Ecology

1.Otero, J. et al. Basin-scale phenology and effects of climate variability on global timing of initial seaward migration of Atlantic salmon (Salmo salar). Glob. Change Biol. 20, 61–75 (2014).ADS 
Article 

Google Scholar 
2.Thorstad, E. B., Whoriskey, F., Rikardsen, A. H. & Aarestrup, K. Aquatic nomads: The life and migrations of the Atlantic salmon. In Atlantic Salmon Ecology (eds Aas, Ø. et al.) 1–32 (Wiley-Blackwell, 2010) https://doi.org/10.1002/9781444327755.ch1.
Google Scholar 
3.Harvey, A. C., Glover, K. A., Wennevik, V. & Skaala, Ø. Atlantic salmon and sea trout display synchronised smolt migration relative to linked environmental cues. Sci. Rep. 10, 3529 (2020).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
4.Jensen, A. J. et al. Timing of smolt migration in sympatric populations of Atlantic salmon (Salmo salar), brown trout (Salmo trutta), and Arctic char (Salvelinus alpinus). Can. J. Fish. Aquat. Sci. 69, 711–723 (2012).Article 

Google Scholar 
5.Hansen, L. P. & Jonsson, B. Salmon ranching experiments in the River Imsa: Effect of timing of Atlantic salmon (Salmo salar) smolt migration on survival to adults. Aquaculture 82, 367–373 (1989).Article 

Google Scholar 
6.Hvidsten, N. A. et al. Influence of sea temperature and initial marine feeding on survival of Atlantic salmon (Salmo salar) post-smolts from the Rivers Orkla and Hals, Norway. J. Fish Biol. 74, 1532–1548 (2009).CAS 
PubMed 
Article 

Google Scholar 
7.Hvidsten, N. A., Heggberget, T. & Jensen, A. J. Sea water temperatures at Atlantic salmon smolt enterance. Nord. J. Freshw. Res. 74, 79–86 (1998).8.Otero, J. et al. Quantifying the ocean, freshwater and human effects on year-to-year variability of one-sea-winter Atlantic salmon angled in multiple Norwegian rivers. PLoS ONE 6, e24005 (2011).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
9.Rikardsen, A. H. & Dempson, J. B. Dietary life-support: the food and feeding of Atlantic salmon at sea. In Atlantic Salmon Ecology (eds. Aas, Ø., Klemetsen, A., Einum, S. & Skurdal, J.) 115–143 (Wiley, 2011).10.Edwards, M. & Richardson, A. J. Impact of climate change on marine pelagic phenology and trophic mismatch. Nature 430, 881–884 (2004).ADS 
CAS 
PubMed 
Article 

Google Scholar 
11.Piou, C. & Prévost, E. Contrasting effects of climate change in continental vs. oceanic environments on population persistence and microevolution of Atlantic salmon. Glob. Change Biol. 19, 711–723 (2013).ADS 
Article 

Google Scholar 
12.McCormick, S. D., Hansen, L. P., Quinn, T. P. & Saunders, R. L. Movement, migration, and smolting of Atlantic salmon (Salmo salar). Can. J. Fish. Aquat. Sci. 55, 77–92 (1998).Article 

Google Scholar 
13.Thorstad, E. B. et al. A critical life stage of the Atlantic salmon (Salmo salar): Behaviour and survival during the smolt and initial post-smolt migration. J. Fish Biol. 81, 500–542 (2012).CAS 
PubMed 
Article 

Google Scholar 
14.Aldrin, M., Storvik, B., Kristoffersen, A. B. & Jansen, P. A. Space-time modelling of the spread of salmon lice between and within Norwegian marine salmon farms. PLoS ONE 8, e64039 (2013).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
15.Jansen, P. A. et al. Sea lice as a density-dependent constraint to salmonid farming. Proc. R. Soc. B Biol. Sci. 279, 2330–2338 (2012).Article 

Google Scholar 
16.Kristoffersen, A. B. et al. Large scale modelling of salmon lice (Lepeophtheirus salmonis) infection pressure based on lice monitoring data from Norwegian salmonid farms. Epidemics 9, 31–39 (2014).PubMed 
Article 

Google Scholar 
17.Bøhn, T. et al. Timing is everything: Survival of Atlantic salmon (Salmo salar) postsmolts during events of high salmon lice densities. J. Appl. Ecol. https://doi.org/10.1111/1365-2664.13612 (2020).Article 

Google Scholar 
18.Berge, Å. I. et al. Development of salinity tolerance in underyearling smolts of Atlantic salmon (Salmo salar) reared under different photoperiods. Can. J. Fish. Aquat. Sci. 52, 243–251 (1995).Article 

Google Scholar 
19.Hoar, W. S. 4 The physiology of smolting salmonids. In Fish Physiology Vol. 11 (eds Hoar, W. S. & Randall, D. J.) 275–343 (Academic Press, 1988).
Google Scholar 
20.Saunders, R. L. & Henderson, E. B. Influence of photoperiod on smolt development and growth of Atlantic salmon (Salmo solar). J. Fish. Res. Board Can. 27, 1295–1311 (1970).Article 

Google Scholar 
21.Strand, J. E. T., Hazlerigg, D. & Jørgensen, E. H. Photoperiod revisited: Is there a critical day length for triggering a complete parr-smolt transformation in Atlantic salmon (Salmo salar)?. J. Fish Biol. 93, 440–448 (2018).CAS 
PubMed 
Article 

Google Scholar 
22.Antonsson, T. & Gudjonsson, S. Variability in timing and characteristics of Atlantic salmon smolt in Icelandic rivers. Trans. Am. Fish. Soc. 131, 643–655 (2002).Article 

Google Scholar 
23.Kennedy, R. & Crozier, W. Evidence of changing migratory patterns of wild Atlantic salmon Salmo salar smolts in the River Bush, Northern Ireland, and possible associations with climate change. J. Fish Biol. 76, 1786–1805 (2010).CAS 
PubMed 
Article 

Google Scholar 
24.Hvidsten, N. A., Jensen, A. J., Vivås, H. & Bakke, Ø. Downstream migration of Atlantic salmon smolts in relation to water flow, water temperature, moon phase and social interaction. Nord. J. Freshw. Res. 70, 38–48 (1995).25.Urke, H. A., Kristensen, T., Ulvund, J. B. & Alfredsen, J. A. Riverine and fjord migration of wild and hatchery-reared Atlantic salmon smolts. Fish. Manag. Ecol. 20, 544–552 (2013).Article 

Google Scholar 
26.Carlsen, K. T., Berg, O. K., Finstad, B. & Heggberget, T. G. Diel periodicity and environmental influence on the smolt migration of Arctic charr, Salvelinus alpinus, Atlantic salmon, Salmo salar, and Brown Trout, Salmo trutta, Northern Norway. Environ. Biol. Fishes 70, 403–413 (2004).Article 

Google Scholar 
27.Birnie-Gauvin, K., Larsen, M. H., Thomassen, S. T. & Aarestrup, K. Testing three common stocking methods: Differences in smolt size, migration rate and timing of two strains of stocked Atlantic salmon (Salmo salar). Aquaculture 483, 163–168 (2018).Article 

Google Scholar 
28.Nielsen, C., Holdensgaard, G., Petersen, H. C., Bjornsson, BTh. & Madsen, S. S. Genetic differences in physiology, growth hormone levels and migratory behaviour of Atlantic salmon smolts. J. Fish Biol. 59, 28–44 (2001).CAS 
Article 

Google Scholar 
29.Orciari, R. D. & Leonard, G. H. Length characteristics of smolts and timing of downstream migration among three strains of Atlantic salmon in a southern New England stream. N. Am. J. Fish. Manag. 16, 851–860 (1996).Article 

Google Scholar 
30.Skaala, Ø. et al. An extensive common-garden study with domesticated and wild Atlantic salmon in the wild reveals impact on smolt production and shifts in fitness traits. Evol. Appl. 12, 1001–1016 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
31.Cooke, S. J. et al. Tracking animals in freshwater with electronic tags: Past, present and future. Anim. Biotelemetry 1, 5 (2013).MathSciNet 
Article 

Google Scholar 
32.Lennox, R. J. et al. Envisioning the future of aquatic animal tracking: Technology, science, and application. Bioscience 67, 884–896 (2017).Article 

Google Scholar 
33.Finstad, B., Okland, F., Thorstad, E. B., BjOrn, P. A. & McKinley, R. S. Migration of hatchery-reared Atlantic salmon and wild anadromous brown trout post-smolts in a Norwegian fjord system. J. Fish Biol. 66, 86–96 (2005).Article 

Google Scholar 
34.McMichael, G. A. et al. The juvenile salmon acoustic telemetry system: A new tool. Fisheries 35, 9–22 (2010).Article 

Google Scholar 
35.Welch, D. W. et al. Freshwater and marine migration and survival of endangered Cultus Lake sockeye salmon (Oncorhynchus nerka) smolts using POST, a large-scale acoustic telemetry array. Can. J. Fish. Aquat. Sci. 66, 736–750 (2009).Article 

Google Scholar 
36.Nilsen, F. et al. Vurdering av lakselusindusert villfiskdødelighet per produksjonsområde i 2018. Rapp. Fra Ekspertgruppe Vurder. Av Lusepåvirkning Append 2, 62 (2018).
Google Scholar 
37.Mulcahy, D. M. Surgical implantation of transmitters into fish. ILAR J. 44, 295–306 (2003).CAS 
PubMed 
Article 

Google Scholar 
38.Cooke, S. J. & Wagner, G. N. Training, experience, and opinions of researchers who use surgical techniques to implant telemetry devices into fish. Fisheries 29, 10–18 (2004).Article 

Google Scholar 
39.Percie du Sert, N. et al. Reporting animal research: Explanation and elaboration for the ARRIVE guidelines 2.0. PLOS Biol. 18, e3000411 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
40.Grün, B. & Leisch, F. FlexMix version 2: Finite mixtures with concomitant variables and varying and constant parameters. J. Stat. Softw. 28, 1–35 (2008).Article 

Google Scholar 
41.Leisch, F. FlexMix: A general framework for finite mixture models and latent class regression in R. J. Stat. Softw. 11, 1–18 (2004).Article 

Google Scholar 
42.Zuur, A., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer Science & Business Media, 2009).
Google Scholar 
43.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting Linear Mixed-Effects Models using lme4. arXiv:1406.5823Stat (2014).44.Lebreton, J.-D., Burnham, K. P., Clobert, J. & Anderson, D. R. Modeling survival and testing biological hypotheses using marked animals: A unified approach with case studies. Ecol. Monogr. 62, 67–118 (1992).Article 

Google Scholar 
45.Laake, J. L. RMark: An R Interface for Analysis of Capture-Recapture Data with MARK. 25 http://www.afsc.noaa.gov/Publications/ProcRpt/PR2013-01.pdf (2013).46.White, G. C. & Burnham, K. P. Program MARK: Survival estimation from populations of marked animals. Bird Study 46, S120–S139 (1999).Article 

Google Scholar 
47.Burnham, K. P. Design and analysis methods for fish survival experiments based on release-recapture. Am. Fish. Soc. Monogr. 5, 1–437 (1987).
Google Scholar 
48.Michel, C. J. et al. Chinook salmon outmigration survival in wet and dry years in California’s Sacramento River. Can. J. Fish. Aquat. Sci. 72, 1749–1759 (2015).Article 

Google Scholar 
49.Persson, L., Kagervall, A., Leonardsson, K., Royan, M. & Alanärä, A. The effect of physiological and environmental conditions on smolt migration in Atlantic salmon Salmo salar. Ecol. Freshw. Fish 28, 190–199 (2019).Article 

Google Scholar 
50.Whalen, K. G., Parrish, D. L. & McCormick, S. D. Migration timing of Atlantic salmon smolts relative to environmental and physiological factors. Trans. Am. Fish. Soc. 128, 289–301 (1999).Article 

Google Scholar 
51.Haraldstad, T., Kroglund, F., Kristensen, T., Jonsson, B. & Haugen, T. O. Diel migration pattern of Atlantic salmon (Salmo salar) and sea trout (Salmo trutta) smolts: An assessment of environmental cues. Ecol. Freshw. Fish 26, 541–551 (2017).Article 

Google Scholar 
52.Skilbrei, O. T., Wennevik, V., Dahle, G., Barlaup, B. & Wiers, T. Delayed smolt migration of stocked Atlantic salmon parr. Fish. Manag. Ecol. 17, 493–500 (2010).Article 

Google Scholar 
53.Freshwater, C. et al. Individual variation, population-specific behaviours and stochastic processes shape marine migration phenologies. J. Anim. Ecol. 88, 67–78 (2019).PubMed 
Article 

Google Scholar 
54.Chapman, B. B., Brönmark, C., Nilsson, J. -Å. & Hansson, L.-A. The ecology and evolution of partial migration. Oikos 120, 1764–1775 (2011).Article 

Google Scholar 
55.Urke, H. A., Arnekleiv, J. V., Nilsen, T. O. & Nilssen, K. J. Development of seawater tolerance and subsequent downstream migration in wild and stocked young-of-the-year derived Atlantic salmon Salmo salar smolts. J. Fish Biol. 84, 178–192 (2014).CAS 
PubMed 
Article 

Google Scholar 
56.Virtanen, E., Söderholm-Tana, L., Soivio, A., Foreman, L. & Muona, M. Effect of physiological condition and smoltification status at smolt release on subsequent catches of adult salmon. Aquaculture 97, 231–257 (1991).Article 

Google Scholar 
57.Björnsson, B. T., Stefansson, S. O. & McCormick, S. D. Environmental endocrinology of salmon smoltification. Gen. Comp. Endocrinol. 170, 290–298 (2011).PubMed 
Article 
CAS 

Google Scholar 
58.Stefansson, S. O., Björnsson, B. T., Ebbesson, L. O. E. & McCormick, S. D. Smoltification. In Fish Larval Physiology (eds. Finn, R. N. & Kapoor, B. G.) 639–681 (Science Publishers, 2008).59.McCormick, S. D., Shrimpton, J. M., Nilsen, T. O. & Ebbesson, L. O. Advances in our understanding of the parr-smolt transformation of juvenile salmon: A summary of the 10th International Workshop on Salmon Smoltification. J. Fish Biol. 93, 437–439 (2018).CAS 
PubMed 
Article 

Google Scholar 
60.Simons, A. Playing smart vs. playing safe: The joint expression of phenotypic plasticity and potential bet hedging across and within thermal environments. J. Evol. Biol. 27, 1047–1056 (2014).CAS 
PubMed 
Article 

