Ecology

Willcox, G., Fornite, S. & Herveux, L. Early Holocene cultivation before domestication in northern Syria. Veg. Hist. Archaeobot. 17, 313–325 (2008).Article 

Google Scholar 
Fuller, D. Q., Willcox, G. & Allaby, R. G. Cultivation and domestication had multiple origins: arguments against the core area hypothesis for the origins of agriculture in the Near East. World Archaeol. 43, 628–652 (2011).Article 

Google Scholar 
Ibáñez, J. J., Anderson, P. C., González-Urquijo, J. & Gibaja, J. Cereal cultivation and domestication as shown by microtexture analysis of sickle gloss through confocal microscopy. J. Archaeol. Sci. 73, 62–81 (2016).Article 

Google Scholar 
Weiss, E., Kislev, M. E. & Hartmann, A. Autonomous cultivation before domestication. Science 312, 1608–1610 (2006).CAS 
PubMed 
Article 

Google Scholar 
Willcox, G. Measuring grain size and identifying Near Eastern cereal domestication: evidence from the Euphrates Valley. J. Archaeol. Sci. 31, 145–150 (2004).Article 

Google Scholar 
White, C. E. & Makarewicz, C. A. Harvesting practices and early Neolithic barley cultivation at el-Hemmeh, Jordan. Veg. Hist. Archaeobot. 21, 85–94 (2012).Article 

Google Scholar 
Colledge, S., Conolly, J., Finlayson, B. & Kuijt, I. New insights on plant domestication, production intensification, and food storage: the archaeobotanical evidence from PPNA Dhra‘. Levant 50, 14–31 (2018).Article 

Google Scholar 
Kuijt, I. & Finlayson, B. Evidence for food storage and predomestication granaries 11,000 years ago in the Jordan Valley. Proc. Natl Acad. Sci. USA 106, 10966–10970 (2009).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Willcox, G. & Stordeur, D. Large-scale cereal processing before domestication during the tenth millennium cal bc in northern Syria. Antiquity 86, 99–114 (2012).Article 

Google Scholar 
Colledge, S. in The Origins of Agriculture and Crop Domestication (eds Damania, A. B. et al.) 121–131 (ICARDA, 1998).Hillman, G. C., Hedges, R., Moore, A. M. T., Colledge, S. & Pettitt, P. New evidence of Lateglacial cereal cultivation at Abu Hureyra on the Euphrates. Holocene 11, 383–393 (2001).Article 

Google Scholar 
Willcox, G. Searching for the origins of arable weeds in the Near East. Veg. Hist. Archaeobot. 21, 163–167 (2012).Article 

Google Scholar 
Snir, A. et al. The origin of cultivation and proto-weeds, long before neolithic farming. PLoS ONE 10, e0131422 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Harris, D. R. & Fuller, D. Q. in Encyclopedia of Global Archaeology (ed. Smith, C.) 104–113 (Springer, 2014).Grime, J. P., Hodgson, J. G. & Hunt, R. Comparative Plant Ecology: A Functional Approach to Common British Species (Springer, 2014).Harlan, J. R., de Wet, J. M. J. & Price, E. G. Comparative evolution of cereals. Evolution 27, 311–325 (1973).PubMed 
Article 

Google Scholar 
Fuller, D. Q. Contrasting patterns in crop domestication and domestication rates: recent archaeobotanical insights from the Old World. Ann. Bot. 100, 903–924 (2007).PubMed 
PubMed Central 
Article 

Google Scholar 
Asouti, E. in Neolithic Corporate Identities. Studies in Early Near Eastern Production, Subsistence and Environment 20 (eds Benz, M. et al.) 21–53 (Ex oriente, 2017).Harris, D. R. in Foraging and Farming: the Evolution of Plant Exploitation (eds Harris, D. R. & Hillman, G.) 11–26 (Unwin Hyman, 1989).Smith, B. D. Low-level food production. J. Archaeol. Res. 9, 1–43 (2001).Article 

Google Scholar 
Rindos, D. The Origins of Agriculture: an Evolutionary Perspective (Academic, 1984).Weide, A. Towards a socio-economic model for southwest Asian cereal domestication. Agronomy 11, 2432 (2021).Article 

Google Scholar 
Hillman, G. C. & Davies, M. S. Measured domestication rates in wild wheats and barley under primitive cultivation, and their archaeological implications. J. World Prehist. 4, 157–222 (1990).Article 

Google Scholar 
Kislev, M. E., Hartmann, A. & Weiss, E. Impetus for sowing and the beginning of agriculture: ground collecting of wild cereals. Proc. Natl Acad. Sci. USA 101, 2692–2695 (2004).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Weide, A. et al. The association of arable weeds with modern wild cereal habitats: implications for reconstructing the origins of plant cultivation in the Levant. Environ. Archaeol. https://doi.org/10.1080/14614103.2021.1882715 (2021).Zohary, M. The segetal plant communities of Palestine. Vegetatio 2, 387–411 (1950).Article 

Google Scholar 
Abbo, S., Lev-Yadun, S. & Gopher, A. Plant domestication and crop evolution in the Near East: on events and processes. Crit. Rev. Plant Sci. 31, 241–257 (2012).Article 

Google Scholar 
Wood, D. & Lenné, J. M. A natural adaptive syndrome as a model for the origins of cereal agriculture. Proc. R. Soc. Lond. B 285, 20180277 (2018).
Google Scholar 
Bogaard, A., Palmer, C., Jones, G., Charles, M. & Hodgson, J. G. A FIBS approach to the use of weed ecology for the archaeobotanical recognition of crop rotation regimes. J. Archaeol. Sci. 26, 1211–1224 (1999).Article 

Google Scholar 
Jones, G., Bogaard, A., Charles, M. & Hodgson, J. G. Distinguishing the effects of agricultural practices relating to fertility and disturbance: a functional ecological approach in archaeobotany. J. Archaeol. Sci. 27, 1073–1084 (2000).Article 

Google Scholar 
Díaz, S. et al. The global spectrum of plant form and function. Nature 529, 167–171 (2016).PubMed 
Article 
CAS 

Google Scholar 
Garnier, E., Navas, M.-L. & Grigulis, K. Plant Functional Diversity: Organism Traits, Community Structure, and Ecosystem Properties (Oxford Univ. Press, 2016).Bogaard, A. Neolithic Farming in Central Europe (Routledge, 2004).Bogaard, A. et al. From traditional farming in Morocco to early urban agroecology in northern Mesopotamia: combining present-day arable weed surveys and crop isotope analysis to reconstruct past agrosystems in (semi-)arid regions. Environ. Archaeol. 23, 303–322 (2018).Article 

Google Scholar 
Hamerow, H. et al. An integrated bioarchaeological approach to the medieval ‘agricultural revolution’: a case study from Stafford, England, c. ad 800–1200. Eur. J. Archaeol. 23, 585–609 (2020).Article 

Google Scholar 
Green, L., Charles, M. & Bogaard, A. Exploring the agroecology of Neolithic Çatalhöyük, Central Anatolia: an archaeobotanical approach to agricultural intensity based on functional ecological analysis of arable weed flora. Paléorient 44, 29–44 (2018).
Google Scholar 
Green, L. Assessing the Nature of Early Farming in Neolithic Western Asia: A Functional Ecological Approach to Emerging Arable Weeds. Univ. of Oxford (2017).Atran, S. Hamula organisation and masha’a tenure in Palestine. Man 21, 271–295 (1986).Article 

Google Scholar 
Palmer, C. ‘Following the plough’: the agricultural environment of northern Jordan. Levant 30, 129–165 (1998).Article 

Google Scholar 
Håkansson, S. in Biology and Ecology of Weeds (eds Holzner, W. & Numata, M.) 123–135 (Springer Netherlands, 1982).Charles, M., Bogaard, A., Jones, G., Hodgson, J. & Halstead, P. Towards the archaeobotanical identification of intensive cereal cultivation: present-day ecological investigation in the mountains of Asturias, northwest Spain. Veg. Hist. Archaeobot. 11, 133–142 (2002).Article 

Google Scholar 
Hartmann-Shenkman, A., Kislev, M. E., Galili, E., Melamed, Y. & Weiss, E. Invading a new niche: obligatory weeds at Neolithic Atlit-Yam, Israel. Veg. Hist. Archaeobot. 24, 9–18 (2015).Article 

Google Scholar 
Kuijt, I. in The Neolithic Demographic Transition and its Consequences (eds Bocquet-Appel, J.-P. & Bar-Yosef, O.) 287–313 (Springer Netherlands, 2008).Bogaard, A. et al. Private pantries and celebrated surplus: storing and sharing food at Neolithic Çatalhöyük, Central Anatolia. Antiquity 83, 649–668 (2009).Article 

Google Scholar 
Jones, G. et al. The origins of agriculture: intentions and consequences. J. Archaeol. Sci. 125, 105290 (2021).Article 

Google Scholar 
Weiss, E., Kislev, M. E., Simchoni, O., Nadel, D. & Tschauner, H. Plant-food preparation area on an Upper Paleolithic brush hut floor at Ohalo II, Israel. J. Archaeol. Sci. 35, 2400–2414 (2008).Article 

Google Scholar 
Kluyver, T. A., Charles, M., Jones, G., Rees, M. & Osborne, C. P. Did greater burial depth increase the seed size of domesticated legumes? J. Exp. Bot. 64, 4101–4108 (2013).CAS 
PubMed 
Article 

Google Scholar 
Preece, C., Jones, G., Rees, M. & Osborne, C. P. Fertile Crescent crop progenitors gained a competitive advantage from large seedlings. Ecol. Evol. 11, 3300–3312 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Halstead, P. Two Oxen Ahead: Pre-mechanized Farming in the Mediterranean (Wiley, 2014).Anderson, P. C. in The Origins of Agriculture and Crop Domestication (eds Damania, A. B. et al.) 145–159 (ICARDA, 1998).Mercuri, A. M., Fornaciari, R., Gallinaro, M., Vanin, S. & di Lernia, S. Plant behaviour from human imprints and the cultivation of wild cereals in Holocene Sahara. Nat. Plants 4, 71–81 (2018).PubMed 
Article 

Google Scholar 
Spengler, R. N. & Mueller, N. G. Grazing animals drove domestication of grain crops. Nat. Plants 5, 656–662 (2019).PubMed 
Article 

Google Scholar 
Smith, B. D. General patterns of niche construction and the management of ‘wild’ plant and animal resources by small-scale pre-industrial societies. Phil. Trans. R. Soc. Lond. B 366, 836–848 (2011).Article 

Google Scholar 
Bogaard, A. et al. Reconsidering domestication from a process archaeology perspective. World Archaeol. https://doi.org/10.1080/00438243.2021.1954990 (2021).Coqueugniot, E. in Espace Naturel, Espace Habité En Syrie Du Nord (10e–2e millénaires av. J.-C.) (eds M. Fortin & O. Aurenche) 109–114 (Maison de l’Orient et de la Méditerranée, 1998).Douché, C. Émergence et développement des sociétés agricoles au Néolithique acéramique (Xe-VIIIe millénaires av. n. ère) étude archéobotanique de Dja’de El-Mughara et Tell Aswad, Syrie. PhD thesis (Archaeological Mission of Dja’de el Mughara, 2018).Noy, T. Gilgal I: a pre-pottery Neolithic site, Israel. The 1985–1987 seasons. Paléorient 15, 11–18 (1989).Article 

Google Scholar 
Bar-Yosef, O. & Gopher, A. in An Early Neolithic Village in the Jordan Valley (eds Bar-Yosef, O. & Gopher, A.) 41–69 (Harvard Univ., 1997).Wright, K. I. The social origins of cooking and dining in early villages of western Asia. Proc. Prehist. Soc. 66, 89–121 (2000).Article 

Google Scholar 
Finlayson, B. Egalitarian societies and the earliest Neolithic of southwest Asia. Prehist. Archaeol. J. Interdiscip. Stud. 3, 27–43 (2020).Article 

Google Scholar 
Bowles, S. & Choi, J.-K. The Neolithic agricultural revolution and the origins of private property. J. Polit. Econ. 127, 2186–2228 (2019).Article 

Google Scholar 
Kuijt, I. The Neolithic refrigerator on a Friday night: how many people are coming to dinner and just what should I do with the slimy veggies in the back of the fridge? Environ. Archaeol. 20, 321–336 (2015).Article 

Google Scholar 
Danin, A. Flora and vegetation of Israel and adjacent areas. Zoogeogr. Isr. 30, 251–276 (1988).
Google Scholar 
Noy-Meir, I., Gutman, M. & Kaplan, Y. Responses of Mediterranean grassland plants to grazing and protection. J. Ecol. 77, 290–310 (1989).Article 

Google Scholar 
Noy-Meir, I. The effect of grazing on the abundance of wild wheat, barley and oat in Israel. Biol. Conserv. 51, 299–310 (1990).Article 

Google Scholar 
Jones, G., Bogaard, A., Halstead, P., Charles, M. & Smith, H. Identifying the intensity of crop husbandry practices on the basis of weed floras. Annu. Br. Sch. Athens 94, 167–189 (1999).Article 

Google Scholar 
Sternberg, M., Gutman, M., Perevolotsky, A., Ungar, E. D. & Kigel, J. Vegetation response to grazing management in a Mediterranean herbaceous community: a functional group approach. J. Appl. Ecol. 37, 224–237 (2000).Article 

Google Scholar 
Sternberg, M. et al. Testing the limits of resistance: a 19-year study of Mediterranean grassland response to grazing regimes. Glob. Change Biol. 21, 1939–1950 (2015).Article 

Google Scholar 
Calev, A. et al. High-intensity thinning treatments in mature Pinus halepensis plantations experiencing prolonged drought. Eur. J. For. Res. 135, 551–563 (2016).Article 

Google Scholar 
Osem, Y., Perevolotsky, A. & Kigel, J. Grazing effect on diversity of annual plant communities in a semi-arid rangeland: interactions with small-scale spatial and temporal variation in primary productivity. J. Ecol. 90, 936–946 (2002).Article 

Google Scholar 
Temper, L. Creating facts on the ground: agriculture in Israel and Palestine (1882–2000). Hist. Agrar. 48, 75–110 (2009).
Google Scholar 
Dan, J., Yaalon, D., Koyumdjisky, H. & Raz, Z. The soil association map of Israel (1:1,000,000). Isr. J. Earth Sci. 21, 29–49 (1970).
Google Scholar 
Sans, F. X. & Masalles, R. M. Phenological patterns in an arable land weed community related to disturbance. Weed Res. 35, 321–332 (1995).Article 

Google Scholar 
Zohary, M. & Feinbrun-Dothan, N. Flora Palaestina Vol. 1–4 (Israel Academy of Sciences and Humanities, 1966).Davis, P. Flora of Turkey and the East Aegean Islands Vol. 1–10 (Edinburgh Univ. Press, 1965).Mortimer, A. M. in Weed Control Handbook: Principles (eds Hance, R. J. & Holly, K.) 1–42 (Blackwell, 1990).Douché, C. & Willcox, G. New archaeobotanical data from the Early Neolithic sites of Dja’de el-Mughara and Tell Aswad (Syria): a comparison between the northern and the southern Levant. Paléorient 44, 45–58 (2018).
Google Scholar 
Jones, G. The application of present-day cereal processing studies to charred archaeobotanical remains. Circaea 6, 91–96 (1990).
Google Scholar 
Bogaard, A., Jones, G. & Charles, M. The impact of crop processing on the reconstruction of crop sowing time and cultivation intensity from archaeobotanical weed evidence. Veg. Hist. Archaeobot. 14, 505–509 (2005).Article 

