Ecology

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

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

Wilson, E. O. Success and dominance in ecosystems: the case of the social insects. Vol. 2 I-XXI (Ecology Institute, 1990).Anderson, C., Franks, N. R. & McShea, D. W. The complexity and hierarchical structure of tasks in insect societies. Anim. Behav. 62, 643–651. https://doi.org/10.1006/anbe.2001.1795 (2001).Article 

Google Scholar 
Theraulaz, G. & Deneubourg, J.-L. in The Ethological roots of Culture (eds Gardner RA, Chiarelli AB, Gardner BT, & Ploojd FX) 1–19 (Kluwer Academic Publishers, 1994).Theraulaz, G. & Bonabeau, E. Modelling the collective building of complex architectures in social insects with lattice swarms. J. Theor. Biol. 177, 381–400. https://doi.org/10.1006/jtbi.1995.0255 (1995).Article 
ADS 

Google Scholar 
Bonabeau, E., Theraulaz, G., Deneubourg, J.-L., Aron, S. & Camazine, S. Self-organization in social insects. Trends Ecol. Evol. 12, 188–193 (1997).Article 

Google Scholar 
Gordon, D. M. The organization of work in social insect colonies. Nature 380, 121–124 (1996).Article 
ADS 

Google Scholar 
Gordon, D. M. The evolution of the algorithms for collective behavior. Cell Syst. 3, 514–520 (2016).Article 

Google Scholar 
Grüter, C. et al. Negative feedback enables fast and flexible collective decision-making in ants. PLoS ONE 7, e44501. https://doi.org/10.1371/journal.pone.0044501 (2012).Article 
ADS 

Google Scholar 
Wehner, R., Harkness, R. D. & Schmid-Hempel, P. Foraging Strategies in Individually Searching Ants. (Fischer, 1983).Oster, G. F. & Wilson, E. O. Caste and Ecology in the Social Insects. (Princeton University Press, 1978).Anderson, C. & Franks, N. R. Teams in animal societies. Behav. Ecol. 12, 534–540. https://doi.org/10.1093/beheco/12.5.534 (2001).Article 

Google Scholar 
Jeanne, R. L. The evolution of the organization of work in social insects. Monitore Zool. Italiano-Ital. J. Zool. 20, 119–133. https://doi.org/10.1080/00269786.1986.10736494 (1986).Article 

Google Scholar 
Ratnieks, F. L. & Anderson, C. Task partitioning in insects societies. Insectes Soc. 46, 95–108 (1999).Article 

Google Scholar 
Anderson, C., Boomsma, J. J. & Bartholdi, J. J. Task partitioning in insect societies: bucket brigades. Insectes Soc. 49, 171–180. https://doi.org/10.1007/s00040-002-8298-7 (2002).Article 

Google Scholar 
Jeanson, R. & Weidenmüller, A. Interindividual variability in social insects–proximate causes and ultimate consequences. Biol. Rev. 89, 671–687 (2014).Article 

Google Scholar 
Leighton, G. M., Charbonneau, D. & Dornhaus, A. Task switching is associated with temporal delays in Temnothorax rugatulus ants. Behav. Ecol. 28, 319–327. https://doi.org/10.1093/beheco/arw162 (2017).Article 

Google Scholar 
Grassé, P.-P. La reconstruction du nid et les coordinations interindividuelles chez Bellicositermes natalensis et Cubitermes sp. la théorie de la stigmergie: Essai d’interprétation du comportement des termites constructeurs. Insectes Soc. 6, 41–80 (1959).Article 

Google Scholar 
Theraulaz, G. & Bonabeau, E. A brief history of stigmergy. Artif. Life 5, 97–116. https://doi.org/10.1162/106454699568700 (1999).Article 

Google Scholar 
Karsai, I. Decentralized control of construction behavior in paper wasps: an overview of the stigmergy approach. Artif. Life 5, 117–136. https://doi.org/10.1162/106454699568719 (1999).Article 

Google Scholar 
Karsai, I. & Penzes, Z. Comb Building in Social Wasps – Self-Organization and Stigmergic Script. J. Theor. Biol. 161, 505–525. https://doi.org/10.1006/jtbi.1993.1070 (1993).Article 
ADS 

Google Scholar 
Dorigo, M., Bonabeau, E. & Theraulaz, G. Ant algorithms and stigmergy. Fut. Gen. Comput. Syst. 16, 851–871 (2000).Article 

Google Scholar 
Camazine, S. Self-organizing pattern-formation on the combs of Honey-Bee Colonies. Behav. Ecol. Sociobiol. 28, 61–76. https://doi.org/10.1007/bf00172140 (1991).Article 

Google Scholar 
Camazine, S., Sneyd, J., Jenkins, M. J. & Murray, J. D. A Mathematical-model of self-organized pattern-formation on the combs of Honeybee Colonies. J. Theor. Biol. 147, 553–571. https://doi.org/10.1016/S0022-5193(05)80264-4 (1990).Article 
ADS 

Google Scholar 
Deneubourg, J.-L. et al. in Simulation of Adaptive Behavior: From Animals to Animats (eds J.A. Meyer & S.W. Wilson) 356–365 (The MIT Press/Bradford Books, 1991).Franks, N. R. & Sendovafranks, A. B. Brood Sorting by Ants – Distributing the Workload over the Work-Surface. Behav. Ecol. Sociobiol. 30, 109–123 (1992).Article 

Google Scholar 
Sendova-Franks, A. B., Scholes, S. R., Franks, N. R. & Melhuish, C. Brood sorting by ants: two phases and differential diffusion. Anim. Behav. 68, 1095–1106. https://doi.org/10.1016/j.anbehav.2004.02.013 (2004).Article 

Google Scholar 
Lan, T., Liu, S. & Yang, S. X. in 2006 6th World Congress on Intelligent Control and Automation. 441–445 (IEEE).Renucci, M., Tirard, A. & Provost, E. Complex undertaking behavior in Temnothorax lichtensteini ant colonies: from corpse-burying behavior to necrophoric behavior. Insectes Soc. 58, 9–16 (2011).Article 

Google Scholar 
Detrain, C. & Deneubourg, J. L. Collective decision-making and foraging patterns in Ants and Honeybees. Advances in Insect Physiology 35(35), 123–173. https://doi.org/10.1016/S0065-2806(08)00002-7 (2008).Article 

