More stories

  • in

    Nutrient content and stoichiometry of pelagic Sargassum reflects increasing nitrogen availability in the Atlantic Basin

    1.Ryther, J. H. The Sargasso Sea. Sci. Am. 194, 98–108 (1956).ADS 
    Article 

    Google Scholar 
    2.Littler, D. S. & Littler, M. M. Caribbean Reef Plants (Offshore Graphics, 2000).3.Winge, O. The Sargasso Sea, Its Boundaries and Vegetation In Report of the Danish Oceanographic Expedition, Vol. III, 1908–1910, (Copenhagen: Andr. Fred. Hòst and Sòn) 34 pp. Miscellaneous Paper Number 2. (1923).4.Parr, A. E. Quantitative observations on the pelagic Sargassum vegetation of the western North Atlantic. Bull. Bingham Oceanogr. Collect. 6, 1–94 (1939).
    Google Scholar 
    5.Lapointe, B. E. A comparison of nutrient-limited productivity in Sargassum natans from neritic vs. oceanic waters of the western North Atlantic Ocean. Limnol. Oceanogr. 40, 625–633 (1995).ADS 
    CAS 
    Article 

    Google Scholar 
    6.Lapointe, B. E., West, L. E., Sutton, T. T. & Hu, C. Ryther revisited: nutrient excretions by fishes enhance productivity of pelagic Sargassum in the western North Atlantic Ocean. J. Exp. Mar. Biol. Ecol. 458, 46–56 (2014).CAS 
    Article 

    Google Scholar 
    7.Gower, J., Hu, C., Borstad, G. & King, S. Ocean color satellites show extensive lines of floating Sargassum in the Gulf of Mexico. IEEE Trans. Geosci. Remote Sens. 44, 3619–3625 (2006).ADS 
    Article 

    Google Scholar 
    8.Williams, A., Feagin, R. & Stafford, A. W. Environmental impacts of beach raking of Sargassum spp. on Galveston Island, TX. Shore Beach 76, 63–69 (2008).
    Google Scholar 
    9.Moritsugu, K. Tampa Bay Times (Times Publishing Company, 1991).10.Turner, R. E. & Rabalais, N. N. Coastal eutrophication near the Mississippi river delta. Nature 368, 619–621 (1994).ADS 
    Article 

    Google Scholar 
    11.Gower, J. F. R. & King, S. A. Distribution of floating Sargassum in the Gulf of Mexico and the Atlantic Ocean mapped using MERIS. Int. J. Remote Sens. 32, 1917–1929 (2011).ADS 
    Article 

    Google Scholar 
    12.Johnson, D. R., Ko, D. S., Franks, J. S., Moreno, P. & Sanchez-Rubio, G. The Sargassum invasion of the Eastern Caribbean and dynamics of the Equatorial North Atlantic. In Proceedings of the 65th Annual Gulf and Caribbean Fisheries Institute Conference pp. 102–103 (2013). http://aquaticcommons.org/21444/1/GCFI_65-17.pdf.13.Gower, J., Young, E. & King, S. Satellite images suggest a new Sargassum source region in 2011. Remote Sens. Lett. 4, 764–773 (2013).Article 

    Google Scholar 
    14.Johns, E. M. et al. The establishment of a pelagic Sargassum population in the tropical Atlantic: biological consequences of a basin-scale long distance dispersal event. Prog. Oceanogr. 182, 102269–102269 (2020).Article 

    Google Scholar 
    15.Wang, M. et al. The great Atlantic Sargassum belt. Science 364, 83–87 (2019).ADS 
    Article 
    CAS 

    Google Scholar 
    16.Djakouré, S., Araujo, M., Hounsou-Gbo, A., Noriega, C. & Bourlès, B. On the potential causes of the recent Pelagic Sargassum blooms events in the tropical North Atlantic Ocean. Biogeosci. Discuss. https://doi.org/10.5194/bg-2017-346 (2017).17.Oviatt, C. A., Huizenga, K., Rogers, C. S. & Miller, W. J. What nutrient sources support anomalous growth and the recent Sargassum mass stranding on Caribbean beaches? A review. Mar. Pollut. Bull. 145, 517–525 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    18.McGillicuddy, D. J., Jr, Anderson, L. A., Doney, S. C. & Maltrud, M. E. Eddy‐driven sources and sinks of nutrients in the upper ocean: results from a 0.1 resolution model of the North Atlantic. Global Biogeochem. Cycles 17, 1035 (2003).19.Barkley, A. E. et al. African biomass burning is a substantial source of phosphorus deposition to the Amazon, Tropical Atlantic Ocean, and Southern Ocean. Proc. Natl Acad. Sci. USA 116, 16216–16221 (2019).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    20.Qi, L., Hu, C., Xing, Q. & Shang, S. Long-term trend of Ulva prolifera blooms in the western Yellow Sea. Harmful Algae 58, 35–44 (2016).PubMed 
    Article 

    Google Scholar 
    21.Qi, L., Hu, C., Wang, M., Shang, S. & Wilson, C. Floating algae blooms in the East China Sea. Geophys. Res. Lett. 44, 501–511,509 (2017).Article 
    CAS 

    Google Scholar 
    22.Smetacek, V. & Zingone, A. Green and golden seaweed tides on the rise. Nature 504, 84–88 (2013).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    23.Van Tussenbroek, B. I. et al. Severe impacts of brown tides caused by Sargassum spp. on near-shore Caribbean seagrass communities. Mar. Pollut. Bull. 122, 272–281 (2017).PubMed 
    Article 
    CAS 

    Google Scholar 
    24.Alvarez-Filip, L., Estrada-Saldívar, N., Pérez-Cervantes, E., Molina-Hernández, A. & González-Barrios, F. J. A rapid spread of the stony coral tissue loss disease outbreak in the Mexican Caribbean. PeerJ 7, e8069–e8069 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    25.Cabanillas-Terán, N., Hernández-Arana, H. A., Ruiz-Zárate, M.-Á., Vega-Zepeda, A. & Sanchez-Gonzalez, A. Sargassum blooms in the Caribbean alter the trophic structure of the sea urchin Diadema antillarum. PeerJ 7, e7589–e7589 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    26.Maurer, A. S., De Neef, E. & Stapleton, S. Sargassum accumulation may spell trouble for nesting sea turtles. Front. Ecol. Environ. 13, 394–395 (2015).Article 

    Google Scholar 
    27.Webster, R. K. & Linton, T. Development and implementation of Sargassum early advisory system (SEAS). Shore Beach 81, 1–1 (2013).
    Google Scholar 
    28.Resiere, D. et al. Sargassum seaweed on Caribbean islands: an international public health concern. Lancet 392, 2691–2691 (2018).Article 

    Google Scholar 
    29.Glibert, P. et al. The role of in the global proliferation of harmful algal blooms: new perspectives and approaches. Oceanography 18, 196–207 (2005).
    Google Scholar 
    30.Glibert, P. M. Eutrophication, harmful algae and biodiversity — Challenging paradigms in a world of complex nutrient changes. Mar. Pollut. Bull. 124, 591–606 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    31.Steffen, W. et al. Planetary boundaries: guiding human development on a changing planet. Science 347, 6223 https://doi.org/10.1126/science.1259855 (2015).32.Ryther, J. H. The ecology of phytoplankton blooms in Moriches bay and Great South bay, Long Island, New York. Biol. Bull. 106, 198–209 (1954).Article 

    Google Scholar 
    33.Ryther, J. H. & Dunstan, W. M. Nitrogen, Phosphorus, and Eutrophication in the coastal marine environment. Science 171, 1008 LP-1013 (1971).Article 

    Google Scholar 
    34.Howarth, R. W. & Marino, R. Nitrogen as the limiting nutrient for eutrophication in coastal marine ecosystems: evolving views over three decades. Limnol. Oceanogr. 51, 364–376 (2006).ADS 
    CAS 
    Article 

    Google Scholar 
    35.Oelsner, G. P. & Stets, E. G. Recent trends in nutrient and sediment loading to coastal areas of the conterminous U.S.: insights and global context. Sci. Total Environ. 654, 1225–1240 (2019).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    36.Falkowski, P. G. Evolution of the nitrogen cycle and its influence on the biological sequestration of CO2 in the ocean. Nature 387, 272–275 (1997).ADS 
    CAS 
    Article 

    Google Scholar 
    37.Tyrrell, T. The relative influences of nitrogen and phosphorus on oceanic primary production. Nature 400, 525–531 (1999).ADS 
    CAS 
    Article 

    Google Scholar 
    38.Lapointe, B. E., Littler, M. M. & Littler, D. S. A comparison of nutrient-limited productivity in macroalgae from a Caribbean barrier reef and from a mangrove ecosystem. Aquat. Bot. 28, 243–255 (1987).Article 

    Google Scholar 
    39.Culliney, J. L. Measurements of reactive phosphorus associated with pelagic Sargassum in the Northwest Sargasso Sea1. Limnol. Oceanogr. 15, 304–305 (1970).ADS 
    CAS 
    Article 

    Google Scholar 
    40.Schaffelke, B. Particulate organic matter as an alternative nutrient source for tropical Sargassum species (Fucales, Phaeophyceae). J. Phycol. 35, 1150–1157 (1999).CAS 
    Article 

    Google Scholar 
    41.Vonk, J. A., Middelburg, J. J., Stapel, J. & Bouma, T. J. Dissolved organic nitrogen uptake by seagrasses. Limnol. Oceanogr. 53, 542–548 (2008).ADS 
    CAS 
    Article 

    Google Scholar 
    42.Han, T., Qi, Z., Huang, H., Liao, X. & Zhang, W. Nitrogen uptake and growth responses of seedlings of the brown seaweed Sargassum hemiphyllum under controlled culture conditions. J. Appl. Phycol. 30, 507–515 (2018).CAS 
    Article 

    Google Scholar 
    43.Fujita, R., Wheeler, P. & Edwards, R. Assessment of macroalgal nitrogen limitation in a seasonal upwelling region. Mar. Ecol. Prog. Ser. 53, 293–303 (1989).ADS 
    Article 

    Google Scholar 
    44.Prospero, J. M. et al. in Nitrogen Cycling in the North Atlantic Ocean and its Watersheds (ed. Robert, W. H.) (Springer, 1996).45.Howarth, R. W. Coastal nitrogen pollution: a review of sources and trends globally and regionally. Harmful Algae 8, 14–20 (2008).CAS 
    Article 

    Google Scholar 
    46.Rockström, J. & Karlberg, L. The quadruple squeeze: defining the safe operating space for freshwater use to achieve a triply green revolution in the Anthropocene. Ambio 39, 257–265 (2010).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    47.Hanisak, M. D. & Samuel, M. A. Twelfth International Seaweed Symposium (Springer, 1986).48.Rabalais, N. N. et al. Hypoxia in the northern Gulf of Mexico: does the science support the plan to reduce, mitigate, and control hypoxia? Estuar. Coasts 30, 753–772 (2007).CAS 
    Article 

