Ecology

Experiment 1—larval response to a rapid attenuation of stimulus light
Acropora tenuis (Dana, 1846) is a common reef-building scleractinian coral in shallow water habitats throughout the Indo-Pacific Ocean. Seven adult colonies of A. tenuis were collected at 2–5 m depth from Backnumbers Reef (S18°29.26′, E147°09.18′), a mid-shelf reef in the central Great Barrier Reef (GBR) in November 2018, and transferred in flow-through tanks over 8 h by ship to flow-through aquaria in the National Sea Simulator (SeaSim), Australian Institute of Marine Science (AIMS), Queensland, Australia. This facility uses natural coastal seawater filtered to 1 µm and the range of water quality parameters matched that of mid-shelf reefs including Backnumbers Reef in November: temperature 26.5–27.5 °C, salinity 36.4–36.5 psu and pH 8.13–8.17. Immediately after spawning on November 5, gamete bundles were mixed to fertilize eggs, and cultures of embryos then larvae were maintained in 500 L flow through seawater tanks (0.5 µm filtered), with the motile aposymbiotic planula larvae becoming competent to settle four days after the spawning. Five to nine day old larvae were used in this experiment, with 10–15 larvae transferred into a rectangle polystyrene chamber (6.5 cm × 3.5 cm × 1 cm) filled with 15 mL 0.5 µm-filtered seawater (FSW). To examine whether larvae respond to rapid changes in the photon flux density of stimulus light, the swimming behavior of larvae was observed under the following light scheme. Firstly, a single long side of the test chamber was illuminated for 120 s using a 50 µmol/m2/s white LED light (ISC-201-2; CCS Inc., Kyoto, Japan), and the normal swimming activity of the larvae was recorded. Subsequently, the stimulus light was rapidly ( More

1.
FAO. The State of Mediterranean and Black Sea Fisheries (FAO, Rome, 2018).
Google Scholar 
2.
Coll, M., Albo-Puigserver, M., Navarro, J., Palomera, I. & Dambacher, J. M. Who is to blame? Plausible pressures on small pelagic fish population changes in the northwestern Mediterranean Sea. Mar. Ecol. Prog. Ser. 617–618, 277–294 (2019).
ADS  Article  Google Scholar 

3.
Palomera, I. et al. Small pelagic fish in the NW Mediterranean Sea: an ecological review. Prog. Oceanogr. 74, 377–396 (2007).
ADS  Article  Google Scholar 

4.
Coll, M., Palomera, I., Tudela, S. & Dowd, M. Food-web dynamics in the South Catalan Sea ecosystem (NW Mediterranean) for 1978–2003. Ecol. Modell. 217, 95–116 (2008).
Article  Google Scholar 

5.
Cardona, L., Martínez-Iñigo, L., Mateo, R. & González-Solís, J. The role of sardine as prey for pelagic predators in the western Mediterranean Sea assessed using stable isotopes and fatty acids. Mar. Ecol. Prog. Ser. 531, 1–14 (2015).
ADS  CAS  Article  Google Scholar 

6.
Quattrocchi, F. Modelling the relationships of medium and long-term variations of the Anchovy and Sardine catches in the Catalan Sea (NW Mediterranean) with the environmental drivers. (Polytechnique University of Catalunya & Instituto de Ciencias del Mar (ICM), CSIC, 2017).

7.
Van Beveren, E. et al. The fisheries history of small pelagics in the Northern Mediterranean. ICES J. Mar. Sci. 73, 1474–1484 (2016).
Article  Google Scholar 

8.
SAC-WGSASP. Technical Report of the Working Group on Stock Assessment of Small Pelagic Species (WGSASP). (2018).

9.
Coll, M. & Bellido, J. M. Evaluation of the population status and specific management alternatives for the small pelagic fish stocks in the Northwestern Mediterranean Sea (SPELMED). (2018).

10.
Pennino, M. G. et al. Current and future influence of environmental factors on small pelagic fish distributions in the Northwestern Mediterranean Sea. Front. Mar. Sci. https://doi.org/10.3389/fmars.2020.00622 (2020).
Article  Google Scholar 

11.
Brosset, P. et al. Spatio-temporal patterns and environmental controls of small pelagic fish body condition from contrasted Mediterranean areas. Prog. Oceanogr. 151, 149–162 (2017).
ADS  Article  Google Scholar 

12.
Saraux, C. et al. Small pelagic fish dynamics: a review of mechanisms in the Gulf of Lions. Deep. Res. Part II(159), 52–61 (2018).
Google Scholar 

13.
Costalago, D. & Palomera, I. Feeding of European pilchard (Sardina pilchardus) in the northwestern Mediterranean: from late larvae to adults. Sci. Mar. 78, 41–54 (2014).
Article  Google Scholar 

14.
Le Bourg, B. et al. Trophic niche overlap of sprat and commercial small pelagic teleosts in the Gulf of Lions (NW Mediterranean Sea). J. Sea Res. 103, 138–146 (2015).
Article  Google Scholar 

15.
Nikolioudakis, N., Palomera, I., Machias, A. & Somarakis, S. Diel feeding intensity and daily ration of the sardine Sardina pilchardus. Mar. Ecol. Prog. Ser. 437, 215–228 (2011).
ADS  Article  Google Scholar 

16.
Nikolioudakis, N., Isari, S., Pitta, P. & Somarakis, S. Diet of sardine Sardina pilchardus: an ‘end-to-end’ field study. Mar. Ecol. Prog. Ser. 453, 173–188 (2012).
ADS  Article  Google Scholar 

17.
Nikolioudakis, N., Isari, S. & Somarakis, S. Trophodynamics of anchovy in a non-upwelling system: direct comparison with sardine. Mar. Ecol. Prog. Ser. 500, 215–229 (2014).
ADS  Article  Google Scholar 

18.
Tudela, S. & Palomera, I. Diel feeding intensity and daily ration in the anchovy Engraulis encrasicolus in the northwest Mediterranean Sea during the spawning period. Mar. Ecol. Prog. Ser. 129, 55–61 (1995).
ADS  Article  Google Scholar 

19.
Tudela, S. & Palomera, I. A Trophic ecology of the European anchovy Engraulis encrasicolus in the Catalan Sea (northwest Mediterranean). Mar. Ecol. Prog. Ser. 160, 121–134 (1997).
ADS  Article  Google Scholar 

20.
Brosset, P. et al. Linking small pelagic dietary shifts with ecosystem changes in the Gulf of Lions. Mar. Ecol. Prog. Ser. 554, 157–171 (2016).
ADS  Article  Google Scholar 

21.
Albo-Puigserver, M. Ecological and functional role of small and medium pelagic fish in the northwestern Mediterranean Sea. (Polytechnique University of Catalunya & Instituto de Ciencias del Mar (ICM), CSIC, 2019).

22.
Amundsen, P. & Sánchez-Hernández, J. Feeding studies take guts: critical review and recommendations of methods for stomach contents analysis in fish. J. Fish Biol. 95, 1364–1373 (2019).
PubMed  Article  Google Scholar 

23.
Jakubavičiūtė, E., Bergström, U., Eklöf, J. S., Haenel, Q. & Bourlat, S. J. DNA metabarcoding reveals diverse diet of the three-spined stickleback in a coastal ecosystem. PLoS ONE 12, e0186929 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

24.
Nielsen, J. M., Clare, E. L., Hayden, B., Brett, M. T. & Kratina, P. Diet tracing in ecology: Method comparison and selection. Methods Ecol. Evol. 9, 278–291 (2018).
Article  Google Scholar 

25.
Leray, M. et al. A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: application for characterizing coral reef fish gut contents. Front. Zool. 10, 34 (2013).
PubMed  PubMed Central  Article  CAS  Google Scholar 

26.
Berry, O. et al. Comparison of morphological and DNA metabarcoding analyses of diets in exploited marine fishes. Mar. Ecol. Prog. Ser. 540, 167–181 (2015).
ADS  CAS  Article  Google Scholar 

27.
Casey, J. M. et al. Reconstructing hyperdiverse food webs: Gut content metabarcoding as a tool to disentangle trophic interactions on coral reefs. Methods Ecol. Evol. 10, 1157–1170 (2019).
Article  Google Scholar 

28.
Thomas, A. C., Deagle, B. E., Eveson, J. P., Harsch, C. H. & Trites, A. W. Quantitative DNA metabarcoding: improved estimates of species proportional biomass using correction factors derived from control material. Mol. Ecol. Resour. 16, 714–726 (2016).
CAS  PubMed  Article  Google Scholar 

29.
Baker, R., Buckland, A. & Sheaves, M. Fish gut content analysis: Robust measures of diet composition. Fish Fish 15, 170–177 (2014).
Article  Google Scholar 

30.
Pacella, S. R. et al. Incorporation of diet information derived from Bayesian stable isotope mixing models into mass-balanced marine ecosystem models: A case study from the Marennes-Oléron Estuary France. Ecol. Modell. 267, 127–137 (2013).
CAS  Article  Google Scholar 

31.
Parnell, A. C. et al. Bayesian stable isotope mixing models. Environmetrics 24, 387–399 (2013).
MathSciNet  Google Scholar 

32.
Stock, B. C. et al. Analyzing mixing systems using a new generation of Bayesian tracer mixing models. PeerJ 6, e5096 (2018).
PubMed  PubMed Central  Article  Google Scholar 

33.
Carreon-Martinez, L., Johnson, T. B., Ludsin, S. A. & Heath, D. D. Utilization of stomach content DNA to determine diet diversity in piscivorous fishes. J. Fish Biol. 78, 1170–1182 (2011).
CAS  PubMed  Article  Google Scholar 

34.
Hunter, J. R. & Kimbrell, C. A. Egg cannibalism in the Northern anchovy Engraulis mordax. Fish. Bull. 78, 811–816 (1980).
Google Scholar 

35.
Vander Zanden, M. J., Clayton, M. K., Moody, E. K., Solomon, C. T. & Weidel, B. C. Stable isotope turnover and half-life in animal tissues: a literature synthesis. PLoS ONE 10, e0116182 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

36.
Bearhop, S., Adams, C. E., Waldron, S., Fuller, R. A. & MacLeod, H. Determining trophic niche width: a novel approach using stable isotope analysis. J. Anim. Ecol. 73, 1007–1012 (2004).
Article  Google Scholar 

37.
Jackson, A. L., Inger, R., Parnell, A. C. & Bearhop, S. Comparing isotopic niche widths among and within communities: SIBER—Stable Isotope Bayesian Ellipses in R. J. Anim. Ecol. 80, 595–602 (2011).
PubMed  Article  Google Scholar 

38.
Cannas, R. et al. Report on bioinformatic analyses of the GBS data and report of the population genetic analyses. Evaluation of the population status and specific management alternatives for the small pelagic fish stocks in the Northwestern Mediterranean Sea (SPELMED). (2018).

39.
Koleff, P., Gaston, K. J. & Lennon, J. J. Measuring beta diversity for presence-absence data. J. Anim. Ecol. 72, 367–382 (2003).
Article  Google Scholar 

40.
Southwood, T. R. E. & Henderson, P. A. Ecological Methods (Backwell Science, USA, 2000).
Google Scholar 

41.
Pianka, E. R. Niche Overlap and Diffuse Competition. Proc. Natl. Acad. Sci. U. S. A. 71, 2141–2145 (1974).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

42.
Bachiller, E. & Irigoien, X. Allometric relations and consequences for feeding in small pelagic fish in the Bay of Biscay. ICES J. Mar. Sci. 70, 232–243 (2013).
Article  Google Scholar 

43.
Bachiller, E. & Irigoien, X. Trophodynamics and diet overlap of small pelagic fish species in the Bay of Biscay. Mar. Ecol. Prog. Ser. 534, 179–198 (2015).
ADS  Article  Google Scholar 

44.
Raab, K. et al. Anchovy Engraulis encrasicolus diet in the North and Baltic Seas. J. Sea Res. 65, 131–140 (2011).
ADS  Article  Google Scholar 

45.
Karachle, P. K. & Stergiou, K. I. Feeding and ecomorphology of three clupeoids in the N Aegean Sea. Mediterr. Mar. Sci. 15, 9 (2013).
Article  Google Scholar 

46.
Costalago, D., Garrido, S. & Palomera, I. Comparison of the feeding apparatus and diet of European sardines Sardina pilchardus of Atlantic and Mediterranean waters: ecological implications. J. Fish Biol. 86, 1348–1362 (2015).
CAS  PubMed  Article  Google Scholar 

47.
Costalago, D., Navarro, J., Álvarez-Calleja, I. & Palomera, I. Ontogenetic and seasonal changes in the feeding habits and trophic levels of two small pelagic fish species. Mar. Ecol. Prog. Ser. 460, 169–181 (2012).
ADS  Article  Google Scholar 

48.
Garrido, S. et al. Trophic ecology of pelagic fish species off the Iberian coast: diet overlap, cannibalism and intraguild predation. Mar. Ecol. Prog. Ser. 539, 271–285 (2015).
ADS  Article  Google Scholar 

49.
Bacha, M. & Amara, R. Spatial, temporal and ontogenetic variation in diet of anchovy (Engraulis encrasicolus) on the Algerian coast (SW Mediterranean). Estuar. Coast. Shelf Sci. 85, 257–264 (2009).
ADS  CAS  Article  Google Scholar 

50.
Brosset, P. et al. Body reserves mediate trade-offs between life-history traits: New insights from small pelagic fish reproduction. R. Soc. Open Sci. 3, 160202 (2016).
ADS  PubMed  PubMed Central  Article  Google Scholar 

51.
Bachiller, E., Cotano, U., Boyra, G. & Irigoien, X. Spatial distribution of the stomach weights of juvenile anchovy (Engraulis encrasicolus L.) in the Bay of Biscay. ICES J. Mar. Sci. 70, 362–378 (2013).
Article  Google Scholar 

