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opencc-by-4.0May 2019View details →

Network analysis highlights increased generalisation and evenness of plant-pollinator interactions after conservation measures

DATASET used in the article entitled “Network analysis highlights increased generalisation and evenness of plant-pollinator interactions after conservation measures”. We supply weighted and binary matrices used for plant-pollinator network analyses, before and after the implementation of conservation measures. We also supply the list of plant and pollinator species recorded in this study.

opencc-by-4.0Dec 2012View details →

Fig. 3 in Review paper Stimulation of Plant Growth through Interactions of Bacteria and Protozoa: Testing the Auxiliary Microbial Loop Hypothesis

Fig. 3. Difference in growth responses of 16 cultivars of rice (Oryza sativa L.) grown in autoclaved soil and with a diverse soil bacterial filtrate reinoculated into the farmland soil in presence (black bars) and absence (white bars) of Acanthamoeba sp. Shoot dry weight (a), total root length (b), number of laterals at seminal root (c), and total nitrogen uptake (d). Vertical error bars represent standard deviation (n = 4–9). The symbols * and ** indicate a significant difference at P <0.05 and 0.01 by one way ANOVA, respectively. Data from Somasundaram et al. (2008).

opencc-by-4.0Dec 2012View details →

Fig. 1 in Review paper Stimulation of Plant Growth through Interactions of Bacteria and Protozoa: Testing the Auxiliary Microbial Loop Hypothesis

Fig. 1. Respiration of glucose-C (µg CO -C * g–1 soil) after addi2 tion of 1,000, 2,000, 4,000, and 8,000 ppm glucose to soil from the Heteren field site (Scheu 1992). 1,000 ppm glucose are completely respired by soil microorganisms within a single day, but glucose was not lasting longer than 4 days after saturation of the soil with glucose at 2,000–8,000 ppm (mean of 3 replicates ± 1 SD, see Ekelund et al. (2009) for a characterization of the soil).

opencc-by-4.0Aug 2024View details →

Interactions between enrichment planted seedlings and mature trees in selectively logged lowland dipterocarp forest - dataset

