Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
27
datasets available to search
ShareScore release 0.9.0
Dataset results
27 results for “mixed conifer”
Pre- and post-fire vegetation and fuel loading data from mixed conifer plots in Arizona and New Mexico: 2010-2023
A permanent plot network was installed in mixed conifer stands across the U.S. Southwest (Arizona and New Mexico) between 2010-2013, primarily to monitor the spread and severity of white pine blister rust (WPBR), a disease caused by the fungal pathogen Cronartium ribicola on southwestern white pine (Pinus strobiformis). Study sites were mid-to-high elevation mixed conifer stands composed of southwestern white pine, Douglas-fir (Pseudotsuga menziesii), white fir (Abies concolor), ponderosa pine (Pinus ponderosa), quaking aspen (Populus tremuloides), Gambel oak (Quercus gambelii), blue spruce (Picea pungens), Engelmann spruce (Picea engelmannii), corkbark fir (Abies latifolia var. arizonica), Rocky Mountain bristlecone pine (Pinus aristata), and New Mexico locust (Robinia neomexicana). After plot installation, 6 fires occurred in the study area, burning an estimated total of 489,390 acres and 30 plots. We remeasured plots at 1-, 5- and 10-year intervals post-fire, quantifying burn severity via a composite burn index (CBI) at the first year post-fire. We also assessed regeneration, overstory mortality, and fuel loading. Overstory variables collected included tree species, status, diameter at breast height (DBH), and mortality, as well as height, height to live crown base, strata, and crown class on a subset of trees. Understory trees were tallied by species. Fuel load was measured via transect and calculated in megagrams per hectare categorically based on fuel type. Other variables such as basal area and trees per hectare were derived and calculated. This dataset was utilized in the manuscript "Climate, fire, and the future of mixed conifer ecosystems in the U.S. Southwest" (currently in review), and R code used for analyses is included in the dataset.
Variations in tree growth provide limited evidence of species mixture effects in Interior West U.S.A. mixed-conifer forests
<p>1. In mixed stands, species complementarity (e.g., facilitation and competition reduction) may enhance forest tree productivity. Although positive mixture effects have been identified in forests worldwide, the majority of studies have focused on two-species interactions in managed systems with high functional diversity. We extended this line of research to examine mixture effects on tree productivity across landscape-scale compositional and environmental gradients in the low functional diversity, fire-suppressed, mixed-conifer forests of the U.S. Interior West.</p> <p>2. We investigated mixture effects on the productivity of <i>Pinus ponderosa</i>, <i>Pseudotsuga menziesii</i>, and <i>Abies concolor</i>. Using region-wide forest inventory data, we created individual-tree generalized linear mixed models and examined the growth of these species across community gradients. We compared the relative influences of stand structure, age, competition, and environmental stress on mixture effects using multi-model inference. We analyzed growth of neighboring tree species to infer whether a mixture effect in a single species translated to the stand-level.</p> <p>3. We found support for a positive mixture effect in <i>P. menziesii</i>, although our results were equivocal in light of a weaker but still plausible alternative model. Growth of <i>P. menziesii</i> neighboring species in mixed stands declined or held constant depending on aridity, suggesting that a positive mixture effect in <i>P. menziesii</i> does not necessarily extend to the stand level. We found no evidence for mixture effects in <i>P. ponderosa</i>, <i>A. concolor</i> or their neighboring species.</p> <p>4. Complementarity appears to have a limited influence on tree growth in the mixed-conifer systems of the U.S. Interior West, reflecting limited functional diversity. Historical changes in stand structure following fire exclusion, particularly high stand densities, may limit the potential for positive species mixture effects. The limited species pool of Interior West forests increases the risk that, without careful management, what functional diversity exists could be lost to compositional changes resulting from stand dynamics or disturbance.</p>
Data from: Thinning and prescribed burning increase shade-tolerant conifer regeneration in a fire excluded mixed-conifer forest
<p>Fire exclusion and past management have altered the composition, structure, and function of frequent-fire forests throughout western North America. In mixed-conifer forests of the California Sierra Nevada, fire exclusion has exacerbated the effects of drought and endemic bark beetles, resulting in extensive mortality of fire-adapted pine species. Thinning and prescribed fire are widely used in these forests to reduce fuels, moderate fire behavior, and restore ecosystems. Tree regeneration influences future forest composition and structure, and therefore future resilience to disturbances, but long-term effects of thinning and prescribed burning on tree regeneration after prolonged fire exclusion are poorly understood. We measured tree regeneration one year prior to, and periodically for 16 years following thinning and prescribed burning in a mixed-conifer forest in the Sierra Nevada, California, USA. We asked three questions. How did the composition and density of tree regeneration change after thinning and prescribed burning? Did pretreatment vegetation types influence conifer regeneration density after treatments? Did planting after overstory thinning increase regeneration density of native pine species?