    Google Scholar 
    49.Tian, H. et al. Long-term trajectory of nitrogen loading and delivery from Mississippi river basin to the Gulf of Mexico. Glob. Biogeochem. Cycles 34, e2019GB006475–e002019GB006475 (2020).ADS 
    CAS 
    Article 

    Google Scholar 
    50.Lapointe, B. E., Brewton, R. A., Herren, L. W., Porter, J. W. & Hu, C. Nitrogen enrichment, altered stoichiometry, and coral reef decline at Looe Key, Florida Keys, USA: a 3-decade study. Mar. Biol. 166, 108–108 (2019).Article 
    CAS 

    Google Scholar 
    51.Lapointe, B. E., Barile, P. J. & Littler, M. M. & Littler, D. S. Macroalgal blooms on southeast Florida coral reefs: II. Cross-shelf discrimination of nitrogen sources indicates widespread assimilation of sewage nitrogen. Harmful Algae 4, 1106–1122 (2005).CAS 
    Article 

    Google Scholar 
    52.Dunn, D. E. Trends in Nutrient Inflows to the Gulf of Mexico from Streams Draining the Conterminous United States, 1972-93. Report No. 96-4113 (Austin, TX, 1996).53.Turner, R. E. & Rabalais, N. N. Changes in Mississippi River water quality this century: implications for coastal food webs. Bioscience 41, 140–147 (1991).Article 

    Google Scholar 
    54.Rabalais, N. N. et al. Nutrient changes in the Mississippi River and system responses on the adjacent continental shelf. Estuaries 19, 386–407 (1996).CAS 
    Article 

    Google Scholar 
    55.Weber, S. C. et al. Amazon River influence on nitrogen fixation and export production in the western tropical North Atlantic. Limnol. Oceanogr. 62, 618–631 (2017).ADS 
    Article 

    Google Scholar 
    56.Ryther, J. H., Menzel, D. W. & Corwin, N. Influence of Amazon River outflow on ecology of Western Tropical Atlantic. I. Hydrography and nutrient chemistry. J. Mar. Res. 25, 69–69 (1967).
    Google Scholar 
    57.Subramaniam, A. et al. Amazon River enhances diazotrophy and carbon sequestration in the tropical North Atlantic Ocean. Proc. Natl Acad. Sci.USA 105, 10460 LP–10410465 (2008).ADS 
    Article 

    Google Scholar 
    58.Barichivich, J. et al. Recent intensification of Amazon flooding extremes driven by strengthened Walker circulation. Sci. Adv. 4, eaat8785–eaat8785 (2018).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    59.Howarth, R. W. et al. Regional nitrogen budgets and riverine N & P fluxes for the drainages to the North Atlantic Ocean: Natural and human influences. In Nitrogen Cycling in the North Atlantic Ocean and its Watersheds (ed. Robert, W. Howarth) (Springer, Dordrecht, 1996). https://doi.org/10.1007/978-94-009-1776-7_3.60.Galloway, J. N. et al. Regional nitrogen budgets and riverine N & P fluxes for the drainages to the North Atlantic Ocean: Natural and human influences. Biogeochemistry (ed. Robert, W. Howarth) 35, 181–226 (Springer, 1996).61.Gower, J. & King, S. Satellite images show the movement of floating Sargassum in the Gulf of Mexico and Atlantic Ocean. Nat. Preced. https://doi.org/10.1038/npre.2008.1894.1 (2008).62.Chapman, A. R. O. & Craigie, J. S. Seasonal growth in Laminaria longicruris: relations with dissolved inorganic nutrients and internal reserves of nitrogen. Mar. Biol. 40, 197–205 (1977).CAS 
    Article 

    Google Scholar 
    63.Zimmerman, R. C. & Kremer, J. N. Episodic nutrient supply to a kelp forest ecosystem in Southern California. J. Mar. Res. 42, 591–604 (1984).Article 

    Google Scholar 
    64.Kain, J. M. The seasons in the subtidal. Br. Phycol. J. 24, 203–215 (1989).Article 

    Google Scholar 
    65.Dorado, S., Rooker, J. R., Wissel, B. & Quigg, A. Isotope baseline shifts in pelagic food webs of the Gulf of Mexico. Mar. Ecol. Prog. Ser. 464, 37–49 (2012).ADS 
    CAS 
    Article 

    Google Scholar 
    66.Kendall, C., Elliott, E. M. & Wankel, S. D. Wiley Online Books 375-449 (2007).67.Altieri, K. E., Hastings, M. G., Peters, A. J., Oleynik, S. & Sigman, D. M. Isotopic evidence for a marine ammonium source in rainwater at Bermuda. Glob. Biogeochem. Cycles 28, 1066–1080 (2014).ADS 
    CAS 
    Article 

    Google Scholar 
    68.Bateman, A. S. & Kelly, S. D. Fertilizer nitrogen isotope signatures. Isotopes Environ. Health Stud. 43, 237–247 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    69.Knapp, A. N., DiFiore, P. J., Deutsch, C., Sigman, D. M. & Lipschultz, F. Nitrate isotopic composition between Bermuda and Puerto Rico: implications for N2 fixation in the Atlantic Ocean. Global Biogeochem. Cycles 22, GB3014 https://doi.org/10.1029/2007GB003107 (2008).70.Knapp, A. N., Sigman, D. M. & Lipschultz, F. N isotopic composition of dissolved organic nitrogen and nitrate at the Bermuda Atlantic Time-series Study site. Global Biogeochem. Cycles 19, GB1018 https://doi.org/10.1029/2004GB002320 (2005).71.Montoya, J. P. Nitrogen stable isotopes in marine environments. Nitrogen Mar. Environ. 2, 1277–1302 (2008).Article 

    Google Scholar 
    72.Wissel, B. & Fry, B. Sources of particulate organic matter in the Mississippi River, USA. Large Rivers 15 105–118 (2003).73.Zaia Alves, G. H., Hoeinghaus, D. J., Manetta, G. I. & Benedito, E. Dry season limnological conditions and basin geology exhibit complex relationships with δ13C and δ15N of carbon sources in four Neotropical floodplains. PLoS ONE 12, e0174499 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    74.Smith, N. P. Upwelling in Atlantic shelf waters of South Florida. Florida Scientist 45, 125–138 (1982).75.Atkinson, L. P., O’Malley, P. G., Yoder, J. A. & Paffenhöfer, G. A. The effect of summertime shelf break upwelling on nutrient flux in southeastern United States continental shelf waters. J. Mar. Res. 42, 969–993 (1984).Article 

    Google Scholar 
    76.Subramaniam, A., Mahaffey, C., Johns, W. & Mahowald, N. Equatorial upwelling enhances nitrogen fixation in the Atlantic Ocean. Geophys. Res. Lett. 40, 1766–1771 (2013).ADS 
    Article 

    Google Scholar 
    77.Carpenter, E. J. Nitrogen fixation by a blue-green epiphyte on Pelagic Sargassum. Science 178, 1207–1209 (1972).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    78.Phlips, E. J., Willis, M. & Verchick, A. Aspects of nitrogen fixation in Sargassum communities off the coast of Florida. J. Exp. Mar. Biol. Ecol. 102, 99–119 (1986).CAS 
    Article 

    Google Scholar 
    79.Subramaniam, A., Montoya, J. P., Foster, R. A. & Capone, D. G. Nitrogen fixation in the eastern equatorial Atlantic: who and how much? European Geosciences Union General Assembly 11, 10156–10156 (2009).80.Carpenter, E. J. et al. Extensive bloom of a N2-fixing diatom/cyanobacterial association in the tropical Atlantic Ocean. Mar. Ecol. Prog. Ser. 185, 273–283 (1999).ADS 
    CAS 
    Article 

    Google Scholar 
    81.Zubkova, M., Boschetti, L., Abatzoglou, J. T. & Giglio, L. Changes in fire activity in Africa from 2002 to 2016 and their potential drivers. Geophys. Res. Lett. 46, 7643–7653 (2019).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    82.Baker, A. R., French, M. & Linge, K. L. Trends in aerosol nutrient solubility along a west–east transect of the Saharan dust plume. Geophys. Res. Lett. 33 L07805, https://doi.org/10.1029/2005GL024764 (2006).83.Baker, A. R., Jickells, T. D., Witt, M. & Linge, K. L. Trends in the solubility of iron, aluminium, manganese and phosphorus in aerosol collected over the Atlantic Ocean. Mar. Chem. 98, 43–58 (2006).CAS 
    Article 

    Google Scholar 
    84.Shelley, R. U., Morton, P. L. & Landing, W. M. Elemental ratios and enrichment factors in aerosols from the US-GEOTRACES North Atlantic transects. Deep Sea Res. Part II Top. Stud. Oceanogr. 116, 262–272 (2015).ADS 
    CAS 
    Article 

    Google Scholar 
    85.Giglio, L., Schroeder, W. & Justice, C. O. The collection 6 MODIS active fire detection algorithm and fire products. Remote Sens. Environ. 178, 31–41 (2016).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    86.Giglio, L., Descloitres, J., Justice, C. O. & Kaufman, Y. J. An enhanced contextual fire detection algorithm for MODIS. Remote Sens. Environ. 87, 273–282 (2003).ADS 
    Article 

    Google Scholar 
    87.Giglio, L., van der Werf, G. R., Randerson, J. T., Collatz, G. J. & Kasibhatla, P. Global estimation of burned area using MODIS active fire observations. Atmos. Chem. Phys. 6, 957–974 (2006).ADS 
    CAS 
    Article 

    Google Scholar 
    88.Roberts, G., Wooster, M. J. & Lagoudakis, E. Annual and diurnal african biomass burning temporal dynamics. Biogeosciences 6, 849–866 (2009).ADS 
    Article 

    Google Scholar 
    89.Baker, A. R. & Jickells, T. D. Atmospheric deposition of soluble trace elements along the Atlantic Meridional Transect (AMT). Prog. Oceanogr. 158, 41–51 (2017).ADS 
    Article 

    Google Scholar 
    90.Chance, R., Jickells, T. D. & Baker, A. R. Atmospheric trace metal concentrations, solubility and deposition fluxes in remote marine air over the south-east Atlantic. Mar. Chem. 177, 45–56 (2015).CAS 
    Article 

    Google Scholar 
    91.Myriokefalitakis, S., Nenes, A., Baker, A. R., Mihalopoulos, N. & Kanakidou, M. Bioavailable atmospheric phosphorous supply to the global ocean: a 3-D global modeling study. Biogeosciences 13, 6519–6543 (2016).ADS 
    CAS 
    Article 

    Google Scholar 
    92.Kanakidou, M., Myriokefalitakis, S. & Tsigaridis, K. Aerosols in atmospheric chemistry and biogeochemical cycles of nutrients. Environ. Res. Lett. 13, 063004 (2018).93.Rosenzweig, M. L. Paradox of enrichment: destabilization of exploitation ecosystems in ecological time. Science 171, 385–387 (1971).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    94.McCann, K. S. et al. Landscape modification and nutrient‐driven instability at a distance. Ecol. Lett. 24, 398–414 (2021).PubMed 
    Article 

