Intraspecific trait variation and changing life-history strategies explain host community disease risk along a temperature gradient

Abstract

Predicting how climate change will affect disease risk is complicated by the fact that changing environmental conditions can affect disease through direct and indirect effects. Species with fast-paced life-history strategies often amplify disease, and changing climate can modify life-history composition of communities thereby altering disease risk. However, individuals within a species can also respond to changing conditions with intraspecific trait variation. To test the effect of temperature, as well as inter- and intraspecifc trait variation on community disease risk, we measured foliar disease and specific leaf area (SLA; a proxy for life-history strategy) on more than 2500 host (plant) individuals in 199 communities across a 1101 m elevational gradient in southeastern Switzerland. There was no direct effect of increasing temperature on disease. Instead, increasing temperature favoured species with higher SLA, fast-paced life-history strategies. This effect was balanced by intraspecific variation in SLA: on average, host individuals expressed lower SLA with increasing temperature, and this effect was stronger among species adapted to warmer temperatures and lower latitudes. These results demonstrate how impacts of changing temperature on disease may depend on how temperature combines and interacts with host community structure while indicating that evolutionary constraints can determine how these effects are manifested under global change. This article is part of the theme issue 'Infectious disease ecology and evolution in a changing world'.

Keywords: climate change; disease; elevation; functional traits; intraspecific trait variation.

Figure 1.

Hypothesized pathways through which changing host species composition and intraspecific trait variation (ITV) can mediate changing disease risk across a temperature gradient. (a) High levels of ITV in warm-adapted species offset changes owing to shifting species composition, resulting in similar levels of community functional traits and disease across the gradient. (b) There is no ITV, so only changes in species composition alter disease. (c) High levels of ITV in warm-adapted species amplify changes in community functional traits and disease owing to shifting species composition. The colours represent an individual's placement along a functional trait axis from fast to slow. Beside the colour bar, species are arranged according to their mean trait value and coloured according to the range of traits that each species can express. Black points represent disease severity. (Online version in colour.)

Figure 2.

Relationships between elevation (metres above sea level (m.a.s.l.), mean soil surface temperature and community weighted mean (CWM) specific leaf area (SLA). (a) Relationship between elevation and mean-soil-surface temperature in the CBO. (b) Results from a model testing how CWM SLA, calculated using species mean traits, is influenced by increasing mean soil surface temperature. Lines are model-estimated means and ribbons are model-estimated 95% confidence intervals. Open circles in (a) are the raw data for individual sites. Open circles in (b) are the raw data for individual host communities (i.e. small plots). Soil surface temperature and elevation are collinear (r = −0.95), and CWM SLA calculated using species means increases with increasing soil surface temperature.

Figure 3.

Results from the analysis testing whether increasing temperature consistently affects intraspecific changes in specific leaf area (SLA). The y-axis is a standardized effect size (Fisher's z), with values below zero corresponding to a negative effect of increasing temperature on SLA. Solid points and error bars (a) and solid lines and ribbons (b,c) represent model-estimated means and 95% confidence intervals. Raw data are represented in the electronic supplementary material, figure S3. (a) Results from an intercept-only model. (b) Results from a model exploring lower-elevation limits of a species within the CBO (omitting the non-significant effect of higher-elevation limits). (c) Results from a model exploring northern range limits of a species (omitting the non-significant effect of southern range limits). On average, SLA becomes lower (i.e. leaves become thicker, more well defended) as temperature increases, with that effect being stronger for species able to colonize the lowest elevation meadows and for species with lower-latitude northern range limits.

Figure 4.

Results from models comparing how (a) changing values of specific leaf area (SLA), (b) changing relative abundances of species, and (c) community weighted mean (CWM) SLA calculated using local trait measurements are influenced by increasing temperature. Lines are model-estimated means and ribbons are model-estimated 95% confidence intervals, with colours representing an aggregate of Ellenberg indicator scores for species thermal preference from the Flora Helvetica (alpine = 1, 2 and 2+; subalpine = 3 and 3+; montane = 4 and 4+). Open circles in (c) are the raw data for individual host communities (i.e. small plots). As temperature increases, the average SLA of montane species declines and the relative abundance of alpine species declines, resulting in no net change in CWM SLA across the gradient. (Online version in colour.)

Figure 5.

Results from the models testing how community weighted mean (CWM) specific leaf area (SLA) and soil surface temperature jointly influence community-level disease. (a) Results from a model fitted using species-level means to calculate CWM SLA. (b) Results from a model using local estimates to calculate CWM SLA. Lines represent the model-estimated effect of CWM SLA estimated at one standard deviation above the mean (orange), one standard deviation below the mean (purple), and at the mean temperature (fuchsia). Points represent the raw data coloured by soil surface temperature of the site. Increasing CWM SLA increased disease, but only at high temperature when CWM SLA was calculated using species means (a). By contrast, increasing CWM SLA consistently increased disease when CWM SLA was calculated using local estimates (b). (Online version in colour.)