Philippa Kaur

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

Bowman, D. M. J. S. et al. Vegetation fires in the Anthropocene. Nat. Rev. Earth Environ. 1, 500–515 (2020).ADS 
Article 

Google Scholar 
Andela, N. et al. A human-driven decline in global burned area. Science 356, 1356–1362 (2017).ADS 
CAS 
Article 

Google Scholar 
United Nations Environment Programme. Spreading like Wildfire–The Rising Threat of Extraordinary Landscape Fires. A UNEP Rapid Response Assessment. (United Nations Environment Programme, Nairobi, 2022).Williams, A. P. et al. Observed impacts of anthropogenic climate change on wildfire in California. Earth’s Future 7, 892–910 (2019).ADS 
Article 

Google Scholar 
Abatzoglou, J. T. & Williams, A. P. Impact of anthropogenic climate change on wildfire across western US forests. Proc. Natl Acad. Sci. USA 113, 11770–11775 (2016).ADS 
CAS 
Article 

Google Scholar 
Dennison, P. E., Brewer, S. C., Arnold, J. D. & Moritz, M. A. Large wildfire trends in the western United States, 1984–2011. Geophys. Res. Lett. 41, 2928–2933 (2014).ADS 
Article 

Google Scholar 
Westerling, A. L. Increasing western US forest wildfire activity: sensitivity to changes in the timing of spring. Philos. Trans. R. Soc. B: Biol. Sci. 371, 20150178 (2016).Article 

Google Scholar 
Pyne, S. J. Fire in America: A Cultural History of Wildland and Rural Fire. (University of Washington Press, 2017).Fire and Resource Assessment Program. Fire Perimeters. Available: https://frap.fire.ca.gov/frap-projects/fire-perimeters/. (California Department of Forestry & Fire Protection, 2018).Westerling, A. L., Hidalgo, H. G., Cayan, D. R. & Swetnam, T. W. Warming and earlier spring increase Western U.S. forest wildfire activity. Science 313, 940–943 (2006).ADS 
CAS 
Article 

Google Scholar 
Starrs, C. F., Butsic, V., Stephens, C. & Stewart, W. The impact of land ownership, firefighting, and reserve status on fire probability in California. Environ. Res. Lett. 13, 034025 (2018).ADS 
Article 

Google Scholar 
Lydersen, J. M. et al. Evidence of fuels management and fire weather influencing fire severity in an extreme fire event. Ecol. Appl. 27, 2013–2030 (2017).Article 

Google Scholar 
Parsons, D. J. & DeBenedetti, S. H. Impact of fire suppression on a mixed-conifer forest. For. Ecol. Manag. 2, 21–33 (1979).Article 

Google Scholar 
Vose, R., Easterling, D. R., Kunkel, K. & Wehner, M. Temperature Changes in the United States. (NASA, 2017).Balch, J. K. et al. Human-started wildfires expand the fire niche across the United States. Proc. Natl Acad. Sci. USA 114, 2946–2951 (2017).ADS 
CAS 
Article 

Google Scholar 
Stephens, S. L., Martin, R. E. & Clinton, N. E. Prehistoric fire area and emissions from California’s forests, woodlands, shrublands, and grasslands. For. Ecol. Manag. 251, 205–216 (2007).Article 

Google Scholar 
Sugihara, N. G., Van Wagtendonk, J. W., Fites-Kaufman, J., Shaffer, K. E. & Thode, A. E. Fire in California’s Ecosystems. (University of California Press, 2006).Jin, Y. et al. Identification of two distinct fire regimes in Southern California: implications for economic impact and future change. Environ. Res. Lett. 10, 094005 (2015).ADS 
Article 

Google Scholar 
Trollope, W. in Ecological Effects of Fire In South African Ecosystems. 199–217 (Springer, 1984).Byram, G. M. in Forest Fire: Control and Use (ed. Davis, K. P.) 155–182 (McGraw-Hill, 1959).McLauchlan, K. K. et al. Fire as a fundamental ecological process: Research advances and frontiers. J. Ecol. https://doi.org/10.1111/1365-2745.13403 (2020).Brando, P. M. et al. Abrupt increases in Amazonian tree mortality due to drought–fire interactions. Proc. Natl Acad. Sci. USA 111, 6347–6352 (2014).ADS 
CAS 
Article 

Google Scholar 
Schroeder, W., Oliva, P., Giglio, L. & Csiszar, I. A. The New VIIRS 375m active fire detection data product: Algorithm description and initial assessment. Remote Sens. Environ. 143, 85–96 (2014).ADS 
Article 

Google Scholar 
Rothermel, R. C. A Mathematical Model for Predicting Fire Spread in Wildland Fuels (USFS, 1972).Hood, S. M., Varner, J. M., van Mantgem, P. & Cansler, C. A. Fire and tree death: understanding and improving modeling of fire-induced tree mortality. Environ. Res. Lett. 13, 113004 (2018).ADS 
Article 

Google Scholar 
Cattau, M. E., Wessman, C., Mahood, A., Balch, J. K. & Poulter, B. Anthropogenic and lightning‐started fires are becoming larger and more frequent over a longer season length in the USA. Glob. Ecol. Biogeogr. 29, 668–681 (2020).Article 

Google Scholar 
Abatzoglou, J. T., Balch, J. K., Bradley, B. A. & Kolden, C. A. Human-related ignitions concurrent with high winds promote large wildfires across the USA. Int. J. Wildland Fire 27, 377–386 (2018).Article 

Google Scholar 
Fried, J. S. et al. Predicting the effect of climate change on wildfire behavior and initial attack success. Clim. Change 87, 251–264 (2008).Article 

Google Scholar 
van Wagtendonk, J. W. The history and evolution of wildland fire use. Fire Ecol. 3, 3–17 (2007).Article 

Google Scholar 
Sullivan, A. L. Wildland surface fire spread modelling, 1990–2007. 2: Empirical and quasi-empirical models. Int. J. Wildland Fire 18, 369–386 (2009).Article 

Google Scholar 
Wang, X. et al. Projected changes in fire size from daily spread potential in Canada over the 21st century. Environ. Res. Lett. 15, 104048 (2020).ADS 
Article 

