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    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

    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

    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

    Novel approach to enhance coastal habitat and biotope mapping with drone aerial imagery analysis

    1.
    Hooper, D. U. et al. A global synthesis reveals biodiversity loss as a major driver of ecosystem change. Nature 486, 105–108 (2012).
    ADS  CAS  PubMed  Article  Google Scholar 
    2.
    Lefcheck, J. S., Wilcox, D. J., Murphy, R. R., Marion, S. R. & Orth, R. J. Multiple stressors threaten the imperiled coastal foundation species eelgrass (Zostera marina) in Chesapeake Bay, USA. Glob. Change Biol. 32, 202–3483 (2017).
    Google Scholar 

    3.
    Duarte, C. M. et al. Rebuilding marine life. Nature 580, 39–51 (2020).
    ADS  CAS  PubMed  Article  Google Scholar 

    4.
    Balvanera, P. et al. Quantifying the evidence for biodiversity effects on ecosystem functioning and services. Ecol. Lett. 9, 1146–1156 (2006).
    PubMed  Article  Google Scholar 

    5.
    Liquete, C. et al. Current status and future prospects for the assessment of marine and coastal ecosystem services: A systematic review. PLoS ONE 8, e67737 (2013).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    6.
    Bayley, D. T. I. & Mogg, A. O. M. Chapter 6—New Advances in Benthic Monitoring Technology and Methodology. World Seas: An Environmental Evaluation 121–132 (Elsevier, Amsterdam, 2018). https://doi.org/10.1016/B978-0-12-805052-1.00006-1.
    Google Scholar 

    7.
    González-Rivero, M. et al. The Catlin Seaview Survey—Kilometre-scale seascape assessment, and monitoring of coral reef ecosystems. Aquat. Conserv. Mar. Freshw. Ecosyst. 24, 184–198 (2014).
    Article  Google Scholar 

    8.
    Ventura, D., Bruno, M., Jona Lasinio, G., Belluscio, A. & Ardizzone, G. A low-cost drone based application for identifying and mapping of coastal fish nursery grounds. Estuar. Coast. Shelf Sci. 171, 85–98 (2016).
    ADS  Article  Google Scholar 

    9.
    Pyle, R. L. in Mesophotic Coral Ecosystems (eds. Loya, Y., Puglise, K. A. & Bridge, T. C. L.) 12, 959–972 (Springe, Berlin, 2019).

    10.
    Lam, K. et al. A comparison of video and point intercept transect methods for monitoring subtropical coral communities. J. Exp. Mar. Biol. Ecol. 333, 115–128 (2006).
    Article  Google Scholar 

    11.
    Dumas, P., Bertaud, A., Peignon, C., Léopold, M. & Pelletier, D. A ‘quick and clean’ photographic method for the description of coral reef habitats. J. Exp. Mar. Biol. Ecol. 368, 161–168 (2009).
    Article  Google Scholar 

    12.
    Monteiro, J. G., Almeida, C., Freitas, R., Delgado, A. & Porteiro, F. Coral assemblages of Cabo Verde: preliminary assessment and description. Proceedings of the 11th ICRS (2009).

    13.
    Beijbom, O. et al. Towards automated annotation of benthic survey images: Variability of human experts and operational modes of automation. PLoS ONE 10, e0130312 (2015).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    14.
    Chennu, A., Färber, P., De’ath, G., de Beer, D. & Fabricius, K. E. A diver-operated hyperspectral imaging and topographic surveying system for automated mapping of benthic habitats. Sci. Rep. 7, 1–12 (2017).
    CAS  Article  Google Scholar 

    15.
    Purkis, S. J. Remote sensing tropical coral reefs: The view from above. Annu. Rev. Mar. Sci. 10, 149–168 (2018).
    ADS  Article  Google Scholar 

    16.
    Kao, H.-M. et al. Determination of shallow water depth using optical satellite images. Int. J. Remote Sens. 30, 6241–6260 (2009).
    ADS  Article  Google Scholar 

    17.
    Saul, S. & Purkis, S. Semi-automated object-based classification of coral reef habitat using discrete choice models. Remote Sens. 7, 15894–15916 (2015).
    ADS  Article  Google Scholar 

    18.
    Marcello, J., Eugenio, F. & Marques, F. Benthic mapping using high resolution multispectral and hyperspectral imagery. In IGARSS 2018—2018 IEEE International Geoscience and Remote Sensing Symposium 1535–1538 (2018). https://doi.org/10.1109/IGARSS.2018.8519166

    19.
    Chénier, R., Faucher, M.-A. & Ahola, R. Satellite-derived bathymetry for improving canadian hydrographic service charts. ISPRS Int. J. Geo-Inf. 7, 306–315 (2018).
    Article  Google Scholar 

    20.
    Casella, E. et al. Mapping coral reefs using consumer-grade drones and structure from motion photogrammetry techniques. Coral Reefs 36, 269–275 (2016).
    ADS  Article  Google Scholar 

    21.
    Chust, G., Galparsoro, I., Borja, Á., Franco, J. & Uriarte, A. Coastal and estuarine habitat mapping, using LIDAR height and intensity and multi-spectral imagery. Estuar. Coast. Shelf Sci. 78, 633–643 (2008).
    ADS  Article  Google Scholar 

    22.
    Garcia, R., Hedley, J., Tin, H. & Fearns, P. A method to analyze the potential of optical remote sensing for benthic habitat mapping. Remote Sens. 7, 13157–13189 (2015).
    ADS  Article  Google Scholar 

    23.
    Hernandez, W. & Armstrong, R. Deriving bathymetry from multispectral remote sensing data. JMSE 4, 8 (2016).
    Article  Google Scholar 

