More stories

  • in

    Fish predators control outbreaks of Crown-of-Thorns Starfish

    Large-scale, long-term field data from the GBR Marine ParkThe field data for CoTS, hard coral cover (here referred to as coral cover) and coral reef fish were obtained from the Australian Institute of Marine Science’s (AIMS) Long-Term Monitoring Programme (LTMP), while fisheries retained catch data were supplied by the Queensland Department of Agriculture and Fisheries (QDAF). The LTMP has been surveying CoTS populations and coral cover at reefs across the length and breadth of the GBR Marine Park since 198350 and has quantified the status and trend of benthic and reef fish assemblages since 1995. Specific examination of the effectiveness of zoning within the GBR Marine Park has also been undertaken24. The surveyed reefs are located within zones open to fishing (i.e. General Use, Habitat Protection and Conservation Park) and zones closed to fishing (i.e. Marine National Park Zones, Preservation and Scientific Research Zones) (Supplementary Table 1). The QDAF fisheries data comprise annual retained catch data from the Coral Reef Fin Fish Fishery including commercial, recreational (including charters) and Indigenous fisheries, as well as the Marine Aquarium Fish Fishery (Supplementary Data 1–3). Monthly catch return logbooks became compulsory for all trawlers and line fisheries on 1 January 198830. Retained catch data from each of these fisheries is collected separately and differently by QDAF (please see details below). Use of these data is by courtesy of the State of Queensland, Australia, through the Department of Agriculture and Fisheries.For both the LTMP and QDAF data, the data sets are chronologically divided into report (LTMP) or financial (QDAF) years, respectively, from 01 July to 30 June. This means that, for instance, the second semester of 2017 belongs to the 2018 report or financial year. Hereafter we will refer to report or financial year as simply year. Below we explain each of these data sets in more detail.LTMP CoTS and coral cover dataLTMP CoTS and coral cover data are available from 1983 to 2020. Both observed CoTS and coral cover data are based on field observations that employ manta tow surveys around the perimeter of each reef following AIMS’ Standard Operational Procedure51. Within this period, manta tows were conducted once per year but not all reefs were sampled every year. Briefly, manta tow surveys are a broad-scale technique that covers large areas of reef quickly and provides an assessment of broad changes in the distribution and abundance of corals and CoTS. During surveys, two boats each tow an observer clockwise and anti-clockwise around reef perimeters in a series of 2-min tows until they meet at the other end of the reef. Each observer records categorical coral cover (Supplementary Table 8) and the number and size of any CoTS observed (Supplementary Table 9) at the end of each 2-min tow51. Manta tow surveys are a non-targeting, rapid assessment method, and therefore it under-samples CoTS individuals that are More

  • in

    A constraint on historic growth in global photosynthesis due to increasing CO2

    1.Friedlingstein, P. et al. Global carbon budget 2019. Earth Syst. Sci. Data 11, 1783–1838 (2019).ADS 

    Google Scholar 
    2.Ballantyne, A. P., Alden, C. B., Miller, J. B., Tans, P. P. & White, J. W. C. Increase in observed net carbon dioxide uptake by land and oceans during the past 50 years. Nature 488, 70–72 (2012).CAS 
    PubMed 
    ADS 

    Google Scholar 
    3.Sitch, S. et al. Recent trends and drivers of regional sources and sinks of carbon dioxide. Biogeosciences 12, 653–679 (2015).ADS 

    Google Scholar 
    4.Keenan, T. F. et al. Recent pause in the growth rate of atmospheric CO2 due to enhanced terrestrial carbon uptake. Nat. Commun. 7, 13428 (2016).CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    5.Schimel, D., Stephens, B. B. & Fisher, J. B. Effect of increasing CO2 on the terrestrial carbon cycle. Proc. Natl Acad. Sci. USA 112, 436–441 (2015).CAS 
    PubMed 
    ADS 

    Google Scholar 
    6.Huntzinger, D. N. et al. Uncertainty in the response of terrestrial carbon sink to environmental drivers undermines carbon-climate feedback predictions. Sci. Rep. 7, 4765 (2017).CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    7.Walker, A. P. et al. Integrating the evidence for a terrestrial carbon sink caused by increasing atmospheric CO2. New Phytol. 229, 2383–2385 (2020).
    Google Scholar 
    8.Sun, Z. et al. Evaluating and comparing remote sensing terrestrial GPP models for their response to climate variability and CO2 trends. Sci. Total Environ. 668, 696–713 (2019).CAS 
    PubMed 
    ADS 

    Google Scholar 
    9.Smith, W. K. et al. Large divergence of satellite and Earth system model estimates of global terrestrial CO2 fertilization. Nat. Clim. Change 6, 306–310 (2016).ADS 

    Google Scholar 
    10.Li, W. et al. Recent changes in global photosynthesis and terrestrial ecosystem respiration constrained from multiple observations. Geophys. Res. Lett. 45, 1058–1068 (2018).ADS 

    Google Scholar 
    11.Wenzel, S., Cox, P. M., Eyring, V. & Friedlingstein, P. Projected land photosynthesis constrained by changes in the seasonal cycle of atmospheric CO2. Nature 538, 499–501 (2016).PubMed 
    ADS 

    Google Scholar 
    12.Ehlers, I. et al Detecting long-term metabolic shifts using isotopomers: CO2-driven suppression of photorespiration in C3 plants over the 20th century. Proc. Natl Acad. Sci. USA 112, 15585–15590 (2015).CAS 
    PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    13.Campbell, J. E. et al. Large historical growth in global terrestrial gross primary production. Nature 544, 84–87 (2017).CAS 
    PubMed 
    ADS 

    Google Scholar 
    14.Eyring, V. et al. Taking climate model evaluation to the next level. Nat. Clim. Change 9, 102–110 (2019).ADS 

    Google Scholar 
    15.Winkler, A. J., Myneni, R. B. & Brovkin, V. Investigating the applicability of emergent constraints. Earth Syst. Dyn. 10, 501–523 (2019).ADS 

    Google Scholar 
    16.Hall, A., Cox, P., Huntingford, C. & Klein, S. Progressing emergent constraints on future climate change. Nat. Clim. Change 9, 269–278 (2019).ADS 

    Google Scholar 
    17.Keenan, T. F. & Williams, C. A. The terrestrial carbon sink. Annu. Rev. Environ. Resour. 43, 219–243 (2018).
    Google Scholar 
    18.Ryu, Y., Berry, J. A. & Baldocchi, D. D. What is global photosynthesis? History, uncertainties and opportunities. Remote Sens. Environ. 223, 95–114 (2019).ADS 

    Google Scholar 
    19.Winkler, A. J., Myneni, R. B., Alexandrov, G. A. & Brovkin, V. Earth system models underestimate carbon fixation by plants in the high latitudes. Nat. Commun. 10, 95 (2019).ADS 

    Google Scholar 
    20.Ainsworth, E. A. & Long, S. P. What have we learned from 15 years of free-air CO2 enrichment (FACE)? A meta-analytic review of the responses of photosynthesis, canopy properties and plant production to rising CO2. New Phytol. 165, 351–372 (2005).PubMed 

    Google Scholar 
    21.De Kauwe, M. G., Keenan, T. F., Medlyn, B. E., Prentice, I. C. & Terrer, C. Satellite based estimates underestimate the effect of CO2 fertilization on net primary productivity. Nat Clim. Change 6, 892–893 (2016).ADS 

    Google Scholar 
    22.Cernusak, L. A. et al Robust response of terrestrial plants to rising CO2. Trends Plant Sci. 24, 578–586 (2019).CAS 
    PubMed 

    Google Scholar 
    23.Piao, S. et al. Evaluation of terrestrial carbon cycle models for their response to climate variability and to CO2 trends. Glob. Change Biol. 19, 2117–2132 (2013).ADS 

    Google Scholar 
    24.Haverd, V. et al. Higher than expected CO2 fertilization inferred from leaf to global observations. Glob. Change Biol. 26, 2390–2402 (2020).ADS 

    Google Scholar 
    25.Friedlingstein, P. et al. Uncertainties in CMIP5 climate projections due to carbon cycle feedbacks. J. Clim. 27, 511–526 (2014).ADS 

    Google Scholar 
    26.Zhao, F. et al. Role of CO2, climate and land use in regulating the seasonal amplitude increase of carbon fluxes in terrestrial ecosystems: a multimodel analysis. Biogeosciences 13, 5121–5137 (2016).CAS 
    ADS 

    Google Scholar 
    27.Le Quéré, C. et al. Global carbon budget 2017. Earth Syst. Sci. Data 10, 405–448 (2018).ADS 

    Google Scholar 
    28.Running, S. W. & Zhao, M. Daily GPP and Annual NPP (MOD17A2/A3) Products NASA Earth Observing System MODIS Land Algorithm User’s Guide v. 3 (MODIS Land Team, 2015).29.Jung, M. et al. Global patterns of land-atmosphere fluxes of carbon dioxide, latent heat, and sensible heat derived from eddy covariance, satellite, and meteorological observations. J. Geophys. Res. 116, https://doi.org/10.1029/2010JG001566 (2011).30.Zeng, N. et al. Agricultural Green Revolution as a driver of increasing atmospheric CO2 seasonal amplitude. Nature 515, 394–397 (2014).CAS 
    PubMed 
    ADS 

    Google Scholar 
    31.Long, S. P. Modification of the response of photosynthetic productivity to rising temperature by atmospheric CO2 concentrations: has its importance been underestimated? Plant Cell Environ. 14, 729–739 (1991).CAS 

    Google Scholar 
    32.Stevens, N., Lehmann, C. E. R., Murphy, B. P. & Durigan, G. Savanna woody encroachment is widespread across three continents. Glob. Change Biol. 23, 235–244 (2017).ADS 

    Google Scholar 
    33.Fleischer, K. et al. Amazon forest response to CO2 fertilization dependent on plant phosphorus acquisition. Nat. Geosci. 12, 736–741 (2019).CAS 
    ADS 

    Google Scholar 
    34.Myneni, R. B. et al. Global products of vegetation leaf area and fraction absorbed PAR from year one of MODIS data. Remote Sens. Environ. 83, 214–231 (2002).ADS 

    Google Scholar 
    35.Cernusak, L. A. et al. Tropical forest responses to increasing atmospheric CO2: current knowledge and opportunities for future research. Funct. Plant Biol. 40, 531–551 (2013).CAS 
    PubMed 

    Google Scholar 
    36.Ainsworth, E. A. & Rogers, A. The response of photosynthesis and stomatal conductance to rising [CO2]: mechanisms and environmental interactions. Plant Cell Environ. 30, 258–270 (2007).CAS 
    PubMed 

    Google Scholar 
    37.Baig, S., Medlyn, B. E., Mercado, L. M. & Zaehle, S. Does the growth response of woody plants to elevated CO2 increase with temperature? A model-oriented meta-analysis. Glob. Change Biol. 21, 4303–4319 (2015).ADS 