Google Scholar 
61.Finstad, B. & Jonsson, N. Factors influencing the yield of smolt releases in Norway. Nord. J. Freshw. Res. 75, 37–55 (2001).62.Diserud, O. H., Hindar, K., Karlsson, S., Glover, K. A. & Skaala, Ø. Genetic impact of escaped farmed Atlantic salmon on wild salmon populations—status 2017. NINA Rapp. 1337, 55 (2017).
Google Scholar 
63.Vollset, K. W. et al. Can the river location within a fjord explain the density of Atlantic salmon and sea trout?. Mar. Biol. Res. 10, 268–278 (2014).Article 

Google Scholar 
64.Lacroix, G. L., Knox, D. & McCurdy, P. Effects of implanted dummy acoustic transmitters on juvenile Atlantic salmon. Trans. Am. Fish. Soc. 133, 211–220 (2004).Article 

Google Scholar 
65.Newton, M. et al. Does size matter? A test of size-specific mortality in Atlantic salmon Salmo salar smolts tagged with acoustic transmitters. J. Fish Biol. 89, 1641–1650 (2016).CAS 
PubMed 
Article 

Google Scholar 
66.Hansen, L. P., Holm, M., Hoist, J. C. & Jacobsen, J. A. The ecology of post-smolts of Atlantic salmon. In Salmon at the Edge (ed. Mills, D.) 25–39 (Blackwell Science Ltd., 2003) https://doi.org/10.1002/9780470995495.ch4.
Google Scholar 
67.Gregory, S. D. et al. Atlantic salmon return rate increases with smolt length. ICES J. Mar. Sci. 76, 1702–1712 (2019).Article 

Google Scholar 
68.Bjørn, P. A. et al. Metodeutvikling for overvåkning og telling av lakselus på viltlevende laksefisk: Ekstrainnsats i 2010 med midler fra FKD. (2011).69.Riley, W. D. et al. Development of schooling behaviour during the downstream migration of Atlantic salmon Salmo salar smolts in a chalk stream: Development of schooling in Salmo salar smolts. J. Fish Biol. 85, 1042–1059 (2014).CAS 
PubMed 
Article 

Google Scholar 
70.Daniels, J., Sutton, S., Webber, D. & Carr, J. Extent of predation bias present in migration survival and timing of Atlantic salmon smolt (Salmo salar) as suggested by a novel acoustic tag. Anim. Biotelemetry 7, 16 (2019).Article 

Google Scholar 
71.Halttunen, E. et al. Migration of Atlantic salmon post-smolts in a fjord with high infestation pressure of salmon lice. Mar. Ecol. Prog. Ser. 592, 243–256 (2018).ADS 
Article 

Google Scholar 
72.Harvey, A. C. et al. Inferring Atlantic salmon post-smolt migration patterns using genetic assignment. R. Soc. Open Sci. 6, 190426 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

Strains and growth conditionsSingle gene deletion strains were taken from the Keio collection [34] (Supplementary Table 1), which consists of all non-essential single gene deletions in E. coli K-12 strain BW25113. MreB and mrdA point mutant strains were from Ref. [35] (Supplementary Table 2). Plasmids pQY10 and pQY11 were created by Gibson assembly of Venus YFP A206K (for pQY10) or Venus CFP A206K (for pQY11) [31], and SpecR from pKDsgRNA-ack (gift from Kristala Prather, Addgene plasmid # 62654, http://n2t.net/addgene:62654; RRID:Addgene_62654) [36]. Plasmids pQY12 and pQY13 were created similarly but additionally with CmR from pACYC184.All E. coli experiments were performed in LB (Merck 110285, Kenilworth, New Jersey) with the appropriate antibiotics and experiments with S. cerevisae were performed in YPD [37]. All agar plates were prepared in OmniTrays (Nunc 242811, Roskilde, Denmark, 12.8 cm × 8.6 cm) or 12 cm × 12 cm square petri dishes (Greiner 688102, Kremsmuenster, Austria) filled with 70 mL media solidified with 2% Bacto Agar (BD 214010, Franklin Lakes, New Jersey). After solidifying, the plates were dried upside-down in the dark for 2 days and stored wrapped at 4 °C in the dark for 7–20 days before using.Tracking lineages with fluorescent tracer beadsIn order to track lineages, we spread fluorescent tracer beads with a similar size to the cells on the surface of an agar plate, allowed them to dry, then inoculated and grew a colony on top of the agar plate and imaged the tracer beads to track lineages. In this way, we are able to track lineages without genetic labels at low density (i.e. sparsely) in the colony so that we can distinguish individual lineages without needing high-resolution microscopy. We find that the bead trajectories track cell lineages over the course of one hour both at the colony front and behind the front (Figs. 1c, S1c, d, and S2). We chose to spread fluorescent tracer beads on the surface of the agar so that they could continue to be incorporated into the colony as it grew, which would allow us to track lineages even as existing beads and lineages get lost from the front. Even though behind the front many cells will be piled up on top of other cells rather than in contact with the agar, we don’t expect this to affect the ability of the beads to measure demographic noise, since lineages at the front (where cells are in a monolayer) are the most likely to contribute offspring to future generations [26].Fig. 1: Label-free method of measuring demographic noise in microbial colonies.a Schematic of bead-based sparse lineage tracing method for measuring demographic noise. b Schematic of existing method for measuring fraction of diversity preserved [26]. c (Top) The trajectory of a single bead (black) and the lineages of the cells neighboring it in the final-timepoint (colors) traced backwards in time in the Keio collection wild type strain. (Bottom) The deviation of the distance between the cell lineages and the bead from the final distance, backwards in time. Colors are the same as in the time series images. The gray shaded region shows a single cell width away or towards the bead. All cells that neighbor the bead in the final timepoint, except for one (orange), are neighbors of the bead in the first timepoint and stay within a single cell width of the final distance to the bead. d Example neutral mixtures of YFP and CFP tagged strains grown for 1 day and bead trajectories for strains highlighted in e. Black lines show the colony front at 12 and 23 hours. e Comparison of MSD at window size L = 50 µm to the fraction of diversity preserved for 3 E. coli strain backgrounds and 6 single gene deletions on the Keio collection wild type background (BW25113). Error bars in MSD represent the standard error of the weighted mean (N = 7–8, see Methods) and error bars in the fraction of diversity preserved represent the standard error of the weighted mean (N = 8) where weights come from uncertainties in counting the number of sectors.Full size imageFluorescent tracer beadsFor experiments with E. coli, 1 µm red fluorescent polystyrene beads from Magsphere (PSF-001UM, Pasadena, CA, USA) were diluted to 3 µg/mL in molecular grade water and 920 µL was spread on the surface of the prepared OmniTray agar plates with sterile glass beads. Excess bead solution was poured out, and the plates were dried under the flow of a class II biosafety cabinet (Nuaire, NU-425-300ES, Plymouth, MN, USA) for 45 min. The bead density was chosen to achieve ~250 beads in a 56x field of view. For experiments with S. cerervisiae, 2 µm dragon green fluorescent polystyrene beads from Bang’s labs (FSDG005, Fishers, IN, USA) were used at a similar surface density.Measurement of the distribution of demographic noiseWe randomly selected 352 single gene deletion strains from the Keio collection. For each experiment, cells were thawed from glycerol stock (see Supplementary Methods), mixed, and 5 µL was transferred into a 96-well flat bottom plate with 100 µL LB and the appropriate antibiotics. Plates were covered with Breathe-Easy sealing membrane (Diversified Biotech BEM-1, Doylestown, PA, USA) and grown for 12 h at 37 °C without shaking. A floating pin replicator (V&P Scientific, FP12, 2.36 mm pin diameter, San Diego, CA, USA) was used to inoculate a 2–3 mm droplet from each well of the liquid culture onto a prepared OmniTray covered with fluorescent tracer beads. Droplets were dried and the plates were incubated upside down at 37 °C for 12 h before timelapse imaging.To account for systematic differences between plates, we also put 8 wild type BW25113 wells in each 96-well plate in different positions on each plate. The mean squared displacement (MSD, see below) of each gene deletion colony was normalized to the weighted average MSD of the wild type BW25113 colonies on that plate, 〈MSD〉WT, and this “relative MSD” is reported. We performed three biological replicates for each strain (grown from the same glycerol stock, Fig. S3), and their measurements were averaged together weighted by the inverse of the square of their individual error in relative MSD. The reported error for the strain is the standard error of the mean. During the experiment, several experimental challenges impede our ability to measure demographic noise, including the appearance of beneficial sectors (identified as diverging bead trajectories that correspond to bulges at the colony front) either due to de novo beneficial mutations or standing variation from glycerol stock (see Supplementary Section 2.4, Figs. S4 and S5), slow growth rate leading to bead tracks that were too short for analysis, no cells transferred during inoculation with our pinning tool, inaccurate particle tracking due to beads being too close together, or out of focus images. In order to keep only the highest quality data points, we focused on the 191 strains that had at least 2 replicates free of such issues.Timelapse imaging of fluorescent beadsPlates were transferred to an ibidi stagetop incubator (Catalog number 10918, Gräfelfing, Germany) set to 37 °C for imaging. Evaporation was minimized by putting wet Kim wipes in the chamber and sealing the chamber with tape. The fluorescent tracer beads at the front of the colony were imaged with a Zeiss Axio Zoom.V16 (Oberkochen, Germany) at 56x magnification. A custom macro program written using the Open Application Development for Zen software was used to find the initial focal position for each colony and adjust for deterministic focus drift over time due to slight evaporation. Timelapse imaging was performed at an interval of 10 min for 12 h, during which time the colony grew about halfway across the field of view. Two z slices were taken for each colony and postprocessed to find the most in-focus image to adjust for additional focus drift. Subpixel-resolution particle tracking of the bead trajectory was achieved using a combination of particle image velocimetry and single particle tracking [38] and is described in detail in the Supplementary Methods.Measurement of bead trajectory mean squared displacementThe measurement of mean squared displacement (MSD) is adapted from [31] and is illustrated in Figs. 1a and S1a. Points in a trajectory that fall within a window of length L are fit to a line of best fit. The MSD is given by$$MSDleft( L right) = leftlangle {leftlangle {frac{1}{L}mathop {int}nolimits_l^{l + L} {left( {{Delta}wleft( {L^prime } right)} right)^2dL^prime } } rightrangle _{windows}} rightrangle _{trajectories}$$where Δw(L’) is the displacement of the bead trajectory from the line of best fit at each point, 〈〉windows is an average over all possible definitions of a window with length L along the trajectory (window definitions are overlapping), and 〈〉trajectories is a weighted average over all trajectories in a field of view, where the weight is the inverse squared standard error of the mean for each trajectory’s MSD(L) (Fig. S1a). We use 200 linearly spaced window sizes from L = 6 to 1152 μm. Window sizes that fit in fewer than 5 trajectories are dropped due to the noisiness in calculating the averaged MSD(L). The combined MSD(L) for all trajectories reflects that of bead trajectories at the colony front, which will have the largest contribution to the strength of demographic noise [26] (Fig. S6). Because we expect the trajectories to follow an anomalous random walk [31], the combined MSD(L) for all trajectories across the field of view is fit using weighted least squares to a power law, where the weight is the inverse square of the propagated standard error of the mean. Colonies with data in fewer than 5 window sizes are dropped due to the noisiness in fitting to a power law. The fit is extrapolated or interpolated to L = 50 µm to give a single summary statistic for each colony, and this quantity is reported as MSD(L = 50 µm) (see Supplementary Section 2.2, Figs. S7 and S8), and the error is calculated as half the difference in MSD (L = 50 µm) from using the upper and lower bounded coefficients to the fit. For calculating the distribution of demographic noise effects, only MSD values where the error is less than half of the value are kept.Measurement of phenotypic traitsFor the phenotypic trait measurements, in addition to the 191 single gene deletions, we also measured 41 additional strains of E. coli which included 4 strain backgrounds, 1 mreB knockout in the MC1000 background, 2 adhesin mutants, and 34 single gene knockouts from the Keio collection that we predicted may have large changes to demographic noise because of an altered biofilm forming ability in liquid culture [39] or altered cell shape from the wild type (using the classification on the Keio website, https://shigen.nig.ac.jp/ecoli/strain/resource/keioCollection/list). We normalized all phenotypic trait values to the average value measured from the wild type colonies on the same plate. The reported values for each strain are averages across 2–3 replicate colonies on different plates and the errors are the standard error of the mean. See the Supplementary Methods for more details of the specific phenotypic trait measurements.Measurement of neutral fraction of diversity preservedNeutral fluorescent pairs were created by transforming background strains with plasmids pQY10 (YFP, SpecR) or pQY11 (CFP, SpecR). Cells were streaked from glycerol stock and a single colony of each strain was inoculated into a 96 well plate with 600 µL LB and 120 µg/mL spectinomycin for plasmid retention. Plates were covered with Breathe-Easy sealing membrane and grown for 12 h at 37 °C without shaking. 50 µL of culture from each strain in a neutral pair were mixed and a floating pin replicator was used to inoculate a 2–3 mm droplet from the liquid culture onto a prepared OmniTray covered with fluorescent tracer beads. Droplets were dried and the plates were incubated at 37 °C.Colonies were imaged after 24 h with fluorescence microscopy using a Zeiss Axio Zoom.V16 and the number of sectors of each color was manually counted. The fraction of diversity preserved was calculated as in Ref. [26] by dividing the number of neutral sectors by one-half times the estimated initial number of cells at the inoculum front (see Fig. 1b). The factor of one-half accounts for the probability that two neighboring cells at the inoculum front share the same color label. The initial number of cells is estimated by measuring the inoculum size of each colony (manually measured by fitting a circle to a brightfield backlight image at the time of inoculation) divided by the effective cell size for E. coli (sqrt(length*width) taken to be 1.7 µm, Ref. [26]).Colony fitnessThe colony fitness coefficient between two strains was measured using a colony collision assay as described in Refs. [26, 40] by growing colonies next to one another and measuring the curvature of the intersecting arc upon collision. Cells were streaked from glycerol stock and a single colony for each strain was inoculated into LB with 120 µg/mL spectinomycin for plasmid retention and incubated at 37 °C for 15 h. The culture was back diluted 1:500 in 1 mL fresh LB with 120 µg/mL spectinomycin and grown at 37 °C for 4 h. 1 µL of the culture was then inoculated onto the prepared 12cmx12cm square petri dishes containing LB with different concentrations of chloramphenicol (0 µg/mL, 1 µg/mL, 2 µg/mL, 3 µg/mL) in pairs that were 5 mm apart, with 32 pairs per plate, then the colonies were incubated at 37 °C. After half of a day, bright field backlight images are taken and were used to fit circles to each colony to determine the distance between the two colonies. After 6 days, the colonies were imaged with fluorescence microscopy using a Zeiss Axio Zoom.V16. The radius of curvature of the intersecting arc between the two colonies was determined with image segmentation and was used to calculate the fitness coefficient between the two strains (Fig. S9a).Measurement of non-neutral establishment probabilityWe transformed 9 gene deletion strains from the Keio collection (gpmI, recB, pgm, tolQ, ychJ, lpcA, dsbA, rfaF, tatB) and 3 strain backgrounds (BW25113, MG1655, DH5α) with pQY11 (CFP, SpecR) or pQY12 (YFP, SpecR, CmR). Cells were streaked from glycerol stock and a single colony of each strain was inoculated into media with 120 µg/mL spectinomycin for plasmid retention, then incubated at 37 °C for 16 h. The culture was back-diluted 1:1000 in 1 mL fresh media with 120 µg/mL spectinomycin and grown at 37 °C for 4 h. YFP chloramphenicol-resistant and CFP chloramphenicol-sensitive cells from the same strain background were mixed respectively at approximately 1:500, 1:200, and 1:50 and distributed in a 96-well plate. A floating pin replicator was used to inoculate a 2–3 mm droplet from the liquid culture onto prepared OmniTrays with varying concentrations of chloramphenicol (0 µg/mL, 1 µg/mL, 2 µg/mL, 3 µg/mL). Droplets were dried and the plates were incubated at 37 °C for 3 days, then imaged by fluorescence microscopy using a Zeiss Axio Zoom.V16.The establishment probability of the resistant strain can be measured by counting the number of established resistant sectors normalized by the initial number of resistant cells at the inoculum front [26], which gives the probability that any given resistant cell in the inoculum escaped genetic drift and grew to a large enough size to create a sector. Briefly,$$p_{est} = N_{sectors}/N_0$$
(1)
where Nsectors is the number of resistant sectors after 3 days (counted by eye) and N0 is the estimated initial number of cells of the resistant type at the inoculum front. Because the establishment probability can only be accurately measured when the initial number of resistant cells is low enough that the resistant sectors do not interact with one another, we only keep colonies where neighboring resistant sectors are distinguishable at the colony front. In cases where we could see that a sector had coalesced from multiple sectors, we counted the number of sectors pre-coalescence. We also did not find a clear downward bias in the establishment probability as a function of initial mutant fraction (Fig. S10), suggesting that the probability of sector coalescence is low in the regime of these experimental parameters. The initial number N0 of cells of the resistant type is estimated by multiplying the initial number of cells at the inoculum front (see measurement of neutral fraction of diversity preserved) by the fraction of resistant cells in the inoculum (measured by plating and counting CFUs). More

