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Selective logging: do rates of forest turnover in stems, species composition and functional traits decrease with time

Oyomoare L Osazuwa-Peters¹, Iván Jiménez², Brad Oberle³

¹Department of Biology, One University Boulevard, University of Missouri Saint Louis, Saint Louis, Missouri, 63121, USA.

Forest Ecology and Management

|September 5, 2015

Summary

This summary is machine-generated.

Selective logging increases tropical forest turnover rates, challenging recovery predictions. These long-term dynamics in Kibale National Park suggest logging impacts persist, potentially affecting carbon storage and forest resilience.

Keywords:

Beta-diversity Kibale National Park disturbance functional traits null model succession

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Area of Science:

Tropical ecology
Forestry science
Conservation biology

Background:

Selective logging is increasing globally, with unknown long-term impacts on tropical forest dynamics.
Understanding forest resilience to logging is crucial for balancing conservation and anthropogenic use.
Succession theory predicts declining turnover rates post-disturbance, but its applicability to logged tropical forests is unclear.

Purpose of the Study:

To investigate the long-term temporal dynamics of forest structure and composition in a selectively logged tropical forest.
To assess the resilience of tropical forests to selective logging over 45 years.
To test predictions of succession theory in logged versus unlogged forest plots.

Main Methods:

Studied temporal dynamics of stems, species composition, and functional traits (wood density, diameter at breast height) over 45 years in Kibale National Park, Uganda.
Utilized data from four censuses (1989, 1999, 2006, 2013) of stems ≥ 10 cm diameter in logged and unlogged plots.
Employed null models to analyze interdependencies among turnover rates and tested predictions regarding turnover rates and temporal variability.

Main Results:

Logged forests exhibited higher turnover rates in stems, species composition, and functional traits compared to unlogged forests.
Turnover rates did not decline through time in logged forests, contradicting succession theory predictions.
Temporal variability in turnover rates was not lower in unlogged forests as predicted.

Conclusions:

Successional models assuming recovery are inadequate for predicting the long-term effects of selective logging on tropical forest dynamics.
Selective logging leads to persistently high turnover rates, potentially compromising forest carbon storage capacity.
The long-term impacts of selective logging may interact with global changes, necessitating a re-evaluation of its perceived benign nature in forest management.