    Google Scholar 
    95.Meybeck, M. Carbon, nitrogen, and phosphorus transport by world rivers. Am. J. Sci. 282, 401–450 (1982).ADS 
    CAS 
    Article 

    Google Scholar 
    96.Fanning, K. A. Nutrient provinces in the sea: concentration ratios, reaction rate ratios, and ideal covariation. J. Geophys. Res. Oceans 97, 5693–5712 (1992).ADS 
    Article 

    Google Scholar 
    97.Ammerman, J. W., Hood, R. R., Case, D. A. & Cotner, J. B. Phosphorus deficiency in the Atlantic: an emerging paradigm in oceanography. Eos, Trans. Am. Geophys. Union 84, 165–170 (2003).ADS 
    Article 

    Google Scholar 
    98.Lomas, M. W., Bonachela, J. A., Levin, S. A. & Martiny, A. C. Impact of ocean phytoplankton diversity on phosphate uptake. Proc. Natl Acad. Sci. USA 111, 17540–17545 (2014).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    99.Richey, J. E. et al. (ORNL Distributed Active Archive Center, 2008).100.Cochonneau, G. et al. The environmental observation and research project, ORE HYBAM, and the rivers of the Amazon basin. In Climate Variability and Change—Hydrological Impacts (eds Demuth, S. et al.) vol. 308, 44–50 (2006). More

  • in

    Genome-wide SNPs redefines species boundaries and conservation units in the freshwater mussel genus Cyprogenia of North America

    1.Frankham, R. Challenges and opportunities of genetic approaches to biological conservation. Biol. Conserv. 143, 1919–1927 (2010).Article 

    Google Scholar 
    2.Goldstein, P. Z., Desalle, R., Amato, G. & Vogler, A. P. Conservation genetics at the species boundary. Conserv. Biol. 14, 120–131 (2000).Article 

    Google Scholar 
    3.Isaac, N. J. B., Mallet, J. & Mace, G. M. Taxonomic inflation: Its influence on macroecology and conservation. Trends Ecol. Evol. 19, 464–469 (2004).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    4.Lydeard, C. et al. The global decline of nonmarine mollusks. Bioscience 54, 321–330 (2004).Article 

    Google Scholar 
    5.Haag, W. R. & Williams, J. D. Biodiversity on the brink: An assessment of conservation strategies for North American freshwater mussels. Hydrobiologia 735, 45–60 (2014).Article 

    Google Scholar 
    6.Ricciardi, A. & Rasmussen, J. Extinction rates of North American freshwater fauna. Conserv. Biol. 13, 1220–1222 (1999).7.Spooner, D. E. & Vaughn, C. C. Context-dependent effects of freshwater mussels on stream benthic communities. Freshw. Biol. 51, 1016–1024 (2006).CAS 
    Article 

    Google Scholar 
    8.Vaughn, C. C., Spooner, D. E. & Galbraith, H. S. Contex-dependent species identity effects within a functional group of filter-feeding bivalves. Ecology 88, 1654–1662 (2007).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    9.Vaughn, C. C., Nichols, S. J. & Spooner, D. E. Community and foodweb ecology of freshwater mussels. J. N. Am. Benthol. Soc. 27, 409–423 (2008).Article 

    Google Scholar 
    10.McMahon, R. F. Ecology and Classification of North American Freshwater Invertebrates (Academic Press, 1991).
    Google Scholar 
    11.Watters, G. T. Unionids, fishes, and the species-area curve. J. Biogeogr. 19, 481–490 (1992).Article 

    Google Scholar 
    12.Haag, W. R. & Warren, M. L. Host fishes and reproductive biology of 6 freshwater mussel species from the Mobile Basin, USA. J. N. Am. Benthol. Soc. 16, 576–585 (1997).Article 

    Google Scholar 
    13.Eckert, N. L. Reproductive biology and host requirement differences among isolated populations of Cyprogenia aberti (Conrad, 1850). MS Thesis, Southwest Missouri State University, Springfield (2003).14.Barnhart, M. C., Haag, W. R. & Roston, W. N. Adaptations to host infection and larval parasitism in Unionoida. J. N. Am. Benthol. Soc. 27, 370–394 (2008).Article 

    Google Scholar 
    15.Rogers, S. O., Watson, B. T. & Neves, R. J. Life history and population biology of the endangered tan riffleshell (Epioblasma florentina walkeri) (Bivalvia: Unionidae). J. N. Am. Benthol. Soc. 20, 582–594 (2001).Article 

    Google Scholar 
    16.Burr, B. M. & Mayden, R. L. Phylogenetics and North American freshwater fishes. In: Systematics, Historical Ecology, and North American Freshwater Fishes. (Stanford University Press, 1992).17.Oesch, R. D. Missouri Naiades: A Guide to the Mussels of Missouri (Missouri Department of Conservation, 1995).
    Google Scholar 
    18.Harris, J. L. et al. Unionoida (Mollusca: Margaritiferidae, Unionidae) in Arkansas, third status review. J. Ark. Acad. Sci. 63, 50–86 (2009).
    Google Scholar 
    19.Obermeyer, B. K. Recovery plan for four freshwater mussels in southeast Kansas: Neosho mucket (Lampsilis rafinesqueana), Ouachita kidneyshell (Ptychobranchus occidentalis), rabbitsfoot (Quadrula cylindrica cylindrica), and western fanshell (Cyprogenia aberti). Kansas Department of Parks and Wildlife (2000).20.Serb, J. M. Discovery of genetically distinct sympatric lineages in the freshwater mussel Cyprogenia aberti (Bivalvia: Unionidae). J. Molluscan Stud. 72, 425–434 (2006).Article 

    Google Scholar 
    21.Grobler, J. P., Jones, J. W., Johnson, N. A., Neves, R. J. & Hallerman, E. M. Homogeneity at nuclear microsatellite loci masks mitochondrial haplotype diversity in the endangered fanshell pearlymussel (Cyprogenia stegaria). J. Hered. 102, 196–206 (2011).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    22.Serb, J. M. & Barnhart, M. C. Congruence and conflict between molecular and reproductive characters when assessing biological diversity in the Western Fanshell Cyprogenia aberti (Bivalvia, Unionidae)1. Ann. Missouri Bot. Gard. 95, 248–261 (2008).Article 

    Google Scholar 
    23.Chong, J. P., Harris, J. L. & Roe, K. J. Incongruence between mtDNA and nuclear data in the freshwater mussel genus Cyprogenia (Bivalvia: Unionidae) and its impact on species delineation. Ecol. Evol. 6, 2439–2452 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    24.Hohenlohe, P. A. et al. Population genomics of parallel adaptation in threespine stickleback using sequenced RAD tags. PLoS Genet. 6, e1000862 (2010).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    25.Leaché, A. D., Fujita, M. K., Minin, V. N. & Bouckaert, R. R. Species delimitation using genome-wide SNP Data. Syst. Biol. 63, 534–542 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    26.Bruneaux, M. et al. Molecular evolutionary and population genomic analysis of the nine-spined stickleback using a modified restriction-site-associated DNA tag approach. Mol. Ecol. 22, 565–582 (2013).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    27.Wagner, C. et al. Genome-wide RAD sequence data provide unprecedented resolution of species boundaries and relationships in the Lake Victoria cichlid adaptive radiation. Mol. Ecol. 22, 787–798 (2013).28.Larson, W. A. et al. Genotyping by sequencing resolves shallow population structure to inform conservation of Chinook salmon (Oncorhynchus tshawytscha). Evol. Appl. 7, 355–369 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    29.Lee, S.-R., Jo, Y.-S., Park, C.-H., Friedman, J. M. & Olson, M. S. Population genomic analysis suggests strong influence of river network on spatial distribution of genetic variation in invasive saltcedar across the southwestern United States. Mol. Ecol. 27, 636–646 (2017).Article 
    CAS 

    Google Scholar 
    30.Massatti, R., Reznicek, A. A. & Knowles, L. L. Utilizing RADseq data for phylogenetic analysis of challenging taxonomic groups: A case study in carex sect. Racemosae. Am. J. Bot. 103, 337–347 (2016).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    31.Razkin, O. et al. Species limits, interspecific hybridization and phylogeny in the cryptic land snail complex Pyramidula: The power of RADseq data. Mol. Phylogenet. Evol. https://doi.org/10.1016/j.ympev.2016.05.002 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    32.Rubin, B. E. R., Ree, R. H. & Moreau, C. S. Inferring phylogenies from RAD sequence data. PLoS ONE 7, e33394 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    33.Takahashi, T., Nagata, N. & Sota, T. Application of RAD-based phylogenetics to complex relationships among variously related taxa in a species flock. Mol. Phylogenet. Evol. https://doi.org/10.1016/j.ympev.2014.07.016 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    34.Boucher, F. C., Casazza, G., Szövényi, P. & Conti, E. Sequence capture using RAD probes clarifies phylogenetic relationships and species boundaries in Primula sect. Auricula. Mol. Phylogenet. Evol. https://doi.org/10.1016/j.ympev.2016.08.003 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    35.Combosch, D. J., Lemer, S., Ward, P. D., Landman, N. H. & Giribet, G. Genomic signatures of evolution in Nautilus—An endangered living fossil. Mol. Ecol. 26, 5923–5938 (2017).PubMed 
    Article 

    Google Scholar 
    36.Cruaud, A. et al. Empirical assessment of RAD sequencing for interspecific phylogeny. Mol. Biol. Evol. 31, 1272–1274 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    37.Eaton, D. A. R. & Ree, R. H. Inferring phylogeny and introgression using RADseq data: An example from flowering plants (Pedicularis: Orobanchaceae). Syst. Biol. 62, 689–706 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    38.Emerson, K. J. et al. Resolving postglacial phylogeography using high-throughput sequencing. Proc. Natl. Acad. Sci. USA 107, 16196–16200 (2010).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    39.Herrera, S. & Shank, T. M. RAD sequencing enables unprecedented phylogenetic resolution and objective species delimitation in recalcitrant divergent taxa. Mol. Phylogenet. Evol. 100, 70–79 (2016).PubMed 
    Article 

    Google Scholar 
    40.Hipp, A. L. et al. A framework phylogeny of the American Oak Clade based on sequenced RAD data. PLoS ONE 9, e93975 (2014).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    41.Jones, J. C., Fan, S., Franchini, P., Schartl, M. & Meyer, A. The evolutionary history of Xiphophorus fish and their sexually selected sword: A genome-wide approach using restriction site-associated DNA sequencing. Mol. Ecol. 22, 2986–3001 (2013).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    42.Funk, W. C. et al. Adaptive divergence despite strong genetic drift: Genomic analysis of the evolutionary mechanisms causing genetic differentiation in the island fox (Urocyon littoralis). Mol. Ecol. 25, 2176–2194 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    43.Taberlet, P. & Luikart, G. Non-invasive genetic sampling and individual identification. Biol. J. Linn. Soc. 68, 41–55 (1999).Article 

