Ecology

1.
Huang, H. Study on mechanized production engineering mode for paddy rice in double-cropping areas in south china. Published doctorial dissertation, China Agricultural University, Beijing. https://kns.cnki.net/KCMS/detail/detail.aspx?dbname=CDFD1214&filename=1014223520.nh (2014).
2.
Gao, Y. M., Yan, T. & Liu, W. J. Research and progress of direct rice seeding mechanization at home and abroad. Agric. Sci. Technol. Equip. 1, 28–29. https://doi.org/10.16313/j.cnki.nykjyzb.2013.01.020 (2013).
Article  Google Scholar 

3.
Fawzy, S., Osman, A. I., Doran, J. & Rooney, D. W. Strategies for mitigation of climate change: A review. Environ. Chem. Lett. 6, 2069–2094. https://doi.org/10.1007/s10311-020-01059-w (2020).
CAS  Article  Google Scholar 

4.
Hussain, S. et al. Rice production under climate change: Adaptations and mitigating strategies. In Environment, Climate, Plant and Vegetation Growth (eds Fahad, S. et al.) 659–686 (Springer, Berlin, 2020).
Google Scholar 

5.
Vicente-Serrano, S. M., Quiring, S. M., Pena-Gallardo, M., Yuan, S. S. & Dominguez-Castro, F. A review of environmental droughts: Increased risk under global warming?. Earth Sci. Rev. 201, 102953. https://doi.org/10.1016/j.earscirev.2019.102953 (2020).
Article  Google Scholar 

6.
Ault, T. R. On the essentials of drought in a changing climate. Science 6488, 256–260. https://doi.org/10.1126/science.aaz5492 (2020).
ADS  CAS  Article  Google Scholar 

7.
Zhang, L. X. & Zhou, T. J. Drought over east Asia: A review. J. Clim. 8, 3375–3399. https://doi.org/10.1175/JCLI-D-14-00259.1 (2015).
ADS  Article  Google Scholar 

8.
Zhang, X. et al. Urban drought challenge to 2030 sustainable development goals. Sci. Total Environ. 693, 133536. https://doi.org/10.1016/j.scitotenv.2019.07.342 (2019).
ADS  CAS  Article  PubMed  Google Scholar 

9.
Chakraborty, D. et al. A global analysis of alternative tillage and crop establishment practices for economically and environmentally efficient rice production. Sci. Rep. 7, 9342. https://doi.org/10.1038/s41598-017-09742-9 (2017).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

10.
Peng, S. B., Tang, Q. Y. & Zou, Y. B. Current status and challenges of rice production in China. Plant Prod. Sci. 12, 3–8. https://doi.org/10.1626/pps.12.3 (2009).
Article  Google Scholar 

11.
Sun, L. M. et al. Implications of low sowing rate for hybrid rice varieties under dry direct-seeded rice system in central China. Field Crops Res. 175, 87–95. https://doi.org/10.1016/j.fcr.2015.02.009 (2015).
Article  Google Scholar 

12.
Farooq, M. et al. Rice direct seeding: Experiences, challenges and opportunities. Soil Tillage Res. 111, 87–98. https://doi.org/10.1016/j.still.2010.10.008 (2011).
Article  Google Scholar 

13.
Sandhu, N. et al. Deciphering the genetic basis of root morphology, nutrient uptake, yield, and yield-related traits in rice under dry direct-seeded cultivation systems. Sci. Rep. 9, 9334. https://doi.org/10.1038/s41598-019-45770-3 (2019).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

14.
Liu, H. Y. et al. Dry direct-seeded rice as an alternative to transplanted-flooded rice in Central China. Agron. Sustain. Dev. 35, 285–294. https://doi.org/10.1007/s13593-014-0239-0 (2015).
Article  Google Scholar 

15.
Muhammad, S. et al. The effect of different weed management strategies on the growth and yield of direct-seeded dry rice (Oryza sativa). Planta Daninha. 34, 57–64. https://doi.org/10.1590/S0100-83582016340100006 (2016).
Article  Google Scholar 

16.
Kakumanu, K. R., Kotapati, G. R., Nagothu, U. S., Kuppanan, P. & Kallam, S. R. Adaptation to climate change and variability: A case of direct seeded rice in Andhra Pradesh, India. J. Water Clim. Change. 10, 419–430. https://doi.org/10.2166/wcc.2018.141 (2019).
Article  Google Scholar 

17.
Yamane, K. et al. Seed vigour contributes to yield improvement in dry direct-seeded rainfed lowland rice. Ann Appl. Biol. 172, 100–110. https://doi.org/10.1111/aab.12405 (2018).
CAS  Article  Google Scholar 

18.
Nakano, H., Hattori, I. & Morita, S. Dry matter yield response to seeding rate and row spacing in direct-seeded and double-harvested forage rice. Jpn. Agric. Res. Q. 53, 255–264. https://doi.org/10.6090/jarq.53.255 (2019).
CAS  Article  Google Scholar 

19.
Sun, C. L. et al. Implications of low sowing rate for hybrid rice varieties under drydirect-seeded rice system in Central China. Field Crops Res. 175, 87–95. https://doi.org/10.1016/j.fcr.2015.02.009 (2015).
Article  Google Scholar 

20.
Jabran, K. et al. Mulching improves water productivity, yield and quality of fine rice under water-saving rice production systems. J. Agron. Crop Sci. 201, 389–400. https://doi.org/10.1111/jac.12099 (2015).
Article  Google Scholar 

21.
Fawibe, O. O., Hiramatsu, M., Taguchi, Y., Wang, J. & Isoda, A. Grain yield, water-use efficiency, and physiological characteristics of rice cultivars under drip irrigation with plastic-film-mulch. J. Crop Improv. 34, 414–436. https://doi.org/10.1080/15427528.2020.1725701 (2020).
Article  Google Scholar 

22.
He, H. B. et al. Rice performance and water use efficiency under plastic mulching with drip irrigation. PLoS ONE 8, e83103. https://doi.org/10.1371/journal.pone.0083103 (2013).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

23.
Farooqi, Z. U. R., Sabir, M., Zeeshan, N., Naveed, K. & Hussain, M. M. Enhancing carbon sequestration using organic amendments and agricultural practices. In Carbon Capture, Utilization and Sequestration (ed. Agarwal, R. K.) 17–35 (IntechOpen, London, 2018).
Google Scholar 

24.
Fuss, S. et al. Negative emissions-part 2: Costs, potentials and side effects. Environ. Res. Lett. 6, 063002. https://doi.org/10.1088/1748-9326/aabf9f (2018).
ADS  CAS  Article  Google Scholar 

25.
Li, Y. S. et al. Influence of continuous plastic film mulching on yield, water use efficiency and soil properties of rice fields under non-flooding condition. Soil Tillage Res. 93, 370–378. https://doi.org/10.1016/j.still.2006.05.010 (2007).
Article  Google Scholar 

26.
Huang, Y., Liu, Q., Jia, W. Q., Yan, C. R. & Wang, J. Agricultural plastic mulching as a source of microplastics in the terrestrial environment. Environ. Pollut. 260, 114096. https://doi.org/10.1016/j.envpol.2020.114096 (2020).
CAS  Article  PubMed  Google Scholar 

27.
Yu, Q. et al. Distribution, abundance and risks of microplastics in the environment. Chemosphere 249, 126059. https://doi.org/10.1016/j.chemosphere.2020.126059 (2020).
ADS  CAS  Article  PubMed  Google Scholar 

28.
Yan, X. Y. et al. Downward transport of naturally-aged light microplastics in natural loamy sand and the implication to the dissemination of antibiotic resistance genes. Environ. Pollut. 262, 114270. https://doi.org/10.1016/j.envpol.2020.114270 (2020).
CAS  Article  PubMed  Google Scholar 

29.
Geyer, R., Jambeck, J. R. & Law, K. L. Production, use, and fate of all plastics ever made. Sci. Adv. 7, e1700782. https://doi.org/10.1126/sciadv.1700782 (2017).
ADS  CAS  Article  Google Scholar 

30.
Osman, A. I. et al. Pyrolysis kinetic modelling of abundant plastic waste (PET) and in-situ emission monitoring. Environ. Sci. Eur. 1, 112. https://doi.org/10.1186/s12302-020-00390-x (2020).
CAS  Article  Google Scholar 

31.
Kumar, U. S. U. et al. Neem leaves extract based seaweed bio-degradable composite films with excellent antimicrobial activity for sustainable packaging material. BioResources 1, 700–713. https://doi.org/10.15376/biores.14.1.700-713 (2019).
CAS  Article  Google Scholar 

32.
Qasim, U. et al. Renewable cellulosic nanocomposites for food packaging to avoid fossil fuel plastic pollution: A review. Environ. Chem. Lett. https://doi.org/10.1007/s10311-020-01090-x (2020).
Article  Google Scholar 

33.
Wang, Y. J., He, K., Zhang, J. B. & Chang, H. Y. Environmental knowledge, risk attitude, and households’ willingness to accept compensation for the application of degradable agricultural mulch film: Evidence from rural China. Sci. Total Environ. 744, 140616. https://doi.org/10.1016/j.scitotenv.2020.140616 (2020).
ADS  CAS  Article  PubMed  Google Scholar 

34.
Cirujeda, A. et al. Biodegradable mulch instead of polyethylene for weed control of processing tomato production. Agron. Sustain. Dev. 32, 889–897. https://doi.org/10.1007/s13593-012-0084-y (2012).
CAS  Article  Google Scholar 

35.
Yin, M. H., Li, Y. N., Fang, H. & Chen, P. P. Biodegradable mulching film with an optimum degradation rate improves soil environment and enhances maize growth. Agric. Water Manag. 216, 127–137. https://doi.org/10.1016/j.agwat.2019.02.004 (2019).
Article  Google Scholar 

36.
Daryanto, S., Wang, L. X. & Jacinthe, P. A. Can ridge-furrow plastic mulching replace irrigation in dryland wheat and maize cropping systems?. Agric. Water Manag. 190, 1–5. https://doi.org/10.1016/j.agwat.2017.05.005 (2017).
Article  Google Scholar 

37.
Qin, S. H., Zhang, J. L., Dai, H. L., Wang, D. & Li, D. M. Effect of ridge–furrow and plastic-mulching planting patterns on yield formation and water movement of potato in a semi-arid area. Agric. Water Manag. 131, 87–94. https://doi.org/10.1016/j.agwat.2013.09.015 (2014).
Article  Google Scholar 

38.
Fan, Y. L. et al. Effects of ridge and furrow film mulching on soil environment and yield under potato continuous cropping system. Plant Soil Environ. 65, 523–529. https://doi.org/10.17221/481/2019-PSE (2019).
Article  Google Scholar 

39.
Fan, T. L. et al. Film mulched furrow-ridge water harvesting planting improves agronomic productivity and water use efficiency in rainfed areas. Agric. Water Manag. 217, 1–10. https://doi.org/10.1016/j.agwat.2019.02.031 (2019).
Article  Google Scholar 

40.
Diaz-Perez, J. C. Root zone temperature, plant growth and yield of broccoli [Brassica oleracea (plenck) var. italica] as affected by plastic film mulches. Sci. Hortic. 123, 156–163. https://doi.org/10.1016/j.scienta.2009.08.014 (2009).
Article  Google Scholar 

41.
Gholamhoseini, M., Dolatabadian, A. & Habibzadeh, F. Ridge-furrow planting system and wheat straw mulching effects on dryland sunflower yield, soil temperature, and moisture. Agron. J. 111, 3383–3392. https://doi.org/10.2134/agronj2019.02.0097 (2019).
CAS  Article  Google Scholar 

42.
Mo, F. et al. Alternating small and large ridges with full film mulching increase linseed (Linum usitatissimum L.) productivity and economic benefit in a rainfed semiarid environment. Field Crops Res. 219, 120–130. https://doi.org/10.1016/j.fcr.2018.01.036 (2018).
Article  Google Scholar 

43.
Gu, X. B., Li, Y. N., Du, Y. D. & Yin, M. H. Ridge-furrow rainwater harvesting with supplemental irrigation to improve seed yield and water use efficiency of winter oilseed rape (Brassica napus L.). J. Integr. Agric. 16, 1162–1172. https://doi.org/10.1016/S2095-3119(16)61447-8 (2017).
Article  Google Scholar 

44.
Mo, F., Wang, J. Y., Xiong, Y. C., Nguluu, S. N. & Li, F. M. Ridge-furrow mulching system in semiarid Kenya: A promising solution to improve soil water availability and maize productivity. Eur. J. Agron. 80, 124–136. https://doi.org/10.1016/j.eja.2016.07.005 (2016).
Article  Google Scholar 

45.
Zhang, X. D. et al. Ridge-furrow mulching system regulates diurnal temperature amplitude and wetting-drying alternation behavior in soil to promote maize growth and water use in a semiarid region. Field Crops Res. 233, 121–130. https://doi.org/10.1016/j.fcr.2019.01.009 (2019).
Article  Google Scholar 

46.
Li, F. M., Wang, J., Xu, J. Z. & Xu, H. L. Productivity and soil response to plastic film mulching durations for spring wheat on entisols in the semiarid loess plateau of China. Soil Tillage Res. 78, 9–20. https://doi.org/10.1016/j.still.2003.12.009 (2004).
CAS  Article  Google Scholar 

47.
Li, C. J. et al. Towards the highly effective use of precipitation by ridge-furrow with plastic film mulching instead of relying on irrigation resources in a dry semi-humid area. Field Crops Res. 188, 62–73. https://doi.org/10.1016/j.fcr.2016.01.013 (2016).
ADS  Article  Google Scholar 

48.
Li, Y. Z. et al. The effect of tillage on nitrogen use efficiency in maize (Zea mays L.) in a ridge–furrow plastic film mulch system. Soil Tillage Res. 195, 104409. https://doi.org/10.1016/j.still.2019.104409 (2019).
Article  Google Scholar 

49.
Zheng, J., Fan, J. L., Zou, Y. F., Chau, H. W. & Zhang, F. C. Ridge-furrow plastic mulching with a suitable planting density enhances rainwater productivity, grain yield and economic benefit of rainfed maize. J. Arid Land. 12, 181–198. https://doi.org/10.1007/s40333-020-0001-1 (2020).
CAS  Article  Google Scholar 

50.
Zhao, H. et al. Ridge-furrow with full plastic film mulching improves water use efficiency and tuber yields of potato in a semiarid rainfed ecosystem. Field Crops Res. 161, 137–148. https://doi.org/10.1016/j.fcr.2014.02.013 (2014).
ADS  Article  Google Scholar 

51.
Ren, X., Chen, X. & Jia, Z. Effect of rainfall collecting with ridge and furrow on soil moisture and root growth of corn in semiarid northwest China. J. Agron. Crop Sci. 196, 109–122. https://doi.org/10.1111/j.1439-037X.2009.00401.x (2010).
Article  Google Scholar 

52.
Dong, W. L. et al. Ridge and furrow systems with film cover increase maize yields and mitigate climate risks of cold and drought stress in continental climates. Field Crops Res. 207, 71–78. https://doi.org/10.1016/j.fcr.2017.03.003 (2017).
Article  Google Scholar 

53.
Tian, Y., Su, D. R., Li, F. M. & Li, X. L. Effect of rainwater harvesting with ridge and furrow on yield of potato in semiarid areas. Field Crops Res. 84, 385–391. https://doi.org/10.1016/S0378-4290(03)00118-7 (2003).
Article  Google Scholar 

54.
Zhang, X. D. et al. Ridge-furrow mulching system drives the efficient utilization of key production resources and the improvement of maize productivity in the loess plateau of China. Soil Tillage Res. 190, 10–21. https://doi.org/10.1016/j.still.2019.02.015 (2019).
Article  Google Scholar 

55.
Li, R., Hou, X. Q., Jia, Z. K. & Han, Q. F. Soil environment and maize productivity in semi-humid regions prone to drought of Weibei Highland are improved by ridge-and-furrow tillage with mulching. Soil Tillage Res. 196, 104476. https://doi.org/10.1016/j.still.2019.104476 (2020).
Article  Google Scholar 

56.
Gu, X. B., Li, Y. N. & Du, Y. D. Film-mulched continuous ridge-furrow planting improves soil temperature, nutrient content and enzymatic activity in a winter oilseed rape field, northwest China. J. Arid Land. 10, 362–374. https://doi.org/10.1007/s40333-018-0055-5 (2018).
Article  Google Scholar 

57.
Li, M. Study on dynamic of maize (Zea mays L.) yield, soil water and soil carbon under the dry-farming plastic mulching system of ridge and furrow. Published doctorial dissertation, LanZhou University, Lanzhou. https://kns.cnki.net/KCMS/detail/detail.aspx?dbname=CDFDTEMP&filename=1020655864.nh (2020).

