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38 results for “Vegetation disturbances”
Critical slowing down: vegetation height, cover and composition assess resilience of tidal fresh, brackish and salt marshes following experimental disturbance at twelve sites across the estuarine landscape
We investigated the relative recovery rates of vegetation cover, vegetation height and community composition over a period of >10 years after experimental disturbance in 12 tidal marshes (two oligohaline, three mesohaline and seven polyhaline sites) across the estuarine landscape on the coast of Georgia, USA. Vegetation was removed from three by three meter plots (four per site) with herbicide in 2006 followed by repeated clipping through 2009. Vegetation height, cover and composition was assessed in experimental and control plots (4 of each per site) from 2010 to 2020. The goal of the study was to assess how quickly the sites recovered from disturbance, how this varied in different types of marshes, and how results varied depending on whether recovery was assessed by vegetation height, cover or composition.
Plant Removal Study: Recovery of Vegetation Following Disturbance at the Sevilleta National Wildlife Refuge, New Mexico
In 1995, a removal study was initiated at the Sevilleta LTER to examine the response of vegetation following the removal of dominant species. Five sites were selected that were dominated by either blue grama (site 1), blue and black grama (site 2), black grama (site 3), black grama and creosote (site 4), or creosote (site 5). A sixth site was later added in the blue grama community along the foothills of the Los Pinos Mountains (site 6). At sites 1, 3, 5, and 6, five 3m x 4m plots had all plants of the dominant species removed; five 3m x 4m plots were controls. At site 2, 5 plots had blue grama removed, 5 plots had black grama removed, and 5 plots were controls. At site 4, 5 plots had black grama removed, 5 plots had creosote removed, and 5 plots were controls. Initial cover prior to removal was estimated by species for each plot. Grass was removed using a shovel to collect above-ground biomass and crowns just below the soil surface. Shrubs were removed using large clippers to collect above-ground biomass to the soil surface. All biomass removed was bagged, dried, and weighed. Plot maintenance or removal of the target dominant species is performed annually or as needed. Rain gauges were installed at each site and the corners of the areas containing each set of plots GPS'd. Plot corners are marked by nails and are flagged periodically to aid identification and minimize foot traffic in the plots. Each northeast nail has a metal tag with site and plot number on it. Erosion bridges (1 m long) were installed in plots 1, 3 and 5 (removals and controls) at sites 1-5. Initial measurements were made in 1996.
Disturbances in vegetation detected with BFAST in the Purapel fluvial catchment
<p>This dataset contains the results (69 TIFF files) of seasonal disturbances detected in vegetation in the Purapel catchment (southern Chile) for the period from 2002 to 2019. These disturbances were obtained by applying the Breaks for Additive Season and Trend (BFAST, Verbesselt et al., 2010) algorithm to 745 Landsat 5, 7 and 8 imagery. We used Collection 2 Level 2 surface reflectance products and applied the CFMask algorithm (Foga et al., 2017) for cloud masking before utilizing the BFAST algorithm.</p> <p>The BFAST algorithm detects changes in the NDVI time series of each pixel. To determine which event was considered a disturbance, we used the same intensity thresholds as in Cabezas and Fassnacht (2018). We then filtered the results to keep just the disturbances with areas greater than 1 hectare, eliminating noisy data.</p> <p>Except for 2 big wildfires (2015 and 2017) it was assumed that all of the disturbances were clear cuts, since forestry is the main productive activity in the region. This was confirmed by validating the data with 35 manually drawn polygons that were randomly distributed across the catchment. Then, we performed an accuracy assessment, obtaining a confusion matrix with a balanced accuracy of 0,86 and a F1 score of 0,69.</p> <p>Each TIFF file is a binary grid with “zeros” representing no disturbance and “ones” representing a disturbance in the season that the name of the file indicates.</p> <p>A GIF file is also included, which contains the time series of the disturbances for easier graphical purposes.