More stories

  • in

    Stoichiometric niche, nutrient partitioning and resource allocation in a solitary bee are sex-specific and phosphorous is allocated mainly to the cocoon

    1.
    Stearns, S. C. The Evolution of Life Histories (Oxford University Press, Oxford, 1996).
    Google Scholar 
    2.
    Sterner, R. W. & Elser, J. J. Ecological Stoichiometry: The Biology of Elements from Molecules to the Biosphere (Princeton University Press, Princeton, 2002).
    Google Scholar 

    3.
    Kaspari, M. & Powers, J. S. Biogeochemistry and geographical ecology: Embracing all twenty-five elements required to build organisms. Am. Nat. 188, S62–S73 (2016).
    PubMed  Article  PubMed Central  Google Scholar 

    4.
    Kozlowski, J. Why life histories are diverse. Polish J. Ecol. 54, 585–605 (2006).
    Google Scholar 

    5.
    Ejsmond, M. J., Varpe, Ø., Czarnoleski, M. & Kozłowski, J. Seasonality in offspring value and trade-offs with growth explain capital breeding. Am. Nat. 186, E111–E125 (2015).
    Article  Google Scholar 

    6.
    Filipiak, M. A better understanding of bee nutritional ecology is needed to optimize conservation strategies for wild bees-the application of ecological stoichiometry. Insects 9, 85 (2018).
    PubMed Central  Article  Google Scholar 

    7.
    Filipiak, Z. M. & Filipiak, M. The scarcity of specific nutrients in wild bee larval food negatively influences certain life history traits. Biology (Basel). 9, 462 (2020).

    8.
    Simpson, S. J. & Raubenheimer, D. The Nature of Nutrition: A Unifying Framework from Animal Adaptation to Human Obesity (Princeton University Press, Princeton, 2012).
    Google Scholar 

    9.
    Bärlocher, F. & Rennenberg, H. Food chains and nutrient cycles. In Ecological biochemistry (eds Krauss, G. J. & Nies, D. H.) 92–122 (Wiley, New York, 2014).
    Google Scholar 

    10.
    DeAngelis, D. L. Dynamics of Nutrient Cycling and Food Webs (Springer Netherlands, Amsterdam, 1992).
    Google Scholar 

    11.
    Schlesinger, W. H. & Bernhardt, E. S. Biogeochemistry (Academic Press, London, 2020).
    Google Scholar 

    12.
    Jeyasingh, P. D., Cothran, R. D. & Tobler, M. Testing the ecological consequences of evolutionary change using elements. Ecol. Evol. 4, 528–538 (2014).
    PubMed  PubMed Central  Article  Google Scholar 

    13.
    Jeyasingh, P. D., Goos, J. M., Thompson, S. K., Godwin, C. M. & Cotner, J. B. Ecological stoichiometry beyond redfield: An ionomic perspective on elemental homeostasis. Front. Microbiol. 8, 722 (2017).
    PubMed  PubMed Central  Article  Google Scholar 

    14.
    González, A. L. et al. Ecological mechanisms and phylogeny shape invertebrate stoichiometry: A test using detritus-based communities across Central and South America. Funct. Ecol. 32, 2448–2463 (2018).
    Article  Google Scholar 

    15.
    Peñuelas, J. et al. The bioelements, the elementome, and the biogeochemical niche. Ecology 100, e02652 (2019).
    PubMed  Article  PubMed Central  Google Scholar 

    16.
    Fagan, W. F. & Denno, R. F. Stoichiometry of actual vs. potential predator-prey interactions: Insights into nitrogen limitation for arthropod predators. Ecol. Lett. 7, 876–883 (2004).
    Article  Google Scholar 

    17.
    Kay, A. D. et al. Toward a stoichiometric framework for evolutionary biology. Oikos 109, 6–17 (2005).
    Article  Google Scholar 

    18.
    Cherif, M. et al. An operational framework for the advancement of a molecule-to-biosphere stoichiometry theory. Front. Mar. Sci. 4, 1–16 (2017).
    ADS  Article  Google Scholar 

    19.
    Welti, N. et al. Bridging food webs, ecosystem metabolism, and biogeochemistry using ecological stoichiometry theory. Front. Microbiol. 8, 1298 (2017).
    PubMed  PubMed Central  Article  Google Scholar 

    20.
    Hessen, D. O., Elser, J. J., Sterner, R. W. & Urabe, J. Ecological stoichiometry: An elementary approach using basic principles. Limnol. Oceanogr. 58, 2219–2236 (2013).
    ADS  CAS  Article  Google Scholar 

    21.
    Lemoine, N. P., Giery, S. T. & Burkepile, D. E. Differing nutritional constraints of consumers across ecosystems. Oecologia 174, 1367–1376 (2014).
    ADS  PubMed  Article  PubMed Central  Google Scholar 

    22.
    Morehouse, N. I., Nakazawa, T., Booher, C. M., Jeyasingh, P. D. & Hall, M. D. Sex in a material world: Why the study of sexual reproduction and sex-specific traits should become more nutritionally-explicit. Oikos 119, 766–778 (2010).
    Article  Google Scholar 

    23.
    Filipiak, M. Key pollen host plants provide balanced diets for wild bee larvae: A lesson for planting flower strips and hedgerows. J. Appl. Ecol. 56, 1410–1418 (2019).
    CAS  Article  Google Scholar 

    24.
    Goos, J. M., Cothran, R. D. & Jeyasingh, P. D. Within-population variation in the chemistry of life: The stoichiometry of sexual dimorphism in multiple dimensions. Evol. Ecol. 31, 635–651 (2017).
    Article  Google Scholar 

    25.
    Halvorson, H. M., Scott, J. T., Sanders, A. J. & Evans-White, M. A. A stream insect detritivore violates common assumptions of threshold elemental ratio bioenergetics models. Freshw. Sci. 34, 508–518 (2015).
    Article  Google Scholar 

    26.
    Meunier, C. L. et al. From elements to function: Toward unifying ecological stoichiometry and trait-based ecology. Front. Environ. Sci. 5, 1–10 (2017).
    Article  Google Scholar 

    27.
    Sperfeld, E., Wagner, N. D., Halvorson, H. M., Malishev, M. & Raubenheimer, D. Bridging ecological stoichiometry and nutritional geometry with homeostasis concepts and integrative models of organism nutrition. Funct. Ecol. 31, 286–296 (2017).
    Article  Google Scholar 

    28.
    Filipiak, M. & Weiner, J. Plant–insect interactions: The role of ecological stoichiometry. Acta Agrobot. 70, 1–16 (2017).
    Article  Google Scholar 

    29.
    Elser, J. J., Dobberfuhl, D. R., MacKay, N. A. & Schampel, J. H. Organism size, life history, and N: P stoichiometry: Toward a unified view of cellular and ecosystem processes. Bioscience 46, 674–684 (1996).
    Article  Google Scholar 

    30.
    Polidori, C. et al. Strong phylogenetic constraint on transition metal incorporation in the mandibles of the hyper-diverse Hymenoptera (Insecta). Org. Divers. Evol. https://doi.org/10.1007/s13127-020-00448-x (2020).
    Article  Google Scholar 

    31.
    Bosch, J., Sgolastra, F. & Kemp, W. P. Life cycle ecophysiology of Osmia mason bees used as crop pollinators. In Bee Pollination in Agricultural Eco-systems (eds James, R. & Pitts-Singer, T. L.) 83–105 (Oxford Scholarship Online, Oxford, 2008).
    Google Scholar 

    32.
    Giejdasz, K. & Wilkaniec, Z. Individual development of the red mason bee (Osmia rufa L., Megachilidae) under natural and laboratory conditions. J. Apic. Sci. 46, 51–57 (2002).
    Google Scholar 

    33.
    Gruber, B., Eckel, K., Everaars, J. & Dormann, C. F. On managing the red mason bee (Osmia bicornis) in apple orchards. Apidologie 42, 564–576 (2011).
    Article  Google Scholar 

    34.
    Kaspari, M. The seventh macronutrient: How sodium shortfall ramifies through populations, food webs and ecosystems. Ecol. Lett. 23, 1153–1168 (2020).
    PubMed  Article  PubMed Central  Google Scholar 

    35.
    Rizzuto, M. et al. Patterns and potential drivers of intraspecific variability in the body C, N, and P composition of a terrestrial consumer, the snowshoe hare (Lepus americanus). Ecol. Evol. 9, 14453–14464 (2019).
    PubMed  PubMed Central  Article  Google Scholar 

    36.
    Sitters, J. & Olde Venterink, H. The need for a novel integrative theory on feedbacks between herbivores, plants and soil nutrient cycling. Plant Soil 396, 421–426 (2015).
    CAS  Article  Google Scholar 

    37.
    Sitters, J. et al. Nutrient availability controls the impact of mammalian herbivores on soil carbon and nitrogen pools in grasslands. Glob. Change Biol. 26, 2060–2071 (2020).
    ADS  Article  Google Scholar 

    38.
    Sitters, J. et al. The stoichiometry of nutrient release by terrestrial herbivores and its ecosystem consequences. Front. Earth Sci. 5, 1–8 (2017).
    Article  Google Scholar 

    39.
    González, A. L., Fariña, J. M., Kay, A. D., Pinto, R. & Marquet, P. A. Exploring patterns and mechanisms of interspecific and intraspecific variation in body elemental composition of desert consumers. Oikos 120, 1247–1255 (2011).
    Article  Google Scholar 

    40.
    Seidelmann, K. Optimal progeny body size in a solitary bee, Osmia bicornis (Apoidea: Megachilidae). Ecol. Entomol. 39, 656–663 (2014).
    Article  Google Scholar 

    41.
    Kim, J. Y. Female size and fitness in the leaf-cutter bee Megachile apicalis. Ecol. Entomol. 22, 275–282 (1997).
    Article  Google Scholar 

    42.
    Markow, T. et al. Elemental stoichiometry of Drosophila and their hosts. Funct. Ecol. 13, 78–84 (1999).
    Article  Google Scholar 

    43.
    Bergwitz, C. & Jüppner, H. Phosphate sensing. Adv. Chronic Kidney Dis. 18, 132–144 (2011).
    PubMed  PubMed Central  Article  Google Scholar 

    44.
    Werner, A. & Kinne, R. K. H. Evolution of the Na-Pi cotransport systems. Am. J. Physiol. Regul. Integr. Comp. Physiol. 280, R301–R312 (2001).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    45.
    Morgan, A. J., Kille, P. & Stürzenbaum, S. R. Microevolution and ecotoxicology of metals in invertebrates. Environ. Sci. Technol. 41, 1085–1096 (2007).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    46.
    Bednarska, A. J., Świątek, Z. M. & Labecka, A. M. Effects of cadmium bioavailability in food on its distribution in different tissues in the ground beetle Pterostichus oblongopunctatus. Bull. Environ. Contam. Toxicol. 103, 421–427 (2019).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    47.
    Świątek, Z. M. & Bednarska, A. J. Energy reserves and respiration rate in the earthworm Eisenia andrei after exposure to zinc in nanoparticle or ionic form. Environ. Sci. Pollut. Res. Int. 26, 24933–24945 (2019).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    48.
    Cohen, A. C. Insect Diets: Science and Technology (CRC Press, Boca Raton, 2005).
    Google Scholar 

    49.
    Seidelmann, K. Optimal resource allocation, maternal investment, and body size in a solitary bee, Osmia bicornis. Entomol. Exp. Appl. 166, 790–799 (2018).
    Article  Google Scholar 

    50.
    Bosch, J. & Vicens, N. Relationship between body size, provisioning rate, longevity and reproductive success in females of the solitary bee Osmia cornuta. Behav. Ecol. Sociobiol. 60, 26–33 (2006).
    Article  Google Scholar 

    51.
    Seidelmann, K., Ulbrich, K. & Mielenz, N. Conditional sex allocation in the Red Mason bee, Osmia rufa. Behav. Ecol. Sociobiol. 64, 337–347 (2010).
    Article  Google Scholar 

    52.
    González, A. L., Dézerald, O., Marquet, P. A., Romero, G. Q. & Srivastava, D. S. The multidimensional stoichiometric niche. Front. Ecol. Evol. 5, 110 (2017).
    Article  Google Scholar 

    53.
    Lemmen, K. D., Butler, O. M., Koffel, T., Rudman, S. M. & Symons, C. C. Stoichiometric traits vary widely within species: A meta-analysis of common garden experiments. Front. Ecol. Evol. 7, 1–15 (2019).
    Article  Google Scholar 

    54.
    Prater, C., Wagner, N. D. & Frost, P. C. Interactive effects of genotype and food quality on consumer growth rate and elemental content. Ecology 98, 1399–1408 (2017).
    PubMed  Article  PubMed Central  Google Scholar 

    55.
    Sherman, R. E., Chowdhury, P. R., Baker, K. D., Weider, L. J. & Jeyasingh, P. D. Genotype-specific relationships among phosphorus use, growth and abundance in Daphnia pulicaria. R. Soc. Open Sci. 4, 170770 (2017).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    56.
    Zajitschek, F. & Connallon, T. Partitioning of resources: The evolutionary genetics of sexual conflict over resource acquisition and allocation. J. Evol. Biol. 30, 826–838 (2017).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    57.
    Moe, S. J. et al. Recent advances in ecological stoichiometry: Insights for population and community ecology. Oikos 109, 29–39 (2005).
    Article  Google Scholar 

    58.
    Peñuelas, J., Sardans, J., Ogaya, R. & Estiarte, M. Nutrient stoichiometric relations and biogeochemical niche in coexisting plant species: Effect of simulated climate change. Polish J. Ecol. 56, 613–622 (2008).
    Google Scholar 

    59.
    Urbina, I. et al. Plant community composition affects the species biogeochemical niche. Ecosphere 8, e01801 (2017).
    Article  Google Scholar 

    60.
    Jeyasingh, P. D., Goos, J. M., Lind, P. R., Roy Chowdhury, P. & Sherman, R. E. Phosphorus supply shifts the quotas of multiple elements in algae and Daphnia: Ionomic basis of stoichiometric constraints. Ecol. Lett. 23, 1064–1072 (2020).
    PubMed  Article  PubMed Central  Google Scholar 

