Philippa Kaur

Newton, I. The Migration Ecology of Birds (Academic Press, USA, 2010).
Google Scholar 
Somveille, M., Rodrigues, A. S. L. & Manica, A. Why do birds migrate? A macroecological perspective. Glob. Ecol. Biogeogr. 24(6), 664–674 (2015).Article 

Google Scholar 
Hahn, S., Bauer, S. & Liechti, F. The natural link between Europe and Africa – 2.1 billion birds on migration. Oikos 118(4), 624–626 (2009).Article 

Google Scholar 
DeLuca, W. V. et al. Transoceanic migration by a 12 g songbird. Biol. Let. 11(4), 20141045 (2015).Article 

Google Scholar 
Deppe, J. L. et al. Fat, weather, and date affect migratory songbirds’ departure decisions, routes, and time it takes to cross the Gulf of Mexico. Proc. Natl. Acad. Sci. USA 112(46), E6331–E6338 (2015).Article 
CAS 

Google Scholar 
Sutherland, W. J. The heritability of migration. Nature 334, 471–472 (1988).Article 
ADS 

Google Scholar 
Alerstam, T. & Lindström, Å. Optimal bird migration: the relative importance of time, energy, and safety. In Bird Migration 331–351 (Springer, 1990).Chapter 

Google Scholar 
Thorup, K. Vagrancy of yellow-browed warbler Phylloscopus inornatus and Pallas’s Warbler Ph. proregulusin north-west Europe: misorientation on great circles. Ring. Migr. 19(1), 7–12 (1998).Article 

Google Scholar 
del Hoyo, J., Elliott, A. & Christie, D. Handbook of the Birds of the World (Lynx Edicions, 2008).
Google Scholar 
Rabøl, J. Reversed migration as the cause of westward vagrancy by four Phylloscopus warblers. British Birds 62, 89–92 (1969).
Google Scholar 
Thorup, K. Reverse migration as a cause of vagrancy: capsule reverse migration in autumn does not occur to the same degree in all species of migrants, but is related to migratory direction. Bird Study 51(3), 228–238 (2004).Article 

Google Scholar 
BirdLife International and Handbook of the Birds of the World, Bird species distribution maps of the world. Version 6.0. Available at http://datazone.birdlife.org/species/requestdis. (2016).R Core Team, R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. (2017).Thorup, K. et al. Orientation of vagrant birds on the Faroe Islands in the Atlantic Ocean. J. Ornithol. 153(4), 1261–1265 (2012).Article 

Google Scholar 
Able, K. The concepts and terminology of bird navigation. J. Avian. Biol. 32(2), 174–183 (2001).Article 

Google Scholar 
Griffin, D. R. & Hock, R. J. Experiments on bird navigation. Science 107(2779), 347–349 (1948).Article 
ADS 
CAS 

Google Scholar 
Kishkinev, D. Sensory mechanisms of long-distance navigation in birds: a recent advance in the context of previous studies. J. Ornithol. 156(S1), 145–161 (2015).Article 

Google Scholar 
Thorup, K. et al. Juvenile songbirds compensate for displacement to oceanic islands during autumn migration. PLoS One 6(3), e17903 (2011).Article 
ADS 
CAS 

Google Scholar 
Wingfield, J. & Sapolsky, R. Reproduction and resistance to stress: when and how. J. Neuroendocrinol. 15(8), 711–724 (2003).Article 
CAS 

Google Scholar 
Sapolsky, R. M., Romero, L. M. & Munck, A. U. How do glucocorticoids influence stress responses? Integrating permissive, suppressive, stimulatory, and preparative actions. Endocr. Rev. 21(1), 55–89 (2000).CAS 

Google Scholar 
Jenni, L. & Jenni-Eiermann, S. Fuel supply and metabolic constraints in migrating birds. J. Avian Biol. 29(4), 521–528 (1998).Article 

Google Scholar 
Casagrande, S. et al. Dietary antioxidants attenuate the endocrine stress response during long-duration flight of a migratory bird. Proc. Biol. Sci. 2020(287), 20200744 (1929).
Google Scholar 
Gwinner, E. et al. Corticosterone levels of passerine birds during migratory flight. Naturwissenschaften 79(6), 276–278 (1992).Article 
ADS 
CAS 

Google Scholar 
Jenni, L. et al. Regulation of protein breakdown and adrenocortical response to stress in birds during migratory flight. Am. J. Physiol. Regul. Integr. Comp. Physiol. 278(5), R1182–R1189 (2000).Article 
CAS 

Google Scholar 
Holberton, R. L., Boswell, T. & Hunter, M. J. Circulating prolactin and corticosterone concentrations during the development of migratory condition in the Dark-eyed Junco Junco hyemalis. Gen. Comp. Endocrinol. 155(3), 641–649 (2008).Article 
CAS 

Google Scholar 
Ramenofsky, M., J. Moffat, and G. Bentley, Corticosterone and migratory behaviour of captive white-crowned sparrows. In International proceedings of ICA-CPB, Pressures of Life: Molecules to Migration. Masai, Mara Game Reserve, p. 575–82 (2008).Eikenaar, C., Klinner, T. & Stowe, M. Corticosterone predicts nocturnal restlessness in a long-distance migrant. Horm. Behav. 66(2), 324–329 (2014).Article 
CAS 

Google Scholar 
Ramenofsky, M. Fat storage and fat metabolism in relation to migration. In Bird Migration 214–231 (Springer, 1990).Chapter 

Google Scholar 
Eikenaar, C., Fritzsch, A. & Bairlein, F. Corticosterone and migratory fueling in Northern wheatears facing different barrier crossings. Gen. Comp. Endocrinol. 186, 181–186 (2013).Article 
CAS 

Google Scholar 
Landys, M. M., Ramenofsky, M. & Wingfield, J. C. Actions of glucocorticoids at a seasonal baseline as compared to stress-related levels in the regulation of periodic life processes. Gen. Comp. Endocrinol. 148(2), 132–149 (2006).Article 
CAS 

Google Scholar 
Romero, L. M. & Reed, J. M. Collecting baseline corticosterone samples in the field: Is under 3 min good enough?. Comp. Biochem. Physiol. A Mol. Integr. Physiol. 140(1), 73–79 (2005).Article 

Google Scholar 
Wingfield, J. C., Kelley, J. P. & Angelier, F. What are extreme environmental conditions and how do organisms cope with them?. Curr. Zool. 57(3), 363–374 (2011).Article 

Google Scholar 
Wingfield, J. C. & Hunt, K. E. Arctic spring: hormone–behavior interactions in a severe environment. Comp. Biochem. Physiol. B Biochem. Mol. Biol. 132(1), 275–286 (2002).Article 

Google Scholar 
Hammer, S. et al. Færøsk Trækfugleatlas: the Faroese bird migration atlas. Fróðskapur spf. (2014).DeSante, D. Vagrants: when orientation or navigation goes wrong. Point Reyes Bird Observ. Newsl. 61, 12–16 (1983).
Google Scholar 
Wingfield, J. C. et al. A mechanistic approach to understanding range shifts in a changing world: What makes a pioneer?. Gen. Comp. Endocrinol. 222, 44–53 (2015).Article 
CAS 

Google Scholar 
Cramp, S. Handbook of the Birds of Europe, the Middle east and North Africa: Birds of the western Palearctic (University Press, 1988).
Google Scholar 
Svensson, L., Identification guide to European passerines. L. Svensson. (1992).Helbig, A. J. & Seibold, I. Molecular phylogeny of Palearctic-African Acrocephalus and Hippolais warblers (Aves: Sylviidae). Mol. Phylogenet. Evol. 11(2), 246–260 (1999).Article 
CAS 

Google Scholar 
Baker, K. Identification of Siberian and other forms of lesser whitethroat. Brit. Birds 81, 382–390 (1988).
Google Scholar 
Olsson, U. et al. New insights into the intricate taxonomy and phylogeny of the Sylvia curruca complex. Mol. Phylogenet. Evol. 67(1), 72–85 (2013).Article 

Google Scholar 
Tsvey, A., Loshchagina, J. & Naidenko, S. Migratory species show distinct patterns in corticosterone levels during spring and autumn migrations. Anim. Migr. 6(1), 4–18 (2019).Article 

Google Scholar 
Owen, J. C. Collecting, processing, and storing avian blood: a review. J. Field Ornithol. 82(4), 339–354 (2011).Article 

Google Scholar 
Pettersson, J. & Hasselquist, D. Fat deposition and migration capacity of robins Erithacus rebecula and goldcrests Regulus regulus at Ottenby Sweden. Ring Migr. 6(2), 66–76 (1985).Article 

Google Scholar 
Bairlein, F. et al. European-African Songbird Migration Network: Manual of Field Methods (Wilhelmshaven, 1995).
Google Scholar 
Wingfield, J. C., Vleck, C. M. & Moore, M. C. Seasonal changes of the adrenocortical response to stress in birds of the Sonoran Desert. J. Exp. Zool. A Comp. Exp. Biol. 264(4), 419–428 (1992).Article 
CAS 

Google Scholar 
SAS Institute, SAS for windows, version 9.4. (2014).Cook, R. D. Detection of influential observation in linear regression. Technometrics 19(1), 15–18 (1977).MathSciNet 
MATH 

Google Scholar 
Rawlings, J. O., Pantula, S. G. & Dickey, D. A. Applied Regression Analysis: A Research Tool (Springer Science & Business Media, 2001).MATH 

Google Scholar 
Grubbs, F. E. Procedures for detecting outlying observations in samples. Technometrics 11(1), 1–21 (1969).Article 

Google Scholar 
Wingfield, J. C. & Kitaysky, A. S. Endocrine responses to unpredictable environmental events: stress or anti-stress hormones?. Integr. Comp. Biol. 42(3), 600–609 (2002).Article 
CAS 

Google Scholar 
Angelier, F. & Wingfield, J. C. Importance of the glucocorticoid stress response in a changing world: theory, hypotheses and perspectives. Gen. Comp. Endocrinol. 190, 118–128 (2013).Article 
CAS 

Google Scholar 
Ralph, C. J. Disorientation and possible fate of young passerine coastal migrants. Bird-Banding 49(3), 237–247 (1978).Article 

Google Scholar 
Atwell, J. W. et al. Boldness behavior and stress physiology in a novel urban environment suggest rapid correlated evolutionary adaptation. Behav. Ecol. 23(5), 960–969 (2012).Article 

Google Scholar 
Krause, J. S. et al. Breeding on the leading edge of a northward range expansion: differences in morphology and the stress response in the arctic Gambel’s white-crowned sparrow. Oecologia 180(1), 33–44 (2016).Article 
ADS 

