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113 results for “Forestry”
Supplementary material 1 from: Motloung R, Robertson M, Rouget M, Wilson J (2014) Forestry trial data can be used to evaluate climate-based species distribution models in predicting tree invasions. NeoBiota 20: 31-48. https://doi.org/10.3897/neobiota.20.5778
Current and potential distributions of sixteen species that are not widespread in southern Africa arranged on the basis of their suitable range size : a) Acacia paradoxa, b) A. cultriformis, c) A. falciformis, d) A. pendula, e) A. rubida, f) A. stricta, g) A. retinodes, h) A. fimbriata, i) A. aneura, j) A. viscidula, k) A. acuminata, l) A. adunca, m) A. binervata, n) A. schinoides, o) A. prominens, p) A. mangium. The grey shading indicates areas that SDMs have identified as suitable by SDMs while the white ones are unsuitable.
Dataset: iShares Global Timber & Forestry ETF (WOOD) Stock Performance
This dataset provides historical stock market performance data for specific companies. It enables users to analyze and understand the past trends and fluctuations in stock prices over time. This information can be utilized for various purposes such as investment analysis, financial research, and market trend forecasting.
Fig. 2 in Coniferous Forest Annual Growth Under Impact Of Beaver-Made Inundations In Dobele Forestry, Latvia
Fig. 2. Radial increment average values of sample Fig. 3. Average trend line of radial increment pine stands. average values of sample pine stands.
Fig. 4 in Coniferous Forest Annual Growth Under Impact Of Beaver-Made Inundations In Dobele Forestry, Latvia
Fig. 4. Radial increment average values of Fig. 5. Average trend line of radial increment sample spruce stands. average values of sample spruce stands.
Agriculture - Forestry 2
<p>Amani Nature Reserve is located 55 km west of the coastal city of Tanga by road and 25 km from Muheza town. It is in Muheza and Korogwe districts in the mountain block of East Usambara between latitudes 5º 04’ 30" – 5º 14’ 10" S and longitudes 38º 30’ 34" - 38º 40’ 06" E with an area of 8,380ha. On the average the altitude of ANR ranges from 300 – 1128 m a.s.l (URT, 2010). It was formed in 1997 by combining six forest reserves i.e. Kwamkoro, Kwamsambia, Mnyuzi scarp, Amani Zigi, Amani East, Amani West and the 1,065 ha of submontane forest that was donated by the East Usambara Tea Company under a Forest Dedication Covenant. Amani is the wettest of all the East Usambara forest blocks,with at least 100 mm rain falling in most months. Monthly rainfall peaks in excess of 300 mm in April-May and reaches about 150 mm in October-December. Mean annual humidity is 87 % in the morning and 77 % at midday. Mean annual temperature at Amani Conservation Centre (900 m) is 21o C, with mean daily minimum and maximum temperature of 16.3o C and 24.9o C, respectively. The hottest season is January-February and the coolest is July-September. The two main forest types are semi-deciduous forests in the lowlands, particularly Mnyuzi Scarp with its lower rainfall, and tall luxuriant submontane evergreen forests in the mountains above 750 m, where rainfall is higher and the largest trees reach 65 m in height. Other biotopes include dry bushland (2%), grassland, barren rocky area and water bodies (all &lt; 1%). Dense submontane forest covers about half and dense lowland forest about one third of the Nature Reserve. Amani Botanical Gardens (340 ha) lie within the periphery of the Nature Reserve which was established in 1902, and it has over 1,000 species from around the world. This has contributed to some 6 % (520 ha) of former submontane forests having been invaded by exotic species, such as Maesopsis eminii, Cedrella odorata and palm trees. Common tree species include Cephalosphaera usambarensis, Allanblackia stuhlmannii, Albizia gummifera, Beilschmiedia kweo, Diospyros abyssinica, Englerodendron usambarense and Drypetes gerrardii. Epiphytic lichens and bryophytes are abundant especially in steep summits. According to census conducted in 2012, the population of Muheza and Korogwe districts are 202,038 and 492,441 respectivelly that make a total of 694479. The main economic activity of the people sorounding the reserve is agriculture (URT, 2013). Lyimo P, Munishi P, Bugingo A, Masaka K, Emily C, Paul C, Mtalikwa S (2018). Endemic plant species of Amani Nature Reserve in Eastern Arc Mountains of Tanzania. Version 1.1. Sokoine University of Agriculture, Department of Ecosystems and Conservation. Occurrence dataset <a href="https://doi.org/10.15468/fj6180">https://doi.org/10.15468/fj6180</a> accessed via GBIF.org</p>
Agriculture - Forestry
