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TERN Surveillance Training 2019 - Day 5, Final Lectures
- 1. Training Session 5 โ Almost There! ๏ง TERN Surveillance โ Data and Sample Curation (Christina Macdonald) ๏ง TERN Surveillance specimen and sample loads (Ben Sparrow for Sally OโNeill) ๏ง Woodlands Protocols (ben Sparrow, for Sally OโNeill) ๏ง Condition Protocols (Ben Sparrow, for Sally OโNeill) ๏ง Future Protocols โ Invertebrates (Ben Sparrow, for Sally OโNeill) ๏ง TERN Surveillance โ New tech (Andrew Tokmakoff) ๏ง Remote Sensing Validation (David Summers) ๏ง The Role of Citizen Science (Katie Irvine) ๏ง TERN Surveillance Research and Applications (Samantha Munroe)
- 3. Data Curation Data comes back from the field in components โข The Tablet (requires little curation) โข Camera- Photo panoramas- JPG and CR2 files โข Soil analysis data โ Soil site spreadsheet (.xlsx) โข Leaf Area Index wand- text file
- 4. The Field Data Collection App โ The Tabletโ โข Developed in house by Andrew Tokmakoff running on consumer grade Android tablets. โข Collects data according to TERNโs protocol โข An efficient data collection method that avoids double- handling and transcription errors โข Maximises integrity of data with drop down menus to record site attributes and characteristics. โข Scans barcode numbers, provenancing all plant, DNA, soil and metagenomic samples. โข Functions without a network (remote locations) then uploads data to safe storage when coverage is available.
- 5. The Tablet Modules โข Site description โข Vegetation Vouchering โ field names & barcodes โข Genetic Vouchering โ DNA sample barcodes โข Point intercept data- 1010 points of observation โข Basal wedge recordings โข Vegetation structural summary
- 6. Soil Site Spreadsheet โข Soil data is recorded on paper then entered to an excel template. โข The soil site sheet has 33 dropdown menus. Records soil surface attributes, disturbance and analysis results. Back in the lab, barcodes are added. โข Uploaded to the Database. โข Looking to move this process to the Field App.
- 7. Excel Soil Site Spreadsheet
- 8. Pick lists control the quality of data and eliminate transcription error. Surface Disturbance 0 - No effective disturbance 1H - HEAVY grazing by hoofed animals 1M - MEDIUM grazing by hoofed animals 1L - LIGHT grazing by hoofed animals 2 - Limited clearing 3 - Extensive clearing 4 - Cleared for pasture, but never cultivated 5 - Cleared for pasture, cultivated at some stage 6 - Cultivated, rain fed 7 - Cultivated, irrigated, past or present 8 - Highly disturbed, e.g. Mining, urban NC - not collected Surface Coarse Fragment Abundance 0 - No coarse fragments 1 - Very slightly or very few (< 2%) 2 - Slightly or few (2-10%) 3 - No qualifier or common (10 - 20%) 4 - Moderately or many (20 - 50%) 5 - Abundant (50 - 90%) 6 - Extreme abundance (> 90%) NC - not collected Surface Coarse Fragment Size 1 - Fine gravelly or small pebbles (2-6mm) 2 - Medium gravelly or medium pebbles (6-20mm) 3 - Coarse gravelly or large pebbles (20-60mm) 4 - Cobbly or cobbles (60-200mm) 5 - Stony or stones (200-600mm) 6 - Bouldery or boulders (600-2000mm) 7 - Large Boulders (>2000mm) NA - not applicable NC - not collected
- 9. Pick lists control the quality of data and eliminate transcription error. Pick lists control the quality of data and eliminate transcription error. Surface Coarse Fragment Lithology AD - Adamellite AG - Agglomerate AC - Alcrete (bauxite) AM - Amphibolite AN - Andesite AH - Anhydrite AP - Aplite AR - Arkose BA - Basalt BB - Bombs (volcanic) BR - Breccia KA - Calcarenite KM - Calcareous mudstone KL - Calcilutite KR - Calcirudite KC - Calcrete CH - Chert CO - Coal CG - Conglomerate CU - Consolidated rock (unidentified) SD - Detrital sedimentary rock (unidentified) Erosion Severity X - Not apparent 0 - No erosion 1 - Minor or present 2 - Moderate 3 - Severe 4 - Very severe NC - not collected Erosion Type W - Wind C - Scald M - Mass movement S - Sheet R - Rill G - Gully T - Tunnel B - Stream bank V - Wave E - (see site notes) NC - not collected NA - not applicable Erosion State A - Active P - Partially stabilised S - Stabilised NC - not collected NA - not applicable Pick lists control the quality of data and eliminate transcription error.
