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Landslides are a central component of tropical montane forest disturbance regimes, including in the tropical Andes biodiversity hotspot, one of the most biodiverse ecosystems in the world. Technological developments in remote sensing have made landscape-scale landslide studies possible, unlocking new avenues for understanding montane biodiversity, ecosystem functioning, and the future effects of climate change. Here, we outline three axes of inquiry for future landslide ecology research in Andean tropical montane forest. We focus exclusively on the Andes due to the vast floral diversity and high endemicity of the tropical Andes biodiversity hotspot, and its importance for global biodiversity and regional ecosystem service provisioning; the broad elevational, latitudinal, and topographic gradients across which landslide dynamics play out; and the existence of long-term plot networks that provide the necessary baseline data on mature forest structure, composition, and functioning to contextualize disturbance impacts. The three lines of study we outline, which draw heavily on remote sensing data and techniques, will deepen scientific understanding of tropical montane forest biodiversity and ecosystem functioning, and the potential impacts of climate change on both. They are: (1) tracking landslide biodiversity dynamics across time and space with high spatial and temporal resolution satellite and unoccupied aerial vehicle imagery; (2) assessing the ecological influence of landslides through the lens of plant functional diversity with imaging spectroscopy; and (3) understanding current and predicting future landslide regimes at scale by building a living landslide inventory spanning the tropical Andes. The research findings from these three axes of inquiry will shed light on the role of landslides and the process of forest recovery from them in both the Andes and worldwide.
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Landslide age, elevation and residual vegetation determine tropical montane forest canopy recovery and biomass accumulation after landslide disturbances in the Peruvian Andes
Abstract Landslides are common natural disturbances in tropical montane forests. While the geomorphic drivers of landslides in the Andes have been studied, factors controlling post‐landslide forest recovery across the steep climatic and topographic gradients characteristic of tropical mountains are poorly understood.Here we use a LiDAR‐derived canopy height map coupled with a 25‐year landslide time‐series map to examine how landslide, topographic and biophysical factors, along with residual vegetation, affect canopy height and heterogeneity in regenerating landslides. We also calculate above‐ground biomass accumulation rates and estimate the time for landslides to recover to mature forest biomass levels.We find that age and elevation are the biggest determinants of forest recovery, and that the jump‐start in regeneration that residual vegetation provides lasts for at least 18 years. Our estimates of time to biomass recovery (31.6–37.1 years) are surprisingly rapid, and as a result we recommend that future research pair LiDAR with hyperspectral imagery to estimate forest above‐ground biomass in frequently disturbed landscapes.Synthesis. Using a high‐resolution LiDAR dataset and a time‐series inventory of 608 landslides distributed across a wide elevational gradient in Andean montane forest, we show that age and elevation are the most influential predictors of forest canopy height and canopy variability. Other features of landslides, in particular the presence of residual vegetation, shape post‐landslide regeneration trajectories. LiDAR allows for a detailed analysis of forest structural recovery across large landscapes and numbers of disturbances, and provides a reasonable upper bound on above‐ground biomass accumulation rates. However, because this method does not capture the effect of compositional change through succession on above‐ground biomass, wherein high‐wood density species gradually replace light‐wooded pioneer species, it overestimates above‐ground biomass. Given previously estimated stem turnover rates along this elevational gradient, we posit that above‐ground biomass recovery takes at least three times as long as our recovery time estimates based on LiDAR‐derived structure alone.
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- Award ID(s):
- 1754647
- PAR ID:
- 10369113
- Publisher / Repository:
- Wiley-Blackwell
- Date Published:
- Journal Name:
- Journal of Ecology
- Volume:
- 109
- Issue:
- 10
- ISSN:
- 0022-0477
- Page Range / eLocation ID:
- p. 3555-3571
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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