58.
Liu, X. E. et al. Film-mulched ridge-furrow management increases maize productivity and sustains soil organic carbon in a dryland cropping system. Soil Sci. Soc. Am. J. 4, 1434–1441. https://doi.org/10.2136/sssaj2014.04.0121 (2014).
CAS  Article  Google Scholar 

59.
Wang, Y. P. et al. Multi-site assessment of the effects of plastic-film mulch on the soil organic carbon balance in semiarid areas of China. Agric. For. Meteorol. 228, 42–51. https://doi.org/10.1016/j.agrformet.2016.06.016 (2016).
ADS  Article  Google Scholar 

60.
Li, S. P., Cai, Z. C., Yang, H. & Wang, J. K. Effects of long-term fertilization and plastic film covering on some soil fertility and microbial properties. Acta Ecol. Sin. 5, 2489–2498 (2009).
Google Scholar  More

1.
Rahmstorf, S. & Coumou, D. Increase of extreme events in a warming world. Proc. Natl. Acad. Sci. U. S. A. 108(44), 17905–17909 (2011).
ADS  CAS  Article  Google Scholar 
2.
Hodgson, J. A. et al. Predicting insect phenology across space and time. Glob. Change Biol. 17, 1289–1300 (2011).
ADS  Article  Google Scholar 

3.
Lane, J. E., Kruuk, L. E. B., Charmantier, A., Murie, J. O. & Dobson, F. S. Delayed phenology and reduced fitness associated with climate change in a wild hibernator. Nature 489, 554–557 (2012).
ADS  CAS  Article  Google Scholar 

4.
IPCC, 2014: Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Core Writing Team, R.K. Pachauri and L.A. Meyer (eds.)]. IPCC, Geneva, Switzerland, 151.

5.
Ge, Q. S., Wang, H. J., Rutishauser, T. & Dai, J. H. Phenological response to climate change in China: a meta-analysis. Glob. Chang. Biol. 21, 265–274 (2015).
ADS  Article  Google Scholar 

6.
Thackeray, S. J. et al. Phenological sensitivity to climate across taxa and trophic levels. Nature 535, 241–245 (2016).
ADS  CAS  Article  Google Scholar 

7.
Berzitis, E. A., Minigan, J. N., Hellett, R. H. & Newman, J. A. Climate and host plant availability impact the future distribution of the bean leaf beetle (Cerotoma trifurcata). Glob. Change Biol. 20, 2778–2792 (2014).
ADS  Article  Google Scholar 

8.
Fand, B. B., Tonnang, H. E. Z., Bal, S. K. & Dhawan, A. K. Shift in the Manifestations of Insect Pests Under Predicted Climatic Change Scenarios: Key Challenges and Adaptation Strategies. In Advances in Crop Environment Interaction (eds Bal, S. et al.). (Springer, Singapore, 2018).

9.
Cayton, H. L., Haddad, N. M., Gross, K., Diamond, S. E. & Ries, L. Do growing degree days predict phenology across butterfly species?. Ecology 96(6), 1473–1479 (2015).
Article  Google Scholar 

10.
Arnold, C. Y. The determination and significance of the base temperature in a linear heat unit system. Am. Soc. Hort. Sci. 74, 430–445 (1959).
Google Scholar 

11.
Higley, L. G., Pedigo, L. P. & Ostile, K. R. DEGDAY: a program for calculating degree–days, and assumptions behind the degree–day approach. Environ. Entomol. 15, 999–1016 (1986).
Article  Google Scholar 

12.
Campbell, A., Frazer, B. D., Gilbert, N., Gutierrez, A. P. & Mackauer, M. Temperature requirements of some aphids and their parasites. J. Appl. Ecol. 11, 431–438 (1974).
Article  Google Scholar 

13.
Stinner, R. E., Gutierrez, A. P. & Butler, G. D. An algorithm for temperature-dependent growth rate simulation. Can. Entomol. 106, 519–524 (1974).
Article  Google Scholar 

14.
Sharpe, P. J. H. & DeMichele, D. W. Reaction kinetics of poikilotherm development. J. Theor. Biol. 64, 649–670 (1977).
CAS  Article  Google Scholar 

15.
Huber, R. T. Heat units and insect population prediction. In Proceedings of Beltwide cotton Production Mechanization Conference, 6–7 Jan, 1982. Las Vegas (1982).

16.
Peddu, H., Fand, B. B., Sawai, H. R. & Lave, N. V. Estimation and validation of developmental thresholds and thermal requirements for cotton pink bollworm Pectinophora gossypiella. Crop Prot. 127, 104984. https://doi.org/10.1016/j.cropro.2019.104984 (2020).
CAS  Article  Google Scholar 

17.
Beasley, C. A. & Adams, C. J. Field–based, degree–day model for pink bollworm (Lepidoptera: Gelechiidae) development. J. Econ. Entomol. 89, 881–890 (1996).
Article  Google Scholar 

18.
Trnka, M. et al. European corn borer life stage model: regional estimates of pest development and spatial distribution under present and future climate. Ecol. Model. 207, 61–84 (2007).
Article  Google Scholar 

19.
Fand, B. B., Tonnang, H. E. Z., Kumar, M., Kamble, A. L. & Bal, S. K. A temperature-based phenology model for predicting development, survival and population growth potential of mealybug, Phenacoccus solenopsis Tinsley (Hemiptera: Pseudococcidae). Crop Prot. 55, 98–108 (2014).
Article  Google Scholar 

20.
Fand, B. B., Sul, N. T., Bal, S. K. & Minhas, P. S. Temperature Impacts the development and survival of common cutworm (Spodoptera litura): Simulation and visualization of potential population growth in India under warmer temperatures through life cycle modelling and spatial mapping. PLoS ONE 10(4), e0124682 (2015).
Article  Google Scholar 

21.
Sporleder, M., Simon, R., Juarez, H. & Kroschel, J. Regional and seasonal forecasting of the potato tuber moth using a temperature-driven phenology model linked with geographic information systems. In Integrated Pest Management for the Potato Tuber Moth, Phthorimaea operculella Zeller – A Potato Pest of Global Importance Zeller – A Potato Pest of Global Importance (eds Kroschel, J. & Lacey, L.) 15–30 (Margraf Publishers, Weikersheim (Germany), 2008).
Google Scholar 

22.
Khadioli, N. et al. Effect of temperature on the phenology of Chilo partellus (Swinhoe) (Lepidoptera, Crambidae): simulation and visualization of the potential future distribution of C. partellus in Africa under warmer temperatures through the development of life-table parameters. Bulle Entomol. Res. 104, 809–822 (2014).
CAS  Article  Google Scholar 

23.
Chimel, S. M. & Wilson, M. C. Estimation of the lower and upper developmental threshold temperatures and duration of the nymphal stages of the meadow spittlebug, Philaenus spumarius. Environ. Entomol. 8, 682–685 (1979).
Article  Google Scholar 

24.
Henneberry, T. J. & Hutchison, W. D. Tobacco budworm (Lepidoptera: Noctuidae): phenology of fall and summer diapausing and degree-day requirements for larval development and adult emergence. Environ. Entomol. 18, 563–569 (1989).
Article  Google Scholar 

25.
Sevacherian, V., Toscano, N. C., Van Steenwyk, R. A., Sharma, R. K. & Sanders, R. R. Forecasting pink bollworm emergence by thermal summation. Environ. Entomol. 6, 545–546 (1977).
Article  Google Scholar 

26.
Zalom, F. G. et al. Degree–days: the calculation and use of heat units in pest management. University of California DANR Leaflet 21373. (1983).

27.
Allen, J. C. A modified sine wave method for calculating degree days. Environ. Ent. 5, 388–396 (1976).
Article  Google Scholar 

28.
Fry, K. E. Heat-unit calculations in cotton cropand insect models. USDA-ARS AAT-W-23. Agricultural Research Service (Western Region), U. S. Department of Agriculture, Oakland, CA. (1983).

29.
Pruess, K. P. Day-degree methods for pest management. Environ. Entomol. 12, 613–619 (1983).
Article  Google Scholar 

30.
CABI. (2020). Invasive species compendium: Pectinophora gossypiella (pink bollworm). https://www.cabi.org/isc/datasheet/39417#70AF7142-7A8B-4F36-A0BA-4F14FA270EED. Accessed 29 April 2020.

31.
Naik, V. C. B. N., Dhara Jothi, B., Dabhade, P. L. & Kranthi, S. Pink Boll worm Pectinophora gossypiella (saunders) infestation on Bt and Non Bt Hybrids in India in 2011–2012. Cotton Res. J. 6, 37–40 (2014).
Google Scholar 

32.
Fand, B. B. et al. Widespread infestation of pink bollworm, Pectinophora gossypiella (Saunders) (Lepidoptera: Gelechidae) on Bt cotton in Central India: a new threat and concerns for cotton production. Phytoparasitica 47, 313–325 (2019).
CAS  Article  Google Scholar 

33.
Huber, R. T., Moore, L. & Hoffman, M. P. Feasibility study of wide–area pheromone trapping of male pink bollworm moths in a cotton insect pest management program. J. Econ. Entomol. 72, 222–227 (1979).
Article  Google Scholar 

34.
Hutchinson, W. D., Butler, G. D. Jr. & Martin, J. M. Age–specific developmental times for pink bollworm (Lepidoptera: Gelechiidae): three age classes of eggs, five larval instars, and pupae. Ann. Entomol. Soc. Am. 79, 482–487 (1986).
Article  Google Scholar 

35.
Bryant, S. R., Thomas, C. D. & Bale, J. S. The influence of thermal ecology on the distribution of three nymphalid butterflies. J. Appl. Ecol. 39, 43–55 (2002).
Article  Google Scholar 

36.
Gergis, M. F., Soliman, M. A., Moftah, E. A. & Naby, A. A. Temperature dependent development and functional responses of pink bollworm Pectinophora gossypiella (Saund.). Assiut. J. Agric. Sci. 21, 119–126 (1990).
Google Scholar 

37.
Yones, M. S., Rahman, H. A., AbouHadid, A. F., Arafat, S. M. & Dahi, H. F. Heat unit requirements for development of the pink bollworm, Pectinophora gossypiella (Saunds.). Egypt Acad. J. Biol. Sci. 4(1), 115–122 (2011).
Google Scholar 

38.
El-Lebody, K. A., Mostafa, H. Z. & Rizk, A. M. Study the biology and thermal requirements of Pectinophora gossypiella (Saunders), infestated cotton bolls var giza 90, under natural conditions. Egypt. Acad. J. Biol. Sci. 8(3), 115–125 (2015).
Google Scholar 

39.
Higley, L. G. & Peterson, R. K. D. Initiating sampling programs. In Handbook of Sampling Methods for Arthropods in Agriculture (eds Pedigo, L. P. & Buntin, G. D.) 119–136 (CRC, Boca Raton, FL, 1994).
Google Scholar 

40.
Nath, V. & Agarwal, R. A. Insect pests of crops and their control. Bharati Publications, Delhi 1:139 (1982).

41.
Sevacherian, V. & El-Zik, K. M. A slide rule for cotton crop and insect management. Univ. Calif. Div. Agric. Sci. Coop. Ext. Leaf. 21361 (1983).

42.
Klein, Z. & Applebaum, S. W. pink bollworm (Pectinophora gossypiella) lifecycle and diapause induction in Israel 1990. Hassadeh 71(2), 210–213 (1990).
Google Scholar 

43.
Kranthi, K. R. Bt Cotton: Questions and Answers 70 (Indian Society for Cotton Improvement (ISCI), Mumbai, India, 2012).
Google Scholar 

44.
Kranthi, K. R. Pink bollworm strikes Bt cotton. Cotton Stat. News 35, 1–6 (2015).
Google Scholar 

45.
Jha, R. C. & Bisen, R. S. Effect of climatic factors on the seasonal incidence of thepink bollworm on cotton crop. Annu. Plant Prot. Sci. 2, 12–14 (1994).
Google Scholar 

46.
Sarwar, M. Biological parameters of pink bollworm Pectinophora gossypiella (Saunders) (Lepidoptera: Gelechiidae): a looming threat for cotton and its eradication opportunity. Int. J. Res. Agric. For. I7, 25–36 (2017).
Google Scholar 

47.
Ellsworth, P., et al. 2006. Pink Bollworm Management. Newsletter of the Pink Bollworm Action Committee. A Project of The Cotton Foundation Produced by the University of Arizona – Cooperative Extension, 1(2): 1–2.

48.
Edwards, M. & Richardson, A. J. Impact of climate change on marine pelagic phenology and trophic mismatch. Nature 430, 881–884 (2004).
ADS  CAS  Article  Google Scholar 

49.
Snyder, R. L. 2002. DegDay.xls, Version 1.01. University of California Department of Land, Air and Water Resources, Atmospheric Science, Davis, California, USA. http://biomet.ucdavis.edu/DegreeDays/DegDay.htm. Accessed 10 Jan 2018.

50.
ICAR-CICR. 2006. Approved Package of practices for Cotton: Maharashtra State. ICAR-Central Institute for Cotton Research, Nagpur, Maharashtra, India. 440 010. http://www.cicr.org.in/pop/mh.pdf. Accessed 22 April 2020.

