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80 results for “enrichment planting”
Effects of chronic nutrient enrichment on plant diversity and ecosystem productivity, 2008-2019
Human activities are enriching many of Earth's ecosystems with biologically limiting mineral nutrients such as nitrogen (N) and phosphorus (P). In grasslands, this enrichment generally reduces plant diversity and increases productivity. The widely demonstrated positive effect of diversity on productivity suggests a potential negative feedback, whereby nutrient induced declines in diversity reduce the initial gains in productivity arising from nutrient enrichment. In addition, plant productivity and diversity can be inhibited by accumulations of dead biomass, which may be altered by nutrient enrichment. Over longer timeframes, nutrient addition may increase soil fertility by increasing soil organic matter and nutrient pools. We examined the effects of 5-11 years of nutrient addition at 47 grasslands in twelve countries. Nutrient enrichment increased aboveground live biomass and reduced plant diversity at nearly all sites, and these effects became stronger over time. We did not find evidence that nutrient induced losses of diversity reduced the positive effects of nutrients on biomass, however nutrient effects on live biomass increased more slowly at sites where litter was also increasing, regardless of plant diversity. This work suggests that short-term experiments may underestimate the long term nutrient enrichment effects on global, grassland ecosystems.
Plant species percent cover data: BioCON : Biodiversity, Elevated CO2, and N Enrichment
BioCON (Biodiversity, CO2, and Nitrogen) is an ecological experiment started in 1997 at the University of Minnesota's Cedar Creek Ecosystem Science Reserve. BioCON's goal is to explore the ways in which plant communities will respond to three environmental changes that are known to be occurring on a global scale: increasing nitrogen deposition, increasing atmospheric CO2, and decreasing biodiversity. Why Biodiversity, CO2, and Nitrogen? While there are many uncertainties in global change biology, there are also some well documented facts. Some of these are: 1. The amount of carbon dioxide (CO2) in the atmosphere is rising. Since the industrial revolution, the CO2 concentration in the atmosphere has increased from approximately 275 parts per million (ppm) to about 378 ppm today. This has been largely the result of fossil fuel burning. It is expected that CO2 levels will continue to rise, and that by the year 2050 these levels will be approximately 550 ppm. CO2 is the raw material for photosynthesis and is known to affect plant growth and development. 2. The amount of nitrogen moving through terrestrial ecosystems has increased in the recent past. While natural "background" levels of nitrogen fixation have remained constant, human additions to the system through fertilizer production and fossil fuel use have increased dramatically. Nitrogen is a key nutrient for plant growth and plays a critical role in plant community structure and composition in many environments. 3. Biodiversity levels are falling. While the research and data are not as complete as they are for CO2 and nitrogen, data indicate that the number of species globally, is being reduced. Perhaps more important for ecosystem function, diversity levels on local to regional scales have fallen due to land use change, biotic invasion and many other drivers. While much is known about how each of these factors affects ecosystem functioning, many questions remain. There is also little data on how these issues affe
Presence or absence of marsh plant species along transects through a nutrient enriched marsh receiving wastewater effluent and a reference (unenriched) marsh, Plum Island Ecosystems LTER.
Presence or absence of marsh plant species along transects through a nutrient enriched marsh receiving wastewater effluent and a reference (unenriched) marsh. Nutrient enrichment comes from the Ipswich Wastewater Treatment Facility on Greenwood Creek in Ipswich. The marsh around Clubhead Creek, Rowley, MA was used as a reference.
Data for 'Floral color and family drive contrasting plant-pollinator responses to nutrient enrichment' by Rebecca A. Nelson, Elizabeth T. Borer, and Eric W. Seabloom 2025. Collected in California grasslands 2023 and 2024.
Data for analysis of how flower color and family mediate plant-pollinator response to nutrient enrichment. Data on pollinator visitation and flower abundance were collected in three California grasslands in 2023 and 2024 from a factorial experimental in which combinations of nitrogen, phosphorus, and potassium with micronutrients were applied.
Plant aboveground biomass data: BioCON : Biodiversity, Elevated CO2, and N Enrichment
BioCON (Biodiversity, CO2, and Nitrogen) is an ecological experiment started in 1997 at the University of Minnesota's Cedar Creek Ecosystem Science Reserve. BioCON's goal is to explore the ways in which plant communities will respond to three environmental changes that are known to be occurring on a global scale: increasing nitrogen deposition, increasing atmospheric CO2, and decreasing biodiversity. Why Biodiversity, CO2, and Nitrogen? While there are many uncertainties in global change biology, there are also some well documented facts. Some of these are: 1. The amount of carbon dioxide (CO2) in the atmosphere is rising. Since the industrial revolution, the CO2 concentration in the atmosphere has increased from approximately 275 parts per million (ppm) to about 378 ppm today. This has been largely the result of fossil fuel burning. It is expected that CO2 levels will continue to rise, and that by the year 2050 these levels will be approximately 550 ppm. CO2 is the raw material for photosynthesis and is known to affect plant growth and development. 2. The amount of nitrogen moving through terrestrial ecosystems has increased in the recent past. While natural "background" levels of nitrogen fixation have remained constant, human additions to the system through fertilizer production and fossil fuel use have increased dramatically. Nitrogen is a key nutrient for plant growth and plays a critical role in plant community structure and composition in many environments. 3. Biodiversity levels are falling. While the research and data are not as complete as they are for CO2 and nitrogen, data indicate that the number of species globally, is being reduced. Perhaps more important for ecosystem function, diversity levels on local to regional scales have fallen due to land use change, biotic invasion and many other drivers. While much is known about how each of these factors affects ecosystem functioning, many questions remain. There is also little data on how these issues affe