Google Scholar 
Parks, S. A. et al. High-severity fire: evaluating its key drivers and mapping its probability across western US forests. Environ. Res. Lett. 13, 044037 (2018).ADS 
Article 

Google Scholar 
Hantson, S. et al. The status and challenge of global fire modelling. Biogeosciences 13, 3359–3375 (2016).ADS 
Article 

Google Scholar 
Reinhardt, E. D. First Order Fire Effects Model: FOFEM 4.0, User’s Guide. (Intermountain Forest and Range Experiment Station, Forest Service, US …, 1997).Jolly, W. M. et al. Climate-induced variations in global wildfire danger from 1979 to 2013. Nat. Commun. 6, 1–11 (2015).CAS 
Article 

Google Scholar 
Pateiro-Lopez, B. & Rodriguez-Casal, A. alphahull: Generalization of the Convex Hull of a Sample of Points in the Plane v. R package version 2.2 (2019).Edelsbrunner, H., Kirkpatrick, D. & Seidel, R. On the shape of a set of points in the plane. IEEE Trans. Inf. theory 29, 551–559 (1983).MathSciNet 
Article 

Google Scholar 
Rodríguez Casal, A. & Pateiro López, B. Generalizing the Convex Hull of A Sample: the R Package alphahull. (2010).Bell, D. M. et al. Multiscale divergence between Landsat-and lidar-based biomass mapping is related to regional variation in canopy cover and composition. Carbon Balance Manag. 13, 15 (2018).Article 

Google Scholar 
Abatzoglou, J. T. Development of gridded surface meteorological data for ecological applications and modelling. Int. J. Climatol. 33, 121–131 (2013).Article 

Google Scholar 
MTBS. Monitoring Trends in Burn Severity Data Access: Fire Level Geospatial Data. (MTBS). (2018).Miller, J. D. et al. Calibration and validation of the relative differenced Normalized Burn Ratio (RdNBR) to three measures of fire severity in the Sierra Nevada and Klamath Mountains, California, USA. Remote Sens. Environ. 113, 645–656 (2009).ADS 
Article 

Google Scholar 
Homer, C. et al. Completion of the 2011 National Land Cover Database for the conterminous United States–representing a decade of land cover change information. Photogrammetric Eng. Remote Sens. 81, 345–354 (2015).
Google Scholar  More

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

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

Synthesis and data extractionData were collected using a literature search of Web of Science for peer-reviewed journal articles published between 1970 and November 2019. We conducted two sets of searches to capture grazing with discrete comparisons (e.g., grazed/ungrazed, moderate vs. heavy intensity grazing) and a range of grazing intensities. The search terms used for each were as follows 1) (graz* OR livestock) AND (exclosure* OR exclusion OR exclude* OR ungrazed OR retire* OR fallow* OR fence* OR paddock*), 2) (“grazing intensity” OR “grazing gradient” OR “stocking rate” OR “rotation*grazing”). Our synthesis includes domesticated and wild grazer species, with the latter defined as an undomesticated species naturally occurring in the study area during the study. Wild grazers are typically native species to the region (e.g., the American bison in Western North America) but can include non-native species that are naturalized in the area (e.g., feral horses on Sable Island).We excluded any study that did not test the effect of grazing animals. A grazer was defined using the definition provided by the authors of the respective study to account for the proportion of forage types in a herbivore’s diet that varies between seasons and habitats. For example, we included animals where their diet is assumed to come from all (e.g., cattle, sheep), most (e.g., wapiti, kangaroos), or some (e.g., deer species) grass species. However, within the included studies, these animals were classified as grazers as most of their diet was grass for the duration of the study. For added clarity about the herbivore composition in each study, we extracted a list of any herbivores listed in the paper regardless of foraging type or if any data was provided.We only included studies that measured animal diversity or abundance as a response variable and included data we could extract or contact the author to obtain9. We included any study with a grazing treatment and included observations within these studies of any grazed and ungrazed sites. All studies with grazing included a comparison to either ungrazed sites, different grazing practices (e.g., cattle vs. sheep), and/or differences in intensities (e.g., heavy/light, extensive/intensive). Studies that only measured plants or soil biota were excluded because syntheses of grazing effects on these groups have already been conducted7,11,12, and our goal was to provide a robust inventory of animal diversity. However, if a study included plants, lichens, or fungi in addition to animals, we included this data. Studies discussing marine grazing or aquatic systems were also excluded. From these preliminary filters, we identified 3,489 published manuscripts. We reviewed these 3,489 published articles and found 245 studies that surveyed animals in grazed sites. In total, we extracted 16,105 observations for over 1,200 species.We extracted 28 variables that focus on management systems, assemblages of grazer species, ecosystem characteristics, and survey type (Table 1). The latitudes, longitudes, and elevations of each study were included when provided for use with geospatial data. In addition, we included variables about the study site’s disturbance history, including last time grazed, if a flood event or fire had occurred, if fertilization was used, if the area was open or fenced off, and if the area was publicly or privately owned. Furthermore, the timeline for the study (i.e., the years the authors initiated and completed the study) was also provided. Study initiation was described by the authors and could include when the grazing treatment started, another treatment was applied, and/or animal surveys began. These timeline columns can be useful in identifying long-term studies and differentiating single grazing events or multi-year experiments. Finally, we generalized the characteristics of the ecosystem of the sites used in each study based on the climate and dominant vegetation.Table 1 The attributes and description of the metadata.csv file that lists the general characteristics of each study.Full size tableWithin the grazing data, we included information about the grazer when provided, including any measurement of the intensity of grazing (e.g., animals per hectare, the height of residual vegetation). We also provided two columns that detailed whether the study tested grazing effects using a discrete comparison or gradient of intensities (Table 2). The value for the target specimens extracted may represent either a single observation or a summarized statistic (e.g., mean animals per site). We identify unique observations as “count” and summarized statistics by the metric used, such as mean, median, standard deviation (column stat in grazingData.csv). When possible, we also included any record of other grazers that co-occurred with the observed grazer species. The data for these variables were extracted from the papers by a single researcher who read through each paper and filled in available data on the mentioned variables.Table 2 The attributes and description of the grazingData.csv file that has the extracted data from each study.Full size tableWe extracted information about the target specimen, site, year, experimental replicate, and response estimate (Table 2). We included multiple categorizations of the target species to assist future users in synthesizing similar taxa (Table 2). When a species name or genus was provided, we conducted a search query (see detailedTaxa.r) through the global biodiversity information facility (GBIF.org) to determine the taxonomic classification of the species, including kingdom, phylum, order, class, and family. When a species name was not included, we provided the lowest taxonomic resolution available. We also included a broader classification of ‘higherTaxon’ to distinguish plants, fungi, vertebrates, and invertebrates. These columns may help group similar species together for community-level analyses. Lastly, we included the characteristic of the plant community (i.e., planted or self-assembled, tilled, and its vegetation class) when plant data was reported.Patterns among studiesMost of the studies took place in the United States (26%), Australia (9%), and the United Kingdom (7%) (Fig. 1). As expected, most studies were conducted in grasslands (n = 206), followed by forests (n = 92) and shrublands (n = 82) (Fig. 2). We included publications from the entire range of years (i.e., 1970–2019), but most were published after 2000 (76%). The number of sites in a study and the study duration showed a bimodal distribution with a long tail (Fig. 3). Most studies included one to eight different sites, and few were conducted longer than five years (Fig. 3). A few studies were highly replicated, while many were limited in their replication (Fig. 3).Fig. 1The locations of studies that measured the response of animals to domestic or wild grazing.Full size imageFig. 2The number of grazing studies conducted in ecosystems around the world. We generalized the characteristics of the ecosystem of the sites used in each study based on the climate and dominant vegetation community. We separated grassland communities into those that were (a) semi-natural without recent cultivation or seeding (self-assembled), (b) recently cultivated or had supplemental seeding (planted/cultivated), and (c) a combination of both. In most grasslands, the cultivation history was unclear.Full size imageFig. 3The number of independent sites surveyed and the duration of each study. Most studies were conducted at either a single site or with some replication (e.g., 6–8 sites). Similarly, most studies were either conducted in one year ( >30%) or over a few years (e.g., 3–6 years). Very few studies (32) or lasted longer than 15 years.Full size imageSite and management data were not reported in all studies, as found in other reviews of grazing impacts on ecological processes10. Of the studies that mentioned the ownership status of the land used, 46% were on private land, 42% were on public land, and 12% had a history of both public and private ownership. Most studies included binary comparisons (56%) of grazed vs. ungrazed plots or sites, though some also included a discrete (22%) or a continuous estimate of grazing intensities (18%).Of the studies that reported plant community origin, 76% were self-assembled, 17% were planted communities, and the remaining included sites were a combination of the two. Domesticated grazers as the focal herbivore made up 67% of the studies, with 12% of the studies having wild grazers as the focal herbivore, and 21% having both present. Domesticated livestock were the most frequently surveyed grazers including cattle (n = 164), sheep (n = 83), and horses (n = 21), but studies are included that examined wild grazers, such as kangaroos (n = 6), elephants (n = 5), and pronghorn (n = 5) (Fig. 4).Fig. 4The frequency in which a study reported herbivores. We included any mention of herbivores regardless of being a grazer, browser, granivore, or other class. This list was obtained by the text within the manuscript and is different than the representation of species in the database (i.e., the measured species).Full size image More

