Selective Logging In Tropical Forests Research Articles

Destructive selective logging in tropical forests causes soil carbon loss through forest degradation and soil redox change

Soil organic C (SOC) stock in tropical forests can be reduced significantly after logging, not only due to lowered above ground biomass (AGB) and consequentially reduced organic matter (OM) input, but also consumption on sequestrated soil C protected by OM-mineral association. In this study, we studied the loss of mineral-associated OM after logging in the main rooting zone (e.g., 0–30 cm), and examined a hypothetical twofold underlying mechanism of SOC reduction: first, decrease in AGB limiting OM input; second, decrease in soil redox potential (Eh) leading to the reductive dissolution of short-range-ordered iron (SRO Fe) minerals that destabilize some mineral-associated OM. Thirty-five plots at different degradation levels were investigated for both above- and belowground properties. AGB and tree community composition were employed as indicators of forest degradation gradient; multiple physicochemical properties in top-30 cm soils were measured every 10 cm depth. We analysed the relationship between AGB and soil C with the mediator SRO Fe, and investigated the role of soil Eh in linking the changes in AGB and SRO Fe. Our results revealed simultaneous decreases in AGB, SRO Fe and soil C in top-30 cm soils, and SRO Fe mediated 43 % of the hypothetical causal effect of AGB on soil C in deeper (20–30 cm) soil. Soil Eh decreased with decreasing AGB in a logistic pattern, dropping to a reductive regime (Eh7 ≤ 0.05 V) when more than half of the AGB was removed from the pristine level. Mean SRO Fe stock in the reductive soils was significantly lower than that in the oxic soils (Eh7 > 0.05 V), and the loss of SRO Fe correspond to the decomposition of OM in 10–30 cm soils. We suggest that the reductive shift in soil is caused by the weakened evapotranspiration as destructive selective logging inflicts canopy openness and succession failure. This study presents the possibility that forest degradation may reduce the size of protected soil C pool resulting from the loss of soil SRO Fe minerals.

LoggingLab: An R package to simulate reduced-impact selective logging in tropical forests using forest inventory data

Even where Reduced-Impact Logging (RIL) practices are applied, selective logging causes substantial damage to tropical forests. To further reduce selective logging damage, the practices that cause the most damage need to be identified and alternatives tested. To this end, we developed the R package LoggingLab, a spatially-explicit and individual tree-based selective logging simulator and demonstrated its functions using data from French Guiana. LoggingLab explicitly simulates damage during each stage of the selective logging process taking into account topography and hydrography, which are main constraints on logging. Most LoggingLab parameters can be easily adjusted to a wide range of local contexts. LoggingLab can also be coupled with forest dynamics models to simulate the long- term effects of different selective logging scenarios.

Towards the use of satellite-based tropical forest disturbance alerts to assess selective logging intensities

Illegal logging is an important driver of tropical forest loss. A wide range of organizations and interested parties wish to track selective logging activities and verify logging intensities as reported by timber companies. Recently, free availability of 10 m scale optical and radar Sentinel data has resulted in several satellite-based alert systems that can detect increasingly small-scale forest disturbances in near-real time. This paper provides insight in the usability of satellite-based forest disturbance alerts to track selective logging in tropical forests. We derive the area of tree cover loss from expert interpretations of monthly PlanetScope mosaics and assess the relationship with the RAdar for Detecting Deforestation (RADD) alerts across 50 logging sites in the Congo Basin. We do this separately for various aggregation levels, and for tree cover loss from felling and skidding, and logging roads. A strong linear relationship between the alerts and visually identified tree cover loss indicates that with dense time series satellite data at 10 m scale, the area of tree cover loss in logging concessions can be accurately estimated. We demonstrate how the observed relationship can be used to improve near-real time tree cover loss estimates based on the RADD alerts. However, users should be aware that the reliability of estimations is relatively low in areas with few disturbances. In addition, a trade-off between aggregation level and accuracy requires careful consideration. An important challenge regarding remote verification of logging activities remains: as opposed to tree cover loss area, logging volumes cannot yet be directly observed by satellites. We discuss ways forward towards satellite-based assessment of logging volumes at high spatial and temporal detail, which would allow for better remote sensing based verification of reported logging intensities and tracking of illegal activities.

