Developing Cost-Effective Field Assessments of Carbon Stocks in Human-Modified Tropical Forests

Assessing invertebrate herbivory in human-modified tropical forest canopies

Studies on the effects of human-driven forest disturbance usually focus on either biodiversity or carbon dynamics but much less is known about ecosystem processes that span different trophic levels. Herbivory is a fundamental ecological process for ecosystem functioning, but it remains poorly quantified in human-modified tropical rainforests.Here, we present the results of the largest study to date on the impacts of human disturbances on herbivory. We quantified the incidence (percentage of leaves affected) and severity (the percentage of leaf area lost) of canopy insect herbivory caused by chewers, miners, and gall makers in leaves from 1,076 trees distributed across 20 undisturbed and human-modified forest plots in the Amazon.We found that chewers dominated herbivory incidence, yet were not a good predictor of the other forms of herbivory at either the stem or plot level. Chewing severity was higher in both logged and logged-and-burned primary forests when compared to undisturbed forests. We found no difference in herbivory severity between undisturbed primary forests and secondary forests. Despite evidence at the stem level, neither plot-level incidence nor severity of the three forms of herbivory responded to disturbance. Synthesis. Our large-scale study of canopy herbivory confirms that chewers dominate the herbivory signal in tropical forests, but that their influence on leaf area lost cannot predict the incidence or severity of other forms. We found only limited evidence suggesting that human disturbance affects the severity of leaf herbivory, with higher values in logged and logged-and-burned forests than undisturbed and secondary forests. Additionally, we found no effect of human disturbance on the incidence of leaf herbivory.

Rainforest-to-pasture conversion stimulates soil methanogenesis across the Brazilian Amazon

The Amazon rainforest is a biodiversity hotspot and large terrestrial carbon sink threatened by agricultural conversion. Rainforest-to-pasture conversion stimulates the release of methane, a potent greenhouse gas. The biotic methane cycle is driven by microorganisms; therefore, this study focused on active methane-cycling microorganisms and their functions across land-use types. We collected intact soil cores from three land use types (primary rainforest, pasture, and secondary rainforest) of two geographically distinct areas of the Brazilian Amazon (Santarém, Pará and Ariquemes, Rondônia) and performed DNA stable-isotope probing coupled with metagenomics to identify the active methanotrophs and methanogens. At both locations, we observed a significant change in the composition of the isotope-labeled methane-cycling microbial community across land use types, specifically an increase in the abundance and diversity of active methanogens in pastures. We conclude that a significant increase in the abundance and activity of methanogens in pasture soils could drive increased soil methane emissions. Furthermore, we found that secondary rainforests had decreased methanogenic activity similar to primary rainforests, and thus a potential to recover as methane sinks, making it conceivable for forest restoration to offset greenhouse gas emissions in the tropics. These findings are critical for informing land management practices and global tropical rainforest conservation.

Belowground changes to community structure alter methane-cycling dynamics in Amazonia

Amazonian rainforest is undergoing increasing rates of deforestation, driven primarily by cattle pasture expansion. Forest-to-pasture conversion has been associated with increases in soil methane (CH₄) emission. To better understand the drivers of this change, we measured soil CH₄ flux, environmental conditions, and belowground microbial community structure across primary forests, cattle pastures, and secondary forests in two Amazonian regions. We show that pasture soils emit high levels of CH₄ (mean: 3454.6 ± 9482.3 μg CH₄ m^-2 d^-1), consistent with previous reports, while forest soils on average emit CH₄ at modest rates (mean: 9.8 ± 120.5 μg CH₄ m^-2 d^-1), but often act as CH₄ sinks. We report that secondary forest soils tend to consume CH₄ (mean: -10.2 ± 35.7 μg CH₄ m^-2 d^-1), demonstrating that pasture CH₄ emissions can be reversed. We apply a novel computational approach to identify microbial community attributes associated with flux independent of soil chemistry. While this revealed taxa known to produce or consume CH₄ directly (i.e. methanogens and methanotrophs, respectively), the vast majority of identified taxa are not known to cycle CH₄. Each land use type had a unique subset of taxa associated with CH₄ flux, suggesting that land use change alters CH₄ cycling through shifts in microbial community composition. Taken together, we show that microbial composition is crucial for understanding the observed CH₄ dynamics and that microorganisms provide explanatory power that cannot be captured by environmental variables.