1.Sharma VK. Adaptive significance of circadian clocks. Chronobiol Int. 2003;20:901–19.PubMed 
Article 

Google Scholar 
2.Paranjpe DA, Kumar Sharma V. Evolution of temporal order in living organisms. J Circadian Rhythms. 2005;3:1–13.Article 
CAS 

Google Scholar 
3.Nobs SP, Tuganbaev T, Elinav E. Microbiome diurnal rhythmicity and its impact on host physiology and disease risk. EMBO Rep. 2019;20:e47129.PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
4.Sartor F, Eelderink-Chen Z, Aronson B, Bosman J, Hibbert LE, Dodd AN, et al. Are there circadian clocks in non-photosynthetic bacteria? Biology. 2019;8:41.CAS 
PubMed Central 
Article 
PubMed 

Google Scholar 
5.Soriano M, Roibas B, Garcia A, Espinosa-Urgel M. Evidence of circadian rhythms in non-photosynthetic bacteria? J Circadian Rhythms. 2010;8:1–4.Article 

Google Scholar 
6.Thaiss CA, Zeevi D, Levy M, Zilberman-Schapira G, Suez J, Tengeler AC, et al. Transkingdom control of microbiota diurnal oscillations promotes metabolic homeostasis. Cell. 2014;159:514–29.CAS 
PubMed 
Article 

Google Scholar 
7.Ishiura M, Kutsuna S, Aoki S, Iwasaki H, Andersson CR, Tanabe A, et al. Expression of a gene cluster kaiABC as a circadian feedback process in cyanobacteria. Science. 1998;281:1519–23.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
8.Aylward FO, Boeuf D, Mende DR, Wood-Charlson EM, Vislova A, Eppley JM, et al. Diel cycling and long-term persistence of viruses in the ocean’s euphotic zone. Proc Natl Acad Sci USA. 2017;114:11446–51.CAS 
PubMed 
Article 

Google Scholar 
9.Dvornyk V, Vinogradova O, Nevo E. Origin and evolution of circadian clock genes in prokaryotes. Proc Natl Acad Sci USA. 2003;100:2495–500.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
10.Maniscalco M, Nannen J, Sodi V, Silver G, Lowrey PL, Bidle KA. Light-dependent expression of four cryptic archaeal circadian gene homologs. Front Microbiol. 2014;5:79.PubMed 
PubMed Central 
Article 

Google Scholar 
11.Bernal P, Allsopp LP, Filloux A, Llamas MA. The Pseudomonas putida T6SS is a plant warden against phytopathogens. ISME J. 2017;11:972–87.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
12.Schmelling NM, Lehmann R, Chaudhury P, Beck C, Albers S-V, Axmann IM, et al. Minimal tool set for a prokaryotic circadian clock. BMC Evol Biol. 2017;17:1–20.Article 
CAS 

Google Scholar 
13.Hong L, Vani BP, Thiede EH, Rust MJ, Dinner AR. Molecular dynamics simulations of nucleotide release from the circadian clock protein KaiC reveal atomic-resolution functional insights. Proc Natl Acad Sci USA. 2018;115:11475–84.Article 
CAS 

Google Scholar 
14.Edgar RS, Green EW, Zhao Y, Ooijen Gvan, Olmedo M, Qin X, et al. Peroxiredoxins are conserved markers of circadian rhythms. Nature. 2012;485:459–64.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
15.Harmer SL, Hogenesch JB, Straume M, Chang H-S, Han B, Zhu T, et al. Orchestrated transcription of key pathways in Arabidopsis by the circadian clock. Science. 2000;290:2110–3.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
16.Farré EM, Weise SE. The interactions between the circadian clock and primary metabolism. Curr Opin Plant Biol. 2012;15:293–300.PubMed 
Article 
CAS 
PubMed Central 

Google Scholar 
17.Harmer SL. The circadian system in higher plants. Annu Rev Plant Biol. 2009;60:357–77.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
18.Haydon MJ, Mielczarek O, Robertson FC, Hubbard KE, Webb AAR. Photosynthetic entrainment of the Arabidopsis circadian clock. Nature. 2013;502:689–92.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
19.DeAngelis KM, Brodie EL, DeSantis TZ, Andersen GL, Lindow SE, Firestone MK. Selective progressive response of soil microbial community to wild oat roots. ISME J. 2009;3:168–78.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
20.Lundberg DS, Lebeis SL, Paredes SH, Yourstone S, Gehring J, Malfatti S, et al. Defining the core Arabidopsis thaliana root microbiome. Nature. 2012;488:86–90.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
21.Zhalnina K, Louie KB, Hao Z, Mansoori N, da Rocha UN, Shi S, et al. Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly. Nat Microbiol. 2018;3:470–80.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
22.Wu G, Tang W, He Y, Hu J, Gong S, He Z, et al. Light exposure influences the diurnal oscillation of gut microbiota in mice. Biochem Biophys Res Commun. 2018;501:16–23.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Teichman EM, O’Riordan KJ, Gahan CGM, Dinan TG, Cryan JF. When rhythms meet the blues: circadian interactions with the microbiota-gut-brain axis. Cell Metab. 2020;31:448–71.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
24.Zarrinpar A, Chaix A, Yooseph S, Panda S. Diet and feeding pattern affect the diurnal dynamics of the gut microbiome. Cell Metab. 2014;20:1006–17.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
25.Leone V, Gibbons SM, Martinez K, Hutchison AL, Huang EY, Cham CM, et al. Effects of diurnal variation of gut microbes and high-fat feeding on host circadian clock function and metabolism. Cell Host Microbe. 2015;17:681–9.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
26.Liang X, Bushman FD, FitzGerald GA. Rhythmicity of the intestinal microbiota is regulated by gender and the host circadian clock. Proc Natl Acad Sci USA. 2015;112:10479–84.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
27.Kaczmarek JL, Musaad SM, Holscher HD. Time of day and eating behaviors are associated with the composition and function of the human gastrointestinal microbiota. Am J Clin Nutr. 2017;106:1220–31.CAS 
PubMed 
PubMed Central 

Google Scholar 
28.Deaver JA, Eum SY, Toborek M. Circadian disruption changes gut microbiome taxa and functional gene composition. Front Microbiol. 2018;9:737.PubMed 
PubMed Central 
Article 

Google Scholar 
29.Hubbard CJ, Brock MT, van Diepen LT, Maignien L, Ewers BE, Weinig C. The plant circadian clock influences rhizosphere community structure and function. ISME J. 2018;12:400–10.PubMed 
Article 
PubMed Central 

Google Scholar 
30.Staley C, Ferrieri AP, Tfaily MM, Cui Y, Chu RK, Wang P, et al. Diurnal cycling of rhizosphere bacterial communities is associated with shifts in carbon metabolism. Microbiome. 2017;5:1–13.Article 

Google Scholar 
31.Feng J, Xu Y, Ma B, Tang C, Brookes PC, He Y, et al. Assembly of root-associated microbiomes of typical rice cultivars in response to lindane pollution. Environ Int. 2019;131:104975.CAS 
PubMed 
Article 

Google Scholar 
32.Gremion F, Chatzinotas A, Harms H. Comparative 16S rDNA and 16S rRNA sequence analysis indicates that Actinobacteria might be a dominant part of the metabolically active bacteria in heavy metal-contaminated bulk and rhizosphere soil. Environ Microbiol. 2003;5:896–907.CAS 
PubMed 
Article 

Google Scholar 
33.Lavecchia A, Curci M, Jangid K, Whitman WB, Ricciuti P, Pascazio S, et al. Microbial 16S gene-based composition of a sorghum cropped rhizosphere soil under different fertilization managements. Biol Fertil Soils. 2015;51:661–72.CAS 
Article 

Google Scholar 
34.Wang B, Zhao J, Guo Z, Ma J, Xu H, Jia Z. Differential contributions of ammonia oxidizers and nitrite oxidizers to nitrification in four paddy soils. ISME J. 2015;9:1062–75.CAS 
PubMed 
Article 

Google Scholar 
35.Rognes T, Flouri T, Nichols B, Quince C, Mahé F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;4:e2584.PubMed 
PubMed Central 
Article 

Google Scholar 
36.Cole JR, Wang Q, Fish JA, Chai B, McGarrell DM, Sun Y, et al. Ribosomal Database Project: data and tools for high throughput rRNA analysis. Nucleic Acids Res. 2014;42:633–42.Article 
CAS 

Google Scholar 
37.Edgar RC. SINTAX: a simple non-Bayesian taxonomy classifier for 16S and ITS sequences. 2016. https://www.biorxiv.org/content/10.1101/074161v1.38.Deng Y, Ruan Y, Ma B, Timmons MB, Lu H, Xu X, et al. Multi-omics analysis reveals niche and fitness differences in typical denitrification microbial aggregations. Environ Int. 2019;132:105085.CAS 
PubMed 
Article 

Google Scholar 
39.Yu M, Meng J, Yu L, Su W, Afzal M, Li Y, et al. Changes in nitrogen related functional genes along soil pH, C and nutrient gradients in the charosphere. Sci Total Environ. 2019;650:626–32.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
40.Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:1–12.Article 
CAS 

Google Scholar 
41.Hamady M, Lozupone C, Knight R. Fast UniFrac: facilitating high-throughput phylogenetic analyses of microbial communities including analysis of pyrosequencing and PhyloChip data. ISME J. 2010;4:17–27.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
42.Zhang J, Zhang N, Liu Y-X, Zhang X, Hu B, Qin Y, et al. Root microbiota shift in rice correlates with resident time in the field and developmental stage. Sci China Life Sci. 2018;61:613–21.PubMed 
Article 
PubMed Central 

Google Scholar 
43.Liaw A, Wiener M. Classification and regression by random Forest. R News. 2002;2:18–22.
Google Scholar 
44.Breiman L. Random forests. Mach Learn. 2001;45:5–32.Article 

Google Scholar 
45.Ma B, Wang H, Dsouza M, Lou J, He Y, Dai Z, et al. Geographic patterns of co-occurrence network topological features for soil microbiota at continental scale in eastern China. ISME J. 2016;10:1891–901.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Wang B, Pourshafeie A, Zitnik M, Zhu J, Bustamante CD, Batzoglou S, et al. Network enhancement as a general method to denoise weighted biological networks. Nat Commun. 2018;9:1–8.Article 
CAS 

Google Scholar 
47.Luo F, Zhong J, Yang Y, Scheuermann RH, Zhou J. Application of random matrix theory to biological networks. Phys Lett A. 2006;357:420–3.CAS 
Article 

Google Scholar 
48.Csardi G, Nepusz T. The igraph software package for complex network research. InterJournal. Complex Syst. 2006;1695:1–9.
Google Scholar 
49.Bastian M, Heymann S, Jacomy M. Gephi: an open source software for exploring and manipulating networks. Icwsm. 2009;8:361–2.
Google Scholar 
50.Dixon P. VEGAN, a package of R functions for community ecology. J Veg Sci. 2003;14:927–30.Article 

Google Scholar 
51.Li H, Su J-Q, Yang X-R, Zhu Y-G. Distinct rhizosphere effect on active and total bacterial communities in paddy soils. Sci Total Environ. 2019;649:422–30.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
52.Vieira S, Sikorski J, Dietz S, Herz K, Schrumpf M, Bruelheide H, et al. Drivers of the composition of active rhizosphere bacterial communities in temperate grasslands. ISME J. 2019; 1–13.53.Yerushalmi S, Green RM. Evidence for the adaptive significance of circadian rhythms. Ecol Lett. 2009;12:970–81.PubMed 
Article 
PubMed Central 