52.
Ventero, A., Iglesias, M. & Villamor, B. Anchovy (Engraulis encrasicolus) otoliths reveal growth differences between two areas of the Spanish Mediterranean Sea. Sci. Mar. 81, 327 (2017).
Article  Google Scholar 

53.
Costalago, D., Palomera, I. & Tirelli, V. Seasonal comparison of the diets of juvenile European anchovy Engraulis encrasicolus and sardine Sardina pilchardus in the Gulf of Lions. J. Sea Res. 89, 64–72 (2014).
ADS  Article  Google Scholar 

54.
Borme, D., Tirelli, V., Brandt, S. B., Umani, S. F. & Arneri, E. Diet of Engraulis encrasicolus in the northern Adriatic Sea (Mediterranean): ontogenetic changes and feeding selectivity. Mar. Ecol. Prog. Ser. 392, 193–209 (2009).
ADS  Article  Google Scholar 

55.
Layman, C. A. et al. Applying stable isotopes to examine food-web structure: an overview of analytical tools. Biol. Rev. 87, 545–562 (2012).
PubMed  Article  Google Scholar 

56.
Albo-Puigserver, M., Navarro, J., Coll, M., Layman, C. A. & Palomera, I. Trophic structure of pelagic species in the northwestern Mediterranean Sea. J. Sea Res. 117, 27–35 (2016).
ADS  Article  Google Scholar 

57.
Petta, J. C. et al. Are you really what you eat? Stomach content analysis and stable isotope ratios do not uniformly estimate dietary niche characteristics in three marine predators. Oecologia 192, 1111–1126 (2020).
ADS  PubMed  Article  Google Scholar 

58.
Demestre, M. Growth and distribution of Solenocera membranacea (Risso, 1816) (Decapoda, Dendrobranchiata) in the northwestern Mediterranean Sea. Sci. Mar. 57, 161–166 (1993).
Google Scholar 

59.
Tsagarakis, K., Giannoulaki, M., Somarakis, S. & Machias, A. Variability in positional, energetic and morphometric descriptors of European anchovy Engraulis encrasicolus schools related to patterns of diurnal vertical migration. Mar. Ecol. Prog. Ser. 446, 243–258 (2012).
ADS  Article  Google Scholar 

60.
Coll, M., Shannon, L. J., Moloney, C. L., Palomera, I. & Tudela, S. Comparing trophic flows and fishing impacts of a NW Mediterranean ecosystem with coastal upwelling systems by means of standardized models and indicators. Ecol. Modell. 198, 53–70 (2006).
Article  Google Scholar 

61.
Checkley, D. M., Asch, R. G. & Rykaczewski, R. R. Climate, Anchovy, and Sardine. Ann. Rev. Mar. Sci. 9, 469–493 (2017).
PubMed  Article  Google Scholar 

62.
Irigoien, X. & De Roos, A. The role of intraguild predation in the population dynamics of small pelagic fish. Mar. Biol. 158, 1683–1690 (2011).
Article  Google Scholar 

63.
Bachiller, E., Cotano, U., Ibaibarriaga, L., Santos, M. & Irigoien, X. Intraguild predation between small pelagic fish in the Bay of Biscay: impact on anchovy (Engraulis encrasicolus L.) egg mortality. Mar. Biol. 162, 1351–1369 (2015).
Article  Google Scholar 

64.
Purcell, J. E. Jellyfish and ctenophore blooms coincide with human proliferations and environmental perturbations. Ann. Rev. Mar. Sci. 4, 209–235 (2012).
PubMed  Article  Google Scholar 

65.
Naman, S. M. et al. Stable isotope-based trophic structure of pelagic fish and jellyfish across natural and anthropogenic landscape gradients in a fjord estuary. Ecol. Evol. 6, 8159–8173 (2016).
PubMed  PubMed Central  Article  Google Scholar 

66.
Albo-Puigserver, M. et al. Trophic ecology of range-expanding round sardinella and resident sympatric species in the NW Mediterranean. Mar. Ecol. Prog. Ser. 620, 139–154 (2019).
ADS  CAS  Article  Google Scholar 

67.
Morote, E., Olivar, M. P., Villate, F. & Uriarte, I. A comparison of anchovy (Engraulis encrasicolus) and sardine (Sardina pilchardus) larvae feeding in the Northwest Mediterranean: influence of prey availability and ontogeny. ICES J. Mar. Sci. 67, 897–908 (2010).
Article  Google Scholar 

68.
Robinson, M. L., Gomez-Raya, L., Rauw, W. M. & Peacock, M. M. Fulton’s body condition factor K correlates with survival time in a thermal challenge experiment in juvenile Lahontan cutthroat trout (Oncorhynchus clarki henshawi). J. Therm. Biol. 33, 363–368 (2008).
Article  Google Scholar 

69.
Siokou-Frangou, I. et al. Plankton in the open Mediterranean Sea: a review. Biogeosciences 7, 1543–1586 (2010).
ADS  Article  Google Scholar 

70.
Vila, M. & Masó, M. Phytoplankton functional groups and harmful algal species in anthropogenically impacted waters of the NW Mediterranean Sea. Sci. Mar. 69, 31–45 (2005).
Article  Google Scholar 

71.
Percopo, I., Siano, R., Cerino, F., Sarno, D. & Zingone, A. Phytoplankton diversity during the spring bloom in the northwestern Mediterranean Sea. Bot. Mar. 54, 243–267 (2011).
Article  Google Scholar 

72.
Devloo-Delva, F. et al. How does marker choice affect your diet analysis: comparing genetic markers and digestion levels for diet metabarcoding of tropical-reef piscivores. Mar. Freshw. Res. 70, 8–18 (2019).
Article  Google Scholar 

73.
Forin-Wiart, M.-A. et al. Evaluating metabarcoding to analyse diet composition of species foraging in anthropogenic landscapes using Ion Torrent and Illumina sequencing. Sci. Rep. 8, 17091 (2018).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

74.
Thuo, D. et al. Food from faeces: evaluating the efficacy of scat DNA metabarcoding in dietary analyses. PLoS ONE 14, e0225805 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

75.
Coll, M., Pennino, M. G., Steenbeek, J., Sole, J. & Bellido, J. M. Predicting marine species distributions: complementarity of food-web and Bayesian hierarchical modelling approaches. Ecol. Modell. 405, 86–101 (2019).
Article  Google Scholar 

76.
Feuilloley, G. et al. Concomitant changes in the environment and small pelagic fish community of the Gulf of Lions. Prog. Oceanogr. 186, 102375 (2020).
Article  Google Scholar 

77.
Pennino, M. G. et al. Ingestion of microplastics and occurrence of parasite association in Mediterranean anchovy and sardine. Mar. Pollut. Bull. 158, 111399 (2020).
CAS  PubMed  Article  Google Scholar 

78.
Compa, M., Ventero, A., Iglesias, M. & Deudero, S. Ingestion of microplastics and natural fibres in Sardina pilchardus (Walbaum, 1792) and Engraulis encrasicolus (Linnaeus, 1758) along the Spanish Mediterranean coast. Mar. Pollut. Bull. 128, 89–96 (2018).
CAS  PubMed  Article  Google Scholar 

79.
Bertrand, J., Leonori, I., Dremière, P. Y. & Cosimi, G. The general specifications of the MEDITS surveys. Sci. Mar. 66, 9–17 (2002).
Article  Google Scholar 

80.
Bertrand, J. A., De Sola, L. G., Papaconstantinou, C., Relini, G. & Souplet, A. The general specifications of the MEDITS surveys. Sci. Mar. 66, 9–17 (2002).
Article  Google Scholar 

81.
Cole, M. et al. Isolation of microplastics in biota-rich seawater samples and marine organisms. Sci. Rep. 4, 4528 (2014).
PubMed  PubMed Central  Article  CAS  Google Scholar 

82.
Hyslop, E. J. Stomach contents analysis-a review of methods and their application. J. Fish Biol. 17, 411–429 (1980).
Article  Google Scholar 

83.
Bachiller, E., Skaret, G., Nøttestad, L. & Slotte, A. Feeding ecology of Northeast Atlantic mackerel, Norwegian spring-spawning herring and blue whiting in the Norwegian Sea. PLoS ONE 11, e0149238 (2016).
PubMed  PubMed Central  Article  CAS  Google Scholar 

84.
Somarakis, S. et al. Daily egg production of anchovy in European waters. ICES J. Mar. Sci. 61, 944–958 (2004).
Article  Google Scholar 

85.
Palomera, I., Tejeiro, B. & Alemany, F. Size at first maturity of the NW Mediterranean anchovy. (2003).

86.
Silva, A. et al. Temporal and geographic variability of sardine maturity at length in the northeastern Atlantic and the western Mediterranean. ICES J. Mar. Sci. 63, 663–676 (2006).
Article  Google Scholar 

87.
Lawlor, L. R. Overlap, similarity, and competition coefficients. Ecology 61, 245–251 (1980).
Article  Google Scholar 

88.
Barroeta, Z., Olivar, M. P. & Palomera, I. Energy density of zooplankton and fish larvae in the southern Catalan Sea (NW Mediterranean). J. Sea Res. 124, 1–9 (2017).
ADS  Article  Google Scholar 

89.
Sabatés, A. Distribution pattern of larval fish populations in the Northwestern Mediterranean. Mar. Ecol. Prog. Ser. 59, 75–82 (1990).
ADS  Article  Google Scholar 

90.
Wangensteen, O. S., Palacín, C., Guardiola, M. & Turon, X. DNA metabarcoding of littoral hard-bottom communities: high diversity and database gaps revealed by two molecular markers. PeerJ 6, e4705 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

91.
Leray, M. et al. A new versatile primer set targeting a short fragment of the mitochondrial COI region for metabarcoding metazoan diversity: application for characterizing coral reef fish gut contents. Front. Zool. 10, 34 (2013).
PubMed  PubMed Central  Article  CAS  Google Scholar 

92.
Vasselon, V., Rimet, F., Tapolczai, K. & Bouchez, A. Assessing ecological status with diatoms DNA metabarcoding: Scaling-up on a WFD monitoring network (Mayotte island, France). Ecol. Indic. 82, 1–12 (2017).
CAS  Article  Google Scholar 

93.
Vierna, J., Doña, J., Vizcaíno, A., Serrano, D. & Jovani, R. PCR cycles above routine numbers do not compromise high-throughput DNA barcoding results. Genome 60, 868–873 (2017).
CAS  PubMed  Article  Google Scholar 

94.
Magoc, T. & Salzberg, S. L. FLASH: fast length adjustment of short reads to improve genome assemblies. Bioinformatics 27, 2957–2963 (2011).
CAS  PubMed  PubMed Central  Article  Google Scholar 

95.
Martin, M. Cutadapt removes adapter sequences from high-throughput sequencing reads. EMB net.journal 17, 10 (2011).
Article  Google Scholar 

96.
Caporaso, J. G. et al. QIIME allows analysis of high-throughput community sequencing data. Nat. Methods 7, 335–336 (2010).
CAS  PubMed  PubMed Central  Article  Google Scholar 

97.
Rognes, T., Flouri, T., Nichols, B., Quince, C. & Mahé, F. VSEARCH: a versatile open source tool for metagenomics. PeerJ 4, e2584 (2016).
PubMed  PubMed Central  Article  Google Scholar 

98.
Mahé, F., Rognes, T., Quince, C., de Vargas, C. & Dunthorn, M. Swarm v2: highly-scalable and high-resolution amplicon clustering. PeerJ 3, e1420 (2015).
PubMed  PubMed Central  Article  Google Scholar 

99.
Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011).
CAS  PubMed  PubMed Central  Article  Google Scholar 

100.
Edgar, R. C. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 26, 2460–2461 (2010).
CAS  PubMed  PubMed Central  Article  Google Scholar 

101.
Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007).
CAS  PubMed  PubMed Central  Article  Google Scholar 

102.
Bokulich, N. A. et al. Quality-filtering vastly improves diversity estimates from Illumina amplicon sequencing. Nat. Methods 10, 57–59 (2013).
CAS  PubMed  Article  Google Scholar 

103.
Esling, P., Lejzerowicz, F. & Pawlowski, J. Accurate multiplexing and filtering for high-throughput amplicon-sequencing. Nucleic Acids Res. 43, 2513–2524 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

104.
Illumina, Inc [internet]. Effects of index Misassignment on multiplexing and downstream analysis (2017). Available from: https://www.illumina.com/. Accessed October 2020.

105.
National Center for Biotechnology Information (NCBI) [internet]. Bethesda (MD): National Library of Medicine (US), National Center for Biotechnology Information (2018). Available from: https://www.ncbi.nlm.nih.gov/. Accessed August 2020.

106.
Coplen, T. B. Guidelines and recommended terms for expression of stable-isotope-ratio and gas-ratio measurement results. Rapid Commun. Mass Spectrom. 25, 2538–2560 (2011).
ADS  CAS  PubMed  Article  Google Scholar 

107.
Post, D. M. et al. Getting to the fat of the matter: models, methods and assumptions for dealing with lipids in stable isotope analyses. Oecologia 152, 179–189 (2007).
ADS  PubMed  PubMed Central  Article  Google Scholar 

108.
Hastie, T. & Tibshirani, R. Generalized Additive Models. Stat. Sci. 1, 297–318 (1986).
MathSciNet  MATH  Article  Google Scholar 

109.
Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).
Article  Google Scholar 

110.
Fox, J. & Weisberg, S. An R Companion to Applied Regression (Sage, Thousand Oaks, 2011).
Google Scholar 

111.
Wood, S. N. Generalized additive models: an introduction with R. J. Stat. Softw. 16, 2 (2006).
Google Scholar 

112.
R Core Team. R: A Language and Environment for Statistical Computing. (2019).

113.
Wickham, H. ggplot2: elegant graphics for data analysis. Springer (Springer, 2009).

114.
Gotelli, N. J., Hart, E. M. & Ellison, A. M. EcoSimR: Null model analysis for ecological data. (2015). doi:https://doi.org/10.5281/zenodo.16522

115.
Wood, S. N. Fast stable restricted maximum likelihood and marginal likelihood estimation of semiparametric generalized linear models. J. R. Stat. Soc. Ser. B (Statistical Methodol).73, 3–36 (2011).

