245 research outputs found

    Rethinking climate engineering categorization in the context of climate change mitigation and adaptation

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    The portfolio of approaches to respond to the challenges posed by anthropogenic climate change has broadened beyond mitigation and adaptation with the recent discussion of potential climate engineering options. How to define and categorize climate engineering options has been a recurring issue in both public and specialist discussions. We assert here that current definitions of mitigation, adaptation, and climate engineering are ambiguous, overlap with each other and thus contribute to confusing the discourse on how to tackle anthropogenic climate change. We propose a new and more inclusive categorization into five different classes: anthropogenic emissions reductions (AER), territorial or domestic removal of atmospheric CO2 and other greenhouse gases (D-GGR), trans-territorial removal of atmospheric CO2 and other greenhouse gases (T-GGR), regional to planetary targeted climate modification (TCM), and climate change adaptation measures (including local targeted climate and environmental modification, abbreviated CCAM). Thus, we suggest that techniques for domestic greenhouse gas removal might better be thought of as forming a separate category alongside more traditional mitigation techniques that consist of emissions reductions. Local targeted climate modification can be seen as an adaptation measure as long as there are no detectable remote environmental effects. In both cases, the scale and intensity of action are essential attributes from the technological, climatic, and political viewpoints. While some of the boundaries in this revised classification depend on policy and judgement, it offers a foundation for debating on how to define and categorize climate engineering options and differentiate them from both mitigation and adaptation measures to climate change

    Detecting Change in Forest Structure with Simulated GEDI Lidar Waveforms: A Case Study of the Hemlock Woolly Adelgid (HWA; Adelges tsugae) Infestation

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    The hemlock woolly adelgid (HWA; Adelges tsugae) is an invasive insect infestation that is spreading into the forests of the northeastern United States, driven by the warmer winter temperatures associated with climate change. The initial stages of this disturbance are difficult to detect with passive optical remote sensing, since the insect often causes its host species, eastern hemlock trees (Tsuga canadensis), to defoliate in the midstory and understory before showing impacts in the overstory. New active remote sensing technologies-such as the recently launched NASA Global Ecosystem Dynamics Investigation (GEDI) spaceborne lidar-can address this limitation by penetrating canopy gaps and recording lower canopy structural changes. This study explores new opportunities for monitoring the HWA infestation with airborne lidar scanning (ALS) and GEDI spaceborne lidar data. GEDI waveforms were simulated using airborne lidar datasets from an HWA-infested forest plot at the Harvard Forest ForestGEO site in central Massachusetts. Two airborne lidar instruments, the NASA G-LiHT and the NEON AOP, overflew the site in 2012 and 2016. GEDI waveforms were simulated from each airborne lidar dataset, and the change in waveform metrics from 2012 to 2016 was compared to field-derived hemlock mortality at the ForestGEO site. Hemlock plots were shown to be undergoing dynamic changes as a result of the HWA infestation, losing substantial plant area in the middle canopy, while still growing in the upper canopy. Changes in midstory plant area (PAI 11-12 m above ground) and overall canopy permeability (indicated by RH10) accounted for 60% of the variation in hemlock mortality in a logistic regression model. The robustness of these structure-condition relationships held even when simulated waveforms were treated as real GEDI data with added noise and sparse spatial coverage. These results show promise for future disturbance monitoring studies with ALS and GEDI lidar data

    An Assessment of the Representation of Ecosystems in Global Protected Areas Using New Maps of World Climate Regions and World Ecosystems

