Ecology

1.Clarke, A. R., Powell, K. S., Weldon, C. W. & Taylor, P. W. The ecology of Bactrocera tryoni (Diptera: Tephritidae): What do we know to assist pest management?. Ann. Appl. Biol. 158, 26–54. https://doi.org/10.1111/j.1744-7348.2010.00448.x (2011).Article 

Google Scholar 
2.Jessup, A. J. et al. in Area-Wide Control of Insect Pests: From Research to Field Implementation (eds M. J. B. Vreysen, A. S. Robinson, & J. Hendrichs) 685–697 (Springer Netherlands, 2007).3.HIA. Australian Horticulture Statistics Handbook 2019/20. Horticulture Innovation Australia Limited (2020).4.Fanson, B. G., Sundaralingam, S., Jiang, L., Dominiak, B. C. & D’Arcy, G. A review of 16 years of quality control parameters at a mass-rearing facility producing Queensland fruit fly, Bactrocera tryoni. Entomol. Exp. Appl. 151, 152–159. https://doi.org/10.1111/eea.12180 (2014)5.Knipling, E. F. Possibilities of Insect Control or Eradication Through the Use of Sexually Sterile Males. J. Econ. Entomol. 48, 459–462. https://doi.org/10.1093/jee/48.4.459 (1955).Article 

Google Scholar 
6.Shelly, T. & McInnis, D. Sterile Insect Technique and Control of Tephritid Fruit Flies: Do Species With Complex Courtship Require Higher Overflooding Ratios?. Ann. Entomol. Soc. Am. 109, 1–11. https://doi.org/10.1093/aesa/sav101 (2015).CAS 
Article 

Google Scholar 
7.Hendrichs, J. & Robinson, A. in Encyclopedia of Insects (Second Edition) (eds Vincent H. Resh & Ring T. Cardé) 953–957 (Academic Press, 2009).8.Dominiak, B., Clifford, C. S. & Nielsen, S. G. Queensland fruit fly (Bactrocera tryoni Froggatt) attraction to and chemical analysis of male annihilation blocks using three concentratios of cuelure at Dubbo, NSW, Australia. Plant Prot. Q. 24, 157–160 (2009).9.Dominiak, B. C., Ekman, J. & Broughton, S. Mass trapping and other management option for mediterranean fruit fly and Queensland fruit fly in Australia. Gen. Appl. Entomol. 44, 1–8 (2016).
Google Scholar 
10.Khan, M. A. M. et al. Semiochemical mediated enhancement of males to complement sterile insect technique in management of the tephritid pest Bactrocera tryoni (Froggatt). Sci. Rep. 7, 13366. https://doi.org/10.1038/s41598-017-13843-w (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
11.Vargas, R. I., Shelly, T. E., Leblanc, L. & Piñero, J. C. in Vitamins & Hormones Vol. 83 (ed Gerald Litwack) Ch. 23, 575–595 (Academic Press, 2010).12.Benelli, G. et al. Sexual communication and related behaviours in Tephritidae: current knowledge and potential applications for Integrated Pest Management. J. Pest. Sci. 87, 385–405. https://doi.org/10.1007/s10340-014-0577-3 (2014).Article 

Google Scholar 
13.Shelly, T. E. Consumption of methyl eugenol by male Bactrocera dorsalis (Diptera: Tephritidae): low incidence of repeat feeding. Florida Entomologist 77, 201–208 (1994).CAS 
Article 

Google Scholar 
14.Shelly, T. E. Trapping male oriental fruit flies (Diptera: Tephritidae): does feeding on a natural source of methyl eugenol reduce capture probability?. Florida Entomologist 83, 109–111 (2000).Article 

Google Scholar 
15.Tan, K. H., & Toong, Y. C. Floral synomone of a wild orchid, Bulbophyllum cheiri, lures Bactrocera fruit flies for pollination. J. Chem. Ecol. 28, 1161–1172 (2002).16.Shelly, T. E. Evaluation of a Genetic Sexing Strain of the Oriental Fruit Fly as a Candidate for Simultaneous Application of Male Annihilation and Sterile Insect Techniques (Diptera: Tephritidae). J. Econ. Entomol. 113, 1913–1921. https://doi.org/10.1093/jee/toaa099 (2020).CAS 
Article 
PubMed 

Google Scholar 
17.Joseph, R. M. & Carlson, J. R. Drosophila Chemoreceptors: A Molecular Interface Between the Chemical World and the Brain. Trends in genetics : TIG 31, 683–695. https://doi.org/10.1016/j.tig.2015.09.005 (2015).CAS 
Article 
PubMed 

Google Scholar 
18.Venthur, H., & Zhou, J.-J. Odorant receptors and odorant-binding proteins as insect pest control targets: A comparative analysis. Front. Physiol. 9, 1. https://doi.org/10.3389/fphys.2018.01163 (2018).19.Leal, W. S. Odorant reception in insects: roles of receptors, binding proteins, and degrading enzymes. Annu Rev. Entomol. 58, 373–391. https://doi.org/10.1146/annurev-ento-120811-153635 (2013).CAS 
Article 
PubMed 

Google Scholar 
20.Zheng, W. et al. Identification and Expression Profile Analysis of Odorant Binding Proteins in the Oriental Fruit Fly Bactrocera dorsalis. Int. J. Mol. Sci. 14, 14936 (2013).Article 

Google Scholar 
21.Siciliano, P. et al. Identification of pheromone components and their binding affinity to the odorant binding protein CcapOBP83a-2 of the Mediterranean fruit fly, Ceratitis capitata. Insect Biochem. Mol. Biol. 48, 51–62. https://doi.org/10.1016/j.ibmb.2014.02.005 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
22.Siciliano, P. et al. Sniffing Out Chemosensory Genes from the Mediterranean Fruit Fly, Ceratitis capitata. Plos One 9, e85523https://doi.org/10.1371/journal.pone.0085523 (2014)23.Campanini, E. B. & de Brito, R. A. Molecular evolution of Odorant-binding proteins gene family in two closely related Anastrepha fruit flies. BMC Evol. Biol. 16, 198–198. https://doi.org/10.1186/s12862-016-0775-0 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
24.Park, K., et al. Expression patterns of two putative odorant-binding proteins in the olfactory organs of Drosophila have different implications for their functions. Vol. 300 (2000).25.Shanbhag, S. R. et al. Expression mosaic of odorant-binding proteins in Drosophila olfactory organs. Microsc. Res. Tech. 55, 297–306. https://doi.org/10.1002/jemt.1179 (2001).CAS 
Article 
PubMed 

Google Scholar 
26.Wu, Z. et al. Discovery of chemosensory genes in the oriental fruit fly, Bactrocera dorsalis. PloS One 10, e0129794-e0129794.https://doi.org/10.1371/journal.pone.0129794 (2015)27.Liu, Z., Smagghe, G., Lei, Z. & Wang, J.-J. Identification of male- and female-specific olfaction genes in antennae of the Oriental Fruit Fly (Bactrocera dorsalis). PLoS ONE 11, e0147783–e0147783. https://doi.org/10.1371/journal.pone.0147783 (2016).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
28.Zhang, J. et al. Identification and expression profiles of novel odorant binding proteins and functional analysis of OBP99a in Bactrocera dorsalis. Arch. Insect Biochem. Physiol. 98, e21452. https://doi.org/10.1002/arch.21452 (2018).ADS 
CAS 
Article 
PubMed 

Google Scholar 
29.Cheng, J. et al. Identification and expression analysis of chemosensory genes in the citrus fruit fly Bactrocera (Tetradacus) minax. PeerJ Preprints 6, e27297v1. https://doi.org/10.7287/peerj.preprints.27297v1 (2018).30.Kumaran, N., Prentis, P. J., Mangalam, K. P., Schutze, M. K. & Clarke, A. R. Sexual selection in true fruit flies (Diptera: Tephritidae): transcriptome and experimental evidences for phytochemicals increasing male competitive ability. Mol. Ecol. 23, 4645–4657. https://doi.org/10.1111/mec.12880 (2014).CAS 
Article 
PubMed 

Google Scholar 
31.Liu, H. et al. BdorOBP2 plays an indispensable role in the perception of methyl eugenol by mature males of Bactrocera dorsalis (Hendel). Sci. Rep. 7, 15894. https://doi.org/10.1038/s41598-017-15893-6 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
32.Kumaran, N. et al. Plant-Mediated Female Transcriptomic Changes Post-Mating in a Tephritid Fruit Fly, Bactrocera tryoni. Genome biology and evolution 10, 94–107.https://doi.org/10.1093/gbe/evx257 (2017)33.Idrees, A. et al. Protein baits, volatile compounds and irradiation influence the expression profiles of odorant-binding protein genes in Bactrocera dorsalis (Diptera: Tephritidae). Appl. Ecol. Environ. Res. 15, 1883–1899 (2017).Article 

Google Scholar 
34.Pavlidi, N. et al. Transcriptomic responses of the olive fruit fly Bactrocera oleae and its symbiont Candidatus Erwinia dacicola to olive feeding. Sci. Rep. 7, 42633. https://doi.org/10.1038/srep42633 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
35.Arya, G. H. et al. The genetic basis for variation in olfactory behavior in Drosophila melanogaster. Chem Senses 40, 233–243. https://doi.org/10.1093/chemse/bjv001 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
36.Akter, H., Adnan, S., Morelli, R., Rempoulakis, P. & Taylor, P. W. Suppression of cuelure attraction in male Queensland fruit flies provided raspberry ketone supplements as immature adults. PLoS ONE 12, e0184086. https://doi.org/10.1371/journal.pone.0184086 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
37.Gilchrist, A. S. et al. The draft genome of the pest tephritid fruit fly Bactrocera tryoni: resources for the genomic analysis of hybridising species. BMC Genomics 15, 1153. https://doi.org/10.1186/1471-2164-15-1153 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
38.Fleischer, J. & Krieger, J. Insect pheromone receptors—Key elements in sensing intraspecific chemical signals. Front. Cell. Neurosci. 12. https://doi.org/10.3389/fncel.2018.00425 (2018).39.Liu, Z. et al. An antennae-specific odorant-binding protein is involved in Bactrocera dorsalis Olfaction. Front. Ecol. Evol. 8.https://doi.org/10.3389/fevo.2020.00063 (2020).40.Tan, K. H. Recaptures of feral Bactrocera dorsalis and B. umbrosa (Diptera: Tephritidae) males after feeding on methyl eugenol. Bull. Entomol. Res. 110, 15–21, https://doi.org/10.1017/S0007485319000208 (2019).41.Liu, L. et al. Contribution of Drosophila DEG/ENaC genes to salt taste. Neuron 39, 133–146. https://doi.org/10.1016/s0896-6273(03)00394-5 (2003).CAS 
Article 
PubMed 

Google Scholar 
42.Swarup, S., Huang, W., Mackay, T. F. C. & Anholt, R. R. H. Analysis of natural variation reveals neurogenetic networks for Drosophila olfactory behavior. Proc Natl Acad Sci USA 110, 1017–1022. https://doi.org/10.1073/pnas.1220168110 (2013).ADS 
Article 
PubMed 

Google Scholar 
43.Vijayan, V., Thistle, R., Liu, T., Starostina, E. & Pikielny, C. W. Drosophila pheromone-sensing neurons expressing the ppk25 Ion channel subunit stimulate male courtship and female receptivity. PLoS Genet. 10, e1004238. https://doi.org/10.1371/journal.pgen.1004238 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
44.Thistle, R., Cameron, P., Ghorayshi, A., Dennison, L. & Scott, K. Contact chemoreceptors mediate male-male repulsion and male-female attraction during Drosophila courtship. Cell 149, 1140–1151. https://doi.org/10.1016/j.cell.2012.03.045 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
45.Khan, M. A. M. et al. Raspberry ketone accelerates sexual maturation and improves mating performance of sterile male Queensland fruit fly, Bactrocera tryoni (Froggatt). Pest Manag. Sci. 75, 1942–1950. https://doi.org/10.1002/ps.5307 (2019).CAS 
Article 
PubMed 

Google Scholar 
46.Weldon, C. W., Perez-Staples, D. & Taylor, P. W. Feeding on yeast hydrolysate enhances attraction to cue-lure in Queensland fruit flies, Bactrocera tryoni. Entomol. Experim. et Applicata 129, 200–209. https://doi.org/10.1111/j.1570-7458.2008.00768.x (2008)47.Najar-Rodriguez, A. J., Galizia, C. G., Stierle, J. & Dorn, S. Behavioral and neurophysiological responses of an insect to changing ratios of constituents in host plant-derived volatile mixtures. J. Exp. Biol. 213, 3388 (2010).CAS 
Article 

Google Scholar 
48.Bertschy, C., Turlings, T. C., Bellotti, A. C. & Dorn, S. Chemically-mediated attraction of three parasitoid species to mealybug-infested cassava leaves. Florida Entomologist 80, 383–395 (1997).Article 

Google Scholar 
49.Butler, D. G., Cullis, B. R., Gilmour, A. R. & Gogel, B. J. (Queensland Department of Primary Industries and Fisheries, Australia, 2009).50.Bolger, A. M., Lohse, M. & Usadel, B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics 30, 2114–2120. https://doi.org/10.1093/bioinformatics/btu170 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
51.Zhao, Q.-Y. et al. Optimizing de novo transcriptome assembly from short-read RNA-Seq data: a comparative study. BMC Bioinf. 12, S2. https://doi.org/10.1186/1471-2105-12-S14-S2 (2011).CAS 
Article 

Google Scholar 
52.Fu, L., Niu, B., Zhu, Z., Wu, S. & Li, W. CD-HIT: accelerated for clustering the next-generation sequencing data. Bioinformatics 28, 3150–3152. https://doi.org/10.1093/bioinformatics/bts565 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
53.Simão, F. A., Waterhouse, R. M., Ioannidis, P., Kriventseva, E. V. & Zdobnov, E. M. BUSCO: assessing genome assembly and annotation completeness with single-copy orthologs. Bioinformatics 31, 3210–3212. https://doi.org/10.1093/bioinformatics/btv351 (2015).CAS 
Article 
PubMed 

