Who's Where in North America?

Conservation biology is a discipline with a deadline. Suitable habitats for nearly all species on nearly all continents are being altered or destroyed at rates unprecedented in human history (Wilson 1988, Ehrlich and Wilson 1991, Myers 1994). With limited time and resources, ecologists and conservation planners are faced with making difficult decisions and setting priorities to conserve what habitat remains. Conservationists have proposed numerous criteria to identify areas of highest conservation priority, including high species richness, high levels of endemism, high concentrations of rare species, unique habitats, unusual ecological or evolutionary phenomena, extraordinary richness or endemism in higher taxa, high ecosystem service value, or intense levels of threat (Myers 1988, Williams and Gastón 1994, Dinerstein et al. 1995, Long et al. 1996, Williams et al. 1996, Daily 1997, Olson and Dinerstein 1998, Ricketts et al. in press). Ideally, conservation priorities will be established based on as many of these factors as possible. Species richness, however, will likely remain a central component of most priority-setting studies because of the intuitive importance of biodiversity “hotspots”: if species extinction is the ultimate crisis to avoid, areas that contain many species are logical targets for biodiversity conservation. The problem with using overall species richness as a criterion for setting conservation priorities is that for the vast majority of species, range data are not available. This situation is unlikely to improve; conducting exhaustive biotic surveys with the limited time and resources normally available for conservation is almost certainly impossible. Indeed, one effort to accomplish an exhaustive biotic survey in Costa Rica self-destructed almost immediately (Kaiser 1997). Conservationists have responded to this lack of data with a widespread search for indicator taxa: relatively well known groups of organisms (i.e., those with relatively well understood ranges and taxonomies) whose distributions can be used as a surrogate measure of the distribution patterns of other taxa, or of biodiversity overall (e.g., Prendergast et al. 1993b, Scott et al. 1993, Sisk et al. 1994, Beccaloni and Gastón 1995, Williams et al. 1996, Carroll and Pearson 1998, Lawton et al. 1998). For example, Sisk et al. (1994) used a richness index based on mammals and butterflies to estimate overall species richness for 135 nations. They assumed that the overall species richness of a country could be estimated by counting the number of species present from these two groups. In other words, they assumed that mammals and butterflies are good indicator taxa of overall species richness at broad scales. The United States Gap Analysis Project also employs indicator taxa (vertebrates, butterflies, and occasionally plants) as proxies for overall biodiversity patterns (Scott et al. 1993). Despite the common and increasing use of indicator taxa, few studies have tested explicitly the utility of different groups as indicators at broad scales. Prendergast et al. (1993b) tested the coincidence of “hotspots” (areas of extraordinarily high species richness) for birds, butterflies, dragonflies, liverworts, and aquatic angiosperms on a grid of 100 km2 squares covering Britain. They found only weak support for any pairwise concordance and no grid squares that were hotspots for all groups. Pearson and Carroll (1998) compared data on butterfly, bird, and tiger beetle richness for sets of large (approximately 90,000 km2) grid squares in North America, India, and Australia. Their findings were equivocal. For instance, they found that tiger beetles are useful indicators of butterfly species richness, but not bird species richness, in North America. Jaarsveld et al. (1998) studied the distributions of eight taxa (mammals, birds, plants, butterflies, termites, antlions, scarab beetles, and buprestid beetles) on a grid of 625 km2 squares in the Transvaal region of South Africa. For each taxon, a rarity-based selection algorithm identified the minimum set of grid squares required to capture all species in that taxon at least once, thus selecting the most efficient, or complementary, set of grid squares. The authors found that the mean overlap of the resulting complementary sets between pairs of taxa was less than 10%. In these and other studies (e.g., Currie 1991, Balmford and Long 1994, Kerr 1997), the authors made a variety of pairwise comparisons among taxa; however, few studies have examined any one taxon's ability to predict species richness in all other taxa, which is an issue of more direct interest to biologists setting priorities for conservation activities. In this article, we test not only pairwise correlations among taxa but also the utility of different groups of organisms as indicators of overall species richness. We use a database originally compiled for a World Wildlife Fund (WWF) conservation assessment of North America north of Mexico (Ricketts et al. in press). The data set includes distribution or richness data for almost all species in nine major groups—birds, butterflies, mammals, reptiles, amphibians, land snails, trees, nontree vascular plants, and tiger beetles—totaling over 20,000 species. To our knowledge, this database is the most taxonomically diverse in existence for species distributions at a continental scale. The taxonomic groups included in the database represent plant and animal, large and small, vagile and sessile, homeothermic and poikilothermic, and vertebrate and invertebrate organisms. Indeed, North America is one of the few regions for which such comprehensive data are available. From the data on these nine taxa, we calculate an index of overall richness, which we use as a surrogate for species richness in all taxa. Because the index includes so many and such diverse taxonomic groups, it may be a more accurate measure of total richness than indexes based on fewer or more closely related taxa (Scott et al. 1993, Sisk et al. 1994, Long et al. 1996). The availability of this index therefore provides a unique opportunity to test not only pairwise correlations among groups, but also the utility of distribution patterns in