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    Representation of ecosystems in protected area networks and conservation strategies is a core principle of global conservation priority setting approaches and a commitment in Aichi Target 11 of the Convention on Biological Diversity. The 2030 Sustainable Development Goals (SDGs) explicitly call for the conservation of terrestrial, freshwater, and marine ecosystems. Accurate ecosystem distribution maps are required to assess representation of ecosystems in protected areas, but standardized, high spatial resolution, and globally comprehensive ecosystem maps have heretofore been lacking. While macroscale global ecoregions maps have been used in global conservation priority setting exercises, they do not identify distinct localized ecosystems at the occurrence (patch) level, and instead describe large ecologically meaningful areas within which additional conservation planning and management are necessary. We describe a new set of maps of globally consistent climate regions and ecosystems at a much finer spatial resolution (250 m) than existing ecological regionalizations. We then describe a global gap analysis of the representation of these ecosystems in protected areas. The new map of terrestrial World Ecosystems was derived from the objective development and integration of 1) global temperature domains, 2) global moisture domains, 3) global landforms, and 4) 2015 global vegetation and land use. These new terrestrial World Ecosystems do not include either freshwater or marine ecosystems, but analog products for the freshwater and marine domains are in development. A total of 431 World Ecosystems were identified, and of these a total of 278 units were natural or semi-natural vegetation/environment combinations, including different kinds of forestlands, shrublands, grasslands, bare areas, and ice/snow regions. The remaining classes were different kinds of croplands and settlements. Of the 278 natural and semi-natural classes, 9 were not represented in global protected areas with a strict biodiversity conservation management objective (IUCN management categories I-IV), and an additional 206 were less than 8.5% protected (half way to the 17% Aichi Target 11 goal). Forty four classes were between 8.5% and 17% protected (more than half way towards the Aichi 17% target), and only 19 classes exceeded the 17% Aichi target. However, when all protected areas (IUCN management categories I-VI plus protected areas with no IUCN designation) were included in a separate global gap analysis, representation of ecosystems increases substantially, with a third of the ecosystems exceeding the 17% Aichi target, and another third between 8.5% and 17%. The overall protection (representation) of global ecosystems in protected areas is considerably less when assessed using only strictly conserved protected areas, and more if all protected areas are included in the analysis. Protected area effectiveness should be included in further evaluations of global ecosystem protection. The ecosystems with the highest representation in protected areas were often bare or sparsely vegetated and found in inhospitable environments (e.g. cold mountains, deserts), and the eight most protected ecosystems were all snow and ice ecosystems. In addition to the global gap analysis of World Ecosystems in protected areas, we report on the representation results for the ecosystems in each biogeographic realm (Neotropical, Nearctic, Afrotropical, Palearctic, Indomalayan, Australasian, and Oceania)

    Microarray Analysis of HIV Resistant Female Sex Workers Reveal a Gene Expression Signature Pattern Reminiscent of a Lowered Immune Activation State

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    To identify novel biomarkers for HIV-1 resistance, including pathways that may be critical in anti-HIV-1 vaccine design, we carried out a gene expression analysis on blood samples obtained from HIV-1 highly exposed seronegatives (HESN) from a commercial sex worker cohort in Nairobi and compared their profiles to HIV-1 negative controls. Whole blood samples were collected from 43 HIV-1 resistant sex workers and a similar number of controls. Total RNA was extracted and hybridized to the Affymetrix HUG 133 Plus 2.0 micro arrays (Affymetrix, Santa Clara CA). Output data was analysed through ArrayAssist software (Agilent, San Jose CA). More than 2,274 probe sets were differentially expressed in the HESN as compared to the control group (fold change ≥1.3; p value ≤0.0001, FDR <0.05). Unsupervised hierarchical clustering of the differentially expressed genes readily distinguished HESNs from controls. Pathway analysis through the KEGG signaling database revealed a majority of the impacted pathways (13 of 15, 87%) had genes that were significantly down regulated. The most down expressed pathways were glycolysis/gluconeogenesis, pentose phosphate, phosphatidyl inositol, natural killer cell cytotoxicity and T-cell receptor signaling. Ribosomal protein synthesis and tight junction genes were up regulated. We infer that the hallmark of HIV-1 resistance is down regulation of genes in key signaling pathways that HIV-1 depends on for infection
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