More stories

  • in

    Optimization of the flow conditions in the spawning ground of the Chinese sturgeon (Acipenser sinensis) through Gezhouba Dam generating units

    Flow velocity thresholdThere were 92 Chinese sturgeon signals from 2016 to 2019, which were identified with the DIDSON dual-frequency video sonar system. The distribution map of Chinese sturgeon signals was shown in Fig. 1. The number of monitored signals in 2016 was significantly higher than in 2017–2019. The latest wild reproduction of the Chinese sturgeon occurred in 2016. Overall, most Chinese sturgeon signals were distributed within 500 m downstream from Gezhouba Dam, and there were more in the right side(facing downstream) than in the left side. The flow field of each sturgeon signal was simulated by the model, and the velocity of each signal location was obtained. According to the statistical analysis of the flow velocity values, the frequency of the sturgeon signal at different flow velocity values was shown in Fig. 2. The results show that most signals were concentrated in areas with flow velocities of 0.6–1.5 m/s, which accounted for 88.1% of the signals; areas with flow velocities below 0.6 m/s accounted for 4.3% of the signals, and areas with flow velocities above 1.5 m/s accounted for 7.6%. Therefore, 0.6–1.5 m/s was selected as the preferred flow velocity range of the Chinese sturgeon for spawning activity. This result was approximately consistent with the ranges proposed by most other researchers. The low limit of the velocity range was lower than that of other researchers. There may be two reasons for this result: the first was that the bottom velocity we analysed was lower than the surface velocity and vertical average velocity under the same conditions; the second was that our research time was after 2016, and the discharge during the spawning period was relatively low, so the velocity of the Chinese sturgeon signal was also relatively low.Figure 1Distribution map of Chinese sturgeon signals, where ○ indicates Chinese sturgeon signals monitored in 2016, ∆ indicates those in 2017, □ indicates those in 2018, and ✩ indicates those in 2019. Map generated in ArcGIS Pro (https://www.esri.com/en-us/arcgis/products/arcgis-pro/overview).Full size imageFigure 2Plots of the frequency for the different flow velocity ranges of Chinese sturgeon signals.Full size imageDifferent opening modes with identical dischargeThe discharge of 6150 m3/s on November 24, 2016, when the latest wild reproduction of Chinese sturgeon occurred, was used to study the flow velocity distribution with different opening modes. The specific opening mode cases are shown in Table 1. Case 1 was the actual situation, and the Dajiang Plant featured 7 open units: #8, #11, #13, #14, #16, #19, and #21. According to the amounts of electricity generated by Dajiang Plant and Erjiang Plant on that day, the proportion of the Dajiang River flow was 58.8%, and the average discharge of each unit was 516.6 m3/s. Case 2 and case 3 featured 7 open units with the same discharge, but in case 2, units #15–21 were continuously open on the right-side (facing downstream), and in case 3, units #8–14 were continuously open near the left side. Case 4 and case 5 were the most concentrated conditions with the discharge of 6150 m3/s because the maximum through-discharge for each unit in the Dajiang Plant is 825 m3/s19. In these cases, at least 5 units were open with an average discharge of 723 m3/s per unit. Case 4 involved continuously opening units #8–12 on the left side, and case 5 involved continuously opening units #17–21 on the right side. Case 6 involved simultaneously opening 14 units on Dajiang River, and the average discharge of each unit was 258.3 m3/s.Table 1 Calculation cases with different opening modes of units under the identical discharge.Full size tableFigure 3 shows the flow fields of the spawning ground under different opening modes with identical discharge. By comparing the areas with a velocity threshold range of 0.6–1.5 m/s in different cases, the most favourable opening mode was determined. In case 1, the velocity at the outlet of the units was higher than the 1.5 m/s velocity threshold, but the discharge of each unit was only 516.6 m3/s, so the high-velocity range was limited, and most areas were suitable. In case 2 and case 3, there was a large difference in proportions of suitable area. Because the left side was deeper than the right side, the flow velocity on the right side was higher under the same discharge, and case 3 more easily exceeded the flow threshold, which resulted in a larger unsuitable area. Case 2 was more suitable than case 1, which also demonstrated that opening the left-side units was more favourable. In case 4 and case 5, the proportions of suitable area were small. Because the units were concentrated, the discharge of each unit was too high, and the outlet velocity was more than 2 m/s, so a large area of high velocity appeared downstream of the units with backflow under the shut-down units. The proportion of suitable area in case 5 was larger than those in case 4 and case 3, which further indicates that opening the left-side units was more favourable than opening the right-side units. Case 6 was greater than that of any other case. Because the discharge of each unit was only 258.3 m3/s, the velocity of the unit outlet was less than 1.5 m/s, and almost all areas were suitable except for the small areas on both sides. The suitable-velocity area was the largest when all units of the Dajiang Plant of Gezhouba Dam were open; therefore, for a given discharge, it was best to open all units.Figure 3Flow field of the spawning ground in different opening modes with identical discharge, where the numbers at the top of each picture are the numbers of units to open, and the arrows indicate the direction of the water flow. Maps generated in Tecplot360 EX 2020 R1 (https://www.tecplot.com/products/tecplot-360/).Full size imageDifferent discharges under identical opening modeThe velocity distribution of the spawning field is affected by the opening mode of the units and discharge of Gezhouba Dam. To study the effect of different discharges, 14 cases were simulated, as shown in Table 2. All units of the Dajiang Plant were considered open because the proportion of suitable area was expected to be maximal under such circumstances. From 1982 to the present, the discharge during the spawning day of Chinese sturgeon under Gezhouba Dam has a wide range: the highest discharge was 27,290 m3/s in 1990, and the lowest discharge was 5590 m3/s in 2012. However, the highest design discharge of the Gezhouba Dam units is 17,930 m3/s20. Once the design discharge is exceeded, the spillway on Erjiang River discharges water, and the velocity distribution of the study area is not affected. Therefore, case 1 represents the lowest discharge of 5590 m3/s, and case 2 represents a discharge of 6000 m3/s. For each subsequent case, the discharge was increased by 1000 m3/s to case 13 with the highest flow of 17,930 m 3/s. In case 14, all units reached the design discharge, and the discharge of each unit was 825 m3/s19.Table 2 Calculation cases with the same opening mode under different discharges.Full size tableFigure 4 shows the proportion of suitable-velocity area with all units open under different discharges. According to the calculation results, the proportion of suitable area slightly fluctuated at approximately 96.2% for discharges of 5590–11,000 m3/s. Because the discharge of each unit was low, the velocity of the unit outlet was low, and most areas were within the velocity threshold. Therefore, it is advantageous to open all units when the discharge is low. After the discharge reached 12,000 m3/s, the proportion of suitable area rapidly decreased. Because the discharge of each unit was high, on the right side of Dajiang River, the velocity of the unit outlet exceeded the velocity threshold and increased with increases in discharge, and the range of effect gradually increased. In the last case, the proportion of suitable area was only 6% when the units reached the designed discharge of 825 m3/s. Because the discharge of each unit was too high, almost all areas exceeded the velocity threshold except for small areas on both sides. Therefore, at discharges below 12,000 m3/s, opening all units is favourable, and at discharge above 12,000 m3/s, a higher discharge corresponds to more unfavourable conditions.Figure 4Proportions of the suitable-velocity area with all units opened under different discharges.Full size imageOptimal scheme under high-flow conditionsHigh-flow conditions at Gezhouba Dam are considered those that exceed 12,000 m3/s because of the substantive decline in suitable habitat area at higher discharges. Because opening the units on the left side of the Dajiang Plant provides a more uniform, suitable habitat, we evaluated 20 cases with a left-side opening mode under different discharge, as shown in Table 3. Because the highest discharge of each unit in the Dajiang Plant is 825 m3/s, at least 9 units must be open when the discharge is 12,000 m3/s. Case 1 was designed to open 9 units on the left, i.e., units #13–21, and the discharge of each unit was 784 m3/s. Cases 2–5 increased by 1 unit from left to right until 13 units were opened. For discharges of 13,000 m3/s, 14,000 m3/s, 15,000 m3/s, and 16,000 m3/s, at least 10, 10, 11, and 12 units were opened. When the discharge was 17,000 m3/s and 17,930 m3/s, at least 13 units were open.Table 3 Calculation cases with different opening modes under high-flow conditions.Full size tableFigure 5 shows the proportions of suitable area for different opening modes under high-flow conditions. The calculation results show that when the discharge was 12,000 m3/s, 13,000 m3/s, and 14,000 m3/s, the proportion of suitable area showed a parabolic trend with the increase in number of units. When the discharge was 12,000 m3/s, the proportion of suitable area with 11 open units on the left was the largest, which was 8.7% larger than the value for all open units and 15% larger than the value for the lowest number of open units. When the discharge was 13,000 m3/s, 12 open units on the left had the largest proportion of suitable-flow-velocity area. When the discharge was 14,000 m3/s, the proportions of suitable area produced by opening 12 and 13 units on the left were the largest. The proportion of suitable area of the lowest number of open units was usually minimal because the discharge of each unit was too high, which resulted in a large area of high velocity that was not suitable for Chinese sturgeon to spawn. Because of the underwater topography, opening the left-side units was more favourable than opening the right-side units, so for all open units, the proportions of suitable area will be lower, and the number of units opened in the middle will be the most advantageous. For a discharge of 15,000 m3/s, with the increase in number of units, the proportion of suitable area increased, and there was no parabolic trend because the discharge of each unit exceeded 678 m3/s; thus, on the left side, there was a large area of high velocity, and the effect extended very far, which was not suitable for Chinese sturgeon.Figure 5Proportions of the suitable area for different opening modes under high-flow conditions, where 12,000–09 on the x-axis indicates that the discharge is 12,000 m3/s, and 9 units are open on the left.Full size image More

  • in

    Climate change drives mountain butterflies towards the summits

    1.Maxwell, S. L., Fuller, R. A., Brooks, T. M. & Watson, J. E. M. Biodiversity: The ravages of guns, nets and bulldozers. Nature 536, 143–145 (2016).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    2.Ripple, W. J., Wolf, C., Newsome, T. M., Barnard, P. & Moomaw, W. R. World scientists’ warning of a climate emergency. Bioscience https://doi.org/10.1093/biosci/biz088 (2019).Article 

    Google Scholar 
    3.Seneviratne, S. I., Lüthi, D., Litschi, M. & Schär, C. Land–atmosphere coupling and climate change in Europe. Nature 443, 205–209 (2006).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    4.Liu, H. et al. Shifting plant species composition in response to climate change stabilizes grassland primary production. Proc. Natl. Acad. Sci. 115, 4051–4056 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    5.Schweiger, O., Settele, J., Kudrna, O., Klotz, S. & Kühn, I. Climate change can cause spatial mismatch of trophically interacting species. Ecology 89, 3472–3479 (2008).PubMed 
    Article 

    Google Scholar 
    6.Parmesan, C. et al. Poleward shifts in geographical ranges of butterfly species associated with regional warming. Nature 399, 579–583 (1999).ADS 
    CAS 
    Article 

    Google Scholar 
    7.Dieker, P., Drees, C. & Assmann, T. Two high-mountain burnet moth species (Lepidoptera, Zygaenidae) react differently to the global change drivers climate and land-use. Biol. Conserv. 144, 2810–2818 (2011).Article 

    Google Scholar 
    8.Habel, J. C., Rödder, D., Schmitt, T. & Nève, G. Global warming will affect the genetic diversity and uniqueness of Lycaena helle populations. Glob. Change Biol. 17, 194–205 (2011).ADS 
    Article 

    Google Scholar 
    9.Grabherr, G., Gottfried, M. & Pauli, H. Climate change impacts in alpine environments. Geogr. Compass 4, 1133–1153 (2010).Article 

    Google Scholar 
    10.Alexander, J. M. et al. Lags in the response of mountain plant communities to climate change. Glob. Change Biol. 24, 563–579 (2018).11.Renner, S. S. & Zohner, C. M. Climate change and phenological mismatch in trophic interactions among plants, insects, and vertebrates. Annu. Rev. Ecol. Evol. Syst. 49, 165–182 (2018).Article 

