Telomeres in a spatial context: a tool for understanding ageing pattern variation in wild populations

Ageing refers to the loss of organismal functionality with age, a process that is characterised by decreased reproduction and survival probability. In natural populations, it is expected that environmental conditions influence an individual's ageing trajectory. Understanding the role of environmental heterogeneity on ageing variation could provide critical insights into population resilience and species distribution but remains overlooked. Telomeres, the end cap of chromosomes, are a promising integrative physiological marker of an individual's health and a possible proxy to aid the understanding of variation in ageing trajectories. Here, we review the existing information on telomere length and its dynamics in wild populations distributed across spatial scales. Despite a relative scarcity of information, there is evidence for divergence in telomere length between populations facing contrasting environments. Nonetheless, a higher spatial resolution and temporal replication are needed to fully understand the role that environmental conditions play on telomere length variation. Since most of the existing studies are correlational, future field and laboratory experiments are required. For the first time, we demonstrate the use of population telomere data to predict species habitat suitability through species distribution models (SDMs). This represents a promising new research area in the study of ageing pattern variation in wild populations. Furthermore, the inclusion of telomere data in future physiological SDMs may improve our understanding of species distribution and population resilience. However, the use of telomeres within this context could be limited if no previous knowledge on the relevance of telomeres as markers of health and survival at the species level is available. Finally, we suggest some key practical and theoretical considerations that, ideally, future studies combining biogeographic and telomere data should pay attention.