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217 results for “forest model”
Fig. 2 in Predictive distribution modelling for rufous-necked hornbill Aceros nipalensis (Hodgson, 1829) in the core area of the Western Forest Complex, Thailand
Fig. 2. Diagram showing the conceptual framework used to model the distribution of the rufous-necked hornbill in the Western Forest Complex (WEFCOM), Thailand.
Fig. 1 in Predictive distribution modelling for rufous-necked hornbill Aceros nipalensis (Hodgson, 1829) in the core area of the Western Forest Complex, Thailand
Fig. 1. Map of research locations showing the study areas where the point counts were conducted along14 line-transects, using a hand-held GPS to accurately measure the count locations or extent and position of evergreen forest where the RNH lives, in order to predict RNH distribution.
Fig. 3 in Predictive distribution modelling for rufous-necked hornbill Aceros nipalensis (Hodgson, 1829) in the core area of the Western Forest Complex, Thailand
Fig. 3. Presence-absence binary models for RNH distributions after introducing equal sensitivity-specific threshold values to the continuous MaxEnt model based on the smallest home range size of the male RNH #15, as derived from Tifong (2007), during: a, the breeding season; b, the non-breeding season; and c, showing the combined habitat classification.
A boreal forest model benchmarking dataset for North America: a case study with the Canadian Land Surface Scheme including Biogeochemical Cycles (CLASSIC)
<p>A boreal forest model benchmarking dataset for North America by harmonizing eddy covariance and supporting measurements from black spruce (Picea mariana)-dominated mature forest stands.</p> <p>Dataset glossary and users’ instructions are documented in ‘README.md’. </p>
Modeling the recent drought and thinning impacts on energy, water and carbon fluxes in a boreal forest
<p>This dataset includes the data used for model calibration and validation, as well as the simulation files with accepted runs, which are available for the readers to re-generate the results of this work. The *.bin files are the data for driving the model and for calibration and validation. They are specifically in the format for the CoupModel. Therefore, to check the data the CoupModel software needs to be installed. </p> <p>Additionally, we provide the software for CoupModel, which the readers could install on local computers to check the simulations. For detailed instructions on how to run CoupModel, please visit the CoupModel website www.coupmodel.com.</p>
Data for: Nitrogen deposition in forests: Statistical modeling of total deposition from throughfall loads
<p><strong>Introduction:</strong> Nitrogen (N) gradient studies in some cases use N deposition in throughfall as measure of N deposition to forests. For evaluating critical loads of N, however, information on total N deposition is required, i.e., the sum of estimates of dry, wet and occult deposition.</p> <p><strong>Methods: </strong>The present paper collects a number of studies in Europe where throughfall and total N deposition were compared in different forest types. From this dataset a function was derived which allows to estimate total N deposition from throughfall N deposition.</p> <p><strong>Results: </strong>At low throughfall N deposition values, the proportion of canopy uptake is high and thus the underestimation of total deposition by throughfall N needs to be corrected. At throughfall N deposition values >20 kg N ha<sup>-1</sup> yr<sup>-1</sup> canopy uptake is getting less important.</p> <p><strong>Conclusions: </strong>This work shows that throughfall clearly underestimates total deposition of nitrogen. With the present data set covering large parts of Europe it is possible to derive a critical load estimate from gradient studies using throughfall data.</p>
Point sample database with spatial holdbacks for global forest edge model training
