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    Tropical and Mediterranean biodiversity is disproportionately sensitive to land-use and climate change

    1.
    Newbold, T. et al. Global effects of land use on local terrestrial biodiversity. Nature 520, 45–50 (2015).
    CAS  Google Scholar 
    2.
    Grooten, M. & Almond, R. E. A. (eds) Living Planet Report ‒ 2018: Aiming Higher (WWF, 2018).

    3.
    Hallmann, C. A. et al. More than 75 percent decline over 27 years in total flying insect biomass in protected areas. PLoS ONE 12, e0185809 (2017).
    PubMed  PubMed Central  Google Scholar 

    4.
    Soroye, P., Newbold, T. & Kerr, J. Climate change contributes to widespread declines among bumble bees across continents. Science 367, 685–688 (2020).
    CAS  PubMed  Google Scholar 

    5.
    Tittensor, D. P. et al. A mid-term analysis of progress toward international biodiversity targets. Science 346, 241–244 (2014).
    CAS  PubMed  Google Scholar 

    6.
    Newbold, T. et al. Climate and land-use change homogenise terrestrial biodiversity, with consequences for ecosystem functioning and human well-being. Emerg. Top. Life Sci. 3, 207–219 (2019).
    Google Scholar 

    7.
    Nicholson, E. et al. Scenarios and models to support global conservation targets. Trends Ecol. Evol. 34, 57–68 (2019).
    PubMed  Google Scholar 

    8.
    Ferrier, S. et al. (eds) The Methodological Assessment Report on Scenarios and Models of Biodiversity and Ecosystem Services (Secretariat of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services, 2016).

    9.
    Newbold, T. Future effects of climate and land-use change on terrestrial vertebrate community diversity under different scenarios. Proc. R. Soc. B Biol. Sci. 285, 20180792 (2018).
    Google Scholar 

    10.
    Marshall, L. et al. The interplay of climate and land use change affects the distribution of EU bumblebees. Glob. Change Biol. 24, 101–116 (2018).
    Google Scholar 

    11.
    Meyer, C., Kreft, H., Guralnick, R. & Jetz, W. Global priorities for an effective information basis of biodiversity distributions. Nat. Commun. 6, 8221 (2015).
    PubMed  PubMed Central  Google Scholar 

    12.
    Visconti, P. et al. Projecting global biodiversity indicators under future development scenarios. Conserv. Lett. 9, 5–13 (2016).
    Google Scholar 

    13.
    Mace, G. M. et al. Aiming higher to bend the curve of biodiversity loss. Nat. Sustain. 1, 448–451 (2018).
    Google Scholar 

    14.
    Araújo, M. B., Alagador, D., Cabeza, M., Nogués-Bravo, D. & Thuiller, W. Climate change threatens European conservation areas. Ecol. Lett. 14, 484–492 (2011).
    PubMed  PubMed Central  Google Scholar 

    15.
    Pinsky, M. L., Eikeset, A. M., McCauley, D. J., Payne, J. L. & Sunday, J. M. Greater vulnerability to warming of marine versus terrestrial ectotherms. Nature 569, 108–111 (2019).
    CAS  PubMed  Google Scholar 

    16.
    Alkemade, R. et al. GLOBIO3: a framework to investigate options for reducing global terrestrial biodiversity loss. Ecosystems 12, 374–390 (2009).
    Google Scholar 

    17.
    Martins, I. S. & Pereira, H. M. Improving extinction projections across scales and habitats using the countryside species-area relationship. Sci. Rep. 7, 12899 (2017).
    PubMed  PubMed Central  Google Scholar 

    18.
    Newbold, T. et al. Widespread winners and narrow-ranged losers: land use homogenizes biodiversity in local assemblages worldwide. PLoS Biol. 16, e2006841 (2018).
    PubMed  PubMed Central  Google Scholar 

    19.
    Blowes, S. A. et al. The geography of biodiversity change in marine and terrestrial assemblages. Science 366, 339–345 (2019).
    CAS  PubMed  Google Scholar 

    20.
    Klein Goldewijk, K., Beusen, A., Van Drecht, G. & De Vos, M. The HYDE 3.1 spatially explicit database of human-induced global land-use change over the past 12,000 years. Glob. Ecol. Biogeogr. 20, 73–86 (2011).
    Google Scholar 

    21.
    Balmford, A. Extinction filters and current resilience: the significance of past selection pressures for conservation biology. Trends Ecol. Evol. 11, 193–196 (1996).
    CAS  PubMed  Google Scholar 

    22.
    Stevens, G. C. The latitudinal gradient in geographic range: how so many species coexist in the tropics. Am. Nat. 133, 240–256 (1989).
    Google Scholar 

    23.
    Thuiller, W., Lavorel, S. & Araújo, M. B. Niche properties and geographical extent as predictors of species sensitivity to climate change. Glob. Ecol. Biogeogr. 14, 347–357 (2005).
    Google Scholar 

    24.
    Forister, M. L. et al. The global distribution of diet breadth in insect herbivores. Proc. Natl Acad. Sci. USA 112, 442–447 (2015).
    CAS  PubMed  Google Scholar 

    25.
    Newbold, T. et al. Ecological traits affect the response of tropical forest bird species to land-use intensity. Proc. R. Soc. Lond. B Biol. Sci. 280, 20122131 (2013).
    Google Scholar 

    26.
    Rader, R., Bartomeus, I., Tylianakis, J. M. & Laliberté, E. The winners and losers of land use intensification: pollinator community disassembly is non-random and alters functional diversity. Divers. Distrib. 20, 908–917 (2014).
    Google Scholar 

    27.
    Pacifici, M. et al. Species’ traits influenced their response to recent climate change. Nat. Clim. Change 7, 205–208 (2017).
    Google Scholar 

    28.
    Wiersma, P., Munoz-Garcia, A., Walker, A. & Williams, J. B. Tropical birds have a slow pace of life. Proc. Natl Acad. Sci. USA 104, 9340–9345 (2007).
    CAS  PubMed  Google Scholar 

    29.
    Sunday, J. M. et al. Thermal-safety margins and the necessity of thermoregulatory behavior across latitude and elevation. Proc. Natl Acad. Sci. USA 111, 5610–5615 (2014).
    CAS  PubMed  Google Scholar 

    30.
    Orme, C. D. L. et al. Distance to range edge determines sensitivity to deforestation. Nat. Ecol. Evol. 3, 886–891 (2019).
    PubMed  Google Scholar 

    31.
    Frishkoff, L. O., Hadly, E. A. & Daily, G. C. Thermal niche predicts tolerance to habitat conversion in tropical amphibians and reptiles. Glob. Change Biol. 21, 3901–3916 (2015).
    Google Scholar 

    32.
    Frishkoff, L. O. et al. Climate change and habitat conversion favour the same species. Ecol. Lett. 19, 1081–1090 (2016).
    PubMed  Google Scholar 

    33.
    Williams, J. J. & Newbold, T. Local climatic changes affect biodiversity responses to land use: a review. Divers. Distrib. 26, 76–92 (2020).
    Google Scholar 

    34.
    De Frenne, P. et al. Global buffering of temperatures under forest canopies. Nat. Ecol. Evol. 3, 744–749 (2019).
    PubMed  Google Scholar 

    35.
    Williams, J. J., Bates, A. E. & Newbold, T. Human‐dominated land uses favour species affiliated with more extreme climates, especially in the tropics. Ecography 43, 391–405 (2020).
    Google Scholar 

    36.
    Janzen, D. H. Why mountain passes are higher in the tropics. Am. Nat. 101, 233–249 (1967).
    Google Scholar 

    37.
    Srinivasan, U., Elsen, P. R. & Wilcove, D. S. Annual temperature variation influences the vulnerability of montane bird communities to land‐use change. Ecography 42, 2084–2094 (2019).
    Google Scholar 

    38.
    Newbold, T. et al. Global patterns of terrestrial assemblage turnover within and among land uses. Ecography 39, 1151–1163 (2016).
    Google Scholar 

    39.
    Hillebrand, H. et al. Biodiversity change is uncoupled from species richness trends: consequences for conservation and monitoring. J. Appl. Ecol. 55, 169–184 (2018).
    Google Scholar 

    40.
    Hudson, L. N. et al. The database of the PREDICTS (Projecting Responses of Ecological Diversity In Changing Terrestrial Systems) project. Ecol. Evol. 7, 145–188 (2017).
    Google Scholar 

    41.
    Chen, I.-C., Hill, J. K., Ohlemuller, R., Roy, D. B. & Thomas, C. D. Rapid range shifts of species associated with high levels of climate warming. Science 333, 1024–1026 (2011).
    CAS  Google Scholar 

    42.
    Senior, R. A., Hill, J. K., González del Pliego, P., Goode, L. K. & Edwards, D. P. A. Pantropical analysis of the impacts of forest degradation and conversion on local temperature. Ecol. Evol. 7, 7897–7908 (2017).
    PubMed  PubMed Central  Google Scholar 

    43.
    Trenberth, K. E. Changes in precipitation with climate change. Clim. Res. 47, 123–138 (2011).
    Google Scholar 

    44.
    Fu, B., Wang, J., Chen, L. & Qiu, Y. The effects of land use on soil moisture variation in the Danangou catchment of the Loess Plateau, China. Catena 54, 197–213 (2003).
    Google Scholar 

    45.
    Hurtt, G. C. et al. Harmonization of land-use scenarios for the period 1500–2100: 600 years of global gridded annual land-use transitions, wood harvest, and resulting secondary lands. Clim. Change 109, 117–161 (2011).
    Google Scholar 

    46.
    Mora, C. et al. The projected timing of climate departure from recent variability. Nature 502, 183–187 (2013).
    CAS  PubMed  Google Scholar 

    47.
    García-Vega, D. & Newbold, T. Assessing the effects of land use on biodiversity in the world’s drylands and Mediterranean environments. Biodivers. Conserv. 29, 393–408 (2020).
    Google Scholar 

    48.
    Jenkins, C. N., Pimm, S. L. & Joppa, L. N. Global patterns of terrestrial vertebrate diversity and conservation. Proc. Natl Acad. Sci. USA 110, E2602–E2610 (2013).
    CAS  PubMed  Google Scholar 

    49.
    Myers, N., Mittermeier, R. A., Mittermeier, C. G., Da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853–858 (2000).
    CAS  PubMed  Google Scholar 

    50.
    Dornelas, M. et al. BioTIME: A database of biodiversity time series for the Anthropocene. Glob. Ecol. Biogeogr. 27, 760–786 (2018).
    PubMed  PubMed Central  Google Scholar 

    51.
    Pearson, R. G. & Dawson, T. P. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful? Glob. Ecol. Biogeogr. 12, 361–371 (2003).
    Google Scholar 

    52.
    Newbold, T., Sanchez-Ortiz, K., De Palma, A., Hill, S. L. L. & Purvis, A. Reply to ‘The biodiversity intactness index may underestimate losses’. Nat. Ecol. Evol. 3, 864–865 (2019).
    PubMed  Google Scholar 

    53.
    Roslin, T. et al. Higher predation risk for insect prey at low latitudes and elevations. Science 356, 742–744 (2017).
    CAS  PubMed  Google Scholar 

    54.
    The IUCN Red List of Threatened Species Version 2013.7 (IUCN, 2013); http://www.iucnredlist.org/

    55.
    Bird Species Distribution Maps of the World Version 2.0 (BirdLife International & NatureServe, 2012); http://www.birdlife.org/datazone/info/spcdownload

    56.
    Hudson, L. N. et al. The PREDICTS database: a global database of how local terrestrial biodiversity responds to human impacts. Ecol. Evol. 4, 4701–4735 (2014).
    PubMed  PubMed Central  Google Scholar 

    57.
    Zero Draft of the Post-2020 Global Biodiversity Framework Resolution CBD/WG2020/2/3 (Convention on Biological Diversity, 2020).

    58.
    Holt, B. G. et al. An update of Wallace’s zoogeographic regions of the world. Science 339, 74–78 (2013).
    CAS  PubMed  Google Scholar 

    59.
    Kissling, W. D., Sekercioglu, C. H. & Jetz, W. Bird dietary guild richness across latitudes, environments and biogeographic regions. Glob. Ecol. Biogeogr. 21, 328–340 (2012).
    Google Scholar 

    60.
    Smith, J. R. et al. A global test of ecoregions. Nat. Ecol. Evol. 2, 1889–1896 (2018).
    PubMed  Google Scholar 

    61.
    Dinerstein, E. et al. An ecoregion-based approach to protecting half the terrestrial realm. BioScience 67, 534–545 (2017).
    PubMed  PubMed Central  Google Scholar 

    62.
    Terrestrial Ecoregions of the World (The Nature Conservancy, 2009); http://maps.tnc.org/gis_data.html

    63.
    Hudson, L. N. et al. Dataset: The 2016 Release of the PREDICTS Database (Natural History Museum Data Portal, 2016); https://doi.org/10.5519/0066354

    64.
    Powers, R. P. & Jetz, W. Global habitat loss and extinction risk of terrestrial vertebrates under future land-use-change scenarios. Nat. Clim. Change 9, 323–329 (2019).
    Google Scholar 

    65.
    Bolker, B. M. et al. Generalized linear mixed models: a practical guide for ecology and evolution. Trends Ecol. Evol. 24, 127–135 (2008).
    Google Scholar 

    66.
    Rigby, R. A., Stasinopoulos, D. M. & Akantziliotou, C. A framework for modelling overdispersed count data, including the Poisson-shifted generalized inverse Gaussian distribution. Comput. Stat. Data Anal. 53, 381–393 (2008).
    Google Scholar 

    67.
    Herkt, K. M. B., Skidmore, A. K. & Fahr, J. Macroecological conclusions based on IUCN expert maps: a call for caution. Glob. Ecol. Biogeogr. 26, 930–941 (2017).
    Google Scholar 

    68.
    Van Vuuren, D. P. et al. The representative concentration pathways: an overview. Clim. Change 109, 5–31 (2011).
    Google Scholar 

    69.
    Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).
    Google Scholar 

    70.
    Andrén, H. Effects of habitat fragmentation on birds and mammals in landscapes with different proportions of suitable habitat: a review. Oikos 71, 355–366 (1994).
    Google Scholar 

    71.
    Bivand, R. & Wong, D. W. S. Comparing implementations of global and local indicators of spatial association. TEST 27, 716–748 (2018).
    Google Scholar  More

  • in

    Ecology shapes epistasis in a genotype–phenotype–fitness map for stick insect colour

    1.
    Barrett, R. D. H. & Hoekstra, H. E. Molecular spandrels: tests of adaptation at the genetic level. Nat. Rev. Genet. 12, 767–780 (2011).
    CAS  PubMed  Google Scholar 
    2.
    Martin, A. & Orgogozo, V. The loci of repeated evolution: a catalog of genetic hotspots of phenotypic variation. Evolution 67, 1235–1250 (2013).
    CAS  PubMed  Google Scholar 

    3.
    Barrett, R. D. H., Rogers, S. M. & Schluter, D. Natural selection on a major armor gene in threespine stickleback. Science 322, 255–257 (2008).
    CAS  PubMed  Google Scholar 

    4.
    Barrett, R. D. H. et al. Linking a mutation to survival in wild mice. Science 363, 499–504 (2019).
    CAS  PubMed  Google Scholar 

    5.
    Gratten, J. et al. A localized negative genetic correlation constrains microevolution of coat color in wild sheep. Science 319, 318–320 (2008).
    CAS  PubMed  Google Scholar 

    6.
    Lamichhaney, S. et al. A beak size locus in Darwin’s finches facilitated character displacement during a drought. Science 352, 470–474 (2016).
    CAS  PubMed  Google Scholar 

