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    Investigating the benthic megafauna in the eastern Clarion Clipperton Fracture Zone (north-east Pacific) based on distribution models predicted with random forest

    Wedding, L. M. et al. From principles to practice: a spatial approach to systematic conservation planning in the deep sea. Proc. R. Soc. B Biol. Sci. 280, 20131684 (2013).CAS 
    Article 

    Google Scholar 
    Kaiser, S., Smith, C. R. & MartínezArbizu, P. Editorial: Biodiversity of the Clarion Clipperton Fracture Zone. Mar. Biodivers. 47, 259–264 (2017).Article 

    Google Scholar 
    Bluhm, H. Monitoring megabenthic communities in abyssal manganese nodule sites of the East Pacific Ocean in association with commercial deep-sea mining. Aquat. Conserv. Mar. Freshw. Ecosyst. 4, 187–201 (1994).Article 

    Google Scholar 
    Simon-Lledó, E. et al. Multi-scale variations in invertebrate and fish megafauna in the mid-eastern Clarion Clipperton Zone. Prog. Oceanogr. 187, 102405 (2020).Article 

    Google Scholar 
    Simon-Lledó, E. et al. Megafaunal variation in the abyssal landscape of the Clarion Clipperton Zone. Prog. Oceanogr. 170, 119–133 (2019).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Hein, J. R., Mizell, K., Koschinsky, A. & Conrad, T. A. Deep-ocean mineral deposits as a source of critical metals for high- and green-technology applications: Comparison with land-based resources. Ore Geol. Rev. 51, 1–14 (2013).Article 

    Google Scholar 
    Kuhn, T., Wegorzewski, A., Rühlemann, C. & Vink, A. Composition, formation, and occurrence of polymetallic nodules. In Deep-Sea Mining: Resource Potential Technical and Environmental Considerations (ed. Sharma, R.) 23–63 (Springer, 2017). https://doi.org/10.1007/978-3-319-52557-0_2.Chapter 

    Google Scholar 
    Simon-Lledó, E. et al. Ecology of a polymetallic nodule occurrence gradient: Implications for deep-sea mining. Limnol. Oceanogr. 64, 1883–1894 (2019).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    International Seabed Authority. Deep Seabed Minerals Contractors. https://www.isa.org.jm/deep-seabed-minerals-contractors?qt-contractors_tabs_alt=0#qt-contractors_tabs_alt (2020).Jones, D. O. B. et al. Biological responses to disturbance from simulated deep-sea polymetallic nodule mining. PLoS ONE 12, e0171750 (2017).ADS 
    PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Niner, H. J. et al. Deep-sea mining with no net loss of biodiversity: An impossible aim. Front. Mar. Sci. 5, 53 (2018).ADS 
    Article 

    Google Scholar 
    Kuhn, T., Uhlenkott, K., Vink, A., Rühlemann, C. & MartínezArbizu, P. Manganese nodule fields from the Northeast Pacific as benthic habitats. In Seafloor Geomorphology as Benthic Habitat: GeoHab Atlas of Seafloor Geomorphic Features and Benthic Habitats (eds Harris, P. T. & Baker, E.) 933–947 (Elsevier, 2020).Chapter 

    Google Scholar 
    Vanreusel, A., Hilario, A., Ribeiro, P. A., Menot, L. & Martínez Arbizu, P. Threatened by mining, polymetallic nodules are required to preserve abyssal epifauna. Sci. Rep. 6, 26808 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Amon, D. J. et al. Insights into the abundance and diversity of abyssal megafauna in a polymetallic-nodule region in the eastern Clarion-Clipperton Zone. Sci. Rep. 6, 30492 (2016).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    De Forges, B. R., Koslow, J. A. & Poore, G. C. B. Diversity and endemism of the benthic seamount fauna in the southwest Pacific. Nature 405, 944–947 (2000).ADS 
    PubMed 
    Article 
    CAS 

    Google Scholar 
    Lodge, M. et al. Seabed mining: International Seabed Authority environmental management plan for the Clarion-Clipperton Zone: A partnership approach. Mar. Policy 49, 66–72 (2014).Article 

    Google Scholar 
    Cuvelier, D. et al. Are seamounts refuge areas for fauna from polymetallic nodule fields?. Biogeosciences 17, 2657–2680 (2020).ADS 
    Article 

    Google Scholar 
    Wedding, L. M. et al. Managing mining of the deep seabed. Science 349, 144–145 (2015).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    International Seabed Authority. Decision of the Council of the International Seabed Authority relating to amendments to the Regulations on the Prospecting and Exploration for Polymetallic Nodules in the Area and related matters. (2013).International Seabed Authority. Recommendations for the guidance of contractors for the assessment of the possible environmental impacts arising from exploration for marine minerals in the Area. (2020).Jones, D. O. B., Ardron, J. A., Colaço, A. & Durden, J. M. Environmental considerations for impact and preservation reference zones for deep-sea polymetallic nodule mining. Mar. Policy 118, 103312 (2020).Article 

    Google Scholar 
    Uhlenkott, K., Vink, A., Kuhn, T. & Martínez Arbizu, P. Predicting meiofauna abundance to define preservation and impact zones in a deep-sea mining context using random forest modelling. J. Appl. Ecol. 57, 1210–1221 (2020).Article 

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

    Google Scholar 
    Ostmann, A. & Martínez Arbizu, P. Predictive models using random forest regression for distribution patterns of meiofauna in Icelandic waters. Mar. Biodivers. 48, 719–735 (2018).Article 

    Google Scholar 
    Uhlenkott, K., Vink, A., Kuhn, T., Gillard, B. & Martínez Arbizu, P. Meiofauna in a potential deep-sea mining area: Influence of temporal and spatial variability on small scale abundance models. Diversity 13, 3 (2021).CAS 
    Article 

    Google Scholar 
    Gazis, I.-Z., Schoening, T., Alevizos, E. & Greinert, J. Quantitative mapping and predictive modeling of Mn nodules’ distribution from hydroacoustic and optical AUV data linked by random forests machine learning. Biogeosciences 15, 7347–7377 (2018).ADS 
    Article 

    Google Scholar 
    Ellis, N., Smith, S. J. & Pitcher, C. R. Gradient forests: Calculating importance gradients on physical predictors. Ecology 93, 156–168 (2012).PubMed 
    Article 

    Google Scholar 
    Miljutina, M. A., Miljutin, D. M., Mahatma, R. & Galéron, J. Deep-sea nematode assemblages of the Clarion-Clipperton Nodule Province (Tropical North-Eastern Pacific). Mar. Biodivers. 40, 1–15 (2010).Article 

    Google Scholar 
    Miljutin, D., Miljutina, M. & Messié, M. Changes in abundance and community structure of nematodes from the abyssal polymetallic nodule field, Tropical Northeast Pacific. Deep Sea Res. Oceanogr. Res. Pap. 106, 126–135 (2015).ADS 
    Article 

    Google Scholar 
    Pape, E., Bezerra, T. N., Hauquier, F. & Vanreusel, A. Limited spatial and temporal variability in meiofauna and nematode communities at distant but environmentally similar sites in an area of interest for deep-sea mining. Front. Mar. Sci. 4, 205 (2017).Article 

    Google Scholar 
    Hauquier, F. et al. Distribution of free-living marine nematodes in the Clarion-Clipperton Zone: Implications for future deep-sea mining scenarios. Biogeosciences 16, 3475–3489 (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    Uhlenkott, K., Vink, A., Kuhn, T. & Martínez Arbizu, P. Meiofauna abundance and distribution predicted with random forest regression in the German exploration area for polymetallic nodule mining, Clarion Clipperton Fracture Zone, Pacific. (2020).Calinski, T. & Harabasz, J. A dendrite method for cluster analysis. Commun. Stat. Theory Methods 3, 1–27 (1974).MathSciNet 
    MATH 
    Article 

    Google Scholar 
    Thiel, H. et al. The large-scale environmental impact experiment DISCOL: Reflection and foresight. Deep Sea Res. 48, 3869–3882 (2001).ADS 
    Article 

    Google Scholar 
    Brown, A., Wright, R., Mevenkamp, L. & Hauton, C. A comparative experimental approach to ecotoxicology in shallow-water and deep-sea holothurians suggests similar behavioural responses. Aquat. Toxicol. 191, 10–16 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    McClain, C. R. Seamounts: identity crisis or split personality?. J. Biogeogr. 34, 2001–2008 (2007).Article 

    Google Scholar 
    Rogers, A. D. The biology of seamounts: 25 years on. In Advances in Marine Biology Vol. 79 (ed. Sheppard, C.) 137–224 (Academic Press, 2018).
    Google Scholar 
    Durden, J. M., Bett, B. J., Jones, D. O. B., Huvenne, V. A. I. & Ruhl, H. A. Abyssal hills–hidden source of increased habitat heterogeneity, benthic megafaunal biomass and diversity in the deep sea. Prog. Oceanogr. 137, 209–218 (2015).ADS 
    Article 

    Google Scholar 
    Durden, J. M. et al. Megafaunal ecology of the western Clarion Clipperton Zone. Front. Mar. Sci. 8, 671062 (2021).ADS 
    Article 

    Google Scholar 
    Jones, D. O. B. et al. Environment, ecology, and potential effectiveness of an area protected from deep-sea mining (Clarion Clipperton Zone, abyssal Pacific). Prog. Oceanogr. 197, 102653 (2021).Article 

    Google Scholar 
    Lutz, M. J., Caldeira, K., Dunbar, R. B. & Behrenfeld, M. J. Seasonal rhythms of net primary production and particulate organic carbon flux to depth describe the efficiency of biological pump in the global ocean. J. Geophys. Res. Oceans 112, C10011 (2007).ADS 
    Article 
    CAS 

    Google Scholar 
    Volz, J. B. et al. Natural spatial variability of depositional conditions, biogeochemical processes and element fluxes in sediments of the eastern Clarion-Clipperton Zone. Pacific Ocean. Deep Sea Res. 140, 159–172 (2018).CAS 
    Article 

    Google Scholar 
    Smith, C. R., De Leo, F. C., Bernardino, A. F., Sweetman, A. K. & Martínez Arbizu, P. Abyssal food limitation, ecosystem structure and climate change. Trends Ecol. Evol. 23, 518–528 (2008).PubMed 
    Article 

    Google Scholar 
    Ramirez-Llodra, E. et al. Deep, diverse and definitely different: unique attributes of the world’s largest ecosystem. Biogeosciences 7, 2851–2899 (2010).ADS 
    Article 

    Google Scholar 
    Kharbush, J. J. et al. Particulate organic carbon deconstructed: Molecular and chemical composition of particulate organic carbon in the ocean. Front. Mar. Sci. 7, 518 (2020).Article 

    Google Scholar 
    Smith, C. R. et al. Latitudinal variations in benthic processes in the abyssal equatorial Pacific: Control by biogenic particle flux. Deep Sea Res. 44, 2295–2317 (1997).ADS 
    CAS 
    Article 

    Google Scholar 
    Kuhn, T. & Rühlemann, C. Exploration of polymetallic nodules and resource assessment: A case study from the German contract area in the Clarion-Clipperton Zone of the tropical Northeast Pacific. Minerals 11, 618 (2021).ADS 
    CAS 
    Article 

    Google Scholar 
    Christodoulou, M. et al. Unexpected high abyssal ophiuroid diversity in polymetallic nodule fields of the northeast Pacific Ocean and implications for conservation. Biogeosciences 17, 1845–1876 (2020).ADS 
    CAS 
    Article 

    Google Scholar 
    Valavi, R., Elith, J., Lahoz-Monfort, J. J. & Guillera-Arroita, G. Modelling species presence-only data with random forests. Ecography 44, 1731–1742 (2021).Article 

    Google Scholar 
    Wiedicke-Hombach, M. & Shipboard Scientific Party. Campaign “MANGAN 2008” with R/V Kilo Moana. (2009).Hijmans, R. J. raster: Geographic Data Analysis and Modeling. (2017).Kaufman, L. & Rousseeuw, P. J. Clustering Large Applications (Program CLARA). in Finding Groups in Data 126–163 (Wiley, 1990). https://doi.org/10.1002/9780470316801.ch3.Maechler, M., Rousseeuw, P., Struyf, A., Hubert, M. & Hornik, K. cluster. (2019).Langenkämper, D., Zurowietz, M., Schoening, T. & Nattkemper, T. W. BIIGLE 2.0: Browsing and annotating large marine image collections. Front. Mar. Sci. 4, 83 (2017).Article 

    Google Scholar 
    Simon-Lledó, E. et al. Preliminary observations of the abyssal megafauna of Kiribati. Front. Mar. Sci. 6, 605 (2019).Article 

    Google Scholar 
    Amon, D. J. et al. Megafauna of the UKSRL exploration contract area and eastern Clarion-Clipperton Zone in the Pacific Ocean: Annelida, Arthropoda, Bryozoa, Chordata, Ctenophora, Mollusca. Biodivers. Data J. 5, e14598 (2017).Article 

    Google Scholar 
    Molodtsova, T. N. & Opresko, D. M. Black corals (Anthozoa: Antipatharia) of the Clarion-Clipperton Fracture Zone. Mar. Biodivers. 47, 349–365 (2017).Article 

    Google Scholar 
    Kersken, D., Janussen, D. & MartínezArbizu, P. Deep-sea glass sponges (Hexactinellida) from polymetallic nodule fields in the Clarion-Clipperton Fracture Zone (CCFZ), northeastern Pacific: Part II—Hexasterophora. Mar. Biodivers. 49, 947–987 (2019).Article 

    Google Scholar 
    Horton, T. et al. Recommendations for the standardisation of open taxonomic nomenclature for image-based identifications. Front. Mar. Sci. 8, 620702 (2021).Article 

    Google Scholar 
    Hughes, J. A. & Gooday, A. J. Associations between living benthic foraminifera and dead tests of Syringammina fragilissima (Xenophyophorea) in the Darwin Mounds region (NE Atlantic). Deep Sea Res. 51, 1741–1758 (2004).Article 

    Google Scholar 
    Liaw, A. & Wiener, M. Classification and regression by random Forest. R News 2, 18–22 (2002).
    Google Scholar 
    Oksanen, J. et al. vegan: Community Ecology Package. (2019).R Core Team. R: A Language and Environment for Statistical Computing. (R Foundation for Statistical Computing, 2019).Wickham, H. Reshaping data with the reshape package. J. Stat. Softw. 21, 1–20 (2007).Article 

    Google Scholar 
    Wickham, H. The split-apply-combine strategy for data analysis. J. Stat. Softw. 40, 1–29 (2011).
    Google Scholar 
    Garnier, S. viridisLite: Default Color Maps from ‘matplotlib’ (Lite Version). (2018).Rabosky, A. R. D. et al. Coral snakes predict the evolution of mimicry across New World snakes. Nat. Commun. 7, 1–9 (2016).
    Google Scholar 
    Smith, M. R. Ternary: An R package for creating ternary plots. Zenodo https://doi.org/10.5281/zenodo.1068996 (2017). More

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    The effects of aqueous extract from watermelon (Citrullus lanatus) peel on the growth and physiological characteristics of Dolichospermum flos-aquae

    Barrington, D. J. & Ghadouani, A. Application of hydrogen peroxide for the removal of toxic cyanobacteria and other phytoplankton from wastewater. Environ. Sci. Technol. 42, 8916–8921. https://doi.org/10.1021/es801717y (2008).CAS 
    Article 
    PubMed 

    Google Scholar 
    Vikrant, K. et al. Engineered/designer biochar for the removal of phosphate in water and wastewater. Sci. Total Environ. 616–617, 1242–1260. https://doi.org/10.1016/j.scitotenv.2017.10.193 (2018).CAS 
    Article 
    PubMed 