    24.
    Gonzalez, L. et al. Unmanned Aerial Vehicles (UAVs) and artificial intelligence revolutionizing wildlife monitoring and conservation. Sensors 16, 97 (2016).
    Article  Google Scholar 

    25.
    Jiménez López, J. & Mulero-Pázmány, M. Drones for conservation in protected areas: Present and future. Drones 3, 10 (2019).
    Article  Google Scholar 

    26.
    Chirayath, V. & Earle, S. A. Drones that see through waves—Preliminary results from airborne fluid lensing for centimetre-scale aquatic conservation. Aquat. Conserv. Mar. Freshw. Ecosyst. 26, 237–250 (2016).
    Article  Google Scholar 

    27.
    Giordano, F., Mattei, G., Parente, C., Peluso, F. & Santamaria, R. Integrating sensors into a marine drone for bathymetric 3D surveys in shallow waters. Sensors 16, 41–17 (2016).
    Article  Google Scholar 

    28.
    Collin, A. et al. Very high resolution mapping of coral reef state using airborne bathymetric LiDAR surface-intensity and drone imagery. Int. J. Remote Sens. 00, 1–13 (2018).
    Google Scholar 

    29.
    Konar, B. & Iken, K. The use of unmanned aerial vehicle imagery in intertidal monitoring. Deep-Sea Res. Part II(147), 79–86 (2018).
    Article  Google Scholar 

    30.
    Parsons, M., Bratanov, D., Gaston, K. J. & Gonzalez, F. UAVs, hyperspectral remote sensing, and machine learning revolutionizing reef monitoring. Sensors 18, 2026 (2018).
    Article  Google Scholar 

    31.
    Rossiter, T., Furey, T., McCarthy, T. & Stengel, D. B. UAV-mounted hyperspectral mapping of intertidal macroalgae. Estuar. Coast. Shelf Sci. https://doi.org/10.1016/j.ecss.2020.106789 (2020).
    Article  Google Scholar 

    32.
    United Nations Environment Programme. Out of the Blue. 1–96 (UNEP, 2020).

    33.
    Monteiro, J. G. & Lopez, J. J. Map of Quinta do Lorde Bay—Madeira Island. 1–3 (2020). doi:https://doi.org/10.22541/au.158939921.14824633

    34.
    Stumpf, R. P., Holderied, K. & Sinclair, M. Determination of water depth with high-resolution satellite imagery over variable bottom types. Limnol. Oceanogr. 48, 547–556 (2003).
    ADS  Article  Google Scholar 

    35.
    Conger, C. L., Hochberg, E. J., Fletcher, C. H. & Atkinson, M. J. Decorrelating remote sensing color bands from bathymetry in optically shallow waters. IEEE Trans. Geosci. Remote Sens. 44, 1655–1660 (2006).
    ADS  Article  Google Scholar 

    36.
    Clarke, K. & Warwick, R. Change in Marine Communities: An Approach to Statistical Analysis (Primer-e Ltd, London, 2014).
    Google Scholar 

    37.
    Baldwin, C. C., Tornabene, L. & Robertson, D. R. Below the mesophotic. Sci. Rep. https://doi.org/10.1038/s41598-018-23067-1 (2018).
    Article  PubMed  PubMed Central  Google Scholar 

    38.
    Olenin, S. & Ducrotoy, J.-P. The concept of biotope in marine ecology and coastal management. J. Exp. Mar. Biol. Ecol. 53, 20–29 (2006).
    CAS  Google Scholar 

    39.
    Frazão Santos, C. et al. in World Seas: An Environmental Evaluation 571–592 (Elsevier, Amsterdam, 2019). https://doi.org/10.1016/B978-0-12-805052-1.00033-4

    40.
    Mumby, P. J. et al. Remote sensing of coral reefs and their physical environment. Mar Polut Bull 48, 219–228 (2004).
    CAS  Article  Google Scholar 

    41.
    Hayes, R. & Goreau, T. Satellite-derived sea surface temperature from Caribbean and Atlantic coral reef sites, 1984–2003. Rev. Biol. Trop. 56, 97–118 (2008).
    Google Scholar 

    42.
    Sugara, A. A., Siregar, V. P. V. & Agus, S. B. S. Classification of benthic habitat of shallow water using worldview-2 image with in-situ and drone data. Jurnal Ilmu dan Teknologi Kelautan Tropis 12, 135–150 (2020).
    Article  Google Scholar 

    43.
    Murfitt, S. L. et al. Applications of unmanned aerial vehicles in intertidal reef monitoring. Sci. Rep. https://doi.org/10.1038/s41598-017-10818-9 (2017).
    Article  PubMed  PubMed Central  Google Scholar 

    44.
    Kaplanis, N. J., Edwards, C. B., Eynaud, Y. & Smith, J. E. Future sea-level rise drives rocky intertidal habitat loss and benthic community change. PeerJ 8, e9186–e9221 (2020).
    PubMed  PubMed Central  Article  Google Scholar 

    45.
    Chatzinikolaou, E. Use and limitations of ecological models. Transit. Waters Bull. 6, 34–41 (2012).
    Google Scholar 

    46.
    de Carneiro, L. R. A., Lima, A. P., Machado, R. B. & Magnusson, W. E. Limitations to the use of species-distribution models for environmental-impact assessments in the Amazon. PLoS ONE 11, e0146543 (2016).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    47.
    van der Wal, D., van Dalen, J., Dool, den, A. W.-V., Dijkstra, J. T. & Ysebaert, T. Biophysical control of intertidal benthic macroalgae revealed by high-frequency multispectral camera images. J. Sea Res. 90, 111–120 (2014).

    48.
    Goldberg, J. & Wilkinson, C. in Status of coral reefs of the World (ed. Wilkinson, C.) 1, 67–92 (Status of coral reefs of the World, 2004).

