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430 results for “forest structure”
Warming Effects on Microbial Structure and Decomposition at Harvard Forest 2011
Because microorganisms are sensitive to temperature, ongoing global warming is predicted to influence microbial community structure and function. We used large-scale warming experiments established at two sites near the northern and southern boundaries of US eastern deciduous forests to explore how microbial communities and their function respond to warming at sites with differing climatic regimes. Soil microbial community structure and function responded to warming at the southern but not the northern site. However, changes in microbial community structure and function at the southern site did not result in changes in cellulose decomposition rates. While most global change models rest on the assumption that taxa will respond similarly to warming across sites and their ranges, these results suggest that the responses of microorganisms to warming may be mediated by differences across the geographic boundaries of ecosystems.
Mangrove Coast Collaborative Project, Post-hurricane Irma mangrove forest structure data, Rookery Bay NERR, February 2022 - March 2023
The dataset describes the structure, composition, and condition of mangrove forests in Rookery Bay National Estuarine Research Reserve (NERR) assessed Feb 2022 - Mar 2023, approximately 5 years post Hurricane Irma (2017) and concurrent with Hurricane Ian (Sep 2022). The dataset includes information on each stem greater than or equal to 1 cm DBH (diameter at breast height) rooted within 69 100 m2 circular plots. Information collected includes site ID, location, species, DBH, status (live or dead), damage associated with the hurricane, presence/absence of regrowth, presence/absence of adventitious roots, whether or not stem is part of a multi-stemmed individual, and the canopy conditions (whether the tree has a canopy or is only sprouting at the base of the tree or trunk). This dataset is associated with the Mangrove Coast Collaborative project (2020 - 2024).
Mangrove Coast Collaborative Project, Post-hurricane Maria mangrove forest structure data, Jobos Bay NERR, March 2022 - August 2022
The dataset describes the structure, composition, and condition of mangrove forests in Jobos Bay National Estuarine Research Reserve (NERR) assessed approximately 5 years after disturbance from Hurricane Maria (2017). The dataset includes information on each stem greater than or equal to 1 cm DBH (diameter at breast height) rooted within 64 100 m2 circular plots. Information collected includes site ID, location, species, DBH, status (live or dead), damage associated with the hurricane, presence/absence of regrowth, presence/absence of adventitious roots, whether or not stem is part of a multi-stemmed individual, and the canopy conditions (whether the stem has a leafed canopy or is only sprouting at the base if live). This dataset is associated with the Mangrove Coast Collaborative project (2020 - 2024).
Forest structural diversity at NEON sites in the continuous USA that experienced recent moderate disturbance
Disturbances can change the structural diversity of forests through time, which can be measured from three-dimensional data provided by LiDAR. Discrete-return LiDAR was used to measure a suite of 19 structural diversity metrics that describe the height, cover and openness, vegetation density, and internal and external heterogeneity of forest vegetation at NEON base plots. Discrete-return LiDAR point clouds from the NEON Aerial Observation Platform (DP1.30003.001) were downloaded September of 2020 and used to estimate the metrics within 40 x 40 m base plots. Metrics were estimated from base plots at 15 NEON forested sites from provisional LiDAR data available from 2014 to 2020. The workflow that produced the data was developed in the program R.