116.
Wei, T. et al. Package ‘corrplot’. Am. Stat. 56, 316–324 (2017).
Google Scholar 

117.
QGIS Development Team. QGIS (Version 3.2.1-Bonn). (2018).

118.
Hsieh, T. C., Ma, K. H., Chao, A. & McInerny, G. iNEXT: an R package for rarefaction and extrapolation of species diversity (ill numbers). Methods Ecol. Evol. 7(12), 1451–1456 (2016).
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Reaction-diffusion modeling predicts short metabolic interaction distances in three-dimensional systems with a metabolite-sink
The basic ideas behind diffusion and the resulting concentration gradients are well-understood. To better understand the biological impact of these concentration gradients, we made reaction-diffusion models where concentration gradient profiles around a producer cell were calculated either in a cube to mimic a three-dimensional system, or in a thin plate to mimic a two-dimensional system (plate thickness of 1.1 µm, roughly matching the producer cell diameter of 1 µm, Fig. 1). In both cases the total volume was 1 nL (106 cells/mL). We used the diffusion coefficient of glucose as it is similar to that of other sugars, organic acids, and amino acids [30], compounds relevant in many metabolic interactions. Factors like viscosity, the presence of extracellular polymeric substances and the local cell concentration vary between environmental conditions, and affect the diffusion coefficient [30, 32]. To visualize their effect on concentration gradient profiles we modeled diffusion in conditions representing aqueous, biofilm, and colony environments (Table S2).
Fig. 1: Predicted concentration gradients in two- and three-dimensional reaction-diffusion systems.

Glucose producing cells were placed in a two- or a three-dimensional space, in the presence and absence of a metabolite-sink. Different environments were simulated by altering the diffusion coefficient. The diffusion coefficient of glucose in water (Ds) was set to 6.7 × 10−10 [30], the diffusion coefficient of glucose in a biofilm (Deff,biofilm,s) was set to 0.25 times Ds [30], and the diffusion coefficient of glucose in a colony (Deff,colony,s) was set to 0.10 times Ds [29, 30] (Table S2). A time-dependent study in COMSOL Multiphysics yielded concentration gradients at several moments. The figure shows the concentration over a horizontal line crossing the producer cell, after 5 h of incubation. To aid visibility the x-axis-range was made similar for both plots, so for the two-dimensional system only part of the concentration gradient profile is shown. The dashed horizontal line indicates a concentration of 10 µM.

Full size image

In simulations without a metabolite-sink the produced glucose accumulated (Fig. 1). After 5 h the minimal glucose concentration was around 1500 µM in both two- and three-dimensional systems (Fig. 1, Table S2), indicating that glucose is biologically available (i.e. above the threshold for growth based on transporter affinities) in the whole system. The introduction of a metabolite-sink limited the glucose accumulation, resulting in concentrations close to 0 µM. In the aqueous two-dimensional system the glucose concentration dropped below a threshold of 10 µM (approximate threshold for growth based on transporter affinities) at a distance of 269 µm from the producer, while in a three-dimensional system this threshold was already reached at 0.7 µm. A decrease in the diffusion coefficient increased the distance at which the glucose concentration dropped below 10 µM, but predicted distances were still in the low µm-range ( More

1.
Rolig AS, Parthasarathy R, Burns AR, Bohannan BJ, Guillemin K. Individual members of the microbiota disproportionately modulate host innate immune responses. Cell Host Microbe. 2015;18:613–20.
CAS  PubMed  PubMed Central  Article  Google Scholar 
2.
McFall-Ngai MJ. Unseen forces: the influence of bacteria on animal development. Dev Biol. 2002;242:1–14.
CAS  PubMed  Article  PubMed Central  Google Scholar 

3.
van der Waaij D, Berghuis-de Vries JM, Lekkerkerk-van der Wees JEC. Colonization resistance of the digestive tract and the spread of bacteria to the lymphatic organs in mice. J Hyg. 1972;70:335–42.
PubMed  Article  PubMed Central  Google Scholar 

4.
Cani PD, Delzenne NM. The role of the gut microbiota in energy metabolism and metabolic disease. Curr Pharm Des. 2009;15:1546–58.
CAS  PubMed  Article  PubMed Central  Google Scholar 

5.
Stecher B, Hardt WD. The role of microbiota in infectious disease. Trends Microbiol. 2008;16:107–14.
CAS  PubMed  Article  PubMed Central  Google Scholar 

6.
Stecher B, Hardt WD. Mechanisms controlling pathogen colonization of the gut. Curr Opin Microbiol. 2011;14:82–91.
CAS  PubMed  Article  PubMed Central  Google Scholar 

7.
Falcinelli S, Rodiles A, Unniappan S, Picchietti S, Gioacchini G, Merrifield DL, et al. Probiotic treatment reduces appetite and glucose level in the zebrafish model. Sci Rep. 2016;6:18061.
CAS  PubMed  PubMed Central  Article  Google Scholar 

8.
Olsan EE, Byndloss MX, Faber F, Rivera-Chavez F, Tsolis RM, Baumler AJ. Colonization resistance: The deconvolution of a complex trait. J Biol Chem. 2017;292:8577–81.
CAS  PubMed  PubMed Central  Article  Google Scholar 

9.
Littman DR, Pamer EG. Role of the commensal microbiota in normal and pathogenic host immune responses. Cell Host Microbe. 2011;10:311–23.
CAS  PubMed  PubMed Central  Article  Google Scholar 

10.
Heselmans M, Reid G, Akkermans LM, Savelkoul H, Timmerman H, Rombouts FM. Gut flora in health and disease: potential role of probiotics. Curr Issues Intest Microbiol. 2005;6:1–7.
CAS  PubMed  PubMed Central  Google Scholar 

11.
Boirivant M, Strober W. The mechanism of action of probiotics. Curr Opin Gastroenterol. 2007;23:679–92.
PubMed  Article  PubMed Central  Google Scholar 

12.
Robinson CJ, Bohannan BJ, Young VB. From structure to function: the ecology of host-associated microbial communities. Microbiol Mol Biol Rev. 2010;74:453–76.
CAS  PubMed  PubMed Central  Article  Google Scholar 

13.
Vollaard EJ, Clasener HA. Colonization resistance. Antimicrob Agents Chemother. 1994;38:409–14.
CAS  PubMed  PubMed Central  Article  Google Scholar 

14.
Gill HS. Probiotics to enhance anti-infective defences in the gastrointestinal tract. Best Pr Res Clin Gastroenterol. 2003;17:755–73.
CAS  Article  Google Scholar 

15.
Rawls JF, Samuel BS, Gordon JI. Gnotobiotic zebrafish reveal evolutionarily conserved responses to the gut microbiota. Proc Natl Acad Sci USA. 2004;101:4596–601.
CAS  PubMed  Article  Google Scholar 

16.
Milligan-Myhre K, Charette JR, Phennicie RT, Stephens WZ, Rawls JF, Guillemin K, et al. Study of host-microbe interactions in zebrafish. Methods cell Biol. 2011;105:87–116.
PubMed  PubMed Central  Article  Google Scholar 

17.
Burns AR, Guillemin K. The scales of the zebrafish: host-microbiota interactions from proteins to populations. Curr Opin Microbiol. 2017;38:137–41.
CAS  PubMed  PubMed Central  Article  Google Scholar 

18.
Douglas AE. Simple animal models for microbiome research. Nat Rev Microbiol. 2019;17:764–75.
CAS  PubMed  Article  Google Scholar 

19.
Flores EM, Nguyen AT, Odem MA, Eisenhoffer GT, Krachler AM. The zebrafish as a model for gastrointestinal tract-microbe interactions. Cell Microbiol. 2020;22:e13152.
CAS  PubMed  Article  Google Scholar 

20.
Melancon E, Gomez De La Torre Canny S, Sichel S, Kelly M, Wiles TJ, Rawls JF, et al. Best practices for germ-free derivation and gnotobiotic zebrafish husbandry. Methods cell Biol. 2017;138:61–100.
CAS  PubMed  PubMed Central  Article  Google Scholar 

21.
Cantas L, Sorby JR, Alestrom P, Sorum H. Culturable gut microbiota diversity in zebrafish. Zebrafish. 2012;9:26–37.
CAS  PubMed  PubMed Central  Article  Google Scholar 

22.
Rendueles O, Ferrieres L, Fretaud M, Begaud E, Herbomel P, Levraud JP, et al. A new zebrafish model of oro-intestinal pathogen colonization reveals a key role for adhesion in protection by probiotic bacteria. PLoS Pathog. 2012;8:e1002815.
CAS  PubMed  PubMed Central  Article  Google Scholar 

23.
Caruffo M, Navarrete NC, Salgado OA, Faundez NB, Gajardo MC, Feijoo CG, et al. Protective Yeasts Control V. anguillarum Pathogenicity and Modulate the Innate Immune Response of Challenged Zebrafish (Danio rerio) Larvae. Front Cell Infect Microbiol. 2016;6:127.
PubMed  PubMed Central  Article  Google Scholar 

24.
Perez-Ramos A, Mohedano ML, Pardo MA, Lopez P. Beta-glucan-producing pediococcus parvulus 2.6: test of probiotic and immunomodulatory properties in zebrafish models. Front Microbiol. 2018;9:1684.
PubMed  PubMed Central  Article  Google Scholar 

25.
Chu W, Zhou S, Zhu W, Zhuang X. Quorum quenching bacteria Bacillus sp. QSI-1 protect zebrafish (Danio rerio) from Aeromonas hydrophila infection. Sci Rep. 2014;4:5446.
PubMed  PubMed Central  Article  CAS  Google Scholar 

26.
Wang Y, Ren Z, Fu L, Su X. Two highly adhesive lactic acid bacteria strains are protective in zebrafish infected with Aeromonas hydrophila by evocation of gut mucosal immunity. J Appl Microbiol. 2016;120:441–51.
CAS  PubMed  Article  PubMed Central  Google Scholar 

27.
Qin C, Zhang Z, Wang Y, Li S, Ran C, Hu J, et al. EPSP of L. casei BL23 Protected against the Infection Caused by Aeromonas veronii via Enhancement of Immune Response in Zebrafish. Front Microbiol. 2017;8:2406.
PubMed  PubMed Central  Article  Google Scholar 

28.
Girija V, Malaikozhundan B, Vaseeharan B, Vijayakumar S, Gobi N, Del Valle Herrera M, et al. In vitro antagonistic activity and the protective effect of probiotic Bacillus licheniformis Dahb1 in zebrafish challenged with GFP tagged Vibrio parahaemolyticus Dahv2. Microb Pathogenesis. 2018;114:274–80.
Article  Google Scholar 

29.
Lin YS, Saputra F, Chen YC, Hu SY. Dietary administration of Bacillus amyloliquefaciens R8 reduces hepatic oxidative stress and enhances nutrient metabolism and immunity against Aeromonas hydrophila and Streptococcus agalactiae in zebrafish (Danio rerio). Fish Shellfish Immunol. 2019;86:410–9.
CAS  PubMed  Article  PubMed Central  Google Scholar 

30.
Declercq AM, Haesebrouck F, Van den Broeck W, Bossier P, Decostere A. Columnaris disease in fish: a review with emphasis on bacterium-host interactions. Vet Res. 2013;44:27.
PubMed  PubMed Central  Article  Google Scholar 

31.
Decostere A, Haesebrouck F, Devriese LA. Characterization of four Flavobacterium columnare (Flexibacter columnaris) strains isolated from tropical fish. Vet Microbiol. 1998;62:35–45.
CAS  PubMed  Article  PubMed Central  Google Scholar 

32.
Figueiredo HC, Klesius PH, Arias CR, Evans J, Shoemaker CA, Pereira DJ Jr, et al. Isolation and characterization of strains of Flavobacterium columnare from Brazil. J fish Dis. 2005;28:199–204.
CAS  PubMed  Article  PubMed Central  Google Scholar 

33.
Soto E, Mauel MJ, Karsi A, Lawrence ML. Genetic and virulence characterization of Flavobacterium columnare from channel catfish (Ictalurus punctatus). J Appl Microbiol. 2008;104:1302–10.
CAS  PubMed  Article  PubMed Central  Google Scholar 

34.
Suomalainen LR, Bandilla M, Valtonen ET. Immunostimulants in prevention of columnaris disease of rainbow trout, Oncorhynchus mykiss (Walbaum). J fish Dis. 2009;32:723–6.
PubMed  Article  PubMed Central  Google Scholar 

35.
Pacha RE, Ordal EJ Myxobacterial infections of salmonids. American Fisheries Society, Diseases of Fishes and Shellfishes 1970:12.

