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217 results for “forest model”
Diurnal rainfall response to the physiological and radiative effects of CO2 in tropical forests in the Energy Exascale Earth System Model v1
<p>Necessary outputs and scripts for recreating the figures for the journal article with the same title.</p>
Occupancy modeling of habitat use by white-tailed deer after more than a decade of exclusion in the boreal forest
<p>The exclusion of herbivores in forest areas is a strategy used to reduce the impact of selective browsing and increase the regeneration of desired plant species. On Anticosti Island (Québec, Canada), selective browsing by white-tailed deer prevents the regeneration of balsam fir – white birch forests leading to their conversion into white spruce forests. Large deer exclosures were established for ca . 10 to 12 years in clear-cuts with patches of residual forest from 2001 to 2006 to assist in the natural regeneration of fir stands and to provide shelter and food resources for deer. Our objective was to assess how deer use exclosures after the removal of fences according to their spatial configuration and habitat composition. We randomly distributed automatic cameras for periods of 14 days during summer in six exclosures ranging from 3.1 to 11.2 km2 (n=25 cameras per exclosure) from which deer were reduced for 10 to 12 years. We compared candidate occupancy models that included spatial configuration and food resource variables while simultaneously controlling for variables affecting detection probability. We obtained weak evidence that deer habitat use increased by 19% when forage resources, represented by the cover of <em>Cornus canadensis</em>, increased from 0 to 100%. None of the other variables (distance between the border of exclosures and cameras and distance between forest patches and cameras) was retained, suggesting that the use of regenerating forests by deer in summer after a period of exclusion is related to forage availability and therefore, any forest management that improves food production during summer should help maintain or increase habitat use by deer.</p>
Growth model used in Guzy et al. Increased growth rates of stream salamanders following forest harvesting
<p>Timber harvesting can influence headwater streams by altering stream productivity, with cascading effects on the food web and predators within, including stream salamanders. Although studies have examined shifts in occupancy or abundance following timber harvest, few examine sublethal effects such as changes in growth and demography. To examine the effect of upland harvesting on growth of the stream-associated Ouachita dusky salamander (<em>Desmognathus brimleyorum</em>), we used capture-mark-recapture over three years at three headwater streams embedded in intensely managed pine forests in west-central Arkansas. The pine stands surrounding two of the streams were harvested, with retention of a 14 and 21 m-wide forested stream buffer on each side of the stream, whereas the third stream was an unharvested control. At the two treatment sites, measurements of newly-metamorphosed salamanders were on average 4.0 and 5.7 mm larger post-harvest compared to pre-harvest. We next assessed the influence of timber harvest on growth of post-metamorphic salamanders with a hierarchical von Bertalanffy growth model that included an effect of harvest on growth rate. Using measurements from 839 individual <em>D. brimleyorum</em> recaptured between 1 and 6 times (total captures n=1,229) we found growth rates to be 1.4 times higher post-harvest. Our study is among the first to examine responses of individual stream salamanders to timber harvesting and we discuss mechanisms that may be responsible for observed shifts in growth. Our results suggest timber harvest that includes retention of a riparian buffer (i.e., Streamside Management Zone) may have short term positive effects on juvenile stream salamander growth, potentially offsetting negative sublethal effects associated with harvest.</p>
Data for Streamflow Prediction: Comparison of SWAT vs. Random Forest Models in Diverse Catchments
<p>This study introduces a time-lag-informed Random Forest (RF) framework for streamflow time series prediction across diverse catchments, and compares its results against SWAT predictions. We found strong evidence of RF's better performance by adding historical flows and time-lags for meteorological values over using only actual meteorological values. On a daily scale, RF demonstrated robust performance (Nash–Sutcliffe efficiency [<em>NSE</em>] > 0.5), whereas SWAT generally yielded unsatisfactory results (<em>NSE</em> < 0.5) and tended to overestimate daily streamflow by up to 27% (<em>PBIAS</em>). However, SWAT provided better monthly predictions, particularly in catchments with irregular flow patterns. Although both models faced challenges in predicting peak flows in snow-influenced catchments, RF outperformed SWAT in an arid catchment. RF also exhibited a notable advantage over SWAT in terms of computational efficiency. Overall, RF is a good choice for daily predictions with limited data, whereas SWAT is preferable for monthly predictions and understanding hydrological processes in depth.</p> <p>This repository contains the input data used for building the RF and SWAT models and the files describing the modeling results.</p> <p>The corresponding Zenodo code repository is available at <a href="../doi/10.5281/zenodo.11064973" target="_blank" rel="noopener">https://zenodo.org/doi/10.5281/zenodo.11064973</a>.</p>
Data used for analysis in "Calibrating tropical forest coexistence in ecosystem demography models using multi-objective optimization through population-based parallel surrogate search"
Open the record for dataset details and reuse information.
