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    Bacteria incorporated with calcium lactate pentahydrate to improve the mortar properties and self-healing occurrence

    1.
    Monteiro, P. J. M., Miller, S. A. & Horvath, A. Towards sustainable concrete. Nat. Mater. 16, 698–699 (2017).
    ADS  CAS  Article  Google Scholar 
    2.
    Schneider, M., Romer, M., Tschudin, M. & Bolio, H. Sustainable cement production—Present and future. Cem. Concr. Res. 41, 642–650 (2011).
    CAS  Article  Google Scholar 

    3.
    Worrell, E., Price, L., Martin, N., Hendriks, C. & Meida, L. O. Carbon dioxide emissions from the global cement industry. Annu. Rev. Energy Environ. 26, 303–329 (2001).
    Article  Google Scholar 

    4.
    Mehta, P. K. & Monteiro, P. J. M. Concrete: Microstructure, Properties, and Materials (McGraw-Hill, New York, 2005).
    Google Scholar 

    5.
    Chen, C., Habert, G., Bouzidi, Y. & Jullien, A. Environmental impact of cement production: Detail of the different processes and cement plant variability evaluation. J. Clean. Prod. 18, 478–485 (2010).
    CAS  Article  Google Scholar 

    6.
    Bu, J., Tian, Z., Zheng, S. & Tang, Z. Effect of sand content on strength and pore structure of cement mortar. J. Wuhan Univ. Technol. Mater. Sci. Ed. 32, 382–390 (2017).
    CAS  Article  Google Scholar 

    7.
    Khaliq, W. & Ehsan, M. B. Crack healing in concrete using various bio influenced self-healing techniques. Constr. Build. Mater. 102, 349–357 (2016).
    CAS  Article  Google Scholar 

    8.
    Vijay, K. & Murmu, M. Effect of calcium lactate on compressive strength and self-healing of cracks in microbial concrete. Front. Struct. Civ. Eng. https://doi.org/10.1007/s11709-018-0494-2 (2018).
    Article  Google Scholar 

    9.
    Vahabi, A., Ramezanianpour, A. A. & Noghabi, K. A. A preliminary insight into the revolutionary new line in improving concrete properties using an indigenous bacterial strain Bacillus licheniformis AK01, as a healing agent. Eur. J. Environ. Civ. Eng. 19, 614–627 (2015).
    Article  Google Scholar 

    10.
    Schlangen, E. & Joseph, C. Self-Healing Processes in Concrete. Self-Healing Materials: Fundamentals, Design Strategies, and Applications (WILEY-VCH Verlag Gmbh & Co. KGaA, New York, 2009). https://doi.org/10.1002/9783527625376.ch5.
    Google Scholar 

    11.
    Mehta, P. K. High-performance, high-volume fly ash concrete for sustainable development. Int. Work. Sustain. Dev. Concr. Technol. 31, 3–14 (2008).
    Google Scholar 

    12.
    Achal, V. & Mukherjee, A. A review of microbial precipitation for sustainable construction. Constr. Build. Mater. 93, 1224–1235 (2015).
    Article  Google Scholar 

    13.
    Burne, R. A. & Chen, Y. Y. M. Bacterial ureases in infectious diseases. Microbes Infect. 2, 533–542 (2000).
    CAS  Article  Google Scholar 

    14.
    Dick, J. et al. Bio-deposition of a calcium carbonate layer on degraded limestone by Bacillus species. Biodegradation 17, 357–367 (2006).
    CAS  Article  Google Scholar 

    15.
    De Muynck, W., De Belie, N. & Verstraete, W. Microbial carbonate precipitation in construction materials: A review. Ecol. Eng. 36, 118–136 (2010).
    Article  Google Scholar 

    16.
    Van Tittelboom, K., De Belie, N., De Muynck, W. & Verstraete, W. Use of bacteria to repair cracks in concrete. Cem. Concr. Res. 40, 157–166 (2010).
    Article  Google Scholar 

    17.
    Dhami, N. K., Reddy, M. S. & Mukherjee, M. S. Biomineralization of calcium carbonates and their engineered applications: A review. Front. Microbiol. 4, 1–13 (2013).
    Article  Google Scholar 

    18.
    Ramachandran, S. K., Ramakrishnan, V. & Bang, S. S. Remediation of concrete using micro-organism. Aci Mater. J. 1, 1. https://doi.org/10.14359/10154 (2001).
    Article  Google Scholar 

    19.
    Dhami, N. K., Reddy, M. S. & Mukherjee, A. Bacillus megaterium mediated mineralization of calcium carbonate as biogenic surface treatment of green building materials. World J. Microbiol. Biotechnol. 29, 2397–2406 (2013).
    CAS  Article  Google Scholar 

    20.
    De Muynck, W., Cox, K., De Belie, N. & Verstraete, W. Bacterial carbonate precipitation as an alternative surface treatment for concrete. Constr. Build. Mater. 22, 875–885 (2008).
    Article  Google Scholar 

    21.
    Achal, V., Mukerjee, A. & Reddy, M. S. Biogenic treatment improves the durability and remediates the cracks of concrete structures. Constr. Build. Mater. 48, 1–5 (2013).
    Article  Google Scholar 

    22.
    Chahal, N., Siddique, R. & Rajor, A. Influence of bacteria on the compressive strength, water absorption and rapid chloride permeability of concrete incorporating silica fume. Constr. Build. Mater. 37, 645–651 (2012).
    Article  Google Scholar 

    23.
    van Paassen, L. A. et al. Potential soil reinforcement by biological denitrification. Ecol. Eng. 36, 168–175 (2010).
    Article  Google Scholar 

    24.
    Erşan, Y. Ç, Hernandez-Sanabria, E., Boon, N. & De Belie, N. Enhanced crack closure performance of microbial mortar through nitrate reduction. Cem. Concr. Compos. 70, 159–170 (2016).
    Article  Google Scholar 

    25.
    Glass, C. & Silverstein, J. Denitrification kinetics of high nitrate concentration water: pH effect on inhibition and nitrite accumulation. Water Res. 32, 831–839 (1998).
    CAS  Article  Google Scholar 

    26.
    van Paassen, L. Biogrout: Ground Improvement by Microbially Induced Carbonate Precipitation (Delft University of Technology, Delft, 2009).
    Google Scholar 

    27.
    Li, M., Fu, Q. L., Zhang, Q., Achal, V. & Kawasaki, S. Bio-grout based on microbially induced sand solidification by means of asparaginase activity. Sci. Rep. 5, 1–9 (2015).
    Google Scholar 

    28.
    Jonkers, H. M. & Schlangen, E. A two component bacteria-based self-healing concrete. In Concrete Repair, Rehabilitation and Retroftting II (eds Alexander, M. G. et al.) (CRC Press, Taylor and Francis Group, Boca Raton, 2009).
    Google Scholar 

    29.
    Jonkers, H. M., Thijssen, A., Muyzer, G., Copuroglu, O. & Schlangen, E. Application of bacteria as self-healing agent for the development of sustainable concrete. Ecol. Eng. 36, 230–235 (2010).
    Article  Google Scholar 

    30.
    Jonkers, H. M. Bacteria-based self-healing concrete. Heron 56, 1–12 (2011).
    Google Scholar 

    31.
    Chaurasia, L., Bisht, V., Singh, L. P. & Gupta, S. A novel approach of biomineralization for improving micro and macro-properties of concrete. Constr. Build. Mater. 195, 340–351 (2019).
    CAS  Article  Google Scholar 

    32.
    Seifan, M., Samani, A. K. & Berenjian, A. Induced calcium carbonate precipitation using Bacillus species. Appl. Microbiol. Biotechnol. 100, 9895–9906 (2016).
    CAS  Article  Google Scholar 

    33.
    Wang, J., Ersan, Y. C., Boon, N. & De Belie, N. Application of microorganisms in concrete: A promising sustainable strategy to improve concrete durability. Appl. Microbiol. Biotechnol. 100, 2993–3007 (2016).
    CAS  Article  Google Scholar 

    34.
    Mondal, S. & Ghosh, A. Investigation into the optimal bacterial concentration for compressive strength enhancement of microbial concrete. Constr. Build. Mater. 183, 202–214 (2018).
    Article  Google Scholar 

    35.
    Andalib, R. et al. Optimum concentration of Bacillus megaterium for strengthening structural concrete. Constr. Build. Mater. 118, 180–193 (2016).
    CAS  Article  Google Scholar 

    36.
    Achal, V. & Pan, X. Characterization of urease and carbonic anhydrase producing bacteria and their role in calcite precipitation. Curr. Microbiol. 62, 894–902 (2011).
    CAS  Article  Google Scholar 

    37.
    Sharma, A. & Bhattacharya, A. Enhanced biomimetic sequestration of CO2 into CaCO3 using purified carbonic anhydrase from indigenous bacterial strains. J. Mol. Catal. B Enzym. 67, 122–128 (2010).
    CAS  Article  Google Scholar 

    38.
    Morandeau, A., Thiéry, M. & Dangla, P. Investigation of the carbonation mechanism of CH and C–S–H in terms of kinetics, microstructure changes and moisture properties. Cem. Concr. Res. 56, 153–170 (2014).
    CAS  Article  Google Scholar 

    39.
    Ameri, F., Shoaei, P., Bahrami, N., Vaezi, M. & Ozbakkaloglu, T. Optimum rice husk ash content and bacterial concentration in self-compacting concrete. Constr. Build. Mater. 222, 796–813 (2019).
    CAS  Article  Google Scholar 

    40.
    Syarif, R., Rizki, I. N., Wattimena, R. K. & Chaerun, S. K. Selection of bacteria inducing calcium carbonate precipitation for self-healing concrete application. Curr. Res. Biosci. Biotechnol. 1, 26–30 (2019).
    Google Scholar 

    41.
    SNI 15-2049-2004. Semen Portland. BSN – National Standardization Agency of Indonesia (2004).

    42.
    Stephen, H. & Stephen, T. Solubilities of Inorganic and Organic Compounds. Binary Systems Part 1 Vol. 1 (Pergamon Press, Oxford, 1979).
    Google Scholar 

    43.
    ASTM C642-13. Standard test method for density, absorption, and voids in hardened concrete. Am. Society Testing Mater. https://doi.org/10.1520/C0642-13.5 (2013).
    Article  Google Scholar 

    44.
    ISRM. Suggested methods for determining the uniaxial compressive strength and deformability of rock materials. Int. J. Rock Mech. Min. Sci. Geomech. 16, 137–140 (1979).
    Google Scholar 

    45.
    ISRM. Suggested methods for determining tensile strength of rock materials. Int. J. Rock Mech. Min. Sci. Geomech. 15, 99–103 (1978).
    Article  Google Scholar  More

  • in

    The transboundary nature of the world’s exploited marine species

    1.
    Hutchinson, G. E. Concluding remarks. Cold Spring Harbor Symp. Quant. Biol. 22, 415–427 (1957).
    Article  Google Scholar 
    2.
    Nelson, J. S., Grande, T. C. & Wilson, M. V. H. Fishes of the World (Wiley, Hoboken, 2016).
    Google Scholar 

    3.
    Song, A. M., Scholtens, J., Stephen, J., Bavinck, M. & Chuenpagdee, R. Transboundary research in fisheries. Mar. Policy 76, 8–18 (2017).
    Article  Google Scholar 

    4.
    Fredston-Hermann, A., Gaines, S. D. & Halpern, B. S. Biogeographic constraints to marine conservation in a changing climate. Ann. N. Y. Acad. Sci. 367, 49–13 (2018).
    Google Scholar 

    5.
    Østhagen, A. Maritime boundary disputes: what are they and why do they matter?. Mar. Policy 120, 104118 (2020).
    Article  Google Scholar 

    6.
    United Nations. United Nations Convention on the Law of the Sea (UNCLOS)—Part V. (1986).

    7.
    Munro, G., Van Houtte, A. & Willmann, R. The Conservation and Management of Shared Fish Stocks: Legal and Economic Aspects. FAO Fisheries Technical Paper No. 456. Food and Agriculture Organization of the United Nations, Rome (2004).

    8.
    Miller, K. & Munro, G. Cooperation and Conflicts in the Management of Transboundary Fishery Resources. (Proceeding of the Second World Conference of the Second World Congress of the American; European Associations of Environmental; Resource Economics, 2002).

    9.
    Englander, G. Property rights and the protection of global marine resources. Nature Sustainability 2, 981–987 (2019).
    Article  Google Scholar 

    10.
    Spijkers, J. & Boonstra, W. J. Environmental change and social conflict: the northeast Atlantic mackerel dispute. Reg. Environ. Change 17, 1835–1851 (2017).
    Article  Google Scholar 

    11.
    Pinsky, M. L. et al. Preparing ocean governance for species on the move. Science 360, 1189–1191 (2018).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    12.
    Miller, K. A., Munro, G. R., Sumaila, U. R. & Cheung, W. W. L. Governing marine fisheries in a changing climate: a game-theoretic perspective. Can J Agric Econ 61, 309–334 (2013).
    Article  Google Scholar 

    13.
    United Nations. Agreement for the Implementation of the Provisions of the United Nations Convention on the Law of the Sea of 10 December 1982 Relating to the Conservation and Management of Straddling Fish Stocks and Highly Migratory Fish Stocks. (1995).

    14.
    Caddy, J. Establishing a consultative mechanism or arrangement for managing shared stocks within the jurisdiction of contiguous states. In Taking stock Defining and Managing Shared Resources (ed. Hancock, D. A.) 80–123 (Australian Society for Fish Biology, Adelaide, 1997).
    Google Scholar 

    15.
    Teh, L. S. L. & Sumaila, U. R. Trends in global shared fisheries. Mar. Ecol. Prog. Ser. 530, 243–254 (2015).
    ADS  Article  Google Scholar 

    16.
    Diario Oficial de la Federación (DOF). Carta Nacional Pesquera. Poder Ejecutivo—Secreataría de Agricultura, Ganadería, Desarrollo Rural, Pesca (SAGARPA). Diario Oficial de la Federación DOF, 1–268 (2018).

    17.
    MAP. Dictamen de Extracción No Perjudicial (DENP) de la población de “tiburón martillo” Sphyrna zygaena. Oficio N. 1038–2017-PRODUCE/DGPCHDI (Tra. N. 18254–2017). Ministerio del Ambiente, Viceministerio de Desarrollo Estratégico de los Recursos Naturales, Peru (2017).

    18.
    Ramesh, N., Rising, J. A. & Oremus, K. L. The small world of global marine fisheries: The cross-boundary consequences of larval dispersal. Science 364, 1192–1196 (2019).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    19.
    Levin, N., Beger, M., Maina, J., McClanahan, T. & Kark, S. Evaluating the potential for transboundary management of marine biodiversity in the Western Indian Ocean. Australas. J. Environ.Manag. 25, 62–85 (2018).
    Article  Google Scholar 

    20.
    Popova, E. et al. Ecological connectivity between the areas beyond national jurisdiction and coastal waters: safeguarding interests of coastal communities in developing countries. Mar. Policy 104, 90–102 (2019).
    Article  Google Scholar 

    21.
    Dunn, D. C. et al. The importance of migratory connectivity for global ocean policy. Proc. R. Soc. B: Biol. Sci. 286, 20191472 (2019).
    Article  Google Scholar 

    22.
    Kaplan, D. M. et al. Uncertainty in empirical estimates of marine larval connectivity. ICES J. Mar. Sci. 74, 1723–1734 (2016).
    Article  Google Scholar 

    23.
    Archambault, B. et al. Adult-mediated connectivity affects inferences on population dynamics and stock assessment of nursery-dependent fish populations. Global Environ. Change 181, 198–213 (2016).
    Google Scholar 

    24.
    Cashion, T. et al. Establishing company level fishing revenue and profit losses from fisheries: A bottom-up approach. Journals Plos.Org 13, e0207768 (2018).
    Google Scholar 

    25.
    FAO. The State of World Fisheries and Aquaculture: Meeting the Sustainable Development Goals. 1–227 (2018).

    26.
    UNDP. Chile and Peru sign Landmark Agreement to Sustain world’s Largest Single Species Fishery (2016).

    27.
    NOAA FIsheries. Bilateral Agreement Between the United States and Russia (2019).

    28.
    Kleisner, K. & Pauly, D. Stock-Status Plots of Fisheries for Regional Seas. in The State of Biodiversity and Fisheries in Regional Seas (eds. Christensen, V., Lai, S., Palomares, M. L. D., Zeller, D. & Pauly, D.) 37–40 (The Fisheries Center, University of British Columbia; Fisheries Centre Research Reports, 2011).

    29.
    Jensen, F., Frost, H., Thogersen, T., Andersen, P. & Andersen, J. L. Game theory and fish wars: the case of the Northeast Atlantic mackerel fishery. Fisheries 172, 7–16 (2015).
    Google Scholar 

    30.
    Munro, G. R. The management of shared fishery resources under extended jurisdiction. Mar. Resour. Econ. 3, 271–296 (2015).
    Article  Google Scholar 

    31.
    Eide, A., Heen, K., Armstrong, C., Flaaten, O. & Vasiliev, A. Challenges and successes in the management of a shared fish stock—the case of the Russian-Norwegian barents sea cod fishery. Acta Borealia 30, 1–20 (2013).
    Article  Google Scholar 

    32.
    Sumaila, U. R., Ninnes, C. & Oelofsen, B. Management of Shared Hake Stocks in the Benguela Marine Ecosystem. In Papers presented at the norway-fao expert consultation on the management of shared fish stocks, 143–159 (2003).

    33.
    Clark, C. W. Restricted Access to Common-Property Fishery Resources: A Game-Theoretic Analysis. In Dynamic optimization and mathematical economics, 117–132 (Springer, Boston, MA, 1980).

    34.
    Spijkers, J. et al. Global patterns of fisheries conflict: forty years of data. Global Environ. Change 57, 101921 (2019).
    Article  Google Scholar 

    35.
    Oremus, K. L. et al. Governance challenges for tropical nations losing fish species due to climate change. Nat. Sustain. 6, 1–4 (2020).
    Google Scholar 

    36.
    Palacios-Abrantes, J., Rashid Sumaila, U. & Cheung, W. W. L. Challenges to transboundary fisheries management in North America under climate change. Ecol. Soc. (in press).

    37.
    Sumaila, U. R., Palacios-Abrantes, J. & Cheung, W. W. L. Climate change, shifting threat points and the management of transboundary fish stocks. Ecol. Soc. (in press).

    38.
    Reygondeau, G. Current and future biogeography of marine exploited groups under climate change. In Predicting Future Oceans Sustainability of Ocean and Human Systems Amidst Global Environmental Change (eds. Cheung, W. W. L., Ota, Y. & Cisneros-Montemayor, A. M.) 87–99 (2019).