36.
Amin NE, Abdallah IS, Faisal M, Easa Me-S, Alaway T, Alyan SA. Columnaris infection among cultured Nile tilapia Oreochromis niloticus. Antonie Van Leeuwenhoek. 1988;54:509–20.
CAS  PubMed  Article  PubMed Central  Google Scholar 

37.
Decostere A, Haesebrouck F, Charlier G, Ducatelle R. The association of Flavobacterium columnare strains of high and low virulence with gill tissue of black mollies (Poecilia sphenops). Vet Microbiol. 1999;67:287–98.
CAS  PubMed  Article  PubMed Central  Google Scholar 

38.
Bernardet J-F, Bowman JP. The genus flavobacterium. Prokaryotes. 2006;7:481–531.
Article  Google Scholar 

39.
Li N, Zhu Y, LaFrentz BR, Evenhuis JP, Hunnicutt DW, Conrad RA, et al. The type IX secretion system is required for virulence of the fish pathogen flavobacterium columnare. Appl Environ Microbiol. 2017;83:e01769–17.
PubMed  PubMed Central  Google Scholar 

40.
Garcia JC, LaFrentz BR, Waldbieser GC, Wong FS, Chang SF. Characterization of atypical Flavobacterium columnare and identification of a new genomovar. J Fish Dis. 2018;41:1159–64.
CAS  PubMed  Article  Google Scholar 

41.
van der Vaart M, van Soest JJ, Spaink HP, Meijer AH. Functional analysis of a zebrafish myd88 mutant identifies key transcriptional components of the innate immune system. Dis Models Mech. 2013;6:841–54.
Article  CAS  Google Scholar 

42.
Pham LN, Kanther M, Semova I, Rawls JF. Methods for generating and colonizing gnotobiotic zebrafish. Nat Protoc. 2008;3:1862–75.
CAS  PubMed  PubMed Central  Article  Google Scholar 

43.
Lemon KP, Armitage GC, Relman DA, Fischbach MA. Microbiota-targeted therapies: an ecological perspective. Sci Transl Med. 2012;4:137rv5.
PubMed  PubMed Central  Article  CAS  Google Scholar 

44.
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinforma. 2014;30:2114–20.
CAS  Article  Google Scholar 

45.
Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc, Ser B,57, 289-300.1995;57:289–300.
Google Scholar 

46.
Ha SM, Kim CK, Roh J, Byun JH, Yang SJ, Choi SB, et al. Application of the whole genome-based bacterial identification system, TrueBac ID, using clinical isolates that were not identified with three matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) systems. Ann Lab Med. 2019;39:530–6.
CAS  PubMed  PubMed Central  Article  Google Scholar 

47.
Volant S, Lechat P, Woringer P, Motreff L, Campagne P, Malabat C, et al. SHAMAN: a user-friendly website for metataxonomic analysis from raw reads to statistical analysis. BMC Bioinforma. 2020;21:345.
Article  Google Scholar 

48.
Rognes T, Flouri T, Nichols B, Quince C, Mahe F. VSEARCH: a versatile open source tool for metagenomics. PeerJ. 2016;4:e2584.
PubMed  PubMed Central  Article  Google Scholar 

49.
Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 2012;41:D590–D6.
PubMed  PubMed Central  Article  CAS  Google Scholar 

50.
Yarza P, Yilmaz P, Pruesse E, Glockner FO, Ludwig W, Schleifer KH, et al. Uniting the classification of cultured and uncultured bacteria and archaea using 16S rRNA gene sequences. Nat Rev Microbiol. 2014;12:635–45.
CAS  PubMed  Article  PubMed Central  Google Scholar 

51.
Love MI, Huber W, Anders S. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol. 2014;15:550.
PubMed  PubMed Central  Article  CAS  Google Scholar 

52.
Olivares-Fuster O, Bullard SA, McElwain A, Llosa MJ, Arias CR. Adhesion dynamics of Flavobacterium columnare to channel catfish Ictalurus punctatus and zebrafish Danio rerio after immersion challenge. Dis Aquat Org. 2011;96:221–7.
Article  Google Scholar 

53.
Cheesman SE, Neal JT, Mittge E, Seredick BM, Guillemin K. Epithelial cell proliferation in the developing zebrafish intestine is regulated by the Wnt pathway and microbial signaling via Myd88. Proc Natl Acad Sci USA. 2011;108:4570–7.
CAS  PubMed  Article  PubMed Central  Google Scholar 

54.
Stephens WZ, Burns AR, Stagaman K, Wong S, Rawls JF, Guillemin K, et al. The composition of the zebrafish intestinal microbial community varies across development. ISME J. 2016;10:644–54.
PubMed  Article  CAS  PubMed Central  Google Scholar 

55.
Mena KD, Gerba CP. Risk assessment of pseudomonas aeruginosa in water. Rev Environ contamination Toxicol. 2009;201:71–115.
CAS  Google Scholar 

56.
Goncalves Pessoa RB, de Oliveira WF, Marques DSC, Dos Santos Correia MT, de Carvalho E, Coelho L. The genus Aeromonas: a general approach. Microb pathogenesis. 2019;130:81–94.
CAS  Article  Google Scholar 

57.
Russell AB, Wexler AG, Harding BN, Whitney JC, Bohn AJ, Goo YA, et al. A type VI secretion-related pathway in Bacteroidetes mediates interbacterial antagonism. Cell Host Microbe. 2014;16:227–36.
CAS  PubMed  PubMed Central  Article  Google Scholar 

58.
Chatzidaki-Livanis M, Coyne MJ, Comstock LE. An antimicrobial protein of the gut symbiont Bacteroides fragilis with a MACPF domain of host immune proteins. Mol Microbiol. 2014;94:1361–74.
CAS  PubMed  PubMed Central  Article  Google Scholar 

59.
Roelofs KG, Coyne MJ, Gentyala RR, Chatzidaki-Livanis M, Comstock LE. Bacteroidales secreted antimicrobial proteins target surface molecules necessary for gut colonization and mediate competition in vivo. mBio. 2016;7:e01055–16.
CAS  PubMed  PubMed Central  Article  Google Scholar 

60.
Jacobson A, Lam L, Rajendram M, Tamburini F, Honeycutt J, Pham T, et al. A gut commensal-produced metabolite mediates colonization resistance to salmonella infection. Cell host microbe. 2018;24:296–307.e7.
CAS  PubMed  PubMed Central  Article  Google Scholar 

61.
Larsbrink J, McKee LS. Bacteroidetes bacteria in the soil: glycan acquisition, enzyme secretion, and gliding motility. Adv Appl Microbiol. 2020;110:63–98.
PubMed  Article  PubMed Central  Google Scholar 

62.
Kamada N, Seo SU, Chen GY, Nunez G. Role of the gut microbiota in immunity and inflammatory disease. Nat Rev Immunol. 2013;13:321–35.
CAS  PubMed  Article  PubMed Central  Google Scholar 

63.
Ganz J, Melancon E, Eisen JS. Zebrafish as a model for understanding enteric nervous system interactions in the developing intestinal tract. Methods cell Biol. 2016;134:139–64.
CAS  PubMed  Article  PubMed Central  Google Scholar 

64.
Bates JM, Mittge E, Kuhlman J, Baden KN, Cheesman SE, Guillemin K. Distinct signals from the microbiota promote different aspects of zebrafish gut differentiation. Dev Biol. 2006;297:374–86.
CAS  PubMed  Article  PubMed Central  Google Scholar 

65.
Yan Q, van der Gast CJ, Yu Y. Bacterial community assembly and turnover within the intestines of developing zebrafish. PLoS ONE. 2012;7:e30603.
CAS  PubMed  PubMed Central  Article  Google Scholar 

66.
Costello EK, Stagaman K, Dethlefsen L, Bohannan BJ, Relman DA. The application of ecological theory toward an understanding of the human microbiome. Science. 2012;336:1255–62.
CAS  PubMed  PubMed Central  Article  Google Scholar 

67.
Rogers GB, Hoffman LR, Carroll MP, Bruce KD. Interpreting infective microbiota: the importance of an ecological perspective. Trends Microbiol. 2013;21:271–6.
CAS  PubMed  Article  PubMed Central  Google Scholar 

68.
Burns AR, Stephens WZ, Stagaman K, Wong S, Rawls JF, Guillemin K, et al. Contribution of neutral processes to the assembly of gut microbial communities in the zebrafish over host development. ISME J. 2016;10:655–64.
CAS  PubMed  Article  PubMed Central  Google Scholar 

69.
Hibbing ME, Fuqua C, Parsek MR, Peterson SB. Bacterial competition: surviving and thriving in the microbial jungle. Nat Rev Microbiol. 2010;8:15–25.
CAS  PubMed  PubMed Central  Article  Google Scholar 

70.
Shafquat A, Joice R, Simmons SL, Huttenhower C. Functional and phylogenetic assembly of microbial communities in the human microbiome. Trends Microbiol. 2014;22:261–6.
CAS  PubMed  PubMed Central  Article  Google Scholar 

71.
Shade A, Peter H, Allison SD, Baho DL, Berga M, Burgmann H, et al. Fundamentals of microbial community resistance and resilience. Front Microbiol. 2012;3:417.
PubMed  PubMed Central  Article  Google Scholar 

72.
Willing BP, Russell SL, Finlay BB. Shifting the balance: antibiotic effects on host-microbiota mutualism. Nat Rev Microbiol. 2011;9:233–43.
CAS  PubMed  Article  PubMed Central  Google Scholar 

73.
Brugman S, Liu KY, Lindenbergh-Kortleve D, Samsom JN, Furuta GT, Renshaw SA, et al. Oxazolone-induced enterocolitis in zebrafish depends on the composition of the intestinal microbiota. Gastroenterology. 2009;137:1757–67.e1.
CAS  PubMed  Article  PubMed Central  Google Scholar 

74.
Salonen A, Nikkila J, Jalanka-Tuovinen J, Immonen O, Rajilic-Stojanovic M, Kekkonen RA, et al. Comparative analysis of fecal DNA extraction methods with phylogenetic microarray: effective recovery of bacterial and archaeal DNA using mechanical cell lysis. J Microbiol Methods. 2010;81:127–34.
CAS  PubMed  Article  PubMed Central  Google Scholar 

75.
Kunin V, Engelbrektson A, Ochman H, Hugenholtz P. Wrinkles in the rare biosphere: pyrosequencing errors can lead to artificial inflation of diversity estimates. Environ Microbiol. 2010;12:118–23.
CAS  PubMed  Article  PubMed Central  Google Scholar 

76.
Shabgah AG, Navashenaq JG, Shabgah OG, Mohammadi H, Sahebkar A. Interleukin-22 in human inflammatory diseases and viral infections. Autoimmun Rev. 2017;16:1209–18.
CAS  PubMed  Article  PubMed Central  Google Scholar  More

1.
Wyatt, T. D. Pheromones and Animal Behavior 104 (Cambridge University Press, Cambridge, 2013).
Google Scholar 
2.
Hughes, N. K., Korpimäki, E. & Banks, P. B. The predation risks of interspecific eavesdropping: weasel-vole interactions. Oikos 119, 1210–1216 (2010).
Article  Google Scholar 

3.
Garvey, P. M. et al. Exploiting interspecific olfactory communication to monitor predators. Ecol. Appl. 27, 389–402 (2017).
PubMed  Article  Google Scholar 

4.
Parsons, M. H. et al. Biologically meaningful scents: a framework for understanding predator–prey research across disciplines. Biol. Rev. 93, 98–114 (2018).
PubMed  Article  Google Scholar 

5.
Varner, E., Gries, R., Takács, S., Fan, S. & Gries, G. Identification and field testing of volatile components in the sex attractant pheromone blend of female house mice. J. Chem. Ecol. 45, 18–27 (2019).
CAS  PubMed  Article  Google Scholar 

6.
Takács, S., Gries, R., Zhai, H. & Gries, G. The sex attractant pheromone of male brown rats: identification and field experiment. Angew. Chemie Int. Ed. 55, 6062–6066 (2016).
Article  CAS  Google Scholar 

7.
Din, W. et al. Origin and radiation of the house mouse: clues from nuclear genes. J. Evol. Biol. 9, 519–539 (1996).
CAS  Article  Google Scholar 

8.
Puckett, E. E. et al. Global population divergence and admixture of the brown rat (Rattus norvegicus). Proc. R. Soc. B Biol. Sci. 283, 20161762 (2016).
Article  Google Scholar 

9.
Karli, P. The Norway rat’s killing response to the white mouse: an experimental analysis. Source Behav. 102, 81–103 (1956).
Google Scholar 

10.
Papes, F., Logan, D. W. & Stowers, L. The vomeronasal organ mediates interspecies defensive behaviors through detection of protein pheromone homologs. Cell 141, 692–703 (2010).
CAS  PubMed  PubMed Central  Article  Google Scholar 

11.
Slotnick, B. Animal cognition and the rat olfactory system. Med. J. Aust. 5, 216–222 (2001).
CAS  Google Scholar 

12.
Hughes, N. K., Price, C. J. & Banks, P. B. Predators are attracted to the olfactory signals of prey. PLoS ONE 5, e13114 (2010).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

13.
Osada, K., Tashiro, T., Mori, K. & Izumi, H. The identification of attractive volatiles in aged male mouse urine. Chem. Senses 33, 815–823 (2008).
CAS  PubMed  Article  Google Scholar 

14.
Kavaliers, M., Choleris, E. & Pfaff, D. W. Recognition and avoidance of the odors of parasitized conspecifics and predators: differential genomic correlates. Neurosci. Biobehav. Rev. 29, 1347–1359 (2005).
PubMed  Article  Google Scholar 

15.
Hurst, J. L. The complex network of olfactory communication in populations of wild house mice Mus domesticus Rutty: urine marking and investigation within family groups. Anim. Behav. 37, 705–725 (1989).
Article  Google Scholar 

16.
Mossman, C. A. & Drickamer, L. C. Odor preferences of female house mice (Mus domesticus) in seminatural enclosures. J. Comp. Psychol. 110, 131–138 (1996).
CAS  PubMed  Article  Google Scholar 

17.
Jones, R. B. & Nowell, N. W. Aversive and aggression-promoting properties of urine from dominant and subordinate male mice. Anim. Learn. Behav. 1, 207–210 (1973).
Article  Google Scholar 

