More stories

  • in

    Escaping Darwin’s shadow: how Alfred Russel Wallace inspires Indigenous researchers

    A map of the Amazon River and its tributaries, as published in Alfred Russel Wallace’s 1853 book.Credit: Mary Evans/Natural History Museum

    Dzoodzo Baniwa, a member of an Indigenous community in Brazil’s Amazonas state, has been collecting data on the region’s biodiversity for around 15 years. He lives in a remote village called Canadá on the Ayari River, a tributary of the Içana, which in turn feeds the Rio Negro, one of the main branches of the Amazon. The nearest city, São Gabriel da Cachoeira, is a three-day trip by motor boat.Dzoodzo (who goes by his Indigenous name but is also known as Juvêncio Cardoso) takes inspiration for his work from many cross-cultural sources. A perhaps unexpected one is a 170-year-old book by the British naturalist Alfred Russel Wallace, who visited the Amazon and Negro rivers on his expeditions in 1848–52. A Narrative of Travels on the Amazon and Rio Negro gives detailed accounts of the wildlife and people Wallace encountered near Dzoodzo’s home, including the Guianan cock-of-the-rock (Rupicola rupicola), a bright orange bird that Wallace describes as “magnificent … sitting amidst the gloom, shining out like a mass of brilliant flame”1.Dzoodzo’s passion for local biodiversity is reflected in his work at Baniwa Eeno Hiepole School, an internationally praised education centre for Indigenous people. He dreams of one day turning it into a research institute and university that might increase scientific understanding of the region’s species, including R. rupicola.
    Alfred Russel Wallace’s first expedition ended in flames
    Wallace, who was born 200 years ago, on 8 January 1823, is best known for spurring Charles Darwin into finally publishing On the Origin of Species, after Wallace sent Darwin his own independent discovery of evolution by natural selection in 1858. Most of Wallace’s subsequent work drew on observations from his 1854–62 expeditions in southeast Asia; his earlier work in Amazonia is much less well known.Yet there are lessons from Wallace’s time in Brazil that are especially relevant for conservationists and other scientists today — notably, what can come from paying attention to what local people say about their own territory.Barriers and boundariesWallace made two key contributions that still shape thinking about Amazonia, the world’s most biodiverse region, which covers parts of Bolivia, Brazil, Colombia, Ecuador, Peru, Venezuela, Guyana, Suriname and French Guiana.On 14 December 1852, Wallace read out his manuscript ‘On the monkeys of the Amazon’ at a meeting of the Zoological Society of London. In this study, which was later published2, Wallace relays observations that form the basis of the most debated hypothesis for how Amazonian organisms diversified: the riverine barrier hypothesis.His paper refers to the large Amazonian rivers as spatial boundaries to the ranges of several primate species. “I soon found that the Amazon, the Rio Negro and the Madeira formed the limits beyond which certain species never passed,” he writes. Since 1852, Wallace’s observations that large rivers could act as geographical barriers that shape the distribution of species have been corroborated, criticized and debated by many. The phenomenon he described clearly holds for some groups, such as monkeys and birds3,4, but not for other groups, such as plants and insects5.Subsequent researchers have explored whether the distribution patterns of species, such as those observed by Wallace, indicate that the evolution of the Amazonian drainage system has itself driven the diversification of species6. Work in the past few years by geologists and biologists show that this drainage system, which includes some of the largest rivers in the world, is dynamic7, and that its rearrangements lead to changes in the distribution ranges of species8. Current species ranges thus hold information about how the Amazonian landscape has changed over time.

    The Guianan cock-of-the-rock (Rupicola rupicola), which Wallace likened to a “brilliant flame”.Credit: Hein Nouwens/Getty

    The second crucial observation made by Wallace, also in his 1852 paper, was that the composition of species varies in different regions. He describes how “several Guiana species come up to the Rio Negro and Amazon, but do not pass them; Brazilian species on the contrary reach but do not pass the Amazon to the north. Several Ecuador species from the east of the Andes reach down into the tongue of land between the Rio Negro and Upper Amazon, but pass neither of those rivers, and others from Peru are bounded on the north by the Upper Amazon, and on the east by the Madeira.” From these observations, he concluded that “there are four districts, the Guiana, the Ecuador, the Peru and the Brazil districts, whose boundaries on one side are determined by the rivers I have mentioned.”
    Evolution’s red-hot radical
    Even though Amazonia is presented as a single, large, green ellipse in most world maps, it is actually a heterogeneous place, with each region and habitat type holding a distinct set of species9,10. The four districts proposed by Wallace are bounded by the region’s largest rivers: the Amazon, Negro and Madeira. But further studies of species ranges since then have revealed more districts, now called areas of endemism, some of which are also bounded by these and other large Amazonian rivers, such as the Tapajós, Xingu and Tocantins9,11.This recognition of spatial heterogeneity in Amazonian species distributions — first accomplished by Wallace — is essential for today’s research, conservation and planning10. Each area of endemism includes species that occur only in that area. And different areas of endemism are affected differently by anthropogenic impacts, such as deforestation, fires and development10. More than half of Amazonia is now within federal or state reserves or Indigenous lands — territories that are recognized by current governments as belonging to Indigenous people. But nearly half of the region’s areas of endemism are located in the south of the region, close to the agricultural frontier, and the species they contain are severely threatened by habitat loss10 (see also www.raisg.org/en).Local knowledgeAlthough Wallace’s writings indicate that in many ways he admired most of the Indigenous people he met, especially those from the upper Rio Negro basin, he still viewed Indigenous people through the European colonial lens of his time. In A Narrative of Travels on the Amazon and Rio Negro1, Wallace describes the Indigenous communities he encountered as “in an equally low state of civilization” — albeit seemingly “capable of being formed, by education and good government, into a peaceable and civilized community”.Yet he did better than many of his contemporaries when it came to respecting local knowledge. In his 1852 paper, for example, Wallace notes that his fellow European naturalists often give vague information about the locality of their collected specimens, and fail to specify such localities in relation to river margins. By contrast, he writes, the “native hunters are perfectly acquainted” with the impact of rivers on the distribution of species, “and always cross over the river when they want to procure particular animals, which are found even on the river’s bank on one side, but never by any chance on the other.” Likewise, in his 1853 book1, Wallace frequently corroborates his findings with information he has obtained from Indigenous people — for example, about the habitat preferences of umbrellabirds (Cephalopterus ornatus) or of “cow-fish” (manatees; Trichechus inunguis).Considering the vastness and complexity of Amazonia, it is hard to see how Wallace could have gained the insights he did after working in the region for only four years, had he not paid close attention to local knowledge.
    The other beetle-hunter
    Amazonian Indigenous peoples have had to endure invasion of their lands, enslavement, violence from invaders and the imposition of other languages and cultures. Despite this, numerous Indigenous researchers wish to expand their knowledge about Amazonia by combining Indigenous and European world views. Meanwhile, a better understanding of how the Amazonian socio-ecological system is organized, and how it is being affected by climate change and local and regional impacts12, hinges on the ability of researchers worldwide to learn from and to be led by Indigenous scientists.The 98 Indigenous lands in the Rio Negro basin cover more than 33 million hectares (see go.nature.com/3wkkftu). If the hopes of Dzoodzo and others to build a research institute and university for the region are met, school students will no longer have to leave their homeland to pursue higher education. The community would have a way to document its own knowledge and that of its ancestors in a more systematic way. And the legitimization of Indigenous people’s research efforts in the legal and academic frameworks recognized by non-Indigenous scientists — such as through the awarding of degrees — would make it easier for Indigenous researchers to partner with other organizations, both nationally and internationally.Indigenous people in the Rio Negro basin today are no longer objects of observation — they have taken charge of their own research using tools from different cultures. Indeed, Dzoodzo is turning to Wallace’s writings, in part, to learn more about how his own ancestors lived.Perhaps the thread between Wallace and Dzoodzo, spanning so many years and such disparate cultures, could seed new kinds of partnership in which learning is reciprocal and for the benefit of all. More

  • in

    Global-scale parameters for ecological models

    EU Commission. Achieve Good Environmental Status. EU Commission Web site https://ec.europa.eu/environment/marine/good-environmental-status/index_en.htm (2008).Olenin, S. et al. Marine strategy framework directive. Task Group 2 (2010).Long, R. The marine strategy framework directive: a new european approach to the regulation of the marine environment, marine natural resources and marine ecological services. Journal of Energy & Natural Resources Law 29, 1–44 (2011).Article 
    ADS 

    Google Scholar 
    Borja, A. et al. Good environmental status of marine ecosystems: what is it and how do we know when we have attained it? Marine Pollution Bulletin 76, 16–27 (2013).Article 

    Google Scholar 
    Froese, R., Demirel, N., Coro, G. & Kleisner, K. M. & Winker, H. Estimating fisheries reference points from catch and resilience. Fish and Fisheries 18, 506–526 (2017).Article 

    Google Scholar 
    Coro, G. Open science and artificial intelligence supporting blue growth. Environmental Engineering & Management Journal (EEMJ) 19 (2020).JRC. EU data collection Web site https://datacollection.jrc.ec.europa.eu/ – Accessed June 2022 (2021).EcoScope Consortium. The EcoScope EU project Web site https://ecoscopium.eu/ – Accessed June 2022 (2021).Pikitch, E. K. et al. Ecosystem-based fishery management. Science 305, 346–347 (2004).Article 

    Google Scholar 
    McLeod, K. L. & Leslie, H. M. Why ecosystem-based management. Ecosystem-based management for the oceans 3–12 (2009).Coro, G., Magliozzi, C., Ellenbroek, A. & Pagano, P. Improving data quality to build a robust distribution model for architeuthis dux. Ecological modelling 305, 29–39 (2015).Article 

    Google Scholar 
    Coro, G., Ellenbroek, A. & Pagano, P. An open science approach to infer fishing activity pressure on stocks and biodiversity from vessel tracking data. Ecological Informatics 64, 101384 (2021).Article 

    Google Scholar 
    Elith, J. & Leathwick, J. R. Species distribution models: ecological explanation and prediction across space and time. Annual Review of Ecology, Evolution and Systematics 40, 677–697 (2009).Article 

    Google Scholar 
    Coro, G., Bove, P. & Ellenbroek, A. Habitat distribution change of commercial species in the adriatic sea during the covid-19 pandemic. Ecological Informatics 101675 (2022).Stanton, J. C., Pearson, R. G., Horning, N., Ersts, P. & Reşit Akçakaya, H. Combining static and dynamic variables in species distribution models under climate change. Methods in Ecology and Evolution 3, 349–357 (2012).Article 

    Google Scholar 
    Coro, G., Magliozzi, C., Ellenbroek, A., Kaschner, K. & Pagano, P. Automatic classification of climate change effects on marine species distributions in 2050 using the aquamaps model. Environmental and ecological statistics 23, 155–180 (2016).Article 
    MathSciNet 

    Google Scholar 
    Coro, G., Pagano, P. & Ellenbroek, A. Detecting patterns of climate change in long-term forecasts of marine environmental parameters. International Journal of Digital Earth 13, 567–585 (2020).Article 
    ADS 

    Google Scholar 
    Wayte, S. E. Management implications of including a climate-induced recruitment shift in the stock assessment for jackass morwong (nemadactylus macropterus) in south-eastern australia. Fisheries Research 142, 47–55 (2013).Article 

    Google Scholar 
    Tanaka, K. R. Integrating environmental information into stock assessment models for fisheries management. Predicting Future Oceans 193–206 (2019).Szuwalski, C. S. & Hollowed, A. B. Climate change and non-stationary population processes in fisheries management. ICES Journal of Marine Science 73, 1297–1305 (2016).Article 

    Google Scholar 
    Bevilacqua, A. H. V., Carvalho, A. R., Angelini, R. & Christensen, V. More than anecdotes: fishers’ ecological knowledge can fill gaps for ecosystem modeling. PLoS One 11, e0155655 (2016).Article 

    Google Scholar 
    Heymans, J. J. et al. Best practice in ecopath with ecosim food-web models for ecosystem-based management. Ecological Modelling 331, 173–184 (2016).Article 

    Google Scholar 
    Piroddi, C. et al. Historical changes of the mediterranean sea ecosystem: modelling the role and impact of primary productivity and fisheries changes over time. Scientific reports 7, 1–18 (2017).Article 
    ADS 

    Google Scholar 
    Campana, E. F., Ciappi, E. & Coro, G. The role of technology and digital innovation in sustainability and decarbonization of the blue economy. Bulletin of Geophysics and Oceanography 123 (2021).Van Vuuren, D. P. et al. The representative concentration pathways: an overview. Climatic change 109, 5–31 (2011).Article 
    ADS 