Google Scholar 
Falsone, K., Jenni-Eiermann, S. & Jenni, L. Corticosterone in migrating songbirds during endurance flight. Horm. Behav. 56(5), 548–556 (2009).Article 
CAS 

Google Scholar 
Long, J. A. & Holberton, R. L. Corticosterone secretion, energetic condition, and a test of the migration modulation hypothesis in the hermit thrush (Catharus Guttatus), a short-distance migrant. Auk 121(4), 1094 (2004).Article 

Google Scholar 
Romero, L. M., Ramenofsky, M. & Wingfield, J. C. Season and migration alters the corticosterone response to capture and handling in an Arctic migrant, the white-crowned sparrow (Zonotrichia leucophrys gambelii). Comp. Biochem. Physiol. C Pharmacol. Toxicol. Endocrinol. 116(2), 171–177 (1997).Article 
CAS 

Google Scholar 
Schwabl, H. Individual variation of the acute adrenocortical response to stress in the white-throated sparrow. Zool.-Anal. Complex Syst. 99(2), 113–120 (1995).CAS 

Google Scholar 
Wingfield, J. et al. Environmental stress, field endocrinology, and conservation biology. In Behavioral approaches to conservation in the wild 95–131 (Cambridge University Press, 1997).
Google Scholar 
Wingfield, J. C., Suydam, R. & Hunt, K. The adrenocortical responses to stress in snow buntings (Plectrophenax nivalis) and Lapland longspurs (Calcarius lapponicus) at Barrow, Alaska. Comp. Biochem. Physiol. C: Pharmacol. Toxicol. Endocrinol. 108(3), 299–306 (1994).
Google Scholar 
Krause, J. S. et al. Weathering the storm: Do arctic blizzards cause repeatable changes in stress physiology and body condition in breeding songbirds?. Gen. Comp. Endocrinol. 267, 183–192 (2018).Article 
CAS 

Google Scholar 
Krause, J. S. et al. The effect of extreme spring weather on body condition and stress physiology in Lapland longspurs and white-crowned sparrows breeding in the Arctic. Gen. Comp. Endocrinol. 237, 10–18 (2016).Article 
CAS 

Google Scholar 
Romero, L. M., Reed, J. M. & Wingfield, J. C. Effects of weather on corticosterone responses in wild free-living passerine birds. Gen. Comp. Endocrinol. 118(1), 113–122 (2000).Article 
CAS 

Google Scholar 
Wingfield, J. C., Moore, M. C. & Farner, D. S. Endocrine responses to inclement weather in naturally breeding populations of white-crowned sparrows (Zonotrichia leucophrys pugetensis). Auk 100(1), 56–62 (1983).Article 

Google Scholar 
Schwabl, H., Bairlein, F. & Gwinner, E. Basal and stress-induced corticosterone levels of garden warblers, Sylvia borin, during migration. J. Comp. Physiol. B. 161(6), 576–580 (1991).Article 
CAS 

Google Scholar 
Wingfield, J. C. et al. Ecological bases of hormone—behavior interactions: the “emergency life history stage”. Am. Zool. 38(1), 191–206 (1998).Article 
CAS 

Google Scholar 
Silverin, B., Arvidsson, B. & Wingfield, J. The adrenocortical responses to stress in breeding willow warblers Phylloscopus trochilus in Sweden: effects of latitude and gender. Funct. Ecol. 11(3), 376–384 (1997).Article 

Google Scholar 
Krause, J. S. et al. Effects of short-term fasting on stress physiology, body condition, and locomotor activity in wintering male white-crowned sparrows. Physiol. Behav. 177, 282–290 (2017).Article 
CAS 

Google Scholar 
Fokidis, H. B. et al. Effects of captivity and body condition on plasma corticosterone, locomotor behavior, and plasma metabolites in curve-billed thrashers. Physiol. Biochem. Zool. 84(6), 595–606 (2011).Article 
CAS 

Google Scholar 
Buttemer, W. A., Astheimer, L. B. & Wingfield, J. C. The effect of corticosterone on standard metabolic rates of small passerine birds. J. Comp. Physiol. B. 161(4), 427–431 (1991).Article 
CAS 

Google Scholar 
Snell, K. R. S. Physiology of avian migratory processes, in Center for Macroecology, Evolution and Climate. University of Copenhagen. (2018).Krause, J. S. et al. The stress response is attenuated during inclement weather in parental, but not in pre-parental, Lapland longspurs (Calcarius lapponicus) breeding in the Low Arctic. Horm. Behav. 83, 68–74 (2016).Article 
CAS 

Google Scholar 
Wingfield, J. C. et al. How birds cope physiologically and behaviourally with extreme climatic events. Philos. Trans. R. Soc. London Ser. B Biol. Sci. 372(1723), 20160140 (2017).Article 

Google Scholar 
Walker, J. J. et al. Rapid intra-adrenal feedback regulation of glucocorticoid synthesis. J. R. Soc. London Interface 12(102), 20140875 (2015).Article 
MathSciNet 
CAS 

Google Scholar 
Holberton, R. L., Parrish, J. D. & Wingfield, J. C. Modulation of the adrenocortical stress response in Neotropical migrants during autumn migration. Auk 113(3), 558–564 (1996).Article 

Google Scholar 
Cornelius, J. M. et al. Contributions of endocrinology to the migration life history of birds. Gen. Comp. Endocrinol. 190, 47–60 (2013).Article 
CAS 

Google Scholar 
Landys-Ciannelli, M. M. et al. Baseline and stress-induced plasma corticosterone during long-distance migration in the bar-tailed godwit Limosa lapponica. Physiol. Biochem. Zool. 75(1), 101–110 (2002).Article 
CAS 

Google Scholar 
Jenni-Eiermann, S. et al. Are birds stressed during long-term flights? A wind-tunnel study on circulating corticosterone in the red knot. Gen. Comp. Endocrinol. 164(2–3), 101–106 (2009).Article 
CAS 

Google Scholar  More

Deng, Y., Li, X., Shi, F. & Hu, X. Woody plant encroachment enhanced global vegetation greening and ecosystem water-use efficiency. Glob. Ecol. Biogeogr. 30, 2337–2353 (2021).Article 

Google Scholar 
Brandt, J., Haynes, M., Kuemmerle, T., Waller, D. & Radeloff, V. Regime shift on the roof of the world: alpine meadows converting to shrublands in the southern Himalayas. Biol. Conserv. 158, 116–127 (2013).Article 

Google Scholar 
García Criado, M., Myers-Smith, I. H., Bjorkman, A. D., Lehmann, C. E. R. & Stevens, N. Woody plant encroachment intensifies under climate change across tundra and savanna biomes. Glob. Ecol. Biogeogr. 29, 925–943 (2020).Article 

Google Scholar 
van Auken, O. Causes and consequences of woody plant encroachment into western North American grasslands. J. Environ. Manage. 90, 2931–2942 (2009).Article 
CAS 

Google Scholar 
Bond, W. J., Midgley, G. F. & Woodward, F. I. The importance of low atmospheric CO2 and fire in promoting the spread of grasslands and savannas. Glob. Chang. Biol. 9, 973–982 (2010).Article 

Google Scholar 
D’Odorico, P., Okin, G. S. & Bestelmeyer, B. T. A synthetic review of feedbacks and drivers of shrub encroachment in arid grasslands. Ecohydrology 5, 520–530 (2012).Article 

Google Scholar 
Kulmatiski, A. & Beard, K. H. Woody plant encroachment facilitated by increased precipitation intensity. Nat. Clim. Change 3, 833–837 (2013).Article 
CAS 

Google Scholar 
Eldridge, D. J. & Soliveres, S. Are shrubs really a sign of declining ecosystem function? Disentangling the myths and truths of woody encroachment in Australia. Aust. J. Bot. 62, 594–608 (2015).Article 

Google Scholar 
Domine, F., Barrere, M. & Morin, S. The growth of shrubs on high Arctic tundra at Bylot Island: impact on snow physical properties and permafrost thermal regime. Biogeosciences 13, 6471–6486 (2016).Article 

Google Scholar 
Maestre, F. T., Callaway, R. M., Valladares, F. & Lortie, C. J. Refining the stress-gradient hypothesis for competition and facilitation in plant communities. J. Ecol. 97, 199–205 (2009).Article 

Google Scholar 
Eldridge, D. J. et al. Impacts of shrub encroachment on ecosystem structure and functioning: towards a global synthesis. Ecol. Lett. 14, 709–722 (2011).Article 

Google Scholar 
Archer, S. R. & Predick, K. I. An ecosystem services perspective on brush management: research priorities for competing land-use objectives. J. Ecol. 102, 1394–1407 (2014).Article 

Google Scholar 
Eldridge, D. J. & Ding, J. Remove or retain: ecosystem effects of woody encroachment and removal are linked to plant structural and functional traits. N. Phytol. 229, 2637–2646 (2020).Article 

Google Scholar 
Albrecht, M. A., Becknell, R. E. & Long, Q. Habitat change in insular grasslands: woody encroachment alters the population dynamics of a rare ecotonal plant. Biol. Conserv. 196, 93–102 (2016).Article 

Google Scholar 
Stanton, R. A. et al. Shrub encroachment and vertebrate diversity: a global meta-analysis. Glob. Ecol. Biogeogr. 27, 368–379 (2017).Article 

Google Scholar 
Archer, S. R. et al. in Rangeland Systems: Processes, Management and Challenges (ed. Briske, D.) 25–84 (Springer, 2017).Anadón, J. D., Sala, O. E., Turner, B. L. & Bennett, E. M. Effect of woody-plant encroachment on livestock production in North and South America. Proc. Natl Acad. Sci. USA 111, 12948–12953 (2014).Article 

Google Scholar 
Maestre, F. T. et al. Structure and functioning of dryland ecosystems in a changing world. Annu. Rev. Eco. Evol. Syst. 47, 215–237 (2016).Article 

Google Scholar 
Teague, W. et al. Sustainable management strategies for mesquite rangeland: the Waggoner Kite project. Rangelands 19, 4–9 (1997).
Google Scholar 
Hamilton, W. T., McGinty, A., Ueckert, D. N., Hanselka, C. W. & Lee, M. R. Brush Management: Past, Present, Future (A&M Univ. Press, 2004).Bestelmeyer, B. T. et al. The grassland–shrubland regime shift in the southwestern United States: misconceptions and their implications for management. BioScience 68, 678–690 (2018).Article 

Google Scholar 
Ding, J. & Eldridge, D. J. Contrasting global effects of woody plant removal on ecosystem structure, function and composition. Perspect. Plant Ecol. Evol. Syst. 39, 125460 (2019).Article 

Google Scholar 
Huxman, T. E. et al. Ecohydrological implication of woody plant encroachment. Ecology 86, 308–319 (2005).Article 

