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135 results for “forest land”
Rapid ant community re-assembly in a Neotropical forest: recovery dynamics and land-use legacy
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Nitrification and denitrification in the Community Land Model compared to observations at Hubbard Brook Forest
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FAOSTAT Forest Land Emissions
<p>The <a href="http://www.fao.org/faostat/en/#data/GF">Forest Land</a> dataset disseminates information on emissions and area change due to gain and losses of carbon stocks in living (aboveground and belowground) tree biomass. Estimates are computed following land Approach 1 and Tier 3 ( stock difference method) of the 2006 IPCC Guidelines for National GHG Inventories (IPCC, 2006). Data on carbon stocks and forest area used as input are those of the Forest Resources Assessment 2020 (FAO, 2020). Data are available by country, with global coverage and relative to the period 1990-2020.</p> <p>This domain contains data on net CO<sub>2</sub> emissions/removals, associated implied emission factors and underlying activity data.</p> <p>The FAOSTAT Emissions data are estimated by FAO and do not coincide with GHG data reported by Parties to UNFCCC. FAOSTAT data and estimates support member countries to assess and report their emissions and removals. FAOSTAT emissions data are a global knowledge product.</p>
Forest inventory, leaf area index, and leaf functional traits of various land cover classes in Kulen, Cambodia
<ol><li><strong>Sub-title 1: </strong>Forest inventory of evergreen forest, regrowth forest, and evergreen forest in Kulen, Cambodia. (<strong>File name: </strong><i>Forest_Inventory_Pub.txt)</i> <strong> </strong></li><li><strong>Sub-title 2: </strong>Species leaf area, chlorophyll a and b and leaf dry matter content of 30 species collected from evergreen forests, regrowth forests, and cashew plantation in Kulen, Cambodia. (<strong>File name:</strong> <i>Leaf_Trait_Species_Pub.txt)</i></li><li><strong>Sub-title 3: </strong>Canopy and total leaf area index from evergreen forests, regrowth forests, and cashew plantation in Kulen, Cambodia. (<strong>File name: </strong><i>LAI_Pub.txt </i>)</li></ol>
Existing land uses constrain climate change mitigation potential of forest restoration in India
<p>The datasets were developed as part of the publication "Existing land uses constrain climate change mitigation potential of forest restoration in India". Please refer to the manuscript for processing details.<br> <br> ForestBioclimaticEnvelop_ProjectionUTM is the bioclimatic envelope of forests developed. The data is in raster format (GeoTIFF 32bit Float) where pixel values = 1 represent the bioclimatic envelope of forests and remaning pixel values are NA. The spatial resolution is 60m in WGS 84 UTM 43N projection system.</p> <p>FinalOpportunity_AfterExclusions_ProjectionUTM is the feasible area of opportunity, after all exclusions of land uses and covers that cannot naturally regenerate to forests. The data is in raster format (GeoTIFF 32bit Float) where pixel values = 1 represent the bioclimatic envelope of forests and remaning pixel values are NA. The spatial resolution is 60m in WGS 84 UTM 43N projection system.</p>
Data from: Patterns and drivers of recent land cover change on two trailing-edge forest landscapes
<p>Climate change is altering the distribution of woody plants by influencing demographic processes and modifying disturbance regimes. Trailing-edge forests may be particularly vulnerable to these effects because they exist at warm, dry margins of tree distributions. To better understand recent climate-driven changes in trailing-edge forests, we used Landsat time series and 1,558 field reference plots to develop annual land cover maps from 1985 to 2020 in two large, biodiverse landscapes in central Arizona, USA. We then combined annual land cover maps with tree ring records and spatial data describing interannual climate, terrain, bark beetle (Curculionidae: Scolytinae) activity, wildfire, and harvest to quantify drivers of forest change. Throughout the two landscapes, forest extent declined by 0.3% and 0.8% from 1985 to 2020. However, considerable variation occurred within the study period, with abrupt (ca. 1–2 years) declines in forest extent followed by gradual (ca. 10 