Fruit and Seed fall in southeast Amazonia following experimental fires
Data files
Dec 21, 2023 version files 196.12 KB
-
Annual_Fruit_Seed_Production_Proportion.csv
15.09 KB
-
README.md
2.20 KB
-
Species_Richness_Treatment_Time.csv
1.70 KB
-
Standardized_WTrait.csv
177.14 KB
Abstract
This dataset was used to investigate how wildfires and edge effects affected fruit and seed (FS) production and diversity of a tropical forest. The research was conducted in southeast Amazonia from 2005-2018, using three 50-hectare plots with varying fire regimes: a control plot, a plot burned annually, and a plot burned every three years (with exception of 2008). These plots also experienced edge effects, two droughts, and a blowdown event. The findings indicate that while low-intensity fires alone had a minor impact on FS production, the combined effects of fires, droughts, and windthrow led to significant declines in FS diversity and production. Additionally, the species composition shifted towards trees with acquisitive strategies, characterized by faster growth, thinner leaves and bark, and shorter stature, particularly along the edges of the burned plots. This research highlights the complex interactions between different types of disturbances and their cumulative impact on tropical forest ecosystems
The dataset associated with this study on forest disturbances in southeast Amazonia provides comprehensive data collected between 2005 and 2018. It includes detailed records from three 50-hectare plots subjected to different fire treatments: a control plot with no fire, a plot burned annually, and a plot burned every three years, with exception of 2008. The data encompasses:
1. Fruit and Seed (FS) Production: Average weight of fruit and seed production across different plots and years, illustrating how different fire regimes impact FS output.
2. FS Species Diversity: Information species richness in the FS rain in each plot, showcasing changes in biodiversity due to varying disturbance levels.
This dataset is instrumental in understanding the intricate dynamics of forest resilience and species adaptation in response to various disturbances, offering valuable insights into the long-term ecological consequences of such events in tropical forests.
Description of the data and file structure
The data are stored as “csv” files:
1) “Annual_Fruit_Seed_Production.csv”
Treat: treatment plots
Edge_Forest: Whether the measurement was taken along the forest edge < 200 m from an agricultural field into the forest) or in the forest core.
Lower: Lower confidence limits based on a nonparametric bootstrap.
Mean: Mean values
Upper: Upper confidence limits based on a nonparametric bootstrap.
Var: The variable of interest, either FS production or FS in litter fall (%)
2) “Species_Richness_Treatment_Time.csv”
Treat: treatment plots
Year: year of measurement
Edge_Forest: Whether the measurement was taken along the forest edge < 200 m from an agricultural field into the forest) or in the forest core.
sp_richness: number of species
3) “Standardized_WTrait.csv”
Treat: treatment plots
Year: year of measurement
var: type of trait measured
x_axis: location of inventory
Edge_Forest: Whether the measurement was taken along the forest edge < 200 m from an agricultural field into the forest) or in the forest core.
w_trait: weight mean trait values (units: standard deviations)
Litterfall was collected biweekly from August 2004 to August 2018 using 0.5-m2 screen litter traps (N: 90 per treatment plot) suspended 1 m above the forest floor and distributed systematically in grids throughout the plots to capture spatial variability random variability and potential edge effects (details in Balch et al., 2008). Litter was oven-dried (65oC for 48 hrs) and weighed to calculate dry mass. Fruits and seeds were separated from the litter and identified to species. Of the total fruit/seed fall, we could not identify 11 % of the sampled species. Nine of these were treated as different morpho species. The same technician sorted and identified all species in order to reduce identification errors
Annual or biannual inventories in the unburned Control (details in Balch et al., 2008) were used to calculate maximum tree size (based on dbh, 97.5% percentile) and relative tree growth per species. In addition, we sampled 413 individuals in the unburned Control and 451 in the burned plots for stem-specific density (SSD), maximum tree height (MTH), relative growth rate (RGR), specific leaf area (SLA) and bark thickness (BT), following Pérez-Harguindeguy et al. (2013). To calculate the community-weighted mean, we weighted trait values by the relative abundance of each species, which was estimated as the number of traps containing the FS of a given species in each year divided by the total number of FS traps. We then evaluated how community-weighted traits changed over time across the three treatments.