    Google Scholar 
    Intergovernmental Panel on Climate Change https://www.academia.edu/download/60673993/climate_change_emission_Special_scenarios20190922-59363-1j1i98f.pdf – Accessed October 2022. IPCC Special Report (2000).Scarcella, G. et al. The potential effects of covid-19 lockdown and the following restrictions on the status of eight target stocks in the adriatic sea. Frontiers in Marine Science 1963 (2022).Wikipedia. ESRI-GRID formats description. Wikipedia https://en.wikipedia.org/wiki/Esri_grid (2022).QGIS. Qgis software version 3.20.0. QGIS Web site https://www.qgis.org/en/site/ (2022).ESRI. Arcgis software version 10.7. ArcGIS Web site https://www.esri.com/it-it/arcgis/products/arcgis-desktop/overview (2022).American Museum of Natural History. Maxent software for modelling species distributions. AMNH Web site https://biodiversityinformatics.amnh.org/open_source/maxent/ (2022).Christensen, V. et al. Ecopath with ecosim: a user’s guide. Fisheries Centre, University of British Columbia, Vancouver 154, 31 (2005).
    Google Scholar 
    Coll, M., Bundy, A. & Shannon, L. J. Ecosystem modelling using the ecopath with ecosim approach. In Computers in fisheries research, 225–291 (Springer, 2009).Colléter, M. et al. Global overview of the applications of the ecopath with ecosim modeling approach using the ecobase models repository. Ecological Modelling 302, 42–53 (2015).Article 

    Google Scholar 
    VanDerWal, J., Falconi, L., Januchowski, S., Shoo, L. & Storlie, C. Sdmtools: Species distribution modelling tools: Tools for processing data associated with species distribution modelling exercises. R package version 1, 1 (2014).
    Google Scholar 
    US National Institutes of Health. ImageJ software for image analysis with Java and the Terrain Cartography plugin for reading ASC files. ImageJ Web site https://imagej.nih.gov/ij/index.html (2018).GDAL. Translator library for raster and vector geospatial data, version 3.5.0. GDAL Web site https://gdal.org/ (2022).Claus, S. et al. Marine regions: towards a global standard for georeferenced marine names and boundaries. Marine Geodesy 37, 99–125 (2014).Article 

    Google Scholar 
    VLIZ. World marine regions definitions and geospatial data. Marine Regions Web site www.marineregions.org (2022).Coro, G. The ASCFileManagement GitHub repository. GitHub https://github.com/cybprojects65/ASCFileManagement (2022).Ready, J. et al. Predicting the distributions of marine organisms at the global scale. Ecological Modelling 221, 467–478 (2010).Article 

    Google Scholar 
    Selig, E. R. et al. Global priorities for marine biodiversity conservation. PloS one 9, e82898 (2014).Article 
    ADS 

    Google Scholar 
    O’hara, C. C., Afflerbach, J. C., Scarborough, C., Kaschner, K. & Halpern, B. S. Aligning marine species range data to better serve science and conservation. PLoS One 12, e0175739 (2017).Article 

    Google Scholar 
    Scarponi, P., Coro, G. & Pagano, P. A collection of aquamaps native layers in netcdf format. Data in brief 17, 292–296 (2018).Article 

    Google Scholar 
    CMEMS. Copernicus Marine Service ocean products data. Copernicus Marine Service Web site https://marine.copernicus.eu/ (2022).E.U. Copernicus Marine Service Information. Global Ocean 1/12° Physics Analysis and Forecast updated Daily. Copernicus Marine Service Web site https://doi.org/10.48670/moi-00016 (2021).Article 

    Google Scholar 
    E.U. Copernicus Marine Service Information. Global Ocean Biogeochemistry Analysis and Forecast. Copernicus Marine Service Web site https://doi.org/10.48670/moi-00015 (2021).Article 

    Google Scholar 
    Hijmans, R. J. et al. Terra: Spatial data analysis. R Spatial Data Science Web site https://rspatial.org/terra/ (2022).MacLeod, C. D. Habitat representativeness score (hrs): a novel concept for objectively assessing the suitability of survey coverage for modelling the distribution of marine species. Journal of the Marine Biological Association of the United Kingdom 90, 1269–1277 (2010).Article 

    Google Scholar 
    Abdi, H. & Williams, L. J. Principal component analysis. Wiley interdisciplinary reviews: computational statistics 2, 433–459 (2010).Article 

    Google Scholar 
    Coro, G., Pagano, P. & Ellenbroek, A. Combining simulated expert knowledge with neural networks to produce ecological niche models for latimeria chalumnae. Ecological modelling 268, 55–63 (2013).Article 

    Google Scholar 
    Coro, G., Pagano, P. & Ellenbroek, A. Automatic procedures to assist in manual review of marine species distribution maps. In International Conference on Adaptive and Natural Computing Algorithms, 346–355 (Springer, 2013).Magliozzi, C., Coro, G., Grabowski, R. C., Packman, A. I. & Krause, S. A multiscale statistical method to identify potential areas of hyporheic exchange for river restoration planning. Environmental Modelling & Software 111, 311–323 (2019).Article 

    Google Scholar 
    Coro, G. & Bove, P. Global-Scale Parameters for Ecological Models, FigShare, https://doi.org/10.6084/m9.figshare.c.6039275.v4 (2022).Coro, G. Means, standard deviations, geometric means, and log-normal standard deviation of the data produced for the present publication. D4Science distributed storage system https://data.d4science.net/foLS (2022).Mann, M. E., Bradley, R. S. & Hughes, M. K. Global-scale temperature patterns and climate forcing over the past six centuries. Nature 392, 779–787 (1998).Article 
    ADS 

    Google Scholar 
    Biskaborn, B. K. et al. Permafrost is warming at a global scale. Nature communications 10, 1–11 (2019).Article 

    Google Scholar 
    Nemani, R. R. et al. Climate-driven increases in global terrestrial net primary production from 1982 to 1999. science 300, 1560–1563 (2003).Article 
    ADS 

    Google Scholar 
    Huang, Y., Zhang, W., Sun, W. & Zheng, X. Net primary production of chinese croplands from 1950 to 1999. Ecological Applications 17, 692–701 (2007).Article 

    Google Scholar 
    Sunlu, U., Aksu, M., Buyukisik, B. & Sunlu, F. S. Spatio-temporal variations of organic carbon and chlorophyll degradation products in the surficial sediments of izmir bay (aegean sea/turkey). Environmental monitoring and assessment 146, 423–432 (2008).Article 

    Google Scholar 
    Kubryakov, A., Mikaelyan, A., Stanichny, S. & Kubryakova, E. Seasonal stages of chlorophyll-a vertical distribution and its relation to the light conditions in the black sea from bio-argo measurements. Journal of Geophysical Research: Oceans 125, e2020JC016790 (2020).ADS 

    Google Scholar 
    Gamo, T. Global warming may have slowed down the deep conveyor belt of a marginal sea of the northwestern pacific: Japan sea. Geophysical Research Letters 26, 3137–3140 (1999).Article 
    ADS 

    Google Scholar 
    Mahaffey, C., Palmer, M., Greenwood, N. & Sharples, J. Impacts of climate change on dissolved oxygen concentration relevant to the coastal and marine environment around the uk. MCCIP Science Review 2002, 31–53 (2020).
    Google Scholar 
    Zhang, W., Dunne, J. P., Wu, H., Zhou, F. & Huang, D. Using timescales of deficit and residence to evaluate near-bottom dissolved oxygen variation in coastal seas. Journal of Geophysical Research: Biogeosciences 127, e2021JG006408 (2022).ADS 

    Google Scholar 
    Helm, K. P., Bindoff, N. L. & Church, J. A. Changes in the global hydrological-cycle inferred from ocean salinity. Geophysical Research Letters 37 (2010).Ren, L., Speer, K. & Chassignet, E. P. The mixed layer salinity budget and sea ice in the southern ocean. Journal of Geophysical Research: Oceans 116 (2011).Mahmuduzzaman, M. et al. Causes of salinity intrusion in coastal belt of bangladesh. International Journal of Plant Research 4, 8–13 (2014).
    Google Scholar 
    Podymov, O., Zatsepin, A. & Ocherednik, V. Increase of temperature and salinity in the active layer of the north-eastern black sea from 2010 to 2020. Physical Oceanography 28, 257–265 (2021).Article 

    Google Scholar 
    Mizyuk, A. & Puzina, O. Sea ice modeling in the sea of azov for a study of long-term variability. In IOP Conference Series: Earth and Environmental Science, vol. 386, 012023 (IOP Publishing, 2019).Pärn, O., Friedland, R., Rjazin, J. & Stips, A. Regime shift in sea-ice characteristics and impact on the spring bloom in the baltic sea. Oceanologia 64, 312–326 (2022).Article 

    Google Scholar 
    Lundesgaard, Ø., Sundfjord, A. & Renner, A. H. Drivers of interannual sea ice concentration variability in the atlantic water inflow region north of svalbard. Journal of Geophysical Research: Oceans 126, e2020JC016522 (2021).ADS 

    Google Scholar 
    Schwegmann, S. & Holfort, J. Regional distributed trends of sea ice volume in the baltic sea for the 30-year period 1982 to 2019. Meteorologische Zeitschrift 33–43 (2021).Simon, S. Interpretation of the correlation coefficient. PMean Web site http://www.pmean.com/definitions/correlation.htm (2020).Yacobi, Y. et al. Chlorophyll distribution throughout the southeastern mediterranean in relation to the physical structure of the water mass. Journal of Marine Systems 6, 179–190, https://doi.org/10.1016/0924-7963(94)00028-A (1995).Article 
    ADS 

    Google Scholar 
    Kucuksezgin, F., Balci, A., Kontas, A. & Altay, O. Distribution of nutrients and chlorophyll-a in the aegean sea. Oceanologica Acta 18, 343–352 (1995).
    Google Scholar 
    Villate, F., Aravena, G., Iriarte, A. & Uriarte, I. Axial variability in the relationship of chlorophyll a with climatic factors and the north atlantic oscillation in a basque coast estuary, bay of biscay (1997–2006). Journal of Plankton Research 30, 1041–1049 (2008).Article 

    Google Scholar 
    Iriarte, A. et al. Dissolved oxygen in contrasting estuaries of the bay of biscay: effects of temperature, river discharge and chlorophyll a. Marine Ecology Progress Series 418, 57–71 (2010).Article 
    ADS 

    Google Scholar 
    Stanev, E. V. Black sea dynamics. Oceanography 18, 56–75 (2005).Article 

    Google Scholar 
    Tsimplis, M. N. & Rixen, M. Sea level in the mediterranean sea: The contribution of temperature and salinity changes. Geophysical research letters 29, 51–1 (2002).Article 

    Google Scholar 
    Schneider, A., Wallace, D. W. & Körtzinger, A. Alkalinity of the mediterranean sea. Geophysical Research Letters 34 (2007).Sara, G., Porporato, E. M., Mangano, M. C. & Mieszkowska, N. Multiple stressors facilitate the spread of a non-indigenous bivalve in the mediterranean sea. Journal of Biogeography 45, 1090–1103 (2018).Article 

    Google Scholar 
    Soto-Navarro, J. et al. Evolution of mediterranean sea water properties under climate change scenarios in the med-cordex ensemble. Climate Dynamics 54, 2135–2165 (2020).Article 
    ADS 

    Google Scholar 
    Dietze, H. & Löptien, U. Retracing hypoxia in eckernförde bight (baltic sea). Biogeosciences 18, 4243–4264 (2021).Article 
    ADS 

    Google Scholar 
    Ulses, C. et al. Oxygen budget of the north-western mediterranean deep-convection region. Biogeosciences 18, 937–960 (2021).Article 
    ADS 

    Google Scholar 
    Jaskulak, M., Sotomski, M., Michalska, M., Marks, R. & Zorena, K. The effects of wastewater treatment plant failure on the gulf of gdansk (southern baltic sea). International Journal of Environmental Research and Public Health 19, 2048 (2022).Article 

    Google Scholar 
    Mihanović, H. et al. Observation, preconditioning and recurrence of exceptionally high salinities in the adriatic sea. Frontiers in Marine Science 8, 834 (2021).Article 

    Google Scholar 
    De Leo, F., Besio, G. & Mentaschi, L. Trends and variability of ocean waves under rcp8. 5 emission scenario in the mediterranean sea. Ocean Dynamics 71, 97–117 (2021).Article 
    ADS 

    Google Scholar 
    Omar, A. M. et al. Trends of ocean acidification and pco2 in the northern north sea, 2003–2015. Journal of Geophysical Research: Biogeosciences 124, 3088–3103 (2019).Article 

    Google Scholar 
    Kröncke, I. et al. Comparison of biological and ecological long-term trends related to northern hemisphere climate in different marine ecosystems. Nature Conservation (2019).Bonnet, D. et al. Comparative seasonal dynamics of centropages typicus at seven coastal monitoring stations in the north sea, english channel and bay of biscay. Progress in oceanography 72, 233–248 (2007).Article 
    ADS 

    Google Scholar 
    Borja, Á. et al. Implementation of the european marine strategy framework directive: A methodological approach for the assessment of environmental status, from the basque country (bay of biscay). Marine Pollution Bulletin 62, 889–904, https://doi.org/10.1016/j.marpolbul.2011.03.031 (2011).Article 

    Google Scholar 
    Coro, G. An open-source re-implementation of the habitat representativeness score. GitHub https://github.com/cybprojects65/HabitatRepresentativenessScore (2022).Coro, G. An OGC-WPS compliant interface to calculate Habitat Representativeness Score. D4Science RPrototypingLab VRE https://services.d4science.org/group/rprototypinglab/data-miner?OperatorId = org.gcube.dataanalysis.wps.statisticalmanager.synchserver.mappedclasses.transducerers.HABITAT_REPRESENTATIVENESS_SCORE (2022).Assante, M. et al. Enacting open science by d4science. Future Generation Computer Systems 101, 555–563 (2019).Article 