- 10. Data modules in the tablet pushed to a cloud based staging warehouse then ingested into the Postgres Database Site Description Veg Vouchering Genetic Vouchering Point intercept Basal Wedge Structural Summary Uploaded Staging Warehouse Checks for anomalies ingested Site description Veg vouchering Genetic Vouchering Point intercept Basal wedge Structural summary Database SITE SAAVVP0001
- 11. Text and .xls files curated and added to Database Herbarium idโs Taxonomic name check Soil analysis data Soil samples assigned barcodes GPS coordinates uploaded & linked to site data Herbarium idโs Taxonomic name check Soil analysis data & barcodes GPS coordinates Site description Veg vouchering Genetic Vouchering Point intercept Basal wedge Structural summary Database Site SAAVVP0001
- 12. Curatorial checks are made and itโs OK to Publish Content curated OKโed to Publish Content curated Herbarium idโs Taxonomic name check Soil analysis data GPS coordinates Site description Veg vouchering Genetic Vouchering Point intercept Basal wedge Structural summary Database OK to Publish ๏ผ Site SAAVVP0001 Available to TERNโs Data Portal TDDP (AEKOS) R-Package S2S Contains all data from 660 sites
- 13. All data is stored on a server
- 14. Photo panorama images โข 3 x photo panoramas set in an equilateral triangle around the plot centre pole. โข 75 -100 images stored as .JPG and CR2 files at TERN on a managed filesystem.
- 15. Leaf Area Index measures plant canopy leaf area or density โข LAI data is downloaded to conversion software. โข Results stored at TERN. โข Available for access, not yet integrated within DB.
- 17. Sample Management When samples arrive back from the field โข Vouchered plants are dried in a plant press, information tags added to each specimen and then they are sent as a batch to the State Herbarium for id.
- 19. Vouchered Specimens sent to Herbariums for Identification (then returned)
- 20. โข Leaf tissue samples (linked to each vouchered plant by a corresponding barcode) are dried and stored in Sistema boxes containing silica and vacuum sealed. Sample Management
- 21. Labelled with adhesive barcode label and scanned with app silica granules dry the sample Boxes are vacuum sealed with some silica in them. Labelled and stored. 6. Samples can then be used for isotope and DNA analyses +
- 22. โข Soil samples are dried in tins, assigned barcodes, tubbed, labelled and stored at the Roseworthy campus. Sample Management
- 24. โข Metagenomic samples are assigned barcodes, their wet silica replaced with dry until all moisture has been removed. โข Stored at the TERN facility. Sample Management
- 27. Samples available for researchers as of May 2019 From 691 Long Term Monitoring Sites Herbarium Verified Plant Specimens 34,846 Herbarium Verified Leaf Tissue Samples 30,000 Field analysed Soil Pit Samples 2,912 Soil Sampled from 3 Depths 14,878 Metagenomic (genetic material) samples 6,371
- 28. Example : All Drosera species collected in WA available as DNA and Voucher samples
- 30. 674+ plots 36,837 plant vouchers 7317 soil metagenomic samples 56,272 leaf tissue samples Data from 559+ plots 16,276 500 g soil samples 1* method
- 41. 1.45 m 1.3 m Position 3
- 44. Woodlands are commonly considered to be ecosystems with 10-30% projected foliage cover, and trees ranging in height from 10-30 m
- 52. โa relative measure of the status of the biota at an assessment site against a natural reference state, described by key attributes that represent the structure, function and composition for the relevant type.โ
- 69. Morning Tea
- 71. Up/Down App โข Taking images of canopy and substrate alternately, along a path. โข Potential for leveraging existing Photopoints Analysis system (+dev)
- 72. Camera Traps โข Standards for Camera Traps Metadata: โProjects and data management (TERN, CSIRO, Aust. Museum) โReady for trials/validation
- 73. Soils (in App) โข A new App module (+ re-factoring of existing) โข Collect characterisation, subsites, metagenomics and bulk density โBarcode scanning, Pictures, Drop-downs, number entry, etc. โข Why? โCompleteness and correctness โข Less curation effort
- 74. Multi-Tablet App (exploratory) โข Within each field team, there are two tablets โข Right now, they are not synchronised โSite definitions (are not synched between tablets) โVouchers within a site (are not synched between tablets) โข Potential to connect and synch tablets via WiFi-Direct (an adhoc WiFi network)
- 75. IGSN (International Geo Sample Number ) โข CSIRO and ARDC โข Designed to provide an unambiguous globally unique persistent identifier for physical samples. โข Facilitates the location, identification, and citation of physical samples used in research. โข Impetus largely from the earth science community โ IGSN are assigned to geological and environmental samples such as rocks, drill cores and soils, as well as related sampling features such as sections, dredges, wells and drill holes.