    49.
    Fabry, V. J., Seibel, B. A. & Feely, R. A. Impacts of ocean acidification on marine fauna and ecosystem processes. ICES J. Mar. Sci. 65, 414–432 (2008).
    CAS  Article  Google Scholar 

    50.
    Radeta, M. et al. in Human-Computer Interaction—INTERACT 2019, vol. 11748, 237–248 (Springer, Cham, 2019).

    51.
    Rusu, E. & Guedes Soares, C. Wave energy pattern around the Madeira Islands. Energy 45, 771–785 (2012).
    Article  Google Scholar 

    52.
    Pullen, J., Caldeira, R., Doyle, J. D., May, P. & Tomé, R. Modeling the air-sea feedback system of Madeira Island. J. Adv. Model. Earth Syst. 9, 1641–1664 (2017).
    ADS  Article  Google Scholar 

    53.
    Kahng, S. E. et al. Community ecology of mesophotic coral reef ecosystems. Coral Reefs 29, 255–275 (2010).
    Article  Google Scholar 

    54.
    Earth Systems Research Institute (ESRI). ArcGIS Desktop: Release 10 (2011).

    55.
    Blaschke, T. Object based image analysis for remote sensing. ISPRS J. Photogram. Remote Sens. 65, 2–16 (2010).
    ADS  Article  Google Scholar 

    56.
    Darwish, A., Leukert, K. & Reinhardt, W. Image segmentation for the purpose of object-based classification. in 3, 2039–2041 (IEEE, 2003).

    57.
    Qian, Y., Zhou, W., Yan, J., Li, W. & Han, L. Comparing machine learning classifiers for object-based land cover classification using very high resolution imagery. Remote Sens. 7, 153–168 (2015).
    ADS  Article  Google Scholar 

    58.
    Masi, B., Macedo, I. & Zalmon, I. Benthic community zonation in a breakwater on the North Coast of the State of Rio de Janeiro, Brazil. Braz. Arch. Biol. Technol. 52, 637–646 (2009).
    Article  Google Scholar 

    59.
    Sangil, C. et al. Shallow subtidal macroalgae in the North-eastern Atlantic archipelagos (Macaronesian region): A spatial approach to community structure. Eur. J. Phycol. 00, 1–16 (2018).
    Google Scholar 

    60.
    Su, T.-C. & Chou, H.-T. Application of multispectral sensors carried on unmanned aerial vehicle (UAV) to trophic state mapping of small reservoirs: A case study of Tain-Pu Reservoir in Kinmen, Taiwan. Remote Sens. 7, 10078–10097 (2015).
    ADS  Article  Google Scholar 

    61.
    Kohler, K. & Gill, S. Coral Point Count with Excel Extensions (CPCe): A Visual Basic Program for the determination of coral and substrate coverage using random point count methodology. Comput. Geosci. 32, 1259–1269 (2006).
    ADS  Article  Google Scholar 

    62.
    Clarke, K. R. & Gorley, R. N. Getting started with PRIMER V7 (PRIMER-E, Plymouth, 2015).
    Google Scholar 

    63.
    Berman, J. & Bell, J. J. Spatial Variability of Sponge Assemblages on the Wellington South Coast, New Zealand. Open Mar. Biol. J. 4, 12–25 (2010). https://doi.org/10.2174/1874450801004010012.

    64.
    Rawson, C. A. et al. Benthic macroinvertebrate assemblages in remediated wetlands around Sydney, Australia. Ecotoxicology 19, 1589–1600 (2010).
    CAS  PubMed  Article  Google Scholar 

    65.
    Anderson, M. J., Gorley, R. N. & Clarke, K. R. PERMANOVA for PRIMER: a guide to software and statistical methods. (PRIMER-E Ltd, 2008). More

  • in

    Modelling seasonal patterns of larval fish parasitism in two northern nearshore areas in the Humboldt Current System

    1.
    Moyano, M., Rodríguez, J. M. & Hernández-León, S. Larval fish abundance and distribution during the late winter bloom off Gran Canaria Island, Canary Islands. Fish Oceanogr. 18, 51–61 (2009).
    Article  Google Scholar 
    2.
    Sutherland, K., Strydom, N. A. & Wooldridge, T. H. Composition, abundance, distribution and seasonality of larval fishes in the Sundays Estuary, South Africa. Afr. Zool. 47, 229–244 (2012).
    Article  Google Scholar 

    3.
    Sun, D., Blomberg, S. P., Cribb, T. H., McCormick, M. I. & Grutter, A. S. The effects of parasites on the early life stages of a damselfish. Coral Reefs 31, 1065–1075 (2012).
    ADS  Article  Google Scholar 

    4.
    Palacios-Fuentes, P., Landaeta, M. F., Muñoz, G., Plaza, G. & Ojeda, F. P. The effects of a parasitic copepod on the recent larval growth of a fish inhabiting rocky coasts. Parasitol. Res. 111, 1661–1671 (2012).
    PubMed  Article  Google Scholar 

    5.
    Muñoz, G., Landaeta, M. F., Palacios-Fuentes, P., López, Z. & González, M. T. Parasite richness in fish larvae from the nearshore waters of central and northern Chile. Folia Parasite. 62, 029 (2015).
    Google Scholar 

    6.
    Ribeiro, F., Hilton, E. J. & Carnegie, R. B. High prevalence and potential impacts of caligid ectoparasites on larval atlantic croaker (Micropogonias undulatus) in the Chesapeake Bay. Estuar. Coasts 39, 583–588 (2016).
    Article  Google Scholar 

    7.
    Jahnsen-Guzmán, N., Bernal-Durán, V. & Landaeta, M. F. Parasitic copepods affect morphospace and diet of larvae of a temperate reef fish. J. Fish. Biol. 92, 330–346 (2018).
    PubMed  Article  PubMed Central  Google Scholar 