    61.
    Ruedenauer, F. A. et al. Best be (e) on low fat: Linking nutrient perception, regulation and fitness. Ecol. Lett. 23, 545–554 (2020).
    PubMed  Article  PubMed Central  Google Scholar 

    62.
    Trinkl, M. et al. Floral species richness correlates with changes in the nutritional quality of larval diets in a stingless bee. Insects 11, E125 (2020).
    PubMed  Article  PubMed Central  Google Scholar 

    63.
    Roswell, M., Dushoff, J. & Winfree, R. Male and female bees show large differences in floral preference. PLoS ONE 14, e0214909 (2019).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    64.
    Vaudo, A. D. et al. Pollen protein: Lipid macronutrient ratios may guide broad patterns of bee species floral preferences. Insects 11, 132 (2020).
    PubMed Central  Article  Google Scholar 

    65.
    Hammer, Ø., Harper, D. A. & Ryan, P. D. PAST: Paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4, 9 (2001).
    Google Scholar 

    66.
    Smilauer, P. & Lepš, J. Multivariate Analysis of Ecological Data using CANOCO 5 (Cambridge University Press, Cambridge, 2014).
    Google Scholar  More

  • in

    Species versus within-species niches: a multi-modelling approach to assess range size of a spring-dwelling amphibian

    1.
    Araújo, M. B. et al. Standards for distribution models in biodiversity assessments. Sci. Adv. 5, eaat4858 (2019).
    ADS  PubMed  PubMed Central  Article  Google Scholar 
    2.
    Peterson, M. L., Doak, D. F. & Morris, W. F. Incorporating local adaptation into forecasts of species’ distribution and abundance under climate change. Glob. Change. Biol 25, 775–793 (2019).
    ADS  Article  Google Scholar 

    3.
    Rodríguez-Rodríguez, E. J. et al. Niche models at inter- and intraspecific levels reveal hierarchical niche differentiation in midwife toads. Sci. Rep. 10, 10942 (2020).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    4.
    Harvey, P. H. & Pagel, M. D. The Comparative Method in Evolutionary Biology Vol. 239 (Oxford University Press, Oxford, 1991).
    Google Scholar 

    5.
    Banerjee, A. K., Mukherjee, A., Guo, W., Ng, W. L. & Huang, Y. Combining ecological niche modeling with genetic lineage information to predict potential distribution of Mikania micrantha Kunth in South and Southeast Asia under predicted climate change. Glob. Ecol. Conserv. 20, e00800 (2019).
    Article  Google Scholar 

    6.
    Martínez-Freiría, F. et al. Climatic refugia boosted allopatric diversification in western Mediterranean vipers. J. Biogeogr. https://doi.org/10.1111/jbi.13861 (2020).
    Article  Google Scholar 

    7.
    Groom, Q. J., Marsh, C. J., Gavish, Y. & Kunin, W. E. How to predict fine resolution occupancy from coarse occupancy data. Methods Ecol. Evol. 9, 2273–2284 (2018).
    Article  Google Scholar 

    8.
    Li, Y. et al. Climate and topography explain range sizes of terrestrial vertebrates. Nat. Clim. Change 6, 498–502 (2016).
    ADS  Article  Google Scholar 

    9.
    Cardoso, P., Borges, P. A. V., Triantis, K. A., Ferrández, M. A. & Martín, J. L. Adapting the IUCN Red List criteria for invertebrates. Biol. Conserv. 144, 2432–2440 (2011).
    Article  Google Scholar 

    10.
    Burbidge, A., Woinarski, J. & Harrison, P. The Action Plan for Australian Mammals 2012 (Csiro Publishing, Clayton, 2014).
    Google Scholar 

    11.
    Jiménez-Alfaro, B., Draper, D. & Nogués-Bravo, D. Modeling the potential area of occupancy at fine resolution may reduce uncertainty in species range estimates. Biol. Conserv. 147, 190–196 (2012).
    Article  Google Scholar 

    12.
    Kamino, L. H. Y., Siqueira, M., Sánchez-Tapia, A. & Stehmann, J. R. Reassessment of the extinction risk of endemic species in the Neotropics: how can modelling tools help us. Nat. Conserv. 10, 191–198 (2012).
    Article  Google Scholar 

    13.
    Kluber, M. R., Olson, D. H. & Puettmann, K. J. Amphibian distributions in riparian and upslope areas and their habitat associations on managed forest landscapes in the Oregon Coast Range. For. Ecol. Manage 256, 529–535 (2008).
    Article  Google Scholar 

    14.
    Hijmans, R. J., Cameron, S. E., Parra, J. L., Jones, P. G. & Jarvis, A. Very high resolution interpolated climate surfaces for global land areas. Int. J. Climatol. 25, 1965–1978 (2005).
    Article  Google Scholar 

    15.
    Guisan, A. & Thuiller, W. Predicting species distribution: offering more than simple habitat models. Ecol. Lett. 8, 993–1009 (2005).
    Article  Google Scholar 

    16.
    Steinfartz, S., Hwang, U. W., Tautz, D., Öz, M. & Veith, M. Molecular phylogeny of the salamandrid genus Neurergus: evidence for an intrageneric switch of reproductive biology. Amphib-Reptilia. 23, 419–431 (2002).
    Article  Google Scholar 

    17.
    Goudarzi, F. et al. Geographic separation and genetic differentiation of populations are not coupled with niche differentiation in threatened Kaiser’s spotted newt (Neurergus kaiseri). Sci. Rep. 9, 6239 (2019).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    18.
    IUCN SSC Amphibian Specialist Group. Neurergus kaiseri. The IUCN Red List of Threatened Species 2016: e.T59450A49436271. https://doi.org/10.2305/IUCN.UK.2016-3.RLTS.T59450A49436271.en. Downloaded on 29 November 2018.

    19.
    Vaissi, S. & Sharifi, M. Integrating multi-criteria decision analysis with a GIS-based siting procedure to select a protected area for the Kaiser’s mountain newt, Neurergus kaiseri (Caudata: Salamandridae). Glob. Ecol. Conserv. 20, e00738 (2019).
    Article  Google Scholar 

    20.
    Rancilhac, L. et al. Phylogeny and species delimitation of Near Eastern Neurergus newts (Salamandridae) based on genome-wide RADseq data analysis. Mol. Phylogenet. Evol. 133, 189–197 (2019).
    PubMed  Article  PubMed Central  Google Scholar 

    21.
    Pearman, P. B., D’Amen, M., Graham, C. H., Thuiller, W. & Zimmermann, N. E. Within-taxon niche structure: niche conservatism, divergence and predicted effects of climate change. Ecography 33, 990–1003 (2010).
    Article  Google Scholar 

    22.
    Lecocq, T., Harpke, A., Rasmont, P. & Schweiger, O. Integrating intraspecific differentiation in species distribution models: Consequences on projections of current and future climatically suitable areas of species. Divers. Distrib. 25, 1088–1100 (2019).
    Article  Google Scholar 

    23.
    Rodríguez-Rodríguez, E. J. et al. Climate change challenges IUCN conservation priorities: A test with western Mediterranean amphibians. SN Appl. Sci. 2, 216 (2020).
    Article  Google Scholar 

    24.
    Joppa, L. N. et al. Impact of alternative metrics on estimates of extent of occurrence for extinction risk assessment. Conserv. Biol. 30, 362–370 (2016).
    PubMed  Article  PubMed Central  Google Scholar 

    25.
    Denoël, M. & Ficetola, G. F. Landscape-level thresholds and newt conservation. Ecol. Appl. 17, 302–309 (2007).
    PubMed  Article  PubMed Central  Google Scholar 

    26.
    Denoël, M. et al. A multi-scale approach to facultative paedomorphosis of European newts (Salamandridae) in the Montenegrin karst: distribution pattern, environmental variables, and conservation. Biol. Conserv. 142, 509–517 (2009).
    Article  Google Scholar 

    27.
    Ildos, A. S. & Ancona, N. Analysis of amphibian habitat preferences in a farmland area (Po plain, northern Italy). Amphib-Reptilia. 15, 307–316 (1994).
    Article  Google Scholar 

    28.
    Beebee, T. J. Discriminant analysis of amphibian habitat determinants in South-East England. Amphib-Reptilia. 6, 35–43 (1985).
    Article  Google Scholar 

    29.
    Manzoor, S. A., Griffiths, G. & Lukac, M. Species distribution model transferability and model grain size—finer may not always be better. Sci. Rep. 8, 7168 (2018).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    30.
    Chardon, N. I., Pironon, S., Peterson, M. L. & Doak, D. F. Incorporating intraspecific variation into species distribution models improves distribution predictions, but cannot predict species traits for a wide-spread plant species. Ecography 43, 60–74 (2020).
    Article  Google Scholar 

    31.
    Maguire, K. C., Shinneman, D. J., Potter, K. M. & Hipkins, V. D. Intraspecific niche models for ponderosa pine (Pinus ponderosa) suggest potential variability in population-level response to climate change. Syst. Biol 67, 965–978 (2018).
    PubMed  Article  PubMed Central  Google Scholar 

    32.
    Barria, A. M. et al. The importance of intraspecific variation for niche differentiation and species distribution models: The ecologically diverse frog pleurodema thaul as study case. Evol. Biol. 47, 206–219 (2020).
    Article  Google Scholar 

    33.
    Austin, M. P. & Van Niel, K. P. Impact of landscape predictors on climate change modelling of species distributions: A case study with Eucalyptus fastigata in southern New South Wales, Australia. J. Biogeogr. 38, 9–19 (2011).
    Article  Google Scholar 

    34.
    Fournier, A., Barbet-Massin, M., Rome, Q. & Courchamp, F. Predicting species distribution combining multi-scale drivers. Glob. Ecol. Conserv. 12, 215–226 (2017).
    Article  Google Scholar 

    35.
    Hernandez, P. A., Graham, C. H., Master, L. L. & Albert, D. L. The effect of sample size and species characteristics on performance of different species distribution modeling methods. Ecography 29, 773–785 (2006).
    Article  Google Scholar 

    36.
    Wisz, M. S. et al. Effects of sample size on the performance of species distribution models. Divers. Distrib. 14, 763–773 (2008).
    Article  Google Scholar 

    37.
    Dinis, M. et al. Allopatric diversification and evolutionary melting pot in a North African Palearctic relict: the biogeographic history of Salamandra algira. Mol. Phylogenet. Evol. 130, 81–91 (2019).
    PubMed  Article  PubMed Central  Google Scholar 

    38.
    Schulte, U. et al. Cryptic niche conservatism among evolutionary lineages of an invasive lizard. Glob. Ecol. Biogeogr 21, 198–211 (2012).
    Article  Google Scholar 

    39.
    Breiner, F. T., Guisan, A., Nobis, M. P. & Bergamini, A. Including environmental niche information to improve IUCN Red List assessments. Divers. Distrib. 23, 484–495 (2017).
    Article  Google Scholar 

    40.
    IUCN Standards and Petitions Committee. Guidelines for Using the IUCN Red List Categories and Criteria, ver. 14. The Standards and Petitions Committee. https://www.iucnredlist.org/documents/RedListGuidelines.pdf (accessed 22 March 2020). (2019).

    41.
    Hartley, S. & Kunin, W. E. Scale dependency of rarity, extinction risk, and conservation priority. Conserv. Biol. 17, 1559–1570 (2003).
    Article  Google Scholar 

    42.
    Raeisi, E. & Stevanovic, Z. Groundwater Hydrology of Springs 498–515 (Elsevier, Amsterdam, 2010).
    Google Scholar 

    43.
    Chen, J. et al. Global land cover mapping at 30 m resolution: A POK-based operational approach. ISPRS J. Photogramm. Remote Sens. 103, 7–27 (2015).
    ADS  Article  Google Scholar 

    44.
    Sharifi, M., Farasat, H., Barani-Beiranvand, H., Vaissi, S. & Foroozanfar, E. Notes on the distribution and abundance of the endangered kaiser’s mountain newt, neurergus kaiseri (caudata: salamandridae), in southwestern Iran. Herpetol. Conserv. Biol 8, 724–731 (2013).
    Google Scholar 

    45.
    Mobaraki, A. et al. A conservation reassessment of the Critically Endangered, Lorestan newt Neurergus kaiseri (Schmidt 1952) in Iran. Amphib. Reptile Conserv. 9, 16–25 (2014).
    Google Scholar 

    46.
    Casula, P., Vignoli, L., Luiselli, L. & Lecis, R. Local abundance and observer’s identity affect visual detectability of Sardinian mountain newts. Herpetol. J. 27, 258–265 (2017).
    Google Scholar 

    47.
    Joly, P., Morand, C. & Cohas, A. Habitat fragmentation and amphibian conservation: Building a tool for assessing landscape matrix connectivity. BC. R. Biol. 326, 132–139 (2003).
    Google Scholar 

    48.
    Pearson, R. G. & Dawson, T. P. Predicting the impacts of climate change on the distribution of species: are bioclimate envelope models useful?. Glob. Ecol. Biogeogr 12, 361–371 (2003).
    Article  Google Scholar 

    49.
    Hijmans, R. J., Phillips, S., Leathwick, J. & Elith, J. dismo: Species distribution modeling. R package version 1.0-12. The R Foundation for Statistical Computing, Vienna. http://cran.r-project.org (2015).

    50.
    R Core Team. R: A Language and Environment for Statistical Computing (R Foundation for Statistical Computing, Vienna, 2013).

