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    Effects of water stress on spectral reflectance of bermudagrass

    Figure 1 shows the reflectance spectra collected over turfgrass at three different levels of water stress, specifically turfgrass at 16 days without watering, the intermediate situation at 7 days and at the end of the trial with the saturated cores (0 days without water), which serves as control. The differences across the curves are well evident. The major difference is the increase of reflectance at all wavelengths at 16 days without watering, where LRWC was at about 18% (Fig. 2), with respect to the other two spectral reflectance curves. It is so evident from the three different curves that in the Near-infrared (NIR 750–1,300 nm) and Short-wavelength infrared (SWIR 1,300–2,500 nm) four major absorption troughs are present. These strong reflectance troughs, located approximately in the NIR at 970 and 1,175, in the SWIR at 1,450 and 1,950 nm, are due to the absorption by water11. The troughs around 1,450 and 1,950 nm are less accentuated in the turf with high degree of desiccation (16 days without watering). Also González-Fernández et al.47 recommend calculating the band area for 1,450 nm and for 1,950 nm because of its link to equivalent water thickness, thus to estimate vine water status. Rallo et al.48 observed typical spectral responses in the SWIR region, where at leaf scale, absorbance bands near 1,450 and 1,900 nm could be related to the leaf water content of an olive grove.
    Figure 2

    Decline in volumetric soil water content (SWC) (%) and leaf relative water content (LRWC) (%) after watering ceased. Each point is the mean of six replications. Bars indicate one standard deviation error.

    Full size image

    However, in the regions of 1,350–1,480, 1,800–2,000 and 2,350–2,500 nm measurements of spectral reflectance of crop leaves are not possible in nature, also with fully sun-light conditions, because of the strong atmospheric absorption of light due to water vapor14,32,49 and are generally not exploited for landscape level studies. Consequently, to correctly measure these regions of wavelengths, a portable spectroradiometer system with an artificial light source must be chosen49. In fact, in our experiment an artificial light source was used, thus 1,430 and 1,950 can be considered key wavelengths for the measurements under artificial light source.
    In the NIR spectral region there is a more commonly exploited troughs around 970 nm and in the region of 1,150–1,260, which are the most studied spectral ranges for estimation of vegetation water content14. It was interesting to note that the troughs of reflectance spectra underwent a gradual reduction in depth as the turfgrass desiccation increased, up to almost disappear in most cases, as showed in the 16 days without water curve. Some of the wavelengths associated with these troughs are, in fact, exploited by the spectral indices used in this study (see Table 1).
    Figure 2 shows SWC and LRWC values, averaged over each set of six replicates with one standard deviation error bars, plotted with respect to the number of days without watering. Volumetric SWC declined as the days without watering increased. Starting from a value of 43.78% for the control cores with 0 days without watering, it decreased reaching a much lower value of 5.19% after two weeks without watering. Similarly, also LRWC declined as the number of days without watering increased. LRWC rate of decline was smaller than SWC as the days without watering were 4 or less (LRWC equal to 98.7%, 94.3% and 94.2% for 0, 1 and 4 days without watering, respectively). Then LRWC steeply decreased as the number of days without watering increased above 4. Observing the two parameters it is interesting to note that, with the exception of data collected in cores at 4 days without water, the trend of SWC and LRWC is similar (Fig. 2). In fact, from 1 to 4 days without water, turfgrass leaves try to preserve more water even if the soil water content decreases.
    Figure 3 plots bar graphs of the selected indices in Table 1, where the indices are averaged over each set of six replicates of turfgrass at same water stress condition. One standard deviation error bars are also plotted. As is evident, all selected indices correlate with water stress level (Fig. 3).
    Figure 3

    Bar graphs of spectral indices averaged over each set of six replicates at same water stress condition, with one standard deviation error bar. (a) NDVI, (b) WI, (c) NDWI2130, (d) NDWI1240, (e) WI/NDVI.

    Full size image

    A quantitative analysis of these correlations, and specifically with respect to SWC, LRWC and SM, is reported in Table 2, which reports the Pearson product-moment correlation coefficients evaluated among the various parameters and indexes studied in this work.
    Table 2 Pearson product-moment correlation coefficients (r) among volumetric soil water content (%) (SWC) measured using a time domain reflectometry (TDR); leaf relative water content (%) (LRWC); soil moisture (%) (SM) and vegetation indices selected for the study.
    Full size table

    Volumetric soil water content (SWC)
    As expected, SWC was found to be highly correlated with SM (r = 0.98, p  More

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    Response of vertebrate scavengers to power line and road rights-of-way and its implications for bird fatality estimates

    1.
    Dulac, J. Global land transport infrastructure requirements: estimating road and railway infrastructure capacity and costs to 2050. (International Energy Agency, Paris, France, 2013).
    2.
    D’Amico, M. et al. Bird on the wire: landscape planning considering costs and benefits for bird populations coexisting with power lines. AMBIO A J. Hum. Environ. 47, 650–656 (2018).
    Google Scholar 

    3.
    Morelli, F., Beim, M., Jerzak, L., Jones, D. & Tryjanowski, P. Can roads, railways and related structures have positive effects on birds? A review. Transp. Res. Part D Transp. Environ. 30, 21–31 (2014).
    Google Scholar 