18.
Barnard, C. J. & Fitzsimons, J. Kin recognition and mate choice in mice: the effects of kinship, familiarity and social interference on intersexual interaction. Anim. Behav. 36, 1078–1090 (1988).
Article  Google Scholar 

19.
He, J., Ma, L., Kim, S., Nakai, J. & Yu, C. R. R. Encoding gender and individual information in the mouse vomeronasal organ. Science 320, 535–538 (2008).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

20.
Yang, M. et al. The rat exposure test: a model of mouse defensive behaviors. Physiol. Behav. 81, 465–473 (2004).
CAS  PubMed  Article  Google Scholar 

21.
Amaral, V. C. S., Santos Gomes, K. & Nunes-de-Souza, R. L. Increased corticosterone levels in mice subjected to the rat exposure test. Horm. Behav. 57, 128–133 (2010).
CAS  PubMed  Article  Google Scholar 

22.
Takács, S., Gries, R. & Gries, G. Sex hormones function as sex attractant pheromones in house mice and brown rats. ChemBioChem 18, 1391–1395 (2017).
PubMed  Article  CAS  Google Scholar 

23.
Jemiolo, B., Alberts, J., Sochinski-Wiggins, S., Harvey, S. & Novotny, M. Behavioural and endocrine responses of female mice to synthetic analogues of volatile compounds in male urine. Anim. Behav. 33, 1114–1118 (1985).
Article  Google Scholar 

24.
Novotny, M. et al. Synthetic pheromones that promote inter-male aggression in mice. Proc. Natl. Acad. Sci. USA 82, 2059–2061 (1985).
ADS  CAS  PubMed  Article  Google Scholar 

25
Banks, P. B., Daly, A. & Bytheway, J. P. Predator odours attract other predators, creating an olfactory web of information. Biol. Lett. 12, 20151053 (2016).
PubMed  PubMed Central  Article  Google Scholar 

26.
Roitberg, B. D. Chemical communication. In Insect Behavior: From Mechanisms to Ecological and Evolutionary Consequences (eds Córdoba-Aguilar, A. et al.) 557–575 (Oxford University Press, Oxford, 2018).
Google Scholar 

27
Vasudevan, A. & Vyas, A. Kairomonal communication in mice is concentration-dependent with a proportional discrimination threshold. F1000Research 2, 195 (2013).
PubMed  PubMed Central  Article  Google Scholar 

28.
Danci, A., Schaefer, P. W., Schopf, A. & Gries, G. Species-specific close-range sexual communication systems prevent cross-attraction in three species of Glyptapanteles parasitic wasps (Hymenoptera: Braconidae). Biol. Control 39, 225–231 (2006).
Article  Google Scholar 

29
Wen, X.-L.L., Wen, P., Dahlsjö, C., Sillam-Dussès, D. & Šobotník, J. Breaking the cipher: ant eavesdropping on the variational trail pheromone of its termite prey. Proc. R. Soc. B Biol. Sci. 284, 20170121 (2017).
Article  CAS  Google Scholar 

30.
Haynes, K. F. & Yeargan, K. V. Exploitation of intraspecific communication systems: illicit signalers and receivers. Ann. Entomol. Soc. Am 92, 960–970 (1999).
Article  Google Scholar 

31.
Dong, S. et al. Olfactory eavesdropping of predator alarm pheromone by sympatric but not allopatric prey. Anim. Behav. 141, 115–125 (2018).
Article  Google Scholar 

32.
Sbarbati, A. & Osculati, F. Allelochemical communication in vertebrates: kairomones, allomones and synomones. Cells Tissues Organs 183, 206–219 (2006).
CAS  PubMed  Article  PubMed Central  Google Scholar 

33.
Apps, P., Rafiq, K. & McNutt, J. W. Do carnivores have a world wide web of interspecific scent signals? in Chemical Signals in Vertebrates (ed. Buesching, C. D.) 182–202 (2019).

34.
Apfelbach, R., Blanchard, C. D., Blanchard, R. J., Hayes, R. A. & Mcgregor, I. S. The effects of predator odors in mammalian prey species: a review of field and laboratory studies. Neurosci. Biobehav. Rev. 29, 1123–1144 (2005).
PubMed  Article  Google Scholar 

35
Jones, M. E. et al. A nose for death: Integrating trophic and informational networks for conservation and management. Front. Ecol. Evol. 4, 124 (2016).
Article  Google Scholar 

36
McGregor, P. K. Communication networks and eavesdropping in animals. In Encyclopedia of Neuroscience (ed. Larry, R. S.) 1179–1184 (Academic Press, Cambridge, 2009).
Google Scholar 

37.
Peake, T. M. Eavesdropping in communication networks. in Animal Communication Networks (ed. McGregor, P. K.) 13–37 (2005).

38.
Tsunoda, M. et al. Identification of an intra- and inter-specific tear protein signal in rodents. Curr. Biol. 28, 1213–1223 (2018).
CAS  PubMed  Article  Google Scholar 

39.
Ardeh, M. J., De Jong, P. W. D., Loomans, A. J. M. & Van Lenteren, J. C. Inter- and intraspecific effects of volatile and nonvolatile sex pheromones on males, mating behavior, and hybridization in Eretmocerus mundus and E. eremicus (Hymenoptera: Aphelinidae). J. Insect Behav. 17, 745–759 (2004).
Article  Google Scholar 

40.
Ylönen, H., Sundell, J., Tiilikainen, R., Eccard, J. A. & Horne, T. Weasels’ (Mustela nivalis nivalis) preference for olfactory cues of the vole (Clethrionomys glareolus). Ecology 84, 1447–1452 (2003).
Article  Google Scholar 

41.
Zhang, Y.-H.H., Liang, H.-C.C., Guo, H.-L.L. & Zhang, J.-X.X. Exaggerated male pheromones in rats may increase predation cost. Curr. Zool. 62, 431–437 (2016).
PubMed  PubMed Central  Article  Google Scholar 

42.
Hughes, N. K., Kelley, J. L. & Banks, P. B. Receiving behaviour is sensitive to risks from eavesdropping predators. Oecologia 160, 609–617 (2009).
ADS  PubMed  Article  PubMed Central  Google Scholar 

43.
Koivula, M., Korpimäki, E. & Korpimaki, E. Do scent marks increase predation risk of microtine rodents?. Oikos 95, 275–281 (2001).
Article  Google Scholar 

44.
May, M. D., Bowen, M. T., Mcgregor, I. S. & Timberlake, W. Rubbings deposited by cats elicit defensive behavior in rats. Physiol. Behav. 107, 711–718 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

45.
Schwende, F. J., Wiesler, D., Jorgenson, J. W., Carmack, M. & Novotny, M. Urinary volatile consituents of the house mouse, Mus musculus, and their endocrine dependency. J. Chem. Ecol. 12, 277–296 (1986).
CAS  PubMed  Article  PubMed Central  Google Scholar 

46.
Musso, A. E., Gries, R., Zhai, H., Takács, S. & Gries, G. Effect of male house mouse pheromone components on behavioral responses of mice in laboratory and field experiments. J. Chem. Ecol. 43, 215–224 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

47.
Ferrero, D. M. et al. Detection and avoidance of a carnivore odor by prey. PNAS 108, 11235–11240 (2011).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

48.
Wolff, J. O. Laboratory studies with rodents: facts or artifacts? Bioscience 53, 421 (2003).
Article  Google Scholar 

49.
Calisi, R. M. & Bentley, G. E. Lab and field experiments: are they the same animal? Horm. Behav. 56, 1–10 (2009).
PubMed  Article  Google Scholar 

50.
Kondrakiewicz, K., Kostecki, M., Szadzińska, W. & Knapska, E. Ecological validity of social interaction tests in rats and mice. Genes Brain Behav. 18, 1–14 (2019).
Article  Google Scholar 

51.
de Masi, E., Vilaça, P. & Razzolini, M. T. P. Environmental conditions and rodent infestation in Campo Limpo district, São Paulo municipality, Brazil. Int. J. Environ. Health Res. 19, 1–16 (2009).
PubMed  Article  Google Scholar 

52.
Stryjek, R., Mioduszewska, B., Spaltabaka-Gędek, E. & Juszczak, G. R. Wild norway rats do not avoid predator scents when collecting food in a familiar habitat: a field study. Sci. Rep. 8, 1–11 (2018).
CAS  Article  Google Scholar 

53.
Lima, S. L. & Bednekoff, P. A. Temporal variation in danger drives antipredator behavior: the predation risk allocation hypothesis. Am. Nat. 153, 649–659 (1999).
PubMed  Article  Google Scholar 

54.
Pérez-Gómez, A. et al. Innate predator odor aversion driven by parallel olfactory subsystems that converge in the ventromedial hypothalamus. Curr. Biol. 25, 1340–1346 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

55.
Isogai, Y. et al. Molecular organization of vomeronasal chemoreception. Nature 478, 241–245 (2011).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

56.
Bacchini, A., Gaetani, E. & Cavaggioni, A. Pheromone binding proteins of the mouse, Mus musculus. Experientia 48, 419–421 (1992).
CAS  PubMed  Article  Google Scholar 

57.
Novotny, M. V., Ma, W., Wiesler, D. & Žídek, L. Positive identification of the puberty-accelerating pheromone of the house mouse: the volatile ligands associating with the major urinary protein. Proc. R. Soc. B Biol. Sci. 266, 2017–2022 (1999).
CAS  Article  Google Scholar 

58.
Hurst, J., Robertson, D., Tolladay, U. & Beynon, R. Proteins in urine scent marks of male house mice extend the longevity of olfactory signals. Anim. Behav. 55, 1289–1297 (1998).
CAS  PubMed  Article  Google Scholar 

59.
Beynon, R. J. & Hurst, J. L. Urinary proteins and the modulation of chemical scents in mice and rats. Peptides 25, 1553–1563 (2004).
CAS  PubMed  Article  Google Scholar 

60.
Caut, S. et al. Rats dying for mice: modelling the competitor release effect. Austral. Ecol. 32, 858–868 (2007).
Article  Google Scholar 

61.
Campbell-Palmer, R. & Rosell, F. The importance of chemical communication studies to mammalian conservation biology: a review. Biol. Conserv. 144, 1919–1930 (2011).
Article  Google Scholar 

62.
Sparrow, E. E., Parsons, M. H. & Blumstein, D. T. Novel use for a predator scent: preliminary data suggest that wombats avoid recolonising collapsed burrows following application of dingo scent. Aust. J. Zool. 64, 192–197 (2016).
Article  Google Scholar 

63.
Friesen, M. R., Beggs, J. R. & Gaskett, A. C. Sensory-based conservation of seabirds: a review of management strategies and animal behaviours that facilitate success. Biol. Rev. 92, 1769–1784 (2017).
PubMed  Article  Google Scholar 

64.
Campbell-Palmer, R. & Rosell, F. Conservation of the Eurasian beaver Castor fiber: an olfactory perspective. Mamm. Rev. 40, 293–312 (2010).
Article  Google Scholar 

65.
Loss, S. R., Will, T. & Marra, P. P. The impact of free-ranging domestic cats on wildlife of the United States. Nat. Commun. 4, 1396 (2013).
ADS  PubMed  Article  CAS  Google Scholar 

66.
BirdLife International. State of the world’s birds: taking the pulse of the planet. (2018).

67.
Courchamp, F., Langlais, M. & Sugihara, G. Cats protecting birds: modelling the mesopredator release effect. J. Anim. Ecol. 68, 282–292 (1999).
Article  Google Scholar 

68.
MacInnes, C. D. et al. Elimination of rabies from red foxes in eastern Ontario. J. Wildl. Dis. 37, 119–132 (2001).
CAS  PubMed  Article  Google Scholar 

69.
Takács, S. et al. New food baits for trapping house mice, black rats and brown rats. Appl. Anim. Behav. Sci. 200, 130–135 (2017).
Article  Google Scholar 

70.
Safranski, T. J., Lamberson, W. R. & Keisler, D. H. Correlations among three measures of puberty in mice and relationships with estradiol concentration and ovulation. Biol. Reprod. 48, 669–673 (1993).
CAS  PubMed  Article  Google Scholar 

71.
Schneider, J. E., Wysocki, C. J., Nyby, J. & Whitney, G. Determining the sex of neonatal mice (Mus musculus). Behav. Res. Methods Instrum. 10, 105 (1978).
Article  Google Scholar 

72.
Dhakal, P. & Soares, M. J. Single-step PCR-based genetic sex determination of rat tissues and cells. Biotechniques 62, 232–233 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

73.
Varner, E, Gries, R. & Gries, G. Attractant blend composition, devices and methods for attracting female mice. US provisional patent application (filed 17 August 2020; Patent App. Serial No. 63/066,716) (2020).