    Google Scholar 
    Assante, M. et al. The gcube system: delivering virtual research environments as-a-service. Future Generation Computer Systems 95, 445–453 (2019).Article 

    Google Scholar 
    Assante, M. et al. Virtual research environments co-creation: The d4science experience. Concurrency and Computation: Practice and Experience e6925 (2022).Coro, G., Candela, L., Pagano, P., Italiano, A. & Liccardo, L. Parallelizing the execution of native data mining algorithms for computational biology. Concurrency and Computation: Practice and Experience 27, 4630–4644 (2015).Article 

    Google Scholar 
    Coro, G., Panichi, G., Scarponi, P. & Pagano, P. Cloud computing in a distributed e-infrastructure using the web processing service standard. Concurrency and Computation: Practice and Experience 29, e4219 (2017).Article 

    Google Scholar 
    Gačić, M., Borzelli, G. E., Civitarese, G., Cardin, V. & Yari, S. Can internal processes sustain reversals of the ocean upper circulation? the ionian sea example. Geophysical research letters 37 (2010).Grilli, F. et al. Seasonal and interannual trends of oceanographic parameters over 40 years in the northern adriatic sea in relation to nutrient loadings using the emodnet chemistry data portal. Water 12, 2280 (2020).Article 

    Google Scholar 
    Cozzi, S. et al. Climatic and anthropogenic impacts on environmental conditions and phytoplankton community in the gulf of trieste (northern adriatic sea). Water 12, 2652 (2020).Article 

    Google Scholar 
    Ducrotoy, J.-P. & Elliott, M. The science and management of the north sea and the baltic sea: Natural history, present threats and future challenges. Marine pollution bulletin 57, 8–21 (2008).Article 

    Google Scholar 
    Dupont, N. & Aksnes, D. L. Centennial changes in water clarity of the baltic sea and the north sea. Estuarine, Coastal and Shelf Science 131, 282–289 (2013).Article 
    ADS 

    Google Scholar 
    Dippner, J. W., Möller, C. & Hänninen, J. Regime shifts in north sea and baltic sea: a comparison. Journal of Marine Systems 105, 115–122 (2012).Article 
    ADS 

    Google Scholar 
    Sisma-Ventura, G. et al. Post-eastern mediterranean transient oxygen decline in the deep waters of the southeast mediterranean sea supports weakening of ventilation rates. Frontiers in Marine Science 1202 (2021).Mavropoulou, A.-M., Vervatis, V. & Sofianos, S. Dissolved oxygen variability in the mediterranean sea. Journal of Marine Systems 208, 103348 (2020).Article 

    Google Scholar 
    Tyberghein, L. et al. Bio-oracle: a global environmental dataset for marine species distribution modelling. Global ecology and biogeography 21, 272–281 (2012).Article 

    Google Scholar 
    Assis, J. et al. Bio-oracle v2. 0: Extending marine data layers for bioclimatic modelling. Global Ecology and Biogeography 27, 277–284 (2018).Article 

    Google Scholar 
    Coro, G. & Bove, P. A high-resolution global-scale model for covid-19 infection rate. ACM Transactions on Spatial Algorithms and Systems (TSAS) 8, 1–24 (2022).Article 

    Google Scholar 
    Inness, A. et al. The cams reanalysis of atmospheric composition. Atmospheric Chemistry and Physics 19, 3515–3556 (2019).Article 
    ADS 

    Google Scholar 
    Karger, D. N., Schmatz, D. R., Dettling, G. & Zimmermann, N. E. High-resolution monthly precipitation and temperature time series from 2006 to 2100. Scientific data 7, 1–10 (2020).Article 

    Google Scholar 
    Kesner-Reyes, K. et al. AquaMaps Environmental Dataset: Half-Degree Cells Authority File (HCAF ver. 7, 10/2019). AquaMaps Web site https://www.aquamaps.org/main/envt_data.php (2019).Kesner-Reyes, K. et al. AquaMaps Environmental Dataset: Half-Degree Cells Authority File (HCAF ver. 6, 08/2016). AquaMaps Web site https://www.aquamaps.org/main/envt_data.php (2016).NASA-NEX. NASA Earth Exchange data. NASA-NEX Web site https://www.nasa.gov/nex/data – data were publicly accessible up to 2020 (2020).Coro, G. A global-scale ecological niche model to predict sars-cov-2 coronavirus infection rate. Ecological modelling 431, 109187 (2020).Article 

    Google Scholar 
    CAMS. Global inversion-optimised greenhouse gas fluxes and concentrations. Copernicus Atmosphere Web site https://ads.atmosphere.copernicus.eu/cdsapp#/dataset/cams-global-greenhouse-gas-inversion?tab=doc (2020).NOAA. ETOPO2 Topography and Bathymetry. NOAA Web site https://sos.noaa.gov/catalog/datasets/etopo2-topography-and-bathymetry-natural-colors/ (2010).Coro, G. & Trumpy, E. Predicting geographical suitability of geothermal power plants. Journal of Cleaner Production 267, 121874 (2020).Article 

    Google Scholar 
    NOAA. World Vector Shorelines. NOAA Web site https://shoreline.noaa.gov/data/datasheets/wvs.html (2019).Tozer, B. et al. Global bathymetry and topography at 15 arc sec: Srtm15+. Earth and Space Science 6, 1847–1864, https://doi.org/10.1029/2019EA000658 (2019).Article 
    ADS 

    Google Scholar 
    Ramesh, R. et al. Land–ocean interactions in the coastal zone: Past, present & future. Anthropocene 12, 85–98 (2015).Article 

    Google Scholar 
    Spalding, M. et al. World atlas of coral reefs (Univ of California Press, 2001).Laske, G. A global digital map of sediment thickness. Eos Trans. AGU 78, F483 (1997).
    Google Scholar 
    Davies, J. H. Global map of solid earth surface heat flow. Geochemistry, Geophysics, Geosystems 14, 4608–4622 (2013).Article 
    ADS 

    Google Scholar 
    Rybach, L. & Muffler, L. J. P. Geothermal systems: principles and case histories. Chichester, Sussex, England and New York, Wiley-Interscience, 1981. 371 p. (1981).Glassley, W. E. Geology and hydrology of geothermal energy. Power Stations Using Locally Available Energy Sources: A Volume in the Encyclopedia of Sustainability Science and Technology Series, Second Edition 23–34 (2018).Barbier, E. Geothermal energy technology and current status: an overview. Renewable and sustainable energy reviews 6, 3–65 (2002).Article 
    ADS 

    Google Scholar 
    Engdahl, E. R., van der Hilst, R. & Buland, R. Global teleseismic earthquake relocation with improved travel times and procedures for depth determination. Bulletin of the Seismological Society of America 88, 722–743 (1998).
    Google Scholar 
    Engdahl, E. R. Global seismicity: 1900–1999. International handbook of earthquake and engineering seismology 665–690 (2002).Richts, A., Struckmeier, W. F. & Zaepke, M. WHYMAP and the groundwater resources map of the world 1: 25,000,000. In Sustaining groundwater resources, 159–173 (Springer, 2011).Warszawski, L. et al. Center for international earth science information network—ciesin—columbia university.(2016). gridded population of the world, version 4 (gpwv4): Population density. palisades. ny: Nasa socioeconomic data and applications center (sedac). Atlas of Environmental Risks Facing China Under Climate Change 228, https://doi.org/10.7927/h4np22dq (2017). More

  • in

    Citizen science helps in the study of fungal diversity in New Jersey

    Martinez-Garcia, L. B., De Deyn, G. B., Pugnaire, F. I., Kothamasi, D. & van der Heijden, M. G. A. Symbiotic soil fungi enhance ecosystem resilience to climate change. Glob. Chang. Biol. 23, 5228–5236 (2017).Article 
    ADS 

    Google Scholar 
    Averill, C. & Hawkes, C. V. Ectomycorrhizal fungi slow soil carbon cycling. Ecol. Lett. 19, 937–947 (2016).Article 

    Google Scholar 
    Cairney, J. W. G. Extramatrical mycelia of ectomycorrhizal fungi as moderators of carbon dynamics in forest soil. Soil Biol. Biochem. 47, 198–208 (2012).Article 
    CAS 

    Google Scholar 
    Milovic, M., Kebert, M. & Orlovic, S. How mycorrhizas can help forests to cope with ongoing climate change? Sumar. List 145, 279–286 (2021).Article 

    Google Scholar 
    Hawksworth, D. L. & Luecking, R. Fungal diversity revisited: 2.2 to 3.8 million species. Microbiol. Spectr. 5, 5.4.10 (2017).Article 

    Google Scholar 
    Stork, N. E. How many species of insects and other terrestrial arthropods are there on Earth? Annu. Rev. Entomol. 63, 31–45 (2018).Article 
    CAS 

    Google Scholar 
    Christenhusz, M. J. M. & Byng, J. W. The number of known plants species in the world and its annual increase. Phytotaxa 261, 201–217 (2016).Article 

    Google Scholar 
    Terrer, C., Vicca, S., Hungate, B. A., Phillips, R. P. & Prentice, I. C. Mycorrhizal association as a primary control of the CO2 fertilization effect. Science 353, 72–74 (2016).Article 
    ADS 
    CAS 

    Google Scholar 
    van der Heijden, M. G. A., Martin, F. M., Selosse, M. A. & Sanders, I. R. Mycorrhizal ecology and evolution: the past, the present, and the future. New Phytol. 205, 1406–1423 (2015).Article 

    Google Scholar 
    Braghiere, R. K. et al. Modeling global carbon costs of plant nitrogen and phosphorus acquisition. J. Adv. Model. Earth Syst. 14, e2022MS003204 (2022).Article 
    ADS 
    CAS 

    Google Scholar 
    Jaouen, G. et al. Fungi of French Guiana gathered in a taxonomic, environmental and molecular dataset. Sci. Data 6, 206 (2019).Article 

    Google Scholar 
    Beninde, J. et al. CaliPopGen: A genetic and life history database for the fauna and flora of California. Sci. Data 9, 380 (2022).Article 

    Google Scholar 
    Gyeltshen, C. & Prasad, K. Biodiversity checklists for Bhutan. Biodivers. Data J. 10, e83798 (2022).Article 

    Google Scholar 
    Steidinger, B. S. et al. Climatic controls of decomposition drive the global biogeography of forest-tree symbioses. Nature 569, 404–408 (2019).Article 
    ADS 
    CAS 

    Google Scholar 
    Soudzilovskaia, N. A. et al. Global mycorrhizal plant distribution linked to terrestrial carbon stocks. Nat. Commun. 10, 5077 (2019).Article 
    ADS 

    Google Scholar 
    Melo, C. D., Walker, C., Freitas, H., Machado, A. C. & Borges, P. A. V. Distribution of arbuscular mycorrhizal fungi (AMF) in Terceira and Sao Miguel Islands (Azores). Biodivers. Data J. 8, e49759 (2020).Article 

    Google Scholar 
    Ordynets, A. et al. Aphyllophoroid fungi in insular woodlands of eastern Ukraine. Biodivers. Data J. 5, e22426 (2017).Article 

    Google Scholar 
    Monteiro, M. et al. A database of the global distribution of alien macrofungi. Biodivers. Data J. 8, e51459 (2020).Article 

    Google Scholar 
    Filippova, N. et al. Yugra State University Biological Collection (Khanty-Mansiysk, Russia): general and digitisation overview. Biodivers. Data J. 10, e77669 (2022).Article 

    Google Scholar 
    Wu, B. et al. Current insights into fungal species diversity and perspective on naming the environmental DNA sequences of fungi. Mycology 10, 127–140 (2019).Article 

    Google Scholar 
    Nilsson, R. H. et al. The UNITE database for molecular identification of fungi: handling dark taxa and parallel taxonomic classifications. Nucleic Acids Res. 47, D259–D264 (2019).Article 
    CAS 

    Google Scholar 
    Gorczak, M. et al. 18th Congress of European Mycologists Bioblitz 2019 – naturalists contribute to the knowledge of mycobiota and lichenobiota of Białowieża Primeval Forest. Acta Mycol. 55, 1–26 (2020).
    Google Scholar 
    Goncalves, S. C., Haelewaters, D., Furci, G. & Mueller, G. M. Include all fungi in biodiversity goals. Science 373, 403–403 (2021).Article 
    ADS 

    Google Scholar 
    Hochkirch, A. et al. A strategy for the next decade to address data deficiency in neglected biodiversity. Conserv. Biol. 35, 502–509 (2021).Article 

    Google Scholar 
    Allen, E. B. et al. Patterns and regulation of mycorrhizal plant and fungal diversity. Plant Soil 170, 47–62 (1995).Article 
    CAS 

    Google Scholar 
    Mueller, G. M. & Schmit, J. P. Fungal biodiversity: what do we know? What can we predict? Biodivers. Conserv. 16, 1–5 (2007).Article 