<div> <h2>Description</h2> 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. 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: 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. IDPlots: The plot number the tree in question is located in. Line_number: The specific line within a plot that the tree in question is located in. sp.plot: Column specifying both the species and the plot number of the tree in question, separated by a period. sp.cohort: Column specifying both the species and the cohort of the tree in question, separated by a comma. richness: If the plot the tree is located in is a plot enrichment-planted with one (mono) or 16 species (sixteen). X_m: Relative x coordinate - the first position of Field-Map had x,y,z-coordinates (0,0,0). Y_m: Relative y coordinate. Z_m: Relative z coordinate. genus: The genus-level identity of the seedling. species: The species-level identity of the seedling survival: If the seedling was alive by 2015 (1 = alive, 0 = dead). planting.date: The date of planting of the seedling. survey_2002: the date of the survey for the 2002 census survey_2011: the date of the survey for the 2011 census. survey_2012: the date of the survey for the 2012 census. survey_2015: the date of the survey for the 2015 census. 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. age_2002: the age (in days) of seedlings at the time of their survey in 2002 age_2011: the age (in days) of seedlings at the time of their survey in 2011 age_2012: the age (in days) of seedlings at the time of their survey in 2012 age_2015: the age (in days) of seedlings at the time of their survey in 2015 diam_2002: Basal diameter of seedling in 2002, measured in mm. diam_2011: Basal diameter of seedling in 2011, measured in mm. diam_2012: Basal diameter of seedling in 2012, measured in mm. diam_2015: Basal diameter of seedling in 2015, measured in mm. DBH_2002: DBH of seedling in 2002, measured in mm. DBH_2011: DBH of seedling in 2011, measured in mm. DBH_2012: DBH of seedling in 2012, measured in mm. DBH_2015: DBH of seedling in 2015, measured in mm. 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). 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). 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). openness: The average canopy openness score across 4 cardinal directions, taken during the second census. ba_total_5: the total basal area of surrounding trees within 5 m of the focal seedling ba_total_10: the total basal area of surrounding trees between 5 and 10 m of the focal seedling ba_total: the total basal area of surrounding trees within 10 m of the focal seedling ba_mean_5: the mean basal area of surrounding trees within 5 m of the focal seedling ba_mean_10: the mean basal area of surrounding trees between 5 and 10 m of the focal seedling ba_mean: the mean basal area of surrounding trees within 10 m of the focal seedling dbh_max_5: The basal area of the largest tree within 5 m of the focal seedling dbh_max_10: The basal area of the largest tree between 5 and 10 m of the focal seedling dbh_max: The basal area of the largest tree within 10 m of the focal seedling no_trees_5: The number of trees within 5 m of the focal seedling no_trees_10: The number of trees between 5 and 10 m of the focal seedling no_trees: The number of trees between 5 and 10 m of the focal seedling ba_total_5_nondip: the total basal area of surrounding trees within 5 m of the focal seedling, considering only surrounding non-dipterocarps 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 ba_total_nondip: the total basal area of surrounding trees within 10 m of the focal seedling , considering only surrounding non-dipterocarps ba_mean_5_nondip: the mean basal area of surrounding trees within 5 m of the focal seedling, considering only surrounding non-dipterocarps 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 ba_mean_nondip: the mean basal area of surrounding trees within 10 m of the focal seedling, considering only surrounding non-dipterocarps dbh_max_5_nondip: The basal area of the largest tree within 5 m of the focal seedling, considering only surrounding non-dipterocarps 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 dbh_max_nondip: The basal area of the largest tree within 10 m of the focal seedling, considering only surrounding non-dipterocarps no_trees_5_nondip: The number of trees within 5 m of the focal seedling, considering only surrounding non-dipterocarps no_trees_10_nondip: The number of trees between 5 and 10 m of the focal seedling, considering only surrounding non-dipterocarps no_trees_nondip: The number of trees within 10 m of the focal seedling, considering only surrounding non-dipterocarps ba_total_5_dip: the total basal area of surrounding trees within 5 m of the focal seedling, considering only surrounding dipterocarps ba_total_10_dip: the total basal area of surrounding trees between 5 and 10 m of the focal seedling, considering only surrounding dipterocarps ba_total_dip: the total basal area of surrounding trees within 10 m of the focal seedling , considering only surrounding dipterocarps ba_mean_5_dip: the mean basal area of surrounding trees within 5 m of the focal seedling, considering only surrounding dipterocarps ba_mean_10_dip: the mean basal area of surrounding trees between 5 and 10 m of the focal seedling, considering only surrounding dipterocarps ba_mean_dip: the mean basal area of surrounding trees within 10 m of the focal seedling, considering only surrounding dipterocarps dbh_max_5_dip: The basal area of the largest tree within 5 m of the focal seedling, considering only surrounding dipterocarps dbh_max_10_dip: The basal area of the largest tree between 5 and 10 m of the focal seedling, considering only surrounding dipterocarps dbh_max_dip: The basal area of the largest tree within 10 m of the focal seedling, considering only surrounding dipterocarps no_trees_5_dip: The number of trees within 5 m of the focal seedling, considering only surrounding dipterocarps no_trees_10_dip: The number of trees between 5 and 10 m of the focal seedling, considering only surrounding dipterocarps no_trees_dip: The number of trees within 10 m of the focal seedling, considering only surrounding dipterocarps openness_log: the openness column now expressed on a natural log scale. ba_total_log: The ba_total column expressed on a natural log scale. ba_total_log_scaled: The ba_total_log column scaled using the scale() function (center = TRUE, scale = FALSE). prop_dip: The proportion of basal area of surrounding trees that belong to dipterocarps ba_max: The basal area of the largest tree, calculated as pi * ((dbh_max / 10) ^ 2) / 40000. prop_ba_max: the proportion of the basal area that belongs to the largest single tree <h2>Contact Person</h2> Ryan Veryard (ryan@veryard.co.uk) <h2>Funding</h2> These data were collected as part of research funded by: <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>   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. <h2>Files</h2> This dataset consists of 1 file: fieldmap_dataset.xlsx <h3>fieldmap_dataset.xlsx</h3> This file contains dataset metadata and 1 data tables: <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> This dataset contains data associated with taxa and these have been validated against appropriate taxonomic authority databases. <h3>GBIF taxa details</h3> 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. <div>&ensp;-&ensp;Plantae &ensp;-&ensp;&ensp;-&ensp;Tracheophyta &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;Magnoliopsida &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;Malvales &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;Dipterocarpaceae &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;Dipterocarpus &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;Dipterocarpus conformis &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;Dryobalanops &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;Dryobalanops lanceolata &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;Hopea &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;Hopea ferruginea &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;Hopea sangal &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;Parashorea &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;Parashorea malaanonan &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;Parashorea warburgii (as synonym: Parashorea tomentella) &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;Shorea &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;Shorea argentifolia &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;Shorea beccariana &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;Shorea faguetiana &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;Shorea gibbosa &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;Shorea johorensis &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;Shorea leprosula &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;Shorea macrophylla &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;Shorea macroptera &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;Shorea ovalis &ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;&ensp;-&ensp;Shorea parvifolia</div> </div>