    Google Scholar 
    44.Palsbøll, P. J., Bérubé, M. & Allendorf, F. W. Identification of management units using population genetic data. Trends Ecol. Evol. 22, 11–16 (2007).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    45.Gibbs, J., Jr. Hunter, M. & Sterling, E. Population genetics: Diversity within versus diversity among populations. In: Problem-Solving in Conservation Biology and Wildlife Management: Exercises for Class, Field, and Laboratory 29–35 (Blackwell Publishing Ltd., 2008). https://doi.org/10.1002/9781444319576.ch4.46.Berendzen, P. B., Simons, A. M., Wood, R. M., Dowling, T. E. & Secor, C. L. Recovering cryptic diversity and ancient drainage patterns in eastern North America: Historical biogeography of the Notropis rubellus species group (Teleostei: Cypriniformes). Mol. Phylogenet. Evol. 46, 721–737 (2008).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    47.Ray, J. M., Wood, R. M. & Simons, A. M. Phylogeography and post-glacial colonization patterns of the rainbow darter, Etheostoma caeruleum (Teleostei: Percidae). J. Biogeogr. 33, 1550–1558 (2006).Article 

    Google Scholar 
    48.Strange, R. M. & Burr, B. M. Intraspecific phylogeography of North American highland fishes: A test of the pleistocene vicariance hypothesis. Evolution (N. Y.) 51, 885–897 (1997).
    Google Scholar 
    49.Pflieger, W. L. A distributional study of missouri fishes. Univ. Kans. Publ. Mus. Nat. Hist. 20 (1971).50.Thornbury, W. D. Regional geomorphology of the United States. J. Geol. 73, 815–816 (1965).Article 

    Google Scholar 
    51.Mayden, R. Vicariance biogeography, parsimony, and evolution in North American freshwater fishes. Syst. Zool. 37, 329–355 (1988).52.Echelle, A. A., Echelle, A. F., Smith, M. H. & Hill, L. G. Analysis of genic continuity in a headwater fish, Etheostoma radiosum (Percidae). Copeia 1975, 197–204 (1975).Article 

    Google Scholar 
    53.Haponski, A. E., Bollin, T. L., Jedlicka, M. A. & Stepien, C. A. Landscape genetic patterns of the rainbow darter Etheostoma caeruleum: A catchment analysis of mitochondrial DNA sequences and nuclear microsatellites. J. Fish Biol. 75, 2244–2268 (2010).Article 
    CAS 

    Google Scholar 
    54.Turner, T. F. & Trexler, J. C. Ecological and historical associations of gene flow in darters (Teleostei: Percidae). Evolution (N. Y.) 52, 1781–1801 (1998).
    Google Scholar 
    55.Turner, T. F., Trexler, J. C., Kuhn, D. N. & Robison, H. W. Life-history variation and comparative phylogeography of darters (Pisces: Percidae) From the North American Central Highlands. Evolution (N. Y.) 50, 2023–2036 (1996).
    Google Scholar 
    56.Cross, F., Mayden, R. & Stewart, J. Fishes in the Western Mississippi Drainage. The Zoogeography of North American Freshwater Fishes (Wiley, 1986).
    Google Scholar 
    57.Barnhart, M. C. Reproduction and Fish Host of the Western Fanshell, Cyprogenia aberti (Conrad 1850) (Kansas Department of Wildlife and Parks, 1997).
    Google Scholar 
    58.Inoue, K., Monroe, E. M., Elderkin, C. L. & Berg, D. J. Phylogeographic and population genetic analyses reveal Pleistocene isolation followed by high gene flow in a wide ranging, but endangered, freshwater mussel. Heredity (Edinb). 112, 282–290 (2014).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    59.Catchen, J. M., Amores, A., Hohenlohe, P. A., Cresko, W. A. & Postlethwait, J. H. Stacks: Building and genotyping Loci de novo from short-read sequences. G3 Genes Genomes Genet. 1, 171–182 (2011).CAS 

    Google Scholar 
    60.Catchen, J. M., Hohenlohe, P. A., Bassham, S., Amores, A. & Cresko, W. A. Stacks: an analysis tool set for population genomics. Mol. Ecol. 22, 3124–3140 (2013).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    61.Peterson, B. K., Weber, J. N., Kay, E. H., Fisher, H. S. & Hoekstra, H. E. Double digest RADseq: An inexpensive method for De Novo SNP discovery and genotyping in model and non-model species. PLoS ONE 7, e37135 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    62.Mastretta-Yanes, A. et al. Restriction site-associated DNA sequencing, genotyping error estimation and de novo assembly optimization for population genetic inference. Mol. Ecol. Resour. 15, 28–41 (2015).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    63.Meirmans, P. G. & Van Tienderen, P. H. Genotype and genodive: two programs for the analysis of genetic diversity of asexual organisms. Mol. Ecol. Notes 4, 792–794 (2004).Article 

    Google Scholar 
    64.Weir, B. S. & Cockerham, C. C. Estimating F-statistics for the analysis of population structure. Evolution (N. Y.) 38, 1358–1370 (1984).CAS 

    Google Scholar 
    65.Raymond, M. & Rousset, F. GENEPOP (Version 1.2): Population genetics software for exact tests and ecumenicism. J. Hered. 86, 248–249 (1995).Article 

    Google Scholar 
    66.Excoffier, L., Smouse, P. E. & Quattro, J. M. Analysis of molecular variance inferred from metric distances among DNA haplotypes: Application to human mitochondrial DNA restriction data. Genetics 131, 479–491 (1992).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    67.Jombart, T. adegenet: A R package for the multivariate analysis of genetic markers. Bioinformatics 24, 1403–1405 (2008).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    68.R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing. Vienna, Austria. Available online at https://www.R-project.org/ (2018).69.Jombart, T., Devillard, S. & Balloux, F. Discriminant analysis of principal components: A new method for the analysis of genetically structured populations. BMC Genet. 11, 94 (2010).70.Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    71.Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software structure: A simulation study. Mol. Ecol. 14, 2611–2620 (2005).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    72.Earl, D. A. & Vonholdt, B. M. Structure Harvester: A website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361 (2012).Article 

    Google Scholar 
    73.Rosenberg, N. A. distruct: A program for the graphical display of population structure. Mol. Ecol. Notes 4, 137–138 (2004).Article 

    Google Scholar 
    74.Minh, B., Nguyen, M.-A. & von Haeseler, A. Ultrafast approximation for phylogenetic bootstrap. Mol. Biol. Evol. 30, 1188–1195 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    75.Nguyen, L.-T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: A fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274 (2015).CAS 
    Article 

    Google Scholar 
    76.Ronquist, F. & Huelsenbeck, J. P. MrBayes 3: Bayesian phylogenetic inference under mixed models. Bioinformatics 19, 1572–1574 (2003).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    77.Bryant, D., Bouckaert, R., Felsenstein, J., Rosenberg, N. A. & Roychoudhury, A. Inferring species trees directly from biallelic genetic markers: Bypassing gene trees in a full coalescent analysis. Mol. Biol. Evol. 29, 1917–1932 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    78.Bouckaert, R. et al. BEAST 2: A software platform for Bayesian evolutionary analysis. PLoS Comput. Biol. 10, 1–6 (2014).Article 
    CAS 

    Google Scholar 
    79.Kass, R. E. & Raftery, A. E. kass1995BayesFactors. J. Am. Stat. Assoc. 90, 773–795 (1995).Article 

    Google Scholar 
    80.Cornuet, J.-M. et al. DIYABC v2.0: A software to make approximate Bayesian computation inferences about population history using single nucleotide polymorphism, DNA sequence and microsatellite data. Bioinformatics 30, 1187–1189 (2014).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    81.Cabrera, A. A. & Palsbøll, P. J. Inferring past demographic changes from contemporary genetic data: A simulation-based evaluation of the ABC methods implemented in diyabc. Mol. Ecol. Resour. 17, e94–e110 (2017).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    82.Jones, J. W. & Neves, R. J. Life history and propagation of the endangered fanshell pearlymussel, Cyprogenia stegaria Rafinesque (Bivalvia: Unionidae) The University of Chicago Press on behalf of the Society for F. J. N. Am. Benthol. Soc. 21, 76–88 (2002).Article 

    Google Scholar  More

  • in

    Temperatures that sterilize males better match global species distributions than lethal temperatures

    1.Pecl, G. T. et al. Biodiversity redistribution under climate change: impacts on ecosystems and human well-being. Science 355, eaai9214 (2017).Article 

    Google Scholar 
    2.Kearney, M. & Porter, W. Mechanistic niche modelling: combining physiological and spatial data to predict species’ ranges. Ecol. Lett. 12, 334–350 (2009).Article 

    Google Scholar 
    3.Nowakowski, A. J. et al. Thermal biology mediates responses of amphibians and reptiles to habitat modification. Ecol. Lett. 21, 345–355 (2018).Article 

    Google Scholar 
    4.Metelmann, S. et al. The UK’s suitability for Aedes albopictus in current and future climates. J. R. Soc. Interface 16, 20180761 (2019).CAS 
    Article 

    Google Scholar 
    5.Kellermann, V. et al. Upper thermal limits of Drosophila are linked to species distributions and strongly constrained phylogenetically. Proc. Natl Acad. Sci. USA 109, 16228–16233 (2012).CAS 
    Article 

    Google Scholar 
    6.Lancaster, L. T. & Humphreys, A. M. Global variation in the thermal tolerances of plants. Proc. Natl Acad. Sci. USA 117, 13580–13587 (2020).CAS 
    Article 

    Google Scholar 
    7.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 
    Article 

    Google Scholar 
    8.Rezende, E. L., Bozinovic, F., Szilàgyi, A. & Santos, M. Predicting temperature mortality and selection in natural Drosophila populations. Science 369, 1242–1245 (2020).CAS 
    Article 

    Google Scholar 
    9.Jørgensen, L. B., Malte, H. & Overgaard, J. How to assess Drosophila heat tolerance: unifying static and dynamic tolerance assays to predict heat distribution limits. Funct. Ecol. 33, 629–642 (2019).Article 

    Google Scholar 
    10.Rezende, E. L., Castañeda, L. E. & Santos, M. Tolerance landscapes in thermal ecology. Funct. Ecol. 28, 799–809 (2014).Article 

    Google Scholar 
    11.Terblanche, J. S. & Hoffmann, A. A. Validating measurements of acclimation for climate change adaptation. Curr. Opin. Insect Sci. 41, 7–16 (2020).Article 

    Google Scholar 
    12.Walsh, B. S. et al. The impact of climate change on fertility. Trends Ecol. Evol. 34, 249–259 (2019).Article 

    Google Scholar 
    13.Sage, T. L. et al. The effect of high temperature stress on male and female reproduction in plants. Field Crops Res. 182, 30–42 (2015).Article 