Change Detection of Selective Logging in the Brazilian Amazon Using X-Band SAR Data and Pre-Trained Convolutional Neural Networks

It is estimated that, in the Brazilian Amazon, forest degradation contributes three times more than deforestation for the loss of gross above-ground biomass. Degradation, in particular those caused by selective logging, result in features whose detection is a challenge to remote sensing, due to its size, space configuration, and geographical distribution. From the available remote sensing technologies, SAR data allow monitoring even during adverse atmospheric conditions. The aim of this study was to test different pre-trained models of Convolutional Neural Networks (CNNs) for change detection associated with forest degradation in bitemporal products obtained from a pair of SAR COSMO-SkyMed images acquired before and after logging in the Jamari National Forest. This area contains areas of legal and illegal logging, and to test the influence of the speckle effect on the result of this classification by applying the classification methodology on previously filtered and unfiltered images, comparing the results. A method of cluster detections was also presented, based on density-based spatial clustering of applications with noise (DBSCAN), which would make it possible, for example, to guide inspection actions and allow the calculation of the intensity of exploitation (IEX). Although the differences between the tested models were in the order of less than 5%, the tests on the RGB composition (where R = coefficient of variation; G = minimum values; and B = gradient) presented a slightly better performance compared to the others in terms of the number of correct classifications for selective logging, in particular using the model Painters (accuracy = 92%) even in the generalization tests, which presented an overall accuracy of 87%, and in the test on RGB from the unfiltered image pair (accuracy of 90%). These results indicate that multitemporal X-band SAR data have the potential for monitoring selective logging in tropical forests, especially in combination with CNN techniques.

A Comparative Assessment of Machine-Learning Techniques for Forest Degradation Caused by Selective Logging in an Amazon Region Using Multitemporal X-Band SAR Images

The near-real-time detection of selective logging in tropical forests is essential to support actions for reducing CO2 emissions and for monitoring timber extraction from forest concessions in tropical regions. Current operating systems rely on optical data that are constrained by persistent cloud-cover conditions in tropical regions. Synthetic aperture radar data represent an alternative to this technical constraint. This study aimed to evaluate the performance of three machine learning algorithms applied to multitemporal pairs of COSMO-SkyMed images to detect timber exploitation in a forest concession located in the Jamari National Forest, Rondônia State, Brazilian Amazon. The studied algorithms included random forest (RF), AdaBoost (AB), and multilayer perceptron artificial neural network (MLP-ANN). The geographical coordinates (latitude and longitude) of logged trees and the LiDAR point clouds before and after selective logging were used as ground truths. The best results were obtained when the MLP-ANN was applied with 50 neurons in the hidden layer, using the ReLu activation function and SGD weight optimizer, presenting 88% accuracy both for the pair of images used for training (images acquired in June and October) of the network and in the generalization test, applied on a second dataset (images acquired in January and June). This study showed that X-band SAR images processed by applying machine learning techniques can be accurately used for detecting selective logging activities in the Brazilian Amazon.

Prospective carbon balance of the wood sector in a tropical forest territory using a temporally-explicit model

Selective logging in tropical forests is often perceived as a source of forest degradation and carbon emissions. Improved practices, such as reduced-impact logging (RIL), and alternative timber production strategies (e.g. plantations) can drastically change the overall carbon impact of the wood production sector. Assessing the carbon balance of timber production is crucial but highly dependent on methodological approaches, especially regarding system boundaries and temporality. We developed a temporally-explicit and territory scale model of carbon balance calibrated with long-term local data using Bayesian inference. The model accounts for carbon fluxes from selective logging in natural forest, timber plantation, first transformation and avoided emissions through energy substitution. We used it to compare prospective scenarios of development for the wood sector in French Guiana. Results show that intensification of practices, through increased logging intensity conducted with RIL and establishment of timber plantations, are promising development strategies to reduce the carbon emissions of the French-Guianese wood sector, as well as the area needed for wood production and hence the pressure on natural forests. By reducing logging damage by nearly 50%, RIL allows increasing logging intensity in natural forest from 20 m3 ha−1 to 30 m3 ha−1 without affecting the carbon balance. The use of logging byproducts as fuelwood also improved the carbon balance of selective logging, when substituted to fossil fuel. Allocating less than 30000 ha to plantation would allow producing 200000 m3 of timber annually, while the same production in natural forest would imply logging more than 400000 ha over 60 years. Timber plantation should be preferentially established on non-forested lands, as converting natural forests to plantation leads to high carbon emission peak over the first three decades.We recommend a mixed-strategy combining selective logging in natural forests and plantations as a way to improve long-term carbon balance while reducing short-term emissions. This strategy can reduce the pressure on natural forests while mitigating the risks of changing practices and allowing a diversified source of timber for a diversity of uses. It requires adaptation of the wood sector and development of technical guidelines. Research and monitoring efforts are also needed to assess the impacts of changing practices on other ecosystem services, especially biodiversity conservation.

