Abstract
Plant functional traits regulate ecosystem functions but little is known about how co-occurring gradients of land use and edaphic conditions influence their expression. We test how gradients of logging disturbance and soil properties relate to community-weighted mean traits in logged and old-growth tropical forests in Borneo.
•We studied 32 physical, chemical and physiological traits from 284 tree species in eight 1 ha plots and measured long-term soil nutrient supplies and plant-available nutrients.
•Logged plots had greater values for traits that drive carbon capture and growth, whilst old-growth forests had greater values for structural and persistence traits. Although disturbance was the primary driver of trait expression, soil nutrients explained a statistically independent axis of variation linked to leaf size and nutrient concentration. Soil characteristics influenced trait expression via nutrient availability, nutrient pools, and pH.
•Our finding, that traits have dissimilar responses to land use and soil resource availability, provides robust evidence for the need to consider the abiotic context of logging when predicting plant functional diversity across human-modified tropical forests. The detection of two independent axes was facilitated by the measurement of many more functional traits than have
been examined in previous studies.
•We studied 32 physical, chemical and physiological traits from 284 tree species in eight 1 ha plots and measured long-term soil nutrient supplies and plant-available nutrients.
•Logged plots had greater values for traits that drive carbon capture and growth, whilst old-growth forests had greater values for structural and persistence traits. Although disturbance was the primary driver of trait expression, soil nutrients explained a statistically independent axis of variation linked to leaf size and nutrient concentration. Soil characteristics influenced trait expression via nutrient availability, nutrient pools, and pH.
•Our finding, that traits have dissimilar responses to land use and soil resource availability, provides robust evidence for the need to consider the abiotic context of logging when predicting plant functional diversity across human-modified tropical forests. The detection of two independent axes was facilitated by the measurement of many more functional traits than have
been examined in previous studies.
Original language | English |
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Pages (from-to) | 1853-1865 |
Number of pages | 13 |
Journal | New Phytologist |
Volume | 221 |
Issue number | 4 |
Early online date | 20 Sept 2018 |
DOIs | |
Publication status | Published - Mar 2019 |
Bibliographical note
We acknowledge financial support by the UK Natural Environment Research Council (NE/K016253/1), with additional support through an ERC Advanced Investigator Award to YM (GEM-TRAIT; 321131). We are indebted to the Sabah Biodiversity Council, Yayasan Sabah, the Maliau Basin and Danum Valley Management Committees, the Institute for Tropical Biology and Conservation at the University of Malaysia, Sabah, and the Sabah Forest Research Centre at Sepilok. We thank Glen Reynolds and the South East Asia Rainforest Research Partnership (SEARRP). This study was supported by funding from the Sime Darby Foundation to the Stability of Altered Forest Ecosystems (SAFE) Project. This project would not have been possible without the indispensable support from dozens of research assistants in Sabah. The support from Laura Kruitbos, Unding Jami, Lisa P. Bentley, Benjamin Blonder, Puikiat Hoo, Palasiah Jotan, Alexander Shenkin and Chun Xing Wong is gratefully acknowledged. We thank Bernadus Bala Ola, Bill McDonald, Alexander Karolus and MinSheng Khoo for species identification. This publication is a contribution from the UK NERC-funded Biodiversity And Land-use Impacts on Tropical Ecosystem Function (BALI) consortium (http://bali.hmtf.info) through its Human Modified Tropical Forests thematic programme.Keywords
- anthropogenic disturbance
- Borneo
- functional diversity
- functional traits
- and use
- Rao’s Q
- tropical rainforest
- variance partitioning