & Sogard, S. M. Maternal age as a determinant of larval growth and survival in a marine fish, Sebastes melanops. Ecology 85, 1258–1264 (2004).
Google Scholar 
20.Longhurst, A. Murphy’s law revisited: longevity as a factor in recruitment to fish populations. Fish. Res. 56, 125–131 (2002).
Google Scholar 
21.Stawitz, C. C. & Essington, T. E. Somatic growth contributes to population variation in marine fishes. J. Anim. Ecol. 88, 315–329 (2019).PubMed 

Google Scholar 
22.Estes, J. A. et al. Trophic downgrading of planet Earth. Science 333, 301–306 (2011).CAS 
PubMed 

Google Scholar 
23.Hollowed, A. B., Hare, S. R. & Wooster, W. S. Pacific basin climate variability and patterns of Northeast Pacific marine fish production. Prog. Oceanogr. 49, 257–282 (2001).
Google Scholar 
24.Holsman, K. K., Aydin, K., Sullivan, J., Hurst, T. & Kruse, G. H. Climate effects and bottom-up controls on growth and size-at-age of Pacific halibut (Hippoglossus stenolepis) in Alaska (USA). Fish. Oceanogr. 28, 345–358 (2019).
Google Scholar 
25.Whitten, A. R., Klaer, N. L., Tuck, G. N. & Day, R. W. Accounting for cohort-specific variable growth in fisheries stock assessments: A case study from south-eastern Australia. Fish. Res. 142, 27–36 (2013).
Google Scholar 
26.Heessen, H. J. L., Daan, N. & Ellis, J. R. Fish atlas of the Cebtic Sea, North Sea, and Baltic Sea (KNNV Publishing and Wageningen Academic Publishers, 2015).27.Froese, R. & Pauly, D. FishBase, version (01/2021) https://www.fishbase.org (2021).28.Munch, S. B. & Salinas, S. Latitudinal variation in lifespan within species is explained by the metabolic theory of ecology. Proc. Natl Acad. Sci. USA 106, 13860–13864 (2009).CAS 
PubMed 
PubMed Central 

Google Scholar 
29.Beukhof, E. et al. Marine fish traits follow fast-slow continuum across oceans. Sci. Rep. 9, 17878 (2019).30.Pauly, D. On the interrelationships between natural mortality, growth parameters, and mean environmental temperature in 175 fish stocks. J. Cons. int. Explor. Mer. 39, 175–192 (1980).
Google Scholar 
31.Audzijonyte, A. et al. Fish body sizes change with temperature but not all species shrink with warming. Nat. Ecol. Evol. 4, 1–6 (2020).
Google Scholar 
32.Audzijonyte, A. et al. Is oxygen limitation in warming waters a valid mechanism to explain decreased body sizes in aquatic ectotherms? Glob. Ecol. Biogeogr. 28, 64–77 (2019).
Google Scholar 
33.Forster, J., Hirst, A. G. & Atkinson, D. Warming-induced reductions in body size are greater in aquatic than terrestrial species. Proc. Natl Acad. Sci. USA 109, 19310–19314 (2012).CAS 
PubMed 
PubMed Central 

Google Scholar 
34.Block, B. A. et al. Tracking apex marine predator movements in a dynamic ocean. Nature 475, 86–90 (2011).CAS 
PubMed 

Google Scholar 
35.Behrenfeld, M. J. & Falkowski, P. G. Photosynthetic rates derived from satellite-based chlorophyll concentration. Limnol. Oceanogr. 42, 1–20 (1997).CAS 

Google Scholar 
36.Ives, A. R. Measuring resilience in stochastic-systems. Ecol. Monogr. 65, 217–233 (1995).
Google Scholar 
37.Alheit, J. & Niquen, M. Regime shifts in the Humboldt Current ecosystem. Prog. Oceanogr. 60, 201–222 (2004).
Google Scholar 
38.Pinsky, M. L., Jensen, O. P., Ricard, D. & Palumbi, S. R. Unexpected patterns of fisheries collapse in the world’s oceans. Proc. Natl Acad. Sci. USA 108, 8317–8322 (2011).CAS 
PubMed 
PubMed Central 

Google Scholar 
39.Spencer, P. D. & Collie, J. S. Patterns of population variability in marine fish stocks. Fish. Oceanogr. 6, 188–204 (1997).
Google Scholar 
40.FAO. The State of World Fisheries and Aquaculture 2018 – Meeting the Sustainable Development Goals. (Food and Agriculture Organization of the United Nations, Rome, 2018).41.Barnett, L. A. K., Branch, T. A., Ranasinghe, R. A. & Essington, T. E. Old-growth fishes become scarce under fishing. Curr. Biol. 27, 2843–2848 (2017).CAS 
PubMed 

Google Scholar 
42.Rouyer, T. et al. Shifting dynamic forces in fish stock fluctuations triggered by age truncation? Glob. Change Biol. 17, 3046–3057 (2011).
Google Scholar 
43.Coumou, D. & Rahmstorf, S. A decade of weather extremes. Nat. Clim. Change 2, 491–496 (2012).
Google Scholar 
44.Easterling, D. R. et al. Climate extremes: Observations, modeling, and impacts. Science 289, 2068–2074 (2000).CAS 
PubMed 

Google Scholar 
45.Portner, H. O. & Peck, M. A. Climate change effects on fishes and fisheries: towards a cause-and-effect understanding. J. Fish. Biol. 77, 1745–1779 (2010).CAS 
PubMed 

Google Scholar 
46.Pinsky, M. L., Worm, B., Fogarty, M. J., Sarmiento, J. L. & Levin, S. A. Marine taxa track local climate velocities. Science 341, 1239–1242 (2013).CAS 
PubMed 

Google Scholar 
47.de Gee, A. & Kikkert, A. H. Analysis of the grey gurnard (Eutrigla gurnardus) samples collected during the 1991 international stomach sample project. ICES Document CM 1993/G:14, 25 (1993).48.Sparholt, H. In Fish Atlas of the Celtic Sea, North Sea, and Baltic Sea (eds Heessen, H., Daan, N., & Ellis, J. R.) 377–381 (KNNV Publishiing and Wageningen Academic Publishers, 2015).49.Arnott, S. A. & Ruxton, G. D. Sandeel recruitment in the North Sea: demographic, climate and trophic effects. Mar. Ecol. Prog. Ser. 238, 199–210 (2002).
Google Scholar 
50.van Deurs, M., van Hal, R., Tomczak, M. T., Jonasdottir, S. H. & Dolmer, P. Recruitment of lesser sandeel Ammodytes marinus in relation to density dependence and zooplakton composition. Mar. Ecol. Prog. Ser. 381, 249–258 (2009).
Google Scholar 
51.Capuzzo, E. et al. A decline in primary production in the North Sea over 25 years, associated with reductions in zooplankton abundance and fish stock recruitment. Glob. Change Biol. 24, E352–E364 (2018).
Google Scholar 
52.Rayner, N. A. et al. Global analyses of sea surface temperature, sea ice, and night marine air temperature since the late nineteenth century. J. Geophys. Res. D: Atmospheres 108, ACL 2-1–ACL 2–29 (2003).
Google Scholar 
53.Papworth, D. J., Marini, S. & Conversi, A. Novel, unbiased analysis approach for investigating population dynamics: A case study on Calanus finmarchicus and its decline in the North Sea. PLoS One 11, e0158230 (2016).PubMed 
PubMed Central 

Google Scholar 
54.Bergstad, O. A., Hoines, A. S. & Jorgensen, T. Growth of sandeel Ammodytes marinus, in the northern North Sea and Norwegian coastal waters. Fish. Res. 56, 9–23 (2002).
Google Scholar 
55.Wright, P. J. Otolith microstructure of the lesser sandeel, Ammodytes marinus. J. Mar. Biol. Assoc. U.K. 73, 245–248 (1993).
Google Scholar 
56.Sell, A. & Heessen, H. in Fish atlas of the Celtic Sea, North Sea, and Baltic Sea (eds Heessen, H., Daan, N., & Ellis, J. R.) 295−299 (KNNV Publishing and Wageningen Academic Publishers, 2015).57.Bergstad, O. A., Hoines, A. S. & Kruger-Johnsen, E. M. Spawning time, age and size at maturity, and fecundity of sandeel, Ammodytes marinus, in the north-eastern North Sea and in unfished coastal waters off Norway. Aquat. Living Resour. 14, 293–301 (2001).
Google Scholar 
58.Pyper, B. J. & Peterman, R. M. Comparison of methods to account for autocorrelation in correlation analyses of fish data. Can. J. Fish. Aquat. Sci. 55, 2127–2140 (1998).
Google Scholar 
59.van der Sleen, P. et al. Non-stationary responses in anchovy (Engraulis encrasicolus) recruitment to coastal upwelling in the Southern Benguela. Mar. Ecol. Prog. Ser. 596, 155–164 (2018).
Google Scholar 
60.Cushing, D. H. Upwelling and production on fish. Adv. Mar. Biol. 9, 255–334 (1971).
Google Scholar 
61.Pauly, D. & Lam, V. W. Y. In Large marine ecosystems: Status and Trends (eds IOC-UNESCO and UNEP) 113–137 (United Nations Environmental Programme, 2016). More

1.Rodrigues, A. S. B. et al. New mitochondrial and nuclear evidences support recent demographic expansion and an atypical phylogeographic pattern in the spittlebug Philaenus spumarius (Hemiptera, Aphrophoridae). PLoS ONE 9, 1–12. https://doi.org/10.1371/journal.pone.0098375 (2014).CAS 
Article 

Google Scholar 
2.Saponari, M., Boscia, D., Nigro, F. & Martelli, G. P. Identification of DNA sequences related to Xylella fastidiosa in oleander, almond and olive trees exhibiting leaf scorch symptoms in Apulia (southern Italy). J. Plant Pathol. 95, 659–668. https://doi.org/10.4454/JPP.V95I3.034 (2013).Article 