    Google Scholar 
    38.Yang, J. et al. Low sensitivity of gross primary production to elevated CO2 in a mature eucalypt woodland. Biogeosciences 17, 265–279 (2020).CAS 
    ADS 

    Google Scholar 
    39.McMurtrie, R. E., Comins, H. N., Kirschbaum, M. U. F. & Wang, Y. P. Modifying existing forest growth models to take account of effects of elevated CO2. Aust. J. Bot. 40, 657–677 (1992).CAS 

    Google Scholar 
    40.Luo, Y., Sims, D. A., Thomas, R. B., Tissue, D. T. & Ball, J. T. Sensitivity of leaf photosynthesis to CO2 concentration is an invariant function for C3 plants: a test with experimental data and global applications. Global Biogeochem. Cycles 10, 209–222 (1996).CAS 
    ADS 

    Google Scholar 
    41.Li, Q. et al. Leaf area index identified as a major source of variability in modeled CO2 fertilization. Biogeosciences 15, 6909–6925 (2018).CAS 
    ADS 

    Google Scholar 
    42.Graven, H. D. et al. Enhanced seasonal exchange of CO2 by northern ecosystems since 1960. Science 341, 1085–1089 (2013).CAS 
    PubMed 
    ADS 

    Google Scholar 
    43.Zaehle, S. et al. Evaluation of 11 terrestrial carbon-nitrogen cycle models against observations from two temperate free-air CO2 enrichment studies. New Phytol. 202, 803–822 (2014).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    44.De Kauwe, M. G. et al. Where does the carbon go? A model-data intercomparison of vegetation carbon allocation and turnover processes at two temperate forest free-air CO2 enrichment sites. New Phytol. 203, 883–899 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    45.Stocker, B. D. et al Drought impacts on terrestrial primary production underestimated by satellite monitoring. Nat. Geosci. 12, 264–270 (2019).CAS 
    ADS 

    Google Scholar 
    46.Williamson, M. S. et al Emergent constraints on climate sensitivities. Rev. Mod. Phys. 93, 025004 (2021).MathSciNet 
    CAS 
    ADS 

    Google Scholar 
    47.Sanderson, B. et al. On structural errors in emergent constraints. Earth Syst. Dyn. Discuss. https://doi.org/10.5194/esd-2020-85 (2021).48.Fisher, J. B., Huntzinger, D. N., Schwalm, C. R. & Sitch, S. Modeling the terrestrial biosphere. Annu. Rev. Environ. Resour. 39, 91–123 (2014).
    Google Scholar 
    49.Arora, V. K. et al. Carbon-concentration and carbon-climate feedbacks in CMIP5 earth system models. J. Clim. 26, 5289–5314 (2013).ADS 

    Google Scholar 
    50.Ballantyne, A. et al. Accelerating net terrestrial carbon uptake during the warming hiatus due to reduced respiration. Nat. Clim. Change 7, 148–152 (2017).CAS 
    ADS 

    Google Scholar 
    51.Forkel, M. et al. Enhanced seasonal CO2 exchange caused by amplified plant productivity in northern ecosystems. Science 351, 696–699 (2016).CAS 
    PubMed 
    ADS 

    Google Scholar 
    52.Friedlingstein, P. et al. On the contribution of CO2 fertilization to the missing biospheric sink. Global Biogeochem. Cycles 9, 541–556 (1995).CAS 
    ADS 

    Google Scholar 
    53.Farquhar, G. D., von Caemmerer, S. & Berry, J. A. A biochemical model of photosynthetic CO2 assimilation in leaves of C3 species. Planta 149, 78–90 (1980).CAS 
    PubMed 

    Google Scholar 
    54.Myneni, R. B., Keeling, C. D., Tucker, C. J., Asrar, G. & Nemani, R. R. Increased plant growth in the northern high latitudes from 1981 to 1991. Nature 386, 698–702 (1997).CAS 
    ADS 

    Google Scholar 
    55.Zhu, Z. et al. Greening of the Earth and its drivers. Nat. Clim. Change 6, 791–795 (2016).CAS 
    ADS 

    Google Scholar 
    56.Keenan, T. F. et al. Increase in forest water-use efficiency as atmospheric carbon dioxide concentrations rise. Nature 499, 324–327 (2013).CAS 
    PubMed 
    ADS 

    Google Scholar 
    57.Ukkola, A. M., Keenan, T. F., Kelley, D. I. & Prentice, I. C. Vegetation plays an important role in mediating future water resources. Environ. Res. Lett. 11, 094022 (2016).ADS 

    Google Scholar 
    58.Donohue, R. J., Roderick, M. L., McVicar, T. R. & Farquhar, G. D. Impact of CO2 fertilization on maximum foliage cover across the globe’s warm, arid environments. Geophys. Res. Lett. 40, 3031–3035 (2013).CAS 
    ADS 

    Google Scholar 
    59.Smith, N. G. & Dukes, J. S. Plant respiration and photosynthesis in global-scale models: incorporating acclimation to temperature and CO2. Glob. Change Biol. 19, 45–63 (2013).ADS 

    Google Scholar 
    60.De Kauwe, M. G. et al. A test of the ‘one-point method’ for estimating maximum carboxylation capacity from field-measured, light-saturated photosynthesis. New Phytol. 210, 1130–1144 (2016).PubMed 

    Google Scholar 
    61.Maire, V. et al. The coordination of leaf photosynthesis links C and N fluxes in C3 plant species. PLoS ONE 7, e0038345 (2012).ADS 

    Google Scholar 
    62.Smith, N. G. & Keenan, T. F. Mechanisms underlying leaf photosynthetic acclimation to warming and elevated CO2 as inferred from least-cost optimality theory. Glob. Change Biol. 26, 806–834 (2020).
    Google Scholar 
    63.Lloyd, J. & Farquhar, G. The CO2 dependence of photosynthesis, plant growth responses to elevated atmospheric CO2 concentrations and their interaction with soil nutrient status. I. General principles and forest ecosystems. Funct. Ecol. 10, 4–32 (1996).
    Google Scholar 
    64.Ehleringer, J. & Björkman, O. Quantum yields for CO2 uptake in C3 and C4 plants: dependence on temperature, CO2, and O2 concentration. Plant Physiol. 59, 86–90 (1997).
    Google Scholar 
    65.Bernacchi, C. J., Singsaas, E. L., Pimentel, C., Portis, A. R. Jr & Long, SP. Improved temperature response functions for models of Rubisco-limited photosynthesis. Plant, Cell Environ. 24, 253–259 (2001).CAS 

    Google Scholar 
    66.Prentice, I. C., Dong, N., Gleason, S. M., Maire, V. & Wright, I. J. Balancing the costs of carbon gain and water transport: testing a new theoretical framework for plant functional ecology. Ecol. Lett. 17, 82–91 (2014).PubMed 

    Google Scholar 
    67.Wang, H. et al. Towards a universal model for carbon dioxide uptake by plants. Nat. Plants 3, 734–741 (2017).CAS 
    PubMed 

    Google Scholar 
    68.Huber, M. L. et al. New international formulation for the viscosity of H2O. J. Phys. Chem. Ref. Data 38, 101–125 (2009).CAS 
    ADS 

    Google Scholar 
    69.Still, C. J., Berry, J. A., Collatz, G. J. & DeFries, R. S. Global distribution of C3 and C4 vegetation: carbon cycle implications. Global Biogeochem. Cycles 17, 6-1–6-14 (2003).ADS 

    Google Scholar 
    70.Zhu, Z. et al. Global data sets of vegetation leaf area index (LAI)3g and fraction of photosynthetically active radiation (FPAR)3g derived from global inventory modeling and mapping studies (GIMMS) normalized difference vegetation index (NDVI3g) for the period 1981 to 2. Remote Sens. 5, 927–948 (2013).ADS 

    Google Scholar 
    71.Zhao, M. & Running, S. W. Drought-induced reduction in global terrestrial net primary production from 2000 through 2009. Science 329, 940–943 (2010).CAS 
    PubMed 
    ADS 

    Google Scholar 
    72.Gallego-Sala, A. et al. Bioclimatic envelope model of climate change impacts on blanket peatland distribution in Great Britain. Clim. Res. 45, 151–162 (2010).
    Google Scholar 
    73.Veroustraete, F. On the use of a simple deciduous forest model for the interpretation of climate change effects at the level of carbon dynamics. Ecol. Modell. 75–76, 221–237 (1994).
    Google Scholar 
    74.Jiang, C. & Ryu, Y. Multi-scale evaluation of global gross primary productivity and evapotranspiration products derived from Breathing Earth System Simulator (BESS). Remote Sens. Environ. 186, 528–547 (2016).ADS 

    Google Scholar 
    75.Zhang, S. et al. Evaluation and improvement of the daily boreal ecosystem productivity simulator in simulating gross primary productivity at 41 flux sites across Europe. Ecol. Modell. 368, 205–232 (2018).CAS 

    Google Scholar 
    76.Liu, Y., Hejazi, M., Li, H., Zhang, X. & Leng, G. A hydrological emulator for global applications-HE v1.0.0. Geosci. Model Dev. 11, 1077–1092 (2018).ADS 

    Google Scholar 
    77.Yuan, W. et al. Increased atmospheric vapor pressure deficit reduces global vegetation growth. Sci. Adv. 5, aax1396 (2019).ADS 

    Google Scholar 
    78.Haverd, V. et al. A new version of the CABLE land surface model (Subversion revision r4601) incorporating land use and land cover change, woody vegetation demography, and a novel optimisation-based approach to plant coordination of photosynthesis. Geosci. Model Dev. 11, 2995–3026 (2018).CAS 
    ADS 

    Google Scholar 
    79.Melton, J. R. & Arora, V. K. Competition between plant functional types in the Canadian Terrestrial Ecosystem Model (CTEM) v. 2.0. Geosci. Model Dev. 9, 323–361 (2016).CAS 
    ADS 

    Google Scholar 
    80.Oleson, K. W. et al. Technical Description of Version 4.0 of the Community Land Model (CLM) (National Center for Atmospheric Research, 2013).81.Tian, H. et al. North American terrestrial CO2 uptake largely offset by CH4 and N2O emissions: toward a full accounting of the greenhouse gas budget. Clim. Change 129, 413–426 (2015).CAS 
    PubMed 
    ADS 

    Google Scholar 
    82.Jain, A. K., Meiyappan, P., Song, Y. & House, J. I. CO2 emissions from land-use change affected more by nitrogen cycle, than by the choice of land-cover data. Glob. Change Biol. 19, 2893–2906 (2013).ADS 

    Google Scholar 
    83.Reick, C. H., Raddatz, T., Brovkin, V. & Gayler, V. Representation of natural and anthropogenic land cover change in MPI-ESM. J. Adv. Model Earth Syst. 5, 459–482 (2013).ADS 

