Ecology

1.
Crawley, M. Herbivory: The Dynamics of Animal–Plant Interactions (Blackwell Scientific, Oxford, 1983).
Google Scholar 
2.
Putman, R. Grazing in Temperate Ecosystems: Large Herbivores and the Ecology of the New Forest (Springer, Berlin, 1986).
Google Scholar 

3.
Huntly, N. Herbivores and the dynamics of communities and ecosystems. Annu. Rev. Ecol. Syst. 1, 477–503 (1991).
Article  Google Scholar 

4.
Skarpe, C. Impact of grazing in savanna ecosystems. Ambio 20, 351–356 (1991).
Google Scholar 

5.
Agrawal, A. A. Macroevolution of plant defense strategies. Trends Ecol. Evol. 22, 103–109 (2007).
PubMed  Article  Google Scholar 

6.
Bruce, T. C. Interplay between insects and plants–dynamic and complex interactions that have coevolved over millions of years but act in milliseconds. J. Exp. Bot. 2, 391 (2014).
Google Scholar 

7.
Mason, N. W. H., Peltzer, D. A., Richardson, S. J., Bellingham, P. J. & Allen, R. B. Stand development moderates effects of ungulate exclusion on foliar traits in the forests of New Zealand: Ungulate impacts on foliar traits. J. Ecol. 98, 1422–1433 (2010).
Article  Google Scholar 

8.
Faison, E. K., DeStefano, S., Foster, D. R., Motzkin, G. & Rapp, J. M. Ungulate browsers promote herbaceous layer diversity in logged temperate forests. Ecol. Evol. 6, 4591–4602 (2016).
PubMed  PubMed Central  Article  Google Scholar 

9.
Simončič, T., Bončina, A., Jarni, K. & Klopčič, M. Assessment of the long-term impact of deer on understory vegetation in mixed temperate forests. J. Veg. Sci. 30, 108–120 (2019).
Article  Google Scholar 

10.
Schmitz, O. J. Herbivory from individuals to ecosystems. Annu. Rev. Ecol. Evol. Syst. 39, 133–152 (2008).
Article  Google Scholar 

11.
Riginos, C. & Grace, J. B. Savanna tree density, herbivores, and the herbaceous community: Bottom-up vs. top-down effects. Ecology 89, 2228–2238 (2008).
PubMed  Article  Google Scholar 

12.
Turkington, R. Top-down and bottom-up forces in mammalian herbivore–vegetation systems: An essay review. Botany 87, 723–739 (2009).
Article  Google Scholar 

13.
Kos, M. et al. Relative importance of plant-mediated bottom-up and top-down forces on herbivore abundance on Brassica oleracea: Bottom-up and top-down effects on herbivores. Funct. Ecol. 25, 1113–1124 (2011).
Article  Google Scholar 

14.
Kuijper, D. P. J. et al. Bottom-up versus top-down control of tree regeneration in the Białowieża Primeval Forest, Poland: Abiotic and biotic control of tree regeneration. J. Ecol. 98, 888–899 (2010).
Article  Google Scholar 

15.
Churski, M., Bubnicki, J. W., Jędrzejewska, B., Kuijper, D. P. J. & Cromsigt, J. P. G. M. Brown world forests: Increased ungulate browsing keeps temperate trees in recruitment bottlenecks in resource hotspots. New Phytol. 214, 158–168 (2017).
PubMed  Article  Google Scholar 

16.
Fretwell, S. D. Food chain dynamics: The central theory of ecology?. Oikos 50, 291–301 (1987).
Article  Google Scholar 

17.
Reimoser, F. & Putman, R. Impacts of wild ungulates on vegetation: Costs and benefits. In Ungulate Management in Europe—Problems and Practices (eds Putman, R. et al.) 144–191 (Cambridge University Press, Cambridge, 2011).
Google Scholar 

18.
Bellingham, P. J. & Allan, C. N. Forest regeneration and the influences of white-tailed deer (Odocoileus virginianus) in cool temperate New Zealand rain forests. For. Ecol. Manag. 175, 71–86 (2003).
Article  Google Scholar 

19.
Russell, F. L. & Fowler, N. L. Effects of white-tailed deer on the population dynamics of acorns, seedlings and small saplings of Quercus buckleyi. Plant Ecol. 173, 59–72 (2004).
Article  Google Scholar 

20.
Casabon, C. & Pothier, D. Browsing of tree regeneration by white-tailed deer in large clearcuts on Anticosti Island, Quebec. For. Ecol. Manag. 253, 112–119 (2007).
Article  Google Scholar 

21.
Pellerin, M. et al. Impact of deer on temperate forest vegetation and woody debris as protection of forest regeneration against browsing. For. Ecol. Manag. 260, 429–437 (2010).
Article  Google Scholar 

22.
Tschöpe, O., Wallschläger, D., Burkart, M. & Tielbörger, K. Managing open habitats by wild ungulate browsing and grazing: A case-study in North-Eastern Germany: Managing open habitats by wild ungulate browsing and grazing. Appl. Veg. Sci. 14, 200–209 (2011).
Article  Google Scholar 

23.
Millett, J. & Edmondson, S. The impact of 36 years of grazing management on vegetation dynamics in dune slacks. J. Appl. Ecol. 50, 1367–1376 (2013).
Article  Google Scholar 

24.
Beck, H., Snodgrass, J. W. & Thebpanya, P. Long-term exclosure of large terrestrial vertebrates: Implications of defaunation for seedling demographics in the Amazon rainforest. Biol. Conserv. 163, 115–121 (2013).
Article  Google Scholar 

25.
Charles, G. K., Porensky, L. M., Riginos, C., Veblen, K. E. & Young, T. P. Herbivore effects on productivity vary by guild: Cattle increase mean productivity while wildlife reduce variability. Ecol. Appl. 27, 143–155 (2017).
PubMed  Article  Google Scholar 

26.
Castleberry, S. B., Ford, W. M., Miller, K. V. & Smith, W. P. Influences of herbivory and canopy opening size on forest regeneration in a southern bottomland hardwood forest. For. Ecol. Manag. 131, 57–64 (2000).
Article  Google Scholar 

27.
Filazzola, A., Tanentzap, A. J. & Bazely, D. R. Estimating the impacts of browsers on forest understories using a modified index of community composition. For. Ecol. Manag. 313, 10–16 (2014).
Article  Google Scholar 

28.
Nishizawa, K., Tatsumi, S., Kitagawa, R. & Mori, A. S. Deer herbivory affects the functional diversity of forest floor plants via changes in competition-mediated assembly rules. Ecol. Res. 31, 569–578 (2016).
CAS  Article  Google Scholar 

29.
Boulanger, V. et al. Ungulates increase forest plant species richness to the benefit of non-forest specialists. Glob. Change Biol. 24, e485–e495 (2018).
Article  Google Scholar 

30.
McGarvey, J. C., Bourg, N. A., Thompson, J. R., McShea, W. J. & Shen, X. Effects of twenty years of deer exclusion on woody vegetation at three life-history stages in a mid-atlantic temperate deciduous forest. Northeast. Nat. 20, 451–468 (2013).
Article  Google Scholar 

31.
Kabeya, D. & Sakai, S. The role of roots and cotyledons as storage organs in early stages of establishment in Quercus crispula: a quantitative analysis of the nonstructural carbohydrate in cotyledons and roots. Ann. Bot. 92, 537–545 (2003).
CAS  PubMed  PubMed Central  Article  Google Scholar 

32.
Boege, K. & Marquis, R. J. Facing herbivory as you grow up: The ontogeny of resistance in plants. Trends Ecol. Evol. 20, 441–448 (2005).
PubMed  Article  Google Scholar 

33.
Hanley, M. E., Lamont, B. B., Fairbanks, M. M. & Rafferty, C. M. Plant structural traits and their role in anti-herbivore defence. Perspect. Plant Ecol. Evol. Syst. 8, 157–178 (2007).
Article  Google Scholar 

34.
Diggle, P. J. Statistical Analysis of Spatial Point Patterns. (Arnold, 2003).

35.
Gratzer, G. & Waagepetersen, R. Seed dispersal, microsites or competition—What drives gap regeneration in an old-growth forest? An application of spatial point process modelling. Forests 9, 230 (2018).
Article  Google Scholar 

36.
Szwagrzyk, J., Gratzer, G., Stępniewska, H., Szewczyk, J. & Veselinovic, B. High reproductive effort and low recruitment rates of European beech: Is there a limit for the superior competitor?. Pol. J. Ecol. 63, 198–212 (2015).
Article  Google Scholar 

37.
Nopp-Mayr, U., Kempter, I., Muralt, G. & Gratzer, G. Herbivory on young tree seedlings in old-growth and managed mountain forests. Ecol. Res. 30, 479–491 (2015).
CAS  Article  Google Scholar 

38.
Shugart, H. H. A theory of forest dynamics. (Springer, 1984).

39.
Lertzman, K. B. Patterns of gap-phase replacement in a subalpine, old-growth forest. Ecology 73, 657–669 (1992).
Article  Google Scholar 

40.
Kneeshaw, D. D. & Bergeron, Y. Canopy gap characteristics and tree replacement in the Southeastern Boreal forest. Ecology 79, 783–794 (1998).
Article  Google Scholar 

41.
Wakeling, J. L., Staver, A. C. & Bond, W. J. Simply the best: The transition of savanna saplings to trees. Oikos 120, 1448–1451 (2011).
Article  Google Scholar 

42.
Kobe, R. K., Pacala, S. W., Silander, J. A. Jr. & Canham, C. D. Juvenile tree survivorship as a component of shade tolerance. Ecol. Appl. 5, 517–532 (1995).
Article  Google Scholar 

43.
Zuidema, P. A., Brienen, R. J. W., During, H. J. & Güneralp, B. Do persistently fast-growing juveniles contribute disproportionately to population growth? A new analysis tool for matrix models and its application to rainforest trees. Am. Nat. 174, 709–719 (2009).
PubMed  Article  Google Scholar 

44.
Tremblay, J.-P., Huot, J. & Potvin, F. Density-related effects of deer browsing on the regeneration dynamics of boreal forests. J. Appl. Ecol. 44, 552–562 (2007).
Article  Google Scholar 

45.
Speed, J. D. M., Austrheim, G., Hester, A. J., Solberg, E. J. & Tremblay, J.-P. Regional-scale alteration of clear-cut forest regeneration caused by moose browsing. For. Ecol. Manag. 289, 289–299 (2013).
Article  Google Scholar 

46.
Shelton, A. L., Henning, J. A., Schultz, P. & Clay, K. Effects of abundant white-tailed deer on vegetation, animals, mycorrhizal fungi, and soils. For. Ecol. Manag. 320, 39–49 (2014).
Article  Google Scholar 

47.
Reimoser, F. & Reimoser, S. Ergebnisse aus dem Vergleichsflächenverfahren (‘Wildschaden-Kontrollzäune’) – ein Beitrag zur Objektivierung der Wildschadensbeurteilung. In Ist die natürliche Verjüngung des Bergwaldes gesichert? (ed. Müller, F.) 151–159 (Austrian Research Centre for Forests, Vienna, 2003).
Google Scholar 

48.
ZAMG. Klimadaten von Österreich 1971–2000. (2013).

49.
Mucina, L., Grabherr, G. & Wallnöfer, S. Die Pflanzengesellschaften Österreichs. Teil III – Wälder und Gebüsche (Gustav Fischer Verlag, Stuttgart, 1993).
Google Scholar 

50.
Reimoser, F., Schodterer, H. & Reimoser, S. Beurteilung des Schalenwildeinflusses auf die Waldverjüngung – Vergleich verschiedener Methoden des Wildeinfluss-Monitorings („WEM – Methodenvergleich”) (Austrian Research Centre for Forests, Vienna, 2014).
Google Scholar 

51.
Reimoser, F., Armstrong, H. & Suchant, R. Measuring forest damage of ungulates: What should be considered. For. Ecol. Manag. 120, 47–58 (1999).
Article  Google Scholar 

52.
Long, Z. T., Pendergast, T. H. & Carson, W. P. The impact of deer on relationships between tree growth and mortality in an old-growth beech-maple forest. For. Ecol. Manag. 252, 230–238 (2007).
Article  Google Scholar 

53.
Van den Brink, P. J. & Ter Braak, C. J. Principal response curves: Analysis of time-dependent multivariate responses of biological community to stress. Environ. Toxicol. Chem. 18, 138–148 (1999).
Article  Google Scholar 

