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1 What to protect? - Systematics and the agony of choice.

Politicians and scientists alike now agree that a priority list of global centres for preservation of biological diversity is required. Diversity has generally been measured only in terms of species richness, or in the form of indices combining richness with abundance. Such measures are considered inadequate for the task in hand. A novel index, based on the information content of cladistic classifications and giving a measure of taxonomic distinctness, is introduced. This taxic diversity measure, when coupled with detailed knowledge of distribution, can be used in modified analyses of the type previously developed as 'critical faunas analysis' or 'network analysis'. Central to all such analyses is the concept of complementarity of floras or faunas. By employing complementarity, step-wise procedures can identify optimally efficient, single-site sequences of priority areas for a group, taking existing reserves into account or not, as required. For practical planning it is concluded that two basic rounds of analysis are required: first, recognition of global priority areas by taxic diversity techniques; secondly, within any such area, analysis without taxic weighting (as being developed by Margules and his co-workers) to identify a network of reserves to contain all local taxa and ecosystems. The paper concludes with a brief discussion of some immediate prospects for development of a systematic approach to global conservation evaluation.

2 Do conservationists and molecular biologists value differences between organisms in the same way?

Recent proponents of the idea of including weightings for species 'distinctiveness' in biodiversity measures for conservation priorities have been quick to adopt genetic-attribute richness as the 'currency' of value to be maximised. However, the consequences of this choice may be at odds with the aims of biodiversity conservation, bringing into question whether choice of another currency might be more appropriate. Among the candidates, morphological attributes most strongly influence the popular perception of diversity (because they are amenable to the senses) and may be most directly useful to people. On the other hand, functional, ecological attributes may be more relevant to the goal of maintaining ecosystem services.

In practice, the direct use of these currencies is usually prevented by the enormous sampling effort required for adequate direct measurement. Instead, the challenge is to improve on unweighted species richness as a measure of diversity, but using the best information on differences among organisms that is actually available in any particular case. Thus, using only the near-universal taxonomic classifications for multicellular organisms, useful indirect measures of genetic or morphological attribute richness can be identified, although the precise choice of measure depends on whether or not users are prepared to assume 'clock'-like patterns of attribute change.

3 Measuring biodiversity value for conservation.

Practical approaches to measuring biodiversity are reviewed in relation to the present debate on systematic approaches to conservation, to fulfil the goal of representativeness: to identify and include the broadest possible sample of components that make up the biota of a given region. Rather than adapting earlier measures that had been developed for other purposes, the most recent measures result from a fresh look at what exactly is of value to conservationists. Although the debate will continue as to where precisely these values lie, more of the discussion has been devoted to ways of estimating values in the absence of ideal information. We discuss the current principles by assuming that the currency of biodiversity is characters, that models of character distribution among organisms are required for comparisons of character diversity, and that character diversity measures can be calculated using taxonomic and environmental surrogates.

4 Measuring biodiversity value.

Measuring biodiversity has become a growth industry during the last five years. We need measures of biodiversity so we can determine the 'where' of in situ conservation action rather than the 'how', to help us decide which combinations of available areas could represent the most biodiversity for the future.

5 Promise and problems in applying quantitative complementary areas for representing the diversity of some Neotropical plants (families Dichapetalaceae, Lecythidaceae, Caryocaraceae, Chrysobalanaceae and Proteaceae).

Priority areas for in situ conservation are an unavoidable consequence of competition with other land uses, although they are certainly not to be seen as the only areas of value for conservation. In 1990 an international workshop was convened in Manaus, Brazil, to identify priority areas within Amazonia by committee (Workshop-90). A substantial part of the data for this assessment came from five plant families recorded for the Flora Neotropica. We compare the success of the Workshop-90 method in representing these plant species with the results of using a simple quantitative method for seeking complementary areas. The promises of quantitative methods are twofold. First, they force people to make their values explicit, which is important because priorities are dependent on the values and goals of individuals and are not universal. Secondly, quantitative methods can achieve representation of more of what is valued. For example, within the 90 top-priority areas (an arbitrary but convenient figure taken from Workshop-90), species representation is shown to be increased when using the complementary areas method by 83%. Simple computer implementations of this method can provide the means for fast inter-active exploration of flexibility in the many alternative area choices. This permits monitoring and review with minimum effort as new data on species and threats are acquired. On the other hand, the problem for all methods is the need for very large numbers of data, whether based on species or on any other surrogates for biodiversity, if well-informed decisions are to be made. This is not a particular problem of quantitative methods, but their explicit nature does highlight the shortcomings of data. For example, patterns in the Flora Neotropica data show effects from small samples even though these data are among the best available for any large tropical wet-forest region. Furthermore, in order to assess the longer-term consequences of area choices, quantitative methods will require many explicit local data on factors affecting viability, threat and cost.

