Clara Antón Fernández

Research Scientist

(+47) 974 30 351
clara.anton.fernandez@nibio.no

Place
Ås H8

Visiting address
Høgskoleveien 8, 1433 Ås

Abstract

Forest structural properties largely govern surface fluxes of moisture, energy, and momentum that strongly affect regional climate and hydrology. Forest structural properties are greatly shaped by forest management activities, especially in the Fennoscandia (Norway, Sweden, and Finland). Insight into transient developments in forest structure in response to management intervention is therefore essential to understanding the role of forest management in mitigating regional climate change. The aim of this study is to present a simple grid-based framework – the Fennoscandic Forest State Simulator (F2S2) -- for predicting time-dependent forest structural trajectories in a manner compatible with land models employed in offline or asynchronously coupled climate and hydrological research. F2S2 enables the prescription of future regional forest structure as a function of: i) exogenously defined scenarios of forest harvest intensity; ii) forest management intensity; iii) climate forcing. We demonstrate its application when applied as a stand-alone tool for forecasting three alternative future forest states in Norway that differ with respect to background climate forcing, forest harvest intensity (linked to two Shared Socio-economic Pathways (SSPs)), and forest management intensity. F2S2 captures impacts of climate forcing and forest management on general trends in forest structural development over time, and while climate is the main driver of longer-term forest structural dynamics, the role of harvests and other management-driven effects cannot be overlooked. To our knowledge this is the first paper presenting a method to map forest structure in space and time in a way that is compatible with land surface or hydrological models employing sub-grid tiling.

Abstract

A new stand-level growth and yield model, consisting of component equations for stand volume, basal area, survival, and dominant stand height, was developed from a dataset of long-term trials for managed thinned and unthinned even-aged Norway spruce (Picea abies (L.) Karst.) forests in Norway. The developed models predict considerably faster growth rates than the existing Norwegian models. Further, it was found that the existing Norwegian stand-level models do not match the data from the thinning trails. The significance of thinning response functions indicated that thinning increases basal area growth while reducing competition related mortality. No significant effects of thinning were found in the dominant stand height growth. Model examination by means of cross-validation indicated that the models were unbiased and performed well within the data range. An application of the developed stand-level model highlights the potential use for these models in comparing different management scenarios.

Abstract

As a carbon dioxide removal measure, the Norwegian government is currently considering a policy of large-scale planting of spruce (Picea abies (L) H. Karst) on lands in various states of natural transition to a forest dominated by deciduous broadleaved tree species. Given the aspiration to bring emissions on balance with removals in the latter half of the 21st century in effort to limit the global mean temperature rise to “well below” 2°C, the effectiveness of such a policy is unclear given relatively low spruce growth rates in the region. Further convoluting the picture is the magnitude and relevance of surface albedo changes linked to such projects, which typically counteract the benefits of an enhanced forest CO2 sink in high-latitude regions. Here, we carry out a rigorous empirically based assessment of the terrestrial carbon dioxide removal (tCDR) potential of large-scale spruce planting in Norway, taking into account transient developments in both terrestrial carbon sinks and surface albedo over the 21st century and beyond. We find that surface albedo changes would likely play a negligible role in counteracting tCDR, yet given low forest growth rates in the region, notable tCDR benefits from such projects would not be realized until the second half of the 21st century, with maximum benefits occurring even later around 2150. We estimate Norway's total accumulated tCDR potential at 2100 and 2150 (including surface albedo changes) to be 447 (±240) and 852 (±295) Mt CO2-eq. at mean net present values of US$ 12 (±3) and US$ 13 (±2) per ton CDR, respectively. For perspective, the accumulated tCDR potential at 2100 represents around 8 years of Norway's total current annual production-based (i.e., territorial) CO2-eq. emissions.