    39.
    Schill, S. R. et al. No reef is an island: integrating coral reef connectivity data into the design of regional-scale marine protected area networks. PLoS ONE 10, e0144199 (2015).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    40.
    Perez, A. U., Schmitter-Soto, J. J., Adams, A. J. & Heyman, W. D. Connectivity mediated by seasonal bonefish (Albula vulpes) migration between the Caribbean Sea and a tropical estuary of Belize and Mexico. Environ. Biol. Fishes 102, 197–207 (2019).
    Article  Google Scholar 

    41.
    Cisneros-Montemayor, A. M., Pauly, D., Weatherdon, L. V. & Ota, Y. A Global estimate of seafood consumption by coastal indigenous peoples. PLoS ONE 11, e0166681 (2016).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    42.
    Hanich, Q. et al. Small-scale fisheries under climate change in the Pacific Islands region. Mar. Policy 88, 279–284 (2018).
    Article  Google Scholar 

    43.
    Cabral, R. B. & Geronimo, R. C. How important are coral reefs to food security in the Philippines? Diving deeper than national aggregates and averages. Mar. Policy 91, 136–141 (2018).
    Article  Google Scholar 

    44.
    Zeller, D. et al. Still catching attention: Sea Around Us reconstructed global catch data, their spatial expression and public accessibility. Mar. Policy 70, 145–152 (2016).
    Article  Google Scholar 

    45.
    Thuiller, W., Lafourcade, B., Engler, R. & Araújo, M. B. BIOMOD—a platform for ensemble forecasting of species distributions. Ecography 32, 369–373 (2009).
    Article  Google Scholar 

    46.
    Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).
    Article  Google Scholar 

    47.
    Beaugrand, G., Lenoir, S., Ibanez, F. & Manté, C. A new model to assess the probability of occurrence of a species, based on presence-only data. Mar. Ecol. Prog. Ser. 424, 175–190 (2011).
    ADS  Article  Google Scholar 

    48.
    Asch, R. G., Cheung, W. W. L. & Reygondeau, G. Future marine ecosystem drivers, biodiversity, and fisheries maximum catch potential in Pacific Island countries and territories under climate change. Mar. Policy 88, 285–294 (2018).
    Article  Google Scholar 

    49.
    Close, C. et al. Distribution ranges of commercial fishes and invertebrates. In Fisheries Centre Research Reports. Fishes in Databases and Ecosystems (eds. Palomares, M. D., Stergiou, K. I. & Pauly, D.) 27–37 (2006).

    50.
    Pauly, D. & Zeller, D. Global Atlas of Marine Fisheries 1–520 (Island Press, Washington, D.C., 2016).
    Google Scholar 

    51.
    Kaschner, K. et al. AquaMaps: Predicted range maps for aquatic species www.aquamaps.org (2016).

    52.
    Pauly, D. & Zeller, D. Catch reconstructions reveal that global marine fisheries catches are higher than reported and declining. Nat. Commun. 7:10244,1–9 (2019).

    53.
    Pebesma, E. et al. Package sf; Simple Features for R. R ( >= 3.3.0), (2018).

    54.
    Tai, T. C., Cashion, T., Lam, V. W. Y. & Sumaila, U. R. Ex-vessel fish price database: disaggregating prices for low-priced species from reduction fisheries. Front. Mar. Sci. 4, 1–10 (2017).
    Article  Google Scholar 

    55.
    Sumaila, U. R., Teh, L., Zeller, D. & Pauly, D. The global ex-vessel fish price database. In Catch Reconstructions: Concepts, Methods and Data Sources (eds. Pauly D., & Zeller, S.) www.searoundus.org (2015).

    56.
    Grainger, R. J. R. & Garcia, S. M. Chronicles of Marine Fishery Landings (1950 1994) Trend Analysis and Fisheries Potential (1996).

    57.
    Pauly, D., Hilborn, R. & Branch, T. A. Fisheries: does catch reflect abundance?. Nature 494, 303–306 (2013).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    58.
    Branch, T. A. Not all fisheries will be collapsed in 2048. Mar. Policy 32, 38–39 (2008).
    Article  Google Scholar 

    59.
    Dowle, M. et al. Package data.table; Extension of ‘data.frame‘. R ( >= 3.1.0), MPL–2.0 | file LICENSE (2019).

    60.
    Firke, S., Haid, C., Knight, R. & Denney, B. Package janitor; Simple tools for examining and cleaning dirty data. R ( >= 3.1.2), (2018).

    61.
    Ram, K., Wickham, H., Richards, C. & Baggett, A. Package wesanderson; A Wes Anderson Palette Generator. R ( >= 3.0), MIT + file LICENSE (2018).

    62.
    Boettiger, C., Chamberlain, S., Lang, D. T. & Wainwright, P. Package rfishbase; R Interface to ’FishBase’. R ( >= 3.0), (2019).

    63.
    Bengtsson, H., Jacobson, A. & Riedy, J. Package R.matlab: Read and Write MAT Files and Call MATLAB from Within R. R ( 2.14.0), LGPL–2.1 | LGPL–3 (2018).

    64.
    Pebesma, E. et al. Package sp; Classes and methods for Spatial Data. R ( 3.0.0), GPL–2 | GPL–3 (2019).

    65.
    Wickham, H. Package tidyverse; Easily Install and Load the ’Tidyverse’. R (3.5.0), MIT + file LICENSE (2017).

    66.
    De Queiroz, G. et al. Package tidytext; Text Mining using ’dplyr’, ’ggplot2’, and Other Tidy Tools. R ( 2.10), MIT (2019).

    67.
    Zeileis, A., Grothendieck, G., Ryan, J. A., Ulrich, J. M. & Andrews, F. Package zoo; S3 Infrastructure for Regular and Irregular Time Series (Z’s Ordered Observations). R ( >= 3.1.0), GPL–2 | GPL–3 (2019).

    68.
    Chambers, J. M., Freeny, A. E. & Heiberger, R. M. Analysis of Variance; Designed Experiments. In Statistical models in s (eds Chambers, J. M. & Hastie, T. J.) 145–193 (Routledge, London, 1992).
    Google Scholar 

    69.
    Krzanowski, W. J. Principles of Multivariate Analysis (Oxford University Press, Oxford, 1990).
    Google Scholar 

    70.
    Hollander, M. & Wolfe, D. A. Nonparametric Statistical Methods (Wiley, Hoboken, 2013).
    Google Scholar 

    71.
    Moore, B. R. et al. Defining the stock structures of key commercial tunas in the Pacific Ocean I: current knowledge and main uncertainties. Global Environ. Change 230, 105525 (2020).
    Google Scholar 

    72.
    Sepulveda, C. A., Wang, M., Aalbers, S. A. & Alvarado-Bremer, J. R. Insights into the horizontal movements, migration patterns, and stock affiliation of California swordfish. Fish. Oceanogr. 29, 152–168 (2019).
    Article  Google Scholar 

    73.
    Vandeperre, F. et al. Movements of Blue Sharks (Prionace glauca) across Their Life History. PLoS ONE 9, e103538–e103614 (2014).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    74.
    Chavez, F. P., Ryan, J., Lluch-Cota, S. E. & Niquen, C. M. From anchovies to sardines and back: multidecadal change in the Pacific Ocean. Science 299, 217–221 (2003).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar  More

  • in

    Linking structural and compositional changes in archaeological human bone collagen: an FTIR-ATR approach

    1.
    Boskey, A. L., Wright, T. M. & Blank, R. D. Collagen and bone strength. J. Bone Miner. Res. 14, 330–335. https://doi.org/10.1359/jbmr.1999.14.3.330 (1999).
    CAS  Article  PubMed  Google Scholar 
    2.
    Fratzl, P. In Collagen (ed Fratzl, P.) 1–13 (Springer, Berlin, 2008).

    3.
    Dehring, K. A., Smukler, A. R., Roessler, B. J. & Morris, M. D. correlating changes in collagen secondary structure with aging and defective type II collagen by Raman spectroscopy. Appl. Spectrosc. 60, 366–372 (2006).
    ADS  CAS  Article  Google Scholar 

    4.
    Shoulders, M. D. & Raines, R. T. Collagen structure and stability. Annu. Rev. Biochem. 78, 929–958. https://doi.org/10.1146/annurev.biochem.77.032207.120833 (2009).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    5.
    Mostaço-Guidolin, L. B. et al. Collagen morphology and texture analysis: From statistics to classification. Sci. Rep. 3, 2190. https://doi.org/10.1038/srep02190 (2013).
    Article  PubMed  PubMed Central  Google Scholar 

    6.
    Schrof, S., Varga, P., Galvis, L., Raum, K. & Masic, A. 3D Raman mapping of the collagen fibril orientation in human osteonal lamellae. J. Struct. Biol. 187, 266–275. https://doi.org/10.1016/j.jsb.2014.07.001 (2014).
    CAS  Article  PubMed  Google Scholar 

    7.
    Viguet-Carrin, S., Garnero, P. & Delmas, P. D. The role of collagen in bone strength. Osteoporos. Int. 17, 319–336. https://doi.org/10.1007/s00198-005-2035-9 (2006).
    CAS  Article  PubMed  Google Scholar 

    8.
    West, P., Torzilli, P., Chen, C., Lin, P. & Camacho, N. Fourier transform infrared imaging spectroscopy analysis of collagenase-induced cartilage degradation. J. Biomed. Opt. 10, 014015 (2005).
    ADS  CAS  Article  Google Scholar 

    9.
    Wang, X., Zhai, M., Zhao, Y. & Yin, J. A review of articular cartilage and osteoarthritis studies by Fourier transform infrared spectroscopic imaging. Ann. Joint 3, 1–9 (2018).
    Article  Google Scholar 

    10.
    Lee, Y.-C. et al. Evidence of preserved collagen in an Early Jurassic sauropodomorph dinosaur revealed by synchrotron FTIR microspectroscopy. Nat. Commun. 8, 14220. https://doi.org/10.1038/ncomms14220 (2017).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    11.
    Longin, R. New method of collagen extraction for radiocarbon dating. Nature 230, 241–242 (1971).
    ADS  CAS  Article  Google Scholar 

    12.
    Ambrose, S. H. & Krigbaum, J. Bone chemistry and bioarchaeology. J. Anthropol. Archaeol. 22, 193–199. https://doi.org/10.1016/S0278-4165(03)00033-3 (2003).
    Article  Google Scholar 

    13.
    13Katzenberg, M. A. In Biological Anthropology of the Human Skeleton (eds M. Katzenberg, A. & Saunders, S. R.) 413–441 (Wiley-Liss, Hoboken, 2000).

    14.
    Fewlass, H. et al. Pretreatment and gaseous radiocarbon dating of 40–100 mg archaeological bone. Sci. Rep. 9, 5342. https://doi.org/10.1038/s41598-019-41557-8 (2019).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    15.
    Pothier Bouchard, G. et al. Portable FTIR for on-site screening of archaeological bone intended for ZooMS collagen fingerprint analysis. J. Archaeol. Sci. Rep. 26, 101862. https://doi.org/10.1016/j.jasrep.2019.05.027 (2019).
    Article  Google Scholar 

    16.
    Kaal, J., López-Costas, O. & Martínez, A. Diagenetic effects on pyrolysis fingerprints of extracted collagen in archaeological human bones from NW Spain, as determined by pyrolysis-GC-MS. J. Archaeol. Sci. 65, 1–10. https://doi.org/10.1016/j.jas.2015.11.001 (2016).
    CAS  Article  Google Scholar 

    17.
    Van Klinken, G. J. Bone collagen quality indicators for palaeodietary and radiocarbon measurements. J. Archaeol. Sci. 26, 687–695 (1999).
    Article  Google Scholar 

    18.
    Dobberstein, R. C. et al. Archaeological collagen: Why worry about collagen diagenesis?. Archaeol. Anthropol. Sci. 1, 31–42. https://doi.org/10.1007/s12520-009-0002-7 (2009).
    Article  Google Scholar 

    19.
    Harbeck, M. & Grupe, G. Experimental chemical degradation compared to natural diagenetic alteration of collagen: Implications for collagen quality indicators for stable isotope analysis. Archaeol. Anthropol. Sci. 1, 43–57. https://doi.org/10.1007/s12520-009-0004-5 (2009).
    Article  Google Scholar 

    20.
    Collins, M. J., Riley, M. S., Child, A. M. & Turner-Walker, G. A basic mathematical simulation of the chemical degradation of ancient collagen. J. Archaeol. Sci. 22, 175–183. https://doi.org/10.1006/jasc.1995.0019 (1995).
    Article  Google Scholar 

    21.
    France, C. A. M., Thomas, D. B., Doney, C. R. & Madden, O. FT-Raman spectroscopy as a method for screening collagen diagenesis in bone. J. Archaeol. Sci. 42, 346–355. https://doi.org/10.1016/j.jas.2013.11.020 (2014).
    CAS  Article  Google Scholar 

    22.
    Chadefaux, C., Le Hô, A.-S., Bellot-Gurlet, L. & Reiche, I. Curve-fitting Micro-ATR-FTIR studies of the amide I and II bands of type I collagen in archaeological bone materials. E-Preserv. Sci. Morana RTD 6, 129–137 (2009).
    CAS  Google Scholar 

    23.
    Sponheimer, M. et al. Saving old bones: A non-destructive method for bone collagen prescreening. Sci. Rep. 9, 13928. https://doi.org/10.1038/s41598-019-50443-2 (2019).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    24.
    Goldenberg, L., Regev, L., Mintz, E. & Boaretto, E. Dating reassembled collagen from fossil bones. Radiocarbon 59, 1487–1496. https://doi.org/10.1017/rdc.2017.69 (2017).
    CAS  Article  Google Scholar 

    25.
    Yizhaq, M. et al. Quality controlled radiocarbon dating of bones and charcoal from the early pre-pottery neolithic B (PPNB) of Motza (Israel). Radiocarbon 47, 193–206. https://doi.org/10.1017/s003382220001969x (2005).
    CAS  Article  Google Scholar 

    26.
    Baker, M. J. et al. Using Fourier transform IR spectroscopy to analyze biological materials. Nat. Protoc. 9, 1771–1791. https://doi.org/10.1038/nprot.2014.110 (2014).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    27.
    Belbachir, K., Noreen, R., Gouspillou, G. & Petibois, C. Collagen types analysis and differentiation by FTIR spectroscopy. Anal. Bioanal. Chem. 395, 829–837. https://doi.org/10.1007/s00216-009-3019-y (2009).
    CAS  Article  PubMed  Google Scholar 

    28.
    de Campos Vidal, B. & Mello, M. L. S. Collagen type I amide I band infrared spectroscopy. Micron 42, 283–289. https://doi.org/10.1016/j.micron.2010.09.010 (2011).
    CAS  Article  Google Scholar 

    29.
    Figueiredo, M., Gamelas, J. & Martins, A. In Infrared Spectroscopy-Life and Biomedical Sciences (ed Theophile, T.) (InTech, 2012).

    30.
    Hanifi, A., McCarthy, H., Roberts, S. & Pleshko, N. Fourier transform infrared imaging and infrared fiber optic probe spectroscopy identify collagen type in connective tissues. PLoS ONE 8, e64822. https://doi.org/10.1371/journal.pone.0064822 (2013).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    31.
    Kong, J. & Yu, S. Fourier transform infrared spectroscopic analysis of protein secondary structures. Acta Biochim. Biophys. Sin. 39, 549–559. https://doi.org/10.1111/j.1745-7270.2007.00320.x (2007).
    CAS  Article  PubMed  Google Scholar 

    32.
    Stani, C., Vaccari, L., Mitri, E. & Birarda, G. FTIR investigation of the secondary structure of type I collagen: New insight into the amide III band. Spectrochim. Acta Part A Mol. Biomol. Spectrosc. 229, 118006. https://doi.org/10.1016/j.saa.2019.118006 (2020).
    CAS  Article  Google Scholar 

    33.
    Ramachandran, G. & Kartha, G. Structure of collagen. Nature 174, 269–270 (1954).
    ADS  CAS  Article  Google Scholar 

    34.
    Ramachandran, G. & Kartha, G. Structure of collagen. Nature 176, 593–595 (1955).
    ADS  CAS  Article  Google Scholar 

    35.
    Rich, A. & Crick, F. The molecular structure of collagen. J. Mol. Biol. 3, 483–484 (1961).
    CAS  Article  Google Scholar 

    36.
    Egli, J., Schnitzer, T., Dietschreit, J. C., Ochsenfeld, C. & Wennemers, H. Why proline? Influence of ring-size on the collagen triple helix. Org. Lett. 22, 348–351 (2019).
    Article  Google Scholar 

    37.
    Barth, A. Infrared spectroscopy of proteins. Biochim. Biophys. Acta Bioenergetics 1767, 1073–1101. https://doi.org/10.1016/j.bbabio.2007.06.004 (2007).
    CAS  Article  Google Scholar 

    38.
    Surovell, T. A. & Stiner, M. C. Standardizing infra-red measures of bone mineral crystallinity: An experimental approach. J. Archaeol. Sci. 28, 633–642. https://doi.org/10.1006/jasc.2000.0633 (2001).
    Article  Google Scholar 

    39.
    Garvie-Lok, S. J., Varney, T. L. & Katzenberg, M. A. Preparation of bone carbonate for stable isotope analysis: The effects of treatment time and acid concentration. J. Archaeol. Sci. 31, 763–776. https://doi.org/10.1016/j.jas.2003.10.014 (2004).
    Article  Google Scholar 

    40.
    Hollund, H. I., Ariese, F., Fernandes, R., Jans, M. M. E. & Kars, H. Testing an alternative high-throughput tool for investigating bone diagenesis: FTIR in attenuated total reflection (ATR) mode. Archaeometry 55, 507–532. https://doi.org/10.1111/j.1475-4754.2012.00695.x (2013).
    CAS  Article  Google Scholar 

    41.
    Berna, F., Matthews, A. & Weiner, S. Solubilities of bone mineral from archaeological sites: The recrystallization window. J. Archaeol. Sci. 31, 867–882. https://doi.org/10.1016/j.jas.2003.12.003 (2004).
    Article  Google Scholar 

    42.
    Lebon, M., Reiche, I., Frohlich, F., Bahain, J. J. & Falgueres, C. Characterization of archaeological burnt bones: Contribution of a new analytical protocol based on derivative FTIR spectroscopy and curve fitting of the nu1nu3 PO4 domain. Anal. Bioanal. Chem. 392, 1479–1488 (2008).
    CAS  Article  Google Scholar 

    43.
    Thompson, T. J. U., Gauthier, M. & Islam, M. The application of a new method of Fourier Transform Infrared Spectroscopy to the analysis of burned bone. J. Archaeol. Sci. 36, 910–914. https://doi.org/10.1016/j.jas.2008.11.013 (2009).
    Article  Google Scholar 

    44.
    Lebon, M. et al. New parameters for the characterization of diagenetic alterations and heat-induced changes of fossil bone mineral using Fourier transform infrared spectrometry. J. Archaeol. Sci. 37, 2265–2276. https://doi.org/10.1016/j.jas.2010.03.024 (2010).
    Article  Google Scholar 

    45.
    Dal Sasso, G. et al. Bone diagenesis variability among multiple burial phases at Al Khiday (Sudan) investigated by ATR-FTIR spectroscopy. Palaeogeogr. Palaeoclimatol. Palaeoecol. 463, 168–179. https://doi.org/10.1016/j.palaeo.2016.10.005 (2016).
    Article  Google Scholar 

    46.
    Toffolo, M. B., Brink, J. S. & Berna, F. Bone diagenesis at the Florisbad spring site, Free State Province (South Africa): Implications for the taphonomy of the Middle and Late Pleistocene faunal assemblages. J. Archaeol. Sci. Rep. 4, 152–163. https://doi.org/10.1016/j.jasrep.2015.09.001 (2015).
    Article  Google Scholar 

    47.
    Lebon, M., Reiche, I., Gallet, X., Bellot-Gurlet, L. & Zazzo, A. Rapid quantification of bone collagen content by ATR-FTIR spectroscopy. Radiocarbon 58, 131–145. https://doi.org/10.1017/rdc.2015.11 (2016).
    CAS  Article  Google Scholar 

    48.
    Pestle, W. J. et al. Hand-held Raman spectroscopy as a pre-screening tool for archaeological bone. J. Archaeol. Sci. 58, 113–120. https://doi.org/10.1016/j.jas.2015.03.027 (2015).
    CAS  Article  Google Scholar 

    49.
    Madden, O., Chan, D. M. W., Dundon, M. & France, C. A. M. Quantifying collagen quality in archaeological bone: Improving data accuracy with benchtop and handheld Raman spectrometers. J. Archaeol. Sci. Rep. 18, 596–605. https://doi.org/10.1016/j.jasrep.2017.11.034 (2018).
    Article  Google Scholar 

    50.
    Dal Sasso, G., Angelini, I., Maritan, L. & Artioli, G. Raman hyperspectral imaging as an effective and highly informative tool to study the diagenetic alteration of fossil bones. Talanta 179, 167–176. https://doi.org/10.1016/j.talanta.2017.10.059 (2018).
    CAS  Article  Google Scholar 

    51.
    López-Costas, O. & Müldner, G. Fringes of the empire: Diet and cultural change at the Roman to post-Roman transition in NW Iberia. Am. J. Phys. Anthropol. 161, 141–154. https://doi.org/10.1002/ajpa.23016 (2016).
    Article  PubMed  Google Scholar 

    52.
    López-Costas, O. Antropología de los restos óseos humanos de Galicia: estudio de la población romano y medieval gallega. Doctoral thesis, University of Granada, (2012).

