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175 results for “tree structure”
Regional and local variation in chemical, structural, and physical leaf traits for tree species in the northeastern United States, 2016-2023.
This dataset is a compilation of leaf trait measurements for 25 different Northern American tree species in the northeastern United States collected between 2016 and 2023 by the Terrestrial Ecosystems Analysis Lab at the University of New Hampshire. Currently, this dataset contains measurements for 2,006 samples across 18 chemical, physical, and structural traits. Measured traits include stable isotopes for carbon (C) and nitrogen (N), chlorophyll estimates, leaf and petiole dimensions, and leaf and petiole water content. Traits have been measured at plots spanning a wide range of latitude, longitude, elevation, and forest types. A simple table containing these plot descriptions has been included. Additional leaf physiological and optical traits have been measured concurrently on many of these samples and have been or will be published separately. This is a continuous dataset that will be updated on an as needed basis.
Annual summer single-day measurements of the thermal environment with a bio-meteorological sensor under trees, shade structures, and sun-exposed areas in the Rio Salado Park in Tempe, AZ, USA
We have measured the thermal environment/bio-meteorological conditions under trees, shade structures, and at sun-exposed locations in the Rio Salado Park in Tempe, AZ, USA annually since 2018 on a clear sky, hot, sunny day in June/July to track shade performance of newly planted trees over time. Measurements were taken with a mobile bio-meteorological weather station, known as MaRTy (Middel & Krayenhoff, 2019; DOI: 10.1016/j.scitotenv.2019.06.085). Since the measurement campaign began, some of the trees have died, were removed, or their environment has changed due to external events such as a bridge collapse (2020-07-29).
Data for investigating structural complexity of individual Scots pine trees
<p>Tree functional traits together with processes such as forest regeneration, growth, and mortality affect forest and tree structure. Forest management inherently impacts these processes. Moreover, forest structure, biodiversity, resilience, and carbon uptake can be sustained and enhanced with forest management activities. To assess structural complexity of individual trees, comprehensive and quantitative measures are needed, and they are often lacking for current forest management practices. Fractal analysis and a single scale, independent metric called box dimension offer means for assessing structural complexity of individual trees. Terrestrial laser scanning (TLS) point clouds provide three-dimensional (3D) information on trees that can be utilized in generating the box dimension metric. This data set includes information needed for generating the box dimension from 741 individual Scots pine (<em>Pinus sylvestris</em> L.) trees from 9 sample plots with different thinning treatments located in southern boreal forests. The thinning treatments include two intensities of thinning and control treatment (i.e., no thinning treatment since the establishment). The data set can be used in characterizing structural complexity of individual Scots pine trees of various size as well as assessing effects of various thinning treatments on it.</p> <p>Please see the data descriptor for more information on the data structure and its possibilities.</p> <p>Please keep the designated corresponding author informed of any plans to use the data. Consultation or collaboration with the original investigators is strongly encouraged. Publications and data products that make use of the data must include proper acknowledgement.</p>
Tree and habitat structure data from rainforest fragments and coffee plantations in the Anamalai Hills, Western Ghats, India
<p><strong>TITLE</strong></p><p><strong>Tree and habitat structure data from rainforest fragments and coffee plantations in the Anamalai Hills, Western Ghats, India</strong><br> </p><p><strong>DESCRIPTION</strong></p><p>This dataset contains point-centred quarter (PCQ) data on trees and habitat structure measurements data from rainforest fragments and some coffee plantations in the Valparai Plateau and Anamalai Tiger Reserve, Tamil Nadu, India. The data were gathered to quantity habitat parameters for bird and small carnivorous mamm community studies. Data were gathered mainly by T. R. Shankar Raman and Divya Mudappa (2000 to 2003), Hari Sridhar (2005), and Akshay Surendra (2019).