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7 results for “ForestGEO”
Ants of the CTFS-ForestGEO Plot at Harvard Forest 2018
Foundation species in forests are hypothesized to control the diversity of associated organisms and modulate core ecosystem processes. At Harvard Forest, eastern hemlock (Tsuga canadensis) is a foundation species, but its relationship to local biodiversity has not been explored in detail. This project examined co-occurrence relationships (using codispersion statistics) between forest trees and ants in the 35-hectare Forest Dynamics Plot in the Prospect Hill tract at Harvard Forest ("megaplot"), in which more than 100,000 woody stems have been identified, mapped, and measured.
Crown Geometry Measurements of 14 Species in the CTFS-ForestGEO Plot at Harvard Forest 2013
Tree crown geometry and height, especially when coupled with remotely sensed data, can aid in the characterization of tree and forest structure. In this study, we collected crown geometry data (tree height, crown radius, and crown depth) in order to develop mixed-effects model allometric equations. We leveraged the already existing Center for Tropical Forest Science (CTFS) and Smithsonian Institute’s Forest Global Earth Observatory (ForestGEO) MegaPlot on Prospect Hill at Harvard Forest, Massachusetts to apply allometric equations. In total, we sampled 374 trees across 14 species. Developed allometry was applied to 2014 CTFS-ForestGEO census data to develop allometric canopy height models, which were compared to a lidar canopy height model acquired by NASA’s G-LiHT.
Gene Expression and Tree Growth in the CTFS-ForestGEO Plot at Harvard Forest 2017-2019
Major goals in ecosystem ecology have been to scale from leaves to canopies and to determine whether individual-level, intra-species and inter-specific variation is critical for models projecting ecosystem processes now and in the future. The project is important in that it examines these issues in detail considering genotypes and levels of gene expression all the way up to canopy level CO2 flux. Ecological genomics and transcriptomics are nascent fields that have been primarily restricted to model species in natural and (mostly) controlled environments. To date, we have very few studies of non-model organisms in nature and/or studies of functional genomics through space and time. The research is producing extraordinarily rich datasets regarding the gene expression of trees across populations, through space in each population, across the growing season and across years and linking this information to growth and gas exchange. It will, therefore, provide tremendous insights into how much variation exists in nature thereby guiding sampling designs in future ecological 'omics projects. More importantly, it will provide unusually detailed phenotypic information for important non-model species that have large impacts on the CO2 flux of eastern US forests.
Seedling Survey of the CTFS-ForestGEO Plot at Harvard Forest since 2017
Within a species, trees can vary in size by many orders of magnitude, from tiny seedlings to multi-ton adults. This intraspecific variation in tree size has important consequences for forest structure and function, as it governs patterns of tree abundance, growth rate and energy flux. Recent theoretical models make predictions about size-energy patterns of forests but available evidence indicates deviation from predictions for the smallest and largest trees. The goal of this project is to examine seedling and canopy tree demography at Harvard Forest and explore their theoretical implications. We will link early remote sensing data from the National Ecological Observatory Network (NEON) with boots-on-the-ground measurements at Harvard Forest plots. We will also fit demographic models of the trees of this forest in transition.
Terrestrial LiDAR Scans in the CTFS-ForestGEO Plot at Harvard Forest 2021
In heavily forested and jungle environments where GPS reception is unavailable due to dense canopy cover, it is difficult to determine one's location. Currently, either visual landmarks are used, or open clearings are found where GPS reception can be reestablished. Alternatively, dead-reckoning systems that rely on Inertial Measurement Unit sensor suites can help over moderate distances, but these devices cannot retain positional accuracy over extended ranges. Creare proposes to address this problem by developing the Tree Positioning System. This technological solution will combine a metrology system for determining local tree maps, and geolocalization algorithms that perform spatial pattern matching of local tree maps against a georegistered reference tree map of the area. This system was tested by scanning trees in the ForestGEO plot at Harvard Forest in June 2021.
