Ecology

The videos were imported from digital videotapes using iMovie 4 and iMovie HD 6.0.3. They were deinterlaced using JES Deinterlacer 3.8.4. Images are processed here using QuickTime Player 7.3.3, GraphicConverter 8.8.3, and GIMP 2.10. Within these applications, it is possible to interpolate and adjust brightness, contrast, color, and other parameters. The simple processing applied here is effective for some cases. With advanced processing techniques that involve greater control and analysis of parameters, experts in image processing might be able to extract additional information.
The 2006 video
The first video was obtained from a kayak with a Sony DCR-HC36 standard video camera (which captures interlaced video at 720 × 480 pixels) in the Pearl River swamp in Louisiana on February 20, 2006, in an area along English Bayou where there were five sightings that week; the ‘kent’ calls of the Ivory-billed Woodpecker were heard twice during the same period, once coming simultaneously from different directions. The 2006 video shows a large woodpecker perched on a tree, climbing upward, taking a short flight between limbs, and then taking off into a longer flight. Part of the perch tree, which includes two forks that facilitated scaling, was used in the size comparison in Fig. 2; the bird in the video appears to be larger than a Pileated Woodpecker specimen8. According to Julie Zickefoose, whose paintings of the Ivory-billed Woodpecker have appeared on the covers of the January 2006 issue of the Auk and both editions of Ref.3, the “long but fluffy and squared-off crest,” “extremely long, erect head and neck,” “large, long bill,” “bill to head proportions,” “rared-back pose,” “long and thin” wings, “flapping leap” between limbs, and “ponderous and heavy” flight are suggestive of the Ivory-billed Woodpecker but not the Pileated Woodpecker13.
Figure 2

A pileated Woodpecker specimen is mounted on part of the perch tree. Frames from the 2006 video were scaled using forks in the tree (dashed lines). A meter stick is placed at the point where the flight between limbs occurred. The inset shows Pileated Woodpecker and Ivory-billed Woodpecker specimens that were photographed side by side at the National Museum of Natural History. The bird in the video is partially hidden by vegetation in the image on the lower left, but it is fully in view in the images at the top when it took the flight between limbs.

Full size image

The 2008 video
A short distance up the same bayou, another video was obtained with the same camera on March 29, 2008, from 23 m up a tree that was used as an observation platform for keeping watch for Ivory-billed Woodpeckers flying over the treetops in the distance. A large bird that flew along the bayou and passed below was identified as an Ivory-billed Woodpecker on the basis of two white stripes on the back and black leading edges and white trailing edges on the dorsal surfaces of the wings (those definitive field marks were observed from an ideal vantage point at close range and nearly directly above). The appearance in the video of the bird, its reflection from the still surface of the bayou, and reference objects made it possible to determine positions along the flight path and obtain estimates of the flight speed and wingspan. The bird in the 2008 video folded its wings closed during the middle of each upstroke as illustrated in Fig. 3. The two large woodpeckers are the only large birds north of the Rio Grande that have this distinctive wing motion, which is clearly resolved in the video. Using an approach that he had previously developed and applied to other woodpeckers17, Bret Tobalske, an expert on woodpecker flight mechanics, digitized the horizontal and vertical motions of the wingtips and concluded that the bird in the video is a large woodpecker13. The flap rate of the bird in the video is about ten standard deviations greater than the mean flap rate of the Pileated Woodpecker13.
Figure 3

Illustrations of large woodpeckers in flight. Left: The Pileated Woodpecker typically swoops upward a short distance before landing on a surface that faces the direction of approach; the Ivory-billed Woodpecker has long vertical ascents that allow time for maneuvering and landing on surfaces that do not face the direction of approach. Center: An Ivory-billed Woodpecker takes off with rapid wingbeats into a horizontal flight that quickly transitions into an upward swooping flight. Right: Illustration of a flight in the Pearl River swamp on March 29, 2008, that was viewed from 23 m up in a cypress tree. When the wings are folded closed in flight, the dorsal stripes and the white triangular patch have the same appearance as they do for the perched birds in Fig. 1. As discussed in Movie S6 of Ref.8, the wings of an Ivory-billed Woodpecker in a historical photo and of the bird in the 2008 video have the swept-back appearance of the wings in the middle image.

Full size image

Additional characteristics of the bird in the video that are consistent with the Ivory-billed Woodpecker but not the Pileated Woodpecker are the high flight speed, narrow wings, swept back wings, and prominent white patches on the dorsal surfaces of the wings8,13. There is one characteristic of the bird in the video that was initially thought to be inconsistent with the Ivory-billed Woodpecker. On the basis of historical accounts of a ‘duck-like’ flight, the Ivory-billed Woodpecker was thought to have a duck-like wing motion in which the wings remain extended throughout the flap cycle. In a series of paintings of the large woodpeckers in flight by Zickefoose18, the wings of the Pileated Woodpecker are correctly shown folding closed during the middle of the upstroke; in a proper representation of conventional wisdom at the time, the wings of the Ivory-billed Woodpecker are shown remaining extended throughout the flap cycle (duck-like flaps). An apparent paradox arose during the initial inspection of the video, which revealed an unexpected wing motion. The paradox was resolved after the discovery that a photo from 1939 shows an Ivory-billed Woodpecker in flight at an instant when the wings are nearly folded closed13.
The 2007 video
The other video was obtained with a Sony HDR-HC3 high-definition video camera (which captures interlaced video at 1,440 × 1,080 pixels) that was mounted on kayak paddles8 in the Choctawhatchee River swamp in Florida on January 19, 2007, in an area where an ornithologist and his colleagues had recently reported a series of sightings7. During an encounter with a pair of birds that were identified as Ivory-billed Woodpeckers on the basis of field marks and remarkable swooping flights, the camera captured a series of events that involve flights, field marks, and other behaviors and characteristics that are consistent with the Ivory-billed Woodpecker but no other species of the region. The analysis of the 2007 video is based in part on the fact that the probability of a series of unlikely events becomes extremely small as the number of events increases12. There is a downward swooping takeoff with a long horizontal glide that is consistent with the following account by Audubon15: “The transit from one tree to another, even should the distance be as much as a hundred yards, is performed by a single sweep, and the bird appears as if merely swinging from the top of the one tree to that of the other, forming an elegantly curved line.” There are upward swooping landings with long vertical ascents that are not consistent with the Pileated Woodpecker but are consistent with an account by Eckleberry of an Ivory-billed Woodpecker that “alighted with one magnificent upward swoop”19.
A long vertical ascent allows time for maneuvering, and the bird appears to rotate about its axis during two of the ascents as illustrated in Fig. 3. In a film of the closely related Magellanic Woodpecker (Campephilus magellanicus)20, there is maneuvering during a landing with a long vertical ascent. During and after one of the ascents, a woodpecker in the 2007 video shows field marks and body proportions that are consistent with the Ivory-billed Woodpecker but no other species of the region. There is a takeoff into horizontal flight with deep and rapid flaps that are not consistent with the Pileated Woodpecker but are similar to the deep and rapid flaps during a takeoff of the closely related Imperial Woodpecker (Campephilus imperialis)21. In another event, a woodpecker climbs upward and engages in a series of behaviors that are consistent with the Ivory-billed Woodpecker but no other species of the region, including delivering a blow that produces an audible double knock and taking off with rapid wingbeats into a flight that immediately transitions into an upward swooping flight that is illustrated in Fig. 3. More

1.
Hairston NG Jr, Ellner SP, Geber MA, Yoshida T, Fox JA. Rapid evolution and the convergence of ecological and evolutionary time. Ecol Lett. 2005;8:1114–27.
Google Scholar 
2.
Ellner SP, Geber MA, Hairston NG Jr. Does rapid evolution matter? Measuring the rate of contemporary evolution and its impacts on ecological dynamics. Ecol Lett. 2011;14:603–14.
PubMed  Google Scholar 

3.
Gómez P, Paterson S, De Meester L, Liu X, Lenzi L, Sharma MD, et al. Local adaptation of a bacterium is as important as its presence in structuring a natural microbial community. Nat Commun. 2016;7:12453.
PubMed  PubMed Central  Google Scholar 

4.
Buckling A, Craig Maclean R, Brockhurst MA, Colegrave N. The beagle in a bottle. Nature. 2009;457:824–9.
CAS  PubMed  Google Scholar 

5.
Gómez P, Buckling A. Real-time microbial adaptive diversification in soil. Ecol Lett. 2013;16:650–5.
PubMed  Google Scholar 

6.
Lawrence D, Fiegna F, Behrends V, Bundy JG, Phillimore AB, Bell T, et al. Species interactions alter evolutionary responses to a novel environment. PLoS Biol. 2012;10:e1001330.
CAS  PubMed  PubMed Central  Google Scholar 

7.
Lankau RA. Rapid evolutionary change and the coexistence of species. Annu Rev Ecol Evol Syst. 2011;42:335–54.
Google Scholar 

8.
Pantel JH, Duvivier C, Meester LD. Rapid local adaptation mediates zooplankton community assembly in experimental mesocosms. Ecol Lett. 2015;18:992–1000.
PubMed  Google Scholar 

9.
Hart SP, Turcotte MM, Levine JM. Effects of rapid evolution on species coexistence. Proc Natl Acad Sci. 2019;116:2112–7.
CAS  PubMed  Google Scholar 

10.
Rainey PB, Travisano M. Adaptive radiation in a heterogeneous environment. Nature. 1998;394:69.
CAS  PubMed  Google Scholar 

11.
Hughes AR, Inouye BD, Johnson MT, Underwood N, Vellend M. Ecological consequences of genetic diversity. Ecol Lett. 2008;11:609–23.
PubMed  Google Scholar 

12.
Bolnick DI, Amarasekare P, Araújo MS, Bürger R, Levine JM, Novak M, et al. Why intraspecific trait variation matters in community ecology. Trends Ecol evolution. 2011;26:183–92.
Google Scholar 

13.
Violle C, Enquist BJ, McGill BJ, Jiang LIN, Albert CH, Hulshof C, et al. The return of the variance: intraspecific variability in community ecology. Trends Ecol Evol. 2012;27:244–52.
PubMed  Google Scholar 

14.
Bolnick DI, Ingram T, Stutz WE, Snowberg LK, Lau OL, Paull JS. Ecological release from interspecific competition leads to decoupled changes in population and individual niche width. Proc R Soc B Biol Sci. 2010;277:1789–97.
Google Scholar 

15.
Bailey SF, Dettman JR, Rainey PB, Kassen R. Competition both drives and impedes diversification in a model adaptive radiation. Proc R Soc B Biol Sci. 2013;280:20131253.
Google Scholar 

16.
Jousset A, Eisenhauer N, Merker M, Mouquet N, Scheu S. High functional diversity stimulates diversification in experimental microbial communities. Sci Adv. 2016;2:e1600124.
PubMed  PubMed Central  Google Scholar 

17.
Schluter D. Experimental evidence that competition promotes divergence in adaptive radiation. Science. 1994;266:798–801.
CAS  PubMed  Google Scholar 