    8.
    González, M. T. & Acuña, E. Influence of host size and sex on the endohelminth infracommunities of the red rockfish Sebastes capensis off Northern Chile. J. Parasitol. 86, 854–857 (2000).
    PubMed  Article  Google Scholar 

    9.
    Poulin, R. Are there general laws in parasite ecology?. Parasitology 134, 763–776 (2007).
    CAS  PubMed  Article  Google Scholar 

    10.
    Fogelman, R. M. & Grutter, A. S. Mancae of the parasitic cymothoid isopod, Anilocra apogonae: Early life history, host-specificity, and effect on growth and survival of preferred young cardinal fishes. Coral Reefs 27, 685 (2008).
    ADS  Article  Google Scholar 

    11.
    Muñoz, G., Landaeta, M. F., Palacios-Fuentes, P. & George-Nascimento, M. Parasites of fish larvae: Do they follow metabolic energetic laws?. Parasitol. Res. 114, 3977–3987 (2015).
    PubMed  Article  Google Scholar 

    12.
    Felley, S. M., Vecchione, M. & Hare, S. G. F. Incidence of ectoparasitic copepods on ichthyoplankton. Copeia 3, 778–782 (1987).
    Article  Google Scholar 

    13.
    Landaeta, M. F. et al. Spatial and temporal variations of coastal fish larvae, ectoparasites and oceanographic conditions off central Chile. Rev. Biol. Mar. Oceanogr. 50, 563–574 (2015).
    Article  Google Scholar 

    14.
    Boxshall, G.A. Crustacean parasites (Copepoda). In Marine Parasitology. (ed. Rohde, K.) 23–138 (CABI, Oxon, 2005).

    15.
    Brooker, A. J., Shinn, A. P. & Bron, J. E. A review of the biology of the parasitic copepod Lernaeocera branchialis (L., 1767) (Copepoda, Pennellidae). Adv. Parasit. 65, 297–341 (2007).
    Article  Google Scholar 

    16.
    Rohde, K. Marine Parasitology (Csiro Publishing, Collingwood, 2005).
    Google Scholar 

    17.
    Poulin, R. Variation in infection parameters among populations within parasite species: Intrinsic properties versus local factors. Int. J. Parasitol. 36, 877–885 (2006).
    PubMed  Article  Google Scholar 

    18.
    Begon, M., Harper, J. L. & Townsend, C. R. Ecology: Individuals, Populations and Communities (Blackwell Scientific Publications, Hoboken, 1986).
    Google Scholar 

    19.
    Thiel, M. et al. The Humboldt Current System of northern and central Chile: Oceanographic processes, ecological interactions and socioeconomic feedback. Oceanogr. Mar. Biol. Annu. Rev. 45, 195–344 (2007).
    Google Scholar 

    20.
    Paredes, L. D., Landaeta, M. F. & González, M. T. Larval fish assemblages in two nearshore areas of the Humboldt Current System during autumn-winter in northern Chile. Rev. Biol. Mar. Oceanogr. 53, 63–76 (2018).
    Article  Google Scholar 

    21.
    Escribano, R., Rosales, S. A. & Blanco, J. L. Understanding upwelling circulation off Antofagasta (northern Chile): A three-dimensional numerical-modeling approach. Cont. Shelf. Res. 24, 37–53 (2004).
    ADS  Article  Google Scholar 

    22.
    Pérez, R. Desarrollo embrionario y larval de los pejesapos Sycyases sanguineus y Gobiesox marmoratus en la bahía de Valparaíso, Chile, con notas sobre su reproducción (Gobiesocidae: Pisces). Lat. Am. J. Aquat. Res. 9, 1–24 (1981).
    Google Scholar 

    23.
    Herrera, G. Descripción de estados post-embrionales de Ophiogobius jenynsi Hoese 1976 (Gobiidae: Blennioidei). Rev. Biol. Mar. Oceanogr. 20, 159–168 (1984).
    Google Scholar 

    24.
    Zavala-Muñoz, F., Landaeta, M. F., Bernal-Durán, V., Herrera, G. A. & Brown, D. I. Larval development and shape variation of the kelpfish Myxodes viridis (Teleostei: Clinidae). Sci. Mar. 80, 39–49 (2016).
    Article  Google Scholar 

    25.
    González, M. T., Castro, R., Muñoz, G. & López, Z. Sea lice (Siphonostomatoida: Caligidae) diversity on littoral fishes from the south-eastern Pacific coast determined from morphology and molecular analysis, with description of a new species (Lepeophtheirus confusum). Parasitol. Int. 65, 685–695 (2016).
    PubMed  Article  CAS  Google Scholar 

    26.
    Bush, A. O., Lafferty, K. D., Lotz, J. M. & Shostak, A. W. Parasitology meets ecology on its own terms: Margolis et al. revisited. J. Parasitol. 83, 575–583 (1997).
    CAS  PubMed  Article  Google Scholar 

    27.
    Zar, J. H. Biostatistical Analysis (Prentice Hall, Upper Saddle River, 1999).
    Google Scholar 

    28.
    Stefansson, G. Analysis of groundfish survey abundance data: Combining the GLM and delta approaches. ICES J. Mar. Sci. 53, 577–588 (1996).
    Article  Google Scholar 

    29.
    Aitchison, J. On the distribution of a positive random variable having a discrete probability mass at the origin. J. Am. Stat. Assoc. 50, 901–908 (1955).
    MathSciNet  MATH  Google Scholar 

    30.
    Pennington, J. T. Efficient estimators of abundance, for fish and plankton surveys. Biometrics 39, 281–286 (1983).
    Article  Google Scholar 