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Abstract

Late-spring frosts (LSFs) affect the performance of plants and animals across the world’s temperate and boreal zones, but despite their ecological and economic impact on agriculture and forestry, the geographic distribution and evolutionary impact of these frost events are poorly understood. Here, we analyze LSFs between 1959 and 2017 and the resistance strategies of Northern Hemisphere woody species to infer trees’ adaptations for minimizing frost damage to their leaves and to forecast forest vulnerability under the ongoing changes in frost frequencies. Trait values on leaf-out and leaf-freezing resistance come from up to 1,500 temperate and boreal woody species cultivated in common gardens. We find that areas in which LSFs are common, such as eastern North America, harbor tree species with cautious (late-leafing) leaf-out strategies. Areas in which LSFs used to be unlikely, such as broad-leaved forests and shrublands in Europe and Asia, instead harbor opportunistic tree species (quickly reacting to warming air temperatures). LSFs in the latter regions are currently increasing, and given species’ innate resistance strategies, we estimate that ∼35% of the European and ∼26% of the Asian temperate forest area, but only ∼10% of the North American, will experience increasing late-frost damage in the future. Our findings reveal region-specific changes in the spring-frost risk that can inform decision-making in land management, forestry, agriculture, and insurance policy.

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Abstract

New mortality models were developed for the purpose of improving long-term growth and yield simulations in Finland, Norway, and Sweden and were based on permanent national forest inventory plots from Sweden and Norway. Mortality was modelled in two steps. The first model predicts the probability of survival, while the second model predicts the proportion of basal area in surviving trees for plots where mortality has occurred. In both models, the logistic function was used. The models incorporate the variation in prediction period length and in plot size. Validation of both models indicated unbiased mortality rates with respect to various stand characteristics such as stand density, average tree diameter, stand age, and the proportion of different tree species, Scots pine (Pinus sylvestris L.), Norway spruce (Picea abies (L.) Karst.), and broadleaves. When testing against an independent dataset of unmanaged spruce-dominated stands in Finland, the models provided unbiased prediction with respect to stand age.

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Abstract

An understanding of the relationship between volume increment and stand density (basal area, stand density index, etc.) is of utmost importance for properly managing stand density to achieve specific management objectives. There are two main approaches to analyse growth–density relationships. The first relates volume increment to stand density through a basic relationship, which can vary with site productivity, age, and potentially incorporates treatment effects. The second is to relate the volume increment and density of thinned experimental plots relative to that of an unthinned experimental plot on the same site. Using a dataset of 229 thinned and unthinned experimental plots of Norway spruce, a growth model is developed describing the relationship between gross or net volume increment and basal area. The models indicate that gross volume increases with increasing basal area up to 50 m2 and thereafter becomes constant out to the maximum basal area. Alternatively, net volume increment was maximized at a basal area of 43 m2 and decreased with further increases in basal area. However, the models indicated a wide range where net volume increment was essentially constant, varying by less than 1 m3 ha−1 year−1. An analysis of different thinning scenarios indicated that the relative relationship between volume increment and stand density was dynamic and changed over the course of a rotation.

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Abstract

The identity of the dominant root-associated microbial symbionts in a forest determines the ability of trees to access limiting nutrients from atmospheric or soil pools1,2, sequester carbon3,4 and withstand the effects of climate change5,6. Characterizing the global distribution of these symbioses and identifying the factors that control this distribution are thus integral to understanding the present and future functioning of forest ecosystems. Here we generate a spatially explicit global map of the symbiotic status of forests, using a database of over 1.1 million forest inventory plots that collectively contain over 28,000 tree species. Our analyses indicate that climate variables—in particular, climatically controlled variation in the rate of decomposition—are the primary drivers of the global distribution of major symbioses. We estimate that ectomycorrhizal trees, which represent only 2% of all plant species7, constitute approximately 60% of tree stems on Earth. Ectomycorrhizal symbiosis dominates forests in which seasonally cold and dry climates inhibit decomposition, and is the predominant form of symbiosis at high latitudes and elevation. By contrast, arbuscular mycorrhizal trees dominate in aseasonal, warm tropical forests, and occur with ectomycorrhizal trees in temperate biomes in which seasonally warm-and-wet climates enhance decomposition. Continental transitions between forests dominated by ectomycorrhizal or arbuscular mycorrhizal trees occur relatively abruptly along climate-driven decomposition gradients; these transitions are probably caused by positive feedback effects between plants and microorganisms. Symbiotic nitrogen fixers—which are insensitive to climatic controls on decomposition (compared with mycorrhizal fungi)—are most abundant in arid biomes with alkaline soils and high maximum temperatures. The climatically driven global symbiosis gradient that we document provides a spatially explicit quantitative understanding of microbial symbioses at the global scale, and demonstrates the critical role of microbial mutualisms in shaping the distribution of plant species.

