the economics of biodiversity - ucsb

Chapter 29

THE ECONOMICS OF BIODIVERSITY

STEPHEN POLASKY

University of Minnesota, USA

CHRISTOPHER COSTELLO

University of California at Santa Barbara, USA

ANDREW SOLOW

Woods Hole Oceanographic Institution, USA

Contents

Abstract 1518Keywords 15181. Introduction 15192. Measures of biodiversity 1520

2.1. Measures based on relative abundance 15202.2. Measures based on joint dissimilarity 1522

3. Sources of value from biodiversity 15253.1. Use value and existence values of individual species 15253.2. Biological prospecting 15263.3. Biodiversity and ecosystem services 1528

4. Strategies to conserve biodiversity 15324.1. Terrestrial habitat protection 15324.2. Marine biodiversity and reserves 15384.3. Introduced species 1540

5. Incentives to conserve and conservation policy 15446. Conclusions 1551References 1552

Handbook of Environmental Economics, Volume 3. Edited by K.-G. Mäler and J.R. Vincent© 2005 Elsevier B.V. All rights reservedDOI: 10.1016/S1574-0099(05)03029-9

1518 S. Polasky et al.

Abstract

The conservation of biodiversity is a major environmental issue, one that promises toremain at or near the top of the environmental agenda for the foreseeable future. Theloss of biodiversity affects human welfare as well as being lamentable for its own sake.Humans depend on natural systems to produce a wide variety of ecosystem goods andservices, ranging from direct use of certain species for food or medicines to ecosys-tem functions that provide water purification, nutrient retention or climate regulation.Threats to biodiversity include habitat loss and fragmentation, the introduction of non-indigenous species, over-harvesting, pollution, changes in geochemical cycles and cli-mate change. Sustaining biodiversity in the face of increasing human populations andincreased human economic activity promises to be a major challenge. Economists havean important role to play in helping to develop and evaluate conservation strategies.Because biodiversity is at risk in large part because of human activity, finding waysto conserve biodiversity will come from better understanding and management of hu-man affairs, not from better biology alone. Economists can help set priorities to allocatescarce conservation resources where they will do the most good. Economists can helpdesign incentive schemes to make conservation policy both effective and efficient. Eco-nomic methods can shed light on what are the most valuable components of biodiversity,including analysis of species existence value, the value of bioprospecting and the valueof ecosystem services.

Keywords

biodiversity measures, valuation, ecosystem services, habitat conservation,conservation policy

JEL classification: Q20, Q22, Q23, Q24, Q28

Ch. 29: The Economics of Biodiversity 1519

1. Introduction

The second half of the 20th century was, in many respects, good to Homo sapiens.Human population more than doubled between 1950 and 2000, growing from approxi-mately 2.5 billion in 1950 to just over 6 billion in 2000 (U.S. Bureau of Census). Humanhealth, nutrition and average life expectancy improved dramatically [Johnson (2000)].The value of economic activity increased by over 400% over the second half of the 20thcentury [Delong (2003))]. The same period was not so good to many of the other specieson the planet. Some ecologists fear that we may now be witnessing the sixth great ex-tinction wave on the planet. Though evidence is fragmentary, current rates of speciesextinction are estimated to be several orders of magnitude above background or naturalextinction rates [NRC (1995), Lawton and May (1995), Pimm et al. (1995)]. The areaof natural habitat has declined as humans have converted lands to agriculture, managedforests or urban development. Roughly half of useable terrestrial land (i.e., land that isnot tundra, ice, boreal, rock, desert) is devoted to grazing livestock or growing crops[Tilman et al. (2001)]. There is roughly half the area of forest now than existed whenagriculture began 8000 years ago [WRI (1998)]. Increased movement of goods has ledto introductions of nonindigenous species with occasional substantial consequences fornative ecosystems. Over-harvesting of fish and game species, climate change, pollu-tion, and changes in geochemical cycling has also threatened many species. Given thisexperience, ecology may supplant economics as “the dismal science”.

Conserving biodiversity has become a major environmental issue. In the words ofecologist Simon Levin: “The central environmental challenge of our time is embodiedin the staggering losses, both recent and projected of biological diversity at all levels,from the smallest organisms to charismatic large animals and towering trees.” [Levin(1999, p. 1)] The loss of biodiversity may be lamentable for its own sake but it alsohas impacts on human welfare. Humans depend on natural systems to produce a widevariety of ecosystem goods and services, ranging from direct use of certain speciesfor food or medicines to ecosystem functions that provide water purification, nutrientretention or climate regulation. Cultural and spiritual values are also tied to elements ofbiodiversity.

The great concern for conserving biodiversity, in contrast to concerns for specificendangered species or particular ecosystems, is a relatively recent phenomenon. Theformation of the Society for Conservation Biology in 1985, the beginning of the jour-nal Conservation Biologyin 1987, and the publication of the edited volume BioDi-versity [Wilson (1988)] serve as useful benchmarks signaling the beginning of broadscientific and policy interests in the conservation of biodiversity. By now there arethousands of journal articles and books devoted to various aspects of conservation (apartial biodiversity bibliography containing approximately 5000 entries can be found athttp://www.apec.umn.edu/faculty/spolasky/Biobib.html).

While biological scientists have a central role in researching biodiversity, economistshave begun to play an important and expanding role [a collection of recent biodiversityarticles by economists is contained in Polasky (2002)]. Biodiversity is at risk largely

http://www.apec.umn.edu/faculty/spolasky/Biobib.html


because of human activity. Therefore, conservation solutions will come from better un-derstanding and management of human affairs, not from better biology alone. Sincethere are limited budgets for conservation that cannot support all worthy conservationprojects, economists can help set priorities to allocate scarce resources where they willdo the most good. Economists can help design incentive schemes to make conservationpolicy both effective and efficient. Economic methods can shed light on what are themost valuable components of biodiversity.

In this chapter we review the recent economics literature on biodiversity. We begin inSection 2 by discussing various ways to define and measure biodiversity. In Section 3 wediscuss the sources of value generated by biodiversity and various empirical measuresof value. Section 4 covers strategies to conserve biodiversity in light of the main threats,namely habitat loss and invasive species. Section 5 discusses incentives (and disincen-tives) for conservation as well as policies whose goal is to conserve biodiversity. Weoffer brief concluding comments in Section 6.

2. Measures of biodiversity

The term biodiversity has been defined in a number of different ways. Most of the mea-sures of diversity developed and used by economists are defined as measures of thejoint dissimilarity among a set of species. There is, however, another strand of eco-logical literature that defines diversity in terms of the relative abundances of specieswithin a community. We begin this section with a review of measures based on relativeabundance and then review measures based on joint dissimilarity.

2.1. Measures based on relative abundance

Perhaps the most common way in which the word diversity has been used in ecologyis to characterize the relative abundances of species within a community [e.g., May(1972), Magurran (1988)]. The relative abundance of a species in a community is de-fined as the proportion of individual organisms in the community that belong to thatspecies. Consider a community containing s species and let π = (π1π2 . . . πs) be thevector of relative abundances with for all j and

∑sj=1 πj = 1. In qualitative terms, the

community is said to be diverse if all of the elements of π are close to 1/s. There issome evidence that communities that have recently been subjected to disturbance havelow diversity. A possible explanation is that, following disturbance, recolonization is ledby opportunistic species with high fecundity (so-called r-strategists) whose numbersquickly dominate the community. Over time, less fecund, but longer-lived species (so-called K-strategists) are able to compete successfully, thereby making the communitymore diverse. The extent to which diversity reflects dynamical properties like stabilityand resilience remains an open question. The chapter on ecosystem dynamics by SimonLevin and Stephen Pacala (2003) in Volume 1 of this handbook discusses some aspectsof this question and other matters related to the measurement of biodiversity.


This qualitative notion of diversity is not amenable to analysis and, during the 1970s,there was a burst of attention to devising quantitative measures that capture this notion.An important contribution by Patil and Tailie (1977) was to define the diversity of acommunity with relative abundance vector π by average rarity:

D(π) =s∑

j=1

rjπj ,

where rj is a measure of the rarity of species j . Different choices of rarity measure yielddifferent diversity measures. Patil and Tailie (1977) focused on the one-parameter fam-ily rj = (1 − π

βj )/β with β � −1. This gives rise to the family of diversity measures:

Dβ(π) =(

1 −s∑

j=1

πβ+1j

)/β.

Special cases include:

D−1(π) = s − 1

which is essentially species richness;

D1(π) = 1 −s∑

j=1

π2j

which is called the Simpson index and gives the probability that two individuals sampledat random are of different species; and the limiting form

D0(π) = −s∑

j=1

πj log πj

which is called variously the Shannon–Weaver index, the Shannon–Wiener index, andthe entropy index.

It is fair to say that the notion that a high level of diversity of this type is preferableto a low level is without a clear basis in either ecology or economics. Weitzman (2000)attempts to provide such a basis through a model of the relationship between the abun-dance of a crop and the number of pests or pathogens specific to that crop. Briefly, underthis model:

Sj = kBzj ,

where Sj is the number of pests specific to crop j and Bj is the total biomass of the crop.Weitzman (2000) imagines that this biomass is divided into Bj separate patches eachof unit biomass. If each pathogen has a probability ε of destroying a patch of biomass,then (under two independence assumptions) the probability of complete extinction of


the crop is:

Pj (Bj ) =(

1 − (1 − ε)kBz

j

)Bj

.

Weitzman (2000) uses the approximation that, for small ε, Pj (Bj ) ∼= (kBzj ε)

Bj to showthat the set of relative abundances that minimizes the probability of a loss of all cropsmaximizes the diversity measure D0(π). The essential tension in this optimization prob-lem is between the safety provided by a large number of patches and the correspondinglarge number of potentially harmful pests.

2.2. Measures based on joint dissimilarity

The focus of the rest of this section is on quantitative measures of diversity that areintended to reflect the joint dissimilarity of a collection of species. One practical moti-vation for work in this area has been the need to evaluate policies aimed at protectingspecies from extinction. It is worth stressing that, as the goal of such policies is to pre-serve species from extinction, this kind of diversity measure should be sensitive only toextinctions (or changes in extinction probabilities) and not to ecological changes (e.g.,in population size or abundance distributions). This is not to say that maintaining popu-lations is unimportant – indeed, it is a critical policy instrument in preventing extinction– only that it is not the ultimate goal. In cases where species are harvested for food orsport, increasing the population size will have utility in and of itself. There is a largebioeconomics literature that analyze these issues [Clark (1990)] and we will not con-sider population size issues further in this section.

The standard measure by which policies aimed at preventing extinction are evalu-ated is the number of species that they protect. However, this measure of diversity –species number – takes no account of the relative differences between species. A pol-icy that protects a large number of species covering a small number of genera maybe, in some sense, inferior to a policy that protects a smaller number of species cover-ing a larger number of genera. This point was made by Vane-Wright, Humphries andWilliams (1991), who went on to propose measures of the diversity of a collection ofspecies based on the phylogenetic tree connecting them. Under the simplest of thesemeasures, each species in the tree is assigned a numerical value inversely proportionalto the number of nodes in the tree associated with it. For a given species, this valuereflects the number of closely related species. The diversity of a collection of species isthen found by summing these values for the species in the collection. By measuring thediversity of a collection of species by combining values assigned to the species in thecollection, this measure cannot distinguish between the case in which a pair of closelyrelated species are both in the collection and the case in which only one is. This cre-ates a problem: it would not make sense to depreciate the value of preserving a specieswith many close relatives when these close relatives are doomed to extinction. Otherproposed measures suffer from a similar problem [e.g., Haney and Eiswerth (1992)].