Fig. 2 in LED grow lights alter sorghum growth and sugarcane aphid (Hemiptera: Aphididae) plant interactions in a controlled environment

Fig. 2. Growth characteristics of grain sorghum grown under conventional lighting (A) from within an environmental chamber, fitted with a W2238 LED grow panel (B and C, see Fig. 1 for light spectrum measured), and for sorghum cv MORHC 858, DKS 37-07, TX 2783, and WSH117 afer 21 d in a growth chamber fitted with a W2238 LED grow panel.

Fig. 3 in LED grow lights alter sorghum growth and sugarcane aphid (Hemiptera: Aphididae) plant interactions in a controlled environment

Fig. 3. Number of true leaves on 4 different sorghum entries grown under conventional and LED light sources.

Fig. 4 in LED grow lights alter sorghum growth and sugarcane aphid (Hemiptera: Aphididae) plant interactions in a controlled environment

Fig. 4. Plant height (cm) for 2 different sorghum entries grown under conventional and LED light sources.

Fig. 1 in LED grow lights alter sorghum growth and sugarcane aphid (Hemiptera: Aphididae) plant interactions in a controlled environment

Fig. 1. Light emission spectrum of the W2238 LED grow panel over the visible spectrum and into the near infrared. The inset spectrum is zoomed vertically to show details of any weaker emissions.

opencc-by-4.0Nov 2023View details →

Rhizobia-legume symbiosis mediates direct and indirect interactions between plants, herbivores and their parasitoids

Data and R scripts for statistical analyses for the article: Rhizobia-legume symbiosis mediates direct and indirect interactions between plants, herbivores and their parasitoids By: Carlos Bustos-Segura, Adrienne L. Godschalx, Lucas Malacari, Fanny Deiss, Sergio Rasmann, Daniel J. Ballhorn, Betty Benrey    Abstract Microorganisms associated with plant roots significantly impact the quality and quantity of plant defences. However, the bottom-up effects of soil microbes on the aboveground multitrophic interactions remain largely under studied. To address this gap, we investigated the chemically-mediated effects of nitrogen-fixing rhizobia on legume-herbivore-parasitoid multitrophic interactions. To address this, we initially examined the cascading effects of the rhizobia bean association on herbivore caterpillars, their parasitoids, and subsequently investigated how rhizobia influence on plant volatiles and extrafloral nectar. Our goal was to understand how these plant-mediated effects can affect parasitoids. Lima bean plants (Phaseoulus lunatus) inoculated with rhizobia exhibited better growth, and the number of root nodules positively correlated with defensive cyanogenic compounds. Despite increase of these chemical defences, Spodoptera latifascia caterpillars preferred to feed and grew faster on rhizobia-inoculated plants. Moreover, the emission of plant volatiles after leaf damage showed distinct patterns between inoculation treatments, with inoculated plants producing more sesquiterpenes and benzyl nitrile than non-inoculated plants. Despite these differences, Euplectrus platyhypenae parasitoid wasps were similarly attracted to rhizobia- or no rhizobia-treated plants. Yet, the oviposition and offspring development of E. platyhypenae was better on caterpillars fed with rhizobia-inoculated plants. We additionally show that rhizobia-inoculated common bean plants (Phaseolus vulgaris) produced more extrafloral nectar, with higher hydrocarbon concentration, than non-inoculated plants. Consequently, parasitoids performed better when fed with extrafloral nectar from rhizobia-inoculated plants. While the overall effects of bean-rhizobia symbiosis on caterpillars were positive, rhizobia also indirectly benefited parasitoids through the caterpillar host, and directly through the improved production of high quality extrafloral nectar. This study underscores the importance of exploring diverse facets and chemical mechanisms that influence the dynamics between herbivores and predators. This knowledge is crucial for gaining a comprehensive understanding of the ecological implications of rhizobia symbiosis on these interactions.