    Google Scholar 
    14.Sales, K. et al. Experimental heatwaves compromise sperm function and cause transgenerational damage in a model insect. Nat. Commun. 9, 4771 (2018).Article 

    Google Scholar 
    15.Porcelli, D., Gaston, K. J., Butlin, R. K. & Snook, R. R. Local adaptation of reproductive performance during thermal stress. J. Evol. Biol. 30, 422–429 (2016).Article 

    Google Scholar 
    16.Saxon, A. D., O’Brien, E. K. & Bridle, J. R. Temperature fluctuations during development reduce male fitness and may limit adaptive potential in tropical rainforest Drosophila. J. Evol. Biol. 31, 405–415 (2018).CAS 
    Article 

    Google Scholar 
    17.Breckels, R. D. & Neff, B. D. The effects of elevated temperature on the sexual traits, immunology and survivorship of a tropical ectotherm. J. Exp. Biol. 216, 2658–2664 (2013).Article 

    Google Scholar 
    18.Paxton, C. W., Baria, M. V. B., Weis, V. M. & Harii, S. Effect of elevated temperature on fecundity and reproductive timing in the coral Acropora digitifera. Zygote 24, 511–516 (2016).Article 

    Google Scholar 
    19.Hurley, L. L., McDiarmid, C. S., Friesen, C. R., Griffith, S. C. & Rowe, M. Experimental heatwaves negatively impact sperm quality in the zebra finch. Proc. R. Soc. Lond. B 285, 20172547 (2018).
    Google Scholar 
    20.Yogev, L. et al. Seasonal variations in pre‐ and post‐thaw donor sperm quality. Hum. Reprod. 19, 880–885 (2004).CAS 
    Article 

    Google Scholar 
    21.Terblanche, J. S., Deere, J. A., Clusella Trullas, S., Janion, C. & Chown, S. L. Critical thermal limits depend on methodological context. Proc. R. Soc. Lond. B 274, 2935–2942 (2007).
    Google Scholar 
    22.Ives, A. R. R2s for correlated data: phylogenetic models, LMMs, and GLMMs. Syst. Biol. 68, 234–251 (2019).Article 

    Google Scholar 
    23.Dillon, M. E., Wang, G., Garrity, P. A. & Huey, R. B. Thermal preference in Drosophila. J. Therm. Biol. 34, 109–119 (2009).Article 

    Google Scholar 
    24.Tratter-Kinzner, M. et al. Is temperature preference in the laboratory ecologically relevant for the field? The case of Drosophila nigrosparsa. Glob. Ecol. Conserv. 18, e00638 (2019).Article 

    Google Scholar 
    25.van Heerwaarden, B. & Sgrò, C. M. Male fertility thermal limits predict vulnerability to climate warming. Nat. Commun. 12, 2214 (2021).CAS 
    Article 

    Google Scholar  More

  • in

    Author Correction: Priority list of biodiversity metrics to observe from space

    Faculty of Geo-Information Science and Earth Observation (ITC), University of Twente, Enschede, the NetherlandsAndrew K. Skidmore, Elnaz Neinavaz, Abebe Ali, Roshanak Darvishzadeh, Marcelle C. Lock & Tiejun WangDepartment of Earth and Environmental Science, Macquarie University, Sydney, New South Wales, AustraliaAndrew K. Skidmore & Marcelle C. LockDepartment of Forest Resources Management, University of British Columbia, Vancouver, British Columbia, CanadaNicholas C. CoopsDepartment of Geography and Environmental Studies, Wollo University, Dessie, EthiopiaAbebe AliRemote Sensing Laboratories, Department of Geography, University of Zurich, Zurich, SwitzerlandMichael E. SchaepmanEuropean Space Research Institute (ESRIN), European Space Agency, Frascati, ItalyMarc PaganiniInstitute for Biodiversity and Ecosystem Dynamics (IBED), University of Amsterdam, Amsterdam, the NetherlandsW. Daniel KisslingBiodiversity Centre, Finnish Environment Institute (SYKE), Helsinki, FinlandPetteri VihervaaraInstitute of Geographical Sciences, Freie Universität Berlin, Berlin, GermanyHannes FeilhauerRemote Sensing Center for Earth System Research, University of Leipzig, Leipzig, GermanyHannes FeilhauerNatureServe, Arlington, VA, USAMiguel FernandezGeorge Mason University, Fairfax, VA, USAMiguel FernandezGerman Centre for Integrative Biodiversity Research (iDiv), Leipzig, GermanyNéstor FernándezInstitute of Biology, Martin Luther University Halle-Wittenberg, Halle (Saale), GermanyNéstor FernándezGoogle, Zurich, SwitzerlandNoel GorelickTour du Valat, Arles, FranceIlse GeijzendorfferEarth Observation Center (EOC), Remote Sensing Technology Institute, German Aerospace Center (DLR), Oberpfaffenhofen, GermanyUta Heiden & Stefanie HolzwarthDepartment of Visitor Management and National Park Monitoring, Bavarian Forest National Park Administration, Grafenau, GermanyMarco HeurichAlbert Ludwigs University of Freiburg, Freiburg, GermanyMarco HeurichGBIF Secretariat, Copenhagen, DenmarkDonald HobernCollege of Marine Science, University of South Florida, St Petersburg, FL, USAFrank E. Muller-KargerFlemish Institute for Technological Research (VITO), Mol, BelgiumRuben Van De KerchoveComputational Landscape Ecology, Helmholtz Centre for Environmental Research (UFZ), Leipzig, GermanyAngela LauschGeography Department, Humboldt University of Berlin, Berlin, GermanyAngela LauschTechnische Universität Braunschweig, Braunschweig, GermanyPedro J. LeitãoHumboldt-Universität zu Berlin, Berlin, GermanyPedro J. LeitãoWageningen Environmental Research, Wageningen University & Research, Wageningen, the NetherlandsCaspar A. MücherUN Environment World Conservation Monitoring Centre, Cambridge, UKBrian O’ConnorDepartment of Biological, Geological and Environmental Sciences, University of Bologna, Bologna, ItalyDuccio RocchiniDepartment of Applied Geoinformatics and Spatial Planning, Faculty of Environmental Sciences, Czech University of Life Sciences, Prague, Czech RepublicDuccio RocchiniEarth Science Division, NASA, Washington DC, USAWoody TurnerUnilever Europe B.V., Rotterdam, the NetherlandsJan Kees VisInstitute of Geography and Geology, University of Wuerzburg, Würzburg, GermanyMartin WegmannLand Systems and Sustainable Land Management, Geographisches Institut, Universität Bern, Bern, SwitzerlandVladimir Wingate More

  • in

    Effects of long-term integrated agri-aquaculture on the soil fungal community structure and function in vegetable fields

    Effects of the two planting systems on soil fungal diversityIn this study, 561,254 sequences were generated from 15 samples obtained from 5 treatments. Base sequences with a length of 201–300 bp accounted for 97.82% of all sequences (Table S1a,b). Rarefaction curves at a similarity level of 97% indicated that the number of sequences extracted from most samples tended to plateau above 10,000. The number of sequences extracted in the test exceeded 30,000, suggesting that the sequencing data were close to saturation, sequencing depth was reasonable, and the results reflected true sample conditions (Fig. 1). The coverage of all samples was above 99.84%. The range of reads in each sample was between 34,390 and 43,510. The range of Operational Taxonomic Units (OTUs) in each sample was between 145 and 318 (Table 1).Figure 1α-Diversity comparison. Rarefaction curves for OTUs were calculated using Mothur (v1.27.0) with reads normalized to more than 30,000 for each sample using a distance of 0.03 OTU.Full size imageTable 1 Comparison of α-diversity indices in TPP and VEE soil samples.Full size tableThe analysis of alpha diversity showed that with increasing planting time, soil fungal OTUs, the Chao index, and the ACE index in TPP-treated plots increased and then decreased with time. In the VEE-IPBP-treated plots, these 3 indexes increased with time and were 56.94%, 33.81%, and 32.50% higher than those in the TPP-treated plots, respectively, after 6 years of implementation (p  More

  • in

    Distinguishing anthropogenic and natural contributions to coproduction of national crop yields globally

    1.Pellegrini, P. & Fernández, R. J. Crop intensification, land use, and on-farm energy-use efficiency during the worldwide spread of the green revolution. Proc. Natl. Acad. Sci. U. S. A. 115(10), 2335–2340 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    2.Ray, D. K., Ramankutty, N., Mueller, N. D., West, P. C. & Foley, J. A. Recent patterns of crop yield growth and stagnation. Nat. Commun. 3(1), 1293 (2012).ADS 
    PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    3.Palomo, I., Felipe-Lucia, M. R., Bennett, E. M., Martín-López, B. & Pascual, U. Chapter six—disentangling the pathways and effects of ecosystem service co-production. In Advance Ecology Research (eds Woodward, G. & Bohan, D. A.) 245–283 (Academic Press, 2016).
    Google Scholar 
    4.Lavorel, S., Locatelli, B., Colloff, M. J. & Bruley, E. Co-producing ecosystem services for adapting to climate change. Philos. T. Roy. Soc. B. 375(1794), 20190119 (2020).Article 

    Google Scholar 
    5.Boerema, A., Rebelo, A. J., Bodi, M. B., Esler, K. J. & Meire, P. Are ecosystem services adequately quantified?. J. Appl. Ecol. 54(2), 358–370 (2017).Article 

    Google Scholar 
    6.Maes, J. et al. An indicator framework for assessing ecosystem services in support of the EU Biodiversity Strategy to 2020. Ecosyst. Serv. 17, 14–23 (2016).Article 

    Google Scholar 
    7.Jones, L. et al. Stocks and flows of natural and human-derived capital in ecosystem services. Land Use Policy 52, 151–162 (2016).Article 

    Google Scholar 
    8.Barot, S., Yé, L., Abbadie, L., Blouin, M. & Frascaria-Lacoste, N. Ecosystem services must tackle anthropized ecosystems and ecological engineering. Ecol. Eng. 99, 486–495 (2017).Article 

    Google Scholar 
    9.Remme, R. P., Edens, B., Schröter, M. & Hein, L. Monetary accounting of ecosystem services: a test case for Limburg province, the Netherlands. Ecol. Econ. 112, 116–128 (2015).Article 

    Google Scholar 
    10.Gaiser, T. & Stahr, K. Soil organic carbon, soil formation and soil fertility. In Ecosystem Services and Carbon Sequestration in the Biosphere (eds Lal, R. et al.) 407–418 (Springer, 2013).
    Google Scholar 
    11.FAO and ITPS. Status of the World’s Soil Resources (SWSR)—Main Report (Food and Agriculture Organization of the United Nations and Intergovernmental Technical Panel on Soils, 2015).
    Google Scholar 
    12.Dainese, M. et al. A global synthesis reveals biodiversity-mediated benefits for crop production. Sci. Adv. 5(10), eaax0121 (2019).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    13.Bommarco, R., Kleijn, D. & Potts, S. G. Ecological intensification: harnessing ecosystem services for food security. Trends Ecol. Evol. 28(4), 230–238 (2013).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    14.Zabel, F., Putzenlechner, B. & Mauser, W. Global agricultural land resources—a high resolution suitability evaluation and its perspectives until 2100 under climate change conditions. PLoS ONE 9(9), e107522 (2014).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    15.Pelletier, N. et al. Energy intensity of agriculture and food systems. Annu. Rev. Environ. Resour. 36(1), 223–246 (2011).Article 

