Predicting the future distributions of species under climate change requires an understanding of the factors that determine the dimensions of the species’ ecological niche. From a demographic perspective, the ecological niche is the set of conditions under which a species has a positive population growth rate. Most models of species distributions assume climate is the primary determinant of the ecological niche and therefore range limits, but biotic factors, such as competition, can also be important determinants of range limits. We used Forest Inventory and Analysis data to test for the effects of both climate and competition on the vital rates of Pinus edulis (common pinyon pine). We then used integral projection models to estimate population growth rates and build demographic range models to project the distribution of the species.
Results/Conclusions
Precipitation had a positive effect on the survival, growth, and reproduction of P. edulis. Temperature had a negative effect on survival and growth, but only a weak effect on reproduction. Competition from trees had a negative effect of growth and recruitment, but only a weak negative effect on survival. Distribution maps based on projected population growth rates generally predicted the lower-elevation limit for P. edulis, except in regions with high precipitation, but they failed to predict the upper-elevation limit. This suggests that other factors, such as competition from grasses at low elevations with high precipitation and disturbance (i.e., fire) at high elevations, are also important for determining the distribution of P. edulis. Mismatches between model predictions and observed distributions could also be caused by limited data on the regeneration stage of the life cycle and the difficulty of accurately estimating vital rates at the edges of the distribution and outside the range. Inverse modeling is a potential solution for addressing these challenges. For many species, incorporating disturbance processes and biotic interactions, in addition to climate effects, may be necessary for predicting distributions under future climate change.