54.
van den Brink, P. J., den Besten, P. J., de Bij, V. A. & ter Braak, C. J. F. Principal response curves technique for the analysis of multivariate biomonitoring time series. Environ. Monit. Assess. 152, 271–281 (2009).
CAS  PubMed  Article  Google Scholar 

55.
Poulin, M., Andersen, R. & Rochefort, L. A new approach for tracking vegetation change after restoration: A case study with peatlands. Restor. Ecol. 21, 363–371 (2013).
Article  Google Scholar 

56.
Borcard, D., Gillet, F. & Legendre, P. Numerical Ecology with R (Springer International Publishing, Berlin, 2018).
Google Scholar 

57.
Van den Brink, P. J., Van den Brink, N. W. & Ter Braak, C. J. Multivariate analysis of ecotoxicological data using ordination: demonstrations of utility on the basis of various examples. Austr. J. Ecotoxicol. 9, 141–156 (2003).
Google Scholar 

58.
R Development Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2020).

59.
Oksanen, J. et al. vegan: Community Ecology Package. (2019).

60.
RStudio Team. RStudio: Integrated Development Environment for R. (RStudio, Inc., 2016).

61.
Wickham, H., François, R., Henry, L. & Müller, K. dplyr: A Grammar of Data Manipulation. (2019).

62.
Wickham, H. forcats: Tools for Working with Categorical Variables (Factors). (2018).

63.
Wickham, H. ggplot2: Elegant Graphics for Data Analysis (Springer, Berlin, 2016).
Google Scholar 

64.
Henry, L. & Wickham, H. purrr: Functional Programming Tools. (2019).

65.
Wickham, H., Hester, J. & Francois, R. readr: Read Rectangular Text Data. (2018).

66.
Wickham, H. stringr: Simple, Consistent Wrappers for Common String Operations. (2019).

67.
Müller, K. & Wickham, H. tibble: Simple Data Frames. (2019).

68.
Wickham, H. & Henry, L. tidyr: Easily Tidy Data with ‘spread()’ and ‘gather()’ Functions. (2019).

69.
McNamara, A., Rubia, E. A. de la, Zhu, H., Ellis, S. & Quinn, M. skimr: Compact and Flexible Summaries of Data. (2019).

70.
Allaire, J. J., Wickham, H., Ushey, K. & Ritchie, G. rstudioapi: Safely Access the RStudio API. (2017).

71.
Allaire, J. J. et al. rmarkdown: Dynamic Documents for R. (2018).

72.
Xie, Y. knitr: A General-Purpose Package for Dynamic Report Generation in R. (2018).

73.
Baumgartner, J. hues: Distinct Colours Palettes Based on ‘iwanthue’. (2017).

74.
Jones, O. R. et al. Diversity of ageing across the tree of life. Nature 505, 169–173 (2014).
ADS  CAS  PubMed  Article  Google Scholar 

75.
Pacala, S. W. et al. Forest models defined by field measurements: Estimation error analysis and dynamics. Ecol. Monogr. 66, 1–43 (1996).
Article  Google Scholar 

76.
Beckage, B. & Clark, J. S. Seedling survival and growth of three forest tree species: The role of spatial heterogeneity. Ecology 84, 1849–1861 (2003).
Article  Google Scholar 

77.
Peltzer, D. A. et al. Disentangling drivers of tree population size distributions. For. Ecol. Manag. 331, 165–179 (2014).
Article  Google Scholar 

78.
Reimoser, F., Odermatt, O., Roth, R. & Suchant, R. Die Beurteilung von Wildverbiss durch SOLL-IST-Vergleich. Allg Forst Jagdztg 168, 214–227 (1997).
Google Scholar 

79.
Pépin, D. et al. Relative impact of browsing by red deer on mixed coniferous and broad-leaved seedlings—An enclosure-based experiment. For. Ecol. Manag. 222, 302–313 (2006).
Article  Google Scholar 

80.
Jurena, P. N. & Archer, S. Woody plant establishment and spatial heterogeneity in grasslands. Ecology 84, 907–919 (2003).
Article  Google Scholar 

81.
Cramer, M. D., Chimphango, S. B. M., Cauter, A. V., Waldram, M. S. & Bond, W. J. Grass competition induces N2 fixation in some species of African Acacia. J. Ecol. 95, 1123–1133 (2007).
CAS  Article  Google Scholar 

82.
Hegland, S. J., Lilleeng, M. S. & Moe, S. R. Old-growth forest floor richness increases with red deer herbivory intensity. For. Ecol. Manag. 310, 267–274 (2013).
Article  Google Scholar 

83.
Lilleeng, M. S., Hegland, S. J., Rydgren, K. & Moe, S. R. Red deer mediate spatial and temporal plant heterogeneity in boreal forests. Ecol. Res. 31, 777–784 (2016).
Article  Google Scholar 

84.
Laurent, L., Mårell, A., Balandier, P., Holveck, H. & Saïd, S. Understory vegetation dynamics and tree regeneration as affected by deer herbivory in temperate hardwood forests. IForest – Biogeosciences For. 10, 837–844 (2017).
Article  Google Scholar 

85.
Holladay, C.-A., Kwit, C. & Collins, B. Woody regeneration in and around aging southern bottomland hardwood forest gaps: Effects of herbivory and gap size. For. Ecol. Manag. 223, 218–225 (2006).
Article  Google Scholar 

86.
Smit, C., Gusberti, M. & Müller-Schärer, H. Safe for saplings; safe for seeds?. For. Ecol. Manag. 237, 471–477 (2006).
Article  Google Scholar 

87.
Pröll, G., Darabant, A., Gratzer, G. & Katzensteiner, K. Unfavourable microsites, competing vegetation and browsing restrict post-disturbance tree regeneration on extreme sites in the Northern Calcareous Alps. Eur. J. For. Res. 134, 293–308 (2015).
Article  Google Scholar 

88.
Stephan, J. G. et al. Long-term deer exclosure alters soil properties, plant traits, understorey plant community and insect herbivory, but not the functional relationships among them. Oecologia 184, 685–699 (2017).
ADS  PubMed  PubMed Central  Article  Google Scholar 

89.
Hidding, B., Tremblay, J.-P. & Côté, S. D. A large herbivore triggers alternative successional trajectories in the boreal forest. Ecology 94, 2852–2860 (2013).
PubMed  Article  Google Scholar 

90.
Augustine, D. J. & McNaughton, S. J. Ungulate effects on the functional species composition of plant communities: Herbivore selectivity and plant tolerance. J. Wildl. Manag. 62, 1165–1183 (1998).
Article  Google Scholar 

91.
Owen-Smith, N. R. Adaptive Herbivore Ecology. From Resources to Populations in Variable Environments (Cambridge University Press, Cambridge, 2002).
Google Scholar 

92.
Reimoser, F. & Reimoser, S. Richtiges Erkennen von Wildschäden am Wald (Zentralstelle Österr, Landesjagdverbände, 2017).
Google Scholar 

93.
Ramirez, J. I. et al. Above- and below-ground cascading effects of wild ungulates in temperate forests. Ecosystems https://doi.org/10.1007/s10021-020-00509-4 (2020).
Article  Google Scholar 

94.
Kral, F. Spät- und postglaziale Waldgeschichte der Alpen aufgrund der bisherigen Pollenanalysen (Österreichischer Agrarverlag, Vienna, 1979).
Google Scholar 

95.
Mayer, H. & Ott, E. Gebirgswaldbau, Schutzwaldpflege: ein waldbaulicher Beitrag zur Landschaftsökologie und zum Umweltschutz (G. Fischer, Mumbai, 1991).
Google Scholar 

96.
Mayer, M., Keßler, D. & Katzensteiner, K. Herbivory modulates soil CO2 fluxes after windthrow: A case study in temperate mountain forests. Eur. J. For. Res. 139, 383–391 (2020).
CAS  Article  Google Scholar  More

1.
Hutchinson, G. E. Homage to Santa Rosalia or why are there so many kinds of animals?. Am. Nat. 93, 145–159 (1959).
Article  Google Scholar 
2.
Volterra, V. Variations and fluctuations of the number of individuals in animal species living together. ICES J. Mar. Sci. 3, 3–51 (1928).
Article  Google Scholar 

3.
MacArthur, R. & Levins, R. Competition, habitat selection, and character displacement in a patchy environment. Proc. Natl. Acad. Sci. USA. 51, 1207–1210 (1964).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

4.
Levin, S. A. Community equilibria and stability, and an extension of the competitive exclusion principle. Am. Nat. 104, 413–423 (1970).
Article  Google Scholar 

5.
Chesson, P. Mechanisms of maintenance of species diversity. Annu. Rev. Ecol. Syst. 31, 343–366 (2000).
Article  Google Scholar 

6.
Chase, J. M. et al. The interaction between predation and competition: a review and synthesis. Ecol. Lett. 5, 302–315 (2002).
Article  Google Scholar 

7.
Amarasekare, P. Competitive coexistence in spatially structured environments: a synthesis. Ecol. Lett. 6, 1109–1122 (2003).
Article  Google Scholar 

8.
Hutchinson, G. E. The paradox of the plankton. Am. Nat. 95, 137–145 (1961).
Article  Google Scholar 

9.
Connell, J. H. Diversity in tropical rain forests and coral reefs. Science 199, 1302–1310 (1978).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

10.
Armstrong, R. A. & McGehee, R. Competitive exclusion. Am. Nat. 115, 151–170 (1980).
MathSciNet  Article  Google Scholar 

11.
Huisman, J. & Weissing, F. J. Biodiversity of plankton by species oscillations and chaos. Nature 402, 407–410 (1999).
ADS  Article  Google Scholar 

12.
Abrams, P. A. & Holt, R. D. The impact of consumer–resource cycles on the coexistence of competing consumers. Theor. Popul. Biol. 62, 281–295 (2002).
PubMed  MATH  Article  Google Scholar 

13.
Schwartz, M. D. et al. Phenology: An Integrative Environmental Science (Kluwer Academic Publishers, New York, 2003).
Google Scholar 

14.
McMeans, B. C., McCann, K. S., Humphries, M., Rooney, N. & Fisk, A. T. Food web structure in temporally-forced ecosystems. Trends Ecol. Evol. 30, 662–672 (2015).
PubMed  Article  Google Scholar 

15.
White, E. R. & Hastings, A. Seasonality in Ecology: Progress and Prospects in Theory (Springer, New York, 2018).
Google Scholar 

16.
Rudolf, V. H. W. The role of seasonal timing and phenological shifts for species coexistence. Ecol. Lett. 22, 1324–1338 (2019).
PubMed  Google Scholar 

17.
Stewart, F. M. & Levin, B. R. Partitioning of resources and the outcome of interspecific competition: a model and some general considerations. Am. Nat. 107, 171–198 (1973).
Article  Google Scholar 

18.
Abrams, P. Variability in resource consumption rates and the coexistence of competing species. Theor. Popul. Biol. 25, 106–124 (1984).
MATH  Article  Google Scholar 

19.
Cushing, J. M. Periodic two-predator, one-prey interactions and the time sharing of a resource niche. SIAM J. Appl. Math. 44, 392–410 (1984).
MathSciNet  MATH  Article  Google Scholar 

20.
Grover, J. P. Resource competition in a variable environment: phytoplankton growing according to Monod’s model. Am. Nat. 136, 771–789 (1990).
Article  Google Scholar 

21.
Loreau, M. Time scale of resource dynamics and coexistence through time partitioning. Theor. Popul. Biol. 41, 401–412 (1992).
MATH  Article  Google Scholar 

22.
Namba, T. & Takahashi, S. Competitive coexistence in a seasonally fluctuating environment II. Multiple stable states and invasion success. Theor. Popul. Biol. 44, 374–402 (1993).
MathSciNet  MATH  Article  Google Scholar 

23.
Chesson, P. Multispecies competition in variable environments. Theor. Popul. Biol. 45, 227–276 (1994).
MATH  Article  Google Scholar 

24.
Abrams, P. A. When does periodic variation in resource growth allow robust coexistence of competing consumer species?. Ecology 85, 372–382 (2004).
Article  Google Scholar 

25.
Gravel, D., Guichard, F. & Hochberg, M. E. Species coexistence in a variable world. Ecol. Lett. 14, 828–839 (2011).
PubMed  Article  PubMed Central  Google Scholar 

26.
Sakavara, A., Tsirtsis, G., Roelke, D. L., Mancy, R. & Spatharis, S. Lumpy species coexistence arises robustly in fluctuating resource environments. Proc. Natl. Acad. Sci. USA. 115, 738–743 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

27.
Dunlap, J. C., Loros, J. J. & DeCoursey, P. J. Chronobiology: Biological Timekeeping (Sinauer Associates, London, 2004).
Google Scholar 

