THE PROTEA ATLAS of southern Africa
Anthony G Rebelo (Ed.)
South African National Biodiversity Institute, Kirstenbosch THE PROTEA ATLAS of southern Africa
Anthony G Rebelo (Ed.)
South African National Biodiversity Institute, Pretoria (Title Page) Standard SANBI copyright page
(Copyright page) Foreword By whom? CONTENTS
ACKNOWLEDGEMENTS ...... x Sponsors ...... x Organisation ...... x Atlassers ...... x
1. INTRODUCTION...... x Background ...... x Scope (objectives) ...... x Species...... x Geographical extent...... x The Record Locality...... x
2. HISTORY...... How the Protea Atlas Project was organized and run ...... x Recruiting and training...... x Recording kit ...... x Data collection and validation...... x Historical data...... x Data processing ...... x Data vetting ...... x Feedback...... x Data availability...... x Maintenance and accessibility...... x
3. ASSESSING PROTEA ATLAS INFORMATION ...... x Biases ...... x Geographical sampling biases ...... x Seasonal biases ...... x Current data biases ...... x Coverage...... x Evaluation of problems experienced ...... x Identification...... x The Sight Record Sheet...... x Core data/optional data...... x Size of Record locality ...... x Point (co-ordinate) versus raster (grid square) locality references...... x Map work problems...... x Locality and altitude resolution ...... x Conservation status...... x Altitude...... x Landform ...... x Aspect...... x Soils: type and colour ...... x Vegetation structure...... x Extent of veld ...... x Age of veld ...... x Population size...... x Population distribution ...... x Flowering...... x New Growth ...... x Seedlings ...... x Fire survival...... x Picking x Average Height...... x Pollinators...... x Recommendations for future Atlasses...... x
4. ACHIEVEMENTS AND FINDINGS...... x New Taxonomic discoveries ...... x Range Densification ...... x Range Extensions ...... x Conservation and Red Data List status...... x Taxonomical problem areas ...... x Hybridization...... x Variation within populations ...... x Atlas efficiency ...... x Area ...... x Season...... x
5. GUIDE TO THE SPECIES ACCOUNTS ...... x
6. INTERPRETING THE GRAPHS ...... x
7. SPECIES ACCOUNTS...... x Brabejum ...... x Hakea...... x Banksia ...... x Aulax...... x Faurea ...... x Protea ...... x Serruria ...... x Paranomus...... x Sorocephalus ...... x Spatalla...... x Leucadendron...... x Vexatorella ...... x Leucospermum...... x Diastella ...... x Mimetes ...... x Orothamnus ...... x
8. REFERENCES...... x 9. APPENDICES...... x 10. ABBREVIATIONS, SYMBOLS AND GLOSSARY ...... x 11. CD OF ATLAS DATA...... x
INDEX: COMMON NAMES AND SYNONYMS...... x INDEX: SCIENTIFIC NAMES AND SYNONYMS...... x ACKNOWLEDGEMENTS We thank all who have contributed to the Protea Atlas Project. The major contributors are thanked below. In addition we thank Eugene Moll (Bot Dept, UCT and Botanical Society), Kobus Eloff (NBG and NBI), Dale Parker (Flora Conservation Committee, Botanical Society) and Roy Siegfried (Percy FitzPatrick Institute of African Ornithology, UCT) for support in developing and bringing the project to fruition.
Sponsors Dept Environmental Affairs and Tourism WWF-SA Pick & Pay Mazda Wildlife Fund National Botanical Institute-South African National Biodiversity Institute Botany Dept, University of Cape Town Botanical Society of South Africa
Organisation A project as large as the Protea Atlas Project took lots of planning, organisation, co-ordination and hard work. We thank the following for their stalwart effort and dedication: Brian Huntley, NBI and SANBI (1991-2001) Project Director Bruce McKenzie, Bot Soc (1997-2001) Tony Rebelo Liza Padfield, WWF-SA (1991-2001)
National Co-ordinators Technical Assistants Leanne Gallard (1991-1992) Ismail Ebrahim (1999-2001) Chris Berens (1992-1994) Nicholas Cole (1994-1998) Regional Co-ordinators Pieter Du Toit (1998) Eastern Cape Ivan Massyn (1998-1999) Dave Jones (1997-1999) Valerie Charlton (1999-2001) Libby McGill (1996-2001) Gauteng Software Development Reuben Heydenrych (1999-2001) Tony Rebelo KwaZulu-Natal Nigel Forshaw Southern Cape GIS Development Dave Jones (1999-2001) Tony Rebelo Swaziland Kate Braun (1992-1998) Website Development Linda Dobson (1998-2001) Nigel Forshaw Regular Volunteers Secretaries Data capture checking Ingrid Neale (1991-1992) Lucy Mackenzie (1992-1994) Helen Campbell (1996-1998) Mary Digby (1994) Lyn McCallum (1995-2001) Heather Webber (1994-1998) Ruth Smart (1996-2001) Ingrid Neale (1998) Species validation Valerie Charlton (1998-1999) Lyn McCallum (1995-2003) Luthfia Frieslaar (1999-2001) Ruth Smart (1996-2006)) Co-ordinate checking UCT Accounts David Louw (1992-1999) Wendy Paisley (1993-1998) Peter Ross (1992-2001) Sandy Smuts (1991-2001) Chris van Vuuren (1992-2001) Sight Record Sheet filing Taxonomical Advisors Ruth Smart (1998-2006)) John Rourke Atlas compilation Ted Oliver Ruth Smart (2004-2006)) Atlas editing Chair of the Steering Committee ?? Eugene Moll (1985-1994) Starfish Richard Cowling (1994-1998) Luthfia Frieslaar (1998) Willie Stock (1998-2001) Ismail Ebrahim (1998)
Tony Abbott, Port Edward, 132, 239, 12 Marc Caplan, Rondebosch, 3, 7, 6 Joggie Ackerman, Kokstad, 1, 2, 2 Cedarberg Rangers, Clanwilliam, 231, 909, 41 Andries Adonis, Malmesbury, 3, 10, 10 Sipiwo Cekiso, Uniedale, 1, 3, 3 Rory Allardice, Bredasdorp, 29, 130, 12 Christopher Charlton, Fish Hoek, 5, 28, 17 Pippin Anderson, Observatory, 1, 1, 1 Valerie Charlton, Fish Hoek, 537, 2177, 208 7 Anysberg Conservation Club, Touwsrivier, 3, 13, 6 Rob Clarke, Heidelberg, 4, 16, 12 Liz Ashton, Higgovale, 4, 4, 2 Gail Cleaver, Uniondale, 12, 51, 15 Ryan Askew, Simonstown, 3, 19, 9 Karin Cockburn, Grahamstown, 17, 44, 11 Osman Asmal, Roggebaai, 1, 1, 1 Archie Coetzee, Lakeside, 2, 11, 7 Atlas Grassroots Research, Kirstenbosch, 16269 ], Johann Coetzer, Oudtshoorn, 32, 73, 11 58540, 530 7 Callan Cohen, Claremont, 17, 120, 42 Atlas Project Education, Kirstenbosch, 1042 ], Nicholas Cole, Hoekwil, 598, 1896, 185 5445, 279 7 Ted and Pam Cole, ***, 11, 11, 4 Atlas Special Projects, Kirstenbosch, 921, 4009, *** Conrade, Worcester, 1, 4, 4 215 7 Stiaan Conradie, Knysna, 12, 65, 11 Elsabe Aucamp, Thabazimbi, 5, 6, 3 Guy and Yvonne Courtin, Askam, 163, 802, 87 Angela Baatjes, Wetton, 1, 8, 8 Carly Cowell, Kirstenbosch, 1, 1, 1 Fotini Babaletakis, Bantry Bay, 5, 16, 12 Richard Cowling Port Elizabeth, 12, 34, 23 Jaana-Maria Ball, Wynberg, 5, 8, 6 Antoinette Crane, Vredehoek, 4, 21, 14 Patricia Baraqwanath, Haenertsburg, 31, 59, 6 Johan Cronje, Roggebaai, 8, 23, 16 Bonnie Barnard, Brooklyn, 22, 54, 6 Hildegard Crous, Claremont, 24, 75, 49 Mark Baron, Plumstead, 4, 22, 10 John Crowson, Underberg, 5, 8, 3 Arthur and Pat Barret, Pinelands, 9, 23, 18 Gia Cumming, Villiersdorp, 38, 145, 28 Thomas Barry, Oudtshoorn, 36, 186, 14 Adam Cupido, Robertson, 7, 36, 17 Mark Beeston, Edinburgh, 1, 1, 1 John Cuthill, Scarborough, 1, 3, 3 Zalisile Bemtele, Loerie, 1, 2, 2 Michael Dalton, Bredasdorp, 6, 37, 15 Muriel Bendel, Bern, 39, 234, 65 Charles Davids, Elgin, 7, 32, 16 Chris Berens, Green Point, 145, 532, 109 David Davids, Bredasdorp, 6, 23, 7 Penny and Neil Berens, Muizenburg, 9, 38, 23 John Davids, Paarl, 6, 32, 15 Bergboegoeklub, Thabazimbi, 1, 2, 2 Chris De Beer, Ugie, 9, 18, 4 Peter Bertram, Caledon, 2, 15, 11 Morne De Boer, Genadendal, 1, 9, 9 Carol Beuchat, Rondebosch, 1, 4, 4 Mortimar De Boer, Genadendal, 1, 9, 9 Hillary Blaauw, Oudtshoorn, 7, 28, 12 Afrika De Bruin, Porterville, 1, 10, 10 Neil Blackshaw, Pinelands, 2, 2, 2 Mariette De Goede, Underberg, 1, 1, 1 Jill Blignaut, Constantia, 183, 925, 92 Nick De Goede, Underberg, 2, 3, 3 Laurel Bloch, Montagu, 1, 3, 3 Isobel De Gruchy, Rosebank, 5, 15, 6 Hilton Blumeris, Grassy Park, 1, 5, 5 De Hoop Rangers, Bredasdorp, 6, 15, 9 Bolus Herbarium Team, Rondebosch, 2, 8, 8 Jan De Jaar, Uniondale, 37, 164, 25 Anthony Borrel, Mbabane, 2, 5, 3 Thomas De Jongh, Oudtshoorn, 43, 286, 23 Robin Borrowdale, Kokstad, 15, 31, 4 Timeke De Kleijn, Rivonia, 39, 64, 9 De Wet Bosenberg, Claremont, 2, 2, 1 Cheryl De Lange, Port Elizabeth, 75, 249, 12 Johnny Boy Botha, Mitchell’s Plain, 1, 6, 6 Louise De Roubaix, Wellington, 1, 7, 7 Mark Botha, Parkview, 2, 13, 10 Sandra De Swardt, Riebeek-West, 4, 23, 12 Pat Bowerbank, Observatory, 45, 278, 58 Dawid De Swart, Bloemfontein, 99, 159, 7 Lindsay Boyd, Nelspruit, 9, 13, 4 Barbara De Villiers, Oudtshoorn, 9, 49, 17 Marius Brand, Clanwilliam, 53, 211, 37 Johan and Christine De Villiers, Igogo, 93, 115, 3 Kate Braun, Mbabane, 80, 129, 7 Luana De Villiers, Simonstown, 4, 28, 17 Ian Bredell, Grabouw, 1, 2, 2 Anna De Wet, Newcastle, 5, 8, 2 Craig Breedt, Weza, 46, 91, 5 Philip Desmet, Scarborough, 26, 70, 34 Kobus Bresler, Uniondale, 73, 219, 26 Mary Digby, Mowbray, 3, 7, 3 Jacobus Brink, Oudtshoorn, 18, 82, 21 Roger Dilgee, Mitchells Plain, 1, 7, 7 Peter Bruyns, Rondebosch, 1, 2, 2 Jim and Judy Dixon, Sedgefield, 2, 9, 9 Richard and Sue Buchanan, Newlands, 3, 5, 4 Ngwane Dlamini, Mbabane, 2, 6, 4 Lindie Buirski, Waterfront, 7, 53, 25 Linda Dobson, Mbabane, 145, 195, 11 Brian and Heron Burger, Fish Hoek, 193, 777, 66 Pixie Doering, Wilderness, 42, 204, 59 Johan Burger, Clanwilliam, 3, 6, 6 Boet Dommisse, Newlands, 2, 15, 11 John Burrows, Lydenburg, 38, 74, 20 Jean Donovan, Fish Hoek, 5, 16, 8 Judy Burrows, Port Elizabeth, 87, 360, 50 Barry Du Plessis, Sedgefield, 6, 10, 8 Barry Busby, Durban North, 167, 304, 42 Pieter Du Toit, Stellenbosch, 15, 66, 22 Ruby Cadle, Port Elizabeth, 3, 8, 8 Ndoda Dubula, Kokstad, 1, 2, 2 Kathleen Calf, Barrydale, 1, 13, 13 Trevor Duckham, Hout Bay, 2, 4, 3 Helen Campbell, Rondebosch, 325, 1558, 152 Bill Duminy, Somerset West, 3, 4, 4 Jimmy Dunn, Fishhoek, 558, 2569, 195 Goliath Highburg, Heidelberg, 30, 129, 30 Timothy Dunne, Rondebosch, 3, 15, 6 Craig Hilton-Taylor, Bergvliet, 4, 7, 7 Ydie, Durandt, Bellville, 5, 48, 17 Kathy Hitchings, Pinelands, 56, 244, 64 Ismail Ebrahim, Phillipi, 540, 2164, 220 7 Tierck Hoekstra, Robertson, 8, 47, 23 Stefan Eggenberg, Bern, 3, 4, 3 Warren Holland, Simonstown, 5, 19, 9 Brian Elms, Pretoria, 4, 4, 1 Patricia Holmes, Bergvliet, 551, 2498, 213 7 Colin Engel, Claremont, 2, 4, 4 Maryke Honig, Claremont, 26, 79, 36 Daniel Engelbrecht, Eerste Rivier, 2, 6, 6 Christopher Howell, Underberg, 3, 5, 3 Michelle Engelbrecht, ***, 2, 11, 9 Ben Human, Heidelberg, 8, 33, 20 Adie