Google Scholar 
Schmutz, E. M., Cable, D. R. & Warwick, J. J. Effect of shrub removal on the vegetation of a semidesert grass-shrub range. Rangel. Ecol. Manag. 12, 34–37 (1959).Article 

Google Scholar 
Noble, J. C. & Walker, P. Integrated shrub management in semi-arid woodlands of eastern Australia: a systems-based decision support model. Agric. Syst. 88, 332–359 (2006).Article 

Google Scholar 
Eldridge, D. J. et al. The pervasive and multifaceted influence of biocrusts on water in the world’s drylands. Glob. Chang. Biol. 26, 6003–6014 (2020).Article 

Google Scholar 
Bestelmeyer, B. T., Goolsby, D. P. & Archer, S. R. Spatial perspectives in state-and-transition models: a missing link to land management. J. Appl. Ecol. 48, 746–757 (2011).Article 

Google Scholar 
Riginos, C. & Young, T. P. Positive and negative effects of grass, cattle, and wild herbivores on Acacia saplings in an East African savanna. Oecologia 153, 985–995 (2007).Article 

Google Scholar 
Soliveres, S. et al. Plant diversity and ecosystem multifunctionality peak at intermediate levels of woody cover in global drylands. Glob. Ecol. Biogeogr. 23, 1408–1416 (2014).Article 

Google Scholar 
Soliveres, S. & Eldridge, D. J. Do changes in grazing pressure and the degree of shrub encroachment alter the effects of individual shrubs on understorey plant communities and soil function? Funct. Ecol. 28, 530–537 (2013).Article 

Google Scholar 
Maestre, F. T., Bowker, M. A., Puche, M., Hinojosa, M. B. & Escudero, A. Shrub encroachment can reverse desertification in semi-arid Mediterranean grasslands. Ecol. Lett. 12, 930–941 (2010).Article 

Google Scholar 
Abreu, R. C. R., Durigan, G., Melo, A. C. G., Pilon, N. A. L. & Hoffmann, W. A. Facilitation by isolated trees triggers woody encroachment and a biome shift at the savanna-forest transition. J. Appl. Ecol. 58, 2650–2660 (2021).Article 

Google Scholar 
North, M., Oakley, B., Fiegener, R. & Barbour, G. M. Influence of light and soil moisture on Sierran mixed-conifer understory communities. Plant Ecol. 177, 13–24 (2005).Article 

Google Scholar 
Muvengwi, J., Mbiba, M., Jimu, L., Mureva, A. & Dodzo, B. An assessment of the effectiveness of cut and ring barking as a method for control of invasive Acacia mearnsii in Nyanga National Park, Zimbabwe. For. Ecol. Manag. 427, 1–6 (2018).Article 

Google Scholar 
Abella, S. R. & Chiquoine, L. P. The good with the bad: when ecological restoration facilitates native and non-native species. Restor. Ecol. 27, 343–351 (2019).Article 

Google Scholar 
Bestelmeyer, B., Ward, J., Herrick, E. J. & Tugel, A. J. Fragmentation effects on soil aggregate stability in a patchy arid grassland. Rangel. Ecol. Manag. 59, 406–415 (2006).Article 

Google Scholar 
Okin, G. S., Gillette, D. A. & Herrick, J. E. Multi-scale controls on and consequences of aeolian processes in landscape change in arid and semi-arid environments. J. Arid. Environ. 65, 253–275 (2006).Article 

Google Scholar 
Hu, X., Li, X. Y., Zhao, Y., Gao, Z. & Zhao, S. J. Changes in soil microbial community during shrub encroachment process in the Inner Mongolia grassland of northern China. Catena 202, 105230 (2021).Article 
CAS 

Google Scholar 
D’Odorico, P. et al. Positive feedback between microclimate and shrub encroachment in the northern Chihuahuan desert. Ecosphere 1, 1–11 (2010).Article 

Google Scholar 
Eldridge, D. J., Soliveres, S., Bowker, M. A. & Val, J. Grazing dampens the positive effects of shrub encroachment on ecosystem functions in a semi‐arid woodland. J. Appl. Ecol. 50, 1028–1038 (2013).Article 

Google Scholar 
Daryanto, S., Eldridge, D. J. & Throop, H. L. Managing semi-arid woodlands for carbon storage: grazing and shrub effects on above- and belowground carbon. Agric. Ecosyst. Environ. 169, 1–11 (2013).Article 

Google Scholar 
Paynter, Q. & Flanagan, G. J. Integrating herbicide and mechanical control treatments with fire and biological control to manage an invasive wetland shrub, Mimosa pigra. J. Appl. Ecol. 41, 615–629 (2004).Article 

Google Scholar 
Throop, H. L., Reichmann, L. G., Sala, O. E. & Archer, S. R. Response of dominant grass and shrub species to water manipulation: an ecophysiological basis for shrub invasion in a Chihuahuan Desert grassland. Oecologia 169, 373–383 (2012).Article 

Google Scholar 
Brantley, S. T. & Young, D. R. Shifts in litterfall and dominant nitrogen sources after expansion of shrub thickets. Oecologia 155, 337–345 (2008).Article 

Google Scholar 
Ding, J. & Eldridge, D. J. The fertile island effect varies with aridity and plant patch type across an extensive continental gradient. Plant Soil 459, 173–183 (2020).Article 

Google Scholar 
Mihoč, M. et al. Soil under nurse plants is always better than outside: a survey on soil amelioration by a complete guild of nurse plants across a long environmental gradient. Plant Soil 408, 31–41 (2016).Article 

Google Scholar 
Ochoa-Hueso, R. et al. Soil fungal abundance and plant functional traits drive fertile island formation in global drylands. J. Ecol. 106, 242–253 (2018).Article 
CAS 

Google Scholar 
Soliveres, S., Eldridge, D. J., Hemmings, F. & Maestre, F. T. Nurse plant effects on plant species richness in drylands: the role of grazing, rainfall and species specificity. Perspect. Plant Ecol. Evol. Syst. 14, 402–410 (2012).Article 

Google Scholar 
Schlesinger, W. et al. Biological feedbacks in global desertification. Science 147, 1043–1048 (1990).Article 

Google Scholar 
Ding, J. & Eldridge, D. J. Climate and plants regulate the spatial variation in soil multifunctionality across a climatic gradient. Catena 201, 105233 (2021).Article 
CAS 

Google Scholar 
Ding, J., Travers, S. K., Delgado-Baquerizo, M. & Eldridge, D. J. Multiple trade-offs regulate the effects of woody plant removal on biodiversity and ecosystem functions in global rangelands. Glob. Chang. Biol. 26, 709–720 (2020).Article 

Google Scholar 
De Soyza, A. G., Whitford, W. G., Martinez-Meza, E. & Van Zee, J. W. Variation in creosotebush (Larrea tridentata) canopy morphology in relation to habitat, soil fertility and associated annual plant communities. Am. Nat. 137, 13–26 (1997).Article 

Google Scholar 
Breemen, N. V. Nutrient cycling strategies. Plant Soil 168, 321–326 (1995).Li, J., Gilhooly, W. P. III., Okin, G. S. & Blackwell, J. III. Abiotic processes are insufficient for fertile island development: a 10-year artificial shrub experiment in a desert grassland. Geophys. Res. Lett. 44, 2245–2253 (2017).Article 

Google Scholar 
Ward, D. et al. Large shrubs increase soil nutrients in a semi-arid savanna. Geoderma 310, 153–162 (2018).Article 
CAS 

Google Scholar 
Miwa, C. Persistence of Western Juniper Resource Islands following Canopy Removal. MSc thesis, Oregon State Univ. (2007).Zhou, L. et al. Shrub-encroachment induced alterations in input chemistry and soil microbial community affect topsoil organic carbon in an Inner Mongolian grassland. Biogeochemistry 136, 311–324 (2017).Article 
CAS 

Google Scholar 
Kwok, A. B. C. & Eldridge, D. J. The influence of shrub species and fine-scale plant density on arthropods in a semiarid shrubland. Rangel. J. 38, 381–389 (2016).Article 

Google Scholar 
Young, J. A., Evans, R. A. & Rimbey, C. Weed control and revegetation following western juniper (Juniperus occidentalis) control. Weed Sci. 33, 513–517 (1985).Article 

Google Scholar 
Wiedemann, H. T. & Kelly, P. J. Turpentine (Eremophila sturtii) control by mechanical uprooting. Rangel. J. 23, 173–181 (2001).Article 

Google Scholar 
Bowker, M. A., Belnap, J., Chaudhary, V. B. & Johnson, N. C. Revisiting classic water erosion models in drylands: the strong impact of biological soil crusts. Soil Biol. Biochem. 40, 2309–2316 (2008).Article 
CAS 

Google Scholar 
Ding, J. & Eldridge, D. J. Biotic and abiotic effects on biocrust cover vary with microsite along an extensive aridity gradient. Plant Soil 450, 429–441 (2020).Article 
CAS 

Google Scholar 
Blaum, N., Seymour, C., Rossmanith, E., Schwager, M. & Jeltsch, F. Changes in arthropod diversity along a land use driven gradient of shrub cover in savanna rangelands: identification of suitable indicators. Biodivers. Conserv. 18, 1187–1199 (2009).Article 

Google Scholar 
Eldridge, D. J., Poore, A., Ruiz-Colmenero, M., Letnic, M. & Soliveres, S. Ecosystem structure, function and composition in rangelands are negatively affected by livestock grazing. Ecol. Appl. 26, 1273–1283 (2016).Article 

Google Scholar 
Maestre, F. T. & Cortina, J. Insights into ecosystem composition and function in a sequence of degraded semiarid steppes. Restor. Ecol. 12, 494–502 (2004).Article 

Google Scholar 
Nakagawa, S. in Ecological Statistics: Contemporary Theory and Application (eds Fox, G. A. et al.) Ch. 4 (Oxford Univ. Press, 2015).Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1-km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37, 4302–4315 (2017).Article 

Google Scholar 
Zomer, R. J., Trabucco, A., Bossio, D. A. & Verchot, L. V. Climate change mitigation: a spatial analysis of global land suitability for clean development mechanism afforestation and reforestation. Agric. Ecosyst. Environ. 126, 67–80 (2008).Article 

Google Scholar 
Tavşanoğlu, Ç. & Pausas, J. G. A functional trait database for mediterranean basin plants. Sci. Data 5, 180135 (2018).Article 

Google Scholar 
The PLANTS Database (USDA, 2019); https://plants.usda.gov/Kattge, J. et al. TRY—a global database of plant traits. Glob. Chang. Biol. 17, 2905–2935 (2011).Article 

