A common approach to investigating species' niches is to examine relationships between spatial variation in environmental conditions and contemporary species occurrences, using species distribution models (SDM or niche models). The relationships between past species distributions and environmental variation over time are less commonly explored. One way to examine effects on species changes over time is to use paleo-datasets to parameterize niche models, where the use of temporal variation allows for making more direct links between past species and environmental conditions through records of past changes. We examined the impact of five environmental variables (temperature, incidence of external nutrient input, local [within bog] moisture, incidence of regionally dry periods, and fire activity) on temporal variation in peatland species composition, occurrences, and abundances (Sphagnum, Eriophorum, Carex, and Ericaceous dwarf shrubs) using a high-resolution peat macrofossil paleo-record spanning the last ~10,000 years from the Store Mosse bog (south-central Sweden). Our results showed that species composition was affected by external nutrient input, local moisture conditions and incidence of regionally dry conditions. The presence and abundance of different species groups were mainly affected by external nutrient input and the incidence of regionally dry periods. Moreover, hummock Sphagna benefited from external nutrient input and low moisture, and in one species, warmer temperatures. Intermediate Sphagna from cooler temperatures with no external nutrient input, and hollow Sphagna from cooler temperatures and external nutrient input. Lastly, our results showed that environmental effects differed between the successional stages of the peatland in one case. Overall, the observed species' responses imply that peatland carbon dynamics will shift with future changes in climate. By examining links between climate and species responses of the past, this study demonstrates that the paleo-data approach in SDMs can contribute to a better understanding of the environmental effects influencing species distributions on longer time scales, thereby providing a valuable tool to improve predictions of future climate change effects.