The present
chapter is structured in two parts. The first one explains different methods
used for collecting data and the second one is related at the data analysis,
statistical methods and software utilized for data treatment. 


3.1 Data collecting

They were relative to ethnobotanic and economic
surveys as well as entomologic data collecting. Before all this, literature
survey were done in different documentation centers of FLASH, ABEE, BIDOC, LEA,
IITA, Faculties of Forestry and of Biology I at the University of Freiburg.
Furthermore deeply understand of the topic were worked out via Internet
websites research. Climatic and demographic data were collected at ASECNA and
INSAE respectively. The section will be presented according to objectives. The
research questions were investigated within the mixed method framework that
used different tools and approaches for collecting quantitative and qualitative
data from field respondents by medicinal plant users living near to forests. In
our study data were collected through structured survey questionnaires,  focus group discussions key informant
interviews and, personal fieldwork observations (entomofauna collecting) .


3.1.1 Ethnobotanic survey

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According their
proximity to different forests, five (05) and four (04) surrounded villages
were selected near Lama Protected Forest (Lama Forest) and Lokoli Swampy Forest
(Lokoli Forest) respectively for ethnobotanical study.  A total of nine villages surrounding Lama
Forest (Akpè, Koto, Massi, Agadjaligbo, Zalimè) and Lokoli Forest (Koussoukpa ,Lokoli,
Dèmè, Samionta) were covered for the survey. Structured and semi-structured
interviews were carried out from September 2012 to February 2013 to identify
the main tree species used to harvest tree barks for medicinal purposes. At
least 30 informants per village have been interviewed. They included different
socio-professional groups (Table1), such as collectors (harvesting bark to sell
it), Traditional healers (harvesting bark for medicinal purposes), farmers and
others (craftsmen, housewives etc) from different socio-cultural groups (Fon,
Holli and Aïzo). As numbers of different target groups were unknown, we chose
30 informants per village in order to cover all groups since none statistical
analysis was competed at village scale. To recruit Traditional healers and
collectors we relied on a snowball technique, to know where recruits are
participated and answered the getting names of them. Farmers and others were
randomly questioned. The first part of our questionnaire asked for socio-demographic
details of the respondent. The second part addressed the purpose, harvesting
method, harvesting frequency (daily, weekly, monthly etc.) and identity of
trees bark-harvested, different diseases healed, the processing method
(decoction, maceration etc.) as well as ‘awareness’ questions ( you remark
some debarking impacts on the tree after harvesting?). Variables such as: Sap Loss, Crown Decimation, Flowers
Abortion, Leaves Loss, Insect Infection and Change Shapewere asked (for details see Annex 1). A total of 261 informants were
interviewed during this survey, 104 from the Lama Forest and 157 from the Lokoli
Forest. It is worthwhile mentioning that in the Lama Forest people were less
willing to participate, therefore we could not get higher sample sizes. Using
data from the ‘Institut National de la
Statistique et de l’Analyse Economique’ (INSAE, 2004), the
population sizes in the villages surrounding Lokoli Forest and Lama Forest were
estimated to 1452 and 1919 inhabitants,
respectively. Hence, the sample sizes correspond to 10.8 % of Lama Forest’s and
5.4  % of Lokoli Forest’s inhabitants.