    Google Scholar 
    Waters, D. P. & Lendemer, J. C. The lichens and allied fungi of Mercer County, New Jersey. Opusc. Philolichenum 18, 17–51 (2019).
    Google Scholar 
    Waters, D. P. & Lendemer, J. C. A revised checklist of the lichenized, lichenicolous and allied fungi of New Jersey. Bartonia, 1–62 (2019).Schwarze, C. A. The parasitic fungi of New Jersey. (New Jersey Agricultural Experiment Stations, 1917).Moose, R. A., Schigel, D., Kirby, L. J. & Shumskaya, M. Dead wood fungi in North America: an insight into research and conservation potential. Nat. Conserv. 32, 1–17 (2019).Article 

    Google Scholar 
    Hibbett, D. S. et al. A higher-level phylogenetic classification of the Fungi. Mycol. Res. 111, 509–547 (2007).Article 

    Google Scholar 
    Hibbett, D. The invisible dimension of fungal diversity. Science 351, 1150–1151 (2016).Article 
    ADS 
    CAS 

    Google Scholar 
    James, T. Y., Stajich, J. E., Hittinger, C. T. & Rokas, A. Toward a Fully Resolved Fungal Tree of Life. Annu. Rev. Microbiol. 74, 291–313 (2020).Article 
    CAS 

    Google Scholar 
    Braghiere, R. K. et al. Mycorrhizal distributions impact global patterns of carbon and nutrient cycling. Geophys. Res. Lett. 48, e2021GL094514 (2021).Article 
    ADS 
    CAS 

    Google Scholar 
    Bonney, R. et al. Citizen science: A developing tool for expanding science knowledge and scientific literacy. Bioscience 59, 977–984 (2009).Article 

    Google Scholar 
    Van Vliet, K. & Moore, C. Citizen science initiatives: engaging the public and demystifying science. J. Microbiol. Biol. Educ. 17, 13–16 (2016).Article 

    Google Scholar 
    Feldman, M. J. et al. Trends and gaps in the use of citizen science derived data as input for species distribution models: A quantitative review. PLoS One 16, e0234587 (2021).Article 
    CAS 

    Google Scholar 
    Shumskaya, M. et al. Fungi of parks, forests and reserves of New Jersey (2007–2019). Version 1.4. Sampling event dataset. Kean University https://doi.org/10.15468/7scek4 (2022).Heilmann-Clausen, J. et al. How citizen science boosted primary knowledge on fungal biodiversity in Denmark. Biol. Conserv. 237, 366–372 (2019).Article 

    Google Scholar 
    GBIF.Org User. NJMA dataset. GBIF Occurrence Download. GBIF https://doi.org/10.15468/dl.93232n (2022).GBIF.Org User. New Jersey Agaricomycetes. GBIF Occurrence Download. Dataset. GBIF https://doi.org/10.15468/dl.6j6382 (2022).GBIF.Org User. USA Agaricomycetes. GBIF Occurrence Download. GBIF https://doi.org/10.15468/dl.ncukzy (2022).GBIF.Org User. Global records Agaricomycetes. GBIF Occurrence Download. GBIF https://doi.org/10.15468/dl.nk54e7 (2022).Meyke, E. When data management meets project management. Biodivers. Inf. Sci. Stand. 3, e37224 (2019).
    Google Scholar 
    Wieczorek, J. et al. Darwin Core: an evolving community-developed biodiversity data standard. PLoS One 7, e29715 (2012).Article 
    ADS 
    CAS 

    Google Scholar 
    Pagad, S., Genovesi, P., Carnevali, L., Schigel, D. & McGeoch, M. A. Data Descriptor: introducing the global register of introduced and invasive species. Sci. Data 5, 170102 (2018).Article 

    Google Scholar 
    Registry-Migration.Gbif.Org.GBIF Backbone Taxonomy. GBIF Secretariat. https://doi.org/10.15468/39omei (2021).Mesibov, R. Archived websites: A Data Cleaner’s Cookbook (version 3) and all BASHing data blog posts 1–200. Zenodo https://doi.org/10.5281/zenodo.6423347 (2022).Chamberlain, S. A. & Boettiger, C. R Python, and Ruby clients for GBIF species occurrence data. PeerJ Preprints 5, e3304v3301 (2017).
    Google Scholar 
    Chamberlain, S. et al. rgbif: Interface to the Global Biodiversity Information Facility API. R package version 3.7.1. Available from https://cran.rproject.org/package=rgbif (2022).Nguyen, N. H. et al. FUNGuild: An open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecol. 20, 241–248 (2016).Article 

    Google Scholar 
    Sousa, D. et al. Tree canopies reflect mycorrhizal composition. Geophys. Res. Lett. 48, e2021GL092764 (2021).Article 
    ADS 

    Google Scholar 
    R Core Team. R: A language and environment for statistical computing. https://www.R-project.org/ (2020).Wickham, H. ggplot2: Elegant Graphics for Data Analysis. https://ggplot2.tidyverse.org (2016).Bederson, B. B., Shneiderman, B. & Wattenberg, M. Ordered and quantum treemaps: Making effective use of 2D space to display hierarchies. ACM Trans. Graph. 21, 833–854 (2002).Article 

    Google Scholar 
    Simpson, H. J. & Schilling, J. S. Using aggregated field collection data and the novel r package fungarium to investigate fungal fire association. Mycologia 113, 842–855 (2021).Article 

    Google Scholar 
    Robertson, T. et al. The GBIF Integrated Publishing Toolkit: Facilitating the efficient publishing of biodiversity data on the Internet. PLoS One 9, e102623 (2014).Article 
    ADS 

    Google Scholar  More

  • in

    Myzomyia and Pyretophorus series of Anopheles mosquitoes acting as probable vectors of the goat malaria parasite Plasmodium caprae in Thailand

    Asada, M. et al. Close relationship of Plasmodium sequences detected from South American pampas deer (Ozotoceros bezoarticus) to Plasmodium spp. in North American white-tailed deer. Int. J. Parasitol. 7, 44–47. https://doi.org/10.1016/j.ijppaw.2018.01.001 (2018).Article 

    Google Scholar 
    Boundenga, L. et al. Haemosporidian parasites of antelopes and other vertebrates from Gabon, Central Africa. PLoS ONE 11, e0148958. https://doi.org/10.1371/journal.pone.0148958 (2016).Article 
    CAS 

    Google Scholar 
    Martinsen, E. S., Perkins, S. L. & Schall, J. J. A three-genome phylogeny of malaria parasites (Plasmodium and closely related genera): Evolution of life-history traits and host switches. Mol. Phylogen. Evol. 47, 261–273. https://doi.org/10.1016/j.ympev.2007.11.012 (2008).Article 
    CAS 

    Google Scholar 
    Templeton, T. J. et al. Ungulate malaria parasites. Sci. Rep. 6, 23230. https://doi.org/10.1038/srep23230 (2016).Article 
    ADS 
    CAS 

    Google Scholar 
    Templeton, T. J., Martinsen, E., Kaewthamasorn, M. & Kaneko, O. The rediscovery of malaria parasites of ungulates. Parasitology 143, 1501–1508. https://doi.org/10.1017/s0031182016001141 (2016).Article 

    Google Scholar 
    Bruce, D., Harvey, D., Hamerton, A. E. & Bruce, L. Plasmodium cephalophi, sp. nov. Proc. R. Soc. B. 87, 45–47 (1913).ADS 

    Google Scholar 
    Sheather, A. L. A malarial parasite in the blood of a buffalo. J. Comp. Pathol. 32, 223–229 (1919).Article 

    Google Scholar 
    Kandel, R. C. et al. First report of malaria parasites in water buffalo in Nepal. Vet. Parasitol. Reg. Stud. Rep. 18, 100348. https://doi.org/10.1016/j.vprsr.2019.100348 (2019).Article 

    Google Scholar 
    de Mello, F. & Paes, S. Sur une plasmodiae du sang des chèvres. C. R. Séanc. Soc. Biol 88, 829–830 (1923).
    Google Scholar 
    Kaewthamasorn, M. et al. Genetic homogeneity of goat malaria parasites in Asia and Africa suggests their expansion with domestic goat host. Sci. Rep. 8, 5827. https://doi.org/10.1038/s41598-018-24048-0 (2018).Article 
    ADS 
    CAS 

    Google Scholar 
    Garnham, P. C. & Edeson, J. F. Two new malaria parasites of the Malayan mousedeer. Riv. Malariol. 41, 1–8 (1962).CAS 

    Google Scholar 
    Garnham, P. C. & Kuttler, K. L. A malaria parasite of the white-tailed deer (Odocoileus virginianus) and its relation with known species of Plasmodium in other ungulates. Proc. R. Soc. Lond. B 206, 395–402. https://doi.org/10.1098/rspb.1980.0003 (1980).Article 
    ADS 
    CAS 

    Google Scholar 
    Martinsen, E. et al. Hidden in plain sight: Cryptic and endemic malaria parasites in North American white-tailed deer (Odocoileus virginianus). Sci. Adv. 2, e1501486. https://doi.org/10.1126/sciadv.1501486 (2016).Article 
    ADS 
    CAS 

    Google Scholar 
    Rattanarithikul, R. et al. Illustrated keys to the mosquitoes of Thailand. IV. Anopheles. Southeast Asian. Trop. Med. Public Health 37, 1–128 (2006).
    Google Scholar 
    Walter Reed Biosystematics Unit. Systematic catalogue of Culicidae. http://mosquitocatalog.org (2021).Manguin, S., Garros, C., Dusfour, I., Harbach, R. E. & Coosemans, M. Bionomics, taxonomy, and distribution of the major malaria vector taxa of Anopheles subgenus Cellia in Southeast Asia: An updated review. Infect. Genet. Evol. 8, 489–503. https://doi.org/10.1016/j.meegid.2007.11.004 (2008).Article 
    CAS 

    Google Scholar 
    Brosseau, L. et al. A multiplex PCR assay for the identification of five species of the Anopheles barbirostris complex in Thailand. Parasit. Vectors 12, 223. https://doi.org/10.1186/s13071-019-3494-8 (2019).Article 

    Google Scholar 
    Paredes-Esquivel, C., Donnelly, M. J., Harbach, R. E. & Townson, H. A molecular phylogeny of mosquitoes in the Anopheles barbirostris Subgroup reveals cryptic species: implications for identification of disease vectors. Mol. Phylogen. Evol. 50, 141–151. https://doi.org/10.1016/j.ympev.2008.10.011 (2009).Article 
    CAS 

    Google Scholar 
    Taai, K. & Harbach, R. E. Systematics of the Anopheles barbirostris species complex (Diptera: Culicidae: Anophelinae) in Thailand. Zool. J. Linn. Soc. 174, 244–264. https://doi.org/10.1111/zoj.12236 (2015).Article 

    Google Scholar 
    Garros, C., Van Bortel, W., Trung, H. D., Coosemans, M. & Manguin, S. Review of the Minimus Complex of Anopheles, main malaria vector in Southeast Asia: From taxonomic issues to vector control strategies. Trop. Med. Int. Health 11, 102–114. https://doi.org/10.1111/j.1365-3156.2005.01536.x (2006).Article 
    CAS 

    Google Scholar 
    Dahan-Moss, Y. et al. Member species of the Anopheles gambiae complex can be misidentified as Anopheles leesoni. Malar. J. 19, 89. https://doi.org/10.1186/s12936-020-03168-x (2020).Article 
    CAS 

    Google Scholar 
    Van Bortel, W. et al. Confirmation of Anopheles varuna in Vietnam, previously misidentified and mistargeted as the malaria vector Anopheles minimus. Am. J. Trop. Med. Hyg. 65, 729–732. https://doi.org/10.4269/ajtmh.2001.65.729 (2001).Article 

    Google Scholar 
    Wharton, R. H., Eyles, D. E., Warren, M., Moorhouse, D. E. & Sandosham, A. A. Investigations leading to the identification of members of the Anopheles umbrosus group as the probable vectors of mouse deer malaria. Bull. 29, 357–374 (1963).CAS 

    Google Scholar 
    Nugraheni, Y. R. et al. Myzorhynchus series of Anopheles mosquitoes as potential vectors of Plasmodium bubalis in Thailand. Sci. Rep. 12, 5747. https://doi.org/10.1038/s41598-022-09686-9 (2022).Article 
    ADS 
    CAS 

    Google Scholar 
    Tu, H. L. C. et al. Development of a novel multiplex PCR assay for the detection and differentiation of Plasmodium caprae from Theileria luwenshuni and Babesia spp. in goats. Acta Trop. 220, 105957. https://doi.org/10.1016/j.actatropica.2021.105957 (2021).Article 
    CAS 

    Google Scholar 
    Cywinska, A., Hunter, F. F. & Hebert, P. D. Identifying Canadian mosquito species through DNA barcodes. Med. Vet. Entomol. 20, 413–424. https://doi.org/10.1111/j.1365-2915.2006.00653.x (2006).Article 
    CAS 

    Google Scholar 
    Hebert, P. D., Cywinska, A., Ball, S. L. & de Waard, J. R. Biological identifications through DNA barcodes. Proc. R. Soc. Lond. B 270, 313–321. https://doi.org/10.1098/rspb.2002.2218 (2003).Article 
    CAS 

