More stories

  • in

    Global conservation prioritization areas in three dimensions of crocodilian diversity

    Ackerly, D. D., Schwilk, D. W. & Webb, C. O. Niche evolution and adaptive radiation: Testing the order of trait divergence. Ecology 87, 50–61 (2006).Article 

    Google Scholar 
    Somaweera, R. et al. The ecological importance of crocodylians: Towards evidence-based justification for their conservation. Biol. Rev. Camb. Philos. Soc. 95, 936–959. https://doi.org/10.1111/brv.12594 (2020).Article 

    Google Scholar 
    Swain, S. et al. Anthropogenic influence on the physico-chemical parameters of Dhamra estuary and adjoining coastal water of the Bay of Bengal. Mar. Pollut. Bull. 162, 111826. https://doi.org/10.1016/j.marpolbul.2020.111826 (2021).Article 
    CAS 

    Google Scholar 
    IUCN. IUCN Red List of Threatened Species. Version 2022.1. www.iucnredlist.org (2022).Markich, S. J. & Jeffree, R. A. (eds) The Finnis River. A Natural Laboratory of Mining Impact—Past, Present and Future (Australian Nuclear Science and Technology Organisation, 2002).
    Google Scholar 
    Vieira, L. M. et al. Mercury and methyl mercury ratios in caimans (Caiman crocodilus yacare) from the Pantanal area, Brazil. J. Environ. Monitor. 13, 280–287. https://doi.org/10.1039/c0em00561d (2011).Article 
    CAS 

    Google Scholar 
    Quintela, F. M. et al. Arsenic, lead and cadmium concentrations in caudal crests of the yacare caiman (Caiman yacare) from Brazilian Pantanal. Sci. Total Environ. 707, 135479. https://doi.org/10.1016/j.scitotenv.2019.135479 (2020).Article 
    CAS 

    Google Scholar 
    Briggs-Gonzalez, V. S., Basille, M., Cherkiss, M. S. & Mazzotti, F. J. American crocodiles (Crocodylus acutus) as restoration bioindicators in the Florida Everglades. PLoS ONE 16, e0250510. https://doi.org/10.1371/journal.pone.0250510 (2021).Article 
    CAS 
    PubMed Central 

    Google Scholar 
    Grigg, G. & Kirshner, D. Biology and Evolution of Crocodylians (CSIRO Publishing, 2015).Book 

    Google Scholar 
    Subalusky, A. L., Fitzgerald, L. A. & Smith, L. L. Ontogenetic niche shifts in the American alligator establish functional connectivity between aquatic systems. Biol. Conserv. 142, 1507–1514 (2009).Article 

    Google Scholar 
    Villamarín, F., Escobedo-Galván, A. H., Siroski, P. & Magnusson, W. E. Geographic distribution, habitat, reproduction, and conservation status of crocodilians in the Americas. In Conservation Genetics of New World Crocodilians (eds Zucoloto, R. B. et al.) (Springer, 2021).
    Google Scholar 
    Albert, C., Luque, G. M. & Courchamp, F. The twenty most charismatic species. PLoS ONE 13, e0199149. https://doi.org/10.1371/journal.pone.0199149 (2018).Article 
    CAS 
    PubMed Central 

    Google Scholar 
    Verissimo, D., MacMillan, D. C. & Smith, R. J. Toward a systematic approach for identifying conservation flag ships. Conserv. Lett. 4, 1–8. https://doi.org/10.1111/j.1755-263X.2010.00151.x (2011).Article 

    Google Scholar 
    Fleishman, E., Murphy, D. D. & Brussard, P. F. A new method for selection of umbrella species for conservation planning. Ecol. Appl. 10, 569–579 (2000).Article 

    Google Scholar 
    Pressey, R. L., Cabeza, M., Watts, M. E., Cowling, R. M. & Wilson, K. A. Conservation planning in a changing world. Trents Ecol. Evol. 2211, 583–592 (2007).Article 

    Google Scholar 
    Petchey, O. L. & Gaston, K. J. Functional diversity: Back to basics and looking forward. Ecol. Lett. 9, 741–758. https://doi.org/10.1111/j.1461-0248.2006.00924.x (2006).Article 

    Google Scholar 
    Magurran, A. E. Measuring Biological Diversity 2nd edn. (Blackwell Publishing, 2004).
    Google Scholar 
    Campos, F. S., Lourenço-de-Moraes, R., Llorente, G. A. & Solé, M. Cost-effective conservation of amphibian ecology and evolution. Sci. Adv. 36, e1602929 (2017).Article 

    Google Scholar 
    Dietz, M. S., Belote, R. T., Aplet, G. H. & Aycrigg, J. L. The world’s largest wilderness protection network after 50 years: An assessment of ecological system representation in the US National Wilderness Preservation System. Biol. Conserv. 184, 431–438 (2015).Article 

    Google Scholar 
    UNEP-WCMC, IUCN. Protected Planet Report 2016 (UNEP-WCMC and IUCN, 2016).
    Google Scholar 
    Jones, K. R. et al. One-third of global protected land is under intense human pressure. Science 360, 788–791. https://doi.org/10.1126/science.aap9565 (2018).Article 
    CAS 

    Google Scholar 
    Rodrigues, A. et al. Effectiveness of the global protected area network in representing species diversity. Nature 428, 640–643. https://doi.org/10.1038/nature02422 (2004).Article 
    CAS 

    Google Scholar 
    Ladle, R. J. & Whittaker, R. J. Conservation Biogeography 301 (Wiley-Blackwell, 2011).Book 

    Google Scholar 
    Dinerstein, E. et al. A “global safety net” to reverse biodiversity loss and stabilize Earth’s climate. Sci. Adv. 6, 2824 (2020).Article 

    Google Scholar 
    Lourenço-de-Moraes, R. et al. No more trouble: An economic strategy to protect taxonomic, functional and phylogenetic diversity of continental turtles. Biol. Conserv. 261, 109241. https://doi.org/10.1016/j.biocon.2021.109241 (2021).Article 

    Google Scholar 
    Brochu, C. A. Phylogenetic relationships of Necrosuchus ionensis Simpson, 1937 and the early history of caimanines. Zool. J. Linn. Soc. 163, 228–256. https://doi.org/10.1111/j.1096-3642.2011.00716.x (2011).Article 

    Google Scholar 
    Buffetaut, E. Systématique, origine et evolution des Gavialidae sud-américains. In Phylógenie et Paléobiogeography: Livre Jubilaire en l´honneur de Robert Hoffstetter (ed. Buffetaut, E.) 127–140 (Géobios, 1982).
    Google Scholar 
    Griffith, P., Lang, J. W., Turvey, S. T. & Gumbs, R. Data from: Using functional traits to identify conservation priorities for the world’s crocodylians. Zenodo. https://doi.org/10.5281/zenodo.6645415 (2022).Griffith, P., Lang, J. W., Turvey, S. T. & Gumbs, R. Using functional traits to identify conservation priorities for the world’s crocodylians. Funct. Ecol. 37, 112. https://doi.org/10.1111/1365-2435.14140 (2022).Article 
    CAS 

    Google Scholar 
    Milian-Garcia, Y. et al. Evolutionary history of Cuban crocodiles Crocodylus rhombifer and Crocodylus acutus inferred from multilocus markers. J. Exp. Zool. A 315, 358–375. https://doi.org/10.1002/jez.683 (2011).Article 

    Google Scholar 
    Rodrıguez-Soberon, R., Ross, P. & Seal, U. IUCN/SSC Conservation Breeding Specialist Group (2000).Milián-García, Y., Ramos-Targarona, R., Pérez-Fleitas, E., Espinosa-López, G. & Russello, M. A. Genetic evidence of hybridization between the critically endangered Cuban crocodile and the American crocodile: Implications for population history and in situ/ex situ conservation. Heridity 114, 272–280 (2015).Article 

    Google Scholar 
    Pacheco-Sierra, G., Gompert, Z., Dominguez-Laso, J. & Vazquez-Dominguez, E. Genetic and morphological evidence of a geographically widespread hybrid zone between two crocodile species, Crocodylus acutus and Crocodylus moreletii. Mol. Ecol. 25, 3484–3498. https://doi.org/10.1111/mec.13694 (2016).Article 

    Google Scholar 
    Borges, V. S. et al. Evolutionary significant units within populations of Neotropical broad-snouted caimans (Caiman latirostris, Daudin, 1802). J. Herpetol. 52, 282–288 (2018).Article 

    Google Scholar 
    Palmer, M. L. & Mazzoti, F. J. Structure of everglades alligator holes. Wetlands 24, 115–122 (2004).Article 

    Google Scholar 
    Marques, T. S. et al. Intraspecific isotopic niche variation in broad-snouted caiman (Caiman latirostris). Isot. Environ. Health Stud. 49, 325–335 (2013).Article 
    CAS 

    Google Scholar 
    Mascarenhas-Junior, P. B. et al. Conflicts between humans and crocodilians in urban areas across Brazil: A new approach to support management and conservation. Ethnobiol. Conserv. 10, 19. https://doi.org/10.15451/ec2021-12-10.37-1-19 (2021).Article 

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

    Google Scholar 
    Ribeiro, M. C., Metzger, J. P., Martensen, A. C., Ponzoni, F. J. & Hirota, M. M. The Brazilian Atlantic Forest: How much is left, and how is the remaining forest distributed? Implications for conservation. Biol. Conserv. 142, 1141–1153 (2009).Article 

    Google Scholar 
    Filogonio, R., Assis, V. B., Passos, L. F. & Coutinho, M. E. Distribution of populations of broad-snouted caiman (Caiman latirostris, Daudin 1802, Alligatoridae) in the São Francisco River basin, Brazil. Braz. J. Biol. https://doi.org/10.1590/S1519-69842010000500007 (2010).Article 

    Google Scholar 
    Marques, J. F. et al. Fires dynamics in the Pantanal: Impacts of anthropogenic activities and climate change. J. Environ. Manag. 299, 113586. https://doi.org/10.1016/j.jenvman.2021.113586 (2021).Article 

    Google Scholar 
    Mataveli, G. A. V. et al. 2020 Pantanal’s widespread fire: Short- and long-term implications for biodiversity and conservation. Biodivers. Conserv. https://doi.org/10.1007/s10531-021-02243-2 (2021).Article 
    PubMed Central 

    Google Scholar 
    Ripple, W. J. et al. Status and ecological effects of the world’s largest carnivores. Science 343, 124–148 (2014).Article 

    Google Scholar 
    Estes, J. A. et al. Trophic downgrading of planet earth. Science 333, 301–306 (2011).Article 
    CAS 

    Google Scholar 
    Canning, A. & Death, R. Trophic cascade direction and flow determine network flow stability. Ecol. Model. 355, 18–23 (2017).Article 

    Google Scholar 
    Wang, Y. Q., Zhu, W. Q., Huang, L., Zhou, K. Y. & Wang, R. P. Genetic diversity of Chinese alligator (Alligator sinensis) revealed by AFLP analysis: An implication on the management of captive conservation. Biodivers. Conserv. 15, 2945–2955 (2006).Article 

    Google Scholar 
    Zhai, T. et al. Effects of population bottleneck and balancing selection on the chinese alligator are revealed by locus-specific characterization of MHC genes. Sci. Rep. 7, 5549. https://doi.org/10.1038/s41598-017-05640-2 (2017).Article 
    CAS 
    PubMed Central 

    Google Scholar 
    Sharma, S. P. et al. Microsatellite analysis reveals low genetic diversity in managed populations of the critically endangered gharial (Gavialis gangeticus) in India. Sci. Rep. https://doi.org/10.1038/s41598-021-85201-w (2021).Article 
    PubMed Central 

    Google Scholar 
    Nair, T. & Krishna, Y. C. Vertebrate fauna of the Chambal River basin, with emphasis on the National Chambal Sanctuary, India. J. Threat. Taxa 5, 3620–3641 (2013).Article 

    Google Scholar 
    Sharma, R. & Singh, L. Status of mugger crocodile (Crocodylus palustris) in National Chambal Sanctuary after thirty years and its implications on conservation of Gharial (Gavialis gangeticus). Zoo’s Print 30, 9–16 (2015).
    Google Scholar 
    Sinhg, H. & Rao, R. Status, threats and conservation challenges to key aquatic fauna (crocodile and dolphin) in National Chambal Sanctuary, India. Aquat. Ecosyst. Health Manag. 20, 59–70 (2017).Article 

    Google Scholar 
    UNEP-WCMC, IUCN. Protected Planet: The World Database on Protected Areas (WDPA) (UNEP-WCMC, IUCN, 2021).
    Google Scholar 
    Smolensky, N. L., Hurtado, L. A. & Fitzgerald, L. A. DNA barcoding of Cameroon samples enhances our knowledge on the distributional limits of putative species of Osteolaemus (African dwarf crocodiles). Conserv. Genet. 16, 235–240. https://doi.org/10.1007/s10592-014-0639-3 (2014).Article 
    CAS 

    Google Scholar 
    Shirley, M. H., Villanova, V. L., Vliet, K. A. & Austin, J. D. Genetic barcoding facilitates captive and wild management of three cryptic African crocodile species complexes. Anim. Conserv. 18, 322–330 (2015).Article 

    Google Scholar 
    Shirley, M. H., Carr, A. N., Nestler, J. H., Vliet, K. A. & Brochu, C. A. Systematic revision of the living African Slender-snouted Crocodiles (Mecistops Gray, 1844). Zootaxa 4504, 151–193. https://doi.org/10.11646/zootaxa.4504.2.1 (2018).Article 

    Google Scholar 
    Murray, C. M., Russo, P., Zorrilla, A. & McMahan, C. D. Divergent morphology among populations of the New Guinea crocodile, Crocodylus novaeguineae (Schmidt, 1928): Diagnosis of an independent lineage and description of a new species. Copeia 107, 517–523. https://doi.org/10.1643/CG-19-240 (2019).Article 

    Google Scholar 
    Hekkala, E. H. et al. An ancient icon reveals new mysteries: Mummy DNA resurrects a cryptic species within the Nile crocodile. Mol. Ecol. 20, 4199–4215 (2011).Article 
    CAS 

    Google Scholar 
    Mobaraki, A. et al. Conservation status of the mugger crocodile Crocodylus palustris: Establishing a task force for a poster species of climate change. Crocodile Specialist Group Newslett. 40(3), 12–20 (2021).
    Google Scholar 
    Cunningham, S. W., Shirley, M. H. & Hekkala, E. R. Fine scale patterns of genetic partitioning in the rediscovered African crocodile, Crocodylus suchus (Saint-Hilaire 1807). PeerJ 12, e1901 (2016).Article 

    Google Scholar 
    Platt, S. G. et al. Siamese Crocodile Crocodylus siamensis. In Crocodiles. Status Survey and Conservation Action Plan 4th edn (eds Manolis, S. C. & Stevenson, C.) (Crocodile Specialist Group, 2019).
    Google Scholar 
    Arcgis Software v. Version 10.1 (2011).Lourenço-de-Moraes, R. et al. Functional traits explain amphibian distribution in the Brazilian Atlantic Forest. J. Biogeogr. 47, 275–287 (2020).Article 

