Can historical names and fishers knowledge help to reconstruct the distribution of fish populations in lakes? (Spens 2007)

Review of focal paper Can historical names and fishers knowledge help to reconstruct the distribution of fish populations in lakes? (Spens 2007) Review of general topic Historical, indigenous and traditional knowledge Examples and expansion Discussion Questions

Can historical names and fishers knowledge help to reconstruct the distribution of fish populations in lakes? Background Information Objectives Hypothesis Methods Results Discussion Conclusion Critical Comments

Aquatic ecosystems threatened Historical dimensions in conservation Brown trout (Salmo trutta) rehabilitation

Spatial Dimensions o Extension with local knowledge o Conventional methods o Landscape- scale studies Historical Dimensions o Interviews and archival records o Maps and place names

Demonstrate value of supplementing aquatic data with other validated sources in studies of past and present fish distribution o Maps and place names o Interviews and archives

The proportion of historical brown trout term lakes (or Rö- lakes) with/without brown trout populations is the same as for other lakes with/without such populations o Null hypothesis

Study Area o Focus on single boreal region in northern Sweden o 1509 lakes with 700,000 ha o Sparsely population o Private Fishery Management Organizations (FMO)

Study Area N F 10 km FIGURE 16.2 Coverage of sixty-three fishery management organizations (FMO:s) within the study area. Shading indicates area boundaries, 10 km scale bar. FIGURE 16.1 The 1,509 lakes within the study area., the northern boreal region of Sweden. Note: Upper box illustrates position of study area within Sweden and Nordic countries. Typesetter 80 km scale bar divided into10 km intervals.

Methods: Interviews (1985 2001) o Face- to- face with fishers and fishing- right owners o Current and historical brown trout populations o Other information sought o Provided information in return

Methods: Maps o Discrete presence / absence records o 1:50,000 Topographic maps with place names o Other map sources o GIS referenced

Methods: Field Studies o Gill- net sampling o Trapping, rod, and electro- fishing o Limnological surveys o Fish habitat surveys and tributary classification

Methods: Other Sources o Field meetings and observations o Archival and historical information o Folklore and more

Methods: Validation o Data confirmation with multiple sources o Interviews and map names o Statistical validation o Further quality control

Results o Brown trout lakes in the region o Introduced, self- sustaining, and non- reproducing o Rö as a traditional term for brown trout in Sweden o Null hypothesis rejected Lill-Rödtjärnen Lill-Rödvattenssjön Lill-Rödvattnet 1 Lill-Rötjern* Lill-Rötjärnen 1 Norra Rötjärn* Rödingtjärnen Rödtjärnarna Rödtjärnarna Rödtjärnen

Results: Temporal Perspective o Conservative historical information o Temporal coverage o Limitations

Results: Temporal Perspective FIGURE 16.4 Scale bar (A.D.) illustrating temporal range of methods to reconstruct brown trout distribution in lakes within the current study. a. Lakes names. b. Palaeontology: Lack of fish fossil evidence makes reconstruction impossible for individual lakes. c. Models are not yet developed for reconstruction of fish fauna. d. Archival data. e. First hand Interviews. f. Field Surveys. Black dotted line = pre-industrial times.

Results: Extinctions (1920-1990) o Permanent extinctions of brown trout o Anthropogenic stressors TABLE 16.3 Factors associated with the extinction of brown trout populations in Rö-named lakes. Anthropogenic Impact Brown trout habitat L a Biological Brook char Spawning area overtaken 5 b Pike introduced Strong predation 3 Chemical Acidification Impossible 2 c Rotenone Impossible 1 d Physical Barrier Impossible 2 b a. Number of extinct brown trout lakes (n = 12) aff ected by specific impact. b. One lake was classified in two categories. c. Permanently acidified ph = 4.7 to 4.9. d. Once impossible, now brown trout has been reintroduced.

Results: Extinctions N 10 km FIGURE 16.5 Rö-named lakes with brown trout populations. Filled circles = brown trout populations present. Crosses = brown trout populations extinct. Typessetter 10 km scale bar. North arrow.

Results: Historical extinction rate (1672 1930) o Natural and anthropogenic stressors o Permanent extinctions

Results: Historical extinction rate (1672 1930) TABLE 16.2 Estimating the maximum (E MAX ) of permanent extinctions 1672-1920, occurring 1672 1920, from lake names from (before lake names the scope (before of the possible scope detection of possible by detection interviews by interviews and historical and documents). historical documents). Self-sustainable brown trout populations Number of lakes Present a 35 Extinct a 12 Possibly extinct or never existed (P) 2 Max. number of Rö brown trout lakes (M) 49 Never existed (impossible habitat) 2 Total number of Rö-lakes 51 Non-Rö brown trout lakes a 115 Total number of brown trout lakes a 162 E MAX (1672-1930) (P/M) (4.2%) (4.2%) 2/49 E MAX (1672-1930) Estimated No. of pop. 7 Max. brown trout lakes (1672-1920) 169 a = Brown trout confirmed 1920 2001.