years) recovery on each landscape. Pinyon-juniper (<em>Pinus</em> <em>edulis</em>, <em>Pinus</em> <em>monophylla</em>, and/or <em>Juniperus</em> spp.) cover increased from 1985 to ca. 2000 but declined after 2000, a period of extreme drought and regional tree die-off. In contrast, pine-oak (<em>Pinus</em> <em>ponderosa</em> and <em>Quercus</em> spp.) cover increased from 2000 to 2020, primarily due to declines in ponderosa pine and mixed conifer cover over the same period. Wildfire was a key driver of transitions from forest to non-forest cover in our study area, with the occurrence of multiple compounded drought years playing an important role in unburned areas. By driving transitions to alternative forest types or non-forest cover, disturbance and drought will increasingly shape forest dynamics and ecosystem transformations throughout the southwestern US.</p>
Forest quality and land use intensity indicators
<b>Description: </b><p>Trends in Biophysical Vegetation Traits of Tropical Forests under Logging and Fragmentation</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/81"><b>Trends in Biophysical Vegetation Traits of Tropical Forests under Logging and Fragmentation</b></a></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=22">here</a></p><p><b>Data worksheets: </b>There are 4 data worksheets in this dataset:</p><ol><li><p><b>Canopy-based forest quality metrics</b> (Worksheet Canopy)</p><p>Dimensions: 213 rows by 6 columns</p><p>Description: Fractional canopy cover and leaf area index</p><p>Fields: </p><ul><li><b>Plot</b>: SAFE project plot number (Field type: Location)</li><li><b>Date</b>: Date images were collected (Field type: Date)</li><li><b>LAI</b>: Leaf area index, corrected for clumping of leaves at plot level (25 m x 25 m) (Field type: Numeric)</li><li><b>fcover</b>: Fractional canopy cover (mean) (Field type: Numeric)</li><li><b>sdfcov</b>: Fractional canopy cover (standard deviation) (Field type: Numeric)</li></ul><br></li><li><p><b>Above-ground biomass</b> (Worksheet AGB)</p><p>Dimensions: 203 rows by 11 columns</p><p>Description: Was derived from DBH and Height of trees for individuals >= 10 cm DBH using five different algorithms on the raw data. We additionally binned heights of trees to account for uncertainties in tree height measurements and used multiple published equations combined with wood density estimates drawn from a distribution of wood density values that differs for unogged, logged and severely logged forest stands. We used oil palm specific equations for biomass estimations in oil palm plots. They will be identical estimates across the five algorithms used. Oil palms have a fundamentally different physical structure to forest trees, so we estimated AGB in oil palm plantations separately using the equation 〖AGB〗_palm= (0.3747*height*100+3.6334)/1000 (Thenkabail et al. 2004). See Pfeifer M, Lefebvre V, Turner E, Cusack J, Khoo M, Chey VK, Peni M, Ewers RMet al. 2015, Deadwood biomass: an underestimated carbon stock in degraded tropical forests?, ENVIRONMENTAL RESEARCH LETTERS, Vol: 10:044019. </p><p>Fields: </p><ul><li><b>Plot</b>: SAFE project plot number (Field type: Location)</li><li><b>Date</b>: Date field plot data were collected (Field type: Date)</li><li><b>AGB_Saner</b>: AGB estimates developed for mixed-species forest stands in East Kalimantan, Indonesia (Field type: Numeric)</li><li><b>AGB_Chave_wet</b>: AGB estimates developed for wet forest (Field type: Numeric)</li><li><b>AGB_Chave_moist</b>: AGB estimates developed for moist forest (Field type: Numeric)</li><li><b>AGB_K09</b>: AGB estimates developed for logged over old growth forest in Malaysian Sabah (Field type: Numeric)</li><li><b>AGB_N10</b>: AGB estimate developed for old growth forest in Malaysia for forests 110 km south-east of Kuala Lumpur (Field type: Numeric)</li><li><b>AGB_Chave14</b>: AGB estimates developed for pantropical forest assuming a wood density of 0.64 (Field type: Numeric)</li><li><b>AGB_Chave14_simulWD</b>: AGB estimates developed for pantropical forest and reflecting disturbance-induced changes to wood density (Field type: Numeric)</li><li><b>AGB_Chave14_Bin5_simulWD</b>: AGB estimates developed for pantropical forest and reflecting disturbance-induced changes to wood density (Field type: Numeric)</li></ul><br></li><li><p><b>SAFE project forest quality scores</b> (Worksheet