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    Effect of salinity on the zinc(II) binding efficiency of siderophore functional groups and implications for salinity tolerance mechanisms in barley

    1.McLean, J. E., Pabst, M. W., Miller, C. D., Dimkpa, C. O. & Anderson, A. J. Effect of complexing ligands on the surface adsorption, internalization, and bioresponse of copper and cadmium in a soil bacterium, Pseudomonas Putida. Chemosphere 91(3), 374–382. https://doi.org/10.1016/j.chemosphere.2012.11.071 (2013).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    2.Clemens, S. Metal ligands in micronutrient acquisition and homeostasis. Plant. Cell Environ. 42(10), 2902–2912. https://doi.org/10.1111/pce.13627 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    3.Ma, H. et al. Elucidation of the mechanisms into effects of organic acids on soil fertility, cadmium speciation and ecotoxicity in contaminated soil. Chemosphere 239, 124706. https://doi.org/10.1016/j.chemosphere.2019.124706 (2020).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    4.Ahmed, E. & Holmström, S. J. M. Siderophores in environmental research: Roles and applications. Microb. Biotechnol. 7(3), 196–208. https://doi.org/10.1111/1751-7915.12117 (2014).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    5.Butler, A. & Theisen, R. M. Iron(III)-siderophore coordination chemistry: Reactivity of marine siderophores. Coord. Chem. Rev. 254(3–4), 288–296. https://doi.org/10.1016/j.ccr.2009.09.010 (2010).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    6.Hider, R. C. & Kong, X. Chemistry and biology of siderophores. Nat. Prod. Rep. 27(5), 637. https://doi.org/10.1039/b906679a (2010).CAS 
    Article 
    PubMed 

    Google Scholar 
    7.Kirby, M. E., Sonnenberg, J. L., Simperler, A. & Weiss, D. J. Stability series for the complexation of six key siderophore functional groups with uranyl using density functional theory. J. Phys. Chem. A 124(12), 2460–2472. https://doi.org/10.1021/acs.jpca.9b10649 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    8.Harrington, J. et al. Structural dependence of Mn complexation by siderophores: Donor group dependence on complex stability and reactivity. GCA. 88, 106–119 (2012).ADS 
    CAS 

    Google Scholar 
    9.McRose, D. L., Seyedsayamdost, M. R. & Morel, F. M. M. Multiple siderophores: Bug or feature?. JBIC J. Biol. Inorg. Chem. 23(7), 983–993. https://doi.org/10.1007/s00775-018-1617-x (2018).CAS 
    Article 
    PubMed 

    Google Scholar 
    10.Johnstone, T. C., Nolan, E. M. Beyond iron: Non-classical biological functions of bacterial siderophores. In Dalton Transactions. Royal Society of Chemistry April 14, 2015, pp 6320–6339. https://doi.org/10.1039/c4dt03559c.11.Northover, G. H. R., Garcia-España, E. & Weiss, D. J. Unravelling the modus operandi of phytosiderophores during zinc uptake in rice: The importance of geochemical gradients and accurate stability constants. J. Exp. Bot. https://doi.org/10.1093/jxb/eraa580 (2020).Article 

    Google Scholar 
    12.Ghavami, N., Alikhani, H. A., Pourbabaee, A. A. & Besharati, H. Study the effects of siderophore-producing bacteria on zinc and phosphorous nutrition of canola and maize plants. Commun. Soil Sci. Plant Anal. 47(12), 1517–1527. https://doi.org/10.1080/00103624.2016.1194991 (2016).CAS 
    Article 

    Google Scholar 
    13.Weiss, D. et al. Isotope fractionation of zinc in the paddy rice soil-water environment and the role of 2’deoxymugineic acid (DMA) as zincophore under Zn limiting conditions. Chem. Geol. 577, 120271. https://doi.org/10.1016/j.chemgeo.2021.120271 (2021).ADS 
    CAS 
    Article 

    Google Scholar 
    14.Suzuki, M. et al. Biosynthesis and secretion of mugineic acid family phytosiderophores in zinc-deficient barley. Plant J. 48(1), 85–97. https://doi.org/10.1111/j.1365-313X.2006.02853.x (2006).CAS 
    Article 
    PubMed 

    Google Scholar 
    15.Zaman, M. , Shahid, S. A., Heng, L., Shahid, S. A., Zaman, M., Heng, L. Soil salinity: Historical perspectives and a world overview of the problem. In Guideline for Salinity Assessment, Mitigation and Adaptation Using Nuclear and Related Techniques 43–53 (Springer, 2018). https://doi.org/10.1007/978-3-319-96190-3_2.16.Alfarrah, N. & Walraevens, K. Groundwater overexploitation and seawater intrusion in coastal areas of arid and semi-arid regions. Water 10(2), 143. https://doi.org/10.3390/w10020143 (2018).CAS 
    Article 

    Google Scholar 
    17.Trenberth, K. Changes in precipitation with climate change. Clim. Res. 47(1), 123–138. https://doi.org/10.3354/cr00953 (2011).Article 

    Google Scholar 
    18.Pendergrass, A. G., Knutti, R., Lehner, F., Deser, C. & Sanderson, B. M. Precipitation variability increases in a warmer climate. Sci. Rep. 7(1), 1–9. https://doi.org/10.1038/s41598-017-17966-y (2017).CAS 
    Article 

    Google Scholar 
    19.Errabii, T., Gandonou, C. H., Essalmani, H., Jamal; Senhaji, N. S. Effects of NaCl and mannitol induced stress on sugarcane (Saccharum Sp.) Callus Cultures. https://doi.org/10.1007/s11738-006-0006-1.20.Saboora, A., Hajihashemi, S. & Khatam, B. NaCl tolerance of wheat genotypes at germination and early seedling growth article in Pakistan. J. Biol. Sci. https://doi.org/10.3923/pjbs.2006.2009.2021 (2006).Article 

    Google Scholar 
    21.Chand, M., Randhawa, N. S. & Bhumbla, D. R. Effectiveness of zinc chelates in zinc nutrition of greenhouse rice crop in a saline-sodic soil. Plant Soil 59(2), 217–225. https://doi.org/10.1007/BF02184195 (1981).CAS 
    Article 

    Google Scholar 
    22.Lores, E. M. & Pennock, J. R. The effect of salinity on binding of Cd, Cr, Cu and Zn to dissolved organic matter. Chemosphere 37(5), 861–874. https://doi.org/10.1016/S0045-6535(98)00090-3 (1998).ADS 
    CAS 
    Article 

    Google Scholar 
    23.Cigala, R. M. et al. Zinc(II) complexes with hydroxocarboxylates and mixed metal species with Tin(II) in different salts aqueous solutions at different ionic strengths: Formation, stability, and weak interactions with supporting electrolytes. Monatshefte fur Chemie 146(4), 527–540. https://doi.org/10.1007/s00706-014-1394-3 (2015).CAS 
    Article 

    Google Scholar 
    24.Laird, D. A., Koskinen, I. W. C. Triazine Soil Interactions. In The Triazine Herbicides 275–299 (Elsevier, 2008). https://doi.org/10.1016/B978-044451167-6.50024-6.25.Cigala, R. M. et al. Speciation of Tin(II) in aqueous solution: Thermodynamic and spectroscopic study of simple and mixed hydroxocarboxylate complexes. Monatshefte fur Chemie 144(6), 761–772. https://doi.org/10.1007/s00706-013-0961-3 (2013).CAS 
    Article 

    Google Scholar 
    26.Daniele, P. G., Rigano, C. & Sammartano, S. Ionic strength dependence of formation constants-I protonation constants of organic and inorganic acids. Talanta 30(2), 81–87. https://doi.org/10.1016/0039-9140(83)80023-X (1983).CAS 
    Article 
    PubMed 

    Google Scholar 
    27.Bretti, C., Foti, C. & Sammartano, S. A new approach in the use of sit in determining the dependence on ionic strength of activity coefficients. Application to Some Chloride Salts Of Interest In The Speciation Of Natural Fluids. Chem. Speciat. Bioavailab. 16(3), 105–110. https://doi.org/10.3184/095422904782775036 (2004).CAS 
    Article 

    Google Scholar 
    28.Bretti, C., De Stefano, C., Foti, C. & Sammartano, S. Critical evaluation of protonation constants. Literature analysis and experimental potentiometric and calorimetric data for the thermodynamics of phthalate protonation in different ionic media. J. Solution Chem. 35(9), 1227–1244. https://doi.org/10.1007/s10953-006-9057-6 (2006).CAS 
    Article 

    Google Scholar 
    29.Cigala, R. M. et al. Quantitative study on the interaction of Sn2+ and Zn2+ with some phosphate ligands, in aqueous solution at different ionic strengths. J. Mol. Liq. 165, 143–153. https://doi.org/10.1016/j.molliq.2011.11.002 (2012).CAS 
    Article 

    Google Scholar 
    30.Northover, G. H. R., Mao, Y., Hanif M. D., Blasco, S., Vilar, R., Garcia-Espana, E. & Weiss, D. J. The control of pH and ionic strength gradients on the interaction of low-molecular-weight organic acids and siderophores. ChemRxiv. Preprint (2021). https://doi.org/10.26434/chemrxiv.14706036.v1.31.Domenico, P. A., Harris, D. R., Schwartz, F. W., Wiley, J., Chichester, N. Y., Brisbane, W. & Singapore, T. Physical and Chemical Hydrogeology 2nd edn.32.Pankow, J.; Taylor & Francis Group. Aquatic Chemistry Concepts 2nd edn.33.Graziano, G. Role of salts on the strength of pairwise hydrophobic interaction. Chem. Phys. Lett. 483(1–3), 67–71. https://doi.org/10.1016/j.cplett.2009.10.040 (2009).ADS 
    CAS 
    Article 

    Google Scholar 
    34.Mancera, R. L. Does salt increase the magnitude of the hydrophobic effect? A computer simulation study. Chem. Phys. Lett. 296(5–6), 459–465. https://doi.org/10.1016/S0009-2614(98)01080-X (1998).ADS 
    CAS 
    Article 

    Google Scholar 
    35.Mancera, R. L. Computer simulation of the effect of salt on the hydrophobic effect. J. Chem. Soc. Faraday Trans. 94(24), 3549–3559. https://doi.org/10.1039/a806899b (1998).CAS 
    Article 

    Google Scholar 
    36.Ghosh, T., Kalra, A. & Garde, S. On the salt-induced stabilization of pair and many-body hydrophobic interactions. J. Phys. Chem. B 109(1), 642–651. https://doi.org/10.1021/jp0475638 (2005).CAS 
    Article 
    PubMed 

    Google Scholar 
    37.Papaneophytou, C. P., Grigoroudis, A. I., McInnes, C. & Kontopidis, G. Quantification of the effects of ionic strength, viscosity, and hydrophobicity on protein-ligand binding affinity. ACS Med. Chem. Lett. 5(8), 931–936. https://doi.org/10.1021/ml500204e (2014).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    38.Ghafoor, K., AL-Juhaimi, F., Ozcan, M. M. & Jahurul, M. H. A. Some nutritional characteristics and mineral contents in Barley (Hordeum Vulgare L.) seeds cultivated under salt stress. Qual. Assur. Saf. Crop. Foods 7(3), 363–368. https://doi.org/10.3920/QAS2013.0380 (2015).CAS 
    Article 

    Google Scholar 
    39.Akman, Z. Effects of plant growth regulators on nutrient content of young wheat and barley plants under
    saline conditions. J. Anim. Vet. Adv. 8(10), 2018–2021 (2009).CAS 

    Google Scholar 
    40.Yousfi, S., Houmani, H., Zribi, F., Abdelly, C. & Gharsalli, M. Physiological responses of wild and cultivated barley to the interactive effect of salinity and iron deficiency. (2012). https://doi.org/10.5402/2012/121983.41.Alderighi, L. et al. Hyperquad simulation and speciation (HySS): A utility program for the investigation of equilibria involving soluble and partially soluble species. Coord. Chem. Rev. 184(1), 311–318. https://doi.org/10.1016/S0010-8545(98)00260-4 (1999).CAS 
    Article 

    Google Scholar 
    42.Gans, P. & O’Sullivan, B. GLEE: A new computer program for glass electrode calibration. Talanta 51(1), 33–37. https://doi.org/10.1016/s0039-9140(99)00245-3 (2000).CAS 
    Article 
    PubMed 

    Google Scholar 
    43.Gans, P., Sabatini, A. & Vacca, A. Investigation of equilibria in solution. Determination of equilibrium constants with the HYPERQUAD suite of programs. Talanta 43(10), 1739–1753. https://doi.org/10.1016/0039-9140(96)01958-3 (1996).CAS 
    Article 
    PubMed 

    Google Scholar 
    44.Hu, W., Xie, J., Chau, H. W. & Si, B. C. Evaluation of parameter uncertainties in nonlinear regression using Microsoft excel spreadsheet. Environ. Syst. Res. 4(1), 1–12. https://doi.org/10.1186/s40068-015-0031-4 (2015).ADS 
    CAS 
    Article 

    Google Scholar 
    45.Harris, W. R., Raymond, K. N. & Weitl, F. L. Ferric ion sequestering agents. 6. The spectrophotometric and potentiometric evaluation of sulfonated tricatecholate ligands. J. Am. Chem. Soc. 103(10), 2667–2675. https://doi.org/10.1021/ja00400a030 (1981).CAS 
    Article 

    Google Scholar 
    46.Bravin, M. N., Tentscher, P., Rose, J. & Hinsinger, P. Rhizosphere PH Gradient Controls Copper Availability in a Strongly Acidic Soil. Environ. Sci. Technol. 43(15), 5686–5691. https://doi.org/10.1021/es900055k (2009).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    47.Gollany, H. T. & Schumacher, T. E. Combined use of colorimetric and microelectrode methods for evaluating rhizosphere PH. Plant Soil 154(2), 151–159. https://doi.org/10.1007/BF00012520 (1993).CAS 
    Article 

    Google Scholar 
    48.Kirk, G. J. D. Root ventilation, rhizosphere modification, and nutrient uptake by rice. In Systems Approaches for Agricultural Development 221–232 (Springer, Netherlands, 1993). https://doi.org/10.1007/978-94-011-2842-1_13.49.Li, J. & Heap, A. D. Spatial interpolation methods applied in the environmental sciences: A review. In Environmental Modelling and Software 173–189 (Elsevier, 2014). https://doi.org/10.1016/j.envsoft.2013.12.008.50.Gergely, A., Kiss, T. & Deák, G. Complexes of 3,4-dihydroxyphenyl derivatives. II. Complex formation processes in the Nickel(II)-L-DOPA and Zinc(II)-L-DOPA systems. Inorganica Chim. Acta 36(1), 113–120. https://doi.org/10.1016/S0020-1693(00)89379-2 (1979).CAS 
    Article 

    Google Scholar 
    51.Griesser, R. & Sigel, H. Ternary complexes in solution. XI. complex formation between the cobalt(h)-, nickel(ii)-, copper(ii)-, and zinc(II)-2,2′-bipyridyl 1:1 complexes and ethylenediamine, glycinate, or pyrocatecholate. Inorg. Chem. 10(10), 2229–2232. https://doi.org/10.1021/ic50104a028 (1971).CAS 
    Article 

    Google Scholar 
    52.Das, A. K. Studies on mixed ligand complexes of cobalt(II), nickel(II), copper(II) and zinc(II) involving 8-hydroxyquinoline-5-sulphonic acid as a primary ligand and substituted catechols as secondary ligands. Transition Met. Chem. 14, 200–209 (1989).CAS 
    Article 

    Google Scholar 
    53.Das, A. K. Astatistical aspects of the stabilities of ternary complexes of cobalt(II), nickel(II), copper(II) and zinc(II) involving amino-polycarboxylic acids and heteroaromatic N-bases as primary ligands and acetohydroxamic acid as a secondary ligand. Transition Met. Chem. 14, 66–68 (1989).CAS 
    Article 

    Google Scholar 
    54.Cannan, R. K. & Kibrick, A. Complex formation between carboxylic acids and divalent metal cations. J. Am. Chem. Soc. 60(10), 2314–2320. https://doi.org/10.1021/ja01277a012 (1938).CAS 
    Article 

    Google Scholar 
    55.Farkas, E., Brown, D. A., Cittaro, R. & Glass, W. K. Metal complexes of glutamic acid-γ-hydroxamic acid (Glu-γ-Ha) (N-hydroxyglutamine) in aqueous solution. J. Chem. Soc. Dalt. Trans. 18, 2803–2807. https://doi.org/10.1039/DT9930002803 (1993).Article 

    Google Scholar 
    56.Farkas, E., Enyedy, É. A. & Csóka, H. Some factors affecting metal ion-monohydroxamate interactions in aqueous solution. J. Inorg. Biochem. 79(1–4), 205–211. https://doi.org/10.1016/S0162-0134(99)00158-0 (2000).CAS 
    Article 
    PubMed 

    Google Scholar 
    57.Warnke, Z. Investigation on divalent metal complexes with oxyacids in aqueous solutions. 6. Potentiometric investigation on copper(II), zinc(II), and cadmium(II) complexes with glycolic acd. Rocz. Chem. 43, 1939 (1969).CAS 

    Google Scholar 
    58.Lengyel, T. Investigations on ion exchange equilibria with radioactive tracer method. 15. Liquid ion exchange technique for investigating mixed complex species of zinc with glycolic and alpha-hydroxyisobutyric acid. Acta Chim. Acad. Sci. Hung. 60, 373 (1969).CAS 

    Google Scholar 
    59.Athavale, V. T., Prabhu, L. H. & Vartak, D. G. Solution stability constants of some metal complexes of derivatives of catechol. J. Inorg. Nucl. Chem. 28(5), 1237–1249. https://doi.org/10.1016/0022-1902(66)80450-5 (1966).CAS 
    Article 

    Google Scholar 
    60.Portanova, R., Lajunen, L. H. J., Tolazzi, M. & Piispanen, J. Critical evaluation of stability constants for α-hydroxycarboxylic acid complexes with protons and metal ions and the accompanying enthalpy changes: Part II. Aliphatic 2-hydroxycarboxylic acids (IUPAC technical report). Pure Appl. Chem. 75(4), 495–540. https://doi.org/10.1351/pac200375040495 (2003).CAS 
    Article 

    Google Scholar 
    61.Krężel, A. & Maret, W. The biological inorganic chemistry of zinc ions. Arch. Biochem. Biophys. 611, 3–19. https://doi.org/10.1016/j.abb.2016.04.010 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    62.Al-Sogair, F. M.; Operschall, B. P.; Sigel, A.; Sigel, H.; Schnabl, J.; Sigel, R. K. O. Probing the Metal-Ion-Binding Strength of the Hydroxyl Group. In Chemical Reviews. American Chemical Society August 10, 964–5003 (2011). https://doi.org/10.1021/cr100415s.63.Gries, D., Brunn, S., Crowley, D. E. & Parker, D. R. Phytosiderophore release in relation to micronutrient metal deficiencies in Barley. Plant Soil 172(2), 299–308. https://doi.org/10.1007/BF00011332 (1995).CAS 
    Article 

    Google Scholar 
    64.Welch, R. M. & Shuman, L. Micronutrient nutrition of plants. CRC Crit. Rev. Plant Sci. 14(1), 49–82. https://doi.org/10.1080/07352689509701922 (1995).CAS 
    Article 

    Google Scholar 
    65.Arnold, T. et al. Evidence for the mechanisms of zinc uptake by rice using isotope fractionation. Plant. Cell Environ. 33(3), 370–381. https://doi.org/10.1111/j.1365-3040.2009.02085.x (2010).CAS 
    Article 
    PubMed 

    Google Scholar 
    66.Haas, H. Fungal siderophore metabolism with a focus on Aspergillus fumigatus. Nat. Prod. Rep. 31(10), 1266–1276. https://doi.org/10.1039/c4np00071d (2014).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    67.Griffin, A. S., West, S. A. & Buckling, A. Cooperation and competition in pathogenic bacteria. Nature 430(7003), 1024–1027. https://doi.org/10.1038/nature02744 (2004).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    68.Wu, D. et al. Tissue metabolic responses to salt stress in wild and cultivated barley. PLoS ONE 8(1), e55431. https://doi.org/10.1371/journal.pone.0055431 (2013).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    69.Widodo, Patterson, J. H.; Newbigin, E. et al.. Metabolic responses to salt stress of Barley (Hordeum Vulgare L.) cultivars, sahara and clipper, which differ in salinity tolerance. J. Exp. Bot. 60(14), 4089–4103 (2009). https://doi.org/10.1093/jxb/erp243CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    70.Yang, C.-W. et al. Comparative effects of salt-stress and alkali-stress on the growth, photosynthesis, solute accumulation, and ion balance of Barley plants. Phytosynthetica 47, 79–86 (2009).CAS 
    Article 

    Google Scholar  More

  • in

    Highly restricted dispersal in habitat-forming seaweed may impede natural recovery of disturbed populations

    1.Wernberg, T. & Filbee-Dexter, K. Missing the marine forest for the trees. Mar. Ecol. Prog. Ser. 612, 209–215 (2019).ADS 
    Article 