To formalize the measurement problem, consider a collection S = (s1, s2, . . . , sn)

of n species or other types and let d(sj , sk) be the distance or dissimilarity between


species sj and sk (neither of which needs be in S). The problem is to define a non-negative real-valued function D(S) that measures the diversity of S. It is natural torequire D to satisfy the following conditions [Weitzman (1992)]. First, diversity shouldnot be reduced by the addition of a species to s. That is, if S and S′ are two collectionsof species with S ⊂ S′, then D(S) < D(S′). Second, diversity should not be increasedby the addition of a species that is identical (in the sense of having 0 dissimilarity) toa species already in S. That is, if s◦ is a species not in S, then D(S ∪ s◦) = D(S)

if and only if d(s◦, si) = 0 for some si ⊂ S. Third, diversity should not decreasewith an unambiguous increase in the dissimilarities between species. That is, for a one-to-one mapping of S onto S′ such that d(si, sj ) � d(s′

i , s′j ) with at least one strict

inequality, D(S) � D(S′). Other requirements are possible (e.g., involving continuity ofthe measure with respect to increasing dissimilarity), but these three seem fundamental.

The first measure to satisfy these requirements was proposed by Weitzman (1992).With the diversity of a single species defined as 0, this measure is given by the recursion

DW(S) = max(DW(S − si) + d(si, S − si)

),

where S − si denotes the collection S with species si removed; the distance d(si, S − si)

between species si and the collection S − si is defined as the minimum of the distancesbetween si and the species in S − si ; and the maximum is taken over the species si in S.In the important case where the distances between species are ultrametric (so that, forany set of three species, the largest two distances between species are equal), the speciescan be represented by a planar tree, and then Weitzman’s measure corresponds to thetotal length of the tree. This is arguably the only sensible measure of pure diversity inthis case. Other diversity measures have been proposed [e.g., Crozier and Kusmierski(1996), Faith (2002)], but these tend to be ad hoc.

It is worth noting at this point that Weitzman’s measure and other early measures ofdiversity were not directly connected to any theory of economic (or ecological) value.This is not to say that the qualitative connection had not been made. Ecologists havelong believed that diversity in nature supports the stability and resilience of ecosystems[despite some surprising suggestions to the contrary, May (1972)]. Diversity, therefore,has economic value arising not only from direct benefits but also from indirect benefitsfrom ecosystem functions that generate valuable ecosystem services, which we discussin more detail in Section 3.3. Economists have developed the argument that, to theextent that similar species provide similar benefits and suffer similar susceptibilities, it issensible to maintain a diverse portfolio of species. The first attempt to base a measure ofdiversity on a theory of value was by Polasky, Solow and Broadus (1993). Using a highlystylized probability model of substitutability of species in providing a single benefit,Polasky, Solow and Broadus (1993) derived a measure of the diversity of a collection ofspecies that reflected the probability that at least one species in the collection providedthe benefit. The measure satisfied the three requirements listed above.

The diversity measure of Polasky, Solow and Broadus (1993) was based on a com-plete (if stylized) model of substitutability. In later work, Solow and Polasky (1994)took a slightly different approach. Suppose that interest in species conservation arises


from the possibility of species providing a benefit, such as a cure for a disease, in thefuture. Suppose further that having more than one species that provides this benefit isno better than having a single species that provides it. Let Bi be the event that species siprovides this benefit. The event that the collection S also provides this benefit is givenby

B(S) =n⋃

i=1

Bi.

The expected benefit of S is p(S)V , where p(S) = prob(B(S)) and V is the fixed unitvalue of the benefit. Because V is fixed, p(S) provides a basis for comparing differentcollections of species.

In the absence of specific information, Solow and Polasky (1994) assumed that theprobability of the event Bi that species si would provide the benefit is an unknownconstant p that does not depend on i. They also assumed that the conditional probabilityof Bi given the event Bj that species sj provides the benefit is:

prob(Bi | Bj ) = p + (1 − p)f(d(si, sj )

),

where f is a known function satisfying f (0) = 1; f (∞) = 0; and f ′ � 0. Although itis not possible to obtain the n-variate probability p(S) from the univariate probability p

and the set of conditional probabilities prob(Bi | Bj ), Solow and Polasky (1994) used aprobability inequality due to Gallot (1966) to show that a lower bound on p(S) is givenby p2DSP (S), where

DSP (S) = etF−1e

for the n-by-n matrix F = [f (d(si, sj ))]. In summary, a lower bound on the expectedvalue of a collection S is an increasing function of the quantity DSP (S). Solow and Po-lasky (1994) went on to show that, under reasonable assumptions about the function f ,this quantity also meets the three main requirements of a diversity measure. Moreover,DSP (S) is bounded below by 1 and above by n, so it has the interpretation as the effec-tive number of species in S.

The main disadvantage of DSP (S) as a diversity measure is that it requires the specifi-cation of the function f that, in some sense, measures the “correlation” between speciesas a function of the dissimilarity between them. On the other hand, from a practical per-spective, it may be a disadvantage of DW(S) that it can increase without bound withdissimilarity. A more serious objection to the practical use of these and other diversitymeasures in actual conservation decision-making is that their information requirementsare utterly unrealistic. Except in extremely unusual situations, conservation decisionsinvolve large numbers of species from a wide variety of taxonomic groups whose iden-tities – let alone genetic dissimilarities – are simply unknown. The numbers of speciesthemselves are also unknown and comparisons based on estimated species number arefraught with problems arising from sampling. An attractive option in this situation is toaim conservation efforts at conserving a diverse collection of habitats, on the assump-tion that dissimilar habitats tend to support dissimilar species. Of course, this merely


transforms the problem to one of assessing the diversity of a collection of habitats. Inthis case, however, the information needed to construct dissimilarities between habitats(e.g., topography, climate, etc.) may be more readily available. Note that, in contrastto the species case, there is no reason to assume that habitats are related through ananalogue to a phylogenetic tree. For this reason, diversity measures based on such astructure have no particular appeal.

3. Sources of value from biodiversity

Some issues related to the value of biodiversity were touched upon in previous sec-tions, for example the utilitarian motivation for the diversity measure of Solow andPolasky (1994). For the most part, however, discussion of diversity measures and valueof biodiversity have been conducted in largely separate literatures. In this section wereview economics literature on the value of biodiversity. (For background on valuationmethods, see Volume 2 of this handbook, titled Valuing Environmental Changes.) Bio-diversity is a broad term encompassing everything from genes to species to ecosystems.Value from biodiversity can arise at any of these levels. We begin with value generatedat the species level.

3.1. Use value and existence values of individual species

Humans have recognized the direct use value of other species, at least implicitly, foras long as our species has existed. For millennia, humans depended upon successfullyhunting animal species and gathering plants species. The switch to domesticated agri-culture changed the form but not the substance of our reliance on other species. Many ofthe direct use values of species which humanity relies upon for food and fiber are welldocumented by agricultural economists, fishery economists and forestry economists.Even for species that are not grown or harvested commercially, there is a long traditionwithin economics of estimating the value of recreational hunting and fishing [see, forexample, Walsh, Johnson and McKean (1988), Markowski et al. (1997), Rosenbergerand Loomis (2001)].

The increased interest in the conservation of biodiversity, especially conserving en-dangered species, brought about a new type of species valuation effort focused onspecies existence value rather than on direct use value. Beginning in the 1980s, econo-mists began to use contingent valuation surveys to ask people what they were willing-to-pay to conserve particular rare or endangered species. In reviewing studies covering18 different species, Loomis and White (1996, p. 249) concluded “that the contingentvaluation method can provide meaningful estimates of the anthropocentric benefits ofpreserving rare and endangered species.” Estimates of annual willingness-to-pay variedfrom a low of $6 per household for the striped shiner to a high of $95 per householdfor the spotted owl. The estimates of willingness-to-pay tended to be higher for more


charismatic species and for situations with greater increases in population sizes, as onewould expect.

Not all economists agree that the contingent valuation method can “provide meaning-ful estimates” of value for conserving species. Brown and Shogren (1998) note that byaggregating the estimates reported by Loomis and White (1996) over all households, theimplied willingness-to-pay to protect less than 2% of endangered species exceeds 1%of GDP, which they remark seems “suspiciously high”. Responses to contingent valu-ation surveys on conserving species may exhibit “embedding effects” [Kahneman andKnetsch (1992)]. A survey of willingness-to-pay for protecting a collection of speciesmight generate estimates similar to the willingness-to-pay for protecting an individualspecies. Desvouges et al. (1993) found similar estimates for willingness-to-pay for pre-venting 2000, 20,000 and 200,000 bird deaths. Survey responses may reflect the valueof protecting an ecosystem (e.g., the value of old growth forests rather than the value ofspotted owls), the environmental more generally, or the “warm-glow” of contributing toa worthy cause [Andreoni (1989, 1990)].

On a different tack, Stevens et al. (1991) found that many people object to surveyquestions that try to elicit a monetary value for species existence. They found that thevast majority of survey respondents thought conserving species was important but theyrefused to state that they would pay a positive amount for conservation. Stevens et al.(1991, p. 268) attribute the unwillingness to state a willingness-to-pay as arising frommoral or ethical concerns about asking “people to choose between ordinary goods (in-come) and a moral principle.”

3.2. Biological prospecting

With arguments about existence values unlikely to be viewed as conclusive, conser-vationists looked for other means to show that conserving biodiversity would makefinancial, as well as moral, sense. One argument used extensively in the early 1990swas that conserving species preserved option value: species might contain valuablecompounds that would yield valuable pharmaceuticals or other products at some fu-ture date [see, for example, Wilson (1992)]. If the species were to go extinct this optionvalue would be lost. Determining the magnitude of this option value was the centralfocus of a literature on bioprospecting.

The early bioprospecting literature produced a wide range of estimates of the value ofconserving a species for pharmaceutical purposes, from $44 [Aylward (1993)] to $23.7million [Principe (1989)] per untested species. The value of conserving an untestedspecies was derived by multiplying the probability of successfully identifying a com-mercially valuable product with the average revenue generated by a successful product.Simpson, Sedjo and Reid (1996) criticized this method. This simple procedure generatesestimates of the average value rather than the marginal value of an untested species. Be-cause multiple species may contain the same compound and in this sense be redundant,marginal values are likely to be far less than average values.


Simpson, Sedjo and Reid (1996) developed a model of sequential search that takesspecies redundancy into account. In their model each of N species has an identicalprobability p of containing a success. Testing each species costs c. In the event of asuccess, revenue R is obtained and the search is terminated. Under this model, it isshown that the value of a collection of N species is

V (N) = pR − c + (1 − p)(pR − c) + (1 − p)2(pR − c) + · · ·+ (1 − p)N−1(pR − c) = pR − c

p

[1 − (1 − p)N

].