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

Google Scholar 
Shi, Z. Gut microbiota: an important link between Western diet and chronic diseases. Nutrients 11, 2287 (2019).CAS 
PubMed Central 
Article 

Google Scholar 
Lynch, S. V. & Pedersen, O. The human intestinal microbiome in health and disease. N. Engl. J. Med. 375, 2369–2379 (2016).CAS 
PubMed 
Article 

Google Scholar 
Glowacki, R. W. P. & Martens, E. C. In sickness and health: effects of gut microbial metabolites on human physiology. PLoS Pathog. 16, e1008370 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Nazaries, L. et al. Evidence of microbial regulation of biogeochemical cycles from a study on methane flux and land use change. Appl. Environ. Microbiol. 79, 4031–4040 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Konopka, A. What is microbial community ecology? ISME J. 3, 1223–1230 (2009).PubMed 
Article 

Google Scholar 
Flemming, H.-C. et al. Biofilms: an emergent form of bacterial life. Nat. Rev. Microbiol. 14, 563–575 (2016).CAS 
PubMed 
Article 

Google Scholar 
Bauer, E., Zimmermann, J., Baldini, F., Thiele, I. & Kaleta, C. BacArena: individual-based metabolic modeling of heterogeneous microbes in complex communities. PLoS Comput. Biol. 13, e1005544 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
van Hoek, M. J. A. & Merks, R. M. H. Emergence of microbial diversity due to cross-feeding interactions in a spatial model of gut microbial metabolism. BMC Syst. Biol. 11, 56 (2017).Article 
CAS 

Google Scholar 
Gorter, F. A., Manhart, M. & Ackermann, M. Understanding the evolution of interspecies interactions in microbial communities. Philos. Trans. R. Soc. Lond. B Biol. Sci. 375, 20190256 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Wintermute, E. H. & Silver, P. A. Emergent cooperation in microbial metabolism. Mol. Syst. Biol. 6, 407 (2010).PubMed 
PubMed Central 
Article 

Google Scholar 
Chen, J., Yoshinaga, M. & Rosen, B. P. The antibiotic action of methylarsenite is an emergent property of microbial communities. Mol. Microbiol. 111, 487–494 (2019).CAS 
PubMed 
Article 

Google Scholar 
Konstantinidis, D. et al. Adaptive laboratory evolution of microbial co-cultures for improved metabolite secretion. Mol. Syst. Biol. 17, e10189 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Park, H. et al. Artificial consortium demonstrates emergent properties of enhanced cellulosic-sugar degradation and biofuel synthesis. NPJ Biofilms Microbiomes 6, 59 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Schwartzman, J. A. et al. Bacterial growth in multicellular aggregates leads to the emergence of complex lifecycles. Preprint at bioRxiv https://doi.org/10.1101/2021.11.01.466752 (2021).Levins, R. & Lewontin, R. The Dialectical Biologist (Harvard Univ. Press, 1985).Diaz, P. I. & Valm, A. M. Microbial interactions in oral communities mediate emergent biofilm properties. J. Dent. Res. 99, 18–25 (2020).CAS 
PubMed 
Article 

