Biodiversity Hotspots and Conservation Priorities
Summary and Keywords
The concept of biodiversity hotspots arose as a science-based framework with which to identify high-priority areas for habitat protection and conservation—often in the form of nature reserves. The basic idea is that with limited funds and competition from humans for land, we should use range maps and distributional data to protect areas that harbor the greatest biodiversity and that have experienced the greatest habitat loss. In its early application, much analysis and scientific debate went into asking the following questions: Should all species be treated equally? Do endemic species matter more? Should the magnitude of threat matter? Does evolutionary uniqueness matter? And if one has good data on one broad group of organisms (e.g., plants or birds), does it suffice to focus on hotspots for a few taxonomic groups and then expect to capture all biodiversity broadly? Early applications also recognized that hotspots could be identified at a variety of spatial scales—from global to continental, to national to regional, to even local. Hence, within each scale, it is possible to identify biodiversity hotspots as targets for conservation.
In the last 10 years, the concept of hotspots has been enriched to address some key critiques, including the problem of ignoring important areas that might have low biodiversity but that certainly were highly valued because of charismatic wild species or critical ecosystem services. Analyses revealed that although the spatial correlation between high-diversity areas and high-ecosystem-service areas is low, it is possible to use quantitative algorithms that achieve both high protection for biodiversity and high protection for ecosystem services without increasing the required area as much as might be expected.
Currently, a great deal of research is aimed at asking about what the impact of climate change on biodiversity hotspots is, as well as to what extent conservation can maintain high biodiversity in the face of climate change. Two important approaches to this are detailed models and statistical assessments that relate species distribution to climate, or alternatively “conserving the stage” for high biodiversity, whereby the stage entails regions with topographies or habitat heterogeneity of the sort that is expected to generate high species richness.
Finally, conservation planning has most recently embraced what is in some sense the inverse of biodiversity hotspots—what we might call conservation wastelands. This approach recognizes that in the Anthropocene epoch, human development and infrastructure are so vast that in addition to using data to identify biodiversity hotspots, we should use data to identify highly degraded habitats and ecosystems. These degraded lands can then become priority development areas—for wind farms, solar energy facilities, oil palm plantations, and so forth. By specifying degraded lands, conservation plans commonly pair maps of biodiversity hotspots with maps of degraded lands that highlight areas for development. By putting the two maps together, it should be possible to achieve much more effective conservation because there will be provision of habitat for species and for economic development—something that can obtain broader political support than simply highlighting biodiversity hotspots.
In an ideal world with unlimited resources, conservation could stave off species extinctions by protecting habitats everywhere on the planet. However, resources are limited, so priorities must be set for where to invest in conservation. One of the most influential and compelling systems for setting priorities at the global level is the notion of biodiversity hotspots, which were originally defined qualitatively as areas with high plant endemism and high levels of historic habitat loss (Myers, 1988). Eventually, qualitative criteria were replaced with two key quantitative criteria: a hotspot must contain at least 1,500 endemic plant species and have at most 30% of its original vegetation (Myers, Mittermeier, Mittermeier, Da Fonseca, & Kent, 2000). Based on this criteria, 34 hotspots were identified, which comprise 2.3% of all the world’s land and are estimated to account for approximately 44% of all vascular plants and 35% of all vertebrate species (Mittermeier, Myers, Thomsen, Da Fonseca, & Olivieri, 1998). The conservation implications were clear: failure to protect these relatively small, threatened areas would result in a devastating loss of species richness.
The idea of biodiversity hotspots was not restricted to global analyses—the same notion could be applied at a hierarchy of scales, in all cases serving to focus efforts where habitat had been severely degraded and large numbers of species were imperiled (Kareiva & Marvier, 2015). It is important to emphasize that the consistent motivation for applying the concept of biodiversity hotspots was to establish priorities systematically in a manner that would make the best use of limited funds. In particular, hotspots were the answer to the question, “How can we protect the most species per dollar invested?” (Myers et al., 2000). The original global hotspots were all terrestrial, and grew from 10 tropical forests (proposed in 1988) to as many as 35 in 2011 (Williams et al., 2011; Mittermeier et al., 2011; also see Figure 1).