Interactions between enrichment planted seedlings and mature trees in selectively logged lowland dipterocarp forest - dataset
<div> <h2>Description</h2> <p>Old-growth forests in Southeast Asia are dominated by trees of the Dipterocarpaceae family which are targeted by selective logging. Their traits (supra-annual mast fruiting, limited dispersal, and recalcitrant seeds that form no seed bank) mean they can have poor natural regeneration rates in some selectively logged forests. Enrichment planting is commonly used to overcome recruitment limitation and increase restoration success. However, it is still unclear what factors influence the success rate of planted seedlings, including the characteristics of the surrounding tree matrix and local neighbourhood. <br>Here, we examine the growth and survival of 721 enrichment line-planted seedlings within 24 plots of the selectively logged forest of the Sabah Biodiversity Experiment, in Malaysian Borneo, in relation to their species identity and local neighbourhoods. We mapped the spatial location, size, and identity of nearly 5,000 surrounding matrix trees (≥ 10 cm diameter at breast height ) within a 10 m radius of focal planted seedlings in 2012 and 2015. We analysed levels of tree density-dependence (in terms of canopy openness and total basal area of surrounding matrix trees), asymmetric competition with naturally occurring trees (the proportion of total basal area within a 10 m radius belonging to the largest tree), and confamilial density-dependence (the proportion of total basal area within a 10 m radius belonging to dipterocarps) for each seedling. The following research questions were addressed: how do changes in (a) light availability, (b) local competition, (c) asymmetric competition, and (d) confamilial density-dependence affect the (1) growth and (2) survival of enrichment planted dipterocarp seedlings? To answer these questions, we mapped the tree size, identity (dipterocarp or non-dipterocarp), and spatial location of surrounding matrix trees with DBH ≥ 10 cm within a 10 m radius of each planted seedling. Column descriptions:<br>tree.id: The ID of the tree in question. This takes the format [plot].[line].[number] with cohort (Old [O] or new [N]) at the end.<br>IDPlots: The plot number the tree in question is located in.<br>Line_number: The specific line within a plot that the tree in question is located in.<br>sp.plot: Column specifying both the species and the plot number of the tree in question, separated by a period.<br>sp.cohort: Column specifying both the species and the cohort of the tree in question, separated by a comma.<br>richness: If the plot the tree is located in is a plot enrichment-planted with one (mono) or 16 species (sixteen).<br>X_m: Relative x coordinate - the first position of Field-Map had x,y,z-coordinates (0,0,0).<br>Y_m: Relative y coordinate.<br>Z_m: Relative z coordinate.<br>genus: The genus-level identity of the seedling.<br>species: The species-level identity of the seedling<br>survival: If the seedling was alive by 2015 (1 = alive, 0 = dead).<br>planting.date: The date of planting of the seedling.<br>survey_2002: the date of the survey for the 2002 census<br>survey_2011: the date of the survey for the 2011 census.<br>survey_2012: the date of the survey for the 2012 census.<br>survey_2015: the date of the survey for the 2015 census.<br>cohort: if this seedling was part of the initial planted cohort planted in 2002 (cohort 1) or the second cohort (cohort 2) which was planted later to replace those that had died.<br>age_2002: the age (in days) of seedlings at the time of their survey in 2002<br>age_2011: the age (in days) of seedlings at the time of their survey in 2011<br>age_2012: the age (in days) of seedlings at the time of their survey in 2012<br>age_2015: the age (in days) of seedlings at the time of their survey in 2015<br>diam_2002: Basal diameter of seedling in 2002, measured in mm.<br>diam_2011: Basal diameter of seedling in 2011, measured in mm.<br>diam_2012: Basal diameter of seedling in 2012, measured in mm.<br>diam_2015: Basal diameter of seedling in 2015, measured in mm.<br>DBH_2002: DBH of seedling in 2002, measured in mm.<br>DBH_2011: DBH of seedling in 2011, measured in mm.<br>DBH_2012: DBH of seedling in 2012, measured in mm.<br>DBH_2015: DBH of seedling in 2015, measured in mm.<br>rgr_2002: the relative growth rate of seedling, calculated as (ln(diameter in 2015)-ln(diameter in 2002) / (age in days in 2015 - age in days in 2002).<br>rgr_2011: the relative growth rate of seedling, calculated as (ln(diameter in 2015)-ln(diameter in 2011) / (age in days in 2015 - age in days in 2011).<br>rgr_2012: the relative growth rate of seedling, calculated as (ln(diameter in 2015)-ln(diameter in 2012) / (age in days in 2015 - age in days in 2012).<br>openness: The average canopy openness score across 4 cardinal directions, taken during the second census.