Google Scholar 
Bogaard, A. et al. in Humans and Landscapes of Çatalhöyük: Reports from the 2000–2008 Seasons (ed. Hodder, I.) 93–128 (Cotsen Institute of Archaeology/British Institute at Ankara, 2013).Filipović, D. Early Farming in Central Anatolia: an Archaeobotanical Study of Crop Husbandry, Animal Diet and Land Use at Neolithic Çatalhöyük (British Archaeological Reports, 2014).Helmer, D. et al. in New Methods and the First Steps of Mammal Domestication (eds Vigne, J.-D. et al.) 86–95 (Oxbow Books, 2005).Charles, M. Fodder from dung: the recognition and interpretation of dung-derived plant material from archaeological sites. Environ. Archaeol. 1, 111–122 (1998).Article 

Google Scholar 
Kislev, M. E. in An Early Neolithic Village in the Jordan Valley (eds Ofer Bar-Yosef & Avi Gopher) 209–236 (Harvard Univ., 1997).Kislev, M. E. et al. in Gilgal: Early Neolithic Occupations in the Lower Jordan Valley. The Excavations of Tamar Noy (eds Bar-Yosef, O. et al.) 251–257 (Oxbow Books, 2010).Snir, A., Nadel, D. & Weiss, E. Plant-food preparation on two consecutive floors at Upper Paleolithic Ohalo II, Israel. J. Archaeol. Sci. 53, 61–71 (2015).Article 

Google Scholar 
Jones, G., Charles, M., Bogaard, A. & Hodgson, J. Crops and weeds: the role of weed functional ecology in the identification of crop husbandry methods. J. Archaeol. Sci. 37, 70–77 (2010).Article 

Google Scholar 
Šmilauer, P. & Lepš, J. Multivariate Analysis of Ecological Data Using CANOCO 5 (Cambridge Univ. Press, 2014).Galili, E. et al. Atlit-Yam: a Prehistoric site on the sea floor off the Israeli coast. J. Field Archaeol. 20, 133–157 (1993).
Google Scholar 
Brenet, M., Sanchez-Priego, J. & Ibáñez-Estévez, J. J. in Préhistoire et Approche Expérimentale (eds Bourguignon, L. et al.) 121–164 (Monique Mergoil, 2001).Bar-Yosef, O., Gopher, A., Goring-Morris, A. N. & Kozlowski, S. K. in Gilgal: Early Neolithic Occupations in the Lower Jordan Valley. The Excavations of Tamar Noy (eds Bar-Yosef, O. et al.) 11–26 (Oxbow Books, 2010). More

Lattice data geoprocessing and temporal extentWe latticed the data49 using a worldwide grid composed of 18,874 hexagonal 7774 km2 units, built using Discrete Global for R (https://github.com/r-barnes/dggridR)50. All the information we processed on yellow fever cases, on urban and sylvatic vectors presences, and on zoogeographic, spatial and environmental variables (see details on this information below) was aggregated at this spatial resolution. We used zonal statistics to calculate average variable values using ArcMAP 10.7.The temporal extent for our analysis was divided into three periods: the late 20th century (1970–2000), the early 21st century (2001–2017), and the period 2018–2020. Predictions estimated by the late 20th century models were validated using cases reported in the early 21st century, and predictions from the early 21st century models were validated with records from 2018‒2020. Although the limit between periods at the turn of the century is arbitrary, it reflects: 1) Distributional changes in the ranges of the Ae. aegypti and Ae. albopictus vectors51; 2) after 1999, the yellow fever genotype I has spread outside the endemic regions, and the genotype I modern-lineage has caused all major yellow fever outbreaks detected in non-endemic regions of South America since 200013; 3) the maximum potential of globalization was realised at the beginning of the 21st century with the opening of international borders, the widespread access to the Internet and to cell phones, and the generalization of online travel booking and of low-cost flights34. The end of the second period, 2017, was chosen in order to include three years with occurrence of yellow fever cases in south-western Brazil (and two since its occurrence in Angola and the DRC), while retaining three later years for predictive testing purposes (details on this testing are given below).Yellow fever datasetsWe used georeferenced cases of yellow fever in humans for a period of 51 years (from 1970 to 2020). This study period starts immediately after the suspension of the use of DDT due to to the appearance of resistance of Ae. aegypti in the late 1960s in several countries, after 50 years of eradication efforts10. We took from Shearer et al.6 the distribution of yellow fever cases for the period 1970–2016. We extracted additional cases for the period 1970–2020 from various sources (Supplementary data 1), including ProMED-mail: Program of International society for infectious diseases; World Health Organization (WHO): Yellow fever outbreak weekly situation reports, Rapport de situation fievre jaune en RD Congo and Weekly epidemiological record; Health Ministry of different countries: Epidemiological Bulletins of yellow fever in Brazil, Peru, Colombia, and Paraguay; Pan American Health Organization (PAHO): Epidemiological Update Yellow Fever; European Centre for Disease Prevention and Control (ECDC): Communicable disease threats report and Rapid risk assessment report; Nigeria Centre for Disease Control (NCDC): Situation report, yellow fever outbreak in Nigeria and Global Infectious Disease and Epidemiology Online Network (GIDEON). The reported cases were complemented with publications available since 2016 with geo-referenced information on case location (Supplementary data 1). In addition, information was also sought on cases reported in French and Portuguese from local news reports in Africa.We only represented in the hexagonal lattice the reported cases of yellow fever that had a precise location or that were referred to administrative unit was smaller than or of similar size to the hexagons. This dataset was transformed into a binary variable per study period representing the presence (n = 218 hexagons in the late 20th century; 493 hexagons in the early 21st century, see Supplementary data 2) or absence (n = 18,656 hexagons in the late 20th century; 18,381 hexagons in the early 21st century), hereafter the distribution of reported cases of yellow fever.Vector datasetThe georeferenced presences of vectors involved in the urban cycle of yellow fever (i.e., the mosquitoes Ae. aegypti and Ae. albopictus) were taken from “The global compendium of the Ae. aegypti and Ae. Albopictus occurrence”26 for the period 1970–2014. We complemented these records with georeferenced data scientifically validated for the period 2014–2017, taken from VectorBase (https://www.vectorbase.org/) and Mosquito Alert (http://www.mosquitoalert.com/). We included both species because, although Ae. Aegypti is the main vector of yellow fever, Ae. albopictus can also transmit the yellow fever virus to humans4,52.In addition, we included georeferenced occurrence data of sylvatic vectors (Haemagogus janthinomys, H. leucocelaenus and Sabethes chloropterus in South America; Ae. africanus and Ae. vittatus in Africa), which were obtained from Vectormap (vectormap.si.edu) and Gbif (https://gbif.org).We represented in the hexagonal lattice the reported occurrence of mosquitoes that had a precise location or were located in administrative smaller than or of similar size to the hexagons. With this information, we built binary variables representing the presence or absence of each mosquito species in each hexagon. For species involved in the urban cycle, we built two binary variables per species: one for the late 20th century, and another for the early 21st century. For species involved in the sylvatic cycle, we merged the data of late 20th century and early 21st century in order to build a binary variable per species, due the scarcity of data and under the assumption that their distributions have been stable during the four last decades53,54,55.Zoogeographic, spatial and environmental variablesWe built zoogeographic variables based on chorotypes, or types of distribution ranges, of all non-human primate species, as all are potentially vulnerable to yellow fever56. A chorotype is a distribution pattern shared by a group of species57. For obtaining these zoogeographic variables, we proceeded in 4 steps: (1) Distribution maps of non-human primates were obtained from the IUCN for South-America and Africa; (2) the species ranges were classified hierarchically using the classification algorithm UPGMA according to the Baroni-Urbani & Buser´s similarity index58; (3) we evaluated the statistical significance of all clusters obtained as a result of the classification using RMacoqui 1.0 software (http://rmacoqui.r-forge.r-project.org/)59; (4) in each hexagon, the number of species belonging to each chorotype was quantified. We generated a zoogeographic model based on the non-human primates chorotypes by running a forward-backward stepwise logistic regression using presence/absence of yellow fever cases and the number of species of each chorotype as dependent and predictor variables, respectively. This procedure was made for two periods: late 20th century and early 21st century. Henceforth, only the selected chorotype variables were considered in the baseline disease favourability models explained below.We built a yellow fever spatial variable for each continent (South-America and Africa), which were calculated through the trend surface approach, by performing a backward-stepwise logistic regression of the distribution of yellow fever cases on a ensemble of variables defined for polynomial combinations of longitude (X) and latitude (Y) up to the third degree: X, Y, XY, X2, Y2, X2Y, XY2, X3, and Y3. We transformed probability values derived from logistic regression into spatial favourability values by applying the Favourability Function60,61, using the following equation:$$F=frac{P}{1-P}Big/left(frac{{n}_{1}}{{n}_{0}}+frac{P}{1-P}right)$$
(1)
where P is the spatial probability of occurrence of at least a case of yellow fever at each hexagon, and n1 and n0 are the numbers of hexagons with presence and absence of yellow fever cases, respectively. We built a different spatial variable for each continent and time period.We used environmental variables related to the following factors: climate, human activity, topography, hydrography, biome, ecosystem type, and forest loss. For details about the source and description of the environmental variables selected, see Supplementary Table 3.Pathogeographical approach to transmission risk modellingOur objectives were to construct a global yellow fever transmission risk map, and to identify areas where primates contribute to increased risk, using the methodology previously used to analyse the worldwide dynamic biogeography of zoonotic and anthroponotic dengue34 (see flowchart in Fig. 1 and Supplementary Methods). We produced a transmission model focused on the late 20th century and another for the early 21st century.The risk of transmission was assessed by combining a first model describing areas favourable to the presence of yellow fever, i.e., the “baseline disease model”; and another model describing areas favourable to the presence of mosquitoes known to act as vectors, i.e., the “vector model”. For this combination, we used the fuzzy intersection62, i.e., the risk of transmission at each hexagon was valued at the minimum between favourability in the baseline disease model and favourability in the vector model.In this way, we considered that the vectors are a limiting factor, and that the risk of transmission derives from the degree to which the environmental conditions are simultaneously favourable for the presence of vectors and for disease cases to occur63. In order to analyze the spatio-temporal dynamic of yellow fever, we made comparable models for the late 20th century and the early 21st century, using predictor variables that are available for both periods. Later, we made a 21st-century enhanced model that optimized the predictive capacity of availabe information in the search for current risk areas. For this purpose, we included, in the variable set, predictors that are only accessible for the 21st century (e.g., high-resolution population density, livestock, irrigation, infrastructures, intact forest, and GlobCover land cover classes; see Supplementary Table 3).Baseline disease modelsThe baseline disease model in the late 20th century was expressed in terms of favourability values, using the Eq. (1) (see above). This time, P was calculated through a multivariable forward-backward stepwise logistic regression of the 20th-century yellow fever presences/absences on a set of zoogeographic, environmental and spatial variables. This was made in two blocks: 1) a stepwise selection of environmental and spatial variables; 2) a later stepwise addition of chorotypes whose presence contribute to improve significantly the likelihood of the model based only on the first block. Variables for each block were preselected using RAO´s score tests (which estimated the significance of its association to the distribution of yellow fever cases), and Benjamini and Hochberg´s (1995) false discovery rate (FDR) to control for Type I errors, which could pass due to the number of variables analysed. We also avoided excesive multicollinearity by preventing that variables with Spearman correlation values >0.8 were included in the same model. In case this happened, only the variable with the most significant RAO´s score-test value was retained, and the multivariable model was re-run. The parameters in the models were estimated using a gradient ascent machine learning algorithm, and the significance of these paremeters was assessed using the test of Wald. The goodness of fit of the models was established using the test of Hosmer and Lemeshow, which checks the significance of the difference between expected and observed values, so that non significant differences mean that the fit is good. We used IBM-SPSS Statistics 24 software package to perform the models and all the associated tests.We subsequently updated the baseline disease model to explain the distribution of yellow fever cases in the early 21st century. Compared to the procedure described for the 20th-century model, we included a new block before the two ones mentioned above. Thus, the methodological sequence was as follows: (1) forcing the input, as a predictor variable, of the logit of the late 20th century baseline disease model (the logit being the linear combination of variables in the 20th-century model); (2) making a later stepwise selection of spatial and environmental variables; and (3) a stepwise addition of chorotypes that contribute to improving the model’s likelihood. In this way, we took into account that the current spread of yellow fever is influenced by the inertia of previous situations. This is equivalent to assuming that there is temporal autocorrelation (i.e., disease cases in the early 21st century are more probable to occur in areas where they already occurred in the late 20th century). In the 21st-century model, the variables entering in blocks (2) and (3) represent the drivers potentially favouring the spread34. The preselection of variables for blocks (2) and (3) and the control for excessive multicollinearity between environmental variables were made as explained for the late 20th-century model.Vector modelsWe produced a favourabuility model for each vector species for the late 20th century and for the early 21st century separately. We built multivariable favourability models for urban vectors using the distribution of each urban mosquito species in the late 20th century and the spatial and environmental variables for the late 20th century, following the same procedure used for block (1) in the 20th-century baseline disease model. We also updated each urban vector model for the early 21st century as in the baseline disease model, using the procedure described for blocks (1) and (2).A single model, referred to both the late 20th and the early 21st centuries, was made for sylvatic vectors, for the reasons explained above. Finally, we built up the vector models for the late 20th century and for the early 21st century by joining all individual vector models of each period using the fuzzy union64 (i.e., considering for each hexagon the maximum value shown by any of the species models). This criterion was taken into account because, if the pathogen were present, the occurrence of a single vector species would involve potential for yellow fever transmission.Model fit assessment and validation of its predictive capacityFavourability models were assessed according to their classification and discrimination capacities respect to the training data set (i.e., to the observations used for model training). The classification capacity was based on two classification thresholds: F = 0.5, which represents the neutral favourability, and F = 0.2, below which the risk of transmission was considered to be low61. Six classification assessment indices were used65: (1) sensitivity (i.e., proportion of presences correctly classified in favourable hexagons), (2) specificity (i.e., proportion of absences correctly classified in unfavourable hexagons), (3) CCR (i.e., proportion of presences and absences correctly classified in favourable hexagons respectively), (4) TSS (that is sensitivity + specifity – 1), (5) underprediction rate (i.e., proportion of favourable areas that are recorded to have presences), and (6) overprediction rate (i.e., proportion of favourable areas that are not recorded to have presences). The discrimination capacity was assessed using the area under the receiver operating characteristic (ROC) curve (AUC)66.The validation of the predictive capacity of the late 20th century disease and transmission-risk models was done by evaluating, with the same indices used above, classification and discrimination capacities with respect to the cases of the period 2001‒2020. The predictive capacity of the models for the early 21st century was validated with respect to the yellow fever cases reported in the period 2018‒2020.Relative importance of the zoogeographical factorWe estimated the pure contribution of non-human primates to the baseline disease model, i.e., how much of the variation in favourability for yellow fever cases was explained exclusively by the zoogeographical factor, by performing a variation partitioning analysis67. This implied the use of the zoogeographic model and a spatio-environmental model constructed with the environmental and spatial variables that entered the baseline disease model. This approach also allowed us to calculate how much is the variation of the baseline disease model attributable simultaneously to the zoogeographical and other factors. We built maps identifying the zones where the non-human primates could increase yellow fever cases in humans, that is, where the presence of primates could favour the occurrence of yellow fever regardless of correlations with other factors. To map these areas we identified the hexagons that fulfilled these conditions: 1) favourability values for the baseline disease model were ≥ 0.2; and 2) the difference between the favourability values provided by the baseline disease model and the spatio-environmental model was positive and ≥ 0.01.Reporting summaryFurther information on research design is available in the Nature Research Reporting Summary linked to this article. More