    Google Scholar 
    Ogola, E. O., Chepkorir, E., Sang, R. & Tchouassi, D. P. A previously unreported potential malaria vector in a dry ecology of Kenya. Parasit. Vectors 12, 80. https://doi.org/10.1186/s13071-019-3332-z (2019).Article 

    Google Scholar 
    Maquart, P. O., Fontenille, D., Rahola, N., Yean, S. & Boyer, S. Checklist of the mosquito fauna (Diptera, Culicidae) of Cambodia. Parasite 28, 60. https://doi.org/10.1051/parasite/2021056 (2021).Article 

    Google Scholar 
    Tainchum, K. et al. Diversity of Anopheles species and trophic behavior of putative malaria vectors in two malaria endemic areas of northwestern Thailand. J. Vector. Ecol. 39, 424–436. https://doi.org/10.1111/jvec.12118 (2014).Article 

    Google Scholar 
    Vantaux, A. et al. Anopheles ecology, genetics and malaria transmission in northern Cambodia. Sci. Rep. 11, 6458. https://doi.org/10.1038/s41598-021-85628-1 (2021).Article 
    ADS 
    CAS 

    Google Scholar 
    Chookaew, S. et al. Anopheles species composition in malaria high-risk areas in Ranong Province. Dis. Control J. 46, 483–493. https://doi.org/10.14456/dcj.2020.45 (2020).Article 

    Google Scholar 
    Makanga, B. et al. Ape malaria transmission and potential for ape-to-human transfers in Africa. Proc. Natl. Acad. Sci. USA. 113, 5329–5334. https://doi.org/10.1073/pnas.1603008113 (2016).Article 
    ADS 
    CAS 

    Google Scholar 
    Ariey, F., Gay, F. & Ménard, R. Malaria Control and Elimination Vol. 254 (Springer, 2020).
    Google Scholar 
    Williams, J. & Pinto, J. Training Manual on Malaria Entomology (Springer, 2012).
    Google Scholar 
    Rigg, C. A., Hurtado, L. A., Calzada, J. E. & Chaves, L. F. Malaria infection rates in Anopheles albimanus (Diptera: Culicidae) at Ipetí-Guna, a village within a region targeted for malaria elimination in Panamá. Infect. Genet. Evol. 69, 216–223. https://doi.org/10.1016/j.meegid.2019.02.003 (2019).Article 

    Google Scholar 
    Torres-Cosme, R. et al. Natural malaria infection in anophelines vectors and their incrimination in local malaria transmission in Darién Panama. PLoS ONE 16, e0250059. https://doi.org/10.1371/journal.pone.0250059 (2021).Article 
    CAS 

    Google Scholar 
    Beebe, N. W. & Saul, A. Discrimination of all members of the Anopheles punctulatus complex by polymerase chain reaction-restriction fragment length polymorphism analysis. Am. J. Trop. Med. Hyg. 53, 478–481. https://doi.org/10.4269/ajtmh.1995.53.478 (1995).Article 
    CAS 

    Google Scholar 
    Perkins, S. L. & Schall, J. J. A molecular phylogeny of malarial parasites recovered from cytochrome b gene sequences. J. Parasitol. 88, 972–978. https://doi.org/10.1645/0022-3395(2002)088[0972:AMPOMP]2.0.CO;2 (2002).Article 
    CAS 

    Google Scholar 
    Snounou, G. et al. High sensitivity of detection of human malaria parasites by the use of nested polymerase chain reaction. Mol. Biochem. Parasitol. 61, 315–320. https://doi.org/10.1016/0166-6851(93)90077-B (1993).Article 
    CAS 

    Google Scholar 
    Hall, T. A. BioEdit: A user-friendly biological sequence alignment editor and analysis program for windows 95/98/NT. Nucleic. Acids. Symp. Ser. 41, 95–98 (1999).CAS 

    Google Scholar 
    Huelsenbeck, J. P. & Ronquist, F. MRBAYES: Bayesian inference of phylogenetic trees. Bioinformatics 17, 754–755 (2001).Article 
    CAS 

    Google Scholar 
    Rambaut, A., Drummond, A. J., Xie, D., Baele, G. & Suchard, M. A. Posterior summarization in Bayesian phylogenetics using tracer 1.7. Syst. Biol. 67, 901–904. https://doi.org/10.1093/sysbio/syy032 (2018).Article 
    CAS 

    Google Scholar 
    Nguyen, L. T., Schmidt, H. A., von Haeseler, A. & Minh, B. Q. IQ-TREE: A fast and effective stochastic algorithm for estimating maximum-likelihood phylogenies. Mol. Biol. Evol. 32, 268–274. https://doi.org/10.1093/molbev/msu300 (2015).Article 
    CAS 

    Google Scholar 
    Ventim, R. et al. Avian malaria infections in western European mosquitoes. Parasitol. Res. 111, 637–645. https://doi.org/10.1007/s00436-012-2880-3 (2012).Article 

    Google Scholar  More

  • in

    Alfred Russel Wallace’s first expedition ended in flames

    Naturalist Alfred Russel Wallace went on an expedition to Amazonas state in Brazil in 1848–52.Credit: Mondadori Portfolio via Getty

    Best known for formulating the theory of evolution by natural selection, independently of Charles Darwin, Alfred Russel Wallace is an appealing if enigmatic figure. The appeal stems in part from his underdog status: poor and self-educated, Wallace had none of Darwin’s social and financial advantages. The enigma comes from his keen embrace of a range of eccentric non-scientific causes, including spiritualism, phrenology and anti-vaccination (for smallpox).Scientists do not like their scientific heroes to bear the taint of irrational thinking. Wallace’s enthusiasms have therefore contributed to him becoming marginalized in the history of evolutionary thought. Most people know about Darwin and the HMS Beagle. But what about Wallace and the Helen?The Helen story is worth revisiting because it shows Wallace at his resolute best. Despite numerous disastrous career setbacks — of which the Helen episode was the most severe — he persevered and eventually succeeded as a scientist.More than 150 years after Wallace’s experience on the Helen, doing science continues to be hard and can be disappointing. Wallace’s misadventure provides both perspective and an object lesson in how to navigate setbacks. His response to problems showcases his most inspiring traits: his commitment to science, his almost superhuman resilience and his refusal to mire himself in self-pity.Tropical explorationsIn his first job as a land surveyor, Wallace developed an interest in the plants he encountered as he tramped across the countryside. Then, in 1844 at the age of 21, he met Henry Walter Bates, who would later discover ‘Batesian mimicry’ (whereby members of a palatable prey species gain protection by mimicking an unpalatable one).Bates, two years Wallace’s junior, had a fixation with beetles, and he catalysed Wallace’s transformation from hobbyist naturalist to serious collector. Wallace’s new-found focus on beetles transcended mere entomological stamp-collecting; he developed an interest in some of the great scientific questions of the time. He was particularly inspired by the anonymously published Vestiges of the Natural History of Creation (1844) by Robert Chambers, which put forward a vision of a transmutational process, with life progressing from simple to complex.Without money or connections, Wallace and Bates aspired to careers in science at a time when the field was the preserve of the moneyed elite. They would have to fund their scientific explorations by collecting and selling specimens. After a hasty choice of destination — tropical South America — and a crash course in collecting methods, Wallace, aged 25, and Bates, aged 23, arrived in Belém, Brazil, in May 1848 (see ‘Doggedly determined’).
    Doggedly determined

    Alfred Russel Wallace tends to be unjustly relegated to a footnote in the Charles Darwin story. He was, in fact, a pioneering biologist who refused to let disadvantage or disaster prevent him from pursuing his scientific dreams.
    January 1823: Alfred Russel Wallace is born in Usk in Wales.
    May 1848: Wallace and Henry Walter Bates arrive in Belém, Brazil.
    July 1852: Wallace boards the Helen, which catches fire three weeks later while at sea.
    October 1852: Wallace reaches Deal, England, aboard the Jordeson.
    March 1854: Wallace leaves Southampton for southeast Asia.
    September 1855: Wallace’s first evolutionary paper describing his ‘Sarawak Law’ is published.
    May 1856: Citing the Sarawak Law paper, geologist Charles Lyell alerts Darwin to the possibility that Wallace is developing ideas similar to Darwin’s.
    February 1858: Wallace sends his paper on natural selection to Darwin from Ternate in the Maluku islands (Moluccas), Indonesia.
    July 1858: The joint Darwin–Wallace paper is presented at the Linnean Society in London.
    November 1859: Darwin’s On the Origin of Species is published.
    March 1862: Wallace returns from southeast Asia.
    November 1913: Wallace dies in Broadstone, England.

    The two split up early on, with Wallace concentrating on the Amazon River’s northern tributary, the Rio Negro, and Bates on the southern fork, the Solimões.Collecting was challenging. The Amazon’s ubiquitous ants often deprived science of hard-won specimens. Crucial collecting materials also disappeared: Wallace once recovered from a bout of fever to discover that local people had drunk the cachaça (a Brazilian rum) he’d been using to pickle specimens. Transport was a constant headache, with travel upstream past rapids requiring unwieldy portages of canoes and cargo. And thanks to his collecting, the cargo became ever more voluminous and unwieldy.Wallace and Bates sporadically sent back shipments of material to their agent in London, Samuel Stevens, who publicized their adventures in scientific journals and sold their specimens, taking a 20% commission.
    Escaping Darwin’s shadow: how Alfred Russel Wallace inspires Indigenous researchers
    Wallace’s journeys on the Rio Negro and its tributaries took him into areas that had not yet been visited by Europeans. He saw (and collected) an extraordinary array of species, many of them new to science. He had a chance to observe and collect artefacts from several Indigenous groups with little or no previous contact with Europeans. As he travelled, Wallace capitalized on his surveying skills to map the terrain. But the remoteness took its toll. He made an “inward vow never to travel again in such wild, unpeopled districts without some civilised companion or attendant”1.Wallace was frequently ill, on one occasion nearly lethally so. His younger brother came out to join him as an assistant in 1849 but died of yellow fever two years later in Belém, on his way back to England. Wallace learnt that his brother was sick but had to wait many anxious months before news of his death made it upriver.In 1852, after four years of exploring and collecting, it was time for Wallace himself to head home. He envisaged a triumphant return. He would complement his collections of preserved organisms with a menagerie of living ones. Mr Wallace’s biological wonders would surely be the toast of scientific London.On 12 July in Belém, Wallace boarded the Helen, a freighter ship bound for London. The trip across the continent to Belém had not gone smoothly. The authorities in Manaus, Brazil, had had to be persuaded to release some of his earlier shipments meant for London, which they had impounded, making the final haul aboard the Helen even larger. But now all that remained was the long voyage back across the Atlantic. Wallace, who shared Captain Turner’s cabin, was the only passenger.Disaster strikesThree weeks into the voyage, Captain Turner interrupted Wallace’s morning routine to tell him that the ship was on fire.Friction caused by the rocking of the ship had ignited poorly stowed cargo. Attempts to intervene were counterproductive — removing the hold covers merely oxygenated the fire — and soon the ship became what Wallace later called “a most magnificent conflagration”1.Captain Turner gave the order to abandon ship, and the scramble to prepare two small wooden boats began. Having been stored on deck in the tropical sunshine, both boats leaked badly. The cook had to find corks to plug their hulls.Before he left the ship, Wallace “went down into the cabin, now suffocatingly hot and full of smoke, to see what was worth saving”1. He retrieved his “watch and a small tin box containing some shirts and a couple of old note-books, with some drawings of plants and animals, and scrambled up with them on deck”1. He tried to lower himself on a rope into one of the small boats, but fever-weakened, he ended up sliding down the rope, stripping the skin off his hands.

