• Energy Research

We analyze here how much carbon is being accumulated annually by secondary tropical dry forests (TDFs) and how structure, composition, time since abandonment, and climate can influence the dynamics of forest carbon accumulation. The study was carried out in Santa Rosa National Park in Guanacaste province, Costa Rica and Mata Seca State Park in Minas Gerais, Brazil. Total carbon storage and carbon accumulation were obtained for both sites from the sum of the aboveground carbon and belowground carbon gain plus the annual litterfall. Carbon accumulation of these TDFs varied from 2.6 Mg C ha−1 y−1 to 6.3 Mg C ha−1 y−1, depending on the age of the forest stands. Time since abandonment and number of stems per plot were the best predictors for carbon storage, annual carbon gains, and losses. Mortality rates and carbon losses were also associated with seasonal climate variability. We found significant correlations between tree mortality, carbon losses and mean seasonal temperature, mean seasonal precipitation, potential evapotranspiration, and the Oceanic Niño Index. Carbon dynamics in tropical dry forests are driven by time since abandonment and forest structure; however, rising temperature and El Niño Southern Oscillation (ENSO) events can have a significant impact on tree mortality and carbon losses. Depending on their location and land-use history, some dry forests are more impacted by climatic extremes than others, and differences between secondary stages are expected.

Remote environmental monitoring systems require effective energy management to allow reliable long-term operation without frequent maintenance to replace or recharge batteries. To design and analyze relevant energy management strategies, we have developed Simulink-based models of a recently constructed monitoring device to evaluate its potential performance. The model uses long-term solar energy data from two locations, Chamela, Mexico, and Fairview, Canada, to estimate the energy harvesting capabilities of the device. Using the simulator, we have developed and evaluated a fuzzy energy management strategy that determines how the device should operate to match the solar energy profile in each location. Solar energy in Chamela, Mexico is abundant and consistent so an energy harvesting remote monitoring device could have a high activity level without risking device failure. Fairview, Canada, has limited solar resources in the winter but plenty in the summer; a device dependent upon this energy source must adapt its activity level to match energy availability or risk running out of energy. While the simulated device in Mexico outperforms the one in Canada, both succeed in matching the available environmental resources and largely avoid energy related device failure. In the future, their performance can be improved by optimizing the designed strategies and further improving the details of the simulation.

Models reveal the high carbon mitigation potential of tropical forest regeneration.

Abstract Tropical dry forests (TDFs) worldwide have an environment-sensitive phenological signal, which easily marks their response to the changing climatic conditions, especially precipitation and temperature. Using TDF phenological characteristics as a proxy, this study aims to evaluate their current continental response to climate change across the Americas. Here, we show that TDFs are resilient to water stress and droughts by increasing their rain use efficiency (RUE) in drier years and recovering to average RUE in the year following the drought. Additionally, we find that TDF productivity trends over the past 18 years are spatially clustered, with sites in the northern hemisphere experiencing increased productivity, while equatorial regions have no change, and the southern hemisphere exhibiting decreased productivity. The results indicate that the TDF will be resilient under future climatic conditions, particularly if there are increasing drought conditions.

Research Highlights: This study advances the effort to accurately estimate the biomass of trees in peatlands, which cover 13% of Canada’s land surface. Background and Objectives: Trees remove carbon from the atmosphere and store it as biomass. Terrestrial laser scanning (TLS) has become a useful tool for modelling forest structure and estimating the above ground biomass (AGB) of trees. Allometric equations are often used to estimate individual tree AGB as a function of height and diameter at breast height (DBH), but these variables can often be laborious to measure using traditional methods. The main objective of this study was to develop allometric equations using TLS-measured variables and compare their accuracy with that of other widely used equations that rely on DBH. Materials and Methods: The study focusses on small black spruce trees (<5 m) located in peatland ecosystems of the Taiga Plains Ecozone in the Northwest Territories, Canada. Black spruce growing in peatlands are often stunted when compared to upland black spruce and having models specific to them would allow for more precise biomass estimates. One hundred small trees were destructively sampled from 10 plots and the dry weight of each tree was measured in the lab. With this reference data, we fitted biomass models specific to peatland black spruce using DBH, crown diameter, crown area, height, tree volume, and bounding box volume as predictors. Results: Our best models had crown size and height as predictors and outperformed established AGB equations that rely on DBH. Conclusions: Our equations are based on predictors that can be measured from above, and therefore they may enable the plotless creation of accurate biomass reference data for a prominent tree species in a common ecosystem (treed peatlands) in North America’s boreal.

AbstractLianas are a key growth form in tropical forests. Their lack of self-supporting tissues and their vertical position on top of the canopy make them strong competitors of resources. A few pioneer studies have shown that liana optical traits differ on average from those of colocated tree. Those trait discrepancies were hypothesized to be responsible for the competitive advantage of lianas over trees. Yet, in the absence of reliable modelling tools, it is impossible to unravel their impact on the forest energy balance, light competition and on the liana success in Neotropical forests. To bridge this gap, we performed a meta-analysis of the literature to gather all published liana leaf optical spectra, as well as all canopy spectra measured over different levels of liana infestation. We then used a Bayesian data assimilation framework applied to two radiative transfer models (RTMs) covering the leaf and canopy scales to derive tropical tree and liana trait distributions, which finally informed a full dynamic vegetation model. According to the RTMs inversion, lianas grew thinner, more horizontal leaves with lower pigment concentrations. Those traits made the lianas particularly efficient at light interception and completely modified the forest energy balance and its carbon cycle. While forest albedo increased by 14% in the shortwave, light availability was dramatically reduced in the understory (−30% of the PAR radiation) and soil temperature decreased by 0.5°C. Those liana-specific traits were also responsible for a significant reduction of tree (−19%) and ecosystem (−7%) gross primary productivity (GPP) while lianas benefited from them (their GPP increased by +27%). This study provides a novel mechanistic explanation to the increase in liana abundance, new evidence of the impact of structural parasitism on forest functioning, and paves the way for the evaluation of the large-scale impacts of woody vines on forest biogeochemical cycles.