Climate change effects on animal and plant phylogenetic diversity in southern Africa

Climate change effects on animal and plant phylogeneticdiversity in southern AfricaDOROTHEA V . P IO 1 , 2 , 3 , † , ROB IN ENGLER 1 , † , H . P ETER L INDER 4 , ARA MONAD JEM5 ,

F ENTON P .D . COTTER I LL 6 , P ETER J . TAYLOR 7 , 8 , M . CORR IE SCHOEMAN9 ,

BEN JAMIN W . PR ICE 1 0 , 1 1 , MART IN H . V I LLET 1 1 , GEETA E ICK 1 2 ,

N ICOLAS SALAMIN 1 , 2 , ‡ and ANTOINE GUISAN1 , 1 3 ,‡1Department of Ecology & Evolution, University of Lausanne, Lausanne, 1015, Switzerland, 2Swiss Institute of Bioinformatics,

University of Lausanne, Lausanne 1015, Switzerland, 3Fauna & Flora International, London, WC2N 6DF, UK, 4Institute for

Systematic Botany, University of Zurich, Zollikerstrasse 107, Zurich 8008, Switzerland, 5All Out Africa Research Unit,

Department of Biological Sciences, University of Swaziland, Private Bag 4, Kwaluseni, Swaziland, 6AEON - Africa Earth

Observatory Network, Geoecodynamics Research Hub, Department of Botany and Zoology, University of Stellenbosch, Private Bag

X1, Matieland 7602, South Africa, 7Durban Natural Science Museum, P. O. Box 4085, Durban, South Africa , 8Department of

Ecology and Resource Management, School of Environmental Sciences, University of Venda, P/Bag X5050, Thohoyandou 0950,

South Africa, 9School of Life Sciences, University of Kwazulu-Natal, Westville campus, Durban 4000, South Africa, 10Department

of Ecology and Evolutionary Biology, University of Connecticut, 75 North Eagleville Road, Storrs, CT 06269, USA, 11Department

of Zoology & Entomology, Rhodes University, Grahamstown 6140, South Africa, 12Institute of Ecology and Evolution, University

of Oregon, Eugene Oregon USA, 13Institute of Earth Sciences, University of Lausanne, Lausanne 1015, Switzerland

Abstract

Much attention has been paid to the effects of climate change on species’ range reductions and extinctions. There is

however surprisingly little information on how climate change driven threat may impact the tree of life and result in

loss of phylogenetic diversity (PD). Some plant families and mammalian orders reveal nonrandom extinction pat-

terns, but many other plant families do not. Do these discrepancies reflect different speciation histories and does cli-

mate induced extinction result in the same discrepancies among different groups? Answers to these questions

require representative taxon sampling. Here, we combine phylogenetic analyses, species distribution modeling, and

climate change projections on two of the largest plant families in the Cape Floristic Region (Proteaceae and Restiona-

ceae), as well as the second most diverse mammalian order in Southern Africa (Chiroptera), and an herbivorous

insect genus (Platypleura) in the family Cicadidae to answer this question. We model current and future species distri-

butions to assess species threat levels over the next 70 years, and then compare projected with random PD survival.

Results for these animal and plant clades reveal congruence. PD losses are not significantly higher under predicted

extinction than under random extinction simulations. So far the evidence suggests that focusing resources on climate

threatened species alone may not result in disproportionate benefits for the preservation of evolutionary history.

Keywords: Chiroptera, Cicadidae, climate change, extinction, niche models, phylogenetic diversity, Platypleura, Proteaceae,

Restionaceae, southern Africa

Received 30 August 2012 and accepted 1 November 2013

Introduction

The effects of climate change on the distribution, abun-

dance, and migration of plant and animal species are

already apparent both in terrestrial and marine ecosys-

tems (Parmesan & Yohe, 2003; Perry et al., 2005; Chen

et al., 2011) and these effects are predicted to become

even more striking in the coming decades (Thomas

et al., 2004; Thuiller et al., 2005, 2011; Broennimann

et al., 2006; Engler et al., 2011). Warming experiments

also suggest negative effects of climate change on

native plant diversity (Gedan & Bertness, 2009).

In Southern Africa, the effects of climate warming are

particularly daunting (West et al., 2012), as magnified

by human demographic pressure on intact landscapes

with widespread and burgeoning land-use change that

converts wild lands into agro-ecosystems (Vetter, 2009;

Willis & Bhagwat, 2009). Together, these two factors

have been leading to the gradual desertification of huge

portions of the landscape (Vetter, 2009). Subtropical

thickets are turning into pseudosavannas of scattered

woody species, where tree mortality rates exceed

recruitment. In the Karoo region of South Africa, heavy

Correspondence: Antoine Guisan, Nicolas Salamin, tel. +41

21 692 4154, fax +41 21 692 4165, e-mail: [email protected],

[email protected]†Co-first authors‡Co-last authors

1© 2014 John Wiley & Sons Ltd

Global Change Biology (2014), doi: 10.1111/gcb.12524

Global Change Biology

grazing and drought have led to the loss of palatable

shrubs and increased dominance by unpalatable woody

species and annuals (Vetter, 2009; Willis & Bhagwat,

2009). A significant portion of miombo savanna wood-

lands across south-central Africa have been converted

to agro-ecosystems since the mid-20th century and this

trend continues (Campbell, 1996; Du Toit & Cumming,

1999; Willis & Bhagwat, 2009).