Google Scholar 
54.Pedersen O, Sand‐Jensen K, Revsbech NP. Diel pulses of O2 and CO2 in sandy lake sediments inhabited by Lobelia dortmanna. Ecology. 1995;76:1536–45.Article 

Google Scholar 
55.Hernandez ME, Beck DAC, Lidstrom ME, Chistoserdova L. Oxygen availability is a major factor in determining the composition of microbial communities involved in methane oxidation. PeerJ. 2015;3:e801.PubMed 
PubMed Central 
Article 

Google Scholar 
56.Saifuddin M, Bhatnagar JM, Segrè D, Finzi AC. Microbial carbon use efficiency predicted from genome-scale metabolic models. Nat Commun. 2019;10:1–10.CAS 
Article 

Google Scholar 
57.Fierer N, Bradford MA, Jackson RB. Toward an ecological classification of soil bacteria. Ecology. 2007;88:1354–64.PubMed 
Article 
PubMed Central 

Google Scholar 
58.Saleem M, Hu J, Jousset A. More than the sum of its parts: microbiome biodiversity as a driver of plant growth and soil health. Annu Rev Ecol Evol Syst. 2019;50:145–68.Article 

Google Scholar 
59.Cozzi G, Broekhuis F, McNutt JW, Turnbull LA, Macdonald DW, Schmid B. Fear of the dark or dinner by moonlight? Reduced temporal partitioning among Africa’s large carnivores. Ecology. 2012;93:2590–9.PubMed 
Article 
PubMed Central 

Google Scholar 
60.Kohl MT, Ruth TK, Metz MC, Stahler DR, Smith DW, White PJ, et al. Do prey select for vacant hunting domains to minimize a multi-predator threat? Ecol Lett. 2019;22:1724–33.PubMed 
Article 
PubMed Central 

Google Scholar 
61.de Vries FT, Griffiths RI, Bailey M, Craig H, Girlanda M, Gweon HS, et al. Soil bacterial networks are less stable under drought than fungal networks. Nat Commun. 2018;9:1–12.Article 
CAS 

Google Scholar 
62.Schmidt JE, Kent AD, Brisson VL, Gaudin ACM. Agricultural management and plant selection interactively affect rhizosphere microbial community structure and nitrogen cycling. Microbiome. 2019;7:1–18.Article 

Google Scholar 
63.DeCoursey PJ, Walker JK, Smith SA. A circadian pacemaker in free-living chipmunks: essential for survival? J Comp Physiol A. 2000;186:169–80.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
64.Worden BD, Skemp AK, Papaj DR. Learning in two contexts: the effects of interference and body size in bumblebees. J Exp Biol. 2005;208:2045–53.PubMed 
Article 
PubMed Central 

Google Scholar 
65.Yerushalmi S, Bodenhaimer S, Bloch G. Developmentally determined attenuation in circadian rhythms links chronobiology to social organization in bees. J Exp Biol. 2006;209:1044–51.PubMed 
Article 
PubMed Central 

Google Scholar 
66.Lone SR, Sharma VK. Exposure to light enhances pre-adult fitness in two dark-dwelling sympatric species of ants. BMC Dev Biol. 2008;8:1–11.Article 

Google Scholar 
67.Yadav P, Choudhury D, Sadanandappa MK, Sharma VK. Extent of mismatch between the period of circadian clocks and light/dark cycles determines time-to-emergence in fruit flies. Insect Sci. 2015;22:569–77.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
68.Yadav P, Thandapani M, Sharma VK. Interaction of light regimes and circadian clocks modulate timing of pre-adult developmental events in Drosophila. BMC Dev Biol. 2014;14:1–12.Article 
CAS 

Google Scholar 
69.Woelfle MA, Ouyang Y, Phanvijhitsiri K, Johnson CH. The adaptive value of circadian clocks: an experimental assessment in cyanobacteria. Curr Biol. 2004;14:1481–6.CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
70.Lambert G, Chew J, Rust MJ. Costs of clock-environment misalignment in individual cyanobacterial cells. Biophys J. 2016;111:883–91.CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
71.Lai AG, Doherty CJ, Mueller-Roeber B, Kay SA, Schippers JHM, Dijkwel PP. CIRCADIAN CLOCK-ASSOCIATED 1 regulates ROS homeostasis and oxidative stress responses. Proc Natl Acad Sci USA. 2012;109:17129–34.CAS 
PubMed 
Article 

Google Scholar 
72.Tanaka K, Ishikawa M, Kaneko M, Kamiya K, Kato S, Nakanishi S. The endogenous redox rhythm is controlled by a central circadian oscillator in cyanobacterium Synechococcus elongatus PCC7942. Photosynth Res. 2019;142:203–10.CAS 
PubMed 
Article 

Google Scholar 
73.Tanaka K, Nakanishi S Time-of-day dependent responses of cyanobacterial cellular viability against oxidative stress. 2019. https://www.biorxiv.org/content/10.1101/851774v2.74.Krittika S, Yadav P. Circadian clocks: an overview on its adaptive significance. Biol Rhythm Res 2019;0:1–24.CAS 

Google Scholar 
75.Koilraj AJ, Sharma VK, Marimuthu G, Chandrashekaran MK. Presence of circadian rhythms in the locomotor activity of a cave-dwelling millipede Glyphiulus cavernicolus sulu (Cambalidae, Spirostreptida). Chronobiol Int. 2000;17:757–65.CAS 
PubMed 
Article 

Google Scholar 
76.Roenneberg T, Merrow M. Life before the clock: modeling circadian evolution. J Biol Rhythms. 2002;17:495–505.PubMed 
Article 

Google Scholar 
77.Espinasa L, Jeffery WR. Conservation of retinal circadian rhythms during cavefish eye degeneration. Evol Dev. 2006;8:16–22.PubMed 
Article 

Google Scholar 
78.Hubbard CJ, McMinn RL, Weinig C. Rhizosphere microbes influence host circadian clock function. 2018. https://www.biorxiv.org/content/10.1101/444539v1. More

Mosquito abundance, biting rate and morphological identificationsA total of 3920 Anopheles sp. females, 1167 and 2753 during the dry and rainy seasons respectively, were captured on a total of 60 collection days. Overall 81% (3187/3920) of the samples were collected in the cow odor-baited double net traps (CBNTs) and while this relative abundance was rather consistent between different collection sites for the CBNTs, 67% (490/733) of the Anopheles from the human odor-baited double net traps (HBNTs) were collected in the forest sites (Table S1).The biting rate (# of females/trap/day) for the HBNTs was consistently higher in the forest sites compared to all other locations during both the rainy and the dry seasons (Table 1). However, for the CBNTs, while the biting rate was the highest in the forest sites during the dry season, the tendency changed during the rainy season with a higher biting rate in the villages and the forests near the villages compared to the forest sites (Table 1).Table 1 Biting and infectious rates of Anopheles mosquitoes collected by HBNTs and CBNTs across sites and seasons.Full size tableA total of 3131 females were morphologically identified as 14 different Anopheles species or complexes of morphologically indistinguishable sibling species. Based on these morphological identifications, species thought to be primary vectors comprised only 10.2% of the collected mosquitoes: Anopheles dirus s.l. (8.1%, n = 319), A. minimus s.l. (0.4%, n = 15) and A. maculatus s.l. (1.7%, n = 67). The most abundant species (represented by more than a hundred individuals in our collection) constituted 75.8% of the collected Anopheles mosquitoes and were represented by 6 species complexes: A. barbirostris (21.2%, n = 831), A. philippinensis (14.6%, n = 571), A. hyrcanus (13.6%, n = 535), A. kochi (10.5%, n = 412), A. dirus (8.1%), A. aconitus (7.7%, n = 303).Molecular determination of mosquito speciesA total of 844 females were molecularly characterized for species in the random subset and represent 26 distinct Anopheles species as determined by ITS2 and CO1 (Table S2). The most abundant species (representing ≥ 5% of the samples; n ≥ 42) comprise 77.8% of the molecularly typed individuals and represent 8 species from 6 different species complexes. These most abundant species included A. dirus (13.2%, n = 112) from the Dirus complex. From the Barbirostris complex; A. dissidens (13.2%, n = 112), and A. campestris-wejchoochotei (8.1%, n = 69). From the Hyrcanus Group, A. peditaeniatus (12.8%, n = 108), and A. nitidus (5.7%, n = 48). The Annularis, Funestus, and Kochi Groups were each represented by a single species A. nivipes (9.2%, n = 78), A. aconitus (6.7%, n = 57), and A. kochi (8.7%, n = 74), respectively. The 18 less abundant species, represent by fewer than 42 samples and in many cases just a handful of samples included A. philippinensis (n = 17) and A. annularis (n = 1) from the Annularis Group, A. jamesii (n = 16), A. pseudojamesi (n = 1), and A. splendidus (n = 1) from the Jamesii Group and A. saeungae (n = 29) and A. barbirostris (n = 2) from the Barbirostris Group. From the Hyrcanus Group A. crawfordi (n = 40), A. argyropus (n = 1), An. nigerrimus (n = 28), and A. sinensis (n = 3) were sampled. Anopheles maculatus (n = 22), A. sawadwongporni (n = 4), and A. rampae (n = 2) from the Maculatus Group. Anopheles tessellatus from the Tessellatus Group and A. interruptus from the Asiaticus Group were each sampled once. A. vagus (n = 12) and A. karwari (n = 3) were also present. There were 2 mosquitoes that had 99.9% identical ITS2 and 99.4% identical CO1 sequences but matched no species in the NCBI database. In addition to the random subset, 79 Plasmodium sp. infected samples were molecularly characterized for species which resulted in a total of 29 Anopheles species as determined by ITS2 and CO1.Day biting rateOverall 20.2 ± 1.2% of the Anopheles females were captured during the daytime (between 06:00 and 18:00). Indeed, while the majority of Anopheles mosquitoes bite at night, an important proportion was active during the day (Fig. 2). Excluding species with extremely low sample sizes and unidentified samples, day biting behaviour was observed for all the Anopheles species and varied from 13 to 68% (Table S3).Figure 2Average number of Anopheles females collected per trap per hour in the different collection sites in the HBNTs and the CBNTs.Full size imageThe day biting rate in the HBNTs was not different across collection sites (19.6 ± 2.9%; χ2 = 3.6, df = 3, p = 0.3; Fig. 3a) but was higher during the dry season (25.9 ± 4.6%) compared to the rainy season (13.8 ± 3.5%; χ2 = 19.08, df = 1, p  More

1.Grossnickle, D. M., Smith, S. M. & Wilson, G. P. Untangling the multiple ecological radiations of early mammals. Trends Ecol. Evol. 34, 936–949 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
2.Maor, R., Dayan, T., Ferguson-Gow, H. & Jones, K. E. Temporal niche expansion in mammals from a nocturnal ancestor after dinosaur extinction. Nat. Ecol. Evol. 1, 1889–1895 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
3.Faurby, S. et al. PHYLACINE 1.2: the phylogenetic atlas of mammal macroecology. Ecology 99, 2626–2626 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
4.Refinetti, R. The diversity of temporal niches in mammals. Biol. Rhythm Res. 39, 173–192 (2008).Article 

Google Scholar 
5.DeCoursey, P. J. Diversity of function of SCN pacemakers in behavior and ecology of three species of sciurid rodents. Biol. Rhythm Res. 35, 13–33 (2004).Article 

Google Scholar 
6.Hut, R. A., Kronfeld-Schor, N., van der Vinne, V. & De la Iglesia, H. In search of a temporal niche: environmental factors. In Neurobiology of Circadian Timing, Vol. 199 (eds. Kalsbeek, A., Merrow, M., Roenneberg, T. & Foster, R. G.) 281–304 (Elsevier, 2012).7.Pianka, E. R., Vitt, L. J., Pelegrin, N., Fitzgerald, D. B. & Winemiller, K. O. Toward a periodic table of niches, or exploring the lizard niche hypervolume. Am. Nat. 190, 601–616 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
8.Violle, C. et al. Functional rarity: the ecology of outliers. Trends Ecol. Evol. 32, 356–367 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
9.Cooke, R. S. C., Bates, A. E. & Eigenbrod, F. Global trade-offs of functional redundancy and functional dispersion for birds and mammals. Glob. Ecol. Biogeogr. 28, 484–495 (2019).Article 

Google Scholar 
10.Flynn, D. F. B. et al. Loss of functional diversity under land use intensification across multiple taxa. Ecol. Lett. 12, 22–33 (2009).PubMed 
Article 
PubMed Central 

Google Scholar 
11.Brum, F. T. et al. Global priorities for conservation across multiple dimensions of mammalian diversity. Proc. Natl Acad. Sci. USA 114, 7641–7646 (2017).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
12.Cooke, R. S. C., Eigenbrod, F. & Bates, A. E. Projected losses of global mammal and bird ecological strategies. Nat. Commun. 10, 2279 (2019).ADS 
PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
13.Hutchinson, G. E. Concluding remarks. Cold Spring Harb. Symp. Quant. Biol. 22, 415–427 (1957).Article 

Google Scholar 
14.Mouillot, D. et al. Niche overlap estimates based on quantitative functional traits: a new family of non-parametric indices. Oecologia 145, 345–353 (2005).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
15.Gaynor, K. M., Hojnowski, C. E., Carter, N. H. & Brashares, J. S. The influence of human disturbance on wildlife nocturnality. Science 360, 1232–1235 (2018).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
16.Levy, O., Dayan, T., Porter, W. P. & Kronfeld-Schor, N. Time and ecological resilience: can diurnal animals compensate for climate change by shifting to nocturnal activity? Ecol. Monogr. 89, e01334 (2019).Article 

Google Scholar 
17.Ankel-Simons, F. & Rasmussen, D. T. Diurnality, nocturnality, and the evolution of primate visual systems. Am. J. Phys. Anthropol. 137, 100–117 (2008).Article 

Google Scholar 
18.Russo, D., Maglio, G., Rainho, A., Meyer, C. F. J. & Palmeirim, J. M. Out of the dark: diurnal activity in the bat Hipposideros ruber on São Tomé island (West Africa). Mamm. Biol. 76, 701–708 (2011).Article 

Google Scholar 
19.Halle, S. Ecological relevance of daily activity patterns. In Activity Patterns in Small Mammals: An Ecological Approach (eds. Halle, S. & Stenseth, N. C.) 67–90 (Springer, 2000).20.Mammola, S. Assessing similarity of n-dimensional hypervolumes: which metric to use? J. Biogeogr. 46, 2012–2023 (2019).Article 