    Google Scholar 
    Pavoine, S., Vallet, J., Dufour, A. B., Gachet, S. & Daniel, H. On the challenge of treating various types of variables: Application for improving the measurement of functional diversity. Oikos 118, 391–402. https://doi.org/10.1111/j.1600-0706.2008.16668.x (2009).Article 

    Google Scholar 
    Colston, T. J., Kulkarni, P., Jetz, W. & Pyron, R. A. Phylogenetic and spatial distribution of evolutionary diversification, isolation, and threat in turtles and crocodilians (non-avian archosauromorphs). BMC Evol. Biol. 20(1), 1–16 (2020).Article 

    Google Scholar 
    R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, 2022).Faith, D. P. Conservation evaluation and phylogenetic diversity. Biol. Conserv. 61, 1–10 (1992).Article 

    Google Scholar 
    Pio, D. V. et al. Spatial predictions of phylogenetic diversity in conservation decision making. Conserv. Biol. 256, 1229–1239 (2011).Article 

    Google Scholar 
    Rodrigues, A. S. L. & Gaston, K. J. Maximising phylogenetic diversity in the selection of networks of conservation areas. Biol. Conserv. 105, 103–111 (2002).Article 

    Google Scholar 
    Safi, K. et al. Understanding global patterns of mammalian functional and phylogenetic diversity. Philos. Trans. R. Soc. B 366, 2536–2544 (2011).Article 

    Google Scholar 
    Trindade-Filho, J., Carvalho, R. A., Brito, D. & Loyola, R. D. How does the inclusion of data deficient species change conservation priorities for amphibians in the Atlantic Forest?. Biodivers. Conserv. 21, 2709–2718 (2012).Article 

    Google Scholar 
    Devictor, V. et al. Spatial mismatch and congruence between taxonomic, phylogenetic and functional diversity: The need for integrative conservation strategies in a changing world. Ecol. Lett. 13, 1030–1040 (2010).
    Google Scholar 
    Swenson, N. G. Functional and Phylogenetic Ecology in R (Springer, 2014).Book 
    MATH 

    Google Scholar 
    Mouchet, M., Villéger, S., Mason, N. W. H. & Mouillo, D. Functional diversity measures: An overview of their redundancy and their ability to discriminate community assembly rules. Funct. Ecol. 24, 867–876 (2010).Article 

    Google Scholar 
    Chaplin-Kramer, R. et al. Global modeling of nature’s contributions to people. Science 366, 255–258 (2019).Article 
    CAS 

    Google Scholar 
    Sharp, R. et al. InVEST 3.10.2.post28+ug.ga4e401c.d20220324 User’s Guide (The Natural Capital Project, Stanford University, University of Minnesota, The Nature Conservancy, and World Wildlife Fund, 2020).
    Google Scholar 
    Lourenço-de-Moraes, R. et al. Climate change will decrease the range size of snake species under negligible protection in the Brazilian Atlantic Forest hotspot. Sci. Rep. 9, 8523. https://doi.org/10.1038/s41598-019-44732-z (2019).Article 
    CAS 
    PubMed Central 

    Google Scholar 
    Sánchez-Fernandez, D. & Abellán, P. Using null models to identify underrepresented species in protected areas: A case study using European amphibians and reptiles. Biol. Conserv. 184, 290–299 (2015).Article 

    Google Scholar  More

  • in

    Effects of thinning on soil nutrient availability and fungal community composition in a plantation medium-aged pure forest of Picea koraiensis

    Yang, B., Pang, X. Y., Hu, B., Bao, W. K. & Tian, G. L. Does thinning-induced gap size result in altered soil microbial community in pine plantation in eastern Tibetan Plateau? Ecol. Evol. 7(9), 2986–2993 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Andrés, B. O., Ricardo, R. P., Raquel, O. & Mirendel, R. Thinning alters the early-decomposition rate and nutrient immobilization-release pattern of foliar litter in Mediterranean oak-pine mixed stands. For. Ecol. Manag. 391, 309–320 (2017).Article 

    Google Scholar 
    Hart, B. T. N., Smith, J. E., Luoma, D. L. & Hatten, J. A. Recovery of ectomycorrhizal fungus communities fifteen years after fuels reduction treatments in ponderosa pine forests of the Blue Mountains, Oregon. For. Ecol. Manag. 422, 11–22 (2018).Article 

    Google Scholar 
    Ge, Z. M. et al. Effects of varying thinning regimes on carbon uptake, total stem wood growth, and timber production in Norway spruce (Picea abies) stands in southern Finland under the changing climate. Ann. For. Sci. 68(2), 371–383 (2011).Article 

    Google Scholar 
    Panayotov, M. et al. Climate extremes during high competition contribute to mortality in unmanaged self-thinning Norway spruce stands in Bulgaria. For. Ecol. Manag. 369, 74–88 (2016).Article 

    Google Scholar 
    Depauw, L. et al. Interactive effects of past land use and recent forest management on the understorey community in temperate oak forests in South Sweden. J. Veg. Sci. 30(5), 917–928 (2019).Article 

    Google Scholar 
    Soalleiro, R. R., Murias, M. B. & Gonzalez, J. G. A. Evaluation through a simulation model of nutrient exports in fast-growing southern European pine stands in relation to thinning intensity and harvesting operations. Ann. For. Sci. 64(4), 375–384 (2007).Article 

    Google Scholar 
    Trentini, C. P. et al. Thinning of loblolly pine plantations in subtropical Argentina: Impact on microclimate and understory vegetation. For. Ecol. Manag. 384, 236–247 (2017).Article 

    Google Scholar 
    Baena, C. W. et al. Thinning and recovery effects on soil properties in two sites of a Mediterranean forest, in Cuenca Mountain (South-eastern of Spain). For. Ecol. Manag. 308, 223–230 (2013).Article 

    Google Scholar 
    He, Z. B. et al. Responses of soil organic carbon, soil respiration, and associated soil properties to long-term thinning in a semi-arid spruce plantation in northwestern China. Land Degrad. Dev. 29(12), 4387–4396 (2018).Article 

    Google Scholar 
    Rambo, T. R. & North, M. P. Canopy microclimate response to pattern and density of thinning in a Sierra Nevada forest. For. Ecol. Manag. 257(2), 435–442 (2009).Article 

    Google Scholar 
    Zhou, L. L. et al. Thinning increases understory diversity and biomass, and improves soil properties without decreasing growth of Chinese fir in southern China. Environ. Sci. Pollut. Res. 23(23), 24135–24150 (2016).Article 
    CAS 

    Google Scholar 
    Collins, C. G., Carey, C. J., Aronson, E. L., Kopp, C. W. & Diez, J. M. Direct and indirect effects of native range expansion on soil microbial community structure and function. J. Ecol. 104(5), 1271–1283 (2016).Article 

    Google Scholar 
    Çömez, A., Tolunay, D. & Güner, ŞT. Litterfall and the effects of thinning and seed cutting on carbon input into the soil in Scots pine stands in Turkey. Eur. J. Forest Res. 138(1), 1–14 (2019).Article 

    Google Scholar 
    Ulvcrona, K. A., Karlsson, K. & Ulvcrona, T. Identifying the biological effects of pre-commercial thinning on diameter growth in young Scots pine stands. Scand. J. For. Res. 29(5), 427–435 (2014).Article 

    Google Scholar 
    Chen, X. L. et al. Soil microbial functional diversity and biomass as affected by different thinning intensities in a Chinese fir plantation. Appl. Soil. Ecol. 92, 35–44 (2015).Article 

    Google Scholar 
    Veselá, P., Vašutová, M., Edwards- Jonášová, M. & Cudlin, P. Soil fungal community in norway spruce forests under bark beetle attack. Forests 10(2), 109 (2019).Article 

    Google Scholar 
    Ardestani, M. M., Jílková, V., Bonkowski, M. & Frouz, J. The effect of arbuscular mycorrhizal fungi Rhizophagus intraradices and soil microbial community on a model plant community in a post-mining soil. Plant Ecol. 220(9), 789–800 (2019).Article 

    Google Scholar 
    Sapsford, S. J., Paap, T., Hardy, G. E. S. J. & Burgess, T. I. The “chicken or the egg”: Which comes first, forest tree decline or loss of mycorrhizae? Plant Ecol. 218(9), 1093–1106 (2017).Article 

    Google Scholar 
    Jirout, J., Šimek, M. & Elhottová, D. Inputs of nitrogen and organic matter govern the composition of fungal communities in soil disturbed by overwintering cattle. Soil Biol. Biochem. 43(3), 647–656 (2011).Article 
    CAS 

    Google Scholar 
    Averill, C., Turner, B. L. & Finzi, A. C. Mycorrhiza-mediated competition between plants and decomposers drives soil carbon storage. Nature 505(7484), 543–545 (2014).Article 
    CAS 
    PubMed 

    Google Scholar 
    Iwaoka, C. et al. The impacts of soil fertility and salinity on soil nitrogen dynamics mediated by the soil microbial community beneath the halophytic Shrub Tamarisk. Microb. Ecol. 75(4), 985–996 (2017).Article 
    PubMed 

    Google Scholar 
    Bahnmann, B. et al. Effects of oak, beech and spruce on the distribution and community structure of fungi in litter and soils across a temperate forest. Soil Biol. Biochem. 119, 162–173 (2018).Article 
    CAS 

    Google Scholar 
    Ling, J. J. et al. Genotype by environment interaction analysis of growth of Picea koraiensis families at different sites using BLUP-GGE. New For. 52(1), 113–127 (2021).Article 

    Google Scholar 
    Zhang, J. B., Wang, L. F., Na, X., Zhang, T. T. & San-Ping, A. N. Primary report on introduction of Picea balfouriana and Picea koraiensis in Gansu. J. Gansu For. Sci. Technol. 44(02), 16–19+29 (2019).
    Google Scholar 
    Yin, L. M. et al. Arbuscular mycorrhizal trees cause a higher carbon to nitrogen ratio of soil organic matter decomposition via rhizosphere priming than ectomycorrhizal trees. Soil Biol. Biochem. 157, 108246 (2021).Article 
    CAS 

    Google Scholar 
    Zhou, L. & Wang, S. L. Effects of mixed tree species on soil nutrients in Picea koraiensis plantations. J. Northeast For. Univ. 47(2), 37–41 (2019).MathSciNet 
    CAS 

    Google Scholar 
    Cabon, A. et al. Thinning increases tree growth by delaying drought-induced growth cessation in a Mediterranean evergreen oak coppice. For. Ecol. Manag. 409, 333–342 (2018).Article 

    Google Scholar 
    Splawinski, T. B. et al. Precommercial thinning of Picea mariana and Pinus banksiana: Impact of treatment timing and competitors on growth response. For. Sci. 63(1), 62–70 (2017).Article 

    Google Scholar 
    Bai, S. H. et al. Effects of forest thinning on soil-plant carbon and nitrogen dynamics. Plant Soil 411(1–2), 437–449 (2016).
    Google Scholar 
    D’Amato, A. W., Troumbly, S. J., Saunders, M. R., Puettmann, K. J. & Albers, M. A. Growth and survival of Picea glauca following thinning of plantations affected by eastern spruce budworm. North. J. Appl. For. 28(2), 72–78 (2011).Article 

    Google Scholar 
    Olivar, J., Bogino, S., Rathgeber, C., Bonnesoeur, V. & Bravo, F. Thinning has a positive effect on growth dynamics and growth–climate relationships in Aleppo pine (Pinus halepensis) trees of different crown classes. Ann. For. Sci. 71(3), 395–404 (2014).Article 

    Google Scholar 
    Weiskittel, A. R., Kenefic, L. S., Seymour, R. S. & Phillips, L. M. Long-term effects of precommercial thinning on the stem dimensions, form and branch characteristics of red spruce and balsam fir crop trees in Maine, USA. Silva Fennica 43(3), 397–409 (2009).Article 

    Google Scholar 
    Repola, J., Hökkä, H. & Penttilä, T. Thinning intensity and growth of mixed spruce-birch stands on drained peatlands in Finland. Silva Fennica 40(1), 83–99 (2006).Article 

    Google Scholar 
    Misson, L., Vincke, C. & Devillez, F. Frequency responses of radial growth series after different thinning intensities in Norway spruce (Picea abies (L.) Karst.) stands. For. Ecol. Manag. 177(1–3), 51–63 (2003).Article 

    Google Scholar 
    Kim, S., Kim, C., Han, S. H., Lee, S. T. & Son, Y. A multi-site approach toward assessing the effect of thinning on soil carbon contents across temperate pine, oak, and larch forests. For. Ecol. Manag. 424, 62–70 (2018).Article 

    Google Scholar 
    Gliksman, D. et al. Litter decomposition in Mediterranean pine forests is enhanced by reduced canopy cover. Plant Soil 422(1–2), 317–329 (2018).Article 
    CAS 

    Google Scholar 
    Achat, D. L., Fortin, M., Landmann, G., Ringeval, B. & Augusto, L. Forest soil carbon is threatened by intensive biomass harvesting. Sci. Rep. 5, 15991 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Jurgensen, M. F. et al. Impacts of timber harvesting on soil organic matter, nitrogen, productivity, and health of inland northwest forests. For. Sci. 43(2), 234–251 (1997).
    Google Scholar 
    Blanco, J. A., Imbert, J. B. & Castillo, F. J. Thinning affects nutrient resorption and nutrient-use efficiency in two Pinus sylvestris stands in the pyrenees. Ecol. Appl. 19(3), 682–698 (2009).Article 
    PubMed 

    Google Scholar 
    Steer, J. & Harris, J. A. Shifts in the microbial community in rhizosphere and non-rhizosphere soils during the growth of Agrostis stolonifera. Soil Biol. Biochem. 32(6), 869–878 (2000).Article 
    CAS 

    Google Scholar 
    Coulombe, D., Sirois, L. & Paré, D. Effect of harvest gap formation and thinning on soil nitrogen cycling at the boreal–temperate interface. Can. J. For. Res. 47(3), 308–318 (2017).Article 
    CAS 

    Google Scholar 
    Hagerman, S. M., Jones, M. D., Bradfield, G. E. & SMSakakibara, S. M. Ectomycorrhizal colonization of Picea engelmannii × Picea glauca seedlings planted across cut blocks of different sizes. Can. J. For. Res. 29(12), 1856–1870 (1999).Article 

    Google Scholar 
    Ogo, S., Yamanaka, T., Akama, K., Nagakura, J. & Yamaji, K. Influence of ectomycorrhizal colonization on cesium uptake by Pinus densiflora seedlings. Mycobiology 46(4), 388–395 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Sebastiana, M. et al. Ectomycorrhizal inoculation with Pisolithus tinctorius reduces stress induced by drought in cork oak. Mycorrhiza 28(3), 247–258 (2018).Article 
    CAS 
    PubMed 

