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Ranking of habitats for the assessment of ecological impact in land use planning
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ELSEVIER
0006-3207(95)00139-5
Biological Conservation 77 (1996) 227-234 Copyright © 1996 Published by Elsevier Science Limited Printed in Great Britain. All rights reserved 0006-3207/96/$15.00+0.00
R A N K I N G OF HABITATS FOR THE ASSESSMENT OF ECOLOGICAL IMPACT IN LAND USE PLANNING E. Rossi & M. Kuitunen Department of Biological and Environmental Science, University of Jyviiskylii, PO Box 35, SF- 40351, Jyvdskyl(i, Finland (Received 14 June 1994: accepted 30 August 1995)
Abstract
combining this method with geographical information systems. Copyright © 1996 Published by Elsevier Science Ltd
A simple habitat ranking method suitable for use in the early stages of land use planning is developed here. The method is designed for assessment of the biological impacts of infrastructure and urban development. The objectives are to formulate an assessment procedure, to minimise fieldwork and to avoid error due to species remaining unnoticed in the field. Ranking takes place in terms of a habitat value (HI/) index calculated on the basis of the species present (vascular plants, amphibians, reptiles, birds and mammals), the threat categories to which they belong and the likelihood of their occupying specific habitats. The existence of the species in different habitats was predicted on the basis of the literature. To calculate the H V index, threat categories and preferred habitats were defined for each species', so that the species could be weighted according to their relative threat category in the region concerned. The method was tested in Finland (60-70 ° N), using several weights for each threat category in order to reveal the effect of differences in weighting. The results appeared to be relatively insensitive to the weights used, supporting the objectivity of the method. Herb-poor dry meadows, riparian habitats, herb-rich deciduous forests, industrial~urban habitats and cultivated areas were ranked highest in the southern regions of the country. Arctic alpine Jells, riparian habitats, spruce mires and herb-rich deciduous forests" were ranked highest in the northernmost region. In addition to potential species composition, the total area of a habitat and its vulnerability to the development concerned also has an influence on its ranking, and therefore separate rankings must be calculated for different types of development. The results suggest that habitat ranking of this kind could be profitable in the early stages of environmental impact assessment, because it helps one to concentrate on the most valuable habitats. It is also reasonable to assume that our method may be of" help in evaluating Siberian boreal habitats by
Keywords: conservation biology, habitat ranking, threat category, conservation value, GIS, EIA, ecological impact analysis. INTRODUCTION
Societal concern about environmental degradation has given rise to requirements for environmental impact assessment (EIA) in the case of road construction or urban development projects, etc. Most of the existing habitat evaluation methods have been developed for conservation needs (Usher, 1986; Spellerberg, 1992), and the necessity for practical methods has increased due to the extensive application of EIA. Consideration of the ecological impacts of large-scale construction projects is typically restricted to an evaluation of specific sites recorded by the environmental authorities as valuable for nature conservation purposes (cf. Box & Forbes, 1992). Therefore, only a small part of the affected area is usually evaluated, and the lesser known nature values may very well be overlooked. As many kinds of habitat will be disturbed in more extensive constructional projects (motorways, factory areas, oil pipelines, power lines, housing estates, etc.), an appreciation of the conservation value of different habitats is necessary in order to assess and compare them and thereby to minimise biological damage. Margules and Usher (1981) summarised the criteria most often used to assign habitat values. These were species diversity, species rarity, naturalness, area, threat of human interference, representativeness, research and educational value, recorded history and potential value. Only some of these criteria are biological, the others being more related to social needs. Margules (1984) explored the criteria used by experienced evaluators and found that they chiefly used only a few, species diversity, species rarity and area of site being the most frequent. For evaluation of the fauna, the simplest indicators are species richness or the number of reproductive individuals. Species diversity, measured by the Shannon index, for example includes both the numbers of
Correspondence to: M. Kuitunen. Present address: NRRI, University of Minnesota, 5013 Miller Trunk Highway, Duluth, Minnesota 55811, USA; Fax: 218 7204217; e-mail [email protected] 227
228
E. Rossi, M. Kuitunen
species and species abundance, but not their threat category. This is because only site-specific information is used in the Shannon calculation. Occupation by rare species (for definition, see Rabinowitz, 1981; Rabinowitz et al., 1986; Fiedler & Ahouse, 1992; Gaston, 1994) is generally accepted as indicating that a habitat has a high biological value, while the validity of species diversity indices has been criticised (e.g. by Alatalo, 1981). Furthermore, the collection of data for cal-culating diversity indices is laborious in the case of large areas. When the region influenced by a project covers a large area, it is impractical to census all species and individuals. This is the case with many infrastructure projects (e.g. powerlines, roads, railways, natural gas pipelines). It is therefore more practicable to conduct the evaluation in stages, applying simple, inexpensive methods first to reveal the potentially critical areas, and using more precise and consequently more expensive methods at the later stages to examine these in detail. The aim of this paper is to present an easy-to-use habitat ranking approach for land use planning. The basic idea was to minimise fieldwork by making thorough use of reported data on species existing in the region concerned. The method is based on the probability of occurrence of given species in different types of habitat. METHODS The model
The ranking system is based on a habitat value (HV) formed using the number of species occurring in the
habitat with a threat category (1-8) weighting for each species (see below). HV is calculated by summing the weights for all the species potentially living in each habitat. Moreover, each species is classified according to its four most preferred habitats out of 26 defined habitats (Table 1). A coefficient accounting for the species preference of a habitat is defined so that the species living mainly in that habitat can be weighted the highest. Equal weights are assigned to each species included in a threat category. In this way the weight assigned to a species is independent of its taxonomy. All the information was obtained from the literature. H V ~ : (n~,~.k . w ; c k ) : i = 1..... 2 6 ; j : 1..... 8 ; k = 1..... 4 ; where HV~ = value of habitat i, ni,i.~ -- number of species preferring habitat i at level k and belonging to threat category j, wi -- weight assigned to threat category j, c, = coefficient assigned to a species preference order k of a habitat i. The computational procedure may be illustrated with an example: let us assume that we have defined four habitats A, B, C and D. Threat categories (here three categories only, I-III; in the proper procedure there are eight categories, j) and preferred habitats (primary and secondary for each species; in the proper procedure there are four categories, k) are defined for all the species recorded in that region according to the best available data. This yields the following:
Table 1. Rank orders of 26 habitats in the four Finnish regions (Fig. 1) using all species
Habitat number and name 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.
