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

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Vegetation community development eight years after harvesting in small streams buffers at the Malcolm Knapp Research Forest

Riparian areas connect terrestrial and aquatic environments. The objectives of this research were to compare the vegetation community composition and structure eight years after harvesting and to explore successional trends among buffer widths at year eight after disturbance and in a chronosequence. A series of small clearcuts were harvested in 1998 in a 70 year old second growth stand at the Malcolm Knapp Research Forest and 0m, 10m and 30m reserve zones were established adjacent to the streams. Each treatment was replicated 3 times and 3 unharvested streams were identified as controls. Overstory and understory vegetation was measured annually from the year of harvest. Canopy density was measured using a densiometer. For comparative purposes, four vegetation plots were added in riparian areas within an 1868 and an old-growth stand during the summer of 2006. Eight years after harvesting, understory vegetation development is affected by buffer width due to higher light levels, and species richness in the 10m and 0m buffers is higher than in the 30m buffer and control. Shrubs and deciduous trees dominate the 0m and 10m buffer treatments. Proximity to the stream does not affect the composition and abundance of species with the exception of herbs and mosses. In the 10m and 30m buffer treatments, up to 15% overstory trees were windthrown in the first 2 years after harvest producing large canopy gaps. Consequently, the understory development in the 10m and 30m buffers is more like that in the 1868 and old-growth stands than in the controls, but these treatments still lack the very large trees and microsite heterogeneity of the older stands. In the unharvested controls, self-thinning continues and there has been 30% mortality of mostly smaller trees over the past 8 years. However, overstory density remains high. The 0m buffer was quickly colonized by shrubs and ferns and within the last 2 years has become dominated by juvenile deciduous trees. Overall, the 10m buffer balances timber production with the maintenance of overstory and understory structure dynamics. The combined effect of light from the edge and partial windthrow is accelerating succession towards a more mature or ‘old-growth’ condition.Forestry, Faculty ofGraduat

FireintensitiesPLOSONE.csv

Historical (1994-2010) fire intensity data provided by SOPFEU who has agrred to share it for research purposes.<div>The dataset contains fire intensity records by fire region and fuel type.The geographical coordinates as well as the final size of each fire event are also available.  </div><div>SupFin: Final size (ha)</div><div>Comb: Fuel type (C-2, C-3)</div><div>FIRE_CODE: fire region</div><div>INT: intensit</div

Modelling Variable Fire Severity in Boreal Forests: Effects of Fire Intensity and Stand Structure.

It is becoming clear that fires in boreal forests are not uniformly stand-replacing. On the contrary, marked variation in fire severity, measured as tree mortality, has been found both within and among individual fires. It is important to understand the conditions under which this variation can arise. We integrated forest sample plot data, tree allometries and historical forest fire records within a diameter class-structured model of 1.0 ha patches of mono-specific black spruce and jack pine stands in northern Québec, Canada. The model accounts for crown fire initiation and vertical spread into the canopy. It uses empirical relations between fire intensity, scorch height, the percent of crown scorched and tree mortality to simulate fire severity, specifically the percent reduction in patch basal area due to fire-caused mortality. A random forest and a regression tree analysis of a large random sample of simulated fires were used to test for an effect of fireline intensity, stand structure, species composition and pyrogeographic regions on resultant severity. Severity increased with intensity and was lower for jack pine stands. The proportion of simulated fires that burned at high severity (e.g. >75% reduction in patch basal area) was 0.80 for black spruce and 0.11 for jack pine. We identified thresholds in intensity below which there was a marked sensitivity of simulated fire severity to stand structure, and to interactions between intensity and structure. We found no evidence for a residual effect of pyrogeographic region on simulated severity, after the effects of stand structure and species composition were accounted for. The model presented here was able to produce variation in fire severity under a range of fire intensity conditions. This suggests that variation in stand structure is one of the factors causing the observed variation in boreal fire severity

Regression tree for black spruce.

<p>Regression tree for simulated fire severity in black spruce patches. The first split in the tree, or the root, is defined by the covariate with the strongest relationship with severity. Box plots at terminal nodes show the distribution of the fire severity data within each branch of the tree. The number of observations within each branch is shown at the top of each boxplot. The total number of simulated fires was 12,000.</p

Distribution of the historical and initial fire intensities by fire region and fuel type.

<p>Boxplots summarizing the distribution of the recorded historical head fire intensities (kW m^-1) for a) fire region, b) fuel type, and the distributions of initial fire intensities (I_i, kW m^-1) for c) fire region, and d) fuel type. Mean values are shown within the boxes. Boxes represent the inter-quartile ranges; horizontal lines within the boxes represent medians; whiskers extend to the most extreme data point that is no more than 1.5 times greater than the 3^rd quartile or less than the 1^st quartile. Dots above whiskers represent extreme values.</p

Regression tree for jack pine.

<p>Regression tree for simulated fire severity in jack pine patches. The first split in the tree, or the root, is defined by the covariate with the strongest relationship with fire severity. Box plots at terminal nodes show the distribution of the fire severity data within each branch of the tree. The number of observations within each branch is shown at the top of each boxplot. The total number of simulated fires was 12,000.</p