    Google Scholar 
    84.Clark, D. B. et al. The Joint UK Land Environment Simulator (JULES), model description—Part 2: Carbon fluxes and vegetation dynamics. Geosci. Model Dev. 4, 701–722 (2011).ADS 

    Google Scholar 
    85.Smith, B. et al. Implications of incorporating N cycling and N limitations on primary production in an individual-based dynamic vegetation model. Biogeosciences 11, 2027–2054 (2014).ADS 

    Google Scholar 
    86.Sitch, S. et al. Evaluation of ecosystem dynamics, plant geography and terrestrial carbon cycling in the LPJ dynamic global vegetation model. Glob. Chang. Biol. 9, 161–185 (2003).ADS 

    Google Scholar 
    87.Keller, K. M. et al. 20th century changes in carbon isotopes and water-use efficiency: tree-ring-based evaluation of the CLM4.5 and LPX-Bern models. Biogeosciences 14, 2641–2673 (2017).CAS 
    ADS 

    Google Scholar 
    88.Krinner, G. et al. A dynamic global vegetation model for studies of the coupled atmosphere-biosphere system. Global Biogeochem. Cycles 19, GB1015 (2005).ADS 

    Google Scholar 
    89.Guimberteau, M. et al. ORCHIDEE-MICT (v8.4.1), a land surface model for the high latitudes: model description and validation. Geosci. Model Dev. 11, 121–163 (2018).CAS 
    ADS 

    Google Scholar 
    90.Zeng, N., Mariotti, A. & Wetzel, P. Terrestrial mechanisms of interannual CO2 variability. Global Biogeochem. Cycles 19, https://doi.org/10.1029/2004GB002273 (2005).91.Kato, E., Kinoshita, T., Ito, A., Kawamiya, M. & Yamagata, Y. Evaluation of spatially explicit emission scenario of land-use change and biomass burning using a process-based biogeochemical model. J. Land Use Sci. 8, 104–122 (2013).
    Google Scholar 
    92.Fernández-Martínez, M. et al. Atmospheric deposition, CO2, and change in the land carbon sink. Sci. Rep. 7, 9632 (2017).PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    93.Ciais, P. et al. Large inert carbon pool in the terrestrial biosphere during the Last Glacial Maximum. Nat. Geosci. 5, 74–79 (2012).CAS 
    ADS 

    Google Scholar 
    94.Cheng, L. et al. Recent increases in terrestrial carbon uptake at little cost to the water cycle. Nat. Commun. 8, 110 (2017).PubMed 
    PubMed Central 
    ADS 

    Google Scholar 
    95.Ueyama, M. et al. Inferring CO2 fertilization effect based on global monitoring land-atmosphere exchange with a theoretical model. Environ. Res. Lett. 15, 084009 (2020).CAS 
    ADS 

    Google Scholar 
    96.Pastorello, G. et al. The FLUXNET2015 dataset and the ONEFlux processing pipeline for eddy covariance data. Sci. Data 7, 225 (2020).PubMed 
    PubMed Central 

    Google Scholar  More

  • in

    Ecological adaptation and phylogenetic analysis of microsymbionts nodulating Polhillia, Wiborgia and Wiborgiella species in the Cape fynbos, South Africa

    1.Stirton, C. H. Polhillia, a new genus of papilionoid legumes endemic to South Africa. South African J. Bot. 52, 167–180 (1986).
    Google Scholar 
    2.Boatwright, J. S., Tilney, P. M. & Van Wyk, B.-E. Taxonomy of Wiborgiella (Crotalarieae, Fabaceae), a genus endemic to the greater Cape Region of South Africa. Syst. Bot. 35, 325–340 (2010).
    Google Scholar 
    3.Moiloa, N. A., Chimphango, S. B. M. & Muasya, A. M. A phylogenetic study of the genus Wiborgia (Crotalarieae, Fabaceae). South African J. Bot. 115, 179–193 (2018).
    Google Scholar 
    4.Myers, N., Mittermeier, R. A., Mittermeier, C. G., Da Fonseca, G. A. B. & Kent, J. Biodiversity hotspots for conservation priorities. Nature 403, 853 (2000).ADS 
    CAS 
    PubMed 

    Google Scholar 
    5.Goldblatt, P. & Manning, J. C. Plant diversity of the Cape region of southern Africa. Ann. Missouri Bot. Gard. 281–302 (2002).6.Forest, F., Colville, J. F. & Cowling, R. M. Evolutionary diversity patterns in the Cape flora of South Africa. in Phylogenetic Diversity 167–187 (Springer, 2018).7.Boatwright, J. S. & Cupido, C. N. Aspalathus crewiana sp. Nov. (Crotalarieae, Fabaceae) from the Western Cape Province, South Africa. Nord. J. Bot. 29, 513–517 (2011).
    Google Scholar 
    8.Mpai, T., Jaiswal, S. K. & Dakora, F. D. Accumulation of phosphorus and carbon and the dependency on biological N-2 fixation for nitrogen nutrition in Polhillia, Wiborgia and Wiborgiella species growing in natural stands in cape fynbos, South Africa. SYMBIOSIS (2020).9.Van Zwieten, L. et al. Enhanced biological N 2 fixation and yield of faba bean (Vicia faba L.) in an acid soil following biochar addition: Dissection of causal mechanisms. Plant Soil 395, 7–20 (2015).
    Google Scholar 
    10.Jaiswal, S. K., Naamala, J. & Dakora, F. D. Nature and mechanisms of aluminium toxicity, tolerance and amelioration in symbiotic legumes and rhizobia. Biol. Fertil. Soils https://doi.org/10.1007/s00374-018-1262-0 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    11.Araújo, S. S. et al. Abiotic stress responses in legumes: Strategies used to cope with environmental challenges. CRC. Crit. Rev. Plant Sci. 34, 237–280 (2015).
    Google Scholar 
    12.Etesami, H., Alikhani, H. & Akbari, A. Evaluation of plant growth hormones production (IAA) ability by Iranian soils rhizobial strains and effects of superior strains application on wheat growth. World Appl. Sci. J. 6, 1576–1584 (2009).CAS 

    Google Scholar 
    13.Ibny, F. Y. I., Jaiswal, S. K., Mohammed, M. & Dakora, F. D. Symbiotic effectiveness and ecologically adaptive traits of native rhizobial symbionts of Bambara groundnut (Vigna subterranea L. Verdc.) in Africa and their relationship with phylogeny. Sci. Rep. 9, 1–17 (2019).CAS 

    Google Scholar 
    14.Kanu, S. A. & Dakora, F. D. Symbiotic nitrogen contribution and biodiversity of root-nodule bacteria nodulating Psoralea species in the Cape Fynbos, South Africa. Soil Biol. Biochem. 54, 68–76 (2012).CAS 

    Google Scholar 
    15.Lemaire, B. et al. Symbiotic diversity, specificity and distribution of rhizobia in native legumes of the Core Cape Subregion (South Africa). FEMS Microbiol. Ecol. 91, 2–17 (2015).
    Google Scholar 
    16.Brink, C., Postma, A. & Jacobs, K. Rhizobial diversity and function in rooibos (Aspalathus linearis) and honeybush (Cyclopia spp.) plants: A review. South African J. Bot. 110, 80–86 (2017).
    Google Scholar 
    17.Dludlu, M. N., Chimphango, S. B. M., Walker, G., Stirton, C. H. & Muasya, A. M. Horizontal gene transfer among rhizobia of the Core Cape Subregion of southern Africa. South African J. Bot. 118, 342–352 (2018).CAS 

    Google Scholar 
    18.Aliero, B. L. Effects of sulphuric acid, mechanical scarification and wet heat treatments on germination of seeds of African locust bean tree, Parkia biglobosa. African J. Biotechnol. 3, 179–181 (2004).CAS 

    Google Scholar 
    19.Hematifar, M., Tehranifar, A. & Abedi, B. Facilitating Seed Germination of Eight Species of Hawthorn (Crataegus spp.) Native of Iran, Using Chemical Scarification and Cold Stratification. Iran. J. Seed Res. 4, 13–22 (2018).
    Google Scholar 
    20.Vincent, J. M. A Manual for the Practical Study of Root-Nodule Bacteria: A Manual for the Practical Study of Root-Nodule Bacteria Vol. 15 (Blackwell Scientific, 1970).
    Google Scholar 
    21.Unkovich, M. & Baldock, J. Measurement of asymbiotic N2 fixation in Australian agriculture. Soil Biol. Biochem. 40, 2915–2921 (2008).CAS 

    Google Scholar 
    22.Somasegaran, P. & Hoben, H. J. Handbook for Rhizobia: Methods in Legume-Rhizobium Technology (Springer, 2012).
    Google Scholar 
    23.Sneath, P. H. A., Sokal, R. R. Numerical taxonomy. The principles and practice of numerical classification. (1973).24.Rohlf, F. J., Applied Biostatistics, I. & Exeter Software (Firm). NTSYS-pc : Numerical taxonomy and multivariate analysis system. (Applied Biostatistics, Inc., 2009).25.Hall, T. BioEdit version 7.0. 0. Distributed by the author, website: www.mbio.ncsu.edu/BioEdit/bioedit.html. (2004).26.Edgar, R. C. MUSCLE: Multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    27.Tamura, K., Stecher, G., Peterson, D., Filipski, A. & Kumar, S. MEGA6: Molecular evolutionary genetics analysis version 6.0. Mol. Biol. Evol. 30, 2725–2729 (2013).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    28.Nei, M. & Kumar, S. Molecular Evolution and Phylogenetics (Oxford University Press, 2000).
    Google Scholar 
    29.Saitou, N. & Nei, M. The neighbor-joining method : A new method for reconstructing phylogenetic trees. Mol. Biol. Evol. 4, 406–425 (1987).CAS 
    PubMed 

    Google Scholar 
    30.Felsenstein, J. Confidence limits on phylogenies: An approach using the bootstrap. Evolution 39, 783–791 (1985).PubMed 

    Google Scholar 
    31.Morón, B. et al. Low pH changes the profile of nodulation factors produced by Rhizobium tropici CIAT899. Chem. Biol. 12, 1029–1040 (2005).PubMed 

    Google Scholar 
    32.Moroenyane, I., Chimphango, S. B. M., Wang, J., Kim, H. K. & Adams, J. M. Deterministic assembly processes govern bacterial community structure in the Fynbos, South Africa. Microb. Ecol. 72, 313–323 (2016).CAS 
    PubMed 

