Showing papers by "G. Arturo Sánchez-Azofeifa published in 2021"

Dynamics of Carbon Accumulation in Tropical Dry Forests under Climate Change Extremes

Abstract: 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 26 Mg C ha−1 y−1 to 63 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 Nino Index Carbon dynamics in tropical dry forests are driven by time since abandonment and forest structure; however, rising temperature and El Nino 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

Integration of remote-sensing based metrics and econometric models to assess the socio-economic contributions of carbon sequestration in unmanaged tropical dry forests

Abstract: Carbon sequestration by forests is one of the vital ecosystem services regulating the global climate. Equally important are the socio-economic co-benefits of carbon sequestration, given their implications for designing policies focused on conservation or restoration of tropical forests. Much debate has been around how to account for, and maximize, the co-benefits of carbon sequestration. Prior research suggests that a better understanding of the spatial relationship between carbon sequestration potential and forest types and dynamics - as a function of geographical context and time - is needed to better estimate their socio-economic benefits. Hence, this paper uses the Tropical Dry Forests of Central and South America to propose a new approach to quantify carbon sequestration of this biome, and its efficiency, using time series of the Terra-MODIS satellite. Our estimations of carbon sequestration are then coupled with a benefit transfer approach to infer carbon sequestration’s monetary cost. Results reveal that these tropical forests sequester an annual average of 22.3 ​± ​3.3 tCO2 ha-1 yr-1 or in total, 1.16 GtCO2. The associated social cost of carbon, calculated using three econometric models, ranges from USD 489 ​ha-1 ​yr-1 to USD 2828 ​ha-1 ​yr-1. These results can open new perspectives regarding the benefits of carbon sequestration against the costs of the negative impacts of climate change for national welfare accounts, their relevance for environmental policy-making, and the implementation or monitoring of carbon-based incentive programs (e.g., WAVES).

Liana optical traits increase tropical forest albedo and reduce ecosystem productivity.

Abstract: Lianas 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 trees. 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 very efficient at light interception and significantly modified the forest energy balance and its carbon cycle. While forest albedo increased by 14% in the shortwave, light availability was reduced in the understorey (-30% of the PAR radiation) and soil temperature decreased by 0.5 degrees 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 lianas on forest functioning, and paves the way for the evaluation of the large-scale impacts of lianas on forest biogeochemical cycles.

Using TLS-Measured Tree Attributes to Estimate above Ground Biomass in Small Black Spruce Trees

Abstract: 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.

Tropical dry forest resilience and water use efficiency: an analysis of productivity under climate change

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.

Hyperspectral and Full-Waveform LiDAR Improve Mapping of Tropical Dry Forest’s Successional Stages

Abstract: Accurate estimation of the degree of regeneration in tropical dry forest (TDF) is critical for conservation policymaking and evaluation. Hyperspectral remote sensing and light detection and ranging (LiDAR) have been used to characterize the deterministic successional stages in a TDF. These successional stages, classified as early, intermediate, and late, are considered a proxy for mapping the age since the abandonment of a given forest area. Expanding on the need for more accurate successional forest mapping, our study considers the age attributes of a TDF study area as a continuous expression of relative attribute scores/levels that vary along the process of ecological succession. Specifically, two remote-sensing data sets: HyMap (hyperspectral) and LVIS (waveform LiDAR), were acquired at the Santa Rosa National Park Environmental Monitoring Super Site (SRNP-EMSS) in Costa Rica, were used to generate age-attribute metrics. These metrics were then used as entry-level variables on a randomized nonlinear archetypal analysis (RNAA) model to select the most informative metrics from both data sets. Next, a relative attribute learning (RAL) algorithm was adapted for both independent and fused metrics to comparatively learn the relative attribute levels of the forest ages of the study area. In this study, four HyMap indices and five LVIS metrics were found to have the potential to map the forest ages of the study area, and compared with these results, a significant improvement was found through the fusion of the metrics on the accuracy of the generated forest age maps. By linking the age group mapping and the relative attribute mapping results, a dynamic gradient of the age-attribute transition patterns emerged.

Leaf Anatomical Traits of Lianas and Trees at the Canopy of Two Contrasting Lowland Tropical Forests in the Context of Leaf Economic Spectrum

Abstract: The leaf economic spectrum describes a comprehensive framework of how the surrounding environment modulates leaf functional traits (LFT) and how these are associated between them. This framework has traditionally focused on physiological, chemical, and biomass assignation traits, but rarely in leaf anatomical traits. Here we compare leaf anatomical metrics and traits of 40 liana and tree species from two lowland tropical forests in Panama with contrasting rainfall regimes: Parque Natural Metropolitano (dry-forest) and Parque Nacional San Lorenzo (wet-forest). Then we evaluate how anatomical traits are associated with well-established LFTs. Anatomical metrics were collected from leaf cross-section images estimating the area, thickness, cell count, and size of the upper and lower epidermis and palisade and spongy mesophyll. Ratios between metrics were performed as potential anatomical traits to reduce the leaf size effect between species. Our results suggest that anatomical changes between life forms are associated with increases in the palisade area and thickness of trees in comparison with lianas, while anatomical changes between forest type species are related to increases in the spongy area and thickness of wet-forest species than dry-forest. These differences could be associated with the high photosynthetic rates of trees or the need to enhance the gas exchange in humid environments. Our results also suggest that anatomical traits are related to well-established LFT; however, the degree of association between them may depend on the life forms and forest type. For example, our results suggest that reductions in the palisade and spongy cell density are associated with increases in leaf mass area and maximum photosynthetic capacity, but this association was not observed when we compared life forms or forest types. The use of leaf anatomical information may facilitate to describe the mechanism that drives the leaf economy, improving our understanding of the resource allocation strategies embedded in functional groups.