Access the full text.
Sign up today, get DeepDyve free for 14 days.
A. Lombard, R. Cowling, R. Pressey, A. Rebelo (2003)
Effectiveness of land classes as surrogates for species in conservation planning for the Cape Floristic RegionBiological Conservation, 112
Cummings Cummings, Huskamp Huskamp (2005)
Grid computingEDUCAUSE Rev., 40
S. Freitag, A. Jaarsveld (1998)
Sensitivity of selection procedures for priority conservation areas to survey extent, survey intensity and taxonomic knowledgeProceedings of the Royal Society of London. Series B: Biological Sciences, 265
S. Freitag, A. Nicholls, A. Jaarsveld (1998)
Dealing with established reserve networks and incomplete distribution data sets in conservation planningSouth African Journal of Science, 94
Michael Stefano (2005)
What Is Grid Computing
B. Manly (1997)
Randomization, Bootstrap and Monte Carlo Methods in Biology
R. Pressey, H. Possingham, J. Day (1997)
Effectiveness of alternative heuristic algorithms for identifying indicative minimum requirements for conservation reservesBiological Conservation, 80
A. Rodrigues, K. Gaston (2002)
Rarity and Conservation Planning across Geopolitical UnitsConservation Biology, 16
(1999)
Environmental Systems Research Institute
A. Bazinet, Daniel Myers, John Fuetsch, M. Cummings (2007)
Grid Services Base Library: A high-level, procedural application programming interface for writing Globus-based Grid servicesFuture Gener. Comput. Syst., 23
Spearman Spearman (1904)
The proof and measurement of association between two thingsAm. J. Psychol., 15
J. Maritz (1996)
Distribution-Free Statistical Methods
Future Gener. Comp. Sy
J. Gentle, B. Manly (1990)
Randomization and Monte Carlo Methods in Biology.Biometrics, 48
(2004)
South African National Biodiversity Assessment Technical Report
Romola Stewart, T. Noyce, H. Possingham (2003)
Opportunity cost of ad hoc marine reserve design decisions: an example from South AustraliaMarine Ecology Progress Series, 253
Romola Stewart, H. Possingham (2005)
Efficiency, costs and trade-offs in marine reserve system designEnvironmental Modeling & Assessment, 10
W. Gladstone, Jennifer Davis (2003)
Reduced survey intensity and its consequences for marine reserve selectionBiodiversity & Conservation, 12
R. Pressey, H. Possingham, V. Logan, J. Day, P. Williams (1999)
Effects of data characteristics on the results of reserve selection algorithmsJournal of Biogeography, 26
T. Ricketts, E. Dinerstein, T. Boucher, T. Brooks, S. Butchart, M. Hoffmann, John Lamoreux, J. Morrison, M. Parr, J. Pilgrim, A. Rodrigues, W. Sechrest, G. Wallace, Ken Berlin, J. Bielby, N. Burgess, D. Church, N. Cox, D. Knox, C. Loucks, G. Luck, L. Master, Robin Moore, R. Naidoo, R. Ridgely, G. Schatz, G. Shire, H. Strand, Wesley Wettengel, E. Wikramanayake (2005)
Pinpointing and preventing imminent extinctions.Proceedings of the National Academy of Sciences of the United States of America, 102 51
D. Rabinowitz, S. Cairns, T. Dillon (1986)
Seven forms of rarity and their frequency in the flora of the British Isles
R. Cowling, R. Pressey, R. Sims-Castley, A. Roux, E. Baard, C. Burgers, G. Palmer (2003)
The expert or the algorithm?—comparison of priority conservation areas in the Cape Floristic Region identified by park managers and reserve selection softwareBiological Conservation, 112
R. Cowlinga, R. Presseyb, M. Rougetc, A. Lombarda (2003)
A conservation plan for a global biodiversity hotspot — the Cape Floristic Region , South Africa
(2007)
Letter Biased data and reserve selection 373 Ó
A. Rodrigues, K. Gaston (2001)
How large do reserve networks need to beEcology Letters, 4
R. Pressey, C. Humphries, C. Margules, R. Vane-Wright, P. Williams (1993)
Beyond opportunism: Key principles for systematic reserve selection.Trends in ecology & evolution, 8 4
S. Freitag, A. Nicholls, A. Jaarsveld (1996)
Nature reserve selection in the Transvaal, South Africa: what data should we be using?Biodiversity & Conservation, 5
A. Rebelo, W. Siegfried (1992)
