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    Publisher Correction: Principles, drivers and opportunities of a circular bioeconomy

    AffiliationsAnimal Production Systems group, Wageningen University & Research, Wageningen, The NetherlandsAbigail Muscat, Evelien M. de Olde, Raimon Ripoll-Bosch & Imke J. M. de BoerFarming Systems Ecology group, Wageningen University & Research, Wageningen, The NetherlandsHannah H. E. Van ZantenPublic Administration and Policy group, Wageningen University & Research, Wageningen, The NetherlandsTamara A. P. Metze & Catrien J. A. M. TermeerPlant Production Systems group, Wageningen University & Research, Wageningen, The NetherlandsMartin K. van IttersumAuthorsAbigail MuscatEvelien M. de OldeRaimon Ripoll-BoschHannah H. E. Van ZantenTamara A. P. MetzeCatrien J. A. M. TermeerMartin K. van IttersumImke J. M. de BoerCorresponding authorCorrespondence to
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    Wolves, dogs and humans in regular contact can mutually impact each other’s skin microbiota

    Humans have the least and pet dogs have the most diverse skin microbiotaOut of all four groups, species richness and diversity were lowest in human skin microbiota, whereas the pet dog group had the highest species richness and diversity. All four groups differed significantly to each other (Kruskal–Wallis; Chao1, chi-squared = 20.828, df = 3, p  More

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    Nonlinear shifts in infectious rust disease due to climate change

    1.Harvell, C. D. et al. Climate warming and disease risks for terrestrial and marine Biota. Science 296, 2158–2162 (2002).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    2.Gautam, H. R., Bhardwaj, M. L. & Kumar, R. Climate change and its impact on plant diseases. Curr. Sci. 105, 1685–1691 (2013).
    Google Scholar 
    3.Bebber, D. P. & Gurr, S. J. Crop-destroying fungal and oomycete pathogens challenge food security. Fungal Genet. Biol. 74, 62–64 (2015).PubMed 
    Article 

    Google Scholar 
    4.Lukanda, M. et al. First report of maize chlorotic mottle virus infecting maize in the Democratic Republic of the Congo. Plant Dis. 98, 1448–1448 (2014).CAS 
    PubMed 
    Article 

    Google Scholar 
    5.Brasier, C. M. in The Elms: Breeding,Conservation, and Disease Management (ed. Dunn, C. P.) 61–72 (Springer US, 2000). https://doi.org/10.1007/978-1-4615-4507-1_4.6.Boyd, I. L., Freer-Smith, P. H., Gilligan, C. A. & Godfray, H. C. J. The consequence of tree pests and diseases for ecosystem services. Science 342, 1235773 (2013).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    7.Chaloner, T. M., Gurr, S. J. & Bebber, D. P. Geometry and evolution of the ecological niche in plant-associated microbes. Nat. Commun. 11, 2955 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    8.Donald, F., Green, S., Searle, K., Cunniffe, N. J. & Purse, B. V. Small scale variability in soil moisture drives infection of vulnerable juniper populations by invasive forest pathogen. Ecol. Manag. 473, 118324 (2020).Article 

    Google Scholar 
    9.Sturrock, R. N. et al. Climate change and forest diseases. Plant Pathol. 60, 133–149 (2011).Article 

    Google Scholar 
    10.Pathak, R., Singh, S. K., Tak, A. & Gehlot, P. Impact of climate change on host, pathogen and plant disease adaptation regime: a review. Biosci. Biotechnol. Res. Asia 15, 529–540 (2018).Article 

    Google Scholar 
    11.Lafferty, K. D. The ecology of climate change and infectious diseases. Ecology 90, 888–900 (2009).PubMed 
    Article 

    Google Scholar 
    12.Ghelardini, L., Pepori, A. L., Luchi, N., Capretti, P. & Santini, A. Drivers of emerging fungal diseases of forest trees. Ecol. Manag. 381, 235–246 (2016).Article 

    Google Scholar 
    13.Wyka, S. A. et al. Emergence of white pine needle damage in the northeastern United States is associated with changes in pathogen pressure in response to climate change. Glob. Change Biol. 23, 394–405 (2017).ADS 
    Article 

    Google Scholar 
    14.Garrett, K. A. et al. in Climate Change 2nd edn (ed. Letcher, T. M.) 325–338 (Elsevier, 2016). https://doi.org/10.1016/B978-0-444-63524-2.00021-X.15.Bebber, D. P. Range-expanding pests and pathogens in a warming world. Annu. Rev. Phytopathol. 53, 335–356 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    16.Bebber, D. P., Ramotowski, M. A. T. & Gurr, S. J. Crop pests and pathogens move polewards in a warming world. Nat. Clim. Change 3, 985–988 (2013).ADS 
    Article 

    Google Scholar 
    17.Altizer, S., Ostfeld, R. S., Johnson, P. T. J., Kutz, S. & Harvell, C. D. Climate change and infectious diseases: from evidence to a predictive framework. Science 341, 514–519 (2013).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    18.Mordecai, E. A. et al. Optimal temperature for malaria transmission is dramatically lower than previously predicted. Ecol. Lett. 16, 22–30 (2013).PubMed 
    Article 

    Google Scholar 
    19.Huey, R. B. & Berrigan, D. Temperature, demography, and ectotherm fitness. Am. Nat. 158, 204–210 (2001).CAS 
    PubMed 
    Article 

    Google Scholar 
    20.Huey, R. B. & Stevenson, R. D. Integrating thermal physiology and ecology of ectotherms: a discussion of approaches. Integr. Comp. Biol. 19, 357–366 (1979).
    Google Scholar 
    21.Rohr, J. R. et al. Frontiers in climate change—disease research. Trends Ecol. Evol. 26, 270–277 (2011).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    22.Peterson, A. T. Shifting suitability for malaria vectors across Africa with warming climates. BMC Infect. Dis. 9, 59 (2009).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    23.Garamszegi, L. Z. Climate change increases the risk of malaria in birds. Glob. Change Biol. 17, 1751–1759 (2011).ADS 
    Article 

    Google Scholar 
    24.Cook, B. I., Mankin, J. S. & Anchukaitis, K. J. Climate change and drought: from past to future. Curr. Clim. Change Rep. 4, 164–179 (2018).Article 

    Google Scholar 
    25.Desprez-Loustau, M.-L., Marçais, B., Nageleisen, L.-M., Piou, D. & Vannini, A. Interactive effects of drought and pathogens in forest trees. Ann. Sci. 63, 597–612 (2006).Article 

    Google Scholar 
    26.Brodribb, T. J. & McAdam, S. A. M. Passive origins of stomatal control in vascular plants. Science 331, 582–585 (2011).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    27.Jactel, H. et al. Drought effects on damage by forest insects and pathogens: a meta-analysis. Glob. Change Biol. 18, 267–276 (2012).ADS 
    Article 

    Google Scholar 
    28.Baptista-Rosas, R. C. et al. Molecular detection of Coccidioides spp. from environmental samples in Baja California: linking Valley Fever to soil and climate conditions. Fungal Ecol. 5, 177–190 (2012).Article 

    Google Scholar 
    29.Cohen, J. M. et al. The thermal mismatch hypothesis explains host susceptibility to an emerging infectious disease. Ecol. Lett. 20, 184–193 (2017).PubMed 
    Article 

    Google Scholar 
    30.Mcelrone, A. J., Reid, C. D., Hoye, K. A., Hart, E. & Jackson, R. B. Elevated CO2 reduces disease incidence and severity of a red maple fungal pathogen via changes in host physiology and leaf chemistry. Glob. Change Biol. 11, 1828–1836 (2005).ADS 
    Article 

    Google Scholar 
    31.Berzitis, E. A., Minigan, J. N., Hallett, R. H. & Newman, J. A. Climate and host plant availability impact the future distribution of the bean leaf beetle (Cerotoma trifurcata). Glob. Change Biol. 20, 2778–2792 (2014).ADS 
    Article 

    Google Scholar 
    32.Bebber, D. P. & Gurr, S. J. Biotic interactions and climate in species distribution modelling. bioRxiv 520320 https://doi.org/10.1101/520320 (2019).33.Parker, I. M. et al. Phylogenetic structure and host abundance drive disease pressure in communities. Nature 520, 542–544 (2015).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    34.Morgan, E. R., Milner-Gulland, E. J., Torgerson, P. R. & Medley, G. F. Ruminating on complexity: macroparasites of wildlife and livestock. Trends Ecol. Evol. 19, 181–188 (2004).PubMed 
    Article 

    Google Scholar 
    35.Paull, S. H., LaFonte, B. E. & Johnson, P. T. J. Temperature-driven shifts in a host-parasite interaction drive nonlinear changes in disease risk. Glob. Change Biol. 18, 3558–3567 (2012).ADS 
    Article 

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

    Google Scholar 
    37.Bebber, D. P. Climate change effects on Black Sigatoka disease of banana. Philos. Trans. R. Soc. B: Biol. Sci. 374, 20180269 (2019).Article 

    Google Scholar 
    38.Soberón, J. & Peterson, A. T. Interpretation of models of fundamental ecological niches and species’ distributional areas. https://doi.org/10.17161/bi.v2i0.4 (2005).39.Garrett, K. A. et al. Complexity in climate-change impacts: an analytical framework for effects mediated by plant disease. Plant Pathol. 60, 15–30 (2011).Article 

    Google Scholar 
    40.Scherm, H. Climate change: can we predict the impacts on plant pathology and pest management? Can. J. Plant Pathol. 26, 267–273 (2004).Article 

    Google Scholar 
    41.Simler-Williamson, A. B., Rizzo, D. M. & Cobb, R. C. Interacting effects of global change on forest pest and pathogen dynamics. Annu. Rev. Ecol. Evol. Syst. https://doi.org/10.1146/annurev-ecolsys-110218-024934 (2019).42.Campbell, E. M. & Antos, J. A. Distribution and severity of white pine blister rust and mountain pine beetle on whitebark pine in British Columbia. Can. J. Res. 30, 1051–1059 (2000).Article 

    Google Scholar 
    43.Larsen, A. E., Meng, K. & Kendall, B. E. Causal analysis in control–impact ecological studies with observational data. Methods Ecol. Evol. 10, 924–934 (2019).Article 

    Google Scholar 
    44.McDonald, G. I., Richardson, B. A., Zambino, P. J., Klopfenstein, N. B. & Kim, M.-S. Pedicularis and Castilleja are natural hosts of Cronartium ribicola in North America: a first report. Pathol. 36, 73–82 (2006).Article 

    Google Scholar 
    45.Geils, B. W., Hummer, K. E. & Hunt, R. S. White pines, Ribes, and blister rust: a review and synthesis. Pathol. 40, 147–185 (2010).Article 

    Google Scholar 
    46.Kinloch, B. B. White pine blister rust in North America: past and prognosis. Phytopathology 93, 1044–1047 (2003).PubMed 
    Article 

