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Plot-based monitoring has yielded much information on the taxonomic diversity and carbon (C) storage in tropical lowland forests of the Amazon basin. This has resulted in an improved understanding of the relationship between lowland forest biomass dynamics and global change drivers, such as climate change and atmospheric CO2 concentration. Much less attention has been paid to the mountain ecosystems of South America that comprise montane forests and alpine vegetation (páramo, puna, high Andean grasslands, wetlands, and alpine heath). This vegetation complex provides a variety of ecosystem services and forms a natural laboratory along various physiographic, geological and evolutionary history/biogeography, and land use history gradients. Here, we review existing empirical understanding and model-based approaches to quantify the contribution of mountain ecosystems to ecosystem service provision in the rapidly changing socioecological setting of the South American mountains. The objective of this paper is to outline a broad road map for the implementation of mountain vegetation into dynamic global vegetation models (DGVM) for use in Earth System Models (ESM), based on our current understanding of their structure and function and of their responsiveness to global change drivers. We also identify treeline processes, critical in mountain ecosystems, as key missing elements in DGVMs/ESMs, and thus explore in addition a treeline model. Stocktaking of the availability of empirical data was undertaken from eight research sites along the Andes and in south-eastern Brazil. Out of eight sites, two (one each in Venezuela and Brazil) had some climate, ecological and ecophysiological data potentially suitable to parametrise a DGVM. Tree biomass data were available for six sites. A preliminary assessment of the Joint UK Land Environment Simulator (JULES) DGVM was made to identify gaps in available data and their impacts on model parametrisation and calibration. Additionally, the potential climate-determined elevation of the treeline was modelled to check the DGVM for its ability to identify the transition between the montane forest and alpine vegetation. Outcomes of the evaluation of the JULES land surface model identified the following key processes in montane forests: temperature-related decrease in net primary production, respiration, and allocation to above-ground biomass and increase in soil C stocks with elevation. There was a variable agreement between simulated biomass and those derived from field measurements via allometric equations. We identified major gaps between data availability and the needs for process-based modelling of South American mountain vegetation and its dynamics in DGVMs. To bridge this gap, we propose a transdisciplinary network, composed of members of the theoretical/modelling and empirical scientific communities, to study the natural dynamics of mountain ecosystems and their responses to global change drivers locally, regionally and at the continental scale, within a social-ecological system framework. The work presented here forms the basis for the design of data collection from field measurements and instrumental monitoring stations to parametrise and verify DGVMs. The network is designed to collaborate with and complement existing long-term research initiatives in the region and will adopt existing standard field protocols. Complementary protocols will ensure compatibility between field data collection and data needed for process-based and empirical models. [ABSTRACT FROM AUTHOR]

Allometric equations are required for a rapid estimation of commercial timber volume and forest biomass stocks. In order to preserve the forest ecosystem, this study applied a non-destructive sampling approach to measure biophysical properties of living trees. From these measurements, volume and biomass models were developed for 11 dominant tree species in a semi-deciduous natural forest and for Acacia auriculiformis in a plantation located in southern Benin. The observations were combined to develop also generic models applicable to non-dominant tree species. Wood samples of the tree species were collected with an increment borer and analysed in the laboratory to determine species-specific wood densities. The sample size was composed of 243 trees in natural forest and 21 trees in plantation. The measurements were conducted in 30 plots of 50 m × 50 m. The graphical assessment of correlation between model outputs (biomass and volume) and variables (diameter and height) and the statistical analysis confirmed that the logarithmic model with two variables had the best predictions. The assessment also confirmed that the model using diameter only as a variable had good predictions when observations on height were unavailable. The comparative analysis of model predictions showed that the generic model in this study over-estimated biomass by up to 74.80% for certain species and under-estimated biomass by 21.18% for other species. The study shows that there are no statistically significant differences between the wood densities in this research and that published in previous studies. [ABSTRACT FROM AUTHOR]

Vascular epiphyte biomass is poorly studied in Congo Basin forest. The aim of this study was to develop a protocol to estimate epiphyte biomass. Epiphytes were collected on 77 phorophytes of diameter 57-169.5 cm, oven-dried for biomass. Allometric equations for epiphyte biomass were developed with diameter and phorophyte height as predictor variables using regression analysis. The mean epiphyte biomass was 24.48 kg/phorophyte with a standard deviation of ±49.99. Cylicodiscus gabunensis and Triplochiton scleroxylon hosted the greatest epiphyte biomass with a mean vascular epiphyte biomass of 71.65 and 62.3 kg/phorophyte, respectively. Kruskal-Wallis test (p = 0.005) showed that there was a significant difference between epiphyte biomass with respect to phorophyte species. The relationship found between epiphyte biomass and dendrometric variables of the phorophytes were intermediate (Adj.R2 < 0.630) meaning epiphyte biomass increased with increasing tree diameter. However, models with diameter input variables had greater prediction than input variables of total height. Based on the Akaike Information Criterion (AIC), the allometry lnY = −21.37 + 5.21 x ln(D) −0.35 x ln(H) with AIC (183) was the best equation for the prediction of epiphyte biomass. These findings shall contribute to REDD+ (Reducing Emissions from Deforestation and Forest Degradation) strategy for climate change mitigation in Congo Basin. [ABSTRACT FROM AUTHOR]

Recent climate change, environmental design, and ecological conservation policies require new and existing urban developments to mitigate and offset carbon dioxide emissions and for cities to become carbon neutral. Some North American models and tools are available and can be used to quantify the carbon offset function of urban trees. But, little information on urban tree carbon storage and sequestration exists from the European Southern Alps. Also, the use of these North American models in Europe has never been assessed. This study developed a protocol to quantify aboveground carbon (C) storage and sequestration using a subsample of urban trees in Bolzano, Italy, and assessed two existing and available C estimation models. Carbon storage and sequestration were estimated using city-specific dendrometrics and allometric biomass equations primarily from Europe and two other United States models; the UFORE (Urban Forest Effects Model) and the CUFR Tree Carbon Calculator (CTCC). The UFORE model carbon storage estimates were the lowest while the CUFR Tree Carbon Calculator (CTCC) C sequestration estimates were the highest. Results from this study can be used to plan, design, and manage urban forests in northern Italy to maximize C offset potential, provide ecosystem services, and for developing carbon neutral policies. Findings can also be used to predict greenhouse gas emissions from tree maintenance operations as well as estimating green waste yield from landscape maintenance activities and its use as biofuel and compost. Managers need to be aware that available models and methods can produce statistically different C storage and sequestration estimates. [ABSTRACT FROM AUTHOR]