Quality)</p><p>Dimensions: 203 rows by 4 columns</p><p>Description: Visual assessment of forest disturbance</p><p>Fields: </p><ul><li><b>Plot</b>: SAFE project plot number (Field type: Location)</li><li><b>Date</b>: Date of assessment (Field type: Date)</li><li><b>ForestQuality</b>: SAFE Project forest quality scores (Field type: Ordered Categorical)</li></ul><br></li><li><p><b>Caneye software analyses carried out using Caneye v6.3.8 in August/September 2013</b> (Worksheet LAI_Caneye)</p><p>Dimensions: 237 rows by 16 columns</p><p>Description: LAI, fcover and fAPAR estimates derived from hemispherical images or using Sunscan Delta T device (Cambridge) if applicable</p><p>Fields: </p><ul><li><b>Plotname</b>: SAFE project plot number (Field type: Location)</li><li><b>Date</b>: Date on which photographs were taken (Field type: Date)</li><li><b>HemiUp</b>: Number of sample points = number of pictures taken - upward looking fisheye pictures (Field type: Numeric)</li><li><b>LAI_eff_v6</b>: LAI effective estimated following algorithm of Caneye version 6 (v6.3.8) (Field type: Numeric)</li><li><b>LAI_true_v6</b>: LAI true (accounting for vegetation clumping) estimated following algorithm of Caneye version 6 (v6.3.8) (Field type: Numeric)</li><li><b>LAI_eff_v5</b>: LAI effective estimated according to Caneye version 5 (Field type: Numeric)</li><li><b>LAI_true_v5</b>: LAI true (accounting for vegetation clumping) estimated according to Caneye version 5 (Field type: Numeric)</li><li><b>ALAeffv5</b>: Effective average leaf inclination angle following alogorith used in Caneye v5 (Field type: Numeric)</li><li><b>ALAtruev5</b>: True average leaf inclination angle following alogorith used in Caneye v5 (Field type: Numeric)</li><li><b>Fap_meas_Dir</b>: Black - sky direct fAPAR (fraction of absorbed photosynthetically active radiation) measured (Field type: Numeric)</li><li><b>Fap_mod_Dir</b>: Black - sky direct fAPAR (fraction of absorbed photosynthetically active radiation) modelled (Field type: Numeric)</li><li><b>Fap_meas_Dif</b>: White - sky diffuse fAPAR (fraction of absorbed photosynthetically active radiation) measured (Field type: Numeric)</li><li><b>Fap_mod_Dif</b>: White - sky diffuse fAPAR (fraction of absorbed photosynthetically active radiation) modelled (Field type: Numeric)</li><li><b>fcover</b>: Fractional canopy cover (mean). Cover fraction (fcover) is defined as the fraction of the soil covered by the vegetation viewed in the nadir direction. Using hemispherical images, the cover fraction must be integrated over a range of zenith angles (0-10 degrees) (Field type: Numeric)</li><li><b>sdfcov</b>: Fractional canopy cover (standard deviation). Cover fraction (fcover) is defined as the fraction of the soil covered by the vegetation viewed in the nadir direction. Using hemispherical images, the cover fraction must be integrated over a range of zenith angles (0-10 degrees) (Field type: Numeric)</li></ul><br></li></ol><p><b>Date range: </b>2010-07-01 to 2014-01-10</p><p><b>Latitudinal extent: </b>4.4245 to 4.7714</p><p><b>Longitudinal extent: </b>116.9477 to 117.7028</p>
Quantifying the spatial heterogeneity of forest conversion costs and how it relates to biodiversity, conservation and land use history
<b>Description: </b><p>Start and end dates of salvage logging activity at the SAFE Project experimental site</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/6"><b>Quantifying the spatial heterogeneity of forest conversion costs and how it relates to biodiversity, conservation and land use history</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>Sime Darby (grant)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>Permits: </b>These data were collected under permit from the following authorities:</p><ul><li>Sabah Biodiversity Council (Research licence na)</li></ul><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=3266827">here</a></p><p><b>Files: </b>This dataset consists of 2 files: template_Symes.xlsx, SAFE_COUPE.zip</p><p><b>template_Symes.