The value of a “marginal species” can be found by comparing V (N + 1) with V (N):

v(N) = V (N + 1) − V (N) = (pR − c)(1 − p)N .

The value of the final species is the expected profit of testing the species, pR − c,multiplied by the probability that tests on all prior species tested have been unsuccessful,(1 − p)N . If p is small, the expected profit of testing a species is likely to be small,leading to low marginal value. On the other hand, if p is large, the species are likely to beredundant for bioprospecting purposes, leading to low marginal value. Using evidenceto assign reasonable parameter values for revenues and costs, the number of speciesand the expected number of potential products, Simpson, Sedjo and Reid (1996) solvefor the probability that generates the maximum marginal species value. For floweringplants (with an N of 250,000), they find that when p = 0.000012, the marginal valueof a species may be as high as $9431.

Simpson, Sedjo and Reid (1996) then use this maximum estimate for value of mar-ginal species along with species–area curves and estimates of endemism (species uniqueto an area) to estimate the maximum marginal value of a hectare of land in each of18 global biodiversity hotspots. These estimates range from a maximum of $20.63per hectare in Western Ecuador to only $0.20 per hectare in the California FloristicProvince. On the basis of their theoretical and empirical results, Simpson, Sedjo andReid (1996) conclude that the incentive to conserve biodiversity for bioprospecting pur-poses is almost certainly too small to offset the opportunity cost of development.

Polasky and Solow (1995) used a similar model to value a collection of species. If theprobability of success on any given trial is p and the revenue upon success is R, thenthe expected value of a collection of N species is V (N) = R[1 − (1 − p)N ], which isthe same as by Simpson, Sedjo and Reid (1996) when c = 0. Polasky and Solow (1995)considered two variants of the simple model to allow for imperfect substitutes amongspecies that generate success for the same product, and dependence in probabilities ofsuccess across species that relate to genetic similarity. Both extensions are motivatedby the experience of bioprospecting. When taxol was found in the bark of the Pacificyew tree, there was an intensified search of related species. It was found that the needlesof the European yew tree could be used to get taxotare, an imperfect substitute fortaxol. With imperfect substitutes, the marginal value of species need not fall as fast asindicated by Simpson, Sedjo and Reid (1996). On the other hand, accounting for speciesinterrelationships tends to reduce the marginal value of species.


Rausser and Small (2000) challenge the empirical conclusions of low value frombioprospecting found in Simpson, Sedjo and Reid (1996). The existence of prior in-formation makes it unlikely that all species will have the same probability of successin yielding a valuable product. Under the assumption that the probability of success isindependent and differs across species, it is optimal to organize the search in order ofdescending success probability of success. When this is done, the value of conserving aspecies with a high probability of success may be large. Rausser and Small (2000) applytheir model to the empirical case examined by Simpson, Sedjo and Reid (1996), withthe assumption that probabilities are proportional to the density of endemic species ineach region, which range from one in ten thousand (Western Ecuador) to one in a mil-lion (California Floristic Province). Rausser and Small find optimal search yields anincremental value of $9177 for the most promising hectare of land in Western Ecuadorcompared with a marginal value of only $20.63 for the same hectare in Simpson, Sedjoand Reid (1996). This result suggests that the benefits of protecting biodiversity hotspotsfor future biological prospecting may indeed outweigh the costs.

Costello and Ward (2003) show, however, that the difference in results betweenRausser and Small (2000) and Simpson, Sedjo and Reid (1996) does not come fromwhether search is optimally ordered or random, but rather from an assumption of differ-ent parameter values. In fact, the value of a hectare in Western Ecuador is $9177 whenconducting an optimal ordered search and only drops to $8840 with random search.

It appears as though the answer to the bioprospecting question may be context-dependent, and will likely hinge critically on the quality of information available tothe bioprospector, for example indigenous knowledge about what species are likelyprospects, and the opportunity cost of land. Neither of these two features has been care-fully analyzed empirically in this literature. We will return to the issue of bioprospectingin Section 5, where we consider its impacts on incentives for conservation.

3.3. Biodiversity and ecosystem services

In the previous sections, it was the elements of biodiversity, the genes or the species, thatwere the focus of analysis as being the sources of value. An alternative line of reasoningfocuses on ecosystems as a whole, rather than the individual parts (genes or species),as being the primary sources of value. Ecosystems provide a wide range of goods andservices of potential value to people. For example, a wetland may provide flood control,absorbing high waters and gradually releasing water over time. It may also filter andretain nutrients and pollutants thereby providing cleaner water downstream. Ecosystemservices include provision of clean air and water, climate regulation, mitigation of nat-ural disturbances, waste decomposition, maintenance of soil fertility, pollination, andpest control, among other things [Daily (1997)].

In principle, quantifying and valuing ecosystem services is no different from quan-tifying and valuing human produced goods or services. In practice, however, valuingecosystem services is problematic. To date there have been few reliable estimates of thevalue for most ecosystem services like those mentioned in the previous paragraph. There


are several difficulties in estimating the value of ecosystem services. First, the currentstate of ecological knowledge may be insufficient to link ecosystem condition and func-tioning to the provision of ecosystem services. In other words, we may not understandecosystem “production functions” well enough to quantify how much service is pro-duced, or how changes in ecosystem condition or function will translate into changesin amounts of ecosystem services produced [Daily et al. (2000)]. Second, even if quan-tification of the services is possible, the current state of economic methods may not besufficient to yield reliable estimates of the value of services. For some ecosystem ser-vices, such as flood control benefits, establishing reasonable estimates of value may notpresent great difficulties. For other services it may be exceedingly difficult to assign avalue, such as the value of habitat to lower the probability of species extinctions. Finally,valuing ecosystem services requires integrating ecological knowledge with economics,which requires cooperation between ecologists and economists. Such cooperation hasbegun [e.g., Brown and Roughgarden (1995), Perrings and Walker (1995), Carpenter,Ludwig and Brock (1999), Tilman et al. (2001)] but is still more the exception than therule.

Some of the most successful efforts to estimate the values of ecosystem serviceshave focused on the production of specific tangible outputs, such as the production offish and game species. Such outputs tend to be readily measurable and may even havemarket prices (e.g., commercially harvested fish). The ecosystem (habitat) is a necessaryinput into the production of the output (species). Focusing on the input side, such worknaturally fits into a classification of ecosystem services. One can just as easily focus onthe output side, however, in which case such studies are best considered as studies ofthe values of individual species (as covered in Section 3.1). Some research in economicshas focused on how changes in ecosystem conditions translate into changes in the valueof output [e.g., Hammack and Brown (1974), Lynne and Prochaska (1981), Ellis andFisher (1987) and Swallow (1990)]. Barbier (2000) provides a review of a number ofpapers that estimate the change in the value of fisheries production with changes incoastal wetlands and mangrove ecosystems.

One widely cited example of valuing an ecosystem service is the provision of cleandrinking water for New York City. New York City gets a sizeable fraction of its waterfrom watersheds in the Catskills. Increased housing development with septic systems,runoff from roads, and agriculture were causing water quality to decline. Continueddeclines in water quality would have forced the U.S. Environmental Protection Agencyto require New York City to build a water filtration plant. The total present value costof building and operating the water filtration plant was estimated to be roughly $6 to$8 billion [Chichilnisky and Heal (1998)]. Instead, New York City decided to invest$1 to 1.5 billion to conserve the Catskills watersheds to avoid building the water filtra-tion plant. Preserving the ecosystem was a far cheaper way to provide clean drinkingwater. The value of the ecosystem service here is the savings provided by avoided cost.Avoided cost is not the value of clean water. Rather it is the cost of replacing the ecosys-tem service with some human engineered alternative. Measures of avoided cost can beused as measures of the value of ecosystem services, but only under certain conditions,


namely that an alternative human engineered alternative exists and that the cost of suchan alternative does not exceed the value of the benefit provided by the service. Bothconditions hold for the New York City example.

At the other end of the spectrum are controversial efforts that attempt broad scaleor even global valuation of ecosystem services. Costanza et al. (1997) estimated thatthe mean value of global ecosystem services was $33 trillion annually, which is greaterthan the value of global GDP at the time ($18 trillion). The paper garnered a lot ofattention and it remains probably the most widely known work on valuing ecosystemservices, especially among non-economists, despite being roundly criticized by manyeconomists [Bockstael et al. (2000), Toman (1998)]. If what is being valued is the life-support system provided by the Earth’s ecosystems, then as Michael Toman noted, theestimate of $33 trillion is “a serious underestimate of infinity”.

Other papers have since stressed the importance of more focused analysis thatmatches the scale of analysis for ecosystem valuation to the scale of management ques-tions [Daily et al. (2000), Balmford et al. (2002)]. For example, Balmford et al. (2002)used studies of specific ecosystems to ask whether conservation or development optionsgenerate greater value. Their study shows that conservation options are preferred, oftenby wide margins. Earlier work by Peters, Gentry and Mendelsohn (1989) reached a sim-ilar conclusion on conserving tropical forests. However, much of the work to date onecosystem services requires making large leaps to overcome lack of data or understand-ing. Much greater understanding of ecosystem functioning, how these functions changewith management actions, and how such changes impact on human values is requiredbefore firm conclusions can be reached.

One question that has received considerable attention in ecology over the past decadeis whether systems with greater diversity, in the sense of having more species, arealso more productive. Results from experiments in grassland plots [Tilman, Wedin andKnops (1996)] and in controlled environments [Naeem et al. (1994, 1995)] found thatincreasing the number of species in the system tended to increase system productivity.Similar results were reported by Hector et al. (1999) in experiments across a number ofEuropean countries.

Several reasons have been put forward to explain the diversity–productivity link[Tilman, Lehman and Thomson (1997), Tilman (1999)]. When species differ in theirproductivity, a collection with more species is more likely to include high productiv-ity species in the mix (the sampling effect). In heterogeneous environments, havingmore species will generally allow the collection of species to better utilize all ecologi-cal niches and so be more productive (the niche differentiation effect). Tilman, Lehmanand Thomson (1997) develop a model of niche differentiation that is formally similarto the Polasky, Solow and Broadus (1993) model of the probability that a set of specieswill contain a given trait. Both models show how coverage increases, either in nichespace or genetic space, when more species are added. The model can also be interpretedas showing that diversity allows greater productivity on average when environmentalconditions vary over time.


Greater diversity has also been linked to lower variance of ecosystem productivity[Tilman and Downing (1994), McGrady-Steed, Harris and Morin (1997), Naeem and Li(1997)]. Tilman, Lehman and Bristow (1998) explain this result using reasoning fromeconomics, which they call the portfolio effect. Increasing the number of species thatfluctuate independently will decrease system volatility, just as increasing the number ofindependent assets in a financial portfolio will decrease the volatility of returns. Greaterdiversity leads to diversification, which leads to lower variance.