    31.
    Zuur, A., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effect Models and Extensions in Ecology with R (Springer Science & Business Media, New York, 2009).
    Google Scholar 

    32.
    Akaike, H. A. new look at the statistical model identification. IEEE Trans. Autom. Control. 19(6), 716–723 (1974).
    ADS  MathSciNet  MATH  Article  Google Scholar 

    33.
    McCullagh, P. & Nelder, J. A. Generalized Linear Models (Chapman & Hall, London, 1989).
    Google Scholar 

    34.
    Burnham, K. P. & Anderson, D. R. Multimodel inference: Understanding AIC and BIC in model selection. Sociol. Method. Res. 33, 261–304 (2004).
    MathSciNet  Article  Google Scholar 

    35.
    Fox, J., & Sanford, W. Car: Companion to applied regression. R Package Version 2, 0–2 http://CRAN.R-project.org/package=car (Accessed 15 Nov 2013) (2010).

    36.
    Pinheiro, J., Bates, D., DebRoy, S. & Sarkar, D. The R Development Core Team. nlme: Linear and nonlinear mixed effects models. R Package Version 3.1-111. in: 3.1-111, R.p.v (Ed.) (2013).

    37.
    Mansur, L., Plaza, G., Landaeta, M. F. & Ojeda, F. P. Planktonic duration in fourteen species of intertidal rocky fishes from the south-eastern Pacific Ocean. Mar. Freshw. Res. 65, 901–909 (2014).
    Article  Google Scholar 

    38.
    Raventós, N. & Macpherson, E. Planktonic larval duration and settlement marks on the otoliths of Mediterranean littoral fishes. Mar. Biol. 38, 1115–1120 (2001).
    Google Scholar 

    39.
    Grutter, A. S., Cribb, T. H., McCallum, H., Pickering, J. L. & McCormick, M. I. Effects of parasites on larval and juvenile stages of the coral reef fish Pomacentrus moluccensis. Coral Reefs 29, 31–40 (2010).
    ADS  Article  Google Scholar 

    40.
    Duong, B. et al. Parasites of coral reef fish larvae: Its role in the pelagic larval stage. Coral Reefs 38, 199–214 (2019).
    ADS  Article  Google Scholar 

    41.
    Palacios-Fuentes, P., Landaeta, M. F., Jahnsen-Guzmán, N., Plaza, G. & Ojeda, F. P. Hatching patterns and larval growth of a triplefin from central Chile inferred by otolith microstructure analysis. Aquat. Ecol. 48, 259–266 (2014).
    CAS  Article  Google Scholar 

    42.
    Landaeta, M.F., Díaz-Richter, C. & Muñoz, G. Larval parasitic copepods affect early life history traits of a temperate clingfish. Parasitol. Res. 119(12), 3977–3985 (2020).

    43.
    Díaz-Astudillo, M. et al. The influence of regional and local oceanography in early stages of marine fishes from temperate rocky reefs. Mar. Biol. 166, 42 (2019).
    Article  Google Scholar 

    44.
    Fields, D. M., Skiftesvik, A. B. & Browman, H. I. Behavioural responses of infective-stage copepodids of the salmon louse (Lepeophtheirus salmonis, Copepoda: Caligidae) to host-related sensory cues. J. Fish. Dis. 41, 875–884 (2018).
    CAS  PubMed  Article  Google Scholar 

    45.
    Palacios-Fuentes, P. et al. Is ectoparasite burden related to host density? Evidence from nearshore fish larvae off the coast of central Chile. Aquat. Ecol. 49, 91–98 (2015).
    Article  Google Scholar 

    46.
    Montory, J. A. et al. Early development of the ectoparasite Caligus rogercresseyi under combined salinity and temperature gradients. Aquaculture 486, 68–74 (2018).
    Article  Google Scholar 

    47.
    Uribe, R. A., Ortiz, M., Macaya, E. C. & Pacheco, A. S. Successional patterns of hard-bottom microbenthic communities at kelps bed (Lessonia trabeculata) and barren ground sublittoral systems. J. Exp. Mar. Biol. Ecol. 472, 180–188 (2015).
    Article  Google Scholar 

    48.
    Landaeta, M. F., Nowajewski, V., Paredes, L. D. & Bustos, C. A. Early life history traits of the blenny Auchenionchus crinitus (Teleostei: Labrisomidae) off northern Chile. J. Mar. Biol. Assoc. U. K. 99, 969–974 (2019).
    CAS  Article  Google Scholar 

    49.
    Muñoz, G. & Olmos, V. Revisión bibliográfica de especies ectoparásitas y hospedadoras de sistemas acuáticos de Chile. Rev. Biol. Mar. Oceanogr. 42, 89–148 (2007).
    Article  Google Scholar  More

  • in

    Distinct bacterial community structure and composition along different cowpea producing ecoregions in Northeastern Brazil

    1.
    Bender, S. F., Wagg, C. & van der Heijden, M. G. A. An underground revolution: biodiversity and soil ecological engineering for agricultural sustainability. Trend Ecol. Evol. 31, 440–452. https://doi.org/10.1016/j.tree.2016.02.016 (2016).
    Article  Google Scholar 
    2.
    El Mujtar, V., Muñoz, N., Mc Cormick, B. P., Pulleman, M. & Tittonell, P. Role and management of soil biodiversity for food security and nutrition; where do we stand?. Glob. Food Secur. 20, 132–144. https://doi.org/10.1016/j.gfs.2019.01.007 (2019).
    Article  Google Scholar 

    3.
    Schimel, J. Playing scales in the methane cycle: from microbial ecology to the globe. Proc. Natl. Acad. Sci. USA 101, 12400–12401. https://doi.org/10.1073/pnas.0405075101 (2004).
    ADS  CAS  Article  PubMed  Google Scholar 