18.
Ellis CN, Traverse CC, Mayo-Smith L, Buskirk SW, Cooper VS. Character displacement and the evolution of niche complementarity in a model biofilm community. Evolution. 2015;69:283–93.
PubMed  PubMed Central  Google Scholar 

19.
Zee PC, Fukami T. Priority effects are weakened by a short, but not long, history of sympatric evolution. Proc R Soc B Biol Sci. 2018;285:20171722.
Google Scholar 

20.
Schluter D. Ecological character displacement in adaptive radiation. Am Nat. 2000;156:S4–S16.
Google Scholar 

21.
Urban MC, De Meester L. Community monopolization: local adaptation enhances priority effects in an evolving metacommunity. Proc R Soc B Biol Sci. 2009;276:4129–38.
Google Scholar 

22.
De Meester L, Vanoverbeke J, Kilsdonk LJ, Urban MC. Evolving perspectives on monopolization and priority effects. Trends Ecol Evol. 2016;31:136–46.
PubMed  Google Scholar 

23.
Luján AM, Gómez P, Buckling A. Siderophore cooperation of the bacterium Pseudomonas fluorescens in soil. Biol Lett. 2015;11:20140934.
PubMed  PubMed Central  Google Scholar 

24.
O’Brien S, Hesse E, Luján A, Hodgson DJ, Gardner A, Buckling A. No effect of intraspecific relatedness on public goods cooperation in a complex community. Evolution. 2018;72:1165–73.
PubMed  PubMed Central  Google Scholar 

25.
Martin M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J. 2011;17:10–12.
Google Scholar 

26.
Li H Aligning sequence reads, clone sequences and assembly contigs with BWA-MEM. arXiv preprint arXiv:13033997 2013.

27.
Garrison E, Marth G Haplotype-based variant detection from short-read sequencing. arXiv preprint arXiv:12073907 2012.

28.
Garrison E Vcflib: A C++ library for parsing and manipulating VCF files. GitHub https://www.githubcom/ekg/vcflib 2012.

29.
Callahan BJ, Sankaran K, Fukuyama JA, McMurdie PJ, Holmes SP Bioconductor workflow for microbiome data analysis: from raw reads to community analyses. F1000Research 2016;5:1492.

30.
Maidak BL, Cole JR, Lilburn TG, Parker CT Jr, Saxman PR, Stredwick JM, et al. The RDP (ribosomal database project) continues. Nucleic Acids Res. 2000;28:173–4.
CAS  PubMed  PubMed Central  Google Scholar 

31.
Schliep KP. phangorn: phylogenetic analysis in R. Bioinformatics. 2010;27:592–3.
PubMed  PubMed Central  Google Scholar 

32.
Hall AR, Colegrave N. How does resource supply affect evolutionary diversification? Proc R Soc B Biol Sci. 2006;274:73–78.
Google Scholar 

33.
Venail PA, MacLean RC, Bouvier T, Brockhurst MA, Hochberg ME, Mouquet N. Diversity and productivity peak at intermediate dispersal rate in evolving metacommunities. Nature. 2008;452:210.
CAS  PubMed  Google Scholar 

34.
Robertson A. Experimental design on the measurement of heritabilities and genetic correlations: biometrical genetics. Biometrics. 1959;15:219–26.
Google Scholar 

35.
Barrett RD, MacLean RC, Bell G. Experimental evolution of pseudomonas fluorescens in simple and complex environments. Am Naturalist. 2005;166:470–80.
Google Scholar 

36.
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B (Methodol). 1995;57:289–300.
Google Scholar 

37.
Lozupone C, Lladser ME, Knights D, Stombaugh J, Knight R. UniFrac: an effective distance metric for microbial community comparison. ISME J. 2011;5:169–72.
PubMed  Google Scholar 

38.
McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS ONE. 2013;8:e61217.
CAS  PubMed  PubMed Central  Google Scholar 

39.
Oksanen J, Kindt R, Legendre P, O’Hara B, Stevens MHH, Oksanen MJ, et al. The vegan package. Community Ecol Package. 2007;10:631–7.
Google Scholar 

40.
Paradis E, Schliep K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics. 2019;35:526–8.
CAS  PubMed  Google Scholar 

41.
Cailliez F. The analytical solution of the additive constant problem. Psychometrika. 1983;48:305–8.
Google Scholar 

42.
Love M, Anders S, Huber W. Differential analysis of count data–the DESeq2 package. Genome Biol. 2014;15:10–1186.
Google Scholar 

43.
McMurdie PJ, Holmes S. Waste not, want not: why rarefying microbiome data is inadmissible. PLoS computational Biol. 2014;10:e1003531.
Google Scholar 

44.
Jombart T, Balloux F, Dray S. Adephylo: new tools for investigating the phylogenetic signal in biological traits. Bioinformatics. 2010;26:1907–9.
CAS  PubMed  Google Scholar 

45.
Lenth R Emmeans: Estimated marginal means, aka least-squares means. R Package Version 2018; 1.

46.
R Core Team. R: A language and environment for statistical computing. 2013.

47.
Wickham H ggplot2: elegant graphics for data analysis. 2016. Springer.

48.
Vellend M. The consequences of genetic diversity in competitive communities. Ecology. 2006;87:304–11.
PubMed  Google Scholar 

49.
Hunt DE, David LA, Gevers D, Preheim SP, Alm EJ, Polz MF. Resource partitioning and sympatric differentiation among closely related bacterioplankton. Science. 2008;320:1081–5.
CAS  PubMed  Google Scholar 

50.
Narwani A, Alexandrou MA, Herrin J, Vouaux A, Zhou C, Oakley TH, et al. Common ancestry is a poor predictor of competitive traits in freshwater green algae. PLoS ONE. 2015;10:e0137085.
PubMed  PubMed Central  Google Scholar 

51.
Buckling A, Kassen R, Bell G, Rainey PB. Disturbance and diversity in experimental microcosms. Nature. 2000;408:961.
CAS  PubMed  Google Scholar 

52.
Castledine M, Buckling A, Padfield D. A shared coevolutionary history does not alter the outcome of coalescence in experimental populations of Pseudomonas fluorescens. J Evol Biol. 2019;32:58–65.
CAS  PubMed  Google Scholar  More

1.
Bhatt, S. et al. The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015. Nature 526, 207–211 (2015).
CAS  Article  ADS  Google Scholar 
2.
World Health Organization. World Malaria Report 2019 (World Health Organization, Geneva, 2019).
Google Scholar 

3.
Sinka, M. E. et al. A global map of dominant malaria vectors. Parasit. Vectors 5, 69 (2012).
Article  Google Scholar 

4.
Killeen, G. F. et al. Measuring, manipulating and exploiting behaviours of adult mosquitoes to optimise malaria vector control impact. BMJ Glob. Health https://doi.org/10.1136/bmjgh-2016-000212 (2017).
Article  PubMed  PubMed Central  Google Scholar 

5.
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, 62. https://doi.org/10.1186/1475-2875-9-62 (2010).
Article  PubMed  PubMed Central  Google Scholar 

6.
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. https://doi.org/10.1186/1475-2875-12-13 (2013).
Article  PubMed  PubMed Central  Google Scholar 

7.
Russell, T. L. et al. Impact of promoting longer-lasting insecticide treatment of bed nets upon malaria transmission in a rural Tanzanian setting with pre-existing high coverage of untreated nets. Malar. J. 9, 20 (2010).
Article  Google Scholar 

8.
Killeen, G. F. Characterizing, controlling and eliminating residual malaria transmission. Malar. J. https://doi.org/10.1186/1475-2875-13-330 (2014).
Article  PubMed  PubMed Central  Google Scholar 

9.
Sherrard-Smith, E. et al. Mosquito feeding behavior and how it influences residual malaria transmission across Africa. Proc. Natl. Acad. Sci. 116, 15086–15095. https://doi.org/10.1073/pnas.1820646116 (2019).
CAS  Article  PubMed  Google Scholar 

10.
Knox, T. B. et al. An online tool for mapping insecticide resistance in major Anopheles vectors of human malaria parasites and review of resistance status for the Afrotropical region. Parasit. Vectors 7, 76. https://doi.org/10.1186/1756-3305-7-76 (2014).
CAS  Article  PubMed  PubMed Central  Google Scholar 

11.
Moyes, C. L. et al. Analysis-ready datasets for insecticide resistance phenotype and genotype frequency in African malaria vectors. Sci. Data 6, 121. https://doi.org/10.1038/s41597-019-0134-2 (2019).
CAS  Article  PubMed  PubMed Central  Google Scholar 

12.
Russell, T. L., Beebe, N. W., Cooper, R. D., Lobo, N. F. & Burkot, T. R. Successful malaria elimination strategies require interventions that target changing vector behaviours. Malar. J. https://doi.org/10.1186/1475-2875-12-56 (2013).
Article  PubMed  PubMed Central  Google Scholar 

13.
Govella, N. J. & Ferguson, H. Why use of interventions targeting outdoor biting mosquitoes will be necessary to achieve malaria elimination. Front. Physiol. https://doi.org/10.3389/fphys.2012.00199 (2012).
Article  PubMed  PubMed Central  Google Scholar 

14.
Killeen, G. F. & Chitnis, N. Potential causes and consequences of behavioural resilience and resistance in malaria vector populations: A mathematical modelling analysis. Malar. J. https://doi.org/10.1186/1475-2875-13-97 (2014).
Article  PubMed  PubMed Central  Google Scholar 

15.
Gatton, M. L. et al. The importance of mosquito behavioural adaptations to malaria control in Africa. Evolution https://doi.org/10.1111/evo.12063 (2013).
Article  PubMed  PubMed Central  Google Scholar 

16.
Ranson, H. & Lissenden, N. Insecticide resistance in African Anopheles mosquitoes: A worsening situation that needs urgent action to maintain malaria control. Trends Parasitol. 32, 187–196. https://doi.org/10.1016/j.pt.2015.11.010 (2016).
CAS  Article  PubMed  Google Scholar 

17.
Pates, H. & Curtis, C. Mosquito behavior and vector control. Annu. Rev. Entomol. https://doi.org/10.1146/annurev.ento.50.071803.130439 (2005).
Article  PubMed  Google Scholar 

18.
Gordicho, V. et al. First report of an exophilic Anopheles arabiensis population in Bissau City, Guinea-Bissau: Recent introduction or sampling bias?. Malar. J. 13, 423. https://doi.org/10.1186/1475-2875-13-423 (2014).
Article  PubMed  PubMed Central  Google Scholar 

19.
Kitau, J. et al. Species shifts in the Anopheles gambiae complex: Do LLINs successfully control Anopheles arabiensis?. PLoS One 7, e31481 (2012).
CAS  Article  ADS  Google Scholar 

20.
Smith, A. The preferential indoor resting habitats of Anopheles gambiae in the Umbugwe area of Tanganyika. East Afr. Med. J. 39, 631–635 (1962).
CAS  PubMed  Google Scholar 

21.
Govella, N., Chaki, P. & Killeen, G. Entomological surveillance of behavioural resilience and resistance in residual malaria vector populations. Malar. J. 12, 124 (2013).
Article  Google Scholar 