Alieva, N. O. et al. Diversity and evolution of coral fluorescent proteins. PLoS ONE 3, e2680 (2008).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Dove, S., Hoegh-Guldberg, O. & Ranganathan, S. Major colour patterns of reef-building corals are due to a family of GFP-like proteins. Coral Reefs 19, 197–204 (2001).Article 

Google Scholar 
Shimomura, O., Johnson, F. H. & Saiga, Y. Extraction, purification, and properties of aequorin, a bioluminescent protein from the luminous hydromedusan, Aequorea. J. Cell. Physiol. 59, 223–239 (1962).CAS 
Article 

Google Scholar 
Salih, A., Larkum, A., Cox, G., Kühl, M. & Hoegh-Guldberg, O. Fluorescent pigments in corals are photoprotective. Nature 408, 850–853 (2000).CAS 
PubMed 
Article 

Google Scholar 
Kawaguti, S. Effect of the green fluorescent pigment on the productivity of the reef corals. Micronesica 5, 121 (1969).
Google Scholar 
Gittins, J. R., D’Angelo, C., Oswald, F., Edwards, R. J. & Wiedenmann, J. Fluorescent protein-mediated colour polymorphism in reef corals: multicopy genes extend the adaptation/acclimatization potential to variable light environments. Mol. Ecol. 24, 453–465 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Roth, M. S., Latz, M. I., Goericke, R. & Deheyn, D. D. Green fluorescent protein regulation in the coral Acropora yongei during photoacclimation. J. Exp. Biol. 213, 3644–3655 (2010).CAS 
PubMed 
Article 

Google Scholar 
Quick, C., D’Angelo, C. & Wiedenmann, J. Trade-offs associated with photoprotective green fluorescent protein expression as potential drivers of balancing selection for color polymorphism in reef corals. Front. Mar. Sci. 5, 11 (2018).Article 

Google Scholar 
Schlichter, D., Fricke, H. W. & Weber, W. Light harvesting by wavelength transformation in a symbiotic coral of the Red Sea twilight zone. Mar. Biol. 91, 403–407 (1986).Article 

Google Scholar 
Bollati, E., Plimmer, D., D’Angelo, C. & Wiedenmann, J. FRET-mediated long-range wavelength transformation by photoconvertible fluorescent proteins as an efficient mechanism to generate orange-red light in symbiotic deep water corals. Int. J. Mol. Sci. 18, 1174 (2017).PubMed Central 
Article 
CAS 

Google Scholar 
Palmer, C. V., Modi, C. K. & Mydlarz, L. D. Coral fluorescent proteins as antioxidants. PLoS ONE 4, e7298 (2009).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Bou-Abdallah, F., Chasteen, N. D. & Lesser, M. P. Quenching of superoxide radicals by green fluorescent protein. Biochim. Biophys. Biochim. Biophys. Acta Gen. Subj. 1760, 1690–1695 (2006).CAS 
Article 

Google Scholar 
Matz, M. V., Marshall, N. J. & Vorobyev, M. Are corals colorful? Photochem. Photobiol. 82, 345–350 (2006).CAS 
PubMed 
Article 

Google Scholar 
Aihara, Y. et al. Green fluorescence from cnidarian hosts attracts symbiotic algae. Proc. Natl Acad. Sci. USA 116, 2118–2123 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Yamashita, H., Koike, K., Shinzato, C., Jimbo, M. & Suzuki, G. Can Acropora tenuis larvae attract native Symbiodiniaceae cells by green fluorescence at the initial establishment of symbiosis? PLoS ONE 16, e0252514 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
D’Angelo, C. et al. Blue light regulation of host pigment in reef-building corals. Mar. Ecol. Prog. Ser. 364, 97–106 (2008).Article 
CAS 

Google Scholar 
Ben-Zvi, O., Eyal, G. & Loya, Y. Light-dependent fluorescence in the coral Galaxea fascicularis. Hydrobiologia 759, 15–26 (2014).Article 

Google Scholar 
Muscatine, L., Porter, J. & Kaplan, I. Resource partitioning by reef corals as determined from stable isotope composition. Mar. Biol. 100, 185–193 (1989).Article 

Google Scholar 
Smith, E. G., D’Angelo, C., Sharon, Y., Tchernov, D. & Wiedenmann, J. Acclimatization of symbiotic corals to mesophotic light environments through wavelength transformation by fluorescent protein pigments. Proc. R. Soc. Lond. Ser. B: Biol. Sci. 284, 20170320 (2017).Schlichter, D., Meier, U. & Fricke, H. Improvement of photosynthesis in zooxanthellate corals by autofluorescent chromatophores. Oecologia 99, 124–131 (1994).CAS 
PubMed 
Article 

Google Scholar 
Gilmore, A. M. et al. Simultaneous time resolution of the emission spectra of fluorescent proteins and zooxanthellar chlorophyll in reef-building corals. Photochem. Photobiol. 77, 515–523 (2003).CAS 
PubMed 
Article 

Google Scholar 
Mazel, C. H. et al. Green-fluorescent proteins in Caribbean corals. Limnol. Oceanogr. 48, 402–411 (2003).CAS 
Article 

Google Scholar 
Dubinsky, Z. & Falkowski, P. Light as a Source of Information and Energy in Zooxanthellate Corals. In Coral Reefs: An Ecosystem in Transition (eds Dubinsky, Z. & Stambler, N.) 107–118 (Springer Science & Business Media, 2011).Kahng, S. E. et al. Light, Temperature, Photosynthesis, Heterotrophy, and the Lower Depth Limits of Mesophotic Coral Ecosystemsin. In Mesophotic Coral Ecosystems. Ch. 42 (eds Loya, Y., Puglise, K. A. & Bridge, T. C. L.) 801–828 (Springer International publishing, 2019).Loya, Y., Poglise, K. & Bridge, T. C. L. Mesophotic Coral Ecosystems (Springer International Publishing, 2019).Eyal, G. et al. Spectral diversity and regulation of coral fluorescence in a mesophotic reef habitat in the Red Sea. PLoS ONE 10, e0128697 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Roth, M. S. et al. Fluorescent proteins in dominant mesophotic reef-building corals. Mar. Ecol. Prog. Ser. 521, 63–79 (2015).CAS 
Article 

Google Scholar 
Ben-Zvi, O., Wangpraseurt, D., Bronstein, O., Eyal, G. & Loya, Y. Photosynthesis and bio-optical properties of fluorescent mesophotic corals. Front. Mar. Sci. 8, 651601 (2021).Article 

Google Scholar 
Ben-Zvi, O., Eyal, G. & Loya, Y. Response of fluorescence morphs of the mesophotic coral Euphyllia paradivisa to ultra-violet radiation. Sci. Rep. 9, 5245 (2019).PubMed 
PubMed Central 
Article 
CAS 

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

Google Scholar 
Goreau, T. F., Goreau, N. I. & Yonge, C. M. Reef corals: autotrophs or heterotrophs? Biol. Bull. 141, 247–260 (1971).Article 

Google Scholar 
Price, J. T., McLachlan, R. H., Jury, C. P., Toonen, R. J. & Grottoli, A. G. Isotopic approaches to estimating the contribution of heterotrophic sources to Hawaiian corals. Limnol. Oceanogr. 66, 2393–2407 (2021).CAS 
Article 

Google Scholar 
Anthony, K. R. N. Coral suspension feeding on fine particulate matter. J. Exp. Mar. Biol. Ecol. 232, 85–106 (1999).Article 

Google Scholar 
Ferrier-Pagès, C., Rottier, C., Beraud, E. & Levy, O. Experimental assessment of the feeding effort of three scleractinian coral species during a thermal stress: effect on the rates of photosynthesis. J. Exp. Mar. Biol. Ecol. 390, 118–124 (2010).Article 

Google Scholar 
Palardy, E. J., Grottoli, G. A. & Matthews, A. K. Effects of upwelling, depth, morphology and polyp size on feeding in three species of Panamanian corals. Mar. Ecol. Prog. Ser. 300, 79–89 (2005).Article 

Google Scholar 
Mies, M. et al. In situ shifts of predominance between autotrophic and heterotrophic feeding in the reef-building coral Mussismilia hispida: an approach using fatty acid trophic markers. Coral Reefs 37, 677–689 (2018).Article 

Google Scholar 
Jerlov, N. G. Optical Oceanography Vol. 5 (Elsevier, 1968).Crandall, J. B., Teece, M. A., Estes, B. A., Manfrino, C. & Ciesla, J. H. Nutrient acquisition strategies in mesophotic hard corals using compound specific stable isotope analysis of sterols. J. Exp. Mar. Biol. Ecol. 474, 133–141 (2016).CAS 
Article 

Google Scholar 
Martinez, S. et al. Energy sources of the depth-generalist mixotrophic coral Stylophora pistillata. Front. Mar. Sci. 7, 566663 (2020).Article 

Google Scholar 
Williams, G. J. et al. Biophysical drivers of coral trophic depth zonation. Mar. Biol. 165, 60 (2018).Article 

Google Scholar 
Lesser, M. P. et al. Photoacclimatization by the coral Montastraea cavernosa in the mesophotic zone: light, food, and genetics. Ecology 91, 990–1003 (2010).PubMed 
Article 

Google Scholar 
Mass, T. et al. Photoacclimation of Stylophora pistillata to light extremes: metabolism and calcification. Mar. Ecol. Prog. Ser. 334, 93–102 (2007).CAS 
Article 

Google Scholar 
Sturaro, N., Hsieh, Y. E., Chen, Q., Wang, P. L. & Denis, V. Trophic plasticity of mixotrophic corals under contrasting environments. Funct. Ecol. 35, 2841–2855 (2021).Article 

Google Scholar 
Lewis, J. B. & Price, W. S. Feeding mechanisms and feeding strategies of Atlantic reef corals. J. Zool. 176, 527–544 (1975).Article 

Google Scholar 
Levy, O., Mizrahi, L., Chadwick-Furman, N. E. & Achituv, Y. Factors controlling the expansion behavior of Favia favus (Cnidaria: Scleractinia): Effects of light, flow, and planktonic prey. Biol. Bull. 200, 118–126 (2001).CAS 
PubMed 
Article 

Google Scholar 
Levy, O., Dubinsky, Z. & Achituv, Y. Photobehavior of stony corals: responses to light spectra and intensity. J. Exp. Biol. 206, 4041–4049 (2003).CAS 
PubMed 
Article 

Google Scholar 
Turak, E. & DeVantier, L. Reef-Building Corals of the Upper Mesophotic Zone of the Central Indo-West Pacificin. In Mesophotic Coral Ecosystems. Ch. 34 (eds Loya, Y., Puglise, K. A. & Bridge, T. C. L.) 621–651 (Springer International Publishing, 2019).Haddock, S. H. D. & Dunn, C. W. Fluorescent proteins function as a prey attractant: experimental evidence from the hydromedusa Olindias formosus and other marine organisms. Biol. Open 4, 1094–1104 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Eyal, G. et al. Euphyllia paradivisa, a successful mesophotic coral in the northern Gulf of Eilat/Aqaba, Red Sea. Coral Reefs 35, 91–102 (2016).Article 

Google Scholar 
Cronin, T. W. Invertebrate Vision in the Water. In Invertebrate Vision (eds Warran, E. & Nilsson, D.-E.) 6, 211–249 (Cambridge University Press, 2006).Bradley, D. J. & Forward, R. B. Jr. Phototaxis of adult brine shrimp Artemia salina. Can. J. Zool. 62, 2357–2359 (1984).Article 

Google Scholar 
Audzijonytė, A., Pahlberg, J., Väinölä, R. & Lindström, M. Spectral sensitivity differences in two Mysis sibling species (Crustacea, Mysida): adaptation or phylogenetic constraints? J. Exp. Mar. Biol. Ecol. 325, 228–239 (2005).Article 

Google Scholar 
Beeton, A. M. Photoreception in the opossum shrimp, Mysis relicta Loven. Biol. Bull. 116, 204–216 (1959).Article 

Google Scholar 
Lindström, M. Eye function of Mysidacea (Crustacea) in the northern Baltic Sea. J. Exp. Mar. Biol. Ecol. 246, 85–101 (2000).PubMed 
Article 

Google Scholar 
Marshall, N. J. & Vorobyev, M. The Design of Color Signals and Color Vision in Fishes. In Sensory Processing in Aquatic Environments. Ch. 10 (eds Collin, S. P. & Marshall, N. J.) 10, 194–222 (Springer, 2003).Denton, E. J. & Warren, F. J. The photosensitive pigments in the retinae of deep-sea fish. J. Mar. Biol. Assoc. UK 36, 651–662 (1957).CAS 
Article 

Google Scholar 
Kelber, A. Invertebrate Colour Vision. In Invertebrate Vision (eds Warran, E. & Nilsson, D.-E.) 250–290 (Cambridge University Press, 2006).Kim, H. J., Araki, T., Suematsu, Y. & Satuito, C. G. Ontogenic phototactic behaviors of larval stages in intertidal barnacles. Hydrobiologia 849, 747–761 (2021).Cohen, J. H. & Forward, R. B. Jr. Spectral sensitivity of vertically migrating marine copepods. Biol. Bull. 203, 307–314 (2002).PubMed 
Article 

Google Scholar 
Su, Z., Huang, L., Yan, Y. & Li, H. The effect of different substrates on pearl oyster Pinctada martensii (Dunker) larvae settlement. Aquaculture 271, 377–383 (2007).Article 

Google Scholar 
Marangoni, R., Puntoni, S., Favati, L. & Colombetti, G. Phototaxis in Fabrea salina I. Action spectrum determination. J. Photochem. Photobiol. B: Biol. 23, 149–154 (1994).CAS 
Article 

Google Scholar 
Hollingsworth, L. L., Kinzie, R. A., Lewis, T. D., Krupp, D. A. & Leong, J. A. C. Phototaxis of motile zooxanthellae to green light may facilitate symbiont capture by coral larvae. Coral Reefs 24, 523–523 (2005).Article 

Google Scholar 
Smith, F. E. & Taylor, E. R. B. Color responses in the Cladocera and their ecological significance. Am. Nat. 87, 49–55 (1953).Article 

Google Scholar 
Feller, K. D. & Cronin, T. W. Spectral absorption of visual pigments in stomatopod larval photoreceptors. J. Comp. Physiol. A 202, 215–223 (2016).CAS 
Article 

