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Phil. Trans. R. Soc. B (2011) 366, 2451–2461
doi:10.1098/rstb.2011.0090
Introduction
Ecology and evolution of mammalian
biodiversity
Kate E. Jones1 and Kamran Safi1,2,*
1
Institute of Zoology, Zoological Society of London, Regents Park, London NW1 4RY, UK
Max Planck Institute for Ornithology, Vogelwarte Radolfzell, Schlossallee 2, 78315 Radolfzell, Germany
2
Mammals have incredible biological diversity, showing extreme flexibility in eco-morphology, physiology, life history and behaviour across their evolutionary history. Undoubtedly, mammals play an
important role in ecosystems by providing essential services such as regulating insect populations,
seed dispersal and pollination and act as indicators of general ecosystem health. However, the
macroecological and macroevolutionary processes underpinning past and present biodiversity patterns are only beginning to be explored on a global scale. It is also particularly important, in the face
of the global extinction crisis, to understand these processes in order to be able to use this knowledge to prevent future biodiversity loss and loss of ecosystem services. Unfortunately, efforts to
understand mammalian biodiversity have been hampered by a lack of data. New data compilations
on current species’ distributions, ecologies and evolutionary histories now allow an integrated
approach to understand this biodiversity. We review and synthesize these new studies, exploring
the past and present ecology and evolution of mammalian biodiversity, and use these findings to
speculate about the mammals of our future.
Keywords: biogeography; conservation; diversification; phylogeny; life-history traits;
species richness
1. INTRODUCTION
Mammals (class Mammalia) are an extraordinary
group, showing an amazing diversity of species,
forms, ecologies, physiologies, life histories and behaviours. Their taxonomic diversity of 5416 extant or
recently extinct species [1] increases every year with
the discovery of new ones [2], even in well-known
groups such as primates (e.g. [3]). The greatest numbers of extant species (99%) are in the subclass Theria
which consists of 5136 eutherian mammal species (i.e.
placental species such as rodents, bats, carnivores,
primates, cows, whales and elephants), and a smaller
proportion (346 species) of metatherian species (i.e.
marsupial mammals such as kangaroos and opossums). However, where the rest of the known
mammalian diversity fit taxonomically is more controversial. Traditionally, the rest of the class Mammalia
has been classified into the subclass ‘Prototheria’,
which is wholly extinct apart from five species of
egg-laying mammals—the platypus and echidnas
(Monotremata) [4]. However, the validity of Prototheria has been questioned and other arrangements
have been proposed, for example, grouping monotremes with recently discovered fossil forms into a
new clade Australosphenida [5].
* Author for correspondence ([email protected]).
One contribution of 12 to a Theme Issue ‘Global biodiversity of
mammals’.
Equally important for understanding mammal ecology and evolution is that the extant diversity of
mammals hides an even more interesting past, where
the number of extinct genera known outnumbers
those now present [4]. Mammals split from
mammal-like reptiles in the Mesozoic (Late Triassic)
around 210 Ma and are thought to have evolved in
successive waves of diversification, involving many
dead-end lineages, short-lived clades and convergent
ecological specializations [6]. For example, most of
the now extinct Cretaceous placental and marsupial
species are phylogenetically distant from the later Cenozoic marsupial and placental forms [7,8], as both
groups underwent independent waves of diversification. Even the extant mammalian diversity is a subset
of the original Late Quaternary ‘pre-human impact’
fauna, where a number of mostly large-bodied species
underwent prehistoric and historical-era humanmediated extinctions [9].
Mammals show extreme morphological diversity,
for example, ranging in size from one of the smallest
extant species, the bumble-bee bat (Craseonycteris
thonglongyai) at under 2 g to the largest known to
have ever lived, the blue whale (Balaenoptera musculus)
at over 154 000 kg [10]. Tongues, teeth, fingers, faces
and feet (among many other body parts) show similar
extremes of adaptations. For example, the tube-lipped
nectar bat (Anoura fistulata) has a tongue so long that
this species has evolved a special chest cavity to store it
when not feeding on nectar [11]. Fingers have been
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K. E. Jones & K. Safi Introduction. Mammalian biodiversity
modified to type this, fly (the bats) and to form the
mammal analogue of woodpecker’s beak to find and
locate insects from under tree bark (the aye-aye,
Daubentonia madagascariensis), among many other
adaptations. Large morphological diversity is present
even in very closely related groups. For example, the
feet of Cetartiodactyla (cows, pigs, whales and dolphins) have been modified into the extremes of
hooves and flippers in a relatively short evolutionary
time span [12]. Although diversification of mammal
eco-morphologies is thought to have accelerated with
the vacation of niches occupied by the non-avian dinosaurs at the end of the Cretaceous, there is evidence
that even the earliest mammals were highly specialized
[6]; for example, species with a beaver-like tail for
swimming (Castorocauda [13]) and wings for gliding
(Volaticotherium [14]). This suggests many independent convergent evolutionary adaptations and
extreme flexibility in mammals over the course of
their evolutionary history.