22.
Coluzzi, M. & Sabatini, A. Chromosomal differentiation and adaptation to human environments in the Anopheles gambiae complex. Trans. R. Soc. Trop. Med. Hyg. 73, 483–497 (1979).
CAS  Article  Google Scholar 

23.
Main, B. J. et al. The genetic basis of host preference and resting behavior in the major African Malaria vector, Anopheles arabiensis. PLOS Genet. 12, e1006303. https://doi.org/10.1371/journal.pgen.1006303 (2016).
CAS  Article  PubMed  PubMed Central  Google Scholar 

24.
Lindblade, K. et al. Impact of sustained use of insecticide-treated bednets on malaria vector species distribution and culicine mosquitoes. J. Med. Entomol. 43, 428–432 (2006).
CAS  Article  Google Scholar 

25.
Russell, T. 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, 80 (2011).
Article  Google Scholar 

26.
Norris, L. C. & Norris, D. E. Heterogeneity and changes in inequality of malaria risk after introduction of insecticide-treated bed nets in Macha, Zambia. Am. J. Trop. Med. Hyg. https://doi.org/10.4269/ajtmh.11-0595 (2013).
Article  PubMed  PubMed Central  Google Scholar 

27.
Tirados, I., Costantini, C., Gibson, G. & Torr, S. J. Blood-feeding behaviour of the malarial mosquito Anopheles arabiensis: Implications for vector control. Med. Vet. Entomol. 20, 425–437. https://doi.org/10.1111/j.1365-2915.2006.652.x (2006).
CAS  Article  PubMed  Google Scholar 

28.
Pates, H. & Curtis, C. Mosquito behavior and vector control. Ann. Rev. Entomol. 50, 53–70 (2004).
Article  Google Scholar 

29.
Gillies, M. & Meillon, B. The Anophelinae of Africa south of the Sahara (Ethiopian Zoogeographical Region). S. Afr. Inst. Med. Res. 20, 20 (1968).
Google Scholar 

30.
Meyrowitsch, D. W. et al. Is the current decline in malaria burden in sub-Saharan Africa due to a decrease in vector population?. Malar. J. 10, 188 (2011).
Article  Google Scholar 

31.
Kaindoa, M. N. et al. Interventions that effectively target Anopheles funestus mosquitoes could significantly improve control of persistent malaria transmission in south–eastern Tanzania. PLoS One 12(5), e0177807. https://doi.org/10.1371/journal.pone.0177807 (2017).
CAS  Article  PubMed  PubMed Central  Google Scholar 

32.
Port, G. R. & Boreham, P. F. L. The effects of bednets on feeding by Anopheles gambiae Giles (Diptera: Culicidae). Bull. Entomol. Res. 72, 20 (1982).
Article  Google Scholar 

33.
Lefevre, T. et al. Beyond nature and nurture: Phenotypic plasticity in blood-feeding behavior of Anopheles gambiae s.s. when humans are not readily accessible. Am. J. Trop. Med. Hyg. 81, 1023–1029 (2009).
Article  Google Scholar 

34.
Moiroux, N. et al. Changes in Anopheles funestus biting behavior following universal coverage of long-lasting insecticidal nets in Benin. J. Infect. Dis. https://doi.org/10.1093/infdis/jis565 (2012).
Article  PubMed  Google Scholar 

35.
Petrarca, V. & Beier, J. C. Intraspecific chromosomal polymorphism in the Anopheles gambiae complex as a factor affecting malaria transmission in the Kisumu area of Kenya. Am. J. Trop. Med. Hyg. 46, 20 (1992).
Article  Google Scholar 

36.
Faye, O. et al. Impact of the use of permethrin pre-impregnated mosquito nets on malaria transmission in a hyperendemic village of Senegal. Med. Trop. (Mars) 58, 355–360 (1998).
CAS  Google Scholar 

37.
Cuzin-Ouattara, N. et al. Wide-scale installation of insecticide-treated curtains confers high levels of protection against malaria transmission in a hyperendemic area of Burkina Faso. Trans. R. Soc. Trop. Med. Hyg. 93, 473–479. https://doi.org/10.1016/S0035-9203(99)90343-7 (1999).
CAS  Article  PubMed  Google Scholar 

38.
Ilboudo-Sanogo, E. et al. Insecticide-treated materials, mosquito adaptation and mass effect: Entomological observations after five years of vector control in Burkina Faso. Trans. R. Soc. Trop. Med. Hyg. 95, 353–360. https://doi.org/10.1016/S0035-9203(01)90179-8 (2001).
CAS  Article  PubMed  Google Scholar 

39.
Mathenge, E. et al. Effect of permethrin-impregnated nets on exiting behavior, blood feeding success and time of feeding of malaria mosquitoes (Diptera: Culicidae) in western Kenya. J. Med. Entomol. https://doi.org/10.1603/0022-2585-38.4.531 (2001).
Article  PubMed  Google Scholar 

40.
Renggli, S. et al. Design, implementation and evaluation of a national campaign to deliver 18 million free long-lasting insecticidal nets to uncovered sleeping spaces in Tanzania. Malar. J. 12, 20 (2013).
Article  Google Scholar 

41.
Kramer, K. et al. Effectiveness and equity of the Tanzania National Voucher Scheme for mosquito nets over 10 years of implementation. Malar. J. 16, 255. https://doi.org/10.1186/s12936-017-1902-0 (2017).
Article  PubMed  PubMed Central  Google Scholar 

42.
Schmidt, C. A., Comeau, G., Monaghan, A. J., Williamson, D. J. & Ernst, K. C. Effects of desiccation stress on adult female longevity in Aedes aegypti and Ae. albopictus (Diptera: Culicidae): Results of a systematic review and pooled survival analysis. Parasit. Vectors 11, 267 (2018).
Article  Google Scholar 

43.
Kalra, B. & Parkash, R. Effects of saturation deficit on desiccation resistance and water balance in seasonal populations of the tropical drosophilid Zaprionus indianus. J. Exp. Biol. 219, 3237. https://doi.org/10.1242/jeb.141002 (2016).
Article  PubMed  Google Scholar 

44.
Lwetoijera, H. C. et al. Increasing role of Anopheles funestus and Anopheles arabiensis in malaria transmission in the Kilombero Valley, Tanzania. Malar. J. 331, 20 (2014).
Google Scholar 

45.
Mayagaya, V. et al. The impact of livestock on the abundance, resting behaviour and sporozoite rate of malaria vectors in southern Tanzania. Malar. J. 14, 17 (2015).
Article  Google Scholar 

46.
Mayagaya, V. The impact of livestock on the ecology of malaria vectors and malaria transmission in the Kilombero Valley. Tanzania MSc thesis, University of Dar es Salaam (2010).

47.
Corbel, V. et al. Combination of malaria vector control interventions in pyrethroid resistance area in Benin: A cluster randomised controlled trial. Lancet Infect. Dis. https://doi.org/10.1016/s1473-3099(12)70081-6 (2012).
Article  PubMed  Google Scholar 

48.
Ngowo, H., Kaindoa, E., Matthiopoulos, J., Ferguson, H. & Okumu, F. Variations in household microclimate affect outdoor-biting behaviour of malaria vectors [version 1; referees: 1 approved, 1 approved with reservations]. Vol. 2 (2017).

49.
Russell, T. et al. Impact of promoting long-lasting insecticide treatment of bednets upon malaria transmission in a rural Tanzania setting with pre existing high coverage of untreated nets. Malar. J. 9, 187 (2010).
Article  Google Scholar 

50.
Katharina Sophia, K. et al. Impact of ENSO 2016–17 on regional climate and malaria vector dynamics in Tanzania. Environ. Res. Lett. 20, 20 (2019).
Google Scholar 

51.
Kessler, S. & Guerin, P. M. Responses of Anopheles gambiae, Anopheles stephensi, Aedes aegypti, and Culex pipiens mosquitoes (Diptera: Culicidae) to cool and humid refugium conditions. J. Vector Ecol. https://doi.org/10.3376/1081-1710(2008)33[145:roagas]2.0.co;2 (2008).
Article  PubMed  Google Scholar 

52.
Chaccour, C. & Killeen, G. F. Mind the gap: Residual malaria transmission, veterinary endectocides and livestock as targets for malaria vector control. Malar. J. 15, 24 (2016).
Article  Google Scholar 

53.
Lyimo, I. N., Kessy, S. T., Mbina, K. F., Daraja, A. A. & Mnyone, L. L. Ivermectin-treated cattle reduces blood digestion, egg production and survival of a free-living population of Anopheles arabiensis under semi-field condition in south-eastern Tanzania. Malar. J. https://doi.org/10.1186/s12936-017-1885-x (2017).
Article  PubMed  PubMed Central  Google Scholar 

54.
Meza, F. C. et al. Mosquito electrocuting traps for directly measuring biting rates and host-preferences of Anopheles arabiensis and Anopheles funestus outdoors. Malar. J. 18, 83. https://doi.org/10.1186/s12936-019-2726-x (2019).
Article  PubMed  PubMed Central  Google Scholar 

55.
Iwashita, H. et al. Push by a net, pull by a cow: Can zooprophylaxis enhance the impact of insecticide treated bed nets on malaria control?. Parasit. Vectors 7, 52 (2014).
Article  Google Scholar 

56.
Tirados, I., Gibson, G., Young, S. & Torr, S. Are herders protected by their herds? An experimental analysis of zooprophylaxis against the malaria vector Anopheles arabiensis. Malar. J. 10, 68 (2011).
Article  Google Scholar 

57.
Donnelly, B., Berrang-Ford, L., Ross, N. A. & Michel, P. A systematic, realist review of zooprophylaxis for malaria control. Malar. J. 14, 313. https://doi.org/10.1186/s12936-015-0822-0 (2015).
Article  PubMed  PubMed Central  Google Scholar 

58.
Killeen, G. F. et al. Developing an expanded vector control toolbox for malaria elimination. BMJ Glob. Health 2, e000211. https://doi.org/10.1136/bmjgh-2016-000211 (2017).
Article  PubMed  PubMed Central  Google Scholar 

59.
Ayala, D., Ullastres, A. & Gonzalez, J. Adaptation through chromosomal inversions in Anopheles. Front. Genet. https://doi.org/10.3389/fgene.2014.00129 (2014).
Article  PubMed  PubMed Central  Google Scholar 

60.
Carrasco, D. et al. Behavioural adaptations of mosquito vectors to insecticide control. Curr. Opin. Insect Sci. 34, 48–54. https://doi.org/10.1016/j.cois.2019.03.005 (2019).
Article  PubMed  Google Scholar 

61.
Matowo, N. S. et al. Fine-scale spatial and temporal heterogeneities in insecticide resistance profiles of the malaria vector, Anopheles arabiensis in rural south-eastern Tanzania. Wellcome Open Res. 2, 20 (2017).
Article  Google Scholar 

62.
Okumu, F. et al. Comparative field evaluation of combinations of long-lasting insecticide treated nets and indoor residual spraying, relative to either method alone, for malaria prevention in an area where the main vector is Anopheles arabiensis. Parasit. Vectors 6, 46 (2013).
Article  Google Scholar 

63.
Briët, O. J. T. et al. Applications and limitations of Centers for Disease Control and Prevention miniature light traps for measuring biting densities of African malaria vector populations: A pooled-analysis of 13 comparisons with human landing catches. Malar. J. 14, 1–13. https://doi.org/10.1186/s12936-015-0761-9 (2015).
Article  Google Scholar 

64.
Clark, G. G., Seda, H. & Gubler, D. J. Use of the “CDC backpack aspirator” for surveillance of Aedes aegypti in San Juan, Puerto Rico. J. Am. Mosq. Control Assoc. 10, 119–124 (1994).
CAS  PubMed  Google Scholar 

65.
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, 462. https://doi.org/10.1186/s13071-015-1066-0 (2015).
Article  PubMed  PubMed Central  Google Scholar 

66.
Allen, R., Pereira, L. S., Raes, D., & Smith, M. Crop evapotranspiration—guidelines for computing crop water requirements. Food and Agriculture Organization (FAO); United Nations, FAO, Irrigation and Drainage Paper 56 (1998).

