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    Near-term climate change impacts on sub-national malaria transmission

    1.
    World Health Organization. Global Health Observatory (GHO) data: Malaria. (2018). Available at: https://www.who.int/gho/malaria/en/.
    2.
    World Health Organization. Climate Change and health. (2018). Available at: https://www.who.int/news-room/fact-sheets/detail/climate-change-and-health. Accessed 31st December 2019.

    3.
    World Health Organization. World Malaria Report 2018. WHO/HTM/GM (World Health Organization, Geneva, 2018).
    Google Scholar 

    4.
    Aal, R. & Elshayeb, A. A. The effects of climate changes on the distribution and spread of malaria in Sudan. Am. J. Environ. Eng. 1, 15–20 (2012).
    Article  Google Scholar 

    5.
    Abeku, T. A. et al. Effects of meteorological factors on epidemic malaria in Ethiopia: a statistical modelling approach based on theoretical reasoning. Parasitology 128, 585–593 (2004).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    6.
    Parham, P. E. & Michael, E. Modelling climate change and malaria transmission. Model. Parasite Transm. Control 673, 184–199 (2010).
    Article  Google Scholar 

    7.
    Gething, P. W. et al. Climate change and the global malaria recession. Nature 465, 342–345 (2010).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    8.
    Zhai, J. X. et al. Development of an empirical model to predict malaria outbreaks based on monthly case reports and climate variables in Hefei, China, 1990–2011. Acta Trop. 178, 148–154 (2018).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    9.
    Tompkins, A. M. & Thomson, M. C. Uncertainty in malaria simulations in the highlands of Kenya: relative contributions of model parameter setting, driving climate and initial condition errors. PLoS ONE 13, 16831 (2018).
    Article  CAS  Google Scholar 

    10.
    Moukam Kakmeni, F. M. et al. Spatial panorama of malaria prevalence in Africa under climate change and interventions scenarios. Int. J. Health Geogr. 17, 1–13 (2018).
    Article  Google Scholar 

    11.
    Hurtado, L. A., Calzada, J. E., Rigg, C. A., Castillo, M. & Chaves, L. F. Climatic fluctuations and malaria transmission dynamics, prior to elimination, in Guna Yala, República de Panamá. Malar. J. 17, 1–12 (2018).
    Article  Google Scholar 

    12.
    Ferrao, J. L., Niquisse, S., Mendes, J. M. & Painho, M. Mapping and modelling malaria risk areas using climate, socio-demographic and clinical variables in Chimoio, Mozambique. Int. J. Environ. Res. Public Health 15, 1–15 (2018).
    Article  Google Scholar 

    13.
    Semakula, H. M. et al. Prediction of future malaria hotspots under climate change in sub-Saharan Africa. Clim. Change 143, 415–428 (2017).
    ADS  CAS  Article  Google Scholar 

    14.
    Imai, C. et al. Associations between malaria and local and global climate variability in five regions in Papua New Guinea. Trop. Med. Health 44, 1–9 (2016).
    Article  Google Scholar 

    15.
    Caminade, C. et al. Impact of climate change on global malaria distribution. Proc. Natl. Acad. Sci. https://doi.org/10.1073/pnas.1302089111 (2014).
    Article  PubMed  Google Scholar 

    16.
    World Health Organization. World Malaria Report 2008 (World Health Organization, Geneva, 2008). ISBN 978 92 4 1564403

    17.
    Chizema-Kawesha, E. et al. Scaling up malaria control in Zambia: progress and impact 2005–2008. Am. J. Trop. Med. Hyg. 83, 480–488 (2010).
    PubMed  PubMed Central  Article  Google Scholar 

    18.
    Mukonka, V. et al. Diagnostic approaches to malaria in Zambia, 2009–2014. Geospat. Health 10, 330 (2015).
    PubMed  Article  Google Scholar 

    19.
    Chanda, E. et al. Insecticide resistance and the future of malaria control in Zambia. PLoS ONE 6, 1–9 (2011).
    Article  CAS  Google Scholar 

    20.
    Kamuliwo, M. et al. The changing burden of malaria and association with vector control interventions in Zambia using district-level surveillance data, 2006–2011. Malar. J. 12, 1–9 (2013).
    Article  Google Scholar 

    21.
    Shimaponda-Mataa, N. M., Tembo-Mwase, E., Gebreslasie, M., Achia, T. N. O. & Mukaratirwa, S. Modelling the influence of temperature and rainfall on malaria incidence in four endemic provinces of Zambia using semiparametric Poisson regression. Acta Trop. 166, 81–91 (2017).
    PubMed  Article  Google Scholar 

    22.
    President’s Malaria Initiative. President’s Malaria Initiative Zambia Malaria Operational Plan FY 2019 (2019).

    23.
    Pinchoff, J. et al. Predictive malaria risk and uncertainty mapping in Nchelenge District, Zambia: evidence of widespread, persistent risk and implications for targeted interventions. Am. J. Trop. Med. Hyg. 93, 1260–1267 (2015).
    PubMed  PubMed Central  Article  Google Scholar 

    24.
    Nkumama, I. N., O’Meara, W. P. & Osier, F. H. A. Changes in malaria epidemiology in Africa and new challenges for elimination. Trends Parasitol. 33, 128–140 (2017).
    PubMed  Article  Google Scholar 

    25.
    Bennett, A. et al. The relative contribution of climate variability and vector control coverage to changes in malaria parasite prevalence in Zambia 2006–2012. Parasites Vectors 9, 431 (2016).
    PubMed  PubMed Central  Article  Google Scholar 

    26.
    Ashton, R. A., Prosnitz, D., Andrada, A., Herrera, S. & Yé, Y. Evaluating malaria programmes in moderate- and low-transmission settings: practical ways to generate robust evidence. Malar. J. https://doi.org/10.1186/s12936-020-03158-z (2020).
    Article  PubMed  PubMed Central  Google Scholar 

    27.
    Carpenter, C. C. J., Pearson, G. W., Mitchell, V. S. & Oaks, S. C. Jr. Malaria: Obstacles and Opportunities (National Academies Press, Washington, 1991).
    Google Scholar 

    28.
    Benelli, G., Jeffries, C. L. & Walker, T. Biological control of mosquito vectors: past, present, and future. Insects 7, 52 (2016).
    PubMed Central  Article  PubMed  Google Scholar 

    29.
    Ukawuba, I. et al. Using rainfall and temperature data in the evaluation of national malaria control programs in Africa. Am. J. Trop. Med. Hyg. 97, 32–45 (2017).
    PubMed  PubMed Central  Article  Google Scholar 

    30.
    Martens, W. J., Jetten, T. H. & Focks, D. A. Sensitivity of malaria, schistosomiasis and dengue to global warming. Clim. Change 35, 145–156 (1997).
    Article  Google Scholar 

    31.
    Martens, W., Niessen, L. W., Rotmans, J., Jetten, T. H. & McMichael, A. J. Potential impact of global climate change on malaria risk. Environ. Health Perspect. 103, 458–464 (1995).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    32.
    Van Lieshout, M., Kovats, R. S., Livermore, M. T. J. & Martens, P. Climate change and malaria: analysis of the SRES climate and socio-economic scenarios. Glob. Environ. Change 14, 87–99 (2004).
    Article  Google Scholar 

    33.
    Martens, P. et al. Climate change and future populations at risk of malaria. Glob. Environ. Change 9, S89–S107 (1999).
    Article  Google Scholar 

    34.
    Arab, A., Jackson, M. C. & Kongoli, C. Modelling the effects of weather and climate on malaria distributions in West Africa. Malar. J. 13, 126 (2014).
    PubMed  PubMed Central  Article  Google Scholar 

    35.
    Central Statistical Office. 2010 census of population and housing: Population and Demographic Projections 2011–2035. 199 (2013).

    36.
    Maude, R. J., Mercado, C. E. G., Rowley, J., Ekapirat, N. & Dondorp, A. Estimating malaria disease burden in the Asia-Pacific. Wellcome Open Res. 4, 59 (2019).
    Article  Google Scholar 

    37.
    Van Buuren, S. Flexible Imputation of Missing Data (Chapman and Hall/CRC, Boca Raton, 2018).
    Google Scholar 

    38.
    Stekhoven, D. J. & Bühlmann, P. MissForest—non-parametric missing value imputation for mixed-type data. Bioinformatics 28, 112–118 (2011).
    PubMed  Article  CAS  Google Scholar 

    39.
    Funk, C. et al. The climate hazards infrared precipitation with stations—a new environmental record for monitoring extremes. Sci. Data 2, 150066 (2015).
    PubMed  PubMed Central  Article  Google Scholar 

    40.
    Saha, S. et al. NCEP Climate Forecast System Version 2 (CFSv2) Monthly Products (2012). https://doi.org/10.5065/D69021ZF

    41.
    Smets, B., Jacobs, T., Swinnen, E., Toté, C. & Wolfs, D. Gio Global Land Component-Lot I “Operation of the Global Land Component”, Framework Service Contract N° 388533 (JRC), Product User Manual Normalized Difference Vegetation Index (NDVI). 2.2 (2018).

    42.
    Smets, B. et al. A 10-daily 1km NDVI from METOP-AVHRR. 10 (2013).

    43.
    Hijmans, R. J. raster: Geographic data analysis and modeling. R package version 2.8–19. Vienna, Austria R Found. Retrieved from https://CRAN.R-project.org/package=rasterImage (2019).

    44.
    Colón-González, F. J., Tompkins, A. M., Biondi, R., Bizimana, J. P. & Namanya, D. B. Assessing the effects of air temperature and rainfall on malaria incidence: an epidemiological study across Rwanda and Uganda. Geospat. Health 11, 1–2 (2016).
    Article  Google Scholar 

    45.
    Suk, J. E. Climate change, malaria, and public health: accounting for socioeconomic contexts in past debates and future research. Wiley Interdiscip. Rev. Clim. Change 7, 551–568 (2016).
    Article  Google Scholar 

    46.
    Mohammadkhani, M., Khanjani, N., Bakhtiari, B. & Sheikhzadeh, K. The relation between climatic factors and malaria incidence in Kerman, South East of Iran. Parasite Epidemiol. Control 1, 205–210 (2016).
    PubMed  PubMed Central  Article  Google Scholar 

    47.
    Okuneye, K. & Gumel, A. B. Analysis of a temperature- and rainfall-dependent model for malaria transmission dynamics. Math. Biosci. 287, 72–92 (2017).
    MathSciNet  PubMed  MATH  Article  Google Scholar 

    48.
    Krefis, A. C. et al. Modeling the relationship between precipitation and malaria incidence in children from a holoendemic area in Ghana. Am. J. Trop. Med. Hyg. 84, 285–291 (2011).
    PubMed  PubMed Central  Article  Google Scholar 

    49.
    Abiodun, G. J., Maharaj, R., Witbooi, P. & Okosun, K. O. Modelling the influence of temperature and rainfall on the population dynamics of Anopheles arabiensis. Malar. J. 15, 1–15 (2016).
    Article  Google Scholar 

    50.
    Blanford, J. I. et al. Implications of temperature variation for malaria parasite development across Africa. Sci. Rep. 3, 1300 (2013).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    51.
    Odongo-Aginya, E., Ssegwanyi, G., Kategere, P. & Vuzi, P. C. Relationship between malaria infection intensity and rainfall pattern in Entebbe peninsula, Uganda. Afr. Health Sci. 5, 238–245 (2005).
    CAS  PubMed  PubMed Central  Google Scholar 

    52.
    Darkoh, E. L., Larbi, J. A. & Lawer, E. A. A weather-based prediction model of malaria prevalence in Amenfi West District, Ghana. Malar. Res. Treat. https://doi.org/10.1155/2017/7820454 (2017).
    Article  PubMed  PubMed Central  Google Scholar 

    53.
    Kilian, A. H., Langi, P., Talisuna, A. & Kabagambe, G. Rainfall pattern, El Nino and malaria in Uganda. Trans. R. Soc. Trop. Med. Hyg. 93, 22–23 (1999).
    CAS  PubMed  Article  Google Scholar 

    54.
    Phung, D., Talukder, M. R. R., Rutherford, S. & Chu, C. A climate-based prediction model in the high-risk clusters of the Mekong Delta region, Vietnam: towards improving dengue prevention and control. Trop. Med. Int. Health 21, 1324–1333 (2016).
    PubMed  Article  Google Scholar 

    55.
    Wu, X., Lu, Y., Zhou, S., Chen, L. & Xu, B. Impact of climate change on human infectious diseases: empirical evidence and human adaptation. Environ. Int. 86, 14–23 (2016).
    PubMed  Article  Google Scholar 

    56.
    Brooks, M. E. et al. glmmTMB balances speed and flexibility among packages for zero-inflated generalized linear mixed modeling. R J. 9, 378–400 (2017).
    Article  Google Scholar 

    57.
    Jiang, Z., Raymond, M., Shi, D. & DiStefano, C. Using a linear mixed-effect model framework to estimate multivariate generalizability theory parameters in R. Behav. Res. Methods https://doi.org/10.3758/s13428-020-01399-z (2020).
    Article  PubMed  Google Scholar 

    58.
    Napier, G., Lee, D., Robertson, C. & Lawson, A. A Bayesian space-time model for clustering areal units based on their disease trends. Biostatistics 00, 1–17 (2018).
    CAS  Google Scholar 

    59.
    Gelman, A., Carlin, J. B., Stern, H. S. & Rubin, D. B. Bayesian data analysis. Technometrics 46, 696 (2004).
    MATH  Google Scholar 

    60.
    Hamra, G., MacLehose, R. & Richardson, D. Markov chain monte carlo: an introduction for epidemiologists. Int. J. Epidemiol. 42, 627–634 (2013).
    PubMed  PubMed Central  Article  Google Scholar 

    61.
    Lee, D., Rushworth, A. & Napier, G. Spatio-temporal areal unit modeling in R with conditional autoregressive priors using the CARBayesST package. J. Stat. Softw. 84, 1–39 (2018).
    CAS  Article  Google Scholar 

    62.
    Jaiswal, R. K., Lohani, A. K. & Tiwari, H. L. Statistical analysis for change detection and trend assessment in climatological parameters. Environ. Process. 2, 729–749 (2015).
    Article  Google Scholar 

    63.
    Wijngaard, J. B., Klein Tank, A. M. G. & Können, G. P. Homogeneity of 20th century European daily temperature and precipitation series. Int. J. Climatol. 23, 679–692 (2003).
    Article  Google Scholar 

    64.
    Hachigonta, S. & Reason, C. J. C. Interannual variability in dry and wet spell characteristics over Zambia. Clim. Res. 32, 49–62 (2006).
    Article  Google Scholar 

    65.
    Kaluba, P., Verbist, K. M. J., Cornelis, W. M. & Van Ranst, E. Spatial mapping of drought in Zambia using regional frequency analysis. Hydrol. Sci. J. https://doi.org/10.1080/02626667.2017.1343475 (2017).
    Article  Google Scholar 

    66.
    Waldman, K. B. et al. Cognitive biases about climate variability in smallholder farming systems in Zambia. Weather Clim. Soc. https://doi.org/10.1175/WCAS-D-18-0050.1 (2019).
    Article  Google Scholar 

    67.
    Musonda, B. Rainfall and Temperature Characteristic Over Zambia (2013).

    68.
    Mubanga, K. H. & Umar, B. B. Climate variability and change in Southern Zambia: 1910 to 2009 Kabwe. In 2014 International Conference on Intelligent Agriculture (ICOIA) (2015). https://doi.org/10.7763/IPCBEE

    69.
    Zambian Ministry of Health. Zambia National Malaria Indicator Survey 2006. 38–41 (2006).

    70.
    Zambian Ministry of Health. The Zambia National Malaria Indicator Survey 2008 (2008).

    71.
    Zambian Ministry of Health. Zambia National Malaria Indicator Survey 2012 (2012).

    72.
    Zambian Ministry of Health. Zambia Malaria Indicator Survey 2015 (2015).

    73.
    Zambian Ministry of Health. Zambia National Malaria Indicator Survey 2010. Malariasurveys.org (2010).

    74.
    Kilian, A. et al. Evidence for a useful life of more than three years for a polyester-based long-lasting insecticidal mosquito net in Western Uganda. Malar. J. 10, 299 (2011).
    PubMed  PubMed Central  Article  Google Scholar 

    75.
    Tan, K. R. et al. A longitudinal study of the durability of long-lasting insecticidal nets in Zambia. Malar. J. 15, 1–12 (2016).
    Article  CAS  Google Scholar 

    76.
    Pulkki-Brännström, A.-M., Wolff, C., Brännström, N. & Skordis-Worrall, J. Cost and cost effectiveness of long-lasting insecticide-treated bed nets-a model-based analysis. Cost Eff. Resour. Alloc. 10, 5 (2012).
    PubMed  PubMed Central  Article  Google Scholar 

    77.
    Stuckey, E. M. et al. Simulation of malaria epidemiology and control in the highlands of western Kenya. Malar. J. https://doi.org/10.1186/1475-2875-11-357 (2012).
    Article  PubMed  PubMed Central  Google Scholar 

    78.
    Carter, R., Mendis, K. N. & Roberts, D. Spatial targeting of interventions against malaria. Bull. World Health Organ. 78, 1401–1411 (2000).
    CAS  PubMed  PubMed Central  Google Scholar 

    79.
    Bousema, T. et al. The impact of hotspot-targeted interventions on malaria transmission: study protocol for a cluster-randomized controlled trial. Trials 14, 36 (2013).
    PubMed  PubMed Central  Article  Google Scholar 

    80.
    Bousema, T. et al. The impact of hotspot-targeted interventions on malaria transmission in Rachuonyo South District in the Western Kenyan Highlands: a cluster-randomized controlled trial. PLoS Med. 13, e1001993 (2016).
    PubMed  PubMed Central  Article  Google Scholar 

    81.
    Walker, P. G. T., Griffin, J. T., Ferguson, N. M. & Ghani, A. C. Estimating the most efficient allocation of interventions to achieve reductions in Plasmodium falciparum malaria burden and transmission in Africa: a modelling study. Lancet Glob. Health 4, e474–e484 (2016).
    PubMed  Article  Google Scholar 

    82.
    World Health Organisation (WHO). Malaria Prevention Works: Let’s Close the Gap (WHO, Geneva, 2017).
    Google Scholar 

    83.
    Kitojo, C. et al. Estimating malaria burden among pregnant women using data from antenatal care centres in Tanzania: a population-based study. Lancet Glob. Health 7, e1695–e1705 (2019).
    PubMed  Article  Google Scholar 

    84.
    Coldiron, M. E., Von Seidlein, L. & Grais, R. F. Seasonal malaria chemoprevention: successes and missed opportunities. Malar. J. https://doi.org/10.1186/s12936-017-2132-1 (2017).
    Article  PubMed  PubMed Central  Google Scholar 

    85.
    Ndiaye, J. L. A. et al. Seasonal malaria chemoprevention combined with community case management of malaria in children under 10 years of age, over 5months, in south-east senegal: a cluster randomized trial. PLoS Med. https://doi.org/10.1371/journal.pmed.1002762 (2019).
    Article  PubMed  PubMed Central  Google Scholar 

    86.
    Issiaka, D. et al. Impact of seasonal malaria chemoprevention on hospital admissions and mortality in children under 5 years of age in Ouelessebougou, Mali. Malar. J. https://doi.org/10.1186/s12936-020-03175-y (2020).
    Article  PubMed  PubMed Central  Google Scholar 

    87.
    Lasry, E. et al. Seasonal malaria chemoprevention, three years of implementation. Am. J. Trop. Med. Hyg. 51, 523–532 (2015).
    Google Scholar 

    88.
    Cissé, B. et al. Effectiveness of seasonal malaria chemoprevention in children under ten years of age in senegal: a stepped-wedge cluster-randomised trial. PLoS Med. https://doi.org/10.1371/journal.pmed.1002175 (2016).
    Article  PubMed  PubMed Central  Google Scholar 

    89.
    Chandramohan, D. et al. Effect of adding azithromycin to seasonal malaria chemoprevention. N. Engl. J. Med. https://doi.org/10.1056/nejmoa1811400 (2019).
    Article  PubMed  Google Scholar 

    90.
    Ndiaye, J. L. A. et al. Impact of seasonal malaria chemoprevention after 3 years at scale in Southern Senegal. Am. J. Trop. Med. Hyg. 19, 103 (2017).
    Google Scholar 

    91.
    Braganza, K., Karoly, D. J. & Arblaster, J. M. Diurnal temperature range as an index of global climate change during the twentieth century. Geophys. Res. Lett. 31, 1–4 (2004).
    Article  Google Scholar 

    92.
    Roget, E. & Khan, V. M. Decadal differences of the diurnal temperature range in the Aral Sea region at the turn of the century. Tellus A Dyn. Meteorol. Oceanogr. 70, 1–12 (2018).
    Article  Google Scholar 

    93.
    Lubinda, J. The spatio-temporal impact of climate change on malaria transmission, control and elimination in southern Africa: the case of Zambia (Unpublished doctoral dissertation). (Ulster University, 2020).

