Guarino, Luigi

Jones, Peter G.

Peters, Michael

Segura, Sergio D.

CGSpace

15010050JANFEBMARAPRMAYJUNJULAUGSEPOCTNOVDEC15010050JANFEBMARAPRMAYJUNJULAUGSEPOCTNOVDECNorthville SouthvillePolar plots of hypothetical rainfall recordsNorthville might be in California, while Southville could be in Chile15010050JANFEBMARAPRMAYJUNJULAUGSEPOCTNOVDECNorthville Southville15010050AUGSEPOCTNOVDECJANFEBMARAPRMAYJUNJULPolar plots of hypothetical rainfall recordsAfter the rigid rotation they are seen to be identicalDesmodium barbatum Cross Continent AdaptabilityMappingThis  example i s ma de  with da ta  from the C IAT ForageGermpla sm Database . T he  genus De smodium  has shownpromise as a  pasture and cove r le gume in a  number of si tuat ions.It is found throughout  t he  tropi cs with spe cie s e ndemic in  bothLa tin Am eric a a nd Asia.Desmodium he te roc arpum subsp. ov al if oli um has  beenextensively coll ect ed in Ma la ysi a, and al so Vietnam, Thail andand Indone sia . T he  probabil it y di stri but ion for South Ea st Asi a,at  t he  right, fol lows the  ac cession point s qui te  wel l.  Someout lying points in  t he  Thai  and Sumat ra n highlands , a nd a  fewpoints on the  low lying coast  in  Vietna m might  indic ate  t ha tthe re  are sub-populat ions i n the a cce ssion se t, but  for t hisexerci se we have not appl ie d the clust er analysis.In Lat in  Ame ri ca  it  shows a large area of probable a da ptabil it yin the B razi li an, Peruvia n and Colombian Amaz on where i twould possibly not  be we lcome d as a  pasture option forconservation re asons.  Howe ve r it  would see m to be  a use fulposs ibil it y in  t he  C olom bian and Venez ue lan L la nos a nd a lso i nthe  wet ter c oa stl and area s of North East  B ra zi l a nd we tt erregions of Ce nt ral Americ a.It’s pote nt ia l ra nge in  Afri ca  appears t o  be quit e l imi te d.  Thehighly signifi cant  area  in  s outhern Nigeri a i s i nt erest ing, a s a re  afew points in  t he  R epubli c of C ongo and Togo.  Although m ostof these  are Tset se areas  whe re  t he  curre nt  uti li sat ion for cat tl epastures may be limit ed; however t he re  ma y othe r niches for t hespeci es,  for example  as ground cover i n pl anta ti ons.Desmodium barbatum is widely re pres ented through L atinAmeri ca,  as shown by the a cce ssions points on the  ma p to  t hel eft. In marke d cont rast  to   D. het erocarpum s ubsp. ovali fol iumt hi s spec ies see ms to be  wel l a da pted for many regions in  Afric a.Import ant a re as through the moi st savanna hs of We st Afri ca,  theC entral  Afri ca n Republ ic  right through to  parts of northernUganda , southern Suda n and even into the l owlands of southe rnEthiopia.In Asia , a lt hough the  area s of pot enti al  adapta ti on a re notext ensive, it  might  find a pl ace  i n Suma tra,  Java and thePhil ippines.These  result s  quic kly indic ate  an i nit ial  a ssessme nt  of t heposs ible  adapta ti on of t he se l egumes for tra ns-contine ntali nt roduc tion. Ini ti al  tri al materia l could be  t arge ted prec isely toarea s where t hey might be  expect ed to yi eld a good re sponse forvarious, t o  be defined, us es.Desmodium heterocarpumsubsp. ovalifoliumThe method   The climate at the each collection point is extracted from a climate database, and used as a calibration set to calculate a climate probability model.  We  standardise the climate data to a common time-scale  We use the 12 point Fourier transform to fit 6 frequencies to the climate data. The phase angle of the first frequency for the rainfall record is subtracted from each climate record. This produces a rigid rotation which sets the common time for rainfall, temperature and diurnal temperature range.  We calculate a model of the probability that a map pixel belongs to the calibration set After a transformation to normalise the rainfall data,  a Principal Components Analysis (PCA) produces a a set of normalised orthogonal components. Any number of these can be selected to represent the probability distribution of the climate data from the calibration set. As these probability calculations are made with models embedded in a varying number of dimensions, we have derived the formulae for calculating the normal probability integral in spaces of varying dimensions.   We then map the probability surface from a set of interpolated climate grids The eigenvectors of each pixel on the interpolated climate surface are used to calculate the probability that the climate at that pixel could belong to the probability distribution defined by the calibration set embedded in the space of dimension determined by the number of principal components chosen by the user.   