</p> <p>Sixteen years after treatments, combined natural regeneration of shade-tolerant white fir (Abies concolor) and incense-cedar (<em>Calocedrus</em> <em>decurrens</em>) averaged 2,032 trees per hectare (tph) after understory thinning, and 7,745 tph after understory thinning combined with prescribed burning, increases of 37% and 146% from pretreatment densities. In contrast, combined natural regeneration of white fir and incense-cedar averaged 497 tph after overstory thinning, 780 tph after overstory thinning with prescribed burning, 113 tph after prescribed burning alone, and 807 tph in untreated controls, all of which were declines from pretreatment densities. Natural regeneration of white fir and incense-cedar was consistently an order of magnitude greater than Jeffrey pine (<em>Pinus</em> <em>jeffreyi</em>) and sugar pine (<em>Pinus</em> <em>lambertiana</em>), whose combined densities 16 years after treatments averaged 37 tph across treatments and did not significantly respond to thinning and/or prescribed burning. Natural conifer regeneration after treatments varied by pre-treatment vegetation type (closed canopy, <em>Ceanothus</em> <em>cordulatus</em> shrub-dominated, and open sparse), with large increases of natural regeneration after understory thinning in closed canopy and <em>Ceanothus</em> shrub vegetation types. Planting increased sugar pine regeneration density after overstory thinning, marginally increased Jeffrey pine regeneration after overstory thinning combined with prescribed burning, and increased white fir regeneration after overstory thinning with and without burning. No treatments reduced white fir and incense-cedar natural regeneration while simultaneously increasing natural pine regeneration, suggesting new thinning, burning, and planting approaches may be required to meet regeneration restoration objectives.</p>
A Convolutional Neural Network classifier identifies tree species in mixed-conifer forest from hyperspectral imagery
<p>Published online: <a href="https://www.mdpi.com/2072-4292/11/19/2326">https://www.mdpi.com/2072-4292/11/19/2326</a></p> <p>DOI: 10.3390/rs11192326</p> <p><strong>Abstract:</strong></p> <p>In this study, we automate tree species classification and mapping using field-based training data, high spatial resolution airborne hyperspectral imagery, and a convolutional neural network classifier (CNN). We tested our methods by identifying seven dominant trees species as well as dead standing trees in a mixed-conifer forest in the Southern Sierra Nevada Mountains, CA (USA) using training, validation, and testing datasets composed of spatially-explicit transects and plots sampled across a single strip of imaging spectroscopy. We also used a three-band ‘Red-Green-Blue’ pseudo true-color subset of the hyperspectral imagery strip to test the classification accuracy of a CNN model without the additional non-visible spectral data provided in the hyperspectral imagery. Our classifier is pixel-based rather than object based, although we use three-dimensional structural information from airborne Light Detection and Ranging (LiDAR) to identify trees (points > 5 m above the ground) and the classifier was applied to image pixels that were thus identified as tree crowns. By training a CNN classifier using field data and hyperspectral imagery, we were able to accurately identify tree species and predict their distribution, as well as the distribution of tree mortality, across the landscape. Using a window size of 15 pixels and eight hidden convolutional layers, a CNN model classified the correct species of 713 individual trees from hyperspectral imagery with an average F-score of 0.87 and F-scores ranging from 0.67–0.95 depending on species. The CNN classification model performance increased from a combined F-score of 0.64 for the Red-Green-Blue model to a combined F-score of 0.87 for the hyperspectral model. The hyperspectral CNN model captures the species composition changes across ~700 meters (1935 to 2630 m) of elevation from a lower-elevation mixed oak conifer forest to a higher-elevation fir-dominated coniferous forest. High resolution tree species maps can support forest ecosystem monitoring and management, and identifying dead trees aids landscape assessment of forest mortality resulting from drought, insects and pathogens. We publicly provide our code to apply deep learning classifiers to tree species identification from geospatial imagery and field training data</p> <p>Digital Publication of the training data polygons and hyperspectral imagery used in the manuscript "A Convolutional Neural Network classifier identifies tree species in mixed-conifer forest from hyperspectral imagery".</p> <p>Code is available in a Jupyter Notebook and can be found here: <a href="https://github.com/jonathanventura/canopy">https://github.com/jonathanventura/canopy</a></p> <p>National Ecological Observatory Network. 2018. Provisional data downloaded from <a href="http://data.neonscience.org/">http://data.neonscience.org</a> on 22 June 2018. Battelle, Boulder, CO, USA</p>
Variations in tree growth provide limited evidence of species mixture effects in Interior West U.S.A. mixed-conifer forests
Open the record for dataset details and reuse information.