</p> <p>References</p> <p>J. Cabezas and F. E. Fassnacht. Reconstructing the Vegetation Disturbance History of a Biodiversity Hotspot in Central Chile Using Landsat, Bfast and Landtrendr. In IGARSS 2018 - 2018 IEEE International Geoscience and Remote Sensing Symposium, pages 7636–7639. IEEE, 7 2018. ISBN 978-1-5386-7150-4. doi: 10.1109/IGARSS.2018.8518863.</p> <p>S. Foga, P. L. Scaramuzza, S. Guo, Z. Zhu, R. D. Dilley, T. Beckmann, G. L. Schmidt, J. L. Dwyer, M. Joseph Hughes, and B. Laue. Cloud detection algorithm comparison and validation for operational Landsat data products. Remote Sensing of Environment, 194:379–390, 6 2017. ISSN 00344257. doi: 10.1016/j.rse.2017.03.026.</p> <p>J. Verbesselt, R. Hyndman, G. Newnham, and D. Culvenor. Detecting trend and seasonal changes in satellite image time series. Remote Sensing of Environment, 114(1):106–115, 1 2010. ISSN 00344257. doi: 10.1016/ j.rse.2009.08.014. URL http://linkinghub.elsevier.com/retrieve/pii/S003442570900265X.</p>
Model output, drivers and parameters for Ecosystem Recovery from Disturbance is Constrained by N Cycle Openness, Vegetation-Soil N Distribution, Form of N Losses, and the Balance Between Vegetation and Soil-Microbial Processes
Files used to generate the data for figures in: Rastetter, EB, Kling, GW, Shaver, GR, Crump, BC, Gough, L. Ecosystem Recovery from Disturbance Is Constrained by N Cycle Openness, Vegetation-Soil N Distribution, Form of N Losses, and the Balance between Vegetation and Soil-Microbial Processes. Ecosystems (2020). https://doi.org/10.1007/s10021-020-00542-3. This paper present a framework for assessing biogeochemical recovery of terrestrial ecosystems from disturbance. We identify three recovery phases. In Phase 1, nitrogen is redistributed from soil organic matter to vegetation, but the ecosystem continues to lose nitrogen because the recovering vegetation cannot take up nitrogen as fast as it is released from soil. In Phase 2, the ecosystem begins re-accumulating nitrogen and converges on a quasi-steady state in which vegetation and soil-microbial processes are in balance. In Phase 3, vegetation and soil-microbial processes remain in balance and the ecosystem slowly re-accumulates the remaining nitrogen.
Experimental disturbance treatments of wetland vegetation
<p><em>Study sites</em></p> <p>Our study sites were located in Kastbjerg Ådal (river valley) in Eastern Jutland, Denmark. It is within the Natura 2000 and habitat area no. 223 appointed because of the wide stretch of fens and mires among other qualities. The water course is in good ecological status according to the Water Framework Directive. Nitrogen deposition in this area is low to moderate, 12.5-14.5 kgN/ha/yr (Ellermann et al. 2021). Meadows and fens dominate the study area, known for ‘the longest stretch of rich fen’ in Denmark. Large parts of the river valley are heavily degraded by drainage, fertilization and scrub encroachment, but there have also been recent efforts to restore the watercourse and the valuable rich fens in the valley. Most fens and wet meadows have been abandoned and are now increasingly dominated by tall grasses, tall forbs and willow scrub, but summer grazing occurs in some areas and efforts are made to ensure grazing in the most valuable fens. The drier meadows are typically mown by heavy machinery. The sites were selected to represent gradients in soil moisture from moist to wet and gradients in nutrient status or productivity from poor to rich and included rich fens with characteristic species, fens dominated by <em>Juncus subnodulosus</em> and by <em>Equisetum fluviatile</em>, drained fens encroached by <em>Phragmites australis</em> and natural meadows with characteristic species and encroached by <em>Epilobium hirsutum</em> and meadows characterized by clovers and cultural grasses.</p> <p>The nine sites were of 10 m<sup>2</sup>, each with ten 1 m<sup>2</sup> plots. The 10 plots within each site had treatments assigned randomly. Despite the location in the same river valley, the sites were considered independent because of their different management history and starting conditions and a typical inter-site distance of c. 225 meters. The experiment was established in June 2017 and treatments were repeated monthly during summer and bimonthly during winter, depending on treatment. Responses were recorded in July 2019.