Mapping Forest Disturbance Due to Selective Logging in the Congo Basin with RADARSAT-2 Time Series

Dense time series of stripmap RADARSAT-2 data acquired in the Multilook Fine mode were used for detecting and mapping the extent of selective logging operations in the tropical forest area in the northern part of the Republic of the Congo. Due to limited radiometric sensitivity to forest biomass variation at C-band, basic multitemporal change detection approach was supplemented by spatial texture analysis to separate disturbed forest from intact. The developed technique primarily uses multi-temporal aggregation of orthorectified synthetic aperture radar (SAR) imagery that are acquired before and after the logging operations. The actual change analysis is based on textural features of the log-ratio image calculated using two SAR temporal composites compiled of SAR scenes acquired before and after the logging operations. Multitemporal aggregation and filtering of SAR scenes decreased speckle and made the extracted textural features more prominent. The overall detection accuracy was around 80%, with some underestimation of the area of forest disturbance compared to reference based on optical data. The user’s accuracy for disturbed forest varied from 76.7% to 94.9% depending on the accuracy assessment approach. We conclude that change detection utilizing RADARSAT-2 time series represents a useful instrument to locate areas of selective logging in tropical forests.

Remote sensing of selective logging in tropical forests: current state and future directions

Abstract: This paper reviews and discusses the status of remote sensing techniques applied in detecting and monitoring selective logging disturbance in tropical forests. The analyses concentrated on the period 1992-2019. Accurate and precise detection of selectively logged sites in a forest is crucial for analyzing the spatial distribution of forest disturbances and degradation. Remote sensing can be used to monitor selective logging activities and associated forest fires over tropical forests, which otherwise requires labor-intensive and time-consuming field surveys, that are costly and difficult to undertake. The number of studies on remote sensing for selective logging has grown steadily over the years, thus, the need for their review so as to guide forest management practices and current research. A variety of peer reviewed articles are discussed so as to evaluate the applicability and accuracy of different methods in different circumstances. Major challenges with existing approaches are singled out and future needs are discussed.

The life cycle carbon balance of selective logging in tropical forests of Costa Rica

AbstractThe effect of logging on atmospheric carbon concentrations remains highly contested, especially in the tropics where it is associated to forest degradation. To contribute to this discussion, we estimated the carbon balance from logging natural tropical forests in Costa Rica through a life cycle accounting approach. Our system included all major life cycle processes at a regional level during one rotation period (15 years). We used mass flow analysis to trace biogenic carbon. Data were gathered from all logging operations in the Costa Rican NW region (107 management plants), a sample of industries transforming wood into final products (20 sawmills), and national reports. We estimated a surplus of −3.06 Mg C ha−1 15 year−1 stored within the system. When accounting for uncertainty and variability in a Monte Carlo analysis, the average balance shifted to −2.19 Mg C ha−1 15 year−1 with a 95% CI of −5.26 to 1.86. This confidence interval reveals probabilities of a net increase in atmospheric carbon due to harvesting although these are smaller than those from a system that acts as a reservoir. Our results provide evidence for the carbon neutrality of bio‐materials obtained from natural forests. We found that anthropogenic reservoirs play a determinant role in delaying carbon emissions and that these may explain differences with previous carbon balance studies on tropical forest management. Therefore, the climate mitigation potential of forest‐derived products is not exclusive to forest management, but measures should be considered throughout the processes of wood transformation, use, and disposal.