51.
Fife, L. C. Factors influencing pink bollworm pupation and emergence from overwintering larvae in central Texas. J. Econ. Entomol. 54, 908–918 (1961).
Article  Google Scholar 

52.
Fand, B. B. et al. A simple and low-cost laboratory rearing technique for cotton pink bollworm, Pectinophora gossypiella (Suanders) (Lepidoptera: Gelechidae) using detached green bolls of cotton. Phytoparasitica https://doi.org/10.1007/s12600-019-00779-2 (2020).
Article  Google Scholar 

53.
Box, G. E. P. & Jenkins, G. M. Time Series Analysis: Forecasting and Control (Holden-Day, San Francisco, 1970).
Google Scholar  More

Chemicals and reagents
Sodium chloride (S3014), potassium chloride (P9541), sodium phosphate dibasic (S3264), potassium phosphate monobasic (P9791), potassium bicarbonate (12602), ammonium chloride (A9434), ethylene diamine tetra acetic acid di-sodium salt (E6635), bovine serum albumin (BSA) (5482), absolute ethanol (100983), methanol (34860) were purchased from Sigma–Aldrich, St. Louis, MO, United States. CD 34, Sca 1, and CBA Flex Set which contains IL-3 (558346), IL-6 (558301), TNFα (558299), IFN γ (562233), G-CSF (560152), GM-CSF (558347), IL-1α (560157), IL-1β (560232), Mouse/Rat Soluble Protein Master Buffer Kit (558266) were procured from BD Biosciences, United States. May-Grunwalds Stain (S039) and Giemsa Stain Solution (TCL083) were procured from Hi-Media India.
1 L 1X PBS (8 g of sodium chloride, 0.2 g of potassium chloride, 1.44 g of sodium phosphate dibasic, 0.25 g of potassium phosphate monobasic to 1 L at pH 7.4), 1 L 1X RBC lysis buffer (1 g of potassium bicarbonate, 8 g of ammonium chloride and 0.03 g of di-sodium EDTA), 1% BSA in PBS, 70% ethanol in PBS, and may-grunwald-giemsa stain mixed in 3:1 ratio were prepared in the laboratory.
Animals and groupings
Pathogen free Strain ‘A’ male mice weighing 25.0–30.0 gm were received from Institute of Nuclear Medicine and Allied Science (INMAS) experimental animal facility at the age of 9 weeks, which is equivalent to young adult humans. Six mice per cage were housed under 25 ± 3 °C temperature and relative humidity of 30–70% in 12 h light/dark cycle with standard food and water. The study design strictly adhered to the guidelines approved by Institutional Animal Care and Use Committee (IACUC) of our institute, Institute of Nuclear Medicine and Allied Sciences (INMAS), Defence Research and Development Organization (DRDO), Delhi, India (Institute Animals Ethics Committee number: INM/IAEC/2019/01).
After 1 week of acclimatization under ambient conditions, mice were grouped for the survival assay according to localized gamma radiation dose i.e. 7, 9, 10, 12, 15, 17 and 19 Gy. Eighteen animals were assigned in each group for this assay.
For all other parameters mice were divided into two groups with six animals in each. Group 1, consisted of untreated or sham irradiated animals. Group 2 was exposed to 15 Gy localized irradiation. Mice were sacrificed at different time points (1, 2, 4, 7, 10, 15, 20, 25, and 30 days) post treatment. Hematopoietic stem cell marker CD 34 and Sca1 were measured on 1, 4, 7, and 10th day post-irradiation.
Irradiation
A Cobalt-60 teletherapy (Model: Bhabhatron-II, Panacea Medical Technologies Pvt Ltd, India) machine was used to irradiate the hinds of the three mice at a time (Fig. 1). The cobalt-60 beam was calibrated following IAEA TRS-398 protocol, with measurement done in water phantom (30 cm × 30 cm × 30 cm), for a field size 10 cm × 10 cm at source to surface distance (SSD) of 80 cm. A Farmer type, 0.6 cc volume, ionization chamber was utilised and the same was recently calibrated for absorbed dose to water (NDW) at Secondary Standard Dosimetry Laboratory (SSDL) of India. The measured absorbed dose rate (water, 10 cm × 10 cm, 80 SSD, Dmax) was 1.39 Gy/min.
Figure 1

Representative image of animal exposure with γ-rays in a field size of 2 cm × 35 cm.

Full size image

The localized bone marrow irradiation was done using single Posteroanterior (PA) beam with 2 cm × 35 cm field size at 80 cm SSD opened through secondary collimators of the machine. The 5 mm thick acrylic re-strainers were used to immobilize each mouse which also provides the build-up thickness for the irradiation. The dose prescription point was the surface of the hinds i.e. 5 mm below the surface of the re-strainer. The whole irradiation platform was placed on 5 cm thick acrylic slab to provide sufficient back scatter and all the air gaps between the three mice were filled with tissue equivalent gel bolus or wet cotton to compensate for missing side scatter. The prescription doses were absorbed dose to water and not corrected for any tissue heterogeneity.
The absolute and relative dosimetry of collimated field (2 cm × 35 cm) were performed using pin point (0.04 cc volume) ionization chamber and EBT3 (ISP, Wayne, USA) Gafchromic dosimetry films in solid water phantom. The response of the pin point chamber and EBT3 films was calibrated against the SSDL calibrated ionization chamber in solid water phantom at the depth of 5 cm and field size 10 cm × 10 cm in the same beam quality (i.e. cobalt-60). The film scanning was performed using Epson 10000XL flatbed scanner as per the manufacturers scanning protocol. The beam profiles were obtained from the scanned films and the full width at half maximum (FWHM) in transverse direction was 1.78 cm for the opening of 2.0 cm. The dose uniformity was better than 4% in central 80% of the field. The absolute dose rate (dose to water, dmax, 2 cm × 35 cm, SSD 80 cm) measured at the centre of the field was 1 Gy/min. The overall uncertainty in dose calculations is 6.5% with enhancement or coverage factor (k = 2) at 95% confidence. The main body of the mice was covered with 15 mm lead to prevent any scatter radiation reaching at other body parts of the animal. The surface dose reaching at the shielded body parts of the mice was measured using 0.4 cc ionisation chamber. The measured mean surface dose was 0.024 ± 0.014% of the open field dose. This dose is negligible and considered to be acceptable.
Survival assay in mice
For survival assay, 18 mice from each experimental group i.e. 7, 9, 10, 12, 15, 17 and 19 Gy, were irradiated at a fixed dose rate of 1 Gy/min. Groups were inspected daily for their morbidity and mortality status for a total duration of 30 days. All the surviving mice were euthanized humanely using intra-peritoneal injection (i.p.) with dexmedetomidine (0.3 mg/kg) in combination with ketamine (190 mg/kg) at the completion of the observation period.
Validation of hematopoietic syndrome model
The model of hematopoietic syndrome was validated with localized radiation of 15 Gy as this was the maximum dose at which all the animals survived as observed in the survival results. Mice were euthanized using intra-peritoneal injection (i.p.) with dexmedetomidine (0.3 mg/kg) in combination with ketamine (190 mg/kg) at 1, 2, 4, 7, 10, 15, 20, 25, and 30th day time points. Further analysis such as organ weight, cell counting, and serum cytokines measurement assays were conducted on various organs such as femur bone, spleen, thymus, lungs, kidneys (L), kidneys (R), liver and heart and blood at above mentioned time points. Hematopoietic stem cell markers (CD 34 and Sca 1) were measured in bone marrow cells of mice at 1, 4, 7, and 10th day time points.
Body and organ weights
The control and 15 Gy locally irradiated group animals were sacrificed at 1, 4, 7, 10, 15, 20, 25, and 30th day time points. The organs (spleen, thymus, lungs, kidneys (L), kidneys (R), liver and heart) were dissected out and weighed individually. Relative organ weight was calculated as the ratio between organ weight and body weight.
Haematology
The blood samples were collected from untreated and 15 Gy localized radiation group at 1, 4, 7, 10, 15, 20, 25, and 30th day time points in EDTA vial by cardiac puncture. 20 µl of collected blood from each experimental animal was taken for haematology and analysed using Nihon Kohden Celltac α, Tokyo, Japan, a fully automatic 3 part haematology analyzer.
Bone marrow cell counting
Total bone marrow cells were flushed out at 1, 4, 7, 10, 15, 20, 25 and 30th day points in 1 ml PBS from both femurs of the untreated mice and 15 Gy localized radiation group. The PBS containing cells was centrifuged at 2000 rpm for 8 min and the supernatant was discarded. The cells were washed with 1 ml PBS and the cell pellet was re-suspended in 1 ml of PBS. The re-suspended sample was diluted as required. The cells were then counted using improved Neubauer chamber under Olympus BX-63 microscope.
Bone marrow smears study
Bone marrow cells collected at 1, 4, 7, 10, 15, 20, 25, and 30th day post-irradiation from different treatment groups were centrifuged at 2000 rpm for 8 min and re-suspended in 50 μl of Fetal Bovine Serum (FBS). A small drop of cells suspension was dropped on a clean microscopic slide and then smeared thinly over an area of 2–3 cm by pulling another glass slide held at a 45° angle. Cells were fixed with methanol and stained with may-grunwald-giemsa. Slides were then analysed using Olympus BX-63 microscope.
Splenocytes and thymocytes counts
The organs excised at 1, 4, 7, 10, 15, 20, 25, and 30th day time-points were cleaned in chilled PBS. Using sterile-chilled frosted slides both the thymus and spleen were minced into a cell suspension and which was further centrifuged at 2000 rpm for 8 min. After centrifugation the supernatant was discarded. The RBCs were lysed using RBC lysis buffer and the cells were washed with 1 ml PBS. The cells were then counted using improved Neubauer chamber under Olympus BX-63 microscope.
Measurement of hematopoietic stem cell marker CD 34 and Sca1
Bone marrow cells were isolated from both the femurs from various treatment groups at different time points. Cell were washed with chilled PBS and fixed with 70% chilled ethanol and stored overnight at – 20 °C. Following day, five million cells were blocked with 1% BSA-PBS and stained with CD 34 and Sca 1 cell-surface markers for 20 min at room temperature. 10,000 cells from each sample were analyzed using FACS LSR II (BD Biosciences, San Jose, CA) and results were visualized by the BD FACSDiva V7 software (BD Biosciences, San Jose, CA).
Cytokines expression changes
Mice blood samples were collected in BD serum separation tube at different time-points post treatment (1, 4, 7, 10, 15, 25, and 30 days) via. cardiac puncture from different treatment groups. Samples were incubated at room temperature for 2 h and after incubation centrifuged at 6000g for 15 min. Serum was separated and stored at − 80 °C until analysis. The Levels of IL-3, IL-6, TNFα, IFN γ, G-CSF, GM-CSF, IL-1α,and IL-1β cytokines in mice serum were determined by using respective BD Cytometric Bead Array (CBA) Flex Set (BD Biosciences, USA) on dual laser flow analyzer (LSR-II, Becton Dickinson Biosciences, USA) according to manufacturer’s instructions. Results were analysed using FCAP Array V3 software (BD Biosciences, San Jose, CA).
Statistical analysis
All values obtained in our results are represented as mean ± SD of three independent replicates. The 30 day survival data was plotted using Kaplan–Meier analysis. The difference between the experimental groups was evaluated by one-way analysis of variance, with Newman–Keuls multiple comparison test (V, 8.01; GraphPad Prism, San Diego, CA, USA). The comparisons were made among the untreated and 15 Gy locally irradiated groups for all experimental parameters except the survival study. A value of p  More

1.
Lawrence, W. S. Dispersal: an alternative mating tactic conditional on sex ratio and body size. Behav. Ecol. Sociobiol. 21, 367–373 (1987).
Article  Google Scholar 
2.
Russell, E. M. & Rowley, I. Philopatry or dispersal: competition for territory vacancies in the splendid fairy-wren, Malurus splendens. Anim. Behav. 45, 519–539 (1993).
Article  Google Scholar 

3.
Herzig, A. L. Effects of population density on long-distance dispersal in the goldenrod beetle Trirhabda virgata. Ecology 76, 2044–2054 (1995).
Article  Google Scholar 

4.
Verhulst, S., Perrins, C. M. & Riddington, R. Natal dispersal of great tits in a patchy environment. Ecology 78, 864–872 (1997).
Article  Google Scholar 

5.
Johnson, M. L. & Gaines, M. S. Evolution of dispersal: theoretical models and empirical tests using birds and mammals. Annu. Rev. Ecol. Evol. Sys. 21, 449–480 (1990).
Article  Google Scholar 

6.
Clobert, J., Danchin, E., Dhondt, A. A. & Nichols, J. Dispersal (Oxford University Press, Oxford, 2001).
Google Scholar 

7.
Bowler, D. E. & Benton, T. G. Causes and consequences of animal dispersal strategies: relating individual behaviour to spatial dynamics. Biol. Rev. 80, 205–225 (2005).
PubMed  Article  PubMed Central  Google Scholar 

8.
Szulkin, M. & Sheldon, B. C. Dispersal as a means of inbreeding avoidance in a wild bird population. Proc. R. Soc. B 275, 703–711 (2008).
PubMed  Article  PubMed Central  Google Scholar 

9.
Cornwallis, C. K. Cooperative breeding and the evolutionary coexistence of helper and nonhelper strategies. Proc. Nat. Acad. Sc. USA 115, 1684–1686 (2018).
CAS  Article  Google Scholar 

10.
Nelson-Flower, M. J., Ridley, A. R., Wiley, E. M. & Flower, T. P. Individual dispersal delays in a cooperative breeder: ecological constraints, the benefits of philopatry and the social queue for dominance. J. Anim. Ecol. 87, 1227–1238 (2018).
PubMed  Article  PubMed Central  Google Scholar 

11.
Komdeur, J. Importance of habitat saturation and territory quality for evolution of cooperative breeding in the Seychelles warbler. Nature 358, 493 (1992).
ADS  Article  Google Scholar 

12.
Kokko, H. & Lundberg, P. Dispersal, migration, and offspring retention in saturated habitats. Am. Nat. 157, 188–202 (2001).
CAS  PubMed  Article  PubMed Central  Google Scholar 

13.
Kokko, H. & Ekman, J. Delayed dispersal as a route to breeding: territorial inheritance, safe havens, and ecological constraints. Am. Nat. 160, 468–484 (2002).
PubMed  Article  PubMed Central  Google Scholar 

14.
Lemel, J. Y., Belichon, S., Clobert, J. & Hochberg, M. E. The evolution of dispersal in a two-patch system: some consequences of differences between migrants and residents. Evol. Ecol. 11, 613–629 (1997).
Article  Google Scholar 

15.
Forero, M. G., Donázar, J. A. & Hiraldo, F. Causes and fitness consequences of natal dispersal in a population of black kites. Ecology 83, 858–872 (2002).
Article  Google Scholar 

16.
Kokko, H. & López-Sepulcre, A. From individual dispersal to species ranges: perspectives for a changing world. Science 313, 789–791 (2006).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

17.
Bonte, D. et al. Costs of dispersal. Biol. Rev. 87, 290–312 (2012).
PubMed  Article  PubMed Central  Google Scholar 

18.
Serrano, D. & Tella, J. L. Lifetime fitness correlates of natal dispersal distance in a colonial bird. J. Anim. Ecol. 81, 97–107 (2012).
PubMed  Article  PubMed Central  Google Scholar 

19.
Kubisch, A., Holt, R. D., Poethke, H. J. & Fronhofer, E. A. Where am I and why? Synthesizing range biology and the eco-evolutionary dynamics of dispersal. Oikos 123, 5–22 (2014).
Article  Google Scholar 

20.
Drobniak, S. M., Wagner, G., Mourocq, E. & Griesser, M. Family living: an overlooked but pivotal social system to understand the evolution of cooperative breeding. Behav. Ecol. 26, 805–811 (2015).
Article  Google Scholar 

