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907 results for “Pine”
Fig. 7 in Stakeholder contributions to conservation of threatened Northern Pine Snakes (Pituophis melanoleucus, Daudin, 1803) in the New Jersey Pine Barrens as a case study
Fig. 7. Volunteers contribute directly to conservation efforts by helping to remove trees that are obstructing sun penetration to nests or hibernation sites (Fig. 7a), or taking data on snake behavior (Fig. 7b).
Fig. 6 in Stakeholder contributions to conservation of threatened Northern Pine Snakes (Pituophis melanoleucus, Daudin, 1803) in the New Jersey Pine Barrens as a case study
Fig. 6. Volunteers of all ages are involved in our Pine Snake research, and the handling and measuring of snakes contributes to their education, and results in their providing information about conservation to their families, friends, classmates, and others. Following hibernation studies, the children (and adults) put the snakes back into their hibernation chambers.
Fig. 4 in Stakeholder contributions to conservation of threatened Northern Pine Snakes (Pituophis melanoleucus, Daudin, 1803) in the New Jersey Pine Barrens as a case study
Fig. 4. Female Pine Snakes sometimes remain in their nests for several days after egg-laying is complete, perhaps protecting their clutch from being disrupted by other females that lay in the same nest.
Fig. 2 in Stakeholder contributions to conservation of threatened Northern Pine Snakes (Pituophis melanoleucus, Daudin, 1803) in the New Jersey Pine Barrens as a case study
Fig. 2. Female Northern Pine Snakes dig their own nests in the New Jersey Pine Barrens, although in the southern part of their range they do not do so. They bend their neck such that the head forms a scoop capable of bringing sand out the entrance (Fig 2a). While digging their body is hidden below ground, and the dump pile of sand is visible (and serves to attract poachers; Fig 2b).
Agroforestry system with Pine trees and grapevine (vitiforestry system)
<p>An agroforestry system with 4 rows of grapevine (<em>Vitis vinifera</em>) between rows of stone pine trees (<em>Pinus pinea</em>) in plot B7 of Domaine de Restinclières (France) (coordinates 43.724202 , 3.859748), grapevine and trees were planted in 1996. Picture taken on 2020-06-16</p>
Silvopastoral system with pine trees and sheep (Mértola) - Portugal
Open the record for dataset details and reuse information.
Silvopastoral system with pine trees and sheep (Mértola) - Portugal
Open the record for dataset details and reuse information.
Low-input breeding in stone pine, a multipurpose forest tree with low genome diversity
<p><span>Stone pine (<em>Pinus pinea</em> L.) is an emblematic tree species within the Mediterranean basin, with high ecological and economic relevance due to the production of edible nuts. Breeding programmes to improve pine nut production started decades ago in Southern Europe but have been hindered by the near absence of polymorphisms in the species genome and the lack of suitable genomic tools. In this study, we assessed new stone pine’s genomic resources and their utilisation in breeding and sustainable use, by using a commercial SNP-array (5,671 SNPs). Firstly, we confirmed the accurate clonal identification and identity check of 99 clones from the Spanish breeding programme. Secondly, we successfully estimated genomic relationships in</span><span> clonal collections, an information needed for </span><span>low-input breeding and genomic prediction. Thirdly, we applied this information to genomic prediction for total number of cones unspoiled by pests and their weight measured in three Spanish clonal tests. Genomic prediction accuracy depends on the trait under consideration and possibly on the number of genotypes included in the test. Predictive ability (<em>r</em><sub><span>y</span></sub>) was significant for the mean cone weight measured in the three clonal tests, while solely significant for the number of cones in one clonal test. The combination of a new SNP-array together with the phenotyping of relevant commercial traits into genomic prediction models, proved to be very promising to identify superior clones for cone weight. This approach opens new perspectives for early selection. </span></p>
