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    Invertebrates dominate the animal world in terms of abundance, diversity and biomass and play critical roles in maintaining ecosystem function. Despite their obvious importance, disproportionate research attention remains focused on vertebrates, with knowledge and understanding of invertebrate ecology still lacking. Due to their inherent advantages, usage of camera traps in ecology has risen dramatically over the last three decades, especially for research on mammals. However, few studies have used cameras to reliably detect fauna such as invertebrates or used cameras to examine specific aspects of invertebrate ecology. Twenty-four Reconyx PC800 HyperfireTM cameras were deployed on 7th July 2016 at Main Camp and left until 12th October 2016 (98 days, or 2352 h of deployment) in the Simpson Desert, south-western Queensland, capturing 372 time-lapse images of Wolf spiders (Family Lycosidae). Images were tagged with camera location, position, angle, camera ID and presence of lycosids. Additionally, spotlight surveys were conducted in October 2016 every hour between dusk (19:30 h) and dawn (05:30 h) over three nights with a total of 352 lycosids observed. This data set was used to determine whether: 1) camera traps provide a viable method for detecting wolf spiders, 2) diel activity patterns of the spiders can be ascertained, and 3) patterns in spider activity vary with environmental conditions, specifically between burned and unburned habitats and the crests and bases of sand dunes. This data presents a useful example of the utility of cameras as a tool for determining the diel activity patterns and habitat use of larger arthropods such as wolf spiders. Please note: Camera trap images are not provided and only species occurrence records are included. Also, image files were renamed after collection, resulting in a number versus time conflict. However, dates and times of sightings provided are correct.

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    High quality passive infrared wildlife cameras were used to acquire information on faunal biodiversity at the Robson Creek site. Two camera traps were deployed at the site between 17-03-2018 and 25-07-2018. The first camera located in proximity to the acoustic sensor SM2/SM4 which is around 100m from the flux tower and at a height of 1.5 meter above ground, on a star picket. The second camera located for a short while near the tower (10 meter) and was attached on a bungy cord tied to a tree, at a height of 0.3 meter above ground.<br><br> The Robson Creek site lies on the Atherton Tablelands in the wet tropical rain forests of Australia at 680-740 m elevation. It is situated in Danbulla National Park within the Wet Tropics World Heritage Area. The Wet Tropics Bioregion of Australia is situated on the north-eastern coast of Queensland, between Cooktown to the north and Townsville to the south. Approximately 40% (7200 km2) of the region is covered by rain forest. Features of the region include very high plant and animal endemism, characteristics of both Gondwanan and Indo-Malaysian forests, and frequent cyclonic disturbance. The site includes core 1 ha plot (100 m x 100 m) which is located within the fetch of the flux tower and is the focal site of recurrent monitoring, and 25 ha vegetation survey plot. The vegetation survey plot has been set up for inclusion in the Smithsonian Tropical Research Institute’s Center for Tropical Forest Science – Forest Global Earth Observatory (CTFS-ForestGEO) global network of forest research plots. <br><br> For additional site information, see https://www.tern.org.au/tern-observatory/tern-ecosystem-processes/robson-creek-rainforest-supersite/ . <br /><br /> Other images collected at the site include time-lapse images taken from 3 phenocams (above canopy). <br /><br /> <iframe frameborder="0" src="https://www.youtube.com/embed/WW-cpPMhMz4" title="TERN Robson Creek SuperSite Wildlife 2017" style="height:248px;width:462px;"></iframe> <br />Camera trap results for the TERN FNQ Rainforest SuperSite - Robson Creek, Jan - Feb 2017.

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    The Daintree Rainforest SuperSite comprises two sites (a) the Daintree Rainforest Observatory at Cape Tribulation, comprising a long-term monitoring sites, canopy crane, and extensive researcher and teaching infrastructure and (b) research facilities at the Daintree Discovery Centre at Cow Bay, an award winning ecotourism interpretive centre featuring a canopy tower, aerial walkway and scientific monitoring. This dataset contains high quality passive infrared wildlife cameras were used to acquire information on faunal biodiversity at the Daintree Discovery Centre at Cow Bay. One camera trap was deployed adjacent to the 1 ha core plot between 22/01/2017 and 08/06/2017. The core plot is located within the fetch of the flux tower and is the focal site of recurrent monitoring. The camera (Reconyx HC600 HyperFire) was attached to a tree at 0.5 metre high. <br> The forest is classed as complex mesophyll vine forest (type 1a) and has an average canopy height of 25m. The dominant canopy trees belong to the Arecaceae, Euphorbiaceae, Rutaceae, Meliaceae, Myristicaceae and Icacinaceae families. It is continuous for several kilometres around the Cow Bay Tower except for an area 600m north-east of the flux tower, which is cleared agricultural land used for a cattle farm. For additional information on the Daintree Rainforest SuperSite, see https://www.tern.org.au/tern-observatory/tern-ecosystem-processes/daintree-rainforest-supersite/ <br /><br /> Other images collected at the site include digital cover photography, phenocam time-lapse images (3 above canopy, 1 under canopy), panoramic landscape and photopoints. <br /><br /> <iframe frameborder="0" src="https://www.youtube.com/embed/7VkIFIWfrkQ" title="Camera trap results for the Daintree Discovery Centre between January and April 2017" style="height:248px;width:462px;"></iframe> <br />Camera trap results for the Daintree Discovery Centre between January and April 2017.

