A multivariate approach to assessing threat and for priority setting in threatened species conservation

Biological Conservation 1993, 64, 57--66

A MULTIVARIATE APPROACH TO ASSESSING THREAT A N D FOR PRIORITY SETTING IN THREATENED SPECIES CONSERVATION David R. Given* Botany Institute, DSIR Land Resources, Private Bag, Christchurch, New Zealand

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David A. NortonJ~ Conservation Research Group, School of Forestry, University of Canterbury, Private Bag 4800, Christchurch, New Zealand (Received 2 December 1991; revised version received 28 April 1992; accepted 9 May 1992)

and often reflects imminent extinction, although other factors such as cultural values can also be important. The difference between threat and priority is important. Priority setting is often based on factors in addition to those that actually threaten a species (Mace & Lande, 1991), and can include cultural and economic considerations. Species that are most threatened biologically are usually considered as having high priority for management action, although some species experiencing relatively low biological threat may still have high priority for management action because of other perceived values. Priority setting for threatened species has become a major issue as people have started to appreciate that there are many species at risk, with many threatened species lists numbering hundreds and some thousands. Lists can be subdivided into more manageable blocks (see Munton, 1987, for review); reasons for subdividing lists include (Holt, 1987, in part):

Abstract

The use of multivariate techniques for assessing the threats facing species and for determining priority groupings in threatened species conservation is evaluated Scores based on different criteria provide species profiles that allow species to be placed in multidimensional space. Species that group together are likely to be threatened for similar reasons. Multivariate techniques identify the main factors threatening species and highlight problems with linear ranking schemes, especially where species can have the same total score for very different reasons. Multi-variate techniques also allow identification of redundant criteria used in deriving species scores, and are free of problems associated with interdependence between criteria. Multivariate techniques provide a powerful additional tool for determining management priorities and for assessing threat and can also be used for assessing changes in species status with time and for modelling potential management actions for improving the security of a ~pecies. However, in using these techniques it is essential to make a clear distinction between threat and priority.

(1) To convey a perception to the public of which species are most threatened. (2) To draft and eventually implement legislation on threatened species. (3) To set priorities for funding and action to preserve species. (4) To break up large lists into groups which can be readily perceived by those using them.

Key words: Priority setting, threatened species, ranking, rarity, multivariate. INTRODUCTION One of the central problems in threatened species conservation is deciding how to allocate limited resources to saving the many species that are threatened with extinction. Ideally, we would like to protect all threatened species, but as this is impossible we need to develop ranking systems (Given, 1990a). These assume that some species are more important than others, in having higher priority for management action. This higher priority is usually based on a greater perceived threat,

Several schemes have been proposed for ranking threatened species. Perhaps the most widely used are those based on the IUCN Red Data Book categories (e.g. Lucus & Synge, 1978; IUCN, 1988; Wilson & Given, 1989; see also Mace & Lande, 1991). A drawback of the IUCN categories is that they do not provide an actual ranking of species from most threatened to least threatened, and, especially when dealing with large numbers of species, are often too coarse to assist managers in decision-making. The IUCN system is also subjective, with differences in application of specific categories in different regions and to different taxonomic

* Present address: David Given & Associates, 101 Jeffreys Road, Christchurch, New Zealand. :~ To whom correspondence should be addressed. Biological Conservation 0006-3207/93/$06.00 © 1993 Elsevier Science Publishers Ltd, England. Printed in Great Britain 57

58

David R. Given, David A. Norton

groups (Mace & Lande, 1991), and is not actually intended as a prioritising system but rather as providing an indication of threat. A number of schemes have been developed that list or rank species in linear order of threat and/or priority; these include that used in the second edition of the British Isles Red Data Book for plants (Perring & Farrell, 1983), one associated with the current Australian threatened plant list (Briggs & Leigh, 1988) and a New Zealand threatened species priority list (Molloy & Davis, 1992). These systems use multiple scoring over a range of criteria to derive total scores for each species. A number of different criteria can be scored and include aspects of the species distribution, vulnerability, and perceived values (e.g. Given, 1990a). Linear ranking schemes are attractive, especially for managers having to allocate scarce resources, but they do have general problems. As Holt (1987) said 'Categorizations forced into linear form force linear thinking by professionals and bureaucrats. Their determination of priorities and subsequent decision-making rest only partly on any index or category assigned to the species in question; they have to take into account such matters as the means available for action, the likelihood of success in a given time-frame, public opinion and other political considerations. It seems to me unlikely (our emphasis) that forcing all we know or don't know about a species into a linear straight-jacket is particularly helpful in their difficult and often thankless task'. The main problems are: (1) Categorisation using a linear ranking system imposes an artificial linearity onto a naturally non-linear biological system. (2) Linear schemes are statistically unsatisfactory, as there is no objective means for identifying where resources should be applied in relation to the different threats species face. (3) Many species tend to rank together, making it difficult to separate them. (4) Linear ranking schemes conceal the reasons for a particular species being threatened. (5) The total scores used in most linear ranking schemes may be effectively artefacts, as the individual criteria used to derive the scores are not necessarily equivalent and criteria are not independent. The individual criteria scored do, however, provide a 'threat or priority profile' for each species. It would therefore seem possible to group together species with similar threat or priority profiles, as such species may have similar management problems (A. V. Hall, pers. comm.), and to use these groups as a basis for management action. One approach is to regard each species as lying somewhere in multidimensional 'threat or priority space' (cf. Holt, 1987), and to use multivariate techniques to look at the arrangement of these species in order to identify groupings of species with similar profiles. In this paper we critically examine the use of