    94.
    Chaves, L. F. & Koendraat, C. J. Climate change and highland malaria: fresh air for a hot debate the quarterly review of bilology. J. Chem. Inf. Model. 53, 1689–1699 (2010).
    Google Scholar 

    95.
    Murdock, C. C., Sternberg, E. D. & Thomas, M. B. Malaria transmission potential could be reduced with current and future climate change. Sci. Rep. 6, 27771 (2016).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    96.
    Paaijmans, K. P. et al. Influence of climate on malaria transmission depends on daily temperature variation. Proc. Natl. Acad. Sci. 107, 15135–15139 (2010).
    ADS  CAS  PubMed  Article  Google Scholar 

    97.
    Thomson, M. C. et al. Using rainfall and temperature data in the evaluation of national malaria control programs in Africa. Am. J. Trop. Med. Hyg. https://doi.org/10.4269/ajtmh.16-0696 (2017).
    Article  PubMed  PubMed Central  Google Scholar 

    98.
    Sena, L., Deressa, W. & Ali, A. Correlation of climate variability and malaria: a retrospective comparative study, Southwest Ethiopia. Ethiop. J. Health Sci. 25, 129 (2015).
    PubMed  PubMed Central  Article  Google Scholar 

    99.
    Kiszewski, A. E. & Teklehaimanot, A. A review of the clinical and epidemiologic burdens of epidemic malaria. Am. J. Trop. Med. Hyg. 71, 128–135 (2004).
    PubMed  Article  Google Scholar 

    100.
    Lobo, N. F. et al. Unexpected diversity of Anopheles species in Eastern Zambia: implications for evaluating vector behavior and interventions using molecular tools. Sci. Rep. 5, 17952 (2015).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    101.
    Moyes, C. L. et al. Analysis-ready datasets for insecticide resistance phenotype and genotype frequency in African malaria vectors. Sci. Data https://doi.org/10.1038/s41597-019-0134-2 (2019).
    Article  PubMed  PubMed Central  Google Scholar 

    102.
    President’s Malaria Initiative. President’s Malaria Initiative 2016—Zambia. 1–45 (2016).

    103.
    Hancock, P. A. et al. Mapping trends in insecticide resistance phenotypes in African malaria vectors. PLoS Biol. https://doi.org/10.1371/journal.pbio.3000633 (2020).
    Article  PubMed  PubMed Central  Google Scholar 

    104.
    World Health Organization. INDOOR RESIDUAL SPRAYING: An Operational Manual for Indoor Residual Spraying (IRS) for Malaria Transmission Control and Elimination (WHO Press, Cleveland, 2015).
    Google Scholar 

    105.
    Mukonka, V. M. et al. High burden of malaria following scale-up of control interventions in Nchelenge District, Luapula Province, Zambia. Malar. J. 13, 153 (2014).
    PubMed  PubMed Central  Article  Google Scholar  More

  • in

    Fine-scale genetic structure in the critically endangered red-fronted macaw in the absence of geographic and ecological barriers

    1.
    Orsini, L., Vanoverbeke, J., Swillen, I., Mergeay, J. & De Meester, L. Drivers of population genetic differentiation in the wild: isolation by dispersal limitation, isolation by adaptation and isolation by colonization. Mol. Ecol. 22, 5983–5999. https://doi.org/10.1111/mec.12561 (2013).
    Article  PubMed  Google Scholar 
    2.
    Legrand, D. et al. Eco-evolutionary dynamics in fragmented landscapes. Ecography 40, 9–25. https://doi.org/10.1111/ecog.02537 (2017).
    Article  Google Scholar 

    3.
    Slatkin, M. Gene flow and the geographic structure of natural populations. Science 236, 787–792. https://doi.org/10.1126/science.3576198 (1987).
    CAS  Article  PubMed  PubMed Central  ADS  Google Scholar 

    4.
    Dolby, G. A., Dorsey, R. J. & Graham, M. R. A legacy of geo-climatic complexity and genetic divergence along the lower Colorado River: Insights from the geological record and 33 desert-adapted animals. J. Biogeogr. 46, 2479–2505. https://doi.org/10.1111/jbi.13685 (2019).
    Article  Google Scholar 

    5.
    Stevens, V. M. et al. A comparative analysis of dispersal syndromes in terrestrial and semi-terrestrial animals. Ecol. Lett. 17, 1039–1052. https://doi.org/10.1111/ele.12303 (2014).
    Article  PubMed  Google Scholar 

    6.
    Ross, K. G. Molecular ecology of social behaviour: analyses of breeding systems and genetic structure. Mol. Ecol. 10, 265–284. https://doi.org/10.1046/j.1365-294X.2001.01191.x (2001).
    CAS  Article  PubMed  Google Scholar 

    7.
    Beck, N. R., Peakall, R. & Heinsohn, R. Social constraint and an absence of sex-biased dispersal drive fine-scale genetic structure in white-winged choughs. Mol. Ecol. 17, 4346–4358. https://doi.org/10.1111/j.1365-294X.2008.03906.x (2008).
    CAS  Article  PubMed  Google Scholar 

    8.
    Morinha, F. et al. Extreme genetic structure in a social bird species despite high dispersal capacity. Mol. Ecol. 26, 2812–2825. https://doi.org/10.1111/mec.14069 (2017).
    Article  PubMed  Google Scholar 

    9.
    Marzluff, J. M. & Angell, T. Cultural coevolution: how the human bond with crows and ravens extends theory and raises new questions. J. Ecol. Anthropol. 9, 69–75 (2005).
    Google Scholar 

    10.
    Toft, C. A. & Wright, T. F. Parrots of the wild: A natural history of the world’s most captivating birds (Univ. California Press, Oakland, California, USA, 2015).
    Google Scholar 

    11.
    Armansin, N. C. et al. Social barriers in ecological landscapes: The social resistance hypothesis. Trends Ecol. Evol. 35, 137–148. https://doi.org/10.1016/j.tree.2019.10.001 (2020).
    Article  PubMed  Google Scholar 

    12.
    Abdelkrim, J., Hunt, G. R., Gray, R. D. & Gemmell, N. J. Population genetic structure and colonisation history of the tool-using New Caledonian Crow. PLoS ONE 7, e36608. https://doi.org/10.1371/journal.pone.0036608 (2012).
    CAS  Article  PubMed  PubMed Central  ADS  Google Scholar 

    13.
    Rutz, C., Ryder, T. B. & Fleischer, R. C. Restricted gene flow and fine-scale population structuring in tool using New Caledonian crows. Naturwissenschaften 99, 313–320. https://doi.org/10.1007/s00114-012-0904-6 (2012).
    CAS  Article  PubMed  ADS  Google Scholar 

    14.
    Wright, T. F., Rodriguez, A. M. & Fleischer, R. C. Vocal dialects, sex-biased dispersal, and microsatellite population structure in the parrot Amazona auropalliata. Mol. Ecol. 14, 1197–1205. https://doi.org/10.1111/j.1365-294X.2005.02466.x (2005).
    CAS  Article  PubMed  Google Scholar 

    15.
    Hobson, E. A., Avery, M. L. & Wright, T. F. The socioecology of Monk Parakeets: Insights into parrot social complexity. Auk 131, 756–775. https://doi.org/10.1642/AUK-14-14.1 (2014).
    Article  Google Scholar 

    16.
    Wright, T. F. & Dahlin, C. R. Vocal dialects in parrots: patterns and processes of cultural evolution. Emu 118, 50–66. https://doi.org/10.1080/01584197.2017.1379356 (2018).
    Article  PubMed  Google Scholar 

    17.
    Smith-Vidaurre, G., Araya-Salas, M. & Wright, T. F. Individual signatures outweigh social group identity in contact calls of a communally nesting parrot. Behav. Ecol. 31, 448–458. https://doi.org/10.1093/beheco/arz202 (2020).
    Article  Google Scholar 

    18.
    Lowe, W. H., Kovach, R. P. & Allendorf, F. W. Population genetics and demography unite ecology and evolution. Trends Ecol. Evol. 32, 141–152. https://doi.org/10.1016/j.tree.2016.12.002 (2017).
    Article  PubMed  Google Scholar 

    19.
    Liedvogel, M., Åkesson, S. & Bensch, S. The genetics of migration on the move. Trends Ecol. Evol. 26, 561–569. https://doi.org/10.1016/j.tree.2011.07.009 (2011).
    Article  PubMed  Google Scholar 

    20.
    Méndez, M., Vögeli, M., Tella, J. L. & Godoy, J. A. Joint effects of population size and isolation on genetic erosion in fragmented populations: finding fragmentation thresholds for management. Evol. Appl. 7, 506–518. https://doi.org/10.1111/eva.12154 (2014).
    Article  PubMed  PubMed Central  Google Scholar 

    21.
    Klauke, N., Schaefer, H. M., Bauer, M. & Segelbacher, G. Limited dispersal and significant fine-scale genetic structure in a tropical montane parrot species. PLoS ONE 11, e0169165. https://doi.org/10.1371/journal.pone.0169165 (2016).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    22.
    Monge, O., Schmidt, K., Vaughan, C. & Gutiérrez-Espeleta, G. Genetic patterns and conservation of the Scarlet Macaw (Ara macao) in Costa Rica. Conserv. Genet. 17, 745–750. https://doi.org/10.1007/s10592-015-0804-3 (2016).
    Article  Google Scholar 

    23.
    Kopps, A. M. et al. Cultural transmission of tool use combined with habitat specializations leads to fine-scale genetic structure in bottlenose dolphins. Proc. R. Soc. Lond., B, Biol. Sci. 281, 20133245. https://doi.org/10.1098/rspb.2013.3245 (2014).

    24.
    Foote, A. D. et al. Genome-culture coevolution promotes rapid divergence of killer whale ecotypes. Nat. Commun. 7, 1–12. https://doi.org/10.1038/ncomms11693 (2016).
    CAS  Article  Google Scholar 

    25.
    Pilot, M., Dahlheim, M. E. & Hoelzel, A. R. Social cohesion among kin, gene flow without dispersal and the evolution of population genetic structure in the killer whale (Orcinus orca). J. Evol. Biol. 23, 20–31. https://doi.org/10.1111/j.1420-9101.2009.01887.x (2010).
    CAS  Article  PubMed  Google Scholar 

    26.
    Estrada, A. Reintroduction of the scarlet macaw (Ara macao cyanoptera) in the tropical rainforests of Palenque, Mexico: Project design and first year progress. Trop. Conserv. Sci. 7, 342–364. https://doi.org/10.1177/194008291400700301 (2014).
    Article  Google Scholar 

    27.
    Lopes, A. R. et al. The influence of anti-predator training, personality and sex in the behavior, dispersion and survival rates of translocated captive-raised parrots. Glob Ecol. Conserv. 11, 146–157. https://doi.org/10.1016/j.gecco.2017.05.001 (2017).
    Article  Google Scholar 

    28.
    Pitter, E. & Christiansen, M. B. Ecology, status and conservation of the Red-fronted Macaw Ara rubrogenys. Bird Conserv. Int. 5, 61–78. https://doi.org/10.1017/S0959270900002951 (1995).
    Article  Google Scholar 

    29.
    Meyer, C. Spatial ecology and conservation of the endemic and endangered Red-fronted Macaw (Ara rubrogenys) in the Bolivian Andes. Diploma Thesis. Centre for Nature Conservation, Faculty of Biology, Georg-August University Göttingen (2010).

    30.
    Tella, J. L., Rojas, A., Carrete, M. & Hiraldo, F. Simple assessments of age and spatial population structure can aid conservation of poorly known species. Biol. Conserv. 167, 425–434. https://doi.org/10.1016/j.biocon.2013.08.035 (2013).
    Article  Google Scholar 

    31.
    Leite, K. C. E., Seixas, G. H. F., Berkunsky, I., Collevatti, R. G. & Caparroz, R. Population genetic structure of the blue-fronted Amazon (Amazona aestiva, Psittacidae: Aves) based on nuclear microsatellite loci: Implications for conservation. Genet. Mol. Res. 7, 819–829. https://doi.org/10.4238/vol7-3gmr474 (2008).
    CAS  Article  PubMed  Google Scholar 

    32.
    Masello, J. F. et al. The high Andes, gene flow and a stable hybrid zone shape the genetic structure of a wide-ranging South American parrot. Front. Zool. 8, 16. https://doi.org/10.1186/1742-9994-8-16 (2011).
    Article  PubMed  PubMed Central  Google Scholar 

    33.
    Olah, G., Heinsohn, R. G., Brightsmith, D. J. & Peakall, R. The application of non-invasive genetic tagging reveals new insights into the clay lick use by macaws in the Peruvian Amazon. Conserv. Genet. 18, 1037–1046. https://doi.org/10.1007/s10592-017-0954-6 (2017).
    Article  Google Scholar 

    34.
    Ellegren, H. et al. Microsatellite evolution: A reciprocal study of repeat lengths at homologous loci in cattle and sheep. Mol. Biol. Evol. 14, 854–860. https://doi.org/10.1093/oxfordjournals.molbev.a025826 (1997).
    CAS  Article  PubMed  Google Scholar 

    35.
    Mills, L. S., Citta, J. J., Lair, K. P., Schwartz, M. K. & Tallmon, D. A. Estimating animal abundance using noninvasive DNA sampling: Promise and pitfalls. Ecol. Appl. 10, 283–294. https://doi.org/10.1890/1051-0761(2000)010[0283:EAAUND]2.0.CO;2 (2000).
    Article  Google Scholar 

    36.
    Alcaide, M., Serrano, D., Tella, J. L. & Negro, J. J. Strong philopatry derived from capture-recapture methods does not lead to fine-scale genetic differentiation in lesser kestrels. J. Anim. Ecol. 78, 468–475. https://doi.org/10.1111/j.1365-2656.2008.01493.x (2009).
    Article  PubMed  Google Scholar 

    37.
    Barrowclough, G. F. Gene flow, effective population sizes, and genetic variance components in birds. Evolution 34, 789–798. https://doi.org/10.2307/2408033 (1980).
    Article  PubMed  Google Scholar 

    38.
    Frankham, R., Ballou, J. D. & Briscoe, D. A. Introduction to conservation genetics (Cambridge University Press, Cambridge, 2010).
    Google Scholar 

    39.
    Jones, O. R. & Wang, J. A comparison of four methods for detecting weak genetic structure from marker data. Ecol. Evol. 2, 1048–1055. https://doi.org/10.1002/ece3.237 (2012).
    Article  PubMed  PubMed Central  Google Scholar 

    40.
    van Rees, C. B., Reed, J. M., Wilson, R. E., Underwood, J. G. & Sonsthagen, S. A. Small-scale genetic structure in an endangered wetland specialist: possible effects of landscape change and population recovery. Conserv. Genet. 19, 129–142. https://doi.org/10.1007/s10592-017-1020-0 (2018).
    Article  Google Scholar 

    41.
    Hubisz, M. J., Falush, D., Stephens, M. & Pritchard, J. K. Inferring weak population structure with the assistance of sample group information. Mol. Ecol. Resour. 9, 1322–1332. https://doi.org/10.1111/j.1755-0998.2009.02591.x (2009).
    Article  PubMed  PubMed Central  Google Scholar 

    42.
    Graciá, E. et al. Genetic signatures of demographic changes in an avian top predator during the last century: Bottlenecks and expansions of the Eurasian Eagle Owl in the Iberian Peninsula. PLoS ONE 10, e0133954. https://doi.org/10.1371/journal.pone.0133954 (2015).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    43.
    Williamson-Natesan, E. G. Comparison of methods for detecting bottlenecks from microsatellite loci. Conserv. Genet. 6, 551–562. https://doi.org/10.1007/s10592-005-9009-5 (2005).
    Article  Google Scholar 

    44.
    Peery, M. Z. et al. Reliability of genetic bottleneck tests for detecting recent population declines. Mol. Ecol. 21, 3403–3418. https://doi.org/10.1111/j.1365-294X.2012.05635.x (2012).
    Article  PubMed  Google Scholar 

    45.
    Garza, J. C. & Williamson, E. G. Detection of reduction in population size using data from microsatellite loci. Mol. Ecol. 10, 305–318. https://doi.org/10.1046/j.1365-294X.2001.01190.x (2001).
    CAS  Article  PubMed  Google Scholar 

    46.
    BirdLife International. Ara rubrogenys. The IUCN Red List of Threatened Species 2018: e.T22685572A131382876. Downloaded on 30 May 2020 (2018). https://doi.org/10.2305/IUCN.UK.2018-2.RLTS.T22685572A131382876.en (2018).