Herb-rich and other deciduous forests Esker forests (Pine) Dry upland forest sites (Pine) Moist upland forest sites (Spruce) Rich fen Open mires Pine mires Spruce mires Baltic Sea and its outer islands Oligotrophic lakes Eutrophic lakes Streams and rivers Springs Shore of the Baltic Sea Riparian habitats Flooded areas Beaches Non calcareous rocky areas Calcareous rocks and quarries Arctic-alpine fells Herb-rich dry meadows Herb-poor dry meadows Wet meadows Cultivated areas Parks and gardens Industrial/Urban
Region I
II
III
IV
3 24 23 13 11 20 26 10 16 25 14 21 15 7 2 19 18 8 17 22 12 1 9 5 6 4
3 19 23 14 10 21 26 8 15 25 12 22 13 9 2 18 20 6 17 24 16 1 11 4 7 5
2 25 24 13 8 17 26 7 16 23 14 18 10 12 1 20 22 4 21 15 19 6 9 3 11 5
3 24 23 8 4 15 26 6 18 20 13 21 12 17 1 19 22 5 9 2 25 11 10 7 16 14
Habitat ranking in land use planning
229
Number of species for which a habitat is Primary (k -- 1) Threat category A B C D
Secondary (k = 2)
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The following totally arbitrary weights (wj) are assigned to threat and to habitat preference (q): wl= 1, wu-- 3, Wlllz 10 and q = 1, c2= 0.5 The HV index of habitat A is then calculated as follows: HVA=8 × 1 × 1 + 2 X 3 X 1 +1 × 1 0 × 1 + 7 × 0 - 5 + 2 × 3 X 0 . 5 + 0 X 10 X 0 . 5 = 3 5 . 5
1X
Repetition of the calculation for all the habitats gives the following results: HVA= 35.5, HVB= 24.0, HVc= 12.0 and HVD = 52.0 The habitats can thus be ranked in the following order: D, A, B, C. Application o f the model
Information used for the calculations We tested this method with the flora and fauna of Finland (c. 330,000 km z in area - - situated between latitudes 60 and 70°N), using vascular plants, mammals, birds, amphibians and reptiles. Excellent information is available on the occurrence and habitat requirements of vascular plants (H~imet-Ahti et al., 1986), birds (Hyyti~i et al., 1983; Koskimies, 1989) and mammals (Koivisto, 1987), and on amphibians and reptiles (Koli, 1987), but arthropods were omitted from the calculations because of the restricted nature of the available data. Potential habitats for each species were recorded exactly as presented in the literature. While there was minor ambiguity in the original classification, e.g. with some overlapping between habitats, no modification was made, in order to maintain objectivity. The resulting total number of habitats was 26 (Table 1). Because of the significant variations in the abundance of some species in different parts of the country, a division was made into four regions, defined in accordance with the phytogeographical zones (Fig. 1; Ahti et.al., 1968), region I corresponding to the hemiboreal zone, region II to the southern boreal, region III to mid-boreal and region IV to the northern boreal zone and arctic-alpine areas. Vascular plants dominated the number of species in each region (Table 2). Threat categories All the species were classified in terms of their regional degree of threat, the most threatened species being divided into five groups according to the IUCN (1980)
% Fig. 1. Regions used for compiling the species vs habitat data
in the application of the model.
categories by reference to the Finnish Red Data Book (Rassi et al., 1992; threat categories 4-8) and the remainder, those not designated by the IUCN as endangered, were placed in three rarity categories in terms of their distribution and abundance in the geographical area concerned (Rabinowitz, 1981; Rabinowitz et al., 1986; Fiedler & Ahouse 1992; Gaston 1994; see also Mace & Lande, 1991 for a criticism of the subjective IUCN categories). The criteria for the threat categories were as follows: 1 Abundant: the species is abundant over the whole region; 2 moderately abundant: the species exists over the whole region but is rare in one-third of it; 3 moderately rare: the species is nonexistent in one-third of the region; 4 insufficiently known: the species is thought to belong to one of the next categories, but because of lack of information it cannot be defined any more exactly (IUCN, 1980); 5 indeterminate: the species is known to belong to one of the next categories, but because of lack of information it cannot be defined any more exactly (IUCN, 1980); 6 rare: populations of the species are very small, but are not at present endangered
E. Rossi, M. Kuitunen
230
Table 2. Numbers of species in the taxonomic groups and geographical regions of Finland (Fig. 1) used in the application of the model
Taxon Region I
Region II
Region III
Region IV
Vascular plants Birds Mammals Amphibians and reptiles
1063 198 44 10
923 194 51 9
787 191 44 8
629 153 35 3
Total
1315
1177
1030
820
or vulnerable (IUCN, 1980); 7 vulnerable: the species will probably become endangered in the not too distant future if its living conditions are not improved (IUCN, 1980); 8 endangered: the species will probably become extinct soon, if its living conditions are not improved. Classification data for threat categories 1-3 were obtained from Hfimet-Ahti et al. (1986), Hyyti~i et aL (1983), Koskimies (1989), Koivisto (1987) and Koli (1987). Distribution of species abundance among the threat categories varied considerably between the regions (Fig. 2), with the greatest differences appearing between the northernmost and the two southernmost regions. Numbers of species decreased in general from south to north, mainly as consequence of a decline in abundant species (known as core species in community ecology; Hanski, 1982) and threatened species (these and others behaving in a similar manner are often called satellite species in community ecology; Hanski, 1982), while those of intermediate species remained almost constant (Fig. 2).
understand a threat category as a measure of the probability of extinction, the scale of measurement is not quantitative. The only indisputable fact is that the threat categories are ordinal. Therefore, mathematical operations such as addition are not possible without cardinalisation of the ordinal data (weighting). Several cardinalisation methods have been presented but there is no generally agreed practice for determining the weights. We calculated expected weights by assuming that the probability density function of the weights is equal for all the threat categories. Nijkamp et aL (1990) have shown that the expected weights can then be calculated as: E(wl) = l l f E(w2) = l l f
+ ll[J(J-l)]
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+ ll[a(J-1)] + .,. + l l ( d
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In order to test the effect of different weightings for threat (%), six sets of weights were assigned to each of the threat categories (Fig. 3). Although it is possible to
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Regions I-IV as in Fig. 1. Threat categories are: 1, abundant; 2, moderately abundant; 3, moderately rare; 4, insufficiently known; 5, indeterminate; 6, rare; 7, vulnerable; 8, endangered.
