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Dryad

Temporal patterns of forest seedling emergence across different disturbance histories data

Cite this dataset

Bowd, Elle (2021). Temporal patterns of forest seedling emergence across different disturbance histories data [Dataset]. Dryad. https://doi.org/10.5061/dryad.0cfxpnw1n

Abstract

Forest ecosystems experience a myriad of natural and anthropogenic disturbances that shape ecological communities. Seedling emergence is a critical, preliminary stage in the recovery of forests post-disturbance and is triggered by a series of abiotic and biotic changes. However, the long-term influence of different disturbance histories on patterns of seedling emergence is poorly understood.

Here, we address this research gap by using an 11-year dataset gathered between 2009 and 2020 to quantify the influence of different histories of natural (wildfire) and anthropogenic (clearcut and post-fire salvage logging) disturbances on emerging seedlings in early successional Mountain Ash forests in south-eastern Australia. We also describe patterns of seedling emergence across older successional forests varying in stand age (stands that regenerated in <1900s, 1939, 1970-90 and 2007-11).

Seedling emergence was highest in the first three years post-disturbance. Stand age and disturbance history significantly influenced the composition and abundance of plant seedlings. Specifically, in salvage logged forests, plant seedlings were the most different from similarly aged forests with other disturbance histories. For instance, relative to clearcut and unlogged, burnt forests of the same age, salvage logging had the lowest overall richness, the lowest counts of Acacia seedlings, and an absence of common species including Acacia obliquinervia, Acacia frigescens, Cassinia arcuealta, Olearia argophylla, Pimelea axiflora, Polyscias sambucifolia and Prosanthera melissifolia over the survey period.

Synthesis: Our findings provide important new insights into the influence of different disturbance histories on regenerating forests and can help predict plant community responses to future disturbances, which may influence forest recovery under altered disturbance regimes.

Methods

Please see manuscript for detailed description of methods.