</p><p><strong>Publications</strong></p><p>Specific portions of the dataset have been used in the following publications:</p><ul><li>Mudappa, D. 2001. <a href="https://hdl.handle.net/10603/101890">Ecology of the brown palm civet <i>Paradoxurus jerdoni</i> in the tropical rainforests of the Western Ghats, India</a>. Ph. D. thesis, Bharathiar University, Coimbatore. https://hdl.handle.net/10603/101890</li><li>Raman, T. R. S. 2001. <a href="https://archive.org/details/raman-2001-ph-d-thesis-iisc">Community ecology and conservation of mid-elevation tropical rainforest bird communities in the southern Western Ghats, India</a>. PhD thesis, Indian Institute of Science, Bangalore. https://archive.org/details/raman-2001-ph-d-thesis-iisc</li><li>Raman, T.R.S. 2006. <a href="https://doi.org/10.1007/s10531-005-2352-5">Effects of Habitat Structure and Adjacent Habitats on Birds in Tropical Rainforest Fragments and Shaded Plantations in the Western Ghats, India</a>. <i>Biodiversity and Conservation</i> 15: 1577–1607. https://doi.org/10.1007/s10531-005-2352-5</li><li>Sridhar, H., & Sankar, K. 2008. <a href="https://doi.org/10.1017/S0266467408004823">Effects of habitat degradation on mixed-species bird flocks in Indian rain forests</a>. <i>Journal of Tropical Ecology</i> 24: 135-147. https://doi.org/10.1017/S0266467408004823</li><li>Surendra, A. & Raman, T. R. S. 2022. <a href="https://doi.org/10.1101/2022.10.22.513365">Forest bird decline and community change over 19 years in long-isolated South Asian tropical rainforest fragments</a>. Preprint. <i>BioRxiv</i> 2022.10.22.513365. https://doi.org/10.1101/2022.10.22.513365<br> </li></ul><p>A related dataset is the following:<br>Raman, T. R. S. (2020). Data from: Effects of Habitat Structure and Adjacent Habitats on Birds in Tropical Rainforest Fragments and Shaded Plantations in the Western Ghats, India. <i>Dryad Dataset.</i> https://doi.org/10.5061/dryad.4mw6m907q<br> </p><p><strong>Curation and corrections</strong></p><p>Data were collated, curated, and corrected before this upload. Besides addition of new columns, explanations of metadata, and other corrections included few related to canopy measurements, effective girth of multi-stem trees, and species identification.</p><p><strong>Acknowledgements</strong></p><p>We are grateful to P. Jeganathan and P. R. Shankar for assistance with data collection in 2000. Others who assisted with field research, and funding agencies related to the specific studies, are acknowledged in the above publications. The data compilation and publication was carried out as part of a grant from Fondation Franklinia to NCF.</p><p><br><strong>CONTACTS</strong><br> </p><p>CONTACT #1<br>1. Name: T. R. Shankar Raman<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: trsr@ncf-india.org<br>5. ORCID: https://orcid.org/0000-0002-1347-3953</p><p>CONTACT #2<br>1. Name: Divya Mudappa<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: divya@ncf-india.org<br>5. ORCID: https://orcid.org/0000-0001-9708-4826</p><p>CONTACT #3<br>1. Name: Hari Sridhar<br>2. Work Address: Wildlife Institute of India, Post Bag #18, Chandrabani, Dehradun – 248001, Uttarakhand, India; Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India<br>3. Work Phone: +91 821 2515601<br>4. Email address: harisridhar1982@gmail.com<br>5. ORCID: https://orcid.org/0000-0003-3286-0120</p><p>CONTACT #4<br>1. Name: Akshay Surendra<br>2. Work Address: Nature Conservation Foundation, 1311, 12th A Main, Vijayanagar 1st Stage, Mysuru 570017, Karnataka, India; School of the Environment, Yale University, New Haven, CT – 06511, USA; New York Botanical Garden, 2900 Southern Blvd, Bronx, NY 10458<br>3. Work Phone: +91 821 2515601<br>4. Email address: akshaysurendra1@gmail.com<br>5. ORCID: https://orcid.org/0000-0003-2719-7432<br> </p><p><br><strong>GEOGRAPHIC COVERAGE</strong></p><p>1. Location/Study Area: Valparai Plateau, Tamil Nadu, India; Anamalai Tiger Reserve, Tamil Nadu, India</p><p>2. GPS coordinates: Valparai Plateau (10°15'- 10°22'N, 76°52' - 76°59'E); Anamalai Tiger Reserve (10°12' - 10°35'N, 76°49' - 77°24'E)</p><p><br><strong>TEMPORAL COVERAGE</strong></p><p>1. Begins: 2000-01-01 (Year, Month, Day)<br>2. Ends: 2019-12-31 (Year, Month, Day)</p><p><br><strong>METHODS</strong></p><p>Methods involved are described in the publications listed above. The vegetation sampling methods are briefly described below.</p><p>PCQ data: Trees ≥30cm girth at breast height (gbh, at 1.3 m) were sampled in replicate point-centred quarter (PCQ) points in each of the sites (fragments or coffee plantations).</p><p>All trees in the PCQ plots were identified to species, or in a few cases to genus, using available field guides. Using a tape measure, distance from plot centre to the middle of the bole and GBH were recorded for each tree. At each of the PCQ plots, circular plots were laid to enumerate shrubs and cut trees and record presence or absence of lianas, cane, Lantana etc as described in the metadata. Canopy and leaf litter variables were measured at replicate points, spaced 25 to 50 m apart, in each site. Elevation readings were also taken at these points using an altimeter or handheld GPS. Canopy height was measured using a rangefinder. Percentage canopy cover was measured using a spherical densiometer at each of the 25 points in each site. Vertical stratification was assessed by noting presence or absence of foliage in the following height intervals (in metres): 0–1, 1–2, 2–4, 4–8, 8–16, 16–24, 24–32, and > 32, directly above and in a 0.5 m radius around each point. Leaf litter depth on the forest floor was measured using a calibrated wooden probe at each point. Where ground vegetation and litter were disturbed along trails, the samples were taken away from trails in the forest floor.