The probability surfaces can be output to other GIS applications The probability surfaces are  ESRI® Shapefiles, with each pixel defined as a square polygon. These can be readily exported to other GIS applications where they can by overlaid with other information such as soils, natural vegetation, human intervention etc. This feature is also useful for producing individually crafted output maps. FloraMap does not pretend to be a complete GIS but is a tool for use with other systems. The Map Window Tool The map window is the central tool in FloraMap. It displays the maps and has controls for managing the map layers including probability surfaces and accession points used in the model. The political boundaries of Latin America, Africa and Asia can be displayed as back ground to the probability layers. Roads, rivers and cities can be overlaid as aids in geographically representing the study areas. Control of colours and characteristics of all layers is available through the Layer Control Window. There are facilities for saving and reloading complete maps, or individual map layers. The Principal Components Window Tool This window controls the principal components analysis. The scaling, weighting and transformation of the variates, choice of components and map probability limits are all at the control of the user, using handy sliders or by entering the values desired. A scatter diagram show the distribution of the accession points in any combinations of the principal component spaces. This is useful for identifying discontinuities in the distribution and for finding errors in the data. A powerful tool for selection of sub sets of the data works merely by drawing round the points in question. This also works from the Map Window. The Cluster Analysis Tool An accession set is often found to contain sub-populations that have distinct climatic ranges. The Cluster Analysis Tool gives the user access to seven powerful clustering techniques to investigate these possibilities. When clusters are found the user has only to point and select a cluster for reanalysis in the PCA or directly on the map. The Climate Diagram Tool The user can view the climate of a point from the map, an accession point from the scatter diagram or map and also the mean climate for groups of points from the scatter diagram or the map. The climate diagram can be displayed in Cartesian or polar co-ordinates and can show the data either by real date, or in rotated form. A FloraMap and its Users The Users Group FloraMap has a users group of over 80 individuals throughout the World. To join the group listserver, please send a message to: listserv@cgiar.org with the following text in the body of the message: subscribe FloraMap <your email address>. The listserver is used to answer users questions, disseminate new information about FloraMap and as a platform for users world- wide to exchange experiences and hints on its use.  The Web Site http://www.floramap-ciar.org FloraMap has a Web site with information on the product, examples of its use and a growing list of Frequently Asked Questions with their answers.Your solution to difficulties with FloraMap could be merely a few mouse clicks away. 1 )) 2 ( ( 1 1 ) 6 ( 1 ) 4 ( 1 ) 2 ( 1 2 2 2 2 2 + − − + + − + − + − r n n r r n r n r n  e r p 2 2 1 − − = 1 )) 3 ( ( 1 1 ) 6 ( 1 ) 4 ( 1 ) 2 ( 1 2 2 2 2 2 + − − + + − + − + − r n n r r n r n r n  p r r r = − ⋅ erf e / 2 2 2 1 2 2 π In even dimensions In odd dimensions The normal probability integral in multiple dimensions Differentiating bands (αβ acid phosphatase) Group 1: S.guianensis var. vulgaris Group 12: S. guianensis var pauciflora Distribution study of Stylosanthes guianensis This example shows a selection from a study in which FloraMap was used to map the predicted range of five important tropical fruit species in the genus Passiflora. This was another case where the Cluster Tool proved useful in identifying differences in climatic adaptation which were also reflected in measured genetic differences. Stylosanthes is the most widely used tropical legume genus for pasture and soil improvement (Edye 1987). The collection of germplasm of the genus has been a high priority over the last 25 years for a number of institutions throughout the world. Stylosanthes guianensis is one of the most sought-after  species in the genus. It has a wide natural