opencc-zeroJun 2021View details →

Data supporting: Methodological overview and data-merging approaches in the study of plant-frugivore interactions

Recording species interactions is one of the main challenges in ecological studies. Frugivory has received much attention for decades as a model for mutualisms among free-living species, and a variety of methods have been designed and developed for sampling and monitoring plant–frugivore interactions. The diversity of techniques poses an important challenge when comparing, combining or replicating results from different sources with different methodologies. With the emergence of modern techniques, such as molecular analysis or multimedia remote recorders, issues when combining data from different sources have become especially relevant. We provide an overview of all the techniques used for monitoring endozoochorous primary seed dispersal, focusing on a critical appraisal of the advantages and limitations, as well as the context-dependency nature, of the different methods. We propose five data merging approaches potentially useful to combine frugivory interactions data from different methodologies. Additionally, we provide two case studies where we combine empirical data from plant–animal interactions in Mediterranean shrublands using different methodologies. Data merging resulted in a net increase in the number of distinct pairwise interactions recorded and compensated biases inherent to different methods, resulting in a more robust estimation of network topological descriptors. These case studies clarify the context-dependent character of the merging approaches, highlighting the value of collecting detailed information on the sampling effort in terms of reliable results and reproducibility. Finally, we discuss the trends with different methodological approaches used in the last decades and future perspectives in this field.

opencc-zeroDec 2021View details →

Data from: Fertiliser application modulates the impact of interannual climate fluctuations and plant-to-plant interactions on the dynamics of annual species in a Mediterranean grassland

Background: Climate and land-use changes, which include the application of various types of organic and inorganic fertilisers, have been reducing the species diversity of Mediterranean grasslands and threatening their conservation. Annual plants are one of the most diverse functional groups of species in these grasslands, despite suffering competitive pressure from perennial herbaceous and woody species, and they are essential for ecosystem functioning and stability. Aims: To quantify how fertilisation modulates the impact of plant-to-plant interactions and climate fluctuations on the dynamics of annuals in Mediterranean grasslands. We hypothesised that the application of sewage sludge would increase competition between functional groups, reducing the abundance of annuals in the long-term, but would buffer the negative impacts of drought on the year-to-year fluctuation of the diversity of annuals. Methods: In a semi-natural species-rich Mediterranean grassland in northern Spain, we analysed the changes in the taxonomical and functional composition and diversity of annuals over 14 years in response to variations in the abundance of perennial herbaceous and woody species, climate fluctuations, and fertilisation with sewage sludge. We quantified separately the patterns of year-to-year fluctuations and long-term trends. Results: The frequency and diversity of annuals decreased with a higher abundance of perennial herbaceous species, drought in June, and cold winters. The addition of sewage sludge decreased the abundance of annuals in the long-term, seemed to promote competition between annuals and other functional groups at an interannual scale, and mitigated the negative effects of drought and cold. Conclusions: Fertilisation influences differently the temporal response of annuals to climate fluctuations and plant-to-plant interactions.

opencc-zeroJan 2023View details →

Phenotypic divergence of traits that mediate antagonistic and mutualistic interactions between island and continental populations of the tropical plant, Tribulus cistoides (Zygophyllaceae)

Premise: Island systems have long served as a model for evolutionary processes due to their unique species interactions. Many studies of the evolution of species interactions on islands have focused on endemic taxa. Fewer studies have focused on how antagonistic and mutualistic interactions shape the phenotypic divergence of widespread non-endemic species living on island populations. Methods: We used the widespread plant Tribulus cistoides (Zygophyllaceae) to test phenotypic divergence in traits that mediate antagonistic interactions with vertebrate granivores (birds) and mutualistic interactions with pollinators and how this is explained by bioclimatic variables. We used both herbarium specimens and field-collected samples to compare phenotypic divergence between continental and island populations. Results: Fruits from island populations were larger than on continents, but the presence of lower spines on mericarps was lower on islands. The presence of spines was largely explained by environmental variation among islands. Petal length was on average 9% smaller on island than continental populations, an effect that was especially accentuated on the Galápagos Islands. Conclusions: Tribulus cistoides exhibits phenotypic divergence between island and continental habitats for antagonistic traits (seed defence) and mutualistic traits (floral traits). Further, the evolution of phenotypic traits that mediate antagonistic and mutualistic interactions depended on the abiotic characteristics of specific islands. This study shows the potential of using a combination of herbarium and field samples for comparative studies on a globally distributed species to test phenotypic divergence on island habitats.

