Wed, Aug 04, 2021:On Demand
Background/Question/Methods
Understanding how climate drivers influence vital rates and population dynamics of plants and animals is critical to predicting the vulnerability of species to future climate changes. Population responses to climate will depend on: the strength of the climate driver effects on vital rates, climate variability, climate temporal auto-correlation, the time window linking climate drivers to vital rates, and the correlation between vital rates. Recent evidence shows that the response of plant vital rates to climate might often be lagged by one or more years. These lagged effects of climate often affect only some vital rates, while others respond to recent climate drivers. We addressed how climate drivers with such diverse temporal windows can affect long-term population growth rate. In principle, the combination of auto-correlated climatic variables, and vital rates responding to different temporal climate windows could decrease positive vital rate correlations, or even impose negative correlations, subsequently buffering long-term population growth. We tested this hypothesis by estimating the effects of lagged vital rates, vital rate correlations, and temporal auto-correlation in climate on stochastic population growth rates through simulation of matrix population models. We then used data from existing population models to link these results to real empirical systems.
Results/Conclusions Our simulations show that in an environment without auto-correlation populations with vital rates that respond similarly to climate (i.e., in the same direction) benefit from responding to both recent and lagged climate. Such a response to climate in different time windows decreases, or removes the positive correlation between the vital rates, and increases the stochastic population growth rate. When we include climate auto-correlation, we see that the same population, but with a highly negatively auto-correlated climate (-0.9) have an even larger advantage. On the other hand, this advantage almost disappears in simulations with a highly positively auto-correlated climate (0.9). We found similar results from simulation based on matrix populations models retrieved from the COMPADRE Plant Matrix Database. We conclude that responding to climate in different time frames can function as a buffering mechanism against increasing climate variability, such as predicted in many areas due to climate change. As a result, not considering such diverse climatic responses can lead to underestimation of population growth rates.
Results/Conclusions Our simulations show that in an environment without auto-correlation populations with vital rates that respond similarly to climate (i.e., in the same direction) benefit from responding to both recent and lagged climate. Such a response to climate in different time windows decreases, or removes the positive correlation between the vital rates, and increases the stochastic population growth rate. When we include climate auto-correlation, we see that the same population, but with a highly negatively auto-correlated climate (-0.9) have an even larger advantage. On the other hand, this advantage almost disappears in simulations with a highly positively auto-correlated climate (0.9). We found similar results from simulation based on matrix populations models retrieved from the COMPADRE Plant Matrix Database. We conclude that responding to climate in different time frames can function as a buffering mechanism against increasing climate variability, such as predicted in many areas due to climate change. As a result, not considering such diverse climatic responses can lead to underestimation of population growth rates.