    Google Scholar 
    2.Thompson, R. C., Wilson, B. J., Tobin, M. L., Hill, A. S. & Hawkins, S. J. Biologically generated habitat provision and diversity of rocky shore organisms at a hierarchy of spatial scales. J. Exp. Mar. Biol. Ecol. 202, 73–84 (1996).Article 

    Google Scholar 
    3.Christie, H., Jørgensen, N. M. & Norderhaug, K. M. Bushy or smooth, high or low; importance of habitat architecture and vertical position for distribution of fauna on kelp. J. Sea Res. 58, 198–208 (2007).ADS 
    Article 

    Google Scholar 
    4.Steneck, R. S. et al. Kelp forest ecosystems: Biodiversity, stability, resilience and future. Environ. Conserv. 29, 436–459 (2002).Article 

    Google Scholar 
    5.Strain, E. M. A., Thomson, R. J., Micheli, F., Mancuso, F. P. & Airoldi, L. Identifying the interacting roles of stressors in driving the global loss of canopy-forming to mat-forming algae in marine ecosystems. Glob. Change Biol. 20, 3300–3312 (2014).ADS 
    Article 

    Google Scholar 
    6.Mineur, F. et al. European seaweeds under pressure: Consequences for communities and ecosystem functioning. J. Sea Res. 98, 91–108 (2015).ADS 
    Article 

    Google Scholar 
    7.Krumhansl, K. A. et al. Global patterns of kelp forest change over the past half-century. PNAS 113, 13785–13790 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    8.Straub, S. C. et al. Resistance, extinction, and everything in between—The diverse responses of seaweeds to marine heatwaves. Front. Mar. Sci. 6, 763 (2019).Article 

    Google Scholar 
    9.Cheminée, A. et al. Nursery value of Cystoseira forests for Mediterranean rocky reef fishes. J. Exp. Mar. Biol. Ecol. 442, 70–79 (2013).Article 

    Google Scholar 
    10.Piazzi, L. et al. Biodiversity in canopy-forming algae: Structure and spatial variability of the Mediterranean Cystoseira assemblages. Estuar. Coast. Shelf Sci. 207, 132–141 (2018).ADS 
    Article 

    Google Scholar 
    11.Thibaut, T., Pinedo, S., Torras, X. & Ballesteros, E. Long-term decline of the populations of Fucales (Cystoseira spp. and Sargassum spp.) in the Albères coast (France, North-western Mediterranean). Mar. Pollut. Bull. 50, 1472–1489 (2005).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    12.Gianni, F. et al. Conservation and restoration of marine forests in the Mediterranean Sea and the potential role of marine protected areas. Adv. Oceanogr. Limnol. 4, 83–101 (2013).Article 

    Google Scholar 
    13.Blanfuné, A., Boudouresque, C. F., Verlaque, M. & Thibaut, T. The fate of Cystoseira crinita, a forest-forming Fucale (Phaeophyceae, Stramenopiles), in France (North Western Mediterranean Sea). Estuar. Coast. Shelf Sci. 181, 196–208 (2016).ADS 
    Article 

    Google Scholar 
    14.Gubbay, S. et al. European Red List of Habitats. Part 1. Marine habitats. Luxembourg: Publications Office of the European Union (2016).15.Perkol-Finkel, S., Ferrario, F., Nicotera, V. & Airoldi, L. Conservation challenges in urban seascapes: Promoting the growth of threatened species on coastal infrastructures. J. Appl. Ecol. 49, 1457–1466 (2012).Article 

    Google Scholar 
    16.Falace, A., Kaleb, S., Fuente, G. D. L., Asnaghi, V. & Chiantore, M. Ex situ cultivation protocol for Cystoseira amentacea var. stricta (Fucales, Phaeophyceae) from a restoration perspective. PLoS ONE 13, e0193011 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    17.Gianni, F., Bartolini, F., Airoldi, L. & Mangialajo, L. Reduction of herbivorous fish pressure can facilitate focal algal species forestation on artificial structures. Mar. Environ. Res. 138, 102–109 (2018).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    18.Gianni, F. et al. Optimizing canopy-forming algae conservation and restoration with a new herbivorous fish deterrent device. Restor. Ecol. 28, 750–756 (2020).Article 

    Google Scholar 
    19.Verdura, J., Sales, M., Ballesteros, E., Cefalì, M. E. & Cebrian, E. Restoration of a canopy-forming alga based on recruitment enhancement: Methods and long-term success assessment. Front. Plant Sci. 9, 1832 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    20.Fuente, G. D. L., Chiantore, M., Asnaghi, V., Kaleb, S. & Falace, A. First ex situ outplanting of the habitat-forming seaweed Cystoseira amentacea var. stricta from a restoration perspective. PeerJ 7, e7290 (2019).Article 

    Google Scholar 
    21.Tamburello, L. et al. Are we ready for scaling up restoration actions? An insight from Mediterranean macroalgal canopies. PLoS ONE 14, e0224477 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    22.Medrano, A. et al. From marine deserts to algal beds: Treptacantha elegans revegetation to reverse stable degraded ecosystems inside and outside a No-Take marine reserve. Restor. Ecol. 28, 632–644 (2020).Article 

    Google Scholar 
    23.Chryssovergis, F. & Panayotidis, P. Évolution des peuplements macrophytobenthiques le long d’un gradient d’eutrophisation. Oceanol. Acta 18, 649–658 (1995).
    Google Scholar 
    24.Sales, M., Cebrian, E., Tomas, F. & Ballesteros, E. Pollution impacts and recovery potential in three species of the genus Cystoseira (Fucales, Heterokontophyta). Estuar. Coast. Shelf Sci. 92, 347–357 (2011).ADS 
    CAS 
    Article 

    Google Scholar 
    25.Díez, I., Santolaria, A., Secilla, A. & Gorostiaga, J. M. Recovery stages over long-term monitoring of the intertidal vegetation in the ‘Abra de Bilbao’ area and on the adjacent coast (N. Spain). Eur. J. Phycol. 44, 1–14 (2009).Article 

    Google Scholar 
    26.Bringloe, T. T. et al. Phylogeny and evolution of the brown algae. Crit. Rev. Plant Sci. 39, 281–321 (2020).CAS 
    Article 

    Google Scholar 
    27.Guern, M. Embryologie de quelques espèces du genre Cystoseira Agardh 1821 (FUCALES). Vie et Milieu 649–680 (1962).28.Dudgeon, S., Kübler, J. E., Wright, W. A., Vadas, R. L. & Petraitis, P. S. Natural variability in zygote dispersal of Ascophyllum nodosum at small spatial scales. Funct. Ecol. 15, 595–604 (2001).Article 

    Google Scholar 
    29.Mangialajo, L. et al. Zonation patterns and interspecific relationships of fucoids in microtidal environments. J. Exp. Mar. Biol. Ecol. 412, 72–80 (2012).Article 

    Google Scholar 
    30.Capdevila, P. et al. Recruitment patterns in the Mediterranean deep-water alga Cystoseira zosteroides. Mar. Biol. 162, 1165–1174 (2015).CAS 
    Article 

    Google Scholar 
    31.Assis, J. et al. A fine-tuned global distribution dataset of marine forests. Sci. Data 7, 119 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    32.Fabbrizzi, E. et al. Modeling macroalgal forest distribution at Mediterranean scale: Present status, drivers of changes and insights for conservation and management. Front. Mar. Sci. 7, 20 (2020).Article 

    Google Scholar 
    33.Benedetti-Cecchi, L., Tamburello, L., Maggi, E. & Bulleri, F. Experimental perturbations modify the performance of early warning indicators of regime shift. Curr. Biol. 25, 1867–1872 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    34.Bulleri, F., Benedetti-Cecchi, L., Ceccherelli, G. & Tamburello, L. A few is enough: A low cover of a non-native seaweed reduces the resilience of Mediterranean macroalgal stands to disturbances of varying extent. Biolical Invasions 19, 2291–2305 (2017).Article 

    Google Scholar 
    35.Rindi, L., Bello, M. D., Dai, L., Gore, J. & Benedetti-Cecchi, L. Direct observation of increasing recovery length before collapse of a marine benthic ecosystem. Nat. Ecol. Evol. 1, 1–7 (2017).Article 

    Google Scholar 
    36.Draisma, S. G. A., Ballesteros, E., Rousseau, F. & Thibaut, T. DNA sequence data demonstrate the polyphyly of the genus Cystoseira and other Sargassaceae genera (Phaeophyceae). J. Phycol. 46, 1329–1345 (2010).Article 

    Google Scholar 
    37.Bruno de Sousa, C. et al. Improved phylogeny of brown algae Cystoseira (Fucales) from the Atlantic-Mediterranean region based on mitochondrial sequences. PLoS ONE 14, e0210143 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    38.Jódar-Pérez, A. B., Terradas-Fernández, M., López-Moya, F., Asensio-Berbegal, L. & López-Llorca, L. V. Multidisciplinary analysis of Cystoseira sensu lato (SE Spain) suggest a complex colonization of the Mediterranean. J. Mar. Sci. Eng. 8, 961 (2020).Article 

    Google Scholar 
    39.Hughes, A. R. & Stachowicz, J. J. Genetic diversity enhances the resistance of a seagrass ecosystem to disturbance. PNAS 101, 8998–9002 (2004).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    40.Reusch, T. B. H. & Hughes, A. R. The emerging role of genetic diversity for ecosystem functioning: Estuarine macrophytes as models. Estuaries and Coasts J ERF 29, 159–164 (2006).Article 

    Google Scholar 
    41.Reusch, T. B. H., Ehlers, A., Hämmerli, A. & Worm, B. Ecosystem recovery after climatic extremes enhanced by genotypic diversity. PNAS 102, 2826–2831 (2005).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    42.Ehlers, A., Worm, B. & Reusch, T. B. H. Importance of genetic diversity in eelgrass Zostera marina for its resilience to global warming. Mar. Ecol. Prog. Ser. 355, 1–7 (2008).ADS 
    Article 

    Google Scholar 
    43.Hughes, A. R., Inouye, B. D., Johnson, M. T. J., Underwood, N. & Vellend, M. Ecological consequences of genetic diversity. Ecol. Lett. 11, 609–623 (2008).PubMed 
    Article 

    Google Scholar 
    44.Frankham, R., Ballou, J. D. & Briscoe, D. A. Introduction to Conservation Genetics (Cambridge University Press, 2002) https://doi.org/10.1017/CBO9780511808999
    .Book 

    Google Scholar 
    45.Cowen, R., Gawarkiewicz, G., Pineda, J., Thorrold, S. & Werner, F. Population connectivity in marine systems: An overview. Oceanography 20, 14–21 (2007).Article 

    Google Scholar 
    46.Mayr, E. Animal Species and Evolution. Animal Species and Evolution (Harvard University Press, 2013).
    Google Scholar 
    47.Kimura, M. The Neutral Theory of Molecular Evolution (Cambridge University Press, 1983) https://doi.org/10.1017/CBO9780511623486
    .Book 

    Google Scholar 
    48.Frankham, R. Conservation genetics. Annu. Rev. Genet. 29, 305–327 (1995).CAS 
    PubMed 
    Article 

    Google Scholar 
    49.Lacy, R. C. Loss of genetic diversity from managed populations: Interacting effects of drift, mutation, immigration, selection, and population subdivision. Conserv. Biol. 1, 143–158 (1987).Article 

    Google Scholar 
    50.Frankham, R. et al. Genetic Management of Fragmented Animal and Plant Populations (Oxford University Press, 2017).Book 

    Google Scholar 
    51.Planes, S., Jones, G. P. & Thorrold, S. R. Larval dispersal connects fish populations in a network of marine protected areas. PNAS https://doi.org/10.1073/pnas.0808007106 (2009).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    52.Pineda, J., Hare, J. A. & Sponaugle, S. Larval transport and dispersal in the coastal ocean and consequences for population connectivity. Oceanography 20, 22–39 (2007).Article 

    Google Scholar 
    53.Caughley, G. Directions in conservation biology. J. Anim. Ecol. 63, 215–244 (1994).Article 

    Google Scholar 
    54.Buonomo, R. et al. Predicted extinction of unique genetic diversity in marine forests of Cystoseira spp. Mar. Environ. Res. 138, 119–128 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    55.Buonomo, R. et al. Habitat continuity and stepping-stone oceanographic distances explain population genetic connectivity of the brown alga Cystoseira amentacea. Mol. Ecol. 26, 766–780 (2017).PubMed 
    Article 

    Google Scholar 
    56.Bermejo, R. et al. Marine forests of the Mediterranean-Atlantic Cystoseira tamariscifolia complex show a southern Iberian genetic hotspot and no reproductive isolation in parapatry. Sci. Rep. 8, 10427 (2018).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    57.Engelen, A. H. et al. A population genetics toolbox for the threatened canopy-forming brown seaweeds Cystoseira tamariscifolia and C. amentacea (Fucales, Sargassaceae). J. Appl. Phycol. 29, 627–629 (2017).Article 

    Google Scholar 
    58.Thibaut, T. et al. Connectivity of populations of the seaweed Cystoseira amentacea within the Bay of Marseille (Mediterranean Sea): Genetic structure and hydrodynamic connections. crya 37, 233–255 (2016).Article 

    Google Scholar 
    59.Guiry, M.D. & Guiry, G.M. AlgaeBase. World-wide electronic publication (National University of Ireland, 2021) http://www.algaebase.org (Accessed 21 Jan 2021).60.Sales, M. & Ballesteros, E. Shallow Cystoseira (Fucales: Ochrophyta) assemblages thriving in sheltered areas from Menorca (NW Mediterranean): Relationships with environmental factors and anthropogenic pressures. Estuar. Coast. Shelf Sci. 84, 476–482 (2009).ADS 
    CAS 
    Article 

    Google Scholar 
    61.Robvieux, P. et al. First characterization of eight polymorphic microsatellites for Cystoseira amentacea var. stricta (Fucales, Sargassaceae). Conserv. Genet. Resour. 4, 923–925 (2012).Article 

    Google Scholar 
    62.Sadogurska, S. S., Neiva, J., Falace, A., Serrão, E. A. & Israel, Á. The genus Cystoseira s.l. (Ochrophyta, Fucales, Sargassaceae) in the Black Sea: Morphological variability and molecular taxonomy of Gongolaria barbata and endemic Ericaria crinita f. bosphorica comb. nov. Phytotaxa 480, 1–21 (2021).Article 

    Google Scholar 
    63.Bologa, A. S. & Sava, D. Progressive decline and present trend of Romanian Black Sea macroalgal flora. Cercetari Mar. 36, 31–60 (2006).
    Google Scholar 
    64.Irving, A. D., Balata, D., Colosio, F., Ferrando, G. A. & Airoldi, L. Light, sediment, temperature, and the early life-history of the habitat-forming alga Cystoseira barbata. Mar. Biol. 156, 1223–1231 (2009).Article 

    Google Scholar 
    65.Allendorf, F. W. Genetics and the conservation of natural populations: Allozymes to genomes. Mol. Ecol. 26, 420–430 (2017).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    66.Ellegren, H. Microsatellites: Simple sequences with complex evolution. Nat. Rev. Genet. 5, 435–445 (2004).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    67.De Meeûs, T. et al. Deceptive combined effects of short allele dominance and stuttering: An example with Ixodes scapularis, the main vector of Lyme disease in the USA. bioRxiv https://doi.org/10.1101/622373 (2019).Article 

    Google Scholar 
    68.De Meeûs, T. Revisiting FIS, FST, Wahlund effects, and null alleles. J. Hered. 109, 446–456 (2018).PubMed 
    Article 
    PubMed Central 

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

    Google Scholar 
    70.Selkoe, K. A. & Toonen, R. J. Microsatellites for ecologists: A practical guide to using and evaluating microsatellite markers. Ecol. Lett. 9, 615–629 (2006).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    71.Manangwa, O. et al. Detecting Wahlund effects together with amplification problems: Cryptic species, null alleles and short allele dominance in Glossina pallidipes populations from Tanzania. Mol. Ecol. Resour. 19, 757–772 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    72.Chapuis, M.-P. & Estoup, A. Microsatellite null alleles and estimation of population differentiation. Mol. Biol. Evol. 24, 621–631 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    73.Engel, C. R., Brawley, S. H., Edwards, K. J. & Serrão, E. Isolation and cross-species amplification of microsatellite loci from the fucoid seaweeds Fucus vesiculosus, F. serratus and Ascophyllum nodosum (Heterokontophyta, Fucaceae). Mol. Ecol. Notes 3, 180–182 (2003).CAS 
    Article 

    Google Scholar 
    74.Paulino, C. et al. Characterization of 12 polymorphic microsatellite markers in the sugar kelp Saccharina latissima. J. Appl. Phycol. 28, 3071–3074 (2016).Article 

    Google Scholar 
    75.Coleman, M. A., Dolman, G., Kelaher, B. P. & Steinberg, P. D. Characterisation of microsatellite loci in the subtidal habitat-forming alga, Phyllospora comosa (Phaeophyceae, Fucales). Conserv. Genet. 9, 1015–1017 (2008).CAS 
    Article 

    Google Scholar 
    76.Coleman, M. A. & Brawley, S. H. Are life history characteristics good predictors of genetic diversity and structure? A case study of the intertidal alga Fucus spiralis (heterokontophyta; Phaeophyceae). J. Phycol. 41, 753–762 (2005).Article 

    Google Scholar 
    77.Coleman, M. A. & Brawley, S. H. Spatial and temporal variability in dispersal and population genetic structure of a rockpool alga. Mar. Ecol. Prog. Ser. 300, 63–77 (2005).ADS 
    Article 

    Google Scholar 
    78.Engel, C. R., Daguin, C. & Serrão, E. A. Genetic entities and mating system in hermaphroditic Fucus spiralis and its close dioecious relative F. vesiculosus (Fucaceae, Phaeophyceae). Mol. Ecol. 14, 2033–2046 (2005).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    79.Medrano, A. et al. Ecological traits, genetic diversity and regional distribution of the macroalga Treptacantha elegans along the Catalan coast (NW Mediterranean Sea). Sci. Rep. 10, 19219 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    80.Engelen, A. H. et al. Periodicity of propagule expulsion and settlement in the competing native and invasive brown seaweeds, Cystoseira humilis and Sargassum muticum (Phaeophyta). Eur. J. Phycol. 43, 275–282 (2008).Article 

    Google Scholar 
    81.Assis, J., Serrão, E. A., Claro, B., Perrin, C. & Pearson, G. A. Climate-driven range shifts explain the distribution of extant gene pools and predict future loss of unique lineages in a marine brown alga. Mol. Ecol. 23, 2797–2810 (2014).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    82.Neiva, J. et al. Genes left behind: Climate change threatens cryptic genetic diversity in the canopy-forming seaweed Bifurcaria bifurcata. PLoS ONE 10, e0131530 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    83.Coleman, M. A. & Kelaher, B. P. Connectivity among fragmented populations of a habitat-forming alga, Phyllospora comosa (Phaeophyceae, Fucales) on an urbanised coast. Mar. Ecol. Prog. Ser. 381, 63–70 (2009).ADS 
    Article 

    Google Scholar 
    84.Boissin, E. et al. Chaotic genetic structure and past demographic expansion of the invasive gastropod Tritia neritea in its native range, the Mediterranean Sea. Sci. Rep. 10, 21624 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    85.Olsen, J. L. et al. North Atlantic phylogeography and large-scale population differentiation of the seagrass Zostera marina L. Mol. Ecol. 13, 1923–1941 (2004).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    86.Peijnenburg, K. T. C. A., Breeuwer, J. A. J., Pierrot-Bults, A. C. & Menken, S. B. J. Phylogeography of the planktonic chaetognath Sagitta setosa reveals isolation in European Seas. Evolution 58, 1472–1487 (2004).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    87.Luttikhuizen, P. C., Campos, J., van Bleijswijk, J., Peijnenburg, K. T. C. A. & van der Veer, H. W. Phylogeography of the common shrimp, Crangon crangon (L.) across its distribution range. Mol. Phylogenet. Evol. 46, 1015–1030 (2008).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    88.Wilson, A. B. & Eigenmann Veraguth, I. The impact of Pleistocene glaciation across the range of a widespread European coastal species. Mol. Ecol. 19, 4535–4553 (2010).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    89.Riquet, F. et al. Parallel pattern of differentiation at a genomic island shared between clinal and mosaic hybrid zones in a complex of cryptic seahorse lineages. Evolution 73, 817–835 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    90.Hewitt, G. M. Hybrid zones-natural laboratories for evolutionary studies. Trends Ecol. Evol. 3, 158–167 (1988).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    91.Johannesson, K., Le Moan, A., Perini, S. & André, C. A Darwinian laboratory of multiple contact zones. Trends Ecol. Evol. https://doi.org/10.1016/j.tree.2020.07.015 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    92.McCoy, S. J., Krueger-Hadfield, S. A. & Mieszkowska, N. Evolutionary phycology: Toward a macroalgal species conceptual framework. J. Phycol. 56, 1404–1413 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    93.Neiva, J., Pearson, G. A., Valero, M. & Serrão, E. A. Fine-scale genetic breaks driven by historical range dynamics and ongoing density-barrier effects in the estuarine seaweed Fucus ceranoides L. BMC Evol. Biol. 12, 78 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    94.Whitlock, M. C. & McCauley, D. E. Indirect measures of gene flow and migration: FST ≠1/(4 Nm + 1). Heredity 82, 117–125 (1999).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    95.Lowe, W. H. & Allendorf, F. W. What can genetics tell us about population connectivity?. Mol. Ecol. 19, 3038–3051 (2010).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    96.Durrant, H. M. S. et al. Implications of macroalgal isolation by distance for networks of marine protected areas. Conserv. Biol. 28, 438–445 (2014).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    97.Engelen, A., Olsen, J., Breeman, A. & Stam, W. Genetic differentiation in Sargassum polyceratium (Fucales: Phaeophyceae) around the island of Curaçao (Netherlands Antilles). Mar. Biol. 139, 267–277 (2001).CAS 
    Article 

