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Articles by Mikko Peltoniemi

Category: Research article

article id 10496, category Research article
Jouni Siipilehto, Harri Mäkinen, Kjell Andreassen, Mikko Peltoniemi. (2021). Models for integrating and identifying the effect of senescence on individual tree survival probability for Norway spruce. Silva Fennica vol. 55 no. 2 article id 10496. https://doi.org/10.14214/sf.10496
Highlights: The effect of senescence was integrated into an individual tree survival model; The best model showed good fit for managed, unmanaged and old-growth stands; The probability for a large tree to survive decreased with increasing stand age; The best performed model included an interaction term between stem diameter and stand age and also stand age as a separate independent variable.

Ageing and competition reduce trees’ ability to capture resources, which predisposes them to death. In this study, the effect of senescence on the survival probability of Norway spruce (Picea abies (L.) Karst.) was analysed by fitting alternative survival probability models. Different model formulations were compared in the dataset, which comprised managed and unmanaged plots in long-term forest experiments in Finland and Norway, as well as old-growth stands in Finland. Stand total age ranged from 19 to 290 years. Two models were formulated without an age variable, such that the negative coefficient for the squared stem diameter described a decreasing survival probability for the largest trees. One of the models included stand age as a separate independent variable, and three models included an interaction term between stem diameter and stand age. According to the model including stand age and its interaction with stem diameter, the survival probability curves could intersect each other in stands with a similar structure but a different mean age. Models that did not include stand age underestimated the survival rate of the largest trees in the managed stands and overestimated their survival rate in the old-growth stands. Models that included stand age produced more plausible predictions, especially for the largest trees. The results supported the hypothesis that the stand age and senescence of trees decreases the survival probability of trees, and that the ageing effect improves survival probability models for Norway spruce.

  • Siipilehto, Natural Resources Institute Finland (Luke), Natural resources, Latokartanonkaari 9, P.O. Box 2, FI-00790 Helsinki, Finland ORCID ID:E-mail: jouni.siipilehto@luke.fi (email)
  • Mäkinen, Natural Resources Institute Finland (Luke), Production systems, Latokartanonkaari 9, P.O. Box 2, FI-00790 Helsinki, Finland ORCID ID: https://orcid.org/0000-0002-1820-6264 E-mail: harri.makinen@luke.fi
  • Andreassen, Norwegian Institute of Bioeconomy Research (NIBIO), NO-1431 Ås, Norway ORCID ID:E-mail: kjellandreassen@gmail.com
  • Peltoniemi, Natural Resources Institute Finland (Luke), Bioeconomy and environment, Latokartanonkaari 9, P.O. Box 2, FI-00790 Helsinki, Finland ORCID ID: https://orcid.org/0000-0003-2028-6969 E-mail: mikko.peltoniemi@luke.fi