    47.
    El, D. O. reto del espacio andino (Instituto de Estudios Peruanos, Lima, Perú, 1981).
    Google Scholar 

    48.
    Williams, J. J., Gosling, W. D., Coe, A. L., Brooks, S. J. & Gulliver, P. Four thousand years of environmental change and human activity in the Cochabamba Basin Bolivia. Quat. Res. 76, 58–68. https://doi.org/10.1016/j.yqres.2011.03.004 (2011).
    Article  Google Scholar 

    49.
    Flantua, S. G. et al. Climate variability and human impact in South America during the last 2000 years: synthesis and perspectives from pollen records. Clim. Past 12, 483–523. https://doi.org/10.5194/cp-12-483-2016 (2016).
    Article  Google Scholar 

    50.
    Schlaifer, M., Las especies nativas y la deforestación en los Andes. Una visión histórica, social y cultural en Cochabamba, Bolivia. Bulletin de l’Institut français d’études andines 22, 585–610 (1993).

    51.
    Sánchez Canedo, W. Inkas,“flecheros” y mitmaqkuna: Cambio social y paisajes culturales en los Valles y en los Yungas de Inkachaca/Paracti y Tablas Monte (Cochabamba-Bolivia, siglos XV-XVI) (Doctoral dissertation, Institutionen för arkeologi och antik historia) Universitetstryckeriet, Uppsala, Sweden (2008).

    52.
    Cobo, B. Historia del Nuevo Mundo (Obras del P. Bernabé Cobo) II Tomos. Estudio preliminar y edición del P. Francisco Mateos. Biblioteca de Autores Españoles, Madrid. Disponible en: http://www.bibliotecavirtualdeandalucia.es/catalogo/consulta/registro.cmd?id=1014725 (1964) [1652].

    53.
    Guaman Poma de Ayala, F. El primer Nueva corónica y buen gobierno [1615] (eds J. V. Murra and R. Adorno, Quechua trans. J. L. Urioste), 3 vols. Mexico City: Siglo Veintiuno 1980 [1615].

    54.
    Tella, J. L. The unknown extent of ancient bird introductions. Ardeola 58, 399–404. https://doi.org/10.13157/arla.58.2.2011.399 (2011).

    55.
    Wilkinson, D., The influence of Amazonia on state formation in the ancient Andes. Antiquity 92, 1362–1376. https://doi.org/10.15184/aqy.2018.194 (2018).

    56.
    Gomez Casaverde, Y. Textiles Chimú con aplicaciones de plumas del Sitio Huaca de la Luna (Circa 800 dc-1470 dc): caracterización tecnológica y aproximación a las rutas de intercambio amazónico-andinas (Modelización y Técnicas Analíticas. Universidad Nacional de Trujillo. Trujillo, Perú, Maestría en Arqueología Sudamericana mención Arqueometría, 2020).
    Google Scholar 

    57.
    Boakes, E. H., Wang, J. & Amos, W. An investigation of inbreeding depression and purging in captive pedigreed populations. Heredity 98, 172–182. https://doi.org/10.1038/sj.hdy.6800923 (2007).
    CAS  Article  PubMed  Google Scholar 

    58.
    Witzenberger, K. A. & Hochkirch, A. Ex situ conservation genetics: a review of molecular studies on the genetic consequences of captive breeding programmes for endangered animal species. Biodivers. Conserv. 20, 1843–1861. https://doi.org/10.1007/s10531-011-0074-4 (2011).
    Article  Google Scholar 

    59.
    Thévenon, S., Bonnet, A., Claro, F. & Maillard, J. C. Genetic diversity analysis of captive populations: The Vietnamese sika deer (Cervus nippon pseudaxis) in zoological parks. Zool. Biol. 22, 465–475. https://doi.org/10.1002/zoo.10091 (2003).
    CAS  Article  Google Scholar 

    60.
    Kekkonen, J., Wikström, M. & Brommer, J. E. Heterozygosity in an isolated population of a large mammal founded by four individuals is predicted by an individual-based genetic model. PLoS ONE 7, e43482. https://doi.org/10.1371/journal.pone.0043482 (2012).
    CAS  Article  PubMed  PubMed Central  ADS  Google Scholar 

    61.
    Jackson, N. D. & Fahrig, L. Habitat amount, not habitat configuration, best predicts population genetic structure in fragmented landscapes. Landsc. Ecol. 31, 951–968. https://doi.org/10.1007/s10980-015-0313-2 (2016).
    Article  Google Scholar 

    62.
    Gibbs, J. P. Demography versus habitat fragmentation as determinants of genetic variation in wild populations. Biol. Conserv. 100, 15–20. https://doi.org/10.1016/S0006-3207(00)00203-2 (2001).
    Article  Google Scholar 

    63.
    Blanco, G., Hiraldo, F. & Tella, J. L. Ecological functions of parrots: an integrative perspective from plant life cycle to ecosystem functioning. Emu 118, 36–49. https://doi.org/10.1080/01584197.2017.1387031 (2018).
    Article  Google Scholar 

    64.
    Storfer, A. et al. Putting the “landscape” in landscape genetics. Heredity 98, 128–142. https://doi.org/10.1038/sj.hdy.6800917 (2007).
    CAS  Article  PubMed  Google Scholar 

    65.
    Sexton, J. P., Hangartner, S. B. & Hoffmann, A. A. Genetic isolation by environment or distance: which pattern of gene flow is most common?. Evolution 68, 1–15. https://doi.org/10.1111/evo.12258 (2014).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    66.
    Rojas, A., Yucra, E., Vera, I., Requejo, A. & Tella, J. A new population of the globally endangered red-fronted Macaw Ara rubrogenys unusually breeding in palms. Bird Conserv. Int. 24, 389–392. https://doi.org/10.1017/S095927091200038X (2014).
    Article  Google Scholar 

    67.
    Blanco, G., Hiraldo, F., Rojas, A., Dénes, F. V. & Tella, J. L. Parrots as key multilinkers in ecosystem structure and functioning. Ecol. Evol. 5, 4141–4160. https://doi.org/10.1002/ece3.1663 (2015).
    Article  PubMed  PubMed Central  Google Scholar 

    68.
    Andrews, K. Population genetics in the conservation of cetaceans and primates in Primates and Cetaceans: Field Research and Conservation of Complex Mammalian Societies (eds. Yamagiwa, J. & Karczmarski, L.) 289–30 (Springer, Japan, 2014).

    69.
    Manel, S. & Holderegger, R. T. years of landscape genetics. Trends Ecol. Evol. 28, 614–621. https://doi.org/10.1016/j.tree.2013.05.012 (2013).
    Article  PubMed  Google Scholar 

    70.
    Lowe, W. H. & Allendorf, F. W. What can genetics tell us about population connectivity?. Mol. Ecol. 19, 3038–3051. https://doi.org/10.1111/j.1365-294X.2010.04688.x (2010).
    Article  PubMed  Google Scholar 

    71.
    Hatchwell, B. J. Cryptic kin selection: kin structure in vertebrate populations and opportunities for kin-directed cooperation. Ethology 116, 203–216. https://doi.org/10.1111/j.1439-0310.2009.01732.x (2010).
    Article  Google Scholar 

    72.
    Bicknell, A. W. J. et al. Population genetic structure and long-distance dispersal among seabird populations: Implications for colony persistence. Mol. Ecol. 21, 2863–2876. https://doi.org/10.1111/j.1365-294X.2012.05558.x (2012).
    CAS  Article  PubMed  Google Scholar 

    73.
    Welch, A. J. et al. Population divergence and gene flow in an endangered and highly mobile seabird. Heredity 109, 19–28. https://doi.org/10.1038/hdy.2012.7 (2012).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    74.
    Bonilla, L. M. Monitoreo de la nidificación de la Paraba Frente Roja (Ara rubrogenys) en dos sitios de reproducción en los valles de los Departamentos de Santa Cruz y Cochabamba) en dos sitios de reproducción en los valles de los Departamentos de Santa Cruz y Cochabamba (Universidad Autónoma Gabriel René Moreno, Santa Cruz de La Sierra, Bolivia, 2007).
    Google Scholar 

    75.
    Caparroz, R., Miyaki, C. Y. & Baker, A. J. Contrasting phylogeographic patterns in mitochondrial DNA and microsatellites: evidence of female philopatry and male-biased gene flow among regional populations of the blue-and-yellow macaw (Psittaciformes: Ara ararauna) in Brazil. Auk 126, 359–370. https://doi.org/10.1525/auk.2009.07183 (2009).
    Article  Google Scholar 

    76.
    Alcaide, M. et al. Population fragmentation leads to isolation by distance but not genetic impoverishment in the philopatric Lesser Kestrel: a comparison with the widespread and sympatric Eurasian Kestrel. Heredity 102, 190–198. https://doi.org/10.1038/hdy.2008.107 (2009).
    CAS  Article  PubMed  Google Scholar 

    77.
    Olah, G. et al. Exploring dispersal barriers using landscape genetic resistance modelling in scarlet macaws of the Peruvian Amazon. Landsc. Ecol. 32, 445–456. https://doi.org/10.1007/s10980-016-0457-8 (2017).
    Article  Google Scholar 

    78.
    Pitter, E. & Christiansen, M. B. Behavior of individuals and social interactions of the Red-fronted Macaw Ara rubrogenys in the wild during the mid-day rest. Ornitol. Neotrop. 8, 133–143 (1997).
    Google Scholar 

    79.
    Keighley, M. V., Heinsohn, R., Langmore, N. E., Murphy, S. A. & Peñalba, J. V. Genomic population structure aligns with vocal dialects in Palm Cockatoos (Probosciger aterrimus); evidence for refugial late-Quaternary distribution?. EMU 119, 24–37. https://doi.org/10.1080/01584197.2018.1483731 (2019).
    Article  Google Scholar 

    80.
    Pacífico, E. C. et al. Breeding to non-breeding population ratio and breeding performance of the globally endangered Lear’s Macaw (Anodorhynchus leari): conservation and monitoring implications. Bird Conserv. Int. 24, 466–476. https://doi.org/10.1017/S095927091300049X (2014).
    Article  Google Scholar 

    81.
    Stutchbury, B. J. & Zack, S. Delayed breeding in avian social systems: the role of territory quality and” floater” tactics. Behaviour 123, 194–219. https://doi.org/10.1163/156853992X00020 (1992).
    Article  Google Scholar 

    82.
    Kokko, H. & Sutherland, W. J. Optimal floating and queuing strategies: consequences for density dependence and habitat loss. Am. Nat. 152, 354–366. https://doi.org/10.1086/286174 (1998).
    CAS  Article  PubMed  Google Scholar 

    83.
    Blanco, G., Laiolo, P. & Fargallo, J. A. Linking environmental stress, feeding-shifts and the ‘island syndrome’: a nutritional challenge hypothesis. Popul. Ecol. 56, 203–216. https://doi.org/10.1007/s10144-013-0404-3 (2014).
    Article  Google Scholar 

    84.
    Koenig, W. D. & Dickinson, J. L. Cooperative breeding in vertebrates: studies of ecology, evolution, and behavior. Cambridge University Press (2016).

    85.
    Gao, H., Bryc, K. & Bustamante, C. D. On identifying the optimal number of population clusters via the deviance information criterion. PLoS ONE 6, e21014. https://doi.org/10.1371/journal.pone.0021014 (2011).
    CAS  Article  PubMed  PubMed Central  ADS  Google Scholar 

    86.
    Rodríguez-Ramilo, S. T. & Wang, J. The effect of close relatives on unsupervised Bayesian clustering algorithms in population genetic structure analysis. Mol. Ecol. Resour. 12, 873–884. https://doi.org/10.1111/j.1755-0998.2012.03156.x (2012).
    Article  PubMed  Google Scholar 

    87.
    Harrisson, K. A. et al. Fine-scale effects of habitat loss and fragmentation despite large-scale gene flow for some regionally declining woodland bird species. Landsc. Ecol. 27, 813–827. https://doi.org/10.1007/s10980-012-9743-2 (2012).
    Article  Google Scholar 

    88.
    Rull, V. Microrefugia. J. Biogeogr. 36, 481–484. https://doi.org/10.1111/j.1365-2699.2008.02023.x (2009).
    Article  Google Scholar 

    89.
    Nadachowska-Brzyska, K., Li, C., Smeds, L., Zhang, G. & Ellegren, H. Temporal dynamics of avian populations during Pleistocene revealed by whole-genome sequences. Curr. Biol. 25, 1375–1380. https://doi.org/10.1016/j.cub.2015.03.047 (2015).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    90.
    James, J. E., Lanfear, R. & Eyre-Walker, A. Molecular evolutionary consequences of island colonization. Genome Biol. Evol. 8, 1876–1888. https://doi.org/10.1093/gbe/evw120 (2016).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    91.
    Gregory-Wodzicki, K. M. Uplift history of the central and Northern Andes: A review. Geol. Soc. Am. Bull. 112, 1091–1105. https://doi.org/10.1130/0016-7606(2000)112%3c1091:UHOTCA%3e2.0.CO;2 (2000).
    Article  ADS  Google Scholar 

    92.
    Navarro, G. & Maldonado M. Geografía ecológica de Bolivia: vegetación y ambientes acuáticos. Edit.: Centro de Ecología Simón I. Patiño-Departamento de Difusión. Cochabamba, Bolivia (2002).

    93.
    López, R. P. Phytogeographical relations of the Andean dry valleys of Bolivia. J. Biogeogr. 30, 1659–1668. https://doi.org/10.1046/j.1365-2699.2003.00919.x (2003).
    Article  Google Scholar 

    94.
    Montesinos-Navarro, A., Hiraldo, F., Tella, J. L. & Blanco, G. Network structure embracing mutualism–antagonism continuums increases community robustness. Nat. Ecol. Evol. 1, 1661–1669. https://doi.org/10.1038/s41559-017-0320-6 (2017).
    Article  PubMed  Google Scholar 

    95.
    Da Silva, A. G., Eberhard, J. R., Wright, T. F., Avery, M. L. & Russello, M. A. Genetic evidence for high propagule pressure and long-distance dispersal in monk parakeet (Myiopsitta monachus) invasive populations. Mol. Ecol. 19, 3336–3350. https://doi.org/10.1111/j.1365-294X.2010.04749.x (2010).
    Article  Google Scholar 

    96.
    Russello, M., Calcagnotto, D., DeSalle, R. & Amato, G. Characterization of microsatellite loci in the endangered St. Vicent parrot, Amazona guildingii. Mol. Ecol. Notes 1, 13–13. https://doi.org/10.1046/j.1471-8278.2001.00061.x (2001).

    97.
    Bergner, L. M., Jamieson, I. G. & Robertson, B. C. Combining genetic data to identify relatedness among founders in a genetically depauperate parrot, the Kakapo (Strigops habroptilus). Conserv. Genet. 15, 1013–1020. https://doi.org/10.1007/s10592-014-0595-y (2014).
    Article  Google Scholar 

    98.
    Stojanovic, D., Olah, G., Webb, M., Peakall, R. & Heinsohn, R. Genetic evidence confirms severe extinction risk for critically endangered swift parrots: implications for conservation management. Anim. Conserv. 21, 313–323. https://doi.org/10.1111/acv.12394 (2018).
    Article  Google Scholar 

    99.
    Väli, Ü., Einarsson, A., Waits, L. & Ellegren, H. To what extent do microsatellite markers reflect genome-wide genetic diversity in natural populations?. Mol. Ecol. 17, 3808–3817. https://doi.org/10.1111/j.1365-294X.2008.03876.x (2008).
    Article  PubMed  Google Scholar 

    100.
    Young, A. M., Hobson, E. A., Lackey, L. B. & Wright, T. E. Survival on the ark: Life-history trends in captive parrots. Anim. Conserv. 15, 28–43. https://doi.org/10.1111/j.1469-1795.2011.00477.x (2012).
    Article  PubMed  PubMed Central  Google Scholar 

    101.
    Fraser, D. J. & Bernatchez, L. Adaptive evolutionary conservation: Towards a unified concept for defining conservation units. Mol. Ecol. 10, 2741–2752. https://doi.org/10.1046/j.0962-1083.2001.01411.x (2001).
    CAS  Article  PubMed  Google Scholar 

    102.
    Palsbøll, P. J., Bérubé, M. & Allendorf, F. W. Identification of management units using population genetic data. Trends Ecol. Evol. 22, 11–16. https://doi.org/10.1016/j.tree.2006.09.003 (2007).
    Article  PubMed  Google Scholar 

    103.
    Schiegg, K. Environmental autocorrelation: curse or blessing?. Trends Ecol. Evol. 18, 212–214. https://doi.org/10.1016/S0169-5347(03)00074-0 (2004).
    Article  Google Scholar 

    104.
    Shafer, A. B. A. et al. Genomics and the challenging translation into conservation practice. Trends Ecol. Evol. 30, 78–87. https://doi.org/10.1016/j.tree.2014.11.009 (2015).
    Article  PubMed  Google Scholar 

    105.
    Valière, N. GIMLET: a computer program for analysing genetic individual identification data. Mol. Ecol. Notes 2, 377–379. https://doi.org/10.1046/j.1471-8286.2002.00228.x-i2 (2002).
    Article  Google Scholar 

    106.
    Jones, O. R. & Wang, J. COLONY: a program for parentage and sibship inference from multilocus genotype data. Mol. Ecol. Resour. 10, 551–555. https://doi.org/10.1111/j.1755-0998.2009.02787.x (2010).
    Article  PubMed  Google Scholar 

    107.
    Keller, L. F. & Waller, D. M. Inbreeding effects in wild populations. Trends Ecol. Evol. 17, 230–241. https://doi.org/10.1016/S0169-5347(02)02489-8 (2002).
    Article  Google Scholar 

    108.
    Weir, B. S. & Cockerham, C. C. Estimating F-statistics for the analysis of population structure. Evolution 38, 1358–1370. https://doi.org/10.2307/2408641 (1984).
    CAS  Article  PubMed  Google Scholar 

    109.
    Peakall, R. & Smouse, P. E. GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research–an update. Bioinformatics 28, 2537–2539. https://doi.org/10.1111/j.1471-8286.2005.01155.x (2012).