    Google Scholar 
    98.Billot, C., Engel, C. R., Rousvoal, S., Kloareg, B. & Valero, M. Current patterns, habitat discontinuities and population genetic structure: The case of the kelp Laminaria digitata in the English Channel. Mar. Ecol. Prog. Ser. 253, 111–121 (2003).ADS 
    Article 

    Google Scholar 
    99.Tatarenkov, A., Jönsson, R. B., Kautsky, L. & Johannesson, K. Genetic structure in populations of Fucus vesiculosus (phaeophyceae) over spatial scales from 10 m to 800 km. J. Phycol. 43, 675–685 (2007).CAS 
    Article 

    Google Scholar 
    100.Susini, M.-L., Thibaut, T., Meinesz, A. & Forcioli, D. A preliminary study of genetic diversity in Cystoseira amentacea (C. Agardh) Bory var. stricta Montagne (Fucales, Phaeophyceae) using random amplified polymorphic DNA. Phycologia 46, 605–611 (2007).Article 

    Google Scholar 
    101.Korotenko, K., Bowman, M. & Dietrich, D. High-resolution numerical model for predicting the transport and dispersal of oil spilled in the Black Sea. Terrest. Atmos. Oceanic Sci. J. 21, 123–136 (2010).Article 

    Google Scholar 
    102.Barale, V., Schiller, C., Tacchi, R. & Marechal, C. Trends and interactions of physical and bio-geo-chemical features in the Adriatic Sea as derived from satellite observations. Sci. Total Environ. 353, 68–81 (2005).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    103.Hauser, L. & Carvalho, G. R. Paradigm shifts in marine fisheries genetics: Ugly hypotheses slain by beautiful facts. Fish Fish. 9, 333–362 (2008).Article 

    Google Scholar 
    104.Orellana, S., Hernández, M. & Sansón, M. Diversity of Cystoseira sensu lato (Fucales, Phaeophyceae) in the eastern Atlantic and Mediterranean based on morphological and DNA evidence, including Carpodesmia gen. emend. and Treptacantha gen. emend. Eur. J. Phycol. 54, 447–465 (2019).CAS 
    Article 

    Google Scholar 
    105.Richard, B., A. & Wilks, A., R. Maps in S. AT&T Bell Laboratories Statistics Research Report [93.2] (1993).106.Richard, B., A. & Wilks, A., R. Constructing a Geographical Database. AT&T Bell Lab-oratories Statistics Research Report [95.2] (1995).107.R Core Team. R: A Language and Environment for Statistical Computing https://www.R-project.org/ (R Foundation for Statistical Computing, 2017).108.Holleley, C. E. & Geerts, P. G. Multiplex Manager 1.0: A cross-platform computer program that plans and optimizes multiplex PCR. Biotechniques 46, 511–517 (2009).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    109.Peakall, R. & Smouse, P. E. genalex 6: Genetic analysis in Excel. Population genetic software for teaching and research. Mol. Ecol. Notes 6, 288–295 (2006).Article 

    Google Scholar 
    110.Goudet, J. Fstat (Version 1.2): A computer program to calculate F-Statistics. J. Hered. 86, 485–486 (1995).Article 

    Google Scholar 
    111.De Meeûs, T., Guégan, J.-F. & Teriokhin, A. T. MultiTest V.1.2, a program to binomially combine independent tests and performance comparison with other related methods on proportional data. BMC Bioinform. 10, 443 (2009).Article 
    CAS 

    Google Scholar 
    112.Benjamini, Y. & Hochberg, Y. Controlling the False Discovery Rate: A practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B Methodol. 57, 289–300 (1995).MathSciNet 
    MATH 

    Google Scholar 
    113.Oosterhout, C. V., Weetman, D. & Hutchinson, W. F. Estimation and adjustment of microsatellite null alleles in nonequilibrium populations. Mol. Ecol. Notes 6, 255–256 (2006).Article 

    Google Scholar 
    114.Petit, R. J., Mousadik, A. E. & Pons, O. Identifying populations for conservation on the basis of genetic markers. Conserv. Biol. 12, 844–855 (1998).Article 

    Google Scholar 
    115.El Mousadik, A. & Petit, R. J. High level of genetic differentiation for allelic richness among populations of the argan tree [Argania spinosa (L.) Skeels] endemic to Morocco. Theor. Appl. Genet. 92, 832–839 (1996).PubMed 
    Article 
    PubMed Central 

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

    Google Scholar 
    117.Szulkin, M., Bierne, N. & David, P. Heterozygosity-fitness correlations: A time for reappraisal. Evolution 64, 1202–1217 (2010).PubMed 
    PubMed Central 

    Google Scholar 
    118.David, P., Pujol, B., Viard, F., Castella, V. & Goudet, J. Reliable selfing rate estimates from imperfect population genetic data. Mol. Ecol. 16, 2474–2487 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    119.Wright, S. The interpretation of population structure by F-statistics with special regard to systems of mating. Evolution 19, 395–420 (1965).Article 

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

    Google Scholar 
    121.Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    122.Falush, D., Stephens, M. & Pritchard, J. K. Inference of population structure using multilocus genotype data: Linked loci and correlated allele frequencies. Genetics 164, 1567–1587 (2003).CAS 
    PubMed 
    PubMed Central 
    Article 

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

    Google Scholar 
    124.Jakobsson, M. & Rosenberg, N. A. Clumpp: A cluster matching and permutation program for dealing with label switching and multimodality in analysis of population structure. Bioinformatics 23, 1801–1806 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    125.Goudet, J. hierfstat, a package for r to compute and test hierarchical F-statistics. Mol. Ecol. Notes 5, 184–186 (2005).Article 

    Google Scholar 
    126.Séré, M., Thévenon, S., Belem, A. M. G. & De Meeûs, T. Comparison of different genetic distances to test isolation by distance between populations. Heredity 119, 55–63 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    127.Rousset, F. & Raymond, M. Statistical analyses of population genetic data: New tools, old concepts. Trends Ecol. Evol. 12, 313–317 (1997).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    128.Hijmans, R. J. Geosphere: Spherical Trigonometry. https://CRAN.R-project.org/package=geosphere. R package version 1.5–5. (2016).129.Korotenko, K. A. Effects of mesoscale eddies on behavior of an oil spill resulting from an accidental deepwater blowout in the Black Sea: An assessment of the environmental impacts. PeerJ 6, e5448 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    130.López-Márquez, V. et al. Seascape genetics and connectivity modelling for an endangered Mediterranean coral in the northern Ionian and Adriatic seas. Landsc. Ecol. 34, 2649–2668 (2019).Article 

    Google Scholar 
    131.Rousset, F. Genetic differentiation and estimation of gene flow from F-statistics under isolation by distance. Genetics 145, 1219–1228 (1997).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    132.Watts, P. C. et al. Compatible genetic and ecological estimates of dispersal rates in insect (Coenagrion mercuriale: Odonata: Zygoptera) populations: Analysis of ‘neighbourhood size’ using a more precise estimator. Mol. Ecol. 16, 737–751 (2007).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    133.Hill, W. G. Estimation of effective population size from data on linkage disequilibrium. Genet. Res. 38, 209–216 (1981).Article 

    Google Scholar 
    134.Waples, R. S. Seed banks, salmon, and sleeping genes: Effective population size in semelparous, age-structured species with fluctuating abundance. Am. Nat. 167, 118–135 (2006).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    135.Waples, R. S. & Do, C. ldne: A program for estimating effective population size from data on linkage disequilibrium. Mol. Ecol. Resour. 8, 753–756 (2008).PubMed 
    Article 

    Google Scholar 
    136.Do, C. et al. NeEstimator v2: Re-implementation of software for the estimation of contemporary effective population size (Ne) from genetic data. Mol. Ecol. Resour. 14, 209–214 (2014).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    137.Cavalli-Sforza, L. L., & Edwards, A. W. F. Phylogenetic analysis: Model and estimation procedures. Am. J. Hum. Genet. 19, 233–257 (1967).CAS 
    PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Grazing intensity drives plant diversity but does not affect forage production in a natural grassland dominated by the tussock-forming grass Andropogon lateralis Nees

    1.IBGE. Instituto Brasileiro de Geografia e Estatística – Censo Agro 2017. IBGE | Censo Agro 2017, Dados preliminares https://censos.ibge.gov.br/agro/2017/ (2017).2.Boldrini, I. I. et al. Flora. In Biodiversidade dos Campos do Planalto das Araucárias 39–94 (2009).3.Iganci, J. R. V., Heiden, G., Miotto, S. T. S. & Pennington, R. T. Campos de Cima da Serra: The Brazilian subtropical highland Grasslands show an unexpected level of plant endemism. Bot. J. Linn. Soc. 167, 378–393 (2011).Article 

    Google Scholar 
    4.Borer, E. T. et al. Herbivores and nutrients control grassland plant diversity via light limitation. Nature 508, 517–520 (2014).ADS 
    CAS 
    Article 

    Google Scholar 
    5.Alhamad, M. N. & Alrababah, M. A. Defoliation and competition effects in a productivity gradient for a semiarid Mediterranean annual grassland community. Basic Appl. Ecol. 9, 224–232 (2008).Article 

    Google Scholar 
    6.Fedrigo, J. K. et al. Temporary grazing exclusion promotes rapid recovery of species richness and productivity in a long-term overgrazed Campos grassland. Restor. Ecol. https://doi.org/10.1111/rec.12635 (2017).Article 

    Google Scholar 
    7.Mavromihalis, J. A., Dorrough, J., Clark, S. G., Turner, V. & Moxham, C. Manipulating livestock grazing to enhance native plant diversity and cover in native grasslands. Rangel. J. 35, 95–108 (2013).Article 

    Google Scholar 
    8.Bircham, J. S. & Hodgson, J. The influence of sward condition on rates of herbage growth and senescence in mixed swards under continuous stocking management. Grass Forage Sci. 38, 323–331 (1983). Article 

    Google Scholar 
    9.Sbrissia, A. F. et al. Defoliation strategies in pastures submitted to intermittent stocking method: Underlying mechanisms buffering forage accumulation over a range of grazing heights. Crop Sci. 58, 945–954 (2018).Article 

    Google Scholar 
    10.Jaurena, M. et al. Native grasslands at the core: A new paradigm of intensification for the Campos of Southern South America to increase economic and environmental sustainability. Front. Sustain. Food Syst. 5, 11 (2021).Article 

    Google Scholar 
    11.Cruz, P. et al. Leaf traits as functional descriptors of the intensity of continuous grazing in native grasslands in the South of Brazil. Rangel. Ecol. Manag. 63, 350–358 (2010).Article 

    Google Scholar 
    12.Benitez, C. A. & Fernandez, J. G. Espécies forrageiras de la pradera natural: Fenologia y respuesta a la frequência e severidad de corte (1970).13.Herve, A. M. B. & Valls, J. F. M. Genêro Andropogon L. (Gramineae) no Rio Grande do Sul. Anuario tecnico do Instituto de Pesquisas Zootecnicas Francisco Osorio (1980).14.Zanin, A. & Longhi-Wagner, H. M. Revisão de Andropogon (Poaceae – Andropogoneae) para o Brasil. Rodriguesia 62, 171–202 (2011).Article 

    Google Scholar 
    15.Augustine, D. J. & McNaughton, S. J. Ungulate effects on the functional species composition of plant communities: Herbivore selectivity and plant tolerance. J. Wildl. Manag. 62, 1165 (1998).Article 

    Google Scholar 
    16.Fraser, L. H. et al. Worldwide evidence of a unimodal relationship between productivity and plant species richness. Science 350, 1177b (2015).ADS 
    Article 

    Google Scholar 
    17.Connell, J. H. Diversity in tropical rain forests and coral reefs: High diversity of trees and corals is maintained only in a nonequilibrium state. Science 199, 1302–1310 (1978).ADS 
    CAS 
    Article 

    Google Scholar 
    18.Milchunas, D. G., Sala, O. E. & Lauenroth, W. K. A generalized model of the effects of grazing by large herbivores on grassland community structure. Am. Nat. 132, 87–106 (1988).Article 

    Google Scholar 
    19.Liu, J. et al. Impacts of grazing by different large herbivores in grassland depend on plant species diversity. J. Appl. Ecol. 52(4), 1053–1062 (2015).Article 

    Google Scholar 
    20.Ren, H., Schönbach, P., Wan, H., Gierus, M. & Taube, F. Effects of grazing intensity and environmental factors on species composition and diversity in typical Steppe of Inner Mongolia, China. PLoS ONE 7(12), e52180 (2012).ADS 
    CAS 
    Article 

    Google Scholar 
    21.Sbrissia, A. F., Silva, S. C., Schmitt, D. & Duchini, P. G. Unravelling the relationship between a seasonal environment and the dynamics of forage growth in grazed swards. J. Agron. Crop Sci. 206, 630–639 (2020).Article 

    Google Scholar 
    22.Hernández-Lambraño, R. E., González-Moreno, P. & Sánchez-Agudo, J. Á. Towards the top: Niche expansion of Taraxacum officinale and Ulex europaeus in mountain regions of South America. Austral. Ecol. 42, 577–589 (2017).Article 

    Google Scholar 
    23.Pinto, L. F. M. et al. Dinâmica do acúmulo de matéria seca em pastagens de Tifton 85 sob pastejo. Sci. Agric. 58, 439–447 (2001).Article 

    Google Scholar 
    24.Duchini, P. G., Guzatti, G. C., Ribeiro Filho, H. M. N. & Sbrissia, A. F. Tiller size/density compensation in temperate climate grasses grown in monoculture or in intercropping systems under intermittent grazing. Grass Forage Sci. 69, 655–665 (2014).CAS 
    Article 

    Google Scholar 
    25.Briske, D. D. & Anderson, V. J. Competitive ability of the bunchgrass Schizachyrium scoparium as affected by grazing history and defoliation. Vegetatio 103, 41–49 (1992).
    Google Scholar 
    26.Altesor, A., Oesterheld, M., Leoni, E., Lezama, F. & Rodriguez, C. Effect of grazing on community structure and productivity of a Uruguayan grassland. Plant Ecol. 179, 83–91 (2005).Article 

    Google Scholar 
    27.Lezama, F. et al. Variation of grazing-induced vegetation changes across a large-scale productivity gradient. J. Veg. Sci. 25, 8–21 (2014).Article 

    Google Scholar 
    28.Lattanzi, F. A. et al. 13C-labeling shows the effect of hierarchy on the carbon gain of individuals and functional groups in dense field stands. Ecology 93, 169–179 (2012).Article 

    Google Scholar 
    29.Roscher, C. et al. Functional composition has stronger impact than species richness on carbon gain and allocation in experimental grasslands. PLoS ONE 14(1), e0204715 (2019).CAS 
    Article 

    Google Scholar 
    30.Wan, C. & Sosebee, R. E. Central dieback of the dryland bunchgrass Eragrostis curvula (weeping lovegrass) re-examined: The experimental clearance of tussock centres. J. Arid Environ. 46, 69–78 (2000).ADS 
    Article 

    Google Scholar 
    31.Angassa, A. Effects of grazing intensity and bush encroachment on herbaceous species and rangeland condition in Southern Ethiopia. L. Degrad. Dev. 25, 438–451 (2014).Article 

    Google Scholar 
    32.Schultz, N. L., Morgan, J. W. & Lunt, I. D. Effects of grazing exclusion on plant species richness and phytomass accumulation vary across a regional productivity gradient. J. Veg. Sci. 22, 130–142 (2011).Article 

    Google Scholar 
    33.Chaneton, E. J. & Facelli, J. M. Disturbance effects on plant community diversity: Spatial scales and dominance hierarchies. Vegetatio 93, 143–155 (1991).Article 

    Google Scholar 
    34.Tow, P. G. & Lazenby, A. Competition and Succession in Pastures (CAB International, 2001). doi:https://doi.org/10.1079/9780851994413.0000.35.Briske, D. D. & Hendrickson, J. R. Does selective defoliation mediate competitive interactions in a semiarid savannah? A demographic evaluation. J. Veg. Sci. 9, 611–622 (1998).Article 

    Google Scholar 
    36.Baer, S. G., Blair, J. M. & Collins, S. L. Environmental heterogeneity has a weak effect on diversity during community assembly in tallgrass prairie. Ecol. Monogr. 86, 94–106 (2016).Article 

    Google Scholar 
    37.Alvares, C. A., Stape, J. L., Sentelhas, P. C., Gonçalves, J. L. M. & Sparovek, G. Köppen’s climate classification map for Brazil. Meteorol. Zeitschrift 22, 711–728 (2013).ADS 
    Article 

    Google Scholar 
    38.Pallarés, O. R., Berretta, E. J. & Maraschin, G. The South American Campos ecosystem BT—Grasslands of the World. Grasslands of the World 1–49 (2005). 39.Allen, V. G. et al. An international terminology for grazing lands and grazing animals. Grass Forage Sci. 66, 2–28 (2011).Article 

    Google Scholar 
    40.Zanini, G. D., Santos, G. T., Schmitt, D. & Padilha, D. A. Distribuição de colmo na estrutura vertical de pastos de capim Aruana e azevém anual submetidos a pastejo intermitente por ovinos. Ciênc. Rural 42, 882–887 (2012).Article 

    Google Scholar 
    41.Carvalho, P. C. F. Harry Stobbs Memorial Lecture: Can grazing behaviour support innovations in grassland management?. Trop. Grassl. Forrajes Trop. 1, 137–155 (2013).Article 

    Google Scholar 
    42.Barthram, G. T. Experimental techniques: The HFRO sward stick. In The Hill Farming Research Organization Biennial Report 1984/1985 29–30 (HFRO, 1985).43.Haydock, K. P. & Shaw, N. H. The comparative yield method for estimating dry matter yield of pasture. Aust. J. Exp. Agric. 15, 663–670 (1975).
    Google Scholar 
    44.Williams, R. J. Gap dynamics in subalpine heathland and grassland vegetation in south-eastern Australia. J. Ecol. 80, 343–352 (1992).Article 

    Google Scholar 
    45.Derner, J. D., Briske, D. D. & Polley, H. W. Tiller organization within the tussock grass Schizachyrium scoparium: A field assessment of competition–cooperation tradeoffs. Botany 90, 669–677 (2012).Article 

    Google Scholar 
    46.Mueller-Dombois, D. & Ellenberg, D. Aims and methods of vegetation ecology. In Community Sampling: The Relevé Method 45–66 (1974).47.Tothill, J. C., Hargreaves, J. N. G., Jones, R. M. & McDonald, C. K. Botanal—A comprehensive sampling and computing procedure for estimating pasture yield and composition. 1. Field sampling. Trop. Agron. Tech. Mem. 78, 1–24 (1992).
    Google Scholar 
    48.’t Mannetje, L. Measuring biomass of grassland vegetation. In Field and Laboratory Methods for Grassland and Animal Production Research 151–177 (CABI, 2000). doi:https://doi.org/10.1079/9780851993515.0151.49.Oksanen, F.J., Blanchet, G., Friendly, M., Kindt, R., Legendre, P. et al. vegan: Community Ecology Package. R package version 2.5-7. (2020). https://CRAN.R-project.org/package=vegan.50.Kindt, R. & Coe, R. Tree diversity analysis. A manual and software for common statistical methods for ecological and biodiversity studies. World Agroforestry Centre (ICRAF), Nairobi. ISBN: 92-9059-179-X (2005).51.R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. (2021). https://www.R-project.org/.52.Watkins, A. J. & Wilson, J. B. Plant community structure, and its relation to the vertical complexity of communities: dominance/diversity and spatial rank consistency. Oikos 70, 91–98 (1994).Article 

    Google Scholar 
    53.Bates, D., Mächler, M., Zurich, E., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

    Google Scholar 
    54.Sbrissia, A. F., Zanella, P. G., Pinto, C. E., Baldissera, T. C. & Garagorry, F. C. Natural grasslands experiment – 2015 – 2017 – Pablo. figshare. https://doi.org/10.6084/m9.figshare.14055419.v1 (2021). More

  • in

    Opportunities and challenges of macrogenetic studies

    1.Brown, J. H. & Maurer, B. A. Macroecology: the division of food and space among species on continents. Science 243, 1145–1150 (1989).CAS 
    Article 

