Current Constraints and Future Directions
in Estimating Coextinction
MELINDA L. MOIR,∗†† PETER A. VESK,∗KARL E. C. BRENNAN,† DAVID A. KEITH,‡
LESLEY HUGHES,§ AND MICHAEL A. MCCARTHY∗
∗School of Botany, University of Melbourne, Parkville, VIC 3010, Australia
†Western Australian Department of Environment & Conservation, P.O. Box 10173, Kalgoorlie, WA 6430, Australia
‡NSW National Parks & Wildlife Service, P.O. Box 1967, Hurstville, NSW 2220, Australia
§Department of Biological Sciences, Macquarie University, North Ryde, NSW 2109, Australia
losses to global biodiversity through the loss of dependent species when hosts go extinct. There are critical gaps
in coextinction theory, and we outline these in a framework to direct future research toward more accurate
estimates of coextinction rates. Specifically, the most critical priorities include acquisition of more accurate
host data, including the threat status of host species; acquisition of data on the use of hosts by dependent
species across a wide array of localities, habitats, and breadth of both hosts and dependents; development
of models that incorporate correlates of nonrandom host and dependent extinctions, such as phylogeny and
traits that increase extinction-proneness; and determination of whether dependents are being lost before their
hosts and adjusting models accordingly. Without synergistic development of better empirical data and more
realistic models to estimate the number of cothreatened species and coextinction rates, the contribution of
coextinction to global declines in biodiversity will remain unknown and unmanaged.
Coextinction is a poorly quantified phenomenon, but results of recent modeling suggest high
Keywords: extinction risk, host specificity, host switching, insects, invertebrates, plant–insect interactions,
parasites, species loss
Restricciones Actuales y Directrices Futuras en la Estimaci´ on de la Coextinci´ on
sugieren grandes p´ erdidas de biodiversidad local mediante la p´ erdida de especies dependientes cuando los
hospederos se extinguen. Hay vac´ ıos cr´ ıticos en la teor´ ıa de coextinc´ ı´ on, y los delineamos en un marco de
referencia para dirigir la investigaci´ on futura hacia estimaciones m´ as precisas de las tasas de coextinci´ on.
Espec´ ıficamente, las prioridades m´ as cr´ ıticas incluyen la obtenci´ on de datos m´ as precisos de los hospederos,
incluyendo el estatus de amenaza de las especie hospedera; obtenci´ on de datos sobre el uso de hospederos
por especies dependientes en una amplia gama de localidades, h´ abitats y amplitud tanto de hospederos como
dependientes; desarrollo de modelos que incorporen correlaciones de extinciones no aleatorias de hospederos
y dependientes, como la filogenia y atributos que incrementan la susceptibilidad a la extinci´ on; y determinar
s´ ı los dependientes se pierden antes que sus hospederos y consecuentemente ajustar los modelos. Sin el
desarrollo sin´ ergico de mejores datos emp´ ıricos y modelos m´ as realistas para estimar el n´ umero de especies
coamenazadas y las tasas de coextinci´ on, la contribuci´ on de coextinci´ on a las declinaciones globales de
biodiversidad permanecer´ a desconocida y no podr´ a ser manejada.
La coextinci´ on es un fen´ omeno poco cuantificado, pero los resultados de modelos recientes
Palabras Clave: alternancia de hospedero, especificidad de hospedero, insectos, interacciones planta-insecto,
invertebrados, par´ asitos, p´ erdida de especies, riesgo de extinci´ on
Paper submitted April 6, 2009; revised manuscript accepted August 17, 2009.
Conservation Biology, Volume 24, No. 3, 682–690
C ?2010 Society for Conservation Biology
Moir et al.
The loss of species through extinction is the only truly
irreversible global environmental change occurring to-
day (Dirzo & Raven 2003). Of the many mechanisms of
species extinction, the least understood is coextinction.
Coextinction occurs when species go extinct because
the host on which they depend becomes extinct (Pimm
1986), or at least undergoes significant decline. Our def-
inition of coextinction is the loss of dependent species
due to a change in their host population, such as reduced
host abundance or removal of individual hosts from the
host-induced extinction, however, should be recognized
as a form of coextinction because it requires the same
management measures as extinction of both host and
dependent simultaneously to maintain a viable host pop-
ulation accessible to the dependent species.
