The Plankton Lifeform Extraction Tool: a digital tool to increase the discoverability and usability of plankton time-series data

Publication history: Accepted - 25 October 2021; Published online - 6 December 2021.Plankton form the base of the marine food web and are sensitive indicators of environmental change. Plankton time series are therefore an essential part of monitoring progress towards global biodiversity goals, such as the Convention on Biological Diversity Aichi Targets, and for informing ecosystem-based policy, such as the EU Marine Strategy Framework Directive. Multiple plankton monitoring programmes exist in Europe, but differences in sampling and analysis methods prevent the integration of their data, constraining their utility over large spatio-temporal scales. The Plankton Lifeform Extraction Tool brings together disparate European plankton datasets into a central database from which it extracts abundance time series of plankton functional groups, called “lifeforms”, according to shared biological traits. This tool has been designed to make complex plankton datasets accessible and meaningful for policy, public interest, and scientific discovery. It allows examination of large-scale shifts in lifeform abundance or distribution (for example, holoplankton being partially replaced by meroplankton), providing clues to how the marine environment is changing. The lifeform method enables datasets with different plankton sampling and taxonomic analysis methodologies to be used together to provide insights into the response to multiple stressors and robust policy evidence for decision making. Lifeform time series generated with the Plankton Lifeform Extraction Tool currently inform plankton and food web indicators for the UK's Marine Strategy, the EU's Marine Strategy Framework Directive, and for the Convention for the Protection of the Marine Environment of the North-East Atlantic (OSPAR) biodiversity assessments. The Plankton Lifeform Extraction Tool currently integrates 155 000 samples, containing over 44 million plankton records, from nine different plankton datasets within UK and European seas, collected between 1924 and 2017. Additional datasets can be added, and time series can be updated. The Plankton Lifeform Extraction Tool is hosted by The Archive for Marine Species and Habitats Data (DASSH) at https://www.dassh.ac.uk/lifeforms/ (last access: 22 November 2021, Ostle et al., 2021). The lifeform outputs are linked to specific, DOI-ed, versions of the Plankton Lifeform Traits Master List and each underlying dataset.Funding that supports this work and the data collected has come from the European Commission, European Union (EU) grant no. 11.0661/2015/712630/SUB/ENVC.2 OSPAR; UK Natural Environment Research Council (grant nos. NE/R002738/1 and NE/M007855/1); EMFF, Climate Linked Atlantic Sector Science (grant no. NE/R015953/1), Department for Environment, Food and Rural Affairs, UK Government (grant nos. ME-5308 and ME-414135), NSF USA OCE-1657887, DFO CA F5955150026/001/HAL, Natural Environment Research Council UK (grant no. NC-R8/H12/100); Horizon 2020 (MISSION ATLANTIC (grant no. 862428)); iCPR (grant no. SBFF-2019-36526), IMR Norway; DTU Aqua Denmark; and the French Ministry of Environment, Energy, and the Sea (MEEM). Recent funding for the development of PLET and the Pelagic Habitats Indicator has been provided by HBDSEG/Defra and MMO/EMFF. The MSS Scottish Coastal Observatory data and analyses are funded and maintained by the Scottish Government Schedules of Service (grant nos. ST05a and ST02H), MSS Stonehaven Samplers, North Atlantic Fisheries College, Shetland, Orkney Islands Harbour Council, and Isle Ewe Shellfish

Automatic surface age dating of impact events on Mars

Counting impact craters on surfaces of terrestrial bodies is currently the only way to estimate the age of a planetary surface and the duration of geological processes occurred in the past. This approach requires a tedious mapping and morphological inspection of a large number of impact craters. We created a Crater Detection Algorithm trained on Martian orbital imagery in order to compile all small impact craters on the Martian surface down to around 100m in diameter. We applied our algorithm on the CTX mosaic (6m/px) between 45 degrees of North and South covering more than 70% of the entire Martian surface, and detected around 17M of impact structures >50m. From these detection, we are now able to obtain an estimation of the age of any geological structures having shaped the surface of Mars at different spatial scales. We primarily focused on impact event dating. Results on the estimation of the age of two impact craters formed recently in the geological history of Mars, Tooting and Mojave crater, will be presented. A spatial analysis of the distribution of impact craters detected on these two regions will be also introduced in the aim to distinguish primary impact crater population from secondaries