Google Scholar 
3.Saponari, M. et al. Infectivity and transmission of Xylella fastidiosa by Philaenus spumarius (Hemiptera: Aphrophoridae) in Apulia Italy. J. Econ. Entomol. 107, 1316–1319. https://doi.org/10.1603/EC14142 (2014).Article 
PubMed 

Google Scholar 
4.Saponari, M., Giampetruzzi, A., Loconsole, G., Boscia, D. & Saldarelli, P. Xylella fastidiosa in olive in apulia: Where we stand. Phytopathology 109, 175–186. https://doi.org/10.1094/PHYTO-08-18-0319-FI (2019).CAS 
Article 
PubMed 

Google Scholar 
5.Cavalieri, V. et al. Transmission of Xylella fastidiosa subspecies pauca sequence type 53 by different insect species. Insects 10, 324. https://doi.org/10.3390/insects10100324 (2019).Article 
PubMed Central 

Google Scholar 
6.Almeida, R. P. P., Blua, M. J., Lopes, J. R. S. & Purcell, A. H. Vector transmission of Xylella fastidiosa: Applying fundamental knowledge to generate disease management strategies. Entomol. Soc. Am. 98, 775–786. https://doi.org/10.1603/0013-8746(2005)098[0775:vtoxfa]2.0.co;2 (2005).Article 

Google Scholar 
7.Schneider, K. et al. Impact of Xylella fastidiosa subspecies pauca in European olives. Proc. Natl. Acad. Sci. U. S. A. 117, 9250–9259. https://doi.org/10.1073/pnas.1912206117 (2020).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
8.Dongiovanni, C. et al. Evaluation of insecticides for the control of juveniles of Philaenus spumarius L., 2015–2017. Arthropod Manag. Tests 43, 2015–2017. https://doi.org/10.1093/amt/tsy073 (2018).Article 

Google Scholar 
9.Fierro, A., Liccardo, A. & Porcelli, F. A lattice model to manage the vector and the infection of the Xylella fastidiosa on olive trees. Sci. Rep. 9, 1–14. https://doi.org/10.1038/s41598-019-44997-4 (2019).CAS 
Article 

Google Scholar 
10.Nyffeler, M. & Benz, G. Spiders in natural pest control: A review. J. Appl. Entomol. 103, 321–339. https://doi.org/10.1111/j.1439-0418.1987.tb00992.x (1987).Article 

Google Scholar 
11Nyffeler, M. & Birkhofer, K. An estimated 400–800 million tons of prey are annually killed by the global spider community. Sci. Nat. https://doi.org/10.1007/s00114-017-1440-1 (2017).Article 

Google Scholar 
12.Nyffeler, M. Ecological impact of spider predation: A critical assessment of Bristowe’s and Turnbull’s estimates. Bull. Br. Arachnol. Soc. 11, 367–373 (2000).
Google Scholar 
13.Phillipson, J. A contribution to the feeding biology of Mitopus morio (F) (Phalangida). J. Anim. Ecol. 29, 35–43. https://doi.org/10.2307/2269 (1960).Article 

Google Scholar 
14Harper, G. & Whittaker, J. The role of natural enemies in the colour polymorphism of Philaenus spumarius (L.). J. Anim. Ecol. 45, 91–104. https://doi.org/10.2307/3769 (1976).Article 

Google Scholar 
15.Benhadi-Marín, J. et al. A guild-based protocol to target potential natural enemies of Philaenus spumarius (Hemiptera: Aphrophoridae), a vector of Xylella fastidiosa (Xanthomonadaceae): A case study with spiders in the olive grove. Insects 11, 100. https://doi.org/10.3390/insects11020100 (2020).Article 
PubMed Central 

Google Scholar 
16.King, R. A., Read, D. S., Traugott, M. & Symondson, W. O. C. Molecular analysis of predation: A review of best practice for DNA-based approaches. Mol. Ecol. 17, 947–963. https://doi.org/10.1111/j.1365-294X.2007.03613.x (2008).CAS 
Article 
PubMed 

Google Scholar 
17.Sint, D., Raso, L., Kaufmann, R. & Traugott, M. Optimizing methods for PCR-based analysis of predation. Mol. Ecol. Resour. 11, 795–801. https://doi.org/10.1111/j.1755-0998.2011.03018.x (2011).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
18.Rejili, M. et al. A PCR-based diagnostic assay for detecting DNA of the olive fruit fly, Bactrocera oleae, in the gut of soil-living arthropods. Bull. Entomol. Res. 106, 695–699. https://doi.org/10.1017/S000748531600050X (2016).CAS 
Article 
PubMed 

Google Scholar 
19Albertini, A. et al. Detection of Bactrocera oleae (Diptera: Tephritidae) DNA in the gut of the soil species Pseudoophonus rufipes (coleoptera: Carabidae). Span. J. Agric. Res. https://doi.org/10.5424/sjar/2018163-12860 (2018).Article 

Google Scholar 
20.Symondson, W. O. C. Molecular identification of prey in predator diets. Mol. Ecol. 11, 627–641. https://doi.org/10.1046/j.1365-294X.2002.01471.x (2002).CAS 
Article 
PubMed 

Google Scholar 
21.Sousa, L. L., Silva, S. M. & Xavier, R. DNA metabarcoding in diet studies: Unveiling ecological aspects in aquatic and terrestrial ecosystems. Environ. DNA 1, 199–214. https://doi.org/10.1002/edn3.27 (2019).Article 

Google Scholar 
22.Juen, A. & Traugott, M. Amplification facilitators and multiplex PCR: Tools to overcome PCR-inhibition in DNA-gut-content analysis of soil-living invertebrates. Soil Biol. Biochem. 38, 1872–1879. https://doi.org/10.1016/j.soilbio.2005.11.034 (2006).CAS 
Article 

Google Scholar 
23.Monzó, C., Sabater-Muñoz, B., Urbaneja, A. & Castańera, P. Tracking medfly predation by the wolf spider, Pardosa cribata Simon, in citrus orchards using PCR-based gut-content analysis. Bull. Entomol. Res. 100, 145–152. https://doi.org/10.1017/S0007485309006920 (2010).CAS 
Article 
PubMed 

Google Scholar 
24.Lantero, E., Matallanas, B., Pascual, S. & Callejas, C. PCR species-specific primers for molecular gut content analysis to determine the contribution of generalist predators to the biological control of the vector of Xylella fastidiosa. Sustainability 10, 4–11. https://doi.org/10.3390/su10072207 (2018).CAS 
Article 

Google Scholar 
25.Cohen, A. C. Extra-oral digestion in predaceous terrestrial Arthropoda. Annu Rev Entomol. 40, 85–103. https://doi.org/10.1146/annurev.en.40.010195.000505 (1995).ADS 
CAS 
Article 

Google Scholar 
26.Krehenwinkel, H., Rödder, N. & Tautz, D. Eco-genomic analysis of the poleward range expansion of the wasp spider Argiope bruennichi shows rapid adaptation and genomic admixture. Glob. Chang. Biol 21, 4320–4332. https://doi.org/10.1111/gcb.13042 (2015).ADS 
Article 
PubMed 

Google Scholar 
27.Kennedy, S. R. et al. High-throughput sequencing for community analysis: the promise of DNA barcoding to uncover diversity, relatedness, abundances and interactions in spider communities. Dev. Genes Evol. 230, 185–201. https://doi.org/10.1007/s00427-020-00652-x (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
28.Hoogendoorn, M. & Heimpel, G. E. PCR-based gut content analysis of insect predators: Using ribosomal ITS-1 fragments from prey to estimate predation frequency. Mol. Ecol. 10, 2059–2067. https://doi.org/10.1046/j.1365-294X.2001.01316.x (2001).CAS 
Article 
PubMed 

Google Scholar 
29.Eitzinger, B., Unger, E. M., Traugott, M. & Scheu, S. Effects of prey quality and predator body size on prey DNA detection success in a centipede predator. Mol. Ecol. 23, 3767–3776. https://doi.org/10.1111/mec.12654 (2014).CAS 
Article 
PubMed 

Google Scholar 
30.Unruh, T. R. et al. Gut content analysis of arthropod predators of codling moth in Washington apple orchards. Biol. Control. 102, 85–92. https://doi.org/10.1016/j.biocontrol.2016.05.014 (2016).Article 

Google Scholar 
31.Rowley, C. et al. PCR-based gut content analysis to identify arthropod predators of Haplodiplosis marginata. Biol. Control 115, 112–118. https://doi.org/10.1016/j.biocontrol.2017.10.003 (2017).CAS 
Article 

Google Scholar 
32.Macías-Hernández, N. et al. Molecular gut content analysis of different spider body parts. PLoS ONE 13, 1–16. https://doi.org/10.1371/journal.pone.0196589 (2018).CAS 
Article 

Google Scholar 
33.Troedsson, C., Simonelli, P., Nägele, V., Nejstgaard, J. C. & Frischer, M. E. Quantification of copepod gut content by differential length amplification quantitative PCR (dla-qPCR). Mar. Biol. 156, 253–259. https://doi.org/10.1007/s00227-008-1079-8 (2009).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
34.Agustí, N., De Vicente, M. C. & Gabarra, R. Development of sequence amplified characterized region (SCAR) markers of Helicoverpa armigera: A new polymerase chain reaction-based technique for predator gut analysis. Mol. Ecol. 8, 1467–1474. https://doi.org/10.1046/j.1365-294X.1999.00717.x (1999).Article 
PubMed 

Google Scholar 
35.Agustí, N., Unruh, T. R. & Welter, S. C. Detecting Cacopsylla pyricola (Hemiptera: Psyllidae) in predator guts using COI mitochondrial markers. Bull. Entomol. Res. 93, 179–185. https://doi.org/10.1079/ber2003236 (2003).Article 
PubMed 