Google Scholar 
Buerger, A. N. et al. Gastrointestinal dysbiosis following diethylhexyl phthalate exposure in zebrafish (Danio rerio): altered microbial diversity, functionality, and network connectivity. Environ. Pollut. 265, 114496 (2020).CAS 
PubMed 
Article 

Google Scholar 
Kim, M. K., Ingremeau, F., Zhao, A., Bassler, B. L. & Stone, H. A. Local and global consequences of flow on bacterial quorum sensing. Nat. Microbiol. 1, 15005 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ebrahimi, A. & Or, D. Hydration and diffusion processes shape microbial community organization and function in model soil aggregates. Water Resour. Res. 51, 9804–9827 (2015).Article 

Google Scholar 
Falconer, R. E. et al. Microscale heterogeneity explains experimental variability and non-linearity in soil organic matter mineralisation. PLoS ONE 10, e0123774 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Fredrickson, J. K. Ecological communities by design. Science 348, 1425–1427 (2015).CAS 
PubMed 
Article 

Google Scholar 
Singer, E. et al. Next generation sequencing data of a defined microbial mock community. Sci. Data 3, 160081 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Marino, S., Baxter, N. T., Huffnagle, G. B., Petrosino, J. F. & Schloss, P. D. Mathematical modeling of primary succession of murine intestinal microbiota. Proc. Natl Acad. Sci. USA 111, 439–444 (2014).CAS 
PubMed 
Article 

Google Scholar 
Cariboni, J., Gatelli, D., Liska, R. & Saltelli, A. The role of sensitivity analysis in ecological modelling. Ecol. Modell. 203, 167–182 (2007).Article 

Google Scholar 
Oreskes, N., Shrader-Frechette, K. & Belitz, K. Verification, validation, and confirmation of numerical models in the Earth sciences. Science 263, 641–646 (1994).CAS 
PubMed 
Article 

Google Scholar 
Machado, D., Andrejev, S., Tramontano, M. & Patil, K. R. Fast automated reconstruction of genome-scale metabolic models for microbial species and communities. Nucleic Acids Res. 46, 7542–7553 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Buffie, C. G. et al. Precision microbiome reconstitution restores bile acid mediated resistance to Clostridium difficile. Nature 517, 205–208 (2015).CAS 
PubMed 
Article 

Google Scholar 
Hammarlund, S. P., Chacón, J. M. & Harcombe, W. R. A shared limiting resource leads to competitive exclusion in a cross-feeding system. Environ. Microbiol. 21, 759–771 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Coyte, K. Z., Schluter, J. & Foster, K. R. The ecology of the microbiome: networks, competition, and stability. Science 350, 663–666 (2015).CAS 
PubMed 
Article 

Google Scholar 
Machado, D. et al. Polarization of microbial communities between competitive and cooperative metabolism. Nat. Ecol. Evol. 5, 195–203 (2021).PubMed 
PubMed Central 
Article 

Google Scholar 
Gu, C., Kim, G. B., Kim, W. J., Kim, H. U. & Lee, S. Y. Current status and applications of genome-scale metabolic models. Genome Biol. 20, 121 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
O’Brien, E. J., Monk, J. M. & Palsson, B. O. Using genome-scale models to predict biological capabilities. Cell 161, 971–987 (2015).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Fang, X., Lloyd, C. J. & Palsson, B. O. Reconstructing organisms in silico: genome-scale models and their emerging applications. Nat. Rev. Microbiol. 18, 731–743 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Colarusso, A. V., Goodchild-Michelman, I., Rayle, M. & Zomorrodi, A. R. Computational modeling of metabolism in microbial communities on a genome-scale. Curr. Opin. Syst. Biol. 26, 46–57 (2021).Article 

Google Scholar 
García-Jiménez, B., Torres-Bacete, J. & Nogales, J. Metabolic modelling approaches for describing and engineering microbial communities. Comput. Struct. Biotechnol. J. 19, 226–246 (2020).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Frioux, C., Singh, D., Korcsmaros, T. & Hildebrand, F. From bag-of-genes to bag-of-genomes: metabolic modelling of communities in the era of metagenome-assembled genomes. Comput. Struct. Biotechnol. J. 18, 1722–1734 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Chaffron, S., Rehrauer, H., Pernthaler, J. & von Mering, C. A global network of coexisting microbes from environmental and whole-genome sequence data. Genome Res. 20, 947–959 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Faust, K. & Raes, J. Microbial interactions: from networks to models. Nat. Rev. Microbiol. 10, 538–550 (2012).CAS 
PubMed 
Article 

Google Scholar 
Li, J. et al. Distinct mechanisms shape soil bacterial and fungal co-occurrence networks in a mountain ecosystem. FEMS Microbiol. Ecol. 96, fiaa030 (2020).CAS 
PubMed 
Article 

Google Scholar 
Berry, D. & Widder, S. Deciphering microbial interactions and detecting keystone species with co-occurrence networks. Front. Microbiol. 5, 219 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Stein, R. R. et al. Ecological modeling from time-series inference: insight into dynamics and stability of intestinal microbiota. PLoS Comput. Biol. 9, e1003388 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Barbier, M., Arnoldi, J.-F., Bunin, G. & Loreau, M. Generic assembly patterns in complex ecological communities. Proc. Natl Acad. Sci. USA 115, 2156–2161 (2018).Gralka, M., Szabo, R., Stocker, R. & Cordero, O. X. Trophic interactions and the drivers of microbial community assembly. Curr. Biol. 30, R1176–R1188 (2020).CAS 
PubMed 
Article 

Google Scholar 
Madeo, D., Comolli, L. R. & Mocenni, C. Emergence of microbial networks as response to hostile environments. Front. Microbiol. 5, 407 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Ratzke, C. & Gore, J. Modifying and reacting to the environmental pH can drive bacterial interactions. PLoS Biol. 16, e2004248 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Wang, B. & Allison, S. D. Emergent properties of organic matter decomposition by soil enzymes. Soil Biol. Biochem. 136, 107522 (2019).CAS 
Article 