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Threat category of a species Fig. 3. Graphical presentation of the weight sets (I-VI) assigned to threat categories in the application of the model. Concurrently used weight sets are connected by lines. Threat categories as in Fig 2,
Habitat ranking in land use planning For the sake of convenience, the calculated weights were multiplied by 64 in order to adjust the smallest weight to 1. This enabled the following weights to be generated for the eight threat categories: I, 2.1, 3.5, 5.1, 7-1, 9.7, 13.8, and 21.7. As one cannot be certain that such a uniform distribution holds good, modified sets were constructed from the above weights to test the effect of distribution assumptions. Thus a second weight set (II) was taken to test a contentious weight distribution, and a third (III) was based on the overall rarity distribution of the species. Since this distribution was concave (Fig. 3), a logistic weight distribution was used as an appropriate model. As the difference between the weights for endangered and abundant species was relatively small compared with those used in other studies (e.g. Wheeler, 1988), the other three weight sets (IV-VI) were generated by rescaling the previous ones (I-III) so that the maximum weights became 100.
Species habitat preference The degree of species-specific habitat preference (cD was tested by introducing two sets of coefficients. First, all the species were treated equally independent of the degree of preference for the habitat concerned, i.e. the coefficient set was 1.0, 1.0, 1.0, 1.0. Secondly, all the weights of a specific threat category group were taken into account only when the habitat in question was the preferred one for a species, while at the other levels of preference only a portion of the weight was taken, using the following coefficients in descending order of preference: 1.0, 0.75, 0.5, 0.25. The six threat category weight (wj) sets (I-VI; Fig 3) and two habitat preference (cD coefficient sets produce a total of 12 habitat values, and it is these that were used to calculate a ranking order of habitats for each region (I-IV; Fig 1). Spearman rank correlations were calculated to compare sensitivity of the weighting for the ranking of habitats. As an activity may have a different impact on different taxa, it may be necessary to weight critical taxonomic groups more than less sensitive taxa. We therefore tested whether the ranking of habitats would be different if only bird species were included in the computation procedure, implementing it in other respects in the same manner as before. RESULTS Ranking of habitats by HV appeared to be relatively insensitive to the weightings used for the threat category groups, the Spearman rank correlation coefficients varying from 0.934 to 0.993 in region I, from 0.928 to 0-984 in region II, from 0.853 to 0.978 in region III and from 0.928 to 0.996 in region IV. Although the ranking of habitats in region III appeared slightly less certain than in the other regions, all the correlations were highly significant (p <0.001).
231
When the habitats were arranged according to the average HVs achieved using the 12 weight sets described, the five most important in the two most southern regions were herb-poor dry meadows, riparian habitats, herb-rich deciduous forests, industrial/urban areas and cultivated areas (Fig. 4, Table 1). Riparian habitats, arctic-alpine fells, herb-rich deciduous forests and rich fens were the most important in the northeastern region. There was an overall trend for the relative importance of culturally influenced habitats to increase from north to south whereas that of arctic-alpine, fells, rich fens, spruce forests, calcareous rocks and quarries and oligotrophic lakes increased from south to north. In spite of discernible variation in the rankings due to the different weightings, the overall order of importance appeared to be indisputable. When only bird species were included in the calculation of the HVs, a slightly different ranking was obtained (Fig. 5). The most notable differences were in the ranking of vegetationally rich, small-sized habitat types such as rich fens (Habitat 5) or springs (Habitat 13). These may be of minor importance for birds and are not mentioned as the habitats of any bird species in the literature consulted. DISCUSSION The ranking of habitats prior to development seems to be a realistic and useful approach for comparing the value of different components of the natural environment. Our method is applicable to ecological impact assessment, but the key to its successful use lies in application early in the planning process. It should be used as a screening tool but not as an end product. For practical application of the method, habitats of roughly equal conservation value must be clustered so that they can be easily identified in the area concerned. This will reduce uncertainty in the ranking order, e.g. on account of the subjective element in the weighting. The tests conducted here demonstrate that the ranking of the habitats is relatively insensitive to weighting, but in spite of this the results must be applied cautiously, because some degree of substitutability is assumed between criteria. Endangered species are not given special prominence, for example (cf. Cable et al., 1989), because we were not considering specific sites, and any general condemnation of potential habitats of endangered species as prohibited areas would be an exaggeration. Due to changes in species threat category status, habitat rankings must be adjusted to finite regions. Arctic-alpine fells, for example, were ranked low in southern Finland for the simple reason that there are none, and therefore there are no specifically arcticalpine fell species there. Thus this habitat value is composed of those common, primarily arctic-alpine fell species which also exist in other habitats over a wide area. However, in addition to these species there are
232
E. Rossi, M. Kuitunen
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Habitat number Fig. 4. Average, maximum and minimum rankings of the habitats when using 12 sets of weightings to compute the HV index values. Vascular plant, mammal, bird, amphibian and reptile species were included in the computation. Regions I-IV as in Fig. 1. some which originally favoured arctic-alpine areas and which now occur as rare species in secondary habitats in southern Finland. Only vascular plants, mammals, birds, amphibians and reptiles were included in the present calculations, invertebrates, for example, being ignored because of the scarcity of data. This is a common practice in environmental evaluations (e.g. Van der Ploeg & Vlijm, 1978; Smith & Theberge, 1986; Pressey & Nicholls, 1989). The loss of accuracy should not be great, however, as invertebrate animals are typically linked with host species. However the species depending on dead wooden material may change that pattern. It must in any case be remembered that ranking by only one taxon may differ decisively from combined ranking by reference to a number of taxa. Therefore the potential effects of an activity must be evaluated taxon by taxon. The ideal way would be to evaluate the effects separately for each species, but that is obviously unrealistic. The differences in the sensitivities of the taxa could be included in the weighting procedure, or alternatively habitats occupied by sensitive taxa could be evaluated separately after transferring the original ranking to maps depicting the location of the planned activity.