    110.
    Pritchard, J. K., Stephens, M. & Donnelly, P. Inference of population structure using multilocus genotype data. Genetics 155, 945–959 (2000).
    CAS  PubMed  PubMed Central  Google Scholar 

    111.
    Falush, D., Stephens, M. & Pritchard, J. K. Inference of population structure using multilocus genotype data: linked loci and correlated allele frequencies. Genetics 164, 1567–1587 (2003).
    CAS  PubMed  PubMed Central  Google Scholar 

    112.
    Kopelman, N. M., Mayzel, J., Jakobsson, M., Rosenberg, N. A. & Mayrose, I. Clumpak: a program for identifying clustering modes and packaging population structure inferences across K. Mol. Ecol. Resour. 15, 1179–1191. https://doi.org/10.1111/1755-0998.12387 (2015).
    CAS  Article  PubMed  PubMed Central  Google Scholar 

    113.
    Earl, D. A. & von Holdt, B. M. STRUCTURE HARVESTER: a website and program for visualizing STRUCTURE output and implementing the Evanno method. Conserv. Genet. Resour. 4, 359–361. https://doi.org/10.1007/s12686-011-9548-7 (2012).
    Article  Google Scholar 

    114.
    Tishkoff, S. A., Reed, F. A., Friedlaender, F. R., Ehret, C., Ranciaro, A., Froment, et al. The genetic structure and history of Africans and African Americans. Science 324, 1035–1044. https://doi.org/10.1126/science.1172257 (2009).

    115.
    Evanno, G., Regnaut, S. & Goudet, J. Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol. Ecol. 14, 2611–2620. https://doi.org/10.1111/j.1365-294X.2005.02553.x (2005).
    CAS  Article  PubMed  Google Scholar 

    116.
    Rousset, F. genepop’007: a complete re-implementation of the genepop software for Windows and Linux. Mol. Ecol. Resour. 8, 103–106. https://doi.org/10.1111/j.1471-8286.2007.01931.x (2008).
    Article  PubMed  Google Scholar 

    117.
    Botstein, D., White, R. L., Skolnick, M. & Davis, R. W. Construction of a genetic linkage map in man using restriction fragment length polymorphisms. Am. J. Hum. Genet. 32, 314–331 (1980).
    CAS  PubMed  PubMed Central  Google Scholar 

    118.
    Kalinowski, S. T., Taper, M. L. & Marshall, T. C. Revising how the computer program CERVUS accommodates genotyping error increases success in paternity assignment. Mol. Ecol. 16, 1099–1106. https://doi.org/10.1111/j.1365-294X.2007.03089.x (2007).
    Article  PubMed  Google Scholar 

    119.
    Ciofi, C., Beaumontf, M. A., Swingland, I. R. & Bruford, M. W. Genetic divergence and units for conservation in the Komodo dragon Varanus komodoensis. Proc. R. Soc. Lond. B Biol. Sci. 266, 2269–2274. https://doi.org/10.1098/rspb.1999.0918 (1999).

    120.
    Piry, S., Luikart, G. & Cornuet, J. M. BOTTLENECK: a computer program for detecting recent reductions in the effective population size using allele frequency data. J. Hered. 90, 502–503. https://doi.org/10.1093/jhered/90.4.502 (1999).
    Article  Google Scholar 

    121.
    Queller, D. C. & Goodnight, K. F. Estimating relatedness using genetic markers. Evolution 43, 258–275. https://doi.org/10.1111/j.1558-5646.1989.tb04226.x (1989).
    Article  PubMed  Google Scholar 

    122.
    Wilson, G. A. & Rannala, B. Bayesian inference of recent migration rates using multilocus genotypes. Genetics 163, 1177–1191 (2003).
    PubMed  PubMed Central  Google Scholar 

    123.
    Piry, S. et al. GENECLASS2: a software for genetic assignment and first-generation migrant detection. J. Hered. 95, 536–539. https://doi.org/10.1093/jhered/esh074 (2004).
    CAS  Article  PubMed  Google Scholar 

    124.
    Waples, R. S. & Do, C. H. I. LDNE: a program for estimating effective population size from data on linkage disequilibrium. Mol. Ecol. Resour. 8, 753–756. https://doi.org/10.1111/mec.12561 (2008).
    Article  PubMed  Google Scholar 

    125.
    Waples, R. S. & Do, C. H. I. Linkage disequilibrium estimates of contemporary Ne using highly variable genetic markers: a largely untapped resource for applied conservation and evolution. Evol. Appl. 3, 244–262. https://doi.org/10.1111/j.1752-4571.2009.00104.x (2010).
    Article  Google Scholar 

    126.
    Rousset, F. Genetic differentiation and estimation of gene flow from F-statistics under isolation by distance. Genetics 145, 1219–1228 (1997).
    CAS  PubMed  PubMed Central  Google Scholar 

    127.
    Bohonak, A. J. IBD (isolation by distance): a program for analyses of isolation by distance. J. Hered. 93, 153–154. https://doi.org/10.1093/jhered/93.2.153 (2002).
    CAS  Article  Google Scholar  More

  • in

    Uncovering multi-faceted taxonomic and functional diversity of soil bacteriomes in tropical Southeast Asian countries

    The soil bacterial diversity
    The soil bacteriome dataset in this study included 558 soil samples collected from Thailand, the Philippines, Malaysia, and Indonesia (Fig. 1).
    Figure 1

    The number of soil samples from the selected Southeast Asian countries which were included in this study. The number in each circle represented the number of samples from each country. The Southeast Asia map was redrawn from “Southeast Asia” map (Google Maps retrieved 7 May 2020, from https://www.google.com/maps/@8.2763609,98.123781,4z).

    Full size image

    Mapping to the global gridded soil information system: SoilGrids21, the soil samples of each selected country encompassed different soil classes (Supplementary Figure S2). The soil from Thailand samples were mostly Acrisols, which comprise clay-rich subsoil with low fertility and high aluminium content. The soil from the Philippines samples were mostly Gleysols, iron-rich wetland soil saturated with groundwater or underwater or in tidal areas. The soil from Malaysia samples were mostly Ferralsols. The soils from Indonesia samples were of mixed soil classes; nearly half (45%) of them belonged to Nitisols, well-drained soil with a moderate-to-high clay content and limited phosphorus availability. Ferralsols took up about 20% of the Indonesia soil samples while another 18% were Histosols (moist soils with thick organic layers). The soil pH levels were significantly different among the soil samples of 4 selected countries (ANOVA, P value  More

  • in

    Pseudogymnoascus destructans growth in wood, soil and guano substrates

    1.
    Fisher, M. C. et al. Emerging fungal threats to animal, plant and ecosystem health. Nature 484, 186–194 (2012).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 
    2.
    Fisher, M. C., Gow, N. A. R. & Gurr, S. J. Tackling emerging fungal threats to animal health, food security and ecosystem resilience. Philos. Trans. R. Soc. B Biol. Sci. 371, 20160332 (2016).
    Article  Google Scholar 

    3.
    Ghosh, P. N., Fisher, M. C. & Bates, K. A. Diagnosing emerging fungal threats: A one health perspective. Front. Genet. 9, 376 (2018).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    4.
    Seyedmousavi, S. et al. Aspergillus and aspergilloses in wild and domestic animals: A global health concern with parallels to human disease. Med. Mycol. 53, 765–797 (2015).
    PubMed  Article  Google Scholar 

    5.
    Stephen, C., Lester, S., Black, W., Fyfe, M. & Raverty, S. Multispecies outbreak of cryptococcosis on southern Vancouver Island, British Columbia. Can. Vet. J. 43, 792–794 (2002).
    PubMed  PubMed Central  Google Scholar 

    6.
    Speare, R., Thomas, A. D., O’Shea, P. & Shipton, W. A. Mucor amphibiorum in the toad, Bufo marinus Australia. J. Wildl. Dis. 30, 399–407 (1994).
    CAS  PubMed  Article  Google Scholar 

    7.
    Connolly, J. H. A review of mucormycosis in the platypus (Ornithorhynchus anatinus). Aust. J. Zool. 57, 235–244 (2009).
    Article  Google Scholar 

    8.
    Gust, N. & Griffiths, J. Platypus mucormycosis and its conservation implications. Austral. Mycol. 28, 1–8 (2009).
    Google Scholar 

    9.
    Thiel, R. P., Mech, L. D., Ruth, G. R., Archer, J. R. & Kaufman, L. Blastomycosis in wild wolves. J. Wildl. Dis. 23, 321–323 (1987).
    CAS  PubMed  Article  Google Scholar 

    10.
    Storms, T. N., Victoria L. Clyde, Linda Munson & Edward C. Ramsay. Blastomycosis in nondomestic felids. J. Zool. Wildl. Med. 34, 231–238 (2003).

    11.
    Guillot, J., Guérin, C. & Chermette, R. Histoplasmosis in Animals. in Emerging and Epizootic Fungal Infections in Animals (eds. Seyedmousavi, S., de Hoog, G. S., Guillot, J. & Verweij, P. E.) 115–128 (Springer International Publishing, 2018). doi:https://doi.org/10.1007/978-3-319-72093-7_5.

    12.
    Scheele, B. C. et al. Amphibian fungal panzootic causes catastrophic and ongoing loss of biodiversity. Science 363, 1459 (2019).
    ADS  CAS  PubMed  Article  Google Scholar 

    13.
    Martel, A. et al. Batrachochytrium salamandrivorans sp. nov. causes lethal chytridiomycosis in amphibians. Proc. Natl. Acad. Sci. USA 110, 15325 (2013).

    14.
    Riley, S. Large-scale spatial-transmission models of infectious disease. Science 316, 1298 (2007).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    15.
    Johnson, P. T. J., de Roode, J. C. & Fenton, A. Why infectious disease research needs community ecology. Science 349, 1259504 (2015).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    16.
    Engering, A., Hogerwerf, L. & Slingenbergh, J. Pathogen–host–environment interplay and disease emergence. Emerg. Microbes Infect. 2, 1–7 (2013).
    Article  CAS  Google Scholar 

    17.
    Shikano, I. & Cory, J. S. Impact of environmental variation on host performance differs with pathogen identity: Implications for host-pathogen interactions in a changing climate. Sci. Rep. 5, 15351 (2015).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    18.
    Kraay, A. N. M. et al. Fomite-mediated transmission as a sufficient pathway: A comparative analysis across three viral pathogens. BMC Infect. Dis. 18, 540 (2018).
    PubMed  PubMed Central  Article  Google Scholar 

    19.
    Stephens, B. et al. Microbial exchange via fomites and implications for human health. Curr. Pollut. Rep. 5, 198–213 (2019).
    CAS  Article  Google Scholar 

    20.
    Langwig, K. E. et al. Host and pathogen ecology drive the seasonal dynamics of a fungal disease, white-nose syndrome. Proc. Biol. Sci. 282, (2015).

    21.
    Huebschman, J. J. et al. Detection of Pseudogymnoascus destructans during Summer on Wisconsin Bats. J. Wildl. Dis. https://doi.org/10.7589/2018-06-146 (2019).
    Article  PubMed  Google Scholar 

    22.
    Hoyt, J. R. et al. Environmental reservoir dynamics predict global infection patterns and population impacts for the fungal disease white-nose syndrome. Proc. Natl. Acad. Sci. USA 117, 7255 (2020).
    ADS  CAS  PubMed  Article  Google Scholar 

    23.
    Foley, J., Clifford, D., Castle, K., Cryan, P. & Osfeld, R. S. Investigating and managing the rapid emergence of white nose syndrome, a novel, fatal, infectious disease of hibernating bats. Conserv. Biol. 25, 223–231 (2011).
    PubMed  Google Scholar 

    24.
    Blanco, C. M. & Garrie, J. Species specific effects of prescribed burns on bat occupancy in northwest Arkansas. For. Ecol. Manage. 460, 117890 (2020).
    Article  Google Scholar 

    25.
    Gargas, A., Trest, M., Christensen, M., Volk, T. J. & Blehert, D. Geomyces destructans sp. nov. associated with bat white-nose syndrome. Mycotaxon 108, 147–154 (2009).

    26.
    Blehert, D. S. et al. Bat white-nose syndrome: An emerging fungal pathogen?. Science 323, 227 (2009).
    CAS  PubMed  Article  Google Scholar 

    27.
    Cryan, P. M. et al. Electrolyte depletion in white-nose syndrome bats. J. Wildl. Dis. 49, 398–402 (2013).
    CAS  PubMed  Article  Google Scholar 

    28.
    Warnecke, L. et al. Pathophysiology of white-nose syndrome in bats: A mechanistic model linking wing damage to mortality. Biol. Lett. 9, 20130177 (2013).
    PubMed  PubMed Central  Article  Google Scholar 

    29.
    Verant, M. L. et al. White-nose syndrome initiates a cascade of physiologic disturbances in the hibernating bat host. BMC Physiol. 14, 10 (2014).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    30.
    Frick, W. F. et al. An emerging disease causes regional population collapse of a common North American bat species. Science 329, 679 (2010).
    ADS  CAS  PubMed  Article  Google Scholar 

    31.
    Turner, G. G., Reeder, D. M. & Coleman, J. T. H. A Five-year assessment of mortality and geographic spread of white-nose syndrome in North American Bats, with a Look at the Future. Update of white-nose syndrome in bats. Bat Res. News 52, 13–27 (2011).

    32.
    Langwig, K. E. et al. Sociality, density-dependence and microclimates determine the persistence of populations suffering from a novel fungal disease, white-nose syndrome. Ecol. Lett. 15, 1050–1057 (2012).
    PubMed  Article  Google Scholar 

    33.
    Langwig, K. E. et al. Invasion dynamics of white-nose syndrome fungus, midwestern United States. Emerg. Infect. Dis. 21, 1023–1026 (2015).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    34.
    USFW. U.S. Fish and Wildlife Service. 2019. White-nose syndrome: The devastating disease of hibernating bats in North America. Accessed 1 May 2020. https://www.whitenosesyndrome.org/mmedia-education/white-nose-syndrome-fact-sheet-june-2018. (2019).

    35.
    Lorch, J. M. et al. Experimental infection of bats with Geomyces destructans causes white-nose syndrome. Nature 480, 376 (2011).
    ADS  CAS  PubMed  Article  Google Scholar 

    36.
    Lorch, J. M. et al. Distribution and environmental persistence of the causative agent of white-nose syndrome, geomyces destructans, in bat hibernacula of the Eastern United States. Appl. Environ. Microbiol. 79, 1293–1301 (2013).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    37.
    Hoyt, J. R. et al. Long-term persistence of Pseudogymnoascus destructans, the Causative Agent of white-nose syndrome, in the absence of bats. EcoHealth 12, 330–333 (2015).
    PubMed  Article  Google Scholar 

    38.
    Campbell, L. J., Walsh, D., Blehert, D. S. & Lorch, J. M. Long-term survival of Pseudogymnoascus destructans at elevated temperatures. J. Wildl. Dis. 56, 278–287 (2020).
    PubMed  Article  Google Scholar 

    39.
    Urbina, J., Chestnut, T., Schwalm, D., Allen, J. & Levi, T. Experimental evaluation of genomic DNA degradation rates for the pathogen Pseudogymnoascus destructans (Pd) in bat guano. PeerJ 8, e8141 (2020).
    PubMed  PubMed Central  Article  Google Scholar 

    40.
    Lorch, J. M. et al. A culture-based survey of fungi in soil from bat hibernacula in the eastern United States and its implications for detection of Geomyces destructans, the causal agent of bat white-nose syndrome. Mycologia 105, 237–252 (2013).
    CAS  PubMed  Article  Google Scholar 

    41.
    Reynolds, H. T., Ingersoll, T. & Barton, H. A. Modeling the environmental growth of Pseudogymnoascus destructans and its impact on the White-nose syndrome epidemic. J. Wildl. Dis. 51, 318–331 (2015).
    PubMed  Article  Google Scholar 

    42.
    Warnecke, L. et al. Inoculation of bats with European Geomyces destructans supports the novel pathogen hypothesis for the origin of white-nose syndrome. Proc. Natl. Acad. Sci. USA 109, 6999 (2012).
    ADS  CAS  PubMed  Article  Google Scholar 

    43.
    WNS Multiagency decontamination team. https://www.whitenosesyndrome.org/mmedia-education/united-states-national-white-nose-syndrome-decontamination-protocol-april-2016-2. (2018).

    44.
    Verant, M., Bohuski, E., Lorch, J. & Blehert, D. Optimized methods for total nucleic acid extraction and quantification of the bat white-nose syndrome fungus, Pseudogymnoascus destructans, from swab and environmental samples. J. VET Diagn. Invest. 28, 110–118 (2016).
    CAS  PubMed  Article  Google Scholar 

    45.
    Rocke, T. E. et al. Virally-vectored vaccine candidates against white-nose syndrome induce anti-fungal immune response in little brown bats (Myotis lucifugus). Sci. Rep. 9, 6788 (2019).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    46.
    Zhelyazkova, V. L. et al. Screening and biosecurity for white-nose Fungus Pseudogymnoascus destructans (Ascomycota: Pseudeurotiaceae) in Hawai‘i. Pac. Sci. 73, 357–365 (2019).
    Article  Google Scholar 

    47.
    Muller, L. K. et al. Bat white-nose syndrome: A real-time TaqMan polymerase chain reaction test targeting the intergenic spacer region of Geomyces destructans. Mycologia 105, 253–259 (2013).
    CAS  PubMed  Article  Google Scholar 

    48.
    Vanderwolf, K. J., Malloch, D. & McAlpine, D. F. Detecting viable Pseudogymnoascus destructans (Ascomycota: Pseudeurotiaceae) from walls of bat hibernacula: Effect of culture media. J. Cave Karst Stud. 78, 158 (2016).
    CAS  Article  Google Scholar 

    49.
    Cheng, T. L. et al. Efficacy of a probiotic bacterium to treat bats affected by the disease white-nose syndrome. J. Appl. Ecol. 54, 701–708 (2017).
    Article  Google Scholar 

    50.
    Micalizzi, E. W., Mack, J. N., White, G. P., Avis, T. J. & Smith, M. L. Microbial inhibitors of the fungus Pseudogymnoascus destructans, the causal agent of white-nose syndrome in bats. PLoS ONE 12, e0179770 (2017).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    51.
    Singh, A., Lasek-Nesselquist, E., Chaturvedi, V. & Chaturvedi, S. Trichoderma polysporum selectively inhibits white-nose syndrome fungal pathogen Pseudogymnoascus destructans amidst soil microbes. Microbiome 6, 139 (2018).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    52.
    De Mandal, S., Zothansanga, Panda, A. K., Bisht, S. S. & Senthil Kumar, N. First report of bacterial community from a Bat Guano using Illumina next-generation sequencing. Genom. Data 4, 99–101. (2015).