    4.
    Laurance, W. F. et al. Reducing the global environmental impacts of rapid infrastructure expansion. Curr. Biol. 25, R259–R262 (2015).
    CAS  PubMed  Google Scholar 

    5.
    Ascensão, F. et al. Beware that the lack of wildlife mortality records can mask a serious impact of linear infrastructures. Glob. Ecol. Conserv. 19, e00661 (2019).
    Google Scholar 

    6.
    Bernardino, J. et al. Bird collisions with power lines: state of the art and priority areas for research. Biol. Conserv. 222, 1–13 (2018).
    Google Scholar 

    7.
    Loss, S. R., Will, T. & Marra, P. P. Estimation of bird-vehicle collision mortality on U.S. roads. J. Wildl. Manag. 78, 763–771 (2014).
    Google Scholar 

    8.
    Collinson, W. J., Parker, D. M., Bernard, R. T. F., Reilly, B. K. & Davies-Mostert, H. T. Wildlife road traffic accidents: a standardized protocol for counting flattened fauna. Ecol. Evol. 4, 3060–3071 (2014).
    PubMed  PubMed Central  Google Scholar 

    9.
    Barrientos, R., Alonso, J. C., Ponce, C. & Palacín, C. Meta-analysis of the effectiveness of marked wire in reducing avian collisions with power lines. Conserv. Biol. 25, 893–903 (2011).
    PubMed  Google Scholar 

    10.
    Ponce, C., Alonso, J. C., Argandoña, G., García Fernández, A. & Carrasco, M. Carcass removal by scavengers and search accuracy affect bird mortality estimates at power lines. Anim. Conserv. 13, 603–612 (2010).
    Google Scholar 

    11.
    Borner, L. et al. Bird collision with power lines: estimating carcass persistence and detection associated with ground search surveys. Ecosphere 8, e01966 (2017).
    Google Scholar 

    12.
    Guinard, É, Julliard, R. & Barbraud, C. Motorways and bird traffic casualties: carcasses surveys and scavenging bias. Biol. Conserv. 147, 40–51 (2012).
    Google Scholar 

    13.
    Santos, S. M., Carvalho, F. & Mira, A. How long do the dead survive on the road? Carcass persistence probability and implications for road-kill monitoring surveys. PLoS ONE 6, e25383 (2011).
    ADS  CAS  PubMed  PubMed Central  Google Scholar 

    14.
    Barrientos, R. et al. A review of searcher efficiency and carcass persistence in infrastructure-driven mortality assessment studies. Biol. Conserv. 222, 146–153 (2018).
    Google Scholar 

    15.
    Huso, M., Dalthorp, D., Miller, T. J. & Bruns, D. Wind energy development: methods to assess bird and bat fatality rates post-construction. Hum. Wildl. Interact. 10, 62–70 (2016).
    Google Scholar 

    16.
    Smallwood, K. S. Estimating wind turbine-caused bird mortality. J. Wildl. Manag. 71, 2781–2791 (2007).
    Google Scholar 

    17.
    Costantini, D., Gustin, M., Ferrarini, A. & Dell’Omo, G. Estimates of avian collision with power lines and carcass disappearance across differing environments. Anim. Conserv. 20, 173–181 (2017).
    Google Scholar 

    18.
    Schutgens, M., Shaw, J. M. & Ryan, P. G. Estimating scavenger and search bias for collision fatality surveys of large birds on power lines in the Karoo, South Africa. Ostrich 85, 39–45 (2014).
    Google Scholar 

    19.
    Loss, S. R., Will, T. & Marra, P. P. Direct human-caused mortality of birds: improving quantification of magnitude and assessment of population impact. Front. Ecol. Environ. 10, 357–364 (2012).
    Google Scholar 

    20.
    Smallwood, K. S., Bell, D. A., Snyder, S. A. & DiDonato, J. E. Novel scavenger removal trials increase wind turbine—caused avian fatality estimates. J. Wildl. Manag. 74, 1089–1096 (2010).
    Google Scholar 

    21.
    Farfán, M. A., Duarte, J., Fa, J. E., Real, R. & Vargas, J. M. Testing for errors in estimating bird mortality rates at wind farms and power lines. Bird Conserv. Int. 27, 431–439 (2017).
    Google Scholar 

    22.
    Flint, P. L., Lance, E. W., Sowl, K. M. & Donnelly, T. F. Estimating carcass persistence and scavenging bias in a human-influenced landscape in western Alaska. J. F. Ornithol. 81, 206–214 (2010).
    Google Scholar 

    23.
    Paula, J. et al. Camera-trapping as a methodology to assess the persistence of wildlife carcasses resulting from collisions with human-made structures. Wildl. Res. 41, 717–725 (2015).
    Google Scholar 

    24.
    Shaw, J. M., van der Merwe, R., van der Merwe, E. & Ryan, P. G. Winter scavenging rates under power lines in the Karoo, South Africa. Afr. J. Wildl. Res. 45, 122–126 (2015).
    Google Scholar 

    25.
    Stevens, B. S., Reese, K. P. & Connelly, J. W. Survival and detectability bias of avian fence collision surveys in sagebrush steppe. J. Wildl. Manag. 75, 437–449 (2011).
    Google Scholar 