    Some of Alfred Russel Wallace’s sketches were salvaged from the fire aboard the Helen on his return journey from South America in 1852.Credit: The Natural History Museum/Alamy

    With fine weather, the best hope of rescue lay in other ships seeing the fire. The two boats duly circled the burning wreck for the next 24 hours, meaning that Wallace got to witness every moment of the tragedy. The animals he had brought with him on the long river journey across the continent, now free from their cages, sought refuge on the one part of the ship still untouched by the flames, the bowsprit. Wallace watched as the monkeys, parrots and more — his pets as well as his best hope of impressing London’s scientific elite — were incinerated.The hoped-for rescue did not immediately materialize, and Captain Turner turned the two open boats towards Bermuda, 1,100 kilometres away to the northwest.As the days ticked by, the situation became increasingly desperate. Water ran low and the tropical sun left Wallace’s “hands and face very much blistered”1. Wallace nevertheless remained upbeat, later recalling that during one night, he “saw several meteors, and in fact could not be in a better position for observing them, than lying on [his] back in a small boat in the middle of the Atlantic”1.Finally, ten days into the ordeal, salvation appeared on the horizon in the form of the Jordeson, a creaking and already overladen cargo ship bound for London.With the immediate crisis past, the magnitude of what had happened started to sink in. In a letter2 written aboard the Jordeson to botanist Richard Spruce (see go.nature.com/3prhbdk), Wallace tallied his catastrophic losses — “almost all the reward of my four years of privation & danger was lost” — and concluded with characteristic understatement, “I have some need of philosophic resignation to bear my fate with patience and equanimity.”
    Evolution’s red-hot radical
    The Jordeson finally limped into Deal, England, on 1 October 1852. Wallace had been at sea for 80 days. His outward voyage with Bates had taken only 29 days.Wallace added a PS to his letter to Spruce. First there was immediate exhilaration about the return — “Such a dinner! Oh! beef steaks & damson tart”. But then came thoughts about the future: “Fifty times since I left Pará [Belém] have I vowed if I once reached England never to trust myself more on the ocean.” Even then, he noted that “good resolutions soon fade”.Stevens had thoughtfully taken out insurance. So Wallace had £200 (US$980 at the time) — a fraction of his collections’ actual value — to cover his costs for a year in London while he tried to salvage what he could from the disaster and make future plans.He rushed out two books, one a travelogue, the other a more technical account of the palm trees of the Amazon. Neither did well — 250 copies remained unsold a decade later from the travel book’s print run of 750. But he was getting his name out there. Stevens, too, had a done a good job of publicizing Wallace’s discoveries while Wallace had been away.Perhaps most crucially, the positive response of the UK Royal Geographical Society to his mapping work of the Rio Negro yielded a free steamship ticket to Singapore.In March 1854, less than 18 months since the Jordeson’s bedraggled arrival at Deal, Wallace departed from Southampton in England for what he would call the “central and controlling incident”2 of his life.Eight more years of perilous travel awaited. So, too, did the discoveries of what came to be known as Wallace’s Line (a boundary between the Asian and Australasian biogeographic regions) and of the theory of evolution by natural selection3,4.The scientific acclaim that greeted Wallace’s return from southeast Asia in 1862 was a just reward both for his contributions and for that phenomenal doggedness — his determination, despite everything, to be a scientist. More

  • in

    Study on adsorption of hexavalent chromium by composite material prepared from iron-based solid wastes

    Material characterization resultsTo investigate the structural composition of NMC-2, XRD analysis plots were performed. Figure 1a shows the XRD pattern of the NMC-2 composite before adsorption. The XRD pattern shows the corresponding strong and narrow peaks, from which it can be seen that the peaks of broad diffraction NMC-2 can correspond to the standard cards of Fe, C, Fe7C3, Fe2C, and FeC, indicating that the synthesized adsorbent is an iron-carbon composite. It can be indicated that mesoporous nitrogen-doped composites were formed during the carbonization process. During the experiments, it was found that the materials are magnetic, probably because of the presence of Fe, FeC, Fe7C3, Fe2C. Due to the magnetic properties of this type of material, rapid separation and recovery can be obtained under the conditions of an applied magnetic field, which allows easy separation of the adsorbent and metal ions from the wastewater15.Figure 1XRD and nitrogen adsorption and desorption tests on materials: (a) XRD pattern of NMC-2 adsorbent before adsorption, (b) pore size distribution of NMC-2, (c) nitrogen adsorption–desorption curve of NMC-2 adsorbent.Full size imageFrom the adsorption–desorption curves of adsorbent N2 in Fig. 1b, it can be seen that the NMC-2 isotherm belongs to the class IV curve, and the appearance of H3-type hysteresis loops is observed at the medium pressure end, and H3 is commonly found in aggregates with laminar structure, producing slit mesoporous or macroporous materials, which indicates that N2 condenses and accumulates in the pore channels, and these phenomena prove that NMC-2 is a porous material16. Figure 1c shows the pore size distribution of the adsorbent NMC-2 obtained according to the BJH calculation method, from which it can be seen that the pore size distribution is not uniform in the range, and most of them are concentrated below 20 nm, while according to Table 1, the specific surface area of the original sample of Fenton sludge and fly ash is 124.08 m2/g and 3.79 m2/g, respectively, and the specific surface area of NMC-2 is 228.65 m2/g. The Fenton The pore volume of the original samples of Fenton sludge and fly ash were 0.18 cm3/g and 0.006 cm3/g respectively, while the pore volume of NMC-2 was 0.24 cm3/g. The pore diameters of the original sample of Fenton sludge and fly ash were 5.72 nm and 6.70 nm respectively, while the pore diameter of NMC-2 was 4.22 nm. The above data indicated that the synthetic materials have increased the specific surface area and pore volume compared with the original samples, indicating that the doping of nitrogen can increase the specific surface area of the material. Since the pore size of mesoporous materials is 2–50 nm, NMC-2 is a porous material with main mesopores. Thanks to the large specific surface area provided by the mesopores, the material has a large number of active sites, and in addition, the mesopores can store more Cr(VI)16, which contributes to efficient removal.Table 1 Total pore-specific surface area, pore volume, and pore size of BJH adsorption and accumulation of Fenton sludge, fly ash and NMC-2.Full size tableThe morphological analysis of the material surface using SEM can see the surface structure and the pore structure of NMC-2. And Fig. 2a–d shows the swept electron microscope image of NMC-2. Figure 2a shows that the surface of the material is not smooth, and there are more lint-like fiber structures. The fibers in Fig. 2b are loosely and irregularly arranged, which may be due to the irregular morphology caused by the small particles of the NMC-2 sample. As shown in Fig. 2c and Fig. 2a there are more pores generated on the surface of NMC-2, which may be due to the addition of K2CO3 to urea and, Fenton sludge solution to generate CO217.Figure 2SEM, TEM and EDS testing of materials: (a–d) SEM image of NMC-2 adsorbent, (e) TEM image of NMC-2; (g–i) TEM-EDS spectrum of NMC-2, (j) TEM-EDS spectra of NMC-2 obtained from.Full size imageThese pores can provide many active sites, which is consistent with the results derived in Fig. 1, where NMC-2 is a mesoporous-dominated porous material, and also demonstrates that the addition of urea can provide a nitrogen source for the material, providing abundant active sites. Figure 2j depicts the TEM of NMC-2. the TEM images show that the synthesized NMC-2 has a folded structure with a surface covered by a carbon film, and the HRTEM (Fig. 2e) also confirms this result with a lattice spacing of 0.13, 0.15, 0.20, 0.23, 0.24, and 0.25 nm, corresponding to the (4 5 2) and (1 0 2) of C, the (2 0 1) of FeC) surface, the (2 1 0) surface of Fe7C3, the (5 3 1) surface of Fe2C, and the (2 0 1) surface of FeC, which also confirms the synthesis of the above substances. The corresponding EDS spectra of the dark field diagram NMC-2 were obtained from Fig. 2j, and the EDS spectra proved the presence of various elements: carbon (C) (Fig. 2f) from fly ash, iron (Fe) (Fig. 2g) from Fenton sludge, nitrogen (N) (Fig. 2h) from urea, and the presence of (O) (Fig. 2i), further confirming the successful preparation of NMC-2.The type of functional groups and chemical bonding on the surface of the material can be analyzed by IR spectrogram analysis. Figure 3b shows the FTIR image of NMC-2 adsorbent 3440 cm−1 wide and strong absorption peak is due to the stretching vibration of –OH, there is a large amount of –OH present on the surface of the material; the peak appearing at 1640 cm−1 is –COOH. Characterization reveals that the –OH absorption peak is wider18,19. In addition, the absorptions at 1390 cm−1 and 1000 cm−1 were attributed to the bending of –OH vibrations of alcohols and phenol and the stretching vibration of C–O20. The above results indicate that the surface of NMC-2 contains a large number of oxygen-containing functional groups, and these functional groups can provide many active sites for the removal of Cr(VI). It was also found that the weak peaks corresponding to 573 cm−1 and 550 cm−1 were attributed to Fe–O groups21. The stretching of Fe–O may be due to the oxidation of loaded Fe0 and Fe2+ to Fe3+22. Figure 3a shows the Fenton sludge and fly ash FTIR images. It can be seen from the figure that the surfaces of Fenton sludge and fly ash contain a large number of oxygen-containing functional groups, the surface functional groups of the two raw materials are more abundant, and the functional groups of NMC-2 around 3441 cm−1, 1632 cm−1, and 1400 cm−1 are not significantly different from those of the raw materials, and the C–H stretching vibration peaks of NMC-2 around 873 cm−1 and 698 cm−1 is not obvious, which may be because the material the C–H bond on the surface of the raw material was oxidized to C–O in the process of synthesis.Figure 3FTIR testing of materials: (a) FTIR image of Fenton sludge, fly ash, (b) Ftir image of NMC-2 adsorbent.Full size imageCr(VI) adsorption experimentSelection of adsorbentTo select the best adsorbent, Cr(VI) adsorption tests were performed on four adsorbents. Figure 4a shows the effect of Fenton sludge and the urea addition on the adsorption efficiency. The Cr(VI) removal rates of the four adsorbents were ranked from low to high: MC-1  More

  • in

    Genetic diversity and structure in wild Robusta coffee (Coffea canephora A. Froehner) populations in Yangambi (DR Congo) and their relation to forest disturbance

    Aguilar R, Cristóbal-Pérez ED, Balvino-Olvera FJ, Aguilar-Aguilar MDJ, Aguirre-Acosta N, Ashworth L et al. (2019) Habitat fragmentation reduces plant progeny quality: a global synthesis. Ecol Lett 22:1163–1173Article 

    Google Scholar 
    Andrews S (2010) FastQC: a quality control tool for high throughput sequence data. http://www.bioinformatics.babraham.ac.uk/projects/fastqcBarlow J, Lennow GD, Ferreira J, Berenguer E, Lees AC, Nally RM et al. (2016) Anthropogenic disturbance in tropical forests can double biodiversity loss from deforestation. Nature 535:144–147Article 
    CAS 

    Google Scholar 
    Barret SC, Eckert CG (1990) Current issues in plant reproductive ecology. Isr J Plant Sci 39:5–12
    Google Scholar 
    Bawa KS, Bullock SH, Perry DR, Coville RE, Grayum MH (1985) Reproductive biology of tropical lowland rain forest trees II. Pollination systems. Am J Bot 72:346–356Article 

    Google Scholar 
    Bello C, Galetti M, Pizo MA, Magnago LFS, Roch MF, Lima RA, et al. (2015) Defaunation affects carbon storage in tropical forests. Sci Adv 1:e1501105. https://doi.org/10.1126/sciadv.1501105Blouin MS (2003) DNA-based methods for pedigree reconstruction and kinship analysis in natural populations. Trends Ecol Evol 18:503–511Article 

    Google Scholar 
    Born C, Kjellberg F, Chevallier M-H, Vignes H, Dikangadissi J-T, Sanguié J et al. (2008) Colonization processes and the maintenance of genetic diversity: insight from a pioneer rainforest tree, Aucoumea Klaineana. Proc R Soc B 275:2171–2179Article 

    Google Scholar 
    Braun M, Dantas L, Esposito T, Pedrosa-Harand A (2020) Strong genetic differentiation on a small geographic scale in the Neotropical rainforest understory tree Paypayrola blanchetiana (Violaceae). Tree Genet Genomes. https://doi.org/10.1007/s11295-020-01477-5Campbell AJ, Carvalheiro LG, Maués MM, Jaffé R, Giannini TC, Freitas MAB et al. (2018) Anthropogenic disturbance of tropical forests threatens pollination services to açai palm in the Amazon river delta. J Appl Ecol 55:1725–1736Article 

    Google Scholar 
    Chang CC, Chow CC, Tellier LC, Vattikuti S, Purcell SM, Lee JJ (2015) Second-generation PLINK: rising to the challenge of larger and richer datasets. Gigascience https://doi.org/10.1186/s13742-015-0047-8Chiriboga-Arroyo F, Jansen M, Bardales-Lozano R, Ismail SA, Thomas E, Garcia M et al. (2021) Genetic threats to the Forest Giants of the Amazon: Habitat degradation effects on the socio-economically important Brazil nut tree (Bertholletia excelsa). Plants People Planet 3:194–210Article 

    Google Scholar 
    Cramer PJS, Wellman FL (1957) Review of literature of coffee research in Indonesia. SIC Editorial, Inter-American Institute of Agricultural SciencesCraparo ACW, Van Asten PJ, Läderach P, Jassogne LT, Grab SW (2015) Coffea arabica yields decline in Tanzania due to climate change: Global implications. Agric Meteorol 207:1–10Article 

    Google Scholar 
    Cubry P, De Bellis F, Pot D, Musoli P, Leroy P (2013) Global analysis of Coffea canephora Pierre ex Froehner (Rubiaceae) from the Guineo-Congolese region reveals impacts from climatic refuges and migration effects. Genet Resour Crop Evol 60:483–501Article 

    Google Scholar 
    Curtis PG, Slay CM, Harris NL, Tyukavina A, Hansen MC (2018) Classifying drivers of global forest loss. Science 361:1108–1111Article 
    CAS 

    Google Scholar 
    Da Silva JMC, Tabarelli M (2000) Tree species impoverishment and the future flora of the Atlantic forest of northeast Brazil. Nature 404:72–74Article 

    Google Scholar 
    Danecek P, Auton A, Abecasis G, Albers CA, Banks E, DePristo MA et al. (2011) The variant call format and VCFtools. Bioinformatics 27:2156–2158Article 
    CAS 

    Google Scholar 
    Davis AP, Gole TW, Baena S, Moat J (2012) The impact of climate change on indigenous arabica coffee (Coffea arabica): predicting future trends and identifying priorities. PLoS One. https://doi.org/10.1371/journal.pone.0047981Denoeud F, Carretero-Paulet L, Dereeper A, Droc G, Guyot R, Pietrella M et al. (2014) The coffee genome provides insight into the convergent evolution of caffeine biosynthesis. Science 345:1181–1184Article 
    CAS 