certain taxa for predicting overall species distribution patterns at broad scales. We use this data set to address three initial questions. First, to what extent are patterns of species richness in the nine taxa correlated with each other? Second, how well is the pattern of species richness of each taxon correlated with total species richness (as approximated by our overall richness index)? Third, how reliable are birds, butterflies, and mammals, when used together, as proxies for overall richness patterns? The taxonomy and distribution of these three taxa are known well enough to allow their use as indicator groups in most parts of the world; thus, they are used most often as indicators of species distributions in other taxa (Scott et al. 1993, Sisk et al. 1994, Long et al. 1996). We then examine two additional questions that illuminate two potential pitfalls associated with the use of indicator taxa at broad scales. First, to what degree are any correlations influenced by the effects of latitude on species richness? In other words, would holding latitude constant severely weaken any correlations we observe? If a correlation in richness patterns among taxa were attributable primarily to the strong North American latitudinal gradient, then applying these results to other regions would be misleading. Second, is the residual error of each index-by-taxon relationship distributed at random across North America? Or is there a geographic pattern to each relationship? Even if the species richness pattern in a taxon were correlated with overall richness, a strong geographic pattern of residual error could still result in substantial and nonrandom over- or underprediction in certain areas. Awareness of any patterns underlying the simple correlation coefficient statistic may deepen the understanding of the predictive ability of indicator taxa and improve the ability of conservation biologists to use them wisely. The geographic units we used are the 110 terrestrial ecoregions composing the continental United States and Canada (Figure 1). This ecoregion map was first introduced as the framework for a WWF conservation assessment of North America (Ricketts et al. in press). Map of the terrestrial ecoregions of the continental United States and Canada. Ecoregion numbers relate to the box on pages 372–373. Ecoregion names and full descriptions are given in Ricketts et al. (in press). Ecoregions are relatively coarse biogeographic divisions of a landscape. They delineate geographically distinct areas that share broadly similar environmental conditions and, often, natural communities. Because of the complexity with which environmental and ecological factors vary across a landscape, most efforts to map ecoregion boundaries combine equal parts quantitative data and gestalt (Bailey 1996). The boundaries are necessarily approximate and usually represent areas of transition rather than sharp divisions. Nevertheless, ecoregions are extremely useful because they allow broad-scale conservation planning, reporting, and monitoring for biologically meaningful geographic units as opposed to arbitrary political boundaries or grid cells. The WWF ecoregions are based on three established ecoregion mapping projects: Omernik (1995), for the conterminous United States; Ecological Stratification Working Group for and et al. (1995), for two the WWF the a framework and certain areas. of the mapping can be found in Ricketts et al. (in press). We and data on the distribution of species in nine taxonomic groups 1). For vascular plants, and a richness estimate for each from their For land snails, of of richness for the ecoregions of the from database of and For all other taxa for land we compared the range data for each species, in the of range to the ecoregion we then the species as present or in each The range in many represent only coarse of species of a range often not support of the species. We therefore used the habitat found in the of most species to the species ranges on the range this a certain of any error in our is likely to be of a than the error in the Ricketts et al. in for a we the resulting database to a species richness estimate for each taxon in all ecoregions box distribution of species richness for all taxa are given in Ricketts et al. in press). groups used in this with number of total species in each and of distribution Ecoregion numbers to the numbers in and those in Ricketts et al. (in press). ecoregion numbers and to the ecoregions in and which were not in the The all nine taxa, mammals, birds, and butterflies only and butterflies, trees, and only for is that of the groups in are not and not represent the majority of species in certain taxa. For example, there is over the of tiger beetles are only a of beetle in North America and and are a of vascular Nevertheless, we the species as we because distribution data are often to these groups. For instance, distributions are often those of the of the vascular are To be useful as a to the of indicator taxa, the groups we test be the groups for which data are likely to be available in other regions of the The richness we for vascular because we For however, we to this nontree vascular as this article, we use the with the to to the species groups in We tested pairwise correlations among all taxonomic groups as well as between each taxon and the overall richness Because in taxa the richness of ecoregions are not distributed we used to test for pairwise correlations among the nine taxa are correlation and most are strong In major taxa to have broadly correlated patterns of species richness across North America. at least two additional in are First, the correlation in the all mammals, a one of the most for an indicator taxon to be a relatively of species richness in other taxa. Second, the correlation between and the of the vascular is relatively the two groups are assumed to (e.g., Currie correlation for all pairwise comparisons among taxa and between each taxon and the overall richness between taxa and the overall richness a more central than predict richness patterns in is patterns of species richness To address this and to test the ability of different taxa to as indicators of overall we used our database to an overall richness from