Quantifying Growth and Structure along Forest Edges in the Northeastern USA 2010-2021
Fragmentation transforms the environment along forest edges. The prevailing narrative, driven by tropical research, suggests that edge environments increase tree mortality and structural degradation resulting in net decreases in ecosystem productivity. We show that temperate forest edges exhibit increased forest growth (basal area increment; BAI) and biomass (basal area; BA) with no change in total mortality relative to the forest interior. To assess forest edges, we analyzed more than 48,000 forest inventory plots (USDA FIA) across the north-eastern US using a quasi-experimental matching design. At forest edges adjacent to anthropogenic land covers, we report increases of 36.3% and 24.1% in forest growth and biomass, respectively. We then scale the edge impacts on growth (along anthropogenic edges only) across our study area using maps of land-cover and forest type. We find large variability in the effect of including edges on estimates of total forest growth, largely driven by differences in the prevalence of fragmentation. Estimated increases in forest growth range from a 23% increase in the agricultural-dominated western areas, a 2% increase in the least-fragmented northern regions, and a 15% increase within the metropolitan east coast. Finally, we also quantify forest fragmentation globally, at 30-m resolution, showing that temperate forests contain 52% more edge forest area than tropical forests. We provide two tables containing the post-matched dataset of FIA subplots, including subplot BA, BAI, and edge status. We include the associated environmental covariates, extracted from gridded raster data, and used in our matching and statistical analyses. Due to plot confidentiality restrictions we do not provide spatial locations of the FIA subplots, but we do provide unique plot identifiers that allow users to link each record to the publically-available data provided by the USDA FIA database (https://apps.fs.usda.gov/fia/datamart/). This dataset can be used to re
Structure of Ant Communities in Declining Hemlock Stands at Harvard Forest 2003
In biomass and ecological dominance, ants are the most important invertebrate taxon in terrestrial ecosystems and they can alter significantly fundamental processes and dynamics of soil ecosystems. Relative to deciduous stands, hemlock stands are depauperate in ant species that are linked to differences in rates of soil turnover and nutrient mineralization between these forest types. In both hemlock and deciduous stands, we will document ant species richness and abundance; assess their role in soil nutrient cycling; determine temporal trajectories of ant community assembly as hemlock declines following woolly adelgid infestation, and is subsequently replaced by birch; and discover how these trajectories influence nutrient availability during this transition. For more details see: Ellison, A. M., J. Chen, D. Dz, C. Kammerer-Burnham, and M. Lau. 2005. Changes in ant community structure and composition associated with hemlock decline in New England. Pages 280-289 in B. Onken and R. Reardon, editors. Proceedings of the 3rd Symposium on Hemlock Woolly Adelgid in the Eastern United States. US Department of Agriculgure - US Forest Service - Forest Health Technology Enterprise Team, Morgantown, West Virginia.
Data from: Functional structure of European forest beetle communities is enhanced by rare species
<p>From article abstract:</p> <p><a href="https://doi.org/10.1016/j.biocon.2022.109491">https://doi.org/10.1016/j.biocon.2022.109491</a></p> <p><strong>ABSTRACT</strong></p> <p>Biodiverse communities have been shown to sustain high levels of multifunctionality and thus a loss of species likely negatively impacts ecosystem functions. For most taxa, however, the roles of individual species are poorly known. Rare species, often the most likely to go extinct, may have unique traits leading to unique functional roles. Alternatively, rare species may be functionally redundant, such that their loss would not disrupt ecosystem functions. We quantified the functional role of rare species by using capture records of wood-living (saproxylic) beetle species, combined with recent databases of their morphological and ecological traits, from three regions in central and northern Europe. Using a rarity index based on species’ local abundance, geographic range, and habitat breadth, we used local and regional species removal simulations to examine the contributions of both the rarest and the most common beetle species to three measures of community functional structure: functional richness, functional specialization, and functional originality. In both regional species pools and local communities, all three of these measures declined more rapidly when rare species were removed than under common (or random) species removal scenarios. These consistent patterns across scales and among several forest types give evidence that rare species provide unique functional contributions, and that their loss may disproportionately impact ecosystem functions. This implies that conservation measures targeting rare and endangered species, such as preserving intact forests with dead wood and mature trees, can provide broader ecosystem-level benefits. Experimental research linking functional structure to ecosystem processes should be prioritized to increase our understanding of the functional consequences of species loss and to develop more effective conservation strategies.