Ecologically, mammals have colonized all continents including the most inhospitable environments
on earth. For example, jerboas (small rodents belonging to the family Dipodidae) inhabiting harsh desert
environments, polar bears (Ursus maritimus) adapting
to polar ice caps [15], some carnivores and Cetartiodactyla living successfully in both marine and
freshwater ecosystems, and bats sharing the air with
the birds and insects. Physiologically and neurologically, mammals have also evolved remarkably diverse
adaptations. For example, the use of electro-receptors
for orientation, electrolocation (e.g. the platypus,
Ornithorhynchus anatinus) [16] and repeated evolution
of echolocation as a means to orient in the absence of
visual cues (many bats and cetaceans, some shrews
and rodents) [17]. Mammals also show extremes of
life histories and behaviours along a fast – slow continuum, where some groups live faster and die much
younger (e.g. rabbits and hares, rodents) than others
(e.g. elephants, primates, whales, bats) [18,19]. Mammals undoubtedly play an important role in
ecosystems by providing essential services such as
seed dispersal, pollination and regulating insect populations, and reducing disease transmission [20 – 22]
and there is some evidence that some groups act as
indicators of general ecosystem health [23].
The macroecological and macroevolutionary processes underpinning past and present mammal
biodiversity patterns are only beginning to be explored
on a global scale. For example, studies in diversification rates [24,25], extinction risk [15,26,27], spatial
species-richness patterns [15,28], body-size evolution
[29– 31], and life-history evolution [32,33]. It is also
particularly important, in the face of the global extinction crisis [34], to understand these processes in order
to be able to use this knowledge to prevent future
biodiversity loss and loss of ecosystem services.
Unfortunately, efforts to understand mammalian biodiversity have been hampered by a lack of data, for
example, global datasets on species’ distributions, life
histories, ecologies and evolutionary histories. Recent
advances in collating these data for all mammals
[10,15,24,28] (www.utheria.org) mean that there is
now a unique and exciting opportunity to explore
Phil. Trans. R. Soc. B (2011)
factors determining mammalian biodiversity. Here, we
review recent studies in this volume [35 – 44] that are
among the first to integrate these new ecological,
evolutionary and spatial datasets. We conclude by
synthesizing these investigations of the ecology and
evolution of mammalian biodiversity to understand
the mammals of our future.
2. MAMMALS IN THE PAST
Understanding the past evolutionary history of mammals is challenging. Palaeontological approaches have
been used successfully to investigate mammalian origins and the processes of subsequent diversification,
extinction and biogeographic spread (e.g. [45,46]).
However, relying solely on fossil evidence can be problematic as fossils may be unevenly distributed and
sampled taxonomically, temporally and spatially
[4,47]. Another approach to understanding the past
can be gained by also examining the relationships
among living species and using these phylogenetic
trees to test hypotheses about divergence and morphological, ecological and behavioural diversification [48].
The past few decades have seen a revolution in our
understanding of mammalian evolutionary histories
with the development and analysis of comprehensive
molecular datasets (reviewed in [49]). For example,
the consensus of recent studies recognizes four major
clades of placental mammals: Afrotheria (e.g. golden
moles, tenrecs, elephant shrews, elephants, dugongs),
Xenarthra (e.g. armadillos, anteaters), and the sister
taxa of Laurasiatheira (e.g. shrews, hedgehogs,
whales, cows, dogs, bats) and Euarchontoglires (e.g.
tree shrews, flying lemurs, primates, rodents)
[50,51]. With the exception of Xenarthra, these
groupings were never suspected on the basis of morphology. These re-arrangements have fundamentally
changed our understanding of mammalian evolution,
where more surprising examples include grouping
whales Cetacea (whales and dolphins) within Artiodactyla (cows and sheep) to form Cetartiodactyla [52].
