GEARING UP TO PREDICT THE IMPACT OF CLIMATE CHANGE ON THE BIODIVERISTY OF SIKKIM

- Pradeep Kumar IFS, Conservator of Forests (WP)

Anthropogenic emissions of Green House Gases (GHGs) are the primary drivers of climate change.  Climate is probably the most important determinant of vegetation pattern on a broad scale and exerts a significant influence on the structure and ecology of forests and other natural ecosystems. Given the rate of projected environmental change for the 21st century, urgent adaptation and mitigation measures are required to slow down the on-going erosion of biodiversity.

How serious is climate change compared with other factors affecting biodiversity?

Very — but it tends to act over a longer time scale. The ecological disruption wrought by climate change is generally slower than that caused by other factors. Such factors include habitat destruction through changes in land use; pollution, for example by nitrogen deposition; the invasion of ecosystems by non-native plant and animal species (biotic exchange); and the biological consequences of increased levels of carbon dioxide in the atmosphere. In the short to- medium term, human-induced fragmentation of natural habitat and invasive species are particular threats to biodiversity. But looking 50 years into the future and beyond, the effects of climate are likely to become increasingly prominent relative to the other factors.

Even though increasing evidence shows that recent human-induced environmental changes have already triggered species’ range shifts, changes in phenology and species’ extinctions, accurate projections of species’ responses to future environmental changes are more difficult to ascertain. One of the greatest concerns in ecological science today is the impact of climate change on ecosystems; this presents a particularly difficult challenge for foresters and ecologists. Fourth Assessment Report of IPCC concluded that resilience of many ecosystems is likely to be exceeded this century by an unprecedented combination of climate change, associated disturbances (e.g. flooding, drought, wildfire and  insects) and other global change drivers (e.g. land use change, pollution, fragmentation of natural systems and over exploitation of resources). Over the course of this century, net carbon uptake by terrestrial ecosystems is likely to peak before mid-century and then weaken or even reverse, thus amplifying climate change. For increase in global average temperature exceeding 1.5-2.5 degree centigrade and the concomitant  CO2 concentration, there are projected major changes in the ecosystem structure and function, species ecological interactions and shifts in species’ geographical ranges with predominantly  negative consequences for biodiversity and ecosystem goods and services, e.g. water and food supply. The most predictable impact includes upward migration of several taxa leading to changes in species composition and competition at higher altitudes. Many alpine species in the process of edging uphill in Sikkim, may run over rarer species near mountain summits. Some species would not be able to migrate due to topographic and manmade  barriers . The changed phenology would affect the process of pollination. It has been established that increase in atmospheric temperature by 1°C would cause 100-150 km shift in latitude and as much as 500 km shift in isotherm. Since Himalayan Mountains exhibit a great compression of climatic zones owing to sharp altitudinal gradients, one would expect a shift of 250-300 m in the distribution of species with increase in temperature by 1°C. Increased temperature will have a direct bearing on snowfall, snowmelt and rainfall regimes affecting soil moisture and habitat conditions which would affect species regeneration and establishment.