    53.
    Banskar, S., Bhute, S. S., Suryavanshi, M. V., Punekar, S. & Shouche, Y. S. Microbiome analysis reveals the abundance of bacterial pathogens in Rousettus leschenaultii guano. Sci. Rep. 6, 36948 (2016).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    54.
    Newman, M. M., Kloepper, L. N., Duncan, M., McInroy, J. A. & Kloepper, J. W. Variation in bat guano bacterial community composition with depth. Front. Microbiol. 9, 914 (2018).
    PubMed  PubMed Central  Article  Google Scholar 

    55.
    Cruz, M. R., Graham, C. E., Gagliano, B. C., Lorenz, M. C. & Garsin, D. A. Enterococcus faecalis inhibits hyphal morphogenesis and virulence of Candida albicans. Infect. Immun. 81, 189 (2013).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    56.
    Graham, C. E., Cruz, M. R., Garsin, D. A. & Lorenz, M. C. Enterococcus faecalis bacteriocin EntV inhibits hyphal morphogenesis, biofilm formation, and virulence of Candida albicans. Proc. Natl. Acad. Sci. USA 114, 4507 (2017).
    CAS  PubMed  Article  Google Scholar 

    57.
    Khan, N. et al. Antifungal activity of bacillus species against fusarium and analysis of the potential mechanisms used in biocontrol. Front. Microbiol. 9, 2363 (2018).
    PubMed  PubMed Central  Article  Google Scholar 

    58.
    Kerr, J. R. Bacterial inhibition of fungal growth and pathogenicity. Microb. Ecol. Health Dis. 11, 129–142 (1999).
    Google Scholar 

    59.
    Wheatley, R. E. The consequences of volatile organic compound mediated bacterial and fungal interactions. Antonie Van Leeuwenhoek 81, 357–364 (2002).
    CAS  PubMed  Article  Google Scholar 

    60.
    Cornelison, C. T., Gabriel, K. T., Barlament, C. & Crow, S. A. Inhibition of pseudogymnoascus destructans growth from conidia and mycelial extension by bacterially produced volatile organic compounds. Mycopathologia 177, 1–10 (2014).
    CAS  PubMed  Article  Google Scholar 

    61.
    Sussman, A. & Douthit, H. Dormancy in microbial spores. Annu. Rev. Plant Physiol. 24, 311–352 (1973).
    CAS  Article  Google Scholar 

    62.
    Feofilova, E. P., Ivashechkin, A. A., Alekhin, A. I. & Sergeeva, Ya. E. Fungal spores: Dormancy, germination, chemical composition, and role in biotechnology (review). Appl. Biochem. Microbiol. 48, 1–11 (2012).

    63.
    Gasch, A. P. Comparative genomics of the environmental stress response in ascomycete fungi. Yeast 24, 961–976 (2007).
    CAS  PubMed  Article  Google Scholar 

    64.
    Marroquin, C. M., Lavine, J. O. & Windstam, S. T. Effect of humidity on development of pseudogymnoascus destructans, the causal agent of bat white-nose syndrome. Northeastern Nat. 24, 54–64 (2017).
    Article  Google Scholar 

    65.
    Raudabaugh, D. B. & Miller, A. N. Nutritional capability of and substrate suitability for pseudogymnoascus destructans, the causal agent of bat white-nose syndrome. PLoS ONE 8, e78300 (2013).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    66.
    Gabriel, K. T., Kartforosh, L., Crow, S. A. & Cornelison, C. T. Antimicrobial activity of essential oils against the fungal pathogens ascosphaera apis and pseudogymnoascus destructans. Mycopathologia 183, 921–934 (2018).
    CAS  PubMed  Article  Google Scholar 

    67.
    Boire, N. et al. Potent inhibition of pseudogymnoascus destructans, the causative agent of white-nose syndrome in bats, by cold-pressed, terpeneless valencia orange oil. PLoS ONE 11, e0148473 (2016).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    68.
    Turbill, C. & Welbergen, J. A. Anticipating white-nose syndrome in the Southern Hemisphere: Widespread conditions favourable to Pseudogymnoascus destructans pose a serious risk to Australia’s bat fauna. Austral. Ecol. 45, 89–96 (2020).
    Article  Google Scholar  More

  • in

    Assumptions about fence permeability influence density estimates for brown hyaenas across South Africa

    1.
    Brumfield, R. T. & Edwards, S. V. Evolution into and out of the Andes: a Bayesian analysis of historical diversification in Thamnophilus antshrikes. Evolution 61, 346–367 (2007).
    CAS  PubMed  Article  Google Scholar 
    2.
    Machado, A. P., Clément, L., Uva, V., Goudet, J. & Roulin, A. The Rocky Mountains as a dispersal barrier between barn owl (Tyto alba) populations in North America. J. Biogeogr. 45, 1288–1300 (2018).
    Article  Google Scholar 

    3.
    Patton, J. L., Da Silva, M. N. F. & Malcolm, J. R. Gene genealogy and differentiation among arboreal spiny rats (Rodentia: Echimyidae) of the Amazon basin: a test of the riverine barrier hypothesis. Evolution 48, 1314–1323 (1994).
    PubMed  Article  Google Scholar 

    4.
    Trinkel, M. et al. Inbreeding and density-dependent population growth in a small, isolated lion population. Anim. Conserv. 13, 374–382 (2010).
    Article  Google Scholar 

    5.
    Vanak, A. T., Thaker, M. & Slotow, R. Do fences create an edge-effect on the movement patterns of a highly mobile mega-herbivore?. Biol. Conserv. 143, 2631–2637 (2010).
    Article  Google Scholar 

    6.
    Parchizadeh, J. et al. Roads threaten Asiatic cheetahs in Iran. Curr. Biol. 28, R1141–R1142 (2018).
    CAS  PubMed  Article  Google Scholar 

    7.
    Williams, S. T., Collinson, W., Patterson-Abrolat, C., Marneweck, D. G. & Swanepoel, L. H. Using road patrol data to identify factors associated with carnivore roadkill counts. PeerJ 7, e6650 (2019).
    PubMed  PubMed Central  Article  Google Scholar 

    8.
    Hayward, M. W. & Kerley, G. I. H. Fencing for conservation: restriction of evolutionary potential or a riposte to threatening processes?. Biol. Conserv. 142, 1–13 (2009).
    Article  Google Scholar 

    9.
    Taylor, A., Lindsey, P., Davies-Mostert, H. & Goodman, P. An assessment of the economic, social and conservation value of the wildlife ranching industry and its potential to support the green economy in South Africa. 1–163 (The Endangered Wildlife Trust, Johannesburg, South Africa, 2015).

    10.
    Pekor, A. et al. Fencing Africa’s protected areas: costs, benefits, and management issues. Biol. Conserv. 229, 67–75 (2019).
    Article  Google Scholar 

    11.
    Woodroffe, R., Hedges, S. & Durant, S. M. To fence or not to fence. Science 344, 46–48 (2014).
    ADS  CAS  PubMed  Article  Google Scholar 

    12.
    Hayward, M. W. & Somers, M. J. An introduction to fencing for conservation. In Fencing for Conservation: Restriction of Evolutionary Potential or a Riposte to Threatening Processes? (eds Somers, M. J. & Hayward, M.) 1–6 (Springer, Berlin, 2012).
    Google Scholar 

    13.
    Cozzi, G., Broekhuis, F., McNutt, J. W. & Schmid, B. Comparison of the effects of artificial and natural barriers on large African carnivores: implications for interspecific relationships and connectivity. J. Anim. Ecol. 82, 707–715 (2013).
    PubMed  Article  Google Scholar 

    14.
    Kesch, M. K., Bauer, D. T. & Loveridge, A. J. Break on through to the other side: the effectiveness of game fencing to mitigate human—wildlife conflict. Afr. J. Wildl. Res. 45, 76–87 (2015).
    Article  Google Scholar 

    15.
    Pirie, T. J., Thomas, R. L. & Fellowes, M. D. Game fence presence and permeability influences the local movement and distribution of South African mammals. Afr. Zool. 52, 217–227 (2017).
    Article  Google Scholar 

    16.
    Lindsey, P. A., Masterson, C. L., Beck, A. L. & Romañach, S. Ecological, social, and financial issues related to fencing as a conservation tool in Africa. In Fencing for Conservation: Restriction of Evolutionary Potential or a Riposte to Threatening Processes? (eds Somers, M. J. & Hayward, M.) 215–234 (Springer, Berlin, 2012).
    Google Scholar 

    17.
    Connolly, T. A., Day, T. D. & King, C. M. Estimating the potential for reinvasion by mammalian pests through pest-exclusion fencing. Wildl. Res. 36, 410–421 (2009).
    Article  Google Scholar 

    18.
    Kesch, K. M., Bauer, D. T. & Loveridge, A. J. Undermining game fences: who is digging holes in Kalahari sands?. Afr. J. Ecol. 52, 144–150 (2013).
    Article  Google Scholar 

    19.
    Edwards, S., Noack, J., Heyns, L. & Rodenwoldt, D. Evidence of a high-density brown hyena population within an enclosed reserve: the role of fenced systems in conservation. Mammmal Res. 64, 519–527 (2019).
    Article  Google Scholar 

    20.
    Kent, V. T. & Hill, R. A. The importance of farmland for the conservation of brown hyaena, Parahyaena brunnea. Oryx 47, 431–440 (2013).
    Article  Google Scholar 

    21.
    Welch, R. J. & Parker, D. M. Brown hyaena population explosion: rapid population growth in a small, fenced system. Wildl. Res. 43, 178–187 (2016).
    Article  Google Scholar 

    22.
    Rogan, M. S. et al. The influence of movement on the occupancy–density relationship at small spatial scales. Ecosphere 10, e02807 (2019).
    Article  Google Scholar 

    23.
    Efford, M. G. & Fewster, R. M. Estimating population size by spatially explicit capture–recapture. Oikos 122, 918–928 (2013).
    Article  Google Scholar 

    24.
    Noack, J., Heyns, L., Rodenwoldt, D. & Edwards, S. Leopard density estimation within an enclosed reserve, Namibia using spatially explicit capture-recapture models. Animals 9, 724 (2019).
    Article  Google Scholar 

    25.
    Balme, G. et al. Big cats at large: Density, structure, and spatio-temporal patterns of a leopard population free of anthropogenic mortality. Popul. Ecol. 61, 256–267 (2019).
    Article  Google Scholar 

    26.
    Noss, A. J. et al. Comparison of density estimation methods for mammal populations with camera traps in the Kaa-Iya del Gran Chaco landscape. Anim. Conserv. 15, 527–535 (2012).
    Article  Google Scholar 

    27.
    Foster, R. J. & Harmsen, B. J. A critique of density estimation from camera-trap data. J. Wildl. Manag. 76, 224–236 (2012).
    Article  Google Scholar 

    28.
    Wiesel, I. Parahyaena brunnea. The IUCN Red List of Threatened Species 2015: e.T10276A82344448., Available from http://dx.doi.org/https://doi.org/10.2305/IUCN.UK.2015-4.RLTS.T10276A82344448.en [Accessed 1 March 2020] (2015).

    29.
    Yarnell, R. et al. A conservation assessment of Parahyaena brunnea. In The Red List of Mammals of South Africa, Swaziland and Lesotho (eds Child, M. F. et al.) (South African National Biodiversity Institute and Endangered Wildlife Trust, Midrand, 2016).
    Google Scholar 

    30.
    QGIS Development Team. QGIS Geographic Information System version 3.10.10. Open Source Geospatial Foundation Project (Available from http://qgis.org) (2020).

    31.
    Natural Earth.Available from http://www.naturalearthdata.com [Accessed Feb 01 2020] (2020).

    32.
    Thorn, M., Scott, D. M., Green, M., Bateman, P. W. & Cameron, E. Z. Estimating brown hyaena occupancy using baited camera traps. Afr. J. Wildl. Res. 39, 1–10 (2009).
    Article  Google Scholar 

    33.
    Yarnell, R. W. et al. The influence of large predators on the feeding ecology of two African mesocarnivores: the black-backed jackal and the brown hyaena. Afr. J. Wildl. Res. 43, 155–166 (2013).
    Article  Google Scholar 

    34.
    Falkena, H. B. & van Hoven, W. Bulls, bears and lions: game ranch profitability in southern Africa (The South Africa Financial Sector Forum, Midrand, 2000).
    Google Scholar 

    35.
    Thorn, M., Green, M., Bateman, P. W., Waite, S. & Scott, D. M. Brown hyaenas on roads: estimating carnivore occupancy and abundance using spatially auto-correlated sign survey replicates. Biol. Conserv. 144, 1799–1807 (2011).
    Article  Google Scholar 

    36.
    Wiesel, I. Predatory and foraging behaviour of brown hyenas (Parahyaena brunnea (Thunberg, 1820)) at cape fur seal (Arctocephalus pusillus pusillus Schreber, 1776) colonies PhD thesis, University of Hamburg, (2006).

    37.
    Brassine, E. & Parker, D. Trapping elusive cats: using intensive camera trapping to estimate the density of a rare African felid. PLoS ONE 10, e0142508 (2015).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    38.
    Ramesh, T., Kalle, R., Rosenlund, H. & Downs, C. T. Low leopard populations in protected areas of Maputaland: a consequence of poaching, habitat condition, abundance of prey, and a top predator. Ecol. Evol. 7, 1964–1973 (2017).
    PubMed  PubMed Central  Article  Google Scholar 

    39.
    Miller, J. R., Pitman, R. T., Mann, G. K., Fuller, A. K. & Balme, G. A. Lions and leopards coexist without spatial, temporal or demographic effects of interspecific competition. J. Anim. Ecol. 87, 1709–1726 (2018).
    PubMed  Article  Google Scholar 

    40.
    Trinkel, M. et al. Translocating lions into an inbred lion population in the Hluhluwe-iMfolozi Park, South Africa. Anim. Conserv. 11, 138–143 (2008).
    Article  Google Scholar 

    41.
    Thompson, S., Avent, T. & Doughty, L. S. Range analysis and terrain preference of adult southern white rhinoceros (Ceratotherium simum) in a South African private game reserve: insights into carrying capacity and future management. PLoS ONE 11, e0161724 (2016).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    42.
    Balme, G. A., Slotow, R. & Hunter, L. T. B. Edge effects and the impact of non-protected areas in carnivore conservation: leopards in the Phinda-Mkhuze Complex, South Africa. Anim. Conserv. 13, 315–323 (2010).
    Article  Google Scholar 

    43.
    Royle, J. A., Chandler, R. B., Sun, C. C. & Fuller, A. K. Integrating resource selection information with spatial capture–recapture. Methods Ecol. Evol. 4, 520–530 (2013).
    Article  Google Scholar 

    44.
    Proffitt, K. M. et al. Integrating resource selection into spatial capture-recapture models for large carnivores. Ecosphere 6, 1–15 (2015).
    Article  Google Scholar 

    45.
    Davies-Mostert, H. T. et al. Long-distance transboundary dispersal of African wild dogs among protected areas in southern Africa. Afr. J. Ecol. 50, 500–506 (2012).
    Article  Google Scholar 

    46.
    Williams, K. S. et al. Utilizing bycatch camera-trap data for broad-scale occupancy and conservation: a case study of the brown hyaena Parahyaena brunnea. Oryx, 1–11, (2020).

    47.
    Sollmann, R., Mohamed, A., Samejima, H. & Wilting, A. Risky business or simple solution – Relative abundance indices from camera-trapping. Biol. Conserv. 159, 405–412 (2013).
    Article  Google Scholar 

    48.
    Palmer, M. S., Swanson, A., Kosmala, M., Arnold, T. & Packer, C. Evaluating relative abundance indices for terrestrial herbivores from large-scale camera trap surveys. Afr. J. Ecol. 56, 791–803 (2018).
    Article  Google Scholar 

    49.
    Swanepoel, L. H. et al. A conservation assessment of Panthera pardus. In The Red List of South Africa, Swaziland and Lesotho (eds Child, M. F. et al.) (South African National Biodiversity Institute and Endangered Wildlife Trust, Midrand, 2016).
    Google Scholar 

    50.
    Williams, K. S. Human-brown hyaena relationships and the role of mountainous environments as refuges in a postcolonial landscape PhD thesis, Durham University, (2017).

    51.
    Richmond-Coggan, L. Comparative abundance and ranging behaviour of brown hyaena (Parahyaena brunnea) inside and outside protected areas in South Africa PhD thesis, Nottingham Trent University, (2014).

    52.
    WorldPop.South Africa 100m population, Available from https://www.worldpop.org/doi/https://doi.org/10.5258/SOTON/WP00246. [Accessed 30 May 2020] (2013).

    53.
    Welch, R. J. Population estimates and spatial ecology of brown hyaenas in Kwandwe Private Game Reserve MSc thesis, Rhodes University, (2014).

    54.
    Karanth, K. U., Nichols, J. D. & Samba-Kumar, N. Ch.7: Estimating tiger abundance from camera trap data: field surveys and analytical issues. In Camera traps in animal ecology: methods and analyses (eds O’Connell, A. F. et al.) 97–118 (Springer, Berlin, 2011).
    Google Scholar 

    55.
    Edwards, S. et al. Making the most of by-catch data: assessing the feasibility of utilising non-target camera trap data for occupancy modelling of a large felid. Afr. J. Ecol. 56, 885–894 (2018).
    Article  Google Scholar 

    56.
    Mazzamuto, M. V., Valvo, M. L. & Anile, S. The value of by-catch data: how species-specific surveys can serve non-target species. Eur. J. Wildl. Res. 65, 68 (2019).
    Article  Google Scholar 

    57.
    Sun, C. C., Fuller, A. K. & Royle, J. A. Trap configuration and spacing influences parameter estimates in spatial capture-recapture models. PLoS ONE 10, e0141634 (2014).
    Article  CAS  Google Scholar 

    58.
    Otis, D. L., Burnham, K. P., White, G. C. & Anderson, D. R. Statistical inference from capture data on closed animal populations. Wildlife Monogr. 62, 3–135 (1978).

    59.
    Kays, R. W. & Slauson, K. M. Ch.5: Remote cameras. In Noninvasive survey methods for carnivores (eds Long, R. A. et al.) 110–140 (Island Press, Washington, 2008).
    Google Scholar 

    60.
    Williams, S. T., Williams, K. S., Lewis, B. P. & Hill, R. A. Population dynamics and threats to an apex predator outside of protected areas: Implications for carnivore management. Roy. Soc. Open. Sci. 4, 1–10 (2017).