Google Scholar 
Walsh, A. M. et al. Microbial succession and flavor production in the fermented dairy beverage kefir. mSystems 1, e00052-16 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Oliveira, N. M., Niehus, R. & Foster, K. R. Evolutionary limits to cooperation in microbial communities. Proc. Natl Acad. Sci. USA 111, 17941–17946 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Leigh, E. R. in Some Mathematical Problems in Biology (ed. Gerstenhaber, M.) 1–61 (American Mathematical Society, 1968).Nedorezov, L. The dynamics of the lynx–hare system: an application of the Lotka–Volterra model. Biophys. 61, 149–154 (2016).CAS 
Article 

Google Scholar 
Mühlbauer, L. K., Schulze, M., Harpole, W. S. & Clark, A. T. gauseR: simple methods for fitting Lotka–Volterra models describing Gause’s “struggle for existence”. Ecol. Evol. 10, 13275–13283 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Belovsky, G. E. Moose and snowshoe hare competition and a mechanistic explanation from foraging theory. Oecologia 61, 150–159 (1984).CAS 
PubMed 
Article 

Google Scholar 
May, R. M. Limit cycles in predator–prey communities. Science 177, 900–902 (1972).CAS 
PubMed 
Article 

Google Scholar 
Friedman, J., Higgins, L. M. & Gore, J. Community structure follows simple assembly rules in microbial microcosms. Nat. Ecol. Evol. 1, 109 (2017).PubMed 
Article 

Google Scholar 
Voit, E. O., Davis, J. D. & Olivença, D. V. Inference and validation of the structure of Lotka–Volterra models. Preprint at bioXriv https://doi.org/10.1101/2021.08.14.456346 (2021).Bucci, V. & Xavier, J. B. Towards predictive models of the human gut microbiome. J. Mol. Biol. 426, 3907–3916 (2014).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Fisher, C. K. & Mehta, P. Identifying keystone species in the human gut microbiome from metagenomic timeseries using sparse linear regression. PLoS ONE 9, e102451 (2014).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Bucci, V. et al. MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses. Genome Biol. 17, 121 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Gao, X., Huynh, B.-T., Guillemot, D., Glaser, P. & Opatowski, L. Inference of significant microbial interactions from longitudinal metagenomics data. Front. Microbiol. 9, 2319 (2018).PubMed 
PubMed Central 
Article 

Google Scholar 
Li, C. et al. An expectation-maximization algorithm enables accurate ecological modeling using longitudinal microbiome sequencing data. Microbiome 7, 118 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Joseph, T. A., Shenhav, L., Xavier, J. B., Halperin, E. & Pe’er, I. Compositional Lotka–Volterra describes microbial dynamics in the simplex. PLoS Comput. Biol. 16, e1007917 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hosoda, S., Fukunaga, T. & Hamada, M. Umibato: estimation of time-varying microbial interaction using continuous-time regression hidden Markov model. Bioinformatics 37, i16–i24 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Remien, C. H., Eckwright, M. J. & Ridenhour, B. J. Structural identifiability of the generalized Lotka–Volterra model for microbiome studies. R. Soc. Open Sci. 8, 201378 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
White, J. R. Novel Methods for Metagenomic Analysis. PhD thesis, Univ. of Maryland (2010).Sousa, A., Frazão, N., Ramiro, R. S. & Gordo, I. Evolution of commensal bacteria in the intestinal tract of mice. Curr. Opin. Microbiol. 38, 114–121 (2017).PubMed 
Article 

Google Scholar 
Mounier, J. et al. Microbial interactions within a cheese microbial community. Appl. Environ. Microbiol. 74, 172–181 (2008).CAS 
PubMed 
Article 

Google Scholar 
Momeni, B., Xie, L. & Shou, W. Lotka–Volterra pairwise modeling fails to capture diverse pairwise microbial interactions. eLife 6, e25051 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Hoek, T. A. et al. Resource availability modulates the cooperative and competitive nature of a microbial cross-feeding mutualism. PLoS Biol. 14, e1002540 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Piccardi, P., Vessman, B. & Mitri, S. Toxicity drives facilitation between 4 bacterial species. Proc. Natl Acad. Sci. USA 116, 15979–15984 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mai, T. S. N. Impact of Metabolic Plasticity on Microbial Community Diversity and Stability. MSc thesis, Univ. of Groningen (2021).Sanchez-Gorostiaga, A., Bajić, D., Osborne, M. L., Poyatos, J. F. & Sanchez, A. High-order interactions distort the functional landscape of microbial consortia. PLoS Biol. 17, e3000550 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Mickalide, H. & Kuehn, S. Higher-order interaction between species inhibits bacterial invasion of a phototroph-predator microbial community. Cell Syst. 9, 521–533.e10 (2019).CAS 
PubMed 
Article 

Google Scholar 
Guo, X. & Boedicker, J. Q. The contribution of high-order metabolic interactions to the global activity of a four-species microbial community. PLoS Comput. Biol. 12, e1005079 (2016).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Meroz, N., Tovi, N., Sorokin, Y. & Friedman, J. Community composition of microbial microcosms follows simple assembly rules at evolutionary timescales. Nat. Commun. 12, 2891 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zomorrodi, A. R. & Segrè, D. Synthetic ecology of microbes: mathematical models and applications. J. Mol. Biol. 428, 837–861 (2016).CAS 
PubMed 
Article 

Google Scholar 
Song, H. S., Cannon, W. R., Beliaev, A. S. & Konopka, A. Mathematical modeling of microbial community dynamics: a methodological review. Processes 2, 711–752 (2014).Article 

Google Scholar 
Descheemaeker, L., Grilli, J. & de Buyl, S. Heavy-tailed abundance distributions from stochastic Lotka–Volterra models. Phys. Rev. E 104, 034404 (2021).CAS 
PubMed 
Article 

Google Scholar 
Bairey, E., Kelsic, E. D. & Kishony, R. High-order species interactions shape ecosystem diversity. Nat. Commun. 7, 12285 (2016).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ji, B., Herrgård, M. J. & Nielsen, J. Microbial community dynamics revisited. Nat. Comput. Sci. 1, 640–641 (2021).Article 