    Google Scholar 
    Merel, S. et al. State of knowledge and concerns on cyanobacterial blooms and cyanotoxins. Environ. Int. 59, 303–327. https://doi.org/10.1016/j.envint.2013.06.013 (2013).CAS 
    Article 
    PubMed 

    Google Scholar 
    Paerl, H. W. & Otten, T. G. Harmful cyanobacterial blooms: Causes, consequences, and controls. Microb. Ecol. 65, 995–1010. https://doi.org/10.1007/s00248-012-0159-y (2013).CAS 
    Article 
    PubMed 

    Google Scholar 
    Monchamp, M. E. et al. Homogenization of lake cyanobacterial communities over a century of climate change and eutrophication. Nat. Ecol. Evol. 2, 317–324. https://doi.org/10.1038/s41559-017-0407-0 (2018).Article 
    PubMed 

    Google Scholar 
    Paerl, H. W. & Fulton, R. S. Ecology of harmful cyanobacteria. In Ecology of Harmful Algae (eds Granéli, E. & Turner, J. T.) 95–109 (Springer, 2006).Chapter 

    Google Scholar 
    Guan, Y., Zhang, M., Yang, Z., Shi, X. & Zhao, X. Intra-annual variation and correlations of functional traits in Microcystis and Dolichospermum in Lake Chaohu. Ecol. Indic. 111, 106052. https://doi.org/10.1016/j.ecolind.2019.106052 (2020).Article 

    Google Scholar 
    Zhang, M. et al. Spatial and seasonal shifts in bloom-forming cyanobacteria in Lake Chaohu: Patterns and driving factors. Phycol. Res. 64, 44–55. https://doi.org/10.1111/pre.12112 (2016).Article 

    Google Scholar 
    Krishnamurthy, T., Carmichael, W. W. & Sarver, E. W. Toxic peptides from freshwater cyanobacteria (blue-green algae) I. Isolation, purification and characterization of peptides from Microcystis aeruginosa and Anabaena flos-aquae. Toxicon 24, 865–873. https://doi.org/10.1016/0041-0101(86)90087-5 (1986).CAS 
    Article 
    PubMed 

    Google Scholar 
    Mahmood, N. A. & Carmichael, W. W. Anatoxin-a(s), an anticholinesterase from the cyanobacterium Anabaena flos-aquae NRC 525–17. Toxicon 25, 1221–1227. https://doi.org/10.1016/0041-0101(87)90140-1 (1987).CAS 
    Article 
    PubMed 

    Google Scholar 
    Li, X., Dreher, T. W. & Li, R. An overview of diversity, occurrence, genetics and toxin production of bloom-forming Dolichospermum (Anabaena) species. Harmful Algae 54, 54–68. https://doi.org/10.1016/j.hal.2015.10.015 (2016).CAS 
    Article 
    PubMed 

    Google Scholar 
    Buratti, F. M. et al. Cyanotoxins: Producing organisms, occurrence, toxicity, mechanism of action and human health toxicological risk evaluation. Arch. Toxicol. 91, 1049–1130. https://doi.org/10.1007/s00204-016-1913-6 (2017).CAS 
    Article 
    PubMed 

    Google Scholar 
    Iredale, R. S., McDonald, A. T. & Adams, D. G. A series of experiments aimed at clarifying the mode of action of barley straw in cyanobacterial growth control. Water Res. 46, 6095–6103. https://doi.org/10.1016/j.watres.2012.08.040 (2012).CAS 
    Article 
    PubMed 

    Google Scholar 
    Zhang, S. H., Zhang, S. Y. & Li, G. Acorus calamus root extracts to control harmful cyanobacteria blooms. Ecol. Eng. 94, 95–101. https://doi.org/10.1016/j.ecoleng.2016.05.053 (2016).Article 

    Google Scholar 
    Mecina, G. F. et al. Effect of flavonoids isolated from Tridax procumbens on the growth and toxin production of Microcystis aeruginosa. Aquat. Toxicol. 211, 81–91. https://doi.org/10.1016/j.aquatox.2019.03.011 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    Yuan, R. et al. The allelopathic effects of aqueous extracts from Spartina alterniflora on controlling the Microcystis aeruginosa blooms. Sci. Total Environ. 712, 13622. https://doi.org/10.1016/j.scitotenv.2019.136332 (2020).CAS 
    Article 

    Google Scholar 
    Tan, K. et al. A review of allelopathy on microalgae. Microbiology 165, 587–592. https://doi.org/10.1099/mic.0.000776 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    Mecina, G. F. et al. Response of Microcystis aeruginosa BCCUSP 232 to barley (Hordeum vulgare L.) straw degradation extract and fractions. Sci. Total. Environ. 599–600, 1837–1847. https://doi.org/10.1016/j.scitotenv.2017.05.156 (2017).CAS 
    Article 
    PubMed 

    Google Scholar 
    Zhao, W., Zheng, Z., Zhang, J., Roger, S. F. & Luo, X. Allelopathically inhibitory effects of eucalyptus extracts on the growth of Microcystis aeruginosa. Chemosphere 225, 424–433. https://doi.org/10.1016/j.chemosphere.2019.03.070 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    Bottino, F. et al. Effects of macrophyte leachate on Anabaena sp. and Chlamydomonas moewusii growth in freshwater tropical ecosystems. Limnology 19, 171–176. https://doi.org/10.1007/s10201-017-0532-0 (2018).CAS 
    Article 

    Google Scholar 
    Zhang, K., Yu, M., Xu, P., Zhang, S. & Benoit, G. Physiological and morphological response of Aphanizomenon flos-aquae to watermelon (Citrullus lanatus) peel aqueous extract. Aquat. Toxicol. 225, 105548. https://doi.org/10.1016/j.aquatox.2020.105548 (2020).CAS 
    Article 
    PubMed 

    Google Scholar 
    Lichtenthaler, H. K. & Buschmann, C. Chlorophylls and carotenoids: Measurement and characterization by UV-VIS spectroscopy. Curr. Protoc. Food Anal. Chem. 1, F4.3.1-F4.38 (2001).Article 

    Google Scholar 
    Ozaki, K. et al. Electron microscopic study on lysis of a cyanobacterium Microcystis. J. Health Sci. 55, 578–585. https://doi.org/10.1248/jhs.55.578 (2009).CAS 
    Article 

    Google Scholar 
    Staats, N., De Winder, B., Stal, L. J. & Mur, L. R. Isolation and characterization of extracellular polysaccharides from the epipelic diatoms Cylindrotheca closterium and Navicula salinarum. Eur. J. Phycol. 34, 161–169. https://doi.org/10.1080/09670269910001736212 (1999).Article 

    Google Scholar 
    Hellebust, J. & Craigie, J. (eds) Handbook of Phycological Methods. Physiological and Biochemical Methods (Cambridge University, 1978).
    Google Scholar 
    Roháček, K. & Barták, M. Technique of the modulated chlorophyll fluorescence: Basic concepts, useful parameters, and some applications. Photosynthetica 37, 339–363. https://doi.org/10.1023/A:1007172424619 (1999).Article 

    Google Scholar 
    Zhang, T. T., He, M., Wu, A. P. & Nie, L. W. Inhibitory effects and mechanisms of Hydrilla verticillata (Linn.f.) royle extracts on freshwater algae. Bull. Environ. Contam. Toxicol. 88, 477–481. https://doi.org/10.1007/s00128-011-0500-z (2012).CAS 
    Article 
    PubMed 

    Google Scholar 
    Zhao, S., Pan, W. & Ma, C. Stimulation and inhibition effects of algae-lytic products from Bacillus cereus strain L7 on Anabaena flos-aquae. J. Appl. Phycol. 24, 1015–1021. https://doi.org/10.1007/s10811-011-9725-9 (2012).CAS 
    Article 

    Google Scholar 
    Kaminski, A. et al. Aquatic macrophyte Lemna trisulca (L.) as a natural factor for reducing anatoxin-a concentration in the aquatic environment and biomass of cyanobacterium Anabaena flos-aquae (Lyngb.) de Bréb. Algal Res. 9, 212–217. https://doi.org/10.1016/j.algal.2015.03.014 (2015).Article 

    Google Scholar 
    Gumbo, J. R., Cloete, T. E., van Zyl, G. J. J. & Sommerville, J. E. M. The viability assessment of Microcystis aeruginosa cells after co-culturing with Bacillus mycoides B16 using flow cytometry. Phys. Chem. Earth. 72–75, 24–33. https://doi.org/10.1016/j.pce.2014.09.004 (2014).Article 

    Google Scholar 
    Fan, J., Ho, L., Hobson, P. & Brookes, J. Evaluating the effectiveness of copper sulphate, chlorine, potassium permanganate, hydrogen peroxide and ozone on cyanobacterial cell integrity. Water Res. 47, 5153–5164. https://doi.org/10.1016/j.watres.2013.05.057 (2013).CAS 
    Article 
    PubMed 

    Google Scholar 
    Lu, Z. Studies on oxidative stress and programmed cell death of Microcystis aeruginosa induced by polyphenolic allelochemicals (D). Institute of Hydrobiology, Chinese Academy of Sciences (2014).Lu, Z. et al. Polyphenolic allelochemical pyrogallic acid induces caspase-3(like)-dependent programmed cell death in the cyanobacterium Microcystis aeruginosa. Algal Res. 21, 148–155. https://doi.org/10.1016/j.algal.2016.11.007 (2017).Article 

    Google Scholar 
    Chen, Y. et al. Vitamin C modulates Microcystis aeruginosa death and toxin release by induced Fenton reaction. J. Hazard. Mater. 321, 888–895. https://doi.org/10.1016/j.jhazmat.2016.10.010 (2017).CAS 
    Article 
    PubMed 

    Google Scholar 
    Latifi, A., Ruiz, M. & Zhang, C. C. Oxidative stress in cyanobacteria. FEMS Microbiol. Rev. 33, 258–278. https://doi.org/10.1111/j.1574-6976.2008.00134.x (2009).CAS 
    Article 
    PubMed 

    Google Scholar 
    Shao, J. H., Wu, X. Q. & Li, R. H. Physiological responses of Microcystis aeruginosa PCC7806 to nonanoic acid stress. Environ. Toxicol. 24, 610–617. https://doi.org/10.1002/tox.20462 (2009).CAS 
    Article 
    PubMed 

    Google Scholar 
    Hua, Q. et al. Allelopathic effect of the rice straw aqueous extract on the growth of Microcystis aeruginosa. Ecotox. Environ. Safe. 148, 953–959. https://doi.org/10.1016/j.ecoenv.2017.11.049 (2018).CAS 
    Article 

    Google Scholar 
    Chen, L., Wang, Y., Shi, L., Zhao, J. & Wang, W. Identification of allelochemicals from pomegranate peel and their effects on Microcystis aeruginosa growth. Environ. Sci. Pollut. Res. 26, 22389–22399. https://doi.org/10.1007/s11356-019-05507-1 (2019).CAS 
    Article 

    Google Scholar 
    Zhang, S. H., Xu, P. Y. & Chang, J. J. Physiological responses of Aphanizomenon flos-aquae under the stress of Sagittaria sagittifolia extract. Bull. Environ. Contam. Toxicol. 97, 870–875. https://doi.org/10.1007/s00128-016-1948-7 (2016).CAS 
    Article 
    PubMed 

    Google Scholar 
    Li, J. et al. Growth inhibition and oxidative damage of Microcystis aeruginosa induced by crude extract of Sagittaria trifolia tubers. J. Environ. Sci. 43, 40–47. https://doi.org/10.1016/j.jes.2015.08.020 (2016).CAS 
    Article 

    Google Scholar 
    Shao, J. et al. Inhibitory effects of sanguinarine against the cyanobacterium Microcystis aeruginosa NIES-843 and possible mechanisms of action. Aquat. Toxicol. 142–143, 257–263. https://doi.org/10.1016/j.aquatox.2013.08.019 (2013).CAS 
    Article 
    PubMed 

    Google Scholar 
    Apel, K. & Hirt, H. Reactive oxygen species: Metabolism, oxidative stress, and signal transduction. Annu. Rev. Plant. Biol. 55, 373–399. https://doi.org/10.1146/annurev.arplant.55.031903.141701 (2004).CAS 
    Article 
    PubMed 

    Google Scholar 
    Zhang, S. & Benoit, G. Comparative physiological tolerance of unicellular and colonial Microcystis aeruginosa to extract from Acorus calamus rhizome. Aquat. Toxicol. 215, 105271. https://doi.org/10.1016/j.aquatox.2019.105271 (2019).CAS 
    Article 
    PubMed 

    Google Scholar 
    Derks, A., Schaven, K. & Bruce, D. Diverse mechanisms for photoprotection in photosynthesis. Dynamic regulation of photosystem II excitation in response to rapid environmental change. BBA-Bioenergetics 1847, 468–485. https://doi.org/10.1016/j.bbabio.2015.02.008 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    Jiang, H. & Qiu, B. Photosynthetic adaptation of a bloom-forming cyanobacterium Microcystis aeruginosa (cyanophyceae) to prolonged uv-b exposure. J. Phycol. 41, 983–992. https://doi.org/10.1111/j.1529-8817.2005.00126.x (2005).Article 

    Google Scholar 
    Azizullah, A., Richter, P. & Häder, D. P. Photosynthesis and photosynthetic pigments in the flagellate Euglena gracilis: As sensitive endpoints for toxicity evaluation of liquid detergents. J. Photochem. Photobiol. B Biol. 133, 18–26. https://doi.org/10.1016/j.jphotobiol.2014.02.011 (2014).CAS 
    Article 

    Google Scholar 
    Singh, D. P., Khattar, J. I. S., Gupta, M. & Kaur, G. Evaluation of toxicological impact of cartap hydrochloride on some physiological activities of a non-heterocystous cyanobacterium Leptolyngbya foveolarum. Pestic. Biochem. Phys. 110, 63–70. https://doi.org/10.1016/j.pestbp.2014.03.002 (2014).CAS 
    Article 

    Google Scholar 
    Movasaghi, Z., Rehman, S. & Rehman, I. U. Raman spectroscopy of biological tissues. Appl. Spectrosc. Rev. 42, 493–541. https://doi.org/10.1080/05704920701551530 (2007).CAS 
    Article 

    Google Scholar 
    Li, K. et al. In vivo kinetics of lipids and astaxanthin evolution in Haematococcus pluvialis mutant under 15% CO2 using Raman microspectroscopy. Bioresource Technol. 244, 1439–1444. https://doi.org/10.1016/j.biortech.2017.04.116 (2017).CAS 
    Article 

    Google Scholar 
    Beutner, S. et al. Quantitative assessment of antioxidant properties of natural colorants and phytochemicals: Carotenoids, flavonoids, phenols and indigoids. The role of beta-carotene in antioxidant functions. J. Sci. Food. Agric. 81, 559–568. https://doi.org/10.1002/jsfa.849 (2001).CAS 
    Article 

    Google Scholar 
    Kelman, D., Ben-Amotz, A. & Berman-Frank, I. Carotenoids provide the major antioxidant defence in the globally significant N2-fixing marine cyanobacterium Trichodesmiumem. Environ. Microbiol. 11, 1897–1908. https://doi.org/10.1111/j.1462-2920.2009.01913.x (2009).CAS 
    Article 
    PubMed 

    Google Scholar 
    Zhou, T. et al. Growth suppression and apoptosis-like cell death in Microcystis aeruginosa by H2O2: A new insight into extracellular and intracellular damage pathways. Chemosphere 211, 1098–1108. https://doi.org/10.1016/j.chemosphere.2018.08.042 (2018).CAS 
    Article 
    PubMed 