    Google Scholar 
    47.Arsdel, E. P. V., Geils, B. W. & Zambino, P. J. Epidemiology for hazard rating of white pine blister rust. In: Guyon JC Comp Proc. 53rd Western International Forest Disease Work Conference 2005 September 26–30 Jackson WY USA (Department of Agriculture, Forest Service, Intermountain Region, Ogden UT, 2006).48.Dudney, J. Characterizing and Managing Drivers of Change in Mediterranean Forest and Grassland Communities (UC Berkeley, 2019).49.Kreyling, J. et al. To replicate, or not to replicate—that is the question: how to tackle nonlinear responses in ecological experiments. Ecol. Lett. 21, 1629–1638 (2018).PubMed 
    Article 

    Google Scholar 
    50.Larson, E. R. & Kipfmueller, K. F. Ecological disaster or the limits of observation? reconciling modern declines with the long-term dynamics of whitebark pine communities. Geogr. Compass 6, 189–214 (2012).Article 

    Google Scholar 
    51.Parmesan, C. & Yohe, G. A globally coherent fingerprint of climate change impacts across natural systems. Nature 421, 37–42 (2003).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    52.Kinloch, B. B. et al. Patterns of variation in blister rust resistance in sugar pine (Pinus lambertiana). In: Proc. IUFRO joint conference: Genetics of five-needle pines, rusts of forest trees, and Strobusphere; 2014 June 15–20; Fort Collins, CO. Proc. RMRS-P-76 (eds Schoettle, A. W., Sniezko, R. A. & Kliejunas, J. T.) 124–128 (Department of Agriculture, Forest Service, Rocky Mountain Research Station, 2018).53.King, J. N., David, A., Noshad, D. & Smith, J. A review of genetic approaches to the management of blister rust in white pines. Pathol. 40, 292–313 (2010).Article 

    Google Scholar 
    54.Maloney, P. E. Incidence and distribution of white pine blister rust in the high-elevation forests of California. Forest Pathol. 41, 308–316 (2011).Article 

    Google Scholar 
    55.Dunlap, J. M. Variability in and environmental correlates to white pine blister rust incidence in five California white pine species. Northwest Sci. 86, 248–263 (2012).Article 

    Google Scholar 
    56.Thoma, D. P., Shanahan, E. K. & Irvine, K. M. Climatic correlates of white pine blister rust infection in whitebark pine in the greater yellowstone ecosystem. Forests 10, 666 (2019).Article 

    Google Scholar 
    57.Talley, S. M., Coley, P. D. & Kursar, T. A. The effects of weather on fungal abundance and richness among 25 communities in the Intermountain West. BMC Ecol. 2, 7 (2002).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    58.Davis, J. K. et al. Improving the prediction of arbovirus outbreaks: A comparison of climate-driven models for West Nile virus in an endemic region of the United States. Acta Trop. 185, 242–250 (2018).PubMed 
    Article 

    Google Scholar 
    59.Manstretta, V. & Rossi, V. Effects of weather variables on ascospore discharge from Fusarium graminearum Perithecia. PLoS ONE 10, e0138860 (2015).PubMed 
    PubMed Central 
    Article 
    CAS 

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

    Google Scholar 
    61.Taylor, K. E., Stouffer, R. J. & Meehl, G. A. An overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc. 93, 485–498 (2012).ADS 
    Article 

    Google Scholar 
    62.Dudney, J. C. et al. Compounding effects of white pine blister rust, mountain pine beetle, and fire threaten four white pine species. Ecosphere 11, e03263 (2020).Article 

    Google Scholar 
    63.Schwandt, J. W., Lockman, I. B., Kliejunas, J. T. & Muir, J. A. Current health issues and management strategies for white pines in the western United States and Canada. Forest Pathol. 40, 226–250 (2010).Article 

    Google Scholar 
    64.Dudney, J. et al. Overstory removal and biological legacies influence long-term forest management outcomes on introduced species and native shrubs. Forest Ecol. Manag. 491, 119149 (2021).Article 

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

    Google Scholar 
    66.Goodsman, D. W., Lusebrink, I., Landhäusser, S. M., Erbilgin, N. & Lieffers, V. J. Variation in carbon availability, defense chemistry and susceptibility to fungal invasion along the stems of mature trees. N. Phytol. 197, 586–594 (2013).CAS 
    Article 

    Google Scholar 
    67.McDowell, N. et al. Mechanisms of plant survival and mortality during drought: why do some plants survive while others succumb to drought? N. Phytol. 178, 719–739 (2008).Article 

    Google Scholar 
    68.Bockino, N. K. & Tinker, D. B. Interactions of white pine blister rust and mountain pine beetle in whitebark pine ecosystems in the southern Greater Yellowstone Area. Nat. Areas J. 32, 31–40 (2012).Article 

    Google Scholar 
    69.Stephenson, N. L., Das, A. J., Ampersee, N. J., Bulaon, B. M. & Yee, J. L. Which trees die during drought? The key role of insect host-tree selection. J. Ecol. 107, 2383–2401 (2019).70.Griffin, D. & Anchukaitis, K. J. How unusual is the 2012–2014 California drought? Geophys. Res. Lett. 41, 2014GL062433 (2014).Article 

    Google Scholar 
    71.Paz‐Kagan, T. et al. What mediates tree mortality during drought in the southern Sierra Nevada? Ecol. Appl. 27, 2443–2457 (2017).PubMed 
    Article 

    Google Scholar 
    72.Zambino, P. J. Biology and pathology of Ribes and their implications for management of white pine blister rust. Pathol. 40, 264–291 (2010).Article 

    Google Scholar 
    73.Anderegg, W. R. L., Anderegg, L. D. L., Kerr, K. L. & Trugman, A. T. Widespread drought-induced tree mortality at dry range edges indicates that climate stress exceeds species’ compensating mechanisms. Glob. Change Biol. 25, 3793–3802 (2019).ADS 
    Article 

    Google Scholar 
    74.Bebber, D. P., Holmes, T. & Gurr, S. J. The global spread of crop pests and pathogens. Glob. Ecol. Biogeogr. 23, 1398–1407 (2014).Article 

    Google Scholar 
    75.Deyle, E. R., May, R. M., Munch, S. B. & Sugihara, G. Tracking and forecasting ecosystem interactions in real time. Proc. R. Soc. B: Biol. Sci. 283, 20152258 (2016).Article 

    Google Scholar 
    76.Deyle, E. R., Maher, M. C., Hernandez, R. D., Basu, S. & Sugihara, G. Global environmental drivers of influenza. Proc. Natl Acad. Sci. 113, 13081–13086 (2016).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    77.Cohen, J. M., Civitello, D. J., Venesky, M. D., McMahon, T. A. & Rohr, J. R. An interaction between climate change and infectious disease drove widespread amphibian declines. Glob. Change Biol. 25, 927–937 (2019).ADS 
    Article 

    Google Scholar 
    78.Kolb, T. E. et al. Observed and anticipated impacts of drought on forest insects and diseases in the United States. Ecol. Manag. 380, 321–334 (2016).Article 

    Google Scholar 
    79.Kutz, S. J. et al. The Arctic as a model for anticipating, preventing, and mitigating climate change impacts on host–parasite interactions. Vet. Parasitol. 163, 217–228 (2009).PubMed 
    Article 

    Google Scholar 
    80.Flower, C. E. & Gonzalez-Meler, M. A. Responses of temperate forest productivity to insect and pathogen disturbances. Annu. Rev. Plant Biol. 66, 547–569 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    81.Trant, A., Higgs, E. & Starzomski, B. M. A century of high elevation ecosystem change in the Canadian Rocky Mountains. Sci. Rep. 10, 9698 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    82.Wong, C. M. & Daniels, L. D. Novel forest decline triggered by multiple interactions among climate, an introduced pathogen and bark beetles. Glob. Change Biol. 23, 1926–1941 (2017).ADS 
    Article 

    Google Scholar 
    83.Endangered and Threatened Wildlife and Plants; Threatened Species Status for Pinus albicaulis (Whitebark Pine) With Section 4(d) Rule. Federal Register https://www.federalregister.gov/documents/2020/12/02/2020-25331/endangered-and-threatened-wildlife-and-plants-threatened-species-status-for-pinus-albicaulis (2020).84.Garrett, K. A., Dendy, S. P., Frank, E. E., Rouse, M. N. & Travers, S. E. Climate change effects on plant disease: genomes to ecosystems. Annu. Rev. Phytopathol. 44, 489–509 (2006).CAS 
    PubMed 
    Article 

    Google Scholar 
    85.PRISM Climate Group. PRISM Climate Group, Oregon State U. http://www.prism.oregonstate.edu/normals/.86.Abatzoglou, J. T. & Brown, T. J. A comparison of statistical downscaling methods suited for wildfire applications. Int. J. Climatol. 32, 772–780 (2012).Article 

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

    Google Scholar 
    88.Mitchell, K. E. et al. The multi-institution North American Land Data Assimilation System (NLDAS): Utilizing multiple GCIP products and partners in a continental distributed hydrological modeling system. J. Geophys. Res. Atmospheres https://doi.org/10.1029/2003JD003823@10.1002/(ISSN)2169-8996.GCIP3 (2018).89.Ritchie, J. & Dowlatabadi, H. Why do climate change scenarios return to coal? Energy 140, 1276–1291 (2017).Article 

    Google Scholar 
    90.R Core Team. R: A Language and Environment for Statistical Computing https://www.rproject.org/ (2017).91.Burns, K. S., Schoettle, A. W., Jacobi, W. R. & Mahalovich, M. F. White pine blister rust in the Rocky Mountain Region and options for management. Management. https://www.fs.fed.us/rm/pubs/rmrs_gtr206.pdf (2007).92.Fox, J. et al. car: Companion to applied regression (2019).93.Nakagawa, S. & Schielzeth, H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol. Evol. 4, 133–142 (2013).Article 

    Google Scholar 
    94.Zuur, A. F., Ieno, E. N., Walker, N., Saveliev, A. A. & Smith, G. M. Mixed Effects Models and Extensions in Ecology with R (Springer New York, 2009).95.Baker-Austin, C. et al. Emerging Vibrio risk at high latitudes in response to ocean warming. Nat. Clim. Change 3, 73–77 (2013).ADS 
    Article 

    Google Scholar 
    96.Lüdecke, D. ggeffects: Tidy data frames of marginal effects from regression models. J. Open Source Softw. 3, 772 (2018).ADS 
    Article 

    Google Scholar 
    97.Rohr, J. R., Raffel, T. R., Romansic, J. M., McCallum, H. & Hudson, P. J. Evaluating the links between climate, disease spread, and amphibian declines. Proc. Natl Acad. Sci. 105, 17436–17441 (2008).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    98.Wooldridge, J. M. Introductory Econometrics: A Modern Approach. 6th ed. (Cengage learning. Boston, MA, 2015).99.Berge, L. fixest: Fast Fixed-Effects Estimations. https://cran.rproject.org/web/packages/fixest/index.html (2020).100.Harrell, F. E. rms: Regression Modeling Strategies https://CRAN.R-project.org/package=rms (2020).101.Kelly, M., Guo, Q., Liu, D. & Shaari, D. Modeling the risk for a new invasive forest disease in the United States: An evaluation of five environmental niche models. Comput. Environ. Urban Syst. 31, 689–710 (2007).Article 