- 76. ausplotsR โ new data products
- 77. ausplotsR โ new data products
- 78. Photopoints: Scalable Cloud Analysis โข Current: โAnalysis was developed on a PC โProduction system runs on a VM in the cloud โIsnโt fully automated, from file collection to results โข Solution: โMigrate to a scalable container-based approach with automation from file upload in Dropbox, Google Drive, etc.
- 79. Our Focii: โข Less curation effort โข Easier to access data โข New and richer data sources Looking forwardโฆ
- 81. Remote sensing validation Topics: โข Remote sensing validation โข SLATS integration โข Unmanned aerial vehicles (UAV/drones) David Summers david.summers@adelaide.edu.au
- 82. Remote sensing validation โข Trade-off in data collection โ Scale and extent โ Accuracy and precision โข Remote sensing collects spectral information (EM Radiation) about the land surface โ Fine scale and large extent โ Low accuracy and precision โข This spectral information is interpreted/processed into other derived products These products require validation!!
- 83. Remote sensing validation โข What am I looking at? UAV Landsat Modis
- 85. The use of TERN Surveillance Plots for remote sensing validation โข Provides Remote Sensing compatible data โข Point intercepts convert to pixel โ Homogeneous โ 1 hectare โ Aligned with grid โข Image classification โ Vegetation communities โ Vegetation cover Landsat ~ 30m Spot ~ 10m MODIS ~250m to ~ 1000m TERN Surveillance Plot 1 ha (100m x 100m) Not to scale!!
- 86. The use of TERN Surveillance Plots for remote sensing validation โข Collaboration with TERN Landscape regarding field methods โ Will use our data to validate many of their products โ LAI Meter, Cover Measures etc โ Potential for Spectrometer/operator to join in some of our field trips โ โTag along tourโ concept โข Data particularly useful for validating cover products....
- 87. SLATS โข Statewide Landcover and Trees Study โข Queensland tree cover mapping โข Star Transect Protocol โข Methodology for quantitative measurement of vegetation cover โข Used to validate remote sensing image classification S1 NEN5N4N3N2N1NW W5 W4 W3 W2 W1 SW S2 S3 S4 S5 SE E1 E2 E3 E4 E5 100 m 100 m
- 88. โข 1 metre samples โข Three vegetation categories โข Non-woody โข Ground cover โข Woody >2 m SLATS โข Site description โข Topography โข Vegetation structure โข Erosion characteristics โข Tree basal area
- 89. Why SLATS? โข National standard for vegetation cover field survey โข Collected for different purposes โ SLATS purpose build for remote sensing validation โ highly refined โ Surveillance Plots method designed for maximum widespread utility of data, including ecological research AND remote sensing validation โข Surveillance Plots can be summarised into SLATS form
- 90. Unmanned Aerial Vehicles - UAV โข Revolutionary data source โข Remote sensing at the site scale โข <1 m pixels โข Spectral information โข Multispectral โข Hyperspectral* โข Surface/Structural information โข Optical 3D point cloud โข Lidar* *Research only
- 91. Spectral and structural information โข Small pixels less likely to be mixed (made up of a mix of materials) โข Non-mixed pixels easier to interpret โข Small pixels can be โup-scaledโ or resampled to larger pixels UAV Landsat Modis
- 92. Spectral and structural information โข Overlapping flight paths provide stereo image โข 3D image from numerous 2D images โข Provides enhanced information on: โข Structure โข Density โข Height โข Spectral properties
- 93. Spectral and structural information
- 94. Thanks!
- 95. The Role of Citizen Science katie.irvine@adelaide.edu.au info@wildorchidwatch.org
- 96. 1.Quality science 2.Linked with community 3.Makes the world a better place Dr. Alan Finkel, Australiaโs Chief Scientist, Australian Citizen Science Association Conference 2018 Citizen Science isโฆ
- 99. Method
- 100. Little forest bat โข 150 participants โข 400 sites โข 13 microbat species identified
- 102. TERN Ecosystem Surveillance: Research & Applications
- 103. TERN Plot PI Data!
- 105. Photo-panorama place holder LiDAR (light detection and ranging)
- 106. International Space Station Meanwhile, somewhere in Australia Basal Area
- 107. The extent of forest in dryland biomes Poor estimates in dryland biomes Dryland forest cover ex. Baobab Trees TERN tree cover data Very high spatial resolution and Google tools
- 108. The extent of forest in dryland biomes Increase current estimates of global forest extent by 9% (400-500 Mha) Plots with forest are in green
- 110. Soil and Landscape Grid of Australia Estimated % clay content 0-5 cm depth 5-15 cm depth < 10 % 10 โ 20% 20 โ 30% 30 โ 35% 35 โ 45% >45%
- 111. ~400 samples Variables โข Latitude โข Longitude โข Altitude โข Soil pH โข MAP โข MAT (maximum) Eucalypt Woodlands Chenopod Shrublands Acacia Woodlands Tussock Grasslands Eucalypt Open Woodlands Other ShrublandsMallee Woodlands
- 112. Geographic trends in metabolite composition Starting points for future discoveries!