Many phylogenetic methods that investigate the
past require complete or near-complete trees of all
the taxa under consideration [53]. Complete phylogenies only started to appear for individual large
clades of mammals in the last decade, at first using
now-controversial techniques [54,55] to combine previously published phylogenies into one meta-analysis
or ‘supertree’ (e.g. [56 – 58]). More recently with an
increase in the availability of data, other methods
which analyse original data in the phylogenetic studies
are starting to be used for some mammalian clades
(e.g. [59 –61]). Our understanding of the ecology
and evolution of these groups has seen a huge
leap forward with the analysis of these complete
trees. For example, the first supertree of nearly all
extant mammals was published in 2007 [24] investigating evidence for shifts in diversification rates over
the course of mammalian evolution. Controversially,
this was the first to suggest that diversification rates
of present-day mammals did not change at the Cretaceous-Tertiary boundary (as traditionally thought) but
around 10 Myr after. However, which clades in particular were responsible for changes in diversification
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Introduction. Mammalian biodiversity
rates, and why diversification rates change is little
understood.
In this volume, Purvis et al. [35] re-examine the
mammalian supertree to demonstrate significant
imbalance (i.e. that diversification rates are not equal
across contemporaneous groups) and identify 15
points in the tree where diversification rate has
increased (radiations) and 12 clades that may be
downshifts. For example, a significant radiation was
found in the clade containing the Laurasiatheira and
Euarchontoglires compared to the other mammals.
Purvis et al. [35] also compare multiple lines of evidence (similarity of the imbalance of real phylogenies
to simulated ones, relationship of spatial scale to phylogenetic imbalance) to hypothesize that the
opportunity of accessing new regions of geographical
or niche space has more influence on diversification
rates than trait variation (e.g. a single key innovation).
Under this model, phylogenies are unbalanced
because regions and niches vary in the diversity they
can support, because new radiations will probably
originate in already diverse clades, and because
lineages that are relics are hard to replace. Different
clades radiate in different places so the global phylogeny will tend to be less unbalanced than for the
phylogeny of species within a smaller region.
Purvis et al.’s [35] innovative idea is consistent with
a number of independent lines of evidence, e.g. dispersal ability has been found to be the most important
predictor of diversification rate in birds [62], and yet
other trait variations are not strong predictors of diversification rates across different taxa [63]. Importantly,
the model sets up testable predictions to explain
global diversification rate asymmetries and highlights
the importance of spatial processes in determining
mammalian biodiversity. Springer et al. [36] in this
volume, turn this approach on its head, and review
how phylogenetic information, timing of divergences,
and present day and fossil distributions can be used
to reconstruct the ancestral distributions of mammals
to test the importance of dispersal and vicariance in
shaping biodiversity. Reconstructing ancestral states
is problematic [64 –66] and reconstructing geographical ranges even more so, because of the lack of a robust
hypothesis about how ranges might evolve [67]. However, new more suitable methods are becoming
available (e.g. [68 – 70]) and our growing understanding of mammalian phylogenetics is changing our
views on mammalian palaeobiogeography.
To illustrate these new methods and this approach,
Springer et al. [36] review and use nine different geographical reconstruction methods and an order-level
phylogeny for 43 placental mammals [71]. They conclude that ambiguous reconstructions are a problem
for all of the current methods and that important
differences result from coding areas based on extant
ranges versus fossil lineages. Using these new
methods, Springer et al. [36] show that out of the
four major clades of placental mammals, Africa was
reconstructed as the ancestral area for Afrotheria,
while South America was more probable for Xenarthra. Most methods reconstructed Eurasia as the
ancestral area for Laurasiatheira and Euarchontoglires.
Springer et al. [36] also propose that the coincidence
Phil. Trans. R. Soc. B (2011)
K. E. Jones & K. Safi
2453
of molecular dates for the separation of Afrotheria
and Xenarthra at approximately 100 Ma with the
plate tectonic sundering of Africa and South America
hints at the importance of vicariance in the early history of placental mammals. The importance of
dispersal events in addition to vicariance is nicely
shown for Madagascar through the co-occurrence of
the four lineages of terrestrial mammals (tenrecs,
lemurs, carnivores and rodents) as well as for South
America (platyrhines and caviomorphs) [36]. As
Springer et al.’s [36] review concludes, it is clearly an
exciting time for mammalian palaeobiogeography,
with the rapid development and application of new
methodologies to our increasing knowledge and
completeness of mammalian phylogenies.