67.
Funk, C. et al. The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Sci. Data 2, 150066. https://doi.org/10.1038/sdata.2015.66 (2015).
Article  PubMed  PubMed Central  Google Scholar 

68.
Scott, J., Brodgon, W. & Collins, F. Identification of single specimens of Anopheles gambiae complex by polymerase chain reaction. Am. J. Trop. Med. Hyg. 49, 520–529 (1993).
CAS  Article  Google Scholar 

69.
Kaindoa, E. W. et al. New evidence of mating swarms of the malaria vector, Anopheles arabiensis in Tanzania. Wellcome Open Res. 2, 88. https://doi.org/10.12688/wellcomeopenres.12458.1 (2017).
Article  PubMed  PubMed Central  Google Scholar 

70.
Lee, Y., Weakley, A. M., Nieman, C. C., Malvick, J. & Lanzaro, G. C. A multi-detection assay for malaria transmitting mosquitoes. J. Vis. Exp. https://doi.org/10.3791/52385 (2015).
Article  PubMed  PubMed Central  Google Scholar 

71.
Cameron, A. C. & Trivedi, P. K. Regression-based tests for overdispersion in the Poisson model. J. Econom. 46, 347–364. https://doi.org/10.1016/0304-4076(90)90014-K (1990).
MathSciNet  Article  Google Scholar 

72.
R-Core-Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2016).
Google Scholar 

73.
Pinheiro, J., Bates, D., DebRoy, S. & Sarkar, D. Linear and nonlinear mixed effects models. R Packag. Vers. 3, 57 (2007).
Google Scholar 

74.
Brooks, M. E. et al. Modeling zero-inflated count data with glmmTMB. BioRxiv 132753, 20 (2017).
Google Scholar  More

1.
Ho A, Di Lonardo DP, Bodelier PLE. Revisiting life strategy concepts in environmental microbial ecology. FEMS Microbiol Ecol. 2017;93:fix006.
Google Scholar 
2.
Poindexter JS. Oligotrophy. In: Alexander M, editor. Advances in microbial ecology. Springer US, Boston, MA: Springer US; 1981. pp. 63–89.

3.
Madigan MT, Bender KS, Buckley DH, Sattley WM, Stahl DA. Brock Biology of Microorganisms, 15th Global edition. Boston, US: Benjamin Cummins. 2018.

4.
Navarro Llorens JM, Tormo A, Martínez-García E. Stationary phase in gram-negative bacteria. FEMS Microbiol Rev. 2010;34:476–95.
PubMed  Google Scholar 

5.
Wood TK, Knabel SJ, Kwan BW. Bacterial persister cell formation and dormancy. Appl Environ Microbiol. 2013;79:7116–21.
CAS  PubMed  PubMed Central  Google Scholar 

6.
Klotz A, Georg J, Bučinská L, Watanabe S, Reimann V, Januszewski W, et al. Awakening of a dormant cyanobacterium from nitrogen chlorosis reveals a genetically determined program. Curr Biol. 2016;26:2862–72.
CAS  PubMed  Google Scholar 

7.
Setlow P, Wang S, Li Y-Q. Germination of spores of the orders Bacillales and Clostridiales. Annu Rev Microbiol. 2017;71:459–77.
CAS  PubMed  Google Scholar 

8.
Song S, Wood TK. ppGpp ribosome dimerization model for bacterial persister formation and resuscitation. bioRxiv. 2019. https://doi.org/10.1101/663658.

9.
Fenton AK, Kanna M, Woods R, Aizawa S, Sockett RE. Shadowing the actions of a predator: Backlit fluorescent microscopy reveals synchronous nonbinary septation of predatory Bdellovibrio inside prey and exit through discrete bdelloplast pores. J Bacteriol. 2010;192:6329–35.
CAS  PubMed  PubMed Central  Google Scholar 

10.
Makowski Ł, Donczew R, Weigel C, Zawilak-Pawlik A, Zakrzewska-Czerwinska J. Initiation of chromosomal replication in predatory bacterium Bdellovibrio bacteriovorus. Front Microbiol. 2016;7.

11.
Rotem O, Pasternak Z, Jurkevitch E. The genus Bdellovibrio and like organisms. The prokaryotes: deltaproteobacteria and epsilonproteobacteria. 2014. pp. 3–17.

12.
Lambert C, Evans KJ, Till R, Hobley L, Capeness M, Rendulic S, et al. Characterizing the flagellar filament and the role of motility in bacterial prey-penetration by Bdellovibrio bacteriovorus. Mol Microbiol. 2006;60:274–86.
CAS  PubMed  PubMed Central  Google Scholar 

13.
Thomashow LS, Rittenberg SC. Waveform analysis and structure of flagella and basal complexes from Bdellovibrio bacteriovorus 109J. J Bacteriol. 1985;163:1038–46.
CAS  PubMed  PubMed Central  Google Scholar 

14.
Hespell RB, Rosson RA, Thomashow MF, Rittenberg SC.  Respiration of Bdellovibrio bacteriovorus strain 109J and its energy substrates for intraperiplasmic growth. J Bacteriol. 1973;113:1280–8.
CAS  PubMed  PubMed Central  Google Scholar 

15.
Hespell RB, Thomashow MF, Rittenberg SC. Changes in cell composition and viability of Bdellovibrio bacteriovorus during starvation. Arch Microbiol. 1974;97:313–27.
CAS  PubMed  Google Scholar 

16.
Paix B, Ezzedine JA, Jacquet S. Diversity, dynamics, and distribution of Bdellovibrio and like organisms in perialpine lakes. Appl Environ Microbiol. 2019;85.

17.
Varon M, Fine M, Stein A. The maintenance of Bdellovibrio at low prey density. Microb Ecol. 1984;10:95–8.
CAS  PubMed  Google Scholar 

18.
Varon M, Zeigler BP. Bacterial predator-prey interaction at low prey density. Appl Environ Microbiol. 1978;36:11–7.
CAS  PubMed  PubMed Central  Google Scholar 

19.
Chen H, Young S, Berhane TK, Williams HN. Predatory Bacteriovorax communities ordered by various prey species. PLoS ONE. 2012;7.

20.
Rogosky AM, Moak PL, Emmert EAB. Differential predation by Bdellovibrio bacteriovorus 109J. Curr Microbiol. 2006;52:81–5.
CAS  PubMed  Google Scholar 

21.
Jurkevitch E, Minz D, Ramati B, Barel G. Prey range characterization, ribotyping, and diversity of soil and rhizosphere Bdellovibrio spp. isolated on phytopathogenic bacteria. Appl Environ Microbiol. 2000;66:2365–71.
CAS  PubMed  PubMed Central  Google Scholar 

22.
Kandel PP, Pasternak Z, van Rijn J, Nahum O, Jurkevitch E. Abundance, diversity and seasonal dynamics of predatory bacteria in aquaculture zero discharge systems. FEMS Microbiol Ecol. 2014;89:149–61.
CAS  PubMed  Google Scholar 

23.
Pineiro SA, Williams HN, Stine OC, Piñeiro SA, Williams HN, Stine OC. Phylogenetic relationships amongst the saltwater members of the genus Bacteriovorax using rpoB sequences and reclassification of Bacteriovorax stolpii as Bacteriolyticum stolpii gen. nov., comb. nov. Int J Syst Evol Microbiol. 2008;58:1203–9.
CAS  PubMed  Google Scholar 

24.
Chen H, Athar R, Zheng G, Williams HN. Prey bacteria shape the community structure of their predators. ISME J. 2011;5:1314–22.
PubMed  PubMed Central  Google Scholar 

25.
Shatzkes K, Connell ND, Kadouri DE. Predatory bacteria: a new therapeutic approach for a post-antibiotic era. Future Microbiol. 2017;12:469–72.
CAS  PubMed  Google Scholar 

26.
Guo Y, Yan L, Cai J. Effects of Bdellovibrio and like organisms on survival and growth performance of juvenile turbot, scophthalmus maximus. J World Aquac Soc. 2016;47:633–45.
Google Scholar 

27.
Youdkes D, Helman Y, Burdman S, Matan O, Jurkevitch E. Potential control of potato soft rot disease by the obligate predators Bdellovibrio and like organisms. Appl Environ Microbiol. 2020;86.

28.
Sathyamoorthy R, Maoz A, Pasternak Z, Im H, Huppert A, Kadouri D, et al. Bacterial predation under changing viscosities. Environ Microbiol. 2019;21:2997–3010.
CAS  PubMed  Google Scholar 

29.
Hobley L, Fung RKY, Lambert C, Harris MATS, Dabhi JM, King SS, et al. Discrete cyclic di-GMP-dependent control of bacterial predation versus axenic growth in Bdellovibrio bacteriovorus. PLoS Pathog. 2012;8.

30.
Karunker I, Rotem O, Dori-Bachash M, Jurkevitch E, Sorek R. A global transcriptional switch between the attack and growth forms of Bdellovibrio bacteriovorus. PLoS ONE. 2013;8.

31.
Amikam D, Galperin MY. PilZ domain is part of the bacterial c-di-GMP binding protein. Bioinformatics. 2006;22:3–6.
CAS  PubMed  Google Scholar 

32.
Ko J, Ryu K-S, Kim H, Shin J-S, Lee J-O, Cheong C, et al. Structure of PP4397 reveals the molecular basis for different c-di-GMP binding modes by PilZ domain proteins. J Mol Biol. 2010;398:97–110.
CAS  PubMed  Google Scholar 

33.
Wirebrand L, Österberg S, López-Sánchez A, Govantes F, Shingler V. PP4397/FlgZ provides the link between PP2258 c-di-GMP signalling and altered motility in Pseudomonas putida. Sci Rep. 2018;8:1–10.
CAS  Google Scholar 

34.
Shanks RMQ, Davra VR, Romanowski EG, Brothers KM, Stella NA, Godboley D, et al. An eye to a kill: using predatory bacteria to control gram-negative pathogens associated with ocular infections. PLOS ONE. 2013;8:e66723.
CAS  PubMed  PubMed Central  Google Scholar 

35.
Wurtzel O, Dori-Bachash M, Pietrokovski S, Jurkevitch E, Sorek R. Mutation detection with next-generation resequencing through a mediator genome. PLoS ONE. 2010;5.