opencc-zeroMar 2023View details →

The turnover of plant-frugivore interactions along plant range expansion: consequences for natural colonisation processes

Plant-animal mutualisms such as seed dispersal are key interactions for sustaining plant range shifts. Whether the organisation of interactions with seed dispersers is reconfigured along the expansion landscape template, and its effects accelerating or slowing colonisation, remain elusive. Here we analyse plant-frugivore interactions in a scenario of rapid population expansion of a Mediterranean juniper. We combined complex network analyses with intensive field surveys, sampling interactions between individual plants and frugivores by DNA-Barcoding and phototrapping over two seasons. We assess the role of intrinsic and extrinsic intraspecific variability in shaping interactions and we estimate the contribution of individual plants to seed rain. The whole interaction network was highly structured, with a distinct set of modules including individual plants and frugivore species arranged concordantly along the expansion gradient. The modular configuration found was partially shaped by individual neighbourhood context (density and fecundity) and phenotypic traits (cone size). Interaction reconfiguration resulted in a higher and uneven contribution to seed dispersal rain by individuals of the expansion boundaries, providing signals of the colonisation local-history. Our study provides novel insights into the key role of mutualistic interactions in colonisation scenarios by promoting fast plant expansion processes.

opencc-zeroJun 2023View details →

Multilevel analysis between Physcomitrium patens and Mortierella explores potential long-standing interaction among land plants and fungi

<a name="_Hlk83717523"></a>The model moss species Physcomitrium patens has long been used for studying divergence and evolution of land plants spanning from bryophytes to angiosperms. In addition to its phylogenetic relationships, the limited number of differential tissues, and comparable morphology to the earliest embryophytes make it an ideal candidate for modeling plant terrestrialization 500 million years ago. Based on how plants and fungi interact today, it is predicted that early interactions may have aided in overcoming the barriers present for initial plant colonization on land. This may have manifested similar to present day, where fungi enabled easier uptake of nitrogen, phosphorous, micronutrients, and water retention in exchange for a reliable carbon source. However, identifiable fungal symbionts in P. patens, despite mutualistic interaction widespread among all present day embryophyte families, have remained elusive. To test modern representatives of early land fungal lineages, two Mortierella species (Linnemannia elongata and Benniella eriona), with strains lacking and containing endobacterial symbionts, were grown in coculture with P. patens. We illustrate the interaction between P. patens and Mortierella through high-throughput phenomics, microscopy, RNA-sequencing, differential expression profiling, gene ontology enrichment, and comparisons among 99 other P. patens transcriptomic studies. Our study provides insights into the earliest plant-fungal interactions may have looked like and ways P. patens and Mortierella communicate today.

opencc-by-4.0Jul 2023View details →

Disruption of an ant-plant mutualism shapes interactions between lions and their primary prey