Trained Random Forest model and scaler parameters on new physical and tsfel features from seismic data of 150s length.
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Trained random forest models on 5000 traces per class based on updated features
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A global 0.05° gross primary productivity of sunlit and shaded leaves dataset via combining two-leaf light use efficiency model with random forest over 2002~2020
<p>The TL-CRF model generated a global 0.05´0.05° product for eight-day gross primary productivity (GPP) of sunlit and shaded canopies from 2002 to 2020 by embedding the random forest (RF) submodule into the two-leaf light use efficiency (TL-LUE) model while considering the seasonal differences in the clumping index. The RF technique was used to integrate various environmental stress factors including meteorological, hydrological, soil properties, and elevation, thereby improving the overall scale of the complex environmental conditions to the maximum LUE. This novel GPP product could support further research on spatial and temporal patterns of the carbon cycle and its association with climate change.</p> <p> </p> <p> </p> <p>Variable: GPP, GPP<sub>sh</sub>, and GPP<sub>su</sub></p> <p>Spatial coverage: global</p> <p>Temporal coverage: 2002 to 2020</p> <p>Spatial resolution: 0.05×0.05°</p> <p>Temporal resolution: eight-day</p> <p>Unite: g C m<sup>−2</sup> d<sup>−1</sup></p> <p>Data format: raster (.tif)</p>
Data for 'Post-disturbance recovery drives 21st century vegetation shifts in the Boreal forest in a dynamic vegetation model'
<p>This is the database containing all the LPJ-GUESS output data used in the paper. For LPJ-GUESS model code, refer to https://zenodo.org/record/8065737. For data processing refer to https://github.com/lucialayr/borealRecovery </p> <p> </p> <p>If you are interested in the raw data, please contact me. </p>
Forest Carbon Modeling Improved through Hierarchical Integration of Pool-Based Measurements
<p>This folder contains data from </p> <ol> <li>measured carbon stocks (carbonpools) from forest inventories</li> <li>carbon stocks (modeled_C_stock_40) estimated by each HDA constraint</li> <li>posterior parameter sets (para_posterior) after burn-in</li> <li>carbon fluxes (modeled_C_HD) estimated by 500 randomly selected posterior parameter sets from each HDA step.</li> <li>carbon stocks (modeled_C_stock_40_DEF) estimated by the default model settings (uninformed)</li> </ol> <p>figures.R to reproduce the figures in the manuscript. </p>
Data from: A mistletoe tale: postglacial invasion of Psittacanthus schiedeanus (Loranthaceae) to Mesoamerican cloud forests revealed by molecular data and species distribution modeling
Background: Ecological adaptation to host taxa is thought to result in mistletoe speciation via race formation. However, historical and ecological factors could also contribute to explain genetic structuring particularly when mistletoe host races are distributed allopatrically. Using sequence data from nuclear (ITS) and chloroplast (trnL-F) DNA, we investigate the genetic differentiation of 31 Psittacanthus schiedeanus (Loranthaceae) populations across the Mesoamerican species range. We conducted phylogenetic, population and spatial genetic analyses on 274 individuals of P. schiedeanus to gain insight of the evolutionary history of these populations. Species distribution modeling, isolation with migration and Bayesian inference methods were used to infer the evolutionary transition of mistletoe invasion, in which evolutionary scenarios were compared through posterior probabilities. Results: Our analyses revealed shallow levels of population structure with three genetic groups present across the sample area. Nine haplotypes were identified after sequencing the trnL-F intergenic spacer. These haplotypes showed phylogeographic structure, with three groups with restricted gene flow corresponding to the distribution of individuals/populations separated by habitat (cloud forest localities from San Luis Potosí to northwestern Oaxaca and Chiapas, localities with xeric vegetation in central Oaxaca, and localities with tropical deciduous forests in Chiapas), with post-glacial population expansions and potentially corresponding to post-glacial invasion types. Similarly, 44 ITS ribotypes suggest phylogeographic structure, despite the fact that most frequent ribotypes are widespread indicating effective nuclear gene flow via pollen. Gene flow estimates, a significant genetic signal of demographic expansion, and range shifts under past climatic conditions predicted by species distribution modeling suggest post-glacial invasion of P. schiedeanus mistletoes to cloud forests. However, Approximate Bayesian Computation (ABC) analyses strongly supported a scenario of simultaneous divergence among the three groups isolated recently. Conclusions: Our results provide support for the predominant role of isolation and environmental factors in driving genetic differentiation of Mesoamerican parrot-flower mistletoes. The ABC results are consistent with a scenario of post-glacial mistletoe invasion, independent of host identity, and that habitat types recently isolated P. schiedeanus populations, accumulating slight phenotypic differences among genetic groups due to recent migration across habitats. Under this scenario, climatic fluctuations throughout the Pleistocene would have altered the distribution of suitable habitat for mistletoes throughout Mesoamerica leading to variation in population continuity and isolation. Our findings add to an understanding of the role of recent isolation and colonization in shaping cloud forest communities in the region.