    Google Scholar 
    Jurgensen, M., Tarpey, R., Pickens, J., Kolka, R. & Palik, B. Long-term effect of silvicultural thinnings on soil carbon and nitrogen pools. Soil Sci. Soc. Am. J. 76(4), 1418–1425 (2012).Article 
    CAS 

    Google Scholar 
    Mosca, E., Montecchio, L., Barion, G., Dal Cortivo, C. & Vamerali, T. Combined effects of thinning and decline on fine root dynamics in a Quercus robur L. forest adjoining the Italian Pre-Alps. Ann. Bot. 119(7), 1235–1246 (2017).Article 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Li, X. & Christie, P. Changes in soil solution Zn and pH and uptake of Zn by arbuscular mycorrhizal red clover in Zn-contaminated soil. Chemosphere 42(2), 201–207 (2001).Article 
    CAS 
    PubMed 

    Google Scholar 
    Hawkes, C. V. et al. Fungal community responses to precipitation. Glob. Change Biol. 17(4), 1637–1645 (2011).Article 

    Google Scholar 
    McGuire, K. L., Fierer, N., Bateman, C., Treseder, K. K. & Turner, B. L. Fungal community composition in Neotropical rain forests: The influence of tree diversity and precipitation. Microb. Ecol. 63(4), 804–812 (2012).Article 
    PubMed 

    Google Scholar 
    Allison, S. D., Hanson, C. A. & Treseder, K. K. Nitrogen fertilization reduces diversity and alters community structure of active fungi in boreal ecosystems. Soil Biol. Biochem. 39(8), 1878–1887 (2007).Article 
    CAS 

    Google Scholar 
    Van Wyk, D. A. B., Adeleke, R., Rhode, O. H. J., Bezuidenhout, C. C. & Mienie, C. Ecological guild and enzyme activities of rhizosphere soil microbial communities associated with Bt-maize cultivation under field conditions in North West Province of South Africa. J. Basic Microbiol. 57(9), 781–792 (2017).Article 
    PubMed 

    Google Scholar 
    Zhao, C. C. et al. Soil microbial community composition and respiration along an experimental precipitation gradient in a semiarid steppe. Sci. Rep. https://doi.org/10.1038/srep24317 (2016).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    Kowalchuk, G. A., Buma, D. S. & Boer, W. D. Peter GLK & van Veen JA (2002) Effects of above-ground plant species composition and diversity on the diversity of soil-borne microorganisms. Antonie Van Leeuwenhoek 81(1–4), 509 (2002).Article 
    PubMed 

    Google Scholar  More

  • in

    A latitudinal gradient of deep-sea invasions for marine fishes

    Hillebrand, H. On the generality of the latitudinal diversity gradient. Am. Nat. 163, 192–211 (2004).
    Google Scholar 
    Pianka, E. R. Latitudinal gradients in species diversity: a review of concepts. Am. Nat. 100, 33–46 (1966).
    Google Scholar 
    Mannion, P. D., Upchurch, P., Benson, R. B. J. & Goswami, A. The latitudinal biodiversity gradient through deep time. Trends Ecol. Evol. 29, 42–50 (2014).
    Google Scholar 
    Jablonski, D., Roy, K. & Valentine, J. W. Out of the tropics: evolutionary dynamics of the latitudinal diversity gradient. Science 314, 102–106 (2006).ADS 
    CAS 

    Google Scholar 
    Alexander Pyron, R. & Wiens, J. J. Large-scale phylogenetic analyses reveal the causes of high tropical amphibian diversity. Proc. R. Soc. B Biol. Sci. 280, 1–10 (2013).
    Google Scholar 
    Allen, A. P. & Gillooly, J. F. Assessing latitudinal gradients in speciation rates and biodiversity at the global scale. Ecol. Lett. 9, 947–954 (2006).
    Google Scholar 
    Wright, S., Keeling, J. & Gillman, L. The road from Santa Rosalia: a faster tempo of evolution in tropical climates. Proc. Natl Acad. Sci. USA 103, 7718–7722 (2006).ADS 
    CAS 

    Google Scholar 
    Rolland, J., Condamine, F. L., Jiguet, F. & Morlon, H. Faster speciation and reduced extinction in the tropics contribute to the mammalian latitudinal diversity gradient. PLoS Biol. 12, e1001775 (2014).
    Google Scholar 
    Rabosky, D. L. et al. An inverse latitudinal gradient in speciation rate for marine fishes. Nature 559, 392–395 (2018).ADS 
    CAS 

    Google Scholar 
    Igea, J. & Tanentzap, A. J. Angiosperm speciation speeds up near the poles. Ecol. Lett. 23, 1–40 (2020).
    Google Scholar 
    Weir, J. T. & Schluter, D. The latitudinal gradient in recent speciation and extinction rates of birds and mammals. Science 315, 1574–1576 (2007).ADS 
    CAS 

    Google Scholar 
    Rabosky, D. L. & Huang, H. A robust semi-parametric test for detecting trait-dependent diversification. Syst. Biol. 65, 181–193 (2016).
    Google Scholar 
    Hansen, J. et al. Global temperature change. Proc. Natl Acad. Sci. USA 103, 14288–14293 (2006).ADS 
    CAS 

    Google Scholar 
    Huey, R. B. & Kingsolver, J. G. Climate warming, resource availability, and the metabolic meltdown of ectotherms. Am. Nat. 194, E140–E150 (2019).
    Google Scholar 
    Gerringer, M. E., Linley, T. D., Jamieson, A. J., Goetze, E. & Drazen, J. C. Pseudoliparis swirei sp. Nov.: A newly-discovered hadal snailfish (Scorpaeniformes: Liparidae) from the Mariana Trench. Zootaxa 4358, 161–177 (2017).
    Google Scholar 
    Childress, J. J. Are there physiological and biochemical adaptations of metabolism in deep-sea animals? Trends Ecol. Evol. 10, 30–36 (1995).CAS 

    Google Scholar 
    Seibel, B. A. & Drazen, J. C. The rate of metabolism in marine animals: environmental constraints, ecological demands and energetic opportunities. Philos. Trans. R. Soc. B Biol. Sci. 362, 2061–2078 (2007).CAS 

    Google Scholar 
    Eme, D., Anderson, M. J., Myers, E. M. V., Roberts, C. D. & Liggins, L. Phylogenetic measures reveal eco-evolutionary drivers of biodiversity along a depth gradient. Ecography 43, 689–702 (2020).
    Google Scholar 
    Costello, M. J. & Chaudhary, C. Marine biodiversity, biogeography, deep-sea gradients, and conservation. Curr. Biol. 27, R511–R527 (2017).CAS 

    Google Scholar 
    Brown, A. & Thatje, S. Explaining bathymetric diversity patterns in marine benthic invertebrates and demersal fishes: Physiological contributions to adaptation of life at depth. Biol. Rev. 89, 406–426 (2014).
    Google Scholar 
    Zintzen, V., Anderson, M. J., Roberts, C. D., Harvey, E. S. & Stewart, A. L. Effects of latitude and depth on the beta diversity of New Zealand fish communities. Sci. Rep. 7, 1–10 (2017).CAS 

    Google Scholar 
    Coleman, R. R., Copus, J. M., Coffey, D. M., Whitton, R. K. & Bowen, B. W. Shifting reef fish assemblages along a depth gradient in Pohnpei, Micronesia. PeerJ 2018, 1–30 (2018).
    Google Scholar 
    Neat, F. C. & Campbell, N. Proliferation of elongate fishes in the deep sea. J. Fish. Biol. 83, 1576–1591 (2013).CAS 

    Google Scholar 
    Martinez, C. M. et al. The deep sea is a hot spot of fish body shape evolution. Ecol. Lett. 24, 1788–1799 (2021).
    Google Scholar 
    Webb, P. Introduction to Oceanography (Online OER textbook, 2017).Hanly, P. J., Mittelbach, G. G. & Schemske, D. W. Speciation and the latitudinal diversity gradient: Insights from the global distribution of endemic fish. Am. Nat. 189, 604–615 (2017).
    Google Scholar 
    Tedesco, P. A., Paradis, E., Lévêque, C. & Hugueny, B. Explaining global-scale diversification patterns in actinopterygian fishes. J. Biogeogr. 44, 773–783 (2017).
    Google Scholar 
    Cooney, C. R., Seddon, N. & Tobias, J. A. Widespread correlations between climatic niche evolution and species diversification in birds. J. Anim. Ecol. 85, 869–878 (2016).
    Google Scholar 
    Title, P. O. & Burns, K. J. Rates of climatic niche evolution are correlated with species richness in a large and ecologically diverse radiation of songbirds. Ecol. Lett. 18, 433–440 (2015).
    Google Scholar 
    Seeholzer, G. F., Claramunt, S. & Brumfield, R. T. Niche evolution and diversification in a Neotropical radiation of birds (Aves: Furnariidae). Evolution 71, 702–715 (2017).
    Google Scholar 
    Kozak, K. H. & Wiens, J. J. Accelerated rates of climatic-niche evolution underlie rapid species diversification. Ecol. Lett. 13, 1378–1389 (2010).
    Google Scholar 
    Schnitzler, J., Graham, C. H., Dormann, C. F., Schiffers, K. & Peter Linder, H. Climatic niche evolution and species diversification in the cape flora, South Africa. J. Biogeogr. 39, 2201–2211 (2012).
    Google Scholar 
    Ghezelayagh, A. et al. Prolonged morphological expansion of spiny-rayed fishes following the end-Cretaceous. Nat. Ecol. Evol. 1–10. https://doi.org/10.1038/s41559-022-01801-3 (2022).Polato, N. R. et al. Narrow thermal tolerance and low dispersal drive higher speciation in tropical mountains. Proc. Natl Acad. Sci. USA 115, 12471–12476 (2018).ADS 
    CAS 

    Google Scholar 
    Rohde, K. Latitudinal gradients in species diversity: the search for the primary cause. Oikos 65, 514–527 (1992).
    Google Scholar 
    O’Hara, T. D., Hugall, A. F., Woolley, S. N. C., Bribiesca-Contreras, G. & Bax, N. J. Contrasting processes drive ophiuroid phylodiversity across shallow and deep seafloors. Nature 565, 636–639 (2019).ADS 

    Google Scholar 
    Losos, J. B. Adaptive radiation, ecological opportunity, and evolutionary determinism. Am. Nat. 175, 623–639 (2010).
    Google Scholar 
    Hulsey, C. D., Roberts, R. J., Loh, Y. H. E., Rupp, M. F. & Streelman, J. T. Lake Malawi cichlid evolution along a benthic/limnetic axis. Ecol. Evol. 3, 2262–2272 (2013).CAS 

    Google Scholar 
    Woolley, S. N. C. et al. Deep-sea diversity patterns are shaped by energy availability. Nature 533, 393–396 (2016).ADS 
    CAS 

    Google Scholar 
    Pigot, A. L., Owens, I. P. F. & Orme, C. D. L. The environmental limits to geographic range expansion in birds. Ecol. Lett. 13, 705–715 (2010).
    Google Scholar 
    Gerringer, M. E., Linley, T. D. & Nielsen, J. G. Revision of the depth record of bony fishes with notes on hadal snailfishes (Liparidae, Scorpaeniformes) and cusk eels (Ophidiidae, Ophidiiformes). Mar. Biol. 168, 1–9 (2021).
    Google Scholar 
    Kolora, S. R. R. et al. Origins and evolution of extreme life span in Pacific Ocean rockfishes. Science 374, 842–847 (2021).ADS 
    CAS 

    Google Scholar 
    Rutschmann, S. et al. Parallel ecological diversification in Antarctic notothenioid fishes as evidence for adaptive radiation. Mol. Ecol. 20, 4707–4721 (2011).
    Google Scholar 
    Wilson, L. A. B., Colombo, M., Hanel, R., Salzburger, W. & Sánchez-Villagra, M. R. Ecomorphological disparity in an adaptive radiation: opercular bone shape and stable isotopes in Antarctic icefishes. Ecol. Evol. 3, 3166–3182 (2013).
    Google Scholar 
    Ingram, T. Speciation along a depth gradient in a marine adaptive radiation. Proc. R. Soc. B. 278, 613–618 (2011).
    Google Scholar 
    Hyde, J. R., Kimbrell, C. A., Budrick, J. E., Lynn, E. A. & Vetter, R. D. Cryptic speciation in the vermilion rockfish (Sebastes miniatus) and the role of bathymetry in the speciation process. Mol. Ecol. 17, 1122–1136 (2008).CAS 

    Google Scholar 
    Kai, Y., Orr, J. W., Sakai, K. & Nakabo, T. Genetic and morphological evidence for cryptic diversity in the Careproctus rastrinus species complex (Liparidae) of the North Pacific. Ichthyol. Res. 58, 143–154 (2011).
    Google Scholar 
    Gerringer, M. E. et al. Habitat influences skeletal morphology and density in the snailfishes (family Liparidae). Front. Zool. 18, 1–22 (2021).
    Google Scholar 
    Saveliev, P. A. & Metelyov, E. A. Species composition and distribution of eelpouts (Zoarcidae, Perciformes, Actinopterygii) in the northwestern Sea of Okhotsk in summer. Prog. Oceanogr. 196, 102605 (2021).
    Google Scholar 
    Quattrini, A. M. et al. Niche divergence by deep-sea octocorals in the genus Callogorgia across the continental slope of the Gulf of Mexico. Mol. Ecol. 22, 4123–4140 (2013).
    Google Scholar 
    Zardus, J. D., Etter, R. J., Chase, M. R., Rex, M. A. & Boyle, E. E. Bathymetric and geographic population structure in the pan-Atlantic deep-sea bivalve Deminucula atacellana (Schenck, 1939). Mol. Ecol. 15, 639–651 (2006).CAS 

    Google Scholar 
    Schüller, M. Evidence for a role of bathymetry and emergence in speciation in the genus Glycera (Glyceridae, Polychaeta) from the deep Eastern Weddell Sea. Polar Biol. 34, 549–564 (2011).
    Google Scholar 
    Smith, W. L., Everman, E. & Richardson, C. Phylogeny and taxonomy of flatheads, scorpionfishes, sea robins, and stonefishes (Percomorpha: Scorpaeniformes) and the evolution of the lachrymal saber. Copeia 106, 94–119 (2018).
    Google Scholar 
    Jamon, M., Renous, S., Gasc, J. P., Bels, V. & Davenport, J. Evidence of force exchanges during the six-legged walking of the bottom-dwelling fish,Chelidonichthys lucerna. J. Exp. Zool. 307A, 542–547 (2007).
    Google Scholar 
    McCune, A. R. & Carlson, R. L. Twenty ways to lose your bladder: common natural mutants in zebrafish and widespread convergence of swim bladder loss among teleost fishes. Evol. Dev. 6, 246–259 (2004).
    Google Scholar 
    Rabosky, D. L. Speciation rate and the diversity of fishes in freshwaters and the oceans. J. Biogeogr. 47, 1207–1217 (2020).
    Google Scholar 
    Daane, J. M. et al. Historical contingency shapes adaptive radiation in Antarctic fishes. Nat. Ecol. Evol. 3, 1102–1109 (2019).
    Google Scholar 
    Mu, Y. et al. Whole genome sequencing of a snailfish from the Yap Trench (~7,000 m) clarifies the molecular mechanisms underlying adaptation to the deep sea. PLoS Genet. 17, e1009530 (2021).CAS 