    7.
    Coberly, L. C. & Rausher, M. D. Pleiotropic effects of an allele producing white flowers in Ipomoea purpurea. Evolution 62, 1076–1085 (2008).
    PubMed  Google Scholar 

    8.
    Korves, T. M., others. Fitness effects associated with the major flowering time gene FRIGIDA in Arabidopsis thaliana in the field. Am. Nat. 169, 141–157 (2007).
    Google Scholar 

    9.
    Rockman, M. V. The QTN program and the alleles that matter for evolution: all that’s gold does not glitter. Evolution 66, 1–17 (2012).
    PubMed  Google Scholar 

    10.
    de Visser, J. C. F. T. & Elena, S. F. The causes of epistasis. Proc. R. Soc. B 278, 3617–3624 (2011).
    PubMed  Google Scholar 

    11.
    Arnegard, M. E. et al. Genetics of ecological divergence during speciation. Nature 511, 307–311 (2014).
    CAS  PubMed  PubMed Central  Google Scholar 

    12.
    Storz, J. F. Causes of molecular convergence and parallelism in protein evolution. Nat. Rev. Genet. 17, 239–250 (2016).
    CAS  PubMed  PubMed Central  Google Scholar 

    13.
    Kryazhimskiy, S., Rice, D. P., Jerison, E. R. & Desai, M. M. Global epistasis makes adaptation predictable despite sequence-level stochasticity. Science 344, 1519–1522 (2014).
    CAS  PubMed  PubMed Central  Google Scholar 

    14.
    Marques, D. A. et al. Experimental evidence for rapid genomic adaptation to a new niche in an adaptive radiation. Nat. Ecol. Evol. 2, 1128–1138 (2018).
    PubMed  PubMed Central  Google Scholar 

    15.
    Natarajan, C. et al. Epistasis among adaptive mutations in deer mouse hemoglobin. Science 340, 1324–1327 (2013).
    CAS  PubMed  PubMed Central  Google Scholar 

    16.
    Dettman, J. R., Sirjusingh, C., Kohn, L. M. & Anderson, J. B. Incipient speciation by divergent adaptation and antagonistic epistasis in yeast. Nature 447, 585–588 (2007).
    CAS  PubMed  Google Scholar 

    17.
    Orr, H. A. The population genetics of speciation— the evolution of hybrid incompatibilities. Genetics 139, 1805–1813 (1995).
    CAS  PubMed  PubMed Central  Google Scholar 

    18.
    Gavrilets, S. Evolution and speciation on holey adaptive landscapes. Trends Ecol. Evol. 12, 307–312 (1997).
    CAS  PubMed  Google Scholar 

    19.
    Schwander, T., Libbrecht, R. & Keller, L. Supergenes and complex phenotypes. Curr. Biol. 24, R288–R294 (2014).
    CAS  PubMed  Google Scholar 

    20.
    Wilfert, L. & Schmid-Hempel, P. The genetic architecture of susceptibility to parasites. BMC Evol. Biol. 8, 187 (2008).
    PubMed  PubMed Central  Google Scholar 

    21.
    Weinreich, D. M., Delaney, N. F., DePristo, M. A. & Hartl, D. L. Darwinian evolution can follow only very few mutational paths to fitter proteins. Science 312, 111–114 (2006).
    CAS  PubMed  Google Scholar 

    22.
    Gavrilets, S. Fitness Landscapes and the Origin of Species (Princeton Univ. Press, 2004); https://doi.org/10.2307/j.ctv39x541

    23.
    Wright, S. The roles of mutation, inbreeding, crossbreeding, and selection in evolution. Proc. Sixth Int. Congr. Genet. 1, 356–366 (1932).
    Google Scholar 

    24.
    Lehner, B. Molecular mechanisms of epistasis within and between genes. Trends Genet. 27, 323–331 (2011).
    CAS  PubMed  Google Scholar 

    25.
    Whitlock, M. C., Phillips, P. C., Moore, F. B. & Tonsor, S. J. Multiple fitness peaks and epistasis. Annu. Rev. Ecol. Syst. 26, 601–629 (1995).
    Google Scholar 

    26.
    Whitlock, M. C. Founder effects and peak shifts without genetic drift: adaptive peak shifts occur easily when environments fluctuate slightly. Evolution 51, 1044–1048 (1997).
    PubMed  Google Scholar 

    27.
    Kingsolver, J. G. et al. The strength of phenotypic selection in natural populations. Am. Nat. 157, 245–261 (2001).
    CAS  PubMed  Google Scholar 

    28.
    Sinervo, B. & Svensson, E. Correlational selection and the evolution of genomic architecture. Heredity 89, 329–338 (2002).
    CAS  PubMed  Google Scholar 

    29.
    Poelwijk, F. J., Kiviet, D. J., Weinreich, D. M. & Tans, S. J. Empirical fitness landscapes reveal accessible evolutionary paths. Nature 445, 383–386 (2007).
    CAS  PubMed  Google Scholar 

    30.
    Plucain, J. et al. Epistasis and allele specificity in the emergence of a stable polymorphism in Escherichia coli. Science 343, 1366–1369 (2014).
    CAS  PubMed  Google Scholar 

    31.
    Kirkpatrick, M. How and why chromosome inversions evolve. PLoS Biol. 8, e1000501 (2010).
    PubMed  PubMed Central  Google Scholar 

    32.
    Sandoval, C. P. Differential visual predation on morphs of Timema cristinae (Phasmatodeae:Timemidae) and its consequences for host range. Biol. J. Linn. Soc. 52, 341–356 (1994).
    Google Scholar 

    33.
    Sandoval, C. P. The effects of the relative geographic scales of gene flow and selection on morph frequencies in the walking‐stick Timema cristinae. Evolution 48, 1866–1879 (1994).
    PubMed  Google Scholar 

    34.
    Sandoval, C. P. & Nosil, P. Counteracting selective regimes and host preference evolution in ecotypes of two species of walking-sticks. Evolution 59, 2405–2413 (2005).
    CAS  PubMed  Google Scholar 

    35.
    Comeault, A. A. et al. Selection on a genetic polymorphism counteracts ecological speciation in a stick insect. Curr. Biol. 25, 1975–1981 (2015).
    CAS  PubMed  Google Scholar 

    36.
    Nosil, P. et al. Natural selection and the predictability of evolution in Timema stick insects. Science 359, 765–770 (2018).
    CAS  PubMed  Google Scholar 

    37.
    Villoutreix, R. et al. Large-scale mutation in the evolution of a gene complex for cryptic coloration. Science 369, 460–466 (2020).
    CAS  PubMed  Google Scholar 

    38.
    Lindtke, D. et al. Long-term balancing selection on chromosomal variants associated with crypsis in a stick insect. Mol. Ecol. 26, 6189–6205 (2017).
    CAS  PubMed  Google Scholar 

    39.
    Endler, J. A. A framework for analysing colour pattern geometry: adjacent colours. Biol. J. Linn. Soc. 107, 233–253 (2012).
    Google Scholar 

    40.
    Endler, J. A. On the measurement and classification of colour in studies of animal colour patterns. Biol. J. Linn. Soc. 41, 315–352 (1990).
    Google Scholar 

    41.
    Hurvich, L. M. Color Vision (Sinauer Associates, 1981).

    42.
    Gompert, Z. et al. Experimental evidence for ecological selection on genome variation in the wild. Ecol. Lett. 17, 369–379 (2014).
    PubMed  Google Scholar 

    43.
    Zhou, X., Carbonetto, P. & Stephens, M. Polygenic modeling with Bayesian sparse linear mixed models. PLoS Genet. 9, e1003264 (2013).
    CAS  PubMed  PubMed Central  Google Scholar 

    44.
    Crawford, L., Zeng, P., Mukherjee, S. & Zhou, X. Detecting epistasis with the marginal epistasis test in genetic mapping studies of quantitative traits. PLoS Genet. 13, e1006869 (2017).
    PubMed  PubMed Central  Google Scholar 

    45.
    Comeault, A. A., Ferreira, C., Dennis, S., Soria-Carrasco, V. & Nosil, P. Color phenotypes are under similar genetic control in two distantly related species of Timema stick insect. Evolution 70, 1283–1296 (2016).
    CAS  PubMed  Google Scholar 

    46.
    Nosil, P. & Crespi, B. J. Experimental evidence that predation promotes divergence in adaptive radiation. Proc. Natl Acad. Sci. USA 103, 9090–9095 (2006).
    CAS  PubMed  Google Scholar 

    47.
    Rennison, D. J., Heilbron, K., Barrett, R. D. H. & Schluter, D. Discriminating selection on lateral plate phenotype and its underlying gene, ectodysplasin, in threespine stickleback. Am. Nat. 185, 150–156 (2015).
    PubMed  Google Scholar 

    48.
    Wright, S. The shifting balance theory and macroevolution. Annu. Rev. Genet. 16, 1–19 (1982).
    CAS  PubMed  Google Scholar 

    49.
    Coyne, J. A., Barton, N. H. & Turelli, M. Perspective: a critique of Sewall Wright’s shifting balance theory of evolution. Evolution 51, 643–671 (1997).
    PubMed  Google Scholar 

    50.
    Wade, M. J. & Goodnight, C. J. Perspective: the theories of Fisher and Wright in the context of metapopulations: when nature does many small experiments. Evolution 52, 1537–1553 (1998).
    PubMed  Google Scholar 

    51.
    Reimchen, T. E. Predator-induced cyclical changes in lateral plate frequencies of Gasterosteus. Behaviour 132, 1079–1094 (1995).
    Google Scholar 

    52.
    Coyne, J. A. & Orr, H. A. Speciation (Sinauer Associates, 2004).

    53.
    Sackman, A. M. & Rokyta, D. R. Additive phenotypes underlie epistasis of fitness effects. Genetics 208, 339–348 (2018).
    CAS  PubMed  Google Scholar 

    54.
    Knief, U. et al. Epistatic mutations under divergent selection govern phenotypic variation in the crow hybrid zone. Nat. Ecol. Evol. 3, 570–576 (2019).
    PubMed  PubMed Central  Google Scholar 

    55.
    Hench, K., Vargas, M., Höppner, M. P., McMillan, W. O. & Puebla, O. Inter-chromosomal coupling between vision and pigmentation genes during genomic divergence. Nat. Ecol. Evol. 3, 657–667 (2019).
    PubMed  Google Scholar 

    56.
    Lewontin, R. C. The Genetic Basis of Evolutionary Change (Columbia Univ. Press, 1974).

    57.
    Scheffer, M. Critical Transitions in Nature and Society (Princeton Univ. Press, 2009).

    58.
    Scheffer, M. et al. Anticipating critical transitions. Science 338, 344–348 (2012).
    CAS  PubMed  Google Scholar 

    59.
    Parchman, T. L. et al. Genome-wide association genetics of an adaptive trait in lodgepole pine. Mol. Ecol. 21, 2991–3005 (2012).
    CAS  PubMed  Google Scholar 

    60.
    Li, H. A statistical framework for SNP calling, mutation discovery, association mapping and population genetical parameter estimation from sequencing data. Bioinformatics 27, 2987–2993 (2011).

    61.
    Soria-Carrasco, V. et al. Stick insect genomes reveal natural selection’s role in parallel speciation. Science 344, 738–742 (2014).
    CAS  PubMed  Google Scholar 

    62.
    Guan, Y. & Stephens, M. Bayesian variable selection regression for genome-wide association studies and other large-scale problems. Ann. Appl. Stat. 5, 1780–1815 (2011).
    Google Scholar 

    63.
    Nosil, P. Reproductive isolation caused by visual predation on migrants between divergent environments. Proc. R. Soc. B 271, 1521–1528 (2004).
    PubMed  Google Scholar 

    64.
    Nosil, P. et al. Genomic consequences of multiple speciation processes in a stick insect. Proc. R. Soc. B 279, 5058–5065 (2012).
    PubMed  Google Scholar 

    65.
    Sandoval, C. P. Persistence of a walking-stick population (Phasmatoptera: Timematodea) after a wildfire. Southwest. Nat. 45, 123–127 (2000).
    Google Scholar 

    66.
    Plummer, M. rjags: Bayesian graphical models using MCMC. R package version 4-8 (2018).

    67.
    Lande, R. & Arnold, S. J. The measurement of selection on correlated characters. Evolution 37, 1210–1226 (1983).
    PubMed  Google Scholar 

    68.
    Janzen, F. J. & Stern, H. S. Logistic regression for empirical studies of multivariate selection. Evolution 52, 1564–1571 (1998).
    PubMed  Google Scholar 

    69.
    Zeugner, S. & Feldkircher, M. Bayesian model averaging employing fixed and flexible priors: the BMS package for R. J. Stat. Softw. 68, 1–37 (2015).
    Google Scholar 

    70.
    Zellner, A. in Bayesian Inference and Decision Techniques: Essays in Honor of Bruno de Finetti (eds Goel, P. & Zellner, A.) 233–243 (1986).

    71.
    Csardi, G. & Nepusz, T. The igraph software package for complex network research. InterJ. Complex Syst. 1695, 1–9 (2006).
    Google Scholar 

    72.
    Weinberger, E. Correlated and uncorrelated fitness landscapes and how to tell the difference. Biol. Cybern. 63, 325–336 (1990).
    Google Scholar 

    73.
    Vassilev, V. K., Fogarty, T. C. & Miller, J. F. Information characteristics and the structure of landscapes. Evol. Comput. 8, 31–60 (2000).
    CAS  PubMed  Google Scholar 

    74.
    Kouyos, R. D. et al. Exploring the complexity of the HIV-1 fitness landscape. PLoS Genet. 8, e1002551–e1002551 (2012).
    CAS  PubMed  PubMed Central  Google Scholar 

    75.
    Malan, K. M. & Engelbrecht, A. P. A survey of techniques for characterising fitness landscapes and some possible ways forward. Inf. Sci. 241, 148–163 (2013).
    Google Scholar 

    76.
    Kondrashov, D. A. & Kondrashov, F. A. Topological features of rugged fitness landscapes in sequence space. Trends Genet. 31, 24–33 (2015).
    CAS  PubMed  Google Scholar 

    77.
    Poursoltan, S. & Neumann, F. in Evolutionary Constrained Optimization (eds Datta, R. & Deb, K.) 29–50 (Springer, 2015); https://doi.org/10.1007/978-81-322-2184-5_2

    78.
    Paten, B. et al. Cactus: algorithms for genome multiple sequence alignment. Genome Res. 21, 1512–1528 (2011).
    CAS  PubMed  PubMed Central  Google Scholar 

    79.
    Hickey, G., Paten, B., Earl, D., Zerbino, D. & Haussler, D. HAL: a hierarchical format for storing and analyzing multiple genome alignments. Bioinformatics 29, 1341–1342 (2013).
    CAS  PubMed  Google Scholar 

    80.
    Endler, J. A. & Mielke, P. W. Comparing entire colour patterns as birds see them. Biol. J. Linn. Soc. 86, 405–431 (2005).
    Google Scholar  More

  • in

    Proteome specialization of anaerobic fungi during ruminal degradation of recalcitrant plant fiber

    1.
    Stewart RD, Auffret MD, Warr A, Wiser AH, Press MO, Langford KW, et al. Assembly of 913 microbial genomes from metagenomic sequencing of the cow rumen. Nat Commun. 2018;9:870.
    PubMed  PubMed Central  Google Scholar 
    2.
    Pulina G, Francesconi AHD, Stefanon B, Sevi A, Calamari L, Lacetera N, et al. Sustainable ruminant production to help feed the planet. Ital J Anim Sci. 2017;16:140–71.
    Google Scholar 

    3.
    Cantarel BL, Coutinho PM, Rancurel C, Bernard T, Lombard V, Henrissat B. The Carbohydrate-Active EnZymes database (CAZy): an expert resource for Glycogenomics. Nucleic Acids Res. 2009;37:D233–8.
    CAS  PubMed  Google Scholar 

    4.
    Haitjema CH, Gilmore SP, Henske JK, Solomon KV, de Groot R, Kuo A, et al. A parts list for fungal cellulosomes revealed by comparative genomics. Nat Microbiol. 2017;2:1–8.
    Google Scholar 

    5.
    Solden LM, Naas AE, Roux S, Daly RA, Collins WB, Nicora CD et al. Interspecies cross-feeding orchestrates carbon degradation in the rumen ecosystem. Nat Microbiol. 2018;3. https://doi.org/10.1038/s41564-018-0225-4.