Presence, precipitation, and temperature data used to estimate eastern forest songbird historical distributions using climatic niche modeling
<p>Boundaries between vegetation types, known as ecotones, can be dynamic in response to climatic changes. The North American Great Plains includes a forest-grassland ecotone in the south-central United States that has expanded and contracted in recent decades in response to historical periods of drought and pluvial conditions. This dynamic region also marks a western distributional limit for many passerine birds that typically breed in forests of the eastern United States. To better understand the influence that variability can exert on broad-scale biodiversity, we explored historical longitudinal shifts in the western extent of breeding ranges of eastern forest songbirds in response to the variable climate of the southern Great Plains. We used climatic niche modeling to estimate current distributional limits of nine species of forest-breeding passerines from 30-year average climate conditions from 1980 to 2010. During this time the southern Great Plains experienced an unprecedented wet period without periodic multi-year droughts that characterized the region's long-term climate from the early 1900s. Species' climatic niche models were then projected onto two historical drought periods: 1952–1958 and 1966–1972. Threshold models for each of the three time periods revealed dramatic breeding range contraction and expansion along the forest-grassland ecotone. Precipitation was the most important climate variable defining breeding ranges of these nine eastern forest songbirds. Range limits extended farther west into southern Great Plains during the more recent pluvial conditions of 1980–2010 and contracted during historical drought periods. An independent dataset from BBS was used to validate 1966–1972 range limit projections. Periods of lower precipitation in the forest-grassland ecotone are likely responsible for limiting the western extent of eastern forest songbird breeding distributions. Projected increases in temperature and drought conditions in the southern Great Plains associated with climate change may reverse range expansions observed in the past 30 years.</p>
Simulation results for "Modeling the joint effects of vegetation characteristics and soil properties on ecosystem dynamics in a Panama tropical forest"
<p>This dataset is the ELM-FATES simulation outputs for the paper entitled "Modeling the joint effects of vegetation characteristics and soil properties on ecosystem dynamics in a Panama tropical forest". </p>
Data for: Temperature and hygrometry of amphibian agar models in behavioral simulation and operational temperature of two forested areas
<p>We investigated how thermoregulatory behaviors affect hydro-thermoregulation in anurans, using agar models as a sampling unit, simulating four behaviors related to behavioral fever and sickness behavior. We collected data in two forest environments (Wet forest and transitional forest) in the Parque Estadual Intervales (PEI), an Integral Conservation Unit of the Atlantic Forest (24°12' - 24°25' S; 48°03 - 48°30' W). The Wet forest is a mature Atlantic Forest, and the Transitional forest is a young secondary forest adjacent to open areas. We measured operational temperatures (temperatures of inanimate objects comparable to real frog species in size and shape) of agar models across 8 replicates in the two forest environments. We also measured agar models' temperature and water loss in different behavior simulations. In each transect, we used eight sampling unit (called tetrad) that was composed of two sets of four sensor-fit agar models. One of the sets was used to collect the operational temperature of the forest areas. These agar models were fitted with a 170 cm HOBO® Data Logger (U12-008) sensor programmed to record the temperature every 15 min. The second set of tetrads was used to collect the agar models' body temperature and water loss in different behavior simulations. The behavioral simulations were defined as: (a) strong behavioral fever (SBF); (b) apathy behavior (AB); (c) single thermoregulatory event (STE); and (d) control model (CO).