    Google Scholar 
    Schreiber, U., Quayle, P., Schmidt, S., Escher, B. I. & Mueller, J. F. Methodology and evaluation of a highly sensitive algae toxicity test based on multiwell chlorophyll fluorescence imaging. Biosens. Bioelectron. 22, 2554–2563. https://doi.org/10.1016/j.bios.2006.10.018 (2007).CAS 
    Article 
    PubMed 

    Google Scholar 
    Kumar, K. S. et al. Algal photosynthetic responses to toxic metals and herbicides assessed by chlorophyll a fluorescence. Ecotox. Environ. Safe. 104, 51–71. https://doi.org/10.1016/j.ecoenv.2014.01.042 (2014).CAS 
    Article 

    Google Scholar 
    Maxwell, K. & Johnson, G. N. Chlorophyll fluorescence: A practical guide. J Exp Bot 51, 659–668. https://doi.org/10.1093/jxb/51.345.659 (2000).CAS 
    Article 
    PubMed 

    Google Scholar 
    Lürling, M. & Roessink, I. On the way to cyanobacterial blooms: Impact of the herbicide metribuzin on the competition between a green alga (Scenedesmus) and a cyanobacterium (Microcystis). Chemosphere 65, 618–626. https://doi.org/10.1016/j.chemosphere.2006.01.073 (2006).CAS 
    Article 
    PubMed 

    Google Scholar 
    Zhu, J. Y., Liu, B. Y., Wang, J., Gao, Y. N. & Wu, Z. B. Study on the mechanism of allelopathic influence on cyanobacteria and chlorophytes by submerged macrophyte (Myriophyllum spicatum) and its secretion. Aquat. Toxicol. 98, 196–203. https://doi.org/10.1016/j.aquatox.2010.02.011 (2010).CAS 
    Article 
    PubMed 

    Google Scholar 
    Wan, J., Guo, P., Peng, X. & Wen, K. Effect of erythromycin exposure on the growth, antioxidant system and photosynthesis of Microcystis flos-aquae. J. Hazard. Mater. 283, 778–786. https://doi.org/10.1016/j.jhazmat.2014.10.026 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    Wang, R. et al. Evaluating the effects of allelochemical ferulic acid on Microcystis aeruginosa by pulse-amplitude-modulated (PAM) fluorometry and flow cytometry. Chemosphere 147, 264–271. https://doi.org/10.1016/j.chemosphere.2015.12.109 (2016).CAS 
    Article 
    PubMed 

    Google Scholar 
    Long, M. et al. Allelochemicals from Alexandrium minutum induce rapid inhibition of metabolism and modify the membranes from Chaetoceros muelleri. Algal Res. 35, 508–518. https://doi.org/10.1016/j.algal.2018.09.023 (2018).Article 

    Google Scholar 
    Cosgrove, J. & Borowitzka, M. A. Chloreophyll fluorescence terminology: An introduction. In Chlorophyll a Fluorescence in Aquatic Sciences: Methods and Applications, Developments in Applied Phycology Vol. 4 (eds Sugget, D. J. et al.) 1–18 (Springer, 2010).
    Google Scholar 
    Kumar, K. S. & Han, T. Physiological response of Lemna species toherbicides and its probable use in toxicity testing. Toxicol. Environ. Health Sci. 2, 39–49. https://doi.org/10.1007/BF03216512 (2010).Article 

    Google Scholar 
    Ricart, M. et al. Primary and complex stressors in polluted mediterranean rivers: Pesticide effects on biological communities. J. Hydrol. 383, 52–61. https://doi.org/10.1016/j.jhydrol.2009.08.014 (2010).CAS 
    Article 

    Google Scholar 
    Deng, C., Pan, X. & Zhang, D. Influence of of loxacin on photosystems I and II activities of Microcystis aeruginosa and the potential role of cyclic electron flow. J. Biosci. Bioeng. 119, 159–164. https://doi.org/10.1016/j.jbiosc.2014.07.014 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    Pereira, S. et al. Complexity of cyanobacterial exopolysaccharides: Composition, structures, inducing factors and putative genes involved in their biosynthesis and assembly. FEMS Microbiol. Rev. 33, 917–941. https://doi.org/10.1111/j.1574-6976.2009.00183.x (2009).CAS 
    Article 
    PubMed 

    Google Scholar 
    Gao, L. et al. Extracellular polymeric substances buffer against the biocidal effect of H2O2 on the bloom-forming cyanobacterium Microcystis aeruginosa. Water Res. 69, 51–58. https://doi.org/10.1016/j.watres.2014.10.060 (2015).CAS 
    Article 
    PubMed 

    Google Scholar 
    Zhang, S. et al. Ameliorating effects of extracellular polymeric substances excreted by Thalassiosira pseudonana on algal toxicity of CdSe quantum dots. Aquat. Toxicol. 126, 214–223. https://doi.org/10.1016/j.aquatox.2012.11.012 (2013).CAS 
    Article 
    PubMed 

    Google Scholar 
    Henriques, I. D. S. & Love, N. G. The role of extracellular polymeric substances in the toxicity response of activated sludge bacteria to chemical toxins. Water Res. 41, 4177–4185. https://doi.org/10.1016/j.watres.2007.05.001 (2007).CAS 
    Article 
    PubMed 

    Google Scholar 
    Zheng, S. M. et al. Role of extracellular polymeric substances on the behavior and toxicity of silver nanoparticles and ions to green algae Chlorella vulgaris. Sci. Total Environ. 660, 1182–1190. https://doi.org/10.1016/j.scitotenv.2019.01.067 (2019).CAS 
    Article 
    PubMed 

    Google Scholar  More

  • in

    Non-linear relationships between density and demographic traits in three Aedes species

    Hutchinson, G. E. An Introduction to Population Ecology (Yale University Press, 1978).MATH 

    Google Scholar 
    Fussman, G. F. & Heber, G. Food web complexity and chaotic population dynamics. Ecol. Lett. 5, 394–401 (1978).Article 

    Google Scholar 
    Maron, J. L. & Crone, E. Herbivory: effects on plant abundance, distribution, and population growth. Proc. R. Soc. B. 272, 2575–2584 (1978).
    Google Scholar 
    Johst, K., Berryman, A. & Lima, M. From individual interactions to population dynamics: Individual resource partitioning simulation exposes the causes of nonlinear intra-specific competition. Pop. Ecol. 50, 79–90 (2008).Article 

    Google Scholar 
    McIntire, K. M. & Juliano, S. A. How can mortality increase population size? A test of two hypotheses. Ecology 99, 1660–1670 (2018).PubMed 
    Article 

    Google Scholar 
    Mylius, S. D. & Deikmann, O. On evolutionary stable life histories, optimization and the need to be specific about density dependence. Oikos 74, 218–224 (1995).Article 

    Google Scholar 
    Courchamp, F., Clutton-Brock, T. & Grenfell, B. Inverse density dependence and the Allee effect. Trends Ecol. Evol. 14, 405–410 (1999).CAS 
    PubMed 
    Article 

    Google Scholar 
    MacLean, R. C. & Gudelj, I. Resource competition and social conflict in experimental populations of yeast. Nature 44, 498–501 (2006).ADS 
    Article 
    CAS 

    Google Scholar 
    Khatchikian, C. E. et al. Recent and rapid population growth and range expansion of the Lyme disease tick vector, Ixodes scapularis North America. Evolution 69, 1678–1689 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lafferty, K. D. & Holt, R. D. How should environmental stress affect the population dynamics of disease?. Ecol. Lett. 6, 654–664 (2003).Article 

    Google Scholar 
    Sibley, R. M., Barker, D., Denham, M. C., Hone, J. & Pagel, M. On the regulation of populations of mammals, birds, fish, and insects. Science 309, 607–610 (2005).ADS 
    Article 
    CAS 

    Google Scholar 
    Bjorndal, K., Bolten, A. B. & Chaloupka, M. Y. Green turtle somatic growth model: evidence for density-dependence. Ecol. App. 10, 269–282 (2000).
    Google Scholar 
    Lamb, J. S., Satgé, Y. G. & Jodice, P. G. R. Influence of density-dependent competition on foraging and migratory behavior of a subtropical colonial seabird. Ecol. Evol. 7, 6469–6481 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Kobayashi, K. Sexual selection sustains biodiversity via producing negative density-dependent population growth. J. Ecol. 107, 1433–1438 (2018).Article 

    Google Scholar 
    López-Sepulcre, A. & Kokko, H. Territorial defense, territory size, and population regulation. Am. Nat. 166, 317–325 (2005).PubMed 
    Article 

    Google Scholar 
    Maag, N., Cozzi, G., Clutton-Brock, T. & Ozgul, A. Density-dependent dispersal strategies in a cooperative breeder. Ecology 99, 1932–1941 (2018).PubMed 
    Article 

    Google Scholar 
    Bonenfant, C. et al. Empirical evidence of density- dependence in populations of large herbivores. Adv. Ecol. Res. 41, 313–357 (2009).Article 

    Google Scholar 
    Legros, M., Lloyd, A. L., Huang, Y. & Gould, F. Density-dependent intraspecific competition in the larval stage of Aedes aegypt (Diptera: Culicidae): Revisiting the current paradigm. J. Med. Entomol. 46, 409–419 (2009).PubMed 
    Article 

    Google Scholar 
    Hixon, M. A. & Jones, G. P. Competition, predation, and density-dependent mortality in demersal marine fishes. Ecology 86, 2847–2859 (2006).Article 

    Google Scholar 
    Vonesh, J. R. & De La Cruz, O. Complex life cycles and density dependence: Assessing the contribution of egg mortality to amphibian declines. Oecologia 133, 325–333 (2002).ADS 
    PubMed 
    Article 

    Google Scholar 
    Southwood, T. R., Murdie, G., Yasuno, M., Tonn, R. J. & Reader, P. M. Studies on the life budget of Ae. aegypti in Wat Samphaya, Bangkok, Thailand. Bull. World Health Organ. 46, 211–226 (1972).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    Dye, C. Intraspecific competition amongst larval Aedes aegypti: food exploitation or chemical interference. Ecol. Entomol. 7, 39–46 (1982).Article 

    Google Scholar 
    Dye, C. Models for the population dynamics of the yellow fever mosquito, Aedes aegypti. J. Anim. Ecol. 53, 247–268 (1984).Article 

    Google Scholar 
    Livdahl, T. P. & Willey, M. S. Prospects for an invasion: competition between Aedes albopictus and native Aedes triseriatus. Science 253, 189–191 (1991).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    Alto, B. W., Lounibos, L. P., Higgs, S. & Juliano, S. A. Larval competition differentially affects arbovirus infection in Aedes mosquito. Ecology 86, 3279–3288 (2005).PubMed 
    Article 

    Google Scholar 
    Juliano, S. A. Population dynamics. J. Am. Mosq. Control Assoc. 23, 265–275 (2007).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Focks, D. A., Haile, D. G., Daniels, E. & Mount, G. A. Dynamics life table model for Aedes aegypti (diptera: Culicidae): simulation results and validation. J. Med. Entomol. 30, 1018–1028 (1993).CAS 
    PubMed 
    Article 

    Google Scholar 
    Ellis, A. M., Garcia, A. J., Focks, D. A., Morrison, A. C. & Scott, T. W. Parameterization and sensitivity analysis of a complex simulation model for mosquito population dynamics, dengue transmission, and their control. Am. J. Trop. Med. Hyg. 85, 257–264 (2011).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Gilpin, M. E. & McClelland, G. A. H. Systems analysis of the yellow fever mosquito Aedes aegypti. Fortschr. Zool. 25, 355–388 (1979).CAS 
    PubMed 

    Google Scholar 
    Juliano, S. A. Species introduction and replacement among mosquitoes: Interspecific resource competition or apparent competition?. Ecology 79, 255–268 (1998).Article 

    Google Scholar 
    Lord, C. C. Density dependence in larval Aedes albopictus (Diptera: Culicidae). J. Med. Entomol. 35, 825–829 (1998).CAS 
    PubMed 
    Article 

    Google Scholar 
    Agnew, P., Hide, M., Sidobre, C. & Michalakis, Y. A minimalist approach to the effects of density-dependent competition on insect life-history traits. Ecol. Entomol. 27, 396–402 (2002).Article 

    Google Scholar 
    Walsh, R. K., Facchinelli, L., Ramsey, J. M., Bond, J. G. & Gould, F. Assessing the impact of density dependence in field populations of Aedes aegypti. J. Vect. Ecol. 36, 300–307 (2011).CAS 
    Article 

    Google Scholar 
    Walsh, R. K., Bradley, C., Apperson, C. S. & Gould, F. An experimental field study of delayed density dependence in natural populations of Aedes albopictus. PLoS ONE 7, e35959 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Walsh, R. K. et al. Regulation of Aedes aegypti population dynamics in field systems: Quantifying direct and delayed density dependence. Am. J. Trop. Med. Hyg. 89, 68–77 (2013).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Livdahl, T. P. & Sugihara, G. Non-linear interactions of populations and the importance of estimating per capita rates of change. J. Anim. Ecol. 53, 573–580 (1984).Article 

    Google Scholar 
    Getz, W. M. A hypothesis regarding the abruptness of density dependence and the growth rate of populations. Ecology 77, 2014–2026 (1996).Article 

    Google Scholar 
    Tenan, S., Tavecchia, G., Oro, D. & Pradel, R. Assessing the effect of density on population growth when modeling individual encounter data. Ecology 100, e02595 (2019).PubMed 
    Article 

    Google Scholar 
    Arditi, R., Bersier, L. & Rohr, R. P. The perfect mixing paradox and the logistic equation: Verhulst vs. Lotka. Ecosphere 7, e01599 (2016).Article 

    Google Scholar 
    Cortés, E. Perspectives on the intrinsic rate of population growth. Meth. Ecol. Evol. 7, 1136–1145 (2016).Article 

    Google Scholar 
    Smith, F. E. Population dynamics in Daphnia magna and a new model for population growth. Ecology 4, 651–663 (1963).Article 

    Google Scholar 
    Ayala, F. J., Gilpin, M. E. & Ehrenfeld, J. G. Competition between species: Theoretical models and experimental tests. Theor. Pop. Biol. 4, 331–356 (1973).MathSciNet 
    CAS 
    Article 

    Google Scholar 
    Borlestean, A., Frost, P. C. & Murray, D. L. A mechanistic analysis of density dependence in algal population dynamics. Front. Ecol. Evol. 3, 37 (2015).Article 

    Google Scholar 
    Clark, F., Brook, B. W., Delean, S., Akçakaya, H. R. & Bradshaw, C. J. A. The theta-logistic is unreliable for modelling most census data. Methods Ecol. Evol. 1, 253–262 (2010).Article 

    Google Scholar 
    Chmielewski, M. W., Khatchikian, C. & Livdahl, T. Estimating the per capita rate of population change: How well do life-history surrogates perform?. Ann. Entomol. Soc. Am. 103, 734–741 (2010).Article 

    Google Scholar 
    Neale, J. T. & Juliano, S. A. Finding the sweet spot: What levels of larval mortality lead to compensation or overcompensation in adult production?. Ecosphere. 10, e02855 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Armistead, J. S., Arias, J. R., Nishimura, N. & Lounibos, L. P. Interspecific larval competition between Aedes albopictus and Aedes japonicus (Diptera: Culicidae) in northern Virginia. J. Med. Entomol. 45, 629–637 (2008).CAS 
    PubMed 
    Article 

    Google Scholar 
    Kaplan, L., Kendell, D., Robertson, D., Livdahl, T. & Khatchikian, C. Aedes aegypti and Aedes albopictus in Bermuda: Extinction, invasion, invasion and extinction. Bio. Invasions. 12, 3277–3288 (2010).Article 