    Google Scholar 
    12.Fleishman, E. & Murphy, D. D. A realistic assessment of the indicator potential of butterflies and other charismatic taxonomic groups. Conserv. Biol. 23, 1109–1116 (2009).PubMed 
    Article 

    Google Scholar 
    13.Sexton, J. P., Montiel, J., Shay, J. E., Stephens, M. R. & Slatyer, R. A. Evolution of ecological niche breadth. Annu. Rev. Ecol. Evol. Syst. 48, 183–206 (2017).Article 

    Google Scholar 
    14.Herrera, J. M., Ploquin, E. F., Rasmont, P. & Obeso, J. R. Climatic niche breadth determines the response of bumblebees (Bombus spp.) to climate warming in mountain areas of the Northern Iberian Peninsula. J. Insect Conserv. 22, 771–779 (2018).Article 

    Google Scholar 
    15.Habel, J. C. et al. Butterfly community shifts over two centuries. Conserv. Biol. 30, 754–762 (2016).PubMed 
    Article 

    Google Scholar 
    16.Descombes, P., Pradervand, J. N., Golay, J., Guisan, A. & Pellissier, L. Simulated shifts in trophic niche breadth modulate range loss of alpine butterflies under climate change. Ecography 39, 796–804 (2016).Article 

    Google Scholar 
    17.Kerr, J. T. Racing against change: Understanding dispersal and persistence to improve species’ conservation prospects. Proc. R. Soc. B 287, 20202061 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    18.Dapporto, L., Cini, A., Voda, R., Dinca, V., Wiemers, M., Menchetti, M., Magini, G., Talavera, G., Shreeve, T., Bonelli, S., Casacci, L. P., Balletto, E., Scalercio, S. & Vila, R. Data from: Integrating three comprehensive datasets shows that mitochondrial DNA variation is linked to species traits and paleogeographic events in European butterflies. (Version 2, p. 4647103 bytes). Dryad (2019).19.Wiemers, M. et al. An updated checklist of the European butterflies (Lepidoptera, Papilionoidea). ZooKeys 811, 9–45 (2018).Article 

    Google Scholar 
    20.Wiemers, M., Chazot, N., Wheat, C., Schweiger, O. & Wahlberg, N. A complete time-calibrated multi-gene phylogeny of the European butterflies. ZooKeys 938, 97–124 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    21.Middleton-Welling, J. et al. A new comprehensive trait database of European and Maghreb butterflies, Papilionoidea. Sci. Data 7, 351 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    22.Weckström, K. et al. Impacts of climate warming on alpine lake biota over the past decade. Arct. Antarct. Alp. Res. 48, 361–376 (2016).Article 

    Google Scholar 
    23.Steinbauer, K., Lamprecht, A., Winkler, M., Bardy-Curchhalter, M., Kreiner, D., Suen, M. & Pauli, H. Shifting composition and functioning in alpine plant communities—Evidence of climate warming effects from 14 years biodiversity observation in the Northeastern Alps. In Conference Vol. 621–622 (2017).24.Bräu, M., Arbeitsgemeinschaft Bayerischer Entomologen & Bayerisches Landesamt für Umwelt (Eds.). Tagfalter in Bayern: 26 Tabellen. (Ulmer, 2013).25.Weidemann, H.-J. Tagfalter Vol. 1 (Neumann-Neudamm, 1986).
    Google Scholar 
    26.Weidemann, H.-J. Tagfalter: Biologie-Ökologie-Biotopschutz Vol. 2 (Neumann-Neudamm, 1988).
    Google Scholar 
    27.Konvicka, M., Maradova, M., Benes, J., Fric, Z. & Kepka, P. Uphill shifts in distribution of butterflies in the Czech Republic: Effects of changing climate detected on a regional scale. Glob. Ecol. Biogeogr. 12, 403–410 (2003).Article 

    Google Scholar 
    28.Wilson, R. J., Gutiérrez, D., Gutiérrez, J. & Monserrat, V. J. An elevational shift in butterfly species richness and composition accompanying recent climate change. Glob. Change Biol. 13, 1873–1887 (2007).ADS 
    Article 

    Google Scholar 
    29.Wilson, R. J. et al. Changes to the elevational limits and extent of species ranges associated with climate change: Elevational shifts accompany climate change. Ecol. Lett. 8, 1138–1146 (2005).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    30.Forister, M. L. et al. Compounded effects of climate change and habitat alteration shift patterns of butterfly diversity. Proc. Natl. Acad. Sci. 107, 2088–2092 (2010).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    31.Warren, M. S. et al. Rapid responses of British butterflies to opposing forces of climate and habitat change. Nature 414, 65–69 (2001).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    32.Hill, J. K. et al. Responses of butterflies to twentieth century climate warming: Implications for future ranges. Proc. R. Soc. Lond. Ser. B Biol. Sci. 269, 2163–2171 (2002).CAS 
    Article 

    Google Scholar 
    33.Essens, T., van Langevelde, F., Vos, R. A., Van Swaay, C. A. M. & WallisDeVries, M. F. Ecological determinants of butterfly vulnerability across the European continent. J. Insect Conserv. 21, 439–450 (2017).Article 

    Google Scholar 
    34.van Swaay, C., Warren, M. & Loïs, G. Biotope use and trends of European butterflies. J. Insect Conserv. 10, 189–209 (2006).Article 

    Google Scholar 
    35.Pyke, G. H., Thomson, J. D., Inouye, D. W. & Miller, T. J. Effects of climate change on phenologies and distributions of bumble bees and the plants they visit. Ecosphere 7, e01267 (2016).Article 

    Google Scholar 
    36.Biella, P. et al. Distribution patterns of the cold adapted bumblebee Bombus alpinus in the Alps and hints of an uphill shift (Insecta: Hymenoptera: Apidae). J. Insect Conserv. 21, 357–366 (2017).Article 

    Google Scholar 
    37.Parolo, G. & Rossi, G. Upward migration of vascular plants following a climate warming trend in the Alps. Basic Appl. Ecol. 9, 100–107 (2008).Article 

    Google Scholar 
    38.Filazzola, A., Matter, S. F. & Roland, J. Inclusion of trophic interactions increases the vulnerability of an alpine butterfly species to climate change. Glob. Change Biol. 26, 2867–2877 (2020).ADS 
    Article 

    Google Scholar 
    39.Schweiger, O. et al. Multiple stressors on biotic interactions: How climate change and alien species interact to affect pollination. Biol. Rev. 85, 777–795 (2010).PubMed 

    Google Scholar 
    40.Inouye, B. D., Ehrlén, J. & Underwood, N. Phenology as a process rather than an event: From individual reaction norms to community metrics. Ecol. Monogr. 89, e01352 (2019).Article 

    Google Scholar 
    41.Birkhofer, K. et al. Land-use type and intensity differentially filter traits in above- and below-ground arthropod communities. J. Anim. Ecol. 86, 511–520 (2017).PubMed 
    Article 

    Google Scholar 
    42.Dapporto, L. & Dennis, R. L. H. The generalist–specialist continuum: Testing predictions for distribution and trends in British butterflies. Biol. Conserv. 157, 229–236 (2013).Article 

    Google Scholar 
    43.Bartoňová, A., Benes, J. & Konvicka, M. Generalist–specialist continuum and life history traits of Central European butterflies (Lepidoptera)—Are we missing a part of the picture?. Eur. J. Entomol. 111, 543–553 (2014).Article 

    Google Scholar 
    44.Bartoňová, A. et al. Isolated Asian steppe element in the Balkans: Habitats of Proterebia afra (Lepidoptera: Nymphalidae: Satyrinae) and associated butterfly communities. J. Insect Conserv. 21, 559–571 (2017).Article 

    Google Scholar 
    45.Hodkinson, I. D. Terrestrial insects along elevation gradients: Species and community responses to altitude. Biol. Rev. 80, 489 (2005).PubMed 
    Article 

    Google Scholar 
    46.Roth, T., Plattner, M. & Amrhein, V. Plants, birds and butterflies: Short-term responses of species communities to climate warming vary by taxon and with altitude. PLoS ONE 9, e82490 (2014).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    47.Biesmeijer, J. C. et al. Parallel declines in pollinators and insect-pollinated plants in Britain and the Netherlands. Science 313, 351–354 (2006).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    48.Filz, K. J., Engler, J. O., Stoffels, J., Weitzel, M. & Schmitt, T. Missing the target? A critical view on butterfly conservation efforts on calcareous grasslands in south-western Germany. Biodivers. Conserv. 22, 2223–2241 (2013).Article 

    Google Scholar 
    49.Hiebl, J. & Frei, C. Daily temperature grids for Austria since 1961—Concept, creation and applicability. Theor. Appl. Climatol. 124, 161–178 (2016).ADS 
    Article 

    Google Scholar 
    50.Hiebl, J. & Frei, C. Daily precipitation grids for Austria since 1961—Development and evaluation of a spatial dataset for hydroclimatic monitoring and modelling. Theor. Appl. Climatol. 132, 327–345 (2018).ADS 
    Article 

    Google Scholar 
    51.Bivand, R. & Yu, D. spgwr: Geographically Weighted Regression (R Package Version 0.6-34) [Computer Software]. https://CRAN.R-project.org/package=spgwr (2019).52.Hijmans, R. J. raster: Geographic Data Analysis and Modeling (R Package Version 3.3-13) [Computer Software]. https://CRAN.R-project.org/package=raster (2019).53.Hijmans, R. J., Phillips, S., Leathwick, J. & Elith, J. dismo: Species Distribution Modeling (R Package Version 1.1-4) [Computer Software]. https://CRAN.R-project.org/package=dismo (2017)54.Höttinger, H. & Pennerstorfer, J. Rote Liste der Tagschmetterlinge Österreichs (Lepidoptera: Papilionoidea & Hesperioidea). In Rote Listen gefährdeter Tiere Österreichs. Checklisten, Gefährdungsanalysen, Handlungsbedarf. Teil 1: Säugetiere, Vögel, Heuschrecken, Wasserkäfer, Netzflügler, Schnabelfliegen, Tagfalter. Grüne Reihe des Bundesministeriums für Land- und Forstwirtschaft, Umwelt und Wasserwirtschaft (Gesamtherausgeberin Ruth Wallner) Band 14/1 (ed. Zulka, K. P.) 313–354 (Böhlau, 2005).55.Blonder, B. & Harris, D. J. hypervolume: High Dimensional Geometry and Set Operations Using Kernel Density Estimation, Support Vector Machines, and Convex Hulls (R Package Version 2.0.12) [Computer Software]. https://CRAN.R-project.org/package=hypervolume (2019).56.Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).Article 

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

    Google Scholar 
    58.Phillips, S. J., Dudík, M. & Schapire, R. E. Maxent Software for Modeling Species Niches and Distributions (Version 3.4.1) [Computer Software]. http://biodiversityinformatics.amnh.org/open_source/maxent/ (2017).59.Swets, J. Measuring the accuracy of diagnostic systems. Science 240, 1285–1293 (1988).ADS 
    MathSciNet 
    CAS 
    PubMed 
    MATH 
    Article 

    Google Scholar 
    60.Weiss, M. & Banko, G. Ecosystem Type Map v3.1—Terrestrial and Marine Ecosystems. ETC/BD report to the EEA (2018). More

  • in

    Wet-dry cycles protect surface-colonizing bacteria from major antibiotic classes

    1.Or D, Smets BF, Wraith J, Dechesne A, Friedman S. Physical constraints affecting bacterial habitats and activity in unsaturated porous media–a review. Adv Water Resour. 2007;30:1505–27.Article 