</p> <p>After the temperature data were collected at 0600 h, three of the agar models (SBF, AB, and STE) were moved immediately, each one according to their corresponding protocol (SBF: Warmest Neighboring Site, AB: closest shelter, mainly small burrows, or accumulations of leaf litter; STE: Alternative Warmest Neighboring site). The selection of the places was made by using a FLIR TG165 Thermal Imaging Thermometer. One hour later, at about 0700 h, and hereafter hourly, a similar procedure was repeated, but only the SBF model required movement. We placed each model within 5 cm of another in this set to ensure similar initial thermal conditions. These tetrads were left undisturbed overnight, and no manipulation occurred after 2000 h. On the next day, at 0600 h, we measured the surface temperatures of the models and immediately applied the corresponding behavioral rule. This procedure was performed hourly until 2000 h. To analyze water loss, we recorded the mass of each model at 0600 h just after measuring temperature and repeated this every two hours. We used a portable balance (A&D Newton EJ-123, 0.01g accuracy) and calculated water loss rates from the difference between the initial model mass and mass measured at each subsequent 2-hour period. We express water loss as a percentage of maximum hydration.</p>
Pearl River Delta FVCOM model mangrove forests scenarios
<p>Model data presented in: De Dominicis, M., Wolf, J., van Hespen, R., Zheng, P., Hu. Z. "Mangrove forests can be an effective coastal defence in the Pearl River Delta, China", <em>Communications Earth & Environment</em> (2023).</p> <p>To explore the effects of vegetation on storm surge dynamics and currents, we used a Finite Volume Community Ocean Model implementation for the South China Sea and the Pearl River Delta and simulated Typhoon Hato (2017) one of the strongest typhoons to affect the coastal areas of the Pearl River Delta in recent decades. We numerically modelled the protection capability of the mangrove wetlands in Shenzhen Bay and in the upper estuary river branches close to Guangzhou to determine their ability to mitigate coastal flooding. Additionally, we analyzed how the effectiveness of mangroves changes under different sea level rise scenarios. </p> <p>The dataset consists of water elevation and horizontal currents for 40 model experiments (see Table 1 in De Dominicis et al, 2023).</p>
Forest Types Show Divergent Biophysical Responses After Fire: Challenges to Ecological Modeling
<p>Datasets and scripts (MATLAB and R) used to document patterns of post-fire biophysical dynamics in seven forest types and 21 Level III ecoregions of the western United States.</p>
Evolutionary adaptation of trees and modelled future larch forest extent in Siberia. Code and simulation data
<p>Code and datset used for the publication: "Evolutionary adaptation of trees and modelled future larch forest extent in Siberia" 2023 Gloy et al.</p>
Modelling potential growth of forest restoration options in Indonesia
<p>Potential growth of secondary forest and tree species typology were assessed using biophysical productivity model developed by IIASA's Agriculture, Forestry, and Ecosystems Services Group. The methodology involved the integration of random forest algorithm, ground data, remote sensing products, soil properties, and literature on yield tables (more detailed methodology publication in preparation). To calibrate the model, the MODIS NPP dataset was adjusted using forest biomass and land cover maps. The model utilized ERA5-Land monthly averaged meteorological data from 2006 to 2015, with a resolution of 0.1° x 0.1°, in addition to soil properties, land cover, and elevation. This comprehensive approach allowed for the determination of spatially explicit site index values for plantations, secondary forests, and primary forests. The resulting productivity information are reflected in growth curves for both fast and slow-growing commercial species as well as native tree species. The parametrization of Chapman-Richards growth curves was conducted using data from representative tree species available in the literature. In the case of peatland areas, potential growth are also modelled using <a href="https://doi.org/10.5281/zenodo.7355835">water regime scenarios</a> provided by IIASA's EPIC model, offering insights into different implications of the potentially varying water table conditions in peatland.</p>
