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Models of the spatio-temporal dynamics of the high altitude Alpine fauna in the climate change context

Models of the spatio-temporal dynamics of the high altitude Alpine fauna in the climate change context
Andrea Mignatti


Department of Electronics, Information and Bioengineering, Polytechnic University of Milan, 20133 Milano MI, ITALY.


High altitude Alpine regions are hotspots of biodiversity and are very sensitive to the occurring climate change, displaying a warming rate higher than the global average. The most evident response of Alpine species is an uphill movement towards higher elevations. Summit species are the most vulnerable because they cannot shift over the ridges or the perennial snow. This thesis’s aim is to develop innovative models for the occurred and expected responses of high altitude Alpine fauna to the climate change.

We developed both species distribution models and dynamic demographic models. Species distribution models describe the relationship be- tween environmental variables and the suitability of a territory to host a given species. Temporal dynamics is instead taken into account in demographic models, which show how the abundance of individuals changes in time. In both cases, there is a need for appropriate methods that identify, from data, which environmental and/or climatic variables have an important influence on the spatial distribution and on the demographic parameters of the target species. The identification of the best predictive models has been carried out by using standard selection criteria (e.g. the Akaike Information Criterion). When the model selection was uncertain we relied on multimodel techniques to produce predictions. Namely, we used the Bayesian Model Averaging (BMA) or, alternatively, the multimodel inference based on the Akaike weights. Three high altitude species, which are vulnerable to climate change, have been chosen for our study: Alpine ibex (Capra ibex), Alpine marmot (Marmota marmota) and black grouse (Tetrao tetrix). The choice has also been motivated by data availability.

In the Alpine marmot case study, we investigated the fine scale characteristics that determine the suitability of the habitat for the species in a high altitude Alpine valley near the Stelvio National Park (North-western Italy). Since there were no available data on marmot distribution in the valley, we performed field surveys to locate burrows. Using available data, we developed species distribution models using BMA applied to logistic regression. Results show that the position of marmot burrows is mainly dependent on the vegetation type, thus suggesting that the speed of marmot uphill shift is limited by the colonization dynamics of the vegetation.

As for the black grouse, we studied the influence of spatial position, population density and meteorological conditions on four demographic rates (growth rate and three components of fertility) that characterize the populations of 17 Alpine districts in the Piedmont region (Italy). Our results are mostly consistent with past results obtained for lowland populations. The meteorological variables that have the main influence on the demographic rates are linked with key periods of the black grouse life cycle; namely the breeding season, the hatching period and the winter season (usually characterized by a high mortality). Moreover, we found that direct density dependence is the main driver of population growth rate.

Alpine ibex populations are characterized by a strong age and sex structure, that has never been considered in a population dynamics model. On the other hand, past studies show that the main drivers of population growth rate are the population density and the accumulation of snow during winter, while survival and fertility are not constant with the age, but are typically smaller and more variable for the youngest and the oldest individuals. Using the Gran Paradiso National Park (Italy) population data, we developed models that, alongside with density and snow depth, take into account the age and sex-structure of the population at different levels of complexity. We first separate the population into four subgroups according to the sex and/or the maturation state of the individuals, and we accordingly define four demographic rates: survival of adult males, adult females and kids, and weaning success. The model identification procedure shows that population density and snow depth are still crucial for the separate population groups, and that intraspecific competition occurs mainly among the individuals of the groups that share the same environment for most of the year. Moreover, our results show that weaning success and survival of kids are maximal for intermediate levels of snow depth. We also developed models that take into account the fine age structure of the population, thus allowing the incorporation of senescence. Results show that the inclusion of senescence is particularly important for adult females survival and for the ability of adult females to breed their kids. Moreover, we found that the effect of the population density and the snow depth on survival and fertility increases with the age of the adults.

Overall, our approach permitted to detect and take into account the environmental (climatic, vegetational, etc.) and the population-specific (density, senescence, etc.) characteristics that drive the species distribution and the demography of the case-study species. Making models that take into account the specificity of each species is a key step to understand the expected impacts of climate change on the Alpine biome. However, a lot of work is still needed in this field, in particular to include the interactions be- tween species, which is certainly of paramount importance to explain their spatial and temporal distribution.