- 113. Veg Voucher Data: Australia's newest weed Golden Clover (Trifolium aureum)
- 114. PAR MAT MAP MI LMA Narea
- 115. ~450 leaf samples from 27 sites PARCO2 (โC13) MAT LMA N-fixer
- 116. Leaf waxes paint picture of past to inform future Leaf wax n-alkanes
- 117. Leaf waxes paint picture of past to inform future
- 119. โข Future and Wrap up
Editor's Notes
- The botanists (Emrys / Michael) search the entire plot, collecting a sample of all plant species present Herbarium quality samples, ideally grabbing diagnostic material, flowers, seed pods, bark if necessary Full newspaper sized
- Also for every species present in the plot, one tea bag sample is collected, or for the dominant species of the plot, 4 samples are collected to enable access to within population dna Around 10 cm of high quality leaf material, ideally fresh green stuff โ which isnโt always available Placed into the synthetic tea bag, labelled with a barcode, scanned into the app, dried on silica in the field, and maintained until dry Stored in air tight containers
- Once collected, samples are dried back in our facility, tubed up, barcoded, scanned into our database Stored at our Roseworthy facility
- These figures are a little out of date and donโt include data/specimens/samples collected so far in 2019 Essentially, we have a lot of specimens and samples to share with researchers
- Just a reminder โ there is no application process to obtain and use our data
- Just a reminder โ there is no application process to obtain and use our data
- Covers all these topics
- Key components of the application
- Agreement includes these key areas
- Woodlands update Background One component of the Enhancing long-term surveillance monitoring programme is a project to develop field survey protocols (an โAusPlots Woodlandsโ method) that will provide a consistent national approach to fixed surveillance site monitoring of Australian woodlands, focussed on vegetation and soils. Any approach must be meaningful but still simple and affordable, and the Department also indicated that this work should build on existing approaches and capacity, and specifically identified the AusPlots Rangelands and Forests methods as existing methods to use as a basis for the Woodlands work. With this in mind, a working group from various government agencies, Universities, and private industry have provided expert input to the development of this method, recommending a trial protocol that builds on existing AusPlots methods and adds new protocols specific to woodlandsโ ecosystems to provide a cohesive woodlands survey method. What we did The trial woodlands method includes two entirely new protocols not used by AusPlots before, to assess trees and coarse woody debris (CWD). The new woodlands protocols for CWD and trees were trialled at two 100 m x 100 m AusPlots, one in a black box woodland and one in a mallee woodland: CWD was sampled along 4 x 100 m line intercepts. Any CWD that intercepted the line, and that had diameter >10 cm was measured for diameter at widest point, diameter at narrowest point (down to 10cm), length, and a decay class was assigned. Large trees (DBH >10 cm) were surveyed in the full 1ha plot. For each large tree the species, alive/dead status, DBH, and height were recorded. Small trees (DBH<10 cm, height>2 m) were measured in 2 x 5 m wide x 50 m long belt transects arranged in a cross at the centre of the plot. Species, alive/dead status, DBH and height were recorded. Seedlings/saplings of each tree species were counted in the belt transects Based on what worked well and not (see notes below), edits to the method were suggested by the field team and then trialled at another 100 m x 100 m AusPlot in black box woodland. What worked well The CWD method was easy to implement in the field and integrated well with existing AusPlots methods that are recommended for the woodlands protocols. This is because the CWD method aligned with existing transect lines established in the plot for use in the Point Intercept. Marking trees and CWD with bright soluble paint once they had been measured provided an easy visual cue to avoid remeasure and assisted in navigation through the plot for the 1ha large tree survey. Use of a vertex hypsometer to measure tree height was quick, easy, and accurate, and can be done by one person. Edited โversion 2โ (see โSuggested editsโ below) of the tree survey worked incredibly well when trialled โ it was logical and simple to carry out in the field, and aligned well with existing AusPlots methods that will be included in the woodlands protocols. Suggested edits Suggested changes to the tree method that were subsequently tested in the field were: large trees measured in 4 x 5 m wide x 100 m belt transects, aligned with the existing 30 m and 70 m transect lines laid out for the point intercept method small trees and seedlings/saplings measured in 4 x 2.5 m wide x 100 m belt transects, aligned with the existing 30 m and 70 m transect lines laid out for the point intercept method location of large trees, small trees, and CWD along the transect line recorded to the closest metre to give a better indication of spatial distribution in plot. Take callipers for CWD and small trees โ quicker/easier than wrapping a tape around, and sometimes you canโt get tape around CWD on the ground.