3. MAMMALS IN THE PRESENT
Spatial variation in species, phylogenetic and functional richness, ecological community composition,
species – area relationships (SARs), geographical
range size, diversification rates, and rates of molecular
evolution are all components of biodiversity that are
somehow inextricably linked together. Here, we
review studies that take an integrated ecological and
evolutionary approach to explore these linkages to
determine common factors underlying present-day
mammalian biodiversity patterns [37 – 42].
Spatial variation in species richness is one of the
most studied macroecological patterns in nature
[72]. Consistent with many other taxa, the highest
species richness of mammals is found in the tropics
[15,28]. Many hypotheses have been suggested to
explain this latitudinal gradient found across many
different organisms; these include the effects of speciation, extinction, colonization and dispersal, as well as
more subtle processes such as range shifts and associations with primary productivity, environmental
seasonality, and habitat complexity (e.g. [73 – 79]).
Investigating the spatial variation in species richness
is therefore a complex multifaceted problem.
In this issue, Bromham [37] starts dissecting this
problem with a review of the factors influencing the
variation in the rates of molecular evolution. Molecular evolution rate may influence speciation rates and
therefore, indirectly, the spatial variation in species
richness. Body size is the most notable correlate,
where small-bodied species generally have faster molecular rates than large-bodied relatives [80], but the
mechanism behind this pattern continues to be
debated. Many life-history traits covary with body
size and there is evidence that factors such as generation time, fecundity, longevity, population size,
metabolic rate and environmental energy also influence rate of molecular evolution [37]. Bromham’s
[37] review concludes that although it is still unclear
as to how different mechanisms determine the rate of
molecular evolution, it is evident that a significant
part of this rate variation is connected to the species’
life history. This has important implications on the
estimates of divergence times on molecular phylogenies. This suggests that further research is needed
to investigate any systematic bias on the dates obtained
by not accounting for life history of the species under
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K. E. Jones & K. Safi Introduction. Mammalian biodiversity
study, for example, dates obtained for mammal divergence times (e.g. [24]).
It has been demonstrated over different spatial
scales and in many taxa that the number of species
in an area increases as the size of the region increases
[81– 84]. This SAR might also help to explain the
puzzle of the spatial variation in species richness.
SARs have mostly been treated from an ecological perspective focusing on immigration, local extinction and
resource-based limits to species co-existence [81,82].
However, over large spatial scales, rates of speciation
and extinction could also contribute to these patterns
of biodiversity [83,85]. In this issue, Kisel et al. [38]
take a new look at the SAR, and present a framework
for understanding how evolutionary processes could
contribute to the creation of large-scale SARs based
on a density-dependent model of clade diversification.
They suggest that SARs result from the direct and
positive effects of area on diversification (increasing
the probability of allopatric speciation and decreasing
the probability of global extinction). Kisel et al. [38]
use the new mammal datasets to show that these
hypothesized effects are apparent in the histories of
mammal diversification—clades occupying larger
areas had higher initial diversification rates and lower
rates of decline in diversification. They propose that
SARs also result from the indirect effects of area
through population size, habitat diversity and fragmentation. Again using the mammal data they found
that energy availability and environmental heterogeneity had the effect of increasing species richness, and
increased the slopes and intercepts of the species –
area relationships. Kisel et al. [38] also highlight the
importance of historical contingency in SARs and
use the mammal datasets to show that clades able to
colonize new, competitor-free regions are more diverse
than their stay-at-home sister clades. Interestingly, this
is predicted from Purvis et al.’s [35] model of clade
diversification presented earlier in this volume, and is
consistent with Springer et al.’s [36] suggestion that
vicariance was an important driver of early placental
mammal evolution.
Although, as Kisel et al. [38] show, current environmental variation influences the spatial variation of
mammalian species richness, Davies et al. [39], in
this volume, highlight the importance of past as well
as present climatic conditions on determining both
range size and species richness. Geographical range
sizes of mammals are highly non-random, where terrestrial mammals with the smallest ranges are mostly
restricted to the tropics, and species with the largest
ranges are found across high latitudes [77,86]. Using
the new mammalian datasets, Davies et al. [39] confirm a Rapoport-like pattern [87] for terrestrial
mammals (and most of the separate species-rich
orders), where latitudinal range extents are greatest
at mid- to high latitudes and narrower at more equatorial latitudes (with the effect more pronounced at
higher latitudes). Their analysis reveals that range
size at high latitudes correlates strongly with the magnitude of Quaternary temperature oscillations,
indicating that Rapoport’s rule may be a product of
long-term climate cycles rather than seasonal variability. Davies et al. [39] also show that species richness is
Phil. Trans. R. Soc. B (2011)
similarly influenced by past climate. For example, the
relationship between species richness and present
environmental conditions is quite different in areas
where Quaternary temperatures fluctuated. The
authors suggest that the Quaternary climatic oscillations in the high northern-latitude areas directly
limited diversity below carrying capacity through
repeated extinctions and range contractions. On the
other hand, the climatic stability of the tropical areas
during the same time allowed for the persistence of
small-ranged specialist species by a tight packing
of species in ecological niche space allowing for the
evolution of high local species richness [39].