Where Should Nature Reserves Be Located in the Cape Floristic Region, South Africa? Models for the Spatial Configuration of a Reserve Network Aimed at Maximizing the Protection of Floral DiversityConservation Biology, 6
E. Meir, S. Andelman, H. Possingham (2004)
Does conservation planning matter in a dynamic and uncertain worldEcology Letters, 7
K. Gaston, A. Rodrigues (2003)
Reserve Selection in Regions with Poor Biological DataConservation Biology, 17
D. Wilcove, D. Rothstein, J. Dubow, A. Phillips, E. Losos (1998)
QUANTIFYING THREATS TO IMPERILED SPECIES IN THE UNITED STATESBioScience, 48
Mark McDonnell, Hugh Possingham, Ian Ball, Elizabeth Cousins (2002)
Mathematical Methods for Spatially Cohesive Reserve DesignEnvironmental Modeling & Assessment, 7
H. Possingham, I. Ball, S. Andelman (2000)
Mathematical Methods for Identifying Representative Reserve Networks
H. Possingham, J. Day, M. Goldfinch, F. Salzborn (1993)
The mathematics of designing a network of protected areas for conservation
Leanna Warman, A. Sinclair, G. Scudder, B. Klinkenberg, R. Pressey (2004)
Sensitivity of Systematic Reserve Selection to Decisions about Scale, Biological Data, and Targets: Case Study from Southern British ColumbiaConservation Biology, 18
(1984)
The world coverage of protected areas : development goals and environmental needs
S. Polasky, J. Camm, A. Solow, B. Csuti, D. White, Rugang Ding (2000)
Choosing reserve networks with incomplete species informationBiological Conservation, 94
K. Virolainen, Teija Virola, J. Suhonen, M. Kuitunen, Antti Lammi, P. Siikamäki (1999)
Selecting networks of nature reserves: methods do affect the long-term outcomeProceedings of the Royal Society of London. Series B: Biological Sciences, 266
R. Pressey, S. Tully (1994)
The cost of ad hoc reservation: a case study in western New South Wales.Austral Ecology, 19
P. Jaccard (1912)
THE DISTRIBUTION OF THE FLORA IN THE ALPINE ZONE.1New Phytologist, 11
James Justus, S. Sarkar (2002)
The principle of complementarity in the design of reserve networks to conserve biodiversity: A preliminary historyJournal of Biosciences, 27
P. Goldblatt, J. Manning (2000)
Cape plants: A conspectus of the Cape flora of South Africa
Complementarity‐based reserve selection algorithms efficiently prioritize sites for biodiversity conservation, but they are data‐intensive and most regions lack accurate distribution maps for the majority of species. We explored implications of basing conservation planning decisions on incomplete and biased data using occurrence records of the plant family Proteaceae in South Africa. Treating this high‐quality database as ‘complete’, we introduced three realistic sampling biases characteristic of biodiversity databases: a detectability sampling bias and two forms of roads sampling bias. We then compared reserve networks constructed using complete, biased, and randomly sampled data. All forms of biased sampling performed worse than both the complete data set and equal‐effort random sampling. Biased sampling failed to detect a median of 1–5% of species, and resulted in reserve networks that were 9–17% larger than those designed with complete data. Spatial congruence and the correlation of irreplaceability scores between reserve networks selected with biased and complete data were low. Thus, reserve networks based on biased data require more area to protect fewer species and identify different locations than those selected with randomly sampled or complete data.
Ecology Letters – Wiley
Published: May 1, 2007
Read and print from thousands of top scholarly journals.
Already have an account? Log in
Bookmark this article. You can see your Bookmarks on your DeepDyve Library.
To save an article, log in first, or sign up for a DeepDyve account if you don’t already have one.
Copy and paste the desired citation format or use the link below to download a file formatted for EndNote
Access the full text.
Sign up today, get DeepDyve free for 14 days.
All DeepDyve websites use cookies to improve your online experience. They were placed on your computer when you launched this website. You can change your cookie settings through your browser.