Ecology

1.
Le Quéré, C. et al. Global Carbon Budget 2018. Earth Syst. Sci. Data 10, 2141–2194 (2018).
ADS  Article  Google Scholar 
2.
Cook-Patton, S. C. et al. Mapping carbon accumulation potential from global natural forest regrowth. Nature 585, 545–550 (2020).
ADS  CAS  PubMed  Article  Google Scholar 

3.
Houghton, R. A., Byers, B. & Nassikas, A. A. A role for tropical forests in stabilizing atmospheric CO2. Nat. Clim. Chang. 5, 1022–1023 (2015).
ADS  Article  Google Scholar 

4.
Chazdon, R. L. et al. Carbon sequestration potential of second-growth forest regeneration in the Latin American tropics. Sci. Adv. 2, e1501639 (2016).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

5.
Philipson, C. D. et al. Active restoration accelerates the carbon recovery of human-modified tropical forests. Science 369, 838–841 (2020).
ADS  CAS  PubMed  Article  Google Scholar 

6.
Taubert, F. et al. Global patterns of tropical forest fragmentation. Nature 554, 519–522 (2018).
ADS  CAS  PubMed  Article  Google Scholar 

7.
Tabarelli, M., Lopes, A. V. & Peres, C. A. Edge-effects drive tropical forest fragments towards an early-successional system. Biotropica 40, 657–661 (2008).
Article  Google Scholar 

8.
Arroyo-Rodríguez, V. et al. Multiple successional pathways in human-modified tropical landscapes: new insights from forest succession, forest fragmentation and landscape ecology research. Biol. Rev. Camb. Philos. Soc. 92, 326–340 (2017).
PubMed  Article  Google Scholar 

9.
Collins, C. D. et al. Fragmentation affects plant community composition over time. Ecography 40, 119–130 (2017).
Article  Google Scholar 

10.
Hansen, M. C. et al. The fate of tropical forest fragments. Sci. Adv. 6 eaax8574 (2020).

11.
Qie, L. et al. Long-term carbon sink in Borneo’s forests halted by drought and vulnerable to edge effects. Nat. Commun. 8, 1966 (2017).
ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

12.
Thirumalai, K., DiNezio, P. N., Okumura, Y. & Deser, C. Extreme temperatures in Southeast Asia caused by El Niño and worsened by global warming. Nat. Commun. 8, 15531 (2017).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

13.
Cai, W. et al. Increased variability of eastern Pacific El Niño under greenhouse warming. Nature 564, 201–206 (2018).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

14.
Vogel, M. M., Hauser, M. & Seneviratne, S. I. Projected changes in hot, dry and wet extreme events’ clusters in CMIP6 multi-model ensemble. Environ. Res. Lett. 15, 094021 (2020).
ADS  Article  Google Scholar 

15.
McDowell, N. et al. Drivers and mechanisms of tree mortality in moist tropical forests. N. Phytol. https://doi.org/10.1111/nph.15027@10.1111/(ISSN)1469-8137.DroughtImpactsonTropicalForests (2018).

16.
Walker, A. P. et al. Decadal biomass increment in early secondary succession woody ecosystems is increased by CO2 enrichment. Nat. Commun. 10, 454 (2019).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

17.
Riutta, T. et al. Logging disturbance shifts net primary productivity and its allocation in Bornean tropical forests. Glob. Chang. Biol. 24, 2913–2928 (2018).
ADS  PubMed  Article  Google Scholar 

18.
Both, S. et al. Logging and soil nutrients independently explain plant trait expression in tropical forests. N. Phytol. 221, 1853–1865 (2019).
CAS  Article  Google Scholar 

19.
Swinfield, T. et al. Imaging spectroscopy reveals the effects of topography and logging on the leaf chemistry of tropical forest canopy trees. Glob. Chang. Biol. 26, 989–1002 (2020).
ADS  PubMed  Article  Google Scholar 

20.
Jotan, P., Maycock, C. R., Burslem, D., Berhaman, A. & Both, S. Comparative vessel traits of macaranga gigantea and vatica dulitensis from Malaysian Borneo. J. Trop. Sci. 32, 25–34 (2020).
Google Scholar 

21.
Uriarte, M. et al. Impacts of climate variability on tree demography in second growth tropical forests: the importance of regional context for predicting successional trajectories. Biotropica 48, 780–797 (2016).
Article  Google Scholar 

22.
Park Williams, A. et al. Temperature as a potent driver of regional forest drought stress and tree mortality. Nat. Clim. Chang. 3, 292–297 (2013).
ADS  Article  Google Scholar 

23.
Wolfe, B. T., Sperry, J. S. & Kursar, T. A. Does leaf shedding protect stems from cavitation during seasonal droughts? A test of the hydraulic fuse hypothesis. N. Phytol. 212, 1007–1018 (2016).
Article  Google Scholar 

24.
Slik, J. W. F. El Niño droughts and their effects on tree species composition and diversity in tropical rain forests. Oecologia 141, 114–120 (2004).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

25.
Jucker, T. et al. Topography shapes the structure, composition and function of tropical forest landscapes. Ecol. Lett. 21, 989–1000 (2018).
PubMed  PubMed Central  Article  Google Scholar 

26.
Itoh, A. et al. Importance of topography and soil texture in the spatial distribution of two sympatric dipterocarp trees in a Bornean rainforest. Ecol. Res. 18, 307–320 (2003).
Article  Google Scholar 

27.
Stovall, A. E. L., Shugart, H. H. & Yang, X. Reply to ‘Height-related changes in forest composition explain increasing tree mortality with height during an extreme drought’. Nat. Commun. 11, 3401 (2020).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

28.
Maréchaux, I. et al. Drought tolerance as predicted by leaf water potential at turgor loss point varies strongly across species within an Amazonian forest. Funct. Ecol. 29, 1268–1277 (2015).
Article  Google Scholar 

29.
Cosme, L. H. M., Schietti, J., Costa, F. R. C. & Oliveira, R. S. The importance of hydraulic architecture to the distribution patterns of trees in a central Amazonian forest. N. Phytol. 215, 113–125 (2017).
Article  Google Scholar 

30.
Bittencourt, P. R. L. et al. Amazonia trees have limited capacity to acclimate plant hydraulic properties in response to long-term drought. Glob. Chang. Biol. https://doi.org/10.1111/gcb.15040 (2020).

31.
Luke, S. H. et al. Riparian buffers in tropical agriculture: Scientific support, effectiveness and directions for policy. J. Appl. Ecol. 56, 85–92 (2019).
Article  Google Scholar 

32.
Padfield, R. et al. Co-Producing a Research Agenda for Sustainable Palm Oil. https://doi.org/10.3389/ffgc.2019.00013 (2019).

33.
Zhao, K. et al. Utility of multitemporal lidar for forest and carbon monitoring: Tree growth, biomass dynamics, and carbon flux. Remote Sens. Environ. 204, 883–897 (2018).
ADS  Article  Google Scholar 

34.
Simonson, W., Ruiz-Benito, P., Valladares, F. & Coomes, D. Modelling above-ground carbon dynamics using multi-temporal airborne lidar: insights from a Mediterranean woodland. Biogeosciences 13, 961–973 (2016).
ADS  CAS  Article  Google Scholar 

35.
Leitold, V. et al. El Niño drought increased canopy turnover in Amazon forests. N. Phytol. 219, 959–971 (2018).
CAS  Article  Google Scholar 

36.
Moura, Y. Mde et al. Carbon dynamics in a human-modified tropical forest: a case study using multi-temporal LiDAR data. Remote Sens. 12, 430 (2020).
ADS  Article  Google Scholar 

37.
Simonson, W., Allen, H. & Coomes, D. Effect of tree phenology on LiDAR measurement of mediterranean forest structure. Remote Sens. 10, 659 (2018).
ADS  Article  Google Scholar 

38.
Sullivan, M. J. P. et al. Long-term thermal sensitivity of Earth’s tropical forests. Science 368, 869–874 (2020).
ADS  CAS  PubMed  Article  Google Scholar 

39.
Coomes, D. A. et al. Area-based vs tree-centric approaches to mapping forest carbon in Southeast Asian forests from airborne laser scanning data. Remote Sens. Environ. 194, 77–88 (2017).
ADS  Article  Google Scholar 

40.
Asner, G. P. et al. Mapped aboveground carbon stocks to advance forest conservation and recovery in Malaysian Borneo. Biol. Conserv. 217, 289–310 (2018).
Article  Google Scholar 

41.
Burton, C., Rifai, S. & Malhi, Y. Inter-comparison and assessment of gridded climate products over tropical forests during the 2015/2016 El Niño. Philosophical Transactions of the Royal Society B: Biological Sciences. 373, 1760 (2018).
Article  Google Scholar 

42.
Ordway, E. M. & Asner, G. P. Carbon declines along tropical forest edges correspond to heterogeneous effects on canopy structure and function. Proc. Natl Acad. Sci. USA 117, 7863–7870 (2020).
CAS  PubMed  Article  Google Scholar 

43.
Phillips, O. L. et al. Drought sensitivity of the Amazon rainforest. Science 323, 1344–1347 (2009).
ADS  CAS  PubMed  Article  Google Scholar 

44.
Sevanto, S. Phloem transport and drought. J. Exp. Bot. 65, 1751–1759 (2014).
CAS  PubMed  Article  Google Scholar 

45.
Aleixo, I. et al. Amazonian rainforest tree mortality driven by climate and functional traits. Nat. Clim. Chang. 9, 384–388 (2019).
ADS  Article  Google Scholar 

46.
Woodgate, W. et al. Quantifying the impact of woody material on leaf area index estimation from hemispherical photography using 3D canopy simulations. Agric. Meteorol. 226-227, 1–12 (2016).
Article  Google Scholar 

47.
Nunes, M. H. et al. Changes in leaf functional traits of rainforest canopy trees associated with an El Niño event in Borneo. Environ. Res. Lett. 14, 085005 (2019).
ADS  CAS  Article  Google Scholar 

48.
Tang, H. & Dubayah, R. Light-driven growth in Amazon evergreen forests explained by seasonal variations of vertical canopy structure. Proc. Natl Acad. Sci. USA 114, 2640–2644 (2017).
CAS  PubMed  Article  Google Scholar 

49.
Doughty, C. E. et al. Drought impact on forest carbon dynamics and fluxes in Amazonia. Nature 519, 78–82 (2015).
ADS  CAS  PubMed  Article  Google Scholar 

50.
Marvin, D. C. & Asner, G. P. Branchfall dominates annual carbon flux across lowland Amazonian forests. Environ. Res. Lett. 11, 094027 (2016).
ADS  Article  CAS  Google Scholar 

51.
Roussel, J.-R., Caspersen, J., Béland, M., Thomas, S. & Achim, A. Removing bias from LiDAR-based estimates of canopy height: accounting for the effects of pulse density and footprint size. Remote Sens. Environ. 198, 1–16 (2017).
ADS  Article  Google Scholar 

52.
Sist, P. & Nguyen-Thé, N. Logging damage and the subsequent dynamics of a dipterocarp forest in East Kalimantan (1990–1996). Ecol. Manag. 165, 85–103 (2002).
Article  Google Scholar 

53.
Rutishauser, E. et al. Rapid tree carbon stock recovery in managed Amazonian forests. Curr. Biol. 25, R787–R788 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

54.
Giambelluca, T. W., Ziegler, A. D., Nullet, M. A., Truong, D. M. & Tran, L. T. Transpiration in a small tropical forest patch. Agric. Meteorol. 117, 1–22 (2003).
Article  Google Scholar 

55.
Ewers, R. M. & Banks-Leite, C. Fragmentation impairs the microclimate buffering effect of tropical forests. PLoS ONE 8, e58093 (2013).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

56.
Laurance, W. F. & Curran, T. J. Impacts of wind disturbance on fragmented tropical forests: a review and synthesis. Austral. Ecol. 33, 399–408 (2008).
Article  Google Scholar 

57.
Jucker, T. et al. Canopy structure and topography jointly constrain the microclimate of human-modified tropical landscapes. Glob. Chang. Biol. 24, 5243–5258 (2018).
ADS  PubMed  Article  PubMed Central  Google Scholar 

58.
Laurance, W. F. Theory meets reality: how habitat fragmentation research has transcended island biogeographic theory. Biol. Conserv. 141, 1731–1744 (2008).
Article  Google Scholar 

59.
Muscarella, R., Kolyaie, S., Morton, D. C., Zimmerman, J. K. & Uriarte, M. Effects of topography on tropical forest structure depend on climate context. J. Ecol. 108, 145–159 (2020).
Article  Google Scholar 

60.
Werner, F. A. & Homeier, J. Is tropical montane forest heterogeneity promoted by a resource‐driven feedback cycle? Evidence from nutrient relations, herbivory and litter decomposition along a topographical gradient. Funct. Ecol. 29, 430–440 (2015).
Article  Google Scholar 

61.
Gonzalez‐Akre, E. et al. Patterns of tree mortality in a temperate deciduous forest derived from a large forest dynamics plot. Ecosphere 7, G04014 (2016).
Article  Google Scholar 

62.
Hansen, M. C. et al. High-resolution global maps of 21st-century forest cover change. Science 342, 850–853 (2013).
ADS  CAS  Article  Google Scholar 

63.
Williamson, J. et al. Riparian Buffers Act as Microclimatic Refugia in Oil Palm Landscapes. https://doi.org/10.17863/CAM.57796 (2020).

64.
Hurst, M. D., Mudd, S. M., Walcott, R., Attal, M. & Yoo, K. Using hilltop curvature to derive the spatial distribution of erosion rates: hilltop curvature predicts erosion rates. J. Geophys. Res. 117, F2 (2012).
Google Scholar 

65.
Gaveau, D. L. A. et al. Rapid conversions and avoided deforestation: examining four decades of industrial plantation expansion in Borneo. Sci. Rep. 6, 32017 (2016).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

66.
Ewers, R. M. et al. A large-scale forest fragmentation experiment: the Stability of Altered Forest Ecosystems Project. Philos. Trans. R. Soc. Lond. B Biol. Sci. 366, 3292–3302 (2011).
PubMed  PubMed Central  Article  Google Scholar 

67.
Pfeifer, M. et al. Mapping the structure of Borneo’s tropical forests across a degradation gradient. Remote Sens. Environ. 176, 84–97 (2016).
ADS  Article  Google Scholar 

68.
Reynolds, G., Payne, J., Sinun, W., Mosigil, G. & Walsh, R. P. D. Changes in forest land use and management in Sabah, Malaysian Borneo, 1990-2010, with a focus on the Danum Valley region. Philos. Trans. R. Soc. Lond. B Biol. Sci. 366, 3168–3176 (2011).
PubMed  PubMed Central  Article  Google Scholar 

69.
Clarke, A. Principles of thermal ecology: temperature, energy, and life. Oxford Scholarship Online https://doi.org/10.1093/oso/9780199551668.001.0001 (2017).

70.
Bolton, D. The computation of equivalent potential temperature. Mon. Weather Rev. 108, 1046–1053 (1980).
ADS  Article  Google Scholar 

71.
Asner, G. P. et al. Carnegie airborne observatory-2: increasing science data dimensionality via high-fidelity multi-sensor fusion. Remote Sens. Environ. 124, 454–465 (2012).
ADS  Article  Google Scholar 

72.
Jucker, T. et al. Estimating aboveground carbon density and its uncertainty in Borneo’s structurally complex tropical forests using airborne laser scanning. Biogeosciences, 15, 3811–3830 (2018).
ADS  Article  Google Scholar 

73.
Khosravipour, A., Skidmore, A. K., Isenburg, M., Wang, T. & Hussin, Y. A. Generating pit-free canopy height models from airborne lidar. Photogramm. Eng. Remote Sens. 80, 863–872 (2014).
Article  Google Scholar 

74.
Asner, G. P. & Mascaro, J. Mapping tropical forest carbon: calibrating plot estimates to a simple LiDAR metric. Remote Sens. Environ. 140, 614–624 (2014).
ADS  Article  Google Scholar 

75.
Pearson, T. R. H., Brown, S. & Casarim, F. M. Carbon emissions from tropical forest degradation caused by logging. Environ. Res. Lett. 9, 034017 (2014).
ADS  Article  CAS  Google Scholar 

76.
Gobakken, T. G. & Næsset, E. N. Assessing effects of laser point density, ground sampling intensity, and field sample plot size on biophysical stand properties derived from airborne laser scanner data. Can. J. For. Res. https://doi.org/10.1139/X07-219 (2008).

