| 1. |
- Lehsten, Dörte, et al.
(author)
-
Modelling the Holocene migrational dynamics of Fagus sylvatica L. and Picea abies (L.) H. Karst
- 2014
-
In: Global Ecology and Biogeography. - : Wiley. - 1466-8238 .- 1466-822X. ; 23:6, s. 658-668
-
Journal article (peer-reviewed)abstract
- Aim Vegetation dynamics and the competitive interactions involved are assumed to restrict the ability of species to migrate. But in most migration modelling approaches disturbance-driven succession and competition processes are reduced to simple assumptions or are even missing. The aim of this study was to test a combination of a migration model and a dynamic vegetation model to estimate the migration of tree species controlled by climate, environment and local species dynamics such as succession and competition. Location Europe. Methods To estimate the effect of vegetation dynamics on the migration of European beech and Norway spruce, we developed a post-process migration tool (LPJ-CATS). This tool integrates outputs of the migration model CATS and the dynamic vegetation model LPJ-GUESS. The model LPJ-CATS relies on a linear dependency between the dispersal kernel and migration rate and is based on the assumption that competition reduces fecundity. Results Simulating potential migration rates with the CATS model, which does not account for competition and disturbance, resulted in mean Holocene migration rates of 435 +/- 55 and 330 +/- 95 m year(-1) for the two species Picea abies and Fagus sylvatica, respectively. With LPJ-CATS, these mean migration rates were reduced to 250 +/- 75 and 170 +/- 60 m year(-1) for spruce and beech, respectively. Moreover, LPJ-CATS simulated migration pathways of these two species that generally comply well with those documented in the palaeo-records. Main conclusions Our 'hybrid' modelling approach allowed for the simulation of generally realistic Holocene migration rates and pathways of the two study species on a continental scale. It suggests that competition can considerably modify spread rates, but also the magnitude of its effect depends on how close climate conditions are to the niche requirements of a particular species.
|
|
| 2. |
- Lehsten, Dörte, et al.
(author)
-
Simulation of Water Level Fluctuations in Kettle Holes Using a Time Series Model
- 2011
-
In: Wetlands. - : Springer Science and Business Media LLC. - 0277-5212 .- 1943-6246. ; 31:3, s. 511-520
-
Journal article (peer-reviewed)abstract
- Kettle holes are widespread in moraine landscapes. Their hydrological properties may be very vulnerable to changes in climatic conditions. To increase our knowledge of how kettle holes function and how they may be affected by climate change requires a model that can be applied to a variety of them regardless of their properties. We used the PIRFICT time series model to simulate the water levels in kettle holes over the last 50 years. For model calibration we applied time series of two-year lengths. We observed correlations between climate indices and water level statistics with a delayed response of one year. The results show a decrease in autumn low water levels and an increase in water level fluctuations. These effects correspond to observed increased summer evaporation and winter precipitation, and imply that the habitat quality dynamic of kettle holes depends on climatic conditions. With the prognosis of even warmer and dryer summers in Europe in the future, conservation strategies for kettle holes should include the effects of climate change.
|
|
| 3. |
- Sallaba, Florian, et al.
(author)
-
A rapid NPP meta-model for current and future climate and CO2 scenarios in Europe
- 2015
-
In: Ecological Modelling. - : Elsevier BV. - 0304-3800. ; 302, s. 29-41
-
Journal article (peer-reviewed)abstract
- Net primary production (NPP) is the difference in gross photosynthetic assimilation of carbon and carbon loss due to autotrophic respiration, and is an important ecosystem variable that facilitates understanding of climate change impacts on terrestrial ecosystem productivity and ecosystem services. The aim of this study is to rapidly estimate the NPP of European potential natural vegetation for current and future climate and carbon-dioxide scenarios (CO2). A NPP meta-model was developed and evaluated based on the dynamic global vegetation model LPJ-GUESS. LPJ-GUESS was used to simulate NPP under current and future climate change as well as CO2 scenarios. The NPP dataset produced from these simulations was used to determine the empirical relationships between NPP and driving climate variables (maximum temperature, minimum temperature, summer precipitation, winter precipitation) along with CO2 concentration. The climate variables’ relationships were combined in a synergistic function including CO2 relationships to estimate NPP. The meta-model was compared with randomly chosen NPP data originated from LPJ-GUESS. Furthermore, the meta-model's performance was evaluated on the European level with LPJ-GUESS simulations. The meta-model performed reasonably well with regard to estimating total NPP while performances for species-specific NPP were poor. For total NPP, the meta-model generated an agreement of R2 = 0.68 and RMSE = 0.06 at CO2 = 350 ppm in comparison to LPJ-GUESS simulations. The consideration of all CO2 concentration scenarios yielded R2 = 0.62 and RMSE = 0.08. A rapid synergistic approach is suggested that enables interactions between climate variables and their intra-annual variability to estimate NPP. This is a useful alternative to traditional empirical models that control NPP with the most limiting climate variable. The meta-model performed reasonably well for estimating total NPP for future climate change and CO2 scenarios. However, species-specific NPP estimates were unsatisfactory, implying that the synergistic approach cannot account for species specific dynamics. Comparison between the meta-model and LPJ-GUESS at the European scale showed that additional environmental variables (e.g. solar radiation) would be necessary to improve the meta-model.
|
|
