Draft
Ver 0.1
Steps:
1) Cleaned data
a. Look for out to seas
b. Out of country
c. District problems
2) Data set return to
3) Clean dataset returned
4) Marked species that are endemic and near endemic (note some species not in specimen database)
a. Joined and clean up
b. Data set return from
Taxa based results
5) Preliminary conservation assessment for near and endemic species (excel sheet) 373 specie and 2871 specimens (Willis etc al.)
6) Results excel sheet and jpg image maps for all 373 species
7) Check which specimens are in protected areas (only Sapo, forest reserves not used, NB the boundary of this are is looking very poor!) Added to excel sheet
Grid Based (Area) results
8) A grid of 0.1 degree size cells was created (seems the best compromise between resolution and the locational error of the data).
9) Layers where queried to get:
a. Area of country
b. % of area
c. Area of Primary vegetation
i. Based on Lib_landcover_frm.shp (seems the best)
ii. Assume that following are primary vegetation types
1. Closed dense forest
2. open dense forest
3. littoral ecosystem complex
d. % of vegetation area
e. Collects of the endemic/near endemics
f. Number of endemic species per cell
g. No of total collections
h. No of Species per grid cell
Additional analysis
10) Highlight areas for conservation (from specimen data)
a. Areas of vegetation > 20% and number of endemic species/taxa over 25
b. Areas of Vegetation > 20% and number of species/taxa over 50
11) Collection per vegetation type
- See Excel sheet for all results
Only the endemic and near endemics were analysis. These results are equivalent to preliminary regional conservation assessments, For endemic species assessment is ovious at all levels (country, regional and global). Summary is below and with pie chart
|
NB. All DD species have been put into VU DD
Map: All georeference specimens. Background is reserves (hatched) with satellite imagery as a back drop.
Map: All endemic and near endemics mapped.
Map:
Country area (to reduce edge effects)
Map: Area of Primary vegetation More green the more area of primary vegetation.
Map: Number of collection per grid cell. More green the more collection. Based on all collections
Map: Number of taxa (species and below) per grid cell. More green the more taxa. Based on all collections
Map:
Number of endemics/near endemic taxa per grid cell.
Map: Areas of vegetation > 20% and number of endemic species/taxa over 25. More green the more taxa. Based on all endemics
Map: Areas of Vegetation > 20% and number of species/taxa over 50
10. Collections per vegetation type
|
Vegetation type |
Number collections All |
% of collections |
Number collection Endemic/near endemics |
% Endemic |
Area % |
|
Primary |
1776 |
15.0 % |
493 |
17.3 % |
37.5 % |
|
Others |
10038 |
85.0 % |
2356 |
82.7 % |
62.5% |
- The major the endemic and near endemics have preliminary conservation assessment that make them threatened.
- If would be usual to look at threaten species as well are endemic/near separately, but we can see from pie chart not many of the endemics/near are not endangered. The same goes for protected/unprotected species (not many protected) So I have only concentrated on Endemics/Near and all specimens/species.
- Many of the taxa are very poorly represented (30% have one or less locality)
- Taxa hotspots are often in areas of low % of primary vegetation.
- The map of endemic (grid map), shows 3 main areas of concentration. This would seem to be the best of the lot.
- We really need more information for the vegetated areas.
- Really the specimen data is too biases to areas of poor vegetation, probably a reflection the historic nature of the collections
- For a real desktop survey (for area not collection), Predictive analysis could be done to highlight areas that have high potential. We really need to have more collection per species if possible? This will take some time to do?
- We should be using a optimisation algorithm for reserve selection, but realistically the specimen data (Absence of a taxa from a cell is most like due to not collecting, rather than actual) will not be enough to do this. Predictive mapping would to some degree help with this? See: http://www.ecology.uq.edu.au/index.html?page=27710
- One other possibility would be to use a eco-tone or eco-region approach to decide on reserve selection (i.e. conservation ?% of all eco-tones regions). Also you could use this with specimen data (i.e. assign taxa to a eco tone/region) – a simpler way of using predictions.
1) Specimen data is only the georeference specimens
2) Can you get open dense forest????
3) We should take into account threat (we can use Lib_threats_frm.shp, but I am not sure what weights to put on the categories).