Google Scholar 
54.Conesa, A. et al. Blast2GO: a universal tool for annotation, visualization and analysis in functional genomics research. Bioinformatics 21, 3674–3676. https://doi.org/10.1093/bioinformatics/bti610 (2005).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
55.Li, H. & Durbin, R. Fast and accurate short read alignment with Burrows-Wheeler transform. Bioinformatics 25, 1754–1760. https://doi.org/10.1093/bioinformatics/btp324 (2009).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
56.Patro, R., Duggal, G., Love, M. I., Irizarry, R. A. & Kingsford, C. Salmon provides fast and bias-aware quantification of transcript expression. Nat. Methods 14, 417–419. https://doi.org/10.1038/nmeth.4197 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
57.Risso, D., Ngai, J., Speed, T. P. & Dudoit, S. Normalization of RNA-seq data using factor analysis of control genes or samples. Nat. Biotechnol. 32, 896–902. https://doi.org/10.1038/nbt.2931 (2014).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
58.Al-Shahrour, F., Díaz-Uriarte, R. & Dopazo, J. FatiGO: a web tool for finding significant associations of Gene Ontology terms with groups of genes. Bioinformatics 20, 578–580. https://doi.org/10.1093/bioinformatics/btg455 (2004).CAS 
Article 
PubMed 

Google Scholar 
59.Ritchie, M. E. et al. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucl. Acids Res. 43, e47–e47. https://doi.org/10.1093/nar/gkv007 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar  More

Data collectionData on the birds’ reproductive success and the number of humans present at nest sites were collected as part of a long-term, ongoing monitoring project in Hungary, in which we investigate the impacts of urbanization on populations of great tits. The great tit is an insectivorous passerine bird that is widespread across the Western Palearctic, occupies both urban and forest habitats, readily accepts nestboxes, and shares many important ecological traits with other tit or chickadee species also occurring in urban habitats27. These traits make this species an ideal model organism for studying the effects of the anthropause on wildlife in different environments.Study sitesWe monitored breeding great tit populations and also collected human presence data in two urban areas and at one forest study site. In one of the urban sites, Veszprém (47°05′17.29″N, 17°54′29.66″E; human population: c. 56,000; the monitoring scheme started in 2013), the nestboxes were placed in public green spaces (public parks, university campuses, a bus station, and a cemetery) that are surrounded by built-up areas and roads, and experience frequent anthropogenic disturbance. At the other urban size, Budapest (47°30′27.4″N, 19°01′03.4″E; the capital city of Hungary, human population: c. 1.75 million; the monitoring scheme started in 2019), the nestboxes were placed in two public urban parks, located c. 400 m from each other in the city core area and separated by high-traffic roads. The parks are freely accessible to residents and are heavily embedded within the urban matrix. At both urban sites, most of the nestboxes are distributed along paths or walking trails. Even though the two cities greatly differ in their size and human population, our urban study plots in both cities have similar general characteristics: these are surrounded by built-up areas, are at a similar distance (c. 3–4 km) from the nearest forested areas (for Veszprém, this is the forest at Vilma-puszta: 47°05′06.7″N, 17°51′51.4″E; for Budapest, this is the forest at Normafa: 47°30′27.7″N 18°57′51.1″E), and nests also experienced a similar level of human disturbance in the pre-COVID reference period (Fig. 1b). The forest site, Szentgál (47°06′39.75″N, 17°41′17.94″E; the monitoring scheme started in 2013), is a mature woodland, dominated by beech (Fagus sylvatica) and hornbeam (Carpinus betulus), located 3 km away from the nearest human habitation (Szentgál, human population: c. 2.800), c. 20 km and 110 km away from Veszprém and Budapest, respectively. There are no paved roads in the forest, and the area is relatively free from human disturbance although it experiences occasional hunting and logging activity.Human presence around nestsTo quantify human presence at our study sites for 2020 and the reference years we counted the number of humans (motorized vehicles excluded) during each nest check, for 30 s, in the proximity of the nestboxes (for similar approach see Corsini et al. 2019). The number of humans was recorded within a 50-m radius of the nestboxes between 2013 and 2018 (Veszprém, Szentgál), and within a 15-m radius distance in 2019–2020 (all sites). We changed the counting distance in 2019 due to methodological reasons following28. However, to be able to compare the human presence data of 2020 in Veszprém and Szentgál to that recorded in earlier years, in 2020 we performed the counts with both the 15-m and the 50-m radius distances at these two sites. Thus, for 2020 in Veszprém, we have human presence data both for the 50-m and the 15-m radius areas that were used in the forest-city and the between-cities comparisons, respectively (see below). For each year and study site, we used human presence data only from seasonally first broods (defined below), and only from nests where there were either already eggs or nestlings in the nest, resulting in 9.4 ± 3.6 (mean ± SD) observations per brood which is a reliable indicator of human presence28.Birds’ reproductive successWe monitored nestboxes each year at least twice a week from mid-March to early June to record laying and hatching dates, clutch size, hatching success, and the number of nestlings in active great tit nests. We ringed nestlings at day 14–16 post-hatch (i.e. a few days before fledging; hatching day of the first chick = day 1) with a numbered metal ring and also recorded their body mass (to the nearest 0.1 g), tarsus length (to the nearest 0.1 mm and following Svensson’s ‘alternative’ method29) and wing length (from the bend of the wing to the longest primary; to nearest 1 mm). Shortly after the expected date of fledging we carefully examined the nest material to identify and count the number of chicks that died after ringing (due to e.g. starvation, predation) that we included in the calculation of nestling survival (detailed below). The aim of this is to get a more accurate estimate for the number of offspring that could indeed fledge from the nest. The number of broods (nestlings) that suffered partial or complete mortality between ringing and fledging were: n = 6 (13) in Budapest (2019–2020), n = 70 (152) in Veszprém (2013–2020), and n = 25 (83) in the Szentgál forest.From these data we determined clutch size (the maximum number of eggs observed in a brood), hatching success (the proportion of chicks hatched / eggs laid), the number of fledglings, and nestling survival (the proportion of fledged young / hatched chicks). The number of fledglings (i.e. the number of young fledged successfully) was calculated as the number of chicks ringed minus the number of chicks found dead in the nest after the ringing. We involved only seasonally first breeding attempts (as this period overlapped with the lockdown period; detailed at the Statistical analyses), and defined first broods as follows. In our study system breeding great tits are captured on their nests and receive a unique combination of colour rings. Active nests are also routinely equipped with a small, concealed video camera enabling us to reliably identify over 80% of breeding individuals each year30. Thus, relying on this setup, we considered a clutch as a first breeding attempt of a pair if it was initiated before the date of the first egg laid in the earliest second clutch at that site by an individually identifiable (i.e. colour-ringed) female that successfully raised her first clutch (i.e. fledged at least one young) in that year.Air pollution and meteorological conditionsTo describe the levels of traffic-related air pollution (nitrogen dioxide [NO2], nitrogen oxides [NOX] and ozone [O3]) and the meteorological conditions (temperature and precipitation) at the two urban study sites (Veszprém and Budapest), we used data provided by the Hungarian Air Quality Monitoring Network and the Hungarian Meteorological Service, respectively. To better understand which aspect of the anthropause might have affected great tits’ breeding success we thus assessed if the lockdown affected air pollution levels differently at the two urban study sites (compared to 2019), or if weather conditions showed different fluctuations between 2019 and 2020 at the two cities. For more details on the statistical analyses and results, see ESM: Sect. 1.Statistical analysesThe duration of the official restrictions on human mobility (lockdown) spanned between 28 March–4 May in Veszprém (calendar date: 88–125; 01 January = 1) and 28 March–18 May (88–139) in Budapest. During this period people were allowed to leave their homes e.g. to run essential errands including individual sport and recreational activities in public green spaces, although with keeping at least 1.5 m from each other (social distancing). Very importantly, from the point of view of our study, the period of movement restrictions had almost completely overlapped with the seasonally first breeding attempts (from egg-laying to fledging) of great tits at both urban sites. The date of laying the 1st egg (calendar date, mean ± SD) in Veszprém was 94.2 ± 6.4, while in Budapest 97 ± 7.8; the date of chick ringing and measuring in Veszprém was 128 ± 5.3, while in Budapest: 133 ± 9.1. Thus, we decided not to exclude any first broods based on the date in order to maximize our sample size. Similarly, the period from which we involved human presence data was also strongly overlapped with the duration of the movement restrictions in both cities. Therefore, in Veszprém, the calendar dates of the first and the last human count at each nest were 87–108 (median: 100) and 121–142 (median: 132), respectively, while in Budapest 87–128 (median: 98) and 118–155 (median: 128).Human presence around nestsIn accordance with our first objective (forest-city comparisons), we explored if the lockdown in 2020 caused any changes in human disturbance around the great tit nests. To do so, we compared the number of humans (50-m radius of the nests) between 2020 and the 2013–2018 reference period, separately for the forest (Szentgál) and urban (Veszprém) study sites. Note that in 2019, we did not collect data on human presence within a 50-m radius at Veszprém and Szentgál (see above: Data collection), therefore 2019 was not included in the reference period of this analysis. We, however, also compared human presence in Veszprém between 2019 and 2020 using the 15-m radius data which indicates a change that is consistent with the differences found using the 50-m radius data (detailed below).First, we built generalized linear mixed-effects (GLM, lme4 R package) models with Poisson error distribution with the number of humans as the response variable, including year as a fixed factor and nestbox ID as random factor to control for non-independence of the data. Next, we extracted the mean values (least-squares means; package emmeans31) and associated standard errors for each year as estimated by the model. We computed the mean of these yearly mean estimates for the 2013–2018 reference period (i.e. calculated a single overall mean describing the whole reference period) and compared this long-term mean to the mean estimate of 2020 by calculating the linear contrast between them (with the ‘contrast’ function of the emmeans package), and expressed linear contrasts as 2020 minus the reference period.For our second objective (between-cities comparisons), we compared the changes in human disturbance around the nestboxes at the two urban study sites, Veszprém and Budapest, using the number of humans recorded within the 15-m radius of the active nests in 2019 and 2020. We analysed the data from Budapest and Veszprém separately and built generalized linear mixed-effects models with Poisson error distribution with the number of humans (15-m radius of the nests) as the response variable, including year as a fixed factor and nestbox ID as random factor to control for non-independence.Birds’ reproductive successWe used data from 2019 (reference; for justification see below in this section) and 2020 (lockdown). First, we constructed separate linear models to analyse each component of reproductive success (response variables), and for the forest-city and the between-cities comparisons. We used linear models (LM) for clutch size and the number of fledglings, generalized linear models (GLM, with quasi-binomial error distribution) for hatching success and nestling survival, and linear-mixed effects models (LME) for nestling body size traits (body mass, tarsus length, and wing length). Models on nestling body size traits contained nestlings’ age at ringing as a confounding variable (three-level factor: 14, 15, or 16 d of age) and brood ID as a random factor to control for the non-independence of chicks raised in the same brood. Finally, these models always contained a habitat (Veszprém or Szentgál) × year (2019 or 2020) interaction term for forest-city comparisons and a city (Budapest or Veszprém) × year (2019 or 2020) interaction term for between-cities comparisons. We checked assumptions of residuals’ normality and homogeneity of variance by inspecting the residuals plots which were respected for all models.Next, to test the prediction for our first objective (forest-city comparisons), we extracted the mean values (least-squares means) and associated standard errors of each response variable for each habitat × year combination as estimated by the linear model’s interaction. Then, from these estimates, we calculated habitat contrasts, i.e. the mean forest-city difference (forest minus urban) for each year (i.e. for 2019 and 2020), and compared the mean habitat contrast for the 2019 reference year to the mean habitat contrast of 2020; for similar approach see14,32,33.For our second objective (between-cities comparisons), we followed the same procedure as for the forest-city comparisons (detailed above) except that here we compared the differences between cities (Budapest minus Veszprém) in 2020 and 2019. These full models (i.e. for the forest-city and between-cities comparisons) are presented in Table S1–S2 (ESM: Sect. 2).In our study, we chose 2019 as a reference year for multiple reasons. First, because this was temporally the closest year without a lockdown. Second, because for Budapest we have monitoring data only from 2019 to 2020, using 2019 and 2020 in all analyses makes the results more comparable. Finally, although we have monitoring data from a total of eight years (2013–2020) for Szentgál (forest site) and Veszprém (urban site), for the forest-city comparisons we did not include years before 2019 in the reference because we noticed a negative trend in birds’ reproductive success throughout the study years (Fig. S3). This trend was especially apparent in the forest population, and may have reduced the forest-city difference by the end of the study period. Indeed, 2019 and 2020 were amongst the poorest years and resulted in a very similar reproductive success between both years within both habitats (Fig. S3). Because such temporal trend may have confounded the comparisons of 2020 with earlier years, to take account for its effect, and to further justify our approach of using 2019 as the reference year, we conducted additional analyses on the birds’ reproductive success by comparing both 2019 and 2020 (separately) to the 2013–2018 long-term reference period. We predict that if 2019 and 2020 are similarly affected by the decreasing trend in reproductive success than then the differences between the long-term reference period and 2019 and 2020, respectively, should be similar. For the details of these long-term forest-city comparisons see ESM: Sect. 3 and Table S3).Finally, we did not conduct the forest-city comparisons (first objective) between the forest site (Szentgál) and the other urban site (Budapest) for two reasons. First, because unlike to the Szentgál vs. Veszprém setup, we did not have an appropriate forest (control) location which is close to Budapest. Second, because conducting comparisons between the long-term data and 2019 and 2020, respectively (see: ESM Sect. 3) was not possible for Budapest because we do not have similar long-term data for the latter site.Clutches that failed before reaching the incubation stage (due to predation or desertion; i.e. final clutch size was uncertain), suffered complete mortality due to weather (e.g. nestbox fall from the tree due to strong wind), and cases when complete or partial clutch or brood loss may have occurred due to the monitoring process (e.g. when a nestbox was dropped or when complete brood failure occurred soon after capturing a parent on the nest) were excluded from all analyses. In the analyses investigating the number of fledglings, fledging success, and nestling body size traits we involved nests only where at least one nestling hatched, and excluded broods that were involved in a food-supplementation experiment (as treatment group) during the nestling rearing period in 201714. We used the R 4.0.5 software environment for statistical analysis and creating figures34.Ethical statementAll procedures were in accordance with Hungarian laws, and adhered to the ASAB/ABS guidelines for the use of animals in behavioural research and teaching. Permit to the use of animals in this study was provided by the National Scientific Ethical Committee on Animal Experimentation (permit number: PE-06/KTF/997–8/2018, FPH061/1329–5/2018, PE-06/KTF/06,543–7/2020 and FPH061/3036–4/2020). Permits to study protected species and access to protected areas were provided by the Middle Transdanubian Inspectorate for Environmental Protection, Natural Protection and Water Management (permit numbers: 31559/2011, 24,861/2014 and VE-09Z/03,454–8/2018, for working in Veszprém and Szentgál) and the Environment Protection and Nature Conservation Department of the Pest County Bureau of the Hungarian Government and the Mayor’s Office of Budapest (permit numbers: PE-06/KTF/997–8/2018, FPH061/1329–5/2018, PE-06/KTF/06,543–7/2020 and FPH061/3036–4/2020, for working in Budapest). More