Google Scholar 
Couzin, I. D. & Franks, N. R. Self-organized lane formation and optimized traffic flow in army ants. Proc Biol Sci 270, 139–146. https://doi.org/10.1098/rspb.2002.2210 (2003).Article 

Google Scholar 
Gulyas, L., Laufer, L. & Szabo, R. in International Workshop on Engineering Self-Organising Applications 50–65 (Springer).Langridge, E. A., Franks, N. R. & Sendova-Franks, A. B. Improvement in collective performance with experience in ants. Behav. Ecol. Sociobiol. 56, 523–529. https://doi.org/10.1007/s00265-004-0824-3 (2004).Article 

Google Scholar 
Oberst, S. et al. Revisiting stigmergy in light of multi-functional, biogenic, termite structures as communication channel. Comput. Struct. Biotechnol. J. 18, 2522–2534 (2020).Article 

Google Scholar 
Hart, A., Anderson, C. & Ratnieks, F. Task partitioning in leafcutting ants. Acta Ethologica 5, 1–11. https://doi.org/10.1007/s10211-002-0062-5 (2002).Article 

Google Scholar 
Hart, A. G. & Ratnieks, F. L. Leaf caching in the leafcutting ant Atta colombica: organizational shift, task partitioning and making the best of a bad job. Anim. Behav. 62, 227–234 (2001).Article 

Google Scholar 
Röschard, J. & Roces, F. Sequential load transport in grass-cutting ants (Atta vollenweideri): maximization of plant delivery rate or improved information transfer? Psyche 2011 (2011).Nickele, M. A., Reis Filho, W. & Pie, M. R. Sequential load transport during foraging in Acromyrmex (Hymenoptera: Formicidae) leaf-cutting ants. Myrmecol News 21, 73–82 (2015).Ferrante, E., Turgut, A. E., Duenez-Guzman, E., Dorigo, M. & Wenseleers, T. Evolution of Self-Organized Task Specialization in Robot Swarms. PLoS Comp. Biol. 11, e1004273. https://doi.org/10.1371/journal.pcbi.1004273 (2015).Article 
ADS 

Google Scholar 
Grueter, C. et al. Negative feedback enables fast and flexible collective decision-making in ants. (2012).Holcombe, M. et al. Modelling complex biological systems using an agent-based approach. Integr. Biol. 4, 53–64 (2012).Article 

Google Scholar 
Fourcassié, V., Dussutour, A. & Deneubourg, J.-L. Ant traffic rules. J. Exp. Biol. 213, 2357–2363 (2010).Article 

Google Scholar 
Modlmeier, A. P., Keiser, C. N., Shearer, T. A. & Pruitt, J. N. Species-specific influence of group composition on collective behaviors in ants. Behav. Ecol. Sociobiol. 68, 1929–1937 (2014).Article 

Google Scholar 
Modlmeier, A. P., Liebmann, J. E. & Foitzik, S. Diverse societies are more productive: a lesson from ants. Proc. R. Soc. B 279, 2142–2150 (2012).Article 

Google Scholar 
Walsh, J. T., Garnier, S. & Linksvayer, T. A. Ant collective behavior is heritable and shaped by selection. Am. Nat. 196, 541–554 (2020).Article 

Google Scholar 
Tannenbaum, E. When does division of labor lead to increased system output?. J. Theor. Biol. 247, 413–425 (2007).Article 
ADS 
MathSciNet 
MATH 

Google Scholar 
Wahl, L. M. Evolving the division of labour: Generalists, specialists and task allocation. J. Theor. Biol. 219, 371–388 (2002).Article 
ADS 
MathSciNet 

Google Scholar 
Wakano, J., Nakata, K. & Yamamura, N. Dynamic model of optimal age polyethism in social insects under stable and fluctuating environments. J. Theor. Biol. 193, 153–165 (1998).Article 
ADS 

Google Scholar 
Goldsby, H. J., Dornhaus, A., Kerr, B. & Ofria, C. Task-switching costs promote the evolution of division of labor and shifts in individuality. Proc. Natl. Acad. Sci. 109, 13686–13691 (2012).Article 
ADS 

Google Scholar 
Rueffler, C., Hermisson, J. & Wagner, G. P. Evolution of functional specialization and division of labor. Proc. Natl. Acad. Sci. 109, E326–E335 (2012).Article 
ADS 

Google Scholar 
Lopes, J. F., Forti, L. C., Camargo, R. S., Matos, C. A. & Verza, S. S. The effect of trail length on task partitioning in three Acromyrmex species (Hymenoptera: Formicidae). Sociobiology 42, 87–92 (2003).
Google Scholar 
Duarte, A., Weissing, F. J., Pen, I. & Keller, L. An evolutionary perspective on self-organized division of labor in social insects. Annu. Rev. Ecol. Evol. Syst. 42(42), 91–110. https://doi.org/10.1146/annurev-ecolsys-102710-145017 (2011).Article 

Google Scholar 
Duarte, A., Pen, I., Keller, L. & Weissing, F. J. Evolution of self-organized division of labor in a response threshold model. Behav. Ecol. Sociobiol. 66, 947–957. https://doi.org/10.1007/s00265-012-1343-2 (2012).Article 

Google Scholar 
Floreano, D. & Keller, L. Evolution of adaptive behaviour in robots by means of Darwinian selection. PLoS Biol. 8, e1000292 (2010).Article 

Google Scholar 
Floreano, D., Mitri, S., Magnenat, S. & Keller, L. Evolutionary conditions for the emergence of communication in robots. Curr. Biol. 17, 514–519 (2007).Article 

Google Scholar 
Mitri, S., Floreano, D. & Keller, L. The evolution of information suppression in communicating robots with conflicting interests. Proc. Natl. Acad. Sci. 106, 15786–15790 (2009).Article 
ADS 

Google Scholar 
Abiodun, O. I. et al. State-of-the-art in artificial neural network applications: A survey. Heliyon 4, e00938 (2018).Article 

Google Scholar 
Dingemanse, N. J., Kazem, A. J., Réale, D. & Wright, J. Behavioural reaction norms: Animal personality meets individual plasticity. Trends Ecol. Evol. 25, 81–89 (2010).Article 

Google Scholar 
Van den Berg, P. & Weissing, F. J. The importance of mechanisms for the evolution of cooperation. Proc. R. Soc. B 282, 20151382 (2015).Article 

