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Data requirements and data sources for biodiversity priority area selection

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Abstract

The data needed to prioritize areas for biodiversity protection are records of biodiversity features — species, species assemblages, environmental classes — for each candidate area. Prioritizing areas means comparing candidate areas, so the data used to make such comparisons should be comparable in quality and quantity. Potential sources of suitable data include museums, herbariums and natural resource management agencies. Issues of data precision, accuracy and sampling bias in data sets from such sources are discussed and methods for treating data to minimize bias are reviewed.

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References

  • Aleksander I and Morton H 1990An introduction to neural computing (London: Chapman and Hall)

    Google Scholar 

  • Anderberg M R 1973Cluster analysis for applications (New York: Academic Press)

    Google Scholar 

  • Austin M P 1994Modelling of landscape patterns and processes using biological data. Sub-project 3: data capability (Consultancy report for ERIN) (Canberra: CSIRO Division of Wildlife and Ecology)

    Google Scholar 

  • Austin M P and Heyligers P C 1989 Vegetation survey design for conservation: gradsect sampling of forests in north-eastern New South Wales;Biol. Conserv. 50 13–32

    Google Scholar 

  • Austin M P and Meyers J A 1996 Current approaches to modelling the environmental niche of eucalyptus: implications for management of forest biodiversity;For. Ecol. Manag. 85 95–106

    Google Scholar 

  • Austin M P, Meyers J A and Doherty M D 1994Modelling of landscape patterns and processes using biological data. Subproject 2: predictive models for landscape patterns and processes (Consultancy report for ERIN) (Canberra: CSIRO Division of Wildlife and Ecology)

    Google Scholar 

  • Austin M P, Nicholls A O and Margules C R 1990 Measurement of the realized qualitative niche: environmental niches of fiveEucalyptus species;Ecol. Monogr. 60 161–177

    Google Scholar 

  • Bedward M, Pressey R L and Keith D A 1992 A new approach for selecting fully representative reserve networks: addressing efficiency, reserve design and land suitability with an iterative analysis;Biol. Conserv. 62 115–125

    Google Scholar 

  • Belbin L 1987 The use of non-hierarchical allocation methods for clustering large sets of data;Aust. J. Ecol. 19 32–39

    Google Scholar 

  • Belbin L, Austin M P, Margules C R, Cresswell I D and Thackway R 1994Modelling of landscape patterns and processes using biological data. Sub-project I: data suitability (Consultancy report for ERIN) (Canberra: CSIRO Division of Wildlife and Ecology)

    Google Scholar 

  • Brieiman L, Friedman J H, Olshen R A and Stone C J 1984Classification and regression trees (Belmont, California: Wadsworth International Group)

    Google Scholar 

  • Brown D G, Lusch D P and Duda K A 1998 Supervised classification of types of glaciated landscapes using digital elevation data;Geomorphology 21 233–250

    Google Scholar 

  • Burrough P A 1986Principles of Geographic Information Systems for Land Resources Assessment (Oxford: Clarendon Press)

    Google Scholar 

  • Busby J R 1986 A biogeoclimatic analysis ofNothofagus cunninghamii (Hook.) Oerst. in southeastern Australia;Aust. J. Ecol. 11 1–7

    Google Scholar 

  • Busby J R 1991 BIOCLIM — a bioclimatic analysis and prediction system; inNature conservation: cost effective biological surveys and data analysis (eds) C R Margules and M P Austin (Melbourne: CSIRO) pp 64–68

    Google Scholar 

  • Carpenter G, Gillison A N and Winter J 1993 DOMAIN: a flexible modelling procedure for mapping potential distributions of plants and animals;Biodiver. Conserv. 2 667–680

    Google Scholar 

  • Caudill M 1990A1 EXPERT: neural networks primer (San Francisco: Miller Freeman)

    Google Scholar 

  • Caughley G 1994 Directions in conservation biology;J. Anim. Ecol. 63 215–224

    Google Scholar 

  • Chapman A D and Busby J R 1994 Linking plant species information to continental biodiversity, inventory, climate modelling and environmental monitoring; inMapping the diversity of nature (ed.) R I Miller (London: Chapman and Hall) pp 179–195

    Google Scholar 

  • Colwell R K and Coddington J A 1994 Estimating terrestrial biodiversity through extrapolation;Philos. Trans. R. Soc. London B345 101–118