In a closely related line of reasoning, Perrings et al. (1995, p. 4) state that “The im-portance of biodiversity is argued to lie in its role in preserving ecosystem resilience,by underwriting the provision of key ecosystem functions under a range of environmen-tal conditions.” Conserving biodiversity maintains species that may look unimportantfor ecosystem function under current conditions, but who may play a crucial role ina drought, pest infestation or other shock [Walker, Kinzig and Langridge (1999)]. Asabove, conserving diversity can lead to a greater probability of having a productive mixof species under a range of potential environmental conditions. Resilience has been de-fined in two main ways in the ecological literature. First, resilience can be defined interms of how quickly an ecosystem returns to equilibrium after a shock. Second, re-silience can be defined in terms of the magnitude of shock that can be absorbed withoutthe ecosystem flipping into the basin of attraction of another equilibrium state. Undereither definition, if the equilibrium is desirable, then greater resilience will tend to in-crease welfare, which is what most of the literature on biodiversity and resilience hasimplicitly assumed. However, increased resilience in an ecosystem in an undesirableequilibrium will not in general be beneficial.

Some ecologists have been skeptical of the findings linking diversity to greater pro-ductivity, stability, or resilience [e.g., Grime (1997), Wardle et al. (1997)]. Huston(1994) notes that there is an inverse relationship between plant diversity and ecosystemproductivity across ecosystems. Examples of low-diversity, high-productivity systemsinclude salt marshes, redwood, bamboo and Douglas fir forests. Critics of the “pots andplots” experiments point out that the processes operating under artificial selection in di-versity experiments are quite different from processes operating under natural selectionin real ecosystems. Therefore, experimental results may not be very informative aboutthe role of diversity in ecosystem functioning.

If the experimental results are correct, this suggests that the loss of biodiversity willlead to the loss of ecosystem function, with the consequent loss of ecosystem services.There remain questions, however, about each of these links. As noted above, there arequestions about the link between diversity and ecosystem function. Further, the linkbetween ecosystem function and ecosystem services is not well understood. In somecases, productivity in an ecological sense is fairly directly linked to value of ecosystemservices in an economic sense. For example, the production of biomass ties fairly di-rectly to the provision of forage and the level of carbon sequestration [Tilman, Polaskyand Lehman (2005)]. In other cases, the mapping between functions and services maybe quite complex.


In sum, there remain a number of unanswered questions about the production andvalue of ecosystem services. We currently lack understanding of the link between man-agement actions and changes and ecosystem functioning. There remain questions aboutthe link between ecosystem functioning and provision of ecosystem services. Finally,there remain questions about the link between the provision of ecosystem services andvalue of these services to humans. Provision of valuable ecosystem services may ul-timately prove to be the most important reason to conserve biodiversity. At present,however, it is hard to know with any precision either the benefits of the provision ofecosystem services or the opportunity costs involved in ensuring their continued provi-sion.

4. Strategies to conserve biodiversity

A number of human actions cause threats to biodiversity. Though over-harvesting is aculprit for some very high profile species (e.g., elephant, rhino, various fish stocks),and climate change poses a threat on the horizon, habitat loss is widely thought to bethe primary cause of biodiversity decline followed by introduction of invasive species[Wilcove et al. (1998), Wilson (1992)]. We begin this section looking at strategies aimedat conserving habitat.

4.1. Terrestrial habitat protection

Because habitat loss and degradation is viewed as the leading threat to biodiversity,a major strategy of conservation groups is targeted toward to the goal of preservinghabitat. A number of private conservation groups, such as the Nature Conservancy, andgovernment agencies set aside land for biological reserves. Additionally, governmentsestablish national parks and other areas where biodiversity is protected. Globally, it isestimated that 6.4% of land (excluding Greenland and Antarctica) is in some form ofprotected areas management [UNDP (2000)]. Much of the land is set aside becauseit is of low value, or because it is of high aesthetic appeal, or for other reasons unre-lated to biodiversity conservation. For example, high alpine ecosystems are very wellrepresented in national park systems and wilderness areas, but there are relatively fewecosystems with high agricultural potential conserved.

Increasing human population and expansion of economic activity make it unlikelythat habitat loss and fragmentation will cease anytime in the near future, especially indeveloping countries. Threats to habitat are generally less in the developed countriesthan in tropical developing countries, where much of biodiversity resides. Threats alsovary by type of ecosystem. Forests in most developed countries, and some developingcountries are expanding even while tropical rainforests, coastal mangroves and wetlandscontinue to disappear. Given limited budgets and facing large threats, conservation plan-ning agencies need to set priorities to ensure that conservation efforts are directed where


they will do the most good. Though conservation organizations typically have not re-lied on the advice of economists, the problem facing these organizations is a classiceconomic problem: how can the greatest conservation return be achieved given limitedresources?

One common approach to addressing habitat conservation is to analyze the “reservesite selection problem.” In the standard formulation of the reserve site selection prob-lem, the goal is to choose a collection of reserve sites that among them contain as manyspecies as possible, subject to a constraint on the number of sites that may be included.In other words, the selected sites represent a kind of Noah’s ark. Species that inhabitat least one selected site (in the ark) survive but those that inhabit sites outside of thereserve network (out of the ark) do not. This problem of maximizing the coverage ofspecies within a reserve network is what is called a maximal coverage problem in op-erations research [Church and ReVelle (1974), Church, Stoms and Davis (1996)]. Theproblem can be written formally as follows. Define yi as an indicator variable for speciessurvival: yi = 1 if species i survives and yi = 0 if species i goes extinct, for all i ∈ I ,where I is the set of all species. Define xj as an indicator variable for whether site j isselected: xj = 1 if site j is selected in the reserve network and xj = 0 if site j is notselected, for all j ∈ J , where J is the set of all potential reserve sites. The reserve siteselection problem is:

Maxm∑

i=1

yi s.t.m∑

j∈Ni

xj � yi,

n∑j=1

xj � k,

where Ni is the set of sites in which species i occurs, and k is the number of sites thatmay be included in the reserve network. The reserve site selection problem written inthis way is an integer-programming problem that can be solved using linear program-ming based branch and bound algorithms [Csuti et al. (1997)].

Much of the early reserve site selection literature did not use optimization methodsto solve for optimal solutions but rather used heuristic methods. Two popular heuristicmethods are choosing “hotspots” and the “greedy algorithm”. Hotspots are sites thathave the greatest numbers of species. The greedy algorithm begins with the site withthe greatest number of species. It then picks sites that add the greatest complementof species to existing reserve sites. To see why neither approach guarantees choosingan optimal reserve network, consider the following simple example from Polasky andSolow (1999) with four potential sites, labeled A through D, of which only two can beincluded in a reserve network. Species, which are labeled 1 through 6, that inhabit eachsite are listed in the column for that site (see Table 1).

A hotspots approach would select sites A and B since they have four species eachwhile the other sites only have three. The greedy algorithm would start by selecting ei-ther site A or site B and then add either site C or site D. The optimal reserve networkinvolves choosing sites C and D. The hotspots approach fails because it does not con-sider the complementarity among sites [Pressey et al. (1993)]. The greedy algorithm,though better than hotspots, fails because it does not allow discarding sites once theyincluded.


Table 1

Site A Site B Site C Site D

1 1 1 32 2 2 43 3 5 64 4

Applications of optimization methods (maximal coverage problem) to the reservesite selection problem include Church, Stoms and Davis (1996), Csuti et al. (1997) andPressey, Possingham and Day (1997). These studies have generally found that optimalreserve networks span a broad range of ecosystem types to include the broadest arrayof species. Accumulation curves, which show the number of species included in reservenetworks of various sizes, have a rapid falloff in the accumulation rate after the first fewsites. For example, Csuti et al. (1997) found that over 90% of all terrestrial vertebratespecies in Oregon were included in a reserve network of five sites, and over 95% wereincluded in 10 sites, but that it took 23 sites to include all species.

The approach outlined in the previous paragraph implicitly assumes that all sites havethe same cost for inclusion in the reserve network. It is a simple matter to change froma constraint on the number of sites,

∑xj � k, to a budget constraint,

∑cj xj � B,

where cj is the cost of selecting site j and B is the total conservation budget [Ando etal. (1998), Polasky, Camm and Garber-Yonts (2001)]. Ando et al. (1998) used this ap-proach to find the minimum cost way of covering endangered species within a reservenetwork in the U.S. In an earlier study, Dobson et al. (1997) showed that endangeredspecies hotspots occurred largely along the coast of California and in Hawaii. Such bio-logical hotspots happen to coincide with real estate hotspots. These places include someof the most expensive real estate in the U.S. Ando et al. (1998) show that choosing sitesthat are not necessarily the most biologically rich sites but have a high species per dol-lar ratio is a cost-effective conservation strategy. Doing so thereby shifted the focus ofconservation toward the inner-mountain west and away from expensive coastal areas.The cost-effective approach resulted in the same number of endangered species in se-lected sites at one-third to one-half the cost of an approach that included the biologicallyrichest sites regardless of cost.

Balmford et al. (2003) recently estimated the costs of acquiring reserve sites for bio-diversity protection worldwide. They develop a model relates annual management cost[shown by James, Gaston and Balmford (2001) to be proportional to acquisition cost ata ratio of about 50 : 1, i.e., a site costing $50 has annual management costs of about $1]to variables such as GNP per unit area and purchasing power parity. Balmford et al.(2003) conclude that for acquisition, the highest benefit to cost ratios appear to be al-most exclusively in the developing world.

The reserve site selection problem can also be modified to incorporate different ob-jectives besides just maximizing the number of species. Polasky et al. (2001) comparedsite selection results when the objective was to maximize phylogenetic diversity versus


maximizing number of species. They found that similar patterns emerged, in large partbecause the two objectives are highly correlated. Sites with high richness will also tendto have high phylogenetic diversity and vice versa. Adding more species to the mix addsbranch lengths to the phylogenetic tree. Rodrigues and Gaston (2002) showed how to in-corporate phylogenetic diversity measure into an integer-programming problem so thatmaximizing diversity is no more difficult than maximizing species richness. They alsofound similar site selection results under richness and diversity objectives. In discussedearlier, there are a number of reasons why biodiversity generates both direct and indirectbenefits. To date, there have been only limited attempts to tie the objective function ofsite selection problem to the value of conserving various elements of biodiversity.

It is also worth noting that most of the literature on conserving biodiversity byprotecting habitat focuses on conserving specific taxonomic groups (e.g., terrestrial ver-tebrates) and does not include all of biodiversity. If conserving all of biodiversity is thegoal, the question arises whether conserving a particular taxonomic group is a good sur-rogate for conserving all of biodiversity. The conclusion from the biological literatureis that taxonomic groups are not generally reliable proxies for other taxonomic groupsor for all of biodiversity [Prendergast et al. (1993), Howard et al. (1998), Andelman andFagan (2000)]. It may be possible to use environmental surrogates or ecosystem typesrather than species as the units of analysis. As yet, however, there is not a solution for agood biodiversity surrogate that commands widespread acceptance.

A key simplification of the reserve site selection approach is embedded in the as-sumptions that species within reserves will survive for sure while those outside of thereserve network will go extinct for sure. A more reasonable approach is to model speciesconservation in probabilistic terms. One reason for viewing the problem of conservationin probabilistic terms is that there is generally uncertainty about the geographic rangeof most species. Biologists may only have limited information about whether a givenspecies exists at a given site. Polasky et al. (2000) used heuristic methods to find so-lutions to the site selection problem when the goal was to maximize expected speciescovered in a reserve network when there was uncertainty about the geographic rangeof species. They used the same data on terrestrial vertebrates in Oregon as Csuti et al.(1997) but used probabilities of occurrence. The broad geographic pattern of the optimalsolution was similar but there was in general more value to adding similar sites underuncertainty in order to increase the probability that at least one site would contain cer-tain species. Camm et al. (2002) showed how to use linear approximations that achievea solution arbitrarily close to the optimal solution using linear programming techniquesthat enable rapidly finding the solution of a probabilistic reserve site selection problem.