Soil and understory CO2 respiration, CH4, and N2O fluxes, tree biomass and litter, and soil carbon stock after a long-term N fertilization of a Scots pine forest in Finland
<p>Data of forest soil respiration, soil and undestory respiration, CH4, and N2O fluxes, soil temperature and volumetric water content (Data_Karstula_GHG_temp.swc.csv), continuous soil temperature and moisture data (Data_Karstula_measured_temperature_2021_2023.csv, Data_Karstula_measured_moisture_2021_2023.csv), forest biomass and litter (Data_Karstula_total_biomass_litter.csv, Data_Karstula_measured_litter_2021_2023.csv), and soil C stocks (Data_Karstula_soc.csv) from the boreal Scots pine forest site Karstula after a long-term N fertilization in Finland (62°54'43.343"N; 24°34'16.021"E).</p> <p>The dataset is used for the publication "Tupek et al. : <strong>Lower sensitivity of microbial respiration to soil moisture after long-term N fertilization increases soil carbon retention in a Scots pine forest</strong>. 2024".</p>
MITgcm model setup and output for "Impact of subglacial freshwater discharge on Pine Island Ice Shelf"
<p>Here, it contains the results of the regional PIG simulation. Model grid is lat-lon similar to Nakayama et al., 2019 roughly 200 m in the region. See Nakayama et al., submitted to GRL for detail. </p> <p>For complete model output (10*Qsg and 50*Qsg cases), please access NASA data (Registration is required).<br> https://ecco.jpl.nasa.gov/drive/files/ECCO2/High_res_PIG/PIG_only_200m </p> <p>Contents can be downloaded easily using wget (see link below). <br> https://ecco-group.org/docs/wget_download_multiple_files_and_directories.pdf</p> <p><strong>(Contents)</strong><br> code.zip (code to run this simulation)<br> input.zip (input file required for this simulation)<br> latlon_run23.zip (CTRL)<br> latlon_run24.zip (Qsg case)<br> latlon_run25.zip (2*Qsg case)<br> <br> <strong>(How to build and run)</strong><br> mkdir build<br> ./../../tools/genmake2 -of ../../../tools/build_options/linux_amd64_ifort+mpi_ice_nas -mpi -mods ../code/<br> make depend<br> make -j 16<br> cd ..</p> <p>mkdir test<br> cd test<br> ln -sf ../input/* .<br> ln -sf /nobackup/hzhang1/forcing/era_xx_it50/ .<br> cp ../build/mitgcm_uv .<br> qsub latlon_run23.sh</p>
R script to simulate the phenology of the pine processionary moth, Thaumetopoea pityocampa, from the egg to the last larval instar (L5).
<p>This folder contains :</p> <p>- ReadMe file with the following explanation</p> <p>- the R script to simulate the phenology of the pine processionary moth (PPM_phenological_model.R),</p> <p>- the flight curve of the pine processionary moth in Orleans (France) in 2019 (Flight_Orleans_2019.csv ; col 1 = date, col 2 = number of adult catches, col 3 = cumulated number of catches) </p> <p>- temperature dataset from June 2019 to December 2020 (Temperature.csv; col 1 = mean daily temperature, col 2 = Date, col 3 = Julian day continuously counted from one year to another). These meteorological data were recorded by the agroclimatic station at Orleans (number 45234, north latitude 47.827 °, east longitude 1.909 °), part of the INRAE national agroclimatic network managed by the service unit AgroClim (Avignon, France).</p> <p>The R script was used in R version 3.5.1 (2018-07-02)</p> <p>The R script will call automatically the flight curve and the temperature dataset given that you have placed them in your working directory, it requires the following R packages : lubridate, hydroGOF, ggplot2 (to be installed beforehand). The function coded to simulate the phenology of the pine processionary moth is called « param ». Input parameters are :<br> - Rm (Maximal development rate in days^(-1) ),<br> - Tm (Optimum temperature in °C)<br> - To (Spread of curve in °C)<br> for each life stage from the egg to L5.<br> This phenology model accumulates the daily development rate given by the Taylor equation and provides for each life stage (resegg, resL1,… resL5) : "min First day" (first day with an individual entering at this stage), "max First day" (last day with an individual entering at this stage), "min Last day" (first day with an individual achieving this stage development), "max Last day" (last day with an individual achieving this stage development), "mean duration" (mean duration of this life stage across all individuals). This is a summary of the phenology but “tabnb” provides the number of individuals present in each stage according to the date.