</p><p><br><strong>FILES INCLUDED</strong><br>Besides the 00_README.txt file that contains this metadata, the dataset includes the following 7 files, whose details and contents are explained below. (Wherever used in the various files, NA implies not available.)<br> </p><p><strong>01) sites.csv -- Details of study sites</strong><br>verbatimLocality: Name of locality as originally used<br>Fragment: Name of rainforest fragment or coffee plantation<br>decimalLongitude: Longitude in decimal degrees North (WGS 84 datum)<br>decimalLatitude: Latitude in decimal degrees East (WGS 84 datum)<br>habitat: Habitat type as mature tropical rainforest, tropical rainforest fragment, or coffee plantation<br>Description: Description of the place<br> </p><p><strong>02) allpcqdata.csv -- Tree data from point-centred quarter (PCQ) surveys</strong><br>Year: Year of survey for bird and vegetation study<br>verbatimLocality: Name of locality as originally used<br>Fragment: Name of rainforest fragment or coffee plantation<br>Point_name: Name ID of point-centred quarter (PCQ) point as used within a survey year<br>pointID: Unique ID of point-centred quarter (PCQ) point including year of survey<br>Tree_no: Tree number ID given to the four trees in each PCQ plot (T1 to T4)<br>verbatimIdentification: Scientific name of tree species as originally written or identified<br>scientificName: Scientific name as currently identified under updated taxonomy<br>nativeAlien: Category indicating whether species is native or alien to the region/country<br>kingdom: Taxonomic Kingdom<br>phylum: Taxonomic Phylum<br>Distance_eff: Distance in metres from centre of PCQ plot to centre of tree trunk<br>Girth_eff: Girth in centimetres (cm) at breast height (1.3 m) of the tree after correction (using appropriate formula) in the case of multi-stemmed individuals<br>locationRemarks: Code for site name as originally used<br>SpCode: Species code as originally used during data entry<br>TreeHeight: Tree height in metres (only available in 2019 survey)<br>identificationRemarks: Notes related to identification if available<br>occurrenceRemarks: Notes related to multi-stemmed individuals (girths in cm) if available and note on one possibly errorneous girth<br> </p><p><strong>03) pcqlocations.csv -- Locations of sample PCQ points</strong><br>pointID: Unique ID of point-centred quarter (PCQ) point including year of survey<br>note: Site name code<br>decimalLatitude: Latitude in decimal degrees East (WGS 84 datum)<br>decimalLongitude: Longitude in decimal degrees North (WGS 84 datum)<br>coordinateUncertaintyInMeters: Approximate uncertainty of the location in metres<br> </p><p><strong>04) allhabitat.csv -- Data on habitat structure variables</strong><br>Year: Year of survey for bird and vegetation study<br>verbatimLocality: Name of locality as originally used<br>Fragment: Name of rainforest fragment or coffee plantation<br>Point: ID of replicate survey point within the Fragment<br>0-1m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 0-1 m above ground<br>1-2m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 1-2 m above ground<br>2-4m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 2-4 m above ground<br>4-8m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 4-8 m above ground<br>8-16m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 8-16 m above ground<br>16-24m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 16-24 m above ground<br>24-32m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band 24-32 m above ground<br>over32m: Presence (1) or absence (0) of foliage within 0.5 m of point in the vertical band greater than 32 m above ground<br>VertStrata: Number of vertical strata with foliage (sum of preceding 8 columns)<br>CanopyHeight: Canopy height in metres<br>CanopyOpenness: Canopy openness in percentage as measured using a spherical densiometer<br>CanopyCover: Canopy cover (closure) in percentage as measured using a spherical densiometer<br>CanopyOverlap: Canopy overlap rank: 0-open sky above; 1-branches above barely touching; 2-overlapping branches above, sky visible; 3-overlapping branches, sky not visible<br>UC: Canopy overlap rank as above, for understorey vegetation only<br>MC: Canopy overlap rank as above, for the midstorey only<br>CC: Canopy overlap rank as above, for the upper canopy only<br>Altitude: Altitude above sea leavel in metres, measued from hand-held altimeter or GPS device<br>RfShrub: Number of shrubs (woody stems at least 1 m in height, GBH < 30 cm) within 2 m radius of point<br>Coffee: Number of coffee bushes (woody stems at least 