<p>Global point dataset sampling 50 biophysical parameters to train the global forest carbon edge model at https://github.com/springinnovate/carbon_edge_model/releases/tag/1.2.0</p> <p>Field schema:</p> <p>accessibility_to_cities_2015_30sec_compressed (Integer64)<br> altitude_10sec_compressed (Integer64)<br> baccini_carbon_data_2014_compressed (Integer64)<br> bio_01_30sec_compressed (Real)<br> bio_02_30sec_compressed (Real)<br> bio_03_30sec_compressed (Real)<br> bio_04_30sec_compressed (Real)<br> bio_05_30sec_compressed (Real)<br> bio_06_30sec_compressed (Real)<br> bio_07_30sec_compressed (Real)<br> bio_08_30sec_compressed (Real)<br> bio_09_30sec_compressed (Real)<br> bio_10_30sec_compressed (Real)<br> bio_11_30sec_compressed (Real)<br> bio_12_30sec_compressed (Real)<br> bio_13_30sec_compressed (Real)<br> bio_14_30sec_compressed (Real)<br> bio_15_30sec_compressed (Real)<br> bio_16_30sec_compressed (Real)<br> bio_17_30sec_compressed (Real)<br> bio_18_30sec_compressed (Real)<br> bio_19_30sec_compressed (Real)<br> cec_0-5cm_mean_compressed (Integer64)<br> cec_5-15cm_mean_compressed (Integer64)<br> cfvo_0-5cm_mean_compressed (Integer64)<br> cfvo_5-15cm_mean_compressed (Integer64)<br> clay_0-5cm_mean_compressed (Integer64)<br> clay_5-15cm_mean_compressed (Integer64)<br> fc_stack_hansen_forest_cover2014_compressed (Integer64)<br> gf_0.4_masked_forest_ESACCI-LC-L4-LCCS-Map-300m-P1Y-2014-v2.0.7 (Real)<br> gf_1.45_masked_forest_ESACCI-LC-L4-LCCS-Map-300m-P1Y-2014-v2.0.7 (Real)<br> gf_5.0_fc_stack_hansen_forest_cover2014_compressed (Real)<br> gf_5.0_masked_forest_ESACCI-LC-L4-LCCS-Map-300m-P1Y-2014-v2.0.7 (Real)<br> hillshade_10sec_compressed (Integer64)<br> masked_forest_ESACCI-LC-L4-LCCS-Map-300m-P1Y-2014-v2.0.7 (Integer64)<br> night_lights_10sec_compressed (Real)<br> night_lights_5min_compressed (Real)<br> nitrogen_0-5cm_mean_compressed (Integer64)<br> nitrogen_10sec_compressed (Integer64)<br> nitrogen_5-15cm_mean_compressed (Integer64)<br> phh2o_0-5cm_mean_compressed (Integer64)<br> phh2o_5-15cm_mean_compressed (Integer64)<br> sand_0-5cm_mean_compressed (Integer64)<br> silt_0-5cm_mean_compressed (Integer64)<br> silt_5-15cm_mean_compressed (Integer64)<br> slope_10sec_compressed (Real)<br> soc_0-5cm_mean_compressed (Integer64)<br> soc_5-15cm_mean_compressed (Integer64)<br> tri_10sec_compressed (Real)<br> wind_speed_10sec_compressed (Real)</p>
National forest inventory data for a size-structured forest population model
<p>In forest communities, light competition is a key process for community assembly. Species' differences in seedling and sapling tolerance to shade cast by overstory trees is thought to determine species composition at late-successional stages. Most forests are distant from these late-successional equilibria, impeding a formal evaluation of their potential species composition. To extrapolate competitive equilibria from short-term data, we therefore introduce the JAB model, a parsimonious dynamic model with interacting size-structured populations, which focuses on sapling demography including the tolerance to overstory competition. We apply the JAB model to a two-"species" system from temperate European forests, i.e. the shade-tolerant species Fagus sylvatica L. and the group of all other competing species. Using Bayesian calibration with prior information from external Slovakian national forest inventory (NFI) data, we fit the JAB model to short timeseries from the German NFI. We use the posterior estimates of demographic rates to extrapolate that F. sylvatica will be the predominant species in 94% of the competitive equilibria, despite only predominating in 24% of the initial states. We further simulate counterfactual equilibria with parameters switched between species to assess the role of different demographic processes for competitive equilibria. These simulations confirm the hypothesis that the higher shade-tolerance of F. sylvatica saplings is key for its long-term predominance. Our results highlight the importance of demographic differences in early life stages for tree species assembly in forest communities.</p>
Spreadsheets to model counterfactual tropical forest losses (1990-2019) for Brazil, Democratic Republic of Congo and Indonesia