Bioacoustic monitoring reveals shifts in breeding songbird populations and singing behaviour with selective logging in tropical forests

Abstract Selective logging is the primary cause of tropical forest degradation and is rapidly expanding worldwide. While the impacts of logging on species diversity and distributions are well understood, little is known about the effects of logging on animal behaviours central to individual fitness and population persistence. The song rate of breeding songbirds is a behavioural trait that is often positively associated with male density and used by conspecific females as an indicator of territory and male quality. Thus, contrasting logging‐induced adjustments in song rates of individual birds with population shifts may illuminate potential mechanisms underlying population distributions. We present a novel application of bioacoustic monitoring, integrating counts of individuals, songs and duets from single automated recording units (ARUs) with N‐mixture models, to estimate shifts in population parameters (occupancy, abundance) and singing behaviours (per‐capita song rates, per‐pair duet rates) of 32 Bornean songbird species with logging. We tested hypotheses on the relationships between adjustments in behavioural and population parameters with logging, and further tested the extent to which species traits predicted behavioural and population shifts. Adjustments to singing behaviour in 59 and 53% of species (57% of duetting species) were concordant with differences in occupancy and abundance respectively, such that species showing reduced populations with logging also produced fewer songs per‐capita, and vice versa. Species known to prefer undisturbed habitats and large‐bodied species showed the most negative effects of logging on singing behaviour and population distributions. Species known to exploit degraded habitats exhibited the opposite pattern. Subdued singing in logged forests by species of conservation concern suggests limited competition between territorial males in small populations and may also signal low‐quality territories. Synthesis and applications. We demonstrate that bioacoustic monitoring can be used to not only estimate important population parameters of occupancy and abundance across a diverse tropical songbird community, but also enables quantification of behaviours considered relevant to individual fitness, yet unobtainable with conventional methods (e.g. point counts). Bioacoustics provides a viable approach to reliable automated large‐scale monitoring of hyperdiverse tropical forest systems under logging operations and other land‐use pressures.

Residual stand damage from selective logging of tropical forests: A comparative case study in central Kalimantan and West Sumatra, Indonesia

Selective logging in natural forests in Indonesia involves logging trees with a diameter at breast height (dbh) of >50 cm and leads to damage to residual stands. This research was conducted on forest concession areas in West Sumatra and Central Kalimantan to analyze residual stand damage. The type and percentage of damage were calculated at both locations by evaluating 10 plots of 1 ha at each site. All trees with a dbh of >20 cm were measured and classified before and after felling and skidding. The stand density and felling intensity were analyzed to determine the factors affecting the amount of damage. Results indicated six types of damages: broken stem, crown injury, fallen or uprooted trees, leaning, bark and stem injuries, and buttress injury. All six types of damages were found in Central Kalimantan, whereas five types of damages (except buttress injury) were found in West Sumatra. On comparing pre- and post-logging conditions, a higher percentage of damage was identified in Central Kalimantan (27.8%) than in West Sumatra (22.4%). The percentage of tree damage increased in line with the increasing felling intensity at both locations. It was concluded that a higher tree density and felling intensity resulted in a greater residual stand damage both in Central Kalimantan and West Sumatra.

Estimating selective logging impacts on aboveground biomass in tropical forests using digital aerial photography obtained before and after a logging event from an unmanned aerial vehicle

Selective logging is one of the factors contributing to deforestation and forest degradation in tropical forests. A low-cost methodology to monitor selective logging is clearly required. However, this poses a challenge because only a few trees are felled at a given time. Here, we investigate the potential of using repeatedly acquired digital aerial photographs (DAPs) from a lightweight unmanned aerial vehicle (UAV) to detect selective logging in tropical forests in Myanmar. Selective logging was conducted within two 9-ha plots. DAPs were acquired immediately before and after selective logging using a lightweight UAV in this case study. The aboveground biomass (AGB) change related to selective logging was regressed against metrics expressing forest changes calculated at a 0.25-ha resolution from a photogrammetric point cloud created using the DAPs before and after selective logging. The root-mean-square error and coefficient of determination were 0.77 and 9.32 Mg/ha, respectively. This study demonstrates that repeated DAPs taken from a lightweight UAV can be used to estimate changes in the AGB linked to selective logging. This method could be used to quantify the impacts of both legal selective logging and illegal logging in tropical forests.