    Google Scholar 
    16.Díaz, S. et al. The IPBES conceptual framework—connecting nature and people. Curr. Opin. Environ. Sustain. 14, 1–16 (2015).Article 

    Google Scholar 
    17.Bennett, E. M. Research frontiers in ecosystem service science. Ecosystems 20(1), 31–37 (2017).Article 

    Google Scholar 
    18.Woods, J., Williams, A., Hughes, J. K., Black, M. & Murphy, R. Energy and the food system. Philos. T. Roy. Soc. B. 365(1554), 2991–3006 (2010).Article 

    Google Scholar 
    19.Foley, J. A. et al. Global consequences of land use. Science 309(5734), 570–574 (2005).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    20.Seppelt, R., Manceur, A. M., Liu, J., Fenichel, E. P. & Klotz, S. Synchronized peak-rate years of global resources use. Ecol. Soc. 19(4), 50 (2014).Article 

    Google Scholar 
    21.Meyfroidt, P. et al. Middle-range theories of land system change. Glob. Environ. Chang. 53, 52–67 (2018).Article 

    Google Scholar 
    22.Fitter, A. H. Are ecosystem services replaceable by technology?. Environ. Res. Econ. 55(4), 513–524 (2013).Article 

    Google Scholar 
    23.Cohen, F., Hepburn, C. J. & Teytelboym, A. Is natural capital really substitutable?. Annu. Rev. Environ. Resour. 44(1), 425–448 (2019).Article 

    Google Scholar 
    24.Ekins, P., Simon, S., Deutsch, L., Folke, C. & De Groot, R. A framework for the practical application of the concepts of critical natural capital and strong sustainability. Ecol. Econ. 44(2–3), 165–185 (2003).Article 

    Google Scholar 
    25.Lassaletta, L., Billen, G., Grizzetti, B., Anglade, J. & Garnier, J. 50 year trends in nitrogen use efficiency of world cropping systems: the relationship between yield and nitrogen input to cropland. Environ. Res. Lett. 9(10), 105011 (2014).ADS 
    Article 

    Google Scholar 
    26.Levers, C., Butsic, V., Verburg, P. H., Müller, D. & Kuemmerle, T. Drivers of changes in agricultural intensity in Europe. Land Use Policy 58, 380–393 (2016).Article 

    Google Scholar 
    27.Coomes, O. T., Barham, B. L., MacDonald, G. K., Ramankutty, N. & Chavas, J.-P. Leveraging total factor productivity growth for sustainable and resilient farming. Nat. Sustain. 2(1), 22–28 (2019).Article 

    Google Scholar 
    28.Fuglie, K. R&D capital, RD spillovers, and productivity growth in world agriculture. Appl. Econ. Perspect. Policy 40(3), 421–444 (2018).Article 

    Google Scholar 
    29.Mueller, N. D. et al. Closing yield gaps through nutrient and water management. Nature 490, 254 (2012).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    30.German, R. N., Thompson, C. E. & Benton, T. G. Relationships among multiple aspects of agriculture’s environmental impact and productivity: a meta-analysis to guide sustainable agriculture. Biol. Rev. 92(2), 716–738 (2017).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    31.Lee, H. & Lautenbach, S. A quantitative review of relationships between ecosystem services. Ecol. Indic. 66, 340–351 (2016).Article 

    Google Scholar 
    32.Lobell, D. B., Schlenker, W. & Costa-Roberts, J. Climate trends and global crop production since 1980. Science 333(6042), 616–620 (2011).ADS 
    CAS 
    Article 

    Google Scholar 
    33.Erb, K.-H. et al. A conceptual framework for analysing and measuring land-use intensity. Curr. Opin. Environ. Sustain. 5(5), 464–470 (2013).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    34.Loos, J. et al. Putting meaning back into “sustainable intensification”. Front. Ecol. Environ. 12(6), 356–361 (2014).Article 

    Google Scholar 
    35.Kleijn, D. et al. Ecological intensification: bridging the gap between science and practice. Trends Ecol. Evol. 34(2), 154–166 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    36.Stirzaker, R., Biggs, H., Roux, D. & Cilliers, P. Requisite simplicities to help negotiate complex problems. Ambio 39(8), 600–607 (2010).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    37.Kuemmerle, T. et al. Challenges and opportunities in mapping land use intensity globally. Curr. Opin. Environ. Sustain. 5(5), 484–493 (2013).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    38.Garibaldi, L. A., Aizen, M. A., Klein, A. M., Cunningham, S. A. & Harder, L. D. Global growth and stability of agricultural yield decrease with pollinator dependence. Proc. Natl. Acad. Sci. U. S. A. 108(14), 5909–5914 (2011).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    39.Bengtsson, J. Biological control as an ecosystem service: partitioning contributions of nature and human inputs to yield. Ecol. Entomol. 40(S1), 45–55 (2015).Article 

    Google Scholar 
    40.Seppelt, R., Arndt, C., Beckmann, M., Martin, E. A. & Hertel, T. Deciphering the biodiversity-production mutualism in the global food security debate. Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2020.06.012 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    41.Poore, J. & Nemecek, T. Reducing food’s environmental impacts through producers and consumers. Science 360(6392), 987–992 (2018).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    42.Beckmann, M. et al. Conventional land-use intensification reduces species richness and increases production: a global meta-analysis. Glob. Chang. Biol. 25(6), 1941–1956 (2019).ADS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    43.Garibaldi, L. A. et al. Farming approaches for greater biodiversity, livelihoods, and food security. Trends Ecol. Evol. 32(1), 68–80 (2017).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    44.Monfreda, C., Ramankutty, N. & Foley, J. A. Farming the planet: 2. Geographic distribution of crop areas, yields, physiological types, and net primary production in the year 2000. Glob. Biogeochem. Cycles 22(1), 1–19 (2008).Article 
    CAS 

    Google Scholar 
    45.IFA, IFDC, IPI, PPI, FAO. Fertilizer Use by Crop (FAO, 2002).
    Google Scholar 
    46.IFA. Assessment of Fertilizer Use by Crop at the Global Level 2006/07–2007/08 (IFA, 2009).
    Google Scholar 
    47.IFA. Assessment of Fertilizer Use by Crop at the Global Level 2010–2010/11 (IFA, 2013).
    Google Scholar 
    48.IFA and IPNI. Assessment of Fertilizer Use by Crop at the Global Level (IFA and IPNI, 2017).
    Google Scholar 
    49.FAO. Crops. http://www.fao.org/faostat/en/#data/QC (2018).50.FAO. Capital Stock. http://www.fao.org/faostat/en/#data/CS (2018).51.U.S. Bureau of Labor Statistics. CPI Inflation Calculator. https://data.bls.gov/cgi-bin/cpicalc.pl?cost1=1.00&year1=200001&year2=201401 (2020).52.FAO. Livestock Manure. http://www.fao.org/faostat/en/#data/EMN (2018).53.FAO. Food Balance Sheets: A Handbook 95 (FAO, 2001).
    Google Scholar 
    54.World Bank. The World by Income and Region. https://datatopics.worldbank.org/world-development-indicators/the-world-by-income-and-region.html (2019).55.R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2019).
    Google Scholar 
    56.RStudio Team. RStudio: Integrated Development for R (RStudio, Inc., 2018).
    Google Scholar 
    57.Cook, R. D. Detection of influential observation in linear regression. Technometrics 19(1), 15–18 (1977).MathSciNet 
    MATH 

    Google Scholar 
    58.Natural Earth. Admin 0—Countries. Version 4.0.0 (accessed 22 October 2017); https://www.naturalearthdata.com/ (2017). More

  • in

    Understanding anatomical plasticity of Argan wood features at local geographical scale in ecological and archaeobotanical perspectives