28.
Kronfeld-Schor, N. & Dayan, T. Partitioning of time as an ecological resource. Annu. Rev. Ecol. Evol. Syst. 34, 153–181 (2003).
Article  Google Scholar 

29.
Kronfeld-Schor, N. et al. Chronobiology by moonlight. Proc. R. Soc. Lond. B 280, 20123088 (2013).
Google Scholar 

30.
Welch, K. D. & Harwood, J. D. Temporal dynamics of natural enemy-pest interactions in a changing environment. Biol. Control 75, 18–27 (2014).
Article  Google Scholar 

31.
Raible, F., Takekata, H. & Tessmar-Raible, K. An overview of monthly rhythms and clocks. Front. Neurol. 8, 189 (2017).
PubMed  PubMed Central  Article  Google Scholar 

32.
Körtner, G. & Geiser, F. The temporal organization of daily torpor and hibernation: circadian and circannual rhythms. Chronobiol. Int. 17, 103–128 (2000).
PubMed  Article  Google Scholar 

33.
Holt, R. D. & Polis, G. A. A theoretical framework for intraguild predation. Am. Nat. 149, 745–764 (1997).
Article  Google Scholar 

34.
Holt, R. D. Predation, apparent competition, and the structure of prey communities. Theor. Popul. Biol. 12, 197–229 (1977).
MathSciNet  CAS  PubMed  Article  Google Scholar 

35.
Connell, J. H. Some mechanisms producing structure in natural communities: a model and evidence from field experiments. Ecol. Evol. Commun. 1, 460–490 (1975).
Google Scholar 

36.
Cozzi, G. et al. Fear of the dark or dinner by moonlight? Reduced temporal partitioning among africa’s large carnivores. Ecology 93, 2590–2599 (2012).
PubMed  Article  Google Scholar 

37.
Campera, M. et al. Temporal niche separation between the two ecologically similar nocturnal Primates Avahi meridionalis and Lepilemur fleuretae. Behav. Ecol. Sociobiol. 73, 1–10 (2019).
Article  Google Scholar 

38.
Leonard, J. P., Tewes, M. E., Lombardi, J. V., Wester, D. W. & Campbell, T. A. Effects of sun angle, lunar illumination, and diurnal temperature on temporal movement rates of sympatric ocelots and bobcats in South Texas. PLoS ONE 15, e0231732 (2020).
CAS  PubMed  PubMed Central  Article  Google Scholar 

39.
Shimadzu, H., Dornelas, M., Henderson, P. A. & Magurran, A. E. Diversity is maintained by seasonal variation in species abundance. BMC Biol. 11, 98 (2013).
PubMed  PubMed Central  Article  Google Scholar 

40.
Gaston, K. J., Bennie, J., Davies, T. W. & Hopkins, J. The ecological impacts of nighttime light pollution: a mechanistic appraisal. Biol. Rev. 88, 912–927 (2013).
PubMed  Article  PubMed Central  Google Scholar 

41.
Lovegrove, B. G. et al. Are tropical small mammals physiologically vulnerable to Arrhenius effects and climate change?. Physiol. Biochem. Zool. 87, 30–45 (2014).
PubMed  Article  PubMed Central  Google Scholar 

42.
Yerushalmi, S. & Green, R. M. Evidence for the adaptive significance of circadian rhythms. Ecol. Lett. 12, 970–981 (2009).
PubMed  Article  PubMed Central  Google Scholar 

43.
Bradshaw, W. E. & Holzapfel, C. M. Genetic response to rapid climate change: it’s seasonal timing that matters. Mol. Ecol. 17, 157–166 (2008).
CAS  PubMed  Article  PubMed Central  Google Scholar 

44.
Sauve, D., Divoky, G. & Friesen, V. L. Phenotypic plasticity or evolutionary change? An examination of the phenological response of an arctic seabird to climate change. Funct. Ecol. 33, 2180–2190 (2019).
Article  Google Scholar 

45.
Abbey-Lee, R. N. & Dingemanse, N. J. Adaptive individual variation in phenological responses to perceived predation levels. Nat. Commun. 10, 1601 (2019).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar  More

1.
Smith, S. E. & Read, D. J. Mycorrhizal Symbiosis 3rd edn. (Academic Press, London, 2008).
Google Scholar 
2.
Gianinazzi, S. et al. Agroecology: the key role of arbuscularmycorrhizas in ecosystem services. Mycorrhiza 20, 519–530 (2010).
Article  Google Scholar 

3.
Lenoir, I., Fontaine, J. & Sahraoui, A. L. H. Arbuscularmycorrhizal fungal responses to abiotic stresses: a review. Phytochem 123, 4–15 (2016).
CAS  Article  Google Scholar 

4.
Song, Y., Chen, D., Lu, K., Sun, Z. & Zeng, R. Enhanced tomato disease resistance primed by arbuscularmycorrhizal fungus. Front. Plant Sci. 6, 786 (2015).
PubMed  PubMed Central  Google Scholar 

5.
Van Geel, M. et al. Abiotic rather than biotic filtering shapes the arbuscularmycorrhizal fungal communities of European seminatural grasslands. New Phytol. 220, 1262–1272 (2018).
Article  Google Scholar 

6.
Varma, A., Prasad, R. & Tuteja, N. Mycorrhiza—Nutrient Uptake (Biocontrol, Ecorestoration Fourth Edition, Springer, 2017).
Google Scholar 

7.
Yu, L., Nicolaisen, J., Larsen, J. & Ravnskov, S. Molecular characterization of root-associated fungal communities in relation to health status of Pisum sativum using barcoded pyrosequencing. Plant Soil 357, 395–405 (2012).
CAS  Article  Google Scholar 

8.
Corredor, A. H., Van Rees, K. & Vujanovic, V. Host genotype and health status influence on the composition of the arbuscularmycorrhizal fungi in Salix bioenergy plantations. For. Ecol. Manag. 314, 112–119 (2014).
Article  Google Scholar 

9.
Martinez, N. & Johnson, N. C. Agricultural management influences propagule densities and functioning of arbuscularmycorrhizas in low- and high-input agroecosystems in arid environments. Appl. Soil Ecol. 46, 300–306 (2010).
Article  Google Scholar 

10.
Hontoria, C., García-González, I., Quemada, M., Roldánd, A. & Alguacil, M. M. The cover crop determines the AMF community composition in soil and in roots of maize after a ten-year continuous crop rotation. Sci. Total Environ. 660, 913–922 (2019).
ADS  CAS  Article  Google Scholar 

11.
Lumini, E., Vallino, M., Alguacil, M. M., Romani, M. & Bianciotto, V. Different farming and water regimes in Italian rice fields affect arbuscularmycorrhizal fungal soil communities. Ecol. Appl. 21, 1696–1707 (2011).
Article  Google Scholar 

12.
Manoharan, L., Rosenstock, N. P., Williams, A. & Hedlund, K. Agricultural management practices influence AMF diversity and community composition with cascading effects on plant productivity. Appl. Soil Ecol. 115, 53–59 (2017).
Article  Google Scholar 

13.
Dai, M., Bainard, L. D., Hamel, C., Gan, Y. & Lynch, D. Impact of land use on arbuscularmycorrhizal fungal communities in rural Canada. Appl. Environ. Microbiol. 79, 6719–6729 (2013).
CAS  Article  Google Scholar 

14.
Aghili, F. et al. Wheat plants invest more in mycorrhizae and receive more benefits from them under adverse than favorable soil conditions. Appl. Soil Ecol. 84, 93–111 (2014).
Article  Google Scholar 

15.
Gaudin, J., Semetey, O., Foissac, X. & Eveillard, S. Phytoplasmatiter in diseased lavender is not correlated to lavender tolerance to stolburphytoplasma. Bull. Insectol. 64(Supplement), S179–S180 (2011).
Google Scholar 

16.
Kamińska, M., Klamkowski, K., Berniak, H. & Treder, W. Effect of arbuscularmycorrhizal fungi inoculation on aster yellows phytoplasma-infected tobacco plants. Sci. Hortic. 125, 500–503 (2010).
Article  Google Scholar 

17.
Batlle, A. et al. Tolerance increase to Candidatus phytoplasma prunorum in mycorrhizal plums fruit trees. Bull. Insectol. 64, 125–126 (2011).
Google Scholar 

18.
D’ameli, R. et al. Increased plant tolerance against chrysanthemum yellows phytoplasma (Candidatus Phytoplasma asteris) following double inoculation with Glomusmosseae BEG12 and Pseudomonas putida S1Pf1Rif. Plant. Pathol. 60, 1014–1022 (2011).
Article  Google Scholar 

19.
Fiorilli, V. et al. Omics approaches revealed how arbuscularmycorrhizal symbiosis enhances yield and resistance to leaf pathogen in wheat. Sci. Rep. 8, 9625 (2018).
ADS  Article  Google Scholar 

20.
Bødker, L., Kjøller, R., Kristensen, K. & Rosendahl, S. Interactions between indigenous arbuscularmycorrhizal fungi and Aphanomyces euteiches in field-grown pea. Mycorrhiza 12, 7–12 (2002).
Article  Google Scholar 

21.
Al-Askar, A. A. & Rashad, Y. M. Arbuscularmycorrhizal fungi: a biocontrol agent against common bean Fusarium root rot disease. Plant Pathol. J. 9, 31–38 (2010).
Article  Google Scholar 

22.
Hugoni, M., Luis, P., Guyonnet, J. & Haichar, F. Z. Plant host habitat and root exudates shape fungal diversity. Mycorrhiza 28, 451–463 (2018).
Article  Google Scholar 

23.
Bertaccini, A. & Duduk, B. Phytoplasma and phytoplasma diseases: A review of recent research. Phytopathol. Mediterr. 48, 355–378 (2009).
CAS  Google Scholar 

24.
Stierlin, E., Nicolè, F., Costes, T., Fernandez, X. & Michel, T. Metabolomic study of volatile compounds emitted by lavender grown under open-field conditions: a potential approach to investigate the yellow decline disease. Metabolomics 16, 31 (2020).
CAS  Article  Google Scholar 

25.
Lopez-Garcia, A. et al. Plant traits determine the phylogenetic structure of arbuscularmycorrhizal fungal communities. Mol. Ecol. 26, 6948–6959 (2017).
Article  Google Scholar 

26.
Alguacil, M. M., Díaz, G., Torres, P., Rodríguez-Caballero, G. & Roldan, A. Host identity and functional traits determine the community composition of the arbuscularmycorrhizal fungi in facultative epiphytic plant species. Fungal Ecol. 39, 307–315 (2019).
Article  Google Scholar 

27.
Neuenkamp, L. et al. The role of plant mycorrhizal type and status in modulating the relationship between plant and arbuscularmycorrhizal fungal communities. New Phytol. 220, 1236–1247 (2018).
CAS  Article  Google Scholar 

28.
Alguacil, M. M., Torrecillas, E., García-Orenes, F. C. & Roldán, A. Changes in the composition and diversity of AMF communities mediated by management practices in a Mediterranean soil are related with increases in soil biological activity. Soil Biol. Biochem. 76, 34–44 (2014).
CAS  Article  Google Scholar 

29.
Giri, B. & Mukerji, K. G. Mycorrhizal inoculant alleviates salt stress in Sesbania aegyptiaca and Sesbania grandiflora under field conditions: evidence for reduced sodium and improved magnesium uptake. Mycorrhiza 14, 307–312 (2004).
Article  Google Scholar 

30.
Phillips, J. M. & Hayman, D. S. Improved procedure for clearing roots and staining parasitic and vesicular–arbuscularmycorrhizal fungi for rapid assessment of infection. Trans. Br. Mycol. Soc. 55, 158–163 (1970).
Article  Google Scholar 

31.
Trouvelot, A., Kough, J. L. & Gianinazzi-Pearson, V. Mesure du taux de mycorhization VA d’un système radiculaire. Recherche de méthodes ayant une signification fonctionnelle. In Physiological and genetical aspects of mycorrhizae (eds Gianinazzi-Pearson, V. & Gianinazzi, S.) 217–221 (INRA Press, Paris, 1986).
Google Scholar 

32.
Gollotte, A., van Tuinen, D. & Atkinson, D. Diversity of arbuscularmycorrhizalfungicolonisingroots of the grassspeciesAgrostis capillaris and Lolium perenne in a fieldexperiment. Mycorrhiza 14, 111–117 (2004).
Article  Google Scholar 

33.
Binet, M. N. et al. Responses of above- and below-ground fungal symbionts to cessation of mowing in subalpine grassland. Fungal Ecol. 25, 14–21 (2017).
Article  Google Scholar 

34.
Mouhamadou, B. et al. Effects of two grass species on the composition of soil fungal communities. Biol. Fertil. Soils 49, 1131–1139 (2013).
Article  Google Scholar 

35.
Boyer, F. et al. Obitools: a unix- inspired software package for DNA metabarcoding. Mol. Ecol. Resour. 16, 176–182 (2016).
CAS  Article  Google Scholar 

36.
Lentendu, G. et al. Assessment of soil fungal diversity in different alpine tundra habitats by means of pyrosequencing. Fungal Div. 49, 113–123 (2011).
Article  Google Scholar 

37.
van Dongen, S. Graph clustering by flow simulation. Ph.D. thesis, University of Utrecht (2000).