    Google Scholar 
    2.Gaston, K. J., Robinson, D. & Chown, S. L. Macrophysiology: large-scale patterns in physiological traits and their ecological implications. Funct. Ecol. 18, 159–167 (2004).Article 

    Google Scholar 
    3.Chown, S. L. & Gaston, K. J. Macrophysiology–progress and prospects. Funct. Ecol. 30, 330–344 (2016).Article 

    Google Scholar 
    4.Avise, J. C. Phylogeography: the History and Formation of Species (Harvard University Press, 2000).5.Ebach, M. C. Origins of Biogeography. Vol. 13 (Springer, 2015).6.Brundin, L. On the real nature of transantarctic relationships. Evolution 19, 496–505 (1965).
    Google Scholar 
    7.Beheregaray, L. B. Twenty years of phylogeography: the state of the field and the challenges for the Southern Hemisphere. Mol. Ecol. 17, 3754–3774 (2008).PubMed 
    PubMed Central 

    Google Scholar 
    8.Hickerson, M. J. et al. Phylogeography’s past, present, and future: 10 years after Avise, 2000. Mol. Phylogenet. Evol. 54, 291–301 (2010).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    9.Gaston, K. J. & Blackburn, T. M. A critique for macroecology. Oikos 84, 353–368 (1999).Article 

    Google Scholar 
    10.Lovegrove, B. G. The zoogeography of mammalian basal metabolic rate. Am. Nat. 156, 201–219 (2000).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    11.Reich, P. B., Walters, M. B. & Ellsworth, D. S. From tropics to tundra: Global convergence in plant functioning. Proc. Natl Acad. Sci. USA 94, 13730–13734 (1997).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    12.Chown, S. L. & Gaston, K. J. Macrophysiology for a changing world. Proc. Biol. Sci. 275, 1469–1478 (2008).PubMed 
    PubMed Central 

    Google Scholar 
    13.Kerr, J. T., Kharouba, H. M. & Currie, D. J. The macroecological contribution to global change solutions. Science 316, 1581–1584 (2007).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    14.Blanchet, S., Prunier, J. G. & De Kort, H. Time to go bigger: Emerging patterns in macrogenetics. Trends Genet. 33, 579–580 (2017). This study coined the term ‘macrogenetics’ and illustrated, through three study examples, how shifting toward macrogenetics should generate new perspectives and theories concerning genetic diversity patterns.CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    15.Blanchet, S. et al. A river runs through it: the causes, consequences, and management of intraspecific diversity in river networks. Evol. Appl. 13, 1195–1213 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    16.Frankham, R. Resolving conceptual issues in conservation genetics: the roles of laboratory species and meta-analyses. Hereditas 130, 195–201 (2004).Article 

    Google Scholar 
    17.Arnqvist, G. & Wooster, D. Meta-analysis: synthesizing research findings in ecology and evolution. Trends Ecol. Evol. 10, 236–240 (1995).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    18.Paz-Vinas, I. et al. Systematic conservation planning for intraspecific genetic diversity. Proc. Biol. Sci. 285, 20172746 (2018).PubMed 
    PubMed Central 

    Google Scholar 
    19.Pelletier, T. A. & Carstens, B. C. Geographical range size and latitude predict population genetic structure in a global survey. Biol. Lett. 14, 20170566 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    20.Miraldo, A. et al. An anthropocene map of genetic diversity. Science 353, 1532–1535 (2016). This paper is thought to be the first published study to massively repurpose public mtDNA sequences to explore global genetic patterns (100,791 sequences from >4,500 terrestrial mammal and amphibian species).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    21.Yiming, L. et al. Latitudinal gradients in genetic diversity and natural selection at a highly adaptive gene in terrestrial mammals. Ecography 44, 206–218 (2021). This study found that adaptive IGV is higher at low latitudes and in smaller mammal species using repurposed MHC gene data from 93 mammal species.Article 

    Google Scholar 
    22.Manel, S. et al. Global determinants of freshwater and marine fish genetic diversity. Nat. Commun. 11, 692 (2020). This study repurposed 58,565 public mtDNA sequences from 5,912 freshwater and marine fish to explore the effects of environmental drivers (temperature, species diversity) on intraspecific genetic diversity.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    23.Theodoridis, S. et al. Evolutionary history and past climate change shape the distribution of genetic diversity in terrestrial mammals. Nat. Commun. 11, 2557 (2020). This study revealed a negative effect of past rapid climate change and a positive effect of interannual precipitation variability in shaping the genetic diversity of terrestrial mammals using 46,965 mtDNA sequences.CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    24.Barrow, L. N., da Fonseca, E. M., Thompson, C. E. P. & Carstens, B. C. Predicting amphibian intraspecific diversity with machine learning: Challenges and prospects for integrating traits, geography, and genetic data. Mol. Ecol. Resour. https://doi.org/10.1111/1755-0998.13303 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    25.De Kort, H. et al. Life history, climate and biogeography interactively affect worldwide genetic diversity of plant and animal populations. Nat. Commun. 12, 516 (2021). This study found weak support for latitudinal IGV gradients, taxonomic-specific effects of temperature stability and life-history traits, and higher IGV in animals compared to plants using microsatellite and amplified fragment length polymorphism data from 8,386 local populations from 727 animal and plant species.PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    26.Schmidt, C., Domaratzki, M., Kinnunen, R. P., Bowman, J. & Garroway, C. J. Continent-wide effects of urbanization on bird and mammal genetic diversity. Proc. Biol. Sci. 287, 20192497 (2020). This study used archived microsatellite data from 85 studies (66 species) to explore the effects of urbanization in mammals and birds.CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    27.Millette, K. L. et al. No consistent effects of humans on animal genetic diversity worldwide. Ecol. Lett. 23, 55–67 (2020). The authors of this article conducted spatial and temporal analysis of the effects of humans on animal genetic diversity worldwide, by repurposing 175,247 mtDNA sequences from >17,000 animal species.PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    28.Taberlet, P. et al. Genetic diversity in widespread species is not congruent with species richness in alpine plant communities. Ecol. Lett. 15, 1439–1448 (2012). This paper reports a Class I macrogenetic study based on amplified fragment length polymorphism genetic data from 27 alpine plant species that tested whether genetic and species diversities co-vary.PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    29.Manel, S. et al. Broad-scale adaptive genetic variation in alpine plants is driven by temperature and precipitation. Mol. Ecol. 21, 3729–3738 (2012).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    30.Gugerli, F. et al. Relationships among levels of biodiversity and the relevance of intraspecific diversity in conservation – a project synopsis. Perspect. Plant. Ecol. Evol. Syst. 10, 259–281 (2008).Article 

    Google Scholar 
    31.Schlaepfer, D. R., Braschler, B., Rusterholz, H.-P. & Baur, B. Genetic effects of anthropogenic habitat fragmentation on remnant animal and plant populations: a meta-analysis. Ecosphere 9, e02488 (2018).Article 

    Google Scholar 
    32.González, A. V., Gómez-Silva, V., Ramírez, M. J. & Fontúrbel, F. E. Meta-analysis of the differential effects of habitat fragmentation and degradation on plant genetic diversity. Conserv. Biol. 34, 711–720 (2020).PubMed 
    Article 

    Google Scholar 
    33.Ratnasingham, S. & Hebert, P. D. N. Bold: the barcode of life data system. Mol. Ecol. Notes 7, 355–364 (2007).CAS 
    PubMed 
    PubMed Central 
    Article 

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

    Google Scholar 
    35.Kattge, J. et al. TRY plant trait database–enhanced coverage and open access. Glob. Change Biol. 26, 119–188 (2020).Article 

    Google Scholar 
    36.Theodoridis, S., Rahbek, C. & Nogues-Bravo, D. Exposure of mammal genetic diversity to mid-21st century global change. Ecography 44, 817–831 (2021).Article 

    Google Scholar 
    37.Rissler, L. J. Union of phylogeography and landscape genetics. Proc. Natl Acad. Sci. USA 113, 8079–8086 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    38.Hubbell, S. P. The unified neutral theory of biodiversity and biogeography (Princeton University Press, 2001).39.Haldane, J. B. S. A mathematical theory of natural and artificial selection, Part V: selection and mutation. Math. Proc. Camb. Philos. Soc. 23, 838–844 (1927).Article 

    Google Scholar 
    40.Wright, S. Evolution in Mendelian populations. Genetics 16, 97–159 (1931).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    41.Fisher, R. A. On the dominance ratio. Proc. R. Soc. Edinburgh 42, 321–341 (1922).Article 

    Google Scholar 
    42.Kimura, M. & Weiss, G. H. The stepping stone model of population structure and the decrease of genetic correlation with distance. Genetics 49, 561–576 (1964).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    43.Kingman, J. F. C. The coalescent. Stoch. Process. Their Appl. 13, 235–248 (1982).Article 

    Google Scholar 
    44.Kimura, M. Evolutionary rate at the molecular level. Nature 217, 624–626 (1968).CAS 
    PubMed 
    Article 

    Google Scholar 
    45.Soulé, M. E. in Molecular Evolution (ed. Ayala, F. J.) 60–77 (Sinauer Associates, 1976).46.Brown, A. H. Isozymes, plant population genetic structure and genetic conservation. Tag. Theor. Appl. Genet. Theor. Angew. Genet. 52, 145–157 (1978).CAS 
    Article 

    Google Scholar 
    47.Mullis, K. et al. Specific enzymatic amplification of DNA in vitro: the polymerase chain reaction. Cold Spring Harb. Symp. Quant. Biol. 51, 263–273 (1986).CAS 
    PubMed 
    Article 

    Google Scholar 
    48.Sanger, F., Nicklen, S. & Coulson, A. R. DNA sequencing with chain-terminating inhibitors. Proc. Natl Acad. Sci. USA 74, 5463–5467 (1977).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    49.Miller, M. R., Dunham, J. P., Amores, A., Cresko, W. A. & Johnson, E. A. Rapid and cost-effective polymorphism identification and genotyping using restriction site associated DNA (RAD) markers. Genome Res. 17, 240–248 (2007).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    50.Carroll, E. L. et al. Genetic and genomic monitoring with minimally invasive sampling methods. Evol. Appl. 11, 1094–1119 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    51.Hebert, P. D. N., Cywinska, A., Ball, S. L. & deWaard, J. R. Biological identifications through DNA barcodes. Proc. Biol. Sci. 270, 313–321 (2003).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    52.Taberlet, P., Coissac, E., Pompanon, F., Brochmann, C. & Willerslev, E. Towards next-generation biodiversity assessment using DNA metabarcoding. Mol. Ecol. 21, 2045–2050 (2012).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    53.Gauthier, J. et al. Museomics identifies genetic erosion in two butterfly species across the 20th century in Finland. Mol. Ecol. Resour. 20, 1191–1205 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    54.Wandeler, P., Hoeck, P. E. A. & Keller, L. F. Back to the future: museum specimens in population genetics. Trends Ecol. Evol. 22, 634–642 (2007).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    55.Strasser, B. J. The experimenter’s museum: GenBank, natural history, and the moral economies of biomedicine. Isis 102, 60–96 (2011).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    56.Whitlock, M. C. Data archiving in ecology and evolution: best practices. Trends Ecol. Evol. 26, 61–65 (2011).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    57.Wilkinson, M. D. et al. The FAIR guiding principles for scientific data management and stewardship. Sci. Data 3, 160018 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    58.Deck, J. et al. The Genomic Observatories Metadatabase (GeOMe): A new repository for field and sampling event metadata associated with genetic samples. PLoS Biol. 15, e2002925 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    59.R Core Team. R: a language and environment for statistical computing, R Foundation for Statistical Computing http://www.r-project.org/index.html (2021).60.Manel, S. & Holderegger, R. Ten years of landscape genetics. Trends Ecol. Evol. 28, 614–621 (2013).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    61.Prunier, J. G., Colyn, M., Legendre, X., Nimon, K. F. & Flamand, M. C. Multicollinearity in spatial genetics: separating the wheat from the chaff using commonality analyses. Mol. Ecol. 24, 263–283 (2015).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    62.Stanley, R. R. E. et al. A climate-associated multispecies cryptic cline in the northwest Atlantic. Sci. Adv. 4, eaaq0929 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    63.Fenderson, L. E., Kovach, A. I. & Llamas, B. Spatiotemporal landscape genetics: investigating ecology and evolution through space and time. Mol. Ecol. 29, 218–246 (2020).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    64.Daza, J. M., Castoe, T. A. & Parkinson, C. L. Using regional comparative phylogeographic data from snake lineages to infer historical processes in middle America. Ecography 33, 343–354 (2010).
    Google Scholar 
    65.Riddle, B. R. Comparative phylogeography clarifies the complexity and problems of continental distribution that drove A. R. Wallace to favor islands. Proc. Natl Acad. Sci. USA 113, 7970–7977 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    66.Carstens, B. C., Morales, A. E., Field, K. & Pelletier, T. A. A global analysis of bats using automated comparative phylogeography uncovers a surprising impact of Pleistocene glaciation. J. Biogeogr. 45, 1795–1805 (2018).Article 

    Google Scholar 
    67.Smith, B. T., Seeholzer, G. F., Harvey, M. G., Cuervo, A. M. & Brumfield, R. T. A latitudinal phylogeographic diversity gradient in birds. PLoS Biol. 15, e2001073 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    68.Smith, B. T. et al. The drivers of tropical speciation. Nature 515, 406–409 (2014).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    69.Ballin, M., Barcaroli, G., Masselli, M. & Scarnó, M. Redesign Sample for Land Use/Cover Area Frame Survey (LUCAS) 2018 (EU Publications, 2018).70.Buchhorn, M. et al. Copernicus global land cover layers — Collection 2. Remote. Sens. 12, 1044 (2020).Article 

    Google Scholar 
    71.Jones, K. E. et al. PanTHERIA: a species-level database of life history, ecology, and geography of extant and recently extinct mammals: Ecological Archives E090-184. Ecology 90, 2648–2648 (2009).Article 

    Google Scholar 
    72.Tedesco, P. A. et al. A global database on freshwater fish species occurrence in drainage basins. Sci. Data 4, 170141 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    73.Vellend, M. & Geber, M. A. Connections between species diversity and genetic diversity: species diversity and genetic diversity. Ecol. Lett. 8, 767–781 (2005).Article 

    Google Scholar 
    74.Fourtune, L., Paz-Vinas, I., Loot, G., Prunier, J. G. & Blanchet, S. Lessons from the fish: a multi-species analysis reveals common processes underlying similar species-genetic diversity correlations. Freshw. Biol. 61, 1830–1845 (2016).Article 

    Google Scholar 
    75.Bertin, A. et al. Genetic variation of loci potentially under selection confounds species-genetic diversity correlations in a fragmented habitat. Mol. Ecol. 26, 431–443 (2017).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    76.Lawrence, E. R. & Fraser, D. J. Latitudinal biodiversity gradients at three levels: linking species richness, population richness and genetic diversity. Glob. Ecol. Biogeogr. 29, 770–788 (2020).Article 

    Google Scholar 
    77.Schmidt, C., Dray, S. & Garroway, C. J. Genetic and species-level biodiversity patterns are linked by demography and ecological opportunity. bioRxiv https://doi.org/10.1101/2020.06.03.132092 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    78.Hillebrand, H. On the generality of the latitudinal diversity gradient. Am. Nat. 163, 192–211 (2004).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    79.Pontarp, M. et al. The latitudinal diversity gradient: novel understanding through mechanistic eco-evolutionary models. Trends Ecol. Evol. 34, 211–223 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    80.Toews, D. P. L. & Brelsford, A. The biogeography of mitochondrial and nuclear discordance in animals. Mol. Ecol. 21, 3907–3930 (2012).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    81.Schmidt, C. & Garroway, C. J. The conservation utility of mitochondrial genetic diversity in macrogenetic research. Conserv. Genet. 22, 323–327 (2021).Article 

    Google Scholar 
    82.Gratton, P. et al. Which latitudinal gradients for genetic diversity? Trends Ecol. Evol. 32, 724–726 (2017). This response to Miraldo et al.20 identified a limitation of that article in that it did not account for the decay of genetic similarity with distance and represents the first critique of the downsides of the macrogenetic approach and the need for rigorous statistics.PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    83.Loveless, M. D. & Hamrick, J. L. Ecological determinants of genetic structure in plant populations. Annu. Rev. Ecol. Syst. 15, 65–95 (1984).Article 

    Google Scholar 
    84.Hu, Y. et al. Spatial patterns and conservation of genetic and phylogenetic diversity of wildlife in China. Sci. Adv. 7, eabd5725 (2021).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    85.Johnson, M. T. J. & Munshi-South, J. Evolution of life in urban environments. Science 358, eaam8327 (2017).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    86.Aguilar, R., Quesada, M., Ashworth, L., Herrerias-Diego, Y. & Lobo, J. Genetic consequences of habitat fragmentation in plant populations: susceptible signals in plant traits and methodological approaches. Mol. Ecol. 17, 5177–5188 (2008).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    87.Pinsky, M. L. & Palumbi, S. R. Meta-analysis reveals lower genetic diversity in overfished populations. Mol. Ecol. 23, 29–39 (2014).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    88.Leigh, D. M., Hendry, A. P., Vázquez-Domínguez, E. & Friesen, V. L. Estimated six per cent loss of genetic variation in wild populations since the industrial revolution. Evol. Appl. 12, 1505–1512 (2019). This study estimated the magnitude of the loss of genetic variation over a century-scale using microsatellite data from 91 species.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    89.Schmidt, C. & Garroway, C. J. The population genetics of urban and rural amphibians in north America. Mol. Ecol. https://doi.org/10.1111/mec.16005 (2021).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    90.Bazin, E., Glémin, S. & Galtier, N. Population size does not influence mitochondrial genetic diversity in animals. Science 312, 570–572 (2006).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    91.Galtier, N., Nabholz, B., Glémin, S. & Hurst, G. D. D. Mitochondrial DNA as a marker of molecular diversity: a reappraisal. Mol. Ecol. 18, 4541–4550 (2009).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    92.Allio, R., Donega, S., Galtier, N. & Nabholz, B. Large variation in the ratio of mitochondrial to nuclear mutation rate across animals: implications for genetic diversity and the use of mitochondrial DNA as a molecular marker. Mol. Biol. Evol. 34, 2762–2772 (2017).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    93.Almeida-Rocha, J. M., Soares, L. A. S. S., Andrade, E. R., Gaiotto, F. A. & Cazetta, E. The impact of anthropogenic disturbances on the genetic diversity of terrestrial species: a global meta-analysis. Mol. Ecol. 29, 4812–4822 (2020).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    94.Landguth, E. L. et al. Quantifying the lag time to detect barriers in landscape genetics. Mol. Ecol. 19, 4179–4191 (2010).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    95.Paz-Vinas, I. et al. Macrogenetic studies must not ignore limitations of genetic markers and scale. Ecol. Lett. 24, 1282–1284 (2021).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    96.Crandall, E. D. et al. The molecular biogeography of the Indo-Pacific: testing hypotheses with multispecies genetic patterns. Glob. Ecol. Biogeogr. 28, 943–960 (2019).Article 

    Google Scholar 
    97.Excoffier, L. & Foll, M. fastsimcoal: a continuous-time coalescent simulator of genomic diversity under arbitrarily complex evolutionary scenarios. Bioinformatics 27, 1332–1334 (2011).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    98.Guillaume, F. & Rougemont, J. Nemo: an evolutionary and population genetics programming framework. Bioinformatics 22, 2556–2557 (2006).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    99.Phillips, J. D., French, S. H., Hanner, R. H. & Gillis, D. J. HACSim: an R package to estimate intraspecific sample sizes for genetic diversity assessment using haplotype accumulation curves. PeerJ Comput. Sci. 6, e243 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    100.Gratton, P. et al. A world of sequences: can we use georeferenced nucleotide databases for a robust automated phylogeography? J. Biogeogr. 44, 475–486 (2017).Article 

    Google Scholar 
    101.Kimura, M. On the probability of fixation of mutant genes in a population. Genetics 47, 713–719 (1962).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    102.Baguette, M. & Van Dyck, H. Landscape connectivity and animal behavior: functional grain as a key determinant for dispersal. Landsc. Ecol. 22, 1117–1129 (2007).Article 

    Google Scholar 
    103.Crow, J. F. & Aoki, K. Group selection for a polygenic behavioral trait: estimating the degree of population subdivision. Proc. Natl Acad. Sci. USA 81, 6073–6077 (1984).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    104.Lanner, R. Why do trees live so long? Ageing Res. Rev. 1, 653–671 (2002).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    105.Nabholz, B., Mauffrey, J.-F., Bazin, E., Galtier, N. & Glemin, S. Determination of mitochondrial genetic diversity in mammals. Genetics 178, 351–361 (2008).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    106.Lasne, C., Heerwaarden, B., Sgrò, C. M. & Connallon, T. Quantifying the relative contributions of the X chromosome, autosomes, and mitochondrial genome to local adaptation. Evolution 73, 262–277 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    107.Phillips, J. D., Gillis, D. J. & Hanner, R. H. Incomplete estimates of genetic diversity within species: implications for DNA barcoding. Ecol. Evol. 9, 2996–3010 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    108.Humphries, P. & Winemiller, K. O. Historical impacts on river fauna, shifting baselines, and challenges for restoration. BioScience 59, 673–684 (2009).Article 