21.
Taborsky, M. Sneakers, satellites, and helpers: parasitic and cooperative behavior in fish reproduction. Adv. Study Behav. 23, e100 (1994).
Google Scholar 

22.
Clutton-Brock, T. H. & Lukas, D. The evolution of social philopatry and dispersal in female mammals. Mol. Ecol. 21, 472–492 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

23.
Pruett-Jones, S. G. & Lewis, M. J. Sex ratio and habitat limitation promote delayed dispersal in superb fairy-wrens. Nature 348, 541–542 (1990).
ADS  Article  Google Scholar 

24.
Bergmüller, R., Heg, D. & Taborsky, M. Helpers in a cooperatively breeding cichlid stay and pay or disperse and breed, depending on ecological constraints. Proc. R. Soc. B 272, 325–331 (2005).
PubMed  Article  PubMed Central  Google Scholar 

25.
Carrete, M., Donázar, J. A., Margalida, A. & Bertran, J. Linking ecology, behaviour and conservation: does habitat saturation change the mating system of bearded vultures?. Biol. Lett. 2, 624–627 (2006).
PubMed  PubMed Central  Article  Google Scholar 

26.
Covas, R., Doutrelant, C. & du Plessis, M. A. Experimental evidence of a link between breeding conditions and the decision to breed or to help in a colonial cooperative bird. Proc. R. Soc. B 271, 827–832 (2004).
PubMed  Article  PubMed Central  Google Scholar 

27.
Baglione, V. et al. Does yearround territoriality rather than habitat saturation explain delayed natal dispersal and cooperative breeding in the carrion crow?. J. Anim. Ecol. 74, 842–851 (2005).
Article  Google Scholar 

28.
Hatchwell, B. J. & Komdeur, J. Ecological constraints, life history traits and the evolution of cooperative breeding. Anim. Behav. 59, 1079–1086 (2000).
CAS  PubMed  Article  PubMed Central  Google Scholar 

29.
Pen, I. & Weissing, F. J. Towards a unified theory of cooperative breeding: the role of ecology and life history re-examined. Proc. R. Soc. B 267, 2411–2418 (2000).
Article  Google Scholar 

30.
Covas, R. & Griesser, M. Life history and the evolution of family living in birds. Proc. R. Soc. B 274, 1349–1357 (2007).
PubMed  Article  PubMed Central  Google Scholar 

31.
Griesser, M. & Barnaby, J. Role of Nepotism Cooperation and Competition in the Avian Families. Birds-Evolution, Behavior and Ecology Series (Nova Science Pub Inc, New York, 2010).
Google Scholar 

32.
Baglione, V., Canestrari, D., Marcos, J. M., Griesser, M. & Ekman, J. History, environment and social behaviour: experimentally induced cooperative breeding in the carrion crow. Proc. R. Soc B 269, 1247–1251 (2002).
PubMed  Article  PubMed Central  Google Scholar 

33.
Tchabovsky, A. & Bazykin, G. Females delay dispersal and breeding in a solitary gerbil, Meriones tamariscinus. J. Mammal. 85, 105–112 (2004).
Article  Google Scholar 

34.
Ellis, E. C., Fuller, D. Q., Kaplan, J. O. & Lutters, W. G. Dating the Anthropocene: Towards an empirical global history of human transformation of the terrestrial biosphere. Elem. Sci. Anth 1, 18 (2013).
Article  Google Scholar 

35.
Bernardo-Madrid, R. et al. Human activity is altering the world’s zoogeographical regions. Ecol. Lett. 22, 1297–1305 (2019).
PubMed  PubMed Central  Google Scholar 

36.
Rodríguez-Martínez, S., Carrete, M., Roques, S., Rebolo-Ifrán, N. & Tella, J. L. High urban breeding densities do not disrupt genetic monogamy in a bird species. PLoS ONE 9, e91314 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

37.
McKinney, M. L. Urbanization as a major cause of biotic homogenization. Biol. Conserv. 127, 247–260 (2006).
Article  Google Scholar 

38.
Grimm, N. B. et al. Global change and the ecology of cities. Science 319, 756–760 (2008).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

39.
McKinney, M. L. & Lockwood, J. L. Biotic homogenization: a few winners replacing many losers in the next mass extinction. Trends Ecol. Evol. 14, 450–453 (1999).
CAS  PubMed  Article  Google Scholar 

40.
Sol, D., González-Lagos, C., Moreira, D., Maspons, J. & Lapiedra, O. Urbanisation tolerance and the loss of avian diversity. Ecol. Lett. 17, 942–950 (2014).
PubMed  Article  PubMed Central  Google Scholar 

41.
Carrete, M. & Tella, J. L. Inter-individual variability in fear of humans and relative brain size of the species are related to contemporary urban invasion in birds. PLoS ONE 6, e18859 (2011).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

42.
Díaz, M. et al. The geography of fear: a latitudinal gradient in anti-predator escape distances of birds across Europe. PLoS ONE 8, e64634 (2013).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

43.
Oro, D., Genovart, M., Tavecchia, G., Fowler, M. S. & Martínez-Abraín, A. Ecological and evolutionary implications of food subsidies from humans. Ecol. Lett. 16, 1501–1514 (2013).
PubMed  Article  PubMed Central  Google Scholar 

44.
Marzluff, J. M. Worldwide urbanization and its effects on birds. Avian ecology and conservation in an urbanizing world (pp. 19–47) (Springer, Boston, MA. 2001).

45.
Haskell, D. G., Knupp, A. M. & Schneider, M. C. Nest predator abundance and urbanization. Avian ecology and conservation in an urbanizing world (pp. 243–258). Springer, Boston, MA. (2001).

46.
Luna, Á., Romero-Vidal, P., Hiraldo, F. & Tella, J. L. Cities may save some threatened species but not their ecological functions. PeerJ 6, e4908 (2018).
PubMed  PubMed Central  Article  Google Scholar 

47.
Muhly, T. B., Semeniuk, C., Massolo, A., Hickman, L. & Musiani, M. Human activity helps prey win the predator–prey space race. PLoS ONE 6, e17050 (2011).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

48.
Rebolo-Ifrán, N., Tella, J. L. & Carrete, M. Urban conservation hotspots: predation release allows the grassland-specialist burrowing owl to perform better in the city. Sci. Rep. 7, 3527 (2017).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

49.
Tuomainen, U. & Candolin, U. Behavioural responses to human-induced environmental change. Biol. Rev. 86, 640–657 (2011).
PubMed  Article  PubMed Central  Google Scholar 

50.
Rodewald, A. D., Kearns, L. J. & Shustack, D. P. Anthropogenic resource subsidies decouple predator-prey relationships. Ecol. Appl. 21, 936–943 (2011).
PubMed  Article  PubMed Central  Google Scholar 

51.
Sih, A., Ferrari, M. C. & Harris, D. J. Evolution and behavioural responses to human-induced rapid environmental change. Evol. Appl. 4, 367–387 (2011).
PubMed  PubMed Central  Article  Google Scholar 

52.
Carrete, M. & Tella, J. L. Behavioural correlations associated with fear of humans differ between rural and urban burrowing owls. Front. Ecol. Evol. 5, 54 (2017).
Article  Google Scholar 

53.
Moore, J. A., Kamarainen, A. M., Scribner, K. T., Mykut, C. & Prince, H. H. The effects of anthropogenic alteration of nesting habitat on rates of extra-pair fertilization and intraspecific brood parasitism in Canada geese branta Canadensis. Ibis 154, 354–362 (2012).
Article  Google Scholar 

54.
Ryder, T. B., Fleischer, R. C., Shriver, W. G. & Marra, P. P. The ecological–evolutionary interplay: density-dependent sexual selection in a migratory songbird. Ecol. Evol. 2, 976–987 (2012).
PubMed  PubMed Central  Article  Google Scholar 

55.
Luna, Á., Palma, A., Sánz-Aguilar, A., Tella, J. L. & Carrete, M. Personality-dependent breeding dispersal in rural but not urban burrowing owls. Sci. Rep. 9, 2886 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

56.
Luna, Á., Palma, A., Sánz-Aguilar, A., Tella, J. L. & Carrete, M. Sex, personality and conspecific density influence natal dispersal with lifetime fitness consequences in urban and rural burrowing owls. PLoS ONE 15, e0226089 (2020).
CAS  PubMed  PubMed Central  Article  Google Scholar 

57.
Mueller, J. C. et al. Evolution of genomic variation in the burrowing owl in response to recent colonization of urban areas. Proc. R. Soc. B 285, 20180206 (2018).
PubMed  Article  PubMed Central  Google Scholar 

58.
Miles, L. S., Rivkin, L. R., Johnson, M. T., Munshi-South, J. & Verrelli, B. C. Gene flow and genetic drift in urban environments. Mol. Ecol. 28, 4138–4151 (2019).
PubMed  Article  PubMed Central  Google Scholar 

59.
Henger, C. S. et al. Genetic diversity and relatedness of a recently established population of eastern coyotes (Canis latrans) in New York City. Urban Ecosyst. 23, 1–12 (2019).
Google Scholar 

60.
Carrete, M. & Tella, J. L. Individual consistency in flight initiation distances in burrowing owls: a new hypothesis on disturbance-induced habitat selection. Biol. Lett. 6, 167–170 (2010).
PubMed  Article  PubMed Central  Google Scholar 

61.
Cockburn, A. Evolution of helping behavior in cooperatively breeding birds. Annu. Rev. Ecol. Syst. 29, 141–177 (1998).
Article  Google Scholar 

62.
Clutton-Brock, T. Breeding together: kin selection and mutualism in cooperative vertebrates. Science 296, 69–72 (2002).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

63.
Browning, L. E. et al. Career provisioning rules in an obligate cooperative breeder: prey type, size and delivery rate. Behav. Ecol. Sociobiol. 66, 1639–1649 (2012).
Article  Google Scholar 

64.
Richardson, D. S., Burke, T. & Komdeur, J. Direct benefits and the evolution of female-biased cooperative breeding in Seychelles warblers. Evolution 56, 2313–2321 (2002).
PubMed  Article  PubMed Central  Google Scholar 

65.
Gamero, A., Székely, T. & Kappeler, P. M. Delayed juvenile dispersal and monogamy, but no cooperative breeding in white-breasted mesites (Mesitornis variegata). Behav. Ecol. Sociobiol. 68, 73–83 (2014).
Article  Google Scholar 

66.
Brown, J. L. Territorial behavior and population regulation in birds: a review and re-evaluation. Wilson Bull. 81, 293–329 (1969).
Google Scholar 

67.
Emlen, S. T. The evolution of helping. I. An ecological constraints model. Am. Nat. 119, 29–39 (1982).
Article  Google Scholar 

68.
Emlen, S. T. An evolutionary theory of the family. Proc. Natl. Acad. Sci. USA 92, 8092–8099 (1995).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

69.
Ekman, J., Eggers, S., Griesser, M. & Tegelström, H. Queuing for preferred territories: delayed dispersal of Siberian jays. J. Anim. Ecol. 70(2), 317–324. https://doi.org/10.1111/j.1365-2656,2001.00490.x (2001).
Article  Google Scholar 

70.
Baglione, V., Canestrari, D., Marcos, J. M., Griesser, M. & Ekman, J. History, environment and social behaviour: experimentally induced cooperative breeding in the carrion crow. Proc. R. Soc. B 269, 1247–1251 (2002).
PubMed  Article  PubMed Central  Google Scholar 

71.
Gardner, J. L., Magrath, R. D. & Kokko, H. Stepping stones of life: natal dispersal in the group-living but noncooperative speckled warbler. Anim. Behav. 66, 521–530 (2003).
Article  Google Scholar 

72.
Russell, E. M., Yom-Tov, Y. & Geffen, E. Extended parental care and delayed dispersal: northern, tropical, and southern passerines compared. Behav. Ecol. 15, 831–838 (2004).
Article  Google Scholar 

73.
Lucia, K. E. et al. Philopatry in prairie voles: an evaluation of the habitat saturation hypothesis. Behav. Ecol. 19, 774–783 (2008).
Article  Google Scholar 

74.
Clobert, J., Le Galliard, J. F., Cote, J., Meylan, S. & Massot, M. Informed dispersal, heterogeneity in animal dispersal syndromes and the dynamics of spatially structured populations. Ecol. Lett. 12, 197–220 (2009).
PubMed  Article  PubMed Central  Google Scholar 

75.
Bocedi, G., Heinonen, J. & Travis, J. M. Uncertainty and the role of information acquisition in the evolution of context-dependent emigration. Am. Nat. 179, 606–620 (2012).
PubMed  Article  PubMed Central  Google Scholar 

76.
Bergmüller, R., Taborsky, M., Peer, K. & Heg, D. Extended safe havens and between-group dispersal of helpers in a cooperatively breeding cichlid. Behaviour 142, 1643–1667 (2005).
Article  Google Scholar 

77.
Tanaka, H., Frommen, J. G., Engqvist, L. & Kohda, M. Task-dependent workload adjustment of female breeders in a cooperatively breeding fish. Behav. Ecol. 29, 221–229 (2018).
Article  Google Scholar 

78.
Suárez-Seoane, S., Osborne, P. E. & Alonso López, J. C. Large-scale habitat selection by agricultural steppe birds in Spain: identifying species–habitat responses using generalized additive models. J. Appl. Ecol. 39, 755–771 (2002).
Article  Google Scholar 

79.
Boyce, M. S. et al. Can habitat selection predict abundance?. J. Anim. Ecol. 85, 11–20 (2016).
PubMed  Article  PubMed Central  Google Scholar 

80.
Muller, K. L., Stamps, J. A., Krishnan, V. V. & Willits, N. H. The effects of conspecific attraction and habitat quality on habitat selection in territorial birds (Troglodytes aedon). Am. Nat. 150, 650–661 (1997).
CAS  PubMed  Article  PubMed Central  Google Scholar 

81.
Danchin, E., Boulinier, T. & Massot, M. Conspecific reproductive success and breeding habitat selection: implications for the study of coloniality. Ecology 79, 2415–2428 (1998).
Article  Google Scholar 

82.
Brown, C. R., Brown, M. B. & Danchin, E. Breeding habitat selection in cliff swallows: The effect of conspecific reproductive success on colony choice. J. Anim. Ecol. 69, 133–142 (2000).
Article  Google Scholar 

83.
Danchin, E., Giraldeau, L. A., Valone, T. J. & Wagner, R. H. Public information: from nosy neighbors to cultural evolution. Science 305, 487–491 (2004).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

84.
Serrano, D., Forero, M. G., Donázar, J. A. & Tella, J. L. Dispersal and social attraction affect colony selection and dynamics of lesser kestrels. Ecology 85, 3438–3447 (2004).
Article  Google Scholar 

85.
Farrell, S. L., Morrison, M. L., Campomizzi, A. J. & Wilkins, R. N. Conspecific cues and breeding habitat selection in an endangered woodland warbler. J. Anim. Ecol. J. Anim. Ecol. 81, 1056–1064 (2012).
PubMed  Article  PubMed Central  Google Scholar 

86.
Pärt, T. The importance of local familiarity and search costs for age-and sex-biased philopatry in the collared flycatcher. Anim. Behav. 49, 1029–1038 (1995).
Article  Google Scholar 