    51.
    ESRI. Using ecological niche modeling. (2016).

    52.
    Blank, L. & Blaustein, L. Using ecological niche modeling to predict the distributions of two endangered amphibian species in aquatic breeding sites. Hydrobiologia 693, 157–167 (2012).
    Article  Google Scholar 

    53.
    Bradie, J. & Leung, B. A quantitative synthesis of the importance of variables used in MaxEnt species distribution models. J. Biogeogr. 44, 1344–1361 (2017).
    Article  Google Scholar 

    54.
    Cunningham, H. R., Rissler, L. J., Buckley, L. B. & Urban, M. C. Abiotic and biotic constraints across reptile and amphibian ranges. Ecography 39, 1–8 (2015).
    Article  Google Scholar 

    55.
    Peterman, W. E. & Semlitsch, R. D. Fine-scale habitat associations of a terrestrial salamander: the role of environmental gradients and implications for population dynamics. PLoS ONE 8, e62184 (2013).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    56.
    Vasconcelos, T. S., Rodríguez, M. Á. & Hawkins, B. A. Species distribution modelling as a macroecological tool: A case study using New World amphibians. Ecography 35, 539–548 (2012).
    Article  Google Scholar 

    57.
    Keating, K. A., Gogan, P. J. P., Vore, J. M. & Irby, L. R. A simple solar radiation index for wildlife habitat studies. J. Wildl. Manage. 71, 1344–1348 (2007).
    Article  Google Scholar 

    58.
    Jenness, J., Brost, B. & Beier, P. Land Facet Corridor Designer: Extension for ArcGIS. Jenness Enterprises. http://www.jennessent.com/arcgis/land_facets.htm. (2013).

    59.
    Marnell, F. Discriminant analysis of the terrestrial and aquatic habitat determinants of the smooth newt (Triturus vulgaris) and the common frog (Rana temporaria) in Ireland. J Zool 244, 1–6 (2001).
    Article  Google Scholar 

    60.
    Phillips, S. J., Anderson, R. P. & Schapire, R. E. Maximum entropy modeling of species geographic distributions. Ecol. Model. 190, 231–259 (2006).
    Article  Google Scholar 

    61.
    Warren, D. L., Wright, A. N., Seifert, S. N. & Shaffer, H. B. Incorporating model complexity and spatial sampling bias into ecological niche models of climate change risks faced by 90 C alifornia vertebrate species of concern. Divers. Distrib. 20, 334–343 (2014).
    Article  Google Scholar 

    62.
    Merow, C., Smith, M. J. & Silander, J. A. A practical guide to MaxEnt for modeling species’ distributions: What it does, and why inputs and settings matter. Ecography 36, 1058–1069 (2013).
    Article  Google Scholar 

    63.
    Radosavljevic, A. & Anderson, R. P. Making better Maxent models of species distributions: Complexity, overfitting and evaluation. J. Biogeogr. 41, 629–643 (2014).
    Article  Google Scholar 

    64.
    Morales, N. S., Fernández, I. C. & Baca-González, V. MaxEnt’s parameter configuration and small samples: Are we paying attention to recommendations? A systematic review. PeerJ 5, e3093 (2017).
    PubMed  PubMed Central  Article  Google Scholar 

    65.
    Shcheglovitova, M. & Anderson, R. P. Estimating optimal complexity for ecological niche models: A jackknife approach for species with small sample sizes. Ecol. Modell. 269, 9–17 (2013).
    Article  Google Scholar 

    66.
    Moreno-Amat, E. et al. Impact of model complexity on cross-temporal transferability in Maxent species distribution models: An assessment using paleobotanical data. Ecol. Model. 312, 308–317 (2015).
    Article  Google Scholar 

    67.
    Elith, J. et al. A statistical explanation of MaxEnt for ecologists. Divers. Distrib. 17, 43–57 (2011).
    Article  Google Scholar 

    68.
    Allouche, O., Tsoar, A. & Kadmon, R. Assessing the accuracy of species distribution models: Prevalence, kappa and the true skill statistic (TSS). J. Appl. Ecol. 43, 1223–1232 (2006).
    Article  Google Scholar 

    69.
    Schoener, T. W. The Anolis lizards of Bimini: Resource partitioning in a complex fauna. Ecology 49, 704–726 (1968).
    Article  Google Scholar 

    70.
    Warren, D. L., Glor, R. E. & Turelli, M. Environmental niche equivalency versus conservatism: Quantitative approaches to niche evolution. Evolution 62, 2868–2883 (2008).
    PubMed  Article  PubMed Central  Google Scholar 

    71.
    Lee, C. K. F., Keith, D. A., Nicholson, E. & Murray, N. J. Redlistr: tools for the IUCN Red Lists of ecosystems and threatened species in R. Ecography 42, 1050–1055 (2019).
    Article  Google Scholar  More

  • in

    A record of vapour pressure deficit preserved in wood and soil across biomes

    1.
    Almeida, A. C. & Landsberg, J. J. Evaluating methods of estimating global radiation and vapor pressure deficit using a dense network of automatic weather stations in coastal Brazil. Agric. For. Meteorol. 118, 237–250 (2003).
    ADS  Article  Google Scholar 
    2.
    Hashimoto, H. et al. Satellite-based estimation of surface vapor pressure deficits using MODIS land surface temperature data. Remote Sens. Environ. 112, 142–155 (2008).
    ADS  Article  Google Scholar 

    3.
    Silva, L. C. R. & Lambers, H. Soil-plant-atmosphere interactions : structure, function, and predictive scaling for climate change mitigation. Plant Soil https://doi.org/10.1007/s11104-020-04427-1 (2020).
    Article  Google Scholar 

    4.
    Maxwell, T. M. & Silva, L. C. R. A state factor model for ecosystem carbon: water relations. Trends Plant Sci. 25, 652–660 (2020).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    5.
    Penuelas, J. & Sardans, J. Developing holistic models of the structure and function of the soil/plant/atmosphere continuum. Plant Soil https://doi.org/10.1007/s11104-020-04641-x (2020).
    Article  PubMed  PubMed Central  Google Scholar 

    6.
    Seager, R. et al. Climatology, variability, and trends in the U.S. vapor pressure deficit, an important fire-related meteorological quantity. J. Appl. Meteorol. Climatol. 54, 1121–1141 (2015).
    ADS  Article  Google Scholar 

    7.
    Retallack, G. J. Greenhouse crises of the past 300 million years. Bull. Geol. Soc. Am. 121, 1441–1455 (2009).
    CAS  Article  Google Scholar 

    8.
    Barbour, M. M., Walcroft, A. S. & Farquhar, G. D. Seasonal variation in δ13C and δ18O of cellulose from growth rings of Pinus radiata. Plant. Cell Environ. 25, 1483–1499 (2002).
    Article  Google Scholar 

    9.
    Breecker, D. O., Sharp, Z. D. & McFadden, L. D. Seasonal bias in the formation and stable isotopic composition of pedogenic carbonate in modern soils from central New Mexico, USA. Bull. Geol. Soc. Am. 121, 630–640 (2009).
    CAS  Article  Google Scholar 

    10.
    Farquhar, G. D., Ehleringer, J. R. & Hubick, K. T. Carbon isotope discrimination and photosynthesis. Annu. Rev. Plant Physiol. Plant Mol. Biol. 40, 503–537 (1989).
    CAS  Article  Google Scholar 

    11.
    Cerling, T. E. Use of carbon isotopes in paleosols as an indicator of the P(CO2) of the paleoatmosphere. Global Biogeochem. Cycles 6, 307–314 (1992).
    ADS  CAS  Article  Google Scholar 

    12.
    Scheidegger, Y., Saurer, M., Bahn, M. & Siegwolf, R. Linking stable oxygen and carbon isotopes with stomatal conductance and photosynthetic capacity: a conceptual model. Oecologia 125, 350–357 (2000).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    13.
    Maxwell, T. M., Silva, L. C. R. & Horwath, W. R. Using multielement isotopic analysis to decipher drought impacts and adaptive management in ancient agricultural systems: Fig. 1. Proc. Natl. Acad. Sci. 111, E4807–E4808 (2014).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    14.
    Barbour, M. M. & Farquhar, A. Relative humidity- and ABA-induced variation in carbon and oxygen isotope ratios of cotton leaves. Plant Cell Environ. https://doi.org/10.1046/j.1365-3040.2000.00575.x (2000).
    Article  Google Scholar 

    15.
    Roden, J. S., Lin, G. & Ehleringer, J. R. A mechanistic model for interpretation of hydrogen and oxygen isotope ratios in tree-ring cellulose. Geochim. Cosmochim. Acta 64, 21–35 (2000).
    ADS  CAS  Article  Google Scholar 

    16.
    Roden, J. S. & Farquhar, G. D. A controlled test of the dual-isotope approach for the interpretation of stable carbon and oxygen isotope ratio variation in tree rings. Tree Physiol. 32, 490–503 (2012).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    17.
    Saurer, M., Aellen, K. & Siegwolf, R. Correlating δ13C and δ18O in cellulose of trees. Plant Cell Environ. 20, 1543–1550 (1997).
    Article  Google Scholar 

    18.
    Johnstone, J. A., Roden, J. S. & Dawson, T. E. Oxygen and carbon stable isotopes in coast redwood tree rings respond to spring and summer climate signals. J. Geophys. Res. Biogeosciences 118, 1438–1450 (2013).
    ADS  CAS  Article  Google Scholar 

    19.
    Sidorova, O. V. et al. Do centennial tree-ring and stable isotope trends of Larix gmelinii (Rupr.) Rupr. indicate increasing water shortage in the Siberian north?. Oecologia 161, 825–835 (2009).
    ADS  PubMed  Article  PubMed Central  Google Scholar 

    20.
    Yakir, D. & Sternberg, L. D. S. L. The use of stable isotopes to study ecosystem gas exchange. Oecologia 123, 297–311 (2000).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    21.
    McCarroll, D. & Loader, N. J. Stable isotopes in tree rings. Quat. Sci. Rev. 23, 771–801 (2004).
    ADS  Article  Google Scholar 

    22.
    Koch, P. L. Isotopic reconstruction of past continental environments. Annu. Rev. Earth Planet. Sci. 26, 573–613 (1998).
    ADS  CAS  Article  Google Scholar 

    23.
    Hook, B. A., Halfar, J., Gedalof, Z., Bollmann, J. & Schulze, D. J. Stable isotope paleoclimatology of the earliest Eocene using kimberlite-hosted mummified wood from the Canadian Subarctic. Biogeosciences 12, 5899–5914 (2015).
    ADS  Article  Google Scholar 

    24.
    Zhang, H. & Nobel, P. S. Dependency of cI/ca and leaf transpiration efficiency on the vapour pressure deficit. Funct. Plant Biol. 23, 561–568 (1996).
    Article  Google Scholar 

    25.
    Silva, L. C. R., Pedroso, G., Doane, T. A., Mukome, F. N. D. & Horwath, W. R. Beyond the cellulose: oxygen isotope composition of plant lipids as a proxy for terrestrial water balance. Geochemical Perspect. Lett. https://doi.org/10.7185/geochemlet.1504 (2015).
    Article  Google Scholar 

    26.
    Breecker, D. O., Sharp, Z. D. & McFadden, L. D. Atmospheric CO2 concentrations during ancient greenhouse climates were similar to those predicted for A.D. 2100. Proc. Natl. Acad. Sci. 107, 576–580 (2010).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    27.
    Breecker, D. O., McFadden, L. D., Sharp, Z. D., Martinez, M. & Litvak, M. E. Deep autotrophic soil respiration in shrubland and woodland ecosystems in central New Mexico. Ecosystems 15, 83–96 (2012).
    CAS  Article  Google Scholar 

    28.
    Abels, H. A. et al. Carbon isotope excursions in paleosol carbonate marking five early Eocene hyperthermals in the Bighorn Basin, Wyoming. Clim. Past Discuss. 11, 1857–1885 (2015).
    Article  Google Scholar 

    29.
    Leary, R. J., Quade, J., DeCelles, P. G. & Reynolds, A. Evidence from paleosols for low to moderate elevation of the India-Asia suture zone during mid-Cenozoic time. Geology 45, 399–402 (2017).
    ADS  Article  Google Scholar 

    30.
    Silva, L. C. R. et al. Expansion of gallery forests into central Brazilian savannas. Glob. Chang. Biol. 14, 2108–2118 (2008).
    ADS  Article  Google Scholar 

    31.
    Oerter, E. J. & Amundson, R. Climate controls on spatial temporal variations in the formation of pedogenic carbonate in the western Great Basin of North Americ. Bull. Geol. Soc. Am. 128, 1095–1104 (2016).
    Article  Google Scholar 

    32.
    Quade, J., Cerling, T. E. & Bowman, J. R. Systematic variations in the carbon and oxygen isotopic composition of pedogenic carbonate along elevation trasects in the southern Great Basin, United States. Geol. Soc. Am. Bull. 101, 464–475 (1989).
    ADS  CAS  Article  Google Scholar 

    33.
    Zamanian, K., Pustovoytov, K. & Kuzyakov, Y. Pedogenic carbonates : forms and formation processes. Earth Sci. Rev. 157, 1–17 (2016).
    ADS  CAS  Article  Google Scholar 

    34.
    Botsyun, S. et al. Revised paleoaltimetry data show low Tibetan Plateau elevation during the Eocene. Science 80, 363 (2019).
    Google Scholar 

    35.
    Maxwell, T. M., Silva, L. C. R. & Horwath, W. R. Predictable oxygen isotope exchange between plant lipids and environmental water: implications for ecosystem water balance reconstruction. J. Geophys. Res. Biogeosciences https://doi.org/10.1029/2018JG004553 (2018).
    Article  Google Scholar 

    36.
    Nyachoti, S., Jin, L., Tweedie, C. E. & Ma, L. Insight into factors controlling formation rates of pedogenic carbonates: a combined geochemical and isotopic approach in dryland soils of the US Southwest. Chem. Geol. https://doi.org/10.1016/j.chemgeo.2017.10.014 (2017).
    Article  Google Scholar 

    37.
    Sanyal, P., Bhattacharya, S. K., Kumar, R., Ghosh, S. K. & Sangode, S. J. Mio-Pliocene monsoonal record from Himalayan foreland basin (Indian Siwalik) and its relation to vegetational change. Palaeogeogr. Palaeoclimatol. Palaeoecol. 205, 23–41 (2004).
    Article  Google Scholar 