    Google Scholar 
    2.Burkhardt J, Hunsche M. “Breath figures” on leaf surfaces—formation and effects of microscopic leaf wetness. Front plant Sci. 2013;4:422.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    3.Wolf AB, Vos M, de Boer W, Kowalchuk GA. Impact of matric potential and pore size distribution on growth dynamics of filamentous and non-filamentous soil bacteria. PloS One. 2013;8:e83661.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    4.Forsberg KJ, Reyes A, Wang B, Selleck EM, Sommer MO, Dantas G. The shared antibiotic resistome of soil bacteria and human pathogens. Science. 2012;337:1107–11.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    5.Williams S, Vickers J. The ecology of antibiotic production. Microb Ecol. 1986;12:43–52.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    6.Raaijmakers JM, Weller DM, Thomashow LS. Frequency of antibiotic-producing Pseudomonas spp. in natural environments. Appl Environ Microbiol. 1997;63:881–7.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    7.Wells JS, Hunter JC, Astle GL, Sherwood JC, Ricca cM, Trejo WH, et al. Distribution of β-lactam and β-lactone producing bacteria in nature. The. J Antibiot. 1982;35:814–21.CAS 
    Article 

    Google Scholar 
    8.Kinkel LL, Schlatter DC, Xiao K, Baines AD. Sympatric inhibition and niche differentiation suggest alternative coevolutionary trajectories among Streptomycetes. ISME J. 2014;8:249–56.CAS 
    PubMed 
    Article 

    Google Scholar 
    9.Vetsigian K, Jajoo R, Kishony R. Structure and evolution of Streptomyces interaction networks in soil and in silico. PLoS Biol. 2011;9:e1001184.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    10.Traxler MF, Kolter R. Natural products in soil microbe interactions and evolution. Nat Prod Rep. 2015;32:956–70.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    11.Franklin AM, Aga DS, Cytryn E, Durso LM, McLain JE, Pruden A, et al. Antibiotics in agroecosystems: introduction to the special section. J Environ Qual. 2016;45:377–93.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    12.Jechalke S, Heuer H, Siemens J, Amelung W, Smalla K. Fate and effects of veterinary antibiotics in soil. Trends Microbiol. 2014;22:536–45.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    13.Mompelat S, Le Bot B, Thomas O. Occurrence and fate of pharmaceutical products and by-products, from resource to drinking water. Environ Int. 2009;35:803–14.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    14.Kelsic ED, Zhao J, Vetsigian K, Kishony R. Counteraction of antibiotic production and degradation stabilizes microbial communities. Nature. 2015;521:516–9.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    15.Cordero OX, Wildschutte H, Kirkup B, Proehl S, Ngo L, Hussain F, et al. Ecological populations of bacteria act as socially cohesive units of antibiotic production and resistance. Science. 2012;337:1228–31.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    16.Schlatter DC, Song Z, Vaz-Jauri P, Kinkel LL. Inhibitory interaction networks among coevolved Streptomyces populations from prairie soils. Plos One. 2019;14:e0223779.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    17.Abrudan MI, Smakman F, Grimbergen AJ, Westhoff S, Miller EL, Van Wezel GP, et al. Socially mediated induction and suppression of antibiosis during bacterial coexistence. Proc Natl Acad Sci. 2015;112:11054–9.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    18.Brauner A, Fridman O, Gefen O, Balaban NQ. Distinguishing between resistance, tolerance and persistence to antibiotic treatment. Nat Rev Microbiol. 2016;14:320.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    19.Andersson DI, Levin BR. The biological cost of antibiotic resistance. Curr Opin Microbiol. 1999;2:489–93.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    20.Handwerger S, Tomasz A. Antibiotic tolerance among clinical isolates of bacteria. Annu Rev Pharmacol Toxicol. 1985;25:349–80.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    21.Kester JC, Fortune SM. Persisters and beyond: mechanisms of phenotypic drug resistance and drug tolerance in bacteria. Crit Rev Biochem Mol Biol. 2014;49:91–101.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    22.Wood KB, Cluzel P. Trade-offs between drug toxicity and benefit in the multi-antibiotic resistance system underlie optimal growth of E. coli. BMC Syst Biol. 2012;6:1–11.Article 

    Google Scholar 
    23.Nguyen D, Joshi-Datar A, Lepine F, Bauerle E, Olakanmi O, Beer K, et al. Active starvation responses mediate antibiotic tolerance in biofilms and nutrient-limited bacteria. Science. 2011;334:982–6.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    24.Meredith HR, Srimani JK, Lee AJ, Lopatkin AJ, You L. Collective antibiotic tolerance: mechanisms, dynamics and intervention. Nat Chem Biol. 2015;11:182.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    25.Nagarajan R, Boeck LD, Gorman M, Hamill RL, Higgens CE, Hoehn MM, et al. beta.-Lactam antibiotics from Streptomyces. J Am Chem Soc. 1971;93:2308–10.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    26.Imada A, Kitano K, Kintaka K, Muroi M, Asai M. Sulfazecin and isosulfazecin, novel β-lactam antibiotics of bacterial origin. Nature. 1981;289:590–1.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    27.Sykes R, Cimarusti C, Bonner D, Bush K, Floyd D, Georgopapadakou N, et al. Monocyclic β-lactam antibiotics produced by bacteria. Nature. 1981;291:489.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    28.Wells JS, TREJO WH, PRINCIPE PA, Bush K, Georgopapadakou N, Bonner DP, et al. EM5400, a family of monobactam antibiotics produced by Agrobacterium radiobacter. J Antibiot. 1982;35:295–9.CAS 
    Article 

    Google Scholar 
    29.ThaKurIa B, Lahon K. The beta lactam antibiotics as an empirical therapy in a developing country: An update on their current status and recommendations to counter the resistance against them. J Clin Diagn Res. 2013;7:1207.PubMed 
    PubMed Central 

    Google Scholar 
    30.Russ D, Glaser F, Tamar ES, Yelin I, Baym M, Kelsic ED, et al. Escape mutations circumvent a tradeoff between resistance to a beta-lactam and resistance to a beta-lactamase inhibitor. Nat Commun. 2020;11:1–9.Article 
    CAS 

    Google Scholar 
    31.Grinberg M, Orevi T, Steinberg S, Kashtan N. Bacterial survival in microscopic surface wetness. eLife. 2019;8:e48508.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    32.Orevi T, Kashtan N. Life in a droplet: microbial ecology in microscopic surface wetness. Front Microbiol. 2021;12:797.Article 

    Google Scholar 
    33.Mauer LJ, Taylor LS. Water-solids interactions: deliquescence. Annu Rev food Sci Technol. 2010;1:41–63.CAS 
    PubMed 
    Article 

    Google Scholar 
    34.Wise ME, Martin ST, Russell LM, Buseck PR. Water uptake by NaCl particles prior to deliquescence and the phase rule. Aerosol Sci Technol. 2008;42:281–94.CAS 
    Article 

    Google Scholar 
    35.Burkhardt J, Koch K, Kaiser H. Deliquescence of deposited atmospheric particles on leaf surfaces. J Water, Air Soil Pollut: Focus. 2001;1:313–21.CAS 
    Article 

    Google Scholar 
    36.Beattie GA. Water relations in the Interaction of foliar bacterial pathogens with plants. Annu Rev Phytopathol. 2011;49:533–55.CAS 
    PubMed 
    Article 

    Google Scholar 
    37.Davila AF, Hawes I, Ascaso C, Wierzchos J. Salt deliquescence drives photosynthesis in the hyperarid A tacama D esert. Environ Microbiol Rep. 2013;5:583–7.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    38.Dai S, Shin H, Santamarina JC. Formation and development of salt crusts on soil surfaces. Acta Geotechnica. 2016;11:1103–9.Article 

    Google Scholar 
    39.Trechsel HR. Moisture control in buildings. ASTM International; West Conshohocken, PA19428-2959, USA; 1994.40.Schwartz-Narbonne H, Donaldson DJ. Water uptake by indoor surface films. Sci Rep. 2019;9:1–10.CAS 
    Article 

    Google Scholar 
    41.Patrick D, Findon G, Miller T. Residual moisture determines the level of touch-contact-associated bacterial transfer following hand washing. Epidemiol Infect. 1997;119:319–25.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    42.Tang IN, Munkelwitz HR. Composition and temperature dependence of the deliquescence properties of hygroscopic aerosols. Atmos Environ Part A Gen Top. 1993;27:467–73.Article 

    Google Scholar 
    43.Pöschl U. Atmospheric aerosols: composition, transformation, climate and health effects. Angew Chem Int Ed. 2005;44:7520–40.Article 
    CAS 

    Google Scholar 
    44.Tecon R. Bacterial survival: life on a leaf. eLife. 2019;8:e52123.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    45.Vejerano EP, Marr LC. Physico-chemical characteristics of evaporating respiratory fluid droplets. J R Soc Interface. 2018;15:20170939.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    46.Rubasinghege G, Grassian VH. Role (s) of adsorbed water in the surface chemistry of environmental interfaces. Chem Commun. 2013;49:3071–94.CAS 
    Article 

    Google Scholar 
    47.Campbell TD, Febrian R, McCarthy JT, Kleinschmidt HE, Forsythe JG, Bracher PJ. Prebiotic condensation through wet–dry cycling regulated by deliquescence. Nat Commun. 2019;10:1–7.Article 
    CAS 

    Google Scholar 
    48.Alsved M, Holm S, Christiansen S, Smidt M, Rosati B, Ling M, et al. Effect of aerosolization and drying on the viability of pseudomonas syringae cells. Front Microbiol. 2018;9:3086.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    49.Xie X, Li Y, Zhang T, Fang HH. Bacterial survival in evaporating deposited droplets on a teflon-coated surface. Appl Microbiol Biotechnol. 2006;73:703–12.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    50.Runkel S, Wells HC, Rowley G. Living with stress: a lesson from the enteric pathogen Salmonella enterica. Adv Appl Microbiol. 2013;83:87–144.51.Amaeze N, Akinbobola A, Chukwuemeka V, Abalkhaila A, Ramage G, Kean R, et al. Development of a high throughput and low cost model for the study of semi-dry biofilms. Biofouling. 2020:36:403–15.52.Tuomanen E, Cozens R, Tosch W, Zak O, Tomasz A. The rate of killing of Escherichia coli byβ-lactam antibiotics is strictly proportional to the rate of bacterial growth. Microbiology. 1986;132:1297–304.CAS 
    Article 

    Google Scholar 
    53.Eng R, Padberg F, Smith S, Tan E, Cherubin C. Bactericidal effects of antibiotics on slowly growing and nongrowing bacteria. Antimicrobial Agents Chemother. 1991;35:1824–8.CAS 
    Article 

    Google Scholar 
    54.Lee S, Foley E, Epstein JA. Mode of action of penicillin: I. Bacterial growth and penicillin activity—Staphylococcus aureus FDA. J Bacteriol. 1944;48:393.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    55.Lopatkin AJ, Stokes JM, Zheng EJ, Yang JH, Takahashi MK, You L, et al. Bacterial metabolic state more accurately predicts antibiotic lethality than growth rate. Nat Microbiol. 2019;4:2109–17.56.Yoon H, Park B-Y, Oh M-H, Choi K-H, Yoon Y. Effect of NaCl on heat resistance, antibiotic susceptibility, and Caco-2 cell invasion of Salmonella. BioMed Res Int. 2013;2013:274096.57.Zhu M, Dai X. High salt cross-protects Escherichia coli from antibiotic treatment through increasing efflux pump expression. mSphere 3: e00095-18. mSphere. 2018;3:e00095–18.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    58.Lee AJ, Wang S, Meredith HR, Zhuang B, Dai Z, You L. Robust, linear correlations between growth rates and β-lactam–mediated lysis rates. Proc Natl Acad Sci. 2018;115:4069–74.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    59.Loftin KA, Adams CD, Meyer MT, Surampalli R. Effects of ionic strength, temperature, and pH on degradation of selected antibiotics. J Environ Qual. 2008;37:378–86.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    60.Thonus IP, Fontijne P, Michel MF. Ampicillin susceptibility and ampicillin-induced killing rate of Escherichia coli. Antimicrobial Agents Chemother. 1982;22:386–90.CAS 
    Article 