Google Scholar 
Pietsch, T. W. Bioluminescence and Luring. In Oceanic Anglerfishes: Extraordinary Diversity in the Deep Sea (ed. Pietsch, T. W.) 6, 229–252 (Berkeley: University of California Press, 2009).Johnsen, S., Balser, E. J., Fisher, E. C. & Widder, E. A. Bioluminescence in the deep-sea cirrate octopod Stauroteuthis syrtensis Verrill (Mollusca: Cephalopoda). Biol. Bull. 197, 26–39 (1999).CAS 
PubMed 
Article 

Google Scholar 
Robison, B. H., Reisenbichler, K. R., Hunt, J. C. & Haddock, S. H. Light production by the arm tips of the deep-sea cephalopod Vampyroteuthis infernalis. Biol. Bull. 205, 102–109 (2003).PubMed 
Article 

Google Scholar 
Haddock, S. H. D., Dunn, C. W., Pugh, P. R. & Schnitzler, C. E. Bioluminescent and red-fluorescent lures in a deep-sea siphonophore. Science 309, 263 (2005).CAS 
PubMed 
Article 

Google Scholar 
Hastings, J. & Nealson, K. H. Bacterial bioluminescence. Annu. Rev. Microbiol. 31, 549–595 (1977).CAS 
PubMed 
Article 

Google Scholar 
Zarubin, M., Belkin, S., Ionescu, M. & Genin, A. Bacterial bioluminescence as a lure for marine zooplankton and fish. Proc. Natl Acad. Sci. USA 109, 853–857 (2012).CAS 
PubMed 
Article 

Google Scholar 
Nakaema, S. & Hidaka, M. Fluorescent protein content and stress tolerance of two color morphs of the coral Galaxea fascicularis. Galaxea 17, 1–11 (2015).Article 

Google Scholar 
Vermeij, M. J. A., Delvoye, L., Nieuwland, G. & Bak, R. P. M. Patterns in fluorescence over a Caribbean reef slope: the coral genus. Madracis. Photosynthetica 40, 423–429 (2002).CAS 
Article 

Google Scholar 
Kahng, S. & Salih, A. Localization of fluorescent pigments in a nonbioluminescent, azooxanthellate octocoral suggests a photoprotective function. Coral Reefs 24, 435–435 (2005).Article 

Google Scholar 
Glynn, P. W. Ecology of a Caribbean coral reef. The Porites reef-flat biotope: Part II. Plankton community with evidence for depletion. Mar. Biol. 22, 1–21 (1973).Article 

Google Scholar 
Holzman, R., Reidenbach, M. A., Monismith, S. G., Koseff, J. R. & Genin, A. Near-bottom depletion of zooplankton over a coral reef II: relationships with zooplankton swimming ability. Coral Reefs 24, 87–94 (2005).Article 

Google Scholar 
Mazel, C. H. Spectral measurements of fluorescence emission in Caribbean cnidarians. Mar. Ecol. Prog. Ser. 120, 185–191 (1995).Article 

Google Scholar 
R Core Team. R: a language and environment for statistical computing (R Foundation for Statistical Computing, 2013).Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 1 1–48 (2015).Kleiman, E. EMAtools: data management tools for real-time monitoring/ecological momentary assessment data. R package version 0.1.4 (2021). More

Von Humboldt, A. Cosmos: A Sketch of a Physical Description of the Universe Vol. 5 (H.G. Bohn Press, 1895).Körner, C. Alpine Treelines: Functional Ecology of the Global High Elevation Tree Limits (Springer, 2012).Peñuelas, J., Ogaya, R., Boada, M. & Jump, A. S. Migration, invasion and decline: changes in recruitment and forest structure in a warming‐linked shift of European beech forest in Catalonia (NE Spain). Ecography 30, 829–837 (2007).Article 

Google Scholar 
Körner, C. Alpine Plant Life: Functional Plant Ecology of High Mountain Ecosystems (Springer, 2021).Körner, C. A re-assessment of high elevation treeline positions and their explanation. Oecologia 115, 445–459 (1998).PubMed 
Article 

Google Scholar 
Körner, C. The cold range limit of trees. Trends Ecol. Evol. 36, 979–989 (2021).PubMed 
Article 

Google Scholar 
Körner, C. & Paulsen, J. A world-wide study of high altitude treeline temperatures. J. Biogeogr. 31, 713–732 (2004).Article 

Google Scholar 
Paulsen, J. & Körner, C. A climate-based model to predict potential treeline position around the globe. Alp. Bot. 124, 1–12 (2014).Article 

Google Scholar 
Feeley, K. J. & Rehm, E. M. Downward shift of montane grasslands exemplifies the dual threat of human disturbances to cloud forest biodiversity. Proc. Natl Acad. Sci. USA 112, E6084–E6084 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lenoir, J. et al. A significant upward shift in plant species optimum elevation during the 20th century. Science 320, 1768–1771 (2008).CAS 
PubMed 
Article 

Google Scholar 
Macias Fauria, M. & Johnson, E. A. Warming-induced upslope advance of subalpine forest is severely limited by geomorphic processes. Proc. Natl Acad. Sci. USA 110, 8117–8122 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Morueta Holme, N. et al. Strong upslope shifts in Chimborazo’s vegetation over two centuries since Humboldt. Proc. Natl Acad. Sci. USA 112, 12741–12745 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Greenwood, S. & Jump, A. S. Consequences of treeline shifts for the diversity and function of high altitude ecosystems. Arct. Antarct. Alp. Res. 46, 829–840 (2014).Article 

Google Scholar 
Körner, C. & Hiltbrunner, E. Why is the alpine flora comparatively robust against climatic warming? Diversity 13, 383 (2021).Article 

Google Scholar 
Miehe, G. et al. Highest treeline in the northern hemisphere found in southern Tibet. Mt. Res. Dev. 27, 169–173 (2007).Article 

Google Scholar 
Myers, N. et al. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).CAS 
PubMed 
Article 

Google Scholar 
Wang, F. et al. Add Himalayas’ Grand Canyon to China’s first national parks. Nature 592, 353–353 (2021).CAS 
PubMed 
Article 

Google Scholar 
Zhu, L. et al. Regional scalable priorities for national biodiversity and carbon conservation planning in Asia. Sci. Adv. 7, eabe4261 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Yao, T. et al. Different glacier status with atmospheric circulations in Tibetan Plateau and surroundings. Nat. Clim. Change 2, 663–667 (2012).Article 

Google Scholar 
Dirnböeck, T., Essl, F. & Rabitsch, W. Disproportional risk for habitat loss of high-altitude endemic species under climate change. Glob. Change Biol. 17, 990–996 (2011).Article 

Google Scholar 
Schickhoff, U. et al. Do Himalayan treelines respond to recent climate change? An evaluation of sensitivity indicators. Earth Syst. Dynam. 6, 245–265 (2015).Article 

Google Scholar 
Singh, S., Sharma, S. & Dhyani, P. Himalayan arc and treeline: distribution, climate change responses and ecosystem properties. Biodivers. Conserv. 28, 1997–2016 (2019).Article 

Google Scholar 
Schickhoff, U. The Upper Timberline in the Himalayas, Hindu Kush and Karakorum: A Review of Geographical and Ecological Aspects (Springer, 2005).Liang, E. et al. Species interactions slow warming-induced upward shifts of treelines on the Tibetan Plateau. Proc. Natl Acad. Sci. USA 113, 4380–4385 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lu, X. et al. Mountain treelines climb slowly despite rapid climate warming. Glob. Ecol. Biogeogr. 30, 305–315 (2021).Article 

Google Scholar 
Harsch, M. A., Hulme, P. E., McGlone, M. S. & Duncan, R. P. Are treelines advancing? A global meta-analysis of treeline response to climate warming. Ecol. Lett. 12, 1040–1049 (2009).PubMed 
Article 

Google Scholar 
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).CAS 
PubMed 
Article 

Google Scholar 
Wan, Z. & Li, Z. A physics-based algorithm for retrieving land-surface emissivity and temperature from EOS/MODIS data. IEEE Trans. Geosci. Remote Sens. 35, 980–996 (1997).Article 

Google Scholar 
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 
Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).Article 

Google Scholar 
Venter, O. et al. Global terrestrial Human Footprint maps for 1993 and 2009. Sci. Data 3, 160067 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Sigdel, S. R. et al. Moisture-mediated responsiveness of treeline shifts to global warming in the Himalayas. Glob. Change Biol. 24, 5549–5559 (2018).Article 

Google Scholar 
Dolezal, J. et al. Sink limitation of plant growth determines tree line in the arid Himalayas. Funct. Ecol. 33, 553–565 (2019).Article 

Google Scholar 
Dolezal, J. et al. Annual and intra-annual growth dynamics of Myricaria elegans shrubs in arid Himalaya. Trees 30, 761–773 (2016).Article 

Google Scholar 
Malcolm, J. R. et al. Global warming and extinctions of endemic species from biodiversity hotspots. Conserv. Biol. 20, 538–548 (2006).PubMed 
Article 

Google Scholar 
Ding, W., Ree, R. H., Spicer, R. A. & Xing, Y. Ancient orogenic and monsoon-driven assembly of the world’s richest temperate alpine flora. Science 369, 578–581 (2020).CAS 
PubMed 
Article 

Google Scholar 
Pirnat, J. Conservation and management of forest patches and corridors in suburban landscapes. Landsc. Urban Plan. 52, 135–143 (2000).Article 

Google Scholar 
Potapov, P. V. et al. Quantifying forest cover loss in Democratic Republic of the Congo, 2000–2010, with Landsat ETM plus data. Remote Sens. Environ. 122, 106–116 (2012).Article 

Google Scholar 
Paulsen, J. & Körner, C. GIS-analysis of tree-line elevation in the Swiss Alps suggests no exposure effect. J. Veg. Sci. 12, 817–824 (2001).Article 

Google Scholar 
FAO. FRA 2000: On Definitions of Forest and Forest Change. Forest Resource Assessment Programme Working Paper, Rome (Food and Agriculture Organization, 2000).Luedeling, E., Siebert, S. & Buerkert, A. Filling the voids in the SRTM elevation model—a TIN-based delta surface approach. ISPRS-J. Photogramm. Remote Sens. 62, 283–294 (2007).Article 

Google Scholar 
Canny, J. Collision detection for moving polyhedra. IEEE Trans. Pattern Anal. Mach. Intell. 8, 200–209 (1986).CAS 
PubMed 
Article 

Google Scholar 
More, J. J. & Sorensen, D. C. Computing a trust region step. SIAM J. Sci. Comput. 4, 553–572 (1983).Article 

Google Scholar 
Theobald, D. M., Harrison-Atlas, D., Monahan, W. B. & Albano, C. M. Ecologically-relevant maps of landforms and physiographic diversity for climate adaptation planning. PLoS ONE 10, e0143619 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Hersbach, H. et al. The ERA5 global reanalysis. Q. J. R. Meteorol. Soc. 146, 1999–2049 (2020).Article 

Google Scholar 
Muñoz-Sabater, J. et al. ERA5-Land: a state-of-the-art global reanalysis dataset for land applications. Earth Syst. Sci. Data 13, 4349–4383 (2021).Article 

Google Scholar 
Liang, E., Wang, Y., Eckstein, D. & Luo, T. Little change in the fir tree-line position on the southeastern Tibetan Plateau after 200 years of warming. New Phytol. 190, 760–769 (2011).PubMed 
Article 

Google Scholar 
Anderegg, W. R. L. et al. Tree mortality predicted from drought-induced vascular damage. Nat. Geosci. 8, 367–371 (2015).CAS 
Article 

Google Scholar 
Abatzoglou, J. T. et al. TerraClimate, a high-resolution global dataset of monthly climate and climatic water balance from 1958–2015. Sci. Data 5, 170191 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Case, B. S. & Buckley, H. L. Local-scale topoclimate effects on treeline elevations: a country-wide investigation of New Zealand’s southern beech treelines. PeerJ 3, e1334 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
Bush, M. B. et al. Fire and climate: contrasting pressures on tropical Andean timberline species. J. Biogeogr. 42, 938–950 (2015).Article 

Google Scholar 
Herrero, A., Zamora, R., Castro, J. & Hodar, J. A. Limits of pine forest distribution at the treeline: herbivory matters. Plant Ecol. 213, 459–469 (2012).Article 

Google Scholar 
Wang, Y. et al. The stability of spruce treelines on the eastern Tibetan Plateau over the last century is explained by pastoral disturbance. For. Ecol. Manag. 442, 34–45 (2019).Article 

Google Scholar 
Wei, Y. et al. Chinese caterpillar fungus (Ophiocordyceps sinensis) in China: current distribution, trading, and futures under climate change and overexploitation. Sci. Total Environ. 755, 142548 (2021).CAS 
PubMed 
Article 

Google Scholar 
Miehe, G. et al. How old is the human footprint in the world’s largest alpine ecosystem? A review of multiproxy records from the Tibetan Plateau from the ecologists’ viewpoint. Quat. Sci. Rev. 86, 190–209 (2014).Article 

Google Scholar 
Willemann, R. J. & Storchak, D. A. Data collection at the international seismological centre. Seismol. Res. Lett. 72, 440–453 (2001).Article 

Google Scholar 
Chen, A., Huang, L., Liu, Q. & Piao, S. Optimal temperature of vegetation productivity and its linkage with climate and elevation on the Tibetan Plateau. Glob. Change Biol. 27, 1942–1951 (2021).Article 

Google Scholar 
Lehmkuhl, F. & Owen, L. A. Late Quaternary glaciation of Tibet and the bordering mountains: a review. Boreas 34, 87–100 (2005).Article 

Google Scholar 
Owen, L. A. & Dortch, J. M. Nature and timing of Quaternary glaciation in the Himalayan–Tibetan orogen. Quat. Sci. Rev. 88, 14–54 (2014).Article 

Google Scholar 
Strobl, C. et al. Conditional variable importance for random forests. BMC Bioinform. 9, 307 (2008).Article 
CAS 

Google Scholar 
Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).Article 

Google Scholar 
Vassallo, D., Krishnamurthy, R. & Fernando, H. J. S. Decreasing wind speed extrapolation error via domain-specific feature extraction and selection. Wind Energy Sci. 5, 959–975 (2020).Article 

Google Scholar 
Ramirez-Villegas, J. & Jarvis, A. Downscaling Global Circulation Model Outputs: The Delta Method Decision and Policy Analysis Working Paper No. 1 (CIAT, 2010).Wu, Z. & Raven, P. Flora of China (Science Press and Missouri Botanical Garden Press, 1994–2006).Wu, Z. Flora of Tibet (Science Press, 1987).Maclean, I. M. D. et al. Microclimates buffer the responses of plant communities to climate change. Glob. Ecol. Biogeogr. 24, 1340–1350 (2015).Article 

Google Scholar 
Randin, C. F. et al. Climate change and plant distribution: local models predict high-elevation persistence. Glob. Change Biol. 15, 1557–1569 (2009).Article 

Google Scholar 
Scherrer, D. & Körner, C. Topographically controlled thermal-habitat differentiation buffers alpine plant diversity against climate warming. J. Biogeogr. 38, 406–416 (2011).Article 

Google Scholar  More

Di Giacomo, G., Hadrich, J., Hutchison, W. D., Peterson, H. & Rogers, M. Economic impact of spotted wing drosophila (Diptera: Drosophilidae) yield loss on minnesota raspberry farms: A grower survey. J. Integr. Pest Manag. https://doi.org/10.1093/jipm/pmz006 (2019).Article 