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>Salvage logging records</b> (described in worksheet Data)</p><p>Description: Dates of earliest and latest known salvage logging activity in logging coupes</p><p>Number of fields: 10</p><p>Number of data rows: 187</p><p>Fields: </p><ul><li><b>CoupeNumber</b>: Coupe number (Field type: Location)</li><li><b>StartDateTrack</b>: Earliest date of logging activity recorded through GPS loggers on bulldozers (Field type: Date)</li><li><b>EndDateTrack</b>: Last date of logging activity recorded through GPS loggers on bulldozers (Field type: Date)</li><li><b>StartDateLocation</b>: Earliest date of logging activity recorded through 'Location' method (Field type: Date)</li><li><b>EndDateLocation</b>: Last date of logging activity recorded through 'Location' method (Field type: Date)</li><li><b>StartDateMeasurement</b>: Earliest date of logging activity recorded through 'Measurement' method (Field type: Date)</li><li><b>EndDateMeasurement</b>: Last date of logging activity recorded through 'Measurement' method (Field type: Date)</li><li><b>Contractor</b>: Name of the contractor responsible for the coupe (Field type: ID)</li><li><b>GlobalStart</b>: Earliest date of any recorded salvage logging activity (Field type: Date)</li><li><b>GlobalEnd</b>: Last date of any recorded salvage logging activity (Field type: Date)</li></ul></li></ol><p><b>SAFE_COUPE.zip</b></p><p>Description: SAFE coupe data</p><p><b>Date range: </b>2013-01-02 to 2015-12-31</p><p><b>Latitudinal extent: </b>4.5000 to 5.0700</p><p><b>Longitudinal extent: </b>116.7500 to 117.8200</p>
Data from: Tropical forest soundscapes as testimonies of past land use
<p><span>Habitat loss is considered one of the factors that causes a decrease in biodiversity in the tropics. Many efforts have been made to protect and restore tropical forests, but it is difficult to quantify biodiversity and assess restoration areas. Studies have used soundscape analyses to gain information about the landscape, using acoustic indices as indicators of the health of faunal communities. We aimed to assess the changes in acoustic indices in habitats with different types of human exploitation and to evaluate the variation in acoustic indices during the hours of the day among these different habitats. The recordings were performed using passive acoustic monitoring (PAM), deriving 15 acoustic indices to assess the characteristics of each environment. The results suggest that in rubber plantations (RP) there was less acoustic activity, followed by rubber-forest plantations (RFP) and light selectively logged areas (LSL), while in habitats of young secondary forests (YSF), mature secondary forests (MSF) and intensive selectively logged forests (ISL) there was more acoustic activity, which indicates greater faunal activity.<span> </span>This study demonstrates that across the various indices tested, the plantation areas (RP and RFP) presented lower values, which indicate reduced acoustic activity compared to forested areas, with the exception of the area lightly selective logged (LSL) which showed lower values of the indices that measure the activity of sonoriferous specie. Therefore, assessing landscape use by monitoring the soundscape can be useful to timely evaluate the ecological dynamics of areas with high species richness, such as the Atlantic Forest.<span> </span></span></p>
Projected impacts of climate and land use changes on the habitat of Atlantic Forest plants in Brazil
<p>Aim:<b> </b>To provide novel evidence on the average impact of climate and land use changes on habitat suitability for tropical plants and to test previous conclusions on the relative importance of these two drivers in shaping future availability of habitat for tropical plant species.</p> <p>Location<b>: </b>Brazil's Atlantic Forest domain.</p> <p>Time period: Plant occurrences recorded between 1960 and 2014. Baseline climate from 1960-2000 and land use from 2015. Projected scenarios of climate for 2041-2060 and land use for 2050.</p> <p>Major taxa studied: Angiosperms.</p> <p>Results: Our results suggest that climate change alone will, surprisingly, have only a modest negative impact on the mean habitat suitability, decreasing it by 2% (median = -5% to -7%, variation associated with scenarios). Land use change alone had a more consistent negative impact on habitat suitability, causing mean and median reductions of 4% to 6%. When the effects of climate and land use are combined, the mean habitat suitability was reduced by 4% (median = -9% to -11%).</p> <p>Main conclusions: The combined impacts of climate and land use changes were substantial, although smaller than expected. Habitat suitability decreased for most species, but it increased substantially for some species, suggesting that the distribution of impacts across species is markedly right skewed. The impacts were typically detrimental to small-ranged species and neutral or beneficial to widespread species. Land use change rather than climate change will likely cause more losses to the habitat of Atlantic Forest plant species within the next several decades.</p>