Over the past decade, several authors have predicted

that native species richness would be reduced and turn-

over rates would increase in the Southern African biota

as a result of global change (Midgley et al., 2003, 2006;

Bomhard et al., 2005; Broennimann et al., 2006; Thuiller

et al., 2006; Midgley & Thuiller, 2007). Declines in

groups of species with similarities in life history traits,

in association with particular environments or host spe-

cies, and susceptibility to anthropogenic pressures are

likely to be influenced by their shared evolutionary his-

tory (Purvis et al., 2000). This can lead to differential

probabilities of (nonrandom) extinction between lin-

eages and can have consequences on future levels of

species and genetic diversity. Conversely, a phyloge-

netically random risk pattern implies that species’ fates

are not largely determined by traits that show a strong

tendency to have similar values among closely related

species.

Assessing climate threat on phylogenetic diversity

(PD) allows predicting whether related taxonomic

groups will require more urgent attention, thus

enabling conservation efforts to be more targeted and

effective for specific groups. There are other advanta-

ges to quantifying the effects of climate change on

PD. Phylogenies represent the evolutionary history of

a taxonomic group and thus PD should encompass

information on multiple traits, represented within

phylogenies into a simple index of ecological,

phenotypic, and functional similarity. PD is thought

to represent an objective and cost effective proxy for

functional diversity and ecosystem function (Srivast-

ava et al., 2012). Although studies demonstrating a

clear link between PD and ecosystem function are

still largely lacking (Winter et al., 2012), there is now

some evidence that PD has an important influence on

aboveground productivity and ecosystem stability

(Cadotte et al., 2012). Ecologically, a phylogenetic

approach to measuring diversity makes it possible to

move beyond traditional taxonomic classification,

toward understanding where on the tree of life

habitat differentiation and adaptation are taking place

(Kembel et al., 2011). Moreover, several studies

suggest that larger PD should increase the evolution-

ary potential and resilience of species to adapt to

environmental change (Meynard et al., 2011; Sgro’

et al., 2011). The use of PD in making inferences

about complex evolutionary processes in a system, as

well as its role as a proxy for ecological function

makes it very powerful.

Despite the important effect that differential threat

probabilities can have on future biodiversity levels,

little attention has been paid so far to exploring the

relationship between predicted climate change effects

and PD (Thuiller et al., 2011). Using IUCN threat

status across taxonomic groups, some authors have

found evidence for nonrandom extinction processes

in angiosperm, avian, and mammalian phylogenies

worldwide (Purvis et al., 2000; Vamosi & Wilson,

2008; Bromham et al., 2012), and in angiosperm

phylogeny in Australia (Sjostrom & Gross, 2006). For

mammals, threat status has been shown to be related

both to anthropogenic pressures and life history

traits, hinting that extinction might be nonrandom

(Davies et al., 2008). Yet, recent results for the same

three taxonomic groups (plants, birds and mammals)

in Europe, but using climate-specific predicted extinc-

tions provide a contrasting view, by showing no

higher loss of evolutionary history in these groups

than expected by chance alone (Thuiller et al., 2011).

This reopens the question of random vs. nonrandom

threat in the tree of life and calls for additional stud-

ies on the effects of climate-specific threat.

Here, we aim to contribute to this debate by provid-

ing evidence from Southern African animal and plant

taxa. We used two major components of South Africa’s

Cape flora (Proteaceae and Restionaceae), the second

most diverse mammal order in Southern Africa (Chi-

roptera) as well as an insect genus (Platypleura) in the

family Cicadidae to make predictions on how threat

and PD may be affected by climate change 70 years

from now. The choice of data was dictated by the

availability and reliability of novel distribution and

genetic datasets for a diverse set of plants and animals.

Moreover, Platypleura were selected because insect

datasets are generally underrepresented in this type of

study and can be expected to provide an interesting

comparison with plants and small mammals. Cicadas

are large-bodied, sap-sucking insects with relatively

high population densities and specific in their plant

associations, (e.g., Price et al., 2007; Price, 2010). In

Southern Africa, almost all species show some degree

of endemism (Price et al., 2007; Price, 2010).