Deriving Surface Ages on Mars Using Automated Crater Counting

Impact craters on solar system bodies are used to determine the relative ages of surfaces. The smaller the limiting primary crater size, the higher the spatial resolution in surface/resurfacing age dating. A manually counted database (Robbins & Hynek, 2012, https://doi.org/10.1029/2011JE003966) of >384,000 craters on Mars >1 km in diameter exists. But because crater size scales as a power law, the number of impact craters in the size range 10 m to 1 km is in the tens of millions, a number making precise analysis of local variations of age, over an entire surface, impossible to perform by manual counting. To decode this crater size population at a planetary scale, we developed an automated Crater Detection Algorithm based on the You Only Look Once v3 object detection system. The algorithm was trained by annotating images of the controlled Thermal Emission Imaging System daytime infrared data set. This training data set contains 7,048 craters that the algorithm used as a learning benchmark. The results were validated against the manually counted database as the ground truth data set. We applied our algorithm to the Thermal Emission Imaging System global mosaic between ±65° of latitude, returning a true positive detection rate of 91% and a diameter estimation error (~15%) consistent with typical manual count variation. Importantly, although a number of automated crater counting algorithms have been published, for the first time we demonstrate that automatic counting can be routinely used to derive robust surface ages

Deriving Surface Ages on Mars using Automated Crater Counting

Impact craters on solar system bodies are used to determine the relative ages of surfaces. The smaller the limiting primary crater size, the higher the spatial resolution in surface/resurfacing age dating. A manually counted database (Robbins & Hynek, 2012, https://doi.org/10.1029/2011JE003966) of >384,000 craters on Mars >1 km in diameter exists. But because crater size scales as a power law, the number of impact craters in the size range 10 m to 1 km is in the tens of millions, a number making precise analysis of local variations of age, over an entire surface, impossible to perform by manual counting. To decode this crater size population at a planetary scale, we developed an automated Crater Detection Algorithm based on the You Only Look Once v3 object detection system. The algorithm was trained by annotating images of the controlled Thermal Emission Imaging System daytime infrared data set. This training data set contains 7,048 craters that the algorithm used as a learning benchmark. The results were validated against the manually counted database as the ground truth data set. We applied our algorithm to the Thermal Emission Imaging System global mosaic between ±65° of latitude, returning a true positive detection rate of 91% and a diameter estimation error (~15%) consistent with typical manual count variation. Importantly, although a number of automated crater counting algorithms have been published, for the first time we demonstrate that automatic counting can be routinely used to derive robust surface ages

A National Wastewater Monitoring Program for a better understanding of public health: A case study using the Australian Census

Wastewater contains a large range of biological and chemical markers of human activity and exposures. Through systematic collection and analysis of these markers within wastewater samples it is possible to measure the public health of whole populations. The analysis of effluent and biosolids can also be used to understand the release of chemicals from wastewater treatment plants into the environment. Wastewater analysis and comparison with catchment specific data (e.g. demographics) however remains largely unexplored. This manuscript describes a national wastewater monitoring study that combines influent, effluent and biosolids sampling with the Australian Census. An archiving program allows estimation of per capita exposure to and consumption of chemicals, public health information, as well as per capita release of chemicals into the environment. The paper discusses the study concept, critical steps in setting up a coordinated national approach and key logistical and other considerations with a focus on lessons learnt and future applications. The unique combination of archived samples, analytical data and associated census-derived population data will provide a baseline dataset that has wide and potentially increasing applications across many disciplines that include public health, epidemiology, criminology, toxicology and sociology

A National Wastewater Monitoring Program for a better understanding of public health: a case study using the Australian Census

Wastewater contains a large range of biological and chemical markers of human activity and exposures. Through systematic collection and analysis of these markers within wastewater samples it is possible to measure the public health of whole populations. The analysis of effluent and biosolids can also be used to understand the release of chemicals from wastewater treatment plants into the environment. Wastewater analysis and comparison with catchment specific data (e.g. demographics) however remains largely unexplored. This manuscript describes a national wastewater monitoring study that combines influent, effluent and biosolids sampling with the Australian Census. An archiving program allows estimation of per capita exposure to and consumption of chemicals, public health information, as well as per capita release of chemicals into the environment. The paper discusses the study concept, critical steps in setting up a coordinated national approach and key logistical and other considerations with a focus on lessons learnt and future applications. The unique combination of archived samples, analytical data and associated census-derived population data will provide a baseline dataset that has wide and potentially increasing applications across many disciplines that include public health, epidemiology, criminology, toxicology and sociology