- Australian woodlands are both culturally and ecologically important to the nation, playing home to a significant portion of Australiaโs biodiversity (Lindenmayer et al. 2010). Woodlands are commonly considered to be ecosystems with 10-30% projected foliage cover, and trees ranging in height from 10-30m (Specht 1970, Yates and Hobbs 1997). Woodlands make up a significant portion of Australian ecosystems, encompassing ten of the major vegetation groups defined under the National Vegetation Information System. They are present in at least 80 IBRA bioregions, encompassing climate ranges from the monsoonal tropics of the north, through to temperate zones of southern Australia. Woodlands habitats have been severely affected by a range of processes since European settlement. For example large areas of woodlands habitats โ especially in temperate zones - were cleared for agriculture and settlements, or heavily modified by grazing (Lindenmayer et al. 2010). Ongoing threats to Australian woodlands include: ๏ท Clearing, result in both loss and fragmentation of habitat ๏ท Grazing ๏ท Removal of timber and debris (e.g. for firewood) ๏ท Climate change ๏ท Changes to nutrient cycles (e.g. due to agricultural fertilisers in the environment) ๏ท Invasive and exotic species ๏ท Habitat modification Environmental monitoring in woodlands habitats has been varied. A range of state/territory-based programs have included woodlands habitats in broader monitoring programs. Other long-term research projects have investigated dynamics of specific woodlands ecosystems in defined locations (e.g. the Great Western Woodlands of WA, Cumberland Plain Woodland Restoration Plot Network in NSW, PPBio woodlands research plots at Karawatha Forest in Queensland, and Nanangroe Plantation Plot Network in NSW; see Lindenmayer et al. 2014 for an overview of some long-term woodlands research). Undertaken by universities or other research groups, these projects are designed to investigate site-specific issues or particular species and processes of interest. Thus, availability of consistent information about woodlands habitats across a wide geographic range is lacking.
- Components of the AusPlots Woodlands survey method The modules included in the AusPlots Woodlands survey method are: 1. Plot and physical descriptions 2. Plot layout and positioning 3. Photo-panoramas 4. Vegetation species survey and vouchering 5. Point intercept 6. Tree survey 7. Coarse woody debris 8. Structural summary and homogeneity 9. Soils and Landscapes 10. Soil metagenomics 11. Post field trip procedures
- Note โ the manual defines what to do with โproblemโ trees with atypical trunks / trunks half within the plot/half out of plot etc.
- Note โ the manual defines what to do with irregularly shaped CWD = multi-stem measurements as per pic
- The AusPlots Condition Protocols were developed as part of the Enhancing Long-term Surveillance Monitoring Across Australia Programme funded by the Commonwealth Department of the Environment (through the 2014/5 National Environmental Research Program) to enhance the breadth and depth of Australiaโs terrestrial ecosystem condition monitoring and reporting at national and regional scales. This is hoped to be achieved by increasing the range and type of AusPlots field sites and monitoring, and through providing guidelines and protocols manuals that will enhance environmental data quality.
- . The attributes are: age class clearing cover coarse woody debris fauna fire floristic composition groundcover land use landscape configuration phenology soil vegetation health vegetation structure woody cover.
- Invertebrates are the largest component of global biodiversity, play a major role in herbivory, nutrient cycling and maintaining soil structure and are an important food source for many vertebrate species Incorporating information about invertebrates into surveillance monitoring makes sense!
- For now โ so we can implement soon-ish, it will be an add-on activity for the field team to conduct whilst doing the current veg and soils methods, therefore something quick and easy โ something that could be set-up in less than 30 minutes, with pitfalls left in place for just a few hours, and then 20 minutes of active work, sampling by tree and shrub beating.