Google Scholar 
Farnsworth, D. et al. Economic analysis of revenue losses and control costs associated with the spotted wing drosophila, Drosophila suzukii (Matsumura), in the California raspberry industry. Pest. Manag. Sci. 73, 1083–1090. https://doi.org/10.1002/ps.4497 (2017).CAS 
Article 
PubMed 

Google Scholar 
Beers, E. H., Van Steenwyk, R. A., Shearer, P. W., Coates, W. W. & Grant, J. A. Developing Drosophila suzukii management programs for sweet cherry in the western United States. Pest Manag. Sci. 67, 1386–1395. https://doi.org/10.1002/ps.2279 (2011).CAS 
Article 
PubMed 

Google Scholar 
Tait, G. et al. Drosophila suzukii (Diptera: Drosophilidae): A decade of research towards a sustainable integrated pest management program. J. Econ. Entomol. 114, 1950–1974. https://doi.org/10.1093/jee/toab158 (2021).Article 
PubMed 

Google Scholar 
Daane, K. M. et al. First exploration of parasitoids of Drosophila suzukii in South Korea as potential classical biological agents. J. Pest Sci. 89, 823–835. https://doi.org/10.1007/s10340-016-0740-0 (2016).ADS 
Article 

Google Scholar 
Abram, P. K. et al. New records of Leptopilina, Ganaspis, and Asobara species associated with Drosophila suzukii in North America, including detections of L. japonica and G. brasiliensis. J. Hymenoptera Res. 78, 1–17. https://doi.org/10.3897/jhr.78.55026 (2020).Article 

Google Scholar 
Chabert, S., Allemand, R., Poyet, M., Eslin, P. & Gibert, P. Ability of European parasitoids (Hymenoptera) to control a new invasive Asiatic pest, Drosophila suzukii. Biol. Control 63, 40–47. https://doi.org/10.1016/j.biocontrol.2012.05.005 (2012).Article 

Google Scholar 
Gonzalez-Cabrera, J., Moreno-Carrillo, G., Sanchez-Gonzalez, J. A., Mendoza-Ceballos, M. Y. & Arredondo-Bernal, H. C. Single and Combined Release of Trichopria drosophilae (Hymenoptera: Diapriidae) to Control Drosophila suzukii (Diptera: Drosophilidae). Neotrop. Entomol. 48, 949–956. https://doi.org/10.1007/s13744-019-00707-3 (2019).CAS 
Article 
PubMed 

Google Scholar 
Rossi Stacconi, M. V., Grassi, A., Ioriatti, C. & Anfora, G. Augmentative releases of Trichopria drosophilae for the suppression of early season Drosophila suzukii populations. Biocontrol 64, 9–19. https://doi.org/10.1007/s10526-018-09914-0 (2018).CAS 
Article 

Google Scholar 
Poyet, M. et al. The wide potential trophic niche of the asiatic fruit fly Drosophila suzukii: The key of its invasion success in temperate Europe?. PLoS ONE 10, e0142785. https://doi.org/10.1371/journal.pone.0142785 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Mazzetto, F. et al. Drosophila parasitoids in northern Italy and their potential to attack the exotic pest Drosophila suzukii. J. Pest Sci. 89, 837–850. https://doi.org/10.1007/s10340-016-0746-7 (2016).Article 

Google Scholar 
Wang, X. G., Kacar, G., Biondi, A. & Daane, K. M. Foraging efficiency and outcomes of interactions of two pupal parasitoids attacking the invasive spotted wing drosophila. Biol. Control 96, 64–71. https://doi.org/10.1016/j.biocontrol.2016.02.004 (2016).Article 

Google Scholar 
Rossi Stacconi, M. V. et al. Host location and dispersal ability of the cosmopolitan parasitoid Trichopria drosophilae released to control the invasive spotted wing Drosophila. Biol. Control 117, 188–196. https://doi.org/10.1016/j.biocontrol.2017.11.013 (2018).Article 

Google Scholar 
Esteban-Santiago, J. M., Rodríguez-Leyva, E., Lomeli-Flores, J. R. & González-Cabrera, J. Demographic parameters of Trichopria drosophilae in three host species. Entomol. Exp. Appl. 169, 330–337. https://doi.org/10.1111/eea.13026 (2021).CAS 
Article 

Google Scholar 
Häussling, B. J. M., Lienenluke, J. & Stokl, J. The preference of Trichopria drosophilae for pupae of Drosophila suzukii is independent of host size. Sci. Rep. 11, 995. https://doi.org/10.1038/s41598-020-80355-5 (2021).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Wang, X. G., Kacar, G., Biondi, A. & Daane, K. M. Life-history and host preference of Trichopria drosophilae, a pupal parasitoid of spotted wing drosophila. Biocontrol 61, 387–397. https://doi.org/10.1007/s10526-016-9720-9 (2016).CAS 
Article 

Google Scholar 
Woltz, J. M. & Lee, J. C. Pupation behavior and larval and pupal biocontrol of Drosophila suzukii in the field. Biol. Control 110, 62–69. https://doi.org/10.1016/j.biocontrol.2017.04.007 (2017).Article 

Google Scholar 
Ballman, E. S., Collins, J. A. & Drummond, F. A. Pupation behavior and predation on Drosophila suzukii (Diptera: Drosophilidae) Pupae in Maine wild blueberry fields. J. Econ. Entomol. 110, 2308–2317. https://doi.org/10.1093/jee/tox233 (2017).Article 
PubMed 

Google Scholar 
Guillén, L., Aluja, M. N., Equihua, M. & Sivinski, J. Performance of two fruit fly (Diptera: Tephritidae) pupal parasitoids (Coptera haywardi [Hymenoptera: Diapriidae] and Pachycrepoideus vindemiae [Hymenoptera: Pteromalidae]) under different environmental soil conditions. Biol. Control 23, 219–227. https://doi.org/10.1006/bcon.2001.1011 (2002).Article 

Google Scholar 
Yi, C. et al. Life history and host preference of Trichopria drosophilae from Southern China, one of the effective pupal parasitoids on the Drosophila species. Insects 11, 103. https://doi.org/10.3390/insects11020103 (2020).Article 
PubMed Central 

Google Scholar 
BoychevaWoltering, S., Romeis, J. & Collatz, J. Influence of the rearing host on biological parameters of Trichopria drosophilae, a potential biological control agent of Drosophila suzukii. Insects. https://doi.org/10.3390/insects10060183 (2019).Article 

Google Scholar 
Otto, M. & Mackauer, M. The developmental strategy of an idiobiont ectoparasitoid, Dendrocerus carpenteri: Influence of variations in host quality on offspring growth and fitness. Oecologia 117, 353–364. https://doi.org/10.1007/s004420050668 (1998).ADS 
Article 
PubMed 

Google Scholar 
Bates, D., Machler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

Google Scholar 
R: A Language and Environment for Statistical Computing (Vienna, Austria, 2008).Johnson, S. N. & Gregory, P. J. Chemically-mediated host-plant location and selection by root-feeding insects. Physiol. Entomol. 31, 1–13. https://doi.org/10.1111/j.1365-3032.2005.00487.x (2006).CAS 
Article 

Google Scholar 
Bezerra Da Silva, C. S., Park, K. R., Blood, R. A. & Walton, V. M. Intraspecific competition affects the pupation behavior of spotted-wing drosophila (Drosophila suzukii). Sci. Rep. 9, 7775. https://doi.org/10.1038/s41598-019-44248-6 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
Renkema, J. M. & Devkota, S. Pupation depth of spotted wing drosophila (Drosophila suzukii) and effects of field sanitation in Florida strawberries. Viii Int. Strawberry Symp. 1156, 849–855. https://doi.org/10.17660/ActaHortic.2017.1156.125 (2017).Article 

Google Scholar 
Tsitsipis, J. A. & Papanicolaou, E. P. Pupation depth in artificially reared olive fruits-flies Dacus-oleae (Diptera, Tephritidae), as affected by several physical characteristics of the substrates. Annales De Zoologie Ecologie Animale 11, 31–40 (1979).
Google Scholar 
Dimou, I., Koutsikopoulos, C., Economopoulos, A. P. & Lykakis, J. Depth of pupation of the wild olive fruit fly, Bactrocera (Dacus) oleae (Gmel.) (Dipt., Tephritidae), as affected by soil abiotic factors. J. Appl. Entomol. 127, 12–17. https://doi.org/10.1046/j.1439-0418.2003.00686.x (2003).Article 

Google Scholar 
de Belle, J. S., Hilliker, A. J. & Sokolowski, M. B. Genetic localization of foraging (for): A major gene for larval behavior in Drosophila melanogaster. Genetics 123, 157–163. https://doi.org/10.1093/genetics/123.1.157 (1989).Article 
PubMed 

Google Scholar 
Sokolowski, M. B. et al. Ecological genetics and behaviour of Drosophila melanogaster larvae in nature. Anim. Behav. 34, 403–408. https://doi.org/10.1016/S0003-3472(86)80109-9 (1986).Article 

Google Scholar 
Rodriguez, L., Sokolowski, M. B. & Shore, J. S. Habitat selection by Drosophila melanogaster larvae. J. Evol. Biol. 5, 61–70. https://doi.org/10.1046/j.1420-9101.1992.5010061.x (1992).Article 

Google Scholar 
McIntosh, H., Atucha, A., Townsend, P. A., Hills, W. B. & Guédot, C. Plastic mulches reduce adult and larval populations of Drosophila suzukii in fall-bearing raspberry. J. Pest. Sci. 95, 525–536. https://doi.org/10.1007/s10340-021-01456-2 (2021).Article 

Google Scholar 
Ballman, E. & Drummond, F. Larval movement of spotted wing drosophila, Drosophila suzukii (Matsumura) (Diptera: Drosophilidae). J. Kansas Entomol. Soc. 92, 412–421. https://doi.org/10.2317/0022-8567-92.1.412 (2019).Article 