    Google Scholar 
    109.Stoffel, M. A. et al. Demographic histories and genetic diversity across pinnipeds are shaped by human exploitation, ecology and life-history. Nat. Commun. 9, 4836 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    110.Collier-Robinson, L., Rayne, A., Rupene, M., Thoms, C. & Steeves, T. Embedding indigenous principles in genomic research of culturally significant species: a conservation genomics case study. N. Z. J. Ecol. 43, 3389 (2019).
    Google Scholar 
    111.Des Roches, S., Pendleton, L. H., Shapiro, B. & Palkovacs, E. P. Conserving intraspecific variation for nature’s contributions to people. Nat. Ecol. Evol. 5, 574–582 (2021).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    112.Gonzalez, A. et al. Estimating local biodiversity change: a critique of papers claiming no net loss of local diversity. Ecology 97, 1949–1960 (2016).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    113.Pope, L. C., Liggins, L., Keyse, J., Carvalho, S. B. & Riginos, C. Not the time or the place: the missing spatio-temporal link in publicly available genetic data. Mol. Ecol. 24, 3802–3809 (2015).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    114.Yilmaz, P. et al. Minimum information about a marker gene sequence (MIMARKS) and minimum information about any (x) sequence (MIxS) specifications. Nat. Biotechnol. 29, 415–420 (2011).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    115.Sibbett, B., Rieseberg, L. H. & Narum, S. The genomic observatories metadatabase. Mol. Ecol. Resour. 20, 1453–1454 (2020).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    116.Eichenberg, D. et al. Widespread decline in Central European plant diversity across six decades. Glob. Change Biol. 27, 1097–1110 (2020).Article 

    Google Scholar 
    117.Cornwell, W. K., Pearse, W. D., Dalrymple, R. L. & Zanne, A. E. What we (don’t) know about global plant diversity. Ecography 42, 1819–1831 (2019).Article 

    Google Scholar 
    118.Li, X. et al. Plant DNA barcoding: from gene to genome. Biol. Rev. 90, 157–166 (2015).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    119.Vasquez-Gross, H. A. et al. CartograTree: connecting tree genomes, phenotypes and environment. Mol. Ecol. Resour. 13, 528–537 (2013).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    120.Lawrence, E. R. et al. Geo-referenced population-specific microsatellite data across American continents, the MacroPopGen Database. Sci. Data 6, 14 (2019). This paper reports a compilation of georeferenced vertebrate microsatellite data, summary statistics and meta-data across the Americas for 897 species and 9,090 genetically distinct populations.PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    121.Zellweger, F., De Frenne, P., Lenoir, J., Rocchini, D. & Coomes, D. Advances in microclimate ecology arising from remote sensing. Trends Ecol. Evol. 34, 327–341 (2019).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    122.Barber, P. H. et al. Advancing biodiversity research in developing countries: the need for changing paradigms. Bull. Mar. Sci. 90, 187–210 (2014).Article 

    Google Scholar 
    123.Bork, P. et al. Tara Oceans. Tara Oceans studies plankton at planetary scale. Introduction. Science 348, 873–873 (2015).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    124.Lotterhos, K. E. & Whitlock, M. C. The relative power of genome scans to detect local adaptation depends on sampling design and statistical method. Mol. Ecol. 24, 1031–1046 (2015).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    125.Hoban, S. et al. Genetic diversity targets and indicators in the CBD post-2020 Global Biodiversity Framework must be improved. Biol. Conserv. 248, 108654 (2020).Article 

    Google Scholar 
    126.Holmes, M. W. et al. Natural history collections as windows on evolutionary processes. Mol. Ecol. 25, 864–881 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    127.Boukhdoud, L. et al. First DNA sequence reference library for mammals and plants of the Eastern Mediterranean Region. Genome 64, 39–49 (2021).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    128.Colella, J. P. et al. The Open-Specimen movement. BioScience 71, 405–414 (2020).Article 

    Google Scholar 
    129.Wright, S. Correlation and causation. J. Agric. Res. 20, 557–585 (1921).
    Google Scholar 
    130.Fourtune, L. et al. Inferring causalities in landscape genetics: an extension of Wright’s causal modeling to distance matrices. Am. Nat. 191, 491–508 (2018).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    131.Paz-Vinas, I., Loot, G., Stevens, V. M. & Blanchet, S. Evolutionary processes driving spatial patterns of intraspecific genetic diversity in river ecosystems. Mol. Ecol. 24, 4586–4604 (2015).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    132.Beaumont, M. A., Zhang, W. & Balding, D. J. Approximate Bayesian computation in population genetics. Genetics 162, 2025–2035 (2002).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    133.Breiman, L. Random forests. Mach. Learn. 45, 5–32 (2001).Article 

    Google Scholar 
    134.Proença, V. et al. Global biodiversity monitoring: From data sources to Essential Biodiversity Variables. Biol. Conserv. 213, 256–263 (2017).Article 

    Google Scholar 
    135.Ve˅trovský, T. et al. A meta-analysis of global fungal distribution reveals climate-driven patterns. Nat. Commun. 10, 5142 (2019).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    136.Hanson, J. O. et al. Conservation planning for adaptive and neutral evolutionary processes. J. Appl. Ecol. 57, 2159–2169 (2020).Article 

    Google Scholar 
    137.Xuereb, A., D’Aloia, C. C., Andrello, M., Bernatchez, L. & Fortin, M. Incorporating putatively neutral and adaptive genomic data into marine conservation planning. Conserv. Biol. 35, 909–920 (2021).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    138.Carvalho, S. B., Torres, J., Tarroso, P. & Velo-Antón, G. Genes on the edge: a framework to detect genetic diversity imperiled by climate change. Glob. Change Biol. 25, 4034–4047 (2019).Article 

    Google Scholar 
    139.Adams, W. M. & Sandbrook, C. Conservation, evidence and policy. Oryx 47, 329–335 (2013).Article 

    Google Scholar 
    140.Laikre, L. et al. Post-2020 goals overlook genetic diversity. Science 367, 1083.2–1085 (2020).Article 
    CAS 

    Google Scholar 
    141.Thomson, A. I. et al. Charting a course for genetic diversity in the UN Decade of Ocean Science. Evol. Appl. 14, 1497–1518 (2021).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    142.Hoban, S. M. et al. Bringing genetic diversity to the forefront of conservation policy and management. Conserv. Genet. Resour. 5, 593–598 (2013).Article 

    Google Scholar 
    143.Carroll, S. R. et al. The CARE principles for indigenous data governance. Data Sci. J. 19, 43 (2020).Article 

    Google Scholar 
    144.Fargeot, L. et al. Patterns of epigenetic diversity in two sympatric fish species: genetic vs. environmental determinants. Genes 12, 107 (2021).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    145.Gaggiotti, O. E. et al. Diversity from genes to ecosystems: a unifying framework to study variation across biological metrics and scales. Evol. Appl. 11, 1176–1193 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    146.Waples, R. S., Antao, T. & Luikart, G. Effects of overlapping generations on linkage disequilibrium estimates of effective population size. Genetics 197, 769–780 (2014).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    147.Waples, R. S. & Yokota, M. Temporal estimates of effective population size in species with overlapping generations. Genetics 175, 219–233 (2007).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    148.Antao, T., Pérez-Figueroa, A. & Luikart, G. Early detection of population declines: high power of genetic monitoring using effective population size estimators. Evol. Appl. 4, 144–154 (2011).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    149.Cornuet, J. M. & Luikart, G. Description and power analysis of two tests for detecting recent population bottlenecks from allele frequency data. Genetics 144, 2001–2014 (1996).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    150.Phillips, J. D., Gwiazdowski, R. A., Ashlock, D. & Hanner, R. An exploration of sufficient sampling effort to describe intraspecific DNA barcode haplotype diversity: examples from the ray-finned fishes (Chordata: Actinopterygii). DNA Barcodes 3, 66–73 (2015).Article 

    Google Scholar 
    151.Tajima, F. The effect of change in population size on DNA polymorphism. Genetics 123, 597–601 (1989).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    152.Jordan, R., Breed, M. F., Prober, S. M., Miller, A. D. & Hoffmann, A. A. How well do revegetation plantings capture genetic diversity? Biol. Lett. 15, 20190460 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    153.Holderegger, R. & Di Giulio, M. The genetic effects of roads: a review of empirical evidence. Basic. Appl. Ecol. 11, 522–531 (2010).Article 

    Google Scholar 
    154.Hale, M. L., Burg, T. M. & Steeves, T. E. Sampling for microsatellite-based population genetic studies: 25 to 30 individuals per population is enough to accurately estimate allele frequencies. PLoS One 7, e45170 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    155.Jackson, T. M., Roegner, G. C. & O’Malley, K. G. Evidence for interannual variation in genetic structure of Dungeness crab (Cancer magister) along the California Current System. Mol. Ecol. 27, 352–368 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    156.Hoban, S. et al. Comparative evaluation of potential indicators and temporal sampling protocols for monitoring genetic erosion. Evol. Appl. 7, 984–998 (2014).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    157.Anderson, C. N. K., Ramakrishnan, U., Chan, Y. L. & Hadly, E. A. Serial SimCoal: a population genetics model for data from multiple populations and points in time. Bioinformatics 21, 1733–1734 (2005).CAS 
    PubMed 
    Article 

    Google Scholar 
    158.Hortal, J. et al. Seven shortfalls that beset large-scale knowledge of biodiversity. Annu. Rev. Ecol. Evol. Syst. 46, 523–549 (2015).Article 

    Google Scholar 
    159.Elbrecht, V., Vamos, E. E., Steinke, D. & Leese, F. Estimating intraspecific genetic diversity from community DNA metabarcoding data. PeerJ 6, e4644 (2018).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    160.Shum, P. & Palumbi, S. R. Testing small-scale ecological gradients and intraspecific differentiation for hundreds of kelp forest species using haplotypes from metabarcoding. Mol. Ecol. https://doi.org/10.1111/mec.15851 (2021).Article 
    PubMed 

    Google Scholar 
    161.Yamahara, K. M. et al. In situ autonomous acquisition and preservation of marine environmental DNA using an autonomous underwater vehicle. Front. Mar. Sci. 6, 373 (2019).Article 

    Google Scholar 
    162.Breed, M. F. et al. Mating patterns and pollinator mobility are critical traits in forest fragmentation genetics. Heredity 115, 108–114 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    163.Hoban, S., Gaggiotti, O. & Bertorelle, G. Sample Planning Optimization Tool for conservation and population Genetics (SPOTG): a software for choosing the appropriate number of markers and samples. Methods Ecol. Evol. 4, 299–303 (2013).Article 

    Google Scholar 
    164.Peck, S. L. Simulation as experiment: a philosophical reassessment for biological modeling. Trends Ecol. Evol. 19, 530–534 (2004).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    165.Reid, B. N., Naro-Maciel, E., Hahn, A. T., FitzSimmons, N. N. & Gehara, M. Geography best explains global patterns of genetic diversity and postglacial co-expansion in marine turtles. Mol. Ecol. 28, 3358–3370 (2019).PubMed 
    PubMed Central 

    Google Scholar 
    166.Kardos, M., Luikart, G. & Allendorf, F. W. Measuring individual inbreeding in the age of genomics: marker-based measures are better than pedigrees. Heredity 115, 63–72 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    167.Willing, E.-M., Dreyer, C. & van Oosterhout, C. Estimates of genetic differentiation measured by FST do not necessarily require large sample sizes when using many SNP markers. PLoS One 7, e42649 (2012).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    168.Shafer, A. B. A. et al. Bioinformatic processing of RAD-seq data dramatically impacts downstream population genetic inference. Methods Ecol. Evol. 8, 907–917 (2017).Article 

    Google Scholar 
    169.Cariou, M., Duret, L. & Charlat, S. How and how much does RAD-seq bias genetic diversity estimates? BMC Evol. Biol. 16, 240 (2016).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    170.De-Kayne, R. et al. Sequencing platform shifts provide opportunities but pose challenges for combining genomic data sets. Mol. Ecol. Resour. 21, 653–660 (2021).PubMed 
    Article 
    CAS 

    Google Scholar 
    171.Leigh, D. M., Lischer, H. E. L., Grossen, C. & Keller, L. F. Batch effects in a multiyear sequencing study: false biological trends due to changes in read lengths. Mol. Ecol. Resour. 18, 778–788 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    172.Linck, E. & Battey, C. J. Minor allele frequency thresholds strongly affect population structure inference with genomic data sets. Mol. Ecol. Resour. 19, 639–647 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    173.Benestan, L. M. et al. Conservation genomics of natural and managed populations: building a conceptual and practical framework. Mol. Ecol. 25, 2967–2977 (2016).PubMed 
    Article 

    Google Scholar 
    174.Feng, S. et al. Dense sampling of bird diversity increases power of comparative genomics. Nature 587, 252–257 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    175.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 
    PubMed Central 
    Article 

    Google Scholar  More

  • in

    Quantitative mapping and spectroscopic characterization of particulate organic matter fractions in soil profiles with imaging VisNIR spectroscopy

    1.Smith, P. et al. The changing faces of soil organic matter research. Eur. J. Soil Sci. 69, 23–30. https://doi.org/10.1111/ejss.12500 (2018).Article 

    Google Scholar 
    2.Kögel-Knabner, I. The macromolecular organic composition of plant and microbial residues as inputs to soil organic matter. Soil Biol. Biochem. 34, 139–162 (2002).Article 

    Google Scholar 
    3.Schmidt, M. W. I. et al. Persistence of soil organic matter as an ecosystem property. Nature 478, 49–56. https://doi.org/10.1038/nature10386 (2011).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    4.Lehmann, J. & Kleber, M. The contentious nature of soil organic matter. Nature 528, 60–68. https://doi.org/10.1038/nature16069 (2015).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    5.Lehmann, J. et al. Persistence of soil organic carbon caused by functional complexity. Nat. Geosci. 13, 529–534. https://doi.org/10.1038/s41561-020-0612-3 (2020).ADS 
    CAS 
    Article 

    Google Scholar 
    6.Dong, L. et al. Effect of grazing exclusion and rotational grazing on labile soil organic carbon in north China. Eur. J. Soil Sci. https://doi.org/10.1111/ejss.12952 (2020).Article 

    Google Scholar 
    7.Leifeld, J. & Kogel-Knabner, I. Soil organic matter fractions as early indicators for carbon stock changes under different land-use?. Geoderma 124, 143–155 (2005).ADS 
    CAS 
    Article 

    Google Scholar 
    8.Poeplau, C. & Don, A. Sensitivity of soil organic carbon stocks and fractions to different land-use changes across Europe. Geoderma 192, 189–201. https://doi.org/10.1016/j.geoderma.2012.08.003 (2013).ADS 
    CAS 
    Article 

    Google Scholar 
    9.Besnard, E., Chenu, C., Balesdent, J., Puget, P. & Arrouays, D. Fate of particulate organic matter in soil aggregates during cultivation. Eur. J. Soil Sci. 47, 495–503 (1996).CAS 
    Article 

    Google Scholar 
    10.von Lützow, M. et al. Stabilization of organic matter in temperate soils: mechanisms and their relevance under different soil conditions—a review. Eur. J. Soil Sci. 57, 426–445. https://doi.org/10.1111/j.1365-2389.2006.00809.x (2006).CAS 
    Article 

    Google Scholar 
    11.Peng, X. H., Zhu, Q. H., Zhang, Z. B. & Hallett, P. D. Combined turnover of carbon and soil aggregates using rare earth oxides and isotopically labelled carbon as tracers. Soil Biol. Biochem. 109, 81–94. https://doi.org/10.1016/j.soilbio.2017.02.002 (2017).CAS 
    Article 

    Google Scholar 
    12.Dynarski, K. A., Bossio, D. A. & Scow, K. M. Dynamic stability of soil carbon: reassessing the “permanence” of soil carbon sequestration. Front. Environ. Sci. 8, 1. https://doi.org/10.3389/fenvs.2020.514701 (2020).Article 

    Google Scholar 
    13.Basile-Doelsch, I., Balesdent, J. & Pellerin, S. Reviews and syntheses: The mechanisms underlying carbon storage in soil. Biogeosci. Discuss. https://doi.org/10.5194/bg-2020-49 (2020).14.Poeplau, C. et al. Isolating organic carbon fractions with varying turnover rates in temperate agricultural soils—a comprehensive method comparison. Soil Biol. Biochem. 125, 10–26. https://doi.org/10.1016/j.soilbio.2018.06.025 (2018).CAS 
    Article 

    Google Scholar 
    15.Rumpel, C. & Kögel-Knabner, I. Deep soil organic matter-a key but poorly understood component of terrestrial C cycle. Plant Soil 338, 143–158. https://doi.org/10.1007/s11104-010-0391-5 (2011).CAS 
    Article 

    Google Scholar 
    16.Steffens, M., Kölbl, A., Schörk, E., Gschrey, B. & Kögel-Knabner, I. Distribution of soil organic matter between fractions and aggregate size classes in grazed semiarid steppe soil profiles. Plant Soil 338, 63–81. https://doi.org/10.1007/s11104-010-0594-9 (2011).CAS 
    Article 

    Google Scholar 
    17.Soriano-Disla, J. M., Janik, L. J., Rossel, R. A. V., Macdonald, L. M. & McLaughlin, M. J. The performance of visible, near-, and mid-infrared reflectance spectroscopy for prediction of soil physical, chemical, and biological properties. Appl. Spectrosc. Rev. 49, 139–186. https://doi.org/10.1080/05704928.2013.811081 (2014).ADS 
    CAS 
    Article 

    Google Scholar 
    18.Stenberg, B., Rossel, R. A. V., Mouazen, A. M. & Wetterlind, J. Visible and near infrared spectroscopy in soil science. Adv. Agron. 107(107), 163–215. https://doi.org/10.1016/s0065-2113(10)07005-7 (2010).CAS 
    Article 

    Google Scholar 
    19.Mouazen, A. M., Steffens, M. & Borisover, M. Reflectance and fluorescence spectroscopy in soil science-Current and future research and developments. Soil Tillage Res. 155, 448–449 (2016).Article 

    Google Scholar 
    20.Viscarra Rossel, R. A. & Bouma, J. Soil sensing: A new paradigm for agriculture. Agric. Syst. 148, 71–74. https://doi.org/10.1016/j.agsy.2016.07.001 (2016).Article 

    Google Scholar 
    21.Nocita, M. et al.. Soil spectroscopy: An alternative to wet chemistry for soil monitoring. Adv. Agron. 132, 139–159 (2015)Article 

    Google Scholar 
    22.Gholizadeh, A., Boruvka, L., Saberioon, M. & Vasat, R. Visible, near-infrared, and mid-infrared spectroscopy applications for soil assessment with emphasis on soil organic matter content and quality: state-of-the-art and key issues. Appl. Spectrosc. 67, 1349–1362. https://doi.org/10.1366/13-07288 (2013).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    23.Hermansen, C. et al. Complete soil texture is accurately predicted by visible near-infrared spectroscopy. Soil Sci. Soc. Am. J. 81, 758–769. https://doi.org/10.2136/sssaj2017.02.0066 (2017).ADS 
    CAS 
    Article 

    Google Scholar 
    24.Zimmermann, M., Leifeld, J. & Fuhrer, J. Quantifying soil organic carbon fractions by infrared-spectroscopy. Soil Biol. Biochem. 39, 224–231. https://doi.org/10.1016/j.soilbio.2006.07.010 (2007).CAS 
    Article 

    Google Scholar 
    25.Madhavan, D. B. et al. Rapid prediction of particulate, humus and resistant fractions of soil organic carbon in reforested lands using infrared spectroscopy. J. Environ. Manage. 193, 290–299. https://doi.org/10.1016/jjenvman.2017.02.013 (2017).CAS 
    Article 
    PubMed 

    Google Scholar 
    26.St. Luce, M. et al. Rapid determination of soil organic matter quality indicators using visible near infrared reflectance spectroscopy. Geoderma 232–234, 449–458. https://doi.org/10.1016/j.geoderma.2014.05.023 (2014).ADS 
    CAS 
    Article 

    Google Scholar 
    27.Terhoeven-Urselmans, T., Michel, K., Helfrich, M., Flessa, H. & Ludwig, B. Near-infrared spectroscopy can predict the composition of organic matter in soil and litter. J. Plant Nutr. Soil Sci. 169, 168–174. https://doi.org/10.1002/jpln.200521712 (2006).CAS 
    Article 

    Google Scholar 
    28.Margenot, A., O’Neill, T., Sommer, R. & Akella, V. Predicting soil permanganate oxidizable carbon (PDXC) by coupling DRIFT spectroscopy and artificial neural networks (ANN). Comput. Electron. Agric. https://doi.org/10.1016/j.compag.2019.105098 (2020).Article 