Google Scholar 
Wetterer, J. K. Ontogenetic changes in forager polymorphism and foraging ecology in the leaf-cutting ant Atta cephalotes. Oecologia 98, 235–238. https://doi.org/10.1007/BF00341478 (1994).Article 
ADS 

Google Scholar 
Wetterer, J. K. Forager size and ecology of Acromyrmex coronatus and other leaf-cutting ants in Costa Rica. Oecologia 104, 409–415. https://doi.org/10.1007/BF00341337 (1995).Article 
ADS 

Google Scholar 
Evison, S. E. F. & Hughes, W. O. Genetic caste polymorphism and the evolution of polyandry in Atta leaf-cutting ants. Naturwissenschaften 98, 643–649 (2011).Article 
ADS 

Google Scholar 
Hughes, W. O., Oldroyd, B. P., Beekman, M. & Ratnieks, F. L. Ancestral monogamy shows kin selection is key to the evolution of eusociality. Science 320, 1213–1216 (2008).Article 
ADS 

Google Scholar 
Villesen, P., Murakami, T., Schultz, T. R. & Boomsma, o. J. Identifying the transition between single and multiple mating of queens in fungus-growing ants. Proc. R. Soc. Lond. Ser. B: Biol. Sci. 269, 1541–1548 (2002).Mueller, U. G. & Rabeling, C. A breakthrough innovation in animal evolution. Proc. Natl. Acad. Sci. 105, 5287–5288 (2008).Article 
ADS 

Google Scholar 
Schultz, T. R. & Brady, S. G. Major evolutionary transitions in ant agriculture. Proc. Natl. Acad. Sci. 105, 5435–5440 (2008).Article 
ADS 

Google Scholar 
Fowler, H. G. Latitudinal gradients and diversity of the leaf-cutting ants (Atta and Acromyrmex)(Hymenoptera: Formicidae). Rev. Biol. Trop. 31, 213–216 (1983).
Google Scholar 
Jackson, D. E. & Ratnieks, F. L. Communication in ants. Curr. Biol. 16, R570–R574 (2006).Article 

Google Scholar 
Roces, F. & Hölldobler, B. Vibrational communication between hitchhikers and foragers in leaf-cutting ants (Atta cephalotes). Behav. Ecol. Sociobiol. 37, 297–302 (1995).Article 

Google Scholar 
Hubbell, S. P., Johnson, L. K., Stanislav, E., Wilson, B. & Fowler, H. Foraging by bucket-brigade in leaf-cutter ants. Biotropica 1, 210–213 (1980).Article 

Google Scholar 
Boi, S., Couzin, I. D., Buono, N. D., Franks, N. & Britton, N. Coupled oscillators and activity waves in ant colonies. Proc. R. Soc. Lond. Ser. B: Biol. Sci. 266, 371–378 (1999).Cole, B. J. Short-term activity cycles in ants: generation of periodicity by worker interaction. Am. Nat. 137, 244–259 (1991).Article 

Google Scholar 
Cornejo, A., Dornhaus, A., Lynch, N. & Nagpal, R. in International Symposium on Distributed Computing. 46–60 (Springer).Franks, N. R., Bryant, S., Griffiths, R. & Hemerik, L. Synchronization of the behaviour within nests of the antleptothorax acervorum (fabricius)—I. Discovering the phenomenon and its relation to the level of starvation. Bull. Math. Biol. 52, 597–612 (1990).Pagliara, R., Gordon, D. M. & Leonard, N. E. Regulation of harvester ant foraging as a closed-loop excitable system. PLoS Comp. Biol. 14, e1006200 (2018).Article 
ADS 

Google Scholar 
Schmickl, T. & Karsai, I. Integral feedback control is at the core of task allocation and resilience of insect societies. Proc. Natl. Acad. Sci. 115, 13180–13185 (2018).Article 
ADS 

Google Scholar 
Solé, R. V., Miramontes, O. & Goodwin, B. C. Oscillations and chaos in ant societies. J. Theor. Biol. 161, 343–357 (1993).Article 
ADS 

Google Scholar 
Gordon, D. M., Goodwin, B. C. & Trainor, L. E. A parallel distributed model of the behaviour of ant colonies. J. Theor. Biol. 156, 293–307 (1992).Article 
ADS 

Google Scholar 
Aoki, S. K. et al. A universal biomolecular integral feedback controller for robust perfect adaptation. Nature 570, 533–537 (2019).Article 

Google Scholar 
Ma, W., Trusina, A., El-Samad, H., Lim, W. A. & Tang, C. Defining network topologies that can achieve biochemical adaptation. Cell 138, 760–773 (2009).Article 

Google Scholar 
Niemeyer, N., Schleimer, J.-H. & Schreiber, S. Biophysical models of intrinsic homeostasis: Firing rates and beyond. Curr. Opin. Neurobiol. 70, 81–88 (2021).Article 

Google Scholar 
Rombouts, J., Vandervelde, A. & Gelens, L. Delay models for the early embryonic cell cycle oscillator. PLoS ONE 13, e0194769 (2018).Article 

Google Scholar 
Tyson, J. J., Chen, K. C. & Novak, B. Sniffers, buzzers, toggles and blinkers: dynamics of regulatory and signaling pathways in the cell. Curr. Opin. Cell Biol. 15, 221–231 (2003).Article 

Google Scholar 
Bryant, B. D. & Miikkulainen, R. Foundations of Trusted Autonomy 87–115 (Springer, 2018).
Google Scholar 
Masad, D. & Kazil, J. in 14th PYTHON in Science Conference. 53–60 (Citeseer).Knaden, M. & Graham, P. The sensory ecology of ant navigation: from natural environments to neural mechanisms. Annu. Rev. Entomol. 61, 63–76 (2016).Article 

Google Scholar  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

Ribic, C. A., Thompson, F. R. & Pietz, P. J. Video Surveillance of Nesting Birds (University of California Press, 2012).Book 

Google Scholar 
O’Brien, T. G. & Kinnaird, M. F. A picture is worth a thousand words: The application of camera trapping to the study of birds. Bird Conserv. Int. 18, S144–S162 (2008).Article 