Currently inhabiting a reserve site, however, does not necessarily guarantee the long-term survival of a viable population of the species. Survival probabilities can be modeledas a function of the amount, spatial pattern and quality of habitat. Such an approachrequires much more biological information than is required for the reserve site selec-tion problem, which requires only presence/absence data for species at each site. Moststudies that incorporate species survival probabilities into a decision analysis aboutwhich lands to protect focus on a single species, largely because of the difficulty of


constructing reasonable spatial population biology models for species. Several stud-ies analyzed the optimal spatial pattern of timber harvests to maximize the survivalprobability for a species of concern for a given value of timber harvests, or viceversa [e.g., Hyde (1989), Hof and Raphael (1997), Hof and Bevers (1998), Marshall,Homans and Haight (2000), Rohweder, McKetta and Riggs (2000), Calkin et al. (2002)].Montgomery, Brown and Adams (1994) combined timber harvest and demand modelswith a population biology model for the spotted owl to develop an estimated marginalcost curve for increasing survival probabilities. They found that the marginal cost in-creased sharply for survival probabilities above 90%. Combining population biologymodels with economic analysis allows for greater biological realism as well as allowingfor marginal analysis, showing how slight increases in protected area change survivalprobability and cost.

One of the large challenges facing conservation researchers is how to bridge the gapbetween single species models, which often include detailed biological and economicmodels and data, with the larger breadth and scale of reserve site selection models. Con-servation biologists have proposed doing conservation at both a coarse scale and a finescale [Noss (1987)]. At the coarse scale, the results of large-scale multi-species analy-sis can direct attention to particular high priority areas for conservation. Then a morein-depth fine scale analysis of those particular sites can help develop on the groundconservation plans for those sites. Such a two-step process has merit but it does notnecessarily result in optimal or near optimal conservation plans. Suppose the fine scaleanalysis of a particular site shows that that conservation is actually more difficult or ex-pensive to accomplish than assumed in the coarse scale analysis. If this site is discardedit may require not just including one different site, but because of the overlap of speciesamong sites, it may change the entire pattern of sites considered to be high priority.

Several recent studies have tried to bridge the gap between fine scale and coarse scalein a single unified analysis. Montgomery et al. (1999) analyzed the effects of land usein Monroe County Pennsylvania on 147 native bird species. The study incorporatedinformation on species–habitat associations and territory size to estimate a viabilityfunction for each species based on land use decisions. Lichtenstein and Montgomery(2003) modeled the effect of forestry harvesting decisions on both economic returnsand viability for 166 terrestrial vertebrates in the Coast Range of Oregon. Polasky etal. (2003) have expanded this approach to analyze economic returns and viability forland uses including agriculture, development, forestry as well as conserving land in itsnatural state. They find that a strategy that partially integrates conservation within theworking landscape of agricultural and forestry production in addition to having somenatural areas is more efficient than strategies that enforces complete separation of landsinto natural areas, devoted solely to conservation purposes, and production areas, de-voted solely to maximizing economic returns. The complete separation of land is anunderlying assumption of the reserve site selection approach.

A general question in land management with multiple outputs is whether it is betterto have specialized management, with some land devoted to particular outputs, versusuniform management, with integrated joint production [Boscolo and Vincent (2003)].


Nonconvexities in production may give rise to specialization. One simple example of anonconvexity in the joint production of timber and biodiversity occurs with species thatrequire large tracts of undisturbed forests. The first units of timber production may resultin a fall in the species population to near zero. Further increases in timber productionwould not therefore affect the species population significantly. In this case, it is betterto have some undisturbed tracts with intensive timber production elsewhere rather thannon-intensive timber production everywhere. Forestry models with joint production ofbiodiversity and timber typically find that it is optimal to have at least some degree ofspecialization [Bowes and Krutilla (1989), Swallow, Parks and Wear (1990), Swallowand Wear (1993), Vincent and Binkley (1993), Swallow, Talukdar and Wear (1997),Boscolo and Vincent (2003)]. Similar results on specialization have been found for al-locating conservation funds across different areas [Wu and Boggess (1999), Wu, Adamsand Boggess (2000)].

Another challenge to conservation researchers is to include dynamics into the analy-sis. Development activity, climate change, biological invasions, changes in relativeprices, and chance events such as forest fires and drought may all play a role in chang-ing the landscape that require changes in conservation priorities and an ability to adapt.Work is beginning to appear that combines predictions of regional climate change and itsimplications for conservation [Parmesan and Yohe (2003), Root et al. (2003)]. Costelloand Polasky (2003) analyze a dynamic reserve site selection problem in which each sitethat is not protected has a probability of being developed during that period. A conserva-tion agency facing a budget constraint each period must choose sites knowing that it willget to choose again in the future, but also knowing that not all remaining high priorityconservation sites will remain undeveloped. Meir, Andelman and Possingham (2003)analyze a similar problem except that in their paper the probability is whether a site willbecome available during the period. When a site becomes available it must either bepurchased by the conservation agency that period or face a development risk. For thesetypes of problems, the optimal sites to choose are those that combine high biologicalvalue added per unit cost and face a high development threat. This conclusion providessome support for the strategy of giving high priority to conserving biodiversity hotspots,defined as areas of high biodiversity or high endemism that face large threats of habitatloss [e.g., Mittermeier et al. (1998), Myers (1988), Myers et al. (2000)]. Kareiva andMarvier (2003) criticize this approach for ignoring other important issues in conser-vation, such as cost and other conserving objectives beyond species (e.g., ecosystemfunctions and services). As noted above, hotspots will fail to be optimal if it does notfactor in complementarity among sites. Many of these criticisms can be addressed cor-rectly specifying the objective function and the set of constraints. For any dynamicconservation problem, biological value added, cost and threat will be essential elementsthat will drive the analysis.

There is a fast-growing literature analyzing habitat conservation issues. Some workin this vein has begun to incorporate both biological and economic objectives in an inte-grated fashion. A key challenge, though, is to be able to incorporate realistic economicand biological details while maintaining the broad-scale focus necessary for setting


biodiversity conservation priorities. At present, most models are too stylized to be ofuse in helping decision-makers choose conservation strategies on the ground, thoughthe gap between models and reality is closing. Another challenge in habitat conserva-tion models is to incorporate dynamics and stochastic events in the analysis.

4.2. Marine biodiversity and reserves

While similar in many respects to the terrestrial environment, marine systems presenta host of challenges for biodiversity conservation and reserve design. Ecologists warnthat marine biodiversity, and the coastal ecosystems on which it depends, is in peril.A recent paper cites a precipitous decline in many marine consumer species from “fan-tastically large” historical levels [Jackson et al. (2001)]. Analyzing data beginning inthe 1950s, Myers and Worm (2003) find that the global ocean has lost more than 90%of large predatory fish, and that industrialized fisheries have typically reduced com-munity biomass by 80% since large scale exploitation began approximately 50 yearsago. Although this collapse has been attributed to several anthropogenic sources suchas pollution, degradation of habitat quality, and changes in climatic regimes, it is hu-man overexploitation of marine resources that tops the list of threats [Jackson et al.(2001)]. The traditional approach to managing marine resources – with single speciesmanagement and little account of uncertainty, learning, or dynamic ecological effects[popularized for example by the early models of Gordon (1954), Smith (1969), amongothers] – has largely failed. And an emerging body of literature makes it clear that newapproaches to the management of marine resources will be required if biological diver-sity is to be protected.

In response to this call, attention has recently shifted from single-species policy de-sign to the implementation of marine reserves. Botsford, Hastings and Gaines (2001)argue that perhaps 35% of marine ecosystems must be set aside in order to protectbiodiversity. Currently only about 0.5% is protected [IUCN (1997), Kelleher, Bleakleyand Wells (1995)]. In addition, some claim that by setting aside large tracts of produc-tive marine ecosystems, we may earn a “double payoff” through enhanced biodiversitywithin the reserve and increased fishery yield from spillovers outside the reserve, thoughthis claim is subject to some dispute [Sanchirico and Wilen (2001)].

Several key features distinguish the design of marine reserves from the design of ter-restrial reserves. Important differences include the biology of target organisms, threatsto native biodiversity, and political economy considerations. Many marine organismsare characterized by planktonic larval stages, where larvae may drift with ocean cur-rents for several weeks, often traveling hundreds of miles before settling. And in theiradult stages, many marine species are highly mobile. This high level of mobility dur-ing several life-history stages has at least two important implications for marine reservedesign. First, even if marine reserves are sited in the most productive areas, small (con-tiguous) reserves may not protect biodiversity as effectively as a reserve around sessilespecies [see, e.g., Botsford, Hastings and Gaines (2001)]. And second, there may ex-ist a “spillover” effect where larvae produced within a reserve spill into adjacent areas


thus enhancing fishery production. Terrestrial species are also mobile (e.g., migratoryspecies) and there may be some spillover effects from conservation, however, these is-sues tend to be of lesser significance in terrestrial than in marine systems.

A second important difference between terrestrial and marine reserves concerns thethreats to biodiversity. In terrestrial environments, reserves are primarily seen as a wayof preventing harmful land conversion. In the marine environment, a primary threatto biodiversity is overexploitation [Jackson et al. (2001)]. While reserves are likely asecond best instrument for reducing fishing pressure, they may be justified on the basisof spatial nonconvexities, though this has not been well explored.

Sanchirico and Wilen (2002) address the political economy of marine reserve de-sign. They argue that the most productive sites (i.e. those that should be protected onthe basis of biological criteria) are also the most important sites to the fishing indus-try, and therefore that these will likely be the areas that attract the greatest politicalpressure against reserve designation. Similar conflicts may exist in terrestrial environ-ments, where hotspots of biodiversity may coincide with areas that are highly desirableeconomically [Ando et al. (1998)].

An emerging body of literature focuses on the principles of design of marine reservenetworks, accounting for the biological (and in a few cases economic) idiosyncrasiesof the marine environment. Botsford, Hastings and Gaines (2001) develop a theoreticalmodel of an infinite linear coastline along which reserves are placed at periodic inter-vals in order achieve either biodiversity or fishery production objectives (accounting forlarval drift). Results suggest over 35% of the coastline would need to be protected inreserves in order to conserve biodiversity. Boersma and Parrish (1999) review the acad-emic and gray literature on marine reserve design and draw several general conclusions.Factors increasing the efficacy of marine reserves on a local scale include:

(1) an intimate knowledge of the biology of protected organisms,(2) the ability to control threats, and(3) the scale of protection exceeds the scale of threat [Boersma and Parrish (1999)].