    Google Scholar 
    Yancey, P. H., Gerringer, M. E., Drazen, J. C., Rowden, A. A. & Jamieson, A. Marine fish may be biochemically constrained from inhabiting the deepest ocean depths. Proc. Natl Acad. Sci. USA 111, 4461–4465 (2014).ADS 
    CAS 

    Google Scholar 
    Janzen, D. Why mountain passes are higher in the tropics. Am. Nat. 101, 233–249 (1967).
    Google Scholar 
    Kozak, K. H. & Wiens, J. J. Climatic zonation drives latitudinal variation in speciation mechanisms. Proc. R. Soc. B: Biol. Sci. 274, 2995–3003 (2007).
    Google Scholar 
    Sheldon, K. S., Huey, R. B., Kaspari, M. & Sanders, N. J. Fifty years of mountain passes: a perspective on Dan Janzen’s classic article. Am. Nat. 191, 553–565 (2018).
    Google Scholar 
    Muñoz, M. M. & Bodensteiner, B. L. Janzen’s hypothesis meets the bogert effect: connecting climate variation, thermoregulatory behavior, and rates of physiological evolution. Integr. Organ. Biol. 1, oby002 (2019).
    Google Scholar 
    Santidrián Tomillo, P., Fonseca, L., Paladino, F. V., Spotila, J. R. & Oro, D. Are thermal barriers ‘higher’ in deep sea turtle nests? PLoS ONE 12, 1–14 (2017).
    Google Scholar 
    Brown, J. H. Why marine islands are farther apart in the tropics. Am. Nat. 183, 842–846 (2014).
    Google Scholar 
    Jablonski, D. et al. Out of the tropics, but how? Fossils, bridge species, and thermal ranges in the dynamics of the marine latitudinal diversity gradient. Proc. Natl Acad. Sci. USA 110, 10487–10494 (2013).ADS 
    CAS 

    Google Scholar 
    Hattermann, T. Antarctic thermocline dynamics along a narrow shelf with easterly winds. J. Phys. Oceanogr. 48, 2419–2443 (2018).ADS 

    Google Scholar 
    Robison, B. H. What drives the diel vertical migrations of Antarctic midwater fish? J. Mar. Biol. Ass. 83, 639–642 (2003).
    Google Scholar 
    Bourgeaud, L. et al. Climatic niche change of fish is faster at high latitude and in marine environments. Preprint at bioRxiv https://doi.org/10.1101/853374 (2019).Pie, M. R. et al. The evolution of latitudinal range limits in tropical reef fishes: heritability, limits, and inverse Rapoport’s rule. J. Biogeogr. 00, 1–12 (2021).
    Google Scholar 
    Powell, M. G. & Glazier, D. S. Asymmetric geographic range expansion explains the latitudinal diversity gradients of four major taxa of marine plankton. Paleobiology 43, 196–208 (2017).
    Google Scholar 
    Lawson, A. M. & Weir, J. T. Latitudinal gradients in climatic-niche evolution accelerate trait evolution at high latitudes. Ecol. Lett. 17, 1427–1436 (2014).
    Google Scholar 
    Boag, T. H., Gearty, W. & Stockey, R. G. Metabolic tradeoffs control biodiversity gradients through geological time. Curr. Biol. 31, 2906–2913.e3 (2021).CAS 

    Google Scholar 
    Near, T. J. et al. Ancient climate change, antifreeze, and the evolutionary diversification of Antarctic fishes. Proc. Natl Acad. Sci. USA 109, 3434–3439 (2012).ADS 
    CAS 

    Google Scholar 
    Hotaling, S., Borowiec, M. L., Lins, L. S. F., Desvignes, T. & Kelley, J. L. The biogeographic history of eelpouts and related fishes: Linking phylogeny, environmental change, and patterns of dispersal in a globally distributed fish group. Mol. Phylogenet. Evol. 162, 107211 (2021).
    Google Scholar 
    Thatje, S., Hillenbrand, C.-D., Mackensen, A. & Larter, R. Life hung by a thread: endurance of Antarctic fauna in glacial periods. Ecology 89, 682–692 (2008).
    Google Scholar 
    Keller, I. & Seehausen, O. Thermal adaptation and ecological speciation. Mol. Ecol. 21, 782–799 (2012).CAS 

    Google Scholar 
    Deutsch, C., Penn, J. L. & Seibel, B. Metabolic trait diversity shapes marine biogeography. Nature 585, 557–562 (2020).ADS 
    CAS 

    Google Scholar 
    Labeyrie, L. D., Duplessy, J. C. & Blanc, P. L. Variations in mode of formation and temperature of oceanic deep waters over the past 125,000 years. Nature 327, 477–482 (1987).ADS 
    CAS 

    Google Scholar 
    Boag, T. H., Stockey, R. G., Elder, L. E., Hull, P. M. & Sperling, E. A. Oxygen, temperature and the deep-marine stenothermal cradle of Ediacaran evolution. Proc. R. Soc. B: Biol. Sci. 285, 2011724 (2018).
    Google Scholar 
    Koslow, J. A. Community structure in North Atlantic deep-sea fishes. Prog. Oceanogr. 31, 321–338 (1993).ADS 

    Google Scholar 
    Brunn, A. The abyssal fauna: its ecology, distribution, and origin. Nature 177, 1105–1108 (1956). Fr.ADS 

    Google Scholar 
    Gaither, M. R. et al. Depth as a driver of evolution in the deep sea: Insights from grenadiers (Gadiformes: Macrouridae) of the genus Coryphaenoides. Mol. Phylogenet. Evol. 104, 73–82 (2016).
    Google Scholar 
    Eastman, J. T. Evolution and diversification of Antarctic notothenioid fishes. Am. Zool. 31, 93–110 (1991).
    Google Scholar 
    Quattrini, A. M., Gómez, C. E. & Cordes, E. E. Environmental filtering and neutral processes shape octocoral community assembly in the deep sea. Oecologia 183, 221–236 (2017).ADS 

    Google Scholar 
    Stefanoudis, P. V. et al. Depth-dependent structuring of reef fish assemblages from the shallows to the rariphotic zone. Front. Mar. Sci. 6, 1–16 (2019).
    Google Scholar 
    Zintzen, V., Anderson, M. J., Roberts, C. D. & Diebel, C. E. Increasing variation in taxonomic distinctness reveals clusters of specialists in the deep sea. Ecography 34, 306–317 (2011).
    Google Scholar 
    Price, S. A., Claverie, T., Near, T. J. & Wainwright, P. C. Phylogenetic insights into the history and diversification of fishes on reefs. Coral Reefs 34, 997–1009 (2015).ADS 

    Google Scholar 
    Weber, M. G., Wagner, C. E., Best, R. J., Harmon, L. J. & Matthews, B. Evolution in a Community Context: On Integrating Ecological Interactions and Macroevolution. Trends Ecol. Evol. 32, 291–304 (2017).
    Google Scholar 
    Linley, T. D. et al. Fishes of the hadal zone including new species, in situ observations and depth records of Liparidae. Deep Sea Res. Part I Oceanogr. Res. Pap. 114, 99–110 (2016).ADS 

    Google Scholar 
    Jamieson, A. J., Linley, T. D., Eigler, S. & Macdonald, T. A global assessment of fishes at lower abyssal and upper hadal depths (5000 to 8000 m). Deep Sea Res. Part I Oceanogr. Res. Pap. 103642. https://doi.org/10.1016/j.dsr.2021.103642 (2021).Boers, N. Observation-based early-warning signals for a collapse of the Atlantic meridional overturning circulation. Nat. Clim. Chang. 11, 680–688 (2021).ADS 

    Google Scholar 
    Paulus, E. Shedding light on deep-sea biodiversity—a highly vulnerable habitat in the face of anthropogenic change. Front. Mar. Sci. 8, 667048 (2021).Froese, R. & Pauly, D. FishBase. FishBase www.fishbase.org (2019).Boettiger, C., Lang, D. T. & Wainwright, P. C. Rfishbase: exploring, manipulating and visualizing FishBase data from R. J. Fish. Biol. 81, 2030–2039 (2012).CAS 

    Google Scholar 
    Revell, L. J. phytools: An R package for phylogenetic comparative biology (and other things). Methods Ecol. Evol. 3, 217–223 (2012).
    Google Scholar 
    Harmon, L. J., Weir, J. T., Brock, C. D., Glor, R. E. & Challenger, W. GEIGER investigating evolutionary radiations. Bioinformatics 24, 129–131 (2008).CAS 

    Google Scholar 
    Karstensen, J., Stramma, L. & Visbeck, M. Oxygen minimum zones in the eastern tropical Atlantic and Pacific oceans. Prog. Oceanogr. 77, 331–350 (2008).ADS 

    Google Scholar 
    Sutton, T. T. et al. A global biogeographic classification of the mesopelagic zone. Deep Sea Res. Part I: Oceanogr. Res. Pap. 126, 85–102 (2017).ADS 

    Google Scholar 
    Alfaro, M. E. et al. Explosive diversification of marine fishes at the Cretaceous–Palaeogene boundary. Nat. Ecol. Evol. 2, 688–696 (2018).
    Google Scholar 
    Magnuson-Ford, K. & Otto, S. P. Linking the investigations of character evolution and species diversification. Am. Nat. 180, 225–245 (2012).
    Google Scholar 
    Goldberg, E. E. & Igić, B. Tempo and mode in plant breeding system evolution. Evolution 66, 3701–3709 (2012).
    Google Scholar 
    Rabosky, D. L. & Goldberg, E. E. Model inadequacy and mistaken inferences of trait-dependent speciation. Syst. Biol. 64, 340–355 (2015).CAS 

    Google Scholar 
    Beaulieu, J. M. & O’Meara, B. C. Detecting hidden diversification shifts in models of trait-dependent speciation and extinction. Syst. Biol. 65, 583–601 (2016).
    Google Scholar 
    Adams, D. C., Collyer, M. L. & Kaliontzopoulou, A. Geomorph: Software for geometric morphometric analyses. R package version 3.1.0. (2019).Collyer, M. L. & Adams, D. C. RRPP: An r package for fitting linear models to high-dimensional data using residual randomization. Methods Ecol. Evol. 9, 1772–1779 (2018).
    Google Scholar 
    Title, P. O. & Rabosky, D. L. Tip rates, phylogenies and diversification: What are we estimating, and how good are the estimates? Methods Ecol. Evol. 10, 821–834 (2019).
    Google Scholar 
    Freckleton, R. P., Phillimore, A. B. & Pagel, M. Relating traits to diversification: a simple test. Am. Nat. 172, 102–115 (2008).
    Google Scholar 
    Jetz, W., Thomas, G. H., Joy, J. B., Hartmann, K. & Mooers, A. O. The global diversity of birds in space and time. Nature 491, 444–448 (2012).ADS 
    CAS 

    Google Scholar 
    Louca, S. & Pennell, M. W. Extant timetrees are consistent with a myriad of diversification histories. Nature 580, 502–505 (2020).ADS 
    CAS 

    Google Scholar 
    May, M. R. & Moore, B. R. A Bayesian approach for inferring the impact of a discrete character on rates of continuous-character evolution in the presence of background-rate variation. Syst. Biol. 69, 530–544 (2020).
    Google Scholar 
    Höhna. et al. RevBayes: Bayesian phylogenetic inference using graphical models and an interactive model-specification language. Syst. Biol. 65, 726–736 (2016).
    Google Scholar 
    Burress, E. D. & Muñoz, M. M. Ecological opportunity from innovation, not islands, drove the anole lizard adaptive radiation. Syst. Biol. 0, 1–12 (2021).
    Google Scholar 
    Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarization in Bayesian phylogenetics using Tracer 1.7. Syst. Biol. 67, 901–904 (2018).CAS 

    Google Scholar 
    Ives, A. R. & Helmus, M. R. Phylogenetic metrics of community similarity. Am. Nat. 176, E128–E142 (2010).
    Google Scholar 
    Costello, M. J. & Breyer, S. Ocean depths: the mesopelagic and implications for global warming. Curr. Biol. 27, R36–R38 (2017).CAS 

    Google Scholar  More

  • in

    User-focused evaluation of National Ecological Observatory Network streamflow estimates