    6.
    Seshadri R, Leahy SC, Attwood GT, Teh KH, Lambie SC, Cookson AL et al. Cultivation and sequencing of rumen microbiome members from the Hungate1000 Collection. Nat Biotechnol. 2018;36. https://doi.org/10.1038/nbt.4110.

    7.
    Solomon KV, Haitjema CH, Henske JK, Gilmore SP, Borges-Rivera D, Lipzen A, et al. Early-branching gut fungi possess a large, comprehensive array of biomass-degrading enzymes. Science. 2016;351:1192–5.
    CAS  PubMed  PubMed Central  Google Scholar 

    8.
    Hanafy RA, Lanjekar VB, Dhakephalkar PK, Callaghan TM, Dagar SS, Griffith GW et al. Seven new Neocallimastigomycota genera from wild, zoo-housed, and domesticated herbivores greatly expand the taxonomic diversity of the phylum. Mycologia 2020:1–28. https://doi.org/10.1080/00275514.2019.1696619.

    9.
    Wilken SE, Swift CL, Podolsky IA, Lankiewicz TS, Seppälä S, O’Malley MA. Linking ‘omics’ to function unlocks the biotech potential of non-model fungi. Curr Opin Syst Biol. 2019;14:9–17.
    Google Scholar 

    10.
    Seppälä S, Wilken SE, Knop D, Solomon KV, O’Malley MA. The importance of sourcing enzymes from non-conventional fungi for metabolic engineering and biomass breakdown. Metab Eng. 2017;44:45–59.
    PubMed  Google Scholar 

    11.
    Podolsky IA, Seppälä S, Lankiewicz TS, Brown JL, Swift CL, O’Malley MA. Harnessing nature’s anaerobes for biotechnology and bioprocessing. Annu Rev Chem Biomol Eng. 2019;10:105–28.
    CAS  PubMed  Google Scholar 

    12.
    Kumar S, Indugu N, Vecchiarelli B, Pitta DW. Associative patterns among anaerobic fungi, methanogenic archaea, and bacterial communities in response to changes in diet and age in the rumen of dairy cows. Front Microbiol. 2015;6:781.
    PubMed  PubMed Central  Google Scholar 

    13.
    Nagaraja TG. Microbiology of the Rumen. In: Rumenology. Cham: Springer International Publishing; 2016. p. 39–61.

    14.
    Edwards JE, Forster RJ, Callaghan TM, Dollhofer V, Dagar SS, Cheng Y, et al. PCR and omics based techniques to study the diversity, ecology and biology of anaerobic fungi: insights, challenges and opportunities. Front Microbiol. 2017;8:1657.
    PubMed  PubMed Central  Google Scholar 

    15.
    Paul SS, Bu D, Xu J, Hyde KD, Yu Z. A phylogenetic census of global diversity of gut anaerobic fungi and a new taxonomic framework. Fungal Divers. 2018;89:253–66.
    Google Scholar 

    16.
    Hanafy RA, Elshahed MS, Liggenstoffer AS, Griffith GW, Youssef NH. Pecoramyces ruminantium, gen. nov., sp. nov., an anaerobic gut fungus from the feces of cattle and sheep. Mycologia. 2017;109:231–43.
    PubMed  Google Scholar 

    17.
    Youssef NH, Couger MB, Struchtemeyer CG, Liggenstoffer AS, Prade RA, Najar FZ, et al. The genome of the anaerobic fungus Orpinomyces sp. strain C1A reveals the unique evolutionary history of a remarkable plant biomass degrader. Appl Environ Microbiol. 2013;79:4620–34.
    CAS  PubMed  PubMed Central  Google Scholar 

    18.
    John Wallace R, Sasson G, Garnsworthy PC, Tapio I, Gregson E, Bani P et al. A heritable subset of the core rumen microbiome dictates dairy cow productivity and emissions. Sci Adv 2019;5. https://doi.org/10.1126/sciadv.aav8391.

    19.
    Gordon GLR, Phillips MW. Removal of anaerobic fungi from the rumen of sheep by chemical treatment and the effect on feed consumption and in vivo fibre digestion. Lett Appl Microbiol. 1993;17:220–3.
    Google Scholar 

    20.
    Söllinger A, Tveit T, Poulsen M, Noel J, Bengtsson M, Bernhardt J, et al. Holistic assessment of rumen microbiome dynamics through quantitative metatranscriptomics reveals multifunctional redundancy during key steps of anaerobic feed degradation. mSystems. 2018;3:1–19.
    Google Scholar 

    21.
    Dai X, Tian Y, Li J, Luo Y, Liu D, Zheng H, et al. Metatranscriptomic analyses of plant cell wall polysaccharide degradation by microorganisms in the cow rumen. Appl Environ Microbiol. 2015;81:1375–86.
    PubMed  PubMed Central  Google Scholar 

    22.
    Comtet-Marre S, Parisot N, Lepercq P, Chaucheyras-Durand F, Mosoni P, Peyretaillade E et al. Metatranscriptomics reveals the active bacterial and eukaryotic fibrolytic communities in the rumen of dairy cow fed a mixed diet. Front Microbiol. 2017;8. https://doi.org/10.3389/fmicb.2017.00067.

    23.
    Gruninger RJ, Nguyen TTM, Reid ID, Yanke JL, Wang P, Abbott DW, et al. Application of transcriptomics to compare the carbohydrate active enzymes that are expressed by diverse genera of anaerobic fungi to degrade plant cell wall carbohydrates. Front Microbiol. 2018;9:1581.
    PubMed  PubMed Central  Google Scholar 

    24.
    Henske JK, Wilken SE, Solomon KV, Smallwood CR, Shutthanandan V, Evans JE, et al. Metabolic characterization of anaerobic fungi provides a path forward for bioprocessing of crude lignocellulose. Biotechnol Bioeng. 2018;115:874–84.
    CAS  PubMed  Google Scholar 

    25.
    Morrison JM, Elshahed MS, Youssef NH. Defined enzyme cocktail from the anaerobic fungus Orpinomyces sp. Strain C1A effectively releases sugars from pretreated corn stover and switchgrass. Sci Rep. 2016;6:1–12.
    Google Scholar 

    26.
    O’Malley MA, Theodorou MK, Kaiser CA. Evaluating expression and catalytic activity of anaerobic fungal fibrolytic enzymes native topiromyces sp E2 inSaccharomyces cerevisiae. Environ Prog Sustain Energy. 2012;31:37–46.
    Google Scholar 

    27.
    Hess M, Sczyrba A, Egan R, Kim T-W, Chokhawala H, Schroth G, et al. Metagenomic discovery of biomass-degrading genes and genomes from cow rumen. Science. 2011;463:463–7.
    Google Scholar 

    28.
    Piao H, Lachman M, Malfatti S, Sczyrba A, Knierim B, Auer M, et al. Temporal dynamics of fibrolytic and methanogenic rumen microorganisms during in situ incubation of switchgrass determined by 16S rRNA gene profiling. Front Microbiol. 2014;5:307.
    PubMed  PubMed Central  Google Scholar 

    29.
    Cox J, Neuhauser N, Michalski A, Scheltema RA, Olsen JV, Mann M. Andromeda: a peptide search engine integrated into the MaxQuant environment. J Proteome Res. 2011;10:1794–805.
    CAS  PubMed  Google Scholar 

    30.
    Cox J, Mann M. MaxQuant enables high peptide identification rates, individualized p.p.b.-range mass accuracies and proteome-wide protein quantification. Nat Biotechnol. 2008;26:1367–72.
    CAS  PubMed  Google Scholar 

    31.
    Kunath BJ, Minniti G, Skaugen M, Hagen LH, Vaaje-Kolstad G, Eijsink VGH et al. Metaproteomics: sample preparation and methodological considerations. In: Capelo-Martínez JL. et al. editors. Emerging Sample Treatments in Proteomics. Advances in Experimental Medicine and Biology. Vol. 1073. Springer, Cham; 2019. p. 187–215.

    32.
    Parks DH, Rinke C, Chuvochina M, Chaumeil P-A, Woodcroft BJ, Evans PN et al. Recovery of nearly 8,000 metagenome-assembled genomes substantially expands the tree of life. Nat Microbiol. 2017; 2. https://doi.org/10.1038/s41564-017-0012-7.

    33.
    Parks DH, Imelfort M, Skennerton CT, Hugenholtz P, Tyson GW. CheckM: assessing the quality of microbial genomes recovered from isolates, single cells, and metagenomes. Genome Res. 2015;25:1043–55.
    CAS  PubMed  PubMed Central  Google Scholar 

    34.
    Zhu W, Lomsadze A, Borodovsky M. Ab initio gene identification in metagenomic sequences. Nucleic Acids Res. 2010;38:e132–e132.
    PubMed  PubMed Central  Google Scholar 

    35.
    Deusch S, Camarinha-Silva A, Conrad J, Beifuss U, Rodehutscord M, Seifert J. A structural and functional elucidation of the rumen microbiome influenced by various diets and microenvironments. Front Microbiol. 2017;8:1605.
    PubMed  PubMed Central  Google Scholar 

    36.
    Li F, Hitch TCA, Chen Y, Creevey CJ, Guan LL. Comparative metagenomic and metatranscriptomic analyses reveal the breed effect on the rumen microbiome and its associations with feed efficiency in beef cattle 06 Biological Sciences 0604 Genetics 06 Biological Sciences 0605 Microbiology. Microbiome. 2019;7:6.
    PubMed  PubMed Central  Google Scholar 

    37.
    Suen G, Weimer PJ, Stevenson DM, Aylward FO, Boyum J, Deneke J, et al. The complete genome sequence of fibrobacter succinogenes S85 reveals a cellulolytic and metabolic specialist. PLoS One. 2011;6:e18814.
    CAS  PubMed  PubMed Central  Google Scholar 

    38.
    Leahy SC, Kelly WJ, Altermann E, Ronimus RS, Yeoman CJ, Pacheco DM, et al. The genome sequence of the rumen methanogen methanobrevibacter ruminantium reveals new possibilities for controlling ruminant methane emissions. PLoS ONE. 2010;5:e8926.
    PubMed  PubMed Central  Google Scholar 

    39.
    Mondo SJ, Dannebaum RO, Kuo RC, Louie KB, Bewick AJ, LaButti K, et al. Widespread adenine N6-methylation of active genes in fungi. Nat Genet. 2017;49:964–8.
    CAS  PubMed  Google Scholar 

    40.
    Grigoriev IV, Nikitin R, Haridas S, Kuo A, Ohm R, Otillar R, et al. MycoCosm portal: gearing up for 1000 fungal genomes. Nucleic Acids Res. 2014;42:D699–D704.
    CAS  PubMed  Google Scholar 

    41.
    Edgar RC. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 2004;32:1792–7.
    CAS  PubMed  PubMed Central  Google Scholar 

    42.
    Talavera G, Castresana J. Improvement of phylogenies after removing divergent and ambiguously aligned blocks from protein sequence alignments. Syst Biol. 2007;56:564–77.
    CAS  PubMed  Google Scholar 

    43.
    Kumar S, Stecher G, Li M, Knyaz C, Tamura K. MEGA X: molecular evolutionary genetics analysis across computing platforms. Mol Biol Evol. 2018;35:1547–9.
    CAS  PubMed  PubMed Central  Google Scholar 

    44.
    Stamatakis A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics. 2014;30:1312–3.
    CAS  PubMed  PubMed Central  Google Scholar 

    45.
    Letunic I, Bork P. Interactive tree of life (iTOL) v3: an online tool for the display and annotation of phylogenetic and other trees. Nucleic Acids Res. 2016;44:W242–W245.
    CAS  PubMed  PubMed Central  Google Scholar 

    46.
    Cox J, Hein MY, Luber CA, Paron I, Nagaraj N, Mann M. Accurate proteome-wide label-free quantification by delayed normalization and maximal peptide ratio extraction, Termed MaxLFQ. Mol Cell Proteom. 2014;13:2513–26.
    CAS  Google Scholar 

    47.
    Tyanova S, Temu T, Sinitcyn P, Carlson A, Hein MY, Geiger T, et al. The Perseus computational platform for comprehensive analysis of (prote)omics data. Nat Methods. 2016;13:731–40.
    CAS  PubMed  Google Scholar 

    48.
    Piao H, Meng Markillie L, Culley DE, Mackie RI, Hess M. Improved method for isolation of microbial RNA from biofuel feedstock for metatranscriptomics. Adv Microbiol. 2013;3:101–7.
    CAS  Google Scholar 

    49.
    Li D, Liu C-M, Luo R, Sadakane K, Lam T-W. MEGAHIT: an ultra-fast single-node solution for large and complex metagenomics assembly via succinct de Bruijn graph. Bioinformatics. 2015;31:1674–6.
    CAS  PubMed  Google Scholar 

    50.
    Li H, Durbin R. Fast and accurate long-read alignment with Burrows–Wheeler transform. Bioinformatics. 2010;26:589–95.
    PubMed  PubMed Central  Google Scholar 

    51.
    Jones P, Binns D, Chang H-Y, Fraser M, Li W, McAnulla C, et al. InterProScan 5: genome-scale protein function classification. Bioinformatics. 2014;30:1236–40.
    CAS  PubMed  PubMed Central  Google Scholar 

    52.
    Lombard V, Golaconda Ramulu H, Drula E, Coutinho PM, Henrissat B. The carbohydrate-active enzymes database (CAZy) in 2013. Nucleic Acids Res. 2014;42:D490–D495.
    CAS  PubMed  Google Scholar 

    53.
    R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. 2018. Available online at https://www.R-project.org/.

    54.
    Hagen LH, Frank JA, Zamanzadeh M, Eijsink VGH, Pope PB, Horn SJ et al. Quantitative metaproteomics highlight the metabolic contributions of uncultured phylotypes in a thermophilic anaerobic digester. Appl Environ Microbiol. 2017; 83. https://doi.org/10.1128/AEM.01955-16.

    55.
    Murphy CL, Youssef NH, Hanafy RA, Couger MB, Stajich JE, Wang Y et al. Horizontal gene transfer as an indispensable driver for evolution of neocallimastigomycota into a distinct gut-dwelling fungal lineage. Appl Environ Microbiol. 2019; 85. https://doi.org/10.1128/AEM.00988-19.

    56.
    Wang Y, Youssef NH, Couger MB, Hanafy RA, Elshahed MS, Stajich JE. Molecular dating of the emergence of anaerobic rumen fungi and the impact of laterally acquired genes. mSystems 2019; 4. https://doi.org/10.1128/msystems.00247-19.

