A new predictor of the irreplaceability of areas for achieving a conservation goal, its application to real-world planning, and a research agenda for further refinement

Abstract A new statistical approach is described for predicting the irreplaceability of areas (or ‘sites') within a region, defined as the likelihood that a given site will need to be protected to ensure achievement of a set of regional conservation targets. The paper begins by clarifying the relationship between irreplaceability and other conservation planning concepts such as flexibility, rarity, endemism and complementarity. We explain why direct measurement of irreplaceability is currently intractable for most real-world applications, and hence the need for prediction. A new predictive approach is proposed which overcomes a number of major shortcomings of previous approaches to predicting irreplaceability. The new approach employs the central limit theorem to estimate the expected frequency distribution of the area of a feature protected by all possible combinations of a set of sites. This expected distribution is used to estimate the total number of site combinations that would achieve target for the feature. The distribution is then used, for each site in turn, to estimate the number of these combinations for which the site of interest is a critical component. This latter number, expressed as a proportion of the estimated total number of representative combinations, provides a measure of the irreplaceability of a site for a single feature. Two techniques are presented for extending this approach to measure irreplaceability in terms of multiple features. Recent application of the new predictor to regional conservation planning in eastern New South Wales and elsewhere is described, with examples. We then present results of a preliminary evaluation of the accuracy of the predictor. Finally, we outline a future research agenda for further validation and refinement of the new technique.

[1]  Paul H. Williams,et al.  What to protect?—Systematics and the agony of choice , 1991 .

[2]  Jon Fjeldså,et al.  Priorities for conservation in Bolivia, Illustrated by a continent-wide analysis of bird distributions , 2001 .

[3]  C. Read,et al.  Handbook of the normal distribution , 1982 .

[4]  R L Pressey,et al.  Beyond opportunism: Key principles for systematic reserve selection. , 1993, Trends in ecology & evolution.

[5]  Denis White,et al.  Conservation Prioritization Using GAP Data , 1996 .

[6]  Jerome Cornfield,et al.  On Samples from Finite Populations , 1944 .

[7]  William F. Laurance,et al.  Tropical Forest Remnants: Ecology, Management, and Conservation of Fragmented Communities , 1998 .

[8]  P. J. Green,et al.  Probability and Statistical Inference , 1978 .

[9]  K. Loague,et al.  Statistical and graphical methods for evaluating solute transport models: Overview and application , 1991 .

[10]  Manuela M. P. Huso,et al.  A comparison of reserve selection algorithms using data on terrestrial vertebrates in Oregon , 1997 .

[11]  B. Smit,et al.  Land-Use Criticality Measures Based on an Interior Point in a Convex Polytope , 1988 .

[12]  Daniel P. Faith,et al.  Application of a taxon priority system for conservation planning by selecting areas which are most distinct from environments already reserved , 1996 .

[13]  William G. Cochran,et al.  Sampling Techniques, 3rd Edition , 1963 .

[14]  F. James Rohlf,et al.  Biometry: The Principles and Practice of Statistics in Biological Research , 1969 .

[15]  Elizabeth Finkel,et al.  Software Helps Australia Manage Forest Debate , 1998, Science.

[16]  A. O. Nicholls,et al.  Dealing with established reserve networks and incomplete distribution data sets in conservation planning , 1998 .

[17]  R. H. Hamre,et al.  Spatial Accuracy Assessment in Natural Resources and Environmental Sciences , 1996 .

[18]  William C. McComb,et al.  IDENTIFYING GAPS IN CONSERVATION NETWORKS: OF INDICATORS AND UNCERTAINTY IN GEOGRAPHIC-BASED ANALYSES , 1997 .

[19]  Andrew R. Solow,et al.  A note on optimal algorithms for reserve site selection , 1996 .

[20]  D. Faith Conservation evaluation and phylogenetic diversity , 1992 .

[21]  Elizabeth Finkel,et al.  Forest Pact Bypasses Computer Model , 1998, Science.

[22]  Paul H. Williams,et al.  Promise and problems in applying quantitative complementary areas for representing the diversity of some Neotropical plants (families Dichapetalaceae, Lecythidaceae, Caryocaraceae, Chrysobalanaceae and Proteaceae) , 1996 .

[23]  Barry Smit,et al.  IDENTIFYING IMPORTANT AGRICULTURAL LANDS: A CRITIQUE , 1987 .

[24]  A. O. Nicholls,et al.  An upgraded reserve selection algorithm , 1993 .

[25]  Hugh P. Possingham,et al.  Effectiveness of alternative heuristic algorithms for identifying indicative minimum requirements for conservation reserves , 1997 .

[26]  T. W. Anderson An Introduction to Multivariate Statistical Analysis , 1959 .

[27]  Robert L. Pressey,et al.  From representation to persistence: requirements for a sustainable system of conservation areas in the species‐rich mediterranean‐climate desert of southern Africa , 1999 .

[28]  John L. Craig,et al.  Nature conservation: the role of networks: Geraldton, W.A., Australia, 16–20 May 1994 , 1995 .

[29]  Wilhelm Barthlott,et al.  Biodiversity: a challenge for development research and policy. , 1998 .

[30]  M. Schervish Multivariate normal probabilities with error bound , 1984 .

[31]  Anthony G. Rebelo,et al.  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 Diversity , 1992 .

[32]  Mark A. Burgman,et al.  Coping with uncertainty in forest wildlife planning , 1995 .

[33]  David A. Keith,et al.  A new approach for selecting fully representative reserve networks: addressing efficiency, reserve design and land suitability with an iterative analysis , 1992 .

[34]  A. O. Nicholls,et al.  Selecting networks of reserves to maximise biological diversity , 1988 .

[35]  Sokal Rr,et al.  Biometry: the principles and practice of statistics in biological research 2nd edition. , 1981 .

[36]  William G. Madow,et al.  On the Limiting Distributions of Estimates Based on Samples from Finite Universes , 1948 .

[37]  Amanda T. Lombard,et al.  Reserve systems for limestone endemic flora of the Cape Lowland Fynbos: Iterative versus linear programming , 1996 .

[38]  Hugh P. Possingham,et al.  Effects of data characteristics on the results of reserve selection algorithms , 1999 .

[39]  Peter L. Forey,et al.  Systematics and conservation evaluation , 1994 .

[40]  David N. Cole,et al.  Threats to Wilderness Ecosystems: Impacts and Research Needs , 1996 .

[41]  Robert L. Pressey,et al.  A Comparison of Richness Hotspots, Rarity Hotspots, and Complementary Areas for Conserving Diversity of British Birds , 1996 .