    Google Scholar 
    102.Meentemeyer, R. K. et al. Epidemiological modeling of invasion in heterogeneous landscapes: spread of sudden oak death in California (1990–2030). Ecosphere 2, 1–24 (2011).Article 

    Google Scholar 
    103.QGIS Development Team. QGIS Geographic Information System. http://qgis.osgeo.org/ (2020).104.Bates, D., Mächler, M., Bolker, B. & Walker, S. Fitting linear mixed-effects models using lme4. J. Stat. Softw. 67, 1–48 (2015).105.Hülsmann, L., Bugmann, H., Cailleret, M. & Brang, P. How to kill a tree: empirical mortality models for 18 species and their performance in a dynamic forest model. Ecol. Appl. 28, 522–540 (2018).PubMed 
    Article 

    Google Scholar 
    106.Cribbs, J., Nesmith, J., van Mantgem, P. & Dudney, J. Using stable isotope analysis and foliar growth measurements to understand physiological responses to drought in whitebark pine. Presented at the Ecological Society of America Symposium (2020).107.Farquhar, G. D. & Richards, R. A. Isotopic composition of plant carbon correlates with water-use efficiency of wheat genotypes. Funct. Plant Biol. 11, 539–552 (1984).CAS 
    Article 

    Google Scholar 
    108.Dudney, J. et al. Climate change and white pine blister rust. https://doi.org/10.17605/OSF.IO/PC9FM. (2021).109.Soberón, J. & Nakamura, M. Niches and distributional areas: concepts, methods, and assumptions. Proc. Natl Acad. Sci. 106, 19644–19650 (2009).ADS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar  More

  • in

    Non-lethal effects of entomopathogenic nematode infection

    1.Gaugler, R. Entomopathogenic nematology (2002).2.Gaugler, R. Entomopathogenic Nematodes in Biological Control (CRC Press, 2018).Book 

    Google Scholar 
    3.Grewal, P. S., Ehlers, R.-U. & Shapiro-Ilan, D. I. Nematodes as Biocontrol Agents (CABI, 2005).Book 

    Google Scholar 
    4.Duncan, L. & McCoy, C. Vertical distribution in soil, persistence, and efficacy against citrus root weevil (coleoptera: Curculionidae) of two species of entomogenous nematodes (rhabditida: Steinernematidae; heterorhabditidae). Environ. Entomol. 25, 174–178 (1996).Article 

    Google Scholar 
    5.Duncan, L., McCoy, C. & Terranova, A. Estimating sample size and persistence of entomogenous nematodes in sandy soils and their efficacy against the larvae of Diaprepes abbreviatus in Florida. J. Nematol. 28, 56 (1996).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    6.Bullock, R., Pelosi, R. & Killer, E. Management of citrus root weevils (coleoptera: Curculionidae) on florida citrus with soil-applied entomopathogenic nematodes (nematoda: Rhabditida). Florida Entomologist 1–7 (1999).7.Koppenhöfer, A. M. & Fuzy, E. M. Steinernema scarabaei for the control of white grubs. Biol. Control 28, 47–59 (2003).Article 

    Google Scholar 
    8.Grewal, P., Power, K., Grewal, S., Suggars, A. & Haupricht, S. Enhanced consistency in biological control of white grubs (coleoptera: Scarabaeidae) with new strains of entomopathogenic nematodes. Biol. Control 30, 73–82 (2004).Article 

    Google Scholar 
    9.Georgis, R. et al. Successes and failures in the use of parasitic nematodes for pest control. Biol. Control 38, 103–123 (2006).Article 

    Google Scholar 
    10.Labaude, S. & Griffin, C. T. Transmission success of entomopathogenic nematodes used in pest control. Insects 9, 72 (2018).Article 

    Google Scholar 
    11.Li, X.-Y., Cowles, R., Cowles, E., Gaugler, R. & Cox-Foster, D. Relationship between the successful infection by entomopathogenic nematodes and the host immune response. Int. J. Parasitol. 37, 365–374 (2007).CAS 
    Article 

    Google Scholar 
    12.Castillo, J. C., Reynolds, S. E. & Eleftherianos, I. Insect immune responses to nematode parasites. Trends Parasitol. 27, 537–547 (2011).CAS 
    Article 

    Google Scholar 
    13.Ribeiro, C. et al. Insect immunity-effects of factors produced by a nematobacterial complex on immunocompetent cells. J. Insect Physiol. 45, 677–685 (1999).CAS 
    Article 

    Google Scholar 
    14.Garriga, A., Mastore, M., Morton, A., Garcia del Pino, F. & Brivio, M. F. Immune response of drosophila suzukii larvae to infection with the nematobacterial complex steinernema carpocapsae-xenorhabdus nematophila. Insects 11, 210 (2020).Article 

    Google Scholar 
    15.Ebrahimi, L., Niknam, G., Dunphy, G. & Toorchi, M. Side effects of immune response of colorado potato beetle, leptinotarsa decemlineata against the entomopathogenic nematode, steinernema carpocapsae infection. Invertebr. Surviv. J. 11, 132–142 (2014).
    Google Scholar 
    16.Ebrahimi, L., Niknam, G. & Lewis, E. Lethal and sublethal effects of iranian isolates of steinernema feltiae and heterorhabditis bacteriophora on the colorado potato beetle, leptinotarsa decemlineata. Biocontrol 56, 781–788 (2011).Article 

    Google Scholar 
    17.Chen, S., Li, J., Han, X. & Moens, M. Effect of temperature on the pathogenicity of entomopathogenic nematodes (Steinernema and Heterorhabditis spp.) to delia radicum. Biocontrol 48, 713–724 (2003).Article 

    Google Scholar 
    18.Mastore, M., Quadroni, S., Toscano, A., Mottadelli, N. & Brivio, M. F. Susceptibility to entomopathogens and modulation of basal immunity in two insect models at different temperatures. J. Therm. Biol 79, 15–23 (2019).CAS 
    Article 

    Google Scholar 
    19.Wojda, I. Temperature stress and insect immunity. J. Therm. Biol 68, 96–103 (2017).CAS 
    Article 

    Google Scholar 
    20.Lee, J. H., Dillman, A. R. & Hallem, E. A. Temperature-dependent changes in the host-seeking behaviors of parasitic nematodes. BMC Biol. 14, 1–17 (2016).Article 

    Google Scholar 
    21.Girling, R., Ennis, D., Dillon, A. & Griffin, C. The lethal and sub-lethal consequences of entomopathogenic nematode infestation and exposure for adult pine weevils, Hylobius abietis (coleoptera: Curculionidae). J. Invertebr. Pathol. 104, 195–202 (2010).CAS 
    Article 

    Google Scholar 
    22.Mastore, M., Arizza, V., Manachini, B. & Brivio, M. F. Modulation of immune responses of Rhynchophorus ferrugineus (insecta: Coleoptera) induced by the entomopathogenic nematode Steinernema carpocapsae (nematoda: Rhabditida). Insect Sci. 22, 748–760 (2015).CAS 
    Article 

    Google Scholar 
    23.Willett, D. S., Filgueiras, C. C., Nyrop, J. P. & Nault, B. A. Attract and kill: spinosad containing spheres to control onion maggot (Delia antiqua). Pest Manag. Sci. 76, 2720–2725 (2020).CAS 
    Article 

    Google Scholar 
    24.Willett, D. S., Filgueiras, C. C., Nyrop, J. P. & Nault, B. A. Field monitoring of onion maggot (Delia antiqua) fly through improved trapping. J. Appl. Entomol. 144, 382–387 (2020).Article 

    Google Scholar 
    25.Kaya, H. K. & Stock, S. P. Techniques in insect nematology. In Manual of Techniques in Insect Pathology, 281–324 (Elsevier, 1997).26.White, G. et al. A method for obtaining infective nematode larvae from cultures. Science (Washington) 66, 302–303 (1927).ADS 
    CAS 
    Article 

    Google Scholar 
    27.R Core Team. R: A. Language and Environment for Statistical Computing. R Foundation for Statistical Computing (Vienna, Austria, 2021).28.Wickham, H. et al. Welcome to the tidyverse. J. Open Sour. Softw. 4, 1686 (2019). https://doi.org/10.21105/joss.01686ADS 
    Article 

    Google Scholar 
    29.Fox, J. & Weisberg, S. An R Companion to Applied Regression third. (Sage, 2019).
    Google Scholar 
    30.Lenth, R. V. emmeans: Estimated Marginal Means, aka Least-Squares Means (2021). R package version 1.5.5-1.31.Franceschi, C. et al. Genes involved in immune response/inflammation, igf1/insulin pathway and response to oxidative stress play a major role in the genetics of human longevity: The lesson of centenarians. Mech. Ageing Dev. 126, 351–361 (2005).CAS 
    Article 

    Google Scholar 
    32.Kumar, S. et al. Lifespan extension in C. elegans caused by bacterial colonization of the intestine and subsequent activation of an innate immune response. Dev. Cell 49, 100–117 (2019).CAS 
    Article 

    Google Scholar 
    33.Bruno, P. et al. Entomopathogenic nematodes from Mexico that can overcome the resistance mechanisms of the western corn rootworm. Sci. Rep. 10, 1–12 (2020).Article 

    Google Scholar 
    34.Stock, S. P., Campos-Herrera, R., El-Borai, F. & Duncan, L. Steinernema khuongi n. sp. (panagrolaimomorpha, steinernematidae), a new entomopathogenic nematode species from Florida, USA. J. Helminthol. 93, 226–241 (2019).CAS 
    Article 

    Google Scholar 
    35.Nagelkerke, N. J. et al. A note on a general definition of the coefficient of determination. Biometrika 78, 691–692 (1991).MathSciNet 
    Article 

    Google Scholar  More

  • in

    Nano/microparticles in conjunction with microalgae extract as novel insecticides against Mealworm beetles, Tenebrio molitor

    1.Köhler, H. R. & Triebskorn, R. Wildlife ecotoxicology of pesticides: can we track effects to the population level and beyond?. Science 341(6147), 759–765 (2013).ADS 
    PubMed 
    Article 
    CAS 

    Google Scholar 
    2.Tilman, D., Cassman, K. G., Matson, P. A., Naylor, R. & Polasky, S. Agricultural sustainability and intensive production practices. Nature 418(6898), 671–677 (2002).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    3.Khan, M. N., Mobin, M., Abbas, Z. K., AlMutairi, K. A. & Siddiqui, Z. H. Role of nanomaterials in plants under challenging environments. Plant Physiol. Biochem. 110, 194–209 (2017).CAS 
    PubMed 
    Article 

    Google Scholar 
    4.Monica, R. C. & Cremonini, R. Nanoparticles and higher plants. Caryologia 62(2), 161–165 (2009).Article 