<p>The Eastern Arc comprises the ancient crystalline mountains that run from the Taita Hills in Kenya to the Makambako Gap just to the south-west of the Udzungwa Mountains, Tanzania and that are under the direct climatic influence of the Indian Ocean (Lovett, 1990). These mountains support forest and some grassland and heathland vegetation, although large areas have been converted from natural vegetation to agriculture and plantations. There is a chain of somewhat similar montane forest areas extending from southern Tanzania to Malawi, Mozambique and Zimbabwe. However, these more southern forests are not so directly under the Indian Ocean climatic regime, are subject to more variable convectional rainfall patterns, and the forests do not possess the species richness and endemism of the Eastern Arc. Together, the Eastern Arc and these other montane forest areas comprise the Tanganyika-Nyasa Mountain Forest Group. Lyimo P, Munishi P (2018). Occurrence data of tree species for crystalline Mountain Forest of Tanzania.. Version 1.4. Sokoine University of Agriculture, Department of Ecosystems and Conservation. Occurrence dataset <a href="https://doi.org/10.15468/tjq4xs">https://doi.org/10.15468/tjq4xs</a> accessed via GBIF.org</p>
DeDuCE: Deforestation and carbon emissions due to agriculture and forestry activities from 2001-2022
<h2>Overview</h2> <p>This dataset provides country-level estimates of agriculture and forestry-driven deforestation and associated carbon emissions for the period 2001-2022. A sub-national level attribution dataset is available for Brazil. Generated by the Deforestation Driver and Carbon Emission (DeDuCE) model, it amalgamates remotely sensed datasets with extensive agricultural statistics to estimate deforestation attributable to agricultural and forestry activities globally. Developed utilizing Google Earth Engine and Python, DeDuCE comprehensively covers over 9300 unique country-commodity footprints across <strong>179 countries and 184 commodities</strong> within the specified period, presenting an unmatched scope and granularity of data.</p> <h2>Documentation</h2> <p>The manuscript detailing the dataset is currently archived at EarthArXiV: <strong><em>Singh, C., & Persson, U. M. (2024). Global patterns of commodity-driven deforestation and associated carbon emissions</em></strong>. <a href="https://doi.org/10.31223/X5T69B" target="_blank" rel="noopener">https://doi.org/10.31223/X5T69B</a></p> <p>The insights from this dataset can also be viewed at: <strong><a href="https://www.deforestationfootprint.earth" target="_blank" rel="noopener">https://www.deforestationfootprint.earth</a></strong></p> <h2>Repository contents</h2> <p>The input and output/data generated by the model are archived here at <strong>Zenodo, </strong>and their description is available in <strong>'README (files in the directory).txt'</strong>.</p> <p>The columns of the (final) dataset '<em>DeDuCE_Deforestation_attribution_v1.0.1 (2001-2022).xlsx</em>' in the folder <em><strong>'Final Attribution Results'</strong></em> represent the following:</p> <ul> <li><strong>Continent/Country group: </strong>All countries are divided into 8 geographical regions</li> <li><strong>ISO: </strong>Three-letter country codes defined by ISO</li> <li><strong>Producer country: </strong>Country of deforestation</li> <li><strong>Year: </strong>Year of deforestation, ranges from 2001-2022 </li> <li><strong>Commodity group: </strong>All commodities are divided into 11 commodity groups</li> <li><strong>Commodity: </strong>Name of commodity aligning with FAOSTAT</li> <li><strong>Deforestation attribution, unamortized (ha): </strong>Annual deforestation estimates</li> <li><strong>Deforestation risk, amortized (ha): </strong>5-year amortised deforestation estimates</li> <li><strong>Deforestation emissions excl. peat drainage, unamortized (MtCO2): </strong>Annual estimates of carbon emissions (based on AGB, BGB, deadwood, litter, soil organic carbon and carbon stock of replacing commodity)</li> <li><strong>Deforestation emissions excl. peat drainage, amortized (MtCO2): </strong>5-year amortised carbon emission estimates, excluding carbon emissions from peatland drainage<strong> </strong></li> <li><strong>Peatland drainage emissions (MtCO2): </strong>Annual estimates of carbon emissions from peatland drainage<strong> </strong></li> <li><strong>Deforestation emissions incl. peat drainage, amortized (MtCO2): </strong>5-year amortised carbon emission estimates, including emissions from peatland drainage</li> <li><strong>Quality Index: </strong>Flagging deforestation estimates </li> </ul> <h2>Contact</h2> <p>If you have any questions, you can contact us at: </p> <p> Chandrakant Singh and U. Martin Persson <br> <em><strong><a href="mailto:chandrakant.singh@chalmers.se;martin.persson@chalmers.se">Email</a></strong>: chandrakant.singh@chalmers.se and martin.persson@chalmers.se </em><br> Physical Resource Theory, Department of Space, Earth & Environment, <br> Chalmers University of Technology, Gothenburg, Sweden</p>
Linked collectors and determiners for: Plant Specimen Database of Tama Forest Science Garden, Forestry and Forest Products Research Institute, Japan.