    Google Scholar 
    61.Cho H, Uehara T, Bernhardt TG. Beta-lactam antibiotics induce a lethal malfunctioning of the bacterial cell wall synthesis machinery. Cell. 2014;159:1300–11.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    62.Yao Z, Kahne D, Kishony R. Distinct single-cell morphological dynamics under beta-lactam antibiotics. Mol Cell. 2012;48:705–12.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    63.Battesti A, Majdalani N, Gottesman S. The RpoS-mediated general stress response in Escherichia coli. Annu Rev Microbiol. 2011;65:189–213.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    64.Bernier SP, Lebeaux D, DeFrancesco AS, Valomon A, Soubigou G, Coppée J-Y, et al. Starvation, together with the SOS response, mediates high biofilm-specific tolerance to the fluoroquinolone ofloxacin. PLoS Genet. 2013;9:e1003144.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    65.Pu Y, Zhao Z, Li Y, Zou J, Ma Q, Zhao Y, et al. Enhanced efflux activity facilitates drug tolerance in dormant bacterial cells. Mol Cell. 2016;62:284–94.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    66.Martins D, McKay G, Sampathkumar G, Khakimova M, English AM, Nguyen D. Superoxide dismutase activity confers (p) ppGpp-mediated antibiotic tolerance to stationary-phase Pseudomonas aeruginosa. Proc Natl Acad Sci. 2018;115:9797–802.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    67.Page R, Peti W. Toxin-antitoxin systems in bacterial growth arrest and persistence. Nat Chem Biol. 2016;12:208–14.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    68.Liao X, Ma Y, Daliri EB-M, Koseki S, Wei S, Liu D, et al. Interplay of antibiotic resistance and food-associated stress tolerance in foodborne pathogens. Trends Food Sci Technol. 2020;95:97–106.CAS 
    Article 

    Google Scholar 
    69.Levin-Reisman I, Brauner A, Ronin I, Balaban NQ. Epistasis between antibiotic tolerance, persistence, and resistance mutations. Proc Natl Acad Sci. 2019;116:14734–9.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    70.Levin-Reisman I, Ronin I, Gefen O, Braniss I, Shoresh N, Balaban NQ. Antibiotic tolerance facilitates the evolution of resistance. Science. 2017;355:826–30.CAS 
    PubMed 
    Article 

    Google Scholar  More

  • in

    Year-round high abundances of the world’s smallest marine vertebrate (Schindleria) in the Red Sea and worldwide associations with lunar phases

    1.Giltay, L. Les larves de Schindler sont-elles des Hemirhamphidae?. Notes Ichthyol. Mus. Roy. d’Hist. Nat Belgique 10, 1–10 (1934).
    Google Scholar 
    2.Johnson, G. D. & Brothers, E. B. Schindleria: a paedomorphic goby (Teleostei: Gobioidei). Bull. Mar. Sci. 52, 441–471 (1993).
    Google Scholar 
    3.Kon, T. & Yoshino, T. Diversity and evolution of life histories of gobioid fishes from the viewpoint of heterochrony. Mar. Freshw. Res. 53, 377–402 (2002).Article 

    Google Scholar 
    4.Randall, J. E. & Cea, A. Shore fishes of Easter Island. (University of Hawaii Press, 2011).5.Kon, T., Yoshino, T., Mukai, T. & Nishida, M. DNA sequences identify numerous cryptic species of the vertebrate: a lesson from the gobioid fish Schindleria. Mol. Phylogenet. Evol. 44, 53–62 (2007).CAS 
    Article 

    Google Scholar 
    6.Robitzch, V., Schröder, M. & Ahnelt, H. Morphometrics reveal inter- and intraspecific sexual dimorphisms in two Hawaiian Schindleria, the long dorsal finned S. praematura and the short dorsal finned S. pietschmanni. Zool. Anz. 292, 197–206 (2021).Article 

    Google Scholar 
    7.Schindler, O. Ein neuer Hemirhamphus aus dem Pazifischen Ozean. Anzeiger der Akad. der Wissenschaften Wien 67, 79–80 (1930).
    Google Scholar 
    8.Schindler, O. Sexually mature larval Hemiramphidae from the Hawaiian Islands. Bull. Bernice P. Bish. Museum 1–28 (1932).9.Landaeta, M. F., Veas, R. & Castro, L. R. First record of the paedomorphic goby Schindleria praematura, Easter Island, South Pacific. J. Fish Biol. 61, 289–292 (2002).Article 

    Google Scholar 
    10.Watson, W. & Walker, H. J. J. The world’s smallest vertebrate, Schindleria brevipinguis, a new paedomorphic species in the family Schindleriidae (Perciformes: Gobioidei). Rec. Aust. Museum 56, 139–142 (2004).Article 

    Google Scholar 
    11.Kon, T., Yoshino, T. & Nishida, M. Cryptic species of the gobioid paedomorphic genus Schindleria from Palau, Western Pacific Ocean. Ichthyol. Res. https://doi.org/10.1007/s10228-010-0178-y (2010).Article 

    Google Scholar 
    12.Ahnelt, H. & Sauberer, M. Deep-water, offshore, and new records of Schindler’s fishes, Schindleria (Teleostei, Gobiidae), from the Indo-west Pacific collected during the Dana-Expedition, 1928–1930. Zootaxa 4731, 451–470 (2020).Article 

    Google Scholar 
    13.Bruun, A. F. A study of a collection of the fish Schindleria from South Pacific waters. Dana Rep. 21, 1–12 (1940).
    Google Scholar 
    14.Jones, S. & Kumaran, M. On the fishes of the genus Schindleria (Giltay) from the Indian Ocean. J. Mar. Biol. 6, 257–264 (1964).
    Google Scholar 
    15.Leis, J. M. Coral Sea atoll lagoons: closed nurseries for the larvae of a few coral reef fishes. Bull. Mar. Sci. 54, 206–227 (1994).ADS 

    Google Scholar 
    16.Belyanina, T. P. Ichthyoplankton in the regions of the Nazca and Salas y Gomez submarine ridges. J. Ichthyol. 29, 84–90 (1989).
    Google Scholar 
    17.Parin, N. V., Mironov, A. N. & Nesis, K. N. Biology of the Nazca and Salas y Gomez submarine ridges, an outpost of the Indo-West Pacific fauna in the Eastern Pacific Ocean: composition and distribution of the fauna, its communities and history. Adv. Mar. Biol. 32, 147–242 (1997).
    Google Scholar 
    18.Ahnelt, H. & Sauberer, M. A new species of Schindler’s fish (Teleostei: Gobiidae: Schindleria) from the Malay archipelago (Southeast Asia), with notes on the caudal fin complex of Schindleria. Zootaxa 4531, 95–108 (2018).Article 

    Google Scholar 
    19.Leis, J. M., Goldman, B. & Read, S. E. Epibenthic fish larvae in the Great Barrier Reef Lagoon near Lizard Island, Australia. Japanese J. Ichthyol. 35, 428–433 (1989).
    Google Scholar 
    20.Thacker, C. & Grier, H. Unusual gonad structure in the paedomorphic teleost Schindleria praematura (Teleostei Gobioidei): a comparison with other gobioid fishes. J. Fish Biol. 66, 378–391 (2005).Article 

    Google Scholar 
    21.Young, S.-S. & Chiu, T.-S. New records of a paedomorphic fish Schindleria praematura (Pisces: Schindleriidae), from Waters of Taiwan. Acta Zool. Taiwanica 11, 127–137 (2000).
    Google Scholar 
    22.Watson, W. & Leis, J. M. Ichthyoplankton of Kaneohe Bay, Hawaii. A one-year study of fish eggs and larvae. 1–178 (University of Hawaiʻi Sea Grant Program, 1974).23.Leis, J. M. & Trnski, T. The larvae of Indo-Pacific shorefishes. (New South Wales Univ. Press, Sydney & Univ. of Hawaii Press, 1989).24.Fricke, R. & Abu El-Regal, M. A. Schindleria nigropunctata, a new species of paedomorphic gobioid fish from the Red Sea (Teleostei: Schindleriidae). Mar. Biodivers. https://doi.org/10.1007/s12526-017-0831-z (2017).Article 

    Google Scholar 
    25.Fricke, R. & Abu El-Regal, M. A. Schindleria elongata, a new species of paedomorphic gobioid from the Red Sea (Teleostei: Schindleriidae). J Fish Biol 2, 1–8. https://doi.org/10.1111/jfb.13280 (2017).Article 

    Google Scholar 
    26.Abu El-Regal, M. A. & Kon, T. First record of the Schindler’s fish, Schindleria praematura (Actinopterygii: Perciformes: Schindleriidae), from the Red Sea. Acta Ichthyol. Piscat. 49, 75–78 (2019).Article 

    Google Scholar 
    27.EAbu El-Regal, M. & Kon, T. First record of the paedomorphic fish Schindleria (Gobioidei, Schindleriidae) from the Red Sea. J. Fish Biol. 72, 1539–1543 (2008).Article 

    Google Scholar 
    28.Ahnelt, H. Redescription of the paedomorphic goby Schindleria nigropunctata Fricke & El-Regal 2017 (Teleostei: Gobiidae) from the Red Sea. Zootaxa 4615, 450–456 (2019).Article 

    Google Scholar 
    29.Contreras, J. E., Landaeta, M. F., Plaza, G., Ojeda, F. P. & Bustos, C. A. The contrasting hatching patterns and larval growth of two sympatric clingfishes inferred by otolith microstructure analysis. Mar. Freshw. Res. 64, 157–167 (2013).Article 

    Google Scholar 
    30.Team, R. C. R: a language and environment for statistical computing (version 3.6). https://www.R-project.org (2020).31.Kleiber, C. & Zeileis, A. Applied econometrics with R. (Springer Science & Business Media, 2008).32.Kleiber, C. & Zeileis, A. AER: applied econometrics with R. R package version 1.1. (2009).33.Batschelet, E. Circular statistics in biology. (Academic Press, New York, 1981).34.Hammer, Ø., Harper, D. A. T. & Ryan, P. D. PAST: paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 9 (2001).
    Google Scholar 
    35.Robitzch, V. & Berumen, M. L. Recruitment of coral reef fishes along a cross-shelf gradient in the Red Sea peaks outside the hottest season. Coral Reefs 39, 1565–1579 (2020).Article 

    Google Scholar 
    36.Whittle, A. G. Ecology, abundance, diversity, and distribution of larval fishes and Schindleriidae (Teleostei: Gobioidei) at two sites on O’ahu, Hawai’i. (University of Hawaiʻi, 2003).37.Depczynski, M. & Bellwood, D. R. Shortest recorded vertebrate lifespan found in a coral reef fish. Curr. Biol. 15, 10 (2005).Article 

    Google Scholar 
    38.Isari, S. et al. Exploring the larval fish community of the central Red Sea with an integrated morphological and molecular approach. PLoS ONE 12, 1–24 (2017).Article 

    Google Scholar 
    39.Depczynski, M. & Bellwood, D. R. Extremes, plasticity, and invariance in vertebrate life history traits: insights from coral reef fishes. Ecology 87, 3119–3127 (2006).Article 

    Google Scholar 
    40.Nanninga, G. B., Saenz-Agudelo, P., Zhan, P., Hoteit, I. & Berumen, M. L. Not finding Nemo: limited reef-scale retention in a coral reef fish. Coral Reefs 34, 383–392 (2015).ADS 
    Article 

    Google Scholar 
    41.Hernaman, V. & Munday, P. L. Life-history characteristics of coral reef gobies. I. Growth and life-span. Mar. Ecol. Prog. Ser. 290, 207–221 (2005).ADS 
    Article 

    Google Scholar 
    42.Lefèvre, C. D., Nash, K. L., González-Cabello, A. & Bellwood, D. R. Consequences of extreme life history traits on population persistence: do short-lived gobies face demographic bottlenecks?. Coral Reefs 35, 399–409 (2016).ADS 
    Article 

    Google Scholar  More

  • in

    Tipping point realized in cod fishery

    1.Heinze, C. et al. The quiet crossing of ocean tipping points. Proc. Natl. Acad. Sci. 118, e2008478118 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    2.Dakos, V. et al. Ecosystem tipping points in an evolving world. Nat. Ecol. Evol. 3, 355–362 (2019).PubMed 
    Article 

    Google Scholar 
    3.Myers, R., Hutchings, J. & Barrowman, N. Hypotheses for the decline of cod in the North Atlantic. Mar. Ecol. Prog. Ser. 138, 293–308 (1996).ADS 
    Article 