36.
Pasternak Z, Njagi M, Shani Y, Chanyi R, Rotem O, Lurie-Weinberger MN, et al. In and out: an analysis of epibiotic vs periplasmic bacterial predators. ISME J. 2014;8:625–35.
CAS  PubMed  Google Scholar 

37.
Pletnev P, Osterman I, Sergiev P, Bogdanov A, Dontsova O. Survival guide: Escherichia coli in the stationary phase. Acta Nat. 2015;7:22–33.
CAS  Google Scholar 

38.
Kazmierczak MJ, Wiedmann M, Boor KJ. Alternative sigma factors and their roles in bacterial virulence. Microbiol Mol Biol Rev. 2005;69:527–43.
CAS  PubMed  PubMed Central  Google Scholar 

39.
Paget MS. Bacterial sigma factors and anti-sigma factors: structure, function and distribution. Biomolecules. 2015;5:1245–65.
CAS  PubMed  PubMed Central  Google Scholar 

40.
Avidan O, Petrenko M, Becker R, Beck S, Linscheid M, Pietrokovski S, et al. Identification and characterization of differentially-regulated type IVb pilin genes necessary for predation in obligate bacterial predators. Sci Rep. 2017;7:1–12.

41.
Barembruch C, Hengge R. Cellular levels and activity of the flagellar sigma factor FliA of Escherichia coli are controlled by FlgM-modulated proteolysis. Mol Microbiol. 2007;65:76–89.
CAS  PubMed  Google Scholar 

42.
Rendulic S, Jagtap P, Rosinus A, Eppinger M, Baar C, Lanz C, et al. A predator unmasked: life cycle of Bdellovibrio bacteriovorus from a genomic perspective. Science. 2004;303:689–92.
CAS  PubMed  Google Scholar 

43.
Nyström T. Stationary-phase physiology. Annu Rev Microbiol. 2004;58:161–81.
PubMed  Google Scholar 

44.
Browning AP, Sharp JA, Mapder T, Baker CM, Burrage K, Simpson MJ. Persistence is an optimal hedging strategy for bacteria in volatile environments. bioRxiv. 2019. https://doi.org/10.1101/2019.12.19.883645.

45.
Ratcliff WC, Denison RF. Individual-level bet hedging in the bacterium Sinorhizobium meliloti. Curr Biol. 2010;20:1740–4.
CAS  PubMed  Google Scholar 

46.
Zhang X-X, Rainey PB. Bet hedging in the underworld. Genome Biol. 2010;11:137.
PubMed  PubMed Central  Google Scholar 

47.
Franklin RB, Mills AL. Multi-scale variation in spatial heterogeneity for microbial community structure in an eastern Virginia agricultural field. FEMS Microbiol Ecol. 2003;44:335–46.
CAS  PubMed  Google Scholar 

48.
Manderscheid B, Matzner E. Spatial heterogeneity of soil solution chemistry in a mature Norway spruce (Picea abies (L.) Karst.) stand. Water Air Soil Pollut. 1995;85:1185–90.
CAS  Google Scholar 

49.
Ranjard L, Lejon DPH, Mougel C, Schehrer L, Merdinoglu D, Chaussod R. Sampling strategy in molecular microbial ecology: Influence of soil sample size on DNA fingerprinting analysis of fungal and bacterial communities. Environ Microbiol. 2003;5:1111–20.
CAS  PubMed  Google Scholar 

50.
Jenal U, Reinders A, Lori C. Cyclic di-GMP: second messenger extraordinaire. Nat Rev Microbiol. 2017;15:271–84.
CAS  PubMed  Google Scholar 

51.
Boehm A, Kaiser M, Li H, Spangler C, Kasper CA, Ackermann M, et al. Second messenger-mediated adjustment of bacterial swimming velocity. Cell. 2010;141:107–16.
CAS  PubMed  Google Scholar 

52.
Paul K, Nieto V, Carlquist WC, Blair DF, Harshey RM. The c-di-GMP binding protein YcgR controls flagellar motor direction and speed to affect chemotaxis by a ‘Backstop Brake’ mechanism. Mol Cell. 2010;38:128–39.
CAS  PubMed  PubMed Central  Google Scholar 

53.
Dattner I, Miller E, Petrenko M, Kadouri DE, Jurkevitch E, Huppert A. Modelling and parameter inference of predator–prey dynamics in heterogeneous environments using the direct integral approach. J R Soc Interface. 2017;14:20160525.
PubMed  PubMed Central  Google Scholar 

54.
Hol FJH, Rotem O, Jurkevitch E, Dekker C, Koster DA. Bacterial predator–prey dynamics in microscale patchy landscapes. Proc R Soc B Biol Sci. 2016;283:20152154.
Google Scholar 

55.
Gabel CV, Berg HC. The speed of the flagellar rotary motor of Escherichia coli varies linearly with protonmotive force. Proc Natl Acad Sci USA. 2003;100:8748–51.
CAS  PubMed  Google Scholar 

56.
Gadkari D, Stolp H. Energy metabolism of Bdellovibrio bacteriovorus. I. Energy production, ATP pool, energy charge. Arch Microbiol. 1975;102:179–85.
CAS  PubMed  Google Scholar 

57.
Shioi JI, Galloway RJ, Niwano M, Chinnock RE, Taylor BL. Requirement of ATP in bacterial chemotaxis. J Biol Chem. 1982;257:7969–75.

58.
Fang X, Gomelsky M. A post-translational, c-di-GMP-dependent mechanism regulating flagellar motility. Mol Microbiol. 2010;76:1295–305.
CAS  PubMed  Google Scholar 

59.
Varon M. Interaction of Bdellovibrio with its prey in mixed microbial populations. Microb Ecol. 1981;7:97–105.
CAS  PubMed  Google Scholar 

60.
Kessel M, Shilo M. Relationship of Bdellovibrio elongation and fission to host cell size. J Bacteriol. 1976;128:477–80.
CAS  PubMed  PubMed Central  Google Scholar 

61.
LaMarre AG, Straley SC, Conti SF. Chemotaxis toward amino acids by Bdellovibrio bacteriovorus. J Bacteriol. 1977;131:201–7.
CAS  PubMed  PubMed Central  Google Scholar 

62.
Chauhan A, Williams HN. Response of Bdellovibrio and like organisms (BALOs) to the migration of naturally occurring bacteria to chemoattractants. Curr Microbiol. 2006;53:516–22.
CAS  PubMed  Google Scholar 

63.
Feng S, Tan CH, Constancias F, Kohli GS, Cohen Y, Rice SA. Predation by Bdellovibrio bacteriovorus significantly reduces viability and alters the microbial community composition of activated sludge flocs and granules. FEMS Microbiol Ecol. 2017;93.

64.
Kadouri DE, O’Toole GA. Susceptibility of biofilms to Bdellovibrio bacteriovorus attack. Appl Environ Microbiol. 2005;71:4044–51.
CAS  PubMed  PubMed Central  Google Scholar 

65.
Szabó E, Liébana R, Hermansson M, Modin O, Persson F, Wilén B-MB-MB-M, et al. Comparison of the bacterial community composition in the granular and the suspended phase of sequencing batch reactors. AMB Express. 2017;7:168.
PubMed  PubMed Central  Google Scholar 

66.
Lambert C, Smith MCM, Sockett RE. A novel assay to monitor predator-prey interactions for Bdellovibrio bacteriovorus 109 J reveals a role for methyl-accepting chemotaxis proteins in predation. Environ Microbiol. 2003;5:127–32.
CAS  PubMed  Google Scholar 

67.
Petrenko M, Friedman SP, Fluss R, Pasternak Z, Huppert A, Jurkevitch E. Spatial heterogeneity stabilizes predator–prey interactions at the microscale while patch connectivity controls their outcome. Environ Microbiol. 2019;22:694–704.

68.
Mukherjee S, Brothers KM, Shanks RMQQ, Kadouri DE. Visualizing Bdellovibrio bacteriovorus by using the tdTomato fluorescent protein. Appl Environ Microbiol. 2015;82:1653–61.
PubMed  Google Scholar 

69.
Jurkevitch E. Isolation and classification of Bdellovibrio and like organisms. Curr Protoc Microbiol. 2012;Chapter 7:Unit 7B.1.
Google Scholar 

70.
Peters JM, Koo B-M, Patino R, Heussler GE, Hearne CC, Qu J, et al. Enabling genetic analysis of diverse bacteria with Mobile-CRISPRi. Nat Microbiol. 2019;4:244–50.
CAS  PubMed  PubMed Central  Google Scholar 

71.
Copeland MF, Weibel DB. Bacterial swarming: a model system for studying dynamic self-assembly. Soft Matter. 2009;5:1174–87.
CAS  PubMed  PubMed Central  Google Scholar 

72.
Rotem O, Pasternak Z, Shimoni E, Belausov E, Porat Z, Pietrokovski S, et al. Cell-cycle progress in obligate predatory bacteria is dependent upon sequential sensing of prey recognition and prey quality cues. Proc Natl Acad Sci. 2015;112:E6028–37.
CAS  PubMed  Google Scholar  More

Bioleaching kinetics
Comparison of Li bioleaching by three various types of organisms (Fig. 1) revealed that the leaching kinetics in systems with yeast R. mucilaginosa was the fastest. Presence of Li in solution was detected at 6th day of the process. After initial faster bioleaching within first 6 days (285.5 µg l−1), there was a gradual decrease of Li concentration in solution due to Li bioaccumulation into the biomass up to 13th day and later stable Li concentration in range of 240–250 µg l−1 was observed suggesting that the rate of bioleaching and bioaccumulation were equal.
Figure 1

Kinetics of Li bioleaching from lepidolite by consortium of A. ferrooxidans and A. thiooxidans (bacteria), A. niger (fungi) and R. mucilaginosa (yeast) (A), long-term kinetics of Li bioleaching by bacteria (B) (fungi: initial ore concentration 10 g l−1, t = 21 °C, pH = 5.1, statically, standard medium, yeast: 10 g l−1, t = 21 °C, pH = 5.1, shaking 160 rpm, rich medium and bacteria: 10 g l−1, t = 30 °C, pH = 1.5, statically, poor medium).