    Google Scholar 

  • Cowling R M (ed.) 1992The ecology of fynbos (Cape Town: Oxford University Press)

    Google Scholar 

  • Crawley M J 1993GLIM for ecologists (Oxford: Blackwell)

    Google Scholar 

  • Deadman P J and Gimblett H R 1997 Applying neural networks to vegetation management plan development;AI Appl. 11 107–112

    Google Scholar 

  • Eversham B C, Harding P T, Loder N, Arnold H R and Fenton R W 1992 Research applications using data from species surveys in Britain; inFaunal inventories of sites for cartography and nature conservation (eds) J L van Goethem and P Grootaert (Brussels: Proceedings of the 8th International Colloquium of the European Invertebrate Survey) pp 29–40

    Google Scholar 

  • Faith D P 1991 Effective pattern analysis methods for nature conservation; inNature conservation: cost effective biological surveys and data analysis (eds) C R Margules and M P Austin (Melbourne: CSIRO) pp 47–53

    Google Scholar 

  • Fitzgerald R W and Lees B G 1993 Assessing the classification accuracy of multicourse remote sensing data;Remote Sensing Environ. 47 1–25

    Google Scholar 

  • Gaston K J, Pressey R L and Margules C R 2002 Persistence and vulnerability: retaining biodiversity in the landscape and in protected areas;J. Biosci. (Suppl. 2)27 361–384

    PubMed  CAS  Google Scholar 

  • Gibbons D W, Reid J B and Chapman R A 1993The new atlas of breeding birds in Britain and Ireland: 1988-1991 (London: Poyser)

    Google Scholar 

  • Gillison A N and Brewer K R W 1985 The use of gradient directed transects or gradsects in natural resource surveys;J. Environ. Manag. 20 103–127

    Google Scholar 

  • Gower J C 1971 A general coefficient of similarity and some of its properties;Biometrics 27 857–871

    Google Scholar 

  • Grinnell J 1922 The role of the ’accidental’;Auk 39 373–380

    Google Scholar 

  • Hanski I 1994 Patch-occupancy dynamics in fragmented landscapes;Trends Ecol. Evol. 9 131–135

    Google Scholar 

  • Hastie T and Tibshirani R 1990Generalised additive models (London: Chapman and Hall)

    Google Scholar 

  • Haung W Y and Lippman R P 1988 Comparisons between neural net and conventional classifiers;Proc. Int. Joint Conf. Neural Networks, San Diego 4 485–493

    Google Scholar 

  • Hecht-Nielson R 1991Neurocomputing (Reading, Massachusetts: Addison-Wesley)

    Google Scholar 

  • Higgs A J and Usher M B 1980 Should nature reserves be large or small?;Nature (London)258 586

    Google Scholar 

  • Hilbert D W, Graham A and Parker T 2000Forest and woodland habitats of the Northern Bettong (Bettongia tropica) in the past, present, and future (Report prepared for the Queensland Parks and Wildlife Service) (Atherton: CSIRO)

    Google Scholar 

  • Hilbert D W and Ostendorf B 2001 The utility of empirical, artificial neural network approaches for modelling the distribution of regional to global vegetation in past, present and future climates;Ecol. Modelling 146 311–327

    Google Scholar 

  • Hilbert D W, Ostendorf B and Hopkins M S 2001 Sensitivity of tropical forests to climate change in the humid tropics of North Queensland;Aust. Ecol. 26 590–603

    Google Scholar 

  • Hilbert D W and van den Muyzenberg J 1999 Using an artificial neural network to characterise the relative suitability of environments for forest types in a complex tropical vegetation mosaic;Diversity Distrib. 5 263–274

    Google Scholar 

  • Hill R S, Read J and Busby J R 1988 The temperature-dependence of photosynthesis of some Australian temperate rainforest trees and its biogeographical significance;J. Biogeogr. 15 431–439

    Google Scholar 

  • Holland J H 1992 Genetic algorithms;Sci. Am. 267 44–50

    Google Scholar 

  • Hurlbert S H 1971 The non-concept of species diversity: a critique and alternative parameters;Ecology 52 577–586

    Google Scholar 

  • Hutchinson G E 1957 Concluding remarks;Cold Spring Harbor Symp. Quant. Biol. 22 415–427