87.
Clarke, A. L., Sæther, B. E. & Røskaft, E. Sex biases in avian dispersal: a reappraisal. Oikos 79, 429–438 (1997).
Article  Google Scholar 

88.
Piper, W. H., Walcott, C., Mager, J. N. & Spilker, F. J. Nestsite selection by male loons leads to sex-biased site familiarity. J. Anim. Ecol. J. Anim. Ecol. 77, 205–210 (2008).
PubMed  Article  PubMed Central  Google Scholar 

89.
Stacey, P. B. & Ligon, J. D. The benefits-of-philopatry hypothesis for the evolution of cooperative breeding: variation in territory quality and group size effects. Am. Nat. 137, 831–846 (1991).
Article  Google Scholar 

90.
Goldstein, J. M., Woolfenden, G. E. & Hailman, J. P. A same-sex stepparent shortens a prebreeder’s duration on the natal territory: tests of two hypotheses in Florida scrub-jays. Behav. Ecol. Sociobiol. 44, 15–22 (1998).
Article  Google Scholar 

91.
Ekman, J. & Griesser, M. Why offspring delay dispersal: experimental evidence for a role of parental tolerance. Proc. R. Soc. B 269, 1709–1713 (2002).
PubMed  Article  PubMed Central  Google Scholar 

92.
Griesser, M. & Ekman, A. Nepotistic alarm calling in the Siberian jay, Perisoreus infaustus. Anim. Behav. 67, 933–939 (2004).
Article  Google Scholar 

93.
Griesser, M. Referential calls signal predator behavior in a group-living bird species. Curr. Biol. 18, 69–73 (2008).
CAS  PubMed  Article  PubMed Central  Google Scholar 

94.
Emlen, S. T. & Wrege, P. H. Breeding biology of white-fronted bee-eaters at Nakuru: the influence of helpers on breeder fitness. J. Anim. Ecol. 60, 309–326 (1991).
Article  Google Scholar 

95.
Cockburn, A. et al. Can we measure the benefits of help in cooperatively breeding birds: the case of superb fairy-wrens Malurus cyaneus?. J. Anim. Ecol. J. Anim. Ecol. 77, 430–438 (2008).
PubMed  Article  PubMed Central  Google Scholar 

96.
Kingma, S. A., Hall, M. L., Arriero, E. & Peters, A. Multiple benefits of cooperative breeding in purple-crowned fairy-wrens: a consequence of fidelity?. J. Anim. Ecol. 79, 757–768 (2010).
PubMed  PubMed Central  Google Scholar 

97.
Fitch, M. A. & Shugart, G. W. Comparative biology and behavior of monogamous pairs and one male-two female trios of Herring Gulls. Behav. Ecol. Sociobiol. 14, 1–7 (1983).
Article  Google Scholar 

98.
Del Hoyo, J., Elliot, A. & Sargatal, J. Handbook of the Birds of the World. Barn Owls to Hummingbirds. Vol. 5.Lynx Edicions, Barcelona (1999).

99.
Clayton, K. M. & Schmutz, J. K. Is the decline of Burrowing Owls Speotyto cunicularia in prairie Canada linked to changes in Great Plains ecosystems?. Bird Conserv. Int. 9, 163–185 (1999).
Article  Google Scholar 

100.
Haug, E. A., Millsap, B. A. & Martell, M. S. The burrowing owl (Speotyto cunicularia). In Poole, A. & F. Gill (editors). The birds of North America. Philadelphia: The Academy of Natural Sciences; Washington, D. C. The American Ornithologists’ Union. Washington, D. C. The American Ornithologists’ Union (1993).

101.
Carrete, M. & Tella, J. L. High individual consistency in fear of humans throughout the adult lifespan of rural and urban burrowing owls. Sci. Rep. 3, 3524 (2013).
ADS  PubMed  PubMed Central  Article  Google Scholar 

102.
Rebolo-Ifrán, N. et al. Links between fear of humans, stress and survival support a non-random distribution of birds among urban and rural habitats. Sci. Rep. 5, 13723 (2015).
ADS  PubMed  PubMed Central  Article  Google Scholar 

103.
Kalinowski, S. T., Wagner, A. P. & Taper, M. L. ML-Relate: Software for estimating relatedness and relationship from multilocus genotypes. Mol. Ecol. Notes 6, 576–579 (2006).
CAS  Article  Google Scholar 

104.
Kalinowski, S. T., Taper, M. L. & Marshall, T. C. Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment. Mol. Ecol. 16, 1099–1106 (2007).
PubMed  Article  PubMed Central  Google Scholar 

105.
Grueber, C. E., Nakagawa, S., Laws, R. J. & Jamieson, I. G. Multimodel inference in ecology and evolution: challenges and solutions. J. Evol. Biol. 24, 699–711 (2011).
CAS  PubMed  Article  PubMed Central  Google Scholar 

106.
Oliehoek, P. A., Windig, J. J., Van Arendonk, J. A. & Bijma, P. Estimating relatedness between individuals in general populations with a focus on their use in conservation programs. Genetics 173, 483–496 (2006).
CAS  PubMed  PubMed Central  Article  Google Scholar 

107.
Laake, J. L. RMark: an R interface for analysis of capture-recapture data with MARK (2013).

108.
Zar, J. H. Statistical significance of mutation frequencies, and the power of statistical testing, using the Poisson distribution. Biometr. J. 26, 83–88 (1984).
Article  Google Scholar 

109.
Labocha, M. K. & Hayes, J. P. Morphometric indices of body condition in birds: a review. J. Ornithol. 153, 1–22 (2012).
Article  Google Scholar 

110.
Choquet, R., Lebreton, J. D., Gimenez, O., Reboulet, A. M. & Pradel, R. U-CARE: Utilities for performing goodness of fit tests and manipulating CApture–REcapture data. Ecography 32, 1071–1074 (2009).
Article  Google Scholar 

111.
Choquet, R., Rouan, L., Pradel, R. Program E-SURGE: a software application for fitting multievent models. In Modeling demographic processes in marked populations. pp. 845–865. Springer, Boston, MA, 2009.

112.
White, G. C. & Burnham, K. P. Program MARK: survival estimation from populations of marked animals. Bird Study 46, S120–S139 (1999).
Article  Google Scholar 

113.
Hadfield, J. D. MCMC methods for multi-response generalized linear mixed models: the MCMCglmm R package. J. Stat. Softw. 33, 1–22 (2010).
Article  Google Scholar 

114.
Mollie, E. B. et al. glmmTMB balances speed and flexibility among Packages for zeroinflated generalized linear mixed modeling. R J. 9, 378–400 (2017).
Article  Google Scholar 

115.
Hartig F. DHARMa: Residual Diagnostics for Hierarchical (Multi-Level/Mixed) Regression Models. R Package. 2018. https://www.cran.r-project.org/package=DHARMa, Version 0. 2. 0.

116.
Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference. A Practical Information-Theoretic Approach (Springer, New York, 2002).
Google Scholar 

117.
Barton, K. MuMIn: Multi-Model Inference. R package version 1. 40. 0. https://CRANhttps://CRAN.R-project.org/package=MuMIn (2017). More

1.
Wittenberg, R. & Cock, M. J. W. Invasive Alien Species: A Toolkit of Best Prevention and Management Practices (CABI, Wallingford, 2001).
Google Scholar 
2.
Genovesi, P. Eradications of invasive alien species in Europe: a review. Biol. Invas. 7, 127–133 (2005).
Article  Google Scholar 

3.
Butchart, S. H. M. et al. Global biodiversity: indicators of recent declines. Science 328, 1164–1168 (2010).
ADS  CAS  PubMed  Article  Google Scholar 

4.
Pejchar, L. & Mooney, H. A. Invasive species, ecosystem services and human well-being. Trends Ecol. Evol. 24, 497–504 (2009).
PubMed  Article  Google Scholar 

5.
Vilà, M. et al. How well do we understand the impacts of alien species on ecosystem services? A pan-European, cross-taxa assessment. Front. Ecol. Environ. 8, 135–144 (2010).
Article  Google Scholar 

6.
David, P. et al. Impacts of invasive species on food webs: a review of empirical data. In Advances in Ecological Research (eds Bohan, D. A. et al.) 1–60 (Academic Press, Cambridge, 2017).
Google Scholar 

7.
Doherty, T. S., Glen, A. S., Nimmo, D. G., Ritchie, E. G. & Dickman, C. R. Invasive predators and global biodiversity loss. Proc. Natl. Acad. Sci. 113, 11261–11265 (2016).
CAS  PubMed  Article  Google Scholar 

8.
Blackburn, T. M. & Ewen, J. G. Parasites as drivers and passengers of human-mediated biological invasions. EcoHealth 14, 61–73 (2017).
PubMed  Article  Google Scholar 

9.
Volponi, S. & Emiliani, D. Presenza e riproduzione di due specie di Ciconiiformi esotici con formazione di ibridi nelle zone umide ravennati del Parco del Delta del Po. Convegno Inanellatori Ital. 10, 1 (2008).
Google Scholar 

10.
Negri, A. et al. Mitochondrial DNA and microsatellite markers evidence a different pattern of hybridization in red-legged partridge (Alectoris rufa) populations from NW Italy. Eur. J. Wildl. Res. 59, 407–419 (2013).
Article  Google Scholar 

11.
Raffini, F. et al. From nucleotides to satellite imagery: approaches to identify and manage the invasive pathogen Xylella fastidiosa and its insect vectors in Europe. Sustainability 12, 4508 (2020).
CAS  Article  Google Scholar 

12.
Little, M. N. et al. The influence of riparian invasion by the terrestrial shrub Lonicera maackii on aquatic macroinvertebrates in temperate forest headwater streams. Biol. Invas. https://doi.org/10.1007/s10530-020-02349-8 (2020).
Article  Google Scholar 

13.
Falk-Petersen, J., Bøhn, T. & Sandlund, O. T. On the numerous concepts in invasion biology. Biol. Invas. 8, 1409–1424 (2006).
Article  Google Scholar 

14.
Helmstedt, K. J. et al. Prioritizing eradication actions on islands: it’s not all or nothing. J. Appl. Ecol. 53, 733–741 (2016).
Article  Google Scholar 

15.
Moon, K., Blackman, D. A. & Brewer, T. D. Understanding and integrating knowledge to improve invasive species management. Biol. Invas. 17, 2675–2689 (2015).
Article  Google Scholar 

16.
European Commission DG ENV. More rigorous studies needed to evaluate impact of invasive birds. Sci. Environ. Policy 1 (2011).

17.
Strubbe, D., Shwartz, A. & Chiron, F. Concerns regarding the scientific evidence informing impact risk assessment and management recommendations for invasive birds. Biol. Conserv. 144, 2112–2118 (2011).
Article  Google Scholar 

18.
Bertolino, S. et al. Spatially explicit models as tools for implementing effective management strategies for invasive alien mammals. Mamm. Rev. 50, 187–199 (2020).
Article  Google Scholar 

19.
Graham, S. et al. Opportunities for better use of collective action theory in research and governance for invasive species management. Conserv. Biol. 33, 275–287 (2019).
PubMed  PubMed Central  Article  Google Scholar 

20.
del Hoyo, J., Elliot, A. & Sargatal, J. Handbook of the Birds of the World (Lynx Edicions, Barcelona, 1992).
Google Scholar 

21.
Kopij, G. Breeding ecology of the Sacred Ibis Threskiornis aethiopicus in the Free State, South Africa. S. Afr. J. Wildl. Res. 29, 25–30 (1999).
Google Scholar 

22.
del Hoyo, J. & Collar, N. HBW and Birdlife International Illustrated Checklist of the Birds of the World. Vol: Non-passerines (2014).

23.
Robert, H., Lafontaine, R., Delsinne, T. & Beudels-Jamar, R. Risk analysis of the Sacred ibis Threskiornis aethiopicus (Royal Belgian Institute of Natural Sciences, Brussels, 2013).
Google Scholar 

24.
Yésou, P. & Clergeau, P. Sacred Ibis: a new invasive species in Europe. Bird. World 18, 517–526 (2006).
Google Scholar 

25.
Clergeau, P. & Yésou, P. Behavioural flexibility and numerous potential sources of introduction for the Sacred Ibis: causes of concern in Western Europe?. Biol. Invas. 8, 1381–1388 (2006).
Article  Google Scholar 

26.
Nentwig, W., Bacher, S., Kumschick, S., Pyšek, P. & Vilà, M. More than “100 worst” alien species in Europe. Biol. Invas. 20, 1611–1621 (2018).
Article  Google Scholar 

27.
Carpegna, F., Della Toffola, M., Alessandria, G. & Re, A. L’Ibis sacro Threskiornis aethiopicus nel Parco Naturale Lame del Sesia e sua presenza in Piemonte. Avocetta 23, 82 (1999).
Google Scholar 

28.
Marion, L. Is the Sacred ibis a real threat to biodiversity? Long-term study of its diet in non-native areas compared to native areas. C. R. Biol. 336, 207–220 (2013).
PubMed  Article  Google Scholar 

29.
Novarini, N. & Stival, E. Wading birds predation on Bufotes viridis (Laurenti, 1768) in the Ca’ Vallesina wetland (Ca’ Noghera, Venice, Italy). Boll. Mus. Storia Nat. Venezia 67, 71–75 (2017).
Google Scholar 

30.
Clergeau, P., Reeber, S., Bastian, S. & Yésou, P. L. profil alimentaire de l’Ibis sacré Threskiornis aethiopicus introduit en France métropolitaine: espèce généraliste ou spécialiste. Rev. Ecol. Terre Vie 65, 331–342 (2010).
Google Scholar 

31.
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 

32.
Pavia, M. L. nuova Banca Dati del Gruppo Piemontese Studi Ornitologici. Tichodroma 6, 152 (2017).
Google Scholar 

33.
Saporetti, F. Abundance, phenology and geographical distribution in relation to habitat of Tringa species in N Italy: a summary of data from the Italian online portal www.ornitho.it. Wader Study 122, 60–70 (2015).
Article  Google Scholar 

34.
iNaturalist. iNaturalist (2020) (accessed 29 April 2020). https://www.inaturalist.org.

35.
Bibby, C., Burgess, N., Hill, D. & Mustoe, S. Bird Census Techniques 2nd edn. (Academic Press, Cambridge, 2000).
Google Scholar 

36.
Buckland, S. T. et al. Introduction to Distance Sampling: Estimating Abundance of Biological Populations (Oxford University Press, Oxford, 2001).
Google Scholar 

37.
Thomas, L. et al. Distance software: design and analysis of distance sampling surveys for estimating population size. J. Appl. Ecol. 47, 5–14 (2010).
PubMed  Article  Google Scholar 

38.
Fasola, M. & Ruiz, X. The value of rice fields as substitutes for natural wetlands for Waterbirds in the Mediterranean region. Colon. Waterbirds 19, 122–128 (1996).
Article  Google Scholar 

39.
Fasola, M., Rubolini, D., Merli, E., Boncompagni, E. & Bressan, U. Long-term trends of heron and egret populations in Italy, and the effects of climate, human-induced mortality, and habitat on population dynamics. Popul. Ecol. 52, 59–72 (2010).
Article  Google Scholar 

40.
Fasola, M. & Cardarelli, E. Long-term changes in the food resources of a guild of breeding Ardeinae (Aves) in Italy. Ital. J. Zool. 82, 238–250 (2014).
Google Scholar 

41.
Fasola, M., Merli, E., Boncompagni, E. & Rampa, A. Monitoring heron populations in Italy, 1972–2010. J. Heron Biol. Conserv. 1, 1–10 (2011).
Google Scholar 

42.
Ter Braak, C., Van Strien, A., Meijer, R. & Verstrael, T. Analysis of monitorng data with many missing values: which method? In Proc. 12th Int. Conf. IBCC EOAC Noordwijkerhout Neth. 663–673 (1994).