ScopeWe compared disposable face masks that were used once with face masks that were sterilized and used five more times (six times in total). Sterilisation and PFE test data of the Aura 1862+ (3M, Saint Paul, Minnesota, USA) face mask indicate that this type of face mask shows good performance after multiple sterilisation cycles10,11,12. In a previous pilot study, the company CSA Services (Utrecht, the Netherlands), a sterilization facility for cleaning, disinfection and sterilization of medical instruments, was rebuild to process FFP2 face masks. In total, 18,166 single use FFP2 masks were sterilised after use in a medical autoclave. As the majority (n = 7993) were Aura 1862+ (3M, Saint Paul, Minnesota, USA), this particular type of face mask was chosen for the LCA.The total weight of the face masks and packaging together during end-of-life consists of incineration for the face masks (97%) and landfill for the carton box packaging of new face masks (3%). There is no recycling potential used in our model since the materials coming from the operating room and its packaging is commonly disposed as medical waste. In the Netherlands, no energy recovery takes place at the incineration of regulated medical waste. Therefore, no co-function was applicable for the end-of-life scenario.Recycling is often a multi-functional process that produces two or more goods. To deal with the multi-functionality in the background processes, the cut-off approach was applied to exclude the allocation of the greenhouse gas emissions to additional goods. This means that potential rest materials such as energy gained during incineration are cut-off and that the greenhouse gas emissions are fully allocated to the waste treatment processes itself.In the LCA, the ‘functional unit’ defines the primary function that is fulfilled by the investigational products and indicates how much of this function is considered18. In this study, we pragmatically chose as a definition for the protection of 100 health care workers against airborne viruses, using one FFP2 certified face mask, each during one working shift of an average of 2 h in a hospital in the Netherlands.Table 1 shows the differences between the two scenarios:

1.

100 masks including packaging, transported from production to the hospital, used and disposed.

2.

100 times use of reprocessed masks. We calculated that 27.1 masks are being produced and transported from production to the hospital. The 27.1 are being reprocessed five times, taking into account that 20% of the batch cannot be reprocessed. Therefore 80% of the batch could be used for reprocessing after each step resulting in: 27.1 (new) + 21.7 (repro 1) + 17.3 (repro 2) + 13.9 (repro 3) + 11.1 (repro 4) + 8.9 (repro 5) = 100 times of use. For each time of reprocessing the batch is transported from the hospital to the (hospital) Central Sterilization Services Department (CSSD) and disposed after five times of reprocessing.

Table 1 Comparison between reference flow 1 and 2.Full size tableCombining the functional unit with the two alternative scenarios results in the reference flows for the protection of 100 health care workers against airborne viruses, either using a face mask one single time (100 virgin masks produced for the 1st scenario), or reusing a face mask for five additional times (27.1 virgin masks produced for the 2nd scenario). For both reference flows, only FFP2 certified face masks are considered. For the calculations each mask is used for a single two hours working shift in an average hospital in the Netherlands.Life cycle inventory (LCI) analysisThe inventory data includes all phases from production (including material production and part production), transport, sterilisation to end-of-life of the life cycle of the single use and reprocessed face masks. We disassembled one face mask to obtain the weight of each individual component on a precision scale (Fit Evolve, Bangosa Digital, Groningen, the Netherlands) with a calibrated inaccuracy of 1.5%. Component information and materials were obtained from the data fact sheet provided by the manufacturer. We conducted a separate validation experiment to establish the material composition in the filtering fabric (Supplement file).This LCA with the Aura 3M masks was based on steam sterilization by means of a hospital autoclave and therefore part of this study. Therefore, face masks were placed in a sterilization bag that contained up to five masks. A total of 1000 masks were placed into an autoclave (Getinge, GSS6713H-E, Sweden) per cycle. After sterilization, the masks were transported to the hospital. Masks were reprocessed for a maximum of five times before final disposal10,11.The assessment of climate change impact is done following as closely as possible the internationally accepted Life Cycle Assessment (LCA) method following the ISO 14040 and 14044 standards19,20. The LCA examines all the phases of the product’s life cycle from raw material extraction to production, packaging, transport, use and reprocessing until final disposal19. The LCA was modelled using SimaPro 9.1.0.7 (PRé Sustainability, Amersfoort, The Netherlands). The background life cycle inventory data were retrieved from the ecoinvent database (Ecoinvent version 3.6, Zürich, Switzerland)21.To make a valid comparison between the disposable and reprocessing face masks, the system boundaries should be equal in both scenarios. The system boundaries in this study consisted of the production, the use and the disposal and waste treatment of the masks. For the reprocessed face masks, the lifecycle is extended due to the sterilisation process (Fig. 1). Therefore, the additional PPE’s and materials needed to safely process the masks (e.q. masks, gloves and protective sheets) are included in the production phase. The production of machinery for the manufacturing of the face masks and the autoclave were not included in this study.Figure 1System boundary overview of new and reprocessed face masks including waste treatment by incineration.Full size imageThe production facility for the face masks is located in Shanghai, China22,23. Further distribution took place from Bracknell, UK to Neuss, Germany and the final destination was set in Rotterdam, the Netherlands.The packaging materials were disposed in the hospital where the face masks are used primarily. After first use, face masks were transported to the sterilisation department. All masks were manually checked before reprocessing by personnel wearing PPE. Of all used Aura 1862+ facemasks that entered the CSA, approximately 10% was discarded. To remain conservative, the LCA was conducted based on a 20% rejection rate as a result of face masks which could not be reused anymore due to deformities, lipstick, and broken elastic bands.A full overview of the life cycle inventory table for the two scenarios and details on model assumptions are added in the Supplemental file (Supplemental file, Part B).Life cycle impact assessmentThe carbon footprint (kg CO2 eq) was chosen as the primary unit in the impact category. ReCiPe was applied at midpoint level and used to translate greenhouse gas emissions into climate change impact16.Uncertainty analysisThe final LCA model contains several uncertainties based on assumptions and measurement inaccuracies24. The included uncertainties were based on weighted components of the masks as well as the packaging which were measured with 1.5% inaccuracy of the precision scale apparatus. A Monte Carlo sampling25 was conducted for both alternatives (disposable and reprocessing) where input parameters for the LCA were sampled randomly from their respective statistical distributions in for 10,000 ‘runs’. Because input parameters between scenarios were partly overlapping, we compared these two scenarios directly using a discernibility analysis. This technique, establishes which scenario is beneficial for each of 10,000 Monte Carlo runs. We report the percentage of instances where the reprocessing scenario has a lower carbon footprint than the disposable scenario.Sensitivity analysisA sensitivity analysis was conducted to check the sensitivity of the outcome measures to variation in the input parameters. To determine which parameters are interesting to investigate, three aspects were considered: the variations in number of face masks per sterilization cycle (autoclave capacity), rejection rate (number of losses per cycle) and transport distance to the CSSD. Finally, we included the relative contribution of these variations. The following three parameter variations were chosen for the sensitivity analysis:

1.

Rejection percentage. The rejection rate was defined based on experiences from the participating sterilisation department and studies that show that sterilisation of the face masks up to 5 times is possible. Masks were re-used for 5 times, approximately 10% was discarded during the total life cycle. Out of this experience and to remain conservative, the total rejection rate was set on 20%. Therefore it is interesting to investigate whether variation in PFE testing outcomes or differences in user protocols influence the outcomes. This should indicate if masks from higher or lower quality can also be suitable candidates for reprocessing.

2.

Autoclave capacity, which largely depends on the loading of the autoclave. To mimic different loads of the autoclave, it is interesting to know the influence of sterilizing fewer masks per run on the model.

3.

Transport. As it is likely that many hospitals have a Central Sterilisation Services Department (CSSD) it is interesting to know the effect of having zero transportation. Moreover, in case hospitals are not willing to change the routing in their CSSD it is interesting to observe how outcomes are influenced if transportation is set on the maximal realistic value of 200 km.

The parameters have been varied with 250 and 500 face masks per sterilisation batch. A rate varying with 10% and 30% of the face masks being rejected due to quality reasons and variation in transport kilometres of 0–200 km.There is a small difference between the baselines of the sensitivity, LCIA and contribution analyses because all these are performed using separate Monte-Carlo simulations. The output of the different simulations may show minor differences due to statistical distribution.Cost price comparisonA cost analysis was made to give insight in costing from a procurement perspective. The cost analysis is conducted with five face masks that were steam sterilized per batch in a permeable laminate bag, Halyard type CLFP150X300WI-S20 and includes the expenses of energy, depreciation, water consumption, cost of personnel, overhead and compared to the prices for a new disposable 3M Aura face mask during the first and second Corona waves. Five pieces per bag were chosen in order to have enough space between the masks to sterilise each mask properly. The cost analysis is based on actual sterilization as well as associated costs compared to the prices of new disposable face masks. The costs were then related to the functional unit of protecting 100 health care workers by calculating the difference in the amount of Euros per 100 face masks. More

1.Lalli, C. & Parsons, T. R. Biological Oceanography: An Introduction (Elsevier, 1997).
Google Scholar 
2.Mackas, D. L., Denman, K. L. & Abbott, M. R. Plankton patchiness: Biology in the physical vernacular. Bull. Mar. Sci. 37, 652–674 (1985).
Google Scholar 
3.Kjerfve, B. & Magill, K. E. Geographic and hydrodynamic characteristics of shallow coastal lagoons. Mar. Geol. 88, 187–199 (1989).ADS 
Article 

Google Scholar 
4.Kjerfve, B. Chapter 1, Coastal lagoons. In Elsevier Oceanography Series Vol. 60 (ed. Kjerfve, B.) 1–8 (Elsevier, 1994).
Google Scholar 
5.McManus, M. A. & Woodson, C. B. Plankton distribution and ocean dispersal. J. Exp. Biol. 215, 1008–1016 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
6.Carr, M.-E. Estimation of potential productivity in Eastern Boundary Currents using remote sensing. Deep Sea Res. Part II 49, 59–80 (2001).ADS 
Article 

Google Scholar 
7.Chavez, F. P. & Messié, M. A comparison of Eastern Boundary Upwelling Ecosystems. Prog. Oceanogr. 83, 80–96 (2009).ADS 
Article 

Google Scholar 
8.Schubert, H. & Telesh, I. Estuaries and coastal lagoons. In Biological Oceanography of the Baltic Sea (eds Snoeijs-Leijonmalm, P. et al.) 483–509 (Springer Netherlands, 2017). https://doi.org/10.1007/978-94-007-0668-2_13.Chapter 

Google Scholar 
9.Pecqueur, D. et al. Dynamics of microbial planktonic food web components during a river flash flood in a Mediterranean coastal lagoon. Hydrobiologia 673, 13–27 (2011).CAS 
Article 

Google Scholar 
10.Deininger, A. et al. Simulated terrestrial runoff triggered a phytoplankton succession and changed seston stoichiometry in coastal lagoon mesocosms. Mar. Environ. Res. 119, 40–50 (2016).CAS 
PubMed 
Article 

Google Scholar 
11.Newton, A. & Mudge, S. M. Temperature and salinity regimes in a shallow, mesotidal lagoon, the Ria Formosa, Portugal. Estuar. Coast. Shelf Sci. 57, 73–85 (2003).ADS 
CAS 
Article 

Google Scholar 
12.Huang, J., Gao, J. & Hörmann, G. Hydrodynamic-phytoplankton model for short-term forecasts of phytoplankton in Lake Taihu, China. Limnologica 42, 7–18 (2012).CAS 
Article 

Google Scholar 
13.Pérez-Ruzafa, A. et al. Connectivity between coastal lagoons and sea: Asymmetrical effects on assemblages’ and populations’ structure. Estuar. Coast. Shelf Sci. 216, 171–186 (2019).ADS 
Article 

Google Scholar 
14.Dube, A., Jayaraman, G. & Rani, R. Modelling the effects of variable salinity on the temporal distribution of plankton in shallow coastal lagoons. J. Hydro-environ. Res. 4, 199–209 (2010).Article 

Google Scholar 
15.Pulina, S., Satta, C. T., Padedda, B. M., Sechi, N. & Lugliè, A. Seasonal variations of phytoplankton size structure in relation to environmental variables in three Mediterranean shallow coastal lagoons. Estuar. Coast. Shelf Sci. 212, 95–104 (2018).ADS 
Article 

Google Scholar 
16.Millet, B. & Cecchi, P. Wind-induced hydrodynamic control of the phytoplankton biomass in a lagoon ecosystem. Limnol. Oceanogr. 37, 140–146 (1992).ADS 
Article 

Google Scholar 
17.Souchu, P. et al. Influence of shellfish farming activities on the biogeochemical composition of the water column in Thau lagoon. Mar. Ecol. Prog. Ser. 218, 141–152 (2001).ADS 
CAS 
Article 

Google Scholar 
18.Paphitis, D. & Collins, M. B. Sediment resuspension events within the (microtidal) coastal waters of Thermaikos Gulf, northern Greece. Cont. Shelf Res. 25, 2350–2365 (2005).ADS 
Article 

Google Scholar 
19.Trombetta, T. et al. Water temperature drives phytoplankton blooms in coastal waters. PLoS One 14, e0214933 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
20.Faust, K. & Raes, J. Microbial interactions: From networks to models. Nat. Rev. Microbiol. 10, 538–550 (2012).CAS 
PubMed 
Article 

Google Scholar 
21.Fuhrman, J. A., Cram, J. A. & Needham, D. M. Marine microbial community dynamics and their ecological interpretation. Nat. Rev. Microbiol. 13, 133–146 (2015).CAS 
PubMed 
Article 