    Google Scholar 
    5.Zheng, L., Hong, F., Lu, S. & Liu, C. Effect of nano-TiO2 on strength of naturally aged seeds and growth of spinach. Biol. Trace Elem. Res. 104(1), 83–91 (2005).CAS 
    PubMed 
    Article 

    Google Scholar 
    6.Lin, D. & Xing, B. Phytotoxicity of nanoparticles: inhibition of seed germination and root growth. Environ. Pollut. 150(2), 243–250 (2007).CAS 
    PubMed 
    Article 

    Google Scholar 
    7.Kah, M. Nanopesticides and nanofertilizers: emerging contaminants or opportunities for risk mitigation?. Front. Chem. 3, 64 (2015).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    8.Sirelkhatim, A. et al. Review on zinc oxide nanoparticles: antibacterial activity and toxicity mechanism. Nano-micro letters 7(3), 219–242 (2015).CAS 
    PubMed 
    Article 

    Google Scholar 
    9.Selvarajan, V., Obuobi, S. & Ee, P. L. R. Silica Nanoparticles—A Versatile Tool for the Treatment of Bacterial Infections. Front. Chem. 8, 602 (2020).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    10.Lykov, A. et al. Silica Nanoparticles as a Basis for Efficacy of Antimicrobial Drugs. Nanostruct. Antimicrob. Therapy 1, 551–575 (2017).Article 

    Google Scholar 
    11.Kim, J. S. et al. Antimicrobial effects of silver nanoparticles. Nanomed. Nanotechnol. Biol. Med. 3(1), 95–101 (2007).CAS 
    Article 

    Google Scholar 
    12.Sharma, A., Patni, B., Shankhdhar, D. & Shankhdhar, S. C. Zinc–an indispensable micronutrient. Physiol. Mol. Biol. Plants 19(1), 11–20 (2013).CAS 
    PubMed 
    Article 

    Google Scholar 
    13.Kawachi, M. et al. A mutant strain Arabidopsis thaliana that lacks vacuolar membrane zinc transporter MTP1 revealed the latent tolerance to excessive zinc. Plant Cell Physiol. 50(6), 1156–1170 (2009).CAS 
    PubMed 
    Article 

    Google Scholar 
    14.Yan, A. & Chen, Z. Impacts of silver nanoparticles on plants: a focus on the phytotoxicity and underlying mechanism. Int. J. Mol. Sci. 20(5), 1003 (2019).CAS 
    PubMed Central 
    Article 
    PubMed 

    Google Scholar 
    15.Vigneron, A., Jehan, C., Rigaud, T. & Moret, Y. Immune defenses of a beneficial pest: the mealworm beetle Tenebrio molitor. Front. Physiol. 10, 138 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    16.Renukadevi, K. P., Saravana, P. S. & Angayarkanni, J. Antimicrobial and antioxidant activity of Chlamydomonas reinhardtii sp. Int. J. Pharm. Sci. Res. 2(6), 1467 (2011).
    Google Scholar 
    17.Jayshree, A., Jayashree, S. & Thangaraju, N. Chlorella vulgaris and Chlamydomonas reinhardtii: effective antioxidant, antibacterial and anticancer mediators. Indian J. Pharm. Sci. 78(5), 575–581 (2016).CAS 
    Article 

    Google Scholar 
    18.Kamble, P., Cheriyamundath, S., Lopus, M. & Sirisha, V. L. Chemical characteristics, antioxidant and anticancer potential of sulfated polysaccharides from Chlamydomonas reinhardtii. J. Appl. Phycol. 30(3), 1641–1653 (2018).CAS 
    Article 

    Google Scholar 
    19.Vishwakarma, J., Parmar, V. & Vavilala, S. L. Nitrate stress-induced bioactive sulfated polysaccharides from Chlamydomonas reinhardtii. Biomed. Res. J. 6(1), 7 (2019).
    Google Scholar 
    20.Burghardt, M., Schreiber, L. & Riederer, M. Enhancement of the diffusion of active ingredients in barley leaf cuticular wax by monodisperse alcohol ethoxylates. J. Agric. Food Chem. 46(4), 1593–1602 (1998).CAS 
    Article 

    Google Scholar 
    21.Henderson, C. F. & Tilton, E. W. Tests with acaricides against the brown wheat mite. J. Econ. Entomol. 48(2), 157–161 (1955).CAS 
    Article 

    Google Scholar 
    22.Debnath, N. et al. Entomotoxic effect of silica nanoparticles against Sitophilus oryzae (L.). J. Pest Sci. 84(1), 99–105 (2011).Article 

    Google Scholar 
    23.Aktar, M. W., Sengupta, D. & Chowdhury, A. Impact of pesticides use in agriculture: their benefits and hazards. Interdiscip. Toxicol. 2(1), 1 (2009).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    24.Majumder, D. D. et al. Current status and future trends of nanoscale technology and its impact on modern computing, biology, medicine and agricultural biotechnology. In 2007 International Conference on Computing: Theory and Applications (ICCTA’07), 563–573 (2007).25.Rahman, A. et al. Surface functionalized amorphous nanosilica and microsilica with nanopores as promising tools in biomedicine. Naturwissenschaften 96(1), 31–38 (2009).ADS 
    CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    26.Pérez-de-Luque, A. & Rubiales, D. Nanotechnology for parasitic plant control. Pest Manag. Sci.: Formerly Pesticide Sci. 65(5), 540–545 (2009).Article 
    CAS 

    Google Scholar 
    27.Chakravarthy, A. K. et al. Bio efficacy of inorganic nanoparticles CdS, Nano-Ag and Nano-TiO2 against Spodoptera litura (Fabricius) (Lepidoptera: Noctuidae). Current Biotica 6(3), 271–281 (2012).
    Google Scholar 
    28.Benelli, G. Mode of action of nanoparticles against insects. Environ. Sci. Pollut. Res. 25(13), 12329–12341 (2018).CAS 
    Article 

    Google Scholar 
    29.Karthiga, P., Rajeshkumar, S. & Annadurai, G. Mechanism of larvicidal activity of antimicrobial silver nanoparticles synthesized using Garcinia mangostana bark extract. J. Cluster Sci. 29(6), 1233–1241 (2018).CAS 
    Article 

    Google Scholar 
    30.Rouhani, M., Samih, M. A. & Kalantari, S. Insecticide effect of silver and zinc nanoparticles against Aphis nerii Boyer De Fonscolombe (Hemiptera: Aphididae). Chil. J. Agric. Res. 72(4), 590 (2012).Article 

    Google Scholar 
    31.Rouhani, M., Samih, M. A. & Kalantari, S. Insecticidal effect of silica and silver nanoparticles on the cowpea seed beetle, Callosobruchus maculatus F(Col: Bruchidae). J. Entomol. Res. 4(4), 297–305 (2013).
    Google Scholar 
    32.Sabbour, M. M. Entomotoxicity assay of two nanoparticle materials 1-(Al2O3 and TiO2) against Sitophilus oryzae under laboratory and store conditions in Egypt. J. Novel Appl. Sci. 1(4), 103–108 (2012).
    Google Scholar 
    33.Stadler, T., Buteler, M. & Weaver, D. K. Novel use of nanostructured alumina as an insecticide. Pest Manag. Sci.: Formerly Pesticide Sci. 66(6), 577–579 (2010).CAS 
    Article 

    Google Scholar 
    34.Xu, R. ISO International standards for particle sizing. China Particuol. 2(4), 164–167 (2004).CAS 
    Article 

    Google Scholar 
    35.Lee, Y. S., Kang, M. H., Cho, S. Y. & Jeong, C. S. Effects of constituents of Amomum xanthioides on gastritis in rats and on growth of gastric cancer cells. Arch. Pharmacal Res. 30(4), 436–443 (2007).CAS 
    Article 

    Google Scholar 
    36.Hussein, H. A. et al. Phytochemical screening, metabolite profiling and enhanced antimicrobial activities of microalgal crude extracts in co-application with silver nanoparticle. Bioresour. Bioprocess. 7(1), 1–17 (2020).MathSciNet 
    Article 

    Google Scholar 
    37.Jeevanandam, J., Barhoum, A., Chan, Y. S., Dufresne, A. & Danquah, M. K. Review on nanoparticles and nanostructured materials: history, sources, toxicity and regulations. Beilstein J. Nanotechnol. 9(1), 1050–1074 (2018).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    38.Servin, A. et al. A review of the use of engineered nanomaterials to suppress plant disease and enhance crop yield. J. Nanopart. Res. 17(2), 1–21 (2015).MathSciNet 
    CAS 
    Article 

    Google Scholar 
    39.Barik, T. K., Kamaraju, R. & Gowswami, A. Silica nanoparticle: a potential new insecticide for mosquito vector control. Parasitol. Res. 111(3), 1075–1083 (2012).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    40.Gao, Y. et al. Thermoresponsive polymer-encapsulated hollow mesoporous silica nanoparticles and their application in insecticide delivery. Chem. Eng. J. 383, 1269 (2020).
    Google Scholar 
    41.Debnath, N., Das, S., Patra, P., Mitra, S. & Goswami, A. Toxicological evaluation of entomotoxic silica nanoparticle. Toxicol. Environ. Chem. 94(5), 944–951 (2012).CAS 
    Article 

    Google Scholar 
    42.Debnath, N., Mitra, S., Das, S. & Goswami, A. Synthesis of surface functionalized silica nanoparticles and their use as entomotoxic nanocides. Powder Technol. 221, 252–256 (2012).CAS 
    Article 

    Google Scholar 
    43.Chang, J. S., Chang, K. L. B., Hwang, D. F. & Kong, Z. L. In vitro cytotoxicitiy of silica nanoparticles at high concentrations strongly depends on the metabolic activity type of the cell line. Environ. Sci. Technol. 41(6), 2064–2068 (2007).ADS 
    CAS 
    PubMed 
    Article 

    Google Scholar 
    44.Gogos, A., Knauer, K. & Bucheli, T. D. Nanomaterials in plant protection and fertilization: current state, foreseen applications, and research priorities. J. Agric. Food Chem. 60(39), 9781–9792 (2012).CAS 
    PubMed 
    Article 

    Google Scholar 
    45.Mondal, K. K. & Mani, C. Investigation of the antibacterial properties of nanocopper against Xanthomonas axonopodis pv punicae, the incitant of pomegranate bacterial blight. Ann. Microbiol. 62(2), 889–893 (2012).CAS 
    Article 

    Google Scholar 
    46.Norman, D. J. & Chen, J. Effect of foliar application of titanium dioxide on bacterial blight of geranium and Xanthomonas leaf spot of poinsettia. HortScience 46(3), 426–428 (2011).CAS 
    Article 

    Google Scholar 
    47.Salem, H. F., Kam, E. & Sharaf, M. A. Formulation and evaluation of silver nanoparticles as antibacterial and antifungal agents with a minimal cytotoxic effect. Int. J. Drug Deliv. 3(2), 293 (2011).CAS 

    Google Scholar 
    48.Lamsa, K. et al. Inhibition effects of silver nanoparticles against powdery mildews on cucumber and pumpkin. Mycobiology 39(1), 26–32 (2011).Article 
    CAS 