Google Scholar 
Kristan, D. M., Golightly Jr, R. T. & Tomkiewicz Jr, S. M. A solar-powered transmitting video camera for monitoring raptor nests. Wildl. Soc. Bull. 24, 284–290 (1996).
Google Scholar 
Grubb, T. An infrared video camera system for monitoring diurnal and nocturnal raptors. J. Raptor Res. 32, 290–296 (1998).
Google Scholar 
Margalida, A. et al. A solar-powered transmitting video camera for monitoring cliff-nesting raptors. J. Field Ornithol. 77, 7–12 (2006).Article 

Google Scholar 
Bolton, M., Butcher, N., Sharpe, F., Stevens, D. & Fisher, G. Remote monitoring of nests using digital camera technology. J. Field Ornithol. 78, 213–220 (2007).Article 

Google Scholar 
Pierce, A. J. & Pobprasert, K. A portable system for continuous monitoring of bird nests using digital video recorders. J. Field Ornithol. 78, 322–328 (2007).Article 

Google Scholar 
Benson, T. J., Brown, J. D. & Bednarz, J. C. Identifying predators clarifies predictors of nest success in a temperate passerine. J. Anim. Ecol. 79, 225–234 (2010).Article 

Google Scholar 
Lewis, S. B., Fuller, M. R. & Titus, K. A comparison of 3 methods for assessing raptor diet during the breeding season. Wildl. Soc. Bull. 32, 373–385 (2004).Article 

Google Scholar 
Rogers, A. S. Quantifying Northern Goshawk diets using remote cameras and observations from blinds. J. Raptor Res. 39, 303–309 (2005).ADS 

Google Scholar 
Tornberg, R. & Reif, V. Assessing the diet of birds of prey: A comparison of prey items found in nests and images. Ornis Fenn. 84, 21 (2007).
Google Scholar 
López-López, P. & Urios, V. Use of digital trail cameras to study Bonelli’s eagle diet during the nestling period. Ital. J. Zool. 77, 289–295 (2010).Article 

Google Scholar 
Harrison, J. T., Kochert, M. N., Pauli, B. P. & Heath, J. A. Using motion-activated trail cameras to study diet and productivity of cliff-nesting Golden Eagles. J. Raptor Res. 53, 26–37 (2019).Article 

Google Scholar 
McRae, S. B., Weatherhead, P. J. & Montgomerie, R. American robin nestlings compete by jockeying for position. Behav. Ecol. Sociobiol. 33, 101–106 (1993).Article 

Google Scholar 
Nathan, A., Legge, S. & Cockburn, A. Nestling aggression in broods of a siblicidal kingfisher, the laughing kookaburra. Behav. Ecol. 12, 716–725 (2001).Article 

Google Scholar 
Grivas, C. et al. An audio–visual nest monitoring system for the study and manipulation of siblicide in bearded vultures Gypaetus barbatus on the island of Crete (Greece). J. Ethol. 27, 105–116 (2009).Article 

Google Scholar 
Gula, R., Theuerkauf, J., Rouys, S. & Legault, A. An audio/video surveillance system for wildlife. Eur. J. Wildl. Res. 56, 803–807 (2010).Article 

Google Scholar 
Sanaiotti, T. M., Seixas, G. H., Duleba, S. & Martins, F. D. Camera trapping at harpy eagle nests: Interspecies interactions under predation risk. J. Raptor Res. 51, 72–78 (2017).Article 

Google Scholar 
Allen, M. L., Inagaki, A. & Ward, M. P. Cannibalism in raptors: A review. J. Raptor Res. 54, 424–430 (2020).Article 

Google Scholar 
Academia, M. H. & Dalgleish, H. J. Use of nest web cameras and citizen science to quantify osprey prey delivery rate and nest success. J. Raptor Res. 56, 212–219 (2022).Article 

Google Scholar 
Gysel, L. W. & Davis, E. M. A simple automatic photographic unit for wildlife research. J. Wildl. Manag. 20, 451–453 (1956).Article 

Google Scholar 
Royama, T. A device of an auto-cinematic food-recorder. Jpn. J. Ornithol. 15, 172–176 (1959).Article 

Google Scholar 
Cox, W. A. et al. Development of camera technology for monitoring nests. In Chapter 15. Video Surveill. Nesting Birds Stud. Avian Biol. (eds Ribic, C. A. et al.) 185–210 (Univ. Calif. Press, 2012).Sanders, M. D. & Maloney, R. F. Causes of mortality at nests of ground-nesting birds in the Upper Waitaki Basin, South Island, New Zealand: A 5-year video study. Biol. Conserv. 106, 225–236 (2002).Article 

Google Scholar 
Reif, V. & Tornberg, R. Using time-lapse digital video recording for a nesting study of birds of prey. Eur. J. Wildl. Res. 52, 251–258 (2006).Article 

Google Scholar 
McKinnon, L. & Bêty, J. Effect of camera monitoring on survival rates of High-Arctic shorebird nests. J. Field Ornithol. 80, 280–288 (2009).Article 

Google Scholar 
Powell, L. A., Giovanni, M. D., Groepper, S. R., Reineke, M. & Schacht, W. H. Attendance Patterns and Survival of Western Meadowlark Nests (University of California Press, 2012).Book 

Google Scholar 
Herranz, J., Yanes, M. & Suárez, F. Does Photo-Monitoring Affect Nest Predation? J. Field Ornithol. 73, 97–101 (2002).Article 

Google Scholar 
Richardson, T. W., Gardali, T. & Jenkins, S. H. Review and meta-analysis of camera effects on avian nest success. J. Wildl. Manag. 73, 287–293 (2009).Article 

Google Scholar 
Cain, S. L. Nesting activity time budgets of Bald Eagles in southeast Alaska. (1985).García-Salgado, G. et al. Evaluation of trail-cameras for analyzing the diet of nesting raptors using the Northern Goshawk as a model. PLoS ONE 10, e0127585 (2015).Article 

