Fig 1.
Non-linear effects of climate on population growth rate.
Population growth rate is in general a non-linear function of climate (solid black lines), either: A. accelerating; or B. decelerating. λC and λF are the current and future growth rates at the current and future values of a climate variable; CC represents current and CF represents future climate. Blue lines show the slope of the population growth rate vs. climate evaluated at the current climate (i.e., “climate sensitivity”). The slope of the red line (“climate responsiveness”) is the ratio of the actual change in population growth Δλ to the actual change in climate ΔC (CF -CC). λL is the linear projection of the future population growth rate using the sensitivity, which underestimates λF when population growth is an accelerating function of climate but overestimates λF when population growth is a decelerating function.
Fig 2.
Range of Douglasia alaskana and location of study.
Map of southcentral Alaska in grey, with the approximate range of D. alaskana indicated by the black polygon [17]. Populations used in this work are indicated by dots, and the labels of populations correspond to those of other figures. The E2 and E1 populations are offset from one another to allow readability.
Fig 3.
Effects of climate on vital rates.
Vital rate relationships with climate for probability of survival (A), mean size after one year of growth (B), probability of fruiting (C), and number of fruits given fruiting (D) for a plant of median size (or for C, median size of fruiting plant). For A, we show how the impact of precipitation in the coldest month changes with coldest month conditions in the year prior. For prior year conditions, we use the observed temperature and precipitation for the coldest, wettest, and warmest/ driest populations (coldest: -9° C, 56.0 mm, wettest: -7° C, 158.2 mm, warmest/ driest: 1° C, 54.4 mm). For B and C, we show how the impact of temperature varies with the associated precipitation during the same interval, using values of precipitation from the driest or wettest populations: B driest: 662.8 mm, B wettest: 1431 mm, C driest: 43.2 mm, C wettest: 180.8 mm. See Table 1 for all parameter estimates, and S1 Fig in S1 Appendix for raw data.
Table 1.
Vital rate functions.
Fig 4.
Change (mean future, 2086–2100, minus mean current, 2008–2022) in precipitation (A) and temperature (B) conditions for five populations for each climate driver present in the vital rate functions. Populations are arranged along the x-axis by increasing current average annual temperature; for example, the E1 population has the coldest average annual temperature over the current period, and the S population has the warmest average annual temperature over this period. See S1 Table in S1 Appendix for raw data.
Fig 5.
Change in population growth rate due to climate change.
Impact of climate change on population growth rate for all populations, expressed as the difference between future (2086–2100) and current (2008–2022) population growth rates. Points indicate the mean difference and error bars indicate 95% confidence intervals of differences (calculated across 500 bootstrapped samples from the distributions of model coefficients). As in other figures, populations are arranged by increasing current average annual temperature. See S2 Fig in S1 Appendix for population growth rates, and S3 Fig in S1 Appendix for GCM-specific results.
Fig 6.
Sensitivity to climate variables at current (2008–2022) climate conditions.
Sensitivities were calculated using a perturbation approach using kernels from the current time period. We show the mean sensitivity across 500 bootstrap replicates; bars indicate the 95% confidence intervals on sensitivities. As in other figures, populations are arranged by increasing current average annual temperature.
Fig 7.
Nonlinear responses of D. alaskana to climate.
Response of annual population growth rates (λ) to changes in aspects of temperature and precipitation that affect vital rates. Unfilled points represent medians (across bootstrapped regression coefficients) of the annual population growth rates vs. the current and future GCM climate values for each year from 2008 to 2099; curves represent predictions of population-specific GAMs with non-focal climate variables held at their mean values. Arrows on the top of the figures represent the magnitude of climate change at each population; start of the arrow is at the mean climate condition for the current period (2008–2022) and end of the arrow is at the mean climate condition for the future period (2086–2100). Filled points represent population growth rates predicted by the GAM at current and future mean climates, with an asterisk indicating future population growth rate predicted by an LTRE approach.
Fig 8.
Δλc, the effect of each climate variable on the change in population growth rate.
We show Δλc, calculated for each climate variable changing current (2008–2022) climate variables to future (2084–2099) climate variables, one climate variable at a time. Bars indicate the mean Δλc and error bars indicate the 95% confidence intervals of Δλcs, calculated across bootstrapped parameter estimates. Horizontal lines indicate the mean Δλ, future- current population growth rate, and dotted lines indicate the 95% confidence intervals of Δλ, calculated across bootstrapped parameter estimates. In both panels, populations are arranged in the same order as in prior figures (increasing current average annual temperature).
Table 2.
Role of climate responsiveness v. rate of climate change.