</p> <p> </p> <p><em>Experimental set-up and treatments</em></p> <p>Each of the 9 sites were divided into ten 1 m × 1 m plots each with a 0.5 m × 0.5 m inner square and a surrounding plot buffer zone with a control and the following treatments: burning, mowing, trampling, intensive summer grazing (SI), intensive summer grazing with trampling (SIT), extensive summer grazing (SE), extensive summer grazing with trampling (SET, year-round grazing (YR), and year-round grazing with trampling (YRT). Treatments were allocated randomly to each plot with the restriction that the control plot was always in one corner. The experiment was multifactorial with respect to grazing and trampling, whereas burning and mowing were stand-alone treatments. Initial biomass in each plot was estimated at the beginning of the experiment in June 2017 as follows: all standing biomass and litter was removed from the plots by manual cutting at the soil surface and following the micro-topography. Bryophytes were harvested by hand plucking. Biomass, litter and bryophytes from the plot buffer zone were cut separately from the inner square. To estimate the species abundances, a representative sample of the inner square was sorted into litter and live biomass (including bryophytes) by species as sorting the complete biomass was not feasible. All species, litter and biomass from the buffer zone were dried at 55° C and weighed. Using the relative abundance of species in the representative sample and with respect to the weight of the total biomass in the inner square, we estimated the abundance of the species in the inner square.</p> <p>Burning was simulated in March 2018 and 2019. We used wooden boards to shield and adjacent areas were watered before burning the focal plot with a gas weed burner. We burned on a calm day following a dry period with frost to ensure minimum risk of igniting underlying peat and fire spreading over ground, but ensuring that the standing biomass and litter would be dry enough to ignite. This is not a simulation of a naturally occurring wildfire, but corresponds to the conditions that managers would prefer for prescribed conservation burning at larger scales. We simulated mowing as a biomass removal in June 2018. Biomass was removed uniformly across the whole plot in a height of c. 5 cm depending on microtopography. This corresponds to conservation mowing in management but without the added disturbance and pressure from machines. Trampling disturbance was applied using short stilts that could be attached to the field biologist’s boot. The surface of the stilt was 49 cm<sup>2</sup> which corresponds to a pressure of 1.3-1.5 kg/cm<sup>2</sup> with the added weight of the field biologist. This again corresponds to the pressure of a hoof of cattle weighing c. 300-400 kg. Trampling was applied by stepping into the field randomly 60 times once every month from May to September and was the same treatment in combination with intensive, extensive and year round grazing. Grazing was simulated by cutting the above-ground biomass using a 1 m<sup>2</sup> frame divided into a 10 cm coordinate system using the letters A-J on the x-axis and the numbers 1-10 on the y-axis. We cut tufts of biomass within the coordinate system using a list of random combinations of letters and numbers. This system enables “ungrazed” individuals to flower and set seeds. Based on our experience with grazing as an agri-environmental management practice in Denmark, we defined intensive summer grazing as taking place between May and September with the goal of removing all standing biomass by September. Extensive summer grazing also takes place May-September, but we carried this out at half the intensity as intensive summer grazing. Year-round grazing obviously takes place during the whole year (here administered May-September and November, January and March) with the goal of removing all standing biomass by the end of winter (March) before the beginning of a new growing season. We used the initial standing biomass (June 2017) as a measurement of plot productivity and estimated the amount of biomass to be removed during “grazing” as c. 20 % of the initial productivity each month May-September in intensive plots and with all standing biomass “grazed” in September. For extensive plots, we estimated removed biomass as c. 10 % of the initial productivity each month May-September leaving some standing biomass in September. Year-round grazing biomass removal was estimated as c. 10 % of yearly productivity removed every month May-September and November and 20 % removed in January and March resulting in no standing biomass at the end of the winter. As expected plot productivity changed as a result of the treatments, the amount of biomass removed had to be adjusted throughout the experiment. In practice, we aimed for removing twice the amount of biomass in intensive plots relative to extensive plots within the same site and always ensuring that no standing biomass was left in intensive plots in September, c. 50 % of the standing biomass was left in extensive plots in September and no standing biomass was left in year-round grazing plots in March (see actual removed biomass by treatment in Appendix A). All treatments were applied to the whole plot (1 m × 1 m), while the biomass response was only measured in the inner square (0.5 m × 0.5 m), leaving a buffer zone between plots with different treatments.