    4.
    Delgado-Baquerizo, M. et al. Microbial diversity drives multifunctionality in terrestrial ecosystems. Nat. Commun. 7, 10541. https://doi.org/10.1038/ncomms10541 (2016).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    5.
    Xue, P. P., Carrillo, Y., Pino, V., Minasny, B. & McBratney, A. B. Soil properties drive microbial community structure in a large scale transect in South Eastern Australia. Sci. Rep. 8, 11725. https://doi.org/10.1038/s41598-018-30005-8 (2018).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    6.
    Araujo, A. S. F. et al. Bacterial community associated with rhizosphere of maize and cowpea in a subsequent cultivation. Appl. Soil Ecol. 143, 26–34. https://doi.org/10.1016/j.apsoil.2019.05.019 (2019).
    Article  Google Scholar 

    7.
    Mendes, L. W. et al. Using metagenomics to connect microbial community biodiversity and functions. Curr. Issues Mol. Biol. 24, 103–118. https://doi.org/10.21775/cimb.024.103 (2017).
    Article  PubMed  Google Scholar 

    8.
    Miranda, A. R. L. et al. Responses of soil bacterial community after seventh yearly applications of composted tannery sludge. Geoderma 318, 1–8. https://doi.org/10.1016/j.geoderma.2017.12.026 (2018).
    ADS  CAS  Article  Google Scholar 

    9.
    Pajares, S., Campo, J., Bohannan, B. J. M. & Etchevers, J. D. Environmental controls on soil microbial communities in a seasonally dry tropical forest. Appl. Environ. Microbiol. 84, e00342-e418. https://doi.org/10.1128/AEM.00342-18 (2018).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    10.
    Dequiedt, S. et al. Biogeographical patterns of soil bacterial communities. Environ. Microbiol. Rep. 1, 251–255. https://doi.org/10.1111/j.1758-2229.2009.00040.x (2009).
    Article  PubMed  Google Scholar 

    11.
    Barnett, S. E., Youngblut, N. D. & Buckley, D. H. Soil characteristics and land-use drive bacterial community assembly patterns. FEMS Microbiol. Ecol. 96, fiz194. https://doi.org/10.1093/femsec/fiz194 (2020).
    Article  PubMed  Google Scholar 

    12.
    Araújo Filho, J. C. et al. Levantamento de reconhecimento de baixa e média intensidade dos solos do Estado de Pernambuco. Boletim de Pesquisa N 11 (2000).

    13.
    Alvares, C. A., Stape, J. L., Sentelhas, P. C., De Moraes Gonçalves, J. L. & Sparovek, G. Köppen’s climate classification map for Brazil. Meteorol. Zeitschrift 22, 711–728 (2013).
    ADS  Article  Google Scholar 

    14.
    Lopes, M. B. S., Tavares, T. C. D. O., Veloso, D. A., Silva, N. C. & Fidelis, R. R. Cowpea bean production under water stress using hydrogels. Pesq. Agropec. Trop. 47, 87–92. https://doi.org/10.1590/1983-40632016v4743398 (2017).
    Article  Google Scholar 

    15.
    Bezerra, A. A. C. et al. Morfologia e produção de grãos em linhagens modernas de feijão-caupi submetidas a diferentes densidades populacionais Morphology and grain yield in modern lines of cowpea under different planting densities. Biologia (Bratisl) 8, 85–93 (2008).
    Google Scholar 

    16.
    Cardoso, E. J. B. N. et al. Soil health: looking for suitable indicators. What should be considered to assess the effects of use and management on soil health?. Sci. Agric. 70, 274–289 (2013).
    Article  Google Scholar 

    17.
    Pereira, A. P. A. et al. Acacia changes microbial indicators and increases C and N in soil organic fractions in intercropped Eucalyptus plantations. Front. Microbiol. 9, 1–13 (2018).
    Article  Google Scholar 

    18.
    Bockheim, J. G. & Hartemink, A. E. Alfisols BT. The Soils of Wisconsin. in (eds. Bockheim, J. G. & Hartemink, A. E.) 129–147 (Springer International Publishing, 2017). https://doi.org/10.1007/978-3-319-52144-2_8

    19.
    Kallenbach, C. M., Frey, S. D. & Grandy, A. S. Direct evidence for microbial-derived soil organic matter formation and its ecophysiological controls. Nat. Commun. 7, 13630. https://doi.org/10.1038/ncomms13630 (2016).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    20.
    Zheng, Q. et al. Soil multifunctionality is affected by the soil environment and by microbial community composition and diversity. Soil Biol. Biochem. 136, 107521. https://doi.org/10.1016/j.soilbio.2019.107521 (2019).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    21.
    Pérez-Jaramillo, J. E., Carrión, V. J., de Hollander, M. & Raaijmakers, J. M. The wild side of plant microbiomes. Microbiome 6, 143. https://doi.org/10.1186/s40168-018-0519-z (2018).
    Article  PubMed  PubMed Central  Google Scholar 

    22.
    Hartman, K., van der Heijden, M. G. A., Roussely-Provent, V., Walser, J. C. & Schlaeppi, K. Deciphering composition and function of the root microbiome of a legume plant. Microbiome 5, 2. https://doi.org/10.1186/s40168-016-0220-z (2017).
    Article  PubMed  PubMed Central  Google Scholar 

    23.
    Kolton, M. et al. Draft genome sequence of Flavobacterium sp. strain F52, isolated from the rhizosphere of bell pepper (Capsicum annuum L. Cv. Maccabi). J. Bacteriol. 194, 5462–5463. https://doi.org/10.1128/JB.01249-12 (2012).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    24.
    Schaefer, C. E. G. R., Fabris, J. D. & Ker, J. C. Minerals in the clay fraction of Brazilian Latosols (Oxisols): a review. Clay Miner. 43, 137–154. https://doi.org/10.1180/claymin.2008.043.1.11 (2008).
    ADS  CAS  Article  Google Scholar 