multivariate techniques for assessing the types of threats facing species and for determining priority groupings for threatened species conservation. METHODS

To illustrate the use of multivariate techniques in threatened species conservation, we use data for threatened plants f r o m northeastern South Island, New Zealand. However, the approach we use could be applied to any group of threatened species, plant or animal. Our study area is defined by the boundaries of the Molesworth, Clarence, Kaikoura, Lowry, Hawdon, Puketeraki, Canterbury Foothills, Canterbury Plains and Banks Ecological Regions (McEwen, 1987). This area is characterised by dry mountains in the west and north, extensive plains in the southeast, and a hilly peninsula in the far east. The climate is generally dry, and the environment has been extensively modified by human activities. In some areas, natural communities are now largely absent, and in others they have been severely disrupted. Only in the higher mountains are relatively natural and undisturbed plant communities still present (Knox, 1969). Within this area, at least 47 species of vascular plant, including two of subspecific or varietal status, are considered threatened to some degree, comprising mainly shrubs and herbs (Appendix 1). All New Zealand threatened plants have been categorised using the IUCN system, with the addition of an extra category 'Local' which includes species that are approaching threat status or occur in habitats prone to loss or damage (Given, 1990b). Applying IUCN categories to our study data, four species are regarded nationally as Endangered, nine as Vulnerable, eight as Rare, and 19 as Local. Because insufficient was known about them, seven are classed as Indeterminate (Appendix 1). Each species was scored for 17 criteria using the system developed by the New Zealand Department of Conservation (Molloy & Davis, 1992). This is based on five criteria types which reflect the taxonomic distinctiveness, population features, vulnerability to extinction, ex situ propagation potential and cultural values of each species (Table 1). Full definitions of these criteria are provided in Molloy and Davis (1992). There has been considerable debate over the value of different criteria used for ranking species (see papers in Fitter and Fitter (1987) and Given (1990a) for reviews), but it was not the objective of this study to address these questions and the Department of Conservation system was used without modification. The main aim of this system is to enable priority setting in the allocation of resources to threatened species conservation. All scores range between 1 (least threat) and 5 (most threat), except for cultural values which range from 1 to 4. All species were scored by the authors, with some additional input from botanists familiar with species. All scoring was for the study area only, and scores are therefore different from those determined nationally for New Zealand (cf. Molloy & Davis, 1992).

Assessing threat and priority setting in threatened species conservation Table 1. Criteria used for determining priorities for the recovery of threatened taxa in the wild in New Zealand (after MoHoy & Davis (1992), who provide full details of the classes for each criteria)

Distinctiveness Taxonomic distinctiveness Geographical distribution Population features Number of populations Mean population size Largest population size Condition of largest population Wild population decline rate Vulnerability Legal protection of habitat Habitat loss rate Predator/harvest impact Competition Habitat specificity Reproductive specificity Other factors affecting survival Potential Propagation/protection ex situ Values Maori cultural values Pakeha cultural values

Species scores were summed without any weighting to give total scores in order to derive a linear ranking scheme. The individual species score profiles were used for multivariate analyses. Groups of species with similar score profiles were identified using agglomerative hierarchical cluster analysis as implemented in PATN (Belbin, 1989). The Bray and Curtis association measure and a flexible unweighted pair group centroid method of cluster fusion were used. Indirect ordination of the species score profiles was performed using detrended correspondence analysis as implemented in CANOCO (ter Braak, 1988). The individual criteria scores were correlated with the species ordination after analysis to assess the spatial direction and strength of correlations. As not all the criteria scored relate directly to 'threat', the cluster and ~ordination analyses were run twice; with all 17 criteria (priority data set) and with a subset of 12 criteria that related specifically to threat Ithreat data set). The criteria excluded from the threat data set were taxonomic distinctiveness, legal protection of habitat, propagation/protection ex situ, Maori cultural values and pakeha (the Maori name for New Zealanders of European descent) cultural values. Based on the result of the priority data set ordination, one additional ordination was undertaken to assess the effect of removing redundant criteria from the analysis. Redundant criteria are those that were not strongly correlated with the initial ordination. For those species that had definite I U C N categories assigned, discriminant analysis was undertaken on the score profiles (for all 17 criteria) to assess how well the previously allocated I U C N categories fitted the scores for our study area. Discriminant analysis was imple-