    Sampling, preparation and treatment of modern reference materialA total of 53 modern wood samples were analyzed. The modern reference samples were collected in 2014 during the annual archaeological field mission, from 36 individuals (Table S1). For some trees, two wood samples of different diameters were collected in order to take into account anatomical variability within individual.The collected individuals showed different conditions of growth described in the introduction section and detailed in the Table 1. With the agreement of the Tifigit inhabitants and local authorities, wood sampling was achieved but samples were not collected from trunks, to avoid injuring trees of major symbolic, ecological and economic importance. Only section samples with perfect axial symmetry were retained to avoid any impact of biomechanical constraints (formation of reaction wood) on wood characters.Once collected, the samples were air-dried during a month at the laboratory. The samples were separately wrapped in tin foil and buried in the sand and then charred without oxygen, at 450 °C for 15 to 20 min depending on the size of the sample. As a result, samples were enriched in carbon (content  > 90%)20,26, reached their maximal shrinkage27, and thus are considered to become morphologically comparable to charcoal produced in medieval fires27,28,29,30,31. The minimum and the maximum diameter of wood samples were measured (mm) using a digital measuring calliper before and after carbonization. The diameter used in the following analyses is the mean of the two measurements carried out before carbonization.Archaeological materialTwenty archaeological charcoal fragments of Argan tree identified during a previous analysis session13 were included in this study (Table S2). All the Argan charcoal fragments were collected in the medieval archaeological deposits of Îgîlîz13. They come from various contexts, for the most part from living units, and belong to the period of highest human activity at the site, between the late 11th and early thirteenth centuries.Quantitative eco-anatomical analysis of wood applied to the Argan treeThe approach consists in measuring constitutive elements of wood and aims to understand variations according to intrinsic (inferred by the branch diameter mainly age of tree18, linked to the existence of growth rings that are often difficult to distinguish) and environmental parameters affecting the cambial activity and thus, rate of growth and wood development20,28,29,30. This high resolution analysis of wood structure, particularly of conductive elements, allows addressing numerous questions that have been successfully solved in the case of the olive tree and the grapevine, such as phenology, ecology, climate, impact of human activities and agricultural practices20,24,25,31,32,33.Argania spinosa wood is diffuse-porous with a dendritic and diagonal arrangement of vessel elements in transversal section34. The axial parenchyma bands are in tangential alignment and composed of multicellular strands. In radial alignment, woody rays are 1–3 cells wide and of heterocellular composition (Fig. 6).Figure 6Wood anatomical features of the Argan tree (in blue) and measured anatomical characters (in red).Full size imageTo apply a quantitative eco-anatomy approach to the Argan tree, both modern charred samples and archaeological charcoal are broken manually in transverse anatomical section. The following wood constitutive elements and anatomical characters related to sap conduction and reserve storage are observed and measured under a reflected-light microscope connected with an image analysis system (DFC300 FX Leica camera and LAS Leica software) (Fig. 6): (1) vessel density (DVS—number of vessels / mm2), (2) vessel surface area (SVS, µm2), (3) ray density (DRA—number of rays / mm2), (4) axial parenchyma density (DPA, number of bands / mm2), (5) Density of wood fenestrated zones bordered on one side by the radial alignment of axial parenchyma cells and on the other side, tangentially, by rays (DWF—number of fenestrated zones / mm2).These anatomical features were measured several times (see ‘Statistical analyses’ section) following radial lines from the cambium inwards the sample and crossing a small number of growth rings (i.e. functional rings from a sap conduction point of view). Moreover, the hydraulic conductivity or vascular conductivity (CD) was assessed using the following formula: CD = (SVS/π)2/DVS (after32,35,36,37). Finally, the ratio ‘Conductive surface / total wood area’ (SC) was calculated.Statistical analysesIn order to determine the number of measurements required for an optimal assessment of anatomical features, a rarefaction method was carried out from the analysis of test wood samples. For each one, repeated measurements of anatomical characters (Surface vessel area (SVS), Density of vessels (DVS), Ray density (DRA), Axial parenchyma density (DPA) and Density of wood fenestrated zones (DWF)) were performed following the aforementioned method and the cumulative mean value was then calculated for each character20,29. For each test sample and anatomical character, the number of measurements of a character required for an optimal assessment was quantified as the minimum number of measurements required to stabilize the mean value (rarefaction curve or cumulative mean curve).Furthermore, different measurement sessions were carried out with the aim of testing possible errors and reproducibility of measurements taken by one or various observers, respectively. The data sets produced were tested using the PCA performed to evaluate the Argan anatomical variability. The ARG8-2 sample was used as test sample. In addition to the initial measurements. The ARG8-2 sample was analyzed 4 times: twice by one operator (ARG8-2 (1-OP1) and ARG8-2 (2-OP1)) and twice by another (ARG8-2 (3-OP2) and ARG8-2 (3-OP2)) at different times. The additional data were incorporated into the PCA as additional individuals for comparison with initial anatomical features of ARG8-2.After showing that measurement errors have no impact on the validity of results and the measurements are reproducible, quantitative eco-anatomical data were processed using a principal component analysis (PCA) in order to evaluate anatomical plasticity in the reference modern material, to appreciate relationships between characters and wood sample caliber and to confront archaeological data to the reference model. PCA was applied on 53 reference modern samples and 7 quantitative variables (anatomical characters) to: (1) validate the hypothesis that there is a significant relationship between the size of the branch and anatomy, as previously demonstrated by analyses of wood development and structure18,20,38 and dendrochronology39; (2) identify the anatomical characters most affected by the age of the branch and, in that case, model the ‘anatomical characters—caliber of the branch’ relationship; (3) develop predictive model that might estimate the minimum branch caliber from eco-anatomical data of archaeological charcoal.Finally, data from analysis of the 20 archaeological charcoal fragments were included in PCA as additional statistical samples. They do not contribute to the development of the reference model, but are compared to the modern reference samples in order to infer the ecological conditions under which Argan trees grew during the Middle Ages. More

  • in

    Changes in taxonomic and functional diversity of plants in a chronosequence of Eucalyptus grandis plantations

    1.Sala, O. E. et al. Global biodiversity scenarios for the year 2100. Science (80- ) 287, 1770–1774 (2000).CAS 
    Article 

    Google Scholar 
    2.Wall, D. H. & Nielsen, U. N. Biodiversity and ecosystem services: is it the same below ground?. Nat. Educ. Knowl. 12, 3–8 (2012).
    Google Scholar 
    3.FAO. Global Forest Resources Assessment 2015: Desk Reference. http://www.fao.org/3/a-i4808e.pdf (2015).4.Filloy, J., Zurita, G. A., Corbelli, J. M. & Bellocq, M. I. On the similarity among bird communities: testing the influence of distance and land use. Acta Oecol. 36, 333–338 (2010).ADS 
    Article 

    Google Scholar 
    5.Santoandré, S., Filloy, J., Zurita, G. A. & Bellocq, M. I. Ant taxonomic and functional diversity show differential response to plantation age in two contrasting biomes. For. Ecol. Manag. 437, 304–313 (2019).Article 

    Google Scholar 
    6.Calviño-Cancela, M. Effectiveness of eucalypt plantations as a surrogate habitat for birds. For. Ecol. Manag. 310, 692–699 (2013).Article 

    Google Scholar 
    7.Santoandré, S., Filloy, J., Zurita, G. A. & Bellocq, M. I. Taxonomic and functional β-diversity of ants along tree plantation chronosequences differ between contrasting biomes. Basic Appl. Ecol. 41, 1–12 (2019).Article 

    Google Scholar 
    8.Corbelli, J. M. et al. Integrating taxonomic, functional and phylogenetic beta diversities: interactive effects with the biome and land use across taxa. PLoS ONE 10, 1–17 (2015).Article 
    CAS 

    Google Scholar 
    9.Phifer, C. C., Knowlton, J. L., Webster, C. R., Flaspohler, D. J. & Licata, J. A. Bird community responses to afforested eucalyptus plantations in the Argentine pampas. Biodivers. Conserv. https://doi.org/10.1007/s10531-016-1126-6 (2016).Article 

    Google Scholar 
    10.Tererai, F., Gaertner, M., Jacobs, S. M. & Richardson, D. M. Eucalyptus invasions in riparian forests: effects on native vegetation community diversity, stand structure and composition. For. Ecol. Manag. 297, 84–93 (2013).Article 

    Google Scholar 
    11.Brancalion, P. H. S. et al. Intensive silviculture enhances biomass accumulation and tree diversity recovery in tropical forest restoration. Ecol. Appl. 29, 1–12 (2019).Article 

    Google Scholar 
    12.Zhang, C., Liu, G., Xue, S. & Wang, G. Soil bacterial community dynamics reflect changes in plant community and soil properties during the secondary succession of abandoned farmland in the Loess Plateau. Soil Biol. Biochem. 97, 40–49 (2016).CAS 
    Article 

    Google Scholar 
    13.Zhu, Y., Wang, Y. & Chen, L. Effects of non-native tree plantations on the diversity of understory plants and soil macroinvertebrates in the Loess Plateau of China. Plant Soil 446, 357–368 (2019).Article 
    CAS 

    Google Scholar 
    14.Zhang, W. et al. Plant functional composition and species diversity affect soil C, N, and P during secondary succession of abandoned farmland on the Loess Plateau. Ecol. Eng. 122, 91–99 (2018).Article 

    Google Scholar 
    15.Munévar, A., Rubio, G. D. & Andrés, G. Changes in spider diversity through the growth cycle of pine plantations in the semi-deciduous Atlantic forest: the role of prey availability and abiotic conditions. For. Ecol. Manag. 424, 536–544 (2018).Article 

    Google Scholar 
    16.Vega, E., Baldi, G., Jobbágy, E. G. & Paruelo, J. Land use change patterns in the Río de la Plata grasslands: the influence of phytogeographic and political boundaries. Agric. Ecosyst. Environ. 134, 287–292 (2009).Article 

    Google Scholar 
    17.Ntshuxeko, V. E. & Ruwanza, S. Physical properties of soil in Pine elliottii and Eucalyptus cloeziana plantations in the Vhembe biosphere, Limpopo Province of South Africa. J. For. Res. https://doi.org/10.1007/s11676-018-0830-3 (2018).Article 

    Google Scholar 
    18.Kerr, T. F. & Ruwanza, S. Does Eucalyptus grandis invasion and removal affect soils and vegetation in the Eastern Cape Province, South Africa?. Austral. Ecol. 41, 328–338 (2016).Article 

    Google Scholar 
    19.Zhang, D. J., Zhang, J., Yang, W. Q. & Wu, F. Z. Potential allelopathic effect of Eucalyptus grandis across a range of plantation ages. Ecol. Res. 25, 13–23 (2010).Article 

    Google Scholar 
    20.Díaz, S. & Cabido, M. Vive la difference: plant functional diversity matters to ecosystem processes: plant functional diversity matters to ecosystem processes. Trends Ecol. Evol. 16, 646–655 (2001).Article 

    Google Scholar 
    21.Petchey, O. L. & Gaston, K. J. Functional diversity: back to basics and looking forward. Ecol. Lett. 9, 741–758 (2006).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    22.Luck, G. W., Lavorel, S., Mcintyre, S. & Lumb, K. Improving the application of vertebrate trait-based frameworks to the study of ecosystem services. J. Anim. Ecol. 81, 1065–1076 (2012).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    23.Lindenmayer, D. et al. Richness is not all: how changes in avian functional diversity reflect major landscape modification caused by pine plantations. Divers. Distrib. 21, 836–847 (2015).Article 

    Google Scholar 
    24.Whittaker, R. H. Vegetation of the Siskiyou Mountains, Oregon and California. Ecol. Monogr. 30, 280–338 (1960).Article 

    Google Scholar 
    25.Swenson, N. G. Functional and Phylogenetic Ecology in R. Use R! (2014). https://doi.org/10.1007/978-1-4614-9542-0.26.Vaccaro, A. S., Filloy, J. & Bellocq, M. I. What land use better preserves taxonomic and functional diversity of birds in a grassland biome?. Avian Conserv. Ecol. 14, 1 (2019).Article 

    Google Scholar 
    27.Blair, J., Nippert, J. & Briggs, J. Grassland Ecology. Ecology and the Environment vol. 8 (Springer, 2014).28.Nic Lughadha, E. et al. Measuring the fate of plant diversity: towards a foundation for future monitoring and opportunities for urgent action. Philos. Trans. R. Soc. B Biol. Sci. 360, 359–372 (2005).CAS 
    Article 

    Google Scholar 
    29.Marteinsdóttir, B. & Eriksson, O. Trait-based filtering from the regional species pool into local grassland communities. J. Plant Ecol. 7, 347–355 (2014).Article 

    Google Scholar 
    30.Salgado Negret, B. La Ecología Funcional como aproximación al estudio, manejo y conservación de la biodiversidad: protocolos y aplicaciones. La ecología funcional como aproximación al estudio, manejo y conservación de la biodiversidad: protocolos y aplicaciones (2015).31.Barbier, S., Gosselin, F. & Balandier, P. Influence of tree species on understory vegetation diversity and mechanisms involved—a critical review for temperate and boreal forests. For. Ecol. Manag. 254, 1–15 (2008).Article 

    Google Scholar 
    32.Zhang, D., Zhang, J., Yang, W., Wu, F. & Huang, Y. Plant and soil seed bank diversity across a range of ages of Eucalyptus grandis plantations afforested on arable lands. Plant Soil 376, 307–325 (2014).CAS 
    Article 

    Google Scholar 
    33.Zhang, C. & Fu, S. Allelopathic effects of eucalyptus and the establishment of mixed stands of eucalyptus and native species. For. Ecol. Manag. 258, 1391–1396 (2009).Article 

