Tree Height Reduction After Selective Logging in a Tropical Forest

Rutishauser, Ervan; Hérault, Bruno; Pétronelli, Pascal; Sist, Plínio

doi:10.1111/btp.12326

Cited by 39 publications

(33 citation statements)

References 38 publications

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“…Despite previous work linking forest disturbance to variation in D : H relationships (Rutishauser et al. , Kearsley et al. ), we found that, across Gabon as a whole, forest disturbance history (primary, logged, or secondary) only weakly influenced the selection of D : H model.…”

Section: Discussioncontrasting

confidence: 99%

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Landscape‐level validation of allometric relationships for carbon stock estimation reveals bias driven by soil type

Beirne

Miao

Núñez

et al. 2019

Ecological Applications

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Mitigation of climate change depends on accurate estimation and mapping of terrestrial carbon stocks, particularly in carbon dense tropical forests. Allometric equations can be used to robustly estimate biomass of tropical trees, but often require tree height, which is frequently unknown. Researchers and practitioners must, therefore, decide whether to directly measure a subset of tree heights to develop diameter : height (D:H) equations or rely on previously published generic equations. To date, studies comparing the two approaches have been spatially restricted and/or not randomly allocated across the landscape of interest, making the implications of deciding whether or not to measure tree heights difficult to determine. To address this issue, we use inventory data from a systematic-random forest inventory across Gabon (102 forest sites; 42,627 trees, including 7,036 height-measured trees). Using plot-specific models of D:H as a benchmark, we compare the performance of a suite of locally fitted and commonly used generic models (parameterized national, georegional, and pantropical equations) across a variety of scales, and assess which abiotic, anthropogenic, and topographical covariates contribute the most to bias in height estimation. We reveal marked spatial structure in the magnitude and direction of bias in tree height estimation using all generic models, due at least in part to soil type, which compounded to substantial error in site-level AGB estimates (of up to 38% or 150 Mg/ha). However, two generic pantropical models (Chave 2014; Feldpausch 2012) converged to within 2.5% of mean AGB at the landscape scale. Our results suggest that some (not all) pantropical equations can extrapolate AGB across large spatial scales with minimal bias in estimated mean AGB. However, extreme caution must be taken when interpreting the AGB estimates from generic models at the site-level as they fail to capture substantial spatial variation in D:H relationships, which could lead to dramatic under-or over-estimation of site-level carbon stocks. Validated allometric models derived at site-or soil-type-levels may be the best way to reduce such biases arising from landscape-level heterogeneity in D:H model fit in the Afrotropics.

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Section: Discussioncontrasting

confidence: 99%

“…, Rutishauser et al. ). For these reasons, generic models cannot accurately represent the heterogeneity of an entire georegion or nation of interest, making the landscape‐scale implications of site‐to‐site variation, disturbance history, and habitat types on allometric relationships impossible to determine.…”

Section: Introductionmentioning

confidence: 99%

Landscape‐level validation of allometric relationships for carbon stock estimation reveals bias driven by soil type

Beirne

Miao

Núñez

et al. 2019

Ecological Applications

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“…Attention should be taken to high harvest intensities and/or substantial incidental damage due to poor harvesting practices that diminish stocks of survivors, even if they promote recruitment. Most trees that recruit are fast-growing pioneers that are favored by disturbance but are vulnerable to water stress (Bonal et al, 2016) and competition (Valladares and Niinemets, 2008), and because their height is lower than in mature forests (Rutishauser et al, 2016), they might have reduced carbon sequestration potential. With ongoing climate change and increased frequencies and intensities of droughts in Amazonia (Malhi et al, 2008), betting on recruits to store C in forests disturbed by selective logging might thus be a risky gamble.…”

Section: Discussionmentioning

confidence: 99%

Carbon recovery dynamics following disturbance by selective logging in Amazonian forests

Piponiot

Sist

Mazzei

et al. 2016

eLife

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When 2 Mha of Amazonian forests are disturbed by selective logging each year, more than 90 Tg of carbon (C) is emitted to the atmosphere. Emissions are then counterbalanced by forest regrowth. With an original modelling approach, calibrated on a network of 133 permanent forest plots (175 ha total) across Amazonia, we link regional differences in climate, soil and initial biomass with survivors’ and recruits’ C fluxes to provide Amazon-wide predictions of post-logging C recovery. We show that net aboveground C recovery over 10 years is higher in the Guiana Shield and in the west (21 ±3 Mg C ha-1) than in the south (12 ±3 Mg C ha-1) where environmental stress is high (low rainfall, high seasonality). We highlight the key role of survivors in the forest regrowth and elaborate a comprehensive map of post-disturbance C recovery potential in Amazonia.DOI: http://dx.doi.org/10.7554/eLife.21394.001

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“…Our biomass estimates based on the Chave et al. () allometry for our logged forests are potentially positively biased as the database of direct‐harvest trees are primarily from undisturbed forests with only five secondary forest sites, and may not capture the reduction in tree heights observed in logged forests (Rutishauser, Hérault, Petronelli, & Sist, ). The wood density parameter ( p ) captures differences in species composition (Baker et al., ).…”

Section: Methodsmentioning

confidence: 99%

Trade‐offs between carbon stocks and timber recovery in tropical forests are mediated by logging intensity

Roopsind

Caughlin

Hout³

et al. 2018

Global Change Biology

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Forest degradation accounts for ~70% of total carbon losses from tropical forests. Substantial emissions are from selective logging, a land-use activity that decreases forest carbon density. To maintain carbon values in selectively logged forests, climate change mitigation policies and government agencies promote the adoption of reduced-impact logging (RIL) practices. However, whether RIL will maintain both carbon and timber values in managed tropical forests over time remains uncertain. In this study, we quantify the recovery of timber stocks and aboveground carbon at an experimental site where forests were subjected to different intensities of RIL (4, 8, and 16 trees/ha). Our census data span 20 years postlogging and 17 years after the liberation of future crop trees from competition in a tropical forest on the Guiana Shield, a globally important forest carbon reservoir. We model recovery of timber and carbon with a breakpoint regression that allowed us to capture elevated tree mortality immediately after logging. Recovery rates of timber and carbon were governed by the presence of residual trees (i.e., trees that persisted through the first harvest). The liberation treatment stimulated faster recovery of timber albeit at a carbon cost. Model results suggest a threshold logging intensity beyond which forests managed for timber and carbon derive few benefits from RIL, with recruitment and residual growth not sufficient to offset losses. Inclusion of the breakpoint at which carbon and timber gains outpaced postlogging mortality led to high predictive accuracy, including out-of-sample R values >90%, and enabled inference on demographic changes postlogging. Our modeling framework is broadly applicable to studies that aim to quantify impacts of logging on forest recovery. Overall, we demonstrate that initial mortality drives variation in recovery rates, that the second harvest depends on old growth wood, and that timber intensification lowers carbon stocks.

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Tree Height Reduction After Selective Logging in a Tropical Forest

Cited by 39 publications

References 38 publications

Landscape‐level validation of allometric relationships for carbon stock estimation reveals bias driven by soil type

Landscape‐level validation of allometric relationships for carbon stock estimation reveals bias driven by soil type

Carbon recovery dynamics following disturbance by selective logging in Amazonian forests

Trade‐offs between carbon stocks and timber recovery in tropical forests are mediated by logging intensity

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