Google Scholar 
22.Trombetta, T., Vidussi, F., Roques, C., Scotti, M. & Mostajir, B. Marine microbial food web networks during phytoplankton bloom and non-bloom periods: Warming favors smaller organism interactions and intensifies trophic cascade. Front. Microbiol. 11, 502336 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
23.Needham, D. M. & Fuhrman, J. A. Pronounced daily succession of phytoplankton, archaea and bacteria following a spring bloom. Nat. Microbiol. 1, 16005 (2016).CAS 
PubMed 
Article 

Google Scholar 
24.Hemraj, D. A., Hossain, A., Ye, Q., Qin, J. G. & Leterme, S. C. Anthropogenic shift of planktonic food web structure in a coastal lagoon by freshwater flow regulation. Sci. Rep. 7, 44441 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
25.Bec, B., Husseini Ratrema, J., Collos, Y., Souchu, P. & Vaquer, A. Phytoplankton seasonal dynamics in a Mediterranean coastal lagoon: Emphasis on the picoeukaryote community. J. Plankton Res. 27, 881–894 (2005).CAS 
Article 

Google Scholar 
26.Collos, Y. et al. Oligotrophication and emergence of picocyanobacteria and a toxic dinoflagellate in Thau lagoon, southern France. J. Sea Res. 61, 68–75 (2009).ADS 
Article 

Google Scholar 
27.Collos, Y. et al. Pheopigment dynamics, zooplankton grazing rates and the autumnal ammonium peak in a Mediterranean lagoon. Hydrobiologia 550(1), 83–93 (2005).CAS 
Article 

Google Scholar 
28.Gangnery, A. et al. Growth model of the Pacific oyster, Crassostrea gigas, cultured in Thau Lagoon (Méditerranée, France). Aquaculture 215, 267–290 (2003).Article 

Google Scholar 
29.Pernet, F. et al. Marine diatoms sustain growth of bivalves in a Mediterranean lagoon. J. Sea Res. 68, 20–32 (2012).ADS 
CAS 
Article 

Google Scholar 
30.Rose, J. M. & Caron, D. A. Does low temperature constrain the growth rates of heterotrophic protists? Evidence and implications for algal blooms in cold waters. Limnol. Oceanogr. 52, 886–895 (2007).ADS 
Article 

Google Scholar 
31.Kordas, R. L., Harley, C. D. G. & O’Connor, M. I. Community ecology in a warming world: The influence of temperature on interspecific interactions in marine systems. J. Exp. Mar. Biol. Ecol. 400, 218–226 (2011).Article 

Google Scholar 
32.Jones, K. J. & Gowen, R. J. Influence of stratification and irradiance regime on summer phytoplankton composition in coastal and shelf seas of the British Isles. Estuar. Coast. Shelf Sci. 30, 557–567 (1990).ADS 
Article 

Google Scholar 
33.Bosak, S., Godrijan, J. & Šilović, T. Dynamics of the marine planktonic diatom family Chaetocerotaceae in a Mediterranean coastal zone. Estuar. Coast. Shelf Sci. 180, 69–81 (2016).ADS 
Article 

Google Scholar 
34.Wagner, C. & Adrian, R. Consequences of changes in thermal regime for plankton diversity and trait composition in a polymictic lake: A matter of temporal scale. Freshw. Biol. 56, 1949–1961 (2011).Article 

Google Scholar 
35.Rynearson, T. A., Flickinger, S. A. & Fontaine, D. N. Metabarcoding reveals temporal patterns of community composition and realized thermal niches of Thalassiosira spp. (Bacillariophyceae) from the Narragansett Bay long-term plankton time series. Biology 9, 19 (2020).CAS 
PubMed Central 
Article 

Google Scholar 
36.Delaney, M. P. Effects of temperature and turbulence on the predator–prey interactions between a heterotrophic flagellate and a marine bacterium. Microb. Ecol. 45, 218–225 (2003).CAS 
PubMed 
Article 

Google Scholar 
37.Peter, K. H. & Sommer, U. Phytoplankton cell size: Intra- and interspecific effects of warming and grazing. PLoS One 7, e49632 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
38.van Donk, E. & Kilham, S. S. Temperature effects on silicon- and phosphorus-limited growth and competitive interactions among three diatoms. J. Phycol. 26, 40–50 (1990).Article 

Google Scholar 
39.Stelzer, C.-P. Population growth in planktonic rotifers. Does temperature shift the competitive advantage for different species? In Rotifera VIII: A Comparative Approach (eds Wurdak, E. et al.) 349–353 (Springer Netherlands, 1998).Chapter 

Google Scholar 
40.Arandia-Gorostidi, N., Weber, P. K., Alonso-Sáez, L., Morán, X. A. G. & Mayali, X. Elevated temperature increases carbon and nitrogen fluxes between phytoplankton and heterotrophic bacteria through physical attachment. ISME 11, 641–650 (2017).CAS 
Article 

Google Scholar 
41.Peacock, E. E., Olson, R. J. & Sosik, H. M. Parasitic infection of the diatom Guinardia delicatula, a recurrent and ecologically important phenomenon on the New England Shelf. Mar. Ecol. Prog. Sci. 503, 1–10 (2014).ADS 
Article 

Google Scholar 
42.Käse, L. et al. Host-parasitoid associations in marine planktonic time series: Can metabarcoding help reveal them?. PLoS One 16, e0244817 (2021).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
43.Caron, D. A., Dennett, M. R., Lonsdale, D. J., Moran, D. M. & Shalapyonok, L. Microzooplankton herbivory in the Ross Sea, Antarctica. Deep Sea Res. Part II 47, 3249–3272 (2000).ADS 
Article 

Google Scholar 
44.Vidussi, F. et al. Effects of experimental warming and increased ultraviolet B radiation on the Mediterranean plankton food web. Limnol. Oceanogr. 56, 206–218 (2011).ADS 
CAS 
Article 

Google Scholar 
45.Balas, L. & Özhan, E. Three-dimensional modelling of stratified coastal waters. Estuar. Coast. Shelf Sci. 54, 75–87 (2002).ADS 
Article 

Google Scholar 
46.Dalu, T., Richoux, N. B. & Froneman, P. W. Distribution of benthic diatom communities in a permanently open temperate estuary in relation to physico-chemical variables. S. Afr. J. Bot. 107, 31–38 (2016).Article 

Google Scholar 
47.Sommer, U., Peter, K. H., Genitsaris, S. & Moustaka-Gouni, M. Do marine phytoplankton follow Bergmann’s rule sensu lato?. Biol. Rev. 92, 1011–1026 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
48.Kirk, J. T. O. Light and Photosynthesis in Aquatic Ecosystems (Cambridge University Press, 1994).Book 

Google Scholar 
49.Litchman, E., de TezanosPinto, P., Klausmeier, C. A., Thomas, M. K. & Yoshiyama, K. Linking traits to species diversity and community structure in phytoplankton. In Fifty years after the “Homage to Santa Rosalia”: Old and new paradigms on biodiversity in aquatic ecosystems (eds Naselli-Flores, L. & Rossetti, G.) 15–28 (Springer Netherlands, 2010).Chapter 

Google Scholar 
50.Unrein, F., Gasol, J. M., Not, F., Forn, I. & Massana, R. Mixotrophic haptophytes are key bacterial grazers in oligotrophic coastal waters. ISME 8, 164–176 (2014).CAS 
Article 

Google Scholar 
51.Polimene, L. et al. Modelling a light-driven phytoplankton succession. J. Plankton Res. 36, 214–229 (2014).CAS 
Article 

Google Scholar 
52.Hatzaki, M. et al. Seasonal aspects of an objective climatology of anticyclones affecting the Mediterranean. J. Clim. 27, 9272–9289 (2014).ADS 
Article 

Google Scholar 
53.Mostajir, B. et al. Experimental test of the effect of ultraviolet-B radiation in a planktonic community. Limnol. Oceanogr. 44, 586–596 (1999).ADS 
Article 

Google Scholar 
54.Lacuna, D. G. & Uye, S.-I. Influence of mid-ultraviolet (UVB) radiation on the physiology of the marine planktonic copepod Acartia omorii and the potential role of photoreactivation. J. Plankton Res. 23, 143–156 (2001).Article 

Google Scholar 
55.Halac, S. et al. An in situ enclosure experiment to test the solar UVB impact on plankton in a high-altitude mountain lake. I. Lack of effect on phytoplankton species composition and growth. J. Plankton Res. 19, 1671–1686 (1997).CAS 
Article 

Google Scholar 
56.Souchu, P. et al. Patterns in nutrient limitation and chlorophyll a along an anthropogenic eutrophication gradient in French Mediterranean coastal lagoons. Can. J. Fish. Aquat. Sci. 67, 743–753 (2010).CAS 
Article 

Google Scholar 
57.Litchman, E. & Klausmeier, C. A. Trait-based community ecology of phytoplankton. Annu. Rev. Ecol. Evol. Syst. 39, 615–639 (2008).Article 

Google Scholar 
58.Reid, P. C., Lancelot, C., Gieskes, W. W. C., Hagmeier, E. & Weichart, G. Phytoplankton of the North Sea and its dynamics: A review. Neth. J. Sea Res. 26, 295–331 (1990).Article 

Google Scholar 
59.Derolez, V. et al. Two decades of oligotrophication: Evidence for a phytoplankton community shift in the coastal lagoon of Thau (Mediterranean Sea, France). Estuar. Coast. Shelf Sci. 241, 106810 (2020).Article 

Google Scholar 
60.Yool, A., Martin, A. P., Fernández, C. & Clark, D. R. The significance of nitrification for oceanic new production. Nature 447, 999–1002 (2007).ADS 
CAS 
PubMed 
Article 

Google Scholar 
61.Constantin, S., Constantinescu, Ș & Doxaran, D. Long-term analysis of turbidity patterns in Danube Delta coastal area based on MODIS satellite data. J. Mar. Syst. 170, 10–21 (2017).Article 

Google Scholar 
62.de Jorge, V. N. & van Beusekom, J. E. E. Wind- and tide-induced resuspension of sediment and microphytobenthos from tidal flats in the Ems estuary. Limnol. Oceanogr. 40, 776–778 (1995).ADS 
Article 

Google Scholar 
63.Ubertini, M. et al. Spatial variability of benthic-pelagic coupling in an estuary ecosystem: Consequences for microphytobenthos resuspension phenomenon. PLoS One 7, e44155 (2012).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
64.Madoni, P. Benthic ciliates in Adriatic Sea lagoons. Eur. J. Protistol. 42, 165–173 (2006).PubMed 
Article 

Google Scholar 
65.Cruz, J. et al. Plankton community and copepod production in a temperate coastal lagoon: What is changing in a short temporal scale?. J. Sea Res. 157, 101858 (2020).Article 

Google Scholar 
66.Audouin, J. Hydrologie de l’étang de Thau. Rev. Trav. Inst. Pêches Marit. 26, 5–104 (1962).
Google Scholar 
67.Byun, D. S., Wang, X. H. & Holloway, P. E. Tidal characteristic adjustment due to dyke and seawall construction in the Mokpo Coastal Zone, Korea. Estuar. Coast. Shelf Sci. 59, 185–196 (2004).ADS 
Article 

Google Scholar 
68.Stefanidou, N., Genitsaris, S., Lopez-Bautista, J., Sommer, U. & Moustaka-Gouni, M. Unicellular eukaryotic community response to temperature and salinity variation in mesocosm experiments. Front. Microbiol. 9, 2444 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
69.Xu, N. et al. Effects of temperature, salinity and irradiance on the growth of the harmful dinoflagellate Prorocentrum donghaiense Lu. Harmful Algae 9, 13–17 (2010).Article 

Google Scholar 
70.Greenwald, G. M. & Hurlbert, S. H. Microcosm analysis of salinity effects on coastal lagoon plankton assemblages. In Saline Lakes V (ed. Hurlbert, S. H.) 307–335 (Springer Netherlands, 1993).Chapter 

Google Scholar 
71.Fiandrino, A., Giraud, A., Robin, S. & Pinatel, C. Validation d’une méthode d’estimation des volumes d’eau échangés entre la mer et les lagunes et définition d’indicateurs hydrodynamiques associés (2012).72.Mostajir, B., Mas, S., Parin, D. & Vidussi, F. High-Frequency physical, biogeochemical and meteorological data of Coastal Mediterranean Thau Lagoon Observatory. SEANOE (2018).73.Données Publiques de Météo-France—Accueil. https://donneespubliques.meteofrance.fr/.74.Kraberg, A., Baumann, M. & Dürselen, C.-D. Coastal phytoplankton: Photo guide for Northern European seas (Univerza v Ljubljani, 2010).
Google Scholar 
75.Bérard-Therriault, L., Poulin, M. & Bossé, L. Guide d’identification du phytoplancton marin de l’estuaire et du golfe du Saint-Laurent incluant également certains protozoaires Canadian Special Publication of Fisheries and Aquatic Sciences No. 128 (NRC Research Press, 1999).
Google Scholar  More

1.EFSA. Effectiveness of in planta control measures for Xylella fastidiosa. EFSA J. 17(5). https://doi.org/10.2903/j.efsa.2019.5666 (2019).2.Hopkins, D. L. Xylella fastidiosa: Xylem-limited bacterial pathogen of plants. Annu. Rev. Phytopathol. 27(1), 271–290. https://doi.org/10.1146/annurev.py.27.090189.001415 (1989).Article 

Google Scholar 
3.Saponari, M., Boscia, D., Nigro, F. & Martelli, G. P. Identification of Dna sequences related to Xylella fastidiosa in oleander, almond and olive trees exhibiting leaf scorch symptoms in Apulia (southern Italy). J. Plant Pathol. 95(3), 668. https://doi.org/10.4454/JPP.V95I3.035 (2013).Article 

Google Scholar 
4.EPPO. Xylella fastidiosa in EPPO region. EPPO Bulletin. 49(2) (2019).5.Fierro, A., Liccardo, A. & Porcelli, F. A lattice model to manage the vector and the infection of the Xylella fastidiosa on olive trees. Sci. Rep. 9, 8723. https://doi.org/10.1038/s41598-019-44997-4 (2019).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
6.Saponari, M., Giampetruzzi, A., Loconsole, G., Boscia, D. & Saldarelli, P. Xylella fastidiosa in olive in apulia: Where we stand. Phytopathology 109(2), 175–186. https://doi.org/10.1094/PHYTO-08-18-0319-FI (2019).CAS 
Article 
PubMed 