Google Scholar  More

In this study, the five main characteristics of green spaces that were measured were area, perimeter, perimeter-area ratio, leaf area index, and canopy density. The structure of parameter between them is shown in Table 3.Table 3 Parameter structure of the cooling and humidification effect based on the spatial characteristics of green spaces.Full size tableCorrelation between various spatial characteristics and cooling and humidifying intensity in green spacesSmall-size green spacesFigures 4 and 6 shows the results of linear regressions between spatial characteristics and the cooling effect in small-size green spaces. There were relatively weak correlations between area, perimeter, perimeter-area ratio, leaf area index and cooling intensity, and a strong correlation between canopy density and cooling intensity. Small-size green space has the weakest positive correlation between perimeter-area ratio and cooling intensity (R2 = 0.11), and its canopy density and cooling intensity have the strongest positive correlation (R2 = 0.64). Meanwhile, small-size green space has weakest negative correlation between perimeter and humidifying intensity (R2 = 0.17), and its leaf area index and humidifying intensity have significant positive correlation (R2 = 0.42). Figures 4a and 5a show that for every 1 ha increase in area of small-size green spaces, the cooling intensity increased by 1.026 °C, and the humidifying intensity decreased by 1.56%. Figures 4b and 5b show that for every 100 m increase in perimeter, the cooling intensity decreases by 1.06 °C, and the humidifying intensity decreased by 1.19%. Figures 4c and 5c show that for every 0.01 increase in the perimeter-area ratio, the cooling intensity increases by 1.12 °C, and the humidifying intensity increased by 1.46%. Figures 4d and 5d show that for every 0.1 increase in the leaf area index, the cooling intensity increases by 1.11 °C, and the humidifying intensity increased by 1.12%. Figures 4e and 5e show that each 0.01 increase in the canopy density, the cooling intensity increases by 1.60 °C, and each 0.1 increase in canopy density, the humidifying intensity increased by 1.15% (Fig. 6).
Figure 4Linear regressions between spatial characteristics and cooling intensity of small-size green spaces.Full size imageFigure 5Linear regressions of spatial characteristics and humidifying intensity of small-size green spaces.Full size imageFigure 6The correlation between the spatial characteristics of small-size green spaces and the intensity of cooling and humidifying (GA means green area; GP means green perimeter; GPAR means green perimeter-area ratio; LAI means leaf area index; CD means canopy density).Full size imageMedium-size green spacesFigures 7 and 9 shows the linear regressions between spatial characteristics and cooling intensity in medium-size green spaces. There was an extremely significant positive correlation between area and cooling intensity, an insignificant positive correlation between the leaf area index and cooling intensity, and a relatively weak negative correlation between the other three characteristics and cooling intensity. Medium-size green space has the weakest negative correlation between canopy density and cooling intensity (R2 = 0.12), and its green area and cooling intensity have the strongest positive correlation (R2 = 0.83). Meanwhile, medium-size green space has weakest negative correlation between perimeter-area ratio and humidifying intensity (R2 = 0.41), and its area and humidifying intensity have most significant positive correlation (R2 = 0.81). Figures 7a and 8a show that for every 1 ha increase in area of medium-size green spaces, the cooling intensity increased by 1.19 °C, and the humidifying intensity increased by 1.24%. Figures 7b and 8b show that for every 100 m increase in perimeter, the cooling intensity decreases by 1.02 °C, and the humidifying intensity increased by 1.17%. Figures 7c and 8c show that for every 0.01 increase in the perimeter-area ratio, the cooling intensity decreases by 1.29 °C, and the humidifying intensity decreased by 2.40%. Figures 7d and 8d show that for every 0.1 increase in the leaf area index, the cooling intensity increases by 1.37 °C, and the humidifying intensity decreased by 1.92%. Figures 7e and 8e show that each 0.01 increase in the canopy density, increases the cooling intensity decreases by 1.23 °C, and the humidifying intensity decreased by 6.48% (Fig. 9).Figure 7Linear regressions between spatial characteristics and cooling intensity of medium-size green spaces.Full size imageFigure 8Linear regressions of spatial characteristics and humidifying intensity of medium-size green spaces.Full size imageFigure 9The correlation between the spatial characteristics of medium-size green spaces and the intensity of cooling and humidifying (GA means green area; GP means green perimeter; GPAR means green perimeter-area ratio; LAI means leaf area index; CD means canopy density).Full size imageLarge-size green spacesFigures 10 and 12 shows the linear regressions between spatial characteristics and cooling intensity in large-size green spaces. There was an insignificant correlation between area and cooling intensity, a weak correlation between canopy density and cooling intensity, and a significant correlation between perimeter, perimeter-area ratio and the leaf area index and cooling intensity. Medium-size green space has the weakest negative correlation between green area and cooling intensity (R2 = 0.35), and its leaf area index and cooling intensity have the strongest positive correlation (R2 = 0.92). Meanwhile, medium-size green space has weakest negative correlation between perimeter-area ratio and humidifying intensity (R2 = 0.11), and its leaf area index and humidifying intensity have most significant positive correlation (R2 = 0.39). Figures 10a and 11a show that for every 1 ha increase in area of large-size green spaces, the cooling intensity decreased by 1.02 °C, and the humidifying intensity decreased by 1.22%. Figures 10b and 11b show that for every 100 m increase in perimeter, the cooling intensity decreases by 1.05 °C, and the humidifying intensity decreased by 1.34%. Figures 10c and 11c show that for every 0.005 increase in the perimeter-area ratio, the cooling intensity decreases by 1.43 °C, and each 0.01 increase in perimeter-area ratio, the humidifying intensity decreased by 1.27%. Figures 10d and 11d show that for every 0.1 increase in the leaf area index, the cooling intensity increases by 2.41 °C, and the humidifying intensity increased by 1.37%. Figures 10e and 11e show that each 0.1 increase in the canopy density, the cooling intensity increased by 3.69 °C, and the humidifying intensity decreased by 2.84% (Fig. 12).Figure 10Linear regressions of spatial characteristics and cooling intensity of large-size green spaces.Full size imageFigure 11Linear regressions of spatial characteristics and humidifying intensity of large-size green spaces.Full size imageFigure 12The correlation between the spatial characteristics of large-size green spaces and the intensity of cooling and humidifying (GA means green area; GP means green perimeter; GPAR means green perimeter-area ratio; LAI means leaf area index; CD means canopy density).Full size imageQuantitative analysis of the microclimatic effects of different types of green spacesQuantitative analysis of the effects of different types of green space on cooling intensityFigure 13 shows the linear regressions between the different types of green spaces and cooling intensity. There were negative correlations between green spaces a short, medium, and long distance from a water body and cooling intensity in small-size green spaces, medium-size green spaces and large-size green spaces. The negative correlation between the distance to a water body and cooling intensity in medium-size green spaces was most significant (R2 = 0.985). The greater the distance to a water body, the lower the cooling intensity. For medium-size green spaces, for every 1/4 increase in the distance ratio, the cooling intensity decreased by 0.81 °C. For small-size green spaces, for every 1/4 increase in the distance ratio, the cooling intensity decreased by 1.04 °C. For large-size green spaces, for every 1/4 increase in the distance ratio, the cooling intensity decreased by 1.36 °C. For small-, medium-, and large-size green spaces, there was a positive correlation between canopy density and cooling intensity. There was a most significant positive correlation between canopy density and cooling intensity in large-size green spaces (R2 = 0.941). The greater the canopy density, the greater the cooling intensity. For large green spaces, for every 0.5 increase in canopy density, the cooling intensity increased by 0.16 °C. For small-size green spaces, for every 0.5 increase in canopy density, the cooling effect increased by 0.15 °C. For medium-size green spaces, for every 0.5 increase in canopy density, the cooling intensity increased by 0.16 °C.Figure 13Linear regressions between the distance from different types of green spaces to water areas, canopy density and cooling intensity.Full size imageQuantitative analysis of the effects of different types of green space on humidifying intensityFigure 14 shows the linear regression between the distance of a green space from a water body, canopy density and humidifying intensity. There was a negative correlation between the distance to a water body and humidifying intensity in small, medium, and large green spaces. The negative correlation between the distance to a water body and humidifying intensity in small green spaces was most significant (R2 = 0.996). The longer the distance, the lower the humidifying intensity. For small green spaces, for every 1/4 in-crease in the distance ratio, the humidifying intensity decreased by 4.23%. For medium-size green spaces, for every 1/4 increase in the distance ratio, the humidifying intensity decreased by 3.02%. For large-size green spaces, for every 1/4 increase in the distance ratio, the humidifying intensity de-creased by 6.14%. For small, medium, and large green spaces, there was a positive correlation between canopy density and humidifying intensity. The positive correlation between canopy density and humidifying intensity in medium-size green spaces was extremely significant (R2 = 0.925). The greater the canopy density, the greater the humidifying intensity. For medium-size green spaces, for every 0.5 increase in canopy density, the humidifying intensity increased by 3.29%. For small-size green spaces, for every 0.5 increase in canopy density, the humidifying intensity increased by 3.17%. For large-size green spaces, for every 0.5 increase in canopy density, the humidifying intensity increased by 4.06% (Fig. 15).
Figure 14Linear regressions between the distance from different types of green space to water area, canopy density and humidifying intensity.Full size imageFigure 15Correlation of different green space types with water distance, canopy density and cooling and humidifying intensity.Full size imageEffect of shape and area of water bodies on microclimatic effects based on numerical simulationBanded waterWe constructed a numerical simulation model to explore the effects of a simulated increase in water body area on cooling and humidification. Figure 16 shows the simulated distribution characteristics of temperature and relative humidity after a 5% and 10% increase in water area at 14:00 when temperatures were high. The results suggest that between 7:00 and 10:00, with a 5% and 10% increase in water area, the air temperature was basically the same and the cooling effect was insignificant. However, between 12:00 and 19:00 and particularly in the hours between 13:00 and 16:00 when temperatures were highest, a 5% increase in water area produced a significant cooling effect, with a daily average value of 0.05 °C and a maximum value of 0.09 °C. A 10% increase in water area produced an extremely significant cooling effect, with a daily average value of 0.07 °C and a maximum value of 0.14 °C. From 11:00 to 19:00, a 5% increase in water area produced a significant humidifying effect, with a daily average value of 0.08% and a maximum value of 0.17%. A 10% increase produced an extremely significant humidifying effect, with a daily average value of 0.13% and a maximum value of 0.26% (See supplementary file).Figure 16Distribution characteristics of cooling and humidifying effects of simulated increase of banded water area at 14:00. (a) original cooling effect of banded water in the sample area; (b) cooling effect of 5% increase in water area; (c) cooling effect of 10% increase in water area; (d) original humidifying effect of banded water in the sample area; (e) humidifying effect of 5% increase in water area; (f) humidifying effect of 10% increase of water area.Full size imageMassive waterFigure 17 shows the simulated distribution characteristics of the cooling and humidifying effects after a 5% and 10% increase in the water area at 14:00 when temperatures were high. Between 8:00 and 19:00, a 5% and 10% increase in water area produced a significant cooling effect. At 19:00, the numerical simulation result was abnormal when the water area increased by 5% and 10%; at 13:00, the numerical simulation result was also ab-normal when the water area increased by 10%. After excluding the abnormal simulated data, a 5% increase in water area produced a cooling effect, with a daily average value of 0.06 °C and a maximum value of 0.10 °C. A 10% increase in water area produced an extremely significant cooling effect, with a daily average value of 0.10 °C and a maximum value of 0.18 °C. Between 11:00 and 19:00, a 5% increase in water area produced a significant humidifying effect, with a daily average value of 0.05% and a maximum value of 0.13%. A 10% increase in water area produced an extremely significant humidifying effect, with a daily average value of 0.13% and a maximum value of 0.27% (See supplementary file).Figure 17Distribution characteristics of cooling and humidifying effects of simulated increase of massive water area at 14:00. (a) original cooling effect of massive water in the sample area; (b) cooling effect of 5% increase in water area; (c) cooling effect of 10% increase in water area; (d) original humidifying effect of massive water in the sample area; (e) humidifying effect of 5% increase in water area; (f) humidifying effect of 10% increase of water area.Full size imageAnnular waterFigure 18 shows the simulated distribution characteristics of the cooling and humidifying effects after a 5% and 10% increase in the area of the annular water body at 14:00 when temperatures were high. Between 7:00 and 19:00, a 5% and 10% increase in water area produced a significant cooling effect. Between 11:00 and 16:00 when temperatures were high, a 5% increase in water area produced a cooling effect, with a daily average value of 0.06 °C and a maximum value of 0.14 °C°C and a 10% increase in water area produced an extremely significant cooling effect, with a daily average value of 0.13 °C and a maximum value of 0.28 °C. Between 7:00 and 19:00, a 5% and 10% increase in water area produced significant humidifying effects. Between 11:00 and 16:00 when temperatures were high, a 5% increase in water area produced an extremely significant humidifying effect, with a daily average value of 0.17% and a maximum value of 0.39% and a 10% increase in water area produced an extremely significant humidifying effect with a daily average value of 0.38% and a maximum value of 0.81% (See supplementary file).Figure 18Distribution characteristics of cooling and humidifying effects of simulated increase of annular water area at 14:00. (a) original cooling effect of annular water in the sample area; (b) cooling effect of 5% increase in water area; (c) cooling effect of 10% increase in water area; (d) original humidifying effect of annular water in the sample area; (e) humidifying effect of 5% increase in water area; (f) humidifying effect of 10% increase of water area.Full size image More

Canadell, J. G. et al. Multi-decadal increase of forest burned area in Australia is linked to climate change. Nat. Commun. 12, 6921 (2021).CAS 
Article 

Google Scholar 
Jolly, W. M. et al. Climate-induced variations in global wildfire danger from 1979 to 2013. Nat. Commun. 6, 7537 (2015).CAS 
Article 

Google Scholar 
Jain, P., Wang, X. & Flannigan, M. D. Trend analysis of fire season length and extreme fire weather in North America between 1979 and 2015. Int. J. Wildland Fire 26, 1009–1020 (2017).Article 

Google Scholar 
Wotton, B. M. & Flannigan, M. D. Length of the fire season in a changing climate. For. Chronicle 69, 187–192 (1993).
Google Scholar 
Collins, L. et al. The 2019/2020 mega-fires exposed Australian ecosystems to an unprecedented extent of high-severity fire. Environ. Res. Lett. 16, 044029 (2021).Article 

Google Scholar 
Higuera, P. E. & Abatzoglou, J. T. Record‐setting climate enabled the extraordinary 2020 fire season in the western United States. Glob. Change Biol. 27, 1–2 (2021).Article 

Google Scholar 
Nolan, R. H. et al. Limits to post-fire vegetation recovery under climate change. Plant Cell Environ. 44, 3471–3489 (2021).CAS 
Article 

Google Scholar 
Abram, N. J. et al. Connections of climate change and variability to large and extreme forest fires in southeast Australia. Commun. Earth Environ. 2, 8 (2021).Article 

Google Scholar 
Dickman, C. R. Ecological consequences of Australia’s “Black Summer” bushfires: managing for recovery. Int. Environ. Assess. Manag. 17, 1162–1167 (2021).Article 

Google Scholar 
Swain, D. L. A shorter, sharper rainy season amplifies California wildfire risk. Geophys. Res. Lett. 48, e2021GL092843 (2021).
Google Scholar 
Keeley, J. E. Fire intensity, fire severity and burn severity: a brief review and suggested usage. Int. J. Wildland Fire 18, 116–126 (2009).Article 

Google Scholar 
He, T., Pausas, J. G., Belcher, C. M., Schwilk, D. W. & Lamont, B. B. Fire-adapted traits of Pinus arose in the fiery Cretaceous. New Phytol. 194, 751–759 (2012).Article 

Google Scholar 
Bradstock, R. A. A biogeographic model of fire regimes in Australia: current and future implications. Glob. Ecol. Biogeogr. 19, 145–158 (2010).Article 

Google Scholar 
Bowman, D. M., Murphy, B. P., Neyland, D. L., Williamson, G. J. & Prior, L. D. Abrupt fire regime change may cause landscape-wide loss of mature obligate seeder forests. Glob. Change Biol. 20, 1008–1015 (2014).Article 

Google Scholar 
Keeley, J. E., Pausas, J. G., Rundel, P. W., Bond, W. J. & Bradstock, R. A. Fire as an evolutionary pressure shaping plant traits. Trends Plant Sci. 16, 406–411 (2011).CAS 
Article 

Google Scholar 
Barrett, K. et al. Postfire recruitment failure in Scots pine forests of southern Siberia. Remote Sens. Environ. 237, 111539 (2020).Article 

Google Scholar 
Miller, R. G., Fontaine, J. B., Merritt, D. J., Miller, B. P. & Enright, N. J. Experimental seed sowing reveals seedling recruitment vulnerability to unseasonal fire. Ecol. Appl. 31, e02411 (2021).
Google Scholar 
Prior, L. D., Williamson, G. J. & Bowman, D. M. Impact of high-severity fire in a Tasmanian dry eucalypt forest. Austral. J. Bot. 64, 193–205 (2016).Article 

Google Scholar 
Brewer, J. S. Long-term population changes of a fire-adapted plant subjected to different fire seasons. Nat. Areas J. 26, 267–273 (2006).Article 

Google Scholar 
Keith, D. A., Holman, L., Rodoreda, S., Lemmon, J. & Bedward, M. Plant functional types can predict decade‐scale changes in fire‐prone vegetation. J. Ecol. 95, 1324–1337 (2007).Article 

Google Scholar 
Savage, M., Mast, J. N. & Feddema, J. J. Double whammy: high-severity fire and drought in ponderosa pine forests of the Southwest. Can. J. For. Res. 43, 570–583 (2013).Article 

Google Scholar 
Miller, R. G. et al. Mechanisms of fire seasonality effects on plant populations. Trends Ecol. Evol. 34, 1104–1117 (2019).Article 

Google Scholar 
Tangney, R., Merritt, D. J., Fontaine, J. B. & Miller, B. P. Seed moisture content as a primary trait regulating the lethal temperature thresholds of seeds. J. Ecol. 107, 1093–1105 (2019).Article 

Google Scholar 
Tangney, R. et al. Seed dormancy interacts with fire seasonality mechanisms. Trends Ecol. Evol. 35, 1057–1059 (2020).Article 

Google Scholar 
Bowman, D. M. et al. The human dimension of fire regimes on Earth. J. Biogeogr. 38, 2223–2236 (2011).Article 

Google Scholar 
Knapp, E. E., Estes, B. L. & Skinner, C. N. Ecological effects of prescribed fire season: a literature review and synthesis for managers. Gen. Tech. Rep. https://doi.org/10.2737/PSW-GTR-224 (2009).Miller, R. G. et al. Fire seasonality mechanisms are fundamental for understanding broader fire regime effects. Trends Ecol. Evol. 35, 869–871 (2020).Article 

Google Scholar 
Keeley, J. E. & Syphard, A. D. Twenty-first century California, USA, wildfires: fuel-dominated vs. wind-dominated fires. Fire Ecol. 15, 24 (2019).Article 

Google Scholar 
Lamont, B. B., Enright, N. J. & He, T. Fitness and evolution of resprouters in relation to fire. Plant Ecol. 212, 1945–1957 (2011).Article 

Google Scholar 
Pausas, J. G. & Bradstock, R. A. Fire persistence traits of plants along a productivity and disturbance gradient in mediterranean shrublands of south‐east Australia. Glob. Ecol. Biogeogr. 16, 330–340 (2007).Article 

Google Scholar 
Pausas, J. G. & Keeley, J. E. Evolutionary ecology of resprouting and seeding in fire-prone ecosystems. New Phytol. 204, 55–65 (2014).Article 

Google Scholar 
Fairman, T. A., Bennett, L. T. & Nitschke, C. R. Short-interval wildfires increase likelihood of resprouting failure in fire-tolerant trees. J. Environ. Manag. 231, 59–65 (2019).Article 

Google Scholar 
Pyke, G. H. Fire-stimulated flowering: a review and look to the future. Critic. Rev. Plant Sci. 36, 179–189 (2017).Article 

Google Scholar 
Zirondi, H. L., Ooi, M. K. J. & Fidelis, A. Fire-triggered flowering is the dominant post-fire strategy in a tropical savanna. J. Veg. Sci. 32, e12995 (2021).Article 

Google Scholar 
Howe, H. F. Response of Zizia aurea to seasonal mowing and fire in a restored Prairie. Am. Midl. Nat. 141, 373–380 (1999).Article 

Google Scholar 
Thompson, K. Seeds and seed banks. New Phytol. 106, 23–34 (1987).Article 

