Drivers of large‐scale spatial demographic variation in a perennial plant

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.

Life-history predicts global population responses to the weather in terrestrial mammals

With the looming threat of abrupt ecological disruption due to a changing climate, predicting which species are most vulnerable to environmental change is critical. The life-history of a species is an evolved response to its environmental context, and therefore a promising candidate for explaining differences in climate-change responses. However, we need broad empirical assessments from across the world's ecosystems to explore the link between life history and climate-change responses. Here, we use long-term abundance records from 157 species of terrestrial mammals and a two-step Bayesian meta-regression framework to investigate the link between annual weather anomalies, population growth rates, and species-level life history. Overall, we found no directional effect of temperature or precipitation anomalies or variance on annual population growth rates. Furthermore, population responses to weather anomalies were not predicted by phylogenetic covariance, and instead there was more variability in weather responses for populations within a species. Crucially, however, long-lived mammals with smaller litter sizes had smaller absolute population responses to weather anomalies compared with their shorter living counterparts with larger litters. These results highlight the role of species-level life history in driving responses to the environment.

The macroecology of plant populations from local to global scales

Population ecologists develop theoretical and pragmatic knowledge of how and why populations change or remain stable, how life histories evolve and devise management strategies for populations of concern. However, forecasting the effects of global change or recommending management strategies is often urgent, requiring ecologists to work without detailed local evidence while using data and models from outside the focal location or species. Here we explore how the comparative ecology of populations, population macroecology, can be used to develop generalisations within and between species across different scales, using available demographic, environmental, life history, occurrence and trait data. We outline the strengths and weaknesses of using broad climatic variables and suitability inferred from probability of occupancy models to represent environmental variation in comparative analyses. We evaluate the contributions of traits, environment and their interaction as drivers of life history strategy. We propose that insights from life history theory, together with the adaptive capacity of populations and individuals, can inform on 'persist in place' vs 'shift in space' responses to changing conditions. As demographic data accumulate at landscape and regional scales for single species, and throughout plant phylogenies, we will have new opportunities for testing macroecological generalities within and across species.