Identifying the vulnerable forests of Southeast Asia, and transforming them into a conservation and climate change mitigation priority

Facing the threat of climate change, preventing land use change in tropical forest areas has been identified as one of the main strategies to reduce carbon emissions to the atmosphere. However, the rate of tropical forest loss has increased rather than decreased over the recent decades, questioning the effectiveness of current approaches in bringing about the necessary changes. To obtain better forest protection and ensure a reduction in emissions, new approaches need to be explored. In Southeast Asia, forest loss is particularly pronounced due to the dominance of agriculture and plantation forestry. The region has experienced a total loss of 11.3% of its forest cover since the beginning of the 21st century, and the rate of loss shows little sign of slowing. Therefore I use Southeast Asia as a case study to present a pragmatic approach to identify and measure forests at risk from deforestation. My aspiration is to develop an approach applicable to the region, which can then be easily adapted globally. I present three core chapters in this thesis. After the introduction, in Chapter 2, I examine whether the current international incentive-based mechanism to reduce emissions from deforestation and forest degradation (REDD+) is well suited to identify historically vulnerable forests, and whether it is likely to lead to real emission reductions. First, I identify and measure the current areas of forests under REDD+ in the Asia and Pacific region. I compare the benchmark emissions from forests (‘reference levels’) submitted by the governments to the United Nations Framework Convention on Climate Change (UNFCCC) with forest area change estimates using the Global Forest Change v1.4 (GFC) dataset. The v results show consistent differences, with most countries reporting considerably less historic forest loss than the GFC-based analysis. These differences are due to: the countries’ selection of activities to report; as well as their choice of forest types and land use; and the selected definitions of the forests to be monitored. Therefore, even if REDD+ is successfully implemented, it will not necessarily lead to emission reductions. In Chapter 3, I identify these vulnerable forests and the drivers of deforestation. I use publicly available satellite data (Sentinel-2) to map 13,330 ha in southern Myanmar. This area is a mixed landscape combining large areas of both natural forest and commercial plantations (mostly of oil palm and rubber). I use Google Earth Engine as a data analysis platform to conduct supervised land cover classifications using a machine learning algorithm. The classifier is able to detect the differences between visibly similar tree crops (e.g. oil palm, rubber, betel nut, and forests) with high accuracy (95.5% - 96.0%) at a 20 m resolution. Based on the results of this initial study, I then scale up the analysis to all of southern Myanmar (more than 4 million ha) and add radar (Sentinel-1 and the Shuttle Radar Topography Mission) datasets. The classifier successfully map the region, achieving a high overall accuracy of 94% against an independent test dataset (84-96% and 81-95% accuracy for oil palm and rubber respectively). In Chapter 4, the method presented in Chapter 3 is used to identify and estimate the area that is actually planted with oil palm within oil palm concession areas in southern Myanmar. The distinction between plantations and concession areas matter, as plantations have been already deforested and converted to oil palm or rubber. Meanwhile, concessions have been allocated to oil palm production, but have yet to be converted. My results show that only 17% of the total concession areas has so far been planted with oil palm (15%, 75,000 ha) or rubber (2%, 7,800 ha). Furthermore, my analyses show that approximately 25,000 ha of oil palm are planted outside formal concessions. This highlights an urgent need to clearly demarcate and enforce concession boundaries. It also reveals that about 200,000 ha of unconverted forests still exist within oil palm concessions that are at high risk of conversion in the future. Hence, these unconverted forests represent an ideal target for conservation and legal protection. The application of this approach for other regions and crop types could result in substantial protection of forests and carbon stocks. For example, in Kalimantan, Indonesia alone, more than three million ha of intact forests are estimated to lie inside oil palm concessions, mostly with little to no legal protection. It is therefore crucial to understand why some concessions remain unexploited, and to evaluate the possibility of changing the status of these areas to protect the forests. This would not affect current levels of production, yet it could considerably contribute to mitigating climate change. Overall, the methods developed and findings presented in my thesis offer a route for countries to improve their forest protection plans and reference levels. If implemented across the tropics, this approach could significantly aid policy makers in developing and implementing policies that reduce the loss of forest carbon stocks. I conclude that risk-based approaches considering tree location, land use and legal status, rather than narrowly defined forest areas, could offer a more transparent means for forest conservation, and a better route to achieving the overarching objectives of climate change mitigation

An alternative gauged U(1)RU(1)_R symmetric model in light of the CDF II WW boson mass anomaly