Erasmus, Pretoria, 22, 83, 17 Christine Hunt, ***, 156, 768, 116 Susan Erwee, Port Elizabeth, 4, 15, 12 Mark Hyde, Harare, 3, 7, 5 Booi Esau, Oudtshoorn, 93, 533, 23 Itala Research, Louwsberg, 24, 43, 7 Jacobus Esau, Oudtshoorn, 8, 35, 15 Chumisa Jackson, Simonstown, 3, 11, 9 Douglas Euston Brown, Woodstock, 639, 2534, 91 Adam Jacobs, Paarl, 7, 58, 20 Mike Euston-Brown, Sedgefield, 56, 181, 38 Eugene Jacobs, Ocean View, 1, 4, 4 *** Faasen, Clanwilliam, 5, 23, 15 Solly Jacobs, Mosselbay, 31, 63, 20 Hans and Penny Faesler, Walchwil-Suisse, 4, 16, 13 Virgil Jacobs, Bonteheuwel, 1, 2, 2 John and Muffy Featherstone, Bishops Court, 3, 14, James Jafta, Oudtshoorn, 127, 772, 30 12 Roelof Jalving, Bredasdorp, 3, 15, 13 Ed February, Cape Town, 2, 7, 7 Hanneke Jamieson, Claremont, 4, 17, 11 Ramon Ferreira, Genadendal, 1, 9, 9 Ivan Jansen, Paarl, 6, 42, 14 Louis Fielies, Heidelberg, 1, 5, 5 Andries Januarie, Thornhill, 1, 6, 6 Trish Fleming, Pretoria, 2, 11, 9 Cora Jardine, Rondebosch, 33, 164, 40 Adrian Flett, Pietermaritzburg, 20, 26, 4 Ivor Jardine, Rondebosch, 4213 ], 18293, 394 7 Ann Forshaw, Bergvliet, 20, 99, 33 Roger Jeffrey, Kenilworth, 27, 91, 36 Nigel Forshaw, Oakridge, 2429 ], 11841, 444 7 Mark and Amida Johns, Kleinmond, 425, 2052, 156 Fanie Fouche, Boesmansriviermond, 1, 7, 7 Chris Jones, Greenacres, 44, 206, 26 Di Franklin, Constantia Hills, 21, 112, 35 Dave and Fay Jones, Sedgefield, 435, 1667, 120 Willie Fredericks, Ocean View, 1, 4, 4 Lee Jones, Claremont, 4, 22, 20 Karin Frehse, Table View, 121, 515, 67 Kira Josua, Athlone, 3, 18, 10 Luthfia Frieslaar, Mitchells Plain, 38, 174, 49 Suzanne Joubert, Fish Hoek, 13, 74, 26 Rolf-Dieter Fuchsmann, Dama Bay, 133, 627, 98 Cornelius Julies, Oudtshoorn, 26, 121, 21 William Fullard, Ladismith, 83, 257, 17 Raymond Julies, Paarl, 1, 6, 6 *** Galant, ***, 1, 2, 2 *** Juries, Swellendam, 1, 3, 3 Ray Gama, Lobamba, 1, 1, 1 Agnes Kalipa, Scarborough, 1, 6, 6 Simon Gardner, Lancaster, 510, 2166, 178 Anneke Kearney, Claremont, 8, 25, 7 Walter Gcabashe, Pietermaritzburg, 1, 2, 2 Victor and Jean Keightley, Barrydale, 651, 2948, Gerhard Gerber, Stellenbosch, 5, 45, 23 116 Ivor Gerrard, Mouille Point, 3, 3, 3 *** Keurinek, ***, 3, 13, 7 Preston Geswint, Port Elizabeth, 20, 47, 6 Howard Kewley, Kwambonambi, 3, 5, 3 Bradley Gibbons, Port Alfred, 10, 46, 17 Gek Hoon Khoo, Singapore, 125, 449, 74 Dalton Gibbs, Zeekoeivlei, 1, 1, 1 Johann Kikillus, Oakridge, 2, 10, 10 *** Gildenhuys, Wydgelee, 1, 7, 7 Darrell Kilian, Rondebosch, 4, 53, 30 Moiragh Girdwood, Newlands, 69, 299, 75 Chrisette Kleynhans, Ladismith, 21, 62, 12 Willem Goemas, Oudtshoorn, 14, 80, 16 Kerstin Koch Borgel and Hendrik Krebs, Bonn, 2, 3, June Good, Rondebosch, 25, 144, 54 3 Jeff Goy, Newlands, 11, 50, 28 Johan Kok, Somerset West, 4, 14, 8 Benedicta Graves, Raffingora, 13, 25, 5 Rynhard Kok, Knysna, 197, 589, 40 Noel Gray, Port Elizabeth, 24, 106, 14 Ines Koker, Somerset West, 9, 33, 17 Paul Gray, Bergvliet, 4, 5, 5 Vibeke Kragh, Clanwilliam, 31, 165, 21 Richard Greene, Nelspruit, 4, 10, 8 Lucille Krige, Claremont, 288, 1042, 128 Nick Grobler, Maclear, 4, 8, 4 Noelline and Doug Kroon, Sasolburg, 65, 65, 2 Grootvadersbos Team, Heidelberg, 2, 8, 8 Kim and Chris Kruyshaar, Clovelly, 23, 77, 12 Cosmos Gumede, Underberg, 1, 1, 1 Sonnette Krynauw, Lydenburg, 3, 3, 1 Iain Guthrie, Umhlali, 56, 79, 8 Christopher Kumalo, Underberg, 1, 2, 2 David Gwynne-Evans, Claremont, 363, 1544, 191 Renate Kustner, Pretoria, 35, 68, 5 Pippa Haarhoff, Langebaanweg, 5, 20, 10 Kathy La Grange, Simonstown, 104, 406, 25 Quintus Hahndiek, Uitenhage, 47, 138, 12 Barend Le Roux, Vishoek, 3, 20, 16 Grant Hall, Wynberg, 11, 15, 4 Mariann Lear, Walmer, 1, 1, 1 Hazel Hall, Citrusdal, 1, 2, 2 Stephen and Mariann Lear, Walmer, 17, 77, 19 Nell Hanekom, Voelklip, 9, 33, 11 Irene Lincoln-Fick, Worcester, 34, 155, 53 Ingrid Hansen, Tamboerskloof, 8, 16, 12 Fred Lingenfelder, Uniondale, 1, 4, 4 Gerard Hansford, Tulbagh, 283, 1509, 66 Tom Lloyd-Evans, Dorset, 1293 ], 6036, 165 Adam Harrower, Rondebosch, 1, 2, 2 Joop Loeve, Pretoria, 146, 250, 21 Bill Hartley, Sedgefield, 20, 90, 22 Amos Lombo, Simonstown, 1, 3, 3 Ernst Hartwig, Bloubergstrand, 50, 178, 40 Louis Loock, Badplaas, 1, 1, 1 Michelle Hatherly, Grahamstown, 1, 4, 4 Mervyn Lotter, Lydenburg, 231, 361, 55 Kasuls Hawkins, Scarborough, 3, 18, 9 *** Lottering, Ladismith, 21, 69, 14 Nic Heideman, Grahamstown, 10, 24, 7 Maryke Lourens, Kuilsrivier, 16, 86, 18 Helderberg Ramblers, Somerset West, 12, 70, 22 David Louw, Newlands, 913, 4103, 231 7 Nick Helme, Scarborough, 953, 4450, 356 7 Barrie Low, Rondebosch, 16, 35, 25 Marianne Heron, Constantia, 3, 14, 9 Hannie Low, Newcastle, 17, 22, 4 Margy Herron, George, 42, 127, 39 Roy Lubke, Grahamstown, 12, 23, 7 Nicky Hess, Bredasdorp, 38, 178, 37 Kerry Lynham, Bergvliet, 1, 9, 9 Ursula Heuston, Constantia, 4, 26, 13 Mellisa Maccay, ***, 21, 118, 22 Reuben Heydenrych, Pretoria, 1137 ], 1694, 72 Lucy Mackenzie, Scarborough, 14, 62, 26 Joseph Heymans, Onverwacht, 46, 73, 9 Washiela Mackenzie, Mitchells Plain, 3, 20, 14 Dumisani Makhunga, Cascades, 108, 166, 6 Fiona Powrie, Constantia, 2, 9, 8 *** Makier, ***, 1, 2, 2 Wessel Pretorius, Nieuwoudtville, 1, 1, 1 Ockert Malan, Stellenbosch, 226, 746, 98 Catherine Price, Fish Hoek, 5, 14, 7 Zukile Malusi, Guguletu, 3, 30, 18 Petrus Prins, Bellville, 1, 3, 3 Vivien and Gerald Manson-Kullin, Llandudno, 1, 9, Sean Privett, Muizenberg, 8, 33, 24 9 Domitilla Raimondo, Bishops Court, 1, 1, 1 Andre Marais, Nelspruit, 24, 45, 3 Daniel Rasmesi, Thornhill, 1, 4, 4 Hannes Marais, Graskop, 36, 47, 6 Diana Rau, Fish Hoek, 1, 5, 5 Tony Marshall, George, 13, 58, 21 Alanna Rebelo, Bergvliet, 12, 40, 20 Alan, Martin, Ladismith, 62, 227, 24 Grace and Tony Rebelo, Bellville, 3, 8, 3 *** Mase, Scarborough, 1, 3, 3 Harold Rees, Atlasville, 1, 1, 1 Dumisane Masikane, Pietermaritzburg, 1, 2, 2 Gail Reeves, Surrey, 64, 299, 73 Peta Masson, Mbabane, 10, 19, 6 Pierre Retief, Rustenburg, 9, 12, 2 Ivan Massyn, Oranjezicht, 322, 1185, 164 Jo Richards, Sunvalley, 2, 4, 3 Gordon Mathews, Franschhoek, 21, 76, 35 Stephen, Richardson Tamboerskloof, 777, 4338, Eugene Matthews, George East, 1, 9, 9 342 7 Lyn McCallum, Bergvliet, 1844 ], 10147, 365 7 Andre Riley, Storms River, 2, 5, 4 Shaun McCartney, Sabie, 4, 6, 3 Sue Rivett, Kokstad, 78, 127, 6 Ian McCausland, Bulawayo, 1, 2, 2 Rocher Pan Rangers, Aurora, 39, 132, 35 David McDonald, Claremont, 5, 19, 12 Carol Rodger, Marina Da Gama, 10, 24, 10 Douglas McGarr, Gillits, 1, 2, 2 Rooiberg Field Rangers, Ladismith, 375, 1805, 68 Libby McGill, Port Elizabeth, 772, 2748, 116 Peter and Virginia Ross, Newlands, 1398 ], 5807, Jill and Geoff McIlleron, The Craggs, 3, 5, 3 220 7 Wendy Mckeag, Rondebosch, 4, 9, 9 Wendy Rowles, Greenacres =(***), 17, 73, 18 Gavin McLachlan, Port Elizabeth, 23, 60, 22 Barbara and Richard Rudman, Port Elizabeth, 1, 2, 2 Cameron McMaster, Stutterheim, 47, 88, 19 Charlie Ruiters, Heidelberg, 19, 77, 29 Llewelyn Michaels, Heidelberg, 7, 25, 8 Gerhard Salvester, Bereaville, 1, 8, 8 Guy Midgley, Rondebosch, 1, 1, 1 Samuel Sampson, Kleinmond, 32, 228, 35 Sabine Minder, Guemligen, 7, 26, 19 Clifford Sauls, Steenberg, 1, 5, 5 Ellie Mitchell, Ottery, 20, 105, 33 Rionaldi Saunders, Oudtshoorn, 91, 421, 29 Vuyiseka Mkonto, Scarborough, 3, 14, 10 Elsa Schaffer, Constantia, 7, 39, 20 Werner and Joan Modinger, Betty's Bay, 2, 13, 10 Ann Scott, Gansbaai, 1, 4, 4 Jaco and Erica Moelich, Stellenbosch, 16, 94, 19 Gill Scott, Constantia, 4, 11, 11 Adrian Mohl, Bern, 1654 ], 7291, 292 7 Martin Scott, Oudtshoorn, 61, 263, 57 Eugene Moll, Lawes , 1, 1, 1 Jean Senogles, Westville, 3, 11, 6 Eva Molzen, George, 3, 21, 14 Phyll Sephton, Maclear, 1, 3, 3 Beth Mommsen, Mclear, 7, 34, 12 Sidney Shlomowitz, Parow, 40, 186, 39 Ara Monadjem, Kwaluseni, 2, 2, 2 Pindar Sidisunthorn, Gardens, 246, 972, 95 Graham Moodie, Heidelberg, 1, 2, 2 Bartholomew Siebrits, Bredasdorp, 4, 7, 5 Conrad Mooney, Oudtshoorn, 62, 314, 25 Maureen Silva Jones, Belvedere, 6, 15, 6 Alasdair Moore, London, 19, 73, 32 Ruth Smart, Wynberg, 1266 ], 5705, 276 7 Antoinette Morgan, Braamfontein, 1, 1, 1 Koos and Jenny Smit, Betty's Bay, 12, 67, 31 Clive Morris, Mitchells Plain, 2, 6, 6 Anthony Smith, Rondebosch, 4, 14, 12 Wayne Mudge, Elgin, 1, 5, 5 Brian Smith, Newton Park, 18, 107, 17 Riana Muire, Emerald Hill, 11, 27, 10 Tim Smulian, Wydgelee, 2, 6, 5 Dirk Muller, Roggebaai, 9, 12, 10 Larisa Smuts, Stellenbosch, 2, 3, 3 Penny Mustart, Mowbray, 20, 106, 29 Rowena Smuts, Cape Town, 3, 6, 6 Brett Myrdal, Muizenberg, 2, 9, 8 Sandy Smuts, Rondebosch, 334, 1665, 157 *** Naik Clanwilliam, 8, 35, 17 Darelle Snyman, Kleinmond, 9, 34, 14 Ingrid Nanni, Claremont, 2, 2, 1 Zyta Soomer, Elsenberg, 1, 8, 8 Willem Naude, Vrede, 11, 11, 2 Craig Spencer, Kleinmond, 51, 188, 43 Victor Ndlangisa, Underberg, 1, 1, 1 Jeff Spencer, Pinelands, 6, 21, 17 Waldemar Neuwirth, Fish Hoek, 31, 183, 42 Sarel Spies, Tygerberg, 20, 81, 38 Bheki Ngubane, Louwsburg, 3, 5, 3 Michael Sprenger, Claremont, 10, 24, 12 Gerrit Nieuwoudt, Humansdorp, 56, 220, 31 Marc Stalmans, Nelspruit, 124, 203, 13 Patricia Nisbet, Cathcart, 92, 130, 10 Joan Stanford, Gouritzmond, 3, 9, 6 Agreement Nyembe, Mbabane, 3, 5, 3 Ann Steele, Pinelands =(***), 591, 2476, 169 John and Nerine Oakes, Greyton, 14, 87, 22 Charl Steenkamp, Touwsrivier, 31, 72, 6 Rudi Oberholser, Ladismith, 11, 50, 15 Koos Steenkamp, Tulbagh, 2, 13, 8 Derrick Odendaal, Bloemfontein, 24, 25, 4 Jeanette Stewart, Howick, 1, 2, 2 Ted Oliver, Claremont, 68, 231, 73 Marthinus Steyn, Pietersburg, 1, 2, 2 Deirdre Opie, Heidelberg, 12, 48, 16 Tommie Steyn, Lydenburg, 19, 39, 10 Graham Opperman, Hout Bay, 1, 5, 5 Sandra Steytler, Wynberg, 9, 24, 19 David Osborne, Still Bay, 3318 ], 12648, 244 7 Craig Stobie, Observatory, 91, 363, 78 Karen Osborne, Ladismith, 47, 145, 22 Marinus Stoltz, Stellenbosch, 5, 20, 12 Outeniqua Tramps (Outramps), Hoekwil, 1732 ], Johann Strauss, Bellville, 12, 63, 16 8032, 219 7 Michael Stroud, Bredasdorp, 143, 1007, 65 Wendy Paisley, Rondebosch, 850, 3765, 230 7 Vanessa Stroud, Bredasdorp, 14, 87, 19 Istvan Pajor, Pietermaritzburg, 1, 1, 1 Irene Strydom, Pinetown, 1, 3, 3 Penny Palmer, Kleinmond, 120, 931, 61 Carolyn Sunners, Nhlangano, 1, 1, 1 Zohra Parker, Parow East, 3, 15, 9 Tim Sutton, Hermanus, 7, 30, 17 Lionel Pero, Halfway House, 1, 1, 1 Merek Svacha, Inchanga, 