A comparison of surface moisture budget and structural equation models in high latitudes: evapotranspiration and atmospheric drivers

Thesis (M.S.) University of Alaska Fairbanks, 2021Arctic soil moisture is one of the most impactful and unknown aspects of the Arctic climate system. As the climate changes, surface soil moisture can impact water supplies, wildfire risk, and vegetation stress, all of which have consequences for terrestrial ecosystems and human activities. The present analysis is intended to (1) document seasonal and interannual variations of surface moisture fluxes in the Arctic region and (2) clarify the drivers of variations of net Precipitation minus Evapotranspiration (P-ET) across Arctic tundra and boreal vegetation and permafrost status. Forty-five flux tower sites were examined across boreal and tundra ecosystems across the Arctic and sub-arctic. The surface moisture budget at boreal forest sites in permafrost areas generally shows a moisture deficit in late spring and early summer, followed by a moisture surplus from late summer into autumn. The annual net P-ET is generally positive but can vary interannually by more than an order of magnitude. A factor analysis found the primary drivers of variations in evapotranspiration to be radiative fluxes, air temperature, and relative humidity, while a path analysis found windspeed to have the largest independent influence on evapotranspiration. Overall, the ET at boreal forest sites shows a stronger dependence on relative humidity, and ET at tundra sites shows the stronger dependence on air temperature. These differences imply that tundra sites are more temperature-limited and boreal sites are more humidity-dependent. Relative to nearby unburned sites, the recovery time of ET after disturbance by wildfire was found to vary from several years on the Alaska tundra to nearly a decade in the Alaska boreal forest.National Science Foundation, Office of Polar Programs Grant ARC-183013

Diagnosis of Atmospheric Drivers of High-Latitude Evapotranspiration Using Structural Equation Modeling

Evapotranspiration (ET) is a relevant component of the surface moisture budget and is associated with different drivers. The interrelated drivers cause variations at daily to interannual timescales. This study uses structural equation modeling to diagnose the drivers over an ensemble of 45 high-latitude sites, each of which provides at least several years of in situ measurements, including latent heat fluxes derived from eddy covariance flux towers. The sites are grouped by vegetation type (tundra, forest) and the presence or absence of permafrost to determine how the relative importance of different drivers depends on land surface characteristics. Factor analysis is used to quantify the common variance among the variables, while a path analysis procedure is used to assess the independent contributions of different variables. The variability of ET at forest sites generally shows a stronger dependence on relative humidity, while ET at tundra sites is more temperature-limited than moisture-limited. The path analysis shows that ET has a stronger direct correlation with solar radiation than with any other measured variable. Wind speed has the largest independent contribution to ET variability. The independent contribution of solar radiation is smaller because solar radiation also affects ET through various other drivers. The independent contribution of wind speed is especially apparent at forest wetland sites. For both tundra and forest vegetation, temperature loads higher on the first factor when permafrost is present, implying that ET will become less sensitive to temperature as permafrost thaws

Allergic Asthmatics Show Divergent Lipid Mediator Profiles from Healthy Controls Both at Baseline and following Birch Pollen Provocation

<div><h3>Background</h3><p>Asthma is a respiratory tract disorder characterized by airway hyper-reactivity and chronic inflammation. Allergic asthma is associated with the production of allergen-specific IgE and expansion of allergen-specific T-cell populations. Progression of allergic inflammation is driven by T-helper type 2 (Th2) mediators and is associated with alterations in the levels of lipid mediators.</p> <h3>Objectives</h3><p>Responses of the respiratory system to birch allergen provocation in allergic asthmatics were investigated. Eicosanoids and other oxylipins were quantified in the bronchoalveolar lumen to provide a measure of shifts in lipid mediators associated with allergen challenge in allergic asthmatics.</p> <h3>Methods</h3><p>Eighty-seven lipid mediators representing the cyclooxygenase (COX), lipoxygenase (LOX) and cytochrome P450 (CYP) metabolic pathways were screened via LC-MS/MS following off-line extraction of bronchoalveolar lavage fluid (BALF). Multivariate statistics using OPLS were employed to interrogate acquired oxylipin data in combination with immunological markers.</p> <h3>Results</h3><p>Thirty-two oxylipins were quantified, with baseline asthmatics possessing a different oxylipin profile relative to healthy individuals that became more distinct following allergen provocation. The most prominent differences included 15-LOX-derived ω-3 and ω-6 oxylipins. Shared-and-Unique-Structures (SUS)-plot modeling showed a correlation (R² = 0.7) between OPLS models for baseline asthmatics (R²Y[cum] = 0.87, Q²[cum] = 0.51) and allergen-provoked asthmatics (R²Y[cum] = 0.95, Q²[cum] = 0.73), with the majority of quantified lipid mediators and cytokines contributing equally to both groups. Unique structures for allergen provocation included leukotrienes (LTB₄ and 6-<em>trans</em>-LTB₄), CYP-derivatives of linoleic acid (epoxides/diols), and IL-10.</p> <h3>Conclusions</h3><p>Differences in asthmatic relative to healthy profiles suggest a role for 15-LOX products of both ω-6 and ω-3 origin in allergic inflammation. Prominent differences at baseline levels indicate that non-symptomatic asthmatics are subject to an underlying inflammatory condition not observed with other traditional mediators. Results suggest that oxylipin profiling may provide a sensitive means of characterizing low-level inflammation and that even individuals with mild disease display distinct phenotypic profiles, which may have clinical ramifications for disease.</p> </div

Evaluation of Integrated Ecological-Economic Models - What are they used for?