Google Scholar 
Abreu, C. I., Anderen Woltz, V. L., Friedman, J. & Gore, J. Microbial communities display alternative stable states in a fluctuating environment. PLoS Comput. Biol. 16, e1007934 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Xu, L., Xu, X., Kong, D., Gu, H. & Kenney T. Stochastic generalized Lotka–Volterra model with an application to learning microbial community structures. Preprint at arXiv https://doi.org/10.48550/arXiv.2009.10922 (2020).Brunner, J. D. & Chia, N. Metabolite-mediated modelling of microbial community dynamics captures emergent behaviour more effectively than species–species modelling. J. R. Soc. Interface 16, 20190423 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
MacArthur, R. Species packing and competitive equilibrium for many species. Theor. Popul. Biol. 1, 1–11 (1970).CAS 
PubMed 
Article 

Google Scholar 
Tilman, D. Resource competition and community structure. Monogr. Popul. Biol. 17, 1–296 (1982).CAS 
PubMed 

Google Scholar 
Chesson, P. MacArthur’s consumer-resource model. Theor. Popul. Biol. 37, 26–38 (1990).Article 

Google Scholar 
Goldford, J. E. et al. Emergent simplicity in microbial community assembly. Science 361, 469–474 (2018).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Marsland, R.3rd et al. Available energy fluxes drive a transition in the diversity, stability, and functional structure of microbial communities. PLoS Comput. Biol. 15, e1006793 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Marsland, R.3rd, Cui, W. & Mehta, P. A minimal model for microbial biodiversity can reproduce experimentally observed ecological patterns. Sci. Rep. 10, 3308 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Estrela, S., Sanchez-Gorostiaga, A., Vila, J. C. & Sanchez, A. Nutrient dominance governs the assembly of microbial communities in mixed nutrient environments. eLife 10, e65948 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Cui, W., Marsland, R. & Mehta, P. Diverse communities behave like typical random ecosystems. Phys. Rev. E 104, 034416 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Haygood, R. Coexistence in MacArthur-style consumer–resource models. Theor. Popul. Biol. 61, 215–223 (2002).PubMed 
Article 

Google Scholar 
Dubinkina, V., Fridman, Y., Pandey, P. P. & Maslov, S. Multistability and regime shifts in microbial communities explained by competition for essential nutrients. eLife 8, e49720 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Pacheco, A. R., Osborne, M. L. & Segrè, D. Non-additive microbial community responses to environmental complexity. Nat. Commun. 12, 2365 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zelezniak, A. et al. Metabolic dependencies drive species co-occurrence in diverse microbial communities. Proc. Natl Acad. Sci. USA 112, 6449–6454 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Crowther, T. W. et al. Untangling the fungal niche: the trait-based approach. Front. Microbiol. 5, 579 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Pacciani-Mori, L., Suweis, S., Maritan, A. & Giometto, A. Constrained proteome allocation affects coexistence in models of competitive microbial communities. ISME J. 15, 1458–1477 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Marsland, R. et al. The Community Simulator: a Python package for microbial ecology. PLoS ONE 15, e0230430 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Obadia, B. et al. Probabilistic invasion underlies natural gut microbiome stability. Curr. Biol. 27, 1999–2006.e8 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
D’Andrea, R., Gibbs, T. & O’Dwyer, J. P. Emergent neutrality in consumer-resource dynamics. PLoS Comput. Biol. 16, e1008102 (2020).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Mancuso, C. P., Lee, H., Abreu, C. I., Gore, J. & Khalil, A. S. Environmental fluctuations reshape an unexpected diversity-disturbance relationship in a microbial community. eLife 10, e67175 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Lajoie, G. & Kembel, S. W. Making the most of trait-based approaches for microbial ecology. Trends Microbiol. 27, 814–823 (2019).CAS 
PubMed 
Article 

Google Scholar 
Zakharova, L., Meyer, K. M. & Seifan, M. Trait-based modelling in ecology: a review of two decades of research. Ecol. Modell. 407, 108703 (2019).Article 

Google Scholar 
Merico, A., Brandt, G., Lan Smith, S. L. & Oliver, M. Sustaining diversity in trait-based models of phytoplankton communities. Front. Ecol. Evol. 2, 59 (2014).Grigoratou, M. et al. A trait-based modelling approach to planktonic foraminifera ecology. Biogeosciences 16, 1469–1492 (2019).Article 

Google Scholar 
Muscarella, M. E., Howey, X. M. & Lennon, J. T. Trait-based approach to bacterial growth efficiency. Environ. Microbiol. 22, 3494–3504 (2020).CAS 
PubMed 
Article 

Google Scholar 
Shao, P., Lynch, L., Xie, H., Bao, X. & Liang, C. Tradeoffs among microbial life history strategies influence the fate of microbial residues in subtropical forest soils. Soil Biol. Biochem. 153, 108112 (2021).CAS 
Article 

Google Scholar 
Malik, A. A. et al. Defining trait-based microbial strategies with consequences for soil carbon cycling under climate change. ISME J. 14, 1–9 (2020).CAS 
PubMed 
Article 

Google Scholar 
Le Roux, X. et al. Predicting the responses of soil nitrite-oxidizers to multi-factorial global change: a trait-based approach. Front. Microbiol. 7, 628 (2016).PubMed 
PubMed Central 
Article 

Google Scholar 
Bouskill, N. J., Tang, J., Riley, W. J. & Brodie, E. L. Trait-based representation of biological nitrification: model development, testing, and predicted community composition. Front. Microbiol. 3, 364 (2012).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kyker-Snowman, E., Wieder, W. R., Frey, S. D. & Grandy, A. S. Stoichiometrically coupled carbon and nitrogen cycling in the MIcrobial-MIneral Carbon Stabilization model version 1.0 (MIMICS-CN v1.0). Geosci. Model Dev. 13, 4413–4434 (2020).CAS 
Article 

Google Scholar 
Kruk, C. et al. A trait-based approach predicting community assembly and dominance of microbial invasive species. Oikos 130, 571–586 (2021).Article 

Google Scholar 
Litchman, E., Ohman, M. D. & Kiørboe, T. Trait-based approaches to zooplankton communities. J. Plankton Res. 35, 473–484 (2013).Article 

Google Scholar 
Garcia, C. A. et al. Linking regional shifts in microbial genome adaptation with surface ocean biogeochemistry. Philos. Trans. R. Soc. Lond. B Biol. Sci. 375, 20190254 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Moreno, A. R., Hagstrom, G. I., Primeau, F. W., Levin, S. A. & Martiny, A. C. Marine phytoplankton stoichiometry mediates nonlinear interactions between nutrient supply, temperature, and atmospheric CO2. Biogeosciences 15, 2761–2779 (2018).CAS 
Article 

