The Utilization of Bryophytes in Bioclimatic Modeling: Predicted Northward Migration of Peatlands in the Mackenzie River Basin, Canada, as a Result of Global Warming

Gignac, L. Dennis; Nicholson, Barbara J.; Bayley, Suzanne E.

doi:10.2307/3244531

Cited by 13 publications

(17 citation statements)

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“…All 7 simulations run with climate projections based on a scenario of 2.0°C warming by 2050 predict the disappearance or shrinkage of the areas inside the blanket peatland bioclimatic envelope in England and Wales, and eastern parts of Scotland. Our study is consistent with previous model-based studies, which have suggested that peatlands in Canada may 'migrate' northwards as a result of elevated temperatures and drought (Gignac et al 1998). Some field studies also suggest that peatland ecosystems at lower latitudes may not be well adapted to survive the more frequent extreme heat waves predicted in Europe and North America as part of global warming (Meehl & Tebaldi 2004, Bragazza 2008.…”

Section: The Future Of Blanket Peatlands In Great Britainsupporting

confidence: 81%

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Bioclimatic envelope model of climate change impacts on blanket peatland distribution in Great Britain

Gallego‐Sala¹,

Clark²,

House³

et al. 2010

Clim. Res.

112

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Blanket peatlands are rain-fed mires that cover the landscape almost regardless of topography. The geographical extent of this type of peatland is highly sensitive to climate. We applied a global process-based bioclimatic envelope model, PeatStash, to predict the distribution of British blanket peatlands. The model captures the present areal extent (Kappa = 0.77) and is highly sensitive to both temperature and precipitation changes. When the model is run using the UKCIP02 climate projections for the time periods 2011-2040, 2041-2070 and 2071-2100, the geographical distribution of blanket peatlands gradually retreats towards the north and the west. In the UKCIP02 high emissions scenario for 2071-2100, the blanket peatland bioclimatic space is ~84% smaller than contemporary conditions ; only parts of the west of Scotland remain inside this space. Increasing summer temperature is the main driver of the projected changes in areal extent. Simulations using 7 climate model outputs resulted in generally similar patterns of declining aereal extent of the bioclimatic space, although differing in degree. The results presented in this study should be viewed as a first step towards understanding the trends likely to affect the blanket peatland distribution in Great Britain. The eventual fate of existing blanket peatlands left outside their bioclimatic space remains uncertain.

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Section: The Future Of Blanket Peatlands In Great Britainsupporting

confidence: 81%

“…However, blanket peatlands are special ecosystems in that they are ombrotrophic mires dependent on high water table levels, which are maintained solely by the right climatic conditions, i.e. high precipitation and low temperature, and as such, they are highly vulnerable to climate change (Gignac et al 1998, Ellis & Tallis 2000.…”

Section: Model Limitationsmentioning

confidence: 99%

Bioclimatic envelope model of climate change impacts on blanket peatland distribution in Great Britain

Gallego‐Sala¹,

Clark²,

House³

et al. 2010

Clim. Res.

112

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“…Bioclimatic envelope models (BCEM) have been used to map the current and possible future distribution of various types of peatlands (Gignac et al 1998, Fronzek et al 2006, Parviainen & Luoto 2007. Envelope models are static statistical models used to define the 'environmental space' where a habitat or species is present (Guisan & Zimmermann 2000).…”

Section: Introductionmentioning

confidence: 99%

Assessing the vulnerability of blanket peat to climate change using an ensemble of statistical bioclimatic envelope models

et al. 2010

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ABSTRACT:We assessed the vulnerability of blanket peat to climate change in Great Britain using an ensemble of 8 bioclimatic envelope models. We used 4 published models that ranged from simple threshold models, based on total annual precipitation, to Generalised Linear Models (GLMs, based on mean annual temperature). In addition, 4 new models were developed which included measures of water deficit as threshold, classification tree, GLM and generalised additive models (GAM). Models that included measures of both hydrological conditions and maximum temperature provided a better fit to the mapped peat area than models based on hydrological variables alone. Under UKCIP02 projections for high (A1F1) and low (B1) greenhouse gas emission scenarios, 7 out of the 8 models showed a decline in the bioclimatic space associated with blanket peat. Eastern regions (Northumbria, North York Moors, Orkney) were shown to be more vulnerable than higher-altitude, western areas (Highlands, Western Isles and Argyle, Bute and The Trossachs). These results suggest a long-term decline in the distribution of actively growing blanket peat, especially under the high emissions scenario, although it is emphasised that existing peatlands may well persist for decades under a changing climate. Observational data from long-term monitoring and manipulation experiments in combination with process-based models are required to explore the nature and magnitude of climate change impacts on these vulnerable areas more fully.