    Google Scholar 
    Juliano, S. A. Coexistence, exclusion, or neutrality? A meta-analysis of competition between Aedes albopictus and resident mosquitoes. Isr. J. Ecol. Evol. 56, 325–351 (2010).PubMed 
    Article 

    Google Scholar 
    Murrell, E. G. & Juliano, S. A. Competitive abilities in experimental microcosms are accurately predicted by a demographic index for R*. PLoS ONE 7, e43458 (2012).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Leisnham, P. T. & Juliano, S. A. Interpopulation differences in competitive effect and response of the mosquito Aedes aegypti and resistance to invasion of a superior competitor. Oecologia 164, 221–230 (2010).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Leisnham, P. T., Lounibos, L. P., O’Meara, G. F. & Juliano, S. A. Interpopulation divergence in competitive interactions of the mosquito Aedes albopictus. Ecology 90, 2405–2413 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    Evans, M. V., Drake, J. M., Jones, L. & Murdock, C. C. Assessing temperature-dependent competition between two invasive mosquito species. Ecol. Appl. 31, e02334 (2021).PubMed 

    Google Scholar 
    Léonard, P. M. & Juliano, S. A. Effects of leaf litter and density on fitness and population performance of the hole mosquito Aedes triseriatus. Ecol. Entomol. 20, 125–136 (1995).Article 

    Google Scholar 
    Chandrasegaran, K. & Juliano, S. A. How do trait-mediated non-lethal effects of predation affect population-level performance of mosquitoes?. Front. Ecol. Evol. 7, 25 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Yee, D. A., Kaufman, M. G. & Juliano, S. A. The significance of ratios of detritus types and microorganism productivity to competitive interactions between aquatic insect detritivores. J. Anim. Ecol. 76, 1105–1115 (2007).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Fader, J. E. & Juliano, S. A. An empirical test of the aggregation model of coexistence and consequences for competing container-dwelling mosquitoes. Ecology 94, 478–488 (2013).PubMed 
    Article 

    Google Scholar 
    Murrell, E. G., Damal, K., Lounibos, L. P. & Juliano, S. A. Distributions of competing container mosquitoes depend on detritus types, nutrient ratios, and food availability. Ann. Entomol. Soc. Am. 104, 688–698 (2011).PubMed 
    Article 

    Google Scholar 
    Tjørve, K. M. C. & Tjørve, E. The use of Gompertz models in growth analyses, and new Gompertz-model approach: An addition to the Unified-Richards family. PLoS ONE 12, e0178691 (2017).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Motulsky, H. & Christopoulos, A. Fitting Models to Biological Data using Linear and Nonlinear Regression: A Practical Guide to Curve Fitting (Oxford University Press, 2004).MATH 

    Google Scholar 
    Osenberg, C. W. et al. Rethinking ecological inference: density dependence in reef fishes. Ecol. Lett. 5, 715–721 (2002).Article 

    Google Scholar 
    Schmitt, R. J., Holbrook, S. J. & Osenberg, C. W. Quantifying the effects of multiple processes on local abundance: A cohort approach for open populations. Ecol. Lett. 2, 294–303 (1999).CAS 
    PubMed 
    Article 

    Google Scholar 
    Fish, D. An analysis of adult size variation within natural mosquito population. In Ecology of Mosquitoes: Proceedings of a Workshop (eds Lounibos, L. P. et al.) 419–429 (Medical Entomology Laboratory, 1985).
    Google Scholar 
    Schneider, J. R., Chadee, D. D., Mori, A., Romero-Severson, J. & Severson, D. W. Heritability and adaptive phenotypic plasticity of adult body size in the mosquito Aedes aegypti with implications for dengue vector competence. Infect. Genet. Evol. 11, 11–16 (2011).PubMed 
    Article 

    Google Scholar 
    Wormington, J. D. & Juliano, S. A. Sexually dimorphic body size and development time plasticity in Aedes mosquitoes (Diptera: Culicidae). Evol. Ecol. Res. 16, 1–12 (2014).
    Google Scholar 
    Steinwascher, K. Competition and growth among Aedes aegypti larvae: Effects of distributing food inputs over time. PLoS ONE 15, e0234676 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Barrera, R. Competition and resistance to starvation in larvae of container-inhabiting Aedes mosquitoes. Ecol. Entomol. 21, 117–127 (1996).Article 

    Google Scholar 
    Servanty, S. et al. Assessing whether mortality is additive using marked animals: A Bayesian state-space modeling approach. Ecology 91, 1916–1923 (2010).PubMed 
    Article 

    Google Scholar 
    Wolfe, M. L. et al. Is anthropogenic cougar mortality compensated by changes in natural mortality in Utah? Insights from long-term studies. Biol. Conserv. 182, 187–196 (2015).Article 

    Google Scholar 
    Kogan, M. Integrated pest management: Historical perspectives and contemporary developments. Ann. Rev. Entomol. 43, 243–270 (1998).CAS 
    Article 

    Google Scholar 
    Lounibos, L. P. Invasions by insect vectors of human diseases. Ann. Rev. Entomol. 47, 233–266 (2002).CAS 
    Article 

    Google Scholar 
    Juliano, S. A. & Lounibos, L. P. Ecology of invasive mosquitoes: Effects on resident species and on human health. Ecol. Lett. 8, 558–574 (2005).PubMed 
    PubMed Central 
    Article 

    Google Scholar  More

  • in

    A collaborative agenda for archaeology and fire science

    Benali, A. et al. Glob. Ecol. Biogeogr. 26, 799–811 (2017).Article 

    Google Scholar 
    Parker, C. H., Keefe, E. R., Herzog, N. M., O’Connell, J. F. & Hawkes, K. Evol. Anthropol. 25, 54–63 (2016).Article 

    Google Scholar 
    MacDonald, K., Scherjon, F., van Veen, E., Vaesen, K. & Roebroeks, W. Proc. Natl Acad. Sci. USA 118, e2101108118 (2021).CAS 
    Article 

    Google Scholar 
    Ellis, E. C. et al. Proc. Natl Acad. Sci. USA 118, e2023483118 (2021).CAS 
    Article 

    Google Scholar 
    Kelly, L. T. et al. Science 370, eabb0355 (2020).CAS 
    Article 

    Google Scholar 
    Roos, C. I., Zedeño, M. N., Hollenback, K. L. & Erlick, M. M. H. Proc. Natl Acad. Sci. USA 115, 8143–8148 (2018).CAS 
    Article 

    Google Scholar 
    Bliege Bird, R. & Bird, D. W. Am. J. Hum. Biol. 33, 4 (2021).Article 

    Google Scholar 
    Gragson, T. L., Coughlan, M. R. & Leigh, D. S. Sustainability 12, 3882 (2020).Article 

    Google Scholar 
    Long, J. W. et al. For. Ecol. Manage. 500, 119597 (2021).Article 

    Google Scholar 
    Adlam, C. et al. Soc. Nat. Resour. https://doi.org/10.1080/08941920.2021.2006385 (2021).Article 

    Google Scholar 
    Long, J. W. et al. Ecopsychology 12, 71–82 (2020).Article 

    Google Scholar 
    Sullivan, A. P. III & Olszewski, D. I. (eds) Archaeological Variability and Interpretation in Global Perspective (Univ. Press Colorado, 2016).Carter, V. A. et al. Commun. Earth. Environ. 2, 72 (2021).Article 

    Google Scholar 
    Roos, C. I. et al. Proc. Natl Acad. Sci. USA 118, e2018733118 (2021).CAS 
    Article 

    Google Scholar 
    Maezumi, S. Y. et al. Nat. Plants 4, 540–547 (2018).Article 

    Google Scholar 
    Hoffman, K. M. et al. Proc. Natl Acad. Sci. USA 118, e2105073118 (2021).CAS 
    Article 

    Google Scholar 
    Sullivan, A. P. III & Mink, P. B. Am. Antiq. 83, 619–638 (2018).Article 

    Google Scholar 
    Klimaszewski-Patterson, A. & Mensing, S. Landsc. Ecol. 35, 2659–2678 (2020).Article 

    Google Scholar 
    Bliege Bird, R. et al. Biol. Conserv. 219, 110–118 (2018).Article 

    Google Scholar 
    McCaffrey, S. et al. Int. J. Wildland Fire 22, 15 (2013).CAS 
    Article 

    Google Scholar 
    Anderson, M. K. Tending the Wild (Univ. California Press, 2005)Marks-Block, T. et al. Fire Ecol. 17, 6 (2021).Article 

    Google Scholar 
    Liebmann, M. J. et al. Proc. Natl Acad. Sci. USA 113, E696–E704 (2016).CAS 
    Article 

    Google Scholar 
    Coughlan, M. R. J. Ethnobiol. 33, 86–104 (2013).Article 

    Google Scholar 
    Klimaszewski-Patterson, A. & Mensing, S. A. Anthropocene 15, 37–48 (2016).Article 

    Google Scholar 
    Derr, K. M. Can. J. Arch. 38, 250–279 (2014).
    Google Scholar 
    Snitker, G. J. Archaeol. Sci. 95, 1–15 (2018).Article 

    Google Scholar 
    Coughlan, M. R. For. Ecol. Manage. 312, 55–66 (2014).Article 

    Google Scholar 
    Le Couédic, M. et al. Rapport de Prospection et Sondages, Larrau, Pyrénées-Atlantiques. Campagne 2014 (ITEM, EA 3002, Université de Pau et des Pays de l’Adour, 2014).Leigh, D. S. et al. Quat. Int. 402, 61–71 (2016).Article 

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    New integrated hydrologic approach for the assessment of rivers environmental flows into the Urmia Lake

    Specifications of the study areaUrmia Lake, as the largest inland lake of Iran, is a national park and one of the largest Ramsar sites of Iran (Ramsar, 1971). The lake is formed in a natural depression within the catchment area in the northwest of Iran. The basin of the lake covers an area of 52,000 km2 and its area is about 5,700 km249. In addition, its maximum length and width are 140 and 50 km, respectively. Further, the lake catchment is a closed inland basin in which all rainwater runoff flows to the central saline lake, and evaporation from the surface of the lake is the only way out. More importantly, it is the largest saltwater lake in Iran and the second largest saltwater lake in the world.The current surface flow system to Urmia Lake consists of 10 main rivers with permanent flow potential, including Zola, Nazlu, Rozeh, Shahrchai, Baranduz, Gadar, Mahabad, Simineh, Zarrineh, and Aji. In terms of the water supply potential of Urmia Lake, Zarrineh, Simineh, Aji, and Nazlu rivers with a flow allocation of 41, 11, 10, and 6% have a key role, respectively.The rivers of this basin are originated from mountains and pass through the heights and enter the agricultural plains. The main usage in plains are for agriculture which cause the changes in natural rivers flow regime. On the other hand, the natural flow regime of the rivers should be considered as the basis for e-flow calculation. So, in the current study the obtained data from the stations situated in the upstream of the rivers and the stations before the agricultural plains are utilized to alleviate the effects of agricultural use on natural flow regime of the rivers. Also, to eliminate the effects of dam rule curve on river flow regime, stations situated in the upstream of the dams are considered as the main scale in the upstream of the dammed rivers like Zarrineh, Mahabad and Zola. Despite all the efforts made to select stations with the least human impact, the two stations related to Aji and Shahar Rivers have been affected by the structures built above them. Therefore, in order to eliminate the effects of the constructed structures at the upstream of the stations, flow naturalization methods were used only for the two stations of Venyar of Aji River and the Band Urmia station of Shahar River. There are several ways to naturalize hydrometric station data. Terrier et al.51 by studying flow naturalization methods in various researches were able to provide a comprehensive study of naturalization methods and selection criteria for each of these methods. According to their studies, the first and the most important prerequisite for stream naturalization is to identify the factors affecting the river and the quality of data in the region, which play a major role in choosing the flow naturalization method. Two factors play a major role in affecting river hydrology. The first factor is the construction of hydraulic structures along the path of rivers and the second factor is the change of land use that has occurred in the rivers basin. In the current study, the purpose of flow naturalization is to eliminate the effects of large dams built on the inlet rivers of the lake, which can affect the hydrology of the river flow. It should be noted that it is not possible to eliminate the effects of land use change due to the gradual nature of the changes, the inability to determine the exact amount and time of the changes and the lack of required data as well. Therefore, in this study, the effects of land use change at the upstream of the stations have been neglected. The most important reason that the Aji River needs to naturalize is the existence of several small dams upstream of Venyar station. To eliminate the effects of dams and flow naturalization at the upstream of this station, the spatial interpolation method introduced by Hughes and Smakhtin52 was used. In this method, Sahzab hydrometric station located at the upstream of the river was used as a base station to naturalize the flow. The next station which needs to be naturalized the flow is the Band Urmia station Shahar River. The main problem for this river has been the construction of a dam upstream of Band Urmia river station since 2004. The drainage area ratio method introduced by Hirsch53 was used to eliminate the effect of this dam on the station data. This method has been used by various researchers to naturalize river flow54,55,56 which is based on the upstream drainage area of the stations. In this method the ratio of the drainage area of the two stations is used to naturalize the flow in the affected station. For this purpose, the data of Bardehsoor station located upstream of the dam was used to naturalize the data of the Band Urmia station. So, anthropogenic effects are at the minimum level in calculations. The utilized stations to calculate the e-flow as upstream stations are illustrated in Fig. 1.Figure 1An overview of the Urmia Lake basin, the rivers, and selected gauging stations. Figure 1 was generated by ArcGIS v10.2 software50 (Environmental Systems Research Institute, Inc., USA, URL http://www.esri.com/).Full size imageAppropriate criteria for allocating the EWR of the Urmia LakeDue to the high salinity of Urmia Lake, only a small number of invertebrates make up the living organisms of this huge water body. Saltwater shrimp or Artemia is a type of aquatic crustacean which can be found in saltwater lakes or coastal lagoons worldwide. Artemia can tolerate salinity less than 10 gl−1 up to 340 gl−1 and adapt to environmental conditions. Artemia Urmiana, the most well-known species of the Urmia Lake, is considered as the main food of migratory birds that spend part of their wintering period on the lake and surrounding wetlands. The presence of this species in the Urmia Lake was first reported by Gunter (1899), and many researchers have confirmed the existence of this bisexual creature in this lake57,58,59,60,61.One of the key factors in estimating the EWR of Urmia Lake is to create an appropriate environmental condition for its dominant species. Abbaspour and Nazari Doost39 identified the EWR of the Urmia Lake by considering the living conditions of Artemia as its dominant species. In this study, Artemia Urmaina was selected as a biological indicator, along with NaCl and elevation above mean sea level (AMSL) as the indicators of water quality and quantity, respectively. The combination of these three indicators forms the ecological basis of Urmia Lake. Therefore, salinity is considered to be equal to 240 gl−1 as the tolerable limit of the biological index. Using long-term statistics in the Urmia Lake and the relationship between quantitative and qualitative water indicators, the water level of 1274.1 m (AMSL) was chosen as the ecological level of the lake so that the balance of these three indicators remained within the allowable range. The study indicated that the calculated environmental water demand of Urmia Lake was equal to 3084 Mm3 per year provided by main rivers entering the lake. Therefore, the proposed new methods should be able to deliver this volume of water to the lake and simultaneously feed the EFR of the river. To supply this water volume, government has programs in order to mitigate the water consumption especially in agriculture. The most important program is 40 percent reduction in agricultural water consumption which is accompanied with the increase of efficiency. Also the government pursues urban wastewater treatment to retrieve some of domestic water to the lake. The mentioned programs are time consuming, however, the new methods presented in this study can be useful for managers in determining the allocation patterns and consumption management. Ordinary method of flow duration curve shifting (FDCS) in estimating e-flowSince the early 1990s, various methods have been developed based on the hydrological indices62 in order to determine the e-flow by taking into account the flow variability and adaptation to the ecological conditions of rivers. One of the intended diagrams in the study of the hydrological characteristics is the flow duration curve (FDC), which is used to assess the fluctuations and variability of water flow from an environmental point of view. Given the importance of the presence of flood currents in the restoration of the river and wetland ecosystems63,64, the FDC is one of the most practical methods to show the full range of river discharge characteristics from water shortage to flood events. This diagram also demonstrates the relationship between the amount and frequency of the flow which can be prepared for daily, annual, and monthly time intervals65. The FDCS is a method in which FDC is employed to estimate the river flow. This method was introduced by Smakhtin and Anputhas66 to evaluate the e-flow in the river system. The method, which is called FDCS, provides a hydrological regime to protect the river in the desired ecological conditions.In the previous research, most of the rivers in the Urmia Lake basin have been compatible with FDCS, and due to the lack of biological data regarding these rivers, it is always one of the top priorities among the methods of estimating the e-flow in rivers leading to the Urmia Lake67,68. It is noteworthy that the characteristics of calculation steps of the ordinary method are provided as follows.This method consists of four main steps:

    1.

