Overall Conclusions: Too Hot to Handle?

Throughout the course of this blog, the emphasis has changed somewhat. To begin with I introduced the general concepts around species, their migrationary capacity and their response to predicted changes in climate. Migration rates were examined, including concepts such as Reid’s paradox, which describes post glacial species migration rates of up to 1000 m per year. Spatial variability of future climate change led to theories of favourable microclimates and possible refugia.

I have shown how climate change has already started to effect species, such as the Turdus migratorius and Marmota flaviventris due to changes in phenology in the Colardo rockies. Other species such as tree populations in North America were analyzed in the context of their range shift due to climate change.

From there, I decided to link past migrations from glacial refugia to possible future modelled migrations. Southern glacial refugia in the mediterranean peninsulas of Iberia, Italy and the Balkans were discussed. Methods of locating glacial refugia such as phylogeographic techniques and reconstructing post glacial colonization through macrofossils and pollen records have been demonstrated. This information was used to infer whether migration rates described in Reid’s paradox could be feasile. I concluded that the existence of cryptic northern glacial refugia was the most likely explanation.

Having shown evidence from past climate change and post glacial recolonization, I reviewed current vegetation modelling techniques. Bioclimate models were found to be the most commonly used, however several criticisms were apparent. These were mainly aimed at their simplicity, as they did not take into account: land use, inter species interaction and evolutionary change and other such parameters. Dynamic global vegetation models are the next generation of model from which future climate impacts on ecosystems should be drawn. An example of how DGVMs may be used was shown, although significant further research into modelling processes needs to be undertaken.

From this blog I hope to have shown the possible effects of climate change on species, and how it will force them to migrate to higher latitudes and altitudes. If i were to have continued this blog I would have focussed on theories and findings as they are published to further my understanding of the topic at the forefront of the field. I have found this experience to be interesting and exciting and would like to thank Dr. Anson Mackay for giving us this unique way of exhibiting our knowledge as well as contributing to academic debate.

Application of a Dynamic Global Vegetation Model

Today’s post will look at the future impacts of Climate Change on the earths ecosystems from predictions from dynamic global vegetation models (DGVMs). A paper by Gonzalez et al. (2010) attempts to model future changes in global biomes by comparing observed changes in 20th century climate with modelled predictions of 21st century vegetation changes. Three global climate models (GCMs) were used in cooperation with the MC1 DGVM.

Results from the projected climate data (Figure 2) and projected vegetation changes (Figure 3) are shown. The GCMs project widespread temperature increase and precipitation changes by 2100. This includes global average temperature increases of 2.4-4 degrees. Average precipitation increases at rates of 0.03-0.04 mm, but becomes increasingly spatially variable. The model also predicts that wildfire frequency will increase in around 1/3 of the global area.

Results from global vegetation modelling follows observed patterns of global biomes. MC1 projections show potential extensive changes under 2071-2100 scenarios. Temperate mixed forest shows the highest areas of potential change, with desert showing the lowest. Gonzalez et al. (2010) believe between one-tenth and one-half of the earths biomes may be highly to very highly vulnerable to change.

Vegetation projections suggest potential latitudinal biome shifts of up to 400 km. Temperate mixed forest shows high vulnerability due to projected loss of coniferous species leading to conversion to temperate broadleaf forest. Tropical ecosystems show low vulnerability to change due to their high temperature tolerance combined with projections of increased precipitation around the equator (Malhi et al. 2008). MC1 data is congruent with data from other DGVMs which agree on shift of boreal forest into tundra at high latitudes and some forest lost in the Amazon.

As for effects, a large proportion of the world’s population live in areas in high vulnerability of potential biome changes. Biome change may alter ecosystem services, such as wood for timber, grass species preferred for grazing and water retention capacity of watersheds for human consumption.

Analysing the Lund-Potsdam-Jena Model (LPJ)

Todays post will be to do with analysing one of the dynamic global vegetation models (DGVM) previously discussed. The one chosen is the LPJ model discussed in Sitch et al. (2003). It is one of many fully integrated DGVMs that have been developed following the criticism of bioclimate envelope models. The model describes vegetation in terms of fractional coverage of a grid cell, taking into account different plant functional types (PFTs) (Table 1).

Each PFT is then assigned bioclimatic limits (Table 2) which determines whether the species can survive/regenerate under the climatic conditions of the particular grid cell.

LPJ addresses the limitations of bioclimate envelope models be including representation of vegetation structure, dynamics, competition between PFTs and soil biogeography into the model. Figure 1 shows the model logic.

The model can predict changes on different spatial scales. My post will focus on those on the global scale. Figure 2 shows a simulated map of potential natural vegetation for the modern climate. It accurately shows the boreal evergreen forests in Canada and northern Eurasia, the boreal deciduous forests in Siberia, and the transition into temperate ecosystems of north America, western Europe and China. LPJ simulates the transition from savanna into evergreen rainforests near the equator, as well as northern tundra, and grasslands in drier areas.

Figure 2. LPJ predictions of PFT distribution

LPJ has been shown to be able to reproduce relatively accurate models of current global ecosystems. This proves the model’s effectivness and provides some reliability for its application to future modelling. Looking forward, attempts should be made to use this information to examine possible effects of climate on species on a global scale. Having shown an example of a DGVM and how they work and my next post will show how one can be applied to the modelling of a certain area, with hopefully some results.