    53.
    Petibois, C., Gouspillou, G., Wehbe, K., Delage, J.-P. & Déléris, G. Analysis of type I and IV collagens by FT-IR spectroscopy and imaging for a molecular investigation of skeletal muscle connective tissue. Anal. Bioanal. Chem. 386, 1961–1966. https://doi.org/10.1007/s00216-006-0828-0 (2006).
    CAS  Article  PubMed  Google Scholar 

    54.
    Haris, P. I. & Severcan, F. FTIR spectroscopic characterization of protein structure in aqueous and non-aqueous media. J. Mol. Catal. B Enzym. 7, 207–221. https://doi.org/10.1016/S1381-1177(99)00030-2 (1999).
    CAS  Article  Google Scholar 

    55.
    Goormaghtigh, E., Ruysschaert, J.-M. & Raussens, V. Evaluation of the information content in infrared spectra for protein secondary structure determination. Biophys. J . 90, 2946–2957. https://doi.org/10.1529/biophysj.105.072017 (2006).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    56.
    Paschalis, E. P. et al. Spectroscopic characterization of collagen cross-links in bone. J. Bone Miner. Res. 16, 1821–1828. https://doi.org/10.1359/jbmr.2001.16.10.1821 (2001).
    CAS  Article  PubMed  Google Scholar 

    57.
    D’Elia, M. et al. Evaluation of possible contamination sources in the 14C analysis of bone samples by FTIR spectroscopy. Radiocarbon 49, 201–210. https://doi.org/10.1017/s0033822200042120 (2007).
    CAS  Article  Google Scholar 

    58.
    Karkanas, P., Bar-Yosef, O., Goldberg, P. & Weiner, S. Diagenesis in prehistoric caves: The use of minerals that form in situ to assess the completeness of the archaeological record. J. Archaeol. Sci. 27, 915–929. https://doi.org/10.1006/jasc.1999.0506 (2000).
    Article  Google Scholar 

    59.
    López-Costas, O., Lantes-Suárez, Ó. & Martínez Cortizas, A. Chemical compositional changes in archaeological human bones due to diagenesis: Type of bone vs soil environment. J. Archaeol. Sci. 67, 43–51. https://doi.org/10.1016/j.jas.2016.02.001 (2016).
    CAS  Article  Google Scholar 

    60.
    Trueman, C. N., Privat, K. & Field, J. Why do crystallinity values fail to predict the extent of diagenetic alteration of bone mineral?. Palaeogeogr. Palaeoclimatol. Palaeoecol. 266, 160–167. https://doi.org/10.1016/j.palaeo.2008.03.038 (2008).
    Article  Google Scholar 

    61.
    Trueman, C. N. G., Behrensmeyer, A. K., Tuross, N. & Weiner, S. Mineralogical and compositional changes in bones exposed on soil surfaces in Amboseli National Park, Kenya: Diagenetic mechanisms and the role of sediment pore fluids. J. Archaeol. Sci. 31, 721–739. https://doi.org/10.1016/j.jas.2003.11.003 (2004).
    Article  Google Scholar 

    62.
    Salesse, K. et al. Variability of bone preservation in a confined environment: The case of the catacomb of Sts Peter and Marcellinus (Rome, Italy). Palaeogeogr. Palaeoclimatol. Palaeoecol. 416, 43–54. https://doi.org/10.1016/j.palaeo.2014.07.021 (2014).
    Article  Google Scholar 

    63.
    Weiner, S. Microarchaeology: Beyond the Visible Archaeological Record (Cambridge University Press, Cambridge, 2010).
    Google Scholar 

    64.
    Pate, F. D., Hutton, J. T. & Norrish, K. Ionic exchange between soil solution and bone: Toward a predictive model. Appl. Geochem. 4, 303–316. https://doi.org/10.1016/0883-2927(89)90034-6 (1989).
    CAS  Article  Google Scholar 

    65.
    Nielsen-Marsh, C. M. & Hedges, R. E. M. Patterns of diagenesis in bone I: The effects of site environments. J. Archaeol. Sci. 27, 1139–1150. https://doi.org/10.1006/jasc.1999.0537 (2000).
    Article  Google Scholar 

    66.
    Weiner, S. & Bar-Yosef, O. States of preservation of bones from prehistoric sites in the Near East: A survey. J. Archaeol. Sci. 17, 187–196. https://doi.org/10.1016/0305-4403(90)90058-D (1990).
    Article  Google Scholar 

    67.
    Weiner, S., Goldberg, P. & Bar-Yosef, O. Bone preservation in Kebara cave, Israel using on-site Fourier transform infrared spectrometry. J. Archaeol. Sci. 20, 613–627. https://doi.org/10.1006/jasc.1993.1037 (1993).
    Article  Google Scholar 

    68.
    Weiner, S., Goldberg, P. & Bar-Yosef, O. Three-dimensional distribution of minerals in the sediments of Hayonim Cave, Israel: Diagenetic processes and archaeological implications. J. Archaeol. Sci. 29, 1289–1308. https://doi.org/10.1006/jasc.2001.0790 (2002).
    Article  Google Scholar 

    69.
    Jans, M. M. E., Nielsen-Marsh, C. M., Smith, C. I., Collins, M. J. & Kars, H. Characterisation of microbial attack on archaeological bone. J. Archaeol. Sci. 31, 87–95. https://doi.org/10.1016/j.jas.2003.07.007 (2004).
    Article  Google Scholar 

    70.
    Ambrose, S. H. Preparation and characterization of bone and tooth collagen for isotopic analysis. J. Archaeol. Sci. 17, 431–451. https://doi.org/10.1016/0305-4403(90)90007-r (1990).
    Article  Google Scholar 

    71.
    López-Costas, O., Müldner, G. & Martínez Cortizas, A. Diet and lifestyle in Bronze Age Northwest Spain: The collective burial of Cova do Santo. J. Archaeol. Sci. 55, 209–218. https://doi.org/10.1016/j.jas.2015.01.009 (2015).
    Article  Google Scholar 

    72.
    Lopez-Costas, O. Taphonomy and burial context of the Roman/post-Roman funerary areas (2nd to 6th centuries AD) of A Lanzada, NW Spain. Estudos do Quaternário, APEQ 12, 55–67 (2015).
    Article  Google Scholar 

    73.
    Collins, M. J. & Galley, P. Towards an optimal method of archaeological collagen extraction: The influence of pH and grinding. Ancient Biomolecules 2, 209–222 (1998).
    CAS  Google Scholar 

    74.
    Boskey, A. & Camacho, N. P. FT-IR imaging of native and tissue-engineered bone and cartilage. Biomaterials 28, 2465–2478. https://doi.org/10.1016/j.biomaterials.2006.11.043 (2007).
    CAS  Article  PubMed  Google Scholar 

    75.
    Kim, M., Bi, X., Horton, W., Spencer, R. & Camacho, N. Fourier transform infrared imaging spectroscopic analysis of tissue engineered cartilage: Histologic and biochemical correlations. J. Biomed. Opt. 10, 031105 (2005).
    ADS  Article  Google Scholar 

    76.
    Heinly, J. H., Guerin, H. L., Auerbach, J. D., Siskey, R. L. & Villarraga, M. L. In 56th Annual Meeting of the Orthopaedic Research Society Poster No. 1466 (2010.).

    77.
    Mark, H. & Workman, J. Jr. Chemometrics: Derivatives in spectroscopy, Part I-the behavior of the derivative. Spectrosc. Eugene 18, 32–37 (2003).
    CAS  Google Scholar 

    78.
    Rieppo, L. et al. Application of second derivative spectroscopy for increasing molecular specificity of fourier transform infrared spectroscopic imaging of articular cartilage. Osteoarthr. Cartil. 20, 451–459. https://doi.org/10.1016/j.joca.2012.01.010 (2012).
    CAS  Article  Google Scholar 

    79.
    Ami, D., Mereghetti, P. & Doglia, S. M. In Multivariate Analysis in Management, Engineering and the Sciences (eds de Freitas, L. V. & de Freitas, A. P. B. R.) https://www.intechopen.com/books/multivariate-analysis-in-management-engineering-and-the-sciences/multivariate-analysis-for-fourier-transform-infrared-spectra-of-complex-biological-systems-and-proce (Intech Open, 2013).

    80.
    Saarakkala, S., Rieppo, L., Rieppo, J. & Jurvelin, J. In Microscopy: Science, Technology, Applications and Education Vol. 1 (eds Méndez-Vilas, A. & Díaz, J.) 403–414 (Formatex, 2010).

    81.
    Smith, B. C. (CRC Press, Boca Raton, 2011).

    82.
    Eriksson, L., Johansson, E., Kettaneh-Wold, N. & Wold, S. Introduction to Multi- and Megavariate Data Analysis using Projection Methods (PCA & PLS) (Umetrics AB, Umeå, 1999).
    Google Scholar 

    83.
    Garson, G. D. In Blue Book Series (Statistical Associates Publishers, Asheboro, 2016).

    84.
    SmartPLS 3 (SmartPLS GmbH, Boenningstedt, 2015). More

  • in

    Behaviours indicating cannibalistic necrophagy in ants are modulated by the perception of pathogen infection level

    1.
    Fox, L. R. Cannibalism in natural populations. Annu. Rev. Ecol. Syst. 6, 87–106 (1975).
    Article  Google Scholar 
    2.
    Polis, G. A. The evolution and dynamics of intraspecific predation. Annu. Rev. Ecol. Evol. Syst. 12, 225–251 (1981).
    Article  Google Scholar 

    3.
    Elgar, M. A. & Crespi, B. J. Ecology and evolution of cannibalism. In Cannibalism: ecology and evolution among diverse taxa (eds Elgar, M. A. & Crespi, B. J.) 1–12 (Oxford University Press, Oxford, 1992).
    Google Scholar 

    4.
    Richardson, M. L., Mitchell, R. F., Reagel, P. F. & Hanks, L. M. Causes and consequences of cannibalism in noncarnivorous insects. Annu. Rev. Entomol. 55, 39–53 (2010).
    CAS  PubMed  Article  Google Scholar 

    5.
    Vilaça, A. Relations between funerary cannibalism and warfare cannibalism: The question of predation. Ethnos 65, 83–106 (2000).
    Article  Google Scholar 

    6.
    Lopez-Riquelme, G. O. & Fanjul-Moles, M. L. The funeral ways of social insects. Social strategies for corpse disposal. Trends Entomol. 9, 71–129 (2013).
    Google Scholar 

    7.
    Walls, S. C. & Roudebush, R. E. Reduced aggression toward siblings as evidence of kin recognition in cannibalistic salamanders. Am. Nat 138, 1027–1038 (1991).
    Article  Google Scholar 

    8.
    Pfennig, D. W. Cannibalistic tadpoles that pose the greatest threat to kin are most likely to discriminate kin. Proc. R. Soc. Lond. B 266, 57–61 (1999).
    Article  Google Scholar 

    9.
    Bilde, T. & Lubin, Y. Kin recognition and cannibalism in a subsocial spider. J. Evolut. Biol. 14, 959–966 (2001).
    Article  Google Scholar 

    10.
    Santana, A. F. K., Roselino, A. C., Cappelari, F. A. & Zucoloto, F. S. Cannibalism in insects. In Insect bioecology and nutrition for integrated pest management (eds Panizzi, A. R. & Parra, J. R. P.) 177–190 (CRC Press, Boca Raton, 2012).
    Google Scholar 

    11.
    Hölldobler, B. & Wilson, E. O. The ants (The Belknap Press of Harvard University, London, 1990).
    Google Scholar 

    12.
    Schmickl, T. & Crailsheim, K. Cannibalism and early capping: strategy of honeybee colonies in times of experimental pollen shortage. J. Comput. Physiol. A 187, 541–547 (2001).
    CAS  Article  Google Scholar 

    13.
    Sun, Q. & Zhou, X. Corpse management in social insects. Int. J. Biol. Sci. 9, 313–321 (2013).
    PubMed  PubMed Central  Article  Google Scholar 

    14.
    Davis, H. E., Meconcelli, S., Rudek, R. & McMahon, D. P. Termites shape their collective behavioural response based on stage of infection. Sci. Rep. 8, 14433. https://doi.org/10.1038/s41598-018-32721-7 (2018).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    15.
    Mabelis, A. A. Wood ant wars: the relationship between aggression and predation in the red wood ant (Formica polyctena Först.). Neth. J. Zool. 29, 451–620 (1979).
    Article  Google Scholar 

    16.
    Driessen, G. J. J., Van Raalte, ATh. & De Bruyn, G. Cannibalism in the red wood ant, Formica polyctena (Hymenoptera: Formicidae). Oecologia 63, 13–22 (1984).
    ADS  PubMed  Article  Google Scholar 

    17.
    Yao, M. et al. The ancient chemistry of avoiding risks of predation and disease. Evol. Biol. 36, 267–281 (2009).
    Article  Google Scholar 

    18.
    Visscher, P. K. The honey bee way of death: Necrophoric behaviour in Apis mellifera colonies. Anim. Behav. 31, 1070–1076 (1983).
    Article  Google Scholar 

    19.
    Oi, D. H. & Pereira, R. M. Ant behavior and microbial pathogens (Hymenoptera: Formicidae). Florida Entomol. 76, 63–74 (1993).
    Article  Google Scholar 

    20.
    Nazzi, F., Della Vedova, G. & D’Agaro, M. A semiochemical from brood cells infested by Varroa destructor triggers hygienic behaviour in Apis mellifera. Apidologie 35, 65–70 (2004).
    CAS  Article  Google Scholar 

    21.
    Renucci, M., Tirrard, A. & Provost, E. Complex undertaking behavior in Temnothorax lichtensteini ant colonies: From corpse-burying behavior to necrophoric behavior. Insect. Soc. 58, 9–16 (2011).
    Article  Google Scholar 

    22.
    Diez, L., Le Borgne, H., Lejeune, P. & Detrain, C. Who brings out the dead? Necrophoresis in the red ant Myrmica rubra. Anim. Behav. 6, 1259–1264 (2013).
    Article  Google Scholar 

    23.
    Baracchi, D., Fadda, A. & Turillazzi, S. Evidence for antiseptic behaviour towards sick adult bees in honey bee colonies. J. Insect. Physiol. 58, 1589–1596 (2012).
    CAS  PubMed  Article  Google Scholar 

    24.
    Pull, Ch. D. et al. Destructive disinfection of infected brood prevents systemic disease spread in ant colonies. eLife 7, e32073. https://doi.org/10.7554/eLife.32073 (2018).
    Article  PubMed  PubMed Central  Google Scholar 

    25.
    Leclerc, J.-B. & Detrain, C. Ants detect but do not discriminate diseased workers within their nest. Sci. Nat. 103, 70. https://doi.org/10.1007/s00114-016-1394-8 (2016).
    CAS  Article  Google Scholar 

    26.
    Williams, T. & Hernandez, O. Costs of cannibalism in the presence of an iridovirus pathogen of Spodoptera frugiperda. Ecol. Entomol. 31, 106–113 (2006).
    Article  Google Scholar 

    27.
    Rudolf, V. H. W. & Antonovics, J. Disease transmission by cannibalism: rare event or common occurrence?. Proc. R. Soc. Lond. B 274, 1205–1210 (2007).
    Google Scholar 

    28.
    Sadeh, A. & Rosenheim, J. A. Cannibalism amplifies the spread of vertically transmitted pathogens. Ecology 97, 1994–2002 (2016).
    PubMed  Article  Google Scholar 

    29.
    Claessen, D., de Roos, A. M. & Persson, L. Population dynamic theory of size-dependent cannibalism. Proc. R. Soc. Lond. B 271, 333–340 (2004).
    Article  Google Scholar 

    30.
    Pfennig, D. W., Ho, S. G. & Hoffman, E. A. Pathogen transmission as a selective force against cannibalism. Anim. Behav. 55, 1255–1261 (1998).
    CAS  PubMed  Article  Google Scholar 

    31.
    Loreto, R. G. & Hughes, D. P. Disease in the society: infectious cadavers result in collapse of ant sub-colonies. PLoS ONE 11, e0160820 (2016).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    32.
    Hughes, W. H., Eilenberg, J. & Boomsmal, J. J. Trade-offs in group living: Transmission and disease resistance in leaf-cutting ants. Proc. R. Soc. Lond. B 269, 1811–1819 (2002).
    Article  Google Scholar 

    33.
    Cremer, S. & Sixt, M. Analogies in the evolution of individual and social immunity. Proc. R. Soc. Lond. B 364, 129–142 (2009).
    Google Scholar 

    34.
    Konrad, M. et al. Social transfer of pathogenic fungus promotes active immunisation in ant colonies. PLoS ONE 10, 1–15 (2012).
    Google Scholar 

    35.
    Liu, L., Ganghua, L., Pengdong, S., Chaoliang, L. & Quiying, H. Experimental verification and molecular basis of active immunization against fungal pathogens in termites. Sci. Rep. 5, 15106. https://doi.org/10.1038/srep15106 (2015).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    36.
    Marikovsky, P. I. On some features of behaviour of the ants Formica rufa L. infected with fungus disease. Insect. Soc. 2, 173–179 (1962).
    Article  Google Scholar 

    37.
    Rutkowski, T. et al. Ants trapped for years in an old bunker; survival by cannibalism and eventual escape. J. Hymenopt. Res. 72, 177–184 (2019).
    Article  Google Scholar 

    38.
    Seifert, B. Die Ameisen Mittel- und Nordeuropas (Lutra-Verlags-und Vertriebsgesellschaft, Görlitz, 2007).
    Google Scholar 

    39.
    Czechowski, W., Radchenko, A., Czechowska, W. & Vepsäläinen, K. The ants of Poland with reference to the myrmecofauna of Europe. Fauna Poloniae (n.s.) 4. (Natura Optima Dux Foundation, 2012).