<br>ba_total_5: the total basal area of surrounding trees within 5 m of the focal seedling <br>ba_total_10: the total basal area of surrounding trees between 5 and 10 m of the focal seedling <br>ba_total: the total basal area of surrounding trees within 10 m of the focal seedling <br>ba_mean_5: the mean basal area of surrounding trees within 5 m of the focal seedling <br>ba_mean_10: the mean basal area of surrounding trees between 5 and 10 m of the focal seedling <br>ba_mean: the mean basal area of surrounding trees within 10 m of the focal seedling <br>dbh_max_5: The basal area of the largest tree within 5 m of the focal seedling <br>dbh_max_10: The basal area of the largest tree between 5 and 10 m of the focal seedling <br>dbh_max: The basal area of the largest tree within 10 m of the focal seedling <br>no_trees_5: The number of trees within 5 m of the focal seedling<br>no_trees_10: The number of trees between 5 and 10 m of the focal seedling<br>no_trees: The number of trees between 5 and 10 m of the focal seedling<br>ba_total_5_nondip: the total basal area of surrounding trees within 5 m of the focal seedling, considering only surrounding non-dipterocarps<br>ba_total_10_nondip: the total basal area of surrounding trees between 5 and 10 m of the focal seedling , considering only surrounding non-dipterocarps<br>ba_total_nondip: the total basal area of surrounding trees within 10 m of the focal seedling , considering only surrounding non-dipterocarps<br>ba_mean_5_nondip: the mean basal area of surrounding trees within 5 m of the focal seedling, considering only surrounding non-dipterocarps<br>ba_mean_10_nondip: the mean basal area of surrounding trees between 5 and 10 m of the focal seedling, considering only surrounding non-dipterocarps<br>ba_mean_nondip: the mean basal area of surrounding trees within 10 m of the focal seedling, considering only surrounding non-dipterocarps<br>dbh_max_5_nondip: The basal area of the largest tree within 5 m of the focal seedling, considering only surrounding non-dipterocarps<br>dbh_max_10_nondip: The basal area of the largest tree between 5 and 10 m of the focal seedling, considering only surrounding non-dipterocarps<br>dbh_max_nondip: The basal area of the largest tree within 10 m of the focal seedling, considering only surrounding non-dipterocarps<br>no_trees_5_nondip: The number of trees within 5 m of the focal seedling, considering only surrounding non-dipterocarps<br>no_trees_10_nondip: The number of trees between 5 and 10 m of the focal seedling, considering only surrounding non-dipterocarps<br>no_trees_nondip: The number of trees within 10 m of the focal seedling, considering only surrounding non-dipterocarps<br>ba_total_5_dip: the total basal area of surrounding trees within 5 m of the focal seedling, considering only surrounding dipterocarps<br>ba_total_10_dip: the total basal area of surrounding trees between 5 and 10 m of the focal seedling, considering only surrounding dipterocarps<br>ba_total_dip: the total basal area of surrounding trees within 10 m of the focal seedling , considering only surrounding dipterocarps<br>ba_mean_5_dip: the mean basal area of surrounding trees within 5 m of the focal seedling, considering only surrounding dipterocarps<br>ba_mean_10_dip: the mean basal area of surrounding trees between 5 and 10 m of the focal seedling, considering only surrounding dipterocarps<br>ba_mean_dip: the mean basal area of surrounding trees within 10 m of the focal seedling, considering only surrounding dipterocarps<br>dbh_max_5_dip: The basal area of the largest tree within 5 m of the focal seedling, considering only surrounding dipterocarps<br>dbh_max_10_dip: The basal area of the largest tree between 5 and 10 m of the focal seedling, considering only surrounding dipterocarps<br>dbh_max_dip: The basal area of the largest tree within 10 m of the focal seedling, considering only surrounding dipterocarps<br>no_trees_5_dip: The number of trees within 5 m of the focal seedling, considering only surrounding dipterocarps<br>no_trees_10_dip: The number of trees between 5 and 10 m of the focal seedling, considering only surrounding dipterocarps<br>no_trees_dip: The number of trees within 10 m of the focal seedling, considering only surrounding dipterocarps<br>openness_log: the openness column now expressed on a natural log scale.<br>ba_total_log: The ba_total column expressed on a natural log scale.<br>ba_total_log_scaled: The ba_total_log column scaled using the scale() function (center = TRUE, scale = FALSE).<br>prop_dip: The proportion of basal area of surrounding trees that belong to dipterocarps<br>ba_max: The basal area of the largest tree, calculated as pi * ((dbh_max / 10) ^ 2) / 40000.<br>prop_ba_max: the proportion of the basal area that belongs to the largest single tree</p> <h2>Contact Person</h2> <p>Ryan Veryard (ryan@veryard.co.uk)</p> <h2>Funding</h2> <p>These data were collected as part of research funded by:</p> <ul> <li>National Environmental Research Council (Standard grant , NE/S007474/1 , <a href="https://www.environmental-research.ox.ac.uk/">https://www.environmental-research.ox.ac.uk/</a> )</li> <li>Agencia Estatal de Investigación de España (Standard grant , RYC2021-032049-I , <a href="https://www.aei.gob.es/">https://www.aei.gob.es/</a> )</li> <li>Ministry of Education, Youth and Sports of the Czech Republic (Standard grant , INTER-TRANSFER LTT17017 , <a href="https://msmt.gov.cz/">https://msmt.gov.cz/</a> )</li> </ul> <p> </p> <p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p> <h2>Files</h2> <p>This dataset consists of 1 file: fieldmap_dataset.xlsx</p> <h3>fieldmap_dataset.xlsx</h3> <p>This file contains dataset metadata and 1 data tables:</p> <h3>Interactions between enrichment planted seedlings and mature trees in selectively logged lowland dipterocarp forest - dataset</h3> <ul> <li>Worksheet: Fieldmap_data</li> <li>Description: Dataset for the published paper "Interactions between enrichment planted seedlings and mature trees in selectively logged lowland dipterocarp forest"</li> <li>Number of fields: 74</li> <li>Number of data rows: 721</li> <ul> <li>tree.id: The ID of the tree in question. This takes the format [plot].[line].