    57.
    Shinkai T, Mitsumori M, Sofyan A, Kanamori H, Sasaki H, Katayose Y, et al. Comprehensive detection of bacterial carbohydrate-active enzyme coding genes expressed in cow rumen. Anim Sci J. 2016;87:1363–70.
    CAS  PubMed  Google Scholar 

    58.
    Naas AE, Solden LM, Norbeck AD, Brewer H, Hagen LH, Heggenes IM, et al. ‘Candidatus Paraporphyromonas polyenzymogenes’ encodes multi-modular cellulases linked to the type IX secretion system. Microbiome. 2018;6:1–13.
    Google Scholar 

    59.
    Parsiegla G, Reverbel C, Tardif C, Driguez H, Haser R. Structures of mutants of cellulase Cel48F of clostridium cellulolyticum in complex with long hemithiocellooligosaccharides give rise to a new view of the substrate pathway during processive action. J Mol Biol. 2008;375:499–510.
    CAS  PubMed  Google Scholar 

    60.
    Steenbakkers PJM, Freelove A, Van Cranenbroek B, Sweegers BMC, Harhangi HR, Vogels GD, et al. The major component of the cellulosomes of anaerobic fungi from the genus Piromyces is a family 48 glycoside hydrolase. Mitochondrial DNA. 2002;13:313–20.
    CAS  Google Scholar 

    61.
    Guimarães BG, Souchon H, Lytle BL, David Wu JH, Alzari PM. The crystal structure and catalytic mechanism of cellobiohydrolase celS, the major enzymatic component of the Clostridium thermocellum cellulosome. J Mol Biol. 2002;320:587–96.
    PubMed  Google Scholar 

    62.
    Pope PB, Denman SE, Jones M, Tringe SG, Barry K, Malfatti SA, et al. Adaptation to herbivory by the Tammar wallaby includes bacterial and glycoside hydrolase profiles different from other herbivores. Proc Natl Acad Sci. 2010;107:14793–8.
    CAS  PubMed  Google Scholar 

    63.
    Qi M, Wang P, O’Toole N, Barboza PS, Ungerfeld E, Leigh MB, et al. Snapshot of the eukaryotic gene expression in muskoxen rumen—a metatranscriptomic approach. PLoS ONE. 2011;6:e20521.
    CAS  PubMed  PubMed Central  Google Scholar 

    64.
    Benoit I, Coutinho PM, Schols HA, Gerlach JP, Henrissat B, de Vries RP. Degradation of different pectins by fungi: correlations and contrasts between the pectinolytic enzyme sets identified in genomes and the growth on pectins of different origin. BMC Genom. 2012;13:321.
    CAS  Google Scholar 

    65.
    Shi H, Ding H, Huang Y, Wang L, Zhang Y, Li X, et al. Expression and characterization of a GH43 endo-arabinanase from Thermotoga thermarum. BMC Biotechnol. 2014;14:35.
    PubMed  PubMed Central  Google Scholar 

    66.
    Israeli-Ruimy V, Bule P, Jindou S, Dassa B, Moraïs S, Borovok I, et al. Complexity of the Ruminococcus flavefaciens FD-1 cellulosome reflects an expansion of family-related protein-protein interactions. Sci Rep. 2017;7:42355.
    CAS  PubMed  PubMed Central  Google Scholar 

    67.
    Flint HJ, Bayer EA, Rincon MT, Lamed R, White BA. Polysaccharide utilization by gut bacteria: potential for new insights from genomic analysis. Nat Rev Microbiol. 2008;6:121–31.
    CAS  PubMed  Google Scholar 

    68.
    Arntzen M, Várnai A, Mackie RI, Eijsink VGH, Pope PB. Outer membrane vesicles from S85 contain an array of carbohydrate-active enzymes with versatile polysaccharide-degrading capacity. Environ Microbiol. 2017;19:2701–14.
    CAS  PubMed  Google Scholar 

    69.
    Devillard E, Goodheart DB, Karnati SKR, Bayer EA, Lamed R, Miron J, et al. Ruminococcus albus 8 mutants defective in cellulose degradation are deficient in two processive endocellulases, Cel48A and Cel9B, both of which possess a novel modular architecture. J Bacteriol. 2004;186:136–45.
    CAS  PubMed  PubMed Central  Google Scholar 

    70.
    Vodovnik M, Duncan SH, Reid MD, Cantlay L, Turner K, Parkhill J, et al. Expression of Cellulosome Components and Type IV Pili within the Extracellular Proteome of Ruminococcus flavefaciens 007. PLoS ONE. 2013;8:e65333.
    CAS  PubMed  PubMed Central  Google Scholar 

    71.
    Henske JK, Gilmore SP, Haitjema CH, Solomon KV, O’Malley MA. Biomass-degrading enzymes are catabolite repressed in anaerobic gut fungi. AIChE J. 2018;64:4263–70.
    CAS  Google Scholar 

    72.
    Altschul SF, Gish W, Miller W, Myers EW, Lipman DJ. Basic local alignment search tool. J Mol Biol. 1990;215:403–10.
    CAS  Google Scholar 

    73.
    Garcia-Vallvé S, Romeu A, Palau J. Horizontal gene transfer of glycosyl hydrolases of the rumen fungi. Mol Biol Evol. 2000;17:352–61.
    PubMed  Google Scholar 

    74.
    Murphy CL, Youssef NH, Hanafy RA, Couger MB, Stajich JE, Wang Y et al. Horizontal gene transfer as an indispensable driver for evolution of Neocallimastigomycota into a distinct gutdwelling fungal lineage. Appl Environ Microbiol. 2019;85. https://doi.org/10.1128/AEM.00988-19.

    75.
    Hart EH, Creevey CJ, Hitch T, Kingston-Smith AH. Meta-proteomics of rumen microbiota indicates niche compartmentalisation and functional dominance in a limited number of metabolic pathways between abundant bacteria. Sci Rep. 2018;8. https://doi.org/10.1038/s41598-018-28827-7.

    76.
    Perez-Riverol Y, Csordas A, Bai J, Bernal-Llinares M, Hewapathirana S, Kundu DJ, et al. The PRIDE database and related tools and resources in 2019: improving support for quantification data. Nucleic Acids Res. 2019;47:D442–50.
    CAS  PubMed  Google Scholar  More

  • in

    Molecular trade-offs in soil organic carbon composition at continental scale

    1.
    Baldock, J. A., Masiello, C. A., Gélinas, Y. & Hedges, J. I. Cycling and composition of organic matter in terrestrial and marine ecosystems. Mar. Chem. 92, 39–64 (2004).
    Google Scholar 
    2.
    Sutton, R. & Sposito, G. Molecular structure in soil humic substances: the new view. Environ. Sci. Technol. 39, 9009–9015 (2005).
    Google Scholar 

    3.
    Lehmann, J. & Kleber, M. The contentious nature of soil organic matter. Nature 528, 60–68 (2015).
    Google Scholar 

    4.
    Baldock, J. A. et al. Assessing the extent of decomposition of natural organic materials using solid-state 13C NMR spectroscopy. Aust. J. Soil Res. 35, 1061–1084 (1997).
    Google Scholar 

    5.
    Mahieu, N., Randall, E. W. & Powlson, D. S. Statistical analysis of published carbon-13 CPMAS NMR spectra of soil organic matter. Soil Sci. Soc. Am. J. 63, 307–319 (1999).
    Google Scholar 

    6.
    Grandy, A. S. & Neff, J. C. Molecular C dynamics downstream: the biochemical decomposition sequence and its impact on soil organic matter structure and function. Sci. Total Environ. 404, 297–307 (2008).
    Google Scholar 

    7.
    Baldock, J. A. et al. Aspects of the chemical structure of soil organic materials as revealed by solid-state 13C NMR spectroscopy. Biogeochemistry 16, 1–42 (1992).
    Google Scholar 

    8.
    Ahmad, R., Nelson, P. N. & Kookana, R. S. The molecular composition of soil organic matter as determined by 13C NMR and elemental analyses and correlation with pesticide sorption. Eur. J. Soil Sci. 57, 883–893 (2006).
    Google Scholar 

    9.
    Rasmussen, C. et al. Beyond clay: towards an improved set of variables for predicting soil organic matter content. Biogeochemistry 137, 297–306 (2018).
    Google Scholar 

    10.
    Cotrufo, M. F., Ranalli, M. G., Haddix, M. L., Six, J. & Lugato, E. Soil carbon storage informed by particulate and mineral-associated organic matter. Nat. Geosci. 12, 989–994 (2019).
    Google Scholar 

    11.
    Wagai, R. et al. Linking temperature sensitivity of soil organic matter decomposition to its molecular structure, accessibility, and microbial physiology. Glob. Change Biol. 19, 1114–1125 (2013).
    Google Scholar 

    12.
    Waksman, S. A. & Iyer, K. R. N. Contribution to our knowledge of the chemical nature and origin of humus: I. on the synthesis of the “humus nucleus”. Soil Sci. 34, 43–69 (1932).
    Google Scholar 

    13.
    Kirk, T. K. & Farrell, R. L. Enzymatic “combustion”: the microbial degradation of lignin. Annu. Rev. Microbiol. 41, 465–501 (1987).
    Google Scholar 

    14.
    Amelung, W., Brodowski, S., Sandhage-Hofmann, A. & Bol, R. in Advances in Agronomy Vol. 100 (ed. Sparks, D. L.) 155–250 (Elsevier, 2008).

    15.
    Thevenot, M., Dignac, M.-F. & Rumpel, C. Fate of lignins in soils: a review. Soil Biol. Biochem. 42, 1200–1211 (2010).
    Google Scholar 

    16.
    Bosatta, E. & Ågren, G. I. Soil organic matter quality interpreted thermodynamically. Soil Biol. Biochem. 31, 1889–1891 (1999).
    Google Scholar 

    17.
    Miltner, A., Bombach, P., Schmidt-Brücken, B. & Kästner, M. SOM genesis: microbial biomass as a significant source. Biogeochemistry 111, 41–55 (2011).
    Google Scholar 

    18.
    Cotrufo, M. F., Wallenstein, M. D., Boot, C. M., Denef, K. & Paul, E. The microbial efficiency-matrix stabilization (MEMS) framework integrates plant litter decomposition with soil organic matter stabilization: do labile plant inputs form stable soil organic matter? Glob. Change Biol. 19, 988–995 (2013).
    Google Scholar 

    19.
    Kallenbach, C. M., Frey, S. D. & Grandy, A. S. Direct evidence for microbial-derived soil organic matter formation and its ecophysiological controls. Nat. Commun. 7, 13630 (2016).
    Google Scholar 

    20.
    Ma, T. et al. Divergent accumulation of microbial necromass and plant lignin components in grassland soils. Nat. Commun. 9, 3480 (2018).
    Google Scholar 

    21.
    Liang, C., Amelung, W., Lehmann, J. & Kästner, M. Quantitative assessment of microbial necromass contribution to soil organic matter. Glob. Change Biol. 25, 3578–3590 (2019).
    Google Scholar 

    22.
    Khan, K. S., Mack, R., Castillo, X., Kaiser, M. & Joergensen, R. G. Microbial biomass, fungal and bacterial residues, and their relationships to the soil organic matter C/N/P/S ratios. Geoderma 271, 115–123 (2016).
    Google Scholar 

    23.
    Malik, A. A. et al. Land use driven change in soil pH affects microbial carbon cycling processes. Nat. Commun. 9, 3591 (2018).
    Google Scholar 

    24.
    Córdova, S. C. et al. Plant litter quality affects the accumulation rate, composition, and stability of mineral-associated soil organic matter. Soil Biol. Biochem. 125, 115–124 (2018).
    Google Scholar 

    25.
    Huang, W. et al. Enrichment of lignin-derived carbon in mineral-associated soil organic matter. Environ. Sci. Technol. 53, 7522–7531 (2019).
    Google Scholar 

    26.
    Wan, D. et al. Iron oxides selectively stabilize plant-derived polysaccharides and aliphatic compounds in agricultural soils. Eur. J. Soil Sci. 70, 1153–1163 (2019).
    Google Scholar 

    27.
    Hernes, P. J., Kaiser, K., Dyda, R. Y. & Cerli, C. Molecular trickery in soil organic matter: hidden lignin. Environ. Sci. Technol. 47, 9077–9085 (2013).
    Google Scholar 

    28.
    Klotzbücher, T., Kalbitz, K., Cerli, C., Hernes, P. J. & Kaiser, K. Gone or just out of sight? The apparent disappearance of aromatic litter components in soils. SOIL 2, 325–335 (2016).
    Google Scholar 

    29.
    Preston, C. M. & Schmidt, M. W. I. Black (pyrogenic) carbon: a synthesis of current knowledge and uncertainties with special consideration of boreal regions. Biogeosciences 3, 397–420 (2006).
    Google Scholar 

    30.
    Lehmann, J. et al. Australian climate–carbon cycle feedback reduced by soil black carbon. Nat. Geosci. 1, 832–835 (2008).
    Google Scholar 

    31.
    Mikutta, R., Kleber, M., Torn, M. S. & Jahn, R. Stabilization of soil organic matter: association with minerals or chemical recalcitrance? Biogeochemistry 77, 25–56 (2006).
    Google Scholar 

    32.
    Kleber, M. What is recalcitrant soil organic matter? Environ. Chem. 7, 320–332 (2010).
    Google Scholar 

    33.
    Schmidt, M. W. I. et al. Persistence of soil organic matter as an ecosystem property. Nature 478, 49–56 (2011).
    Google Scholar 

    34.
    DiDonato, N., Chen, H., Waggoner, D. & Hatcher, P. G. Potential origin and formation for molecular components of humic acids in soils. Geochim. Cosmochim. Acta 178, 210–222 (2016).
    Google Scholar 

    35.
    Scatena, F. An Introduction to the Physiography and History of the Bisley Experimental Watersheds in the Luquillo Mountains of Puerto Rico General Technical Report SO-72 (USDA, 1989).