Subsequently, the hotspot approach has been applied to marine systems. In marine systems, hotspots focused on coral reefs. Using a similar approach to the terrestrial hotspots, Roberts et al. (2002) found that the 10 richest centers of endemic reef species cover only 15.8% of the world’s reef systems but capture between 44.8% and 53.6% of the restricted range reef species. Unlike their terrestrial counterparts, the delineation of marine hotspots did not include any threat cutoff, such as “less than 30% of the habitat remaining.” One especially interesting finding of the marine analysis was that 8 of the 10 marine biodiversity hotspots were adjacent to terrestrial hotspots, which suggests that extending terrestrial efforts seaward could be an effective strategy.
The influence of the hotspot concept in conservation is enormous. The work of Myers et al. (2000) has been cited over 15,000 times (according to Google Scholar). Perhaps more important than academic citations is the suggestion that the hotspot concept directed large amounts of conservation funding: Myers (2003) estimated that as of 2003, the hotspot strategy had directed the allocation of over $750 million, although this estimate is not based on analysis of exactly where money is actually spent. In fact, when researchers sought to test how money was actually spent (Halpern et al., 2006, p. 58), they found, “according to the finance officers of these organizations [WWF, Conservation International, and Birdlife International], they have no way of tracking spending at the regional or national level.” That said, funding organizations such as Moore Foundation, MacArthur Foundation, World Bank, and Global Environmental Facility did heed the biodiversity hotspot concept in their grant making in the late 1990s and early 2000s (Myers, 2003). For instance, the World Bank, which spends an average of $309 million annually on biodiversity projects, invested the most money in Brazil, which contains three hotspots: The Amazon, Cerrado, and Atlantic forests (Hickey & Pimm, 2011). The Philippines and Madagascar, both hotspots themselves, received the third and sixth most funds from the World Bank. Thus, biodiversity hotspots appear to inform World Bank funding to an extent, but as Hickey and Pimm (2011, p. 269) note, the World Bank does not fund “biodiversity priorities in a vacuum,” but instead balances biodiversity conservation with other goals, such as supporting poor or local communities.
As might be expected of any conservation strategy, thinking and science evolve and new ideas come into play. The four largest conservation nongovernmental organizations (NGOs)—the Nature Conservancy, WWF, Conservation International, and the Wildlife Conservation Society (WCS)—may all be said to have preservation of the world’s biodiversity as a key goal in their respective missions. But the trend in conservation has been to use hotspots to inform broader, more integrative conservation goals, rather than to focus on them exclusively. Indeed, the WWF website states that it has “evolved from saving species and landscapes to addressing the larger global threats and forces that impact them” (WWF, 2016). The Nature Conservancy takes a similar approach in using an integrative strategy that aims to protect both biodiversity and human communities that depend on it. Even the WCS, which remains focused on conserving species, has placed an emphasis on “reversing the decline of six priority groups of species across their range– elephants, apes, big cats, sharks and rays, whales and dolphins, and tortoises and freshwater turtles” (WCF, 2016). But the very nature of these animals demands an approach that accounts for their broad ranges. That is, one that protects large, contiguous swathes of land, not just hotspots.
This does not mean that the concept of hotspots is flawed, but the decades following the 1990s have seen NGOs adapt their application of it. Apart from the scientific evolution of hotspot thinking, which is examined later in this article, there is the practical limitation that hotspots are on such huge spatial scales that they cannot offer much guidance to project-level or local work, which is where conservation is actually implemented. The World Database of Protected Areas states that there are 197,368 protected terrestrial sites in its database, which account for 20.6 million square kilometers of land (Juffe-Bignoli et al., 2014). This equates to an average size of about 100 square kilometers per protected area. However, according to the Critical Ecosystem Partnership Fund, an organization focused on protecting hotspots, the smallest hotspot (New Caledonia) has roughly 5,000 square kms of habitat remaining, and the largest (Cerrado) has 438,000 square kms remaining (Critical Ecosystem Partnership, 2016). Thus, to fully protect even the smallest of the hotspots would require acquiring and protecting land at a scale substantially greater than what is the norm.