1 m in height, GBH < 30 cm) within 2 m radius of point<br>Maesopsis: Number of alien Maesopsis eminii stems (woody stems at least 1 m in height, GBH < 30 cm) within 2 m radius of point<br>Strobilanthes: Number of Strobilanthes shrubs (woody stems at least 1 m in height, GBH < 30 cm) within 2 m radius of point<br>TotalShrub: Total number of shrubs within 2 m radius of point<br>Liana: Presence (1) or absence (0) of woody lianas within 5 m radius of point<br>Cane: Presence (1) or absence (0) of cane (Calamus sp.) within 2 m radius of point<br>Lantana: Presence (1) or absence (0) of Lantana camara shrubs within 2 m radius of point<br>Bamboo: Presence (1) or absence (0) of bamboo culms within 2 m radius of point<br>LeafLitter: Depth of leaf litter in cm (to 0.5 cm accuracy) measured using a calibrated wooden probe<br>CutTrees: Number of cut trees within 5 m radius of point<br> </p><p><strong>05) gbifnames.csv -- Results of GBIF name matching tool</strong><br>sno: Serial number<br>verbatimScientificName: Scientific name of tree species as originally written or identified<br>scientificName: Scientific name after matching with Global Biodiversity Information Facility (GBIF) database to lowest taxonomic level<br>sciNameWithAuthor: Scientific name with author as provided by GBIF name matching tool<br>key: GBIF key as provided by GBIF name matching tool<br>matchType: Type of match as provided by GBIF name matching tool<br>confidence: Confidence as provided by GBIF name matching tool<br>status: Status as accepted name or synonym as provided by GBIF name matching tool<br>rank: Taxonomic rank as provided by GBIF name matching tool<br>kingdom: Kingdom as provided by GBIF name matching tool<br>phylum: Phylum as provided by GBIF name matching tool<br>class: Class as provided by GBIF name matching tool<br>order: Order as provided by GBIF name matching tool<br>family: Family as provided by GBIF name matching tool<br>genus: Genus as provided by GBIF name matching tool<br>species: Species as provided by GBIF name matching tool<br>canonicalName: Canonical name as provided by GBIF name matching tool<br>authorship: Author of name as provided by GBIF name matching tool<br> </p><p><strong>06) plots2000.csv -- Data from 5 m radius circular plots in select sites</strong><br>verbatimLocality: Name of locality as originally used<br>Fragment: Name of rainforest fragment or coffee plantation<br>PlotID: ID of 5 m radius plot<br>Treeno: Serial number of tree in the plot<br>verbatimIdentification: Scientific name of tree species as originally written or identified<br>scientificName: Scientific name as currently identified under updated taxonomy<br>Girth_eff: Girth in centimetres (cm) at breast height (1.3 m) of the tree after correction (using appropriate formula) in the case of multi-stemmed individuals<br>nativeAlien: Category indicating whether species is native or alien to the region/country<br>kingdom: Kingdom as provided by GBIF name matching tool<br>phylum: Phylum as provided by GBIF name matching tool<br>occurrenceRemarks: Notes related to multi-stemmed individuals (girths in cm) if available and identification</p><p> </p><p><strong>07) anampcqs4gbif.rmd -- Text file with code in the R statistical and programming language</strong> </p><p>This R code was used for converting data in this Zenodo dataset into Darwin Core occurrence dataset for upload to the Global Biodiversity Information Facility (GBIF, https://www.gbif.org). The published dataset can now be accessed at: https://doi.org/10.15468/cmsveh</p><p> </p><p><strong>Changes in Version 2</strong></p><p>In sites.csv, changed habitat from "Rainforest" to "Tropical rainforest fragment" for Puthuthottam</p><p>Added the anampcqs4gbif.rmd file with R code</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>
Data for: The structure of evolutionary model space for proteins across the tree of life
<p>Supporting data for "The structure of evolutionary model space for proteins across the tree of life," submitted by GE Scolaro and EL Braun. The data files correspond to three gzipped tarballs including protein multiple sequence alignments, PAML format models of protein evolution, and model fit data; see included README for details.</p>
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.
Alaskan Peatland Experiment: Community structure and productivity data for 2007-2010 VI - Tree Biomass and NPP
This dataset contains both tree biomass and net primary productivity for Picea mariana (living and standing dead) within the bog of the Alaskan Peatland Experiment as measured in the fall of 2010. Plot within the bog include a plot established within the lowland black spruce permafrost plateau (permafrost), and two plots established within collapse scars embedded within the plateau. One collapse scar formed ~ 45 years ago (old collapse) and the other formed ~ 25 years ago based upon aerial photography provided by the BCEF LTER. The data provided in this data set can be sorted by site and plot.