The degree of species specialization “is arguably the
most fundamental concept in the history of thought
on extinction risk” (McKinney 1997). Host specificity is
therefore a crucial factor in coextinction. Every animal
species may host a range of external and internal para-
sites, including mites, lice, fleas, and nematodes. Many of
these dependent species have restricted or unique host
relationships and hence face coextinction if their hosts
become extinct (Nunn et al. 2004; Hughes & Page 2007;
to at least one host-specific herbivorous species of insect
(Strong et al. 1984). Approximately 22,100 (7.4%) of the
world’s 300,000 plant species are considered threatened
(Smith et al. 1993; Myers et al. 2000), so the potential for
coextinction of numerous host-dependent insect species
is high (Koh et al. 2004a). Despite the critical implica-
tions of coextinction for global biodiversity, the topic is
poorly studied. More generally, species interactions are
usually ignored when the extinction risks of species are
assessed (Sabo 2008).
Current conservation strategies overlook most inverte-
brates and microorganisms, including pathogens (many
of which depend on hosts). Inefficient use of scarce con-
servation resources and unforeseen extinctions may re-
and other taxa reliant on trophic processes in which de-
pendent species are involved (the latter are often termed
extinction cascades; Diamond 1989). Some conservation
for their dependent species either directly (actively re-
moving the dependent species from hosts [e.g., Bevill et
al. 1999]) or indirectly (removal of the host from the wild
[e.g., Gompper & Williams 1998]). Although the loss of a
dependent species, such as a parasite or herbivore, may
be expected to benefit the short-term survival of a threat-
ened host (Bevill et al. 1999), unexpected consequences
other parasites or herbivores (Gonz´ alez-Meg´ ıas & G´ omez
2003). Insects with parasitic larval stages that depend
on plants or animals may also perform important ecosys-
tem functions, for example as pollinators, during other
stages of their life cycle (Hudson et al. 2006). In some
circumstances, conservation of dependent species may
benefit the host species because parasites can increase
the genetic diversity of hosts (Nunn et al. 2004; Poulin
& Morand 2004; Duffy et al. 2008) and thereby reduce
susceptibility of host populations to extinction through
epidemics of novel pathogens (e.g., Altizer et al. 2003).
Thus, dependent species, themselves a large and impor-
tant component of biodiversity, should be conserved to
sustain an even wider array of species and ecological
Because coextinction rates must be estimated with
models of dependent species and their hosts, here we
first examine current models. Second, we propose fu-
ture directions for empirical studies that are needed to
structure and parameterize realistic models of coextinc-
Linear versus Nonlinear Relationships in Models
most of them are undescribed (Hammond 1995). Thus,
estimating coextinction rates for invertebrates will re-
quire modeling to supplement available data and direct
further data collection (Koh et al. 2004a). There is no
standard model for estimating the potential loss of de-
pendent species through coextinction of their hosts. The
simplest approach assumes a linear extrapolation of ex-
tinction in which the number of threatened dependents
reflects the number of hosts that are threatened (Stork &
Lyal 1993; Poulin & Morand 2004; Thacker et al. 2006).
The linear approach assumes unique host dependency
(monophagy), and departure from this assumption ad-
versely affects the accuracy of estimates of threatened
For most species the linear approach to estimating co-
extinction risk is overly simplistic for several reasons.
First, dependent species may require several different
hosts during their life cycle; thus, their extinction risk
is compounded if any one host is threatened. For ex-
ample, the endangered butterfly Maculinea arion de-
pends on a host plant and a tending ant species, but
extinction probability of the butterfly greatly increases
if either the host–plant cover falls below 5% or host–ant
density is <500 nests/ha (Griebeler & Seitz 2002). The
assumption of a linear relationship between host and
dependent extinctions is therefore likely to underesti-
mate extinction rates of dependents because dependents
with more complex life histories are likely to have higher
Volume 24, No. 3, 2010
Coextinction: Constraints and Directions
Figure 1. Hypothetical examples
of the proportion of dependent
species expected to be extin-
guished through coextinction
with increasing proportions of
host extinctions derived from a
probabilistic model approach
(sensu Koh et al. 2004a) (line a);
dependent species having a 1:1
ratio with hosts (i.e., monophagy)
or a linear relationship (line b);
and dependent species becoming
extinct before hosts (line c).
coextinction rates (Koh et al. 2004a). Second, a depen-
dent species may be capable of using several alternative
host species, only one of which may be threatened. In
this case linear extrapolation of coextinction risks over-
estimating the threat (Dobson et al. 2008). Moreover,
alternative hosts may vary in suitability, such that de-
pendent populations may have different rates of survival,
growth, and reproduction on different host species. For
example, if the endangered butterfly Maculinea rebeli is
tended by ant species other than Myrmica schencki, it
has 29 times higher larval mortality (Steiner et al. 2003).