    Google Scholar 
    49.Schofield, R. M. S. Metals in cuticular structures. Scorp. Biol. Res. 1, 234–256 (2001).
    Google Scholar 
    50.Oonincx, D. G. A. B. & Van der Poel, A. F. B. Effects of diet on the chemical composition of migratory locusts (Locusta migratoria). Zoo Biol. 30(1), 9–16 (2011).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    51.Van Broekhoven, S., Oonincx, D. G., Van Huis, A. & Van Loon, J. J. Growth performance and feed conversion efficiency of three edible mealworm species (Coleoptera: Tenebrionidae) on diets composed of organic by-products. J. Insect Physiol. 73, 1–10 (2015).PubMed 
    Article 
    CAS 
    PubMed Central 

    Google Scholar 
    52.Locke, M. & Nichol, H. Iron economy in insects: transport, metabolism, and storage. Annu. Rev. Entomol. 37(1), 195–215 (1992).CAS 
    Article 

    Google Scholar 
    53.Jones, M. W., de Jonge, M. D., James, S. A. & Burke, R. Elemental mapping of the entire intact Drosophila gastrointestinal tract. J. Biol. Inorg. Chem. 20(6), 979–987 (2015).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    54.Mir, A. H., Qamar, A., Qadir, I., Naqvi, A. H. & Begum, R. Accumulation and trafficking of zinc oxide nanoparticles in an invertebrate model, Bombyx mori, with insights on their effects on immuno-competent cells. Sci. Rep. 10(1), 1–14 (2020).Article 
    CAS 

    Google Scholar 
    55.Zhang, X. F., Shen, W. & Gurunathan, S. Silver nanoparticle-mediated cellular responses in various cell lines: an in vitro model. Int. J. Mol. Sci. 17(10), 1603 (2016).PubMed Central 
    Article 
    CAS 

    Google Scholar 
    56.Liau, S. Y., Read, D. C., Pugh, W. J., Furr, J. R. & Russell, A. D. Interaction of silver nitrate with readily identifiable groups: relationship to the antibacterialaction of silver ions. Lett. Appl. Microbiol. 25(4), 279–283 (1997).CAS 
    PubMed 
    Article 

    Google Scholar 
    57.Matsumura, Y., Yoshikata, K., Kunisaki, S. I. & Tsuchido, T. Mode of bactericidal action of silver zeolite and its comparison with that of silver nitrate. Appl. Environ. Microbiol. 69(7), 4278–4281 (2003).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    58.Gupta, A., Maynes, M. & Silver, S. Effects of halides on plasmid-mediated silver resistance in Escherichia coli. Appl. Environ. Microbiol. 64(12), 5042–5045 (1998).ADS 
    CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    59.Lee, J. H. et al. Biopersistence of silver nanoparticles in tissues from Sprague-Dawley rats. Part. Fibre Toxicol. 10(1), 1–14 (2013).Article 
    CAS 

    Google Scholar 
    60.Vinluan, R. D. III. & Zheng, J. Serum protein adsorption and excretion pathways of metal nanoparticles. Nanomedicine 10(17), 2781–2794 (2015).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    61.Armstrong, N., Ramamoorthy, M., Lyon, D., Jones, K. & Duttaroy, A. Mechanism of silver nanoparticles action on insect pigmentation reveals intervention of copper homeostasis. PLoS ONE 8(1), 53186 (2013).ADS 
    Article 
    CAS 

    Google Scholar 
    62.Chun, J. P., Choi, J. S. & Ahn, Y. J. Utilization of fruit bags coated with nano-silver for controlling black stain on fruit skin of ‘niitaka’pear (Pyrus pyrifolia). Hortic. Environ. Biotechnol. 51(4), 245–248 (2010).
    Google Scholar 
    63.Jo, Y. K., Kim, B. H. & Jung, G. Antifungal activity of silver ions and nanoparticles on phytopathogenic fungi. Plant Dis. 93(10), 1037–1043 (2009).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar  More

  • in

    Comparing recurrent convolutional neural networks for large scale bird species classification

    1.Rosenberg, K. V. et al. Decline of the North American avifauna. Science 366, 120–124 (2019).ADS 
    CAS 
    Article 

    Google Scholar 
    2.Inger, R. et al. Common European birds are declining rapidly while less abundant species numbers are rising. Ecol. Lett. 18, 28–36 (2015).Article 

    Google Scholar 
    3.Leach, E. C., Burwell, C. J., Ashton, L. A., Jones, D. N. & Kitching, R. L. Comparison of point counts and automated acoustic monitoring: Detecting birds in a rainforest biodiversity survey. Emu 116, 305–309 (2016).Article 

    Google Scholar 
    4.Drake, K. L., Frey, M., Hogan, D. & Hedley, R. Using digital recordings and sonogram analysis to obtain counts of yellow rails. Wildl. Soc. Bull. 40, 346–354 (2016).Article 

    Google Scholar 
    5.Lambert, K. T. & McDonald, P. G. A low-cost, yet simple and highly repeatable system for acoustically surveying cryptic species. Austral. Ecol. 39, 779–785 (2014).Article 

    Google Scholar 
    6.Burnett, K. Distribution, abundance, and acoustic characteristics of Kohala forest birds. Ph.D. thesis, University of Hawaii at Hilo (2020).7.Owen, K. et al. Bioacoustic analyses reveal that bird communities recover with forest succession in tropical dry forests. Avian Conserv. Ecol. 15, 25 (2020).Article 

    Google Scholar 
    8.Furnas, B. J., Landers, R. H. & Bowie, R. C. Wildfires and mass effects of dispersal disrupt the local uniformity of type I songs of hermit warblers in California. Auk 137, ukaa031 (2020).Article 

    Google Scholar 
    9.Aide, T. M. et al. Real-time bioacoustics monitoring and automated species identification. PeerJ 1, e103 (2013).Article 

    Google Scholar 
    10.Potamitis, I., Ntalampiras, S., Jahn, O. & Riede, K. Automatic bird sound detection in long real-field recordings: Applications and tools. Appl. Acoust. 80, 1–9 (2014).Article 

    Google Scholar 
    11.Stowell, D. & Plumbley, M. D. Automatic large-scale classification of bird sounds is strongly improved by unsupervised feature learning. PeerJ 2, e488 (2014).Article 

    Google Scholar 
    12.Tachibana, R. O., Oosugi, N. & Okanoya, K. Semi-automatic classification of birdsong elements using a linear support vector machine. PLoS ONE 9, e92584 (2014).ADS 
    Article 

    Google Scholar 
    13.Zheng, A. & Casari, A. Feature Engineering for Machine Learning: Principles and Techniques for Data Scientists (OReilly, London, 2018).
    Google Scholar 
    14.Najafabadi, M. M. et al. Deep learning applications and challenges in big data analytics. J. Big Data 2, 1 (2015).Article 

    Google Scholar 
    15.Dieleman, S., Brakel, P. & Schrauwen, B. Audio-based music classification with a pretrained convolutional network. In ISMIR (2011).16.Lee, H., Pham, P., Largman, Y. & Ng, A. Unsupervised feature learning for audio classification using convolutional deep belief networks. In Advances in Neural Information Processing Systems22 (2009).17.Bergler, C. et al. Orca-spot: An automatic killer whale sound detection toolkit using deep learning. Sci. Rep. 9, 10997 (2019).ADS 
    Article 

    Google Scholar 
    18.Zhong, M. et al. Beluga whale acoustic signal classification using deep learning neural network models. J. Acoust. Soc. Am. 147, 1834–1841 (2020).ADS 
    Article 

    Google Scholar 
    19.Strout, J. et al. Anuran call classification with deep learning. In 2017 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2662–2665 (2017).20.Salamon, J., Bello, J. P., Farnsworth, A. & Kelling, S. Fusing shallow and deep learning for bioacoustic bird species classification. In IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (2017).21.Stowell, D., Wood, M. D., Pamuła, H., Stylianou, Y. & Glotin, H. Automatic acoustic detection of birds through deep learning: The first bird audio detection challenge. Methods Ecol. Evol. 10, 368–380. https://doi.org/10.1111/2041-210X.13103 (2019).Article 

    Google Scholar 
    22.[Dataset] Cornell Lab of Ornithology. Cornell birdcall identification. https://www.kaggle.com/c/birdsong-recognition (accessed 15 Jun 2020).23.McFee, B. et al. librosa: Audio and music signal analysis in python. In Proceedings of the 14th Python in Science Conference 8 (2015).24.Simonyan, K. & Zisserman, A. Very deep convolutional networks for large-scale image recognition. In ICLR (2015).25.He, K., Zhang, X., Ren, S. & Sun, J. Deep residual learning for image recognition. In CVPR 770–778 (2016).26.Billerman, S. M., Keeney, B. K., Rodewald, P. G. & Schulenberg, T. S. (eds.) Birds of the World Cornell Laboratory of Ornithology, Ithaca, NY, USA, 2020). https://birdsoftheworld.org/bow/home.27.Gu, A., Dao, T., Ermon, S., Rudra, A. & Re, C. Hippo: Recurrent memory with optimal polynomial projections (2020). arXiv:2008.07669.28.Molau, S., Pitz, M., Schluter, R. & Ney, H. Computing mel-frequency cepstral coefficients on the power spectrum. In 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221) 1, 73–76 (2001). https://doi.org/10.1109/ICASSP.2001.940770.29.Choi, K., Fazekas, G. & Sandler, M. Automatic tagging using deep convolutional neural networks (2016). arXiv:1606.00298.30.Dieleman, S. & Schrauwen, B. End-to-end learning for music audio. In 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) 6964–6968 (2014).31.Voelker, A., Kajic, I. & Eliasmith, C. Legendre memory units: Continuous-time representation in recurrent neural networks. In NeurIPS (2019).32.Huang, G., Liu, Z., van der Maaten, L. & Weinberger, K. Q. Densely connected convolutional networks. In CVPR 4700–4708 (2017).33.Doriana, C., Leforta, R., Bonnela, J., Zaraderb, J.-L. & Adam, O. Bi-class classification of humpback whale sound units against complex background noise with deep convolution neural network (2017). arXiv:1702.02741.34.Narasimhan, R., Fern, X. Z. & Raich, R. Simultaneous segmentation and classification of bird song using cnn. In Proc. Int. Conf. Acoust. Speech, Signal Process 146–150 (2017).35.Sankupellay, M. & Konovalov, D. Bird call recognition using deep convolutional neural network, resnet-50 (2018).36.Zhang, L., Wang, D., Bao, C., Wang, Y. & Xu, K. Large-scale whale-call classification by transfer learning on multi-scale waveforms and time-frequency features. Appl. Sci. 9, 1020 (2019).Article 

    Google Scholar 
    37.Berman, P. C., Bronstein, M. M., Wood, R. J., Gero, S. & Gruber, D. F. Deep machine learning techniques for the detection and classification of sperm whale bioacoustics. Sci. Rep. 9, 12588 (2019).ADS 
    Article 