Natural history specimen data linked to collectors and determiners held within, "Plant Specimen Database of Tama Forest Science Garden, Forestry and Forest Products Research Institute, Japan". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/38e8b720-9074-4471-a016-73cae18a6c1c">https://bionomia.net/dataset/38e8b720-9074-4471-a016-73cae18a6c1c</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/38e8b720-9074-4471-a016-73cae18a6c1c">https://gbif.org/dataset/38e8b720-9074-4471-a016-73cae18a6c1c</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Herbarium of Taiwan Forestry Research Institute.
Natural history specimen data linked to collectors and determiners held within, "Herbarium of Taiwan Forestry Research Institute". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/eb7681c5-5c9c-4e28-954f-f328991c7004">https://bionomia.net/dataset/eb7681c5-5c9c-4e28-954f-f328991c7004</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/eb7681c5-5c9c-4e28-954f-f328991c7004">https://gbif.org/dataset/eb7681c5-5c9c-4e28-954f-f328991c7004</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Northern Forestry Centre Arthropod Collection, Edmonton.
Natural history specimen data linked to collectors and determiners held within, "Northern Forestry Centre Arthropod Collection, Edmonton". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/84676332-f762-11e1-a439-00145eb45e9a">https://bionomia.net/dataset/84676332-f762-11e1-a439-00145eb45e9a</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/84676332-f762-11e1-a439-00145eb45e9a">https://gbif.org/dataset/84676332-f762-11e1-a439-00145eb45e9a</a>. Formatted as a Frictionless Data package.
Linked collectors and determiners for: Diversity and composition of preserved angiosperm specimens at Tanzania Forestry Research Institute (TAFORI) Herbarium.
Natural history specimen data linked to collectors and determiners held within, "Diversity and composition of preserved angiosperm specimens at Tanzania Forestry Research Institute (TAFORI) Herbarium". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/e5dfbff2-8ce1-4393-8c51-cc9c678ec8be">https://bionomia.net/dataset/e5dfbff2-8ce1-4393-8c51-cc9c678ec8be</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/e5dfbff2-8ce1-4393-8c51-cc9c678ec8be">https://gbif.org/dataset/e5dfbff2-8ce1-4393-8c51-cc9c678ec8be</a>. Formatted as a Frictionless Data package.
MAgPIE v4.3.x model run outputs including dynamic forestry sector
<p>Archive of runs produced for the forestry paper using MAgPIE 4.3.1+</p> <p><a href="https://github.com/magpiemodel/magpie">Model Code</a></p> <p><a href="https://rse.pik-potsdam.de/doc/magpie/4.3/index.htm">Model documentation</a></p> <p><a href="https://github.com/magpiemodel/tutorials">Model tutorials</a></p>
Finnish cooperatives active in the forestry and real estate sector
<p>58 cooperatives incorporated in Finland (osuuskunta / osk) active in the forestry or real estate business, with name and business ID (y-tunnus).</p> <p>The companies have been identified by searching all 200+ cooperatives in the agriculture or real estate field as listed on OpenCorporates and manually identifying those most likely to do business relevant for climate change and climate sink preservation (industry codes 02100, 02400, 68201, 68202, 68209 in Finland TOL 2008). Some extra notes were added manually, including an URL with more information.</p> <p>This is a first version of the dataset. Future versions may include more companies and more extracts from the trade register (PRH).</p>
Dataset from: Ultraviolet refractive index values of organic aerosol extracted from deciduous forestry, urban and marine environments
<p>The refractive index values of atmospheric aerosols are required to address the large uncertainties in the magnitude of atmospheric radiative forcing and measurements of the refractive index dispersion with wavelength of particulate matter sampled from the atmosphere are rare over ultraviolet wavelengths. An ultraviolet-optimized spectroscopic system illuminates optically-trapped single particles from a range of tropospheric environments to determine the particle’s optical properties. Aerosol from remote marine, polluted urban, and forestry environments is collected on quartz filters, and the organic fraction is extracted and nebulized to form micron-sized spherical particles. The radius and the real component of refractive index dispersion with wavelength of the optically trapped particles are determined to a precision of 0.001 µm and 0.002 respectively over a near-ultraviolet-visible wavelength range of 0.320–0.480 µm. Remote marine aerosol is observed to have the lowest refractive index (n=1.442 (λ=0.350 µm)), with above-canopy rural forestry aerosol (n=1.462–1.481 (λ=0.350 µm)) and polluted urban aerosol (n=1.444–1.485 (λ=0.350 µm)) showing similar refractive index dispersions with wavelength. In-canopy rural forestry aerosol is observed to have the highest refractive index value (n=1.508 (λ=0.350 µm)). The study presents the first single particle measurements of the dispersion of refractive index with wavelength of atmospheric aerosol samples below wavelengths of 0.350 µm. The Cauchy dispersion equation, commonly used to describe the visible refractive index variation of aerosol particles, is demonstrated to extend to ultraviolet wavelengths below 0.350 µm for the urban, forestry, and atmospheric aerosol water-insoluble extracts from these environments. A 1D radiative-transfer calculation of the difference in top-of-the-atmosphere albedo between atmospheric core-shell mineral aerosol with and without films of this material demonstrates the importance of organic films forming on mineral aerosol.</p> <p>The raw experimental spectra collected and analysed in this study are provided, as well as files for the calibrated wavelengths.</p>
Documenting twenty years of the contracted labor-intensive forestry workforce on National Forest System lands in the United States
Open the record for dataset details and reuse information.