</p> <p> </p> <p><em>Response variables</em></p> <p>A full plot (1 m × 1 m) species list was recorded in the field at the end of the experiment. From this total plot richness, vascular plant plot richness, bryophyte plot richness and number of indicator species per plot were calculated. Indicator species of conservation status are species considered moderately to very sensitive towards habitat degradation as defined by Fredshavn et al. (2010, see Appendix C). Indicator species are often adapted to relatively infertile habitats revealed by low Ellenberg N values and high Grime’s S values reflecting tolerance to nutrient shortage.</p> <p>Mean plot Grime’s C and S values (Grime et al. 1989) were calculated based on vascular plant species lists. We converted Grime’s life strategies to numerical values based on Ejrnæs and Bruun (2000).</p> <p>We performed a Nonmetric Multi-dimensional Scaling analysis (NMDS) on the presence-absence of vascular plant and bryophyte species at the end of the experiment using the function metaMDS in R-package ‘vegan’ (Oksanen et al. 2017) in R version 4.0.3 (R Core Team 2017), using Sørensen dissimilarity and a four-dimensional solution (k =4). The plot coordinates at the three first NMDS axes were extracted (NMS4 was discarded as noise) and these, along with the four richness variables as well as Grime’s C and S values, were used as response variables in Linear Mixed Models (LME) as described in ‘Statistical analyses’.</p> <p>Supplementary to regression models of single response variables we carried out a quadratic discriminant analysis (QDA) as described in ‘Statistical analyses’ using the change in six indicators during the course of the experiment. The difference between plot species richness at the beginning and end of the experiment was calculated based on the species lists from sorted initial biomass and end biomass (0.5 m × 0.5 m). Start-end differences were also calculated separately for vascular plant species richness, bryophyte species richness, richness of indicator species, the ratio between biomass of forbs and graminoids (grasses, sedges and rushes) and Grime’s C and S mean site values.</p> <p> </p> <p><em>Explanatory and co-variables</em></p> <p>Leaf nitrogen, carbon and phosphorous were determined from plot level sampling of leaf plates of grasses, i.e., the most abundant species group across sites. Fresh leaf plates were collected at the beginning and end of the project and then dried, ground and analyzed in the lab. Soil moisture (% volumetric water content) was measured as the mean of four measurements per plot at the beginning and end of the project using a FieldScout TDR 300 Soil Moisture Meter.</p> <p>The total number of species found in each site was used as a co-variable in species richness models reflecting the local species pool.</p> <p> </p> <p><em>Data processing</em></p> <p>All species names were checked for synonyms using the national database arter.dk.</p> <p> </p> <p>References:</p> <p> </p> <p>Ejrnæs, R. and H. H. Bruun (2000). "Gradient analysis of dry grassland vegetation in Denmark." Journal of Vegetation Science <strong>11</strong>(4): 573-584.</p> <p>Ellermann, T., R. Bossi, J. Nygaard, J. H. Christensen, P. Løfstrøm, C. Monies, C. Geels, I. E. Nielsen and M. B. Poulsen (2021). Atmosfærisk deposition 2019. NOVANA. Aarhus, Aarhus Universitet, DCE - Nationalt Center for Miljø og Energi.</p> <p>Fredshavn, J., R. Ejrnæs and B. Nygaard (2010). "Teknisk anvisning for kortlægning af terrestriske naturtyper. TA-N3, Version 1.04. Fagdatacenter for Biodiversitet og Terrestriske Naturdata, Danmarks Miljøundersøgelser. 18 s. ."</p> <p>Grime, J. P., J. G. Hodgson and R. Hunt (1989). Comparative plant ecology: a functional approach to common British species. London, Unwin Hyman.</p> <p>Oksanen, J., F. G. Blanchet, R. Kindt, P. Legendre, R. B. O'Hara, G. L. Simpson, P. Solymos, M. H. H. Stevens and H. Wagner (2017). "Package 'vegan': Community Ecology Package. Version 2.4-3. <a href="http://cran.r-project.org/web/packages/vegan/vegan.pdf">http://cran.r-project.org/web/packages/vegan/vegan.pdf</a>."</p> <p> </p> <p>R Core Team (2017). R: A language and environment for statistical computing. Vienna, Austria, R Foundation for Statistical Computing.</p>
Hurricane Disturbance Vegetation Anomaly (HDVA) [Data set for Turner et al.]