Google Scholar 
Hedges, L. V., Gurevitch, J. & Curtis, P. S. The meta-analysis of response ratios in experimental ecology. Ecology 80, 1150–1156 (1999).Article 

Google Scholar 
Mallen-Cooper, M. et al. Global synthesis reveals strong multifaceted effects of eucalypts on soils. Glob. Ecol. Biogeogr. 31, 1667–1678 (2022).Article 

Google Scholar 
Chen, X., Chen, H. Y. & Chang, S. X. Meta-analysis shows that plant mixtures increase soil phosphorus availability and plant productivity in diverse ecosystems. Nat. Ecol. Evol. 6, 1112–1121 (2022).Article 

Google Scholar 
Noble, D. W. A., Lagisz, M., O’dea, R. E. & Nakagawa, S. Nonindependence and sensitivity analyses in ecological and evolutionary meta-analyses. Mol. Ecol. 26, 2410–2425 (2017).Article 

Google Scholar 
Nakagawa, S. & Santos, E. Methodological issues and advances in biological meta-analysis. Ecol. Evol. 26, 1253–1274 (2012).Article 

Google Scholar 
Grace, J. B. Structural Equation Modeling and Natural Systems (Cambridge Univ. Press, 2006).Viechtbauer, W. Conducting meta-analyses in R with the metafor package. J. Stat. Softw. 36, 1–48 (2010).Article 

Google Scholar 
Archer, E. rfPermute v2.1.1 (R Foundation for Statistical Computing, 2010).Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, 2009).Stefan, V. & Levin, S. plotbiomes: plot Whittaker biomes with ggplot2 (R package version 0009001, 2021).Kahle, D. & Wickham, H. ggmap: spatial visualization with ggplot2. R. J. 5, 144–161 (2013).Article 

Google Scholar 
R Core Team. MOSR connections (R Foundation for Statistical Computing, 2013). More

Cantwell-Jones, A. et al. Global plant diversity as a reservoir of micronutrients for humanity. Nat. Plants https://doi.org/10.1038/s41477-022-01100-6 (2022).Swenson, N. G. Phylogenetic imputation of plant functional trait databases. Ecography 37, 105–110 (2014).Article 

Google Scholar 
Swenson, N. G. et al. Phylogeny and the prediction of tree functional diversity across novel continental settings. Glob. Ecol. Biogeogr. 26, 553–562 (2017).Article 

Google Scholar 
Molina-Venegas, R. et al. Assessing among-lineage variability in phylogenetic imputation of functional trait datasets. Ecography 41, 1740–1749 (2018).Article 

Google Scholar 
Guénard, G., Legendre, P. & Peres-Neto, P. Phylogenetic eigenvector maps: a framework to model and predict species traits. Methods Ecol. Evol. 4, 1120–1131 (2013).Article 

Google Scholar 
Guénard, G., Ohe, P. C., von der, Walker, S. C., Lek, S. & Legendre, P. Using phylogenetic information and chemical properties to predict species tolerances to pesticides. Proc. R. Soc. B 281, 20133239 (2014).Article 

Google Scholar 
Ezekiel, M. Methods of Correlation Analysis (John Wiley and Sons, 1930).Johnson, T. F., Isaac, N. J. B., Paviolo, A. & González-Suárez, M. Handling missing values in trait data. Glob. Ecol. Biogeogr. 30, 51–62 (2021).Article 

Google Scholar 
Debastiani, V. J., Bastazini, V. A. G. & Pillar, V. D. Using phylogenetic information to impute missing functional trait values in ecological databases. Ecol. Inform. 63, 101315 (2021).Article 

Google Scholar 
Goolsby, E. W., Bruggeman, J. & Ané, C. Rphylopars: fast multivariate phylogenetic comparative methods for missing data and within-species variation. Methods Ecol. Evol. 8, 22–27 (2017).Article 

Google Scholar  More

Baker, A. C., Glynn, P. W. & Riegl, B. Climate change and coral reef bleaching: an ecological assessment of long-term impacts, recovery trends and future outlook. Estuar. Coast Shelf Sci. 80, 435–471 (2008).Article 

Google Scholar 
Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science 359, 80–83 (2018).Article 
CAS 

Google Scholar 
Normille, D. El Niño’s warmth devastating reefs worldwide. Science 352, 2015–2016 (2016).
Google Scholar 
Morikawa, M. K. & Palumbi, S. R. Using naturally occurring climate resilient corals to construct bleaching-resistant nurseries. Proc Natl Acad Sci USA 116, 10586–10591 (2019).Safaie, A. et al. High frequency temperature variability reduces the risk of coral bleaching. Nat. Commun. 9, 1671 (2018).Article 

Google Scholar 
Thomas, L. et al. Mechanisms of thermal tolerance in Reef-building corals across a fine-grained environmental mosaic: lessons from Ofu. Am. Samoa. Front Mar. Sci. 4, 434 (2018).Article 

Google Scholar 
Kenkel, C. D., Meyer, E. & Matz, M. V. Gene expression under chronic heat stress in populations of the mustard hill coral (Porites astreoides) from different thermal environments. Mol. Ecol. 22, 4322–4334 (2013).Article 
CAS 

Google Scholar 
Gomulkiewicz, R. & Holt, R. D. When does evolution by natural selection prevent extinction? Evolution 49, 201–207 (1995).
Google Scholar 
Bruno, J. F., Siddon, C. E., Witman, J. D., Colin, P. L. & Toscano, M. A. El Niño related coral bleaching in Palau, western Caroline Islands. Coral Reefs 20, 127–136 (2001).Article 

Google Scholar 
Golbuu, Y. et al. Palau’s coral reefs show differential habitat recovery following the 1998-bleaching event. Coral Reefs 26, 319–332 (2007).Article 

Google Scholar 
van Woesik, R. et al. Climate-change refugia in the sheltered bays of Palau: analogs of future reefs. Ecol. Evol. 2, 2474–2484 (2012).Article 

Google Scholar 
Barkley, H. C. & Cohen, A. L. Skeletal records of community-level bleaching in Porites corals from Palau. Coral Reefs 35, 1407–1417 (2016).Article 

Google Scholar 
Gouezo, M. et al. Drivers of recovery and reassembly of coral reef communities. Proc. R. Soc. B Biol. Sci. 286, 20182908 (2019).Shamberger, K. E. F. et al. Diverse coral communities in naturally acidified waters of a Western Pacific reef. Geophys. Res. Lett. 41, 499–504 (2014).Article 

Google Scholar 
Barkley, H. C. et al. Changes in coral reef communities across a natural gradient in seawater pH. Sci. Adv. 1, e1500328 (2015).Article 

Google Scholar 
Fabricius, K. E., Mieog, J. C., Colin, P. L., Idip, D. & van Oppen, M. J. H. Identity and diversity of coral endosymbionts (zooxanthellae) from three Palauan reefs with contrasting bleaching, temperature and shading histories. Mol. Ecol. 13, 2445–2458 (2004).Article 
CAS 

Google Scholar 
Anthony, K. R. N., Kline, D. I., Diaz-Pulido, G., Dove, S. & Hoegh-Guldberg, O. Ocean acidification causes bleaching and productivity loss in coral reef builders. Proc. Natl Acad. Sci. USA 105, 17442–17446 (2008).Article 
CAS 

Google Scholar 
Gibbin, E. M., Putnam, H. M., Gates, R. D., Nitschke, M. R. & Davy, S. K. Species-specific differences in thermal tolerance may define susceptibility to intracellular acidosis in reef corals. Mar. Biol. 162, 717–723 (2015).Article 
CAS 

Google Scholar 
Boulay, J. N., Hellberg, M. E., Cortés, J. & Baums, I. B. Unrecognized coral species diversity masks differences in functional ecology. Proc. R. Soc. B Biol. Sci. 281, 20131580 (2013).Baums, I. B., Boulay, J. N., Polato, N. R. & Hellberg, M. E. No gene flow across the Eastern Pacific Barrier in the reef-building coral Porites lobata. Mol. Ecol. 21, 5418–5433 (2012).Article 

Google Scholar 
Forsman, Z. H., Wellington, G. M., Fox, G. E. & Toonen, R. J. Clues to unraveling the coral species problem: Distinguishing species from geographic variation in Porites across the Pacific with molecular markers and microskeletal traits. PeerJ 3, e751 (2015).Article 

Google Scholar 
Levas, S. J., Grottoli, A. G., Hughes, A., Osburn, C. L. & Matsui, Y. Physiological and biogeochemical traits of bleaching and recovery in the mounding species of coral Porites lobata: implications for resilience in mounding corals. PLoS ONE 8, e63267 (2013).Article 
CAS 

Google Scholar 
Linsley, B. K. et al. Coral carbon isotope sensitivity to growth rate and water depth with Paleo-sea level implications. Nat. Commun. 10, 1–9 (2019).
Google Scholar 
Peyrot-Clausade, M., Hutchings, P. & Richard, G. Temporal variations of macroborers in massive Porites lobata on Moorea, French Polynesia. Coral Reefs 11, 161–166 (1992).Article 

Google Scholar 
Nanami, A. & Nishihira, M. Microhabitat association and temporal stability in reef fish assemblages on massive Porites microatolls. Ichthyol. Res. 51, 165–171 (2004).Article 

Google Scholar 
Cantin, N. E. & Lough, J. M. Surviving coral bleaching events: porites growth anomalies on the Great Barrier Reef. PLoS ONE 9, e88720 (2014).Article 

Google Scholar 
Carilli, J. E., Norris, R. D., Black, B., Walsh, S. M. & Mcfield, M. Century-scale records of coral growth rates indicate that local stressors reduce coral thermal tolerance threshold. Glob. Chang Biol. 16, 1247–1257 (2010).Article 

Google Scholar 
Cantin, N. E., Cohen, A. L., Karnauskas, K. B., Tarrant, A. M. & McCorkle, D. C. Ocean warming slows coral growth in the central Red Sea. Science 329, 322–325 (2010).Article 
CAS 

Google Scholar 
Lough, J. M. & Cooper, T. F. New insights from coral growth band studies in an era of rapid environmental change. Earth Sci. Rev. 108, 170–184 (2011).Article 
CAS 

Google Scholar 
Mollica, N. R. N. et al. Skeletal records of bleaching reveal different thermal thresholds of Pacific coral reef assemblages. Coral Reefs 38, 743–757 (2019).Article 

Google Scholar 
Barkley, H. C. et al. Repeat bleaching of a central Pacific coral reef over the past six decades (1960–2016). Commun. Biol. 1, 177 (2018).DeCarlo, T. M. & Cohen, A. L. Dissepiments, density bands and signatures of thermal stress in Porites skeletons. Coral Reefs 36, 749–761 (2017).Article 