    26.
    Turner, K. L., Abernethy, E. F., Conner, L. M., Rhodes, O. E. Jr. & Beasley, J. C. Abiotic and biotic factors modulate carrion fate and vertebrate scavenging communities. Ecology 98, 2413–2424 (2017).
    PubMed  Google Scholar 

    27.
    Riding, C. S. & Loss, S. R. Factors influencing experimental estimation of scavenger removal and observer detection in bird-window collision surveys. Ecol. Appl. 28, 2119–2129 (2018).
    PubMed  Google Scholar 

    28.
    Rosene, W. & Lay, D. W. Disappearance and visibility of quail remains. J. Wildl. Manag. 27, 139–142 (1963).
    Google Scholar 

    29.
    Lambertucci, S. A., Speziale, K. L., Rogers, T. E. & Morales, J. M. How do roads affect the habitat use of an assemblage of scavenging raptors?. Biodivers. Conserv. 18, 2063–2074 (2009).
    Google Scholar 

    30.
    Donázar, J. A., Ceballos, O. & Cortes-Avizanda, A. Tourism in protected areas: disentangling road and traffic effects on intra-guild scavenging processes. Sci. Total Environ. 630, 600–608 (2018).
    ADS  PubMed  Google Scholar 

    31.
    Hill, J. E., DeVault, T. L., Beasley, J. C., Rhodes, O. E. & Belant, J. L. Roads do not increase carrion use by a vertebrate scavenging community. Sci. Rep. 8, 16331 (2018).
    ADS  PubMed  PubMed Central  Google Scholar 

    32.
    Huijbers, C. M. et al. Limited functional redundancy in vertebrate scavenger guilds fails to compensate for the loss of raptors from urbanized sandy beaches. Divers. Distrib. 21, 55–63 (2015).
    Google Scholar 

    33.
    Olson, Z. H., Beasley, J. C. & Rhodes, O. E. Jr. Carcass type affects local scavenger guilds more than habitat connectivity. PLoS ONE 11, e0147798 (2016).
    PubMed  PubMed Central  Google Scholar 

    34.
    Smith, J. B., Laatsch, L. J. & Beasley, J. C. Spatial complexity of carcass location influences vertebrate scavenger efficiency and species composition. Sci. Rep. 7, 10250 (2017).
    ADS  PubMed  PubMed Central  Google Scholar 

    35.
    DeVault, T. L., Rhodes Olin, E. & Shivik, J. A. Scavenging by vertebrates: behavioral, ecological, and evolutionary perspectives on an important energy transfer pathway in terrestrial ecosystems. Oikos 102, 225–234 (2003).
    Google Scholar 

    36.
    Joseph, G. S., Seymour, C. L. & Foord, S. H. The effect of infrastructure on the invasion of a generalist predator: pied crows in southern Africa as a case-study. Biol. Conserv. 205, 11–15 (2017).
    Google Scholar 

    37.
    Dean, W. R. J., Milton, S. J. & Anderson, M. D. Use of road kills and roadside vegetation by Pied and Cape Crows in semi-arid South Africa. Ostrich 77, 102–104 (2006).
    Google Scholar 

    38.
    Slater, F. M. An assessment of wildlife road casualties—the potential discrepancy between numbers counted and numbers killed. Web Ecol. 3, 33–42 (2002).
    Google Scholar 

    39.
    Knight, R. L. & Kawashima, J. Y. Responses of raven and red-tailed hawk populations to linear right-of-ways. J. Wildl. Manag. 57, 266–271 (1993).
    Google Scholar 

    40.
    Meunier, F. D., Verheyden, C. & Jouventin, P. Use of roadsides by diurnal raptors in agricultural landscapes. Biol. Conserv. 92, 291–298 (2000).
    Google Scholar 

    41.
    Andersen, G. E., Johnson, C. N., Barmuta, L. A. & Jones, M. E. Use of anthropogenic linear features by two medium-sized carnivores in reserved and agricultural landscapes. Sci. Rep. 7, 11624 (2017).
    ADS  PubMed  PubMed Central  Google Scholar 

    42.
    Frey, S. N. & Conover, M. R. Habitat use by meso-predators in a corridor environment. J. Wildl. Manag. 70, 1111–1118 (2006).
    Google Scholar 

    43.
    Raiter, K. G., Hobbs, R. J., Possingham, H. P., Valentine, L. E. & Prober, S. M. Vehicle tracks are predator highways in intact landscapes. Biol. Conserv. 228, 281–290 (2018).
    Google Scholar 

    44.
    Silva, C., Simões, M. P., Mira, A. & Santos, S. M. Factors influencing predator roadkills: the availability of prey in road verges. J. Environ. Manag. 247, 644–650 (2019).
    Google Scholar 

    45.
    Bautista, L. M. et al. Effect of weekend road traffic on the use of space by raptors. Conserv. Biol. 18, 726–732 (2004).
    Google Scholar 

    46.
    Benítez-López, A., Alkemade, R. & Verweij, P. A. The impacts of roads and other infrastructure on mammal and bird populations: a meta-analysis. Biol. Conserv. 143, 1307–1316 (2010).
    Google Scholar 

    47.
    Tyler, N. et al. Ultraviolet vision and avoidance of power lines in birds and mammals. Conserv. Biol. 28, 630–631 (2014).
    PubMed  PubMed Central  Google Scholar 

    48.
    IPMA. Boletins Climatológicos Mensais (Portugal Continental). Instituto Português do Mar e da Atmosfera, I. P. (IPMA, I. P.). https://www.ipma.pt/pt/publicacoes/ (2017).