Data for "Age effect on tree structure and biomass allocation in Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies [L.] Karst.)"
<p>VAPU dataset for tree biomass was collected from southern Finland in 1988-1990 by the Finnish Forest Research Institute (Metla, now Natural Resources Institute Finland, Luke) (VAPU data set).</p> <p>Those sample trees (162 Scots pine and 163 Norway spruce) are originated from the whole VAPU data set. The sheet 'Pine' and 'Spruce' data have been matched between 'sample branch measurements' and the 'biomass' information (by cluster X, Y, and plot, tree number).</p> <p>Biomass estimation for foliage and branches has been described here: https://doi.org/10.1016/j.ecolmodel.2004.04.024 and https://doi.org/10.1093/treephys/25.7.803<br> </p>
Robot Self-Assembly as Adaptive Growth Process: Collective Selection of Seed Position and Self-Organizing Tree-Structures
<p>Autonomous self-assembly allows to create structures and scaffolds on demand and automatically. The desired structure may be predetermined or alternatively it is the result of an artificial growth process that adapts to environmental features and to the intermediate structure itself. In a self-organizing and decentralized control approach the robots interact only locally and form the structure collectively. Designing a complete approach that allows the robot group to collectively decide on where to start the self-assembly, that adapts at runtime to environmental conditions, and that guarantees the structural stability is challenging and does not yet exist. We present an approach to self-assembly inspired by diffusion-limited aggregation that generates an adaptive structure reacting to environmental conditions in an artificial growth process. During a preparatory stage the robots collectively decide where to start the self-assembly also depending on environmental conditions. In the actual self-assembly stage, the robots create tree-like structures that grow towards light. We report the results of robot self-assembly experiments with 50 Kilobots. Our results demonstrate how an adaptive growth process can be implemented in robots. We explain how our approach will be extended to a 3-d growth process and how robot self-assembly as an open-ended adaptive growth process opens up a multiplicity of future opportunities.</p>
Tree-ring dataset derived from timbers of roof structures at Colegial del Salvador church in Seville and Jaen Cathedral (Spain)
<p>Tree-ring dataset in Heidelberg format derived from pine (Pinus sp.) timbers of the roof structures at Jaen Cathedral (dendro code JCS) and the Colegial del Salvador church in Seville (code SCS). These samples were researched within the scope of the NWO-funded project <em>Filling in the blanks in European dendrochronology</em>. They are documented in the following article:</p> <p>Domínguez-Delmás, M.,<strong> </strong>Van Daalen, S., Alejano-Monge, R., Wazny,T., 2018. Timber resources, transport and woodworking techniques in post-medieval Andalusia (Spain): Insights from dendroarchaeological research on historic roof structures. <em>Journal of Archaeological Science</em> <strong>95</strong>: 64–75. <a href="https://doi.org/10.1016/j.jas.2018.05.002">https://doi.org/10.1016/j.jas.2018.05.002</a></p> <p>This upload includes the supplementary material accompanying the article (tables with sampled elements on each building and their metadata). While most of the timbers from Jaen Cathedral are dated, only two of 71 from the Colegial are dated.If discrepancies exist between the metadata inserted in the headers of the measurements and tables uploaded here and the metadata on the article, the metadata published in the article prevails.</p>
Tree size, microhabitat diversity and landscape structure determine the value of isolated trees for bats in farmland
<p>Isolated trees are increasingly recognised as playing a vital role in supporting biodiversity in agricultural landscapes, yet their occurrence has declined substantially in recent decades. Most bats in Europe are tree-dependent species that rely on woody elements in order to persist in farmlands. However, isolated trees are rarely considered in conservation programs and landscape planning. Further investigations are therefore urgently required to identify which trees – based on both their intrinsic characteristics and their location in the landscape – are particularly important for bats. We acoustically surveyed 57 isolated trees for bats to determine the relative and interactive effects of size, tree-related microhabitat (TreM) diversity and surrounding landscape context on bat activity. Tall trees with large diameter at breast height and crown area positively influenced the activity of <em>Pipistrellus pipistrellus</em> and small Myotis bats (<em>Myotis</em> spp.) while smaller and thinner trees favoured <em>M. myotis</em> activity. The diversity of TreMs that can be used as roosts had a positive effect on (i) <em>Barbastella barbastellus</em> activity only when trees were relatively close (10% within 100 radius scale). The potential benefits of isolated trees for bats result from ecological mechanisms operating at both tree and landscape scales, underlining the crucial need for implementing a multi-scale approach in conservation programs. Maintaining the largest and most TreM-diversified trees located in the most heterogeneous agricultural landscapes will provide the greatest benefits.</p>