Google Scholar 
36.Aebi, A. et al. Detecting arthropod intraguild predation in the field. Biocontrol 56, 429–440. https://doi.org/10.1007/s10526-011-9378-2 (2011).Article 

Google Scholar 
37.Hosseini, R., Schmidt, O. & Keller, M. A. Factors affecting detectability of prey DNA in the gut contents of invertebrate predators: A polymerase chain reaction-based method. Entomol. Exp. Appl. 126, 194–202. https://doi.org/10.1111/j.1570-7458.2007.00657.x (2008).CAS 
Article 

Google Scholar 
38.Agustí, N. et al. Collembola as alternative prey sustaining spiders in arable ecosystems: Prey detection within predators using molecular markers. Mol Ecol. 12, 3467–3475. https://doi.org/10.1046/j.1365-294X.2003.02014.x (2003).CAS 
Article 
PubMed 

Google Scholar 
39.Greenstone, M. H., Tillman, P. G. & Hu, J. S. Predation of the newly invasive pest Megacopta cribraria (Hemiptera: Plataspidae) in soybean habitats adjacent to cotton by a complex of predators. J Econ Entomol. 107, 947–954. https://doi.org/10.1603/EC13356 (2014).CAS 
Article 
PubMed 

Google Scholar 
40.Welch, K. D., Whitney, T. D. & Harwood, J. D. Non-pest prey do not disrupt aphid predation by a web-building spider. Bull. Entomol. Res. 106, 91–98. https://doi.org/10.1017/S0007485315000875 (2016).CAS 
Article 
PubMed 

Google Scholar 
41.Vincent, J. F. V. Arthropod cuticle: A natural composite shell system. Compos. Part A Appl. Sci. Manuf. 33, 1311–1315. https://doi.org/10.1016/S1359-835X(02)00167-7 (2002).Article 

Google Scholar 
42.Cardoso, P. et al. Rapid biodiversity assessment of spiders (Araneae) using semi-quantitative sampling: A case study in a Mediterranean forest. Insect Conserv. Divers. 1, 71–84. https://doi.org/10.1111/j.1752-4598.2007.00008.x (2008).Article 

Google Scholar 
43.Harwood, J. D., Phillips, S. W., Sunderland, K. D. & Symondson, W. O. C. Secondary predation: Quantification of food chain errors in an aphid-spider-carabid system using monoclonal antibodies. Mol. Ecol. 10, 2049–2057. https://doi.org/10.1046/j.0962-1083.2001.01349.x (2001).CAS 
Article 
PubMed 

Google Scholar 
44.Seabra, S. G. et al. Corrigendum to “Molecular phylogeny and DNA barcoding in the meadow-spittlebug Philaenus spumarius (Hemiptera, Cercopidae) and its related species” [Mol. Phylogenet. Evol. 56 (2010) 462–467]. Mol. Phylogenet. Evol. 152, 106888. https://doi.org/10.1016/j.ympev.2020.106888 (2020).CAS 
Article 
PubMed 

Google Scholar 
45.Folmer, O., Black, M., Hoeh, W., Lutz, R. & Vrijenhoek, R. DNA primers for amplification of mitochondrial cytochrome c oxidase subunit I from diverse metazoan invertebrates. Mol. Mar. Biol. Biotechnol. 3, 294–299 (1994).CAS 
PubMed 
PubMed Central 

Google Scholar 
46.Kumar, S., Stecher, G., Li, M., Knyaz, C. & Tamura, K. MEGA X: Molecular evolutionary genetics analysis across computing platforms. Mol. Biol. Evol. 35, 1547–1549. https://doi.org/10.1093/molbev/msy096 (2018).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
47.Untergasser, A. et al. Primer3-new capabilities and interfaces. Nucleic Acids Res. 40, 1–12. https://doi.org/10.1093/nar/gks596 (2012).CAS 
Article 

Google Scholar 
48.Ye, J. et al. Primer-BLAST: A tool to design target-specific primers for polymerase chain reaction. BMC Bioinform. 13, 134. https://doi.org/10.1186/1471-2105-13-134 (2012).CAS 
Article 

Google Scholar 
49.Morente, M. et al. Distribution and relative abundance of insect vectors of Xylella fastidiosa in olive groves of the Iberian Peninsula. Insects 9, 175. https://doi.org/10.3390/insects9040175 (2018).Article 
PubMed Central 

Google Scholar  More

Research model and softwareCLIMEX modelFAW growth and development are primarily related to climate conditions, especially temperature patterns17. The current study used CLIMEX (version 4)42, a semi-mechanistic niche modeling platform, to project FAW distribution in relation to climate. The model parameters that describe the species’ response to climate were overlaid onto FAW occurrence data and climate data to project the species’ potential global distribution. Briefly, the annual growth index (GI) was used to describe the potential for FAW population growth during favorable climatic conditions, while stress indices (SI: cold, wet, hot, and dry) and interaction stresses (SX: hot-dry, hot-wet, cold-dry, and cold-wet) (Table 1) were applied to describe the probability that FAW populations could survive unfavorable conditions. The Ecoclimatic index (EI) was derived from a combination of GI, SI, and SX indices to provide an overall annual index of climatic suitability on a scale of 0–10042. An EI value of 0 indicates that the location is not suitable for the long-term survival of the species, whereas an EI value of 100 indicates maximum climatic suitability comparable to conditions in incubators. EI values of more than 30 indicate the optimal climate for a species. In this study, the climatic suitability was classified into four arbitrary categories; unsuitable for EI = 0, marginal for 0  More

1.Stein, E. D., Cohen, Y. & Winer, A. M. Environmental distribution and transformation of mercury compounds. Crit. Rev. Environ. Sci. Technol. 26, 1–43 (1996).CAS 
Article 

Google Scholar 
2.Ciccarelli, C. et al. Assessment of sampling methods about level of mercury in fish. Ital. J. Food Saf. 8, 153–157 (2019).
Google Scholar 
3.Ditri, F. M. Mercury contamination: What we have learned since Minamata. Environ. Monit. Assess. 19, 165–182 (1991).CAS 
Article 

Google Scholar 
4.Monteiro, L. R. & Furness, R. W. Seabirds as monitors of mercury in the marine environment. Water Air Soil Pollut. 80, 851–870 (1995).CAS 
Article 
ADS 

Google Scholar 
5.Pitter, P. In Hydrochemie 5th edn (ed. Pitter, P.) (VSCHT Praha, 2015).
Google Scholar 
6.Hylander, L. D. & Meili, M. 500 years of mercury production: Global annual inventory by region until 2000 and associated emissions. Sci. Total. Environ. 304, 13–27 (2003).CAS 
Article 
ADS 

Google Scholar 
7.Pacyna, E. G. et al. Global emission of mercury to the atmosphere from anthropogenic sources in 2005 and projections to 2020. Atmos. Environ. 44, 2487–2499 (2010).CAS 
Article 
ADS 

Google Scholar 
8.Pai, P., Niemi, D. & Powers, B. A North American inventory of anthropogenic mercury emissions. Fuel Process. Technol. 65, 101–115 (2000).Article 

Google Scholar 
9.Wang, Q. R., Kim, D., Dionysiou, D. D., Sorial, G. A. & Timberlake, D. Sources and remediation for mercury contamination in aquatic systems: A literature review. Environ. Pollut. 131, 323–336 (2004).Article 

Google Scholar 
10.Buck, D. G. et al. A global-scale assessment of fish mercury concentrations and the identification of biological hotspots. Sci. Total Environ. 687, 956–966 (2019).CAS 
Article 
ADS 

Google Scholar 
11.Gentes, S. et al. Application of European water framework directive: Identification reference sites and bioindicator fish species for mercury in tropical freshwater ecosystems (French Guiana). Ecol. Indic. 106, 105468. https://doi.org/10.1016/j.ecolind.2019.105468 (2019).CAS 
Article 

Google Scholar 
12.Thomas, S. M. et al. Climate and landscape conditions indirectly affect fish mercury levels by altering lake water chemistry and fish size. Environ. Res. 188, 109750. https://doi.org/10.1016/j.envres.2020.109750 (2020).CAS 
Article 
PubMed 

Google Scholar 
13.Zupo, V. et al. Mercury accumulation in freshwater and marine fish from the wild and from aquaculture ponds. Environ. Pollut. 255, 112975. https://doi.org/10.1016/j.envpol.2019.112975 (2019).CAS 
Article 
PubMed 

Google Scholar 
14.Zhang, J. L. et al. Health risk assessment of heavy metals in Cyprinus carpio (Cyprinidae) from the upper Mekong river. Environ. Sci. Pollut. Res. 26, 9490–9499 (2019).CAS 
Article 

Google Scholar 
15.Cerna, M. Opatreni mezinarodnich instituci a Ceske republiky k omezovani rizika znecistovani zivotniho prostredi rtuti. Chem. Listy. 98, 916–921 (2004) ((Article in Czech)).CAS 

Google Scholar 
16.Janouskova, D. & Svehla, J. Mercury concentrations in fish tissues in the water reservoir Rimov, South Bohemia. Crop Sci. 19, 43–48 (2002).
Google Scholar 
17.Purba, J. S., Silalahi, J. & Haro, G. Analysis of mercury in fish, North Sumatera, Indonesia by atomic absorption spectrophotometer. Asian J. Pharm. 8, 21–25 (2020).CAS 
Article 

Google Scholar 
18.Willacker, J. J., Eagles-Smith, C. A. & Blazer, V. S. Mercury bioaccumulation in freshwater fishes of the Chesapeake Bay watershed. Ecotoxicology 29, 459484 (2020).Article 