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Abstract

• Key message A dataset of forest resource projections in 23 European countries to 2040 has been prepared for forest-related policy analysis and decision-making. Due to applying harmonised definitions, while maintaining country-specific forestry practices, the projections should be usable from national to international levels. The dataset can be accessed at https://doi.org/10.5061/dryad.4t880qh . The associated metadata are available at https://metadata-afs.nancy.inra.fr/geonetwork/srv/eng/catalog.search#/metadata/8f93e0d6-b524-43bd-bdb8-621ad5ae6fa9 .

Abstract

The present study aims to develop biologically sound and parsimonious site index models for Norway to predict changes in site index (SI) under different climatic conditions. The models are constructed using data from the Norwegian National Forest Inventory and climate data from the Norwegian meteorological institute. Site index was modeled using the potential modifier functional form, with a potential component (POT) depending on site quality classes and two modifier components (MOD): temperature and moisture. Each of these modifiers was based on a portfolio of candidate variables. The best model for spruce-dominated stands included temperature as modifier (R2 = 0.56). In the case of pine- and deciduous-dominated stands, the best models included both modifiers (R2 = 0.40 and 0.54 for temperature and moisture, respectively). We illustrate the use of the models by analyzing the possible shift in SI for year 2100 under one (RCP4.5) of the benchmark scenarios adopted by the Intergovernmental Panel on Climate Change for its fifth assessment report. The models presented can be valuable for evaluating the effect of climate change scenarios in Norwegian forests.

Abstract

Boreal forests contain 30% of the global forest carbon with the majority residing in soils. While challenging to quantify, soil carbon changes comprise a significant, and potentially increasing, part of the terrestrial carbon cycle. Thus, their estimation is important when designing forest-based climate change mitigation strategies and soil carbon change estimates are required for the reporting of greenhouse gas emissions. Organic matter decomposition varies with climate in complex nonlinear ways, rendering data aggregation nontrivial. Here, we explored the effects of temporal and spatial aggregation of climatic and litter input data on regional estimates of soil organic carbon stocks and changes for upland forests. We used the soil carbon and decomposition model Yasso07 with input from the Norwegian National Forest Inventory (11275 plots, 1960–2012). Estimates were produced at three spatial and three temporal scales. Results showed that a national level average soil carbon stock estimate varied by 10% depending on the applied spatial and temporal scale of aggregation. Higher stocks were found when applying plot-level input compared to country-level input and when long-term climate was used as compared to annual or 5-year mean values. A national level estimate for soil carbon change was similar across spatial scales, but was considerably (60–70%) lower when applying annual or 5-year mean climate compared to long-term mean climate reflecting the recent climatic changes in Norway. This was particularly evident for the forest-dominated districts in the southeastern and central parts of Norway and in the far north. We concluded that the sensitivity of model estimates to spatial aggregation will depend on the region of interest. Further, that using long-term climate averages during periods with strong climatic trends results in large differences in soil carbon estimates. The largest differences in this study were observed in central and northern regions with strongly increasing temperatures.