    Assessing the existing hydrological conditions (preparing the FDC for a natural river flow regime),

    2.

    Selecting the appropriate environmental management class;

    3.

    Acquiring the environmental FDC;

    4.

    Generating e-flow time series.

    The first step is to prepare the FDC in the desired river range using monthly flow data. In this method, FDC for the natural river flow regime is prepared by 17 fixed percentage points of occurrence probabilities (0.01, 0.1, 1, 5, 10, 20, 30, 40, 50, 60, 70, 80, 90, 95, 99, 99.9, 99.99) where P1 = 99.99% and P17 = 0.01% represent the highest and lowest probability of occurrence, respectively. These points ensure that the entire flow range is adequately covered, as well as facilitating the continuation of the next steps.This method, which uses mean monthly flow (MMF) data, considers six environmental management classes (EMC) from A to F. The FDC of EFR (FDC-EFR) for each class in terms of EMC is determined based on the obtained natural river FDC by the MMF. The higher EMC needs more water to maintain the ecosystem. These classes are determined based on empirical relationships between the flow and ecological status of rivers, which currently have no specific criteria for identifying these limits. The selection of the appropriate class individually relies on the expert’s judgment of the river ecosystem condition.After obtaining the natural FDC, the next step is to calculate the FDC-EFR for each EMC using the lateral shifts of FDC to the left along the probabilistic axis. For EMC-A rivers, one lateral shift to the left is applied while two, three, and four lateral shifts are employed for EMC-B, EMC-C, and EMC-D rivers, respectively. It should be noted that the overall hydrological pattern of the flow will be maintained although the flow variation is lost for each shift.In the current study, global e-flow calculation (GEFC) v2.0 software69,70 has been utilized to compute the e-flow by the FDCS method. The long-term data (at least 20 years) of MMF are the required input data for this software.According to the research conducted on the rivers of the Urmia Lake basin, EMC-C is the minimum considered EMC for 10 main rivers of the lake, thus the EMC-C has been considered in this study, and all calculations for classes A, B, C have been performed accordingly.The description of new methods based on ordinary methodThe main purpose of presenting new methods is to combine the EWR of wetlands or lakes and the hydrological method of FDCS, which can be used to calculate the e-flow of rivers and meet the needs of lakes or wetlands in downstream. These methods relies on the FDCS while with the difference that the proposed method includes three fundamental changes compared to the original one.

    1.

    Applying monthly FDC (FDC for each month separately) instead of annual FDC,

    2.

    Employing daily flow data instead of MMF,

    3.

    Considering the downstream EWR in the amount of the lateral shift in the FDCS method.

    The use of the structure of new methods lead to a dynamic process that is based on the selected EMC of the river, the amount of the natural flow, and the date of occurrence and can compute the amount of the e-flow of the river on each day of the year.River hydrology greatly varies depending on the type of the basin, the climate of the area, and the relationship between the basin and the river each exhibiting different behaviors during the months of the year. Accordingly, the proposed methods should provide sufficient comprehensiveness in estimating the e-flow by considering different flow characteristics. Due to the type and timing of precipitation in the Urmia Lake basin, the rivers are full of water from March to June and spend extremely less flows during the other times of the year. For example, Fig. 2 shows the distribution of the Nazlu River flows in the west of Urmia Lake throughout the year. According to the data, 74% of the AF crosses the river from March to June, and the highest and lowest river discharges are related to May with 29% and September and August with 2% of the AF, respectively.Figure 2Historical hydrograph at the Tapik Station, Nazlu River: (a) Daily and mean monthly distribution of flows and (b) Magnified hydrograph for a typical year (1993).Full size imageAccording to the flow distribution throughout the year, the annual FDC is an average FDC of each month of the year. However, the flow of a river during the months of the year represents significant changes. Therefore, the monthly FDC is higher than the annual FDC in the high-water months (e.g., May). Additionally, this curve is lower compared to the annual FDC in the low-water months (e.g., September). Accordingly, the use of monthly FDCs provides more details of changes in the hydrological parameters of the flow and can be a better indicator of the hydrological index of river flows.In the conventional FDCS method, the FDC is obtained using the MMF data of each station. The obtained curve represents the monthly average of river flow and does not illustrates the minimum, maximum and the effect of flow fluctuations in the estimation of e-flows (Fig. 2).In the new methods, all FDC diagrams were obtained by daily data. Both annual (EFR-Ann) and monthly (EFR-Mon) methods are separately utilized to compare the calculation of the e-flow and to choose the best method. The annual FDC is a probabilistic chart for the whole year and the monthly FDC includes 12 probability curves for each year. Due to the use of FDC in e-flow estimations, it has been attempted to perform all calculations from this diagram. Therefore, concepts related to the flow volume can be integrated with the FDCS method. Some of the applied concepts for this purpose are as follows.In the FDCS method, the FDC is defined based on 17 probabilistic percentage points. To calculate the mean AF (MAF) volume, the theorem of the mean value for a definite integral is employed in the FDC diagram. Accordingly, considering that FDC is continuous between the first and seventeenth probability points, the mean flow (Fm) is obtained from Eq. (1) as.
    $$F_{m} = frac{1}{{P_{1} – P_{17} }}mathop smallint limits_{{P_{17} }}^{{p_{1} }} Fleft( p right)dp$$
    (1)
    Fm = Mean flow. P1, P17 = Points of FDC probability that P1 = 99.99 and P17 = 0.01.Given that the FDC consists of 17 probability points and the probability function ‘F(P)’ is unavailable for this curve as a mathematical equation, obtaining this equation for each flow curve increases the computational cost. Therefore, numerical integration methods can be used in this regard. The trapezoidal numerical solution method has been utilized for this purpose. By applying the trapezoidal method in solving Eq. (1), Eq. (2) is obtained, which is used to compute the mean flow of the FDC.$$F_{m} = frac{1}{{P_{1} – P_{17} }}mathop sum limits_{i = 1}^{17} frac{{left( {F_{i} + F_{i + 1} } right)}}{2}{*}left[ {P_{i} – P_{i + 1} } right]$$
    (2)
    Pi = 17 points of FDC probability that P1 = 99.99% and P17 = 0.01%. Fi = The amount of the river flow with the probability of the occurrence of Pi.To calculate the AF volume by monthly and annual FDCs, Eq. (3) can be applied for the AF volume in the EFR-Ann method, as well as employing Eqs. (4) and (5) for the monthly and AF volume in the EFR-Mon method, respectively.$${text{V}}_{{AF_{Ann} }} = frac{365*24*3600}{{P_{1} – P_{17} }}mathop sum limits_{i = 1}^{17} frac{{left( {F_{i} + F_{i + 1} } right)}}{2}{*}left[ {P_{i} – P_{i + 1} } right]$$
    (3)
    $${text{V}}_{Monthly } = frac{{D_{k} *24*3600}}{{P_{1} – P_{17} }}mathop sum limits_{i = 1}^{17} frac{{left( {F_{i} + F_{i + 1} } right)}}{2}{*}left[ {P_{i} – P_{i + 1} } right]$$
    (4)
    $${text{V}}_{{AF_{Mon} }} = mathop sum limits_{k = 1}^{12} left[ {{text{V}}_{Monthly } } right]_{k}$$
    (5)

    VAFAnn = AF volume using annual FDC. VMonthly = Monthly flow volume. VAFMon = AF volume using monthly FDC. Dk = Number of the days of the kth month. k = Number of each month.The required e-flow by wetlands and lakes must have two basic characteristics. The volume of EWR for maintaining their ecological level must be determined and provided by the studies of their ecosystems. In addition, fluctuations must be maintained in water levels in the lake due to hydrological conditions under the basins of the lake supplying rivers given the fact that maintaining the hydrological conditions of the river is one of the major goals of the FDCS method in estimating the e-flow of the river. On the other hand, the rehabilitation of the wetland or lake downstream of rivers requires a certain amount of water, and the new methods must be applied to combine these two goals. In this regard, the AF volume, which can be transferred to the lake (VL Mon or Ann) by these rivers, is calculated by taking into account the natural flow conditions of the rivers in the basin and without considering the consumptions,.$${text{V}}_{{L_{Ann} }} { } = mathop sum limits_{j = 1}^{{text{n}}} left[ {{text{V}}_{{AF_{Ann} }} } right]_{j}$$
    (6)
    $${text{V}}_{{L _{Mon} }} = mathop sum limits_{j = 1}^{{text{n}}} left[ {{text{V}}_{{AF_{Mon} }} } right]_{j}$$
    (7)

    n = Number of input rivers to the lake. VLAnn = AF volume, which can be transferred to the lake using annual FDC. VLMon = AF volume, which can be transferred to the lake using monthly FDC.The ratio of the EWR of the lake or wetland to the average annual volume of the basin should be determined at this stage.$$b = frac{{{text{V}}_{EWR} }}{{{text{V}}_{{L_{Ann} }} or {text{V}}_{{L _{Mon} }} }}$$
    (8)
    b = The ratio of the EWR of the lake or wetland to the average annual volume of the basin. VEWR = Volume of environmental water requirement of the lake or wetland.In the conventional FDCS method, which is determined using GEFC v2.0 software70 (It is then called the GEFC method), depending on the type of the river EMC, the allocation curve is obtained with one or more shifts of the FDC. Each EMC includes a certain ratio of the MAF volume of the river, and changing the flow EMC facilitates changing the flow volume. It is impossible to supply a specific and predetermined downstream water volume of the river. Therefore, in the new methods, a new process must be used to calculate the amount of the FDC shift in order to provide a certain volume of water in the shifting of the FDC. First, a new definition of the EMC was developed for the new methods. In this definition, instead of using a specific shift of the FDC, the range between the two classes was characterized as an EMC. For example, the region between the curve of EMC-A and the natural flow and the region between the EMC-A and EMC-B curves are defined as EMC-A and EMC-B areas, respectively. These regions can be defined for all EMCs (Fig. 3).Figure 3Comparison of the EFR allocated to each of the environmental management classes from this new approach (on the left) with the conventional FDCS methods (on the right).Full size imageBased on the new definition of the range of EMC, the FDC can be shifted as much as needed according to the volume of downstream EWR. The EWR can be defined as the annual percentage river flow respecting the shift of EMCs or a percentage between two specific classes. If the required flow volume is between two specific classes, Eq. (9) can be used to shift the FDC. In fact, with the new definition, any required probable shift can be applied to the FDC ِdiagram to reach a certain volume. In this case, new probable points are determined using Eq. (9), followed by performing the FDC shift similar to the FDCS method in the next step.$$P_{{i_{new} }} = P_{i} + a{*}left( {P_{i – 1} – P_{i} } right)quad i = t, ldots ,16$$
    (9)
    Pinew = New shifted probability point. Pi = 17 points of FDC probability that P1 = 99.99% and P17 = 0.01%. a = Coefficient of shift which defined between 0 and 1. t = Number of shifts performed on the FDC diagram numbered 1–6 for the areas of EMC A, B, C, D, E, F, respectively.The concept of numerical integration and Eqs. (9) and (3) were utilized to calculate the annual volume of different EMCs for each river, and Eqs. (10) and (12) were obtained for the new annual and monthly methods, respectively.$$begin{aligned} & {text{V}}_{{AF class_{t} Ann }} = frac{1}{{P_{1} – left[ {P_{17} + a*left( {P_{16} – P_{17} } right)} right]}} \ & quad quad quad quad quad *left[ {F_{1} *left[ {P_{1} – left[ {P_{t + 1} + a*left( {P_{t} – P_{t + 1} } right)} right]} right] + mathop sum limits_{{i = {text{t}} + 1}}^{16} frac{{left( {F_{i – t} + F_{i – t + 1} } right)}}{2}{*}left[ {P_{i} – P_{i + 1} + a{*}left( {P_{i – 1} – 2P_{i} + P_{i + 1} } right)} right]} right]*365*24*3600 \ end{aligned}$$
    (10)
    $$begin{aligned}&{text{V}}_{{ class_{t} Mon }} = frac{{D_{k} *24*3600}}{{P_{1} – left[ {P_{17} + a*left( {P_{16} – P_{17} } right)} right]}} \ & quad quad quad quad quad *left[ {F_{1} *left[ {P_{1} – left[ {P_{t + 1} + a*left( {P_{t} – P_{t + 1} } right)} right]} right] + mathop sum limits_{{i = {text{t}} + 1}}^{16} frac{{left( {F_{i – t} + F_{i – t + 1} } right)}}{2}{*}left[ {P_{i} – P_{i + 1} + a{*}left( {P_{i – 1} – 2P_{i} + P_{i + 1} } right)} right]} right] end{aligned}$$
    (11)
    $${text{V}}_{{AF class_{t} Mon}} = mathop sum limits_{K = 1}^{12} left[ {{text{V}}_{{ class_{t} Mon}} } right]_{k}$$
    (12)

    VAF classt Ann = AF volume for the related class of selected t for annual method. Vclasst Mon = Monthly flow volume for the related class of selected t for monthly method. VAF classt Mon = AF volume for the related class of selected t for monthly method.where t is the number of shifts performed on the FDC diagram numbered 1–6 for the areas of EMC A, B, C, D, E, F, respectively. To find the exact value of a in these equations, the scope of the EMC must be determined based on the required volume by downstream. Therefore, assuming a = 0 in these equations, the AF volume at the boundary of each class is obtained for both EFR-Mon (Eq. (10)) and EFR-Ann (Eq. (12)) methods. The nearest calculated annual volume is selected as the appropriate EMC which is smaller than the volume of downstream. Further, the corresponding t-class is used to solve the equations, representing the range of the selected EMC.At this stage, the value of the obtained ‘a’ from the FDC shift diagram equals the required volume of downstream. For this purpose, Eqs. (13) and (14) for the EFR-Ann and EFR-Mon methods are obtained from Eqs. (10) and (12), respectively.$$b{text{*V}}_{{AF_{Ann} }} = V_{{AF class_{t } Ann }}$$
    (13)
    $$b{text{*V}}_{{AF_{Mon} }} = V_{{AF class_{t} Mon }}$$
    (14)