    49.
    IPMA. Boletins Climatológicos Mensais (Portugal Continental). Instituto Português do Mar e da Atmosfera, I. P. (IPMA, I. P.). https://www.ipma.pt/pt/publicacoes/ (2018).

    50.
    E.P. Recenseamento de tráfego (2005)—distrito de Évora (Estradas de Portugal, S.A., 2005).

    51.
    R Development Core Team. R: a language and environment for statistical computing, version 3.6.1 (2019).

    52.
    Therneau, T. M. A Package for Survival Analysis in S. version 2.44-1.1 (2019).

    53.
    Bispo, R., Bernardino, J., Marques, T. A. & Pestana, D. Discrimination between parametric survival models for removal times of bird carcasses in scavenger removal trials at wind turbines sites BT. In Advances in Regression, Survival Analysis, Extreme Values, Markov Processes and Other Statistical Applications (eds LitadaSilva, J. et al.) 65–72 (Springer, Berlin, 2013).
    Google Scholar 

    54.
    Dalthorp, D. et al. GenEst statistical models—A generalized estimator of mortality. Techniques and Methods (2018). https://pubs.er.usgs.gov/publication/tm7A2. https://doi.org/10.3133/tm7A2.

    55.
    Gutierrez, R. G. Parametric frailty and shared frailty survival models. Stata J. 2, 22–44 (2002).
    Google Scholar 

    56.
    Kaplan, E. L. & Meier, P. Nonparametric estimation from incomplete observations. J. Am. Stat. Assoc. 53, 457–481 (1958).
    MathSciNet  MATH  Google Scholar 

    57.
    Linz, G. M., Bergman, D. L. & Bleier, W. J. Estimating survival of song bird carcasses in crops and woodlots. Prairie Nat. 29, 7–13 (1997).
    Google Scholar 

    58.
    Lourenço, P. M. Rice field use by raptors in two Portuguese wetlands. Airo 19, 13–18 (2009).
    Google Scholar 

    59.
    Simmons, R. E. Harriers of the World: Their Behaviour and Ecology (Oxford University Press, Oxford, 2000).
    Google Scholar 

    60.
    DeGregorio, B. A., Weatherhead, P. J. & Sperry, J. H. Power lines, roads, and avian nest survival: effects on predator identity and predation intensity. Ecol. Evol. 4, 1589–1600 (2014).
    PubMed  PubMed Central  Google Scholar 

    61.
    Beasley, J. C., Olson, Z. H. & DeVault, T. L. Ecological role of vertebrate scavengers. In Carrion Ecology, Evolution and Their Applications (eds Benbow, M. E. et al.) 107–127 (CRC Press, Boca Raton, 2015).
    Google Scholar 

    62.
    Peisley, R. K., Saunders, M. E., Robinson, W. A. & Luck, G. W. The role of avian scavengers in the breakdown of carcasses in pastoral landscapes. EMU Austral. Ornithol. 117, 68–77 (2017).
    Google Scholar 

    63.
    DeVault, T. L. & Rhodes, O. E. Identification of vertebrate scavengers of small mammal carcasses in a forested landscape. Acta Theriol. (Warsz) 47, 185–192 (2002).
    Google Scholar 

    64.
    Hiraldo, F., Blanco, J. C. & Bustamante, J. Unspecialized exploitation of small carcasses by birds. Bird Study 38, 200–207 (1991).
    Google Scholar 

    65.
    Hager, S. B., Cosentino, B. J. & McKay, K. J. Scavenging affects persistence of avian carcasses resulting from window collisions in an urban landscape. J. F. Ornithol. 83, 203–211 (2012).
    Google Scholar 

    66.
    Prosser, P., Nattrass, C. & Prosser, C. Rate of removal of bird carcasses in arable farmland by predators and scavengers. Ecotoxicol. Environ. Saf. 71, 601–608 (2008).
    CAS  PubMed  Google Scholar 

    67.
    DeVault, T. L., Olson, Z. H., Beasley, J. C. & Rhodes, O. E. Mesopredators dominate competition for carrion in an agricultural landscape. Basic Appl. Ecol. 12, 268–274 (2011).
    Google Scholar 

    68.
    Ratton, P., Secco, H. & da Rosa, C. A. Carcass permanency time and its implications to the roadkill data. Eur. J. Wildl. Res. 60, 543–546 (2014).
    Google Scholar 

    69.
    Santos, R. A. L. et al. Carcass persistence and detectability: reducing the uncertainty surrounding wildlife-vehicle collision surveys. PLoS ONE 11, e0165608 (2016).
    PubMed  PubMed Central  Google Scholar 

    70.
    Linz, G. M., Davis, J. E., Engeman, R. M., Otis, D. L. & Avery, M. L. Estimating survival of bird carcasses in Cattail Marshes. Wildl. Soc. Bull. 19, 195–199 (1991).
    Google Scholar  More

  • in

    A meta-analysis of country-level studies on environmental change and migration

    1.
    Kelley, C. P., Mohtadi, S., Cane, M. A., Seager, R. & Kushnir, Y. Climate change in the Fertile Crescent and implications of the recent Syrian drought. Proc. Natl Acad. Sci. USA 112, 3241–3246 (2015).
    CAS  Google Scholar 
    2.
    Gleick, P. H. Water, drought, climate change, and conflict in Syria. Weather Clim. Soc. 6, 331–340 (2014).
    Google Scholar 