Sharing land via keystone structure: retaining naturally regenerated trees may efficiently benefit birds in plantations
<p class="MsoListParagraph"><span>Meeting food/wood demands with increasing human population and per-capita consumption is a pressing conservation issue, and is often framed as a choice between land sparing and land sharing. Although most empirical studies comparing the efficacy of land sparing and sharing supported land sparing, land sharing may be more efficient if its performance is tested by rigorous experimental design and habitat structures providing crucial resources for various species––keystone structures––are clearly involved. We launched a manipulative experiment to retain naturally regenerated broad-leaved trees when harvesting conifer plantations in central Hokkaido, northern Japan. We surveyed birds in harvested treatments, unharvested plantation controls and natural forest references one-year before the harvest and for three consecutive post-harvest years. We developed a hierarchical community model separating abundance and space-use (territorial proportion overlapping treatment plots) subject to imperfect detection to assess population consequences of retention harvesting. Application of the model to our data showed that retaining some broad-leaved trees increased total abundance of forest birds over the harvest rotation cycle. Specifically, pre-harvest survey showed that the amount of broad-leaved trees increased forest bird abundance in a concave manner (i.e., in a form of diminishing-return). After harvesting, a small amount of retained broad-leaved trees mitigated negative harvesting impacts on abundance though retention harvesting reduced the space-use. Nevertheless, positive retention effects on the post-harvest bird density as the product of abundance and space-use exhibited a concave form. Thus, small profit reductions were shown to yield large increases in forest bird abundance. The difference in bird abundance between clear-cutting and low amounts of broad-leaved tree retention increased slightly from the first to second post-harvesting years. We conclude that retaining a small amount of broad-leaved trees may be a cost-effective on-site conservation approach for the management of conifer plantations. Retention of 20-30 broad-leaved trees per ha may be sufficient to maintain higher forest bird abundance than clear-cutting over the rotation cycle. Retention approaches can be incorporated into management systems using certification schemes and best management practices. Developing an awareness of the roles and values of naturally regenerated trees is needed to diversify plantations.</span></p>
Figure 2: Impedance by means of Bode-plot representation, symmetric (con- tinuous line) and the asymmetric (dashed line) tree.-THE RESPIRATORY IMPEDANCE IN AN ASYMMETRIC MODEL OF THE LUNG STRUCTURE
<p>Figure 2 shows the total impedance by means of its Bode plot, for the symmetric and the asymmetric tree, whereas the airway tubes are modelled by an R ¡ L ¡ C element in both representations.<br> It is signi¯cant to observe that in the frequency interval of clinical interest,<br> ! 2 [25; 300] rad/s, the two impedances tend to behave similarly. For the asymmetric case, we have a decrease of about -10dB/dec and a phase of ap-proximately ¡50o, resulting in a fractional order of n »=0:5. This observation suggests that a combined e®ect of more than one fractal order is present in the lungs and that it leads naturally to values closer to measured data in the low<br> frequency range.</p>
Data and code from: Evaluating genomic offset predictions in a forest tree with high population genetic structure
<p>Predicting how tree populations will respond to climate change is an urgent societal concern. An increasingly popular way to make such predictions is the genomic offset (GO) approach, which aims to use genomic and climate data to identify populations that may experience climate maladaptation in the near future. More precisely, GO tries to represent the change in allele frequencies required to maintain the current gene-climate relationships under climate change. However, the GO approach has major limitations and, despite promising validation of its predictions using height data from common gardens, it still lacks broad empirical testing. In the present study, we evaluated the consistency and empirical validity of GO predictions in maritime pine (<em>Pinus pinaster</em> Ait.), a tree species from southwestern Europe and North Africa with a marked population genetic structure. First, gene-climate relationships were estimated using 9,817 SNPs genotyped in 454 trees from 34 populations; and candidate SNPs potentially involved in climate adaptation were identified. Second, GO was predicted using four methods, namely Gradient Forest (GF), Redundancy Analysis (RDA), latent factor mixed model (LFMM) and Generalised Dissimilarity Modeling (GDM), two sets of SNPs (candidate and control SNPs) and five climate general circulation models (GCMs) to account for uncertainty in future climate predictions. Last, the empirical validity of GO predictions was evaluated within a Bayesian framework by estimating the associations between GO predictions and two independent data sources: mortality data