38.
Thompson, L. A. S-PLUS (and R) manual to accompany Agresti’s Categorical Data Analysis (2002), 2nd ed (2009).

39.
Oksanen, J., Kindt, R., Legendre, P., O’Hara, B. & Gavin, L. vegan: Community Ecology Package. R package version 1.15–4 (2009).

40.
Mouhamadou, B. et al. Molecular screening of xerophilic Aspergillus strains producing mycophenolic acid. Fungal Biol. 121, 103–111 (2017).
CAS  Article  Google Scholar  More

1.
Carpenter, S. R. & Scheffer, M. Critical transitions and regime shifts in ecosystems: consolidating recent advances. New Models for Ecosystem Dynamics and Restoration 22–32 (2009).
2.
Taubert, F. et al. Global patterns of tropical forest fragmentation. Nature 554, 519–522 (2018).
ADS  CAS  Google Scholar 

3.
Geldmann, J., Manica, A., Burgess, N. D., Coad, L. & Balmford, A. A global-level assessment of the effectiveness of protected areas at resisting anthropogenic pressures. Proc. Natl. Acad. Sci. USA 116, 23209–23215 (2019).
ADS  CAS  Google Scholar 

4.
Ellis, E. C. Anthropogenic transformation of the terrestrial biosphere. Philos. Trans. A Math. Phys. Eng. Sci. 369, 1010–1035 (2011).
ADS  Google Scholar 

5.
Stephens, L. et al. Archaeological assessment reveals Earth’s early transformation through land use. Science 365, 897–902 (2019).
ADS  CAS  Google Scholar 

6.
Marchant, R. Archaeological assessment reveals Earth’s early transformation through land use. Science 365, 897–902 (2019).
ADS  Google Scholar 

7.
Kaplan, J. O., Krumhardt, K. M. & Zimmermann, N. The prehistoric and preindustrial deforestation of Europe. Quatern. Sci. Rev. 28, 3016–3034 (2009).
ADS  Google Scholar 

8.
Czerniak, L. & Pyzel, J. Neolithic farmers and the introduction of pottery in the south Baltic. Bericht Römisch-Germanischen Kommission 89, 347–360 (2011).
Google Scholar 

9.
Willis, K. J., Gillson, L. & Brncic, T. M. How, “virgin” is virgin rainforest?. Science 304, 402–403 (2004).
CAS  Google Scholar 

10.
Seddon, A. W. R. What do we mean by regime shift? Distinguishing between extrinsic and intrinsic forcing in paleoecological data. Past Glob. Changes Mag. 25, 94–95 (2017).
Google Scholar 

11.
Loughlin, N. J. D., Gosling, W. D., Mothes, P. & Montoya, E. Ecological consequences of post-Columbian indigenous depopulation in the Andean-Amazonian corridor. Nat. Ecol. Evol. 2, 1233–1236 (2018).
Google Scholar 

12.
Moreno-Mateos, D. et al. Anthropogenic ecosystem disturbance and the recovery debt. Nat. Commun. 8, 14163 (2017).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

13.
Lamentowicz, M. et al. Always on the tipping point—a search for signals of past societies and related peatland ecosystem critical transitions during the last 6500 years in N Poland. Quatern. Sci. Rev. 225, 105954 (2019).
Google Scholar 

14.
Ralska-Jasiewiczowa, M. et al. Late Glacial and Holocene history of vegetation in Poland based on isopollen maps (W. Szafer Institute of Botany, Polish Academy of Sciences, Kraków, 2004).
Google Scholar 

15.
Clifford, M. J. & Booth, R. K. Late-holocene drought and fire drove a widespread change in forest community composition in eastern North America. Holocene 25, 1102–1110 (2015).
ADS  Google Scholar 

16.
Davies, L. J. et al. High-resolution age modelling of peat bogs from northern Alberta, Canada, using pre- and post-bomb 14 C, 210 Pb and historical cryptotephra. Quat. Geochronol. 47, 138–162 (2018).
Google Scholar 

17.
Kołaczek, P., Karpińska-Kołaczek, M., Marcisz, K., Gałka, M. & Lamentowicz, M. Palaeohydrology and the human impact on one of the largest raised bogs complex in the Western Carpathians (Central Europe) during the last two millennia. Holocene 28, 595–608 (2018).
ADS  Google Scholar 

18.
Marcisz, K. et al. Long-term hydrological dynamics and fire history over the last 2000 years in CE Europe reconstructed from a high-resolution peat archive. Quatern. Sci. Rev. 112, 138–152 (2015).
ADS  Google Scholar 

19.
Hildebrandt-Radke, I. & Makohonienko, M. Krajobraz kulturowy Wielkopolski w pradziejach i czasach historycznych: wprowadzenie. Landform Anal. 16, 17–19 (2011).
Google Scholar 

20.
Makohonienko, M. Przyrodnicza historia Gniezna (Homini, Bydgoszcz-Poznań, 2000).
Google Scholar 

21.
Brown, A. & Pluskowski, A. Detecting the environmental impact of the Baltic Crusades on a late-medieval (13th–15th century) frontier landscape: palynological analysis from Malbork Castle and hinterland, Northern Poland. J. Archaeol. Sci. 38, 1957–1966 (2011).
Google Scholar 

22.
Stivrins, N. et al. Palaeoenvironmental evidence for the impact of the crusades on the local and regional environment of medieval (13th-16th century) northern Latvia, eastern Baltic. The Holocene 1–10 (2015).

23.
Wacnik, A. et al. Determining the responses of vegetation to natural processes and human impacts in north-eastern Poland during the last millennium: combined pollen, geochemical and historical data. Veg. Hist. Archaeobot. 25, 479–498 (2016).
Google Scholar 

24.
Woodward, C., Shulmeister, J., Larsen, J., Jacobsen, G. E. & Zawadzki, A. Landscape hydrology The hydrological legacy of deforestation on global wetlands. Science 346, 844–847 (2014).

25.
Colombaroli, D. & Gavin, D. G. Highly episodic fire and erosion regime over the past 2,000 y in the Siskiyou Mountains, Oregon. Proc. Natl. Acad. Sci. 107, 18909–18914 (2010).
ADS  CAS  Google Scholar 

26.
Bonn, A., Allott, T., Evans, M., Joosten, H. & Stoneman, R. Peatland Restoration and Ecosystem Services: Science, Policy and Practice (Cambridge University Press, Cambridge, 2016).
Google Scholar 

27.
Ireland, A. W. & Booth, R. K. Upland deforestation triggered an ecosystem state-shift in a kettle peatland. J. Ecol. 100, 586–596 (2012).
Google Scholar 

28.
Joosten, H., Tanneberger, F. & Moen, A. Mires and peatlands in Europe “Stuttgart, Germany”, 2017).

29.
Swindles, G. T. et al. Widespread drying of European peatlands in recent centuries. Nat. Geosci. 12, 922–928 (2019).
ADS  CAS  Google Scholar 

30.
Marcisz, K., Kołaczek, P., Gałka, M., Diaconu, A.-C. & Lamentowicz, M. Exceptional hydrological stability of a Sphagnum-dominated peatland over the late Holocene. Quatern. Sci. Rev. 231, 106180 (2020).
Google Scholar 

31.
Page, S. E. & Baird, A. J. Peatlands and global change: response and resilience. Annu. Rev. Environ. Resour. 41, 35–57 (2016).
Google Scholar 

32.
Poppick, L. Resilient Peatlands Keep Carbon Bogged Down. Eos 100, (2019).

33.
Gorham, E. & Rochefort, L. Peatland restoration: A brief assessment with special reference to Sphagnum bogs. Wetl. Ecol. Manag. 11, 109–119 (2003).
CAS  Google Scholar 

34.
Calder, W. J. & Shuman, B. Detecting past changes in vegetation resilience in the context of a changing climate. Biol. Lett. 15, 20180768 (2019).
PubMed  PubMed Central  Google Scholar 

35.
de Jong, R. et al. in Changing Climates, Earth Systems and Society. Series: International Year of Planet Earth (ed Dodson, J.) 85–121 (Springer, Heidelberg, 2010).

36.
Marcinkian, A. Ziemia lubuska w dobie cywilizacji łużyckiej, cz. 2 Zielona Góra, 2010).

37.
Urbańska, A. & Kurnatowski, S. in Studia nad początkami i rozplanowaniem miast na środkową Odrą i dolna Warta (województwo zielonogórskie) t. 1: Ziemia Lubuska, Nowa Marchia, Wielkopolska (ed Zdzisław Kaczmarczyk, A. W.) 35–111 Zielona Góra, 1967).

38.
Weiss, A. Organizacja diecezji lubuskiej w średniowieczu Lublin, 1970).

39.
Labuda, G. Zajęcie Ziemi Lubuskiej przez margrabiów brandenburskicj w połowie XIII wieku. Śląski Kwartalnik Historyczny „Sobótka” 28, 311–322 (1973).

40.
Przybył, M. in Cognitioni Gestorum. Studia z dziejów średniowiecza dedykowane Profesorowi Jerzemu Strzelczykowi (eds Sikorski, D. A. & Wyrwa, A. M.) 395–404 Poznań-Warszawa, 2006).

41.
Zajchowska, S. in tudia nad początkami i rozplanowaniem miast na środkową Odrą i dolna Warta (województwo zielonogórskie) t. 1: Ziemia Lubuska, Nowa Marchia, Wielkopolska (eds Kaczmarczyk, Z. & Wędzki, A.) 113–126 Zielona Góra, 1967).

42.
Wasilkiewicz, K. Templariusze i Joannici w biskupstwie lubuskim (XIII-XVI w.) Gniezno, 2016).

43.
Carsten, F. L. Essays in German History (A&C Black, 1985).

44.
Piskorski, J. M. Kolonizacja wiejska Pomorza Zachodniego w XIII i w początkach XIV wieku na tle procesów osadniczych w średniowiecznej Europie (Poznańskie Tow, Przyjaciół Nauk, 1990).
Google Scholar 

45.
Chmarzyński, G. Zamek w Łagowie. Pamiętnik Związku Historyków Sztuki i Kultury 1, 55–87 (1948).
Google Scholar 

46.
Lamentowicz, M. & Mitchell, E. A. D. The ecology of testate amoebae (Protists) in Sphagnum in north-western Poland in relation to peatland ecology. Microb. Ecol. 50, 48–63 (2005).
Google Scholar 

47.
van Geel, B. in Tracking environmental change using lake sediments. Volume 3: Terrestrial, Algal and Siliceous Indicators (eds Smol, J. P., Birks, H. J. B. & Last, W. M.) 99–119 (Kluwer Academic Publishers, Dortrecht, 2001).

48.
Davies, A. L. Dung fungi as an indicator of large herbivore dynamics in peatlands. Rev. Palaeobot. Palynol. 271, 104108 (2019).
Google Scholar 

49.
Cywa, K. Trees and shrubs used in medieval Poland for making everyday objects. Veg. Hist. Archaeobot. 27, 111–136 (2018).
Google Scholar 

50.
Kurnatowska, Z. & Łosińska, A. in Człowiek a środowisko w środkowym i dolnym Nadodrzu 161–173 Wrocław, 1996).

51.
Warner, B. G., Kubiw, H. J. & Hanf, K. I. An anthropogenic cause for quaking mire formation in southwestern Ontario. Nature 340, 380–384 (1989).
ADS  Google Scholar 

52.
Ellis, E. C. et al. Used planet: A global history. Proc. Natl. Acad. Sci. USA 110, 7978–7985 (2013).
ADS  CAS  Google Scholar 

53.
Haldon, J. et al. History meets palaeoscience: Consilience and collaboration in studying past societal responses to environmental change. Proc Natl Acad Sci USA 115, 3210 (2018).
ADS  CAS  Google Scholar 

54.
Czerwiński, S. et al. Znaczenie wspólnych badań historycznych i paleoekologicznych nad wpływem człowieka na środowisko. Przykład ze stanowiska Kazanie we wschodniej Wielkopolsce. Studia Geohistorica 56 (2020).