The habitat ranking order observed in this study is quite expected. However, the most surprising result is the importance of man-made habitats for wildlife in south Finland where urban pressure would be expected to create ahigher prominence for conserving natural habitats. Species originally preferring the herbpoor dry meadows have occupied industrial and urban habitats as secondary sites and these are now threatened. Total number or area of habitats within the each region should be included in the use of the ranking process (cf. Sankovskii, 1992). On the other hand, incorporation of habitat size is problematic because usually only part of the habitat area is affected. Therefore, only variation in the value of the affected habitat should be considered. In view of the log-linear relationship between area size and species number (e.g. MacArthur & Wilson, 1967; Janzen, 1983), this variation is smaller in the case of large areas, and consequently the value of small areas should be emphasised in land use planning. Simplified habitat classification methods are generally based on excessively rough environmental patterns (e.g. Atkinson, 1990) or single taxonomic groups (e.g. Cable et al., 1989) and are inadequate for measuring
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the total ecological value of a habitat. The US Fish and Wildlife Service has developed a Habitat Evaluation Procedure (HEP) which aims to evaluate an area on the basis of key habitat factors for certain species (e.g. Lancia et al., 1982). Sankovskii (1992) introduced an approach to the evaluation of habitats based on the relative probability of their disappearance. He rejected a territorial approach (equal to the sum of the values that are attached to the local objects situated in this territory) and estimated the ecological values of communities using data on relative fragility, uniqueness, size of regeneration unit and intensity of destructive factors. Sankovskii (1992) assigned equal values to each type of community, but it is argued here that the concept of equal value is valid only at the species level. Communities are never alike, and therefore destruction of a single natural community will add to the probability of extinction of some species even if other communities of that type remain unchanged. The increase in the probability of extinction is dependent on the number and threat category of the species living in that community. In addition, the concept of regeneration unit is difficult to define. Although it is reasonable to conduct a ranking of
habitats based on those species occurring in each geographical area, it is also possible to generalise our results obtained from Finland to other geographical areas, especially as the geographical variation in the ranking order of the habitats was low. Finland forms part of the circumpolar boreal phytogeographical zone that also comprises parts of Russia, Alaska, the USA and Canada. Although species composition varies greatly, there are considerable similarities in the case of Russia and Finland in particular, and it may be possible to compare habitats in Russia based on our results, employing the techniques provided by geographical information systems and to produce a biodiversity inventory of at least some parts of Siberia, a region in which environmental evaluation would otherwise be an enormous task. ACKNOWLEDGEMENTS The assistance of Liisa Horppila and Leena Kiuru is gratefully acknowledged. We also thank Yrj6 Haila, Pekka Helle, Timo T6rm~_l~i and two anonymous referees for reading the manuscript and the Finnish Road Administration and the Finnish Academy of Sciences (MK) for financial support of the study.
234
E. Rossi, M. Kuitunen
REFERENCES Ahti, T., Hfimet-Ahti, L. & Jalas, J. (1968). Vegetation zones and their sections in northwestern Europe. Ann. Bot. Fennici, 5, 169-211. Alatalo, R. V. (1981). Problems in the measurement of evenness in ecology. Oikos, 37, 199-204. Atkinson, S. F. (1990). A simplified habitat evaluation for use with remote sensing data. Environ. Prof., 12, 122-30. Box, J. & Forbes, J. (1992). Ecological considerations in the environmental assessment of road proposals. Highways and Transportation, April, 16-22. Cable, T., Brack, V. & Holmes, V. (1989). Simplified method for wetland habitat assessment. Environ. Manage., 13, 207-13. Fiedler, P. L. & Ahouse, J. J. (1992). Hierarchies of cause: toward an understanding of rarity in vascular plant species. In Conservation Biology, ed. P. L. Fiedler & S. K. Jain. Chapman & Hall, London, pp. 23~,7. Gaston, K. J. (1994), Rarity. Chapman & Hall. London. Hanski, I. (1982). Dynamics of regional distribution: on core and satellite species hypothesis. Oikos, 38, 210-21. H~imet-Ahti, L., Suominen, J., Ulvinen, T., Uotila, P. & Vuokko, S. (eds) (1986). Retkeilykasvio. Luonnonsuojelun Tuki, Helsinki (in Finnish). Hyyti~i, K. , Kellom~iki, E. & Koistinen, J. (eds) (1983). The atlas of breeding birds in Finland. Lintutieto Oy, Helsinki (in Finnish with English summary). IUCN (1980). Worm conservation strategy. IUCN, Gland. Janzen, O. (1983). No park is an island: increase in interference from outside as park size decreases. Oikos, 41, 402-10. Koivisto, I. (1987). Suomen eliiimet, osa 1. nisgikkgigit. Weilin & G66s, Helsinki (in Finnish). Koli, L. (1987). Suomen eldimet, osa 3. kalat, sammakkoelgiimet ja matelijat. Weilin & G66s, Helsinki (in Finnish). Koskimies, P. (1989). The distribution of the Finnish breeding birds. Lintutieto, Helsinki. Lancia, R. A., Miller, S. D., Adams, D. A. & Hazel, D. W. (1982). Validating habitat quality assessment: an example. Trans. N. Amer. Wildl. Nat. Resour. Conf., 47, 96-110. MacArthur, R. H. & Wilson, E. O. (1967). The theory of