Data for: Specialist carabids in mixed montane forests are positively associated with biodiversity-oriented forestry and abundance of roe deer
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Replication data for impact evaluation of two large-scale forestry incentive programs in Guatemala
Open the record for dataset details and reuse information.
Small mammals surveys, 1981 - 1996, Adirondack Long-Term Ecological Monitoring Program Project No. 10 by Adirondack Ecological Center of the State University of New York College of Environmental Science and Forestry, Newcomb, New York. Environmental Data Initiative
Small mammals are important in forested ecosystems: they are key predators on seeds and invertebrates, provide food for larger predators and act as disease vectors. The objective of this study was to document small mammal abundance and population changes in managed and unmanaged forests of Huntington Wildlife Forest (HWF). Seven sites were sampled from 1981-1996. Fifty traps per site (250 total) were deployed for 4 nights and checked in the mornings. All captured small mammals were identified, sexed, weighed, and measured for reproductive condition, tagged, and brought into the lab for processing. Females with embryos or placental scars were noted in the lab. Over a five-year period, 671 deer mice; 261 woodland jumping mice, 594 southern redbacked voles, 248 short-tailed shrews, 373 masked shrews, 75 smoky shrews and small numbers of other species were captured and sexed/aged. According to Prachar and Sage (1988), weights of deer mice, redbacked voles, woodland jumping mice, short-tailed shrews, masked shrews and smoky shrews differed among years and age classes for 1983-1987. Weights differed between sexes for mice and voles but not shrews. Placental scar/embryo counts of mice and voles did not differ among years, habitats, mammal age classes or sexes. Reproductive rates of shrews exhibited patterns of fluctuation from 1983-1987.
Songbird surveys , 1952 - 1964, 1983 - 2008 Adirondack Long-Term Ecological Monitoring Program Project No. 2 Breeding Birds by Adirondack Ecological Center of the State University of New York College of Environmental Science and Forestry, Newcomb, New York. Environmental Data Initiative.
Study objectives were to (1) Document long-term trends in relative abundance and diversity of breeding forest birds (songbirds) in forest stands with different harvest histories and (2) Identify bird species that can be used as indicators of habitat change or degradation. Declines in neotropical migrants have been linked to changes in habitat quantity and quality across species' range. Songbirds that nest and forage in different habitat types or at different heights in the forest canopy may not be affected equally by forest change or management. We detected breeding songbirds using point-counts at Huntington Wildlife Forest (HWF) in the central Adirondack Mountains of New York during 1983-2000 and modeled on an original songbird point count dataset from Webb et al. (1977). Relative abundance (RA, the number of individual birds/count) was measured in sites with differing management histories, from an unmanaged >300-year-old stand to a stand cut with the shelterwood/overstory removal method just prior to sampling in 1983). Over eighty bird species were detected during the study duration. Songbird ecology and habitat characteristics can be used to understand long-term changes in relative abundance as related to forest change.
Photographic documentation of forestry treatments of 77 forest plots at Sagehen Creek Field Station, 2016-2019
Data package contains sets of photographs taken at 77 forest monitoring plots within the Sagehen Experimental Forest. These plots are a subset of 500+ forest monitoring plots established in 2004 and 2005 for the purpose of testing strategically-placed land area treatments (SPLATS) that impede forest fire progression (Vaillant 2008, UC Berkeley Doctoral Dissertation). Sites were photographed at various intervals before and after prescribed forestry treatments. Two major types of treatment occurred and additional activity is ongoing. In Summer/Fall 2016, hand thinning or mastication was performed on a subset of plots, and in Summer/Fall 2018, logging was performed on another subset of plots. Some plots received one treatment, and others received none. Information about photo dates and treatment status can be found in the FMP details csv file. This monitoring is ongoing through the Sagehen Forest Project.
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.