Safi et al. [40], in this issue, also explore correlates
of the spatial variation in mammalian richness but
they focus on the linkages between species, functional
and phylogenetic richness at a global scale. Functional
diversity may represent the amount of trait similarity in
an ecological community and phylogenetic diversity,
the amount of shared evolutionary time. The authors
suggest that by comparing the relationship between
functional and phylogenetic diversity, the relative
importance of historical and evolutionary processes
shaping present richness patterns can be teased
apart. Using the new mammal datasets, Safi et al.
[40] find that all richness measures follow the classic
latitudinal gradient. Although there is a high surrogacy
between all the measures, climatic conditions impact
relative phylogenetic and relative function richness differently. They find that tropical areas are characterized
by a lower functional diversity suggesting that under
stable environmental conditions, niche space can be
more tightly packed and higher species richness
levels achieved. It would be interesting to see if the
influence of past climate was similarly heterogeneous
across the different measures of richness as predicted
by Davies et al. [39].
Cardillo [41], in this issue, explores further how
species can be packed into niches (or community
assembly) by reviewing some of the ecological and
macroecological processes that are suggested to play
a role. The attributes of a set of species in a particular
region can give clues to the processes determining
community assembly. For example, if competition
between species for resources occurs in an area, then
less adapted species will be excluded. Species that
are closely related are often more ecologically similar
so may compete more strongly. So if competition is
important in determining species composition, then
we would expect to see fewer closely related species
co-existing [88]. Cardillo [41] notes that this rapidly
growing research area mostly focuses on testing the
phylogenetic community composition of local-scale
assemblages [89]. Macroecology, also a field of
research interested in understanding the structure of
assemblages but at large scales, has historically not
taken a phylogenetic perspective on this problem. Cardillo [41] takes a new look at this to merge the two
approaches and ask whether we would expect largescale assemblages to show significant structure.
Using an example exploring the phylogenetic structure
of carnivore assemblages within Africa he finds that
many assemblages at these large scales are phylogenetically non-random (either clustered or over
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Introduction. Mammalian biodiversity
dispersed). Cardillo [41] suggests that one explanation
of this is that many clades underwent rapid biome-filling radiation, followed by diversification slowdown
and competitive sorting as niche space became saturated. This is consistent with Kisel et al.’s [38]
finding that clades able to colonize new, competitorfree regions are more diverse than their stay-at-home
sister clades and Cardillo’s [41] theory of diversification has the best predictive power in explaining the
asymmetry of mammal phylogenies [35].
The underlying communalities in the findings of all
these recent studies are consistent with the idea of a
unifying theory of biodiversity (UTB) [90]. Jones
et al. [42] explore this idea with a synthetic review of
the mammalian macroecological literature, firstly
reviewing the processes underlying the UTBs and
their predictions, and then reviewing the evidence to
support them. They also assess what other macroecological patterns are known for mammals but are not
predicted by UTBs, and consider how UTBs might
need to be extended to cover them. Their conclusions
point to where our future research should be focused
to reconcile the pattern and the theory.
4. LESSONS FROM THE PAST AND PRESENT
Mammalian biodiversity is rapidly declining [15],
along with the other measured biodiversity on this
planet, as anthropogenic activity monopolizes more
of the earth’s ecosystems and natural resources
[34,91]. Perhaps these new integrated approaches to
understanding the processes determining mammalian
biodiversity can help us to prevent future biodiversity
loss and the loss of ecosystem services that mammals
provide? Previous work using this approach and these
datasets (and others) have examined species-level patterns and processes determining current global
mammalian extinction [15,26,27,77,92 – 96]. These
studies have suggested that extinction risk in mammals
(among other things) is phylogenetically and spatially
non-random, that the trait correlates of extinction
risk depend on the taxonomic group, past extinction
filters, type of extinction threat and that these trait/
extinction risk relationships interact with body size.
Here we review two new studies furthering our understanding of the extinction process, first from the recent
past [43] and the second examining which factors
influence populations to decline and whether these
are the same as those factors that are important at
the species level [44].
