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

Abstract

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 hypothesize 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 richness 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.

Keywords: Neotropics; disturbance; forest; palaeoecology; pollen; resilience.

Figure 1.

Distribution of study sites (black stars) in Mexico, Central and South America. Sites are displayed on top of a map with the current vegetation sensitivity index (VSI) as identified by Seddon et al. [12].

Figure 2.

The relationships describing the two hypotheses to be tested. (a) Palynological richness (number of different pollen taxa per sample), as a proxy for vegetation richness, versus recovery rates (percentage of forest pollen abundance increase per year, relative to pre-disturbance levels, log₁₀(x + 1)-transformed). (b) Vegetation sensitivity index (VSI) versus mean recovery rate (log₁₀(x + 1)-transformed) per each study site. (c) Squared chord distance coefficient measuring similarity between pre- and post-disturbance samples along an altitudinal gradient. The horizontal black line represents the 5th percentile cut-off value of 0.48, below which there is insignificant assemblage change between samples.