Data and file overview: <ol> <li>Kamaru_Path_Analysis_Data.csv</li> <li>Kamaru_Path_Analysis.R</li> <li>Kamaru_Zebra_RSF_Data.csv</li> <li>Kamaru_Zebra_RSF.R</li> </ol> Layers used to build Zebra RSF: <ol> <li>Kamaru_DWater: distance to water</li> <li>Kamaru_DGlade: distance to glade</li> <li>Kamaru_DSettlement: distance to human settlement</li> <li>Kamaru_OPC_Veg: vegetation layer (classes: V. drepanolobium, E. divinorum, others)</li> </ol> SPECIFIC INFORMATION FOR: Kamaru_Path_Analysis_Data.csv <ol> <li>Number of variables: 11</li> <li>Description: This data file includes 105 zebra kill sites and paired random locations from June 2019 to August 2020. It also includes: (A) monthly utilization distributions of lion prides associated with each kill site and paired point; and (B) zebra densities estimated from resource selection functions, associated with each kill site, and paired random location. Please see our supplementary materials for more details on data and methods.</li> <li>Variable list:</li> </ol> (A) rsf.block: Resource Selection Function blocks (block 1: Jan-Apr 2019, block 2: May-Sep 2019, block 3: Oct 2019 – Jan 2020, block 4: Feb-May 2020, block 5: Jun-Sep 2020) (B) Kill_ID: kill identifier. (C) Lion_ID: individual lion pride identifier. (D) Date (Day, Month, Year) when a specific kill occurred. (E) Zebra_kill (1 = kill site, 0 = paired random location). (F). Species: Zebra. (G) Visibility: openness measurement using a rangefinder in (m). (H) Lion_activity: Utilization distributions (UD) of lions. (I) Invasion (1 = invaded by big-headed ants, 0 = uninvaded by big-headed ants). (J) zeb.rsf: resource selection function value. (K) zeb.density: zebra density estimated from resource selection functions. SPECIFIC INFORMATION FOR: Kamaru_Zebra_RSF_Data.csv <ol> <li>Number of variables: 10</li> <li>Description: This data file includes 182 zebra sightings, paired with 10 random points created for each sighting/used point. Also, the data includes actual GPS locations of each sighting and the total number of zebras in each sighting. Please see our supplementary materials for more details on data and methods.</li> <li>Variable list:</li> </ol> (A) Species: Zebra. (B) Date (Day, Month, Year) for that sighting. (C) Survey: count identifier (Survey 2 to 21). (D) GPS location (X and Y), longitude and latitude of that sighting location. (E) Transect: Transect number. (F) Used: (1= zebra sighting, 0 = paired point). (G) zebra.ct: total number of zebras in each sighting.   R CODE SPECIFIC INFORMATION FOR: Kamaru_Path_Analysis.R <ol> <li>Description: Apply this code to Kamaru_Path_Analysis_Data.csv to build nested path models.</li> </ol> SPECIFIC INFORMATION FOR: Kamaru_Zebra_RSF.R <ol> <li>Description: Apply this code to Kamaru_Zebra_RSF_Data.csv to build resource selection functions for zebra. Use the following layers: Kamaru_DWater, Kamaru_DGlade, Kamaru_DSettlement and Kamaru_OPC_Veg to build the Zebra RSF.</li> </ol>

opencc-by-4.0Sep 2023View details →

Data set on the impact of selected plant protection products on ecosystem service providers, including interactive effects

The Excel files contain the results of ecotoxicological tests for the effects of selected insecticides on ESP species. The objective of this dataset is to provide original data from acute and semi-chronic laboratory tests on a few important beneficial species with broad geographic distribution. The data allow the evaluation of delayed effects and possible interactive effects of combined treatments for those pesticides that are commonly used in mixtures or sprayed next to each other in short time intervals, effectively exposing non-target arthropods to combined/sequential effects. Each data file contains the “Description” sheet where all details of the test and the exact meaning of data fields in the database are reported. The data files are named in a self-explanatory manner, starting with the name of the institution that produced the data (UC – University of Coimbra; UJA – Jagiellonian University), followed by the name of the tested species and names of tested products.

openother-openOct 2023View details →

R code and datasets for flower-visitor interactions (pollinators, robbers, thieves) and plant traits from Mount Cameroon

Raw data and R code for: Cheaters among pollinators: Nectar robbing and thieving vary spatiotemporally with floral traits in Afrotropical forests. Ecosphere, 2023. doi: 10.1002/ecs2.4696  When using the dataset for anything, cite the Sakhalkar et al. Ecosphere paper. All related information can be found in the cited paper. For additional information, refer to the paper or write to either robert.tropek@gmail.com or sailee.sakha@gmail.com.

publicMay 2025View details →

Data from: Artificial nighttime lighting and herbivory interactively reduce the biomass production of invasive plants while enhancing that of native plants

Open the record for dataset details and reuse information.

publicNov 2023View details →

Unveiling the genetic networks: Exploring the dynamic interaction of photosynthetic phenotypes in woody plants across varied light gradients

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