3, 8, 8 Michael Peter, Vryheid, 4, 12, 4 Shaun Swanepoel, Mosselbay, 321, 1399, 47 Peter Phillipson, Grahamstown, 2, 6, 6 Petra Swart, Bredasdorp, 1, 3, 3 Stephanie Pienaar, Cotswold, 23, 99, 34 Nicolaas Swarts, Paarl, 4, 16, 7 Deon Pieterse, Newfields, 1, 3, 3 Piet Swatz, Bredasdorp, 2, 7, 6 Milile Plaatjie, Thorn Hill, 1, 6, 6 Murray Taylor, Constantia =(***), 3, 6, 4 Ricky Taylor, Congella, 3, 3, 1 Willie Viljoen, Vryheid, 2, 3, 2 Ken Thomas, Villiersdorp, 11, 65, 25 Fritz Volk, Greyton, 102, 636, 106 Jennetta Tilney, St James, 418, 1911, 141 Caitlin Von Witt, Kirstenbosch, 3, 3, 2 Andrew and Eve Tiltman, Pinelands, 982, 3793, Lewine Megan Walters, Parkwood Estate, 12, 69, 20 205 7 Benjamin Walton, Muizenberg, 14, 35, 15 Jonathan Timberlake, Bulawayo, 1, 1, 1 Paul Waterhouse, ***, 15, 18, 10 Nicholaas Tities, Swellendam, 7, 26, 12 Karen Watkins, Newlands, 20, 72, 30 Peter Toms, Kalk Bay, 2, 9, 9 Denise Wedderburn, Rondebosch, 1, 4, 4 Towerkop Field Rangers, Ladismith, 572, 2766, 73 Adrienne Wedepohl, Somerset West, 2, 18, 17 Miemsie Troskie, Simonstown, 2, 8, 7 Felicity Weir, Ugie, 2, 5, 3 Moira Truter, Tokai, 20, 131, 43 Brian Wells, Port Elizabeth, 3, 6, 5 Roseanne Turner, Kirstenhof, 2, 7, 6 Chris and Jo Welman, Newlands, 744, 2544, 163 Ross Turner, Claremont, 13, 21, 5 Nigel Wessels, George, 153, 578, 80 Jason Valentyn, Robertson, 3, 17, 15 Anita Wheeler, Uniedal, 2, 12, 10 Michael Van Beinum, ***, 3, 9, 8 Louis Willemse, Waterfront, 11, 34, 15 Eva Van Belle, Fish Hoek, 2, 2, 2 Kitty and Austen Williams, George, 1042 ], 5417, *** Van Dalen, Retreat, 1, 8, 8 179 Harry Van Den Berg, Malkerns, 5, 6, 3 Louise Williamson, Mossel Bay, 2, 8, 6 Yvonne Van Den Berg, Riversdal, 3, 24, 11 Crystelle Wilson, Durban, 2, 1, 1 Corey Van Der Laan, Somerset West, 89, 602, 76 Molly Wilson, Diepriver, 3, 24, 16 Cedras Van Der Merwe, Grabouw, 5, 31, 13 Jonas Witbooi, Uniondale, 34, 144, 23 Jenny Van Der Merwe, Knysna, 1, 3, 3 Owen Wittridge, Simonstown, 3, 19, 15 Pierre Van Der Spuy, ***, 43, 177, 51 Johannes Wolligter, Porterville, 2, 3, 3 Riaan Van Der Walt, Porterville, 79, 307, 81 Elmien Wolvaardt, Gardens, 6, 34, 15 Collette Van Deventer, Ladismith, 6, 18, 7 Julia Wood, Tokai, 5, 6, 3 Louis Van Dyk, Citrusdal, 3, 18, 12 Steve Worth, Mowbray, 3, 5, 4 Sunette Van Romburgh, Touwsrivier, 6, 28, 9 Peter Wright, Bredasdorp, 11, 54, 25 Chris Van Vuuren, Simonstown, 610, 1955, 93 Ken Wynne-Dyke, Amanda Glen, 17, 105, 34 Abraham Van Zyl, Robertson, 1, 3, 3 Mary Yates, Port Elizabeth, 2, 5, 3 Appel Van Zyl, Stellenbosch, 8, 19, 10 Bonani Yekani, Scarborough, 1, 2, 2 Johannes Vaughan, Ladismith, 79, 284, 18 Stefenie Yelenik, Rondebosch, 14, 41, 19 Joseph Vena, Loerie, 1, 3, 3 Marilyn Young, Knysna, 1, 3, 3 Wessel Vermeulen, Knysna, 2, 6, 5 Gayle Youthed, Grahamstown, 6, 22, 8 Piet and Sonja,Viljoen, Igogo, 28, 43, 2 Nana Zolbrod, Wynberg, 37, 108, 32
1. INTRODUCTION Background Developments in computing have bridged the gap The Protea Atlas Project was styled on the between simple compilation of raster lists to point successful Banksia Atlas project (Taylor and Hopper sampling and compiling lists for any mappable unit 1988), run from 1984-1986. The excellent of larger size. As the Banksia atlassed had background, history and evaluation of the Banksia pioneered this approach, it made sense to take a fine Atlas gave the Protea Atlas a head start: this scale approach and see what sort of coverage attention to detail is provided here so that future volunteers could contribute to the mapping of the atlasses can learn can benefit from the experiences flora. of these two projects. Rationale Atlassing probably originated on a national scale with the Atlas of the British Flora (Perring and
2. HISTORY Background , Historical data , How the Protea Atlas Project was organised and run Data processing , ,
Recruiting and training Data vetting People were recruited by a variety of means. , Records were only kept of those who completed an application form. Applicants were sent a recording kit and 2-years of Protea Atlas Newsletters. These Feedback included details of courses, and trips. Of the ??? who Data summary applied, Newsletters Web page Interim Distribution Maps, Recording kit , Data availability , Data collection and validation , Maintenance and accessibility ,
3. ASSESSING PROTEA ATLAS INFORMATION ,. co-ordinates versus grid squares
Biases Map work problems , ,
Geographical sampling biases Locality and altitude resolution , ,
Seasonal biases Conservation status , ,
Current data biases Altitude , ,
Coverage Landform , ,
Aspect Evaluation of problems experienced , Superbly guided by Banksia Atlas and its excellent “points to consider for future atlasses” Soils: type and colour , Identification Field guide needed from start, Vegetation structure , The Sight Record Sheet , Extent of veld , Core data/optional data , Age of veld , Size of Record locality , Population size , Point versus raster locality references
Population distribution Picking , ,
Flowering Average Height , ,
New Growth Pollinators , ,
Seedlings , Recommendations for future Atlasses Fire survival , ,
4. ACHIEVEMENTS AND FINDINGS , Hybridization New Taxonomic discoveries , , Variation within populations Range Densification , , Atlas efficiency Range Extensions , , Area Conservation and Red Data List status , , Season Taxonomical problem areas , ,
5. GUIDE TO THE SPECIES ACCOUNTS Guide to the Species Accounts: Interpreting Protea Atlas Data.
therefore capitalized them appropriately. The are Please read this thoroughly! presented in the sequence English, Afrikaans, Without understanding the definitions, assumptions German, and other. and limitations of the Protea Atlas Project you cannot validly interpret the data. See “Data Other Scientific Names: Collection and Validation” and “Assessing Protea Older and invalid scientific names. These may be Information” for further information on the because an older name exists for the same taxon, or background to the project. Herein we summarize because of a technical fault with the publication (e.g. information needed to interpret the data presented in no type specimen, no Latin description, not validly this volume. published). Where the authors are in brackets they assigned the species to another genus. The dates are Scientific or Botanical Name: for the author who named the species. This comprises the genus, species and author of the Listings of names reassigned to older or current name and date of publication. Where the species genera are not given (technically in Botany the two was first described in another genus, the author of names are separate entities). For instance, Protea the first name is presented in brackets, followed by lagopus Thunberg 1781, Paranomus lagopus the author who assigned it to the current genus – in (Thunb.) Salisbury 1809 and Nivenia lagopus such cases both dates are given. (Thunb.) R. Br. 1810 are all different names based Technically the current valid name for any taxon (a on the same type and description. Because the genus, species, subspecies, variety or form) is the species name is identical to the current name, none earliest published name containing the name, a brief of these will be listed as synonyms. Latin description of the species and reference to a herbarium specimen (or illustration) which becomes Sample size the “type”. The Proteaceae were bedeviled by a The sample size is the total number of record feud between supporters of Richard Salisbury who localities for the species, and is an index of both the published in 1809 and Robert Brown who published abundance of the species and of recording effort. in 1810. Brown’s supporters – comprising the Sample sizes are repeated for data below, as they professional scientists and gentry – claimed that often include only subsets of the data. For instance, Salisbury – a cantankerous novo riche – had sat in planted records were excluded for habitat on a talk by Brown to the Linnaean Society and descriptions, but were used for species specific rushed into print a plagiarized work. However, information such as flowering, growth and height. Salisbury clearly had a manuscript prepared or in Note that atlas data are often highly pseudo- preparation by the time of Brown’s talk. The rules replicated: thus for a given area, month or habitat, clearly give Salisbury precedence, but this was only the data may be highly temporally and spatially accepted in the 1930s, requiring extensive revision clumped. For example, most of the data for a month of names – especially since both Salisbury and from a region could have arisen from a single Brown described all species then known. Today we weekend’s hike on a trail. This is not optimal as use Robert Brown’s generic names (which have local idiosyncrasies can bias results: the ideal would been given a “conserved” status, despite not being be if the records were scattered all over the region first) and Richard Salisbury’s species names. Some over the entire month. Bear this in mind when authors have argued that Brown’s generic interpreting the data. delimitation was better than Salisbury, but apart from Leucadendron it is Salisbury’s generic DEMOGRAPHY concepts that are in use today for Cape Proteaceae. The population size of a species has many Common English Name: implications for its biology. For example, species The most popular name in use: the official common comprising very small populations may have name. inbreeding problems. This may result in the loss of genetic variability so that the species becomes very Common Afrikaans Name: specific in its habitat requirements. This The most popular name in use, if any: the official specialization may result in populations which are Afrikaans common name prone to extinction should the climate change or disease infect them. Species characterized by small Other Common Names: populations are often very localized in their Other names encountered by atlassers or published distribution and confined to a very few localities. in the literature. Many species have many different Some more widespread species are also confined to common names and these may vary from region to small populations. These species often vary region or refer to different species in different considerably in their habitat and floral or leaf regions. We have not