An integrated model of stand dynamics, soil carbon and fire regime : pplications to boreal ecosystem response to climate change

Les forêts d'épinettes noires (Picea mariana (Mill.) BSP) contiennent de grandes quantités de carbone stockées dans la biomasse vivante et dans le sol. Les feux de forêt et leur régime (ex. l’intervalle de retour de feu, l’intensité, la saisonnalité et la sévérité) jouent un rôle central dans le stockage et le flux du carbone, en modifiant la distribution et le transfert de carbone. Il y a peu de doute dans la communauté scientifique que le changement climatique provoquera des modifications dans les variables temporelles et spatiales qui contrôlent la fréquence et la sévérité des feux. Un modèle démographique structuré par classes de diamètre a été développé pour simuler le stockage du carbone sous divers régimes de feu. Cette approche intègre l’effet de l’intensité du feu et les mesures de la structure du peuplement sur la sévérité mesurée par la proportion de la mortalité des arbres. Le modèle permet aussi de quantifier et de cartographier les estimations régionales du carbone actuelles et futures pour le domaine bioclimatique de la pessière à mousses du nord du Québec. Les résultats de simulations suggèrent que la sévérité du feu augmente avec l’intensité initiale du feu. La variation de la structure du peuplement est l'un des facteurs qui explique la variation observée dans la sévérité du feu des régions boréales. Nous avons simulé les stocks et fluctuations de carbone sous sept niveaux d’intervalle de retour de feu et deux saisons de feu. Nous avons testé pour un effet de ces paramètres sur la moyenne des stocks de carbone. Les stocks de carbone étaient sensibles aux intervalles entre 60 et 300 ans. Le stock de carbone dans le sol fut plus faible pour les incendies d'été qui se produisaient durant de plus courts IRF. Finalement, les impacts à court terme du changement climatique ont été investigués au cours de quatre périodes climatiques : 1980-2010, 2010-2040, 2040-2070 et 2070-2100. Des cartes d’intervalle de retour du feu historique et futur et des données météorologiques projetées par CanESM2 RCP8.5 ont été utilisées pour simuler la croissance des forêts, le taux de décomposition, le régime du feu et la dynamique du C. Dans nos expériences de simulation, l’accumulation de carbone dans l’écosystème était réduite de 11% d’ici à la fin de 2100. Les forêts d'épinette noire du Québec seraient possiblement en train de perdre leur capacité à séquestrer et à stocker le carbone organique durant les prochaines décennies, à cause des effets du changement climatique sur le régime de feu et la croissance des forêts.Boreal black spruce forests (Picea mariana (Mill.) BSP) store great amounts of carbon in the living biomass and in the soil. Fire regime characteristics (e.g. fire return interval, fire intensity, fire season and severity) play a central role in the storage and flow of carbon, by modifying the distribution and transfer of material among pools. There is little doubt in the scientific community that climate change will cause changes in the temporal and spatial variables that control the frequency and severity of fires. A demographic diameter-class structured model was developed to simulate boreal carbon storage under different fire regimes. This approach incorporates the effect of fire intensity and stand structure measures to simulate fire severity, measured as the proportion of overstory tree mortality. The model allows quantifying and mapping average regional estimates of current and future carbon stocks for the black spruce-feathermoss bioclimatic domain of northern Québec. Simulation results suggest that fire severity increases with fire the intensity. Stand structure is one of the factors that explains the observed variation in boreal fire severity. We simulated carbon stocks and fluxes under seven levels of fire return interval (FRI) and two fire seasons. We tested for an effect of these parameters on average carbon stocks. Carbon stocks were sensitive to IRF's between 60 and 300 years. Soil C stocks were lower for summer fires that occurred during shorter IRF. Finally, we investigated the short-term impacts of climate change under four climatic periods: 1980-2010, 2010-2040, 2040-2070 and 2070-2100. Historical and future FRI maps and historical and forecasted weather data estimated by CanESM2 RCP8.5 were used to drive the growth of forests, decomposition rates, fire regime and C dynamics. In our simulation experiments, the accumulation of carbon in the ecosystem was reduced by 11% by the end of 2100. The results of this study suggest that black spruce forest could be losing their capacity to sequester and store organic C over the next coming decades due to climate change effects on the fire regime and on forest growth

Knowledge Coproduction for Transformative Climate Adaptation: Building Robust Strategies