    Google Scholar 
    33.Dabo, M., Jaiswal, S. K. & Dakora, F. D. Phylogenetic evidence of allopatric speciation of bradyrhizobia nodulating cowpea ( Vigna unguiculata L. walp ) in South African and Mozambican soils Department of Crop Sciences, Tshwane University of Technology, Private Bag Chemistry Department. Tshw. FEMS Microbiol. Ecol. 19, 1–14 (2019).
    Google Scholar 
    34.Singh, S. K., Jaiswal, S. K., Vaishampayan, A. & Dhar, B. Physiological behavior and antibiotic response of soybean (Glycine max L.) nodulating rhizobia isolated from Indian soils. African J. Microbiol. Res. 7, 2093–2102 (2013).
    Google Scholar 
    35.Hayat, R., Ali, S., Amara, U., Khalid, R. & Ahmed, I. Soil beneficial bacteria and their role in plant growth promotion: A review. Ann. Microbiol. 60, 579–598 (2010).
    Google Scholar 
    36.Berendsen, R. L., Pieterse, C. M. J. & Bakker, P. A. H. M. The rhizosphere microbiome and plant health. Trends Plant Sci. 17, 478–486 (2012).CAS 
    PubMed 

    Google Scholar 
    37.Maseko, S. T. & Dakora, F. D. Rhizosphere acid and alkaline phosphatase activity as a marker of P nutrition in nodulated Cyclopia and Aspalathus species in the Cape fynbos of South Africa. South African J. Bot. 89, 289–295 (2013).CAS 

    Google Scholar 
    38.Dludlu, M. N., Chimphango, S., Stirton, C. H. & Muasya, A. M. Differential preference of burkholderia and mesorhizobium to pH and soil types in the core cape subregion, South Africa. Genes 9, 2 (2017).PubMed Central 

    Google Scholar 
    39.Graham, P. H. et al. Acid pH tolerance in strains of Rhizobium and Bradyrhizobium, and initial studies on the basis for acid tolerance of Rhizobium tropici UMR1899. Can. J. Microbiol. 40, 198–207 (1994).CAS 

    Google Scholar 
    40.Fikri-Benbrahim, K., Chraibi, M., Lebrazi, S., Moumni, M. & Ismaili, M. Phenotypic and Genotypic Diversity and Symbiotic Effectiveness of Rhizobia Isolated from Acacia sp. Grown in Morocco. J. Agric. Sci. Technol. 19, (2017).41.Moumni, M., Fikri-Benbrahim, K., Ismaili, M., Lebrazi, S. & Chraibi, M. Phenotypic and G enotypic D iversity and S ymbiotic E ffectiveness of R hizobia I solated from Acacia sp. G rown in Morocco. JKUAT (2018). http://hdl.handle.net/123456789/373842.Farissi, M. et al. Growth, nutrients concentrations, and enzymes involved in plants nutrition of alfalfa populations under saline conditions. (2014).43.Lebrazi, S. & Benbrahim, K. F. Environmental stress conditions affecting the N2 fixing Rhizobium-legume symbiosis and adaptation mechanisms. African J. Microbiol. Res. 8, 4053–4061 (2014).
    Google Scholar 
    44.Bhargava, Y., Murthy, J. S. R., Kumar, T. V. R. & Rao, M. N. Phenotypic, stress tolerance and plant growth promoting characteristics of rhizobial isolates from selected wild legumes of semiarid region, Tirupati, India. Adv. Microbiol. 6, 1 (2016).CAS 

    Google Scholar 
    45.Sankhla, I. S. et al. Molecular characterization of nitrogen fixing microsymbionts from root nodules of Vachellia (Acacia) jacquemontii, a native legume from the Thar Desert of India. Plant Soil 410, 21–40 (2017).CAS 

    Google Scholar 
    46.Rathi, S. et al. Selection of Bradyrhizobium or Ensifer symbionts by the native Indian caesalpinioid legume Chamaecrista pumila depends on soil pH and other edaphic and climatic factors. FEMS Microbiol. Ecol. 94, 1–17 (2018).
    Google Scholar 
    47.Choudhary, D., Rai, M. K., Shekhawat, N. S. & Kataria, V. In vitro propagation of Farsetia macrantha Blatt. & Hallb.: An endemic and threatened plant of Indian Thar Desert. Plant Cell, Tissue Organ Cult. 142, 519–526 (2020).CAS 

    Google Scholar 
    48.de Castro Pires, R. et al. Soil characteristics determine the rhizobia in association with different species of Mimosa in central Brazil. Plant Soil 423, 411–428 (2018).
    Google Scholar 
    49.Verma, J. P., Yadav, J., Tiwari, K. N. & Kumar, A. Effect of indigenous Mesorhizobium spp. and plant growth promoting rhizobacteria on yields and nutrients uptake of chickpea (Cicer arietinum L.) under sustainable agriculture. Ecol. Eng. 51, 282–286 (2013).
    Google Scholar 
    50.Datta, C. & Basu, P. S. Indole acetic acid production by a Rhizobium species from root nodules of a leguminous shrub, Cajanus cajan. Microbiol. Res. 155, 123–127 (2000).CAS 
    PubMed 

    Google Scholar 
    51.Brink, C. J. Plant Growth-Promoting Properties of Fynbos Rhizobia and Their Diversity (Stellenbosch University, 2018).
    Google Scholar 
    52.Naamala, J., Jaiswal, S. K. & Dakora, F. D. Antibiotics resistance in Rhizobium: Type, process, mechanism and benefit for agriculture. Curr. Microbiol. 72, 804–816 (2016).CAS 
    PubMed 

    Google Scholar 
    53.Baba, T. & Schneewind, O. Instruments of microbial warfare: Bacteriocin synthesis, toxicity and immunity. Trends Microbiol. 6, 66–71 (1998).CAS 
    PubMed 

    Google Scholar 
    54.Menezes, K. A. S., Nunes, G. F. O. & Sampaio, A. A. Diversity of new root nodule bacteria from Erythrina velutina Willd., a native legume from the Caatinga dry forest (Northeastern Brazil). Rev Cienc Agrárias 39, 222–233 (2016).
    Google Scholar 
    55.Pagano, M. C. Rhizobia associated with neotropical tree Centrolobium tomentosum used in riparian restoration. Plant, Soil Environ. 54, 498–508 (2008).CAS 

    Google Scholar 
    56.Hong, W., Zeng, J. & Xie, J. Antibiotic drugs targeting bacterial RNAs. Acta Pharm. Sin. B 4, 258–265 (2014).PubMed 
    PubMed Central 

    Google Scholar 
    57.Elliott, G. N. et al. Nodulation of Cyclopia spp. (Leguminosae, Papilionoideae) by Burkholderia tuberum. Ann. Bot. 100, 1403–1411 (2007).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    58.Hassen, A. I., Bopape, F. L., Habig, J. & Lamprecht, S. C. Nodulation of rooibos (Aspalathus linearis Burm. f.), an indigenous South African legume, by members of both the α-proteobacteria and β-proteobacteria. Biol. Fertil. Soils 48, 295–303 (2012).CAS 

    Google Scholar 
    59.Gerding, M., O’Hara, G. W., Bräu, L., Nandasena, K. & Howieson, J. G. Diverse Mesorhizobium spp. with unique nodA nodulating the South African legume species of the genus Lessertia. Plant Soil 358, 385–401 (2012).CAS 

    Google Scholar 
    60.Lemaire, B. et al. Recombination and horizontal transfer of nodulation and ACC deaminase (acdS) genes within Alpha-and Beta-proteobacteria nodulating legumes of the Cape Fynbos biome. FEMS Microbiol. Ecol. 91, (2015).61.Gogarten, J. P., Doolittle, W. F. & Lawrence, J. G. Prokaryotic evolution in light of gene transfer. Mol. Biol. Evol. 19, 2226–2238 (2002).CAS 
    PubMed 

    Google Scholar 
    62.Andrews, M. et al. Horizontal transfer of symbiosis genes within and between rhizobial genera: Occurrence and importance. Genes 9, 321 (2018).PubMed Central 

    Google Scholar 
    63.Turner, S. L. & Young, J. P. W. The glutamine synthetases of rhizobia : Phylogenetics and evolutionary implications. 17, 309–319 (2000).64.Gevers, D. et al. Re-evaluating prokaryotic species. Nat. Rev. Microbiol. 3, 733 (2005).CAS 
    PubMed 

    Google Scholar 
    65.Ormeño-Orrillo, E. et al. Phylogenetic evidence of the transfer of nodZ and nolL genes from Bradyrhizobium to other rhizobia. Mol. Phylogenet. Evol. 67, 626–630 (2013).PubMed 

    Google Scholar 
    66.Parker, M. A., Lafay, B., Burdon, J. J. & Van Berkum, P. Conflicting phylogeographic patterns in rRNA and nifD indicate regionally restricted gene transfer in Bradyrhizobiumaa. Microbiology 148, 2557–2565 (2002).CAS 
    PubMed 

    Google Scholar 
    67.Duran, D. et al. Bradyrhizobium paxllaeri sp. Nov. and Bradyrhizobium icense sp. Nov., nitrogen-fixing rhizobial symbionts of Lima bean (Phaseolus lunatus L.) in Peru. Int. J. Syst. Evol. Microbiol. 64, 2072–2078 (2014).PubMed 

    Google Scholar 
    68.Grönemeyer, J. L., Kulkarni, A., Berkelmann, D., Hurek, T. & Reinhold-Hurek, B. Identification and characterization of rhizobia indigenous to the Okavango region in Sub-Saharan Africa. Appl. Environ. Microbiol. https://doi.org/10.1128/AEM.02417-14 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    69.Rogel, M. A., Ormeno-Orrillo, E. & Romero, E. M. Symbiovars in rhizobia reflect bacterial adaptation to legumes. Syst. Appl. Microbiol. 34, 96–104 (2011).PubMed 

    Google Scholar 
    70.Lindstrom, K., Murwira, M., Willems, A. & Altier, N. The biodiversity of beneficial microbe-host mutualism : The case of rhizobia. Res. Microbiol. 161, 453–463 (2010).PubMed 

    Google Scholar 
    71.Barcellos, F. G., Menna, P., da Silva Batista, J. S. & Hungria, M. Evidence of horizontal transfer of symbiotic genes from a Bradyrhizobium japonicum inoculant strain to indigenous diazotrophs Sinorhizobium (Ensifer) fredii and Bradyrhizobium elkanii in a Brazilian Savannah soil. Appl. Environ. Microbiol. 73, 2635–2643 (2007).ADS 
    CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    72.Jourand, P., Mateille, T., Fargette, M. & Rapior, S. Nematostatic activity of aqueous extracts of West African Crotalaria species. Nematology 6, 765–771 (2004).
    Google Scholar 
    73.Chen, W.-M. et al. Legume symbiotic nitrogen fixation by β-proteobacteria is widespread in nature. J. Bacteriol. 185, 7266–7272 (2003).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    74.Aoki, S., Ito, M. & Iwasaki, W. From β-to α-proteobacteria: The origin and evolution of rhizobial nodulation genes nodIJ. Mol. Biol. Evol. 30, 2494–2508 (2013).CAS 
    PubMed 

    Google Scholar 
    75.Moulin, L., Béna, G., Boivin-Masson, C. & Stkepkowski, T. Phylogenetic analyses of symbiotic nodulation genes support vertical and lateral gene co-transfer within the Bradyrhizobium genus. Mol. Phylogenet. Evol. 30, 720–732 (2004).CAS 
    PubMed 