    Google Scholar 
    4.Sguotti, C. et al. Catastrophic dynamics limit Atlantic cod recovery. Proc. R. Soc. B Biol. Sci. 286, 20182877 (2019).Article 

    Google Scholar 
    5.Levin, P. S. & Möllmann, C. Marine ecosystem regime shifts: Challenges and opportunities for ecosystem-based management. Philos. Trans. R. Soc. B Biol. Sci. 370, 20130275 (2015).Article 

    Google Scholar 
    6.King, J. R., Mcfarlane, G. A. & Punt, A. E. Shifts in fisheries management: Adapting to regime shifts. Philos. Trans. R. Soc. B Biol. Sci. 370, 20130277 (2015).Article 

    Google Scholar 
    7.Döring, R., Berkenhagen, J., Hentsch, S. & Kraus, G. Small-Scale Fisheries in Germany: A Disappearing Profession? In Small-Scale Fisheries in Europe: Status, Resilience and Governance (eds. Pascual-Fernández, J. J., Pita, C. & Bavinck, M.) vol. 23 483–502 (Springer International Publishing, 2020).8.Papaioannou, E. A., Vafeidis, A. T., Quaas, M. F., Schmidt, J. O. & Strehlow, H. V. Using indicators based on primary fisheries’ data for assessing the development of the German Baltic small-scale fishery and reviewing its adaptation potential to changes in resource abundance and management during 2000–09. Ocean Coast. Manag. 98, 38–50 (2014).Article 

    Google Scholar 
    9.EU. Regulation (EU) 2016/1139 of the European Parliament and of the Council of 6 July 2016 establishing a multiannual plan for the stocks of cod, herring and sprat in the Baltic Sea and the fisheries exploiting those stocks, amending Council Regulation (EC) No 2187/2005 and repealing Council Regulation (EC) No 1098/2007. (2016).10.Scheffer, M., Carpenter, S., Foley, J. A., Folke, C. & Walker, B. Catastrophic shifts in ecosystems. Nature 413, 591–596 (2001).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    11.Lenton, T. M. Environmental tipping points. Annu. Rev. Environ. Resour. 38, 1–29 (2013).ADS 
    Article 

    Google Scholar 
    12.Möllmann, C., Folke, C., Edwards, M. & Conversi, A. Marine regime shifts around the globe: Theory, drivers and impacts. Philos. Trans. R. Soc. B Biol. Sci. 370, 20130260 (2015).Article 

    Google Scholar 
    13.ICES. Advice cod in subdivisions 22–24, western Baltic stock (western Baltic Sea). (2019) https://doi.org/10.17895/ICES.ADVICE.5587.14.Conversi, A. et al. A holistic view of marine regime shifts. Philos. Trans. R. Soc. B Biol. Sci. 370, 20130279 (2015).Article 

    Google Scholar 
    15.Ratajczak, Z. et al. Abrupt change in ecological systems: Inference and diagnosis. Trends Ecol. Evol. 33, 513–526 (2018).PubMed 
    Article 

    Google Scholar 
    16.Turner, M. G. et al. Climate change, ecosystems and abrupt change: Science priorities. Philos. Trans. R. Soc. B Biol. Sci. 375, 20190105 (2020).Article 

    Google Scholar 
    17.Scheffer, M. & Carpenter, S. R. Catastrophic regime shifts in ecosystems: Linking theory to observation. Trends Ecol. Evol. 18, 648–656 (2003).Article 

    Google Scholar 
    18.Beisner, B., Haydon, D. & Cuddington, K. Alternative stable states in ecology. Front. Ecol. Environ. 1, 376–382 (2003).Article 

    Google Scholar 
    19.Subbey, S., Devine, J. A., Schaarschmidt, U. & Nash, R. D. Modelling and forecasting stock–recruitment: Current and future perspectives. ICES J. Mar. Sci. 71, 2307–2322 (2014).Article 

    Google Scholar 
    20.Grasman, R. P. P. P., Maas, H. L. J. van der & Wagenmakers, E.-J. Fitting the Cusp Catastrophe in r : A cusp Package Primer. J. Stat. Softw. 32, 1-27 (2009).21.Thom, R. Structural Stability and Morphogenesis—An Outline of a General Theory of Models (Benjamin Inc, 1975).MATH 

    Google Scholar 
    22.Zeeman, E. Catastrophe theory. Sci. Am. 234, 65–83 (1976).Article 

    Google Scholar 
    23.Barunik, J. & Vosvrda, M. Can a stochastic cusp catastrophe model explain stock market crashes?. J. Econ. Dyn. Control 33, 1824–1836 (2009).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    24.Xiaoping, Z., Jiahui, S. & Yuan, C. Analysis of crowd jam in public buildings based on cusp-catastrophe theory. Build. Environ. 45, 1755–1761 (2010).Article 

    Google Scholar 
    25.Guastello, S. J., Boeh, H., Shumaker, C. & Schimmels, M. Catastrophe models for cognitive workload and fatigue. Theor. Issues Ergon. Sci. 13, 586–602 (2012).Article 

    Google Scholar 
    26.Angelis, V., Angelis-Dimakis, A. & Dimaki, K. The Cusp Catastrophe model in describing a bank’s attractiveness as measured by its image. Proc. Econ. Finance 19, 261–277 (2015).Article 

    Google Scholar 
    27.Sideridis, G. D., Simos, P., Mouzaki, A. & Stamovlasis, D. Efficient word reading: Automaticity of print-related skills indexed by rapid automatized naming through cusp-catastrophe modeling. Sci. Stud. Read. 20, 6–19 (2016).Article 

    Google Scholar 
    28.Diks, C. & Wang, J. Can a stochastic cusp catastrophe model explain housing market crashes?. J. Econ. Dyn. Control 69, 68–88 (2016).Article 

    Google Scholar 
    29.Xu, Y. & Chen, X. Protection motivation theory and cigarette smoking among vocational high school students in China: A cusp catastrophe modeling analysis. Glob. Health Res. Policy 1, 3 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    30.Chen, D.-G., Lin, F., Chen, X., Tang, W. & Kitzman, H. Cusp Catastrophe Model: A nonlinear model for health outcomes in nursing research. Nurs. Res. 63, 211–220 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    31.Mostafa, M. M. Catastrophe theory predicts international concern for global warming. J. Quant. Econ. https://doi.org/10.1007/s40953-020-00199-8 (2020).Article 

    Google Scholar 
    32.Sguotti, C. et al. Non-linearity in stock–recruitment relationships of Atlantic cod: Insights from a multi-model approach. ICES J. Mar. Sci. 77, 1492–1502 (2020).Article 

    Google Scholar 
    33.Forster, P. M., Maycock, A. C., McKenna, C. M. & Smith, C. J. Latest climate models confirm need for urgent mitigation. Nat. Clim. Change 10, 7–10 (2020).ADS 
    Article 

    Google Scholar 
    34.Gröger, M., Arneborg, L., Dieterich, C., Höglund, A. & Meier, H. E. M. Summer hydrographic changes in the Baltic Sea, Kattegat and Skagerrak projected in an ensemble of climate scenarios downscaled with a coupled regional ocean–sea ice–atmosphere model. Clim. Dyn. 53, 5945–5966 (2019).Article 

    Google Scholar 
    35.Litzow, M. A., Mueter, F. J. & Hobday, A. J. Reassessing regime shifts in the North Pacific: Incremental climate change and commercial fishing are necessary for explaining decadal-scale biological variability. Glob. Change Biol. 20, 38–50 (2014).ADS 
    Article 

    Google Scholar 
    36.Auber, A., Travers-Trolet, M., Villanueva, M. C. & Ernande, B. Regime shift in an exploited fish community related to natural climate oscillations. PLoS One 10, e0129883 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    37.Karnauskas, M. et al. Evidence of climate-driven ecosystem reorganization in the Gulf of Mexico. Glob. Change Biol. 21, 2554–2568 (2015).ADS 
    Article 

    Google Scholar 
    38.Wernberg, T. et al. Climate-driven regime shift of a temperate marine ecosystem. Science 353, 169–172 (2016).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    39.Kotta, J. et al. Novel crab predator causes marine ecosystem regime shift. Sci. Rep. 8, 4956 (2018).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    40.Vert-pre, K. A., Amoroso, R. O., Jensen, O. P. & Hilborn, R. Frequency and intensity of productivity regime shifts in marine fish stocks. Proc. Natl. Acad. Sci. 110, 1779–1784 (2013).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    41.Perretti, C. et al. Regime shifts in fish recruitment on the Northeast US Continental Shelf. Mar. Ecol. Prog. Ser. 574, 1–11 (2017).ADS 
    Article 

    Google Scholar 
    42.Litzow, M. A., Ciannelli, L., Cunningham, C. J., Johnson, B. & Puerta, P. Nonstationary effects of ocean temperature on Pacific salmon productivity. Can. J. Fish. Aquat. Sci. 76, 1923–1928 (2019).Article 

    Google Scholar 
    43.van der Maas, H. L. J., Kolstein, R. & van der Pligt, J. Sudden transitions in attitudes. Sociol. Methods Res. 32, 125–152 (2003).MathSciNet 
    Article 

    Google Scholar 
    44.Griffith, G. P. Closing the gap between causality, prediction, emergence, and applied marine management. ICES J. Mar. Sci. 77, 1456–1462 (2020).Article 

    Google Scholar 
    45.Hutchings, J. A. Collapse and recovery of marine fishes. Nature 406, 882–885 (2000).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    46.Hilborn, R., Hively, D. J., Jensen, O. P. & Branch, T. A. The dynamics of fish populations at low abundance and prospects for rebuilding and recovery. ICES J. Mar. Sci. 71, 2141–2151 (2014).Article 

    Google Scholar 
    47.Köster, F. Trophodynamic control by clupeid predators on recruitment success in Baltic cod?. ICES J. Mar. Sci. 57, 310–323 (2000).Article 

    Google Scholar 
    48.Rowe, S., Hutchings, J. A., Bekkevold, D. & Rakitin, A. Depensation, probability of fertilization, and the mating system of Atlantic cod (Gadus morhua L.). ICES J. Mar. Sci. 61, 1144–1150 (2004).Article 

    Google Scholar 
    49.Keith, D. M. & Hutchings, J. A. Population dynamics of marine fishes at low abundance. Can. J. Fish. Aquat. Sci. 69, 1150–1163 (2012).Article 

    Google Scholar 
    50.Kuparinen, A., Keith, D. M. & Hutchings, J. A. Allee effect and the uncertainty of population recovery: Allee effect and population recovery. Conserv. Biol. 28, 790–798 (2014).PubMed 
    Article 

    Google Scholar 
    51.Neuenhoff, R. D. et al. Continued decline of a collapsed population of Atlantic cod (Gadus morhua) due to predation-driven Allee effects. Can. J. Fish. Aquat. Sci. 76, 168–184 (2019).Article 

    Google Scholar 
    52.Vergnon, R., Shin, Y.-J. & Cury, P. Cultivation, Allee effect and resilience of large demersal fish populations. Aquat. Living Resour. 21, 287–295 (2008).Article 

    Google Scholar 
    53.Saha, B., Bhowmick, A. R., Chattopadhyay, J. & Bhattacharya, S. On the evidence of an Allee effect in herring populations and consequences for population survival: A model-based study. Ecol. Model. 250, 72–80 (2013).Article 

    Google Scholar 
    54.Perälä, T. & Kuparinen, A. Detection of Allee effects in marine fishes: Analytical biases generated by data availability and model selection. Proc. R. Soc. B Biol. Sci. 284, 20171284 (2017).Article 

    Google Scholar 
    55.Lundquist, C. J. & Botsford, L. W. Estimating larval production of a broadcast spawner: The influence of density, aggregation, and the fertilization Allee effect. Can. J. Fish. Aquat. Sci. 68, 30–42 (2011).Article 

    Google Scholar 
    56.Sæther, B.-E., Engen, S., Lande, R. & Saether, B.-E. Density-dependence and optimal harvesting of fluctuating populations. Oikos 76, 40 (1996).MATH 
    Article 

    Google Scholar 
    57.Rowe, S. & Hutchings, J. A. Mating systems and the conservation of commercially exploited marine fish. Trends Ecol. Evol. 18, 567–572 (2003).Article 