    36.
    Kleber, M. et al. in Advances in Agronomy Vol. 130 (ed. Sparks, D. L.) Ch. 1 (Elsevier, 2015).

    37.
    Slessarev, E. W. et al. Water balance creates a threshold in soil pH at the global scale. Nature 540, 567–569 (2016).
    Google Scholar 

    38.
    Davidson, E. A. & Janssens, I. A. Temperature sensitivity of soil carbon decomposition and feedbacks to climate change. Nature 440, 165–173 (2006).
    Google Scholar 

    39.
    Lundström, U. S., van Breemen, N. & Bain, D. The podzolization process. A review. Geoderma 94, 91–107 (2000).
    Google Scholar 

    40.
    Kramer, M. G., Sanderman, J., Chadwick, O. A., Chorover, J. & Vitousek, P. M. Long-term carbon storage through retention of dissolved aromatic acids by reactive particles in soil. Glob. Change Biol. 18, 2594–2605 (2012).
    Google Scholar 

    41.
    Kaiser, K. & Guggenberger, G. The role of DOM sorption to mineral surfaces in the preservation of organic matter in soils. Org. Geochem. 31, 711–725 (2000).
    Google Scholar 

    42.
    Coward, E. K., Ohno, T. & Plante, A. F. Adsorption and molecular fractionation of dissolved organic matter on iron-bearing mineral matrices of varying crystallinity. Environ. Sci. Technol. 52, 1036–1044 (2018).
    Google Scholar 

    43.
    Throckmorton, H. M., Bird, J. A., Dane, L., Firestone, M. K. & Horwath, W. R. The source of microbial C has little impact on soil organic matter stabilisation in forest ecosystems. Ecol. Lett. 15, 1257–1265 (2012).
    Google Scholar 

    44.
    Moorhead, D. L. & Sinsabaugh, R. L. A theoretical model of litter decay and microbial interaction. Ecol. Monogr. 76, 151–174 (2006).
    Google Scholar 

    45.
    LaRowe, D. E. & Van Cappellen, P. Degradation of natural organic matter: a thermodynamic analysis. Geochim. Cosmochim. Acta 75, 2030–2042 (2011).
    Google Scholar 

    46.
    Ye, C. et al. Reconciling multiple impacts of nitrogen enrichment on soil carbon: plant, microbial and geochemical controls. Ecol. Lett. 21, 1162–1173 (2018).
    Google Scholar 

    47.
    Ayres, E., et al. NEON Field and Lab Procedure and Protocol: TIS Soil Pit Sampling Protocol NEON.DOC.001307 (NEON, 2017); https://data.neonscience.org/data-products/DP1.00097.001

    48.
    Ayres, E. & Durden, D. NEON Field and Lab Procedure and Protocol: TIS Soil Archiving NEON.DOC.000325 (NEON, 2017); https://data.neonscience.org/data-products/DP1.00097.001

    49.
    Ayres, E. NEON Procedure and Protocol: Producing TIS Soil Archive Subsamples for Users NEON.DOC.001306 (NEON, 2017); https://data.neonscience.org/data-products/DP1.00097.001

    50.
    Gélinas, Y., Baldock, J. A. & Hedges, J. I. Demineralization of marine and freshwater sediments for CP/MAS 13C NMR analysis. Org. Geochem. 32, 677–693 (2001).
    Google Scholar 

    51.
    Harbison, G. S. et al. High-resolution carbon-13 NMR of retinal derivatives in the solid state. J. Am. Chem. Soc. 107, 4809–4816 (1985).
    Google Scholar 

    52.
    Mao, J.-D. et al. Quantitative characterization of humic substances by solid-state carbon-13 nuclear magnetic resonance. Soil Sci. Soc. Am. J. 64, 873–884 (2000).
    Google Scholar 

    53.
    Longbottom, T. L. & Hockaday, W. C. Molecular and isotopic composition of modern soils derived from kerogen-rich bedrock and implications for the global C cycle. Biogeochemistry 143, 239–255 (2019).
    Google Scholar 

    54.
    NEON (National Ecological Observatory Network). DP1.00096.001, DP1.10066.001, DP1.10102.001, DP1.10109.001 (accessed September 1, 2019), DP1.10026.001, DP1.10033.001, DP1.10031.001 (accessed May 15, 2020); http://data.neonscience.org

    55.
    Sullivan, P. F. et al. Climate and species affect fine root production with long-term fertilization in acidic tussock tundra near Toolik Lake, Alaska. Oecologia 153, 643–652 (2007).
    Google Scholar 

    56.
    SanClements, M. et al. Collaborating with NEON. BioScience 70, 107–107 (2020).
    Google Scholar 

    57.
    Mu, Q., Zhao, M. & Running, S. W. Improvements to a MODIS global terrestrial evapotranspiration algorithm. Remote Sens. Environ. 115, 1781–1800 (2011).
    Google Scholar 

    58.
    Revelle, W. psych: Procedures for Personality and Psychological Research v.1.8.12 (Northwestern University, 2018).

    59.
    Chittleborough, D. J. Indices of weathering for soils and palaeosols formed on silicate rocks. Aust. J. Earth Sci. 38, 115–120 (1991).
    Google Scholar 

    60.
    Hair, J. F., Risher, J. J., Sarstedt, M. & Ringle, C. M. When to use and how to report the results of PLS-SEM. Eur. Bus. Rev. 31, 2–24 (2019).
    Google Scholar 

    61.
    Lefcheck, J. S.piecewiseSEM: piecewise structural equation modelling in R for ecology, evolution, and systematics. Methods Ecol. Evol. 7, 573–579 (2016).
    Google Scholar  More

  • in

    Seeds attached to refrigerated shipping containers represent a substantial risk of nonnative plant species introduction and establishment

    Changes in propagule pressure from single or multiple regions directly contribute to the success or failure of nonnative species establishment6,8,23,24. In this study, we collected and measured the quantity and diversity of seeds, over time obtained from the air-intake grilles of refrigerated containers, with two seasons for comparison. Targeting the trans-oceanic transport of a single commodity in this industrial trade system that serves as a transport vector for hitchhiking seeds provided reduced variation in which to quantify propagule pressure, including propagule size (Fig. 1) and propagule number (Supplementary Fig. 2) of plant species considered to be of high-risk to agriculture in the USA19. Our key finding is that influx is sufficient and reproduction of these species is high enough to represent a risk of population(s) establishment in and around the shipping port, even with the bottlenecks of escape from the shipping container, subsequent germination, and seedling survival (Figs. 2, 3).
    Over 20,000 shipping containers are moved as import or export daily on the GCT 14, providing ample volumes for passive hitchhikers to establish at the GCT and surrounding areas. In fact, we found steady arrival of shipping containers over the approximately 32-week shipping season (Supplementary Fig. 2). Conversely, we found strong seasonal variation in propagule number (i.e., number of seeds per refrigerated shipping container; Fig. 1). We estimated for the FNW, S. spontaneum, that over 40,000 seeds entered GCT during the two shipping seasons (Table 1). This level of propagule pressure is clearly sufficient to represent introduction and establishment risk of a clonal, perennial, fecund species that likely does not require a large initial propagule size, even if the escape rate from the shipping containers is exceedingly small (Table 1; Fig. 2). In this study, the four focal monocotyledonous taxa all had similar seed sizes, and no other larger-sized propagative material of these species (e.g., rhizomatous material or cuttings) were encountered during our study at the GCT.
    The theoretical literature postulates that increased numbers of propagules (i.e., propagule size6,8,9,10,11) and pressure (which includes propagule size and frequency as a rate) increases the likelihood of nascent population establishment and population size and diversity6,10; however, among our four focal taxa, a nascent population may establish from a single seed during arrival at a suitable terrestrial substrate, such as the GCT’s greenspaces19. Persistence of an extremely small population can, and is likely to, be facilitated by asexual propagation and spatial spread of these particular plant taxa. Theoretical population biology intrinsically includes propagule pressure within the invasion process6,10, and empirical studies measuring propagule pressure have demonstrated its importance as the most important and generalizable predictor of nonnative invasion success24. Propagule pressure in itself is also the factor most influenced by human activity8,9. Therefore, our study adds additional support to the importance of propagule pressure (see Figs. 2 and 3), and in this system, there is sufficient propagule pressure (i.e., influx from Fig. 3) for invasion success, even if escape rates from shipping containers, germination, and survival are low.
    Though S. spontaneum is the only FNW we encountered, we were also able to identify Arundo donax L., a species that is listed as noxious by 46 of the USA’s 50 states25. We were not able to identify seeds to a taxonomic level sufficient to determine origin status for the other 28 taxa encountered (Table S4), but for our three additional focal species, we suggest that two species are native and one is likely introduced. We found Typha domingensis (Pers.) Steud. (native), Andropogon glomeratus (Walter) Britton, Sterns & Poggenb. and A. virginicus L. (both native), and Phragmites australis (Cav.) Steud. (nonnative), already established on-port at the GCT in a previous study that demonstrated that the Port of Savannah is a hub of nonnative species richness19.
    For any of the species collected on the shipping containers, the propagules have the potential of being picked up en route to the GCT or, with the exception of S. spontaneum since it is not established there, at the GCT. Most of the taxa already have cosmopolitan distributions, and actual escape rates from the shipping containers are not yet known, meaning that the seeds could make multiple journeys on cargo ships across oceans before being released from a container. Also, the seasonality of seed dispersal coincides with dispersal time in the northern hemisphere, which may apply to seed sources in Panama, the Caribbean, or the USA. Andropogon glomeratus and A. virginicus occur throughout North and Central America (including Panama and the Caribbean)26. As native species to the southeastern USA, propagules escaping refrigerated shipping containers are not of significant concern, although they could be homogenizing genetic composition if genotypes from other portions of the parental established ranges are introduced here. Additionally, Andropogon propagules may result in introductions of nonnative species to South America if the seeds remain on the containers and are viable for return trips. Typha domingensis has nearly a global distribution, and though it is native to the southeastern USA, its presence at the Port of Savannah could also indicate the presence of admixed genotypes. Moreover, our morphological identification of the seeds could not distinguish to the species level, and T. angustifolia L., a nonnative species, could have been represented in our samples, though this species is well established and widely distributed already in the USA26. Phragmites australis, a noxious weed in 6 USA states25, is already found worldwide26. The genus Phragmites contains 4 species, of which only Phragmites australis is native to portions of North America; however, intra- and inter-specific hybridization among genotypes has resulted in the influx of nonnative lineages from Europe and Asia, which have spread to areas of the continent where it is not native27,28.
    The most interesting case is S. spontaneum, the FNW. This species is established only in Florida on the USA mainland, where it was introduced for historical and extant breeding programs with sugarcane29. This recent report29 showed that it was naturalized in only three counties, but we have documented it growing in six counties and in cultivation in one additional county (Supplementary Fig. 4). We did not find it growing at the GCT at the Port of Savannah. Yet, it is known that S. spontaneum, which is native to the Indian subcontinent, is well established in the Panama Canal region29,30 along the shipping route of interest. The number of propagules we intercepted and estimated, along with nontrivial germination rates and high survivorship of seedlings, indicate that this species represents a real threat of establishment outside of Florida. Combined with other modeled estimates that S. spontaneum can establish throughout the majority of the USA29, we suggest that this species represents a significant risk of negative invasive species impact, earning its FNW listing in the 1980s18,29.
    All four of our focal taxa share common life history features that have been suggested to be characteristic of invasive plant species: asexual reproduction through rhizomes, persistence in a wide range of environmental conditions, prolific seed production (Table 1 and citations within), wind pollination, and wind dispersal31,32,33. These traits have the potential to enhance geographic spread into new ranges and rapidly lead to single-species domination of local plant communities. All of these taxa have a life history and ecology similar to the very successful southeastern USA invasive species cogongrass (Imperata cylindrica (L.) P. Beauv.) that has been demonstrated to benefit from intraspecific heterosis and multiple introductions34,35,36.
    A previous study used molecular barcoding of seedlings germinated from seed collected from Season 1 in this study, and they identified some seedlings as: S. spontaneum, Typha sp(p)., Phragmites sp(p)., and Andropogon sp(p).37, as identified here. Seeds that were grouped as S. spontaneum in this study resulted in seedlings that returned haplotypes for the genus Phragmites (rbcL haplotype 1 and matK haplotypes 3 and 437) and Saccharum along with other genera37. There are two interesting and opposing forces at play here. First, in sorting seeds morphologically, there is the potential to group similar looking seeds of different species. The molecular barcode result that shows Phragmites haplotypes in seeds morphologically identified as S. spontaneum is evidence of misidentification and inaccurate sorting of seed. Second, some haplotypes showed equally correct molecular identification across multiple genera of grasses, indicating that these standard molecular barcode sequences for plants may not have the species-level resolution necessary for molecular identification of some of the highest threat invasive grass species.
    There are two key approaches to mitigating the risk that propagules of nonnative taxa will become established: 1) prevent propagules from hitchhiking on transoceanic cargo ships, in this case, becoming attached to shipping containers at their point-of-origin or stops along the way (that result from “trans-shipping”), and 2) prevent viable propagules from entering and establishing in the USA, via inspection and interception by the “gatekeepers” of biosecurity at international points-of-entry. These agricultural inspectors are tasked with the interception of propagules of insects, fungi, and all other nonnative or “actionable” taxa, in addition to the seeds of plants. One potential solution to reduce invasion risk by vascular plant seed is to employ a scaled-up version of the research approach we implemented here of backpack vacuuming air-intake grilles of refrigerated shipping containers. Another possibility in lieu of labour-intensive vacuuming of intake grilles is to conduct research on efficacy of liquid pre-emergent herbicide application to the air-intake grilles. For either approach, our data support that these interventions may not be needed year-round for important species like S. spontaneum, which have a clear import seasonality on this particular commodity. For example, based on our data, seed removal measures may only be needed in October, November, and early-mid December.
    In the face of poorly resourced capacity for inspection and the potential of diminishing fiscal resources and human capital, consequences include acceleration of biodiversity loss, economic and environmental impacts, and on-going biotic homogenization. The interception efforts to prevent the entry of nonnative propagules of all nonnative taxa worldwide will ultimately conserve local endemism, biodiversity, economic output, and ecosystem services that are interrupted or extirpated by biological invasions1,3. This research aimed to identify key risks and highlights the need for improved strategies for efficacious prevention and interception of nonnative, particularly plant, propagules prior to establishment, though such prevention approaches can be designed and applied for many taxa. Enhancing the capacity, speed, and frequency of successful prevention programs will be required to minimize or eliminate the real risks posed by viable hitchhiking propagules associated with economic trade and sea/air transportation of commodities and people. More

  • in

    Microbial deterioration and sustainable conservation of stone monuments and buildings

    1.
    Dornieden, T., Gorbushina, A. & Krumbein, W. Biodecay of cultural heritage as a space/time-related ecological situation—an evaluation of a series of studies. Int. Biodeterior. Biodegrad. 46, 261–270 (2000).
    CAS  Google Scholar 
    2.
    Warscheid, T. et al. Studies on the temporal development of microbial infection of different types of sedimentary rocks and its effect on the alteration of the physico-chemical properties in building materials. In Conservation of stone and other materials: Proc. of the International RILEM/UNESCO congress held at the UNESCO headquarters (ed. Thiel, M.-J.) 303–310 (E. & F.N. Spon Ltd, 1993).

    3.
    Gadd, G. M. Geomicrobiology of the built environment. Nat. Microbiol. 2, 16275 (2017).
    CAS  Google Scholar 

    4.
    Pinna, D. Coping with Biological Growth on Stone Heritage Objects: Methods, Products, Applications, and Perspectives (Apple Academic Press, 2017).

    5.
    Onofri, S., Zucconi, L., Isola, D. & Selbmann, L. Rock-inhabiting fungi and their role in deterioration of stone monuments in the Mediterranean area. Plant Biosyst. 148, 384–391 (2014).
    Google Scholar 

    6.
    Villa, F., Stewart, P. S., Klapper, I., Jacob, J. M. & Cappitelli, F. Subaerial biofilms on outdoor stone monuments: changing the perspective toward an ecological framework. BioScience 66, 285–294 (2016).
    Google Scholar 

    7.
    Warscheid, T. & Braams, J. Biodeterioration of stone: a review. Int. Biodeterior. Biodegrad. 46, 343–368 (2000).
    CAS  Google Scholar 

    8.
    Saiz-Jimenez, C. Biogeochemistry of weathering processes in monuments. Geomicrobiol. J. 16, 27–37 (1999).
    CAS  Google Scholar 

    9.
    Chen, J., Blume, H.-P. & Beyer, L. Weathering of rocks induced by lichen colonization — a review. Catena 39, 121–146 (2000).
    CAS  Google Scholar 

    10.
    Martino, P. D. What about biofilms on the surface of stone monuments? Open Conf. Proc. J. 6, 14–28 (2016).
    Google Scholar 

    11.
    Gu, J.-D., Ford, T. E. & Mitchell, R. in Uhlig’s Corrosion Handbook 3rd edn (ed. Revie, R. W.) 351–363 (Wiley, 2011).

    12.
    Polynov, B. The first stages of soil formation on massive crystaline rocks. Pochvovedeniye 7, 325–339 (1945).
    Google Scholar 

    13.
    Vernadskiy, V. Geochemical Essays (Ocherki geokhimii) (Leningrad State Publishing House, 1927).

    14.
    Krasil’nikov, N. The role of microorganisms in the weathering of rocks. Mikrobiologiya 18, 318–323 (1949).
    Google Scholar 

    15.
    Yarilova, Y. A. The role of lithophilous lichens in the weathering of massive crystalline rocks. Pochvovedeniye 3, 533–548 (1947).
    Google Scholar 

    16.
    Pochon, J., Tardieux, P., Lajudie, J. & Charpentier, M. Degradation des temples d’Angkor et processus biologiques. Ann. Inst. Pasteur 98, 457–461 (1960).
    Google Scholar 

    17.
    Pochon, J. & Jaton, C. in Biodeterioration of Materials (eds. Wolters, A. H. & Elphich, C. C.) 258–268 (Elsevier, 1968).