    As part of the streamflow data release, NEON released four relevant data products: Gauge Height26, Elevation of Surface Water29, Stage-discharge Rating Curves30, and Continuous Discharge15. Data users are able to download this full suite of information and protocols to inform decisions on data usage and applicability. We evaluated the quality of the Continuous Discharge product using all four relevant NEON data products, considering the validity of model inputs as well as the goodness-of-fit of final streamflow estimates. We analyzed 1) the fit of the regression between manual stage height readings and continuous pressure transducer data used to estimate continuous stream surface elevation, 2) the fit of rating curves transforming stream surface elevation to streamflow, and 3) the proportion of streamflow estimates over the maximum manually-measured streamflow.Stage classificationThe rating curve models predicting streamflow required continuous stream stage estimates as model inputs. NEON predicted continuous gauge height with a two step approach. First, continuous in-stream transducer readings were converted to water height by applying an offset between the transducer elevation and the staff gauge (Eq. 1). This offset is derived from the NEON geolocation database as the difference between the location of the pressure transducer and the staff gauge27. The offset changes only when the location of either the staff gauge or transducer moves.$${h}_{wc}=frac{{P}_{sw}}{p,ast ,g},ast ,1000+{h}_{stage}$$
    (1)
    Conversion of pressure data to water height used by NEON27 where hwc is the estimated water column height (m), Psw is calibrated surface water pressure (kPa), p is the density of water (999 kg/m3), g is the acceleration due to gravity (9.81 m/s2), and hstage is the offset between the pressure transducer and the staff gauge (m).Then, NEON uses a linear regression between manually-measured reference stage height and the calculated gauge height from Eq. 1, yielding final predictions of continuous stream gauge height27. In an ideal setting, stage and gauge height should correlate perfectly28. In the field, sensor uncertainty, manual reference measurement error, and shifting conditions in the stream can convolute the relationship. We tested the goodness of fit between continuously estimated stream gauge height values and manual stage measurements using the Nash-Sutcliffe model efficiency coefficient (Eq. 2). Nash-Sutcliffe coefficient is a commonly used metric in hydrology used to evaluate how well a model performed relative to observed values (manually measured stage and calculated gauge height). For the purposes of this discussion, manual reference measurements will be referred to as ‘stage’ and automated, sensed readings as ‘gauge height’.$$NSE=1-frac{Sigma {left({Q}_{o}-{Q}_{m}right)}^{2}}{Sigma {left({Q}_{o}-{bar{Q}}_{o}right)}^{2}}$$
    (2)
    Equation 2 presents Nash-Sutcliffe model efficiency coefficient, where Qo is an observed value (streamflow or stage height), Qm is a modeled value, and ({bar{Q}}_{o}) is the mean of observed values.Stage, gauge height, and regression data were sourced from the NEON Continuous Discharge product, representing what was directly applied to streamflow estimation. Up to 26 stage measurements were available per year. We examined every regression between stage and gauge height (one per site year in which data was available) and classified each as either ‘good’, ‘fair’, or ‘poor’ quality based on their goodness of fit. Regressions with a NSE (Eq. 2) of 0.90 or greater were considered good, those with a NSE of less than 0.90 but greater than or equal to 0.75 were considered fair, and those with an NSE of less than 0.75 were considered poor (Fig. 2).Drift detectionBecause electronic instruments, such as pressure transducers, can have systematic directional drift, referred to as ‘drift’, during deployment, we developed an approach to detect periods of time when NEON’s Elevation of Surface Water product drifted. We used two methods to assess and flag the potential for instrument drift at monthly time steps. First, we flagged any period the manually measured stage fell outside NEON’s uncertainty bound for gauge height made at the same time. From this, we calculated the proportion of stage measurements outside of the gauge height uncertainty bounds per month. This proved to be a relatively lenient filter that missed periods of manually identified drift. We found adding a second filter that flagged any month where the difference between the manually measured stage and gauge height exceeded 6 cm, was effective in catching the majority of periods where drift was identified. Second, we calculated the average differences between stage and gauge height for each month (Fig. 3). To determine appropriate cut-off values to classify areas of potential drift, we manually audited and flagged periods of observable directional drift. Our goal was to set a maximum cut-off difference which retained as much usable data as possible while still capturing 70% of the manually flagged directional drift periods. Applying this method, we determined a cut-off value of 6 cm average monthly deviation between observed and predicted stage values.Using these two filters in combination, we again classified data into three groups: ‘likely no drift’, ‘potential drift’, and ‘not assessed’. Site-months with no more than 50% of stage measurements outside of the gauge height time series uncertainty and an average difference between stage and gauge height less than 6 cm were considered to have ‘likely no drift’. Site-months with either more than 50% of stage readings outside of the gauge height time series uncertainty or an average difference between stage and gauge height more than 6 cm were deemed to have ‘potential drift’. Site-months with no stage measurements could not be evaluated and were considered ‘not assessed’. Although this approach to identify drift is imperfect, in that slight drift could be missed and times without manual measurements are not possible to assess, we believe this is a helpful method given the data available from NEON and the fact drift has been observed when visually inspecting data (Fig. 3).Rating curve classificationTo evaluate how well rating curves predicted streamflow, we assessed each rating curve used to convert stage to discharge. NEON prepares a new rating curve for each site’s water year (beginning on October 1st)27. In cases where NEON reported multiple rating curves for a site’s water year each curve was assessed separately across the time series which it was used. We classified rating curves into three tiers based on two metrics: the Nash-Sutcliffe coefficient (Eq. 2) between observed and predicted streamflow, and the percentage of continuous discharge values above the maximum manually measured gauging used to construct the rating curve.First, we calculated the Nash-Sutcliffe coefficient for each rating curve to estimate how well rating curves captured the variation in the stage-streamflow relationship. We used the reported values for modeled and manually measured streamflow from the ‘Y1simulated’ and ‘Y1observed’ columns in the ‘sdrc_resultsResiduals’ table of the Stage-discharge rating curves product. NEON generally conducts between 12 and 24 manual gaugings per year to build and maintain the stage-discharge relationship.Second, we calculated the percentage of continuous streamflow values outside the range of manually measured estimates of streamflow. This was useful to assess if the stage-discharge relationship is representative of observed flow conditions. The relationship between discharge and stage is often nonlinear, with inflection points around changes in channel morphology making gauging the stream at high and low flow conditions critical to building a reliable rating curve16. A rating curve based on a large number of direct field measurements all taken during a narrow range of baseflows, for example, could generate a rating curve with a high Nash-Sutcliffe coefficient that is unreliable when extrapolated to high or low flow events. Using these two metrics, we were able to classify rating curves into categories of relative quality. To calculate the percentage of values in the continuous streamflow product that fall outside the range of manually gauged streamflow values, we extracted the maximum and minimum gauging values from the ‘sdrc_resultsResiduals’ table in the Stage-discharge Rating Curve product. We then compared the predicted values derived from each rating curve (as reported in the ‘csd_continuousDischarge’ table) to the extracted range and calculated the proportion of values which fell outside of it.We used the Nash-Sutcliffe coefficient and percentage of streamflow values over the maximum observed field measurements to classify rating curves into three categories outlined in Table 1.To integrate stage-gauge regressions, drift detections, and rating curve classification, we produced a summary table with classifications for all three tests and the corresponding metrics used in each classification (Fig. 5). The table is grouped by month and site so users can query sites and determine which months have the appropriate data for their needs. More

  • in

    Active predation, phylogenetic diversity, and global prevalence of myxobacteria in wastewater treatment plants

    Myxococcota and Bdellovibrionota were active constituents of activated sludge microbiotaTo explore the predating activity and diversity of predatory bacteria in activated sludge, 13C-labeled Escherichia coli and Pseudomonas putida cells (determined as 97.09 and 97.30 atom% 13C, respectively) were added to the sludge microcosms for maximumly eight days of incubation, and 13C incorporation was examined using rRNA-SIP to identify prokaryotic and eukaryotic microorganisms involved in actively consuming the 13C-labeled prey cells. Bacterial 16S rRNA gene amplicon sequencing-based analysis indicated the relative contribution of 47.9% and 42.7% of the obtained sequences by the added biomass upon amendment in the 13C-E. coli (Fig. 1A) and 13C-P. putida (Fig. 1B) microcosms, which dropped below 1.0% after 16 h and eight days of incubation, respectively. The overall bacterial community structure at the steady state was highly comparable to that of the control microcosms (Fig. 1C), indicating that the prey cell amendments did not induce too strong fluctuation in the microbiota structure during the SIP experiment that prevented disentangling the indigenous community dynamics.Fig. 1: The dynamics of the prokaryotic communities and mineralization of the added 13C-biomass during the microcosm experiment.The overall prokaryotic communities were obtained by 16S rRNA gene amplicon sequencing of the total DNA from the activated sludge microcosms amended with 13C-E. coli (A) and 13C-P. putida (B) cells, and the control group (C) without amendment. The structure of the active prokaryotic communities was inferred based on amplicon sequencing of the light rRNA fractions from the microcosms amended with 13C-E. coli (D) and 13C-P. putida (E) cells. The temporal change in the proportion of produced 13CO2 in total CO2 indicated the mineralization of the 13C-labeled cells of E. coli and P. putida in duplicate microcosms (F). Relative sequence abundance of the ten most abundant prokaryotic phyla, together with the genera Escherichia-Shigella and Pseudomonas, was shown.Full size imageThe metabolically active bacterial communities, as inferred by 16S rRNA gene transcripts of the light rRNA fractions from the microcosms, were rather consistent throughout the experiment (Fig. 1D, E), but they showed clear compositional differences compared to the overall prokaryotic communities inferred by 16S rRNA gene amplicon sequences (Fig. 1A, B). Myxococcota and Bdellovibrionota species showed an average relative abundance of 17.5 (±0.7) % and 2.7 (±0.2) % in the 16S rRNA gene transcripts, respectively, which were significantly higher than 5.4 (±0.6) % and 1.3 (±0.1) % in the 16S rRNA genes of bacterial communities (p 1% in the 13C-heavy fractions, strong 13C-labeling was found for the as-yet-uncultivated myxobacterial mle1-27 clade (average EF 2.66 across time and treatments), which contributed to 10.3% to 38.9% of the 16S rRNA gene transcripts in the 13C-heavy fractions, indicating its high metabolic activity in consuming the 13C-labeled biomass of both E. coli and P. putida. Comparatively, Haliangium spp. and uncultured Polyangiaceae belonging to Myxococcota, as well as the as-yet-uncultivated OM27 clade belonging to Bdellovibrionota, also exhibited strong 13C-labeling (maximum EF across time: 2.4–39.5), but almost exclusively in the microcosms amended with 13C-E. coli cells (Fig. 2A). The as-yet-uncultivated myxobacterial VHS-B3-70 clade exhibited the strongest enrichment (average EF 16.67 across time and treatments) but made up only 0.2% to 2.3% of 16S rRNA gene transcripts of the 13C-heavy fraction (Fig. 2A). Overall, our microcosm experiment tracking added 13C-labeled prey bacterial cells with rRNA-SIP suggested prominent predatory activity of Myxococcota and Bdellovibrionota lineages including largely as-yet-uncultivated ones (e.g., the mle1-27, VHS-B3-70, and OM27 clades) in activated sludge.Fig. 2: The enrichment of incorporators of added 13C-biomass in heavy rRNA fractions and the temporal labeling patterns.13C-labeled prokaryotic (A) and micro-eukaryotic (B) genus-level taxa were identified by SIP in the microcosms added with E. coli and P. putida after one, two, and four days of incubation. Enrichment factor (EF) was calculated for microorganisms using heavy and light rRNA gradient fractions of the 13C- and 12C-microcosms to infer 13C-labeling. Taxa with an EF  > 0.1 in at least one of the treatment groups at one sampling time point was considered labeled. The area of circles indicates the relative sequence abundance of the labeled taxa in heavy 13C-rRNA. The negative EFs and positive EFs 1% in the heavy rRNA fractions of at least one of the 13C-E. coli and 13C-P. putida microcosms at a sampling point.Full size imageMyxococcota and Bdellovibrionota predated more selectively than protistsFor the micro-eukaryotes, several taxa belonging to Ciliophora, especially Cyrtophoria spp. and Telotrochidium spp., and also Peritrichia spp., Vaginicola spp., Aspidisca spp., and Epistylis spp., were highly enriched (maximum EF across time and treatments: 0.9–6.7) in the 13C-heavy rRNA fractions (Fig. 3B), in agreement with the dominance of Ciliophora in the micro-eukaryotic rRNA gene transcripts (Fig. 2B). The Candida-Lodderomyces clade and Cyberlindnera-Candida clade within Ascomycota, Magnoliophyta spp. within Phragmoplastophyta, and Poteriospumella spp. and unclassified Chromulinales within Ochrophyta were also strongly labeled (maximum EF: 13.5–242.5, Fig. 2B). Moreover, the 13C-biomass incorporation by micro-eukaryotes was independent of whichever prey bacteria (Fig. 2B, D), revealing no detectable prey preference in the metabolically active micro-eukaryotic predators. On the contrary, differential labeling by 13C-E. coli and 13C-P. putida cells was frequently observed for the predatory bacteria (Fig. 2A, C). The most obvious example was the OM27 clade ASVs belonging to Bdellovibrionota, which were found to incorporate 13C-labeled biomass exclusively of E. coli (Fig. 2C). Comparatively, Haliangium-affiliated ASV27 and ASV63 were labeled only by 13C-E. coli, ASV57 labeled by both 13C-E. coli and 13C-P. putida, while ASV72 and ASV76 were also labeled by 13C-P. putida, but only at a later sampling point (Fig. 2C). These results on the divergent labeling patterns with the tested prey bacteria together strongly implied population-specific predating behaviors of predatory bacteria in activated sludge.Fig. 3: In situ relative abundance of Myxococcota and Bdellovibrionota in aerobic and anaerobic sludge at a local WWTP (WWTP01) based on sampling over two years.The abundance of the abundant genera belonging to Myxococcota and Bdellovibrionota in aerobic and anaerobic sludge were compared according to amplicon sequencing-based analysis of bacterial 16S rRNA gene V3-V4 region. The top 10 abundant genus-level taxa across samples collected from eight samplings are shown, with the putative predators identified by SIP in the microcosm experiment highlighted. The asterisk denotes significant difference in relative abundance between aerobic and anaerobic sludges (p 0.1% in the activated sludge of WWTP01, including the putative predators identified in the microcosm experiment, i.e., Haliangium spp. (2.8 ± 0.7%) which represented the most abundant myxobacterial lineage in the activated sludge, uncultured Polyangiaceae (0.4 ± 0.1%), and the mle1-27 clade (0.2 ± 0.0%; Fig. 3). Moreover, Pajaroellobacter (1.2 ± 0.2%), Nannocystis (0.4 ± 0.1%), Phaselicystis (0.3 ± 0.1%), and several other myxobacterial clades, although not identified as putative predators in the microcosm experiment, were among the abundant myxobacteria in situ in the activated sludge. Although the myxobacterial genera showed comparable relative abundance in the anaerobic tanks, fed by returned activated sludge, to their counterparts in the aerobic tanks, the obligately aerobic myxobacteria were presumably metabolically inactive in the anerobic sludge. Unlike Myxococcota, members of Bdellovibrionota altogether showed significantly higher relative abundance in the aerobic sludge (1.0 ± 0.2%) than in the anaerobic sludge (0.6 ± 0.1%, paired samples Wilcoxon test p  More

  • in

    Soil, leaf and fruit nutrient data for pear orchards located in the Circum-Bohai Bay and Loess Plateau regions