Discussion: Historical Maps and Names o Value o Applications o Limitations

Discussion: Interviews o Value of face- to- face interviews with fishers o Limitations of data collected via interviews o Applications

Conclusion o Historical sources prove useful o Conservative nature of historical sources o Restoration applications

The Good o Clear structure and flow of content o Comprehensive examination of brown trout o Sound validation of historical sources o Relevant discussion and suggestions

The Bad o Confusing (null) hypothesis provided o Bias in methods applied o Difficult extrapolation to other regions o Limited applicability and impact of study o Long time frame

Many examples in fisheries science literature: Reduce effort needed for large- scale studies (Hesthagen et al. 1993) Past occurrence of species (Wallace 1998) Original landscapes (Sousa and Garcia- Murillo 2001)

Definition: The knowledge and insights acquired through extensive observation of and interaction with an area or a species, including knowledge passed down in an oral tradition, or shared among users of a resource (Huntington 2000). Cumulative body of knowledge, practice, and belief (Berkes et al. 2000) Evolving, culturally transmitted (Berkes et al. 2000) Historical, local, indigenous

Semi- directive interview Questionnaire Analytical workshop Collaborative fieldwork Context- specific considerations: intent and local challenges (Huntington 2000)

Key features of TEK: iterative and adaptive Many valuable uses of TEK in the context of fisheries management Opportunities to augment scientific understanding

Potential value- added: Location- specific knowledge (Poizat & Baran 1997) Species- specific knowledge (Aswani & Hamilton 2004) Folk taxonomy and systematics (Drew 2005) Increased knowledge of environmental linkages (Gadgil et al. 1993)

Value- added continued: Evaluating change (Ellis & West 2005) Baseline data (Menzies 2006) Development of new hypotheses (Pers. Com. Dr. Dee Williams, U.S. DOI Bureau of Ocean Energy Management, 2015)

Value- added continued: Expertise in working with animals (Pers. Com. Dr. Dee Williams, U.S. DOI Bureau of Ocean Energy Management, 2015) Local capacity- building and power sharing (Drew 2005)

Barriers to integration: Not empirical, not systematic (Gadgil et al. 1993) Misconception of what TEK is, and can offer (Huntington 2000) Language (Drew 2005) Culture (Houde 2007) Politics (Butler 2006) Lack of effective methodology (Williams 2012)

Case 1: Integrating Local Knowledge in BC Case 2: Bowhead Whale Population Estimates Canadian Policy Northwest Territories British Columbia

Looked at the Herring Fisheries in BC Interviewed 30 people with different back grounds (Mackinson 2001) 9 fishers 7 fisheries Managers 8 fisheries Scientists 6 aboriginal fishers

Fishers Seine netters and 1 gillnetter (Mackinson 2001) Aboriginal Elders from different bands 290 years of Collective Experience

Interviewed the most experienced individuals Asked calibrating questions to ensure the interviewee s honesty (Mackinson 2001) Asked one question that has a well known answer Asked one question that is nearly impossible to answer All interviewee s found to be honest

Differences between groups (Mackinson 2001) Fishers Good with observations Could not say why things happened Scientist Good with interpreting their results Better at interpreting and assigning importance to features

Used a fuzzy logic expert system (CLUPEX) If..Then statements Infered qualitative and quantitative data No instance where information contradicted other interviews (Mackinson 2001)

Bowhead populations managed by DFO in Canada (DFO 2014) Considered a species at risk in Canada Presently with ~10,200 individuals (DFO 2014)

In 1997 US scientist completed a study in Alaska Counted all whales passing Assumed that: No eating on migration to feeding grounds Used available open water to breath All open water appeared near surveying site Short migration period

Esitmated 600-1200 individuals Delcared the species endangered Informed International Whaling Commission (IWC) of their findings The whale hunt canceled to preserve the populations integrity

Inupiat aboriginal band contested the decision to band whale harvest Claimed poor study design vastly underestimated the population size

Inupiat contested that all of the assumptions Whales eat while migrating Whales can break through ice to breath More open water further north then surveyed Migration much longer then presumed in the study Migration corridor hundreds of Kilometers larger

After court case IWC removed the ban After 14 years of research all claims by the Inupiat were found to be correct

What we learned Using local knowledge can help build a more effective study Traditional knowledge can advance aide science and potentially speed up the process

Canada gives environmental assessor choice to use TEK or not (CEAA 2013) Northwest Territories Considered leader in Traditional Ecological Knowledge policy British Columbia No current policy

Recognize the vast knowledge accumulated by Aboriginal peoples The government recognizes that aboriginal traditional knowledge is a valid and essential source of information about the natural environment and its resources (Northwest Territories Policy 53.03 2005)

Traditional Knowledge Policy Framework Coordinate using Traditional Knowledge Working Group Provide training and awareness, collaborate, promote, support and guidance, resources, and accountability

No official policy Trending towards co- management Has been used in specific cases Herring Fisheries Management (Mackinson 2001) Pacific Region Crab Fishery (DFO 2014) White Sturgeon Fisheries (Eco- trust Canada 2012)

Use of TEK can help develop a more effective scientific study but integration will be a challenge Canadian Policy varies and has room for development

Do you think that evaluating historical map data is a viable method for use in fisheries management in BC? What are the largest barriers to integrating TEK with Western science? Do you think that Canada needs a hard policy on Traditional Ecological Knowledge?

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