[number] with cohort (Old [O] or new [N]) at the end. (type: replicate)</li> <li>IDPlots: The plot number the tree in question is located in. (type: location)</li> <li>Line_number: The specific line within a plot that the tree in question is located in. (type: numeric)</li> <li>richness: If the plot the tree is located in is a plot enrichment-planted with one (mono) or 16 species (sixteen). (type: ordered categorical)</li> <li>X_m: Relative x coordinate - the first position of Field-Map had x,y,z-coordinates (0,0,0). (type: numeric)</li> <li>Y_m: Relative y coordinate. (type: numeric)</li> <li>Z_m: Relative z coordinate. (type: numeric)</li> <li>genus: The genus-level identity of the seedling. (type: categorical)</li> <li>species: The species-level identity of the seedling (type: taxa)</li> <li>survival: If the seedling was alive by 2015 (1 = alive, 0 = dead). (type: numeric trait)</li> <li>planting.date: The date of planting of the seedling. (type: date)</li> <li>survey_2002: the date of the survey for the 2002 census (type: date)</li> <li>survey_2011: the date of the survey for the 2011 census. (type: date)</li> <li>survey_2012: the date of the survey for the 2012 census. (type: date)</li> <li>survey_2015: the date of the survey for the 2015 census. (type: date)</li> <li>cohort: if this seedling was part of the initial planted cohort planted in 2002 (cohort 1) or the second cohort (cohort 2) which was planted later to replace those that had died. (type: ordered categorical)</li> <li>age_2002: the age (in days) of seedlings at the time of their survey in 2002 (type: numeric trait)</li> <li>age_2011: the age (in days) of seedlings at the time of their survey in 2011 (type: numeric trait)</li> <li>age_2012: the age (in days) of seedlings at the time of their survey in 2012 (type: numeric trait)</li> <li>age_2015: the age (in days) of seedlings at the time of their survey in 2015 (type: numeric trait)</li> <li>diam_2002: Basal diameter of seedling in 2002, measured in mm. (type: numeric trait)</li> <li>diam_2011: Basal diameter of seedling in 2011, measured in mm. (type: numeric trait)</li> <li>diam_2012: Basal diameter of seedling in 2012, measured in mm. (type: numeric trait)</li> <li>diam_2015: Basal diameter of seedling in 2015, measured in mm. (type: numeric trait)</li> <li>DBH_2002: DBH of seedling in 2002, measured in mm. (type: numeric trait)</li> <li>DBH_2011: DBH of seedling in 2011, measured in mm. (type: numeric trait)</li> <li>DBH_2012: DBH of seedling in 2012, measured in mm. (type: numeric trait)</li> <li>DBH_2015: DBH of seedling in 2015, measured in mm. (type: numeric trait)</li> <li>rgr_2002: the relative growth rate of seedling, calculated as (ln(diameter in 2015)-ln(diameter in 2002) / (age in days in 2015 - age in days in 2002). (type: numeric trait)</li> <li>rgr_2011: the relative growth rate of seedling, calculated as (ln(diameter in 2015)-ln(diameter in 2011) / (age in days in 2015 - age in days in 2011). (type: numeric trait)</li> <li>rgr_2012: the relative growth rate of seedling, calculated as (ln(diameter in 2015)-ln(diameter in 2012) / (age in days in 2015 - age in days in 2012). (type: numeric trait)</li> <li>openness: The average canopy openness score across 4 cardinal directions, taken during the second census. (type: numeric)</li> <li>ba_total_5: the total basal area of surrounding trees within 5 m of the focal seedling (type: numeric)</li> <li>ba_total_10: the total basal area of surrounding trees between 5 and 10 m of the focal seedling (type: numeric)</li> <li>ba_total: the total basal area of surrounding trees within 10 m of the focal seedling (type: numeric)</li> <li>ba_mean_5: the mean basal area of surrounding trees within 5 m of the focal seedling (type: numeric)</li> <li>ba_mean_10: the mean basal area of surrounding trees between 5 and 10 m of the focal seedling (type: numeric)</li> <li>ba_mean: the mean basal area of surrounding trees within 10 m of the focal seedling (type: numeric)</li> <li>dbh_max_5: The basal area of the largest tree within 5 m of the focal seedling (type: numeric)</li> <li>dbh_max_10: The basal area of the largest tree between 5 and 10 m of the focal seedling (type: numeric)</li> <li>dbh_max: The basal area of the largest tree within 10 m of the focal seedling (type: numeric)</li> <li>no_trees_5: The number of trees within 5 m of the focal seedling (type: numeric)</li> <li>no_trees_10: The number of trees between 5 and 10 m of the focal seedling (type: numeric)</li> <li>no_trees: The number of trees between 5 and 10 m of the focal seedling (type: numeric)</li> <li>ba_total_5_nondip: the total basal area of surrounding trees within 5 m of the focal seedling, considering only surrounding non-dipterocarps (type: numeric)</li> <li>ba_total_10_nondip: the total basal area of surrounding trees between 5 and 10 m of the focal seedling, considering only surrounding non-dipterocarps (type: numeric)</li> <li>ba_total_nondip: the total basal area of surrounding trees within 10 m of the focal seedling, considering only surrounding non-dipterocarps (type: numeric)</li> <li>ba_mean_5_nondip: the mean basal area of surrounding trees within 5 m of the focal seedling, considering only surrounding non-dipterocarps (type: numeric)</li> <li>ba_mean_10_nondip: the mean basal area of surrounding trees between 5 and 10 m of the focal seedling, considering only surrounding non-dipterocarps (type: numeric)</li> <li>ba_mean_nondip: the mean basal area of surrounding trees within 10 m of the focal seedling, considering only surrounding non-dipterocarps (type: numeric)</li> <li>dbh_max_5_nondip: The basal area of the largest tree within 5 m of the focal seedling, considering only surrounding non-dipterocarps (type: numeric)</li> <li>dbh_max_10_nondip: The basal area of the largest tree between 5 and 10 m of the focal seedling, considering only surrounding non-dipterocarps (type: numeric)</li> <li>dbh_max_nondip: The basal area of the largest tree within 10 m of the focal seedling, considering only surrounding non-dipterocarps (type: numeric)</li> <li>no_trees_5_nondip: The number of trees within 