We consider an explanation of CDF II W bosom mass anomaly by $Z-Z'$ mixing with $U(1)_R$ gauge symmetry under which right-handed fermions are charged. It is found that $U(1)_R$ is preferred to be leptophobic to accommodate the anomaly while avoiding other experimental constraints. In such a case we require extra charged leptons to cancel quantum anomalies and the SM charged leptons get masses via interactions with the extra ones. These interactions also induce muon $g-2$ and lepton flavor violations. We discuss muon $g-2$, possible flavor constraints, neutrino mass generation via inverse seesaw mechanism, and collider physics regarding $Z'$ production for parameter space explaining the W boson mass anomaly.Comment: 22 pages, 3 figures, 2 tables; version accepted for publication in Physical Review

Fisheries Connectivity Networks to Measure the Adaptive Capacity of Small-Scale Fisheries in the Baja California Peninsula, Mexico

Small-scale fisheries around the world are increasingly facing pressures from a range of environmental, economic, and social sources. In order to sustain the societal benefits of small-scale fisheries, it is imperative to understand how fishing communities adapt to disturbances. Fishermen often catch multiple different species as an adaptation technique because diversifying one’s harvest portfolio like this creates multiple sources of income in case one species becomes unavailable. Here, we apply fisheries connectivity network analysis to characterize the harvest portfolios and timing of landings of dozens of small-scale fisheries in the Baja California Peninsula, Mexico. We found that network metrics like modularity and density varied by region and through time. The Pacific coast of Baja California displayed increasingly modular fisheries connectivity networks over the study period. This indicates that fisheries landings became less covaried with one another, implying an increased capacity to adapt by targeting species that are uncorrelated with each other through time. The remaining three Regions showed the opposite trend, where the covariance between fisheries increase over time. Differences in regional catch compositions also reflected the diversity of species caught around the Baja California Peninsula. A network perspective of small-scale fisheries landings provides insight into the resilience of multi-species fishing communities

More than meets the eye: Using Sentinel-2 to map small plantations in complex forest landscapes.

Many tropical forest landscapes are now complex mosaics of intact forests, recovering forests, tree crops, agroforestry, pasture, and crops. The small patch size of each land cover type contributes to making them difficult to separate using satellite remote sensing data. We used Sentinel-2 data to conduct supervised classifications covering seven classes, including oil palm, rubber, and betel nut plantations in Southern Myanmar, based on an extensive training dataset derived from expert interpretation of WorldView-3 and UAV data. We used a Random Forest classifier with all 13 Sentinel-2 bands, as well as vegetation and texture indices, over an area of 13,330 ha. The median overall accuracy of 1000 iterations was >95% (95.5%⁻96.0%) against independent test data, even though the tree crop classes appear visually very similar at a 20 m resolution. We conclude that the Sentinel-2 data, which are freely available with very frequent (five day) revisits, are able to differentiate these similar tree crop types. We suspect that this is due to the large number of spectral bands in Sentinel-2 data, indicating great potential for the wider application of Sentinel-2 data for the classification of small land parcels without needing to resort to object-based classification of higher resolution data

Neutrinophilic DM annihilation in a model with U(1)Lμ−Lτ×U(1)HU(1)_{L_\mu-L_\tau} \times U(1)_{H} gauge symmetry

We propose a model with two different extra $U(1)$ gauge symmetries; muon minus tauon symmetry $U(1)_{{L_\mu}-L_{\tau}}$ and hidden symmetry $U(1)_H$. Then, we explain muon anomalous magnetic moment, semi-leptonic decays $b\to s\ell\bar\ell$, and dark matter. In particular, we find an intriguing dark matter candidate to be verified by Hyper-Kamiokande and JUNO in the future that request neutrinophilic DM with rather light dark matter mass$\sim{\cal O}(10)$ MeV.Comment: 23 pages, 6 figures, 2 table