from National Forest Inventories (NFI), and mortality and height data from five common gardens in contrasting environments. We found high variability in GO predictions across methods, SNP sets and GCMs. Regarding validation, GO predictions with GDM and GF (and to a lesser extent RDA) based on the candidate SNPs showed the strongest and most consistent associations with mortality rates in common gardens and NFI plots. We found almost no association between GO predictions and tree height in common gardens, most likely due to the overwhelming effect of population genetic structure on tree height in this species. Our study demonstrates the imperative to validate GO predictions with a range of independent data sources before they can be used as informative and reliable metrics in conservation or management strategies.</p>
Data for: Pine trees structure plant biodiversity patterns in savannas
<p>Overstory trees serve multiple functions in grassy savannas. Past research has shown that large pine canopy openings harbor greater plant species richness and different species composition. However, these studies did not examine such patterns at the scale of individual trees. We examined the relationship between understory plant communities and proximity to individual pine trees in dry and mesic pine savannas in frequently burned (1-3 year intervals) and long unburned (>30 years since fire) sites in north central Florida. We recorded the presence and abundance (stem or ramet number) of plant species in 1 m x 1 m plots adjacent to tree boles (basal) or outside crown driplines (open). In addition, we quantified environmental variables, including light transmittance and percent cover of litter, bare ground, and fuel loading classes.</p>
Figure 2. Maximum-likelihood trees for J in Mitochondrial evidence indicates a shallow phylogeographic structure for Jaculus blanfordi (Murray, 1884) populations (Rodentia: Dipodidae)
Figure 2. Maximum-likelihood trees for J. blanfordi mtDNA haplotypes in different datasets for cyt b (1110 bp), COI (618 bp), and COI + cyt b (313 bp + 284 bp). The numbers next to the nodes indicate the bootstrap (>50%) and posterior probability (>0.50) values obtained by maximum-likelihood and Bayesian inference, respectively. The trees are rooted with haplotypes from J. orientalis and J. jaculus. See Table 1 and Figure 1 for the haplotype designations and corresponding localities.
Network of reference tree-ring chronologies for forensic botanical (dendrochronological) examinations and dating of architectural structures in the Tyva Republic.
<p>The database consists of tables. First sheet - general description of tree-ring chronologies (general information: name of chronology, authors, data type, tree-ring parameter, notes, key words; description of sample collection site: site name, location, region, latitude, longitude, height; description of sample collection: collection code designation, number of series, year of first ring, year of last ring, maximum length of sample, average width of year ring; species affiliation - species; support - grant number). Second sheet, first column - years, second column - standardized growth value. The third sheet is a PDF document containing the results of independent testing in the program COFECA (the file is opened by the command: right-click/Acrobat Document object/open). The database is implemented in the OpenOffice.org Calc spreadsheet processor. The table file format is an internal OpenOffice.org Calc format, with the extension .ods. The data is accessed and structured using the standard tools "Sort", "Autofilter", etc. In the database, the integrity restriction control is not implemented, the user is invited to monitor the integrity of the database himself. Computer type: IBM PC. PC; OS: Windows 10.</p> <p>Type and version of the database management system: OpenOffice.org Calc.</p> <p>Database size: 5.6 MB</p>
Terrestrial laser scanning data Wytham Woods: individual trees and quantitative structure models (QSMs)
<p>This dataset was used for the analysis of the following publication:<br> <em>Laser scanning reveals potential underestimation of biomass carbon in temperate forest. Calders, K, Verbeeck, V, Burt, A, Origo, N, Nightingale, J, Malhi, Y, Wilkes, P, Raumonen, P, Bunce, R G H and Disney, M. Ecological Solutions and Evidence (accepted)</em></p> <p><strong>Any use of this dataset should cite the paper above </strong>(Creative Commons Attribution 4.0 International Public License).</p> <p>Contact: kim.calders@ugent.be</p> <p> </p> <p>================================================<br> Dataset<br> ================================================</p> <p><strong>General</strong>: <br> TLS data were collected in leaf-off conditions during late November 2015 - January 2016. Windy days were avoided to ensure data quality. We used a RIEGL VZ-400 terrestrial laser scanner (RIEGL Laser Measurement Systems GmbH). The instrument has a beam divergence of 0.35 mrad and operates in the infrared (wavelength 1550 nm) with a range up to 350 m. The pulse repetition rate for each scan was 300 kHz, the minimum range was 0.5 m and the angular sampling resolution was 0.04°. This resulted in 22,500,000 outgoing pulses for a single scan, resulting in a beam diameter of 2.45 cm and beam spacing of 3.5 cm at 50 m (for example). The azimuth angle range was 0-360° and