island biogeography. Princeton University Press, Princeton, NJ. Mace, G. M. & Lande, R. (1991). Assessing extinction threats: towards a reevaluation of IUCN threatened species categories. Conserv. BioL, 5, 148-57. Margules, C. R. (1984). Conservation evaluation in practice, II. Enclosed grasslands in the Yorkshire Dales, Great Britain, J. Environ. Manage., 18, 169-83. Margules, C. R. & Usher, M. B. (1981). Criteria used in assessing wildlife conservation potential: a review. Biol. Conserv., 21, 79-109. Nijkamp, P., Rietveld, P. & Voogd, H. (1990). Multicriteria evaluation in physical planning. Elsevier, Amsterdam. Pressey, R. L. & Nicholls, A. O. (1989). Efficiency in conservation evaluation: scoring versus iterative approaches. Biol. Conserv., 50, 199-218. Rabinowitz, D. (1981). Seven forms of rarity. In The biological aspects of rare plant conservation, ed. H. Synge. Wiley, Chichester, pp. 205-18. Rabinowitz, D., Cairns, S. & Dillon, T. (1986). Seven forms of rarity and their frequency in the flora of the British Isles. In Conservation biology. The science of scarcity and diversity, ed. M. Soul6. Sinauer Associates, Sunderland, MA, pp. 182 204. Rassi, P., Kaipiainen, H., Mannerkoski, I. & St~hls, G. (ed.) (1992). Report on the monitoring of threatened animals' and plants in Finland Finnish Governmental Publication Board, KomiteanmietintO 1991:30, Helsinki (in Finnish with English summary). Sankovskii, A. (1992). Toward evaluation of natural objects. Environ. Manage., 16, 283-7. Smith, P. G. R. & Theberge, J. B. (1986). A review of criteria for evaluating natural areas. Environ. Manage., 10, 715-34. Spellerberg, I. F. (1992). Evaluation and assessment for conservation. Chapman & Hall, London. Usher, M. B. (ed.) (1986). Wildlife conservation evaluation. Chapman & Hall, London. Van der Ploeg, S. W. F. & Vlijm, L. (1978). Ecological evaluation, nature conservation and land use planning with particular reference to methods used in the Netherlands. Biol. Conserv., 14, 197-221. Wheeler, B. D. (1988). Species richness, species rarity and conservation evaluation of rich-fen vegetation in lowland England and Wales. J. Appl. Ecol., 25, 331-53.
,f
ELSEVIER
0006-3207(95)00139-5
Biological Conservation 77 (1996) 227-234 Copyright © 1996 Published by Elsevier Science Limited Printed in Great Britain. All rights reserved 0006-3207/96/$15.00+0.00
R A N K I N G OF HABITATS FOR THE ASSESSMENT OF ECOLOGICAL IMPACT IN LAND USE PLANNING E. Rossi & M. Kuitunen Department of Biological and Environmental Science, University of Jyviiskylii, PO Box 35, SF- 40351, Jyvdskyl(i, Finland (Received 14 June 1994: accepted 30 August 1995)
Abstract
combining this method with geographical information systems. Copyright © 1996 Published by Elsevier Science Ltd
A simple habitat ranking method suitable for use in the early stages of land use planning is developed here. The method is designed for assessment of the biological impacts of infrastructure and urban development. The objectives are to formulate an assessment procedure, to minimise fieldwork and to avoid error due to species remaining unnoticed in the field. Ranking takes place in terms of a habitat value (HI/) index calculated on the basis of the species present (vascular plants, amphibians, reptiles, birds and mammals), the threat categories to which they belong and the likelihood of their occupying specific habitats. The existence of the species in different habitats was predicted on the basis of the literature. To calculate the H V index, threat categories and preferred habitats were defined for each species', so that the species could be weighted according to their relative threat category in the region concerned. The method was tested in Finland (60-70 ° N), using several weights for each threat category in order to reveal the effect of differences in weighting. The results appeared to be relatively insensitive to the weights used, supporting the objectivity of the method. Herb-poor dry meadows, riparian habitats, herb-rich deciduous forests, industrial~urban habitats and cultivated areas were ranked highest in the southern regions of the country. Arctic alpine Jells, riparian habitats, spruce mires and herb-rich deciduous forests" were ranked highest in the northernmost region. In addition to potential species composition, the total area of a habitat and its vulnerability to the development concerned also has an influence on its ranking, and therefore separate rankings must be calculated for different types of development. The results suggest that habitat ranking of this kind could be profitable in the early stages of environmental impact assessment, because it helps one to concentrate on the most valuable habitats. It is also reasonable to assume that our method may be of" help in evaluating Siberian boreal habitats by
Keywords: conservation biology, habitat ranking, threat category, conservation value, GIS, EIA, ecological impact analysis. INTRODUCTION
Societal concern about environmental degradation has given rise to requirements for environmental impact assessment (EIA) in the case of road construction or urban development projects, etc. Most of the existing habitat evaluation methods have been developed for conservation needs (Usher, 1986; Spellerberg, 1992), and the necessity for practical methods has increased due to the extensive application of EIA. Consideration of the ecological impacts of large-scale construction projects is typically restricted to an evaluation of specific sites recorded by the environmental authorities as valuable for nature conservation purposes (cf. Box & Forbes, 1992). Therefore, only a small part of the affected area is usually evaluated, and the lesser known nature values may very well be overlooked. As many kinds of habitat will be disturbed in more extensive constructional projects (motorways, factory areas, oil pipelines, power lines, housing estates, etc.), an appreciation of the conservation value of different habitats is necessary in order to assess and compare them and thereby to minimise biological damage. Margules and Usher (1981) summarised the criteria most often used to assign habitat values. These were species diversity, species rarity, naturalness, area, threat of human interference, representativeness, research and educational value, recorded history and potential value. Only some of these criteria are biological, the others being more related to social needs. Margules (1984) explored the criteria used by experienced evaluators and found that they chiefly used only a few, species diversity, species rarity and area of site being the most frequent. For evaluation of the fauna, the simplest indicators are species richness or the number of reproductive individuals. Species diversity, measured by the Shannon index, for example includes both the numbers of
Correspondence to: M. Kuitunen. Present address: NRRI, University of Minnesota, 5013 Miller Trunk Highway, Duluth, Minnesota 55811, USA; Fax: 218 7204217; e-mail [email protected] 227
