Partners In Flight (PIF) has developed a system of prioritizing bird species of North America (north of Mexico) on the basis of their demography in several categories (Carter et al. 2000). Beissinger et al. (2000) found that to be a sound system, useful for focusing management attention on those species most in need, and proposed a system for using PIF's categorical scores to derive a priority rank for each species that reflects its risk of local extirpation. We believe those ranks can be of great utility as species weights in an index to compare bird communities' “conservation value” as a whole. Here, we review the history of conservation-value indices and examine attributes of bird communities that make them especially attractive for use in such indices to evaluate and compare sites. We then discuss properties of the PIF ranking system that make it an attractive basis for use as species weights for such an index. Finally, we discuss how using this index in concert with more traditional summary statistics can provide additional information on the structure and status of bird communities.

Utility Of Bird Communities For Site Evaluation

Because of their low birth rates and relatively long life spans, birds are extremely sensitive to spatial and temporal changes in the environment, and thus are generally viewed as important indicators of ecosystem integrity (Maurer 1993). Furthermore, compared to other vertebrate taxa, avian communities are typically quite diverse in both number of species and variety of habitat features used, and thus provide a simultaneous assessment of a wide range of ecosystem attributes. Finally, bird communities are easily monitored, using well-established and easily replicated protocols (e.g. Bibby et al. 1992; Ralph et al. 1993, 1995).

Bird communities are therefore a logical focus for site evaluation or assessment. Sometimes, evaluation criteria are mandated by concerns for threatened and endangered species or similarity to some reference condition (e.g. Bryce et al. 2002); lacking such clearly defined criteria, a more general assessment of overall conservation value of a site is often of interest. In that case, bird community characteristics are assumed to indicate not only the health of the bird community itself, but also the conservation value of the site in general.

Because bird-community data are inherently complex, however, efforts are made to reduce data dimensionality by restricting analyses to such summary statistics as species richness, diversity, territory density, or total abundance, or dividing the community into guilds and discussing patterns therein. Reliance on those measures involves at least one problematic assumption: more diverse or productive habitats are more valuable. Differences in conservation need or priority among species are ignored, because, by their very nature, traditional summary statistics obscure information on species composition. Thus, those summary statistics and guild-based approaches have failed to provide adequate indices of conservation need, because they “often miss important changes in populations of rare species that should be a higher conservation priority” (Beissinger et al. 2000:150). Consequently, such analyses are generally accompanied by discussion of trends of a few high-priority or more abundant species (e.g. Nuttle and Burger 1996a, Leidolf 1999, McShea and Rappole 2000). Interpretation can be difficult, however, because some species are restricted to one or more of the sites examined, resulting in a diminished ability to make entirely objective decisions. Thus, discussion and recommendations are reduced to statements such as “one option is good for some species, whereas another option is good for other species.”

The Conservation Value Concept

One approach that avoids the problematic assumptions of community summary statistics and species lists is that of “conservation value,” reviewed by Götmark et al. (1986). Whereas the true conservation value of any site is unknowable, it is possible to devise indices that are correlates to it, similar to using hatch rates or recruitment of birds as correlates to fitness. To differentiate between true conservation value and indices aimed at approximating it, we henceforth use CV to refer to the indices and the phrase “conservation value” to refer to the true unknowable quantity. Götmark et al. (1986) compared the performance of species richness, diversity indices, and various CV indices in ranking several sites to how a panel of experts ranked the sites. In doing so, they also provided some insights regarding properties good indices should have.

Careful inspection of the CV indices reviewed by Götmark et al. (1986) showed all of them to be of the form

The weighting factor w_i scales the abundance measure to reflect the conservation priority of each species in the community. There were two general types of weighting factors used, both of which use the size of a reference population for each species, which we call N_i. Depending on objectives and focus, the reference population used was either the national or the regional population. The first type of weighting factor scaled N_i to the total population of all species to derive a measure of relative abundance or rarity of the species in the reference population:

In comparing those CV indices and diversity indices to expert opinion, Götmark et al. (1986) concluded that diversity indices, specifically Shannon's index H' (Shannon 1948) and Simpson's index λ (Simpson 1949), often give misleading results and should be avoided. They further concluded that relative abundance is an important component of any index. They cautioned, however, that if additive composite weighting factors are used, the various criteria (such as relative abundance and area importance) may be intercorrelated, resulting in an over-assessment of a site's true conservation value. Intercorrelation may lead to unfounded distinctions between sites. Finally, the authors pointed out that the scale of the reference population (i.e. whether N_i represents regional or national population size) has a large influence on the results. That last point is complicated further by the difficulty in obtaining reliable estimates for population size beyond the local study area.