    18.
    Pochon, J. & Jaton, C. The role of microbiological agencies in the deterioration of stone. Chem. Ind. 9, 1587–1589 (1967).
    Google Scholar 

    19.
    Paquet, J. Contribution a l’etude de la maladie de la pierre: new hypothese sur les causes des transferts et des concentrations de sulfate produisant les effets foliants. Mon. His. France 10, 73–88 (1964).
    Google Scholar 

    20.
    Hueck, H. in Biodeterioration of Materials. Microbiological and Allied Aspects (eds Walters, A. H. & Elphick, J. J.) 6–12 (Elsevier Publishing Co. Ltd, 1968).

    21.
    Gaylarde, P. & Gaylarde, C. Deterioration of siliceous stone monuments in Latin America: microorganisms and mechanisms. Corros. Rev. 22, 395–416 (2004).
    CAS  Google Scholar 

    22.
    Uchida, E., Ogawa, Y., Maeda, N. & Nakagawa, T. Deterioration of stone materials in the Angkor monuments, Cambodia. Eng. Geol. 55, 101–112 (2000).
    Google Scholar 

    23.
    Caneva, G., Bartoli, F., Savo, V., Futagami, Y. & Strona, G. Combining statistical tools and ecological assessments in the study of biodeterioration patterns of stone temples in Angkor (Cambodia). Sci. Rep. 6, 32601 (2016).
    CAS  Google Scholar 

    24.
    Meng, H., Katayama, Y. & Gu, J.-D. More wide occurrence and dominance of ammonia-oxidizing archaea than bacteria at three Angkor sandstone temples of Bayon, Phnom Krom and Wat Athvea in Cambodia. Int. Biodeterior. Biodegrad. 117, 78–88 (2017).
    CAS  Google Scholar 

    25.
    Zammit, G., Sánchez-Moral, S. & Albertano, P. Bacterially mediated mineralisation processes lead to biodeterioration of artworks in Maltese catacombs. Sci. Total Environ. 409, 2773–2782 (2011).
    CAS  Google Scholar 

    26.
    McNamara, C. J., Perry, T. D., Bearce, K. A., Hernandez-Duque, G. & Mitchell, R. Epilithic and endolithic bacterial communities in limestone from a Maya archaeological site. Microb. Ecol. 51, 51–64 (2006).
    Google Scholar 

    27.
    Ortega-Morales, B. O. et al. Bioweathering potential of cultivable fungi associated with semi-arid surface microhabitats of Mayan buildings. Front. Microbiol. 7, 201 (2016).
    Google Scholar 

    28.
    Cappitelli, F., Principi, P., Pedrazzani, R., Toniolo, L. & Sorlini, C. Bacterial and fungal deterioration of the Milan Cathedral marble treated with protective synthetic resins. Sci. Total Environ. 385, 172–181 (2007).
    CAS  Google Scholar 

    29.
    Rosado, T. et al. Pink! Why not? On the unusual colour of Évora Cathedral. Int. Biodeterior. Biodegrad. 94, 121–127 (2014).
    CAS  Google Scholar 

    30.
    Schiavon, N. et al. A multianalytical approach to investigate stone biodeterioration at a UNESCO world heritage site: the volcanic rock-hewn churches of Lalibela, Northern Ethiopia. Appl. Phys. A 113, 843–854 (2013).
    CAS  Google Scholar 

    31.
    Guillitte, O. Bioreceptivity: a new concept for building ecology studies. Sci. Total Environ. 167, 215–220 (1995).
    CAS  Google Scholar 

    32.
    Warscheid, T. & Leisen, H. in Biocolonization of Stone: Control and Preventive Methods: Proceedings from the MCI Workshop Series (eds Charola, A. E. et al.) 1–18 (Smithsonian Institution Scholarly Press, 2011).

    33.
    Warscheid, T., Oelting, M. & Krumbein, W. E. Physico-chemical aspects of biodeterioration processes on rocks with special regard to organic pollutants. Int. Biodeterior. Biodegrad. 28, 37–48 (1991).
    CAS  Google Scholar 

    34.
    Haack, T. K. & McFeters, G. A. Nutritional relationships among microorganisms in an epilithic biofilm community. Microb. Ecol. 8, 115–126 (1982).
    CAS  Google Scholar 

    35.
    Liu, X., Meng, H., Wang, Y., Katayama, Y. & Gu, J.-D. Water is a critical factor in evaluating and assessing microbial colonization and destruction of Angkor sandstone monuments. Int. Biodeterior. Biodegrad. 133, 9–16 (2018).
    CAS  Google Scholar 

    36.
    Prieto, B. & Silva, B. Estimation of the potential bioreceptivity of granitic rocks from their intrinsic properties. Int. Biodeterior. Biodegrad. 56, 206–215 (2005).
    CAS  Google Scholar 

    37.
    Miller, A. Z. et al. Bioreceptivity of building stones: a review. Sci. Total Environ. 426, 1–12 (2012).
    CAS  Google Scholar 

    38.
    Warscheid, T. et al. Biodeterioration studies on soapstone, quartzite & sandstones of historical monuments in Brazil and Germany. Preliminary results and evaluation for restoration practices. In Proc. of the 7th International Congress on Deterioration and Conservation of Stone 491–500 (Laboratório Nacional de Engenharia Civil, 1992).

    39.
    Beck, K., Al-Mukhtar, M., Rozenbaum, O. & Rautureau, M. Characterization, water transfer properties and deterioration in tuffeau: building material in the Loire valley—France. Build. Environ. 38, 1151–1162 (2003).
    Google Scholar 

    40.
    Sousa, L. M. O., Suárez del Río, L. M., Calleja, L., Ruiz de Argandoña, V. G. & Rey, A. R. Influence of microfractures and porosity on the physico-mechanical properties and weathering of ornamental granites. Eng. Geol. 77, 153–168 (2005).
    Google Scholar 

    41.
    Koestler, R., Warscheid, T. & Nieto, F. in Saving our Architectural Heritage: The Conservation of Historic Stone Structures (eds Baer, N. S. & Snethlage, R.) 25–36 (Wiley, 1997).

    42.
    Miller, A. Z., Dionísio, A., Laiz, L., Macedo, M. F. & Saiz-Jimenez, C. The influence of inherent properties of building limestones on their bioreceptivity to phototrophic microorganisms. Ann. Microbiol. 59, 705–713 (2009).
    CAS  Google Scholar 

    43.
    Tiano, P., Accolla, P. & Tomaselli, L. Phototrophic biodeteriogens on lithoid surfaces: an ecological study. Microb. Ecol. 29, 299–309 (1995).
    CAS  Google Scholar 

    44.
    Vázquez-Nion, D., Silva, B. & Prieto, B. Influence of the properties of granitic rocks on their bioreceptivity to subaerial phototrophic biofilms. Sci. Total Environ. 610–611, 44–54 (2018).
    Google Scholar 

    45.
    Miller, A., Dionísio, A. & Macedo, M. F. Primary bioreceptivity: a comparative study of different Portuguese lithotypes. Int. Biodeterior. Biodegrad. 57, 136–142 (2006).
    CAS  Google Scholar 

    46.
    Hunt, J. M. Distribution of hydrocarbons in sedimentary rocks. Geochim. Cosmochim. Acta 22, 37–49 (1961).
    CAS  Google Scholar 

    47.
    Carter, N. & Viles, H. Lichen hotspots: raised rock temperatures beneath Verrucaria nigrescens on limestone. Geomorphology 62, 1–16 (2004).
    Google Scholar 

    48.
    Castanier, S., Le Métayer-Levrel, G. & Perthuisot, J.-P. Ca-carbonates precipitation and limestone genesis—the microbiogeologist point of view. Sediment. Geol. 126, 9–23 (1999).
    CAS  Google Scholar 

    49.
    Leavengood, P., Twilley, J. & Asmus, J. F. Lichen removal from Chinese Spirit Path figures of marble. J. Cult. Herit. 1, S71–S74 (2000).
    Google Scholar 

    50.
    Gu, J.-D., Ford, T. E. & Mitchell, R. in Uhlig’s Corrosion Handbook 3rd edn (ed. Revie, R. W.) 451–460 (Wiley, 2011).

    51.
    Roig, P. B., Regidor Ros, J. L. & Estellés, R. M. Biocleaning of nitrate alterations on wall paintings by Pseudomonas stutzeri. Int. Biodeterior. Biodegrad. 84, 266–274 (2013).
    CAS  Google Scholar 

    52.
    Šimonovičová, A., Gódyová, M. & Ševc, J. Airborne and soil microfungi as contaminants of stone in a hypogean cemetery. Int. Biodeterior. Biodegrad. 54, 7–11 (2004).
    Google Scholar 

    53.
    Lan, W., Li, H., Wang, W.-D., Katayama, Y. & Gu, J.-D. Microbial community analysis of fresh and old microbial biofilms on Bayon Temple Sandstone of Angkor Thom, Cambodia. Microb. Ecol. 60, 105–115 (2010).
    Google Scholar 

    54.
    Bartoli, F. et al. Biological colonization patterns on the ruins of Angkor temples (Cambodia) in the biodeterioration vs bioprotection debate. Int. Biodeterior. Biodegrad. 96, 157–165 (2014).
    Google Scholar 

    55.
    Xu, H.-B. et al. Lithoautotrophical oxidation of elemental sulfur by fungi including Fusarium solani isolated from sandstone Angkor temples. Int. Biodeterior. Biodegrad. 126, 95–102 (2018).
    CAS  Google Scholar 

    56.
    Kusumi, A., Li, X. S. & Katayama, Y. Mycobacteria isolated from Angkor monument sandstones grow chemolithoautotrophically by oxidizing elemental sulfur. Front. Microbiol. 2, 104 (2011).
    CAS  Google Scholar 

    57.
    Caneva, G. et al. Exploring ecological relationships in the biodeterioration patterns of Angkor temples (Cambodia) along a forest canopy gradient. J. Cult. Herit. 16, 728–735 (2015).
    Google Scholar 

    58.
    Kemmling, A., Kämper, M., Flies, C., Schieweck, O. & Hoppert, M. Biofilms and extracellular matrices on geomaterials. Environ. Geol. 46, 429–435 (2004).
    CAS  Google Scholar 

    59.
    Gaylarde, C. C., Rodríguez, C. H., Navarro-Noya, Y. E. & Ortega-Morales, B. O. Microbial biofilms on the sandstone monuments of the Angkor Wat complex, Cambodia. Curr. Microbiol. 64, 85–92 (2012).
    CAS  Google Scholar 

    60.
    Nuhoglu, Y. et al. The accelerating effects of the microorganisms on biodeterioration of stone monuments under air pollution and continental-cold climatic conditions in Erzurum, Turkey. Sci. Total Environ. 364, 272–283 (2006).
    CAS  Google Scholar 

    61.
    Gaylarde, C. et al. Epilithic and endolithic microorganisms and deterioration on stone church facades subject to urban pollution in a sub-tropical climate. Biofouling 33, 113–127 (2017).
    Google Scholar 

    62.
    Mansch, R. & Bock, E. Biodeterioration of natural stone with special reference to nitrifying bacteria. Biodegradation 9, 47–64 (1998).
    CAS  Google Scholar 

    63.
    Viles, H. A. Implications of future climate change for stone deterioration. Geol. Soc. Lond. Spec. Publ. 205, 407–418 (2002).
    Google Scholar 

    64.
    Moroni, B. & Pitzurra, L. Biodegradation of atmospheric pollutants by fungi: a crucial point in the corrosion of carbonate building stone. Int. Biodeterior. Biodegrad. 62, 391–396 (2008).
    CAS  Google Scholar 

    65.
    Saiz-Jimenez, C. Biodeterioration vs biodegradation: the role of microorganisms in the removal of pollutants deposited on historic buidlings. Int. Biodeterior. Biodegrad. 40, 225–232 (1997).
    CAS  Google Scholar 

    66.
    Mitchell, R. & Gu, J.-D. Changes in the biofilm microflora of limestone caused by atmospheric pollutants. Int. Biodeterior. Biodegrad. 46, 299–303 (2000).
    CAS  Google Scholar 

    67.
    Stefanis, N.-A., Theoulakis, P. & Pilinis, C. Dry deposition effect of marine aerosol to the building stone of the medieval city of Rhodes, Greece. Build. Environ. 44, 260–270 (2009).
    Google Scholar 

    68.
    Leysen, L., Roekens, E. & Van Grieken, R. Air-pollution-induced chemical decay of a sandy-limestone Cathedral in Belgium. Sci. Total Environ. 78, 263–287 (1989).
    CAS  Google Scholar 

    69.
    Duan, Y. et al. The microbial community characteristics of ancient painted sculptures in Maijishan Grottoes, China. PLoS ONE 12, e0179718 (2017).
    Google Scholar 

    70.
    Bakr, A. & El Hafez, M. A. Role assessment of bat excretions in degradation of painted surface from Mohamed Ali’s palace, Suez, Egypt. Egypt. J. Archaeol. Restor. Stud. 3, 47–56 (2012).
    Google Scholar 

    71.
    Wierzchos, J. et al. Adaptation strategies of endolithic chlorophototrophs to survive the hyperarid and extreme solar radiation environment of the Atacama Desert. Front. Microbiol. 6, 934 (2015).
    Google Scholar 

    72.
    Aviam, O., Bar-Nes, G., Zeiri, Y. & Sivan, A. Accelerated biodegradation of cement by sulfur-oxidizing bacteria as a bioassay for evaluating immobilization of low-level radioactive waste. Appl. Environ. Microbiol. 70, 6031–6036 (2004).
    CAS  Google Scholar 

    73.
    Vupputuri, S. et al. Isolation of a sulfur-oxidizing Streptomyces sp. from deteriorating bridge structures and its role in concrete deterioration. Int. Biodeterior. Biodegrad. 97, 128–134 (2015).
    CAS  Google Scholar 

    74.
    Sand, W. & Bock, E. Biodeterioration of mineral materials by microorganisms—biogenic sulfuric and nitric acid corrosion of concrete and natural stone. Geomicrobiol. J. 9, 129–138 (1991).
    CAS  Google Scholar 

    75.
    Salvadori, O. & Municchia, A. C. The role of fungi and lichens in the biodeterioration of stone monuments. Open Conf. Proc. J. 7, 39–54 (2016).
    CAS  Google Scholar 

    76.
    Meng, H., Luo, L., Chan, H. W., Katayama, Y. & Gu, J.-D. Higher diversity and abundance of ammonia-oxidizing archaea than bacteria detected at the Bayon Temple of Angkor Thom in Cambodia. Int. Biodeterior. Biodegrad. 115, 234–243 (2016).
    CAS  Google Scholar 

    77.
    Daims, H. et al. Complete nitrification by Nitrospira bacteria. Nature 528, 504–509 (2015).
    CAS  Google Scholar 

    78.
    Gu, J.-D. & Katayama, Y. A microbiological challenge in protection of the sandstone Angkor monuments in Cambodia. IIC Newsletter (15 December 2017).