Full size image

The lowest amount of Li was bioleached by fungi A. niger. Under this bioleaching conditions Li was for the first time observed in solution after 26 days of the process. Its concentration gradually increased later on. Again bioaccumulation was observed affecting the amount of Li in the solution.
In the case of bacteria, medium composition was the most important for Li bioleaching. In nutrient rich medium for acidophilic chemoautotrophic acidithiobacilli which contained energy sources (Fe2+ ions and S0) no Li bioleaching was observed during the whole process time. However, in the medium with limited amount of nutrients and energy sources containing just sulphuric acid and elemental sulphur, Li+ ions presence was observed at 21st day for the first time. Bacteria were probably forced to utilize nutrients necessary for their life directly in the leached material. During the first 77 days the lithium bioleaching kinetics was very slow but this stage was followed by the sharp increase of bioleaching rate (400 times increase of the bioleaching rate was observed) resulting in 11 mg l−1 of solubilised Li at the end of the bioleaching experiments (after 336 days). The rapid change in the bioleaching rate might be attributed to the changes of mineral structure due to bacterial activity. No Li was found in control experiments using the media without microorganisms addition.
Kinetic analysis
To kinetically interpret the heterogeneous non-catalytic reaction for lepidolite bioleaching the shrinking core model (SCM) was used. The assumptions to use the model are based on the three facts—(i) mixed lepidolite particles are considered as nonporous particles, (ii) ore grains gradually shrank and (iii) the product layers form around the unreacted grains20. The development and verification of the model were previously described in details by several authors20,21.
Experimental data obtained for all three studied bioleaching systems were substituted into both equations of SCM model. In the case of bacterial bioleaching a plot of 1−(1−X)1/3 versus time (Fig. 2) was found a straight line suggesting that chemical reaction and outer diffusion are the rate controlling steps of the process of bacterial bioleaching. Changes of rate constant, kr, (apparent from slopes of the plots) can be visible, as well. The linear relationship was obtained in the initial stage of bioleaching (R2 = 0.9944) and later at the day 77 the rate of the process changed but still showed the good fitting obtained by plotting 1−(1−X)1/3 versus time (R2 = 0.9991). This changes are very well visible also in the previous Fig. 1 showing the increase of Li+ ion concentration within the experimental period.
Figure 2

Plot of 1−(1−X)1/3 versus time for Li recovery by consortium of bacteria (initial ore concentration 10 g l−1, t = 30 °C, pH = 1.5, statically, poor media).

Full size image

However, the SCM model did not fit to the bioleaching data of two other bioleaching systems, using fungi and yeasts. Obviously, parallel bioaccumulation of Li+ ions into the biomass was responsible for considerably different bioleaching behaviour.
Changes of pH
Conditions of bioleaching experiments (pH, medium composition) were adjusted according the type of the microorganism used. Independently of conditions, the decrease of pH (Fig. 3) was recorded in all three bioleaching system. The most obvious decrease in pH occurred in bioleaching by microscopic fungi A. niger, with a pH decrease from 5.1 to 3 within first 12 days, followed by slow decrease to 2.5 until the end of the experiment. According to various authors22,23, it can be suggested that organic acids, considered the main fungal bioleaching agents, were produced. In the control medium a small increase in pH (from 5.2 to 5.6) was observed.
Figure 3

Changes of pH during bioleaching of lepidolite by consortium of A. ferrooxidans and A. thiooxidans (bacteria), A. niger (fungi) and R. mucilaginosa (yeast) (fungi: initial ore concentration 10 g l−1, t = 21 °C, pH = 5.1, statically, standard medium, yeast: 10 g l−1, t = 21 °C, pH = 5.1, shaking 160 rpm, rich medium and bacteria: 10 g l−1, t = 30 °C, pH = 1.5, statically, poor medium).

Full size image

A similar pattern was also observed in bacterial bioleaching, in which fast decrease of pH to 1.2 was observed during first 7 days followed by slow decrease to 0.9. Later the pH was stable in range of 0.9–1.2. Probably bacteria A. thiooxidans were mainly responsible for such pH decrease. In the control without bacteria addition the pH initially decreased from 1.5 to 1.3 and later increased and remained at 1.5.
As shown in Fig. 3 fast pH decrease was observed during first 6 days of bioleaching with yeast R. mucilaginosa from initial 5.1 to 4.1. Later pH did not change until 20th day followed by slow decrease to 3.5 at 30th day. In control media, without microorganisms, pH value slowly increased from initial 5.1 to final 5.5.
Bioleaching mechanisms
According to obtained results different mechanisms can be suggested for lepidolite bioleaching by biological systems studied. Mechanisms of Li bioleaching from lepidolite by A. niger fungus may be attributed to combination of biochemical (due to organic acids production) and biomechanical (due to hyphae penetration) leaching mechanisms. Significant drop of pH values indicates increased concentration of organic acids in the media as the result of high metabolic activity of the A. niger cell what was confirmed by various authors studying bioleaching by the microscopic fungi14,22,23,24,25. However, lepidolite interpenetration by A. niger hyphae growing along cleavages was observed by SEM analysis of solid residue after bioleaching, as well (Supplementary Information, Fig. S1), suggesting that direct biomechanical deterioration of lepidolite was also a part of the whole lithium extraction mechanism. However, according to Gadd26 the biochemical activities of microorganisms play more significant role than mechanical degradation.
Mechanisms of lepidolite bioleaching by bacteria is unknown. However, from abovementioned results it is obvious that no other substance except H+ ions contributed to the dissolution of Li+ ions. These results suggested that Li in lepidolite was dissolved by acid. Probably the mechanisms suggested by Liu et al.20 for leaching of lepidolite in sulphuric acid may be applied to bioleaching by acidophilic bacteria with sulphuric acid as a main bioleaching agent, as well. The main reaction of mixed alkali metal bioleaching may be expressed as follows:

$$ {text{M}}_{{2}} {text{O }} + {text{ H}}_{{2}} {text{SO}}_{{4}} = {text{ M}}_{{2}} {text{SO}}_{{4}} + {text{ H}}_{{2}} {text{O}} $$
(1)

where M presents alkali metals. Metallic elements from lepidolite are dissolved to form metal sulphates and mixed alums in the solution resulting just in partial lepidolite dissolution20. Overal reaction of lepidolite bioleaching in sulphuric acid produced by bacteria may be adopted from Onalbaeva et al.11:

$$ {text{3Li}}_{{2}} {text{O}}cdot{text{2K}}_{{2}} {text{O}}cdot{text{5Al}}_{{2}} {text{O}}_{{3}} cdot{1}0{text{SiO}}_{{2}} cdot{text{2SiF}}_{{4}} + { 2}0{text{H}}_{{2}} {text{SO}}_{{4}} = {text{ 3Li}}_{{2}} {text{SO}}_{{4}} + {text{ 2K}}_{{2}} {text{SO}}_{{4}} + {text{ 5Al}}_{{2}} left( {{text{SO}}_{{4}} } right)_{{3}} + {text{ 11SiO}}_{{2}} + {text{ H}}_{{2}} {text{SiF}}_{{6}} + {text{ 18H}}_{{2}} {text{O }} + {text{ 2HF}} $$
(2)

$$ {text{3Li}}_{{2}} {text{O}}cdot{text{2K}}_{{2}} {text{O}}cdot{text{5Al}}_{{2}} {text{O}}_{{3}} cdot{text{12SiO}}_{{2}} cdot{text{4H}}_{{2}} {text{O }} + { 2}0{text{H}}_{{2}} {text{SO}}_{{4}} = {text{ 3Li}}_{{2}} {text{SO}}_{{4}} + {text{ 2K}}_{{2}} {text{SO}}_{{4}} + {text{ 5Al}}_{{2}} left( {{text{SO}}_{{4}} } right)_{{3}} + {text{ 12SiO}}_{{2}} + {text{ 24H}}_{{2}} {text{O}} $$
(3)

Also Guo et al.27observed that increased H+ concentration catalysed the process of Li leaching from lepidolite via accelerating the protonation of the crystal lattices.
X-ray diffraction analysis
XRD analysis was applied in this study for phase identification and structural changes evaluation of samples before and after bioleaching in all three studied systems. Significant differences in mineralogical composition of leaching residue among the three studied bioleaching systems are visible from XRD spectra comparison (Supplementary Information, Fig. S2) suggesting that different mechanisms can be responsible for bioleaching. While bacterial bioleaching led to the disappearing of muscovite phase from XRD spectrum, the fungal bioleaching led to the appearance of new silicate phase (SiO2) and muscovite was found a dominant phase. According to Liu et al.20 presence of quartz in the spectrum at the end of the process may correspond with alkali metal dissolution from the silicate lattice. Phase changes were observed also after bioleaching by yeast R. mucilaginosa. Reallocation and significant decrease of diffraction peaks intensity was observed and similarly as in case of microscopic fungi muscovite has become a dominant phase while polylithionite phase significantly weakened. Based on the results, it can be suggested that the bioleaching mechanisms of lepidolite by fungi and yeast may be similar, however, in the case of bacteria the mechanisms might be significantly different. Further experiments are necessary to understand the mechanisms behind the lepidolite bioleaching.
Li distribution
Bioaccumulation of lithium into the biomass was observed when heterotrophic microorganisms A. niger and R. mucilaginosa were used (Fig. 4A). No bioaccumulation was found when bioleaching by consortium of acidophilic bacteria was studied. It can be suggested that the process of Li recovery by A. niger and R. mucilaginosa is a combination of two basic processes – initial bioleaching (metal solubilisation) followed by rapid bioaccumulation (intracellular lithium accumulation). It is possible that lithium bioaccumulation could significantly contribute to its solubilisation as released Li+ cations were fast accumulated in the cells and thus “pulled” the equilibrium resulting in the increased efficiency of the Li dissolution.
Figure 4

Distribution of Li between solution and biomass during bioleaching of lepidolite (A) and efficiency of the lepidolite bioleaching (B) by consortium of A. ferrooxidans and A. thiooxidans (bacteria), A. niger (fungi) and R. mucilaginosa (yeast) (fungi: initial ore concentration 10 g l−1, t = 21 °C, pH = 5.1, statically, standard medium, yeast: 10 g l−1, t = 21 °C, pH = 5.1, shaking 160 rpm, rich medium and bacteria: 10 g l−1, t = 30 °C, pH = 1.5, statically, poor medium).