discriminated between correct morphology between populations. By contrast, and inconsistently used names in the belief that local species with dense populations comprising millions names arise spontaneously and often have charm and of plants, or with sparsely distributed plants in intimacy not found in the official names. We do not populations covering huge areas are probably consider them less correct or legitimate than the relatively resistant to epidemics, climatic change and official common name. We regard common names human interference. The density of plants in these as proper nouns and not collective nouns and have species may reflect competition for water, space or the distribution of disease organisms in the areas. Flowering: The state of flowering in a species is mainly Most species have a few relatively dense determined by the time of year, water availability, populations, covering a relatively large area (in the plant resource levels, and the age of the plant. core or favoured habitat) and some sparser Different species flower at different times of the populations of smaller size (usually peripheral to its year. Whereas some species only flower at a main distribution range). specific season, other species may flower at different times of the year in different areas or at different Population: altitudes. Whereas some species only flower after Called “Population Number Code” on the SRS, or fires, others may require 1 - 15 years before they “Population size”, this refers to the number of plants produce flowers. within the Record Locality. Seedlings were not In order to determine the flowering code atlassers included, but young plants were. The codes were first needed to check whether the species stores old defined as follows: flowerheads (infructescences or seedheads) for many • A Abundant More than 10 000 plants. years on the plant (serotiny) or discards them before • C Common 101 to 10 000 plants. flowering again. Seedheads are not to be considered • F Frequent 10 to 100 plants. for determining flowering code. They are easily • # Rare 1-9 plants (the actual number recognized as they occur on the same branch below seen was recorded, or for those the current year’s inflorescences. recorded as R, the default value The unit of flowering is the flowerhead: either of 5 was entered). conflorescences (flowerheads comprising many • X Extinct Known localities where plants inflorescences e.g. Mimetes, Paranomus, Serruria, were absent, or Sorocephalus, Spatalla) or inflorescences (flower • Only dead plants or heads e.g. Leucadendron, Leucospermum, Protea) skeletons seen. on different plants. Atlassers had to determine Data are presented as percentages in the order listed whether the majority of the con/inflorescences in the above for categories with data only. Only natural population were: populations (see “Planted Records”) are included. in bud (flowerheads closed or open but no Repeat data are included. florets open) open (some florets open, some closed) or Dispersion: over (all florets open or finished). Called “Population Size Code” on the SRS, or Flowerheads may remain as “buds” for many “Population distribution,” this refers to the density months, stay open for several days to several weeks of plants within the Record Locality. The codes are and remain as over for two to twelve months (any defined as follows: longer than a year and they are considered • E Even Uniformly distributed seedheads). However, it is possible to have a throughout the Record Locality population in which half the flowerheads are in bud but confined to the Record and one third are over and the remainder (one sixth) Locality. are open and yet the population may never have a • W Widespread Uniformly distributed larger proportion open. The flowering codes were throughout the Record Locality defined as follows (flowerhead = conflorescence or but extending well beyond the inflorescence): Record Locality (either into a • B In Bud Majority of flowerheads in bud. different habitat or because the A few may be open but fewer habitat exceeds the maximum are over than are open. Record Locality size of 25 ha). • F Flowering Flowerheads either in bud or • V Variable Varying conspicuously in density within the Record over predominate with some Locality. open. All three classes must be • C Clumped Confined to one or a few present. localized patches within the Record Locality. • P Peak Flowering Some flowerheads in bud It was found that based on the above definition and over but with the majority atlassers used “W” for “V” species when these open. extended beyond the Record Locality. Although • O Over Majority of flowerheads over. A regularly corrected, this bias persisted. few may be open, but fewer are Data are presented as percentages in descending in bud than are open. order by code. Only natural populations (see • C In Cone All of flowerheads over. None “Planted Records”) are included. Repeat data are open or in bud. Seedheads with included. seeds present on plant. • N Nothing No flowerheads visible either as PHENOLOGY in bud, open or over. Seedheads absent or having released all Due to repeat visits to the same site by certain their seeds. atlassers, multiple visits to sites by different Note that the flowering code refers to the total atlassers, and the volume of data received, it was number of flowerheads in the Record Locality. It hoped to be able to compile seasonal patterns of does not refer to the numbers of bushes or plants in flowering and growth patterns and seedling flower! It does not refer to the total number of emergence. These are presented here. individual florets! It does not refer to the florets within a flowerhead! Note that “In Cone” and young leaves which are often hairy, more curved, “Nothing” only apply when there are no smaller, and of a different colour to that of mature flowerheads. Note that “In Bud” includes states in plants. which there many be no flowers or flowerheads The identification of seedlings is not easy. Only open. Planted (see “Planted Records”) and Repeat atlassers who had germinated proteas from seeds, or SRS are included. who intimately knew the pre-fire proteas, would Data are summarized textually and as a graph (see have been able to identify different species. The “Interpreting Flowering Graphs”). Sample sizes are seedlings present codes are defined as follows: presented for each month. Generally at least 10 • N None No seedlings of the protea records are required to have any confidence in the species present. data (ideally these should also not be • R Rare Seedlings fewer than number of pseudoreplicates). For each category, the months parents or skeletons. with over 20% of records are listed. Peak levels are • M Many Seedlings more numerous than presented as the percentage of records, summed over parents or skeletons. Peak Flowering, Flowering, In Bud and Over, in the If atlassers were uncertain as to the identification of highest-scoring month. The previously published any seedlings present then they recorded in the flowering period – usually the latest monograph - is Additional Remarks Box that unidentified protea presented for comparison. seedlings were present. The number of parents is the number of adult plants in a mature stand or the Growth: number of dead plants after a fire. The period of shoot and leaf growth is a vulnerable Very few records of seedlings were obtained. period in the protea’s annual cycle. Heat, lack of Seedlings excluded immature or juvenile plants that water and herbivores may destroy the young leaves had lost their cotyledons but have not yet flowered: and shoots before they may have had time to expand those atlassers wishing to present seedling to parent and harden. In order to protect the young growth, ratios for juvenile plants did so in the Additional they are often covered by hairs or wax and may be a Remarks Box. It was anticipated that useful different colour to the hardened robust leaves and seasonal data would be obtained by recording stem. Typically new growth on a stem lasts only a seedlings, but it transpires that many species retain week or two, although plants may produce several cotyledons for an entire year, so that seasonal data growth flushes over the year, and not all branches are not particularly useful. Furthermore, few will produce shoots. However, it was considered too atlassers were able to identify seedlings and data are complicated to ask atlassers record more than the sparse. Thus seasonality of seedlings is not presence of new growth. The new shoot growth presented. codes are defined as follows: Nevertheless, some useful data on seedlings were • N None No new leaves or fewer than 5% obtained. These are presented as absent, and where of bushes with new leaves. present the seedling to parent ratios are presented. • R Rare New leaves on fewer than half The month of seedling observations is also the bushes. presented. Data include repeat SRS, but exclude • M Much New leaves on more than half the planted (see “Planted Records”) data. bushes. Note that the code refers to bushes, not stems. ECOLOGY Planted (see “Planted Records”) and Repeat SRS are included. Fire survival: Data are summarized textually and as a graph (see Fire is an important phenomenon in shaping “Interpreting Growth Graphs”). Sample sizes are vegetation communities and may determine which presented for each month. Generally at least 10 species are present in any Record Locality. Proteas records are required to have any confidence in the differ in their response to fire. data (ideally these should also not be Some species have dormant buds beneath a thick pseudoreplicates). For each category, the months insulating bark. These buds start growing when the with over 20% of records are listed. Peak levels are foliage has been killed by fire. These are the stem presented as the percentage of records, summed over resprouters. Peak Flowering, Flowering, In Bud and Over, in the Other species have dormant buds below ground level highest-scoring month. There is no previously either on a thick rounded bole or on thin available data on growth phenology for comparison. underground stems. Following a fire which may kill all the above ground parts, the buds start growing Seedlings: from below ground. These are the ground Most protea seeds only germinate following a fire. resprouters. Any seeds which might germinate at any other time Other species do not have dormant buds. When a are probably consumed by rodents. However, some fire occurs the plant may be killed. In these cases species do recruit new plants into the population regeneration is by seeds only (obligate seed between fires. These exceptions are poorly known regenerators). These seeds may have been stored in but are probably more common than currently a dormant state on the plant (waiting for the fire thought. This information is crucial to reserve before being released) or stored dormant in the soil managers who must decide when to burn vegetation (waiting for fire to trigger off their germination). if it appears moribund. Some obligate seed regenerating species may escape For the Protea Atlas purposes, a seedling is a plant death by avoiding fires. They may grow too tall for which still has conspicuous cotyledons present near the fire too affect them, or they may occur in the base of the stem. Cotyledons are the large, flat habitats where fires usually cannot kill them (e.g. 