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Identifying drivers of forest resilience in long-term records from the Neotropics


Adolf, Carole et al. (2020), Identifying drivers of forest resilience in long-term records from the Neotropics , Dryad, Dataset,


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 ( and can be downloaded from there or by using the Neotoma R package ( 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).


Schweizerischer Nationalfonds zur Förderung der Wissenschaftlichen Forschung, Award: P2BEP2_178414