    Google Scholar 
    58.Swain, D. P. & Chouinard, G. A. Predicted extirpation of the dominant demersal fish in a large marine ecosystem: Atlantic cod (Gadus morhua) in the southern Gulf of St. Lawrence. Can. J. Fish. Aquat. Sci. 65, 2315–2319 (2008).Article 

    Google Scholar 
    59.Kuparinen, A. & Hutchings, J. A. Increased natural mortality at low abundance can generate an Allee effect in a marine fish. R. Soc. Open Sci. 1, 140075 (2014).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    60.Swain, D. & Benoît, H. Extreme increases in natural mortality prevent recovery of collapsed fish populations in a Northwest Atlantic ecosystem. Mar. Ecol. Prog. Ser. 519, 165–182 (2015).ADS 
    Article 

    Google Scholar 
    61.Walters, C. & Kitchell, J. F. Cultivation/depensation effects on juvenile survival and recruitment: Implications for the theory of fishing. Can. J. Fish. Aquat. Sci. 58, 39–50 (2001).Article 

    Google Scholar 
    62.Andreasen, H. et al. Diet composition and food consumption rate of harbor porpoises (Phocoena phocoena) in the western Baltic Sea. Mar. Mamm. Sci. 33, 1053–1079 (2017).Article 

    Google Scholar 
    63.Hüssy, K. Review of western Baltic cod (Gadus morhua) recruitment dynamics. ICES J. Mar. Sci. 68, 1459–1471 (2011).Article 

    Google Scholar 
    64.Winter, A., Richter, A. & Eikeset, A. M. Implications of Allee effects for fisheries management in a changing climate: Evidence from Atlantic cod. Ecol. Appl. 30, 1–14 (2020).65.Munch, S. B., Giron-Nava, A. & Sugihara, G. Nonlinear dynamics and noise in fisheries recruitment: A global meta-analysis. Fish Fish. 19, 964–973 (2018).Article 

    Google Scholar 
    66.Szuwalski, C. S., Vert-Pre, K. A., Punt, A. E., Branch, T. A. & Hilborn, R. Examining common assumptions about recruitment: A meta-analysis of recruitment dynamics for worldwide marine fisheries. Fish Fish. 16, 633–648 (2015).Article 

    Google Scholar 
    67.Funk, S., Krumme, U., Temming, A. & Möllmann, C. Gillnet fishers’ knowledge reveals seasonality in depth and habitat use of cod (Gadus morhua) in the Western Baltic Sea. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsaa071 (2020).Article 

    Google Scholar 
    68.Hüssy, K., Hinrichsen, H.-H. & Huwer, B. Hydrographic influence on the spawning habitat suitability of western Baltic cod (Gadus morhua). ICES J. Mar. Sci. 69, 1736–1743 (2012).Article 

    Google Scholar 
    69.Hinrichsen, H.-H., Hüssy, K. & Huwer, B. Spatio-temporal variability in western Baltic cod early life stage survival mediated by egg buoyancy, hydrography and hydrodynamics. ICES J. Mar. Sci. 69, 1744–1752 (2012).Article 

    Google Scholar 
    70.Petereit, C., Hinrichsen, H.-H., Franke, A. & Köster, F. Floating along buoyancy levels: Dispersal and survival of western Baltic fish eggs. Prog. Oceanogr. 122, 131–152 (2014).ADS 
    Article 

    Google Scholar 
    71.Stiasny, M. H. et al. Ocean acidification effects on Atlantic Cod larval survival and recruitment to the fished population. PLoS One 11, e0155448 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    72.Voss, R. et al. Ecological-economic sustainability of the Baltic cod fisheries under ocean warming and acidification. J. Environ. Manag. 238, 110–118 (2019).Article 

    Google Scholar 
    73.Lindegren, M., Möllmann, C., Nielsen, A. & Stenseth, N. C. Preventing the collapse of the Baltic cod stock through an ecosystem-based management approach. Proc. Natl. Acad. Sci. 106, 14722–14727 (2009).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    74.Lindegren, M. et al. Ecological forecasting under climate change: The case of Baltic cod. Proc. R. Soc. B Biol. Sci. 277, 2121–2130 (2010).Article 

    Google Scholar 
    75.Holsman, K. K. et al. Ecosystem-based fisheries management forestalls climate-driven collapse. Nat. Commun. 11, 4579 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    76.Levin, P. S. et al. Building effective fishery ecosystem plans. Mar. Policy 92, 48–57 (2018).Article 

    Google Scholar 
    77.Dawson, C. & Levin, P. S. Moving the ecosystem-based fisheries management mountain begins by shifting small stones: A critical analysis of EBFM on the U.S. West Coast. Mar. Policy 100, 58–65 (2019).Article 

    Google Scholar 
    78.Link, J. S. & Marshak, A. R. Characterizing and comparing marine fisheries ecosystems in the United States: Determinants of success in moving toward ecosystem-based fisheries management. Rev. Fish Biol. Fish. 29, 23–70 (2019).Article 

    Google Scholar 
    79.Townsend, H. et al. Progress on implementing ecosystem-based fisheries management in the United States through the use of ecosystem models and analysis. Front. Mar. Sci. 6, 641 (2019).Article 

    Google Scholar 
    80.Koehn, L. E. et al. Case studies demonstrate capacity for a structured planning process for ecosystem-based fisheries management. Can. J. Fish. Aquat. Sci. 77, 1256–1274 (2020).Article 

    Google Scholar 
    81.Skern-Mauritzen, M. et al. Ecosystem processes are rarely included in tactical fisheries management. Fish Fish. 17, 165–175 (2016).Article 

    Google Scholar 
    82.Marshall, K. N., Koehn, L. E., Levin, P. S., Essington, T. E. & Jensen, O. P. Inclusion of ecosystem information in US fish stock assessments suggests progress toward ecosystem-based fisheries management. ICES J. Mar. Sci. 76, 1–9 (2019).Article 

    Google Scholar 
    83.Otto, S. A., Kadin, M., Casini, M., Torres, M. A. & Blenckner, T. A quantitative framework for selecting and validating food web indicators. Ecol. Ind. 84, 619–631 (2018).Article 

    Google Scholar 
    84.Kadin, M. et al. Trophic interactions, management trade-offs and climate change: The need for adaptive thresholds to operationalize ecosystem indicators. Front. Mar. Sci. 6, 249 (2019).ADS 
    Article 

    Google Scholar 
    85.Samhouri, J. F. et al. Defining ecosystem thresholds for human activities and environmental pressures in the California Current. Ecosphere 8, 1–21 (2017).86.Payne, M. R. et al. Lessons from the first generation of marine ecological forecast products. Front. Mar. Sci. 4, 289 (2017).Article 

    Google Scholar 
    87.Tommasi, D. et al. Managing living marine resources in a dynamic environment: The role of seasonal to decadal climate forecasts. Prog. Oceanogr. 152, 15–49 (2017).ADS 
    Article 

    Google Scholar 
    88.Haltuch, M. et al. Unraveling the recruitment problem: A review of environmentally-informed forecasting and management strategy evaluation. Fish. Res. 217, 198–216 (2019).Article 

    Google Scholar 
    89.Hobday, A. J. et al. A framework for combining seasonal forecasts and climate projections to aid risk management for fisheries and aquaculture. Front. Mar. Sci. 5, 137 (2018).Article 

    Google Scholar 
    90.Hobday, A. J. et al. Ethical considerations and unanticipated consequences associated with ecological forecasting for marine resources. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsy210 (2019).Article 

    Google Scholar 
    91.Punt, A. E., Butterworth, D. S., de Moor, C. L., De Oliveira, J. A. A. & Haddon, M. Management strategy evaluation: Best practices. Fish Fish. 17, 303–334 (2016).Article 

    Google Scholar 
    92.Grüss, A. et al. Recommendations on the use of ecosystem modeling for informing ecosystem-based fisheries management and restoration outcomes in the Gulf of Mexico. Mar. Coast. Fish. 9, 281–295 (2017).Article 

    Google Scholar 
    93.Hollowed, A. B. et al. Integrated modeling to evaluate climate change impacts on coupled social-ecological systems in Alaska. Front. Mar. Sci. 6, 775 (2020).Article 

    Google Scholar 
    94.Okamoto, D. K. et al. Attending to spatial social–ecological sensitivities to improve trade-off analysis in natural resource management. Fish Fish. 21, 1–12 (2020).Article 

    Google Scholar 
    95.Möllmann, C. et al. Implementing ecosystem-based fisheries management: From single-species to integrated ecosystem assessment and advice for Baltic Sea fish stocks. ICES J. Mar. Sci. 71, 1187–1197 (2014).Article 

    Google Scholar 
    96.Voss, R. et al. Assessing social—ecological trade-offs to advance ecosystem-based fisheries management. PLoS One 9, e107811 (2014).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    97.Schmidt, J. O. et al. Future ocean observations to connect climate, fisheries and marine ecosystems. Front. Mar. Sci. 6, 550 (2019).Article 

    Google Scholar 
    98.Hicks, C. C. et al. Engage key social concepts for sustainability. Science 352, 38–40 (2016).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    99.Hornborg, S. et al. Ecosystem-based fisheries management requires broader performance indicators for the human dimension. Mar. Policy 108, 103639 (2019).Article 

    Google Scholar 
    100.Levin, P. S. et al. Conceptualization of social-ecological systems of the california current: An examination of interdisciplinary science supporting ecosystem-based management. Coast. Manag. 44, 397–408 (2016).Article 

    Google Scholar 
    101.ICES. Herring (Clupea harengus) in subdivisions 20-24, spring spawners (Skagerrak, Kattegat, and western Baltic). https://doi.org/10.17895/ICES.ADVICE.4715 (2019).102.Quentin Grafton, R. Adaptation to climate change in marine capture fisheries. Mar. Policy 34, 606–615 (2010).Article 

    Google Scholar 
    103.Lindegren, M. & Brander, K. Adapting fisheries and their management to climate change: A review of concepts, tools, frameworks, and current progress toward implementation. Rev. Fish. Sci. Aquac. 26, 400–415 (2018).Article 

    Google Scholar 
    104.Holsman, K. K. et al. Towards climate resiliency in fisheries management. ICES J. Mar. Sci. https://doi.org/10.1093/icesjms/fsz031 (2019).Article 

    Google Scholar 
    105.Bell, R. J., Odell, J., Kirchner, G. & Lomonico, S. Actions to promote and achieve climate-ready fisheries: Summary of current practice. Mar. Coast. Fish. 12, 166–190 (2020).Article 

    Google Scholar 
    106.Gaichas, S. K., Link, J. S. & Hare, J. A. A risk-based approach to evaluating northeast US fish community vulnerability to climate change. ICES J. Mar. Sci. 71, 2323–2342 (2014).Article 

    Google Scholar 
    107.Pecl, G. T. et al. Rapid assessment of fisheries species sensitivity to climate change. Clim. Change 127, 505–520 (2014).ADS 
    Article 

    Google Scholar 
    108.Hare, J. A. et al. A vulnerability assessment of fish and invertebrates to climate change on the Northeast U.S. Continental Shelf. PLoS One 11, e0146756 (2016).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    109.Johnson, J. E. et al. Assessing and reducing vulnerability to climate change: Moving from theory to practical decision-support. Mar. Policy 74, 220–229 (2016).Article 

    Google Scholar 
    110.Whitney, C. K. et al. Adaptive capacity: From assessment to action in coastal social-ecological systems. Ecol. Soc. 22, art22 (2017).Article 

    Google Scholar 
    111.Johnson, F. A., Eaton, M. J., Mikels-Carrasco, J. & Case, D. Building adaptive capacity in a coastal region experiencing global change. Ecol. Soc. 25, art9 (2020).Article 

    Google Scholar 
    112.ICES. Baltic Fisheries Assessemant Working Group. (2019). https://doi.org/10.17895/ICES.PUB.5949.113.ICES. Baltic Fisheries Assessemant Working Group. ICES CM 2014/ACOM:10 (2014).114.Hüssy, K. et al. Spatio-temporal trends in stock mixing of eastern and western Baltic cod in the Arkona Basin and the implications for recruitment. ICES J. Mar. Sci. J. Conseil 73, 293–303 (2016).Article 