    Google Scholar 
    38.Zhong, M. et al. Improving passive acoustic monitoring applications to the endangered cook inlet beluga whale. J. Acoust. Soc. Am. 146, 3089–3089 (2019).ADS 
    Article 

    Google Scholar 
    39.Efremova, D. B., Sankupellay, M. & Konovalov, D. A. Data-efficient classification of birdcall through convolutional neural networks transfer learning. In 2019 Digital Image Computing: Techniques and Applications (DICTA) 1–8 (2019).40.Zhong, M. et al. Multispecies bioacoustic classification using transfer learning of deep convolutional neural networks with pseudo-labeling. Appl. Acoust. 166, 107375 (2020).Article 

    Google Scholar 
    41.Thakura, A., Thapar, D., Rajan, P. & Nigam, A. Deep metric learning for bioacoustic classification: Overcoming training data scarcity using dynamic triplet loss. J. Acoust. Soc. Am. 146, 534 (2019).ADS 
    Article 

    Google Scholar 
    42.Wang, Z., Yan, W. & Oates, T. Time series classification from scratch with deep neural networks: A strong baseline (2016). arXiv:1611.06455.43.Williams, R. J. & Zipser, D. A learning algorithm for continually running fully recurrent neural networks. Neural Comput. 1, 270–280 (1989).Article 

    Google Scholar 
    44.Rumelhart, D. E., Hinton, G. E. & Williams, R. J. Learning representations by back-propagating errors. Nature 323, 533–536 (1986).ADS 
    Article 

    Google Scholar 
    45.Bengio, Y., Simard, P. & Frasconi, P. Learning long-term dependencies with gradient descent is difficult. IEEE Trans. Neural Netw. 5, 157–166 (1994).CAS 
    Article 

    Google Scholar 
    46.Hochreiter, S. & Schmidhuber, J. Long short-term memory. Neural Comput. 9, 1735–1780 (1997).CAS 
    Article 

    Google Scholar 
    47.Cho, K., Van Merriënboer, B., Bahdanau, D. & Bengio, Y. On the properties of neural machine translation: Encoder–decoder approaches (2014). arXiv:1409.1259.48.Zeng, Y., Mao, H., Peng, D. & Yi, Z. Spectrogram based multi-task audio classification. Multimed. Tools Appl. 78, 3705–3722 (2019).Article 

    Google Scholar 
    49.Voelker, A. R. & Eliasmith, C. Improving spiking dynamical networks: Accurate delays, higher-order synapses, and time cells. Neural Comput. 30, 569–609 (2018).MathSciNet 
    Article 

    Google Scholar 
    50.Xu, Y., Kong, Q., Huang, Q., Wang, W. & Plumbley, M. D. Convolutional gated recurrent neural network incorporating spatial features for audio tagging (2017). arXiv:1702.07787.51.Keren, G. & Schuller, B. Convolutional RNN: An enhanced model for extracting features from sequential data (2016). arXiv:1602.05875.52.Lai, G., Chang, W.-C., Yang, Y. & Liu, H. Modeling long- and short-term temporal patterns with deep neural networks (2017). arXiv:1703.07015.53.Shiu, Y. et al. Deep neural networks for automated detection of marine mammal species. Sci. Rep. 10, 607 (2020).ADS 
    CAS 
    Article 

    Google Scholar 
    54.Espi, M., Fujimoto, M., Kubo, Y. & Nakatani, T. Spectrogram patch based acoustic event detection and classification in speech overlapping conditions. In 2014 4th Joint Workshop on Hands-free Speech Communication and Microphone Arrays (HSCMA) 117–121 (2014).55.Feng, L., Liu, S. & Yao, J. Music genre classification with paralleling recurrent convolutional neural network (2017). arXiv:1712.08370.56.Choi, K., Fazekas, G., Sandler, M. & Cho, K. Convolutional recurrent neural networks for music classification (2016). arXiv:1609.04243.57.Himawan, I., Towsey, M. & Roe, P. 3d convolution recurrent neural networks for bird sound detection. In Wood, M., Glotin, H., Stowell, D. & Stylianou, Y. (eds.) Proceedings of the 3rd Workshop on Detection and Classification of Acoustic Scenes and Events 1–4 (Detection and Classification of Acoustic Scenes and Events, 2018).58.Cakir, E., Adavanne, S., Parascandolo, G., Drossos, K. & Virtanen, T. Convolutional recurrent neural networks for bird audio detection. In 2017 25th European Signal Processing Conference (EUSIPCO) 1744–1748 (2017). More

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    High efficacy of microbial larvicides for malaria vectors control in the city of Yaounde Cameroon following a cluster randomized trial

    1.Guengant, J.-P. & May, J. F. African demography. Glob. J. Emerg. Mark. Econ. 5, 215–267 (2013).
    Google Scholar 
    2.Neiderud, C.-J. How urbanization affects the epidemiology of emerging infectious diseases. Infect. Ecol. Epidemiol. 5, 27060 (2015).PubMed 
    PubMed Central 

    Google Scholar 
    3.Eder, M. et al. Scoping review on vector-borne diseases in urban areas: Transmission dynamics, vectorial capacity and co-infection. Infect. Dis. Poverty 7, 1–24 (2018).Article 

    Google Scholar 
    4.Keiser, J. et al. Urbanization in sub-Saharan Africa and implication for malaria control. Am. J. Trop. Med. Hyg. 71, 118–127 (2004).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    5.Tusting, L. S. et al. Environmental temperature and growth faltering in African children: A cross-sectional study. Lancet Planet. Health 4, e116–e123. https://doi.org/10.1016/S2542-5196(20)30037-1 (2020).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    6.Hay, S. I. et al. Climate variability and malaria epidemics in the highlands of East Africa. Trends Parasitol. 21, 52–53 (2005).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    7.Bhatt, S. et al. The effect of malaria control on Plasmodium falciparum in Africa between 2000 and 2015. Nature 526, 207–211. https://doi.org/10.1038/nature15535 (2015).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    8.Kamau, A., Mogeni, P., Okiro, E. A., Snow, R. W. & Bejon, P. A systematic review of changing malaria disease burden in sub-Saharan Africa since 2000: Comparing model predictions and empirical observations. BMC Med. 18, 1–11 (2020).Article 

    Google Scholar 
    9.Nkumama, I. N., O’Meara, W. P. & Osier, F. H. Changes in malaria epidemiology in Africa and new challenges for elimination. Trends Parasitol. 33, 128–140 (2017).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    10.World Health Organization. World Malaria Report 2020: 20 Years of Global Progress and Challenges (WHO, 2020).Book 

    Google Scholar 
    11.World Health Organization. World Malaria Report 2012 (World Health Organization, 2012).Book 

    Google Scholar 
    12.Report, W. M. . Licence: CC BY-NC-SA 3.0 IGO (World Health Organization, 2019).
    Google Scholar 
    13.Ranson, H. & Lissenden, N. Insecticide resistance in African Anopheles mosquitoes: A worsening situation that needs urgent action to maintain malaria control. Trends Parasitol. 32, 187–196 (2016).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    14.Ranson, H. et al. Pyrethroid resistance in African anopheline mosquitoes: What are the implications for malaria control?. Trends Parasitol. 27, 91–98. https://doi.org/10.1016/j.pt.2010.08.004 (2011).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    15.Moyes, C. L. et al. Evaluating insecticide resistance across African districts to aid malaria control decisions. Proc. Natl. Acad. Sci. 117, 22042–22050 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    16.Moyes, C. L. et al. Contemporary status of insecticide resistance in the major Aedes vectors of arboviruses infecting humans. PLoS Negl. Tropi. Dis. 11, e0005625 (2017).Article 
    CAS 

    Google Scholar 
    17.Staedke, S. G. et al. LLIN evaluation in Uganda Project (LLINEUP)—Impact of long-lasting insecticidal nets with, and without, piperonyl butoxide on malaria indicators in Uganda: Study protocol for a cluster-randomised trial. Trials 20, 1–13 (2019).Article 

    Google Scholar 
    18.Hemingway, J. et al. Country-level operational implementation of the global plan for insecticide resistance management. Proc. Natl. Acad. Sci. 110, 9397–9402. https://doi.org/10.1073/pnas.1307656110 (2013).ADS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    19.Chanda, E. et al. Scale-up of integrated malaria vector control: Lessons from Malawi. Bull. World Health Organ. 94, 475 (2016).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    20.Derua, Y. A., Kweka, E. J., Kisinza, W. N., Githeko, A. K. & Mosha, F. W. Bacterial larvicides used for malaria vector control in sub-Saharan Africa: Review of their effectiveness and operational feasibility. Parasit. Vectors 12, 1–18 (2019).Article 

    Google Scholar 
    21.WHO. Larval Source Management: A Supplementary Measure for Malaria Vector Control (World Health Organization, 2013).
    Google Scholar 
    22.Dambach, P. et al. Reduction of malaria vector mosquitoes in a large-scale intervention trial in rural Burkina Faso using Bti based larval source management. Malar. J. 18, 1–9 (2019).Article 

    Google Scholar 
    23.Fillinger, U. & Lindsay, S. W. Larval source management for malaria control in Africa: Myths and reality. Malar J. https://doi.org/10.1186/1475-2875-10-353 (2011).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    24.Shousha, A. T. Species-eradication: The eradication of Anopheles gambioe from Upper Egypt, 1942–1945. Bull. World Health Organ. 1, 309 (1948).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    25.Utzinger, J., Tozan, Y. & Singer, B. H. Efficacy and cost-effectiveness of environmental management for malaria control. Trop Med. Int. Health 6, 677–687. https://doi.org/10.1046/j.1365-3156.2001.00769.x (2001).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    26.Fillinger, U., Ndenga, B., Githeko, A. & Lindsay, S. W. Integrated malaria vector control with microbial larvicides and insecticide-treated nets in western Kenya: A controlled trial. Bull. World Health Organ. 87, 655–665 (2009).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    27.Choi, L., Majambere, S. & Wilson, A. L. Larviciding to prevent malaria transmission. Cochrane Database Syst. Rev. 8, CD012736 (2019).
    PubMed 
    PubMed Central 

    Google Scholar 
    28.Tusting, L. S. et al. Socioeconomic development as an intervention against malaria: A systematic review and meta-analysis. Lancet 382, 963–972. https://doi.org/10.1016/s0140-6736(13)60851-x (2013).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    29.Choi, L. & Wilson, A. Larviciding to control malaria. Cochrane Database Syst. Rev. 2017, CD012736. https://doi.org/10.1002/14651858.CD012736 (2017).Article 
    PubMed Central 