    79.
    Gu, J.-D., Ford, T. E., Berke, N. S. & Mitchell, R. Biodeterioration of concrete by the fungus Fusarium. Int. Biodeterior. Biodegrad. 41, 101–109 (1998).
    Google Scholar 

    80.
    Li, X. S. et al. Oxidation of elemental sulfur by Fusarium solani strain THIF01 harboring endobacterium Bradyrhizobium sp. Microb. Ecol. 60, 96–104 (2010).
    CAS  Google Scholar 

    81.
    Li, X., Arai, H., Shimoda, I., Kuraishi, H. & Katayama, Y. Enumeration of sulfur-oxidizing microorganisms on deteriorating stone of the Angkor monuments, Cambodia. Microbes Environ. 23, 293–298 (2008).
    Google Scholar 

    82.
    Bourcart, J., Noetzlin, J., Pochon, J. & Berthelier, S. Etude des détériorations des pierres des monuments historiques. In Annales de l’Institut Technique de Bâtiment et des Travaux Publics 1–16 (1949).

    83.
    Lepidi, A. & Schippa, G. Some aspects of the growth of chemotrophic and heterotrophic microorganisms on calcareous surfaces. In Colloque international sur la deterioration des pierres en oeuvre. 1er. International symposium on the deterioration of building stones 143–148 (Les Imprimerie Reunites de Chambery, 1973).

    84.
    Barcellona Vero, L. & Monte Sila, M. Isolation of various sulphur-oxidizing bacteria from stone monuments. In The conservation of stone i. Proceedings of the international symposium (ed. Rossi-Manaresi, R.) 233–244 (Centro per la conservazione delle sculture all’aperto, 1976).

    85.
    Tarantino, M. M. S.-G. The metabolic state of microorganisms of the genus Thiobacillus on stone monuments. In The Conservation of stone II: preprints of the contributions to the international symposium 117–138 (Centro per la conservazione delle sculture all’aperto, 1981).

    86.
    Milde, K., Sand, W., Wolff, W. & Bock, E. Thiobacilli of the corroded concrete walls of the Hamburg sewer system. Microbiology 129, 1327–1333 (1983).
    Google Scholar 

    87.
    Krumbein, W. E. Photolithotropic and chemoorganotrophic activity of bacteria and algae as related to beachrock formation and degradation (gulf of Aqaba, Sinai). Geomicrobiol. J. 1, 139–203 (1979).
    CAS  Google Scholar 

    88.
    Suzuki, D., Li, Z., Cui, X., Zhang, C. & Katayama, A. Reclassification of Desulfobacterium anilini as Desulfatiglans anilini comb. nov. within Desulfatiglans gen. nov., and description of a 4-chlorophenol-degrading sulfate-reducing bacterium, Desulfatiglans parachlorophenolica sp. nov. Int. J. Syst. Evol. Microbiol. 64, 3081–3086 (2014).
    CAS  Google Scholar 

    89.
    Kleindienst, S. et al. Diverse sulfate-reducing bacteria of the Desulfosarcina/Desulfococcus clade are the key alkane degraders at marine seeps. ISME J. 8, 2029–2044 (2014).
    CAS  Google Scholar 

    90.
    Griffin, P., Indictor, N. & Koestler, R. The biodeterioration of stone: a review of deterioration mechanisms, conservation case histories, and treatment. Int. Biodeterior. Biodegrad. 28, 187–207 (1991).
    Google Scholar 

    91.
    Gaylarde, P., Englert, G., Ortega-Morales, O. & Gaylarde, C. Lichen-like colonies of pure Trentepohlia on limestone monuments. Int. Biodeterior. Biodegrad. 58, 119–123 (2006).
    CAS  Google Scholar 

    92.
    Isola, D. et al. Extremotolerant rock inhabiting black fungi from Italian monumental sites. Fungal Divers. 76, 75–96 (2016).
    Google Scholar 

    93.
    Suihko, M.-L. et al. Characterization of aerobic bacterial and fungal microbiota on surfaces of historic Scottish monuments. Syst. Appl. Microbiol. 30, 494–508 (2007).
    CAS  Google Scholar 

    94.
    Morillas, H. et al. Characterization of the main colonizer and biogenic pigments present in the red biofilm from La Galea Fortress sandstone by means of microscopic observations and Raman imaging. Microchem. J. 121, 48–55 (2015).
    CAS  Google Scholar 

    95.
    Hu, H. et al. Occurrence of Aspergillus allahabadii on sandstone at Bayon temple, Angkor Thom, Cambodia. Int. Biodeterior. Biodegrad. 76, 112–117 (2013).
    CAS  Google Scholar 

    96.
    ElBaghdady, K. Z., Tolba, S. T. & Houssien, S. S. Biogenic deterioration of Egyptian limestone monuments: treatment and conservation. J. Cult. Herit. 38, 118–125 (2019).
    Google Scholar 

    97.
    Gonzalez-Pimentel, J. L. et al. Yellow coloured mats from lava tubes of La Palma (Canary Islands, Spain) are dominated by metabolically active Actinobacteria. Sci. Rep. 8, 1944 (2018).
    Google Scholar 

    98.
    Garty, J. Influence of epilithic microorganisms on the surface temperature of building walls. Can. J. Bot. 68, 1349–1353 (1990).
    Google Scholar 

    99.
    Sterflinger, K. Fungi: their role in deterioration of cultural heritage. Fungal Biol. Rev. 24, 47–55 (2010).
    Google Scholar 

    100.
    Ortega-Morales, B. O., Gaylarde, C. C., Englert, G. E. & Gaylarde, P. M. Analysis of salt-containing biofilms on limestone buildings of the Mayan culture at Edzna, Mexico. Geomicrobiol. J. 22, 261–268 (2005).
    CAS  Google Scholar 

    101.
    Cappitelli, F. et al. Improved methodology for bioremoval of black crusts on historical stone artworks by use of sulfate-reducing bacteria. Appl. Environ. Microbiol. 72, 3733–3737 (2006).
    CAS  Google Scholar 

    102.
    Vincke, E. et al. Influence of polymer addition on biogenic sulfuric acid attack of concrete. Int. Biodeterior. Biodegrad. 49, 283–292 (2002).
    CAS  Google Scholar 

    103.
    De Windt, L. & Devillers, P. Modeling the degradation of Portland cement pastes by biogenic organic acids. Cem. Concr. Res. 40, 1165–1174 (2010).
    Google Scholar 

    104.
    Turkington, A. V. & Paradise, T. R. Sandstone weathering: a century of research and innovation. Geomorphology 67, 229–253 (2005).
    Google Scholar 

    105.
    Rossi, F. et al. Characteristics and role of the exocellular polysaccharides produced by five cyanobacteria isolated from phototrophic biofilms growing on stone monuments. Biofouling 28, 215–224 (2012).
    CAS  Google Scholar 

    106.
    Li, W.-W. & Yu, H.-Q. Insight into the roles of microbial extracellular polymer substances in metal biosorption. Bioresour. Technol. 160, 15–23 (2014).
    CAS  Google Scholar 

    107.
    Stone, A. T. Microbial metabolites and the reductive dissolution of manganese oxides: oxalate and pyruvate. Geochim. Cosmochim. Acta 51, 919–925 (1987).
    CAS  Google Scholar 

    108.
    Monte, M. Oxalate film formation on marble specimens caused by fungus. J. Cult. Herit. 4, 255–258 (2003).
    Google Scholar 

    109.
    Cariati, F., Rampazzi, L., Toniolo, L. & Pozzi, A. Calcium oxalate films on stone surfaces: experimental assessment of the chemical formation. Stud. Conserv. 45, 180–188 (2000).
    CAS  Google Scholar 

    110.
    Scherer, G. W. Stress from crystallization of salt. Cem. Concr. Res. 34, 1613–1624 (2004).
    CAS  Google Scholar 

    111.
    Saiz-Jimenez, C. & Laiz, L. Occurrence of halotolerant/halophilic bacterial communities in deteriorated monuments. Int. Biodeterior. Biodegrad. 46, 319–326 (2000).
    CAS  Google Scholar 

    112.
    Favero-Longo, S. E., Borghi, A., Tretiach, M. & Piervittori, R. In vitro receptivity of carbonate rocks to endolithic lichen-forming aposymbionts. Mycol. Res. 113, 1216–1227 (2009).
    Google Scholar 

    113.
    Lisci, M., Monte, M. & Pacini, E. Lichens and higher plants on stone: a review. Int. Biodeterior. Biodegrad. 51, 1–17 (2003).
    Google Scholar 

    114.
    Caneva, G., Danin, A., Ricci, S. & Conti, C. The pitting of Trajan’s column, Rome: an ecological model of its origin. In Conservazione del Patrimonio culturale II, Contributi Centro Linceo Interdisciplinare Beniamino Segre 78–102 (Accademia Nazionale dei Lincei, 1994).

    115.
    Danin, A. Pitting of calcareous rocks by organisms under terrestrial conditions. Isr. J. Earth Sci. 41, 201–207 (1992).
    Google Scholar 

    116.
    Danin, A. & Caneva, G. Deterioration of limestone walls in Jerusalem and marble monuments in Rome caused by cyanobacteria and cyanophilous lichens. Int. Biodeterior. Biodegrad. 26, 397–417 (1990).
    Google Scholar 

    117.
    Lombardozzi, V., Castrignanò, T., D’Antonio, M., Casanova Municchia, A. & Caneva, G. An interactive database for an ecological analysis of stone biopitting. Int. Biodeterior. Biodegrad. 73, 8–15 (2012).
    Google Scholar 

    118.
    Gehrmann, C., Krumbein, W. & Petersen, K. Endolithic lichens and the corrosion of carbonate rocks. A study of biopitting. Int. J. Mycol. Lichenol. 5, 37–48 (1992).
    Google Scholar 

    119.
    McIlroy de la Rosa, J. P., Warke, P. A. & Smith, B. J. Microscale biopitting by the endolithic lichen Verrucaria baldensis and its proposed role in mesoscale solution basin development on limestone. Earth Surf. Process. Landf. 37, 374–384 (2012).
    Google Scholar 

    120.
    Pomar, F., Gómez-Pujol, L., Fornós, J. J., Del Valle, L. & Nogales, B. Limestone biopitting in coastal settings: A spatial, morphometric, SEM and molecular microbiology sequencing study in the Mallorca rocky coast (Balearic Islands, Western Mediterranean). Geomorphology 276, 104–115 (2017).
    Google Scholar 

    121.
    Caneva, G. Ecological approach to the genesis of calcium oxalate patinas on stone monuments. Aerobiologia 9, 149–156 (1993).
    Google Scholar 

    122.
    Bruno, L. & Valle, V. Effect of white and monochromatic lights on cyanobacteria and biofilms from Roman Catacombs. Int. Biodeterior. Biodegrad. 123, 286–295 (2017).
    Google Scholar 

    123.
    Danin, A. Patterns of biogenic weathering as indicators of palaeoclimates in Israel. Proc. R. Soc. Edinb. B 89, 243–253 (1986).
    Google Scholar 

    124.
    de Ferri, L., Lottici, P. P., Lorenzi, A., Montenero, A. & Salvioli-Mariani, E. Study of silica nanoparticles – polysiloxane hydrophobic treatments for stone-based monument protection. J. Cult. Herit. 12, 356–363 (2011).
    Google Scholar 

    125.
    Son, S. et al. Organic−inorganic hybrid compounds containing polyhedral oligomeric silsesquioxane for conservation of stone heritage. ACS Appl. Mater. Inter. 1, 393–401 (2009).
    CAS  Google Scholar 

    126.
    Erkal, A., D’Ayala, D. & Sequeira, L. Assessment of wind-driven rain impact, related surface erosion and surface strength reduction of historic building materials. Build. Environ. 57, 336–348 (2012).
    Google Scholar 

    127.
    Traversetti, L., Bartoli, F. & Caneva, G. Wind-driven rain as a bioclimatic factor affecting the biological colonization at the archaeological site of Pompeii, Italy. Int. Biodeterior. Biodegrad. 134, 31–38 (2018).
    Google Scholar 

    128.
    Ortega-Morales, O., Guezennec, J., Hernández-Duque, G., Gaylarde, C. C. & Gaylarde, P. M. Phototrophic biofilms on ancient Mayan buildings in Yucatan, Mexico. Curr. Microbiol. 40, 81–85 (2000).
    CAS  Google Scholar 

    129.
    Li, Q., Zhang, B., He, Z. & Yang, X. Distribution and diversity of bacteria and fungi colonization in stone monuments analyzed by high-throughput sequencing. PLoS ONE 11, e0163287 (2016).
    Google Scholar 

    130.
    Wu, F., Wang, W., Feng, H. & Gu, J.-D. Realization of biodeterioration to cultural heritage protection in China. Int. Biodeterior. Biodegrad. 117, 128–130 (2017).
    CAS  Google Scholar 

    131.
    Wang, W. et al. Seasonal dynamics of airborne fungi in different caves of the Mogao Grottoes, Dunhuang, China. Int. Biodeterior. Biodegrad. 64, 461–466 (2010).
    Google Scholar 

    132.
    Zamarreño, D. V., Inkpen, R. & May, E. Carbonate crystals precipitated by freshwater bacteria and their use as a limestone consolidant. Appl. Environ. Microbiol. 75, 5981–5990 (2009).
    Google Scholar 

    133.
    Jroundi, F. et al. Protection and consolidation of stone heritage by self-inoculation with indigenous carbonatogenic bacterial communities. Nat. Commun. 8, 279 (2017).
    Google Scholar 

    134.
    Ascaso, C. et al. In situ evaluation of the biodeteriorating action of microorganisms and the effects of biocides on carbonate rock of the Jeronimos Monastery (Lisbon). Int. Biodeterior. Biodegrad. 49, 1–12 (2002).
    Google Scholar 

    135.
    Koestler, R. J., Parreira, E., Santoro, E. D. & Noble, P. Visual effects of selected biocides on easel painting materials. Stud. Conserv. 38, 265–273 (1993).
    Google Scholar 

    136.
    Fidanza, M. R. & Caneva, G. Natural biocides for the conservation of stone cultural heritage: a review. J. Cult. Herit. 38, 271–286 (2019).
    Google Scholar 

    137.
    Silva, M., Rosado, T., Teixeira, D., Candeias, A. & Caldeira, A. T. Production of green biocides for cultural heritage. Novel biotechnological solutions. Int. J. Conserv. Sci. 6, 519–530 (2015).
    CAS  Google Scholar 

    138.
    Silva, M., Rosado, T., Teixeira, D., Candeias, A. & Caldeira, A. T. Green mitigation strategy for cultural heritage: bacterial potential for biocide production. Environ. Sci. Pollut. Res. 24, 4871–4881 (2017).
    CAS  Google Scholar 

    139.
    Marin, E., Vaccaro, C. & Leis, M. Biotechnology applied to historic stoneworks conservation: testing the potential harmfulness of two biological biocides. Int. J. Conserv. Sci. 7, 227–238 (2016).
    Google Scholar 

    140.
    Caneva, G., Fidanza, M. R., Tonon, C. & Favero-Longo, S. E. Biodeterioration patterns and their interpretation for potential applications to stone conservation: a hypothesis from allelopathic inhibitory effects of lichens on the Caestia Pyramid (Rome). Sustainability 12, 1132 (2020).
    CAS  Google Scholar 

    141.
    Alfano, G. et al. The bioremoval of nitrate and sulfate alterations on artistic stonework: the case-study of Matera Cathedral after six years from the treatment. Int. Biodeterior. Biodegrad. 65, 1004–1011 (2011).
    CAS  Google Scholar 

    142.
    Soffritti, I. et al. The potential use of microorganisms as restorative agents: an update. Sustainability 11, 3853 (2019).
    CAS  Google Scholar 

    143.
    Scherer, G. W., Flatt, R. & Wheeler, G. Materials science research for the conservation of sculpture and monuments. MRS Bull. 26, 44–50 (2001).
    CAS  Google Scholar 

    144.
    Gu, J.-D. Microbiological deterioration and degradation of synthetic polymeric materials: recent research advances. Int. Biodeterior. Biodegrad. 52, 69–91 (2003).
    CAS  Google Scholar 

    145.
    Charola, A. E., McNamara, C. & Koestler, R. J. (eds) Biocolonization of Stone: Control and Preventive Methods: Proceedings from the MCI Workshop Series Smithsonian Contributions to Museum Conservation no. 2 (Smithsonian Institution Scholarly Press, 2011).