Google Scholar 
19.Regulation (EU) 2017/852 of European Parliament and of the council of 17 May 2017 on mercury, and repealing Regulation (EC) No 1102/2008. Official Journal of the European Union.20.European Commission. The EU Fish Market. https://www.eumofa.eu/documents/20178/415635/EN_The+EU+fish+market_2020.pdf (2020).21.Nebesky, V., Policar, T., Blecha, M., Kristan, J. & Svacina, P. Trends in import and export of fishery products in the Czech Republic during 2010–2015. Aquacult. Int. 24, 1657–1668 (2016).Article 

Google Scholar 
22.FAO. Fisheries & Aquaculture—National Aquaculture Sector Overview—Czech Republic. http://www.fao.org/fishery/countrysector/naso_czechrepublic/en (accessed April 24 April 2021) (2021).23.Rakmanikhah, Z., Esmaili-Sari, A. & Bahramifar, N. Total mercury and methylmercury concentrations in native and invasive fish species in Shadegan International Wetland, Iran, and health risk assessment. Environ. Sci. Pollut. Res. 27, 6765–6773 (2020).Article 

Google Scholar 
24.Celechovska, O., Svobodova, Z., Zlabek, V. & Macharackova, B. Distribution of metals in tissues of the common carp (Cyprinus carpio L.). Acta Vet. Brno 76, 93–100 (2007).Article 

Google Scholar 
25.Cerveny, D. et al. Fish fin-clips as non-lethal approach for biomonitoring of mercury contamination in aquatic environments and human health risk assessment. Chemosphere 163, 290–295 (2016).CAS 
Article 
ADS 

Google Scholar 
26.WHO. Evaluations of the Joint FAO/WHO Expert Committee on Food Additives (JECFA). https://apps.who.int/food-additives-contaminants-jecfa-database/search.aspx.27.Kannan, K. et al. Distribution of total mercury and methyl mercury in water, sediment, and fish from south Florida estuaries. Arch. Environ. Con. Tox. 34, 109–118 (1998).CAS 
Article 

Google Scholar 
28.US EPA. Guidance for Assessing Chemical Contaminant Data for Use in Fish Advisories Documents. Volume 2: Risk Assessment and Fish Consumption Limits, Third Edition. https://www.epa.gov/fish-tech/guidance-assessing-chemical-contaminant-data-use-fish-advisories-documents (accessed 8 May 2021) (2000).29.Ministry of Agriculture of the Czech Republic. Situacni a vyhledova zprava—Ryby. http://eagri.cz/public/web/file/666957/Ryby_2020_web.pdf (accessed 8 May 2021, in Czech) (2020).30.Novotna, K., Svobodova, Z., Harustiakova, D. & Mikula, P. Spatial and temporal trends in contamination of the Czech part of the Elbe River by mercury between 1991 and 2016. Bull. Environ. Contam. Toxicol. 105, 750–757 (2020).CAS 
Article 

Google Scholar 
31.Raldua, D., Diez, S., Bayona, J. M. & Barcelo, D. Mercury levels and liver pathology in feral fish living in the vicinity of a mercury cell chlor-alkali factory. Chemosphere 66, 1217–1225 (2007).CAS 
Article 
ADS 

Google Scholar 
32.Squadrone, S. et al. Heavy metals distribution in muscle, liver, kidney and gill of European catfish (Silurus glanis) from Italian rivers. Chemosphere 90, 358–365 (2013).CAS 
Article 
ADS 

Google Scholar 
33.Cerveny, D. et al. Contamination of fish in important fishing grounds of the Czech Republic. Ecotoxicol. Environ. Saf. 109, 101–109 (2014).CAS 
Article 

Google Scholar 
34.Marsalek, P., Svobodova, Z. & Randak, T. The content of total mercury and methylmercury in common carp from selected Czech ponds. Aquac. Int. 15, 299–304 (2007).CAS 
Article 

Google Scholar 
35.Vicarova, P., Docekalova, H., Ridoskova, A. & Pelcova, P. Heavy metals in the common carp (Cyprinus carpio L.) from three reservoirs in the Czech Republic. Czech J. Food Sci. 34, 422–428 (2016).CAS 
Article 

Google Scholar 
36.Akerblom, S., Bignert, A., Meili, M., Sonesten, L. & Sundbom, M. Half a century of changing mercury levels in Swedish freshwater fish. Ambio 43, 91–103 (2014).Article 

Google Scholar 
37.Dvorak, P., Andreji, J., Mraz, J. & Dvorakova Liskova, Z. Concentration of heavy and toxic metals in fish and sediments from the Morava river basin, Czech Republic. Neuroendocrinol. Lett. 36, 126–132 (2015).CAS 
PubMed 

Google Scholar 
38.Dusek, L. et al. Bioaccumulation of mercury in muscle tissue of fish in the Elbe River (Czech Republic): Maultispecies monitoring study 1991–1996. Ecotoxicol. Environ. Saf. 61, 256–267 (2005).CAS 
Article 

Google Scholar 
39.Marsalek, P., Svobodova, Z. & Randak, T. Total mercury and methylmercury contamination in fish from various sites along the Elbe River. Acta Vet. Brno. 75, 579–585 (2006).CAS 
Article 

Google Scholar 
40.Wang, X. & Wang, W. X. The three ‘B’ of mercury in China: Bioaccumulation, biodynamics and biotransformation. Environ. Pollut. 250, 216–232 (2019).CAS 
Article 

Google Scholar 
41.Jankovska, I. et al. Importance of fish gender as a factor in environmental monitoring of mercury. Environ. Sci. Pollut. Res. 21, 6239–6242 (2014).CAS 
Article 

Google Scholar 
42.Carrasco, L. et al. Patterns of mercury and methylmercury bioaccumulation in fish species downstream of a long-term mercury-contaminated site in the lower Ebro River (NE Spain). Chemosphere 84, 1642–1649 (2011).CAS 
Article 
ADS 

Google Scholar 
43.Havelkova, M., Dusek, L., Nemethova, D., Poleszczuk, G. & Svobodova, Z. Comparison of mercury distribution between liver and muscle: A biomonitoring of fish from lightly and heavily contaminated localities. Sensors. 8, 4095–4109 (2008).CAS 
Article 
ADS 

Google Scholar 
44.Kruzikova, K. et al. The correlation between fish mercury liver/muscle ratio and high and low levels of mercury contamination in Czech localities. Int. J. Electrochem. Sc. 8, 45–56 (2013).CAS 

Google Scholar 
45.Kensova, R., Kruzikova, K. & Svobodova, Z. Mercury speciation and safety of fish from important fishing locations in the Czech Republic. Czech J. Food Sci. 30, 276–284 (2012).CAS 
Article 

Google Scholar 
46.European Commission. Commission Regulation 1881/2006 Setting Maximum Levels of Certain Contaminants in Foodstuffs. https://eur-lex.europa.eu/ (accessed 2 May 2021) (2006). More