    40.
    Meyling, N. V. & Eilenberg, J. Ecology of the entomopathogenic fungi Beauveria bassiana and Metarhizium anisopliae in temperate agroecosystems: Potential for conservation biological control. Biol. Control 43, 145–155 (2007).
    Article  Google Scholar 

    41.
    Reber, A. & Chapuisat, M. Diversity, prevalence and virulence of fungal entomopathogens in colonies of the ant Formica selysi. Insect. Soc. 59, 231–239 (2012).
    Article  Google Scholar 

    42.
    Hajek, A. E. & St. Leger, R. J. Interactions between fungal pathogens and insect hosts. Annu. Rev. Entomol. 39, 293–322 (1994).
    Article  Google Scholar 

    43.
    Maák, I. et al. Cues or meaningless objects? Differential responses of the ant Formica cinerea to corpses of competitors and enslavers. Anim. Behav. 91, 53–59 (2014).
    Article  Google Scholar 

    44.
    Csata, E. & Dussutour, A. Nutrient regulation in ants (Hymenoptera: Formicidae): A review. Myrmecol. News 29, 111–124 (2019).
    Google Scholar 

    45.
    Nonacs, P. Death in the distance: Mortality risk as information for foraging ants. Behaviour 112, 23–35 (1990).
    Article  Google Scholar 

    46.
    Roces, F. & Núṅez, J. A. Information about food quality influences load-size selection in recruited leaf-cutting ants. Anim. Behav. 45, 135–143 (1993).
    Article  Google Scholar 

    47.
    Song, D., Hu, X. P. & Su, N.-Y. Survivorship, cannibalism, body weight loss, necrophagy, and entombement in laboratory groups of the Formosan subterranean termite, Coptotermes formosanus under starvation (Isoptera: Rhinotermitidae). Sociobiology 47, 27–39 (2006).
    Google Scholar 

    48.
    Heifig, I., Lima, J. T., Janei, V. & Costa-Leonardo, A. M. Effects of group size and starvation on survival of the Asian subterranean termite Coptotermes gestroi (Isoptera: Rhinotermitidae). Austral Entomol. 57, 279–284 (2017).
    Article  Google Scholar 

    49.
    Pompilio, L., Kacelnik, A. & Behmer, S. T. State-dependent learned valuation drives choice in an invertebrate. Science 311, 1613–1615 (2006).
    ADS  CAS  PubMed  Article  Google Scholar 

    50.
    Akino, T. & Yamaoka, R. Origin of oleic acid: Corpse recognition signal in the ant Formica japonica Motschlsky (Hymenoptera: Formicidae). Jpn. J. Appl. Entomol. Z. 40, 265–271 (1996).
    CAS  Article  Google Scholar 

    51.
    Chouvenc, T., Robert, A., Sémon, E. & Bordereau, C. Burial behaviour by dealates of the termite Pseudacanthotermes spiniger (Termitidae, Macrotermitinae) induced by chemical signals from termite corpses. Insect. Soc. 59, 119–125 (2012).
    Article  Google Scholar 

    52.
    Kok-Boon, N., Beng-Keok, Y., Kunio, T., Tsuyoshi, Y. & Chow-Yang, L. Do termites avoid carcasses? Behavioral responses depend on the nature of the carcasses. PLoS ONE 7, 1–11 (2012).
    Google Scholar 

    53.
    Diez, L., Moquet, L. & Detrain, C. Post-mortem changes in chemical profile and their influence on corpse removal in ants. J. Chem. Ecol. 39, 1424–1432 (2013).
    CAS  PubMed  Article  Google Scholar 

    54.
    Bignell, D. E., Roisin, Y. & Lo, N. Biology of Termites: A modern synthesis (Springer, Berlin, 2010).
    Google Scholar 

    55.
    Dlusskij, G. M. Ants of the genus Formica (Hymenoptera, Formicidae, g. Formica) (Nauka, Moscow, 1967) (in Russian).
    Google Scholar 

    56.
    Czechowski, W. Ants cemeteries. Przegląd Zoologiczny 20, 417–427 (1976) (in Polish with English summary).
    Google Scholar 

    57.
    Czechowski, W. Around nest cemeteries of Myrmica schencki Em. (Hymenoptera: Formicidae): their origin and a possible significance. Pol. J. Ecol. 56, 359–363 (2008).
    Google Scholar 

    58.
    Gibb, H. Experimental evidence for mediation of competition by habitat succession. Ecology 92, 1871–1878 (2011).
    CAS  PubMed  Article  Google Scholar 

    59.
    Chouvenc, T., Su, N.-Y. & Elliott, M. L. Interaction between the subterranean termite Reticulitermes flavipes (Isoptera: Rhinotermitidae) and the entomopathogenic fungus Metarhizium anisopliae in foraging arenas. J. Econ. Entomol. 101, 885–893 (2008).
    CAS  PubMed  Article  Google Scholar 

    60.
    Yanagawa, A., Yokohari, F. & Shimizu, S. The role of antennae in removing entomopathogenic fungi from cuticle of the termite Coptotermes formosanus. . J. Insect Sci. 9, 1–9 (2009).
    Article  Google Scholar 

    61.
    Tranter, Ch., LeFevre, L., Evison, S. E. F. & Hughes, W. O. H. Threat detection: Contextual recognition and response to parasites by ants. Behav. Ecol. 26, 396–405 (2015).
    Article  Google Scholar 

    62.
    Bonadies, E., Wcislo, W. T., Gálvez, D., Hughes, W. O. H. & Fernández-Marin, H. Hygiene defense behaviors used by a fungus-growing ant depend on the fungal pathogen stages. Insects 10, 130 (2019).
    PubMed Central  Article  PubMed  Google Scholar 

    63.
    Simone-Finstrom, M. D. & Spivak, M. Increased resin collection after parasite challenge: A case of self-medication in honey bees?. PLoS ONE 7, e34601. https://doi.org/10.1371/journal.pone.0034601 (2012).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    64.
    Brütsch, T. & Chapuisat, M. Wood ants protect their brood with tree resin. Anim. Behav. 93, 157–161 (2014).
    Article  Google Scholar 

    65.
    Ormond, E. L., Thomas, A. P. M., Pell, J. K., Freeman, S. N. & Roy, H. E. Avoidance of a generalist entomopathogenic fungus by the ladybird Coccinella septempunctata. FEMS Microbiol. Ecol. 77, 229–237 (2011).
    CAS  PubMed  Article  Google Scholar 

    66.
    Fernández-Marín, H., Zimmerman, J. K., Rehner, S. A. & Wcislo, W. T. Active use of the metapleural glands by ants in controlling fungal infection. Proc. R. Soc. Lond. B 273, 1689–1695 (2006).
    Google Scholar 

    67.
    Tragust, S. et al. Ants disinfect fungus-exposed brood by oral uptake and spread of their poison. Curr. Biol. 23, 1–7 (2013).
    Article  CAS  Google Scholar 

    68.
    Tragust, S., Herrmann, C., Häfner, J., Braasch, R., Tilgen, Ch., Hoock, M., Milidakis, M. A., Gross, R. & Feldhaar, H. Formicine ants swallow their highly acidic poison for gut microbial selection and control. bioRxiv preprint https://doi.org/10.1101/2020.02.13.947432 (2020).

    69.
    Cremer, S., Pull, Ch. D. & Fürst, M. A. Social immunity: emergence and evolution of colony-level disease protection. Annu. Rev. Entomol. 63, 105–123 (2018).
    CAS  PubMed  Article  Google Scholar 

    70.
    Rosengaus, R. B., Jordan, C., Lefebvre, M. L. & Traniello, J. F. A. Pathogen alarm behavior in a termite: A new form of communication in social insects. Naturwissenschaften 86, 544–548 (1999).
    ADS  CAS  PubMed  Article  Google Scholar 

    71.
    Hernandez-Lopez, J., Reissberger-Gallé, U., Crailsheim, K. & Schuehly, W. Cuticular hydrocarbon cues of immune-challenged workers elicit immune activation in honeybee queens. Mol. Ecol. 26, 3062–3073 (2017).
    CAS  PubMed  Article  Google Scholar 

    72.
    Chouvenc, T. & Su, N.-Y. When subterranean termites challenge the rules of fungal epizootics. PLoS ONE 7, 84. https://doi.org/10.1371/journal.pone.0034484 (2012).
    CAS  Article  Google Scholar 

    73.
    Csata, E., Erős, K. & Markó, B. Effects of the ectoparasitic fungus Rickia wasmannii on its ant host Myrmica scabrinodis: changes in host mortality and behavior. Insect. Soc. 61, 247–252 (2014).
    Article  Google Scholar 

    74.
    Diez, L., Urbain, L., Lejeune, Ph. & Detrain, C. Emergency measures: adaptive response to pathogen intrusion in the ant nest. Behav. Process. 116, 80–86 (2015).
    Article  Google Scholar 

    75.
    Qui, H.-L. et al. Differential necrophoric behaviour of the ant Solenopsis invicta towards fungal infected corpses of workers and pupae. Bull. Entomol. Res. 105, 607–614 (2015).
    Article  CAS  Google Scholar 

    76.
    Pereira, H. & Detrain, C. Pathogen avoidance and prey discrimination in ants. R. Soc. Open Sci. 7, 191705 (2020).
    ADS  PubMed  PubMed Central  Article  Google Scholar 

    77.
    Cremer, S., Armitage, S. A. O. & Schmid-Hempel, P. Social immunity. Curr. Biol. 17, 693–702 (2007).
    Article  CAS  Google Scholar 

    78.
    Pull, Ch. D. & Cremer, S. Co-founding ant queens prevent disease by performing prophylactic undertaking behaviour. BMC Evol. Biol. 219, 17. https://doi.org/10.1186/s12862-017-1062-4 (2017).
    Article  Google Scholar 

    79.
    Kramm, K. R., West, D. F. & Rockenbach, P. G. Pathogens of termites: transfer of the entomopathogen Metarhizium anisopliae between the termites of Reticulitermes sp.. J. Invertebr. Pathol. 40, 1–6 (1982).
    Article  Google Scholar 

    80.
    Kesäniemi, J., Koskimäki, J. J. & Jurvansuu, J. Corpse management of the invasive Argentine ant inhibits growth of pathogenic fungi. Sci. Rep. 9, 7593. https://doi.org/10.1038/s41598-019-44144-z (2019).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    81.
    Greenwald, E. E., Baltiansky, L. & Feinerman, O. Individual crop loads provide local control for collective food intake in ant colonies. eLife 7, e31730 (2018).
    PubMed  PubMed Central  Article  Google Scholar 

    82.
    Horstmann, K. Untersuchungen über den Nahrungserwerb der Waldameisen (Formica polyctena Foerster) im Eichenwald. Oecologia 5, 138–157 (1970).
    ADS  PubMed  Article  Google Scholar 

    83.
    Bhatkar, A. & Whitcomb, W. H. Artificial diet for rearing various species of ants. Florida Entomol. 53, 229–232 (1970).
    Article  Google Scholar 

    84.
    Choe, D. H. & Rust, M. K. Horizontal transfer of insecticides in laboratory colonies of the argentine ant (Hymenoptera: Formicidae). J. Econ. Entomol. 101, 1397–1405 (2008).
    CAS  PubMed  Article  Google Scholar 

    85.
    Pereira, R. M. & Stimac, J. L. Transmission of Beauveria bassiana within nests of Solenopsis invicta (Hymenoptera: Formicidae) in the laboratory. Environ. Entomol. 21, 1427–1432 (1992).
    Article  Google Scholar 

    86.
    Liu, H., Skinner, M., Parker, B. L. & Brownbridge, M. Pathogenicity of Beauveria bassiana, Metarhizium anisopliae (Deuteromycotina: Hyphomycetes), and other entomopathogenic fungi against Lygus lineolaris (Hemiptera: Miridae). J. Econ. Entomol. 95, 675–681 (2002).
    PubMed  Article  Google Scholar 

    87.
    Loreto, R. G. & Hughes, D. P. Disease dynamics in ants. Adv. Genet. 94, 287–306. https://doi.org/10.1016/bs.adgen.2015.12.005 (2016).
    CAS  Article  PubMed  Google Scholar 

    88.
    R Core Team. R: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2017). https://www.R-project.org/.

    89.
    Therneau, T. coxme: Mixed Effects Cox Models. R package version 2.2-5. https://CRAN.R-project.org/package=coxme (2015).

    90.
    Bates, D., Maechler, M., Bolker, B. & Walker, S. lme4: Linear mixed-effects models using Eigen and S4. R package version 1.0-5. https://CRAN.R-project.org/package=lme4 (2013).

    91.
    Bartoń, K. MuMIn: Multi-model inference. R package version 1.9.13. https://CRAN.R-project.org/package=MuMIn (2013).

    92.
    Grueber, C. E., Nakagawa, S., Laws, R. J. & Jamieson, I. G. Multimodel inference in ecology and evolution: Challenges and solutions. J. Evol. Biol. 24, 699–711 (2011).
    CAS  PubMed  Article  Google Scholar  More

  • in

    Multiscale consensus habitat modeling for landscape level conservation prioritization

    1.
    Pimm, S. L. et al. The biodiversity of species and their rates of extinction, distribution, and protection. Science 344, 1246752 (2014).
    CAS  Article  Google Scholar 
    2.
    Smeraldo, S. et al. Modelling risks posed by wind turbines and power lines to soaring birds: the black stork (Ciconia nigra) in Italy as a case study. Biodivers. Conserv. 29, 1959–1976 (2020).
    Article  Google Scholar 

    3.
    Gutierrez, B. L. et al. An island of wildlife in a human-dominated landscape: the last fragment of primary forest on the Osa Peninsula’s Golfo Dulce coastline Costa Rica. PLoS ONE 14, e0214390 (2019).
    CAS  PubMed Central  Article  PubMed  Google Scholar 

    4.
    Padalia, H. et al. Assessment of historical forest cover loss and fragmentation in Asian elephant ranges in India. Environ. Monit. Assess. 191, 802 (2019).
    Article  Google Scholar 

    5.
    Sodhi, N. S., Lee, T. M., Koh, L. P. & Brook, B. W. A meta-analysis of the impact of anthropogenic forest disturbance on Southeast Asia’s biotas. Biotropica 41, 103–109 (2009).
    Article  Google Scholar 

    6.
    Beier, P. Determining minimum habitat areas and habitat corridors for cougars. Conserv. Biol. 7, 94–108 (1993).
    Article  Google Scholar 

    7.
    MacNally, R. & Bennett, A. F. Species-specific prediction of the impact of habitat fragmentation: local extinction of birds in the box-ironbark forests of central Victoria Australia. Biol. Conserv. 82, 147–155 (1997).
    Article  Google Scholar 

    8.
    Hanski, I. Habitat connectivity, habitat continuity, and metapopulations in dynamic landscapes. Oikos 8, 209–219 (1999).
    Article  Google Scholar 

    9.
    Weaver, J. L., Paquet, P. C. & Ruggerio, L. F. Resilience and conservation of large carnivores in the Rocky Mountains. Conserv. Biol. 10, 964–976 (1996).
    Article  Google Scholar 

    10.
    Smith, J. B., Nielsen, C. K. & Hellgren, E. C. Suitable habitat for recolonizing large carnivores in the midwestern USA. Oryx 50, 555–564 (2016).
    Article  Google Scholar 

    11.
    Morehouse, A. T., Hughes, C., Manners, N., Bectell, J. & Bruder, T. Carnivores and communities: a case study of human-carnivore conflict mitigation in southwestern Alberta. Front. Ecol. Evol. 8, 2 (2020).
    Article  Google Scholar 

    12.
    Pelton, M. R. et al. American black bear conservation action plan in Bears (ed. Servheen, C., Herrero, S., & Peyton, B.) 144–146. Status survey and conservation action plan. (IUCN/SSC Bear and Polar Bear Specialist Groups, 1999).

    13.
    Williamson, D. F. In the Black: Status, Management, and Trade of the American Black Bear (Ursus americanus) in North America (TRAFFIC North America. World Wildlife Fund, Washington, DC, 2002).
    Google Scholar 

    14.
    Hristienko, H. & McDonald, J. E. Jr. Going into the 21st century: a perspective on trends and controversies in the management of the American black bear. Ursus 18, 72–88 (2007).
    Article  Google Scholar 

    15.
    Scheick, B. K. & McCown, W. Geographic distribution of American black bears in North America. Ursus 25, 24–33 (2014).
    Article  Google Scholar 

    16.
    Wright, S. Evolution and the genetics of populations (The University of Chicago Press, Chicago, 1984).
    Google Scholar 

    17.
    Wooding, J. B. & Hardisky, T. S. Home range, habitat use, and mortality of black bears in north-central Florida. Int. Conf. Bear Res. Manag. 9, 349–356 (1994).
    Google Scholar 

    18.
    Florida Game and Fresh Water Fish Commission. Management of the Black Bear in Florida: A Staff Report to the Commissioners (Florida Game and Fresh Water Fish Commission, Tallahassee, 1993).
    Google Scholar 

    19.
    Florida Fish and Wildlife Conservation Commission. Florida Black Bear Management Plan (Florida Game and Fresh Water Fish Commission, Tallahassee, 2019).
    Google Scholar 

    20.
    Dixon, J. D. Genetic consequences of habitat fragmentation and loss: the case of the Florida black bear (Ursus americanus floridanus). Conserv. Genet. 8, 455–464 (2007).
    Article  Google Scholar 

    21.
    Brown, J. H. Challenges in Estimating Size and Conservation of Black Bear in West-Central Florida. Thesis, University of Kentucky (2004)

    22.
    Humm, J. M., McCown, J. W., Scheick, B. K. & Clark, J. D. Spatially explicit population estimates for black bears based on cluster sampling. J. Wildl. Manag. 81, 1187–1201 (2017).
    Article  Google Scholar 

    23.
    Florida Fish and Wildlife Conservation Commission. Florida Black Bear Management Plan (Florida Game and Fresh Water Fish Commission, Tallahassee, 2012).
    Google Scholar 

    24.
    Carr, M. H. & Zwick, P. D. Technical Report Florida 2070: Mapping Florida’s Future—Alternative Patterns of Development in 2070 (Geoplan Center at the University of Florida, Gainesville, 2016).
    Google Scholar 

    25.
    Noss, R. E., Quiqley, H. B., Hornocker, M. G., Merrill, T. & Paquet, P. C. Conservation biology and carnivore conservation in the Rocky Mountains. Conserv. Biol. 10, 94–96 (1996).
    Google Scholar 

    26.
    Breitenmoser, U. Large predators in the Alps: the fall and rise of man’s competitors. Biol. Conserv. 83, 279–289 (1998).
    Article  Google Scholar 