    3.
    De Châtel, F. The role of drought and climate change in the Syrian uprising: untangling the triggers of the revolution. Middle East. Stud. 50, 521–535 (2014).
    Google Scholar 

    4.
    Selby, J., Dahi, O. S., Fröhlich, C. & Hulme, M. Climate change and the Syrian civil war revisited. Polit. Geogr. 60, 232–244 (2017).
    Google Scholar 

    5.
    Myers, N. Environmental refugees: a growing phenomenon of the 21st century. Phil. Trans. R. Soc. Lond. B 357, 609–613 (2002).
    Google Scholar 

    6.
    Renaud, F., Bogardi, J. J., Dun, O. & Warner, K. Control, Adapt or Flee: How to Face Environmental Migration? InterSecTions, Publication Series of United Nations University, ENS Vol. 5 (United Nations University Institute for Environment and Human Security, 2007).

    7.
    Stern, N. The Economics of Climate Change: The Stern Review (Cambridge Univ. Press, 2006).

    8.
    Biermann, F. & Boas, L. Preparing for a warmer world: towards a global governance system to protect climate refugees. Glob. Environ. Polit. https://doi.org/10.1162/glep.2010.10.1.60 (2010).

    9.
    Cattaneo, C. et al. Human migration in the era of climate change. Rev. Environ. Econ. Policy 13, 189–206 (2019).
    Google Scholar 

    10.
    Berlemann, M. & Steinhardt, M. F. Climate change, natural disasters, and migration—a survey of the empirical evidence. CESifo Econ. Stud. 63, 353–385 (2017).
    Google Scholar 

    11.
    Hunter, L. M., Luna, J. K. & Norton, R. M. Environmental dimensions of migration. Annu. Rev. Sociol. 41, 377–397 (2015).
    Google Scholar 

    12.
    Piguet, E. Linking climate change, environmental degradation, and migration: a methodological overview. Wiley Interdiscip. Rev. Clim. Change 1, 517–524 (2010).
    Google Scholar 

    13.
    Borderon, M. et al. Migration influenced by environmental change in Africa: a systematic review of empirical evidence. Demogr. Res. 41, 491–544 (2019).
    Google Scholar 

    14.
    Black, R., Stephen, R., Bennett, G., Thomas, S. M. & Beddington, J. R. Migration as adaptation. Nature 478, 447–449 (2011).
    CAS  Google Scholar 

    15.
    Barrios, S., Bertinelli, L. & Strobl, E. Climatic change and rural-urban migration: the case of sub-Saharan Africa. J. Urban Econ. 60, 357–371 (2006).
    Google Scholar 

    16.
    Naudé, W. Natural disasters and international migration from sub-Saharan Africa. Migrat. Lett. 6, 165–176 (2009).
    Google Scholar 

    17.
    Ruyssen, I. & Rayp, G. Determinants of intraregional migration in sub-Saharan Africa 1980-2000. J. Dev. Stud. 50, 426–443 (2014).
    Google Scholar 

    18.
    Backhaus, A., Martinez-Zarzoso, I. & Muris, C. Do climate variations explain bilateral migration? A gravity model analysis. IZA J. Migrat. 4, 3 (2015).
    Google Scholar 

    19.
    Beine, M. & Parsons, C. Climatic factors as determinants of international migration. Scand. J. Econ. 117, 723–767 (2015).
    Google Scholar 

    20.
    Coniglio, N. D. & Pesce, G. Climate variability and international migration: an empirical analysis. Environ. Dev. Econ. 20, 434–468 (2015).
    Google Scholar 

    21.
    Ghimire, R., Ferreira, S. & Dorfman, J. H. Flood-induced displacement and civil conflict. World Dev. 66, 614–628 (2015).
    Google Scholar 

    22.
    Cai, R., Feng, S., Oppenheimer, M. & Pytlikova, M. Climate variability and international migration: the importance of the agricultural linkage. J. Environ. Econ. Manage. 79, 135–151 (2016).
    Google Scholar 

    23.
    Cattaneo, C. & Peri, G. The migration response to increasing temperatures. J. Dev. Econ. 122, 127–146 (2016).
    Google Scholar 

    24.
    Maurel, M. & Tuccio, M. Climate instability, urbanisation and international migration. J. Dev. Stud. 52, 735–752 (2016).
    Google Scholar 

    25.
    Beine, M. & Parsons, C. R. Climatic factors as determinants of international migration: Redux. CESifo Econ. Stud. 63, 386–402 (2017).
    Google Scholar 

    26.
    Cattaneo, C. & Bosetti, V. Climate-induced international migration and conflicts. CESifo Econ. Stud. 63, 500–528 (2017).
    Google Scholar 

    27.
    Reuveny, R. & Moore, W. H. Does environmental degradation influence migration? Emigration to developed countries in the late 1980s and 1990s. Soc. Sci. Q. 90, 461–479 (2009).
    Google Scholar 

    28.
    Damette, O. & Gittard, M. Changement climatique et migrations: les transferts de fonds des migrants comme amortisseurs? Mondes Dev. 179, 85 (2017).
    Google Scholar 