3.1.2 Diversity of Coleoptera xylophagous of most important medicinal
trees debarked Sampling method

As explained and described above, both Lama and Lokoli Forests are
differently managed. While the first one is protected ecosystem with forbidden
access, the second one suffers from human pressure via the use of forest
resources such as tree barks for medicinal purposes because of its free access.
Then, studying the effect of forest disturbance such as debarking on insect
diversity especially the xylophagous and their natural enemies is very important
in order to evaluate the impact of local population on forest resources. This
could be made by several methods and one of the most important is being
associated with better design of field experiments: BACI analyses which mean
Before-After/Control-Impact methods (Stewart-Oaten et al., 1986). In the present study,
we adopted the Control-Impact method by setting up insect traps on debarked
trees and non-debarked tree which served as control. For that, we selected tree
mostly cited in the previous study in inside both forests and added tree harvested
in the field which is more open habitat, since farmers spared some species in
their fields. It is worthwhile to precise that, due to agriculture system, we
could not consider data from open habitat in Lama Forest since sites selected were
burned by farmers. Thus in Lama Forest we considered only trees selected in
forest such as Anogeissus leiocarpa,
Dialium guineense and Khaya
senegalensis while in Lokoli Forest species such as  Nauclea
diderrichii, Syzygium owariense and
Ficus trichopoda were selected in the forest and in the field we worked on Parkia biglobosa, Pterocarpus santalinoides and Bridelia ferruginea. Then closed
habitat here concerned traps established on tree species debarked or not inside
forests and open habitat concerned those set up on tree species in fields. To
allow comparisons, a standardized collecting method was used. It consisted of
establishing interception traps made with funnels surmounted of plastic with an
area of 10cm x 15cm (Picture 3). This funnel leads to a recuperation tube which
contained formol (0.5 %). Per tree species, two treatments with six replicates
each were installed. The first treatment consisted of a debarked tree where an
area of 10 cm x 20 cm was experimentally debarked at breast height. The second
was a control tree where no bark was removed. A total of 108 interception traps
were installed. Traps were sampled every two weeks for a period of four months
during the dry season. Data collected were: insects caught by trap and presence/absence
of insect holes and number of insect holes Sorting scope and identification

We focused on xylophagous
Coleoptera because they were most abundant and are most relevant for tree
health when it concerned bark harvesting. All taxa with a potential relevance
for wood were considered, such as bark beetles (Scolytidae), ambrosia beetles
(Platypodidae), longhicorn (Cerambycidae), jewel beetles (Buprestidae) and
their natural enemies (Cleridae, Staphylinidae, Histeridae etc.). All Coleoptera were first sorted to ‘morphospecies’
(sensu New 1998) and then taxonomically identified at the International
Institute of Tropical Agriculture (IITA) in Benin.

 3.1.3 Economic survey

studying the effect of debarking on tree species, we tried to understand the
reason leading local people to continue harvesting bark on medicinal tree
species in Lokoli Forest. Is the activity of selling bark profitable to collectors
or not. For doing that, a survey was carried
out from January to March 2013 in the villages next to Lokoli Forest: Lokoli,
Dèmè, Samionta and Koussoukpa. It was based on interviews using questionnaires
(Annex 2  Table 1) with 30 collectors as
respondents who harvested bark directly in the forest and sold it at different
markets. In a pre-survey, three groups of collectors were identified: Group 1
(G1) collectors sell their bark locally, in the villages next to the forest;
group 2 (G2) and group 3 (G3) collectors sell it at medium- (< 50 km) and distant markets (> 50 km), respectively.

We collected
following data: (i) Bark species and quantities as well as their prices
(selling price) (ii) All costs induced by the activity, such as purchase, travelling
costs (round trips for bark harvesting and travelling to the market), costs of
material used and their depreciation, number of persons involved in the
activity as well as the number of working days before going to the market, (iii)
All taxes paid by the collectors and the number of times they went to the
market per month (Annex2, Table 2). Annual economic parameters were obtained by
extrapolating monthly values. Since collectors were active only during the dry
season, we considered a period of six months for estimating annual values.


3.1.4 Factors influencing medicinal tree attacks by xylophagous

main factors which influence medicinal tree attacks by xylophagous is an interesting
but in the same time very astringent because of the cryptic life mode of
xylophagous. Biotic (forest compostion, naturel enemies,  and abiotic factors are capable to influence
forest insects. As we could not consider all factors, we emphasized on some which
were relatively easy to follow such as tree species, habitat, season and
harvesting rate. Data were collected in one year allowing following all

traps were set up on tree species for four months and then substituted by
emergence traps for four months during both dry and rainy seasons. Data were
collected from October 2014 to November 2015 in both degraded and non-degraded
forests and in the fields. Here, we followed the description and classification
of Lachat et al, 2006 in selecting of different habitats in Lama Forest. For
better comparing results between habitat types, as both forests are
ecologically different, we selected some common debarked species to both
forests and those belonged to the most tree bark sale by collectors (see table
2). Three different habitats were selected; non-degraded forest, degraded
forest and field. In Lama Forest, species of non degraded forests were
established in Humid dense forest of Synometra megallophylla and species of
degraded forests were selected in typical dense forest. In Lokoli Forest,
species of degraded forest were established both within mash forest and at the
edge of forest while trap of non-degraded forest’ species were settled within
swampy forest were very few human impacts were noted.

following table 2 shows all selected species debarked or not in different
forests. As tree species selected were more important than in previous study,
we limited the replication at three in order to be able to collect entomofauna
and to make sorting of different xylophagous and their potential natural
enemies which have been captured. Per season, 72 and 90 traps were established
in Lama and Lokoli respectively. Data collected were: individual number of
insects caught by the traps, presence or absence of insect holes, number of
insect hole recorded on the tree.