    27.
    Waser, P. M. Patterns and consequences of dispersal in gregarious carnivores. In Carnivore Behavior, Ecology, and Evolution (ed. Gittleman, J. L.) 267–295 (Cornell University Press, Ithaca, 1996).
    Google Scholar 

    28.
    Guisan, A. & Thuiller, W. Predicting species distribution: offering more than simple habitat models. Ecol. Lett. 8, 993–1009 (2005).
    Article  Google Scholar 

    29.
    Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232 (2006).
    Article  Google Scholar 

    30.
    Yackulic, C. B. et al. Presence-only modelling using MAXENT: When can we trust the inferences?. Methods Ecol. Evol. 4, 236–243 (2013).
    Article  Google Scholar 

    31.
    Araújo, M. B. & New, M. Ensemble forecasting of species distributions. Trends Ecol. Evol. 22, 42–47 (2007).
    Article  Google Scholar 

    32.
    De Oliveira Moreira, D. et al. The distributional ecology of the maned sloth: Environmental influences on its distribution and gaps in knowledge. PLoS ONE. 9, 1–12 (2014).
    Google Scholar 

    33.
    Martin, J. et al. Brown bear habitat suitability in the Pyrenees: transferability across sites and linking scales to make the most of scarce data. J. Appl. Ecol. 49, 621–631 (2012).
    Article  Google Scholar 

    34.
    Khosravi, R., Hemami, R. K. M. & Cushman, S. A. Multi-scale niche modeling of three sympatric felids of conservation importance in central Iran. Landsc. Ecol. 34, 2451–2467 (2019).
    Article  Google Scholar 

    35.
    Maehr, D. S., McCown, J. W., Land, E. D. & Roof, J. C. Southwest Florida Black Bear Habitat Use, Distribution, Movements, and Conservation Strategy (Florida Game and Fresh Water Fish Commission, Gainesville, 1992).
    Google Scholar 

    36.
    McCown, W., Kublis, P., Eason, T. & Scheick, B. Black Bear Movements and Habitat Use Relative to Roads in Ocala National Forest (Florida Fish and Wildlife Commission, Gainesville, 2004).
    Google Scholar 

    37.
    Dobey, S. Ecology of Florida black bears in the Okefenokee-Osceola ecosystem. Wildl. Monogr. 158, 1–41 (2005).
    Google Scholar 

    38.
    Ulrey, W. A. Home Range, Habitat Use, and Food Habits of the Black Bear in South-Central Florida. Thesis, University of Kentucky (2008)

    39.
    Karelus, D. L., McCown, J. W., Scheick, B. K., van de Kerk, M. & Oli, M. K. Home ranges and habitat selection by black bears in a newly colonized population in Florida. Southeast Nat. 15, 346–364 (2016).
    Article  Google Scholar 

    40.
    Karelus, D. L., McCown, J. W., Scheick, B. K. & Oli, M. K. Microhabitat features influencing habitat use by Florida black bears. Glob. Ecol. Conserv. 13, e00367 (2018).
    Article  Google Scholar 

    41.
    Olson, D. M. & Dinerstein, E. The Global 200: Priority ecoregions for global conservation. Ann. MO Bot. Gard. 89, 125–126 (2002).
    Article  Google Scholar 

    42.
    U.S. Census Bureau. Population and housing unite estimates vintage 2018. Washington, DC (2018).

    43.
    Burby, R. & May, P. Making Governments Plan (John Hopkins University Press, Baltimore, 1997).
    Google Scholar 

    44.
    Boarnet, M. G., McLaughlin, R. B. & Carruthers, J. I. Does state growth management change the pattern of urban growth? Evidence from Florida. Reg. Sci. Urban Econ. 41, 236–252 (2011).
    Article  Google Scholar 

    45.
    Seibert, S. G. Status and Management of Black Bears in Apalachicola National Forest (Florida Game and Fresh Water Fish Commission, Gainesville, 2013).
    Google Scholar 

    46.
    Land, E. D. Southwest Florida Black Bear Habitat Use, Distribution, Movements, and Conservation Strategy (Florida Game and Fresh Water Fish Commission, Tallahassee, 1994).
    Google Scholar 

    47.
    McCown, W., Eason, T. H. & Cunningham, M. W. Black Bear Movements and Habitat Use Relative to Roads in Ocala National Forest (Florida Fish and Wildlife Conservation Commission, Gainesville, 2001).
    Google Scholar 

    48.
    Stratman, M. R., Alden, C. D., Pelton, M. R. & Sunquist, M. E. Habitat use by American black bears in the sandhills of Florida. Ursus 12, 109–114 (2001).
    Google Scholar 

    49.
    Maehr, D. W. et al. Spatial characteristics of an isolated Florida black bear population. Southeast Nat. 2, 433–446 (2003).
    Article  Google Scholar 

    50.
    Orlando, M. A. The Ecology and Behavior of an Isolated Black Bear Population in West Central Florida. Thesis, University of Kentucky (2003)

    51.
    Annis, K. M. The Impact of Translocation on Nuisance Florida Black Bears. Thesis, University of Florida (2007).

    52.
    Neils, A. M. Florida Black Bear (Ursus americanus floridanus) at the Urban-Wildlife Interface: Are They Different? Thesis, University of Florida (2011).

    53.
    Guthrie, J. M. Modeling Movement Behavior and Road Crossing the Black Bear of South Central Florida. Thesis, University of Kentucky (2012).

    54.
    Baruch-Mordo, S. et al. Stochasticity in natural forage production affects use of urban areas by black bears: implications to management of human-bear conflicts. PLoS ONE 9, e85122 (2014).
    ADS  PubMed Central  Article  CAS  PubMed  Google Scholar 

    55.
    Lewis, J. S., Rachlow, J. L., Garton, E. O. & Vierling, L. A. Effects of habitat on GPS collar performance: using data screening to reduce location error. J. Appl. Ecol. 44, 663–671 (2007).
    Article  Google Scholar 

    56.
    Clark, J. D., Laufenberg, J. S., Davidson, M. & Murrow, J. L. Connectivity among subpopulations of Louisiana black bears as estimated by a step selection function. J. Wildl. Manage. 79, 1347–1360 (2015).
    Article  Google Scholar 

    57.
    Beumer, L. T., van Beest, F. M., Stelvig, M. & Schmidt, N. M. Spatiotemproal dynamics in habitat suitability of a large Arctic herbivore: environmental heterogeneity is key to a sedentary lifestyle. Glob. Ecol. Conserv. 18, e00647 (2019).
    Article  Google Scholar 

    58.
    Hinton, J. W. et al. Space use and habitat selection by resident and transient red wolves (Canis rufus). PLoS ONE 11, e0167603 (2016).
    PubMed Central  Article  CAS  PubMed  Google Scholar 

    59.
    Fourcade, Y., Engler, J. O., Rodder, D. & Secondi, J. Mapping species distributions with MAXENT using a geographically biased sample of presence data: a performance assessment of methods for correcting sampling bias. PLoS ONE 9, e97122 (2014).
    ADS  PubMed Central  Article  CAS  PubMed  Google Scholar 

    60.
    Pellerin, M., Said, S. & Gaillard, J.-M. Roe deer Capreolus capreolus home-range sizes estimated from VHF and GPS data. Wildl. Biol. 14, 101–110 (2009).
    Article  Google Scholar 

    61.
    Signer, J., Fieberg, J. & Avgar, T. Animal movement tools (amt): R package for managing tracking data and conducting habitat selection analyses. Ecol. Evol. 9, 880–890 (2019).
    PubMed Central  Article  PubMed  Google Scholar 

    62.
    Maehr, D. S. & Brady, J. R. Food habits of Florida black bears. J. Wildl. Manag. 48, 230–235 (1984).
    Article  Google Scholar 

    63.
    Hellgren, E. C., Vaughan, M. R. & Stauffer, D. F. Macrohabitat use by black bears in a southeastern wetland. J Wildl. Manag. 55, 442–448 (1991).
    Article  Google Scholar 

    64.
    Karelus, D. L. et al. Effects of environmental factors and landscape features on movement patterns of Florida black bears. J. Mammal. 98, 1463–1478 (2017).
    Article  Google Scholar 

    65.
    Florida Natural Areas Inventory. Florida Forever Board of Trustees Projects (2018).

    66.
    McGarigal, K., Cushman, S. A. & Ene, E. FRAGSTATS v4: Spatial Pattern Analysis Program for Categorical and Continuous Maps. University of Massachusetts, Amherst, MA. https://www.umass.edu/landeco/research/fragstats/fragstats.html. (2012).

    67.
    Riley, S. J., DeGloria, S. D. & Elliot, R. A terrain ruggedness index that quantifies topographic heterogeneity. Intermt. J. Sci. 5, 1–4 (1999).
    Google Scholar 

    68.
    Clark, J. D., Dunn, J. E. & Smith, K. G. A multivariate model of female black bear habitat use for a geographic information system. J. Wildl. Manag. 57, 519–526 (1993).
    Article  Google Scholar 

    69.
    U.S. Geological Survey. National Elevation Dataset. Washington, DC (2016).

    70.
    Ditmer, M. A., Noyce, K. V., Fieberg, J. R. & Garshelisa, D. L Delineating the ecological and geographic edge of an opportunist: The American black bear exploiting an agricultural landscape. Ecol. Model. 387, 205–219 (2018).
    Article  Google Scholar 

    71.
    U.S. Department of Agriculture National Agriculture Statistics Service. Census of Agriculture, Ag Census Web Maps. Washington, DC (2016).

    72.
    Hostetler, J. A. et al. Demographic consequences of anthropogenic influences: Florida black bears in north-central Florida. Biol. Conserv. 142, 2456–2463 (2009).
    Article  Google Scholar 

    73.
    Center for International Earth Science Information Network – CIESIN – Columbia University. Gridded Population of the World, Version 4 (GPWv4): Population Density. Palisades, NY (2016).

    74.
    Brody, A. J. & Pelton, M. R. Effects of roads on black bears in western North Carolina. Wildl. Soc. B 17, 5–10 (1989).
    Google Scholar 

    75.
    U.S. Census Bureau. TIGER/Line Shapefiles (machine readable data files). Washington DC (2016).

    76.
    U.S. Geological Survey. National Hydrology Dataset. Washington, DC (2018).

    77.
    U.S. Fish & Wildlife Service. National Wetlands Inventory Data. St Petersburg, FL (2018).

    78.
    R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/ (2019).

    79.
    Esri. ArcGIS Desktop: Release 10.4. Redlands, CA: Environmental Systems Research Institute (2015).

    80.
    Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151 (2006).
    Article  Google Scholar 

    81.
    Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).
    Article  Google Scholar 

    82.
    Calenge, C. The package adehabitat for the R software: a tool for the analysis of space and habitat use by animals. Ecol. Model. 197, 516–519 (2016).
    Article  Google Scholar 

    83.
    Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43–57 (2011).
    Article  Google Scholar 

    84.
    Phillips, S. J., Dudik, M., & Schapire, R. E. A maximum entropy approach to species distribution modeling in Proceedings of the twenty-first international conference on machine learning (technical coordinators Greiner, R. & Schuurmans, D.) 655–662 (ACM Press, 2004).

    85.
    Hernandez, P. A. et al. Predicting species distributions in poorly-studied landscapes. Biodivers. Conserv. 17, 1353–1366 (2008).
    Article  Google Scholar 

    86.
    Poor, E. E., Loucks, C., Jakes, A. & Urban, D. L. Comparing habitat suitability and connectivity modeling methods for conserving pronghorn migrations. PLoS ONE 7, e49390 (2012).
    ADS  CAS  PubMed Central  Article  PubMed  Google Scholar 

    87.
    Duan, R.-Y., Kong, X.-Q., Huang, M.-Y., Fan, W.-Y. & Wang, Z.-G. The predictive performance and stability of six species distribution models. PLoS ONE 9, e112764 (2014).
    ADS  PubMed Central  Article  CAS  PubMed  Google Scholar 

    88.
    Zhang, J. et al. MaxEnt modeling for predicting the spatial distribution of three raptors in the Sanjiangyuan National Park China. Ecol. Evol. 9, 6643–6654 (2019).
    PubMed Central  Article  PubMed  Google Scholar 

    89.
    Bertolino, S. et al. Spatially-explicit models as tools for implementing effective management strategies for invasive alien mammals. Mammal. Rev. 50, 87–199 (2020).
    Google Scholar 

    90.
    Alsamadisi, A. G., Tran, L. T. & Papes, M. Employing inferences across scales: integrating spatial data with different resolutions to enhance Maxent models. Ecol. Model. 415, 108857 (2020).
    Article  Google Scholar 

    91.
    Peralvo, M. F., Cuesta, F. & van Manen, F. Delineating priority habitat areas for the conservation of Andean bears in northern Ecuador. Ursus 16, 222–233 (2005).
    Article  Google Scholar 

    92.
    Mahalanobis, P. C. On the generalized distance in statistics. Proc. Natl. Aacd. Sci. India 2, 49–55 (1936).
    MATH  Google Scholar 

    93.
    Browning, D. M., Beaupre, S. J. & Duncan, L. Using partitioned Mahalanobis D2 (K) to formulate a GIS-based model of timber rattlesnake hibernacula. J. Wildl. Manag. 69, 33–44 (2005).
    Article  Google Scholar 

    94.
    Griffin, S. C., Taper, M. L., Hoffman, R. & Mills, L. S. Ranking Mahalanobis Distance models for predictions of occupancy from presence-only data. J. Wildl. Manag. 74, 1112–1121 (2010).
    Article  Google Scholar 

    95.
    Phillips, S. J. et al. Sample selection bias and presence-only distribution models: implications for background and pseudo-absence data. Ecol. Appl. 19, 181–197 (2009).
    Article  Google Scholar 

    96.
    Fielding, A. H. & Bell, J. F. A review of methods for the assessment of prediction errors in conservation presence/absence models. Environ. Conserv. 24, 38–49 (1997).
    Article  Google Scholar 

    97.
    Boyce, M. S., Vernier, P. R., Nielsen, S. E. & Schmiegelow, F. K. A. Evaluating resource selection functions. Ecol. Model. 157, 281–300 (2002).
    Article  Google Scholar 

    98.
    Hirzel, A. H., Le Lay, G., Helfer, V., Randin, C. & Guisan, A. Evaluating the ability of habitat suitability models to predict species presences. Ecol. Model. 199, 142–152 (2006).
    Article  Google Scholar 

    99.
    Murrow, J. L. & Clark, J. D. Effects of hurricanes Katrina and Rita on Louisiana black bear habitat. Ursus 23, 192–205 (2012).
    Article  Google Scholar 

    100.
    Sing, T., Sander, O., Beerenwinkel, N. & Lengauer, T. ROCR: visualizing classifier performance in R. Bioinformatics 21, 7881 (2005).
    Article  CAS  Google Scholar 

    101.
    Broennimann, B. & Di Cola, V. A. ecospat: Spatial Ecology Miscellaneous Methods. R package version 3.0 (2018).

    102.
    Liu, C., White, M. & Newell, G. Selecting thresholds for the prediction of species occurrence with presence-only data. J. Biogeogr. 40, 778–789 (2013).
    Article  Google Scholar 

    103.
    Hellgren, E. C., Bales, S. L., Gregory, M. S., Leslie, D. M. Jr. & Clark, J. D. Testing a Mahalanobis Distance model of black bear habitat use in the Ouichita Mountains of Oklahoma. J. Wildl. Manag. 71, 924–928 (2007).
    Article  Google Scholar 

    104.
    Murrow, J. L., Thatcher, C. A., van Manen, F. T. & Clark, J. A data-based conservation planning tool for Florida Panthers. Environ. Model. Assess. 18, 159–170 (2013).
    Article  Google Scholar 

    105.
    NOAA Office for Coastal Management. Detailed method for mapping sea level rise inundation. (NOAA, 2017).

    106.
    Pelton, M. R. 2003. Black bear. In Wild Mammals of North America: Biology, Management, and Conservation (eds Feldhamer, J. A. et al.) 547–555 (Johns Hopkins University, Baltimore, 2003).
    Google Scholar 

    107.
    Thuiller, W., Brotons, L., Araujo, M. B. & Lavorel, S. Effects of restricting environmental range of data to project current and future species distributions. Ecography 27, 165–172 (2004).
    Article  Google Scholar 

    108.
    Liu, C., Berry, P. M., Dawson, T. P. & Pearson, R. G. Selecting thresholds of occurrence in the prediction of species distributions. Ecography 28, 385–393 (2005).
    Article  Google Scholar 

    109.
    Kopp, R. E. et al. Probalistic 21st and 22nd century sea-level projections at a global network of tide-gauge sites. Earth’s Future 2, 383–406 (2014).
    ADS  Article  Google Scholar 

    110.
    Xiao, H. & Tang, Y. Assess the “superposed” effects of storm surge from a Category 3 hurricane. and continuous sea-level rise on saltwater intrusion into the surficial aquifer in coastal east-central Florida (USA). Environ. Sci. Pollut Res. 26, 21882–21889 (2019).
    CAS  Article  Google Scholar 

    111.
    Laurance, W. F. et al. A global strategy for road building. Nature 513, 229–234 (2014).
    ADS  CAS  Article  Google Scholar 

    112.
    Mukul, S. A. et al. Combined effects of climate change and sea-level rise project dramatic habitat loss of the globally endangered Bengal tiger in the Bangladesh Sundarbans. Sci. Total Environ. 663, 830–840 (2019).
    ADS  CAS  Article  Google Scholar 

    113.
    Poor, E. E., Shao, Y. & Kelly, M. J. Mapping and predicting forest loss in a Sumatran tiger landscape from 2002 to 2050. J. Environ. Manag. 231, 397–404 (2019).
    Article  Google Scholar 

    114.
    Durner, G. M. et al. Predicting 21st-century polar bear habitat distribution from global climate models. Ecol. Monogr. 79, 25–58 (2009).