    Google Scholar 
    76.Lu, Y. L. et al. Genetic diversity and biogeography of rhizobia associated with Caragana species in three ecological regions of China. Syst. Appl. Microbiol. 32, 351–361 (2009).CAS 
    PubMed 

    Google Scholar 
    77.Ochman, H., Lawrence, J. G. & Groisman, E. A. Lateral gene transfer and the nature of bacterial innovation. Nature 405, 299–304 (2000).ADS 
    CAS 
    PubMed 

    Google Scholar  More

  • in

    Quantitative mismatch between empirical temperature-size rule slopes and predictions based on oxygen limitation

    ModelAt a given temperature i there should be a maximum body mass, Mmaxi, for which the maximum temperature-dependent surface-specific flux of oxygen, fmaxi (with unit mass O2 area−1 time−1) allows for oxygen uptake to match consumption, and where a further increase in size would lead to an oxygen deficit. This can be expressed as:$$fma{x}_{i}cdot Ama{x}_{i}={k}_{i}Mma{{x}_{i}}^{beta },$$
    (1)
    where the left side of the equation gives oxygen uptake and the right side represents oxygen demand. Amaxi is the maximum surface area used for oxygen uptake. Thus, the exact area of the organism that should be considered here will depend on the type of organism (i.e. gill surface area [e.g. fish] or other specific areas of the body surface where oxygen uptake occurs [e.g. ventral body region of Daphnia]). β is the allometric scaling exponent describing the relationship between body mass and oxygen consumption, and ki is the parameter describing temperature-dependent oxygen consumption (with unit mass O2 body mass−1 time−1). The relationship between A and M can be expressed as A = α∙Mc, where the constant α gives the mass specific surface area used for oxygen uptake (with units area mass−1) when M = 1. The constant c is the allometric scaling exponent describing the relationship between body mass and area over which oxygen can diffuse. Thus, since maximum body size will only be limited by oxygen availability when oxygen demand increases faster than supply with increasing body size, the model is only valid for c  More

  • in

    A food web approach reveals the vulnerability of biocontrol services by birds and bats to landscape modification at regional scale

    1.Foley, J. A. et al. Global consequences of land use. Science 309, 570–574 (2005).CAS 
    Article 
    ADS 

    Google Scholar 
    2.Fischer, J. & Lindenmayer, D. Landscape modification and habitat fragmentation: a synthesis. Global Ecol. Biogepogr. 16, 265–280 (2005).Article 

    Google Scholar 
    3.Dainese, M. et al. A global synthesis reveals biodiversity-mediated benefits for crop production. Sci. Adv. 5, eaax0121 (2019).Article 
    ADS 

    Google Scholar 
    4.Boyles, J., Cryan, P., McCracken, G. F. & Kunz, T. H. Economic importance of bats in agriculture. Science 332, 41–42 (2011).Article 
    ADS 

    Google Scholar 
    5.Puig-Montserrat, X. et al. Pest control service provided by bats in Mediterranean rice paddies: linking agroecosystems structure to ecological functions. Mamm. Biol. 80, 237–245 (2015).Article 

    Google Scholar 
    6.Maas, B. et al. Bird and bat predation services in tropical forests and agroforestry landscapes. Biol. Rev. 91, 1081–1101 (2015).Article 

    Google Scholar 
    7.Maes, J. et al. Mapping ecosystem services for policy support and decision making in the European Union. Ecosyst. Serv. 1, 31–39 (2012).Article 

    Google Scholar 
    8.Alkemade, R., Burkhard, B., Crossman, N. D., Nedkov, S. & Petz, K. Quantifying ecosystem services and indicators for science, policy and practice. Ecol. Indic. 37, 161–162 (2014).Article 

    Google Scholar 
    9.Mandle, L. et al. Assessing ecosystem service provision under climate change to support conservation and development planning in Myanmar. PLoS ONE 12(9), 23 (2017).Article 

    Google Scholar 
    10.Dang, A. N., Jackson, B. M., Benavidez, R. & Tomscha, S. A. Review of ecosystem service assessments: Pathways for policy integration in Southeast Asia. Ecosyst. Serv. 49, 101266 (2021).Article 

    Google Scholar 
    11.Eurostats. Agriculture, Forestry and Fisheries. European Statistics. https://ec.europa.eu/eurostat (2021).12.Eurostats. Pests and diseases in viticulture. EIP-AGRI Focus Group. https://ec.europa.eu/eip/agriculture/ (2019).13.Eurostats. Pests and diseases of the olive tree. EIP-AGRI Focus Group. https://ec.europa.eu/eip/agriculture/ (2019).14.EPPO. EPPO Global Database. https://gd.eppo.int (2018).15.Chaplin-Kramer, R., O’Rourke, M. E., Blitzer, L. J. & Kremen, C. A meta-analysis of crop pest and natural enemy response to landscape complexity. Ecol. Lett. 14, 922–932 (2011).Article 

    Google Scholar 
    16.Equipa Atlas. Atlas das Aves Nidificantes em Portugal (1999–2005). Instituto da Conservação da Natureza e da Biodiversidade, Sociedade Portuguesa para o Estudo das Aves, Parque Natural da Madeira e Secretaria Regional do Ambiente e do Mar. Assírio & Alvim, Lisboa (2008).17.Rainho, A., Alves, P., Amorim, F. & Marques, J. T. Atlas dos morcegos: de Portugal continental. Instituto da Conservação da Natureza e das Florestas (2013).18.Herrera, J. M., Ploquin, E., Rodriguez-Pérez, J. & Obeso, J. R. Determining habitat suitability of a mountain bumblebee fauna: a baseline approach for testing the impact of climate change on species distribution and abundance. J. Biogeogr. 41, 700–712 (2014).Article 

    Google Scholar 
    19.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 
    20.Araújo, M. B. et al. Standards for distribution models in biodiversity assessments. Sci. Adv. 5, eaat4858 (2019).Article 
    ADS 

    Google Scholar 
    21.Jiménez-Valverde, A., Lobo, J. M. & Hortal, J. Not as good as they seem: the importance of concepts in species distribution modelling. Divers. Distrib. 14, 885–890 (2008).Article 

    Google Scholar 
    22.Tylianakis, J. M., Laliberté, E., Nielsen, A. & Bascompte, J. Conservation of species interaction networks. Biol. Conserv. 143, 2270–2279 (2010).Article 

    Google Scholar 
    23.Valiente-Banuet, A. et al. Beyond species loss: the extinction of ecological interactions in a changing world. Funct. Ecol. 29, 299–307 (2015).Article 

    Google Scholar 
    24.Karp, D. S. et al. Forest bolsters bird abundance, pest control, and coffee yield. Ecol. Lett. 16, 1339–1347 (2013).Article 

    Google Scholar 
    25.Maas, B., Clough, Y. & Tscharntke, T. Bats and birds increase crop yield in tropical agroforestry landscapes. Ecol. Lett. 16, 1480–1487 (2013).Article 

    Google Scholar 
    26.Barbaro, L. et al. Avian pest control in vineyards is driven by interactions between bird functional diversity and landscape heterogeneity. J. App. Ecol. 54, 500–508 (2016).Article 

    Google Scholar 
    27.Paiola, A. et al. Exploring the potential of vineyards for biodiversity conservation and delivery of biodiversity-mediated ecosystem services: a global-scale systematic review. Sci. Total Environ. 706, 135839 (2020).CAS 
    Article 
    ADS 

    Google Scholar 
    28.Charbonnier, Y. et al. Pest control services provided by bats in vineyard landscapes. Agric. Ecosyst. Environ. 306, 107207 (2021).CAS 
    Article 

    Google Scholar 
    29.Rey, P. J. et al. Landscape-moderated biodiversity effects of ground herb cover in olive groves: implications for regional biodiversity conservation. Agr. Ecosyst. Environ. 277, 61–73 (2020).Article 

    Google Scholar 
    30.Morgado, R. et al. A Mediterranean silent spring? The effects of olive farming intensification on breeding bird communities. Agric. Ecosyst. Environ. 288, 106694 (2020).Article 

    Google Scholar 
    31.Martínez-Núñez, C. et al. Direct and indirect effects of agricultural practices, landscape complexity and climate on insectivorous birds, pest abundance and damage in olive groves. Agric. Ecosyst. Environ. 304, 107145 (2020).Article 

    Google Scholar 
    32.Herrera, J. M., Costa, P., Medinas, D., Marques, J. T. & Mira, A. Community composition and activity of insectivorous bats in Mediterranean olive farms. Anim. Conserv. 18, 557–566 (2015).Article 

    Google Scholar 
    33.Costa, A. et al. Structural simplification compromises the potential of common insectivorous bats to provide biocontrol services against the major olive pest Prays oleae. Agric. Ecosyst. Environ. 287, 106708 (2020).Article 

    Google Scholar 
    34.Puig-Montserrat, X., Mas, M., Flaquer, C., Tuneu-Corrala, C. & López-Baucells, A. Benefits of organic olive farming for the conservation of gleaning bats. Agric. Ecosyst. Environ. 313, 107361 (2021).Article 

    Google Scholar 
    35.Rey, P. J. Preserving frugivorous birds in agro-ecosystems: lessons from Spanish olive orchards. J. Appl. Ecol. 48, 228–237 (2011).Article 

    Google Scholar 
    36.Rodríguez-San Pedro, A. et al. Influence of agricultural management on bat activity and species richness in vineyards of central Chile. J. Mamm. 99, 1495–1502 (2018).
    Google Scholar 
    37.Pithon, J. A., Beaujouan, V., Daniel, H., Pain, G. & Vallet, J. Are vineyards important habitats for birds at local or landscape scales?. Basic Appl. Ecol. 17, 240–251 (2016).Article 

    Google Scholar 
    38.Froidevaux, J. S. P., Louboutin, B. & Jones, G. Does organic farming enhance biodiversity in Mediterranean vineyards? A case study with bats and arachnids. Agr. Ecosyst. Environ. 249, 112–122 (2017).Article 

    Google Scholar 
    39.Van der Biest, K. et al. Aligning biodiversity conservation and ecosystem services in spatial planning: focus on ecosystem processes. Sci. Total Environ. 712, 136350 (2020).Article 
    ADS 

    Google Scholar 
    40.Janzen, D. H. Latent extinction-the living dead. Encycl. Biodivers. 3, 689–699 (2001).Article 

    Google Scholar 
    41.Herrera, J. M. et al. Generalities of vertebrate responses to landscape composition and configuration gradients in a highly heterogeneous Mediterranean region. J. Biogeogr. 43, 1203–1214 (2016).Article 

    Google Scholar 
    42.Ponti, L., Gutierrez, A. P., Rutid, P. M. & Dell’Aquila, A. Fine-scale ecological and economic assessment of climate change on olive in the Mediterranean Basin reveals winners and losers. Proc. Nat. Acad. Sci. 111, 5598–5603 (2014).CAS 
    Article 
    ADS 