</p> <p> </p> <p><strong>DATASET DESCRIPTION</strong></p> <p>This dataset includes a) beetle capture information and b) beetle trait information from three countries: 1) Norway, 2) Finland, and 3) Germany. </p> <p> </p> <p><strong>FILES</strong></p> <p><strong>readme.txt</strong> -- this has the information from this description section</p> <p><strong>Norway_traits.csv</strong>, <strong>Finland_traits.csv</strong>, <strong>Germany_traits.csv</strong> -- these are the trait files, including all species</p> <p><strong>Norway_sites.species.csv</strong>, <strong>Finland_sites.species.csv</strong>, <strong>Germany_sites.species.csv</strong> -- this has species (rows) by sites (columns); values are the number of beetles caught (for number of traps, dates, and other site covariates, see related dataset: <a href="https://doi.org/10.5061/dryad.tmpg4f50b">https://doi.org/10.5061/dryad.tmpg4f50b</a> and manuscript: <a href="https://doi.org/10.1111/jbi.14272">https://doi.org/10.1111/jbi.14272</a>). Species names follow GBIF taxonomic backbone.</p> <p><strong>Traits_METADATA.csv</strong> -- this has information on all the fields in the trait data</p> <p> </p>
Population genomics reveals differences in genetic structure between two endemic arboreal rodent species in threatened cloud forest habitat
<p>SNPs obtained by UNEAK pipeline for <em>Habromys schmidlyi </em>and <em>Reithrodontomys microdon</em>. </p> <p>Pleae cite as: </p> <p>Colunga-Salas P., T Marines-Macías, G Hernández-Canchola, S Barbosa, C Ramírez, JB Searle, L León-Paniagua. 2022. <strong>Population genomics reveals differences in genetic structure between two endemic arboreal rodent species in threatened cloud forest habitat</strong>. Mammalian Reasearch. Doi: 10.1007/s13364-022-00667-x</p>
Post-logging community structure and biomass accumulation in Watershed 10, Andrews Experimental Forest , 1974 to present
Old-growth and mature Douglas-fir forest were clearcut logged in 1975. Large slash was removed from the site rather than burned. Douglas-fir seedlings were planted in 1976 and 1977, but due to poor survival 4.9 ha were replanted in 1978. Despite planting, most tree stems originated naturally, either as sprouts from cut stumps (hardwoods) or as natural regeneration (conifers and hardwoods). Study plots were established and sampled for cover and frequency of understory vegetation and counts of seedlings and saplings prior to harvest (1973) and resampled annually for cover and biomass of understory vegetation and tree growth from 1976-1981, then in 1983, 1985, and then in 4-6 year intervals. WS10 was intensively studied in the 1970’s and 80’s. The entire watershed was surveyed into a 25 x 25-m grid and then 36 15 by 10 meter plots were established for long-term sampling.
dataset: Responses of the structure and function of the understory plant communities to precipitation reduction across forest ecosystems in Germany
<p><strong>Context</strong>: Understory plant communities play a central role in forest biogeochemistry and the recruitment of trees making up the future forest. It is so far poorly understood how climate change will affect understory structure and functions in forest of different management intensity.</p> <p> </p><p><strong>Aims</strong>: We monitored understory functional traits including transpiration and carbon isotope discrimination, community structure and diversity during two growing seasons as affected by drought in forests subjected to different management intensities. We hypothesized that drought would affect ecophysiological traits such as transpiration but not species richness and diversity. Moreover, we assumed that stand-specific characteristics and forest management intensity modify the drought-resistance of the understory community.</p> <p></p> <p><strong>Methods</strong>: We set up roofs in beech and conifer stands with different management intensity in three different regions across Germany and a drought event close to the 2003 drought was imposed in two consecutive years.