    Google Scholar 

  • Hutchinson M F 1991 Application of thin plate smoothing splines to continent-wide data assimilation; inData assimilation systems (ed.) J D Jasper (Melbourne: Bureau of Meteorology) pp 104–113

    Google Scholar 

  • Hutchinson M F 1993 Development of a continent-wide DEM with applications to terrain and climate analysis; inEnvironmental Modelling with GIS (eds) M F Goodchild, B O Parks and L T Steyaert (New York: Oxford University Press) pp 392–399

    Google Scholar 

  • Hutchinson M F 1995 Interpolating mean rainfall using thin plate smoothing splines;Int. J. Geogr. Inform. Syst. 9 385–403

    Google Scholar 

  • IUCN (The World Conservation Union) 1994Guidelines for protected area management categories (Gland, Switzerland: IUCN)

    Google Scholar 

  • Jalas J and Suominen J (eds) 1972, 1973, 1976, 1979, 1980, 1983, 1986, 1989, 1991, 1994Atlas Florae Europaeae Volumes 1–10 (The Committee for Mapping the Flora of Europe and Societas Biologica Fennica Vanamo)

  • Jongman R T H, ter Braak C J F and van Tongeren O F 1987Data analysis in community and landscape ecology (Wagenigen: Pudoc)

    Google Scholar 

  • Lawton J H, Prendergast J R and Eversham B C 1994 The numbers and spatial distributions of species: analyses of British data; inSystematics and conservation evaluation (eds) P L Forey, C J Humphries and R I Vane-Wright (Oxford: Oxford University Press) pp 177–195

    Google Scholar 

  • Leclercq J 1979 Tous ces atlas, toutes ces cartes, c’est pour quoi faire?;Notes Fauniques Gembloux 2 1–22

    Google Scholar 

  • Lees B G 1994 Decision trees, artificial neural networks and genetic algorithms for classification of remotely sensed and ancillary data; inProceedings of the 7th Australian Remote Sensing Conference (Floreat, Western Australia: Remote Sensing and Photogrammetry Association Australia) pp 51–60

    Google Scholar 

  • Lindenmayer D B, Cunningham R B, Nix H A, Tanton M T and Smith A P 1991a Predicting the abundance of hollow-bearing trees in montane forests of southeastern Australia;Aust. J. Ecol. 16 91–98

    Google Scholar 

  • Lindenmayer D B, Cunningham R B, Tanton M T, Nix H A and Smith A P 1991b The conservation of arboreal marsupials in the montane ash forests of the Central Highlands of Victoria, south-east Australia. III. The habitat requirements of Leadbeater’s possum,Gymnobelideus leadbeateri, and models of the diversity and abundance of arboreal marsupials;Biol. Conserv. 56 295–315

    Google Scholar 

  • Longmore R (ed.) 1986Atlas of elapid snakes of Australia (Canberra: Australian Government Publishing Service)

    Google Scholar 

  • Magurran A E 1988Ecological diversity and its measurement (London: Croom Helm)

    Google Scholar 

  • Maling D H 1974 Personal projections;Geograph. Mag. 46 599–600

    Google Scholar 

  • Margules C R and Austin M P 1994 Biological models for monitoring species decline: the construction and use of data bases;Philos. Trans. R. Soc. London B344 69–75

    Google Scholar 

  • Margules C R, Nicholls A O and Austin M P 1987 Diversity ofEucalyptus species predicted by a multi-variable environmental gradient;Oecologia 71 229–232

    Google Scholar 

  • Margules C R and Pressey R L 2000 Systematic conservation planning;Nature (London)405 243–253

    CAS  Google Scholar 

  • Margules C R, Pressey R L and Williams P H 2002 Representing biodiversity: data and procedures for identifying priority areas for conservation;J. Biosi. (Suppl. 2)27 309–326

    CAS  Google Scholar 

  • Margules C R, Redhead T D, Faith D P and Hutchinson M F 1995BioRap: Guidelines for using the BioRap methodology and tools (Canberra: CSIRO)

    Google Scholar 

  • McAllister D E, Schueler F W, Roberts C M and Hawkins J P 1994 Mapping and GIS analysis of the global distribution of coral reef fishes on an equal-area grid; inMapping the diversity of nature (ed.) R I Miller (London: Chapman and Hall) pp 155–175