More specifically, we investigated the likely effects of

climate-specific threat on PD (by using contractions of

suitable climate as a proxy for extinction risk) and

whether surviving PD was higher or lower than that

expected under random extinction processes. This pro-

vided a timely assessment of potential climate change

effects on the probability of survival between lineages

of different Southern African taxonomic groups.

© 2014 John Wiley & Sons Ltd, Global Change Biology, doi: 10.1111/gcb.12524

2 D. V. PIO et al.

Materials and methods

Species occurrence data

Species occurrence datasets were assembled for four animal

and plant groups occurring in Southern Africa: Proteaceae,

Restionaceae, Chiroptera and Platypleura. Georeferenced

occurrence data were obtained (i) for the Proteaceae from the

Protea Atlas (Rebelo, 2012; covering 82% of all species); (ii) for

the Restionaceae based on herbarium specimens collected by

one of us (H.P. Linder et al., unpublished; last accessed 2006;

covering all species); (iii) for Chiroptera from museum

records, as well as from several unpublished surveys and

represent the most comprehensive distribution dataset for

Southern African bat species existing today (Monadjem et al.,

2010b; 88; % of all species), and (iv) for Platypleura from an

unpublished dataset (M.H. Villet, unpublished; 66% of all spe-

cies). The determination of all specimens of Restionaceae was

re-assessed, and species which were indeterminate, or which

could be named only to generic level, were removed from the

data matrix. No specimens that were not personally checked

by HPL were included. The georeferenced data were then

assessed by mapping the distribution of each species individu-

ally, and distribution points deemed to be unlikely (wrong

soils, geographical outliers, or in the ocean) were revisited and

corrected (they were not simply pruned from the matrix). The

re-identification is done over a period of 14 years. The genus

Platypleura is monophyletic in Africa, and the putative Asian

species are being transferred to other genera because they are

phylogenetically independent linages. The African species

form two sister clades, one (34 species) endemic to South

Africa and the other (12 species) originating in Southern

Africa and spreading northwards into East and West Africa,

but not overlapping geographically with the first clade. Five

species of the latter clade from Western Africa are clustered

on a branch very distant from the root of the tree where they

have limited effect on the phylogenetic distances between the

Southern African species and on PD calculations (Price, 2010).

The spatial accuracy associated with observation records was

of approximately 2 min for the Proteaceae and Restionaceae,

from accurate GPS localities to quarter degree squares

(15′ 9 15′) for the Chiroptera, and of 1 min for the Platypleura.

At the latitude of South Africa, 1 min corresponds to

approximately 1.6 km.

Climatic data

We used fine resolution 1′ 9 1′ (~ 1.6 9 1.6 km at this

latitude) climatic data for calibrating and projecting the Prote-

aceae and Restionaceae models, since the spatial accuracy of

the occurrence data allowed for finer scale predictions. We

used a set of seven little-correlated (correlation coefficient

<0.7), ecologically meaningful variables for plants, adapted

from the Worldclim database for the Cape region, namely:

annual evapotranspiration, evapotranspiration of the wettest

quarter, annual precipitation, precipitation of the wettest

quarter, precipitation of the driest quarter, annual tempera-

ture, and temperature of the coldest quarter. Climatic data for

the calibration and projection of Chiroptera and Platypleura

models were obtained from the CRU CL 2.0 dataset (New

et al., 2000) generated at a grid size of 10′ 9 10′ (~ 16 9 16 km

at this latitude). These data consisted in six little-correlated

variables (correlation coefficient <0.7) representing the major

climatic gradients in Africa, namely: mean annual potential

evapotranspiration, annual growing-degree days, minimum

temperature of the coldest month, maximum temperature of

the warmest month, mean annual temperature, and annual

sum of precipitation.

Climate projections for the year 2080 (average of 2070–2090)

were produced by perturbing the current climatic data with

anomalies derived from climatic projections produced by the

HadCM3 general circulation model using the A1FI IPCC SRES

scenario (Nakicenovic & Swart, 2000). This scenario is consid-

ered severe, with concentrations of CO2 increasing from

380 ppm in 2000 to 800 ppm in 2080, resulting in a global tem-

perature rises of 3.6 K. For South Africa, the A1FI scenario

projects an increase in yearly average temperature of

2.8 � 0.7 K (mean � 1 SD) and a decrease of 59 � 20 mm in

annual sum of precipitations. When considering only the Cape

Floristic region, the projected increase in temperature is of

2.2 � 0.3 K, and the projected decrease in rainfall of

45 � 13 mm.

Species distribution modeling

As our species datasets were lacking true species absences,

we generated random pseudo-absences (i.e. randomly

selected locations that were considered as species absences;

Elith et al., 2006). For each species, pseudo-absences were

generated in a number 10 times more abundant than the

species’ presences. Pseudo-absences were down-weighted

for model calibration to ensure equal prevalence between

presences and pseudo-absences (presences were given a

weight of 1.0, pseudo-absences a weight of 0.1; following

Elith et al., 2006).