77.
Gray, C. L., Slade, E. M., Mann, D. J. & Lewis, O. T. Do riparian reserves support dung beetle biodiversity and ecosystem services in oil palm-dominated tropical landscapes? Ecol. Evol. 4, 1049–1060 (2014).
PubMed  PubMed Central  Article  Google Scholar 

78.
Metcalfe, P., Beven, K. & Freer, J. Dynamic TOPMODEL: a new implementation in R and its sensitivity to time and space steps. Environ. Model. Softw. 72, 155–172 (2015).
Article  Google Scholar 

79.
Condit, R. et al. Tropical forest dynamics across a rainfall gradient and the impact of an El Niño Dry Season. J. Trop. Ecol. 20, 51–72 (2004).
Article  Google Scholar 

80.
H’edl, R. et al. A new technique for inventory of permanent plots in tropical forests: a case study from lowland dipterocarp forest in Kuala Belalong, Brunei Darussalam. Blumea J. Biodivers. Evolut. Biogeogr. Plants 54, 124–130 (2009).
Article  Google Scholar 

81.
Kent, R., Lindsell, J. A., Laurin, G. V., Valentini, R. & Coomes, D. A. Airborne LiDAR detects selectively logged tropical forest even in an advanced stage of recovery. Remote Sens. 7, 8348–8367 (2015).
ADS  Article  Google Scholar 

82.
Gonsamo, A., Walter, J.-M. N. & Pellikka, P. Sampling gap fraction and size for estimating leaf area and clumping indices from hemispherical photographs. Can. J. Res. 40, 1588–1603 (2010).
Article  Google Scholar 

83.
Kalacska, M. E. R., Sanchez-Azofeifa, G. A., Calvo-Alvarado, J. C., Rivard, B. & Quesada, M. Effects of season and successional stage on leaf area index and spectral vegetation indices in three mesoamerican tropical dry forests1. Biotropica 37, 486–496 (2005).
Article  Google Scholar 

84.
Thimonier, A., Sedivy, I. & Schleppi, P. Estimating leaf area index in different types of mature forest stands in Switzerland: a comparison of methods. Eur. J. Res. 129, 543–562 (2010).
Article  Google Scholar 

85.
Chen, J. M. & Cihlar, J. Quantifying the effect of canopy architecture on optical measurements of leaf area index using two gap size analysis methods. IEEE Trans. Geosci. Remote Sens. 33, 777–787 (1995).
ADS  Article  Google Scholar 

86.
Schleppi, P., Conedera, M., Sedivy, I. & Thimonier, A. Correcting non-linearity and slope effects in the estimation of the leaf area index of forests from hemispherical photographs. Agric. Meteorol. 144, 236–242 (2007).
Article  Google Scholar 

87.
Harmon, M. E., Whigham, D. F., Sexton, J. & Olmsted, I. Decomposition and mass of woody detritus in the dry tropical forests of the Northeastern Yucatan Peninsula, Mexico. Biotropica 27, 305–316 (1995).
Article  Google Scholar 

88.
Team, R. C. R: A Language and Environment for Statistical Computing. (Foundation for Statistical Computing, Vienna, Austria. 2017). Available online: www.r-project.org (accessed 14 Febuary 2019) (2018).

89.
Ploton, P. et al. Spatial validation reveals poor predictive performance of large-scale ecological mapping models. Nat. Commun. 11, 4540 (2020).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

90.
Wedeux, B. et al. Dynamics of a human-modified tropical peat swamp forest revealed by repeat lidar surveys. Glob. Chang. Biol. https://doi.org/10.1111/gcb.15108 (2020). More

1.
Grant, P. R. & Grant, B. R. How and why Species Multiply: The Radiation of Darwin’s Finches. (Princeton University Press, 2008).
2.
Baldwin, B. G. & Sanderson, M. J. Age and rate of diversification of the Hawaiian silversword alliance (Compositae). Proc. Natl Acad. Sci. USA 95, 9402–9406 (1998).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

3.
Losos, J. B. & Ricklefs, R. E. Adaptation and diversification on islands. Nature 457, 830–836 (2009).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

4.
Macarthur, R. H. & Wilson, E. O. The Theory of Island Biogeography. (Princeton University Press, 1967).

5.
Lewontin, R. C. The organism as the subject and object of evolution. Scientia 77, 65 (1983).
Google Scholar 

6.
Blows, M. W. & Hoffmann, A. A. A reassessment of genetic limits to evolutionary change. Ecology 86, 1371–1384 (2005).
Article  Google Scholar 

7.
Hansen, T. F. & Houle, D. Measuring and comparing evolvability and constraint in multivariate characters. J. Evol. Biol. 21, 1201–1219 (2008).
CAS  PubMed  Article  PubMed Central  Google Scholar 

8.
West-Eberhard, M. J. Developmental Plasticity and Evolution. (Oxford University Press, 2003).

9.
Wagner, G. P. & Altenberg, L. Perspective: complex adaptations and the evolution of evolvability. Evolution 50, 967–976 (1996).
PubMed  Article  PubMed Central  Google Scholar 

10.
Hendrikse, J. L., Parsons, T. E. & Hallgrímsson, B. Evolvability as the proper focus of evolutionary developmental biology. Evol. Dev. 9, 393–401 (2007).
PubMed  Article  PubMed Central  Google Scholar 

11.
Klingenberg, C. P. Studying morphological integration and modularity at multiple levels: concepts and analysis. Philos. Trans. R. Soc. B 369, 20130249 (2014).
Article  Google Scholar 

12.
Jablonski, D. Approaches to macroevolution: 1. General concepts and origin of variation. Evol. Biol. 44, 427–450 (2017).
PubMed  PubMed Central  Article  Google Scholar 

13.
Uller, T., Moczek, A. P., Watson, R. A., Brakefield, P. M. & Laland, K. N. Developmental bias and evolution: a regulatory network perspective. Genetics 209, 949–966 (2018).
PubMed  PubMed Central  Article  Google Scholar 

14.
Hansen, T. F. Is modularity necessary for evolvability? Remarks on the relationship between pleiotropy and evolvability. Biosystems 69, 83–94 (2003).
PubMed  Article  PubMed Central  Google Scholar 

15.
Goswami, A., Binder, W. J., Meachen, J. & O’Keefe, F. R. The fossil record of phenotypic and modularity: a deep-time perspective on developmental and evolutionary dynamics. Proc. Natl Acad. Sci. USA 112, 4891–4896 (2015).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

16.
Armbruster, W. S., Pelabon, C., Bolstad, G. H. & Hansen, T. F. Integrated phenotypes: understanding trait covariation in plants and animals. Philos. Trans. R. Soc. B 369, 20130245 (2014).
Article  Google Scholar 

17.
Felice, R. N., Randau, M. & Goswami, A. A fly in a tube: macroevolutionary expectations for integrated phenotypes. Evolution 72, 2580–2594 (2018).
PubMed  PubMed Central  Article  Google Scholar 

18.
Goswami, A., Smaers, J. B., Soligo, C. & Polly, P. D. The macroevolutionary consequences of phenotypic integration: from development to deep time. Philos. Trans. R. Soc. B 369, 20130254 (2014).
CAS  Article  Google Scholar 

19.
Cheverud, J. M. Phenotypic, genetic, and environmental morphological integration in the cranium. Evolution 36, 499–516 (1982).
PubMed  Article  PubMed Central  Google Scholar 

20.
Wagner, G. P., Pavlicev, M. & Cheverud, J. M. The road to modularity. Nat. Rev. Genet. 8, 921–931 (2007).
CAS  PubMed  Article  PubMed Central  Google Scholar 

21.
Melo, D., Porto, A., Cheverud, J. M. & Marroig, G. Modularity: genes, development and evolution. Annu. Rev. Ecol. Evol. Syst. 47, 463–486 (2016).
PubMed  PubMed Central  Article  Google Scholar 

22.
Gerhart, J. & Kirschner, M. The theory of facilitated variation. Proc. Natl Acad. Sci. USA 104, 8582–8589 (2007).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

23.
Villmoare, B., Fish, J. & Jungers, W. Selection, morphological integration, and strepsirrhine locomotor adaptations. Evol. Biol. 38, 88–99 (2011).
Article  Google Scholar 

24.
Navalon, G., Marugan-Lobon, J., Bright, J. A., Cooney, C. R. & Rayfield, E. J. The consequences of craniofacial integration for the adaptive radiations of Darwin’s finches and Hawaiian honeycreepers. Nat. Ecol. Evol. 4, 270–278 (2020).
PubMed  Article  PubMed Central  Google Scholar 

25.
Nicholson, K. E. et al. Mainland colonization by island lizards. J. Biogeogr. 32, 929–938 (2005).
Article  Google Scholar 

26.
Poe, S. et al. A phylogenetic, biogeographic, and taxonomic study of all extant species of Anolis (Squamata; Iguanidae). Syst. Biol. 66, 663–697 (2017).
PubMed  Article  PubMed Central  Google Scholar 

27.
Jackman, T., Losos, J. B., Larson, A. & de Queiroz, K. in Molecular Evolution and Adaptive Radiation (eds Givnish, T. & Systma, K.) 535–557 (Cambridge University Press, 1997).

28.
Underwood, G. The anoles of the Eastern Caribbean (Sauria, Iguanidae). Revisionary notes. Bull. Mus. Comp. Zool., Part III 121, 191–226 (1959).
Google Scholar 

29.
Losos, J. B. Lizards in an Evolutionary Tree: Ecology and Adaptive Radiation of Anoles. Vol. 10 (University of California Press, 2009).

30.
Pinto, G., Mahler, D. L., Harmon, L. J. & Losos, J. B. Testing the island effect in adaptive radiation: rates and patterns of morphological diversification in Caribbean and mainland Anolis lizards. Proc. R. Soc. B 275, 2749–2757 (2008).
PubMed  Article  PubMed Central  Google Scholar 

31.
Poe, S. & Anderson, C. G. The existence and evolution of morphotypes in Anolis lizards: coexistence patterns, not adaptive radiations, distinguish mainland and island faunas. PeerJ 6, e6040 (2019).
PubMed  PubMed Central  Article  Google Scholar 

32.
Irschick, D. J., Vitt, L. J., Zani, P. A. & Losos, J. B. A comparison of evolutionary radiations in mainland and Caribbean Anolis lizards. Ecology 78, 2191–2203 (1997).
Article  Google Scholar 

33.
Macrini, T. E., Irschick, D. J. & Losos, J. B. Ecomorphological differences in toepad characteristics between mainland and island anoles. J. Herpetol. 37, 52–58 (2003).
Article  Google Scholar 

34.
Velasco, J. A. & Herrel, A. Ecomorphology of Anolis lizards of the Choco’ region in Colombia and comparisons with Greater Antillean ecomorphs. Biol. Biol. J. Linn. Soc. 92, 403–403 (2007).
Article  Google Scholar 

35.
Williams, E. E. in Evol. Biol. Vol. 6 (eds Theodosius Dobzhansky, MaxK Hecht, & WilliamC Steere) Ch. 3, 47–89 (Springer US, 1972).

36.
Williams, E. E. in Lizard ecology: studies of a model organism (eds Pianka, E. R., Huey, R. B. & Schoener, T. W.) 326–370 (Harvard University Press, 1983).

37.
Losos, J. B., Jackman, T. R., Larson, A., Queiroz, K. & Rodriguez-Schettino, L. Contingency and determinism in replicated adaptive radiations of island lizards. Science 279, 2115–2118 (1998).
ADS  CAS  PubMed  Article  Google Scholar 

38.
Tinius, A. & Russell, A. P. Geometric morphometric analysis of the breast-shoulder apparatus of lizards: a test case using Jamaican anoles (Squamata: Dactyloidae). Anat. Rec. 297, 410–432 (2014).
Article  Google Scholar 

39.
Tinius, A., Russell, A. P., Jamniczky, H. A. & Anderson, J. S. What is bred in the bone: ecomorphological associations of pelvic girdle form in greater Antillean Anolis lizards. J. Morphol. 279, 1016–1030 (2018).
PubMed  Article  Google Scholar 

40.
Adams, D. C. & Collyer, M. L. Phylogenetic comparative methods and the evolution of multivariate phenotypes. Annu. Rev. Ecol. Evol. Syst. 50, 405–425 (2019).
Article  Google Scholar 

41.
Legendre, P. & Legendre, L. Numerical Ecology. (Elsevier, 2012).

42.
Collyer, M. L., Davis, M. A. & Adams, D. C. Making heads or tails of combined landmark configurations in geometric morphometric data. Evol. Biol. 47, 193–205 (2020).
Article  Google Scholar 

43.
Kumar, S., Stecher, G., Suleski, M. & Hedges, S. B. TimeTree: a resource for timelines, timetrees, and divergence times. Mol. Biol. Evol. 34, 1812–1819 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

44.
Nishimoto, S. & Logan, M. P. O. Subdivision of the lateral plate mesoderm and specification of the forelimb and hindlimb forming domains. Semin. Cell Dev. Biol. 49, 102–108 (2016).
PubMed  Article  PubMed Central  Google Scholar 

45.
Shou, S., Scott, V., Reed, C., Hitzemann, R. & Stadler, H. S. Transcriptome analysis of the murine forelimb and hindlimb autopod. Dev. Dyn. 234, 74–89 (2005).
CAS  PubMed  Article  PubMed Central  Google Scholar 

46.
Margulies, E. H., Kardia, S. L. R. & Innis, J. W. A comparative molecular analysis of developing mouse forelimbs and hindlimbs using Serial Analysis of Gene Expression (SAGE). Genome Res. 11, 1686–1698 (2001).
CAS  PubMed  PubMed Central  Article  Google Scholar 

47.
Adams, D. C. & Collyer, M. L. On the comparison of the strength of morphological integration across morphometric datasets. Evolution 70, 2623–2631 (2016).
PubMed  Article  PubMed Central  Google Scholar 

48.
Adams, D. C. Evaluating modularity in morphometric data: challenges with the RV coefficient and a new test measure. Methods Ecol. Evol. 7, 565–572 (2016).
Article  Google Scholar 

49.
Adams, D. C. & Collyer, M. L. Comparing the strength of modular signal, and evaluating alternative modular hypotheses, using covariance ratio effect sizes with morphometric data. Evolution 73, 2352–2367 (2019).
PubMed  Article  PubMed Central  Google Scholar 

50.
Dellinger, A. S. et al. Modularity increases rate of floral evolution and adaptive success for functionally specialized pollination systems. Commun. Biol. 2, 453 (2019).
PubMed  PubMed Central  Article  Google Scholar 

51.
Venditti, C., Meade, A. & Pagel, M. Multiple routes to mammalian diversity. Nature 479, 393–396 (2011).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

52.
Cooney, C. R. et al. Mega-evolutionary dynamics of the adaptive radiation of birds. Nature 542, 344 (2017).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

53.
Marki, P. Z., Kennedy, J. D., Cooney, C. R., Rahbek, C. & Fjeldsa, J. Adaptive radiation and the evolution of nectarivory in a large songbird clade. Evolution 73, 1226–1240 (2019).
PubMed  Article  PubMed Central  Google Scholar 

54.
Brown, R. L. What evolvability really is. Br. J. Philos. Sci. 65, 549–572 (2013).
MathSciNet  Article  Google Scholar 

55.
Watson, R. A. & Szathmary, E. How can evolution learn? Trends Ecol. Evol. 31, 147–157 (2016).
PubMed  Article  PubMed Central  Google Scholar 

56.
Young, N. M. & Hallgrimsson, B. Serial homology and the evolution of mammalian limb covariation structure. Evolution 59, 2691–2704 (2005).
PubMed  Article  PubMed Central  Google Scholar 

57.
Young, N. M., Wagner, G. P. & Hallgrimsson, B. Development and the evolvability of human limbs. Proc. Natl Acad. Sci. USA 107, 3400–3405 (2010).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

58.
Kelly, E. M. & Sears, K. E. Reduced phenotypic covariation in marsupial limbs and the implications for mammalian evolution. Biol. J. Linn. Soc. 102, 22–36 (2011).
Article  Google Scholar 

59.
Bennett, C. V. & Goswami, A. Does developmental strategy drive limb integration in marsupials and monotremes? Mamm. Biol. 76, 79–83 (2011).
Article  Google Scholar 

60.
Martin-Serra, A. & Benson, R. B. J. Developmental constraints do not influence long-term phenotypic evolution of marsupial forelimbs as revealed by interspecific disparity and integration patterns. Am. Nat. 195, 547–560 (2020).
PubMed  Article  PubMed Central  Google Scholar 

61.
Parter, M., Kashtan, N. & Alon, U. Facilitated variation: how evolution learns from past environments to generalize to new environments. PLoS Comp. Biol. 4, e1000206 (2008).
ADS  Article  CAS  Google Scholar 

62.
Kouvaris, K., Clune, J., Kounios, L., Brede, M. & Watson, R. A. How evolution learns to generalise: Using the principles of learning theory to understand the evolution of developmental organisation. PLoS Comp. Biol. 13, e1005358 (2017).
ADS  Article  CAS  Google Scholar 

63.
Brun-Usan, M., Rago, A., Thies, C., Uller, T. & Watson, R. A. Developmental models reveal the role of phenotypic plasticity in explaining genetic evolvability. bioRxiv https://doi.org/10.1101/2020.06.29.179226 (2020).