    115.
    Yovovich, V., Allen, M. L., Macaulay, L. T. & Wilmers, C. C. Using spatial characteristics of apex carnivore communication and reproductive behaviors to predict responses to future human development. Biodivers. Conserv. 29, 2589–2603 (2020).
    Article  Google Scholar 

    116.
    Muhly, T. B. et al. Functional response of wolves to human development across boreal North America. Ecol. Evol. 9, 10801–10815 (2019).
    PubMed Central  Article  PubMed  Google Scholar 

    117.
    Zeller, K. A., Wattles, D. W., Conlee, L. & Destefano, S. Response of female black bears to a high-density road network and identification of long-term road mitigation sites. Anim. Conserv. https://doi.org/10.1111/acv.12621 (2020).
    Article  Google Scholar 

    118.
    Morales-González, A., Ruiz-Villar, H., Ordiz, A. & Penteriani, V. Large carnivores living alongside humans: Brown bears in human-modified landscapes. Glob. Ecol. Conserv. 22, 1–13 (2020).
    Google Scholar 

    119.
    Maletzke, B. et al. Cougar response to a gradient of human development. Ecosphere 8, 1–14 (2017).
    Article  Google Scholar 

    120.
    Barrington-Leigh, C. & Millard-Ball, A. A century of sprawl in the United States. PNAS https://doi.org/10.1073/pnas.1504033112 (2015).
    Article  Google Scholar  More

  • in

    Conspecific recognition of pedal scent in domestic dogs

    1.
    Owen, M. A. et al. An experimental investigation of chemical communication in the polar bear. J. Zool. 295, 36–43. https://doi.org/10.1111/jzo.12181 (2015).
    Article  Google Scholar 
    2.
    Yasui, T., Tsukise, A. & Meyer, W. Histochemical analysis of glycoconjugates in the eccrine glands of the raccoon digital pads. Eur. J. Histochem. 48, 393–402 (2009).
    Google Scholar 

    3.
    Meyer, W. & Bartels, T. Histochemical study on the eccrine glands in the foot pad of the cat. Basic Appl. Histochem. 33, 219–238 (1989).
    CAS  PubMed  Google Scholar 

    4.
    Meyer, W. & Tsukise, A. Lectin histochemistry of snout skin and foot pads in the wolf and the domesticated dog (Mammalia: Canidae). Ann. Anat. Anatomischer Anzeiger 177, 39–49. https://doi.org/10.1016/S0940-9602(11)80129-9 (1995).
    CAS  Article  PubMed  Google Scholar 

    5.
    Parillo, F. & Diverio, S. Glycocomposition of the apocrine interdigital gland secretions in the fallow deer (Dama dama). Res. Vet. Sci. 86, 194–199. https://doi.org/10.1016/j.rvsc.2008.08.004 (2009).
    CAS  Article  PubMed  Google Scholar 

    6.
    Müller-Schwarze, D., Källquist, L., Mossing, T., Brundin, A. & Andersson, G. Responses of reindeer to interdigital secretions of conspecifics. J. Chem. Ecol. 4, 325–335. https://doi.org/10.1007/bf00989341 (1978).
    Article  Google Scholar 

    7.
    Sergiel, A. et al. Histological, chemical and behavioural evidence of pedal communication in brown bears. Sci. Rep. 7, 1052. https://doi.org/10.1038/s41598-017-01136-1 (2017).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    8.
    Kruuk, H. Otters: Ecology, Behaviour and Conservation (Oxford University Press, Oxford, 2006).
    Google Scholar 

    9.
    Gorman, M. L. & Trowbridge, B. J. in Carnivore Behavior, Ecology, and Evolution (ed John L. Gittleman) 57–88 (Springer, New York, 1989).

    10.
    Spotte, S. Societies of Wolves and Free-Ranging Dogs (Cambridge University Press, Cambridge, 2012).
    Google Scholar 

    11.
    Sillero-Zubiri, C. & Macdonald, D. W. Scent-marking and territorial behaviour of Ethiopian wolves Canis simensis. J. Zool. 245, 351–361. https://doi.org/10.1111/j.1469-7998.1998.tb00110.x (1998).
    Article  Google Scholar 

    12.
    Cassidy, K. A., Mech, L. D., MacNulty, D. R., Stahler, D. R. & Smith, D. W. Sexually dimorphic aggression indicates male gray wolves specialize in pack defense against conspecific groups. Behav. Proc. 136, 64–72. https://doi.org/10.1016/j.beproc.2017.01.011 (2017).
    Article  Google Scholar 

    13.
    Rothman, R. J. & Mech, L. D. Scent-marking in lone wolves and newly formed pairs. Anim. Behav. 27, 750–760. https://doi.org/10.1016/0003-3472(79)90010-1 (1979).
    Article  Google Scholar 

    14.
    Udell, M. A. R., Dorey, N. R. & Wynne, C. D. L. What did domestication do to dogs? A new account of dogs’ sensitivity to human actions. Biol. Rev. 85, 327–345. https://doi.org/10.1111/j.1469-185X.2009.00104.x (2010).
    Article  PubMed  Google Scholar 

    15.
    Miklósi, Á. Dog Behaviour, Evolution, and Cognition 2nd edn. (Oxford University Press, Oxford, 2015).
    Google Scholar 

    16.
    Rosell, F. Secrets of the Snout: The Dog’s Incredible Nose (University of Chicago Press, Chicago, 2018).
    Google Scholar 

    17.
    Dunbar, I. F. Olfactory preferences in dogs: the response of male and female beagles to conspecific odors. Behav. Biol. 20, 471–481 (1977).
    CAS  Article  PubMed  Google Scholar 

    18.
    Lisberg, A. E. & Snowdon, C. T. Effects of sex, social status and gonadectomy on countermarking by domestic dogs, Canis familiaris. Anim. Behav. 81, 757–764. https://doi.org/10.1016/j.anbehav.2011.01.006 (2011).
    Article  Google Scholar 

    19.
    Ranson, E. & Beach, F. A. Effects of testosterone on ontogeny of urinary behavior in male and female dogs. Horm. Behav. 19, 36–51. https://doi.org/10.1016/0018-506X(85)90004-2 (1985).
    CAS  Article  PubMed  Google Scholar 

    20.
    Natynczuk, S., Bradshaw, J. W. S. & McDonald, D. W. Chemical constituents of the anal sacs of domestic dogs. Biochem. Syst. Ecol. 17, 83–87. https://doi.org/10.1016/0305-1978(89)90047-1 (1989).
    CAS  Article  Google Scholar 

    21.
    Sherman, C. K., Reisner, I. R., Taliaferro, L. A. & Houpt, K. A. Characteristics, treatment, and outcome of 99 cases of aggression between dogs. Appl. Anim. Behav. Sci. 47, 91–108. https://doi.org/10.1016/0168-1591(95)01013-0 (1996).
    Article  Google Scholar 

    22.
    Pal, S. K., Ghosh, B. & Roy, S. Agonistic behaviour of free-ranging dogs (Canis familiaris) in relation to season, sex and age. Appl. Anim. Behav. Sci. 59, 331–348. https://doi.org/10.1016/S0168-1591(98)00108-7 (1998).
    Article  Google Scholar 

    23.
    Trisko, R. K., Sandel, A. A. & Smuts, B. Affiliation, dominance and friendship among companion dogs. Behaviour 153, 693–725. https://doi.org/10.1163/1568539X-00003352 (2016).
    Article  Google Scholar 

    24.
    Rosvall, K. A. Intrasexual competition in females: evidence for sexual selection?. Behav. Ecol. 22, 1131–1140. https://doi.org/10.1093/beheco/arr106 (2011).
    Article  PubMed  PubMed Central  Google Scholar 

    25.
    Beach, F. A. Coital behaviour in dogs. VIII. Social affinity, dominance and sexual preference in the bitch. Behaviour 36, 131. https://doi.org/10.1163/156853970X00088 (1970).
    Article  Google Scholar 

    26.
    Pageat, P. & Gaultier, E. Current research in canine and feline pheromones. Vet. Clin. Small Anim. Pract. 33, 187–211. https://doi.org/10.1016/s0195-5616(02)00128-6 (2003).
    Article  Google Scholar 

    27.
    Bekoff, M. Ground scratching by male domestic dogs: a composite signal. J. Mammal. 60, 847–848. https://doi.org/10.2307/1380206 (1979).
    Article  Google Scholar 

    28.
    Hepper, P. & Wells, D. in Handbook of Olfaction and Gustation (ed Richard Doty) 591–604 (Wiley-Blackwell, 2015).

    29.
    Nicolaides, N. Skin lipids: their biochemical uniqueness. Science 186, 19–26 (1974).
    ADS  CAS  Article  PubMed  Google Scholar 

    30.
    Fierer, N., Hamady, M., Lauber, C. L. & Knight, R. The influence of sex, handedness, and washing on the diversity of hand surface bacteria. Proc. Natl. Acad. Sci. 105, 17994–17999. https://doi.org/10.1073/pnas.0807920105 (2008).
    ADS  Article  PubMed  Google Scholar 

    31.
    Craig, A. Forebrain emotional asymmetry: a neuroanatomical basis?. Trends Cognit. Sci. 9, 566–571. https://doi.org/10.1016/j.tics.2005.10.005 (2005).
    Article  Google Scholar 

    32.
    Royet, J.-P. & Plailly, J. Lateralization of olfactory processes. Chem. Senses 29, 731–745. https://doi.org/10.1093/chemse/bjh067 (2004).
    Article  PubMed  Google Scholar 

    33.
    Siniscalchi, M. et al. Sniffing with the right nostril: lateralization of response to odour stimuli by dogs. Anim. Behav. 82, 399–404. https://doi.org/10.1016/j.anbehav.2011.05.020 (2011).
    Article  Google Scholar 

    34.
    Lisberg, A. E. & Snowdon, C. T. The effects of sex, gonadectomy and status on investigation patterns of unfamiliar conspecific urine in domestic dogs, Canis familiaris. Anim. Behav. 77, 1147–1154. https://doi.org/10.1016/j.anbehav.2008.12.033 (2009).
    Article  Google Scholar 

    35.
    Fanjul, M. S., Zenuto, R. R. & Busch, C. Use of olfaction for sexual recognition in the subterranean rodent Ctenomys talarum. Acta theriologica 48, 35–46 (2003).
    Article  Google Scholar 

    36.
    Hart, B. L. Environmental and hormonal influences on urine marking behavior in the adult male dog. Behav. Biol. 11, 167–176. https://doi.org/10.1016/S0091-6773(74)90321-6 (1974).
    CAS  Article  PubMed  Google Scholar 

    37.
    Johnston, R. E., Derzie, A., Chiang, G., Jernigan, P. & Lee, H.-C. Individual scent signatures in golden hamsters: evidence for specialization of function. Anim. Behav. 45, 1061–1070. https://doi.org/10.1006/anbe.1993.1132 (1993).
    Article  Google Scholar 

    38.
    Gilfillan, G. D., Vitale, J. D., McNutt, J. W. & McComb, K. Spontaneous discrimination of urine odours in wild African lions, Panthera leo. Anim. Behav. 126, 177–185. https://doi.org/10.1016/j.anbehav.2017.02.003 (2017).
    Article  Google Scholar 

    39.
    Rostain, R. R., Ben-David, M., Groves, P. & Randall, J. A. Why do river otters scent-mark? An experimental test of several hypotheses. Anim. Behav. 68, 703–711. https://doi.org/10.1016/j.anbehav.2003.10.027 (2004).
    Article  Google Scholar 

    40.
    Blundell, G. M., Ben-David, M. & Bowyer, R. T. Sociality in river otters: cooperative foraging or reproductive strategies?. Behav. Ecol. 13, 134–141. https://doi.org/10.1093/beheco/13.1.134 (2002).
    Article  Google Scholar 

    41.
    Mills, M. Behavioural mechanisms in territory and group maintenance of the brown hyaena, Hyaena brunnea, in the southern Kalahari. Anim. Behav. 31, 503–510. https://doi.org/10.1016/s0003-3472(83)80072-4 (1983).
    Article  Google Scholar 

    42.
    Boydston, E. E., Morelli, T. L. & Holekamp, K. E. Sex differences in territorial behavior exhibited by the spotted hyena (Hyaenidae, Crocuta crocuta). Ethology 107, 369–385. https://doi.org/10.1046/j.1439-0310.2001.00672.x (2001).
    Article  Google Scholar 

    43.
    Bamberger, M. & Houpt, K. A. Signalment factors, comorbidity, and trends in behavior diagnoses in dogs: 1,644 cases (1991–2001). J. Am. Vet. Med. Assoc. 229, 1591–1601. https://doi.org/10.2460/javma.229.10.1591 (2006).
    Article  PubMed  Google Scholar 

    44.
    Starling, M. J., Branson, N., Thomson, P. C. & McGreevy, P. D. Age, sex and reproductive status affect boldness in dogs. Vet. J. 197, 868–872. https://doi.org/10.1016/j.tvjl.2013.05.019 (2013).
    Article  PubMed  Google Scholar 

    45.
    Bodnariu, A. L. I. N. A. Indicators of stress and stress assessment in dogs. Lucr. Stiint. Med. Vet. 41, 20–26 (2008).
    Google Scholar 

    46.
    Pal, S. K. Factors influencing intergroup agonistic behaviour in free-ranging domestic dogs (Canis familiaris). Acta Ethol. 18, 209–220. https://doi.org/10.1007/s10211-014-0208-2 (2015).
    Article  Google Scholar 

    47.
    Derix, R. et al. Male and female mating competition in wolves: female suppression vs. male intervention. Behaviour 127(1–2), 141–174 (1993).
    Article  Google Scholar 

    48.
    Udell, M. A. R. & Wynne, C. D. L. A review of domestic dogs’ (Canis familiaris) human-like behaviors: or why behavior analysts should stop worrying and love their dogs. J. Exp. Anal. Behav. 89, 247–261. https://doi.org/10.1901/jeab.2008.89-247 (2008).
    Article  PubMed  PubMed Central  Google Scholar 

    49.
    Kubinyi, E., Turcsán, B. & Miklósi, Á. Dog and owner demographic characteristics and dog personality trait associations. Behav. Proc. 81, 392–401. https://doi.org/10.1016/j.beproc.2009.04.004 (2009).
    Article  Google Scholar 

    50.
    Siniscalchi, M., d’Ingeo, S. & Quaranta, A. The dog nose “KNOWS” fear: asymmetric nostril use during sniffing at canine and human emotional stimuli. Behav. Brain Res. 304, 34–41. https://doi.org/10.1016/j.bbr.2016.02.011 (2016).
    Article  PubMed  Google Scholar 

    51.
    Peters, R. & Mech, L. D. in Wolf and Man (eds Roberta L. Hall & Henry S. Sharp) 133–147 (Academic Press, 1978).

    52.
    Thoß, M. et al. Regulation of volatile and non-volatile pheromone attractants depends upon male social status. Sci. Rep. 9, 489. https://doi.org/10.1038/s41598-018-36887-y (2019).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    53.
    Samuel, L. et al. Fears from the past? The innate ability of dogs to detect predator scents. Anim. Cognit. 23, 1–9 (2020).
    Article  Google Scholar 

    54.
    Thomsett, L. R. Structure of canine skin. Br. Vet. J. 142(2), 116–123 (1986).
    CAS  Article  PubMed  Google Scholar 

    55.
    Traniello, J. F. & Bakker, T. C. Minimizing observer bias in behavioral research: blinded methods reporting requirements for behavioral ecology and sociobiology. Behav. Ecol. 69, 1573–1574. https://doi.org/10.1007/s00265-015-2001-2 (2015).
    Article  Google Scholar 

    56.
    Fugazza, C. & Miklósi, Á. Domestic dog cognition and behavior 177–200 (Springer, Berlin, 2014).
    Google Scholar 

    57.
    Siniscalchi, M., Bertino, D. & Quaranta, A. Laterality and performance of agility-trained dogs. Later. Asymmetries Body Br. Cognit. 19, 219–234. https://doi.org/10.1080/1357650X.2013.794815 (2014).
    Article  Google Scholar 

    58.
    McKinley, J. & Sambrook, T. D. Use of human-given cues by domestic dogs (Canis familiaris) and horses (Equus caballus). Anim. Cogn. 3, 13–22. https://doi.org/10.1007/s100710050046 (2000).
    Article  Google Scholar 

    59.
    Johnen, D., Heuwieser, W. & Fischer-Tenhagen, C. An approach to identify bias in scent detection dog testing. Appl. Anim. Behav. Sci. 189, 1–12 (2017).
    Article  Google Scholar 

    60.
    Mulholland, M. M., Olivas, V. & Caine, N. G. The nose may not know: dogs’ reactions to rattlesnake odours. Appl. Anim. Behav. Sci. 204, 108–112. https://doi.org/10.1016/j.applanim.2018.04.001 (2018).
    Article  Google Scholar 

    61.
    Greer, K. A., Canterberry, S. C. & Murphy, K. E. Statistical analysis regarding the effects of height and weight on life span of the domestic dog. Res. Vet. Sci. 82, 208–214. https://doi.org/10.1016/j.rvsc.2006.06.005 (2007).
    Article  PubMed  Google Scholar 

    62.
    Gardner, M. & McVety, D. Treatment and Care of the Geriatric Veterinary Patient (Wiley, Hoboken, 2017).
    Google Scholar 

    63.
    Crowley, J. & Adelman, B. The Complete Dog Book: Official Publication of the American Kennel Club (Howell House, New York, 1998).
    Google Scholar 

    64.
    Harrison, X. A. et al. A brief introduction to mixed effects modelling and multi-model inference in ecology. PeerJ 6, e4794. https://doi.org/10.7717/peerj.4794 (2018).
    Article  PubMed  PubMed Central  Google Scholar 

    65.
    MuMIn: Multi-Model Inference (2018).

    66.
    Burnham, K. P., Anderson, D. R. & Huyvaert, K. P. AIC model selection and multimodel inference in behavioral ecology: some background, observations, and comparisons. Behav. Ecol. Sociobiol. 65, 23–35. https://doi.org/10.1007/s00265-010-1029-6 (2011).
    Article  Google Scholar 

    67.
    Anderson, D. R. Model Based Inference in the Life Sciences: A Primer on Evidence (Springer, Berlin, 2007).
    Google Scholar 

    68.
    Cumming, G. Understanding the New Statistics: Effect Sizes, Confidence Intervals, and Meta-Analysis (Routledge, London, 2013).
    Google Scholar 

    69.
    Hothorn, T., Bretz, F. & Westfall, P. Simultaneous inference in general parametric models. Biomet. J. 50, 346–363. https://doi.org/10.1002/bimj.200810425 (2008).
    MathSciNet  Article  MATH  Google Scholar  More

  • in

    Reference transcriptomes and comparative analyses of six species in the threatened rosewood genus Dalbergia

    1.
    Vatanparast, M. et al. First molecular phylogeny of the pantropical genus Dalbergia: implications for infrageneric circumscription and biogeography. S. Afr. J. Bot. 89, 143–149 (2013).
    CAS  Article  Google Scholar 
    2.
    Saha, S. et al. Ethnomedicinal, phytochemical, and pharmacological profile of the genus Dalbergia L. (Fabaceae). Phytopharmacology 4, 291–346 (2013).
    Google Scholar 

    3.
    Sprent, J. I. Legume Nodulation: A Global Perspective (Wiley, Hoboken, 2009).
    Google Scholar 

    4.
    Bhagwat, R. M., Dholakia, B. B., Kadoo, N. Y., Balasundaran, M. & Gupta, V. S. Two new potential barcodes to discriminate Dalbergia species. PLoS ONE 10, 1–18 (2015).
    Article  CAS  Google Scholar 

    5.
    EIA. Routes of Extinction: The Corruption and Violence Destroying SIAMESE Rosewood in the Mekong (Environmental Investigation Agency, London, 2014).
    Google Scholar 

    6.
    EIA. The Hongmu Challenge: A Briefing for the 66th Meeting of the CITES Standing Committee, January 2016 (2016).

    7.
    Winfield, K., Scott, M. & Graysn, C. Global status of Dalbergia and Pterocarpus rosewood producing species in trade. in Convention on International Trade in Endangered Species 17th Conference of Parties – Johannesburg (2016).