55.
Brown, A. et al. The ecological impact of conquest and colonization on a medieval frontier landscape: combined palynological and geochemical analysis of lake sediments from Radzyń Chełminski, northern Poland. Geoarchaeology 30, 511–527 (2015).
Google Scholar 

56.
Jaroszewicz, B. et al. Białowieża forest—a relic of the high naturalness of European Forests. Forests 10, 849 (2019).

57.
Sabatini, F. M. et al. Where are Europe’s last primary forests. Divers. Distrib. 24, 1426–1439 (2018).
Google Scholar 

58.
Ludat, H. Das Lebuser Stiftsregister von 1405. Studien zu den Sozial- und Wirtschaftsverhältnissen im mittleren Oderraum zu Beginn des 15 Wiesbaden, 1965).

59.
Zellweger, F. et al. Forest microclimate dynamics drive plant responses to warming. Science 368, 772–775 (2020).
ADS  CAS  Google Scholar 

60.
Hájek, T. in Photosynthesis in Bryophytes and Early Land Plants, Advances in Photosynthesis and Respiration (eds Hanson, D. T. & Rice, S. K.) 233–252 (Springer Science+Business Media, Dordrecht, 2014).

61.
Lamentowicz, M., Tobolski, K. & Mitchell, E. A. D. Palaeoecological evidence for anthropogenic acidification of a kettle-hole peatland in northern Poland. The Holocene 17, 1185–1196 (2007).
ADS  Google Scholar 

62.
Słowiński, M. et al. Paleoecological and historical data as an important tool in ecosystem management. J. Environ. Manag. 236, 755–768 (2019).
Google Scholar 

63.
Gorham, E., Janssens, J. A., Wheeler, G. A. & Glaser, P. H. The natural and anthropogenic acidification of peatlands. Effects of atmospheric pollutants on forests, wetlands and agricultural ecosystems. Proc. Toronto, 1985 493–512 (1987).

64.
Pawlyta, J. & Lamentowicz, M. in Methods of absolute chronology 10th International conference, Gliwice, Poland, 22–25th April 2010 (2010).

65.
Lamentowicz, M. & Obremska, M. A rapid response of testate amoebae and vegetation to inundation of a kettle hole mire. J. Paleolimnol. 43, 499–511 (2010).
ADS  Google Scholar 

66.
Zaccone, C. et al. Highly anomalous accumulation rates of C and N recorded by a relic, free-floating peatland in Central Italy. Sci. Rep. 7, 43040 (2017).
ADS  CAS  PubMed  PubMed Central  Google Scholar 

67.
Korcz, W. Historyczne losy ziem pogranicza lubusko-wielkopolskiego na tle dziejów ziemi lubuskiej. Rocznik Lubuski 40–85 (1966).

68.
Ellis, E. C. Ecology in an anthropogenic biosphere. Ecol. Monogr. 85, 287–331 (2015).
Google Scholar 

69.
Bronk Ramsey, C. Radiocarbon calibration and analysis of stratigraphy: the OxCal program. Radiocarbon 37, 425–430 (1995).
CAS  Google Scholar 

70.
Bronk Ramsey, C. Deposition models for chronological records. Quatern. Sci. Rev. 27, 42–60 (2008).
ADS  Google Scholar 

71.
Ramsey, C. B. & Lee, S. Recent and planned developments of the program OxCal. Radiocarbon 55, 720–730 (2013).
CAS  Google Scholar 

72.
Reimer, P. J. et al. Intcal13 and Marine13 radiocarbon age calibration curves 0–50,000 years Cal BP. Radiocarbon 55, 1869–1887 (2013).
CAS  Google Scholar 

73.
Berglund, B. E. & Ralska-Jasiewiczowa, M. in Handbook of Holocene Paleoecology and Paleohydrology (ed Berglund, B. E.) 455–484 (Wiley & Sons Ltd., Chichester-Toronto, 1986).

74.
Moore, P. D., Webb, J. A. & Collinson, M. E. Pollen Analysis (Blackwell Scientific Publication, 1991).

75.
Beug, H.-J. Leitfaden der Pollenbestimmung für Mitteleuropa und angrenzende Gebiete (Verlag Dr. Friedrich Pfeil, München, 2004).
Google Scholar 

76.
van Geel, B. & Aptroot, A. Fossil ascomycetes in quaternary deposits. Nova Hedwigia 82, 313–329 (2006).
Google Scholar 

77.
Behre, K.-E. The interpretation of anthopogenic indicators in pollen diagrams. Pollen Spores 23, 225–245 (1981).
Google Scholar 

78.
Poska, A., Saarse, L. & Veski, S. Reflections of pre- and early-agrarian human impact in the pollen diagrams of Estonia. Palaeogeogr. Palaeoclimatol. Palaeoecol. 209, 37–50 (2004).
Google Scholar 

79.
Gaillard, M.-J. Pollen methods and studies/archaeological applications. Encyclop. Quatern. Sci. 3, 880–904 (2013).
Google Scholar 

80.
Tinner, W. & Hu, F. S. Size parameters, size-class distribution and area-number relationship of microscopic charcoal: relevance for fire reconstruction. The Holocene 13, 499–505 (2003).
ADS  Google Scholar 

81.
Finsinger, W. & Tinner, W. Minimum count sums for charcoalconcentration estimates in pollen slides: accuracy and potential errors. The Holocene 15, 293–297 (2005).
ADS  Google Scholar 

82.
Davis, M. B. & Deevey, E. S. J. Pollen accumulation rates: estimates from late-glacial sediment of Roger Lake. Science 145, 1293–1295 (1964).
ADS  CAS  Google Scholar 

83.
Feurdean, A. et al. Fire has been an important driver of forest dynamics in the Carpathian Mountains during the Holocene. For. Ecol. Manage. 389, 15–26 (2017).
Google Scholar 

84.
Conedera, M. et al. Reconstructing past fire regimes: methods, applications, and relevance to fire management and conservation. Quatern. Sci. Rev. 28, 555–576 (2009).
ADS  Google Scholar 

85.
Mauquoy, D. & van Geel, B. in Encyclopedia of Quaternary Science (Elsevier, Amsterdam, 2007).

86.
Booth, R. K., Lamentowicz, M. & Charman, D. J. Preparation and analysis of testate amoebae in peatland paleoenvironmental studies. Mires Peat 7, 1–7 (2010).
Google Scholar 

87.
Payne, R. J. & Mitchell, E. A. D. How many is enough? Determining optimal count totals for ecological and palaeoecological studies of testate amoebae. J. Paleolimnol. 42, 483–495 (2008).
Google Scholar 

88.
Clarke, K. J. Guide to Identification of Soil Protozoa – Testate Amoebae (Freshwater Biological Association, Ambleside, 2003).
Google Scholar 

89.
Grospietsch, T. Wechseltierchen (Rhizopoden) (Kosmos Verlag, Stuttgart, 1958).
Google Scholar 

90.
Mazei, Y. & Tsyganov, A. N. Freshwater Testate Amoebae (KMK, Moscow, 2006).
Google Scholar 

91.
Ogden, C. G. & Hedley, R. H. An Atlas of Freshwater Testate Amoebae (Oxford University Press, London, 1980).
Google Scholar 

92.
Meisterfeld, R. in The Illustrated Guide to the Protozoa (eds Lee, J. J., Leedale, G. F. & Bradbury, P.) 827–860 (Allen Press, Lawrence, 2001).

93.
Meisterfeld, R. in The Illustrated Guide to the Protozoa (eds Lee, J. J., Leedale, G. F. & Bradbury, P.) 1054–1084 (Allen Press, Lawrence, 2001).

94.
Siemensma, F. J. Microworld, world of amoeboid organisms. World-wide electronic publication (www.arcella.nl) (Kortenhoef, The Netherlands, 2019).

95.
Juggins, S. C2 User guide. Software for ecological and palaeoecological data analysis and visualisation (University of Newcastle, Newcastle upon Tyne, UK, 2003).

96.
Grimm, E. C. TILIA/TILIA graph. Version 1.2. (1992).

97.
MacAskill, M. R. DataGraph 3.0. J. Stat. Softw. 47, 1–9 (2012).
Google Scholar 

98.
Lara, E., Roussel-Delif, L., Fournier, B., Wilkinson, D. M. & Mitchell, E. A. D. Soil microorganisms behave like macroscopic organisms: patterns in the global distribution of soil euglyphid testate amoebae. J. Biogeogr. 43, 520–532 (2016).
Google Scholar 

99.
Singer, D., Kosakyan, A., Pillonel, A., Mitchell, E. A. D. & Lara, E. Eight species in the Nebela collaris complex: Nebela gimlii (Arcellinida, Hyalospheniidae), a new species described from a Swiss raised bog. Eur. J. Protistol. 51, 79–85 (2015).
Google Scholar 

100.
Dixon, P. VEGAN, a package of R functions for community ecology. J. Veg. Sci. 14, 927–930 (2003).
Google Scholar 

101.
Team R Development Core. R: A language and environment for statistical computing. (2015). More

1.
Kritzer, J. P. & Sale, P. F. Metapopulation ecology in the sea: From Levins’ model to marine ecology and fisheries science. Fish Fish 5, 131–140 (2004).
Article  Google Scholar 
2.
Mumby, P. J. Connectivity of reef fish between mangroves and coral reefs: algorithms for the design of marine reserves at seascape scales. Biol. Conserv. 128, 215–222 (2006).
Article  Google Scholar 

3.
Laegdsgaard, P. & Johnson, C. Why do juvenile fish utilise mangrove habitats? J. Exp. Mar. Bio. Ecol. https://doi.org/10.1016/S0022-0981(00)00331-2 (2001).
Article  Google Scholar 

4.
Cocheret de la Morinière, E. et al. Ontogenetic dietary changes of coral reef fishes in the mangrove-seagrass-reef continuum: stable isotopes and gut-content analysis. Mar. Ecol. Prog. Ser. https://doi.org/10.3354/meps246279 (2003).
Article  Google Scholar 

5.
Karnauskas, M., Chérubin, L. M. & Paris, C. B. Adaptive significance of the formation of multi-species fish spawning aggregations near submerged capes. PLoS ONE https://doi.org/10.1371/journal.pone.0022067 (2011).
Article  PubMed  PubMed Central  Google Scholar 

6.
Wright, P. J. et al. Integrating the scale of population processes into fisheries management, as illustrated in the sandeel, Ammodytes marinus. ICES J. Mar. Sci. 76, 1453–1463 (2019).
Article  Google Scholar 

7.
Thorrold, S. R., Latkoczy, C., Swart, P. K. & Jones, C. M. Natal homing in a marine fish metapopulation. Science 291, 297–299 (2001).
CAS  PubMed  Article  Google Scholar 

8.
Gillanders, B. M. in Ecological Connectivity among Tropical Coastal Ecosystems (ed. Nagelkerken, I.) 457–492 (Springer Netherlands, 2009).