    Orchard site selectionThe survey was conducted from 2018 to 2019 in the Circum-Bohai Bay region, which included Shandong, Hebei, and Liaoning provinces and Beijing, and the Loess Plateau region, which included Shanxi and Shaanxi provinces. Five typical production counties were selected in each province or city. Representative orchards were selected according to the production of the main varieties in each county (orchard area was greater than 1.0 ha; the pear trees were 15 to 25 years old; and the yield of orchards ranged from 40 to 60 t ha−1). A total of 225 orchards were investigated (Fig. 1), including 150 in the Circum-Bohai Bay region and 75 in the Loess Plateau region (Table 1).Fig. 1The locations of the 225 pear orchards.Full size imageTable 1 Numbers of pear orchard and main cultivated varieties investigated in Circum-Bohai Bay and Loess Plateau.Full size tableSample collection and pretreatmentSoil and leaf samples were collected at the stage in which the growth of new shoots ceased, from July 1 to July 1510. Eleven sampling sites were determined in each orchard according to an “S” shape sampling method (Fig. 2), and soil samples from the 0–20 cm, 20–40 cm and 40–60 cm layers were collected. The soil samples of the same soil layer at each sampling site were mixed into one sample. Then, the soil samples were air-dried, ground and sifted with a nylon sieve for determination of nutrient concentrations.Fig. 2The “S” shape sampling method. The red dots are the sampling locations.Full size imageTen to fifteen pear trees in each orchard of the same size and vigour and 5 to 10 mature leaves from the middle of a long shoot from the periphery of each tree were selected for leaf sampling11. Then, all the leaves from the same orchard were mixed into one leaf sample. The leaves were washed with tap water containing a detergent, with deionized water, with 0.01 M hydrochloric acid and then with deionized water again and then dried at 100 °C for 30 min and at 70 °C to a constant weight. Then, the leaf samples were crushed into a powder and sifted with a nylon sieve for nutrient determination.Fruit samples were collected at the ripening stage. Pear trees from which leaf samples were collected from each orchard were selected for fruit sample collection. Three to five peripheral fruits of the same size were collected from each tree, and fruit samples from the same orchard were mixed into one sample. The fruits were washed with tap water containing a detergent, with deionized water, with 0.01 M hydrochloric acid and then with deionized water again, cut into slices and then dried at 100 °C for 30 min and at 70 °C to a constant weight. Then, the fruit samples were crushed into a powder and sifted with a nylon sieve for nutrient determination.Sample determinationVarious indicators of soil and plant samples were determined according to the method of Cui et al.12 and Bao13.Soil pH determinationA potentiometric method was used to measure soil pH. Carbon dioxide-free water was added to soil that had been passed through a 2 mm sieve at a water-soil ratio of 2.5:1. The soil solution was stirred for 1 min and left undisturbed for 30 min. Each soil sample was measured more than three times with a pH meter (FE20K PLUS PH, Mettler-Toledo, Switzerland), and the difference in the parallel determination results was less than 0.2 pH units. The electrode was washed with deionized water and dried with filter paper after each sample measurement. A calibration solution was used to calibrate the electrode between measurements after every 10 soil samples.Soil organic matter determinationSoil organic matter was measured according to the Schollenberger method using chromic acid redox titration. Five millilitres of a 0.8 M 1/6 K2Cr2O7 solution was added to a test tube with approximately 0.5000 g of soil that had been passed through a 0.25 mm sieve. The mixture was then added to 5 mL concentrated sulfuric acid and shaken gently to disperse the soil. The tube was placed in a phosphoric acid bath, heated to 170 °C and boiled for 5 min. To condense the water vapour that escaped during the heating process, a small funnel was placed on the top of the test tube. The substances in the test tube and funnel were transferred to a conical flask after cooling. Then, the solution was added to 1,10-phenanthroline hydrate and titrated with 0.2 M FeSO4 until it turned maroon. A blank experiment was performed when each batch of samples was measured. The soil organic matter content was calculated according to the following formula:$${rm{omega }}left({rm{OM}}right)=frac{left({rm{V}}-{rm{V}}0right)times {rm{c}}times 3times 1.724times {rm{f}}}{{rm{m}}}$$
    (1)
    ω(OM): soil organic matter content; c: standard FeSO4 solution concentration; V: volume of the standard FeSO4 used in titration; V0: volume of standard FeSO4 used in titrating control sample; 3: molar mass of a quarter of carbon; 1.724: the conversion factor from organic carbon to organic matter; f: oxidation correction coefficient (the value was 1.1); m: mass of oven-dried soil sample.Soil total N determinationTotal N was determined by the semitrace Kjeldahl method. Approximately 1.0000 g of air-dried soil that had been passed through a 0.25 mm sieve was added to a digestion tube. Meanwhile, the soil moisture content was measured to calculate the mass of the oven-dried soil. Two grams of accelerator and 5 mL of concentrated sulfuric acid were added to the tube. The tube was then covered with a small funnel, and the sample was digested at 360 °C for 15–20 min. The mixture was digested for 1 h until the colour changed from brown to greyish green or greyish white. Two digested soilless samples were used as controls. After the digestion tube cooled, it was placed in a distiller, and a small amount of deionized water was added. Five millilitres of a 2% boric acid indicator was added to a 150 mL conical flask, and the flask was placed at the end of the condenser tube. Then, the digestion solution was distilled until the distillate volume was approximately 75 mL. The distillate was titrated with 0.01 M standard hydrochloric acid to a purplish red colour endpoint. The soil total N concentration was calculated according to the following formula:$${rm{omega }}({rm{N}})=frac{({rm{V}}-{rm{V}}0)times {rm{c}}times 14}{{rm{m}}}$$
    (2)
    ω(N): soil total N concentration; c: standard acid concentration; V: volume of the standard acid used in titration; V0: volume of standard acid used in titrating control sample; 14: molar mass of N; m: mass of oven-dried soil sample.Soil alkaline hydrolysable N determinationApproximately 2.00 g of air-dried soil that have been passed through a 2 mm sieve was placed in the outer chamber of a diffuser. The diffuser was gently rotated to evenly distribute the soil in the outer chamber. Two millilitres of H3BO3 indicator was placed in the inner chamber of the diffusion dish. The edge of the frosted glass surface of the diffuser was coated with alkaline glycerin and covered with frosted glass. The diffuser was covered tightly and secured with rubber bands after 10.00 mL of 1 M NaOH was injected into the diffuser through a hole in the frosted glass. The diffuser was placed in a 40 °C incubator for alkaline hydrolysis diffusion for 24 h. Then, the mixture was titrated with 0.01 M standard hydrochloric acid until it turned purplish red. A blank test was performed at the same time as the samples. The soil alkaline hydrolysable N concentration was calculated according to the following formula:$${rm{omega }}({rm{N}})=frac{({rm{V}}-{rm{V}}0)times {rm{c}}times 14}{{rm{m}}}$$
    (3)
    ω(N): soil alkaline hydrolysable N concentration; c: standard acid solution concentration; V: volume of the standard acid used in titration; V0: volume of standard acid used in titrating control sample; 14: molar mass of N; m: mass of air-dried soil sample.Soil available P determinationApproximately 2.50 g of air-dried soil that had been passed through a 2 mm sieve was placed in a plastic bottle and 50 mL of 0.5 M NaHCO3 was added. After the bottle was shaken for 30 min, the mixture was immediately filtered with phosphorus-free filter paper. Ten millilitres of the filtrate was accurately measured into a conical flask, and 5.00 mL of Mo-Sb-Vc colour developer and 10 mL of deionized water were added. The absorbance of the mixture was measured at approximately 700 nm after 30 min using a UV-Vis spectrophotometer (UV1900PC, AuCy Instrument, Shanghai, China). Finally, the P concentration was calculated according to a standard curve prepared with solutions of different P concentrations. A blank test was performed at the same time that the samples were determined.Soil available K determinationApproximately 5.00 g of air-dried soil that had been passed through a 2 mm sieve was placed in a plastic bottle, and 50 mL of 1.0 M NH4OAc was added. After the sample was shaken for 30 min, the mixture was immediately filtered with dry filter paper. The concentration of K in the filtrate was determined directly by a flame photometer (LM12-FP6430, Haifuda, China) according to a standard curve prepared with solutions of different K concentrations. A blank test was performed at the same time that the samples were determined.Leaf and fruit N determinationApproximately 0.3000 g of plant powder that had been passed through a 0.5 mm sieve was placed into a digestion tube and 5 mL concentrated sulfuric acid was added. Then, the digestion tube was placed onto a digestion stove at 360 °C after two doses of 2 mL H2O2, and the sample was digested until the mixture turned brown. After the tube cooled, 2 mL H2O2 was added, and the digestion was continued for 5 min. This process was repeated until the mixture turned clear. The mixture was diluted to 100 mL in a volumetric flask for testing after it cooled. Then, 5 to 10 mL of the liquid to be tested was accurately measured into a distiller for distillation. The distillation and titration processes were the same as those used for ammonium in the Soil total N determination section. A blank test was performed at the same time as sample measurement. The leaf or fruit N concentration was calculated according to the following formula:$${rm{omega }}({rm{N}})=frac{({rm{V}}-{rm{V}}0)times {rm{c}}times 14times {rm{V}}1}{{rm{m}}times {rm{V}}2}$$
    (4)
    ω(N): total N concentration; c: standard acid concentration; V: volume of the standard acid used in titration; V0: volume of standard acid used in titrating control sample; 14: molar mass of N; m: mass of oven-dried sample; V1: volume of the digestion solution after constant volume; V2: measured volume of digestion solution after constant volume.Leaf and fruit P, K, Ca, Fe, Mn, Cu, Zn, B determinationApproximately 0.5000 g of plant powder that had been passed through a 0.5 mm sieve was placed in a digestion tube and a 10 mL mixture of concentrated nitric acid and hypochlorous acid (4:1) was added. After the sample was left undisturbed for more than 4 h, it was placed onto a digestion stove and heated to 150 °C so that NO2 could volatilize slowly. Then, the temperature was appropriately increased to a temperature not higher than 250 °C until the digestive solution was transparent and approximately 2 mL remained. The solution was transferred into a volumetric flask after cooling and adjusted to a constant volume of 50 mL. The solution was then filtered, and the concentration of each element in the solution was determined by a plasma emission spectrometer (ICP-OES, OPTIMA 3300 DV, 75 Perkin-Elmer, USA). A blank test was performed at the same time as sample measurement. The leaf or fruit P, K, Ca, Fe, Mn, Cu, Zn, and B concentrations were calculated according to the following formula:$${rm{omega }}({rm{P}},{rm{K}},{rm{Ca}},{rm{Fe}},{rm{Mn}},{rm{Cu}},{rm{Zn}},{rm{B}})=frac{rho ({rm{P}},{rm{K}},{rm{Ca}},{rm{Fe}},{rm{Mn}},{rm{Cu}},{rm{Zn}},{rm{B}})times {rm{V}}times {rm{f}}}{{rm{m}}}$$
    (5)
    ω(P, K, Ca, Fe, Mn, Cu, Zn, B): P, K, Ca, Fe, Mn, Cu, Zn, B concentration in leaf or fruit; ρ(P, K, Ca, Fe, Mn, Cu, Zn, B): the concentration of P, K, Ca, Fe, Mn, Cu, Zn or B in the liquid to be measured; V: volume of the liquid to be measured after constant volume; f: dilution ratio of the liquid to be measured; m: mass of oven-dried sample. More

  • in

    Flickering flash signals and mate recognition in the Asian firefly, Aquatica lateralis

    Flash recordingAll field recording and experiments were performed at the paddy field in the Northern Chita Peninsula, Aichi Prefecture, central Japan, in June and July between 2003 and 2016. The ambient temperature at the firefly’s active period was measured using a thermometer. The flashes were recorded with a digital video camera (NV-GS-400, Panasonic, Japan) mounted on a tripod at a height of 30–50 cm from ground and a distance of 1.0–1.5 m away from the specimen. Isolated specimens were selected for recording to exclude the background light from other nontarget specimens. When another specimen appeared near the target specimen, the video recording was cancelled. When a female copulated during video recording in the field, her flashes until 1 min before copulation were regarded as those of a ‘receptive female’. To record the flashes of a ‘mated female’, the female specimens already mated were prepared in aquariums (because virgin and mated females cannot be distinguished in the field): the eggs were obtained from wild female specimens collected one year before at the same field and reared to adults; immediately after emergence the virgin female was confined in a small container with two cultured males for two nights to facilitate copulation. As the parents of the reared specimens were collected from the observation field (same genetic background), the rearing temperature was almost the same as that of the natural field, the emergence period of the cultured specimens overlapped with that of the natural population, the adult body sizes of the reared and natural specimens were indistinguishable, and the flash pattern of the cultured mated females was indistinguishable from that of the wild (potentially) mated females. Thus, we believe that there was no influence of different rearing environments, i.e., the flash behavior of the cultured mated female specimens is expected to be substantially the same as that of wild mated female specimens. To distinguish them from wild (potentially) mated females, the elytra of cultured mated females were marked with colored ink before placing them in the field, and after three days, the flashes of ink-marked specimens were recorded. Of note, we never observed male attraction and copulation in any of the mated females used for field observation; thus, the mated females were unreceptive.Waveform analysisSequential still images were captured from video files at 30 frames per second using VirtualDub (GPL), and then the light intensities in the images were qualified (8-bit linear gray scaling from black to white at 0–255) using ImageJ software. In this study, we defined ‘flash’ as a luminescent waveform from baseline to baseline and ‘flickering’ as fluctuation above baseline in a single flash. The waveforms containing a saturated signal (255, white) were omitted. The waveforms of the maximum signal value lower than 50 were also omitted because of the difficulty in separating signal and noise. Approximately 10–90 waveforms per individual were analyzed; thus, the effect of the occasional interruption of the flash recording by the specimen’s movement and/or vegetation swinging between the specimen and the video camera is statistically ignorable. FD is defined as the time interval between the beginning and the end of a flash (Fig. S1). Flicker intensity (FI) was defined as$${text{FI}} = left{ {begin{array}{*{20}l} {mathop {max }limits_{1 le i le n} left( {frac{{{text{min}}left( {p_{i} ,p_{i + 1} } right) – t_{i} }}{{min left( {p_{i} , p_{i + 1} } right) + t_{i} }}} right)} hfill & {{text{if}} , n ge 1} hfill \ 0 hfill & {{text{if}} , n = 0} hfill \ end{array} } right.$$where p, t, and n denote the peak and the trough (local extrema) in the waveform of a flash and the number of toughs in the flash, respectively (Fig. S1). In total, we measured the FD and FI values of 347, 94, and 355 waveforms from 13 sedentary males, 7 receptive females, and 8 mated females, respectively. We did not consider the flash brightness as a factor because the measured value of the light intensity depends largely on the distance between the light source and the detector; thus, the actual brightness of the lantern cannot be practically measured in the field.e-FireflyFor male attraction experiments, we built an electronic LED device, the e-firefly, to generate patterned flashes with various FDs and FIs using a chip LED (green type, λmax = 568 nm, Everlight Electronics, Taiwan; Figs. S2 and S3) with a microcontroller PIC16F628A (Microchip Technology, USA) (see Figs. S4-S5). An example of the program for the microcontroller is shown in Supplementary Data S1. The brightness was constant in all programs. Flickering frequency ranged between 5–12 Hz, which corresponds to that of sedentary male flashes (approximately 10 Hz)15. To prevent direct access of the attracted specimen to the light source, the chip LED was covered by a steel net painted green (see Fig. S2). For flying male attraction experiments, when the male landed within a 100-mm distance from the e-firefly, we judged the attraction to be a success; otherwise, it was a failure. For sedentary male attraction experiments, the e-firefly was placed 200–300 mm away from the sedentary male. When the approaching male touched the steel net covering the e-firefly, to warrant a positive approach, we measured the time the male remained on the net. If the male did not move away from the net for more than 2 min, we judged the attraction to be a success (strict criterion for judgment); otherwise, it was a failure.Spectral measurementThe luminescence spectra of e-firefly and A. lateralis were measured using a Flame-S spectrophotometer (Ocean Insight, USA). The living A. lateralis specimens were anesthetized on ice and frozen at − 20 °C until use. The lantern started luminescence by thawing at room temperature, and the spectrum was measured during luminescence (within 5 min).Statistical analysisFirst, we considered a discriminant analysis using a logistic regression model that discriminates between receptive females and others in the observational data. We fitted several models with combinations of FD and FI, quadratic terms of FD and FI (FD2, FI2), interaction of FD and FI (FD (times) FI), and temperature (T) as explanatory variables. Based on Akaike’s information criteria (AIC) values and model simplicity, we chose the logistic regression model with FD, FI, FD2 and T as explanatory variables. Let (p)(({varvec{x}})) denote the conditional probability that a flash is from a receptive female given ({varvec{x}}=left(mathrm{FD},mathrm{ FI},mathrm{ T}right)) and (widehat{p})(({varvec{x}})) denote its estimate. The coefficients of the logistic regression model are estimated as follows.
    [Model for the observational data with temperature (T)]
    $$begin{gathered} {text{log}}frac{{hat{p}}}{{1 – hat{p}}} = begin{array}{*{20}l} { – 32.26 + 69.69 times FD – 43.47 times FI – 76.63 times FD^{2} + 0.87 times T} hfill \ {~quad left( {6.50} right)quad quad left( {15.37} right)quad quad quad left( {8.56} right)quad quad quad quad left( {17.44} right)quad quad quad left( {0.19} right)~~} hfill \ end{array} hfill \ quad {text{AIC: 84}}{text{.75}} hfill \ end{gathered}$$[Model for the observational data without temperature (T)]$$begin{gathered} {text{log}}frac{{hat{p}}}{{1 – hat{p}}} = begin{array}{*{20}l} { – 7.69~ + 47.57 times FD~ – 38.29 times FI~ – 52.86 times FD^{2} ~} hfill \ {~;left( {1.86} right)quad quad left( {9.68} right)quad quad quad left( {7.08} right)quad quad quad quad left( {11.38} right)~~} hfill \ end{array} hfill \ quad {text{AIC: 114}}{text{.89}} hfill \ end{gathered}$$where values in parentheses indicate standard deviations. The same applies hereafter. Temperature (T) is included in the model not because it affects the occurrence of receptive females but because it affects the FD and/or FI of receptive females. The AIC value increased by 30, which is substantial, when temperature was excluded from the model.Figure 2 shows the FD and FI of each flash from receptive females, mated females and males with the discriminant boundaries of receptive females from others for (p=0.5).We next considered a discriminant analysis for the experimental data. Let ({q}^{f}({varvec{x}})) denote the conditional probability that a flying male is attracted to a flash of ({varvec{x}}=left(mathrm{FD},mathrm{ FI},mathrm{ T}right)) and lands, and ({widehat{q}}^{f}({varvec{x}})) denote its estimate. Among several models we fit, the smallest AIC value is attained by the logistic regression model with FD, FI and T as explanatory variables, but the AIC is not much different from the model with FD and FI only.
    [Model for flying males with temperature (T)]
    $$begin{gathered} {text{log}}frac{{hat{q}^{f} }}{{1 – hat{q}^{f} }} = begin{array}{*{20}l} { – 0.74~~ – 2.42 times FD – 16.82 times FI + 0.31 times T} hfill \ {~;left( {4.01} right)quad quad left( {0.83} right)quad quad quad left( {4.88} right)quad quad quad quad left( {0.20} right)~} hfill \ end{array} hfill \ quad {text{AIC}}:66.96 hfill \ end{gathered}$$