Such relationships suggest a departure from simple linear
To move beyond simple linear assumptions of
dependent–host relationships, Koh et al. (2004a) esti-
mated coextinction risks for a range of invertebrates and
their hosts with probabilistic and nomographic models
derived from host–dependent matrices that incorporated
different levels of host specificity. They found that as
the number of host extinctions increases, the number of
dependent species being extinguished increases at an ac-
celerating rate (Fig. 1). The exceptions were assemblages
in which all dependent species were host specific to a
single host species, which produced a linear relationship
(e.g., primates and their nematodes; Fig. 1). From these
models Koh et al. (2004a) estimated that on the basis
of threat status of host taxa (Hilton-Taylor 2000), 4672
beetles, 598 fish parasites, 446 lice, 193 bird mites, 142
butterflies, 20 primate nematodes, nine primate fungi,
and eight fig wasps were threatened globally with coex-
tinction. They suggest that this mechanism of extinction
has most likely already caused the loss of over 200 depen-
dent species since 1500 AD and will become increasingly
important as more host species are extinguished.
Current Gaps in Coextinction Theory
Despite recent advances in estimating potential coextinc-
specificity (Koh et al. 2004a; Dobson et al. 2008), further
refinements are required to improve the empirical basis
date inevitable uncertainties that remain in relationships
between dependents and their hosts. Coextinction esti-
mates are influenced by the host data, dependent data,
and the interactions between these two components
(Fig. 2). Several (often interrelated) factors may alter
hosts, dependents, or their interaction. For hosts these
factors include the accuracy of the threat status of hosts
(Fig. 2a), the breadth of host species examined (Fig. 2b),
and the correlated pattern of host extinctions (Fig. 2c).
For dependents, important factors are breadth of the de-
pendent species assessed (Fig. 2d) and host specificity
of the dependents (Fig. 2e). Important considerations
influencing interactions between hosts and dependent
species include differences in host–dependent interac-
tions across regions (Fig. 2f) and whether hosts or their
dependents go extinct first (Fig. 2g). We discuss each
factor below in further detail.
Estimating Extinction Risks of Hosts
Estimating extinction risks of host taxa is crucial for es-
timating extinction risks of their dependents. Koh et al.
(2004a) used data from the IUCN (International Union
for Conservation of Nature) Red List (Hilton-Taylor 2000)
to estimate the number of threatened species in various
taxonomic groups of hosts. Coextinction is not listed
as a threatening process by the IUCN (IUCN 2008);
Volume 24, No. 3, 2010
Moir et al.
Figure 2. A conceptual framework for the components influencing coextinction estimates. Coextinction is
primarily influenced by hosts, dependent species, and their interactions. These variables are in turn influenced by
several factors. See text for an explanation of factors a through g.
many species are threatened by coextinction. The IUCN
assessments of threat reflect relative extinction risks of
mammals and birds well in 70–80% of cases (Keith et al.
2004), but assessments of plant and many other inverte-
brate hosts are notoriously incomplete, at least relative to
mammals, birds, amphibians, and corals, for which there
are recent global assessments. Because red lists underes-
timate the total number of threatened taxa for particular
host groups (Cuar´ on 1993; Smith et al. 1993), coextinc-
tion rates derived from red-list data will also be severely
underestimated. A more reliable method of determining
the regional number of threatened taxa (both host and
dependent species) is to use local sources (Rodriguez et
al. 2000) such as government and nongovernment orga-
nizations, published literature, and experts (field ecolo-
gists and taxonomists) in the particular taxa of interest.
In addition, such local sources may provide further crit-
ical information about threatened hosts and their associ-
ated dependents for models. For example, a host-specific
species of plant louse (Psyllidae) has been recorded from
only one of six populations of its threatened host and
is therefore at greater risk of extinction than the host
(Taylor & Moir 2009). Combining information from such
a wide range of sources lends itself to Bayesian analysis
(Clark 2007; McCarthy 2007) of the extinction risk of
hosts and their dependents. Undoubtedly, use of more
accurate data on the threat status of host species will
increase the overall number of dependents regarded as
Sampling Variation across Hosts
Estimates of coextinction rates have often been made
across relatively few hosts. To gain insight into which
hosts are more likely to be associated with high co-
extinction rates, the numbers of specialist dependents
need to be examined across a wide range of host taxa.
