Inferring the age and environmental characteristics of fossil sites using citizen science

Not all fossil sites preserve microfossils that can be extracted using acid digestion, which may leave knowledge gaps regarding a site’s age or environmental characteristics. Here we report on a citizen science approach that was developed to identify microfossils in situ on the surface of sedimentary rocks. Samples were collected from McGraths Flat, a recently discovered Miocene rainforest lake deposit located in central New South Wales, Australia. Composed entirely of iron-oxyhydroxide, McGraths Flat rocks cannot be processed using typical microfossil extraction protocols e.g., acid digestion. Instead, scanning electron microscopy (SEM) was used to automatically acquire 25,200 high-resolution images from the surface of three McGraths Flat samples, covering a total area of 1.85 cm2. The images were published on the citizen science portal DigiVol, through which 271 citizen scientists helped to identify 300 pollen and spores. The microfossil information gained in this study is biostratigraphically relevant and can be used to constrain the environmental characteristics of McGraths Flat. Our findings suggest that automated image acquisition coupled with an evaluation by citizen scientists is an effective method of determining the age and environmental characteristics of fossiliferous rocks that cannot be investigated using traditional methods such as acid digestion.

[1]  M. McCurry,et al.  New cicada fossils from Australia (Hemiptera: Cicadoidea: Cicadidae) with remarkably detailed wing surface nanostructure , 2022, Alcheringa: An Australasian Journal of Palaeontology.

[2]  D. Cantrill,et al.  A Lagerstätte from Australia provides insight into the nature of Miocene mesic ecosystems , 2022, Science advances.

[3]  J. Riding A Guide to Preparation Protocols in Palynology , 2021, Palynology.

[4]  F. Marret,et al.  An overview of techniques applied to the extraction of non-pollen palynomorphs, their known taphonomic issues and recommendations to maximize recovery , 2020, Special Publications.

[5]  Charless C. Fowlkes,et al.  Improving the taxonomy of fossil pollen using convolutional neural networks and superresolution microscopy , 2020, Proceedings of the National Academy of Sciences.

[6]  W. Cornwell,et al.  Widespread short‐term persistence of frog species after the 2019–2020 bushfires in eastern Australia revealed by citizen science , 2020, Conservation Science and Practice.

[7]  Kent J. Crippen,et al.  The Belgrade PaleoBlitz: A pilot project to engage amateur paleontologists , 2020, Palaeontologia Electronica.

[8]  D. Lazarus,et al.  NSB (Neptune Sandbox Berlin): An expanded and improved database of marine planktonic microfossil data and deep-sea stratigraphy , 2020 .

[9]  A. Knoll,et al.  The Rhynie chert , 2019, Current Biology.

[10]  Sven Eberhardt,et al.  StomataCounter: a neural network for automatic stomata identification and counting. , 2019, The New phytologist.

[11]  W. Cornwell,et al.  Improving big citizen science data: Moving beyond haphazard sampling , 2019, PLoS Biology.

[12]  L. Soul,et al.  Fossil Atmospheres: a case study of citizen science in question-driven palaeontological research , 2018, Philosophical Transactions of the Royal Society B.

[13]  M. V. Van Kranendonk,et al.  Snapshot of an early Paleoproterozoic ecosystem: Two diverse microfossil communities from the Turee Creek Group, Western Australia , 2018, Geobiology.

[14]  Graham C. Smith,et al.  Economical crowdsourcing for camera trap image classification , 2018, Remote Sensing in Ecology and Conservation.

[15]  Andrew Zisserman,et al.  Time-lapse imagery and volunteer classifications from the Zooniverse Penguin Watch project , 2018, Scientific Data.

[16]  Fiona M. Jones,et al.  Time-lapse imagery and volunteer classifications from the Zooniverse Penguin Watch project , 2018, Scientific Data.

[17]  J. K. Legind,et al.  Contribution of citizen science towards international biodiversity monitoring , 2017 .

[18]  C. Lintott,et al.  Snapshot Serengeti, high-frequency annotated camera trap images of 40 mammalian species in an African savanna , 2015, Scientific Data.

[19]  Trisha Gura,et al.  Citizen science: Amateur experts , 2013, Nature.

[20]  Rick Bonney,et al.  The history of public participation in ecological research , 2012 .

[21]  Paul Flemons,et al.  Image based Digitisation of Entomology Collections: Leveraging volunteers to increase digitization capacity , 2012, ZooKeys.

[22]  R. M. Hodgson,et al.  Progress towards an automated trainable pollen location and classifier system for use in the palynology laboratory , 2011 .

[23]  R. Bonney,et al.  Citizen Science: A Developing Tool for Expanding Science Knowledge and Scientific Literacy , 2009 .

[24]  H. Hass,et al.  Spores of the Rhynie chert plant Aglaophyton (Rhynia) major (Kidston and Lang) D.S. Edwards, 1986 , 2006 .

[25]  A.W.G. Duller,et al.  A new approach to automated pollen analysis , 2000 .

[26]  M. Macphail Palynostratigraphy of the murray basin, inland Southeastern Australia , 1999 .

[27]  R. McLean,et al.  Enhancement of leaf fossilization potential by bacterial biofilms , 1997 .

[28]  D. Gilbertson,et al.  Taphonomy and the palynology of care deposits , 1989 .

[29]  G. Playford,et al.  Laboratory techniques for extraction of palynomorphs from sediments , 1984 .

[30]  B. Kraatz,et al.  Fossil trackways of the Baynunah Formation , 2022 .