    By solving Eqs. (13) and (14), the obtained value of a represents the annual and monthly methods, and the obtained shifted FDC stands for the required annual volume downstream.After determining the appropriate FDC, it is used to calculate the daily e-flow needs of the river using the spatial interpolation algorithm52, which is also employed in the FDCS method. To this end, the probability of the river flow occurrence from the annual or monthly FDCs (according to the selected method) is determined and then the required river flow in the specified probability of occurrence is obtained using the e-flow curve.The range of variability approach (RVA)71,72 is a complex method based on the use of e-flow for achieving the goals of river ecosystem management. This method is applied to compare the methods and select the best one based on the least hydrological change compared to the natural flow of the river. Furthermore, it is based on the importance of the hydrological feature impact of the river on the life, biodiversity of native aquatic species, and the natural ecosystem of the river and aims to provide complete statistical characteristics of the flow regime.In the RVA method, the indicators of hydrologic alteration (IHA) parameters related to the natural river flow are considered as a basis, and changes in the IHA parameters of different EMCs are evaluated accordingly. Richter et al.72 suggested that the distribution of the annual values of IHA parameters for maintaining river environmental conditions must be kept as close as possible to natural flow condition parameters. In several studies, this method was used to investigate changes in the hydrological parameters of a river over time37.Moreover, the total data related to the natural flow of the river for each IHA parameter are classified into three categories in the RVA method. In this study, this classification is based on Default software, and the 17% distance from the median is introduced as the boundary of the classes. By this definition, three classes of the same size are created, in which the middle category is between 34 and 67, and the lower and higher ranges are called the lowest and highest categories, respectively.Using the current change factor obtained from Eq. (15), the RVA method can quantify the change amount in the values of the 33 IHA parameters compared to the natural flow conditions.$$HA = left( {O_{f} – E_{f} } right)/E_{f}$$
    (15)
    HA = Hydrological alteration index. Of = Number of flows occurring within a certain category of the IHA parameter under changed flow conditions. Ef = Number of flows occurring in the same category specified by the parameter under natural flow conditions.In this case, for each IHA parameter, three HA factors are obtained, which can be separately examined for river flows in these three categories. In the analysis of parameters, the positive HA means that the number of occurrences of the phenomenon has increased in a certain IHA category compared to the natural conditions of the river flow. Negative values imply a decrease in the number of occurrences of the same phenomenon. To compare the number of changes in IHA parameters, the HA factor of the RVA method and IHA software (Version 7.1)73 was employed to allocate e-flows in different methods. The obtained results using RVA method calculates and represents HA of each 33 parameters. However, making decision to choose the best method, all parameters need to be assessed and presented as a total index. Due to calculate total HA index based on studies of Xue et al.74 Eq. (16) can be used.$$HA_{o} = sqrt {frac{{mathop sum nolimits_{i = 1}^{33} HA_{i}^{2} }}{33}} *100$$
    (16)
    HAo = Total hydrological alteration index. HAi = Hydrological alteration of each of 33 parameters.Determination of EFR for different EMCs for all methodsInitially, the MMF for each available statistical month was obtained by daily data from stations located in the upstream of the basin rivers of Urmia Lake (Fig. 1). The FDC for the natural flow and various EMCs were obtained using MMF values and GEFC software. Next, to perform the calculations in the EFR-Ann method, the FDC of a natural flow and different EMCs during the year were plotted by daily data. Finally, for the EFR-Mon method, the daily data of each month of the year were examined and the FDC of the natural flow and EMCs were separately plotted for each month.Based on the presented method in this research, Fig. 4 illustrates a step-by-step diagram for determining the e-flows of rivers in the Urmia Lake basin.Figure 4Step-by-step flowchart for determining the environmental flows of rivers in the Urmia Lake basin.Full size image More

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    Defoliation-induced changes in foliage quality may trigger broad-scale insect outbreaks

    Swank, W. T., Waide, J. B., Crossley, D. A. & Todd, R. L. Insect defoliation enhances nitrate export from forest ecosystems. Oecologia 51, 297–299 (1981).CAS 
    PubMed 
    Article 

    Google Scholar 
    Hunter, M. D. Insect population dynamics meets ecosystem ecology: effects of herbivory on soil nutrient dynamics. Agric. For. Entomol. 3, 77–84 (2001).Article 

    Google Scholar 
    Metcalfe, D. B. et al. Herbivory makes major contributions to ecosystem carbon and nutrient cycling in tropical forests. Ecol. Lett. 17, 324–332 (2014).PubMed 
    Article 

    Google Scholar 
    Metcalfe, D. B., Crutsinger, G. M., Kumordzi, B. B. & Wardle, D. A. Nutrient fluxes from insect herbivory increase during ecosystem retrogression in boreal forest. Ecology 97, 124–132 (2016).PubMed 
    Article 

    Google Scholar 
    Lovett, G. M. et al. Insect defoliation and nitrogen cycling in forests. Bioscience 52, 335 (2002).Article 

    Google Scholar 
    Frost, C. J. & Hunter, M. D. Recycling of nitrogen in herbivore feces: Plant recovery, herbivore assimilation, soil retention, and leaching losses. Oecologia 151, 42–53 (2007).PubMed 
    Article 

    Google Scholar 
    Le Mellec, A. & Michalzik, B. Impact of a pine lappet (Dendrolimus pini) mass outbreak on C and N fluxes to the forest floor and soil microbial properties in a Scots pine forest in Germany. Can. J. Res. 38, 1829–1841 (2008).Article 
    CAS 

    Google Scholar 
    Grüning, M. M., Simon, J., Rennenberg, H. & L-M-Arnold, A. Defoliating insect mass outbreak affects soil N fluxes and tree N nutrition in scots pine forests. Front. Plant Sci. 8, 954 (2017).Mikola, J., Yeates, G. W., Barker, G. M., Wardle, D. A. & Bonner, K. I. Effects of defoliation intensity on soil food-web properties in an experimental grassland community. Oikos 92, 333–343 (2001).Article 

    Google Scholar 
    Chapman, S. K., Hart, S. C., Cobb, N. S., Whitham, T. G. & Koch, G. W. Insect herbivory increases litter quality and decomposition: an extension of the acceleration hypothesis. Ecology 84, 2867–2876 (2003).Article 

    Google Scholar 
    Pitman, R. M., Vanguelova, E. I. & Benham, S. E. The effects of phytophagous insects on water and soil nutrient concentrations and fluxes through forest stands of the Level II monitoring network in the UK. Sci. Total Environ. 409, 169–181 (2010).CAS 
    PubMed 
    Article 

    Google Scholar 
    Kaukonen, M. et al. Moth herbivory enhances resource turnover in subarctic mountain birch forests? Ecology 94, 267–272 (2013).PubMed 
    Article 

    Google Scholar 
    Weintraub, M. Biological phosphorus cycling in arctic and alpine soils. In Phosphorus in Action (eds. Bünemann E., Oberson, A. & Frossard, E.) Vol. 26, p. 295–316 (Springer, 2011).Högberg, P., Näsholm, T., Franklin, O. & Högberg, M. N. Tamm review: on the nature of the nitrogen limitation to plant growth in fennoscandian boreal forests. Ecol. Manag. 403, 161–185 (2017).Article 

    Google Scholar 
    Maynard, D. G. et al. How do natural disturbances and human activities affect soils and tree nutrition and growth in the Canadian boreal forest? Environ. Rev. 22, 161–178 (2014).CAS 
    Article 

    Google Scholar 
    Wan, S., Hui, D. & Luo, Y. Fire effects on nitrogen pools and dynamics in terrestrial ecosystems: a Meta-Analysis. Ecol. Appl. 11, 1349–1365 (2001).Article 

    Google Scholar 
    Hart, S. A. & Chen, H. Y. H. Understory vegetation dynamics of North American boreal forests. CRC Crit. Rev. Plant Sci. 25, 381–397 (2006).Article 

    Google Scholar 
    Martineau, C., Beguin, J., Séguin, A. & Paré, D. Cumulative effects of disturbances on soil nutrients: predominance of antagonistic short-term responses to the salvage logging of insect-killed stands. Ecosystems 23, 812–827 (2020).CAS 
    Article 

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

    Google Scholar 
    Grenon, F., Bradley, R. L. & Titus, B. D. Temperature sensitivity of mineral N transformation rates, and heterotrophic nitrification: Possible factors controlling the post-disturbance mineral N flush in forest floors. Soil Biol. Biochem. 36, 1465–1474 (2004).CAS 
    Article 

    Google Scholar 
    Guntiñas, M. E., Leirós, M. C., Trasar-Cepeda, C. & Gil-Sotres, F. Effects of moisture and temperature on net soil nitrogen mineralization: A laboratory study. Eur. J. Soil Biol. 48, 73–80 (2012).Article 
    CAS 

    Google Scholar 
    Houle, D., Duchesne, L. & Boutin, R. Effects of a spruce budworm outbreak on element export below the rooting zone: a case study for a balsam fir forest. Ann. Sci. 66, 707–707 (2009).Article 
    CAS 

    Google Scholar 
    Griffin, J. M. & Turner, M. G. Changes to the N cycle following bark beetle outbreaks in two contrasting conifer forest types. Oecologia 170, 551–565 (2012).PubMed 
    Article 

    Google Scholar 
    Orwig, D. A., Cobb, R. C., D’Amato, A. W., Kizlinski, M. L. & Foster, D. R. Multi-year ecosystem response to hemlock woolly adelgid infestation in southern New England forests. Can. J. Res. 38, 834–843 (2008).Article 

    Google Scholar 
    McMillin, J. D. & Wagner, M. R. Chronic defoliation impacts pine sawfly (Hymenoptera: Diprionidae) performance and host plant quality. Oikos 79, 357 (1997).Article 

    Google Scholar 
    Pureswaran, D. S., Johns, R., Heard, S. B. & Quiring, D. Paradigms in eastern spruce budworm (Lepidoptera: Tortricidae) population ecology: a century of debate. Environ. Entomol. 45, 1333–1342 (2016).PubMed 
    Article 

    Google Scholar 
    Vidal, M. C. & Murphy, S. M. Bottom-up vs. top-down effects on terrestrial insect herbivores: a meta-analysis. Ecol. Lett. 21, 138–150 (2018).PubMed 
    Article 

    Google Scholar 
    White, T. C. R. The abundance of invertebrate herbivores in relation to the availability of nitrogen in stressed food plants. Oecologia 63, 90–105 (1984).CAS 
    PubMed 
    Article 

    Google Scholar 
    White, T. C. R. An alternative hypothesis explains outbreaks of conifer-feeding budworms of the genus Choristoneura (Lepidoptera: Tortricidae) in Canada. J. Appl. Entomol. 142, 725–730 (2018).Article 

    Google Scholar 
    Bouchard, M., Régnière, J. & Therrien, P. Bottom-up factors contribute to large-scale synchrony in spruce budworm populations1. Can. J. Res. 48, 277–284 (2018).CAS 
    Article 

    Google Scholar 
    I-M-Arnold, A. et al. Forest defoliator pests alter carbon and nitrogen cycles. R. Soc. Open Sci. 3, 1–7 (2016).
    Google Scholar 
    Pureswaran, D. S. et al. Climate-induced changes in host tree–insect phenology may drive ecological state-shift in boreal forests. Ecology 96, 1480–1491 (2015).Article 

    Google Scholar 
    MFFP (Ministère des Forêts de la Faune et des Parcs). Aires infestées par la tordeuse des bourgeons de l’épinette au Québec en 2019 – Version 1.1. (2019).Forkner, R. E. & Hunter, M. D. What goes up must come down? Nutrient addition and predation pressure on oak herbivores. Ecology 81, 1588–1600 (2000).Article 

    Google Scholar 
    Schlesinger, W. H. Some thoughts on the biogeochemical cycling of potassium in terrestrial ecosystems. Biogeochemistry 154, 427–432 (2021).Article 

    Google Scholar 
    Kristensen, J. A., Metcalfe, D. B. & Rousk, J. The biogeochemical consequences of litter transformation by insect herbivory in the Subarctic: a microcosm simulation experiment. Biogeochemistry 138, 323–336 (2018).CAS 
    Article 

    Google Scholar 
    Kagata, H. & Ohgushi, T. Ecosystem consequences of selective feeding of an insect herbivore: Palatability-decomposability relationship revisited. Ecol. Entomol. 36, 768–775 (2011).Article 

    Google Scholar 
    Kagata, H. & Ohgushi, T. Positive and negative impacts of insect frass quality on soil nitrogen availability and plant growth. Popul. Ecol. 54, 75–82 (2012).Article 

    Google Scholar 
    Weihrauch, D. & O’Donnell, M. J. Mechanisms of nitrogen excretion in insects. Curr. Opin. Insect Sci. 47, 25–30 (2021).PubMed 
    Article 

    Google Scholar 
    Choudhury, D. Herbivore induced changes in leaf-litter resource quality: a neglected aspect of herbivory in ecosystem nutrient dynamics. Oikos 51, 389–393 (1988).Article 

    Google Scholar 
    Régnière, J. & You, M. A simulation model of spruce budworm (Lepidoptera: Tortricidae) feeding on balsam fir and white spruce. Ecol. Modell. 54, 277–297 (1991).Article 

    Google Scholar 
    Balducci, L. et al. The paradox of defoliation: declining tree water status with increasing soil water content. Agric. Meteorol. 290, 108025 (2020).Article 

    Google Scholar 
    Conant, R. T. et al. Temperature and soil organic matter decomposition rates – synthesis of current knowledge and a way forward. Glob. Change Biol. 17, 3392–3404 (2011).Article 

    Google Scholar 
    Doran, O., MacLean, D. A. & Kershaw, J. A. Needle longevity of balsam fir is increased by defoliation by spruce budworm. Trees – Struct. Funct. 31, 1933–1944 (2017).Article 

    Google Scholar 
    Wu, Y., Maclean, D. A., Hennigar, C. & Taylor, A. R. Interactions among defoliation level, species, and soil richness determine foliage production during and after simulated spruce budworm attack. Can. J. Res. 50, 565–580 (2020).Article 

    Google Scholar 
    Fierravanti, A., Rossi, S., Kneeshaw, D., De Grandpré, L. & Deslauriers, A. Low non-structural carbon accumulation in spring reduces growth and increases mortality in conifers defoliated by spruce budworm. Front. Glob. Change 2, 1–13 (2019).Article 

    Google Scholar 
    Hennigar, C. R., MacLean, D. A., Quiring, D. T. & Kershaw, J. A. Differences in spruce budworm defoliation among balsam fir and white, red, and black spruce. For. Sci. 54, 158–166 (2008).
    Google Scholar 
    Bognounou, F., De Grandpré, L., Pureswaran, D. S. & Kneeshaw, D. Temporal variation in plant neighborhood effects on the defoliation of primary and secondary hosts by an insect pest. Ecosphere 8, e01759 (2017).Article 

    Google Scholar 
    Li, F. et al. Responses of tree and insect herbivores to elevated nitrogen inputs: a meta-analysis. Acta Oecologica 77, 160–167 (2016).Article 

    Google Scholar 
    Shaw, G. G., Little, C. H. A. & Durzan, D. J. Effect of fertilization of balsam fir trees on spruce budworm nutrition and development. Can. J. Res. 8, 364–374 (1978).CAS 
    Article 

    Google Scholar 
    Mattson, W. J., Haack, R. A., Lawrence, R. K. & Slocum, S. S. Considering the nutritional ecology of the spruce budworm in its management. Ecol. Manag. 39, 183–210 (1991).Article 

    Google Scholar 
    Metcalfe, D. B. et al. Ecological stoichiometry and nutrient partitioning in two insect herbivores responsible for large-scale forest disturbance in the Fennoscandian subarctic. Ecol. Entomol. 44, 118–128 (2019).Article 

    Google Scholar 
    Kaitaniemi, P., Ruohomäki, K., Ossipov, V., Haukioja, E. & Pihlaja, K. Delayed induced changes in the biochemical composition of host plant leaves during an insect outbreak. Oecologia 116, 182–190 (1998).PubMed 
    Article 

