<p>This data set provides the photosynthetic pathways for 2428 species recorded across 541 plots surveyed by Australia’s Terrestrial Ecosystem Research Network (TERN) between 2011 and 2017 (inclusive). TERN survey plots are 1 ha (100 x 100 m) permanently established sites located in a homogeneous area of terrestrial vegetation. At each plot, TERN survey teams record vegetation composition and structural characteristics and collect a range of plant samples using a point-intercept method. Species were assigned a photosynthetic pathway using literature and carbon stable isotope analysis of bulk tissue collected by TERN at the survey plots. </p><p>The data set is comprised of two data tables and one data descriptor that defines the values in the two data tables. The first table contains a list of each species and its photosynthetic pathway. The second table includes a list of all the peer-reviewed sources used to create this data set. </p><p>This data set will be updated on an annual basis as TERN’s plot network expands and new information becomes available. </p>

This collection contains the data used in the Multi-Criteria Analysis Shell for Spatial Decision Support (MCAS-S) software tool. From the Data menu, explore and download individual supplementary layers, or download the entire datapack. The Multi-Criteria Analysis Shell for Spatial Decision Support (MCAS-S) is a software tool developed by the Australian Bureau of Agricultural and Resource Economics and Sciences that enables multi-criteria analysis (MCA) using spatial data. It is a powerful, easy-to-use and flexible decision-support tool that promotes: - framework for assessing options <br> - common metric for classifying, ranking and weighting of the data <br> - tools to compare, combine and explore spatial data <br> - live-update of alternative scenarios and trade-offs. <br>

The climate adjusted linear seasonal persistent green trend is derived from analysis of the linear seasonal persistent green trend, adjusted for rainfall. The current version is based on the 1987-2014 period. <br> Seasonal persistent green cover is derived from seasonal cover using a weighted smooth spline fitting routine. This weights a smooth line to the minimum values of the seasonal green cover. This smooth minimum is designed to represent the slower changing green component, ideally consisting of perennial vegetation including over-storey, mid-storey and persistent ground cover. The seasonal persistent green is then summarised using simple linear regression, and the slope of the fitted line is captured in the linear seasonal persistent green product. This product is further processed to produce a climate-adjusted version.

This dataset contains spatial layers describing Forest Connectivity from 1995-2019, in NSW Regional Forest Agreements (RFA) Areas along the eastern coast. Forest Connectivity accounts for the general quality of terrestrial habitats supporting biodiversity at each location, the fragmentation of habitat within its neighbourhood and how its position in the landscape contributes to connectivity among the habitats across a region. <br> These have been based off the National Greenhouse Gas Inventory (NGGI) National Carbon Accounting System (NCAS) National Forest and Sparse Woody Vegetation Data grids (ABARES, 2020). These base grids are Landsat in origin and have a resolution of 25m. <br> Forest Connectivity, including canopy cover connectivity and fragmentation is concerned and linked to forest condition. Concepts applied are to be aligned with definitions as found in the NSW Biodiversity Indicator Program (BIP) and the Spatial Links methodology for calculating connectivity.<br> Base cover extent grids used are from the NSW RFA Historic Forest Canopy Cover Extent – 1995 to 2019 product. <br> Read more about the project on the Natural Resources Commission website:<br> https://www.nrc.nsw.gov.au/fmip-baselines-ecosystem-health-projectfe1<br> This dataset is superseded by 'NSW Forest Monitoring and Improvement Program State-Wide Historic Forest Connectivity - 1995 to 2020'

This dataset consists of images of fauna, flora, fungi or general scenery or events captured at the site on an ad-hoc basis and may provide the researcher with information regarding the species that occupy, frequent or traverse this site.<br /> <br /> The Litchfield Savanna SuperSite was established in 2013 in Litchfield National Park. Site selection was influenced by the history of long-term monitoring work undertaken in this area by the Darwin Centre for Bushfire Research (formerly Bushfires NT). The core 1ha plot is dominated by <em>Eucalyptus miniata</em>. The site is representative of the dominant ecosystem type across northern Australia: frequently burnt tropical savanna in high rainfall areas. For additional site information, see https://www.tern.org.au/tern-observatory/tern-ecosystem-processes/litchfield-savanna-supersite/ . <br /> Phenocam images and photopoints are also collected at the site.

Digital Cover Photography (DCP) upward-looking images will be collected up to twice per year to capture vegetation cover at Boyagin Wandoo Woodland SuperSite. These images can be used to estimate Leaf area index (LAI), Crown Cover or Foliage Projective Cover (FPC). The Boyagin Wandoo Woodland SuperSite was established in 2017 in Wandoo Woodland, which is surrounded by broadacre farming. About 80% of the overstorey cover is <em>Eucalyptus accedens</em>. For additional site information, see https://www.tern.org.au/tern-observatory/tern-ecosystem-processes/boyagin-wandoo-woodland-supersite/ . Digital Hemispheric Photography (DHP) has also been collected at Boyagin SuperSite.