Modeling Species Distribution in Sikkim

Sikkim Himalayas are one of the unique storehouses of biodiversity including many indigenous, vulnerable and endangered species. Therefore, it becomes all the more important to assess the impact of climate change on biodiversity of Sikkim Himalayas. But before we do that let me give a brief as to how it can be done. Only way to know something about future is modeling. It is the most relevant approach to predicting range shifts under global change, but existing models have only coarse spatio-temporal resolution insufficient for the challenges of adaptive management of climate change impacts in Sikkim. Therefore, site specific and high resolution models will be needed for Sikkim. There are basically two methods by which modeling can be done. One is ‘mechanistically’ and the other is ‘correlatively’.  Mechanistic models aim to incorporate physiologically limiting mechanisms in a species’ tolerance to environmental conditions. Such mechanistic models require detailed understanding of the physiological response of species to environmental factors.  Mechanistic models work on the basis  of assessing parameters needed for plants physiological processes i.e. we would have to find out as to how much ( or what range of) temperature, humidity or precipitation etc. is required for  the optimal growth of the plants. Then if we can model future climatic conditions, we can model plants’ response too. But it is not as easy as it looks, more so in Sikkim, where we have a poor understanding, in quantitative terms, of growth parameters of the plants found in Sikkim.  For example one of the mechanistic equilibrium ecosystem model called biome-BGC simulates the storage and fluxes of water, Carbon and Nitrogen within the vegetation, litter and soil components of the terrestrial ecosystems. This model uses daily time step, driven by daily values for maximum and minimum temperatures, precipitation, solar radiation, air humidity and requires definitions of vegetation, climate and site characteristics. We do not have a comprehensive database of the growth parameters of even the main species found in Sikkim. So the mechanistic modeling is more or less ruled out for the time being.

Empirical methods commonly utilize associations between environmental variables and known species’ occurrence records to identify environmental conditions within which populations can be maintained. Correlative models aim to estimate the environmental conditions that are suitable for a species by associating known species’ occurrence records with suites of environmental variables that can reasonably be expected to affect the species’ physiology and probability of persistence. This is basically a bio-climate envelope modeling.  The central premise of this approach is that the observed distribution of a species provides useful information as to the environmental requirements of that species. For example, we may assume that the plant species ‘bojo’ favors wet soils because it has been observed growing in these soils. There are limitations of this approach but this method can yield valuable biogeographical information.

We are used to thinking about the occurrence of species in geographical space; that is, the species’ distribution as plotted on a map. To understand species’ distribution models it is important to also think about species occurring in environmental space, which is a conceptual space defined by the environmental variables to which the species responds. The concept of environmental space has its foundations in ecological niche theory. The term ‘niche’ has a long and varied history of use in ecology. Fundamental niche of a species can be thought of as the set of environmental conditions within which a species can survive and persist. It may be thought of as an ‘n-dimensional hyper volume’, every point in which corresponds to a state of the environment that would permit the species to exist indefinitely. If the environmental conditions encapsulated within the fundamental niche are plotted in geographical space then we have the potential distribution. The species occurrence records and environmental variables can be entered into an algorithm that aims to identify environmental conditions that are associated with species occurrence. Depending on the method used, various decisions and tests will need to be made at this stage to ensure the algorithm gives optimal results.  Having run the modeling algorithm, a map can be drawn showing the predicted species’ distribution. The ability of the model to predict the known species’ distribution should be tested at this stage. A set of species occurrence records that have not previously been used in the modeling should be used as independent test data. The ability of the model to predict the independent data can be assessed using a suitable test statistic.

Since spatially explicit occurrence records are available for a large number of species, the correlative seems more viable option for studying species’ distribution models in Sikkim. With the recent initiatives of the Department of Forests on getting spatially referenced species data through sample plots in forests of Sikkim, this information has become readily available in easily usable format. Having decided species’ distribution model, it can be used to predict the likely redistribution of species under projected climate change over the coming years. In brief, the general approach would consist of calibrating the models based on current distributions of species in Sikkim and then predicting future distributions of those species across Sikkim for which the environmental input variables have been perturbed to reflect expected changes.

Due to complexity of Sikkim in terms of its topography and vegetation, an integrated modeling would be required which   addresses more localized impacts and also addresses fine-scale processes including species’ dispersal and local connectivity. At increasingly smaller scales, parameters like soil factors or land use become increasingly important. We, fortunately, have a good digital data base in Sikkim in this respect. However, one of the biggest drawbacks we face today in Sikkim is that of a regional high resolution climate model. Does that mean that we sit back and let the problems take over? Definitely not. We must try to develop the models on the basis of whatever meteorological data we have. We can create interpolated climate surfaces even by taking a cue from the meteorological data of nearby stations or develop climate models based on atmospheric processes. Not having sufficient points for modeling should not be an excuse for inaction.