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Plant selection for ethnobotanical uses on the Amalfi Coast (Southern Italy).

Savo V, Joy R, Caneva G, McClatchey WC - J Ethnobiol Ethnomed (2015)

Bottom Line: Percentages of agreement were calculated to compare the results of the analyses.All statistical methods in one flora agreed that Malvaceae was over-used and Poaceae under-used, but this was not found to be consistent with results of the second flora in which one statistical result was non-significant.The chi-square analyses were significant for phylogeny, life form and habitat.

View Article: PubMed Central - PubMed

Affiliation: Hakai Institute, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada. vsavo@sfu.ca.

ABSTRACT

Background: Many ethnobotanical studies have investigated selection criteria for medicinal and non-medicinal plants. In this paper we test several statistical methods using different ethnobotanical datasets in order to 1) define to which extent the nature of the datasets can affect the interpretation of results; 2) determine if the selection for different plant uses is based on phylogeny, or other selection criteria.

Methods: We considered three different ethnobotanical datasets: two datasets of medicinal plants and a dataset of non-medicinal plants (handicraft production, domestic and agro-pastoral practices) and two floras of the Amalfi Coast. We performed residual analysis from linear regression, the binomial test and the Bayesian approach for calculating under-used and over-used plant families within ethnobotanical datasets. Percentages of agreement were calculated to compare the results of the analyses. We also analyzed the relationship between plant selection and phylogeny, chorology, life form and habitat using the chi-square test. Pearson's residuals for each of the significant chi-square analyses were examined for investigating alternative hypotheses of plant selection criteria.

Results: The three statistical analysis methods differed within the same dataset, and between different datasets and floras, but with some similarities. In the two medicinal datasets, only Lamiaceae was identified in both floras as an over-used family by all three statistical methods. All statistical methods in one flora agreed that Malvaceae was over-used and Poaceae under-used, but this was not found to be consistent with results of the second flora in which one statistical result was non-significant. All other families had some discrepancy in significance across methods, or floras. Significant over- or under-use was observed in only a minority of cases. The chi-square analyses were significant for phylogeny, life form and habitat. Pearson's residuals indicated a non-random selection of woody species for non-medicinal uses and an under-use of plants of temperate forests for medicinal uses.

Conclusions: Our study showed that selection criteria for plant uses (including medicinal) are not always based on phylogeny. The comparison of different statistical methods (regression, binomial and Bayesian) under different conditions led to the conclusion that the most conservative results are obtained using regression analysis.

No MeSH data available.


Related in: MedlinePlus

Number of under-used or over-used families using the different methods (linear regression, binomial method and Bayesian approach), floras (FL1 and FL2) and datasets (DTS1, DTS2, DTS3)
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Fig8: Number of under-used or over-used families using the different methods (linear regression, binomial method and Bayesian approach), floras (FL1 and FL2) and datasets (DTS1, DTS2, DTS3)

Mentions: In Figs. 7 and 8, we show that for the datasets and floras of the Amalfi Coast, the Bayesian method identifies the highest number of over-used and under-used families, while linear regression identifies the lowest number (the “least common set of families” across the three methods).Fig. 7


Plant selection for ethnobotanical uses on the Amalfi Coast (Southern Italy).

Savo V, Joy R, Caneva G, McClatchey WC - J Ethnobiol Ethnomed (2015)

Number of under-used or over-used families using the different methods (linear regression, binomial method and Bayesian approach), floras (FL1 and FL2) and datasets (DTS1, DTS2, DTS3)
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4508904&req=5

Fig8: Number of under-used or over-used families using the different methods (linear regression, binomial method and Bayesian approach), floras (FL1 and FL2) and datasets (DTS1, DTS2, DTS3)
Mentions: In Figs. 7 and 8, we show that for the datasets and floras of the Amalfi Coast, the Bayesian method identifies the highest number of over-used and under-used families, while linear regression identifies the lowest number (the “least common set of families” across the three methods).Fig. 7

Bottom Line: Percentages of agreement were calculated to compare the results of the analyses.All statistical methods in one flora agreed that Malvaceae was over-used and Poaceae under-used, but this was not found to be consistent with results of the second flora in which one statistical result was non-significant.The chi-square analyses were significant for phylogeny, life form and habitat.

View Article: PubMed Central - PubMed

Affiliation: Hakai Institute, Simon Fraser University, 8888 University Drive, Burnaby, BC, V5A 1S6, Canada. vsavo@sfu.ca.

ABSTRACT

Background: Many ethnobotanical studies have investigated selection criteria for medicinal and non-medicinal plants. In this paper we test several statistical methods using different ethnobotanical datasets in order to 1) define to which extent the nature of the datasets can affect the interpretation of results; 2) determine if the selection for different plant uses is based on phylogeny, or other selection criteria.

Methods: We considered three different ethnobotanical datasets: two datasets of medicinal plants and a dataset of non-medicinal plants (handicraft production, domestic and agro-pastoral practices) and two floras of the Amalfi Coast. We performed residual analysis from linear regression, the binomial test and the Bayesian approach for calculating under-used and over-used plant families within ethnobotanical datasets. Percentages of agreement were calculated to compare the results of the analyses. We also analyzed the relationship between plant selection and phylogeny, chorology, life form and habitat using the chi-square test. Pearson's residuals for each of the significant chi-square analyses were examined for investigating alternative hypotheses of plant selection criteria.

Results: The three statistical analysis methods differed within the same dataset, and between different datasets and floras, but with some similarities. In the two medicinal datasets, only Lamiaceae was identified in both floras as an over-used family by all three statistical methods. All statistical methods in one flora agreed that Malvaceae was over-used and Poaceae under-used, but this was not found to be consistent with results of the second flora in which one statistical result was non-significant. All other families had some discrepancy in significance across methods, or floras. Significant over- or under-use was observed in only a minority of cases. The chi-square analyses were significant for phylogeny, life form and habitat. Pearson's residuals indicated a non-random selection of woody species for non-medicinal uses and an under-use of plants of temperate forests for medicinal uses.

Conclusions: Our study showed that selection criteria for plant uses (including medicinal) are not always based on phylogeny. The comparison of different statistical methods (regression, binomial and Bayesian) under different conditions led to the conclusion that the most conservative results are obtained using regression analysis.

No MeSH data available.


Related in: MedlinePlus