    38.
    Ufnar, D. F., Gröcke, D. R. & Beddows, P. A. Assessing pedogenic calcite stable-isotope values: Can positive linear covariant trends be used to quantify palaeo-evaporation rates?. Chem. Geol. 256, 46–51 (2008).
    ADS  CAS  Article  Google Scholar 

    39.
    Jahren, A. H. & Sternberg, L. S. L. Annual patterns within tree rings of the Arctic middle Eocene (ca. 45 Ma): isotopic signatures of precipitation, relative humidity, and deciduousness. Geology 36, 99–102 (2008).
    ADS  CAS  Article  Google Scholar 

    40.
    Retallack, G. J., Wynn, J. G. & Fremd, T. J. Glacial-interglacial-scale paleoclimatic change without large ice sheets in the Oligocene of central Oregon. Geology 32, 297–300 (2004).
    ADS  Article  Google Scholar 

    41.
    Howell, T. A. & Dusek, D. Comparison of vapor-pressure-deficit calculation methods: Southern high plains. J. Irrig. Drain. Eng. 121, 191–198 (1995).
    Article  Google Scholar 

    42.
    Castellvi, F., Perez, P. J., Villar, J. M. & Rose, J. I. Analysis of methods for estimating vapor pressure deficits and relative humidity. Agric. For. Meteorol. 82, 29–45 (1996).
    ADS  Article  Google Scholar 

    43.
    Jahren, A. H. & Sternberg, L. S. L. Humidity estimate for the middle Eocene Arctic rain forest. Geology 31, 463–466 (2003).
    ADS  Article  Google Scholar 

    44.
    Schubert, B. A. & Jahren, A. H. The effect of atmospheric CO2 concentration on carbon isotope fractionation in C3 land plants. Geochim. Cosmochim. Acta 96, 29–43 (2012).
    ADS  CAS  Article  Google Scholar 

    45.
    Sheldon, N. D., Retallack, G. J. & Tanaka, S. Geochemical climofunctions from North American soils and application to paleosols across the eocene: oligocene boundary in oregon geochemical climofunctions from North American soils and application to paleosols across the eocene-oligocene boundary in Or. J. Geol. 110, 687–696 (2015).
    ADS  Article  Google Scholar 

    46.
    Retallack, G. J., Bestland, E. & Fremd, T. Eocene and oligocene paleosols of central oregon. Geol. Soc. Am. Spec. Pap. 344, 1–192 (2000).
    Google Scholar 

    47.
    White, P. D. & Schiebout, J. A. Paleogene paleosols of Big Bend National Park, Texas. Spec. Pap. Geol. Soc. Am. 369, 537–550 (2003).
    Google Scholar 

    48.
    Fischer-Femal, B. J. & Bowen, G. J. Coupled carbon and oxygen isotope model for pedogenic carbonates. Geochim. Cosmochim. Acta https://doi.org/10.1016/j.gca.2020.10.022 (2020).
    Article  Google Scholar 

    49.
    Cerling, T. E. & Quade, J. Stable carbon and oxygen isotopes in soil carbonates. Clim. Chang. Cont. Isot. Rec. 78, 78 (1993).
    Google Scholar 

    50.
    Sarangi, V., Agrawal, S. & Sanyal, P. The disparity in the abundance of C4 plants estimated using the carbon isotopic composition of paleosol components. Palaeogeogr. Palaeoclimatol. Palaeoecol. 561, 110068 (2021).
    Article  Google Scholar 

    51.
    Huang, C. M., Wang, C. S. & Tang, Y. Stable carbon and oxygen isotopes of pedogenic carbonates in Ustic Vertisols: Implications for paleoenvironmental change. Pedosphere 15, 539–544 (2005).
    CAS  Google Scholar 

    52.
    Werner, C. et al. Progress and challenges in using stable isotopes to trace plant carbon and water relations across scales. Biogeosciences 9, 3083–3111 (2012).
    ADS  CAS  Article  Google Scholar 

    53.
    Wynn, J. G. & Bird, M. I. C4-derived soil organic carbon decomposes faster than its C3 counterpart in mixed C3/C4 soils. Glob. Chang. Biol. 13, 2206–2217 (2007).
    ADS  Article  Google Scholar 

    54.
    Garzione, C. N., Dettman, D. L. & Horton, B. K. Carbonate oxygen isotope paleoaltimetry: evaluating the effect of diagenesis on paleoelevation estimates for the Tibetan plateau. Palaeogeogr. Palaeoclimatol. Palaeoecol. 212, 119–140 (2004).
    Article  Google Scholar 

    55.
    Rice, C. M. et al. A Devonian auriferous hot spring system, Rhynie, Scotland. J. Geol. Soc. Lond. 152, 229–250 (1995).
    CAS  Article  Google Scholar 

    56.
    Bera, M. K., Sarkar, A., Tandon, S. K., Samanta, A. & Sanyal, P. Does burial diagenesis reset pristine isotopic compositions in paleosol carbonates?. Earth Planet. Sci. Lett. 300, 85–100 (2010).
    ADS  CAS  Article  Google Scholar 

    57.
    Cernusak, L. A. et al. Environmental and physiological determinants of carbon isotope discrimination in terrestrial plants. New Phytol. 200, 950–965 (2013).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    58.
    Vargas, A. I., Schaffer, B., Yuhong, L. & Lobo, S. Testing plant use of mobile vs immobile soil water sources using stable isotope experiments. New Phytol. https://doi.org/10.1111/nph.14616 (2017).
    Article  PubMed  PubMed Central  Google Scholar 

    59.
    Flanagan, L. B. & Farquhar, G. D. Variation in the carbon and oxygen isotope composition of plant biomass and its relationship to water-use efficiency at the leaf- and ecosystem-scales in a northern Great Plains grassland. Plant Cell Environ. 37, 425–438 (2014).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    60.
    Sheshshayee, M. S. et al. Oxygen isotope enrichment (Δ18O) as a measure of time-averaged transpiration rate. J. Exp. Bot. 56, 3033–3039 (2005).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    61.
    Sternberg, L., Fernandes, P. & Ellsworth, V. Divergent biochemical fractionation, not convergent temperature , explains cellulose oxygen isotope enrichment across latitudes. 6, (2011).

    62.
    Retallack, G. J. Field and laboratory tests for recognition of Ediacaran paleosols. Gondwana Res. 36, 94–110 (2016).
    Article  CAS  Google Scholar 

    63.
    Farquhar, G. D. & Cernusak, L. A. Ternary effects on the gas exchange of isotopologues of carbon dioxide. Plant Cell Environ. 35, 1221–1231 (2012).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    64.
    Maxwell, T. M., Silva, L. C. R. & Horwath, W. R. Integrating effects of species composition and soil properties to predict shifts in montane forest carbon–water relations. Proc. Natl. Acad. Sci. 201718864 (2018). https://doi.org/10.1073/pnas.1718864115

    65.
    Locatelli, E. R. The exceptional preservation of plant fossils: a review of taphonomic pathways and biases in the fossil record. Paleontol. Soc. Pap. 20, 237–258 (2014).
    Article  Google Scholar 

    66.
    Castruita-Esparza, L. U. et al. Coping with extreme events: growth and water-use efficiency of trees in Western Mexico during the driest and wettest periods of the past one hundred sixty years. J. Geophys. Res. Biogeosci. 124, 3419–3431 (2019).
    Article  Google Scholar 

    67.
    Jahren, A. H. The arctic forest of the middle eocene. Annu. Rev. Earth Planet. Sci. 35, 509–540 (2007).
    ADS  CAS  Article  Google Scholar 

    68.
    Falini, F. On the formation of coal deposits of lacustrine origin. Bull. Geol. Soc. Am. 76, 1317–1346 (1965).
    Article  Google Scholar  More

  • in

    Multiple life-stage inbreeding depression impacts demography and extinction risk in an extinct-in-the-wild species

    1.
    Keller, L. F. & Waller, D. M. Inbreeding effects in wild populations. Trends Ecol. Evol. 17, 230–241 (2002).
    Article  Google Scholar 
    2.
    Boakes, E. H., Wang, J. & Amos, W. An investigation of inbreeding depression and purging in captive pedigreed populations. Heredity (Edinb). 98, 172–182 (2007).
    CAS  PubMed  Article  Google Scholar 

    3.
    Bozzuto, C., Biebach, I., Muff, S., Ives, A. R. & Keller, L. F. Inbreeding reduces long-term growth of Alpine ibex populations. Nat. Ecol. Evol. 3, 1359–1364 (2019).
    PubMed  Article  Google Scholar 

    4.
    Saccheri, I., Kuussaari, M., Kankare, M., Vikman, P. & Hanski, I. Inbreeding and extinction in a butterfly metapopulation. Nature 392, 491–494 (1998).
    ADS  CAS  Article  Google Scholar 

    5.
    Kardos, M., Taylor, H. R., Ellegren, H., Luikart, G. & Allendorf, F. W. Genomics advances the study of inbreeding depression in the wild. Evol. Appl. 9, 1205–1218 (2016).
    PubMed  PubMed Central  Article  Google Scholar 

    6.
    Allendorf, F. W., Luikart, G. & Aitken, S. N. Conservation and the genetics of populations. (Wiley-Blackwell, 2013).

    7.
    Johnson, H. E., Mills, L. S., Wehausen, J. D., Stephenson, T. R. & Luikart, G. Translating effects of inbreeding depression on component vital rates to overall population growth in endangered bighorn sheep. Conserv. Biol. 25, 1240–1249 (2011).
    PubMed  Article  Google Scholar 

    8.
    Frankham, R. Where are we in conservation genetics and where do we need to go?. Conserv. Genet. 11, 661–663 (2010).
    Article  Google Scholar 

    9.
    Pierson, J. C. et al. Incorporating evolutionary processes into population viability models. Conserv. Biol. 29, 755–764 (2015).
    PubMed  Article  Google Scholar 

    10.
    Huisman, J., Kruuk, L. E. B., Ellisa, P. A., Clutton-Brock, T. & Pemberton, J. M. Inbreeding depression across the lifespan in a wild mammal population. Proc. Natl. Acad. Sci. U. S. A. 113, 3585–3590 (2016).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    11.
    Grueber, C. E., Laws, R. J., Nakagawa, S. & Jamieson, I. G. Inbreeding depression accumulation across life-history stages of the endangered takahe. Conserv. Biol. 24, 1617–1625 (2010).
    PubMed  Article  Google Scholar 

    12.
    Harrisson, K. A. et al. Lifetime fitness costs of inbreeding and being inbred in a critically endangered bird. Curr. Biol. 29, 2711-2717.e4 (2019).
    CAS  PubMed  Article  Google Scholar 

    13.
    Ralls, K., Ballou, J. D. & Templeton, A. Estimates of lethal equivalents and the cost of inbreeding in mammals. Conserv. Biol. 2, 185–192 (1988).
    Article  Google Scholar 

    14.
    Hoeck, P. E. A., Wolak, M. E., Switzer, R. A., Kuehler, C. M. & Lieberman, A. A. Effects of inbreeding and parental incubation on captive breeding success in Hawaiian crows. Biol. Conserv. 184, 357–364 (2015).
    Article  Google Scholar 

    15.
    Jimenez, J. A., Hughes, K. A., Alaks, G., Graham, L. & Lacy, R. C. An experimental study of inbreeding depression in a natural habitat. Science (80-. ). 266, 271–273 (1994).

    16.
    Van Oosterhout, C., Zijlstra, W. G., Van Heuven, M. K. & Brakefield, P. M. Inbreeding depression and genetic load in laboratory metapopulations of the butterfly Bicyclus anynana. Evolution (N. Y). 54, 218–225 (2000).

    17.
    Szulkin, M., Garant, D., Mccleery, R. H. & Sheldon, B. C. Inbreeding depression along a life-history continuum in the great tit. J. Evol. Biol. 20, 1531–1543 (2007).
    CAS  PubMed  Article  Google Scholar 

    18.
    Wolak, M. E., Arcese, P., Keller, L. F., Nietlisbach, P. & Reid, J. M. Sex-specific additive genetic variances and correlations for fitness in a song sparrow (Melospiza melodia) population subject to natural immigration and inbreeding. Evolution (N. Y). 72, 2057–2075 (2018).

    19.
    Kennedy, E. S., Grueber, C. E., Duncan, R. P. & Jamieson, I. G. Severe inbreeding depression and no evidence of purging in an extremely inbred wild species-the chatham island black robin. Evolution (N. Y). 68, 987–995 (2014).

    20.
    Jamieson, I. G., Tracy, L. N., Fletcher, D. & Armstrong, D. P. Moderate inbreeding depression in a reintroduced population of North Island robins. Anim. Conserv. 10, 95–102 (2007).
    Article  Google Scholar 

    21.
    Norén, K., Godoy, E., Dalén, L., Meijer, T. & Angerbjörn, A. Inbreeding depression in a critically endangered carnivore. Mol. Ecol. https://doi.org/10.1111/mec.13674 (2016).
    Article  PubMed  Google Scholar 

    22.
    Sæther, B. E. & Bakke, Ø. Avian life history variation and contribution of demographic traits to the population growth rate. Ecology 81, 642–653 (2000).
    Article  Google Scholar 

    23.
    Beissinger, S. R. & McCullough, D. R. Population viability analysis. (University of Chicago Press, 2002).

    24.
    Lacy, R. C. Lessons from 30 years of population viability analysis of wildlife populations. Zoo Biol. 38, 67–77 (2019).
    PubMed  Article  Google Scholar 

    25.
    Traill, L. W., Bradshaw, C. J. A. & Brook, B. W. Minimum viable population size: A meta-analysis of 30 years of published estimates. Biol. Conserv. 139, 159–166 (2007).
    Article  Google Scholar 

    26.
    O’Grady, J. J. et al. Realistic levels of inbreeding depression strongly affect extinction risk in wild populations. Biol. Conserv. 133, 42–51 (2006).
    Article  Google Scholar 

    27.
    Lacy, R. C., Miller, P. S. & Traylor-Holzer, K. Vortex 10 user’s manual. (2017).

    28.
    Ballou, J. D. & Lacy, R. C. in Population management for survival and recovery (eds. Ballou, J. D., Gilpin, M. & Foose, T. J.) 76–111 (Columbia University Press, 1995).

