Methodology

Our methodological framework parameterises local knowledge and scientific data in such a way as to allow a direct comparison, identifies congruencies and differences between local perception and scientific assessment of ecologi-cal items, to then analyse these differences to derive a synthesis between loecologi-cal perception and ecological assessment.

Ecological items which can be used in our methodological framework are a) distinct and roughly equivalent units from a local and a scientific perspective, b) occur in the environment with certain, quantifiable abundances and fre-quencies, in space or time, and c) can be assigned to functional categories.

Our null hypothesis is that local valuation correlates with ecological impor-tance of ecological items. The expectation of a correlation is based on the as-sumption that LEK reflects observable ecological attributes, such as species abundance, that also form the basis for scientific assessment (Mackinson

2001). Observations may be linked to land use activities such as harvesting fruit or fuel wood.

It is important to keep in mind that there are different phases within an inter-disciplinary research process. We recommend that the research questions and main hypotheses are formulated together. A side by side phase of disciplines follows, to gather objective data for each discipline. A fully integrated coop-eration of disciplines follows (Baumgärtner et al. 2008), to assure a synthesis of the different disciplines.

A summary of our framework is presented in Fig. 2.1.

Fig. 2.1: Framework for the integration of LEK into ecological research on ecological items.

2.2.1 Step 1: Collection of ecological knowledge

The first step is the collection of data on the valuation of items in LEK and on ecological performance of the items. When collecting and analysing LEK it is a challenge to account for scientific rigour, because LEK might not lend itself well to mathematical representation (Mackinson 2001). Thus quantitative methods are useful tools. Furthermore LEK should be collected with participa-tory methods to actively include local people in the research process.

Ranking and scoring methods are good tools to parameterise LEK because, rather than answering questions which might be directed by the values of the researcher, local people are encouraged to explore their own versions of their worlds (Pretty 1995). Methods such as free-listing and pile sorting (Borgatti 1994) from Cultural Domain Analysis (CDA) investigate how items are related to each other in people’s minds. The informant is asked to name all the impor-tant items of interest that come to mind. A necessary assumption of this method is that the items mentioned first and most frequently by informants tend to be more salient in their domain of knowledge (Bernard 1994). As a re-sult of the free-listing the items are ranked by local informants. Why a high or a low rank is given can yet not be answered, because the parameter ranking the items is unknown. Thus, LEK is still implicit.

Ecological performance of items can be collected via an ecological assessment, quantified by parameters such as frequency or ground cover. Ecological per-formance can be ranked explicitly by these parameters, e.g. from highest to lowest frequency. Thus two data sets are collected and ranked, the local one where the parameter ranking the items is not known and the ecological one, where the parameter responsible for ranking the items is known. This is impor-tant for the next step, the comparison, because the parameters by which eco-logical performance is ranked must be known to identify the parameter rank-ing the local data set. In addition, interviews and participatory observation concerning the topic of interest are necessary to gain insights into the applica-tion of knowledge, and to interpret the informaapplica-tion provided by the local in-formants (Davis and Wagner 2003; Jones et al. 2008).

2.2.2 Step 2: Comparison of local and ecological salience

a) Salience scores are calculated for the ecological items via salience indices.

This results in two data sets, the local and the ecological salience scores. The salience indices include more information than the ranking, and they transfer the “ecological” and “local” information into salience scores with the same dimension. Salience scores can be plotted as x and y coordinates in a scatter diagram, which can later be used to identify differences and congruencies be-tween the data sets. Keep in mind that the parameter ranking ecological per-formance is still known after the transfer from ranks to salience scores.

We propose three different salience indices: Cognitive Salience Index (CSI) (Sutrop 2001), Smith’s Salience Index (SI) (Borgatti 1999) and the Frequency Index (FI) (Borgatti 1994). The indices differ with respect to the principle pa-rameters they include and in how sensitive they react towards these parame-ters (Schnegg and Lang 2008) (Table 2.1). CSI and SI originate from anthropo-logical research. They determine the salience of an item considering the rank of the item in the free-list of each informant, its frequency in all collected free-lists, and the individual length of the informant’s free-list. This approach can directly be transferred to an ecological data set, if the free-list and the in-formant have equivalents in the ecological research design. If, for example, the analysed items are plant species, the free-list could be equivalent to a species list and the informant could be equivalent to a sample plot. Note that this has to be considered explicitly in the research design, because only if each index parameter has an equivalent in the ecological data set, can a direct comparison be achieved (see case study step 2). FI originates from ecological research, it is based on item frequency only (Table 2.1).

Table 2.1: Salience indices CSI, SI and FI and their sensitivity to input parameters (-- not considered; - not sensitive; + sensitive; ++ very sensitive).

Salience index Source Input parameter

item position in list item frequency in all lists list length

CSI (Sutrop 2001) + - +

SI (Borgatti 1999) + - ++

FI (Borgatti 1994) -- + --

b) The local and the ecological salience scores of the items are compared to identify differences and congruencies between local salience and ecological performance to accept or reject the null hypothesis. The salience scores can be compared visually by placing them in a two-dimensional plot, an importance-performance grid, originally developed for marketing purposes (Martilla and James 1977), or by means of correlation methods. The resulting degree of overlap or correlation is an indication of the relationship between the data sets and thus of how important ecological performance is for the salience of eco-logical items by local people.

The null hypothesis is that a correlation exists. Only if this is rejected the next steps can be applied.

2.2.3 Step 3: Synergy – conceptualisation of functional aspects

To answer the question of why certain items are important in local perception, the data sets must be analysed in further detail. The items need to be charac-terised by adding information of certain functional attributes or features. If the null hypothesis, i.e. that local salience correlates with ecological performance, is rejected, we can assume that the difference between local salience and eco-logical performance of ecoeco-logical items can be explained by other observable (Mackinson 2001) or functional ecological attributes (Walker et al. 2006). If, for example, plant species are these units, they might be classified into plant functional types, according to their habitat preferences, or life forms. For local classification approaches and the assignment of ecological units to scientific classes, further methods from Cultural Domain Analysis, such as pile sorting (Borgatti 1994) can be applied.