    8.
    Bentham, G. Synopsis of Dalbergieae, a Tribe of Leguminosae. J. Proc. Linn. Soc. Lond. Bot. 4, 1–128 (1860).
    MathSciNet  Article  Google Scholar 

    9.
    Lavin, M. et al. The dalbergioid legumes (Fabaceae): delimitation of a pantropical monophyletic clade. Am. J. Bot. 88, 503 (2001).
    CAS  Article  PubMed  Google Scholar 

    10.
    Hartvig, I. et al. Population genetic structure of the endemic rosewoods Dalbergia cochinchinensis and D. oliveri at a regional scale reflects the Indochinese landscape and life-history traits. Ecol. Evol. 8, 530–545 (2018).
    Article  PubMed  Google Scholar 

    11.
    Hartvig, I., Czako, M., Kjær, E. D., Nielsen, L. R. & Theilade, I. The use of DNA barcoding in identification and conservation of rosewood (Dalbergia spp.). PLoS ONE 10, e0138231 (2015).
    Article  CAS  PubMed  PubMed Central  Google Scholar 

    12.
    Wattoo, J. I., Saleem, M. Z., Shahzad, M. S., Arif, A. & Hameed, A. DNA barcoding: amplification and sequence analysis of rbcl and matK genome regions in three divergent plant species. Adv. Life Sci. 4, 03–07 (2016).
    CAS  Google Scholar 

    13.
    Phong, D. T., Tang, D. V., Hien, V. T. T., Ton, N. D. & Van, H. N. Nucleotide diversity of a nuclear and four chloroplast DNA regions in rare tropical wood species of dalbergia in Vietnam: a DNA barcode identifying utility. Asian J. Appl. Sci. 02, 116–125 (2014).
    Google Scholar 

    14.
    Resende, L. C., Ribeiro, R. A. & Lovato, M. B. Diversity and genetic connectivity among populations of a threatened tree (Dalbergia nigra) in a recently fragmented landscape of the Brazilian Atlantic Forest. Genetica 139, 1159–1168 (2011).
    Article  PubMed  Google Scholar 

    15.
    Buzatti, R. S. O., Ribeiro, R. A., Filho, J. P. L. & Lovato, M. B. Fine-scale spatial genetic structure of Dalbergia nigra (Fabaceae), a threatened and endemic tree of the Brazilian Atlantic Forest. Genet. Mol. Biol. 35, 838–846 (2012).
    Article  Google Scholar 

    16.
    Liu, F.-M. et al. De novo transcriptome analysis of Dalbergia odorifera and transferability of SSR markers developed from the transcriptome. Forests 10, 98 (2019).
    Article  Google Scholar 

    17.
    Xu, D.-P., Xu, S.-S., Zhang, N.-N., Yang, Z.-J. & Hong, Z. Chloroplast genome of Dalbergia cochinchinensis (Fabaceae), a rare and Endangered rosewood species in Southeast Asia. Mitochondrial DNA B 4, 1144–1145 (2019).
    Article  Google Scholar 

    18.
    Wariss, H. M., Yi, T.-S., Wang, H. & Zhang, R. Characterization of the complete chloroplast genome of Dalbergia odorifera (Leguminosae), a rare and critically endangered legume endemic to China. Conserv. Genet. Resour. https://doi.org/10.1007/s12686-017-0866-2 (2017).
    Article  Google Scholar 

    19.
    Liu, Y., Huang, P., Li, C.-H., Zang, F.-Q. & Zheng, Y.-Q. Characterization of the complete chloroplast genome of Dalbergia cultrata (Leguminosae). Mitochondrial DNA B 4, 2369–2370 (2019).
    Article  Google Scholar 

    20.
    Deng, C., Xin, G., Zhang, J. & Zhao, D. Characterization of the complete chloroplast genome of Dalbergia hainanensis (Leguminosae), a vulnerably endangered legume endemic to China. Conserv. Genet. Resour. 1, 105–108 (2018).
    Google Scholar 

    21.
    Song, Y., Zhang, Y., Xu, J., Li, W. & Li, M. F. Characterization of the complete chloroplast genome sequence of Dalbergia species and its phylogenetic implications. Sci. Rep. 9, 1–10 (2019).
    ADS  Article  CAS  Google Scholar 

    22.
    Lateef, A., Prabhudas, S. K. & Natarajan, P. RNA sequencing and de novo assembly of Solanum trilobatum leaf transcriptome to identify putative transcripts for major metabolic pathways. Sci. Rep. 8, 15375 (2018).
    ADS  Article  CAS  PubMed  PubMed Central  Google Scholar 

    23.
    Keilwagen, J., Hartung, F., Paulini, M., Twardziok, S. O. & Grau, J. Combining RNA-seq data and homology-based gene prediction for plants, animals and fungi. BMC Bioinform. 19, 189 (2018).
    Article  CAS  Google Scholar 

    24.
    Wang, B., Kumar, V., Olson, A. & Ware, D. Reviving the transcriptome studies: an insight into the emergence of single-molecule transcriptome sequencing. Front. Genet. 10, 384 (2019).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    25.
    Lamble, S. et al. Improved workflows for high throughput library preparation using the transposome-based nextera system. BMC Biotechnol. 13, 104 (2013).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    26.
    Ewels, P., Magnusson, M., Lundin, S. & Käller, M. MultiQC: summarize analysis results for multiple tools and samples in a single report. Bioinformatics 32, 3047–3048 (2016).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    27.
    Buffalo, V. Scythe—a Bayesian adapter trimmer (version 0.994 BETA) [Software] (2011). https://github.com/vsbuffalo/scythe.

    28.
    Joshi, N. A. & Fass, J. N. Sickle: a sliding-window, adaptive, quality-based trimming tool for FastQ files (Version 1.33) [Software] (2011). https://github.com/najoshi/sickle.

    29.
    Carruthers, M. et al. De novo transcriptome assembly, annotation and comparison of four ecological and evolutionary model salmonid fish species. BMC Genomics 19, 32 (2018).
    Article  CAS  PubMed  PubMed Central  Google Scholar 

    30.
    Haas, B. J. et al. De novo transcript sequence reconstruction from RNA seq using the Trinity platform for reference generation and analysis. Nat. Protoc. 8, 1494–1512 (2013).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    31.
    Langmead, B. & Salzberg, S. L. Fast gapped-read alignment with Bowtie 2. Nat. Methods 9, 357–359 (2012).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    32.
    Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006).
    CAS  Article  Google Scholar 

    33.
    Haas, B. J. TransDecoder (2018). https://github.com/TransDecoder/TransDecoder.

    34.
    Kriventseva, E. V. et al. OrthoDB v10: sampling the diversity of animal, plant, fungal, protist, bacterial and viral genomes for evolutionary and functional annotations of orthologs. Nucl. Acids Res. 47, D807–D811 (2019).
    CAS  Article  PubMed  Google Scholar 

    35.
    Waterhouse, R. M. et al. BUSCO applications from quality assessments to gene prediction and phylogenomics. Mol. Biol. Evol. 35, 543 (2017).
    Article  CAS  PubMed Central  Google Scholar 

    36.
    Bertioli, D. J. et al. The genome sequences of Arachis duranensis and Arachis ipaensis, the diploid ancestors of cultivated peanut. Nat. Genet. 48, 438–446 (2016).
    CAS  Article  Google Scholar 

    37.
    Smith-Unna, R., Boursnell, C., Patro, R., Hibberd, J. M. & Kelly, S. TransRate: reference-free quality assessment of de novo transcriptome assemblies. Genome Res. 26, 1134–1144 (2016).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    38.
    UniProt: a worldwide hub of protein knowledge. Nucl. Acids Res.47, D506–D515 (2019).

    39.
    Cheng, C.-Y. et al. Araport11: a complete reannotation of the Arabidopsis thaliana reference genome. Plant J. 89, 789–804 (2017).
    CAS  Article  PubMed  Google Scholar 

    40.
    El-Gebali, S. et al. The Pfam protein families database in 2019. Nucl. Acids Res. 47, D427–D432 (2019).
    CAS  Article  PubMed  Google Scholar 

    41.
    Almagro Armenteros, J. J. et al. SignalP 50 improves signal peptide predictions using deep neural networks. Nat. Biotechnol. 37, 420–423 (2019).
    CAS  Article  PubMed  Google Scholar 

    42.
    Krogh, A., Larsson, B., von Heijne, G. & Sonnhammer, E. L. Predicting transmembrane protein topology with a hidden markov model: application to complete genomes. J. Mol. Biol. 305, 567–580 (2001).
    CAS  Article  PubMed  Google Scholar 

    43.
    Emms, D. M. & Kelly, S. OrthoFinder2: fast and accurate phylogenomic orthology analysis from gene sequences. BioRxiv https://doi.org/10.1101/466201 (2018).
    Article  Google Scholar 

    44.
    Guo, L. et al. The opium poppy genome and morphinan production. Science 362, 343–347 (2018).
    ADS  CAS  Article  PubMed  Google Scholar 

    45.
    Nakamura, T., Yamada, K. D., Tomii, K. & Katoh, K. Parallelization of MAFFT for large-scale multiple sequence alignments. Bioinformatics 34, 2490–2492 (2018).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    46.
    Suyama, M., Torrents, D. & Bork, P. PAL2NAL: robust conversion of protein sequence alignments into the corresponding codon alignments. Nucl. Acids Res. 34, W609–W612 (2006).
    CAS  Article  PubMed  Google Scholar 

    47.
    Darriba, D., Taboada, G. L., Doallo, R. & Posada, D. jModelTest 2: more models, new heuristics and high-performance computing. Nat. Methods 9, 772 (2012).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    48.
    Stamatakis, A. RAxML version 8: a tool for phylogenetic analysis and post-analysis of large phylogenies. Bioinformatics 30, 1312–1313 (2014).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    49.
    Bouckaert, R. et al. BEAST 2.5: an advanced software platform for Bayesian evolutionary analysis. PLoS Comput. Biol. 15, e1006650 (2019).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    50.
    Brea, M., Zamuner, A. B., Matheos, S. D., Iglesias, A. & Zucol, A. F. Fossil wood of the Mimosoideae from the early Paleocene of Patagonia, Argentina. Alcheringa An Australas. J. Palaeontol. 32, 427–441 (2008).
    Article  Google Scholar 

    51.
    Hane, J. K. et al. A comprehensive draft genome sequence for lupin (Lupinus angustifolius), an emerging health food: insights into plant-microbe interactions and legume evolution. Plant Biotechnol. J. 15, 318–330 (2017).
    CAS  Article  PubMed  Google Scholar 

    52.
    Lavin, M., Herendeen, P. S. & Wojciechowski, M. F. Evolutionary rates analysis of leguminosae implicates a rapid diversification of lineages during the tertiary. Syst. Biol. 54, 575–594 (2005).
    Article  PubMed  Google Scholar 

    53.
    Moretzsohn, M. C. et al. A study of the relationships of cultivated peanut (Arachis hypogaea) and its most closely related wild species using intron sequences and microsatellite markers. Ann. Bot. 111, 113–126 (2013).
    CAS  Article  PubMed  Google Scholar 

    54.
    Ye, J. et al. WEGO 2.0: a web tool for analyzing and plotting GO annotations, 2018 update. Nucl. Acids Res. 46, W71 (2018).
    CAS  Article  PubMed  Google Scholar 

    55.
    De Bie, T., Cristianini, N., Demuth, J. P. & Hahn, M. W. CAFE: a computational tool for the study of gene family evolution. Bioinformatics 22, 1269–1271 (2006).
    Article  CAS  PubMed  Google Scholar 

    56.
    Mi, H., Muruganujan, A. & Thomas, P. D. PANTHER in 2013: modeling the evolution of gene function, and other gene attributes, in the context of phylogenetic trees. Nucl. Acids Res. 41, D377–D386 (2013).
    CAS  Article  PubMed  Google Scholar 

    57.
    Yang, Z. PAML 4: phylogenetic analysis by maximum likelihood. Mol. Biol. Evol. 24, 1586–1591 (2007).
    CAS  Article  PubMed  Google Scholar 

    58.
    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B (Methodological) 57(1), 289–300 (1995).
    MathSciNet  MATH  Google Scholar 

    59.
    Sun, J. et al. Adaptation to deep-sea chemosynthetic environments as revealed by mussel genomes. Nat. Ecol. Evol. 1, 0121 (2017).
    Article  Google Scholar 

    60.
    Yu, G., Wang, L. G., Han, Y. & He, Q. Y. ClusterProfiler: an R package for comparing biological themes among gene clusters. Omi. A J. Integr. Biol. 16, 284–287 (2012).
    CAS  Article  Google Scholar 

    61.
    Soltis, D. E., Soltis, P. S., Bennett, M. D. & Leitch, I. J. Evolution of genome size in the angiosperms. Am. J. Bot. 90, 1596–1603 (2003).
    Article  PubMed  Google Scholar 

    62.
    Hiremath, S. C. & Nagasampige, M. H. Genome size variation and evolution in some species of Dalbergia Linn.f. (Fabaceae). Caryologia 57, 367–372 (2004).
    Article  Google Scholar 

    63.
    Lawrence, G. H. M. Taxonomy of Vascular Plants (IBH Publishing Co., Oxford, 1973).
    Google Scholar 

    64.
    Lombello, R. A. & Forni-Martins, E. R. Chromosome studies and evolution in Sapindaceae. Caryologia 51, 89–93 (1998).
    Article  Google Scholar 

    65.
    Sheremet’ev, S. N. & Gamalei, Y. V. Towards angiosperms genome evolution in time. arXiv (2013).

    66.
    Carlquist, S. Anatomy of vine and liana stems: a review and synthesis. In The Biology of Vines (eds Putz, F. E. & Mooney, H. A.) 53–72 (University of Cambridge Press, Cambridge, 1991).
    Google Scholar 

    67.
    Li, Q. et al. The phylogenetic analysis of Dalbergia (Fabaceae: Papilionaceae) based on different DNA barcodes. Holzforschung 71, 939–949 (2017).
    CAS  Article  Google Scholar 

    68.
    Lavin, M. et al. Metacommunity process rather than continental tectonic history better explains geographically structured phylogenies in legumes. Philos. Trans. R. Soc. B Biol. Sci. 359, 1509–1522 (2004).
    CAS  Article  Google Scholar 

    69.
    Kučerová, J. Miocénna flóra z lokalít Kalonda a Mučín. Acta Geol. Slovaca 1, 65–70 (2009).
    Google Scholar 

    70.
    Gao, S.-X. & Zhou, Z.-K. The megafossil legumes from China. In Advances in Legume Systematics (eds Herendeen, P. S. & Dilcher, D. L.) (The Royal Botanic Gardens, Kew, 1992).
    Google Scholar 

    71.
    de Saporta, G. Dalbergia phleboptera Saporta. Muséum national d’Histoire naturelle (2015). https://science.mnhn.fr/institution/mnhn/collection/f/item/14084.?lang=en_US.

    72.
    De Bruyn, M. et al. Borneo and Indochina are major evolutionary hotspots for southeast Asian biodiversity. Syst. Biol. 63, 879–901 (2014).
    Article  PubMed  Google Scholar 

    73.
    Koenen, E. J. M. et al. The origin and early evolution of the legumes are a complex paleopolyploid phylogenomic tangle closely associated with the cretaceous-paleogene (K-Pg) boundary. biorxiv https://doi.org/10.1101/577957 (2019).
    Article  Google Scholar 

    74.
    Lespinet, O., Wolf, Y. I., Koonin, E. V. & Aravind, L. The role of lineage-specific gene family expansion in the evolution of eukaryotes. Genome Res. 12, 1048–1059 (2002).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    75.
    Ming, Y. et al. Molecular footprints of inshore aquatic adaptation in Indo-Pacific humpback dolphin (Sousa chinensis). Genomics https://doi.org/10.1016/j.ygeno.2018.07.015 (2018).
    Article  PubMed  Google Scholar 

    76.
    Force, A. et al. Preservation of duplicate genes by complementary, degenerative mutations. Genetics 151, 1531–1545 (1999).
    CAS  PubMed  PubMed Central  Google Scholar 

    77.
    Luengo, T. M., Mayer, M. P. & Rüdiger, S. G. The Hsp70–Hsp90 chaperone cascade in protein folding. Trends Cell Biol. 29(2), 164–177. https://doi.org/10.1016/j.tcb.2018.10.004 (2019).
    CAS  Article  Google Scholar 

    78.
    Jacob, P., Hirt, H. & Bendahmane, A. The heat-shock protein/chaperone network and multiple stress resistance. Plant Biotechnol. J. 15, 405–414 (2017).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    79.
    Yamada, K. et al. Cytosolic HSP90 regulates the heat shock response that is responsible for heat acclimation in Arabidopsis thaliana. J. Biol. Chem. 282, 37794–37804 (2007).
    CAS  Article  PubMed  Google Scholar 

    80.
    Clément, M. et al. The cytosolic/nuclear HSC70 and HSP90 molecular chaperones are important for stomatal closure and modulate abscisic acid-dependent physiological responses in arabidopsis. Plant Physiol. 156, 1481–1492 (2011).
    Article  CAS  PubMed  PubMed Central  Google Scholar 

    81.
    Hou, Q. & Bartels, D. Comparative study of the aldehyde dehydrogenase (ALDH) gene superfamily in the glycophyte Arabidopsis thaliana and Eutrema halophytes. Ann. Bot. 115, 465–479 (2015).
    CAS  Article  PubMed  Google Scholar 

    82.
    Missihoun, T. D. & Kotchoni, S. O. Aldehyde dehydrogenases and the hypothesis of a glycolaldehyde shunt pathway of photorespiration. Plant Signal. Behav. 13, e1449544 (2018).
    Article  CAS  PubMed  PubMed Central  Google Scholar 

    83.
    Estioko, L. P. et al. Differences in responses to flooding by germinating seeds of two contrasting rice cultivars and two species of economically important grass weeds. AoB Plants 6, plu064 (2014).
    Article  CAS  PubMed  PubMed Central  Google Scholar 

    84.
    Brocker, C. et al. Aldehyde dehydrogenase (ALDH) superfamily in plants: Gene nomenclature and comparative genomics. Planta 237, 189–210 (2013).
    CAS  Article  PubMed  Google Scholar 

    85.
    Sharma, B., Joshi, D., Yadav, P. K., Gupta, A. K. & Bhatt, T. K. Role of ubiquitin-mediated degradation system in plant biology. Front. Plant Sci. 7, 806 (2016).
    PubMed  PubMed Central  Google Scholar 

    86.
    Walters, K. J., Goh, A. M., Wang, Q., Wagner, G. & Howley, P. M. Ubiquitin family proteins and their relationship to the proteasome: a structural perspective. Biochimica et Biophysica Acta Mol. Cell Res. 1695, 73–87 (2004).
    CAS  Article  Google Scholar 

    87.
    Liu, Z.-B. et al. A novel membrane-bound E3 ubiquitin ligase enhances the thermal resistance in plants. Plant Biotechnol. J. 12, 93–104 (2014).
    Article  CAS  PubMed  Google Scholar 

    88.
    Macho, A. P. & Zipfel, C. Plant PRRs and the activation of innate immune signaling. Mol. Cell 54, 263–272 (2014).
    CAS  Article  PubMed  Google Scholar 

    89.
    Martin, G. B., Bogdanove, A. J. & Sessa, G. Understanding the functions of plant disease resistance proteins. Annu. Rev. Plant Biol. 54, 23–61 (2003).
    CAS  Article  PubMed  Google Scholar 

    90.
    Cohn, J., Sessa, G. & Martin, G. B. Innate immunity in plants. Curr. Opin. Immunol. 13, 55–62 (2001).
    CAS  Article  PubMed  Google Scholar 

    91.
    Lehmann, P. Structure and evolution of plant disease resistance genes. J. Appl. Genet. 43, 403–414 (2002).
    ADS  PubMed  Google Scholar 

    92.
    Jeffares, D. C., Tomiczek, B., Sojo, V. & dos Reis, M. A beginners guide to estimating the non-synonymous to synonymous rate ratio of all protein-coding genes in a genome. in Parasite Genomics Protocols: Second Edition 65–90 (Springer Fachmedien, 2014). https://doi.org/10.1007/978-1-4939-1438-8_4.