<p>18 spreadsheets used to simulate the counterfactual forest losses underlying the publication:<br> "Trends in tropical forest loss and the social value of emission reductions"<br> by Thomas Knoke, Nick Hanley, Rosa Maria Roman-Cuesta, Ben Groom, Frank Venmans and Carola Paul published in Nature Sustainability (DOI: 10.1038/s41893-023-01175-9)</p> <p>Each spreadsheet covers a period of five years. The dynamic robust multifunctional land-use allocation model is based on</p> <p>Knoke, T. et al. Accounting for multiple ecosystem services in a simulation of land-use<br> decisions: Does it reduce tropical deforestation? Glob. Chang. Biol. 26, 2403–2420; 10.1111/gcb.15003 (2020).</p> <p>For details see linked publication and README file</p> <p> </p>
Output raster datasets from an application of a fine resolution spatially explicit forest water yield model in Florida's panhandle
<p>These raster datasets are the output results for a spatial water yield model applied to an 11 county area in the state of Florida panhandle. The water yield model is adapted from Acharya, et al. 2022 and the spatial modelling process is detailed in this datasets associated publication. All data are in the WGS 1984 UTM Zone 16N coordinate system and have 10m horizontal spatial resolution. </p> <p>The output raster datasets contained here are water yield estimate informed with 2018 pine basal area, binary depth to water table, and average aridity index input rasters. These rasters have 10m spatial resolution, the raster extent covers 11 counties in the panhandle of Florida, the units are in centimeters of water yield per year. The water yield outputs consist of ten rasters representing the current water yield using the mean aridity raster, the water yield expected from the three pine tree thinning scenarios: 7m/hectare ba, 11 m/hectare, and 18 m/hectare, taken from the mean aridity index. Then rasters representing the water yield expected from the three thinning scenarios under maximum, and minimum aridity indexes.</p> <p> </p> <p>These ten outputs are listed here:</p> <p>"wy_current_mean" Based on 2018 BA conditions; Mean ARID</p> <p>"wy_18_mean" BA reduced to 18m2ha-1; Mean ARID</p> <p>"wy_11_mean" BA reduced to 11m2ha-1; Mean ARID</p> <p>"wy_7_mean" BA reduced to 7m2ha-1; Mean ARID</p> <p>"wy_ 18_max" BA reduced to 18m2ha-1; Maximum ARID</p> <p>"wy_ 11_max" BA reduced to 11m2ha-1; Maximum ARID</p> <p>"wy_7_max" BA reduced to 7m2ha-1; Maximum ARID</p> <p>"wy_ 18_min" BA reduced to 18m2ha-1; Minimum ARID</p> <p>"wy_ 11_min" BA reduced to 11m2ha-1; Minimum ARID</p> <p>"wy_ 7_min" BA reduced to 7m2ha-1; Minimum ARID</p> <p> </p> <p>Water yield was estimated for 2018 using the following datasets to inform the model in the Current Water Yield Calculation tool:</p> <ul> <li>Leaf area index modeled from a 2018 pine species basal area raster,</li> <li>Depth to water table data provided by Florida Geological Survey and reclassified as a binary raster,</li> <li>Average aridity index raster generated with precipitation data from PRISM Climate Group and MODIS PET data.</li> </ul> <p>For detailed information on how the above inputs were developed, please see the associated publication:</p> <p>Vernon, J., St. Peter, J., Crandall, C., Awowale, O.E., Medley, P., Drake, J., & Ibeanusi, V. (2023). Spatial application of southern pine water yield for prioritizing forest management activities. ISPRS International Journal of Geo-Information, 12(2), 34. <a href="https://doi.org/10.3390/ijgi12020034">https://doi.org/10.3390/ijgi12020034</a> </p>
Input raster datasets for an application of a fine resolution spatially explicit forest water yield model in Florida's panhandle
<p>These raster datasets are the inputs for a spatial water yield model applied to an 11 county area in the state of Florida's panhandle. The water yield model is adapted from Acharya, et al. 2022 and the spatial modelling process is detailed in the associated publication. The five input datasets required for this water yield analysis are: 1) a model of pine species basal area, named "ARSA_PineBA_10m" 2) a binary depth to water table raster named "DTW_cm_binary2" , and 3) three spatial aridity index raster dataset named "Aridity_Min", "Aridity_Max" and "Aridity_Mean", created from potential evapotranspiration, and precipitation raster datasets. The min max and mean codifiers relate to the range of aridity values found in our dataset of 7 year temporal range, from MODIS PET and PRISM percipitation yearly data. All input and output data are in the WGS 1984 UTM Zone 16N coordinate system and have 10m horizontal spatial resolution. </p>
