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>

This dataset is a collection of drone RGB and multispectral imagery from plots across Australia (AusPlots, SuperSites, Cal/Val sites to be established in the future). Standardised 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 is a collection of drone lidar data from plots across Australia (AusPlots, SuperSites, Cal/Val sites to be established in the future). The aim of these drone surveys is to capture vegetation structure. The standardised data collection and data processing protocols developed in 2022 are based on the DJI Matrice 300 (M300) RTK drone platform. Lidar sensor DJI Zenmuse L1 is used with DJI Matrice 300 (M300) RTK platform to capture RGB colourised 3D point clouds. The data is georeferenced using the onboard GNSS in M300 and the D-RTK 2 base station. DJI Terra software was used to generate 3D point clouds from the raw lidar data. The protocols include flight planning and data collection guidelines for a 100 x 100 m TERN plot, and the processing workflow used on DJI Terra. Mission-specific metadata for each plot is provided in the imagery/metadata folder (please refer to the imagery collection). The Drone Data Collection and Lidar Processing protocols can be found at <em> https://www.tern.org.au/field-survey-apps-and-protocols/ </em>.

<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>

The record contains information on the five-minute, point-count, bird survey data conducted in the Southern Forests Experimental Forest Landscape (SFEFL), Warra Site for the period between 2010 to 2011. Data such as age class of study plots, Landscape Disturbance Index (LDI), bird survey period, bird species identification details, observation distance, number of individuals, height and direction of observation at a minimum distance of 25 meters are provided.

<p> The dataset aims at studying associations between mating system parameters and fitness in natural populations of trees. Fifty-eight open-pollinated progeny arrays were collected from trees in three populations. Progeny were planted in a reciprocal transplant trial. Fitness was measured by family establishment rates. We genotyped all trees and their progeny at eight microsatellite loci. Planting site had a strong effect on fitness, but seed provenance and seed provenance × planting site did not. Populations had comparable mating system parameters and were generally outcrossed, experienced low biparental inbreeding and high levels of multiple paternity. As predicted, seed families that had more multiple paternities also had higher fitness, and no fitness-inbreeding correlations were detected. Demonstrating that fitness was most affected by multiple paternities rather than inbreeding, we provide evidence supporting the constrained inbreeding hypothesis; i.e. that multiple paternity may impact on fitness over and above that of inbreeding, particularly for preferentially outcrossing trees at life stages beyond seed development. This dataset could potentially be reused for meta-analysis or review of effects of habitat fragmentation on plants (e.g. pollination, mating system, genetic diversity etc). Please contact owner prior to re-use. </p> <p>This is part of the authors' PhD at the University of Adelaide, supervised by Prof Andrew Lowe, Dr Mike Gardner and Dr Kym Ottewell. Main goals of the project were 1. Examine and quantify the impact of fragmentation and tree density on mating patterns, and how this may vary with pollinators of differing mobility 2. Determine the theoretical expectations and perform empirical tests of mating pattern-fitness relationships in trees 3. Explore the plant genetic resource management implications that arise from the observations in aims 1 and 2 </p>

This is a data set on the prescence of Salmonella and the exposure of flavivirus in the Australian White Ibis. The data is presented in an excel file that lists, band numbers, sample dates, age, sex, bill lengths, presence of Salmonella in gut samples, and evidence of exposure to flavivirus for 72 birds sampled in the years 2002, 2003 and 2015 in Sydney, Australia. Detailed results listed in our open accessible manuscript published in the Journal of Urban Ecology in 2019. <em> https://academic.oup.com/jue/article/5/1/juz006/5506280</em>.