<p>The Hurricane Disturbance Vegetation Anomaly (HDVA) is a rapid assessment approach to understand the severity of ecological damage from a high intensity storm event on an otherwise healthy, mature mangrove forest. Data archived here focuses on Cuba, where Hurricane Irma (Category 5) hit the northern coast in September 2017 and caused wide-spread damage to mangroves and coastal forests. Local scientists were not able to assess the full extent or severity of damages through field work due to limited infrastructure and resources, so they turned to remote sensing analysis. We developed a multitemporal, multiresolution approach to assess the damage 1) extent and 2) relative severity using changes from the typical green-leaf phenology represented by the Enhanced Vegetation Index with MODIS and Sentinel-2 data. All data was processed in Google Earth Engine API to access and utilize large amounts of historical data, as well as compare sensors’ spatial resolution impacts on results. This data set includes the HDVA products and categorization of data by quartile of damage (catastrophic, severe, moderate, mild, no loss). </p>
Estimated wetland vegetation cover comparing reference and muskrat disturbed areas
Biota that act as ecosystem engineers are increasingly recognized as effective components of habitat restoration as they can continuously alter and shape their environments without human intervention. Muskrats are considered ecosystem engineers and are relatively common in North American wetlands. While muskrat impacts to large areas have been documented, few studies have quantified the impact of muskrats on ecosystem biodiversity. We conducted a field study in wetlands along the Upper St. Lawrence River (New York, USA) to investigate the effects of muskrat herbivory and structure building on plant biodiversity in cattail-invaded wetlands. The data collected are separated into two main groups, i.e. “reference” vegetation surveys and “muskrat” vegetation surveys. “Reference” surveys were conducted for the partial grant fulfillment funded by the New York Department of Environmental Conservation [contract AM10165] and the National Fish and Wildlife Foundation special project administered by the United States Fish and Wildlife Service, Partners for Fish and Wildlife Program, New York Field Office, Cortland, NY [grant number #2005-0129-055/58859]. “Muskrat” surveys were conducted for the Masters project by Z.X. Kua. The five datasets include 1) Reference quadrat plant taxa estimated cover (n = 1912), 2) Reference quadrat cattail measurements (n = 737), 3) Muskrat-disturbed quadrat plant taxa estimated cover (n = 1886), 4) Muskrat-disturbed quadrat cattail measurements (n = 582), and 5) Wetland plant taxa lookup table for vegetation survey (n = 135).