    146.
    Yang, F. et al. Conservation of weathered historic sandstone with biomimetic apatite. Chin. Sci. Bull. 57, 2171–2176 (2012).
    CAS  Google Scholar 

    147.
    Gherardi, F., Roveri, M., Goidanich, S. & Toniolo, L. Photocatalytic nanocomposites for the protection of European architectural heritage. Materials 11, 65 (2018).
    Google Scholar 

    148.
    Sierra-Fernandez, A., Gomez-Villalba, L., Rabanal, M. & Fort, R. New nanomaterials for applications in conservation and restoration of stony materials: a review. Mater. Construcc. 67, e107 (2017).
    Google Scholar 

    149.
    Grossi, C. M., Bonazza, A., Brimblecombe, P., Harris, I. & Sabbioni, C. Predicting twenty-first century recession of architectural limestone in European cities. Environ. Geol. 56, 455–461 (2008).
    CAS  Google Scholar 

    150.
    de la Rosa, J. P. M., Warke, P. A. & Smith, B. J. Lichen-induced biomodification of calcareous surfaces: bioprotection versus biodeterioration. Prog. Phys. Geog. 37, 325–351 (2013).
    Google Scholar 

    151.
    Gadd, G. M. & Dyer, T. D. Bioprotection of the built environment and cultural heritage. Microb. Biotechnol. 10, 1152–1156 (2017).
    Google Scholar 

    152.
    Pinna, D. Biofilms and lichens on stone monuments: do they damage or protect? Front. Microbiol. 5, 133 (2014).
    Google Scholar 

    153.
    Gadd, G. M. et al. Oxalate production by fungi: significance in geomycology, biodeterioration and bioremediation. Fungal Biol. Rev. 28, 36–55 (2014).
    Google Scholar 

    154.
    Bosch-Roig, P. & Ranalli, G. The safety of biocleaning technologies for cultural heritage. Front. Microbiol. 5, 155 (2014).
    Google Scholar 

    155.
    Zhang, G. et al. Biochemical reactions and mechanisms involved in the biodeterioration of stone world cultural heritage under the tropical climate conditions. Int. Biodeterior. Biodegrad. 143, 104723 (2019).
    CAS  Google Scholar 

    156.
    Zhang, X., Ge, Q., Zhu, Z., Deng, Y. & Gu, J.-D. Microbiological community of the Royal Palace in Angkor Thom and Beng Mealea of Cambodia by Illumina sequencing based on 16S rRNA gene. Int. Biodeterior. Biodegrad. 134, 127–135 (2018).
    CAS  Google Scholar  More

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    Field-based sciences must transform in response to COVID-19

    When only local participation in fieldwork is possible, how effectively can remote collaborations be executed in the field sciences, when so much diverse expertise is required?
    Foremost is a comprehensive investment in the creation of digital archives at different scales. Various government agencies and developer-led fieldwork as well as excavations in extreme locations have been using such methods and techniques for years14. However, practice is neither standardized nor mainstreamed across comparable research projects, and in the context of COVID-19 there are many reasons to push for such goals.
    First, the creation of high-resolution photographic databases for photogrammetry is relatively easy to teach remotely, and is inexpensive, although post-processing time is substantial and requires investment in technicians. These techniques can record and visualize spatial relationships, stratigraphic sequences and, depending on the use of different light, may permit assessments that can even be superior to traditional by-eye illustrations14. Specialists can clearly mark sampling locations on the models for those on site, enabling group assessments. Second, the creation of three-dimensional (3D) geological and sedimentary archives also enables re-assessment of sequences in future. Third, the creation and sharing of both the archives and their interpretations will precipitate the much-needed standardization of sampling and analytical procedures. Within an open data framework, this working model will ensure that novice researchers and non-specialists learn from experts through collaborative, team-based inferences, rather than stitching together the results of individual specialists in a top-down approach.
    Our existing international field season schedules also require change — a long-overdue adjustment in the face of increasing anthropogenically induced climate change. Fieldwork will be based locally and projects will require many short and closely spaced field seasons. Those who can access our field sites within a few hours can conduct short-distance trips, focused on discrete steps in the process of assessment, excavation, sampling and inference. For example, a short initial season would focus on building a high-resolution digital model of the field site that can be shared with remote collaborators to develop excavation strategies. A later season could focus solely on sampling, following remote collaborative assessment of digital archives. Such approaches also in part mitigate the problems faced by less-accessible field sites, where frequent online meetings and the exchange of information are impossible. Effective remote collaboration will require very clear scheduling among remote experts at each phase of the process to minimize the burden on local researchers.
    At a landscape scale, the situation becomes more challenging. 3D models from unmanned aerial vehicles (UAVs), remote-sensing data15 and LiDAR16 are already widely used in prospection and analysis, and may facilitate effective collaboration between remote specialists and local participants. Remotely generated landscape-scale hypotheses and geomorphological maps can be tested at a later stage by specialists on the ground, whenever longer-distance travel becomes an appropriate option. The creation of 3D data using UAVs does not represent a considerable remote training challenge. A major new research focus should address the extent to which these methods and predictive models in geomorphology are able to replicate, complement and validate assessments of landscape-scale processes made in the field. More

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    Climate change increases predation risk for a keystone species of the boreal forest

    1.
    Romero, G. Q. et al. Global predation pressure redistribution under future climate change. Nat. Clim. Change 8, 1087–1091 (2018).
    Article  Google Scholar 
    2.
    Ims, R. A. et al. Arctic greening and bird nest predation risk across tundra ecotones. Nat. Clim. Change 9, 607–610 (2019).
    Article  Google Scholar 

    3.
    Stenseth, N. et al. Snow conditions may create an invisible barrier for lynx. Proc. Natl Acad. Sci. USA 101, 10632–10634 (2004).
    CAS  Article  Google Scholar 

    4.
    Zimova, M., Mills, L. S. & Nowak, J. J. High fitness costs of climate change induced camouflage mismatch in a seasonally colour moulting mammal. Ecol. Lett. 19, 299–307 (2016).
    Article  Google Scholar 

    5.
    Post, E., Peterson, R. O., Stenseth, N. C. & McLaren, B. E. Ecosystem consequences of wolf behavioural response to climate. Nature 401, 905–907 (1999).
    CAS  Article  Google Scholar 

    6.
    Iles, D. T., Rockwell, R. F. & Koons, D. N. Shifting vital rate correlations alter predicted population responses to increasingly variable environments. Am. Nat. 193, E57–E64 (2019).
    Article  Google Scholar 

    7.
    Fisher, J. T. & Burton, A. C. Wildlife winners and losers in an oil sands landscape. Front. Ecol. Environ. 16, 323–328 (2018).
    Article  Google Scholar 

    8.
    Myers, J. H. Population cycles: generalities, exceptions and remaining mysteries. Proc. R. Soc. B 285, 20172841 (2018).
    Article  Google Scholar 

    9.
    Boutin, S. et al. Population changes of the vertebrate community during a snowshoe hare cycle in Canada’s boreal forest. Oikos 74, 69–80 (1995).
    Article  Google Scholar 

    10.
    Murray, D. L. & Boutin, S. The influence of snow on lynx and coyote movements: does morphology affect behavior? Oecologia 88, 463–469 (1991).
    Article  Google Scholar 

    11.
    Penczykowski, R. M., Connolly, B. M. & Barton, B. T. Winter is changing: trophic interactions under altered snow regimes. Food Webs 13, 80–91 (2017).
    Article  Google Scholar 

    12.
    Cornulier, T. et al. Europe-wide dampening of population cycles in keystone herbivores. Science 340, 63–66 (2013).
    CAS  Article  Google Scholar 

    13.
    Kausrud, K. L. et al. Linking climate change to lemming cycles. Nature 456, 93–97 (2008).
    CAS  Article  Google Scholar 

    14.
    Ims, R. A., Henden, J.-A. & Killengreen, S. T. Collapsing population cycles. Trends Ecol. Evol. 23, 79–86 (2008).
    Article  Google Scholar 

    15.
    Hodges, K. et al. in Ecosystem Dynamics of the Boreal Forest (eds Krebs, C. et al.) 141–178 (Oxford Univ. Press, 2001).

    16.
    Oli, M. K. et al. Demography of snowshoe hare population cycles. Ecology 101, e02969 (2020).
    Article  Google Scholar 

    17.
    Peacock, S. Projected twenty-first-century changes in temperature, precipitation, and snow cover over North America in CCSM4. J. Clim. 25, 4405–4429 (2012).
    Article  Google Scholar 

    18.
    Krebs, C. J. et al. What factors determine cyclic amplitude in the snowshoe hare (Lepus americanus) cycle? Can. J. Zool. 92, 1039–1048 (2014).
    Article  Google Scholar 

    19.
    Yan, C., Stenseth, N. C., Krebs, C. J. & Zhang, Z. Linking climate change to population cycles of hares and lynx. Glob. Change Biol. 19, 3263–3271 (2013).
    Google Scholar 

    20.
    Studd, E. K. et al. Use of acceleration and acoustics to classify behavior, generate time budgets, and evaluate responses to moonlight in free-ranging snowshoe hares. Front. Ecol. Evol. 7, e154 (2019).
    Article  Google Scholar 

    21.
    Mills, L. et al. Camouflage mismatch in seasonal coat color due to decreased snow duration. Proc. Natl Acad. Sci. USA 110, 7360–7365 (2013).
    CAS  Article  Google Scholar 

    22.
    Wilson, E. C., Shipley, A. A., Zuckerberg, B., Peery, M. Z. & Pauli, J. N. An experimental translocation identifies habitat features that buffer camouflage mismatch in snowshoe hares. Conserv. Lett. 12, e12614 (2019).
    Article  Google Scholar 

    23.
    Guillaumet, A., Bowman, J., Thornton, D. & Murray, D. L. The influence of coyote on Canada lynx populations assessed at two different spatial scales. Community Ecol. 16, 135–146 (2015).
    Article  Google Scholar 

    24.
    Peers, M. J. L., Thornton, D. H. & Murray, D. L. Reconsidering the specialist–generalist paradigm in niche breadth dynamics: resource gradient selection by Canada lynx and bobcat. PLoS ONE 7, e51488 (2012).
    CAS  Article  Google Scholar 

    25.
    Bowler, B., Krebs, C., O’Donoghue, M. & Hone, J. Climatic amplification of the numerical response of a predator population to its prey. Ecology 95, 1153–1161 (2014).
    Article  Google Scholar 

    26.
    Krebs, C. J., Boutin, S. & Boonstra, R. (eds) Ecosystem Dynamics of the Boreal Forest (Oxford Univ. Press, 2001).

    27.
    O’Donoghue, M., Boutin, S., Krebs, C. & Hofer, E. Numerical responses of coyotes and lynx to the snowshoe hare cycle. Oikos 80, 150–162 (1997).
    Article  Google Scholar 

    28.
    Hodges, K. in Ecology and Conservation of Lynx in the United States (eds Ruggiero, L. F. et al.) 117–161 (Univ. Press of Colorado, 2000).

    29.
    Brown, R. D. & Mote, P. W. The response of Northern Hemisphere snow cover to a changing climate. J. Clim. 22, 2124–2145 (2009).
    Article  Google Scholar 

    30.
    Korpela, K. et al. Nonlinear effects of climate on boreal rodent dynamics: mild winters do not negate high-amplitude cycles. Glob. Change Biol. 19, 697–710 (2013).
    Article  Google Scholar 

    31.
    Kielland, K., Olson, K. & Euskirchen, E. Demography of snowshoe hares in relation to regional climate variability during a 10-year population cycle in interior Alaska. Can. J. Res. 40, 1265–1272 (2010).
    Article  Google Scholar 

    32.
    Humphries, M. M., Studd, E. K., Menzies, A. K. & Boutin, S. To everything there is a season: summer-to-winter food webs and the functional traits of keystone species. Integr. Comp. Biol. 57, 961–976 (2017).
    Article  Google Scholar 

    33.
    Peers, M. J. L. et al. Prey availability and ambient temperature influence carrion persistence in the boreal forest. J. Anim. Ecol. https://doi.org/10.1111/1365-2656.13275 (2020).

    34.
    Krebs, C. J., Boonstra, R. & Boutin, S. Using experimentation to understand the 10-year snowshoe hare cycle in the boreal forest of North America. J. Anim. Ecol. 87, 87–100 (2018).
    Article  Google Scholar 

    35.
    Krebs, C. J. et al. The Community Ecological Monitoring Program Annual Data Report (Univ. of British Columbia, 2018).

    36.
    Zeileis, A., Grothendieck, G., Ryan, J., Ulrich, J. & Andrews, F. zoo: S3 infrastructure for regular and irregular time series (Z’s ordered observations). R package version 1.8-8 (2019).

    37.
    Fieberg, J. & Delgiudice, G. D. What time is it? Choice of time origin and scale in extended proportional hazards models. Ecology 90, 1687–1697 (2009).
    Article  Google Scholar 

    38.
    Murray, D. L. et al. Death from anthropogenic causes is partially compensatory in recovering wolf populations. Biol. Conserv. 143, 2514–2524 (2010).
    Article  Google Scholar 

    39.
    Murray, D. & Bastille-Rousseau, G. in Population Ecology in Practice (eds Murray, D. L. & Sandercock, B.) 123–156 (Wiley-Blackwell, 2020).

    40.
    Burnham, K. & Anderson, D. Model Selection and Multimodel Inference (Springer, 2002).

    41.
    Graham, M. H. Confronting multicollinearity in ecological multiple regression. Ecology 84, 2809–2815 (2003).
    Article  Google Scholar 

    42.
    McLellan, B. N. Some mechanisms underlying variation in vital rates of grizzly bears on a multiple use landscape. J. Wildl. Manag. 79, 749–765 (2015).
    Article  Google Scholar 

    43.
    Lunn, M. & McNeil, D. Applying Cox regression to competing risks. Biometrics 51, 524–532 (1995).
    CAS  Article  Google Scholar 

    44.
    Bastille-Rousseau, G. et al. Phase-dependent climate–predator interactions explain three decades of variation in neonatal caribou survival. J. Anim. Ecol. 85, 445–456 (2016).
    Article  Google Scholar 

    45.
    Murray, D. L., Bastille-Rousseau, G., Hornseth, M., Row, J. & Thornton, D. H. in Population Ecology in Practice (eds Murray, D. L. & Sandercock, B.) 17–46 (Wiley-Blackwell, 2020).

    46.
    Hodges, K. E., Krebs, C. J. & Sinclair, A. R. E. Snowshoe hare demography during a cyclic population low. J. Anim. Ecol. 68, 581–594 (1999).
    Article  Google Scholar 

    47.
    Boutin, S., Gilbert, B. S., Krebs, C. J., Sinclair, A. R. E. & Smith, J. N. M. The role of dispersal in the population dynamics of snowshoe hares. Can. J. Zool. 63, 106–115 (1984).
    Article  Google Scholar 

    48.
    Gillis, E. A. Survival of juvenile hares during a cyclic population increase. Can. J. Zool. 76, 1949–1956 (1998).
    Article  Google Scholar 

    49.
    Graf, P. M., Wilson, R. P., Qasem, L., Hackländer, K. & Rosell, F. The use of acceleration to code for animal behaviours; a case study in free-ranging Eurasian beavers Castor fiber. PLoS ONE 10, 1–17 (2015).
    Google Scholar  More