While the original biodiversity hotspot papers were aimed at these large areas and global priorities, biodiversity hotspots can be found on any spatial scale, although there are issues with data resolution at finer scales (Hurlbert & Jetz, 2007). As a tool for structuring research questions and thinking about conservation priorities, biodiversity hotspots remain an important concept that still plays a central role in conservation discussions (see Figure 2). The reason that the concept remains vital is that it has evolved and is useful in a wide variety of contexts ranging from climate impacts, to ecosystem services, to development mitigation.
Early Debates on and Critiques of the Hotspot Approach
There are four major areas of critique of the hotspot approach:
• Collecting long list of species globally may not reflect everyone’s conservation goals—especially if ecosystem services are of interest.
• The data used to generate the list are often weak and reflect only a few taxa, with little indication that other taxa are correlated.
• The priority list does not reflect a formal decision-theoretic framework that maximizes or minimizes some objective function.
• Although the concept is couched in terms of cost-effectiveness, no costs are actually used when formulating the list.
Each of these critiques, which are considered in detail next, represent specific scientific criticisms. In addition, there is a more political critique, which is that by ignoring nearly 98% of the world’s land area, too many people will feel left out of the conservation movement and see little value in it for themselves. Any prioritization will always designate “low-priority areas.” But it could be argued that global hotspots are at too large a scale and leave out too many people. The easy solution to this issue is to generate hotspot maps at finer spatial scales so that within every country, there are some hotspots worthy of conservation focus.
The most fundamental critique of global hotspots is that simply harboring a long list of species neglects important conservation goals. For instance, people may value iconic or charismatic species such as polar bears and dolphins (Lorimer, 2007), and these species transcend any hotspot for endemic plant species. In other cases, the public may be motivated to support conservation because of the ecosystem services that nature provides (Goldman, Tallis, Kareiva, & Daily, 2008). By simply focusing on maintaining a long global list, the hotspot approach risks allowing economically valuable habitats to be lost. Consider the case of Spartina marshes. These can offer valuable ecosystem services, such as flood regulation and fisheries production. Yet without any endemic plants, such regions are completely neglected in any hotspot analysis (Kareiva & Marvier, 2015). Indeed, the spatial correlation between ecosystem services and biodiversity is often weak to nonexistent (Chan, Shaw, Cameron, Underwood, & Daily, 2006; Naidoo & Ricketts, 2006). For example, analyzing land parcels of 500 hectares on the central coast of California, Chan et al. (2006) found no evidence of any significant correlation between having high biodiversity and greater ecosystem service values. In a global analysis, Naidoo et al. (2008, p. 9495) found that at the level of ecoregions, those “selected to maximize biodiversity provide no more eco-system services than regions chosen randomly.” What this means is that if one were interested in focusing on biodiversity hotspots, there is a good chance that the lands protected for that purpose would not do a good job delivering ecosystem services. It is interesting to note, however, that Chan et al. (2006) found that if one had dual objectives—both biodiversity and ecosystem services—one could meet those objectives in California by expanding the amount of land protected by a modest amount. This is because some species-rich hotspots did in fact coincide with ecosystem service hotspots.