It is often difficult to draw parallels between studies
of extinction risk in current taxa and those already
extinct. These two types of studies are often done at
very different taxonomic and time scales, cover different taxa which are differently sampled, use different
methods and definitions of extinction (actual versus
risk of extinction) [93]. In this issue, Turvey & Fritz
[43] do just this by comparing present day extinction
risk patterns to the spatial, taxonomic and phylogenetic pattern of 241 mammal species known to have
gone extinct from the Holocene (11 500 years ago)
up to the present day. Adding this Holocene data
[97] into the new mammal databases, Turvey & Fritz
[43] find that, as with extinction risk in current taxa,
Phil. Trans. R. Soc. B (2011)
K. E. Jones & K. Safi
2455
Holocene extinctions show a phylogenetic and
spatially non-random pattern. However, there are
important differences in these patterns; specifically,
regional faunas from which susceptible species have
gone extinct now appear less threatened. This is
consistent with the ‘extinction-filter’ hypothesis, originally conceived to explain the patterns in regional
faunas of extant taxa [98]. Turvey & Fritz’s [43]
study highlights the importance of incorporating patterns of extinction over longer time periods when
examining current extinction risk analyses. However,
they also caution against uncritical use of past extinction data without considering the sampling biases
inherent in these kinds of data.
Rates of decline in populations across the world have
provided convincing evidence that biodiversity is rapidly
declining [34]. However, how declining populations
translate to global species-level extinction events is
poorly understood. Collen et al. [44], in this issue,
examine for the first time, the relationship between the
well-documented correlates of extinction risk (e.g.
[92]) and those of population trends. Studies of extinction risk typically use a measure of risk from the
International Union for Conservation of Nature
(IUCN) [99] which is a composite measure for each
species consisting of population size, trends in different
populations and geographical range size. However, this
composite measure is uninformative for estimating
trends in local populations. Collen et al. [44] collated
three measures of population change from 292
mammal species of 1384 different populations from
the living planet index (LPI) database [100], the largest
repository of wildlife population abundance trends
in the world. Combining this with the new mammal
datasets of traits and evolutionary history, they found
that significant traits were similar to studies of extinction
risk, but environmental conditions were generally better
determinants of population declines than intrinsic biological traits. Specifically, areas of high temperature
and evapotranspiration (tropical areas) were associated
with declining species. Collen et al. [44] suggest that
the intensity of the decline may mask a correlation
with intrinsic biological traits, i.e. if pressures are of a
high intensity, then species biology ceases to matter.
Although these are interesting patterns, the authors
conclude that further research is needed to tease apart
these trends with a more even sample of taxa and
geographical coverage than is currently available in the
LPI database.
5. MAMMALS IN OUR FUTURE
So what of the future? With the increasing availability
of global datasets we are learning more about the rules
that determine mammalian biodiversity and beginning
to appreciate that the past plays a central role in our
understanding of the present. However, our ability to
make predictions of future mammalian biodiversity
and the effect any biodiversity change will have on
ecosystem function is only beginning. Examples of
this approach include studies that examine the patterns of biodiversity, such as areas of high richness
(measured taxonomically, functionally or phylogenetically), threat or endemicity, and apply current threat
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K. E. Jones & K. Safi Introduction. Mammalian biodiversity
drivers or models of future changes to these areas to
predict future biodiversity (e.g. [101 – 104]). Only a
few of these studies have focused explicitly on mammals. Niche modelling is another pattern-based
approach which models the environmental conditions
for species presence and/or absence data to predict
possible geographical ranges. These can then be used
to predict changes in distributions under different climate change scenarios [105]. However, these studies
do not often account for competition between species,
species life histories or ability to disperse to new
niches predicted by the models, although research is
progressing in this area [106].
Other studies have tried to apply our understanding
of the processes shaping biodiversity more explicitly
using extinction risk models [27] to identify species
that were currently at lower risk than predicted from
the model—species at ‘latent risk’ [107]. This study proposed 20 regions which were currently intact but
contained these hotspots of latent risk species, assuming
that extinction drivers would intensify linearly. More
realistic scenarios of global change need to be incorporated into such analyses to be more policy relevant,
which is challenging [77]. Davies et al. [77] present
some examples of how this approach could be attempted
and this is clearly an important and timely area of future
research. More studies are needed that incorporate our
knowledge of biodiversity processes into understanding
our future. For example, based on the consensus of the
evidence in this volume, opening of new ecological or
spatial niche space seems to have played a major role in
patterns of mammalian diversification. We would
argue that as a substantial amount of new niches are unlikely to become available in the near future (given the rate
of human population growth and consumption), mammalian diversification may be in a phase of stabilization
or decline. Additionally, evidence suggests that environmental stability and available energy are important
determinants of species composition and richness
which is rapidly changing with anthropogenic global
change. Predicting the future for mammals needs a
more process-based approach.