74.
R Core Team. R: A language and environment for statistical computing. (2019). More

1.
Burgess, S. D., Bowring, S. & Shen, S.-Z. High-precision timeline for Earth’s most severe extinction. Proc. Natl Acad. Sci. USA 111, 3316–3321 (2014).
Article  Google Scholar 
2.
Svensen, H. et al. Siberian gas venting and the end-Permian environmental crisis. Earth Planet. Sci. Lett. 277, 490–500 (2009).
Article  Google Scholar 

3.
Burgess, S. D. & Bowring, S. A. High-precision geochronology confirms voluminous magmatism before, during, and after Earth’s most severe extinction. Sci. Adv. 1, e1500470 (2015).
Article  Google Scholar 

4.
Saunders, A. D. Two LIPs and two Earth-system crises: the impact of the North Atlantic igneous province and the Siberian Traps on the Earth-surface carbon cycle. Geol. Mag. 153, 201–222 (2016).
Article  Google Scholar 

5.
Cui, Y. & Kump, L. R. Global warming and the end-Permian extinction event: proxy and modeling perspectives. Earth Sci. Rev. 149, 5–22 (2015).
Article  Google Scholar 

6.
Sun, Y. et al. Lethally hot temperatures during the early Triassic greenhouse. Science 338, 366–370 (2012).
Article  Google Scholar 

7.
Brand, U. et al. The end-Permian mass extinction: a rapid volcanic CO2 and CH4-climatic catastrophe. Chem. Geol. 323, 121–144 (2012).
Article  Google Scholar 

8.
Winguth, A. M. E., Shields, C. A. & Winguth, C. Transition into a hothouse world at the Permian–Triassic boundary—a model study. Palaeogeogr. Palaeoclimatol. Palaeoecol. 440, 316–327 (2015).
Article  Google Scholar 

9.
Garbelli, C. et al. Neotethys seawater chemistry and temperature at the dawn of the end-Permian mass extinction. Gondwana Res. 35, 272–272 (2016).
Article  Google Scholar 

10.
Wang, W. et al. A high-resolution middle to late Permian paleotemperature curve reconstructed using oxygen isotopes of well-preserved brachiopod shells. Earth Planet. Sci. Lett. 540, 116245 (2020).
Article  Google Scholar 

11.
Lau, K. V. et al. Marine anoxia and delayed Earth system recovery after the end-Permian extinction. Proc. Natl Acad. Sci. USA 113, 2360–2365 (2016).
Article  Google Scholar 

12.
Elrick, M. et al. Global-ocean redox variation during the middle-late Permian through Early Triassic based on uranium isotope and Th/U trends of marine carbonates. Geology 45, 163–166 (2017).
Article  Google Scholar 

13.
Zhang, F. F. et al. Congruent Permian–Triassic δ238U records at Panthalassic and Tethyan sites: confirmation of global-oceanic anoxia and validation of the U-isotope paleoredox proxy. Geology 46, 327–330 (2018).
Article  Google Scholar 

14.
Korte, C. et al. Carbon, sulfur, oxygen and strontium isotope records, organic geochemistry and biostratigraphy across the Permian/Triassic boundary in Abadeh, Iran. Int. J. Earth Sci. 93, 565–581 (2004).
Google Scholar 

15.
Grice, K. et al. Photic zone euxinia during the Permian–Triassic superanoxic event. Science 307, 706–709 (2005).
Article  Google Scholar 

16.
Payne, J. L. et al. Calcium isotope constraints on the end-Permian mass extinction. Proc. Natl Acad. Sci. USA 107, 8543–8548 (2010).
Article  Google Scholar 

17.
Clarkson, M. O. et al. Ocean acidification and the Permo-Triassic mass extinction. Science 348, 229–232 (2015).
Article  Google Scholar 

18.
Garbelli, C., Angiolini, L. & Shen, S.-Z. Biomineralization and global change: a new perspective for understanding the end-Permian extinction. Geology 45, 19–22 (2017).
Article  Google Scholar 

19.
Hönisch, B., Hemming, N. G., Archer, D., Siddall, M. & McManus, J. F. Atmospheric carbon dioxide concentration across the mid-Pleistocene transition. Science 324, 1551–1554 (2009).
Article  Google Scholar 

20.
Rae, J. W. B. et al. CO2 storage and release in the deep Southern Ocean on millennial to centennial timescales. Nature 562, 569–573 (2018).
Article  Google Scholar 

21.
Gutjahr, M. et al. Very larger release of mostly volcanic carbon during the Palaeocene–Eocene thermal maximum. Nature 548, 573–577 (2017).
Article  Google Scholar 

22.
Henehan, M. et al. Rapid ocean acidification and protracted Earth system recovery followed the end-Cretaceous Chicxulub impact. Proc. Natl Acad. Sci. USA 116, 22500–22504 (2019).
Article  Google Scholar 

23.
Müller, T. et al. Ocean acidification during the early Toarcian extinction event: Evidence from boron isotopes in brachiopods. Geology https://doi.org/10.1130/G47781.1 (2020).

24.
Posenato, R. Survival patterns of microbenthic marine assemblages during the end-Permian mass extinction in the western Tethys (Dolomites, Italy). Palaeogeogr. Palaeoclimatol. Palaeoecol. 280, 150–167 (2019).
Article  Google Scholar 

25.
Wallmann, K. et al. Periodic changes in the Cretaceous ocean and climate caused by marine redox see-saw. Nat. Geosci. 12, 456–462 (2019).
Article  Google Scholar 

26.
Retallack, G. J. A 300-million-year record of atmospheric carbon dioxide from fossil plant cuticles. Nature 411, 287–290 (2001).
Article  Google Scholar 

27.
Goddéris, Y. et al. Causal or casual link between the rise of nannoplankton calcification and a tectonically-driven massive decrease in Late Triassic atmospheric CO2? Earth Planet. Sci. Lett. 267, 247–255 (2008).
Article  Google Scholar 

28.
McElwain, J. C., Wagner, P. J. & Hesselbo, S. P. Fossil plant relative abundances indicate sudden loss of Late Triassic biodiversity in East Greenland. Science 324, 1554–1556 (2009).
Article  Google Scholar 

29.
Witkowski, C. R., Weijers, J. W. H., Blais, B., Schouten, S. & Damste, J. S. S. Molecular fossils from phytoplankton reveal secular (p_{{rm{CO}}_2}) trend over the Phanerozoic. Sci. Adv. 4, eaat4556 (2018).

30.
Joachimski, M. M. et al. Climate warming in the latest Permian and the Permian–Triassic mass extinction. Geology 40, 195–198 (2012).
Article  Google Scholar 

31.
Schobben, M., Joachimski, M. M., Korn, D., Leda, L. & Korte, C. Palaeotethys seawater temperature rise and an intensified hydrological cycle following the end-Permian mass extinction. Gondwana Res. 26, 675–683 (2014).
Article  Google Scholar 

32.
Algeo, T. J. et al. Plankton and productivity during the Permian–Triassic boundary crisis: an analysis of organic carbon fluxes. Glob. Planet. Change 105, 52–67 (2013).
Article  Google Scholar 

33.
Saitoh, M. et al. Nitrogen isotope chemostratigraphy across the Permian–Triassic boundary at Chaotian, Sichuan, South China. J. Asian Earth Sci. 93, 113–128 (2014).
Article  Google Scholar 

34.
Sun, Y. D. et al. Ammonium ocean following the end-Permian mass extinction. Earth Planet. Sci. Lett. 518, 211–222 (2019).
Article  Google Scholar 

35.
Shen, J. et al. Marine productivity changes during the end-Permian crisis and Early Triassic recovery. Earth Sci. Rev. 149, 136–162 (2015).
Article  Google Scholar 

36.
Canfield, D. E. Models of oxic respiration, denitrification and sulfate reduction in zones of coastal upwelling. Geochim. Cosmochim. Acta 70, 5753–5765 (2006).
Article  Google Scholar 

37.
Anderson, R. F. et al. Deep-sea oxygen depletion and ocean carbon sequestration during the last ice age. Glob. Biogeochem. Cycles 33, 301–317 (2019).
Article  Google Scholar 

38.
Zeebe, R. & Westbroek, P. A simple model for the CaCO3 saturation state of the ocean: The ‘Strangelove’, the ‘Neritan’, and the ‘Cretan’ Ocean. Geochem. Geophys. Geosyst. 4, 1104 (2003).
Article  Google Scholar 

39.
Vollstaedt, H. et al. The Phanerozoic δ88/86Sr record of seawater: new constraints on past changes in oceanic carbonate fluxes. Geochim. Cosmochim. Acta 128, 249–265 (2014).
Article  Google Scholar 

40.
Silva-Tamayo, J. C. et al. Global perturbation of the marine calcium cycle during the Permian–Triassic transition. Geol. Soc. Am. Bull. 130, 1323–1338 (2018).
Article  Google Scholar 

41.
Woods, A. Assessing Early Triassic paleoceanographic conditions via unusual sedimentary fabrics and features. Earth Sci. Rev. 137, 6–18 (2014).
Article  Google Scholar 

42.
Burgess, S. D., Muirhead, J. D. & Bowring, S. A. Initial pulse of Siberian Traps sills as the trigger of the end-Permian mass extinction. Nat. Commun. 8, 164 (2017).
Article  Google Scholar 

43.
Song, H. J., Wignall, P. B., Tong, J. N. & Yin, H. F. Two pulses of extinction during the Permian–Triassic crisis. Nat. Geosci. 6, 52–56 (2013).
Article  Google Scholar 

44.
Brayard, A. et al. Transient metazoan reefs in the aftermath of the end-Permian mass extinction. Nat. Geosci. 4, 693–697 (2011).
Article  Google Scholar 

45.
Martindale, R. C., Foster, W. J. & Velledits, F. The survival, recovery, and diversification of Metazoan reef ecosystems following the end-Permian mass extinction event. Palaeogeogr. Palaeoclimatol. Palaeoecol. 513, 100–115 (2019).
Article  Google Scholar 

46.
Reynard, S. et al. Interacting effects of CO2 partial pressure and temperature on photosynthesis and calcification in a scleractinian coral. Glob. Change Biol. 9, 1660–1668 (2003).
Article  Google Scholar 

47.
Beatty, T. W., Zonneveld, J.-P. & Henderson, C. M. Anomalously diverse Early Triassic ichnofossil assemblages in northwest Pangea: a case for shallow-marine habitable zone. Geology 36, 771–774 (2008).
Article  Google Scholar 

48.
Le Quéré, C. et al. Global carbon budget 2018. Earth Syst. Sci. Data 10, 2141–2194 (2018).
Article  Google Scholar 

49.
Orr, J. C. et al. Anthropogenic ocean acidification over the twenty-first century and its impact on calcifying organisms. Nature 437, 681–686 (2005).
Article  Google Scholar 

50.
Schmidtko, S., Stramma, L. & Visbeck, M. Decline in global oxygen content during the past five decades. Nature 542, 335–339 (2017).
Article  Google Scholar 

51.
Posenato, R. Marine biotic events in the Lopingian succession and latest Permian extinction in the Southern Alps (Italy). Geol. J. 45, 195–215 (2010).
Article  Google Scholar 

52.
Broglio Loriga, C., Neri, C., Pasini, M. & Posenato, R. in Permian and Permian–Triassic Boundary in the South-Alpine segment of the western Tethys, and Additional Reports (ed. Cassinis, G.) 5–44 (Societa Geologica Italiana, 1988).

53.
Posenato, R. The athyridoids of the transitional beds between Bellerophon and Werfen formations (uppermost Permian, Southern Alps, Italy). Riv. Ital. Paleontol. Soc. 1071, 197–226 (2001).
Google Scholar 

54.
Kearsey, T., Twichett, R. J., Price, G. D. & Grimes, S. T. Isotope excursion and palaeotemperature estimates from the Permian/Triassic boundary in the Southern Alps (Italy). Palaeogeogr. Palaeoclimatol. Palaeoecol. 279, 29–40 (2009).
Article  Google Scholar 

55.
Muttoni, G. et al. Opening of the neo-Tethys ocean and the Pangea B to Pangea A transformation during the Permian. GeoArabia 14, 17–48 (2009).
Google Scholar 

56.
Reichow, M. K. et al. The timing and extent of the eruption of the Siberian Traps large igneous province: implications for the end-Permian environmental crisis. Earth Planet. Sci. Lett. 277, 9–20 (2009).
Article  Google Scholar 

57.
Brand, U., Logan, A., Hiller, N. & Richardson, J. Geochemistry of modern brachiopods: applications and implications for oceanography and paleoceanography. Chem. Geol. 198, 305–334 (2003).
Article  Google Scholar 

58.
Rollion-Bard, C. et al. Assessing the biomineralisation processes in the shell layers of modern brachiopods from oxygen isotopic composition and elemental ratios: implications for their use as paleoenvironmental proxies. Chem. Geol. 524, 49–66 (2019).
Article  Google Scholar 

59.
Jurikova, H. et al. Boron isotope systematics of cultured brachiopods: response to acidification, vital effects and implications for palaeo-pH reconstruction. Geochim. Cosmochim. Acta 248, 370–386 (2019).
Article  Google Scholar 

60.
Jurikova, H. et al. Incorporation of minor and trace elements into cultured brachiopods: implications for proxy application with new insights from a biomineralisation model. Geochim. Cosmochim. Acta 286, 418–440 (2020).
Article  Google Scholar 

61.
Jurikova, H. et al. Boron isotope composition of the cold-water coral Lophelia pertusa along the Norwegian margin: zooming into a potential pH-proxy by combining bulk and high-resolution approaches. Chem. Geol. 513, 143–152 (2019).
Article  Google Scholar 

62.
Kasemann, S. A., Schmidt, D. N., Bijima, J. & Foster, G. L. In situ boron isotope analyses in marine carbonates and its application for foraminifera and palaeo-pH. Chem. Geol. 260, 138–147 (2009).
Article  Google Scholar 

63.
Lemarchand, D., Gaillardet, J., Lewin, É. & Allègre, C. J. The influence of rivers on marine boron isotopes and implications for reconstructing past ocean pH. Nature 408, 951–954 (2000).
Article  Google Scholar 

64.
Joachimski, M. M., Simon, L., van Geldern, R. & Lécuyer, C. Boron isotope geochemistry of Paleozoic brachiopod calcite: implications for a secular change in the boron isotope geochemistry of seawater over the Phanerozoic. Geochim. Cosmochim. Acta 69, 4035–4044 (2005).
Article  Google Scholar 

65.
Klochko, K., Kaufman, A. J., Wengsheng, Y., Byrne, R. H. & Tossell, J. A. Experimental measurement of boron isotope fractionation in seawater. Earth Planet. Sci. Lett. 248, 276–285 (2006).
Article  Google Scholar 

66.
Lécuyer, C., Grandjean, P., Reynard, B., Albarède, F. & Telouk, P. 11B/10B analysis of geological materials by ICP-MS Plasma 54: application to the boron fractionation between brachiopod calcite and seawater. Chem. Geol. 186, 45–55 (2002).
Article  Google Scholar 