    Google Scholar 
    30.Tusting, L. S. et al. Mosquito larval source management for controlling malaria. Cochrane Database Syst. Rev. 8, CD008923 (2013).
    Google Scholar 
    31.Geissbuhler, Y. et al. Microbial larvicide application by a large-scale, community-based program reduces malaria infection prevalence in urban Dar es Salaam. Tanzania. PLoS One. 4, e5107. https://doi.org/10.1371/journal.pone.0005107 (2009).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    32.Castro C. M. et al. Community-based environmental management for malaria control: evidence from a small-scale intervention in Dar es Salaam, Tanzania. Malar J. 8, 57 (2009)PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    33.Maheu-Giroux, M. & Castro, M. C. Impact of community-based larviciding on the prevalence of malaria infection in Dar es Salaam, Tanzania. PLoS ONE 8, e71638. https://doi.org/10.1371/journal.pone.0071638 (2013).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    34.World Health Organization. A Framework for Malaria Elimination (World Health Organization, 2017).
    Google Scholar 
    35.Wold Health Organization. 11th World Malaria Day “Ready to Beat Malaria” We are the Generation that can End Malaria (ed. World Health Organization). (World Health Oraganization, 2018).
    Google Scholar 
    36.Institut National de Statistiques and IFC. Enquête Démographique et de Santé du Cameroun (ed. INS. ICF.) 1–515 (INS, 2020).
    Google Scholar 
    37.Barbazan, P. et al. Control of Culex quinquefasciatus (Diptera: Culicidae) with Bacillus sphaericus in Maroua, Cameroon. J. Am. Mosq. Control Assoc. 13, 263–269 (1997).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    38.Hougard, J.-M. et al. Lutte contre Culex quinquefasciatus par Bacillus sphaericus: résultats d’une campagne pilote dans une agglomération urbaine d’Afrique équatoriale. Bulletin de l’organisation mondiale de la santé 71, 367–375 (1994).
    Google Scholar 
    39.Antonio-Nkondjio, C. et al. Anopheles gambiae distribution and insecticide resistance in the cities of Douala and Yaoundé (Cameroon): Influence of urban agriculture and pollution. Malar J. https://doi.org/10.1186/1475-2875-10-154 (2011).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    40.Tene Fossog, B. et al. Water quality and Anopheles gambiae larval tolerance to pyrethroids in the cities of Douala and Yaounde (Cameroon). J. Trop. Med. 2012, 1–10. https://doi.org/10.1155/2012/429817 (2012).Article 

    Google Scholar 
    41.Fondjo, E., Robert, V., Le Goff, G., Toto, J. & Carnevale, P. Urban malaria transmission in Yaounde (Cameroon). 2. Entomologic study in 2 semi urban districts. Bull. Soc. Pathol. Exot. 85, 57–63 (1992).CAS 
    PubMed 
    PubMed Central 

    Google Scholar 
    42.Manga, L., Robert, V., Messi, J., Desfontaines, M. & Le Carnevale, P. paludisme urbain à Yaoundé, Cameroun. 1-Etude entomologique dans deux quartiers centraux. Mém. Soc. R Belge Entomol. 35, 155–162 (1992).
    Google Scholar 
    43.Doumbe-Belisse, P. et al. High malaria transmission sustained by Anopheles gambiae sl occurring both indoors and outdoors in the city of Yaoundé, Cameroon. Wellcome Open Res. 3, 164 (2018).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    44.Talipouo, A. et al. Malaria prevention in the city of Yaoundé: Knowledge and practices of urban dwellers. Malar. J. 18, 167. https://doi.org/10.1186/s12936-019-2799-6 (2019).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    45.Djamouko-Djonkam, L. et al. Implication of Anopheles funestus in malaria transmission in the city of Yaoundé, Cameroon. Parasite 27, 10 (2020).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    46.Djamouko-Djonkam, L. et al. Spatial distribution of Anopheles gambiae sensu lato larvae in the urban environment of Yaoundé, Cameroon. Infect. Dis. Poverty 8, 1–15 (2019).Article 

    Google Scholar 
    47.Nchoutpouen, E. et al. Culex species diversity, susceptibility to insecticides and role as potential vector of Lymphatic filariasis in the city of Yaoundé, Cameroon. PLoS Negl. Trop. Dis. 13, e0007229 (2019).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    48.Bamou, R. et al. Status of insecticide resistance and its mechanisms in Anopheles gambiae and Anopheles coluzzii populations from forest settings in south Cameroon. Genes 10, 741 (2019).CAS 
    PubMed Central 
    Article 

    Google Scholar 
    49.Ngadjeu, C. S. et al. Influence of house characteristics on mosquito distribution and malaria transmission in the city of Yaoundé, Cameroon. Malar. J. 19, 53 (2020).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    50.Nkahe, D. L. et al. Fitness cost of insecticide resistance on the life-traits of a Anopheles coluzzii population from the city of Yaoundé, Cameroon. Wellcome Open Res. 5, 171 (2020).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    51.Historique-Meteo. https://www.historique-meteo.net/afrique/cameroun/ (2020).52.Govella, N. et al. A new tent trap for sampling exophagic and endophagic members of the Anopheles gambiae complex. Malar. J. 8, 157. https://doi.org/10.1186/1475-2875-8-157 (2009).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    53.Wilson, A. L. et al. Evidence-based vector control? Improving the quality of vector control trials. Trends Parasitol. 31, 380–390 (2015).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    54.Fillinger, U., Sonye, G., Killeen, G. F., Knols, B. G. & Becker, N. The practical importance of permanent and semipermanent habitats for controlling aquatic stages of Anopheles gambiae sensu lato mosquitoes: Operational observations from a rural town in western Kenya. Trop. Med. Int. Health 9, 1274–1289 (2004).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    55.Majambere, S., Lindsay, S. W., Green, C., Kandeh, B. & Fillinger, U. Microbial larvicides for malaria control in The Gambia. Malar. J. 6, 1–14 (2007).Article 

    Google Scholar 
    56.Gillies, M. T. & Coetzee, M. A Supplement to the Anophelinae of Africa South of the Sahara (Afrotropical Region) (South African Institute for Medical Research, 1987).
    Google Scholar 
    57.Majambere, S. et al. Is mosquito larval source management appropriate for reducing malaria in areas of extensive flooding in The Gambia? A cross-over intervention trial. Am. J. Trop. Med. Hyg. 82, 176–184 (2010).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    58.Fossog Tene, B. et al. Resistance to DDT in an urban setting: Common mechanisms implicated in both M and S forms of Anopheles gambiae in the City of Yaoundé, Cameroon. PLoS ONE 8, e61408. https://doi.org/10.1371/journal.pone.0061408 (2013).ADS 
    CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    59.Antonio-Nkondjio, C. et al. Investigation of mechanisms of bendiocarb resistance in Anopheles gambiae populations from the city of Yaoundé, Cameroon. Malar. J. 15, 424. https://doi.org/10.1186/s12936-016-1483-3 (2016).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    60.Antonio-Nkondjio, C. et al. Rapid evolution of pyrethroid resistance prevalence in Anopheles gambiae populations from the cities of Douala and Yaoundé (Cameroon). Malar. J. 14, 155. https://doi.org/10.1186/s12936-015-0675-6 (2015).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    61.Bamou, R. et al. Assessment of the Anophelinae blood seeking bionomic and pyrethroids resistance of local malaria vectors in the forest region of Southern Cameroon. JEZS 8, 1054–1062 (2020).
    Google Scholar 
    62.World Health Organization. The Role of Larviciding for Malaria Control in Sub-Saharan Africa: Interim Position Statement (World Health Organization, 2012).
    Google Scholar 
    63.Talipouo, A. et al. High insecticide resistance mediated by different mechanisms in Culex quinquefasciatus populations from the city of Yaoundé, Cameroon. Sci. Rep. 11, 1–11 (2021).Article 
    CAS 

    Google Scholar 
    64.Haines, A. et al. Promoting health and advancing development through improved housing in low-income settings. J. Urban Health 90, 810–831 (2013).PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    65.Tusting, L. S. et al. Housing improvements and malaria risk in sub-Saharan Africa: A multi-country analysis of survey data. PLoS Med. 14, e1002234. https://doi.org/10.1371/journal.pmed.1002234 (2017).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    66.Tusting, L. S. et al. The evidence for improving housing to reduce malaria: A systematic review and meta-analysis. Malar. J. 14, 209. https://doi.org/10.1186/s12936-015-0724-1 (2015).Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    67.Organization, W. H. Report of the Nineteenth WHOPES Working Group Meeting: WHO/HQ, Geneva, 8–11 February 2016: Review of Veeralin LN, VectoMax GR, Bactivec SC.(World Health Organization, 2016).68.Service M. Mosquito Ecology. Field Sampling Methods (Elsevier Applied Science, 1993).Book 

    Google Scholar 
    69.Edwards, F. W. Mosquitoes of the Ethiopian Region. HI.-Culicine Adults and Pupae. Mosquitoes of the Ethiopian Region. HI.-Culicine Adults and Pupae (1941).70.Edwards, F. W. Mosquitoes of the Ethiopian region. III, Culicine adults and pupae (Brit. Mus. Nat. Hist., 1941).71.Gillies, M. & Coetzee, M. A supplement to the Anophelinae of Africa south of the Sahara (Afrotropical region). Pub South Afr Inst Med Res 55, 143 (1987).
    Google Scholar 
    72.Wagtech, P. Water Quality Testing 1–139. https://www.palintest.com/product-categories/wagtech/ (Wagtech, 2012).73.Gillies, M. T. & DeMeillon, B. The Anopheline of Africa, south of the Sahara (Ethiopian zoogeographical region) Johannesburg: publication of south African Institute of Medical Research no. 54. (SAIMR, 1968).74.Gillies, T. & Coetzee, M. Supplement of the Anopheles of Africa south of Sahara (Afrotropical region) (Publication of the South African Institute of Medical Research, 1987).
    Google Scholar 
    75.Santolamazza, F. et al. Distribution of knock-down resistance mutations in Anopheles gambiae molecular forms in west and west-central Africa. Malar. J. 7, 74 (2008).PubMed 
    PubMed Central 
    Article 
    CAS 

    Google Scholar 
    76.Koekemoer, L., Kamau, L., Hunt, R. & Coetzee, M. A cocktail polymerase chain reaction assay to identify members of the Anopheles funestus (Diptera: culicidae) group. Am. J. Trop. Med. Hyg. 66, 804–811 (2002).CAS 
    PubMed 
    Article 
    PubMed Central 

    Google Scholar 
    77.Livak, K. J. Organization and mapping of a sequence on the Drosophila melanogaster X and Y chromosomes that is transcribed during spermatogenesis. Genetics 107, 611–634 (1984).CAS 
    PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    78.Wirtz, R., Burkot, T., Graves, P. & Andre, R. Field evaluation of enzymelinked immunosorbent assays for P. falciparum and P. vivax sporozoites in mosquitoes (Diptera: Culicidae) from Papua, new Guinea. J. Med. Entomol. 24, 433–437. https://doi.org/10.1093/jmedent/24.4.433 (1987).CAS 
    Article 
    PubMed 
    PubMed Central 

    Google Scholar 
    79.Bass, C. et al. PCR-based detection of Plasmodium in Anopheles mosquitoes: A comparison of a new high-throughput assay with existing methods. Malar. J. 7, 177 (2008).PubMed 
    PubMed Central 
    Article 

    Google Scholar 
    80.WHO. Test Procedures for Insecticide Resistance Monitoring in Malaria Vector Mosquitoes (WHO, 2013). https://doi.org/10.1371/journal.pone.0013140.Book 