Google Scholar 
7.Mannino, M. R. et al. Horizon scanning for plant health: Report on 2017–2020 activities. EFSA Support. Publ. https://doi.org/10.2903/sp.efsa.2021.EN-2010 (2021).Article 

Google Scholar 
8.EFSA. Scientific opinion on the risks to plant health posed by Xylella fastidiosa in the EU territory, with the identification and evaluation of risk reduction options. EFSA J. 13(1), 3989. https://doi.org/10.2903/j.efsa.2015.3989 (2015).9.Cornara, D. et al. An overview on the worldwide vectors of Xylella fastidiosa. Entomol. Gen. 39(3–4), 157–181. https://doi.org/10.1127/entomologia/2019/0811 (2019).Article 

Google Scholar 
10.Finke, D. L. Contrasting the consumptive and non-consumptive cascading effects of natural enemies on vector-borne pathogens. Entomol. Exp. Appl. 144, 45–55. https://doi.org/10.1111/j.1570-7458.2012.01258.x (2012).Article 

Google Scholar 
11.Martini, X., Hoffmann, M., Coy, M. R., Stelinski, L. L. & Pelz-Stelinski, K. S. Infection of an insect vector with a bacterial plant pathogen increases its propensity for dispersal. PLoS ONE 10(6), 1–16. https://doi.org/10.1371/journal.pone.0129373 (2015).CAS 
Article 

Google Scholar 
12.Almeida, R. P. P. et al. Addressing the new global threat of Xylella fastidiosa. Phytopathology 109(2), 172–174. https://doi.org/10.1094/PHYTO-12-18-0488-FI (2019).CAS 
Article 
PubMed 

Google Scholar 
13.Cornara, D., Bosco, D. & Fereres, A. Philaenus spumarius: When an old acquaintance becomes a new threat to European agriculture. J. Pest. Sci. 91(3), 957–972. https://doi.org/10.1007/s10340-018-0966-0 (2018).Article 

Google Scholar 
14.Halkka, O., Raatikainen, M., Vasarainen, A. & Heinonen, L. Ecology and ecological genetics of Philaenus spumarius (L.) (Homoptera). Ann. Zool. Fenn. 4, 1–18 (1967).
Google Scholar 
15.Lavigne, R. Biology of Philaenus leucophthalmus (L.) in Massachusetts. J. Econ. Entomol. 52(5), 904–907. https://doi.org/10.1093/jee/52.5.904 (1959).Article 

Google Scholar 
16.Ossiannilsson, F. The Auchenorrhyncha (Homoptera) of Fennoscandia and Denmark. Part 2: The families Cicadidae, Cercopidae, Membracidae, and Cicadellidae (excl. Deltocephalinae). Fauna Entomol. Scand. 7(2), 223–593 (1981).
Google Scholar 
17.Weaver, C. R. The seasonal behavior of meadow spittlebug and its relation to a control method. J. Econ. Entomol. 44(3), 350–353. https://doi.org/10.1093/jee/44.3.350 (1951).MathSciNet 
CAS 
Article 

Google Scholar 
18.Weaver, C. R. & King, D. R. Meadow spittlebug, Philaenus leucophthalmus (L.). Research Bulletin; Ohio Agricultural Experiment Station. (ed. Wooster, OH, USA, 1954).19.Drosopoulos, S. & Asche, M. Biosystematic studies on the spittlebug genus Philaenus with the description of a new species. Zool. J. Linn. Soc. 101(2), 169–177. https://doi.org/10.1111/j.1096-3642.1991.tb00891.x (2008).Article 

Google Scholar 
20.Grant, J. F., Lambdin, P. L. & Folium, R. A. Infestation levels and seasonal incidence of the meadow spittlebug (Homoptera: cercopidae) on musk thistle in Tennessee. J. Agric. Urban Entomol. 15, 83–91 (1998).
Google Scholar 
21.Halkka, O. Equilibrium populations of Philaenus spumarius L. Nature 193(4810), 93–94. https://doi.org/10.1038/193093a0 (1962).ADS 
Article 

Google Scholar 
22.Freeman, J. A. Studies in the distribution of insects by Aerial currents. J. Anim. Ecol. 14, 128 (1945).Article 

Google Scholar 
23.Reynolds, D. R., Chapman, J. W. & Stewart, A. J. A. Windborne migration of Auchenorrhyncha (Hemiptera) over Britain. Eur. J. Entomol. 114, 554–564. https://doi.org/10.14411/eje.2017.070 (2017).Article 

Google Scholar 
24.Gutierrez, A. P., Nix, H. A., Havenstein, D. E. & Moore, P. A. The ecology of Aphis Craccivora Koch and subterranean clover stunt virus in south-east Australia. III. A regional perspective of the phenology and migration of the Cowpea Aphid. J. Appl. Ecol. 11(1), 21–35. https://doi.org/10.2307/2402002 (1974).Article 

Google Scholar 
25.Pienkowski, R. L. & Medler, J. T. Synoptic weather conditions associated with long-range movement of the potato leafhopper, Empoasca fabae, into Wisconsin. Ann. Entomol. Soc. Am. 57(5), 588–591. https://doi.org/10.1093/aesa/57.5.588 (1964).Article 

Google Scholar 
26.Drake, V. A. Radar observations of moths migrating in a nocturnal low-level jet. Ecol. Entomol. 10(3), 259–265. https://doi.org/10.1111/j.1365-2311.1985.tb00722.x (1985).Article 

Google Scholar 
27.Wallin, J. R. & Loonan, D. V. Low-level jet winds, aphid vectors, local weather, and barley yellow dwarf virus outbreaks. Phytopathology 61(9), 1068. https://doi.org/10.1094/PHYTO-61-1068 (1971).Article 

Google Scholar 
28.Sedlacek, J. D. & Freytag, P. H. Aspects of the field biology of the Blackfaced Leafhopper (Homoptera: Cicadellidae) in corn and pastures in Kentucky. J. Econ. Entomol. 79(3), 605–613. https://doi.org/10.1093/jee/79.3.605 (1986).Article 

Google Scholar 
29.Zhu, M., Radcliffe, E. B., Ragsdale, D. W., MacRae, I. V. & Seeley, M. W. Low-level jet streams associated with spring aphid migration and current season spread of potato viruses in the U.S. northern Great Plains. Agric. For. Meteorol. 138(1–4), 192–202. https://doi.org/10.1016/j.agrformet.2006.05.001 (2006).ADS 
Article 

Google Scholar 
30.Bodino, N. et al. Dispersal of Philaenus spumarius (Hemiptera: Aphrophoridae), a vector of Xylella fastidiosa, in olive grove and meadow agroecosystems. Environ. Entomol. https://doi.org/10.1093/ee/nvaa140 (2020).Article 
PubMed Central 

Google Scholar 
31.Lago, C. et al. Dispersal of Neophilaenus campestris, a vector of Xylella fastidiosa, from olive groves to over-summering hosts. J. Appl. Entomol. https://doi.org/10.1111/jen.12888 (2021).Article 

Google Scholar 
32.Minter, M. et al. The tethered flight technique as a tool for studying life-history strategies associated with migration in insects. Ecol. Entomol. 43(4), 397–411. https://doi.org/10.1111/een.12521 (2018).Article 
PubMed 
PubMed Central 

Google Scholar 
33.Ávalos-Masó, J. A., Martí-Campoy, A. & Soto, T. A. Study of the flying ability of Rhynchophorus ferrugineus (Coleoptera: Dryophthoridae) adults using a computer-monitored flight mill. Bull. Entomol. Res. 104(4), 462–470. https://doi.org/10.1017/S0007485314000121 (2014).Article 

Google Scholar 
34.Yu, E. Y., Gassmann, A. J. & Sappington, T. W. Using flight mills to measure flight propensity and performance of western corn rootworm, diabrotica virgifera virgifera (Leconte). J. Vis. Exp. 152, e59196. https://doi.org/10.3791/59196 (2019).Article 

Google Scholar 
35.Riley, J. R., Downham, M. C. A. & Cooter, R. J. Comparison of the performance of Cicadulina leafhoppers on flight mills with that to be expected in free flight. Entomol. Exp. Appl. 83(3), 317–322. https://doi.org/10.1046/j.1570-7458.1997.00186.x (1997).Article 

Google Scholar 
36.Zhang, Y., Wang, L., Wu, K., Wyckhuys, K. A. G. & Heimpel, G. E. Flight performance of the Soybean Aphid, Aphis glycines (Hemiptera: Aphididae) under different temperature and humidity regimens. Environ. Entomol. 37(2), 301–306. https://doi.org/10.1603/0046-225X(2008)37[301:FPOTSA]2.0.CO;2 (2008).Article 
PubMed 

Google Scholar 
37.Cheng, Y., Luo, L., Jiang, X. & Sappington, T. Synchronized oviposition triggered by migratory flight intensifies larval outbreaks of beet. PLoS ONE https://doi.org/10.1371/journal.pone.0031562 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
38.Jones, C. M. et al. Genomewide transcriptional signatures of migratory flight activity in a globally invasive insect pest. Mol. Ecol. 24(19), 4901–4911. https://doi.org/10.1111/mec.13362 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
39.White, S. M., Bullock, J. M., Hooftman, D. A. P. & Chapman, D. S. Modelling the spread and control of Xylella fastidiosa in the early stages of invasion in Apulia, Italy. Biol. Invasions 19(6), 1825–1837. https://doi.org/10.1007/s10530-017-1393-5 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
40.Jones, V. P., Naranjo, S. E. & Smith, T. J. Insect ecology and behavior: Laboratory flight mill studies. Accessed 22 July 2021. (2010). http://entomology.tfrec.wsu.edu/VPJ_Lab/Flight-Mill41.Martí-Campoy, A. et al. Design of a computerised flight mill device to measure the flight potential of different insects. Sensors (Switzerland) 16(4), 485. https://doi.org/10.3390/s16040485 (2016).ADS 
Article 

Google Scholar 
42.Kees, A. M., Hefty, A. R., Venette, R. C., Seybold, S. J. & Aukema, B. H. Flight capacity of the walnut twig beetle (coleoptera: Scolytidae) on a laboratory flight mill. Environ. Entomol. 46(3), 633–641. https://doi.org/10.1093/ee/nvx055 (2017).Article 
PubMed 

Google Scholar 
43.Morente, M. et al. Distribution and relative abundance of insect vectors of Xylella fastidiosa in olive groves of the Iberian peninsula. Insects 9(4), 175. https://doi.org/10.3390/insects9040175 (2018).Article 
PubMed Central 

Google Scholar 
44.Morente, M., Cornara, D., Moreno, A. & Fereres, A. Continuous indoor rearing of Philaenus spumarius, the main European vector of Xylella fastidiosa. J. Appl. Entomol. 142(9), 901–904. https://doi.org/10.1111/jen.12553 (2018).Article 

Google Scholar 
45.Guthery, F. S., Burnham, K. P. & Anderson, D. R. Model Selection and multimodel inference: A practical information-theoretic approach. J. Wildl. Manag. 67, 655 (2003).Article 

Google Scholar 
46.Zuur, A. F., Ieno, E. N., Walker, N. J., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R. (ed. Springer Sci. Bus. Media, 2009).47.Strona, G., Carstens, C. J. & Beck, P. S. A. Network analysis reveals why Xylella fastidiosa will persist in Europe. Sci. Rep. 7(1), 1–8. https://doi.org/10.1038/s41598-017-00077-z (2017).CAS 
Article 

Google Scholar 
48.Whittaker, J. B. Density regulation in a population of Philaenus spumarius (L.) (Homoptera: Cercopidae). J. Anim. Ecol. 42(1), 163–172. https://doi.org/10.2307/3410 (1973).Article 

Google Scholar 
49.Wiman, N. G., Walton, V. M., Shearer, P. W., Rondon, S. I. & Lee, J. C. Factors affecting flight capacity of brown marmorated stink bug, Halyomorpha halys (Hemiptera: Pentatomidae). J. Pest Sci. 88(1), 37–47. https://doi.org/10.1007/s10340-014-0582-6 (2015).Article 

Google Scholar 
50.Strona, G. et al. Small world in the real world: Long distance dispersal governs epidemic dynamics in agricultural landscapes. Epidemics 30, 100384. https://doi.org/10.1016/j.epidem.2020.100384 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
51.Irwin, M. E. & Tresh, J. M. Long-range aerial dispersal of cereal aphids as virus vectors in North America. Philos. Trans. R. Soc. London. B Biol. Sci. 321(1207), 421–446. https://doi.org/10.1098/rstb.1988.0101 (1988).ADS 
Article 

Google Scholar 
52.Chapman, J. W., Reynolds, D. R. & Wilson, K. Long-range seasonal migration in insects: Mechanisms, evolutionary drivers and ecological consequences. Ecol. Lett. 18(3), 287–302. https://doi.org/10.1111/ele.12407 (2015).Article 
PubMed 

Google Scholar 
53.Fereres, A., Irwin, M. E. & Kampmeier, G. E. Aphid movement: Process and consecuences. in Aphids as crop pests. (ed.2 Emden, H. F. van, Harrington, R.). 196–224. https://doi.org/10.1079/9781780647098.0196 (CABI Publishing, 2017).54.Petrovskii, S., Mashanova, A. & Jansen, V. A. A. Variation in individual walking behavior creates the impression of a Levy flight. PNAS 108, 8704–8707. https://doi.org/10.1073/pnas.1015208108 (2011).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
55.Okano, K. Sublethal effects of a neonicotinoid insecticide on the sharpshooter vectors of Xylella fastidiosa. Doctoral dissertation (UC Berkeley, 2009).56.Robinet, C., David, G. & Jactel, H. Modeling the distances traveled by flying insects based on the combination of flight mill and mark-release-recapture experiments. Ecol. Modell. 402, 85–92. https://doi.org/10.1016/j.ecolmodel.2019.04.006 (2019).Article 

Google Scholar 
57.Taylor, R. A. J., Bauer, L. S., Poland, T. M. & Windell, K. N. Flight performance of agrilus planipennis (Coleoptera: Buprestidae) on a flight mill and in free flight. J. Insect Behav. 23(2), 128–148. https://doi.org/10.1007/s10905-010-9202-3 (2010).Article 