Adaptation is a process of adjustment to actual or expected climate and its effects in order to moderate harm or exploit beneficial opportunities. Most adaptation options are scalable and applicable but may result in inequitable tradeoffs stemming from maladaptation. Thus, climate adaptation and maladaptation are inseparable and are equally likely. Adaptation has been commonly envisioned as coping mechanisms or incremental adjustments from existing strategies. However, both coping and incremental adaptations have failed in explicitly address the underlying drivers of systemic inequalities. Enabling and catalyzing conditions for transformative adaptation, both locally and regionally (i.e. strengthening collaborative governance, building capacities, promoting iterative multi-stakeholder engagement), is, therefore, crucial in building robust climate change adaptations under deep uncertainty. However, the lack of approaches entailing decision analytics, stakeholder engagement/deliberation, and interactive modeling and evaluation may hinder transformative adaptation success. Combining robust decision-making approaches with collaborative research and co-production processes can be constructive in illuminating the decision-rule systems that undergird current adaptation decision-making. This chapter offers some insights into how knowledge coproduction can be used to inform robust climate adaptation strategies under contexts of deep uncertainty while facilitating transformative system change

Exploring the Spatial Distribution of Air Pollution and Its Association with Socioeconomic Status Indicators in Mexico City

Air pollution is one of the most challenging global sustainability problems in the world. Roughly 90 of global citizens live in areas that exceed the acceptable air pollution levels according to the World Health Organization air quality guidelines. However, socially disadvantaged groups are disproportionately located in areas exposed to higher levels of air pollution. Understanding the association between risk exposure to air pollutants and the underlying socio-economic factors determining risk is central for sustainable urban planning. The purpose of this study was to explore environmental inequalities in Mexico City, specifically the spatial association between air pollutants and socioeconomic status (SES) indicators. We propose that SES indicators will be expected to spatially cluster vulnerable individuals and groups into heavily polluted areas. To test this hypothesis, we used 2017–2019 data from governmental records to perform spatial interpolations to explore the spatial distribution of criteria pollutants. We carried out spatial autocorrelations of air pollutants and SES indicators using the bivariate Moran’s I index. Our findings provide strong evidence of spatial heterogeneity in air pollution exposure in Mexico City. We found that socially deprived areas located in the southern periphery of Mexico City were exposed to higher ozone concentrations. On the contrary, wealthiest areas concentrated in the city center were exposed to greater concentrations of nitrogen dioxide and carbon monoxide. Our findings highlight the need for policy-driven approaches that take into consideration not only the geographic variability and meteorological dynamics associated with air pollution exposure, but also the management of socioeconomic risk factors aimed at reducing disparate exposure to air pollution and potential health impacts

Changes in air quality in Mexico City, London and Delhi in response to various stages and levels of lockdowns and easing of restrictions during COVID-19 pandemic

The impacts of COVID-19 lockdown restrictions have provided a valuable global experiment into the extent of improvements in air quality possible with reductions in vehicle movements. Mexico City, London and Delhi all share the problem of air quality failing WHO guideline limits, each with unique situations and influencing factors. We determine, discuss and compare the air quality changes across these cities during the COVID-19, to understand how the findings may support future improvements in their air quality and associated health of citizens. We analysed ground-level PM10, PM2.5, NO2, O3 and CO changes in each city for the period 1st January to August 31, 2020 under different phases of lockdown, with respect to daily average concentrations over the same period for 2017 to 2019. We found major reductions in PM10, PM2.5, NO2 and CO across the three cities for the lockdown phases and increases in O3 in London and Mexico City but not Delhi. The differences were due to the O3 production criteria across the cities, for Delhi production depends on the VOC-limited photochemical regime. Levels of reductions were commensurate with the degree of lockdown. In Mexico City, the greatest reduction in measured concentration was in CO in the initial lockdown phase (40%), in London the greatest decrease was for NO2 in the later part of the lockdown (49%), and in Delhi the greatest decrease was in PM10, and PM2.5 in the initial lockdown phase (61% and 50%, respectively). Reduction in pollutant concentrations agreed with reductions in vehicle movements. In the initial lockdown phase vehicle movements reduced by up to 59% in Mexico City and 63% in London. The cities demonstrated a range of air quality changes in their differing geographical areas and land use types. Local meteorology and pollution events, such as forest fires, also impacted the results

Identification of De Novo Copy Number Variants Associated with Human Disorders of Sexual Development