Deer capture and samplingWe captured 100 mule deer (54 females, 46 males) during November 2018–February 2019, avoiding capture and sampling of juveniles. We attempted to distribute captures throughout the ~23 km2 study area described by Miller et al.5 to minimize spatial disparities in comparing contemporary and past data, and to assure marks were widely distributed for December ground counts to estimate deer abundance5,35,36. Field and sampling methods generally followed those used elsewhere5,31,37. Field procedures were reviewed and approved by the CPW Animal Care and Use Committee (file 14–2018).We pursued deer on foot and darted them opportunistically, delivering sedative combinations intramuscularly via projectile syringe. Premixed immobilization drug combinations included either nalbuphine (N; 0.9 mg/kg) or butorphanol (B; 0.5 mg/kg) combined with azaperone (A; 0.2 mg/kg) and medetomidine (M; 0.2 mg/kg)38, with standard total doses for respective combinations based on an estimated mass of 70 kg (average drug volume per animal was 1.3 ml NMA, 1.4 ml BAM). We collected rectal mucosa biopsies to determine CWD infection status37. We also collected whole blood and marked all deer with individually identifiable ear tags and some with telemetry (n = 51) or visual identification (n = 12) collars. Ages were estimated to the nearest year via tooth replacement and wear patterns39; observers used a pocket reference guide in the field to help assure consistency. To antagonize sedation upon completion of handling and sampling, each deer received 5 mg atipamezole/mg M administered, injected intramuscularly.Prion diagnosticsFormalin-fixed tissue biopsies were processed and analyzed by immunohistochemistry (IHC) at the Colorado State University Veterinary Diagnostic Laboratory (Fort Collins, Colorado USA; CSUVDL) for evidence of CWD-associated prion (PrPCWD) accumulations using monoclonal antibody F99/97.6.1 (VMRD Inc., Pullman, Washington, USA)40 and standard IHC methods24,37,41, except that the CSUVDL’s IHC staining machine (Leica Microsystems Inc., Buffalo Grove, Illinois, USA) was different from that used in earlier studies (Ventana Medical Systems, Oro Valley, Arizona, USA). Biopsies were evaluated microscopically and classified as positive (infected) or not detected (negative) based on PrPCWD presence or absence; the same pathologist (T. R. Spraker) read biopsies for both the current and prior5 studies.We included only data from deer with biopsies providing ≥3 lymphoid follicles in analyses involving infection status in order to maintain a relatively high (≥90%) probability of detecting infected individuals24. Two animals with low follicle counts that died shortly after capture were excepted by substituting postmortem IHC results. Limiting the acceptable follicle count excluded seven females (two 225SS, five 225SF) and two males (one 225SS, one 225SF) from some analyses. One male deer was 225FF and one female deer was missing a blood sample and thus not assigned to a PRNP gene group; these two individuals also were excluded from some analyses (e.g., Table 1).
PRNP genotypingWe used DNA extracted from whole blood buffy coat aliquots (n = 99) to screen for the presence of sequences at PRNP gene codon 225 that encode for serine (S) and/or phenylalanine (F) in the mature prion polypeptide, classifying individuals as 225SS, 225SF, or 225FF16,36,42. Methods generally followed those described by Jewell et al.16. Briefly, we extracted DNA using the DNeasy® blood and tissue kit (Qiagen, Valenica, California, USA). We amplified the complete open reading frame (ORF) plus 25 bp of 5′ flanking sequences and 53 bp of 3′ flanking sequences in the PRNP coding region using polymerase chain reaction (PCR). Purified DNA was combined in a 0.2 ml PCR tube containing a puReTag Ready-To-Go PCR bead (illustra™, GE Healthcare Bio-Sciences Corp, Piscataway, New Jersey, USA). Each PCR bead contained 2.5 units puReTag DNA polymerase, 10 mM Tris-HCI, 50 mM KCl, 1.5 mM MgCl2, 200 µM of each deoxynucleoside triphosphate, and stabilizers, including bovine serum albumin. For each PCR assay, 1 μL of each primer at 200 nM, 22 μL of RNase-free water and 1 μL of approximately 100 ng total genomic DNA was added for a final volume of 25 μL. Primers used for amplification were forward (MD582F, 5′-ACATGGGCATATGATGCTGACACC-3′) and reverse (MD1479RC, 5′-ACTACAGGGCTGCAGGTAGATACT-3′) described by Jewell et al.16. Reactions were thermal-cycled in a PTC 100 (MJ Research) at 94 C for 5 min and then 32 cycles of 94 C for 7 s, 62 C for 15 s, 72 C for 30 s and a final cycle of 72 C for 5 min, and kept at 4 C until inspected for successful amplification by agarose gel electrophoresis. As confirmed by LaCava et al.19, the MD582F and MD1479RC primers developed by Jewell et al.16 specifically amplify the functional PRNP gene ORF, thereby excluding confounding effects that could arise from the presence of a processed pseudogene that occurs in a majority of deer (Odocoileus spp.)42.We used EcoRI restriction digestion of the PCR-amplified PRNP region16—a validated assay targeting the singular polymorphism at codon 225 in mule deer—to screen all 99 samples for presence of S or F codons. Aliquots (10 μl) of completed PCR reactions were incubated with 10 U EcoRI (New England Biolabs) in a total volume of 12 μl containing 50 mM NaCl, 100 mM Tris/HCl, 10 mM MgCl2, 0.025% Triton X-100 (pH 7.5) at 37 C for 2–16 h followed by the addition of 2.5 μl 6× concentrate gel loading solution (Sigma- Aldrich) per sample, and the inspection of products by agarose gel electrophoresis for the presence of one 897bp-sized band for 225SS, two bands—one 897 bp and one 719 bp—for 225SF, or one 719 bp-sized band for 225FF. As noted by Jewell et al.16, occurrence of TTC (the F codon) at position 225 creates an EcoRI recognition DNA sequence and cleavage site GAATTC from codons 224–225, whereas TCC (the S codon) creates the sequence GAATCC, which is not cut by EcoRI. When incubated with EcoRI, PCR products with a TTC codon at position 225 yielded cleavage fragments of the predictable sizes listed16. Because no other sites within the PRNP ORF DNA sequence are potentially transformable to GAATTC with one base change, this represents a specific genotyping method for assessing the S225F polymorphism in mule deer16.To confirm findings from EcoRI screening, we examined sequences of the complete PRNP ORF from 20 samples that showed evidence of cleavage indicating 225*F and 6 samples without cleavage identified as 225SS. For DNA sequencing, we used primers 245 (5′-GGTGGTGACTGACTGTGTGTTGCTTGA-3′), 12 (5′-TGGTGGTGACTGTGTGTTGCTTGA-3′) and 3FL1 (5′-GATTAAGAAGATAATGAAAACAGGAAGG-3′; Integrated DNA Technologies). Sanger sequencing was done on purified PCR product by Eurofins Genomics (Louisville, Kentucky, USA). Sequence chromatograms were viewed and DNA sequence alignments and comparisons were made using the MAFFT multiple sequence alignment program v7.450 module, software platform v2020.2.3 of Geneious Prime. Sequencing confirmed the presence of coding for F in all samples identified as 225*F by EcoRI digestion, as well as the absence of such coding in samples identified by EcoRI digestion as 225SS. Moreover, presence of AGC at codon 138 in all sequenced samples reconfirmed that the primers we used had amplified the functional PRNP gene42.Statistics and reproducibilityFor analyses, we tabulated IHC-positive and -negative results to estimate apparent prevalence of prion infection. We also tabulated the number of individuals assigned to PRNP genotypes and to age groupings as described. Age groupings were selected based on relevance to CWD epidemiology in mule deer1,5,8,12,16,17,18,20,24,31,37. Assuming a ~2-year disease course5,8,17 and relative scarcity of end-stage disease in 225SS deer More

NEWS
11 January 2022

Landmark Colombian bird study repeated to right colonial-era wrongs

A re-run of a 100-year-old, US-led bird survey will inform future conservation efforts — but be helmed by local researchers.

Luke Taylor

Luke Taylor

View author publications

You can also search for this author in PubMed
 Google Scholar

Twitter

Facebook

Email

Download PDF

Ornithologist Andrés Cuervo takes a selfie of a team of Colombia Resurvey Project researchers during an expedition in Caquetá.Credit: Andrés M. Cuervo

Colombia has more animal and plant species per square kilometre than anywhere else in the world. Pioneering US bird scientist Frank Chapman once said that the country was so rich in biodiversity that when his research team explored the area in the early twentieth century, it could have studied a single mountain range for five years and still not have mapped all of its fauna.More than 100 years later, Colombian researchers are redoing his legendary bird survey, which is a reference for ornithologists the world over. They are surveying the areas that Chapman catalogued between 1911 and 1915, to investigate how a century of war, global warming and industrialization has affected the landscape and its biodiversity.
The world’s species are playing musical chairs: how will it end?
But this project will not snatch birds and whisk them to a museum abroad — as Chapman’s team did. Instead, local scientists will keep specimens in Colombia and engage with local communities during their expeditions, to include them in the momentous endeavour, improve the quality of the research and set an ethical standard for future fieldwork.Chapman and at least 5 other collectors shot many of the nearly 16,000 birds that they hauled back to the American Museum of Natural History in New York City, offering local residents little explanation — or credit. “You wouldn’t like it if I came to your house, surveyed it without permission, took photos and then went back to Colombia without telling you what I had found,” says Nelsy Niño-Rodríguez, the Colombia Resurvey Project’s community-relations coordinator, who is an ornithologist at the Alexander von Humboldt Biological Resources Research Institute in Bogotá. Without local guides knowledgeable about Colombia and its birds, Chapman couldn’t possibly have located and collected so many specimens, says Natalia Ocampo-Peñuela, a research partner on the resurvey project and a conservation ecologist at the University of California, Santa Cruz. Yet Chapman’s logs hardly mention guides; when they are discussed, it’s usually in racist or pejorative terms, she says.“His interest was to feed his curiosity, his scientific intellect and the museum,” she adds, but not to inform the wider population — and definitely not the local populations.A changed landscape Colombian researchers have dreamt of re-running Chapman’s expeditions for decades. But it wasn’t possible until the past few years, because many areas were inaccessible owing to armed conflict. Following a landmark peace deal in 2016, remote regions that had been under the control of the Revolutionary Armed Forces of Colombia (FARC), a left-wing guerrilla group, once again opened to exploration. That, and an infusion of funding from the Colombian government and international donors, meant researchers could attempt a resurvey.

Birds of Colombia: top, many-banded araçari (Pteroglossus pluricinctus); left, pileated finch (Coryphospingus pileatus); right, white-fringed antwren (Formicivora grisea).Credit: Andrés M. Cuervo

Chapman visited Colombia because he thought that its geography made it one of the most biodiverse places in the world. He theorized that the presence of the Andes Mountains, combined with the country’s position bridging South and Central America, made it an evolutionary melting pot.
FARC and the forest: Peace is destroying Colombia’s jungle — and opening it to science
Although Colombia is still home to around 10% of the world’s biodiversity, the forests once explored by Chapman have changed immensely. Pristine jungles have been cleared to create uniform pastures resembling golf courses, says Andrés Cuervo, an ornithologist at the National University of Colombia in Bogotá who is one of the directors of the resurvey project. The dirt tracks that Chapman and his team traversed on mules are now roads. And climate change has pushed birds to higher elevations and altered their migratory patterns.Seeking to understand the effects of these changes on biodiversity, researchers launched the Colombia Resurvey Project in 2019. The main objective is to gather bird specimens, including DNA and tissue samples, to compare the modern population with Chapman’s collection. The team, which includes US researchers as well as local ones, has so far conducted 6 expeditions, visiting 14 of Chapman’s original sites — leaving 60 to go. A useful catalogue The researchers are finding that they have to venture deep into the forest to find birds that were once a stone’s throw from Chapman’s campsites, Cuervo says. And some species are nowhere to be found, including the red-ruffed fruitcrow (Pyroderus scutatus) — almost certainly lost when the trees in its territory were cut down to grow avocados, he adds.