    25.
    Mendes, L. W., de Lima Brossi, M. J., Kuramae, E. E. & Tsai, S. M. Land-use system shapes soil bacterial communities in Southeastern Amazon region. Appl. Soil Ecol. 95, 151–160. https://doi.org/10.1016/j.apsoil.2015.06.005 (2015).
    Article  Google Scholar 

    26.
    Gyaneshwar, P., Naresh Kumar, G., Parekh, L. J. & Poole, P. S. Role of soil microorganisms in improving P nutrition of plants. Plant Soil 245, 83–93 (2002).
    CAS  Article  Google Scholar 

    27.
    Germano, M. G. et al. Functional diversity of bacterial genes associated with aromatic hydrocarbon degradation in anthropogenic dark earth of Amazonia. Pesq. Agropec. Bras. 47, 654–664. https://doi.org/10.1590/S0100-204X2012000500004 (2012).
    Article  Google Scholar 

    28.
    Mohammadipanah, F. & Wink, J. Actinobacteria from arid and desert habitats: diversity and biological activity. Front. Microbiol. 6, 1541. https://doi.org/10.3389/fmicb.2015.01541 (2016).
    Article  PubMed  PubMed Central  Google Scholar 

    29.
    Andreote, F. D. & Pereira e Silva, M. D. C. Microbial communities associated with plants: learning from nature to apply it in agriculture. Curr. Opin. Microbiol. 37, 29–34 (2017).
    Article  Google Scholar 

    30.
    Rocha, S. M. B. et al. Nodule microbiome from cowpea and lima bean grown in composted tannery sludge-treated soil. Appl. Soil Ecol. 151, 103542 (2020).
    Article  Google Scholar 

    31.
    Soltani, A.-A. et al. Plant growth promoting characteristics in some Flavobacterium spp. isolated from soils of Iran. J. Agric. Sci. 2, 106–115. https://doi.org/10.5539/jas.v2n4p106 (2010).
    Article  Google Scholar 

    32.
    Liew, K. J. et al. Complete genome sequence of Rhodothermaceae bacterium RA with cellulolytic and xylanolytic activities. 3 Biotech 8, 376. https://doi.org/10.1007/s13205-018-1391-z (2018).
    Article  PubMed  PubMed Central  Google Scholar 

    33.
    Navarrete, A. A. et al. Verrucomicrobial community structure and abundance as indicators for changes in chemical factors linked to soil fertility. Anto. van Leeuwe. 108, 741–752. https://doi.org/10.1007/s10482-015-0530-3 (2015).
    CAS  Article  Google Scholar 

    34.
    Buckley, D. H. & Schmidt, T. M. Environmental factors influencing the distribution of rRNA from Verrucomicrobia in soil. FEMS Microbiol. Ecol. 35, 105–112. https://doi.org/10.1016/S0168-6496(00)00122-7 (2001).
    CAS  Article  PubMed  Google Scholar 

    35.
    Kroeger, M. E. et al. New biological insights into how deforestation in amazonia affects soil microbial communities using metagenomics and metagenome-assembled genomes. Front. Microbiol. 9, 1635. https://doi.org/10.3389/fmicb.2018.01635 (2018).
    Article  PubMed  PubMed Central  Google Scholar 

    36.
    Maron, P.-A. et al. High microbial diversity promotes soil ecosystem functioning. Appl. Environ. Microbiol. 84, 1–13 (2018).
    Article  Google Scholar 

    37.
    Székely, A. J. & Langenheder, S. The importance of species sorting differs between habitat generalists and specialists in bacterial communities. FEMS Microbiol. Ecol. 87, 102–112 (2014).
    Article  Google Scholar 

    38.
    López-Mondéjar, R. et al. Decomposer food web in a deciduous forest shows high share of generalist microorganisms and importance of microbial biomass recycling. ISME J. 12, 1768–1778 (2018).
    Article  Google Scholar 

    39.
    Pasternak, Z. et al. Spatial and temporal biogeography of soil microbial communities in Arid and Semiarid regions. PLoS ONE 8, e69705. https://doi.org/10.1371/journal.pone.0069705 (2013).
    ADS  CAS  Article  PubMed  PubMed Central  Google Scholar 

    40.
    Pandit, S. N., Kolasa, J. & Cottenie, K. Contrasts between habitat generalists and specialists: an empirical extension to the basic metacommunity framework. Ecology 90, 2253–2262. https://doi.org/10.1890/08-0851.1 (2009).
    Article  PubMed  Google Scholar 

    41.
    Yang, W. et al. Response of fungal communities and co-occurrence network patterns to compost amendment in black soil of northeast China. Front. Microbiol. 10, 1–11 (2019).
    Article  Google Scholar 

    42.
    van der Heijden, M. G. A. & Hartmann, M. Networking in the plant microbiome. PLoS Biol. 14, e1002378. https://doi.org/10.1371/journal.pbio.1002378 (2016).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    43.
    Saboya, R. D. C. C. et al. Resposta do feijão-caupi a estirpes fixadoras de nitrogênio em Gurupi-TO. J. Biotechnol. Biodivers. 4, 40–48. https://doi.org/10.20873/jbb.uft.cemaf.v4n1.saboya (2013).
    Article  Google Scholar 

    44.
    IBGE. Levantamento Sistemático da Produção Agrícola Estatística da Produção Agrícola. (2019).

    45.
    Tedesco, M., Gianello, C. & Bissani, C. Análises de solo, plantas e outros materiais (UFRGS, Porto Alegre, 1995).
    Google Scholar 