64.
Shanahan, T. Phylogenetic inertia and Darwin’s higher law. Stud. Hist. Philos. Sci. Part C 42, 60–68 (2011).
Article  Google Scholar 

65.
Houle, D., Bolstad, G. H., van der Linde, K. & Hansen, T. F. Mutation predicts 40 million years of fly wing evolution. Nature 548, 447–450 (2017).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

66.
Braendle, C., Baer, C. F. & Felix, M. A. Bias and evolution of the mutationally accessible phenotypic space in a developmental system. PLoS Genet. 6, e1000877 (2010).
PubMed  PubMed Central  Article  CAS  Google Scholar 

67.
Haber, A. Phenotypic covariation and morphological diversification in the ruminant skull. Am. Nat. 187, 576–591 (2016).
PubMed  Article  PubMed Central  Google Scholar 

68.
Schluter, D. Adaptive radiation along genetic lines of least resistance. Evolution 50, 1766–1774 (1996).
PubMed  Article  PubMed Central  Google Scholar 

69.
Hanot, P., Herrel, A., Guintard, C. & Cornette, R. The impact of artificial selection on morphological integration in the appendicular skeleton of domestic horses. J. Anat. 232, 657–673 (2018).
PubMed  PubMed Central  Article  Google Scholar 

70.
Penna, A., Melo, D., Bernardi, S., Oyarzabal, M. I. & Marroig, G. The evolution of phenotypic integration: How directional selection reshapes covariation in mice. Evolution 71, 2370–2380 (2017).
PubMed  PubMed Central  Article  Google Scholar 

71.
Watson, R. A., Wagner, G. P., Pavlicev, M., Weinreich, D. M. & Mills, R. The evolution of phenotypic correlations and “developmental memory”. Evolution 68, 1124–1138 (2014).
PubMed  PubMed Central  Article  Google Scholar 

72.
Donihue, C. M. et al. Hurricane effects on Neotropical lizards span geographic and phylogenetic scales. Proc. Natl Acad. Sci. USA 117, 10429–10434 (2020).
CAS  PubMed  Article  PubMed Central  Google Scholar 

73.
Feiner, N., Jackson, I. S. C., Munch, K. L., Radersma, R. & Uller, T. Plasticity and evolutionary convergence in the locomotor skeleton of Greater Antillean Anolis lizards. eLife 9, e57468 (2020).
CAS  PubMed  PubMed Central  Article  Google Scholar 

74.
Vanhooydonck, B. & Irschick, D. in Topics in functional and ecological vertebrate morphology (eds Aerts, P., D’Août, K., Herrel, A. & Van Damme, R.) (Shaker Publishing, 2002).

75.
Schluter, D. The Ecology of Adaptive Radiation. (Oxford: Oxford University Press, 2000).

76.
Roughgarden, J. Anolis Lizards of the Caribbean: Ecology, Evolution, and Plate Tectonics. (Oxford University Press, 1995).

77.
Stacklies, W., Redestig, H., Scholz, M., Walther, D. & Selbig, J. pcaMethods-a bioconductor package providing PCA methods for incomplete data. Bioinformatics 23, 1164–1167 (2007).
CAS  PubMed  Article  PubMed Central  Google Scholar 

78.
Losos, J. B. et al. Evolutionary implications of phenotypic plasticity in the hindlimb of the lizard Anolis sagrei. Evolution 54, 301–305 (2000).
CAS  PubMed  PubMed Central  Google Scholar 

79.
Tinius, A. Geometric morphometric analysis of the breast-shoulder apparatus of Greater Antillean anole ecomorphs PhD thesis, (University of Calgary, 2016).

80.
Cignoni, P. et al. in Eurographics Italian Chapter Conference (eds Scarano, V., De Chiara, R. & Erra, U.) (The Eurographics Association, 2008).

81.
Geomorph: Software for geometric morphometric analyses. R package version 3.1.0. (2019).

82.
Olsen, A. M. & Westneat, M. W. StereoMorph: an R package for the collection of 3D landmarks and curves using a stereo camera set-up. Methods Ecol. Evol. 6, 351–356 (2015).
Article  Google Scholar 

83.
Mahler, D. L., Ingram, T., Revell, L. J. & Losos, J. B. Exceptional convergence on the macroevolutionary landscape in island lizard radiations. Science 341, 292–295 (2013).
ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

84.
Rohlf, F. J. Shape statistics: procrustes superimpositions and tangent spaces. J. Classif. 16, 197–223 (1999).
MATH  Article  Google Scholar 

85.
Uetz, P., Freed, P. & Hosek, J. The Reptile Database http://www.reptile-database.org (2019).

86.
Pyron, R. A., Burbrink, F. T. & Wiens, J. J. A phylogeny and revised classification of Squamata, including 4161 species of lizards and snakes. BMC Evol. Biol. 13, 93 (2013).
PubMed  PubMed Central  Article  CAS  Google Scholar 

87.
Köhler, G. & Hedges, S. B. A revision of the green anoles of Hispaniola with description of eight new species (Reptilia, Squamata, Dactyloidae). Nov. Carib. 9, 1–135 (2016).
Google Scholar 

88.
Hofmann, E. P. & Townsend, J. H. Origins and biogeography of the Anolis crassulus subgroup (Squamata: Dactyloidae) in the highlands of Nuclear Central America. BMC Evol. Biol. 17, 267 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

89.
Mahler, D. L. et al. Discovery of a giant chameleon-like lizard (Anolis) on hispaniola and its significance to understanding replicated adaptive radiations. Am. Nat. 188, 357–364 (2016).
PubMed  Article  PubMed Central  Google Scholar 

90.
Kohler, J., Hahn, M. & Kohler, G. Divergent evolution of hemipenial morphology in two cryptic species of mainland anoles related to Anolis polylepis. Salamandra 48, 1–11 (2012).
Google Scholar 

91.
Kohler, G., Perez, R. G. T., Petersen, C. B. P. & De la Cruz, F. R. M. A revision of the Mexican Anolis (Reptilia, Squamata, Dactyloidae) from the Pacific versant west of the Isthmus de Tehuantepec in the states of Oaxaca, Guerrero, and Puebla, with the description of six new species. Zootaxa 3862, 1 (2014).
PubMed  Article  PubMed Central  Google Scholar 

92.
Revell, L. J. phytools: an R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).
Article  Google Scholar 

93.
Nicholson, K. E., Crother, B. I., Guyer, C. & Savage, J. M. It is time for a new classification of anoles (Squamata: Dactyloidae). Zootaxa 3477, 1–108 (2012).
Article  Google Scholar 

94.
Goswami, A. & Finarelli, J. A. EMMLi: a maximum likelihood approach to the analysis of modularity. Evolution 70, 1622–1637 (2016).
PubMed  Article  PubMed Central  Google Scholar 

95.
Bookstein, F. L. et al. Cranial integration in Homo: singular warps analysis of the midsagittal plane in ontogeny and evolution. J. Hum. Evol. 44, 167–187 (2003).
PubMed  Article  PubMed Central  Google Scholar 

96.
Adams, D. C. Quantifying and comparing phylogenetic evolutionary rates for shape and other high-dimensional phenotypic data. Syst. Biol. 63, 166–177 (2014).
PubMed  Article  PubMed Central  Google Scholar 

97.
Xie, W. G., Lewis, P. O., Fan, Y., Kuo, L. & Chen, M. H. Improving marginal likelihood estimation for bayesian phylogenetic model selection. Syst. Biol. 60, 150–160 (2011).
PubMed  Article  PubMed Central  Google Scholar 

98.
Brown, M. B. & Forsythe, A. B. Robust tests for the equality of variances. J. Am. Stat. Assoc. 69, 364–367 (1974).
MATH  Article  Google Scholar 

99.
Levene, H. in Contributions to Probability and Statistics (Stanford University Press, 1960).

100.
Kruskal, W. H. & Wallis, W. A. Use of ranks in one-criterion variance analysis. J. Am. Stat. Assoc. 47, 583–621 (1952).
MATH  Article  Google Scholar  More

1.
Greenwood, P. J. & Harvey, P. H. The natal and breeding dispersal of birds. Annu. Rev. Ecol. Syst. 13, 1–21 (1982).
Article  Google Scholar 
2.
Paradis, E., Baillie, S. R., Sutherland, W. J. & Gregory, R. D. Patterns of natal and breeding dispersal in birds. J. Anim. Ecol. 67, 518–536 (1998).
Article  Google Scholar 

3.
Clobert, J. Dispersal (Oxford University Press, 2001).
Google Scholar 

4.
Clobert, J., Baguette, M., Benton, T. G. & Bullock, J. M. Dispersal Ecology and Evolution (Oxford University Press, 2012).
Google Scholar 

5.
Bowler, D. E. & Benton, T. G. Causes and consequences of animal dispersal strategies: Relating individual behaviour to spatial dynamics. Biol. Rev. 80, 205–225 (2005).
PubMed  Article  Google Scholar 

6.
Ronce, O. How does it feel to be like a rolling stone? Ten questions about dispersal evolution. Annu. Rev. Ecol. Evol. Syst. 38, 231–253 (2007).
Article  Google Scholar 

7.
Nathan, R., Perry, G., Cronin, J. T., Strand, A. E. & Cain, M. L. Methods for estimating long-distance dispersal. Oikos 103, 261–273 (2011).
Article  Google Scholar 

8.
Stevens, V. M. et al. Dispersal syndromes and the use of life-histories to predict dispersal. Evol. Appl. 6, 630–642 (2013).
PubMed  PubMed Central  Article  Google Scholar 

9.
Driscoll, D. A. et al. The trajectory of dispersal research in conservation biology: Systematic review. PLoS ONE 9, e95053 (2014).
ADS  PubMed  PubMed Central  Article  Google Scholar 

10.
Smith, A. L. et al. Managing uncertainty in movement knowledge for environmental decisions. Conserv. Lett. 12, 1–8. https://doi.org/10.1111/conl.12620 (2018).
Article  Google Scholar 

11.
Koenig, W. D., Van Vuren, D. & Hooge, P. N. Detectability, philopatry, and the distribution of dispersal distances in vertebrates. Trends Ecol. Evol. 11, 514–517 (1996).
CAS  PubMed  Article  Google Scholar 

12.
Trakhtenbrotl, A., Nathan, R., Perry, G. & Richardson, D. M. The importance of long-distance dispersal in biodiversity conservation. Divers. Distrib. 11, 173–181 (2005).
Article  Google Scholar 

13.
Nathan, R., Klein, E., Robledo-Arnuncio, J. J. & Revilla, E. Dispersal kernels: Review. In Dispersal Ecology and Evolution (eds Clobert, J. et al.) 187–210 (Oxford University Press, 2012).
Google Scholar 

14.
Van Houtan, K. S., Pimm, S. L., Halley, J. M., Bierregaard, R. O. & Lovejoy, T. E. Dispersal of Amazonian birds in continuous and fragmented forest. Ecol. Lett. 10, 219–229 (2007).
PubMed  Article  Google Scholar 

15.
Matthysen, E. Multicausality of dispersal: A review. Dispersal Ecol. Evol. 3, 18 (2012).
Google Scholar 

16.
Ronce, O., Olivieri, I., Clobert, J. & Danchin, E. Perspectives on the study of dispersal evolution. In Dispersal (eds Clobert, J. et al.) 341–357 (Oxford University Press, 2001).
Google Scholar 

17.
Clobert, J., Le Galliard, J.-F., Cote, J., Meylan, S. & Massot, M. Informed dispersal, heterogeneity in animal dispersal syndromes and the dynamics of spatially structured populations. Ecol. Lett. 12, 197–209 (2009).
PubMed  Article  Google Scholar 

18.
McPeek, M. A. & Holt, R. D. The evolution of dispersal in spatially and temporally varying environments. Am. Midl. Nat. 140, 1010–1027 (1992).
Article  Google Scholar 

19.
Dingle, H. Migration. The Biology of Life on the Move (Oxford University Press, 1996).
Google Scholar 

20.
Verhulst, S., Perrins, C. M. & Riddington, R. Natal dispersal of great tits in a patchy environment. Ecology 78, 864 (1997).
Article  Google Scholar 

21.
Tarwater, C. E., Beissinger, S. R. & Gaillard, J.-M. Dispersal polymorphisms from natal phenotype-environment interactions have carry-over effects on lifetime reproductive success of a tropical parrot. Ecol. Lett. 15, 1218–1229 (2012).
PubMed  Article  Google Scholar 

22.
Baines, C. B., Ferzoco, I. M. C. & McCauley, S. J. Phenotype-by-environment interactions influence dispersal. J. Anim. Ecol. 88, 1263–1274 (2019).
PubMed  Article  Google Scholar 

23.
López-López, P., Zuberogoitia, Í., Alcántara, M. & Gil, J. A. Philopatry, natal dispersal, first settlement and age of first breeding of bearded vultures Gypaetus barbatus in central Pyrenees. Bird Study 60, 555–560 (2013).
Article  Google Scholar 

24.
Poessel, S. A., Bloom, P. H., Braham, M. A. & Katzner, T. E. Age- and season-specific variation in local and long-distance movement behavior of golden eagles. Eur. J. Wildl. Res. 62, 377–393 (2016).
Article  Google Scholar 

25.
Benard, M. F. & McCauley, S. J. Integrating across life-history stages: Consequences of natal habitat effects on dispersal. Am. Nat. 171, 553–567 (2008).
PubMed  Article  Google Scholar 

26.
Matthysen, E. Density-dependent dispersal in birds and mammals. Ecography 28, 403–416 (2005).
Article  Google Scholar 

27.
Stamps, J. A. Conspecific attraction and aggregation in territorial species. Am. Nat. 131, 329–347 (1988).
Article  Google Scholar 

28.
van Horne, B. Density as a misleading indicator of habitat quality. J. Wildl. Manage. 47, 893–901 (1983).
Article  Google Scholar 

29.
Serrano, D. & Tella, J. L. The role of despotism and heritability in determining settlement patterns in the colonial lesser kestrel. Am. Nat. 169, E53–E67 (2007).
PubMed  Article  Google Scholar 

30.
Pyle, P. Age at first breeding and natal dispersal in a declining population of Cassin’s Auklet. Auk 118, 996–1007 (2001).
Article  Google Scholar 

31.
Greenwood, P. J. Mating systems, philopatry and dispersal in birds and mammals. Anim. Behav. 28, 1140–1162 (1980).
Article  Google Scholar 

32.
Clarke, A., Sæther, B.-E. & Røskaft, E. Sex biases in avian dispersal: A reappraisal. Oikos 79, 429–438 (1997).
Article  Google Scholar 

33.
Sanz-Aguilar, A. et al. Sex- and age-dependent patterns of survival and breeding success in a long-lived endangered avian scavenger. Sci. Rep. 7, 40204 (2017).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

34.
Sergio, F., Blas, J. & Hiraldo, F. Predictors of floater status in a long-lived bird: A cross-sectional and longitudinal test of hypotheses. J. Anim. Ecol. 78, 109–118 (2009).
PubMed  Article  Google Scholar 

35.
Zabala, J. & Zuberogoitia, I. Breeding performance and survival in the peregrine falcon Falco peregrinus support an age-related competence improvement hypothesis mediated via an age threshold. J. Avian Biol. 46, 141–150 (2015).
Article  Google Scholar 

36.
Kim, S. Y., Velando, A., Torres, R. & Drummond, H. Effects of recruiting age on senescence, lifespan and lifetime reproductive success in a long-lived seabird. Oecologia 166, 615–626 (2011).
ADS  PubMed  Article  Google Scholar 

37.
Bonte, D. et al. Costs of dispersal. Biol. Rev. Camb. Philos. Soc. 87, 290–312 (2012).
PubMed  Article  Google Scholar 

38.
Spear, L. B., Pyle, P. & Nur, N. Natal dispersal in the western gull: Proximal factors and fitness consequences. J. Anim. Ecol. 67, 165–179 (2009).
Article  Google Scholar 

39.
Forero, M., Donázar, J.A. & Hiraldo, F. Causes and fitness consequences of natal dispersal in a population of black kites. Ecology 83, 858–872 (2002).
Article  Google Scholar 

40.
Barbraud, C., Johnson, A. R. & Bertault, G. Phenotypic correlates of post-fledging dispersal in a population of greater flamingos: The importance of body condition. J. Anim. Ecol. 72, 246–257 (2003).
Article  Google Scholar 

41.
McNamara, J. M. & Dall, S. R. X. The evolution of unconditional strategies via the ‘multiplier effect’. Ecol. Lett. 14, 237–243 (2011).
PubMed  Article  Google Scholar 