Fijian sea krait behavior relates to fine‐scale environmental heterogeneity in old‐growth coastal forest: The importance of integrated land–sea management for protecting amphibious animals
<p><span>Here the data for "Fijian sea krait behaviour relates to fine-scale environmental heterogeneity in old growth forest: the importance of integrated land-sea management for protecting amphibious animals" by</span><span> Lowe, C., Keppel, G., Waqa, K., Peters, S., Fisher, R.N., Scanlon, A., Osborne-Naikatini, T, and Thomas-Moko, N </span><span> is provided. This article investigates the habitat of </span>Yellow Lipped Sea Kraits, <em>Laticauda</em> <em>colubrina</em>, in the terrestrial realm on Leluvia Island, a small, topographically flat atoll in Fiji with coastal forest. The investigation uses concurrent microclimate measurements and behaviour surveys, as well as vegetation surveys, and the data collected for these analyses are provided here. Microclimates were significantly related to canopy cover, leaf litter depth, and distance from the high-water mark (HWM). Sea kraits were almost exclusively observed in coastal forest within 30 m of the HWM. Sloughing of skins only occurred within crevices of mature or dying trees. Resting <em>L</em>. <em>colubrina</em> were significantly more likely to occur at locations with higher mean diurnal temperatures, lower leaf litter depths, and shorter distances from the HWM. On Leleuvia, behaviour of <em>L</em>. <em>colubrina</em> therefore relates to environmental heterogeneity created by old-growth coastal forests, particularly canopy cover and crevices in mature and dead tree trunks. The importance of healthy coastal habitats, both terrestrial and marine, for <em>L</em>. <em>colubrina</em> suggests it could be a good flagship species for advocating integrated land-sea management. Furthermore, our study highlights the importance of coastal forests and topographically flat atolls for biodiversity conservation. Effective conservation management of amphibious species that utilise land- and seascapes is therefore likely to require a holistic approach that incorporates connectivity among ecosystems and environmental heterogeneity at all relevant scales.</p>
APPENDIX 13 in Detangling the effects of patch attributes on bryophyte diversity in fragmented subtropical secondary forests - a case study of land-bridge islands
APPENDIX 13. — Islands with multi-long branched appearance in the Thousand Island Lake, China.
Land-use legacies affect flower visitation network structure after forest restoration
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Fijian sea krait behavior relates to fine‐scale environmental heterogeneity in old‐growth coastal forest: The importance of integrated land–sea management for protecting amphibious animals
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Data from: Land use type, forest cover, and forest edges modulate avian cross-habitat spillover
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Data from: Land-use legacies influence tree water-use efficiency and nitrogen dynamics in recently established European forests
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Land-use change erodes trophic redundancy in tropical forest streams: Evidence from amino acid stable isotope analysis
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Projected impacts of climate and land use changes on the habitat of Atlantic Forest plants in Brazil
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Data from: Avian phylogenetic and functional diversity are better conserved by land-sparing than land-sharing farming in lowland tropical forests
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Data from: Assessing the effects of land‑use intensity on small mammal community composition and genetic variation in Myodesglareolus and Microtus arvalis across grassland and forest habitats
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.