    Google Scholar 
    43.Silva, L. P. et al. Advancing the integration of multi-marker metabarcoding data in dietary analysis of trophic generalists. Mol. Ecol. Resour. 19, 1420–1432 (2019).Article 

    Google Scholar 
    44.Pejchar, L. et al. Net effects of birds in agroecosystems. Bioscience 68, 896–904 (2018).
    Google Scholar 
    45.Alberdi, A. et al. DNA metabarcoding and spatial modelling link diet diversification with distribution homogeneity in European bats. Nat. Comm. 11, 1154 (2020).CAS 
    Article 
    ADS 

    Google Scholar  More

  • in

    Fruiting character variability in wild individuals of Malania oleifera, a highly valued endemic species

    Weight and dimensions of fruit and stoneThe mean weight of a fruit from a particular tree ranged from 21.25 ± 4.26 to 58.26 ± 10.44 g, with the weight of the heaviest mean fruit weight being 2.74 times that of the lightest. Similarly, the mean stone weight ranged from 8.99 ± 2.35 to 20.32 ± 3.14 g, with a 2.26 times difference between the heaviest and lightest stones (Table 2). There were significant differences (p  More

  • in

    The Southern Ocean Exchange: porous boundaries between humpback whale breeding populations in southern polar waters

    1.Clapham, P. J. & Mead, J. G. Sharing the space: Review of humpback whale occurrence in the Amazonian equatorial coast. In: Mammalian Species: Megaptera novaeangliae. American Society of Mammalogists Issue, vol 604, 5 (1999). https://doi.org/10.1016/j.gecco.2019.e00854.2.Rasmussen, K. et al. Southern Hemisphere humpback whales wintering off Central America: Insights from water temperature into the longest mammalian migration. Biol. Lett. 3, 302–305. https://doi.org/10.1098/rsbl.2007.0067 (2007).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    3.De Weerdt, J., Ramos, E. A. & Cheeseman, T. Northernmost records of Southern Hemisphere humpback whales (Megaptera novaeangliae) migrating from the Antarctic Peninsula to the Pacific coast of Nicaragua. Mar. Mamm. Sci. 36, 1015–1021. https://doi.org/10.1111/mms.12677 (2020).Article 

    Google Scholar 
    4.Mikhalev, Y. A. Humpback whales Megaptera novaeangliae in the Arabian Sea. Mar. Ecol. Prog. Ser. 149, 13–21. https://doi.org/10.3354/meps149013 (1997).ADS 
    Article 

    Google Scholar 
    5.Ristau, N. G. et al. Sharing the space: Review of humpback whale occurrence in the Amazonian Equatorial Coast. Glob. Ecol. Conserv. 22, e00854. https://doi.org/10.1016/j.gecco.2019.e00854 (2020).Article 

    Google Scholar 
    6.Kellogg, R. What is known of the migration of some of the whalebone whales U.S.G.P.O. In Publication Smithsonian Institution, 2997 Rex Nan Kivell Collection, NK5765, 467e494, 2997 (2) leaves of plates (Smithsonian Publication, 1929).7.Clapham, P. J. Humpback whale. In Megaptera novaeangliae. Encyclopedia of Marine Mammals, 3rd edn, 489–492. (Academic Press, 2018). https://doi.org/10.1016/B978-0-12-804327-1.00154-0.8.Chereskin, E. et al. Song structure and singing activity of two separate humpback whales populations wintering off the coast of Caño Island in Costa Rica. J. Acoust. Soc. Am. 146, EL509–EL515 (2020).Article 

    Google Scholar 
    9.Jackson, J. et al. Global diversity and oceanic divergence of humpback whales (Megaptera novaeangliae). Proc. R. Soc. B 281, 20133222. https://doi.org/10.1098/rspb.2013.3222 (2014).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    10.Baker, C. S. et al. Abundant mitochondrial DNA variation and world-wide population structure in humpback whales. Proc. Natl. Acad. Sci. 90, 8239–8243 (1993).ADS 
    CAS 
    Article 

    Google Scholar 
    11.Palsbøll, P. J. et al. Distribution of mtDNA haplotypes in North Atlantic humpback whales: The influence of behaviour on population structure. Mar. Ecol. Progr. Ser. 116, 1–10 (1995).ADS 
    Article 

    Google Scholar 
    12.Rosenbaum, H. C. et al. First circumglobal assessment of Southern Hemisphere humpback whale mitochondrial genetic variation and implications for management. Endang. Species Res. 32, 551–567. https://doi.org/10.3354/esr00822 (2017).Article 

    Google Scholar 
    13.Kershaw, F. et al. Multiple processes drive genetic structure of humpback whale (Megaptera novaeangliae) populations across spatial scales. Mol. Ecol. 26, 977–994. https://doi.org/10.1111/mec.13943 (2017).Article 
    PubMed 

    Google Scholar 
    14.Baker, C. S. et al. Strong maternal fidelity and natal philopatry shape genetic structure in North Pacific humpback whales. Mar. Ecol. Progr. Ser. 494, 291–306 (2013).ADS 
    Article 

    Google Scholar 
    15.Garland, E. C. et al. Dynamic horizontal cultural transmission of humpback whale song at the ocean basin scale. Curr. Biol. 21, 687–691. https://doi.org/10.1016/j.cub.2011.03.019 (2011).CAS 
    Article 
    PubMed 

    Google Scholar 
    16.Garland, E. C. et al. Humpback whale song on the Southern Ocean feeding grounds: Implications for cultural transmission. PLoS ONE 8, 11. https://doi.org/10.1371/journal.pone.0079422 (2013).CAS 
    Article 

    Google Scholar 
    17.Donovan, G. A. Review of IWC stock boundaries. In Report of the International Whaling Commission (Special Issue), vol. 13, 39–68 (1991).18.IWC. JCRM (Supplement), vol. 15, 287–288 (2014).19.Félix, F. & Guzmán, H. M. Satellite tracking and sighting data analyses of Southeast Pacific humpback whales (Megaptera novaeangliae): Is the migratory route coastal or oceanic?. Aquat. Mamm. 40, 329–340. https://doi.org/10.1578/AM.40.4.2014.329 (2014).Article 

    Google Scholar 
    20.Albertson, G. R. et al. Temporal stability and mixed-stock analyses of humpback whales (Megaptera novaeangliae) in the nearshore waters of the Western Antarctic Peninsula. Polar Biol. 41, 323–340. https://doi.org/10.1007/s00300-017-2193-1 (2018).Article 

    Google Scholar 
    21.Acevedo, J. et al. First evidence of interchange of humpback whales (Megaptera novaeangliae) between the Magellan Strait and Antarctic Peninsula feeding grounds. Polar Biol. 44, 613–619. https://doi.org/10.1007/s00300-021-02827-2 (2021).Article 

    Google Scholar 
    22.Andriolo, A., Kinas, P. G., Engel, M. H., Martins, C. C. A. & Rufino, A. M. Humpback whales within the Brazilian breeding ground: Distribution and population size estimate. Endanger. Species Res. 11, 233–243. https://doi.org/10.3354/esr00282 (2010).Article 

    Google Scholar 
    23.Martins, C. C. A., Andriolo, A., Engel, M. H., Kinas, P. G. & Saito, C. H. Identifying priority areas for humpback whale conservation at Eastern Brazilian Coast. Ocean Coast. Manag. 75, 63–71. https://doi.org/10.1016/j.ocecoaman.2013.02.006 (2013).Article 

    Google Scholar 
    24.Dalla Rosa, L. et al. Feeding ground of the eastern South Pacific humpback whale population include the south Orkney island. Polar Res. 31, 17324. https://doi.org/10.3402/polar.v31i0.17324 (2012).Article 

    Google Scholar 
    25.Zerbini, A. N. et al. Satellite-monitored movements of humpback whales Megaptera novaeangliae in the southwest Atlantic Ocean. Mar. Ecol. Prog. Ser. 313, 295e304. https://doi.org/10.3354/meps313295 (2006).Article 

    Google Scholar 
    26.Zerbini, A. et al. Migration and summer destinations of humpback whales (Megaptera novaeangliae) in the western South Atlantic Ocean. J. Cetacean Res. Manag. 3, 113–118. https://doi.org/10.47536/jcrm.vi.315 (2011).Article 

    Google Scholar 
    27.Engel, M. H. et al. Mitochondrial DNA diversity of the Southwestern Atlantic humpback whale (Megaptera novaeangliae) breeding area off Brazil, and the potential connections to Antarctic feeding areas. Conserv. Genet. 9, 1253e1262. https://doi.org/10.1007/s10592-007-9453-5 (2008).CAS 
    Article 

    Google Scholar 
    28.Engel, M. H. & Martin, A. R. Feeding grounds of the western South Atlantic humpback whale population. Mar. Mamm. Sci. 25, 964e969. https://doi.org/10.1111/j.1748-7692.2009.00301.x (2009).Article 

    Google Scholar 
    29.IWC. Report of the workshop on the comprehensive assessment of Southern hemisphere humpback whales. J. Cetacean Res. Manag. 1, 1–50 (2011).30.Horton, T., Zerbini, A., Andriolo, A., Danilewicz, D. & Sucunza, F. Multi-decadal humpback whale migratory route fidelity despite oceanographic and geomagnetic change. Front. Mar. Sci. 7, 414. https://doi.org/10.3389/fmars.2020.00414 (2020).Article 

    Google Scholar 
    31.Stevick, P. T. et al. Population spatial structuring on the feeding grounds in North Atlantic humpback whales (Megaptera novaeangliae). J. Zool. 270, 244e255. https://doi.org/10.1111/j.1469-7998.2006.00128.x (2006).Article 

    Google Scholar 
    32.IWC. Report of the scientific committee. Rep. Int. Whal. Commun. 48, 53–118 (1998).33.Cypriano-Souza, A. L. et al. Genetic differentiation between humpback whales (Megaptera novaeangliae) from Atlantic and Pacific breeding grounds of South America. Mar. Mamm. Sci. 33, 457–479. https://doi.org/10.1111/mms.12378 (2017).CAS 
    Article 

    Google Scholar 
    34.IWC. J. Cetacean Res. Manag. (Supplement) 7, 235–246 (2005).35.Dalla Rosa, L., Secchi, E. R., Maia, Y. G., Zerbini, A. N. & Heide-Jørgensen, M. P. Movements of satellite-monitored humpback whales on their feeding ground along the Antarctic Peninsula. Polar Biol. 31, 771–781 (2008).Article 

    Google Scholar 
    36.Bombosch, A. et al. Predictive habitat modelling of humpback (Megaptera novaeangliae) and Antarctic minke (Balaenoptera bonaerensis) whales in the Southern Ocean as a planning tool for seismic surveys. Deep Sea Res. (I Oceanogr. Res. Pap.) 91, 101–114. https://doi.org/10.1016/j.dsr.2014.05.017 (2014).ADS 
    Article 