    Google Scholar 
    115.Weist, P. et al. Assessing SNP-markers to study population mixing and ecological adaptation in Baltic cod. PLoS One 14, e0218127 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    116.R Core Team. R: A Language and Environment for Statistical Computing. (Accessed 2 July 2021); https://www.R-project.org/ (R Foundation for Statistical Computing, 2020).117.Wickham, H. et al. Welcome to the tidyverse. J. Open Source Softw. 4, 1686 (2019).ADS 
    Article 

    Google Scholar 
    118.Killick, R. & Eckley, I. A. Changepoint: An R package for changepoint analysis. J. Stat. Softw. 58, 1–19 (2014).119.Zeileis, A., Kleiber, C., Krämer, W. & Hornik, K. Testing and dating of structural changes in practice. Comput. Stat. Data Anal. 44, 109–123 (2003).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    120.Otto, S. A. Comparison of change point detection methods. (Accessed 2 July 2021); https://www.marinedatascience.co/blog/2019/09/28/comparison-of-change-point-detection-methods/. (2019). More

  • in

    Climate change and tree growth in the Khakass-Minusinsk Depression (South Siberia) impacted by large water reservoirs

    1.IPCC. Climate Change 2007: The Physical Science Basis. Contribution of working group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (Cambridge University Press, 2007).2.IPCC. Special Report on the Impacts of Global Warming of 1.5 °C above Pre-industrial Levels and Related Global Greenhouse Gas Emission Pathways, in the Context of Strengthening the Global Response to the Threat of Climate Change, Sustainable Development, and Efforts to Eradicate Poverty (WMO, 2019).3.Rogers, J. C. & Mosely-Thompson, E. Atlantic Arctic cyclones and mild Siberian winters of the 1980s. Geophys. Res. Lett. 22, 799–802 (1995).ADS 
    Article 

    Google Scholar 
    4.Davi, N. K., Jacoby, G. C., Curtis, A. E. & Baatarbileg, N. Extension of drought records for central Asia using tree rings: West-central Mongolia. J. Clim. 19, 288–299 (2006).ADS 
    Article 

    Google Scholar 
    5.Kattsov, V. M. & Semenov, S. M. Second Roshydromet Assessment Report on Climate Change and its Consequences in Russian Federation (Roshydromet, 2014).
    Google Scholar 
    6.Savelieva, N. I., Semiletov, I. P., Vasilevskaya, L. N. & Pugach, S. P. A climate shift in seasonal values of meteorological and hydrological parameters for Northeastern Asia. Prog. Oceanogr. 47, 279–297 (2000).ADS 
    Article 

    Google Scholar 
    7.Liu, X. et al. Drought evolution and its impact on the crop yield in the North China Plain. J. Hydrol. 564, 984–996 (2018).ADS 
    Article 

    Google Scholar 
    8.Cho, D. J. & Kim, K. Y. Role of Ural blocking in Arctic sea ice loss and its connection with Arctic warming in winter. Clim. Dyn. 56, 1571–1588 (2021).Article 

    Google Scholar 
    9.Savkin, V. M. Reservoirs of Siberia: Consequences of their creation to water ecology and water management facilities. Sib. Ecol. J. 2, 109–121 (2000) (in Russian).
    Google Scholar 
    10.Poff, N. L. & Hart, D. D. How dams vary and why it matters for the emerging science of dam removal: An ecological classification of dams is needed to characterize how the tremendous variation in the size, operational mode, age, and number of dams in a river basin influences the potential for restoring regulated rivers via dam removal. Bioscience 52, 659–668 (2002).Article 

    Google Scholar 
    11.Osika, D. G., Otinova, AYu. & Ponomareva, N. L. About the origin of the global warming and the reasons for the formation of climatic anomalies and disasters. Arid Ecosyst. 19, 104–112 (2013) (in Russian).
    Google Scholar 
    12.Aras, E. Effects of multiple dam projects on river ecology and climate change: Çoruh River Basin, Turkey. Adv. Environ. Res. 7, 121 (2018).
    Google Scholar 
    13.Shen, P. & Zhao, S. 1/4 to 1/3 of observed warming trends in China from 1980 to 2015 are attributed to land use changes. Clim. Change 164, 59. https://doi.org/10.1007/s10584-021-03045-9 (2021).ADS 
    Article 

    Google Scholar 
    14.Ward, J. V. & Stanford, J. A. The Ecology of Regulated Streams (Plenum Press, 1979).Book 

    Google Scholar 
    15.Ligon, F. K., Dietrich, W. E. & Trush, W. J. Downstream ecological effects of dams. Bioscience 45, 183–192 (1995).Article 

    Google Scholar 
    16.Gyau-Boakye, P. Environmental impacts of the Akosombo dam and effects of climate change on the lake levels. Environ. Dev. Sustain. 3, 17–29 (2001).Article 

    Google Scholar 
    17.Muth, R. T. et al. Flow and Temperature Recommendations for Endangered Fishes in the Green River Downstream of Flaming Gorge Dam. Final Report, Upper Colorado River Endangered Fish Recovery Program Project FG-53 (UCREFRP, 2000).18.Degu, A. M. et al. The influence of large dams on surrounding climate and precipitation patterns. Geophys. Res. Lett. 38, L04405. https://doi.org/10.1029/2010GL046482 (2011).ADS 
    Article 

    Google Scholar 
    19.Normatov, I. S., Muminov, A. & Normatov, P. I. The impact of water reservoirs on biodiversity and food security. Creation of adaptation mechanisms. Glob. Perspect. Eng. Manag. 1, 21–25 (2012).
    Google Scholar 
    20.Butorin, N. V., Vendrov, S. L., Dyakonov, K. N., Reteyum, A. Y. & Romanenko, V. I. Effect of the Rybinsk reservoir on the surrounding area. In Man-Made Lakes: Their Problems and Environmental Effects (eds Ackerman, W. C. et al.) 246–250 (American Geophysical Union, 1973).
    Google Scholar 
    21.American Society of Civil Engineers. Guidelines for Retirement of Dams and Hydroelectric Facilities (American Society of Civil Engineers, 1997).
    Google Scholar 
    22.Rosenzweig, C. et al. Assessment of observed changes and responses in natural and managed systems. In Climate Change 2007: Impacts, Adaptation and Vulnerability. Contribution of Working Group II to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change (eds Parry, M. L. et al.) 79–131 (Cambridge UP, 2007).
    Google Scholar 
    23.Piao, S. et al. Plant phenology and global climate change: Current progresses and challenges. Glob. Change Biol. 25, 1922–1940 (2019).ADS 
    Article 

    Google Scholar 
    24.Gill, D. S., Amthor, J. S. & Bormann, F. H. Leaf phenology, photosynthesis, and the persistence of saplings and shrubs in a mature northern hardwood forest. Tree Physiol. 18, 281–289 (1998).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    25.Augspurger, C. K., Cheeseman, J. M. & Salk, C. F. Light gains and physiological capacity of understory woody plants during phenological avoidance of canopy shade. Funct. Ecol. 19, 537–546 (2005).Article 

    Google Scholar 
    26.Zhang, X., Friedl, M. A., Schaaf, C. B. & Strahler, A. H. Climate controls on vegetation phenological patterns in northern mid-and high latitudes inferred from MODIS data. Glob. Chang. Biol. 10, 1133–1145 (2004).ADS 
    Article 

    Google Scholar 
    27.Zeng, H., Jia, G. & Epstein, H. Recent changes in phenology over the northern high latitudes detected from multi-satellite data. Environ. Res. Lett. 6, 045508. https://doi.org/10.1088/1748-9326/6/4/045508 (2011).ADS 
    Article 

    Google Scholar 
    28.Montgomery, R. A., Rice, K. E., Stefanski, A., Rich, R. L. & Reich, P. B. Phenological responses of temperate and boreal trees to warming depend on ambient spring temperatures, leaf habit, and geographic range. PNAS 117, 10397–10405 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    29.Badeck, F.-W. et al. Responses of spring phenology to climate change. New Phytol. 162, 295–309 (2004).Article 

    Google Scholar 
    30.Camarero, J. J., Olano, J. M. & Parras, A. Plastic bimodal xylogenesis in conifers from continental Mediterranean climates. New Phytol. 185, 471–480 (2010).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    31.Rossi, S., Girard, M.-J.J. & Morin, H. Lengthening of the duration of xylogenesis engenders disproportionate increases in xylem production. Glob. Chang. Biol. 20, 2261–2271 (2014).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    32.McCarty, J. P. Ecological consequences of recent climate change. Conserv. Biol. 15, 320–331 (2001).Article 

    Google Scholar 
    33.Aagaard, K. & Carmack, E. C. The role of sea ice and other fresh water in the Arctic circulation. J. Geophys. Res. Oceans 94, 14485–14498 (1989).ADS 
    Article 

    Google Scholar 
    34.Hunt, J. D. et al. Hydropower impact on the river flow of a humid regional climate. Clim. Change 163, 379–393 (2020).ADS 
    Article 

    Google Scholar 
    35.Kosmakov, I. V. Thermal and Ice Regime in the Upper and Lower Reaches of High-Pressure Hydroelectric Power Stations on the Yenisei (Klaretianum, 2001) (in Russian).
    Google Scholar 
    36.Bryzgalov, V. I. From the Experience of Creation and Development of the Krasnoyarsk and Sayano-Shushenskaya Hydroelectric Power Plants (Siberian Publ. House “Surikov,” 1999) (in Russian).
    Google Scholar 
    37.Sheffield, J., Andreadis, K. M. & Wood, E. F. Global and continental drought in the second half of the twentieth century: Severity-area-duration analysis and temporal variability of large-scale events. J. Clim. 22, 1962–1981 (2009).ADS 
    Article 

    Google Scholar 
    38.Liu, H. et al. Rapid warming accelerates tree growth decline in semi-arid forests of Inner Asia. Glob. Change Biol. 19, 2500–2510 (2013).ADS 
    Article 

    Google Scholar 
    39.Stanke, H., Finley, A. O., Domke, G. M., Weed, A. S. & MacFarlane, D. W. Over half of western United States’ most abundant tree species in decline. Nat. Commun. 12, 451. https://doi.org/10.1038/s41467-020-20678-z (2021).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    40.Amrit, K., Pandey, R. P., Mishra, S. K. & Daradur, M. Relationship of drought frequency and severity with range of annual temperature variation. Nat. Hazards 92, 1199–1210 (2018).Article 

    Google Scholar 
    41.Jackson, R. D., Idso, S. B., Reginato, R. J. & Pinter, P. J. Jr. Canopy temperature as a crop water stress indicator. Water Resour. Res. 17(4), 1133–1138 (1981).ADS 
    Article 

    Google Scholar 
    42.Bao, G., Liu, Y. & Linderholm, H. W. April–September mean maximum temperature inferred from Hailar pine (Pinus sylvestris var. mongolica) tree rings in the Hulunbuir region, Inner Mongolia, back to 1868 AD. Palaeogeogr. Palaeoclimatol. Palaeoecol. 313, 162–172 (2012).Article 

    Google Scholar 
    43.de Vrese, P. & Stacke, T. Irrigation and hydrometeorological extremes. Clim. Dyn. 55, 1521–1537 (2020).Article 

    Google Scholar 
    44.Gustokashina, N. N. & Balybina, A. S. Variation in the natural-climatic characteristics of the territory adjacent to the reservoirs of the Angara chain of power plants. Geogr. Nat. Res. 4, 93–100 (2005) (in Russian).
    Google Scholar 
    45.Arzac, A. et al. Increasing radial and latewood growth rates of Larix cajanderi Mayr. and Pinus sylvestris L. in the continuous permafrost zone in Central Yakutia (Russia). Ann. For. Sci. 76, 96 (2019).Article 

    Google Scholar 
    46.Gower, S. T. & Richards, J. H. Larches: Deciduous conifers in an evergreen world. Bioscience 40, 818–826 (1990).Article 