    Google Scholar 
    Depecker J, Asimonyio JA, Miteho R, Hatangi Y, Kambale J-L, Verleysen L, et al. (2022) The association between rainforest disturbance and recovery, tree community composition, and community traits in the Yangambi area in the Democratic Republic of the Congo. J Trop Ecol. https://doi.org/10.1017/S0266467422000347Dick CW, Etchelecu G, Austerlitz F (2003) Pollen dispersal of tropical trees (Dinizia excelsa: Fabaceae) by native insects and African honeybees in pristine and fragmented Amazonian rainforest. Mol Ecol 12:753–764Article 

    Google Scholar 
    Doyle JJ, Doyle JL (1987) A rapid DNA isolation procedure for small quantities of fresh leaf tissue. Phytochemical Bull 19:11–15
    Google Scholar 
    Edwards DP, Socolar JB, Mills SC, Burivalova Z, Koh LP, Wilcove DS (2019) Conservation of tropical forests in the Anthropocene. Curr Biol 29:R1008–R1020Article 
    CAS 

    Google Scholar 
    El Mousadik A, Petit RJ (1996) High level of genetic differentiation for allelic richness among populations of the argan tree [Argania spinosa (L.) Skeels] endemic to Morocco. Theor Appl Genet 92:832–839Article 
    CAS 

    Google Scholar 
    Elshire RJ, Glaubitz JC, Sun Q, Poland JA, Kawamoto K, Buckler ES et al. (2011) A robust, simple genotyping-by-sequencing (GBS) approach for high diversity species. PLoS One. https://doi.org/10.1371/journal.pone.0019379Ernst C, Mayaux P, Verhegghen A, Bodart C, Christophe M, Defourny P (2013) National forest cover change in Congo Basin: deforestation, reforestation, degradation and regeneration for the years 1990, 2000 and 2005. Glob Chang Biol 19:1173–1187Article 

    Google Scholar 
    Evanno G, Regnaut S, Goudet J (2005) Detecting the number of clusters of individuals using the software STRUCTURE: a simulation study. Mol Ecol 14:2611–2620Article 
    CAS 

    Google Scholar 
    FAO, UNEP (2020) The State of the World’s Forests 2020. In Forests, bio-diversity and people. FAO and UNEPFerrão RG, da Fonseca AFA, Ferrão MAG, De Mune LH (2019) Conilon Coffee: the Coffea canephora produced in Brazil. Incaper, Vitória-ES, Brasil
    Google Scholar 
    Gardner TA, Barlow J, Chazdon R, Ewers RM, Harvey CA, Peres CA et al. (2009) Prospects for tropical forest biodiversity in a human‐modified world. Ecol Lett 12:561–582Article 

    Google Scholar 
    García-Fernández C, Sánchez JA, Blanco G (2018) SNP-haplotypes: An accurate approach for parentage and relatedness inference in gilthead sea bream (Sparus aurata). Aquaculture 495:582–591Article 

    Google Scholar 
    Gomez C, Dussert S, Hamon P, Hamon S, De Kochko A, Poncert V (2009) Current genetic differentiation of Coffea canephora pierre ex a. Froehn in the guineo-Congolian african zone: Cumulative impact of ancient climatic changes and recent human activities. BMC Evol Biol 9:167Article 

    Google Scholar 
    Goudet J (2013) hierfstat: estimation and tests of hierarchical F-statistics. R Package version 0:04–10. http://CRAN.R-project.org/package=hierfstatHubbell SP, Foster RB (1986) Biology, chance and history and the structure of tropical rain forest tree communities. In: Diamond JM, Case TJ (eds) Community ecology. Harper and Row, New York, NY, p 314–329
    Google Scholar 
    ICO (2022) Coffee Market Report: August 2022. Donwloaded from International Coffee Organization https://www.ico.org/documents/cy2021-22/cmr-0822-e.pdfIsmail SA, Ghazoul J, Ravikanth G, Kushalappa CG, Uma Shaanker R, Kettle CJ (2017) Evaluating realized seed dispersal across fragmented tropical landscapes: A two‐fold approach using parentage analysis and the neighbourhood model. N Phytol 214:1307–1316Article 
    CAS 

    Google Scholar 
    Jombart T (2008) adegenet: a R package for the multivariate analysis of genetic markers. Bioinformatics 24:1403–1405Article 
    CAS 

    Google Scholar 
    Jombart T, Collins C (2015) Analysing genome-wide SNP data using adegenet 2.0.0. https://adegenet.r-forge.r-project.org/files/tutorial-genomics.pdfJones AG, Small CM, Paczolt KA, Ratterman NL (2010) A practical guide to methods of parentage analysis. Mol Ecol Resour 10:6–30Article 

    Google Scholar 
    Jones OR, Wang J (2012) A comparison of four methods for detecting weak genetic structures from maker data. Ecol Evol 2:1048–1055Article 

    Google Scholar 
    Kalinowski ST, Wagner AP, Taper ML (2006) ML-Relate: a computer program for maximum likelihood estimation of relatedness and relationship. Mol Ecol Notes 6:576–579Article 
    CAS 

    Google Scholar 
    Kearsley E, Verbeeck H, Hufkens K, Van, de Perre F, doetterl S, Baert G et al. (2017) Functional community structure of African monodominant Gilbertiodendron dewevrei forest influenced by local environmental filtering. Ecol Evol 7:295–304Article 

    Google Scholar 
    Kier G, Mutke J, Dinerstein E, Ricketss TH, Küper W, Kreft H et al. (2005) Global patterns of plant diversity and floristic knowledge. J Biogeogr 32:1107–1116Article 

    Google Scholar 
    Kiwuka C, Goudsmit E, Tournebize R, Oliveir de Aquino S, Douma JC, Bellanger L et al. (2021) Genetic diversity of native and cultivated Ugandan Robusta coffee (Coffea canephora Pierre ex A. Froehner): Climate influences, breeding potential and diversity conservation. PLoS One 16:e0245965Article 
    CAS 

    Google Scholar 
    Kreft H, Jetz W (2007) Global patterns and determinants of vascular plant diversity. Proc Natl Acad Sci USA 104:5925–5930Article 
    CAS 

    Google Scholar 
    Lachenaud P, Zhang D (2008) Genetic diversity and population structure in wild stands of cacao trees (Theobroma cacao L.) in French Guiana. Ann For Sci. https://doi.org/10.1051/forest:2008011Lashermes P, Combes MC, Ribas A, Cenci A, Mahé L, Etienne H (2010) Genetic and physical mapping of the SH3 region that confers resistance to leaf rust in coffee tree (Coffea arabica L.). Tree Genet Genomes 6:973–980Article 

    Google Scholar 
    Leroy T, Marraccini P, Dufour M, Montagnon C, Lashermes P, Sabau X et al. (2005) Construction and characterization of a Coffea canephora BAC library to study the organization of sucrose biosynthesis genes. Theor Appl Genet 111:1031–1041Article 

    Google Scholar 
    Li H, Handsaker B, Wysoker A, Fennell T, Ruan J, Homer N et al. (2009) The Sequence Alignment/Map format and SAMtools. Bioinformatics 14:2078–2079Article 

    Google Scholar 
    Li YL, Liu JX (2018) StructureSelector: A web‐based software to select and visualize the optimal number of clusters using multiple methods. Mol Ecol Resour 18:176–177Article 

    Google Scholar 
    Makelele IA, Verheyen K, Boeckx P, Ntaboba LC, Bazirake BM, Ewango C et al. (2021) Afrotropical secondary forests exhibit fast diversity and functional recovery, but slow compositional and carbon recovery after shifting cultivation. J Veg Sci 32:1–13Article 

    Google Scholar 
    Martin M (2011) Cutadapt removes adapter sequences from high-throughput sequencing reads. EMBnet J 17:10–12Article 

    Google Scholar 
    Mateu-Andrés I, De Paco L (2006) Genetic diversity and the reproductive system in related species of Antirrhinum. Ann Bot 98:1053–1060Article 

    Google Scholar 
    Mayr E (1954) Change of genetic environment and evolution. In: Huxley A, Hardy AC, Ford EB (eds) Evolution as a process. Allen and Unwin, London, p 157–180
    Google Scholar 
    McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A et al. (2010) The Genome Analysis Toolkit: A MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res 20:1297–1303Article 
    CAS 

    Google Scholar 
    Merot-L’anthoene V, Tournebize R, Darracq O, Rattina V, Lepelley M, Bellanger L et al. (2019) Development and evaluation of a genome-wide Coffee 8.5K SNP array and its application for high-density genetic mapping and for investigating the origin of Coffea arabica L. Plant Biotechnol J 17:1418–1430Article 

    Google Scholar 
    Musoli P, Cubry P, Aluka P, Billot C, Dufour M, De Bellis F et al. (2009) Genetic differentiation of wild and cultivated populations: diversity of Coffea canephora Pierre in Uganda. Genome 52:634–646Article 
    CAS 

    Google Scholar 
    Neushulz EL, Mueller T, Schleuning M, Böhning-Gaese K (2016) Pollination and seed dispersal are the most threatened processes of plant regeneration. Sci Rep 6:1–6
    Google Scholar 
    Norden N, Chazdon RL, Chao A, Jiang YH, Vilchez-Alvarado B (2009) Resilience of tropical rain forests: tree community reassembly in secondary forests. Ecol Lett 12:385–394Article 

    Google Scholar 
    Nowak MD, Davis AP, Anthony F, Yoder AD (2011) Expression and trans-specific polymorphism of self-incompatibility RNases in Coffea (Rubiaceae). PLoS One. https://doi.org/10.1371/journal.pone.0021019Nyakaana S (2007) Microgeographical genetic structure of forest robusta coffee (Coffea canephora, Pierre), in Kibale National Park, Uganda. Afr J Ecol 45:71–75Article 

    Google Scholar 
    Oberleitner F, Egger C, Oberdorfer S, Dullinger S, Wanek W, Hietz P (2021) Recovery of aboveground biomass, species richness and composition in tropical secondary forests in SW Costa Rica. Ecol Manag 479:118580Article 

    Google Scholar 
    Olsson O, Nuñez-Iturri G, Smith HG, Ottosson U, Effium EO (2019) Competition, seed dispersal and hunting: what drives germination and seedling survival in an Afrotropical forest? AoB Plants https://doi.org/10.1093/aobpla/plz018Oryem-Origa H (1999) Fruit and seed ecology of wild Robusta coffee (Coffea canephora Froehner) in Kibale National Park. Uganda Afr J Ecol 37:439–448Article 

    Google Scholar 
    Peakall R, Smouse RPP (2012) GenAlEx 6.5: genetic analysis in Excel. Population genetic software for teaching and research—an update. Bioinformatics 28:2537–2539Article 
    CAS 

    Google Scholar 
    Podani J (2000) Introduction to the exploration of multivariate biological data. Backhuys Publishers, Kerkwere
    Google Scholar 
    Poland JA, Rife TW (2012) Genotyping-by-sequencing for plant breeding and genetics. Plant Genome. https://doi.org/10.3835/plantgenome2012.05.0005Poorter L, Craven D, Jakovac CC, van der Sande MT, Amissah L, Bongers F et al. (2021) Multidimensional tropical forest recovery. Science 374:1370–1376Article 
    CAS 

    Google Scholar 
    Raj A, Stephens M, Pritchard JK (2014) fastSTRUCTURE: variational inference of population structure in large SNP data sets. Genetics 197:573–589Article 

    Google Scholar 
    RStudio Team (2016) RStudio: Integrated Development for RSasaki N, Putz FE (2009) Critical need for new definitions of “forest” and “forest degradation” in global climate change agreements. Conserv Lett 2:226–232Article 

    Google Scholar 
    Sezen UU, Chazdon RL, Holsinger KE (2007) Multigenerational genetic analysis of tropical secondary regeneration in a canopy palm. Ecology 88:3065–3075Article 

    Google Scholar 
    Schaumont D, Veeckman E, Van der Jeugt F, Haegeman A, van Glabeke S, Bawin Y et al. (2022) Stack Mapping Anchor Points (SMAP): a versatile suite of tools for read-backed haplotyping. Preprint at bioRxiv https://doi.org/10.1101/2022.03.10.483555Shapiro AC, Grantham HS, Aguilar-Amuchastegui N, Murray NJ, Gond V, Bonfils D, et al. (2021) Forest condition in the Congo Basin for the assessment of ecosystem conservation status. Ecol Indic. https://doi.org/10.1016/j.ecolind.2020.107268Silva MDC, Várzea V, Guerra-Guimarães L, Azinheira HG, Fernandez D, Petitot AS et al. (2006) Coffee resistance to the main diseases: leaf rust and coffee berry disease. Braz J Plant Physiol 18:119–147Article 
    CAS 

    Google Scholar 
    Theim TJ, Shirk RY, Givnish TJ (2014) Spatial genetic structure in four understorey Psychotria species (Rubiaceae) and implications for tropical forest diversity. Am J Bot 101:1189–1199Article 