    Google Scholar 
    Fuentealba, A. & Bauce, É. Interspecific variation in resistance of two host tree species to spruce budworm. Acta Oecol. 70, 10–20 (2016).Article 

    Google Scholar 
    Nealis, V. G. & Régnière, J. Insect – host relationships influencing disturbance by the spruce budworm in a boreal mixedwood forest. Can. J. Res. 1882, 1870–1882 (2004).Article 

    Google Scholar 
    Greenbank, D. O. Staminate flowers and the spruce budworm. Mem. Entomol. Soc. Can. 95, 202–218 (1963).Article 

    Google Scholar 
    Sturtevant, B. R., Cooke, B. J., Kneeshaw, D. D. & MacLean, D. A. Modeling insect disturbance across forested landscapes: insights from the spruce budworm. in Simulation Modeling Of Forest Landscape Disturbances. 93–134 (Springer, 2015).Zalucki, M. P., Clarke, A. R. & Malcolm, S. B. Ecology and behavior of first instar larval Lepidoptera. Annu. Rev. Entomol. 47, 361–393 (2002).CAS 
    PubMed 
    Article 

    Google Scholar 
    Despland, E. Effects of phenological synchronization on caterpillar early-instar survival under a changing climate1. Can. J. Res. 48, 247–254 (2018).Article 

    Google Scholar 
    Mattson, W. J. Herbivory in relation to plant nitrogen content. Annu. Rev. Ecol. Syst. 11, 119–161 (1980).Article 

    Google Scholar 
    Greenbank, D. O. The role of climate and dispersal in the initiation of the spruce budworm outbreak in New Brunswick: II. The role of dispersal. Can. J. Zool. 35, 385–403 (1957).Article 

    Google Scholar 
    Boulanger, Y. et al. The use of weather surveillance radar and high-resolution three dimensional weather data to monitor a spruce budworm mass exodus flight. Agric. Meteorol. 234–235, 127–135 (2017).Article 

    Google Scholar 
    Landry, J. S. & Parrott, L. Could the lateral transfer of nutrients by outbreaking insects lead to consequential landscape-scale effects? Ecosphere 7, e01265 (2016).Article 

    Google Scholar 
    Andersen, T., Elser, J. J. & Hessen, D. O. Stoichiometry and population dynamics. Ecol. Lett. 7, 884–900 (2004).Article 

    Google Scholar 
    Environment Canada. Canadian climate normals: 1981-2010 Climate normals and averages. (2015). Available at: http://climate.weather.gc.ca/climate_normals/index_e.html. (Accessed: 5 April 2016).De Grandpré, L., Morissette, J. & Gauthier, S. Long-term post-fire changes in the northeastern boreal forest of Quebec. J. Veg. Sci. 11, 791–800 (2000).Gauthier, S., Boucher, D., Morissette, J. & De Grandpré, L. Fifty-seven years of composition change in the eastern boreal forest of Canada. J. Veg. Sci. 21, 772–785 (2010).
    Google Scholar 
    Bouchard, M. & Pothier, D. Spatiotemporal variability in tree and stand mortality caused by spruce budworm outbreaks in eastern Quebec. Can. J. Res. 40, 86–94 (2010).Article 

    Google Scholar 
    Fettes, J. J. Investigations of sampling techniques for population studies of the spruce budworm on balsam fir in Ontario (Forest Insect Laboratory, 1950).Miller, R. O. High-Temperature oxidation: dry ashing. In Handbook of Reference Methods for Plant Analysis (ed. Karla, Y. P.) 53–56 (CRC Press, Taylor & Francis Group, 1998).Trottier-Picard, A. et al. Amounts of logging residues affect planting microsites: a manipulative study across northern forest ecosystems. Ecol. Manag. 312, 203–215 (2014).Article 

    Google Scholar 
    RCoreTeam. R.: A language and environment for statistical computing. (R Foundation for Statistical Computing, 2021).Pinheiro, J., Bates, D., DebRoy, S., Sarkar, D. & RCoreTeam. _nlme: Linear and Nonlinear Mixed Effects Models_. (2020).Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).Article 

    Google Scholar 
    Lenth, R. V. emmeans: Estimated Marginal Means, aka Least-Squares Means. R package version 1.6.0. (2021). More

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    Suitability of spider mites and green peach aphids as prey for Eriopis connexa (Germar) (Coleoptera: Coccinellidae)

    Sun, M. et al. Reduced phloem uptake of Myzus persicae on an aphid resistant pepper accession. BMC Plant Biol. 18, 1–14 (2018).ADS 
    CAS 
    Article 

    Google Scholar 
    Migeon, A. & Dorkeld, F. Spider Mites Web: A comprehensive database for the Tetranychidae. Available at http://www.montpellier.inra.fr/CBGP/spmweb (2021)Sato, M. E. et al. Spiromesifen resistance in Tetranychus urticae (Acari: Tetranychidae): Selection, stability, and monitoring. Crop Prot. 89, 278–283 (2016).CAS 
    Article 

    Google Scholar 
    Melville, C. C., Andrade, S. C., Oliveira, N. T. & Andrade, D. J. Impact of Tetranychus ogmophallos (Acari: Tetranychidae) on different phenological stages of peanuts. Bragantia 77, 116–123 (2018).Article 

    Google Scholar 
    Savi, P. J., de Moraes, G. J., Melville, C. C. & Andrade, D. J. Population performance of Tetranychus evansi (Acari: Tetranychidae) on African tomato varieties and wild tomato genotypes. Exp. Appl. Acarol. 77, 555–570 (2019).CAS 
    PubMed 
    Article 

    Google Scholar 
    Bass, C. et al. The evolution of insecticide resistance in the peach potato aphid, Myzus persicae. Insect Biochem. Mol. Biol. 51, 41–51 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    Tabet, V. G., Vieira, M. R., Martins, G. L. M. & Sousa, C. G. N. M. Plant extracts with potential to control of two-spotted spider mite. Arq. Inst. Biol. 85, 1–8 (2018).Article 

    Google Scholar 
    Özkara, A., Akyil, D. & Konuk, M. Pesticides, environmental pollution, and health. In Environmental Health Risk—Hazardous Factors to Living Species (eds Larramendy, M. & Soloneski, S.) (InTech, 2016).
    Google Scholar 
    Faraone, N., Evans, R., LeBlanc, J. & Hillier, N. K. Soil and foliar application of rock dust as natural control agent for two-spotted spider mites on tomato plants. Sci. Rep. 10, 12108 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Taghizadeh, M., Iraninejad, K. H., Iranipour, S. & Vahed, M. M. Comparative study on the efficiency and consumption rate of Stethorus gilvifrons (Coleoptera, Coccinellidae) and Orius albidipennis (Hemiptera, Anthocoridae), the predators of Tetranychus urticae Koch (Acari, Tetranychidae). North-West. J. Zool. 16, 125–133 (2020).
    Google Scholar 
    Orr, D. & Lahiri, S. Biological control of insect pests in crops. In Integrated Pest Management: Current Concepts and Ecological Perspective (ed. Abrol, D. P.) 531–548 (Academic Press, 2014).Chapter 

    Google Scholar 
    Oliveira, N. C., Wilcken, C. F. & Matos, C. A. O. Biological cycle and predation of three coccinellid species (Coleoptera, Coccinellidae) on giant conifer aphid Cinara atlantica (Wilson) (Hemiptera, Aphididae). Rev. Bras. Entomol. 48, 529–533 (2004).Article 

    Google Scholar 
    Moghaddam, M. G. et al. Demographic traits of Hippodamia variegata (Goeze) (Coleoptera: Coccinellidae) fed on Sitobion avenae Fabricius (Hemiptera: Aphididae). J. Crop Prot. 5, 431–445 (2016).Article 

    Google Scholar 
    Gómez, W. D. & Polanía, I. Z. Life table of the predatory beetle Eriopis connexa (Germar) (Coleoptera: Coccinellidae). Rev. UDCA Act. Divulg. Cient. 12, 147–155 (2009).
    Google Scholar 
    Silva, R. B. et al. Biological aspects of Eriopis connexa (Germar) (Coleoptera: Coccinellidae) fed on different insect pests of maize (Zea mays L.) and sorghum [Sorghum bicolor L. (Moench)]. Braz. J. Biol. 73, 419–424 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    Nascimento, D. V., Lira, R., Ferreira, E. K. S. & Torres, J. B. Performance of the aphidophagous coccinellid Eriopis connexa fed on single species and mixed-species prey. Biocontrol. Sci. Technol. 31, 951–963 (2021).Article 

    Google Scholar 
    Miller, J. C. A comparison of techniques for laboratory propagation of a South American ladybeetle, Eriopis connexa (Coleoptera: Coccinellidae). Biol. Control 5, 462–465 (1995).MathSciNet 
    Article 

    Google Scholar 
    Miller, J. C. & Paustian, J. W. Temperature-dependent development of Eriopis connexa (Coleoptera: Coccinellidae). Environ. Entomol. 21, 1139–1142 (1992).Article 

    Google Scholar 
    Sarmento, R. A. et al. Fat body morphology of Eriopis connexa (Coleoptera, Coccinellidae) in function of two alimentary sources. Braz. Arch. Biol. Technol. 47, 407–411 (2004).Article 

    Google Scholar 
    Van Driesche, R. et al. Catalog of Species Introduced into Canada, Mexico, the USA, or the USA Overseas Territories for Classical Biological Control of Arthropods 1985–2018 (USDA Forest Service, 2018).
    Google Scholar 
    Fidelis, E. G. et al. Coccinellidae, Syrphidae and Aphidoletes are key mortality factors for Myzus persicae in tropical regions: A case study on cabbage crops. Crop Prot. 112, 288–294 (2018).Article 

    Google Scholar 
    Francesena, N. et al. Potential of predatory Neotropical ladybirds and minute pirate bug on strawberry aphid. Nat. Acad. Bras. Ciênc. 91, e20181001 (2019).Article 

    Google Scholar 
    Reed, D. K. & Pike, K. S. Summary of an exploration trip to South America. IOBC Nearctic Reg. Newsl. 36, 16–17 (1991).
    Google Scholar 
    Li, Y., Zhou, X., Duan, W. & Pang, B. Food consumption and utilization of Hippodamia variegata (Coleoptera: Coccinellidae) is related to host plant species of its prey, Aphis gossypii (Hemiptera: Aphididae). Acta Entomol. Sin. 58, 1091–1097 (2015).
    Google Scholar 
    Tian, M., Wei, Y., Zhang, S. & Liu, T. Suitability of Bemisia tabaci (Hemiptera: Aleyrodidae) biotype-B and Myzus persicae (Hemiptera: Aphididae) as prey for the ladybird beetle, Serangium japonicum (Coleoptera: Coccinellidae). Eur. J. Entomol. 114, 603–608 (2017).Article 

    Google Scholar 
    Chi, H. Life table analysis incorporating both sexes and variable development rates among individuals. Environ. Entomol. 17, 26–34 (1988).Article 

    Google Scholar 
    Midthassel, A., Leather, S. R. & Baxter, I. H. Life table parameters and capture success ratio studies of Typhlodromips swirskii (Acari: Phytoseiidae) to the factitious prey Suidasia medanensis (Acari: Suidasidae). Exp. Appl. Acarol. 61, 69–78 (2013).PubMed 
    Article 

    Google Scholar 
    Hosseini, A. et al. Life history responses of Hippodamia variegata (Coleoptera: Coccinellidae) to changes in the nutritional content of its prey, Aphis gossypii (Hemiptera: Aphididae), mediated by nitrogen fertilization. Biol. Control 130, 27–33 (2019).CAS 
    Article 

    Google Scholar 
    Almeida, D. P., Berber, G. C. M., Aguiar-Menezes, E. L. & Resende, A. L. S. Evaluation of biological parameters of Eriopis connexa (Germar, 1824) and Coleomegilla maculata (DeGeer, 1775) (Coleoptera: Coccinellidae) fed with alternative prey developed at the integrated center for pest management – UFRRJ. Sci. Electron. Arch. 14, 8–16 (2021).Article 

    Google Scholar 
    Duarte, W., Arévalo, H. & Polanía, I. Z. Influence of three aphid species used as prey on some biological aspects of the predator Eriopis connexa. J. Anim. Sci. 3, 193–199 (2013).
    Google Scholar 
    Zazycki, L. C. F. et al. Biology and fertility life table of Eriopis connexa, Harmonia axyridis and Olla v-nigrum (Coleoptera: Coccinellidae). Braz. J. Biol. 75, 969–973 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Chi, H. & Liu, H. Two new methods for the study of insect population ecology. Bull. Inst. Zool. Acad. Sin. 24, 225–240 (1985).
    Google Scholar 
    Santos, N. R. et al. Biological aspects of Harmonia axyridis fed on two prey species and intraguild predation with Eriopis connexa. Pesq. Agropec. Bras. 44, 554–560 (2009).Article 

    Google Scholar 
    Chi, H. TWOSEX-MSChart: A Computer Program for the Age-Stage, Two-Sex Life Table Analysis. National Chung Hsing University, Taichung, Taiwan. http://140.120.197.173/Ecology/prod02.htm (2021)Efron, B. & Tibshirani, R. J. An Introduction to the Bootstrap (Springer, 1993). https://doi.org/10.1007/978-1-4899-4541-9.Book 
    MATH 

    Google Scholar 
    Hesterberg, T. It’s time to retire the ‘n > = 30’ rule. In Proceedings of the American Statistical Association, Statistical Computing Section (CD-ROM). http://home.comcast.net/~timhesterberg/articles/JSM08-n30.pdf (2008)Huang, Y. B. & Chi, H. Age-stage, two-sex life tables of Bactrocera cucurbitae (Coquillett) (Diptera: Tephritidae) with a discussion on the problem of applying female age-specific life tables to insect populations. Insect Sci. 19, 263–273 (2012).Article 

    Google Scholar 
    Akkopru, E. P., Atlıhan, R., Okut, H. & Chi, H. Demographic assessment of plant cultivar resistance to insect pests: A case study of the dusky-veined walnut aphid (Hemiptera: Callaphididae) on five walnut cultivars. J. Econ. Entomol. 108, 378–387 (2015).PubMed 
    Article 

    Google Scholar 
    Smucker, M. D., Allan, J. & Carterette, B. A comparison of statistical significance tests for information retrieval evaluation. In Proceedings of the Sixteenth ACM Conference on Conference on Information and Knowledge Management—CIKM ’07 623 (ACM Press, 2007).Wei, M. et al. Demography of Cacopsylla chinensis (Hemiptera: Psyllidae) reared on four cultivars of Pyrus bretschneideri (Rosales: Rosaceae) and P. communis pears with estimations of confidence intervals of specific life table statistics. J. Econ. Entomol. 113, 2343–2353 (2020).PubMed 
    Article 

    Google Scholar 
    Wu, X., Zhou, X. & Pang, B. Influence of five host plants of Aphis gossypii Glover on some population parameters of Hippodamia variegata (Goeze). J Pest Sci 83, 77–83 (2010).Article 

    Google Scholar 
    Kalushkov, P. & Hodek, I. New essential aphid prey for Anatis ocellata and Calvia quatuordecimguttata (Coleoptera: Coccinellidae). Biocontrol Sci. Technol. 11, 35–39 (2001).Article 

    Google Scholar 
    Hodek, I. & Honěk, A. Ecology of Coccinellidae (Springer, 1996).Book 

    Google Scholar 
    Hodek, I. & Evans, E. Food relationships. In Ecology and Behaviour of the Ladybird Beetles (Coccinellidae) (eds Hodek, I. et al.) (Wiley, 2012).Chapter 