Google Scholar 
21.Langerhans, R. B. & DeWitt, T. J. Shared and unique features of evolutionary diversification. Am. Nat. 164, 335–349 (2004).PubMed 
Article 
PubMed Central 

Google Scholar 
22.Sibly, R. M. & Brown, J. H. Effects of body size and lifestyle on evolution of mammal life histories. Proc. Natl Acad. Sci. USA 104, 17707–17712 (2007).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
23.Bielby, J. et al. The fast-slow continuum in mammalian life history: an empirical reevaluation. Am. Nat. 169, 748–757 (2007).CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
24.Cardillo, M. et al. Multiple causes of high extinction risk in large mammal species. Science 309, 1239–1241 (2005).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
25.Bennie, J. J., Duffy, J. P., Inger, R. & Gaston, K. J. Biogeography of time partitioning in mammals. Proc. Natl Acad. Sci. USA 111, 13727–13732 (2014).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
26.Bonebrake, T. C., Rezende, E. L. & Bozinovic, F. Climate change and thermoregulatory consequences of activity time in mammals. Am. Nat. 196, 45–56 (2020).PubMed 
Article 
PubMed Central 

Google Scholar 
27.Roll, U., Dayan, T. & Kronfeld-Schor, N. On the role of phylogeny in determining activity patterns of rodents. Evol. Ecol. 20, 479–490 (2006).Article 

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

Google Scholar 
29.Wilson, G. P. et al. Adaptive radiation of multituberculate mammals before the extinction of dinosaurs. Nature 483, 457–460 (2012).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
30.Anderson, S. R. & Wiens, J. J. Out of the dark: 350 million years of conservatism and evolution in diel activity patterns in vertebrates. Evolution 71, 1944–1959 (2017).PubMed 
Article 
PubMed Central 

Google Scholar 
31.Pei, Y., Valcu, M. & Kempenaers, B. Interference competition pressure predicts the number of avian predators that shifted their timing of activity. Proc. R. Soc. B 285, 20180744 (2018).PubMed 
Article 
PubMed Central 

Google Scholar 
32.Donati, G. & Borgognini-Tarli, S. M. From darkness to daylight: cathemeral activity in primates. J. Anthropol. Sci. 84, 7–32 (2006).
Google Scholar 
33.Veilleux, C. C. & Cummings, M. E. Nocturnal light environments and species ecology: implications for nocturnal color vision in forests. J. Exp. Biol. 215, 4085 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
34.le Roux, A., Cherry, M. I., Gygax, L. & Manser, M. B. Vigilance behaviour and fitness consequences: comparing a solitary foraging and an obligate group-foraging mammal. Behav. Ecol. Sociobiol. 63, 1097–1107 (2009).Article 

Google Scholar 
35.Voigt, C. C. & Lewanzik, D. Trapped in the darkness of the night: thermal and energetic constraints of daylight flight in bats. Proc. R. Soc. B 278, 2311–2317 (2011).PubMed 
Article 
PubMed Central 

Google Scholar 
36.Rydell, J. & Speakman, J. R. Evolution of nocturnality in bats: potential competitors and predators during their early history. Biol. J. Linn. Soc. 54, 183–191 (1995).Article 

Google Scholar 
37.Kronfeld-Schor, N. & Dayan, T. Partitioning of time as an ecological resource. Annu. Rev. Ecol. Evol. Syst. 34, 153–181 (2003).Article 

Google Scholar 
38.Gaston, K. J. Nighttime ecology: the “nocturnal problem” revisited. Am. Nat. 193, 481–502 (2019).PubMed 
Article 
PubMed Central 

Google Scholar 
39.Muul, I. & Lim, B. L. Comparative morphology, food habits, and ecology of some Malaysian arboreal rodents. In The Ecology of Arboreal Folivores (ed. Montgomery, G. G.) 361–368 (Smithsonian Institution, 1978).40.Hall, M. I., Kamilar, J. M. & Kirk, E. C. Eye shape and the nocturnal bottleneck of mammals. Proc. R. Soc. B 279, 4962–4968 (2012).PubMed 
Article 
PubMed Central 

Google Scholar 
41.Heesy, C. P. & Hall, M. I. The nocturnal bottleneck and the evolution of mammalian vision. Brain. Behav. Evol. 75, 195–203 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
42.Buckley, L. B., Hurlbert, A. H. & Jetz, W. Broad-scale ecological implications of ectothermy and endothermy in changing environments. Glob. Ecol. Biogeogr. 21, 873–885 (2012).Article 

Google Scholar 
43.Frey, S., Volpe, J. P., Heim, N. A., Paczkowski, J. & Fisher, J. T. Move to nocturnality not a universal trend in carnivore species on disturbed landscapes. Oikos 129, 1128–1140 (2020).Article 

Google Scholar 
44.Benítez-López, A. Animals feel safer from humans in the dark. Science 360, 1185–1186 (2018).ADS 
PubMed 
Article 
PubMed Central 

Google Scholar 
45.Rabaiotti, D. & Woodroffe, R. Coping with climate change: limited behavioral responses to hot weather in a tropical carnivore. Oecologia 189, 587–599 (2019).ADS 
CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
46.Gaston, K. J., Visser, M. E. & Hölker, F. The biological impacts of artificial light at night: the research challenge. Philos. Trans. R. Soc. B 370, 20140133 (2015).Article 

Google Scholar 
47.McCain, C. M. & King, S. R. B. Body size and activity times mediate mammalian responses to climate change. Glob. Change Biol. 20, 1760–1769 (2014).ADS 
Article 

Google Scholar 
48.Cox, D. T. C., Maclean, I. M. D., Gardner, A. S. & Gaston, K. J. Global variation in diurnal asymmetry in temperature, cloud cover, specific humidity and precipitation and its association with leaf area index. Glob. Change Biol. 26, 7099–7111 (2020).ADS 
Article 

Google Scholar 
49.Campbell, G. S. & Norman, J. M. Animals and their environment. In An Introduction to Environmental Biophysics (eds. Campbell, G. S. & Norman, J. M.) 185–207 (Springer, 1998).50.Estes, J. A. et al. Trophic downgrading of planet earth. Science 333, 301–306 (2011).ADS 
CAS 
PubMed 
Article 
PubMed Central 

Google Scholar 
51.Shores, C. R., Dellinger, J. A., Newkirk, E. S., Kachel, S. M. & Wirsing, A. J. Mesopredators change temporal activity in response to a recolonizing apex predator. Behav. Ecol. 30, 1324–1335 (2019).Article 

Google Scholar 
52.Carter, N., Jasny, M., Gurung, B. & Liu, J. Impacts of people and tigers on leopard spatiotemporal activity patterns in a global biodiversity hotspot. Glob. Ecol. Conserv. 3, 149–162 (2015).Article 

Google Scholar 
53.Cooke, R. S. C., Eigenbrod, F. & Bates, A. E. Ecological distinctiveness of birds and mammals at the global scale. Glob. Ecol. Conserv. 22, e00970 (2020).Article 

Google Scholar 
54.Petchey, O. L. & Gaston, K. J. Extinction and the loss of functional diversity. Proc. R. Soc. Lond. B 269, 1721–1727 (2002).Article 

Google Scholar 
55.Thuiller, W. et al. Conserving the functional and phylogenetic trees of life of European tetrapods. Philos. Trans. R. Soc. B 370, 20140005 (2015).Article 

Google Scholar 
56.Larsen, T. H., Williams, N. M. & Kremen, C. Extinction order and altered community structure rapidly disrupt ecosystem functioning. Ecol. Lett. 8, 538–547 (2005).PubMed 
Article 
PubMed Central 

Google Scholar 
57.Holker, F., Wolter, C., Perkin, E. K. & Tockner, K. Light pollution as a biodiversity threat. Trends Ecol. Evol. 25, 681–682 (2010).PubMed 
Article 
PubMed Central 

Google Scholar 
58.Mittermeier, R., Rylands, A., Lacher, T. & Wilson, D. Handbook of the Mammals of the World, Vol. 1–4 & 6–9 (Lynx Edicions, 2001).59.R Core Team. R: A Language and Environment for Statistical Computing (R Core Team, 2019).60.Pineda-Munoz, S. & Alroy, J. Dietary characterization of terrestrial mammals. Proc. R. Soc. B 281, 20141173 (2014).PubMed 
Article 
PubMed Central 

Google Scholar 
61.Villéger, S., Mason, N. W. H. & Mouillot, D. New multidimensional functional diversity indices for a multifaceted framework in functional ecology. Ecology 89, 2290–2301 (2008).PubMed 
Article 
PubMed Central 

Google Scholar 
62.Blonder, B., Lamanna, C., Violle, C. & Enquist, B. J. The n-dimensional hypervolume. Glob. Ecol. Biogeogr. 23, 595–609 (2014).Article 

Google Scholar 
63.Penone, C. et al. Imputation of missing data in life-history trait datasets: which approach performs the best? Methods Ecol. Evol. 5, 961–970 (2014).Article 

Google Scholar 
64.Taugourdeau, S., Villerd, J., Plantureux, S., Huguenin-Elie, O. & Amiaud, B. Filling the gap in functional trait databases: use of ecological hypotheses to replace missing data. Ecol. Evol. 4, 944–958 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
65.Filho, J. A. F. D., Rangel, T. F., Santos, T. & Bini, L. M. Exploring patterns of interspecific variation in quantitative traits using sequential phylogenetic eigenvector regressions. Evolution 66, 1079–1090 (2012).Article 

Google Scholar 
66.Duong, T. & Hazelton, M. Plug-in bandwidth matrices for bivariate kernel density estimation. J. Nonparametr. Stat. 15, 17–30 (2003).MathSciNet 
MATH 
Article 

Google Scholar 
67.Blonder, B. Hypervolume concepts in niche- and trait-based ecology. Ecography 41, 1441–1455 (2018).Article 

Google Scholar 
68.Cornwell, W. K., Schwilk, D. W. & Ackerly, D. D. A trait-based test for habitat filtering: convex hull volume. Ecology 87, 1465–1471 (2006).PubMed 
Article 
PubMed Central 

Google Scholar 
69.Wilman, H. et al. EltonTraits 1.0: Species-level foraging attributes of the world’s birds and mammals. Ecology 95, 2027–2027 (2014).Article 

Google Scholar  More

In this work, we performed stochastic simulations of a cyclic nonhierarchical system composed of 5 species. To this purpose, we implemented a standard numerical algorithm largely used to study spatial biological systems11,13,41. We considered a generalisation of the rock-paper-scissors game for 5 species, whose rules are illustrated in Fig. 1a. The arrows indicate a cyclic dominance among the species. Accordingly, individuals of species i beat individuals of species (i+1), with (i=1,2,3,4,5).The dynamics of individuals’ spatial organisation occurs in a square lattice with periodic boundary conditions, following the rules: selection, reproduction, and mobility. We assumed the May-Leonard implementation so that the total number of individuals is not conserved43. Each grid point contains at most one individual, which means that the maximum number of individuals is ({mathcal {N}}), the total number of grid points.Initially, the number of individuals is the same for all species, i.e., (I_i,=,{mathcal {N}}/5), with (i=1,2,3,4,5) (there are no empty spaces in the initial state). We prepared the initial conditions by distributing each individual at a random grid point. At each timestep, one interaction occurs, changing the spatial configuration of individuals. The possible interactions are:

Selection: (i j rightarrow i otimes ,), with (j = i+1), where (otimes) means an empty space; every time one selection interaction occurs, the grid point occupied by the individual of species (i+1) vanishes.

Reproduction: (i otimes rightarrow i i,); when one reproduction is realised an individual of species i fills the empty space.

Mobility: (i odot rightarrow odot i,), where (odot) means either an individual of any species or an empty site; an individual of species i switches positions with another individual of any species or with an empty space.

In our stochastic simulations, selection, reproduction, and mobilities interactions occur with the following probabilities: s, r and m, respectively. We assumed that individuals of all species have the same probabilities of selecting, reproducing and moving. The interactions were implemented by assuming the von Neumann neighbourhood, i.e., individuals may interact with one of their four nearest neighbours. The simulation algorithm follows three steps: i) sorting an active individual; ii) raffling one interaction to be executed; iii) drawing one of the four nearest neighbours to suffer the sorted interaction (the only exception is the directional mobility, where the neighbour is chosen according to the movement tactic). If the interaction is executed, one timestep is counted. Otherwise, the three steps are redone. Our time unit is called generation, defined as the necessary time to ({mathcal {N}}) timesteps to occur.In our model, individuals of one out of the species can move into the direction with more individuals of a target species. The choice is based on the strategy assumed by species. We assumed three sorts of directional movement tactics:

Attack tactic: an individual of species i moves into the direction with more individuals of species (i+1);

Anticipation tactic: an individual of species i goes towards the direction with a larger number of individuals of species (i+2);

Safeguard tactic: an individual of species i walk into the direction with a larger concentration of individuals of species (i-2).