There exist only a few empirical analyses of efficient reserve design for marine systems.Roberts et al. (2002) identify marine biodiversity hotspots in tropical reef ecosystems,in which 0.012% of the world’s oceans contain about 50% of the world’s restricted-range species. As discussed above, however, the hotspot approach is not necessarily anefficient method to choose a set of conservation reserves [Polasky and Solow (1999),Sanchirico and Wilen (2001)]. Sala et al. (2002) conduct what amounts to a reservesite selection (optimization) exercise to design a network of marine reserves in the Gulfof California. Using data on larval production, transport, spawning success, and sev-eral socioeconomic factors, they find, for example that most conservation goals can beachieved with an optimally designed network covering 40% of rocky reef habitat.

The ecology literature on marine reserves focuses primarily on increasing conser-vation, rather than exploitation. There is, however, an emerging body of literature thatsuggests that in some cases fishery yields may be unaffected, or even enhanced, by thecreation of marine reserves. For example, Roberts et al. (2001) show that within 5 yearsof its creation, a small network of marine reserves in St. Lucia has increased harvest of


adjacent fisheries by 46–90%. Theoretical models seem to support these (albeit scarce)empirical findings. Hastings and Botsford (1999) create a simple analytical model inwhich an optimal fraction of the coastline is placed in reserve, and fishers rely on lar-val spillovers for harvest. Under simplifying assumptions, they show an equivalence inharvest between an optimally designed system of marine reserves, where all fish out-side reserves are harvested and a non-reserve system in which a fixed fraction or fixednumber of the population is harvested.

Bioeconomic models give mixed predictions about the effect of reserves on yields[Hannesson (2002), Pezzey, Roberts and Urdal (2000), Sanchirico and Wilen (2001,2002)]. Pezzey, Roberts and Urdal (2000) develop a simple bioeconomic model ofa marine reserve that allows for basic economic behavior outside the reserve (openaccess). They give conditions under which the reserve will decrease (when the openaccess density is large) or increase (when the open access density is small) the harvest.When parameterized with data from three Caribbean sites, the model predicts harvest-maximizing reserves of 20–40% of total area, with dividends of 10–80% increases inyields. Sanchirico and Wilen (2001) more carefully account for both spatial ecologicaland economic considerations. Spatial ecology is modeled using metapopulation theory,where resource patches are distributed through space and connect with each other vialarval dispersal. Economic behavior is also modeled in space such that spatial arbitrageis eliminated via migration of effort. This spatial bioeconomic model allows them toderive several important conclusions about the conditions under which reserves are aneffective management tool. In many cases, these results contradict biological analysesthat focus only on hotspots of biological production.

In summary, overexploitation (among other sources) has caused precipitous declinesin the populations of marine organisms. There is a growing body of literature – spawnedfrom both ecology and economics – that is suggestive of the biodiversity, and in somecases fishery, benefits of marine reserves. In the absence of spatial nonconvexities and/orhabitat-modifying fishing technology, marine reserves are most likely a second-bestinstrument with which to manage a fishery for economic gain. Siting reserves in themost biologically productive areas will likely generate the most opposition from fishers[Sanchirico and Wilen (2002)]. But to achieve multiple objectives of biodiversity pro-tection and fishery production, there is mounting evidence that marine reserves may bean effective, even efficient, policy option.

4.3. Introduced species

A major threat to both marine and terrestrial systems that may be largely uncheckedby reserves alone is the introduction of nonindigenous species. Biological invasionsby nonindigenous species of plants, animals, and microbes cause significant ecologicaland economic damage worldwide. And while the consequences of invasions have beencarefully studied, few workable policy solutions exist. Despite the fact that biologicalinvasions are driven by economic activity and that they cause significant economic dam-age, economists have been largely absent from the discussion about introduced species.


In this section we describe the ecological and economic consequences of introducedspecies and the policy implications from the economics literature. We begin with adescription of the problem from both economic and ecological perspectives. The magni-tude of the problem hinges critically on the rates of introduction, population growth, anddamage. We discuss the various modes of entry, and then turn to economic approachesto controlling damage by employing both ex ante and ex post control instruments.

Introductions of nonindigenous species (also referred to as exotic, alien, transplanted,invasive, and introduced species) have important economic and ecological effects. TheU.S. Office of Technology Assessment (OTA) estimated that the annual monetary lossesassociated with nonindigenous species introductions were approximately $5 billion[OTA (1993)]. A more recent estimate placed this figure at over $100 billion annually[Pimentel et al. (2000)] but this estimate is quite speculative. Losses from nonindige-nous species introductions include damage to municipal infrastructure, losses in agri-cultural and forest production, and reduced abundance of harvested marine resources.For example, Knowler and Barbier (2000) describe how the introduction of the combjelly to the Black Sea caused a decline in the commercial anchovy fishery. [See Perrings,Williamson and Dalmazzone (2000) for a compilation of early papers on the economicsof biological invasions.]

Nonindigenous species can cause significant damage to native ecosystems, mostlyvia competition with and predation on native species [see Cox (1999) for an excellentreview of impacts to North American ecosystems]. Nonindigenous species are widelyacknowledged as the second leading cause of global biodiversity decline, next to habitatconversion [Wilson (1992)]. In the U.S., 42% (400 of 958) of species on the endangeredspecies list are at risk primarily because of adverse interactions with nonindigenousspecies [The Nature Conservancy (1996), Wilcove et al. (1998)]. Globally, the picturemay be worse. Displaced, reduced, or otherwise degraded native communities are oftenlinked to invasion and spread by nonindigenous species.

Estimating the present and future magnitude of the problem is not an easy task.Costello and McAusland (2003) develop a model of stochastic introductions of non-indigenous species where the rate of introduction is linked to the volume of trade. Theyexamine whether agricultural protection (to reduce imports of agricultural products fora net importer of agricultural products) will reduce pecuniary damage to the agriculturalsector from nonindigenous species introductions. They find that while protectionism re-duces the volume of trade and hence the rate of new introductions, the overall damagemay increase because domestic agricultural production both creates a larger platformfor new introductions and creates a larger agricultural base subject to damage.

Others have conducted empirical analyses of the rate of introductions over time. It iswidely believed that the rate of introductions has increased over time. And this beliefappears to be borne out empirically. For example, Cohen and Carlton (1998) show thateven after correcting for extraordinary taxonomic effort, the rate of introductions tothe San Francisco estuary has steadily increased over the past 150 years. However, asCostello and Solow (2003) point out, the observed rate of introductions depends onvariables such as the true rate of introductions, collection effort, and the observability of


newly introduced species. They show that the discovery process of introduced speciestypically does not reflect the true rate of introductions, and that it would appear asthough species were arriving at an increasing rate, even if in fact the rate were constantor even zero.

The design of efficient policy to limit damage from invasive species requires a solidunderstanding of the pathways through which they enter a host country. Entry can occurvia intentional or unintentional means. Non-native species may be intentionally intro-duced to a region as ornamentals, for purposes of habitat modification (e.g. erosioncontrol), to control other pests (as biological control agents), or for other direct eco-nomic gain (e.g. introduced fish species for recreation). Many of these introductionsprovide net benefits. In fact nearly all of the food produced in the United States isof nonnative origin. However, some introductions cause damage that outweighs theirbenefit. For example the OTA reports that approximately 50% of intentional molluskintroductions cause harm. The percentage of introduced species that cause harm inother taxonomic groups ranges from 0% (for plant pathogens, though data are lim-ited) to 62% (for terrestrial vertebrates). Unintentional introductions occur primarilyvia international trade and tourism when individuals “hitchhike” on traded products orin shipping materials. Hitchhikers on agricultural and forest products are common, andmany pests to domestic agriculture and forestry can be linked to infected shipmentsof agricultural products. The percentage of unintentional introductions that cause harmranges from 39% for fish to 98% for plant pathogens [OTA (1993)].

But not all introduced species find their new home to their liking. The conventionalwisdom is the “rule of tens”, which states that approximately 10% of imports escape tobecome introduced, 10% of introduced species become established, and 10% of estab-lished species become harmful. There has been considerable interest among ecologistsin identifying the most important determinants of successful introductions. For example,Brown (1989) links introduction success with both the level of disturbance of the hostenvironment and the similarity of the physical environment between original and hostlocations. Consistent with the rule of tens, Case (1996) correlates success (of avian pop-ulations) with the number of failed attempts in the same region. In a statistical analysisof the determinants of success, Dalmazzone (2000) uses a regression model to explainthe share of alien to native plant species in 26 countries as a function of variables suchas GDP, the volume of imports, percentage of land in agriculture and pasture, whetherthe country is an island, and human population density. She finds that human populationdensity (positive effect), GDP per capita (positive effect), extent of permanent pasture(positive effect), and extent of agriculture (negative effect) may all play a significantrole in determining the percentage of alien to native plant species in a country. Morerecent work has focused on the role of genetics. For example, Sakai et al. (2001) showthat colonization success is enhanced by greater genetic variation, probably because thisvariation allows better exploitation of novel habitats.

With perfect foresight, only species with positive net benefits would be intentionallyintroduced (by a social planner) to a new location. Despite the rules of thumb citedabove, scientists do not have a precise understanding of the conditions under which


species will escape, breed, and cause damage. Therefore whether to admit a specieswith unknown consequences is a situation of decision-making under uncertainty. Oneissue here that has not been well explored is the relative merit of a “black list” versusa “white list” policy. A black list contains all species known to cause damage in aparticular country. Under a black list policy, all species are admitted that do not appearon the list, and it therefore creates a burden on policymakers to identify those species.Conversely, a white list contains all species thought to cause no damage. A white listpolicy is conservative, and will likely reject economically valuable species that are infact benign. It imposes a burden on potential importers to show that their import is infact safe.

Thus far we have considered only the case where damage from species introductionscan be avoided by acquiring ex ante information about the likely economic or ecologicalcosts imposed by the species. Thomas and Randall (2000) contrast this approach withone in which decisions are completely revocable (i.e. extermination of problem speciesis costless). Given that perfect ex ante information is typically impossible to acquire,they propose a management model that balances the two approaches. Their protocol isunique in its explicit treatment of the revocability of outcomes.

While intentional introductions can cause serious damage, the OTA reports that ap-proximately 81% of all harmful new exotics detected from 1980–1993 were uninten-tionally introduced, primarily via international trade. This has led some to suggest thatlarge-scale trade reduction may be required to effectively control the problem [see forexample Jenkins (1996)]. Employing a more formal economic framework, McAuslandand Costello (2003) examine the economic tradeoffs between tariffs and port inspec-tions to reduce imports of species. They find that the optimal policy hinges critically ontwo partner-specific characteristics: the expected damage per species and the proportionof infected traded goods. Among other results they find that while tariffs should alwaysbe positive, there are important cases (e.g. for sufficiently high infectiousness) whereinspections should be zero. They discuss how the optimal partner-specific instrumentsa country would like to pursue would violate the World Trade Organization policy ofnondiscrimination.

Others have focused on decentralized strategies to mitigate damage. For example,Shogren (2000) discusses the risk-reduction strategies an economy may engage in withrespect to harmful biological invasions. He points out that the private sector will engagein self-protection, or averting behavior, to protect itself from risk. This private agentadaptation effect must be accounted for in the policy design process in order to maintaineconomic efficiency.