    61.
    Mills, M. G. L. The comparative behavioural ecology of the brown hyaena Hyaena brunnea and the spotted hyaena Crocuta crocuta in the southern Kalahari. Koedoe 27, 237–247 (1984).
    Google Scholar 

    62.
    Kent, V. T. The status and conservation potential of carnivores in semi-arid rangelands, Botswana the Ghanzi farmlands: a case study PhD thesis, Durham University, (2011).

    63.
    Satter, C. B. et al. Long-term monitoring of ocelot densities in Belize. J. Wildl. Manag. 83, 283–294 (2019).
    Article  Google Scholar 

    64.
    Jordan, M. J., Barrett, R. H. & Purcell, K. L. Camera trapping estimates of density and survival of fishers Martes pennanti. Wildl. Biol. 17, 266–276 (2011).
    Article  Google Scholar 

    65.
    Efford, M. G. secr: Spatially explicit capture-recapture models. R package version 3.2.1. (Available from http://cran.r-project.org/package=secr) (2019).

    66.
    R Development Core Team. R: A language and environment for statistical computing. Version 3.6.0 (Available from https://www.R-project.org/.) (2019).

    67.
    Bahaa-ed-din, L. et al. Effects of human land-use on Africa’s only forest-dependent felid: The African golden cat Caracal aurata. Biol. Conserv. 199, 1–9 (2016).
    Article  Google Scholar 

    68.
    Loock, D. J., Williams, S. T., Emslie, K. W., Matthews, W. S. & Swanepoel, L. H. High carnivore population density highlights the conservation value of industrialised sites. Sci. Rep-UK 8, 16575 (2018).
    ADS  Article  CAS  Google Scholar 

    69.
    Carter, N. H., Shrestha, B. K., Karki, J. B., Pradhan, N. M. B. & Liu, J. G. Coexistence between wildlife and humans at fine spatial scales. Proc. Natl. Acad. Sci. U.S.A. 109, 15360–15365 (2012).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    70.
    Treves, A., Mwima, P., Plumptre, A. J. & Isoke, S. Camera-trapping forest–woodland wildlife of western Uganda reveals how gregariousness biases estimates of relative abundance and distribution. Biol. Conserv. 143, 521–528 (2010).
    Article  Google Scholar 

    71.
    O’Brien, T. G., Kinnaird, M. F. & Wibisono, H. T. Crouching tigers, hidden prey: Sumatran tiger and prey populations in a tropical forest landscape. Anim. Conserv. 6, 131–139 (2003).
    Article  Google Scholar 

    72.
    Williams, K. S., Williams, S. T., Fitzgerald, L. E., Sheppard, E. C. & Hill, R. A. Brown hyaena and leopard diets on private land in the Soutpansberg Mountains, South Africa. Afr. J. Ecol. 56, 1021–1027 (2018).
    Article  Google Scholar 

    73.
    Maddock, A. H. Analysis of brown hyena (Hyaena brunnea) scats from the central Karoo, South Africa. J. Zool. 231, 679–683 (1993).
    Article  Google Scholar 

    74.
    Maude, G. The comparative ecology of the brown hyaena (Hyaena brunnea) in Makgadikgadi National Park and a neighbouring community cattle area in Botswana MSc thesis, University of Pretoria, (2005).

    75.
    Harihar, A. & Pandav, B. Influence of connectivity, wild prey and disturbance on occupancy of tigers in the human-dominated western Terai Arc Landscape. PLoS ONE 7, e40105 (2012).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    76.
    Burnham, K. P. & Anderson, D. R. Model selection and multimodel inference: a practical information-theoretic approach 2nd edn. (Springer, Berlin, 2002).
    Google Scholar 

    77.
    Balme, G. A., Hunter, L. T. B. & Slotow, R. Evaluating methods for counting cryptic carnivores. J. Wildl. Manage. 73, 433–441 (2009).
    Article  Google Scholar 

    78.
    Gopalaswamy, A. M. et al. Program SPACECAP: software for estimating animal density using spatially explicit capture-recapture models. Methods Ecol. Evol. 3, 1067–1072 (2012).
    Article  Google Scholar 

    79.
    Williams, S. T. et al. R code and data for estimating brown hyaena density across South Africa. Available from https://figshare.com/s/f958e721d38dff237bab (2020). More

  • in

    Application of wood ash leads to strong vertical gradients in soil pH changing prokaryotic community structure in forest top soil

    1.
    Silva, F. C., Cruz, N. C., Tarelho, L. A. C. & Rodrigues, S. M. Use of biomass ash-based materials as soil fertilisers: critical review of the existing regulatory framework. J. Clean Prod. 214, 112–124 (2019).
    Article  Google Scholar 
    2.
    Huotari, N., Tillman-Sutela, E., Moilanen, M. & Laiho, R. Recycling of ash—for the good of the environment?. Forest Ecol. Manag. 348, 226–240 (2015).
    Article  Google Scholar 

    3.
    Ingerslev, M., Skov, S., Sevel, L. & Pedersen, L. B. Element budgets of forest biomass combustion and ash fertilisation—a Danish case-study. Biomass Bioenergy 35, 2697–2704 (2011).
    CAS  Article  Google Scholar 

    4.
    Karltun, E. et al. in Sustainable Use of Forest Biomass for Energy (eds Röser, D., Asikainen, A., Raulund-Rasmussen, K. & Stupak, I.) 79–108 (Springer, Berlin, 2008).

    5.
    Thiffault, E. et al. Effects of forest biomass harvesting on soil productivity in boreal and temperate forests—a review. Environ. Rev. 19, 278–309 (2011).
    Article  CAS  Google Scholar 

    6.
    Aronsson, K. A. & Ekelund, N. G. A. Biological effects of wood ash application to forest and aquatic ecosystems. J. Environ. Qual. 33, 1595–1605 (2004).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    7.
    Reimann, C. et al. Element levels in birch and spruce wood ashes—green energy?. Sci. Total Environ. 393, 191–197 (2008).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    8.
    Falkowski, P. G., Fenchel, T. & Delong, E. F. The microbial engines that drive Earth’s biogeochemical cycles. Science 320, 1034–1039 (2008).
    ADS  CAS  Article  Google Scholar 

    9.
    Rønn, R., Vestergard, M. & Ekelund, F. Interactions between bacteria, protozoa and nematodes in soil. Acta Protozool. 51, 223–235 (2012).
    Google Scholar 

    10.
    van der Heijden, M. G. A., Bardgett, R. D. & van Straalen, N. M. The unseen majority: soil microbes as drivers of plant diversity and productivity in terrestrial ecosystems. Ecol. Lett. 11, 296–310 (2008).
    PubMed  Article  PubMed Central  Google Scholar 

    11.
    Wall, D. H. et al. Soil Ecology and Ecosystem Services (Oxford University Press, Oxford, 2012).
    Google Scholar 

    12.
    Fierer, N. Embracing the unknown: disentangling the complexities of the soil microbiome. Nat. Rev. Microbiol. 15, 579–590 (2017).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    13.
    Kaiser, K. et al. Driving forces of soil bacterial community structure, diversity, and function in temperate grasslands and forests. Sci. Rep. 6, 33696 (2016).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    14.
    Waldrop, M. P., Balser, T. C. & Firestone, M. K. Linking microbial community composition to function in a tropical soil. Soil Biol. Biochem. 32, 1837–1846 (2000).
    CAS  Article  Google Scholar 

    15.
    Bang-Andreasen, T. et al. Wood ash induced pH changes strongly affect soil bacterial numbers and community composition. Front. Microbiol. 8, 1400 (2017).
    PubMed  PubMed Central  Article  Google Scholar 

    16.
    Bååth, E. & Arnebrant, K. Growth-rate and response of bacterial communities to pH in limed and ash treated forest soils. Soil. Biol. Biochem. 26, 995–1001 (1994).
    Article  Google Scholar 

    17.
    Cruz-Paredes, C., Wallander, H., Kjøller, R. & Rousk, J. Using community trait-distributions to assign microbial responses to pH changes and Cd in forest soils treated with wood ash. Soil. Biol. Biochem. 112, 153–164 (2017).
    CAS  Article  Google Scholar 

    18.
    Fritze, H., Perkiömäki, J. & Pennanen, T. Distribution of microbial biomass and phospholipid fatty acids in Podzol profiles under coniferous forest. Eur. J. Soil Sci. 51, 565–573 (2000).
    CAS  Article  Google Scholar 

    19.
    Frostegård, A., Bååth, E. & Tunlid, A. Shifts in the structure of soil microbial communities in limed forests as revealed by phospholipid fatty-acid analysis. Soil. Biol. Biochem. 25, 723–730 (1993).
    Article  Google Scholar 

    20.
    Jokinen, H. K., Kiikkilä, O. & Fritze, H. Exploring the mechanisms behind elevated microbial activity after wood ash application. Soil. Biol. Biochem. 38, 2285–2291 (2006).
    CAS  Article  Google Scholar 

    21.
    Noyce, G. L. et al. Soil microbial responses to wood ash addition and forest fire in managed Ontario forests. Appl. Soil Ecol. 107, 368–380 (2016).
    Article  Google Scholar 

    22.
    Perkiömäki, J. & Fritze, H. Short and long-term effects of wood ash on the boreal forest humus microbial community. Soil. Biol. Biochem. 34, 1343–1353 (2002).
    Article  Google Scholar 

    23.
    Vestergård, M. et al. The relative importance of the bacterial pathway and soil inorganic nitrogen increase across an extreme wood-ash application gradient. GBC Bioenergy 10, 320–334 (2018).
    Google Scholar 

    24.
    Fierer, N. & Jackson, R. B. The diversity and biogeography of soil bacterial communities. Proc. Natl. Acad. Sci. USA 103, 626–631 (2006).
    ADS  CAS  PubMed  Article  PubMed Central  Google Scholar 

    25.
    Rousk, J. et al. Soil bacterial and fungal communities across a pH gradient in an arable soil. ISME J. 4, 1340–1351 (2010).
    PubMed  Article  PubMed Central  Google Scholar 

    26.
    Demeyer, A., Nkana, J. C. V. & Verloo, M. G. Characteristics of wood ash and influence on soil properties and nutrient uptake: an overview. Bioresour. Technol. 77, 287–295 (2001).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    27.
    Maresca, A., Hyks, J. & Astrup, T. F. Recirculation of biomass ashes onto forest soils: ash composition, mineralogy and leaching properties. Waste Manag. 70, 127–138 (2017).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    28.
    Fierer, N., Bradford, M. A. & Jackson, R. B. Toward an ecological classification of soil bacteria. Ecology 88, 1354–1364 (2007).
    PubMed  Article  PubMed Central  Google Scholar 

    29.
    Nemergut, D. R., Cleveland, C. C., Wieder, W. R., Washenberger, C. L. & Townsend, A. R. Plot-scale manipulations of organic matter inputs to soils correlate with shifts in microbial community composition in a lowland tropical rain forest. Soil. Biol. Biochem. 42, 2153–2160 (2010).
    CAS  Article  Google Scholar 

    30.
    Philippot, L. et al. The ecological coherence of high bacterial taxonomic ranks. Nat. Rev. Microbiol. 8, 523–529 (2010).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    31.
    Ramirez, K. S., Craine, J. M. & Fierer, N. Consistent effects of nitrogen amendments on soil microbial communities and processes across biomes. Glob. Change Biol. 18, 1918–1927 (2012).
    ADS  Article  Google Scholar 

    32.
    Gömöryová, E., Pichler, V., Tóthová, S. & Gömöry, D. Changes of chemical and biological properties of distinct forest floor layers after wood ash application in a Norway spruce stand. Forests 7, 108 (2016).
    Article  Google Scholar 

    33.
    Hansen, M., Bang-Andreasen, T., Sørensen, H. & Ingerslev, M. Micro vertical changes in soil pH and base cations over time after application of wood ash on forest soil. For. Ecol. Manag. 406, 274–280 (2017).
    Article  Google Scholar 

    34.
    Blume, E. et al. Surface and subsurface microbial biomass, community structure and metabolic activity as a function of soil depth and season. Appl. Soil. Ecol. 20, 171–181 (2002).
    Article  Google Scholar 

    35.
    Ekelund, F., Rønn, R. & Christensen, S. Distribution with depth of protozoa, bacteria and fungi in soil profiles from three Danish forest sites. Soil Biol. Biochem. 33, 475–481 (2001).
    CAS  Article  Google Scholar 

    36.
    Fierer, N., Schimel, J. P. & Holden, P. A. Variations in microbial community composition through two soil depth profiles. Soil Biol. Biochem. 35, 167–176 (2003).
    CAS  Article  Google Scholar 

    37.
    Drew, M. C. Comparison of effects of a localized supply of phosphate, nitrate, ammonium and potassium on growth of seminal root system, and shoot, in Barley. New Phytol. 75, 479–490 (1975).
    CAS  Article  Google Scholar 

    38.
    Hutchings, M. J. & John, E. A. The effects of environmental heterogeneity on root growth and root/shoot partitioning. Ann. Bot. 94, 1–8 (2004).
    PubMed  PubMed Central  Article  Google Scholar 

    39.
    Brunner, I., Zimmermann, S., Zingg, A. & Blaser, P. Wood-ash recycling affects forest soil and tree fine-root chemistry and reverses soil acidification. Plant Soil. 267, 61–71 (2004).
    CAS  Article  Google Scholar 

    40.
    Saarsalmi, A., Smolander, A., Moilanen, M. & Kukkola, M. Wood ash in boreal, low-productive pine stands on upland and peatland sites: long-term effects on stand growth and soil properties. For. Ecol. Manag. 327, 86–95 (2014).
    Article  Google Scholar 

    41.
    Lanzén, A. et al. The community structures of prokaryotes and fungi in mountain pasture soils are highly correlated and primarily influenced by pH. Front. Microbiol. 6, 321 (2015).
    Article  Google Scholar 

    42.
    Lauber, C. L., Hamady, M., Knight, R. & Fierer, N. Pyrosequencing-based assessment of soil pH as a predictor of soil bacterial community structure at the continental scale. Appl. Environ. Microbiol. 75, 5111–5120 (2009).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    43.
    Bang-Andreasen, T., Schostag, M., Prieme, A., Elberling, B. & Jacobsen, C. S. Potential microbial contamination during sampling of permafrost soil assessed by tracers. Sci. Rep. 7, 43338 (2017).
    ADS  CAS  PubMed  PubMed Central  Article  Google Scholar 

    44.
    Saarsalmi, A., Kukkola, M., Moilanen, M. & Arola, M. Long-term effects of ash and N fertilization on stand growth, tree nutrient status and soil chemistry in a Scots pine stand. For. Ecol. Manag. 235, 116–128 (2006).
    Article  Google Scholar 

    45.
    Zimmermann, S. & Frey, B. Soil respiration and microbial properties in an acid forest soil: effects of wood ash. Soil Biol. Biochem. 34, 1727–1737 (2002).
    CAS  Article  Google Scholar 

    46.
    Bååth, E. Adaptation of soil bacterial communities to prevailing pH in different soils. Fems Microbiol. Ecol. 19, 227–237 (1996).
    ADS  Article  Google Scholar 

    47.
    Madigan, M. T., Martinko, J. M., Dunlap, P. V. & Clark, D. P. Brock Biology of Microorganisms 14th edn. (Pearson, Boston, 2014).
    Google Scholar 

    48.
    Rosso, L., Lobry, J. R., Bajard, S. & Flandrois, J. P. Convenient model to describe the combined effects of temperature and pH on microbial-growth. Appl. Environ. Microb. 61, 610–616 (1995).
    CAS  Article  Google Scholar 

    49.
    Kielak, A. M., Barreto, C. C., Kowalchuk, G. A., van Veen, J. A. & Kuramae, E. E. The ecology of acidobacteria: moving beyond genes and genomes. Front. Microbiol. 7, 744 (2016).
    PubMed  PubMed Central  Google Scholar 

    50.
    Kim, J. M. et al. Soil pH and electrical conductivity are key edaphic factors shaping bacterial communities of greenhouse soils in Korea. J. Microbiol. 54, 838–845 (2016).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    51.
    Ochecova, P., Tlustos, P., Szakova, J., Mercl, F. & Maciak, M. Changes in nutrient plant availability in loam and sandy clay loam soils after wood fly and bottom ash amendment. Agron. J. 108, 487–497 (2016).
    CAS  Article  Google Scholar 

    52.
    Pitman, R. M. Wood ash use in forestry—a review of the environmental impacts. Forestry 79, 563–588 (2006).
    Article  Google Scholar 

    53.
    Cederlund, H. et al. Soil carbon quality and nitrogen fertilization structure bacterial communities with predictable responses of major bacterial phyla. Appl. Soil Ecol. 84, 62–68 (2014).
    Article  Google Scholar 

    54.
    Cleveland, C. C., Nemergut, D. R., Schmidt, S. K. & Townsend, A. R. Increases in soil respiration following labile carbon additions linked to rapid shifts in soil microbial community composition. Biogeochemistry 82, 229–240 (2007).
    CAS  Article  Google Scholar 

    55.
    Padmanabhan, P. et al. Respiration of C-13-labeled substrates added to soil in the field and subsequent 16S rRNA gene analysis of C-13-labeled soil DNA. Appl. Environ. Microbiol. 69, 1614–1622 (2003).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    56.
    Lladó, S. & Baldrian, P. Community-level physiological profiling analyses show potential to identify the copiotrophic bacteria present in soil environments. PLoS ONE 12, e0171638 (2017).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    57.
    Starke, R. et al. Bacteria dominate the short-term assimilation of plant-derived N in soil. Soil Biol. Biochem. 96, 30–38 (2016).
    CAS  Article  Google Scholar 

    58.
    Teng, Y., Wang, X. M., Li, L. N., Li, Z. G. & Luo, Y. M. Rhizobia and their bio-partners as novel drivers for functional remediation in contaminated soils. Front. Plant Sci. 6, 32 (2015).
    PubMed  PubMed Central  Article  Google Scholar 

    59.
    Bergmann, G. T. et al. The under-recognized dominance of Verrucomicrobia in soil bacterial communities. Soil Biol. Biochem. 43, 1450–1455 (2011).
    CAS  PubMed  PubMed Central  Article  Google Scholar 

    60.
    Hansen, M., Saarsalmi, A. & Peltre, C. Changes in SOM composition and stability to microbial degradation over time in response to wood chip ash fertilisation. Soil Biol. Biochem. 99, 179–186 (2016).
    CAS  Article  Google Scholar 

    61.
    Reid, C. & Watmough, S. A. Evaluating the effects of liming and wood-ash treatment on forest ecosystems through systematic meta-analysis. Can. J. For. Res. 44, 867–885 (2014).
    CAS  Article  Google Scholar 

    62.
    Levy-Booth, D. J. et al. Cycling of extracellular DNA in the soil environment. Soil Biol. Biochem. 39, 2977–2991 (2007).
    CAS  Article  Google Scholar 

    63.
    Nielsen, K. M., Johnsen, P. J., Bensasson, D. & Daffonchio, D. Release and persistence of extracellular DNA in the environment. Environ. Biosaf. Res. 6, 37–53 (2007).
    CAS  Article  Google Scholar 

    64.
    Carini, P. et al. Relic DNA is abundant in soil and obscures estimates of soil microbial diversity. Nat. Microbiol. 2, 1–6 (2017).
    Article  CAS  Google Scholar 

    65.
    Carvalhais, L. C., Dennis, P. G., Tyson, G. W. & Schenk, P. M. Application of metatranscriptomics to soil environments. J. Microbiol. Methods 91, 246–251 (2012).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    66.
    Urich, T. et al. Simultaneous assessment of soil microbial community structure and function through analysis of the meta-transcriptome. PLoS ONE 3, e2527 (2008).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    67.
    Bang-Andreasen, T. et al. Total RNA sequencing reveals multilevel microbial community changes and functional responses to wood ash application in agricultural and forest soil. FEMS Microbiol. Ecol. 96, 1–13 (2019).
    Google Scholar 

    68.
    Zhang, J., Kobert, K., Flouri, T. & Stamatakis, A. PEAR: a fast and accurate Illumina Paired-End reAd mergeR. Bioinformatics 30, 614–620 (2014).
    CAS  Article  Google Scholar  More

  • in

    Local knowledge as a tool for prospecting wild food plants: experiences in northeastern Brazil

    1.
    Kalle, R. et al. Gaining momentum: Popularization of Epilobium angustifolium as food and recreational tea on the Eastern edge of Europe. Appetite 150, 104638 (2020).
    PubMed  Article  PubMed Central  Google Scholar 
    2.
    FAO. Voluntary Guidelines for the Conservation and Sustainable Use of Crop Wild Relatives and Wild Food Plants. (Food and Agriculture Organization of the United Nations, 2017).

