The Central Appalachian region, USA, contains several high elevation-endemic woodland salamanders (genus Plethodon), which are thought to be particularly vulnerable to climate change due to their restricted distributions and low vagility. In West Virginia, there is a strong management focus on protection and recovery of the federally threatened Cheat Mountain salamander (Plethodon nettingi; CMS). To support this focus, there is a need for improved understanding of CMS occurrence-habitat relationships and spatially explicit projections of fine-scale contemporary and potential future habitat quality to inform management actions. In addition, there is concern among resource managers that climate change may increase habitat quality at high elevations for CMS competitors, particularly the eastern red-backed salamander (Plethodon cinereus; RBS), potentially resulting in increased competition pressure for CMS. To address these knowledge gaps, we created ecological niche models for CMS and RBS using the Random Forest classification algorithm and used the estimated occurrence-habitat relationships to assess ecological niche overlap between the species and project fine-scale contemporary and potential future habitat availability and quality. We estimated that the ecological niches of CMS and RBS were 80.5% similar, and habitat projections indicated the species would exhibit opposite responses to climate change in our region. For CMS, we estimated that amount of high-quality habitat will be reduced by mid-century and potentially lost by end-of-century, but that moderate and low-quality habitat will persist. For RBS, we estimated that amount of high-quality habitat will increase through end-of-century, and that high elevations will become more suitable for the species, indicating that competition pressure for CMS is likely to increase. This study improves understanding of important habitat characteristics for CMS and RBS, and our spatially explicit projections can assist natural resource managers with habitat protection actions, species monitoring efforts, and climate change adaptation strategies.

The dataset includes three csv files: [1] effects of pre-inhabitation and viruses on the feeding behavior of <i>Rhopalosiphum padi</i> and <i>R. maidis</i> (min). [2] effects of pre-inhabitation and viruses on the fecundity of<i> R. padi</i> and <i>R. maidis</i> (total offspring in laboratory and field). [3] effect of pre-inhabitation and viruses on the host plant nutrient content (amino acids, total sterols, and simple sugars-mg/g). These data might be used by researchers studying positive interactions, effects of viruses on host plants and vectors, phytochemistry of the wheat plant, and feeding behavior of phloem-feeders.

<p>Digital Cover Photography (DCP) upward-looking images are collected three times per year to capture vegetation cover at Gingin Banksia Woodland SuperSite. These images can be used to estimate Leaf Area Index (LAI). </p> <p> The Gingin Banksia Woodland SuperSite was established in 2011 and is located in a natural woodland of high species diversity with an overstorey dominated by banksia species. </p><p> Other images collected at the site include digital hemispherical photography (DHP), photopoints, phenocam time-lapse images taken from fixed under and overstorey cameras, and ancillary images of fauna and flora. </p>

<p>Digital Hemispherical Photography (DHP) upward-looking images are collected three times per year to capture vegetation and crown cover at the Gingin Banksia Woodland SuperSite. These images are used to estimate Leaf area index (LAI). </p> <p> The Gingin Banksia Woodland SuperSite was established in 2011 and is located in a natural woodland of high species diversity with an overstorey dominated by Banksia species. For additional site information, see https://www.tern.org.au/tern-observatory/tern-ecosystem-processes/gingin-banksia-woodland-supersite/. </p><p> Other images collected at the site include digital cover photography (DCP), photopoints, phenocam time-lapse images taken from fixed under and overstorey cameras and ancillary images of fauna and flora. </p>

<p>This dataset lists the occurrence of fungi and their abundance identified at rangeland sites across Australia by the TERN Surveillance Monitoring team, using standardised AusPlots methodologies. </p> <p>Fungi occurrences (i.e. a sample of a fungi at a particular point and time) are methodically identified at each site as part of the AusPlots Point intercept method. Fungi occurrences data can be aggregated across the site to calculate relative abundance, fungi ground cover.</p>