3.2 Data analysis


3.2.1Ethnobotanical data analysis

All statistical analyses were
performed in R (R Core Team, 2014) and the significance level was set to ? = 5

uses and tree species: The different diseases cited by respondents were
ordered for each forest using the Fidelity Level (FL) of Friedman et al.

Where nj is
the number of respondents who reported the cure of a given disease using the
bark of a given tree species j and Nf is the total
number of respondents for each forest.

This was made in order to
select tree barks which were cited for diseases with FL ?5 % and presented with
processing method in healing disease. Fisher’s exact test was used to test
significant differences in bark uses in term of disease healed between Lama Forest
and Lokoli Forest. A pattern in bark uses (times a tree was mentioned) in the
two considered forest types were assessed with Correspondence Factor Analysis (CFA) performed on
professional groups and tree species involved in the cure of diseases.

of most debarked tree species : We proceeded by weighting the response (as use score
R) given by respondents as following: all plants harvested daily or weekly are
scored as 5; plants used monthly or quarterly are weighted as 3 and those
harvested biannual or annual are scored as 1. The use intensity (UI) for each
tree species was determined as the average use score (R):

Where Rij is
the use score of the tree species j by the respondent i from a
given group of sample size N.

each forest, using Wilcoxon rank sum test (UIj?0),
we tested in which forest tree species were more debarked. A cluster analysis
was then performed to group the most important debarked species according to
their UIj values computed per professional group for each
forest. Principal components analyses (PCA) was furthermore applied to the UIj
data to characterize relevant clusters in terms of professional group
and forest.

Multiple comparison tests
under Kruskal-Wallis rank sum test were then performed in the R library agricolae
(de Mendiburu, 2014) to assess variations in harvesting score with respect to forest, ethnic group, age, sex and
professional group.

of debarking method: A Correspondence Factor
Analysis (CFA) was performed to highlight the use of bark according to professional
group and harvesting method debarking method recorded during the survey.

of users’ relative impact on debarked tree species and sustainable debarking
methods used: The
perceptions of plant bark users on the impact of debarking on trees were
assessed using Impact01 (yes/no) and six impact type variables namely Sap
Loss, Crown Decimation, Flowers Abortion, Leaves Loss, Insect Infection and
Change Shape. Generalized linear models (probit) were fitted to assess
the variability of these seven binary perception variables against forest type,
ethny, age, sex and profession. The R function step was used to reduce
model through backward selection of model terms.

Furthermore, a Correspondence
Factor Analysis (CFA) was perfomed to relate the use of sustainable debarking
method according to professional group.



3.2.2 Study of diversity of Coleoptera xylophagous and their natural


The species diversity of insects was evaluated using three common
?-diversity indices, the cumulative number (S) of species recorded on a tree,
the Shannon-Weaver (1949) diversity index (H’) and the Evenness (J) of Pielou
(1966). The index of Bray-Curtis was computed to assess dissimilarity between
habitat types (Lama-forest,
Lokoli-swamps and Lokoli-crops).

To assess
the importance of each group for each habitat type, the percentages of all
collected insects (both abundance and species richness) by ecological group
(Predator and xylophagous) and in different insect families were used. Fisher’s
exact tests were performed to test for differences in the frequency
(considering both abundance and species richness) of ecological groups between
habitat types.

indicator value index (IVI) of Dufrêne and Legendre (1997) was used to
determine indicator species for habitats types, tree species and treatments
(debarking). For a given species i
in a group (defined by habitats types, tree species or treatments) indexed k, IVI is given by:

 whereAki and Bki are
respectively the specificity and the fidelity (define!) of a species?

with ?ki the mean number of insects of the species i from
all sampled trees in the group k and p the total number of

with?kithe number of trees on
which the species i has been collected in the group k and ;?k total number of trees
sampled in the group k.

