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217 results for “forest model”
Dataset: Selecting tree species to restore forest under climate change conditions: complementing species distribution models with field experimentation
<p>This repository contains the files associated with the following article:</p> <p>Jesús Sandoval-Martínez, Ernesto I. Badano, Francisco A. Guerra-Coss, Jorge A. Flores Cano, Joel Flores, Sandra Milena Gelviz-Gelvez, Felipe Barragán-Torres, “Selecting tree species to restore forest under climate change conditions: complementing species distribution models with field experimentation”, submitted to <em>Journal of Environmental Management</em>.</p> <p><strong>Supplementary material 01 </strong>is a compressed file that contains two Microsoft Excel files with data that support the results of the study. A file correspond to <em>Vachellia pennatula</em> and the another file correspond to <em>Prosopis laevigata</em>. In both files, the first spreadsheet shows the occurrence data (latitude and longitude) used to calibrate the distribution model (SDM) of the corresponding species, the current values of the 19 bioclimatic variables associated with these coordinates and the Spearman correlation coefficients used to select the variables included in the SDM (selected variables are indicated in green). The second spreadsheet shows the current habitat occupancy probabilities of the target species estimated with the SDM at the geographic coordinates of occurrence points, while the table on the side shows the fraction of true presences dropping at the following probability categories: (1) habitat occupancy probabilities below 0.1 = unsuitable spatial units for the species, (2) habitat occupancy probabilities between 0.1 and 0.4 = barely suitable spatial units for the species, (3) habitat occupancy probabilities between 0.4 and 0.7 = moderately suitable spatial units for the species, and (4) habitat occupancy probabilities above 0.7 = highly suitable spatial units for the species. The third spreadsheet shows the one-thousand random geographic coordinates and the corresponding current and future habitat occupancy probabilities of each species. Future habitat occupancy probabilities are provided for three time periods (2041-2060, 2061-2080 and 2081-2100) at four radiative forcing levels each (2.6, 4.5, 7.0 and 8.5 W/m<sup>2</sup>).</p> <p><strong>Supplementary material 02 </strong>is a compressed file that contains a folder for <em>Vachellia pennatula</em> and another folder for <em>Prosopis laevigata</em>. Each of these folders contains the summaries of the MaxEnt outputs that support the results of the corresponding SDM.</p> <p><strong>Supplementary material 03 </strong>is a compressed Keyhole Markup Language file (KMZ) that contains interactive maps that are optimized for the desktop version of Google Earth. To accelerate visualization of maps, we recommend installing this software in a computer meeting the following requirements: CPU Intel Core i5 9<sup>th</sup> generation or higher, CPU clock speed 1.8 GHz or higher, random-access memory (RAM) 8 GB or higher, and video random access memory (VRAM) 1 GB or higher. Otherwise, opening this file may take several minutes. These maps are organized in a folder for <em>Vachellia pennatula</em> and another folder for <em>Prosopis laevigata</em>, which must be expanded for accessing the following information (click on the arrow on the left of folders to expand them):</p> <ul> <li><strong>Current climate </strong>– Activating this folder (click the fox on the left of the folder) display the map of habitat occupancy probabilities of species across Mexico under the current climate.</li> <li><strong>Period 2041-2060, 2061-2080 and 2081-2100 </strong>– Expanding each of these folders (click on the arrow on the left of folders) shows four subfolders that correspond to different radiative forcing levels (2.6, 4.5, 7.0 and 8.5 W/m<sup>2</sup>). Activating each of these sub folders (click the fox on the left of subfolders) display the map of habitat occupancy probabilities of species across Mexico expected on the corresponding time period and radiative forcing level. These maps also show the areas classified as climatically unsuitable in the multivariate environmental similarity surface (MESS) analysis. Clicking on the names of subfolders displays a figure showing the relationship between current and future habitat occupancy probabilities of the species on the corresponding time period and radiative forcing level. In these figures, the red line is the empirical relationship between these variables and the solid blue line is the theoretical relationship with intercept = 0 and slope = 1. The statistical results that support these relationships are also shown in these figures.</li> </ul> <p><strong>Supplementary material 04 </strong>is a compressed file that contains two Microsoft Excel files with data that support the results of the study. the file labeled as “Microclimate data” contains two spreadsheets, which correspond to the temperature and rainfall values measured in controls under the current climate and climate change simulation plots located of the field experiments. The file levelled as “Seedling emergence and survival” contains a spreadsheet for <em>Vachellia pennatula</em> and another one for <em>Prosopis laevigata</em>, which contains the data used to estimate the seedling emergence and survival rates in controls and climate change simulation plots.</p>