Koh et al. (2004a) initiated this research by compiling
hosts. Further detailed examination is now required to
determine how coextinction risks vary across functional
and taxonomic groups of hosts. Thacker et al. (2006)
found the global risk of coextinction for aphids and scale
insects on trees to be low. Nevertheless, there could be
gaard (2000), for example, found more monophagous
beetles on lianas than trees. Furthermore, host species
Volume 24, No. 3, 2010
Coextinction: Constraints and Directions
from monospecific genera may have more monophagous
dependents than hosts from more speciose genera or
families because the dependents are isolated from po-
tential hosts (Ødegaard et al. 2000; Dobson et al. 2008).
These comparisons are vital because host extinction is
not random and identifying the host taxa with higher
proportions of host-specific dependents will help deter-
mine conservation priorities.
Correlated Host Extinctions
Extinction risks are not distributed randomly across host
are more prone to extinction than others because of
shared phylogeny, life-history characters, habitat depen-
dencies, and geographic distributions (McKinney 1997).
Likewise, dependent species are often clustered on re-
lated hosts (Ødegaard et al. 2005; Poulin et al. 2006;
Mouillot et al. 2008), with host occupancy further con-
strained by host life history, habitat, and distribution.
Therefore, even if dependent taxa are associated with
several alternative but related hosts, extinctions of re-
lated hosts may cause a cascade of coextinctions of re-
lated dependents (Rezende et al. 2007). For example, 62
bird species in New Zealand and 79 birds of the Hawaiian
Islands became extinct after the arrival of humans (Dun-
can & Blackburn 2004; Boyer 2008). The birds most vul-
nerable to extinction were closely related; all species in
the order Dinornithiformes went extinct in New Zealand
(Duncan & Blackburn 2004). Bird parasites, especially ec-
toparasites, such as lice, can often use several bird hosts
Hawaii this characteristic would not have saved the de-
pendent species from coextinction, because the cascade
of related bird extinctions would have resulted in loss of
oligophagous or even polyphagous dependent species.
Thus, the rates of coextinction will increase greatly with
the loss of related hosts and will not be restricted to the
monophagous dependents, a factor overlooked in linear
approaches to modeling coextinction.
Correlated extinctions of host species will also be
expected when groups of hosts are exposed to the
same threatening processes. Examples include predator-
related extinctions of mammals within particular weight
ranges (Chisholm & Taylor 2007) and fire-driven extinc-
tions of shrubs with certain life histories (Keith 1996).
such as body size (Duncan & Blackburn 2004; Chisholm
& Taylor 2007; Boyer 2008), growth form or life his-
tory (Keith 1996; Vamosi & Vamosi 2005), geographic
distribution (McClean et al. 2005; Thuiller et al. 2005;
Boyer 2008), and degree of specialization (Boyer 2008).
Although phylogenetic relationships and other correlates
of extinction risk are well-studied (e.g., Boyer 2008; Va-
Rezende et al. 2007). Future work should consider the
potential for hosts to be extinguished in a nonrandom
way and identify those dependents most prone to coex-
tinction. This may, in turn, reveal particular groups of
dependents that are more prone to coextinction, regard-
less of whether they are monophagous, oligophagous, or
Sampling Variation across Dependents
To determine the coextinction proneness of dependents,
a wide range of taxa must be examined because extinc-
tion risks may vary within and across a range of depen-
dent taxa. These taxa must include even those depen-
dents that rely on hosts that are themselves dependent
on another organism, such as the parasitic nematodes of
gall-inducing flies on plants (Taylor & Davies 2008). Fur-
thermore, the distinctions between invertebrate species
are commonly cryptic and only identifiable from exami-
nation of genitalia or molecular analysis, which typically
uncovers more host-specific-dependent species within
an assemblage (Poulin & Keeney 2008). Therefore, sys-
tematists are essential for accurate estimation of host-
specific fauna. Other issues of sampling variation across
dependent species are analogous to those described for
Measuring Host Specificity
Accurate measures of the host specificity of depen-
dents are crucial for determining coextinction rates.
to coextinction than their oligophagous or polyphagous
relatives (Thacker et al. 2006). The high specificity of Ly-
are considered one of the most extinction-prone families
databases often contain many dependent species with
ambiguous feeding preferences, host specificity indices
need to incorporate uncertainty when assigning depen-
dent species to hosts.