Species distribution models were calibrated for each species

with more than 20 occurrences using six different modeling

techniques: generalized linear models (GLM; Mccullagh &

Nelder, 1989), generalized additive models (GAM; Hastie &

Tibshirani, 1986), boosted regression trees (GBM; Ridgeway,

1999), random forest models (Breiman, 2001), multivariate

adaptive regression splines (MARS; Friedman, 1991) and clas-

sification tree analysis (CTA; Breiman et al., 1984). GLMs and

GAMs were calibrated using a binomial distribution and a

logistic link function. A bidirectional stepwise procedure was

used for explanatory variable selection, based on the Akaike

information criterion (Akaike, 1974). Up to second order poly-

nomials (linear and quadratic terms) were allowed for each

explanatory variable in GLMs, and up to third order splines in

GAMs. GBMs were calibrated with a maximum number of

trees set to 5,000, fivefold cross-validation procedures to select

the optimal numbers of trees to be kept and a value of five as

maximum depth of variable interactions. Random forest mod-

els were fitted by growing 750 trees with half the numbers of

available predictors sampled for splitting at each node. MARS

models were fitted with a maximum interaction degree equal


CLIMATE CHANGE & PHYLOGENETIC DIVERSITY LOSS 3

to 2. All models were calibrated using the BIOMOD package

(Thuiller et al., 2009) in the R software (R Development Core

Team, 2009).

The predictive power of each individual model was evalu-

ated through a repeated data-splitting procedure (for details

see Thuiller et al., 2009). A model was trained on 70% of the

data before being evaluated against the remaining 30% using

two measures: the area under the receiver operating character-

istic curve (AUC; Hanley & Mcneil, 1982) and the true skill sta-

tistic (TSS; Allouche et al., 2006). This data-splitting procedure

was repeated 10 times and the evaluation values averaged.

The final models used to carry out spatial projections were

calibrated using 100% of the data. To avoid using poorly cali-

brated models, only projections from models with AUC >0.75and TSS >0.5 were considered in all subsequent analyses.

Model projections were carried out over various spatial

extents, depending on the group of species: Proteaceae and

Restionaceae models were projected only over the Cape Floris-

tic region, while and Platypleura and Chiroptera models were

projected over respectively all of South Africa and the entire

southern part of the African continent. All models were pro-

jected at the same spatial resolution as the data with which

they were calibrated (1′ for Proteaceae and Restionaceae, 10′

for Platypleura and Chiroptera).

We assumed unlimited (universal) dispersal, which makes

our area loss or extinction rates conservative.

Model averaging (ensemble forecast) was performed by

weighting the individual model projections respectively by

their AUC or TSS scores and averaging the result, a method

shown to be particularly robust (Marmion et al., 2009). Each

ensemble forecast model was then reclassified into two differ-

ent binary projections (i.e. the species is either projected pres-

ent or absent), using the threshold that would respectively

maximize jointly the percentage of presences and absences

correctly predicted (Liu et al., 2005), and maximize the TSS

value.

Since generating pseudo-absence introduced a random

component into our models, the entire modeling procedure

was replicated 11 times, each time with a new set of pseu-

do-absences (pseudo-absence replicates). The final projected

distribution of a species was then obtained by combining

together the projections from our 11 replicates. The number of

replicates was not increased further due to computational and

data storage resources-constraints. Two methods were used:

majority rule and average. In the majority rule method, the

binary projections obtained from each replicate are summed

and the final value is equal to the majority across the 11 repli-

cates, which is why the number of replicates needs to be

uneven (e.g., if six replicates predicted a presence and five and

an absence, the majority method assigns a presence). In the

average method, the continuous ensemble forecasts for each

pseudo-absence replicate were averaged. The resulting aver-

aged projection was then reclassified into binary projections

using the average reclassification threshold of all 11 replicates.

In addition, regressions to test the relationship between the

size of present and future distributions as well as between

present distributions, and the absolute overlap between pres-

ent and future distributions, were run.

Phylogenetic tree building

We used dated species level phylogenies for each taxonomic

group. Reconstructions always had a Bayesian component,

but the exact options used changed between the datasets

because MCMC methods must be adjusted to ensure conver-

gence and appropriate mixing. We measured tree imbalance

and stemminess (Heard, 1992; Pybus & Harvey, 2000; Davies

et al., 2012) of each tree using rescaled Colless values and the

apTreeshape package in R. These measures of the branching

pattern of a tree are interesting attributes because they can

give us indications on macro-evolutionary processes that have

shaped species evolution and can be important when assess-

ing patterns of extinction (e.g., Cadotte & Davies, 2010). For

example, negative rescaled Colless values indicate more

balanced phylogenies (as opposed to pectinate or more comb-

like), negative stemminess values indicate ‘tippy’ trees (trees

with longer branches toward the tips) and positive stemmi-

ness values indicate ‘stemmy’ trees (trees with longer

branches toward the root; Davies et al., 2012).