Google Scholar 
58.Srygley, R. B. & Lorch, P. D. Coping with uncertainty: Nutrient deficiencies motivate insect migration at a cost to immunity. Integr. Comp. Biol. 53, 1002–1013. https://doi.org/10.1093/icb/ict047 (2013).CAS 
Article 
PubMed 

Google Scholar 
59.Nilakhe, S. S. & Buainain, C. M. Observations on movement of spittlebug adults. Pesqui. Agropecuária Bras. Brasília 23, 123–134 (1988).
Google Scholar 
60.Neuman-Lee, L. A., Hopkins, G. R., Brodie, E. D. & French, S. S. Sublethal contaminant exposure alters behavior in a common insect: Important implications for trophic transfer. J. Environ. Sci. Heal. Part B Pestic. Food Contam. Wastes 48(6), 442–448. https://doi.org/10.1080/03601234.2013.761839 (2013).CAS 
Article 

Google Scholar 
61.Wilson, D. M. The central nervous control of flight in a locust. J. Exp. Biol. 38(2), 471–490 (1961).Article 

Google Scholar 
62.Yamanaka, T., Tatsuki, S. & Shimada, M. Flight characteristics and dispersal patterns of fall webworm (Lepidoptera: Arctiidae) males. Environ. Entomol. 30(6), 1150–1157. https://doi.org/10.1603/0046-225X-30.6.1150 (2001).Article 

Google Scholar 
63.Blackmer, J. L., Hagler, J. R., Simmons, G. S. & Henneberry, T. J. Dispersal of Homalodisca vitripennis (Homoptera: Cicacellidae) from a point release site in citrus. Environ. Entomol. 35(6), 1617–1625. https://doi.org/10.1093/ee/35.6.1617 (2006).Article 

Google Scholar 
64.Bodino, N. et al. Phenology, seasonal abundance and stage-structure of spittlebug (Hemiptera: Aphrophoridae) populations in olive groves in Italy. Sci. Rep. 9(1), 1–17. https://doi.org/10.1038/s41598-019-54279-8 (2019).CAS 
Article 

Google Scholar 
65.Minuz, R. L., Isidoro, N., Casavecchia, S., Burgio, G. & Riolo, P. Sex-dispersal differences of four phloem-feeding vectors and their relationship to wild-plant abundance in vineyard agroecosystems. J. Econ. Entomol. 106(6), 2296–2309. https://doi.org/10.1603/ec13244 (2013).CAS 
Article 
PubMed 

Google Scholar 
66.Waloff, N. Dispersal by flight of leafhoppers (Auchenorrhyncha: Homoptera). J. Appl. Ecol. 10, 705 (1973).Article 

Google Scholar 
67.Johnson, C. G. Physiological factors in insect migration by flight. Nature 198(4879), 423–427. https://doi.org/10.1038/198423a0 (1963).ADS 
Article 

Google Scholar 
68.Drake, V. A. & Gatehouse, A. G. Insect Migration. Tracking Resources through Space and Time. (ed. Cambridge University Press). 7(3) Cambridge UK. https://doi.org/10.1007/s10841-006-9039-4 (1995).69.Sappington, T. W. & Showers, W. B. Reproductive maturity, mating status, and long-duration flight behavior of agrotis ipsilon (Lepidoptera: Noctuidae) and the conceptual misuse of the oogenesis flight syndrome by entomologists. Environ. Entomol. 21(4), 677–688. https://doi.org/10.1093/ee/21.4.677 (1992).Article 

Google Scholar 
70.Zhao, X. C. et al. Does the onset of sexual maturation terminate the expression of migratory behaviour in moths? A study of the oriental armyworm, Mythimna separata. J Insect Physiol. 55(11), 1039–432009. https://doi.org/10.1016/j.jinsphys.2009.07.007 (2009).CAS 
Article 
PubMed 

Google Scholar 
71.Tigreros, N. & Davidowitz, G. Flight-fecundity tradeoffs in wing-monomorphic insects. Adv. Insect Phys. 56, 1–41. https://doi.org/10.1016/bs.aiip.2019.02.001 (2019).Article 

Google Scholar 
72.Drake, V. A. & Farrow, R. A. The influence of atmospheric structure and motions on insect migration. Ann. Rev. Entomol. 33(1), 183–210. https://doi.org/10.1146/annurev.en.33.010188.001151 (1988).Article 

Google Scholar 
73.Burt, P. J. A. & Pedgley, D. E. Nocturnal insect migration: Effects of local winds. Adv. Ecol. Res. 27, 61–92. https://doi.org/10.1016/S0065-2504(08)60006-9 (1997).Article 

Google Scholar 
74.Gordh, G. & McKirdy, S. The Handbook of Plant Biosecurity (Springer, 2014). https://doi.org/10.1007/978-94-007-7365-3Book 