Google Scholar 
Follows, M. J., Dutkiewicz, S., Grant, S. & Chisholm, S. W. Emergent biogeography of microbial communities in a model ocean. Science 315, 1843–1846 (2007).CAS 
PubMed 
Article 

Google Scholar 
Coles, V. J. et al. Ocean biogeochemistry modeled with emergent trait-based genomics. Science 358, 1149–1154 (2017).CAS 
PubMed 
Article 

Google Scholar 
Ratzke, C., Barrere, J. & Gore, J. Strength of species interactions determines biodiversity and stability in microbial communities. Nat. Ecol. Evol. 4, 376–383 (2020).PubMed 
Article 

Google Scholar 
Bradford, M. A. et al. Quantifying microbial control of soil organic matter dynamics at macrosystem scales. Biogeochemistry 156, 19–40 (2021).Article 

Google Scholar 
Ward, B. A., Dutkiewicz, S., Moore, C. M. & Follows, M. J. Iron, phosphorus, and nitrogen supply ratios define the biogeography of nitrogen fixation. Limnol. Oceanogr. 58, 2059–2075 (2013).CAS 
Article 

Google Scholar 
Zwart, J. A., Solomon, C. T. & Jones, S. E. Phytoplankton traits predict ecosystem function in a global set of lakes. Ecology 96, 2257–2264 (2015).PubMed 
Article 

Google Scholar 
Nemergut, D. R., Shade, A. & Violle, C. When, where and how does microbial community composition matter. Front. Microbiol. 5, 497 (2014).PubMed 
PubMed Central 
Article 

Google Scholar 
Severin, I., Östman, Ö. & Lindström, E. S. Variable effects of dispersal on productivity of bacterial communities due to changes in functional trait composition. PLoS ONE 8, e80825 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Staley, C. et al. Core functional traits of bacterial communities in the Upper Mississippi River show limited variation in response to land cover. Front. Microbiol. 5, 414 (2014).PubMed 
PubMed Central 

Google Scholar 
Worden, L. Conservation of community functional structure across changes in composition in consumer-resource models. J. Theor. Biol. 493, 110239 (2020).PubMed 
Article 

Google Scholar 
van der Plas, F. et al. Plant traits alone are poor predictors of ecosystem properties and long-term ecosystem functioning. Nat. Ecol. Evol. 4, 1602–1611 (2020).PubMed 
Article 

Google Scholar 
Song, H.-S. et al. Regulation-structured dynamic metabolic model provides a potential mechanism for delayed enzyme response in denitrification process. Front. Microbiol. 8, 1866 (2017).PubMed 
PubMed Central 
Article 

Google Scholar 
Hemelrijk, C. K. & Hildenbrandt, H. Schools of fish and flocks of birds: their shape and internal structure by self-organization. Interface Focus 2, 726–737 (2012).PubMed 
PubMed Central 
Article 

Google Scholar 
Hellweger, F. L., Clegg, R. J., Clark, J. R., Plugge, C. M. & Kreft, J.-U. Advancing microbial sciences by individual-based modelling. Nat. Rev. Microbiol. 14, 461–471 (2016).CAS 
PubMed 
Article 

Google Scholar 
Griesemer, M. & Sindi, S. S. Rules of engagement: a guide to developing agent-based models. Methods Mol. Biol. 2349, 367–380 (2022).PubMed 
Article 

Google Scholar 
Jayathilake, P. G. et al. A mechanistic individual-based model of microbial communities. PLoS ONE 12, e0181965 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Clark, J. R., Daines, S. J., Lenton, T. M., Watson, A. J. & Williams, H. T. P. Individual-based modelling of adaptation in marine microbial populations using genetically defined physiological parameters. Ecol. Modell. 222, 3823–3837 (2011).Article 

Google Scholar 
Nadell, C. D. et al. Cutting through the complexity of cell collectives. Proc. Biol. Sci. 280, 20122770 (2013).PubMed 
PubMed Central 

Google Scholar 
Allen, B., Gore, J. & Nowak, M. A. Spatial dilemmas of diffusible public goods. eLife 2, e01169 (2013).PubMed 
PubMed Central 
Article 

Google Scholar 
Abs, E., Leman, H. & Ferrière, R. A multi-scale eco-evolutionary model of cooperation reveals how microbial adaptation influences soil decomposition. Commun. Biol. 3, 520 (2020).PubMed 
PubMed Central 
Article 

Google Scholar 
Kreft, J.-U. et al. Mighty small: observing and modeling individual microbes becomes big science. Proc. Natl Acad. Sci. USA 110, 18027–18028 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Parise, F., Lygeros, J. & Ruess, J. Bayesian inference for stochastic individual-based models of ecological systems: a pest control simulation study. Front. Environ. Sci. 3, https://doi.org/10.3389/fenvs.2015.00042 (2015).Allison, S. D. & Goulden, M. L. Consequences of drought tolerance traits for microbial decomposition in the DEMENT model. Soil Biol. Biochem. 107, 104–113 (2017).CAS 
Article 

Google Scholar 
Allison, S. D. A trait-based approach for modelling microbial litter decomposition. Ecol. Lett. 15, 1058–1070 (2012).CAS 
PubMed 
Article 

Google Scholar 
Doloman, A., Varghese, H., Miller, C. D. & Flann, N. S. Modeling de novo granulation of anaerobic sludge. BMC Syst. Biol. 11, 69 (2017).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Gogulancea, V. et al. Individual based model links thermodynamics, chemical speciation and environmental conditions to microbial growth. Front. Microbiol. 10, 1871 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Gutierrez, M. & Rodriguez-Paton, A. Simulating multicell populations with an accelerated gro simulator. In Proc. ECAL 2017, Fourteenth European Conf. on Artificial Life, 186–188 (2017).Gutiérrez, M. et al. A new improved and extended version of the multicell bacterial simulator gro. ACS Synth. Biol. 6, 1496–1508 (2017).PubMed 
Article 
CAS 

