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Sámi traditional ecological knowledge as a guide to science: snow, ice and reindeer pasture facing climate change

Published online by Cambridge University Press:  23 December 2010

Jan Åge Riseth
Affiliation:
Norut Tromsø and Sámi University College, P.O. Box 250, N-8504 Narvik, Norway (janar@norut.no)
Hans Tømmervik
Affiliation:
Norwegian Institute for Nature Research, N-9296 Tromsø, Norway
Elina Helander-Renvall
Affiliation:
Arctic Indigenous Peoples and Sámi Research Office, Arctic Centre, University of Lapland, FIN-96101 Rovaniemi, Finland
Niklas Labba
Affiliation:
Moskavuona Sámi Language and Culture Centre, N-9042 Laksvatn, Norway
Cecilia Johansson
Affiliation:
Department of Earth Sciences, Uppsala University, S-75236 Uppsala, Sweden
Eirik Malnes
Affiliation:
Norut Northern Research Institute, N-9294 Tromsø, Norway
Jarle W. Bjerke
Affiliation:
Norwegian Institute for Nature Research, N-9296 Tromsø, Norway
Christer Jonsson
Affiliation:
Abisko Scientific Research Station, S-98107 Abisko, Sweden
Veijo Pohjola
Affiliation:
Department of Earth Sciences, Uppsala University, S-75236 Uppsala, Sweden
Lars-Erik Sarri
Affiliation:
Esrange, Swedish Space Corporation, S-98128 Kiruna, Sweden
Audhild Schanche
Affiliation:
Sámi Parliament, N-9730 Karasjok, Norway
Terry V. Callaghan
Affiliation:
Abisko Scientific Research Station, S-98107 Abisko, Sweden and Department of Animal and Plant Sciences, University of Sheffield, S10 2TN
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Abstract

Scientific studies of challenges of climate change could be improved by including other sources of knowledge, such as traditional ecological knowledge (TEK), in this case relating to the Sámi. This study focuses on local variations in snow and ice conditions, effects of the first durable snow, and long term changes in snow and ice conditions as pre-requisites for understanding potential future changes. Firstly, we characterised snow types and profiles based on Sámi categories and measured their density and hardness. Regression analysis showed that density can explain much of the variation in hardness, while snow depth was not significantly correlated with hardness. Secondly, we found that whether it is dry/cold or warm/wet around the fall of the first durable snow is, according to Sámi reindeer herders, crucial information for forecasting winter grazing conditions, but this has had limited focus within science. Thirdly, elderly herders’ observations of changes in snow and ice conditions by ‘reading nature’ can aid reinterpretation of meteorological data by introducing researchers to alternative perspectives. In conclusion we found remarkable agreement between scientific measurements and Sámi terminology. We also learnt that TEK/science cooperation has much potential for climate change studies, though time and resources are needed to bridge the gap between knowledge systems. In particular, TEK attention to shifts in nature can be a useful guide for science.

Information

Type
Research Article
Copyright
Copyright © Cambridge University Press 2010
Figure 0

Fig. 1. Study area.

Figure 1

Table 1. Sámi snow categories used in our fieldwork and their relevance for reindeer herding. Main informants: Nils Tomas Labba, Gustav Labba (Saarivuoma). Erik Anders Niia, Håkan Kuhmunen (Gabna).

Figure 2

Fig. 2. Uniform snow types (Sámi snow categories). Relationship between the density (g/dm3) and the Integrated Ram Hardness (IRH) (kgcm). This relationship was almost significant (R2 = 0.47, P = 0.06). Values are means +/- SE.

Figure 3

Fig. 3. Stratified snow profiles (Sámi snow categories). Relationship between the density (g/dm3) and Integrated Ram Hardness (IRH) kgcm). Values are means +/- SE. This relationship was found to be significant (IRH: R2 = 0.85, P = 0.001).

Figure 4

Fig. 4. Comparison of snow profiles. Grazed (left) and ungrazed (right).

The upper layers of profiles 1 and 2 are induced by climatic events like wind and heavy snow falls (development of ceavvi) and ‘rain-on-snow’ events leading to the development of gaskageardni, so the greater snow depth at the grazed profile is not a response to grazing. The lower layers of profile 1 are compacted and icy snow (čiegargovvi and jiekᵑa) with high densities (>450g/dm3) caused by grazing compared with ungrazed profile 2, which is dominated by looser snow (seaᵑaš) with lower densities (<350g/dm3).Snow profile 1 (Grazed: Suovdnji) Layer 1 (Top layer): Vahca (150–250g/dm3) Layer 2 (black): Gaskageardni (Ice crust layer, density >500g/dm3) Layer 3: Ceavvi Layer 4 (black): Gaskageardni (Ice crust layer, density >500g/dm3) Layer 5: Ceavvi Layer 6 (black): Gaskageardni (Ice crust layer, density >600g/dm3) Layer 7: čiegargovvi (Completely transformed to icy snow) Layer 8: Jiekᵑa; Ice on the ground.Snow profile 2: (Not grazed) Layer 1 (Top layer): Vahca (150–250g/dm3) Layer 2 (black): Gaskageardni (Ice crust layer, density >500g/dm3) Layer 3: Ceavvi. Layer 4 (black): Gaskageardni (Ice crust layer, density >500g/dm3) Layer 5: Seaᵑaš.
Figure 5

Table 2. Comparison of observations made by elderly and middle-aged Sámi herders from Saarivuoma (Nils Tomas, Gustav, Laila and Agneta Labba, Bror Labba) and Gabna (Erik Anders Niia and Håkan Kuhmunen) together with meteorological data collected at the Abisko Scientific Research Station (latitude 68.35° N, longitude 18.82° E), supplemented by other scientific observations.