67.
Ridgwell, A. A mid Mesozoic revolution in the regulation of ocean chemistry. Mar. Chem. 217, 339–357 (2005).
Google Scholar 

68.
Penman, D. E., Hönisch, B., Rasbury, E. T., Hemming, N. G. & Spero, H. J. Boron, carbon, and oxygen isotopic composition of brachiopod shells: intra-shell variability, controls, and potential as a paleo-pH recorder. Chem. Geol. 340, 32–39 (2013).
Article  Google Scholar 

69.
Garbelli, C., Angiolini, L., Brand, U. & Jadoul, F. Brachiopod fabric, classes and biogeochemistry: implications for the reconstruction and interpretation of seawater carbon-isotope curves and records. Chem. Geol. 371, 60–67 (2014).
Article  Google Scholar 

70.
Khiel, J. T. & Shields, C. A. Climate simulations of the latest Permian: implications for mass extinction. Geology 33, 757–760 (2005).
Article  Google Scholar 

71.
Lowenstein, T. K., Timofeeff, M. N., Brennan, S. T., Hardie, L. A. & Demicco, R. V. Oscillations in Phanerozoic seawater chemistry: evidence from fluid inclusions. Science 294, 1086–1088 (2001).
Article  Google Scholar 

72.
Berner, R. A. & Kothavala, Z. GEOCARB III: A revised model of atmospheric CO2 over Phanerozoic time. Am. J. Sci. 301, 182–204 (2001).
Article  Google Scholar 

73.
Royer, D. L., Donnadieu, Y., Park, J., Kowalczyk, J. & Godderis, Y. Error analysis of CO2 and O2 estimates from the long-term geochemical model GEOCARBSULF. Am. J. Sci. 314, 1259–1283 (2014).
Article  Google Scholar 

74.
Wallmann, K., Schneider, B. & Sarnthein, M. Effects of eustatic sea-level change, ocean dynamics, and nutrient utilization on atmospheric (p_{{rm{CO}}_2}) and seawater composition over the last 130,000 years. Clim. Past 12, 339–375 (2016).
Article  Google Scholar 

75.
Berner, R. A. GEOCARB II: a revised model of atmospheric CO2 over Phanerozoic time. Am. J. Sci. 294, 56–91 (1994).
Article  Google Scholar  More

1.
Bradshaw, C. J. & Brook, B. W. Human population reduction is not a quick fix for environmental problems. Proc. Natl. Acad. Sci. 111, 16610–16615 (2014).
ADS  CAS  PubMed  Article  Google Scholar 
2.
Xu, W. et al. Strengthening protected areas for biodiversity and ecosystem services in China. Proc. Natl. Acad. Sci. USA 114, 1601–1606. https://doi.org/10.1073/pnas.1620503114 (2017).
CAS  Article  PubMed  Google Scholar 

3.
Kong, L. et al. Habitat conservation redlines for the giant pandas in China. Biol. Cons. 210, 83–88. https://doi.org/10.1016/j.biocon.2016.03.028 (2017).
Article  Google Scholar 

4.
4Ji, D. in South China Morning Post(2017).

5.
5Johnson, I. in New York Times(2015).

6.
Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).
ADS  CAS  PubMed  Article  Google Scholar 

7.
Foley, J. A. et al. Global consequences of land use. Science 309, 570–574 (2005).
ADS  CAS  Article  Google Scholar 

8.
Pimm, S. L. & Raven, P. Biodiversity: extinction by numbers. Nature 403, 843–845 (2000).
ADS  CAS  PubMed  Article  Google Scholar 

9.
DeFries, R., Hansen, A., Turner, B., Reid, R. & Liu, J. Land use change around protected areas: management to balance human needs and ecological function. Ecol. Appl. 17, 1031–1038 (2007).
PubMed  Article  Google Scholar 

10.
Juffe-Bignoli, D. et al. Protected planet report 2014 (UNEP-WCMC, Cambridge, 2014).
Google Scholar 

11.
Schulze, K. et al. An assessment of threats to terrestrial protected areas. Conserv. Lett. 11, e12435 (2018).
Article  Google Scholar 

12.
Achiso, Z. Biodiversity and human livelihoods in protected areas: worldwide perspective—a review. SSR Inst. Int. J. Life Sci. 6, 2565–2578 (2020).
Article  Google Scholar 

13.
Ma, Z. et al. Changes in area and number of nature reserves in China. Conserv. Biol. 33, 1066–1075 (2019).
PubMed  PubMed Central  Article  Google Scholar 

14.
State Forestry Administration. Wildlife Conservation and Nature Reserve Construction Project [In Chinese]. http://www.forestry.gov.cn/portal/main/s/438/content-32569.html (2001).

15.
Bruno, J. F. et al. Climate change threatens the world’s marine protected areas. Nat. Clim. Change 8, 499 (2018).
ADS  Article  Google Scholar 

16.
Daskin, J. H. & Pringle, R. M. Warfare and wildlife declines in Africa’s protected areas. Nature 553, 328 (2018).
ADS  CAS  PubMed  Article  Google Scholar 

17.
Riggio, J., Jacobson, A. P., Hijmans, R. J. & Caro, T. How effective are the protected areas of East Africa?. Glob. Ecol. Conserv. 17, e00573 (2019).
Article  Google Scholar 

18.
Wang, W., Ren, G., He, Y. & Zhu, J. Habitat degradation and conservation status assessment of gallinaceous birds in the Trans-Himalayas, China. J. Wildl. Manag. 72, 1335–1341 (2008).
Article  Google Scholar 

19.
Lei, F. & Lu, T. China Endemic Birds 516–522 (Science Press, Beijing, 2006).
Google Scholar 

20.
20IUCN. The IUCN Red List of Threatened Species. https://www.iucnredlist.org (2020).

21.
21CITES. Convention on International Trade in Endangered Species of Wild Fauna and Flora (CITES) Appendices I, II and III. https://cites.org/eng/app/appendices.php (2017).

22.
Zhang, Z. W., Ding, C. Q., Ding, P. & Zheng, G. M. The current statts and a conservation strategy for species of Gralliformes in China. Chin. Biodivers. 11, 414–421 (2003).
Google Scholar 

23.
Zheng, G. M. & Wang, Q. S. China Red Data Book of Endangered Animals (Aves) (Science Press, Beijing, 1998).
Google Scholar 

24.
Zhang, Z. et al. Distribution and Population Status of Brown-Eared Pheasant in China 91–96 (World Pheasant Association, Hexham, 2002).
Google Scholar 

25.
25Zhang, Z. W., Zhang, G. & Song, J. Population status and conservation strategies of Brown Eared pheasant. In: Proceedings of the 4th Annual Symposium on Birds Across the two side of Taiwan Strait (2000).

26.
26Liu, Y. N. Studies on population status and habitat suitablity evalution of Brown Eared pheasants (Crossoptilon mantchuricum) in China. Master degree dissertation thesis, Beijing Normal University (2017).

27.
Li, X. & Liu, R. The Brown Eared Pheasant (International Academic Publishers, Bern, 1993).
Google Scholar 

28.
Wang, X. H. & An, C. L. Study of Brown Eared-pheasant population distribution in Xiaowutaishan Nature Reserve. Wildlife 28, 14–16 (2007) [In Chinese].
Google Scholar 

29.
Zhang, G. et al. Scale-dependent wintering habitat selection by brown eared pheasant in Luyashan Nature Reserve of Shanxi, China. Acta Ecol. Sin. 25, 952–957 (2005).
Google Scholar 

30.
Brown, D. et al. Land Use and Land Cover Change (Pacific Northwest National Laboratory (PNNL), Richland, WA, 2014).
Google Scholar 

31.
Jetz, W., Wilcove, D. S. & Dobson, A. P. Projected impacts of climate and land-use change on the global diversity of birds. PLoS Biol. 5, e157 (2007).
PubMed  PubMed Central  Article  CAS  Google Scholar 

32.
Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

33.
Root, T. L. et al. Fingerprints of global warming on wild animals and plants. Nature 421, 57–60 (2003).
ADS  CAS  Article  Google Scholar 

34.
Warren, M. 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 

35.
Walther, G.-R. et al. Ecological responses to recent climate change. Nature 416, 389–395 (2002).
ADS  CAS  Article  Google Scholar 

36.
Tittensor, D. P. et al. A mid-term analysis of progress toward international biodiversity targets. Science 346, 241–244 (2014).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

37.
Pimm, S. L. et al. The biodiversity of species and their rates of extinction, distribution, and protection. Science 344, 1246752 (2014).
CAS  PubMed  PubMed Central  Article  Google Scholar 

38.
Zhou, C., Zhao, Y., Connelly, J. W., Li, J. & Xu, J. Current nature reserve management in China and effective conservation of threatened pheasant species. Wildl. Biol. 1, wlb.00258. https://doi.org/10.2981/wlb.00258 (2017).
Article  Google Scholar 

39.
Zhou, C., Xu, J. & Zhang, Z. Dramatic decline of the vulnerable Reeves’s pheasant Syrmaticus reevesii, endemic to central China. Oryx 49, 529–534 (2015).
Article  Google Scholar 

40.
Dudley, N., Groves, C., Redford, K. H. & Stolton, S. Where now for protected areas? Setting the stage for the 2014 World Parks Congress. Oryx 48, 496–503 (2014).
Article  Google Scholar 

41.
Ervin, J. The three new R’s for protected areas: repurpose, reposition and reinvest’. Parks 19, 75 (2013).
Article  Google Scholar 

42.
Ervin, J. et al. In: CBD Technical Series Vol. 44 5 (Convention on Biological Diversity, 2010).

43.
Hernandez, P. A., Graham, C. H., Master, L. L. & Albert, D. L. The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29, 773–785 (2006).
Article  Google Scholar 

44.
Viña, A. & Liu, J. Hidden roles of protected areas in the conservation of biodiversity and ecosystem services. Ecosphere 8, e01864 (2017).
Article  Google Scholar 

45.
Li, D. et al. Habitat selection of breeding brown eared-pheasants (Crossoptilon mantchuricum) in Xiaowutaishan National Nature Reserve, Hebei Province, China. Front. Biol. China 4, 102–110. https://doi.org/10.1007/s11515-008-0090-2 (2008).
Article  Google Scholar 

46.
Song, K. et al. Modeling habitat factors and suitability for the Brown Eared Pheasant (Crossoption mantchuricum) in Baihuashan National Nature Reserve, Beijing. Chin. J. Zool. 51, 363–372 (2016).
Google Scholar 

47.
Li, H. Q., Lian, Z. M. & Chen, C. G. Winter foraging habitat selection of brown-eared pheasant (Crossoptilon mantchuricum) and the common pheasant (Phasianus colchicus) in Huanglong Mountains, Shaanxi Province. Acta Ecol. Sin. 29, 335–340 (2009).
CAS  Article  Google Scholar 

48.
Zhang, G., Zhang, Z., Yang, F. & Li, S. Habitat selection of brown-eared pheasant at the Wulushan National Nature Reserve of Shanxi, China. Scientia Silvae Sinicae 46, 100–103 (2010).
Google Scholar 

49.
Mi, C. R., Huettmann, F. & Guo, Y. M. Obtaining the best possible predictions of habitat selection for wintering Great Bustards in Cangzhou, Hebei Province with rapid machine learning analysis. Chin. Sci. Bull. 59, 4323–4331 (2014).
Article  Google Scholar 

50.
Lu, N., Jing, Y., Lloyd, H. & Sun, Y.-H. Assessing the distributions and potential risks from climate change for the Sichuan Jay (Perisoreus internigrans). The Condor 114, 365–376 (2012).
Article  Google Scholar 

51.
Razgour, O., Hanmer, J. & Jones, G. Using multi-scale modelling to predict habitat suitability for species of conservation concern: the grey long-eared bat as a case study. Biol. Cons. 144, 2922–2930 (2011).
Article  Google Scholar 

52.
Phillips, S. J., Dudík, M. & Schapire, S. E. Maxent software for modeling species niches and distributions (Version 3.4. 1). Tillgänglig från. http://biodiversityinformatics.amnh.org/open_source/maxent (2017).

53.
Lu, N., Jia, C.-X., Lloyd, H. & Sun, Y.-H. Species-specific habitat fragmentation assessment, considering the ecological niche requirements and dispersal capability. Biol. Conserv. 152, 102–109 (2012).
Article  Google Scholar 

54.
Fielding, A. H. & Bell, J. F. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ. Conserv. 24, 38–49 (1997).
Article  Google Scholar 

55.
Phillips, S. J. & Dudík, M. Modeling of species distributions with Maxent: new extensions and a comprehensive evaluation. Ecography 31, 161–175 (2008).
Article  Google Scholar 

56.
Fouquet, A., Ficetola, G. F., Haigh, A. & Gemmell, N. Using ecological niche modelling to infer past, present and future environmental suitability for Leiopelma hochstetteri, an endangered New Zealand native frog. Biol. Cons. 143, 1375–1384 (2010).
Article  Google Scholar 

57.
Hu, J., Hu, H. & Jiang, Z. The impacts of climate change on the wintering distribution of an endangered migratory bird. Oecologia 164, 555–565 (2010).
ADS  PubMed  Article  PubMed Central  Google Scholar 

58.
Liu, C., Berry, P. M., Dawson, T. P. & Pearson, R. G. Selecting thresholds of occurrence in the prediction of species distributions. Ecography 28, 385–393 (2005).
Article  Google Scholar 

59.
59Scott, J. M. et al. Gap analysis: a geographic approach to protection of biological diversity. Wildl. Monogr. 3–41 (1993).