9.
Kincaid, K. & Rose, G. Effects of closing bottom trawling on fisheries, biodiversity, and fishing communities in a boreal marine ecosystem: The Hawke box off Labrador, Canada. Can. J. Fish. Aquat. Sci. 74, 1490–1502 (2017).
Article  Google Scholar 

10.
Le Quesne, W. J. F., Hawkins, S. J. & Shepherd, J. G. A comparison of no-take zones and traditional fishery management tools for managing site-attached species with a mixed larval pool. Fish Fish 8, 181–195 (2007).
Article  Google Scholar 

11.
Horwood, J. W., Nichols, J. H. & Milligan, S. Evaluation of closed areas for fish stock conservation. J. Appl. Ecol. 35, 893–903 (2008).
Article  Google Scholar 

12.
Wright, P. J., Tobin, D., Gibb, F. M. & Gibb, I. M. Assessing nursery contribution to recruitment: Relevance of closed areas to haddock Melanogrammus aeglefinus. Mar. Ecol. Prog. Ser. 400, 221–232 (2010).
Article  Google Scholar 

13.
Lipcius, R. N., Stockhausen, W. T., Eggleston, D. B., Marshall, L. S. & Hickey, B. Hydrodynamic decoupling of recruitment, habitat quality and adult abundance in the Caribbean spiny lobster: Source-sink dynamics? in. Mar. Freshw. Res. 48, 807–815 (1997).
Article  Google Scholar 

14.
McBride, R. S. & Able, K. W. Ecology and fate of butterflyfishes, Chaetodon spp., in the temperate, western North Atlantic. Bull. Mar. Sci. 63, 401–416 (1998).
Google Scholar 

15.
Dahlgren, C. P. et al. Marine nurseries and effective juvenile habitats: Concepts and applications. Mar. Ecol. Prog. Ser. 312, 291–295 (2006).
Article  Google Scholar 

16.
Fogarty, M. J., Fogarty, M. J., Botsford, L. W. & Botsford, L. W. Population connectivity and spatial management of marine fisheries. Oceanography 20, 112–123 (2007).
Article  Google Scholar 

17.
Pickett, G. D., Kelley, D. F. & Pawson, M. G. The patterns of recruitment of sea bass, Dicentrarchus labrax L. from nursery areas in England and Wales and implications for fisheries management. Fish. Res. 68, 329–342 (2004).
Article  Google Scholar 

18.
Walther, B. D. & Thorrold, S. R. Water, not food, contributes the majority of strontium and barium deposited in the otoliths of a marine fish. Mar. Ecol. Prog. Ser. https://doi.org/10.3354/meps311125 (2006).
Article  Google Scholar 

19.
Dorval, E., Jones, C. M., Hannigan, R. & Montfrans, J. van. Relating otolith chemistry to surface water chemistry in a coastal plain estuary. Can. J. Fish. Aquat. Sci. 64, 411–424 (2007).
CAS  Article  Google Scholar 

20.
Thomas, O. R. B., Ganio, K., Roberts, B. R. & Swearer, S. E. Trace element–protein interactions in endolymph from the inner ear of fish: implications for environmental reconstructions using fish otolith chemistry. Metallomics 9, 239–249 (2017).
CAS  PubMed  Article  Google Scholar 

21.
Walther, B. D., Kingsford, M. J., O’Callaghan, M. D. & McCulloch, M. T. Interactive effects of ontogeny, food ration and temperature on elemental incorporation in otoliths of a coral reef fish. Environ. Biol. Fishes 89, 441–451 (2010).
Article  Google Scholar 

22.
Sturrock, A. M. et al. Physiological influences can outweigh environmental signals in otolith microchemistry research. Mar. Ecol. Prog. Ser. 500, 245–264 (2014).
CAS  Article  Google Scholar 

23.
Sturrock, A. M. et al. Quantifying physiological influences on otolith microchemistry. Methods Ecol. Evol. 6, 806–816 (2015).
Article  Google Scholar 

24.
Régnier, T. et al. Otolith chemistry reveals seamount fidelity in a deepwater fish. Deep Sea Res. Part I Oceanogr. Res. Pap. 121, 183–189 (2017).
Article  Google Scholar 

25.
Gillanders, B. M. Temporal and spatial variability in elemental composition of otoliths: implications for determining stock identity and connectivity of populations. Can. J. Fish. Aquat. Sci. 59, 669–679 (2002).
CAS  Article  Google Scholar 

26.
Wright, P. J., Régnier, T., Gibb, F. M., Augley, J. & Devalla, S. Assessing the role of ontogenetic movement in maintaining population structure in fish using otolith microchemistry. Ecol. Evol. 8, 7907–7920 (2018).
PubMed  PubMed Central  Article  Google Scholar 

27.
Wright, P. J., Neat, F. C., Gibb, F. M., Gibb, I. M. & Thordarson, H. Evidence for metapopulation structuring in cod from the west of Scotland and North Sea. J. Fish. Biol. 69, 181–199 (2006).
CAS  Article  Google Scholar 

28.
ICES. Working Group for the Celtic Seas Ecoregion (WGCSE). ICES Scientific Reports 1:29, (ICES, 2019).

29.
Tobin, D., Wright, P. J., Gibb, F. M. & Gibb, I. M. The importance of life stage to population connectivity in whiting (Merlangius merlangus) from the northern European shelf. Mar. Biol. 157, 1063–1073 (2010).
Article  Google Scholar 

30.
Burns, N. M., Bailey, D. M. & Wright, P. J. A method to improve fishing selectivity through age targeted fishing using life stage distribution modelling. PLoS ONE https://doi.org/10.1371/journal.pone.0214459 (2019).
Article  PubMed  PubMed Central  Google Scholar 

31.
Symes, D. & Ridgeway, S. Inshore fisheries regulation and management in Scotland; Meeting the challenges of Environmental Integration. Scottish Natural Heritage Commissioned Report F02AA405 (Scottish Natural Heritage and RSPB, 2003).

32.
Thygesen, U. H., Pedersen, M. W. & Madsen, H. in Tagging and Tracking of Marine Animals with Electronic Devices. Vol. 9, 23–34 (Springer, 2009).

33.
Gillanders, B. M. Connectivity between juvenile and adult fish populations: do adults remain near their recruitment estuaries? Mar. Ecol. Prog. Ser. 240, 215–223 (2002).
Article  Google Scholar 

34.
Elsdon, T. et al. Otolith chemistry to describe movements and life-history parameters of fishes. Oceanogr. Mar. Biol. 46, 297–330 (2008).
Google Scholar 

35.
West, J. B., Bowen, G. J., Dawson, T. E. & Tu, K. P. Isoscapes: Understanding Movement, Pattern, and Process on Earth through Isotope Mapping. (Springer, 2010).

36.
Vander Zanden, H. B. et al. Determining origin in a migratory marine vertebrate: A novel method to integrate stable isotopes and satellite tracking. Ecol. Appl. 25, 320–335 (2015).
PubMed  Article  Google Scholar 

37.
Trueman, C. N., MacKenzie, K. M. & St John Glew, K. Stable isotope-based location in a shelf sea setting: accuracy and precision are comparable to light-based location methods. Methods Ecol. Evol. 8, 232–240 (2017).
Article  Google Scholar 

38.
Campana, S. E. & Thorrold, S. R. Otoliths, increments, and elements: keys to a comprehensive understanding of fish populations?. Can. J. Fish. Aquat. Sci. 58, 30–38 (2001).
Article  Google Scholar 

39.
Elsdon, T. S. & Gillanders, B. M. Interactive effects of temperature and salinity on otolith chemistry: challenges for determining environmental histories of fish. Can. J. Fish. Aquat. Sci. 59, 1796–1808 (2002).
CAS  Article  Google Scholar 

40.
Barnes, T. C. & Gillanders, B. M. Combined effects of extrinsic and intrinsic factors on otolith chemistry: Implications for environmental reconstructions. Can. J. Fish. Aquat. Sci. 70, 1159–1166 (2013).
CAS  Article  Google Scholar 

41.
Gibb, F. M., Gibb, I. M. & Wright, P. J. Isolation of Atlantic cod (Gadus morhua) nursery areas. Mar. Biol. 151, 1185–1194 (2007).
Article  Google Scholar 

42.
Higgins, R. M. et al. Multi-disciplinary fingerprints reveal the harvest location of cod Gadus morhua in the Northeast Atlantic. Mar. Ecol. Prog. Ser. 404, 197–206 (2010).
Article  Google Scholar 

43.
Geffen, A. J., Jarvis, K., Thorpe, J. P., Leah, R. T. & Nash, R. D. M. Spatial differences in the trace element concentrations of Irish Sea plaice Pleuronectes platessa and whiting Merlangius merlangus otoliths. J. Sea Res. 50, 247–256 (2003).
Article  CAS  Google Scholar 

44.
Mercier, L. et al. Selecting statistical models and variable combinations for optimal classification using otolith microchemistry. Ecol. Appl. 21, 1352–1364 (2011).
PubMed  Article  Google Scholar 

45.
Balls, P. et al. Ices baseline survey of trace metals in European shelf waters. ICES J. Mar. Sci. https://doi.org/10.1006/jmsc.1993.1047 (1993).
Article  Google Scholar 

46.
IPCS. Barium international programme on chemical safety: environmental health criteria 107. (Environmental Health Criteria, 1990).

47.
Balls, P. W. Composition of suspended particulate matter from Scottish coastal waters-geochemical implications for the transport of trace metal contaminants. Sci. Total Environ. https://doi.org/10.1016/0048-9697(86)90021-5 (1986).
Article  Google Scholar 

48.
Muller, F. L. L., Tranter, M. & Balls, P. W. Distribution and transport of chemical constituents in the Clyde Estuary. Estuar. Coast. Shelf Sci. 39, 105–126 (1994).
CAS  Article  Google Scholar 

49.
Gibb, F. M., Régnier, T., Donald, K. & Wright, P. J. Connectivity in the early life history of sandeel inferred from otolith microchemistry. J. Sea Res. 119, 8–16 (2017).
Article  Google Scholar 

50.
Xiao, J., Tagliabracci, V. S., Wen, J., Kim, S. A. & Dixon, J. E. Crystal structure of the Golgi casein kinase. Proc. Natl Acad. Sci. USA 110, 10574–10579 (2013).
CAS  PubMed  Article  Google Scholar 

51.
Liu, Z. et al. Shape-preserving amorphous-to-crystalline transformation of CaCO3 revealed by in situ TEM. Proc. Natl Acad. Sci. USA 117, 3397–3404 (2020).
CAS  PubMed  Article  Google Scholar 

52.
Altenritter, M. E. & Walther, B. D. The Legacy of Hypoxia: tracking carryover effects of low oxygen exposure in a demersal fish using geochemical tracers. Trans. Am. Fish. Soc. https://doi.org/10.1002/tafs.10159 (2019).
Article  Google Scholar 

53.
Forrester, G. E. & Swearer, S. E. Trace elements in otoliths indicate the use of open- coast versus bay nursery habitats by juvenile California halibut. Mar. Ecol. Prog. Ser. 241, 201–213 (2002).
CAS  Article  Google Scholar 

54.
Hamer, P. A. & Jenkins, G. P. Comparison of spatial variation in otolith chemistry of two fish species and relationships with water chemistry and otolith growth. J. Fish. Biol. 71, 1035–1055 (2007).
CAS  Article  Google Scholar 

55.
White, J. W., Standish, J. D., Thorrold, S. R. & Warner, R. R. Markov chain monte carlo methods for assigning larvae to natal sites using natural geochemical tags. Ecol. Appl. 18, 1901–1913 (2008).
PubMed  Article  Google Scholar 

56.
Stanley, R. R. E. et al. Environmentally mediated trends in otolith composition of juvenile Atlantic cod (Gadus morhua). ICES J. Mar. Sci. 72, 2350–2363 (2015).
Article  Google Scholar 

57.
Xu, Q.-S. & Liang, Y.-Z. Monte Carlo cross validation. Chemom. Intell. Lab. Syst. 56, 1–11 (2001).
CAS  Article  Google Scholar 

58.
Baudron, A. R., Serpetti, N., Fallon, N. G., Heymans, J. J. & Fernandes, P. G. Can the common fisheries policy achieve good environmental status in exploited ecosystems: The west of Scotland demersal fisheries example. Fish. Res. 211, 217–230 (2019).
Article  Google Scholar 

59.
Carlucci, R. et al. Nursery areas of red mullet (Mullus barbatus), hake (Merluccius merluccius) and deep-water rose shrimp (Parapenaeus longirostris) in the Eastern-Central Mediterranean Sea. Estuar. Coast. Shelf Sci. 83, 529–538 (2009).
CAS  Article  Google Scholar 

60.
Heath, M. R. et al. Combination of genetics and spatial modelling highlights the sensitivity of cod (Gadus morhua) population diversity in the North Sea to distributions of fishing. ICES J. Mar. Sci. 71, 794–807 (2014).
Article  Google Scholar 

61.
Hunter, A., Speirs, D. C. & Heath, M. R. Fishery-induced changes to age and length dependent maturation schedules of three demersal fish species in the Firth of Clyde. Fish. Res. 170, 14–23 (2015).
Article  Google Scholar 

62.
Phillipson, J. & Symes, D. ‘A sea of troubles’: Brexit and the fisheries question. Mar. Policy 90, 168–173 (2018).
Article  Google Scholar 

63.
Ellis, J. R., Milligan, S. P., Readdy, L., Taylor, N. & Brown, M. J. Spawning and nursery grounds of selected fish species in UK waters. Science Series Technical Report. Vol. 147 (Cefas, 2012).

64.
European Commission. Impact assessment of discard policy for specific fisheries. Studies and Pilot Projects for Carrying Out the Common Fisheries Policy No FISH/2006/17. 1–289 (IEEP, 2007).