    [Model for flying males without temperature (T)]
    $$begin{gathered} {text{log}}frac{{hat{q}^{f} }}{{1 – hat{q}^{f} }} = begin{array}{*{20}l} { – 5.36~ – 1.72 times FD – 13.69 times FI} hfill \ {~;left( {1.49} right)quad quad left( {0.63} right)~quad quad left( {4.09} right)~~} hfill \ end{array} hfill \ quad {text{AIC}}:67.61 hfill \ end{gathered}$$
    For sedentary males, the model with the smallest AIC value includes all the quadratic terms of FI and FD but not temperature. Let ({q}^{s}({varvec{x}})) denote the conditional probability that a sedentary male is attracted to a flash of ({varvec{x}}=left(mathrm{FD},mathrm{ FI},mathrm{ T}right)) and ({widehat{q}}^{s}left({varvec{x}}right)) denote its estimate. The logistic regression model for ({q}^{s}({varvec{x}})) with the best AIC value is given as follows.
    [Model for sedentary males]
    $${text{log}}frac{{hat{q}~^{s} }}{{1 – hat{q}~^{s} }} = begin{array}{*{20}l} { – 0.68~ + 7.84 times FD~ + 48.17 times FI – 5.35 times FD^{2} – 166.70 times FI^{2} – 65.67 times FD times FI} hfill \ {;left( {0.97} right)quad quad quad left( {2.99} right)quad quad quad left( {17.74} right)quad quad quad left( {1.74} right)quad quad quad quad left( {72.34} right)quad quad quad quad left( {17.67} right)~} hfill \ end{array}$$
    Figure 3 shows successes and failures of attraction of flying males on the left and sedentary males on the right with estimated discriminant boundaries.Let us now estimate probabilities that a flying male is attracted and lands or a sedentary male is attracted to a flash when a flash is from a receptive female or when a flash is either from a sedentary male or mated female. The probability that a flying male is attracted and lands when a flash is from a receptive female is a conditional probability and is expressed as follows.$$begin{aligned} Pleft(left.begin{array}{*{20}c} {text{Flying male}} \ {text{is attracted}} \ end{array} right|begin{array}{*{20}c} {text{Receptive }} \ {{text{female}}} \ end{array} right) & = frac{{Pleft( {begin{array}{*{20}c} {text{Flying male}} \ {text{is attracted}} \ end{array} {text{ and }}begin{array}{*{20}c} {text{Receptive }} \ {{text{female}}} \ end{array} } right) }}{{Pleft( {begin{array}{*{20}c} {{text{Receptive}}} \ {{text{female}}} \ end{array} } right)}}, \ Pleft( {begin{array}{*{20}c} {{text{Receptive}}} \ {{text{female}}} \ end{array} } right) & = mathop int_{Omega } Pleft(left. begin{array}{*{20}c} {{text{Receptive}}} \ {{text{female}}} \ end{array} right|{varvec{x}} right)fleft( {varvec{x}} right)d{varvec{x}} = mathop int_{Omega }pleft( {varvec{x}} right) fleft( {varvec{x}} right)d{varvec{x}} hspace{5mm}{text{and}} \ Pleft( {begin{array}{*{20}c} {text{Flying male}} \ {text{is attracted}} \ end{array} {text{ and }}begin{array}{*{20}c} {text{Receptive }} \ {{text{female}}} \ end{array} } right) & = mathop int_{Omega } Pleft(left. begin{array}{*{20}c} {{text{Receptive}}} \ {{text{female}}} \ end{array} right|varvec{x} right)Pleft(left. begin{array}{*{20}c} {text{Flying male}} \ {text{is attracted}} \ end{array} right|{varvec{x}} right)fleft( {varvec{x}} right)d{varvec{x}} \ & = mathop int_{Omega } pleft( varvec{x} right)q^{f} left( {varvec{x}} right)fleft( {varvec{x}} right)d{varvec{x}}mathbf{.} \ end{aligned}$$Integrals are taken over the domain (Omega) of ({varvec{x}}=(FD, FI, T)) of all females and males, and (f({varvec{x}})) is the joint density function of ({varvec{x}}.) Because (f({varvec{x}})) is unknown, we use the empirical distribution of the observational data, and conditional probabilities given ({varvec{x}}) are replaced with their estimates by logistic regression models. Let ({{varvec{x}}}_{i}=left(F{D}_{i}, F{I}_{i}, {T}_{i}right), i=mathrm{1,2},dots N) denote the (i) th observation in the observational data. The estimates of probabilities are given as follows:$$begin{aligned} hat{P}left( {begin{array}{*{20}c} {{text{Receptive}}} \ {{text{female}}} \ end{array} }right) & = frac{1}{N}mathop sum limits_{i = 1}^{n} hat{p}left( {{varvec{x}}_{i} } right) hspace{15mm} {text{and}} \ hat{P}left( {begin{array}{*{20}c} {text{Flying male}} \ {text{is attracted}} \ end{array} {text{ and }}begin{array}{*{20}c} {text{Receptive }} \ {{text{female}}} \ end{array} } right) & = frac{1}{N}mathop sum limits_{i = 1}^{n} hat{p}left( {{varvec{x}}_{i} } right) hat{q}^{f} left( {{varvec{x}}_{i} } right). \ end{aligned}$$Thus,$$hat{P}left( left. begin{array}{*{20}c} {text{Flying male}} \ {text{is attracted}} \ end{array} right| begin{array}{*{20}c} {text{Receptive }} \ {text{female}} \ end{array} right) = frac{{mathop sum nolimits_{i = 1}^{n} hat{p}left( {{varvec{x}}_{i} } right) hat{q}^{f} left( {{varvec{x}}_{i} } right)}}{{mathop sum nolimits_{i = 1}^{n}hat{p}left(varvec{x}_i right)}}.$$Similarly, we have$$begin{aligned} hat{P}left( left.begin{array}{*{20}c} {text{Flying male}} \ {text{is attracted}} \ end{array}right| {text{Others}} right) & = frac{{mathop sum nolimits_{i = 1}^{n} (1 – hat{p}left( {{varvec{x}}_{i} } right)) hat{q}^{f} left( {{varvec{x}}_{i} } right)}}{{mathop sum nolimits_{i = 1}^{n} (1 – hat{p}left( {{varvec{x}}_{i} } right))}} \ hat{P}left( left. begin{array}{*{20}c} {text{Sedentary male}} \ {text{is attracted}} \ end{array} right| begin{array}{*{20}c} {text{Receptive }} \ {text{female}} \ end{array} right)& = frac{{mathop sum nolimits_{i = 1}^{n} hat{p}left( {{varvec{x}}_{i} } right) hat{q}^{s} left( {{varvec{x}}_{i} } right)}}{{mathop sum nolimits_{i = 1}^{n} hat{p}left( varvec{x}_{i} right)}}hspace{15mm} {text{ and}} \hat{P}left(left. begin{array}{*{20}c} {text{Sedentary male}} \ {text{is attracted}} \ end{array}right| {text{Others}} right) & = frac{{mathop sum nolimits_{i = 1}^{n} left( {1 – hat{p}left( varvec{x}_{i} right)} right) hat{q}^{s} left( {varvec{x}_{i} } right)}}{mathop sum nolimits_{i = 1}^{n} left( {1 – hat{p}left( varvec{x}_{i} right)} right)} . \ end{aligned}$$The estimated probabilities are shown in Table 1.Table 1 Estimated probabilities of a flying male and a sedentary male being attracted to flashes from a receptive female and from others.Full size table More

  • in

    Ecological niche model transferability of the white star apple (Chrysophyllum albidum G. Don) in the context of climate and global changes

    IPBES (2019): Summary for policymakers of the global assessment report on biodiversity and ecosystem services of the Intergovernmental Science-Policy Platform on Biodiversity and Ecosystem Services. S. Díaz, J. Settele, E. S. Brondízio E.S., H. T. Ngo, M. Guèze, J. Agard, A. Arneth, P. Balvanera, K. A. Brauman, S. H. M. Butchart, K. M. A. Chan, L. A. Garibaldi, K. Ichii, J. Liu, S. M. Subramanian, G. F. Midgley, P. Miloslavich, Z. Molnár, D. Obura, A. Pfaff, S. Polasky, A. Purvis, J. Razzaque, B. Reyers, R. Roy Chowdhury, Y. J. Shin, I. J. Visseren-Hamakers, K. J. Willis, and C. N. Zayas (eds.). IPBES secretariat, Bonn, Germany. 56 p.FAO. in Global Forest Resources Assessment 2020: Main report. Rome. https://doi.org/10.4060/ca9825en (2020).Millennium Ecosystem Assessment (MA). Ecosystems and Human Well-Being: Synthesis. Island Press, Washington (2005)CBD. Considerations for Implementing International Standards and Codes of Conduct in National Invasive Species. Strategies and Plans. CBD (2011).Semper-Pascual, A. et al. Using occupancy models to assess the direct and indirect impacts of agricultural expansion on species’ populations. Biodivers. Conserv. 29, 3669–3688 (2020).
    Google Scholar 
    IPCC. Provisional State of the Global Climate. 2022. https://storymaps.arcgis.com/stories/5417cd9148c248c0985a5b6d028b0277, Accessed 23rd December 2022.Nunez, S. & Alkemade, R. Exploring interaction effects from mechanisms between climate and land-use changes and the projected consequences on biodiversity. Biodivers. Conserv. 30, 3685–3696 (2021).
    Google Scholar 
    Liu, C., White, M. & Newell, G. Measuring and comparing the accuracy of species distribution models with presence absence data. Ecography 34, 232–243. https://doi.org/10.1111/j.1600-0587.2010.06354.x (2011).Article 
    CAS 

    Google Scholar 
    Hao, T., Elith, J., Lahoz-Monfort, J. J. & Guillera-Arroita, G. Testing whether ensemble modelling is advantageous for maximising predictive performance of species distribution models. Ecography 43, 549–558. https://doi.org/10.1111/ecog.04890 (2020).Article 

    Google Scholar 
    Pearson, G. R., Raxworthy, J. C., Nakamura, M. & Peterson, A. T. Predicting species distributions from small numbers of occurrence records: A test case using cryptic geckos in Madagascar. J. Biogeogr 34, 102–117 (2007).
    Google Scholar 
    Thuiller, W. et al. Niche-based modelling as a tool for predicting the risk of alien plant invasions at a global scale. Glob. Chang. Biol. 11, 2234–2250 (2005).ADS 

    Google Scholar 
    He, Y. et al. Predicting potential global distribution and risk regions for potato cyst nematodes (Globodera rostochiensis and Globodera pallida). Sci. Rep. 12(1), 1–10 (2022).ADS 
    CAS 

    Google Scholar 
    Elith, J., Kearney, M. & Phillips, S. The art of modelling range-shifting species. Methods Ecol. Evol. 1, 330–342 (2010).
    Google Scholar 
    Ashraf, U., Chaudhry, M. N. & Peterson, A. T. Ecological niche models of biotic interactions predict increasing pest risk to olive cultivars with changing climate. Ecosphere 12, e03714. https://doi.org/10.1002/ecs2.3714 (2021).Article 

    Google Scholar 
    Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43–57 (2011).
    Google Scholar 
    Ganglo, J. C. et al. Ecological niche modeling and strategies for the conservation of Dialium guineense Willd. (Black velvet) in West Africa. Int. J. Biodivers. Conserv. 9, 373–388 (2017).
    Google Scholar 
    Djotan, A. K. G. et al. How far can climate changes help to conserve and restore Garcinia kola Heckel, an extinct species in the wild in Benin (West Africa). Int. J. Biodivers. Conserv. 10, 203–213 (2018).
    Google Scholar 
    Kakpo, S. B. et al. Spatial distribution and impacts of climate change on Milicia excelsa in Benin, West Africa. J. For. Res. 32, 143–150. https://doi.org/10.1007/s11676-019-01069-7 (2021).Article 

    Google Scholar 
    Jung, M. et al. A global map of terrestrial habitat types. Sci. Data 7(1), 1–8 (2020).MathSciNet 