43.
Gregory, R. D. et al. Developing indicators for European birds. Philos. Trans. R. Soc. Lond. B. Biol. Sci. 360, 269–288 (2005).
PubMed  PubMed Central  Article  Google Scholar 

44.
Pannekoek, J. & van Strien, A. TRIM 3 Manual. TRends and Indices for Monitoring data. Stat. Neth. Voorbg. Neth. Research Paper No. 0102, 1–57 (2001).

45.
Sokal, R. R. & Rohlf, F. J. Biometry: The Principles and Practice of Statistics in Biological Research (W.H. Freeman, New York, 1995).
Google Scholar 

46.
La Sorte, F. A. et al. Opportunities and challenges for big data ornithology. The Condor 120, 414–426 (2018).
Article  Google Scholar 

47.
Camerano, P., Giannetti, F., Terzuolo, P. G. & Guiot, E. La Carta Forestale del Piemonte—Aggiornamento 2016 (IPLA S.p.A.—Regione Piemonte, Turin, 2017).
Google Scholar 

48.
Guisan, A., Thuille, W. & Zimmerman, N. Habitat Suitability and Distribution Models: With Applications in R (Cambridge University Press, Cambridge, 2017).
Google Scholar 

49.
Naimi, B. & Araújo, M. B. sdm: a reproducible and extensible R platform for species distribution modelling. Ecography 39, 368–375 (2016).
Article  Google Scholar 

50.
Naimi, B., Hamm, N. A. S., Groen, T. A., Skidmore, A. K. & Toxopeus, A. G. Where is positional uncertainty a problem for species distribution modelling?. Ecography 37, 191–203 (2014).
Article  Google Scholar 

51.
Chatterjee, S. & Hadi, A. S. Regression Analysis by Example (Wiley, Hoboken, 2006).
Google Scholar 

52.
Elith, J. et al. A statistical explanation of MaxEnt for ecologists: statistical explanation of MaxEnt. Divers. Distrib. 17, 43–57 (2011).
Article  Google Scholar 

53.
Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E. & Blair, M. E. Opening the black box: an open-source release of Maxent. Ecography 40, 887–893 (2017).
Article  Google Scholar 

54.
Treggiari, A. A., Gagliardone, M., Pellegrino, I. & Cucco, M. Habitat selection in a changing environment: the relationship between habitat alteration and Scops Owl (Aves: Strigidae) territory occupancy. Ital. J. Zool. 80, 574–585 (2013).
Article  Google Scholar 

55.
Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS): assessing the accuracy of distribution models. J. Appl. Ecol. 43, 1223–1232 (2006).
Article  Google Scholar 

56.
Heckmann, M. & Burk, L. Gridsampler—a simulation tool to determine the required sample size for repertory grid studies. J. Open Res. Softw. 5, 2 (2017).
Article  Google Scholar 

57.
Guisan, A., Graham, C. H., Elith, J. & Huettmann, F. Sensitivity of predictive species distribution models to change in grain size. Divers. Distrib. 13, 332–340 (2007).
Article  Google Scholar 

58.
Marion, L. & Marion, P. Première installation spontanée d’une colonie d’Ibis Sacré Threskiornis aethiopicus, au Lac de Grand-Lieu. Données préliminaires sur la production en jeunes et sur le régime alimentaire. Alauda 62, 275–280 (1994).
Google Scholar 

59.
ONCFS. L’Ibis sacré (2020). http://www.oncfs.gouv.fr/La-lutte-contre-les-especes-exotiques-envahissantes-ru152/LIbis-sacre-ar282. Accessed 31 July 2020.

60.
Yésou, P., Clergeau, P., Bastian, S., Reeber, S. & Maillard, J.-F. The Sacred Ibis in Europe: ecology and management. Br. Birds 110, 197–212 (2017).
Google Scholar 

61.
Herring, G. & Gawlik, D. E. Potential for successful population establishment of the nonindigenous sacred ibis in the Florida Everglades. Biol. Invas. 10, 969–976 (2008).
Article  Google Scholar 

62.
Chen, K. & Hetherington, W. Sacred ibis nest control ineffective: environmentalist. Taipei Times 4 (2018).

63.
Bird Ecology Study Group. African Sacred Ibis in Taiwan (2020). https://besgroup.org/2019/01/28/african-sacred-ibis-in-taiwan/. Accessed 31 July 2020.

64.
Grussu, M. Checklist of the birds of Sardinia. Aves Ichnusae 4, 3–55 (2001).
Google Scholar 

65.
Chane, M. & Balakrishnan, M. Population structure, feeding habits and activity patterns of the African Sacred ibis (Threskiornis aethiopicus) in Dilla Kera area, southern Ethiopia. Ethiop. J. Biol. Sci. 15, 93–105 (2016).
Google Scholar 

66.
Smeraldo, S. et al. Modelling risks posed by wind turbines and power lines to soaring birds: the black stork (Ciconia nigra) in Italy as a case study. Biodivers. Conserv. 29, 1959–1976 (2020).
Article  Google Scholar 

67.
Grussu, M. Gli uccelli alloctoni in Sardegna: una checklist aggiornata. Mem. Della Soc. Ital. Sci. Nat. E Mus. Civ. Storia Nat. Milano 36, 65 (2008).
Google Scholar 

68.
Ranghetti, L. et al. Testing estimation of water surface in Italian rice district from MODIS satellite data. Int. J. Appl. Earth Observ. Geoinform. 52, 284–295 (2016).
ADS  Article  Google Scholar 

69.
Fasola, M., Cardarelli, E., Pellitteri-Rosa, D. & Ranghetti, L. The recent decline of heron populations in Italy and the changes in rice cultivation practice. In 25th International Ornithological Congress. Ornithological Science (The Ornithological Society of Japan, 2014).

70.
Delmastro, G. B. Il gambero della Louisiana Procambarus clarkii (Girard, 1852) in Piemonte: nuove osservazioni su distribuzione, biologia, impatto e utilizzo (Crustacea: Decapoda: Cambaridae). Riv. Piemontese Storia Nat. 38, 61–129 (2017).
Google Scholar 

71.
Bernini, G. et al. Complexity of biogeographic pattern in the endangered crayfish Austropotamobius italicus in northern Italy: molecular insights of conservation concern. Conserv. Genet. 17, 141–154 (2016).
Article  Google Scholar 

72.
Fàbregas, M. C., Guillén-Salazar, F. & Garcés-Narro, C. The risk of zoological parks as potential pathways for the introduction of non-indigenous species. Biol. Invas. 12, 3627–3636 (2010).
Article  Google Scholar 

73.
Brichetti, P. & Fracasso, G. Ornitologia Italiana. 1 Gaviidae-Falconidae (Alberto Perdisa editore, Bologna, 2003).
Google Scholar 

74.
Wasef, S. Mitogenomic diversity in Sacred Ibis Mummies sheds light on early Egyptian practices. PLoS ONE 14, e0223964 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

75.
Fasola, M. & Alieri, R. Conservation of heronry Ardeidae sites in North Italian agricultural landscapes. Biol. Conserv. 62, 219–228 (1992).
Article  Google Scholar 

76.
Cocchi, R. & Volponi, S. Information on measures and related costs in relation to species included on the Union list—Threskiornis aethiopicus. Tech. Note Prep. IUCN Eur. Comm. ISPRA Ozzano Emilia Italy (2020).

77.
Smits, R., Van Horssen, P. & Van Der Winden, J. A Risk Analysis of the Sacred Ibis in the Netherlands: Including Biology and Management Options of this Invasive Species (Bureau Waardenburg Consultants for Environment & Ecology, Culemborg, 2010).
Google Scholar 

78.
Byers, J. E. et al. Directing research to reduce the impacts of nonindigenous species. Conserv. Biol. 16, 630–640 (2002).
Article  Google Scholar 

79.
Heath, J. A. & Frederick, P. C. Trapping White Ibises with rocket nets and mist nets in the Florida Everglades. J. Field Ornithol. 74, 187–192 (2003).
Article  Google Scholar 

80.
Gurevitch, J. & Padilla, D. Are invasive species a major cause of extinctions?. Trends Ecol. Evol. 19, 470–474 (2004).
PubMed  Article  Google Scholar 

81.
Didham, R. K., Tylianakis, J. M., Hutchison, M. A., Ewers, R. M. & Gemmell, N. J. Are invasive species the drivers of ecological change?. Trends Ecol. Evol. 20, 470–474 (2005).
PubMed  Article  Google Scholar 

82.
Vaslin, M. Predaction de l’Ibis sacré sur des colonies des sternes et des guifettes. Ornithos 12, 106–109 (2005).
Google Scholar 

83.
Evans, T. & Blackburn, T. M. Global variation in the availability of data on the environmental impacts of alien birds. Biol. Invas. 22, 1027–1036 (2020).
Article  Google Scholar 

84.
Clergeau, P., Fourcy, D., Reeber, S. & Yésou, P. New but nice? Do alien sacred ibises Threskiornis aethiopicus stabilize nesting colonies of native spoonbills Platalea leucorodia at Grand-Lieu Lake, France?. Oryx 44, 533–538 (2010).
Article  Google Scholar 

85.
Bremner, A. & Park, K. Public attitudes to the management of invasive non-native species in Scotland. Biol. Conserv. 139, 306–314 (2007).
Article  Google Scholar  More

1.
Castro, J. I. The shark nursery of Bulls Bay, South Carolina, with a review of the shark nurseries of the Southeastern coast of the United States. Environ. Biol. Fishes 38, 37–48 (1993).
Article  Google Scholar 
2.
Heupel, M. R. & Simpfendorfer, C. A. Estuarine nursery areas provide a low mortality environment for young bull sharks Carcharhinus leucas. Mar. Ecol. Prog. Ser. 433, 237–244. https://doi.org/10.3354/meps09191 (2011).
ADS  Article  Google Scholar 

3.
Heupel, M. R., Carlson, J. & Simpfendorfer, C. A. Shark nursery areas: concepts, definition, characterization and assumptions. Mar. Ecol. Prog. Ser. 337, 287–297 (2007).
ADS  Article  Google Scholar 

4.
Martins, A. P. B., Heupel, M. R., Chin, A. & Simpfendorfer, C. A. Batoid nurseries: definition, use and importance. Mar. Ecol. Prog. Ser. 595, 253–267. https://doi.org/10.3354/meps12545 (2018).
ADS  Article  Google Scholar 

5.
Simpfendorfer, C. A. & Milward, N. Utilization of a tropical bay as a nursery area by sharks of the families Carcharhinidae and Sphyrnidae. Environ. Biol. Fishes 37, 337–345. https://doi.org/10.1007/BF00005200 (1993).
Article  Google Scholar 

6.
Heupel, M. R. & Simpfendorfer, C. A. Estimation of mortality of juvenile blacktip sharks, Carcharhinus limbatus, within a nursery area using telemetry data. Can. J. Fish. Aquat. Sci. 59(4), 624–632 (2002).
Article  Google Scholar 

7.
Yokota, L. & Lessa, R. P. A nursery area for sharks and rays in Northeastern Brazil. Environ. Biol. Fishes 75(3), 349–356. https://doi.org/10.1007/s10641-006-0038-9 (2006).
Article  Google Scholar 

8.
Cerutti-Pereyra, F. et al. Restricted movements of juvenile rays in the lagoon of Ningaloo Reef, Western Australia, evidence for the existence of a nursery. Environ. Biol. Fishes 97(4), 371–383. https://doi.org/10.1007/s10641-013-0158-y (2014).
Article  Google Scholar 

9.
Stewart, J. D., Nuttall, M., Hickerson, E. L. & Johnston, M. A. Important juvenile manta ray habitat at flower garden banks national marine sanctuary in the northwestern Gulf of Mexico. Mar. Biol. 165, 111. https://doi.org/10.1007/s00227-018-3409-9 (2018).
CAS  Article  Google Scholar 

10.
Childs, J. N. The Occurrence, Habitat Use, and Behavior of Sharks and Rays Associating with Topographic Highs in the Northwestern Gulf of Mexico (Texas A&M University, College town, 2001).
Google Scholar 

11.
Pate, J. H. & Marshall, A. D. Urban manta rays: potential manta ray nursery habitat along a highly developed Florida coastline. Endang. Species Res. 43, 51–64. https://doi.org/10.3354/esr01054 (2020).
Article  Google Scholar 

12.
Germanov, E. S. et al. Contrasting habitat use and population dynamics of reef manta rays within the Nusa Penida Marine Protected Area Indonesia. Front. Mar. Sci. 6, 215. https://doi.org/10.3389/fmars.2019.00215 (2019).
Article  Google Scholar 

13.
Gaskins, L. C. Pregnant giant devil ray (Mobula mobular) bycatch reveals potential Northern Gulf of California pupping ground. Ecology 100, 7. https://doi.org/10.1002/ecy.2689 (2019).
Article  Google Scholar 

14.
Couturier, L. I. E. et al. Biology, ecology and conservation of the Mobulidae. J. Fish. Biol. 80, 1075–1119. https://doi.org/10.1111/j.1095-8649.2012.03264.x (2012).
CAS  Article  PubMed  Google Scholar 

15.
Stewart, J. D. et al. Research priorities to support effective manta and devil ray conservation. Front. Mar. Sci. 5, 314. https://doi.org/10.3389/fmars.2018.00314 (2018).
Article  Google Scholar 

16.
Stevens, J. D., Bonfil, R., Dulvy, N. K. & Walker, P. A. The effects of fishing on sharks, rays, and chimaeras (chondrichthyans), and the implications for marine ecosystems. ICES J. Mar. Sci. 57, 476–494. https://doi.org/10.1006/jmsc.2000.0724 (2000).
Article  Google Scholar 

17.
Dulvy, N. K., Pardo, S. A., Simpfendorfer, C. A. & Carlson, J. K. Diagnosing the dangerous demography of manta rays using life history theory. PeerJ 2, e400. https://doi.org/10.7717/peerj.400 (2014).
Article  PubMed  PubMed Central  Google Scholar 

18.
Notarbartolo di Sciara, G. Natural history of the rays of the genus Mobula in the Gulf of California. Fish Bull. 86(1), 45–66 (1988).
Google Scholar 

19.
Dulvy, N. K. & Reynolds, J. D. Evolutionary transitions among egg-laying, live-bearing and maternal inputs in sharks and rays. Proc. Phys. Soc. B 264, 1309–1315. https://doi.org/10.1098/rspb.1997.0181 (1997).
Article  Google Scholar 

20.
Marshall, A. D. & Bennett, M. B. Reproductive ecology of the reef manta ray Manta alfredi in southern Mozambique. J. Fish. Biol. 77, 169–190. https://doi.org/10.1111/j.1095-8649.2010.02669.x (2010).
CAS  Article  PubMed  Google Scholar 

21.
Croll, D. A. et al. Vulnerabilities and fisheries impacts: the uncertain future of manta and devil rays. Aquat. Conserv. Mar. Freshw. Ecosyst. 26, 562–657. https://doi.org/10.1002/aqc.2591 (2016).
Article  Google Scholar 

22.
White, W., Giles, J. & Potter, I. Data on the bycatch fishery and reproductive biology of mobulid rays (Myliobatiformes) in Indonesia. Fish. Res. 82, 65–73. https://doi.org/10.1016/j.fishres.2006.08.008 (2006).
Article  Google Scholar 

23.
Rohner, C. et al. Trends in sightings and environmental influences on a coastal aggregation of manta rays and whale sharks. Mar. Ecol. Prog. Ser. 482, 153–168. https://doi.org/10.3354/meps10290 (2013).
ADS  Article  Google Scholar 

24.
Paulin, C. D., Habib, G., Carey, C. L., Swanson, P. M. & Voss, G. J. New records of Mobula japanica and Masturus lanceolatus, and further records of Luvaris imperialis. N. Z. J. Mar Freshw. Res. 16, 11–17 (1982).
Article  Google Scholar 

25.
IUCN. The IUCN Red List of Threatened Species. Version 2020-2. https://www.iucnredlist.org/ (2020). Accessed 20 July 2020.

