Population Ecology from Local to Global Scales: a case study using Plantago lanceolata

Abstract

This thesis investigated approaches to infer on demographic processes across varying scales and environmental gradients, pulling information from multiple sources. I used the model organism Plantago lanceolata. This perennial herb is native to Eurasia, but currently near-globally distributed. The PlantPopNet network monitors its demography in over 60 populations worldwide. In addition, I monitored 18 populations along a steep environmental gradient leading to a niche limit in the Alps. I investigated whether the relationship between demography and plant size remains the same across different parts of the range of P. lanceolata. I established that simple size metrics such as leaf number captured patterns in vital rates similarly across populations, climatic gradients and both the native and invasive range of the species. I studied the relationship between the probability of occupancy of the species and its demography using Species Distribution Models (SDM). I found a faster pace of life with increasing probability of occupancy, in both the regional study in the Alps and across the native range of the species: the generation length and reproductive life expectancy decreased, but the yearly reproductive rate increased. In the Alps, these life history changes were shown to be mostly caused by a lower probability of survival in high [robability of occupancy areas. Abundance decreased with increasing probability of occupancy. I investigated the use of functional traits to predict the demographic performance along the alpine gradient. I found that leaf traits were correlated with survival in strong interaction with the climate. High SLA and Leaf Area values were correlated with a lower probability of survival in harsh conditions, but not in high probability of occupancy areas. These results pave the way for investigation of the changes in demographic strategies across scales, and suggest methods to pull information from functional traits and occurrence information to infer on population processes.