    29.
    Armbruster, P. & Reed, D. H. Inbreeding depression in benign and stressful environments. Heredity (Edinb). 95, 235–242 (2005).
    CAS  PubMed  Article  Google Scholar 

    30.
    Fox, C. W. & Reed, D. H. Inbreeding depression increases with environmental stress: an experimental study and meta-analysis. Evolution (N. Y). 65, 246–258 (2011).

    31.
    Baker, R. H. The avifauna of Micronesia, its origin, evolution and distribution. (University of Kansas Publications, 1951).

    32.
    Marshall, J. T. The endemic avifauna of Sapan, Tinian Guam and Palau. Condor 51, 200–221 (1949).
    Article  Google Scholar 

    33.
    Wiles, G. J., Bart, J., Beck, R. E. & Aguon, C. F. Impacts of the brown tree snake: patterns of decline and species persistence in Guam’s avifauna. Conserv. Biol. 17, 1350–1360 (2003).
    Article  Google Scholar 

    34.
    Savidge, J. A. Extinction of an island forest avifauna by an introduced snake. Ecology 68, 660–668 (1987).
    Article  Google Scholar 

    35.
    Haig, S. M., Ballou, J. D. & Casna, N. J. Genetic identification of kin in Micronesian kingfishers. J. Hered. 86, 423–431 (1995).
    Article  Google Scholar 

    36.
    Lacy, R. C., Ballou, J. D. & Pollak, J. P. PMx: Software package for demographic and genetic analysis and management of pedigreed populations. Methods Ecol. Evol. 3, 433–437 (2012).
    Article  Google Scholar 

    37.
    Ferrie, G. Using molecular genetic and demographic tools to improve management of ex situ avian populations. (University of Central Florida, 2017). http://stars.library.ucf.edu/etd/5709

    38.
    Zuur, A. F., Ieno, E. N. & Elphick, C. S. A protocol for data exploration to avoid common statistical problems. Methods Ecol. Evol. 1, 3–14 (2010).
    Article  Google Scholar 

    39.
    Burnham, K. . & Anderson, D. R. Model selection and multimodel inference: a practical information-theoretic approach. (Springer, 2002).

    40.
    Whittingham, M. J., Stephens, P. A., Bradbury, R. B. & Freckleton, R. P. Why do we still use stepwise modelling in ecology and behaviour?. J. Anim. Ecol. 75, 1182–1189 (2006).
    PubMed  Article  Google Scholar 

    41.
    Nietlisbach, P., Muff, S., Reid, J. M., Whitlock, M. C. & Keller, L. F. Nonequivalent lethal equivalents: Models and inbreeding metrics for unbiased estimation of inbreeding load. Evol. Appl. 12, 266–279 (2018).
    PubMed  PubMed Central  Article  Google Scholar 

    42.
    Zou, G. A modified poisson regression approach to prospective studies with binary data. Am. J. Epidemiol. 159, 702–706 (2004).
    PubMed  Article  Google Scholar 

    43.
    Bates, D., Mächler, M., Bolker, B. M. & Walker, S. C. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).
    Article  Google Scholar 

    44.
    R Development Core Team. R: A language and environment for statistical computing. (2019).

    45.
    Lacy, R. C. & Pollak, J. P. Vortex: A stochastic simulation of the extinction process. (2017).

    46.
    Hemmings, N. L., Slate, J. & Birkhead, T. R. Inbreeding causes early death in a passerine bird. Nat. Commun. 3, 1–4 (2012).
    Article  CAS  Google Scholar 

    47.
    Tiira, K., Piironen, J. & Primmer, C. R. Evidence for reduced genetic variation in severely deformed juvenile salmonids. Can. J. Fish. Aquat. Sci. 63, 2700–2707 (2006).
    Article  Google Scholar 

    48.
    Wang, J., Hill, W. G., Charlesworth, D. & Charlesworth, B. Dynamics of inbreeding depression due to deleterious mutations in small populations: mutation parameters and inbreeding rate. Genet. Res. 74, 165–178 (1999).
    CAS  PubMed  Article  Google Scholar 

    49.
    Husband, B. C. & Schemske, D. W. Evolution of the magnitude and timing of inbreeding depression in plants. Evolution (N. Y). 50, 54–70 (1996).

    50.
    de Boer, R. A., Eens, M. & Müller, W. Sex-specific effects of inbreeding on reproductive senescence. Proc. R. Soc. B Biol. Sci. 285, (2018).

    51.
    Keller, L. F., Reid, J. M. & Arcese, P. Testing evolutionary models of senescence in a natural population: Age and inbreeding effects on fitness components in song sparrows. Proc. R. Soc. B Biol. Sci. 275, 597–604 (2008).
    CAS  Article  Google Scholar 

    52.
    Partridge, L. & Mangel, M. Messages from mortality: The evolution of death rates in the old. Trends Ecol. Evol. 14, 438–442 (1999).
    CAS  PubMed  Article  Google Scholar 

    53.
    Charlesworth, B. & Hughes, K. A. Age-specific inbreeding depression and components of genetic variance in relation to the evolution of senescence. Proc. Natl. Acad. Sci. U. S. A. 93, 6140–6145 (1996).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    54.
    Kristensen, T. N., Loeschcke, V. & Hoffmann, A. A. Linking inbreeding effects in captive populations with fitness in the wild: Release of replicated Drosophila melanogaster lines under different temperatures. Conserv. Biol. 22, 189–199 (2008).
    PubMed  Article  Google Scholar 

    55.
    Ryman, N. & Laikre, L. Effects of supportive breeding on the genetically effective population size. Conserv. Biol. 5, 325–329 (1991).
    Article  Google Scholar 

    56.
    Hedrick, P. W. & Garcia-Dorado, A. Understanding inbreeding depression, purging, and genetic rescue. Trends Ecol. Evol. 31, 940–952 (2016).
    PubMed  Article  Google Scholar 

    57.
    Kalinowski, S. T., Hedrick, P. W. & Miller, P. S. Inbreeding Depression in the Speke’s Gazelle Captive Breeding Program. Conserv. Biol. 14, 1375–1384 (2000).
    Article  Google Scholar 

    58.
    Gilligan, D. M. & Frankham, R. Dynamics of individual adaptation processes. Conserv. Genet. 4, 189–197 (2003).
    Article  Google Scholar 

    59.
    Christie, M. R., Marine, M. L., French, R. A. & Blouin, M. S. Genetic adaptation to captivity can occur in a single generation. Proc. Natl. Acad. Sci. U. S. A. 109, 238–242 (2012).
    ADS  CAS  PubMed  Article  Google Scholar 

    60.
    Grueber, C. E., Waters, J. M. & Jamieson, I. G. The imprecision of heterozygosity-fitness correlations hinders the detection of inbreeding and inbreeding depression in a threatened species. Mol. Ecol. 20, 67–79 (2011).
    PubMed  Article  Google Scholar 

    61.
    Milligan, M. C., Wells, S. L. & McNew, L. B. A population viability analysis for sharp-tailed grouse to inform reintroductions. J. Fish Wildl. Manag. 9, 565–581 (2018).
    Article  Google Scholar 

    62.
    Research needs & implications for population management. Moßbrucker, A. M., Imron, M. A., Pudtatmoko, S., Pratje, P. & Sumardi. Modelling the fate of Sumatran elephants in Bukit Tigapuluh, Indonesia. J. For. Sci. 10, 5–18 (2016).
    Google Scholar 

    63.
    Sharpe, M. & Berggren, P. Indian Ocean humpback dolphin in the Menai Bay off the south coast of Zanzibar, East Africa is Critically Endangered. Aquat. Conserv. Mar. Freshw. Ecosyst. 29, 2133–2146 (2019).
    Article  Google Scholar 

    64.
    McQuillan, R. et al. Runs of homozygosity in European populations. Am. J. Hum. Genet. 83, 359–372 (2008).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    65.
    Caballero, A., Bravo, I. & Wang, J. Inbreeding load and purging: Implications for the short-term survival and the conservation management of small populations. Heredity (Edinb). 118, 177–185 (2017).
    CAS  PubMed  Article  Google Scholar 

    66.
    Liao, W. & Reed, D. H. Inbreeding-environment interactions increase extinction risk. Anim. Conserv. 12, 54–61 (2009).
    CAS  Article  Google Scholar 

    67.
    Melbourne, B. A. & Hastings, A. Extinction risk depends strongly on factors contributing to stochasticity. Nature 454, 100–103 (2008).
    ADS  CAS  PubMed  Article  Google Scholar  More

  • in

    Carbon storage and sequestration potential in aboveground biomass of bamboos in North East India

    1.
    International Network for Bamboo and Rattan. Annual Report. www.Inbar.int. (2005).
    2.
    Sharma, M. L. & Nirmala, C. Bamboo diversity of India: An Update. Conference Paper. 10th World Bamboo Congress, Korea (2015).

    3.
    Environment and Forest Department, Government of Mizoram. Bamboos of Mizoram (2010).

    4.
    Jha, L. K. Bamboo based agroforestry systems to reclaim degraded hilly tracts (jhum) land in North Eastern India: Study on uses, species diversity, distribution, and growth performance of Melocanna baccifera, Dendrocalamus hamiltonii, D. longispathus and Bambusa tulda in natural stands and in stands managed on a sustainable basis. Bamboo Science and Culture. J. Am. Bamboo Soc. 23(1), 1–28 (2010).
    Google Scholar 

    5.
    Nigatu, A., Wondei, M., Alemu, A., Gebeyehu, D. & Workagegnehu, H. Productivity of highland bamboo (Yushania alpina) across different plantation niches in West Amhara, Ethiopia. Forest Sci. Tech. 16, 116–122 (2020).
    Article  Google Scholar 

    6.
    Quiroga, R. A. R., Li, T., Lora, G. & Anderson L. E. A measurement of the carbon sequestration potential of Guadua angustifolia in the Carrasco National Park Bolivia. Development Research Working Paper Series 04/2013. Institute for Advanced Development Studies. Bolivia (2013).

    7.
    Nath, A. J., Lal, R. & Das, A. K. Managing woody bamboos for carbon farming and carbon trading. Glob. Ecol. Conserv. 3, 654–663 (2015).
    Article  Google Scholar 

    8.
    Wu, W., Liu, Q., Zhu, Z. & Shen, Y. Managing bamboo for carbon sequestration, bamboo stem and bamboo shoots. Small Scale Forest. 14, 233–243 (2015).
    Article  Google Scholar 

    9.
    Yen, T. M., Ji, Y. J. & Lee, J. S. Estimating biomass production and carbon storage for a fast-growing makino bamboo (Phyllosatchys makinoi) plant based on the diameter distribution model. For. Ecol. Manag. 260, 339–344 (2010).
    Article  Google Scholar 

    10.
    Singnar, P., Das, M. C., Sileshi, G. W., Brahma, B. & Nath, A. J. Allometric scalling, biomass accumulation and carbon stocks in different aged stands of thin-walled bamboos Schiostachyum dullooa, Pseudostachyum polymorphum and Melocanna baccifera. For. Ecol. Manag. 395, 81–91 (2017).
    Article  Google Scholar 

    11.
    Directorate of Science and Technology. Climate profile of Mizoram. A publication by Mizoram State Climate Change Cell, 23 (2018).

    12.
    Soil Survey Staff. Soil taxonomy A basic system of soil classification for making and interpreting soil surveys. U. S. Department of Agriculture Handbook p 436 (1999).

    13.
    Houba, V., VanderLee, J., Novozamsky, I. & Wallinga, I. Soil and plant analysis. A series of Syllabi Part 5. Soil Analysis Procedures Fourth Edition Wageningen, Netherlands (1989).

    14.
    Banik, R. L. Silviculture and Field-Guide to Priority Bamboos of Bangladesh and South Asia. Government of the people’s Republic of Bangladesh. Forest Research Institute, Chittagong, 187. (2000).