In the standard model, individuals of all species move randomly.We considered that only individuals of species 1 perform the directional movement tactics, as illustrated in Fig. 1b. The solid, dashed, and dashed-dotted lines represent the Attack, Anticipation, and Safeguard tactics, respectively. The concentric circumference arcs show that individuals of species 2, 3, 4, and 5 always move randomly. For implementing a directional movement, the algorithm follows the steps: i) it is assumed a disc of radius R (the perception radius), in the active individual’s neighbourhood; ii) it is defined four circular sectors in the directions of the four nearest neighbours; iii) according to the movement tactic, the target species is defined: species 2, 3, and 4, for Attack, Anticipation, and Safeguard tactics, respectively; iv) it is counted the number of individuals of the target species within each circular sector. Individuals on the borders are assumed to be part of both circular sectors; v) the circular sector that contains more individuals of the target species is chosen. In the event of a tie, a draw between the tied directions is made; vi) the active individual switches positions with the immediate neighbour in the chosen direction. The swap is also executed in case of the neighbour grid point is empty.To observe the spatial patterns, we first performed a single simulation for the standard model, Attack, Anticipation, and Safeguard tactics. The realisations run in square lattices with (500^2) grid points, for a timespan of 5000 generations. We captured 500 snapshots of the lattice (in intervals of 10 generations), that were used to make the videos of the dynamics of the spatial patterns showed in https://youtu.be/Ndvk6Rg57m4 (standard), https://youtu.be/JGhkDAHSo74 (Attack), https://youtu.be/ZZp9QlOfv2Q (Anticipation), and https://youtu.be/eFxWdLhIOuQ (Safeguard). The final snapshots were depicted in Fig. 2a–d. Individuals of species 1, 2, 3, 4, and 5 are identified with the colours ruby, blue, pink, green, and yellow, respectively; while white dots represent empty spaces. The simulations were performed assuming selection, reproduction, and mobility probabilities: (s = r = m = 1/3). The perception radius was assumed to be (R=3).The population dynamics were studied by means of the spatial density (rho _i), defined as the fraction of the grid occupied by individuals of species i at time t, i.e., (rho _i = I_i/{mathcal {N}}), where (i=0) stands for empty spaces and (i=1,…,5) represent the species 1, 2, 3, 4, and 5. The temporal changes in spatial densities of the simulations showed in Fig. 2 were depicted in Fig. 3, where the grey, ruby, blue, pink, green, and yellow lines represent the densities of empty spaces and species 1, 2, 3, 4, and 5, respectively. We also computed how the selection risk of individuals of species i changes in time. To this purpose, the algorithm counts the total number of individuals of species i at the beginning of each generation. It is then counted the number of times that individuals of species i are killed during the generation. The ratio between the number of selected individuals and the initial amount is defined as the selection risk of species i, (zeta _i). The results were averaged for every 50 generations. Figure 4 shows (zeta _i,(%)) as a function of the time for the simulations presented in Fig. 2. The ruby, blue, pink, green, and yellow lines show the selection risks of individuals of species 1, 2, 3, 4, and 5, respectively.To quantify the spatial organisation of the species, we studied the spatial autocorrelation function. This quantity measures how individuals of a same species are spatially correlated, indicating spatial domain sizes. Following the procedure carried out in literature41,42,44,45,46, we first calculated the Fourier transform of the spectral density as (C({{vec{r^{prime}}}}) = {mathcal{F}}^{{ – 1}} { S({{vec{k}}})} /C(0)), where the spectral density (S({{vec{k}}})) is given by (S({{vec{k}}}) = sumlimits_{{k_{x} ,k_{y} }} {mkern 1mu} varphi ({{vec{kappa }}})), with (varphi ({{vec{kappa }}}) = {mathcal{F}}{mkern 1mu} { phi ({{vec{r}}}) – langle phi rangle }). The function (phi ({{vec{r}}})) represents the species in the position ({{vec{r}}}) in the lattice (we assumed 0, 1, 2, 3, 4, and 5, for empty sites, and individuals of species 1, 2, 3, 4, and 5, respectively). We then computed the spatial autocorrelation function as$$C(vec{r^{prime}}) = sumlimits_{{|{{vec{r^{prime}}}}| = x + y}} {frac{{C({{vec{r^{prime}}}})}}{{min (2N – (x + y + 1),(x + y + 1))}}}.$$Subsequently, we found the scale of the spatial domains of species i, defined for (C(l_i)=0.15), where (l_i) is the characteristic length for species i.We calculated the autocorrelation function by running 100 simulations using lattices with (500^2) grid points, assuming (s = r = m = 1/3) and (R=3). Each simulation started from different random initial conditions. We then captured each species spatial configuration after 5000 generations to calculate the autocorrelation functions. Finally, we averaged the autocorrelation function in terms of the radial coordinate r and calculated the characteristic length for each species. We also calculated the standard deviation for the autocorrelation functions and the characteristic lengths. Figure 4 shows the comparison of the results for Attack, Anticipation, and Safeguard strategies with the standard model. The ruby, blue, pink, green, and yellow circles indicate the mean values for species 1, 2, 3, 4, and 5, respectively. In the case of standard model, the mean values are represented by grey circles, which are the same for all species. The error bars show that standard deviation. The horizontal black line represents (C(l_i), =, 0.15).To further explore the numerical results, we studied how the perception radius R influences species spatial densities and selection risks. We calculated the mean value of the spatial species densities, (langle , rho _i,rangle) and the mean value of selection risks, (langle , zeta _i,rangle) from a set of 100 simulations in lattices with (500^2) grid points, starting from different initial conditions for (R=1,2,3,4,5). We used (s,=r,=,m,=1/3) and a timespan of (t=5000) generations. The mean values and standard deviation were calculated using the second half of the simulations, thus eliminating the density fluctuations inherent in the pattern formation process. The results were shown in Fig. 6, where the circles represent the mean values and error bars indicate the standard deviation. The colours are the same as in Figs. 3 and 4. Furthermore, to verify the precision of the statistical results, we calculated the variation coefficient – the ratio between the standard deviation and the mean value. Supplementary Tables S1 and S2 show statistical outcomes for species densities and selections risks, respectively.We studied a more realistic scenario where not all individuals of species 1 can perform the directional movement tactics. For this reason, we defined the conditioning factor (alpha), with (0,le ,alpha ,le ,1), representing the proportion of individuals of species 1 that moves directionally. For (alpha =0) all individuals move randomly while for (alpha =1) all individuals move directionally. This means that every time an individual of species 1 is sorted to move, there is a probability (alpha) of the algorithm implementing the directional movement tactic, instead of randomly choosing one of its four immediate neighbours to switch positions. To understand the effects of the conditioning factor, we observed how the density of species 1 changes for the entire range of (alpha), with intervals of (Delta alpha = 0.1). The simulations were implemented for (R=3) and (s,=r,=,m,=1/3). It was computed the mean value of the spatial density of species 1, (langle , rho _1,rangle), and its standard deviation from a set of 100 different random initial conditions. The results were depicted in Fig. 7, where the green, red, and blue dashed lines show (langle , rho _1,rangle) as a function of (alpha). The error bars indicate the standard deviation.Finally, we aimed to investigate how the directional movement tactics jeopardise species coexistence for a wide mobility probability range. Because of this, we run 2000 simulations in lattices with (100^2) grid points for (0.05, More

1.Scholik, A. R. & Yan, H. Y. Effects of boat engine noise on the auditory sensitivity of the fathead minnow, Pimephales promelas. . Environ. Biol. Fish. 63, 203–209. https://doi.org/10.1023/A:1014266531390 (2002).Article 

Google Scholar 
2.Simpson, S. D. et al. Anthropogenic noise increases fish mortality by predation. Nat. Commun. 7, 10544. https://doi.org/10.1038/ncomms10544 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
3.Slabbekoorn, H. et al. A noisy spring: the impact of globally rising underwater sound levels on fish. Trends. Ecol. Evol. 25, 419–427. https://doi.org/10.1016/j.tree.2010.04.005 (2010).Article 
PubMed 

Google Scholar 
4.Halfwerk, W. & Slabbekoorn, H. Pollution going multimodal: the complex impact of the human-altered sensory environment on animal perception and performance. Biol. Lett. 11, 20141051. https://doi.org/10.1098/rsbl.2014.1051 (2015).Article 
PubMed 
PubMed Central 

Google Scholar 
5.Baker, C. F. & Montgomery, J. C. Sensory deficits induced by cadmium in banded kokopu, Galaxias fasciatus, juveniles. Environ. Biol. Fish. 62, 455–464. https://doi.org/10.1023/A:1012290912326 (2001).Article 

Google Scholar 
6.O’Connor, J. J. et al. Sediment pollution impacts sensory ability and performance of settling coral-reef fish. Oecologia 180, 11–21. https://doi.org/10.1007/s00442-015-3367-6 (2016).ADS 
Article 
PubMed 

Google Scholar 
7.Tierney, K. B., Sampson, J. L., Ross, P. S., Sekela, M. A. & Kennedy, C. J. Salmon olfaction is impaired by an environmentally realistic pesticide mixture. Environ. Sci. Tech. 42, 4996–5001. https://doi.org/10.1021/es800240u (2008).CAS 
Article 

Google Scholar 
8.Ward Ashley, J. W., Duff Alison, J., Horsfall Jennifer, S. & Currie, S. Scents and scents-ability: pollution disrupts chemical social recognition and shoaling in fish. Proc. R Soc. B Biol. Sci. 275, 101–105. https://doi.org/10.1098/rspb.2007.1283 (2008).CAS 
Article 

Google Scholar 
9.Besson, M. et al. Exposure to agricultural pesticide impairs visual lateralization in a larval coral reef fish. Sci. Rep. 7, 9165. https://doi.org/10.1038/s41598-017-09381-0 (2017).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
10.Vasconcelos, R. O., Amorim, M. C. P. & Ladich, F. Effects of ship noise on the detectability of communication signals in the Lusitanian toadfish. J. Exp. Biol. 210, 2104–2112. https://doi.org/10.1242/jeb.004317 (2007).Article 
PubMed 

Google Scholar 
11.Bruintjes, R. et al. Rapid recovery following short-term acoustic disturbance in two fish species. R. Soc. Open Sci. 3, 150686. https://doi.org/10.1098/rsos.150686 (2016).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
12.Radford, A. N., Lèbre, L., Lecaillon, G., Nedelec, S. L. & Simpson, S. D. Repeated exposure reduces the response to impulsive noise in European seabass. Glob. Chang. Biol. 22, 3349–3360. https://doi.org/10.1111/gcb.13352 (2016).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
13.Briffa, M., de la Haye, K. & Munday, P. L. High CO2 and marine animal behaviour: Potential mechanisms and ecological consequences. Mar. Pollut. Bull. 64, 1519–1528. https://doi.org/10.1016/j.marpolbul.2012.05.032 (2012).CAS 
Article 
PubMed 

Google Scholar 
14.Simpson, S. D. et al. Ocean acidification erodes crucial auditory behaviour in a marine fish. Biol. Lett. https://doi.org/10.1098/rsbl.2011.0293 (2011).Article 
PubMed 
PubMed Central 

Google Scholar 
15.van der Sluijs, I. et al. Communication in troubled waters: responses of fish communication systems to changing environments. Evol. Ecol. 25, 623–640. https://doi.org/10.1007/s10682-010-9450-x (2011).Article 

Google Scholar 
16.Wysocki, L. E., Dittami, J. P. & Ladich, F. Ship noise and cortisol secretion in European freshwater fishes. Biol. Conserv. 128, 501–508. https://doi.org/10.1016/j.biocon.2005.10.020 (2006).Article 

Google Scholar 
17.Montgomery, J. C., Jeffs, A., Simpson, S. D., Meekan, M. & Tindle, C. Sound as an orientation cue for the pelagic larvae of reef fishes and decapod crustaceans. Adv. Mar. Biol. 51, 143–196. https://doi.org/10.1016/S0065-2881(06)51003-X (2006).Article 
PubMed 

Google Scholar 
18.Burke, L., Reytar, K., Spalding, M. & Perry, A. Reefs at risk revisited. 130, 1 (2011).
Google Scholar 
19.Collin, S. P. & Hart, N. S. Vision and photoentrainment in fishes: The effects of natural and anthropogenic perturbation. Integr. Zool. 10, 15–28. https://doi.org/10.1111/1749-4877.12093 (2015).Article 
PubMed 

Google Scholar 
20.Brodie, J. E. et al. Terrestrial pollutant runoff to the Great Barrier Reef: An update of issues, priorities and management responses. Mar Pollut Bull 65, 81–100. https://doi.org/10.1016/j.marpolbul.2011.12.012 (2012).CAS 
Article 
PubMed 

Google Scholar 
21.Marshall, N. J. in Animal Signals. Signalling and signal design in animal communication (eds Y. Espmark, Y. Amundsen, & G. Rosenqvist) 83–120 (Tapir Academic Press, 2000).22.Marshall, J. Vision and lack of vision in the ocean. Curr. Biol. 27, R494–R502. https://doi.org/10.1016/j.cub.2017.03.012 (2017).CAS 
Article 
PubMed 

Google Scholar 
23.Cortesi, F. et al. Visual system diversity in coral reef fishes. Semin. Cell Dev. Biol. 106, 31–42. https://doi.org/10.1016/j.semcdb.2020.06.007 (2020).CAS 
Article 
PubMed 

Google Scholar 
24.Collier, C., Waycott, M. & Ospina, A. G. Responses of four Indo-West Pacific seagrass species to shading. Mar. Pollut. Bull. 65, 342–354. https://doi.org/10.1016/j.marpolbul.2011.06.017 (2012).CAS 
Article 
PubMed 

Google Scholar 
25.De’ath, G. & Fabricius, K. Water quality as a regional driver of coral biodiversity and macroalgae on the Great Barrier Reef. Ecol. Appl. 20, 840–850. https://doi.org/10.1890/08-2023.1 (2010).Article 
PubMed 

Google Scholar 
26.Fabricius, K. E. Effects of terrestrial runoff on the ecology of corals and coral reefs: review and synthesis. Mar. Pollut. Bull. 50, 125–146. https://doi.org/10.1016/j.marpolbul.2004.11.028 (2005).CAS 
Article 
PubMed 

Google Scholar 
27.Morgan, K. M., Perry, C. T., Johnson, J. A. & Smithers, S. G. Nearshore turbid-zone corals exhibit high bleaching tolerance on the Great Barrier Reef following the 2016 ocean warming event. Front. Mar. Sci. 4, 1–13. https://doi.org/10.3389/fmars.2017.00224 (2017).Article 

Google Scholar 
28.Schartau, J. M., Sjögreen, B., Gagnon, Y. L. & Kröger, R. H. H. Optical plasticity in the crystalline lenses of the cichlid fish Aequidens pulcher. Curr. Biol. 19, 122–126. https://doi.org/10.1016/j.cub.2008.11.062 (2009).CAS 
Article 
PubMed 

Google Scholar 
29.Kröger, R. H. H., Braun, S. C. & Wagner, H.-J. Rearing in different photic and chromatic environments modifies spectral responses of cone horizontal cells in adult fish retina. Vis. Neuro Sci. 18, 857–864. https://doi.org/10.1017/S0952523801186025 (2001).Article 

Google Scholar 
30.Kröger, R. H. H., Knoblauch, B. & Wagner, H.-J. Rearing in different photic and spectral environments changes the optomotor response to chromatic stimuli in the cichlid fish Aequidens pulcher. J. Exp. Biol. 206, 1643–1648. https://doi.org/10.1242/jeb.00337 (2003).Article 
PubMed 

Google Scholar 
31.Borner, K. K. et al. Turbidity affects social dynamics in Trinidadian guppies. Behav. Ecol. Sociobiol. 69, 645–651. https://doi.org/10.1007/s00265-015-1875-3 (2015).Article 

Google Scholar 
32.Kimbell, H. S. & Morrell, L. J. Turbidity influences individual and group level responses to predation in guppies Poecilia reticulata. . Anim. Behav. 103, 179–185. https://doi.org/10.1016/j.anbehav.2015.02.027 (2015).Article 