    Google Scholar 

  • McCullagh P and Nelder J A 1989Generalised linear models 2nd edition (London: Chapman and Hall)

    Google Scholar 

  • McKenzie N L, Belbin L, Margules C R and Keighery G J 1989 Selecting representative reserve systems in remote areas: a case study in the Nullarbor Region, Australia;Biol. Conserv. 50 239–261

    Google Scholar 

  • Mickleburgh S P, Hutson A M and Racey P A 1992Old World fruit bats: an action plan for their conservation (Gland, Switzerland: IUCN)

    Google Scholar 

  • Moore D M, Lees B G and Davey S M 1991 A new method for predicting vegetation distributions using decision tree analysis in a geographic information system;Environ. Manag. 15 59–71

    Google Scholar 

  • Nelson G and Platnick N I 1981Systematics and biogeography: cladistics and vicariance (New York: Columbia University Press)

    Google Scholar 

  • Nicholls A O 1989 How to make biological surveys go further with Generalised Linear Models;Biol. Conserv. 50 51–75

    Google Scholar 

  • Nicholls A O 1991 Examples of the use of Generalised Linear Models in analysis of survey data for conservation evaluation; inNature conservation: cost effective biological surveys and data analysis (eds) C R Margules and M P Austin (Melbourne: CSIRO) pp 54–63

    Google Scholar 

  • Nix H A 1986 A biogeographic analysis of Australian elapid snakes; inAtlas of elapid snakes of Australia (ed.) R Longmore (Canberra: Australian Government Publishing Service) pp 4–15

    Google Scholar 

  • Nix H A, Faith D P, Hutchinson M F, Margules C R, West J, Allison A, Kesteven J L, Natera G, Slater W, Stein J L and Walker P 2000The BioRap toolbox: a national study of biodiversity assessment and planning for Papua New Guinea (Canberra: Centre for Resource and Environmental Studies, Australian National University)

    Google Scholar 

  • Nix H A and Switzer M A (eds) 1991Rainforest animals: atlas of vertebrates endemic to Australia’s wet tropics Kowari (1) (Canberra: Australian National Parks and Wildlife Service)

    Google Scholar 

  • Palmer M W 1990 The estimation of species richness by extrapolation;Ecology 71 1195–1198

    Google Scholar 

  • Panetta F D and Dodd J 1987 Bioclimatic prediction of the potential distribution of skeleton weed,Chondrilla juncea L., in Western Australia;J. Aust. Inst. Agric. Sci. 53 11–16

    Google Scholar 

  • Paruelo J M and Tomasel F 1997 Prediction of functional characteristics of ecosystems: a comparison of artificial neural networks and regression models;Ecol. Model. 98 173–186

    Google Scholar 

  • Pearson D L and Carroll S S 1998 Global patterns of species richness: spatial models for conservation planning using bioindicator and precipitation data;Conserv. Biol. 12 809–821

    Google Scholar 

  • Pekkarinen A, TerÄs I, Viramo J and Paatela J 1981 Distribution of bumblebees (Hymenoptera, Apidae:Bombus andPsithyrus) in eastern Fennoscandia;Not. Entomol. 61 71–89

    Google Scholar 

  • Prendergast H D V and Hattersley P W 1985 Distribution and cytology of AustralianNeurachne and its allies (Poaceae), a group containing C3, C4 and C3–C4 intermediate species;Aust. J. Bot. 33 317–336

    Google Scholar 

  • Prendergast J R and Eversham B C 1997 Species richness covariance in higher taxa: empirical tests of the biodiversity indicator concept;Ecography 20 210–216

    Google Scholar 

  • Prendergast J R, Wood S N, Lawton J H and Eversham B C 1993 Correcting for variation in recording effort in analyses of diversity hotspots;Biodiver. Lett. 1 39–53

    Google Scholar 

  • Pressey R L, Humphries C J, Margules C R, Vane-Wright R I and Williams P H 1993 Beyond opportunism: key principles for systematic reserve selection;Trends Ecol. Evol. 8 124–128

    Google Scholar 

  • Pressey R L and Nicholls A O 1989 Efficiency in conservation evaluation: scoring versus iterative approaches;Biol. Conserv. 50 199–218