59

mented using a DISCRIM procedure in SAS (SAS, 1990). The discriminant model was then applied to the seven species of Indeterminate status to estimate the appropriate IUCN category. RESULTS Priority data set analyses

Linear ranking of total scores (Table 2) lists species from highest to lowest priority. The species with highest priority rank are Carex inopinata, Helichrysum dimorphum and Carmichaelia kirkii, and that with the lowest rank is Hebe raoulii var. macaskillii. Cluster analysis of the priority data set was interpreted at a dissimilarity of 0.22 giving five groups-considered to be the maximum number that could be realistically interpreted. These groups reflect differences in the criteria used to determine priorities; species with similar priority profiles group together (Table 3). The first group includes the two mistletoe species Alepis flavida and Peraxilla tetrapetala, which have high habitat specificity (i.e. require a host tree) and are difficult to propagate, are directly threatened by predation (in this case introduced brushtailed possums Trichosurus vulpecula), but still have good populations left with moderate decline rates in legally protected areas. Both species have moderate pakeha cultural values and are classified nationally as Indeterminate. The second group includes ten species with limited distributions, but moderate numbers of generally large populations in good condition. However, habitat specificity is moderate to high. Most are localised rather than highly threatened species. They have moderate to high pakeha cultural values and one species (Desmoschoenus spiralis) has high Maori cultural values. Celmisia mackaui is typical of this group, restricted to a Table 2. Species for priority data set ranked by total score for 17 criteria

56 49 49 48 48 46 45 44 44 43 43 43 43 43 43 43 42 42 42 42 41 41 41 40

Carex inopinata Helichrysum dimorphum Carmichaeliakirkii Muehlenbeckia astonii Hebe cupressoides Myosotis colensoi Leptinellafiliformis Triglochinpalustre Desrnoschoenusspiralis Australopyrurnsp. Ranunculusgodleyanus Myosurus minimus Juncus holoschoenus Centipeda minima Austrofestuca littoralis Anogramma leptophylla Notospartium torulosum Myosotis arnoldii Melicytus angustifolius Hebe armstrongii Mazus pumilo Leptinella nana Iphigenia novae-zelandiae Chenopodium detestans

40 39 38 38 37 37 37 36 36 36 36 35 35 35 35 34 34 34 34 33 33 33 32

Celmisia mackaui Carmichaeliaappressa Plantagoobconica Carmichaeliaastonii Swainsona novae-zelandiae Raoulia cinerea Notospartium carmichaeliae Urticalinearifolia Olearia capillaris Myosotis australis Alepis flavida Pleurosorus rutifolius Olearia coriacea Crassula multicaulis Chiloglottisgunnii Peraxilla tetrapetala Gnaphaliumnitidulum Coprosma obconica Chenopodiumpusillum Teucridiumparvifolium Pseudopanaxferox Brachyglottis sciadophila Hebe raoulii

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David R. Given, David A. Norton Table 3. Spscies composition of elmer groups for priority data set

GROUP 1 GROUP 2 Pseudopanax ferox Celmisia mackaui Desmoschoenus spiralis GROUP 3

Carmichaelia kirkii Muehlenbeckia astonii GROUP 4

Myosotis australis Juncus hoioschoenus Plantago obconica Olearia coriacea Leptinella nana

GROUP 5 Chilogiottis gunnii Chenopodium detestans Crassula muiticaulis Carmichaelia astonii

Alepis flavida Brachyglottis sciadophila Carmichaelia appressa Notospartium carmichaeliae Swainsona novae-zelandiae Carex inopinata Helichrysum dimorphum

Peraxiila tetrapetala Teucridium parvifolium Hebe armstrongii Hebe raoulii

,4nogramma leptophylla Myosurus minimus Notospartium torulosum Coprosma obconica Iphigenia novae-zelandiae Ranunculus godleyanus

Mazus pumilo Leptinella filiformis Melicytus angustifolius Urtica iinearifolia Pleurosorus rutifolius Olearia capillaris Triglochin palustris Centipeda minima Raoulia cinerea Australopyrum sp.