26.
Ward-Paige, C. A., Davis, B. & Worm, B. Global population trends and human use patterns of manta and mobula rays. PLoS ONE 8(9), e74835. https://doi.org/10.1371/journal.pone.0074835 (2013).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

27.
White, E. R., Myers, M. C., Flemming, J. M. & Baum, J. K. Shifting elasmobranch community assemblage at Cocos Island an isolated marine protected area. Conserv. Biol. 29, 186–1197. https://doi.org/10.1111/cobi.12478 (2015).
Article  Google Scholar 

28.
Stevens, G., Fernando, D., Dando, M. & Sciara, G. Guide to the Manta and Devil Rays of the World (Wild Nature Press, Plymout, 2018).
Google Scholar 

29.
Marshall, A.D. et al. Mobula munkiana. The IUCN Red List of Threatened Species 2019: e.T60198A124450956. (2019). Accessed 8 March 2020.

30.
Hobro, F. The Feeding Ecology, Foraging Behavior and Conservation of Manta Rays (Mobulidae) in Baja California, Mexico (University of Wales, Cardiff, 2002).
Google Scholar 

31.
Broadhurst, M. K., Laglbauer, B. J. L. & Bennett, M. B. Gestation and size at parturition for Mobula kuhlii cf. eregoodootenkee. Environ. Biol. Fish. 102, 1009–1014. https://doi.org/10.1007/s10641-019-00886-3 (2019).
Article  Google Scholar 

32.
Lopez, J. N. Estudio comparativo de la reproducción de tres especies del género Mobula (Chondrichthyes: Mobulidae) en el suroeste del Golfo de California, México (Instituto Politécnico Nacional, México). https://www.repositoriodigital.ipn.mx/bitstream/123456789/14110/1/serranol1.pdf (2009). Accessed 15 July 2020.

33.
Heinrichs, S., O’Malley, M., Medd, H. B. & Hilton, P. Manta Ray of Hope: The Global Threat to Manta and Mobula Rays. Manta Ray of Hope Project. https://wildaid.org/wp-content/uploads/2017/09/The-Global-Threat-to-Manta-and-Mobula-Rays-WEB.pdf (2011). Accessed 15 July 2020.

34.
Notarbartolo di Sciara, G. A revisionary study of the genus Mobula Rafinesque, 1810 (Chondrichthyes: Mobulidae) with the description of a new species. Zool. J. Linn. Soc. 91, 1–91. https://doi.org/10.1111/j.1096-3642.1987.tb01723.x (1987).
Article  Google Scholar 

35.
Smith, W. D., Bizzarro, J. J. & Cailliet, G. M. The artisanal elasmobranch fishery on the east coast of Baja California, Mexico: Characteristics and management considerations. Cienc. Mar. 35(2), 209–236 (2009).
Article  Google Scholar 

36.
Afonso, A. S., Cantareli, C. V., Levy, R. P. & Veras, L. B. Evasive mating behavior by female nurse sharks, Ginglymostoma cirratum (Bonnaterre, 1788), in an equatorial insular breeding ground. Neotrop Ichthyol. 14(4), e160103. https://doi.org/10.1590/1982-0224-20160103 (2016).
Article  Google Scholar 

37.
Hamlett, W. Reproductive biology and phylogeny of chondrichthyes. In Book 3, Reproductive Biology and Phylogeny (ed. Hamlett, W.) (CRC Press, Boca Raton, 2005). https://doi.org/10.1201/9781439856000.
Google Scholar 

38.
Stevens, G. M. W., Hawkins, J. P. & Roberts, C. M. Courtship and mating behaviour of manta rays Mobula alfredi and Mobula birostris in the Maldives. J. Fish. Biol. 93(2), 344–359. https://doi.org/10.1111/jfb.13768 (2018).
Article  PubMed  Google Scholar 

39.
Villavicencio-Garayzar, C. J. Observations on Mobula munkiana (Chondrichthyes: Mobulidae) in the Bahia de la Paz, B.C.S.. Mexico. Rev. Investig. Cient. 2(2), 78–81 (1991).
Google Scholar 

40.
Marshall, A. D., Compagno, L. J. V. & Bennett, M. B. Redescription of the genus Manta with resurrection of Manta alfredi. Zootaxa 28, 1–28. https://doi.org/10.5281/zenodo.191734 (2009).
Article  Google Scholar 

41.
Obeso-Nieblas, M., Gaviño-Rodríguez, J. H., Jiménez-Illescas, A. R. & Shirasago Germán, B. Simulación numérica de la circulación por marea y viento del noroeste y sur en la Bahía de La Paz B.C.S. Oceánides 11, 1–2 (2002).
Google Scholar 

42.
Bass, A. J. Problems in studies of sharks in the Southern Indian Ocean. In Sensory Biology of Sharks, Skates and Rays (ed. Hodgson, E. S.) 545–594 (Tufts University, Medford, 1978).
Google Scholar 

43.
Tenzing, P. The Eco-physiology of Two Species of Tropical Stingrays in an Era of Climate Change (James Cook University, Townsville, 2014).
Google Scholar 

44.
Brinton, E., Fleminger, A. & Siegel-Causey, D. The temperate and tropical planktonic biotas of the Gulf of California. CalCOFI Rep. 27, 228–266 (1986).
Google Scholar 

45.
Deakos, M. H. Paired-laser photogrammetry as a simple and accurate system for measuring the body size of free-ranging manta rays Manta alfredi. Aquat. Biol. 10, 1–10. https://doi.org/10.3354/ab00258 (2010).
Article  Google Scholar 

46.
Salomon-Aguilar, C. A., Villavicencio-Garayzar, C. J. & Reyes-Bonilla, H. Shark breeding grounds and seasons in the Gulf of California: fishery management and conservation strategy. Cienc. Mar. 35(4), 369–388 (2009).
Article  Google Scholar 

47.
Brinton, E. & Townsend, A. W. Euphausiids in the Gulf of California the 1957 cruises. Calif. Coop. Ocean. Fish. Investig. Rep. 21, 211–236 (1980).
Google Scholar 

48.
De Silva-Davila, R. & Palomares-Garcia, R. Unusual larval growth production of Nyctiphanes simplex in Bahia de La Paz, Baja California Mexico. J. Crustac. Biol. 18(3), 490–498. https://doi.org/10.1163/193724098X00313 (1998).
Article  Google Scholar 

49.
Gómez-Gutiérrez, J., Martínez-Gómez, S. & Robinson, C. J. Seasonal growth, molt, and egg production of Nyctiphanes simplex (Crustacea: Euphausiacea) juveniles and adults in the Gulf of California. Mar. Ecol. Prog. Ser. 455, 173–194. https://doi.org/10.3354/meps09631 (2012).
ADS  Article  Google Scholar 

50.
Uchida, S., Toda, M. & Matsumoto, Y. Captive records of manta rays in Okinawa Churaumi Aquarium. In Joint Meeting of Ichthyologists and Herpetologists (Montreal, QC), 23–28 (2008).

51.
Hidalgo-Gonzalez, R. M. & Alvarez-Borrego, S. Total and new production in the Gulf of California estimated from ocean color data from the satellite sensor SeaWIFS. Deep Sea Res. II(51), 739–752. https://doi.org/10.1016/j.dsr2.2004.05.006 (2004).
ADS  CAS  Article  Google Scholar 

52.
Santamaria-Del-Angel, E., Alvarez-Borrego, S., Millán-Nuñez, R. & Muller-Karger, F. E. Sobre el efecto de las surgencias de verano en la biomasa fitoplanctónica del Golfo de California. Rev. Soc. Mex. Hist. Nat. 49, 207–212 (1999).
Google Scholar 

53.
VUE Software Manual, Version 2.5 Vemco. Bedford, Nova Scotia, Canada (2014).

54.
Daly, R., Smale, M. J., Cowley, P. D. & Froneman, P. W. Residency patterns and migration dynamics of adult bull sharks (Carcharhinus leucas) on the east coast of southern Africa. PLoS ONE 9, e109357. https://doi.org/10.1371/journal.pone.0109357 (2014).
ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

55.
Smith, P.E. & Richardson, S.L. Técnicas modelo para prospecciones de huevos y larvas de peces pelágicos. In: FAO Fishing Technical Report. 175, 107 (1979).

56.
Beers, J. R. Volumetric methods. In Book 4, Zooplankton Fixation and Preservation. Monographs on Oceanography methods (ed. Steedman, H. F.) (The UNESCO Press, Paris, 1976).
Google Scholar  More

Leaf profitability
We calculated the economic profitability of leaves and analysed the patterns in relation to leaf size, environment and durability (longevity). There was wide variation in leaf profitability with a mean value (± SD) of 3.4 ± 3.5% day−1 and 5th/95th percentile range of 0.29–10.3% day−1. There are no previous studies reporting leaf profitability values for direct comparison, but they can be re-calculated based on published values for leaf payback time. For example, Williams et al.21 reported payback time values in several Piper species from a Mexican rainforest, corresponding to leaf profitability values between 0.01 and 33% day−1. Poorter et al.23 calculated payback times corresponding to leaf profitability values of 1.25–50% day−1 , depending on species type, light environment, and growth conditions (very high values were observed for seedlings grown under non-limiting, hydroponic conditions). The mean values of our study are lower, but in line with values calculated from payback time of Kikuzawa and Lechowicz25 (2.2 ± 2% day−1), because they consider the mean labour time and the favourable period length, as we also applied in our calculations (see “Methods” and Supplementary File S2 online).
One factor that could influence profitability is the size of a production unit. For example, a large leaf may imply a higher cost required for structural support; therefore, the changes in profitability will depend on how gains and expenses vary with size. As it turned out, we found that leaf profitability was positively related to leaf size (Fig. 2A), but the percentage of variance explained was not especially high (R2 = 0.07, P  More

1.
Musso, D., Rodriguez-Morales, A. J., Levi, J. E., Cao-Lormeau, V.-M. & Gubler, D. J. Unexpected outbreaks of arbovirus infections: lessons learned from the Pacific and tropical America. Lancet Infect. Dis. 18, e355–e361 (2018).
PubMed  Article  Google Scholar 
2.
Mavian, C. et al. Islands as hotspots for emerging mosquito-borne viruses: a one-health perspective. Viruses 11, 11 (2018).

3.
Cao-Lormeau, V.-M. Tropical islands as new hubs for emerging arboviruses. Emerg. Infect. Dis. 22, 913–915 (2016).
PubMed  PubMed Central  Article  Google Scholar 

4.
Cassadou, S. et al. Emergence of chikungunya fever on the French side of Saint Martin island, October to December 2013. Euro Surveill. 19, 20752 (2014).

5.
Dorléans, F. et al. Outbreak of Chikungunya in the French Caribbean Islands of Martinique and Guadeloupe: findings from a Hospital-Based Surveillance System (2013–2015). Am. J. Trop. Med. Hyg. 98, 1819–1825 (2018).

6.
Halstead, S. B. Reappearance of chikungunya, formerly called dengue, in the Americas. Emerg. Infect. Dis. 21, 557–561 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

7.
Faria, N. R. et al. Zika virus in the Americas: early epidemiological and genetic findings. Science 352, 345–349 (2016).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

8.
Grubaugh, N. D., Faria, N. R., Andersen, K. G. & Pybus, O. G. Genomic insights into Zika virus emergence and spread. Cell 172, 1160–1162 (2018).
CAS  PubMed  Article  Google Scholar 

9.
Metsky, H. C. et al. Zika virus evolution and spread in the Americas. Nature 546, 411–415 (2017).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

10.
Hotez, P. J. & Murray, K. O. Dengue, West Nile virus, chikungunya, Zika-and now Mayaro? PLoS Negl. Trop. Dis. 11, e0005462 (2017).
PubMed  PubMed Central  Article  Google Scholar 

11.
Lorenz, C., Freitas Ribeiro, A. & Chiaravalloti-Neto, F. Mayaro virus distribution in South America. Acta Trop. 198, 105093 (2019).
PubMed  Article  Google Scholar 

12.
Ganjian, N. & Riviere-Cinnamond, A. Mayaro virus in Latin America and the Caribbean. Rev. Panam. Salud Publica 44, e14 (2020).
PubMed  PubMed Central  Article  Google Scholar 

13.
Weaver, S. C. & Reisen, W. K. Present and future arboviral threats. Antivir. Res. 85, 328–345 (2010).
CAS  PubMed  Article  Google Scholar 

14.
Long, K. C. et al. Experimental transmission of Mayaro virus by Aedes aegypti. Am. J. Trop. Med. Hyg. 85, 750–757 (2011).
PubMed  PubMed Central  Article  Google Scholar 

15.
Suspected dengue cases by epidemiological week for countries and territories of the America. https://www.paho.org/data/index.php/en/mnu-topics/indicadores-dengue-en/dengue-nacional-en/252-dengue-pais-ano-en.html?start=2.

16.
Five-fold increase in dengue cases in the Americas over the past decade. https://www.paho.org/hq/index.php?option=com_content&view=article&id=9657:2014-los-casos-dengue-americas- (2014).

17.
Obolski, U. et al. MVSE: an R‐package that estimates a climate‐driven mosquito‐borne viral suitability index. Methods Ecol. Evol. 10, 1357–1370 (2019).
PubMed  PubMed Central  Article  Google Scholar 

18.
Kraemer, M. U. G. et al. The global distribution of the arbovirus vectors Aedes aegypti and Ae. albopictus. Elife 4, e08347 (2015).
PubMed  PubMed Central  Article  Google Scholar 

19.
Kraemer, M. U. G. et al. Past and future spread of the arbovirus vectors Aedes aegypti and Aedes albopictus. Nat. Microbiol. 4, 854–863 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

20.
Hamlet, A. et al. The seasonal influence of climate and environment on yellow fever transmission across Africa. PLoS Negl. Trop. Dis. 12, e0006284 (2018).
PubMed  PubMed Central  Article  Google Scholar 

21.
Do, T. T. T., Martens, P., Luu, N. H., Wright, P. & Choisy, M. Climatic-driven seasonality of emerging dengue fever in Hanoi, Vietnam. BMC Public Health 14, 1078 (2014).
PubMed  PubMed Central  Article  Google Scholar 

22.
Perez-Guzman, P. N. et al. Measuring mosquito-borne viral suitability in Myanmar and implications for Local Zika virus transmission. PLoS Curr. 10 (2018).