Sisk et al. The richness index for each is the number of taxonomic groups used in the index is the number of species of in the and is the total number of species of in the This index for each taxon, the of North American species that is found in an ecoregion and then the across all taxa. therefore equal to the taxonomic groups rather than to species, the of taxa. simple or of all species from the nine groups, by would result in an index that is by vascular plants, because in most ecoregions the species richness of vascular is two of than that of any other The overall species richness index is included in the box on pages and is the ecoregions in Map of overall richness This map richness for all nine taxa: mammals, birds, reptiles, amphibians, butterflies, tiger beetles, land snails, trees, and nontree vascular We used this overall index to test the correlation between each taxon and overall species richness. To between the and we the overall richness the taxon in each For example, we from the richness index it for correlation with We also a of three butterflies, and tested the correlation between this index and overall species richness. is these three taxa whose taxonomic and distribution data are known (Scott et al. 1993, Sisk et al. 1994, and 1995, Long et al. which them among the for indicator taxa at broad scales. This is in the as the overall but it includes only mammals, birds, and in the the taxa composing were from the overall index we tested The of that each taxonomic in to being correlated with all other groups, is correlated with the index of overall species richness. is also correlated with overall species richness. results that any taxon or of taxa could be used as an indicator of total species richness at these coarse scales. species data such as these are likely to degree of which the of correlations et al. range with ecoregions are not data in that they not their richness With (e.g., that of the number of of used in Carroll and Pearson (1998) have this problem in a by using a of correlations to test for In this article, we a which a to the of to In to the of the correlations for the of 110 we also test it for the arbitrary that the of over Even this the correlations between each taxon and the overall richness index remain in all The correlations between the richness of each taxon and overall species richness a for in the search for useful indicator taxa. that almost any could well as a surrogate for species richness patterns in other taxa or for overall species richness. patterns these correlation however, that this The of To how latitude species richness in the nine groups, we the species richness of each taxon by the mean latitude of the that latitude between and of the in species richness in the nine taxa. The of the vary among taxa, but they are all (Figure This result that all taxa in richness with increasing at of of taxon richness and for each taxon on in north from an arbitrary the of The among taxa, but all are The for vascular is not because is one or two of than that of the other taxa, but it a We used correlation to test the correlations in species richness that remain latitude is from the data The correlations between each taxon and the overall richness index are when they are for latitude result is similar to that of et al. for North American birds, butterflies, and tiger correlations are still for but the of pairwise correlations among taxa among the taxa whose correlations are most are the groups often used as butterflies, birds, and The correlation coefficient between the overall richness index and the of only these three taxa from (in to (in when latitude effects are correlation with the of latitude comparisons among all taxonomic groups are as are correlations between each taxon and the overall richness index correlation when latitude is the utility of these nine taxa for overall patterns of richness is severely in the three groups (mammals, birds, Because the strong latitudinal in North America is for a large of the correlation among taxa, the of the correlations in may be if they are assumed in other regions of the patterns of one or a few taxa are used to overall species richness one pattern is being based on In this the can be as one of with the taxon in being the and the overall richness index the We the overall richness index the taxon each taxon and each ecoregion a of based on the and of residual these we used the species richness data latitude effects We then the ecoregion map to any geographic patterns of provides an of our using butterflies as an of overall richness index by butterfly richness. an and the the of the is the The on of this the ecoregions have or or The between of were and were constant among all taxa to comparisons among the taxa. the ecoregions are the geographic patterns of residual error (Figure on these the nine taxa three of the first birds, mammals, and vascular plants) have in the of the In this the overall richness index is higher than on the of richness in any of these taxa the overall richness index is than or relatively to the are found to the north and of the of the from of overall richness index on taxa. is as in areas not the taxon is high in species richness, but rather the overall richness index is higher than by the taxon ecoregions contain no species of the taxon in mammals and nontree vascular all in the first broad pattern in which overall richness is higher than in the United and land all in the broad pattern in which overall richness is higher than in the United and tiger beetles not in For taxa of the amphibians, and land ecoregions with (i.e., overall richness higher than are in the central and region of the United States The of two and tiger of which a pattern that in the first or of these taxa to overall richness in the United States and in the and the of the United States Even of the for these in a conservation to the use of indicator taxa can be from the patterns The three taxa birds, and the pattern the richness index of these three groups only the geographic present in each of the three taxa (Figure Map of the from of overall richness index by of mammals, birds, and is as in these to a by a taxon from each of the three geographic we an using butterflies, trees, and The