Google Scholar 
DeCarlo, T. M. et al. Acclimatization of massive reef-building corals to consecutive heatwaves. Proc. R. Soc. B 286, 20190235 (2019).DeCarlo, T. M. The past century of coral bleaching in the Saudi Arabian central Red Sea. PeerJ 8, e10200 (2020).Article 

Google Scholar 
Silverstein, R. N., Cunning, R. & Baker, A. C. Change in algal symbiont communities after bleaching, not prior heat exposure, increases heat tolerance of reef corals. Glob. Chang Biol. 21, 236–249 (2015).Article 

Google Scholar 
Fabricius, K. E. Effects of irradiance, flow, and colony pigmentation on the temperature microenvironment around corals: Implications for coral bleaching? Limnol. Oceanogr. 51, 30–37 (2006).Article 

Google Scholar 
Edmunds, P. J., Putnam, H. M. & Gates, R. D. Photophysiological consequences of vertical stratification of Symbiodinium in tissue of the coral Porites lutea. Biol. Bull. 223, 226–235 (2012).Article 
CAS 

Google Scholar 
Smith, L. W., Wirshing, H., Baker, A. C. & Birkeland, C. Environmental versus genetic influences on growth rates of the corals Pocillopora eydouxi and Porites lobata. Pac. Sci. 62, 57–69 (2008).Article 

Google Scholar 
Kenkel, C. D. & Bay, L. K. Exploring mechanisms that affect coral cooperation: symbiont transmission mode, cell density and community composition. PeerJ 2018, e6047 (2018).Article 

Google Scholar 
Sunde, J., Yıldırım, Y., Tibblin, P. & Forsman, A. Comparing the performance of microsatellites and RADseq in population genetic studies: analysis of data for Pike (Esox lucius) and a synthesis of previous studies. Front. Genet. 11, 218 (2020).Article 

Google Scholar 
Barkley, H. C., Cohen, A. L., McCorkle, D. C. & Golbuu, Y. Mechanisms and thresholds for pH tolerance in Palau corals. J. Exp. Mar. Biol. Ecol. 489, 7–14 (2017).Article 
CAS 

Google Scholar 
Mollica, N. R. et al. Ocean acidification affects coral growth by reducing skeletal density. Proc. Natl Acad. Sci. USA 115, 1754–1759 (2018).Article 
CAS 

Google Scholar 
DeCarlo, T. M. et al. Coral macrobioerosion is accelerated by ocean acidification and nutrients. Geology 43, 7–10 (2014).Article 

Google Scholar 
Manzello, D. P. et al. Role of host genetics and heat-tolerant algal symbionts in sustaining populations of the endangered coral Orbicella faveolata in the Florida Keys with ocean warming. Glob. Chang Biol. 25, 1016–1031 (2019).Article 

Google Scholar 
Rippe, J. P., Dixon, G., Fuller, Z. L., Liao, Y. & Matz, M. Environmental specialization and cryptic genetic divergence in two massive coral species from the Florida Keys Reef Tract. Mol. Ecol. 1–17 https://doi.org/10.1111/mec.15931 (2021).Schoepf, V. et al. Thermally variable, macrotidal Reef habitats promote rapid recovery from mass coral bleaching. Front. Mar. Sci. 7, 245 (2020).Article 

Google Scholar 
Dixon, G. B. et al. Genomic determinants of coral heat tolerance across latitudes. Science 348, 1460–1462 (2015).Article 
CAS 

Google Scholar 
Baums, I. B. et al. Considerations for maximizing the adaptive potential of restored coral populations in the western Atlantic. Ecol. Appl. 29, 1–23 (2019).Article 

Google Scholar 
Gosselin, L. A. & Qian, P.-Y. Juvenile mortality in benthic marine invertebrates. Mar. Ecol. Prog. Ser. 146, 265–282 (1997).Article 

Google Scholar 
Gouezo, M. et al. Modelled larval supply predicts coral population recovery potential following disturbance. Mar. Ecol. Prog. Ser. 661, 127–145 (2021).Golbuu, Y., Gouezo, M., Kurihara, H., Rehm, L. & Wolanski, E. Long-term isolation and local adaptation in Palau’s Nikko Bay help corals thrive in acidic waters. Coral Reefs 35, 909–918 (2016).Article 

Google Scholar 
Golbuu, Y. et al. Predicting coral recruitment in Palau’s complex reef archipelago. PLoS ONE 7, e50998 (2012).Article 
CAS 

Google Scholar 
Barshis, D. J., Birkeland, C., Toonen, R. J., Gates, R. D. & Stillman, J. H. High-frequency temperature variability mirrors fixed differences in thermal limits of the massive coral Porites lobata (Dana, 1846). J. Exp. Biol. jeb.188581 https://doi.org/10.1242/jeb.188581 (2018).Shamberger, K. E. F., Lentz, S. J. & Cohen, A. L. Low and variable ecosystem calcification in a coral reef lagoon under natural acidification. Limnol. Oceanogr. https://doi.org/10.1002/lno.10662 (2017).Cacciapaglia, C. & van Woesik, R. Climate-change refugia: shading reef corals by turbidity. Glob. Chang Biol. 22, 1145–1154 (2016).Article 

Google Scholar 
Anthony, K. R. Enhanced energy status of corals on coastal, high-turbidity reefs. Mar. Ecol. Prog. Ser. 319, 111–116 (2006).Article 

Google Scholar 
Houlbrèque, F. & Ferrier-Pagès, C. Heterotrophy in tropical scleractinian corals. Biol. Rev. Camb. Philos. Soc. 84, 1–17 (2009).Article 

Google Scholar 
Aichelman, H. E. et al. Heterotrophy mitigates the response of the temperate coral Oculina arbuscula to temperature stress. Ecol. Evol. 6, 6758–6769 (2016).Article 

Google Scholar 
Gómez‐Corrales, M. & Prada, C. Cryptic lineages respond differently to coral bleaching. Mol. Ecol. 0, 1–9 (2020).
Google Scholar 
Fifer, J. E., Yasuda, N., Yamakita, T., Bove, C. B. & Davies, S. W. Genetic divergence and range expansion in a western North Pacific coral. Sci. Total Environ. 152423 https://doi.org/10.1016/J.SCITOTENV.2021.152423 (2021).Euclide, P. T. et al. Attack of the PCR clones: rates of clonality have little effect on RAD-seq genotype calls. Mol. Ecol. Resour. 20, 66–78 (2020).Article 
CAS 

Google Scholar 
Noonan, S. H. C., DiPerna, S., Hoogenboom, M. O. & Fabricius, K. E. Effects of variable daily light integrals and elevated CO2 on the adult and juvenile performance of two Acropora corals. Mar. Biol. 169, 1–15 (2022).Article 

Google Scholar 
Martins, C. P. P. et al. Growth response of reef-building corals to ocean acidification is mediated by interplay of taxon-specific physiological parameters. Front. Mar. Sci. 0, 879 (2022).
Google Scholar 
Bairos-Novak, K. R., Hoogenboom, M. O., van Oppen, M. J. H. & Connolly, S. R. Coral adaptation to climate change: meta-analysis reveals high heritability across multiple traits. Glob. Chang. Biol. 27, 5694–5710 (2021).Article 
CAS 

Google Scholar 
Kenkel, C. D., Setta, S. P. & Matz, M. V. Heritable differences in fitness-related traits among populations of the mustard hill coral, Porites astreoides. Heredity 115, 509–516 (2015).Article 
CAS 

Google Scholar 
Dziedzic, K. E., Elder, H., Tavalire, H. & Meyer, E. Heritable variation in bleaching responses and its functional genomic basis in reef-building corals (Orbicella faveolata). Mol. Ecol. 28, 2238–2253 (2019).Article 

Google Scholar 
Quigley, K. M., Bay, L. K. & Oppen, M. J. H. Genome‐wide SNP analysis reveals an increase in adaptive genetic variation through selective breeding of coral. Mol. Ecol. 2176–2188 https://doi.org/10.1111/mec.15482 (2020).Veron, J. E. N. Corals of the World (Australian Institute of Marine Science, 2000).Polato, N. R., Concepcion, G. T., Toonen, R. J. & Baums, I. B. Isolation by distance across the Hawaiian Archipelago in the reef-building coral Porites lobata. Mol. Ecol. 19, 4661–4677 (2010).Article 
CAS 

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).Article 

Google Scholar 
Puritz, J. B., Hollenbeck, C. M. & Gold, J. R. dDocent: a RADseq, variant-calling pipeline designed for population genomics of non-model organisms. PeerJ 2, e431 (2014).Article 

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).Article 
CAS 

Google Scholar 
Li, H. Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. ArXiv (2013).Garrison, E. & Marth, G. Haplotype-based variant detection from short-read sequencing. ArXiv (2012).Danecek, P. et al. The variant call format and VCFtools. Bioinformatics 27, 2156–2158 (2011).Article 
CAS 

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

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

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

Google Scholar 
Puechmaille, S. J. The program STRUCTURE does not reliably recover the correct population structure when sampling is uneven: subsampling and new estimators alleviate the problem. Mol. Ecol. Resour. 16, 608–627 (2016).Article 

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).Article 
CAS 

Google Scholar 
Goudet, J. Hierfstat, a package for R to compute and test hierarchical F-statistics. Molecular Ecology Notes. 5, 184–186 (2005).Zeileis, A. & Grothendieck, G. zoo: S3 infrastructure for regular and irregular time series. J. Stat. Softw. 14, 1–27 (2005).Ryan, J. A. & Ulrich, J. M. xts: eXtensible Time Series. Package at https://cran.r-project.org/package=xts (2018).LaJeunesse, T. C. Diversity and community structure of symbiotic dinoflagellates from Caribbean coral reefs. Mar. Biol. 141, 387–400 (2002).Article 

Google Scholar 
LaJeunesse, T. C. & Trench, R. K. Biogeography of two species of Symbiodinium (Freudenthal) inhabiting the intertidal sea anemone Anthopleura elegantissima (Brandt). Biol. Bull. 199, 126–134 (2000).Article 
CAS 

Google Scholar  More

Direct injection method for ethylene and acetylene δ13C analyses by GC-C-IRMSFollowing the direct injection approach of classical ISARA12 with a few modifications, ARA samples with high ethylene yield ( > 500 ppmv) in 10% v/v acetylene were manually injected into a Thermo Scientific Trace GC Ultra-Isolink with an Agilent HP-PLOT/Q  capillary GC column (30 m, i.d. = 0.32 mm, f.t. = 20 μm) and a combustion reactor connected to a Thermo Scientific Delta V Plus isotope ratio mass spectrometer (GC-C-IRMS; Fig. 1a). Modifications include the replacement of silver ferrules in the GC oven with Valcon polymide (graphite reinforced polymer) ferrules to limit memory effects from acetylene. The combustion reactor was oxidized with pure oxygen for 1 h before each run and brief (15 min) seed oxidations were performed between measurement batches (i.e., required every ~ 6–8 ethylene injections, ~ 4–6 acetylene injections) to regenerate reactor oxidation capacity and minimize drift of δ13C values. See Supplementary Table S1a online for additional instrument settings.Ethylene Pre-Concentration (EPCon) methodARA samples with  More