'seed-leaves' that are the first to appear when a seed between big rocks). germinates. Note that most seedlings also have This data was only recorded if there has been a fire It was hoped that to use this data, together with the recently and it was possible to determine the fire Age Veld, could be used to determine how long after survival strategy. If atlassers were uncertain or if a fire species flower for the first time. they could not determine a strategy then the field Data are presented textually and graphically (see was to be left blank. “Interpreting Age to Flowering Curves”). Three The fire survival codes are defined as follows: values are presented: the age at which the first • K Killed All adults killed by fire. No records of flowering were obtained, an estimated age seedlings or young plants present. from the graph of when 50% of the localities had • S Seedlings All adults killed by fire. Only flowering plants, and the age at which all (100%) of seedlings or young plants present. the localities had flowered. The latter figure • E Escape A few adult plants survived excluded young veld with only a single or 2 data escaping the fire by being too tall points. Only natural populations (see “Planted or growing among rocks. Few to Records”) are included. Repeat data are included. many plants may have been Data for patchy fires and data in the ARB are killed. excluded. • B Bole Most plants not killed by fire. Resprouting from below ground. Height: • T Trunk Most plants killed by fire. This is the distance from the ground level to the top Resprouting from trunk. of the bush. Take the average height of the live Young plants are defined as those that have lost their bushes in the stand. Ignore plants killed be fire or cotyledons but have never flowered. They are thus dead material on resprouting bushes (in which case intermediate between seedlings and adults. record the height of the top-most living shoot). The The escape code “E” is not used plants that survived average height codes are defined as follows: in swathes of unburnt vegetation where a fire burnt • 1 < 0.2m Less than twice ankle height. unevenly. All the above categories only apply in • 2 0.2m - 1m From 0.2m to waist height. areas where a fire had burnt recently. If categories • 3 1m - 2m From 1m to between elbow and “E”, “B” and “T” are accompanied by seedlings, or hand height when arms are held for category “S”, the presence of seedlings must be above head. added to the Seedlings Present code. • 4 2m - 5m From 2m to about three times Data are presented as percentages in descending your height. order by category. Only natural populations (see • 5 > 5m Taller than 5m. “Planted Records”) are included. Repeat data are Assigning height classes was often difficult. Most included. atlassers chose the modal height (that of most of the plants) as this made most sense. Although many Age to first Flowering proteas generally occur on cohorts (plants of the If the Record Locality was burnt within the last few same age, dating from the last fire) of the same years and the age of the vegetation could be worked height, plants of some species may vary out or ascertained from a local resident or forester, considerably in height within the Record Locality. then the period since the last fire was recorded in However, the distribution of height classes usually decimal years (e.g. 4 months was recorded as 0.3). occurs on one of two patterns: If there were signs of a recent fire but it could not be • Two or more distinct height classes. Under these found out when the fire occurred the field was to be circumstances record the average height code of the left blank (the presence of fire was coded most common height class in the Average Height separately). field and in the Additional Remarks Box record the If there was no obvious sign of fire and a non- heights of other distinct classes. resprouting, serotinous female Leucadendron or • No distinct height classes, merely a continuous Protea species was present, then the age of the range in heights. Record the average height in the Record Locality could be estimated as follows: Additional Height field and in the Additional Select an average looking bush and select a branch Remarks Box record that the height of the species tip on this bush. Starting at the branch tip and varied over a wide range. following the stem to the base, count the positions Data are presented as percentages in descending where old cones still occur, or used to occur, order by code. irrespective of whether they have opened or not. Planted records and Repeat data (see “Planted Add 2-4 years to this figure (to account for the time Records”) are included. ARB data are not included. it took the plant to grow before flowering) and you have a rough age for the Record Locality. If there is Pollinators: another serotinous species in the Record Locality Many species of insects, mites, birds and mammals repeat the count and take the oldest age. This age visit protea flowerheads. These visitors are attracted should be accurate to within 5 to 20 years depending by nectar, pollen and sometimes resins and waxes on the age of the vegetation. produced by the flowerheads. In addition, other Occasionally fires burn an area in patches, leaving visitors eat the floral parts. Flowerheads also swatches of vegetation unburnt. These two areas do provide shelter, warmth and protection. Yet other not comprise two Record Localities. In such a case visitors feed on or hitch rides on these visitors. record the age of the most extensive area in the Age Many of these visitors are not pollinators. Others Veld box and in the Additional Remarks Box record carry pollen and are therefore pollinators. To be in the less frequent age. Code the Signs of Fire code as effective pollinator a visitor must brush against the “P” for Patchy. Under such conditions atlassers stigma or the pollen presenter (the modified portion could record in the Additional Remarks Box (ARB) of the style which holds the pollen. Since the stigma whether species in the burnt area have begun and pollen presenter are very close together at the tip flowering. of the style (except in Leucadendron and Aulax) brushing against the one invariably means brushing totally translucent. Mouth parts are sucking against the other. If a visitor has pollen on its head (tube like). or body then it is almost certainly a pollinator. • N No pollinators seen. Not all visitors are diurnal, some are nocturnal. The This was only supposed to be filled in after only nocturnal visitors readily detected during the extensive checks when the species was day are small mammals, as these leave signs of their flowering. Initially many atlassers filled this visits (i.e. faeces, occasional foot spoor and in as a matter of course, whether the species pathways or runs between protea bushes, and was flowering or not. These data were nibbling of florets and bracts). If these signs are discarded and atlassers informed that at least present in, or adjacent to, flowerheads, mammals 2 hours of observation were required to fill in were recorded as pollinators, as it is virtually code “N”. impossible for mammals to take nectar without Ants were not considered pollinators and being dusted with pollen. specifically excluded from “Hymenoptera” to this Any observations of animals visiting a flowerhead, end. They do not move between plants (most or their signs, were recorded in the two pollinator proteas are self-infertile - pollen will not set seed in code fields. Additional pollinators were recorded in inflorescences on the same bush) and have powerful the Additional Remarks Box. Where pollinators fungicidal secretions which kill pollen. Their could be identified to spedies, these were recorded in presence could be recorded in the Additional the “Specify name of pollinator” section. The Remarks Box. pollinator codes are as follows: Data are presented as percentages in decreasing • W Wind. order by category. All data were included, except It is difficult to observe wind pollination. that in ARB. Where sample sizes are unambiguous To determine if wind may pollinate a species, (all records for a single pollinator, or single shake a branch with inflorescences. If pollen observations), they have been omitted. cascades down in considerable quantities (stand upwind if prone to hay fever) the Detailed pollinators: species is both wind pollinated and ready to Atlassers volunteering more detailed information on be pollinated. pollinators did so here. These included common or • S Bird (Sunbirds or Sugarbirds or other Latin names of visitors, and sometimes just more species). information such as moths, Scarab or Monkey Birds are obvious visitors and the easiest to Beetle, or Carpenter Bee. Data were coded at the identify: note the species in the “Specify Protea Atlas office. Additional Remarks Box data name of pollinator” area. were included here also if specific enough. • M Non-flying mammal (rats, mice and shrews). Data are presented in decreasing order with the Most rodents are nocturnal so look for signs sample size in parentheses. Unambiguous sample runs, faeces, spoor, nibbled bracts. sizes (i.e. single records or where all records are