    Google Scholar 
    29.Fang, Q. et al. Visible and near-infrared reflectance spectroscopy for investigating soil mineralogy: a review. J. Spectrosc. https://doi.org/10.1155/2018/3168974 (2018).Article 

    Google Scholar 
    30.Shi, P., Castaldi, F., van Wesemael, B. & van Oost, K. Vis-NIR spectroscopic assessment of soil aggregate stability and aggregate size distribution in the Belgian Loam Belt. Geoderma https://doi.org/10.1016/j.geoderma.2019.113958 (2020).Article 

    Google Scholar 
    31.Canasveras, J. C., Barron, V., del Campillo, M. C., Torrent, J. & Gomez, J. A. Estimation of aggregate stability indices in Mediterranean soils by diffuse reflectance spectroscopy. Geoderma 158, 78–84. https://doi.org/10.1016/j.geoderma.2009.09.004 (2010).ADS 
    CAS 
    Article 

    Google Scholar 
    32.Hermansen, C. et al. Visible-near-infrared spectroscopy can predict the clay/organic carbon and mineral fines/organic carbon ratios. Soil Sci. Soc. Am. J. 80, 1486–1495. https://doi.org/10.2136/sssaj2016.05.0159 (2016).ADS 
    CAS 
    Article 

    Google Scholar 
    33.Jaconi, A., Don, A. & Freibauer, A. Prediction of soil organic carbon at the country scale: stratification strategies for near-infrared data. Eur. J. Soil Sci. 68, 919–929. https://doi.org/10.1111/ejss.12485 (2017).CAS 
    Article 

    Google Scholar 
    34.Jaconi, A., Vos, C. & Don, A. Near infrared spectroscopy as an easy and precise method to estimate soil texture. Geoderma 337, 906–913. https://doi.org/10.1016/j.geoderma.2018.10.038 (2019).ADS 
    Article 

    Google Scholar 
    35.Riedel, F., Denk, M., Muller, I., Barth, N. & Glasser, C. Prediction of soil parameters using the spectral range between 350 and 15,000 nm: A case study based on the Permanent Soil Monitoring Program in Saxony Germany. Geoderma 315, 188–198. https://doi.org/10.1016/j.geoderma.2017.11.027 (2018).ADS 
    CAS 
    Article 

    Google Scholar 
    36.Clairotte, M. et al. National calibration of soil organic carbon concentration using diffuse infrared reflectance spectroscopy. Geoderma 276, 41–52. https://doi.org/10.1016/j.geoderma.2016.04.021 (2016).ADS 
    CAS 
    Article 

    Google Scholar 
    37.Orgiazzi, A., Ballabio, C., Panagos, P., Jones, A. & Fernandez-Ugalde, O. LUCAS soil, the largest expandable soil dataset for Europe: a review. Eur. J. Soil Sci. 69, 140–153. https://doi.org/10.1111/ejss.12499 (2018).Article 

    Google Scholar 
    38.Stevens, A., Nocita, M., Toth, G., Montanarella, L. & van Wesemael, B. Prediction of soil organic carbon at the european scale by visible and near infrared reflectance spectroscopy. PLoS ONE 8, 1. https://doi.org/10.1371/journal.pone.0066409 (2013).CAS 
    Article 

    Google Scholar 
    39.Nocita, M. et al. Prediction of soil organic carbon content by diffuse reflectance spectroscopy using a local partial least square regression approach. Soil Biol. Biochem. 68, 337–347. https://doi.org/10.1016/j.soilbio.2013.10.022 (2014).CAS 
    Article 

    Google Scholar 
    40.Viscarra Rossel, R. A. & Hicks, W. S. Soil organic carbon and its fractions estimated by visible-near infrared transfer functions. Europ. J. Soil Sci. 66, 438–450. https://doi.org/10.1111/ejss.12237 (2015).CAS 
    Article 

    Google Scholar 
    41.Steffens, M. & Buddenbaum, H. Laboratory imaging spectroscopy of a stagnic Luvisol profile – High resolution soil characterisation, classification and mapping of elemental concentrations. Geoderma 195–196, 122–132 (2013).ADS 
    Article 

    Google Scholar 
    42.Steffens, M., Kohlpaintner, M. & Buddenbaum, H. Fine spatial resolution mapping of soil organic matter quality in a Histosol profile. Eur. J. Soil Sci. 65, 827–839. https://doi.org/10.1111/ejss.12182 (2014).CAS 
    Article 

    Google Scholar 
    43.Hobley, E., Steffens, M., Bauke, S. L. & Kogel-Knabner, I. Hotspots of soil organic carbon storage revealed by laboratory hyperspectral imaging. Sci. Rep. 8, 1. https://doi.org/10.1038/s41598-018-31776-w (2018).ADS 
    CAS 
    Article 

    Google Scholar 
    44.Lucas, M., Pihlap, E., Steffens, M., Vetterlein, D. & Kogel-Knabner, I. Combination of imaging infrared spectroscopy and x-ray computed microtomography for the investigation of bio- and physicochemical processes in structured soils. Front. Environ. Sci. 8, 1. https://doi.org/10.3389/fenvs.2020.00042 (2020).Article 

    Google Scholar 
    45.Mueller, C. W., Steffens, M. & Buddenbaum, H. Permafrost soil complexity evaluated by laboratory imaging Vis-NIR spectroscopy. Eur. J. Soil Sci. https://doi.org/10.1111/ejss.12927 (2019).Article 

    Google Scholar 
    46.Schreiner, S., Buddenbaum, H., Emmerling, C. & Steffens, M. VNIR/SWIR laboratory imaging spectroscopy for wall-to-wall mapping of elemental concentrations in soil cores. Photogrammetrie Fernerkundung Geoinformation https://doi.org/10.1127/pfg/2015/0279 (2015).Article 

    Google Scholar 
    47.Askari, M. S., O’Rourke, S. M. & Holden, N. M. A comparison of point and imaging visible-near infrared spectroscopy for determining soil organic carbon. J. Near Infrared Spectrosc. 26, 133–146. https://doi.org/10.1177/0967033518766668 (2018).ADS 
    CAS 
    Article 

    Google Scholar 
    48.O’Rourke, S. M. & Holden, N. M. Determination of soil organic matter and carbon fractions in forest top soils using spectral data acquired from visible-near infrared hyperspectral images. Soil Sci. Soc. Am. J. 76, 586–596. https://doi.org/10.2136/sssaj2011.0053 (2012).ADS 
    CAS 
    Article 

    Google Scholar 
    49.Buddenbaum, H. & Steffens, M. Laboratory imaging spectroscopy of soil profiles. J. Spectral Imag. 2, 1. https://doi.org/10.1255/jsi.2011.a2 (2011).Article 

    Google Scholar 
    50.Buddenbaum, H. & Steffens, M. Mapping the distribution of chemical properties in soil profiles using laboratory imaging spectroscopy SVM and PLS regression. EARSeL eProc. 11, 25–32 (2012).
    Google Scholar 
    51.Poeplau, C. et al. Stocks of organic carbon in German agricultural soils-Key results of the first comprehensive inventory. J. Plant Nutr. Soil Sci. 183, 665–681. https://doi.org/10.1002/jpln.202000113 (2020).CAS 
    Article 

    Google Scholar 
    52.Viscarra Rossel, R. A., Lobsey, C. R., Sharman, C., Flick, P. & McLachlan, G. Novel proximal sensing for monitoring soil organic C stocks and condition. Environ. Sci. Technol. 51, 5630–5641. https://doi.org/10.1021/acs.est.7b00889 (2017).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    53.IUSS Working Group WRB. World reference base for soil resources 2006. Vol. 103 (FAO, 2006).
    Google Scholar 
    54.Steffens, M., Kölbl, A., Totsche, K. U. & Kögel-Knabner, I. Grazing effects on soil chemical and physical properties in a semiarid steppe of Inner Mongolia (P.R. China). Geoderma 143, 63–72 (2008).ADS 
    CAS 
    Article 

    Google Scholar 
    55.Hoffmann, C. et al. Effects of grazing and climate variability on grassland ecosystem functions in Inner Mongolia: Synthesis of a 6-year grazing experiment. J. Arid Environ. 135, 50–63. https://doi.org/10.1016/j.jaridenv.2016.08.003 (2016).ADS 
    Article 

    Google Scholar 
    56.FAO. Guidelines for soil description. 4th edition edn, (FAO, 2006).
    Google Scholar 
    57.Lenhard, K., Baumgartner, A. & Schwarzmaier, T. Independent laboratory characterization of NEO HySpex imaging spectrometers VNIR-1600 and SWIR-320m-e. IEEE Trans. Geosci. Remote Sens. 53, 1828–1841. https://doi.org/10.1109/TGRS.2014.2349737 (2015).ADS 
    Article 

    Google Scholar 
    58.Peddle, D. R., White, H. P., Soffer, R. J., Miller, J. R. & LeDrew, E. F. Reflectance processing of remote sensing spectroradiometer data. Comput. Geosci. 27, 203–213 (2001).ADS 
    Article 

    Google Scholar 
    59.Rogass, C. et al. Translational imaging spectroscopy for proximal sensing. Sensors 17, 1857 (2017).Article 

    Google Scholar 
    60.Steffens, M., Kölbl, A. & Kögel-Knabner, I. Alteration of soil organic matter pools and aggregation in semi-arid steppe topsoils as driven by organic matter input. Eur. J. Soil Sci. 60, 198–212. https://doi.org/10.1111/j.1365-2389.2008.01104.x (2009).CAS 
    Article 

    Google Scholar 
    61.Golchin, A., Oades, J. M., Skjemstad, J. O. & Clarke, P. Soil-structure and carbon cycling. Aust. J. Soil Res. 32, 1043–1068 (1994).Article 

    Google Scholar 
    62.Christensen, B. T. Physical fractionation of soil and structural and functional complexity in organic matter turnover. Eur. J. Soil Sci. 52, 345–353 (2001).CAS 
    Article 

    Google Scholar 
    63.Schmidt, M. W. I., Rumpel, C. & Kögel-Knabner, I. Evaluation of an ultrasonic dispersion procedure to isolate primary organomineral complexes from soils. Eur. J. Soil Sci. 50, 87–94 (1999).Article 

    Google Scholar 
    64.Steffens, M. et al. Spatial variability of topsoils and vegetation in a grazed steppe ecosystem in Inner Mongolia (PR China). J. Plant Nutr. Soil Sci. 172, 78–90. https://doi.org/10.1002/jpln.200700309 (2009).CAS 
    Article 

    Google Scholar 
    65.Six, J., Gregorich, E. & Koegel-Knabner, I. Landmark Papers: No. 1. Tisdall, J. M. & Oades, J. M. 1982. Organic matter and water-stable aggregates in soils. Journal of Soil Science, 33, 141–163 Commentary on the impact of the impact of Tisdall & Oades (1982): by J. Six, E. G. Gregorich & I. Kogel-Knabner. European Journal of Soil Science 63, 3–7 (2012).66.Six, J., Bossuyt, H., Degryze, S. & Denef, K. A history of research on the link between (micro)aggregates, soil biota, and soil organic matter dynamics. Soil Tillage Res. 79, 7–31 (2004).Article 

    Google Scholar 
    67.Wiesmeier, M. et al. Aggregate stability and physical protection of soil organic carbon in semi-arid steppe soils. Eur. J. Soil Sci. 63, 22–31. https://doi.org/10.1111/j.1365-2389.2011.01418.x (2012).CAS 
    Article 

    Google Scholar 
    68.McSherry, M. E. & Ritchie, M. E. Effects of grazing on grassland soil carbon: a global review. Glob. Change Biol. 19, 1347–1357. https://doi.org/10.1111/gcb.12144 (2013).ADS 
    Article 

    Google Scholar 
    69.Viscarra Rossel, R. A. & Behrens, T. Using data mining to model and interpret soil diffuse reflectance spectra. Geoderma 158, 46–54. https://doi.org/10.1016/j.geoderma.2009.12.025 (2010).ADS 
    CAS 
    Article 

    Google Scholar 
    70.Ben-Dor, E., Inbar, Y. & Chen, Y. The reflectance spectra of organic matter in the visible near-infrared and short wave infrared region (400–2500 nm) during a controlled decomposition process. Remote Sens. Environ. 61, 1–15 (1997).ADS 
    Article 

    Google Scholar 
    71.Delegido, J., Verrelst, J., Rivera, J. P., Ruiz-Verdu, A. & Moreno, J. Brown and green LAI mapping through spectral indices. Int. J. Appl. Earth Obs. Geoinf. 35, 350–358. https://doi.org/10.1016/j.jag.2014.10.001 (2015).ADS 
    Article 

    Google Scholar 
    72.Viscarra Rossel, R. A., McGlynn, R. N. & McBratney, A. B. Determing the composition of mineral-organic mixes using UV-vis-NIR diffuse reflectance spectroscopy. Geoderma 137, 70–82. https://doi.org/10.1016/j.geoderma.2006.07.004 (2006).ADS 
    CAS 
    Article 

    Google Scholar 
    73.Ben-Dor, E. et al. Imaging spectrometry for soil applications. Adv. Agronomy 97, 321. https://doi.org/10.1016/s0065-2113(07)00008-9 (2008).CAS 
    Article 

    Google Scholar  More

  • in

    Stochasticity in host-parasitoid models informs mechanisms regulating population dynamics

    1.Benincà, E., Ballantine, B., Ellner, S.P. & Huisman, J. Species fluctuations sustained by a cyclic succession at the edge of chaos. Proc. Natl. Acad. Sci. 112, 6389–6394 (2015).2.Lande, R. et al. Stochastic Population Dynamics in Ecology and Conservation (Oxford University Press, 2003).3.Bonsall, M. B. & Hastings, A. Demographic and environmental stochasticity in predator-prey metapopulation dynamics. J. Anim. Ecol. 73, 1043–1055 (2004).Article 

    Google Scholar 
    4.Nisbet, R. M. & Gurney, W. Modelling Fluctuating Populations: reprint of first Edition (1982) (Blackburn Press, 2003).5.Hening, A. & Nguyen, D. H. Stochastic Lotka–Volterra food chains. J. Math. Biol. 77(1), 135–163 (2018).MathSciNet 
    Article 

    Google Scholar 
    6.Khasminskii, R. et al. Long term behavior of solutions of the Lotka–Volterra system under small random perturbations. Ann. Appl. Probab. 11(3), 952–963 (2001).MathSciNet 
    Article 

    Google Scholar 
    7.Huang, W., Hauert, C. & Traulsen, A. Stochastic game dynamics under demographic fluctuations. Proc. Natl. Acad. Sci., 112(29), 9064–9069 (2015).8.Suvinthra, M. & Balachandran, K. Large deviations for the stochastic predator-prey model with nonlinear functional response. J. Appl. Probab. 54(2), 507 (2017).MathSciNet 
    Article 

    Google Scholar 
    9.Zou, X. & Wang, K. Optimal harvesting for a stochastic Lotka–Volterra predator-prey system with jumps and nonselective harvesting hypothesis. Optim. Control Appl. Methods 37(4), 641–662 (2016).MathSciNet 
    Article 

    Google Scholar 
    10.Larsen, A. E. Modeling multiple nonconsumptive effects in simple food webs: a modified Lotka–Volterra approach. Behav. Ecol. 23(5), 1115–1125 (2012).Article 

    Google Scholar 
    11.Singh, A. Stochastic dynamics of consumer-resource interactions. bioRxiv (2021).12.Bashkirtseva, I., Ryashko, L. & Tsvetkov, I. Analysis of stochastic phenomena in ricker-type population model with delay. In AIP Conference Proceedings, vol. 1895, p. 050003 (2017).13.Halley, J. M. & Iwasa, Y. Extinction rate of a population under both demographic and environmental stochasticity. Theor. Popul. Biol. 53, 1–15 (1998).CAS 
    Article 

    Google Scholar 
    14.Hassell, M. P. (Oxford University Press, 2000).15.Gurney, W. S. C. & Nisbet, R. M. Ecological Dynamics (Oxford University Press, 1998).16.Murdoch, W. W., Briggs, C. J. & Nisbet, R. M. Consumer-Resouse Dynamics (Princeton University Press, 2003).17.Kakehashi, N., Suzuki, Y. & Iwasa, Y. Niche overlap of parasitoids in host-parasitoid systems: its consequence to single versus multiple introduction controversy in biological control. J. Appl. Ecol. 21, 115–131 (1984).Article 

    Google Scholar 
    18.May, R. M. & Hassell, M. P. The dynamics of multiparasitoid-host interactions. Am. Nat. 117(3), 234–261 (1981).MathSciNet 
    Article 

    Google Scholar 
    19.Hackett-Jones, E., Cobbold, C. & White, A. Coexistence of multiple parasitoids on a single host due to differences in parasitoid phenology. Theor. Ecol. 2(1), 19–31 (2009).Article 

    Google Scholar 
    20.van Velzen, E., Pérez-Vila, S. & Etienne, R. S. The role of within-host competition for coexistence in multiparasitoid-host systems. Am. Nat. 187(1), 48–59 (2016).Article 

    Google Scholar 
    21.Nicholson, A. & Bailey, V. A. The balance of animal populations. Part 1. Proc. Zool. Soc. Lond. 3, 551–598 (1935).Article 

    Google Scholar 
    22.Singh, A., Murdoch, W. W. & Nisbet, R. M. Skewed attacks, stability, and host suppression. Ecology 90(6), 1679–1686 (2009).Article 

    Google Scholar 
    23.Bešo, E., Kalabušić, S., Mujić, N. & Pilav, E. Stability of a certain class of a host-parasitoid models with a spatial refuge effect. J. Biol. Dyn. 14(1), 1–31 (2020).MathSciNet 
    Article 

    Google Scholar 
    24.Taylor, A. D. Heterogeneity in host-parasitoid interactions: ‘aggregation of risk’ and the (cv^2 >1) rule. Trends Ecol. Evolu. 8, 400–405 (1993).25.Hassell, M. P., May, R. M., Pacala, S. W. & Chesson, P. L. The persistence of host-parasitoid associations in patchy environments. I. A general criterion. Am. Nat. 138, 568–583 (1991).Article 

    Google Scholar 
    26.Pacala, S. W. & Hassell, M. P. The persistence of host- parasitoid associations in patchy environments. II. Evaluation of field data. Am. Nat. 138, 584–605 (1991).Article 

    Google Scholar 
    27.Bernstein, C. Density dependence and the stability of host-parasitoid systems. Oikos 47, 176–180 (1986).Article 

    Google Scholar 
    28.Free, C., Beddington, J. & Lawton, J. On the inadequacy of simple models of mutual interference for parasitism and predation. J. Anim. Ecol. 46, 543–554 (1977).Article 

    Google Scholar 
    29.Rogers, D. & Hassell, M. General models for insect parasite and predator searching behaviour: interference. J. Anim. Ecol. 43, 239–253 (1974).Article 

    Google Scholar 
    30.Reeve, J. D., Cronin, J. T. & Strong, D. R. Parasitoid aggregation and the stabilization of a salt marsh host- parasitoid system. Ecology 75, 288–295 (1994).Article 

    Google Scholar 
    31.Rohani, P., Godfray, H. C. J. & Hassell, M. P. Aggregation and the dynamics of host-parasitoid systems: A discrete-generation model with within-generation redistribution. Am. Nat. 144(3), 491–509 (1994).Article 

    Google Scholar 
    32.May, R. M. Host-parasitoid systems in patchy environments: A phenomenological model. J. Anim. Ecol. 47, 833–844 (1978).Article 

    Google Scholar 
    33.Singh, A. & Nisbet, R. M. Semi-discrete host-parasitoid models. J. Theor. Biol. 247(4), 733–742 (2007).ADS 
    MathSciNet 
    Article 

    Google Scholar 
    34.Singh, A. Population dynamics of multi-host communities attacked by a common parasitoid, bioRxiv (2021).35.Singh, A. & Emerick, B. Hybrid systems framework for modeling host-parasitoid population dynamics. In 2020 59th IEEE Conference on Decision and Control (CDC), 4628–4633 (2020).36.Lane, S. D., St, C. M. Mary, & Getz, W. M. Coexistence of attack-limited parasitoids sequentially exploiting the same resource and its implications for biological control. Ann. Zool. Fenn. 43, 17–34 (2006).
    Google Scholar 
    37.Pedersen, B. S. & Mills, N. J. Single vs. multiple introduction in biological control: the roles of parasitoid efficiency, antagonism and niche overlap. J. Appl. Ecol. 41(5), 973–984 (2004).Article 

    Google Scholar 
    38.Abram, P. K., Brodeur, J., Burte, V. & Boivin, G. Parasitoid-induced host egg abortion; an underappreciated component of biological control services provided by egg parasitoids. Biol. Control 98, 52–60 (2016).Article 

    Google Scholar 
    39.Jervis, M. A., Hawkin, B. A. & Kidd, N. A. C. The usefulness of destructive host-feeding parasitoids in classical biological control: Theory and observation conflict. Ecol. Entomol. 21(1), 41–46 (1996).Article 