<p>The record comprises data on rabbit warren surveys conducted within the TGB Osborn (Koonamore) Vegetation Reserve (KVR) from 1977 to 2014 as part of ongoing rabbit control efforts. The Reserve was divided into 400x400 m grid squares (with exceptions for row 5 and column 6), and warrens were identified based on the presence of at least four rabbit holes. In some years, the entire Reserve was surveyed; in others, only a subset was covered. All identified warrens or holes were fumigated and filled following detection.</p> <p>This is version 2.0 of the Koonamore Rabbit Warren Survey data release.</p>

This dataset contains RGB and multispectral imagery collected at TERN’s Calperum Mallee SuperSite during a field trial of an Uncrewed Aircraft System (UAS), conducted to assess the use of drone-based imagery for the TERN Drone project across existing and future TERN sites (AusPlots, SuperSites, and Cal/Val sites). Standardised TERN Ecosystem Surveillance Drone Data Collection and Data Processing protocols are used to collect drone imagery and to generate orthomosaics. The protocols developed in 2022 are based on the DJI Matrice 300 (M300) RTK drone platform. DJI Zenmuse P1 and MicaSense RedEdge-MX/Dual sensors are used with M300 to capture RGB and multispectral imagery simultaneously. The data is georeferenced using the DJI D-RTK2 base station and onboard GNSS RTK. In the processing workflow, the multispectral image positions (captured with navigation-grade accuracy) are interpolated using image timestamp and RGB image coordinates. Dense point clouds and the fine-resolution RGB smoothed surface were used to generate co-registered RGB (1 cm/pixel) and multispectral (5 cm/pixel) orthomosaics. Mission-specific metadata for each plot is provided in the imagery/metadata folder. The Drone Data Collection and RGB and Multispectral Imagery Processing protocols can be found at <em> https://www.tern.org.au/field-survey-apps-and-protocols/ </em>.

This dataset comprises a comprehensive collection of plant tissue samples representing vascular plants sampled from TERN Ecosystem Surveillance monitoring plots across Australia. Derived from the plant voucher specimens, these tissue samples are critical for accurate species identification and verification, supporting a complete inventory of vascular plant species at each plot. They also serve as reference material for DNA barcoding and stable isotope analyses. Collected following the standardised Ecosystem Surveillance methodology, the dataset includes over 76,000 archived samples housed at the TERN Australia Soil and Herbarium Collection, located at the University of Adelaide's Waite Campus.<br></br> Each record includes detailed metadata such as voucher barcode, site and visit information and sampling details. Plant tissue samples are accessible and available for loan upon request through the EcoPlots Samples portal via an Expression of Interest.

<p>Ground lidar, also known as Terrestrial Laser Scanning (TLS), is a ranging instrument that provides detailed 3D measurements directly related to the quantity and distribution of plant materials in the canopy. This dataset contains raw instrument data and ancillary data for numerous sites across northern and eastern Australia from 2012 onwards. Scans have been collected using two Riegl VZ400 waveform recording TLS instruments. One is co-owned and operated by the Remote Sensing Centre, Queensland Department of Environment and Science (DES) and the TERN Auscover Brisbane Node, University of Queensland. The second is owned and operated by Wageningen University, Netherlands.</p> <p>Data can be accessed from https://field.jrsrp.com/ by selecting the combinations Field, Ground Lidar. Raw data are accessible by selecting individual locations on the map and then clicking on the TLS scan directory link on the right hand site of the screen. </p>

This dataset comprises a comprehensive collection of plant voucher specimens representing vascular plants sampled from TERN Ecosystem Surveillance monitoring plots across Australia. These specimens are essential for accurate species identification and verification, providing a complete inventory of vascular plant species present at each plot. Collected following the standardised Ecosystem Surveillance methodology, the dataset includes over 57,000 accessioned vascular plant specimens housed at the TERN Australia Soil and Herbarium Collection, located at the University of Adelaide's Waite Campus.<br></br> Each record includes detailed metadata such as voucher barcode, site and visit information, sampling details, and digitised images where available. Plant voucher specimens are accessible and available for loan upon request through the EcoPlots Samples portal via an Expression of Interest.