Google Scholar 
Baskin, C. C. & Baskin, J. M. Seeds: Ecology, Biogeography, and Evolution of Dormancy and Germination 2nd edn (Academic Press, 2001).Alvarado, V. & Bradford, K. J. A hydrothermal time model explains the cardinal temperatures for seed germination. Plant Cell Environ. 25, 1061–1069 (2002).Article 

Google Scholar 
Mackenzie, B. D. E., Auld, T. D., Keith, D. A., Hui, F. K. C. & Ooi, M. K. J. The effect of seasonal ambient temperatures on fire-stimulated germination of species with physiological dormancy: a case study using boronia (Rutaceae). PLoS One 11, e0156142 (2016).Article 
CAS 

Google Scholar 
Ooi, M. K. J. Delayed emergence and post-fire recruitment success: effects of seasonal germination, fire season and dormancy type. Austral. J. Bot. 58, 248–256 (2010).Article 

Google Scholar 
Bond, W. Fire survival of Cape Proteaceae-influence of fire season and seed predators. Vegetatio 56, 65–74 (1984).Article 

Google Scholar 
Keith, D. A., Dunker, B. & Driscoll, D. A. Dispersal: the eighth fire seasonality effect on plants. Trends Ecol. Evol. 35, 305–307 (2020).Article 

Google Scholar 
Paroissien, R. & Ooi, M. K. J. Effects of fire season on the reproductive success of the post-fire flowerer Doryanthes excelsa. Environ. Exp. Bot. 192, 104634 (2021).Article 

Google Scholar 
Furlaud, J. M., Prior, L. D., Williamson, G. J. & Bowman, D. M. J. S. Bioclimatic drivers of fire severity across the Australian geographical range of giant Eucalyptus forests. J. Ecol. 109, 2514–2536 (2021).Article 

Google Scholar 
Thomsen, A. M. & Ooi, M. K. J. Shifting season of fire and its interaction with fire severity: Impacts on reproductive effort in resprouting plants. Ecol. Evol. 12, e8717 (2022).Article 

Google Scholar 
Fill, J. M. & Crandall, R. M. Stronger evidence needed for global fire season effects. Trends Ecol. Evol. 35, 867–868 (2020).Article 

Google Scholar 
Jump, A. S. & Peñuelas, J. Running to stand still: adaptation and the response of plants to rapid climate change. Ecol. Lett. 8, 1010–1020 (2005).Article 

Google Scholar 
Inouye, D. W. Climate change and phenology. Wiley Interdiscip. Rev. Clim. Change n/a, e764 (2022).
Google Scholar 
Enright, N. J., Marsula, R., Lamont, B. B. & Wissel, C. The ecological significance of canopy seed storage in fire-prone environments: a model for non-sprouting shrubs. J. Ecol. 86, 946–959 (1998).Article 

Google Scholar 
Setterfield, S. A. The impact of experimental fire regimes on seed production in two tropical eucalypt species in northern Australia. Austral. J. Ecol. 22, 279–287 (1997).Article 

Google Scholar 
Collette, J. C. & Ooi, M. K. J. Evidence for physiological seed dormancy cycling in the woody shrub Asterolasia buxifolia and its ecological significance in fire-prone systems. Plant Biol. 22, 745–749 (2020).CAS 
Article 

Google Scholar 
Setterfield, S. A. Seedling establishment in an Australian tropical savanna: effects of seed supply, soil disturbance and fire. J. Appl. Ecol. 39, 949–959 (2002).Article 

Google Scholar 
Russell-Smith, J. & Edwards, A. C. Seasonality and fire severity in savanna landscapes of monsoonal northern Australia. Int. J. Wildland Fire 15, 541–550 (2006).Article 

Google Scholar 
Whitehead, P. J., Purdon, P., Russell-Smith, J., Cooke, P. M. & Sutton, S. The management of climate change through prescribed Savanna burning: Emerging contributions of indigenous people in Northern Australia. Public Adm. Dev. 28, 374–385 (2008).Article 

Google Scholar 
Prior, L. D., Williams, R. J. & Bowman, D. M. Experimental evidence that fire causes a tree recruitment bottleneck in an Australian tropical savanna. J. Tropical Ecol. 26, 595–603 (2010).Abatzoglou, J. T., Williams, A. P. & Barbero, R. Global emergence of anthropogenic climate change in fire weather indices. Geophys. Res. Lett. 46, 326–336 (2019).Article 

Google Scholar 
Ferreira, L. N., Vega-Oliveros, D. A., Zhao, L., Cardoso, M. F. & Macau, E. E. N. Global fire season severity analysis and forecasting. Comput. Geosci. 134, 104339 (2020).Article 

Google Scholar 
Flannigan, M. et al. Global wildland fire season severity in the 21st century. For. Ecol. Manag. 294, 54–61 (2013).Article 

Google Scholar 
Ansley, R. J. & Castellano, M. J. Prickly pear cactus responses to summer and winter fires. Rangel. Ecol. Manag. 60, 244–252 (2007).Article 

Google Scholar 
Ansley, R. J., Kramp, B. A. & Jones, D. L. Honey mesquite (Prosopis glandulosa) seedling responses to seasonal timing of fire and fireline intensity. Rangel. Ecol. Manag. 68, 194–203 (2015).Article 

Google Scholar 
Armstrong, G. & Legge, S. The post-fire response of an obligate seeding Triodia species (Poaceae) in the fire-prone Kimberley, north-west Australia. Int. J. Wildland Fire 20, 974–981 (2012).Article 

Google Scholar 
Bellows, R. S., Thomson, A. C., Helmstedt, K. J., York, R. A. & Potts, M. D. Damage and mortality patterns in young mixed conifer plantations following prescribed fires in the Sierra Nevada, California. For. Ecol. Manag. 376, 193–204 (2016).Article 

Google Scholar 
Beyers, J. L. & Wakeman, C. D. Season of burn effects in southern California chaparral. In Second interface between ecology and land development in California 45–55 (Occidental College, CA, 2000).Bowen, B. J. & Pate, J. S. Effect of season of burn on shoot recovery and post‐fire flowering performance in the resprouter Stirlingia latifolia R. Br.(Proteaceae). Austral Ecol. 29, 145–155 (2004).Article 

Google Scholar 
Casals, P., Valor, T., Rios, A. & Shipley, B. Leaf and bark functional traits predict resprouting strategies of understory woody species after prescribed fires. For. Ecol. Manag. 429, 158–174 (2018).Article 

Google Scholar 
Céspedes, B., Torres, I., Luna, B., Pérez, B. & Moreno, J. M. Soil seed bank, fire season, and temporal patterns of germination in a seeder-dominated Mediterranean shrubland. Plant Ecol. 213, 383–393 (2012).Article 

Google Scholar 
Clabo, D. C. & Clatterbuck, W. K. Shortleaf pine (Pinus echinata, Pinaceae) seedling sprouting responses: Clipping and burning effects at various seedling ages and seasons. J. Torrey Bot. Soc. 146, 96–110 (2019).Article 

Google Scholar 
Drewa, P. B. Effects of fire season and intensity on Prosopis glandulosa Torr. var. glandulosa. Int. J. Wildland Fire 12, 147–157 (2003).Article 

Google Scholar 
Drewa, P. B., Platt, W. J. & Moser, E. B. Fire effects on resprouting of shrubs in headwaters of southeastern longleaf pine savannas. Ecology 83, 755–767 (2002).Article 

Google Scholar 
Drewa, P. B., Thaxton, J. M. & Platt, W. J. Responses of root‐crown bearing shrubs to differences in fire regimes in Pinus palustris (longleaf pine) savannas: exploring old‐growth questions in second‐growth systems. Appl. Veg. Sci. 9, 27–36 (2006).
Google Scholar 
Ellsworth, L. M. & Kauffman, J. B. Seedbank responses to spring and fall prescribed fire in mountain big sagebrush ecosystems of differing ecological condition at Lava Beds National Monument, California. J. Arid Environ. 96, 1–8 (2013).Article 

Google Scholar 
Fairfax, R. et al. Effects of multiple fires on tree invasion in montane grasslands. Landsc. Ecol. 24, 1363–1373 (2009).Article 

Google Scholar 
Fill, J. M., Welch, S. M., Waldron, J. L. & Mousseau, T. A. The reproductive response of an endemic bunchgrass indicates historical timing of a keystone process. Ecosphere 3, 1–12 (2012).Article 

Google Scholar 
Grant, C. Post-burn vegetation development of rehabilitated bauxite mines in Western Australia. For. Ecol. Manag. 186, 147–157 (2003).Article 

Google Scholar 
Hajny, K. M., Hartnett, D. C. & Wilson, G. W. Rhus glabra response to season and intensity of fire in tallgrass prairie. Int. J. Wildland Fire 20, 709–720 (2011).Article 

Google Scholar 
Holmes, P. A comparison of the impacts of winter versus summer burning of slash fuel in alien-invaded fynbos areas in the Western Cape. Southern African For. J. 192, 41–50 (2001).Article 

Google Scholar 
Jasinge, N., Huynh, T. & Lawrie, A. Consequences of season of prescribed burning on two spring-flowering terrestrial orchids and their endophytic fungi. Austr. J. Bot. 66, 298–312 (2018).Article 

Google Scholar 
Jasinge, N., Huynh, T. & Lawrie, A. Changes in orchid populations and endophytic fungi with rainfall and prescribed burning in Pterostylis revoluta in Victoria, Australia. Ann. Bot. 121, 321–334 (2018).CAS 
Article 

Google Scholar 
Kauffman, J. & Martin, R. Sprouting shrub response to different seasons and fuel consumption levels of prescribed fire in Sierra Nevada mixed conifer ecosystems. For. Sci. 36, 748–764 (1990).
Google Scholar 
Keyser, T. L., Greenberg, C. H. & McNab, W. H. Season of burn effects on vegetation structure and composition in oak-dominated Appalachian hardwood forests. For. Ecol. Manag. 433, 441–452 (2019).Article 

Google Scholar 
Knox, K. & Clarke, P. J. Fire season and intensity affect shrub recruitment in temperate sclerophyllous woodlands. Oecologia 149, 730–739 (2006).CAS 
Article 

Google Scholar 
Lamont, B. B. & Downes, K. S. Fire-stimulated flowering among resprouters and geophytes in Australia and South Africa. Plant Ecol. 212, 2111–2125 (2011).Article 

Google Scholar 
Lesica, P. & Martin, B. Effects of prescribed fire and season of burn on recruitment of the invasive exotic plant, Potentilla recta, in a semiarid grassland. Restoration Ecol. 11, 516–523 (2003).Article 

Google Scholar 
Moreno, J. M. et al. Rainfall patterns after fire differentially affect the recruitment of three Mediterranean shrubs. Biogeosciences 8, 3721–3732 (2011).Article 

Google Scholar 
Mulligan, M. K. & Kirkman, L. K. Burning influences on wiregrass (Aristida beyrichiana) restoration plantings: natural seedling recruitment and survival. Restor. Ecol. 10, 334–339 (2002).Article 

Google Scholar 
Nield, A. P., Enright, N. J. & Ladd, P. G. Fire-stimulated reproduction in the resprouting, non-serotinous conifer Podocarpus drouynianus (Podocarpaceae): the impact of a changing fire regime. Popul. Ecol. 58, 179–187 (2016).Article 

Google Scholar 
Norden, A. H. & Kirkman, L. K. Persistence and prolonged winter dormancy of the federally endangered Schwalbea Americana L.(Scrophulariaceae) following experimental management techniques. Nat. Areas J. 24, 129–134 (2004).
Google Scholar 
Olson, M. S. & Platt, W. J. Effects of habitat and growing season fires on resprouting of shrubs in longleaf pine savannas. Vegetatio 119, 101–118 (1995).Article 

Google Scholar 
Ooi, M. K. The importance of fire season when managing threatened plant species: a long-term case-study of a rare Leucopogon species (Ericaceae). J. Environ. Manag. 236, 17–24 (2019).Article 

Google Scholar 
Pavlovic, N. B., Leicht-Young, S. A. & Grundel, R. Short-term effects of burn season on flowering phenology of savanna plants. Plant Ecology 212, 611–625 (2011).Article 

Google Scholar 
Payton, I. J. & Pearce, H. G. Fire-Induced Changes to the Vegetation of Tall-Tussock (Chionochloa rigida) Grassland Ecosystems. (Department of Conservation Wellington, New Zealand, 2009).Peguero, G. & Espelta, J. M. Disturbance intensity and seasonality affect the resprouting ability of the neotropical dry-forest tree Acacia pennatula: do resources stored below-ground matter? J. Tropical Ecol. 28, 539–546 (2011).Risberg, L. & Granström, A. Exploiting a window in time. Fate of recruiting populations of two rare fire-dependent Geranium species after forest fire. Plant Ecol. 215, 613–624 (2014).Article 

Google Scholar 
Rodríguez-Trejo, D. A., Castro-Solis, U. B., Zepeda-Bautista, M. & Carr, R. J. First year survival of Pinus hartwegii following prescribed burns at different intensities and different seasons in central Mexico. Int. J. Wildland Fire 16, 54–62 (2007).Article 

Google Scholar 
Russell, M., Vermeire, L., Ganguli, A. & Hendrickson, J. Fire return interval and season of fire alter bud banks. Rangel. Ecol. Manag.72, 542–550 (2019).Article 

Google Scholar 
Russell-Smith, J., Whitehead, P. J., Cook, G. D. & Hoare, J. L. Response of Eucalyptus‐dominated savanna to frequent fires: lessons from Munmarlary, 1973–1996. Ecol. Monogr. 73, 349–375 (2003).Article 

Google Scholar 
Schmidt, I. B., Sampaio, A. B. & Borghetti, F. Effects of the season on sexual reproduction and population structure of Heteropterys pteropetala (Adr. Juss.), Malpiguiaceae, in areas of Cerrado sensu stricto submitted to biennial fires. Acta Bot. Brasilica 19, 927–934 (2005).Article 

Google Scholar 
Shepherd, B. J., Miller, D. L. & Thetford, M. Fire season effects on flowering characteristics and germination of longleaf pine (Pinus palustris) savanna grasses. Restor. Ecol. 20, 268–276 (2012).Article 

Google Scholar 
Spier, L. P. & Snyder, J. R. Effects of wet-and dry-season fires on Jacquemontia curtisii, a south Florida pine forest endemic. Nat. Areas J. 18, 350–357 (1998).
Google Scholar 
Tsafrir, A. et al. Fire season modifies the perennial plant community composition through a differential effect on obligate seeders in eastern Mediterranean woodlands. Appl. Veg. Sci. 22, 115–126 (2019).Article 

Google Scholar 
Vander Yacht, A. L. et al. Vegetation response to canopy disturbance and season of burn during oak woodland and savanna restoration in Tennessee. For. Ecol. Manag. 390, 187–202 (2017).Article 

Google Scholar 
Vidaller, C., Dutoit, T., Ramone, H. & Bischoff, A. Fire increases the reproduction of the dominant grass Brachypodium retusum and Mediterranean steppe diversity in a combined burning and grazing experiment. Appl. Veg. Sci. 22, 127–137 (2019).Article 

Google Scholar 
Williams, P. R., Congdon, R. A., Grice, A. C. & Clarke, P. J. Soil temperature and depth of legume germination during early and late dry season fires in a tropical eucalypt savanna of north‐eastern Australia. Austral Ecol. 29, 258–263 (2004).Article 

