ENVIRONMENTAL FACTORS CORRELATED WITH CHRYSOPHYTE CYST ASSEMBLAGES IN LOW ARCTIC LAKES OF SOUTHWEST GREENLAND1

Abstract

We analyzed the relationship between chrysophyte cyst assemblages in surface sediment samples and limnological and geographical variables for 70 lakes located along Søndre Strømfjord in southwest Greenland. Over 247 stomatocysts were identified and of these, 153 were sufficiently abundant for use in statistical analyses. Eight stomatocysts were considered to be new and are described formally. Canonical correspondence analysis indicated that conductivity was the dominant variable explaining cyst distribution, reflecting the large conductivity gradient in lake water chemistry in this area. High conductivity lakes had distinctive cyst assemblages with lower diversity than low alkalinity lakes, where assemblages were similar to alpine soft‐water lakes elsewhere. The high conductivity lakes, however, had similar cysts to other saline lakes elsewhere in the arctic. Additionally, pH, calcium, maximum depth, longitude, sulfate, total phosphorus, and altitude all explained significant amounts of variability of cyst assemblages. Longitude was the only geographical variable that explained cyst variability independently of other variables (i.e. had a unique effect), which suggests that the climatic gradient from the coast to the head of the fjord has a structuring effect on cyst assemblages. Conductivity (weighted‐averaging partial least squares, r2=0.917; root mean square error=0.142; r2jack=0.861, root mean square error of prediction=0.191) and pH inference models (weighted averaging, r2=0.924; root mean square error=0.158; r2jack=0.826, root mean square error of prediction=0.240) were developed. For the pH model, high conductivity lakes (>800 μS20·cm−1) were removed. Both models are statistically robust and could be applied to lakes in west Greenland to reconstruct conductivity and/or pH. Such paleolimnological reconstructions provide the means of acquiring long‐term data for use in the evaluation of, for example, regional paleoclimatic models.