Google Scholar  More

SubjectsIn total, twenty-two black-capped chickadees (nine males and 13 females) were tested between May and December 2019, and 16 black-capped chickadees (seven males and nine females) completed the experiment. One male and one female failed to learn Pretraining, and one female failed to learn Non-differential training (see descriptions below for training information); as a result, all three were removed from the experiment. In addition, one male and two females died of natural causes during the course of the study (see Ethics Declaration). For all birds, sex was determined by deoxyribonucleic acid analysis of blood samples37. All birds were captured in Edmonton (North Saskatchewan River Valley, 53.53°N, 113.53°W; Mill Creek Ravine, 53.52°N, 113.47°W), Alberta, Canada in January 2018 and January 2019 and were at least one year of age at capture, verified by examining outer tail rectrices38.Prior to the current experiment, all chickadees were individually housed in Jupiter Parakeet cages (30 × 40 × 40 cm; Rolf C. Hagen, Inc., Montreal, QB, Canada) in a single colony room. Therefore, birds did not have physical contact with each other, but did have visual and auditory contact. Birds had ad libitum access to food (Mazuri Small Bird Maintenance Diet; Mazuri, St. Louis, MO, USA), water with vitamins supplemented on alternating days (Monday, Wednesday, Friday; Prime Vitamin Supplement; Hagen, Inc.), a cup containing grit, and a cuttlebone. Additional nutritional supplements included three to five sunflower seeds daily, one superworm (Zophobas morio) three times a week, and a mixture of hard-boiled eggs and greens (spinach or parsley) twice a week. The colony rooms were maintained at approximately 20 °C and on a light:dark cycle that followed the natural light cycle for Edmonton, Alberta, Canada.One bird had previous experience with one operant experiment involving chick-a-dee calls but showed no difference in responding in comparison to the naïve birds. The remaining 15 birds had no previous experimental experience with black-capped chickadee-produced fee-bee songs or any experimental paradigm.Recordings of acoustic stimuliThe following acoustic stimuli were used in our previous published operant study which indicated that male and female chickadees can identify individual females via their song36. Stimuli included the songs of six female black-capped chickadees. All females were captured in Edmonton (North Saskatchewan River Valley, 53.53°N, 113.53°W; Mill Creek Ravine, 53.52°N, 113.47°W), Alberta, Canada in January 2010, 2011, 2012, and 2014, and all females were at least one year of age at capture, verified by examining outer tail rectrices38. Four females were recorded in Spring 2012 and two females were recorded in Fall 2014. Each recording session lasted approximately 1 h and all recordings took place after colony lights turned on at 08:00, specifically at 8:15. All females were recorded in silence, individually, within their respective colony room cages. Colony room cages were placed in sound-attenuating chambers for recording (1.7 m × 0.84 m × 0.58 m; Industrial Acoustics Company, Bronx, NY). An AKG C 1000S (AKG Acoustics, Vienna, Austria) microphone (positioned 0.1 m above and slightly behind the cage) was connected to a Marantz PMD670 (Marantz America, Mahwah, NJ) digital recorder (16 bit, 44,100 Hz sampling rate) and was used for all recordings. Audio recordings were analyzed and cut into individual files (songs) using SIGNAL 5.03.11 software (Engineering Design, Berkley, CA, USA).Acoustic stimuliFor the current study, a total of 150 vocalizations were used as stimuli, these vocalizations were comprised of 25 fee-bee songs produced by each of six recorded female chickadees. We ensured that all 150 were of high quality, meaning no audible interference, and all stimuli were bandpass filtered (lower bandpass 500 Hz, upper bandpass 14,000 Hz) using GoldWave version 6.31 (GoldWave, Inc., St. John’s, NL, Canada) in order to reduce any background noise outside of the song stimuli spectrum. For each song stimulus, 5 ms of silence was added to the leading and trailing portion of the vocalization and each stimulus was tapered to remove transients, in addition amplitude was equalized peak to peak using SIGNAL 5.03.11 software. When triggered, stimuli were presented at approximately 75 dB peak SPL as measured by a calibrated Brüel & Kjær Type 2239 (Brüel & Kjær Sound & Vibration Measurement A/S, Nærum, Denmark) sound pressure meter (A-weighting, slow response), a level that corresponds with the natural chickadee vocalizations amplitudes39,40,41. All dB measurements were made at the level of the request perch where birds trigger stimuli and where birds are required to remain for the length of the stimuli and all dB measurements refer to SPL.Noise stimuliAnthropogenic noise stimuli were originally created and used by Potvin and MacDougall-Shackleton42 and by Potvin, Curcio, Swaddle, and MacDougall-Shackleton43. The stimuli were recorded from an urban area in Melbourne, Victoria, Australia and other anthropogenic noise stimuli of various trains, cars, motorcycles, and lawnmowers downloaded from Soundbible.com were used. Within Victoria44 and Alberta45,46, urban traffic noise averages 60–80 dB SPL. The files used varied in length, with those recorded in Melbourne all being 10 min in length and those downloaded from Soundbible.com varying between 1–10 minutes42,43. In total 10 tracks were used with 30 total minutes of noise stimuli. Three anthropogenic noise conditions were used in the study, including Silence (no noise), Low noise (anthropogenic noise stimuli played at ~ 40 dB peak SPL), and High noise (anthropogenic noise stimuli played at ~ 75 dB peak SPL) replicating the variation of traffic noise experienced in urban areas42,43. For the Low and High noise conditions the 10 tracks repeated on a randomized loop during data collection (natural light of light/dark cycle) with, thus noise exemplars overlapped songs by chance, to further emulate urban areas. Noise stimuli had natural variations and modulations in frequency and amplitude over the course of the sound files. All dB measurements for noise stimuli included in this study refer to SPL. See Fig. 1 for female song and traffic noise stimuli spectrograms and power spectra.Figure 1(A) Spectrogram of a female fee-bee song in silence. (B) Power spectrum of female fee-bee song in silence . (C) Spectrogram of female fee-bee song in low noise. (D) Power spectrum of female fee-bee song (black) in low noise (grey). (E) Spectrogram of female fee-bee song in high noise. (F) Power spectrum of female fee-bee song (black) in high noise (grey).Full size imageApparatusFor the duration of the experiment, birds were housed individually in modified colony room cages (30 × 40 × 40 cm; described above) which were placed inside a ventilated, sound-attenuating operant chamber. See Fig. 2 for illustration of operant conditioning chamber. All chambers were lit with a full spectrum LED bulb (3 W, 250 lm E26, Not-Dim, 5000 K; Lohas LED, Chicago, IL, USA), and maintained the natural light:dark cycle for Edmonton, Alberta. Each cage within each operant chamber contained two perches and an additional perch fitted with an infrared sensor (i.e., the request perch). See Fig. 2C. Each cage also contained a water bottle, grit cup, and cuttlebone See Fig. 2G-2H. Birds had ad libitum access to water (with vitamins supplemented on alternating days; Monday, Wednesday, Friday), grit, and cuttlebone and were provided two superworms daily (a morning and afternoon worm). An opening (11 × 16 cm) located on the left side of the cage allowed the birds to access a motorized feeder, with a red LED light, and equipped with an infrared sensor47. See Fig. 2B,D–F. The purpose of the sensor was so that food was only available as a reward for correct responses to auditory stimuli during the operant discrimination task. We should note that performance of the discrimination task is required for access to food and thus maintains motivation. For operation and data collection, a personal computer connected to a single-board computer48 scheduled trials and recorded responses to stimuli. Stimuli were played from a personal computer hard drive through a Cambridge Integrated Amplifier (model A300 or Azur 640A; Cambridge Audio, London, England). Data is downloaded once a day in order to reduce stress on subjects as all equipment must be tested following download, requiring contact with subjects. Stimuli played in the chamber through a Fostex full-range speaker (model FE108 Σ or FE108E Σ; Fostex Corp., Japan; frequency response range 80–18,000 Hz) located beside the feeder. See Sturdy and Weisman49 for a detailed description of the apparatus. See Fig. 2 for an illustration of the operant conditioning chamber set-up.Figure 2Illustration of the operant conditioning chamber, including: (A) speaker, (B) automated feeder, (C) request perch fitted with infrared photo-beam assembly, (D) feeder cup, (E) electrical inputs, (F) red LED, (G) water bottle, (H) and cuttlebone. Also shown is the feeder opening, and additional perches. To simplify, the sketch the front and floor of the chamber, and the enclosure’s acoustic lining are not included.Full size imageProcedureOperant conditioningOur current operant conditioning go/no-go set-up is used to understand how birds perceive auditory stimuli. By training the birds to respond to particular stimuli and withhold responding to other stimuli we can compare responses to both types of stimuli. The go/no-go paradigm requires the birds to learn which stimuli require correct responses (go), providing reinforcement (food), and which stimuli require birds to withhold responding (no-go), resulting in the avoidance of punishment (lights out).The current study follows nine stages, after learning to use the operant conditioning set-up, birds then go through Non-differential training (stage 1) where they will be exposed to all stimuli that will be used in the experiment and to ensure that the birds respond to the stimuli equivalently. Then birds complete Discrimination training (stage 2) where birds on two categories of sounds. One category is rewarded, the other category is punished. Then the Discrimination-85 (stage 3) phase prepares birds for future trials where there is no reward nor punishment. After this point, birds will follow three series (Silence; Low; High) of Discrimination-85 with noise (stage 4, 6, 8) and a corresponding Probe with noise (stage 5, 7, 9), meaning that that each subject will repeated the two discrimination tasks three times with different noise conditions, with the order of noise conditions randomized among individuals. The detailed procedures for each stage are described in the following.Non-differential trainingThe purpose of Non-differential training is to engender a high level of responding on all trials, across all stimuli. Once a bird learned to use the request perch fitted with a sensor as well as learned to use the feeder to obtain food then Pretraining began. During Pretraining, birds were trained to respond to a 1 s tone (1,000 Hz) in order to receive access to food. Pretraining occurred over an approximately 15-day period in order to allow acclimatization to the chamber, feeder, and speaker. Following Pretraining was Non-differential training. During Non-differential training, birds received food for responding to all fee-bee song stimuli. All trials began when a bird landed on the request perch and remained on the perch for between 900–1100 ms, at which point a randomly-selected song stimulus played. Songs were presented in random order from trial to trial until all 150 stimuli had been triggered and played without replacement; once all 150 stimuli were played, a new random sequence initiated. In the event that the bird left the request perch during a stimulus presentation, the trial was deemed interrupted, and resulted in a 30 s lights out of the operant chamber. If the bird entered the feeder within 1 s after the stimulus (any stimulus) was played, it was given 1 s access to food, followed by a 30 s intertrial interval. If a bird remained on the request perch during the stimulus presentation and the 1 s following the completion of the stimulus, then the bird received a 60 s intertrial interval with the lights on. Birds continued on Non-differential training until they completed six 450-trial blocks at ≥ 60% responding on average to all stimuli, at least four 450-trial blocks at ≤ 3% difference in responding to future rewarded versus future unrewarded Discrimination stimuli, at least four 450-trial blocks at ≤ 3% difference in responding to future rewarded versus unrewarded Discrimination stimuli. Then following a day of free feed (during which birds had ad libitum access to a food cup) birds completed a second round of Non-differential training in which they completed at least one 450-trial block that met each of the above requirements. A 450-trial block consisted of the bird experiencing each of the 150 stimuli three times. For the current study the average time to complete Non-differential training ranged from 10 to 41 days (M = 21.43, SD = 9).Discrimination trainingDiscrimination training procedures included only 114 out of the 150 training stimuli that were previously presented in non-differential training, and responses to these stimuli were now differentially reinforced. Specifically, correct responses to half of the stimuli (“rewarded stimuli”, S+) were positively reinforced with 1 s access to food, and incorrect responses to the other half (“unrewarded stimuli”, S−) were instead punished with a 30-s intertrial interval of lights off within the operant chamber. In regard to criterion, Discrimination training continued until a bird completed six 342-trial blocks with a discrimination ratio between their respective S+ and S− of greater than 0.80 with the last two blocks being consecutive. For discrimination ratio calculations see Response Measures below.The current subjects were randomly assigned to either a True category discrimination group (n = 10) or Pseudo category discrimination group (n = 6). Furthermore, chickadees in the True category discrimination group were divided into two subgroups: (a) True 1 (n = 5; three females and two males) discriminated between 57 rewarded fee-bee songs produced by three individual female chickadees (S+) and 57 unrewarded fee-bee songs produced by another three individual female chickadees (S−); and (b) True 2 (n = 5; two females and three males) discriminated between the same songs with opposite rewards, properly, the 57 rewarded (S+) fee-bee songs were the S− from True 1 and the 57 unrewarded (S−) fee-bee songs were the S+ from True 1. For birds in the True category discrimination the average number of blocks completed per day for Discrimination training ranged from 2.4–4.4 blocks (3.3 ± 0.7 blocks).In similitude, the Pseudo category discrimination group was divided into two subgroups: (a) Pseudo 1 (n = 3; two females and one male) discriminated between 57 randomly-selected rewarded (S+) fee-bee songs and 57 randomly-selected unrewarded (S−) fee-bee songs; and (b) the second subgroup Pseudo 2 (n = 3; one female and two males) discriminated between the same songs with opposite rewards, meaning, the 57 rewarded (S+) fee-bee songs were the S− from Pseudo 1 and the 57 unrewarded (S−) fee-bee songs were the S+ from Pseudo 1 (S+) fee-bee songs and 57 randomly-selected unrewarded (S−) fee-bee songs. To explicate, the purpose of the two Pseudo groups was to include a control in which subjects are required to memorize each vocalization independent of the producer rather than be trained to categorize songs according to individual chickadees as the True groups have been. All birds remained in their respective groups (True 1 and 2; Pseudo 1 and 2) for the duration of the study. For birds in the Pseudo category discrimination the average number of blocks completed per day for Discrimination training ranged from 3.3–6.1 blocks (4.34 ± 1.2 blocks).Discrimination-85 phaseDiscrimination-85 was identical to the above Discrimination training except that rewarded songs were reinforced with a reduced probability, P = 0.85. Therefore, for 15% of trials when a rewarded stimulus was played and a bird correctly responded, no access to food was triggered. Instead, a 30 s lights on intertrial interval occurred. The change in reinforcement occurs in order to prepare birds for Probe trials in which novel song stimuli were neither rewarded with access to food nor unrewarded with a lights out, instead nothing occurs. Discrimination-85 continued until birds completed two consecutive 342-trial blocks with a discrimination ratio of at least 0.80.Discrimination-85 phase with noiseAll subjects followed three series (Silence; Low; High) of Discrimination-85 with noise and a corresponding Probe with noise and the order of noise stimuli was randomly-selected for each bird. Discrimination-85 with noise was identical to the Discrimination-85 phase except one of the three noise stimuli conditions (Silence; Low noise, 40 dB SPL; High noise, 75 dB SPL) was played over the song stimuli. The noise stimuli condition was randomly-selected for each bird. Each bird went through three series of Discrimination-85 with noise (Silence; Low; High) until reaching criteria: two consecutive 342-trial blocks with a discrimination ratio of at least 0.80. Here, we were interested in how the addition of noise would impact discrimination between rewarded and unrewarded female song stimuli.Probe phase with noiseFollowing each Discrimination-85 phase with noise was a corresponding Probe phase with noise. During Probe the reinforcement contingencies from Discrimination-85 were maintained. In addition to the 114 stimuli from Discrimination training, this stage included 12 novel fee-bee songs (i.e., Probe stimuli), two from each of the six individual females. For True groups, six of these novel songs were categorized as P + and the other six as P-, based on whether they were produced by the same birds as the S+ or the S− training stimuli. For Pseudo groups, the novel songs were not assigned to categories. For both groups, the 12 novel stimuli were neither rewarded (no food access) nor unrewarded (no lights out). The birds completed six 126-trial blocks in which the 114 familiar discrimination stimuli repeated once per block and the 12 probe sequences played once per block. In addition, one of the three noise stimuli conditions (Silence; Low noise, 40 dB SPL; High noise, 75 dB SPL) was played over the song stimuli, and each bird went through three series of Probe with noise (Silence; Low; High) which corresponded with the birds previous Discrimination-85 phase with noise condition. Thus, all birds completed all three Discrimination-85 phases with noise conditions followed by the corresponding Probe with noise conditions, and the order of noise stimuli condition was randomly-selected for each bird. In Probe phases we are interested if subjects can categorize novel stimuli to previously rewarded or unrewarded female birds.Response measuresFor each 342-block trial during training (Discrimination-85 with noise; Probe with noise), proportion response was calculated (R + /(N-I)): R + represents the number of trials in which the bird went to the feeder, N represents the total number of trials, and I represents the number of interrupted trials in which the bird left the perch before the entire stimulus played. For Discrimination training and the Discrimination-85 phase, a discrimination ratio was calculated by dividing the mean proportion response to all S+ stimuli by the mean proportion response to S+ stimuli plus the mean proportion response to S− stimuli. A discrimination ratio = 0.50 specifies equal response to rewarded (S+) and unrewarded (S−) stimuli, a discrimination ratio = 1.00 specifies a perfect discrimination between S+ and S− stimuli. We also collected data regarding the number of blocks and days per stage (Discrimination training; Discrimination-85 training with noise) in order to examine the latency of discrimination learning.Statistical analysesAll statistical analyses were conducted using SPSS (Version 20, Chicago, SPSS Inc.). In order to compare the number of trials needed to reach criterion and the discrimination ratios between True and Pseudo groups for Discrimination Training we conducted an analysis of variance (ANOVA). For Discrimination-85 with noise (Silence, Low noise, High noise), an ANOVA was conducted to compare the number of trials needed to reach criterion and the discrimination ratios between True and Pseudo groups. We also conducted post-hoc tests in order to reveal any sex differences between groups.And for Discrimination-85 with noise and Probes with noise repeated measures ANOVA was conducted to compare proportion response to training stimuli and probe stimuli between True groups and Pseudo groups. Lastly, we conducted post-hoc tests in order to reveal any differences in the number of trials to reach criterion during Discrimination training and to Discrimination-85 with noise.Ethics declarationThroughout the experiment, birds remained in the testing apparatus to minimize the transport and handling of each bird. One male and two female subjects died from natural causes during operant training. Following the experiment, healthy birds were returned to the colony room for use in future experiments.All procedures were conducted in accordance with the Canadian Council on Animal Care (CCAC) Guidelines and Policies with approval from the Animal Care and Use Committee for Biosciences for the University of Alberta (AUP 1937), which is consistent with the Animal Care Committee Guidelines for the Use of Animals in Research. Birds were captured and research was conducted under an Environment Canada Canadian Wildlife Service Scientific permit (#13-AB-SC004), Alberta Fish and Wildlife Capture and Research permits (#56,066 and #56,065), and the City of Edmonton Parks permit. All methods are reported in accordance with ARRIVE guidelines. More

Resistance of H. zea populations to Cry1AcTo measure variation in resistance of H. zea populations across field locations during 2017 and 2018, larval offspring of insects collected from non-Bt maize were subjected to a diet-overlay bioassay containing a diagnostic concentration of Cry1Ac (29 µg/cm2) corresponding to the mean LC95 of four Cry1Ac susceptible H. zea populations. Overall, larval survivorship varied significantly among years (Fig. 1).Figure 1Survival of H. zea larvae collected in 2017 and 2018 following exposure to 29 µg/cm2 Cry1Ac in diet-overlay assay. Dashed line is mean survival of a known susceptible field population. ***Years significantly different F = 25.38; df = 1, 58; P  More

1.Lengeler, C. Insecticide-treated bed nets and curtains for preventing malaria. Cochrane Database Syst Rev. https://doi.org/10.1002/14651858.CD000363.pub2 (2004).Article 
PubMed 
PubMed Central 

Google Scholar 
2.Pluess, B. et al. Indoor residual spraying for preventing malaria (Review). Cochrane Rev. https://doi.org/10.1002/14651858.CD006657.pub2 (2010).Article 

Google Scholar 
3.WHO. Guidelines for malaria vector control. (World Health Organization, 2019). https://www.who.int/malaria/publications/atoz/9789241550499/en/. Accessed 08 Sept 2019.4.Bhatt, S. et al. The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015. Nature 526(7572), 207–211 (2015).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
5.Killeen, G. F. et al. Made-to-measure malaria vector control strategies: Rational design based on insecticide properties and coverage of blood resources for mosquitoes. Malar. J. 13(1), 146 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
6.Waite, J. L. et al. Increasing the potential for malaria elimination by targeting zoophilic vectors. Sci. Rep. 7, 40551 (2017).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
7.Cooke, M. K. et al. A bite before bed’: Exposure to malaria vectors outside the times of net use in the highlands of western Kenya. Malar. J. 14(1), 259 (2015).PubMed 
PubMed Central 
Article 

Google Scholar 
8.Milali, M. P., Sikulu-Lord, M. T. & Govella, N. J. Bites before and after bedtime can carry a high risk of human malaria infection. Malar. J. 16(1), 91 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
9.Pates, H. et al. Differential behaviour of Anopheles gambiae sensu stricto (Diptera: Culicidae) to human and cow odours in the laboratory. Bull. Entomol. Res. 91(4), 289–296 (2001).CAS 
PubMed 
Article 

Google Scholar 
10.Costantini, C. et al. Odor-mediated host preferences of West African mosquitoes, with particular reference to malaria vectors. Am. J. Trop. Med. Hyg. 58(1), 56–63 (1998).CAS 
PubMed 
Article 

Google Scholar 
11.Gillies, M. T. & DeMeillon, B. The Anophelinae of Africa South of the Sahara (Ethiopian Zoogeographical Region) (South African Institute for Medical Research, 1968).
Google Scholar 
12.Hamon, J. Les moustiques anthropophiles de la région de Bobo-Dioulasso (République de Haute-Volta): Cycles d’agressivité et variations saisonnières. Ann. Soc. Entomol. (1963).13.Nyarango, P. M. et al. A steep decline of malaria morbidity and mortality trends in Eritrea between 2000 and 2004: the effect of combination of control methods. Malar. J. 5(1), 33 (2006).PubMed 
PubMed Central 
Article 

Google Scholar 
14.WHO, World malaria report 2019, in Licence: CC BY-NC-SA 3.0 IGO. https://www.who.int/news-room/feature-stories/detail/world-malaria-report-2019. Accessed 17 Dec 2019.15.Hancock, P. A. et al. Mapping trends in insecticide resistance phenotypes in African malaria vectors. PLoS Biol. 18(6), e3000633 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
16.Martinez-Torres, D. et al. Molecular characterization of pyrethroid knockdown resistance (kdr) in the major malaria vector Anopheles gambiae ss. Insect Mol. Biol. 7(2), 179–184 (1998).CAS 
PubMed 
Article 

Google Scholar 
17.Balabanidou, V. et al. Cytochrome P450 associated with insecticide resistance catalyzes cuticular hydrocarbon production in Anopheles gambiae. Proc. Natl. Acad. Sci. USA 113(33), 9268–9273 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
18.Edi, C. V. et al. CYP6 P450 enzymes and ACE-1 duplication produce extreme and multiple insecticide resistance in the malaria mosquito Anopheles gambiae. PloS Genet 10(3), e1004236 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
19.Kreppel, K. et al. Emergence of behavioural avoidance strategies of malaria vectors in areas of high LLIN coverage in Tanzania. Sci. Rep. 10(1), 1–11 (2020).Article 
CAS 