Disorders of sexual development (DSD), ranging in severity from genital abnormalities to complete sex reversal, are among the most common human birth defects with incidence rates reaching almost 3%. Although causative alterations in key genes controlling gonad development have been identified, the majority of DSD cases remain unexplained. To improve the diagnosis, we screened 116 children born with idiopathic DSD using a clinically validated array-based comparative genomic hybridization platform. 8951 controls without urogenital defects were used to compare with our cohort of affected patients. Clinically relevant imbalances were found in 21.5% of the analyzed patients. Most anomalies (74.2%) evaded detection by the routinely ordered karyotype and were scattered across the genome in gene-enriched subtelomeric loci. Among these defects, confirmed de novo duplication and deletion events were noted on 1p36.33, 9p24.3 and 19q12-q13.11 for ambiguous genitalia, 10p14 and Xq28 for cryptorchidism and 12p13 and 16p11.2 for hypospadias. These variants were significantly associated with genitourinary defects (P = 6.08×10−12). The causality of defects observed in 5p15.3, 9p24.3, 22q12.1 and Xq28 was supported by the presence of overlapping chromosomal rearrangements in several unrelated patients. In addition to known gonad determining genes including SRY and DMRT1, novel candidate genes such as FGFR2, KANK1, ADCY2 and ZEB2 were encompassed. The identification of risk germline rearrangements for urogenital birth defects may impact diagnosis and genetic counseling and contribute to the elucidation of the molecular mechanisms underlying the pathogenesis of human sexual development

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5 Pág.Peer reviewe

Differential Proteomic Analysis of Mammalian Tissues Using SILAM

Differential expression of proteins between tissues underlies organ-specific functions. Under certain pathological conditions, this may also lead to tissue vulnerability. Furthermore, post-translational modifications exist between different cell types and pathological conditions. We employed SILAM (Stable Isotope Labeling in Mammals) combined with mass spectrometry to quantify the proteome between mammalian tissues. Using 15N labeled rat tissue, we quantified 3742 phosphorylated peptides in nuclear extracts from liver and brain tissue. Analysis of the phosphorylation sites revealed tissue specific kinase motifs. Although these tissues are quite different in their composition and function, more than 500 protein identifications were common to both tissues. Specifically, we identified an up-regulation in the brain of the phosphoprotein, ZFHX1B, in which a genetic deletion causes the neurological disorder Mowat–Wilson syndrome. Finally, pathway analysis revealed distinct nuclear pathways enriched in each tissue. Our findings provide a valuable resource as a starting point for further understanding of tissue specific gene regulation and demonstrate SILAM as a useful strategy for the differential proteomic analysis of mammalian tissues

Reconocimiento a revisoras, revisores y editoras, editores invitados de ECOSISTEMAS del año 2023

4 Pág.Peer reviewe

Integrating Diverse Datasets Improves Developmental Enhancer Prediction

Gene-regulatory enhancers have been identified using various approaches, including evolutionary conservation, regulatory protein binding, chromatin modifications, and DNA sequence motifs. To integrate these different approaches, we developed EnhancerFinder, a two-step method for distinguishing developmental enhancers from the genomic background and then predicting their tissue specificity. EnhancerFinder uses a multiple kernel learning approach to integrate DNA sequence motifs, evolutionary patterns, and diverse functional genomics datasets from a variety of cell types. In contrast with prediction approaches that define enhancers based on histone marks or p300 sites from a single cell line, we trained EnhancerFinder on hundreds of experimentally verified human developmental enhancers from the VISTA Enhancer Browser. We comprehensively evaluated EnhancerFinder using cross validation and found that our integrative method improves the identification of enhancers over approaches that consider a single type of data, such as sequence motifs, evolutionary conservation, or the binding of enhancer-associated proteins. We find that VISTA enhancers active in embryonic heart are easier to identify than enhancers active in several other embryonic tissues, likely due to their uniquely high GC content. We applied EnhancerFinder to the entire human genome and predicted 84,301 developmental enhancers and their tissue specificity. These predictions provide specific functional annotations for large amounts of human non-coding DNA, and are significantly enriched near genes with annotated roles in their predicted tissues and lead SNPs from genome-wide association studies. We demonstrate the utility of EnhancerFinder predictions through in vivo validation of novel embryonic gene regulatory enhancers from three developmental transcription factor loci. Our genome-wide developmental enhancer predictions are freely available as a UCSC Genome Browser track, which we hope will enable researchers to further investigate questions in developmental biology. © 2014 Erwin et al