Category: Special section

article id 290, category Special section
Mikko Peltoniemi, Esther Thürig, Stephen Ogle, Taru Palosuo, Marion Schrumpf, Thomas Wutzler, Klaus Butterbach-Bahl, Oleg Chertov, Alexander Komarov, Aleksey Mikhailov, Annemieke Gärdenäs, Charles Perry, Jari Liski, Pete Smith, Raisa Mäkipää. (2007). Models in country scale carbon accounting of forest soils. Silva Fennica vol. 41 no. 3 article id 290. https://doi.org/10.14214/sf.290
Countries need to assess changes in the carbon stocks of forest soils as a part of national greenhouse gas (GHG) inventories under the United Nations Framework Convention on Climate Change (UNFCCC) and the Kyoto Protocol (KP). Since measuring these changes is expensive, it is likely that many countries will use alternative methods to prepare these estimates. We reviewed seven well-known soil carbon models from the point of view of preparing country-scale soil C change estimates. We first introduced the models and explained how they incorporated the most important input variables. Second, we evaluated their applicability at regional scale considering commonly available data sources. Third, we compiled references to data that exist for evaluation of model performance in forest soils. A range of process-based soil carbon models differing in input data requirements exist, allowing some flexibility to forest soil C accounting. Simple models may be the only reasonable option to estimate soil C changes if available resources are limited. More complex models may be used as integral parts of sophisticated inventories assimilating several data sources. Currently, measurement data for model evaluation are common for agricultural soils, but less data have been collected in forest soils. Definitions of model and measured soil pools often differ, ancillary model inputs require scaling of data, and soil C measurements are uncertain. These issues complicate the preparation of model estimates and their evaluation with empirical data, at large scale. Assessment of uncertainties that accounts for the effect of model choice is important part of inventories estimating large-scale soil C changes. Joint development of models and large-scale soil measurement campaigns could reduce the inconsistencies between models and empirical data, and eventually also the uncertainties of model predictions.
  • Peltoniemi, Finnish Forest Research Institute, Vantaa Research Unit, P.O. Box 18, FI-01301 Vantaa, Finland ORCID ID:E-mail: mikko.peltoniemi@metla.fi (email)
  • Thürig, Swiss Federal Institute for Forest, Snow and Landscape Research (WSL), Birmensdorf, Switzerland; European Forest Institute, Joensuu, Finland ORCID ID:E-mail:
  • Ogle, Natural Resources Ecology Laboratory, Colorado State University, Fort Collins, USA ORCID ID:E-mail:
  • Palosuo, European Forest Institute, Joensuu, Finland ORCID ID:E-mail:
  • Schrumpf, Max-Planck-Institute for Biogeochemistry, Jena, Germany ORCID ID:E-mail:
  • Wutzler, Max-Planck-Institute for Biogeochemistry, Jena, Germany ORCID ID:E-mail:
  • Butterbach-Bahl, Institute for Meteorology and Climate Research, Forschungszentrum Karlsruhe GmbH, Garmisch-Partenkirchen, Germany ORCID ID:E-mail:
  • Chertov, St. Petersburg State University, St. Petersburg-Peterhof, Russia ORCID ID:E-mail:
  • Komarov, Institute of Physicochemical and Biological Problems in Soil Science of Russian Academy of Sciences, Pushchino, Russia ORCID ID:E-mail:
  • Mikhailov, Institute of Physicochemical and Biological Problems in Soil Science of Russian Academy of Sciences, Pushchino, Russia ORCID ID:E-mail:
  • Gärdenäs, Dept. of Soil Sciences, Swedish University of Agricultural Sciences, Uppsala, Sweden ORCID ID:E-mail:
  • Perry, USDA Forest Service, Northern Research Station, St. Paul, MN USA ORCID ID:E-mail:
  • Liski, Finnish Environment Institute, Helsinki, Finland ORCID ID:E-mail:
  • Smith, School of Biological Sciences, University of Aberdeen, Aberdeen, UK ORCID ID:E-mail:
  • Mäkipää, Finnish Forest Research Institute, Vantaa Research Unit, P.O. Box 18, FI-01301 Vantaa, Finland ORCID ID:E-mail: raisa.makipaa@metla.fi
article id 287, category Special section
Mikko Peltoniemi, Juha Heikkinen, Raisa Mäkipää. (2007). Stratification of regional sampling by model-predicted changes of carbon stocks in forested mineral soils. Silva Fennica vol. 41 no. 3 article id 287. https://doi.org/10.14214/sf.287
Monitoring changes in soil C has recently received interest due to reporting under the Kyoto Protocol. Model-based approaches to estimate changes in soil C stocks exist, but they cannot fully replace repeated measurements. Measuring changes in soil C is laborious due to small expected changes and large spatial variation. Stratification of soil sampling allows the reduction of sample size without reducing precision. If there are no previous measurements, the stratification can be made with model-predictions of target variable. Our aim was to present a simulation-based stratification method, and to estimate how much stratification of inventory plots could improve the efficiency of the sampling. The effect of large uncertainties related to soil C change measurements and simulated predictions was targeted since they may considerably decrease the efficiency of stratification. According to our simulations, stratification can be useful with a feasible soil sample number if other uncertainties (simulated predictions and forecasted forest management) can be controlled. For example, the optimal (Neyman) allocation of plots to 4 strata with 10 soil samples from each plot (unpaired repeated sampling) reduced the standard error (SE) of the stratified mean by 9–34% from that of simple random sampling, depending on the assumptions of uncertainties. When the uncertainties of measurements and simulations were not accounted for in the division to strata, the decreases of SEs were 2–9 units less. Stratified sampling scheme that accounts for the uncertainties in measured material and in the correlates (simulated predictions) is recommended for the sampling design of soil C stock changes.
  • Peltoniemi, Finnish Forest Research Institute, Vantaa Research Unit, P.O. Box 18, FI-01301 Vantaa, Finland ORCID ID:E-mail: mikko.peltoniemi@metla.fi (email)
  • Heikkinen, Finnish Forest Research Institute, Vantaa Research Unit, P.O. Box 18, FI-01301 Vantaa, Finland ORCID ID:E-mail:
  • Mäkipää, Finnish Forest Research Institute, Vantaa Research Unit, P.O. Box 18, FI-01301 Vantaa, Finland ORCID ID:E-mail: raisa.makipaa@metla.fi

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