the zenith angle range was 30-130°. Therefore an additional scan was acquired at each scan location with the scanner tilted at 90° from the vertical to complete sampling of the full hemisphere at each location. Scans were done in a larger 6 ha area using an approximate 20 m × 20 m grid, to ensure the best possible data quality within our 1.4 ha study area. Trees which had at least more than half of their stem at tree diameter 1.3 m inside the boundaries of the study area were included</p> <p>[ Note that this dataset contains 876 individual trees, but after applying the boundary conditions, 835 trees within the study area were used in the analysis of the paper >> see TLS_Inventory.ipynb]</p> <p>Full details of the methods to segment individual trees and generate the QSMs can be found in the paper <em>Calders et al. Ecological Solutions and Evidence.</em></p> <p><strong>Tree ID:</strong><br> Tree IDs can have numbers only or numbers + letters. A number only means this was a base with one stem. A number + letter means individual trees (split below 1.3m), that share a common tree base.</p> <p><strong>Datasets:</strong><br> 1) DATA_clouds_txt & DATA_clouds_ply: Individually segmented trees in *txt and *ply format. File naming is [tree_id].*txt or [tree_ply].*tx</p> <p>2) DATA_QSM_opt: optimised QSMs using TreeQSM v2.0 (https://github.com/InverseTampere/TreeQSM). File naming is [tree_id]-[dmin0]-[rcov0]-[nmin0]-[dmin]-[rcov]-[nmin]-[lcyl]-[NoGround]-[iteration].mat </p> <p>3) Raw scan data can be found here: http://dx.doi.org/10.5285/ed9156e1697343e4ad82e83ed550e345</p> <p> </p> <p>================================================<br> Paper analysis<br> ================================================</p> <p>We have provided all scripts (analysis_and_figures) that were used to:</p> <p>1 ) analyse the data (TLS_Inventory.ipynb):<br> ----- Analysis of point clouds and QSMs using TLS_Inventory.py.ipynb > tls_summary.csv (#876 trees)<br> ----- Link with census &1.4ha > trees_summary.csv (#835 trees)</p> <p>2) generate the paper figures:<br> ----- various *.R and *.ipynb scripts in the main folder and /allometriesTLS/</p> <p> </p> <p>================================================<br> Funding<br> ================================================</p> <p>The TLS fieldwork was funded through the Metrology for Earth Observation and Climate project (MetEOC-2), grant number ENV55 within the European Metrology Research Programme (EMRP). The EMRP is jointly funded by the EMRP participating countries within EURAMET and the European Union. Funds for purchase of the UCL RIEGL VZ-400 instrument was provided by the UK NERC National Centre for Earth Observation (NCEO) and UCL Geography. The census of the forest plot was supported by an ERC Advanced Investigator Grant to Yadvinder Malhi (GEM-TRAIT, grant number 321131).</p>
Masting is shaped by tree-level attributes and stand structure, more than climate, in a Rocky Mountain conifer species
<p>Many tree species mast, meaning seed production is highly variable from year to year and synchronous within a stand, but this phenomenon remains poorly understood. To better understand how a changing climate, altered disturbance regimes, or novel management strategies might affect future seed production, we quantified the joint influence of both biotic (tree size, age, and neighborhood competition) and abiotic factors (climate and weather) on seed production in a widespread conifer species, Rocky Mountain ponderosa pine (<em>Pinus ponderosa</em> var. <em>scopulorum</em>). We reconstructed individual-level annual cone production across a large portion of this species' range using the cone abscission scar method, and mixed models were used to test hypotheses related to the causes and drivers of masting in this species. Our results suggest that masting in ponderosa pine is a process shaped at the individual-level, and this leads to high, local-scale variation in annual cone production. The effects of weather were strongest at climatically marginal sites, but overall, the joint effects of weather and climate only weakly described individual-level patterns of annual cone production in ponderosa pine (R<sup>2</sup><sub>m</sub> = 1.6%, R<sup>2</sup><sub>c</sub> = 30.1%). Rather, we found that masting was strongly influenced by tree- and stand-level factors such as diameter, age, and local neighborhood density, all of which were associated with the mean, interannual variability, and between-tree synchrony of cone production at the individual-level. Larger and older trees produced more cones, more frequently, and with less synchrony than smaller and younger trees. Open-grown trees experiencing lower levels of neighborhood competition also produced more cones with less interannual variability, but with higher between-tree synchrony. Because tree- and stand-level traits appear to regulate seed production more strongly than climate or weather in this species, management interventions targeting these factors could be powerful tools to manage future tree recruitment. Thus, current efforts to reduce stand density and conserve large trees in some ponderosa pine forests may enhance tree-level seed production and reduce variability in seed crops among years.</p>
ScienceDex guides
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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.