distribution and can be found from Mexico to Argentina (Williams et al 1984) This work formed part of a collaboration between CIAT, CSIRO in Australia, the University of Birmingham, and Kew Gardens in the UK. It was reported in Jones et al. (1997b) We took an accession set of 438 individuals and using the FloraMap algorithms (this was before they were incorporated in the FloraMap software package), we fitted the projected distribution map. There was a considerable lack of fit of the probability surface. Many accessions fell in areas of very low probability, while large tracts of indicated high probability were void of accessions, even in areas that we knew had been collected. We therefore made a cluster analysis of the climates in the accession set and grouped them into 12 classes. Groups 1 and 12 contained the majority of the collection and were clean, well-defined clusters. We then mapped the two probability surfaces, as shown on the left. After we had finished this exercise we looked over the data set once more and  found that an extensive isozyme study has been made for almost all of the accessions. What is more there was a clear link between the isozyme data and  taxonomic variation in the species, which is differentiated into two botanical varieties, S. guianensis var. vulgaris  and S. guianensis var. pauciflora. These differences also corresponded closely with our cluster analysis. Group 1 corresponded with accessions of  var. vulgaris, while Group 12 corresponded with var. pauciflora. The latter is favored by warmer, drier climates. With some specially introduced errors to extend the exercise, this data set now forms part of the extensive tutorial which constitutes chapter one of the FloraMap Users Manual.   FloraMap A Computer Tool for Predicting the Distribution of Plants and other Organisms in the Wild  P.G. Jones1, S. Segura2, L. Guarino3 & M. Peters4  1. Land Use Consultant, Centro Internacional de Agricultura Tropical (CIAT)  2. CIRAD-FLHOR/IPGRI, c/o CIAT  3. Scientist, International Plant Genetic Resources Institute (IPGRI)  4. Germplasm Specialist, CIAT FloraMap maps the predicted distribution, or areas of possible adaptation, of natural organisms when little is known of the detailed physiology of the organism. The climate at the collection point of a set of individuals is assumed to be representative of the environmental range of the organisms. For plants, these are usually germplasm accessions or herbarium specimens. Bibliography  Edye, L.A. 1987. Potential of Stylosanthes for improving tropical grasslands. Outlook on Agriculture 16:124-130. Escobar, L. A. 1988. Passifloraceae. La flora de Colombia. Universidad Nacional de Colombia, Bogotá, Colombia. Jones, P. G., and A. Gladkov. 1999. FloraMap Version 1.  A computer tool for predicting the distribution of plants and other organisms in the wild. CD-ROM and Manual 99 p. Centro Internacional de Agricultura Tropical, Cali Colombia. Jones, P. G., N. Galwey, S.E. Beebe and J. Tohme.  1997a.  The use of geographical information systems in biodiversity exploration and conservation.  Biodiversity and Conservation 6:947-958. Jones, P. G., M.C. Sawkins, B.L. Maass and P.C. Kerridge.  1997b.  GIS and genetic diversity case studies in Stylosanthes.  Poster presented at  XVIII International Grassland Congress, June 8-19, Winnipeg, Canada.  Segura, S.D., G. Coppens d'Eeckenbrugge and P. Ollitrault.  1998.  Isozyme variation in five species of Passiflora subgenus Tacsonia and Passiflora manicata. In: XLIV Annual Meeting of the Interamer. Soc. Trop., September 28-October 2 1998, Barquisimeto, Venezuela.  Segura, S.D., L. Guarino, G. Coppens d 'Eeckenbrugge, M. Grum and P. Ollitrault.  1999.  Mapping the distribution and regions climatically suitable for four species in  Passiflora subgenus Tacsonia (Passifloraceae) and P. manicata. Poster presented at II Simposio de Recursos Genéticos para América Latina e Caribe - SIRGEALC, 21-26 November 1999, Brasília, Brasil. Williams, R.J, R. Reid, R. Schultze-Kraft, N.M..S. Costa and B.D. Thomas. 1984. Natural distribution of Stylosanthes. In: Stace, H.M. and Edye, L.A. The biology and agronomy of Stylosanthes. AcademicPress, Australia. p 73-101. 

FloraMap : a computer tool for predicting the distribution of plants and other organisms in the wild

https://cgspace.cgiar.org/bitstream/10568/57713/1/POSTER_FLORAMAP.pdf

FloraMap : a computer tool for predicting the distribution of plants and other organisms in the wild

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