While most species introductions cause either no damage or have positive benefits,some cause large damage. For intentional introductions, this is an information prob-lem. For unintentional introductions, economic tradeoffs exist between tariffs (to reducetrade volumes), port inspections or standards of cleanliness, and ex post control. The im-portant ecological and economic consequences of nonindigenous species introductionsmake this a fruitful area of future research for environmental economists.


5. Incentives to conserve and conservation policy

In prior sections we discussed both the value of conserving biodiversity and what opti-mal strategies for conservation might entail. As with many other environmental issues,specifying an optimal outcome and achieving it are two different matters. Not all of thebenefits of conservation accrue to those who make decisions that impact biodiversity.For example, a person who decides to cut down a portion of tropical forest to plant cropsgains the benefits of growing the crops. The benefits of maintaining the tropical forest,however, accrue to a wider community. Nutrient retention, local climate effects, preven-tion of erosion, and water quality all may contribute local or regional benefits. Carbonsequestration and species existence value may contribute global benefits. Conservationoften yields significant positive externalities and in some instances may provide globalpublic goods. Unless there is some way to provide decision-makers with incentives thatappropriately reward them for conservation, decision-makers will generally fail to makeappropriate conservation decisions. As with other externalities, a solution to this prob-lem is to find some way to internalize externalities. A variety of different approaches fordoing so in the context of conservation have been proposed. In some cases, there maybe ways to set up markets that allow conservation to pay for itself (e.g., ecotourism,genetic prospecting). In other cases, it may be necessary for government interventionto overcome market failure and foster conservation. Such intervention may make useof market mechanisms (e.g., payments for ecosystem services, tradable developmentrights), or it may employ more traditional command and control regulatory approaches(e.g., portions of the Endangered Species Act and the Convention on International Tradein Endangered Species).

We begin by considering cases where at least partial solutions may lie within themarket system itself without explicit need for government intervention. Conserving bio-diversity can yield valuable goods and services that can, under the right circumstances,be sold in the market. Doing so may generate enough revenue to make conservation fi-nancially viable. This point is the core thesis behind several recent books [Heal (2000),Daily and Ellison (2002), and Pagiola, Bishop and Landell-Mills (2002)]. One exam-ple in which conservation can be made to be financially attractive is ecotourism. TheWorld Tourism Organization estimates that tourism generated revenues of $463 billionin 2001. One of the fastest growing segments of tourism may be nature-based or eco-tourism. Some areas have had a long history of profiting from the richness of the localbiodiversity, including Yellowstone National Park in the U.S., Krueger National Parkin South Africa and a variety of National Parks in Kenya and Tanzania. Costa Rica hasalso done well promoting ecotourism with approximately 1 million tourists spending $1billion in 2000 [Daily and Ellison (2002, p. 178)]. Several economic studies have foundthat ecotourism can generate significant revenues in a variety of developing countrysettings [e.g., Aylward et al. (1996), Lindberg (2001), Maille and Mendelsohn (1993),Wunder (2000)].

Private landowners can also capture benefits of ecotourism, and contribute signifi-cantly to conservation efforts. There are approximately 5000 game ranches and 4000


mixed game ranch and livestock operations in South Africa. These operations makeup 13% of the land base of South Africa. Notably, the land base in game ranches ismore than double the land area in all the protected areas in the country [Absa EconomicResearch Group (2002)].

Ecotourism works best where there is spectacular scenery or where there arelarge concentrations of charismatic megafauna. Southern African game ranches attracttourists and hunters to who want a shot at the “big five” (lions, leopards, elephants,rhinos, and buffalo). Other areas without star attractions may find it difficult to makeecotourism competitive with other land uses. There are also complaints that ecotourismmay harm the very biodiversity it seeks to promote through over-utilization. Further, ifrevenues from ecotourism do not find their way to the local community or landowners,then the locale community or landowners will not have an incentive to conserve.

A second way in which biodiversity conservation may generate market rewards isfrom bioprospecting in which the search for useful genetic material from plant or animalspecies may lead to the development of valuable pharmaceuticals or other products (seeSection 3.2). Dan Jantzen noted that coffee was “the world’s most popular rain-forestdrug. . . A one-cent tax on each cup worldwide would fund all of tropical conservationforever. Now, no one is going to succeed in imposing that tax. . . but if the bioprospectingcontracts are written right, it’ll be there for the next cup of coffee to come along” [Dailyand Ellison (2002, p. 173)]. Despite some early excitement about the potential for bio-prospecting to provide large incentives for conservation, this potential has failed to panout. Merck and Costa Rica’s Instituto Nacional de Biodiversidad (INBio) signed a $1million deal in 1991. However, few bioprospecting contracts have followed since. Onereason, as discussed in the section on bioprospecting, is that returns from bioprospect-ing are likely to be small and insufficient by themselves to generate much incentive forconservation [Simpson, Sedjo and Reid (1996)].

A second problem with bioprospecting has arisen over the distribution of rents. De-veloping countries have argued strenuously that rents should accrue to the countryproviding the biological material and that failure to do so amounts to “biopiracy”. TheU.S. has argued that there should be strong intellectual property rights protecting theinvestment and discovery process by pharmaceutical companies. Disputes over prop-erty rights were the major reason the U.S. failed to ratify the Convention on BiologicalDiversity.

There are unanswered questions about the optimal allocation of rents from a bio-prospecting agreement. Barrett and Lybbert (2000) conclude that “If the rents do notaccrue to local land users who ultimately make conservation or conversion decisions,the debate surrounding the size of bioprospecting rents is irrelevant since the key ques-tions ultimately surround the calculus of land and labor use in fragile ecosystems”[Barrett and Lybbert (2000, p. 295)]. Consider, for example, the case of the rosy periwin-kle, a plant native to Madagascar that contains vincristine, a powerful cancer-fightingcompound. No synthetic substitute for vincristine exists, and one ounce of vincristinerequires 15 tons of periwinkle leaves. This has resulted in depletion of nearly the en-tire native periwinkle habitat in Madagascar [Koo and Wright (1999)], though the plant


has been extensively cultivated elsewhere. However, if drug companies do not keep asignificant fraction of rents from developing new drugs they may not have sufficient in-centive to develop new drugs via bioprospecting. Mendelsohn and Balick (1995) founda significant difference between likely social and private returns to development of newdrugs. Koo and Wright (1999) also argue that biodiversity will be underprovided by theprivate sector via bioprospecting on the grounds that although the value of biodiversityis very large, market and social values are grossly misaligned.

Both ecotourism and bioprospecting have been subject to criticism that revenuesgenerated by conservation activities have not necessarily resulted in benefits to localcommunities. Local communities with no financial stake in conservation or that infact suffer financial losses from conservation activities (e.g., wildlife damage to crops)might resent or actively oppose such activities, leading to a greater probability thatconservation will fail. Trying to give local communities a stake in conservation hasled to efforts to promote community-based conservation [Western and Wright (1994)]and integrated conservation–development projects [Wells and Brandon (1992)]. Thegoal of community-based conservation is to give local communities control over re-sources, thereby giving the community a stake in conservation. The most well-knowncommunity-based conservation program is the Communal Areas Management Programfor Indigenous Resources (CAMPFIRE) in Zimbabwe [see Barbier (1992) for an earlyreview and economic assessment]. Integrated conservation-development projects try to“link biodiversity conservation in protected areas with local socio-economic develop-ment” [Wells and Brandon (1992)]. Both approaches arose because of the failure oftraditional protected areas conservation strategies that ignored the needs of local com-munities.

The extent to which conservation and local control over resources, or local economicdevelopment, are mutually consistent remains to be seen. Overall, community-basedconservation and integrated conservation-development projects have had mixed successto date. There is no guarantee that once they are given the choice, local communitieswill in fact choose to conserve. Cultural, social or political factors may block conser-vation even when economic factors favor conservation. However, there is no guaranteethat conservation and local economic development are in fact consistent goals. Certainlyin some communities with ecotourism potential or where ecosystems provide valuableecosystem services, conservation and development may go hand-in-hand. In other cases,the conservation of biodiversity and economic development may not be consistent. Be-cause of the pervasive nature of external benefits created by biodiversity conservation,it may require more than just allowing local control and market forces to achieve anefficient level of conservation.

Recognition that the conservation of biodiversity may generate benefits that reachwell beyond the local community provides a rationale for governments and non-governmental organizations to provide resources for conservation, and for the institutionof national or international conservation policies. At present, though there are a num-ber of policies to promote conservation, there are also a number of policies that havethe exact opposite effect. Agricultural subsidies, subsidies to clearing land, resource


extraction and new development, all may contribute to driving a further wedge betweenprivate and social returns from actions that conserve biodiversity. Perhaps the first rulefor policy should be to “do no harm”. Beyond doing no harm by eliminate perverse sub-sidies, however, positive external benefits from conservation require policies that createpositive incentives to conserve.

Both governments and non-governmental organizations, such as the Nature Conser-vancy and World Wildlife Fund, are actively engaged in acquiring land for conservationand in other activities promoting conservation. Buying land is a direct and secure wayto promote conservation but it is often a costly instrument for protecting biodiver-sity. Boyd, Caballero and Simpson (1999) find that acquisition is often “conservationoverkill”. Conservation easements that rule out certain incompatible land uses, but notall land uses, is often a far cheaper route to secure conservation objective than acqui-sition. Recently, interest has shifted away from land acquisition toward conservationeasements and other ways of working with landowners to promote both conservationand landowner interests. For example the Nature Conservancy’s approach, once heavilyweighted toward acquisition, now incorporates mechanisms such as community devel-opment projects to reduce the demand for fuel wood and the purchase of conservationeasements to limit development (see www.nature.org for examples).

Acknowledging that donors from high-income nations invest billions of dollars to-ward ecosystem protection in low-income nations, a related literature debates therelative merits of direct conservation payments versus indirect mechanisms (e.g. pay-ments to promote ecotourism which generates ecosystem protection as a joint prod-uct). Although indirect approaches are the predominant form of intervention in low-income countries, Ferraro and Simpson (2002) argue that direct payments can be farmore cost-effective, often requiring no additional institutional infrastructure or donorsophistication.

Another approach to conservation is to institute a system of transferable developmentrights (TDR). TDR are virtually identical to cap-and-trade schemes to limit pollutionemissions. In a TDR system, the conservation planner determines how much land canbe developed in a given area. Development rights are then allocated and trades for theright to develop are allowed. Developers can increase density in a growth zone (“receiv-ing area”) only by purchasing a development rights from the preservation area (“sendingarea”). The approach was developed and implemented extensively in the 1970s to directdevelopment within urban areas [see Field and Conrad (1975) for what appears to be thefirst economic model of the supply and demand for development rights; see Mills (1980)for a model of TDR and a discussion of their appropriateness for use in protecting publicgoods]. Not until recently have economists explicitly considered TDR as a mechanismto conserve biodiversity. Panayotou (1994) develops the TDR approach for conserva-tion. He argues that “biodiversity conservation is ultimately a development rather thana conservation issue” [Panayotou (1994, p. 91)]. Given that most biodiversity exists inthe developing world, and that the public good nature of biodiversity requires a mecha-nism for paying developing countries to be stewards of this resource, Panayotou arguesthat TDR may also be an effective way to protect global (as well as local) biodiversity.

http://www.nature.org


Merrifield (1996) proposes use of a similar concept where “habitat preservation cred-its” would be required for development. There is no guarantee that TDR schemes, likecap-and-trade schemes, will result in efficient outcomes unless the planner chooses thecorrect amount of rights/permits to allocate. An additional problem faced in TDR forconservation is deciding what are appropriate trades. Land units, unlike air emissions,have unique characteristics and may contribute to a number of conservation objectives.What constitutes an equal trade is not obvious. Similar problems over establishing theproper trading ratios exist in mitigation banking schemes for wetlands.