Google Scholar 
Swann, D. E., Kawanishi, K. & Palmer, J. Evaluating types and features of camera traps in ecological studies: a guide for researchers. In Camera Traps in Animal Ecology 27–43 (Springer, 2011).Dykstra, C., Meyer, M. & Warnke, D. Bald Eagle reproductive performance following video camera placement. J. Raptor Res. 36, 136–139 (2002).
Google Scholar 
Del Moral, J. C. & Molina, B. El águila perdicera en España, población reproductora en 2018 y método de censo (SEO/BirdLife, 2018).
Google Scholar 
Generalitat Valenciana. Orden 2/2022, de 16 de febrero, de la Conselleria de Agricultura, Desarrollo Rural, Emergencia Climática y Transición Ecológica, por la que se actualizan los listados valencianos de especies protegidas de flora y fauna (2022).Real, J. & Mañosa, S. Demography and conservation of western European Bonelli’s eagle Hieraaetus fasciatus populations. Biol. Conserv. 79, 59–66 (1997).Article 

Google Scholar 
Hernández-Matías, A. et al. From local monitoring to a broad-scale viability assessment: A case study for the Bonelli’s Eagle in western Europe. Ecol. Monogr. 83, 239–261 (2013).Article 

Google Scholar 
Rollan, A. et al. Guiding local-scale management to improve the conservation of endangered populations: The example of Bonelli’s Eagle Aquila fasciata. Bird Conserv. Int. 31, 395–409 (2021).Article 

Google Scholar 
López-López, P., García-Ripollés, C. & Urios, V. Population size, breeding performance and territory quality of Bonelli’s Eagle Hieraaetus fasciatus in eastern Spain. Bird Study 54, 335–342 (2007).Article 

Google Scholar 
López-López, P. Informe científico valoración de la inclusión del águila perdicera como especie en peligro de extinción en el Catálogo Valenciano de Especies de Fauna Amenazadahttps://doi.org/10.13140/RG.2.2.32806.04166 (2021).López-López, P., Perona, A., Egea-Casas, O., Morant, J. & Urios, V. Tri-axial accelerometry shows differences in energy expenditure and parental effort throughout the breeding season in long-lived raptors. Curr. Zool. 68, 57–67 (2022).Article 

Google Scholar 
Perona, A. M., Urios, V. & López-López, P. Holidays? Not for all Eagles have larger home ranges on holidays as a consequence of human disturbance. Biol. Conserv. 231, 59–66 (2019).Article 

Google Scholar 
Morollón, S., Urios, V. & López-López, P. Fifteen days are enough to estimate home-range size in some long-lived resident eagles. J. Ornithol. 163, 849–854 (2022).Article 

Google Scholar 
Stewart-Oaten, A., Murdoch, W. W. & Parker, K. R. Environmental impact assessment:” Pseudoreplication” in time?. Ecology 67, 929–940 (1986).Article 

Google Scholar 
Underwood, A. Beyond BACI: The detection of environmental impacts on populations in the real, but variable, world. J. Exp. Mar. Biol. Ecol. 161, 145–178 (1992).Article 

Google Scholar 
López-López, P., García-Ripollés, C., García-López, F., Aguilar, J. M. & Verdejo, J. Patrón de distribución del águila real Aquila chrysaetos y del águila-azor perdicera Hieraaetus fasciatus en la provincia de Castellón. Ardeola 51, 275–283 (2004).
Google Scholar 
López-López, P., García-Ripollés, C., Aguilar, J. M., Garcia-López, F. & Verdejo, J. Modelling breeding habitat preferences of Bonelli’s eagle (Hieraaetus fasciatus) in relation to topography, disturbance, climate and land use at different spatial scales. J. Ornithol. 147, 97–106 (2006).Article 

Google Scholar 
Gil-Sánchez, J. Effects of altitude and prey availability on the laying date of Bonelli’s Eagles (Hieraaetus fasciatus) in Granada (SE Spain). Ardeola 47, 1–8 (2000).
Google Scholar 
Forsman, D. Flight Identification of Raptors of Europe, North Africa and the Middle East (Bloomsbury Publishing, 2016).
Google Scholar 
Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R Vol. 574 (Springer, 2009).Book 
MATH 

Google Scholar 
Harrison, X. A. et al. A brief introduction to mixed effects modelling and multi-model inference in ecology. PeerJ 6, e4794 (2018).Article 

Google Scholar 
Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).Article 

Google Scholar 
Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4, 133–142 (2013).Article 

Google Scholar 
Barton, K. MuMIn: Multi-Model Inference. R package version 1.46.0. (2022).Cutler, T. L. & Swann, D. E. Using remote photography in wildlife ecology: A review. Wildl. Soc. Bull. 27, 571–581 (1999).
Google Scholar 
Richardson, C. T. & Miller, C. K. Recommendations for protecting raptors from human disturbance: A review. Wildl. Soc. Bull. 25, 634–638 (1997).
Google Scholar 
Balbontin, J., Penteriani, V. & Ferrer, M. Variations in the age of mates as an early warning signal of changes in population trends? The case of Bonelli’s eagle in Andalusia. Biol. Conserv. 109, 417–423 (2003).Article 

Google Scholar 
Martinez, J. A. et al. Breeding performance, age effects and territory occupancy in a Bonelli’s eagle Hieraaetus fasciatus population. Ibis 150, 223–233 (2008).Article 

Google Scholar 
Sánchez-Zapata, J., Calvo, J., Carrete, M. & Martínez, J. Age and breeding success of a Golden Eagle Aquila chrysaetos population in southeastern Spain. Bird Study 47, 235–237 (2000).Article 

Google Scholar 
Ferrer, M., Penteriani, V., Balbontin, J. & Pandolfi, M. The proportion of immature breeders as a reliable early warning signal of population decline: Evidence from the Spanish imperial eagle in Donana. Biol. Conserv. 114, 463–466 (2003).Article 

Google Scholar 
Cano, A. & Parrinder, E. Studies of less familiar birds, Bonelli’s Eagle. Br. Birds 54, 422–427 (1961).
Google Scholar 
Blondel, J., Coulon, L., Girerd, B. & Hortigue, M. Deux cents heures d’observation aupre‘s de l’aire de l’Aigle de Bonelli Hieraaetus fasciatus. Nos Oiseaux 30, 37–60 (1969).
Google Scholar 
Vaucher, C. Notes sur 1’ethologie de I’Aigle de Bonelli. Nos Oiseaux 31, 101–111 (1971).
Google Scholar 
Elósegui, J. Informe preliminar sobre alimentación de aves rapaces en Navarra y provincias limítrofes. Ardeola 19, 249–256 (1974).
Google Scholar 
Cheylan, G. L. place trophique de l’Aigle de Bonelli Hieraaetus fasciatus dans les biocénoses méditerranéennes. Alauda 45, 1–15 (1977).
Google Scholar 
Palma, L., Cancela da Fonseca, L. & Oliveira, L. L’alimentation de l’aigle de Bonelli Hieraaetus fasciatus dans la coˆte portugaise. Rapinyaires Mediterranis 2, 87–96 (1984).
Google Scholar 
Real, J. Biases in diet study methods in the Bonelli’s eagle. J. Wildl. Manag. 60, 632–638 (1996).Article 