42.
Shields, W. M. Philopatry, inbreeding, and the evolution of sex (State University of New York, 1982).
Google Scholar 

43.
Elorriaga, J. et al. First documented case of long-distance dispersal in the Egyptian Vulture (Neophron percnopterus). J. Raptor Res. 43, 142–145 (2009).
Article  Google Scholar 

44.
Carrete, M. et al. Habitat, human pressure, and social behavior: Partialling out factors affecting large-scale territory extinction in an endangered vulture. Biol. Conserv. 136, 143–154 (2007).
Article  Google Scholar 

45.
García-Ripollés, C. & López-López, P. Integrating effects of supplementary feeding, poisoning, pollutant ingestion and wind farms of two vulture species in Spain using a population viability analysis. J. Ornithol. 152, 879–888 (2011).
Article  Google Scholar 

46.
Sanz-Aguilar, A. et al. Action on multiple fronts, illegal poisoning and wind farm planning, is required to reverse the decline of the Egyptian vulture in southern Spain. Biol. Conserv. 187, 10–18 (2015).
Article  Google Scholar 

47.
Tauler, H. et al. Identifying key demographic parameters for the viability of a growing population of the endangered Egyptian Vulture Neophron percnopterus. Bird Conserv. Int. 25, 426–439 (2015).
Article  Google Scholar 

48.
Lieury, N., Gallardo, M., Ponchon, C., Besnard, A. & Millon, A. Relative contribution of local demography and immigration in the recovery of a geographically-isolated population of the endangered Egyptian vulture. Biol. Conserv. 191, 349–356 (2015).
Article  Google Scholar 

49.
Agudo, R., Rico, C., Hiraldo, F. & Donázar, J. A. Evidence of connectivity between continental and differentiated insular populations in a highly mobile species. Divers. Distrib. 17, 1–12 (2011).
Article  Google Scholar 

50.
Travis, J. M. J. & Dytham, C. Habitat persistence, habitat availability and the evolution of dispersal. Proc. R. Soc. B Biol. Sci. 266, 723–728 (1999).
Article  Google Scholar 

51.
Poethke, H. J. & Hovestadt, T. Evolution of density- and patch-size-dependent dispersal rates. Proc. R. Soc. B Biol. Sci. 269, 637–645 (2002).
Article  Google Scholar 

52.
Kun, Á. & Scheuring, I. The evolution of density-dependent dispersal in a noisy spatial population model. Oikos 115, 308–320 (2006).
Article  Google Scholar 

53.
Hovestadt, T., Kubisch, A. & Poethke, H. J. Information processing in models for density-dependent emigration: A comparison. Ecol. Modell. 221, 405–410 (2010).
Article  Google Scholar 

54.
Morton, E. R. et al. Dispersal: a matter of scale. Ecology 99, 938–946 (2018).
PubMed  Article  Google Scholar 

55.
Delestrade, A., McCleery, R. H. & Perrins, C. M. Natal dispersal in a heterogeneous environment: The case of the Great tit in Wytham. Acta Oecol. 17, 519–529 (1996).
Google Scholar 

56.
Luna, Á., Palma, A., Sanz-Aguilar, A., Tella, J. L. & Carrete, M. Sex, personality and conspecific density influence natal dispersal with lifetime fitness consequences in urban and rural burrowing owls. PLoS ONE 15, 1–17 (2020).
Google Scholar 

57.
Eikenaar, C., Richardson, D. S., Brouwer, L. & Komdeur, J. Sex-biased natal dispersal in a closed, saturated population of Seychelles warblers Acrocephalus sechellensis. J. Avian Biol. 39, 73–80 (2008).
Article  Google Scholar 

58.
Serrano, D., Tella, J. L., Donázar, J. A. & Pomarol, M. Social and individual features affecting natal dispersal in the colonial Lesser Kestrel. Ecology 84, 3044–3054 (2003).
Article  Google Scholar 

59.
Hernández, M. & Margalida, A. Poison-related mortality effects in the endangered Egyptian vulture (Neophron percnopterus) population in Spain. Eur. J. Wildl. Res. 55, 415–423 (2009).
Article  Google Scholar 

60.
Fattebert, J., Balme, G., Dickerson, T., Slotow, R. & Hunter, L. Density-dependent natal dispersal patterns in a leopard population recovering from over-harvest. PLoS ONE 10, 1–15 (2015).
Article  CAS  Google Scholar 

61.
Gundersen, G., Andreassen, H. P. & Ims, R. A. Individual and population level determinants of immigration success on local habitat patches: An experimental approach. Ecol. Lett. 5, 294–301 (2002).
Article  Google Scholar 

62.
Newby, J. R. et al. Human-caused mortality influences spatial population dynamics: Pumas in landscapes with varying mortality risks. Biol. Conserv. 159, 230–239 (2013).
Article  Google Scholar 

63.
Doligez, B., Danchin, E. & Clobert, J. Public information and breeding habitat selection in a wild bird population. Science 297, 1168–1170 (2002).
ADS  CAS  PubMed  Article  Google Scholar 

64.
Delibes, M., Gaona, P. & Ferreras, P. Effects of an attractive sink leading into maladaptive habitat selection. Am. Nat. 158, 277–285 (2001).
CAS  PubMed  Article  Google Scholar 

65.
Cortés-Avizanda, A., Ceballos, O. & Donázar, J. A. Long-term trends in population size and breeding success in the Egyptian Vulture (Neophron percnopterus) in Northern Spain. J. Raptor Res. 43, 43–49 (2009).
Article  Google Scholar 

66.
Zuberogoitia, I., Zabala, J., Martínez, J. A., Martínez, J. E. & Azkona, A. Effect of human activities on Egyptian vulture breeding success. Anim. Conserv. 11, 313–320 (2008).
Article  Google Scholar 

67.
Schlaepfer, M. A., Runge, M. C. & Sherman, P. W. Ecological and evolutionary traps. Trends Ecol. Evol. 17, 474–480 (2002).
Article  Google Scholar 

68.
Robertson, B. A. & Hutto, R. L. A framework for understanding ecological traps and an evaluation of existing evidence. Ecology 87, 1075–1085 (2006).
PubMed  Article  Google Scholar 

69.
Betts, M. G., Hadley, A. S., Rodenhouse, N. & Nocera, J. J. Social information trumps vegetation structure in breeding-site selection by a migrant songbird. Proc. R. Soc. B Biol. Sci. 275, 2257–2263 (2008).
Article  Google Scholar 

70.
Stodola, K. W. & Ward, M. P. The emergent properties of conspecific attraction can limit a species’ ability to track environmental change. Am. Nat. 189, 726–733 (2017).
PubMed  Article  Google Scholar 

71.
Serrano, D. Dispersal in raptors. In Birds of Prey. Biology and Conservation in the XXI Century (eds Hernán Sarasola, J. et al.) 95–121 (Springer, 2018).
Google Scholar 

72.
Trochet, A., Stevens, V. M. & Baguette, M. Evolution of sex-biased dispersal. Q. Rev. Biol. 91, 297–320 (2016).
PubMed  Article  Google Scholar 

73.
Forsman, E. D., Anthony, R. G., Reid, J. A., Loschl, P. J. & Sovern, S. G. Natal and breeding dispersal of northern spotted owls. Wildl. Monogr. 1, 35 (2002).
Google Scholar 

74.
Steiner, U. K. & Gaston, A. J. Reproductive consequences of natal dispersal in a highly philopatric seabird. Behav. Ecol. 16, 634–639 (2005).
Article  Google Scholar 

75.
González, L. M. et al. Effective natal dispersal and age of maturity in the threatened Spanish Imperial Eagle Aquila adalberti: Conservation implications. Bird Stud. 53, 285–293 (2006).
Article  Google Scholar 

76.
Oro, D., Tavecchia, G. & Genovart, M. Comparing demographic parameters for philopatric and immigrant individuals in a long-lived bird adapted to unstable habitats. Oecologia 165, 935–945 (2011).
ADS  PubMed  Article  Google Scholar 

77.
Grande, J. M. et al. Survival in a long-lived territorial migrant: Effects of life-history traits and ecological conditions in wintering and breeding areas. Oikos 118, 580–590 (2009).
Article  Google Scholar 

78.
Van Noordwijk, A. J. On bias due to observer distribution in the analysis of data on natal dispersal in birds. J. Appl. Stat. 22, 683–694 (1995).
Article  Google Scholar 

79.
Ens, B. J. et al. Despotic distribution and deferred maturity: Two sides of the same coin?. Am. Nat. 146, 625–650 (2015).
Article  Google Scholar 

80.
Maness, T. J. & Anderson, D. J. Predictors of juvenile survival in birds. Ornithol. Monogr. 78, 1–55 (2013).
Article  Google Scholar 

81.
Azpillaga, M., Real, J. & Hernández-Matías, A. Effects of rearing conditions on natal dispersal processes in a long-lived predator bird. Ecol. Evol. 8, 6682–6698 (2018).
PubMed  PubMed Central  Article  Google Scholar 

82.
Delgado, M., Penteriani, V., Revilla, E. & Nams, O. The effect of phenotypic traits and external cues on natal dispersal movements. J. Anim. Ecol. 79, 620–632 (2010).
Article  Google Scholar 

83.
Zuberogoitia, I., Zabala, J., Martínez, J. E., González-Oreja, J. A. & López-López, P. Effective conservation measures to mitigate the impact of human disturbances on the endangered Egyptian vulture. Anim. Conserv. 17, 410–418 (2014).
Article  Google Scholar 

84.
Donázar, J. A. et al. Epizootics and sanitary regulations drive long-term changes in fledgling body condition of a threatened vulture. Ecol. Indic. 113, 106188 (2020).
Article  Google Scholar 

85.
Boulinier, T. & Danchin, E. The use of conspecific reproductive success for breeding patch selection in terrestrial migratory species. Evol. Ecol. 11, 505–517 (1997).
Article  Google Scholar 

86.
Brown, J. H. & Kodric-Brown, A. Turnover rates in insular biogeography: Effect of immigration on extinction. Ecology 58, 445–449 (1977).
Article  Google Scholar 

87.
Benton, T. G. & Bowler, D. E. Linking dispersal to spatial dynamics. In Dispersal Ecology and Evolution (eds Clobert, J. et al.) 251–265 (Oxford University Press, 2012).
Google Scholar 

88.
Delgado, M. D. M., Ratikainen, I. I. & Kokko, H. Inertia: The discrepancy between individual and common good in dispersal and prospecting behaviour. Biol. Rev. 86, 717–732 (2011).
Article  Google Scholar 

89.
Doncaster, C. P., Clobert, J., Doligez, B., Gustafsson, L. & Danchin, E. Balanced dispersal between spatially varying local populations: An alternative to the source-sink model. Am. Nat. 150, 425–445 (1997).
CAS  PubMed  Article  Google Scholar 

90.
Millon, A., Lambin, X., Devillard, S. & Schaub, M. Quantifying the contribution of immigration to population dynamics: A review of methods, evidence and perspectives in birds and mammals. Biol. Rev. 94, 2049–2067 (2019).
PubMed  Article  Google Scholar 

91.
Altwegg, R., Collingham, Y. C., Erni, B. & Huntley, B. Density-dependent dispersal and the speed of range expansions. Divers. Distrib. 19, 60–68 (2013).
Article  Google Scholar 

92.
Tauler-Ametller, H., Hernández-Matías, A., Pretus, J. L. L. & Real, J. Landfills determine the distribution of an expanding breeding population of the endangered Egyptian Vulture Neophron percnopterus. Ibis 159, 757–768 (2017).
Article  Google Scholar 

93.
Gilroy, J. J. & Sutherland, W. J. Beyond ecological traps: Perceptual errors and undervalued resources. Trends Ecol. Evol. 22, 351–356 (2007).
PubMed  Article  Google Scholar 

94.
Patten, M. A. & Kelly, J. F. Habitat selection and the perceptual trap. Ecol. Appl. 20, 2148–2156 (2010).
PubMed  Article  Google Scholar 

95.
Doebeli, M. & Ruxton, G. D. Evolution of dispersal rates in metapopulation models: Branching and cyclic dynamics in phenotype space. Evolution 51, 1730 (1997).
PubMed  Article  Google Scholar 

96.
Murrell, D. J., Travis, J. M. J. & Dytham, C. The evolution of dispersal distance in spatially-structured populations. Oikos 97, 229–236 (2002).
Article  Google Scholar 

97.
Heino, M. & Hanski, I. Evolution of migration rate in a spatially realistic metapopulation model. Am. Nat. 157, 495–511 (2001).
CAS  PubMed  Article  Google Scholar 

98.
Mathias, A., Kisdi, È. & Olivieri, I. Divergent evolution of dispersal in a heterogeneous landscape. Evolution 55, 246–259 (2001).
CAS  PubMed  Article  Google Scholar 

99.
Baguette, M., Clobert, J. & Schtickzelle, N. Metapopulation dynamics of the bog fritillary butterfly: Experimental changes in habitat quality induced negative density-dependent dispersal. Ecography 34, 170–176 (2011).
Article  Google Scholar 

100.
Margalida, A. et al. Uneven large-scale movement patterns in wild and reintroduced pre-adult bearded vultures: Conservation implications. PLoS ONE 8, e65857 (2013).
ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

101.
Buechley, E. R., McGrady, M. J., Çoban, E. & Şekercioğlu, Ç. H. Satellite tracking a wide-ranging endangered vulture species to target conservation actions in the Middle East and East Africa. Biodivers. Conserv. 27, 2293–2310 (2018).
Article  Google Scholar 

102.
Dwyer, J. F., Fraser, J. D. & Morrison, J. L. Evolution of communal roosting: A social refuge-territory prospecting hypothesis. J. Raptor Res. 52, 407–419 (2018).
Article  Google Scholar 

103.
Blanco, G. & Tella, J. L. Temporal, spatial and social segregation of red-billed choughs between two types of communal roost: A role for mating and territory acquisition. Anim. Behav. 57, 1219–1227 (1999).
CAS  PubMed  Article  Google Scholar 

104.
Bocedi, G., Heinonen, J. & Travis, J. M. J. Uncertainty and the role of information acquisition in the evolution of context-dependent emigration. Am. Nat. 179, 606–620 (2012).
PubMed  Article  Google Scholar 

105.
Delgado, M. M., Bartoń, K. A., Bonte, D. & Travis, J. M. J. Prospecting and dispersal: Their eco-evolutionary dynamics and implications for population patterns. Proc. R. Soc. B Biol. Sci. 281, 20132851 (2014).
CAS  Article  Google Scholar 

106.
Kesler, D. C., Walters, J. R. & Kappes, J. J. Social influences on dispersal and the fat-tailed dispersal distribution in red-cockaded woodpeckers. Behav. Ecol. 21, 1337–1343 (2010).
Article  Google Scholar 

107.
Ducros, D. et al. Beyond dispersal versus philopatry? Alternative behavioural tactics of juvenile roe deer in a heterogeneous landscape. Oikos 129, 81–92 (2019).
Article  Google Scholar 

108.
BirdLife International. Species factsheet: Neophron percnopterus. (2019). Available at: http://www.birdlife.org. Accessed 19 Dec 2019.