65.
Hufnagl, M., Peck, M. A., Nash, R. D. M., Pohlmann, T. & Rijnsdorp, A. D. Changes in potential North Sea spawning grounds of plaice (Pleuronectes platessa L.) based on early life stage connectivity to nursery habitats. J. Sea Res. 84, 26–39 (2013).
Article  Google Scholar 

66.
Hannesson, R. Zonal attachment of fish stocks and management cooperation. Fish. Res. 140, 149–154 (2013).
Article  Google Scholar 

67.
ICES. Report of the Workshop of National Age Readings Coordinators (WKNARC). (ICES, 2011).

68.
Longerich, H. P., Jackson, S. E. & Gunnther, D. Laser ablation inductively coupled plasma mass spectrometery transient signal data acquisition and analyte concentration calculation. J. Anal. Spectrom. 11, 899–904 (1996).
CAS  Article  Google Scholar 

69.
Knick, S. T., Leu, M., Rotenberry, J. T., Hanser, S. E. & Fesenmyer, K. A. Diffuse migratory connectivity in two species of shrubland birds: Evidence from stable isotopes. Oecologia 174, 595–608 (2014).
PubMed  Article  Google Scholar 

70.
Burns, N. M., Hopkins, C. R., Bailey, D. M. & Wright, P. J. Connecting fishing grounds to nursery areas using novel otolith isoscape analysis. [Data Collection] University of Glasgow Enlighten database https://doi.org/10.5525/gla.researchdata.1040 (2020).

71.
Burns, N. M. NeilMBurns/Element_chemoscape_geolocation20: Code for Otolith chemoscape analysis in whiting (Version v1.0). Zenodo. https://doi.org/10.5281/zenodo.4088644 (2020). More

Site description
The study site is a typical semiarid grassland representative of the Eurasian steppe17, located in the Xilin River Basin, Inner Mongolia Autonomous Region of China, close to the Inner Mongolia Grassland Ecosystem Research Station (IMGERS, 43°38′ N, 116°42′ E). Mean annual precipitation is 346 mm, with 60–80% of precipitation falling in the growing season (May to September). Mean annual temperature is 0.3 °C, with mean monthly temperatures ranging from −21.6 °C in January to 19.0 °C in July4. The topography at our experimental site consists of two landscape units (i.e. flat block and sloped block), with elevation ranging from 1200 to 1280 m above sea level, and slopes less than 5°18,19. The soil is classified as dark chestnut (Calcic Chernozem, ISSS Working Group RB, 1998) derived from aeolian sediments18,20. The soil substrate is dominated by sandy loam and loamy sand with more than 50% being fine sand and silt21. At the beginning of the experiment, soil organic carbon and total nitrogen contents were higher in the flat block than in the sloped block (Table 1). Plant species richness and above-ground biomass were also greater in the flat block than in the sloped block, although species composition in terms of relative biomass of common species did not differ between the two systems (Table 1). Leymus chinensis (perennial rhizomatous grass) and Stipa grandis (perennial bunchgrass) are the dominant species in the study area, together accounting for more than 70% of community aboveground biomass. Other dominant species include Cleistogenes squarrosa, Agropyron cristatum, Achnatherum sibiricum, and Carex korshinskyi.
Table 1 Soil and vegetation characteristics in the flat and sloped blocks prior to grazing and mowing interventions.
Full size table

Study design
The experimental area was used for moderate sheep grazing (1.5–3 ewes ha−1 year−1) by local herdsmen until 2003. Afterwards, grass swards recovered for two years before the experiment started20,22. At the end of the growing season in 2004, prior to beginning the experiment, swards in the entire area were cut to 3–5 cm in stubble height23. The experiment was established in June 2005 with split plots in a randomized complete block design (Fig. 1). The study area included two blocks (i.e., flat and sloped blocks), with each block further divided into seven plots. We included flat and sloped blocks because our project was designed to assess the impacts of grazing at spatial scales that are both relevant to land management and that can capture ecosystem and landscape-scale effects of grazing24. It is unrealistic to conduct such a study in an area with no variation in topography. Grazing intensity was randomly assigned to the plots, and each plot was divided into two subplots. The grazing or mowing management regime was randomly assigned into each subplot23. In the grazing regime, there were seven levels of grazing intensity (GI: 0, 1.5, 3.0, 4.5, 6.0, 7.5 and 9.0 sheep ha−1), and sheep grazed in the subplots continuously from June to September each year25. The ungrazed plots (0 sheep ha−1) had no sheep grazing for 12 years. Each subplot was 2 ha, except the subplot with 1.5 sheep ha−1, which was enlarged to 4 ha to ensure a minimum herd of six sheep per subplot. In the mowing regime, mowing was done once a year in the middle of August. Plant and soil microbial community data was collected in late July and early August 2017, after 12 years of grazing and mowing treatments.
Fig. 1

Illustration of the grazing experiment design. G: grazing regime, M: mowing regime.

Full size image

Plant community surveys
For each subplot, we randomly laid out ten 1 m × 1 m quadrats at least five meters from the edge of each plot to avoid edge effects. In each quadrat, plant species were identified, and the abundance of each species was counted by bunches (bunchgrasses) or stems (rhizomatous grasses). For each species, five individuals were randomly chosen to measure plant height and the average height of all species was used as plant canopy height. Plant canopy coverage was measured visually.
Soil sampling
For each quadrat, three soil cores (3 cm diameter, 10 cm depth) were collected, and soil was passed through a 2 mm sieve to form one composite soil sample per quadrat. Sieved soil was then divided into three subsamples. One subsample was air-dried for the analysis of soil pH, soil organic carbon (SOC), total nitrogen (TN), and total phosphorus (TP). The second fresh subsample was used for the analysis of microbial community structure and microbial biomass. The third subsample was stored at -20 °C prior to being used for microbial sequencing analysis.
Soil physical and chemical properties
To evaluate soil compaction, we measured soil hardness by using a Yamanaka-style soil hardness tester (Fujiwara Scientific Co., Japan). Soil moisture content was measured by using 10 g of moist soil that was oven-dried at 105 °C for 24 h. Soil pH was measured in a 1:2.5 soil:water suspension using a pH meter (FE20-FiveEasy, Mettler-Toledo, Switzerland).
We measured SOC content with the Walkley-black method, soil TN content by the micro-Kjeldahl digestion, followed by colorimetric determination with a 2300 Kjeltec Analyzer Unit, and soil TP content was by the H2SO4-HClO4 fusion method using a 6505 UV spectrophotometer26.
Soil microbial community structure
Microbial community structure was assessed using phospholipid fatty acids (PLFAs), as described by Bossio and Scow27. First, lipids were extracted from 10 g of fresh soil using a buffer (CHCL3:CH3OH:K2HPO4 = 1:2:0.8, v:v:v). Second, the fatty acid methyl esters (FAMEs) were separated, quantified and identified using a gas chromatograph system (Agilent 7890, Santa Clara, USA) and a MIDI Sherlock Microbial Identification System (MIDI Inc., Newark, USA). Peak areas were converted to nmol g−1 dry soil using the internal standard, methylnon-adecanoate (C19:0). Third, the specific microbial groups were identified according to their representative markers. Specifically, G+ bacteria correspond to iso-, anteiso- and 10Me-branched PLFAs; G- bacteria correspond to monounsaturated and cyclopropyl PLFAs; arbuscular mycorrhizal fungi (AMF) use 16:1ω5c as representative marker; saprotrophic fungi (SF) use 18:1ω9c, 18:2ω6c and 18:3ω6c as representative markers28,29,30. The 12:0, 14:0, 15:0, 16:0, 17:0, 18:0 PLFAs were general markers present in all microorganisms30,31. Bacterial PLFAs included G+ and G− bacteria PLFAs. Fungal PLFAs included arbuscular mycorrhizal and saprotrophic fungi PLFAs. Total microbial PLFAs were the sum of bacterial, fungal, and general PLFAs.
Soil microbial biomass carbon (MBC), nitrogen (MBN), and phosphorus (MBP) were measured using the chloroform-extraction method32,33. For MBC and MBN, two fresh soil samples were used for the analysis. One sample was placed in a chloroform steam bath for 24 h and another sample was kept non-fumigated. Then, organic C and total N were extracted by shaking two soil samples in 0.5 M K2SO4 for 1 h and filtering through a Whatman No. 1 filter paper (9 cm in diameter). The filtered extracts were measured with a total organic carbon (TOC) analyzer (Elementar vario TOC, Hanau, Germany). Microbial biomass P was measured using a similar method as for MBC and MBN except that P was extracted by 0.5 M NaHCO3 and then measured with a UV Spectrometer (6505 spectrometer, Jenway, Stone, UK).
DNA extraction and sequencing
We mixed ten soil samples of each plot to form one composite sample for DNA extraction and sequencing. Total genomic DNA was extracted from 0.5 g soil using a FastDNA Spin kit (MP Biomedical, Santa Ana, California, USA). The DNA quality was checked by 1% agarose gel electrophoresis and quantity was determined with a NanoDrop 2000 UV-vis spectrophotometer (Thermo Scientific, Wilmington). Bacterial 16 S rRNA genes were amplified with PCR primers 338 F (5′- ACTCCTACGGGAGGCAGCAG-3′) and 806 R (5′-GGACTACHVGGGTWTCTAAT-3′). Fungal internal transcribed spacer (ITS) rRNA genes were amplified with PCR primers ITS1F (5′-CTTGGTCATTTAGAGGAAGTAA-3′) and ITS2 (5′-GCTGCGTTCTTCATCGATGC-3′)34,35. The resulting PCR products were extracted from a 2% agarose gel and further purified using the AxyPrep DNA Gel Extraction Kit (Axygen Biosciences, Union City, CA, USA) and quantified using QuantiFluor™-ST (Promega, USA). Purified amplicons were pooled in equimolar concentrations and paired-end sequenced for high-throughput 16 S rRNA or ITS rRNA gene sequencing on an Illumina Hiseq. 2500 platform (Illumina Inc., USA) according to the standard protocols by Novogene Technology Co., Ltd. Operational taxonomic units (OTUs) were clustered with 97% similarity cut-off using UPARSE (version 7.1 https://drive5.com/uparse/), and chimeric sequences were identified and removed using UCHIME. Silva and Unite databases were used as references for bacteria and fungi, respectively34,35. More

1.
Sih, A., Ferrari, M. C. O. & Harris, D. J. Evolution and behavioural responses to human-induced rapid environmental change. Evol. Appl. 4, 367–387. https://doi.org/10.1111/j.1752-4571.2010.00166.x (2011).
Article  PubMed  PubMed Central  Google Scholar 
2.
Sih, A. Effects of early stress on behavioral syndromes: an integrated adaptive perspective. Neurosci. Biobehav. Rev. 35, 1452–1465. https://doi.org/10.1016/j.neubiorev.2011.03.015 (2011).
Article  PubMed  Google Scholar 

3.
Franks, S. J., Weber, J. J. & Aitken, S. N. Evolutionary and plastic responses to climate change in terrestrial plant populations. Evol. Appl. 7, 123–139. https://doi.org/10.1111/eva.12112 (2014).
Article  PubMed  Google Scholar 

4.
Parmesan, C. Ecological and evolutionary responses to recent climate change. Annu. Rev. Ecol. Evol. Syst. 37, 637–669. https://doi.org/10.1146/annurev.ecolsys.37.091305.110100 (2006).
Article  Google Scholar 

5.
Mulholland, P. J. et al. Effects of climate change on freshwater ecosystems of the south-eastern United States and the Gulf Coast of Mexico. Hydrol. Process. 11, 949–970. https://doi.org/10.1002/(SICI)1099-1085(19970630)11:83.0.CO;2-G (1997).

6.
Justić, D., Rabalais, N. N. & Turner, R. E. Coupling between climate variability and coastal eutrophication: evidence and outlook for the northern Gulf of Mexico. J. Sea Res. 54, 25–35. https://doi.org/10.1016/j.seares.2005.02.008 (2005).
ADS  Article  Google Scholar 

7.
Sokolova, I. M. & Lannig, G. Interactive effects of metal pollution and temperature on metabolism in aquatic ectotherms: implications of global climate change. Clim. Res. 37, 181–201. https://doi.org/10.3354/cr00764 (2008).
Article  Google Scholar 

8.
Bradshaw, W. E. & Holzapfel, C. M. Evolutionary response to rapid climate change. Am. Assoc. Adv. Sci. 312, 1477–1478 (2006).
CAS  Google Scholar 

9.
Ghalambor, C. K., Huey, R. B., Martin, P. R., Tewksbury, J. J. & Wang, G. Are mountain passes higher in the tropics? Janzen’s hypothesis revisited. Integr. Comp. Biol. 46, 5–17. https://doi.org/10.1093/icb/icj003 (2006).