Google Scholar 
Gil-Sánchez, J. M., Molino, F., Valenzuela, G. & Moleón, M. Demografía y alimentación del Águila-azor Perdicera (Hieraaetus fasciatus) en la provincia de Granada. Ardeola 47, 69–75 (2000).
Google Scholar 
Ontiveros, D., Pleguezuelos, J. M. & Caro, J. Prey density, prey detectability and food habits: The case of Bonelli’s eagle and the conservation measures. Biol. Conserv. 123, 19–25 (2005).Article 

Google Scholar 
Moleón, M. et al. Large-scale spatio-temporal shifts in the diet of a predator mediated by an emerging infectious disease of its main prey. J. Biogeogr. 36, 1502–1515 (2009).Article 

Google Scholar 
Resano-Mayor, J. et al. Diet–demography relationships in a long-lived predator: From territories to populations. Oikos 125, 262–270 (2016).Article 

Google Scholar 
Di Vittorio, M. et al. Long-term changes in the breeding period diet of Bonelli’s eagle (Aquila fasciata) in Sicily, Italy. Wildl. Res. 46, 409–414 (2019).Article 

Google Scholar  More

IPCC Climate Change The physical science basis. In Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change (eds Masson-Delmotte, V. et al.) (Cambridge University Press, 2021).
Google Scholar 
Doney, S. C. et al. Climate change impacts on marine ecosystems. Annu. Rev. Mar. Sci. 4, 11–37 (2012).Article 
ADS 

Google Scholar 
Song, H. et al. Thresholds of temperature change for mass extinctions. Nat. Commun. 12, 4694 (2021).Article 
ADS 
CAS 

Google Scholar 
Free, C. M. et al. Impacts of historical warming on marine fisheries production. Science 363, 979–983 (2019).Article 
ADS 
CAS 

Google Scholar 
Cheung, W. W. L. et al. Large-scale redistribution of maximum fisheries catch potential in the global ocean under climate change: Climate change impacts on catch potential. Glob. Change Biol. 16, 24–35 (2010).Article 
ADS 

Google Scholar 
Harley, C. D. G. et al. The impacts of climate change in coastal marine systems: Climate change in coastal marine systems. Ecol. Lett. 9, 228–241 (2006).Article 
ADS 

Google Scholar 
Dahlke, F. T., Wohlrab, S., Butzin, M. & Pörtner, H.-O. Thermal bottlenecks in the life cycle define climate vulnerability of fish. Science 369, 65–70 (2020).Article 
ADS 
CAS 

Google Scholar 
Hodgson, E. E., Essington, T. E. & Kaplan, I. C. Extending vulnerability assessment to include life stages considerations. PLoS ONE 11, e0158917 (2016).Article 

Google Scholar 
Peck, M. A., Reglero, P., Takahashi, M. & Catalán, I. A. Life cycle ecophysiology of small pelagic fish and climate-driven changes in populations. Prog. Oceanogr. 116, 220–245 (2013).Article 
ADS 

Google Scholar 
Tsoukali, S., Visser, A. W. & MacKenzie, B. R. Functional responses of North Atlantic fish eggs to increasing temperature. Mar. Ecol. Prog. Ser. 555, 151–165 (2016).Article 
ADS 

Google Scholar 
Pörtner, 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).Article 

Google Scholar 
Pankhurst, N. W. & Munday, P. L. Effects of climate change on fish reproduction and early life history stages. Mar. Freshw. Res. 62, 1015–1026 (2011).Article 
CAS 

Google Scholar 
Brauner, C. J. Acid-base balance. In Fish Larval physiology (eds Finn, R. N. & Kapoor, B. G.) 185–198 (Science Publishers, 2008).
Google Scholar 
Dahlke, F. T. et al. Effects of ocean acidification increase embryonic sensitivity to thermal extremes in Atlantic cod, Gadus morhua. Glob. Chang. Biol. 23, 1499–1510 (2017).Article 
ADS 

Google Scholar 
Shelbourne, J. E. Significance of the subdermal space in pelagic fish embryos and larvae. Nature 176, 743–744 (1955).Article 
ADS 

Google Scholar 
Sundby, S. & Kristiansen, T. The principles of buoyancy in marine fish eggs and their vertical distributions across the world oceans. PLoS ONE 10, e0138821 (2015).Article 

Google Scholar 
Shei, M., Mies, M. & Olivotto, I. Other demersal spawners and mouthbrooders. Marine ornamental species aquaculture, 223–250 (2017).Beaudreau, A. H. The predatory role of lingcod (Ophiodon elongatus) in the San Juan Archipelago, Washington. (University of Washington, 2009).Love, M. Certainly More Than You Want to Know About the Fishes of the Pacific Coast: A Postmodern Experience. (Really Big Press, 2011).Pauly, D. & Christensen, V. Primary production required to sustain global fisheries. Nature 374, 255–257 (1995).Article 
ADS 
CAS 

Google Scholar 
Reum, J. C. et al. Interpretation and design of ocean acidification experiments in upwelling systems in the context of carbonate chemistry co-variation with temperature and oxygen. ICES J. Mar. Sci. 73, 582–595 (2016).Article 

Google Scholar 
Cheresh, J. & Fiechter, J. Physical and biogeochemical drivers of alongshore pH and oxygen variability in the California Current System. Geophys. Res. Lett. 47, e2020089553 (2020).Article 
ADS 

Google Scholar 
Gruber, N. et al. Rapid progression of ocean acidification in the California Current System. Science 337, 220–223 (2012).Article 
ADS 
CAS 

Google Scholar 
Hauri, C. et al. Spatiotemporal variability and long-term trends of ocean acidification in the California Current System. Biogeosciences 10, 193–216 (2013).Article 
ADS 

Google Scholar 
Pepin, P. Effect of temperature and size on development, mortality, and survival rates of the pelagic early life history stages of marine fish. Can. J. Fish. Aquat. Sci. 48, 503–518 (1991).Article 