    Google Scholar 
    37.Stevick, P. et al. Migrations of individually identified humpback whales between the Antarctic Peninsula and South America. J. Cetacean Res. Manag. 6, 109–113 (2004).
    Google Scholar 
    38.Pomilla, C. & Rosenbaum, H. C. Against the current: An inter-oceanic whale migration event. Biol. Lett. 1, 476–479. https://doi.org/10.1098/rsbl.2005.0351 (2005).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    39.Stevick, P. T. et al. A quarter of a world away: Female humpback whale moves 10 000 km between breeding areas. Biol. Lett. 7, 299–302. https://doi.org/10.1098/rsbl.2010.0717 (2011).Article 
    PubMed 

    Google Scholar 
    40.Stevick, P. T. et al. Inter-oceanic movement of an adult female humpback whale between Pacific and Atlantic breeding grounds off South America. J. Cetacean Res. Manag. 13, 159–162 (2013).
    Google Scholar 
    41.Félix, F. et al. A new case of interoceanic movement of a humpback whale in the Southern hemisphere: The El Niño link. Aquat. Mamm. 46, 578–583. https://doi.org/10.1578/AM.46.6.2020.578 (2020).Article 

    Google Scholar 
    42.Castro, C. Engel, M., Martin, A. & Kaufman, G. Comparison of humpback whale catalogues between Ecuador, and South Georgia and Sandwich Island: Evidence of increased feeding area I boundary or overlap between feeding areas I and II? Report of the scientific committee. Rep. Int. Whal. Comm. SC/66/SH (2015).43.Cheeseman, T. et al. Advanced image recognition: A fully automated, high-accuracy photo-identification matching system for humpback whales. Mamm. Biol. https://doi.org/10.1007/s42991-021-00180-9 (in press).44.Gura, T. Citizen science: Amateur experts. Nature 496, 259–261. https://doi.org/10.1038/nj7444-259a (2013).CAS 
    Article 
    PubMed 

    Google Scholar 
    45.Chandler, M. et al. Contribution of citizen science towards international biodiversity monitoring. Biol. Conserv. 213, 280–294. https://doi.org/10.1016/j.biocon.2016.09.004 (2017).Article 

    Google Scholar 
    46.de Sherbinin, A. et al. The critical importance of citizen science data. Front. Clim. 3, 650760. https://doi.org/10.3389/fclim.2021.650760 (2021).Article 

    Google Scholar 
    47.Pallin, L. J., Robbins, J., Kellar, N., Bérubé, M. & Friedlaender, A. Validation of a blubber-based endocrine pregnancy test for humpback whales. Conserv. Physiol. https://doi.org/10.1093/conphys/coy031 (2018).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    48.Gabriele, C. M., Straley, J. M. & Neilson, J. L. Age at first calving of female humpback whales in Southeastern Alaska. In Proceedings of the Fourth Glacier Bay Science Symposium, October 26–28, 2004: U.S. Geological Survey Scientific Investigations Report (eds. Piatt, J. F. & Gende, S. M.) vol. 2007–5047, 159–162 (2007).49.Baker, C. S. & Medrano-González, L. Worldwide distribution and diversity of humpback whale mitochondrial DNA lineages. In Molecular and Cell Biology of Marine Mammals (ed. Pfeiffer, C. J.) 84–99 (Krieger Publishing Company, 2002).
    Google Scholar 
    50.Bettridge, S. et al. Status Review of the Humpback Whale (Megaptera novaeangliae) under the Endangered Species Act. NOAA-TM-NMFS-SWFSC-540, ID#4883, 241. https://repository.library.noaa.gov/view/noaa/4883 (2015).51.IWC. Annex H: Report of the sub-committee on other Southern hemisphere whale stocks. J. Cetacean Res. Manag.(Supplement) 17, 250–282 (2016).52.Zerbini, A. et al. Assessing the recovery of an Antarctic predator from historical exploitation. R. Soc. Open Sci. 6, 190368. https://doi.org/10.1098/rsos.190368 (2019).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    53.Zerbini, A. N., Clapham, P. J. & Wade, P. R. Assessing plausible rates of population growth in humpback whales from life-history data. Mar. Biol. 157, 1432e1793. https://doi.org/10.1007/s00227-010-1403-y (2010).Article 

    Google Scholar 
    54.Gonçalves, M. I. C. et al. Low latitude habitat use patterns of a recovering population of humpback whales. J. Mar. Biol. Assoc. U. K. 98, 1087–1096. https://doi.org/10.1017/S0025315418000255 (2018).Article 

    Google Scholar 
    55.Riekkola, L. et al. Longer migration not necessarily the costliest strategy for migrating humpback whales. Aquat. Conserv. Mar. Freshw. Ecosyst. 1, 12. https://doi.org/10.1002/aqc.3295 (2020).Article 

    Google Scholar 
    56.Pallin, L. J. et al. High pregnancy rates in humpback whales (Megaptera novaeangliae) around the Western Antarctic Peninsula, evidence of a rapidly growing population. R. Soc. Open Sci. 5, 180017. https://doi.org/10.1098/rsos.180017 (2018).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    57.Avila, I. C. et al. Whales extend their stay in a breeding ground in the Tropical Eastern Pacific. ICES J. Mar. Sci. 77, 109–118. https://doi.org/10.1093/icesjms/fsz251 (2020).Article 

    Google Scholar 
    58.Fritsen, C. H., Memmott, J. C. & Stewart, F. J. Inter-annual sea-ice dynamics and micro-algal biomass in winter pack ice of Marguerite Bay, Antarctica. Deep Sea Res II Top. Stud. Oceanogr. 55, 2059–2067. https://doi.org/10.1016/j.dsr2.2008.04.034 (2008).ADS 
    Article 

    Google Scholar 
    59.Meyer, B. The overwintering of Antarctic krill, Euphausia superba, from an ecophysiological perspective. Polar Biol. 35, 15–37. https://doi.org/10.1007/s00300-011-1120-0 (2012).Article 

    Google Scholar 
    60.Seyboth, E. et al. Influence of krill (Euphausia superba) availability on humpback whale (Megaptera novaeangliae) reproductive rate. Mar. Mamm. Sci. https://doi.org/10.1111/mms.12805 (2021).Article 

    Google Scholar 
    61.Atkinson, A. A., Siegel, V., Pakhomov, E. & Rothery, P. Long-term decline in krill stock and increase in salps within the Southern Ocean. Nature 432, 100–103. https://doi.org/10.1038/nature02996 (2004).ADS 
    CAS 
    Article 
    PubMed 

    Google Scholar 
    62.Atkinson, A. et al. Krill (Euphausia superba) distribution contracts southward during rapid regional warming. Nat. Clim. Change 9, 142–147. https://doi.org/10.1038/s41558-018-0370-z (2019).ADS 
    Article 

    Google Scholar 
    63.Loeb, V. J. & Santora, J. A. Climate variability and spatiotemporal dynamics of five Southern Ocean krill species. Prog. Ocean. 134, 93–122. https://doi.org/10.1016/j.pocean.2015.01.002 (2015).Article 

    Google Scholar 
    64.Forcada, J., Trathan, P. & Murphy, E. J. Life history buffering in Antarctic mammals and birds against changing patterns of climate and environmental variation. Glob. Change Biol. 14, 2473–2488 (2008).ADS 
    Article 

    Google Scholar 
    65.Fielding, S. et al. Interannual variability in Antarctic krill (Euphausia superba) density at South Georgia, Southern Ocean: 1997–2013. ICES J. Mar. Sci. 71, 2578–2588. https://doi.org/10.1093/icesjms/fsu104 (2014).MathSciNet 
    Article 

    Google Scholar 
    66.Wedekin, L. L. et al. Running fast in the slow lane: Rapid population growth of humpback whales after exploitation. Mar. Ecol. Prog. Ser. 575, 195–206. https://doi.org/10.3354/meps12211 (2017).ADS 
    Article 

    Google Scholar 
    67.Rogers, A. D. et al. Antarctic futures: An assessment of climate-driven changes in ecosystem structure, function, and service provisioning in the Southern Ocean. Ann. Rev. Mar. Sci. 12, 87–120. https://doi.org/10.1146/annurev-marine-010419-011028 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    68.Glockner, D. A. & Venus, S. Determining the sex of humpback whales (Megaptera novaeangliae) in their natural environment. In Behavior and Communication of Whales. (Westview Press, 1983).69.Darling, J. D. & Berubé, M. Interactions of singing humpback whales with other males. Mar. Mamm. Sci. 17, 570–584. https://doi.org/10.1111/j.1748-7692.2001.tb01005.x (2001).Article 

    Google Scholar 
    70.Noad, M. J., Cato, D. H., Bryden, M. M., Jenner, M. N. & Jenner, K. C. S. Cultural revolution in whale songs. Nature 408, 537–537 (2000).ADS 
    CAS 
    Article 

    Google Scholar 
    71.Darling, D. J. & Sousa-Lima, R. S. Songs indicate interaction between humpback whale (Megaptera novaeangliae) populations in western and eastern South Atlantic Ocean. Mar. Mamm. Sci. 21, 557–566. https://doi.org/10.1111/j.1748-7692.2005.tb01249.x (2006).Article 

    Google Scholar 
    72.McKnight, A., Allyn, A. J., Duffy, D. C. & Irons, D. B. ‘Stepping stone’ pattern in Pacific Arctic tern migration reveals the importance of upwelling areas. Mar. Ecol. Prog. Ser. 491, 253–264. https://doi.org/10.3354/meps10469 (2013).ADS 
    Article 

    Google Scholar 
    73.Groch, K. R. et al. Cetacean morbilivirus in humpback whale’s exhaled breath. Transbound. Emerg. Dis. https://doi.org/10.1111/tbed.13883 (2020).Article 
    PubMed 

    Google Scholar 
    74.Ballance, L. T. Contributions of photographs to cetacean science. Aquat. Mamm. 44, 668–682 (2018).Article 

    Google Scholar 
    75.Kosmala, M., Wiggins, A., Swanson, A. & Simmons, B. Assessing data quality in citizen science. Front. Ecol. Environ. 14, 551–560. https://doi.org/10.1002/fee.1436 (2016).Article 

    Google Scholar 
    76.Vieira, E. A., Souza, L. R. & Longo, G. O. Diving into science and conservation: Recreational divers can monitor reef assemblages. Perspect. Ecol. Conserv. 18, 51–59. https://doi.org/10.1016/j.pecon.2019.12.001 (2020).Article 

