Spatially explicit modeling and prediction of shrub cover increase near Umiujaq, Nunavik

Abstract

AbstractA circumpolar increase in shrub growth and cover has been underway in Arctic and subarctic ecosystems for the last few decades, but there is considerable spatial heterogeneity in this shrubification process. Although topography, hydrology, and edaphic factors are known to influence shrubification patterns, a better understanding of the landscape‐scale factors driving this phenomenon is needed to accurately predict its impacts on ecosystem function. In this study, we generated land cover change models in order to identify variables driving shrub cover increase near Umiujaq (Québec, Canada). Using land cover maps from 1990/1994 and 2010, we modeled observed changes using two contrasting conceptual approaches: binomial modeling of transitions to shrub dominance and multinomial modeling of all land cover transitions. Models were used to generate spatially explicit predictions of transition to shrub dominance in the near future as well as long‐term predictions of the abundance of different land cover types. Model predictions were validated using both field data and current Landsat‐derived trends of normalized difference vegetation index (NDVI) increase in the region in order to assess their consistency with observed patterns of change. We found that both variables related to topography and to vegetation were useful in modeling land cover changes occurring near Umiujaq. Shrubs tended to preferentially colonize low‐elevation areas and moderate slopes, while their cover was more likely to increase in the vicinity of existing shrub patches. Deterministic realizations of the spatially explicit models of land cover change had a good predictive capability, although they performed better at predicting the proportion of different cover types than at predicting the precise location of the changes. Binomial models performed as well as multinomial models, indicating that neglecting land cover changes other than shrubification does not result in decreased prediction accuracy. The predicted probabilities of shrub increase in the region were consistent with patterns of change inferred from field data, but only partly supported by recent local increases in NDVI. Our findings increase the current understanding of the factors driving shrubification, while warranting further research on its impacts on ecosystem function and on the link between land cover changes and shifts in remotely sensed vegetation indices.