- Especially ANTS as they are the dominant terrestrial invertebrate group in the Australian environment, and are by far the most commonly used invertebrate indicators in land management Abundant, diversity and functional importance, sensitive to disturbance, so we know they will be of research interest Baited tubes with a meat slurry attracting ants โ 20 vials placed along the ground, the slurry attracts all ant species but prone to interference by the most dominant species (researchers will be aware of this). Vials will be kept separate, not pooled together Avoiding the hottest weather,
- Shrub beating - beating 6 individual plants of the dominant shrub species (when itโs a woody species) with a standardised number of whacks, catching what falls out
- Some pretty photos
- It wonโt look like this on the right! But rather jars of preserved un-sorted samples
- It wonโt look like this! But rather jars of preserved un-sorted samples
- Comms and engagement officer for the Wild Orchid Watch project based with TERN at UoA. I also run citizen science projects with the Department for Environment and Water, and coordinate the SA chapter of the Australian Citizen Science Association. Citizen Science is the practice of community volunteers participating in the scientific process. Citizen scientists may be retired or student scientists or amateur naturalists who work in a completely different field. Citizen scientist are supported by professional scientists to ensure that data collected is of a high quality. The growing field of citizen science provides us with a unique opportunity to harness community skills and passion We can collect information at far greater temporal and spatial scales with community participation than with trained scientists alone Education and engagement for greater scientific literacy and conservation messaging. People feel empowered when given the opportunity to participate in a project that they are passionate about Citizen science has the potential to cover large temporal and spatial scales in ways that it is impossible for traditional science projects. While many volunteers contribute to TERN data collection and processing, there is scope exciting ways that citizen scientists might be able to get involved in TERN in the future.
- Dr Alan Finkel, Australiaโs chief scientist gave an inspiring presentation at the national citizen science conference held in Adelaide last year. He described the 3 pillars of good citizen science: Quality Science - -Citizen science projects must be designed using robust scientific methodology and must aim to answer important scientific questions. - We use technology such as apps and online databases to streamline information management and ensure accuracy of information collected. -The beauty of citizen science as a method for scientific research is that it allows us to collect and share information at temporal and spatial scales that are otherwise unachievable. Linked with community - Participation in citizen science enriches people and communities. -It fosters greater scientific and technological literacy and provides an avenue for people to get involved in meaningful scientific research and share their skills and knowledge. Makes the world a better place - ย Citizen science volunteers learn about their local conditions, and then can use the information to inspire collaborative action and influence planning and policy decisions. -Information gathered by citizen scientists is relevant to local problems and conditions and can be re-used in research on global issues, such as climate change. -Citizen science is a valuable tool for scientific empowerment, education, and action, and it is growing in momentum in Australia.
- The variety and beauty of orchids is what draws people to his family. These are photos from the WOW Instagram account which are donated by citizen scientists. The online community of orchid enthusiasts is very active and is a great way to communicate with project participants. The data collection is based on photos taken and submitted via the WOW app. Participants will be prompted by the app to enter additional phots and info on habitat etc to provide context and valuable details.
- This is a citizen science bat monitroing project that I have been involved with for the Department for Environment and Water. Bat monitoring is part of the fauna protocols for TERN. Social survey Anabat recorders - image BioCollect App for field data Species ID from sonograms - image Data analysis โ species distributions
- PARTNERS โ very important for the success of citizen science projects, must be properly resourced. Best outcomes when government, community groups and universities work together.
- State and national projects. Some have apps and some submissions are via website. A lot of choice!
- The first example I want to talk about is a research project that is a a great example of how to apply PI data, which in this case was used to look habitat variation in the rangelands. The Australian rangelands, is a exceptionally diverse are home to a wide range of species and habitats. But much of this diversity is not well categorised, which is a problem for conservation or research. Hence the goal of this study, was, using PI data, to develop a habitat classification scheme. The authors clustered or grouped plots based on the species diversity and structural traits of each plot to identify similar habitat types, which could be the basis of a classification scheme. In additional, they correlate the plot structure to \environmental variables to understand the abiotic context for variation in rangeland vegetation. These variables would included things like MAP and MAT, and soil attributions, which were gathered for the slga, like soil ph, and total N and P.
- After quantifying species diversity and structure in roughly 400 plots, the team initially identified 3 โsuperclustersโ or groups of plots, which were primary distinguished by latitude and associated climate variables. These three groups were Mediterranean, savannah, and desert. And as you can see from this multivariate analysis, where each point is plot, these three groups cluster together fairly well, indicating this a logical initial classification. Within each of these superclusters, the authors identified smaller clusters based on differences in species and growth form diversity. This figure showing the number of different clusters that were identified, and the area of the chart slices represent the proportional contribution of the importance of each growth form. potential categories within a classification scheme. Our results offer a tentative classification scheme that is novel, ecologically sound and coherent in terms of floristic composition and structural attributes.
- Been introduced this week to our photo-panoramas. Not going to discuss that in detail But what it is important to known when working with these photo-panoramas, is are they accurate? Can photo panoramas faithfully record structural attributes of a plot In order to answer that question, we compared structures attributes including stem count, diameter at breast height (DBH), and basal area as determined by field ground observations, PP, and high cost survey grade Terrestrial Laser Scanner (TLS) RIEGL VZ-400. Assuming field observations were correct Stem Count Assuming that the ground obs are the best most accurate value we can achieve Neither method did a great job Blockage caused by stems in the foreground In both cases these methods need to be refined Mean DBH Pretty similar for all three Basal Area much better with photopnaorams Good tool for photopanaorams, good alternative ground observations And considering that PP is cheaper ( it takes a little longer, but it far most cost effective).