A second issue is that, even if having long species lists is the only thing you care about, traditional hotspots can be so taxonomically biased that vast segments of biodiversity are neglected. The key weakness is that the original hotspot formulations were based only on endemic plant data. There is no guarantee that efforts to protect these endemic plant hotspots will also capture the diversity of other taxa. In fact, several studies that have assessed the spatial correlation of hotspots among different taxa have found extremely low correlations. Wolters, Bengtsson, and Zaitsev (2006, p. 1890) reported in a metastudy of literature looking at correlations in species richness between different taxa that “the total variance in species richness of one group explained by species richness in another group was, on average, 14%.” If one alters the spatial scale and the groups of animals examined, better correlations can be found. But as Wolters et al. (2006) noted, there is much heterogeneity in effect size. The fact that a certain relationship exists on one scale is hardly enough to suggest that is consistent across all scales. Prendergast, Quinn, Lawton, Eversham, and Gibbons (1993), in looking at the distribution of species in Britain, made similar findings, reporting that “species rich areas (hotspots) frequently do not coincide for different taxa, and many rare species do not occur in the most species rich squares.” (p. 335). These results make clear that hotspots are not a strategy to conserve biodiversity; rather, they are a strategy to conserve endemic plants. When one examines the global distribution of rare and threatened vertebrates, the absence of congruence among bird, mammal, and amphibian species at risk (Grenyer et al., 2006) indicates that what Prendergast found in the United Kingdom is a global problem for any hotspot approach that relies on species lists for selective taxa.
A third critique concerns how loosely the hotspot prioritization is framed. Ideally, priorities should result from a formal decision-theoretic application of some objective function. An example of such an objective function might be: “Secure as many species as possible, with X square kilometers protected.” An alternative formulation might be: “Protect species such that at least 80% of the world’s species are protected, and this is done with the minimum amount of land.” Instead of solving an objective function, hotspots are simply arrived at using two criteria: that the region contains at least 1,500 endemic species of vascular plants and has lost at least 70% of its original habitat. But this definition is not grounded in any decision-theoretic framework. Proponents may argue that by protecting these hotspots, they in turn protect the benefits they harbor. As Mittermeier, Turner, Larsen, Brooks, and Gascon (2011, p. 4) stated, “as species vanish, so too does the health security of every human. Earth’s species are a vast genetic storehouse that may harbor a cure for cancer, malaria, or the next new pathogen.” But these benefits are tangential, not reflecting the actual objective that has been maximized. The actual achievement of protecting a hotspot is what is outlined in its definition: an area with at least 1,500 endemic species of vascular plants and less than 30% of its original habitat has now been protected. Even supposing that protecting endemic species is in fact the end goal, relying solely on hotspots to guide conservation falls short, as the relevant constraints—time, funds, etc.—must be taken into account. When the problem is framed this way, it may turn out that protecting more areas with less endemism can accomplish the same goal as protecting fewer areas with higher endemism. In one analysis, Murdoch, Ranganathan, Polasky, and Regetz (2010) used a similar approach and developed a return on investment (ROI) strategy for preserving ecosystems. Here, the return is determined by the conservation benefit of protecting an area over the cost of doing so. With a limited budget of $500 million, the ROI approach outperformed a “maximize benefit” strategy, wherein the most at-risk areas were given highest priority. This does not mean that hotspots are unimportant—only that more thought needs to be put into evaluating them within a decision-theoretic framework. To use an analogy: It has been said that the current loss of species is akin to burning down the world’s library without knowing the content of 90% of its books. The scramble to protect hotspots, then, is like rushing haphazardly into the burning library to grab all the remaining rare second-edition Encyclopedia Britannicas, but only if 70% of them are already in ashes. The information salvaged is no doubt important, but one wonders if there are not better strategies out there.
As areas for future conservation protection, hotspots are reactive rather than proactive, and in some sense, they may represent areas where it is too late for protection to do much good. Several conservation biologists have pointed out that it might make much more sense to focus on protecting areas with lower vulnerability than areas under imminent threat of development or agricultural expansion (Dobrovolski, Diniz-Filho, Loyola, & Júnior, 2011). Whereas reactive biodiversity hotspots tend to focus on habitat remnants with high species richness, proactive priorities based on low conversion risk tend to focus on wilderness areas or “last-of-the-wild” areas that are likely to be secure against development (in the near term, at least). A global analysis of reactive hotspots versus proactive priorities revealed the two approaches were not spatially congruent—with the proactive approach reducing conflict between protection and expected agricultural expansion (Dobrovolski et al., 2011). Another way of looking at the contrast between reactive and proactive is through the lens of cost-effectiveness, which we discuss next. As it relates to reactive versus proactive, the notion is that if you want to maximize the number of species secured per dollar invested, greater value is likely to be found in relatively pristine regions with minimal rates of habitat loss, as opposed to regions under siege that have already lost most of their vegetation.