    29.
    Gröschl, J. & Steinwachs, T. Do natural hazards cause international migration? CESifo Econ. Stud. 63, 445–480 (2017).
    Google Scholar 

    30.
    Henderson, J. V., Storeygard, A. & Deichmann, U. Has climate change driven urbanization in Africa? J. Dev. Econ. 124, 60–82 (2017).
    Google Scholar 

    31.
    Mahajan, P. & Yang, D. Taken by Storm: Hurricanes, Migrant Networks, and U.S. Immigration Working Paper 23756 (NBER, 2017); https://doi.org/10.3386/w23756

    32.
    Marchiori, L., Maystadt, J.-F. & Schumacher, I. Is environmentally induced income variability a driver of human migration? Migr. Dev. 6, 33–59 (2017).
    Google Scholar 

    33.
    Missirian, A. & Schlenker, W. Asylum applications respond to temperature fluctuations. Science 358, 1610–1614 (2017).
    CAS  Google Scholar 

    34.
    Spencer, N. & Urquhart, M.-A. Hurricane strikes and migration: evidence from storms in central America and the caribbean. Weather Clim. Soc. 10, 569–577 (2018).
    Google Scholar 

    35.
    Peri, G. & Sasahara, A. The Impact of Global Warming in Rural-Urban Migrations: Evidence from Global Big Data Working Paper 25728 (NBER, 2019).

    36.
    Wesselbaum, D. & Aburn, A. Gone with the wind: international migration. Glob. Planet. Change 178, 96–109 (2019).
    Google Scholar 

    37.
    Naudé, W. The determinants of migration from sub-Saharan African countries. J. Afr. Econ. 19, 330–356 (2010).
    Google Scholar 

    38.
    Alexeev, A., Good, D. H. & Reuveny, R. Weather-Related Disasters and International Migration (Indiana Univ., 2011); http://www.umdcipe.org/conferences/Maastricht/conf_papers/Papers/Effects_of_Natural_Disasters.pdf

    39.
    Bettin, G. & Nicolli, F. Does Climate Change Foster Emigration from Less Developed Countries? Evidence from Bilateral Data Working Paper 10 (Univ. degli Stud. di Ferrara, 2012).

    40.
    Brückner, M. Economic growth, size of the agricultural sector, and urbanization in Africa. J. Urban Econ. 71, 26–36 (2012).
    Google Scholar 

    41.
    Gröschl, J. Climate change and the relocation of population. In Beiträge zur Jahrestagung des Vereins für Soc. 2012 Neue Wege und Herausforderungen für den Arbeitsmarkt des 21. Jahrhunderts – Sess. Migr. II, No. D03-V1, ZBW (Verein für Socialpolitik (German Economic Association), 2012).

    42.
    Hanson, G. H. & McIntosh, C. Birth rates and border crossings: Latin American migration to the US, Canada, Spain and the UK. Econ. J. 122, 707–726 (2012).
    Google Scholar 

    43.
    Marchiori, L., Maystadt, J. F. & Schumacher, I. The impact of weather anomalies on migration in sub-Saharan Africa. J. Environ. Econ. Manage. 63, 355–374 (2012).
    Google Scholar 

    44.
    Drabo, A. & Mbaye, L. M. Natural disasters, migration and education: an empirical analysis in developing countries. Environ. Dev. Econ. 20, 767–796 (2015).
    Google Scholar 

    45.
    Black, R. et al. The effect of environmental change on human migration. Glob. Environ. Change 21, 3–11 (2011).
    Google Scholar 

    46.
    Boas, I. et al. Climate migration myths. Nat. Clim. Change 9, 901–903 (2019).
    Google Scholar 

    47.
    Black, R. et al. Foresight: Migration and Global Environmental Change. Future Challenges and Opportunities (The Government Office for Science, 2011).

    48.
    Veroniki, A. A. et al. Methods to estimate the between-study variance and its uncertainty in meta-analysis. Res. Synth. Methods 7, 55–79 (2016).
    Google Scholar 

    49.
    Crespo Cuaresma, J., Fidrmuc, J. & Hake, M. Demand and supply drivers of foreign currency loans in CEECs: a meta-analysis. Econ. Syst. 38, 26–42 (2014).
    Google Scholar 

    50.
    Hsiang, S. M., Burke, M. & Miguel, E. Quantifying the influence of climate on human conflict. Science 341, 1235367 (2013).
    Google Scholar 

    51.
    Nawrotzki, R. J. & Bakhtsiyarava, M. International climate migration: evidence for the climate inhibitor mechanism and the agricultural pathway. Popul. Space Place 23, 1–16 (2016).
    Google Scholar 

    52.
    Beine, M. & Jeusette, L. A Meta-Analysis of the Literature on Climate Change and Migration CREA Discussion Paper Series (Center for Research in Economic Analysis, Univ. Luxembourg, 2018).

    53.
    Auffhammer, M., Hsiangy, S. M., Schlenker, W. & Sobelz, A. Using weather data and climate model output in economic analyses of climate change. Rev. Environ. Econ. Policy 7, 181–198 (2013).
    Google Scholar 

    54.
    Hsiang, S. Climate econometrics. Annu. Rev. Resour. Econ. 8, 43–75 (2016).
    Google Scholar 

    55.
    Hugo, G. Future demographic change and its interactions with migration and climate change. Glob. Environ. Change 21(Suppl.), S21–S33 (2011).