Google Scholar 
33.Johansen, J. L. & Jones, G. P. Sediment-induced turbidity impairs foraging performance and prey choice of planktivorous coral reef fishes. Ecol. Appl. 23, 1504–1517. https://doi.org/10.1890/12-0704.1 (2013).CAS 
Article 
PubMed 

Google Scholar 
34.Chamberlain, A. C. & Ioannou, C. C. Turbidity increases risk perception but constrains collective behaviour during foraging by fish shoals. Anim. Behav. 156, 129–138. https://doi.org/10.1016/j.anbehav.2019.08.012 (2019).Article 

Google Scholar 
35.Gregory, R. S. Effect of turbidity on the predator avoidance behaviour of juvenile Chinook salmon (Oncorhynchus tshawytscha). Can. J. Fish. Aquat. Sci. 50, 241–246. https://doi.org/10.1139/f93-027 (1993).Article 

Google Scholar 
36.Hess, S. et al. Enhanced fast-start performance and anti-predator behaviour in a coral reef fish in response to suspended sediment exposure. Coral Reefs 38, 103–108. https://doi.org/10.1007/s00338-018-01757-6 (2019).ADS 
Article 

Google Scholar 
37.Miner, J. G. & Stein, R. A. Detection of predators and habitat choice by small Bluegills: Effects of turbidity and alternative prey. T Am. Fish. Soc. 125, 97–103. https://doi.org/10.1577/1548-8659(1996)125%3c0097:DOPAHC%3e2.3.CO;2 (1996).Article 

Google Scholar 
38.Utne-Palm, A. C. Visual feeding of fish in a turbid environment: Physical and behavioural aspects. Mar. Freshw. Behav. Phys. 35, 111–128. https://doi.org/10.1080/10236240290025644 (2002).Article 

Google Scholar 
39.Fiksen, Ø., Aksnes, D., Flyum, H. & M. & Giske, J. ,. The influence of turbidity on growth and survival of fish larvae: A numerical analysis. Hydrobiologia 484, 49–59. https://doi.org/10.1023/A:1021396719733 (2002).Article 

Google Scholar 
40.Gregory, R. S. & Levings, C. D. The effects of turbidity and vegetation on the risk of juvenile salmonids, Oncorhynchus spp, to predation by adult cutthroat trout, O. clarkii. Environ Biol Fish 47, 279–288. https://doi.org/10.1007/BF00000500 (1996).Article 

Google Scholar 
41.Wenger, A. S., McCormick, M. I., McLeod, I. M. & Jones, G. P. Suspended sediment alters predator–prey interactions between two coral reef fishes. Coral Reefs 32, 369–374. https://doi.org/10.1007/s00338-012-0991-z (2013).ADS 
Article 

Google Scholar 
42.Asaeda, T., Kyung Park, B. & Manatunge, J. Characteristics of reaction field and the reactive distance of a planktivore, Pseudorasbora parva (Cyprinidae), in various environmental conditions. Hydrobiologia 489, 29–43. https://doi.org/10.1023/A:1023298823106 (2002).Article 

Google Scholar 
43.Barrett, J. C., Grossman, G. D. & Rosenfeld, J. Turbidity-induced changes in reactive distance of rainbow trout. Trans. Am. Fish. Soc. 121, 437–443. https://doi.org/10.1577/1548-8659(1992)121%3c0437:TICIRD%3e2.3.CO;2 (1992).Article 

Google Scholar 
44.Sweka, J. A. & Hartman, K. J. Reduction of reactive distance and foraging success in smallmouth bass, Micropterus dolomieu, exposed to elevated turbidity levels. Environ. Biol. Fish. 67, 341–347. https://doi.org/10.1023/A:1025835031366 (2003).Article 

Google Scholar 
45.Suriyampola, P. S., Cacéres, J. & Martins, E. P. Effects of short-term turbidity on sensory preference and behaviour of adult fish. Anim. Behav. 146, 105–111. https://doi.org/10.1016/j.anbehav.2018.10.014 (2018).Article 

Google Scholar 
46.De Robertis, A., Ryer, C. H., Veloza, A. & Brodeur, R. D. Differential effects of turbidity on prey consumption of piscivorous and planktivorous fish. Can. J. Fish. Aquat. Sci. 60, 1517–1526. https://doi.org/10.1139/f03-123 (2003).Article 

Google Scholar 
47.Huenemann, T. W., Dibble, E. D. & Fleming, J. P. Influence of turbidity on the foraging of largemouth bass. Trans. Am. Fish. Soc. 141, 107–111. https://doi.org/10.1080/00028487.2011.651554 (2012).Article 

Google Scholar 
48.Sekhar, M. A., Singh, R., Bhat, A. & Jain, M. Feeding in murky waters: acclimatization and landmarks improve foraging efficiency of zebrafish (Danio rerio) in turbid waters. Biol. Lett. 15, 20190289. https://doi.org/10.1098/rsbl.2019.0289 (2019).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
49.Wenger, A. S., Johansen, J. L. & Jones, G. P. Increasing suspended sediment reduces foraging, growth and condition of a planktivorous damselfish. J. Exp. Mar. Biol. Ecol. 428, 43–48. https://doi.org/10.1016/j.jembe.2012.06.004 (2012).Article 

Google Scholar 
50.Wenger, A. S. et al. Suspended sediment prolongs larval development in a coral reef fish. J. Exp. Biol. 217, 1122–1128. https://doi.org/10.1242/jeb.094409 (2014).Article 
PubMed 

Google Scholar 
51.Ehlman, S. M., Sandkam, B. A., Breden, F. & Sih, A. Developmental plasticity in vision and behavior may help guppies overcome increased turbidity. J. Comput. Physiol. 201, 1125–1135. https://doi.org/10.1007/s00359-015-1041-4 (2015).Article 

Google Scholar 
52.Hazelton, P. D. & Grossman, G. D. Turbidity, velocity and interspecific interactions affect foraging behaviour of rosyside dace (Clinostomus funduloides) and yellowfin shiners (Notropis lutippinis). Ecol. Freshw. Fish 18, 427–436. https://doi.org/10.1111/j.1600-0633.2009.00359.x (2009).Article 

Google Scholar 
53.Hecht, T. & van der Lingen, C. D. Turbidity-induced changes in feeding strategies of fish in estuaries. S. Afr. J. Zool. 27, 95–107. https://doi.org/10.1080/02541858.1992.11448269 (1992).Article 

Google Scholar 
54.Sweka, J. A. & Hartman, K. J. Effects of turbidity on prey consumption and growth in brook trout and implications for bioenergetics modeling. Can. J. Fish. Aquat. Sci. 58, 386–393. https://doi.org/10.1139/f00-260 (2001).Article 

Google Scholar 
55.Burt de Perera, T. & Macías Garcia, C. Amarillo fish (Girardinichthys multiradiatus) use visual landmarks to orient in space. Ethology 109, 341–350. https://doi.org/10.1046/j.1439-0310.2003.00876.x (2003).Article 

Google Scholar 
56.Warburton, K. The use of local landmarks by foraging goldfish. Anim. Behav. 40, 500–505. https://doi.org/10.1016/s0003-3472(05)80530-5 (1990).Article 

Google Scholar 
57.Burt de Perera, T. & Guilford, T. C. Rapid learning of shelter position in an intertidal fish, the shanny Lipophrys pholis L. J. Fish. Biol. 72, 1386–1392. https://doi.org/10.1111/j.1095-8649.2008.01804.x (2008).Article 

Google Scholar 
58.Hughes, R. N. & Blight, C. M. Two intertidal fish species use visual association learning to track the status of food patches in a radial maze. Anim. Behav. 59, 613–621. https://doi.org/10.1006/anbe.1999.1351 (2000).CAS 
Article 
PubMed 

Google Scholar 
59.Huntingford, F. A. & Wright, P. J. How sticklebacks learn to avoid dangerous feeding patches. Behav. Process. 19, 181–189. https://doi.org/10.1016/0376-6357(89)90040-5 (1989).CAS 
Article 

Google Scholar 
60.Reese, E. Orientation behavior of butterflyfishes (family Chaetodontidae) on coral reefs: spatial learning of route specific landmarks and cognitive maps. Environ. Biol. Fish. 25, 79–86. https://doi.org/10.1007/bf00002202 (1989).Article 

Google Scholar 
61.Silveira, M. M., Oliveira, J. J. & Luchiari, A. C. Dusky damselfish Stegastes fuscus relational learning: evidences from associative and spatial tasks. J. Fish. Biol. 86, 1109–1120. https://doi.org/10.1111/jfb.12618 (2015).CAS 
Article 
PubMed 

Google Scholar 
62.Cyrus, D. P. & Blaber, S. J. M. Turbidity and salinity in a tropical northern Australian estuary and their influence on fish distribution. Estuar. Coast Shelf. S 35, 545–563. https://doi.org/10.1016/S0272-7714(05)80038-1 (1992).ADS 
CAS 
Article 

Google Scholar 
63.Macdonald, R. K., Ridd, P. V., Whinney, J. C., Larcombe, P. & Neil, D. T. Towards environmental management of water turbidity within open coastal waters of the Great Barrier Reef. Mar. Pollut. Bull. 74, 82–94. https://doi.org/10.1016/j.marpolbul.2013.07.026 (2013).CAS 
Article 
PubMed 

Google Scholar 
64.Wenger, A. S. et al. A critical analysis of the direct effects of dredging on fish. Fish Fish 18, 967–985. https://doi.org/10.1111/faf.12218 (2017).Article 

Google Scholar 
65.Wenger, A. S. & McCormick, M. I. Determining trigger values of suspended sediment for behavioral changes in a coral reef fish. Mar. Pollut. Bull. 70, 73–80. https://doi.org/10.1016/j.marpolbul.2013.02.014 (2013).CAS 
Article 
PubMed 

Google Scholar 
66.Gardner, M. B. Mechanisms of size selectivity by planktivorous fish: a test of hypotheses. Ecology 62, 571–578. https://doi.org/10.2307/1937723 (1981).Article 

Google Scholar 
67.Shoup, D. E. & Wahl, D. H. The effects of turbidity on prey selection by piscivorous largemouth bass. Trans. Am. Fish. Soc. 138, 1018–1027. https://doi.org/10.1577/T09-015.1 (2009).Article 

Google Scholar 
68.Cheung, A., Zhang, S., Stricker, C. & Srinivasan, M. V. Animal navigation: the difficulty of moving in a straight line. Biol. Cybern. 97, 47–61. https://doi.org/10.1007/s00422-007-0158-0 (2007).MathSciNet 
Article 
PubMed 
MATH 

Google Scholar 
69.McLean, D. J. & Skowron Volpani, M. A. trajr: An R package for characterisation of animal trajectories. Ethology 124, 40–448. https://doi.org/10.1111/eth.12739 (2018).Article 

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

Google Scholar 
71.jtools: Analysis and presentation of social scientific data v. R package version 2.0.1 (2019).72.Gradall, K. S. & Swenson, W. A. Responses of brook trout and creek chubs to turbidity. Trans. Am. Fish. Soc. 111, 392–395. https://doi.org/10.1577/1548-8659(1982)111%3c392:ROBTAC%3e2.0.CO;2 (1982).Article 

Google Scholar 
73.Berg, L. & Northcote, T. G. Changes in territorial, gill-flaring, and feeding behavior in juvenile Coho salmon (Oncorhynchus kisutch) following short-term pulses of suspended sediment. Can. J. Fish. Aquat. Sci. 42, 1410–1417. https://doi.org/10.1139/f85-176 (1985).Article 

Google Scholar 
74.Paris, C. B. et al. Reef odor: A wake up call for navigation in reef fish larvae. PLoS ONE 8, e72808. https://doi.org/10.1371/journal.pone.0072808 (2013).ADS 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
75.Siebeck, U. E., Parker, A. N., Sprenger, D., Mathger, L. M. & Wallis, G. A species of reef fish that uses ultraviolet patterns for covert face recognition. Curr. Biol. 20, 407–410. https://doi.org/10.1016/j.cub.2009.12.047 (2010).CAS 
Article 
PubMed 

Google Scholar 
76.Cheney, K. L., Grutter, A. S., Blomberg, S. P. & Marshall, N. J. Blue and yellow signal cleaning behavior in Coral Reef Fishes. Curr. Biol. 19, 1283–1287. https://doi.org/10.1016/j.cub.2009.06.028 (2009).CAS 
Article 
PubMed 

Google Scholar 
77.Brown, C. & Braithwaite, V. A. Size matters: a test of boldness in eight populations of the poeciliid Brachyraphis episcopi. Anim. Behav. 68, 1325–1329. https://doi.org/10.1016/j.anbehav.2004.04.004 (2004).Article 

Google Scholar 
78.Harborne, A. R., Rogers, A., Bozec, Y.-M. & Mumby, P. J. Multiple stressors and the functioning of coral reefs. Annu. Rev. Mar. Sci. 9, 445–468. https://doi.org/10.1146/annurev-marine-010816-060551 (2017).ADS 
Article 

Google Scholar  More

1.Kettle, D.S. Medical and Veterinary Entomology 2nd edn (CAB International, 1995).2.Falleroni, D. Fauna anofelica italiana e suo “habitat” (paludi, risaie, canali). Metodi di lotta contro la malaria. Riv. Malariol. 5, 553–559 (1926).
Google Scholar 
3.Severini, F., Toma, L., Di Luca, M. & Le, R. R. Zanzare Italiane: generalità e identificazione degli adulti (Diptera, Culicidae). Fragmenta Entomologica. 41(2), 213–372. https://doi.org/10.4081/FE.2009.92 (2009).Article 

Google Scholar 
4.Beebe, N. W. DNA barcoding mosquitoes: advice for potential prospectors. Parasitology 145(5), 622–633. https://doi.org/10.1017/S0031182018000343 (2018).CAS 
Article 
PubMed 

Google Scholar 
5.Manguin, S. et al. Biodiversity of Malaria in the World (John Libbey Eurotext, 2008).6.Linton, Y. M., Smith, L. & Harbach, E. Observations on the taxonomic status of Anopheles subalpinus Hackett & Lewis and An. melanoon Hacket. Eur. Mosq. Bull. 13, 1–7 (2002).
Google Scholar 
7.Boccolini, D., Di Luca, M., Marinucci, M. & Romi, R. Further molecular and morphological support for the formal synonymy of Anopheles subalpinus Hackett & Lewis with An. melanoon Hackett. Eur. Mosq. Bull. 16, 1–5 (2003).
Google Scholar 
8.Andreeva, I. V., Sibataev, A. K., Rusakova, A. M. & Stegniĭ, V. N. Morpho-cytogenetic characteristic of the mosquito Anopheles artemievi (Diptera: Culicidae), a malaria vector from the complex maculipennis. Parazitologiia. 41(5), 348–363 (2007).PubMed 