Dataset from "How does a warm and low-snow winter impact the snow cover dynamics in a humid and discontinuous boreal forest? Insights from observations and modeling in eastern Canada"
<p>The dataset presented below is described in the publication “<em>How does a warm and low-snow winter impact the snow cover dynamics in a humid and discontinuous boreal forest? An observational study in eastern Canada.</em>” from Bouchard et al. (submitted) in the journal Hydrology and Earth System Science.</p> <p>The original dataset includes <strong>monitoring data</strong> collected at Montmorency Forest (47.29°N, 71.17°W) from 15 October 2020 to 15 June 2021 (W20-21) and from 15 October 2021 to 15 June 2021 (W21-22) in a medium-size gap, the small-size gap and under the canopy. The study site is a balsam fir – whit birch stand on a 12° slope of north-east aspect. In the monitoring dataset you can find at the hourly timestep:</p> <ul> <li>Snow depth (cm)</li> <li>Soil temperature at 20 cm, 10 cm and 5 cm below ground surface (°C)</li> <li>Soil-snow interface temperature (°C)</li> <li>Snow temperature every 15 cm from the ground surface (°C)</li> <li>Snow surface temperature (°C)</li> <li>Air temperature (°C)</li> <li>Relative humidity (%)</li> <li>Soil volumetric water content at 15 cm below the ground surface (0 – 1)</li> </ul> <p>The dataset also includes <strong>snow pit observations</strong> taken at Montmorency Forest during W20-21 and during W21-22. Each winter, four (4) snow pits were dug inside medium-size gaps, small-size gaps and at subcanopy locations. Snow pit measurement dates are presented in Bouchard et al. (submitted). Each snow pit includes the vertical profile of:</p> <ul> <li>Snow stratigraphy</li> <li>Snow temperature</li> <li>Snow density</li> <li>Snow specific surface area (SSA)</li> </ul> <p> </p> <p>The snow pit height corresponds to the upper boundary of the topmost snow layer in the stratigraphy profile. For density measurements, the height value corresponds to the center of the 3-cm thick box cutter. For the SSA, the value is measured optically at the top of the sample. This value is representative of the top 1 cm of the snow sample, as this is the typical e-folding depth of 1310 nm radiation in snow. Grain type codes for the snowpack stratigraphy correspond to the <em>International Classification for Seasonal Snow </em>(Fierz et al., 2009):</p> <ul> <li>PP: precipitation particles </li> <li>DF: decomposed and fragmented precipitation particles</li> <li>RG: rounded grains</li> <li>FC: faceted crystals</li> <li>FCxr: rounding faceted particles</li> <li>DH: depth hoar</li> <li>MFpc: melt forms – rounded polycrystals</li> <li>MF: melt forms – clustered rounded grains</li> <li>MFcr: melt forms – melt-freeze crusts</li> <li>IF: ice formations</li> </ul>
Data set supporting publication "Hydrological Coupling and Decoupling of Hydric Hemi-boreal Forest Sites Inferred from Soil Water Models and Tree-Ring Chronology" by Kalvāns A. and Dauškane I. accepted for publication in the scientific journal Forests
<p>These files are supporting information for the article:</p> <p>Kalvāns, Daukškane (<em>accepted</em>). Hydrological Coupling and Decoupling of Hydric Hemi-boreal Forest Sites Inferred from Soil Water Models and Tree-Ring Chronology. <em>Forests</em></p> <p>The data set comprises following elements:</p> <ol> <li>Hydrus-1D soil water model setup files and calculation results ([1_Hydrus_1D_hydric_forest_soil_water_model_instances.zip]) for two study plots and 12 model instances, with following naming convention: [{Site Identifier}__{proportion of active leaf area index}_kLAI__{forced groundwater exfiltration rate cm/day}_SeepIn_const]. That is model instance named [P1__0.4_kLAI__0.05_SeepIn_const.h1d], considers the study site Plot_1, the active leafe area proportion is 0.4 and it has applied constant rate of groundwater exfiltration at the base of the soil column of 0.05 cm/day. The models are forced by E-OBS v26.0e data set for the period from 1980-01-01 to 2022-06-30.</li> <li>Black alder <em>Alnus glutinosa</em> tree ring chronologies for the two study plots ([2_tree_ring_data.zip])</li> <li>Soil and ground-water observation time series for the two study plots ([3_soil_ground_water_observations.zip])</li> </ol>
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.