Another direct method to create positive incentives for conservation is to institute asystem of payments for the provision of ecosystem services. The country that has movedfurthest in this direction is Costa Rica. The 1996 Forestry Law instituted payments forecosystem services. The law recognizes four ecosystem services: mitigation of green-house gas emissions, watershed protection, biodiversity conservation, and scenic beauty.The National Forestry Financial Fund enters into contracts with landowners that agreeto do forest preservation, reforestation or sustainable timber management. Funds to paylandowners come from taxes on fuel use, sale of carbon credits, payments from indus-try and from the Global Environment Fund. Many developed countries have adoptedsome form of “green payments” in which agricultural support payments are targeted tofarmers who adopt environmentally friendly management practices or land uses [OECD(2001)].

While market oriented policies have been of increasing importance in recent years,other important policies directed at the conservation of biodiversity, including the U.S.Endangered Species Act and the Convention on International Trade in EndangeredSpecies, are at their core largely command and control regulatory regimes. The Endan-gered Species Act (ESA), enacted in 1973, changed conservation policy from a largelyvoluntary and toothless regime that existed prior to 1973 into a powerful environmentallaw capable of stopping large government projects and actions of private landowners[Brown and Shogren (1998)]. Section 7 of the ESA prohibits federal agencies from ac-tions that cause “jeopardy” (i.e., risk of extinction) to species listed as threatened orendangered. Section 9 prohibits public and private parties from “taking” listed species.“Taking” includes causing harm to species through adverse habitat modification fromotherwise legal land uses, such as timber harvesting or building, as well as more obviousprohibitions against killing, injuring or capturing a listed species.

The way the law is written, the ESA appears to have very limited scope for economicconsiderations. Sections 7 and 9 are absolute prohibitions. Biological criteria are thebasis for listing species. In TVA v. Hill, the U.S. Supreme Court wrote: “The plain in-tent of Congress in enacting this statue was to halt or reverse the trend toward speciesextinction, whatever the cost” [437 U.S. 153, 184 (1978)]. When it looked like a smallunremarkable fish (the snail darter) that was previously all but unknown would halt con-struction of a large dam backed by politically powerful members of Congress, Congressamended the ESA. They authorized the formation of the Endangered Species Commit-tee (“The God Squad”) to allow an exemption to the ESA if the benefits of doing so


would clearly outweigh the costs. There are high hurdles to be met for convening thisCommittee and it has been used rarely.

Despite the fact that the law is written in a way that appears to marginalize economicconsiderations, it has proved impossible to administer the Act while totally ignoringeconomics. Several writers have noted that economic and political considerations influ-ence agency actions at all stages of the ESA process including the listing stage, whichis supposed to be done strictly on biological grounds [e.g., Bean (1991, p. 41), Houck(1993, pp. 285–286), Thomas and Verner (1992, p. 628)]. Endangered species whoseprotection threatens to impose large costs run into political opposition that translatesinto pressure on the Fish and Wildlife Service. This pressure appears to translate tolower probability of listing [Ando (1999)]. The benefits side of the equation also seemsto affect listing and recovery spending even though the ESA does not base such deci-sions on the popularity of the species. Metrick and Weitzman (1996) found that morecharismatic species were more likely to be listed than uncharismatic species, and thatonce listed “visceral characteristics play a highly significant role in explaining the ob-served spending patterns, while the more scientific characteristics appear to have littleinfluence” [Metrick and Weitzman (1996, p. 3)].

While much of the early regulatory activity under the ESA targeted government ac-tions under Section 7, the 1990s saw an increase in the emphasis on conservation onprivate lands under Section 9. More than half of endangered species have over 80% oftheir habitat on private land [USFWS (1997)]. Conservation on private lands presents anumber of incentive issues [Innes, Polasky and Tschirhart (1998)]. A landowner whoseparcel contains endangered species habitat may face restrictions on what activities maybe undertaken. The landowner need not be compensated if restrictions are imposed andlosses to the landowner result [though the law on regulatory takings is quite unset-tled, see Polasky and Doremus (1998)]. The potential losses the ESA may impose ona landowner give rise to several perverse incentives. Innes (1997) shows that there canbe a race to develop in order to beat the imposition of an ESA ruling. Similarly, theremay be an incentive to “shoot, shovel and shut up” in order to lower the likelihood ofimposition of restrictions under the ESA [Stroup (1995)]. Further, because current lawstipulates that acquiring specific information about species is a prerequisite to impos-ing restrictions on a landowner, there is no incentive for the landowner to cooperate inallowing biological information to be collected [Polasky and Doremus (1998)].

There are several possible ways to reform the ESA to cure the worst of the perverseincentives. One method is to provide compensation. When eminent domain is used andthere is a physical taking of property, the government is required to provide compen-sation equal to market value of the property. The same approach could be taken whenthe government mandates conservation on private land. There are two potential prob-lems with this approach. First, Blume, Rubinfeld and Shapiro (1984) show that whenlandowners are fully compensated in the event of a taking, there is an incentive to over-invest. It is socially optimal to take account of the probability of future takings thatrender the investment worthless. The landowner, however, is fully reimbursed and soignores this factor. Second, use of government funds to pay for compensation may be


costly. On the other hand, others point out that there is an advantage to forcing reg-ulators to understand the costs of imposing regulations by paying compensation [e.g.,Stroup (1995)]. Rather than tying compensation to market value, paying compensationtied to the value of conservation, along the lines of green payments discussed above,can generate efficient incentives to conserve [Hermalin (1995)].

A different approach to reform is to allow landowners to avoid sanctions if they canprove that their proposed actions will not cause harm [Polasky and Doremus (1998)].This type of approach is exemplified in the ESA by the provision to allow landowneractions that cause some minor and unintended harm to a listed species for landownerswith approved Habitat Conservation Plans. The incentive for filing Habitat ConservationPlans was further sweetened by promises of “no surprises” and “safe harbors” that putthe burden on the government for costs imposed by future regulatory actions.

The Convention on International Trade in Endangered Species (CITES) has arguablybeen the international agreement that has had the greatest impact on conservation out-comes [WCMC (1992)]. CITES authorizes banning international trade in species listedunder Appendix I, and regulating trade in species listed under Appendix II. In 1989,CITES initiated a ban on trade in ivory. In the 1970s and 1980s rampant poaching ofelephants caused a drop in elephant populations of roughly 50% [Barbier et al. (1990)].Particularly threatened were elephant populations in east African countries. Elephantpopulations in southern African countries were less threatened. Imposing the ivory tradeban was controversial. Southern African countries with relatively healthy elephant pop-ulations (Botswana, Malawi, Namibia, South Africa, Zimbabwe) objected and did notsign on to the ban. Opponents of a ban argued that the ban would likely result in highivory prices as supply was choked off, which would increase the rewards of successfulpoaching [Barbier and Swanson (1990)]. Opponents also argued that by denying rightsto sell ivory legally there would be less financial reason to conserve elephant popula-tions and less money available for enforcement efforts against poaching. Proponents ofthe ban, including east African countries and many developed countries, argued thatwithout the ban elephant populations would continue to decline, as it was too easy tosell illegally harvested ivory and because anti-poaching efforts of impoverished govern-ments were no match for well-organized poaching gangs. Van Kooten and Bulte (2000)summarize economic arguments about the ivory ban and present results from applica-tion of several dynamic models.

The ivory ban appears to have been largely successful in halting the decline in ele-phant populations. A main explanation for this apparent success is that by making thepurchase of ivory illegal the ban appears to have decreased demand for ivory. Theincreased price for ivory on the black market never materialized and in fact pricesappeared to fall after the ban was put in place. Kremer and Morcom (2000) offer analternative explanation for the failure of prices to rise with the ban, namely that it cre-ated expectations of lower future prices, which were then self-fulfilling. They point outthat in dynamic renewable resource models with storage it is possible to have multipleequilibria, one leading to sustainable populations and the other leading to extinction. Ifexpectations are that there will be sufficient future stocks there will be low prices and


little incentive to poach, leading to a rise in population and fulfilling expectations. Onthe other hand, if expectations are that there will not be sufficient future stocks, priceswill be high leading to high poaching pressure and falling stocks thereby fulfilling ex-pectations.

How trade affects conservation has been applied to other contexts besides elephantsand ivory. Brown and Layton (2001) argue that a trade ban would fail to work as well forrhino. Rhino horn is ground into a powder and used in traditional medicine in Asia. Thistype of demand would be unlikely to shift with imposition of a trade ban. They arguethat creating expectations of plentiful future supply, as in the sustainable equilibrium ofKremer and Morcom (2000), is really the only hope for rhino conservation. There havealso been analyses of the effect of trade on habitat conservation, particularly for tropicalforests [see, for example, Barbier and Rauscher (1994)].

6. Conclusions

The conservation of biodiversity is a major environmental issue, one that promises toremain at or near the top of the environmental agenda for the foreseeable future. Thethreats to biodiversity from habitat loss and fragmentation, introduction of nonindige-nous species, over-harvesting, pollution, changes in geochemical cycles and climatechange are likely to intensify rather than abate. Sustaining biodiversity in the face ofincreasing human populations and increased human economic activity promises to be amajor challenge. Successful conservation efforts will require management that simulta-neously keeps in mind the needs of Homo sapiens and other species. Economists havean important role to play in helping to develop and evaluate conservation policies.

The economics literature on biodiversity conservation has gone from virtually non-existent to fairly substantial in a short amount of time. A quick glance through thereferences shows that the large majority of articles have been written within the past fewyears. Advances have been made in defining and measuring biodiversity, evaluating bio-prospecting and ecosystem services, analyzing strategies for habitat conservation andcontrolling introduction of nonindigenous species, and analysis of conservation poli-cies. However, several key challenges remain. First, economists face the challenge oftrying to ascertain the values of conservation to society. Getting a clear picture of valuesis problematic at present, particularly for existence values and the value of ecosystemservices. Second, there is the difficult challenge of understanding how various humanactions impact ecosystems and how changes in ecosystems translate into changes inbiodiversity or ecosystem services. This challenge will require advances in ecologicalunderstanding as well as in economics. Third, there is the difficult challenge of incorpo-rating dynamics and uncertainty into conservation planning. Conservation is a long-termissue and strategies will need to adjust to changes in conditions and unforeseen events.Finally, there is the challenge of designing and implementing conservation policies thatprovide proper incentives to conserve. Doing so is particularly important in developing


countries, which contain a large share of biodiversity, may have weak existing institu-tions and have the most urgent need for continued economic development to improvethe welfare of their citizens. These important challenges promise to keep economists,as well as ecologists, busy for a long time to come.

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