Resurvey project researchers Jessica Diaz (right) and Andrés Sierra (left) record data from a mist net, used to collect birds during expeditions.Credit: Andrés M. Cuervo

The team has also confirmed that birds dependent on unique ecosystems are being replaced by generalist species — which are more adaptable to fragmented forest and a disrupted diet — reducing the country’s biodiversity1. Larger species and fruit eaters seem to have been hit particularly hard over the past century, because they require vast expanses of forest to thrive.
Illegal mining in the Amazon hits record high amid Indigenous protests
The effects of climate and landscape changes on bird populations in the tropics are not well understood, so the project will inform future conservation efforts, researchers say. “It’s almost impossible to imagine all the ways in which this data can potentially be used down the road,” says John Bates, curator and head of life sciences at the Field Museum of Natural History in Chicago, Illinois. Members of the resurvey project hope their catalogue will have as much impact as Chapman’s. It will include resources such as a genomic map illustrating birds’ evolution, generated from DNA samples. “We are collecting the most complete set of specimens that one can imagine so that scientists from now and the future can answer questions that we haven’t thought of,” Ocampo says.Taking chargeThe Colombia Resurvey Project team especially hopes that its anti-colonial approach will resonate with the scientific community. The researchers run workshops before each excursion to inform local communities about why they are planning to kill some birds, and how this is important for conservation and science. They are storing the specimens at the National University of Colombia, where the birds will be digitally catalogued, so that people can view them online, listen to audio of their song and scroll through interactive maps of the expeditions. And the team is creating birdwatching tours at the expedition sites to boost tourism.
Brazilian road proposal threatens famed biodiversity hotspot
Involving communities leads to better results, Niño-Rodríguez says. For instance, even if some Indigenous people do not know the scientific names for birds, they might be able to identify them on sight and know where they are most likely to be found. And community knowledge of how the forests have changed has passed from generation to generation, so local residents are able to fill gaps when satellite data and research logs aren’t available.It’s equally important to the researchers that those leading the project are from Colombia. They say it’s common for local experts to help visiting foreign researchers to find new species and make discoveries, but be excluded from the scientific process and the credit. “We don’t want to be the guys with the permits or the guys who facilitate the logistics of someone else’s research,” Cuervo says. “We want to do our own high-quality research, and we want it to be available for people to use.” This time around, the American Museum of Natural History is a partner on the project, rather than its lead. “Although the Chapman expedition was conducted with help and permissions from the Colombian government, today’s expeditions appropriately look much different than they did in Chapman’s time,” says a museum spokesperson, adding that the museum “is proud of the very active relationship it maintains with Colombia’s scientific institutions through education and research”.
Colombia: after the violence
Meanwhile, project researchers are training curious members of local communities in how to identify birds scientifically, so they can continue to log species with their cameras and mobile phones once the researchers leave the forest. Areas previously ruled by FARC guerrillas are now falling under the control of other armed groups, which might not let outsiders in, so local residents could soon be the only people who have access to some of Colombia’s most biodiverse jungles and the birds that inhabit them.“Hopefully we won’t have to wait another hundred years for scientists to return to these sites and assess their bird fauna,” Cuervo says. “Communities can do it with empowerment and interest in their biodiversity and surroundings.”

Nature 601, 178-179 (2022)
doi: https://doi.org/10.1038/d41586-021-03527-x

References1.Gómez, C., Tenorio, E. A. & Cadena, C. D. Conserv. Biol. 35, 1552–1563 (2021).PubMed 
Article 

Google Scholar 
Download references

Related Articles

The world’s species are playing musical chairs: how will it end?

FARC and the forest: Peace is destroying Colombia’s jungle — and opening it to science

Colombia: after the violence

Brazilian road proposal threatens famed biodiversity hotspot

Illegal mining in the Amazon hits record high amid Indigenous protests

Colombia creates its first science ministry

Subjects

Conservation biology

Biodiversity

Climate change

Animal behaviour

Latest on:

Biodiversity

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

Two million species catalogued by 500 experts
Correspondence 11 JAN 22

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

Climate change

How researchers can help fight climate change in 2022 and beyond
Editorial 05 JAN 22

Sustainability at the crossroads
Editorial 21 DEC 21

The science events to watch for in 2022
News 17 DEC 21

Jobs

Postdoctoral Fellowship Positions in Cancer Nanomedicine and Neuro-Engineering

University of Maryland School of Medicine
Baltimore, MD, United States

2022 Call for IBS Directors and Chief Investigators(CI)

Institute for Basic Science (IBS)
Multiple locations

Post-doctoral Fellow – Neurodegeneration

Oklahoma Medical Research Foundation (OMRF)
Oklahoma City, United States

Post-doctoral Fellow – Neurodegeneration

Oklahoma Medical Research Foundation (OMRF)
Oklahoma City, United States More

Abdel-Ghani AH, Parzies HK, Omary A, Geiger HH (2004) Estimating the outcrossing rate of barley landraces and wild barley populations collected from ecologically different regions of Jordan Theor Appl Genet 109(3):588–595PubMed 

Google Scholar 
Akerman A, Bürger R (2014) The consequences of gene flow for local adaptation and differentiation: a two-locus two-deme model J Math Biol 68(5):1135–1198PubMed 

Google Scholar 
Al-Asadi H, Petkova D, Stephens M, Novembre J (2019) Estimating recent migration and population-size surfaces PLoS Genet 15(1):e1007908PubMed 
PubMed Central 

Google Scholar 
Baker HG (1967) Support for Baker’s law-as a rule Evolution 21(4):853–856PubMed 

Google Scholar 
Baker K, Baker K, Bayer M, Cook N, Dreißig S, Dhillon T, Russell J, Hedley PE, Morris J, Ramsay L, Colas I et al. (2014) The low-recombining pericentromeric region of barley restricts gene diversity and evolution but not gene expression Plant J 79(6):981–992CAS 
PubMed 
PubMed Central 

Google Scholar 
Battey C, Ralph PL, Kern AD (2020) Space is the place: effects of continuous spatial structure on analysis of population genetic data Genetics 215(1):193–214CAS 
PubMed 
PubMed Central 

Google Scholar 
Baum M, Grando S, Backes G, Jahoor A, Sabbagh A, Ceccarelli S (2003) QTLs for agronomic traits in the mediterranean environment identified in recombinant inbred lines of the cross’ Arta’ × H. spontaneum 41-1 Theor Appl Genet 107(7):1215–1225CAS 
PubMed 

Google Scholar 
Bedada G, Westerbergh A, Nevo E, Korol A, Schmid KJ (2014) DNA sequence variation of wild barley Hordeum spontaneum (L.) across environmental gradients in Israel Heredity 112(6):646–655CAS 
PubMed 
PubMed Central 

Google Scholar 
Berner D, Roesti M (2017) Genomics of adaptive divergence with chromosome-scale heterogeneity in crossover rate Mol Ecol 26(22):6351–6369CAS 
PubMed 

Google Scholar 
Bhatia G, Patterson N, Sankararaman S, Price AL (2013) Estimating and interpreting FST: the impact of rare variants Genome Res 23(9):1514–1521CAS 
PubMed 
PubMed Central 

Google Scholar 
Bohra A, Kilian B, Kilian B, Sivasankar S, Caccamo M, Mba, C, McCouch SR, Varshney RK (2021) Reap the crop wild relatives for breeding future crops. Trends Biotechnol. https://doi.org/10.1016/j.tibtech.2021.08.009Bradburd GS, Coop GM, Ralph PL (2018) Inferring continuous and discrete population genetic structure across space. Genetics 210(1):33–52PubMed 
PubMed Central 

Google Scholar 
Bürger R, Akerman A (2011) The effects of linkage and gene flow on local adaptation: a two-locus continent–island model. Theor Popul Biol 80(4):272–288PubMed 
PubMed Central 

Google Scholar 
Cabreros I, Storey JD (2019) A likelihood-free estimator of population structure bridging admixture models and principal components analysis. Genetics 212(4):1009–1029PubMed 
PubMed Central 

Google Scholar 
Caldwell KS, Russell J, Langridge P, Powell W (2006) Extreme population-dependent linkage disequilibrium detected in an inbreeding plant species, Hordeum vulgare. Genetics 172(1):557–567CAS 
PubMed 
PubMed Central 

Google Scholar 
Capblancq T, Luu K, Blum MG, Bazin E (2018) Evaluation of redundancy analysis to identify signatures of local adaptation. Mol Ecol Resour 18(6):1223–1233CAS 
PubMed 

Google Scholar 
Caye K, Jumentier B, Lepeule J, François O (2019) LFMM 2: fast and accurate inference of gene-environment associations in genome-wide studies. Mol Biol Evol 36(4):852–860CAS 
PubMed 
PubMed Central 

Google Scholar 
Contreras-Moreira B, Serrano-Notivoli R, Mohammed NE, Cantalapiedra CP, Beguería S, Casas AM, Igartua E (2019) Genetic association with high-resolution climate data reveals selection footprints in the genomes of barley landraces across the Iberian Peninsula. Mol Ecol 28(8):1994–2012PubMed 
PubMed Central 

Google Scholar 
Dawson IK, Russell J, Powell W, Steffenson B, Thomas WTB, Waugh R (2015) Barley: a translational model for adaptation to climate change. New Phytol 206(3):913–931PubMed 

Google Scholar 
Dray S, Legendre P, Peres-Neto PR (2006) Spatial modelling: a comprehensive framework for principal coordinate analysis of neighbour matrices (pcnm). Ecol Model 196(3-4):483–493
Google Scholar 
Dray S, Bauman D, Blanchet G, Borcard D, Clappe S, Guenard G, Jombart T, Larocque G, Legendre P, Madi N, Wagner HH (2019) adespatial: multivariate multiscale spatial analysis. R package version 0.3-7. https://CRAN.R-project.org/package=adespatialElshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES, Mitchell SE (2011) A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS ONE 6(5):e19379CAS 
PubMed 
PubMed Central 

Google Scholar 
Excoffier L, Hofer T, Foll M (2009) Detecting loci under selection in a hierarchically structured population. Heredity 103(4):285–298CAS 
PubMed 

Google Scholar 
Fang Z, Gonzales AM, Clegg MT, Smith KP, Muehlbauer GJ, Steffenson BJ, Morrell PL (2014) Two genomic regions contribute disproportionately to geographic differentiation in wild barley. G34(7):1193–1203PubMed 
PubMed Central 

Google Scholar 
Fick SE, Hijmans RJ (2017) Worldclim 2: new 1-km spatial resolution climate surfaces for global land areas. Int J Climatol 37(12):4302–4315
Google Scholar 
Forester BR, Lasky JR, Wagner HH, Urban DL (2018) Comparing methods for detecting multilocus adaptation with multivariate genotype–environment associations. Mol Ecol 27(9):2215–2233CAS 
PubMed 

