Study area
Ixtlan is located inside the Juarez ridge "Sierra de Juárez" in central Oaxaca, Mexico (Figure 2). For a complete description of its location, territory, climate and vegetation see Valdés et al. [23] and Garibay-Orijel et al. [24]. In general, it has temperate climate, and is located inside a wide and preserved coniferous woodland (Figure 3). In 2002 the village had approximately 2201 habitants with Zapotec origin but today just 50% speak their original language [25].
Zapotecs are an ancient culture that adopted Mayan Quiche cultural elements [26]. Monte Alban (650 – 900 B.C.) was its most representative pre-Columbian city, and after its abandonment their cultural unity became lost and fragmented [27]. Zapotec belongs to the otomague linguistic group, also containing mixtec, otomi, chinantec and mague. Nowadays they are widely distributed in Oaxaca, mainly in the Oaxaca valley, the Tehuantepec isthmus, the Juarez ridge, the Villa Alta district, Yalalag, and Miahuatlan ridge [27].
The economy in the Juarez ridge is based on agriculture, silviculture and cattling with some coffee and fruit plantations. Forest resources are very important to the region, with almost 40% of regional production based on them. In general, regional development is scarce, and health and education services are lacking [28]. Ixtlan is one of the most developed communities where approximately 60.43% of adult people are dedicated to primary activities (forest, agriculture and cattling); 9.02% work in schools; 9.02% in health and government offices; 12.72% in industry and 18.04% in services [25].
Ethnomycological work
Since 2000 we have conducted an exhaustive recompilation of local traditional mycological knowledge. The taxonomy and nomenclature of folk taxa used in this paper are those documented in Garibay-Orijel et al. [24]. In May 2003, we applied 95 questionnaires to a random sample of informants. All informants were twenty years or older and they all lived at least for the last five years in Ixtlan. Fifty-one respondents were female and 44 male. Thirty-nine were between 20 and 39 years, 32 between 40 and 59 years, and 24 were 60 years old or more. Fifty-one were service employees, 18 were service employees and peasants, 12 were peasants, and 14 were forest employees. The questionnaire includes a free list and one question for every CS variable (sub index).
To obtain the free list [29], we asked informants to give us a list of every edible mushroom that they knew. We reviewed the correct taxonomical identity of every folk name given by each informant using high-resolution photographs (1200 dpi, 21.5 cm × 28 cm) as described by Garibay-Orijel et al. [24].
Edible mushrooms cultural significance index
To develop the Edible Mushrooms Cultural Significance Index (EMCSI) we modified Pieroni's [21] model that includes seven cultural variables influencing CS: frequency of mention, perceived availability, frequency of use, taste score appreciation, plant parts used, multifunctional food use, and food-medicinal role. For EMCSI, we included from Pieroni's model the Mention Index (QI), Perceived Abundance Index (PAI), Frequency of Use Index (FUI), Taste Score Appreciation Index (TSAI) and Multifunctional Food Index (MFFI). Details of these variables can be found in Pieroni [21].
We eliminated Pieroni's Part Used Index because in plants, the roots, stem, leaves, flowers and fruits can be eaten alone or combined [12]. In Ixtlan in contrast, mushrooms are eaten as a whole and even if the stipe or cuticle are removed, there are hardly any cultural implications. It is important to mention that maybe in other places or cultures this variable could be useful and meaningful in terms of CS.
We eliminated also the Food-Medicinal Role Index because although in Mexico approximately 30 mushrooms (including lichens) are used with medicinal purposes [30], the food-medicine concept is not applied with them. This is, no mushrooms are consumed as nourishments and medicines at the same time. This contrasts with Asia (Korea, Japan, China) where this is quite common and almost 300 fungal species are used as medicines [31]. Instead of it we used the Health Index (HI). A very relevant factor influencing the CS of edible mushrooms is the possibility of becoming ill or dying after their consumption. Although plant toxicity is common too, people are always conscious that a mistake in mushroom identification could be fatal. HI evaluate where a species was placed by informants in the range between those species that are mislead because their toxicity or its similarity with toxic ones, and those that are eaten for health reasons.
In a general sense, "Culture" is defined as a socially patterned human thought and behavior with the properties of been shared, symbolic, integrated, learned, transmitted cross-generationally and adaptative [32]. From these characteristics, the last three are reflected in the appearance, permanence or extinction of resources uses; a matter not normally been part of CS evaluations. To assess this, we included the Knowledge Transmission Index (KTI).
Wild edible mushrooms are collected in more than 80 countries around the word; its sells estimated value is approximately $2 billion dollars a year. In rural areas, particularly in non-developed countries, the incomes due to mushrooms selling complete the economy of poor families [33]. For that reason, it could be expected that monetary value of mushrooms could affect substantially its CS in places where there are commercialized; we evaluated this with the Economic Index (EI).
Calculation of each variable and final EMCSI computer
The final value of the Pieroni's index (CFSI) is the product its variables. Mathematical considerations in CFSI are: possible extreme values for each sub index are different (different scale); the possibility of zero values in must sub indexes is omitted, thus some characteristics may be overrated and the information of no CS is lost; the weight in the total calculation of each sub index is different.
To compute the compound index (EMCSI), we first categorized informants' responses to the questionnaire. Data for each variable were obtained as follows:
QI = (N°mentions/N°informants) 10.
PAI, informants rank the species perceived abundance based on a graphic stimulus that shows five possibilities on a logarithmic scale (Figure 4).
FUI, informants answer the options question: How often do you eat spi?
TSAI, informants answer the rank question: How much do you like spi? To avoid the subjectivity of each informant, we used graphic stimuli to categorize their answers (Figure 5).
MFFI, informants answer the open question: How do you cook spi?
KTI, we asked our informants how many generations were involved in the knowledge of certain mushroom. If it was a new use, we asked from whom they had learned it.
HI, informants answer the options question: How safe is to eat spi, and, can its consumption be harmful? It is important to notice that the difference between HI and Pieroni's Food-Medicinal Role Index was done because our scale ranges from toxic to healthy foods and his scale ranges from healthy to medicinal foods.
EI, informants answered the option questions: Have you sold/bought spi and at what price?
In EMCSI, all variables are based on a 0 to 10 scale and all indexes have the same weight. PAI, FUI, TSAI, MFFI, KTI, HI and EI are the average of all informants reporting a particular species. The relative value of mentions QI was used to amplify differences and to estimate the CS of species on the whole sample.
Table 1 shows the categorization of the possible responses to the open questions, the alternatives to the choice options, and the values in every variable for each answer.
The formula for the index was: EMCSI = (PAI+FUI+TSAI+MFFI+KTI+HI+EI)QI.
To clarify the procedures, in Table 2 we provide an example of a hypothetical questionnaire of one species for three interviewees, the categorization of answers and the compute process.
Analysis
As argued by Pieroni [21], indexes of CS could carry out more complex and comparative schemes when coupled with multivariate statistics; so in order to analyze relationships between species and sub indexes we developed a set of grouping and ordination techniques. First, with the species-by-sub index matrix, we calculated the Euclidean distances between species. Then we searched for groups of species with the complete linkage amalgamation rule. Second, to identify groups of species based on their similarity [34], we ran a multi-dimensional scaling analysis (MDS) with the Euclidean distances. We inferred the variables that arranged these groups with a Principal Component Analysis (PCA) by variables (columns). To explain the way each sub index is acting on the entire Cultural Significance process, we developed a PCA by OTUS (rows). We also looked for correlations between sub indexes with Spearman correlations [35]. Statistical procedures were performed using STATISTICA 5.1 for Windows [36] and BIODIVERSITY PRO 2 [37].
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