    Google Scholar 
    47.McDowell, N. et al. Mechanisms of plant survival and mortality during drought: Why do some plants survive while others succumb to drought?. New Phytol. 178, 719–739 (2008).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    48.Piper, F. I. & Fajardo, A. Foliar habit, tolerance to defoliation and their link to carbon and nitrogen storage. J. Ecol. 102, 1101–1111 (2014).CAS 
    Article 

    Google Scholar 
    49.Khansaritoreh, E., Schuldt, B. & Dulamsuren, C. Hydraulic traits and tree-ring width in Larix sibirica Ledeb. as affected by summer drought and forest fragmentation in the Mongolian forest steppe. Ann. For. Sci. 75, 30. https://doi.org/10.1007/s13595-018-0701-2 (2018).Article 

    Google Scholar 
    50.Urban, J., Rubtsov, A. V., Urban, A. V., Shashkin, A. V. & Benkova, V. E. Canopy transpiration of a Larix sibirica and Pinus sylvestris forest in Central Siberia. Agric. For. Meteorol. 271, 64–72 (2019).ADS 
    Article 

    Google Scholar 
    51.Kolari, P., Lappalainen, H. K., HäNninen, H. & Hari, P. Relationship between temperature and the seasonal course of photosynthesis in Scots pine at northern timberline and in southern boreal zone. Tellus B Chem. Phys. Meteorol. 59, 542–552 (2007).ADS 
    Article 
    CAS 

    Google Scholar 
    52.Wu, J., Guan, D., Yuan, F., Wang, A. & Jin, C. Soil temperature triggers the onset of photosynthesis in Korean pine. PLoS ONE 8, e65401. https://doi.org/10.1371/journal.pone.0065401 (2013).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    53.Yang, Q. et al. Two dominant boreal conifers use contrasting mechanisms to reactivate photosynthesis in the spring. Nat. Commun. 11, 128. https://doi.org/10.1038/s41467-019-13954-0 (2020).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    54.Tanja, S. et al. Air temperature triggers the recovery of evergreen boreal forest photosynthesis in spring. Glob. Change Biol. 9, 1410–1426 (2003).ADS 
    Article 

    Google Scholar 
    55.Sevanto, S. et al. Wintertime photosynthesis and water uptake in a boreal forest. Tree Physiol. 26, 749–757 (2006).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    56.Rossi, S. et al. Critical temperatures for xylogenesis in conifers of cold climates. Glob. Ecol. Biogeogr. 17, 696–707 (2008).Article 

    Google Scholar 
    57.Babushkina, E. A., Belokopytova, L. V., Zhirnova, D. F. & Vaganov, E. A. Siberian spruce tree ring anatomy: Imprint of development processes and their high-temporal environmental regulation. Dendrochronologia 53, 114–124 (2019).Article 

    Google Scholar 
    58.Cannell, M. G. R. & Smith, R. I. Climatic warming, spring budburst and forest damage on trees. J. Appl. Ecol. 23, 177–191 (1986).Article 

    Google Scholar 
    59.Bertin, R. I. Plant phenology and distribution in relation to recent climate change. J. Torrey Bot. Soc. 135, 126–146 (2008).Article 

    Google Scholar 
    60.Ziaco, E., Biondi, F., Rossi, S. & Deslauriers, A. Environmental drivers of cambial phenology in Great Basin bristlecone pine. Tree Physiol. 36, 818–831 (2016).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    61.Rahman, M. H. et al. Winter-spring temperature pattern is closely related to the onset of cambial reactivation in stems of the evergreen conifer Chamaecyparis pisifera. Sci. Rep. 10, 14341. https://doi.org/10.1038/s41598-020-70356-9 (2020).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    62.Katz, R. W. & Brown, B. G. Extreme events in a changing climate: Variability is more important than averages. Clim. Chang. 21, 289–302 (1992).ADS 
    Article 

    Google Scholar 
    63.Germain, S. J. & Lutz, J. A. Climate extremes may be more important than climate means when predicting species range shifts. Clim. Chang. 163, 579–598 (2020).ADS 
    Article 

    Google Scholar 
    64.Vendrov, S. L., Avakyan, A. B., Dyakonov, K. N. & Reteyum, A. Y. The Role of Reservoirs in Changing Natural Conditions (Znaniye, 1968) (in Russian).
    Google Scholar 
    65.Stivari, S. M., De Oliveira, A. P. & Soares, J. On the climate impact of the local circulation in the Itaipu Lake area. Clim. Chang. 72, 103–121 (2005).ADS 
    Article 

    Google Scholar 
    66.Wilks, D. S. Statistical Methods in the Atmospheric Sciences 4th edn. (Elsevier, 2019).
    Google Scholar 
    67.Arguez, A. & Vose, R. S. The definition of the standard WMO climate normal: The key to deriving alternative climate normals. Bull. Am. Meteorol. Soc. 92, 699–704 (2011).ADS 
    Article 

    Google Scholar 
    68.Rosgidromet. Guidelines for the Compilation of Agrometeorological Yearbook for the Agricultural Zone of the Russian Federation. Guiding Document 52.33.725–2010 (Russian Scientific Research Institute of Hydrometeorological Information, World Data Center, 2010) (in Russian).69.Chae, H. et al. Local variability in temperature, humidity and radiation in the Baekdu Daegan Mountain protected area of Korea. J. Mt. Sci. 9, 613–627 (2012).Article 

    Google Scholar 
    70.Wypych, A., Ustrnul, Z. & Schmatz, D. R. Long-term variability of air temperature and precipitation conditions in the Polish Carpathians. J. Mt. Sci. 15, 237–253 (2018).Article 

    Google Scholar 
    71.Selyaninov, G. T. About climate agricultural estimation. Proc. Agric. Meteorol. 20, 165–177 (1928) (in Russian).
    Google Scholar 
    72.Babushkina, E. A., Belokopytova, L. V., Grachev, A. M., Meko, D. M. & Vaganov, E. A. Variation of the hydrological regime of Bele-Shira closed basin in Southern Siberia and its reflection in the radial growth of Larix sibirica. Reg. Environ. Change. 17, 1725–1737 (2017).Article 

    Google Scholar 
    73.Cook, E. R. & Kairiukstis, L. A. Methods of Dendrochronology. Application in Environmental Sciences (Kluwer Academic Publishers, 1990).Book 

    Google Scholar 
    74.Rinn, F. TSAP-Win: Time Series Analysis and Presentation for Dendrochronology and Related Applications: User Reference (RINNTECH, 2003).
    Google Scholar 
    75.Holmes, R. L. Computer-assisted quality control in tree-ring dating and measurement. Tree-Ring Bull. 43, 69–78 (1983).
    Google Scholar 
    76.Grissino-Mayer, H. D. Evaluating crossdating accuracy: A manual and tutorial for the computer program COFECHA. Tree-Ring Res. 57, 205–221 (2001).
    Google Scholar 
    77.Cook, E. R, Krusic, P. J., Holmes, R. H. & Peters, K. Program ARSTAN Ver. ARS41d. https://www.ldeo.columbia.edu/tree-ring-laboratory/resources/software (2007).78.Strackee, J. & Jansma, E. The statistical properties of mean sensitivity—A reappraisal. Dendrochronologia 10, 121–135 (1992).
    Google Scholar 
    79.Wigley, T. M. L., Briffa, K. R. & Jones, P. D. On the average value of correlated time series, with applications in dendroclimatology and hydrometeorology. J. Appl. Meteorol. Climatol. 23, 201–213 (1984).ADS 
    Article 

    Google Scholar 
    80.Yasmeen, S. et al. Contrasting climate-growth relationship between Larix gmelinii and Pinus sylvestris var. mongolica along a latitudinal gradient in Daxing’an Mountains, China. Dendrochronologia 58, 125645. https://doi.org/10.1016/j.dendro.2019.125645 (2019).Article 

    Google Scholar  More

  • in

    Biosynthetic potential of uncultured Antarctic soil bacteria revealed through long-read metagenomic sequencing

    Soil diversity, taxonomic classification and binning of BGCsNonpareil analysis estimated an abundance-weighted coverage of 85.3% for the 44.4 Gb used in the long-read assembly. To achieve 95% and 99% coverage, respectively, 250 Gb and 1.6 Tb of sequencing were predicted to be necessary. Alpha diversity was estimated at Nd = 21.6. Contigs were binned using CONCOCT, MaxBin2 and MetaBAT2, consensus bins were generated using metaWRAP refine and classified using GTDB-Tk. This yielded 114 bacterial bins with CheckM completeness  > 50% and contamination  More

  • in

    The limits of SARS-CoV-2 predictability

    If an endpoint of continued circulation (endemicity rather than eradication) seems likely, this still leaves us with questions about the range of outbreak sizes, their intensity and seasonality. Surprisingly, some basic epidemiological parameters for predicting these dynamical features are still uncertain. For example, R0, the reproductive number, which captures the infectiousness of the pathogen, is typically measured from the growth of the epidemic and is harder to estimate once non-pharmaceutical interventions (NPIs) are in place. Similarly, changes to R0 for evolved SARS- CoV-2 variants are difficult to ascertain given simultaneous changes to behaviour and interventions. It is not yet clear whether there is an evolutionary limit to strain infectiousness. To date, structural changes to the SARS-CoV-2 spike furin cleavage site7 as well as enhanced binding of the receptor binding domain to the human ACE2 receptor8 have been associated with enhanced transmissibility in variant strains, but in the longer term, transmission increases may saturate and viral evolution may modulate other aspects of disease transmission including host susceptibility. Nevertheless, any present or future changes to R0 will affect long-term epidemic dynamics, including the intensity of outbreaks and the age-structure of infections.The transmission of many respiratory pathogens varies seasonally, driven either by climatic factors or seasonal changes in behaviour such as schooling. The role of climate in driving transmission of SARS-CoV-2 is currently unclear: high susceptibility during the early pandemic likely limited any climate effect9, and statistical analyses of the climate-SARS-CoV-2 link have been confounded by trends in the data and regional differences in reporting and control measures. This has not been helped by the relatively short case time series (that is, just over a year’s worth of data) compared to typical climate–disease studies that look for climate links over many seasons. An alternative line of evidence comes from the four endemic coronaviruses, which exhibit seasonal wintertime outbreaks. It is possible that SARS-CoV-2 will follow suit. Disentangling the climate drivers of SARS-CoV-2 will become easier over time as both longer time series are available, and susceptibility declines9.A further question is the extent to which SARS-CoV-2 endemic dynamics will be affected by interactions with other circulating pathogens, including the endemic coronaviruses. Both modelling and laboratory work implies a degree of cross-immunity between coronaviruses10,11,12. The NPIs put in place to limit the spread of SARS-CoV-2 have also limited the circulation of many other pathogens, such that infection interactions have not been observed in current case trajectories13. However, as NPIs are relaxed, signatures of cross-species interactions will likely become increasingly visible.Beyond cross-immunity with other pathogens, the longitudinal trajectory of immunity, as depicted in Fig. 1, will play a crucial role in determining SARS-CoV-2 endemic dynamics14. For immunizing infections, susceptibility is driven by birth rates, and infections may be concentrated in younger age groups. For infections with waning immunity or antigenic evolution, susceptibility is driven by the rate at which immunity wanes or the rate the pathogen evolves as well as characteristics of secondary infections. The disease dynamics of pathogens with high rates of antigenic evolution are particularly hard to predict: evolved strains may have variable transmission rates and manifest variable immune responses. An analogy can be made with influenza, where the size and intensity of the seasonal influenza peak is typically very difficult to forecast15.The future course of SARS-CoV-2 remains uncertain. The next few months to a year represents a critical time where we will begin to develop an understanding of key parameters, such as the strength and duration of vaccinal and natural immunity, the seasonality of transmission and the possible interaction of SARS-CoV-2 with other circulating pathogens. In combination, these parameters will allow improved prediction of both long-term SARS-CoV-2 epidemic dynamics, as well as the likelihood of elimination and eradication. An area of particular focus will be the rate of antigenic evolution and the extent to which vaccines remain protective against evolved strains. In all scenarios, rapid and equitable distribution of vaccines presents the greatest hope for minimizing future severe outbreaks. More