    15.
    FAO. Guidelines on Destructive Measurement for Forest Biomass Estimation (FAO, Rome, 2012).
    Google Scholar 

    16.
    IPCC Good Practice Guidance for LULUCF Sector. Intergovernmental Panel on Climate Change (Cambridge University Press, Cambridge, 2003).
    Google Scholar 

    17.
    Yuming, Y., Chaomao, H., Jiarong, X. & Fan, D. Techniques of cultivation and integrated development of sympodial bamboo species. In Sustainable Development of Bamboo and Rattan Sectors in Tropical China 48–66 (China Forestry Publishing House, Beijing, 2001).
    Google Scholar 

    18.
    Embaye, K., Weih, M., Ledin, S. & Christersson, L. Biomass and nutrient distribution in a highland bamboo forest in southwest Ethiopia: Implications for management. For. Ecol. Manag. 204, 159–169 (2005).
    Article  Google Scholar 

    19.
    Nath, A. J. & Das, A. K. Carbon pool and sequestration potential of village bamboos in the agroforestry system of northeastern India. Trop. Ecol. 53, 287–293 (2012).
    CAS  Google Scholar 

    20.
    Amoah, M., Assan, F. & Dadzie, K. P. Aboveground biomass, carbon storage and fuel values of Bambusa vulgaris, Oxynanteria abbyssinica and Bambusa vulgaris Var. vitata plantations in the Bobiri forest reserve of Ghana. J. Sustain. For. 38, 1–24 (2019).
    Article  Google Scholar 

    21.
    Xu, M., Ji, H. & Zhuang, S. Carbon stock of Moso bamboo (Phyllostachys pubescens) forests along a latitude gradient in the subtropical region of China. PLoS One 13, 2,e0193024 (2018).
    Google Scholar 

    22.
    Majumdar, K., Choudhary, B. K. & Datta, B. K. Aboveground woody biomass carbon stocks potential in selected tropical forest patches of Tripura, Northeast India. Open J. Ecol. 6, 598–612 (2016).
    Article  Google Scholar 

    23.
    Pathak, P. K., Kumar, H., Kumari, G. & Bilyaminu, H. Biomass production potential in different species of hemicelluloses from bamboo in central Utter Pradesh. Ecoscan 10, 41–43 (2016).
    Google Scholar 

    24.
    Sohel, M. S. I., Alamgir, M., Akhter, S. & Rahman, M. Carbon storage in a bamboo (Bambusa vulgaris) plantation in the degraded tropical forest: Implications for policy development. Land Use Policy 49, 142–151 (2015).
    Article  Google Scholar 

    25.
    Thokchom, A. & Yadava, P. S. Comparing aboveground  carbon sequestration between bamboo forest and Dipterocarpus forests of Manipur, Northeast India. Int. J. Ecol. Environ. Sci. 41, 33–42 (2015).
    Google Scholar 

    26.
    Xu, L. et al. Structural development and carbon dynamics of Moso bamboo forests in Zhejiang Province, China. For. Ecol. Manag. 409, 479–488 (2017).
    Article  Google Scholar 

    27.
    Nath, A. J., Das, G. & Das, A. K. Aboveground standing biomass and carbon storage in village bamboos in North East India. Biom. Bioeng. 33, 1188–1196 (2009).
    Article  Google Scholar 

    28.
    Wang, Y. C. Estimates of biomass and carbon sequestration in Dendrocalamus latiflorus culms. J. For. Prod. Ind. 23(1), 13–22 (2004).
    Google Scholar 

    29.
    Wang, J. et al. The structures, aboveground biomass, carbon storage of Phyllostachys pubescens stands in Huisun Experimental Forest Station and Shi-Zhuo. Q. For. Res. 31, 17–26 (2009).
    CAS  Google Scholar 

    30.
    Sujarwo, W. Stand biomass and carbon storage of bamboo forest in Penglipuram traditional village, Bali (Indonesia). J. For. Res. 27, 913–917 (2016).
    CAS  Article  Google Scholar 

    31.
    Nfornkah, B. N. et al. Culm allometry and carbon storage capacity of Bambusa vulgaris Schrad. ex J. C. WendL. in the tropical evergreen rain forest of Cameroon. J Sustain For. https://doi.org/10.1080/10549811.2020.1795688 (2020).
    Article  Google Scholar  More

  • in

    Monkeys fight more in polluted air

    Monkey conflict data
    We obtained social conflict data ofNorthern China Rhesus Monkeys from Hongshan Forest Zoo of Nanjing, China. Nanjing (31° 14′–32° 37′ N, 118° 22′–119° 14′ E) is located in the central region of the lower Yangtze River and southwest of Jiangsu Province. It is an important national gateway city for the development of the central and western regions in the Yangtze River Delta, with an area of 6587 km2 covering a population of more than 8 Million. Average annual temperature is about 15.4 °C. Annual precipitation is 1106 mm, 60% of which occurs from Jun to Sep.
    There are about 90 monkeys in the Hongshan Zoo in 2017, about 35 adults, 20 sub-adults and 35 juveniles or new-borns. The round monkey park was located in the central part of the zoo, with an area of about 2000 m2. Although a thick and 3-m high glass wall has been built to prevent artificial feedings, visitors sometimes throw food into the monkey park, causing a social conflict due to the food competition. Usually the zookeeper feeds these monkeys twice a day at about 9:30 am and 3:30 pm respectively.
    We established a monitoring camera web (Haikang DS-7104N-SN/P) covering the monkey park in September 2016 and video-recorded the whole population since then. We defined social conflicts of monkeys as aggressive or fighting behaviors between individuals, including chasing (one chases another until it escapes), wrestling (one grapples and wrestles with another, until one escapes or gives up), biting (one opens its mouth and bites or tries to bites another), scratching (One scratches or scrapes another using its hands), threating (One warns or threats another through calling or behavioural display), etc. The age of participants and the occurrence time were recorded for each aggression46. We considered a conflict ends if there is no continuation within 10 s after the aggression. Since these monkeys are inactive during the night, we only recorded their diurnal aggressive behaviors from 6:30 till 18:30 and then summed the fights as daily social conflicts. One-year round data were collected from Mar 2017 to Feb 2018.
    Air Quality Index
    We obtained Air Quality Index (AQI) data of Nanjing from the Data Centre of the Ministry of Environmental Protection of the People’s Republic of China (MEP, http://datacenter.mep.gov.cn/)17. Based on established criteria (GB3095-2012). AQI is calculated for six major air pollutants separately: particle matter  More

  • in

    Prioritizing conservation actions in urbanizing landscapes

    1.
    Game, E. T., Kareiva, P. & Possingham, H. P. Six common mistakes in conservation priority setting. Conserv. Biol. 27, 480–485 (2013).
    PubMed  PubMed Central  Article  Google Scholar 
    2.
    Bottrill, M. C. et al. Is conservation triage just smart decision making?. Trends Ecol. Evol. 23, 649–654 (2008).
    PubMed  Article  PubMed Central  Google Scholar 

    3.
    Wilson, K. A., Carwardine, J. & Possingham, H. P. Setting conservation priorities. Ann. N. Y. Acad. Sci. 1162, 237–264 (2009).
    ADS  PubMed  Article  PubMed Central  Google Scholar 

    4.
    Samhouri, J. F. & Levin, P. S. Linking land-and sea-based activities to risk in coastal ecosystems. Biol. Conserv. 145, 118–129 (2012).
    Article  Google Scholar 

    5.
    Shelton, A. O., Samhouri, J. F., Stier, A. C. & Levin, P. S. Assessing trade-offs to inform ecosystem-based fisheries management of forage fish. Sci. Rep. 4, 7110 (2014).
    ADS  PubMed  PubMed Central  Article  Google Scholar 

    6.
    Tallis, H. Natural Capital: Theory and Practice of Mapping Ecosystem Services. (Oxford University Press, 2011).

    7.
    Murdoch, W. et al. Maximizing return on investment in conservation. Biol. Conserv. 139, 375–388 (2007).
    Article  Google Scholar 

    8.
    Carwardine, J. et al. Prioritizing threat management for biodiversity conservation. Conserv. Lett. 5, 196–204 (2012).
    Article  Google Scholar 

    9.
    Fonner, R., Bellanger, M. & Warlick, A. Economic analysis for marine protected resources management: challenges, tools, and opportunities. Ocean Coast. Manag. 194, 105222 (2020).
    Article  Google Scholar 

    10.
    Chan, K. M., Hoshizaki, L. & Klinkenberg, B. Ecosystem services in conservation planning: targeted benefits vs. co-benefits or costs?. PLoS ONE 6, e24378 (2011).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    11.
    McDonald, R. I., Kareiva, P. & Forman, R. T. The implications of current and future urbanization for global protected areas and biodiversity conservation. Biol. Conserv. 141, 1695–1703 (2008).
    Article  Google Scholar 

    12.
    Economic, U. N. D. of & Social Affairs, P. D. World Urbanization Prospects: The 2018 Revision. (United Nations Publications New York, 2019).

    13.
    Liu, Z., He, C. & Wu, J. The relationship between habitat loss and fragmentation during urbanization: an empirical evaluation from 16 world cities. PLoS ONE 11, e0154613 (2016).
    PubMed  PubMed Central  Article  Google Scholar 

    14.
    Heidt, V. & Neef, M. Benefits of urban green space for improving urban climate. In Ecology, Planning, and Management of Urban Forests 84–96 (Springer, 2008).

    15.
    Wolch, J. R., Byrne, J. & Newell, J. P. Urban green space, public health, and environmental justice: the challenge of making cities ‘just green enough’. Landsc. Urban Plan. 125, 234–244 (2014).
    Article  Google Scholar 

    16.
    Kondo, M. C., Fluehr, J. M., McKeon, T. & Branas, C. C. Urban green space and its impact on human health. Int. J. Environ. Res. Public. Health 15, 445 (2018).
    PubMed Central  Article  Google Scholar 

    17.
    Wood, E. et al. Not all green space is created equal: biodiversity predicts psychological restorative benefits from urban green space. Front. Psychol. 9, 2320 (2018).
    PubMed  PubMed Central  Article  Google Scholar 

    18.
    Pickett, S. T. et al. Urban ecological systems: scientific foundations and a decade of progress. J. Environ. Manag. 92, 331–362 (2011).
    CAS  Article  Google Scholar 

    19.
    Grimm, N. B. et al. Global change and the ecology of cities. Science 319, 756–760 (2008).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    20.
    Walsh, C. J. et al. The urban stream syndrome: current knowledge and the search for a cure. J. North Am. Benthol. Soc. 24, 706–723 (2005).
    Article  Google Scholar 

    21.
    Paul, M. J. & Meyer, J. L. Streams in the urban landscape. Annu. Rev. Ecol. Syst. 32, 333–365 (2001).
    Article  Google Scholar 

    22.
    Schueler, T. R., Fraley-McNeal, L. & Cappiella, K. Is impervious cover still important? Review of recent research. J. Hydrol. Eng. 14, 309–315 (2009).
    Article  Google Scholar 

    23.
    Canessa, S. & Parris, K. M. Multi-scale, direct and indirect effects of the urban stream syndrome on amphibian communities in streams. PLoS ONE 8, e70262 (2013).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    24.
    Bernhardt, E. S. & Palmer, M. A. Restoring streams in an urbanizing world. Freshw. Biol. 52, 738–751 (2007).
    Article  Google Scholar 

    25.
    Hardy, S. D. & Koontz, T. M. Collaborative watershed partnerships in urban and rural areas: different pathways to success?. Landsc. Urban Plan. 95, 79–90 (2010).
    Article  Google Scholar 

    26.
    Ahiablame, L. M., Engel, B. A. & Chaubey, I. Effectiveness of low impact development practices: literature review and suggestions for future research. Integr. Environ. Assess. Manag. Int. J. 223, 4253–4273 (2012).
    CAS  Google Scholar 

    27.
    McIntyre, J. et al. Soil bioretention protects juvenile salmon and their prey from the toxic impacts of urban stormwater runoff. Chemosphere 132, 213–219 (2015).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    28.
    McIntyre, J. K. et al. Severe coal tar sealcoat runoff toxicity to fish is prevented by bioretention filtration. Environ. Sci. Technol. 50, 1570–1578 (2016).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    29.
    Spromberg, J. A. et al. Coho salmon spawner mortality in western US urban watersheds: bioinfiltration prevents lethal storm water impacts. J. Appl. Ecol. 53, 398–407 (2016).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    30.
    Seattle, D. of P. & D. 2015 Environmentally Critical Areas: Best Available Science Review. (2015).

    31.
    Rondinini, C., Wilson, K. A., Boitani, L., Grantham, H. & Possingham, H. P. Tradeoffs of different types of species occurrence data for use in systematic conservation planning. Ecol. Lett. 9, 1136–1145 (2006).
    PubMed  Article  PubMed Central  Google Scholar 

    32.
    Rhodes, J. R. et al. Regional variation in habitat–occupancy thresholds: a warning for conservation planning. J. Appl. Ecol. 45, 549–557 (2008).
    Article  Google Scholar 

    33.
    Carwardine, J., Klein, C. J., Wilson, K. A., Pressey, R. L. & Possingham, H. P. Hitting the target and missing the point: target-based conservation planning in context. Conserv. Lett. 2, 4–11 (2009).
    Article  Google Scholar 

    34.
    Ruckelshaus, M. H., Levin, P., Johnson, J. B. & Kareiva, P. M. The Pacific salmon wars: what science brings to the challenge of recovering species. Annu. Rev. Ecol. Syst. 33, 665–706 (2002).
    Article  Google Scholar 

    35.
    Underwood, E. C. et al. Protecting biodiversity when money matters: maximizing return on investment. PLoS ONE 3, e1515 (2008).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    36.
    Murdoch, W., Ranganathan, J., Polasky, S. & Regetz, J. Using return on investment to maximize conservation effectiveness in Argentine grasslands. Proc. Natl. Acad. Sci. 107, 20855–20862 (2010).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    37.
    Boyd, J., Epanchin-Niell, R. & Siikamäki, J. Conservation planning: a review of return on investment analysis. Rev. Environ. Econ. Policy 9, 23–42 (2015).
    Article  Google Scholar 

    38.
    Samhouri, J. F., Levin, P. S., James, C. A., Kershner, J. & Williams, G. Using existing scientific capacity to set targets for ecosystem-based management: a Puget Sound case study. Mar. Policy 35, 508–518 (2011).
    Article  Google Scholar 

    39.
    Martin, J., Runge, M. C., Nichols, J. D., Lubow, B. C. & Kendall, W. L. Structured decision making as a conceptual framework to identify thresholds for conservation and management. Ecol. Appl. 19, 1079–1090 (2009).
    PubMed  Article  PubMed Central  Google Scholar 

    40.
    Puget Sound Regional Council (PSRC). 2050 Forecast of People and Jobs. https://www.psrc.org/ (2018).

    41.
    Ruckelshaus, M., Essington, T. & Levin, P. 2009 Puget Sound, Washington, USA. in Ecosystem-based Management for the Oceans 201–226 (Island Press, Washington, DC, USA, 2012).

    42.
    Feist, B. E. et al. Roads to ruin: conservation threats to a sentinel species across an urban gradient. Ecol. Appl. 27, 2382–2396 (2017).
    PubMed  PubMed Central  Article  Google Scholar 

    43.
    Scholz, N. L. et al. Recurrent die-offs of adult coho salmon returning to spawn in Puget Sound lowland urban streams. PLoS ONE 6, e28013 (2011).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    44.
    WAECY – Water Resource Inventory Areas (WRIA).