228
E. Rossi, M. Kuitunen
species and species abundance, but not their threat category. This is because only site-specific information is used in the Shannon calculation. Occupation by rare species (for definition, see Rabinowitz, 1981; Rabinowitz et al., 1986; Fiedler & Ahouse, 1992; Gaston, 1994) is generally accepted as indicating that a habitat has a high biological value, while the validity of species diversity indices has been criticised (e.g. by Alatalo, 1981). Furthermore, the collection of data for cal-culating diversity indices is laborious in the case of large areas. When the region influenced by a project covers a large area, it is impractical to census all species and individuals. This is the case with many infrastructure projects (e.g. powerlines, roads, railways, natural gas pipelines). It is therefore more practicable to conduct the evaluation in stages, applying simple, inexpensive methods first to reveal the potentially critical areas, and using more precise and consequently more expensive methods at the later stages to examine these in detail. The aim of this paper is to present an easy-to-use habitat ranking approach for land use planning. The basic idea was to minimise fieldwork by making thorough use of reported data on species existing in the region concerned. The method is based on the probability of occurrence of given species in different types of habitat. METHODS The model
The ranking system is based on a habitat value (HV) formed using the number of species occurring in the
habitat with a threat category (1-8) weighting for each species (see below). HV is calculated by summing the weights for all the species potentially living in each habitat. Moreover, each species is classified according to its four most preferred habitats out of 26 defined habitats (Table 1). A coefficient accounting for the species preference of a habitat is defined so that the species living mainly in that habitat can be weighted the highest. Equal weights are assigned to each species included in a threat category. In this way the weight assigned to a species is independent of its taxonomy. All the information was obtained from the literature. H V ~ : (n~,~.k . w ; c k ) : i = 1..... 2 6 ; j : 1..... 8 ; k = 1..... 4 ; where HV~ = value of habitat i, ni,i.~ -- number of species preferring habitat i at level k and belonging to threat category j, wi -- weight assigned to threat category j, c, = coefficient assigned to a species preference order k of a habitat i. The computational procedure may be illustrated with an example: let us assume that we have defined four habitats A, B, C and D. Threat categories (here three categories only, I-III; in the proper procedure there are eight categories, j) and preferred habitats (primary and secondary for each species; in the proper procedure there are four categories, k) are defined for all the species recorded in that region according to the best available data. This yields the following:
Table 1. Rank orders of 26 habitats in the four Finnish regions (Fig. 1) using all species
Habitat number and name 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.
Herb-rich and other deciduous forests Esker forests (Pine) Dry upland forest sites (Pine) Moist upland forest sites (Spruce) Rich fen Open mires Pine mires Spruce mires Baltic Sea and its outer islands Oligotrophic lakes Eutrophic lakes Streams and rivers Springs Shore of the Baltic Sea Riparian habitats Flooded areas Beaches Non calcareous rocky areas Calcareous rocks and quarries Arctic-alpine fells Herb-rich dry meadows Herb-poor dry meadows Wet meadows Cultivated areas Parks and gardens Industrial/Urban
Region I
II
III
IV
3 24 23 13 11 20 26 10 16 25 14 21 15 7 2 19 18 8 17 22 12 1 9 5 6 4
3 19 23 14 10 21 26 8 15 25 12 22 13 9 2 18 20 6 17 24 16 1 11 4 7 5
2 25 24 13 8 17 26 7 16 23 14 18 10 12 1 20 22 4 21 15 19 6 9 3 11 5
3 24 23 8 4 15 26 6 18 20 13 21 12 17 1 19 22 5 9 2 25 11 10 7 16 14
Habitat ranking in land use planning
229
Number of species for which a habitat is Primary (k -- 1) Threat category A B C D
Secondary (k = 2)
A
B
C
D
1
8
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9
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The following totally arbitrary weights (wj) are assigned to threat and to habitat preference (q): wl= 1, wu-- 3, Wlllz 10 and q = 1, c2= 0.5 The HV index of habitat A is then calculated as follows: HVA=8 × 1 × 1 + 2 X 3 X 1 +1 × 1 0 × 1 + 7 × 0 - 5 + 2 × 3 X 0 . 5 + 0 X 10 X 0 . 5 = 3 5 . 5
1X
Repetition of the calculation for all the habitats gives the following results: HVA= 35.5, HVB= 24.0, HVc= 12.0 and HVD = 52.0 The habitats can thus be ranked in the following order: D, A, B, C. Application o f the model
Information used for the calculations We tested this method with the flora and fauna of Finland (c. 330,000 km z in area - - situated between latitudes 60 and 70°N), using vascular plants, mammals, birds, amphibians and reptiles. Excellent information is available on the occurrence and habitat requirements of vascular plants (H~imet-Ahti et al., 1986), birds (Hyyti~i et al., 1983; Koskimies, 1989) and mammals (Koivisto, 1987), and on amphibians and reptiles (Koli, 1987), but arthropods were omitted from the calculations because of the restricted nature of the available data. Potential habitats for each species were recorded exactly as presented in the literature. While there was minor ambiguity in the original classification, e.g. with some overlapping between habitats, no modification was made, in order to maintain objectivity. The resulting total number of habitats was 26 (Table 1). Because of the significant variations in the abundance of some species in different parts of the country, a division was made into four regions, defined in accordance with the phytogeographical zones (Fig. 1; Ahti et.al., 1968), region I corresponding to the hemiboreal zone, region II to the southern boreal, region III to mid-boreal and region IV to the northern boreal zone and arctic-alpine areas. Vascular plants dominated the number of species in each region (Table 2). Threat categories All the species were classified in terms of their regional degree of threat, the most threatened species being divided into five groups according to the IUCN (1980)
% Fig. 1. Regions used for compiling the species vs habitat data
in the application of the model.