Neighboring edges: interacting edge effects of linear disturbances on vegetation in treed fens
<p>The influence of edges on forest biodiversity is an important environmental effect associated with habitat fragmentation, as edge effects can further reduce the remaining 'interior' habitat. However, extrapolating the influence of edges across the broader landscape has been difficult, especially regarding how to treat multiple edges in close proximity, where edge effects might interact. In this study, we examined the interaction of edge effects for multiple edges from a dense network of narrow (3-8 m wide) and low-severity linear disturbances called 'seismic lines'. Seismic lines are created during oil and gas exploration and are responsible for severe dissection of boreal forests in western Canada. Specifically, our objectives were to: (1) to compare the edge influence of "wide" (~8 m) and "narrow" (~3 m) seismic lines; and (2) to determine whether edges in close proximity show interaction of edge influences, i.e. do multiple narrow seismic lines have a stronger or weaker edge influence than a single narrow seismic line. We sampled vegetation along transects perpendicular to seismic lines in treed moderate-rich and poor fens. We used randomization tests of edge influence to calculate the magnitude and distance of edge effects. In moderate-rich fens, we found a positive edge influence on understory diversity from both wide and narrow seismic lines. We also found a weakening edge interaction on diversity, i.e., single narrow seismic lines had a stronger edge influence on diversity than multiple narrow seismic lines. In treed poor fens, multiple narrow seismic lines had a negative edge effect on tree density, understory abundance, richness, and composition. In addition, we found strengthening edge interactions in treed poor fens on tree density, graminoid cover, and understory composition. Our results show how assessing the edge influence of multiple disturbances can provide a better understanding of the cumulative effects of habitat fragmentation.</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>
source data of "Enhanced vegetation drought resistance induced by elephant disturbance in Africa"
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Landslide age, elevation and residual vegetation determine tropical montane forest canopy recovery and biomass accumulation after landslide disturbances in the Peruvian Andes
<p>Landslides are common natural disturbances in tropical montane forests. While the geomorphic drivers of landslides in the Andes have been studied, factors controlling post-landslide forest recovery across the steep climatic and topographic gradients characteristic of tropical mountains are poorly understood.</p> <p>Here we use a LiDAR-derived canopy height map coupled with a 25-year landslide time series map to examine how landslide, topographic, and biophysical factors, along with residual vegetation, affect canopy height and heterogeneity in regenerating landslides. We also calculate aboveground biomass accumulation rates and estimate the time for landslides to recover to mature forest biomass levels.</p> <p>We find that age and elevation are the biggest determinants of forest recovery, and that the jump-start in regeneration that residual vegetation provides lasts for at least 18 years. Our estimates of time to biomass recovery (31.6-37.1 years) are surprisingly rapid, and as a result we recommend that future research pair LiDAR with hyperspectral imagery to estimate forest aboveground biomass in frequently disturbed landscapes.</p> <p>Synthesis: Using a high-resolution LiDAR dataset and a time-series inventory of 608 landslides distributed across a wide elevational gradient in Andean montane forest, we show that age and elevation are the most influential predictors of forest canopy height and canopy variability. Other features of landslides, in particular the presence of residual vegetation, shape post-landslide regeneration trajectories. LiDAR allows for a detailed analysis of forest structural recovery across large landscapes and numbers of disturbances, and provides a reasonable upper bound on aboveground biomass accumulation rates. However, because this method does not capture the effect of compositional change through succession on aboveground biomass, wherein high-wood density species gradually replace light-wooded pioneer species, it overestimates aboveground biomass. Given previously estimated stem turnover rates along this elevational gradient, we posit that aboveground biomass recovery takes at least three times as long as our recovery time estimates based on LiDAR-derived structure alone.</p>
Similar vegetation-geomorphic disturbance feedbacks shape unstable glacier forelands across mountain regions