This study compares globally available integrated ecological-economic models with focus on use and implementation in scientific and advisory contexts. The Ecosystem based Approach to (Fisheries) Management (EAM) calls for an understanding and management of fisheries and other uses of the marine environment that explicitly take into account ecological, economic and social considerations. Though it is acknowledged that only human activities can be managed, their optimal management will depend on the ecosystem in which they take place and the ecosystem goods and services. Hence, the direct and indirect impact of fisheries on the marine ecosystem and vice versa must be assessed and predicted. This entails a move from single species to multispecies and to ecosystem assessments. Also, successful marine management needs involve economic and social sustainability of the activities. Therefore, explicit incorporation of economic and social components of fisheries and marine management needs be included in management evaluation. Accordingly, tools need to be developed and implemented, which take the ecological-economic-social-governance interactions into account. The aim of this study is to compare relevant models from around the world, to discuss their development and level of implementation, and to develop a sound basis for evaluating and comparing these tools, including their robustness. It will analyse the needed characteristics for the use in advisory context through a comparative and descriptive matrix and summary table. Based on this analysis we make re-commendations for future research, development and advisory structures that can increase the level of use and implementation of the models

Shared and Unique Structures (SUS) plot.

<p>SUS plot correlating the OPLS models of healthy controls versus asthmatic controls (Baseline Asthmatics, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033780#pone-0033780-g003" target="_blank">Figure 3A, X</a>-axis) and healthy controls versus asthmatics following provocation (Provoked Asthmatics, <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033780#pone-0033780-g003" target="_blank">Figure 3B, Y</a>-axis). A complete list of p(corr) values for both models is provided in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033780#pone.0033780.s010" target="_blank">Table S7</a>. Abbreviations are as follows: 15-lipoxygenease (15-LOX), cyclooxygenase (COX), 5-lipoxygenase (5-LOX), cytochrome P450 (CYP), ω-6 fatty acid (ω-6), ω-3 fatty acid (ω-3), healthy controls (HC), asthmatic controls (AC) and asthmatics following provocation (AFP). Colors are as follows: 15-LOX-derived ω-3 oxylipins (blue), 15-LOX-derived ω-6 oxylipins (red), COX-derived ω-6 oxylipins (green), 5-LOX-derived ω-6 oxylipins (orange), CYP-derived ω-6 oxylipins (gold). Variables with p(corr)≥|0.4| are labeled, while those ≤|0.4| are shown as symbols.</p

Sum of 15-LOX metabolites.

<p>(<b>A</b>) Sum of 15-LOX metabolites from both ω-3 and ω-6 pathways. (<b>B</b>) Sum of 15-LOX metabolites from ω-6 fatty acids (12-and 15-HETE, 15-KETE, 5,15-DiHETE, 13-HODE, 9,10,13- and 9,12,13-TriHOME, 9-HODE, 9-KODE and 15-HETrE), (<b>C</b>) Sum of 15-LOX metabolites from ω-3 fatty acids (9- and 13-HOTE, 12- and 15-HEPE and 17-HDoHE). Data are provided as concentration in BALF (pM). HC: Healthy controls, AC: Asthmatic controls, AFP: Asthmatics following provocation. The p-values obtained using Student's T-test (HC vs. AC and HC vs. AFP) and one-sided Cochran-Armitage trend test (HC, AC and AFP) are indicated in the figure.</p

OPLS score and loading column plots with respect to separation according to diagnosis.

<p>Loading column plots visualize variables correlating with healthy (−) or asthmatics (+), error bars indicate 95% confidence interval. The number of variables correlating with the asthmatic population with 95% confidence increases following provocation from n = 21 to n = 32. (<b>A</b>) Healthy controls (green) and asthmatic controls (yellow) (R²Y[cum] = 0.87, Q²[cum] = 0.51). (<b>B</b>) Healthy controls (green) and asthmatics following provocation (red) (R²Y[cum] = 0.95, Q²[cum] = 0.73).</p

Clinical data of participating subjects.

a<p>Subject numbering is presented as originally published by Thunberg <i>et al.</i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033780#pone.0033780-Thunberg1" target="_blank">[42]</a>.</p>b<p>Asthma (A), Healthy (H).</p>c<p>Age in years at time of study inclusion.</p>d<p>Female (F), Male (M).</p>e<p>FEV₁% values at baseline. A Student's t-test of the groups indicated that there was no different between the two populations (p = 0.11).</p>f<p>kU/l required for 20% drop in FEV₁%, as reported by Thunberg <i>et al.</i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033780#pone.0033780-Thunberg1" target="_blank">[42]</a>.</p>g<p>Oxylipin profiling of subject 7 and 8 was performed in a pooled sample.</p>h<p>Individual 9 was incorrectly reported by Thunberg <i>et al.</i><a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0033780#pone.0033780-Thunberg1" target="_blank">[42]</a> and is a 22 year old male healthy individual.</p