categories by reference to the Finnish Red Data Book (Rassi et al., 1992; threat categories 4-8) and the remainder, those not designated by the IUCN as endangered, were placed in three rarity categories in terms of their distribution and abundance in the geographical area concerned (Rabinowitz, 1981; Rabinowitz et al., 1986; Fiedler & Ahouse 1992; Gaston 1994; see also Mace & Lande, 1991 for a criticism of the subjective IUCN categories). The criteria for the threat categories were as follows: 1 Abundant: the species is abundant over the whole region; 2 moderately abundant: the species exists over the whole region but is rare in one-third of it; 3 moderately rare: the species is nonexistent in one-third of the region; 4 insufficiently known: the species is thought to belong to one of the next categories, but because of lack of information it cannot be defined any more exactly (IUCN, 1980); 5 indeterminate: the species is known to belong to one of the next categories, but because of lack of information it cannot be defined any more exactly (IUCN, 1980); 6 rare: populations of the species are very small, but are not at present endangered
E. Rossi, M. Kuitunen
230
Table 2. Numbers of species in the taxonomic groups and geographical regions of Finland (Fig. 1) used in the application of the model
Taxon Region I
Region II
Region III
Region IV
Vascular plants Birds Mammals Amphibians and reptiles
1063 198 44 10
923 194 51 9
787 191 44 8
629 153 35 3
Total
1315
1177
1030
820
or vulnerable (IUCN, 1980); 7 vulnerable: the species will probably become endangered in the not too distant future if its living conditions are not improved (IUCN, 1980); 8 endangered: the species will probably become extinct soon, if its living conditions are not improved. Classification data for threat categories 1-3 were obtained from Hfimet-Ahti et al. (1986), Hyyti~i et aL (1983), Koskimies (1989), Koivisto (1987) and Koli (1987). Distribution of species abundance among the threat categories varied considerably between the regions (Fig. 2), with the greatest differences appearing between the northernmost and the two southernmost regions. Numbers of species decreased in general from south to north, mainly as consequence of a decline in abundant species (known as core species in community ecology; Hanski, 1982) and threatened species (these and others behaving in a similar manner are often called satellite species in community ecology; Hanski, 1982), while those of intermediate species remained almost constant (Fig. 2).
understand a threat category as a measure of the probability of extinction, the scale of measurement is not quantitative. The only indisputable fact is that the threat categories are ordinal. Therefore, mathematical operations such as addition are not possible without cardinalisation of the ordinal data (weighting). Several cardinalisation methods have been presented but there is no generally agreed practice for determining the weights. We calculated expected weights by assuming that the probability density function of the weights is equal for all the threat categories. Nijkamp et aL (1990) have shown that the expected weights can then be calculated as: E(wl) = l l f E(w2) = l l f
+ ll[J(J-l)]
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In order to test the effect of different weightings for threat (%), six sets of weights were assigned to each of the threat categories (Fig. 3). Although it is possible to
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Regions I-IV as in Fig. 1. Threat categories are: 1, abundant; 2, moderately abundant; 3, moderately rare; 4, insufficiently known; 5, indeterminate; 6, rare; 7, vulnerable; 8, endangered.
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Threat category of a species Fig. 3. Graphical presentation of the weight sets (I-VI) assigned to threat categories in the application of the model. Concurrently used weight sets are connected by lines. Threat categories as in Fig 2,
Habitat ranking in land use planning For the sake of convenience, the calculated weights were multiplied by 64 in order to adjust the smallest weight to 1. This enabled the following weights to be generated for the eight threat categories: I, 2.1, 3.5, 5.1, 7-1, 9.7, 13.8, and 21.7. As one cannot be certain that such a uniform distribution holds good, modified sets were constructed from the above weights to test the effect of distribution assumptions. Thus a second weight set (II) was taken to test a contentious weight distribution, and a third (III) was based on the overall rarity distribution of the species. Since this distribution was concave (Fig. 3), a logistic weight distribution was used as an appropriate model. As the difference between the weights for endangered and abundant species was relatively small compared with those used in other studies (e.g. Wheeler, 1988), the other three weight sets (IV-VI) were generated by rescaling the previous ones (I-III) so that the maximum weights became 100.
Species habitat preference The degree of species-specific habitat preference (cD was tested by introducing two sets of coefficients. First, all the species were treated equally independent of the degree of preference for the habitat concerned, i.e. the coefficient set was 1.0, 1.0, 1.0, 1.0. Secondly, all the weights of a specific threat category group were taken into account only when the habitat in question was the preferred one for a species, while at the other levels of preference only a portion of the weight was taken, using the following coefficients in descending order of preference: 1.0, 0.75, 0.5, 0.25. The six threat category weight (wj) sets (I-VI; Fig 3) and two habitat preference (cD coefficient sets produce a total of 12 habitat values, and it is these that were used to calculate a ranking order of habitats for each region (I-IV; Fig 1). Spearman rank correlations were calculated to compare sensitivity of the weighting for the ranking of habitats. As an activity may have a different impact on different taxa, it may be necessary to weight critical taxonomic groups more than less sensitive taxa. We therefore tested whether the ranking of habitats would be different if only bird species were included in the computation procedure, implementing it in other respects in the same manner as before. RESULTS Ranking of habitats by HV appeared to be relatively insensitive to the weightings used for the threat category groups, the Spearman rank correlation coefficients varying from 0.934 to 0.993 in region I, from 0.928 to 0-984 in region II, from 0.853 to 0.978 in region III and from 0.928 to 0.996 in region IV. Although the ranking of habitats in region III appeared slightly less certain than in the other regions, all the correlations were highly significant (p <0.001).