Finally, we point out that any predictive approach to
understand mammals of our future must attempt to
include the mammalian biodiversity that is currently
poorly known or completely unknown. In 1993,
4629 extant or recently extinct species were recognized
[108], 5416 in 2005 [1], which in 3 years had
increased to 5488 for IUCN’s global mammal assessment [15]. In fact, based on the current discovery
rate of 200 – 300 species per decade, Reeder et al. [2]
suggest that over 7000 mammalian species will be
recognized by 2050. Using a species-accumulation
curve and assuming a linear relationship between
total cumulative number of species and time by
decade (figure 1), Reeder et al. [2] estimated an
additional 300 species would be discovered in the
decade following 2000.
Extending this approach, we used the year of discovery for species in IUCN’s global mammal
assessment [15] to estimate how many mammals we
would expect to have discovered in the next decade
from 2008 until 2018. We used a generalized additive
model (GAM) to provide greater precision than a
Phil. Trans. R. Soc. B (2011)
6000
5000
4000
mammal species
2456
3000
2000
1000
0
0
50
100
150
200
years since 1758
250
300
Figure 1. Cumulative number of recognized mammal species
(grey dots) since 1758 using data from [15]. The solid line
represents the fitted generalized additive model. The
dashed line is the projection of the model 50 years into the
future (from 2008 onwards). The stars and circles represent
model projections for models that were built on subsets of
the entire dataset (circles are 10 year projections and stars
25 year projections) to assess how well projections can
potentially represent future values (see text). The dotted
line represents the linear correlation derived in [2].
linear model (figure 1). The GAM was built with the
smooth term being the years since 1785 fitted to the
cumulative number of species. The degrees of freedom
and thus the number of knots determining the
smoothness of the fit were chosen using a restricted
estimated maximum likelihood approach [109]
(smooth term statistics: estimated d.f. ¼ 8.978 [109],
d.f. ¼ 9, F ¼ 74 648, p , 2e – 16). For the next
decade, the GAM predicts an additional discovery of
97 species in 2018 and a total of 336 additional species
in the next 25 years (up until 2033; figure 1). Projections into the future are notoriously unreliable, so we
assess the accuracy of our predictions by fitting GAMs
on the subsets of the known time range (10 and 25
years). Our models tend to underestimate the number
of species in the future subset, but the mean difference
between the actual value and the 10 and 25 year predictions are quite small (figure 1; 10 year mean difference +
standard deviation ¼ 28 + 175 species; 25 year difference + standard deviation ¼ 210 + 432). In these
predictions, we assume that discovery effort will remain
more or less constant. However, the application of new
efficient molecular identification methods may shift the
intercept of the line upwards (a grade-shift), which may
result in perhaps thousands more species.
These new species are likely to be biased both taxonomically, ecologically and spatially, as is the case
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K. E. Jones & K. Safi
2457
richness of
threatened mammals
Introduction. Mammalian biodiversity
richness of the most recently
discovered 1000 mammals
Figure 2. Species richness of all threatened and the most recently discovered 1000 mammals globally, data from [15].
The values on both axes have been standardized between 0 and 1 and binned in 10 equal-sized categories.
for the most recently discovered species [2,110].
Following these patterns, we could predict that most
species to be described will probably be found
among the most species-rich clades (e.g. rodents and
bats) and will be small with a restricted range. Spatial
biases in the 341 species of mammals discovered since
1992, show associations with islands and the tropics
[2]. We also show this for the most recently discovered
1000 species of mammals using data in IUCN’s global
mammal assessment [15], where most species have
been discovered in the lowland Amazonian forests,
East African tropical forests, Indo-Malayan forests,
Papua New Guinea highlands and Australia (figure
2). The expectation is therefore that future discoveries
would be also within these regions, although currently
species-rich areas are likely to be important sources of
new discoveries. Worryingly, most recently discovered
mammals and most threatened mammal species
occur in the tropical belt indicating that some species
may go extinct before even being discovered (figure 2).