5 m of the focal seedling, considering only surrounding non-dipterocarps (type: numeric)</li> <li>no_trees_10_nondip: The number of trees between 5 and 10 m of the focal seedling, considering only surrounding non-dipterocarps (type: numeric)</li> <li>no_trees_nondip: The number of trees within 10 m of the focal seedling, considering only surrounding non-dipterocarps (type: numeric)</li> <li>ba_total_5_dip: the total basal area of surrounding trees within 5 m of the focal seedling, considering only surrounding dipterocarps (type: numeric)</li> <li>ba_total_10_dip: the total basal area of surrounding trees between 5 and 10 m of the focal seedling, considering only surrounding dipterocarps (type: numeric)</li> <li>ba_total_dip: the total basal area of surrounding trees within 10 m of the focal seedling, considering only surrounding dipterocarps (type: numeric)</li> <li>ba_mean_5_dip: the mean basal area of surrounding trees within 5 m of the focal seedling, considering only surrounding dipterocarps (type: numeric)</li> <li>ba_mean_10_dip: the mean basal area of surrounding trees between 5 and 10 m of the focal seedling, considering only surrounding dipterocarps (type: numeric)</li> <li>ba_mean_dip: the mean basal area of surrounding trees within 10 m of the focal seedling, considering only surrounding dipterocarps (type: numeric)</li> <li>dbh_max_5_dip: The basal area of the largest tree within 5 m of the focal seedling, considering only surrounding dipterocarps (type: numeric)</li> <li>dbh_max_10_dip: The basal area of the largest tree between 5 and 10 m of the focal seedling, considering only surrounding dipterocarps (type: numeric)</li> <li>dbh_max_dip: The basal area of the largest tree within 10 m of the focal seedling, considering only surrounding dipterocarps (type: numeric)</li> <li>no_trees_5_dip: The number of trees within 5 m of the focal seedling, considering only surrounding dipterocarps (type: numeric)</li> <li>no_trees_10_dip: The number of trees between 5 and 10 m of the focal seedling, considering only surrounding dipterocarps (type: numeric)</li> <li>no_trees_dip: The number of trees within 10 m of the focal seedling, considering only surrounding dipterocarps (type: numeric)</li> <li>openness_log: the openness column now expressed on a natural log scale. (type: numeric)</li> <li>ba_total_log: The ba_total column expressed on a natural log scale. (type: numeric)</li> <li>ba_total_log_scaled: The ba_total_log column scaled using the scale() function (center = TRUE, scale = FALSE). (type: numeric)</li> <li>prop_dip: The proportion of basal area of surrounding trees that belong to dipterocarps (type: numeric)</li> <li>ba_max: The basal area of the largest tree, calculated as pi * ((dbh_max / 10) ^ 2) / 40000. (type: numeric)</li> <li>prop_ba_max: the proportion of the basal area that belongs to the largest single tree (type: numeric)</li> </ul> </ul> <h2>Extents</h2> <ul> <li>Date range: 2002-07-01 to 2015-05-28</li> <li>Latitudinal extent: 5.07° to 5.1°</li> <li>Longitudinal extent: 117.64° to 117.67°</li> </ul> <h2>Taxonomic coverage</h2> <p>This dataset contains data associated with taxa and these have been validated against appropriate taxonomic authority databases.</p> <h3>GBIF taxa details</h3> <p>The following taxa were validated against the GBIF backbone dataset (version 2023-08-28). If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p> <div> - Plantae<br> -  - Tracheophyta<br> -  -  - Magnoliopsida<br> -  -  -  - Malvales<br> -  -  -  -  - Dipterocarpaceae<br> -  -  -  -  -  - <em>Dipterocarpus</em><br> -  -  -  -  -  -  - <em>Dipterocarpus conformis</em><br> -  -  -  -  -  - <em>Dryobalanops</em><br> -  -  -  -  -  -  - <em>Dryobalanops lanceolata</em><br> -  -  -  -  -  - <em>Hopea</em><br> -  -  -  -  -  -  - <em>Hopea ferruginea</em><br> -  -  -  -  -  -  - <em>Hopea sangal</em><br> -  -  -  -  -  - <em>Parashorea</em><br> -  -  -  -  -  -  - <em>Parashorea malaanonan</em><br> -  -  -  -  -  -  - <em>Parashorea warburgii</em> (as synonym: <em>Parashorea tomentella</em>)<br> -  -  -  -  -  - <em>Shorea</em><br> -  -  -  -  -  -  - <em>Shorea argentifolia</em><br> -  -  -  -  -  -  - <em>Shorea beccariana</em><br> -  -  -  -  -  -  - <em>Shorea faguetiana</em><br> -  -  -  -  -  -  - <em>Shorea gibbosa</em><br> -  -  -  -  -  -  - <em>Shorea johorensis</em><br> -  -  -  -  -  -  - <em>Shorea leprosula</em><br> -  -  -  -  -  -  - <em>Shorea macrophylla</em><br> -  -  -  -  -  -  - <em>Shorea macroptera</em><br> -  -  -  -  -  -  - <em>Shorea ovalis</em><br> -  -  -  -  -  -  - <em>Shorea parvifolia</em></div> </div>
Fig. 4 in Phylogenomics of the tropical plant family Ochnaceae using targeted enrichment of nuclear genes and 250+ taxa
Fig. 4. Distribution of specimen ages and the number of loci recovered in the phylogenomic study of Ochnaceae. A, Histogram of the collection years of all Ochnaceae specimens; B & C, Relationship between the year of collection of the specimens and the number of loci recovered for tissue obtained from herbarium material (excluding specimens with silica-dried leaf material), analysed for Ochneae and all the remaining Ochnaceae separately, either using the consensus-alignment (B) or the sample-specific (C) reference-based assembly approach. Pearson correlation coefficients and confidence intervals are given for each group.
Fig. 2 in Phylogenomics of the tropical plant family Ochnaceae using targeted enrichment of nuclear genes and 250+ taxa
Fig. 2. RAxML trees based on the concatenated nuclear loci of Ochnaceae. A, Early-diverging branches of Ochnaceae and relationships within Quiinoideae based on the FAM dataset; B, Phylogenetic relationships of Sauvagesieae, Luxemburgieae and Testuleeae based on the SLT dataset. Numbers on the branches are bootstrap values>50%. Numbers in parentheses after species names correspond to the specimen IDs (only for species with multiple accessions). The indicated classification of subfamilies and tribes follows Schneider & al. (2014).