by overall richness on a pattern of error (Figure than the by overall richness on (Figure or on any taxon (Figure on in two other is correlated more with the overall richness index and it correlated with overall richness when latitude is constant Map of the from of overall richness index by of butterflies, trees, and The pattern of is than in is as in In selecting butterflies, trees, and to a more we have to and improve by the taxon in each of the pattern The of this article, however, is not to the and use of butterflies, trees, and as indicators of species richness. our to a more there are geographic patterns to the error in of any indicator taxon or these patterns will conservation biologists taxa that will combine to a more accurate surrogate measure for overall richness. This to test indicator taxa because distribution data on a large number of species are available for the United States and Canada. a to what extent can these results be to other range data for the majority of species those used in this are known and indicator taxa would be the continental many factors may the distribution patterns of species. a unique history of and each of which may different taxa in different For example, the in North America and resulting in have a distinct on biogeographic patterns 1988, found to be useful indicators on one be difficult to with in other parts of the Indeed, Pearson and Carroll (1998) found that the indicators for predicting richness in tiger beetles, birds, and butterflies among the United India, and Australia. The and of these three regions Pearson and (1998) an test of the of results from one of the to is likely that for regions with more similar to that of North America as or South the in this will be more Nevertheless, Pearson and (1998) results that found in one region not the utility of taxa as indicators of overall species richness. that vary a however, may be useful in of species richness. For example, we have that latitude is an in North America. studies have it to be almost and Currie 1991, Lawton et al. 1993, et al. 1996, et al. 1997). holding latitude pairs of taxa in our remain taxa in a similar to other of species richness. and have proposed to broad-scale patterns of species richness, including mean potential and 1991, Lawton et al. 1993, et al. 1996, 1997). the factors of species richness in different groups may it to indicator taxa for the region of interest et al. 1997). when applying our results to other regions is the issue of scale. authors have that the among taxa are to the of (e.g., 1995, 1997). the broad of our and factors such as mean and potential are likely to be factors such as and habitat will to the most factors at these two on habitat and other Ideally, it would be to how these the of correlations among taxa as the our results are in other regions at a similar scale. In areas and at the we are less likely to be The data we have used for these all species richness data from compiled species may from at least two that could our correlation First, the ecoregions we used in this three of in from km2 to of studies have as the the species richness of the (e.g., and Wilson it is that a substantial of the correlation we among taxa is to the range of ecoregion in our with large ecoregions relatively species in all groups and ecoregions relatively in all groups. we found no relationship between ecoregion and species richness in any taxon This result may be in to the ecoregions as latitude (Figure 1). correlation between latitude and ecoregion may the correlation between latitude and species richness, the relationship between species richness and in our latitude only of the in ecoregion as compared to of the in species richness the of the ecoregions made less Ecoregions by delineate distinct groups of related and large and relatively the may have the species richness of ecoregions by small, diverse areas as ecoregions (e.g., ecoregion and and by large areas of relatively habitat ecoregions (e.g., ecoregion the from the richness data for correlation would not our results could result from a common problem with the use of species richness effort is not equal among because biologists are likely to have more effort species data in areas than in This an correlations in species richness. studies have to for this in effort (e.g., Prendergast et al. and 1997). for this would be it would be difficult to measure total effort for each In it is unlikely that the data in this from this for two First, we on range map of compiled species for each Because many range to to ranges more species are often assumed to be present in areas if they are by known Second, of the taxa we used mammals, trees, and have studied in North America and are therefore unlikely to be from ecoregions because of among these taxa are not than those among the less well known groups, that correlations among the less well known groups are unlikely to be by indicator taxa the of and indicator by be relatively well known or at least to but it also be and The results in this in the search for indicators of broad-scale patterns of species The three most (i.e., for indicator taxa are not the most are often the and accurate when or in the with using these taxa as indicators are not immediately from simple of correlation that any of the taxa or indexes we tested are of overall species richness at coarse (i.e., scales. two of these latitude effects and geographic patterns of that this initial common of patterns of species richness is to conservation priorities (Scott et al. 1993, Sisk et al. 1994). however, conservation biologists more than richness “hotspots” when priorities for conservation. on species richness be with on of species endemism, with richness and endemism of higher taxa, levels of and ecosystem service species richness, however, will to be in selecting conservation the patterns in this may conservation biologists of taxa for use as proxies for overall species richness and to more with more and in the of conservation We and for with the species data and and data on vascular plants, as Pearson at for tiger beetles and for land and many in the and and two the with their This was at World Wildlife Fund by the for the and an Conservation to at was by the and and a to