Proteaceae. A calibrated phylogenetic tree for the Proteaceae

based on 23 genes was assembled from pre-existing data (see

Valente et al., 2010; Pio et al., 2011) and all other available

sequences for the South African (and some Australian) Protea-

ceae in GenBank following the method of Sanderson & Mcma-

hon (2007). The tree comprising 284 taxa was built using

MrBayes 3.1.2 (Huelsenbeck et al., 2001). Two runs of four

Markov chain Monte Carlo chains were run for 10 million gen-

erations using the GTR+Gamma model of DNA evolution and

default priors. The best-fit model was estimated through like-

lihood-ratio tests. The convergence of the two runs and the

stabilization of the model parameters were assessed using Tra-

cer 1.5 (Drummond & Rambaut, 2007) and we removed the

first 5,000 trees as burn-in. The tree with the highest posterior

probability was then dated with a penalized likelihood

method (Sanderson, 2002) as implemented in the ape package

in R (Paradis et al., 2004) using the fossils described in Sauquet

et al. (2009). Penalized likelihood was used here instead of

relaxed clock analyses to alleviate the computational cost due

to the size of the matrix used. To check the consistency of the

date estimates, we also ran penalized likelihood on 100 ran-

domly sampled trees from the posterior distribution given by

MrBayes (see Pio et al., 2011 for details).

Restionaceae. The tree for this group was published previ-

ously (Hardy et al., 2008) and here we used the tree estimated

based on plastid DNA sequences (ITS). A more recent study

(Litsios et al., accepted, Evolution) has added one nuclear

locus to this tree, but the topology remains unchanged.

Chiroptera. A species level phylogeny for 89 species of bats

was built using two mitochondrial markers: cytochrome b and

16S rRNA. DNA was extracted from fresh skin samples

collected in Malawi and Mozambique between August and

December 2007 (Monadjem et al., 2010a), and muscle samples

obtained from the Durban Science Museum, South Africa.

Both fresh and museum specimens were stored in 95%


4 D. V. PIO et al.

ethanol, and DNA was extracted using a phenolchloroform-

isoamyl procedure (Sambrook et al., 1989). We did not use the

mammal supertree because the taxonomy of Southern African

bat species has undergone recent changes and our species

coverage is more extensive. A set of six custom primers were

used to amplify the two genes, which were sequenced using

the Sanger method (Sanger et al., 1977). Existing sequences

available in GenBank by 1/10/09 were also integrated in the

analysis. Sequences were first aligned with a multiple

alignment program, ClustalX (Larkin et al., 2007) and then

adjusted manually with the sequence alignment editor BioEdit

(Hall, 1999). Sequences from the two genes were concatenated

to form a supermatrix after checking that single gene

phylogenetic analyses did not show incongruent nodes

supported by more than 80% bootstrap.

Trees were built with both Maximum Likelihood (ML) and

Bayesian methods. We used PhyML 2.4.4 (Guindon &

Gascuel, 2003) and MrBayes 3.1.2 (Huelsenbeck et al., 2001).

The best model for the combined dataset was GTR+G+I as

determined by likelihood-ratio tests. Three mammals, the

horse (Equidae, Perissodactyla), the pangolin (Manidae, Pholi-

dota) and the sea lion (Phocidae, Carnivora) were designated

as outgroups. ML bootstrap support was determined from 200

replicates with NNI branch swapping (Salamin et al., 2003).

The MrBayes tree was obtained by running three independent

analyses, each with four Markov chains for 50 million genera-

tions with sampling performed every 1000 trees, thereby gen-

erating 50,000 sample points per analysis. Burn-in values were

determined in Tracer 1.5 by checking for convergence of two

independent analyses as well as the stabilization of the model

parameters. The first 5,000 samples were discarded and a

consensus tree with posterior probability values was obtained

in TreeAnnotator 1.5.3 by pooling trees from the three

independent runs (both extensions of BEAST 1.5.3, see below).

Because of congruence between trees obtained by using

maximum likelihood and Bayesian methods, we chose to use

and date the tree generated with Bayesian methods for

consistency with the other groups in this study.

The relaxed Bayesian clock method (Drummond et al.,

2006) was used to generate a calibrated tree using BEAST 1.5.3

(Drummond & Rambaut, 2007). As for the previous analyses,

the GTR + G + I model was used. Three fossil constraints

were introduced on the tree and prior distributions followed a

log-normal distribution with mean taken from the fossil

record and variance adjusted to include the uncertainty associ-

ated with the fossil. The first constraint (corresponding to the

last shared ancestor of the Chiroptera) was set to 65 � 5 MYA,

at or following the Cretaceous-Tertiary boundary (Eick et al.,

2005; Teeling et al., 2005). The second fossil constraint, corre-

sponding to the last known common ancestor shared by the

families Rhinolophidae and Hipposideridae was set to 46 � 5

MYA (Eick et al., 2005; Teeling et al., 2005) and a third was set

to 75 � 10 MYA for the carnivore–pangolin split (Mckenna &

Bell, 1997).