109.
Donázar, J. A., Ceballos, O. & Tella, J. L. Communal roosts of Egyptian vulture (Neophron percnopterus): Dynamics and implications for the species conservation. In Biología y conservación de las rapaces Mediterráneas (eds Muntaner, J. & Muntaner, J.) 189–201 (SEO/Birdlife, 1996).
Google Scholar 

110.
Hernández-Matías, A. et al. Determinants of territorial recruitment in bonelli’s eagle (Aquila fasciata) populations. Auk 127, 173–184 (2010).
Article  Google Scholar 

111.
Phipps, W. L. et al. Spatial and temporal variability in migration of a soaring raptor across three continents. Front. Ecol. Evol. 7, 1–14 (2019).
Article  Google Scholar 

112.
del Moral, J. C. El Alimoche Común en España Población Reproductora en 2008 y Método de Censo (SEO/Birdlife, 2009).
Google Scholar 

113.
del Moral, J. C. & El Martí, R. Alimoche Común en España y Portugal. (I Censo Coordinado). Año 2000. Monografía no 8 (SEO/Birdlife, 2002).
Google Scholar 

114.
Donázar, J. A. & Ceballos, O. Growth rates of nestling Egyptian Vultures Neophrone percnopterus in relation to brood size, hatching order and environmental factors. Ardea 77, 217–226 (1989).
Google Scholar 

115.
Imdadullah, M., Aslam, M. & Altaf, S. Mctest: An r package for detection of collinearity among regressors. R J. 8, 499–509 (2016).
Article  Google Scholar 

116.
Burnham, K. P. & Anderson, D. R. Model Selection and Multimodel Inference: A Practical Information-Theoretic Approach (Springer, 2002).
Google Scholar 

117.
Giam, X. & Olden, J. D. Quantifying variable importance in a multimodel inference framework. Methods Ecol. Evol. 7, 388–397 (2016).
Article  Google Scholar 

118.
Schabenberger, O. & Pierce, F. J. Contemporary Statistical Models for the Plant and Soil Sciences (CRC Press, 2002).
Google Scholar 

119.
R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, Vienna, 2018).

120.
Hartig, F. DHARMa: Residual Diagnostics for Hierarchical (Multi-Level / Mixed) Regression Models. R package version 0.2.4. (2019). More

Nicholas School of the Environment, Duke University, Durham, NC, USA
James S. Clark, Christopher L. Kilner, Jordan Luongo, Renata Poulton-Kamakura, Ethan Ready, Chantal D. Reid, C. Lane Scher, William H. Schlesinger, Shubhi Sharma, Samantha Sutton, Jennifer J. Swenson & Margaret Swift

INRAE, LESSEM, University Grenoble Alpes, Saint-Martin-d’Heres, France
James S. Clark, Benoit Courbaud, Georges Kunstler, Kyle C. Rodman & Thomas T. Veblen

Department of Geography, University of Colorado Boulder, Boulder, CO, USA
Robert Andrus & Emily Moran

School of Natural Sciences, University of California, Merced, Merced, CA, USA
Melaine Aubry-Kientz

Forest Research Institute, University of Quebec in Abitibi-Temiscamingue, Rouyn-Noranda, QC, Canada
Yves Bergeron

Department of Systematic Zoology, Faculty of Biology, Adam Mickiewicz University, Poznan, Poland
Michal Bogdziewicz

USDA Forest Service, Southern Research Station, Monticello, AR, USA
Don C. Bragg

USDA Forest Service Southern Research Station, Auburn, AL, USA
Dale Brockway & Timothy J. Fahey

Natural Resources, Cornell University, Ithaca, NY, USA
Natalie L. Cleavitt

Institute for the Environment, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
Susan Cohen

Greater Yellowstone Network, National Park Service, Bozeman, MT, USA
Robert Daley, Kristin L. Legg & Erin Shanahan

USGS Western Ecological Research Center, Three Rivers, CA, USA
Adrian J. Das & Nathan L. Stephenson

Earth and Environment, Boston University, Boston, MA, USA
Michael Dietze

Finnish Meteorological Institute, Helsinki, Finland
Istem Fer

Forest Resources, University of Washington, Seattle, WA, USA
Jerry F. Franklin

Department of Biological Science, Northern Arizona University, Flagstaff, AZ, USA
Catherine A. Gehring, Amy V. Whipple & Thomas G. Whitham

University of California, Santa Cruz, Santa Cruz, CA, USA
Gregory S. Gilbert & Kai Zhu

USDA Forest Service, Bent Creek Experimental Forest, Asheville, NC, USA
Cathryn H. Greenberg

USDA Forest Service Southern Research Station, Eastern Forest Environmental Threat Assessment Center, Research Triangle Park, NC, USA
Qinfeng Guo

Department of Biology, University of Washington, Seattle, WA, USA
Janneke HilleRisLambers

School for Environment and Sustainability, University of Michigan, Ann Arbor, MI, USA
Ines Ibanez

Department of Biology, University of Saskatchewan, Saskatoon, SK, Canada
Jill Johnstone

Health and Environmental Sciences Department, Xian Jiaotong-Liverpool University, Suzhou, China
Johannes Knops

Hastings Reservation, University of California Berkeley, Carmel Valley, CA, USA
Walter D. Koenig

Department of Biological Sciences, DePaul University, Chicago, IL, USA
Jalene M. LaMontagne

Department of Wildland Resources, Utah State University Ecology Center, Logan, UT, USA
James A. Lutz

Department of Biology, University of New Mexico, Albuquerque, NM, USA
Diana Macias

Pacific Forestry Centre, Victoria, BC, Canada
Eliot J. B. McIntire

Université du Québec en Abitibi-Témiscamingue, Rouyn-Noranda, Quebec, Canada
Yassine Messaoud

Department of Biology, Colby College, Waterville, ME, USA
Christopher M. Moore

Department of Biology, Washington University in St. Louis, St. Louis, MO, USA
Jonathan A. Myers

University of New Mexico, Albuquerque, NM, USA
Orrin B. Myers

Department for the Ecology of Animal Societies, Max Planck Institute of Animal Behavior, Konstanz, Germany
Chase Nunez

Valles Caldera National Preserve, National Park Service, Jemez Springs, NM, USA
Robert Parmenter

Fort Collins Science Center, Fort Collins, CO, USA
Sam Pearse

Department of Natural Sciences, Mars Hill University, Mars Hill, NC, USA
Scott Pearson

Department of Forest and Rangeland Stewardship, Colorado State University, Fort Collins, CO, USA
Miranda D. Redmond & Andreas P. Wion

Ecology and Evolutionary Biology, University of Toronto, Toronto, ON, Canada
Amanda M. Schwantes

Department of Biology, Wilkes University, Wilkes-Barre, PA, USA
Michael A. Steele

Geography Department and Russian and East European Institute, Bloomington, IN, USA
Roman Zlotin More

1.
Stuart-Smith, R. D., Mellin, C., Bates, A. E. & Edgar, G. J. Nat. Ecol. Evol. https://doi.org/10.1038/s41559-020-01342-7 (2020).
Article  Google Scholar 
2.
Wilson, S. K. et al. J. Anim. Ecol. 77, 220–228 (2008).
Article  Google Scholar 

3.
Dornelas, M. et al. Science 344, 296–299 (2014).
CAS  Article  Google Scholar 

4.
Sommer, B., Harrison, P. L., Beger, M. & Pandolfi, J. M. Ecology 95, 1000–1009 (2014).
Article  Google Scholar 

5.
Feary, D. A. et al. Fish Fish. 15, 593–615 (2013).
Article  Google Scholar 

6.
Brandl, S. J. et al. Science 364, 1189–1192 (2019).
CAS  Article  Google Scholar 

7.
Hughes, T. P. et al. Nature 546, 82–90 (2017).
CAS  Article  Google Scholar 

8.
Beyer, H. et al. Conserv. Lett. 11, e12587 (2018).
Article  Google Scholar 

9.
Bottrill, M. C. et al. Trends Ecol. Evol. 24, 183–184 (2009).
Article  Google Scholar 

10.
Wernberg, T. et al. Nat. Clim. Change 3, 78–82 (2013).
Article  Google Scholar  More

1.
McKinney, M. L. & Lockwood, J. L. Biotic homogenization: a few winners replacing many losers in the next mass extinction. Trends Ecol. Evol. 14, 450–453 (1999).
CAS  PubMed  Article  PubMed Central  Google Scholar 
2.
Magurran, A. E., Dornelas, M., Moyes, F., Gotelli, N. J. & McGill, B. Rapid biotic homogenization of marine fish assemblages. Nat. Commun. 6, 8405 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

3.
Devictor, V. et al. Functional biotic homogenization of bird communities in disturbed landscapes. Glob. Ecol. Biogeogr. 17, 252–261 (2008).
Article  Google Scholar 

4.
Devictor, V., Julliard, R. & Jiguet, F. Distribution of specialist and generalist species along spatial gradients of habitat disturbance and fragmentation. Oikos 117, 507–514 (2008).
Article  Google Scholar 

5.
Richardson, L. E., Graham, N. A. J., Pratchett, M. S., Eurich, J. G. & Hoey, A. S. Mass coral bleaching causes biotic homogenization of reef fish assemblages. Glob. Change Biol. 24, 3117–3129 (2018).
Article  Google Scholar 

6.
Wilson, S. K. et al. Habitat utilization by coral reef fish: implications for specialists vs. generalists in a changing environment. J. Anim. Ecol. 77, 220–228 (2008).
Article  Google Scholar 

7.
Munday, P. L. Habitat loss, resource specialization, and extinction on coral reefs. Glob. Change Biol. 10, 1642–1647 (2004).
Article  Google Scholar 

8.
Jones, G. P., McCormick, M. I., Srinivasan, M. & Eagle, J. V. Coral decline threatens fish biodiversity in marine reserves. Proc. Natl Acad. Sci. USA 101, 8251–8253 (2004).
CAS  PubMed  Article  Google Scholar 

9.
Paddack, M. J. et al. Recent region-wide declines in Caribbean reef fish abundance. Curr. Biol. 19, 590–595 (2009).
CAS  PubMed  Article  Google Scholar 

10.
Hughes, T. P. et al. Spatial and temporal patterns of mass bleaching of corals in the Anthropocene. Science 359, 80–83 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

11.
Hughes, T. P. et al. Coral reefs in the Anthropocene. Nature 546, 82–90 (2017).
CAS  PubMed  PubMed Central  Article  Google Scholar 

12.
Cheal, A. J., MacNeil, M. A., Emslie, M. J. & Sweatman, H. The threat to coral reefs from more intense cyclones under climate change. Glob. Change Biol. 23, 1511–1524 (2017).
Article  Google Scholar 

13.
Oliver, E. C. J. et al. Longer and more frequent marine heatwaves over the past century. Nat. Commun. 9, 1324 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

14.
Ling, S. D., Johnson, C. R., Frusher, S. D. & Ridgway, K. R. Overfishing reduces resilience of kelp beds to climate-driven catastrophic phase shift. Proc. Natl Acad. Sci. USA 106, 22341–22345 (2009).
CAS  PubMed  Article  PubMed Central  Google Scholar 

15.
Sunday, J. M. et al. Species traits and climate velocity explain geographic range shifts in an ocean-warming hotspot. Ecol. Lett. 18, 944–953 (2015).
PubMed  Article  Google Scholar 

16.
Mair, L. et al. Abundance changes and habitat availability drive species’ responses to climate change. Nat. Clim. Change 4, 127–131 (2014).
Article  Google Scholar 

17.
Monaco, C. J. et al. Dietary generalism accelerates arrival and persistence of coral-reef fishes in their novel ranges under climate change. Glob. Change Biol. 26, 5564–5573 (2020).
Article  Google Scholar 

18.
Kleypas, J. A., McManus, J. W. & Menez, L. A. B. Environmental limits to coral reef development: where do we draw the line? Am. Zool. 39, 146–159 (2015).
Article  Google Scholar 

19.
Munday, P. L., Jones, G. P., Pratchett, M. S. & Williams, A. J. Climate change and the future for coral reef fishes. Fish Fish. 9, 261–285 (2008).
Article  Google Scholar 

20.
Edgar, G. J. & Stuart-Smith, R. D. Systematic global assessment of reef fish communities by the Reef Life Survey program. Sci. Data 1, 140007 (2014).
PubMed  PubMed Central  Article  Google Scholar 

21.
Pratchett, M. S. et al. in Oceanography and Marine Biology: Annual Review Vol. 46 (eds Gibson, R. N. et al.) 251–296 (Taylor and Francis, 2008).

22.
Stuart-Smith, R. D., Brown, C. J., Ceccarelli, D. M. & Edgar, G. J. Ecosystem restructuring along the Great Barrier Reef following mass coral bleaching. Nature 560, 92–96 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

23.
Feary, D. A. The influence of resource specialization on the response of reef fish to coral disturbance. Mar. Biol. 153, 153–161 (2007).
Article  Google Scholar 

24.
Mellin, C., Bradshaw, C., Fordham, D. & Caley, M. Strong but opposing β-diversity–stability relationships in coral reef fish communities. Proc. R. Soc. B 281, 20131993 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

25.
Wernberg, T. et al. Climate-driven regime shift of a temperate marine ecosystem. Science 353, 169–172 (2016).
CAS  PubMed  Article  PubMed Central  Google Scholar 

26.
Stuart-Smith, R. D., Edgar, G. J. & Bates, A. E. Thermal limits to the geographic distributions of shallow-water marine species. Nat. Ecol. Evol. 1, 1846–1852 (2017).
PubMed  Article  PubMed Central  Google Scholar 

27.
Stuart-Smith, R. D., Edgar, G. J., Barrett, N. S., Kininmonth, S. J. & Bates, A. E. Thermal biases and vulnerability to warming in the world’s marine fauna. Nature 528, 88–92 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

28.
Vergés, A. et al. Long-term empirical evidence of ocean warming leading to tropicalization of fish communities, increased herbivory, and loss of kelp. Proc. Natl Acad. Sci. USA 113, 13791–13796 (2016).
PubMed  Article  CAS  PubMed Central  Google Scholar 

29.
Booth, D. J., Figueira, W. F., Gregson, M. A., Brown, L. & Beretta, G. Occurrence of tropical fishes in temperate southeastern Australia: role of the East Australian Current. Estuar. Coast. Shelf Sci. 72, 102–114 (2007).
Article  Google Scholar 

30.
Feary, D. A. et al. Latitudinal shifts in coral reef fishes: why some species do and others do not shift. Fish Fish. 15, 593–615 (2014).
Article  Google Scholar 

31.
Guisan, A. et al. Scaling the linkage between environmental niches and functional traits for improved spatial predictions of biological communities. Glob. Ecol. Biogeogr. 28, 1384–1392 (2019).
Article  Google Scholar 

32.
Pratchett, M. S., Hoey, A. S., Wilson, S. K., Messmer, V. & Graham, N. A. J. Changes in biodiversity and functioning of reef fish assemblages following coral bleaching and coral loss. Diversity 3, 424–452 (2011).
Article  Google Scholar 

33.
Johnson, C. R. et al. Climate change cascades: shifts in oceanography, species’ ranges and subtidal marine community dynamics in eastern Tasmania. J. Exp. Mar. Biol. Ecol. 400, 17–32 (2011).
Article  Google Scholar 

34.
Dornelas, M. et al. Assemblage time series reveal biodiversity change but not systematic loss. Science 344, 296–299 (2014).
CAS  Article  Google Scholar 

35.
Blowes, S. A. et al. The geography of biodiversity change in marine and terrestrial assemblages. Science 366, 339–345 (2019).
CAS  PubMed  Article  Google Scholar 

36.
Gilchrist, G. W. Specialists and generalists in changing environments. I. Fitness landscapes of thermal sensitivity. Am. Nat. 146, 252–270 (1995).
Article  Google Scholar 

37.
Pellissier, L. et al. Quaternary coral reef refugia preserved fish diversity. Science 344, 1016–1019 (2014).
CAS  PubMed  Article  Google Scholar 

38.
Graham, M. H., Kinlan, B. P. & Grosberg, R. K. Post-glacial redistribution and shifts in productivity of giant kelp forests. Proc. R. Soc. B 277, 399–406 (2010).
PubMed  Article  PubMed Central  Google Scholar 

39.
Hughes, T. P. et al. Global warming and recurrent mass bleaching of corals. Nature 543, 373–377 (2017).
CAS  Article  Google Scholar 

40.
Wismer, S., Tebbett, S. B., Streit, R. P. & Bellwood, D. R. Spatial mismatch in fish and coral loss following 2016 mass coral bleaching. Sci. Total Environ. 650, 1487–1498 (2019).
CAS  PubMed  Article  Google Scholar 

41.
Waldock, C., Stuart-Smith, R. D., Edgar, G. J., Bird, T. J. & Bates, A. E. The shape of abundance distributions across temperature gradients in reef fishes. Ecol. Lett. 22, 685–696 (2019).
PubMed  PubMed Central  Article  Google Scholar 

42.
Mouillot, D. et al. Rare species support vulnerable functions in high-diversity ecosystems. PLoS Biol. 11, e1001569 (2013).
CAS  PubMed  PubMed Central  Article  Google Scholar 

43.
Robinson, J. P. W. et al. Productive instability of coral reef fisheries after climate-driven regime shifts. Nat. Ecol. Evol. 3, 183–190 (2019).
PubMed  Article  Google Scholar 

44.
Cresswell, A. K. et al. Translating local benthic community structure to national biogenic reef habitat types. Glob. Ecol. Biogeogr. 26, 1112–1125 (2017).
Article  Google Scholar 

45.
Edgar, G. J., Barrett, N. S. & Stuart-Smith, R. D. Exploited reefs protected from fishing transform over decades into conservation features otherwise absent from seascapes. Ecol. Appl. 19, 1967–1974 (2009).
PubMed  Article  Google Scholar 

46.
Althaus, F. et al. A standardised vocabulary for identifying benthic biota and substrata from underwater imagery: the CATAMI classification scheme. PLoS ONE 10, e0141039 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

47.
Carmona, C. P., de Bello, F., Mason, N. W. H. & Lepš, J. Traits without borders: integrating functional diversity across scales. Trends Ecol. Evol. 31, 382–394 (2016).
PubMed  Article  Google Scholar 

48.
Stuart-Smith, R. D. et al. Integrating abundance and functional traits reveals new global hotspots of fish diversity. Nature 501, 539–542 (2013).
CAS  PubMed  Article  Google Scholar 

49.
Spalding, M. D. et al. Marine ecoregions of the world: a bioregionalization of coastal and shelf areas. BioScience 57, 573–583 (2007).
Article  Google Scholar 

50.
Becker, R. A., Wilks, A. R (original S code) & Brownrigg, R. (R version). mapdata: Extra map databases. R package version 2.3.0 (2018).