Although Koh et al. (2004a) substantially improved
estimates of coextinction by incorporating variation in
specificity of dependent species on their hosts, data sets
representing entire assemblages of invertebrates solely
from threatened hosts (distinct from, and in addition to,
nonthreatened host data sets) are lacking. Although mu-
seum collections provide the most readily available host
data for construction of host–dependent matrices, these
fore unlikely to support accurate inferences about host
specificity. Direct inventories of dependent assemblages
on selected hosts offer a more reliable, rapid, and cost-
effective basis for inferring host specificity. If sampling is
suitably structured, probability distributions can be cal-
culated for the number of hosts associated with each
Volume 24, No. 3, 2010
Moir et al.
dependent (i.e., degree of polyphagy). Direct invento-
ries are especially needed in regions where the inverte-
brate fauna is speciose and poorly described (e.g., Aus-
tralia, Austin et al. 2004; Panama, Ødegaard 2003; New
Zealand, Poulin 2004). Because dependent rarity is often
correlated with rarity of its host (Hopkins et al. 2002),
direct inventories of dependent assemblages on threat-
ened hosts will provide crucial data for estimating how
many dependent species are potentially cothreatened
(e.g., Thacker et al. 2006). Nevertheless, direct inven-
tories will be limited when the host is already extinct or
has too few individuals remaining to support destructive
sampling. Because sampling effort will largely determine
the proportion of the dependent assemblage uncovered
(Poulin 1998), we recommend appropriate sampling de-
signs (e.g., collecting methods, number of individuals)
with subsequent tests of inventory completeness (e.g.,
species accumulation curves).
Data sets from threatened hosts are important be-
cause invertebrate assemblages on common hosts may
not be characteristic of those on threatened host species.
Evolutionary, phenotypic, or behavioral changes that
reduce dependence on a single host can allow de-
pendent species to avoid coextinction (Nosil 2002;
Poulin et al. 2006). Thus, the assemblage on a threat-
ened host species may contain a higher proportion of
oligophagous and polyphagous species as some orig-
inally monophagous species adapt to their shrinking
food resource or become extinct. For example, native
have wider host ranges in regions where the trees have
gone locally extinct (Austin et al. 2004). In such cases,
the models of Koh et al. (2004a) would overestimate
But how likely is it that dependent species have
avoided coextinction through mechanisms such as host
switching or, alternatively, have already gone extinct
as their host population shrank? Host switching may
involve phenotypic plasticity or evolutionary mecha-
nisms and is most likely when the dependent can al-
ready feed (albeit perhaps suboptimally) on a closely re-
lated host species or if the dependent species is already
polyphagous. Host switching will be less likely where
there are no closely related hosts within the dependent’s
geographical range (e.g., on species-poor host genera
or families [Vamosi & Wilson 2008]) and with strictly
monophagous dependent species. In a historical con-
text, slower extinctions from events such as past climate
change may have allowed invertebrates to avoid coex-
tinction through host switching. Nevertheless, because
current host extinctions occur more rapidly in response
fragmentation, anthropogenic climate change) (Williams
et al. 2007; Brook et al. 2008), the potential for mass
coextinction of invertebrates may now be substantially
Biogeographical factors influence the accuracy of coex-
tinction estimates in several respects, although princi-
pally through host specificity. Host specificity of depen-
dents may rely on the biotic context in which the organ-
isms occur and can vary between different ecosystems.
The specificity of parasites on fishes, for example, varies
between oceans, lakes, and streams (Dobson et al. 2008).
Host specificity may also vary between tropical and tem-
perate zones and result in over- or underestimation when
dependent and host matrices are extrapolated globally
from few localities. Justine (2007) points out that Koh
et al. (2004a) may have underestimated the global num-
ber of cothreatened monogenean ectoparasites (593 taxa
from 746 threatened fish hosts listed by IUCN) because
the Canadian database of fish parasites from which their
host–dependent matrix was derived failed to account for
large numbers of coral fish species which, on average,
are each hosts to more than 10 species of monogeneans.
This discrepancy highlights how biogeographic variation
and knowledge gaps in dependent species biodiversity
(McKinney 1999; Poulin 2004; Justine 2007) may lead
to biased estimates of coextinction risk (Windsor 1998;
Dobson et al. 2008). Therefore, it is vital that global es-
timates of coextinction be based on host-specificity data
stratified across a range of biomes.