High quality digital images are captured using a digital SLR camera at the plots (core 1 hectare vegetation plot) at the TERN Warra Tall Eucalypt SuperSite using the panoramic photopoint method. The panoramic photopoint method may be the most informative in open forests/woodlands and rangelands. Three photopoints are established configured in an equilateral triangle (2.5m sides) with the centre marked with a star dropper and the location recorded with DGPS. At each photopoint take photographic sequences in a 360° panorama, with up to 40 photographs with a minimum 50% overlap between consecutive photographs. For more information about the method, see <a href= 'http://dx.doi.org/10.13140/2.1.4287.3607'>White, el al. (2012) AusPlots Rangelands Survey Protocols Manual Version 1.2.9.</a> <br> The Warra Tall Eucalypt SuperSite was established in 2012 and is located in a stand of tall, mixed-aged <em>Eucalyptus obliqua</em> forest (1.5, 77 and &gt;250 years-old) with a rainforest / wet sclerophyll understorey and a dense man-fern (<em>Dicksonia antarctica</em>) ground-layer. The site experienced a fire in January 2019, which consumed the ground layer and killed a high proportion of the understorey trees but stimulated dense seedling regeneration. For additional site information, see https://www.tern.org.au/tern-observatory/tern-ecosystem-processes/warra-tall-eucalypt-supersite/. <br /><br /> Other images collected at the site include digital hemispherical photography, phenocam time-lapse images taken from fixed under and overstorey cameras, five-photopoint images, and ancillary images of fauna and flora.

The linear seasonal persistent green trend is derived from analysis of the seasonal persistent green product over time. The current version is based on the 1987-2014 period. <br> Seasonal persistent green cover is derived from seasonal fractional cover using a weighted smooth spline fitting routine. This weights a smooth line to the minimum values of the seasonal green cover. This smooth minimum is designed to represent the slower changing green component, ideally consisting of perennial vegetation including over-storey, mid-storey and persistent ground cover. The seasonal persistent green is then summarized using simple linear regression, and the slope of the fitted line is captured in this product. The original units are percentage points per year. Values are later truncated and scaled.

This dataset contains spatial layers describing Forest Canopy Loss and Recovery from 1998-2019 in NSW Regional Forest Agreements (RFA) Areas along the eastern coast. <br> These have been based off the National Greenhouse Gas Inventory (NGGI) National Carbon Accounting System (NCAS) National Forest and Sparse Woody Vegetation Data grids (ABARES, 2020). These base grids are Landsat in origin and have a resolution of 25m. <br> For this dataset product and the processing of metrics, aspects of canopy loss and disturbances in the forest estate were investigated. Measures of canopy loss and recovery are seen as one of the multiple indicators of forest health. This is related to agents or pressures that affect the capacity of native forests and commercial operations to maintain normal ecosystem functions and sustainably provide productive capacity. <br> To attribute disturbances, as a driver of change, a Multiple Lines of Evidence (MLE) approach was used that leveraged available spatial datasets. This allowed for a project-wide disturbance and disturbance context layer to be generated. This information can be interpreted back against forest cover extent change outputs, in particular the differences between individual years, to identify the areas of change and the likely reasons why. Therefore, landscape trends in forest loss can be potentially assigned or at the very least investigated. <br> The time taken, in terms of years, for areas to recover from losses in forest canopy cover extent can has also been determined. This process identifies the time taken for a patch of forest to return to a 20% canopy cover threshold, and other characteristics such as the forest type and likely disturbance or loss event. <br> Forest Canopy Loss and Recovery uses measures of canopy loss and disturbances which can be interpreted back against forest cover extent change outputs, in particular the differences between individual years, to identify the areas of change and the likely reasons why. Therefore, landscape trends in forest canopy loss can be potentially assigned or at the very least investigated. Time taken in years for areas to recover for losses has also been determined, as-well as other characteristics such as forest type and likely disturbance/loss event. <br> Base cover extent grids used are from the NSW RFA Historic Forest Canopy Cover Extent – 1995 to 2019 product. Read more about the project on the Natural Resources Commission website:<br> https://www.nrc.nsw.gov.au/fmip-baselines-ecosystem-health-projectfe1<br> This dataset is superseded by 'NSW Forest Monitoring and Improvement Program State-Wide Historic Forest Canopy Loss and Recovery - 1998 to 2020'

<p>Digital Hemispherical Photography (DHP) upward-looking images were collected annually to capture vegetation and crown cover at Daintree Rainforest SuperSite. These images are used to estimate Leaf Area Index (LAI). </p><p> The site is located at the Daintree Rainforest Observatory in Lowland Complex Mesophyll Vine Forest near Cape Tribulation. Flux monitoring was established in 2001 with additional monitoring capabilities added over time. The site has more than 80 species including canopy trees belonging to the <em>Arecaceae, Euphorbiaceae, Rutaceae, Meliaceae, Myristicaceae and Icacinaceae</em> families. For additional site information, see https://www.tern.org.au/tern-observatory/tern-ecosystem-processes/daintree-rainforest-supersite/. </p><p> Other images collected at the site include photopoints, phenocam time-lapse images taken from fixed under and overstorey cameras and ancilliary images of fauna and flora. </p>