Cicadidae: Platypleurini: Platypleura. A tree for this group

was based on the larger phylogeny of the cicada tribe

Platypleurini (Price, 2010). From this dataset, 26 South African

species of cicadas in the genus Platypleura (Amyot & Serville

1842) were selected for a species level phylogeny based on

two mitochondrial markers (COI and 16S) and one nuclear

marker (EF-1a), with Albanycada albigera (Walker 1850) used as

an outgroup.

Bayesian Inference analyses were conducted using MrBayes

v.3.1.2 (Huelsenbeck & Ronquist, 2001) under the

GTR + I + G model, carried out on the University of Oslo Bio-

portal facility cluster (www.bioportal.uio.no). The analysis

comprised four independent runs of 10 million generations,

using random starting trees with four chains (one cold, three

hot), sampling every 1000 generations with an initial branch

length prior set, using the ‘brlenspr = Unconstrained : Expo-

nential (100.0)’ command. Stationarity in each analysis was

assessed using the potential scale reduction factor (PSRF) data

and plots of likelihood scores, tree length, and average stan-

dard deviation of split frequencies against generation. Only

trees generated at stationarity were used to calculate the

posterior probabilities.

PD calculation

Although many measures of PD exist (Schweiger et al., 2008;

King, 2009; Vamosi et al., 2009; Magnuson-Ford et al., 2010;

Morlon et al., 2011; Pio et al., 2011), we used rooted PD (Rodri-

gues & Gaston, 2002), as it was recently shown to be one of

the most appropriate ways of accounting for evolutionary his-

tory and relatedness between taxa in a conservation context

(Pio et al., 2011). First, rooted PD (hereafter referred to simply

as PD) was calculated for the entire tree.

Global comparison of predicted vs. random remaining PD

In this analysis predicted and random remaining PD were

compared by sequentially pruning phylogenetic trees and cal-

culating remaining PD after each pruning event. Using the

results from present and future (2080) distribution models,

species were pruned from each tree according to the percent-

age of overall occupancy predicted to be lost over time. The

species predicted to undergo the largest loss in occupancy

from present to future distribution was dropped from the tree

in the first pruning event, the two species predicted to

undergo the largest and second largest losses were dropped

from the tree in the second pruning event and so on. After

each pruning event, the remaining PD was calculated and

compared to the PD remaining from pruning events where

the same number of randomly selected species were dropped.

10′000 replicates of random remaining PD were computed for

each pruning event, allowing us to obtain a P-value of

predicted PD as compared to the distribution of the random

replicates.

Spatially explicit comparison of predicted PD vs. randomPD

The relationship between predicted and random PD was

further investigated by spatially quantifying the difference



between predicted remaining PD and random remaining PD

in each of the study area’s grid cells. First, species richness

and PD were computed in each grid cell under current and

future (A1 2080) climatic conditions. Then, for each grid cell,

which had at least 2 species predicted to be present under

future climatic conditions, and for which a decrease in species

richness is projected under our climate change scenario, a

number of randomly selected species equal to the difference

between the number of species predicted under current and

future climatic conditions was discarded. PD was then com-

puted for the remaining species (random remaining PD). This

operation was replicated 1,000 times for each grid cell. Finally,

we computed the mean PD of the 1,000 replicates, as well as

the 5% quantile and the P -value of predicted PD as compared

to the distribution of the 1,000 random replicates.

Results

Species distribution models

Models were fitted for 167 species of Proteaceae (of

330; 51%), 147 species of Restionaceae (of 297; 50%),

50 species of Chiroptera (of 75; 67%), and 26 species

of Platypleura (44; 59%). These models allowed pre-

dicting the potential distributions of all species under

present and future (year 2080) climatic conditions

(Fig. 1). Most models demonstrated robust predictive

power with a mean AUC of 0.98 and 0.99 and a

mean TSS value of 0.98 and 0.99, for majority rule

and average methods, respectively (evaluations val-

ues are averages obtained from the split-sample pro-

cedure). Less than 2% of models were rejected based

on insufficient predictive power. Since the AUC and

TSS-based projections with both the majority and

average methods were highly correlated (r > 0.98 in

all groups), hereafter we present only results from

the ‘TSS majority’ method. In addition, size of cur-

rent species distributions was found to be a good

predictor for both size of future distributions (R2 of

0.17, 0.26, 0.07 and 0.26 with respective P -values of

<0.0001, <0.0001, 0.045 and <0.001 for Proteaceae,

Restionaceae, Chiroptera, and Platypleura, respec-

tively) and size of the overlap between present and

future distributions (R2 of 0.23, 0.47, 0.39, 0.75 with

P -values of <0.001, <0.0001, <0.0001, and <0.0001 for

Chiroptera, Platypleura, Restionaceae and Proteaceae

respectively.