Data from: Thinning and prescribed burning increase shade-tolerant conifer regeneration in a fire excluded mixed-conifer forest
Open the record for dataset details and reuse information.
FATES projections of forest structure and composition (1830-2098) at a mixed conifer site in the southern Sierra Nevada
Open the record for dataset details and reuse information.
Photochemical Reflectance Index (PRI) captures the ecohydrological sensitivity of a semi-arid mixed conifer forest
The Photochemical Reflectance Index (PRI) corresponds to the de-epoxidation state of the xanthophyll cycle and is one of the few pigment-based vegetation indices sensitive to rapid plant physiological responses. As such, new remotely-sensed PRI products present opportunities to study diurnal and seasonal processes in evergreen conifer forests, where complex vegetation dynamics are not well reflected by the small annual changes in chlorophyll content or leaf structure. Because PRI is tied explicitly to short and long term changes in xanthophyll pigments which are responsible for regulatig stress, this study characterized PRI in a semi-arid, sub-alpine mixed conifer forest, in order to assess its potential as a proxy for water stress by extension of its association with photoprotection. To determine the sensitivity of PRI to seasonal changes in ecohydrological variability and gross primary productivity, canopy spectral measurements were combined with eddy covariance flux and sap flow methods. Seasonally, there was a significant relationship between PRI and sap flow velocity (R2=0.56), and multiple linear regression analysis demonstrated the PRI response to dynamic water and energy limitations in this system. Although PRI was an effective indicator of stomatal response to ecohydrological constraints on a seasonal time scale, top-of-canopy leaf-level gas exchange, chlorophyll fluorescence, and hyperspectral reflectance measurements suggest that diurnal PRI saturates under conditions of severe light stress. This research indicates that remotely-sensed PRI has potential to fill spatial and temporal gaps in the ability to distinguish how water availability influences carbon dynamics of forested ecosystems.
Data from: The 15-year post-treatment response of a mixed-conifer understory plant community to thinning and burning treatments
<p>Disturbance is central to maintaining diversity in forest ecosystems. In the dry forests of the western United States, over a century of fire exclusion has altered the fire regimes of these forests, resulting in high fuel loads and a loss of plant diversity. Mechanical thinning and prescribed fire are widely used to restore structural complexity and species diversity in many western U.S. forests. While studies have shown that the reintroduction of fire into these forests initially promotes plant diversity, there is limited information on the persistence of this effect. We evaluated the effects of thinning and burning treatments on the understory plant community fifteen years after treatment in an old-growth, Sierran mixed-conifer forest. Using a full-factorial design, including three levels of thinning and two levels of burning, we found mechanical thinning and prescribed fire reduced litter depths and increased the availability of bare ground, resulting in an initial increase in herb cover. However, fifteen years after treatment, litter depths and shrub cover increased, resulting in a more homogenous understory community and a loss of herb cover. Overall, our results suggest that while thinning and burning treatments initially promote herbaceous plant cover, these effects are short lived in the absence of a second disturbance.</p>
Data from: Tree growth responses to extreme drought after mechanical thinning and prescribed fire in a Sierra Nevada mixed-conifer forest, USA
<p class="MsoNormal">An estimated 128 M trees died during the 2012-2016 California drought, largely in the southern Sierra Nevada Range. Prescribed burning and mechanical thinning are widely used to reduce fuels and restore ecosystem properties, but it is unclear if these treatments improve tree growth and vigor during extreme drought. This study examined tree growth responses after thinning, prescribed burning, and extreme drought at the Teakettle Experimental Forest, a historically frequent fire mixed-conifer forest in the southern Sierra Nevada of California, USA. Mechanical thinning (no thin, understory thin, and overstory thin) and prescribed burning (unburned, fall burning) were implemented in 2000-2001. Using annual growth data from increment cores, over 10,000 mapped and measured trees, and lidar-derived metrics of solar radiation and topographic wetness, we had two primary questions. First, what were the growth responses to thinning and prescribed burning treatments, and did these responses persist during the 2012-2016 drought? Second, what tree-level attributes and environmental conditions influenced growth responses to treatments and drought?