10.5061/DRYAD.XWDBRV19C
Adolf, Carole
0000-0002-5127-3004
University of Oxford
Tovar, Carolina
Royal Botanic Gardens
Kühn, Nicola
University of Oxford
Behling, Hermann
University of Göttingen
Berrío, Juan Carlos
0000-0002-9198-9277
University of Leicester
Dominguez-Vázquez, Gabriela
0000-0003-2001-7216
Universidad Michoacana de San Nicolás de Hidalgo
Figueroa-Rangel, Blanca
0000-0002-5869-5277
University of Guadalajara
Gonzalez-Carranza, Zaire
University of Amsterdam
Islebe, Gerald Alexander
0000-0002-9612-7756
El Colegio de la Frontera Sur
Hooghiemstra, Henry
University of Amsterdam
Neff, Hector
California State University, Long Beach
Olvera-Vargas, Miguel
0000-0002-7290-1639
University of Guadalajara
Whitney, Bronwen
0000-0002-2329-9645
Northumbria University
Wooller, Matthew J.
University of Alaska Fairbanks
Willis, Kathy J.
University of Oxford
Identifying drivers of forest resilience in long-term records from the
Neotropics
Dryad
dataset
2019
Swiss National Science Foundation
https://ror.org/00yjd3n13
P2BEP2_178414
2020-03-30T00:00:00Z
2020-03-30T00:00:00Z
en
513586 bytes
8
CC0 1.0 Universal (CC0 1.0) Public Domain Dedication
Here we use 30 long-term, high-resolution palaeoecological records from
Mexico, Central and South America to address two hypotheses regarding
possible drivers of resilience in tropical forests as measured in terms of
recovery rates from previous disturbances. First, we hypothesise that
faster recovery rates are associated with regions of higher biodiversity,
as suggested by the insurance hypothesis. And second, that resilience is
due to intrinsic abiotic factors that are location specific, thus regions
presently displaying resilience in terms of persistence to current
climatic disturbances should also show higher recovery rates in the past.
To test these hypotheses, we applied a threshold approach to identify past
disturbances to forests within each sequence. We then compared the
recovery rates to these events with pollen richness before the event. We
also compared recovery rates of each site with a measure of present
resilience in the region as demonstrated by measuring global vegetation
persistence to climatic perturbations using satellite imagery. Preliminary
results indeed show a positive relationship between pre-disturbance
taxonomic diversity and faster recovery rates. However, there is less
evidence to support the concept that resilience is intrinsic to a region;
patterns of resilience apparent in ecosystems presently are not
necessarily conservative through time.
Here you can find csv files with pollen counts from 15 sites of the
neotropics. The additional datasets used in the manuscript were downloaded
from the Neotoma Paleoecology Database (https://www.neotomadb.org/) and
can be downloaded from there or by using the Neotoma R package
(https://cran.r-project.org/web/packages/neotoma/neotoma.pdf). Details of
these datasets can be found in the electronic supplementary material file.
To use the R scripts found in this depository on the data from the Neotoma
Paleoecology Database, the first three rows of the datasets must be 1. the
header row with "name", "group", "element",
etc rows. 2. The "Depth" row. 3. The "Sample ID" row.
After these three rows, the pollen taxa rows should follow. Any other rows
(e.g. "Thickness", "AnalysisUnitName", "Sample
Name", and rows referring to ages of samples should be removed prior
to running the R scripts. You can also find the R codes to retrieve
disturbances from these datasets (*dataset*_mean_sd), diversity
calculations (adapted from Dr. Daniele Colombaroli's diversity
scripts, *dataset*_formatting_div) and the R script for the statistical
modelling of the hypoteses to be tested in the paper
(RecoveryR_Richness_Statistics_Script_12Nov19). Additionally, the data for
the statistical analyses can be found in the .csv filest
"RecoveryRates_BiolLetters_rich.csv" (for the first hypothesis)
and "RecoveryRates_BiolLetters_vsi.csv" (for the second
hypothesis).