10.
Huang, S. L., Hao, Y., Mei, Z., Turvey, S. T. & Wang, D. Common pattern of population decline for freshwater cetacean species in deteriorating habitats. Freshw. Biol. 57, 1266–1276. https://doi.org/10.1111/j.1365-2427.2012.02772.x (2012).
Article  Google Scholar 

11.
Matteson, S. W., Mossman, M. J. & Shealer, D. A. Population decline of black terns in Wisconsin: a 30-year perspective. Waterbirds 35, 185–193. https://doi.org/10.1675/063.035.0201 (2012).
Article  Google Scholar 

12.
Blaustein, A. R. & Bancroft, B. A. Amphibian population declines: evolutionary considerations. Bioscience 57, 437–444. https://doi.org/10.1641/B570517 (2007).
Article  Google Scholar 

13.
Taylor, B. M., Houk, P., Russ, G. R. & Choat, J. H. Life histories predict vulnerability to overexploitation in parrotfishes. Coral Reefs 33, 869–878. https://doi.org/10.1111/j.1752-4571.2010.00166.x0 (2014).
ADS  Article  Google Scholar 

14.
Trzcinski, M. K., Mohn, R. & Bowen, W. K. Continued decline of an Atlantic cod population: how important is gray seal predation?. Ecol. Appl. 16, 2276–2292. https://doi.org/10.1111/j.1752-4571.2010.00166.x1 (2006).
Article  PubMed  Google Scholar 

15.
Kovach, R. P. et al. Climate, invasive species and land use drive population dynamics of a cold-water specialist. J. Appl. Ecol. 54, 638–647. https://doi.org/10.1111/j.1752-4571.2010.00166.x2 (2017).
Article  Google Scholar 

16.
Greenlees, M. J., Phillips, B. L. & Shine, R. An invasive species imposes selection on life-history traits of a native frog. Biol. J. Linn. Soc. 100, 329–336. https://doi.org/10.1111/j.1752-4571.2010.00166.x3 (2010).
Article  Google Scholar 

17.
Deutsch, C. A. et al. Impacts of climate warming on terrestrial ectotherms across latitude. Proc. Natl. Acad. Sci. 105, 6668–6672. https://doi.org/10.1111/j.1752-4571.2010.00166.x4 (2008) (arXiv:1408.1149.).
ADS  Article  PubMed  Google Scholar 

18.
Huey, R. B. et al. Predicting organismal vulnerability to climate warming: roles of behaviour, physiology and adaptation. Phil. Trans. R. Soc. B Biol. Sci. 367, 1665–1679. https://doi.org/10.1111/j.1752-4571.2010.00166.x5 (2012).
Article  Google Scholar 

19.
Somero, G. N. The physiology of climate change: how potentials for acclimatization and genetic adaptation will determine ‘winners’ and ‘losers’. J. Exp. Biol. 213, 912–920. https://doi.org/10.1242/jeb.037473 (2010).

20.
Hoffman, A. A., Hallas, R. J., Dean, J. A. & Schiffer, M. Low potential for climatic stress adaptation in a rainforest Drosophila species. Science 301, 100–102 (2003).
ADS  Article  Google Scholar 

21.
Martinez, E., Porreca, A. P., Colombo, R. E. & Menze, M. A. Tradeoffs of warm adaptation in aquatic ectotherms: live fast, die young?. Comp. Biochem. Physiol. Part A Mol. Integr. Physiol. 191, 209–215. https://doi.org/10.1111/j.1752-4571.2010.00166.x6 (2016).
CAS  Article  Google Scholar 

22.
Payne, N. L. et al. Temperature dependence of fish performance in the wild: links with species biogeography and physiological thermal tolerance. Funct. Ecol. 30, 903–912. https://doi.org/10.1111/j.1752-4571.2010.00166.x7 (2016).
Article  Google Scholar 

23.
Walsh, S. J., Haney, D. C. & Timmerman, C. M. Variation in thermal tolerance and routine metabolism among spring- and stream-dwelling freshwater sculpins (Teleostei: Cottidae) of the southeastern United States. Ecol. Freshw. Fish 6, 84–94. https://doi.org/10.1111/j.1752-4571.2010.00166.x8 (1997).
Article  Google Scholar 

24.
Strange, K. T., Vokoun, J. C. & Noltie, D. B. Thermal tolerance and growth differences in orangethroat darter (Etheostoma spectabile) from thermally contrasting adjoining streams. Am. Midl. Nat. 148, 120–128. https://doi.org/10.1111/j.1752-4571.2010.00166.x9 (2002).
Article  Google Scholar 

25.
Lemoine, N. P. & Burkepile, D. E. Temperature-induced mismatches between consumption and metabolism reduce consumer fitness. Ecology 93, 2483–2489 (2012).
Article  Google Scholar 

26.
Rall, B. Ö. C., Vucic-Pestic, O., Ehnes, R. B., EmmersoN, M. & Brose, U. Temperature, predator–prey interaction strength and population stability. Glob. Change Biol. 16, 2145–2157. https://doi.org/10.1016/j.neubiorev.2011.03.0150 (2010).
ADS  Article  Google Scholar 

27.
Brodnik, R. M. Impacts of Water Warming on the Physiology and Life-History of a Tropical Freshwater Fish. Master’s thesis, The Ohio State University (2015).

28.
O’Reilly, C. M., Alin, S. R., Plisnier, P.-D., Cohen, A. S. & McKee, B. A. Climate change decreases aquatic ecosystem productivity of Lake Tanganika. Afr. Nat. 424, 766–768 (2003).

29.
Stenuite, S. et al. Phytoplankton production and growth rate in Lake Tanganyika: evidence of a decline in primary productivity in recent decades. Freshw. Biol. 52, 2226–2239. https://doi.org/10.1016/j.neubiorev.2011.03.0151 (2007).
CAS  Article  Google Scholar 

30.
Verburg, P. & Hecky, R. E. The physics of the warming of Lake Tanganyika by climate change. Limnol. Oceanogr. 54, 2418–2430. https://doi.org/10.1016/j.neubiorev.2011.03.0152 (2009).
ADS  Article  Google Scholar 

31.
Moritz, C. & Agudo, R. The future of species under climate change: resilience or decline?. Science 341, 504–508. https://doi.org/10.1016/j.neubiorev.2011.03.0153 (2013).
ADS  CAS  Article  PubMed  Google Scholar 

32.
Fournier-Level, A. et al. A map of local adaptation in Arabidopsis thaliana. Science 334, 86–89. https://doi.org/10.1016/j.neubiorev.2011.03.0154 (2011).
ADS  CAS  Article  PubMed  Google Scholar 

33.
Biro, P. A., Beckmann, C. & Stamps, J. A. Small within-day increases in temperature affects boldness and alters personality in coral reef fish. Proc. R. Soc. Biol. 277, 71–77 (2010).
Article  Google Scholar 

34.
Kochhann, D., Campos, D. F. & Val, A. L. Experimentally increased temperature and hypoxia affect stability of social hierarchy and metabolism of the Amazonian cichlid Apistogramma agassizii. Comp. Biochem. Physiol. Part A Mol. Integr. Physiol. 190, 54–60. https://doi.org/10.1016/j.neubiorev.2011.03.0155 (2015).
CAS  Article  Google Scholar 

35.
Ratnasabapathi, D., Burns, J. & Souchek, R. Effects of temperature and prior residence on territorial aggression in the convict cichlid Cichlasoma nigrofasciatum. Aggress. Behav. 18, 365–372. https://doi.org/10.1002/1098-2337(1992)18:53.0.CO;2-E (1992).

36.
Careau, V. & Garland, T. Jr. Performance, personality, and energetics: correlation, causation, and mechanism. Physiol. Biochem. Zool. 85, 543–571 (2012).
Article  Google Scholar 

37.
Biro, P. A. & Stamps, J. A. Do consistent individual differences in metabolic rate promote consistent individual differences in behavior?. Trends Ecol. Evol. 25, 653–659. https://doi.org/10.1016/j.neubiorev.2011.03.0156 (2010).
Article  PubMed  Google Scholar 

38.
Magurran, A. E. & Seghers, B. H. Variation in schooling and aggression amongst guppy (Poecilia reticulata) populations in Trinidad. Behaviour 118, 214–234 (1991).
Article  Google Scholar 

39.
Kieffer, J. D., Kubacki, M. R., Phelan, F. J., Philipp, D. P. & Tufts, B. L. The effect of catch-and-release angling on the parental care behavior of male smallmouth bass. Trans. Am. Fish. Soc. 124, 70–76. https://doi.org/10.1577/1548-8659(1995)124 More

We collected data from all US states where school vaccine exemption information was freely available from the Department of Health website in any format. We were able to locate that data in 24 states (see Table 1 for a list of states included). Within these states, the number of years available varied relatively widely, between 19 years in California and a single year in 6 states. The most represented year in our dataset was 2017 (corresponding to school year 2017–2018). Because the dataset was compiled in June-July 2019, we note that it is likely that additional data for more recent years may be available, or that data may have become available in additional states not included in our dataset.
Table 1 Exemption data reporting varies widely across states.
Full size table

The data format varied widely between states, and exemptions were reported either as a number of exemptions or as a percentage of the enrolled students. We have elected to use number of students rather than percentages, and have transformed data as needed. For most states included in our dataset, the data are provided at the county level. In several states (Arizona, Colorado, Illinois, Maine, Michigan, South Dakota, Tennessee, Vermont, Oregon, and Washington), the data was provided at the school level, which we aggregated to the county.
Additional data processing was necessary in some cases. In Virginia, data was provided by school name, but county or city information was not included. We used a list of public and private schools to match school names with their respective county using fuzzy matching (with the ‘fuzzywuzzy’ Python package) with an 80% matching requirement. Our algorithm was unable to find a suitable match for between 3.8% and 6.8% of schools (depending on year), and these schools were not included in the final counts at the county level. Similarly, in Idaho, data at the school level included city information but county was not provided. We first matched city and county names, before aggregating the exemption data at the county level. Finally in New York state, exemptions were provided as percentages at the school level but enrollment information was not included. We obtained enrollment for public and private schools separately from the New York State Education Department, and used the school unique code to calculate exemption number from enrollment and exemption percentages. We then aggregated these numbers at the county level.
States reported data for exemptions based on varying definitions, so we selected data records based on data availability to make the data comparable across states. We aimed to achieve parsimonious definitions of total medical exemptions (Fig. 1a), total non-medical exemptions (Fig. 1b), and total exemptions (Fig. 1c), which includes both types of exemptions. We define medical exemptions as reported total medical exemptions. In Florida, permanent medical exemptions were reported separately from temporary medical exemptions, so permanent medical exemptions was chosen to represent total medical exemptions. To define total non-medical exemptions, we considered the state law regarding non-medical exemptions and the data availability. If the state reported total aggregated non-medical exemptions, that was selected as total non-medical exemptions. If the state reported only religious exemptions and only allows religious exemptions, that was selected as total non-medical exemptions. If the state reported only religious exemptions, but also allows philosophical exemptions, that was considered missing data. If the state allows philosophical exemptions and only reports philosophical exemptions, that was selected as total non-medical exemptions, as the state may not differentiate religious from philosophical. If the state allows philosophical exemptions and reports both religious and philosophical exemptions separately, these values were summed for total non-medical exemptions. To define total exemptions, if the state reported a total exemptions value, this value was used. If the state did not report a total exemptions value, but reported values for total medical exemptions and total non-medical exemptions, as defined above, these were summed for total exemptions. If the state was missing either medical or non-medical exemptions, but reported the total number of students with completed vaccinations, the total exemptions was the difference between the number of students enrolled and the number of students completed. This classification process is visualized in Fig. 1.
Fig. 1

Exemptions were classified by type to standardize reporting. Exemptions were classified as medical exemptions (a), non-medical exemptions (b), and total exemptions (c) to standardize reporting across states with different values reported.

Full size image

We also considered disease-specific exemptions reports. If a state reported the number of exemptions for a vaccine specific to a given infection, that value was used. If the state did not report exemptions, but did provide the total number complete for that disease, the difference between the enrolled students and the completed students was used. For pertussis-specific vaccination, we used DTaP exemptions where available, and TDaP exemptions where DTaP was not available. For measles-specific vaccination, if separate reports were available for measles, mumps, and rubella, the value for measles was used. If measles was not available, then the mumps or rubella exemptions were used, if available.
The data in the figures is only data reported for kindergartens in states where kindergarten-specific data was available, or K-12 data in states where kindergarten-specific data was not reported. States reported age groups heterogeneously, and data by other age groups is available in the data file. We note that Oregon reports kindergarten-specific data in 2014–2015, then K-12 data in 2016–2018. More