    Google Scholar 
    34.Florentine, S. K. & Fox, J. E. D. Allelopathic effects of Eucalyptus victrix L. on Eucalyptus species and grasses. Allelopath. J. 11, 77–83 (2003).
    Google Scholar 
    35.Jobbágy, E. et al. Forestación en pastizales: hacia una visión integral de sus oportunidades y costos ecológicos. Agrociencia X, 109–124 (2006).36.Ruwanza, S., Gaertner, M., Esler, K. J. & Richardson, D. M. Allelopathic effects of invasive Eucalyptus camaldulensis on germination and early growth of four native species in the Western Cape South Africa. South. For. 77, 91–105 (2015).Article 

    Google Scholar 
    37.Suggitt, A. J. et al. Habitat microclimates drive fi ne-scale variation in extreme temperatures. Oikos https://doi.org/10.1111/j.1600-0706.2010.18270.x (2011).Article 

    Google Scholar 
    38.Zellweger, F., Roth, T., Bugmann, H. & Bollmann, K. Beta diversity of plants, birds and butterflies is closely associated with climate and habitat structure. Glob. Ecol. Biogeogr. 26, 898–906 (2017).Article 

    Google Scholar 
    39.Silveira, L. & Alonso, J. Runoff modifications due to the conversion of natural grasslands to forests in a large basin in Uruguay. Hidrol. Process. 329, 320–329 (2009).ADS 
    Article 

    Google Scholar 
    40.Mendoza, C. A., Gallardo, J. F., Turrión, M. B., Pando, V. & Aceñolaza, P. G. Dry weight loss in leaves of dominant species in a successional sequence of the Mesopotamian Espinal (Argentina). For. Syst. 26, 1–10 (2017).
    Google Scholar 
    41.Rodriguez, E. E., Aceñolaza, P. G., Perea, E. L. & Galán de Mera, A. A phytosociological analysis of Butia yatay (Arecaceae) palm groves and gallery forests in Entre Rios, Argentina. Aust. J. Bot. https://doi.org/10.1071/BT16140 (2017).Article 

    Google Scholar 
    42.Piwczyński, M., Puchałka, R. & Ulrich, W. Influence of tree plantations on the phylogenetic structure of understorey plant communities. For. Ecol. Manag. 376, 231–237 (2016).Article 

    Google Scholar 
    43.Csecserits, A. et al. Tree plantations are hot-spots of plant invasion in a landscape with heterogeneous land-use. Agric. Ecosyst. Environ. 226, 88–98 (2016).Article 

    Google Scholar 
    44.Amazonas, N. T. et al. High diversity mixed plantations of Eucalyptus and native trees: an interface between production and restoration for the tropics. For. Ecol. Manag. 417, 247–256 (2018).Article 

    Google Scholar 
    45.Verstraeten, G. et al. Understorey vegetation shifts following the conversion of temperate deciduous forest to spruce plantation. For. Ecol. Manag. 289, 363–370 (2013).Article 

    Google Scholar 
    46.Grass, I., Brandl, R., Botzat, A., Neuschulz, E. L. & Farwig, N. Contrasting taxonomic and phylogenetic diversity responses to forest modifications: comparisons of taxa and successive plant life stages in south African scarp forest. PLoS ONE 10, 1–20 (2015).Article 
    CAS 

    Google Scholar 
    47.Wu, J. et al. Should exotic Eucalyptus be planted in subtropical China: insights from understory plant diversity in two contrasting Eucalyptus chronosequences. Environ. Manag. 56, 1244–1251 (2015).ADS 
    Article 

    Google Scholar 
    48.Jin, D. et al. High risk of plant invasion in the understory of eucalypt plantations in South China. Sci. Rep. 5, 18492 (2016).ADS 
    Article 
    CAS 

    Google Scholar 
    49.Haughian, S. R. & Frego, K. A. Short-term effects of three commercial thinning treatments on diversity of understory vascular plants in white spruce plantations of northern New Brunswick. For. Ecol. Manag. 370, 45–55 (2016).Article 

    Google Scholar 
    50.Smith, G. F., Iremonger, S., Kelly, D. L., O’Donoghue, S. & Mitchell, F. J. G. Enhancing vegetation diversity in glades, rides and roads in plantation forests. Biol. Conserv. 136, 283–294 (2007).Article 

    Google Scholar 
    51.Aceñolaza, P. G., Rodriguez, E. E. & Diaz, D. Efecto de prácticas de manejo silvícola sobre la diversidad vegetal bajo plantaciones de Eucalyptus grandis. In 4to Congreso Forestal Argentino y Latinoamericano (2013).52.Connell, J. H. & Slatyer, R. O. Mechanisms of succession in natural communities and their role in community stability and organization. Am. Nat. 111, 1119–1144 (1977).Article 

    Google Scholar 
    53.Pedley, S. M. & Dolman, P. M. Multi-taxa trait and functional responses to physical disturbance. J. Anim. Ecol. 83, 1542–1552 (2014).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    54.Birkhofer, K., Smith, H. G., Weisser, W. W., Wolters, V. & Gossner, M. M. Land-use effects on the functional distinctness of arthropod communities. Ecography (Cop.) https://doi.org/10.1111/ecog.01141 (2015).Article 

    Google Scholar 
    55.Mangels, J., Fiedler, K., Schneider, F. D. & Blu, N. Diversity and trait composition of moths respond to land-use intensification in grasslands : generalists replace specialists. Biodivers. Conserv. https://doi.org/10.1007/s10531-017-1411-z (2017).Article 

    Google Scholar 
    56.Morello, J., Matteucci, S. D., Rodriguez, A. F. & Silva, M. Ecorregiones y complejos ecosistemicos argentino. (2012).57.Cabrera, Á. Fitogeografía de la República Argentina. Bol. Soc. Argent. Bot. 14, 1–42 (1971).
    Google Scholar 
    58.Rodriguez, E. E., Aceñolaza, P. G., Picasso, G. & Gago, J. Plantas del bajo Rio Uruguay: árboles, arbustos, herbáceas, lianas y epifitas. (2018).59.Bilenca, D. & Miñarro, F. Identificación de Áreas Valiosas de Pastizal (AVPs) en las Pampas y Campos de Argentina Uruguay y sur de Brasil. Vasa https://doi.org/10.1007/s13398-014-0173-7.2 (2004).Article 

    Google Scholar 
    60.Inta. Plan de Tecnologia Regional 2009–2011. INTA Cent. Reg. Entre Rios (2011).61.Aguerre, M. et al. Manual para productores de Eucaliptos de la Mesopotamia Argentina. (1995).62.Aparicio, J. L., Larocca, F. & Dalla Tea, F. Silvicultura de establecimiento de Eucalyptus grandis. IDIA XXI, Revista de Información sobre Investigación y Desarrollo Agropecuario 66–69 (2005).63.Vilela, E., Leite, H. G. & Jaffe, K. Level of economic damage for leaf-cutting ants (Hymenoptera: Formicidae) in Eucalyptus plantations in Brazil. Sociobiology 42, 1–10 (2015).
    Google Scholar 
    64.Larroca, F., Dalla Tea, F. & Aparicio, J. L. Técnicas de implantación y manejo de eucaliptus para pequeños y medianos forestadores en Entre Ríos y Corrientes. in XIX Jornadas Forestales de Entre Ríos. (2004).65.Burkart, A. Flora ilustrada de la provincia de Entre Ríos. (INTA, 1969).66.Burkart, A. Flora ilustrada de Entre Ríos (Argentina). Parte 2 Gramíneas. Colección Científica del INTA (1969).67.Peyras, M., Vespa, N. I., Bellocq, M. I. & Zurita, G. A. Quantifying edge effects : the role of habitat contrast and species specialization. J. Insect Conserv. 17, 807–820 (2013).Article 

    Google Scholar 
    68.Werenkraut, V., Fergnani, P. N. & Ruggiero, A. Ants at the edge: a sharp forest-steppe boundary influences the taxonomic and functional organization of ant species assemblages along elevational gradients in northwestern Patagonia (Argentina). Biodivers. Conserv. 24, 287–308 (2015).Article 

    Google Scholar 
    69.Diaz, S., Cabido, M. & Casanoves, F. Plant functional traits and environmental filters at a regional scale. J. Veg. Sci. 9, 113–122 (1998).Article 

    Google Scholar 
    70.Grime, J. P. Benefits of plant diversity to ecosystems: immediate, filter and founder effects. J. Ecol. 86, 902–910 (1998).Article 

    Google Scholar 
    71.Carreño-Rocabado, G. et al. Land-use intensification effects on functional properties in tropical plant communities. Ecol. Appl. https://doi.org/10.1007/s11548-012-0737-y (2015).Article 

    Google Scholar 
    72.Pérez-Harguindeguy, N. et al. New Handbook for standardized measurment of plant functional traits worldwide. Aust. J. Bot. 61, 167–234 (2013).Article 

    Google Scholar 
    73.Laliberté, E. & Legendre, P. A distance-based framework for measuring functional diversity from multiple traits. Ecology 91, 299–305 (2010).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    74.Legendre, P. & Legendre, L. F. J. Numerical Ecology. (Elsevier, 2012).75.Kembel, S. W. et al. Package ‘ picante ’: Integrating Phylogenies and Ecology. Cran-R 1–55 (2018). https://doi.org/10.1093/bioinformatics/btq166 >.License.76.Swenson, N. G., Anglada-Cordero, P. & Barone, J. A. Deterministic tropical tree community turnover: evidence from patterns of functional beta diversity along an elevational gradient. Proc. R. Soc. B Biol. Sci. 278, 877–884 (2011).Article 

    Google Scholar 
    77.Cribari-Neto, F. & Zeileis, A. Journal of Statistical Software. J. Stat. Softw. 34, 1–24 (2010).Article 

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

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

    Google Scholar 
    80.Grace, J. B. Structural Equation Modeling and Natural Systems. (Cambridge University Press, 2006).81.Fan, Y. et al. Applications of structural equation modeling (SEM) in ecological studies: an updated review. Ecol. Process. 5, 19 (2016).ADS 
    Article 

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

    Google Scholar 
    83.Lefcheck, J., Byrnes, J. & Grace, J. Package ‘ piecewiseSEM ’. R (2019).84.Brown, A. M. et al. The fourth-corner solution – using predictive models to understand how species traits interact with the environment. Methods Ecol. Evol. 5, 344–352 (2014).Article 

    Google Scholar 
    85.Hastie, T., Tibshirani, R. & Friedman, J. The Elements of Statistical Learning: Data Mining, Inference and Prediction. (2009).86.Barton, K. Package ‘MuMIn’.Multi-Model Inference. (2018).87.Dawson, S. K. et al. Plant traits of propagule banks and standing vegetation reveal flooding alleviates impacts of agriculture on wetland restoration. J. Appl. Ecol. 54, 1907–1918 (2017).Article 

    Google Scholar 
    88.QGIS Development Team. QGIS Geographic Information System. Open Source Geospatial Foundation Project. (2019). http://qgis.osgeo.org More