Reducing terrestrial greenhouse gas emissions: a human dimensions contribution

This paper describes achievements from the human dimensions research within New Zealand's 'Reducing Greenhouse Gas Emissions from the Terrestrial Biosphere' programme, in three parts: (i) regional responses to climate change policy development, (ii) indigenous groups, land use and climate change, and (iii) participation in the Land Use in Rural New Zealand (LURNZ) model development. We then critically review our work, using a post-normal science framework to inform further development of this research.climate change policy; policy development; discourse analysis; econometric modelling; governance; Maori land use; mitigation; post-normal science; greenhouse gas emissions; emissions reduction; rural areas; New Zealand; indigenous groups; terrestrial biosphere.

Individual-Based Allometric Equations Accurately Measure Carbon Storage and Sequestration in Shrublands

Many studies have quantified uncertainty in forest carbon (C) storage estimation, but there is little work examining the degree of uncertainty in shrubland C storage estimates. We used field data to simulate uncertainty in carbon storage estimates from three error sources: (1) allometric biomass equations; (2) measurement errors of shrubs harvested for the allometry; and (3) measurement errors of shrubs in survey plots. We also assessed uncertainty for all possible combinations of these error sources. Allometric uncertainty had the greatest independent effect on C storage estimates for individual plots. The largest error arose when all three error sources were included in simulations (where the 95% confidence interval spanned a range equivalent to 40% of mean C storage). Mean C sequestration (1.73 Mg C ha–1 year–1) exceeded the margin of error produced by the simulated sources of uncertainty. This demonstrates that, even when the major sources of uncertainty were accounted for, we were able to detect relatively modest gains in shrubland C storage

Functional Traits Reveal Processes Driving Natural Afforestation at Large Spatial Scales

<div><p>An understanding of the processes governing natural afforestation over large spatial scales is vital for enhancing forest carbon sequestration. Models of tree species occurrence probability in non-forest vegetation could potentially identify the primary variables determining natural afforestation. However, inferring processes governing afforestation using tree species occurrence is potentially problematic, since it is impossible to know whether observed occurrences are due to recruitment or persistence of existing trees following disturbance. Plant functional traits have the potential to reveal the processes by which key environmental and land cover variables influence afforestation. We used 10,061 survey plots to identify the primary environmental and land cover variables influencing tree occurrence probability in non-forest vegetation in New Zealand. We also examined how these variables influenced diversity of functional traits linked to plant ecological strategy and dispersal ability. Mean annual temperature was the most important environmental predictor of tree occurrence. Local woody cover and distance to forest were the most important land cover variables. Relationships between these variables and ecological strategy traits revealed a trade-off between ability to compete for light and colonize sites that were marginal for tree occurrence. Biotically dispersed species occurred less frequently with declining temperature and local woody cover, suggesting that abiotic stress limited their establishment and that biotic dispersal did not increase ability to colonize non-woody vegetation. Functional diversity for ecological strategy traits declined with declining temperature and woody cover and increasing distance to forest. Functional diversity for dispersal traits showed the opposite trend. This suggests that low temperatures and woody cover and high distance to forest may limit tree species establishment through filtering on ecological strategy traits, but not on dispersal traits. This study shows that ‘snapshot’ survey plot data, combined with functional trait data, may reveal the processes driving tree species establishment in non-forest vegetation over large spatial scales.</p> </div

Partial contributions to Observed vs. Fitted tree occurrences within the simplified BRT model.

<p>The graphs show Observed vs. Fitted tree occurrences (A) and smoothed partial contributions within the simplified BRT model for (B) mean annual temperature (C) percentage woody cover in a 25 m radius and (D) distance to nearest forest. The smoothed partial contribution plots reflect the influence of a predictor variable when all other variables are held constant. CVROC is the cross-validated receiver operator curve (ROC) for the final boosted regression tree model. ROC is a measure of discrimination accuracy when predicting a binary response.</p

Map of survey plots used in boosted regression tree modeling.

<p>Vegetation classes for New Zealand survey plots are mapped based on a reclassification of Dymond and Shepherd [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075219#B58" target="_blank">58</a>]. ‘Other’ is all non-forest vegetation except subalpine scrub.</p

Map of predicted tree occurrence probability in non-forest vegetation in New Zealand.

<p>The map shows model-predicted probability of tree occurrence in non-forest vegetation (the ‘other’ and ‘subalpine scrub’ classes of Dymond and Shepherd [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075219#B58" target="_blank">58</a>]) in New Zealand. Grey areas are covered by indigenous (from [<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0075219#B58" target="_blank">58</a>]) and planted forest (from Land Cover Database 2).</p