High-resolution data are necessary to understand the effects of climate on plant population dynamics of a forest herb.

Climate is assumed to strongly influence species distribution and abundance. Although the performance of many organisms is influenced by the climate in their immediate proximity, the climate data used to model their distributions often have a coarse spatial resolution. This is problematic because the local climate experienced by individuals might deviate substantially from the regional average. This problem is likely to be particularly important for sessile organisms like plants and in environments where small-scale variation in climate is large. To quantify the effect of local temperature on vital rates and population growth rates, we used temperature values measured at the local scale (in situ logger measures) and integral projection models with demographic data from 37 populations of the forest herb Lathyrus vernus across a wide latitudinal gradient in Sweden. To assess how the spatial resolution of temperature data influences assessments of climate effects, we compared effects from models using local data with models using regionally aggregated temperature data at several spatial resolutions (≥1 km). Using local temperature data, we found that spring frost reduced the asymptotic population growth rate in the first of two annual transitions and influenced survival in both transitions. Only one of the four regional estimates showed a similar negative effect of spring frost on population growth rate. Our results for a perennial forest herb show that analyses using regionally aggregated data often fail to identify the effects of climate on population dynamics. This emphasizes the importance of using organism-relevant estimates of climate when examining effects on individual performance and population dynamics, as well as when modeling species distributions. For sessile organisms that experience the environment over small spatial scales, this will require climate data at high spatial resolutions.