    93.
    Andersen, E. J., Ali, S., Byamukama, E., Yen, Y. & Nepal, M. P. Disease resistance mechanisms in plants. Genes 9(7), 339 (2018).
    Article  CAS  PubMed Central  Google Scholar 

    94.
    IUCN. The IUCN Red List of Threatened Species. Veresion 2019–2 (2019). https://www.iucnredlist.org.

    95.
    Federhen, S. The NCBI taxonomy database. Nucl. Acids Res. 40(D1), D136–D143 (2012).
    CAS  Article  PubMed  Google Scholar 

    96.
    Brandies, P., Peel, E., Hogg, C. J. & Belov, K. The value of reference genomes in the conservation of threatened species. Genes 10, 846 (2019).
    CAS  Article  PubMed Central  Google Scholar 

    97.
    Supple, M. A. & Shapiro, B. Conservation of biodiversity in the genomics era. Genome Biol. 19(1), 1–12 (2018).
    Article  Google Scholar 

    98.
    Fuentes-Pardo, A. P. & Ruzzante, D. E. Whole-genome sequencing approaches for conservation biology: advantages, limitations and practical recommendations. Mol. Ecol. 26, 5369–5406 (2017).
    CAS  Article  PubMed  Google Scholar 

    99.
    Wang, Z., Gerstein, M. & Snyder, M. RNA-Seq: a revolutionary tool for transcriptomics. Nat. Rev. Genet. 10, 57–63 (2009).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    100.
    Bragg, J. G., Potter, S., Bi, K. & Moritz, C. Exon capture phylogenomics: efficacy across scales of divergence. Mol. Ecol. Resour. 16, 1059–1068 (2016).
    CAS  Article  PubMed  Google Scholar 

    101.
    İpek, A., İpek, M., Ercişli, S. & Tangu, N. A. Transcriptome-based SNP discovery by GBS and the construction of a genetic map for olive. Funct. Integr. Genomics 17, 493–501 (2017).
    Article  CAS  PubMed  Google Scholar 

    102.
    Vatanparast, M., Powell, A., Doyle, J. J. & Egan, A. N. Targeting legume loci: a comparison of three methods for target enrichment bait design in Leguminosae phylogenomics. Appl. Plant Sci. 6, e1036 (2018).
    Article  PubMed  PubMed Central  Google Scholar 

    103.
    Ouborg, N. J. Integrating population genetics and conservation biology in the era of genomics. Biol. Lett. 6, 3–6 (2010).
    Article  PubMed  Google Scholar 

    104.
    CITES. Consideration of Proposals for Amendment of Appendices I and II. Convention on International Trade in Endangered Species of Wild Fauna and Flora. (Convention on International Trade in Endangered Species of Wild Fauna and Flora, 2017).

    105.
    Asian Regional Workshop (Conservation & Sustainable Management of Trees Viet Nam). Dalbergia cochinchinensis. The IUCN Red List of Threatened Species. e.T32625A9719096 (1998). https://doi.org/10.2305/IUCN.UK.1998.RLTS.T32625A9719096.en.

    106.
    Bernal, R., Gradstein, S. & Celis, M. Catálogo de plantas y líquenes de Colombia (Instituto de Ciencias Naturales, Universidad Nacional de Colombia, Bogotá, 2015).
    Google Scholar 

    107.
    World Conservation Monitoring Centre. Dalbergia melanoxylon. The IUCN Red List of Threatened Species 1998. e.T32504A9710439 (1998). https://doi.org/10.2305/IUCN.UK.1998.RLTS.T32504A9710439.en.

    108.
    ILDIS. International Legume Database and Information Service V10.39 (2011).

    109.
    Nghia, N. H. Dalbergia oliveri. The IUCN Red List of Threatened Species 1998. e.T32306A9693932 (1998). https://doi.org/10.2305/IUCN.UK.1998.RLTS.T32306A9693932.en.

    110.
    Orwa, C., Mutua, A., Kindt, R., Jamnadass, R. & Anthony, S. Agroforestree Database: A Tree Reference and Selection Guide Version 4.0. (2009). https://www.worldagroforestry.org/sites/treedbs/treedatabases.asp. More

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    A possible link between coral reef success, crustose coralline algae and the evolution of herbivory

    The role of CCA as reef consolidators
    We found a significant correlation between the proportion of reefs that contain CCA as secondary reef builders and the proportion of true reefs over the last 150 million years. Coral reefs can benefit from CCA in various ways. Relating to the reef ridge, the stony pavement made up by the algae protects the ridge from onrushing waves and also consolidates the reef flats behind the ridges11. With reference to the whole reef, CCA reinforce the structure created by corals, fill cracks, bind together much of the sand, dead corals and debris, and thereby create a stable substrate and reduce reef erosion22. Larval settlement, metamorphosis, and recruitment of several coral species is strictly determined by chemosensory recognition of specific signal molecules uniquely available in specific CCA23.
    However, it has to be considered that there are modern reefs that cope with wavy, high-energy environments without the aid of CCA, as for example the Alacran reef in Mexico12. CCA are not the only possibility to add rigidity to a reef. Submarine lithification can be more important than CCA in creating calcite precipitates, especially when environmental and ecological conditions are unfavourable for the growth of CCA, e.g. because of the lack of light. Submarine lithification in the form of Mg-calcite precipitates exists in many forms, including cemented micritic crusts and infillings of cracks. Additionally, their respective carbonate sources may be abiotic24 or originate from a great variety of organisms, including reef fish25. Therefore, they do also play an important role for the structural integrity of coral reefs24. CCA abundance may benefit from reef growth in terms of ecological niches provided, additionally increasing the positive correlation. We thus suggest that the significant correlation between the proportion of reefs reinforced by CCA as secondary reef builders and the proportion of true reefs can be interpreted as a mutual benefit. On the one hand, the presence of CCA can add stability to coral reefs, especially when the reef ridge is exposed to heavy wave action. On the other hand, sufficient reef growth can be a prerequisite for a larger abundance of CCA. A shift towards one side in this mutual dependence is subject to the particular features of each reef, as for example if CCA rather benefit from the shelter of crevices in reefs with high grazing pressure or if corals rather benefit from the presence of CCA at sites of intense wave exposure.
    The physicochemical parameters ocean temperature, sea level, and RCO2
    CCA occur worldwide from the tropics10 to polar latitudes26 and temperature is one of the primary determinants in their geographical distribution, and the boundaries of their biogeographical regions are associated with isotherms27. Therefore, the identification of ocean temperature as an important driver of CCA reefs is reasonable. Aguirre, et al.28 reported that throughout the history of CCA, species richness broadly correlates with global mean palaeotemperature. However, only the diversity of the order Sporolithales varies positively with temperature, whereas the diversity of the order Corallinales varies negatively with temperature. Accordingly, the warm-water Sporolithales were most species-rich during the warm Cretaceous, but they declined and were rapidly replaced by the Corallinales as Cenozoic temperatures declined. In recent environments, members of the Sporolithales are confined to greater water depths while in euphotic reefs, they do not play a role as reef stabilizers28 and occupy only cryptic habitats sensu Kobluk29, i.e. cavities that serve as well-protected habitats and are not subject to the full spectrum of environmental and biotic controls that exist on the reef surface28. The wave-pounded intertidal algal ridges are built predominantly by Porolithon onkodes (Heydrich) Foslie 1909, P. gardineri (Foslie) Foslie 1909, P. craspedium (Foslie) Foslie 1909, and Lithophyllum kotschyanum Unger 1858 in the Indo-Pacific. In the Atlantic, the main reef reinforcers are Porolithon onkodes (Heydrich) Foslie 1909 and Lithophyllum congestum (Foslie) Foslie 1900. All these species belong to the ‘cool’-water adapted Corallinales. Thus, the increasing capacity of CCA to stabilize coral reefs is in line with the general trend of decreasing ocean temperatures.
    A change in sea level does not impact the capacity of CCA to reinforce coral reefs, likely because sea level changes measured on the level of geological stages have no effect on reef formation5. On shorter time scales, sea level is expected to influence the formation of coral reefs, but probably not the CCA’s reef enforcing capacity. We conclude this because the environmental tolerances of CCA in terms of sea level fluctuation are much wider than those of reef corals. Most CCA species appear uniquely tolerant of aerial exposure10. Additionally, many CCA are very well adapted to changes in salinity and especially to low photon irradiances30. The environmental tolerances of reef corals are narrower31,32.
    Considering our assumption that there is a mutual relationship between the presence of CCA and the growth of true reefs, another reason might be that one of the most important genera in modern coral reefs, Acropora Oken, 1815, is well adapted to cope with rapid sea-level changes. First observed as an important reef builder in the Oligocene33, Acropora has become a dominant reef builder from the Pleistocene until today, when sea-level fluctuations increased in rate and magnitude34. Indeed, there is a temporal overlap between the first decline in the fraction of CCA reefs—between the Turonian and the Campanian—and a maximum in sea level. Despite this, sea level is not selected as a relevant explanatory variable for the fraction of CCA reefs by the GLM because the relationship between the fraction of CCA reefs and sea level varies inconsistently throughout entire time series of the analysed 150 million years. While the decline in the fraction of CCA reefs may additionally be linked to an increase in temperature before and a significant drop in CCA diversity during the period with a low fraction of CCA reefs, data of the analysis are not suitable to conclusively identify the driver for this particular CCA crisis.
    For the entire time series, RCO2, was identified by the model as a minor driver, which may be explained by the fact that an increase of atmospheric pCO2 has only little to no impact on mean ocean surface pH on timescales exceeding 10,000 years35. A plausible reason is that slow rates of CO2 release lead to a different balance of carbonate chemistry changes and a smaller seawater CaCO3 saturation response. This is because the alkalinity released by rock weathering on land must ultimately be balanced by the preservation and burial of CaCO3 in marine sediments. The burial is controlled by the CaCO3 saturation state of the ocean and therefore, the saturation is ultimately regulated by weathering on long time scales, and not by atmospheric pCO2. The effect of weathering on atmospheric pCO2 is much weaker than the effect of weathering on ocean pH. The much stronger effect of weathering on ocean pH allows pH and CaCO3 saturation to be almost decoupled for slowly increasing atmospheric pCO235.
    The influence of CCA species diversity
    The quantification of CCA species diversity in the geological past is associated to a number of challenges. While for recent CCA the extensive use of molecular phylogenetic methods resolved the four orders (Corallinales, Hapalidiales, Sporolithales, and Rhodogorgonales) currently recognized in the subclass Corallinophycidae as monophyletic lineages36,37, we have to rely on morphological characters since molecular methods are not available for the identification of fossil CCA. Because CCA show a pronounced phenotypic plasticity depending on environmental factors, their taxonomic identification depends on morphological characters like conceptacles (i.e. spore chambers) and the arrangement of cells in different areas of the thallus, features often not adequately preserved in fossil CCA. This has led to a great number of fossil CCA taxa that have been described on the basis of only a few anatomical characters of doubtful taxonomic value38. The inclusion of such taxa precludes fully reliable diversity estimations. To circumvent such problems, we used rarefied species data reviewed by experts on fossil CCA taxonomy28.
    Our results show that high CCA diversity is linked to a higher abundance of CCA in true coral reefs. This might seem to contrast with the fact that in modern reefs, the wave-pounded intertidal algal ridges are built predominantly by only a few species while the ones making up the majority of diversity have a cryptic, hidden mode of life protected from full or direct exposure to major physical environmental factors and therefore do not contribute significantly to reef stabilization. However, if several CCA species were contributing to the same ecosystem function, a higher species diversity may have buffered reef systems from losing all species associated with the key function of supporting reef development39. As discussed in detail in the next section, the abundance of CCA in true reefs was transiently reduced four times since the Early Cretaceous. Except for the earliest crisis, this was likely caused by the origin and diversification of echinoids and parrot fish, prominent groups of bioeroding organisms that denude CCA. However, the CCA-coral reef system successfully recovered all times. We argue that this was supported by functional redundancy of CCA, because a diverse group of abundant species with a wider range of responses can help absorb disturbances39. This redundancy of responses to events among species within a functional group—the reef cementers—is an important component of resilience and the maintenance of ecosystem services. The amount of CCA biomass is critical in terms of the cementing capacity. Multi-species community models40 have shown that with consecutive native species’ extinctions at high diversity levels, species extinction usually only leads to a slight decrease in the total biomass of the native community. However, when starting from a lower initial diversity, a few consecutive species extinctions cause a relatively large biomass loss that ultimately leads to collapse. It should also be stressed that sometimes single species are responsible for the functioning of an ecosystem (i.e., keystone species), even if the ecosystem features a generally high biodiversity. Therefore, such ecosystems will decline if this key species is removed41.
    Experiments with plants in rangelands42 showed that functional diversity maintains ecosystem functioning. At heavily grazed sites, some species dominant in the ungrazed communities were lost or substantially reduced. In four out of five cases, the minor species that replaced these lost ones were their functional analogues. Accordingly, we suggest that formerly less dominant but functionally analogous grazing-tolerant species increased in abundance and contributed to the maintenance of ecosystem functions. CCA species removed or reduced in biomass by grazing pressure can be replaced in terms of their ecosystem service, i.e. reef cementation, by other CCA that are better adapted to grazing.
    This implies that in recent coral reef environments, areas with high CCA diversity—potentially including species occupying cryptic habitats—are more resilient against disturbance. Because the skeletal mineralogies of CCA vary considerably among species43, this resilience possibly applies also to future ocean acidification.
    The evolution of herbivory and transient reef crises
    The data reveal four crises in the abundance of CCA within true reefs, during the Cretaceous (Turonian–Campanian), the Paleocene (Selandian–Thanetian), the Miocene (Serravallian), and the Pliocene (Zanclean–Piacenzian). The reason that the timing of the Paleocene crisis differs from the known Paleocene–Eocene crisis20 might be that our study focuses on the number of true reefs, while the Paleocene-Eocene crisis is expressed by a change in cumulative metazoan reef volume. Except for the first one, all crises observed here occurred synchronous with pronounced evolutionary events in clades of grazing organisms. Cementing and binding is the main function of CCA in the facilitation of true coral reefs. The decline in CCA abundance during the Selandian–Thanetian corresponds with a marked increase in the rate of morphological evolution in echinoids (Fig. 2). This includes major shifts in lifestyle and the evolution of new subclades in this group44, with a net trend towards improved mobility and feeding ability also on CCA16. Regarding the Serravallian and Zanclean–Piacenzian crises, echinoids appear to play a very minor role as their evolutionary rates constantly decreased over time44. However, another important clade of coralline grazers, the parrot fishes (Scarinae Rafinesque, 1810) may have become major players45. Although reef-grazing fish have existed for nearly 400 Ma, specialized detritivores feeding on macroalgae have only been known since the Miocene46. This is also in line with the radiation of acroporid corals since the mid Miocene47, whose branched morphologies create interstitial niches for parrot fish but also for cryptic CCA species. The parrot fishes (Scarinae) first appeared in the Serravallian45, which may have caused the third crisis in CCA reef cementing capacity. The lineage diversification of Scarinae was most pronounced during the Zanclean-Piacenzian, which we deem responsible for the third crisis.
    The abundance of CCA in true coral reefs recovered relatively fast after all crises probably due to morphological adaptations developed within the CCA. Experiments have shown that echinoids are able to graze tissues to depths averaging 88 µm16, which is critical for CCA with thin crust morphologies. The resulting decline of thin crust morphologies led to the occupation of niches by branching CCA16. The twig-like morphologies of branching CCA prevent echinoids from denuding CCA thallus and confine this process to the tips of the branches. CCA are able to transfer nutrients within their thallus16. Therefore, these superficial grazing wounds can be rapidly healed if sufficient nutrient reservoirs are present in other, ungrazed parts of the algae. Meristems and conceptacles engulfed in the thallus may be another adaptation pertinent to the relatively low impact of echinoid grazing, as this is a plausible strategy to protect the reproductive and growth structures of the CCA. The more intense grazing pressure exerted by the parrot fishes, which bite CCA to an average depth of 288 µm16 and are able to eat the tips of branched CCA48 may have resulted in a greater abundance of CCA with very thick crusts. Thick-crust CCA possess larger nutrient reservoirs making them capable to recover also from grazing exerted by parrot fishes. All these adaptations and their development are congruent with the origination and diversification of the grazer clades as already outlined in other studies16,49,50. Today and potentially already during the geological history, CCA did not only successfully adapt to various grazer clades but even required the grazing pressure to stay free of epiphytes49. Here we show for the first time that the process of grazer evolution may also have affected the potential capacity of the CCA to reinforce coral reefs for three times during the geological past.
    Future implications for the capacity of CCA to reinforce coral reefs
    As it concerns some of the most important biodiversity hot spots on our planet2, the potential future impact of the ongoing global change on the capacity of CCA to reinforce coral reefs should become a focal point of reef research. Despite the implementation of numerous mesocosm and aquaria experiments51,52,53, long-term data in the magnitude of months on CCA responses to modified environmental parameters are still sparse. Also, the change from ambient to modified parameters (e.g. pCO2, temperature) happens much faster than at natural rates.
    The impact of elevated pCO2 on CCA depends on the rate of change. While fast rates are critical, slow pCO2 increase may even result in increased net calcification at moderately elevated pCO2 levels54. However, this comes at the cost of structural integrity of the CCA skeleton which, in turn, makes the CCA likely more susceptible to bioerosion. Bioerosion by echinoids and parrot fishes is beneficial to CCA at the present state, as it removes fast growing fleshy algae and other epiphytes49, but nothing is known about the future of this interaction when the integrity of the CCA skeletons is altered. Additionally, it has been shown that elevated pCO2 levels accelerate sponge reef bioerosion55,56,57. Therefore, a combination of increased bioerosion rates affecting corals and CCA might lead to strongly deteriorated conditions for coral reef formation. As outlined above, a greater CCA diversity might also increase their resilience against ocean acidification because of the great variety in skeletal mineralogies.
    Regarding elevated temperatures, the outcome for CCA is unpredictable. Depending on the examined species, elevated temperatures affect CCA primary production in different ways: some species show no or negligible response30, some change their skeletal chemistry in terms of dolomite concentration58, and others respond with strongly impaired germination success59 or declining skeletal densities60. Due to the positive influence of cooler temperatures on CCA’s abundance in true reefs detected in our study, elevated temperatures will likely have a negative outcome but also here, the rate of change might be similarly important as the magnitude.
    To estimate the future of CCA’s potential to facilitate coral reef growth in the face of global change, we encourage long term experiments—preferably in near-natural mesocosm studies—including the main reef stabilizing CCA species. More