Spatial replication should be prioritized to advance our understanding of population responses to climate

Understanding the responses of plant populations dynamics to climatic variability is frustrated by the need for long-term datasets that capture demographic responses to a range of climates. Here, we advocate for new studies that prioritize spatial over temporal replication, but without inferring the effect of temporal climatic gradients from spatial climatic gradients – as usually done in the so called “space-for-time substitutions”. Rather, we advocate to estimate the effects of climate by sampling replicate populations in locations with similar climate. We first use data analysis on spatial locations in the conterminous USA to assess how far apart spatial replicates should be from each other to minimize temporal correlations in climate. We find that spatial locations separated by 250 Km have moderate (0.5) correlations in annual precipitation. Second, we use simulations to demonstrate that spatial replication can lead to substantial gains in the range of climates sampled during a given set of years so long as the climate correlations between the populations are at low to moderate levels. Third, we use simulations to quantify how many spatial replicates would be necessary to achieve the same statistical power of a single-population, long-term data set under different strengths and directions of spatial correlations in climate between spatial replicates. Our results indicate that spatial replication is an untapped opportunity to study the effects of climate on demography and to rapidly fill important knowledge gaps in the field of population ecology.