Austrofestuca littoralis Myosotis arnoldii Chenopodium pusillum Gnaphalium nitiduhan

small area, but extant populations are reasonably stable. This group includes one nationally Vulnerable, one Rare and eight Local species. The third group includes the 'classic' threatened plants. These are species with few populations in poor condition, and high population decfine and habitat loss rates. Legal protection is generally poor. Competition and habitat specificity are moderate to high, and they have low to moderate pakeha cultural values. Hebe cupressoides is a typical example, with two small populations within the study area. Two nationally Endangered, three Vulnerable and one Rare species are included. The fourth group, comprising 17 species, is similar in occurring at only a few sites with generally small population sizes. However, unlike the last group, population condition is moderate, and population decline an~habitat loss rates are low. The fern Pleurosorus rut~olius is typical; although only occurring at a few sites, there appears to be little evidence of overall decline. This group includes one nationally Endangered, three Vulnerable, four Rare, six Local, and three Indeterminate species. The final group of 12 species is also characterised by species with limited numbers of populations, but generally of larger size than in group 4. Population condition is healthy and population decline and habitat loss rates are low. Predation and competition are also low, but habitat specificity is high. The sand grass .4ustrofestuca littoralis belongs to this group, and although known from only one site in the study area, exists as a stable population. One nationally Endangered, two Vulnerable, two Rare, five Local and two Indeterminate species are included. Ordination of these data using detrended correspondence analysis explained 42.8% of the variance with the first three axes. Plots of the first axis against the second and third axes (Fig. 1) separate the species out in twodimensional ordination space and can be interpreted in terms of the most important criteria used to derive the

Hebe cupressoides Myosotis colensoi

species priority profiles. Only those criteria that had correlations of greater than 0-5 with either axis are plotted. The length of the arrows indicates the relative strength of the correlation. In the ordination plot of axis 1 and 2 (Fig. l(a)), species with low values on axis 1 typically have high pakeha cultural value, experience considerable predator or harvester impact, but are generally well-protected legally and are readily propagated (e.g. Hebe armstrongiO. The converse applies to species with high values on axis 1 (e.g. Chiloglottis gunniO. Similar patterns occur in other directions on the ordination plot. For example, Carmichaelia kirkii occurs in the lower left and is a species characterised by high habitat specificity and considerable competition. Species in the lower right of the diagram typically have few populations (e.g. Plantago obconica has only one population in the study area), while species in the upper left are known from many populations (e.g. ,4lepisflavida). Similar interpretations can be drawn from the axis 1-axis 3 ordination plot (Fig. l(b)). From these two ordinations it is apparent that the most important criteria used for scoring the individual species are size of the largest populations, ex situ propagation potential, population decline rate, number of populations and predator impact. Conversely criteria such as geographical distribution, reproductive specificity and Maori cultural values appear to be largely redundant in explaining variation within the study data set. The priority data set was reanalysed without those criteria that were not strongly correlated with the initial (17 criteria) ordination. Deletion of all criteria with correlations of less than 0.5 with the first or second axis (i.e. not shown on Fig. l(a)), resulted in an ordination based on nine criteria. The results of this ordination are very similar to the full (17 criteria) ordination, with Spearman rank correlation coefficients of 0.96, 0.95 and 0-60 obtained between the pairs of species scores for axes 1, 2 and 3, respectively.

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respectively. The considerable overlap between species with a range of scores and the poor correlations between scores and ordination axes suggest that straight linear ranking is oversimplifying the nature of threats facing these species and other factors affecting species priorities. Overlaying of IUCN categories (not illustrated) showed a similar lack of association. Threat data set analyses The linear ranking of total scores for the priority and threat data analyses was very similar, having a Spearman rank correlation coefficient of 0.93. However, the scores of some individual species did change considerably. For example, the sedge Desmoschoenus spiralis, ranked 8 in the priority data set list, dropped to 23 in the threat data set list, reflecting its high cultural values. Conversely some species that had little cultural significance but were threatened increased in their ranking when only 12 criteria were used; for example, the ranking of Leptinella nana increased from 22 to 9. The analysis of the threat data set using cluster analysis was again interpreted for five groups, corresponding to a dissimilarity of 0.26 (Table 4). The first cluster group was the same as in the first analysis containing the two mistletoe species Alepis flavida and

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(b) Fig. 1. Axes 1 and 2(a) and axes 1 and 3(b) of the priority data set species ordination. Species numbering follows Appendix 1. The correlation direction of the most important criteria are shown (length of line is proportional to correlation size). Criteria codes: np, number of populations; mps, mean population size; lps, largest population size; pc, condition of largest population; pd, wild population decline rate; lp, legal protection of habitat; hi, habitat loss rate; pi, predator/ harvest impact; c, competition; hs, habitat specificity; of, other factors affecting survival; pes, propagation/protection ex situ; pv, pakeha cultural values. The species are divided into four groups in Fig. l(a) according to their total scores 30-45+. Overlaying total species scores on the axis 1-axis 2 ordination (Fig. l(a)) shows the complex nature of factors used in prioritising species. Only those species with very high scores are distinct. Spearman rank correlation coefficients of -0-01, -0-25 and -0.38 were obtained between total species scores and the species scores on the first, second and third ordination axes,