Is sustainable logging possible in Africa’s dense forest?

René Catinot, qui fut directeur des recherches forestières au CTFT (Centre Technique Forestier Tropical, qui deviendra le département Forêt du Cirad) et figure historique de la foresterie tropicale française, se posait cette question au début des années 1960. Dès leur arrivée en Afrique Tropicale, nous dit Catinot, « les Forestiers chargés de la gestion de la forêt dense ont cherché à la régénérer ». Les connaissances forestières de milieux tempérés sont insuffisantes face à des forêts qui comportent 200 à 300 espèces, mais qui contiennent nettement moins de bois qu’une forêt aménagée en zone tempérée. Un débat oppose alors les tenants d’une régénération naturelle à ceux de la régénération artificielle. Le problème de l’exploitation sélective tropicale, particulièrement en Afrique, est le faible taux de prélèvement qui limite l’arrivée de lumière au sol et empêche la germination nécessaire au renouvellement des espèces les plus appréciées commercialement, lesquelles sont souvent à tendance héliophile. Catinot est affirmatif « il ne faut pas compter sur la Nature seule pour la régénérer ». Les techniques sylvicoles s’emploient, dès lors, à favoriser ces espèces commerciales, soit en « stimulant la croissance des plants préexistants » (sylviculture utilisant la régénération naturelle), soit en « transplantant dans les forêts épuisées par l’exploitation des plants d’essences nobles préalablement produits en pépinière » (régénération artificielle). Le langage sur la « noblesse » des essences trahit les conceptions de l’époque. Le terme de « diversité biologique » n’apparaitra qu’en 1968 et il faudra encore quelques années pour comprendre que la biodiversité ne se limite pas à la somme des espèces, mais représente l'ensemble des interactions entre les êtres vivants. Que nous dit Catinot, qui exprime bien la pensée des forestiers tropicaux de l’époque ? La sylviculture utilisant la régénération naturelle, « c’est avant tout une destruction lente et prudente du couvert » en utilisant délianage, dégagement et empoisonnement des « espèces gênantes ». Quant à la régénération artificielle, « il faut bien détruire la forêt préexistante pour donner aux plants la lumière indispensable à leur croissance ». Ce langage serait inaudible aujourd’hui, à l’heure où l’on cherche à promouvoir « l’exploitation à faible impact » et où la certification Forest Stewardship Council « de bonne gestion forestière » (FSC) s’interroge sur la compatibilité de l’exploitation, même à faible impact, avec le maintien de « paysages forestiers intacts »...

Impacts of Airborne Lidar Pulse Density on Estimating Biomass Stocks and Changes in a Selectively Logged Tropical Forest

Airborne lidar is a technology well-suited for mapping many forest attributes, including aboveground biomass (AGB) stocks and changes in selective logging in tropical forests. However, trade-offs still exist between lidar pulse density and accuracy of AGB estimates. We assessed the impacts of lidar pulse density on the estimation of AGB stocks and changes using airborne lidar and field plot data in a selectively logged tropical forest located near Paragominas, Para, Brazil. Field-derived AGB was computed at 85 square 50 × 50 m plots in 2014. Lidar data were acquired in 2012 and 2014, and for each dataset the pulse density was subsampled from its original density of 13.8 and 37.5 pulses·m−2 to lower densities of 12, 10, 8, 6, 4, 2, 0.8, 0.6, 0.4 and 0.2 pulses·m−2. For each pulse density dataset, a power-law model was developed to estimate AGB stocks from lidar-derived mean height and corresponding changes between the years 2012 and 2014. We found that AGB change estimates at the plot level were only slightly affected by pulse density. However, at the landscape level we observed differences in estimated AGB change of >20 Mg·ha−1 when pulse density decreased from 12 to 0.2 pulses·m−2. The effects of pulse density were more pronounced in areas of steep slope, especially when the digital terrain models (DTMs) used in the lidar derived forest height were created from reduced pulse density data. In particular, when the DTM from high pulse density in 2014 was used to derive the forest height from both years, the effects on forest height and the estimated AGB stock and changes did not exceed 20 Mg·ha−1. The results suggest that AGB change can be monitored in selective logging in tropical forests with reasonable accuracy and low cost with low pulse density lidar surveys if a baseline high-quality DTM is available from at least one lidar survey. We recommend the results of this study to be considered in developing projects and national level MRV systems for REDD+ emission reduction programs for tropical forests.