    3.
    Gold, K. & McBurney. Conservation of plant diversity for sustainable diets. in Sustainable diets and biodiversity: directions ad solutions for policy, research and action (eds. Burlingame, B. & Dernini, S.) 30–36 (FAO Headquarters, 2010).

    4.
    Soares, W. L. & de Porto, M. F. Estimating the social cost of pesticide use: An assessment from acute poisoning in Brazil. Ecol. Econ. 68, 2721–2728 (2009).
    Article  Google Scholar 

    5.
    Oliveira, B. P. T. & Ranieri, G. R. Narrativa midiática e difusão sobre Plantas Alimentícias Não Convencionais (PANC): Contribuições para avançar no debate. Cad. Agroecol. 13, 7 (2017).
    Google Scholar 

    6.
    de Assis, J. G. A., Galvão, R. F. M., de Castro, I. R. & de Melo, J. F. Plantas Alimentícias Não Convencionais na Bahia: uma rede em consolidação. Agriculturas 13, 16–20 (2016).
    Google Scholar 

    7.
    Kinupp, V. F. & Lorenzi, H. Plantas Alimentícias não Convencionais no Brasil: Guia de identificação, Aspectos Nutricionais e Receitas Ilustradas. (Instituto Plantarum, 2014).

    8.
    Jacob, M. C. M., de Medeiros, M. F. A. & Albuquerque, U. P. Biodiverse food plants in the semiarid region of Brazil have unknown potential: A systematic review. PLoS ONE 15, e0230936 (2020).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    9.
    Pieroni, A. Evaluation of the cultural significance of wild food botanicals traditionally consumed in Northwestern tuscany, Italy. J. Ethnobiol. 21, 89–104 (2001).
    Google Scholar 

    10.
    Jacob, M. C. M. & Albuquerque, U. P. Biodiverse food plants: Which gaps do we need to address to promote sustainable diets?. Ethnobiol. Conserv. 9, 1–6 (2020).
    Google Scholar 

    11.
    Berkes, F., Colding, J. & Folke, C. Rediscovery of traditional ecological knowledge as adaptive management. Ecol. Appl. 10, 1251–1262 (2000).
    Article  Google Scholar 

    12.
    Cavalli-Sforza, L. L. & Feldman, M. W. Cultural Transmission and Evolution: A Quantitative Approach (Princeton University Press, Princeton, 1981).
    Google Scholar 

    13.
    Reyes-García, V. et al. Cultural transmission of ethnobotanical knowledge and skills: An empirical analysis from an Amerindian society. Evol. Hum. Behav. 30, 274–285 (2009).
    Article  Google Scholar 

    14.
    Ladio, A. H. & Lozada, M. Patterns of use and knowledge of wild edible plants in distinct ecological environments: A case study of a Mapuche community from northwestern Patagonia. Biodivers. Conserv. 13, 1153–1173 (2004).
    Article  Google Scholar 

    15.
    Menendez-baceta, G., Pardo-de-santayana, M., Aceituno-mata, L. & Reyes-garcía, V. Trends in wild food plants uses in Gorbeialdea (Basque Country). Appetite 112, 9–16 (2017).
    PubMed  Article  PubMed Central  Google Scholar 

    16.
    Ayantunde, A. A., Briejer, M., Hiernaux, P., Udo, H. M. J. & Tabo, R. Botanical knowledge and its differentiation by age, gender and ethnicity in Southwestern Niger. Hum. Ecol. 36, 881–889 (2008).
    Article  Google Scholar 

    17.
    de Brito, C. C. et al. The use of different indicators for interpreting the local knowledge loss on medical plants. Braz. J. Pharmacogn. 27, 2 (2017).
    Article  Google Scholar 

    18.
    Ghorbani, A., Langenberger, G. & Sauerborn, J. A comparison of the wild food plant use knowledge of ethnic minorities in Naban River Watershed National Nature Reserve, Yunnan SW China. J. Ethnobiol. Ethnomed. 8, 17 (2012).
    PubMed  PubMed Central  Article  Google Scholar 

    19.
    Kang, Y., Łuczaj, Ł, Kang, J. & Zhang, S. Wild food plants and wild edible fungi in two valleys of the Qinling Mountains (Shaanxi, central China). J. Ethnobiol. Ethnomed. 9, 26 (2013).
    PubMed  PubMed Central  Article  Google Scholar 

    20.
    Nascimento, V. T., Lucena, R. F., Maciel, M. I. & Albuquerque, U. P. Knowledge and use of wild food plants in areas of dry seasonal forests in Brazil. Ecol. Food Nutr. 52, 317–343 (2013).
    PubMed  Article  PubMed Central  Google Scholar 

    21.
    Torres-Avilez, W., Medeiros, P. M. D. & Albuquerque, U. P. Effect of gender on the knowledge of medicinal plants: Systematic review and meta-analysis. Evid.-Based Complement. Altern. Med. 2016, 6592363 (2016).
    Article  Google Scholar 

    22.
    Somnasang, P. & Moreno-Black, G. Knowing, gathering and eating: Knowledge and attitudes about wild food in an Isan village in Northeastern Thailand. J. Ethnobiol. 20, 197–216 (2000).
    Google Scholar 

    23.
    Cruz, M. P., Medeiros, P. M., Combariza, I. S., Peroni, N. & Albuquerque, U. P. ‘I eat the manofê so it is not forgotten’: Local perceptions and consumption of native wild edible plants from seasonal dry forests in Brazil. J. Ethnobiol. Ethnomed. 10, 45 (2014).
    PubMed  PubMed Central  Article  Google Scholar 

    24.
    Gomes, D. L., dos Ferreira, R. P. S., da Santos, É. M. C., da Silva, R. R. V. & de Medeiros, P. M. Local criteria for the selection of wild food plants for consumption and sale in Alagoas, Brazil. Ethnobiol. Conserv. 9, 10 (2020).
    Google Scholar 

    25.
    Serrasolses, G. et al. A matter of taste: Local explanations for the consumption of wild food plants in the Catalan pyrenees and the Balearic Islands. Econ. Bot. 70, 176–189 (2016).
    Article  Google Scholar 

    26.
    Balemie, K. & Kebebew, F. Ethnobotanical study of wild edible plants in Derashe and Kucha Districts South Ethiopia. J. Ethnobiol. Ethnomed. 2, 53 (2014).
    Article  Google Scholar 

    27.
    Costa, J. M. S., Melo, Y. N. C. da S. & Navas, R. Agricultura familiar e agroecologia: diversidade na produção do assentamento Dom Helder Câmara. in Gestão dos ambientes nas práticas socioeconômicas (eds. Selva, V. S. F. et al.) 31–37 (Itacaiúnas, 2019).

    28.
    Cavalcanti, B. C., Rocha, R. & Barros, D. A. Desiring the city—the urban imaginary in rural collective settlements in a Brazilian submontane Atlantic forest reserve. Horizontes Antropológicos 3, 217–235 (2007).
    Google Scholar 

    29.
    Lopes, T. V., Cruz, R. R. & Silva, R. J. N. da. Produção agrícola em um assentamento de reforma agrária da zona da mata alagoana: uma análise do uso de agrotóxicos e a alternativa orgânica. in Gestão dos ambientes nas práticas socioeconômicas (eds. Selva, V. S. F. et al.) 88–94 (Itacaiúnas, 2019).

    30.
    Oliveira, J. R. P. M. & Pôrto, K. C. Composição, riqueza e padrões de distribuição das hepáticas (Marchantiophyta) epífitas da Estação Ecológica Murici, AL Brasil. Rev. Bras. Biociências 5, 1041–1043 (2007).
    Google Scholar 

    31.
    IBGE. Manual Técnico da Vegetação Brasileira. (1992).

    32.
    de Campos, L. Z., Albuquerque, U. P., Peroni, N. & Araújo, E. L. Do socioeconomic characteristics explain the knowledge and use of native food plants in semiarid environments in Northeastern Brazil?. J. Arid Environ. 115, 53–61 (2015).
    ADS  Article  Google Scholar 

    33.
    Nascimento, V. T., Pereira, H. C., Silva, A. S., Nunes, A. T. & Medeiros, P. M. Plantas alimentícias espontâneas conhecidas pelos moradores do Vau da Boa Esperança, município de Barreiras, oeste da Bahia, nordeste do Brasil. Ouricuri 5, 86–109 (2015).
    Google Scholar 

    34.
    Bhattarai, S., Chaudhary, R. P. & Taylor, R. S. L. Wild edible plants used by the people of Manang district, central Nepal wild edible. Ecol. Food Nutr. 48, 1–20 (2009).
    PubMed  Article  PubMed Central  Google Scholar 

    35.
    Ladio, A. H. & Lozada, M. Edible wild plant use in a Mapuche community of northwestern Patagonia. Hum. Ecol. 28, 53–71 (2000).
    Article  Google Scholar 

    36.
    Sansanelli, S. & Tassoni, A. Wild food plants traditionally consumed in the area of Bologna (Emilia Romagna region, Italy ). J. Ethnobiol. Ethnomed. 10, 69 (2014).
    PubMed  PubMed Central  Article  Google Scholar 

    37.
    Sousa, D. C. P., Soldati, G. T., Monteiro, J. M., De Sousa Araújo, T. A. & Albuquerque, U. P. Information retrieval during free listing is biased by memory: Evidence from medicinal plants. PLoS ONE 11, e0165838 (2016).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    38.
    Tabuti, J. R. S., Dhillion, S. S. & Lye, K. A. The status of wild food plants in Bulamogi County Uganda. Int. J. Food Sci. Nutr. 55, 485–498 (2004).
    PubMed  Article  CAS  Google Scholar 

    39.
    Hadjichambis, A. C. H. et al. Wild and semi-domesticated food plant consumption in seven circum-Mediterranean areas. Int. J. Food Sci. Nutr. 59, 383–414 (2008).
    PubMed  Article  Google Scholar 

    40.
    Pieroni, A. Gathered wild food plants in the upper valley of the Serchio River (Garfagnana) Central Italy. Econ. Bot. 53, 327–341 (1999).
    Article  Google Scholar 

    41.
    Thakur, D., Sharma, A. & Uniyal, S. K. Why they eat, what they eat: Patterns of wild edible plants consumption in a tribal area of Western Himalaya. J. Ethnobiol. Ethnomed. 13, 70 (2017).
    PubMed  PubMed Central  Article  Google Scholar 

    42.
    Cruz-garcia, G. S. & Price, L. L. Ethnobotanical investigation of ‘wild’ food plants used by rice farmers in Kalasin Northeast Thailand. J. Ethnobiol. Ethnomed. 7, 33 (2011).
    PubMed  PubMed Central  Article  Google Scholar 

    43.
    Ogle, B. M. & Grivetti, L. E. Legacy of the chameleon: Edible wild plants in the kingdom of Swaziland, southern Africa. A cultural, ecological, nutritional study. Part iv—nutritional analysis and conclusions. Ecol. Food Nutr. 17, 41–64 (1985).
    Article  Google Scholar 

    44.
    Price, L. L. Wild plant food in agricultural environments: A study of occurrence, management, and gathering rights in Northeast Thailand. Hum. Organ. 56, 2019–2221 (1997).
    Article  Google Scholar 

    45.
    Ribeiro, J. P. O. et al. Can ecological apparency explain the use of plant species in the semi-arid depression of Northeastern Brazil?. Acta Bot. Brasilica 28, 476–483 (2014).
    Article  Google Scholar 

    46.
    Soldati, G. T., Medeiros, P. M., Duque-Brasil, R., Coelho, F. M. G. & Albuquerque, U. P. How do people select plants for use? Matching the ecological apparency hypothesis with optimal foraging theory. Environ. Dev. Sustain. 19, 2143–2161 (2017).
    Article  Google Scholar 

    47.
    Bezerra, J. E. F., Lederman, I. E., Junior, J. F. da S. & Proença, C. E. B. Araçá. in Frutas Nativas da Região Centro-Oste do Brasil (eds. Vieira, R. F., Costa, T. da S. A., Silva, D. B. da, Ferreira, F. R. & Sano, S. M.) 42–62 (Embrapa Recursos Genéticos e Biotecnologia, 2006). doi:https://doi.org/10.13140/2.1.2141.1206.

    48.
    Peralta-Bohórquezo, A. F. P., Parada, F., Quijano, C. E. & Pino, J. A. Analysis of volatile compounds of sour guava (psidium guineense swartz) fruit. J. Essent. Oil Res. 22, 493–498 (2010).
    Article  Google Scholar 

    49.
    Damiani, C. et al. Characterization of fruits from the savanna: Araça (Psidium guinnensis Sw.) and Marolo (Annona crassiflora Mart.). Cienc. e Tecnol. Aliment. 31, 723–729 (2011).
    Article  Google Scholar 

    50.
    Schmeda-Hirschmann, G., Feresin, G., Tapia, A., Hilgert, N. & Theoduloz, C. Proximate composition and free radical scavenging activity of edible fruits from the Argentinian Yungas. J. Sci. Food Agric. 85, 1357–1364 (2005).
    Article  CAS  Google Scholar 

    51.
    González, A., Ramírez, M. & Sánchez, P. N. Estudio fitoquímico y actividad antibacterial de Psidium guineense Sw (choba) frente a Streptococcus mutans, agente causal de caries dentales. Rev. Cuba. Plantas Med. 10, 11 (2005).
    Google Scholar 

    52.
    Santos, M. A. C., Queiróz, M. A., Bispo, J. S. & Dantas, B. F. Seed germination of Brazilian guava (Psidium guineense Swartz). J. Seed Sci. 37, 214–221 (2015).
    Article  Google Scholar 

    53.
    Keeler, C. Genipa Americana in native tropical medicine. Dermatol. Trop. Ecol. Geogr. 3, 104–107 (1964).
    Google Scholar 

    54.
    Figueiredo, R. W., Maia, G. A., Holanda, L. F. F. & Monteiro, J. C. F. Características físicas e químicas do jenipapo. Pesqui. Agropecuária Bras. 21, 421–428 (1986).
    Google Scholar 

    55.
    Conceição, A. O., Rossi, M. H., Oliveira, F. F., Takser, L. & Lafond, J. Genipa americana (Rubiaceae) fruit extract affects mitogen-activated protein kinase cell pathways in human trophoblast-derived bewo cells: Implications for placental development. J. Med. Food 14, 483–494 (2011).
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    56.
    Hamacek, F. R., Moreira, A. V. B., Martino, H. S. D., Ribeiro, S. M. R. & Pinheiro-Santana, H. M. Valor nutricional, caracterização Física e físico-química de jenipapo (Genipa Americana L.) do cerrado de Minas Gerais. Aliment. e Nutr. 24, 73–77 (2013).
    Google Scholar 

    57.
    Omena, C. M. B. et al. Antioxidant, anti-acetylcholinesterase and cytotoxic activities of ethanol extracts of peel, pulp and seeds of exotic Brazilian fruits. Antioxidant, anti-acetylcholinesterase and cytotoxic activities in fruits. Food Res. Int. 49, 334–344 (2012).
    Article  CAS  Google Scholar 

    58.
    Porto, R. G. C. L. et al. Chemical composition and antioxidant activity of Genipa Americana L. (Jenipapo) of the Brazilian Cerrado. J. Agric. Environ. Sci. 3, 51–61 (2014).
    Google Scholar 

    59.
    Dickson, L. et al. Main human urinary metabolites after genipap (Genipa americana L.) juice intake. Nutrients 10, 2 (2018).
    Article  CAS  Google Scholar 

    60.
    Alves, L. F. & Ming, L. C. Chemistry and pharmacology of some plants mentioned in the letter of Pero Vaz de Caminha. Ethnobiol. Conserv. 4, 1–15 (2015).
    Google Scholar 

    61.
    Li, Z. et al. Genipin inhibits the growth of human bladder cancer cells via inactivation of PI3k/AkT signaling. Oncol. Lett. 15, 2619–2624 (2018).
    PubMed  PubMed Central  Google Scholar 

    62.
    Shanmugam, M. K. et al. Potential role of genipin in cancer therapy. Pharmacol. Res. 133, 195–200 (2018).
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    63.
    Brauch, J. E., Zapata-Porras, S. P., Buchweitz, M., Aschoff, J. K. & Carle, R. Jagua blue derived from Genipa americana L. fruit: A natural alternative to commonly used blue food colorants?. Food Res. Int. 89, 391–398 (2016).
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    64.
    Souza, A. F., Andrade, A. C. S., Ramos, F. N. & Loureiro, M. B. Ecophysiology and morphology of seed germination of the neotropical lowland tree Genipa americana (Rubiaceae). J. Trop. Ecol. 15, 667–680 (1999).
    Article  Google Scholar 