Harvard Forest CTFS-ForestGEO Mapped Forest Plot since 2014
To investigate the forest dynamics across a larger range of scales in which many processes operate, Harvard Forest (HF) researchers, with assistance from the Center for Tropical Forest Science (CTFS) and the Smithsonian Institute’s Forest Global Earth Observatory (ForestGEO), completed an initial census of all woody stems within a 35 ha plot located at the Harvard Forest in 2014. The HF MegaPlot is part of a global array of large-scale plots established by CTFS whose goals are to increase sampling efforts into temperate forests to explore ecosystem processes beyond population dynamics and biodiversity. The geography and size of the HF MegaPlot (500 m x 700 m) is designed to include a continuous, expansive, and varied natural forest landscape. The strategic plot location will yield opportunities for the study of forest dynamics and demography while capturing a large amount of existing NSF funded LTER (Long Term Ecological Research) science infrastructure (e.g. eddy flux towers, gauged sections of a small watershed, existing smaller permanent plots) and a century of observations and studies. The HF MegaPlot will enable an integrated study of ecosystem processes (e.g., biogeochemistry, hydrology, carbon dynamics) and forest dynamics by melding the past with current and future forest research. The second census was conducted during the summers of 2018 and 2019 but did not contain the central swamp portion of the plot.
Simulated forests for the Changbaishan ForestGEO site
<p>This dataset contains tree records generated with the forest model Formind fitted to the forest at the Changbaishan ForestGEO site.</p> <p>The data correspond to one hectare of forest, which was recorded in time intervals of five years. The considered forest is in equilibrium state: prior to the first records being taken, the forest was simulatd for 2000 years. Then, every five years, key properties (position, dimension etc.) of all individual trees in the forest were recorded and their productivity in the succeeding productive period determined.</p> <p>The data set contains 1000 forest states and can be used to analyze how the structure of a forest is related to its productivity. An example script for the data analysis is provided along with the data.</p> <p>The data set comes with the following assets:</p> <ul> <li>A markdown readme file <code>README.md</code> with the contents of this description.</li> <li>The data file <code>TreeLists.csv</code>, which is a comma-separated text file with one header line.</li> <li>A data anaylsis example script <code>data_analysis_example.py</code>, showing how the data can be analyzed.</li> </ul> <p>The data set contains the following fields:</p> <table> <tbody> <tr> <td><strong>Field</strong></td> <td><strong>Descritpion</strong></td> </tr> <tr> <td>State index</td> <td>Index of the forest state. There are five years difference between forest state <em>i</em> and state <em>i+1</em>. Each forest state includes the trees of one hectare of forest. </td> </tr> <tr> <td>X [m]</td> <td><em>x</em> coordinate of the tree in metres.</td> </tr> <tr> <td>Y [m]</td> <td><em>y</em> coordinate of the tree in metres.</td> </tr> <tr> <td>PFT</td> <td>Plant functional type of the tree. 0: small light demanding, 1: large light demanding I, 2: large light demanding II (Q. mongolica), 3: large mid-tolerant, 4: small shade-tolerant, 5: large shade-tolerant. </td> </tr> <tr> <td>DBH [m]</td> <td>iameter at breast height of the tree in metres.</td> </tr> <tr> <td>Height [m]</td> <td>Height of the tree in metres.</td> </tr> <tr> <td>Biomass [t ODM]</td> <td>Biomass of the tree in tons organic dry matter.</td> </tr> <tr> <td>LAI [m² / m²]</td> <td>Leaf area index of the tree.</td> </tr> <tr> <td>LAI above [m² / m²]</td> <td>Leaf area index above the tree's canopy.</td> </tr> <tr> <td>Mature</td> <td>Indicates whether the tree is in the mature (fullgrown) state (<code>True</code> / <code>False</code>). </td> </tr> <tr> <td>GPP [t ODM / yr]</td> <td>Gross primary production of the tree in the upcoming year, provided in tons organic dry matter per year. The value is undefined for trees that died in the upcoming year.</td> </tr> <tr> <td>NPP [t ODM / yr]</td> <td>Net primary production (of above-ground biomass) of the tree in the upcoming year, provided in tons organic dry matter per year. The value is undefined for trees that died in the upcoming year.</td> </tr> <tr> <td>Respiration [t ODM / yr]</td> <td>Total respiration (and other carbon losses) of the tree in the upcoming year, provided in tons organic dry matter per year. The value is undefined for trees that died in the upcoming year. </td> </tr> <tr> <td>Respiration without size limit [t ODM / yr]</td> <td>Total respiration (and other carbon losses) of the tree in the upcoming year, provided in tons organic dry matter per year if the carbon usage of mature trees was not decreased to 0. The value is undefined for trees that died in the upcoming year. </td> </tr> <tr> <td>Stayed alive</td> <td>Indicates whether the tree stayed alive in the upcoming year (<code>True</code> / <code>False</code>).</td> </tr> </tbody> </table> <p> </p> <h3>Data analysis example</h3> <p>An example for the data analysis is provided in the file `data_analysis.py`. To run the script, Python needs to be installed along with the packages <code>pandas</code>, <code>numpy</code>, and <code>matplotlib</code>.</p>
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