An important question in determining species composition in a lake is what species has had the chance to reach the lake in the first place. The aim of this study was to examine natural stream connectivity limitations for whitefish (Coregonus lavaretus) between lakes, and to contribute to the development and evaluation of GIS-based methods to answer this question. 497 lakes with identified downstream source lakes, were classified as naturally colonized or introduced whitefish populations, reflecting the possibilities for whitefish to reach the lake naturally. Data of maximum stream slope and stream length between each lake pair was generated by Stefan Blumentrath, NINA. Maximum stream slope was measured on two different resolutions of stream length for comparison, one on slope over 10 m and one over 150m. A subset of lakes was manually examined in geographic information systems and compared to maps and aerial photos to evaluate the data and compare the results with the full dataset. False slope maximums were corrected and streams with much human alteration around a possible natural slope maximum was removed from the small dataset. The two datasets were analyzed using logistic regression models. Akaikes information criterion (AIC) showed that the optimal model, for both datasets, was the one using only slope maximum as predictor, and slope over 150m gave better results than 10m. A k50-coefficient, the value of a predictor that results in 50% probability of colonization, was introduced as an approximate of when the predictor forms a connectivity hinder. The k50-corefficient was estimated to 2,08±0,22° (±standard error) for slope over 150m for the big dataset and 2,58±0,20 for the small. Weaknesses in the data were distinguished and improvements for future fish connectivity studies are suggested.
| Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:umu-184023 |
| Date | January 2021 |
| Creators | Dynesius, Aron |
| Publisher | Umeå universitet, Institutionen för ekologi, miljö och geovetenskap |
| Source Sets | DiVA Archive at Upsalla University |
| Language | Swedish |
| Detected Language | English |
| Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
| Format | application/pdf |
| Rights | info:eu-repo/semantics/openAccess |
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