    65.
    Oliveira, L. M., Oliveira Silva, E., Bruno, R. & Alves, E. U. Periods and dry environments in the seeds quality of Genipa americana L. Semin. Ciencias Agrar. 32, 495–502 (2011).
    Article  Google Scholar 

    66.
    Jackix, E. A., Monteiro, E. B., Raposo, H. F., Vanzela, E. C. & Amaya-Farfán, J. Taioba (xanthosoma sagittifolium) leaves: Nutrient composition and physiological effects on healthy rats. J. Food Sci. 78, 1929–1934 (2013).
    Article  CAS  Google Scholar 

    67.
    Akonor, P. T., Tortoe, C. & Buckman, E. S. Evaluation of cocoyam-wheat composite flour in pastry products based on proximate composition, physicochemical, functional, and sensory properties. J. Culin. Sci. Technol. 16, 52–65 (2018).
    Article  Google Scholar 

    68.
    Falade, K. O. & Okafor, C. A. Physicochemical properties of five cocoyam (Colocasia esculenta and Xanthosoma sagittifolium) starches. Food Hydrocoll. 30, 173–181 (2013).
    Article  CAS  Google Scholar 

    69.
    Falade, K. O. & Okafor, C. A. Physical, functional, and pasting properties of flours from corms of two Cocoyam (Colocasia esculenta and Xanthosoma sagittifolium) cultivars. J. Food Sci. Technol. 52, 3440–3448 (2015).
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    70.
    Nishanthini, A. & Mohan, V. R. Antioxidant activites of Xanthosoma sagittifolium Schott using various in vitro assay models. Asian Pac. J. Trop. Biomed. 2, S1701–S1706 (2012).
    Article  Google Scholar 

    71.
    Pinto, N. A. V. D., Fernandes, S. M., Thé, P. M. P. & Carvalho, V. D. Variabilidade da composição centesimal, vitamina C, ferro e cálcio de partes da folha de Taioba (Xanthosoma sagittifolium Schott). Rev. Bras. Agrociência 7, 205–208 (2001).
    Google Scholar 

    72.
    Oliveira, G. L., Andrade, L. H. C. & Oliveira, A. F. M. Xanthosoma sagittifolium and Laportea aestuans: Species used to prevent osteoporosis in Brazilian traditional medicine. Pharm. Biol. 50, 930–932 (2012).
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    73.
    Jackix, E. A., Monteiro, E. B., Raposo, H. F. & Amaya-Farfán, J. Cholesterol reducing and bile-acid binding properties of taioba (Xanthosoma sagittifolium) leaf in rats fed a high-fat diet. Food Res. Int. 51, 886–891 (2013).
    Article  CAS  Google Scholar 

    74.
    Arruda, S. F., Souza, E. M. T. & Siqueira, E. M. A. Carotenoids from Malanga (Xanthosoma sagittifolium) leaves protect cells against oxidative stress in rats. Int. J. Vitam. Nutr. Res. 75, 161–168 (2005).
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    75.
    Chai, W. & Liebman, M. Effect of different cooking methods on vegetable oxalate content. J. Agric. Food Chem. 53, 3027–3030 (2005).
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    76.
    de Carvalho, E. F. & Cordeiro, J. A. D. Um método alternativo e eficiente de propagação vegetativa de inhame (Colocasia esculenta (L.) SCHOTT) e de taioba (Xanthosoma sagittifolium (L) SCHOOT). Acta Amaz. 20, 11–18 (1990).
    Article  Google Scholar 

    77.
    Suja, G., John, K. S. & Sundaresan, S. Potential of tannia (Xanthosoma sagittifolium (L.) Schott.) for organic production. J. Root Crop. 35, 36–40 (2009).
    Google Scholar 

    78.
    Ramos-Escudero, F., Santos-Buelga, C., Pérez-Alonso, J. J., Yáñez, J. A. & Dueñas, M. HPLC-DAD-ESI/MS identification of anthocyanins in Dioscorea trifida L. yam tubers (purple sachapapa). Eur. Food Res. Technol. 230, 745–752 (2010).
    Article  CAS  Google Scholar 

    79.
    Bousalem, M. et al. Evidence of diploidy in the wild Amerindian yam, a putative progenitor of the endangered species dioscorea trifida (Dioscoreaceae). Genome 53, 371–383 (2010).
    PubMed  Article  CAS  PubMed Central  Google Scholar 

    80.
    Nascimento, W. F., Rodrigues, J. F., Koehler, S., Gepts, P. & Veasey, E. A. Spatially structured genetic diversity of the Amerindian yam (Dioscorea trifida L.) assessed by SSR and ISSR markers in Southern Brazil. Genet. Resour. Crop Evol. 60, 2405–2420 (2013).
    Article  Google Scholar 

    81.
    Rached, L. B., de Vizcarrondo, C. A., Rincón, A. M. & Padilla, F. Evaluación de harinas y almidones de mapuey (Dioscorea trifida), variedades blanco y morado. Arch. Latinoam. Nutr. 56, 2 (2006).
    Google Scholar 

    82.
    Morada, D. E. S. & Yáñez, J. A. Antocianinas, polifenoles, actividad anti-oxidante de sachapapa morada (Dioscorea trifida L.) y evaluación de lipoperoxidación en suero humano. Rev. la Soc. Química del Perú 76, 61–72 (2010).
    Google Scholar 

    83.
    Mollica, J. Q. et al. Anti-inflammatory activity of American yam Dioscorea trifida L.f. in food allergy induced by ovalbumin in mice. J. Funct. Foods 5, 1975–1984 (2013).
    Article  CAS  Google Scholar 

    84.
    Beyerlein, P., Mendes, A. M. S. & Pereira, H. S. Floral phenology, seed germination and hybrid plants of the amerindian yam (Dioscorea trifida). Acta Amaz. 49, 167–172 (2019).
    Article  Google Scholar 

    85.
    N’Danikou, S., Achigan-dako, E. G. & Wong, J. L. G. Eliciting local values of wild edible plants in southern Bénin to identify priority species for conservation. Econ. Bot. 65, 381–395 (2011).
    Article  Google Scholar  More

  • in

    Vulnerability assessment of nearshore clam habitat subject to storm waves and surge

    1.
    Knutson, T. R. et al. Tropical cyclones and climate change. Nat. Geosci. 3(3), 157–163 (2010).
    ADS  CAS  Article  Google Scholar 
    2.
    Lin, N. & Emanuel, K. Grey swan tropical cyclones. Nat. Clim. Change 6, 106–111 (2016).
    ADS  Article  Google Scholar 

    3.
    de Vet, P. L. M. et al. Variations in storm-induced bed level dynamics across intertidal flats. Sci. Rep. 10, 12877 (2020).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    4.
    Harris, L., Nel, R., Smale, M. & Schoeman, D. Swashed away? storm impacts on sandy beach macrofaunal communities. Estuar. Coast. Shelf Sci. 94(3), 210–221 (2011).
    ADS  Article  Google Scholar 

    5.
    Machado, P. M., Costa, L. L., Suciu, M. C., Tavares, D. C. & Zalmon, I. R. Extreme storm wave influence on sandy beach macrofauna with distinct human pressures. Mar. Pollut. Bull. 107(1), 125–135 (2016).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    6.
    Costa, L. L., Machado, P. M. & Zalmon, I. R. Do natural disturbances have significant effects on sandy beach macrofauna of Southeastern Brazil?. Zoologia (Curitiba) 36(1), e29814 (2019).
    Google Scholar 

    7.
    Ghorai, D. & Sen, H. S. Role of climate change in increasing occurrences oceanic hazards as a potential threat to coastal ecology. Nat Hazards 75, 1223–1245 (2015).
    Article  Google Scholar 

    8.
    Posey, M., Lindberg, W., Alphin, T. & Vose, F. Influence of storm disturbance on an offshore benthic community. Bull. Mar. Sci. 59(3), 523–529 (1996).
    Google Scholar 

    9.
    Saloman, C. H. & Naughton, S. P. Effect of Hurricane Eloise on the benthic fauna of Panama City Beach, Florida, USA. Mar. Biol. 42(4), 357–363 (1977).
    Article  Google Scholar 

    10.
    Abe, H. et al. Impact of the 2011 tsunami on the Manila clam Ruditapes philippinarum population and subsequent population recovery in Matsukawaura Lagoon, Fukushima, northeastern Japan. Region. Stud. Mar. Sci. 9, 97–105 (2017).
    Article  Google Scholar 

    11.
    Dreyer, J., Bailey-Brock, J. H. & McCarthy, S. A. The immediate effects of Hurricane Iniki on intertidal fauna on the south shore of O ‘ahu. Mar. Environ. Res. 59(4), 367–380 (2005).
    CAS  PubMed  Article  PubMed Central  Google Scholar 

    12.
    Izumi, S. & Masabumi, S. Behavioral characteristics of the juvenile Japanese surf clam Pseudocardium sachalinensis in response to sand erosion and deposition associated with oscillatory water flow. Fish. Sci. 64(3), 367–372 (1998).
    Article  Google Scholar 

    13.
    Bricheno, L. M., Wolf, J. & Aldridge, J. Distribution of natural disturbance due to wave and tidal bed currents around the UK. Cont. Shelf Res. 109, 67–77 (2015).
    ADS  Article  Google Scholar 

    14.
    Browning, T. N. et al. Widespread deposition in a coastal bay following three major 2017 hurricanes (Irma, Jose, and Maria). Sci. Rep. 9(1), 7101 (2019).
    ADS  PubMed  PubMed Central  Article  CAS  Google Scholar 

    15.
    Hagerman, G. & Rieger, R. Dispersal of benthic Meiofauna by wave and current action in Bogue sound, North Carolina, USA. Mar. Ecol. 2(3), 245–270 (1981).
    ADS  Article  Google Scholar 

    16.
    Corte, G. N. et al. Storm effects on intertidal invertebrates: Increased beta diversity of few individuals and species. PeerJ 5, e3360 (2017).
    PubMed  PubMed Central  Article  Google Scholar 

    17.
    Murphy, A. E. et al. Quantifying the effects of commercial clam aquaculture on c and n cycling: An integrated ecosystem approach. Estuar. Coasts 39(6), 1–16 (2016).
    Article  CAS  Google Scholar 

    18.
    Turra, A. et al. Population biology and secondary production of the harvested clam Tivela Mactroides (Born, 1778) (Bivalvia, Veneridae) in Southeastern Brazil. Mar. Ecol. 36, 2 (2015).
    Article  Google Scholar 

    19.
    Thomas, S. et al. Does the size structure of venerid clam populations affect ecosystem functions on intertidal sandflats?. Estuar. Coasts 20, 20 (2020).
    Google Scholar 

    20.
    Wong, W. H., Rabalais, N. N. & Turner, R. E. Abundance and ecological significance of the clam Rangia Cuneata (Sowerby, 1831) in the upper Barataria Estuary (Louisiana, USA). Hydrobiologia 651(1), 305–315 (2010).
    CAS  Article  Google Scholar 

    21.
    Adkins, S. C., Marsden, I. D. & Pirker, J. G. Reproduction, growth and size of a burrowing intertidal clam exposed to varying environmental conditions in estuaries. Inverteb. Reprod. Dev. 60(3), 223–237 (2016).
    CAS  Article  Google Scholar 

    22.
    Clements, J. C. & Hunt, H. L. Effects of CO2-driven sediment acidification on infaunal marine bivalves: A synthesis. Mar. Pollut. Bull. 117(1–2), 6–16 (2017).
    CAS  PubMed  Article  Google Scholar 

    23.
    Clements, J. C., Woodard, K. D. & Hunt, H. L. Porewater acidification alters the burrowing behavior and post-settlement dispersal of juvenile soft-shell clams (Mya arenaria). J. Exp. Mar. Biol. Ecol. 477(Apr.), 103–111 (2016).
    Article  Google Scholar 

    24.
    Ocaña, F. A., Pech, D., Simões, N. & Hernández-Ávila, I. Spatial assessment of the vulnerability of benthic communities to multiple stressors in the Yucatan Continental Shelf, Gulf of Mexico. Ocean Coast. Manag. 181, 104900 (2019).
    Article  Google Scholar 

    25.
    Ortega, L., Celentano, E., Delgado, E. & Defeo, O. Climate change influences on abundance, individual size and body abnormalities in a sandy beach clam. Mar. Ecol. Progress Ser. 20, 545 (2016).
    Google Scholar 

    26.
    Hinchey, E. K., Schaffner, L. C., Hoar, C. C., Vogt, B. W. & Batte, L. P. Responses of estuarine benthic invertebrates to sediment burial: The importance of mobility and adaptation. Hydrobiologia 556(1), 85–98 (2006).
    Article  Google Scholar 

    27.
    Redjah, I. et al. The importance of turbulent kinetic energy on transport of juvenile clams (Mya arenaria). Aquaculture 307(1–2), 20–28 (2010).
    Article  Google Scholar 

    28.
    Forêt, M., Tremblay, R., Neumeier, U. & Olivier, F. Temporal variation of secondary migrations potential: Concept of temporal windows in four commercial bivalve species. Aquat. Liv. Resour. 31, 19 (2018).
    Article  Google Scholar 

    29.
    Hunt, H. L. & Chant, F. R. J. Modeling bedload transport of juvenile bivalves: Predicted changes in distribution and scale of postlarval dispersal. Estuar. Coasts 32(6), 1090–1102 (2009).
    Article  Google Scholar 

    30.
    Carolyn, J. L., Conrad, A. P. & Vonda, J. C. Behaviour controls post-settlement dispersal by the juvenile bivalves Austrovenus stutchburyi and Macomona Liliana. J. Exp. Mar. Biol. Ecol. 306, 51–74 (2004).
    Article  Google Scholar 

    31.
    St-Onge, P., Miron, G. & Moreau, G. Burrowing behaviour of the softshell clam (Mya arenaria) following erosion and transport. J. Exp. Mar. Biol. Ecol. 340(1), 103–111 (2007).
    Article  Google Scholar 

    32.
    Bolam, S. G. Burial survival of benthic macrofauna following deposition of simulated dredged material. Environ. Monit. Assess. 181(1–4), 13–27 (2011).
    PubMed  Article  PubMed Central  Google Scholar 

    33.
    Fiori, S. M. & Carcedo, M. C. Influence of grain size on burrowing and alongshore distribution of the yellow clam (Amarilladesma mactroides). J. Shellf. Res. 34(3), 785–789 (2015).
    Article  Google Scholar 

    34.
    Lewis, N. S., Fox, E. W. & Dewitt, T. H. Estimating the distribution of harvested estuarine bivalves with natural-history-based habitat suitability models. Estuar. Coast. Shelf Sci. 219(Apr. 5), 453–472 (2019).
    ADS  PubMed  PubMed Central  Article  Google Scholar 

    35.
    Lundquist, C. J. et al. Spatial variability in recolonisation potential: Influence of organism behaviour and hydrodynamics on the distribution of macrofaunal colonists. Mar. Ecol. Prog. Ser. 324, 67–81 (2006).
    ADS  Article  Google Scholar 

    36.
    Hunt, H. L. Transport of juvenile clams: Effects of species and sediment grain size. J. Exp. Mar. Biol. Ecol. 312(2), 271–284 (2004).
    Article  Google Scholar 

    37.
    Lundquist, C. J., Pilditch, C. A. & Cummings, V. J. Behaviour controls post-settlement dispersal by the juvenile bivalves Austrovenus stutchburyi and Macomona liliana. J. Exp. Mar. Biol. Ecol. 306(1), 51–74 (2004).
    Article  Google Scholar 

    38.
    Sakurai, I., Nakajima, K. & Yamashita, T. Effect of oscillatory water flow on burrowing behaviors of the Japanese surf clam Pseudocardium sachalinensis. Nippon Suisan Gakkaishi 64(3), 406–411 (1998).
    Article  Google Scholar 

    39.
    Alejandro, A., Doris, O. & Pedro, T. Effect of transfer time, temperature, and size on burrowing capacity of juvenile clams, Mulinia edulis, from hatchery. World Aquacult. Soc. 50(4), 1–15 (2018).
    Google Scholar 

    40.
    Zaklan, S. D. & Ydenberg, R. The body size–burial depth relationship in the infaunal clam Mya arenaria. J. Exp. Mar. Biol. Ecol. 215(1), 1–17 (1997).
    Article  Google Scholar 

    41.
    Zwarts, L. & Wanink, J. Siphon size and burying depth in deposit-and suspension-feeding benthic bivalves. Mar. Biol. 100(2), 227–240 (1989).
    Article  Google Scholar 

    42.
    Abarca, A., Oliva, D. & Toledo, P. Effect of transfer time, temperature, and size on burrowing capacity of juvenile clams, Mulinia edulis, from hatchery. J. World Aquacult. Soc. 50(4), 774–788 (2019).
    Article  Google Scholar 

    43.
    Nunez, J. D., Laitano, M. V., Meretta, P. E. & Ocampo, E. H. Burrowing behavior of an infaunal clam species after siphon nipping. J. Exp. Mar. Biol. Ecol. 459(Oct. 4), 45–50 (2014).
    Article  Google Scholar 

    44.
    Maurer, D., Keck, R. T., Tinsman, J. C. & Leathem, W. A. Vertical migration and mortality of benthos in dredged material—part I: Mollusca. Mar. Environ. Res. 4(4), 299–319 (1981).
    Article  Google Scholar 

    45.
    Sakurai, I. & Seto, M. Behavioral characteristics of the juvenile Japanese surf clam Pseudocardium sachalinensis in response to sand erosion and deposition associated with oscillatory water flow. Fish. Sci. 64(3), 367–372 (1998).
    CAS  Article  Google Scholar 

    46.
    Hutchison, Z. L., Hendrick, V. J., Burrows, M. T., Wilson, B. & Last, K. S. Buried alive: The behavioural response of the mussels, modiolus modiolus and mytilus edulis to sudden burial by sediment. PLoS One 11(3), e0151471 (2016).
    PubMed  PubMed Central  Article  CAS  Google Scholar 

    47.
    Ma, D., Shi, W. & Yu, J. Burial effects of Tianjin nangang industrial zone dredging Materialon Macrobenthos. J. Zhejiang Ocean Univ. 20, 20 (2015) ((in Chinese)).
    Google Scholar 

    48.
    Quinn, N., Atkinson, P. & Wells, N. Modelling of tide and surge elevations in the Solent and surrounding waters: The importance of tide–surge interactions. Estuar. Coast. Shelf Sci. 112(112), 162–172 (2012).
    ADS  Article  Google Scholar 

    49.
    Houser and Chris. Alongshore variation in the morphology of coastal dunes: Implications for storm response. Geomorphology 199, 48–61 (2013).
    ADS  Article  Google Scholar  More