    45.
    Spromberg, J. A. & Scholz, N. L. Estimating the future decline of wild coho salmon populations resulting from early spawner die-offs in urbanizing watersheds of the Pacific Northwest, USA. Integr. Environ. Assess. Manag. 7, 648–656 (2011).
    PubMed  Article  PubMed Central  Google Scholar 

    46.
    Bolte, J. & Vache, K. Envisioning Puget Sound Alternative Futures. Or. State Univ. (2010).

    47.
    King, M. A. & Fairfax, S. K. Beyond bucks and acres: land acquisition and water. Tex Rev 83, 1941 (2004).
    Google Scholar 

    48.
    Bottrill, M. C. & Pressey, R. L. The effectiveness and evaluation of conservation planning. Conserv. Lett. 5, 407–420 (2012).
    Article  Google Scholar 

    49.
    Rissman, A. R. & Smail, R. Accounting for results: how conservation organizations report performance information. Environ. Manag. 55, 916–929 (2015).
    ADS  Article  Google Scholar 

    50.
    Dinerstein, E. et al. A global deal for nature: guiding principles, milestones, and targets. Sci. Adv. 5, eaaw2869 (2019).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    51.
    Jones, K. R. et al. The location and protection status of Earth’s diminishing marine wilderness. Curr. Biol. 28, 2506–2512 (2018).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    52.
    Tulloch, V. J. et al. Why do we map threats? Linking threat mapping with actions to make better conservation decisions. Front. Ecol. Environ. 13, 91–99 (2015).
    Article  Google Scholar 

    53.
    Moilanen, A. et al. Balancing alternative land uses in conservation prioritization. Ecol. Appl. 21, 1419–1426 (2011).
    PubMed  Article  PubMed Central  Google Scholar 

    54.
    Rodewald, A. D., Strimas-Mackey, M., Schuster, R. & Arcese, P. Tradeoffs in the value of biodiversity feature and cost data in conservation prioritization. Sci. Rep. 9, 1–8 (2019).
    CAS  Article  Google Scholar 

    55.
    Walsh, J. C. et al. Prioritizing conservation actions for Pacific salmon in Canada. J. Appl. Ecol. (2020).

    56.
    Chow, M. I. et al. An urban stormwater runoff mortality syndrome in juvenile coho salmon. Aquat. Toxicol. 214, 105231 (2019).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    57.
    Battin, J. et al. Projected impacts of climate change on salmon habitat restoration. Proc. Natl. Acad. Sci. 104, 6720–6725 (2007).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    58.
    Council, N. R. et al. Upstream: Salmon and Society in the Pacific Northwest. (National Academies Press, 1996).

    59.
    Benda, L., Andras, K., Miller, D. & Bigelow, P. Confluence effects in rivers: interactions of basin scale, network geometry, and disturbance regimes. Water Resour. Res. 40, (2004).

    60.
    Nel, J. L. et al. Progress and challenges in freshwater conservation planning. Aquat. Conserv. Mar. Freshw. Ecosyst. 19, 474–485 (2009).
    Article  Google Scholar 

    61.
    Booth, D. B., Roy, A. H., Smith, B. & Capps, K. A. Global perspectives on the urban stream syndrome. Freshw. Sci. 35, 412–420 (2016).
    Article  Google Scholar 

    62.
    Feist, B. E., Buhle, E. R., Arnold, P., Davis, J. W. & Scholz, N. L. Landscape ecotoxicology of coho salmon spawner mortality in urban streams. PLoS ONE 6, e23424 (2011).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    63.
    Sethi, S. A., O’Hanley, J. R., Gerken, J., Ashline, J. & Bradley, C. High value of ecological information for river connectivity restoration. Landsc. Ecol. 32, 2327–2336 (2017).
    Article  Google Scholar 

    64.
    Watts, M. E. et al. Marxan with Zones: software for optimal conservation based land-and sea-use zoning. Environ. Model. Softw. 24, 1513–1521 (2009).
    Article  Google Scholar 

    65.
    Beger, M. et al. Incorporating asymmetric connectivity into spatial decision making for conservation. Conserv. Lett. 3, 359–368 (2010).
    Article  Google Scholar 

    66.
    Bower, S. D. et al. Making tough choices: picking the appropriate conservation decision-making tool. Conserv. Lett. 11, e12418 (2018).
    Article  Google Scholar 

    67.
    Schwartz, M. W. et al. Decision support frameworks and tools for conservation. Conserv. Lett. 11, e12385 (2018).
    Article  Google Scholar 

    68.
    Jarden, K. M., Jefferson, A. J. & Grieser, J. M. Assessing the effects of catchment-scale urban green infrastructure retrofits on hydrograph characteristics. Hydrol. Process. 30, 1536–1550 (2016).
    ADS  Article  Google Scholar 

    69.
    Pyke, C. et al. Assessment of low impact development for managing stormwater with changing precipitation due to climate change. Landsc. Urban Plan. 103, 166–173 (2011).
    Article  Google Scholar 

    70.
    Kim, D.-G., Jeong, K. & Ko, S.-O. Removal of road deposited sediments by sweeping and its contribution to highway runoff quality in Korea. Environ. Technol. 35, 2546–2555 (2014).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    71.
    Scheffer, M. Foreseeing tipping points. Nature 467, 411–412 (2010).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    72.
    Halpern, B. S. Addressing Socioecological Tipping Points and Safe Operating Spaces in the Anthropocene. in Conservation for the Anthropocene Ocean 271–286 (Elsevier, 2017).

    73.
    Malhado, A. C. M., Pires, G. F. & Costa, M. H. Cerrado conservation is essential to protect the Amazon rainforest. Ambio 39, 580–584 (2010).
    PubMed  PubMed Central  Article  Google Scholar 

    74.
    Selkoe, K. A. et al. Principles for managing marine ecosystems prone to tipping points. Ecosyst. Health Sustain. 1, 1–18 (2015).
    Article  Google Scholar 

    75.
    Schilling, J. & Logan, J. Greening the rust belt: a green infrastructure model for right sizing America’s shrinking cities. J. Am. Plann. Assoc. 74, 451–466 (2008).
    Article  Google Scholar 

    76.
    Hughes, R. M. et al. A review of urban water body challenges and approaches:(2) mitigating effects of future urbanization. Fisheries 39, 30–40 (2014).
    Article  Google Scholar 

    77.
    Parker, D. P. Land trusts and the choice to conserve land with full ownership or conservation easements. Nat. Resour. J. 483–518 (2004).

    78.
    Kennedy, C. M. et al. Optimizing land use decision-making to sustain Brazilian agricultural profits, biodiversity and ecosystem services. Biol. Conserv. 204, 221–230 (2016).
    Article  Google Scholar 

    79.
    Kaeriyama, M., Seo, H., Kudo, H. & Nagata, M. Perspectives on wild and hatchery salmon interactions at sea, potential climate effects on Japanese chum salmon, and the need for sustainable salmon fishery management reform in Japan. Environ. Biol. Fishes 94, 165–177 (2012).
    Article  Google Scholar 

    80.
    Willson, M. F. & Halupka, K. C. Anadromous fish as keystone species in vertebrate communities. Conserv. Biol. 9, 489–497 (1995).
    Article  Google Scholar 

    81.
    McIntyre, J. K. et al. Interspecies variation in the susceptibility of adult Pacific salmon to toxic urban stormwater runoff. Environ. Pollut. 238, 196–203 (2018).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    82.
    Service (NMFS), N. M. F. Report: 5-Year Review: Summary & Evaluation of Puget Sound Chinook Salmon, Hood Canal Summer-run Chum Salmon, Puget Sound Steelhead. (2016).

    83.
    Spromberg, J. A. & Meador, J. P. Relating results of chronic toxicity responses to population-level effects: modeling effects on wild chinook salmon populations. Integr. Environ. Assess. Manag. Int. J. 1, 9–21 (2005).
    CAS  Article  Google Scholar 

    84.
    Allan, J. D. Landscapes and riverscapes: the influence of land use on stream ecosystems. Annu. Rev. Ecol. Evol. Syst. 35, 257–284 (2004).
    Article  Google Scholar 

    85.
    Bierwagen, B. G. et al. National housing and impervious surface scenarios for integrated climate impact assessments. Proc. Natl. Acad. Sci. 107, 20887–20892 (2010).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    86.
    Walsh, C. J., Fletcher, T. D. & Burns, M. J. Urban stormwater runoff: a new class of environmental flow problem. PLoS ONE 7, e45814 (2012).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar  More

  • in

    Anthropogenic interferences lead to gut microbiome dysbiosis in Asian elephants and may alter adaptation processes to surrounding environments

    1.
    Cardinale, B. J. et al. Biodiversity loss and its impact on humanity. Nature 486, 59 (2012).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 
    2.
    Hooper, D. U. et al. A global synthesis reveals biodiversity loss as a major driver of ecosystem change. Nature 486, 105 (2012).
    ADS  CAS  Article  Google Scholar 

    3.
    Taylor-Brown, A. et al. The impact of human activities on Australian wildlife. PLoS ONE 14(1), e0206958 (2019).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    4.
    Hunter, P. The human impact on biological diversity. How species adapt to urban challenges sheds light on evolution and provides clues about conservation. EMBO Rep. 8(4), 316–318 (2007).

    5.
    Woinarski, J. C. Z., Burbidge, A. A. & Harrison, P. L. Ongoing unraveling of a continental fauna: Decline and extinction of Australian mammals since European settlement. Proc. Natl. Acad. Sci. 112(15), 4531 (2015).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    6.
    Cho, I. & Blaser, M. J. The human microbiome: At the interface of health and disease. Nat. Rev. Genet. 13(4), 260–270 (2012).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    7.
    Cryan, J. F. & Dinan, T. G. Mind-altering microorganisms: The impact of the gut microbiota on brain and behaviour. Nat. Rev. Neurosci. 13(10), 701–712 (2012).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    8.
    Kau, A. L., Ahern, P. P., Griffin, N. W., Goodman, A. L. & Gordon, J. I. Human nutrition, the gut microbiome and the immune system. Nature 474(7351), 327–336 (2011).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    9.
    Inserra, A. et al. Mice lacking Casp 1, Ifngr and Nos2 genes exhibit altered depressive- and anxiety-like behaviour, and gut microbiome composition. Sci. Rep. 9(1), 6456 (2019).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    10.
    Kuti, D. et al. Gastrointestinal (non-systemic) antibiotic rifaximin differentially affects chronic stress-induced changes in colon microbiome and gut permeability without effect on behavior. Brain Behav. Immun. 84, 218–228 (2020).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    11.
    Bharwani, A. et al. Structural & functional consequences of chronic psychosocial stress on the microbiome & host. Psychoneuroendocrinology. 63, 217–227 (2016).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    12.
    Wasimuddin, Menke, S., Melzheimer, J., Thalwitzer, S., Heinrich, S., Wachter, B. et al. Gut microbiomes of free-ranging and captive Namibian cheetahs: Diversity, putative functions and occurrence of potential pathogens. Mol. Ecol. 26(20), 5515–5527 (2017).

    13.
    Sommer, F. et al. The gut microbiota modulates energy metabolism in the hibernating brown bear Ursus arctos. Cell Rep. 14(7), 1655–1661 (2016).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    14.
    Ley, R. E. et al. Evolution of mammals and their gut microbes. Science (New York, NY). 320(5883), 1647–1651 (2008).
    ADS  CAS  Article  Google Scholar 

    15.
    Wang, J. et al. Dietary history contributes to enterotype-like clustering and functional metagenomic content in the intestinal microbiome of wild mice. Proc. Natl. Acad. Sci. U.S.A. 111(26), E2703–E2710 (2014).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    16.
    Koch, H. & Schmid-Hempel, P. Socially transmitted gut microbiota protect bumble bees against an intestinal parasite. Proc. Natl. Acad. Sci. U.S.A. 108(48), 19288–19292 (2011).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    17.
    Schmidt, E., Mykytczuk, N. & Schulte-Hostedde, A. I. Effects of the captive and wild environment on diversity of the gut microbiome of deer mice (Peromyscus maniculatus). ISME J. 13(5), 1293–1305 (2019).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    18.
    Lahdenperä, M., Mar, K.U., Courtiol, A., Lummaa, V. Differences in age-specific mortality between wild-caught and captive-born Asian elephants. Nat. Commun. 9(1), 3023 (2018).

    19.
    Sun, C. H., Liu, H. Y., Liu, B., Yuan, B. D. & Lu, C. H. Analysis of the gut microbiome of wild and captive Pere David’s deer. Front. Microbiol. 10, 2331 (2019).
    PubMed  PubMed Central  Article  Google Scholar 

    20.
    Ryser-Degiorgis, M.-P. Wildlife health investigations: Needs, challenges and recommendations. BMC Vet. Res. 9(1), 223 (2013).
    PubMed  PubMed Central  Article  Google Scholar 

    21.
    Stallknecht, D. E. Impediments to wildlife disease surveillance, research, and diagnostics. Curr. Top. Microbiol. Immunol. 315, 445–461 (2007).
    CAS  PubMed  PubMed Central  Google Scholar 

    22.
    Soulsbury, C. D. et al. The welfare and ethics of research involving wild animals: A primer. Methods Ecol. Evol. 11(10), 1164–1181 (2020).
    Article  Google Scholar 

    23.
    Amato, K. R. et al. Using the gut microbiota as a novel tool for examining colobine primate GI health. Global Ecol. Conserv. 7, 225–237 (2016).
    Article  Google Scholar 

    24.
    Gehrig, J.L., Venkatesh, S., Chang, H.W., Hibberd, M.C., Kung, V.L., Cheng, J. et al. Effects of microbiota-directed foods in gnotobiotic animals and undernourished children. Science (New York, NY). 365(6449) (2019).

    25.
    Choudhury, A., Lahiri Choudhury, D.K., Desai, A., Duckworth, J.W., Easa, P.S., Johnsingh, A.J.T. et al. Elephas maximus. The IUCN red list of threatened species. p. e.T7140A12828813 (2008).