Full size image

The highest amount of lithium was accumulated by R. mucilaginosa cells, representing 92% of the total amount of Li recovered from the ore. In the case of microscopic fungi A. niger, produced biomass accumulated 77% of the total solubilised Li. Distribution of Li between solution and biomass of particular microorganisms is shown in Fig. 4A. It is obvious that in both cases (fungi and yeast) bioaccumulation is dominant process of Li recovery and just small amount of Li+ ions remain in solution.
Bioleaching efficiency
The bioleaching efficiency is given as a sum of two processes – Li dissolution and its accumulation in the biomass. The final bioleaching yields for consortium of A. ferrooxidans and A. thiooxidans, fungi A. niger and R. mucilaginosa were found to be 8.8%, 0.2% and 1.1%, respectively. The results suggested that the most efficient among all three studied systems was the consortium of acidophilic bacteria A. ferrooxidans and A. thiooxidans (Fig. 4B) with the final bioleaching yield of almost 9%. On the other hand, very long time (336 days) was necessary for the process. Reichel et al.15 found 11% Li recovery from zinnwaldite using consortium of sulphur-oxidising bacteria, however, authors reported just 14 days for observed Li bioleaching efficiency although they do not found clear explanation of higher bioleaching efficiency in comparison with chemical leaching.
The lowest bioleaching yield was observed when A. niger was used. Rezza et al.13,14 used A. niger for Li bioleaching from spodumene with highest recovery of 0.75 mg l−1 of lithium, they do not reported any bioaccumulation.
Composition of medium had very strong effect on bioleaching efficiency by R. mucilaginosa as in nutrient rich medium due to significantly higher biomass production majority of Li has accumulated into the biomass resulting in 3 times higher final Li recovery. There were also morphological differences observed between yeasts cultivated in nutrient rich and poor environments with spherical shape and thin exopolymer layer of 0.48 µm for yeast from nutrient rich media in comparison with oval cells and thick exopolymer layer (1.8 µm) when cultivated in nutrient poor medium17.
Despite of quite low bioleaching efficiency there is clearly visible potential of all three biological systems for Li recovery from hard rocks. Even with low Li concentration in solution after bioleaching, the lithium concentration in the leaching solution resembles the lithium concentration of sea water (0.1–0.2 mg l−1) and brines (0.1–2 g l−1) considered for economic recovery28,29. That shows that the leaching solution is generally suitable for further processing15.
Due to the expensive separation of Li from leaching liquor, the conventional processing routes are likely not economic. However, ability of fungus A. niger and especially yeast R. mucilaginosa represent advantageous route of Li recovery after bioleaching. Thermal, chemical or microbiological process can be used to Li extraction from the biomass later on.
Metabolic activity and hyphae penetration of microscopic fungi and yeasts resulted in significant structural changes of mineral enhancing the access of lithium by bioleaching agent. Maybe the combination of heterotrophic microorganisms (microscopic fungi or yeast) bioleaching leading to mineral structure changes with consequent bacterial bioleaching could bring better results in the future. More

Sampling of N. barkeri and related species
Phytoseiid mites inhabit a variety of habitats, such as various plants and soil litters. Individuals were collected from plants and those on substrates and soil litters were isolated using Berlese’ funnels and kept in 95% alcohol. Samples were mounted in Hoyer’s medium and softened and cleaned with lactic acid if the mite body was hard. In addition, specimens were deposited at several institutes: GIABR (Guangdong Institute of Applied Biological Resources, Guangzhou, Guangdong, China), HUM (Hokkaido University Museum, Sapporo, Japan), NMNS (National Museum of Nature and Science, Tsukuba, Japan), NTU (Department of Entomology, National Taiwan University, Taipei, Taiwan), TARL (Taiwan Acari Research Laboratory, Taichung City, Taiwan). Female phytoseiid mites were collected, including 250 specimens of N. barkeri, and 262 specimens of 35 non-target species belonging to subfamily Amblyseiinae, in 6 tribes, and 11 genera. The following numbers of these non-target species were collected: 4 of N. baraki, 10 of N. longispinosus, 10 of N. makuwa, 6 of N. taiwanicus, 9 of N. womersleyi, 9 of Amblyseius alpinia, 10 of A. bellatulus, 10 of A. eharai, 10 of A. herbicolus, 2 of A. pascalis, 10 of A. tamatavensis, 10 of Euseius aizawai, 6 of E. circellatus, 7 of E. daluensis, 11 of E. macaranga, 10 of E. ovalis, 6 of E. paraovalis, 3 of E. nicholsi, 6 of E. oolong, 7 of E. sojaensis, 4 of Gynaeseius liturivorus, 3 of G. santosoi, 10 of Okiseius subtropicus, 4 of Paraamblyseius formosanus, 7 of Paraphytoseius chihpenensis, 10 of Parap. cracentis, 3 of Parap. hualienensis, 10 of Parap. orientalis, 6 of Phytoscutus salebrosus, 10 of Proprioseiopsis asetus, 3 of Prop. ovatus, 8 of Scapulaseius anuwati, 10 of S. cantonensis, 10 of S. okinawanus, and 8 of S. tienhsainensis. In addition, specimens of N. barkeri were collected from the United States, China, Israel, Japan, the Netherlands, Taiwan, and Thailand (including intercepted specimens in plant quarantine).
Quantitative measurements of phytoseiid mites
Specimens were examined under an Olympus BX51 microscope, and measurements were performed using a stage-calibrated ocular micrometer and ImageJ 1.4736. Photos were taken using a Motic Moticam 5+ camera attached to the microscope (Figure S1). All measurements were recorded in micrometres (μm). The general terminology used for morphological descriptions in this study conformed to that of Chant and McMurtry20. The notation for idiosomal setae conformed to that of Lindquist and Evans37 and Lindquist38, as adapted by Rowell et al.39 and Chant and Yoshida-Shaul32. Phytoseiid mites exhibit pronounced sexual dimorphism, and female individuals are more crucial for identification because of their distinguishing features and greater prevalence. In the present study, 22 quantitative measurements were collected from the female specimens: dorsal shield length and width; j1, j3, j4, j6, J5, z2, z4, z5, Z1, Z4, Z5, s4, r3, and R1 setae length; ventrianal shield length and width (at ZV2 level); JV5 length; St IV length; spermatheca calyx length, and spermatheca calyx width (Fig. 1, Table 1).
XGBoost training and computing
We used XGBoost to develop a classification system for target mite species and related species based on their morphological features. Among machine learning methods, XGBoost is the most efficient for implementing the gradient boosting decision tree algorithm from multiple decision trees, which are created successively. For each iteration, a tree enhances its predictive power by minimising the unexplained part of the last tree. First, we determined the number of decision trees through cross-validation. The original sample was randomly partitioned into five equally sized subsamples (Table S1). A single subsample and the other subsamples were retained for use as the validation and training data, respectively. Cross-validation was then performed five times, with each subsample used exactly once as the validation data. The number of decision trees allows the same level of performance to be achieved in training and validation. The number of decision trees was then used for the full dataset to create a final model, and key morphological features were selected for their relative importance. Next, we used ICE plots to indicate the determinative roles of these key features in classification. Plots in which one line represents one specimen indicate changes in predictions (of target species) that occur as a morphological feature change. We generated XGBoost and ICE plots by respectively using the R package “xgboost”40 and “pdp”41.
Drawings
Hand-drawn illustrations (Fig. 1) were made under an optic microscope (Olympus BX51). These drawings were first scanned, then processed and digitized with Photoshop CS6 (Adobe Systems Incorporated, USA). More

1.
King, A. J., Douglas, C. M. S., Huchard, E., Isaac, N. J. B. & Cowlishaw, G. Dominance and affiliation mediate despotism in a social primate. Curr. Biol. 18, 1833–1838 (2008).
CAS  PubMed  Google Scholar 
2.
Harcourt, J. L., Ang, T. Z., Sweetman, G., Johnstone, R. A. & Manica, A. Social feedback and the emergence of leaders and followers. Curr. Biol. 19, 248–252 (2009).
CAS  PubMed  Google Scholar 

3.
Maransky, B. P. & Bildstein, K. L. Follow your elders: age-related differences in the migration behavior of broad-winged hawks at hawk mountain sanctuary, Pennsylvania. Wilson Bull. 113, 350–353 (2001).
Google Scholar 

4.
King, A. J., Johnson, D. D. P. & Van Vugt, M. The origins and evolution of leadership. Curr. Biol. 19, R911–R916 (2009).
CAS  PubMed  Google Scholar 

5.
Dyer, J. R. G. et al. Consensus decision making in human crowds. Anim. Behav. 75, 461–470 (2008).
Google Scholar 

6.
Lusseau, D. & Conradt, L. The emergence of unshared consensus decisions in bottlenose dolphins. Behav. Ecol. Sociobiol. 63, 1067–1077 (2009).
Google Scholar 

7.
Diamond, J. Unwritten knowledge. Nature 410, 521–521 (2001).
ADS  CAS  PubMed  Google Scholar 

8.
McComb, K. et al. Leadership in elephants: the adaptive value of age. Proc. R. Soc. B Biol. Sci. 278, 3270–3276 (2011).
Google Scholar 

9.
McComb, K., Moss, C., Durant, S. M., Baker, L. & Sayialel, S. Matriarchs as repositories of social knowledge in African elephants. Science 292, 491–494 (2001).
ADS  CAS  PubMed  Google Scholar 

10.
Brent, L. J. N. et al. Ecological knowledge, leadership, and the evolution of menopause in killer whales. Curr. Biol. 25, 746–750 (2015).
CAS  PubMed  Google Scholar 

11.
Stephen Dobson, F. Competition for mates and predominant juvenile male dispersal in mammals. Anim. Behav. 30, 1183–1192 (1982).
Google Scholar 

12.
Baker, J. E. Trophy hunting as a sustainable use of wildlife resources in Southern and Eastern Africa. J. Sustain. Tour. 5, 306–321 (1997).
Google Scholar 

13.
Hurt, R. & Ravn, P. Hunting and its benefits: an overview of hunting in Africa with special reference to Tanzania. In Wildlife Conservation by Sustainable Use, 295–313 (Springer, 2000). https://doi.org/10.1007/978-94-011-4012-6_15.

14.
Chiyo, P. I., Obanda, V. & Korir, D. K. Illegal tusk harvest and the decline of tusk size in the African elephant. Ecol. Evol. 5, 5216–5229 (2015).
PubMed  PubMed Central  Google Scholar 

15.
Presotto, A., Fayrer-Hosken, R., Curry, C. & Madden, M. Spatial mapping shows that some African elephants use cognitive maps to navigate the core but not the periphery of their home ranges. Anim. Cogn. 22, 251–263 (2019).
PubMed  Google Scholar 

16.
Von Gerhardt, K., Van Niekerk, A., Kidd, M., Samways, M. & Hanks, J. The role of elephant Loxodonta africana pathways as a spatial variable in crop-raiding location. Oryx 48, 436–444 (2014).
Google Scholar 

17.
Lee, P. C., Poole, J. H., Njiraini, N., Sayialel, C. N. & Moss, C. J. Male social dynamics. In The Amboseli elephants 260–271 (University of Chicago Press, 2011). https://doi.org/10.7208/chicago/9780226542263.003.0017.

18.
Ngene, S. M. et al. The ranging patterns of elephants in Marsabit protected area, Kenya: the use of satellite-linked GPS collars. Afr. J. Ecol. 48, 386–400 (2009).
Google Scholar 

19.
Archie, E. A., Moss, C. J. & Alberts, S. C. The ties that bind: genetic relatedness predicts the fission and fusion of social groups in wild African elephants. Proc. R. Soc. B Biol. Sci. 273, 513–522 (2005).
Google Scholar 

20.
Chiyo, P. I. et al. Association patterns of African elephants in all-male groups: the role of age and genetic relatedness. Anim. Behav. 81, 1093–1099 (2011).
Google Scholar 

21.
Poole, J. H. Rutting behavior in African elephants: the phenomenon of musth. Behaviour 102, 283–316 (1987).
Google Scholar 

22.
Hollister-Smith, J. A. et al. Age, musth and paternity success in wild male African elephants, Loxodonta africana. Anim. Behav. 74, 287–296 (2007).
Google Scholar 

23.
Goldenberg, S. Z., de Silva, S., Rasmussen, H. B., Douglas-Hamilton, I. & Wittemyer, G. Controlling for behavioural state reveals social dynamics among male African elephants, Loxodonta africana. Anim. Behav. 95, 111–119 (2014).
Google Scholar 

24.
Moss, C. J. The demography of an African elephant (Loxodonta africana) population in Amboseli, Kenya. J. Zool. 255, 145–156 (2001).
Google Scholar 

25.
Lee, P. C. & Moss, C. J. Statural growth in known-age African elephants (Loxodonta africana). J. Zool. 236, 29–41 (1995).
Google Scholar 

26.
Moss, C. J. & Lee, P. C. Female reproductive strategies. In The Amboseli Elephants 187–204 (University of Chicago Press, 2011). https://doi.org/10.7208/chicago/9780226542263.003.0012.