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    High quality passive infrared wildlife cameras were used to acquire information on faunal biodiversity at the site. Two cameras were deployed from July to Dec 2018 and between March and May 2019. <br /><br /> 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/. <br /> Other images collected at the site include digital cover photography, phenocam time-lapse images taken from fixed under and overstorey cameras and ancillary images of flora.

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    1. Restoration of degraded landscapes has become increasingly important for the conservation of species and their habitats owing to habitat destruction and rapid environmental change. An increasing focus on restoration activities of old-fields as agricultural land abandonment has expanded in the developed world. Studies examining outcomes of ecological restoration predominantly focus on vegetation structure and plant diversity, and sometimes vertebrate fauna. Fewer studies have systematically investigated the effects of restoration efforts on soil chemical and biophysical condition or ground-dwelling invertebrates and there is a limited synthesis of these data. 2. This dataset comprised data for a global meta-analysis of published studies to assess the effects on soil properties and invertebrates of restoring land that was previously used for agriculture. Studies were included if the site had been either cropped or grazed, restoration was either active (planting) or passive (abandonment, fencing) and if adequate data on soil chemical or physical properties or invertebrate assemblages were reported for restored, control (cropped/grazed) or reference sites. 3. The dataset includes 42 studies, published between 1994 and 2019 that met the inclusion criteria, covering 16 countries across all continents. More studies assessed passive restoration approaches than active planting, and native species were more commonly planted than exotic species.

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    We selected nine study sites, each incorporating three vegetation states: (a) fallow cropland, representing the restoration starting point, (b) planted old field (actively restored site), and (c) reference York gum (E. loxophleba) woodland. Plant species richness and cover All annual and perennial plant species were recorded in spring 2017 within each plot and identified to genus and species level where possible. Nomenclatures follow the Western Australian Herbarium (2017). A point intercept method previously demonstrated to provide objective and repeatable measures of cover (Godínez-Alvarez, Herrick, Mattocks, Toledo & Van Zee 2009; Prober, Standish & Wiehl 2011) was used to quantify cover of individual plant species, total vegetation cover and substrate types (i.e., bare ground, litter cover, plant cover). Ground cover, individual species, and canopy cover intercepting at every 2 m along four parallel, evenly spaced 50 m transects across each plot were recorded using a vertically placed dowel (8 mm wide, 2 m tall), resulting in 100 intercepting points per plot. For planted old fields, transects were placed parallel to planting rows, with two centred on rows and two centred between rows. This approximately represented the relative abundance of planted rows and non-planted inter-rows. If a species was recorded in the plot but did not intercept the dowel on any transect it was assigned 0.5 points. This method provided a measure of relative abundance (percentage cover) of plant species across the plot. To calculate species richness and cover across different life history and growth forms, species were classified into the following groups: total, native trees, native shrubs, native non – planted shrubs, native grasses, native perennial forbs, native annual forbs, exotic grasses and exotic annual forbs using the Western Australian Herbarium (2017) classification. Woody debris and leaf litter surveys Leaf-litter dry mass was estimated by collecting leaf-litter from five randomly placed 25 cm x 25 cm quadrats along two 50 m transects across each plot. Litter was stored in paper bags for transportation and then oven dried for 36 hours at 60 °C. The dried litter was weighed to 3 decimal points. Cover of fine and coarse woody debris and litter depth was estimated at every meter along two 20 m transects for each plot. Woody debris was classified by diameter. Length, max and min diameter was measured for all logs with a diameter greater than 10 cm.

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    <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>

  • Ground layer vascular plant species identity and projective foliage cover (PFC) data were collected from four permanently marked 50x10 metre plots in north Queensland on a three monthly frequency for three years. Ten 0.5 square metre quadrats were used for sampling at each occasion at each site and the data pooled and averaged. Refer to Neldner, V.J., Kirkwood, A.B. and Collyer, B.S. (2004). Optimum time for sampling floristic diversity in tropical eucalypt woodlands of northern Queensland. The Rangeland Journal 26: 190-203 for more information. Note: Spreadsheet compiled in 2021 from original data collection records.