Fine-scale Quantification of Absorbed Photosynthetically Active Radiation (APAR) in Plantation Forests with 3D Radiative Transfer Modeling and LiDAR Data
<p>In recent years, LiDAR technology has gained widespread attention for its ability to provide precise 3D vertical structural data for various objects, particularly forests. In our dataset, we utilized LiDAR data to reconstruct intricately detailed three-dimensional representations of specific larch forest landscapes. These detailed forest structural models enable us to drive three-dimensional radiative transfer models, analyze the radiation budget of the forest canopy, and gain valuable insights into fine-scale forest management strategies.</p> <p>This is the research work we conducted by combining the aforementioned 3D forest scenes with the 3D RTM LESS. If you use our data, please cite our article. You can access our publication via DOI: 10.34133/plantphenomics.0166.</p> <p>We welcome researchers interested in a wide range of fields, such as vegetation ecological applications, to communicate with us by combining 3D vegetation modeling.</p> <p><br><br></p>
Input data files for Dietrich et al. Chl-a and nutrient random forest modeling
<p>Input data for the models originally from:</p> <p>EPA, U. S. <em>WSIO Indicator Data Library</em>, <<a href="https://www.epa.gov/wsio/wsio-indicator-data-library">https://www.epa.gov/wsio/wsio-indicator-data-library</a>> (2023).</p> <p>Platt, L. R., Spaulding, S.A., Covert, A., Murphy, J.C., and Raynor, N. A national harmonized dataset of discrete chlorophyll from lakes and streams (2005-2022). (2023). <a href="https://doi.orghttps">https://doi.org:https://doi.org/10.5066/P9J0ZIOF</a></p> <p>Saad, D. A., Argue, D.M., Schwarz, G.E., Anning, D.W., Ator, S.W., Hoos, A.B., Preston, S.D., Robertson, D.M., and Wise, D.R., 2019. Water-quality and streamflow datasets used for estimating long-term mean daily streamflow and annual loads to be considered for use in regional streamflow, nutrient and sediment SPARROW models, United States, 1999-2014. (2019). <a href="https://doi.orghttps">https://doi.org:https://doi.org/10.5066/F7DN436B</a></p> <p> </p>
Process-based Modeling of Ecosystem-Level Monoterpene from a Japanese Larch (Larix Kaempferi) Forest
<p>Title ''Process-based Modeling of Ecosystem-Level Monoterpene from a Japanese Larch (Larix Kaempferi) Forest''<br>Zhanzhuo Chen 1,2, Tomomichi Kato 3, Akihiko Ito 4,5, Tatsuya Miyauchi 3, Yoshiyuki Takahashi 4, and Jing Tang 2</p> <p>1 Graduate School of Global Food Resources, Hokkaido University, Sapporo, Hokkaido, 060-0809, Japan<br>2 Center for Volatile Interactions (VOLT), Department of Biology, University of Copenhagen, DK-2100, Copenhagen, Denmark<br>3 Research Faculty of Agriculture, Hokkaido University, Sapporo, Hokkaido, 060-8589, Japan<br>4 Earth System Division, National Institute for Environmental Studies (NIES), Onogawa, Tsukuba, Ibaraki, 305-8506, Japan<br>5 Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo-ku, Tokyo 113-8657, Japan<br>Correspondence to: Tomomichi Kato (tkato@agr.hokudai.ac.jp)</p>
Figure 3 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan
Figure 3. Location map of the study area.
Inputs, results data and analysis script for the evaluation of the PDG-Arena forest growth model on beech-fir stands
<p>Supplementary files for simulations in Rouet et al. (2024): PDG-Arena: An eco-physiological model for characterizing tree-tree interactions in heterogeneous and mixed stands (doi: <a href="https://doi.org/10.1101/2024.02.09.579667" target="_blank" rel="noopener">10.1101/2024.02.09.579667</a>).</p> <p>This repository is an archive of the github repository PDG-Arena-extra (release v1.0.3), accessible at <a href="https://github.com/camille-rouet/PDG-Arena-extra/tree/v1.0.3" target="_blank" rel="noopener">https://github.com/camille-rouet/PDG-Arena-extra/tree/v1.0.3</a>.</p>
Figure 2 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan
Figure 2. Schematic view of Kernel Ridge Regression (KRR) model.
Figure 1 in Forest yield prediction under different climate change scenarios using data intelligent models in Pakistan
Figure 1. High-resolution flow chart of the Random Forest (RF) model.