Selective logging in tropical forests decreases the robustness of liana-tree interaction networks to the loss of host tree species.

Selective logging is one of the major drivers of tropical forest degradation, causing important shifts in species composition. Whether such changes modify interactions between species and the networks in which they are embedded remain fundamental questions to assess the 'health' and ecosystem functionality of logged forests. We focus on interactions between lianas and their tree hosts within primary and selectively logged forests in the biodiversity hotspot of Malaysian Borneo. We found that lianas were more abundant, had higher species richness, and different species compositions in logged than in primary forests. Logged forests showed heavier liana loads disparately affecting slow-growing tree species, which could exacerbate the loss of timber value and carbon storage already associated with logging. Moreover, simulation scenarios of host tree local species loss indicated that logging might decrease the robustness of liana-tree interaction networks if heavily infested trees (i.e. the most connected ones) were more likely to disappear. This effect is partially mitigated in the short term by the colonization of host trees by a greater diversity of liana species within logged forests, yet this might not compensate for the loss of preferred tree hosts in the long term. As a consequence, species interaction networks may show a lagged response to disturbance, which may trigger sudden collapses in species richness and ecosystem function in response to additional disturbances, representing a new type of 'extinction debt'.

Fates of trees damaged by logging in Amazonian Bolivia

Estimation of carbon losses from trees felled and incidentally-killed during selective logging of tropical forests is relatively straightforward and well-documented, but less is known about the fates of collaterally-damaged trees that initially survive. Tree response to logging damage is an important and overlooked ecological process potentially affecting 2–5% of all extant tropical trees. Here we report on the fates of damaged trees over the first 8-years after logging in a transitional Amazonian forest in Eastern Bolivia. Mortality rates of damaged trees peaked in the first year after logging, and then slowly declined to background rates by the end of the study, indicating that if a damaged tree survives 8years, it then runs approximately the same annual mortality risk as an undamaged tree. Of all types of logging damage, crown damage reduced growth rates the most while inclined trees suffered the highest mortality rates. Neither wood density nor tree size conferred tolerance to damage, though species with bark exudates were less tolerant of damage. Surprisingly, damaged trees survived droughts better than undamaged trees, perhaps due to their proximity to felling gaps and concomitant reduced above- and below-ground competition or due to their reduced leaf areas and associated reductions in water stress. While this study only tests one interaction between an aspect of climate change and logging, we found a positive signal for forest resilience. This response should be considered amongst others in models of managed forests in climate change scenarios.

Selective logging: does the imprint remain on tree structure and composition after 45 years?

Selective logging of tropical forests is increasing in extent and intensity. The duration over which impacts of selective logging persist, however, remains an unresolved question, particularly for African forests. Here, we investigate the extent to which a past selective logging event continues to leave its imprint on different components of an East African forest 45 years later. We inventoried 2358 stems ≥10 cm in diameter in 26 plots (200 m × 10 m) within a 5.2 ha area in Kibale National Park, Uganda, in logged and unlogged forest. In these surveys, we characterized the forest light environment, taxonomic composition, functional trait composition using three traits (wood density, maximum height and maximum diameter) and forest structure based on three measures (stem density, total basal area and total above-ground biomass). In comparison to unlogged forests, selectively logged forest plots in Kibale National Park on average had higher light levels, different structure characterized by lower stem density, lower total basal area and lower above-ground biomass, and a distinct taxonomic composition driven primarily by changes in the relative abundance of species. Conversely, selectively logged forest plots were like unlogged plots in functional composition, having similar community-weighted mean values for wood density, maximum height and maximum diameter. This similarity in functional composition irrespective of logging history may be due to functional recovery of logged forest or background changes in functional attributes of unlogged forest. Despite the passage of 45 years, the legacy of selective logging on the tree community in Kibale National Park is still evident, as indicated by distinct taxonomic and structural composition and reduced carbon storage in logged forest compared with unlogged forest. The effects of selective logging are exerted via influences on tree demography rather than functional trait composition.