    Google Scholar  More

  • in

    Human skin triglycerides prevent bed bug (Cimex lectularius L.) arrestment

    Bed bugsFour bed bug populations (one laboratory strain and three collected from infested homes) were used in this study (Table 1). All populations were reared in the laboratory as described by DeVries et al.28. Briefly, bed bugs were maintained in 168 cm3 plastic containers on paper substrate at 25 °C, 50% relative humidity, and a photoperiod of 12 h:12 h (Light:Dark). Bed bugs were fed defibrinated rabbit blood (Hemostat Laboratories, Dixon, CA, USA) weekly using an artificial feeding system. This system maintained blood at 35 °C by circulating water through custom-made water-jacketed glass feeders. An artificial membrane (plant budding tape, A.M. Leonared, Piqua, OH, USA) was stretched over the bottom of each glass feeder, containing the blood while simultaneously allowing bed bugs to feed through it. In all experiments, adult males starved for 7–10 days were used. All populations were used for documenting responses to human skin swabs. The WS population was used for bioassays with various human volunteers and hexane extracted swabs, and the JC population was used for testing various lipids.Table 1 Bed bug populations used in this study.Full size tableSkin swab collectionThe North Carolina State University Institutional Review Board approved this study (IRB #14173). Informed consent was obtained from all human participants, and all the methods were performed according to the relevant guidelines and regulations. Six human volunteers (3 males, 3 females) ranging from 25 to 50 years old representing several ethnicities (white/Caucasian, Hispanic, Asian) provided samples for this project. Skin swabs were collected following the exact methods outlined by DeVries et al.16. In our 2019 study, these swabs were reported to attract bed bugs independent of other cues in Y-tube olfactometer assays. Briefly, participants were asked to follow a standard operating procedure, which was reviewed with them prior to sample collection. Before collecting skin swabs, participants were asked to not to eat ‘spicy’ food for at least 24 h, take a morning shower, avoid the use of deodorant and cosmetics after showering, and avoid strenuous physical activity. Skin swabs were collected 4–8 h after showering. Hands were washed with water only before lifting filter paper. Swabs were collected using 4.5 cm diameter filter paper discs (#1; Whatman plc, Madistone, United Kingdom). Both sides of a single filter paper disc were rubbed over the left arm from hand to armpit for 12 s, left leg from lower thigh to ankle for 12 s, and left armpit for 6 s. This procedure was repeated on the right side using a new filter paper disc, so that two samples were collected during each swabbing session. The skin swab samples were then stored in glass vials at − 20 °C, and used within one month of collection. The swabs from all human volunteers were used to compare participants and establish that bed bugs responded similarly to all, and participant A’s skin swabs were used for all subsequent bioassays.Two-choice arrestment bioassaysTwo-choice bioassays were conducted in plastic Petri dishes of 6 cm diameter (Corning Life Sciences, Durham, NC, USA) (Fig. 1). The bottom surface of each Petri dish was roughened so that bed bugs could freely move about the arena. Two tents (3 × 1.5 cm) were created using filter paper (Whatman #1). One tent served as the control tent, and the other served as the treatment tent. Control tents were either untreated (nothing added) or treated with hexane only. Treatment tents were either made directly from human odor swabs, treated with human odor extract (in hexane), or treated with a specific compound (in hexane). Tents were allowed 60 min to acclimate to room conditions and allow for the solvent to evaporate prior to initiating bioassays. The positions of tents (treatment and control) were alternated to account for any side-bias.Figure 1Two-choice behavioral assay (top-view) consisting of two equal size paper shelter tents. A clean filter paper (control) was always paired with a treated filter paper that either represented a human skin swab, hexane extract of swabbed paper, SPE fraction of human skin swab extract, or authentic TAGs. A single male bed bug was introduced into the center of each arena and allowed to select a tent to arrest under.Full size imageAdult male bed bugs were housed in individual vials for 24 h prior to each experiment. A single adult male bed bug was released in the middle of the arena 5 h into the scotophase, by transferring it on its harborage. The harborage material was removed immediately after the bed bug moved off of it (the harborage). Bed bugs were allowed the remaining 7 h of the scotophase to freely move around the arena, with their final position reported 3 h into the photophase. Bed bugs that were in contact with the filter paper with any part of their body were recorded as making a choice (i.e. arrestment state); others not in contact with either filter paper tent were recorded as non-responders, reported in the figures, but not used in data analysis. It should be noted that momentary pauses in movement (feeding or other behaviors) are not referred to as arrestment in this study. In total, 15–39 replicates were performed for each experiment (reported for each bioassay).Bioassays with human skin swabsBioassays with human skin swabs were performed to understand if bed bug arrestment behavior (1) differed among different bed bug populations, and (2) influenced by different host odors. Skin swabs were removed from the freezer, equilibrated to room temperature, divided into three equal parts and trimmed to a rectangular shape corresponding to the size of a shelter tent (Fig. 1). Skin swabs from participant A were used to evaluate the responses of four bed bug populations (Table 1). Skin swabs from all participants A–F were used to evaluate the robustness of our findings across multiple human hosts.Skin swab extraction and fractionationSkin swabs collected from volunteer A were pooled and extracted in hexane. Extraction procedures were carried out sequentially by placing a single skin swab into a 20 ml glass vial containing 5 ml of hexane, vortexing for 30 s, then moving the filter paper to a new 20 ml vial containing 5 ml of hexane and repeating the process. Three sequential extractions were performed for each skin swab, and a minimum of 10 skin swabs (collected over several days) were used for each extraction. After all skin swabs were extracted, all sequential hexane extracts were combined and concentrated to a final concentration of one skin swab equivalent per 300 µl, or one bioassay equivalent (BE) per 100 µl (since each swab was used for 3 bioassays; see “Bioassays with human skin swabs” for more information on the size used for each bioassay). Control swabs were also extracted. These swabs were treated identically to the skin swabs, except they did not contact human skin.To determine what compound classes were responsible for the observed behavior, hexane extracts were fractionated using solid phase extraction (SPE). Extracted samples were concentrated to 1 BE/10 µl hexane, then loaded onto a 1 g silica SPE column (6 ml total volume; J.T. Baker, Phillipsburg, NJ, USA). The column was eluted with the following solvents (4 ml of each, each repeated twice sequentially): hexane, 2% ether (in hexane), 5% ether (in hexane), 10% ether (in hexane), 20% ether (in hexane), 50% ether (in hexane), 100% ether, ethyl acetate, and methanol (all solvents acquired from Sigma Aldrich, St. Louis, MO, USA). Each solvent fraction was then concentrated to a final concentration of 1 BE/100 µl and stored at − 20 °C.Bioassays with extracted and fractionated human skin swabsFor all extraction and fractionation bioassays, filter paper tents were cut to a size of 3 cm × 1.5 cm (Fig. 1) and treated with 100 µl (1 BE) of extracted or fractionated human skin swabs (50 µl on each side). A dose–response bioassay was run first to determine if the compounds responsible for bed bug arrestment responses could be extracted and at what concentration (BE) they were behaviorally active. Dilutions were made in hexane, with control tents receiving extracts of control filter paper. At least 20 replicates were conducted for each concentration. After validating an appropriate BE that could be used in future experiments, SPE fractions were diluted in hexane to 0.1 BE and applied to filter paper tents as previously described (50 µl per side). A minimum of 15 replicates were conducted for each fraction to identify behaviorally active fractions.Compound identificationTo better understand what classes of compounds were present in behaviorally active fractions, we conducted thin layer chromatography (TLC) with known standards. A flexible, silica (250 µm) TLC plate (Whatman) was placed into a glass chamber containing a solvent layer of 1.5 cm. The plate was cleaned twice with acetone, then standards (triglyceride [TAG], wax ester, squalene) and samples (fractions) were each loaded into separate lanes. The plate was developed twice in 10% ether (in hexane), then visualized non-destructively with iodine.In addition, behaviorally active fractions were further evaluated for their composition with GC–MS and LC–MS. GC–MS was employed to analyze free fatty acids, squalene, and cholesterol29, whereas LC–MS was employed to characterize the intact skin lipids as previously described30. Samples were analyzed with a GC 7890A coupled to the MS 5975 VL analyzer (Agilent Technologies, CA, USA) following derivatization. Briefly, 50 µL of the extract dissolved in isopropanol were dried under nitrogen and derivatized with 100 µL BSTFA containing 1% trimethylchlorosilane (TCMS) in pyridine to generate the trimethylsilyl (TMS) derivatives at 60 °C for 60 min. GC separation was performed with a 30 m × 0.250 mm (i.d.) × 0.25 µm film thickness DB-5MS fused silica column (Agilent). Helium was used as the carrier gas. Samples were acquired in scan mode by means of electron impact (EI) MS.Liquid-chromatography coupled to the MS analyzer by means of an electrospray interface (ESI) was used to determine abundance and ESI tandem MS of non-volatile lipids as previously described29,30. LC separation was performed with a reverse phase Zorbax SB-C8 column (2.1 × 100 mm, 1.8 μm particle size, Agilent). Data were acquired in the positive ion mode at unit mass resolving power by scanning ions between m/z 100 and 1000 with G6410A series triple quadrupole (QqQ) (Agilent). LC runs and MS spectra were processed with the Mass Hunter software (B.09.00 version).Bioassays with triglyceridesAfter determining that TAGs were prominent compounds in bioactive skin swab fractions, commercially available TAGs were evaluated for behavioral activity. Filter paper tents were treated with 100 µl of hexane (50 µl to each side) containing TAG standards. First, tripalmitin (16:0/16:0/16:0) (Sigma-Aldrich) was evaluated in a dose–response fashion (60 µg to 0.6 µg) to determine what level of TAG was appropriate for bioassays. The upper level of testing was set at 60 µg as a conservative estimate of the amount of TAGs bed bugs may be exposed to, based on calculations of our arena size and previous reports of TAGs on human skin and sebum. Specifically, previous reports documented that 1.5 mg of sebum could be passively collected using Sebutape from an area of 4.7 cm230,31. Because TAGs typically constitute 60% of human sebum32, it is reasonable to assume that passive collection of sebum can result in  > 190 µg/cm2 of TAGs in a short amount of time (30 min). Our sampling methods involved swabbing rather than passive collection, but our use of 60 µg over a 9 cm2 (two sides of 4.5 cm2) shelter tent (6.67 µg/cm2) is a low-estimate of the amount of TAGs collected (although this was not directly measured in the current study). Other TAGs that we tested at a concentration of 60 µg per 9 cm2 included the saturated TAGs trimyristin (14:0/14:0/14:0) and tristearin (18:0/18:0/18:0) and the unsaturated TAGs triolein (18:1/18:1/18:1), trilinolein (18:2/18:2/18:2), and trilinolenin (18:3/18:3/18:3) (all from Sigma-Aldrich). A minimum of 30 replicates were conducted with each TAG.Statistical analysisA Chi-square goodness of fit test was used to compare the responses of bed bugs to control versus treated tents in all two-choice bioassays, with the null hypothesis that if bed bugs do not respond differentially to treated tents they should display a 1:1 preference ratio for both sides of the assay. All tests were conducted in SPSS Version 26 (IBM Corp., Armonk, NY). More