231
When the habitats were arranged according to the average HVs achieved using the 12 weight sets described, the five most important in the two most southern regions were herb-poor dry meadows, riparian habitats, herb-rich deciduous forests, industrial/urban areas and cultivated areas (Fig. 4, Table 1). Riparian habitats, arctic-alpine fells, herb-rich deciduous forests and rich fens were the most important in the northeastern region. There was an overall trend for the relative importance of culturally influenced habitats to increase from north to south whereas that of arctic-alpine, fells, rich fens, spruce forests, calcareous rocks and quarries and oligotrophic lakes increased from south to north. In spite of discernible variation in the rankings due to the different weightings, the overall order of importance appeared to be indisputable. When only bird species were included in the calculation of the HVs, a slightly different ranking was obtained (Fig. 5). The most notable differences were in the ranking of vegetationally rich, small-sized habitat types such as rich fens (Habitat 5) or springs (Habitat 13). These may be of minor importance for birds and are not mentioned as the habitats of any bird species in the literature consulted. DISCUSSION The ranking of habitats prior to development seems to be a realistic and useful approach for comparing the value of different components of the natural environment. Our method is applicable to ecological impact assessment, but the key to its successful use lies in application early in the planning process. It should be used as a screening tool but not as an end product. For practical application of the method, habitats of roughly equal conservation value must be clustered so that they can be easily identified in the area concerned. This will reduce uncertainty in the ranking order, e.g. on account of the subjective element in the weighting. The tests conducted here demonstrate that the ranking of the habitats is relatively insensitive to weighting, but in spite of this the results must be applied cautiously, because some degree of substitutability is assumed between criteria. Endangered species are not given special prominence, for example (cf. Cable et al., 1989), because we were not considering specific sites, and any general condemnation of potential habitats of endangered species as prohibited areas would be an exaggeration. Due to changes in species threat category status, habitat rankings must be adjusted to finite regions. Arctic-alpine fells, for example, were ranked low in southern Finland for the simple reason that there are none, and therefore there are no specifically arcticalpine fell species there. Thus this habitat value is composed of those common, primarily arctic-alpine fell species which also exist in other habitats over a wide area. However, in addition to these species there are
232
E. Rossi, M. Kuitunen
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Habitat number Fig. 4. Average, maximum and minimum rankings of the habitats when using 12 sets of weightings to compute the HV index values. Vascular plant, mammal, bird, amphibian and reptile species were included in the computation. Regions I-IV as in Fig. 1. some which originally favoured arctic-alpine areas and which now occur as rare species in secondary habitats in southern Finland. Only vascular plants, mammals, birds, amphibians and reptiles were included in the present calculations, invertebrates, for example, being ignored because of the scarcity of data. This is a common practice in environmental evaluations (e.g. Van der Ploeg & Vlijm, 1978; Smith & Theberge, 1986; Pressey & Nicholls, 1989). The loss of accuracy should not be great, however, as invertebrate animals are typically linked with host species. However the species depending on dead wooden material may change that pattern. It must in any case be remembered that ranking by only one taxon may differ decisively from combined ranking by reference to a number of taxa. Therefore the potential effects of an activity must be evaluated taxon by taxon. The ideal way would be to evaluate the effects separately for each species, but that is obviously unrealistic. The differences in the sensitivities of the taxa could be included in the weighting procedure, or alternatively habitats occupied by sensitive taxa could be evaluated separately after transferring the original ranking to maps depicting the location of the planned activity.
The habitat ranking order observed in this study is quite expected. However, the most surprising result is the importance of man-made habitats for wildlife in south Finland where urban pressure would be expected to create ahigher prominence for conserving natural habitats. Species originally preferring the herbpoor dry meadows have occupied industrial and urban habitats as secondary sites and these are now threatened. Total number or area of habitats within the each region should be included in the use of the ranking process (cf. Sankovskii, 1992). On the other hand, incorporation of habitat size is problematic because usually only part of the habitat area is affected. Therefore, only variation in the value of the affected habitat should be considered. In view of the log-linear relationship between area size and species number (e.g. MacArthur & Wilson, 1967; Janzen, 1983), this variation is smaller in the case of large areas, and consequently the value of small areas should be emphasised in land use planning. Simplified habitat classification methods are generally based on excessively rough environmental patterns (e.g. Atkinson, 1990) or single taxonomic groups (e.g. Cable et al., 1989) and are inadequate for measuring
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24 3 14 7 18 26 17 9 10 11 8 16 25 23 22 12 2 Habitat number
5. Average, maximum and minimum rankings of the habitats when using 12 sets of weightings and only avian species to compute the HV index values. Regions I-IV as in Fig. 1.
the total ecological value of a habitat. The US Fish and Wildlife Service has developed a Habitat Evaluation Procedure (HEP) which aims to evaluate an area on the basis of key habitat factors for certain species (e.g. Lancia et al., 1982). Sankovskii (1992) introduced an approach to the evaluation of habitats based on the relative probability of their disappearance. He rejected a territorial approach (equal to the sum of the values that are attached to the local objects situated in this territory) and estimated the ecological values of communities using data on relative fragility, uniqueness, size of regeneration unit and intensity of destructive factors. Sankovskii (1992) assigned equal values to each type of community, but it is argued here that the concept of equal value is valid only at the species level. Communities are never alike, and therefore destruction of a single natural community will add to the probability of extinction of some species even if other communities of that type remain unchanged. The increase in the probability of extinction is dependent on the number and threat category of the species living in that community. In addition, the concept of regeneration unit is difficult to define. Although it is reasonable to conduct a ranking of
habitats based on those species occurring in each geographical area, it is also possible to generalise our results obtained from Finland to other geographical areas, especially as the geographical variation in the ranking order of the habitats was low. Finland forms part of the circumpolar boreal phytogeographical zone that also comprises parts of Russia, Alaska, the USA and Canada. Although species composition varies greatly, there are considerable similarities in the case of Russia and Finland in particular, and it may be possible to compare habitats in Russia based on our results, employing the techniques provided by geographical information systems and to produce a biodiversity inventory of at least some parts of Siberia, a region in which environmental evaluation would otherwise be an enormous task. ACKNOWLEDGEMENTS The assistance of Liisa Horppila and Leena Kiuru is gratefully acknowledged. We also thank Yrj6 Haila, Pekka Helle, Timo T6rm~_l~i and two anonymous referees for reading the manuscript and the Finnish Road Administration and the Finnish Academy of Sciences (MK) for financial support of the study.
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E. Rossi, M. Kuitunen
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