The impact of selective logging and clearcutting on forest structure, tree diversity and above‐ground biomass of African tropical forests

AbstractTropical deforestation is well known to have serious negative consequences for biodiversity, terrestrial carbon sinks and the balance of atmospheric greenhouse gases. By contrast, selective logging of tropical forests is often regarded as having a lesser impact on the ecosystem particularly in long terms, even though there have been few critical evaluations of the practice, particularly in Africa. We compared field data from 511 plots in the tropical forest of Sierra Leone, Ghana, Cameroon and Gabon. These plots were subject to different forest management practices: no recent logging (primary forests), selective logging (up to 30 years old) and re‐grown secondary forests post clear‐cutting (at least 20 years ago). Our findings suggest that the vertical structure and plant richness of the selectively logged and secondary forests change in different amplitude from those of primary forests, but stem density and the prevalence of vine and weed species differ greatly. We show that the effects of selective logging are greater than those expected simply from the removal of commercial species, and can persist for decades. Selective logging, unless it is practiced at very low harvest intensities, can significantly reduce the biomass of a tropical forest for many decades, seriously diminishing aboveground carbon storage capacity, and create opportunities for weeds and vines to spread and slow down the ecological succession.

Forest biomass recovery after conventional and reduced-impact logging in Amazonian Brazil

Growing concerns about unnecessarily destructive selective logging of tropical forests and its impacts on greenhouse gas (GHG) emissions motivated this study on post-logging biomass dynamics over a 16-year period in a control plot and in plots subjected to conventional logging (CL) or reduced-impact logging (RIL) in Paragominas, Pará State, Brazil. All trees >25cm were monitored in 25.4ha plots of each treatment, each with a subplot of 5.25ha for trees >10cmdbh. The commercial timber volumes in felled trees were 38.9 and 37.4m3ha−1 in the RIL and CL plots, respectively, but the extracted volumes were 38.6 and 29.7 m3 ha−1, respectively. Immediately after logging, plots subjected to RIL and CL lost 17% and 26% of their above-ground biomass, respectively. Over the 16years after logging, the average annual increments in above-ground biomass (recruitment plus residual tree growth minus mortality) were 2.8Mgha−1 year−1 in the RIL plot but only 0.5Mgha−1year−1 in the CL plot. By 16years post-logging, the RIL plot recovered 100% of its original above-ground biomass while the CL plot recovered only 77%; over the same period, biomass in the control plot maintained 96% of its initial stock. These findings reinforce the claim that conversion from CL to RIL would represent an efficient forest-based strategy to mitigate climate change under the REDD+ and would be an important step towards sustainable forest management.

Two‐Stage Recovery of Amphibian Assemblages Following Selective Logging of Tropical Forests

There is a lack of quantitative information on the effectiveness of selective-logging practices in ameliorating effects of logging on faunal communities. We conducted a large-scale replicated field study in 3 selectively logged moist semideciduous forests in West Africa at varying times after timber extraction to assess post logging effects on amphibian assemblages. Specifically, we assessed whether the diversity, abundance, and assemblage composition of amphibians changed over time for forest-dependent species and those tolerant of forest disturbance. In 2009, we sampled amphibians in 3 forests (total of 48 study plots, each 2 ha) in southwestern Ghana. In each forest, we established plots in undisturbed forest, recently logged forest, and forest logged 10 and 20 years previously. Logging intensity was constant across sites with 3 trees/ha removed. Recently logged forests supported substantially more species than unlogged forests. This was due to an influx of disturbance-tolerant species after logging. Simultaneously Simpson's index decreased, with increased in dominance of a few species. As time since logging increased richness of disturbance-tolerant species decreased until 10 years after logging when their composition was indistinguishable from unlogged forests. Simpson's index increased with time since logging and was indistinguishable from unlogged forest 20 years after logging. Forest specialists decreased after logging and recovered slowly. However, after 20 years amphibian assemblages had returned to a state indistinguishable from that of undisturbed forest in both abundance and composition. These results demonstrate that even with low-intensity logging (≤3 trees/ha) a minimum 20-year rotation of logging is required for effective conservation of amphibian assemblages in moist semideciduous forests. Furthermore, remnant patches of intact forests retained in the landscape and the presence of permanent brooks may aid in the effective recovery of amphibian assemblages.