    26.
    Zhang, C., Xu, B., Lu, T. & Huang, Z. Metagenomic analysis of the fecal microbiomes of wild asian elephants reveals microflora and enzymes that mainly digest hemicellulose. J. Microbiol. Biotechnol. 29(8), 1255–1265 (2019).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    27.
    Ilmberger, N. et al. A comparative metagenome survey of the fecal microbiota of a breast- and a plant-fed Asian elephant reveals an unexpectedly high diversity of glycoside hydrolase family enzymes. PLoS ONE 9(9), e106707 (2014).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    28.
    Songer, M., Aung, M., Allendorf, T. D., Calabrese, J. M. & Leimgruber, P. Drivers of change in Myanmar’s wild elephant distribution. Trop. Conserv. Sci. 9(4), 1940082916673749 (2016).
    Article  Google Scholar 

    29.
    Crawley, J. A. H. et al. Investigating changes within the handling system of the largest semi-captive population of Asian elephants. PLoS ONE 14(1), e0209701 (2019).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    30.
    Oo, Z. M. Health issues of captive Asian elephants in Myanmar. Gajah. 36, 21–22 (2012).
    Google Scholar 

    31.
    Chel, H.M., Iwaki, T., Hmoon, M., Thaw, Y.N., Chan Soe, N., Win, S.Y., et al. Morphological and molecular identification of cyathostomine gastrointestinal nematodes of Murshidia and Quilonia species from Asian elephants in Myanmar. Int. J. Parasitol. Parasites Wildl. (2020).

    32.
    Sukumar, R., Santiapillai, C. Elephas maximus: Status and distribution. in The Proboscidea: Evolution and Palaeoecology of Elephants and their Relatives 327–331 (Oxford University Press, New York, 1996).

    33.
    Leimgruber, P. et al. Current status of Asian elephants in Myanmar. Gajah. 35, 76–86 (2011).
    Google Scholar 

    34.
    Prakash, T.G.S.L., Indrajith, W.A.A.D.U., Aththanayaka, A.M.C.P., Karunarathna, S., Botejue, M., Nijman, V. et al. Illegal capture and internal trade of wild Asian elephants (Elephas maximus) in Sri Lanka. Nat. Conserv. 42, 51–69 (2020).

    35.
    Clubb, R. & Mason, G. A Review of the Welfare of Zoo Elephants in Europe: A Report Commissioned by the RSPCA (Animal BehaviourResearch Group, University of Oxford, Oxford, 2002).
    Google Scholar 

    36.
    Millspaugh, J.J., Burke, T., Van Dyk, G., Slotow, R., Washburn, B.E., Woods, R.J. Stress response of working African elephants to transportation and safari adventures. J. Wildl. Manag. 1257–1260 (2007).

    37.
    Clubb, R. et al. Compromised survivorship in zoo elephants. Science (New York, NY). 322(5908), 1649 (2008).
    ADS  CAS  Article  Google Scholar 

    38.
    Easton, A.V., Quinones, M., Vujkovic-Cvijin, I., Oliveira, R.G., Kepha, S., Odiere, M.R. et al. The impact of anthelmintic treatment on human gut microbiota based on cross-sectional and pre- and postdeworming comparisons in western Kenya. mBio. 10(2) (2019).

    39.
    Martin, I. et al. Dynamic changes in human-gut microbiome in relation to a placebo-controlled anthelminthic trial in Indonesia. PLoS Negl. Trop. Dis. 12(8), e0006620 (2018).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    40.
    He, F. et al. Variations in gut microbiota and fecal metabolic phenotype associated with Fenbendazole and Ivermectin tablets by 16S rRNA gene sequencing and LC/MS-based metabolomics in Amur tiger. Biochem. Biophys. Res. Commun. 499(3), 447–453 (2018).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    41.
    Kunz, I. G. Z. et al. Equine fecal microbiota changes associated with anthelmintic administration. J. Equine Vet. Sci. 77, 98–106 (2019).
    PubMed  Article  PubMed Central  Google Scholar 

    42.
    Gagliardi, A. et al. Rebuilding the gut microbiota ecosystem. Int. J. Environ. Res. Public Health. 15(8), 1679 (2018).
    PubMed Central  Article  CAS  Google Scholar 

    43.
    Clayton, J. B. et al. Captivity humanizes the primate microbiome. Proc. Natl. Acad. Sci. U.S.A. 113(37), 10376–10381 (2016).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    44.
    McKenzie, V. J. et al. The effects of captivity on the mammalian gut microbiome. Integr. Comp. Biol. 57(4), 690–704 (2017).
    PubMed  PubMed Central  Article  Google Scholar 

    45.
    Monfort, S.L. “Mayday mayday mayday”, the millennium ark is sinking! in (Holt, W.V., Brown, J.L., Comizzoli, P. eds.) Reproductive Sciences in Animal Conservation: Progress and Prospects 15–31 (Springer, New York, 2014).

    46.
    Gerber, L. R. Conservation triage or injurious neglect in endangered species recovery. Proc. Natl. Acad. Sci. U.S.A. 113(13), 3563–3566 (2016).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    47.
    Haworth, S.E., White, K.S., Côté, S.D., Shafer, A.B.A. Space, time and captivity: Quantifying the factors influencing the fecal microbiome of an alpine ungulate. FEMS Microbiol. Ecol. 95(7) (2019).

    48.
    Gibson, K. M. et al. Gut microbiome differences between wild and captive black rhinoceros—Implications for rhino health. Sci. Rep. 9(1), 7570 (2019).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    49.
    Montonye, D. R. et al. Acclimation and institutionalization of the mouse microbiota following transportation. Front. Microbiol. 9, 1085 (2018).
    PubMed  PubMed Central  Article  Google Scholar 

    50.
    Conour, L. A., Murray, K. A. & Brown, M. J. Preparation of animals for research–issues to consider for rodents and rabbits. ILAR J. 47(4), 283–293 (2006).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    51.
    Obernier, J. A. & Baldwin, R. L. Establishing an appropriate period of acclimatization following transportation of laboratory animals. ILAR J. 47(4), 364–369 (2006).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    52.
    Mir, R. A., Kleinhenz, M. D., Coetzee, J. F., Allen, H. K. & Kudva, I. T. Fecal microbiota changes associated with dehorning and castration stress primarily affects light-weight dairy calves. PLoS ONE 14(1), e0210203 (2019).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    53.
    Abhijith, T.V., Ashokkumar, M., Dencin, R.T., George, C. Gastrointestinal parasites of Asian elephants (Elephas maximus L. 1798) in south Wayanad forest division, Kerala, India. J. Parasit. Dis. 42(3), 382–390 (2018).

    54.
    Bansiddhi, P., Brown, J.L., Thitaram, C., Punyapornwithaya, V., Somgird, C., Edwards, K.L. et al. Changing trends in elephant camp management in northern Thailand and implications for welfare. PeerJ. 6, e5996-e (2018).

    55.
    Leung, J. M. & Loke, P. N. A role for IL-22 in the relationship between intestinal helminths, gut microbiota and mucosal immunity. Int. J. Parasitol. 43(3–4), 253–257 (2013).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    56.
    Kreisinger, J., Bastien, G., Hauffe, H.C., Marchesi, J., Perkins, S.E. Interactions between multiple helminths and the gut microbiota in wild rodents. Philos. Trans. R. Soc. Lond. Ser. B Biol. Sci. 370(1675) (2015).

    57.
    Lee, S. C. et al. Helminth colonization is associated with increased diversity of the gut microbiota. PLoS Negl. Trop. Dis. 8(5), e2880 (2014).
    PubMed  PubMed Central  Article  Google Scholar 

    58.
    Ditgen, D. et al. Harnessing the helminth secretome for therapeutic immunomodulators. Biomed. Res. Int. 2014, 964350 (2014).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    59.
    Hewitson, J. P. et al. Proteomic analysis of secretory products from the model gastrointestinal nematode Heligmosomoides polygyrus reveals dominance of venom allergen-like (VAL) proteins. J. Proteom. 74(9), 1573–1594 (2011).
    CAS  Article  Google Scholar 

    60.
    Chong, R. et al. Looking like the locals—Gut microbiome changes post-release in an endangered species. Anim. Microbiome. 1(1), 8 (2019).
    Article  Google Scholar 

    61.
    Wienemann, T. et al. The bacterial microbiota in the ceca of Capercaillie (Tetrao urogallus) differs between wild and captive birds. Syst. Appl. Microbiol. 34(7), 542–551 (2011).
    PubMed  Article  PubMed Central  Google Scholar 

    62.
    Pilla, R. & Suchodolski, J. S. The role of the canine gut microbiome and metabolome in health and gastrointestinal disease. Front. Vet. Sci. 6, 498 (2019).
    PubMed  Article  PubMed Central  Google Scholar 

    63.
    Hemarajata, P. & Versalovic, J. Effects of probiotics on gut microbiota: Mechanisms of intestinal immunomodulation and neuromodulation. Therap. Adv. Gastroenterol. 6(1), 39–51 (2013).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    64.
    Pertoldi, C., Randi, E., Ruiz-González, A., Vergeer, P. & Ouborg, J. How can genomic tools contribute to the conservation of endangered organisms. Int. J. Genomics. 2016, 4712487 (2016).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    65.
    Roth, T. L. et al. Reduced gut microbiome diversity and metabolome differences in Rhinoceros species at risk for iron overload disorder. Front. Microbiol. 10, 2291 (2019).
    PubMed  PubMed Central  Article  Google Scholar 

    66.
    Youngblut, N. D. et al. Host diet and evolutionary history explain different aspects of gut microbiome diversity among vertebrate clades. Nat. Commun. 10(1), 2200 (2019).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    67.
    Tatsika, S., Karamanoli, K., Karayanni, H. & Genitsaris, S. Metagenomic characterization of bacterial communities on ready-to-eat vegetables and effects of household washing on their diversity and composition. Pathogens. 8(1), 37 (2019).
    CAS  PubMed Central  Article  Google Scholar 

    68.
    Allan, N., Knotts, T.A., Pesapane, R., Ramsey, J.J., Castle, S., Clifford, D. et al. Conservation implications of shifting gut microbiomes in captive-reared endangered voles intended for reintroduction into the wild. Microorganisms. 6(3) (2018).

    69.
    Amato, K. R. et al. The gut microbiota appears to compensate for seasonal diet variation in the wild black howler monkey (Alouatta pigra). Microb. Ecol. 69(2), 434–443 (2015).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    70.
    Eid, H. M. et al. Significance of microbiota in obesity and metabolic diseases and the modulatory potential by medicinal plant and food ingredients. Front. Pharmacol. 8, 387 (2017).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    71.
    Lay, C. et al. Design and validation of 16S rRNA probes to enumerate members of the Clostridium leptum subgroup in human faecal microbiota. Environ. Microbiol. 7(7), 933–946 (2005).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    72.
    Kartzinel, T. R., Hsing, J. C., Musili, P. M., Brown, B. R. P. & Pringle, R. M. Covariation of diet and gut microbiome in African megafauna. Proc. Natl. Acad. Sci. 116(47), 23588–23593 (2019).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    73.
    Pope, P. B. et al. Metagenomics of the Svalbard reindeer rumen microbiome reveals abundance of polysaccharide utilization loci. PLoS ONE 7(6), e38571 (2012).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    74.
    Warnecke, F. et al. Metagenomic and functional analysis of hindgut microbiota of a wood-feeding higher termite. Nature 450(7169), 560–565 (2007).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    75.
    Evans, N. J. et al. Characterization of novel bovine gastrointestinal tract Treponema isolates and comparison with bovine digital dermatitis treponemes. Appl. Environ. Microbiol. 77(1), 138 (2011).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    76.
    Kay, G. L. et al. Differences in the faecal microbiome in Schistosoma haematobium infected children vs. uninfected children. PLoS Negl. Trop. Dis. 9(6), 0003861 (2015).
    Article  CAS  Google Scholar 

    77.
    Trevelline, B. K., Fontaine, S. S., Hartup, B. K. & Kohl, K. D. Conservation biology needs a microbial renaissance: A call for the consideration of host-associated microbiota in wildlife management practices. Proc. Biol. Sci. 2019(286), 20182448 (1895).
    Google Scholar 

    78.
    Borody, T. J., Paramsothy, S. & Agrawal, G. Fecal microbiota transplantation: Indications, methods, evidence, and future directions. Curr. Gastroenterol. Rep. 15(8), 337 (2013).
    PubMed  PubMed Central  Article  Google Scholar 

    79.
    Blyton, M. D. J. et al. Faecal inoculations alter the gastrointestinal microbiome and allow dietary expansion in a wild specialist herbivore, the koala. Anim. Microbiome. 1(1), 6 (2019).
    Article  Google Scholar 

    80.
    Guo, W. et al. Fecal microbiota transplantation provides new insight into wildlife conservation. Glob. Ecol. Conserv. 24, e01234 (2020).
    Article  Google Scholar 

    81.
    Bolyen, E. et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat. Biotechnol. 37(8), 852–857 (2019).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    82.
    Callahan, B. J. et al. DADA2: High-resolution sample inference from Illumina amplicon data. Nat. Methods 13(7), 581–583 (2016).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    83.
    Anderson, M. J. A new method for non-parametric multivariate analysis of variance. Austral. Ecol. 26(1), 32–46 (2001).
    Google Scholar 

    84.
    Vazquez-Baeza, Y., Pirrung, M., Gonzalez, A. & Knight, R. EMPeror: A tool for visualizing high-throughput microbial community data. GigaScience. 2(1), 16 (2013).
    PubMed  PubMed Central  Article  Google Scholar 

    85.
    Bokulich, N. A. et al. Optimizing taxonomic classification of marker-gene amplicon sequences with QIIME 2’s q2-feature-classifier plugin. Microbiome. 6(1), 90 (2018).
    PubMed  PubMed Central  Article  Google Scholar 

    86.
    Morton, J. T. et al. Balance trees reveal microbial niche differentiation. mSystems 2(1), e00162-00166 (2017).
    CAS  PubMed  PubMed Central  Google Scholar 

    87.
    Mandal, S. et al. Analysis of composition of microbiomes: A novel method for studying microbial composition. Microb. Ecol. Health Dis. 26, 27663 (2015).
    PubMed  PubMed Central  Google Scholar  More