27.
Chiyo, P. I. & Cochrane, E. P. Population structure and behaviour of crop-raiding elephants in Kibale National Park, Uganda. Afr. J. Ecol. 43, 233–241 (2005).
Google Scholar 

28.
Stevens, J. Understanding Human–Elephant Interactions in and Around Makgadikgadi Pans National Park, Botswana. PhD Thesis, University of Bristol (2018).

29.
Obanda, V. et al. Injuries of free ranging African elephants (Loxodonta africana africana) in various ranges of Kenya. Pachyderm 44, 54–58 (2008).
Google Scholar 

30.
Foley, C., Pettorelli, N. & Foley, L. Severe drought and calf survival in elephants. Biol. Lett. 4, 541–544 (2008).
PubMed  PubMed Central  Google Scholar 

31.
Evans, K. E. Elephants for Africa: male Savannah elephant Loxodonta africana sociality, the Makgadikgadi and resource competition. Int. Zoo Yearb. 53, 200–207 (2019).
Google Scholar 

32.
Shannon, G., Page, B. R., Duffy, K. J. & Slotow, R. The ranging behaviour of a large sexually dimorphic herbivore in response to seasonal and annual environmental variation. Aust. Ecol. 35, 731–742 (2010).
Google Scholar 

33.
Mueller, T., O’Hara, R. B., Converse, S. J., Urbanek, R. P. & Fagan, W. F. Social learning of migratory performance. Science 341, 999–1002 (2013).
ADS  CAS  PubMed  Google Scholar 

34.
Pettit, B., Ákos, Z., Vicsek, T. & Biro, D. Speed determines leadership and leadership determines learning during pigeon flocking. Curr. Biol. 25, 3132–3137 (2015).
CAS  PubMed  Google Scholar 

35.
Hutchinson, J. R. The locomotor kinematics of Asian and African elephants: changes with speed and size. J. Exp. Biol. 209, 3812–3827 (2006).
PubMed  Google Scholar 

36.
Taylor, L. A. et al. Movement reveals reproductive tactics in male elephants. J. Anim. Ecol. 89, 57–67 (2019).
PubMed  PubMed Central  Google Scholar 

37.
King, A. J. et al. Selfish-herd behaviour of sheep under threat. Curr. Biol. 22, R561–R562 (2012).
CAS  PubMed  Google Scholar 

38.
Joubert, D. Hunting behaviour of lions (Panthera leo) on elephants (Loxodonta africana) in the Chobe National Park, Botswana. Afr. J. Ecol. 44, 279–281 (2006).
Google Scholar 

39.
de Boer, W. F. et al. Spatial distribution of lion kills determined by the water dependency of prey species. J. Mammal. 91, 1280–1286 (2010).
Google Scholar 

40.
Wittemyer, G., Daballen, D. & Douglas-Hamilton, I. Comparative demography of an at-risk African elephant population. PLoS ONE 8, e53726 (2013).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

41.
Jones, T. et al. Age structure as an indicator of poaching pressure: Insights from rapid assessments of elephant populations across space and time. Ecol. Ind. 88, 115–125 (2018).
Google Scholar 

42.
Conradt, L., Krause, J., Couzin, I. D. & Roper, T. J. “Leading according to need” self-organizing groups. Am. Nat. 173, 304–312 (2009).
CAS  PubMed  Google Scholar 

43.
Hoare, D., Reeves, P. & Krause, J. Positioning behaviour in roach shoals: the role of body length and nutritional state. Behaviour 135, 1031–1039 (1998).
Google Scholar 

44.
Fischhoff, I. R. et al. Social relationships and reproductive state influence leadership roles in movements of plains zebra, Equus burchellii. Anim. Behav. 73, 825–831 (2007).
Google Scholar 

45.
Leggett, K. Effects of artificial water points on the movement and behaviour of desert-dwelling elephants in north-western Namibia. Pachyderm 40, 40–51 (2006).
Google Scholar 

46.
Bell, R. H. V. The effect of soil nutrient availability on community structure in African ecosystems. In Ecological Studies 193–216 (Springer, Berlin, 1982). https://doi.org/10.1007/978-3-642-68786-0_10.

47.
Mramba, R. P., Andreassen, H. P., Mlingi, V. & Skarpe, C. Activity patterns of African elephants in nutrient-rich and nutrient-poor savannas. Mammal. Biol. 94, 18–24 (2019).
Google Scholar 

48.
Mutinda, H., Poole, J. H. & Moss, C. J. Decision making and leadership in using the ecosystem. In The Amboseli Elephants 246–259 (University of Chicago Press, 2011). https://doi.org/10.7208/chicago/9780226542263.003.0016.

49.
Aureli, F. et al. Fission-fusion dynamics. Curr. Anthropol. 49, 627–654 (2008).
Google Scholar 

50.
Moss, C. J. & Poole, J. H. Relationships and social structure in African elephants. In Primate Social Relationships: An Integrated Approach (ed. Hinde, R. A.) (Blackwell, Oxford, 1983).
Google Scholar 

51.
Wittemyer, G., Douglas-Hamilton, I. & Getz, W. M. The socioecology of elephants: analysis of the processes creating multitiered social structures. Anim. Behav. 69, 1357–1371 (2005).
Google Scholar 

52.
Bates, L. A. et al. African elephants have expectations about the locations of out-of-sight family members. Biol. Let. 4, 34–36 (2007).
Google Scholar 

53.
Couzin, I. D., Krause, J., Franks, N. R. & Levin, S. A. Effective leadership and decision-making in animal groups on the move. Nature 433, 513–516 (2005).
ADS  CAS  PubMed  Google Scholar 

54.
Hanks, J. Growth of the African elephant (Loxodonta africana). Afr. J. Ecol. 10, 251–272 (1972).
Google Scholar 

55.
Muposhi, V. K., Gandiwa, E., Bartels, P., Makuza, S. M. & Madiri, T. H. Trophy hunting and sustainability: temporal dynamics in trophy quality and harvesting patterns of wild herbivores in a tropical semi-arid savanna ecosystem. PLoS ONE 11, e0164429 (2016).
PubMed  PubMed Central  Google Scholar 

56.
DG Ecological Consulting. Policy and Strategy for the Conservation and Management of Elephants in Botswana (Department of Wildlife and National Parks, Gaberone, 2003).
Google Scholar 

57.
Pilgram, T. & Western, D. Inferring the sex and age of African elephants from tusk measurements. Biol. Conserv. 36, 39–52 (1986).
Google Scholar 

58.
De Villiers PA. Aspect of the Behaviour and Ecology of Elephant (Loxodonta Africana, Blumenbach, 1797) in the Eastern Transvaal Lowveld with Special Reference to Environmental Interactions. PhD thesis, University of the Orange Free State (1994).

59.
Lindsey, P. A., Frank, L. G., Alexander, R., Mathieson, A. & Romañach, S. S. Trophy hunting and conservation in Africa: problems and one potential solution. Conserv. Biol. 21, 880–883 (2007).
PubMed  Google Scholar 

60.
Selier, S.-A.J., Page, B. R., Vanak, A. T. & Slotow, R. Sustainability of elephant hunting across international borders in southern Africa: a case study of the greater Mapungubwe Transfrontier Conservation Area. J. Wildl. Manag. 78, 122–132 (2013).
Google Scholar 

61.
Archie, E. A. et al. Fine-scale population genetic structure in a fission–fusion society. Mol. Ecol. 17, 2666–2679 (2008).
PubMed  Google Scholar 

62.
Evans, K. E. & Harris, S. Adolescence in male African elephants, Loxodonta africana, and the importance of sociality. Anim. Behav. 76, 779–787 (2008).
Google Scholar 

63.
Poole, J. H., Lee, P. C., Njiraini, N. & Moss, C. J. Longevity, competition, and musth. In The Amboseli Elephants 272–288 (University of Chicago Press, 2011). https://doi.org/10.7208/chicago/9780226542263.003.0018.

64.
CITES. 2020 CITES national export quotas. https://www.cites.org/eng/resources/quotas/index.php (2020).

65.
Mbaiwa, J. E. Effects of the safari hunting tourism ban on rural livelihoods and wildlife conservation in Northern Botswana. S. Afr. Geogr. J. 100, 41–61 (2017).
Google Scholar 

66.
Thouless C.R. et al. African Elephant Status Report 2016: an update from the African Elephant Database. Occasional Paper Series of the IUCN Species Survival Commission, No. 60 IUCN/SSC Africa Elephant Specialist Group. IUCN, Gland, Switzerland (2016).

67.
Pitfield A. R. The Social and Environmental Factors Affecting the Life of Bull African Elephants (Loxodonta africana) in a ‘Bull Area’—A Social Network Analysis. Masters Thesis, University of Bristol (2017) (email info@elephantsforafrica.org for a PDF)

68.
Moss, C. Getting to know a population. In Studying Elephants (ed. Kangwana, K.) 58–74 (The African Wildlife Foundation, Nairobi, 1996).
Google Scholar 

69.
Lee, H. C. & Teichroeb, J. A. Partially shared consensus decision making and distributed leadership in vervet monkeys: older females lead the group to forage. Am. J. Phys. Anthropol. 161, 580–590 (2016).
PubMed  Google Scholar 

70.
Mayberry, A. L., Hovorka, A. J. & Evans, K. E. Well-being impacts of human-elephant conflict in Khumaga, Botswana: exploring visible and hidden dimensions. Conser. Soc. 15, 280–291. https://doi.org/10.4103/cs.cs_16_132 (2017).
Article  Google Scholar  More

E.D.S. was supported by the International Mobilities of Researchers of the Biology Centre (grant no. CZ.02.2.69/0.0/0.0/16_027/0008357). L.E. is supported by funding from the European Research Council (ERC) under the European Union’s Horizon 2020 research and innovation programme (ERC Starting grant no. 803151). M.W.B. was supported by the United States National Science Foundation Division of Environmental Biology (grant no. 1456054). M.K. was supported by Fellowship Purkyně (Czech Academy of Sciences) and by the project Centre for research of pathogenicity and virulence of parasites r.n.: CZ.02.1.01/0.0/0.0/16_019/0000759. More