    Google Scholar 
    81.Bass, C. et al. Detection of knockdown resistance (kdr) mutations in Anopheles gambiae: A comparison of two new high-throughput assays with existing methods. Malar. J. 6, 1–14. https://doi.org/10.1186/1475-2875-6-111 (2007).CAS 
    Article 

    Google Scholar 
    82.Mulla, M. S., Norland, L. R., Fanara, D. M., Darwazeh, H. A. & McKean, D. W. Control of chironomid midges in recreational lakes. J. Econ. Entomol. 64, 300–307 (1971).CAS 
    Article 

    Google Scholar  More

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    The rate and fate of N2 and C fixation by marine diatom-diazotroph symbioses

    Abundances of N2 fixing symbioses in the WTNATo date, the various marine symbiotic diatoms are notoriously understudied, and hence our understanding of their abundances and distribution patterns is limited [7]. In general, these symbiotic populations are capable of forming expansive blooms, but largely co-occur at low densities in tropical and subtropical waters with a few rare reports in temperate waters [26,27,28,29, 39,40,41,42]. The Rhizosolenia-Richelia symbioses have been more commonly reported in the North Pacific gyre [26, 27, 31], and the western tropical North Atlantic (WTNA) near the Amazon and Orinoco River plumes is an area where widespread blooms of the H. hauckii-Richelia symbioses are consistently recorded [28, 29, 42,43,44,45,46,47].In the summer of 2010, bloom densities (105−106 cells L−1) of the H. hauckii-Richelia symbioses were encountered at multiple stations with mesohaline (30–35 PSU) surface salinities (Supplementary Table 1). The R. clevei-Richelia symbioses were less abundant (2–30 cells L−1). Similar densities of H. hauckii-Richelia have been reported in the WTNA during spring (April–May) and summer seasons (June–July) (28–29; 46). In fall 2011, less dense symbiotic populations (0–50 cells L−1) were observed, and the dominant symbioses was the larger cell diameter (30–50 µm) H. membranaceus associated with Richelia. Previous observations of H. membranaeus-Richelia in this region are limited and reported as total cells (i.e., 12-218 cells) and highest numbers recorded in Aug–Sept in waters near the Bahama Islands [43]. On the other hand, Rhizosolenia-Richelia are even less reported in the WTNA, and most studies by quantitative PCR assays based on the nifH gene (for nitrogenase enzyme for N2 fixation) of the symbiont (44; 46–7). Unlike qPCR which cannot resolve if the populations are symbiotic or active for N2 fixation, the densities and activity reported here represent quantitative counts and measures of activity for symbiotic Rhizosolenia.The WTNA is largely influenced by both riverine and atmospheric dust deposition (e.g., Saharan dust) [48], including the silica necessary for the host diatom frustules, and trace metals (e.g., iron) necessary for photosynthesis by both partners and the nitrogenase enzyme (for N2 fixation) of the symbiont. We observed similar hydrographic conditions (i.e., low to immeasurable concentrations of dissolved N, sufficient concentrations of dissolved inorganic P and silicates, and variable surface salinities; 22; 28–29; 40–47) as reported earlier that favor high densities of H. hauckii-Richelia blooms. Unfortunately our data is too sparse to determine if these conditions are in fact priming and favoring the observed blooms of the H.hauckii-Richelia symbioses in summer 2010, and to a lesser extent in the Fall 2011.A biometric relationship between C and N activity and host biovolumeThe diatom-Richelia symbioses are considered highly host specific [10, 11], however, the driver of the specificity between partners remains unknown. We initially hypothesized that host selectivity could be related to the N2 fixation capacity of the symbiont. Moreover, it would be expected that the larger H. membranaceus and R. clevei hosts which are ~2–2.5 and 3.5–5 times, respectively, larger in cell dimensions than the H. hauckii cells would have higher N requirements (Supplementary Table 2). In fact, recently it was reported that the filament length of Richelia is positively correlated with the diameter of their respective hosts [22]. Thus, to determine if there is also a size dependent relationship between activity and cell biovolume, the enrichment of both 15N and 13C measured by SIMS was plotted as a function of symbiotic cell biovolume.Given the long incubation times (12 h) and previous work [32] that show fixation and transfer of reduced N to the host is rapid (i.e., within 30 min), we expected most if not all of the reduced N, or enrichment of 15N, to be transferred to the host diatom during the experiment (Fig. 1). Therefore, we measured and report the enrichment for the whole symbiotic cell, rather than the enrichment in the individual partners (Supplementary Table 2; Fig. 2). The enrichment of both 13C/12C and 15N/14N was significantly higher in the larger H. membranaceus-Richelia cells (atom % 13C: 1.5628–2.0500; atom % 15N: 0.8645–1.0200) than the enrichment measured in the smaller H. hauckii-Richelia cells (atom % 13C: 1.0700–1.3078; atom % 15N: 0.3642–0.7925) (Fig. 2) (13C, Mann–Whitney p = 0.009; 15N, Mann–Whitney p 50 symbiotic cells in a chain) were reported at station 2 with fully intact symbiotic Richelia filaments (2–3 vegetative cells and terminal heterocyst), and at station 25 chains were short (1–2 symbiotic cells) and associated with short Richelia filaments (only terminal heterocyst). Moreover, the symbiotic H. hauckii hosts possessed poor chloroplast auto-fluorescence at station 25 [46]. Given that the cells selected for NanoSIMS were largely single cells, rather than chains, we suspect that these cells were in a less than optimal cell state, which was also reflected in the low 13C/12C enrichment ratios and low estimated C-based growth rates (0.30–57 div d−1). These are particularly reduced compared to the growth rates recently reported for enrichment cultures of H. hauckii-Richelia (0.74–93 div d−1§) (Supplementary Table 2) [33].In 2011, higher cellular N2 fixation rates (15.4–27.2 fmols N cell−1 h−1) were measured for the large cell diameter H. membranaceus-Richelia, symbioses. Despite high rates of fixation, cell abundances were low (4–19 cells L−1), and resulted in a low overall contribution of the symbiotic diatoms to the whole water N2 ( >1%) and C-fixation ( >0.01%). The estimated C-based growth rates for H. membranaceus were high (1.9–3.5 div d−1), whereas estimated N-based growth rates (0.3–4 div d−1) were lower than previously published (33; 52–53). Hence the populations in 2011 were likely in a pre-bloom condition given the low cell densities.Estimating symbiotically derived reduced N to surface oceanTo date, determining the fate of the newly fixed N from these highly active but fragile symbiotic populations has been difficult. Thus, we attempted to estimate the excess N fixed and potentially available for release to the surround by using the numerous single cell-specific rates of N2 fixation determined by SIMS on the Hemiaulus spp.-Richelia symbioses (Supplementary Materials). Because the populations form chains during blooms and additionally sink, we calculated the size-dependent sinking rates for both single cells and chains ( >50 cells). Initially we hypothesized that sinking rates of the symbiotic associations would be more rapid than the N excretion rates, such that most newly fixed N would contribute less to the upper water column (sunlit).The sinking velocities were plotted (Fig. 5) as a function of cell radius at a range (min, max) of densities and included two different form resistances (∅ = 0.3 and 1.5). As expected, the combination of form resistance and density has a large impact on the sinking velocity. For example, a H. hauckii cell of similar radius (10 μm) and density (3300 kg m−3) but higher form resistance (0.3 vs. 1.5) sinks twice as fast at the lower form resistance (Fig. 5). This points to chain formation (e.g., increased form resistance) as a potential ecological adaptation to reduce sinking rates. Recently, colony formation was identified as an important phenotypic trait that could be traced back ancestrally amongst both free-living and symbiotic diatoms that presumably functions for maintaining buoyancy and enhancing light capture [22].Fig. 5: The influence of cell characteristics on estimated sinking velocity for symbiotic Hemiaulus spp.The range of diatom sinking speed predicted using the modified Stokes approximation for diatoms [74] and accounting for the symbioses (cylinders) having varying cell size characteristics (form resistance by altering chain length, density; Supplementary Table 4). Note that form resistance increases with chain length and that the longest chains would have sinking speeds less than 10 m d−1.Full size imageThe concentration of fixed N surrounding a H. hauckii and H. membranaceus cell were modeled (Supplementary Materials; Supplementary Table 4; Fig. 6). First, the cellular N requirement (QN, mol N cell−1) for a cell of known volume, V, as per the allometric formulation of Menden-Deuer and Lessard [71] is calculated by the following.$${{{{{{{mathrm{Q}}}}}}}}_{{{{{{{mathrm{N}}}}}}}} = (10^{ – 12}/12) times 0.76 ;times, {{{{{{{mathrm{V}}}}}}}}^{^{0.189}}$$
    (1)
    Fig. 6: The simplified case of diffusive nitrogen (N) exudate plumes for non-motile symbioses.The concentration of dissolved N (nmol L−1) is presented at of varying cell sizes (3 µm and 30 µm) for H. hauckii-Richelia (A and B, respectively) and H. membranaceus-Richelia (C and D, respectively) growing at specific growth rates of 0.4 d−1 (dashed red lines) or 0.68 d−1 (solid black lines). Exudation follows the same principle as diffusive uptake as per Kiorboe [72] in the absence of turbulence.Full size imageVolume calculations assume a cylindrical shape; whereas exudation assumes an equivalent spherical volume. Then, using published growth rates of 0.4 d−1 and 0.68 d−1 for the symbioses [52, 53], N uptake rate (VN) necessary to sustain the QN was determined. N loss was assumed to be a constant fraction (f) of the VN; this fraction was assumed to be 7.5% and 11% for H. hauckii and H. membranaceus, respectively, or the estimated excess N which was fixed given the assumed growth rate [31]. The excretion rate (EN) of the individual cells was then calculated as$${{{{{{{mathrm{E}}}}}}}}_{{{{{{{mathrm{N}}}}}}}} = {{{{{{{mathrm{fQ}}}}}}}}_{{{{{{{mathrm{N}}}}}}}}$$
    (2)
    The concentration of fixed N surrounding the cell (Cr) was iteratively calculated by the following:$${{{{{{{mathrm{C}}}}}}}}_{{{{{{{mathrm{r}}}}}}}} = {{{{{{{mathrm{E}}}}}}}}_{{{{{{{mathrm{N}}}}}}}}/(4pi * {{{{{mathrm{D}}}}}}* {{{{{mathrm{r}}}}}}_{{{{{mathrm{{x}}}}}}}) + {{{{{{{mathrm{C}}}}}}}}_{{{{{{{mathrm{i}}}}}}}}$$
    (3)
    The concentric radius (rx) as per Kiørboe [72] uses a diffusivity of N assumed to be 1.860 × 10−5 cm2 sec−1 and the background concentration of N (Ci) is assumed to be negligible. Figure 5 presents the results for the two symbioses: H. membranaceus and H. hauckii at the two growth rates and as chains or singlets. Mean sinking rates for cells with a high form resistance (e.g., chains) are More