    56.
    Martin, P. L. & Taylor, J. E. in Development Strategy, Employment and Migration: Insights from Models 43–62 (OECD, 1996).

    57.
    Feng, S., Krueger, A. B. & Oppenheimer, M. Linkages among climate change, crop yields and Mexico–US cross-border migration. Proc. Natl Acad. Sci. USA 107, 14257–14262 (2010).
    CAS  Google Scholar 

    58.
    Mendelsohn, R. & Dinar, A. Climate change, agriculture, and developing countries: does adaptation matter? World Bank Res. Obs. 14, 277–293 (1999).
    Google Scholar 

    59.
    Schlenker, W. & Roberts, M. J. Nonlinear temperature effects indicate severe damages to U.S. crop yields under climate change. Proc. Natl Acad. Sci. USA 106, 15594–15598 (2009).
    CAS  Google Scholar 

    60.
    Schlenker, W. & Lobell, D. B. Robust negative impacts of climate change on African agriculture. Environ. Res. Lett. 5, 014010 (2010).
    Google Scholar 

    61.
    Abel, G. J., Brottrager, M., Crespo Cuaresma, J. & Muttarak, R. Climate, conflict and forced migration. Glob. Environ. Change 52, 239–249 (2019).
    Google Scholar 

    62.
    Burke, M., Hsiang, S. M. & Miguel, E. Climate and conflict. Annu. Rev. Econ. 7, 577–617 (2015).
    Google Scholar 

    63.
    Barnett, J. & Adger, W. N. Climate change, human security and violent conflict. Polit. Geogr. 26, 639–655 (2007).
    Google Scholar 

    64.
    Schlenker, W., Hanemann, W. M. & Fisher, A. C. The impact of global warming on U.S. agriculture: An econometric analysis of optimal growing conditions. Rev. Econ. Stat. https://doi.org/10.1162/003465306775565684 (2006).

    65.
    Dimitrova, A. & Bora, J. K. Monsoon weather and early childhood health in India. PLoS ONE 15, e0231479 (2020).
    CAS  Google Scholar 

    66.
    Muttarak, R. & Dimitrova, A. Climate change and seasonal floods: potential long-term nutritional consequences for children in Kerala, India. BMJ Glob. Health 4, e001215 (2019).
    Google Scholar 

    67.
    Deschênes, O. & Greenstone, M. Climate change, mortality, and adaptation: evidence from annual fluctuations in weather in the US. Am. Econ. J. Appl. Econ. 3, 152–185 (2011).
    Google Scholar 

    68.
    Deschenes, O. Temperature, human health, and adaptation: a review of the empirical literature. Energ. Econ. https://doi.org/10.1016/j.eneco.2013.10.013 (2014).

    69.
    Burgess, R., Deschênes, O., Donaldson, D. & Greenstone, M. Weather, Climate Change and Death in India Working Paper (LSE, 2017).

    70.
    Zivin, J. G. & Neidell, M. Environment, health, and human capital. J. Econ. Lit. 51, 689–730 (2013).
    Google Scholar 

    71.
    Gemenne, F. Why the numbers don’t add up: a review of estimates and predictions of people displaced by environmental changes. Glob. Environ. Change 21, 41–49 (2011).
    Google Scholar 

    72.
    Findley, S. E. Does drought increase migration? A study of migration from rural Mali during the 1983–1985 drought. Int. Migr. Rev. 28, 539 (1994).
    CAS  Google Scholar 

    73.
    Black, R., Arnell, N. W., Adger, W. N., Thomas, D. & Geddes, A. Migration, immobility and displacement outcomes following extreme events. Environ. Sci. Policy 27, S32–S43 (2013).
    Google Scholar 

    74.
    Bohra-Mishra, P., Oppenheimer, M., Cai, R., Feng, S. & Licker, R. Climate variability and migration in the Philippines. Popul. Environ. 38, 286–308 (2017).
    Google Scholar 

    75.
    Bardsley, D. K. & Hugo, G. J. Migration and climate change: examining thresholds of change to guide effective adaptation decision-making. Popul. Environ. 32, 238–262 (2010).
    Google Scholar 

    76.
    Mertz, O., Mbow, C., Reenberg, A. & Diouf, A. Farmers’ perceptions of climate change and agricultural adaptation strategies in rural Sahel. Environ. Manage. 43, 804–816 (2009).
    Google Scholar 

    77.
    Garcia, A. J., Pindolia, D. K., Lopiano, K. K. & Tatem, A. J. Modeling internal migration flows in sub-Saharan Africa using census microdata. Migr. Stud. 3, 89–110 (2015).
    Google Scholar 

    78.
    Naudé, W. Conflict, Disasters and No Jobs: Reasons for International Migration from sub-Saharan Africa. WIDER Research Paper 85 (United Nations Univ., 2008).