51.
Matis, P. A., Donelson, J. M., Bush, S., Fox, R. J. & Booth, D. J. Temperature influences habitat preference of coral reef fishes: will generalists become more specialised in a warming ocean? Glob. Change Biol. 24, 3158–3169 (2018).
Article  Google Scholar  More

1.
Guidelines for Using the IUCN Red List Categories and Criteria Version 14 (IUCN Standards and Petitions Committee, 2019); http://www.iucnredlist.org/documents/RedListGuidelines.pdf
2.
Dalrymple, S. E., Godefroid, S., Orsenigo, S. & Abeli, T. Frankenstein’s work or everyday conservation? How reintroductions are informing the de-extinction debate. J. Nat. Conserv. 56, 125870 (2020).
Article  Google Scholar 

3.
IUCN SSC. Guiding Principles on Creating Proxies of Extinct Species for Conservation Benefit Version 1.0 (IUCN, 2016).

4.
Dalrymple, S. E. & Abeli, T. Ex situ seed banks and the IUCN Red List. Nat. Plants 5, 122–123 (2019).
Article  Google Scholar 

5.
Humphreys, A. M., Govaerts, R., Ficinski, S. Z., Lughadha, E. N. & Vorontsova, M. S. Global dataset shows geography and life form predict modern plant extinction and rediscovery. Nat. Ecol. Evol. 3, 1043–1047 (2019).
Article  Google Scholar 

6.
Knapp, W. M. et al. Regional records improve data quality in determining plant extinction rates. Nat. Ecol. Evol. 4, 512–514 (2020).
Article  Google Scholar 

7.
Ladle, R. J., Jepson, P., Malhado, A. C. M., Jennings, S. & Barua, M. The causes and biogeographical significance of species’ rediscovery. Front. Biogeogr. 3, 111–118 (2011).
Google Scholar 

8.
Scheffers, B. R., Yong, D. L., Harris, J. B. C., Giam, X. & Sodhi, N. S. The world’s rediscovered species: back from the brink? PLoS ONE 6, e22531 (2011).
CAS  Article  Google Scholar 

9.
Aedo, C., Medina, L., Barberá, P. & Fernández-Albert, M. Extinctions of vascular plants in Spain. Nord. J. Bot. 33, 83–100 (2015).
Article  Google Scholar 

10.
Bawri, A., Gajurel, P. R. & Khan, M. L. Rediscovery of Primula polonensis. Kew Bull. 70, 56–60 (2015).
Article  Google Scholar 

11.
Bonini, F., Lastrucci, L. & Gigante, D. Juncus atratus Krock. (Juncaceae) rediscovered in Italy: a species deserving urgent conservation actions. Biologia 75, 1519–1527 (2020).
Article  Google Scholar 

12.
Abeli, T. et al. Ex situ collections and their potential for the restoration of extinct plants. Conserv. Biol. 34, 303–313 (2020).
Article  Google Scholar 

13.
Liu, U., Breman, E., Cossu, T. A. & Kenney, S. The conservation value of germplasm stored at the Millennium Seed Bank, Royal Botanic Gardens, Kew, UK. Biodivers. Conserv. 27, 1347–1386 (2018).
Article  Google Scholar 

14.
Minteer, B. A., Collins, J. P., Love, K. E. & Puschendorf, R. Avoiding (re)extinction. Science 344, 260–261 (2014).
CAS  Article  Google Scholar 

15.
Rossi, G. et al. Is legal protection sufficient to ensure plant conservation? The Italian Red List of policy species as a case study. Oryx 50, 431–436 (2016).
Article  Google Scholar 

16.
Fos, S., Laguna, E., Jiménez, J. & Gómez-Serrano, M. Á. Plant micro-reserves in Valencia (E. Spain): a model to preserve threatened flora in China? Plant Divers. 39, 383–389 (2017).
Article  Google Scholar 

17.
Keith, D. A. & Burgman, M. A. The Lazarus effect: can the dynamics of extinct species lists tell us anything about the status of biodiversity? Biol. Conserv. 117, 41–48 (2004).
Article  Google Scholar 

18.
Dunkel, F. G. The Ranunculus auricomus L. complex (Ranunculaceae) in northern Italy. Webbia 65, 179–227 (2010).
Article  Google Scholar 

19.
Bartolucci, F. et al. An updated checklist of the vascular flora native to Italy. Plant Biosyst. 152, 179–303 (2018).
Article  Google Scholar 

20.
Lista Vermelha da Flora Vascular de Portugal Continental (Sociedade Portuguesa de Botânica e Associação Portuguesa de Ciência da Vegetação – PHYTOS, em parceria com o Instituto da Conservação da Natureza e das Florestas, 2020); https://listavermelha-flora.pt/

21.
Euro+Med PlantBase—the Information Resource for Euro-Mediterranean Plant Diversity (Euro+Med, 2020); http://ww2.bgbm.org/EuroPlusMed/

22.
Perehrym, M. M. in Vascular Plants of the Emerald Network of Ukraine Under Protection of the Bern Convention (ed. Solomakha, V. A.) (Ministry of Ecology and Natural Resources of Ukraine, 2016).

23.
Andrés-Sánchez, S., Galbany-Casals, M., Rico, E. & Martínez-Ortega, M. M. A nomenclatural treatment for Logfia Cass. and Filago L. (Asteraceae) as newly circumscribed: typification of several names. Taxon 60, 572–576 (2011).

24.
Vladimirov, V., Aybeke, A., Matevski, V. & Tan, K. New floristic records in the Balkans: 33*. Phytol. Balc. 23, 281–329 (2017).
Google Scholar 

25.
Orsenigo, S. et al. Red list of threatened vascular plants in Italy. Plant Biosyst. 152, 310–335 (2021).
Article  Google Scholar 

26.
Barina, Z. (ed.) Distribution Atlas of Vascular Plants in Albania (Hungarian Natural History Museum, 2017).

27.
La Liste Rouge des Espèces Menacées en France—Chapitre Flore Vasculaire de France Métropolitaine (UICN France, FCBN, AFB, MNHN, 2018).

28.
Blanca, G., Gavira, O. & Suárez-Santiago, V. N. Galatella malacitana (Asteraceae): a new species from the peridotitic mountains of southern Spain. Phytotaxa 205, 239–248 (2015).
Article  Google Scholar 

29.
Bogdanović, S., Brullo, S., Ljubičić, I., Rat, M. & Salmeri, C. Cytotaxonomical remarks on Loncomelos visianicum (Hyacinthaceae), a poorly known species endemic to Croatia. Phytotaxa 430, 95–108 (2020).
Article  Google Scholar 

30.
Banfi, E. in Flora d’Italia 2nd edn, Vol. 1 (eds Pignatti, S. et al.) (Edagricole, 2017). More

1.
Leach, J. E., Triplett, L. R., Argueso, C. T. & Trivedi, P. Communication in the phytobiome. Cell 169, 587–596 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 
2.
Bahram, M. et al. Structure and function of the global topsoil microbiome. Nature 560, 233–237 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

3.
Liu, H., Macdonald, C. A., Cook, J., Anderson, I. C. & Singh, B. K. An ecological loop: host microbiomes across multitrophic interactions. Trends Ecol. Evol. 34, 1118–1130 (2019).
PubMed  Article  PubMed Central  Google Scholar 

4.
Banerjee, S., Schlaeppi, K. & van der Heijden, M. G. A. Keystone taxa as drivers of microbiome structure and functioning. Nat. Rev. Microbiol. 16, 567–576 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

5.
Hoeksema, J. D. et al. Evolutionary history of plant hosts and fungal symbionts predicts the strength of mycorrhizal mutualism. Commun. Biol. 1, 116 (2018).
PubMed  PubMed Central  Article  Google Scholar 

6.
Fisher, R. M., Henry, L. M., Cornwallis, C. K., Kiers, E. T. & West, S. A. The evolution of host–symbiont dependence. Nat. Commun. 8, 1–8 (2017).
CAS  Article  Google Scholar 

7.
Bar-On, Y. M., Phillips, R. & Milo, R. The biomass distribution on Earth. Proc. Natl Acad. Sci. USA 115, 6506–6511 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

8.
Fierer, N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat. Rev. Microbiol. 15, 579–590 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

9.
Van Der Heijden, M. G. A., Bardgett, R. D. & Van Straalen, N. M. The unseen majority: soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems. Ecol. Lett. 11, 296–310 (2008).
PubMed  Article  PubMed Central  Google Scholar 

10.
Dubey, A. et al. Soil microbiome: a key player for conservation of soil health under changing climate. Biodivers. Conserv. 28, 2405–2429 (2019).
Article  Google Scholar 

11.
Berendsen, R. L., Pieterse, C. M. J. & Bakker, P. A. H. M. The rhizosphere microbiome and plant health. Trends Plant Sci. 17, 478–486 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

12.
Hartmann, A. et al. Assessment of the structural and functional diversities of plant microbiota: achievements and challenges—a review. J. Adv. Res. 19, 3–13 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

13.
Gurung, K., Wertheim, B. & Falcao Salles, J. The microbiome of pest insects: it is not just bacteria. Entomol. Exp. Appl. 167, 156–170 (2019).
Article  Google Scholar 

14.
Finkel, O. M., Castrillo, G., Herrera Paredes, S., Salas González, I. & Dangl, J. L. Understanding and exploiting plant beneficial microbes. Curr. Opin. Plant Biol. 38, 155–163 (2017).
PubMed  PubMed Central  Article  Google Scholar 

15.
Toju, H. et al. Core microbiomes for sustainable agroecosystems. Nat. Plants 4, 247–257 (2018).
PubMed  Article  PubMed Central  Google Scholar 

16.
Sergaki, C., Lagunas, B., Lidbury, I., Gifford, M. L. & Schäfer, P. Challenges and approaches in microbiome research: from fundamental to applied. Front. Plant Sci. 9, 1205 (2018).
PubMed  PubMed Central  Article  Google Scholar 

17.
Mariotte, P. et al. Plant–soil feedback: bridging natural and agricultural sciences. Trends Ecol. Evol. 33, 129–142 (2018).
PubMed  Article  PubMed Central  Google Scholar 

18.
Porter, S. S. & Sachs, J. L. Agriculture and the disruption of plant–microbial symbiosis. Trends Ecol. Evol. 35, 426–439 (2020).
PubMed  Article  PubMed Central  Google Scholar 

19.
Humphrey, P. T. & Whiteman, N. K. Insect herbivory reshapes a native leaf microbiome. Nat. Ecol. Evol. 4, 221–229 (2020).
PubMed  PubMed Central  Article  Google Scholar 

20.
Lòpez-Fernàndez, S., Mazzoni, V., Pedrazzoli, F., Pertot, I. & Campisano, A. A phloem-feeding insect transfers bacterial endophytic communities between grapevine plants. Front. Microbiol. 8, 834 (2017).
PubMed  PubMed Central  Article  Google Scholar 

21.
Kim, D. R. et al. A mutualistic interaction between Streptomyces bacteria, strawberry plants and pollinating bees. Nat. Commun. 10, 4802 (2019).
PubMed  PubMed Central  Article  CAS  Google Scholar 

22.
Adeleke, R. A., Raimi, A. R., Roopnarain, A. & Mokubedi, S. M. in Biofertilizers for Sustainable Agriculture and Environment Vol 55 (eds Bhoopander, G. et al.) 137–172 (Springer, 2019).

23.
Besset-Manzoni, Y., Rieusset, L., Joly, P., Comte, G. & Prigent-Combaret, C. Exploiting rhizosphere microbial cooperation for developing sustainable agriculture strategies. Environ. Sci. Pollut. Res. 25, 29953–29970 (2018).
Article  Google Scholar 

24.
Hussain, S., Siddique, T., Saleem, M., Arshad, M. & Khalid, A. Impact of pesticides on soil microbial diversity, enzymes, and biochemical reactions. Adv. Agron. 102, 159–200 (2009).
CAS  Article  Google Scholar 

25.
Wolmarans, K. & Swart, W. J. Influence of glyphosate, other herbicides and genetically modified herbicide-resistant crops on soil microbiota: a review. South Afr. J. Plant Soil 31, 177–186 (2014).
Article  Google Scholar 

26.
Kim, N., Zabaloy, M. C., Guan, K. & Villamil, M. B. Do cover crops benefit soil microbiome? A meta-analysis of current research. Soil Biol. Biochem. 142, 107701 (2020).
CAS  Article  Google Scholar 

27.
Venter, Z. S., Jacobs, K. & Hawkins, H. J. The impact of crop rotation on soil microbial diversity: a meta-analysis. Pedobiologia 59, 215–223 (2016).
Article  Google Scholar 

28.
Imfeld, G. & Vuilleumier, S. Measuring the effects of pesticides on bacterial communities in soil: a critical review. Eur. J. Soil Biol. 49, 22–30 (2012).
CAS  Article  Google Scholar 

29.
Bünemann, E. K., Schwenke, G. D. & Van Zwieten, L. Impact of agricultural inputs on soil organisms—a review. Aust. J. Soil Res. 44, 379–406 (2006).
Article  Google Scholar 

30.
Tsiafouli, M. A. et al. Intensive agriculture reduces soil biodiversity across Europe. Glob. Chang. Biol. 21, 973–985 (2015).
PubMed  Article  PubMed Central  Google Scholar 

31.
Chen, H. et al. Global meta-analyses show that conservation tillage practices promote soil fungal and bacterial biomass. Agric. Ecosyst. Environ. 293, 106841 (2020).
CAS  Article  Google Scholar 

32.
Pérez-Jaramillo, J. E., Carrión, V. J., de Hollander, M. & Raaijmakers, J. M. The wild side of plant microbiomes. Microbiome 6, 143 (2018).
PubMed  PubMed Central  Article  Google Scholar 

33.
Ullah, M. & Dijkstra, F. Fungicide and bactericide effects on carbon and nitrogen cycling in soils: a meta-analysis. Soil Syst. 3, 23 (2019).
CAS  Article  Google Scholar 

34.
Wang, Z., Li, Y., Li, T., Zhao, D. & Liao, Y. Conservation tillage decreases selection pressure on community assembly in the rhizosphere of arbuscular mycorrhizal fungi. Sci. Total Environ. 710, 136326 (2020).
CAS  PubMed  Article  PubMed Central  Google Scholar 

35.
Gómez-Gallego, C. et al. Glyphosate-based herbicide affects the composition of microbes associated with Colorado potato beetle (Leptinotarsa decemlineata). FEMS Microbiol. Lett. 367, fnaa050 (2019).
Article  CAS  Google Scholar 

36.
Jenkins, M., Locke, M., Reddy, K., McChesney, D. S. & Steinriede, R. Glyphosate applications, glyphosate resistant corn, and tillage on nitrification rates and distribution of nitrifying microbial communities. Soil Sci. Soc. Am. J. 81, 1371–1380 (2017).
CAS  Article  Google Scholar 

37.
Ramakrishnan, B., Venkateswarlu, K., Sethunathan, N. & Megharaj, M. Local applications but global implications: can pesticides drive microorganisms to develop antimicrobial resistance? Sci. Total Environ. 654, 177–189 (2019).
CAS  PubMed  Article  PubMed Central  Google Scholar 

38.
Felsot, A. S. Enhanced biodegradation of insecticides in soil: implications for agroecosystems. Annu. Rev. Entomol. 34, 453–476 (1989).
CAS  Article  Google Scholar 

39.
Kikuchi, Y. et al. Symbiont-mediated insecticide resistance. Proc. Natl Acad. Sci. USA 109, 8618–8622 (2012).
CAS  PubMed  Article  PubMed Central  Google Scholar 

40.
Tago, K., Kikuchi, Y., Nakaoka, S., Katsuyama, C. & Hayatsu, M. Insecticide applications to soil contribute to the development of Burkholderia mediating insecticide resistance in stinkbugs. Mol. Ecol. 24, 3766–3778 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

41.
Zhang, J. et al. Rapid evolution of symbiotic bacteria populations in spirotetramat-resistant Aphis gossypii glover revealed by pyrosequencing. Comp. Biochem. Physiol. D 20, 151–158 (2016).
Google Scholar 

42.
Xia, X. et al. Gut microbiota mediate insecticide resistance in the diamondback moth, Plutella xylostella (L.). Front. Microbiol. 9, 25 (2018).
PubMed  PubMed Central  Article  Google Scholar 

43.
Almeida, L. G., de, Moraes, L. A. B., de, Trigo, J. R., Omoto, C. & Cônsoli, F. L. The gut microbiota of insecticide-resistant insects houses insecticide-degrading bacteria: a potential source for biotechnological exploitation. PLoS ONE 12, e0174754 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