Google Scholar 
20.Moiroux, N. et al. Human exposure to early morning Anopheles funestus biting behavior and personal protection provided by long-lasting insecticidal nets. PLoS ONE 9, e104967 (2014).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
21.Reddy, M. R. et al. Outdoor host seeking behaviour of Anopheles gambiae mosquitoes following initiation of malaria vector control on Bioko Island, Equatorial Guinea. Malar. J. 10(1), 184–184 (2011).PubMed 
PubMed Central 
Article 

Google Scholar 
22.Rozendaal, J. et al. Behavioral responses of Anopheles darlingi in Suriname to DDT residues on house walls. J. Am. Mosq. Control Assoc. 5(3), 339–350 (1989).CAS 
PubMed 

Google Scholar 
23.Mwangangi, J. M. et al. Shifts in malaria vector species composition and transmission dynamics along the Kenyan coast over the past 20 years. Malar. J. 12(1), 1 (2013).Article 

Google Scholar 
24.Sougoufara, S. et al. Shift in species composition in the Anopheles gambiae complex after implementation of long-lasting insecticidal nets in Dielmo, Senegal. Med. Vet. Entomol. 30(3), 365–368 (2016).CAS 
PubMed 
Article 

Google Scholar 
25.Moiroux, N. et al. Changes in Anopheles funestus biting behaviour following universal coverage of long-lasting insecticidal nets in Benin. J. Infect. Dis. 206, 1622–1629 (2012).CAS 
PubMed 
Article 

Google Scholar 
26.Matowo, N. S. et al. Using a new odour-baited device to explore options for luring and killing outdoor-biting malaria vectors: A report on design and field evaluation of the Mosquito Landing Box. Parasit. Vectors 6(1), 1–16 (2013).Article 

Google Scholar 
27.Russell, T. L. et al. Increased proportions of outdoor feeding among residual malaria vector populations following increased use of insecticide-treated nets in rural Tanzania. Malar. J. 10, 1 (2011).Article 

Google Scholar 
28.Sherrard-Smith, E. et al. Mosquito feeding behavior and how it influences residual malaria transmission across Africa. Proc. Natl. Acad. Sci. 116(30), 15086–15095 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
29.Dambach, P. et al. Reduction of malaria vector mosquitoes in a large-scale intervention trial in rural Burkina Faso using Bti based larval source management. Malar. J. 18(1), 311 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
30.Bayili, K. et al. Evaluation of efficacy of Interceptor (R) G2, a long-lasting insecticide net coated with a mixture of chlorfenapyr and alpha-cypermethrin, against pyrethroid resistant Anopheles gambiae s.l. in Burkina Faso. Malar. J. 16, 9 (2017).Article 
CAS 

Google Scholar 
31.Oumbouke, W. A. et al. Evaluation of an alpha-cypermethrin + PBO mixture long-lasting insecticidal net VEERALIN® LN against pyrethroid resistant Anopheles gambiae s.s.: an experimental hut trial in M’bé, central Côte d’Ivoire. Parasit. Vectors 12(1), 544 (2019).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
32.Yaro, J. B. et al. A cohort study to identify risk factors for Plasmodium falciparum infection in Burkinabe children: Implications for other high burden high impact countries. Malar. J. 19(1), 371 (2020).MathSciNet 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
33.Dabire, K. et al. Anopheles funestus (Diptera: Culicidae) in a humid savannah area of western Burkina Faso: Bionomics, insecticide resistance status, and role in malaria transmission. J. Med. Entomol. 44(6), 990–997 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
34.Badolo, A. et al. Three years of insecticide resistance monitoring in Anopheles gambiae in Burkina Faso: Resistance on the rise?. Malar. J. 11(1), 1 (2012).Article 

Google Scholar 
35.Hamon, J. et al. Présence dans le Sud-Ouest de la Haute-Volta d’une Population d’Anopheles gambiae” A” résistante au DDT (Organisation mondiale de la Santé, 1968).
Google Scholar 
36.Hamon, J. et al. Présence dans le Sud-Ouest de la Haute-Volta de populations d’Anopheles funestus Giles résistantes à la dieldrine. Med. Trop. 28(2), 222–226 (1968).
Google Scholar 
37.Toe, H. Characterisation of Insecticide Resistance in Anopheles gambiae from Burkina Faso and Its Impact on Current Malaria Control Strategies (University of Liverpool, 2015).
Google Scholar 
38.Namountougou, M. et al. Insecticide resistance mechanisms in Anopheles gambiae complex populations from Burkina Faso, West Africa. Acta Trop. 197, 105054 (2019).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
39.Toe, K. H. et al. Increased pyrethroid resistance in malaria vectors and decreased bed net effectiveness, Burkina Faso. Emerg. Infect. Dis. 20, 10 (2014).Article 
CAS 

Google Scholar 
40.Beier, J. C., Killeen, G. & Githure, J. I. Entomologic inoculation rates and Plasmodium falciparum malaria prevalence in Africa. Am. J. Trop. Med. Hyg. 61, 109–113 (1999).CAS 
PubMed 
Article 

Google Scholar 
41.Smith, D. L. et al. The entomological inoculation rate and Plasmodium falciparum infection in African children. Nature 438, 492–495 (2005).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
42.Killeen, G. F. et al. Quantifying behavioural interactions between humans and mosquitoes: Evaluating the protective efficacy of insecticidal nets against malaria transmission in rural Tanzania. BMC Infect. Dis. 6, 161 (2006).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
43.Huho, B. et al. Consistently high estimates for the proportion of human exposure to malaria vector populations occurring indoors in rural Africa. Int. J. Epidemiol. 42(1), 235 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
44.Robert, V. & Carnevale, P. Influence of deltamethrin treatment of bed nets on malaria transmission in the Kou valley, Burkina Faso. Bull. World Health Organ. 69(6), 735 (1991).CAS 
PubMed 
PubMed Central 

Google Scholar 
45.Dabire, K. et al. Year to year and seasonal variations in vector bionomics and malaria transmission in a humid savannah village in west Burkina Faso. J. Vector Ecol. 33(1), 70–75 (2008).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
46.Epopa, P. S. et al. Seasonal malaria vector and transmission dynamics in western Burkina Faso. Malar. J. 18(1), 113 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
47.Dambach, P. et al. Nightly biting cycles of anopheles species in rural northwestern Burkina Faso. J. Med. Entomol. 55(4), 1027–1034 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
48.Guglielmo, F. et al. Quantifying variation in exposure risk to mosquito bites at the individual level in Burkina Faso. Malar. J. (2020)49.Edi, C. A. et al. Long-term trends in Anopheles gambiae insecticide resistance in Côte d’Ivoire. Parasit. Vectors 7(1), 500 (2014).PubMed 
PubMed Central 

Google Scholar 
50.Fuseini, G. et al. Evaluation of the residual effectiveness of Fludora fusion WP-SB, a combination of clothianidin and deltamethrin, for the control of pyrethroid-resistant malaria vectors on Bioko Island, Equatorial Guinea. Acta Trop. 196, 42–47 (2019).CAS 
PubMed 
Article 

Google Scholar 
51.Rongsriyam, Y. & Busvine, J. Cross-resistance in DDT-resistant strains of various mosquitoes (Diptera, Culicidae). Bull. Entomol. Res. 65(3), 459–471 (1975).Article 

Google Scholar 
52.Chandre, F. et al. Status of pyrethroid resistance in Anopheles gambiae sensu lato. Bull. World Health Organ. 77(3), 230–234 (1999).CAS 
PubMed 
PubMed Central 

Google Scholar 
53.Bagi, J. et al. When a discriminating dose assay is not enough: measuring the intensity of insecticide resistance in malaria vectors. Malar. J. 14(1), 210 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
54.Robert, V., et al., Etude des taux de parturité et d’infection du complexe Anopheles gambiae dans la rizière de la vallée du Kou, Burkina Faso. Le paludisme en Afrique de l’Ouest, 1991: p. 17.55.Pombi, M. et al. The Sticky Resting Box, a new tool for studying resting behaviour of Afrotropical malaria vectors. Parasit. Vectors 7(1), 247 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
56.Gnémé, A. et al. Anopheline occurrence and the risk of urban malaria in the city of Ouagadougou, Burkina Faso. (2019).57.Akogbéto, M. C. et al. Blood feeding behaviour comparison and contribution of Anopheles coluzzii and Anopheles gambiae, two sibling species living in sympatry, to malaria transmission in Alibori and Donga region, northern Benin, West Africa. Malar. J. 17(1), 307 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
58.Wamae, P. M. et al. Early biting of the Anopheles gambiae s.s. and its challenges to vector control using insecticide treated nets in western Kenya highlands. Acta Trop. 150, 136–142 (2015).CAS 
PubMed 
Article 

Google Scholar 
59.Gillies, M. Age-groups and the biting cycle in Anopheles gambiae: A preliminary investigation. Bull. Entomol. Res. 48(3), 553–559 (1957).Article 

Google Scholar 
60.Bayoh, M. et al. Persistently high estimates of late night, indoor exposure to malaria vectors despite high coverage of insecticide treated nets. Parasit. Vectors 7, 380 (2014).PubMed 
Article 

Google Scholar 
61.Sougoufara, S. et al. Biting by Anopheles funestus in broad daylight after use of long-lasting insecticidal nets: A new challenge to malaria elimination. Malar. J. 13, 125 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
62.Meyers, J. I. et al. Increasing outdoor host-seeking in Anopheles gambiae over 6 years of vector control on Bioko Island. Malar. J. 15(1), 1 (2016).MathSciNet 
Article 

Google Scholar 
63.Bayoh, M. N. et al. Anopheles gambiae: Historical population decline associated with regional distribution of insecticide-treated bed nets in western Nyanza Province, Kenya. Malar. J. 9(1), 62 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
64.Chinula, D. et al. Proportional decline of Anopheles quadriannulatus and increased contribution of An. arabiensis to the An. gambiae complex following introduction of indoor residual spraying with pirimiphos-methyl: An observational, retrospective secondary analysis of pre-existing data from south-east Zambia. Parasit. Vectors 11(1), 544 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
65.Main, B. J. et al. The genetic basis of host choice and resting behavior in the major African malaria vector Anopheles arabiensis. BioRxiv 2016, 044701 (2016).
Google Scholar 
66.Coluzzi, M. et al. Behavioral divergences between mosquitos with different inversion karyotypes in polymorphic populations of Anopheles gambiae complex. Nature 266, 832–833 (1977).ADS 
CAS 
PubMed 
Article 

Google Scholar 
67.Elanga-Ndille, E. et al. The G119S Acetylcholinesterase (Ace-1) target site mutation confers carbamate resistance in the major malaria vector Anopheles gambiae from cameroon: A challenge for the coming IRS implementation. Genes 10(10), 790 (2019).CAS 
PubMed Central 
Article 
PubMed 

Google Scholar 
68.Toé, K. H. et al. The recent escalation in strength of pyrethroid resistance in Anopheles coluzzi in West Africa is linked to increased expression of multiple gene families. BMC Genomics 16(1), 146 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
69.Finda, M. F. et al. Linking human behaviours and malaria vector biting risk in south-eastern Tanzania. PLoS ONE 14(6), e0217414 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
70.INSD. Annuaire sttistique 2018 Burkina Faso: Institut national de la statistique et de la démographie (2019).71.QGIS.org. QGIS Geographic Information System. QGIS Association. http://www.qgis.org. (2021).72.OpenStreetMap. OpenStreetMap contributors. https://www.openstreetmap.org. (2021).73.Benedict, M. Q. Care and maintenance of anopheline mosquito colonies. In The Molecular Biology of Insect Disease Vectors 3–12 (Springer, 1997).Chapter 

Google Scholar 
74.WHO. Test procedures for insecticide resistance monitoring in malaria vector mosquitoes: 2nd edition. (World Health Organization, 2016). http://www.who.int/malaria.75.WHO. Supplies for Monitoring Insecticide Resistance in Disease Vectors. (World Health Organization: Parasitic Diseases and Vector Control (PVC)/Communicable Disease Control, Prevention and Eradication (CPE), 2001).76.M.W. Service. Mosquito Ecology Field Sampling Methods (Elsevier Science Publishers, 1993).Book 

Google Scholar 
77.Kreppel, K. S. et al. Comparative evaluation of the Sticky-Resting-Box-Trap, the standardised resting-bucket-trap and indoor aspiration for sampling malaria vectors. Parasit. Vectors 8(1), 1–5 (2015).Article 

Google Scholar 
78.Gillies, M. & Coetzee, M. A Supplement to the Anophelinae of Africa south of the Sahara (Afrotropical region). (1987).79.Fanello, C., Santolamazza, F. D. & DellaTorre, A. Simultaneous identification of species and molecular forms of the Anopheles gambiae complex by PCR-RFLP. Medi. Vet. Entomol. 16(4), 461–464 (2002).CAS 
Article 

Google Scholar 
80.Sanou, A. et al. Evaluation of mosquito electrocuting traps as a safe alternative to the human landing catch for measuring human exposure to malaria vectors in Burkina Faso. Malar. J. 18(1), 386 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
81.Wirtz, R. et al. ELISA method for detecting Plasmodium falciparum circumsporozoite antibody. Bull. World Health Organ. 67(5), 535 (1989).CAS 
PubMed 
PubMed Central 

Google Scholar 
82.WHO. Manual on Practical Entomology in Malaria. Part II. Methods and Techniques. 84–85 (WHO Division of Malaria and other Parasitic Diseases, 1975).83.Garrett-Jones, C. The human blood index of malaria vectors in relation to epidemiological assessment. Bull. World Health Organ. 30(2), 241 (1964).CAS 
PubMed 
PubMed Central 

Google Scholar 
84.Beier, J. C. et al. Bloodmeal identification by direct enzyme-linked immunosorbent assay (ELISA), tested on Anopheles (Diptera: Culicidae) in Kenya. J. Med. Entomol. 25(1), 9–16 (1988).ADS 
CAS 
PubMed 
Article 

Google Scholar 
85.Kiszewski, A. et al. A global index representing the stability of malaria transmission. Am. J. Trop. Med. Hyg. 70(5), 486–498 (2004).PubMed 
Article 

Google Scholar 
86.Team, R.C. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2018). https://www.R-project.org/.87.Wood, S. & Wood, M.S. The mgcv package. https://www.R-project.org/ (2007).88.Bates, D. et al. Fitting linear mixed-effects models using lme4. http://arxiv.org/abs/1406.5823 (2014). More