23.
Rodrigues Faria, N. et al. Epidemiology of Chikungunya Virus in Bahia, Brazil, 2014-2015. PLoS Curr. 8 (2016).

24.
Lourenço, J. et al. Epidemiological and ecological determinants of Zika virus transmission in an urban setting. eLife 6, e29820 (2017).

25.
Dengue serotypes by year for countries and territories of the Americas. https://www.paho.org/data/index.php/es/temas/indicadores-dengue/dengue-nacional/549-dengue-serotypes-es.html.

26.
Bowman, L. R., Rocklöv, J., Kroeger, A., Olliaro, P. & Skewes, R. A comparison of Zika and dengue outbreaks using national surveillance data in the Dominican Republic. PLoS Negl. Trop. Dis. 12, e0006876 (2018).
PubMed  PubMed Central  Article  Google Scholar 

27.
Lindsey, N. P., Staples, J. E. & Fischer, M. Chikungunya Virus disease among travelers-United States 2014–2016. Am. J. Trop. Med. Hyg. 98, 192–197 (2018).
PubMed  Article  Google Scholar 

28.
Zingman, M. A., Paulino, A. T. & Payano, M. P. Clinical manifestations of chikungunya among university professors and staff in Santo Domingo, the Dominican Republic. Rev. Panam. Salud Publica 41, e64 (2017).
PubMed  PubMed Central  Article  Google Scholar 

29.
Rosario, V. et al. Chikungunya infection in the general population and in patients with rheumatoid arthritis on biological therapy. Clin. Rheumatol. 34, 1285–1287 (2015).
CAS  PubMed  Article  Google Scholar 

30.
He, A., Brasil, P., Siqueira, A. M., Calvet, G. A. & Kwatra, S. G. The emerging Zika virus threat: a guide for dermatologists. Am. J. Clin. Dermatol. 18, 231–236 (2017).
PubMed  Article  Google Scholar 

31.
Martinez, J. D., Garza, J. A. Cla & Cuellar-Barboza, A. Going viral 2019: Zika, Chikungunya, and Dengue. Dermatol. Clin. 37, 95–105 (2019).
CAS  PubMed  Article  Google Scholar 

32.
Duffy, M. R. et al. Zika virus outbreak on Yap Island, Federated States of Micronesia. N. Engl. J. Med. 360, 2536–2543 (2009).
CAS  PubMed  Article  Google Scholar 

33.
Pineda, C., Muñoz-Louis, R., Caballero-Uribe, C. V. & Viasus, D. Chikungunya in the region of the Americas. A challenge for rheumatologists and health care systems. Clin. Rheumatol. 35, 2381–2385 (2016).
PubMed  Article  Google Scholar 

34.
Langsjoen, R. M. et al. Molecular virologic and clinical characteristics of a chikungunya fever outbreak in La Romana, Dominican Republic, 2014. PLoS Negl. Trop. Dis. 10, e0005189 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar 

35.
Grubaugh, N. D. et al. Tracking virus outbreaks in the twenty-first century. Nat. Microbiol. 4, 10–19 (2019).
CAS  PubMed  Article  Google Scholar 

36.
Kraemer, M. U. G. et al. The global compendium of Aedes aegypti and Ae. albopictus occurrence. Sci. Data 2, 150035 (2015).
PubMed  PubMed Central  Article  Google Scholar 

37.
Liu-Helmersson, J., Stenlund, H., Wilder-Smith, A. & Rocklöv, J. Vectorial capacity of Aedes aegypti: effects of temperature and implications for global dengue epidemic potential. PLoS ONE 9, e89783 (2014).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

38.
Winokur, O. C., Main, B. J., Nicholson, J. & Barker, C. M. Impact of temperature on the extrinsic incubation period of Zika virus in Aedes aegypti. PLoS Negl. Trop. Dis. 14, e0008047 (2020).
PubMed  PubMed Central  Article  CAS  Google Scholar 

39.
Chan, M. & Johansson, M. A. The incubation periods of Dengue viruses. PLoS ONE 7, e50972 (2012).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

40.
Mordecai, E. A. et al. Detecting the impact of temperature on transmission of Zika, dengue, and chikungunya using mechanistic models. PLoS Negl. Trop. Dis. 11, e0005568 (2017).
PubMed  PubMed Central  Article  Google Scholar 

41.
Faria, N. R. et al. Establishment and cryptic transmission of Zika virus in Brazil and the Americas. Nature 546, 406–410 (2017).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

42.
Cauchemez, S. et al. Local and regional spread of chikungunya fever in the Americas. Eur. Surveill. 19, 20854 (2014).
CAS  Article  Google Scholar 

43.
Nishiura, H., Kinoshita, R., Mizumoto, K., Yasuda, Y. & Nah, K. Transmission potential of Zika virus infection in the South Pacific. Int. J. Infect. Dis. 45, 95–97 (2016).
PubMed  Article  Google Scholar 

44.
Liu, Y. et al. Reviewing estimates of the basic reproduction number for dengue, Zika and chikungunya across global climate zones. Environ. Res. 182, 109114 (2020).
CAS  PubMed  Article  Google Scholar 

45.
Rodriguez-Barraquer, I., Salje, H. & Cummings, D. A. Opportunities for improved surveillance and control of dengue from age-specific case data. Elife 8, e45474 (2019).

46.
Plan de preparación y respuesta frente a brotes de Fiebre Chikungunya. Resolución Ministerial N° 427 – 2014/MINSA Lima, Peu (2014).

47.
Low, G. K.-K., Ogston, S. A., Yong, M.-H., Gan, S.-C. & Chee, H.-Y. Global dengue death before and after the new World Health Organization 2009 case classification: a systematic review and meta-regression analysis. Acta Trop. 182, 237–245 (2018).
PubMed  Article  Google Scholar 

48.
Freitas, A. R. R., Alarcón-Elbal, P. M., Paulino-Ramírez, R. & Donalisio, M. R. Excess mortality profile during the Asian genotype chikungunya epidemic in the Dominican Republic. 2014. Trans. R. Soc. Trop. Med. Hyg. 112, 443–449 (2018).
PubMed  Article  Google Scholar 

49.
Imai, N., Dorigatti, I., Cauchemez, S. & Ferguson, N. M. Estimating dengue transmission intensity from sero-prevalence surveys in multiple countries. PLoS Negl. Trop. Dis. 9, e0003719 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

50.
Grubaugh, N. D. et al. Xenosurveillance: a novel mosquito-based approach for examining the human-pathogen landscape. PLoS Negl. Trop. Dis. 9, e0003628 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

51.
Fauver, J. R. et al. The use of xenosurveillance to detect human bacteria, parasites, and viruses in mosquito bloodmeals. Am. J. Trop. Med. Hyg. 97, 324–329 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

52.
Fauver, J. R. et al. Xenosurveillance reflects traditional sampling techniques for the identification of human pathogens: a comparative study in West Africa. PLoS Negl. Trop. Dis. 12, e0006348 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

53.
Grubaugh, N. D. et al. Travel surveillance and genomics uncover a hidden zika outbreak during the waning epidemic. Cell 178, 1057–1071.e11 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

54.
Vogels, C. B. F. et al. Arbovirus coinfection and co-transmission: a neglected public health concern? PLoS Biol. 17, e3000130 (2019).
PubMed  PubMed Central  Article  CAS  Google Scholar 

55.
Bisanzio, D. et al. Spatio-temporal coherence of dengue, chikungunya and Zika outbreaks in Merida, Mexico. PLoS Negl. Trop. Dis. 12, e0006298 (2018).
PubMed  PubMed Central  Article  Google Scholar 

56.
Freitas, L. P., Cruz, O. G., Lowe, R. & Sá Carvalho, M. Space–time dynamics of a triple epidemic: dengue, chikungunya and Zika clusters in the city of Rio de Janeiro. Proc. R. Soc. B: Biol. Sci. 286, 20191867 (2019).
Article  Google Scholar 

57.
Shioda, K. et al. Identifying signatures of the impact of rotavirus vaccines on hospitalizations using sentinel surveillance data from Latin American countries. Vaccine 38, 323–329 (2020).
PubMed  Article  Google Scholar 

58.
Blohm, G. et al. Mayaro as a Caribbean traveler: Evidence for multiple introductions and transmission of the virus into Haiti. Int. J. Infect. Dis. 87, 151–153 (2019).
CAS  PubMed  Article  Google Scholar 

59.
Lednicky, J. et al. Mayaro Virus in Child with Acute Febrile Illness, Haiti, 2015. Emerg. Infect. Dis. 22, 2000–2002 (2016).
PubMed  PubMed Central  Article  Google Scholar 

60.
Weppelmann, T. A. et al. A tale of two flaviviruses: a seroepidemiological study of dengue virus and west nile virus transmission in the ouest and sud-est departments of Haiti. Am. J. Trop. Med. Hyg. 96, 135–140 (2017).
PubMed  PubMed Central  Article  Google Scholar 

61.
Wiggins, K., Eastmond, B. & Alto, B. W. Transmission potential of Mayaro virus in Florida Aedes aegypti and Aedes albopictus mosquitoes. Med. Vet. Entomol. 32, 436–442 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

62.
Pereira, T. N., Carvalho, F. D., De Mendonça, S. F., Rocha, M. N. & Moreira, L. A. Vector competence of Aedes aegypti, Aedes albopictus, and Culex quinquefasciatus mosquitoes for Mayaro virus. PLoS Negl. Trop. Dis. 14, e0007518 (2020).
PubMed  PubMed Central  Article  Google Scholar 

63.
Kantor, A. M., Lin, J., Wang, A., Thompson, D. C. & Franz, A. W. E. Infection pattern of Mayaro Virus in Aedes aegypti (Diptera: Culicidae) and transmission potential of the virus in mixed infections with Chikungunya virus. J. Med. Entomol. 56, 832–843 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

64.
Komar, O. et al. West Nile virus survey of birds and mosquitoes in the Dominican Republic. Vector-Borne Zoonotic Dis. 5, 120–126 (2005).
PubMed  Article  PubMed Central  Google Scholar 

65.
Requena-Méndez, A. et al. Cases of chikungunya virus infection in travellers returning to Spain from Haiti or Dominican Republic, April-June 2014. Eur. Surveill. 19, 20853 (2014).
Article  Google Scholar 

66.
Millman, A. J. et al. Chikungunya and Dengue virus infections among united states community service volunteers returning from the Dominican Republic, 2014. Am. J. Trop. Med. Hyg. 94, 1336–1341 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

67.
Duijster, J. W. et al. Zika virus infection in 18 travellers returning from Surinam and the Dominican Republic, The Netherlands, November 2015–March 2016. Infection 44, 797–802 (2016).
PubMed  PubMed Central  Article  Google Scholar 

68.
Barzon, L. et al. Isolation of infectious Zika virus from saliva and prolonged viral RNA shedding in a traveller returning from the Dominican Republic to Italy, January 2016. Eur. Surveill. 21, 30159 (2016).
Google Scholar 

69.
Goncé, A. et al. Spontaneous abortion associated with Zika virus infection and persistent viremia. Emerg. Infect. Dis. 24, 933–935 (2018).
PubMed  PubMed Central  Article  Google Scholar 

70.
Díaz-Menéndez, M. et al. Initial experience with imported Zika virus infection in Spain. Enfermedades Infecciosas y. Microbiol.ía Cl.ínica 36, 4–8 (2018).
Google Scholar 

71.
Perez, F. et al. The decline of dengue in the Americas in 2017: discussion of multiple hypotheses. Trop. Med. Int. Health 24, 442–453 (2019).
PubMed  PubMed Central  Article  Google Scholar 

72.
Ribeiro, G. S. et al. Does immunity after Zika virus infection cross-protect against dengue?. Lancet Glob. Health 6, e140–e141 (2018).
PubMed  Article  Google Scholar 

73.
Ribeiro, G. S. et al. Influence of herd immunity in the cyclical nature of arboviruses. Curr. Opin. Virol. 40, 1–10 (2020).
CAS  PubMed  Article  Google Scholar 

74.
Gordon, A. et al. Prior dengue virus infection and risk of Zika: a pediatric cohort in Nicaragua. PLoS Med. 16, e1002726 (2019).
PubMed  PubMed Central  Article  CAS  Google Scholar 

75.
Rodriguez-Barraquer, I. et al. Impact of preexisting dengue immunity on Zika virus emergence in a dengue endemic region. Science 363, 607–610 (2019).
ADS  CAS  PubMed  Article  Google Scholar 

76.
Tsang, T. K. et al. Effects of infection history on dengue virus infection and pathogenicity. Nat. Commun. 10, 1246 (2019).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

77.
Katzelnick, L. C., Montoya, M., Gresh, L., Balmaseda, A. & Harris, E. Neutralizing antibody titers against dengue virus correlate with protection from symptomatic infection in a longitudinal cohort. Proc. Natl Acad. Sci. USA 113, 728–733 (2016).
ADS  CAS  PubMed  Article  Google Scholar 

78.
República Dominicana Chikungunya. https://www.paho.org/dor/images/stories/archivos/chikungunya/boletin_chikv_no-13_2014_8_20.pdf?ua=1 (2014).

79.
Verdonschot, P. F. M. & Besse-Lototskaya, A. A. Flight distance of mosquitoes (Culicidae): a metadata analysis to support the management of barrier zones around rewetted and newly constructed wetlands. Limnologica 45, 69–79 (2014).
Article  Google Scholar 

80.
Vazeille, M. et al. Two Chikungunya isolates from the outbreak of La Reunion (Indian Ocean) exhibit different patterns of infection in the mosquito, Aedes albopictus. PLoS ONE 2, e1168 (2007).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

81.
Lamballerie, Xde et al. Chikungunya virus adapts to tiger mosquito via evolutionary convergence: a sign of things to come? Virol. J. 5, 33 (2008).
PubMed  PubMed Central  Article  CAS  Google Scholar 

82.
González, M. A. et al. Micro-environmental features associated to container-dwelling mosquitoes (Diptera: Culicidae) in an urban cemetery of the Dominican Republic. Rev. Biol. Trop. 67, 132–145 (2019).

83.
González, M. A. et al. A survey of tire-breeding mosquitoes (Diptera: Culicidae) in the Dominican Republic: considerations about a pressing issue. Biomédica 40, 507–515 (2020).

84.
IX Censo Nacional De Población Y Vivienda. vol. 1 (2012).

85.
PLISA Health Information Platform for the Americas. https://www.paho.org/data/index.php/en/.

86.
Dominican Republic: Human Development Indicators. http://hdr.undp.org/en/countries/profiles/DOM.

87.
Lipsitch, M. et al. Transmission dynamics and control of severe acute respiratory syndrome. Science 300, 1966–1970 (2003).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

88.
Petrone, M. E. et al. Asynchronicity of endemic and emerging mosquito-borne disease outbreaks in the Dominican Republic. Repository: Arbovirus_Epi_DR. (2020), https://doi.org/10.5281/zenodo.4287651. More