Google Scholar 
Williams, P. R., Congdon, R. A., Grice, A. C. & Clarke, P. J. Germinable soil seed banks in a tropical savanna: seasonal dynamics and effects of fire. Austral Ecol. 30, 79–90 (2005).Article 

Google Scholar 
Zhao, H. et al. Ecophysiological influences of prescribed burning on wetland plants: a case study in Sanjiang Plain wetlands, northeast China. Fresenius Environ. Bull 20, 2932–2938 (2011).CAS 

Google Scholar 
Pick, J. L., Nakagawa, S. & Noble, D. W. Reproducible, flexible and high‐throughput data extraction from primary literature: The metaDigitise r package. Methods in Ecol. Evol. 10, 426–431 (2019).Article 

Google Scholar 
Team, R. C. R: A Language and Environment for Statistical Computing. https://www.R-project.org/ (2020).Higgins, J. P. et al. Cochrane Handbook for Systematic Reviews of Interventions. (John Wiley & Sons, 2019).Lüdecke, D., Lüdecke, M. D. & David, B. W. Package ‘esc’. https://strengejacke.github.io/esc (2017).Schwarzer, G. meta: An R package for meta-analysis. R news 7, 40–45 (2007).
Google Scholar 
Borenstein, M., Hedges, L. V., Higgins, J. P. & Rothstein, H. R. A basic introduction to fixed‐effect and random‐effects models for meta‐analysis. Res. Synth. Methods 1, 97–111 (2010).Article 

Google Scholar 
Harrer, M., Cuijpers, P., Furukawa, T. A. & Ebert, D. D. Doing Meta-Analysis with R: a Hands-on Guide. (Chapman and Hall, 2019).Wilke, C. O., Wickham, H. & Wilke, M. C. O. Package ‘cowplot’. Streamlined Plot Theme and Plot Annotations for ‘ggplot2 (Cowplot, 2019).Fill, J. M., Davis, C. N. & Crandall, R. M. Climate change lengthens southeastern USA lightning‐ignited fire seasons. Glob. Change Biol. 25, 3562–3569 (2019).Article 

Google Scholar 
Halofsky, J. E., Peterson, D. L. & Harvey, B. J. Changing wildfire, changing forests: the effects of climate change on fire regimes and vegetation in the Pacific Northwest, USA. Fire Ecol. 16, 4 (2020).Article 

Google Scholar 
Kraaij, T., Cowling, R. M., van Wilgen, B. W., Rikhotso, D. R. & Difford, M. Vegetation responses to season of fire in an aseasonal, fire-prone fynbos shrubland. PeerJ 5, e3591 (2017).Article 

Google Scholar 
Peel, M. C., Finlayson, B. L. & McMahon, T. A. Updated world map of the Köppen-Geiger climate classification. Hydrol. Earth Syst. Sci. 11, 1633–1644 (2007).Article 

Google Scholar 
Murphy, B. P. et al. Fire regimes of Australia: a pyrogeographic model system. J. Biogeogr. 40, 1048–1058 (2013).Article 

Google Scholar 
McColl-Gausden, S. C., Bennett, L. T., Duff, T. J., Cawson, J. G. & Penman, T. D. Climatic and edaphic gradients predict variation in wildland fuel hazard in south-eastern Australia. Ecography 43, 443–455 (2020).Article 

Google Scholar 
Pausas, J. G. & Keeley, J. E. Evolutionary ecology of resprouting and seeding in fire‐prone ecosystems. New Phytol. 204, 55–65 (2014).Article 

Google Scholar 
Lamont, B. B., Maitre, D. C. L., Cowling, R. M. & Enright, N. J. Canopy seed storage in woody plants. Bot. Rev. 57, 277–317 (1991).Article 

Google Scholar 
Tangney, R. et al. Data supporting: Success of post-fire plant recovery strategies varies with shifting fire seasonality. Zenodo https://doi.org/10.5061/dryad.7sqv9s4t5 (2022).Rothstein, H. R., Sutton, A. J. & Borenstein, M. Publication Bias in Meta-Analysis: Prevention, Assessment and Adjustments (John Wiley & Sons, 2006).Head, M. L., Holman, L., Lanfear, R., Kahn, A. T. & Jennions, M. D. The extent and consequences of P-hacking in science. PLoS Biol. 13, e1002106 (2015).Article 
CAS 

Google Scholar 
Egger, M., Smith, G. D., Schneider, M. & Minder, C. Bias in meta-analysis detected by a simple, graphical test. BMJ 315, 629–634 (1997).CAS 
Article 

Google Scholar 
Simonsohn, U., Nelson, L. D. & Simmons, J. P. P-curve: a key to the file-drawer. J. Exp. Psychol.Gen. 143, 534 (2014).Article 

Google Scholar  More

In the present work, we extend the base model for the spatial mosquito population dynamics24 to include wild male mosquitoes and genetically modified male mosquitoes. Thus, five populations will be considered: the aquatic mosquito population, including larvae and pupae, the egg mosquito population, the reproductive female mosquito population, the wild male mosquito population, and the genetically modified male population. Similar approaches can be found in the literature25,26.In the following system, we represent mosquito population densities (mosquitoes per m(^2)) by: E – in the egg phase, A – in the aquatic phase, F – female in the reproductive phase, M – wild males, and G – genetically modified male mosquitoes. Due to the very high resistance of the egg phase (up to 450 days27) and as we are interested in an urban spatial macro-scale modeling, we do not consider the mortality in the egg phase. The model is described by the following system of partial differential equations:$$begin{aligned} {left{ begin{array}{ll} partial _t E &{} = alpha beta F M -e E, \ partial _t A &{} = e left( 1 – dfrac{A}{k} right) E -(eta _a+{mu _a})A, \ partial _t F &{} = nabla cdot (D_m nabla F) -mu _f F + reta _{a} A, \ partial _t M &{} = nabla cdot (D_m nabla M) -mu _m M + (1-r)eta _{a} A, \ partial _t G &{} = nabla cdot (D_g nabla G) -mu _{g}G + l, end{array}right. } end{aligned}$$
(1)
where ( alpha ) represents the proportion of wild male mosquitoes to the total number of male mosquitoes (wild males + genetically modified males); (beta ) represents the expected quantity of eggs from the successful encounter between wild females and males; e is the egg hatching rate; k is the carrying capacity of the aquatic phase; ( eta _a ) is the emergence rate for mosquitoes from the aquatic phase to the female or male phases; ( mu _a), (mu _f), (mu _m), and (mu _{g}) are the mortality rates of mosquitoes in the aquatic phase, females, males, and genetically modified males, respectively; r is the proportion of females to males (typically (r=0.5)); (l=l(x,y,t)) is the function representing the number of genetically modified mosquitoes released in a unit of time at any point of the domain; (D_m) is the diffusion coefficient of wild mobiles females and males; (D_g) is the diffusion coefficient of genetically modified males. The proposed model (1) can naturally deal with heterogeneous parameters, such as mortality, diffusion, and carrying capacity coefficients. Thus it is possible to model the influence of rain, wind, and human action. In the context of this work, we are considering that the city neighborhood is divided into two environments: houses and streets. Due to lack of data, we restrict the investigated heterogeneity only to the carrying capacity coefficient.The proposed model can be regarded as an extension of other “economic” models20,24 in the effort to qualitatively reproduce the complex phenomena by using as few parameters as possible. Following this idea, the carrying capacity was neglected in the egg phase because of the skip oviposition phenomenon28 i.e., the female lays the number of eggs that the place holds, without more space, she migrates to other environments to finish laying the eggs. We also do not consider this coefficient in the winged phase as limitations in the winged phase were not reported in any study. On the other hand, we consider it in the aquatic phases (larvae and pupae), where it is effective29.The term ( alpha ), which multiplies the probability of encounters between male and female, represents the impact of the insertion of genetically modified males in the mosquito population to the immobile phase and is defined as$$begin{aligned} alpha = left{ begin{array}{cc} 1, &{} text{ if } M=G= 0, \ dfrac{M}{M + G}, &{} text{ otherwise }. end{array} right. end{aligned}$$
(2)
Similar modeling approach can be found in the literature16. As the release rate of genetically modified males increases, the alpha value decreases, and, consequently, the probability of encounter between females and wild males also decreases. Thus, there is a greater probability of encounter between genetically modified males and females. This approach presents an advantage, when compared to the models found in the literature25, as System  (1) does not present singularities at the equilibrium states, allowing mathematical analysis and numerical simulations. From the biological point of view, the increment of male wild mosquitoes over some critical value does not affect the egg deposition. At first glance, the term FM can lead to a misunderstanding that such property is not satisfied in the presented model. However, in Section “Equilibrium points considering the application of genetically modified male mosquitoes,” we argue that both male and female populations possess mathematical attractor equilibria, blocking the wild male population from growing beyond this value.Finally, any acceptable population model should be invariant in the definition domain, meaning its solution does not present senseless values. Setting the variable domain as$$begin{aligned} 0 le E(x,y,t)< infty ,;; 0 le A(x,y,t) le k, ;; 0 le F(x,y,t)< infty ,;; 0 le M(x,y,t)< infty ,;; 0 le G(x,y,t) < infty , end{aligned}$$ (3) we can verify that it is invariant under the time evolution by the System (1). To prove this statement, it is sufficient to verify that the vector field defined by the right side of (1) points into the domain when (E, A, F, M, G) approaches the domain boundary. When E approaches zero, the right side of the first equation in (1) is not negative. When A approaches zero, the right side of the second equation in (1) is not negative. When A approaches k (bottom), the first term on the right side of the second equation in (1) tends to zero, while the second term remains negative. Since the term ( nabla cdot (D_m nabla F) ) cannot change the F sign, when F approaches zero, the right side of the third equation in (1) is not negative Since the term ( nabla cdot (D_m nabla M) ) cannot change the M sign, when M approaches zero, the right side of the fourth equation in (1) is not negative. Since the term ( nabla cdot (D_g nabla G) ) cannot change the G sign, when G approaches zero, the right side of the fifth equation in (1) is not negative. In the rest of this section, let us explain how to estimate one-by-one all the parameters used in this model from experimental data available in the literature. It is a challenging task as, typically, the development of the Ae. aegypti mosquito depends on food variation30, temperature variations14,15 and rainfall31. This data is not available in the literature in the organized and systematic form. Because of that, we assume the environment is under optimal conditions of temperature, availability of food, and humidity.How to estimate emergence rate ((eta _a)) The emergence rate describes the rate at which the aquatic phase of the mosquito emerges into the adult phases. In the present model, for simplicity, it was considered that no mosquito from the crossing between genetically modified males and females reaches adulthood. Thus, the emergence rate is calculated on the crossing between females and wild males. Under optimal conditions and feeding distribution, based on the literature30, the emergence rate is 0.5596 (text{ day}^{-1}).How to estimate diffusion coefficients ((D_m,D_g)) The diffusion coefficient is one of the most important parameters describing the mosquitoes’ movement. We use the methodology proposed in the previous work24 to obtain the diffusion coefficient of adult mosquitoes (females and males) and genetically modified males.The estimate is done by assuming that all mosquitoes are released at (0, 0), and their movement is described by the corresponding equation in (1) neglecting other terms than diffusion. The population starts spreading in all directions. We define the spreading distance R(t) as the radius of the region centered in (0, 0) where (90%) of the initial mosquitoes population density is present. In Silva et al.24 it is shown that$$begin{aligned} R(t) = sqrt{4Dt} ;text {erf}^{-1}(0.9). end{aligned}$$ (4) Now corresponding diffusion coefficient is estimated by using the average flight distance of the mosquitoes and the characteristic time related to their life expectancy. Under favorable weather conditions, the average lifetime flight distance of females and males is approximately32,33 65 m, while the same for GM males is34 67.3 m. Based on the literature, we consider that the characteristic time for wild females and males32 is 7 days, and the same for genetically modified males is34 2.17 days. Using (4) we estimate the values for (D_m) and (D_g) summarized in Table 1. It would be natural to consider that the mosquitoes’ movement changes in different environments. Unfortunately, we were unable to find the corresponding experimental data, and because of that, we considered that (D_m) and (D_g) are the same in streets and house blocks.How to estimate mortality rates ((mu _a), (mu _f), (mu _m), (mu _{g}))The mortality coefficient represents an average quantity of mosquitoes in the corresponding phase dying each day. As mentioned before, we disregard the mortality rate in the egg phase, as it is negligible due to its great durability27, it does not affect the numerical results, and it complicates analytical estimates. Thus, the aquatic phase mortality rate coefficient is equal to the same for larvae’s coefficient, which is approximately29 (mu _a = 0.025) (1/day).There is no solid agreement on the mortality rate of male and female wild mosquitoes in the literature. Although some results29,30 suggest they are similar, we follow these authors and consider them equal. Considering both natural death and accidental ones, approximately (10%) of females and male mosquitoes in the adult phase die at each day35. Under optimal conditions, the mortality coefficient can be estimated from this data by using the proposed model (1) by neglecting diffusion and emergence terms in the corresponding equation; details can be found in the previous work24. The resulting parameter values are summarized in Table 1.It would be natural to consider that the mosquitoes mortality rate depends on the environment. Unfortunately, we were unable to find the corresponding experimental data, and because of that, we considered that (mu _a), (mu _f), (mu _m), and (mu _{g}) are the same in streets and house blocks.How to estimate the expected egg number ((beta ))This coefficient represents the average quantity of eggs a wild female lays per day, assuming a successful meeting with a wild male. Considering the number of times a female lays eggs in its lifetime36, the average quantity of eggs per lay and the mosquito’s life expectancy, under favorable conditions, this coefficient is estimated as (beta = 34).How to estimate the hatching rate (e)This coefficient determines the average number of eggs hatching in one day. Experimental data37 suggest that, under optimal humidity conditions, the mean value of the hatch rate coefficient is 0.24 given a temperature of 28 ((^{circ })C), which is considered ideal for mosquito development. This is the value used in the present work.How to estimate carrying capacity coefficient (k)The carrying capacity k represents the space limitation of one phase due to situations present in the environment37,38, such as competition for food among the larvae39. In general, it depends on external factors such as food availability, climate, terrain properties, making direct estimation almost impossible. In the Analytical results section, we show how to estimate this coefficient for each grid block. When considering spatial population dynamics in a heterogeneous environment, carrying capacity is one of the most influential parameters as it varies significantly. For example, house block offer more food and a shelter against natural predators resulting to a larger carrying capacity when compared with street environment. Following the literature32 we assume that the 80% of the mosquito’s breeding places are in houses resulting in the relation (k_h=5k_s), where (k_h) and (k_s) are the carrying capacities of the house blocks and in the streets.Genetically modified mosquitoes release rate (l)Function l(x, y, t) determines how many genetically modified mosquitoes are released in the location (x, y) at time t.In a normal situation, the sex ratio between males and females is 1 : 1. The increment of this proportion favoring GM males increases the probability of females to mate with these mosquitoes. As reported in the literature12,30 the initial launch size is 11 times larger than the adult female population, and it is done in some spots in the city. In this work, we analyze different release strategies maintaining the (11times 1) proportion in some scenarios.Table 1 All parameter values are directly taken or estimated from the literature as explained in section Modeling.Full size table More