6 Measuring more of biodiversity: can higher-taxon richness predict wholesale species richness ?

To assess conservation priorities, a means of measuring the distribution of a much larger part of overall biodiversity is needed that will at the same time reduce the colossal sampling problems of exhaustive surveys. One possibility is a 'top-down' taxonomic approach, in which the biodiversity of different areas may be compared using measures based on the number of higher taxa present in each. The advantage of this approach is that survey costs should be greatly reduced because identification to species level, particularly within the few hyper-rich higher taxa, would be unnecessary. We report that family richness is a good predictor of species richness for a variety of groups and regions, including both British ferns and British butterflies among 100 km 100 km (10,000 km²) grid squares, Australian passerine birds among 5º 5º grid squares (c. 220,000-310,000 km²) and 10º 10º grid squares (c. 970,000-1,190,000 km²), and North and Central American bats among grid squares of c. 611,000 km². With careful choice of higher-taxon rank, it may be possible to re-deploy effort from taxonomically intensive to taxonomically extensive surveys, in order to estimate the global distribution of a much larger proportion of overall biodiversity at the same cost.

7 Centres of seed-plant diversity: the family way.

Maps of wholesale species richness (numbers) are urgently required to guide the conservation of biodiversity. The problem is that complete counts of organisms are impractical at present. Indirect solutions are needed that will be both cheap and quick. One approach is to re-deploy sampling effort higher up the taxonomic scale, to measure richness among genera, tribes or families. Data for numbers of higher taxa have the advantage over data for species that they are usually not only more complete, but also cheaper to acquire. So for the same cost, surveys could be broadened to encompass more of wholesale biodiversity. Data for seed-plant families were used to map world-wide the regional distribution of (A) richness and (B) a measure of endemism. Differences among areas in plant family lists were then used to rank the top ten complementary areas for maximising cumulative family richness at each step.

8 A comparison of richness hotspots, rarity hotspots and complementary areas for conserving diversity using British birds.

Biodiversity conservation requires that efficient methods be used to choose priority areas for in situ conservation management. We compare three quantitative methods for choosing 5% (an arbitrary but convenient figure) from among the total number of 10 10 km grid cells in Britain to represent the diversity of breeding birds: (1) Hotspots of richness, which simply select the areas richest in species; (2) hotspots of range-size rarity (narrow endemism), which select areas richest in just those species with the most restricted ranges; and (3) sets of complementary areas, which select areas for the greatest combined species richness. Our results show that richness hotspots obtained the highest number of species-in-grid-cell records (with many representations of the more widespread species), whereas the complementary areas method obtained the lowest number. However, whereas richness hotspots included representation of 89% of British species of breeding birds and rarity hotspots included 98% of species, the areas chosen using complementarity represented all of the species, where possible, at least six times over. The complementary areas method is also well suited to supplementing the existing conservation network. As an example, starting with grid cells with better than 50% area cover by existing Sites of Special Scientific Interest, we searched for areas that could complete the representation of all of the most threatened birds in Britain, the Red Data species. The complementary areas method distinguishes between irreplaceable and flexible areas, which helps planners by providing alternatives for negotiation. This method can also show which particular species justify the choice of each area. Yet before the complementary areas method is fully able to select the best areas for conservation management, further development is necessary to integrate some of the more important factors affecting viability, threat, and cost.

9 Mapping variations in the strength and breadth of biogeographic transition zones using species turnover.

Biogeographic regions are widely regarded as real entities, or at least as useful summaries of the complex patterns of spatial concordance amongst species. The problem is that, whereas some parts of the transition zones between regions may be strong and abrupt boundaries, other parts of the same zones may be weak or broad clines, so that the corresponding parts of lines drawn on maps, although convenient, are arbitrary constructs. One approach to investigating transition zones ascribes values to the area units themselves, by quantifying the spatial turnover among species within the surrounding neighbourhoods of areas on maps. Using data for bumble bee distributions world-wide, I show that measures of neighbourhood turnover can discover many of the transition zones that are found by classification techniques when applied to the same data. But unlike classification techniques, turnover measures, when used in combination, can show how a transition zone varies along its length, not only in its strength (the proportion of species contributing to the zone) but also in its breadth (the degree of spatial overlap and the degree of coincidence among species replacements across it). For bumble bees at least, these transition zones are also negatively associated with areas that have a combination of both high species richness and high species nestedness.

10 Mapping biodiversity value worldwide: combining higher-taxon richness from different groups.

Maps of large-scale biodiversity are urgently needed to guide conservation, and yet complete enumeration of organisms is impractical at present. One indirect approach is to measure richness at higher taxonomic ranks, such as families. The difficulty is how to combine information from different groups on numbers of higher taxa, when these taxa may in effect have been defined in different ways, particularly for more distantly related major groups. In this paper the regional family richness of terrestrial and freshwater seed plants, amphibians, reptiles and mammals is mapped world-wide by combining: (i) absolute family richness; (ii) proportional family richness; and (iii) proportional family richness weighted for the total species richness in each major group. The assumptions of the three methods and their effects on the results are discussed, although for these data the broad pattern is surprisingly robust with respect to the method of combination. Scores from each of the methods of combining families are used to rank the top five richness hotspots and complementary areas, and hotspots of endemism are mapped by unweighted combination of range-size rarity scores.