    Google Scholar 
    40.Okuyama, T. Density-dependent distribution of parasitism risk among underground hosts. Bull. Entomol. Res. 109(4), 528–533 (2019).CAS 
    Article 

    Google Scholar 
    41.Cobbold, C. A., Roland, J. & Lewis, M. A. The impact of parasitoid emergence time on host-parastioid population dynamics. Theor. Popul. Biol. 75(2), 201–215 (2009).Article 

    Google Scholar 
    42.Liere, H., Jackson, D. & Vandermeer, J. Ecological complexity in a coffee agroecosystem: Spatial heterogeneity, popoulation persistence and biological control. PLoS One 7(9), e45508 (2012).43.Zoroa, N., Lesigne, E., Fernandez-Saez, M.J., Zoroa, P. & Casas, J. The coupon collector urn model with unequal probabilities in ecology and evolution, J. R. Soc. Interface 14, 20160643 (2017).44.Singh, A. & Emerick, B. Generalized stability conditions for host-parasitoid population dynamics: Implications for biological control. Ecol. Model. 456, 109656 (2021).45.Ledder, G. Mathematics for the Life Sciences: Calculus, Modeling, Probability, and Dynamical Systems (Springer Science & Business Media, 2013).46.Elaydi, S. An Introduction to Difference Equations (Springer, 1996).47.Gajic, Z. & Qureshi, M. T. J. Lyapunov matrix equation in system stability and control. (Courier Corporation, 2008).48.Singh, A. & Nisbet, R. M. Variation in risk in single-species discrete-time models. Math. Biosci. Eng. 5, 859–875 (2008).MathSciNet 
    Article 

    Google Scholar 
    49.Emerick, B. K. & Singh, A. The effects of host-feeding on stability of discrete-time host-parasitoid population dynamic models. Math. Biosci. 272, 54–63 (2016).MathSciNet 
    Article 

    Google Scholar 
    50.Pachepsky, E., Nisbet, R. M. & Murdoch, W. W. Between discrete and continuous: Consumer-resource dynamics with synchronized reproduction. Ecology 89(1), 280–288 (2007).Article 

    Google Scholar 
    51.Emerick, B. K., Singh, A & Chhetri, S. R. Global redistribution and local migration in semi-discrete host-parasitoid population dynamic models. Math. Biosci. 327, 108409 (2020).52.Rogers, D. J. Random searching and incest population models. J. Anim. Ecol. 41, 369–383 (1972).Article 

    Google Scholar 
    53.Hassell, M. P. & Comins, H. N. Sigmoid functional responses and population stability. Theor. Popul. Biol. 14, 62–66 (1978).CAS 
    Article 

    Google Scholar 
    54.Fernández-arhex, V. & Corley, J. C. The functional response of parasitoids and its implications for biological control. Biocontrol Sci. Technol. 13(4), 403–413 (2003).Article 

    Google Scholar 
    55.Okuyama, T. Dilution effects enhance variation in parasitism risk among hosts and stabilize host-parasitoid population dynamics. Ecol. Model. 441, 109425 (2021). More

  • in

    Male sperm storage impairs sperm quality in the zebrafish

    1.Ward, P. I. Intraspecific variation in sperm size characters. Heredity 80, 655–659 (1998).Article 

    Google Scholar 
    2.Schulte-Hostedde, A. I. & Montgomerie, R. Intraspecific variation in ejaculate traits of the northern watersnake (Nerodia sipedon). J. Zool. 270, 147–152. https://doi.org/10.1111/j.1469-7998.2006.00101.x (2006).Article 

    Google Scholar 
    3.Morrow, E. H. & Gage, A. R. Consistent signicant variation between individual males in spermatozoal morphometry. J. Zool. 254, 147–153 (2001).Article 

    Google Scholar 
    4.Locatello, L., Pilastro, A., Deana, R., Zarpellon, A. & Rasotto, M. B. Variation pattern of sperm quality traits in two gobies with alternative mating tactics. Funct. Ecol. 21, 975–981 (2007).Article 

    Google Scholar 
    5.Iglesias-Carrasco, M., Harrison, L., Jennions, M. D. & Head, M. L. Combined effects of rearing and testing temperatures on sperm traits. J. Evol. Biol. 33, 1715–1724. https://doi.org/10.1111/jeb.13710 (2020).Article 
    PubMed 

    Google Scholar 
    6.Evans, J. P. & Magurran, A. E. Geographic variation in sperm production by Trinidadian guppies. Proc. R. Soc. B-Biol. Sci. 266, 2083–2087 (1999).Article 

    Google Scholar 
    7.Morrow, E. H., Leijon, A. & Meerupati, A. Hemiclonal analysis reveals significant genetic, environmental and genotype x environment effects on sperm size in Drosophila melanogaster. J. Evol. Biol. 21, 1692–1702. https://doi.org/10.1111/j.1420-9101.2008.01585.x (2008).CAS 
    Article 
    PubMed 

    Google Scholar 
    8.Firman, R. C., Klemme, I. & Simmons, L. W. Strategic adjustments in sperm production within and between two island populations of house mice. Evolution 67, 3061–3070. https://doi.org/10.5061/dryad.87pk2 (2013).Article 
    PubMed 

    Google Scholar 
    9.Taborsky, M. Sperm competition in fish: ‘bourgeois’ males and parasitic spawning. Trends Ecol. Evol. 13, 222–227 (1998).CAS 
    Article 

    Google Scholar 
    10.Kustra, M. C. & Alonzo, S. H. Sperm and alternative reproductive tactics: a review of existing theory and empirical data. Philos. Trans. R. Soc. B-Biol. Sci. 375, 20200075. https://doi.org/10.1098/rstb.2020.0075 (2020).Article 

    Google Scholar 
    11.Marshall, D. J. Environmentally induced (co)variance in sperm and offspring phenotypes as a source of epigenetic effects. J. Exp. Biol. 218, 107–113. https://doi.org/10.1242/jeb.106427 (2015).Article 
    PubMed 

    Google Scholar 
    12.Vega-Trejo, R. et al. The effects of male age, sperm age and mating history on ejaculate senescence. Funct. Ecol. 33, 1267–1279. https://doi.org/10.1111/1365-2435.13305 (2019).Article 

    Google Scholar 
    13.Macartney, E. L., Crean, A. J., Nakagawa, S. & Bonduriansky, R. Effects of nutrient limitation on sperm and seminal fluid: a systematic review and meta-analysis. Biol. Rev. Camb. Philos. Soc. 94, 1722–1739. https://doi.org/10.1111/brv.12524 (2019).Article 
    PubMed 

    Google Scholar 
    14.Johnson, S. L. et al. Evidence that fertility trades off with early offspring fitness as males age. Proc. R. Soc. B-Biol. Sci. https://doi.org/10.1098/rspb.2017.2174 (2018).Article 

    Google Scholar 
    15.Gasparini, C., Marino, I. A. M., Boschetto, C. & Pilastro, A. Effect of male age on sperm traits and sperm competition success in the guppy (Poecilia reticulata). J. Evol. Biol. 23, 124–135 (2010).CAS 
    Article 

    Google Scholar 
    16.Velando, A., Noguera, J. C., Drummond, H. & Torres, R. Senescent males carry premutagenic lesions in sperm. J. Evol. Biol. 24, 693–697 (2011).CAS 
    Article 

    Google Scholar 
    17.Pilastro, A., Scaggiante, M. & Rasotto, M. B. Individual adjustment of sperm expenditure accords with sperm competition theory. Proc. Natl. Acad. Sci. U.S.A. 99, 9913–9915 (2002).ADS 
    CAS 
    Article 

    Google Scholar 
    18.Nicholls, E. H., Burke, T. & Birkhead, T. R. Ejaculate allocation by male sand martins, Riparia riparia. Proc. R. Soc. B-Biol. Sci. 268, 1265–1270. https://doi.org/10.1098/rspb.2001.1615 (2001).CAS 
    Article 

    Google Scholar 
    19.Oppliger, A., Hosken, D. J. & Ribi, G. Snail sperm production characteristics vary with sperm competition risk. Proc. R. Soc. B-Biol. Sci. 265, 1527–1534 (1998).Article 

    Google Scholar 
    20.Crean, A. J. & Marshall, D. J. Gamete plasticity in a broadcast spawning marine invertebrate. Proc. Natl. Acad. Sci. U.S.A. 105, 13508–13513 (2008).ADS 
    CAS 
    Article 

    Google Scholar 
    21.Fisher, H. S., Hook, K. A., Weber, W. D. & Hoekstra, H. E. Sibling rivalry: males with more brothers develop larger testes. Ecol. Evol. 8, 8197–8203. https://doi.org/10.1002/ece3.4337 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    22.Ramm, S. A. & Stockley, P. Adaptive plasticity of mammalian sperm production in response to social experience. Proc. R. Soc. B-Biol. Sci. 276, 745–751 (2009).Article 

    Google Scholar 
    23.Pizzari, T., Cornwallis, C. K. & Froman, D. P. Social competitiveness associated with rapid fluctuations in sperm quality in male fowl. Proc. R. Soc. B-Biol. Sci. 274, 853–860. https://doi.org/10.1098/rspb.2006.0080 (2007).Article 

    Google Scholar 
    24.Silva, W. et al. The effects of male social environment on sperm phenotype and genome integrity. J. Evol. Biol. 32, 535–544. https://doi.org/10.1111/jeb.13435 (2019).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    25.Firman, R. C., Garcia-Gonzalez, F., Simmons, L. W. & Andre, G. I. A competitive environment influences sperm production, but not testes tissue composition, in house mice. J. Evol. Biol. 31, 1647–1654. https://doi.org/10.1111/jeb.13360 (2018).Article 
    PubMed 

    Google Scholar 
    26.Bozynski, C. C. & Liley, N. R. The effect of female presence on spermiation, and of male sexual activity on “ready” sperm in the male guppy. Anim. Behav. 65, 53–58. https://doi.org/10.1006/Anbe.2002.2024 (2003).Article 

    Google Scholar 
    27.Aitken, R. J. Impact of oxidative stress on male and female germ cells: implications for fertility. Reproduction 159, R189–R201. https://doi.org/10.1530/REP-19-0452 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    28.Reinhardt, K. Evolutionary consequences of sperm cell aging. Q. Rev. Biol. 82, 375–393 (2007).Article 

    Google Scholar 
    29.Pizzari, T., Dean, R., Pacey, A., Moore, H. & Bonsall, M. B. The evolutionary ecology of pre- and post-meiotic sperm senescence. Trends Ecol. Evol. 23, 131–140. https://doi.org/10.1016/j.tree.2007.12.003 (2008).Article 
    PubMed 

    Google Scholar 
    30.Gasparini, C., Dosselli, R. & Evans, J. P. Sperm storage by males causes changes in sperm phenotype and influences the reproductive fitness of males and their sons. Evol. Lett. 1, 16–25. https://doi.org/10.1002/evl3.2 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    31.Reinhardt, K. & Turnell, B. Sperm ageing: a complex business. Funct. Ecol. 33, 1188–1189. https://doi.org/10.1098/rspb.2018.2873 (2019).Article 

    Google Scholar 
    32.Tarin, J. J., Pérez-Albalà, S. & Cano, A. Consequences on offspring of abnormal function in ageing gametes. Hum. Reprod. Update 6, 532–549 (2000).CAS 
    Article 

    Google Scholar 
    33.Li, J. et al. The effect of male sexual abstinence periods on the clinical outcomes of fresh embryo transfer cycles following assisted reproductive technology: a meta-analysis. Male Sexual Reprod. Health 4, 1–8 (2020).
    Google Scholar 
    34.Periyasamy, A. J. et al. Does duration of abstinence affect the live-birth rate after assisted reproductive technology? A retrospective analysis of 1,030 cycles. Fertil. Steril. 108, 988–992. https://doi.org/10.1016/j.fertnstert.2017.08.034 (2017).Article 
    PubMed 

    Google Scholar 
    35.World Health Organization. WHO laboratory manual for the examination and processing of human semen 5th ed. (Geneva: World Health Organization, 2010).36.Comar, V. A. et al. Influence of the abstinence period on human sperm quality: analysis of 2,458 semen samples. JBRA Assist. Reprod. 21, 306–312. https://doi.org/10.5935/1518-0557.20170052 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    37.Gasparini, C., Kelley, J. L. & Evans, J. P. Male sperm storage compromises sperm motility in guppies. Biol. Let. 10, 20140681. https://doi.org/10.1098/rsbl.2014.0681 (2014).Article 

    Google Scholar 
    38.Poli, F., Immler, S., Gasparini, C. & Taborsky, M. Effects of ovarian fluid on sperm traits and its implications for cryptic female choice in zebrafish. Behav. Ecol. 30, 1298–1305. https://doi.org/10.1093/beheco/arz077 (2019).Article 

    Google Scholar 
    39.Riesco, M. F., Valcarce, D. G., Martinez-Vazquez, J. M. & Robles, V. Effect of low sperm quality on progeny: a study on zebrafish as model species. Sci. Rep. https://doi.org/10.1038/s41598-019-47702-7 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    40.Hagedorn, M. & Carter, V. L. Zebrafish reproduction: revisiting in vitro fertilization to increase sperm cryopreservation success. PLoS ONE 6, e21059. https://doi.org/10.1371/journal.pone.0021059 (2011).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    41.Zajitschek, S., Hotzy, C., Zajitschek, F. & Immler, S. Short-term variation in sperm competition causes sperm-mediated epigenetic effects on early offspring performance in the zebrafish. Proc. R. Soc. B-Biol. Sci. 281, 20140422. https://doi.org/10.1098/rspb.2014.0422 (2014).Article 

    Google Scholar 
    42.R Core Team. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. http://www.R-project.org/ (2020).43.Stoffel, M. A., Nakagawa, S. & Schielzeth, H. rptR: repeatability estimation and variance decomposition by generalized linear mixed-effects models. Methods Ecol. Evol. 8, 1639–1644. https://doi.org/10.1111/2041-210X.12797 (2017).Article 

    Google Scholar 
    44.Bates, D., Maechler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48. https://doi.org/10.18637/jss.v067.i01 (2015).Article 

    Google Scholar 
    45.Fox, J. & Weisberg, S. An R companion to applied regression 3rd edn. (Sage, 2019).
    Google Scholar 
    46.Lenth, R. V. Least-squares means: the R package lsmeans. J. Stat. Softw. 69, 1–33. https://doi.org/10.18637/jss.v069.i01 (2016).Article 

    Google Scholar 
    47.White, J. et al. Multiple deleterious effects of experimentally aged sperm in a monogamous bird. Proc. Natl. Acad. Sci. U.S.A. 105, 13947–13952 (2008).ADS 
    CAS 
    Article 

    Google Scholar 
    48.Reinhardt, K. & Siva-Jothy, M. T. An advantage for young sperm in the house cricket Acheta domesticus. Am. Nat. 165, 718–723 (2005).Article 

    Google Scholar 
    49.Gage, M. J. G. et al. Spermatozoal traits and sperm competition in Atlantic salmon: relative sperm velocity is the primary determinant of fertilization success. Curr. Biol. (CB) 14, 44–47 (2004).CAS 

    Google Scholar 
    50.Fitzpatrick, J. L. et al. Female promiscuity promotes the evolution of faster sperm in cichlid fishes. Proc. Natl. Acad. Sci. U.S.A. 106, 1128–1132. https://doi.org/10.1073/pnas.0809990106 (2009).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    51.Alavioon, G. et al. Haploid selection within a single ejaculate increases offspring fitness. Proc. Natl. Acad. Sci. U.S.A. 114, 8053–8058. https://doi.org/10.1073/pnas.1705601114 (2017).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    52.Cosson, J. Frenetic activation of fish spermatozoa flagella entails short-term motility, portending their precocious decadence. J. Fish Biol. 76, 240–279. https://doi.org/10.1111/j.1095-8649.2009.02504.x (2010).CAS 
    Article 
    PubMed 

    Google Scholar 
    53.Levitan, D. R. Sperm velocity and longevity trade off each other and influence fertilization in the sea urchin Lytechinus variegatus. Proc. R. Soc. B-Biol. Sci. 267, 531–534 (2000).CAS 
    Article 

    Google Scholar 
    54.Taborsky, M., Schütz, D., Goffinet, O. & van Doorn, G. S. Alternative male morphs solve sperm performance/longevity trade-off in opposite directions. Sci. Adv. 4, 8563 (2018).ADS 
    Article 

    Google Scholar 
    55.Cardozo, G., Devigili, A., Antonelli, P. & Pilastro, A. Female sperm storage mediates post-copulatory costs and benefits of ejaculate anticipatory plasticity in the guppy. J. Evol. Biol. 33, 1294–1305. https://doi.org/10.1111/jeb.13673 (2020).Article 
    PubMed 

    Google Scholar 
    56.delBarco-Trillo, J. et al. A cost for high levels of sperm competition in rodents: increased sperm DNA fragmentation. Proc. R. Soc. B-Biol. Sci. 283, 20152708 (2016).Article 

    Google Scholar 
    57.Firman, R. C., Young, F. J., Rowe, D. C., Duong, H. T. & Gasparini, C. Sexual rest and post-meiotic sperm ageing in house mice. J. Evol. Biol. 28, 1373–1382. https://doi.org/10.1111/jeb.12661 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    58.Gosálvez, J., López-Fernández, C., Hermoso, A., Fernández, J. L. & Kjelland, M. E. Sperm DNA fragmentation in zebrafish (Danio rerio) and its impact on fertility and embryo viability—implications for fisheries and aquaculture. Aquaculture 433, 173–182. https://doi.org/10.1016/j.aquaculture.2014.05.036 (2014).CAS 
    Article 

    Google Scholar 
    59.Perez-Cerezales, S., Martinez-Paramo, S., Beirao, J. & Herraez, M. P. Fertilization capacity with rainbow trout DNA-damaged sperm and embryo developmental success. Reproduction 139, 989–997. https://doi.org/10.1530/REP-10-0037 (2010).CAS 
    Article 
    PubMed 

    Google Scholar 
    60.Quay, W. Cloacal sperm in spring migrants: occurrence and interpretation. The Condor 87, 273–280 (1985).Article 

    Google Scholar 
    61.Thomsen, R., Soltis, J. & Teltscher, C. Sperm competition and the function of male masturbation in non-human primates. Sexual selection and reproductive competition in primates: New perspectives and directions (Jones, 2003).62.Engeszer, R. E., Patterson, L. B., Rao, A. A. & Parichy, D. M. Zebrafish in the wild: a review of natural history and new notes from the field. Zebrafish 4, 21–40. https://doi.org/10.1089/zeb.2006.9997 (2007).Article 
    PubMed 

    Google Scholar 
    63.Spence, R. & Smith, C. Mating preference of female zebrafish, Danio rerio, in relation to male dominance. Behav. Ecol. 17, 779–783. https://doi.org/10.1093/beheco/arl016 (2006).Article 

    Google Scholar 
    64.Spence, R. & Smith, C. Male territoriality mediates density and sex ratio effects on oviposition in the zebrafish, Danio rerio. Anim. Behav. 69, 1317–1323. https://doi.org/10.1016/j.anbehav.2004.10.010 (2005).Article 

    Google Scholar 
    65.Parker, G. A. Sperm competition and its evolutionary consequences in the insects. Biol. Rev. 45, 525–567 (1970).Article 

    Google Scholar 
    66.Parker, G. A. Sperm competition games: raffles and roles. Proc. R. Soc. B-Biol. Sci. 242, 120–126 (1990).ADS 
    Article 

    Google Scholar  More

  • in

    Predation risk is a function of seasonality rather than habitat complexity in a tropical semiarid forest

    1.Pianka, E. R. Niche relations of desert lizards in Ecology and Evolution of Communities, Cody, M. L. & Diamond, J. M. (Eds). (Harvard University Press, 1975).2.Castilla, A. M. & Labra, A. Predation and spatial distribution of the lizard Podarcis hipanica atrata: an experimental approach. Acta Oecol. 19, 107–114 (1998).ADS 
    Article 

    Google Scholar 
    3.Cantwell, L. R. & Forrest, T. G. Response of Anolis sagrei to acoustic calls from predatory and non-predatory birds. J. Herpetol. 47, 293–298 (2013).Article 

    Google Scholar 
    4.Edmund, M. Defense in animals: A survey of antipredator defenses. (Longman Press, 1974).5.Wilcove, D. Nest predation in forest tracts and the decline of migratory songbirds. Ecology 66, 121l-l214 (1985).Article 

    Google Scholar 
    6.Endler, J. A. Defense against predators in Predator-prey relationships, Feder, M. E. & Lauder, G. V. (Eds). (The University of Chicago Press, 1986).7.Constantini, D., Bruner, E., Fanfani, A. & Dell’Omo, G. Male-biased predation of western green lizards by Eurasian kestrels. Naturwissenschaften 94, 1015–1020. https://doi.org/10.1007/s00114-007-0284-5 (2007).ADS 
    CAS 
    Article 

    Google Scholar 
    8.Barnett, A. A. et al. Run, hide or fight: anti-predation strategies in Endangered red-nosed cuxiú (Chiropotes albinasus, Pitheciidae) in south-eastern Amazonia. Primates 58, 353–360. https://doi.org/10.1007/s10329-017-0596-9 (2017).Article 
    PubMed 