    Google Scholar 

  • Rich T C G 1997 Isad hoc good enough?;Trans. Suffolk Nat. Hist. Soc. 33 14–21

    Google Scholar 

  • Rumelhart D E and McClelland J L 1986Parallel distributed processing: explorations in the micro structures of cognition (Cambridge, Massachusetts: MIT Press)

    Google Scholar 

  • Sanders H L 1968 Marine benthic diversity: a comparative study;Am. Nat. 102 243–282

    Google Scholar 

  • Scott J M, Davis F, Csuti B, Noss R, Butterfield B, Groves C, Anderson H, Caicco S, D’Erchia F, Edwards T C, Ulliman J and Wright R G 1993 Gap analysis: a geographic approach to protection of biological diversity;Wildl. Monogr. 123 1–41

    Google Scholar 

  • Sneath P H A and Sokal R R 1973Numerical taxonomy (San Francisco: W H Freeman)

    Google Scholar 

  • Stockwell D R B and Noble I R 1992 Induction of sets of rules from animal distribution data: a robust and informative method of data analysis;Math. Compu. Simul. 33 385–390

    Google Scholar 

  • Stockwell D R B, Davey S M, Davis J R and Noble I R 1990 Using induction of decision trees to predict greater glider density;A1 Appl. 4 33–43

    Google Scholar 

  • Tufte E R 1990Envisioning information (Cheshire, Connecticut: Graphics Press)

    Google Scholar 

  • Walker P A and Cocks K D 1991 HABITAT: a procedure for modelling a disjoint environmental envelope for a plant or animal species;Global Ecol. Biogeogr. Lett. 1 108–118

    Google Scholar 

  • Wessels K J, van Jaarsveld A S, Grimbeek J D and van der Linde M J 1998 An evaluation of the gradsect biological survey method;Biodiver. Conserv. 7 1093–1121

    Google Scholar 

  • White D, Kimerling A J and Overton W S 1992 Cartographic and geometric components of a global sampling design for environmental monitoring;Cartogr. Geogr. Inform. Syst. 19 5–22

    Google Scholar 

  • Williams J E 1991 Biogeographic patterns of three sub-alpine eucalypts in south-east Australia with special reference toEucalyptus pauciflora, Sieb ex Spreng;J. Biogeogr. 18 223–230

    Google Scholar 

  • Williams P H 1991 An annotated checklist of bumble bees with an analysis of patterns of description (Hymenoptera: Apidae, Bombini);Bull. Br. Mus. Nat. Hist. Entomol. 67 79–152

    Google Scholar 

  • Williams P H and Gaston K J 1994 Measuring more of biodiversity: can higher-taxon richness predict wholesale species richness?;Biol. Conserv. 67 211–217

    Google Scholar 

  • Williams P H and Gaston K J 1998 Biodiversity indicators: graphical techniques, smoothing and searching for what makes relationships work;Ecography 21 551–560

    Google Scholar 

  • Williams P, Gibbons D, Margules C, Rebelo A, Humphries C and Pressey R 1996a A comparison of richness hotspots, rarity hotspots and complementary areas for conserving diversity using British birds;Conserv. Biol. 10 155–174

    Google Scholar 

  • Williams P H, Prance G T, Humphries C J and Edwards K S 1996b Promise and problems in applying quantitative complementary areas for representing the diversity of some Neotropical plants (families Dichapetalaceae, Lecythidaceae, Caryocaraceae, Chrysobalanaceae and Proteaceae);Biol. J. Linnean Soc. 58 125–157

    Google Scholar 

  • Yee T W and Mitchell N D 1991 Generalised additive models in plant ecology;J. Veg. Sci. 2 587–602

    Google Scholar 

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Authors and Affiliations

  1. Tropical Forest Research Centre and the Rainforest Cooperative Research Centre, CSIRO Sustainable Ecosystems, PO Box 780, 4883, Atherton Queensland, Australia

    P. H. Williams, C. R. Margules & D. W. Hilbert

  2. Department of Entomology, The Natural History Museum, Cromwell Road, SW7 5BD, London, UK

    P. H. Williams

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  1. P. H. Williams
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  2. C. R. Margules
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  3. D. W. Hilbert
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Correspondence to C. R. Margules.

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Williams, P.H., Margules, C.R. & Hilbert, D.W. Data requirements and data sources for biodiversity priority area selection. J Biosci 27, 327–338 (2002). https://doi.org/10.1007/BF02704963

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