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Abstract

Key Message. This analysis of the tools and methods currently in use for reporting woody biomass availability in 21 European countries has shown that most countries use, or are developing, National Forest Inventory-oriented models whereas the others use standwise forest inventory--oriented methods. Context. Knowledge of realistic and sustainable wood availability in Europe is highly relevant to define climate change mitigation strategies at national and European level, to support the development of realistic targets for increased use of renewable energy sources and of industry wood. Future scenarios at European level highlight a deficit of domestic wood supply compared to wood consumption, and some European countries state they are harvesting above the increment. Aims. Several country-level studies on wood availability have been performed for international reporting. However, it remains essential to improve the knowledge on the projection methods used across Europe to better evaluate forecasts. Methods. Analysis was based on descriptions supplied by the national correspondentsinvolved in USEWOOD COST Action (FP1001), and further enriched with additionaldata from international reports that allowedcharacterisation of the forests in these countries for the same base year. Results. Methods currently used for projecting wood availability were described for 21 European countries. Projection systems based on National Forest Inventory (NFI) data prevail over methods based on forest management plans. Only a few countries lack nationwide projection tools, still using tools developed for specific areas. Conclusions. A wide range of NFI-based systems for projecting wood availability exists, being under permanent improvement. The validation of projection forecasts and the inclusion of climate sensitive growth models into these tools are common aims for most countries. Cooperation among countries would result in higher efficiency when developing and improving projection tools and better comparability among them.

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Abstract

National Forest Inventories (NFIs) provide estimates of forest parameters for national and regional scales. Many key variables of interest, such as biomass and timber volume, cannot be measured directly in the field. Instead, models are used to predict those variables from measurements of other field variables. Therefore, the uncertainty or variability of NFI estimates results not only from selecting a sample of the population but also from uncertainties in the models used to predict the variables of interest. The aim of this study was to quantify the model-related variability of Norway spruce (Picea abies [L.] Karst) biomass stock and change estimates for the Norwegian NFI. The model-related variability of the estimates stems from uncertainty in parameter estimates of biomass models as well as residual variability and was quantified using a Monte Carlo simulation technique. Uncertainties in model parameter estimates, which are often not available for published biomass models, had considerable influence on the model-related variability of biomass stock and change estimates. The assumption that the residual variability is larger than documented for the models and the correlation of within-plot model residuals influenced the model-related variability of biomass stock change estimates much more than estimates of the biomass stock. The larger influence on the stock change resulted from the large influence of harvests on the stock change, although harvests were observed rarely on the NFI sample plots in the 5-year period that was considered. In addition, the temporal correlation between model residuals due to changes in the allometry had considerable influence on the model-related variability of the biomass stock change estimate. The allometry may, however, be assumed to be rather stable over a 5-year period. Because the effects of model-related variability of the biomass stock and change estimates were much smaller than those of the sampling-related variability, efforts to increase the precision of estimates should focus on reducing the sampling variability. If the model-related variability is to be decreased, the focus should be on the tree fractions of living branches as well as stump and roots.

Abstract

Harvest activity directly impacts timber supply, forest conditions, and carbon stock. Forecasts of the harvest activity have traditionally relied on the assumption that harvest is carried out according to forest management guidelines or to maximize forest value. However, these rules are, in practice, seldom applied systematically, which may result in large discrepancies between predicted and actual harvest in short-term forecasts. We present empirical harvest models that predict final felling and thinning based on forest attributes such as site index, stand age, volume, slope, and distance to road. The logistic regression models were developed and fit to Norwegian national forest inventory data and predict harvest with high discriminating power. The models were consistent with expected landowners behavior, that is, areas with high timber value and low harvest cost were more likely to be harvested. We illustrate how the harvest models can be used, in combination with a growth model, to develop a national business-as-usual scenario for forest carbon. The business-as-usual scenario shows a slight increase in national harvest levels and a decrease in carbon sequestration in living trees over the next decade.