44.
Bowles, T. M., Jackson, L. E., Loeher, M. & Cavagnaro, T. R. Ecological intensification and arbuscular mycorrhizas: a meta-analysis of tillage and cover crop effects. J. Appl. Ecol. 54, 1785–1793 (2017).
Article  Google Scholar 

45.
Valente, J., Gerin, F., Le Gouis, J., Moënne-Loccoz, Y. & Prigent-Combaret, C. Ancient wheat varieties have a higher ability to interact with plant growth-promoting rhizobacteria. Plant. Cell Environ. 43, 246–260 (2020).
CAS  PubMed  Article  PubMed Central  Google Scholar 

46.
Newton, A. C., Gravouil, C. & Fountaine, J. M. Managing the ecology of foliar pathogens: ecological tolerance in crops. Ann. Appl. Biol. 157, 343–359 (2010).
Article  Google Scholar 

47.
Huang, X., Zhao, J., Zhou, X., Zhang, J. & Cai, Z. Differential responses of soil bacterial community and functional diversity to reductive soil disinfestation and chemical soil disinfestation. Geoderma 348, 124–134 (2019).
CAS  Article  Google Scholar 

48.
Karlsson, I., Friberg, H., Steinberg, C. & Persson, P. Fungicide effects on fungal community composition in the wheat phyllosphere. PLoS ONE 9, e111786 (2014).
PubMed  PubMed Central  Article  CAS  Google Scholar 

49.
Schaeffer, R. N., Vannette, R. L., Brittain, C., Williams, N. M. & Fukami, T. Non-target effects of fungicides on nectar-inhabiting fungi of almond flowers. Environ. Microbiol. Rep. 9, 79–84 (2017).
PubMed  Article  PubMed Central  Google Scholar 

50.
Lagnaoui, A. & Radcliffe, E. B. Potato fungicides interfere with entomopathogenic fungi impacting population dynamics of green peach aphid. Am. J. Potato Res. 75, 19–25 (1998).
CAS  Article  Google Scholar 

51.
Sarkar, S., Narayanan, P., Divakaran, A., Balamurugan, A. & Premkumar, R. The in vitro effect of certain fungicides, insecticides, and biopesticides on mycelial growth in the biocontrol fungus Trichoderma harzianum. Turkish J. Biol. 34, 399–403 (2010).
CAS  Google Scholar 

52.
Duke, S. O. Interaction of chemical pesticides and their formulation ingredients with microbes associated with plants and plant pests. J. Agric. Food Chem. 66, 7553–7561 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

53.
Kakumanu, M. L., Reeves, A. M., Anderson, T. D., Rodrigues, R. R. & Williams, M. A. Honey bee gut microbiome is altered by in-hive pesticide exposures. Front. Microbiol. 7, 1255 (2016).
PubMed  PubMed Central  Article  Google Scholar 

54.
del Mar Fernández, M. et al. Influence of microbiota in the susceptibility of parasitic wasps to abamectin insecticide: deep sequencing, esterase and toxicity tests. Pest Manag. Sci. 75, 79–86 (2019).
Article  CAS  Google Scholar 

55.
Motta, E. V. S. & Moran, N. A. Impact of glyphosate on the honey bee gut microbiota: effects of intensity, duration, and timing of exposure. mSystems 5, e00268-20 (2020).
PubMed  PubMed Central  Article  Google Scholar 

56.
Steffan, S. A. et al. Omnivory in bees: elevated trophic positions among all major bee families. Am. Nat. 194, 414–421 (2019).
PubMed  Article  PubMed Central  Google Scholar 

57.
Bernauer, O. M., Gaines-Day, H. R. & Steffan, S. A. Colonies of bumble bees (Bombus impatiens) produce fewer workers, less bee biomass, and have smaller mother queens following fungicide exposure. Insects 6, 478–488 (2015).
PubMed  PubMed Central  Article  Google Scholar 

58.
Yoder, J. A., Nelson, B. W., Jajack, A. J. & Sammataro, D. in Beekeeping – From Science to Practice (eds Vreeland, R. H. & Sammatoro, D.) 73–90 (Springer, 2017).

59.
Vida, C., Vicente, A. & Cazorla, F. M. The role of organic amendments to soil for crop protection: induction of suppression of soilborne pathogens. Ann. Appl. Biol. 176, 1–15 (2020).
Article  Google Scholar 

60.
Hartman, K. et al. Cropping practices manipulate abundance patterns of root and soil microbiome members paving the way to smart farming. Microbiome 6, 14 (2018).
PubMed  PubMed Central  Article  Google Scholar 

61.
Ling, N. et al. Insight into how organic amendments can shape the soil microbiome in long-term field experiments as revealed by network analysis. Soil Biol. Biochem. 99, 137–149 (2016).
CAS  Article  Google Scholar 

62.
Li, X. et al. Legacy of land use history determines reprogramming of plant physiology by soil microbiome. ISME J. 13, 738–751 (2019).
CAS  PubMed  Article  PubMed Central  Google Scholar 

63.
Vukicevich, E., Lowery, T., Bowen, P., Úrbez-Torres, J. R. & Hart, M. Cover crops to increase soil microbial diversity and mitigate decline in perennial agriculture. A review. Agron. Sustain. Dev. 36, 48 (2016).
Article  CAS  Google Scholar 

64.
Osipitan, O. A., Dille, J. A., Assefa, Y. & Knezevic, S. Z. Cover crop for early season weed suppression in crops: systematic review and meta-analysis. Agron. J. 110, 2211–2221 (2018).
Article  Google Scholar 

65.
Hokkanen, H. M. T. & Menzler-Hokkanen, I. Insect pest suppressive soils: buffering pulse cropping systems against outbreaks of Sitona weevils. Ann. Entomol. Soc. Am. 111, 139–143 (2018).
Article  Google Scholar 

66.
Esmaeili Taheri, A., Hamel, C. & Gan, Y. Cropping practices impact fungal endophytes and pathogens in durum wheat roots. Appl. Soil Ecol. 100, 104–111 (2016).
Article  Google Scholar 

67.
Lucas, S. T., D’Angelo, E. M. & Williams, M. A. Improving soil structure by promoting fungal abundance with organic soil amendments. Appl. Soil Ecol. 75, 13–23 (2014).
Article  Google Scholar 

68.
Misra, P. et al. Vulnerability of soil microbiome to monocropping of medicinal and aromatic plants and its restoration through intercropping and organic amendments. Front. Microbiol. 10, 2604 (2019).
PubMed  PubMed Central  Article  Google Scholar 

69.
Nicola, L. et al. Fumigation with dazomet modifies soil microbiota in apple orchards affected by replant disease. Appl. Soil Ecol. 113, 71–79 (2017).
Article  Google Scholar 

70.
Nobbe, F. & Hiltner, L. Inoculation of the soil for cultivating leguminous plants. US Patent 570 (1896).

71.
Thilakarathna, M. S. & Raizada, M. N. A meta-analysis of the effectiveness of diverse rhizobia inoculants on soybean traits under field conditions. Soil Biol. Biochem. 105, 177–196 (2017).
CAS  Article  Google Scholar 

72.
Zhang, S., Lehmann, A., Zheng, W., You, Z. & Rillig, M. C. Arbuscular mycorrhizal fungi increase grain yields: a meta-analysis. N. Phytol. 222, 543–555 (2019).
CAS  Article  Google Scholar 

73.
Veresoglou, S. D. & Menexes, G. Impact of inoculation with Azospirillum spp. on growth properties and seed yield of wheat: a meta-analysis of studies in the ISI Web of Science from 1981 to 2008. Plant Soil 337, 469–480 (2010).
CAS  Article  Google Scholar 

74.
Federici, B. A., Bonning, B. C. & St. Leger, R. J. in Patho-Biotechnology (eds Sleator, R. & Hill, C.) 15–40 (CRC Press, 2008).

75.
Johnson, L. J. et al. The exploitation of epichloae endophytes for agricultural benefit. Fungal Divers. 60, 171–188 (2013).
Article  Google Scholar 

76.
Castillo Lopez, D., Zhu-Salzman, K., Ek-Ramos, M. J. & Sword, G. A. The entomopathogenic fungal endophytes Purpureocillium lilacinum (formerly Paecilomyces lilacinus) and Beauveria bassiana negatively affect cotton aphid reproduction under both greenhouse and field conditions. PLoS ONE 9, e103891 (2014).
PubMed  PubMed Central  Article  Google Scholar 

77.
Sessitsch, A., Pfaffenbichler, N. & Mitter, B. Microbiome applications from lab to field: facing complexity. Trends Plant Sci. 24, 194–198 (2019).
CAS  PubMed  Article  PubMed Central  Google Scholar 

78.
Stephan, J. G. et al. Honeybee-specific lactic acid bacterium supplements have no effect on American foulbrood-infected honeybee colonies. Appl. Environ. Microbiol. 85, e00606-19 (2019).
PubMed  PubMed Central  Article  Google Scholar 

79.
Bacilio, M., Moreno, M., Lopez-Aguilar, D. R. & Bashan, Y. Scaling from the growth chamber to the greenhouse to the field: demonstration of diminishing effects of mitigation of salinity in peppers inoculated with plant growth-promoting bacterium and humic acids. Appl. Soil Ecol. 119, 327–338 (2017).
Article  Google Scholar 

80.
Latz, M. A. C., Jensen, B., Collinge, D. B. & Lyngs Jørgensen, H. J. Identification of two endophytic fungi that control Septoria tritici blotch in the field, using a structured screening approach. Biol. Control 141, 104128 (2020).
CAS  Article  Google Scholar 

81.
Haney, C. H., Samuel, B. S., Bush, J. & Ausubel, F. M. Associations with rhizosphere bacteria can confer an adaptive advantage to plants. Nat. Plants 1, 15051 (2015).
CAS  PubMed  PubMed Central  Article  Google Scholar 

82.
Smith, K. P., Handelsman, J. & Goodman, R. M. Genetic basis in plants for interactions with disease-suppressive bacteria. Proc. Natl Acad. Sci. USA 96, 4786–4790 (1999).
CAS  PubMed  Article  PubMed Central  Google Scholar 

83.
Shrestha, A. et al. Genetic differences in barley govern the responsiveness to N-acyl homoserine lactone. Phytobiomes J. 3, 191–202 (2019).
Article  Google Scholar 

84.
Chowdhury, S. P. et al. Effects of Bacillus amyloliquefaciens FZB42 on lettuce growth and health under pathogen pressure and its impact on the rhizosphere bacterial community. PLoS ONE 8, e68818 (2013).
PubMed  PubMed Central  Article  Google Scholar 

85.
Papavizas, G. C. Survival of Trichoderma harzianum in soil and in pea and bean rhizospheres. Phytopathology 72, 121 (1982).
Article  Google Scholar 

86.
Hungria, M., Campo, R. J., Chueire, L. M. O., Grange, L. & Megías, M. Symbiotic effectiveness of fast-growing rhizobial strains isolated from soybean nodules in Brazil. Biol. Fertil. Soils 33, 387–394 (2001).
CAS  Article  Google Scholar 

87.
Cassán, F. & Diaz-Zorita, M. Azospirillum sp. in current agriculture: from the laboratory to the field. Soil Biol. Biochem. 103, 117–130 (2016).
Article  CAS  Google Scholar 

88.
Ojiambo, P. S., Battilani, P., Cary, J. W., Blum, B. H. & Carbone, I. Cultural and genetic approaches to manage aflatoxin contamination: recent insights provide opportunities for improved control. Phytopathology 108, 1024–1037 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

89.
Karise, R. et al. Reliability of the entomovector technology using Prestop-Mix and Bombus terrestris L. as a fungal disease biocontrol method in open field. Sci. Rep. 6, 31650 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

90.
Hawkes, C. V. & Connor, E. W. Translating phytobiomes from theory to practice: ecological and evolutionary considerations. Phytobiomes J. 1, 57–69 (2017).
Article  Google Scholar 

91.
Mitter, B. et al. A new approach to modify plant microbiomes and traits by introducing beneficial bacteria at flowering into progeny seeds. Front. Microbiol. 8, 11 (2017).
PubMed  PubMed Central  Google Scholar 

92.
Prado, A., Marolleau, B., Vaissière, B. E., Barret, M. & Torres-Cortes, G. Insect pollination: an ecological process involved in the assembly of the seed microbiota. Sci. Rep. 10, 3575 (2020).
CAS  PubMed  PubMed Central  Article  Google Scholar 

93.
Bosworth, A. H. et al. Alfalfa yield response to inoculation with recombinant strains of Rhizobium meliloti with an extra copy of dctABD and/or modified nifA expression. Appl. Environ. Microbiol. 60, 3815–3832 (1994).
CAS  PubMed  PubMed Central  Article  Google Scholar 

94.
Suárez, R. et al. Improvement of drought tolerance and grain yield in common bean by overexpressing trehalose-6-phosphate synthase in rhizobia. Mol. Plant Microbe Interact. 21, 958–966 (2008).
PubMed  Article  CAS  PubMed Central  Google Scholar 

95.
Leonard, S. P. et al. Engineered symbionts activate honey bee immunity and limit pathogens. Science 367, 573–576 (2020).
CAS  PubMed  PubMed Central  Article  Google Scholar 

96.
Sarma, B. K., Yadav, S. K., Singh, S. & Singh, H. B. Microbial consortium-mediated plant defense against phytopathogens: readdressing for enhancing efficacy. Soil Biol. Biochem. 87, 25–33 (2015).
CAS  Article  Google Scholar 

97.
Becker, J., Eisenhauer, N., Scheu, S. & Jousset, A. Increasing antagonistic interactions cause bacterial communities to collapse at high diversity. Ecol. Lett. 15, 468–474 (2012).
PubMed  Article  PubMed Central  Google Scholar 

98.
Hu, J. et al. Probiotic diversity enhances rhizosphere microbiome function and plant disease suppression. mBio 7, e01790-16 (2016).
PubMed  PubMed Central  Article  Google Scholar 

99.
Nuzzo, A., Satpute, A., Albrecht, U. & Strauss, S. L. Impact of soil microbial amendments on tomato rhizosphere microbiome and plant growth in field soil. Microb. Ecol. 80, 398–409 (2020).
CAS  PubMed  Article  PubMed Central  Google Scholar 

100.
Xu, X. M. & Jeger, M. J. Combined use of two biocontrol agents with different biocontrol mechanisms most likely results in less than expected efficacy in controlling foliar pathogens under fluctuating conditions: a modeling study. Phytopathology 103, 108–116 (2013).
PubMed  Article  PubMed Central  Google Scholar 

101.
Guijarro, B. et al. Compatibility interactions between the biocontrol agent Penicillium frequentans Pf909 and other existing strategies to brown rot control. Biol. Control 129, 45–54 (2019).
Article  Google Scholar 

102.
Rubin, R. L., van Groenigen, K. J. & Hungate, B. A. Plant growth promoting rhizobacteria are more effective under drought: a meta-analysis. Plant Soil 416, 309–323 (2017).
CAS  Article  Google Scholar 

103.
Rho, H. et al. Do endophytes promote growth of host plants under stress? A meta-analysis on plant stress mitigation by endophytes. Microb. Ecol. 75, 407–418 (2018).
PubMed  Article  PubMed Central  Google Scholar 

104.
Johnson, K. B., Temple, T. N., Elkins, R. B. & Smith, T. J. Strategy for non-antibiotic fire blight control in U.S.-grown organic pome fruit. Acta Hortic. 1056, 93–100 (2014).
Article  Google Scholar 

105.
Temple, T. N., Thompson, E. C., Uppala, S. S., Granatstein, D. & Johnson, K. Floral colonization dynamics and specificity of Aureobasidium pullulans strains used to suppress fire blight of pome fruit. Plant Dis. 104, 121–128 (2019).
PubMed  Article  PubMed Central  Google Scholar 

106.
Rotolo, C. et al. Use of biocontrol agents and botanicals in integrated management of Botrytis cinerea in table grape vineyards. Pest Manag. Sci. 74, 715–725 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

107.
Abbey, J. A., Percival, D., Asiedu, S. K., Prithiviraj, B. & Schilder, A. Management of Botrytis blossom blight in wild blueberries by biological control agents under field conditions. Crop Prot. 131, 105078 (2020).
CAS  Article  Google Scholar 

108.
Morel, M. A., Cagide, C., Minteguiaga, M. A., Dardanelli, M. S. & Castro-Sowinski, S. The pattern of secreted molecules during the co-inoculation of alfalfa plants with Sinorhizobium meliloti and Delftia sp. strain JD2: an interaction that improves plant yield. Mol. Plant Microbe Interact. 28, 134–142 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

109.
Remans, R. et al. Effect of Rhizobium–Azospirillum coinoculation on nitrogen fixation and yield of two contrasting Phaseolus vulgaris L. genotypes cultivated across different environments in Cuba. Plant Soil 312, 25–37 (2008).
CAS  Article  Google Scholar 