The second group of 17 taxa includes all the species with small and declining populations, and with moderate to high habitat loss rates (the highest of the five groups). Two types of species are included in this group; those that are nationally threatened (e.g. Carex in•pinata and Helichrysum dimorphum), and those that are represented by a few declining populations in the study area but may be more abundant elsewhere (e.g. Desmoschoenus spiralis and Anogramma leptophylla). Nine of the 13 nationally Endangered and Vulnerable species in the data-set are included in this group, as well as three Rare, two Local, and three Indeterminate species. The third group of 15 species, although characterised by few populations, differs from the last group in that these are generally of larger size, and population decline and habitat loss rates are low. Hebe armstrongii, for example, has one large population showing no evidence of decline and a second smaller population. This group includes one nationally Endangered, three Vulnerable, two Rare, seven Local and two Indeterminate taxa. All the remaining Endangered and Vulnerable taxa are included in this group. The fourth group includes local species with several scattered populations that are generally of moderate size with low decline rates in areas of low habitat loss. Pseudopanax ferox and Teucridium parvifolium are typical of this group, occurring scattered through the study area in small but stable populations. One nationally Rare species and seven Local species are included. The final group is represented by five species with only a few populations which are of small size but reasonably stable and experiencing little direct threat.

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David R. Given, David A. Norton Table 4. Species composition of cluster groups for threat data set

GROUP I GROUP 2 Carmichaelia kirkff Helichrysum dimorphum Leptinella filiformis Melicytus angustifolius Myosotis colensoi GROUP 3 Carmichaelia astonii Chenopodium detestans Crassula multicaulis Ranunculus godleyanus Australopyrum sp. GROUP 4 Hebe raoulii Pseudopanax ferox GROUP 5 Olearia coriacea

Alepis flavida Anogramma leptophylla Desmoschoenus spiralis Iphigenia novae-zelandiae Leptinella nana Muehlenbeckia astonii Myosurus minimus Austrofestuca littoralis Celmisia mackaui Chenopodium pusillum Hebe armstrongii Raoulia cinerea

Peraxilla tetrapetala Carex inopinata Hebe cupressoides Juncus holoschoenus Mazus pumilo Myosotis australis Notospartium torulosum Carmichaelia appressa Centipeda minima Chiloglottis gunnii Myosotis arnoldii Triglochin palustris

Brachyglottis sciadophila Notospartium carmichaeliae Swainsona novae-zelandiae

Gnaphalium nitidulum Pleurosorus rutifolius Teucridium parvifolium Olearia capillaris Urtica linearifolia

Coprosma obconica Plantago obconica

This group is largely comprised of species that are at the limits of their range in the study area (e.g. Olearia capillaris). Two nationally Rare and three Local species are included. Ordination of these data resulted in the first three axes explaining 48.3% of the variance in the data set (Fig. 2). These axes can again be interpreted in terms of the indicated correlations on the ordination plots. The most important criteria are population decline rate, habitat specificity and size of the largest population, and the least important factors are reproductive specificity and geographical distribution. Overlaying the species total scores (Fig. 2(a)), or IUCN categories, shows that there is again very little clear separation of scores in ordination space, while correlations between total scores and species scores on the first three ordination axes were again weak (r values of -0-05, -0.34 and -0.05 for axes 1, 2 and 3, respectively). Discriminant analysis of I U C N categories

Multivariate techniques can also assist in assigning Indeterminate species to specific IUCN groupings. Discriminant analysis was used to assess how well our data Table 5. Number of species (percentages in brackets) successfully allocated by the estimated discriminant function

Allocated IUCN category E Original IUCN category E 4 (100-0) V 0 (0.0) R 0 (0'0) L 0 (0.0)

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0 (0.0) 0 (0.0) 1 (12.5) 16 (84-2)

E, Endangered; V, Vulnerable; R, Rare; L, Local

fitted the previously assigned IUCN categories; we found that generally the fit was good with between 84 and 100% of species correctly allocated (Table 5). We then applied the discriminant model to the seven species of Indeterminate status, allocating them to IUCN categories, with the assigned categories making sense with what we know of the biology and likely conservation status of the individual species in the study area (Table 6). DISCUSSION AND CONCLUSIONS These multivariate analyses highlight the complex nature of threats species face and of the criteria we use to determine threatened species priority lists. Many of these threats and criteria are related, but others are not. Reduction of the information for these criteria to a single value by linear ranking results in considerable information loss; species can have the same total score, but for very different reasons. For example, in the iariority data set Austrofestuca littoralis and Anogramma leptophylla both have total scores of 43 (Table 2). However, they are threatened for very different reasons. Austrofestuca littoralis is known from only one population in the study area, but this is reasonably large, while Anogramma leptophylla is known from several populations, but these are small. Furthermore, population condition is worse and habitat loss rate greater for Anogramma leptophylla than for Austrofestuca littoralis. Table 6. Allocation by the discriminant function of indeterminate species into IUCN categories

