| European
Tropical Forest Research Network |
| Study type | Number | Protocols (%) | Biometric (%) |
| Biodiversity | 3 | 66 | 0 |
| Often subjective but justifiable? | |||
| Demographic | 9 | 44 | 22 |
| Often based on single study plots or stands | |||
| Ethnobotany | 10 | 50 | 20 |
| Including quantitative ethnobotany | |||
| Experiments | 5 | 80 | 80 |
| Insufficient replication of treatments | |||
| Harvesting studies | 5 | 80 | 60 |
| Insufficient replication of treatments | |||
| Resource inventory | 42 | 69 | 57 |
| Insufficient plots | |||
| Mapping | 3 | 0 | 33 |
| Biometrics not a major concern? | |||
| Market studies | 2 | 50 | 0 |
| Econometric criteria apply | |||
| Methodology | 11 | 64 | 55 |
| Often use pseudo-replication | |||
| Monitoring | 12 | 50 | 25 |
| Different biometric criteria apply | |||
| Rapid assessment | 1 | 100 | 0 |
| Rapidity and rigour not compatible? | |||
| Remote sensing | 2 | 0 | 0 |
| No sampling protocols reported for ground truthing | |||
| Use of secondary data | 6 | 10 | 17 |
| Did not report original protocols | |||
| Social surveys | 2 | 50 | 50 |
| Sociometric criteria apply | |||
| Yield studies | 13 | 46 | 8 |
| Often sampling is subjective | |||
| TOTAL | 126 | 56 | 38 |
Back
to: Developing needs-based inventory methods for NTFPs
Table
2. Developing and Testing Criteria and Indicators for the
Assessment and Evaluation of Ecotourism in Tropical Rain Forests
Bernd
Stecker italic
= most important indicator
| C I: Integration into national policy and planning |
Malaysia |
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| Ind 1: Political stability/ threats to tourists |  |
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Ind 2: Tourism & nature conservation policy |
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Ind 3: Land use planning |
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Ind 4: Incentives |
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Ind 5: Overall sector co-ordination |
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Ind 6: Involvement of NGOs |
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Ind 7: Nature conservation personnel |
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Ind 8: Education and training |
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Ind 9: Marketing |
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| C II: Suitability of the forest area for ecotourism |
TN |
ER |
| Ind 1: Protection status |
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Ind 2: Size of area |
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Ind 3: Indigenous residents |
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Ind 4: Natural attractions |
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Ind 5: Visibility of wild animals |
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Ind 6: Cultural attractions |
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Ind 7: Accessibility |
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Ind 8: Climatic conditions |
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Ind 9: Health risks |
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| C III: Integration into a comprehensive management plan | ||
| Ind 1: Management Plan |
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| C IV: Ecologically sound management of tourism activities | ||
| Ind 1: Environmental impacts |
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Ind 2: Monitoring and control |
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| Ind 3: Visitor management |
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| Ind 4: Environmental education |
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| Ind 5: Number of staff |
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nd 6: Qualification of staff |
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| C V:
Revenue in support of the protected forest area |
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| Ind 1: Amount/distribution of tourist expenditure |
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Ind 2: Fee takings of the Park administration |
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Ind 3: Revenue to support management costs |
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| C VI: Participation of the local population | ||
| Ind 1: Voice & rights in development decisions |
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Ind 2: Income and employment |
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Ind 3: Level of education and training |
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Ind 4: Capital availability |
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Figure 1: Example of a variogram
Back to: Analysis of the spatial distribution of NTFPs in the tropical forest of Ghana
Table
3. Schematic presentation of the proposed method to identify populations of
bromeliads that may be exploited with sustainability of yield.
After Wolf & Konings (in press), reproduced with permission of the editor.
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Reconnaissance
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Select a group of species of host trees, of similar bark characteristics,
that support dense bromeliad populations.
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Map the area.
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Lay out several parallel transects, covering the total area.
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Establish at least 35 random sampling points on the transects.
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Inventory
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Select the four nearest trees to each sampling point, one per quarter,
with DBH >5 cm, (point-centred quarter method).
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Record for each tree: Mean Distance, MD, to sampling point (cm), species,
DBH and no. of branching points
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Record for the bromeliads: species, no. of rosettes >20 cm tall (in
some cases smaller species may also be included), and the no. of rosettes
in the lower forest stratum, i.e. up to a height of six m or ±
1/3 of the canopy height.
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Analysis
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Calculate the host tree density per ha, TD; TD = 10000/((MD/100)*(MD/100)).
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Calculate per bromeliad species the average occupation, O, per host
tree; O = total no. of rosettes/number of trees.
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Calculate the standard error of the average occupation; SEO = standard
deviation/square root number of trees.
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Calculate per bromeliad species the average density per ha, BD; BD =
TD*O.
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Calculate the lower limit of the 95% confidence interval of the bromeliad
density, LLBD; LLBD = TD*(O-SEO*1.96)
IF LLBD <10.000 THEN STOP |
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Standardise for all trees the DBH and the no. of branching points; standardised
X = (X-mean)/standard deviation.
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Plot Tree Size (= sum of standardised DBH and no. of branching points)
against no. of rosettes.
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Define low sustaining trees, LS, that support <50% of expected maximum
no. of rosettes
IF LS >50% THEN STOP |
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Exclude low sustaining trees from the analysis.
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Calculate Index of Spatial Homogeneity, ISH; ISH = squared correlation
coefficient between Tree Size and square root of no. of rosettes.
IF ISH <0.90 THEN STOP |
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Exploitation
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Harvest bromeliads in the understory, up to six m, in a four year -depending
on the species- rotation cycle.
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Implement
a monitoring program, applying the described method.
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Table
4: NTFPs and Forest Fruits in South-east Mexico
Remi Gauthier
Mean per
cent of income from different sources, by wealth ranking
| Wealth ranking | Number of households | Per cent income from NTFPs | Per cent income from forest fruits |
| Well-off | 1 | 27 | 3 |
| Slightly better-off | 2 | 17 | 0 |
| Poor | 14 | 6 | <1 |
| Very poor | 3 | 5 | 1 |
