The influence of climate change on the productivity of Scots pine, Norway spruce, Pendula birch and Pubescent birch in southern and northern Finland

Kellomäki, Seppo; Kolström, Marja

doi:10.1016/0378-1127(94)90171-6

Cited by 55 publications

(19 citation statements)

References 17 publications

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“…They can also be used to predict various aspects of forest structure and function, such as biomass and productivity (Kellomäki and Kolström, 1994;Peterson and Peterson, 1994). Box 12.2 presents an application of an empirical bioclimatic model in Pakistan.…”

Section: Box 121 Using a Simple Bioclimate Model To Estimate Forest mentioning

confidence: 99%

Temperature Mediated Moose Survival in Northeastern Minnesota

et al. 2009

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The earth is in the midst of a pronounced warming trend and temperatures in Minnesota, USA, as elsewhere, are projected to increase. Northern Minnesota represents the southern edge to the circumpolar distribution of moose (Alces alces), a species intolerant of heat. Moose increase their metabolic rate to regulate their core body temperature as temperatures rise. We hypothesized that moose survival rates would be a function of the frequency and magnitude that ambient temperatures exceeded the upper critical temperature of moose. We compared annual and seasonal moose survival in northeastern Minnesota between 2002 and 2008 with a temperature metric. We found that models based on January temperatures above the critical threshold were inversely correlated with subsequent survival and explained >78% of variability in spring, fall, and annual survival. Models based on late‐spring temperatures also explained a high proportion of survival during the subsequent fall. A model based on warm‐season temperatures was important in explaining survival during the subsequent winter. Our analyses suggest that temperatures may have a cumulative influence on survival. We expect that continuation or acceleration of current climate trends will result in decreased survival, a decrease in moose density, and ultimately, a retreat of moose northward from their current distribution.

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Section: Box 121 Using a Simple Bioclimate Model To Estimate Forest mentioning

confidence: 99%

Temperature Mediated Moose Survival in Northeastern Minnesota

et al. 2009

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“…Year-to-year variations in tree phenology are indeed responsible for large differences in forest productivity and carbon sequestration (Kellomaki & Kolstrom 1994, Lieth 1997, Leinonen & Kramer 2002, Piao et al 2007). In addition, earlier budburst due to global warming may lead to extensive damage from early frost episodes (Hänninen 1995, Kramer et al 2000.…”

Section: Introductionmentioning

confidence: 99%

Modelling the timing of Betula pubescens budburst. II. Integrating complex effects of photoperiod into process-based models

Caffarra¹,

Donnelly²,

Chuine³

2011

Clim. Res.

117

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Despite experimental evidence of the individual and interactive effects of photoperiod and temperature on bud growth, photoperiod has not yet been effectively accounted for in models of budburst. However, in some tree species, such as Betula pubescens (birch), photoperiod has an important role in phenological control, and its inclusion in process-based models of budburst might affect phenological projections under climate change scenarios. The aim of the present study was to integrate photoperiod into a process-based phenological model (Chuine 2000; J Theor Biol 207: 337-347; Unified model), using experimental findings in which photoperiod was found to significantly affect budburst in B. pubescens (Caffarra et al. 2011; Clim Res 46:147-157, this issue). The effect of photoperiod was integrated into the model at 2 levels. Firstly, photoperiod, in interaction with temperature, affects the course of dormancy induction. Secondly, photoperiod modifies the response to temperature during the phase of forcing. The resulting model (DORMPHOT) for the simulation of birch budburst was fitted to a large phenological dataset, including data from different latitudes, and validated with 7 datasets from 4 different European countries. Besides giving more biological realism to the model, the newly introduced mechanisms improved its predictive performance. The DORMPHOT model outperformed the Unified model, the linear regression model (budburst date vs. spring average temperature), and the UniForc model. It also proved to be more accurate at predicting budburst in extremely warm years, which suggests it might be more reliable than previous models when using future climate change scenarios. KEY WORDS: Betula pubescens · Budburst · Calibration · Phenological models · Photoperiod · ValidationResale or republication not permitted without written consent of the publisher Clim Res 46: 159-170, 2011 temperature changes as dormancy progresses, and its responses are, in some cases, proportional to the amount of time of exposure to a particular environmental condition. These findings have resulted in the elaboration of models that describe budburst timing as the end point of 2 consecutive phases: endodormancy, which is released upon the cumulative effect of chilling temperatures (cool, autumn-winter temperatures), and ecodormancy, during which the cumulative effect of forcing temperatures (warm, spring temperatures) promotes cell growth (Hänninen 1990, Battey 2000. These models account for the effects of temperature in terms of developmental units, and different functions relate temperature to the rate of growth or the rate of dormancy release of the buds.Among these models, the simplest consider only the effects of forcing temperatures for the prediction of budburst timing. In the Spring Warming model (Cannell & Smith 1983, Chuine et al. 1998, Pop et al. 2000, also called the Thermal Time model (Hunter & Lechowicz 1992), and in the UniForc model (Chuine 2000), temperature is linearly or sigmoidally related to the rate of growth...

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“…Required to Counteract Negative Impacts In the southern part of the Baltic Sea basin, reduced precipitation in combination with higher temperatures is likely to result in reduced growth and increased risk of fire and pest outbreaks (Kellomäki and Kolström 1994;Lasch et al 2002Lasch et al , 2005BACC Author Team 2008;Köhl et al 2010). However, adaptive forest management may counteract these unfavourable conditions.…”

Section: Adaptive Forest Management Ismentioning

confidence: 99%

Socio-economic Impacts—Forestry and Agriculture

Krug¹,

Eriksson

Heidecke³

et al. 2015

Regional Climate Studies

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Climate change affects the vulnerability and productivity of forestry and agricultural systems, predominantly by changes in precipitation and temperature patterns. Indirect impacts are altered risk of damage, for example, by longer periods of drought stress and other biotic and abiotic disturbances. While southern and eastern parts of the Baltic Sea basin are likely to experience a net impact of climate change that is negative for production, northern and western regions are likely to experience a general increase in production. As a result, land-use potentials will change and will foster adaptation and mitigation measures. In the northern region, forest management adaptation may lead to substantial yield increases, while in the south management, adaptation may be required to counter deteriorating conditions. Comparable conclusions can be drawn for agricultural management: if adaptation potentials are fully exploited, substantial yield increases can be expected for certain crop species. In the southern areas and for certain species, deteriorating conditions and possibly increasing climatic variability are projected. Both climate change impacts and human responses will affect socio-economic conditions in the Baltic Sea basin.

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The influence of climate change on the productivity of Scots pine, Norway spruce, Pendula birch and Pubescent birch in southern and northern Finland

Cited by 55 publications

References 17 publications

Temperature Mediated Moose Survival in Northeastern Minnesota

Temperature Mediated Moose Survival in Northeastern Minnesota

Modelling the timing of Betula pubescens budburst. II. Integrating complex effects of photoperiod into process-based models

Socio-economic Impacts—Forestry and Agriculture

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