Fig. 1 in Phylogenomics of the tropical plant family Ochnaceae using targeted enrichment of nuclear genes and 250+ taxa
Fig. 1. Overview of the phylogenetic relationships of the major clades of Ochnaceae based on the FAM dataset together with images of representative species. The classification follows Schneider & al. (2014). Ochninae is by far the most species-rich clade comprising about two-thirds of the family's species and six genera (Brack. = Brackenridgea; Cmp. = Campylospermum, clades A and B; I. = Idertia; Ochna; Ouratea; Rh. = Rhabdophyllum). Letters around the tree refer to the photos (mostly flowers except where indicated) and the relative position of the displayed taxa on the tree. A, Medusagyne oppositifolia (Medusagynoideae); B, Froesia venezuelensis (Quiinoideae); C, Luxemburgia schwackeana (Luxemburgieae); D, Rhytidanthera sulcata; E, Cespedesia spathulata; F, Poecilandra retusa; G, Godoya antioquiensis; H, Wallacea insignis; I, Sauvagesia semicylindrifolia; J, Sauvagesia erecta (Sauvagesieae); K, Infructescence of Lophira lanceolata with accrescent sepals (Lophirinae); L, Flower of Elvasia kollmannii (Elvasiinae); M, Perissocarpa umbellifera; N, Fruiting Rhabdophyllum arnoldianum; O, Brackenridgea nitida; P, Campylospermum glaberrimum; Q, Ochna serrulata; R, Fruit of Ochna integerrima with drupelets sitting on enlarged receptacle; S, Fruit of Ouratea sp. with enlarged red receptable; T, Ouratea sp. — Photos: A, K & N from www.africanplants.senckenberg.de (Dressler & al., 2014–); B by Julio Schneider; C by William Milliken/ Royal Botanic Gardens, Kew; D by Sandra Reinales; E by Reinaldo Aguilar; F, H & M by Francisco Farroñay; G by John Clark; I, J, S & T by Domingos Cardoso; L by Claudio Nicoletti de Fraga; O by John Elliott; P by Warran McCleland; Q by Marja Broersma; R by Pierre Grard.
Data from: Water regime and nitrogen enrichment facilitate the encroachment of woody plants at various developmental stages in freshwater marshes
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Data from: Water controls the divergent responses of terrestrial plant photosynthesis under nitrogen enrichment
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Plant aboveground biomass carbon and nitrogen: BioCON : Biodiversity, Elevated CO2, and N Enrichment
BioCON (Biodiversity, CO2, and Nitrogen) is an ecological experiment started in 1997 at the University of Minnesota's Cedar Creek Ecosystem Science Reserve. BioCON's goal is to explore the ways in which plant communities will respond to three environmental changes that are known to be occurring on a global scale: increasing nitrogen deposition, increasing atmospheric CO2, and decreasing biodiversity. Why Biodiversity, CO2, and Nitrogen? While there are many uncertainties in global change biology, there are also some well documented facts. Some of these are: 1. The amount of carbon dioxide (CO2) in the atmosphere is rising. Since the industrial revolution, the CO2 concentration in the atmosphere has increased from approximately 275 parts per million (ppm) to about 378 ppm today. This has been largely the result of fossil fuel burning. It is expected that CO2 levels will continue to rise, and that by the year 2050 these levels will be approximately 550 ppm. CO2 is the raw material for photosynthesis and is known to affect plant growth and development. 2. The amount of nitrogen moving through terrestrial ecosystems has increased in the recent past. While natural "background" levels of nitrogen fixation have remained constant, human additions to the system through fertilizer production and fossil fuel use have increased dramatically. Nitrogen is a key nutrient for plant growth and plays a critical role in plant community structure and composition in many environments. 3. Biodiversity levels are falling. While the research and data are not as complete as they are for CO2 and nitrogen, data indicate that the number of species globally, is being reduced. Perhaps more important for ecosystem function, diversity levels on local to regional scales have fallen due to land use change, biotic invasion and many other drivers. While much is known about how each of these factors affects ecosystem functioning, many questions remain. There is also little data on how these issues affe
Plant Photosynthesis: BioCON : Biodiversity, Elevated CO2, and N Enrichment
BioCON (Biodiversity, CO2, and Nitrogen) is an ecological experiment started in 1997 at the University of Minnesota's Cedar Creek Ecosystem Science Reserve. BioCON's goal is to explore the ways in which plant communities will respond to three environmental changes that are known to be occurring on a global scale: increasing nitrogen deposition, increasing atmospheric CO2, and decreasing biodiversity. Why Biodiversity, CO2, and Nitrogen? While there are many uncertainties in global change biology, there are also some well documented facts. Some of these are: 1. The amount of carbon dioxide (CO2) in the atmosphere is rising. Since the industrial revolution, the CO2 concentration in the atmosphere has increased from approximately 275 parts per million (ppm) to about 378 ppm today. This has been largely the result of fossil fuel burning. It is expected that CO2 levels will continue to rise, and that by the year 2050 these levels will be approximately 550 ppm. CO2 is the raw material for photosynthesis and is known to affect plant growth and development. 2. The amount of nitrogen moving through terrestrial ecosystems has increased in the recent past. While natural "background" levels of nitrogen fixation have remained constant, human additions to the system through fertilizer production and fossil fuel use have increased dramatically. Nitrogen is a key nutrient for plant growth and plays a critical role in plant community structure and composition in many environments. 3. Biodiversity levels are falling. While the research and data are not as complete as they are for CO2 and nitrogen, data indicate that the number of species globally, is being reduced. Perhaps more important for ecosystem function, diversity levels on local to regional scales have fallen due to land use change, biotic invasion and many other drivers. While much is known about how each of these factors affects ecosystem functioning, many questions remain. There is also little data on how these issues affe
Plant Nitrogen Isotopes: BioCON : Biodiversity, Elevated CO2, and N Enrichment