</p> <p><strong>Results</strong>: Precipitation reduction decreased soil water content by 2 to 8%, depending on stand and region, in comparison to the control subplots. In the first year, leaf level transpiration was reduced for different functional groups, which scaled to community transpiration modified by additional effects of drought on functional group specific leaf area. Acclimation effects in most functional groups were observed in the second year. We did not observe a significant reduction of plant diversity or a consistent management effect upon drought.</p> <p><strong>Conclusion</strong>: Our results indicate high plasticity and acclimation responses of the forest understory vegetation to changing climate conditions and recurrent drought events.</p> <p><strong>Abbreviations:</strong></p> <p>sp12 - campaign spring 2012; ls12 - campaign late summer 2012; es13 - campaign early summer 2013; ls13-campaign late summer 2013</p> <p>SEW16 - Schorfheide plot 16; SEW49 - Schorfheide plot 49; SEW48 - Schorfheide plot 48;HEW03 - Hainich plot 03; HEW12 - Hainich plot 12; HEW47- Hainich plot 47; AEW13 - Alb plot 13; AEW29 - Alb plot 29; AEW08 - Alb plot 08<br> explo - exploratory<br> SEW - Schorfheide; HEW - Hainich; AEW - Schwäbische Alb<br> in - conifer intensive managed; ma - beech managed; un - beech unmanaged<br> c- control; r - roof<br> LAIs - community leaf area index m<sup>2</sup>/m<sup>2</sup>; H - Shannon´s diversity index; Ts - community transpiration rate (weighted by LAI) mmol H<sub>2</sub>O m-<sup>2</sup> leaf area s-<sup>1</sup>; Ets - Evapotranspiration (mmol/m2/sec); E - Evaporation (mmol/m2/sec); C - leaf photosynthetic carbon isotope discrimination (∆<sup>13</sup>C) according to Farquhar et al. (1982); Cs - community photosynthetic carbon isotope discrimination (∆<sup>13</sup>C) according to Farquhar et al. (1982) (weighted by LAI)</p> <p> </p>
R scripts for analyzing LiDAR data to assess forest canopy structure and perform Principal Component Analysis (PCA) on derived metrics
<p>This repository contains R scripts for analyzing LiDAR data to assess forest canopy structure and perform Principal Component Analysis (PCA) on spectral and LiDAR-derived metrics. The scripts cover LiDAR data processing, canopy height model (CHM) generation, calculation of forest canopy metrics, and PCA analysis.</p>
Stand structure and tree population dynamic attribute dataset of long abandoned strict forest reserves
<p>We provide an integrated dataset of two consecutive forest inventories, both containing plot-level, and individual tree-level data. The first provides the descriptions and measuring units (or categories) of plot-level variables (Table 1). The plot level table contains 233 records (rows), one for each selected permanent plot of six strict forest reserves located in Hungary. This dataset is georeferenced and contains information on inventories and basic stand structure attributes (Table_1_Plots ESRI shape format). </p> <p>The individual tree-level datasets were acquired by the sampling procedure, detailed in section 2.2. Species, dendrometric attributes, relative crown position, health, and decay status were documented for each tree belonging to the samples. Table 2 provides the descriptions and measuring units (or categories) of tree-level datasets in detail. Furthermore, it provides a tree history classification based on the interpretation of tree status changes. According to a simple scheme of the life and dead history of a tree, it could be classified into four main phases: establishment/regeneration phase; developmental phase; death and gradual decay of the tree trunk; terminated in decomposed/disintegrated state. The main events along these phases are ingrowth regeneration; death of the tree (mortality); disaggregation and decomposition of deadwood. We classify each sampled tree individuals into tree history categories (events and phases, Table 3) that can provide population dynamic aspects at stand level by appropriate tree aggregation functions.</p> <p>Relational link can be set between the plot-level and tree-level datasets based on the unique identification code of the site and sampling plots.</p>
Dataset (81 forest parcels) supplementing the publication "Owner attitudes and landscape parameters drive stand structure and valuable habitats in small-scale private forests of Lower Saxony (Germany)"
<p>The dataset about 81 small-scale private forest parcels contains the answer variables and predictors used in the publication "Owner attitudes and landscape parameters drive stand structure and valuable habitats in small-scale private forests of Lower Saxony (Germany)".</p>
Age structure, developmental pathways, and fire regime characterization of Douglas-fir/western hemlock forests in the central western Cascades of Oregon
These data are the raw forest stand- and age-structure data from 124 stands in the central western Cascades of Oregon used to construct a conceptual model of stand development under the mixed-severity fire regime that has operated extensively in this region.