While the hotspot concept proclaims “cost-effectiveness” as one of its merits, it never includes any real data on costs. The reason why this is so crucial is that the costs of conservation (either due to land costs or staffing costs) commonly vary by two or three orders of magnitude across the available options (Murdoch, Polasky, Wilson, Possingham, Kareiva, & Shaw, 2007). This means that if cost-effectiveness is really the goal, it is highly likely that costs will drive the optimal priority set more than range maps and species occurrences. Using data on land costs and habitat diversity in Argentina, Murdoch et al. (2010) contrasted the outcome of priorities established on the basis of maximizing benefits (e.g., habitat variety and hence species diversity) versus minimizing cost. In both cases, there was a budget constraint—as there always is in the real world. Clearly, the “minimize costs” approach ended up costing less. But remarkably, this approach also ended up having more conservation value than did the “maximize the benefits” approach (at least 2 times better, and as high as 4.5 times better for especially limited budgets). This counterintuitive result is directly due to the lack of an explicit cost function in the hotspot methodology. One may snatch up the most valuable piece of land for conservation first, the second-most-valuable piece of land next, and so forth. But as this is done, not only is other habitat being degraded, but available funds are quickly being depleted and large sums of money are spent on tiny fragments of land—money that may well be better spent elsewhere. In short, one cannot claim cost-effectiveness unless cost data are included in the analyses.
New Permutations of Biodiversity Mapping and How the Hotspot Notion Has Evolved
Climate change is already altering the distribution of species and is expected to threaten the persistence of much biodiversity. A key question, then, concerns the extent to which current maps of biodiversity hotspots are appropriate priorities for future conservation, given the predicted range shifts of many species. Species-by-species examinations suggest that a failure to account for climate change could leave many species unprotected (Oliver, Smithers, Beale, & Watts, 2016). As a substitute for a species-by-species approach, Anderson and Feree (2010) recommend what they call “conserving the stage” (p. 9). Arguing that species ranges will change under climate stress in largely unpredictable ways, the authors propose that what is important is to conserve the evolutionary drivers of biodiversity, with geophysical diversity being of particular significance. Anderson and Feree (2010) identify latitude, elevation range, amount of calcareous bedrock, and the number of geologic classes—with geologic class itself being determined by another set of variables (e.g., chemistry, weathering processes, etc.)—as four key geophysical factors that influence species diversity. Thus, to protect biodiversity over the long term, they argue that conservation should seek to maximize protection of “geophysically diverse” (in abstract, p. 1) areas; i.e., areas that encompass these four factors in a range of quantities and combinations.
Just as climate change will alter species distributions on land, so will it in the oceans. To examine the future of marine hotspots, Molinas et al. (2015) used existing distributions of species to identify thermal limits and then predicted how those distributions would shift under different emissions scenarios as represented by the representative concentration pathways of the Intergovernmental Panel on Climate Change (IPCC). Locally, each region of the ocean experiences the loss and gain of species, with range expansions prevailing over range contractions. The result is the formation of no-analog communities, where invasions are common. In other words, the result may not necessarily be a net loss in diversity, but a trend toward homogenization as species leaving one area enter another species’s area. The interactions between these invading species and the local populations then become a new focus for conservation, and conservation attention will need to shift to those regions experiencing the addition of many new species.