Trained Random Forest Model for PNW Seismic Event Classification Trained on 150s waveforms (P-50, P+100), 50 Hz, and 1-10 Hz BP Filtered
<p>This dataset contains three trained random forest models named as following - </p> <ul> <li>P_10_100_F_1_10_50.joblib - This is a model trained on 110s long waveforms (origin time - 10, origin time +100) in case of earthquakes and explosions and (first arrival pick -10, first arrival pick + 100) in case of surface events, the waveforms are tapered using 10% cosine taper, bandpass filtered between 1-10 Hz using Butterworth four corner filter, normalized and resampled to 50 Hz. </li> <li>P_50_100_F_1_10_50.joblib </li> <li>P_10_30_F_1_15_50.joblib. </li> </ul> <p>And also the standard scaler parameters for each features that will be used to normalize them. </p>
Fig. 4 in Preliminary Biophysical Assessment Of Forest Ecosystem Services: Two Model Area Examples
Fig. 4. Ecosystem class: mediation of noise impacts, indicator: Estimated noise reduction.
Model runs - Reconciling the EU forest, biodiversity, and climate strategies
<p>These are the model runs for the paper "Reconciling the EU forest, biodiversity, and climate strategies" published in Global Change Biology in 2024, by Gregor et al.</p> <p>The data can be used to re-run the optimizations.</p>
New Zealand native forest plant cover data for Popovic et al. MEE (2019), Untangling direct species associations from indirect mediator species effects with graphical models.
<p>Forest cover measurements were collected at 1246 native forest sites that form part of a network of permanent 20 x 20 m plots spread throughout New Zealand. A total of 1831 plant species were present in these plots, with the most common being herbs, graminoids, ferns, shrubs and trees. Plant cover (in ordinal categories) was assessed for each species in several tiers at different heights. The cover data we analysed (<em>NZ_native_forest_cover.csv) </em>were the maximum cover recorded over all the tiers at the 964 sites identified as native forests, containing 1311 species with at least one presence. <em>NZ_native_forest_species.csv</em> contains species data including species name, exotic/native, and plant type (tree, shrub, etc.), corresponding to the plant species in the columns of <em>NZ_native_forest_cover.csv</em>.</p> <p>We acknowledge the use of data drawn from the Natural Forest plot data collected between January 2002 and March 2007 by the LUCAS programme for the Ministry for the Environment, New Zealand.</p> <p> </p>
Modelling riparian forest distribution and composition to entire river networks
<p><strong>Aim: </strong>Developing a methodology to map the distribution of riparian forests to entire river networks and determining the main environmental factors controlling their spatial patterns.</p> <p><strong>Location: </strong>Cantabrian region, northern Spain.</p> <p><strong>Methods: </strong>We mapped the riparian forests at a physiognomic and phytosociological levels by delimiting riparian zones and generating vegetation distribution models based on remote sensing data (Landsat 8 OLI and LiDAR PNOA). We built virtual watersheds to define a spatial framework where the catchment environmental information can be routed to each river reach, jointly with the vegetation map. In order to determine the drivers playing a significant role on the observed spatial patterns in the riparian forest we modelled interactions between these datasets of environmental information and riparian vegetation by using the Random Forest algorithm.</p> <p><strong>Results: </strong>The modelling results obtained reproduced a reliable variation of riparian forest structure and composition across Cantabrian watersheds. The produced maps were highly accurate, with more than a 70% overall accuracy for the forest occurrence. A clear differentiation between Eurosiberian (91E0 and 9160 habitats) and Mediterranean (92E0) riparian forests was shown on both sides of the mountain range. Topography and land use were the main drivers defining the distribution of riparian forest as a physiognomic unit. In turn, altitude, climate and percentage of pasture were the most relevant factors determining their composition (phytosociological approach).</p> <p><strong>Conclusions: </strong>Our study confirms that the anthropic control ultimately defines the distribution of the vegetation in the riparian area at a regional to local scale. Human disturbances constrain the extension of forest patches across their potential distribution defined by topoclimatic boundaries, which establish a clear limit between Mediterranean and Eurosiberian biogeographical regions.</p>
Fig. 1 in Interpreting the condition of the forest environment with use of the SCP/MIB model of carabid communities (Coleoptera: Carabidae)
Fig. 1. Correlation between values of SCP index of Carabids beetles and age of inhabiting forests.
Fig. 8 in Interpreting the condition of the forest environment with use of the SCP/MIB model of carabid communities (Coleoptera: Carabidae)
Fig. 8. The status of carabid communities living in different zones of the forest – fallow ecotone.