for • H Hymenoptera (bees and wasps). a single pollinator) are omitted. All data were Bees and wasps have pinched waists, two included. pairs of translucent wings and obvious antennae which are longer than their heads. HABITAT • D Diptera (Flies) Flies have only one pair of translucent wings, Distance to Ocean: very short and inconspicuous (3 segmented) The sea has several major influences on plant life. It antennae and sucking mouth parts (either provides a spray of nutrients, but at the same time tube or mop like). contains dehydrating salts. It provides a stabilizing • L Lepidoptera (Butterflies and Moths) effect on temperature and humidity, so that Butterflies and moths have colourful or extremely hot or cold conditions occur very camouflaged wings covered with fine scales infrequently near the sea. It also provides mist and and long thin coiled mouth parts. moisture. Consequently, plants nearer the sea live in • C Coleoptera (Beetles) a different habitat to those far inland. A distance of Beetles have hard, dark front wings (elytra) 2 km was arbitrarily chosen as the limit of the which are horny or leathery, never veined combined effects that the sea has on plants. and meet in a straight line on the back. Atlassers recorded a “Yes” if the Record Locality is These cover the membranous flying wings within 2 km of the coast or a “No” if the Record which are folded underneath the elytra when Locality is not within 2 km of the coast. not in flight. Mouth parts are biting. Data are presented as the proportion of records • A Acarinae (Mites). within (coastal) or further than (inland) 2 km of the Although at first easily overlooked, close coast. Repeat data are not included. inspection of some Protea flowerheads with mites will reveal the thousands of mites Altitude: present as a swirling mass on the heads, and Altitude determines the frequency of snow and frost, subsequent itchiness on hands (and nose, if the amount of cloud and rainfall, and the amount of smelled). Tapping or breathing on the heads sunlight reaching a plant. results in a drastic increase in activity and Altitude (in metres) is the height above sea level at upwelling of these mites, in the expectation the Record Locality, based on maps, preferably the of a ride on the visitor to another flower. 1:50 000 series topographical maps. Missing data • X Other (Bats, Bugs, etc.). were filled in by data checkers at the atlas office. Bugs (Hemiptera) have the upper half of the Data are presented textually and graphically (see outer wings dark and translucent, the inner “Interpreting Altitude Graphs”). The following syntheses are presented: Minimum and maximum Note that the plant species on the edge of rivers and altitude, followed by the lower quartile (lq), median lakes and pans”, and in swamps, dambos and (med) and upper quartile (uq): these are respectively seepages, are usually totally different from those in the values of one-quarter, one-half and three- the surrounding veld. These are quite different quarters of the sorted altitude, binned into 20m habitats as plants may have access to perennial altitude classes. Repeat and planted (see “Planted water, or may be waterlogged - conditions for which Records”) data are excluded. plants must especially adapted. Thus separate Sight Record Sheets should be completed when a species Landform: occurs both next to a river or lake and in the veld There are a great many potential landforms within a beyond. landscape. For simplicity the Protea Atlas Project Data are presented as percentages in decreasing defined the following Landforms: order by category. . Repeat and planted (see “Planted Records”) data are excluded. • RV Riverine vegetation: on river banks or in river bed. Slope: Rivers (may) have water flowing in a Slope is important in determining species’ clearly defined channel, with boulder beds distributions. Not only the angle of the slope, but or sand banks clearly visible. Typically also the position on the slope. However, codes were riverine vegetation flanks the river and does kept simple. The slope codes used for the Protea Atlas Project are defined as follows: not extend much inland. o • SW Swamps, dambos and seepages: permanent • CL Vertical slope or cliff (> 60 ). • SI Steep incline or slope (> 20 o and < 60 o). or seasonal. o o These are covered with vegetation. Most • GI Gentle incline or slope (> 5 and < 20 ). water movement (when present) occurs • DU A gentle undulating dune landscape. below ground-level or within the This caters for dune fields and fossil dunes. vegetation. There are no conspicuous It refers to cases where the Record Locality channels of water movement. Swamps may comprises an undulating topography but the also occur between rivers or on the edge of vegetation and soil are similar over the rivers and lakes. undulations (alternatively, record the • LE Lake or pan edge. different communities using other codes). • HT Flat or level (< 5 o). Hill or mountain top. These are devoid of vegetation, containing o either expanses of water, salt or bare soil. • VB Flat or level (< 5 ). Valley bottom or bottomlands. Any vegetation present is strictly seasonal. o Typically vegetation on the pan edge is • PL Flat or level (< 5 ). A flat platform on a restricted to a narrow belt of several meters slope. and does not extend much inland. Data are presented as percentages in decreasing • RO Rocky outcrops, boulder screes. order by category. . Repeat and planted (see Rocky outcrops are distinct localized piles “Planted Records”) data are excluded. of big or small rocks or boulders (> 1 m diameter), which intrude on the general Aspect: landscape. It includes boulder screes. The aspect describes the direction that the entire Record Locality faces, when the slope is greater than Typically there is very little soil within the o outcrop, most of which is confined to 5 (i.e. Slope codes CL, SI and GI). Fill in the box crevices. using the 16 compass points. For flat and level • DS Deep soils: no bedrock visible. Record Localities aspect has no meaning, so leave Deep soil is used as a relative term to blank. For highly variable aspects, without clear distinguish areas where there is no bedrock direction, leave blank. or half-buried boulders visible at the soil The 16 compass points are as follows: surface. The actual depth to which the soil N, NNE, NE, ENE, E, ESE, SE, SSE, S, SSW, SW, may extend before encountering the WSW, W, WNW, NW, NNW. In reality, atlassers tended to code aspect if any bedrock is irrelevant to the definition. o • SS Shallow soils: bed rock clearly visible. slope was detectable (a slope of 1-2 ). Also the Shallow soil is used as a relative term to primary compass points were coded more than the distinguish areas where bed-rock is clearly intermediates (see “Interpreting Aspect Pies”). visible and protrudes from the soil: at least Data are presented textually and graphically (see quarter to half the area has to be bedrock. “Interpreting Aspect Pies”). Data are summarized Large half buried boulders are considered to into the 4 major compass points, presented as be part of the bed-rock. The actual depth to percentages in decreasing order by category. For which the soil may occur between the summarizing the data, NE, SE, SW and NW were boulders is irrelevant to the definition. halved and this summed to the respective compass • TM Termite mound. points. Repeat and planted (see “Planted Records”) Used when species are confined to termite data are excluded. mounds. When recording landform codes proceed from top to Soil Type: bottom until correct category is reached. If two The simplified soil types used by the Protea Atlas codes are equally applicable choose the uppermost. Project are defined as follows: Soil depth is considered unimportant in the top three • S Sand Comprising mainly course or fine landforms. sand. Loose when dry, crumbles even when wet, not sticky, water drains through. • L Loam Comprising sand, clay and organic Where exposed bedrock differs from boulders, matter. Fragments into pieces when record the bedrock as the geology code, and record squeezed, malleable when wet, information on boulders in the Additional Remarks slightly sticky, porous to water. Box. • C Clay Comprising mainly clay. Hard when Geologists, engineers and gemmologists added dry, moulds readily when wet additional geological codes, as required. Where without cracking, sticks to fingers species were known to have, or were suspected of when wet. Water tends not to sink having, highly specific geological preferences, into soil – may have cracks when atlassers were encouraged to record the geology as dry. technically as they deemed necessary. • P Peat Often dark, comprising decomposed The primary geology codes used (those marked * organic matter. Very little sand or where not originally defined and - with the clay present. exception of TS - were only used by a few atlassers) • G Gravel Mainly comprising pebbles between and their definitions, are as follows: 2 and 75 mm in diameter, with some • BA Basalt (Igneous - deposited by volcanism). finer material between the pebbles. • R Rocky Mainly comprising pebbles greater Crystals are not visible with the naked than 75 mm in diameter, with some eye. Typically dark green or black in finer material between the pebbles. colour, basalts are not laminated. The top The categories were chosen for ease of use. To of the Drakensberg and Lebombo determine the soil type code, take some surface soil, Mountains were deposited as huge basalt crush and wet it. If a surface layer of leaf litter is lava sheets. present remove it (a kick should suffice) and take the • BC* Banded Chert underlying soil. Record the presence of the litter • CO Conglomerate (Sedimentary - deposited layer in the Additional Remarks Box (This was by water or wind). never done). Additional or more detailed soil types A rock of course fragments or pebbles, could be added to the ARB. cemented together with finer particles. Data are presented as percentages in decreasing Formed by ancient fast flowing rivers, order by category. Repeat and planted (see “Planted rock falls and glaciation. Most commonly Records”) data are excluded. occurs in ancient river valleys and adjacent old mountains. Soil Colour: • DI* Dibase Soil colour may reveal interesting things