110.
Carrión, V. J. et al. Pathogen-induced activation of disease-suppressive functions in the endophytic root microbiome. Science 366, 606–612 (2019).
PubMed  Article  CAS  PubMed Central  Google Scholar 

111.
Santhanam, R. et al. Native root-associated bacteria rescue a plant from a sudden-wilt disease that emerged during continuous cropping. Proc. Natl Acad. Sci. USA 112, E5013–E5020 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

112.
Gould, A. L. et al. Microbiome interactions shape host fitness. Proc. Natl Acad. Sci. USA 115, E11951–E11960 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

113.
Carlström, C. I. et al. Synthetic microbiota reveal priority effects and keystone strains in the Arabidopsis phyllosphere. Nat. Ecol. Evol. 3, 1445–1454 (2019).
PubMed  PubMed Central  Article  Google Scholar 

114.
Niu, B., Paulson, J. N., Zheng, X. & Kolter, R. Simplified and representative bacterial community of maize roots. Proc. Natl Acad. Sci. USA 114, E2450–E2459 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

115.
Sanchez-Gorostiaga, A., Bajić, D., Osborne, M. L., Poyatos, J. F. & Sanchez, A. High-order interactions distort the functional landscape of microbial consortia. PLoS Biol. 17, e3000550 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

116.
Herrera Paredes, S. et al. Design of synthetic bacterial communities for predictable plant phenotypes. PLoS Biol. 16, e2003962 (2018).
PubMed  PubMed Central  Article  CAS  Google Scholar 

117.
Kehe, J. et al. Massively parallel screening of synthetic microbial communities. Proc. Natl Acad. Sci. USA 116, 12804–12809 (2019).
CAS  PubMed  Article  PubMed Central  Google Scholar 

118.
Pineda, A., Kaplan, I. & Bezemer, T. M. Steering soil microbiomes to suppress aboveground insect pests. Trends Plant Sci. 22, 770–778 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

119.
Mueller, U. G. & Sachs, J. L. Engineering microbiomes to improve plant and animal health. Trends Microbiol. 23, 606–617 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

120.
Swenson, W., Wilson, D. S. & Elias, R. Artificial ecosystem selection. Proc. Natl Acad. Sci. USA 97, 9110–9114 (2000).
CAS  PubMed  Article  PubMed Central  Google Scholar 

121.
Jochum, M. D., McWilliams, K. L., Pierson, E. A. & Jo, Y. K. Host-mediated microbiome engineering (HMME) of drought tolerance in the wheat rhizosphere. PLoS ONE 14, e0225933 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

122.
Panke-Buisse, K., Poole, A. C., Goodrich, J. K., Ley, R. E. & Kao-Kniffin, J. Selection on soil microbiomes reveals reproducible impacts on plant function. ISME J. 9, 980–989 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

123.
Arora, J., Mars Brisbin, M. A. & Mikheyev, A. S. Effects of microbial evolution dominate those of experimental host-mediated indirect selection. PeerJ 8, e9350 (2020).
PubMed  PubMed Central  Article  Google Scholar 

124.
Morella, N. M. et al. Successive passaging of a plant-associated microbiome reveals robust habitat and host genotype-dependent selection. Proc. Natl Acad. Sci. USA 117, 1148–1159 (2020).
CAS  PubMed  Article  PubMed Central  Google Scholar 

125.
Mason, C. J. et al. Plant defenses interact with insect enteric bacteria by initiating a leaky gut syndrome. Proc. Natl Acad. Sci. USA 116, 15991–15996 (2019).
CAS  PubMed  Article  PubMed Central  Google Scholar 

126.
Pérez-Jaramillo, J. E. et al. Linking rhizosphere microbiome composition of wild and domesticated Phaseolus vulgaris to genotypic and root phenotypic traits. ISME J. 11, 2244–2257 (2017).
PubMed  PubMed Central  Article  Google Scholar 

127.
Walters, W. A. et al. Large-scale replicated field study of maize rhizosphere identifies heritable microbes. Proc. Natl Acad. Sci. USA 115, 7368–7373 (2018).
PubMed  Article  PubMed Central  Google Scholar 

128.
Wallace, J. G., Kremling, K. A., Kovar, L. L. & Buckler, E. S. Quantitative genetics of the maize leaf microbiome. Phytobiomes J. 2, 208–224 (2018).
Article  Google Scholar 

129.
Huang, R. et al. Natural variation at OsCERK1 regulates arbuscular mycorrhizal symbiosis in rice. N. Phytol. 225, 1762–1776 (2020).
CAS  Article  Google Scholar 

130.
Zhang, J. et al. NRT1.1B is associated with root microbiota composition and nitrogen use in field-grown rice. Nat. Biotechnol. 37, 676–684 (2019).
CAS  PubMed  Article  PubMed Central  Google Scholar 

131.
French, E., Tran, T. & Iyer-Pascuzzi, A. Tomato genotype modulates selection and responses to root microbiota. Phytobiomes J. 4, 314–326.

132.
Wintermans, P. C. A., Bakker, P. A. H. M. & Pieterse, C. M. J. Natural genetic variation in Arabidopsis for responsiveness to plant growth-promoting rhizobacteria. Plant Mol. Biol. 90, 623–634 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

133.
Pérez-Jaramillo, J. E., Mendes, R. & Raaijmakers, J. M. Impact of plant domestication on rhizosphere microbiome assembly and functions. Plant Mol. Biol. 90, 635–644 (2015).
PubMed  PubMed Central  Article  CAS  Google Scholar 

134.
Hacquard, S., Spaepen, S., Garrido-Oter, R. & Schulze-Lefert, P. Interplay between innate immunity and the plant microbiota. Annu. Rev. Phytopathol. 55, 565–589 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

135.
Mendes, L. W., Mendes, R., Raaijmakwers, J. M. & Tsai, S. M. Breeding for soil-borne pathogen resistance impacts active rhizosphere microbiome of common bean. ISME J. 12, 3038–3042 (2018).
CAS  PubMed  PubMed Central  Article  Google Scholar 

136.
Koprivova, A. et al. Root-specific camalexin biosynthesis controls the plant growth-promoting effects of multiple bacterial strains. Proc. Natl Acad. Sci. USA 116, 15735–15744 (2019).
CAS  PubMed  Article  PubMed Central  Google Scholar 

137.
Vílchez, J. I. et al. DNA demethylases are required for myo-inositol-mediated mutualism between plants and beneficial rhizobacteria. Nat. Plants 6, 983–995 (2020).
PubMed  Article  CAS  PubMed Central  Google Scholar 

138.
Zhalnina, K. et al. Dynamic root exudate chemistry and microbial substrate preferences drive patterns in rhizosphere microbial community assembly. Nat. Microbiol. 3, 470–480 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

139.
Esse, H. P., Reuber, T. L. & Does, D. Genetic modification to improve disease resistance in crops. N. Phytol. 225, 70–86 (2020).
Article  Google Scholar 

140.
Ryu, M. et al. Control of nitrogen fixation in bacteria that associate with cereals. Nat. Microbiol. 5, 80–84 (2020).
Article  CAS  Google Scholar 

141.
Murphy, K. A., Tabuloc, C. A., Cervantes, K. R. & Chiu, J. C. Ingestion of genetically modified yeast symbiont reduces fitness of an insect pest via RNA interference. Sci. Rep. 6, 22587 (2016).
CAS  PubMed  PubMed Central  Article  Google Scholar 

142.
Whitten, M. M. A. et al. Symbiont-mediated RNA interference in insects. Proc. R. Soc. B 283, 20160042 (2016).
PubMed  Article  CAS  PubMed Central  Google Scholar 

143.
Chung, S. H., Jing, X., Luo, Y. & Douglas, A. E. Targeting symbiosis-related insect genes by RNAi in the pea aphid–Buchnera symbiosis. Insect Biochem. Mol. Biol. 95, 55–63 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

144.
Lemmon, Z. H. et al. Rapid improvement of domestication traits in an orphan crop by genome editing. Nat. Plants 4, 766–770 (2018).
CAS  PubMed  Article  PubMed Central  Google Scholar 

145.
Li, T. et al. Domestication of wild tomato is accelerated by genome editing. Nat. Biotechnol. 36, 1160–1163 (2018).
CAS  Article  Google Scholar 

146.
Zsögön, A. et al. De novo domestication of wild tomato using genome editing. Nat. Biotechnol. 36, 1211–1216 (2018).
Article  CAS  Google Scholar 

147.
Geddes, B. A. et al. Engineering transkingdom signalling in plants to control gene expression in rhizosphere bacteria. Nat. Commun. 10, 3430 (2019).
PubMed  PubMed Central  Article  CAS  Google Scholar 

148.
Petrosino, J. F. The microbiome in precision medicine: the way forward. Genome Med. 10, 12 (2018).
PubMed  PubMed Central  Article  Google Scholar 

149.
Basso, B. & Antle, J. Digital agriculture to design sustainable agricultural systems. Nat. Sustain. 3, 254–256 (2020).
Article  Google Scholar 

150.
Schlaeppi, K. & Bulgarelli, D. The plant microbiome at work. Mol. Plant Microbe Interact. 28, 212–217 (2015).
CAS  PubMed  Article  PubMed Central  Google Scholar 

151.
Vannette, R. L. The floral microbiome: plant, pollinator, and microbial perspectives. Annu. Rev. Ecol. Evol. Syst. 51, 363–386 (2020).
Article  Google Scholar 

152.
Pineda, A., Kaplan, I., Hannula, S. E., Ghanem, W. & Bezemer, M. T. Conditioning the soil microbiome through plant‐soil feedbacks suppresses an aboveground insect pest. New Phytol. 226, 595–608 (2020).
CAS  PubMed  PubMed Central  Article  Google Scholar 

153.
Blundell, R. et al. Organic management promotes natural pest control through altered plant resistance to insects. Nat. Plants 6, 483–491 (2020).
CAS  PubMed  Article  PubMed Central  Google Scholar 

154.
Mendes, R. et al. Deciphering the rhizosphere microbiome for disease-suppressive bacteria. Science 332, 1097–1100 (2011).
CAS  PubMed  Article  PubMed Central  Google Scholar 

155.
Gu, S. et al. Competition for iron drives phytopathogen control by natural rhizosphere microbiomes. Nat. Microbiol. 5, 1002–1010 (2020).
CAS  PubMed  PubMed Central  Article  Google Scholar 

156.
Zengler, K. et al. EcoFABs: advancing microbiome science through standardized fabricated ecosystems. Nat. Methods 16, 567–571 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

157.
Kaplan, I. et al. Phylogenetic farming: can evolutionary history predict crop rotation via the soil microbiome? Evol. Appl. 13, 1984–1999 (2020).
PubMed  PubMed Central  Article  Google Scholar 

158.
Chang, H. X., Haudenshield, J. S., Bowen, C. R. & Hartman, G. L. Metagenome-wide association study and machine learning prediction of bulk soil microbiome and crop productivity. Front. Microbiol. 8, 519 (2017).
PubMed  PubMed Central  Google Scholar 

159.
Ribière, C., Hegarty, C., Stephenson, H., Whelan, P. & O’Toole, P. W. Gut and whole-body microbiota of the honey bee separate thriving and non-thriving hives. Microb. Ecol. 78, 195–205 (2019).
PubMed  Article  PubMed Central  Google Scholar 

160.
Wei, Z. et al. Initial soil microbiome composition and functioning predetermine future plant health. Sci. Adv. 5, eaaw0759 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

161.
Bainard, L. D., Bainard, J. D., Hamel, C. & Gan, Y. Spatial and temporal structuring of arbuscular mycorrhizal communities is differentially influenced by abiotic factors and host crop in a semi-arid prairie agroecosystem. FEMS Microbiol. Ecol. 88, 333–344 (2014).
CAS  PubMed  Article  PubMed Central  Google Scholar 

162.
Stedtfeld, R. D. et al. Primer set 2.0 for highly parallel qPCR array targeting antibiotic resistance genes and mobile genetic elements. FEMS Microbiol. Ecol. 94, fiy130 (2018).
CAS  Article  Google Scholar 

163.
Shade, A. Diversity is the question, not the answer. ISME J. 11, 1–6 (2017).
PubMed  Article  PubMed Central  Google Scholar 

164.
Saleem, M., Hu, J. & Jousset, A. More than the sum of its parts: microbiome biodiversity as a driver of plant growth and soil health. Annu. Rev. Ecol. Evol. Syst. 50, 145–168 (2019).
Article  Google Scholar 

165.
Shao, H. & Zhang, Y. Non-target effects on soil microbial parameters of the synthetic pesticide carbendazim with the biopesticides cantharidin and norcantharidin. Sci. Rep. 7, 5521 (2017).
PubMed  PubMed Central  Article  CAS  Google Scholar 

166.
Wu, M. et al. Rational dose of insecticide chlorantraniliprole displays a transient impact on the microbial metabolic functions and bacterial community in a silty-loam paddy soil. Sci. Total Environ. 616–617, 236–244 (2018).
PubMed  Article  CAS  PubMed Central  Google Scholar 

167.
Adak, T. et al. Target and non-target effect of commonly used fungicides on microbial properties in rhizospheric soil of rice. Int. J. Environ. Anal. Chem. 100, 1350–1361 (2019).
Article  CAS  Google Scholar 

168.
Wang, Y. et al. Long-term no-tillage and organic input management enhanced the diversity and stability of soil microbial community. Sci. Total Environ. 609, 341–347 (2017).
CAS  PubMed  Article  PubMed Central  Google Scholar 

169.
Hartmann, M., Frey, B., Mayer, J., Mäder, P. & Widmer, F. Distinct soil microbial diversity under long-term organic and conventional farming. ISME J. 9, 1177–1194 (2015).
PubMed  Article  PubMed Central  Google Scholar 

170.
Zhu, S., Vivanco, J. M. & Manter, D. K. Nitrogen fertilizer rate affects root exudation, the rhizosphere microbiome and nitrogen-use-efficiency of maize. Appl. Soil Ecol. 107, 324–333 (2016).
Article  Google Scholar 

171.
Yeoh, Y. K. et al. The core root microbiome of sugarcanes cultivated under varying nitrogen fertilizer application. Environ. Microbiol. 18, 1338–1351 (2016).
PubMed  Article  PubMed Central  Google Scholar 

172.
Liu, Y. & Ludewig, U. Nitrogen-dependent bacterial community shifts in root, rhizome and rhizosphere of nutrient-efficient Miscanthus x giganteus from long-term field trials. GCB Bioenergy 11, 1334–1347 (2019).
CAS  Article  Google Scholar 

173.
Shaharoona, B., Naveed, M., Arshad, M. & Zahir, Z. A. Fertilizer-dependent efficiency of Pseudomonads for improving growth, yield, and nutrient use efficiency of wheat (Triticum aestivum L.). Appl. Microbiol. Biotechnol. 79, 147–155 (2008).
CAS  PubMed  Article  PubMed Central  Google Scholar 

174.
Chen, S. et al. Root-associated microbiomes of wheat under the combined effect of plant development and nitrogen fertilization. Microbiome 7, 136 (2019).
CAS  PubMed  PubMed Central  Article  Google Scholar 

175.
Shen, W. et al. Higher rates of nitrogen fertilization decrease soil enzyme activities, microbial functional diversity and nitrification capacity in a Chinese polytunnel greenhouse vegetable land. Plant Soil 337, 137–150 (2010).
CAS  Article  Google Scholar 

176.
Wang, Z., Li, Y., Li, T., Zhao, D. & Liao, Y. Tillage practices with different soil disturbance shape the rhizosphere bacterial community throughout crop growth. Soil Tillage Res. 197, 104501 (2020).
Article  Google Scholar 

177.
Kraut-Cohen, J. et al. Effects of tillage practices on soil microbiome and agricultural parameters. Sci. Total Environ. 705, 135791 (2020).
CAS  PubMed  Article  PubMed Central  Google Scholar 

178.
Mavrodi, D. V. et al. Long-term irrigation affects the dynamics and activity of the wheat rhizosphere microbiome. Front. Plant Sci. 9, 345 (2018).
PubMed  PubMed Central  Article  Google Scholar 

179.
Hartmann, M. et al. A decade of irrigation transforms the soil microbiome of a semi‐arid pine forest. Mol. Ecol. 26, 1190–1206 (2017).
PubMed  Article  PubMed Central  Google Scholar 

180.
Palacios, O. A. et al. Monitoring of indicator and multidrug resistant bacteria in agricultural soils under different irrigation patterns. Agric. Water Manag. 184, 19–27 (2017).
Article  Google Scholar 

181.
Mavrodi, D. V. et al. Long-term irrigation affects the dynamics and activity of the wheat rhizosphere microbiome. Front. Plant. Sci. 9, 345 (2018).
PubMed  PubMed Central  Article  Google Scholar  More