BioCON (Biodiversity, CO2, and Nitrogen) is an ecological experiment started in 1997 at the University of Minnesota's Cedar Creek Ecosystem Science Reserve. BioCON's goal is to explore the ways in which plant communities will respond to three environmental changes that are known to be occurring on a global scale: increasing nitrogen deposition, increasing atmospheric CO2, and decreasing biodiversity. Why Biodiversity, CO2, and Nitrogen? While there are many uncertainties in global change biology, there are also some well documented facts. Some of these are: 1. The amount of carbon dioxide (CO2) in the atmosphere is rising. Since the industrial revolution, the CO2 concentration in the atmosphere has increased from approximately 275 parts per million (ppm) to about 378 ppm today. This has been largely the result of fossil fuel burning. It is expected that CO2 levels will continue to rise, and that by the year 2050 these levels will be approximately 550 ppm. CO2 is the raw material for photosynthesis and is known to affect plant growth and development. 2. The amount of nitrogen moving through terrestrial ecosystems has increased in the recent past. While natural "background" levels of nitrogen fixation have remained constant, human additions to the system through fertilizer production and fossil fuel use have increased dramatically. Nitrogen is a key nutrient for plant growth and plays a critical role in plant community structure and composition in many environments. 3. Biodiversity levels are falling. While the research and data are not as complete as they are for CO2 and nitrogen, data indicate that the number of species globally, is being reduced. Perhaps more important for ecosystem function, diversity levels on local to regional scales have fallen due to land use change, biotic invasion and many other drivers. While much is known about how each of these factors affects ecosystem functioning, many questions remain. There is also little data on how these issues affe
Plant and soil organic matter responses to ten years of nutrient enrichment in the Nutrient Network:Nutrient Network. A cross-site investigation of bottom-up control over herbaceous plant community dynamics and ecosystem function
This experiment is one implementation of a globally distributed experiment, known as the Nutrient Network. At Cedar Creek, as in over 70 other sites in grasslands around the world, the experiment aims to describe impacts of increased nutrients (nitrogen, phosphorus, potassium, sulfur and other metals) and decreased herbivory (removal of mammals by fencing). Two overarching questions are being explored with these manipulations: 1. To what extent are plant production and diversity co-limited by multiple nutrients in herbaceous-dominated communities? 2. Under what conditions do grazers or fertilization control plant biomass, diversity, and composition? By utilizing identical protocols at diverse grassland sites around the world, NutNet aims to uncover both the generalities in ecosystem functioning, and the contingencies or differences which can obscure those common mechanisms. In addition to the standard NutNet protocol, e247 includes an additional low Nitrogen gradient (1 gram Nitrogen per meter squared per year and 5 grams Nitrogen per meter squared per year in addition to the standard 10 grams Nitrogen per meter squared per year).
Nitrogen enrichment stimulates wetland plant responses whereas salt amendments alter sediment microbial communities and biogeochemical responses
<p>Freshwater wetlands of the temperate north are exposed to a range of pollutants that may alter their function, including nitrogen (N)-rich agricultural and urban runoff, seawater intrusion, and road salt contamination, though it is largely unknown how these drivers of change interact with the vegetation to affect wetland carbon (C) fluxes and microbial communities. We implemented a full factorial mesocosm (378.5 L tanks) experiment investigating C-related responses to three common wetland plants of eastern North America (<i>Phragmites australis</i>, <i>Spartina pectinata</i>, <i>Typha latifolia</i>), and four water quality treatments (fresh water control, N, road salt, sea salt). During the 2017 growing season, we quantified carbon dioxide (CO<sub>2</sub>) and methane (CH<sub>4</sub>) fluxes, above- and below-ground biomass, root porosity, light penetration, pore water chemistry (NH<sub>4</sub><sup>+</sup>, NO<sub>3</sub><sup>-</sup>, SO<sub>4</sub><sup>-</sup>², Cl<sup>-</sup>, DOC), soil C mineralization, as well as sediment microbial communities via 16S rRNA gene sequencing. Relative to freshwater controls, N enrichment stimulated plant biomass, which in turn increased CO<sub>2</sub> uptake and reduced light penetration, especially in <i>Spartina</i> stands. Root porosity was not affected by water quality, but was positively correlated with CH<sub>4 </sub>emissions,<sub> </sub>suggesting that plants can be important conduits for CH<sub>4</sub> from anoxic sediment to the atmosphere. Sediment microbial composition was largely unaffected by N addition, whereas salt amendments induced structural shifts, reduced sediment community diversity, and reduced C mineralization rates, presumably due to osmotic stress. Methane emissions were suppressed by sea salt, but not road salt, providing evidence for the additional chemical control (SO<sub>4</sub><sup>-2</sup> availability) on this microbial-mediated process. Thus, N may have stimulated plant activity while salting treatments preferentially enriched specific microbial populations. Together our findings underpin the utility of combining plant and microbial responses, and highlight the need for more integrative studies to predict the consequences of a changing environment on freshwater wetlands. </p>
Data from: Soil fungi underlie a phylogenetic pattern in plant growth responses to nitrogen enrichment
1. Under increasing anthropogenic nitrogen (N) deposition, some plant species will thrive while others will not. Previous work has shown that plant phylogeny can predict these responses, and that interactions with mycorrhizal fungi are a mechanism that drives variation in plant responses to N enrichment. Yet, much of this work has ignored the roles of other root-associated fungi and whole soil fungal communities in driving these responses. 2. We tested whether soil fungi mediate responses of plant growth and plant-soil feedbacks (between close and distant plant relatives) to N enrichment by implementing a greenhouse experiment in which we applied factorial treatments of N fertilization, host-specific soil inocula, and fungicide to 15 eucalypt tree species that co-occur on the island state of Tasmania, Australia and form two phylogenetic lineages within the subgenus Symphyomyrtus. 3. Conspecific-conditioned soil fungi enhanced growth responses to N enrichment for plants within one lineage (lineage 1) but depressed growth responses to N enrichment for plants within another lineage (lineage 2). Lineage-specific shifts in ectomycorrhizal (ECM) colonization were consistent with previous evidence that more vs. less successful strategies under N enrichment are those where carbon allocation to mycorrhizal fungi is reduced vs. maintained, respectively. The latter was also accompanied by a stronger reduction in root colonization of non-filamentous fungi (of unknown function) under N enrichment. Plant-soil feedbacks were neutral for lineage 1 but negative for lineage 2 (i.e., greater growth in soils conditioned by opposite vs. same lineage individuals), but were not altered by N enrichment or fungicide. Lineage-level differences in root colonization suggest that these feedbacks could be driven by differential plant responsiveness to dark septate endophytes and non-filamentous fungi, the colonization of which seemed to benefit plant growth. 4. Our results confirm that interactions with soil fungi (ECM fungi in particular) underlie phylogenetic patterns in tree species' growth responses to N enrichment and may thus influence which plants win or lose under future N deposition scenarios. Yet, we provide some of the first evidence (albeit from controlled rather than natural conditions) that N deposition may not play a strong role in shifting plant-soil feedbacks.