Conservationists have also recognized that it may not be the direct climate impacts that threaten biodiversity as much as it is the human response to climate change (Poiani et al., 2011). In order to see what this means for biodiversity hotspots, Jantz et al. (2015) used the different IPCC emissions scenarios to predict land-use changes and then used those land-use changes to estimate losses of habitat in hotspots. The land-use change was driven in these IPCC scenarios primarily by increased wood harvest and expansion of pastureland and cropland. They found that these land-use changes were likely to amount to a loss of habitat of between 26% and 58% in current terrestrial hotspots (with the higher losses associated with the business-as-usual scenarios). This land-use change driven by climate could mean an additional loss of between 200 and 6,000 plant species. These analyses also reveal an additional complication—in terms of land conversion, lower-emission scenarios may not be good for biodiversity hotspots. In particular, Jantz et al. (2015) found that crop expansion was worst under the most ambitious mitigation strategy due to the increased demand for biofuel. In other words, the most ambitious mitigation strategy may lose more land, but they also may save more species by direct CO2 reduction.
A second active area of hotspot research is what might be called its inverse—instead of mapping areas of highest conservation value (i.e., biodiversity hotspots), one can use maps of areas with the lowest conservation value. For example, given the need for renewable energy, which can occupy extensive areas of land, Kiesecker et al. (2011) mapped highly disturbed lands and overlaid those lands with wind energy potential. In this way, they were able to show that wind energy development can meet the United States national goals by using only highly disturbed lands, hence never threatening high value lands such as biodiversity hotspots. More generally, an approach called “development by design” (Kiesecker et al., 2009) provides a hierarchy of responses to development pressures, with the aim of protecting biodiversity. This does not replace biodiversity hotspots but rather supplements them. The philosophy is simple: Instead of only identifying what should not be developed (the hotspots), conservation also needs to indicate what can be developed. In this way, instead of being seen as obstructionist to economic and infrastructure development, conservation gains a voice in the decision-making process. One of the features of development by design is that it is often coupled with large amounts of money for offsets, or can be coupled with the creation of new protected areas (Kareiva & Marvier, 2015). For example, development by design led to 400,000 hectares of new protected area in exchange for specification of where mining could occur in Mongolia, and 68,000 hectares of new protected area in Wyoming (Kareiva, 2014).
The Future of Hotspot Research
All formulations of a hotspot approach to conserving biodiversity rely on some form of threat data. For the original biodiversity hotspot approach, threat was taken to be captured by the percent of habitat (especially forest) that had already been converted or lost. However, it is not clear whether this is a good proxy for future threat. Reducing rates of biodiversity loss requires a quantitative understanding of what is threatening biodiversity, where risks are highest, and what are the best actions for averting those threats in the future (Joppa et al., 2016). Moreover, any formal decision-theoretic approach to global biodiversity priorities requires a specific time horizon and some estimate of a probability of land conversion. For example, consider the two following scenarios, each of which has areas of land of equal size, but with different numbers of species and different risks of development or land conversion. For the sake of clarity, further suppose that in each case, the development imparts a well-defined impact. In scenario A, there is an Area A, having 1,000 species and a 10% chance of being developed within the next 100 years. This development will certainly eliminate 10% of Area A’s species. In scenario B, there is another plot of land, Area B, having 500 species and a 50% chance of being developed in the next 100 years. As with the first scenario, this development will certainly eliminate 10% of the current species. Now, the expected value of an outcome is the probability of each possible event multiplied by the value of that event. In this example, we are interested in the conservation impact, or the number of species lost to development. Thus:
Using our example (and assuming that no development entails no species loss), we have:
where we have used Ex[X] to denote the expected impact of scenario X.
Thus, even though Area A has twice as many species, because it is five times less likely to be developed than Area B, Area B is actually expected to account for more species loss. If your objective is to minimize the loss of species, then you should strive to take action that alters the expected number of species lost. In this example, a simple way to do this is to target Area B for protection so that its risk of development and the corresponding expected number of species lost are decreased. However, it is important to note that these estimates were based on the risk of development “over the next 100 years.” Basing the estimate instead on the risk of development over the next 50 years may—particularly if, for instance, Area B is much less likely to be developed in this time frame—yield different results. This simple example makes clear the importance of generating threat estimates for species at multiple time scales (and analogously, the same reasoning could be extended to spatial scales).