Species

A lepis flavida Australopyrum sp. Chenopodium pusillum Iphigenia novae-zelandiae Juncus holoschoenus Leptinella filiformis Peraxilla tetrapetala

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(b) Fig. 2. Axes 1 and 2(a) and axes 1 and 3(b) of the threat data set species ordination. Species numbering follows Appendix I. The correlation direction of the most important criteria are shown (length of line is proportional to correlation size). Criteria codes as in Fig. 1. The species are divided into four groups in Fig 2(a) according to their total scores 20-35+. As first suggested by Holt (1987), a more informative approach for assessing threatened species is to regard them as occurring in multidimensional space, with the dimensions or axes corresponding to different types of threat. This perspective makes it possible to conceptualise areas of space into which species are being driven by human and other factors, pushing them towards extinction. It would then seem desirable for managers to develop strategies that keep species out of these areas. For example, in the threat data set (Fig. 2(a)), the lower part of the axis 1-axis 2 ordination represents an 'undesirable' area, being characterised by high predator impact, high habitat loss and high competi-

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tion and small numbers of small-sized populations. In contrast species in the upper parts of this ordination could be considered to be in more 'desirable' positions, as they are not experiencing these conditions and presumably are less directly threatened with extinction. The ordinations also show that not all criteria used to score species are equally important, and for our study data set at least several appear largely redundant. This redundancy is clearly illustrated when the ordination analysis was repeated without such criteria. Correlation of species scores on the first three axes of the reduced ordination with the species scores for the full priority data set ordination (17 criteria) shows that the axes are virtually identical; removal of these criteria does not affect the relative positions of species in ordination space. It is therefore clear that much of the data we are using for classifying threatened species carries very little information content, and multivariate techniques can provide a powerful means of identifying these redundant criteria. Two additional problems occur with combining individual criteria to obtain a composite or total score. First, there is little evidence that a particular score is equally important for any two criteria. For example, in the Department of Conservation scoring system (Molloy & Davis, 1992) a score of 4 for mean population size reflects 'from 2 to 10 plants, or area 1-10 sq m, or unknown but suspected to be small' and a score of 4 for competition reflects 'competition at many sites; impact high at some sites'. There is, however, no evidence that changes in the levels of these two criteria are equivalent. Combining them assumes this. A similar problem has been recognised in using multiple criteria to evaluate natural areas (Smith & Theberge, 1987; Margules et al., 1991). The second problem occurs because criteria are not independent (Smith & Theberge, 1987). Species that are scored high for some criteria are also likely to be scored high for others. Correlated data are, however, ideal for multivariate analyses, as these techniques are designed to reduce complex multivariate data sets to a few simple variables summarising most of the common variation. The difference between criteria that describe threat to a species and those that are important for determining priorities has been widely recognised (e.g. Mace & Lande, 1991). In most cases the threat criteria are usually a subset of the criteria used to determine priorities for management action. Although managers must consider wider issues in setting priorities, it is also important to understand the threats to species if we are to develop the knowledge and techniques necessary for successful recovery work. The two data sets analysed here highlight the difference between threat and priority, as illustrated in the different species groupings obtained from the two cluster analyses (Tables 3 and 4). In particular, the much larger group of highly threatened species in the threat data set cluster analysis (Table 4, group 2), suggests that although not all may be highpriority species (cf. priority data set analysis) they are

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David R. Given, David A. Norton

all highly threatened within the study area. Whether they all warrant equal management action is another question. The differences highlighted between the threat and priority data sets suggest that combining information on threat and priority is unwise (cf. Mace & Lande, 1991). It has been suggested that for ranking natural areas an hierarchical framework may be more appropriate than a simple index (e.g. Margules & Usher, 1981; Margules, 1986); 'biological' factors, for example, could provide a first level of sorting and 'political' factors a second level. A similar approach would seem profitable for threatened species, with an initial species sorting based on threat, and subsequent sorting based on nonthreat considerations such as cultural concerns and economic considerations. Whitten (1990) took such an approach when he used a combination of threat, recovery potential, and estimated budget for determining priorities for recovery of threatened species in Britain. Although non-threat criteria are used in the New Zealand threatened species priority list (Molloy & Davis, 1992), one of them, Maori cultural values, is also used for a second sorting level to identify species of particular concern to Maori people. We strongly endorse the comments of Mace and Lande (1991) that threat and priority must be clearly distinguished, and believe that an hierarchical approach to priority setting is highly desirable. The multivariate approach used here for assessing threat has similarities to the approach advocated by Rabinowitz (1981) and Rabinowitz et al. (1986) for considering 'types of rarity' in plants. These authors developed an eight-celled table based on range, habitat specificity and local abundance, and suggested that this reflected fundamental differences in the biology of rare plants. Soul6 (1986) points out that the components of Rabinowitz's table are comparable to within-habitat, between-habitat, and geographic components of diversity. Multivariate analysis offers an objective alternative to the approach used by Rabinowitz. The multivariate techniques presented here have two additional applications in threatened species work. First, they provide the opportunity for evaluating trends in threatened species status through time. For example, as habitat protection improves or population decline rates increase or decrease, species threat profiles will change and they will shift in their relative position in multivariate threat space. Multivariate techniques provide a useful means of characterising and assessing these changes relative to other species. The application of multivariate techniques to the same data over a period of time could provide a means for assessing the success of recovery programmes (cf. Whitten, 1990). Multivariate techniques also provide the opportunity to explore the factors that could be modified to improve the status of a species. By knowing the conditions that characterise a reasonably stable or safe group of species, it should be possible to identify and model the management changes that could be made to improve the security of a particular target species. Although