Partners In Flight Species Concern Scores— A Weighty Matter

Fortunately, in North America, a solution to the problem of deriving quantitative population estimates has been provided by PIF. In developing a system for setting conservation priorities for landbirds, PIF determines “concern scores” for each species in several categories relevant to conservation of the species' global or regional population. Carter et al. (2000) provided a thorough explanation of the scoring process. Briefly, scored categories are breeding distribution, nonbreeding distribution, relative abundance, threats to breeding, threats to nonbreeding, population trend, and area importance, each valued 1 (low priority) to 5 (high priority). Note that two of the PIF categories, relative abundance and area importance, are weighting factors in indices reviewed by Götmark et al. (1986). Although the PIF scores use a ranked graduated scale rather than continuous population data (as in Götmark et al. 1986), the formulation of the scales would produce similar trends when applied as weights. Breeding Bird Survey data (Robbins et al. 1986), range maps, and other published data provide the quantitative basis for the scores. Whereas the scoring system is continental in scope, scores are calculated at several scales, including continental, state or province, physiographic area, and bird conservation region, making explicit the issue of spatial scale that was problematic in indices reviewed by Götmark et al. (1986). Scores for all resident (breeding and nonbreeding) bird species at all scales are available from Rocky Mountain Bird Observatory (RMBO; see Acknowledgments).

The sum of all PIF-scored categories for each species (i.e. its composite score, henceforth PIF.comp) was the focal product when the PIF system was first introduced (Hunter et al. 1993), and has been the most common application of the PIF scoring system so far (e.g. Nuttle and Burger 1996a, b; Nuttle 1997; Leidolf 1999; Twedt et al. 1999; McShea and Rappole 2000; Hamel et al. 2002). However, in reintroducing the PIF scoring system (using more quantitatively derived category scores), Carter et al. (2000:545) cautioned that PIF.comp is a “potentially useful number” for highlighting the highest overall priority species, but that it may be misleading if it is not “considered in the context of its component parts.” Indeed, we recognize several instances where PIF.comp does not reflect what we consider to be a reasonable interpretation of a particular species' conservation need, especially regarding treatment of non-native species.

Comparison of composite versus ranked PIF scoring methods

The validity of using PIF.comp (or any other number) as a weight in a CV index rests on there being a real and consistent relationship between that number and conservation value of the site to which the index is applied. Despite its original application for that purpose, it is possible that small errors or inconsistencies in PIF.comp that were not important for grouping species into broad categories of conservation priority (e.g. “extremely high priority,” “very high priority,” “low priority,” etc. sensu table 15 in Hunter et al. 1993) might add up to significant but erroneous differences in CV. P. B. Hamel (pers. comm. during Nuttle and Burger 1996b) suggested that assigning numerical ranks to categories proposed in Hunter et al. (1993), and using those ranks as weights instead of the raw PIF.comp scores, might reduce such a problem.

A less ad hoc approach for reorganizing PIF scores was proposed by Beissinger et al. (2000). In critiquing the PIF scoring process, they pointed out several shortcomings to using a summed score where the biological or statistical relation-ship between categories making up that score is not known, echoing Götmark et al.'s (1986) earlier warning. Bryce et al. (2002) used such a summation of obviously biologically intercorrelated (though not statistically intercorrelated) metrics in developing a “bird integrity index” to evaluate riparian sites. To correct that problem for PIF scores, Beissinger et al. (2000: table 2) provided a system for re-ranking species based on considering relative contributions of each PIF-scored category (e.g. threats to breeding, population trend, etc.) to each species' probability of extirpation. Thus, their revised ranks (henceforth “PIF.rank”) range from 0 to 5, with non-native species receiving a rank of 0, threatened and endangered species receiving a rank of 5, and other species receiving a rank between 1 and 4 depending on combination of PIF-scored categories.

Beissinger et al. (2000) reported a correlation (R = 0.76) between PIF.rank and PIF.comp for birds of New York, indicating a “good but incomplete match” between the two scoring methods. We compared PIF.rank and PIF.comp for breeding birds of the MAV (Bird Conservation Region 26; RMBO, see Acknowledgments) to examine general patterns in their relationship and determined that they were similarly correlated (Pearson's R = 0.70). Here, we point out some features of their revised ranking system that we find especially advantageous, namely treatment of non-native species, or that we disagree with, namely treatment of threatened and endangered species and a logical error that results in misclassification of many species.

Conclusion

As noted by Götmark et al. (1986), the conservation value concept avoids some of the misleading conclusions that may result from over-reliance on summary statistics like species diversity or abundance, because CV indices incorporate more demographic information about each species. Partners in Flight scores provide a more rigorous, objective, and widely available weighting system than any existing alternative for use in a CV index. Furthermore, the ranking scheme of Beissinger et al. (2000: table 2), as modified in Table 1, improves application of the PIF system by correcting for statistical problems from using additive measures such as PIF.comp, and removes some of the dependence on S. Application to CV notwithstanding, further refinement in the PIF scoring process will occur as population data undergo continued analysis and more data become available. We therefore recommend that, as illustrated in the studies reviewed here, CV(PIF.rank) be used in concert with the more traditional summary statistics, to provide more complete understanding of what factors contribute to a site's CV index, and a better approximation of a site's true conservation value. Finally, extension of the PIF scoring process to other taxa would allow an even more thorough evaluation of the conservation value of a site.