Spatiotemporal Evolution of Urban Expansion Using Landsat Time Series Data and Assessment of Its Influences on Forests

Analysis of urban land use dynamics is essential for assessing ecosystem functionalities and climate change impacts. The focus of this study is on monitoring the characteristics of urban expansion in Hang-Jia-Hu and evaluating its influences on forests by applying 30-m multispectral Landsat data and a machine learning algorithm. Firstly, remote sensed images were preprocessed with radiation calibration, atmospheric correction and topographic correction. Then, the C5.0 decision tree was used to establish classification trees and then applied to make land use maps. Finally, spatiotemporal changes were analyzed through dynamic degree and land use transfer matrix. In addition, average land use transfer probability matrix (ATPM) was utilized for the prediction of land use area in the next 20 years. The results show that: (1) C5.0 decision tree performed with precise accuracy in land use classification, with an average total accuracy and kappa coefficient of more than 90.04% and 0.87. (2) During the last 20 years, land use in Hang-Jia-Hu has changed extensively. Urban area expanded from 5.84% in 1995 to 21.32% in 2015, which has brought about enormous impacts on cultivated land, with 198,854 hectares becoming urban, followed by forests with 19,823 hectares. (3) Land use area prediction based on the ATPM revealed that urbanization will continue to expand at the expense of cultivated land, but the impact on the forests will be greater than the past two decades. Rationality of urban land structure distribution is important for economic and social development. Therefore, remotely sensed technology combined with machine learning algorithms is of great significance to the dynamic detection of resources in the process of urbanization.

How informative is the response of Ground Beetles' (Coleoptera: Carabidae) assemblages to anthropogenic land use changes? Insights for ecological status assessments from a case study in the Neotropics

Ground Beetles (Carabidae) have been suggested to be used as indicators of ecological status under the effects of anthropogenic land use and land cover changes in highly biodiverse regions such as the Neotropics. In this study Ground Beetles' assemblages from a region in the Brazilian Amazon were investigated for evaluating their applicability as disturbance indicators. Differences in assemblages between ecosystems, discriminated by complimentary methodologies, demonstrate a sensitive reaction from the most pristine forests to increasingly disturbed systems. Besides capturing the influence of different prevailing conditions between ecosystems, Ground Beetles are easy to communicate and to link with the other components of the system, being effortless and routinely measurable using standard methodologies. This study represents a step forward in using Ground Beetles for the purposes of planning, management and public reporting on the ecological status of Neotropical ecosystems. Additionally, the methods depicted could support projections for trends of relevant ecosystem attributes under realistic social-ecological change scenarios, which can be used to guide effective conservation planning.

The Use of Mixed Effects Models for Obtaining Low-Cost Ecosystem Carbon Stock Estimates in Mangroves of the Asia-Pacific

Mangroves provide extensive ecosystem services that support local livelihoods and international environmental goals, including coastal protection, biodiversity conservation and the sequestration of carbon (C). While voluntary C market projects seeking to preserve and enhance forest C stocks offer a potential means of generating finance for mangrove conservation, their implementation faces barriers due to the high costs of quantifying C stocks through field inventories. To streamline C quantification in mangrove conservation projects, we develop predictive models for (i) biomass-based C stocks, and (ii) soil-based C stocks for the mangroves of the Asia-Pacific. We compile datasets of mangrove biomass C (197 observations from 48 sites) and soil organic C (99 observations from 27 sites) to parameterize the predictive models, and use linear mixed effect models to model the expected C as a function of stand attributes. The most parsimonious biomass model predicts total biomass C stocks as a function of both basal area and the interaction between latitude and basal area, whereas the most parsimonious soil C model predicts soil C stocks as a function of the logarithmic transformations of both latitude and basal area. Random effects are specified by site for both models, which are found to explain a substantial proportion of variance within the estimation datasets and indicate significant heterogeneity across-sites within the region. The root mean square error (RMSE) of the biomass C model is approximated at 24.6 Mg/ha (18.4% of mean biomass C in the dataset), whereas the RMSE of the soil C model is estimated at 4.9 mg C/cm³ (14.1% of mean soil C). The results point to a need for standardization of forest metrics to facilitate meta-analyses, as well as provide important considerations for refining ecosystem C stock models in mangroves.