Lags in the response of mountain plant communities to climate change

Abstract

Rapid climatic changes and increasing human influence at high elevations around the world will have profound impacts on mountain biodiversity. However, forecasts from statistical models (e.g. species distribution models) rarely consider that plant community changes could substantially lag behind climatic changes, hindering our ability to make temporally realistic projections for the coming century. Indeed, the magnitudes of lags, and the relative importance of the different factors giving rise to them, remain poorly understood. We review evidence for three types of lag: "dispersal lags" affecting plant species' spread along elevational gradients, "establishment lags" following their arrival in recipient communities, and "extinction lags" of resident species. Variation in lags is explained by variation among species in physiological and demographic responses, by effects of altered biotic interactions, and by aspects of the physical environment. Of these, altered biotic interactions could contribute substantially to establishment and extinction lags, yet impacts of biotic interactions on range dynamics are poorly understood. We develop a mechanistic community model to illustrate how species turnover in future communities might lag behind simple expectations based on species' range shifts with unlimited dispersal. The model shows a combined contribution of altered biotic interactions and dispersal lags to plant community turnover along an elevational gradient following climate warming. Our review and simulation support the view that accounting for disequilibrium range dynamics will be essential for realistic forecasts of patterns of biodiversity under climate change, with implications for the conservation of mountain species and the ecosystem functions they provide.

Keywords: alpine ecosystems; biotic interactions; climate change; climatic debt; migration; novel interactions; range dynamics; range expansion.

Figure 1

The magnitude and pace of alpine plant community turnover with climate change will be influenced by rates of species dispersal from lower elevations (“dispersal lags”), their rates of establishment and population growth in alpine communities (“establishment lags”) and extinction rates of resident alpine species (“extinction lags”). We include factors highlighted by our review that will increase (pointed arrows) or decrease (flat arrows) the magnitude of lags (factors related to focal species physiology and demography, biotic interactions, and the physical environment coloured orange, green and blue, respectively, in the online version).

Figure 2

Range predictions for a focal alpine species (species A) following climate change. Species A shows a trade-off between population growth rate and tolerance of low temperature, such that it can tolerate a greater range of temperature than species B, but is outcompeted by species B under warmer climatic conditions (i.e. it has a broader fundamental niche than species B, but a realized niche restricted to cooler temperatures; panel a). Following climate warming, a simple species distribution modelling (SDM) approach for species A, that assumes realized niche conservatism, would predict high elevation range expansion and low elevation range contraction (panel b). However, if species B is constrained by a dispersal lag, as shown, then the prediction of local extinction at the lower elevation range margin for species A would be incorrect. Instead species A would persist at its current lower elevation range margin, expanding its realized niche, due to the absence of competition from species B (panel c). This outcome would be predicted by process-based models that account independently for effects of climate and competition on population growth rates.

Figure 3

Temporal community turnover along an elevational temperature gradient (β-diversity, represented by the gradient from white to red, see legend in panel d) following climate warming. The left section of the mountains represents the results of simulations (“Simul.”), while the right section represents the expected turnover obtained by simply stacking species distribution model projections (“SDM”). Shown are eight scenarios that differ depending on dispersal ability (rows) and growth rate within the species pool (a, e: coefficient of variation [CV] = 0.1; b, f: CV = 1/√3; c, g: mean = 0.2; d, h: mean = 0.5; a-d: d = 10^-1; e-h: d = 10^-4). In each panel, all other parameters except the ones specified in the header of their line and column were set to the average value of their respective distribution (see Table S1). The lowest elevation communities are not displayed (see Appendix S1). Communities at the highest elevations resulting from colonization of previously unoccupied habitat are coloured in blue, while communities that remain empty despite warming are in light grey. Note that outcomes of temporal community turnover obtained from stacking SDM projections can also differ slightly among panels due to different initial conditions (i.e. initial burn-in period that allowed the meta-communities to equilibrate).

Figure 4

Temporal community turnover along an elevational temperature gradient (β-diversity, represented by the gradient from white to red, see legend in panel d) following climate warming. The left section of the mountains represents the results of simulations (“Simul.”), while the right section represents the expected turnover obtained by stacking species distribution models projection (“SDM”). Shown are eight scenarios that differ depending on dispersal ability (rows) and sensitivity to competition within the species pool (a, e: coefficient of variation [CV] = 0.1; b, f: CV = 1/√3; c, g: mean = 0.7; d, h: mean = 1.5; a-d: d = 10^-1; e-h: d = 10^-4). In each panel, all other parameters except the ones specified in the header of their line and column were set to the average value of their respective distribution (see Table S1). The lowest elevation communities are not displayed (see Appendix S1). Communities at the highest elevations resulting from colonization of previously unoccupied habitat are coloured in blue, while communities that remain empty despite warming are in light grey. Note that outcomes of temporal community turnover obtained from stacking SDM projections can also differ slightly among panels due to different initial conditions (i.e. initial burn-in period that allowed the meta-communities to equilibrate).