Biogeographical issues other than host specificity also
need to be assessed to provide more accurate global rates
of coextinction. For example, degree of host specificity
of tropical Lycaenidae butterflies (Koh et al. 2004b), but
is this the case in temperate zones where other factors
such as dispersal potential may be more important (e.g.,
Hanski et al. 2006)? Greater host ranges often equate
to a higher species richness of dependents. For exam-
ple, due to their larger geographic ranges and popu-
lation sizes, albatrosses and petrels have higher diver-
sities of louse compared with other seabirds, possibly
because the lice have access to greater numbers of sym-
range size may also play a role in determining the num-
ber of specialist-dependent species. Furthermore, within
the range of a single host species there may be more
specialist-dependent species in different geographical lo-
calities (i.e., Sobhian & Zw¨ olfer 1985). Alternatively, the
host specificity of a single dependent species may vary
within its own range, as has been shown for some flea
species (Krasnov et al. 2004).
Contrasts of the host specificity of the same depen-
dent groups (e.g., within all herbivorous beetles) across
different biomes, ecosystems, habitats, and host species
are specifically required to provide more accurate gen-
eralizations of global coextinction rates. Higher host
specificity values in different habitats or across differ-
ent host taxa would result in hotspots of coextinction
Volume 24, No. 3, 2010
Coextinction: Constraints and Directions
Order of Extinction of Host and Dependent Species
Current models (linear, probabilistic, and nomographic
models) assume dependent taxa are extinguished when,
evidence that dependents may go extinct before their
host, including the extinction of insects before their host
plants (Biedermann 2000; Le´ on-Cort´ es et al. 2003; Koh
et al. 2004c); limpets before their seagrass hosts (Carl-
ton et al. 1991); and microbial pathogens before their
vertebrate hosts (de Castro & Bolker 2005). This early
extinction of a dependent occurs when the host popu-
lation becomes too small to sustain a viable population
of dependents. The level at which an organism will go
extinct due to a change in some required variable (e.g.,
number of habitat patches) has been termed the extinc-
their dependent species remain unexplored for entire de-
pendent species assemblages because few population vi-
ability models have addressed multispecies interactions
(Sabo 2008). More empirical data are needed to address
this deficiency (Benton 2003), and this may illuminate a
need for coextinction models to incorporate declines (as
well as extinctions) in host populations.
els of coextinction may have profound effects on the
shape of curves predicting the rate at which dependents
are lost as their hosts are extinguished. It is possible that
by Koh et al. (2004a) (curve c on Fig. 1, rather than curve
a). This new model would be consistent with predictions
that extinction rates of terrestrial invertebrate species are
high (e.g., between 7 and 30 species lost globally every
week) (Mawdsley & Stork 1995). If Fig. 1c is correct in
some situations, invertebrates may be facing higher ex-
tinction rates than plants or vertebrates (Thomas et al.
2004; Dunn 2005; Dobson et al. 2008). The concept that
coextinction rates are higher for dependents than hosts
urgently requires testing with empirical data.
Understanding and predicting global rates of coextinc-
tion are formidable but crucial challenges if the rate of
extinction is to be slowed. Insufficient taxonomic and
basic ecological knowledge of invertebrate species has
inhibited documentation of extinctions and necessitated
highly speculative estimates by extrapolation, which are
very sensitive to the empirical data used and modeling
assumptions. Therefore, it is critical that there be a rapid
and synergistic development of empirical studies and
modeling approaches to obtain more accurate estimates
of coextinction. The most urgent challenges ahead are to
(1) acquire more accurate host data including the threat
status of host species, (2) acquire host–dependent matri-
ces through targeted sampling (e.g., direct inventories)
across a wide array of localities, habitats, and breadth of
hosts and dependents, (3) develop models that incorpo-
rate correlates of nonrandom host and dependent extinc-
tions, such as phylogeny, biogeographical, and species
traits that increase extinction proneness, and (4) deter-
mine whether dependents are being lost before the hosts
and adjust models accordingly.
Meeting the above challenges requires investment of
key species, host specificity studies, and modeling of de-
pendent population responses to changes in host popula-
tions resulting from anthropogenic actions. Understand-
ing the process of coextinction will allow proactive con-
servation, rather than the often reactive and piecemeal
methods thus far employed.
(DP0772057), Australia & Pacific Science Founda-
tion (APSF 07/3), University of Melbourne Botany
Facility (Applied Environmental Decision Analysis hub),
and New South Wales National Parks & Wildlife Service
supported this work. We thank three anonymous
reviewers and the editors for their valuable suggestions
that substantially improved this paper.
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