Modelling alternative harvest effects on soil CO2 and CH4 fluxes from peatland forests [dataset]
<p>This contains the forest floor soil respiration data and the water level data used in the model simulations in the paper titled "Modelling alternative harvest effects on soil CO2 and CH4 fluxes from peatland forests " (Li et al, 2024; <a title="Persistent link using digital object identifier" href="https://doi.org/10.1016/j.scitotenv.2024.175257" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.scitotenv.2024.175257</a>)</p>
Illustrations for 'Disturbances in the evergreen boreal forest and their impact on 21st century vegetation and climate dynamics - A stochastic modeling approach' (Doctoral thesis)
<p>This repository contains all the original illustrations I created for my doctoral thesis at the Technical University of Munich. This work is published under a Creative Commons CC-BY-SA license, which means that you are free to use and adapt this work under the same license for commercial and non-commercial applications as long as you credit the original work. To credit, please cite this repository as well as my doctoral thesis.</p> <p> </p> <p> </p>
Data analysis & code: Quantifying the impact of climate change and forest management on Swedish forest ecosystems using the dynamic vegetation model LPJ-GUESS
<p><span>This file contains code to optimize the allometric parameters, to plot the figures, and details of the underlying data analysis in "Quantifying the impact of climate change and forest management on Swedish forest ecosystems using the dynamic vegetation model LPJ-GUESS" (Bergkvist et al.). <br></span></p>
Full-coverage 1 km daily ambient PM2.5 and O3 concentrations of China in 2005-2017 based on multi-variable random forest model
<p>The aim of our study was to construct random forest models with high-performance, and estimate daily average PM<sub>2.5</sub> concentration and O<sub>3</sub> daily maximum 8h average concentration (O<sub>3</sub>-8hmax) of China in 2005-2017 at a spatial resolution of 1km×1km. The model variables included meteorological variables, satellite data, chemical transport model output, geographic variables and socioeconomic variables. Random forest model based on ten-fold cross validation was established, and spatial and temporal validations were performed to evaluate the model performance. According to our sample-based division method, the daily, monthly and yearly simulations of PM<sub>2.5</sub> gave average model fitting R<sup>2</sup> values of 0.85, 0.88 and 0.90, respectively; these R<sup>2</sup> values were 0.77, 0.77, and 0.69 for O<sub>3</sub>-8hmax, respectively. The meteorological variables and their lagged values can significantly affect both PM<sub>2.5</sub> and O<sub>3</sub>-8hmax simulations. During 2005-2017, PM<sub>2.5</sub> exhibited an overall downward trend, while ambient O<sub>3</sub> experienced an upward trend. Whilst the spatial patterns of PM<sub>2.5</sub> and O<sub>3</sub>-8hmax barely changed between 2005 and 2017, the temporal trend had spatial characteristic.</p> <p>Each dataset is the annual mean concentration of PM<sub>2.5</sub> or O<sub>3</sub>-8hmax based on the standard grid (Grid.csv) for that year. The coordinate system of the grid is WGS-84.</p>
Limitations of using surrogates for behaviour classification of accelerometer data: refining methods using random forest models in Caprids
<p>Animal-attached devices can be used on cryptic species to measure their movement and behaviour, enabling unprecedented insights into fundamental aspects of animal ecology and behaviour. However, direct observations of subjects are often still necessary to translate biologging data accurately into meaningful behaviours. As many elusive species cannot easily be observed in the wild, captive or domestic surrogates are typically used to calibrate data from devices. However, the utility of this approach remains equivocal. </p> <p>Here, we assess the validity of using captive conspecifics, and phylogenetically-similar domesticated counterparts (surrogate species) for calibrating behaviour classification. Tri-axial accelerometers and tri-axial magnetometers were used with behavioural observations to build random forest models to predict the behaviours. We applied these methods using captive Alpine ibex (Capra ibex) and a domestic counterpart, pygmy goats (Capra aegagrus hircus), to predict the behaviour including terrain slope for locomotion behaviours of captive Alpine ibex. </p> <p>Behavioural classification of captive Alpine ibex and domestic pygmy goats was highly accurate (> 98%). Model performance was reduced when using data split per individual, i.e., classifying behaviour of individuals not used to train models (mean ± sd = 56.1 ± 11%). Behavioural classifications using domestic counterparts, i.e., pygmy goat observations to predict ibex behaviour, however, were not sufficient to predict all behaviours of a phylogenetically similar species accurately (> 55%).</p> <p>We demonstrate methods to refine the use of random forest models to classify behaviours of both captive and free-living animal species. We suggest there are two main reasons for reduced accuracy when using a domestic counterpart to predict the behaviour of a wild species in captivity; domestication leading to morphological differences and the terrain of the environment in which the animals were observed. We also identify limitations when behaviour is predicted in individuals that are not used to train models. Our results demonstrate that biologging device calibration needs to be conducted using: (i) with similar conspecifics, and (ii) in an area where they can perform behaviours on terrain that reflects that of species in the wild.</p>
ScienceDex guides
Understand access before you commit
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.