    Google Scholar 
    9.Barnett, A. A. et al. Honest error, precaution or alertness advertisement? Reactions to vertebrate pseudopredators in red-nosed cuxiús (Chiropotes albinasus), a high-canopy neo-tropical primate. Ethology 124, 177–187. https://doi.org/10.1111/eth.12721 (2018).Article 

    Google Scholar 
    10.Roslin, T. et al. Higher predation risk for insect prey at low latitudes and elevations. Science 356, 742–744. https://doi.org/10.1126/science.aaj1631 (2017).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    11.Shepard, D. B. Habitat but not body shape affects predator attack frequency on lizard models in the Brazilian Cerrado. Herpetologica 63, 193–202. https://doi.org/10.1655/0018-0831(2007)63[193:HBNBSA]2.0.CO;2 (2007).Article 

    Google Scholar 
    12.Salvidio, S., Costa, A. & Romano, A. The use of clay models in amphibian field studies: a short review. Bull. Env. Life Sc. 1, 8 (2019).
    Google Scholar 
    13.Castilla, A. M., Gosá, A., Galán, P. & Pérez-Mellado, V. Green tails in lizards of the genus Podarcis: do they influence the intensity of predation?. Herpetologica 55, 530–537 (1999).
    Google Scholar 
    14.Bateman, P. W., Fleming, P. A. & Wolfe, A. K. A different kind of ecological modelling: the use of clay model organisms to explore predator-prey interactions in vertebrates. J. Zool. 301, 251–262. https://doi.org/10.1111/jzo.12415 (2017).Article 

    Google Scholar 
    15.Rössler, D., Pröhl, H. & Lötters, S. The future of clay model studies. BMC Zool. 3, 6. https://doi.org/10.1186/s40850-018-0033-6 (2018).Article 

    Google Scholar 
    16.Major, R. E. & Kendal, C. E. The contribution of artificial nest experiments to understanding avian reproductive success: a review of methods and conclusions. Ibis 138, 298–307 (1996).Article 

    Google Scholar 
    17.Kuchta, S. R. Experimental support for aposematic coloration in the salamander Ensatina eschscholtzii xanthoptica: implications for mimicry of Pacific newts. Copeia 267–271, 2005. https://doi.org/10.1643/CH-04-173R (2005).Article 

    Google Scholar 
    18.Kraemer, A. C., Serb, J. M. & Adams, D. C. Both novelty and conspicuousness influence selection by mammalian predators on the colour pattern of Plethodon cinereus (Urodela: Plethodontidae). Biol. J. Linn. Soc. 118, 889–900. https://doi.org/10.1111/bij.12780 (2016).Article 

    Google Scholar 
    19.Salvidio, S., Palumbi, G., Romano, A. & Costa, A. Safe caves and dangerous forests? Predation risk may contribute to salamander colonization of subterranean habitats. Sci. Nat. 104, 3–4. https://doi.org/10.1007/s00114-017-1443-y (2017).CAS 
    Article 

    Google Scholar 
    20.Mcelroy, M. T. Teasing apart crypsis and aposematism-evidence that disruptive coloration reduces predation on a noxious toad. Biol. J. Linn. Soc. 17, 285–294. https://doi.org/10.1111/bij.12669 (2016).Article 

    Google Scholar 
    21.Nordberg, E. J. & Schwarzkopf, L. Predation risk is a function of alternative prey availability rather than predator abundance in a tropical savanna woodland ecosystem. Sci. Rep. 9, 7718. https://doi.org/10.1038/s41598-019-44159-6 (2019).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    22.Costa, A., Coroller, S. & Salvidio, S. Comparing day and night predation rates on lizard-Like clay models. Herpetol. Conserv. Biol. 15, 198–203 (2020).
    Google Scholar 
    23.Nour, N., Matthysen, E. & Dhondt, A. A. Artificial nest predation and habitat fragmentation: different trends in birds and mammal predators. Ecography 16, 111–116 (1993).Article 

    Google Scholar 
    24.Castilla, A. M. Intensive predation of Audouin’s Gull nests by the yellow legged gull in the Columbretes islands. Colon Waterbirds 18, 226–230. https://doi.org/10.2307/1521487 (1995).Article 

    Google Scholar 
    25.Diego-Rasilla, F. J. Influence of predation pressure on the escape behaviour of Podarcis muralis lizards. Behav. Processes 63, 1–7. https://doi.org/10.1016/S0376-6357(03)00026-3 (2003).Article 
    PubMed 

    Google Scholar 
    26.Stuart-fox, D. M., Moussalli, A., Marshall, N. J. & Owens, I. P. F. Conspicuous males suffer higher predation risk: Visual modeling and experimental evidence from lizards. Anim. Behav. 66, 541–550. https://doi.org/10.1006/anbe.2003.2235 (2003).Article 

    Google Scholar 
    27.Husak, J. F., Macedonia, J. M., Fox, S. F. & Sauceda, R. C. Predation cost of conspicuous male coloration in collared lizards (Crotaphytus collaris): an experimental test using clay-covered model lizards. Ethology 112, 572–580. https://doi.org/10.1111/j.1439-0310.2005.01189.x (2006).Article 

    Google Scholar 
    28.Keehn, J. E. & Feldman, C. R. Predator attack rates and anti-predator behavior of Side-blotched Lizards (Uta stransbuiana) at Southern California Wind Farms, USA. Herpetol. Conserv. Biol. 13, 194–204 (2018).
    Google Scholar 
    29.Hansen, N. A., Sato, C. F., Michael, D. L., Lindenmayer, D. B. & Driscoll, D. A. Predation risk for reptiles is highest at remnant edges in agricultural landscapes. J. Appl. Ecol. 56, 31–43. https://doi.org/10.1111/1365-2664.13269 (2019).Article 

    Google Scholar 
    30.Hegna, R. H., Saporito, R. A., Gerow, K. G. & Donnelly, M. A. Contrasting colours in an aposematic frog do not affect predation. Ann. Zool. 48, 29–38. https://doi.org/10.5735/086.048.0103 (2011).Article 

    Google Scholar 
    31.Paluh, D. J., Hantak, M. M. & Saporito, R. A. A test of aposematism in the dendrobatid poison frog Oophaga pumilio: the importance of movement in clay model experiments. J. Herpetol. 48, 249–254. https://doi.org/10.1670/13-027 (2014).Article 

    Google Scholar 
    32.Rojas, D. P., Stow, A., Amézquita, A., Simões, P. I. & Lima, A. P. No predatory bias with respect to colour familiarity for the aposematic Adelphobates galactonotus (Anura: Dendrobatidae). Behaviour 152, 1637–165. https://doi.org/10.1163/1568539X-00003297 (2015).Article 

    Google Scholar 
    33.Brodie, E. D. I. I. I. Differential avoidance of coral snake banded patterns by free-ranging avian predators in Costa Rica. Evolution 47, 227–235. https://doi.org/10.1111/j.1558-5646.1993.tb01212.x (1993).Article 
    PubMed 

    Google Scholar 
    34.Brodie, E. D. I. I. I. & Janzen, F. J. Experimental studies of coral snake mimicry: Generalized avoidance of ringed snake patterns by free-ranging avian predators. Funct. Ecol. 9, 186–190. https://doi.org/10.2307/2390563 (1995).Article 

    Google Scholar 
    35.Pfennig, D. W., Harper, G. R. Jr., Brumo, A. F., Harcombe, W. R. & Pfennig, K. S. Population differences in predation on Batesian mimics in allopatry with their model: Selection against mimics is strongest when they are common. Behav. Ecol. Sociobiol. 61, 505–511. https://doi.org/10.1007/s00265-006-0278-x (2006).Article 

    Google Scholar 
    36.Martín, J. & López, P. An experimental test of the costs of antipredatory refuge use in the wall lizard, Podarcis muralis. Oikos 84, 499–505 (1999).Article 

    Google Scholar 
    37.Amo, L., López, P. & Martín, J. Refuge use: a conflict between avoiding predation and losing mass in lizards. Physiol. Behav. 90, 334–343. https://doi.org/10.1016/j.physbeh.2006.09.035 (2007).CAS 
    Article 
    PubMed 

    Google Scholar 
    38.Endler, J. A. Interactions between predators and prey in Behavioural Ecology: An Evolutionary Approach, Krebs, J. R. & Davies, N. B., (Eds). (Blackwell, 1991).39.Denno, R. F., Finke, D. L. & Langellotto, G. A. Direct and indirect effects of vegetation structure and habitat complexity on predator-prey and predator-predator interactions in Ecology of Predator-prey Interactions, Barbosa, P. & Castellanos, I. (Eds). (Oxford University Press, 2005).40.Ruxton, G. D., Sherratt, T. N. & Speed, M. P. Avoiding Attack: The evolutionary ecology of crypsis, warning signals, and mimicry. (Oxford University Press, 2004).41.Sih, A. To hide or not to hide? Refuge use in a fluctuating environment. Trends Ecol. Evol. 12, 375–6 (1997).CAS 
    Article 

    Google Scholar 
    42.Martín, J., López, P. & Cooper, W. E. Jr. When to come out from a refuge: balancing predation risk and foraging opportunities in an alpine lizard. Ethology 109, 77–87. https://doi.org/10.1046/j.1439-0310.2003.00855.x (2003).Article 

    Google Scholar 
    43.Bulova, S. J. Ecological correlates of population and individual variation in antipredator behaviour of two species of desert lizards. Copeia 4, 980–992. https://doi.org/10.2307/1446721 (1994).Article 

    Google Scholar 
    44.Vanhooydonck, B. & Van Damme, R. Relationships between locomotor performance, microhabitat use and antipredator behaviour in lacertid lizards. Func. Ecol. 17, 160–169. https://doi.org/10.1046/j.1365-2435.2003.00716.x (2003).Article 

    Google Scholar 
    45.Vervust, B., Grbac, I. L. & Van Damme, R. Differences in morphology, performance and behavior between recently diverged populations of Podarcis sicula mirror differences in predation pressure. Oikos 116, 1343–1352. https://doi.org/10.1111/j.2007.0030-1299.15989.x (2007).Article 

    Google Scholar 
    46.Smith, G. R. & Ballinger, R. E. The ecological consequences of habitat and microhabitat use in lizards: a review. Contemp. Herpetol. 3, 1–13. https://doi.org/10.1002/3527600213.ch1 (2001).Article 

    Google Scholar 
    47.Wüster, W. et al. Do aposematism and Batesian mimicry require bright colours? A test, using European viper markings. Proc. Roy. Soc. London 271, 2495–2499. https://doi.org/10.1098/rspb.2004.2894 (2004).Article 

    Google Scholar 
    48.Worthington-Hill, O. & Gill, A. Effects of large-scale heathland management on thermal regimes and predation on adders Vipera berus. Anim. Conserv. 22, 481–492. https://doi.org/10.1111/acv.12489 (2019).Article 

    Google Scholar 
    49.Chiang, J. C. H. & Koutavas, A. Tropical flip-flop connection. Nature 432, 684–685. https://doi.org/10.1038/432684a (2004).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    50.Carmo, R. F. R., Amorim, H. P. & Vasconcelos, S. D. Scorpion diversity in two types of seasonally dry tropical forest in the semi-arid region of Northeastern Brazil. Biota. Neotrop. 13, 340–344. https://doi.org/10.1590/S1676-06032013000200037 (2013).Article 

    Google Scholar 
    51.Warrick, G. D., Kato, T. T. & Rose, B. R. Microhabitat use and home range characteristics of Blunt-nosed leopard lizards. J. Herpetol. 32, 183–191 (1998).Article 

    Google Scholar 
    52.Constantini, D. & Dell’Omo, G. Sex-Specific predation on two lizard species by kestrels. Russ. J. Ecol. 41, 99–101. https://doi.org/10.1134/S1067413610010182 (2010).Article 

    Google Scholar 
    53.Poulin, B. et al. Avian predation upon lizards and frogs in a neotropical forest understory. J. Trop. Ecol. 17, 21–40. https://doi.org/10.1017/S026646740100102X (2001).Article 

    Google Scholar 
    54.Araújo, C. S., Candido, D. M., Araújo, H. F. P., Dias, S. C. & Vasconcellos, A. Seasonal variations in scorpion activities (Arachnida: Scorpiones) in an area of Caatinga vegetation in Northeastern Brazil. Zoologia 27, 372–376. https://doi.org/10.1590/S1984-46702010000300008 (2010).Article 

    Google Scholar 
    55.Vasconcellos, A. et al. Seasonality of insects in the semi-arid Caatinga of northeastern Brazil. Rev. Bras. Entomol. 54, 471–476. https://doi.org/10.1590/S0085-56262010000300019 (2010).Article 

    Google Scholar 
    56.Schall, J. J. & Pianka, E. R. Evolution of escape behavior diversity. Am. Nat. 115, 551–566 (1980).Article 

    Google Scholar 
    57.Martín, J. & López, P. Influence of habitat structure on the escape tactics of the lizard Psammodromus algirus. Can. J. Zool. 73, 129–132 (1995).Article 

    Google Scholar 
    58.Rocha, C. F. D. & Bergallo, H. G. Intercommunity variation in the distribution of abundance of dominant lizard species in restinga habitats. Ciencia e Cultura 49, 269–274 (1997).
    Google Scholar 
    59.Van-Sluys, M. Growth and body condition of the saxicolous lizard Tropidurus itambere in southeastern Brazil. J. Herpetol. 32, 359–365 (1998).Article 

    Google Scholar 
    60.Liebezeit, J. R. & Zack, S. Point counts underestimate the importance of arctic foxes as avian nest predators: evidence from remote video cameras in arctic Alaskan oil fields. Arctic 61, 153–161 (2008).
    Google Scholar 
    61.DeGregorio, B. A., Weatherhead, P. J. & Sperry, J. H. Power lines, roads, and avian nest survival: effects on predator identity and predation intensity. Ecol. Evol. 4, 1589–1600. https://doi.org/10.1002/ece3.1049 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    62.Huey, R. B. & Pianka, E. R. Ecological consequences of foraging mode. Ecology 62, 991–999 (1981).Article 

    Google Scholar 
    63.Greene, H. W. Antipredator mechanisms in reptiles in Biology of Reptilian, Gans, C. & Huey, R. B. (Eds.). (Springer, 1998).64.Martín, J. & López, P. Amphibians and reptiles as prey of birds in southwestern Europe. Smit. Herpetol. Inform. Serv. 82, 1–43 (1990).
    Google Scholar 
    65.Steffen, J. E. Perch-height specific predation on tropical lizard clay models: implications for habitat selection in mainland neotropical lizards. Rev. Biol. Trop. 57, 859–864. https://doi.org/10.15517/rbt.v57i3.5498 (2009).Article 
    PubMed 

    Google Scholar 
    66.Dunham, A. E., Grant, B. W. & Overall, K. L. Interfaces between biophysical and physiological ecology and the population ecology of terrestrial vertebrate ectotherms. Physiol. Zool. 62, 335–355 (1989).Article 

    Google Scholar 
    67.Ruiz-Esparza, J. et al. Birds of the Grota do Angico Natural Monument in the semi-arid Caatinga scrublands of northeastern Brazil. Biota. Neotrop. 11, 1–8. https://doi.org/10.1590/S1676-06032011000200027 (2011).Article 

    Google Scholar 
    68.Lima, C. P., Santos, S. S. & Lima, R. C. Levantamento e Anilhamento da Ornitofauna na Pátria da Arara-Azul-de-Lear (Anodorhynchus leari, Bonaparte, 1856): um complemento ao Levantamento realizado por Sick, H., Gonzaga, L. P. e Teixeira, D. M., 1987. Atual. Ornitol. 112, 11–22 (2003).69.Roos, A. L. et al. Avifauna da região do Lago de Sobradinho: composição, riqueza e biologia. Ornithologia 1, 135–160 (2006).
    Google Scholar 
    70.Farias, G. B., Pereira, G. P. & Burgos, K. Q. Aves da Floresta Nacional de Negreiros (Serrita, Pernambuco). Atual. Ornitol. 157, 41–46 (2010).
    Google Scholar 
    71.Sousa, P. A. G. & Freire, E. M. X. Coleodactylus natalensis (NCN). Predation. Herpetol. Rev. 41, 218 (2010).
    Google Scholar 
    72.Ribeiro, L. B., Gogliath, M. & Freire, E. M. X. Hemidactylus brasilianus (Amaral’s Brazilian Gecko) and Cnemidophorus ocellifer (Spix`s Whiptail). Predation. Herpetol. Bull. 117, 31–32 (2011).
    Google Scholar 
    73.De-Carvalho, C. B. et al. Gymnodactylus geckoides (Naked-Toed Gecko): Predation. Herpetol. Bull. 121, 41–43 (2012).
    Google Scholar 
    74.McCormick, S. & Polis, G. A. Arthropods that prey on vertebrates. Biol. Rev. 57, 29–58 (1982).Article 

    Google Scholar 
    75.Rocha, C. F. D. & Vrcibradic, D. Reptiles as predators of vertebrates and as preys in a restinga habitat of southeastern Brazil. Ciencia e Cultura 50, 364–368 (1998).
    Google Scholar 
    76.Armas, L. F. Frogs and lizards as prey of some Greater Antillean arachnids. Rev. Iberica Aracnol. 3, 87–88 (2000).
    Google Scholar 
    77.Schatz, B., Suzzoni, J. P., Corbara, B. & Dejean, A. Selection and capture of prey in the African ponerine ant Plectroctena minor (Hymenoptera: formicidae). Acta Oecol. 22, 55–60. https://doi.org/10.1016/S1146-609X(00)01100-0 (2001).ADS 
    Article 

    Google Scholar 
    78.Nordberg, E. J., Edwards, L. & Schwarzkopf, L. Terrestrial invertebrates: an underestimated predator guild for small vertebrate groups. Food Webs 15, e00080 (2018).Article 

    Google Scholar 
    79.Seifert, C. L., Schulze, C. H., Dreschke, T. C. T., Frötscher, H. & Fiedler, K. Day vs. night predation on artificial caterpillars in primary rainforest habitats-an experimental approach. Entomol. Exp. Appl. 158, 54–59. https://doi.org/10.1111/eea.12379 (2016).Article 

    Google Scholar 
    80.Andrade, L. A., Pereira, I. M., Leite, U. T. & Barbosa, M. R. V. Análise da cobertura de duas fitofisionomias de Caatinga, com diferentes históricos de uso, no município de São João do Cariri, estado da Paraíba. Cerne 11, 253–262 (2005).
    Google Scholar 
    81.Castelletti, C. H. M., Silva, J. M. C., Tabarelli, M. & Santos, A. M. M. Quanto ainda resta da Caatinga? Uma estimative preliminar in Biodiversidade da Caatinga: áreas e ações prioritárias para a conservação, Silva, J. M. C., Tabarelli, M., Fonseca, M. T. & Lins, L. V. (Eds.). (Ministério do Meio Ambiente Publishing, 2004).82.Albuquerque, U. P. et al. Caatinga revisited: ecology and conservation of an important seasonal dry forest. Sci. World J. 1–18, 2012. https://doi.org/10.1100/2012/205182 (2012).Article 

    Google Scholar 
    83.Da Silva, A. C. C., Prata, A. P. N. & Mello, A. A. Flowering plants of the Grota do Angico Natural Monument, Caatinga of Sergipe, Brazil. Check List 9, 733–739 (2013).Article 

    Google Scholar 
    84.Nimer, E. Climatologia da Região Nordeste do Brasil: Introdução à Climatologia Dinâmica. Rev. Bras. Geog. 34, 3–51 (1972).
    Google Scholar 
    85.Santos, A. F. & Andrade, J. A. O quadro natural: caracterização e delimitação do semi-árido sergipano. Sergipe. Brazil. (CNPq/UFS, 1992).86.SEMARH–Secretaria de Estado do Meio Ambiente e dos Recursos Hídricos. Plano de Manejo do Monumento Natural Grota do Angico. Sergipe, Brazil. (Secretaria de Estado do Meio Ambiente e dos Recursos Hídricos, 2011)87.Ferreira, A. S., Silva, A. O., Conceição, B. M. & Faria, R. G. The diet of six species of lizards in an area of Caatiga, Brazil. Herpetol. J. 27, 151–160 (2017).
    Google Scholar 
    88.Rocha, S. M. et al. Lizards from the Alto Sertão region of Sergipe state, northeastern Brazil. Biota Neotrop. 21(2), e20201137 (2021).Article 

    Google Scholar 
    89.Bennett, A. T. D., Cuthill, I. C. & Norris, K. J. Sexual selection and the mismeasure of color. Am. Nat. 144, 848–860 (1994).Article 

    Google Scholar 
    90.Niskanen, M. & Mappes, J. Significance of the dorsal zigzag pattern of Vipera latastei gaditana against avian predators. J. Anim. Ecol. 74, 1091–1101. https://doi.org/10.1111/j.1365-2656.2005.01008.x (2005).Article 

    Google Scholar 
    91.R Core Team. R: A language and environment for statistical computing (2020). More