Artificial night-time lighting and nutrient enrichment synergistically favour the growth of alien ornamental plant species over co-occurring native plant species
<ol> <li>Insights into ecological drivers of alien plant invasions can be gained through comparative studies of growth and fecundity of invasive alien plants versus those of co-occurring non-invasive alien plants and native plants across environmental conditions in common garden settings. Habitats that harbour alien plant species in many ecosystems globally are presently experiencing light pollution resulting from artificial light at night (ALAN) and increased rates of nutrient enrichment of the soil. However, the potential interactive effects of ALAN and nutrient enrichment on invasiveness of alien plant species remain unknown.</li> <li>Here, we performed a common-garden experiment to test the interactive effects of ALAN and soil nutrient enrichment on the growth of a random set of 10 alien (five invasive and five naturalized) and seven co-occurring native ornamental plant species that are commonly cultivated within urban and peri-urban areas of Nairobi city in Kenya. We predicted that a simultaneous increase in photoperiod via ALAN and nutrient enrichment will favor growth of invasive alien plant species over that of non-invasive alien and native plant species. We grew the 17 plant species under natural daylight (ALAN-) vs natural daylight followed by ALAN (ALAN+) and fully crossed with two levels of nutrient enrichment (low vs high) and competition (competition vs no-competition against a native plant <em>Ocimum</em> <em>gratissimum</em>) treatments.</li> <li>Under simultaneous high-nutrient and no-competition treatments, ALAN enhanced mean total biomass of invasive and naturalized alien species by 61.1% and 131.4%, respectively but decreased that of native plant species by 34%. In contrast, under simultaneous high-nutrient and competition treatments, ALAN enhanced mean total biomass of invasive alien plant species by 68.6% and that of naturalized alien species by 51.9% and native species by 35.4%. High-nutrient treatment enhanced flower formation more strongly in invasive and naturalized alien plants than in native plants. The invasive and naturalized alien species grew taller than native species across the light, nutrient, and competition treatments.</li> <li> <em>Synthesis</em>: The present findings suggest that light pollution and nutrient enrichment may jointly confer growth advantage to invasive alien plant species over that of co-occurring native plant species and enhance invasiveness of alien plant species.</li> </ol>
Functional traits explain the consistent resistance of biodiversity to plant invasion under nitrogen enrichment
<p>Elton's biotic resistance hypothesis, which posits that diverse communities should be more resistant to biological invasions, has received considerable experimental support. However, it remains unclear whether such a negative diversity–invasibility relationship would persist under anthropogenic environmental change. By using the common ragweed (Ambrosia artemisiifolia) as a model invader, our four-year grassland experiment demonstrated consistently negative relationships between resident species diversity and community invasibility, irrespective of nitrogen addition, a result further supported by a meta-analysis. Importantly, our experiment showed that plant diversity consistently resisted invasion simultaneously through increased resident biomass, increased trait dissimilarity among residents, and increased community weighted means of resource-conservative traits that strongly resist invasion, pointing to the importance of both trait complementarity and sampling effects for invasion resistance even under resource enrichment. Our study provides unique evidence that considering species functional traits can help further our understanding of biotic resistance to biological invasions in a changing environment.</p>
Data from: Microfluidic Enrichment Barcoding (MEBarcoding): a new method for high throughput plant DNA barcoding
<p>DNA barcoding has become a valuable tool to support species identification with a broad range of applications in fields such as traditional taxonomy, ecology, forensics, food analysis, and environmental science. We introduce Microfluidics Enrichment Barcoding (MEBarcoding) for plant DNA Barcoding, a cost-effective method for high throughput DNA barcoding. MEBarcoding uses the Fluidigm Access Array™ to simultaneously amplify targeted regions for 48 DNA samples and hundreds of PCR primer pairs (a total of 23,040 PCR products) during a single thermal cycling protocol. A second generation instrument from Fluidigm, called the Juno™, can accommodate 192 DNA samples simultaneously. As a proof of concept, we developed a microfluidic PCR workflow using the Fluidigm Access Array™ and Illumina MiSeq to generate new sequences from 96 samples for each of the four primary DNA barcode loci in plants: rbcL, matK, trnH-psbA, and ITS (384 total sequences). This workflow was used to build a reference library that includes 78 families and 96 genera from all major plant lineages, including bryophytes, ferns and lycophytes, gymnosperms, and all major groups of angiosperms, which are currently lacking in public databases. Our results demonstrate that this technique offers a highly efficient alternative method to traditional PCR and Sanger sequencing by increasing the estimated number of plant DNA barcodes that can be sequenced by a single technician in one week by 800%, at a reduced cost, and by generating a barcode library with a more comprehensive taxonomic coverage.</p>
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