Google Scholar 
Lauel, B. J. & Blood, D. M. The Effects of Temperature on Hatching and Survival of Northern Rock Sole Larvae (Lepidopsetta polyxystra) (Springer, 2011).
Google Scholar 
Guevara-Fletcher, C., Alvarez, P., Sanchez, J. & Iglesias, J. Effect of temperature on the development and mortality of European hake (Merluccius merluccius L.) eggs from southern stock under laboratory conditions. J. Exp. Mar. Biol. Ecol. 476, 50–57 (2016).Article 

Google Scholar 
Collins, L. A. & Nelson, S. G. Effects of temperature on oxygen consumption, growth, and development of embryos and yolk-sac larvae of Siganus randalli (Pisces: Siganidae). Mar. Biol. 117, 195–204 (1993).Article 

Google Scholar 
Cook, M. A., Guthrie, K. M., Rust, M. B. & Plesha, P. D. Effects of salinity and temperature during incubation on hatching and development of lingcod Ophiodon elongatus Girard, embryos. Aquac. Res. 36, 1298–1303 (2005).Article 

Google Scholar 
Pörtner, H. Integrating climate-related stressor effects on marine organisms: Unifying principles linking molecule to ecosystem-level changes. Mar. Ecol. Prog. Ser. 470, 273–290 (2012).Article 
ADS 

Google Scholar 
Laurel, B. J., Copeman, L. A., Spencer, M. & Iseri, P. Comparative effects of temperature on rates of development and survival of eggs and yolk-sac larvae of Arctic cod (Boreogadus saida) and walleye pollock (Gadus chalcogrammus). ICES J. Mar. Sci. 75, 2403–2412 (2018).Article 

Google Scholar 
Jordaan, A., Hayhurst, S. E. & Kling, L. J. The influence of temperature on the stage at hatch of laboratory reared Gadus morhua and implications for comparisons of length and morphology. J. Fish Biol. 68, 7–24 (2006).Article 

Google Scholar 
Peña, R., Dumas, S., Zavala-Leal, I. & Contreras-Olguín, M. Effect of incubation temperature on the embryonic development and yolk-sac larvae of the Pacific red snapper Lutjanus peru (Nichols & Murphy, 1922). Aquac Res 45, 519–527 (2014).Article 

Google Scholar 
Breitburg, D. Effects of hypoxia, and the balance between hypoxia and enrichment, on coastal fishes and fisheries. Estuaries 25, 767–781 (2002).Article 

Google Scholar 
Hassell, K. L., Coutin, P. C. & Nugegoda, D. Hypoxia impairs embryo development and survival in black bream (Acanthopagrus butcheri). Mar. Pollut. Bull. 57, 302–306 (2008).Article 
CAS 

Google Scholar 
Giorgi, A. E. The Environmental Biology of the Embryos, Egg Masses and Nesting Sites of the Lingcod, Ophiodon elongatus. (University of Washington, 1981).Oseid, D. M. & Smith, L. L. Survival and hatching of walleye eggs at various dissolved oxygen levels. Progress. Fish-Cult. 33, 81–85 (1971).Article 
CAS 

Google Scholar 
Shumway, D. L., Warren, C. E. & Doudoroff, P. Influence of oxygen concentration and water movement on the growth of steelhead trout and coho salmon embryos. Trans. Am. Fish. Soc. 93, 342–356 (1964).Article 

Google Scholar 
Baumann, H., Talmage, S. C. & Gobler, C. J. Reduced early life growth and survival in a fish in direct response to increased carbon dioxide. Nat. Clim Change 2, 38–41 (2012).Article 
ADS 
CAS 

Google Scholar 
Faria, A. M. et al. Effects of high pCO2 on early life development of pelagic spawning marine fish. Mar. Freshw. Res. 68, 2106–2114 (2017).Article 
CAS 

Google Scholar 
Frommel, A. Y. et al. Severe tissue damage in Atlantic cod larvae under increasing ocean acidification. Nat. Clim. Change 2, 42–46 (2012).Article 
ADS 
CAS 

Google Scholar 
Munday, P. L. et al. Effects of elevated CO2 on early life history development of the yellowtail kingfish, Seriola lalandi, a large pelagic fish. ICES J. Mar. Sci. 73, 641–649 (2016).Article 

Google Scholar 
Hurst, T. P., Fernandez, E. R. & Mathis, J. T. Effects of ocean acidification on hatch size and larval growth of walleye pollock (Theragra chalcogramma). ICES J. Mar. Sci. 70, 812–822 (2013).Article 

Google Scholar 
Wang, X., Song, L., Chen, Y., Ran, H. & Song, J. Impact of ocean acidification on the early development and escape behavior of marine medaka (Oryzias melastigma). Mar. Environ. Res. 131, 10–18 (2017).Article 

Google Scholar 
Franke, A. & Clemmesen, C. Effect of ocean acidification on early life stages of Atlantic herring (Clupea harengus L.). Biogeosciences 8, 3697–3707 (2011).Article 
ADS 
CAS 

Google Scholar 
Forsgren, E., Dupont, S., Jutfelt, F. & Amundsen, T. Elevated CO 2 affects embryonic development and larval phototaxis in a temperate marine fish. Ecol. Evol. 3, 3637–3646 (2013).Article 

Google Scholar 
Bromhead, D. et al. The potential impact of ocean acidification upon eggs and larvae of yellowfin tuna (Thunnus albacares). Deep Sea Res. II 113, 268–279 (2015).Article 
CAS 

Google Scholar 
Garrido, S. et al. Born small, die young: Intrinsic, size-selective mortality in marine larval fish. Sci. Rep. 5, 17065 (2015).Article 
ADS 
CAS 

Google Scholar 
Sampaio, E. et al. Impacts of hypoxic events surpass those of future ocean warming and acidification. Nat. Ecol. Evol. 5, 311–321 (2021).Article 

Google Scholar 
Crain, C. M., Kroeker, K. & Halpern, B. S. Interactive and cumulative effects of multiple human stressors in marine systems. Ecol. Lett. 11, 1304–1315 (2008).Article 

Google Scholar 
Pörtner, H. O. Synergistic effects of temperature extremes, hypoxia, and increases in CO2 on marine animals: From Earth history to global change. J. Geophys. Res. 110, 0910 (2005).Article 