Advancements in QSAR modelling: Decision trees and rotation forest for prediction of Aspergillus anti-inflammatory metabolites
<p>This study presents applications of advancements in QSAR modelling for predicting nitric oxide (NO) inhibitors and anti-inflammatory metabolites from the <em>Aspergillus</em> genus. Inflammation-related diseases remain a pressing concern, necessitating the identification of effective anti-inflammatory compounds. Using decision trees, the Ranker method, and CorrelationAtrributeEval as a base classifier for attribute selection together with Rotation Forest and Adaboost as enhancers, we explored their potential with different classifiers including Artificial Neural Networks and J48 Trees. The proposed QSAR models employed an ensemble approach with Rotation Forest and Adaboost.M1, applying an automated KNIME workflow. Seven molecular descriptors were selected and trained on a comprehensive dataset of diverse anti-inflammatory <em>Aspergillus</em> specialised metabolites. Results showed that the Rotation Forest-enhanced version outperformed other models, capturing complex structure-activity relationships and improving predictive performance. Chemical characteristics of electrotopological state, topological distances, and functional groups including secondary amides and alcohols contribute to important anti-inflammatory effects. The developed QSAR model showed good predictive performance for anti-inflammatory <em>Aspergillus</em> metabolites, focusing on their NO inhibitory activity. These results can contribute to the discovery of novel anti-inflammatory drugs based on computational techniques.</p> <p> </p>
Soil chemistry dataset from the work "Modelling and prediction of major soil chemical properties with Random Forest: machine learning as tool to understand soil-environment relationships in Antarctica"
<p>Bases sum, H+Al (potential acidity), pH, phosphorous, remaining P (P-rem), sodium and total organic carbon distribution in Antarctic soils modeled and predicted through Machine Learning approaches, legacy soil data and environmental covariates. The quantile and prediction interval data represent the spatial uncertainty of the predictions.</p> <p>As soon as the work "Modelling and prediction of major soil chemical properties with Random Forest: machine learning as tool to understand soil-environment relationships in Antarctica" is published, the paper will be cited here. </p> <p>The .zip file contains the following folders:</p> <p>1) soil_chemistry_antarctica: data containing the soil chemical attributes distribution</p> <p>2) soil_chemistry_prediction_interval: uncertainty from the prediction interval 90% (Q95% - Q5%) of the soil attributes prediction</p> <p>4) soil_texture_quantile05: quantile 5% of the soil attributes prediction</p> <p>5) soil_texture_quantile95: quantile 95% of the soil attributes prediction</p>
Data for: Nitrogen deposition in forests: Statistical modeling of total deposition from throughfall loads
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National forest inventory data for a size-structured forest population model
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Data from: Toward spatio-temporal models to support national-scale forest carbon monitoring and reporting
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The western United States large forest-fire stochastic simulator (WULFFSS) 1.0: A monthly gridded forest-fire model using interpretable statistics
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Non-trophic interactions amplify kelp harvest-induced biomass oscillations and biomass changes in a kelp forest ecological network model
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Nitrification and denitrification in the Community Land Model compared to observations at Hubbard Brook Forest
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Random forest climatic modeling of agricultural insurance loss across the inland Pacific Northwest region of the United States
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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.