    Google Scholar 
    Pervez, A. & Kumar, R. Preference of the aphidophagous ladybird Propylea dissecta for two species of aphids reared on toxic host plants. Eur. J. Environ. Sci 7, 130–134 (2017).
    Google Scholar 
    Omkar, & Bind, R. B. Prey quality dependent growth, development and reproduction of a biocontrol agent, Cheilomenes sexmaculata (Fabricius) (Coleoptera: Coccinellidae). Biocontrol Sci. Technol. 14, 665–673 (2004).Article 

    Google Scholar 
    Omkar, & James, B. E. Influence of prey species on immature survival, development, predation and reproduction of Coccinella transversalis Fabricius (Col., Coccinellidae). J. Appl. Entomol. 128, 150–157 (2004).Article 

    Google Scholar 
    Farooq, M., Shakeel, M., Iftikhar, A., Shahid, M. R. & Zhu, X. Age-stage, two-sex life tables of the lady beetle (Coleoptera: Coccinellidae) feeding on different aphid species. J. Econ. Entomol. 111, 575–585 (2018).PubMed 
    Article 

    Google Scholar 
    Giles, K. L. et al. Host plants affect predator fitness via the nutritional value of herbivore prey: Investigation of a plant-aphid-ladybeetle system. Biocontrol 47, 1–21 (2002).Article 

    Google Scholar 
    Savi, P. J., de Moraes, G. J. & Andrade, D. J. Effect of tomato genotypes with varying levels of susceptibility to Tetranychus evansi on performance and predation capacity of Phytoseiulus longipes. Biocontrol 66, 687–700 (2021).CAS 
    Article 

    Google Scholar 
    Hodek, I. & Honěk, A. Scale insects, mealybugs, whiteflies and psyllids (Hemiptera, Sternorrhyncha) as prey of ladybirds. Biol. Control 51, 232–243 (2009).Article 

    Google Scholar 
    Qureshi, J. A. & Stansly, P. A. Three Homopteran pests of citrus as prey for the convergent lady beetle: Suitability and preference. Environ. Entomol. 40, 1503–1510 (2011).PubMed 
    Article 

    Google Scholar 
    Sarmento, R. A. et al. Functional response of the predator Eriopis connexa (Coleoptera: Coccinellidae) to different prey types. Braz. Arch. Biol. Technol. 50, 121–126 (2007).Article 

    Google Scholar 
    Oliveira, E. E. et al. Biological aspects of the predator Cycloneda sanguinea (Linnaeus, 1763) (Coleoptera: Coccinellidae) fed with Tetranychus evansi (Baker and Pritchard, 1960) (Acari: Tetranychidae) and Macrosiphum euphorbiae (Thomas, 1878) (Homoptera: Aphididae). Biosci. J. 21, 33–39 (2005).
    Google Scholar 
    de Moraes, G. J., McMurtry, J. A. & Baker, E. W. Redescription and distribution of the spider mites Tetranychus evansi and T. marianae. Acarologia 28, 333–343 (1987).
    Google Scholar 
    Iperti, G. Biodiversity of predaceous coccinellidae in relation to bioindication and economic importance. Agric. Ecosyst. Environ. 74, 323–342 (1999).Article 

    Google Scholar 
    Cruz-Rivera, E. & Hay, M. E. Prey nutritional quality interacts with chemical defenses to affect consumer feeding and fitness. Ecol. Monogr. 73, 483–506 (2003).Article 

    Google Scholar 
    Munyaneza, J. & Obrycki, J. J. Development of three populations of Coleomegilla maculata (Coleoptera: Coccinellidae) feeding on eggs of colorado potato beetle (Coleoptera: Chrysomelidae). Environ. Entomol. 27, 117–122 (1998).Article 

    Google Scholar 
    Adams, T. S. Effect of diet and mating status on ovarian development in a predaceous stink bug Perillus bioculatus (Hemiptera: Pentatomidae). Ann. Entomol. Soc. Am. 93, 529–535 (2000).Article 

    Google Scholar 
    Lima, M. S., Pontes, W. J. T. & Nóbrega, R. L. Pollen did not provide suitable nutrients for ovary development in a ladybird Brumoides foudrasii (Coleoptera: Coccinellidae). Diversitas J. 5, 1486–1494 (2020).Article 

    Google Scholar 
    Melville, C. C. et al. Peanut cultivars display susceptibility by triggering outbreaks of Tetranychus ogmophallos (Acari: Tetranychidae). Exp. Appl. Acarol. 78, 295–314 (2019).PubMed 
    Article 

    Google Scholar  More

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    Ethical microbiome research with Indigenous communities

    Lewis, C., Obregon-Tito, A., Tito, R., Foster, M. & Spicer, P. The Human Microbiome Project: lessons from human genomics. Trends Microbiol. 20, 1–4 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    Claw, K. et al. A framework for enhancing ethical genomic research with Indigenous communities. Nat. Commun. 9, 2957 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Reardon, J. Race to the Finish (Princeton University Press, 2009).Tsosie, K., Yracheta, J., Kolopenuk, J. & Smith, R. Indigenous data sovereignties and data sharing in biological anthropology. Am. J. Phys. Anthropol. 174, 183–186 (2021).PubMed 
    Article 

    Google Scholar 
    Tsosie, K., Yracheta, J., Kolopenuk, J. & Geary, J. We have ‘gifted’ enough: Indigenous genomic data sovereignty in precision medicine. Am. J. Bioeth. 21, 72–75 (2021).PubMed 
    Article 

    Google Scholar 
    Haring, R. C. et al. Empowering equitable data use partnerships and Indigenous data sovereignties mid pandemic genomics. Front. Public Health 9, 742467 (2021).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Asad, T. in The Politics of Anthropology: from Colonialism and Sexism Toward a View from Below (eds Huizer, G. & Mannheim, B.) 85–96 (Ithica Press, 1973).Deloria, V. Custer Died for Your Sins: an Indian Manifesto (University of Oklahoma Press, 1969).Hymes, D. Reinventing Anthropology (Pantheon, 1974).Trouillot, M. R. Global Transformations (Palgrave Macmillan, 2003).Broesch, T. et al. Navigating cross-cultural research: methodological and ethical considerations. Proc. R. Soc. B https://doi.org/10.1098/rspb.2020.1245 (2020).Urassa, M., Lawson, D., Wamoyi, J., Gurmu, E. & Gibson, M. Cross-cultural research must prioritize equitable collaboration. Nat. Hum. Behav. 5, 668–671 (2021).PubMed 
    Article 

    Google Scholar 
    Blanchard, J. et al. Power sharing, capacity building, and evolving roles in ELSI: The Center for the Ethics of Indigenous Genomic Research. Collaborations 3, 18 (2020).PubMed 

    Google Scholar 
    Hudson, M. et al. in Ethics in Indigenous Research, Past Experiences—Future Challenges (Vaartoe Centre for Sami Research, 2016).Schroeder, D., Chatfield, K., Singh, M., Chennells, R. & Herissone-Kelly, P. Equitable Research Partnerships: a Global Code of Conduct to Counter Ethics Dumping (Springer Nature, 2019); https://doi.org/10.1007/978-3-030-15745-6Jobson, R. The case for letting anthropology burn: sociocultural anthropology in 2019. Am. Anthropologist 122, 259–271 (2020).Article 

    Google Scholar 
    Kowal, E. Orphan DNA: Indigenous samples, ethical biovalue and postcolonial science. Soc. Stud. Sci. 43, 577–597 (2013).Article 

    Google Scholar 
    Smith, L. T. Decolonizing Methodologies: Research and Indigenous Peoples (Bloomsbury Publishing, 2021).Viswanathan, M. et al. Community‐Based Participatory Research: Assessing the Evidence: Summary (AHRQ, 2004).Caniglia, G. et al. A pluralistic and integrated approach to action-oriented knowledge for sustainability. Nat. Sustain. 4, 93–100 (2021).Article 

    Google Scholar 
    Coombes, B., Johnson, J. & Howitt, R. Indigenous geographies III: methodological innovation and the unsettling of participatory research. Prog. Hum. Geogr. 38, 845–854 (2014).Article 

    Google Scholar 
    Sharp, R. & Foster, M. Community involvement in the ethical review of genetic research: lessons from American Indian and Alaska Native populations. Environ. Health Perspect. 110, 145–148 (2002).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Wallerstein, N. & Duran, B. in Community-Based Participatory Research for Health: Advancing Social and Health Equity (eds Wallerstein, N., Duran, B., Oetzel, J. G. & Minkler, M.) 17–29 (John Wiley and Sons, 2017).Obregon-Tito, A. et al. Subsistence strategies in traditional societies distinguish gut microbiomes. Nat. Commun. 6, 6505 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    Mandava, A., Pace, C., Campbell, B., Emanuel, E. & Grady, C. The quality of informed consent: mapping the landscape. A review of empirical data from developing and developed countries. J. Med. Ethics 38, 356–365 (2012).PubMed 
    Article 

    Google Scholar 
    Afolabi, M. O. et al. Informed consent comprehension in African research settings. Tropical Med. Int. Health 19, 625–642 (2014).Article 

    Google Scholar 
    Edwards, T., Cadigan, R., Evans, J. & Henderson, G. Biobanks containing clinical specimens: defining characteristics, policies, and practices. Clin. Biochem. 47, 245–251 (2014).PubMed 
    Article 

    Google Scholar 
    Grady, C. et al. Broad consent for research with biological samples: workshop conclusions. Am. J. Bioeth. 15, 34–42 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Lewis, C., McCall, L.-I., Sharp, R. & Spicer, P. Ethical priority of the most actionable system of biomolecules: the metabolome. Am. J. Phys. Anthropol. 171, 177–181 (2020).PubMed 
    Article 

    Google Scholar 
    McCarty, C., Chapman-Stone, D., Derfus, T., Giampietro, P. & Fost, N. Community consultation and communication for a population-based DNA biobank: the Marshfield clinic personalized medicine research project. Am. J. Med. Genet. A 146A, 3026–3033 (2008).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Tsosie, K. S., Yracheta, J. M. & Dickenson, D. Overvaluing individual consent ignores risks to tribal participants. Nat. Rev. Genet. 20, 497–498 (2019).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Chavis, D. M., Stucky, P. E. & Wandersman, A. Returning basic research to the community: a relationship between scientist and citizen. Am. Psychologist 38, 424 (1983).Article 

    Google Scholar 
    Godoy, R., Reyes-García, V., Byron, E., Leonard, W. & Vadez, V. The effect of market economies on the well-being of indigenous peoples and on their use of renewable natural resources. Annu. Rev. Anthropol. 34, 121–138 (2005).Article 

    Google Scholar 
    Reardon, J. & TallBear, K. ‘Your DNA is our history’ genomics, anthropology, and the construction of whiteness as property. Curr. Anthropol. 53, 233–245 (2012).Article 

    Google Scholar 
    Schnorr, S. et al. Gut microbiome of the Hadza hunter-gatherers. Nat. Commun. 5, 3654 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    O’Doherty, K. C. et al. Opinion: conservation and stewardship of the human microbiome. Proc. Natl Acad. Sci. USA 111, 14312–14313 (2014).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Dubois, G., Girard, C., Lapointe, F.-J. & Shapiro, J. The Inuit gut microbiome is dynamic over time and shaped by traditional foods. Microbiome 5, 151 (2017).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Sprockett, D. et al. Microbiota assembly, structure, and dynamics among Tsimane horticulturalists of the Bolivian Amazon. Nat. Commun. 11, 3772 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Stagaman, K. et al. Market integration predicts human gut microbiome attributes across a gradient of economic development. MSystems 3, 00122-17 (2018).Article 

    Google Scholar 
    Conteville, L. C., Oliveira-Ferreira, J. & Vicente, A. C. P. Gut microbiome biomarkers and functional diversity within an Amazonian semi-nomadic hunter-gatherer group. Front. Microbiol. 30, 1743 (2019).Article 

    Google Scholar 
    Fischer, M. In the science zone: the Yanomami and the fight for representation. Anthropol. Today 17, 9–14 (2001).Article 

    Google Scholar 
    Goncalves Martin, J. Opening a path with papers: Yanomami health agents and their use of medical documents. J. Lat. Am. Caribb. Anthropol. 21, 434–456 (2016).Article 

    Google Scholar 
    Redford, K. & Maclean Stearman, A. Forest-dwelling native Amazonians and the conservation of biodiversity: interests in common or in collision? Conserv. Biol. 7, 248–255 (1993).Article 

    Google Scholar 
    Vega, C., Orellana, J., Oliveira, M., Hacon, S. & Basta, P. Human mercury exposure in Yanomami indigenous villages from the Brazilian Amazon. Int. J. Environ. Res. Public Health 15, 1051 (2018).PubMed Central 
    Article 
    CAS 

    Google Scholar 
    Clemente, J. et al. The microbiome of uncontacted Amerindians. Sci. Adv. 1, 1500183 (2015).Article 
    CAS 

    Google Scholar 
    Gibbons, A. Hadza on the brink. Science 360, 700–704 (2018).CAS 
    PubMed 
    Article 

    Google Scholar 
    Pollom, T. R., Herlosky, K. N., Mabulla, I. A. & Crittenden, A. N. Changes in juvenile foraging behavior among the Hadza of Tanzania during early transition to a mixed-subsistence economy. Hum. Nat. 31, 123–140 (2020).PubMed 
    Article 

    Google Scholar 
    Crittenden, A. N. et al. Harm avoidance and mobility during middle childhood and adolescence among Hadza foragers. Hum. Nat. 32, 150–176 (2021).PubMed 
    Article 

    Google Scholar 
    Wynberg, R. & Chennells, R. in Indigenous Peoples, Consent and Benefit Sharing (eds Wynberg, R., Schroeder, D. & Chennells, R.) 89–124 (Springer, 2009); https://doi.org/10.1007/978-90-481-3123-5_6Rubel, M. et al. Lifestyle and the presence of helminths is associated with gut microbiome composition in Cameroonians. Genome Biol. 21, 122 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Sankaranarayanan, K. et al. Gut microbiome diversity among Cheyenne and Arapaho individuals from Western Oklahoma. Curr. Biol. 25, 3161–3169 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    Rogers, G. B., Ward, J., Brown, A. & Wesselingh, S. L. Inclusivity and equity in human microbiome research. Lancet 39, 728–729 (2019).Article 

    Google Scholar 
    Ambler, J. et al. Including digital sequence data in the Nagoya Protocol can promote data sharing. Trends Biotechnol. 39, 116–125 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Morgera, E. The need for an international legal concept of fair and equitable benefit sharing. Eur. J. Int. Law 27, 353–383 (2016).Article 

    Google Scholar 
    Bissell, W. Engaging colonial nostalgia. Cultural Anthropol. 20, 215–248 (2005).Article 

    Google Scholar 
    Crittenden, A. in The Secret Lives of Anthropologists (ed. Hewlett, B. L.) 299–321 (Routledge, 2019).Redford, K. H. The ecologically noble savage. Cultural Survival Q 15, 46–48 (1991).
    Google Scholar 
    Carmody, R. N., Sarkar, A. & Reese, A. T. Gut microbiota through an evolutionary lens. Science 372, 462–463 (2021).CAS 
    PubMed 
    Article 

    Google Scholar 
    Belmont Report: Ethical Principles and Guidelines for the Protection of Human Subjects of Research (National Commission for the Protection of Human Subjects of Biomedical and Behavioral Research, 1979).McClain, V. W. Patents on life: a brief view of human milk component patenting. World Nutr. 9, 57–69 (2018).Article 

    Google Scholar 
    Hill, J. H. ‘Expert rhetorics’ in advocacy for endangered languages: who is listening, and what do they hear? J. Linguistic Anthropol. 12, 119–133 (2002).Article 

    Google Scholar  More