<p>Glacier forelands are among the most rapidly changing landscapes on Earth. Stable ground is rare as geomorphic processes move sediments across large areas of glacier forelands for decades to centuries following glacier retreat. Yet, most ecological studies sample exclusively on stable terrain to fulfil chronosequence criteria, thus missing potential feedbacks between geomorphic disturbances and vegetation colonization. By influencing vegetation and soil development, such vegetation-geomorphic disturbance feedbacks could be crucial to understand glacier foreland ecosystem development in a changing climate. We surveyed vegetation and environmental properties, including geomorphic disturbance intensities, in 105 plots located on both stable and unstable moraine terrain in two geomorphologically active glacier forelands in New Zealand and Switzerland. Our plot data showed that geomorphic disturbance intensities changed permanently from high/moderate to low/stable when vegetation reached cover values around 40%. Around this cover value, species with response and effect traits adapted to geomorphic disturbances dominated. This suggests that such species can act as 'biogeomorphic' ecosystem engineers that stabilize ground through positive feedback loops. Across floristic regions, biogeomorphic ecosystem engineer traits creating ground stabilization, such as mat growth and association with mycorrhiza, are remarkably similar. Non-metric multidimensional scaling revealed a linked sequence of decreasing geomorphic disturbance intensities and changing species composition from pioneer to late successional species. We interpret this linked geomorphic disturbance-vegetation succession sequence as 'biogeomorphic succession', a common successional pathway in unstable river and coastal ecosystems across the world. Soil and vegetation development were related to this sequence, and only advanced once biogeomorphic ecosystem engineer species covered 40–45% of a plot, indicating a crucial role of biogeomorphic ecosystem engineer stabilization. Different topoclimatic conditions could explain variance in biogeomorphic succession timescales and ecosystem engineer root traits between the glacier forelands. As glacier foreland ground is widely unstable, we propose to consider glacier forelands as 'biogeomorphic ecosystems' in which ecosystem structure and function are shaped by geomorphic disturbances and their feedbacks with adapted plant species, similar to rivers and coasts.</p>
Do harvest retention patches in the boreal forest emulate those resulting from wildfire? A comparison of understory vegetation a decade after disturbance
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Similar vegetation-geomorphic disturbance feedbacks shape unstable glacier forelands across mountain regions
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Neighboring edges: interacting edge effects of linear disturbances on vegetation in treed fens
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Landslide age, elevation and residual vegetation determine tropical montane forest canopy recovery and biomass accumulation after landslide disturbances in the Peruvian Andes
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Walnut Gulch Experimental Watershed site, station Built-up or disturbed vegetation zone in Walnut Gulch Watershed, study of plant cover of Acacia constricta (whitethorn acacia) in units of percent on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Walnut Gulch Experimental Watershed (WGE) contains plant cover of Acacia constricta (whitethorn acacia) measurements in percent units and were aggregated to a yearly timescale.
Walnut Gulch Experimental Watershed site, station Built-up or disturbed vegetation zone in Walnut Gulch Watershed, study of plant cover of Flourensia cernua (American tarwort) in units of percent on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Walnut Gulch Experimental Watershed (WGE) contains plant cover of Flourensia cernua (American tarwort) measurements in percent units and were aggregated to a yearly timescale.
Walnut Gulch Experimental Watershed site, station Built-up or disturbed vegetation zone in Walnut Gulch Watershed, study of plant cover of Fouquieria splendens (ocotillo) in units of percent on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Walnut Gulch Experimental Watershed (WGE) contains plant cover of Fouquieria splendens (ocotillo) measurements in percent units and were aggregated to a yearly timescale.
Walnut Gulch Experimental Watershed site, station Built-up or disturbed vegetation zone in Walnut Gulch Watershed, study of plant cover of Koeberlinia spinosa (crown of thorns) in units of percent on a yearly timescale
The EcoTrends project was established in 2004 by Dr. Debra Peters (Jornada Basin LTER, USDA-ARS Jornada Experimental Range) and Dr. Ariel Lugo (Luquillo LTER, USDA-FS Luquillo Experimental Forest) to support the collection and analysis of long-term ecological datasets. The project is a large synthesis effort focused on improving the accessibility and use of long-term data. At present, there are ~50 state and federally funded research sites that are participating and contributing to the EcoTrends project, including all 26 Long-Term Ecological Research (LTER) sites and sites funded by the USDA Agriculture Research Service (ARS), USDA Forest Service, US Department of Energy, US Geological Survey (USGS) and numerous universities. Data from the EcoTrends project are available through an exploratory web portal (http://www.ecotrends.info). This web portal enables the continuation of data compilation and accessibility by users through an interactive web application. Ongoing data compilation is updated through both manual and automatic processing as part of the LTER Provenance Aware Synthesis Tracking Architecture (PASTA). The web portal is a collaboration between the Jornada LTER and the LTER Network Office. The following dataset from Walnut Gulch Experimental Watershed (WGE) contains plant cover of Koeberlinia spinosa (crown of thorns) measurements in percent units and were aggregated to a yearly timescale.
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.