11 A comparison of reserve selection algorithms using data on terrestrial vertebrates in Oregon.

We compare the number of species represented and the spatial pattern of reserve networks derived using five types of reserve selection algorithms on a set of vertebrate distribution data for the State of Oregon (USA). The algorithms compared are: richness-based heuristic algorithms (four variations), weighted rarity-based heuristic algorithms (two variations), progressive rarity-based heuristic algorithms (11 variations), simulated annealing, and a linear programming-based branch-and-bound algorithm. The linear programming algorithm provided optimal solutions to the reserve selection problem, finding either the maximum number of species for a given number of sites or the minimum number of sites needed to represent all species. Where practical, we recommend the use of linear programming algorithms for reserve network selection. However, several simple heuristic algorithms provided near-optimal solutions for these data. The near-optimality, speed and simplicity of heuristic algorithms suggests that they are acceptable alternatives for many reserve selection problems, especially when dealing with large datasets or complicated analyses.

12 Identifying priorities for the conservation of biodiversity: systematic biological criteria within a socio-political framework.

Systematic goals for the conservation of biological diversity are seen, in relation to the Convention on Biological Diversity, to be linked inextricably to human values. Biodiversity is complex, and cannot be conserved adequately through attention to reserves alone: a system of habitat fragments within a hostile matrix is unlikely to result in long-term persistence of the biological species or attributes for which the areas were initially chosen. Failure to pay attention to biodiversity requirements outside reserves also means that wider human needs for ecosystem services and spiritual well-being cannot be met. If we conclude that all of nature must be managed for biodiversity, is the selection of so-called priority-areas meaningless? Based on human rights and responsibilities, a framework is proposed to integrate individual and corporate actions by means of a hierarchical system of local and national governments, working together with regional federations and global agencies. The intention is to promote biodiversity conservation in all areas of the globe, with funds and resources mobilised at successively wider spatial scales and higher levels of responsibility, in accordance with established area-selection procedures. Essential principles of this integrative framework include a requirement that no legitimate lower level of responsibility is bypassed in allocation of higher-level resources, and that no conflicting priority established at a higher level can be imposed other than through negotiation (if necessary, through payment of incremental or opportunity costs). Such a system permits resolution of conflicts typically encountered between different interest groups, and results in the generation of resource distribution profiles, whereby conservation funds generated at different levels within the hierarchy are allocated to supplement local funds, in accordance with any wider priorities for conservation action that have been agreed. Priorities are thus not all or none, but are built up at successive levels to reflect locally, nationally, regionally and globally perceived biodiversity values for each and every land unit. To ensure that all areas are considered, and that all identified resource allocation problems are retained on an up-dateable agenda, organisational needs for 'national biodiversity agencies' and 'action registers' are also discussed. Such a socio-political system would be transparent, and dependent on democratic engagement in environmental policy and decision making at all levels of society.

13 Key sites for conservation: area-selection methods for biodiversity.

Biodiversity value has been identified with the option value for future use or evolution of different expressible genes or phenotypic characters of organisms. From this viewpoint, the most direct measure will usually be taxonomic or phylogenetic diversity, but species richness and richness in larger biotic or environmental assemblages may be viewed as presenting a scale of surrogacy. In any practical study, the choice of surrogate from this scale will be a compromise between precision on the one hand and cost of data acquisition on the other. The amount of this biodiversity that can be represented for any given expenditure using quantitative area-selection methods are described. When the identity of the surrogate units is known in each area, or a pattern predicting them is known, then complementarity methods can be much more efficient than methods using scoring or hotspots of richness or rarity. The transparency of the area-selection process can also be increased by replacing combinatorial scoring methods with sequential decision methods in order to give greater public accountability. This transparency has made it much easier to justify why any particular area is selected; why it is given a particular level of priority; and precisely which parts within its local biota might deserve local management priority. Approaches to accommodating variation among areas in viability and threat are discussed, in order to avoid selecting areas with biotas that have poor prognoses for persistence. One possibility is to apply existing techniques that use niche-based models of habitat suitability in order to pre-filter data for the 'viability centres' for each species (or other surrogates). In principle, it is an approach that could be automated for datasets with large numbers of species when only general information on factors governing habitat suitability is known.

14 Biodiversity indicators: graphical techniques, smoothing and searching for what makes relationships work.

Knowledge of the distribution of biodiversity remains poor. This situation might more readily be resolved if the species richness of certain groups of organisms indicated the richness of other, less well known groups. A spatially explicit exploration of the pattern in the predictive power that one taxon (a potential ‘indicator group’) might have for the diversity of another has been performed previously. In this paper we respond to three important points that have been raised. First, we describe an additional graphical technique for visualising spatial aspects of indicator relationships. Second, we examine some of the consequences of smoothing species richness data on observed indicator relationships. Third, we consider some of the factors that may contribute to strong indicator relationships.

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