Impacts of tropical selective logging on carbon storage and tree species richness: A meta-analysis☆
Introduction
Over 400 million hectares of tropical forest are designated as timber concessions, making selective logging – the removal of selected trees from a stand – one of the most widespread human disturbances in tropical forests (Asner et al., 2009). Tropical logging produces approximately one eighth of global timber (Blaser et al., 2011), and is an important contributor to many local and national economies. However, logging can have negative impacts on biodiversity (Berry et al., 2010) and leads to increased carbon emissions (Bryan et al., 2010, Nepstad et al., 1999). Poor management of logging concessions can endanger the long-term sustainability of timber production and there have been suggestions that we might be approaching peak timber production in the tropics (Shearman et al., 2012).
Given the large global demand for tropical timber, researchers have proposed modifications to logging techniques to reduce their negative environmental effects, particularly regarding carbon emissions (Putz et al., 2008b) and their impacts on biodiversity (Bicknell et al., 2015). The direct impacts of selective logging are largely the result of the effects of harvesting, skidding of logs, and construction of infrastructure, such as roads, on the mortality and recruitment of trees. The major source of carbon losses is the felling of large trees. However, damage and subsequent death of smaller trees as a result of crushing by felled trees or damage during removal of logs can also be a major contributor of carbon emissions (Putz et al., 2008b). Damage and mortality of non-target trees can also limit forest recovery (Gourlet-Fleury et al., 2013b, Sist et al., 2014) and, if recruitment fails to keep pace with mortality, this can result in altered tree community composition (Ouédraogo et al., 2011). Some of the negative effects of logging on carbon emissions and biodiversity could potentially be minimised by reducing large tree mortality, reducing residual damage to trees that are not felled, or increasing the recruitment of priority species.
One of the most widely accepted means of reducing large tree mortality is to limit the minimum diameter at breast height (DBH) at which trees can be cut (Sist et al., 2003a). Placing such limits decreases logging intensity (volume of trees extracted ha−1). In addition to reducing the number of large trees felled, limiting logging intensity can also reduce residual damage to unfelled trees (Mazzei et al., 2010, Picard et al., 2012). In terms of biodiversity, recent work has shown that increases in logging intensity leads to a linear reduction in animal species richness for most vertebrates while a slight increase in bird species richness is observed at low intensities (Burivalova et al., 2014). Similarly, it is likely that species richness of trees might be enhanced at low intensities owing to an influx of shade intolerant species as suggested by the intermediate disturbance hypothesis (Bongers et al., 2009; but see Fox, 2013 for a full discussion of the intermediate disturbance hypothesis).
In recent years reduced impact logging (RIL) techniques have been considered to reduce the negative environmental impacts of selective logging (Putz et al., 2008a). Though application of RIL is not uniform, it tends to involve one or more of the following activities: cutting lianas prior to logging, felling trees in predetermined directions to minimise the impact to the surrounding forest, limiting road construction, identification and mapping of trees to be cut prior to logging, and planning of roads and skid trails (Pinard and Putz, 1996). Individual studies have suggested that RIL might reduce carbon emissions (Pinard and Putz, 1996), residual tree damage (Sist et al., 2003c), and result in more favourable biodiversity outcomes (Bicknell et al., 2014) when compared to conventional logging. It has also been suggested that RIL could be carried out at similar intensities to conventional logging while causing less damage to residual trees (Pinard and Putz, 1996, Putz et al., 2001; but see Sist et al., 2003a, Sist et al., 2003b, Sist et al., 2003c). Furthermore, it has been proposed that its wide implementation could reduce global carbon emissions from selective logging by 30% (Putz et al., 2008b). If true, these minimisations in the negative consequences of selective logging could be vital in securing long-term sustainability of timber producing tropical forests.
Despite claims made about the benefits of RIL, evidence is conflicting. Studies that investigate the effectiveness of RIL in reducing the negative impacts of conventional logging generally do so by comparing between areas logged using RIL techniques at relatively low intensities. For example, in one of the few studies comparing the effects of RIL and conventional logging on carbon stocks, any treatment effect was confounded by an approximately 50% higher logging intensity in conventionally logged plots (Pinard and Putz, 1996). Moreover, in the studies where differences in the logging intensity have been controlled for, there appears to be little difference in the impacts of RIL on the damage to residual trees (Sist et al., 2003c) and carbon stocks (Griscom et al., 2014). Taken together, these observations bring the value of RIL into question, given that a major aim of RIL is to reduce impact whilst maintaining timber yields (Keller et al., 2003).
Though RIL is widely cited as a method for limiting the negative effects of tropical selective logging there is little information regarding its general impact once logging intensities are controlled for. Though Putz et al. (2012) provided a valuable overview of the impacts of tropical selective logging on biomass and tree species richness, no attempt was made to explain differences in these impacts between sites. The recent meta-analysis by Bicknell et al. (2014) indicated that RIL reduced impacts on animal populations, but there are no equivalent syntheses of effects on trees. Given that REDD+ aims to provide economic incentives to reduce loss of carbon and biodiversity from forests (Harvey et al., 2010) and RIL has been suggested as means of attaining these reductions (Putz et al., 2008b), understanding variation in logging impacts is vital to inform management. In this study, we aim to address this knowledge gap by conducting a meta-analysis to determine which factors relating to logging method and intensity might explain differences in (1) residual stand damage, (2) aboveground biomass loss, and (3) tree species richness.
Section snippets
Systematic review
We defined selectively logged tropical forests as native forests between the latitudes of 40′N and 40′S subjected to selective tree removal for timber. We undertook a standard systematic review as described by Pullin and Stewart (2006) and used the terms (“biomass” OR “carbon” OR “basal area” OR “damage” OR “snag” OR “non-target” OR “tree” OR “species richness” OR biodiversity) AND (selective logg∗ OR felling OR timber extraction OR reduced-impact logging OR degradation) AND “tropical forest”
Results
The systematic review yielded 62 studies, from which we extracted data on residual tree damage from 72 sites, and 43 and 23 paired, replicated sites that measured biomass and tree species richness respectively. In total these data comprised of information on residual damage from 285 plots, comparisons of aboveground biomass from 326 logged and 128 unlogged plots and comparisons of tree species richness from 256 different logged and 161 unlogged plots. Median logged-site age for those sites
Discussion
This study draws on a larger body of evidence than the recent meta-analysis of Putz et al. (2012) on the impacts of selective tropical logging, making it the most precise meta-analysis of the impacts of tropical selective logging on carbon and tree biodiversity to date. In addition, our analyses of the impacts of logging on biomass and species richness accounted for (i) differences in study precision, (ii) study-level pseudoreplication, and (iii) explored the reasons for variation in impacts
Acknowledgements
PM would like to thank NERC for providing PhD funding. MP and MSK thank Sime Darby for funding of the SAFE project. This paper is a contribution to Imperial College’s Grand Challenges in Ecosystems and the Environment initiative. Thanks are due to Louise Barwell for statistical advice and to anonymous reviewers of a previous version of this manuscript.
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This article is part of a special feature entitled “The characteristics, impacts and management of forest fire in China”