Google Scholar 
Piggott, J. J., Townsend, C. R. & Matthaei, C. D. Reconceptualizing synergism and antagonism among multiple stressors. Ecol. Evol. 5, 1538–1547 (2015).Article 

Google Scholar 
Boyd, P. W. et al. Experimental strategies to assess the biological ramifications of multiple drivers of global ocean change: A review. Glob. Change Biol 24, 2239–2261 (2018).Article 
ADS 

Google Scholar 
Giorgi, A. E. & Congleton, J. L. Effects of current velocity on development and survival of lingcod, Ophiodon elongatus, embryos. Environ. Biol. Fish 10, 15–27 (1984).Article 

Google Scholar 
Liu, G., Zhu, S., Liu, D. & Ye, Z. Effect of the C/N ratio on inorganic nitrogen control and the growth and physiological parameters of tilapia s fingerlings, Oreochromis niloticu reared in biofloc systems. Aquac. Res. 49, 2429–2439 (2018).Article 
CAS 

Google Scholar 
Houde, E. D. Fish early life dynamics and recruitment variability. Am. Fish. Soc. Symp. 2, 17–29 (1987).ADS 

Google Scholar 
Miller, T. J., Crowder, L. B., Rice, J. A. & Marschall, E. A. Larval size and recruitment mechanisms in fishes: Toward a conceptual framework. Can. J. Fish. Aquat. Sci. 45, 1657–1670 (1988).Article 

Google Scholar 
Doi, H., Akamatsu, F. & González, A. L. Starvation effects on nitrogen and carbon stable isotopes of animals: An insight from meta-analysis of fasting experiments. R. Soc. open sci. 4, 170633 (2017).Article 
ADS 

Google Scholar 
Pimentel, M. S. et al. Defective skeletogenesis and oversized otoliths in fish early stages in a changing ocean. J. Exp. Biol. 1, 092635. https://doi.org/10.1242/jeb.092635 (2014).Article 

Google Scholar 
Politis, S. N., Dahlke, F. T., Butts, I. A., Peck, M. A. & Trippel, E. A. Temperature, paternity and asynchronous hatching influence early developmental characteristics of larval Atlantic cod, Gadus morhua. J. Exp. Mar. Biol. Ecol. 459, 70–79 (2014).Article 

Google Scholar 
Appelbaum, S. et al. Studies on rearing of lingcod Ophiodon elongatus. Aquaculture 135, 219–227 (1995).Article 

Google Scholar 
Hempel, G. Early life history of marine fish: The egg stage. Washington Sea Grant. (University of Washington Press, 1979)Gadomski, D. M. & Caddell, S. M. Effects of temperature on the development and survival of eggs of four coastal California fishes. Fish. Bull. 94, 41–48 (1996).
Google Scholar 
Parker, L. M. et al. Adult exposure influences offspring response to ocean acidification in oysters. Glob. Change Biol. 18, 82–92 (2012).Article 
ADS 

Google Scholar 
Rombough, P. The effects of temperature on embryonic and larval development. In Global Warming: Implications for Freshwater and Marine Fish (Society for Experimental Biology Seminar Series) (eds Wood, C. & McDonald, D.) 177–224 (Cambridge University Press, 1997).Chapter 

Google Scholar 
Bownds, C., Wilson, R. & Marshall, D. J. Why do colder mothers produce larger eggs? An optimality approach. J. Exp. Biol. 213, 3796–3801 (2010).Article 

Google Scholar 
Longo, G. C. et al. Strong population differentiation in lingcod ( Ophiodon elongatus ) is driven by a small portion of the genome. Evol. Appl. 13, 2536–2554 (2020).Article 
CAS 

Google Scholar 
Silberberg, K. R., Laidig, T. E., Adams, P. B. & Albin, D. Analysis of maturity in lingcod, Ophiodon elongatus. California Fish Game 87, 139–152 (2001).
Google Scholar 
Palumbi, S. R. Why mothers matter. Nature 430, 621–622 (2004).Article 
ADS 
CAS 

Google Scholar 
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).Article 

Google Scholar 
Miller, D. J., & Geibel, J. J. Summary of Blue Rockfish and Lingcod Life Histories, a Reef Ecology Study, and Giant Kelp, Macrocystis Pyrifera, Experiments in Monterey Bay, California. (State of California, Resources Agency, Department of Fish and Game, 1973).Low, C. J. & Beamish, R. J. A study of the nesting behavior of lingcod (Ophiodon elongatus) in the strait of Georgia, British Columbia. Can. Fish. Mar. Serv. Tech. Rep. 843, 1–10 (1978).
Google Scholar 
King, J. R. & Withler, R. E. Male nest site fidelity and female serial polyandry in lingcod (Ophiodon elongatus, Hexagrammidae): Lingcod nest site fidelity. Mol. Ecol. 14, 653–660 (2005).Article 

Google Scholar 
Withler, R. E. et al. Polygamous mating and high levels of genetic variation in lingcod, Ophiodon elongatus of the Strait of Georgia, British Columbia. In Genetics of Subpolar Fish and Invertebrates 345–357 (Springer, 2004).
Google Scholar 
Perkins, M. J. et al. Application of nitrogen and carbon stable isotopes (δ15N and δ13C) to quantify food chain length and trophic structure. PLoS ONE 9, e93281 (2014).Article 
ADS 

Google Scholar 
Earth Systems Research Laboratory (ESRL). NOAA’s Ocean Climate Change Web Portal. http://www.esrl.noaa.gov/psd/ipcc/ocn/ (2019).Feely, R., Doney, S. & Cooley, S. Ocean acidification: Present conditions and future changes in a high-CO2 world. Oceanography 22, 36–47 (2009).Article 

Google Scholar 
Frieder, C. A., Nam, S. H., Martz, T. R. & Levin, L. A. High temporal and spatial variability of dissolved oxygen and pH in a nearshore California kelp forest. Biogeosciences 9, 3917–3930 (2012).Article 
ADS 
CAS 

Google Scholar 
Olito, C., White, C. R., Marshall, D. J. & Barneche, D. R. Estimating monotonic rates from biological data using local linear regression. J. Exp. Biol. 1, 148775. https://doi.org/10.1242/jeb.148775 (2017).Article 

Google Scholar 
Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9(2), 378–400 (2017).Article 

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