about the • DO Dolomite (Sedimentary - deposited by processes which are occurring in soil. Thus rain water or wind) may wash certain minerals from the surface layers of Similar to limestone, but does not foam in soil into deeper layers, changing the colour of the acid unless heated. Typically, crystals are soil with depth. By contrast, a waterlogged soil may too small to see. Caves, blind rivers or have surface colours that normally occur very deep sinkholes may be present in the area. within the soil. Colour also indicates the oxygen Formed by the precipitation of calcium content (essential for root growth) of the soil, and and magnesium in seawater by ancient the origin of the soil. algae, the largest expanses of dolomites Take a handful of soil from the surface (after kicking occur on the Witwatersrand. These away the leaf litter), and determine its colour. dolomites often have thin layers of hard Coloured blocks for this purpose were printed on the chert within them. back of the Protea Atlas manual, but were too • DR* Dolerite extreme to be of practical use. Atlassers were • GR Granite (Igneous - deposited by advised to use the colours relative to the soils that volcanism) they know. The colours used were: Consisting of clearly visible different • K Black kinds of crystals: sparkling flakes (mica), • B Brown colourless pink rectangles (feldspar), and • G Grey black biotite in a background of • W White translucent milky quartz. Common as • Y Yellow large granite domes, which formed deep • O Orange in the earth as magma cooled slowly. • R Red • HE* Haemitite • X Other • HL Hardpan laterite / silcrete (Metamorphic - Often soil colour may vary over remarkably short formed chemically) distances: this was not regarded as indicating a New These hardpans (ouklip) are recognised by Record Locality unless it was accompanied by their dark colour, and their occurrence as changes in vegetation or soil type. thick sheets (crusts) on the tops of hills. Data are presented as percentages in decreasing They usually comprise spherical nodules order by category. Repeat and planted (see “Planted (occasionally honeycombed) cemented in Records”) data are excluded. a reddish matrix. They were formed in deep waterlogged soils where iron, Geology: aluminium or silicon were leached from The geology code refers to the bedrock or boulders the upper soil and deposited as a dorbank, visible. At Record Localities where there are no klipbank, or koffee-bank. With the bedrock or boulders visible, leave this field blank removal of the upper soil, these hardpans unless you know the geology or its decomposition have become the exposed rocks. products. • LI Limestone (Sedimentary - deposited by • DE Desert water or wind) With a total plant cover of less than 30%. A pale rock containing more than 50% The landscape is dominated by the geology, calcium carbonate (lime). It foams when rather than the plants (Synonyms: Arid a drop of acid is placed on it. It usually Karoo). has crystals which are visible and may • FO Forest contain shell fragments. Caves, blind Dominated by trees over 5m tall with rivers or sinkholes may be present in the canopies interlocked. Lianas and epiphytes area. Formed from the shells of marine common (Synonyms: Rainforest). molluscs or by chemical deposition, • GR Grassland limestones are mainly coastal in Few tall shrubs or herbs (comprising less occurrence. This category includes than 10% of the ground cover); dominated calcium based hardpan formations, as it is by perennial grasses and herbs. not easy to tell whether limestones were • PL Plantations. formed by sedimentation or chemically. • SH Shrubland • PY* Pyroxenite Dominated by shrubs less than 2m tall. • QU* Quartzites (Sedimentary/Metamorphic - Grasses may or may not be present, but deposited by water or wind) shrubs dominate the projected canopy Sandstone with sand grains fused and not cover. (Synonyms: Alpine Scrub, clearly visible. Brokenveld, Fynbos, Heathland, Karoo, • RY* Rhyolite Macchia, Renosterveld). • SA Sandstone (Sedimentary - deposited by • SU Suburban / urban. water or wind) • TH Thicket A rock consisting of sand grains, clearly Dominated by shrubs and trees greater than visible to the naked eye, cemented 2m tall, but seldom more than 5m tall. together with finer particles, but generally These categories comprise more than 40% with a lot of space between the grains of the projected ground cover. Grasses may making them porous to water. They be present, or bushes may be so thick as to usually occur in beds from several be impenetrable (Synonyms: Strandveld, centimetres to metres thick. Originally Valley Bushveld). formed at beaches, deserts and deltas, they • WG Wooded grassland usually erode to form steep mountainous Dominated by grasses and herbs, but with terrain, as in the Cape and Drakensberg. some trees or shrubs present. Trees and • SC* Schists shrubs may comprise 10-40% of the • SE* Serpentine projected ground cover (Synonyms: • SH Shale (Sedimentary - deposited by water Kalahariveld, Thornveld). or wind) • WO Woodland Dominated by trees (over A rock consisting of fine grained particles, 5m tall). Tree canopies comprise more than usually too fine to see by eye. Usually 40% of the projected ground cover, but clearly layered, with beds several never interlock. Grasses usually dominate millimetres thick. Formed in lagoons the understorey (Synonyms: Savannah, lakes and flood plains, shales comprise Lowveld). 80% of sedimentary rocks. Typical of the • XX Other. Karoo and grain and fruit producing areas Data are presented as percentages in decreasing in the Cape. order by category. Repeat and planted (see “Planted • SO* Talk schists • TS* Tertiary Sands (Sedimentary - deposited Records”) data are excluded. by water or wind) Loose, unconsolidated sand. Deep
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN Low flowering: P. cynaroides does not produce much High Peak: S. mollis is unusual in having a high flowers, although there is a barely detectable autumn- incidence of Peak Flowering. Flowers are found in winter peak. Note that the proportion of Buds is high most months, but lowest in Dec to Feb, when Cones all year round and that this exceeds the Flowers, (seeds in this non-serotinous species) are prominent. suggesting that many buds produce new growth rather than flowerheads. Although serotinous, about an equal proportion have No Flowering, suggesting either a long period to flowering after a fire, or – more realistically – that flowers are not produced in older veld.
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN Broad season: P. susannae has a broad flowering season centred on Apr to Jul, but extending from Jan to Sep. Prior to Jun it is in Bud, and post Jun it is Over. During the non-flowering season the serotinous cones are prominent, with very few records of No JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN Flowering (probably from young veld). Skewed season: M. pauciflorus has a gradual increase in flowering from Apr to Jul, before reaching maximum levels (in both Flowering and Peak Flowering) from Jul to Nov. Post flowering there is a sudden decline with Over and Cone, with a lull prior to producing new flowers in Autumn. Bimodal Peaks: No examples of bimodal flowering (two peaks within a year) were found in our data.
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN Winter Peak: P. burchellii has a winter peak, centred on Jul and Aug. Note that it is serotinous. JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN Short buds: O. zeyherii is unusual in only producing Buds for a very short period (Apr). By contrast plants are Over from Oct to Feb, with fruit (Cones) from Dec to Mar.
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN Summer Peak: A. umbellata has a summer peak, centred on Nov to Feb, with a strong component of Peak Flowering. Note that it is serotinous.
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN Long buds: L. microcephalum has buds from Feb to Jun, and flowers from Jul to Sep. Only Sep has prominent Over. The species is clearly serotinous.
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN Autumn Peak: P. canaliculata has an autumn peak from Apr to Jun. Note that it is serotinous.
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN Long cone: L. conocarpodendron is non-serotinous, and has an almost symmetrical Bud to Over pattern around the flowering period. Although fruit (Cone) peak in Jan to Feb, some can be found all year.
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN Spring Peak: L. nitidum has a broad spring peak, with most flowers in Sep to Oct. Note that it is not serotinous with Cones (seeds) on the plant from Nov to Jan (the “ant” season), and a brief spell of No Flowering before Buds start in Autumn.
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN Short cone: L. catherinae flowers from Sep to Dec, with Over from Dec to Deb. However, fruit (Cones) are only found in Feb to Mar. The broad obvious steps in the bars suggest that data is limited and the graph should be interpreted cautiously.
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN Insufficient data: L. harpoganatum is so rare that it was visited only infrequently – in four months. Although a pattern is evident, the sample sizes are too small to allow confidence in an Oct flowering peak, and do not allow the duration of the flowering season to be deduced, although the Cones from Jun suggest it might perhaps be as long as 6 months or more.
Interpreting Growth Graphs Like the flowering graphs, the growth graphs extend over 18 months from January to June. The unlabelled y- axis on the graph is a proportion (out of 1.0 or 100%). Data are summarized as the proportion for each month, in ascending order, of the growth categories: “Much” in black; “Rare” in red; and, “None
Winter Peak: No examples of a winter peak were found in our data. Proteas don’t grow in winther!
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN Broad season: L. xanthoconus produces growth from Nov to Apr, although there is some growth all year round.
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN Bimodal Peak: P. montana appears to have two peak growth periods: a minor peak in Sep to Oct and a major peak in Jan to Mar. Not many species exhibit such a pattern.