- Switching gears a bit now, because the first two examples are a bit more localised But our plot data is not just used for research studies, it also contributes to large global survey programs For those of you who are not familiar, GEDI (Global Ecosystems Dynamics Investigation), a High resolution laser which takes globally representative measurements of vertical structure in the worldโs forests. It located on International Space Station (ISS) These measurements are used to quantify aboveground biomass density (AGBD) at the scale approximately a 25 m diameter circle on. TERN ES supplies GEDI with basal wedge data from our plots, which in turn can be used to calculate biomass, which is then used to validate the GEDI imagery.
- But recently, an example of our data being used that we are particularly proud of is TERN Plot data, which was used in a global analysis of the distribution of forests and woodlands in dryland areas, dryland forest extent and cover, in the past, has not been accurately measured, by the scarcity of large-scale studies of dryland biomes But, as we have discussed formally and informal this week, dryland ecosystems contain great deal of biodiversity, in fact the first paper I discussed really drives that point home, with all the different clusters And that includes, in some areas forests And so understanding their extent has substantial implications for understand this like carbon flux and storage capacity To accurately determine how much forest and tree cover remains in dryland biomes, a large team associated with the Food and Agriculture Organization of the United Nations established a global initiative to undertake a Global Drylands Assessment of forest. high spatial resolution imagery and Google geospatial tools to determine tree cover in dry land biomes all around the globe. They did this by targeting a large sample of 0.5-ha plots. So you might be wondering what TERN ES have to do with this. Well, similar to GEDI, TERN was able to provide our tree cover data for 100s of plots across the Australian ranges land, which in turn were used to help validate remote sensing data on this project TERN staff were also heavily involved in that process. TERN data for crucial on-ground verification of satellite-based analyses in Oceania
- So in the end, what did they find? - โfoundโ 467 million hectares of previously unreported forest around the world The discovery increases the known amount of global forest cover by around 9%, and will significantly boost estimates of how much carbon is stored in plants worldwide Global drylands contain 45% more forest than has been found in previous surveys Also by revealing that drylands a greater capacity to support trees and forest than previously perceived and understood, a unique chance to mitigate climate change impacts through large-scale dryland conservation and afforestation actions is presented This has particular relevance for Australia considering much of our country is arid semiarid land.
- Look at the applications of our samples
- Continuing the theme of ES making contributions to much larger programs. Our samples, similar to plot data, also help to validate and develop models of soil and landscape attributers. We have not discussed this much this week, but some of you will be familiar with the slga, this platform also includes data and mapping products of roughly 30 different attributed, modelled for the entire country. It provides easy access to nationally-consistent and comprehensive soil and landscape attribute data at a finer resolution than ever before in Australia, particularly now that it has its own r package. But similar to gedi, even the greatest national data sets, need a ground support. In this case ES soil samples are scanned, and the result data for a wide range of attributions, such as estimate clay content, and used to ultimate build and validate slga models. TERN samples are critical to this landscape product landscape product.
- But our soil samples have been use for a number of different research projects, not just validation, but for new research as well. And a great example of thisis a study that use our samples to help create new and improve drugs derived from secondary metabolites. Secondary metabolites are organic compounds produced by bacteria that are not directly involved in growth, development, or reproduction but help them interact with their environment. These include things like toxins and signalling agents that allow bacteria to obtain nutrients as well as defend against, and communicate with, other organisms. But these metabolites can also be extremely valuable from an economic or more anthropocentric perspective because these natural products can have pharmaceutical applications. In fact bacterial bioactive metabolites have been one of the most prolific sources of small-molecule therapeutics, things like antibiotics, antitumor drugs and immunosuppressive drugs. So there is an enormous incentive to identify areas or environments that may be significant sources for these metabolites. However, that is a big gap in our understanding of the best way to find key sites to look for the metabolites. We know that environmental factors contribute to differences in the composition of natural products, but there is a limited understanding of how bacterial biosynthetic diversity varies from one environment to the next, and what environmental factors correlate with changes in biosynthetic diversity. TERN soil samples provide a unique opportunity to investigate the relationship between metabolite and bacterial diversity and the environment, Using TERN Soil samples, a team from The Rockefeller University (scientific research, primarily in the biological and medical sciences, that provides doctoral and postdoctoral education. Rockefeller is the oldest biomedical research institute in the United States.) Used ~400 top soil samples from ecologically and geographically diverse plots across Australia including samples from the major vegetation groups we often survey, For each sample, eDNA (environmental DNA) was extracted from the soil to look for gene clusters associated secondary metabolite production. They then compared eDNA diversity and composition at each site to the latitude, longitude, altitude, soil pH, MAP and MAMT.