To assess
the significance of computed IVI values, randomization tests each based on
10000 permutations were performed using the R library indicspecies (De
Caceres and Legendre, 2009). Species with IVI ? 0.25 and significant at 5  % level were selected as indicator species
(Lachat et al., 2006). The density of insects (N,
insect abundance) and the diversity indices S and
H’ were used to assess the effect of debarking on the attraction of
beetles and the variability of this effect across habitat types and tree
species. For each of the three dependent variables (N, S and H’), a repeated
measure ANOVA was considered with habitat type, tree species (nested to habitat
type) and debarking as fixed factors and sampling time as random factor. ANOVAs
were performed using the R library nlme (Bates et al., 2014) with
Poisson error distributions for N and S; and a Gaussian error distribution for
H’ after Box and Cox power transformation (Box and Cox, 1964). The mean and
coefficient of variation (cv) of the three dependent variables were calculated
for each habitat type per tree
species considering debarked and non-debarked trees separately.

The abondance
of xylophagous beetles (Nx), the number of predators (Np), the
number of predators per xylophagous (Npx) and the number of holes (NH)
per tree were used to assess the impact of insects on trees, according to tree
species (per habitat type) and debarking. A negative binomial error model was
fitted to each of these variables (Nx, Np, Npx and NH) using the R library MASS (Venables
and Ripley, 2002). Goodness of fit was assessed using R2 and ?2
test on residual deviance from each model. Means of Nx, Np, Npx and NH
were computed per tree species for debarked and controls trees separately and
compared using negative binomial error models.


3.2.3 Economical data analysis Profit and Loss accounts Analysis

The profit
and loss accounts analysis estimates the Net Income (NI) over one year. It is
the Operation Income (OI; OI = Qt*SP; Qt: bark quantity, SP: selling price) after
subtracting different costs.

All costs
(total costs: Ttcost) faced by economic activities can be broken into two main
categories: fixed costs and variable costs. Fixed costs (FC) are those that do
not change, while variable costs (VC) change depending on the company or a
private individual’s activity. Fixed costs are linked to staff expenses, to
taxes and to equipment while variables costs represent costs linked to
transportation, packaging and the purchasing of bark.


Net Income
(NI) is the difference between OI and FC+VC= TtCost.

Economic Profitability (EP) was then calculated as the ratio between NI and the
sum of Ttcost induced by the economic activity: Sensitivity Analysis

economic analysis was based on computation of the Break-even Point (BP) and the
safety margin (SM).

BP is the ratio from the product of OI and FC
divided by OI-VC. SM is OI minus BP.


 ;     SM= OI- BP Analyses of the profitability of bark sale

Statistical analyses were performed in R (R Core
Team, 2014). The significance level was set to ?= 0.05. A mixed model ANOVA (random intercept linear mixed model) was
performed, independently for each variable of the Profit and Loss Account
Analysis and the Sensitivity Analysis using the R library lme4 (Bates et
al. 2014). Each time ‘collector group’ and ‘tree species’ were fixed
factors and ‘respondent’ a random factor. Mean values and standard errors were
computed per collector group and per tree species to highlight significant
differences in returns from bark sale.

3.2.4 Factor influencing medicinal plant attacks by
xylophagous insects

To assess
the distribution and the diversity of insects, abundance (N) and diversity
indices (species richness (S), Shannon-Weaver (1949) diversity index (H), and
equi-repartition index (E) of Pielou (1966)) were computed per habitat within
studied site and per debarking level and insects ecology. Bray-Curtis
and Jaccard similarity indices were also computed between habitat types within
studies sites to assess the resemblance of these habitat types. The proportions
of entomofauna from each recorded family and order were computed to identify
the most prominent insect families and order in the studied habitats. The indicator value index (IVI) of Dufrêne and
Legendre (1997) was used to determine indicator species in different studied
sites and across debarking levels. This analysis was performed with the R
package ‘indicspecies’ (Caceres and Legendre, 2009).

Generalized Linear Mixed Model (GLMM) with binomial error distribution was
adjusted to insect attack event with studied site, presence of water, habitat,
tree species, debarking level and season as fixed factors and collection time
as random factor. Two GLMMs with Poisson error distribution were also adjusted
to the number of beetle individuals per beetle species (offset) and to the
number of beetle individuals on attacked trees using the same set of factors. GLMMs
were fitted using the R package ‘glmmML’ (Broström, 2013) and the
pseudo-R² (coefficient of determination) of Nagelkerke N (1991) was computed to
assess their explanative power. The significance an estimated coefficient was
interpreted as the significance of the effect the corresponding factor on the
response. Mean and standard error of the responses were computed to illustrate
the results of the GLMMs.


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