With development and climate change proceeding at such a rapid pace, biodiversity priorities need to increasingly be based on dynamically updated maps of change and threat. By building a database that projects habitat loss due to urbanization, agriculture, fossil fuel development, renewable energy, and mining, Oakleaf et al. (2015) identified habitats that are especially vulnerable to massive habitat loss. These habitats are tropical and subtropical grasslands, savannas, and shrublands. In terms of geography, central and eastern Africa, southern and western Australia, and the central rocky mountain region of North America are at greatest risk of development. This type of information combined with hotspots has the advantage of being proactive and is a considerable improvement over a reliance on past deforestation rates as a threat index.
Initially, biodiversity hotspots represented an approach to conservation that largely lacked any ideas from evolutionary biology—either data or theories. Genetics, for instance, were understood to be valuable for evaluating population viability, population structure, and genetic drift, but the inferences here were (and still are) limited by the availability of informative genetic markers. But as Shafer et al. (2015, p. 84) noted, we are approaching the $1,000 genome. The access to this genomic data provides conservationist biologists with a powerful tool. A key idea is that, as genes provide the building blocks on which evolution works, it is genetic diversity that is paramount. The loss of an evolutionary distinct species entails not just one less species, but also a loss of a host of potentially adaptive genes. In other words, species are not all equal in their biodiversity value. May-Collado, Zambrana-Torrelio, and Agnarsson (2016) applied this thinking to global priority-setting for the conservation of aquatic mammals, which include representatives from Carnivora, Cetacea, and Sirenia. Altogether, 127 species and two different levels of risk were included in the analysis. They identified 22 priority areas, most of which were concentrated in coastal areas. Interestingly, they were able to compare their priority areas to those obtained from a more traditional hotspot analysis that did not incorporate evolutionary distinctiveness. They found considerable overlap between their results based on phylogenies and the traditional approach based simply on distribution records.
Redding and Mooers (2006) combined both threat status and genetic information to establish priorities for 9,546 species of birds worldwide. Their analysis was, however, framed by a species-by-species perspective and was not converted into spatial or geographic priorities (although doing so would be a simple exercise). It is worth noting that the effect of genetic information on priorities could be dramatic. The plains wanderer (Pedionomus torquatos) rose from the 159th most important species for conservation action to 19th place. This jump was due to the fact this species is the sole member of its family.
Much less developed is the application of within-species data on genetic diversity. This may become more important when considering climate change, because the ability of species to adapt to climate change is expected to be proportional to the amount of genetic variation within populations. Thus, one could imagine using the increasingly available data on genetic diversity to identify if there are evolutionary hotspots that could be the source of evolutionary responses to climate change. Souto et al. (2015) sampled 360 populations of nine species of trees from four of the dominant families across the austral temperate forest hotspot in Argentina and Chile. With isozyme and chloroplast deoxyribonucleic acid (DNA) used as genetic markers, genetic diversity was calculated as a function of heterozygosity, the number of unique alleles, haplotype diversity, and the number of unique haplotypes. When this information was overlaid with spatial geographic information systems (GIS) data, Souto et al. (2015, p. 541) reported that “although levels of genetic polymorphism in widespread phylogenetically independent taxa varied along their ranges, they yielded concordant spatial patterns that revealed genetic hotspots.” They went on to hypothesize that the spatial concordance is the result of similar evolutionary and ecological forces. They also observed that adaptive and neutral variation, observed within a single species, showed spatially concordant patterns.
There are important implications here for the conservationist. First, the map generated by such a “genetic hotspot” approach is at a finer scale than hotspots based solely on species richness, and thus is of more practical significance. Second, if the spatial concordance between adaptive and neutral variation holds true for other environments, then the task of identifying evolutionary important populations is greatly aided. The era of the $1,000 genome may be near, but using that genomic data to infer adaptively significant genes is a computationally hard problem that is outside the realm of many conservation programs.
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