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“…Numerous studies have shown that the C sink capacity of a given peatland site and the source/sink dynamics of CO 2 and CH 4 are dependent on moisture availability (soil water tables; e.g., Joiner et al 1999;Alm et al 1999;Moore and Roulet 1993), which makes peatland C cycling inherently sensitive to climate. Moreover, geographic distributions of peatlands are controlled by climate (Gignac and Vitt 1994;Halsey et al 1998), and bioclimatic modelling results predict a marked northwards displacement of these ecosystems under future climatic change scenarios (Gignac et al 1998). An estimated 60% of Canada's current peatland area is located in regions expected to be severely to extremely severely affected by climatic warming (Kettles and Tarnocai 1999), and the potential release of C stored in these organic soils to the atmosphere is a serious concern.…”

mentioning

confidence: 99%

Developing statistical models to estimate the carbon density of organic soils

Bauer¹,

Bhatti²,

Cash³

et al. 2006

Can. J. Soil. Sci.

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. 2006. Developing statistical models to estimate the carbon density of organic soils. Can. J. Soil Sci. 86: 295-304. Carbon density is a key variable in assessments of local or regional soil carbon (C) stocks, but its direct measurement on large numbers of samples is both time-consuming and expensive. To assess whether the C density of organic soils can be inferred from other parameters, we examined the ability of field-(stratigraphic depth and material type) and lab-(bulk density and ash content) based variables to predict the C density of organic soil samples. Candidate models given three different levels of a priori information about samples were developed from data for continental western Canada and examined using Akaike's information criterion (AIC). Models at each level were then used to predict profile-level C storage in cores from three different regions (continental western Canada, Ontario, and the Northwest Territories). In profiles from western Canada, predictions were unbiased, with mean prediction errors of 0-7% and local precision depending on the amount of a priori information available. Application of models to other regions yielded mixed results, probably reflecting both differences in site characteristics and classification/analytical methods used. Since these error sources are impossible to separate given available data, we recommend that models for C density prediction should be tailored to a given research question and region. The results suggest that simple, field-based variables are sufficient to predict C density for the purpose of regional surveys. To obtain accurate estimates at the profile level, bulk density (and ash or C content) have to be measured in the lab. . Les profils de l'Ouest canadien donnent des prévisions non biaisées avec une erreur moyenne de 0 à 7 %, la précision locale dépendant du volume d'informations a priori disponible. L'application des modèles à d'autres régions donne des résultats mitigés, sans doute à cause des variations au niveau des caractéristiques du site et des méthodes de classification ou d'analyse employées. Ces sources d'erreur ne pouvant être séparées des données existantes, les auteurs préconisent qu'on adapte les modèles de prévision à une région et à un problème scientifique particuliers. Les résultats laissent croire que des variables simples, relevées sur le terrain, suffisent pour prévoir la densité du carbone dans le cadre d'études régionales. La masse volumique apparente (ainsi que la concentration de carbone et la teneur en cendres) devra être mesurée en laboratoire si l'on veut en obtenir une estimation plus précise au niveau du profil. Abbreviations: ASH, ash content (% or proportion of sample weight); AIC, Akaike information criterion; C I , inorganic carbon content (% or proportion of sample weight); C O , organic carbon content (% or proportion of sample weight); C T , total carbon content (% or proportion of sample weight); BD, bulk density (g cm -3 ); COR, carbon/organic matter ratio; CD, carbon density (g cm -3 ); D, depth (cm from...

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The Utilization of Bryophytes in Bioclimatic Modeling: Predicted Northward Migration of Peatlands in the Mackenzie River Basin, Canada, as a Result of Global Warming

Cited by 13 publications

References 0 publications

Bioclimatic envelope model of climate change impacts on blanket peatland distribution in Great Britain

Bioclimatic envelope model of climate change impacts on blanket peatland distribution in Great Britain

Assessing the vulnerability of blanket peat to climate change using an ensemble of statistical bioclimatic envelope models

Developing statistical models to estimate the carbon density of organic soils

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