    Google Scholar 
    Poor, E. E., Scheick, B. K. & Mullinax, J. M. Multiscale consensus habitat modeling for landscape level conservation prioritization. Sci. Rep. 10(1), 1–13 (2020).
    Google Scholar 
    Schüßler, D., Mantilla-Contreras, J., Stadtmann, R., Ratsimbazafy, J. H. & Radespiel, U. Identification of crucial stepping stone habitats for biodiversity conservation in northeastern Madagascar using remote sensing and comparative predictive modeling. Biodivers. Conserv. 29, 2161–2184 (2020).
    Google Scholar 
    Campos-Cerqueira, M. et al. Climate change is creating a mismatch between protected areas and suitable habitats for frogs and birds in Puerto Rico. Biodivers. Conserv. 30, 3509–3528 (2021).
    Google Scholar 
    Biddle, R. et al. The value of local community knowledge in species distribution modelling for a threatened Neotropical parrot. Biodivers. Conserv. 30, 1803–1823 (2021).
    Google Scholar 
    Costa, A. et al. Modelling the amphibian chytrid fungus spread by connectivity analysis: Towards a national monitoring network in Italy. Biodivers. Conserv. 30(10), 2807–2825 (2021).
    Google Scholar 
    Konowalik, K. & Nosol, A. Evaluation metrics and validation of presence-only species distribution models based on distributional maps with varying coverage. Sci. Rep. 11(1), 1–15 (2021).
    Google Scholar 
    Borgelt, J., Sicacha-Parada, J., Skarpaas, O. & Verones, F. Native range estimates for red-listed vascular plants. Sci. Data 9(1), 1–12 (2022).
    Google Scholar 
    Brychkova, G. et al. Climate change and land-use change impacts on future availability of forage grass species for Ethiopian dairy systems. Sci. Rep. 12(1), 1–16 (2022).
    Google Scholar 
    Carrara, R. & Roig-Juñent, S. A. Maps of potential biodiversity: when the tools for regional conservation planning clash with species ecological niches. Biodivers. Conserv. 31(2), 651–665 (2022).
    Google Scholar 
    Critchlow, R. et al. Multi-taxa spatial conservation planning reveals similar priorities between taxa and improved protected area representation with climate change. Biodivers. Conserv. 31(2), 683–702 (2022).
    Google Scholar 
    González-Orozco, C. E., Porcel, M., Rodriguez-Medina, C. & Yockteng, R. Extreme climate refugia: A case study of wild relatives of cacao (Theobroma cacao) in Colombia. Biodivers. Conserv. 31(1), 161–182 (2022).
    Google Scholar 
    Karami, S., Ejtehadi, H., Moazzeni, H., Vaezi, J. & Behroozian, M. Minimal climate change impacts on the geographic distribution of Nepeta glomerulosa, medicinal species endemic to southwestern and central Asia. Sci. Rep. 12(1), 1–10 (2022).ADS 
    CAS 

    Google Scholar 
    Montemayor, S. I., Besteiro, S. I. & del Río, M. G. Integrating ecological and biogeographical tools for the identification of conservation areas in two Neotropical biogeographic provinces in Argentina based on phytophagous insects. Biodivers. Conserv. 31(7), 1969–1986 (2022).
    Google Scholar 
    da Silva, L. B. et al. How future climate change and deforestation can drastically affect the species of monkeys endemic to the eastern Amazon, and priorities for conservation. Biodivers. Conserv. 31(3), 971–988 (2022).
    Google Scholar 
    Yousefi, M. & Naderloo, R. Global habitat suitability modeling reveals insufficient habitat protection for mangrove crabs. Sci. Rep. 12(1), 1–9 (2022).
    Google Scholar 
    Yudaputra, A. et al. Habitat preferences, spatial distribution and current population status of endangered giant flower Amorphophallus titanum. Biodivers. Conserv. 31(3), 831–854 (2022).
    Google Scholar 
    Gomes, V. H. et al. Species distribution modelling: Contrasting presence-only models with plot abundance data. Sci. Rep. 8(1), 1–12 (2018).
    Google Scholar 
    Hoveka, L. N., van der Bank, M., Bezeng, B. S. & Davies, T. J. Identifying biodiversity knowledge gaps for conserving South Africa’s endemic flora. Biodivers. Conserv. 29, 2803–2819 (2020).
    Google Scholar 
    Macdonald, D. W. et al. Predicting biodiversity richness in rapidly changing landscapes: Climate, low human pressure or protection as salvation?. Biodivers. Conserv. 29, 4035–4057 (2020).
    Google Scholar 
    Peng, Y., Feng, J., Sang, W. & Axmacher, J. C. Geographical divergence of species richness and local homogenization of plant assemblages due to climate change in grasslands. Biodivers. Conserv. 31(3), 797–810 (2022).
    Google Scholar 
    Rincón, V. et al. Connectivity of Natura 2000 potential natural riparian habitats under climate change in the Northwest Iberian Peninsula: Implications for their conservation. Biodivers. Conserv. 31(2), 585–612 (2022).MathSciNet 

    Google Scholar 
    Leta, S. et al. Modeling the global distribution of Culicoides imicola: An Ensemble approach. Sci. Rep. 9(1), 1–9 (2019).ADS 
    CAS 

    Google Scholar 
    Messina, J. P. et al. The current and future global distribution and population at risk of dengue. Nat. Microbiol. 4(9), 1508–1515 (2019).CAS 

    Google Scholar 
    Redding, D. W. et al. Impacts of environmental and socio-economic factors on emergence and epidemic potential of Ebola in Africa. Nat. Commun. 10, 4531 (2019).ADS 

    Google Scholar 
    Klitting, R. et al. Predicting the evolution of the Lassa virus endemic area and population at risk over the next decades. Nat. Commun. 13(1), 1–15 (2022).
    Google Scholar 
    Li, Y. P., Gao, X., An, Q., Sun, Z. & Wang, H. B. Ecological niche modeling based on ensemble algorithms to predicting current and future potential distribution of African swine fever virus in China. Sci. Rep. 12(1), 1–11 (2022).ADS 

    Google Scholar 
    Oppel, S., Schaefer, H. M., Schmidt, V. & Schröder, B. How much suitable habitat is left for the last known population of the Pale-headed Brush-Finch?. The Condor 106, 429–434 (2004).
    Google Scholar 
    Heikkinen, R. K., Marmion, M. & Luoto, M. Does the interpolation accuracy of species distribution models come at the expense of transferability?. Ecography 35, 276–288 (2012).
    Google Scholar 
    Manzoor, S. A., Griffiths, G. & Lukac, M. Species distribution model transferability and model grain size–finer may not always be better. Sci. Rep. 8(1), 1–9 (2018).
    Google Scholar 
    Yates, K. L. et al. Outstanding challenges in the transferability of ecological models. Trends Ecol. Evol. 33, 790–802. https://doi.org/10.1016/j.tree.2018.08.001 (2018).Article 

    Google Scholar 
    Gantchoff, M. G. et al. Distribution model transferability for a wide-ranging species, the Gray Wolf. Sci. Rep. 12(1), 1–11 (2022).
    Google Scholar 
    Lyam, P. T., Adeyemi, T. O. & Ogundipe, O. T. Distribution modelling of Chrysophyllum albidum G. Don. in South-West Nigeria. J. Nat. Environ. Sci. 3, 7–14 (2012).
    Google Scholar 
    Orwa, C., Mutua, A., Kindt, R., Jamnadass, R., & Simons, A. Agroforestree Database: a tree reference and selection guide version 4.0. World Agroforestry Centre, Kenya. http://www.worldagroforestry.org/af/treedb/ (2009).Bolanle-Ojo, O. T. & Onyekwelu, J. C. Socio-economic importance of Chrysophyllum albidum G. Don. Rainforest and derived savanna ecosystems of Ondo state, Nigeria. Eur. J. Agric. For. Res. 2, 43–51 (2014).
    Google Scholar 
    Ugwu, J. A. & Umeh, V. C. Assessment of African star apple (Chrysophyllum albidum) fruit damage due to insect pests in Ibadan Southwest Nigeria. Res. J. For. 9, 87–92 (2015).
    Google Scholar 
    Akoegninou, A., Van der Burg, W. J. & Van der Maesen, L. J. G. in Flore Analytique du Bénin (No. 06.2). Backhuys Publishers. (2006).Houessou, L. G., Lougbegnon, T. O., Gbesso, F. G., Anagonou, L. E. & Sinsin, B. Ethno-botanical study of the African star apple (Chrysophyllum albidum G. Don) in the Southern Benin (West Africa). J. Ethnobiol. Ethnomed. 8, 1–10 (2012).
    Google Scholar 
    Lougbégnon, O. T., Nassi, K. M. & Gbesso, G. H. F. Ethnobotanique quantitative de l’usage de Chrysophyllum albidum G. Don par les populations locales au Bénin. J. Appl. Biosci. 95, 9028–9038 (2015).
    Google Scholar 
    Nartey, D., Gyesi, J. N., & Borquaye, L. S. Chemical composition and biological activities of the essential oils of Chrysophyllum albidum G. Don (African star apple). Biochem. Res. Int. 2021 (2021).Olajide, O., Udo, E. S., & Out, D. O. Diversity and population of timber tree species producing valuable non-timber products in two tropical rainforests in cross river state, Nigeria. J. Agric. Soc. Sci. ISSN Print 1813–2235 (2008)Platts, P. J., Omeny, P. & Marchant, R. AFRICLIM: High-resolution climate projections for ecological applications in Africa. Afr. J. Ecol. 53, 103–108 (2015).
    Google Scholar 
    Hajima, T. et al. Development of the MIROC-ES2L Earth system model and the evaluation of biogeochemical processes and feedbacks. Geosci. Model Dev. 13, 2197–2244 (2020).ADS 

    Google Scholar 
    Aiello-Lammens, M. E., Boria, R. A., Radosavljevic, A., Vilela, B. & Anderson, R. P. An R package for spatial thinning of species occurrence records for use in ecological niche models. Ecography 38, 541–545 (2015).
    Google Scholar 
    Lannuzel, G., Balmot, J., Dubos, N., Thibault, M. & Fogliani, B. High-resolution topographic variables accurately predict the distribution of rare plant species for conservation area selection in a narrow-endemism hotspot in New Caledonia. Biodivers. Conserv. 30, 963–990 (2021).
    Google Scholar 
    Scales, K. L. et al. Scale of inference: On the sensitivity of habitat models for wide-ranging marine predators to the resolution of environmental data. Ecography 40, 210–220 (2017).
    Google Scholar 
    Fick, S. E. & Hijmans, R. J. (2017) WorldClim 2: New 1km spatial resolution climate surfaces for global land areas. Int. J. Climatol. 37(12), 4302–4315 (2017).
    Google Scholar 
    Center for International Earth Science Information Network: CIESIN—Columbia University. 2021. Gridded Population of the World, Version 4 (GPWv4): Administrative Unit Center Points with Population Estimates, Revision 11. Palisades, NY: NASA Socioeconomic Data and Applications Center (SEDAC). Last Accessed 7th December, 2021. https://doi.org/10.7927/H4BC3WMT (2018)Eyring, V. et al. Overview of the coupled model intercomparison project phase 6 (CMIP6) experimental design and organization. Geosci. Model Dev. 9, 1937–1958. https://doi.org/10.5194/gmd-9-1937-2016 (2016).Article 
    ADS 

    Google Scholar 
    Dormann, C. F. et al. Collinearity: A review of methods to deal with it and a simulation study evaluating their performance. Ecography 36, 27–46 (2013).
    Google Scholar 
    Naimi, B. & Araújo, M. B. sdm: A reproducible and extensible R platform for species distribution modelling. Ecography 39, 368–375. https://doi.org/10.1111/ecog.01881 (2016).Article 

    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL https://www.R-project.org/ (2020)Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).
    Google Scholar 
    Elith, J., Leathwick, J. R. & Hastie, T. A working guide to boosted regression trees. J. Anim. Ecol. 77, 802–813 (2008).CAS 

    Google Scholar 
    Breiman, L. Random forests. Mach. Learn. 45, 5–32. https://doi.org/10.1023/A:1010933404324 (2001).Article 
    MATH 

    Google Scholar 
    Zheng, B. & Agresti, A. Summarizing the predictive power of a generalized linear model. Stat. Med. 19, 1771–1781 (2000).CAS 

    Google Scholar 
    Hastie, T. J. in Generalized Additive Models, Statistical models, 249–307 (Routledge, 2017).Friedman, J. H. Multivariate adaptive regression splines. Ann. Stat. 19, 1–67 (1991).MathSciNet 
    MATH 

    Google Scholar 
    Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232 (2006).
    Google Scholar 
    Barbet-Massin, M., Jiguet, F., Albert, C. H. & Thuiller, W. Selecting pseudo-absences for species distribution models: How, where and how many?. Methods Ecol. Evol. 3, 327–338 (2012).
    Google Scholar 
    QGIS Development Team. QGIS geographic information system. Open Source Geospatial Foundation Project. http://qgis.osgeo.org (2021)Fandohan, B. et al. Women’s traditional knowledge, use value, and the contribution of tamarind (Tamarindus indica L.) to rural households’ cash income in Benin. Econ. Bot. 64, 248–259 (2010).
    Google Scholar 
    Gouwakinnou, G. N., Lykke, A. M., Assogbadjo, A. E. & Sinsin, B. Local knowledge, pattern and diversity of use of Sclerocarya birrea. J. Ethnobiol. Ethnomed. 7, 1–9 (2011).
    Google Scholar 
    O’Donnell, M. S. & Ignizio, D. A. Bioclimatic predictors for supporting ecological applications in the conterminous United States. US Geological Surv. Data Ser. 691, 4–9 (2012).
    Google Scholar 
    United Nations. 2022. World population projected to reach 9.8 billion in 2050, and 11.2 billion in 2100. https://www.un.org/en/desa/world-population-projected-reach-98-billion-2050-and-112-billion-2100, Accessed 25th December 2022 .Gbesso, F. H. G., Tente, B. H. A., Gouwakinnou, G. N. & Sinsin, B. A. Influence des changements climatiques sur la distribution géographique de Chrysophyllum albidum G. Don (Sapotaceae) au Benin. Int. J. Biol. Chem. Sci. 7, 2007–2018 (2013).
    Google Scholar 
    Elith, J. et al. Novel methods improve prediction of species’ distributions from occurrence data. Ecography 29, 129–151 (2006).
    Google Scholar 
    Mi, C., Huettmann, F., Guo, Y., Han, X. & Wen, L. Why choose random forest to predict rare species distribution with few samples in large undersampled areas? Three Asian crane species models provide supporting evidence. Peer J. 5, e2849. https://doi.org/10.7717/peerj.2849 (2017).Article 

    Google Scholar 
    Segurado, P. & Araujo, M. B. An evaluation of methods for modelling species distributions. J. Biogeogr. 31, 1555–1568 (2004).
    Google Scholar 
    Pearson, R. G. et al. Model-based uncertainty in species range prediction. J. Biogeogr. 33, 1704–1711 (2006).
    Google Scholar 
    Dambros, C. et al. The role of environmental filtering, geographic distance and dispersal barriers in shaping the turnover of plant and animal species in Amazonia. Biodivers. Conserv. 29, 3609–3634 (2020).
    Google Scholar  More