</p> <p class="MsoNormal">Thinning increased residual tree growth and that response persisted through extreme drought 10 -15 years after treatments. Growth responses were higher in overstory versus understory thinning, with differences between thinning types more pronounced during drought. Species-specific growth responses were strongest with overstory thinning, with sugar pine (Pinus lambertiana) and incense-cedar (Calocedrus decurrens) having higher growth responses compared to white fir (Abies concolor) and Jeffery pine (Pinus jeffreyi). For individual trees, factors associated with higher growth responses were declining pretreatment growth trend, smaller tree size, and post-treatment low neighborhood basal area. Growth responses were initially not influenced by topography, but topographic wetness became important during extreme drought. Mechanical thinning resulted in durable increases in residual tree growth rates during extreme drought over a decade after thinning occurred, indicating treatment longevity in mitigating drought stress. In contrast, tree growth did not improve after prescribed burning, likely due to fire effects that reduced surface fuels, but had little effect on reducing tree density. Thinning treatments promoted durable growth responses, but focusing on stand-level metrics may ignore important tree-level attributes such as localized competition and topography associated with higher water availability. Mechanical thinning was effective at improving growth in trees that had been experiencing declining growth trends, but was less effective in improving growth responses in large old higher ecological importance.</p>
Data for: Sierra Nevada mixed-conifer regeneration response to repeated burning varies by species
<p>Fire-exclusion has acted as a major perturbation on dry conifer forests in the western U.S., increasing tree density and, in mixed-conifer forests, the dominance of shade-tolerant species. Restoration efforts aim to reverse these effects by reducing stand density, restoring relative proportions of tree species, and reintroducing recurrent fire, but there are limited long-term data on the effects of repeated burning on tree regeneration. We analyzed two decades of seedling and overstory data from the Teakettle Experimental Forest in the southern Sierra Nevada, California, USA to determine how thinning and repeated burning affect seedling establishment and overstory recruitment. The treatments included three levels of thinning (no thin, understory thin, overstory thin) crossed with two levels of burning (burn, no burn). Treatments were replicated three times in 4 ha treatment units. Seedlings were surveyed at gridpoints within the treatment units for the pre-treatment period (1999-2000), following the first-entry burn (2004-2006), at 10 years post-treatment (2011-2012), and before (2016-2017) and after (2018-2020) the second-entry burn. We analyzed seedling response using mixed models. Across treatments, pine seedling densities remained much lower than shade-tolerant seedling densities. We found that repeated burns led to modest increases in sugar pine (<em>Pinus lambertiana</em>) and substantial increases in incense-cedar (<em>Calocedrus decurrens</em>) seedling densities four years post-burn. No significant differences in seedling densities among repeated burning treatments were detected for Jeffrey pine (<em>P. jeffreyi</em>) or white fir (<em>Abies concolor</em>). We used overstory stem map data and seedling data to estimate recruitment into the mid-story and found that estimates of natural mid-story recruitment were much higher among white fir and incense-cedar than pines, even following treatments. </p>
Data from: Fall prescribed burns deplete surface soil C pools more than spring burns in a young mixed-conifer forest
Open the record for dataset details and reuse information.
Data from: The 15-year post-treatment response of a mixed-conifer understory plant community to thinning and burning treatments
Open the record for dataset details and reuse information.
Photochemical Reflectance Index (PRI) captures the ecohydrological sensitivity of a semi-arid mixed conifer forest
Open the record for dataset details and reuse information.
Repeated fuel treatments fall short of fire-adapted regeneration objectives in a Sierra Nevada mixed conifer forest, USA
Open the record for dataset details and reuse information.
Dead tree removal after drought mortality increases understory plant diversity in a mixed conifer forest
Open the record for dataset details and reuse information.
Data for: Sierra Nevada mixed-conifer regeneration response to repeated burning varies by species
Open the record for dataset details and reuse information.
Data from: Tree growth responses to extreme drought after mechanical thinning and prescribed fire in a Sierra Nevada mixed-conifer forest, USA
Open the record for dataset details and reuse information.
Soil nutrient fluxes and hot spots in a Mediterranean mixed-conifer forest
Open the record for dataset details and reuse information.
Data from: Eigenfeature-Enhanced Deep Learning: Advancing Tree Species Classification in Mixed Conifer Forests with Lidar
<p>Data and code used in <i>Lidar-derived eigenfeatures improve species classification using deep-learning in Southwestern mixed conifer forests</i>. Lidar data is provided for UAV-LS and ALS in <i>lidar_data.zip</i>. Las files are named by site and plot (e.g., 3-2). Field data, shapefiles, and code is provided in <i>data.zip</i>. R code is provided for tree segmentation, tree matching, image creation, and neural network cross validation. </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.