- Didnโt detect any correlations with these factors and diversity, changes in latitude and to a lesser extent changes in pH were found to correlate most consistently with changes in composition on a continent-wide scale. Although further studies are needed to better understand the underlying causes driving this relationship, these findings provide insights into how best to direct future natural product drug discovery efforts. They were also able to identify the sites that had key metabolies often used in current drug therapies, so this is a good clue to look in these areas for future , and thus indentify key hotspots for drug discovery. These data can be useful for identification of environments that are potentially productive starting points in the discovery of novel members of these families of biomedically relevant NPs.ย
- Moving on to plant samples In fact, just this past year, one of the veg voucher samples we collected turned about to be species never before recorded in Austrlaia. Working with plant samples collected duringย TERNโs ecosystem survey of Australiaโs alpine region, Neville Walsh of Royal Botanic Gardens Victoria identified Australiaโs newest weed species: Golden Clover (Trifolium aureum). Native to eastern and central Europe, Golden Clover is not known to occur elsewhere in Australia except where it was found by TERN on Victoriaโs Dargo High Plains south-west of Mt Hotham in January 2018. Similarities to other clover species could have led to Golden Clover being overlooked in the past. The discovery is the result of the systematic monitoring of all species at TERNโs monitoring sites, followed up with retention and formal identification of voucher specimens. The identification of this new species reinforces the value of theย TERN AusPlots methodologyย for the surveillance of Australiaโs ecosystems Itโs not known how common Golden Clover is in the Australian alps, but surveys at the TERN ecosystem surveillance alpine plots this summer by Royal Botanic Gardens Victoria staff are expected to determine if the population is large enough to be of concern to native vegetation. Future repeat TERN surveys will also detect any significant increases in frequency or abundance of the species, a finding that could trigger more targeted monitoring or management.
- Now one of the main things that samples are collected for with plants is N and C, and isotope analysis. And this next example really shows the potential applications of the plant samples in this regard. Nitrogen (N) is an essential nutrient for primary production and plant growth. Nitrogen content per unit leaf area (Narea) is a key variable in plant functional ecology. is important for making perditions about growth, productivity and resilience. In this study, the authors set out to test the predictability of Narea using measurements carried out on dried plant material collected by the Terrestrial Ecosystem Research Network (TERN) ES. Tested and quantified the effects of irradiance, ci V ca ratio (from 13C), temperature, LMA, and N-fixation ability (26% of the species sampled were Nfixers) on variation in Narea.
- Analysis used 442 leaf measurements representing all species found in a 100m100m plot at each of 27 sites on a broad northโsouth transect across Australia. Canโt go through all aspects of their findings, but in brief, regression analysis revealed significant partial relationships Narea vs. ci V ca, MAT, and ln IL (Table 1, Fig. 2). The relationship was also negative for MAT, as expected, because there is an inverse relationship between temperature and the quantity of leaf proteins Our application of trait gradient analysis also points out a way towards process-based treatments of functional trait diversity in next-generation models
- And finally, I wanted to highlight a good example of where plot and sample data can be used together, in a project that as I said early, I am not sure when samples were getting collected that people were thinking paleoclimate applications, but creative people saw the potential. This example actually comes right out of the Uni Adelaide from a phd thesis which was interested in using microfossils in soils to study past environments. Microfossils, are exactly what they the sound like, (they are fossils you can only see with a microscope). Organic microfossils you might find in soils includeย pollen or spores. Scientists can make interpretations about past vegetation composition and environmental change by extracting microfossils because we can assume these trends and relationships are relatively consistent between the past and present. If we know what factors control modern day pollen composition or distribution, we can assume that the processes control pollen composition and distribution in the past. Leaf waxย n-alkanes are one type of compound made by plants that can be preserved in soils and sediments for millions of years. It is a microfossil. But it was unclear how variation in n-alkane composition, structure, and concentrations can be interpreted relative to vegetation structure ย So the authors took soil samples from about TERN from about 20 sites extracted theย n-alkanes and compared them to variables such grass and tree cover at TERN plots to see if alkanes composition changes predicably with either.
- So what did they find, Well Norm33 is a specific group of n-alkanes, they have a certain composition, and the n-alkane values from TERN soils appear to correlate with the general vegetation found over a broad area. This finding suggests that these molecular fossils provide a faithful record of large-scale ecosystem structure such as the predominance of grasses versus trees, which can be very useful in reconstructing past vegetation change.โ So in ancient soils if see high Norm33 values, this could be used to indicate the area used to have a lot grass cover, rather than trees.