    Google Scholar 
    Torti SD, Coley PD, Kursar TA (2001) Causes and consequences of monodominance in tropical lowland forests. Am Nat 157:141–153Article 
    CAS 

    Google Scholar 
    Tyukavina A, Hansen MC, Potapov P, Parker D, Okpa C, Stehman SV, et al. (2018) Congo Basin forest loss dominated by increasing smallholder clearing. Sci Adv. https://doi.org/10.1126/sciadv.aat2993Vanden Abeele S, Janssens SB, Asimonyio Anio J, Bawin Y, Depecker J, Kambale B et al. (2021) Genetic diversity of wild and cultivated Coffea canephora in northeastern DR Congo and the implications for conservation. Am J Bot 108:2425–2434Article 

    Google Scholar 
    Vandepitte K, Gristina AS, De Hert K, Meekers T, Roldán-Ruiz I, Honnay O (2012) Recolonization after habitat restoration leads to decreased genetic variation in populations of a terrestrial orchid. Mol Ecol 21:4206–4215Article 
    CAS 

    Google Scholar 
    Van Vliet N, Muhindo J, Kbale Nyumu J, Mushagalusa O, Nasi R (2018) Mammal depletion processes as evidenced from spatially explicit and temporal local ecological knowledge. Trop Conserv Sci 11:1–16
    Google Scholar 
    Vekemans X, Hardy OJ (2004) New insights from fine-scale spatial genetic structure analyses in plant populations. Mol Ecol 13:921–935Article 
    CAS 

    Google Scholar 
    Vranckx G, Jacquemyn H, Muys B, Honnay O (2012) Meta‐analysis of susceptibility of woody plants to loss of genetic diversity through habitat fragmentation. Conserv Biol 26:228–237Article 

    Google Scholar 
    Weir BS, Cockerham CC (1984) Estimating F-statistics for the analysis of population structure. Evolution 38:1358–1370CAS 

    Google Scholar 
    Wellman FL (1961) Coffee. Botany, cultivation, and utilization. Leonard Hill, London
    Google Scholar 
    Widmer A, Lexer C (2001) Glacial refugia: sanctuaries for allelic richness, but not for gene diversity. Trends Ecol Evol 16:267–269Article 
    CAS 

    Google Scholar 
    Wright S (1932) The role of mutation, inbreeding, crossbreeding and selection in evolution. In: Proceedings of the sixth international congress of genetics. pp 356–366.Zhang J, Kobert K, Flouri T, Stamatakis A (2014) PEAR: a fast and accurate Illumina Paired-End read merger. Bioinformatics 30:614–620Article 
    CAS 

    Google Scholar  More

  • in

    Spatio-temporal patterns of Synechococcus oligotypes in Moroccan lagoonal environments

    In a previous study18, we used bioinformatics tools to analyze the metagenome and the amplicon 16S sequences to gain an insight into microbial diversity in Moroccan lagoons, namely Marchica and Oualidia. 16S rRNA gene classification revealed a high percentage of bacteria in both lagoons. On average, bacteria accounted for 90% of the total prokaryotes in Marchica and ~ 70% in Oualidia. The five phyla that were the most abundant in both lagoons, Marchica and Oualidia, respectively, were Proteobacteria (53.62%, 29.18%), Bacteroidetes (16.46%, 43.49%), Cyanobacteria (0.53%, 34.35%), Verrucomicrobia (1.75%, 15.82%), and Actinobacteria (7.42%, 13.98%). At the genus level, we found that the highest assigned hits were attributed to Synechococcus, which was highly abundant in Marchica (32%) compared to Oualidia (0.07%) in 2014. This amount dropped to 22% in Marchica and 0.04% in Oualidia in 2015. Hence, in this study we performed the analysis of the Synechococcus genus community using oligotyping to investigate their dynamics and understand their co-occurrence and covariation in space and time within fragile ecosystems such as lagoons.We may divide our results into two emerging Synechococcus communities: one dominated in 2014 and the other was less present in 2015, each composed of different cooccurring Synechococcus oligotypes. The abundant Synechococcus community in Marchica in 2014 consisted of clades I, 5.3, III, IV, and VII. These clades are typically found in either warmer or more oligotrophic environments19,20. This result is in accordance with Marchica’s environmental characteristics; it is an oligotrophic ecosystem with high primary production and warmer water in summer21. The community included clades CB5 and WPC1 in Marchica 2014 and 2015 when the number of Synechococcus reads was lower. Strains belonging to the CB5 clade lack phycourobilin (PUB), contain one motile strain22,23, are present in temperate coastal waters and are prevalent in polar/subpolar waters24,25,26. WPC1 strains are observed in open-ocean and near-shore waters1,24,27. Clades IV and I usually co-occur and are more prevalent in cold coastal waters19,28,29,30. Interestingly, Clade III was prominent in Marchica. This clade is known to be motile and restricted to warm, oligotrophic water19,20,30. Although at a smaller read number, clade III was also observed in Oualidia, where the temperature is cooler compared to Marchica. Furthermore, we found that clade III growth has been shown to be severely affected at low temperatures30. Moreover, representatives of both clades I and IV were present in Oualidia in both the summers of 2014 and 2015. Some Synechococcus strains, which are known to prefer cooler water temperatures and salinities, were in higher relative abundance in the waters of Marchica. This result agrees with a previous study showing that Synechococcus isolates of clades I and IV exhibited temperature preferences31. Their growth rates were marginally lower at low temperatures in strains from clades I and IV, which were dominant in temperate regions.Nitrate levels are typically low or undetectable in these lagoons, which allows the persistence of clades that would not typically thrive in coastal waters at other times of the year. In 2014, the nitrate concentration was higher than the average of 10 mg/l, which could be due to increased agricultural activities and wastewater treatment plant effluent21. The decreasing nitrate concentration in Marchica in 2015 could be explained by the newly installed inlet in 2010, which was designed to improve water exchange with the open sea and reduce the amount of suspended matter21. Temperature and salinity have a large effect on nitrate in marine ecosystems32; the highest nitrate degradation rates were observed at 35 °C and at increasing salinity rates. Therefore, we expected to see correlations between salinity, temperature and nitrate concentrations. Interestingly, clades CB5 in Marchica and IV in Oualidia increased in relative abundance in summer 2015 compared to 2014, when the nitrate concentration decreased. Moreover, the Synechococcus microbial community diversity and density are variables depending on the variations in the physical and chemical parameters. These parameters are strongly influenced by the marine waters passing through the artificial inlets, which have an impact on the internal hydrodynamics of both lagoons and hence the distribution and co-occurrence of Synechococcus strains. In addition, anthropogenic activities also have a great influence on Synechococcales population growth and interactions with their viruses33,34.This study revealed some differences between Marchica and Oualidia in identified Synechococcus clades. The Marchica lagoon showed more heterogeneity (clades I, II, III, IV, VII, VIII, 5.3, WPC1, CB5, and IX) than the Oualidia lagoon, where fewer clades were identified (I, III, IV, and VII). There was a clear variation in the pattern of correlation between oligotypes of the same or different clades for both the 2014 and 2015 samplings. Furthermore, we observed complex patterns of co-occurrence among oligotypes; in 2014 (clades I, III, IV, 5.3, VII), and in 2015, we found clades CB5 and WPC1. In Oualidia, values decreased in comparison to Marchica in both 2014 and 2015 summer samplings, following a pattern of co-occurrence, especially for both clades I and IV in both sampling years. Many studies have shown that the relative proportions of cooccurring Synechococcus populations to each other at the clade and subclade levels vary in space and time based on environmental factors such as seasonal temperature fluctuations, nutrient availability and upwelling, circulation patterns, and abundance of other phytoplankton8.We presume that the greater variability in oligotype co-occurrence behavior observed in Marchica Lagoon, especially in the summer of 2014, could be due to the higher abundance and diversity of Synechococcus oligotypes, physico-chemical parameter fluctuations or rehabilitation of the lagoon.Less abundant oligotypes could also be considered potential bioindicators of Synechococcus genetic diversity. Their seasonal occurrence might contribute to changing ecological and biogeochemical characteristics of the marine environment35. The Synechococcus relative abundance count revealed that the Marchica Synechococcus community included the least abundant oligotypes in 2015. For instance, O7 and O8 were detected in 2014 and were absent in 2015 (Table 1). It is unclear which factors served to constrain the relative abundances of these least present oligotypes, but temperature and salinity could have an impact on their distribution in Marchica (Fig. 4) and the opposite for Oualidia, which are cooler-temperature adapted ones. We noticed that the relative abundance of cooccurring Synechococcus was not constant. For instance, oligotype 4 belonging to Clade IV showed higher values in summer 2014 (974 reads) in Marchica compared to summer 2015 (319 reads), and the opposite was observed in Oualidia, with a lower abundance compared to Marchica. Increased values of cooccurring clade I oligotypes (14, 26, and 6) were detected in the summer of 2014 in both lagoons.Figure 4Principle component analysis of Synechococcus oligotype relative abundance. The plot is generated using the relative abundance of each oligotype, T temperature, S Salinity, and NO3− Nitrate. Each point represents an oligotype. Colors represent the year of sampling; red for 2014 and blue for 2015. The shape of point indicates the sampling site; rounded points refer to Marchica lagoon, and triangles refer to Oualidia. Circles represent the normal distribution of oligotypes; the red circle refers to 2014, and the blue one refers to 2015.Full size imageIn comparing our results with a study from Little Sippewissett Marsh (LSM)8 that used oligotyping to investigate the distribution of the genus Synechococcus in space and time sequencing the V4-V6 hypervariable region of the 16S rRNA gene, we found 31 oligotypes, while they identified 12. In both studies, the proportion of Synechococcus oligotypes increased in summer and in coastal waters compared to estuaries. In addition, Clades I and IV were more abundant in saline conditions, such as Marchica Lagoon. However, these clades were found in greater relative abundances at cold temperatures, in contrast to our study, where they were identified in Marchica’s warm waters. Moreover, clade CB5 tended to be prominent at relatively warm temperatures (17–20 °C)6. In our work, it was not prevalent either in cooler or warmer water. Notably, the relative abundance of rare oligotypes was higher in warm hypersaline estuary waters8,18, while in our case study, they occurred in cooler moderately saline Oualidia waters.The dominance of a certain clade could have many different ecological ramifications, especially as the clades can be incredibly diverse in their growth, loss, nutrient utilization and other attributes. The dominant clade’s growth and loss patterns will set the stage for the population dynamics. For instance, if the dominant clade only blooms in a given environmental factor such as temperature, light, or salinity, it will then affect the timing of blooms, and follow-on the effects of subsequent grazing, lysis or even biogeochemical cycling. Even if the population is diverse, the dynamics as a whole will be a composite response of each individual clade’s ecophysiology, making it important to understand their composition and how it changes over space and time.While the rpoC1 gene is a higher resolution diversity marker36, 16S amplicon data can be used for exploring the entire bacterial assemblage including Synechococcus clade designations via oligotyping35. The latter has a great advantage in answering unexplained diversity contained in taxa using 16S rRNA gene sequences. Nevertheless, it has some limitations, as it acts optimally only when performed on taxa that are closely related. Regarding distantly related taxa, the high number of increased-entropy locations makes the supervision steps difficult. In addition, although oligotyping does not rely on clustering conditions or availability of existing reads within reference databases, it demands preliminary operational taxonomic unit clustering to find closely related species appropriate for the analysis. This method is under continuous improvement to better exploit the information within subtle variations in 16S rRNA gene sequences5.In conclusion, we explored the patterns of Synechococcus diversity in space and time using an oligotyping approach to examine these populations in lagoon waters of Mediterranean Marchica and Atlantic Oualidia, in Morocco. Patterns that have been observed at the clade and subclade levels, such as Synechococcus, relative abundance and the co-occurrence of groups from different clades, were shown to occur among oligotypes. The Marchica Lagoon showed a heterogeneous Synechococcus diversity compared to Oualidia in summer 2014. Thirty-one Synechococcus oligotypes were identified. Two distinct communities emerged in the 2014 and 2015 summer samplings, abundant and rare Synechococcus species, each comprising cooccurring Synechococcus oligotypes from different clades. Network analysis showed that six oligotypes were exclusive to Marchica Lagoon. The identified clades I, III, IV, VII, and 5.3 in Marchica were in accordance with its environmental characteristics. In addition, the relative abundance of some cooccurring Synechococcus strains was not constant over time and space (e.g., clades I and IV). Using gene oligotyping, we illustrated some of the challenges associated with the identification of novel Synechococcus strains or studied their co-occurrence in space and time. Oligotyping has been instrumental in discriminating closely related Synechococcus strains. However, this study leaves open questions about how samples differ by location and whether locations differ from year to year. Do cooccurring oligotypes interact with each other and to what extent do they correlate with physicochemical parameters? What triggers the coexistence of clades I and IV with clade III in warm water or 5.3 with VII, which do not know much about. Finally, how do relative abundances change over seasons. Hence, future work needs to consider additional stations and seasons to provide better statistical support for our findings and to better understand their correlation with physical and chemical environmental parameters. Other factors were not considered in this study, such as nutrient availability, chlorophyll, irradiance, viral lysis, and greater sequencing depth, which could also influence the observed seasonal dynamics. More