What of poorly known species? How can these be
included into predictive models? The threat status of
836 out of 5488 species is unassessed by IUCN
because they are too poorly known, and classified as
data deficient [99] (figure 3). It is unclear whether
the proportion of threat categories among these datadeficient species is the same as among the assessed
species. Often data-deficient species have restricted
ranges, so may be more likely to be threatened. Finding ways of assessing the status of these species seems
important for understanding future mammalian biodiversity. Here, we provide an example of such an
approach using methodology developed to assess the
phylogenetic and spatial signal of extinction risk in
carnivores [111]. We used a combination of spatial
eigenvector estimation and phylogenetic eigenvectors
as described in Safi & Pettorelli [111] and modelled
a binary response (threatened/not threatened) for
data-deficient species for each mammalian order separately. Due to the size of some of the mammalian
orders we used a forward selection procedure minimizing the Aikaike’s information criterion of the
Phil. Trans. R. Soc. B (2011)
alternative models, instead of the step-wise forward
selection/backward elimination procedure based on
model deviance originally proposed in Safi & Pettorelli
[111]. The performance of the models was assessed
using a cross-validation among bats. In the validation,
we compared modelled and known threat status by
running 100 models where we removed an assessed
species randomly and compared its probability of
being threatened with its known IUCN rank. The
cross-validation revealed that the procedure was
reliable and consistently modelled threatened species
to have significantly higher probabilities of threat than
non-threatened species (median and interquartile
range of modelled threat probability for non-threatened
species: 0.04 [0.001, 0.1] and for threatened species: 0.52 [0.24, 0.75], Wilcoxon signed-rank test
W ¼ 129, p , 0.0001).
Out of 481 data-deficient species with distribution
and phylogenetic information, we estimated a proportion
of 35 per cent to be threatened (figure 3). The proportion
of threatened species among the data-deficient species is
significantly higher than among the assessed species
(difference in proportions ¼ 20.09, z ¼ 23.14, p ¼
0.002). This suggests that data-deficient species are of
greater conservation concern. Of a total of 836 datadeficient species, 355 species did not have distribution
and/or phylogenetic data. If we assume the same modelled ratio of threatened versus non-threatened species
for the entire set of 836 data-deficient species, and then
add the total number of threatened species in all of the
assessed species, then 28 per cent (1513 species) are in
more or less immediate risk of going extinct. Approaches
such as these seem a promising start on including poorly
known species in any predictive modelling of future
mammalian biodiversity.
6. CONCLUSIONS
This issue brings together biodiversity experts to use new
compilations of global data on mammal species’ distributions, life histories, ecologies and evolutionary
histories to understand the pattern and processes
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2458
K. E. Jones & K. Safi Introduction. Mammalian biodiversity
modelled to be
threatened
(%)
100
no phylogenetic
and/or range
information available
non-threatened
least concern
87.5
75
62.5
near threatened
modelled to be non-threatened
vulnerable
threatened
data deficient
extinct (EW and EX)
critically endangered
endangered
50
37.5
25
12.5
0
Figure 3. Modelling threat status in data-deficient mammal species, data from Schipper et al. [15]. EW, species extinct in the
wild; EX, extinct species.
underlying mammalian biodiversity. The consensus of
evidence suggests that current mammalian biodiversity
is the result of a strong signature of past events and also
the present environmental and ecological conditions.
For example, opening of new ecological or spatial niche
space seems to have played a major role in patterns of
mammalian diversification whose signature can be seen
in the current patterns of biodiversity. There is a pressing
need to understand this complex pattern to allow us to
predict mammals most at risk of extinction to preserve
some of their fascination for our future generations.
We would like to thank all the authors for contributing to this
stimulating volume, for lively discussions, their patience
and enthusiasm. The datasets and much of the research
in this issue was inspired by an original collaboration
to investigate correlates of mammalian extinction and
geographical range size (A. Purvis, G. Mace, J. L.
Gittleman, O. Bininda-Emonds, M. Cardillo, K. E. Jones
and other members of the mammal superteam). It is a
testament to the excellence and generosity of these
researchers and their vision of modern collaborative
research, that these datasets are freely available to others to
further the field of mammalian biodiversity and
conservation. It is our privilege to have been involved. We
would also like to thank Joanna Bolesworth from the
editorial team for her understanding and support while we
put this issue together. Many thanks also to the Max
Planck Institute for Ornithology journal club who helped
us with ideas and comments on this issue. This work was
supported by the Zoological Society of London and the
Max Planck Institute for Ornithology.
3
4
5
6
7
8
9
10
11
12
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