Thermal requirements to develop PDWe examined the response of a wide spectrum of European grapevine varieties to XfPD infection in three independent experiments conducted in 2018, 2019, and 2020. Overall, 86.1% (n = 764) of 886 inoculated plants, comprising 36 varieties and 57 unique scion/rootstock combinations, developed PD symptoms 16 weeks after inoculation. European V. vinifera varieties exhibited significant differences in their susceptibility to XfPD (Supplementary Table S1). All varieties, however, showed PD symptoms to some extent, confirming previous field observations of general susceptibility to XfPD9,12,37. We also found significant differences in virulence (χ2 = 68.73, df = 1, P = 2.2 × 10−16) between two XfPD strains isolated from grapevines in Majorca across grapevine varieties (Supplementary Fig. S1). Full details on the results of the inoculation tests are available in “Methods”, Supplementary Note 1, Supplementary Table S1 and Supplementary Data 1.Growing degree days (GDD) have traditionally been used to describe and predict phenological events of plants and insect pests, but rarely in plant diseases58. We took advantage of data collated in the inoculation trials together with temperature to relate symptom development to the accumulated heat units at weeks eight, 10, 12, 14, and 16 after inoculation (Supplementary Data 1). Rather than counting GDDs linearly above a threshold temperature, we consider Xf ’s specific growth rate in vitro within its cardinal temperatures. The empirical growth rates come from the seminal work by Feil & Purcell38 shown in the inset of Fig. 1a. This Arrhenius plot was transformed, as explained in Supplementary Note 2A, to obtain a a piece-wise function f(T) Eq. (1). Our model and risk maps are based on f(T) (red line in Fig. 1a) because it provides the best fit to the experimental data when compared with the commonly used Beta function (blue line) for representing the thermal response in biological processes59,60. This Modified Growing Degree Day (MGDD) profile Eq. (1) enables to measure the thermal integral from hourly average temperatures, improving the prediction scale of the biological process61. MGDD also provides an excellent metric to link XfPD growth in culture with PD development as, once the pathogen is injected into the healthy vine, symptoms progression mainly depends upon the bacterial load (i.e., multiplication) and the movement through the xylem vessel network, which are fundamentally temperature-dependent processes38,62. Moreover, MGDD can be mathematically related to the exponential or logistic growth of the pathogen within the plant (Supplementary Note 2B).Fig. 1: Climatic and transmission layers composing the epidemiological model.a MGDD profile fitted to the in vitro data of Xf growth rate in Feil & Purcell 200138. The original Arrhenius plot in Kelvin degrees (inset) was converted to Celsius, as explained in (Supplementary Note 2A), to obtain the fit shown in the main plot red line; the blue line represents the fit with a Beta function. b Correlation between CDD and the average ({T}_{min }) of the coldest month between 1981 and 2019. Plotted black dots (worldwide) and yellow dots (main wine-producing zones) depict climatic data from 6,487,200 cells at 0.1∘ × 0.1∘ resolution, spread globally and retrieved from ERA5-Land dataset. The red solid line depicts the fitted exponential function for worldwide data and the blue solid line for main vineyard zones. c Nonlinear relationship between MGDD (red line) and CDD (blue line) and the likelihood of developing chronic infections. Black dots depict the cumulative proportion of grapevine plants in the population of 36 inoculated varieties showing five or more symptomatic leaves at each of the 15 MGDD levels (see Supplementary Information). Vertical bars are the 95% CI. d Combined ranges of MGDD and CDD on the likelihood of developing chronic infection. e Transmission layer in the dynamic equation (1) of the SIR compartmental model. f Relationship between the exponential growth of the number of infected plants with the risk index and their ranks.Full size imageInterannual infection survival in grapevines plays a relevant role when modelling PD epidemiology. In our model, we assumed a threshold of five or more symptomatic leaves for these chronic infections based on the relationship between the timing and severity of the infection during the growing season and the likelihood of winter recovery38,39,42. This five-leaf cut-off was grounded on: (i) the bimodal distribution in the frequency of the number of symptomatic leaves among the population of inoculated grapevines (Supplementary Fig. S1), whereby vines that generally show less than five symptomatic leaves at 12 weeks after inoculation remain so in the following weeks, while those that pass that threshold continue to produce symptomatic leaves, and (ii) the observed correlation between the acropetal and basipetal movement of Xf along the cane (Supplementary Fig. S1). The likelihood of developing chronic infections as a function of accumulated MGDD among the population of grapevine varieties was modelled using survival analysis with data fitted to a logistic distribution ({{{{{{{mathcal{F}}}}}}}}({{{{{rm{MGDD}}}}}})). A minimum window of MGDD = 528 was needed to develop chronic infections (var. Tempranillo), about 975 for a median estimate, while a cumulative MGDD  > 1159 indicate over 90% probability within a growing season (red curve in Fig. 1c and “Methods”).Next, we intended to model the probability of disease recovery by exposure to cold temperatures. Previous works had specifically modelled cold curing on Pinot Noir and Cabernet Sauvignon varieties in California as the effect of temperature and duration39 by assuming a progressive elimination of the bacterial load with cold temperatures42. In the absence of appropriate empirical data to formulate a general average pattern of winter curing among grapevine varieties, we combined the approach of Lieth et al.39 and the empirical observations of Purcell on the distribution of PD in the US related to the average minimum temperature of the coldest month, Tmin, isolines41. To consider the accumulation of cold units in an analogy of the MGDD, we searched for a general correlation between Tmin and the cold degree days (CDDs) with base temperature = 6 ∘C (see “Methods”). We found an exponential relation, ({{{{{rm{CDD}}}}}} sim 230exp (-0.26cdot {T}_{min })), where specifically, CDD ≳ 306 correspond to ({T}_{min } < -1.{1},^{circ }{{{{{rm{C}}}}}}) (Fig. 1b). To transform this exponential relationship to a probabilistic function analogous to ({{{{{{{mathcal{F}}}}}}}}({{{{{rm{MGDD}}}}}})), hereafter denoted ({{{{{{{mathcal{G}}}}}}}}({{{{{rm{CDD}}}}}})), ranging between 0 and 1, we considered the sigmoidal family of functions (f(x)=frac{A}{B+{x}^{C}}) with A = 9 × 106, B = A and C = 3 (Fig. 1c), fulfilling the limit ({{{{{{{mathcal{G}}}}}}}}({{{{{rm{CDD}}}}}}=0)=1), i.e., no winter curing when no cold accumulated, and a conservative 75% of the infected plants recovered at ({T}_{min }=-1.{1},^{circ }{{{{{rm{C}}}}}}) instead of 100% to reflect uncertainties on the effect of winter curing.MGDD/CDD distribution mapsMGDD were used to compute annual risk maps of developing PD during summer for the period 1981–2019 (see “Methods”). The resulting averaged map identifies all known areas with a recent history of severe PD in the US corresponding to ({{{{{{{mathcal{F}}}}}}}}({{{{{rm{MGDD}}}}}}) , > , 90 %) (i.e., high-risk), such as the Gulf Coast states (Texas, Alabama, Mississippi, Louisiana, Florida), Georgia and Southern California sites (e.g., Temecula Valley) (Fig. 2a), while captures areas with a steep gradation of disease endemicity in the north coast of California (({{{{{{{mathcal{F}}}}}}}}({{{{{rm{MGDD}}}}}} , > , 50 % )). Overall, more than 95% of confirmed PD sites (n = 155) in the US (Supplementary Data 2) fall in grid cells with ({{{{{{{mathcal{F}}}}}}}}({{{{{rm{MGDD}}}}}}) , > , 50 %).Fig. 2: Average thermal-dependent maps for Pierce’s disease (PD) development and recovery in North America and Europe.PD development during the growing season based on average ({{{{{{{mathcal{F}}}}}}}}({{{{{rm{MGDD}}}}}})) estimations between 1981 and 2019 in North America (a) and Europe (b) derived from the results of the inoculation experiments on 36 grapevine varieties. Large differences in the areal extension with favourable MGDDs can be observed between the US and Europe. The winter curing effect is reflected in the distribution of the average ({{{{{{{mathcal{G}}}}}}}}({{{{{rm{CDD}}}}}})) for the 1981–2019 period in the United States (c) and Europe (d). A snapshot of the temperature-driven probability of chronic infection averaged for the 1981–2019 period is obtained from the joint effect of MGDD and CDD in North America (e) and Europe (f). Warmer colours indicate more favourable conditions for chronic PD and the dashed line highlights the threshold of chronic infection probability being 0.5.Full size imageThe average MGDD-projected map for Europe during 1981–2019 spots a high risk for the coast, islands and major river valleys of the Mediterranean Basin, southern Spain, the Atlantic coast from Gibraltar to Oporto, and continental areas of central and southeast Europe (Fig. 2b). Of these, however, only some Mediterranean islands, such as Cyprus and Crete, show ({{{{{{{mathcal{F}}}}}}}}({{{{{rm{MGDD}}}}}}) , > , 99 %) comparable to areas with high disease incidence in the Gulf Coast states of the US and California. Almost all the Atlantic coast from Oporto (Portugal) to Denmark are below suitable MGDD, with an important exception in the Garonne river basin in France (Bordeaux Area) with low to moderate MGDD (Fig. 2b).Figure 2a shows how the area with high-risk MGDD values extends further north of the current known PD distribution in the southeastern US, suggesting that winter temperatures limit the expansion of PD northwards9. A comparison between MGDD and CDD maps (Fig. 2a vs. Fig. 2c, Fig. 2e) further supports the idea that winter curing is restricting PD northward migration from the southeastern US. However, consistent with growing concern among Midwest states winegrowers on PD northward migration led by climate change63, we found a mean increase of 0.12% y−1 in the areal extent with CDD  0.075) in 22.3% of the vineyards in Europe. However, no vineyard is in epidemic-risk zones with a high-risk index and only 2.9% of the vineyard surface is at moderate risk (Supplementary Table S8). The areas with the highest risk index (r(t) between 0.70 and 0.88) are mainly located in the Mediterranean islands of Crete, Cyprus and the Balearic Islands or at pronounced peninsulas like Apulia (Italy) and Peloponnese (Greece) in the continent (Fig. 6a and Supplementary Table S8). Most vineyards are in non-risk zones (42.1%), whereas 35.6% are located in transition zones with presently non-risk but where XfPD could become established in the next decades causing some sporadic outbreaks. In Supplementary Data 4 and Supplementary Table S8, we provide full details of the total vineyard areas currently at risk for each country and region.Fig. 6: Intersection between Corine-land-cover vineyard distribution map and PD-risk maps for 2020 and 2050.Data were obtained from Corine-land-cover (2018) and the layer of climatic suitability forP. spumarius in Europe from35. The surface of the vineyard contour has been enlarged to improve the visualisation of the risk zones and disease-incidence growth-rate ranks. a PD risk map for 2019 and its projection for 2050 (b). Blue colours represent non-risk zones and transient risk zones for chronic PD (R0  More