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN Narrow season: L. microcephalum produces growth mainly from Dec to Mar, but peaks strongly in Jan to Feb. There is peak of No Growth in Jul to Aug.
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN No data: D. myrtifolia clearly grows from Dec to Mar, but the Jun and Oct peaks might be spurious based on too little data. Note the large column blocks, suggestive of few records per month, even though 9 of the 12 months have data.
JAN FEB MAR APR MAY JUN JUL AUG SEP OCT NOV DEC JAN FEB MAR APR MAY JUN Low season: A. pallasia produces some new growth all year round, with a Dec to Feb peak, but even during this period only about 60% of populations are active. Interpreting Age to Flowering Curves The age to flowering curves have been calculated from data in which atlassers provided both the veld age and data on flowering. Veld coded as “Patchy” for fire age have been excluded, as have planted records. For each age class from 1 to 20 years old the proportion of records containing “None” for flowering versus the remaining categories for flowering has been calculated. These are presented as points on the graph with the vertical axis being the proportion of flowering records (from 0 to 1) per age class, and the horizontal axis the age of the veld (and by implication the age of the plants) from 1 to 12 years old (the data for 1-20 years are available electronically). The error bars are calculated simply as 1/√n: thus a big error bar of 1.0 implies a single record, an error bar of 0.5 above and 0.5 below would imply 4 records, and progressively smaller bars imply a greater and greater reliability of the data. Although not strictly accurate, it is useful to regard the data as being somewhere along the error bar and not necessarily at the shown point. The curve connecting the points is calculated as a trinomial (ax3+bx2+dx+c). It is not constrained to pass through the axis (thus we do not assume that populations cannot flower within the year following a fire, or that populations cannot have null flowers in much older veld, i.e. we do not assume that c = 0). The curves do not incorporate the sample sizes and consequently we have not presented estimates of accuracy (e.g. R2) of the curves. Note that these data are proportion of populations (or record localities) containing plants that have flowered, and are not proportion of plants within the population. No attempt has been made to check that these patterns are uniform across the species’ distributional range. Note that sample sizes are usually small. Strictly, age to flowering is only useful for serotinous species. These species store the fruit on the plant allowing plants that have flowered in the past to be coded as “In Cone” and not as “None”. For these species the age to 100% flowering is valid. For non-serotinous species, records from the non-flowering period will be coded as “No Flowering” so that values of 100% will rarely be achieved. Non-serotinous species flowering for long periods will attain higher values than those flowering briefly. However, species that are cryptic and overlooked when not flowering (e.g. some Spatalla) may have the proportions overestimated. However, age to first flowering is useful for all species, and so curves are presented for all species. It must be noted though that only a single plant is required for the population to be coded as “Flowering” so that age to first flowering does not necessarily imply an ecological ability to cope with a fire at this age, although it strongly suggests that some plants will survive. The assumption that the age of the plants is the same as the veld age does not apply to resprouters, which may be hundreds of years old. However, the utility of the graphs in determining post-fire flowering patterns is equally useful, and reflects a species ability to recover from the fire. Interestingly, many species exhibit the same patterns of recovery as species killed by fire. Note that the descriptive accounts of the age to flowering included planted or escaped populations for which veld age is known, and thus include marginally more data, resulting in apparent conflict between the descriptive and graphed data.
1 1 g g n i r erin e w o ow l
0.5 fl 0.5
s f s e te i S % Sit %
0 0 0123456789101112 0123456789101112 Age (Years after fire) Age (Years after fire) Typical: A. cancellata produces its first flowers at 2 Non serotinous: P. sceptrum-gustavianus yields a years, but note the high error bars for veld of 2 to 3 curve expected for non-serotinous species, in that years age, and the lack of data for veld one-year old. 100% flowering is not achieved because of records Small error bars at years 6-9 indicate good estimates. outside of the flowering season. Species with shorter About 50% flowering is reached in year 4-5 and flowering seasons will have lower peaks: in this case, 100% is approached, but not reached, in years 7-13. flowering peaks at about 50% of populations, and 1 suggests that after 9 years flower production declines.
g However, the error bars are too large to have much erin w
o confidence in this interpretation. l
0.5 f 1 es t i g n % S i r
0 owe 0.5 0123456789101112 fl Age (Years after fire) tes i
Early flowering: A. pallasia is a resprouter and % S flowers in the second year after fire, reaching 50% at 0 0123456789101112 2-3 years and 100% by year 5, which is maintained Age (Years after fire) thereafter. Resprouter early flowering: S. lineare flowers at 1 100% after flowering. This is more likely to be a g function of the populations not being detected when erin w o
l the plants are not in flower, than a true 100% 0.5 s f e flowering. 1 % Sit g
0 n i 0123456789101112 r Age (Years after fire) owe 0.5 fl
Late flowering: P. stokoei is a late flowerer, with the tes i
first data for flowering in 9-year old veld. Even at 13 % S years 50% flowering has not been attained. 0 0123456789101112 1 Age (Years after fire) g n i Resprouter resembling reseeder: P. nitida (note the r
owe nice short error bars indicative of plenty of data) 0.5 fl tes
i appears to behave as a reseeder, except that in year 1
% S above 20% of the populations have flowered. 0 However, the maximum attained is only 80% of 0123456789101112 Age (Years after fire) populations – as we would expect for a non-serotinous Non-serotinous resembling serotinous: O. zeyheri, species. although myrmecochorous and thus expected to never 1 peak flowering, in fact starts flowering in year 4, g erin w
reaches 50% in year 7, and attains 100% from 8-9 o l
0.5 f es years. The tails on the curve are extrapolation errors. t i % S
0 0123456789101112 Age (Years after fire) Resprouter with decline: B. stellatifolium displays the same pattern as P. nitida up until 7 years, but then declines to zero in year 13. The large error bars indicate the small sample sizes, and the last point is based on a single record which is thus of high uncertainty. Interpretaion of this curve is complicated by the flowers and fruit only being visible for a few months.
1 g erin w o l
0.5 f es t i % S
0 0123456789101112 Age (Years after fire) Resprouter with decline: F. saligna is a tree in grassland and should be largely immune to fire cycles. The data suggest though that most of the plants flower in the first 3 years following a fire, and then declines dramatically. Since most grasslands burn regularly, this might hint that keeping fires out of grasslands results in reduced flowering – however the high variance (error bars) suggests that such a hypothesis might be premature. On the other hand grasslands older than 3 years are relatively rare, and this may explain the lack of data for older veld.
1 g erin w o l
0.5 f es t i % S
0 0123456789101112 Age (Years after fire) Poor data: D. buekii has only two records of veld age, both of which are supported by only a single data point. It is thus difficult to deduce anything from this data other than that some populations have flowered by 3 years.
1 g erin w o l
0.5 f es t i % S
0 0123456789101112 Age (Years after fire) Poor data: F. galpinii has much more records, but most of these are based only on spurious data, as evidence by the large error bars. Little can be inferred other than that it can flower the year following a fire: perhaps not unexpected from a forest margin species.
Interpreting Altitude Graphs Altitude graphs are presented for all species. The vertical axis is the altitude range, which is resolved into 20 m intervals. The Fynbos altitude range of sea level up to 2400 m is used for all species – records from elsewhere higher than 2400 m are binned into the 2400 m class. The horizontal axis is the proportion of records from each altitude class – obviously species with many records from many altitudinal classes will have a low maximum value, whereas species that are poorly atlassed or with narrow altitudinal ranges will have a high maximum value. Planted records are excluded.
Interpreting Aspect Pies Data were coded by Atlassers into 16 compass points. These are summarized as the four major compass points in the text summary. The Aspect Pies summarize these data in 8 compass points. The categories NNE, ENE, ESE, SSE, SSW, WSW, WNW and NNW were halved and the value added to the 8 principal compass points. These are coloured as black for North, grey for South, red for East and West, and white for the four intermediate compass points. The proportions of records are coded as the angle of the pies. Thus a bigger slice implies a larger proportion. This has the disadvantage that the position of the compass points is lost, but is does have advantages. The North slice (when present) is constrained to be centred at 0o. However, aspect is not always relevant. Especially where the landscape is flat aspect may not a major environmental factor to the species. Consequently, the proportion of records from flat landscapes (Valley Bottom, Hill Top, Plateau) has been included as a doughnut. The size of the doughnut varies from < 10% for species that are confined to slopes, to 90% (the limit of legibility of the pie) for those that only occur in flat landscapes. The scale between 10 and 90% is linear by radius and equal to the proportion of flat areas.
N Even South and East bias WNW NE SW Protea lepidocarpodendron has a Protea stokoei occurs E N fairly even distribution of aspect, NW NE predominantly on south and with a very slight bias to the W E SE slopes, but also on east SE S south and south-west. Almost all SE and NE slopes. As a the records are from slopes. SW consequence the larger south S wedge is displaced clockwise.
North bias Largely flat landscapes N N Leucospermum erubescens has a Leucospermum NW NE strong north bias, with over half hypophyllocarpodendron E W of records from the north slope, occurs predominantly SE NW and a good proportion of the rest NE (56%) on flats and low- WSW S SE E SW from the NW and NE slopes. lying areas, with relatively S few records from slopes. This South bias is indicated by the relatively large doughnut. Leucospermum formosum has a N strong south bias, with a good W NWNNEE Exclusively flat landscapes SE NW proportion from the SW and SE SW Leucospermum heterophyllum occurs NE slopes as well. West slopes are W slightly better represented than almost exclusively (> 90%) SW S on low-lying areas and eastern slopes. E although aspect data give a good aspect distribution SE West bias S
Leucadendron sessile has half of N (slightly biased to S and NE), NW NE its records from the west, SW E the size of the doughnut suggests that aspect is SE and NW sector. By contrast, W largely irrelevant for this species. only one quarter of its records S are from the equivalent eastern SW Sampling domain in the Cape. sectors. The distribution of aspect types in the Cape Flora is
not symmetrical and is N East bias shown opposite. Note the NW NE N E Leucadendron argenteum has a NW NE W bias of north and south W third of its records from the SW SE slopes (due to the SW S East, and over half from NE to S Swartberg and Langeberg- SE. E Outeniqua- Tsitsikamma SE mountains ranges) North-South bias compared to east and west. This is the pattern expected for species that are not affected by aspect. Mimetes saxatile has most of its N records from the North and Flat areas are ignored in this summary. South slopes, and very few NW from any other aspect. About W NE SW half of the records (57%) are E from flat areas, as shown by the SE size of the doughnut in the centre. S Interpreting Distribution Maps More to come here.
7. SPECIES ACCOUNTS 6 files to come here! References Appendices Abbreviations, Symbols and Glossary Index: Common Names and synonyms Index: Scientific Names and synonyms
CD of atlas data.