</p> <p>To download the software R, please visit: <a href="https://www.r-project.org/">https://www.r-project.org/</a></p> <p>This work was supported by: the national project called PHENEC (grant from the French Research Agency – ANR – number19-CE32-0007; <a href="https://www6.inrae.fr/phenec/Project-description">https://www6.inrae.fr/phenec/Project-description</a>), the SOERE called TEMPO (<a href="https://tempo.pheno.fr/soere-tempo_eng/">https://tempo.pheno.fr/soere-tempo_eng/</a>), and the French region Val de Loire.</p> <p>Associated scientific article:<br> Poitou L, Laparie M, Pincebourde S, Rousselet J, Suppo C, Robinet C (2022) Warming causes atypical phenology in a univoltine moth with differentially sensitive larval stages. Frontiers in Ecology and Evolution, DOI: 10.3389/fevo.2022.825875.</p>
Data - Co-feeding of VGO and pine-wood derived hydrogenated pyrolysis oils in an Fluid Catalytic Cracking pilot plant to generate olefins and gasoline
<p>Dataset to the corresponding research article with the same title. Research article is submitted to the open research europe platform.</p>
Data for: Hardwoods influence effect of climate and intraspecific competition on growth of woodland longleaf pine trees
<p>Longleaf pine woodlands of the North American Coastal Plain are proposed to be resilient to climate change impacts, but little is known about changes in limiting factors to longleaf pine growth as climate has changed in the late 20<sup>th</sup> and early 21<sup>st</sup> centuries. Moreover, the role that neighborhood trees play in the context of climate change remains largely unexplored. We used static and moving-window tree-ring and climatic analyses to measure the effects of climate on longleaf pine growth at a site in southwest Georgia, USA. We then performed maximum likelihood analysis to examine the influence of neighboring hardwoods on the response of longleaf pine growth to the joint effects of competition and climate. Analysis of climate data from local stations in southwest Georgia over six decades indicated that mean air temperature decreased until the late 20<sup>th </sup>century and then began to rise, and that the variability of spring and summer precipitation has increased. Tree ring and climate analyses indicated longleaf pine radial growth is sensitive to precipitation and air temperature, and that the strength of correlation of longleaf pine growth to summer air temperature and summer precipitation increased since the 1950s. Likelihood models, which were applied over a shorter (23-year) period and explicitly incorporated competition, did not support a link between summer temperature and growth but did indicate summer precipitation increased growth. Furthermore, basal area of neighboring hardwoods was correlated with greater pine growth per millimeter of precipitation. Basal area of neighboring longleaf pine negatively affected the growth of conspecific trees; the presence of hardwoods increased the competitive effect when basal area of neighboring pine trees was low (<10 m<sup>2</sup> ha<sup>-1</sup>) but decreased the competitive effect when basal area of neighboring pine trees was high (≥ 10 m<sup>2</sup> ha<sup>-1</sup>). These results suggest that retention or recruitment of hardwood trees when restoring longleaf pine woodlands may contribute to increased ability to withstand dry summers and may help to allay concerns of managers that retention of hardwoods will unduly affect the growth of residual mature longleaf pines.</p>
Site characterization, water balance modeling, and regeneration attributes of managed and unmanaged ponderosa pine sites in the southwestern United States
<p>This dataset contains biotic and abiotic site characterization data and SOILWAT2 water balance model simulation outputs (two daily outputs: 1915-2011, 1980-2020) for 77 ponderosa pine forest sites in the southwestern United States. Data were collected in summer 2019 and summer 2021. Overviews of the sampling and modeling methodologies are detailed in the following publications:</p> <p>Pirtel NL, Bradford JB, Hubbard RM, Abella SR, Kolb TE, Litvak ME, Porter SL and Petrie MD. 2021. The aboveground and belowground growth characteristics of juvenile conifers in the southwestern United States, Ecosphere 12: e03839, doi:10.1002/ecs2.3839.</p> <p>Petrie MD, Hubbard RM, Bradford JB, Kolb TE, Moser WK, Noel A, Schlaepfer DR, Bowen MA, Fuller LR and Moser WK. 2023. Widespread regeneration failure in ponderosa pine forests of the southwestern United States, Forest Ecology and Management: in press.</p> <p> </p>
