Optimum plot and sample sizes for carbon stock and biodiversity estimation in the lowland tropical forests of Papua New Guinea

Grussu, Giorgio; Testolin, Riccardo; Saulei, Simon; Farcomeni, Alessio; Yosi, Cossey K.; Sanctis, Michele De; Attorre, Fabio

doi:10.1093/forestry/cpv047

Cited by 17 publications

(8 citation statements)

References 40 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In a recent study, Grussu et al . () recommend an optimum plot size of 0.2 ha for 155 replicates for 95% confidence within 5% of the mean for PNG lowland forest systems.…”

Section: Discussionmentioning

confidence: 99%

“…Potential exists, however, to combine our data with that from the existing 50 ha plot, through the development of a mixed plot statistical design, to improve precision of AGLB and carbon estimates. In a recent study, Grussu et al (2016) recommend an optimum plot size of 0.2 ha for 155 replicates for 95% confidence within 5% of the mean for PNG lowland forest systems. Our total carbon estimate for 10 770 ha of the WCA (1 552 347 Mg C) demonstrates the importance of the conservation area in terms of carbon sequestration and the potential for the community to engage with mechanisms such as REDD.…”

Section: Comparison Of Carbon Estimate With Those From Other Png Studiesmentioning

confidence: 99%

See 1 more Smart Citation

Estimating carbon stock in lowland Papua New Guinean forest: Low density of large trees results in lower than global average carbon stock

et al. 2017

View full text Add to dashboard Cite

Papua New Guinean forests (PNG), sequestering up to 3% of global forest carbon, are a focus of climate change mitigation initiatives, yet few field-based studies have quantified forest biomass and carbon for lowland PNG forest. We provide an estimate for the 10 770 ha Wanang Conservation Area (WCA) to investigate the effect of calculation methodology and choice of allometric equation on estimates of above-ground live biomass (AGLB) and carbon. We estimated AGLB and carbon from 43 nested plots at the WCA. Our biomass estimate of 292.2 Mg AGLB ha À1 (95% CI 233.4-350.6) and carbon at 137.3 Mg C ha À1 (95% CI 109.8-164.8) is higher than most estimates for PNG but lower than mean global estimates for tropical forest. Calculation method and choice of allometric model do not significantly influence mean biomass estimates; however, the most recently calibrated allometric equation generates estimates 13% higher for lower 95% confidence intervals of mean biomass than previous allometric modelsa value often used as a conservative estimate of biomass. Although large trees at WCA (>70 cm diameter at breast height) accounted for 1/5 total biomass, their density was lower than that seen in SE Asian and Australia forests. Lower density of large trees accounts for lower AGLB than in neighbouring forestsas large trees contribute disproportionately to forest biomass. Reduced frequency of larger trees at WCA is explained by the lack of diversity of large dipterocarp species common to neighbouring SE Asian forests and, potentially, higher rates of local disturbance dynamics. PNG is susceptible to the El Niño Southern Oscillation (ENSO) extreme drought events to which large trees are particularly sensitive and, with still over 20% carbon in large trees, differential mortality under increasing ENSO drought stress raises the risk of PNG forest switching from carbon sink to source with reduced long-term carbon storage capacity.

show abstract

“…In a recent study, Grussu et al . () recommend an optimum plot size of 0.2 ha for 155 replicates for 95% confidence within 5% of the mean for PNG lowland forest systems.…”

Section: Discussionmentioning

confidence: 99%

Section: Comparison Of Carbon Estimate With Those From Other Png Studiesmentioning

confidence: 99%

Estimating carbon stock in lowland Papua New Guinean forest: Low density of large trees results in lower than global average carbon stock

et al. 2017

View full text Add to dashboard Cite

show abstract

“…The optimal plot size will be one where the desired statistical power (expressed as either a desired precision of the sample mean or ability to test a hypothesis of interest) is achieved for the least overall cost. For example, Grussu et al (2015) re-analysed data from existing lha permanent sample plots in logged and unlogged forests in Papua New Guinea, in order to evaluate the optimal plot size for efficiently achieving estimates of tree species richness and carbon stocks. The original 1-ha plots were not found to be efficient, and optimum plot sizes for estimating tree species richness were 0.08 and 0.2 ha in unlogged and logged forests respectively.…”

Section: Field-based Inventory Methodsmentioning

confidence: 99%

Towards rapid assessments of tree species diversity and structure in fragmented tropical forests: A review of perspectives offered by remotely-sensed and field-based data

Ganivet

Bloomberg

2019

Forest Ecology and Management

View full text Add to dashboard Cite

show abstract

“…For example smaller plot sizes can result in fewer individuals being sampled per plot, and consequently higher within‐treatment variation in observed taxonomic diversity (Connell and Sousa, 1983; Chase and Knight, 2013), in addition to greater beta diversity among plots (Sreekar et al ., 2018). Scale dependence of variance is likely to be strongest in non‐uniform environments with patchy distributions of individuals and species (Král et al ., 2010; Avery and Burkhart, 2015; Grussu et al ., 2016). This high variability among small plots has the potential to weaken observed relationships between environmental gradients and biodiversity (Field et al ., 2009).…”

Section: Scale Dependence In Summary Statistics From Field Studiesmentioning

confidence: 99%

Implications of scale dependence for cross‐study syntheses of biodiversity differences

Spake¹,

Mori

Beckmann

et al. 2020

Ecology Letters

View full text Add to dashboard Cite

Biodiversity studies are sensitive to well‐recognised temporal and spatial scale dependencies. Cross‐study syntheses may inflate these influences by collating studies that vary widely in the numbers and sizes of sampling plots. Here we evaluate sources of inaccuracy and imprecision in study‐level and cross‐study estimates of biodiversity differences, caused by within‐study grain and sample sizes, biodiversity measure, and choice of effect‐size metric. Samples from simulated communities of old‐growth and secondary forests demonstrated influences of all these parameters on the accuracy and precision of cross‐study effect sizes. In cross‐study synthesis by formal meta‐analysis, the metric of log response ratio applied to measures of species richness yielded better accuracy than the commonly used Hedges' g metric on species density, which dangerously combined higher precision with persistent bias. Full‐data analyses of the raw plot‐scale data using multilevel models were also susceptible to scale‐dependent bias. We demonstrate the challenge of detecting scale dependence in cross‐study synthesis, due to ubiquitous covariation between replication, variance and plot size. We propose solutions for diagnosing and minimising bias. We urge that empirical studies publish raw data to allow evaluation of covariation in cross‐study syntheses, and we recommend against using Hedges' g in biodiversity meta‐analyses.

show abstract

Optimum plot and sample sizes for carbon stock and biodiversity estimation in the lowland tropical forests of Papua New Guinea

Cited by 17 publications

References 40 publications

Estimating carbon stock in lowland Papua New Guinean forest: Low density of large trees results in lower than global average carbon stock

Estimating carbon stock in lowland Papua New Guinean forest: Low density of large trees results in lower than global average carbon stock

Towards rapid assessments of tree species diversity and structure in fragmented tropical forests: A review of perspectives offered by remotely-sensed and field-based data

Implications of scale dependence for cross‐study syntheses of biodiversity differences

Contact Info

Product

Resources

About