60.
Riitters, K., Wickham, J., O’Neill, R., Jones, K. B. & Smith, E. Global-scale patterns of forest fragmentation. Conservation Ecology 4(2) (2000). More

1.
IPCC Climate Change 2014: Impacts, Adaptation, and Vulnerability (eds Field, C. B. et al.) (Cambridge Univ. Press, 2014).
2.
Miller-Rushing, A. J. & Primack, R. B. Global warming and flowering times in Thoreau’s Concord: a community perspective. Ecology 89, 332—341 (2008).
Article  Google Scholar 

3.
Menzel, A. et al. European phenological response to climate change matches the warming pattern. Glob. Change Biol. 12, 1969–1976 (2006).
Article  Google Scholar 

4.
Cleland, E. E., Chuine, I., Menzel, A., Mooney, H. A. & Schwartz, M. D. Shifting plant phenology in response to global change. Trends Ecol. Evol. 22, 357–365 (2007).
Article  Google Scholar 

5.
Wolkovich, E. M. et al. Warming experiments underpredict plant phenological responses to climate change. Nature 485, 494–497 (2012).
CAS  Article  Google Scholar 

6.
Rutishauser, T., Luterbacher, J., Defila, C., Frank, D. & Wanner, H. Swiss spring plant phenology 2007: extremes, a multi-century perspective, and changes in temperature sensitivity. Geophys. Res. Lett. 35, L05703 (2008).
Article  Google Scholar 

7.
Yu, H. Y., Luedeling, E. & Xu, J. C. Winter and spring warming result in delayed spring phenology on the Tibetan Plateau. Proc. Natl Acad. Sci. USA 107, 22151–22156 (2010).
CAS  Article  Google Scholar 

8.
Wang, X. et al. No trends in spring and autumn phenology during the global warming hiatus. Nat. Commun. 10, 2389 (2019).
Article  CAS  Google Scholar 

9.
Fu, Y. S. H. et al. Declining global warming effects on the phenology of spring leaf unfolding. Nature 526, 104–107 (2015).
CAS  Article  Google Scholar 

10.
Chuine, I. et al. Can phenological models predict tree phenology accurately in the future? The unrevealed hurdle of endodormancy break. Glob. Change Biol. 22, 3444–3460 (2016).
Article  Google Scholar 

11.
Harrington, C. A. & Gould, P. J. Tradeoffs between chilling and forcing in satisfying dormancy requirements for Pacific Northwest tree species. Front. Plant Sci. 6, 120 (2015).
Article  Google Scholar 

12.
Zohner, C. M., Benito, B. M., Svenning, J. C. & Renner, S. S. Day length unlikely to constrain climate-driven shifts in leaf-out times of northern woody plants. Nat. Clim. Change 6, 1120–1123 (2016).
Article  Google Scholar 

13.
Basler, D. & Körner, C. Photoperiod and temperature responses of bud swelling and bud burst in four temperate forest tree species. Tree Physiol. 34, 377–388 (2014).
Article  Google Scholar 

14.
Caffarra, A., Donnelly, A., Chuine, I. & Jones, M. B. Modelling the timing of Betula pubescens bud-burst. I. Temperature and photoperiod: a conceptual model. Clim. Res. 46, 147–157 (2011).
Article  Google Scholar 

15.
Flynn, D. F. B. & Wolkovich, E. M. Temperature and photoperiod drive spring phenology across all species in a temperate forest community. New Phytol. 219, 1353–1362 (2018).
CAS  Article  Google Scholar 

16.
Caffarra, A., Donnelly, A. & Chuine, I. Modelling the timing of Betula pubescens budburst. II. Integrating complex effects of photoperiod into process-based models. Clim. Res. 46, 159–170 (2011).
Article  Google Scholar 

17.
Fraga, H., Pinto, J. G. & Santos, J. A. Climate Change projections for chilling and heat forcing conditions in European vineyards and olive orchards: a multi-model assessment. Climatic Change 152, 179–193 (2019).
Article  Google Scholar 

18.
Heide, O. Daylength and thermal time responses of budburst during dormancy release in some northern deciduous trees. Physiol. Plant. 88, 531–540 (1993).
CAS  Article  Google Scholar 

19.
Singh, R. K., Svystun, T., AlDahmash, B., Jönsson, A. M. & Bhalerao, R. P. Photoperiod- and temperature-mediated control of phenology in trees—a molecular perspective. New Phytol. 213, 511–524 (2017).
CAS  Article  Google Scholar 

20.
Vitasse, Y. & Basler, D. What role for photoperiod in the bud burst phenology of European beech. Eur. J. For. Res. 132, 1–8 (2013).
Article  Google Scholar 

21.
Vitasse, Y. & Basler, D. Is the use of cuttings a good proxy to explore phenological responses of temperate forests in warming and photoperiod experiments? Tree Physiol. 34, 174–183 (2014).
Article  Google Scholar 

22.
Laube, J. et al. Chilling outweighs photoperiod in preventing precocious spring development. Glob. Change Biol. 20, 170–182 (2014).
Article  Google Scholar 

23.
Basler, D. & Körner, C. Photoperiod sensitivity of bud burst in 14 temperate forest tree species. Agric. For. Meteorol. 165, 73–81 (2012).
Article  Google Scholar 

24.
Caffarra, A. & Donnelly, A. The ecological significance of phenology in four different tree species: effects of light and temperature on bud burst. Int. J. Biometeorol. 55, 711–721 (2011).
Article  Google Scholar 

25.
Ohlemüller, R., Gritti, E. S., Sykes, M. T. & Thomas, C. D. Towards European climate risk surfaces: the extent and distribution of analogous and non-analogous climates 1931–2100. Glob. Ecol. Biogeogr. 15, 395–405 (2006).
Article  Google Scholar 

26.
Williams, J. W. & Jackson, S. T. Novel climates, no-analog communities, and ecological surprises. Front. Ecol. Environ. 5, 475–482 (2007).
Article  Google Scholar 

27.
Williams, J. W., Jackson, S. T. & Kutzbacht, J. E. Projected distributions of novel and disappearing climates by 2100 AD. Proc. Natl Acad. Sci. USA 104, 5738–5742 (2007).
CAS  Article  Google Scholar 

28.
IPCC Climate Change 2013: The Physical Science Basis (eds Stocker, T. F. et al.) (Cambridge Univ. Press, 2013).

29.
Xu, Y., Ramanathan, V. & Victor, D. G. Global warming will happen faster than we think. Nature 564, 30–32 (2018).

30.
Wolkovich, E. M. et al. Observed Spring Phenology Responses in Experimental Environments (OSPREE) (Knowledge Network for Biocomplexity, 2019); https://doi.org/10.5063/F1CZ35KB

31.
Richardson, E. A model for estimating the completion of rest for ‘Redhaven’ and ’Elberta’ peach trees. HortScience 9, 331–332 (1974).
Google Scholar 

32.
Dennis, F. Problems in standardizing methods for evaluating the chilling requirements for the breaking of dormancy in buds of woody plants. HortScience 38, 347–350 (2003).
Article  Google Scholar 

33.
Gelman, A. & Hill, J. Data Analysis Using Regression and Multilevel/Hierarchical Models (Cambridge Univ. Press, 2006).

34.
Fu, Y. H. et al. Short photoperiod reduces the temperature sensitivity of leaf-out in saplings of Fagus sylvatica but not in horse chestnut. Glob. Change Biol. 25, 1696–1703 (2019).
Article  Google Scholar 

35.
Bradley, N. L., Leopold, A. C., Ross, J. & Huffaker, W. Phenological changes reflect climate change in Wisconsin. Proc. Natl Acad. Sci. USA 96, 9701–9704 (1999).
CAS  Article  Google Scholar 

36.
Gauzere, J., Lucas, C., Ronce, O., Davi, H. & Chuine, I. Sensitivity analysis of tree phenology models reveals increasing sensitivity of their predictions to winter chilling temperature and photoperiod with warming climate. Ecol. Model. 441, 108805 (2019).
Article  Google Scholar 

37.
Heide, O. & Prestrud, A. Low temperature, but not photoperiod, controls growth cessation and dormancy induction and release in apple and pear. Tree Physiol. 25, 109–114 (2005).
CAS  Article  Google Scholar 

38.
van der Schoot, C., Paul, L. K. & Rinne, P. L. H. The embryonic shoot: a lifeline through winter. J. Exp. Bot. 65, 1699–1712 (2014).
Article  CAS  Google Scholar 

39.
Fishman, S., Erez, A. & Couvillon, G. The temperature dependence of dormancy breaking in plants: mathematical analysis of a two-step model involving a cooperative transition. J. Theor. Biol. 124, 473–483 (1987).
Article  Google Scholar 

40.
Weinberger, J. H. et al. Chilling requirements of peach varieties. Proc. J. Am. Soc. Hort. Sci. 56, 122–128 (1950).

41.
Polgar, C. A., Primack, R. B., Williams, E. H., Stichter, S. & Hitchcock, C. Climate effects on the flight period of Lycaenid butterflies in Massachusetts. Biol. Conserv. 160, 25–31 (2013).
Article  Google Scholar 

42.
Vitasse, Y. Ontogenic changes rather than difference in temperature cause understory trees to leaf out earlier. New Phytol. 198, 149–155 (2013).
Article  Google Scholar 

43.
Laube, J., Sparks, T. H., Estrella, N. & Menzel, A. Does humidity trigger tree phenology? Proposal for an air humidity based framework for bud development in spring. New Phytol. 202, 350–355 (2014).
Article  Google Scholar 

44.
Li, C., Stevens, B. & Marotzke, J. Eurasian winter cooling in the warming hiatus of 1998–2012. Geophys. Res. Lett. 42, 8131–8139 (2015).
Article  Google Scholar 

45.
Balling, R. C. J., Michaels, P. J. & Knappenberger, P. C. Analysis of winter and summer warming rates in gridded temperature time series. Clim. Res. 9, 175–181 (1998).
Article  Google Scholar 

46.
Hänninen, H. Effects of climatic change on trees from cool and temperate regions: an ecophysiological approach to modelling of bud burst phenology. Can. J. Bot. 73, 183–199 (1995).
Article  Google Scholar 

47.
Güsewell, S., Furrer, R., Gehrig, R. & Pietragalla, B. Changes in temperature sensitivity of spring phenology with recent climate warming in Switzerland are related to shifts of the preseason. Glob. Change Biol.23, 5189–5202 (2017).
Article  Google Scholar 

48.
Roberts, A. M., Tansey, C., Smithers, R. J. & Phillimore, A. B. Predicting a change in the order of spring phenology in temperate forests. Glob. Change Biol.21, 2603–2611 (2015).
Article  Google Scholar 

49.
Moher, D., Liberati, A., Tetzlaff, J. & Altman, D. G. Preferred reporting items for systematic reviews and meta-analyses: the PRISMA statement. Ann. Intern. Med. 151, 264–269 (2009).
Article  Google Scholar 

50.
Kicinski, M. Publication bias in recent meta-analyses. PLoS ONE 8, e81823 (2013).

51.
Gurevitch, J., Morrow, L. L., Wallace, A. & Walsh, J. S. A meta-analysis of competition in field experiments. Am. Nat. 140, 539–572 (1992).
Article  Google Scholar 

52.
Gurevitch, J. & Hedges, L. V. Statistical issues in ecological meta-analyses. Ecology 80, 1142–1149 (1999).
Article  Google Scholar 

53.
Lin, L. F. & Chu, H. T. Quantifying publication bias in meta-analysis. Biometrics 74, 785–794 (2018).
Article  Google Scholar 

54.
Luedeling, E. & Brown, P. H. A global analysis of the comparability of winter chill models for fruit and nut trees. Int. J. Biometeorol. 55, 411–421 (2011).
Article  Google Scholar 

55.
R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2017).

56.
Luedeling, E. chillR: statistical methods for phenology analysis in temperate fruit trees. R package version 0.70.17 (2019).

57.
Cornes, R. C., van der Schrier, G., van den Besselaar, E. J. & Jones, P. D. An ensemble version of the E-OBS temperature and precipitation data sets. J. Geophys. Res. Atmos. 123, 9391–9409 (2018).
Article  Google Scholar 

58.
Livneh, B.et al. A spatially comprehensive, hydrometeorological data set for Mexico, the US, and Southern Canada 1950–2013. Sci. Data 2, 150042 (2015).

59.
Harrington, C. A., Gould, P. J. & St Clair, J. B. Modeling the effects of winter environment on dormancy release of Douglas-fir. For. Ecol. Manag. 259, 798–808 (2010).
Article  Google Scholar 

60.
Carpenter, B. et al. Stan: a probabilistic programming language. J. Stat. Softw. https://doi.org/10.18637/jss.v076.i01(2017).

61.
Stan Development Team. RStan: the R interface to Stan. R package version 2.17.3 (2018).

62.
Gelman, A. et al. Bayesian Data Analysis (CRC Press, 2014).

63.
Gauzere, J. et al. Integrating interactive effects of chilling and photoperiod in phenological process-based models. A case study with two European tree species: Fagus sylvatica and Quercus petraea. Agric. For. Meteorol. 244, 9–20 (2017).
Article  Google Scholar 

64.
Saikkonen, K. et al. Climate change-driven species’ range shifts filtered by photoperiodism. Nat. Clim. Change 2, 239 (2012).
Article  Google Scholar 

65.
Way, D. A. & Montgomery, R. A. Photoperiod constraints on tree phenology, performance and migration in a warming world. Plant Cell Environ. 38, 1725–1736 (2015).
Article  Google Scholar 

66.
Chuine, I., Garcia de Cortazar Atauri, I., Hanninen, H. & Kramer, K. in Phenology: An Integrative Environmental Science (ed. Schwartz M.) 275–293 (Springer, 2013).

67.
Stan Development Team Stan User’s Guide v.2.19 (Stan, 2019). More