Video and accelerometer tree sway data for two ponderosa pine trees in the Manitou Experimental Forest, Colorado
<p>This repository includes video, accelerometer, and sway frequency data for two ponderosa pine trees (Pinus ponderosa, PIPO) in the Manitou Experimental Forest in Colorado. Video and accelerometer samples were selected from data recorded between May and September 2020. Sway frequency data for both trees was extracted from both video and accelerometer data using the methods described in Ammatelli et al. (In review).</p> <p>All times are in Mountain Daylight Time.</p> <p><strong>Video Data </strong></p> <p>The three, 30 s videos were recorded using a video camera attached to the top of a nearby tower. </p> <p>Video camera: GoPro camera, 30 fps, 1080p resolution, 155° FOV</p> <p>Approximate pixel bounding boxes (ymin, ymax, xmin, xmax):</p> <ol> <li>Tree 1: (440, 520, 395, 425)</li> <li>Tree 2: (400, 575, 690, 760)</li> </ol> <p>File naming convention: manitou-X.MP4 where X the video ID (a,b,c)</p> <p>Date and time</p> <ul> <li>manitou-a.MP4 (2020-8-15 12:05:58)</li> <li>manitou-b.MP4 (2020-8-20 17:28:9)</li> <li>manitou-c.MP4 (2020-8-31 11:50:20)</li> </ul> <p><strong>Accelerometer Data</strong></p> <p>Accelerometer data for both trees was recorded using a 3-axis accelerometer. The accelerometers were positioned ~6-8 m above the ground (total tree height ~8-10 m).</p> <p>Accelerometer: Gulf Coast Data Concepts 2g MEL-X2,16 Hz continuous sampling</p> <p>File naming convention: manitou_accelerometer_X_treeY.csv where X is the video ID (a,b,c) and Y is the tree number (1,2)</p> <p>Variables:</p> <ol> <li>datetime - time of acceleration sample (MDT)</li> <li>Ax - raw acceleration along X-axis of sensor</li> <li>Ay - raw acceleration along Y-axis of sensor</li> <li>Az - raw acceleration along Z-axis of sensor</li> </ol> <p><strong>Sway Frequency Data</strong></p> <p>For each video and tree, the tree's sway frequency was extracted from the video and two lengths of accelerometer data: a segment with the same start time and duration as the video and a 30-minute segment.</p> <p>File naming convention: manitou_sway_treeY.csv where Y is the tree number (1,2)</p> <p>Variables:</p> <ol> <li>name - name of video sample</li> <li>datetime - start time of video sample</li> <li>vvs_avg_hz - frequency (Hz) of tree extracted using VVS method with average spectrum aggregation</li> <li>vvs_hist_hz - frequency (Hz) of tree extracted using VVS method with peak frequency histogram aggregation</li> <li>acc_30sec_hz - frequency (Hz) of tree extracted from an accelerometer segment with the same start time and duration as the video</li> <li>acc_30min_hz - frequency (Hz) of tree extracted from a 30-minute accelerometer segment centered on the video start time</li> </ol> <p>See the below paper for more information.</p> <p>Bush, S. A. (2022). Ecohydrologic Processes in the Montane Headwaters of the Upper South<br> Platte River (Doctoral dissertation). Retrieved from ProQuest Dissertations Publishing. (cub.b12869881). Boulder, CO: University of Colorado at Boulder.</p>
Reforestation of high elevation pines: Direct seeding success depends on seed source and sowing environment
<p>Forest persistence in regions impacted by increasing water and temperature stress will depend upon species' ability to either rapidly adjust to novel conditions or migrate to track ecological niches. Predicted, rapid climate change is likely to outpace the adaptive and migratory capacity of long-lived isolated tree species, and reforestation may be critical to species' persistence. Facilitating persistence both within and beyond a species' range requires identification of seed lots best adapted to the current and future conditions predicted with rapid climate change. We evaluate variation in emergent seedling performance that leads to differential survival among species and populations for three high-elevation five-needle pines. We paired a fully reciprocal field common garden experiment with a greenhouse common garden study to a) quantify variation in seedling