梅干し中の有機酸及びｱﾐｸﾞﾀﾞﾘﾝ関連物質の抗菌作用

梅干しの抗菌作用に関与する成分を明らかにするために､塩分20%の梅干しの有機酸とｱﾐｸﾞﾀﾞﾘﾝ関連物質(ｸｴﾝ酸､ﾘﾝｺﾞ酸､酢酸､ｱﾐｸﾞﾀﾞﾘﾝ､ﾍﾞﾝｽﾞｱﾙﾃﾞﾋﾄﾞ､ﾍﾞﾝｼﾞﾙｱﾙｺｰﾙ)の含有量をもとにし､黄色ﾌﾞﾄﾞｳ球菌と大腸菌に対する抗菌作用をﾍﾟｰﾊﾟｰﾃﾞｨｽｸ拡散法及び短時間殺菌法で検討した｡その結果､含有量が多かったｸｴﾝ酸が強い抗菌作用を示し､抗菌作用の主体はｸｴﾝ酸であることが示唆された｡ｸｴﾝ酸､ﾘﾝｺﾞ酸､酢酸の混合液では､ｸｴﾝ酸単独より強い抗菌作用を示した｡また､この混合液に20%の食塩を添加したものでは､さらに抗菌作用が増強した｡一方､ｱﾐｸﾞﾀﾞﾘﾝ､ﾍﾞﾝｽﾞｱﾙﾃﾞﾋﾄﾞ及びﾍﾞﾝｼﾞﾙｱﾙｺｰﾙの単独の含有量では抗菌作用を示さなかった｡以上のことから､梅干しの抗菌作用は梅干しに含まれる有機酸と梅干しの製造工程で添加された食塩の相乗効果によることが示唆された｡To identify ingredients responsible for the antibacterial properties of umeboshi, the inhibitory effects of organic acids and amygdalin-related compounds (citric acid, malic acid, acetic acid, amygdalin, benzaldehyde, and benzyl alcohol) against Staphylococcus aureus FDA 209P and Escherichia coli NIHJ JC-2 were compared at concentrations found in 20% salt pickled Japanese apricot (Umeboshi) using the paper disc diffusion method and an assay of rapid bactericidal capacity. Citric acid showed a potent antibacterial activity, suggesting that this predominant organic acid plays a major role in the antibacterial properties of Umeboshi. A mixture of citric, malic, and acetic acids exhibited higher antibacterial activity than citric acid alone, and the addition of salt to the mixture at a concentration of 20% resulted in a much higher activity. No antibacterial activity was observed with amygdalin, benzaldehyde, or benzyl alcohol. These results suggested that the antibacterial properties of Umeboshi may be attributed to the synergistic effects of its organic acid content and salt added during processing

Automated Tracking of 3-D Overturn Patches in Direct Numerical Simulation of Stratified Homogeneous Turbulence

Abstract. Direct numerical simulation is a valuable tool for modeling turbulence, but like "wet lab" simulation, it does not solve the problem of how to interpret the data. Manual analysis, accompanied by visual aids, is a time consuming, error prone process due to the elaborate timedependent structures appearing in simulations. We describe a technique based on volume tracking, that enables the worker to identify and observe evolving coherent flow structures, eliminating uninteresting background data. Using our techniques we were able to investigate 3-D density overturns in stably stratified homogeneous turbulence, understand entangled physical structures and their dynamical behavior. We describe our technique, which improves on past work by incorporating application-specific knowledge into the identification process. Such knowledge was vital in filtering out spurious information that would have interfered with the experimental method. Representative results are shown which summarize the physical insight gained by the application of the above identification/tracking method

Adsorption of Urinary Proteins on the Conventionally Used Urine Collection Tubes: Possible Effects on Urinary Proteome Analysis and Prevention of the Adsorption by Polymer Coating

One possible factor determining recovery of trace amount of protein biomarker candidates during proteome analyses could be adsorption on urine tubes. This issue, however, has not been well addressed so far. Recently, a new technical device of surface coating by poly(2-methacryloyloxyethyl phosphorylcholine (MPC)-co-n-butyl methacrylate (BMA)) (poly(MPC-co-BMA)) has been developed mainly to prevent the adsorption of plasma proteins. We assessed whether conventionally used urine tubes adsorb trace amount of urinary proteins and, if any, whether the surface coating by poly(MPC-co-BMA) can minimize the adsorption. Proteinuric urine samples were kept in poly(MPC-co-BMA)-coated and noncoated urine tubes for 15 min and possibly adsorbed proteins and/or peptides onto urine tubes were analyzed by SDS-PAGE, 2-DE, and the MALDI-TOF MS. It was found that a number of proteins and/or peptides adsorb on the conventionally used urine tubes and that surface coating by poly(MPC-co-BMA) can minimize the adsorption without any significant effects on routine urinalysis test results. Although it remains to be clarified to what extent the protein adsorption can modify the results of urinary proteome analyses, one has to consider this possible adsorption of urinary proteins when searching for trace amounts of protein biomarkers in urine