Google Scholar 
Momeni, B., Waite, A. J. & Shou, W. Spatial self-organization favors heterotypic cooperation over cheating. eLife 2, e00960 (2013).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Kreft, J.-U., Booth, G. & Wimpenny, J. W. T. BacSim, a simulator for individual-based modelling of bacterial colony growth. Microbiology 144, 3275–3287 (1998).CAS 
PubMed 
Article 

Google Scholar 
Picioreanu, C., Van Loosdrecht, M. C. & Heijnen, J. J. Effect of diffusive and convective substrate transport on biofilm structure formation: a two-dimensional modeling study. Biotechnol. Bioeng. 69, 504–515 (2000).CAS 
PubMed 
Article 

Google Scholar 
Lardon, L. A. et al. iDynoMiCS: next-generation individual-based modelling of biofilms. Environ. Microbiol. 13, 2416–2434 (2011).CAS 
PubMed 
Article 

Google Scholar 
Chacón, J. M., Möbius, W. & Harcombe, W. R. The spatial and metabolic basis of colony size variation. ISME J. 12, 669–680 (2018).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Oyebamiji, O. K. et al. Gaussian process emulation of an individual-based model simulation of microbial communities. J. Comput. Sci. 22, 69–84 (2017).Article 

Google Scholar 
Menon, R. & Korolev, K. S. Public good diffusion limits microbial mutualism. Phys. Rev. Lett. 114, 168102 (2015).PubMed 
Article 
CAS 

Google Scholar 
Dobay, A., Bagheri, H. C., Messina, A., Kümmerli, R. & Rankin, D. J. Interaction effects of cell diffusion, cell density and public goods properties on the evolution of cooperation in digital microbes. J. Evol. Biol. 27, 1869–1877 (2014).CAS 
PubMed 
Article 

Google Scholar 
Canzian, L., Zhao, K., Wong, G. C. L. & van der Schaar, M. A dynamic network formation model for understanding bacterial self-organization into micro-colonies. IEEE Trans. Mol. Biol. Multiscale Commun. 1, 76–89 (2015).Article 

Google Scholar 
Nadell, C. D., Foster, K. R. & Xavier, J. B. Emergence of spatial structure in cell groups and the evolution of cooperation. PLoS Comput. Biol. 6, e1000716 (2010).PubMed 
PubMed Central 
Article 
CAS 

Google Scholar 
Mendoza, S. N., Olivier, B. G., Molenaar, D. & Teusink, B. A systematic assessment of current genome-scale metabolic reconstruction tools. Genome Biol. 20, 158 (2019).PubMed 
PubMed Central 
Article 

Google Scholar 
Orth, J. D., Thiele, I. & Palsson, B. Ø. What is flux balance analysis? Nat. Biotechnol. 28, 245–248 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Feist, A. M. & Palsson, B. O. The biomass objective function. Curr. Opin. Microbiol. 13, 344–349 (2010).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Blasche, S. et al. Metabolic cooperation and spatiotemporal niche partitioning in a kefir microbial community. Nat. Microbiol. 6, 196–208 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Dukovski, I. et al. A metabolic modeling platform for the computation of microbial ecosystems in time and space (COMETS). Nat. Protoc. 16, 5030–5082 (2021).CAS 
PubMed 
Article 

Google Scholar 
Varahan, S., Sinha, V., Walvekar, A., Krishna, S. & Laxman, S. Resource plasticity-driven carbon-nitrogen budgeting enables specialization and division of labor in a clonal community. eLife 9, e57609 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Angeles-Martinez, L. & Hatzimanikatis, V. Spatio-temporal modeling of the crowding conditions and metabolic variability in microbial communities. PLoS Comput. Biol. 17, e1009140 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zimmermann, J., Kaleta, C. & Waschina, S. gapseq: informed prediction of bacterial metabolic pathways and reconstruction of accurate metabolic models. Genome Biol. 22, 81 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zorrilla, F., Buric, F., Patil, K. R. & Zelezniak, A. metaGEM: reconstruction of genome scale metabolic models directly from metagenomes. Nucleic Acids Res. 49, e126 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Ponomarova, O. et al. Yeast creates a niche for symbiotic lactic acid bacteria through nitrogen overflow. Cell Syst. 5, 345–357.e6 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Foster, K. R. & Bell, T. Competition, not cooperation, dominates interactions among culturable microbial species. Curr. Biol. 22, 1845–1850 (2012).CAS 
PubMed 
Article 

Google Scholar 
Borer, B., Ataman, M., Hatzimanikatis, V. & Or, D. Modeling metabolic networks of individual bacterial agents in heterogeneous and dynamic soil habitats (IndiMeSH). PLoS Comput. Biol. 15, e1007127 (2019).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Labhsetwar, P., Cole, J. A., Roberts, E., Price, N. D. & Luthey-Schulten, Z. A. Heterogeneity in protein expression induces metabolic variability in a modeled Escherichia coli population. Proc. Natl Acad. Sci. USA 110, 14006–14011 (2013).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Kehe, J. et al. Positive interactions are common among culturable bacteria. Sci. Adv. 7, eabi7159 (2021).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Zmora, N. et al. Personalized gut mucosal colonization resistance to empiric probiotics is associated with unique host and microbiome features. Cell 174, 1388–1405.e21 (2018).CAS 
PubMed 
Article 

Google Scholar 
Korem, T. et al. Growth dynamics of gut microbiota in health and disease inferred from single metagenomic samples. Science 349, 1101–1106 (2015).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Hamilton, J. J. et al. Metabolic network analysis and metatranscriptomics reveal auxotrophies and nutrient sources of the cosmopolitan freshwater microbial lineage acI. mSystems 2, e00091-17 (2017).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar 
Guerra, C. A. et al. Tracking, targeting, and conserving soil biodiversity. Science 371, 239–241 (2021).CAS 
PubMed 
Article 

Google Scholar 
IPCC. Climate Change and Land: an IPCC Special Report on Climate Change, Desertification, Land Degradation, Sustainable Land Management, Food Security, and Greenhouse Gas Fluxes in Terrestrial Ecosystems (IPCC, 2020).Pacciani-Mori, L., Giometto, A., Suweis, S. & Maritan, A. Dynamic metabolic adaptation can promote species coexistence in competitive microbial communities. PLoS Comput. Biol. 16, e1007896 (2020).CAS 
PubMed 
PubMed Central 
Article 

Google Scholar  More

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

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