emergence and functional traits, b) ask how functional traits affect performance under different establishment conditions, and c) evaluate whether trait and performance variation demonstrates local adaptation and plasticity. Among study species – limber, Great Basin bristlecone, and whitebark pines – we found divergence in emergence and functional traits, though soil moisture was the strongest driver of seedling emergence and abundance across all species. Generalist limber pine had a clear emergence advantage as well as traits associated with drought adaptation, while edaphic specialist bristlecone pine was characterized by low emergence yet high early survival once established. Despite evidence for edaphic specialization, soil characteristics alone did not explain bristlecone success. Across species, trait-environment relationships provided some evidence for local adaptation in drought-adapted traits, but we found no evidence of local adaptation in emergence or survival at this early life stage. For managers looking to promote persistence, sourcing seed from drier environments is likely to impart greater drought resistance into reforestation efforts through strategies such as greater root investment, increasing the probability of early seedling survival. This research demonstrates, through a rigorous reciprocal transplant experimental design, that it may be possible to select climate- and soil-appropriate seed sources for reforestation. However, planting success will ultimately rely on a suitable establishment environment, requiring careful consideration of interannual climate variability for management interventions in these climate- and disturbance-impacted tree species.</p>
Tree-Ring Measurement File: Lodgepole Pine Samples Collected at Steamboat Geyser, Yellowstone National Park
<p>Decade format (Tucson format) tree-ring measurement file (0.001 mm) for lodgepole pine core samples collected adjacent to Steamboat Geyser in Yellowstone National Park, Wyoming, USA.</p>
Data from: Increased aridity is associated with stronger tradeoffs in ponderosa pine vital functions
<p>Trees must allocate resources to core functions, like growth, defense, and reproduction. These allocation patterns have profound effects on forest health, yet little is known about how core functions trade off over time, and even less is known about how a changing climate will impact tradeoffs. We conducted a 21-year survey of growth, defense, and reproduction in 80 ponderosa pine individuals spanning 8 populations across environmental gradients along the Colorado Front Range, USA. We used linear mixed models to describe tradeoffs among these functions and to characterize variability among and within individuals over time. Growth and defense were lower in years of high cone production and local drought conditions amplified year-to-year tradeoffs between reproduction and growth, where trees located at sites with hotter and drier climates showed stronger tradeoffs between reproduction and growth. Our results support the environmental stress hypothesis of masting, which predicts that greater interannual variation in tree functions is associated with more marginal environments, such as those that are prone to drought. With warming temperatures and increased exposure to drought stress, trees will be faced with stronger interannual tradeoffs, which could lead to further decreases in growth and defensive efforts, ultimately increasing risks of mortality.</p>
Fig. 4 in Red wood Ants (Formica rufa-group) prefer mature pine forests in Variscan granite environments (Hymenoptera: Formicidae)
Fig. 4 – Numbers of RWA nests versus medium tree age of primary tree species (TS) for a) MGBSF and b) FBBSF
Fig. 2 in Red wood Ants (Formica rufa-group) prefer mature pine forests in Variscan granite environments (Hymenoptera: Formicidae)
Fig. 2 – Tectonic setting of both study areas with a, major tectonic units, faults (black lines) taken from literature (see list under reference section geological maps), b, detailed geologic setting of the Münchsgrün (MG), and c, Falkenberg (FB) study areas with FB forest sections (1-8). In MG area, the older Mitterteich/Steinwald Granite is overlain by Miocene/Pliocene basin-filling sediments.
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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.