    46.
    Yeomans, J. C. & Bremner, J. M. A rapid and precise method for routine determination of organic carbon in soil. Commun. Soil Sci. Plant Anal. 19, 1467–1476. https://doi.org/10.1080/00103628809368027 (1988).
    CAS  Article  Google Scholar 

    47.
    Caporaso, J. G. et al. Global patterns of 16S rRNA diversity at a depth of millions of sequences per sample. Proc. Natl. Acad. Sci. 108, 4516–4522 (2011).
    ADS  CAS  Article  Google Scholar 

    48.
    Illumina. MiSeq System. Denature and Dilute Libraries Guide. Document 15039740 (2019).

    49.
    Zhang, J., Kobert, K., Flouri, T. & Stamatakis, A. PEAR: A fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics 30, 614–620. https://doi.org/10.1093/bioinformatics/btt593 (2014).
    CAS  Article  Google Scholar 

    50.
    Callahan, B. J. et al. DADA2: high-resolution sample inference from Illumina amplicon data. Nat. Methods https://doi.org/10.1038/nmeth.3869 (2016).
    Article  PubMed  PubMed Central  Google Scholar 

    51.
    Quast, C. et al. The SILVA ribosomal RNA gene database project: Improved data processing and web-based tools. Nucleic Acids Res. 41, 590–596 (2013).
    Article  Google Scholar 

    52.
    Leps, J. & Smilauer, P. Multivariate Analysis of Ecological Data usingCANOCO This. Zhurnal Eksperimental’noi i Teoreticheskoi Fiziki https://doi.org/10.1017/CBO9780511615146 (2003).
    Article  MATH  Google Scholar 

    53.
    Anderson, M. J. A new method for non parametric multivariate analysis of variance. Austral Ecol. 26, 32–46 (2001).
    Google Scholar 

    54.
    Hammer, Ø., Harper, D. A. T. & Ryan, P. D. PAST: paleontological statistics software package for education and data analysis. Palaeontol. Electron. 4(1), 1–9 (2001).
    Google Scholar 

    55.
    Parks, D. H., Tyson, G. W., Hugenholtz, P. & Beiko, R. G. STAMP: Statistical analysis of taxonomic and functional profiles. Bioinformatics 30, 3123–3124 (2014).
    CAS  Article  Google Scholar 

    56.
    Benjamini, Y. & Hochberg, Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J. R. Stat. Soc. Ser. B 57, 289–300. https://doi.org/10.1111/j.2517-6161.1995.tb02031.x (1995).
    MathSciNet  Article  MATH  Google Scholar 

    57.
    R Core Team. R Development Core Team. R: A Language and Environment for Statistical Computing 55, 275–286 (2016).

    58.
    Chazdon, R. L. et al. A novel statistical method for classifying habitat generalists and specialists. Ecology 92, 1332–1343. https://doi.org/10.1890/10-1345.1 (2011).
    Article  PubMed  Google Scholar 

    59.
    Pedrinho, A., Mendes, L. W., Merloti, L. F., Andreote, F. D. & Tsai, S. M. The natural recovery of soil microbial community and nitrogen functions after pasture abandonment in the Amazon region. FEMS Microbiol. Ecol. 96, fiaa149. https://doi.org/10.1093/femsec/fiaa149 (2020).
    Article  PubMed  Google Scholar 

    60.
    Friedman, J. & Alm, E. J. Inferring correlation networks from genomic survey data. PLoS Comput. Biol. 8, 1–11 (2012).
    Article  Google Scholar 

    61.
    Bastian, M., Heymann, S. & Jacomy, M. Gephi: An open source software for exploring and manipulating networks. BT – International AAAI Conference on Weblogs and Social. 361–362 (2009). More

  • in

    Tropical rhodolith beds are a major and belittled reef fish habitat

    The Abrolhos shelf extends ~ 200 km offshore and is SWA’s most biodiverse region, encompassing a large mid-to-outer shelf hard bottom domain with reefs and rhodolith beds (~ 20,900 km2)5,6. Fine-sediment dissipative beaches and a large estuary with mangroves dominate the coastline, and terrigenous-mixed sediments predominate in the inner shelf27. This large and complex seascape (Fig. 1) comprises a representative experimental setting for understanding the distribution and abundance of reef fishes in different habitats, as well as for exploring the drivers and spatial scaling of beta diversity in reef fish assemblages. The high richness of reef fishes off coral reefs that we found in Abrolhos was unexpected, and sheds new light toward the integration of phenomena that occur at different scales and across distinct habitats and groups of organisms11,20. From a practical standpoint, our results are relevant to improve marine management in complex tropical seascapes with rhodolith beds23 and other large marginal habitats.
    The high richness of reef fishes in rhodolith beds, where fish biomass was smaller than on reefs (Supplementary Fig. S1 online; Fig. 4), seems to be primarily related to the much larger area of rhodolith beds, as well as to the broader depth and cross-shelf range of this hard-bottom habitat, contrasting with reefs. Rather than being a regional idiosyncrasy, the relatively larger area and cross-shelf range of non-reef habitat used by reef fishes seems to be recurrent in tropical shelves across all ocean basins8,9,23. However, due to logistical constrains and to the apparent smaller relevance of marginal habitats to fish and other reef-associated organisms, these habitats are still much less sampled than the iconic shallow water reefs20, with the exception of mangroves and seagrass beds3,8,9.
    Compositional variability in biological communities is strongly dependent on spatial scale. Accordingly, beta diversity is expected to be high at biogeographic and local scales, while turnover tends to be lower at regional scales28,29. Reef fish assemblages tend to vary sharply at small spatial scales ( More