    79.
    Dell, M., Jones, B. F. & Olken, B. A. What do we learn from the weather? The new climate-economy literature. J. Econ. Lit. 52, 740–798 (2014).
    Google Scholar 

    80.
    Gemenne, F. & Blocher, J. How can migration serve adaptation to climate change? Challenges to fleshing out a policy ideal. Geogr. J. 183, 336–347 (2017).
    Google Scholar 

    81.
    Zickgraf, C. Keeping people in place: political factors of (im)mobility and climate change. Soc. Sci. 8, 1–17 (2019).
    Google Scholar 

    82.
    Ayeb-Karlsson, S. et al. I will not go, I cannot go: cultural and social limitations of disaster preparedness in Asia, Africa, and Oceania. Disasters 43, 752–770 (2019).
    Google Scholar 

    83.
    Oakes, R. Culture, climate change and mobility decisions in pacific small island developing states. Popul. Environ. 40, 480–503 (2019).
    Google Scholar 

    84.
    Gharad, B., Chowdhury, S. & Mobarak, A. M. Underinvestment in a profitable technology: the case of seasonal migration in Bangladesh. Econometrica 82, 1671–1748 (2014).
    Google Scholar 

    85.
    Kniveton, D., Black, R. & Schmidt-Verkerk, K. Migration and climate change: towards an integrated assessment of sensitivity. Environ. Plan. A 43, 431–450 (2011).
    Google Scholar 

    86.
    Hornbeck, R. The enduring impact of the American Dust Bowl: Short- and long-run adjustments to environmental catastrophe. Am. Econ. Rev. 102, 1477–1507 (2012).
    Google Scholar 

    87.
    Libecap, G. D. & Steckel, R. H. in The Economics of Climate Change: Adaptations Past and Present (eds Libecap, G. D. & Steckel, R. H.) 1–22 (Univ. Chicago Press, 2011).

    88.
    Hsiang, S. M. & Narita, D. Adaptation to cyclone risk: evidence from the global cross-section. Clim. Change Econ. 03, 1250011 (2012).
    Google Scholar 

    89.
    Borenstein, M., Hedges, L. V., Higgins, J. P. T. & Rothstein, H. R. A basic introduction to fixed-effect and random-effects models for meta-analysis. Res. Synth. Methods 1, 97–111 (2010).
    Google Scholar 

    90.
    Hedges, Larry V. & Olkin, I. Statistical Method for Meta-Analysis (Academic Press, 1998).

    91.
    Lipsey, M. W. & Wilson, D. B. Practical meta-analysis (SAGE Publications, 2001).

    92.
    Hedges, L. V. & Olkin, I. Vote-counting methods in research synthesis. Psychol. Bull. 88, 359–369 (1980).
    Google Scholar 

    93.
    Combs, J. G., Ketchen, D. J., Crook, T. R. & Roth, P. L. Assessing cumulative evidence within ‘macro’ research: why meta-analysis should be preferred over vote counting. J. Manage. Stud. 48, 178–197 (2011).
    Google Scholar 

    94.
    Beine, M., Bertoli, S. & Fernández-Huertas Moraga, J. A practitioners’ guide to gravity models of international migration. World Econ. 39, 496–512 (2016).
    Google Scholar 

    95.
    Angrist, J. D. & Pischke, J.-S. Mostly Harmless Econometrics (Princeton Univ. Press, 2009).

    96.
    Dell, M., Jones, B. F. & Olken, B. A. Temperature shocks and economic growth: evidence from the last half century. Am. Econ. J. Macroecon. https://doi.org/10.1257/mac.4.3.66 (2012).

    97.
    Bohra-Mishra, P., Oppenheimer, M. & Hsiang, S. M. Nonlinear permanent migration response to climatic variations but minimal response to disasters. Proc. Natl Acad. Sci. USA 111, 9780–9785 (2014).
    CAS  Google Scholar 

    98.
    World Development Indicators (The World Bank, 2019).

    99.
    Marshall, M. G. Major Episodes of Political Violence (MEPV) and Conflict Regions, 1946–2018 (Center for Systemic Peace, 2019).

    100.
    Sundberg, R. & Melander, E. Introducing the UCDP georeferenced event dataset. J. Peace Res. 50, 523–532 (2013).
    Google Scholar 

    101.
    Högbladh, S. UCDP GED Codebook Version 19.1 (Department of Peace and Conflict Research, Uppsala Univ., 2019).

    102.
    Harris, I., Jones, P. D., Osborn, T. J. & Lister, D. H. Updated high-resolution grids of monthly climatic observations—the CRU TS3.10 dataset. Int. J. Climatol. 34, 623–642 (2014).
    Google Scholar 

    103.
    EM-DAT: The Emergency Events Database (Centre for Research on the Epidemiology of Disasters, 2020); www.emdat.be

    104.
    Bell, M. et al. Internal migration data around the world: assessing contemporary practice. Popul. Space Place 21, 1–17 (2015).
    Google Scholar 

    105.
    Bell, M. & Charles-Edwards, E. Cross-National Comparisons of Internal Migration: An Update of Global Patterns and Trends Technical Paper 2013/1 (United Nations, 2013).

    106.
    Chindarkar, N. Gender and climate change-induced migration: proposing a framework for analysis. Environ. Res. Lett. 7, 025601 (2012).
    Google Scholar 

    107.
    Hoffmann, R., Dimitrova, A., Muttarak, R., Crespo Cuaresma, J. & Peisker, J. A meta-analysis of country level studies on environmental change and migration | replication data and code. Harvard Dataverse, V1 https://doi.org/10.7910/DVN/HYRXVV (2020).

    108.
    R Core Team R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019); https://www.r-project.org/ More

  • in

    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