essentially theoretical, this type of approach can suggest strategies for use in species management. We consider that the multivariate approach advocated here, through allowing analysis of a wide range of factors affecting threatened species, has considerable utility in assessing the complex effects and interactions of these factors. Such techniques provide a valuable additional approach for evaluating the nature of threats species face and for assessing the effectiveness of various management regimes. The use of broadband IUCN categories, linear ranking, population viability analysis and multivariate techniques will collectively provide a much better insight into factors threatening species and in the efficient allocation of resources for their conservation, than the use of one system alone. However, it is essential when using these techniques to make a clear distinction between threat and priority.

ACKNOWLEDGEMENTS We thank Ian Atkinson, Bruce Clarkson, Janice Molloy, Mike Morgan, Peter Williams and an anonymous referee for helpful comments on earlier drafts of this paper, and Richard Woollons for assistance with statistical analyses.

REFERENCES Allan, H. H. (1961). Flora of New Zealand, Vol. 1. Government Printer, Wellington. Belbin, L. (1989). PATN Technical Reference. CSIRO, Division of Wildlife and Ecology, Lyneham, ACT. Briggs, J. & Leigh, J. (1988). Rare and threatened Australian plants. Aust. Nat. Parks & Wild. Serv., Spec. Pubis, No. 14. Brownsey, P. J. & Smith-Dodsworth, J. C. (1989). New Zealand Ferns and Allied Plants. Bateman, Auckland. Connor, H. E. & Edgar, E. (1987). Name changes in the indigenous New Zealand flora, 1960-1986 and nomina Nova IV, 1983-1986. N. Z. J. Bot., 25, 115-70. Fitter, R. & Fitter, M. (1987). The Road to Extinction. IUCNAJNEP,IUCN, Gland. Given, D. R. (1990a). Priorities for threatened plant conservation. Botany Institute, DSIR Land Resources, Christchurch (unpublished report). Given, D. R. (compiler) (1990b). Threatened and local plant lists--New Zealand botanical region. Botany Institute, DSIR Land Resources, Christchurch (unpublished report). Holt, S. J. (1987). Categorization of threats to and status of wild populations. In The Road to Extinction, ed. R. Fitter & M. Fitter. IUCN/UNEP,IUCN, Gland, pp. 19-30. IUCN (1988). 1988 IUCN Red List of Threatened Animals. IUCN, Gland. Knox, G. A. (ed.) (1969). The Natural History of Canterbury. Reed, Wellington. Lucus, G. L. & Synge, H. (1978). The IUCN Plant Red Data Book. IUCN, Gland. Mace, G. M. & Lande, R. (1991). Assessing extinction threats: towards a re-evaluation of IUCN threatened species categories. Conserv. Biol., 5, 148-57. Margules, C. R. (1986). Conservation evaluation in practice. In Wildlife Conservation Evaluation, ed. M. B. Usher. Chapman & Hall, London, pp. 297-314. Margules, C. & Usher, M. B. (1981). Criteria used in assessing wildlife conservation potential: a review. Biol. Conserv., 21, 79-109.

Assessing threat and priority setting in threatened species conservation Margules, C. R., Pressey, R. L. & Nichols, A. O. (1991). Selecting nature reserves. In Nature Conservation: Cost-Effective Biological Surveys and Data Analysis, ed. C. R. Margules & M. P. Austin. CSIRO, Canberra, pp. 90-7. McEwen, W. M. (1987). Ecological regions and districts of New Zealand, 3rd revised edition in four maps. Biol. Resour. Centre Pubis., No. 5. Department of Conservation, Wellington. Molloy, J. & Davis, A. M. (1992). Setting Priorities for the Conservation of New Zealand's Plants and Animals. Department of Conservation, Wellington. Moore, L. B. & Edgar, E. (1970). Flora of New Zealand, Vol. 2., Government Printer, Wellington. Munton, P. (1987). Concepts of threat to the survival of species used in Red Data Books and similar compilations. In The Road to Extinction, ed. R. Fitter & M. Fitter. IUCN/UNEP, IUCN, Gland, pp. 71-111. Perring, F. H. & Farrell, L. (1983). Vascular Plants. British Red Data Books, Vol. 1, 2nd edn. Royal Society for Nature Conservation, Nettleham, Lincoln. Rabinowitz, D. (1981). Seven forms of rarity. In The Biological Aspects of Rare Plant Conservation, ed. H. Synge. John Wiley, Chichester, pp. 205-17. Rabinowitz, D., Cairns, S. & Dillon, T. (1986). Seven forms of