Google Scholar 
Forester BR, Jones MR, Joost S, Landguth EL, Lasky JR (2016) Detecting spatial genetic signatures of local adaptation in heterogeneous landscapes. Mol Ecol 25(1):104–120CAS 
PubMed 

Google Scholar 
Galkin E, Dalal A, Evenko A, Fridman E, Kan I, Wallach R, Moshelion M (2018) Risk-management strategies and transpiration rates of wild barley in uncertain environments. Physiol Plant 164(4):412–428CAS 
PubMed 

Google Scholar 
Gautier M (2015) Genome-wide scan for adaptive divergence and association with population-specific covariates. Genetics 201(4):1555–1579CAS 
PubMed 
PubMed Central 

Google Scholar 
Gibson MJ, Moyle LC (2020) Regional differences in the abiotic environment contribute to genomic divergence within a wild tomato species. Mol Ecol 29(12):2204–2217CAS 
PubMed 

Google Scholar 
Günther T, Coop G (2013) Robust identification of local adaptation from allele frequencies. Genetics 195(1):205–220PubMed 
PubMed Central 

Google Scholar 
Gutaker RM, Groen SC, Bellis ES, Choi JY, Pires IS, Bocinsky RK, Slayton ER, Wilkins O, Castillo CC, Negrão S et al. (2020) Genomic history and ecology of the geographic spread of rice. Nat Plants 6(5):492–502PubMed 

Google Scholar 
Hämälä T, Savolainen O (2019) Genomic patterns of local adaptation under gene flow in arabidopsis lyrata. Mol Biol Evol 36(11):2557–2571
Google Scholar 
Harlan JR, Zohary D (1966) Distribution of wild wheats and barley. Science 153(3740):1074–1080CAS 
PubMed 

Google Scholar 
Hartfield M, Bataillon T, Glémin S (2017) The evolutionary interplay between adaptation and self-fertilization. Trends Genet 33(6):420–431CAS 
PubMed 
PubMed Central 

Google Scholar 
Hendrick MF, Finseth FR, Mathiasson ME, Palmer KA, Broder EM, Breigenzer P, Fishman L (2016) The genetics of extreme microgeographic adaptation: an integrated approach identifies a major gene underlying leaf trichome divergence in yellowstone mimulus guttatus. Mol Ecol 25(22):5647–5662CAS 
PubMed 

Google Scholar 
Hengl T, Mendes de Jesus J, Heuvelink GBM, Ruiperez Gonzalez M, Kilibarda M, Blagotić A, Shangguan W, Wright MN, Geng X, Bauer-Marschallinger B et al. (2017) Soilgrids250m: Global gridded soil information based on machine learning. PLoS ONE 12(2):e0169748PubMed 
PubMed Central 

Google Scholar 
Herzig P, Herzig P, Maurer A, Draba V, Sharma R, Draicchio F, Bull H, Milne L, Thomas WTB, Flavell AJ, Pillen K (2018) Contrasting genetic regulation of plant development in wild barley grown in two European environments revealed by nested association mapping. J Exp Bot 69(7):1517–1531CAS 
PubMed 
PubMed Central 

Google Scholar 
Hill W, Weir B (1988) Variances and covariances of squared linkage disequilibria in finite populations. Theor Popul Biol 33(1):54–78CAS 
PubMed 

Google Scholar 
Hodgins KA, Yeaman S (2019) Mating system impacts the genetic architecture of adaptation to heterogeneous environments. New Phytol 224(3):1201–1214PubMed 

Google Scholar 
House GL, Hahn MW (2018) Evaluating methods to visualize patterns of genetic differentiation on a landscape. Mol Ecol Resour 18(3):448–460PubMed 

Google Scholar 
Hübner S, Korol AB, Schmid KJ (2015) Rna-seq analysis identifies genes associated with differential reproductive success under drought-stress in accessions of wild barley hordeum spontaneum. BMC Plant Biol15(1):1–14
Google Scholar 
Hübner S, Bdolach E, Ein-Gedy S, Schmid KJ, Korol A, Fridman E (2013) Phenotypic landscapes: phenological patterns in wild and cultivated barley. J Evol Biol 26(1):163–174PubMed 

Google Scholar 
Hübner S, Günther T, Flavell A, Fridman E, Graner A, Korol A, Schmid KJ (2012) Islands and streams: clusters and gene flow in wild barley populations from the Levant. Mol Ecol 21(5):1115–1129PubMed 

Google Scholar 
Hübner S, Höffken M, Oren E, Haseneyer G, Stein N, Graner A, Schmid K, Fridman E (2009) Strong correlation of wild barley (Hordeum spontaneum) population structure with temperature and precipitation variation. Mol Ecol 18(7):1523–1536PubMed 

Google Scholar 
Jakob SS, Rödder D, Engler JO, Shaaf S, Özkan H, Blattner FR, Kilian B (2014) Evolutionary history of wild barley (Hordeum vulgare subsp. spontaneum) analyzed using multilocus sequence data and paleodistribution modeling. Genome Biol Evol 6(3):685–702CAS 
PubMed 
PubMed Central 

Google Scholar 
Jayakodi M, Padmarasu S, Haberer G, Bonthala VS, Gundlach H, Monat C, Lux T, Kamal N, Lang D, Himmelbach A, Ens J, Zhang X-Q, Angessa TT, Zhou G, Tan C, Hill C, Wang P, Schreiber M, Boston LB, Plott C, Jenkins J, Guo Y, Fiebig A, Budak H, Xu D, Zhang J, Wang C, Grimwood J, Schmutz J, Guo G, Zhang G, Mochida K, Hirayama T, Sato K, Chalmers KJ, Langridge P, Waugh R, Pozniak CJ, Scholz U, Mayer KFX, Spannagl M, Li C, Mascher M, Stein N (2020) The barley pan-genome reveals the hidden legacy of mutation breeding Nature 588:284–289. https://doi.org/10.1038/s41586-020-2947-8CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Kawecki TJ, Ebert D (2004) Conceptual issues in local adaptation. Ecol Lett 7(12):1225–1241
Google Scholar 
Kilian B, Özkan H, Kohl J, von Haeseler A, Barale F, Deusch O, Brandolini A, Yucel C, Martin W, Salamini F (2006) Haplotype structure at seven barley genes: relevance to gene pool bottlenecks, phylogeny of ear type and site of barley domestication. Mol Genet Genom 276(3):230–241CAS 

Google Scholar 
Lasky JR, Des Marais DL, McKAY JK, Richards JH, Juenger TE, Keitt TH (2012) Characterizing genomic variation of arabidopsis thaliana: the roles of geography and climate. Mol Ecol 21(22):5512–5529PubMed 

Google Scholar 
Lasky JR, Upadhyaya HD, Ramu P, Deshpande S, Hash CT, Bonnette J, Juenger TE, Hyma K, Acharya C, Mitchell SE et al. (2015) Genome-environment associations in sorghum landraces predict adaptive traits. Sci Adv 1(6):e1400218PubMed 
PubMed Central 

Google Scholar 
Lawson DJ, Van Dorp L, Falush D (2018) A tutorial on how not to over-interpret STRUCTURE and ADMIXTURE bar plots. Nat Commun 9(1):1–11CAS 

Google Scholar 
Lee C-R, Mitchell-Olds T (2011) Quantifying effects of environmental and geographical factors on patterns of genetic differentiation. Mol Ecol 20(22):4631–4642PubMed 
PubMed Central 

Google Scholar 
Leek JT (2011) Asymptotic conditional singular value decomposition for high-dimensional genomic data. Biometrics 67(2):344–352PubMed 

Google Scholar 
Legendre P, Legendre L (2012) Canonical analysis. In: Numerical ecology, 3rd English edn, chap. 11. Elsevier Science BV, The Netherlands, pp 625–710López-Goldar X, Agrawal AA (2021) Ecological interactions, environmental gradients, and gene flow in local adaptation Trends Plant Sci 26(8):796–809PubMed 

Google Scholar 
Lotterhos KE, Whitlock MC (2015) The relative power of genome scans to detect local adaptation depends on sampling design and statistical method. Mol Ecol 24(5):1031–1046PubMed 

Google Scholar 
Lundgren E, Ralph PL (2019) Are populations like a circuit? Comparing isolation by resistance to a new coalescent-based method. Mol Ecol Resour 19(6):1388–1406PubMed 

Google Scholar 
Makowski D, Ben-Shachar M, Lüdecke D (2019) bayestestR: describing effects and their uncertainty, existence and significance within the Bayesian framework. J Open Source Softw 4(40):1541
Google Scholar 
Mascher M, Gundlach H, Himmelbach A, Beier S, Twardziok SO, Wicker T, Radchuk V, Dockter C, Hedley PE, Russell J et al. (2017) A chromosome conformation capture ordered sequence of the barley genome. Nature 544(7651):427–433CAS 
PubMed 

Google Scholar 
Mascher M (2019) Pseudomolecules and annotation of the second version of the reference genome sequence assembly of barley cv. morex [morex v2]. https://doi.ipk-gatersleben.de:443/DOI/83e8e186-dc4b-47f7-a820-28ad37cb176b/d1067eba-1d08-42e2-85ec-66bfd5112cd8/2McVean G (2009) A genealogical interpretation of principal components analysis. PLoS Genet 5(10):e1000686Mee JA, Yeaman S (2019) Unpacking conditional neutrality: genomic signatures of selection on conditionally beneficial and conditionally deleterious mutations. Am Nat 194(4):529–540PubMed 

Google Scholar 
Milner SG, Jost M, Taketa S, Mazón ER, Himmelbach A, Oppermann M, Weise S, Knüpffer H, Basterrechea M, König P et al. (2019) Genebank genomics highlights the diversity of a global barley collection. Nat Genet 51(2):319–326CAS 
PubMed 