Aguillon SM, Fitzpatrick JW, Bowman R, Schoech SJ, Clark AG, Coop G, Chen N (2017) Deconstructing isolation-by-distance: The genomic consequences of limited dispersal. PLoS Genet 13:e1006911Article 

Google Scholar 
Browning BL, Browning SR (2016) Genotype imputation with millions of reference samples. Am J Hum Genet 98:116–126Article 

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

Google Scholar 
Carvajal TM, Ogiski K, Yaegeshi S, Hernandez LFT, Viacrusis KM, Ho HT, Amalin DM, Watanable K (2020) Fine-scale population genetic structure of dengue mosquito vector, Aedes aegypti, in metropolitan manila, Philippines. PLOS Neglected Tropical Dis 14:e0008279Article 

Google Scholar 
Christophers SR. 1960. Aedes aegypti: the yellow fever mosquito. CUP Archive.Combs M, Puckett EE, Richardson J, Mims D, Munshi-South J (2018) Spatial population genomics of the brown rat (Rattus norvegicus) in New York City. Mol Ecol 27:83–98Article 

Google Scholar 
Conomos MP, Reiner AP, Weir BS, Thornton TA (2016) Model-free estimation of recent genetic relatedness. Am J Hum Genet 98:127–148Article 

Google Scholar 
Danecek P, Bonfield JK, Liddle J, Marshall J, Ohan V, Pollard MO et al. (2021) Twelve years of SAMtools and BCFtools. GigaScience 10:1–4Article 

Google Scholar 
Doak DF, Marino PC, Kareiva PM (1992) Spatial scale mediates the influence of habitat fragmentation on dispersal success: Implications for conservation. Theor Popul Biol 41:315–336Article 

Google Scholar 
Dobrotworsky NV (1965) The mosquitoes of Victoria (Diptera, Culicidae). Melbourne University Press, London
Google Scholar 
Doggett SL, Russell RC (1997) Aedes notoscriptus can transmit inland and coastal isolates of Ross River and Barmah Forest viruses from New South Wales. Arbovirus Asutrlian Reg 7:79–81
Google Scholar 
Dray S, Dufour A-B (2007) The ade4 package: implementing the duality diagram for ecologists. J Stat Softw 22:1–20Article 

Google Scholar 
Edland T (1983) Attacks by the winter moth group (Operophtera brumata, Agriopis aurantiaria, Erannis defoliaria) in orchards. A system for forecasting the expected attack degree. Gartneryrket 73:208–212
Google Scholar 
Endersby NM, White WL, Chan J, Hust T, Rašić G, Miller A, Hoffmann AA (2013) Evidence of cryptic genetic lineages within Aedes notoscriptus (Skuse). Infect, Genet Evolution 18:191–201Article 

Google Scholar 
Feria-Arroyo T, Aguilar C, Vazquez CQ, Santos-Luna R, Roman-Perez S, Oraby T et al. (2020) A tale of two cities: Aedes Mosquito surveillance across the Texas-Mexico Border. Subtropical Agriculture Environ 71:12
Google Scholar 
Fountain T, Husby A, Nonaka E, DiLeo MF, Korhonen JH, Rastas P et al. (2018) Inferring dispersal across a fragmented landscape using reconstructed families in the Glanville fritillary butterfly. Evolut Appl 11:287–297Article 

Google Scholar 
Goldberg EE, Lande R (2015) Species’ borders and dispersal barriers. Am Naturalist 170:297–304Article 

Google Scholar 
Goslee SC, Urban DL (2007) The ecodist package for dissimilarity-based analysis of ecological data. J Stat Softw 22:1–19Article 

Google Scholar 
Guan D, McCarthy SA, Wood J, Howe K, Wang Y, Durbin R (2020) Identifying and removing haplotypic duplication in primary genome assemblies. Bioinformatics 36(9):2896–2898Article 

Google Scholar 
Guerra CA, Reiner RC, Perkins TA, Lindsay SW, Midega JT, Brady OJ et al. (2014) A global assembly of adult female mosquito mark-release-recapture data to inform the control of mosquito-born pathogens. Parasites Vectors 7(1):1–15Article 

Google Scholar 
Hardy OJ, Vekemans X (2002) spagedi: a versatile computer program to analyse spatial genetic structure at the individual or population levels. Mol Ecol Notes 2:618–620Article 

Google Scholar 
Harrington LC, Edman JD, Scott TW (2001) Why do female Aedes aegypti (Diptera: Culicidae) feed preferentially and frequently on human blood? J Med Entomol 38:411–422Article 

Google Scholar 
Harrington LC, Scott TW, Lerdthusnee K, Coleman RC, Costero A, Clark GG et al. (2005) Dispersal of the dengue vector Aedes aegypti within and between rural communities. Am J tropical Med Hyg 72(2):209–220Article 

Google Scholar 
Harris AF, McKemey AR, Nimmo D, Curtis Z, Black I, Morgan SA et al. (2012) Successful suppression of a field mosquito population by sustained release of engineered male mosquitoes. Nat Biotechnol 30:828–830Article 

Google Scholar 
Hoffmann AA, Montgomery BL, Popovici J, Iturbe-Ormaetxe I, Johnson PH, Muzzi F et al. (2011) Successful establishment of Wolbachia in Aedes populations to suppress dengue transmission. Nature 2011 476 7361 476:454–457
Google Scholar 
Honório AN, da Costa Silva W, José Leite P, Monteiro Gonçalves J, Philip Lounibos L, Lourenço-de-Oliveira R (2003) Dispersal of Aedes aegypti and Aedes albopictus (Diptera: Culicidae) in an Urban Endemic dengue Area in the State of Rio de Janeiro, Brazil. Memórias do Inst Oswaldo Cruz, Rio de Jan 98:191–198Article 

Google Scholar 
Jasper M, Schmidt TL, Ahmad NW, Sinkins SP, Hoffmann AA (2019) A genomic approach to inferring kinship reveals limited intergenerational dispersal in the yellow fever mosquito. Mol Ecol Resour 22.3:1200–1212Jasper M, Schmidt TL, Hoffmann AA (2022) Estimating dispersal using close kin dyads: The KINDISPERSE R package. Mol Ecol Resour 22:1200–1212Article 

Google Scholar 
Jeger MJ (1999) Improved understanding of dispersal in crop pest and disease management: current status and future directions. Agric For Meteorol 97:331–349Article 

Google Scholar 
Johnson MTJ, Munshi-South J (2017) Evolution of life in urban environments. Science 358Juarez JG, Chaves LF, Garcia-Luna SM, Martin E, Badillo-Vargas I, Medeiros MCI, Hamer GL (2021) Variable coverage in an Autocidal Gravid Ovitrap intervention impacts efficacy of Aedes aegypti control. J Appl Ecol 58:2075–2086Article 

Google Scholar 
Kay BH, Watson TM, Ryan PA (2008) Definition of productive Aedes notoscriptus (Diptera: Culicidae) habitats in western Brisbane, and a strategy for their control. Aust J Entomol 47:142–148Article 

Google Scholar 
Kolmogorov M, Yuan J, Lin Y, Pevzner P (2019) Assembly of long error-prone reads using repeat graphs. Nat Biotechnol 37.5:540–546Article 

Google Scholar 
Kotsakiozi P, Evans BR, Gloria-Soria A, Kamgang B, Mayanja M, Lutwama J et al. (2018) Population structure of a vector of human diseases: Aedes aegypti in its ancestral range, Africa. Ecol Evolution 8:7835–7848Article 

Google Scholar 
Krueger F (2021) Trimgalore. GitHub repository, https://github.com/FelixKrueger/TrilGalore.Langmead B, Salzberg SL (2012) Fast gapped-read alignment with Bowtie 2. Nat methods 9:357Article 

Google Scholar 
Lee DJ, Hicks MM, Griffiths M, Debenham ML, Bryan JH, Russel RC et al. (1987) The Culicidae of the Australasian Region: Genus Anopheles (Anopheles, Cellia). Australian Government Publishing Service: 315.Liew CCFC, Curtis CF (2004) Horizontal and vertical dispersal of dengue vector mosquitoes, Aedes aegypti and Aedes albopictus, in Singapore. Med Vetenary Enetomology 18.4:351–360Article 

Google Scholar 
Malinsky M, Trucchi E, Lawson DJ, Falush D (2018) RADpainter and fineRADstructure: Population Inference from RADseq Data. Mol Biol Evolution 35:1284–1290Article 

Google Scholar 
McCarroll L, Paton MG, Karunaratne SHPP, Jayasuryia HTR, Kalpage KSP, Hemingway J (2000) Insecticides and mosquito-borne disease. Nature 407:961–962. 6807 407Article 

Google Scholar 
Metzger ME, Wekesa JW, Kluh S, Fujioka KK, Saviskas R, Arugay A et al. (2021) Detection and establishment of Aedes notoscriptus (Diptera: Culicidae) mosquitoes in Southern California, United States. J Med Entomol 59.1:67–77
Google Scholar 
Muir LE, Kay BH (1998) Aedes aegypti survival and dispersal estimated by mark-release-recapture in northern Australia. Am J Tropical Med Hyg 58:277–282Article 

Google Scholar 
Palsøll P, Zachariah MP, Bérubé M (2010) Detecting populations in the ‘ambiguous’ zone: Kinship-based estimation of population structure at low genetic divergence. Mol Ecol Resourses 10:797–805Article 

Google Scholar 
Rasheed SB, Boots M, Frantz AC, Butlin RK (2013) Population structure of the mosquito Aedes aegypti (Stegomyia aegypti) in Pakistan. Med Vet Entomol 27:430–440Article 

Google Scholar 
Rašić G, Filipović I, Weeks AR, Hoffmann AA (2014) Genome-wide SNPs lead to strong signals of geographic structure and relatedness patterns in the major arbovirus vector, Aedes aegypti. BMC genomics 15(1):1–12Article 