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rarity and their frequency in the flora of the British Isles. In

ConservationBiology; The Scienceof Scarcity and Diversity,ed. M. E. Soulr. Sinauer, Sunderland, Massachusetts, pp. 182-204. SAS (1990). SAS/STA T Guidefor Personal Computers, Version 6 Edition. SAS Institute Inc., Cary, North Carolina. Smith, P. G. R. & Theberge, J. B. (1987). Evaluating natural areas using multiple criteria: theory and practice. Environ. Manage., 11,447-60. Soulr, M. E. (!986). Different approaches, different conclusions. In Conservation Biology; The Science of Scarcity and Diversity, ed. M. E. Soulr. Sinauer, Sunderland, Massachusetts, pp. 117-21. ter Braak, C. J. F. (1988). CANOCO--A FORTRAN Program

for Canonical Community Ordination by (Partial) ( Detrended) (Canonical) Correspondence Analysis, Principle Component Analysis and Redundancy Analysis (Version 3.12-1991). Agricultural Mathematics Group, Wageningen. Whitten, A. J. (1990). Recovery: A proposed programme for Britain's protected species. Nature Conservancy Council CSD Report, No. 1089. Wilson, C. M. & Given, D. R. (1989). Threatened Plants of New Zealand. DSIR Publishing, Wellington.

APPENDIX 1 Species used in analyses Code

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32

Species~

Alepis flavida Anogramma leptophylla Australopyrum sp. Austrofestuca littoralis Brachyglottis sciadophila Carex inopinata Carmichaelia appressa Carmichaelia astonii Carmichaelia kirkii Celmisia mackaui Centipeda minima Chenopodium detestans Chenopodium pusillum Chiloglottis gunnii Coprosma obconica Crassula multicaulis Desmoschoenus spiralis Gnaphalium nitidulum Hebe armstrongii Hebe cupressoides Hebe raoulii var. macaskillii Helichrysum dimorphum Iphigenia novae-zelandiae Juncus holoschoenus Leptinella filiformis Leptinella nana Mazus pumilo Melicytus angustifolius Muehlenbeckia astonii Myosotis arnoldii Myosotis australis Myosotis colensoi

Family Loranthaceae Gymnogrammaceae Poaceae Poaceae Asteraceae Cyperaceae Fabaceae Fabaceae Fabaceae Asteraceae Asteraceae Chenopodiaceae Chenopodiaceae Orchidaceae Rubiaceae Crassulaceae Cyperaceae Asteraceae Scrophulariaceae Scrophulariaceae Scrophulariaceae Asteraceae Colchicaceae Juncaceae Asteraceae Asteraceae Scrophulariaceae Violaceae Polygonaceae Boraginaceae Boraginaceae Boraginaceae

I U C N category b Indeterminate Rare Indeterminate Vulnerable Local Endangered Rare Local Vulnerable Local Local Rare Indeterminate Endangered Rare Local Local Rare Vulnerable Vulnerable Local Endangered Indeterminate Indeterminate Indeterminate Endangered Vulnerable Local Rare Local Vulnerable Vulnerable Continued

David R. Given, David A. Norton

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APPENDIX l---contd

Code 33 34 35 36 37 38 39 40

41 42 43

44 45 46 47

Speciesa

Myosurus minimus subsp, novae-zelandiae Notospartium carmichaeliae Notospartium torulosum Olearia capillaris Olearia coriacea Peraxilla tetrapetala Plantago obconica Pleurosorus rutifolius Pseudopanax ferox Ranunculus godleyanus Raoulia cinerea Swainsona novae-zelandiae Teucridium parvifolium Triglochin palustris Urtica linearifolia

Family

Ranunculaceae Fabaceae Fabaceae Asteraceae Asteraceae Loranthaceae Plantaginaceae Aspleniaceae Aralaceae Ranunculaceae Asteraceae Fabaceae Verbenaceae Juncaginaceae Urticaceae

IUCN categoryb

Vulnerable Local Rare Local Local Indeterminate Rare Local Local Local Local Local Local Vulnerable Local

a IUCN categories based on Given (1990b). b Nomenclature follows Allan (1961), Brownsey and Smith-Dodsworth (1989) and Moore and Edgar (1970), and changes suggested in Connor and Edgar (1987).