Tree stand structure summary at Bonanza Creek Experimental Forest LTER sites
We measure trees and shrubs within 50 x 60 m permanent plots at LTER research sites within the Bonanza Creek Experimental Forest, as a means to monitor vegetation change and to estimate productivity. The 27 LTER research sites represent three replicates each of six successional stages of primary succession on the floodplain of the Tanana River and three stages of succession following wildfire in the uplands. All trees and tall shrubs within the plot are stem mapped, and the dbh is measured at least every 5 years. The condition of each tree is assessed at the time of measurement and following major disturbance events. Band dendrometers have been placed on select trees at each site where applicable. Litterfall and seedfall are collected and measured annually. White spruce seedlings are mapped and their heights measured every 2 years within the tree plots at younger successional stages. The DBH of alders and willows are measured. Shrub heights are measured annually at young floodplain stands. Changes in species composition through succession are a function of life history traits modified by facilitative and competitive interactions. Vegetation-caused changes in resource (light, nutrients, and moisture)availability during succession control vegetation biomass, productivity, and organic matter and nutrient distribution.
Structural cover at snowshoe hare predation sites in Bonanza Creek Experimental Forest from 2008 to 2012
This dataset contains the horizontal and canopy cover measured at predation sites of snowshoe hares that were collared in Bonanza Creek Experimental Forest.
Carbon Dynamics Along a Permafrost Gradient at Caribou-Poker Creeks Research Watershed (CPCRW) in Interior Alaska: Forest stand structure in a 75x75m spatial domain along a permafrost and vegetation gradient.
This dataset includes forest stand structure. Project summary: Specific leaf area (SLA, leaf area per unit dry mass) is a key canopy structural characteristic, a measure of photosynthetic capacity, and an important input into many terrestrial process models. Although many studies have examined SLA variation, relatively few data exist from high latitude, climate-sensitive permafrost regions. We measured SLA and soil and topographic properties across a boreal forest permafrost transition, in which forest composition changed as permafrost deepened from 54 to >150 cm over 75 m hillslope transects in Caribou-Poker Creeks Research Watershed, Alaska. This is an exploratory study to begin understanding SLA variation and controls thereof in a non-contiguous permafrost system.
Hubbard Brook Experimental Forest and Adirondack Mountains: In-stream large wood and riparian forest structure, 2002-2019
This dataset presents data on the in-stream large wood in 16 stream reaches in the Hubbard Brook Experimental Forest as well as the riparian forest structure and composition at these streams. It also provides data on the large wood in 13 stream reaches in old-growth forests in the Adirondack Mountains of New York.
Dataset for "Phylogenetic structure of European forest vegetation" - Journal of Biogeography (DOI: 10.1111/jbi.14046)
<p>This dataset contains the list of plant occurrences and geographical and environmental attributes of the vegetation-plots analyzed in the paper titled “Phylogenetic structure of European forest vegetation” by Padullés Cubino et al. (2021; Journal of Biogeography; DOI: 10.1111/jbi.14046). </p> <p>The dataset contains 3 tables:</p> <ol> <li>“Table_taxa.csv”: It includes the list of angiosperm plant taxa in selected vegetation plots.</li> <li>“Table_sites.csv”: It includes data on the environmental variables of plots, their classification into different forest types, their location in 1<sup>o</sup> × 1<sup>o</sup> grid cells, and the reference to the original datasets archived in the European Vegetation Archive (EVA; http://euroveg.org/eva-database-participating-databases).</li> <li>“Metadata.csv”: It includes a description of the fields found in the two previous tables.</li> </ol>
LiDAR-derived forest structure data and predictions of the locations of old-growth forests for Central Finland.