Google Scholar 
Reiter P, Amador MA, Anderson RA, Clark GG (1995) Short report: dispersal of Aedes aegypti in an urban area after blood feeding as demonstrated by rubidium-marked eggs. Am J Tropical Med Hyg 52:177–179Article 

Google Scholar 
Ribeiro Jr JP, Diggle PJ (2001) geoR: a package for geostatistical analysis. R N. 1.2:14–18
Google Scholar 
Ritchie SA (2001) Effect of some animal feeds and oviposition substrates on Aedes oviposition in ovitraps in Cairns. Aust J Am Mosq Contol Assoc 11:2
Google Scholar 
Rousset (2000) Genetic differentiation between individuals. J Evol Biol 13:58–62Article 

Google Scholar 
RStudio Team (2021) RStudio: Integrated Development Environment for R. RStudio, PBC, Boston, MA, http://www.rstudio.com/Russell RC, Geary MJ (1992) The susceptibility of the mosquitoes Aedes notoscriptus and Culex annulirostris to infection with dog heartworm Dirofilaria immitis and their vector efficiency. Med Vet Entomol 6:154–158Article 

Google Scholar 
Schmidt TL, Filipović I, Hoffmann AA, Rašić G (2018) Fine-scale landscape genomics helps explain the slow spatial spread of Wolbachia through the Aedes aegypti population in Cairns, Australia. Heredity 120:386–395Article 

Google Scholar 
Schmidt TL, Swan T, Chung J, Karl S, Demok S, Yang Q et al. (2021) Spatial population genomics of a recent mosquito invasion. Mol Ecol 30:1174–1189Article 

Google Scholar 
Schmidt TL, Elfekih S, Cao LJ, Wie SJ, Al-Fageeh MB, Nassar M, et al. (2022) Close kin dyads indicate intergenerational dispersal and barriers. The American Naturalist.Shirk AJ, Cushman SA (2011) sGD: software for estimating spatially explicit indices of genetic diversity. Mol Ecol Resour 11:922–934Article 

Google Scholar 
Shirk AJ, Cushman SA (2014) Spatially-explicit estimation of Wright’s neighborhood size in continuous populations. Front Ecol Evolution 2:62Article 

Google Scholar 
Sumner J, Rousset F, Estoup A, Moritz C (2001) ‘Neighbourhood’ size, dispersal and density estimates in the prickly forest skink (Gnypetoscincus queenslandiae) using individual genetic and demographic methods. Mol Ecol 10:1917–1927Article 

Google Scholar 
Sunahara T, Mogi M (2004) Searching clusters of community composition along multiple spatial scales: a case study on aquatic invertebrate communities in bamboo stumps in West Timor. Popul Ecol 46:149–158Article 

Google Scholar 
Tantowijoyo W, Arguni E, Johnson P, Budiwati N, Nurhayati PI, Fitriana I et al. (2016) Spatial and temporal variation in Aedes aegypti and Aedes albopictus (Diptera: Culicidae) numbers in the Yogyakarta area of Java, Indonesia, with implications for Wolbachia Releases. J Med Entomol 53:188–198Article 

Google Scholar 
Trense D, Schmidt TL, Yang Q, Chung J, Hoffmann AA, Fischer K (2021) Anthropogenic and natural barriers affect genetic connectivity in an Alpine butterfly. Mol Ecol 30:114–130Article 

Google Scholar 
Trewin B, Pagendam DE, Darbro JM, Health Q, Devine GJ (2019) Urban Landscape Features Influence the Movement and Distribution of the Australian Container-Inhabiting Mosquito Vectors Aedes aegypti (Diptera: Culicidae) and Aedes notoscriptus (Epidemiology of Ross River virus in South East Queensland, Australia. J Med Entomol 57.2:443–453Verdonschot PFM, Besse-Lototskaya AA (2014) Flight distance of mosquitoes (Culicidae): A metadata analysis to support the management of barrier zones around rewetted and newly constructed wetlands. Limnologica 45:69–79Article 

Google Scholar 
Wallace JR, Mangas KM, Porter JL, Marcsisin R, Pidot SJ, Howden B et al. (2017) Mycobacterium ulcerans low infectious dose and mechanical transmission support insect bites and puncturing injuries in the spread of Buruli ulcer. PLoS Neglected Tropical Dis 11(4):e0005553Article 

Google Scholar 
Watson TM, Kay BH (1999) Vector competence of Aedes notoscriptus (Diptera: Culicidae) for Barmah Forest virus and of this species and Aedes aegypti (Diptera: Culicidae) for dengue 1-4 viruses in Queensland, Australia. J Med Entomol 36:508–514Article 

Google Scholar 
Watson TM, Saul A, Kay BH (2000) Aedes notoscriptus (Diptera: Culicidae) Survival and Dispersal Estimated by Mark-Release-Recapture in Brisbane, Queensland, Australia. J Med Entomol 37:380–384Article 

Google Scholar 
Wright S (1946) Isolation by distance under diverse systems of mating. Genetics 31:39Article 

Google Scholar 
Ye C, Ma ZS, Cannon CH, Pop M, Douglas WV (2012) Exploiting sparseness in de novo genome assembly. BMC Bioinforma 13:1–8Article 

Google Scholar 
Zimin AV, Marçais G, Puiu D, Roberts M, Salzberg SL, Yorke JA (2013) The MaSuRCA genome assembler. Bioinformatics 21:2669–77Article 

Google Scholar  More

Soil studies were carried out on 115,896.2 hectares of agricultural lands in fifteen villages of the municipal district obtained by subtracting from the available area of the village industrial lands, populated areas, forest plots occupied by water, etc.As a result of the land reform and redistribution of land for various purposes for the period from 1972 to 2021, the area of agricultural land decreased by 12.7% compared to the data of the previous survey.In the research area, the largest territories are occupied by black soils, which amount to 52,826.24 ha, including bleached soils—42,605.9 ha, alkaline – 6983.8 ha and shortened – 3236.54 ha. Slightly inferior to the black soils are dark gray forest soils with an area of 37,043.63 hectares, alluvial—12,287.4 hectares, gray forest—6371.96 hectares and forest soils of a rooted profile – 5058.94 hectares. The share of sod-carbonate soils accounts for 7792.7 hectares of land, which is 6.2%. The gradation did not include the soils of the ravine-beam complex, sand and gravel masses, existing ravines and disturbed lands, and quarries that occupy 5,452.4 hectares of territory (4.3%).One of the important indicators of soils, especially used in agricultural production, is the humus state. Thus, over 49 years there has been a slight decrease in the area under obese (high-humus) soils in the hectare ratio, due to a general decrease in the area of farmland, but in the context of the security group, they have increased by 1.3% (Table 1). The remaining levels of security have remained almost at the same level. The increase in the amount of fat chernozems was facilitated by the withdrawal of arable land from circulation and their transfer to perennial plantations. Earlier researches conducted on experimental fields of the Bashkir State Agrarian University identified and revealed changes in the quantitative and qualitative composition of organic matter from 15 to 30% when introducing a land plot for arable land26. To preserve and improve soil fertility, it is recommended to carry out a complex of agrotechnical, agrochemical and reclamation measures and the use of various meliorants, organic and mineral fertilizers27.Studies of the capacity of the humus horizon have shown that low–sized soils have become the most widespread—69,660.2 hectares or 60.1% of the total area of agricultural land (Fig. 2). A smaller area is occupied by medium-sized soils – 38,128.7 hectares (32.9%), not included in the gradation – 8107.3 hectares or 7.0%, respectively. It should be noted that the specific gravity of the soil of the ravine-beam complex, sand and gravel masses, active ravines and disturbed lands, and quarries increased by 2.5%.Figure 2Distribution of soils by humus horizon thickness by region.Full size imageThe granulometric composition of the soil is also of great agronomic importance28. Physical, physico-chemical, physico-mechanical properties and water, air, and nutrient regimes of soils depend on it29,30. In the Salavatskiy district there were practically no changes in soil areas in terms of granulometric composition, mainly clay soil varieties predominate. According to the mechanical composition of the soil there were distributed as follows: light clay – 71,807.38 ha or 62% (in 1972, 86,375 ha or 65.1%) of the total area of agricultural land and heavy loamy – 34,745.24 ha (30%) (in 1972—39,614 ha or 29.8%). The share of medium-loamy varieties accounts for 0.8% (in 1972—0.84%) (Fig. 3).Figure 3Distribution of Salavatskiy district soil areas by granulometric composition, %.Full size imageThe gradation did not include 8362.27 hectares of land. Heavy loamy, medium clay, sandy loam and sandy soils have not been identified.All arable soils of the analyzed territory are slightly susceptible to erosion processes, the processes of water and, to a lesser extent, wind erosion have developed. 67,445.21 hectares of land, or 58.2% (in 1972, 77,702 hectares) of the total area of agricultural lands are occupied under lightly washed soils, the share of medium and heavily washed accounts for 3.9% and 0.1%, respectively. Unwashed soils are distributed on 36,985.46 hectares (31.9%) (Table 2).Table 2 Soil areas by category of erosion feature (Salavatskiy district of the Republic of Bashkortostan).Full size tableAccording to the results of the field research and laboratory agrochemical analyses of soils, land refinements related to agricultural land were carried out. The basis for correcting and digitizing the contours of soil varieties were in the maps made in 1972 (Fig. 4).Figure 4Soil map within the boundaries of the Salavatskiy district of the Republic of Bashkortostan, 1972.Full size imageDigitization included scanning the topographic basis, then assigning coordinates to a raster image, decrypting and digitizing orthophotos (Fig. 5).Figure 5Orthophotoplan within the boundaries of the Salavatskiy district of the Republic of Bashkortostan, 2007.Full size imageAfter the carried-out activities, a soil map was obtained in the digital format of the Mapinfo program, after which it was converted into a raster basis with reference to the local coordinate system MSK 02 zone 1. The digitization of the 1972 soil map was carried out manually by outlining the contours of the topographic base and the scanned map.During digitization, information partially lost due to its wear and distortion during scanning was restored. A necessary condition is the use of the originals of the soil maps of the previous survey (1972).As a planned basis on which the created layers can be opened and information on soils can be obtained, a raster basis was ordinated into a local coordinate system (Fig. 6).Figure 6Completed soil map within the boundaries of the Salavat district of the Republic of Belarus, 2021.Full size imageThe result of the conducted research is the developed electronic digital soil map of the municipal district of Salavatskiy district which unites 15 rural settlements. The electronic soil map is presented in the form of a complex of electronic layers with the names of the type and subtype of soils, soil variety, mechanical or granulometric composition, soil-forming and underlying rocks. It also includes indicators of organic carbon, humus, mobile phosphorus, exchangeable potassium, soil acidity by pH value and the capacity of the humus-accumulative horizon. More