Google Scholar 
Morrell PL, Toleno DM, Lundy KE, Clegg MT (2005) Low levels of linkage disequilibrium in wild barley (Hordeum vulgare ssp. spontaneum) despite high rates of self-fertilization. Proc Natl Acad Sci USA 102(7):2442–2447CAS 
PubMed 
PubMed Central 

Google Scholar 
Navarro JAR, Willcox M, Burgueño J, Romay C, Swarts K, Trachsel S, Preciado E, Terron A, Delgado HV, Vidal V et al. (2017) A study of allelic diversity underlying flowering-time adaptation in maize landraces. Nat Genet 49(3):476
Google Scholar 
Nevo E, Zohary D, Brown A, Haber M (1979) Genetic diversity and environmental associations of wild barley, Hordeum spontaneum, in Israel. Evolution 33(3):815–833CAS 
PubMed 

Google Scholar 
Nevo E, Beharav A, Meyer RC, Hackett CA, Forster BP, Russell JR, Powell W (2005) Genomic microsatellite adaptive divergence of wild barley by microclimatic stress in ‘Evolution Canyon’, Israel. Biol J Linn Soc84(2):205–224
Google Scholar 
Oksanen J, Blanchet FG, Friendly M, Kindt R, Legendre P, McGlinn D, Minchin PR, O’Hara RB, Simpson GL, Solymos P, Stevens MHH, Szoecs E, Wagner H (2019) vegan: community ecology package. R package version 2.5-6. https://CRAN.R-project.org/package=veganPankin A, Altmüller J, Becker C, von Korff M (2018) Targeted resequencing reveals genomic signatures of barley domestication. New Phytol 218(3):1247–1259CAS 
PubMed 
PubMed Central 

Google Scholar 
Pekel J-F, Cottam A, Gorelick N, Belward AS (2016) High-resolution mapping of global surface water and its long-term changes. Nature 540(7633):418–422CAS 

Google Scholar 
Pembleton L, Cogan N, Forster J (2013) StAMPP: an R package for calculation of genetic differentiation and structure of mixed-ploidy level populations Mol Ecol Res 13:946–952. https://doi.org/10.1111/1755-0998.12129CAS 
Article 

Google Scholar 
Peterman WE (2018) Resistancega: an r package for the optimization of resistance surfaces using genetic algorithms. Methods Ecol Evol 9(6):1638–1647
Google Scholar 
Petkova D, Novembre J, Stephens M (2016) Visualizing spatial population structure with estimated effective migration surfaces. Nat Genet 48(1):94CAS 
PubMed 

Google Scholar 
Pham A-T, Maurer A, Pillen K, Brien C, Dowling K, Berger B, Eglinton JK, March TJ (2019) Genome-wide association of barley plant growth under drought stress using a nested association mapping population. BMC Plant Biol 19(1):134PubMed 
PubMed Central 

Google Scholar 
Poland JA, Brown PJ, Sorrells ME, Jannink J-L (2012) Development of high-density genetic maps for barley and wheat using a novel two-enzyme genotyping-by-sequencing approach. PLoS ONE 7(2):e32253CAS 
PubMed 
PubMed Central 

Google Scholar 
Pyhäjärvi T, Hufford MB, Mezmouk S, Ross-Ibarra J (2013) Complex patterns of local adaptation in teosinte. Genome Biol Evol 5(9):1594–1609PubMed 
PubMed Central 

Google Scholar 
Rellstab C, Gugerli F, Eckert AJ, Hancock AM, Holderegger R (2015) A practical guide to environmental association analysis in landscape genomics. Mol Ecol 24(17):4348–4370PubMed 

Google Scholar 
Renaut S, Grassa CJ, Yeaman S, Moyers BT, Lai Z, Kane NC, Bowers JE, Burke JM, Rieseberg LH (2013) Genomic islands of divergence are not affected by geography of speciation in sunflowers. Nat Commun 4(1):1–8
Google Scholar 
Russell J, Mascher M, Dawson IK, Kyriakidis S, Calixto C, Freund F, Bayer M, Milne I, Marshall-Griffiths T, Heinen S et al. (2016) Exome sequencing of geographically diverse barley landraces and wild relatives gives insights into environmental adaptation. Nat Genet 48(9):1024CAS 
PubMed 

Google Scholar 
Samuk K, Samuk K, Owens GL, Delmore KE, Miller SE, Rennison DJ, Schluter D (2017) Gene flow and selection interact to promote adaptive divergence in regions of low recombination. Mol Ecol 26(17):4378–4390PubMed 

Google Scholar 
Sato K, Mascher M, Himmelbach A, Haberer G, Spannagl M, Stein N (2021) Chromosome-scale assembly of wild barley accession ‘OUH602’. G3 11(10):jkab244PubMed 
PubMed Central 

Google Scholar 
Schmid K, Kilian B. Russell J (2018) Barley domestication, adaptation and population genomics. In: The Barley Genome, Springer International Publishing: Cham, pp 317–336Szkiba D, Kapun M, von Haeseler A, Gallach M (2014) SNP2GO: functional analysis of genome-wide association studies. Genetics 197(1):285–289PubMed 
PubMed Central 

Google Scholar 
Terrazas RA, Balbirnie-Cumming K, Morris J, Hedley PE, Russell J, Paterson E, Baggs EM, Fridman E, Bulgarelli D (2020) A footprint of plant eco-geographic adaptation on the composition of the barley rhizosphere bacterial microbiota. Sci Rep 10(1):1–13
Google Scholar 
Tiffin P, Ross-Ibarra J (2014) Advances and limits of using population genetics to understand local adaptation. Trends Ecol Evol 29(12):673–680PubMed 

Google Scholar 
Tsuda Y, Chen J, Stocks M, Källman T, Sønstebø JH, Parducci L, Semerikov V, Sperisen C, Politov D, Ronkainen T et al. (2016) The extent and meaning of hybridization and introgression between siberian spruce (picea obovata) and norway spruce (picea abies): cryptic refugia as stepping stones to the west? Mol Ecol 25(12):2773–2789CAS 
PubMed 

Google Scholar 
Turner-Hissong SD, Mabry ME, Beissinger TM, Ross-Ibarra J, Pires JC (2020) Evolutionary insights into plant breeding Curr Opin Plant Biol 54:93–100. https://doi.org/10.1016/j.pbi.2020.03.003CAS 
Article 
PubMed 

Google Scholar 
de Villemereuil P, Frichot É, Bazin É, François O, Gaggiotti OE (2014) Genome scan methods against more complex models: when and how much should we trust them? Mol Ecol 23(8):2006–2019PubMed 

Google Scholar 
Volis S (2011) Adaptive genetic differentiation in a predominantly self-pollinating species analyzed by transplanting into natural environment, crossbreeding and QST-FST test. New Phytol 192(1):237–248CAS 
PubMed 

Google Scholar 
Volis S, Mendlinger S, Ward D (2002a) Differentiation in populations of Hordeum spontaneum along a gradient of environmental productivity and predictability: life history and local adaptation. Biol J Linn Soc 77(4):479–490
Google Scholar 
Volis S, Mendlinger S, Ward D (2002b) Adaptive traits of wild barley plants of Mediterranean and desert origin. Oecologia 133(2):131–138PubMed 

Google Scholar 
Volis S, Zaretsky M, Shulgina I (2010) Fine-scale spatial genetic structure in a predominantly selfing plant: role of seed and pollen dispersal. Heredity 105(4):384–393CAS 
PubMed 

Google Scholar 
Volis S, Shulgina I, Ward D, Mendlinger S (2003) Regional subdivision in wild barley allozyme variation: adaptive or neutral? J Hered 94(4):341–351CAS 
PubMed 

Google Scholar 
Volis S, Verhoeven K, Mendlinger S, Ward D (2004) Phenotypic selection and regulation of reproduction in different environments in wild barley. J Evol Biol 17(5):1121–1131CAS 
PubMed 

Google Scholar 
Volis S, Yakubov B, Shulgina I, Ward D, Mendlinger S (2005) Distinguishing adaptive from nonadaptive genetic differentiation: comparison of q st and f st at two spatial scales. Heredity 95(6):466–475CAS 
PubMed 

Google Scholar 
Volis S, Yakubov B, Shulgina I, Ward D, Zur V, Mendlinger S (2001) Tests for adaptive RAPD variation in population genetic structure of wild barley, Hordeum spontaneum Koch. Biol J Linn Soc 74(3):289–303
Google Scholar 
Wang X, Chen Z-H, Yang C, Zhang X, Jin G, Chen G, Wang Y, Holford P, Nevo E, Zhang G et al. (2018) Genomic adaptation to drought in wild barley is driven by edaphic natural selection at the Tabigha Evolution Slope. Proc Natl Acad Sci USA 115(20):5223–5228CAS 
PubMed 
PubMed Central 

Google Scholar 
Wiegmann M, Wiegmann M, Maurer A, Pham A, March TJ, Al-Abdallat A, Thomas WTB, Bull HJ, Shahid M, Eglinton J, Baum M, Flavell AJ, Tester M, Pillen K (2019) Barley yield formation under abiotic stress depends on the interplay between flowering time genes and environmental cues Sci Rep 9(1):6397. https://doi.org/10.1038/s41598-019-42673-1CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Yeaman S, Whitlock MC (2011) The genetic architecture of adaptation under migration–selection balance. Evolution 65(7):1897–1911PubMed 

Google Scholar 
Zheng X, Levine D, Shen J, Gogarten S, Laurie C, Weir B (2012) A high-performance computing toolset for relatedness and principal component analysis of snp data Bioinformatics 28(24):3326–3328. https://doi.org/10.1093/bioinformatics/bts606CAS 
Article 
PubMed 
PubMed Central 

Google Scholar  More