Phylogenetic tree imbalance and stemminess

Platypleura and Restionaceae communities were rela-

tively balanced and stemmy, while Restionaceae and

Chiroptera were relatively tippy in comparison (Protea-

ceae: 5.62; 11.32; Restionaceae: 4.77; �3.19; Platypleura:

1.24; 4.13, Chiroptera: �0.43; �3.18).

Global comparison of predicted vs. random remaining PD

Forty-seven, 24, 145, and 167 comparisons between pre-

dicted and random remaining PD (for Chiroptera,

Platypleura, Restionaceae, and Proteaceae, respectively)

were computed from sequential pruning events

(Fig. 2). Two of the comparisons for Chiroptera, three

for Restionaceae and 22 for Proteaceae (none for Platy-

pleura) resulted in predicted remaining PD being signif-

icantly lower than random remaining PD. Although not

significant, predicted remaining PD in Proteaceae is

higher than random as long as 38% of species are still

on the tree. After this threshold and as species continue

to be pruned predicted remaining PD becomes lower

than random. In Restionaceae, predicted remaining PD

is higher than random while the first 15% of species are

pruned (as well as between 25% and 69%) and mostly

lower than random after 69% of species have been

pruned. In Chiroptera predicted remaining PD is on

the contrary, lower until 65% of the species have been

pruned and mostly higher thereafter. In Cicadas,

predicted remaining PD is always slightly higher than

random.

Spatially explicit comparison of predicted PD vs. randomPD

Summary statistics across grid cells revealed that

P -values of predicted PD as compared to the distribu-

tion of the 1,000 random replicates were not correlated

with numbers of species present under current climatic

conditions, numbers of species predicted under future

conditions, PD values under current climatic condi-

tions, or PD values under future climatic conditions.

Grid cells which underwent significantly higher PD

losses as a result of predicted local extinctions as

opposed to randomly simulated local extinctions repre-

sented 12%, 13%, 17%, and 6% for Chiroptera, Platyple-

ura, Proteaceae, and Restionaceae respectively (Fig. 3).

Discussion

This study predicts that high concentrations of PD for

Proteaceae, Restionaceae, Chiroptera, and Platypleura

will contract considerably within the next 70 years but

that climate change will not result in higher nor lower

PD losses, on average, compared to random extinction

simulations (Fig. 1).

Our results for the Proteaceae are consistent with cli-

mate change simulations previously carried out in a

study, which assessed the relative potential impacts of

future land use and climate change on the threat status

of this plant family (Bomhard et al., 2005). However,

we found that additional species are predicted to


6 D. V. PIO et al.

(a)

(b)

Fig. 1 Predicted phylogenetic diversity (PD) and normalized discrepancy [(PD minus species richness (SR)] patterns are represented

for present and future (2080) climatic conditions in four diverse animal and plant groups: Proteaceae (a), Restionaceae (b), Chiroptera

(c), and Platypleura (d). Highest values (in red) denote larger amounts of present and future PD or larger normalized discrepancies.

More specifically, in the latter case, red grid cells denote areas where values for PD are higher than for SR (species present account for a

disproportionately high amount of PD) while blue grid cells denote areas where SR is higher than PD (species present account for a

comparatively low amount of PD).



(c)

(d)

Fig. 1 continued


8 D. V. PIO et al.

become extinct. This is because our simulations extend

for a further 60 years into the future. Our results for

Chiroptera also concur with a study evaluating

whether national parks met their mandate under cli-

mate change and land transformation scenarios by

assessing the sensitivity of 277 (nonvolant) mammals

on the African continent (Thuiller et al., 2006). This

study found that the Kalahari will likely lose the high-

est number of mammals on the African continent (Thu-

iller et al., 2006); similarly our results predicted this

region to become very barren in terms of future surviv-

ing bat PD.

Extinctions have received attention from many

researchers because they result in both a shift in com-

munity structure and interspecies interactions (Wil-

liams & Jackson, 2007; Stralberg et al., 2009), and the

deletion of portions of the history of life (Purvis et al.,

2000). An already existing substantial body of literature

tries to assess whether present species’ extinction risk is

nonrandom, exhibiting patterns of phylogenetic clump-

ing (Bennett & Owens, 1997; Purvis et al., 2000; Sakai

et al., 2002; Pilgrim et al., 2004; Sjostrom & Gross, 2006;

Faith, 2008; Vamosi & Wilson, 2008). Studying how

extinctions are distributed phylogenetically ultimately

allows us to investigate what portions of evolutionary

history may be eliminated and consequently how eco-

systems and their functioning may be affected.