Google Scholar 
9.Artemov, G. N. et al. A standard photomap of ovarian nurse cell chromosomes and inversion polymorphism in Anopheles beklemishevi. Parasites Vectors 11(1), 211. https://doi.org/10.1186/s13071-018-2657-3 (2018).MathSciNet 
CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
10.Naumenko, A. N. et al. Chromosome and genome divergence between the cryptic Eurasian malaria vector-species Anopheles messeae and Anopheles daciae. Genes (Basel) 11(2), 165. https://doi.org/10.3390/genes11020165 (2020).CAS 
Article 

Google Scholar 
11.Nicolescu, G., Linton, Y. M., Vladimirescu, A., Howard, T. M. & Harbach, R. E. Mosquitoes of the Anopheles maculipennis group (Diptera: Culicidae) in Romania, with the discovery and formal recognition of a new species based on molecular and morphological evidence. Bull. Entomol. Res. 94(6), 525–535. https://doi.org/10.1079/ber2004330 (2004).CAS 
Article 
PubMed 

Google Scholar 
12.Gordeev, M. I., Zvantsov, A. B., Goriacheva, I. I., Shaĭkevich, E. V. & Ezhov, M. N. Description of the new species Anopheles artemievi sp.n. (Diptera, Culicidae). Med. Parazitol. (Mosk). 2, 4–5 (2005).
Google Scholar 
13.Djadid, N. D. et al. Molecular identification of Palearctic members of Anopheles maculipennis in northern Iran. Malar. J. 6, 6. https://doi.org/10.1186/1475-2875-6-6 (2007).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
14.Bietolini, S., Candura, F. & Coluzzi, M. Spatial and long term temporal distribution of the Anopheles maculipennis complex species in Italy. Parassitologia 48(4), 581–608 (2006).CAS 
PubMed 

Google Scholar 
15.Romi, R. et al. Status of malaria vectors in Italy. J. Med. Entomol. 34(3), 263–271. https://doi.org/10.1093/jmedent/34.3.263 (1997).CAS 
Article 
PubMed 

Google Scholar 
16.Zamburlini, R. & Cargnus, E. Residual mosquitoes in the northern Adriatic seacoast 50 years after the disappearance of malaria. Parassitologia 40, 431–437 (1998).CAS 
PubMed 

Google Scholar 
17.Gratz, N. G. Vector- and Rodent-Borne Diseases in Europe and North America: Distribution, Public Health Burden, and Control (Cambridge University Press, 2006).18.Zahar, A. R. The WHO European region and the two Eastern Mediterranean Region. Applied field studies. In Vector Bionomics in the Epidemiology and Control of Malaria. Part II. WHO/VBC/90.1 (World Health Organization, 1990).19.NPHO Annual Epidemiological Surveillance Report Malaria in Greece, 2019. https://eody.gov.gr/wp-content/uploads/2019/01/MALARIA_ANNUAL_REPORT_2019_ENG.pdf (2019).20.Romi, R. et al. Assessment of the risk of malaria re-introduction in the Maremma plain (Central Italy) using a multi-factorial approach. Malar. J. 11, 98. https://doi.org/10.1186/1475-2875-11-98 (2012).Article 
PubMed 
PubMed Central 

Google Scholar 
21.Baldari, M. et al. Malaria in Maremma, Italy. Lancet 351(9111), 1246–1247. https://doi.org/10.1016/S0140-6736(97)10312-9 (1998).CAS 
Article 
PubMed 

Google Scholar 
22.Romi, R., Boccolini, D., Menegon, M. & Rezza, G. Probable autochthonous introduced malaria cases in Italy in 2009–2011 and the risk of local vector-borne transmission. Euro Surveill. 17(48), 20325 (2012).PubMed 

Google Scholar 
23.Boccolini, D. et al. Non-imported malaria in Italy: paradigmatic approaches and public health implications following an unusual cluster of cases in 2017. BMC Public Health 20(1), 857. https://doi.org/10.1186/s12889-020-08748-9 (2020).Article 
PubMed 
PubMed Central 

Google Scholar 
24.European Centre for Disease Prevention and Control. Multiple reports of locally-acquired malaria infections in the EU—20 September 2017. (ECDC, 2017).25.Lilja, T., Eklöf, D., Jaenson, T. G. T., Lindström, A. & Terenius, O. Single nucleotide polymorphism analysis of the ITS2 region of two sympatric malaria mosquito species in Sweden: Anopheles daciae and Anopheles messeae. Med. Vet. Entomol. 34(3), 364–368. https://doi.org/10.1111/mve.12436 (2020).CAS 
Article 
PubMed 

Google Scholar 
26.Di Luca, M., Boccolini, D., Marinucci, M. & Romi, R. Intrapopulation polymorphism in Anopheles messeae (An. maculipennis complex) inferred by molecular analysis. J. Med. Entomol. 41(4), 582–586. https://doi.org/10.1603/0022-2585-41.4.582 (2004).Article 
PubMed 

Google Scholar 
27.Scharlemann, J. P. et al. Global data for ecology and epidemiology: a novel algorithm for temporal Fourier processing MODIS data. PLoS ONE 3(1), e1408. https://doi.org/10.1371/journal.pone.0001408 (2008).ADS 
Article 
PubMed 
PubMed Central 

Google Scholar 
28.Fick, S. E. & Hijmans, R. J. WorldClim 2: new 1km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37(12), 4302–4315 (2017).Article 

Google Scholar 
29.Batovska, J., Cogan, N. O., Lynch, S. E. & Blacket, M. J. Using Next-Generation Sequencing for DNA Barcoding: Capturing Allelic Variation in ITS2. G3 (Bethesda) 7(1), 19–29. https://doi.org/10.1534/g3.116.036145 (2017).CAS 
Article 

Google Scholar 
30.Novikov, Iu. M. & Kabanova, V. M. Adaptive association of inversions in a natural population of the malaria mosquito Anopheles messeae Fall. Genetika 15(6), 1033–1045 (1979).PubMed 

Google Scholar 
31.Vaulin, O. V. & Novikov, Y. M. Polymorphism and interspecific variability of cytochrome oxidase subunit I (COI) gene nucleotide sequence in sibling species of A and B Anopheles messeae and An. beklemishevi (Diptera: Culicidae). Russ. J. Genet. Appl. Res. 2(6), 421–429. https://doi.org/10.1134/S2079059712060159 (2012).Article 

Google Scholar 
32.Bezzhonova, O. V. & Goryacheva, I. I. Intragenomic heterogeneity of rDNA internal transcribed spacer 2 in Anopheles messeae (Diptera: Culicidae). J. Med. Entomol. 45(3), 337–341. https://doi.org/10.1603/0022-2585(2008)45[337:ihorit]2.0.co;2 (2008).CAS 
Article 
PubMed 

Google Scholar 
33.Novikov, Y. M. & Shevchenko, A. I. Inversion polymorphism and the divergence of two cryptic forms of Anopheles messeae (Diptera, Culicidae) at the level of genomic DNA repeats. Russ. J. Genet. 37, 754–763 (2001).CAS 
Article 

Google Scholar 
34.Kitzmiller, J. B., Frizzi, G. & Baker, R. Evolution and speciation within the Maculipennis complex of the genus Anopheles. In Genetics of Insect Vectors of Disease ed. (ed. Wright, J.W. & Pal, R.) (Elsevier Publishing, 1967).35.De Queiroz, K. Species concepts and species delimitation. Syst. Biol. 56(6), 879–886. https://doi.org/10.1080/10635150701701083 (2007).Article 
PubMed 

Google Scholar 
36.Alquezar, D. E., Hemmerter, S., Cooper, R. D. & Beebe, N. W. Incomplete concerted evolution and reproductive isolation at the rDNA locus uncovers nine cryptic species within Anopheles longirostris from Papua New Guinea. BMC Evol. Biol. 10, 392. https://doi.org/10.1186/1471-2148-10-392 (2010).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
37.Mallet, J., Besansky, N. & Hahn, M. W. How reticulated are species?. BioEssays 38(2), 140–149. https://doi.org/10.1002/bies.201500149 (2016).Article 
PubMed 

Google Scholar 
38.Fouet, C., Kamdem, C., Gamez, S. & White, B. J. Genomic insights into adaptive divergence and speciation among malaria vectors of the Anopheles nili group. Evol Appl. 10(9), 897–906. https://doi.org/10.1111/eva.12492 (2017).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
39.Pombi, M. et al. Dissecting functional components of reproductive isolation among closely related sympatric species of the Anopheles gambiae complex. Evol. Appl. 10(10), 1102–1120. https://doi.org/10.1111/eva.12517 (2017).Article 
PubMed 
PubMed Central 

Google Scholar 
40.Cohuet, A., Harris, C., Robert, V. & Fontenille, D. Evolutionary forces on Anopheles: What makes a malaria vector?. Trends Parasitol. 26(3), 130–136. https://doi.org/10.1016/j.pt.2009.12.001 (2010).Article 
PubMed 

Google Scholar 
41.Jetten, T. H., Takken, W. Anophelism Without Malaria in Europe: A Review of the Ecology and Distribution of the Genus Anopheles in Europe. (Wageningen Agricultural University, 1994).42.Becker, N. et al. Mosquitoes and Their Control 2nd edn (Springer Science & Business Media, 2010).43.Mosca, A., Balbo L., Grieco C. & Roberto P. Rice-field mosquito control in Northern Italy. In Proc. of 14th E-SOVE Int. Conf. 98 (2010).44.Daskova, N. G. & Rasnicyn, S. P. Review of data on susceptibility of mosquitoes in the USSR to imported strains of malaria parasites. Bull. World Health Organ. 60(6), 893–897 (1982).CAS 
PubMed 
PubMed Central 

Google Scholar 
45.de Zulueta, J., Ramsdale, C. D. & Coluzzi, M. Receptivity to malaria in Europe. Bull. World Health Organ. 52(1), 109–111 (1975).PubMed 
PubMed Central 

Google Scholar 
46.Ramsdale, C. D. & Coluzzi, M. Studies on the infectivity of tropical African strains of Plasmodium falciparum to some southern European vectors of malaria. Parassitologia 17(1–3), 39–48 (1975).CAS 
PubMed 

Google Scholar 
47.Teodorescu, C., Ungureanu, E., Mihai, M. & Tudose, M. Contributions to the study of the receptivity of the vector A. labranchiae atroparvus to two strains of P. vivax. Revista Medico-Chirurgicala din Iasi 52(1), 73–75 (1978).
Google Scholar 
48.Sousa, C. A. G. Malaria Vectorial Capacity and Competence of Anopheles atroparvus Van Thiel, 1927 (Diptera, Culicidae): Implications for the Potential Re-emergence of Malaria in Portugal (Thesis, Universidade Nova de Lisboa, Instituto de Higiene e Medicina Tropical, 2008).49.Toty, C. et al. Malaria risk in Corsica, former hot spot of malaria in France. Malar. J. 9, 231. https://doi.org/10.1186/1475-2875-9-231 (2010).Article 
PubMed 
PubMed Central 

Google Scholar 
50.Calzolari, M. et al. West Nile virus surveillance in 2013 via mosquito screening in Northern Italy and the influence of weather on virus circulation. PLoS ONE 10(10), e0140915. https://doi.org/10.1371/journal.pone.0140915 (2015).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
51.Marinucci, M., Romi, R., Mancini, P., Di Luca, M. & Severini, C. Phylogenetic relationships of seven palearctic members of the maculipennis complex inferred from ITS2 sequence analysis. Insect. Mol. Biol. 8(4), 469–480. https://doi.org/10.1046/j.1365-2583.1999.00140.x (1999).CAS 
Article 
PubMed 

Google Scholar 
52.Jalali, S., Ojha, R. & Venkatesan, T. DNA barcoding for identification of agriculturally important insects. In New Horizons in Insect Science: Towards Sustainable Pest Management (ed. Chakravarthy, A. K.) (Springer, 2015).53.Lühken, R. et al. Distribution of individual members of the mosquito Anopheles maculipennis complex in Germany identified by newly developed real-time PCR assays. Med. Vet. Entomol. 30, 144–154. https://doi.org/10.1111/mve.12161 (2016).Article 
PubMed 

Google Scholar 
54.Katoh, K., Rozewicki, J. & Yamada, K. MAFFT online service: multiple sequence alignment, interactive sequence choice and visualization. Brief Bioinform. 20(4), 1160–1166. https://doi.org/10.1093/bib/bbx108 (2019).CAS 
Article 
PubMed 

Google Scholar 
55.Guindon, S. & Gascuel, O. A simple, fast, and accurate algorithm to estimate large phylogenies by maximum likelihood. Syst. Biol. 52(5), 696–704. https://doi.org/10.1080/10635150390235520 (2003).Article 
PubMed 

Google Scholar 
56.Darriba, D., Taboada, G. L., Doallo, R. & Posada, D. jModelTest 2: more models, new heuristics and parallel computing. Nat. Methods 9(8), 772. https://doi.org/10.1038/nmeth.2109 (2012).CAS 
Article 
PubMed 
PubMed Central 

Google Scholar 
57.Letunic, I. & Bork, P. Interactive Tree Of Life (iTOL) v4: recent updates and new developments. Nucl. Acids Res. 47(W1), W256–W259. https://doi.org/10.1093/nar/gkz239 (2019).CAS 
Article 
PubMed 

Google Scholar 
58.Phillips, S. J., Anderson, R. P., Dudík, M., Schapire, R. E. & Blair, M. E. Opening the black box: an open-source release of Maxent. Ecography 40, 887–893. https://doi.org/10.1111/ecog.03049 (2017).Article 

Google Scholar 
59.Elith, S. J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43–57. https://doi.org/10.1111/j.1472-4642.2010.00725.x (2011).Article 

Google Scholar 
60.Warren, D. L., Glor, R. E. & Turelli, M. ENMTools: a toolbox for comparative studies of environmental niche models. Ecography 33, 607–611. https://doi.org/10.1111/j.1600-0587.2009.06142.x (2010).Article 

Google Scholar 
61.Phillips, S., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190(3–4), 231–259. https://doi.org/10.1016/j.ecolmodel.2005.03.026 (2006).Article 

Google Scholar  More