<p><strong>INTRO</strong><br> This archive contains data and analysis code for the Biodiversity Map -project conducted by Open Knowledge Finland (http://fi.okfn.org/projects/biodiversity-map/)</p> <p><strong>LICENCE</strong><br> The files listed below are all released to the public domain under a CC0 public domain dedication (https://creativecommons.org/publicdomain/zero/1.0/)</p> <p><strong>FILE DESCRIPTIONS</strong></p> <p><em><strong>FILE 1:</strong></em> background.zip<br> Inside the archive is a comma-separated file "background.csv" containing LiDAR-derived forest structure variables for 2/3 of Central Finland. These were derived from 3 raster data sets describing forest canopy maximum height (mh), forest canopy cover (cc) and lidar return intensity (in). The rasters had resolutions of 6 metres, 6 metres and 2 metres, respectfully. An 18 m resolution grid was then used to aggregate the rasters into average, minimum and maximum values + standard deviations of the original variables. The original LiDAR data was made available by the National Land Survey of Finland.</p> <p><br> <em><strong>FILE 2:</strong></em> conservation.lambdas<br> This file contains fitted parameters for the maxent model. For more information, check maxent documentation at https://www.cs.princeton.edu/~schapire/maxent/</p> <p><strong><em>FILE 3:</em></strong> conserved_swd.csv<br> Forest structure variables at 18 meter resolution for old-growth conservation areas in Central Finland. A subset of background.csv. This file still has a header, the variables are the same as in background.csv</p> <p><em><strong>FILE 4:</strong></em> grass_create_forest_rasters_from_las.sh<br> A shell script used to convert LiDAR files to raster maps of forest structure with GRASS 7.</p> <p><em><strong>FILE 5:</strong></em> lidar_coverage.png<br> A map showing the extent of LiDAR data available for Central Finland when we did the analyses.</p> <p><em><strong>FILE 6:</strong></em> maxent_model_run_product.sh<br> A shell script used to fit the maximum entropy model to predict the locations of conservation-area-like forests in Central Finland.</p> <p><em><strong>FILE 7:</strong></em> projection_product.csv<br> The results of the maxent model in a comma separated file. The first row has the variable names: x,y,product_fit. x and y are coordinates in the CRS ETRS-TM35FIN (EPSG:3067). product_fit is "the probablility that this 18*18 meter grid cell is old-growth conservation area".</p> <p><em><strong>FILE 8:</strong></em> README<br> A file with a description of the dataset in human-readable form.</p> <p><strong>VALIDATION FILES</strong><br> The data in these files was collected to validate the results of the aforementioned maxent model. The data were collected in a hierarchical sampling scheme: six randomly determinded unintersecting 9 km * 9 km landscape windows were chosen for sampling. From each window, three samples were taken. One sample from conservation areas, one sample from the "best" 10 % of forests as determined by the maxent model excluding conservation areas and one random sample. Not all windows contained conservation areas, and not all areas were accessible (islands, for example). In addition a few areas were skipped due to time constraints.</p> <p>The sampled points are identified by their lanscape window (suuralue), their sample (otos) and their sample number (mittauspiste).</p> <p><em><strong>FILE 9:</strong></em> validation_felled.csv<br> A comma separated list of those points that were not measured because they were felled.</p> <p><em><strong>FILE 10:</strong></em> validation_gps_results_2016-09-07.csv<br> A list of gps coordinates for all the sample points. product_fit is the value of the geographically closest prediction from the maxent model described above.</p> <p><em><strong>FILE 11:</strong></em> validation_lying_deadwood_transects_2016-08-30.csv<br> A comma separated file with data from deadwood transects. From each validation point, three 30 m long transects were made with 120 degree angles between them, and all lying deadwood more than 2 cm in diameter were measured. For some validation points, there were geographical obstructions which prevented the full 90 m of transect being surveyed, this is also recorded in the data. Each row holds measurements from one lying trunk.<br> </p> <p><em><strong>FILE 12:</strong></em> validation_relascope_2016-08-30.csv<br> Relascope measurements from the validation points. Each row is measurements for one species from one validation point. Dead and alive trees are counted separately.<br> </p> <p><strong>MORE INFORMATION</strong></p> <p>For more in-depth descritions of the files, read the file named README.<br> For some auxilliary files and information, check our old hackathon repository on github: https://github.com/Koalha/bdm_hackathon</p>
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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.