Although evidence that PD constitutes a meaningful

proxy for functional diversity and evolutionary poten-

tial is still scarce, this body of literature is growing rap-

idly (Cadotte et al., 2009; Flynn et al., 2011; Cadotte,

2013) and a recent study sets out clear future research

priorities to create a solid conceptual basis for the use

and relevance of PD in conservation (Winter et al.,

2012).

Fig. 2 Predicted and mean random remaining phylogenetic

diversity (PD) for Proteaceae, Restionaceae, Platypleura, and

Chiroptera are plotted. Species predicted to undergo the largest

losses in total occupancy from present to future distributions

are dropped from their phylogenetic trees sequentially, one at a

time, until only two species are left (the minimum required to

calculate PD).

Fig. 3 All mapped grid cells represent areas that undergo local (only from that specific grid cell) extinctions as a result of climate

change for 4 groups of plants and animals: Restionaceae, Proteaceae, Platypleura, and Chiroptera. Red cells represent areas where larger

amounts of phylogenetic diversity (PD) are predicted to be lost through climate change induced local extinctions as compared to ran-

dom expectations, while black cells represent areas where there is no difference between predicted PD losses and those resulting from

random simulations.



Several studies on birds and nonvolant mammals,

but more recently also on angiosperm families, have

shown evidence for nonrandom extinction processes

(Bennett & Owens, 1997; Russell et al., 1998; Purvis

et al., 2000; Sjostrom & Gross, 2006; Vamosi & Wilson,

2008). Threat in birds for instance seems negatively cor-

related with body size and relative fecundity, which

may have predisposed certain lineages to extinction

(Bennett & Owens, 1997). Studies on angiosperms

found that elevated risk was found in smaller clades

(Vamosi & Wilson, 2008). However, other studies on

regional and global floral datasets found contrasting or

mixed evidence for nonrandom threat or rarity patterns

(Schwartz & Simberloff, 2001; Webb & Pitman, 2002;

Pilgrim et al., 2004; Davies et al., 2011; Thuiller et al.,

2011).

Some of the studies that found evidence for nonran-

dom extinction, have pointed out that once phylogenet-

ically distributed traits have already mediated

considerable extinction, then many monotypic genera

or families might be the last survivors of once-larger

clades and cause disproportionate PD loss (Purvis et al.,

2000). Nonrandom patterns may then be likely if a large

percentage of each group has already been lost. In our

simulations, however, both at the landscape and at the

grid-cell level, there was no evidence of PD losses

depending on the number of species already pruned

from a tree.

Using detailed phylogenetic information, a recent

study shows that plant extinctions from the Cape

Floristic Region may result in little loss of evolutionary

history, and that this may be true because biodiversity

hotspots such as the Cape Floristic Region are the

product of recent speciation (Davies et al., 2011). The

authors further argue that the processes driving extinc-

tion in relict lineages are different from those for young

diversifying lineages and that their findings may be

particularly relevant to recently radiated plant groups.

Although focusing on the same region and some of the

same groups, there are several important differences

which prevent direct comparison of these interesting

findings with the present study. Firstly, Davies et al.

(2011) are investigating extinction as a measure of

accumulated threats, not specifically climate change.

Second, the present study includes an animal group

(Chiroptera) for which Southern Africa is not the center

of speciation.

In the majority of the published literature, extinction

is indeed to be simulated through a threat status as a

measure of accumulated threats and most often

considers drastic land-use changes. Gradually changing

ecosystems, however, (for example, those that change

in response to climate change) may show different

patterns from systems subject to abrupt anthropogenic

use (Winter & Schweiger, 2011). Climate-specific effects

on the tree of life are the focus of this study and have

only been investigated once before (Thuiller et al.,

2011). Using European plants, birds, and mammals,

Thuiller et al. (2011) showed that PD loss predicted to

happen through climate change is neither higher nor

lower than expected by chance (Thuiller et al., 2011).

The present study provides similar evidence from four

Southern African groups. Simulated extinction events

based on sequential pruning of species predicted to

undergo range contractions in this study did not result

in higher (nor lower) PD losses when compared to ran-

dom simulations, except in a small number of cases. A

possible explanation of this pattern is that a species’

range, the trait which places species at risk of extinction

from climate change in this study, is a trait with very

weak or no phylogenetic signal (Carotenuto et al.,

2010).

With the ranges of most species in this study being

reduced in response to climatic changes, the degree of

habitat connectivity will likely play an important role

in the maintenance of diversity. Because of the limita-

tions intrinsic to predicting range contractions,

increased threat, and indeed to incomplete sampling,

this study does not claim specific conservation recom-

mendations for particular areas or taxonomic groups in

Southern Africa. Moreover, we do not investigate the

entire tree of life, and some species may occur outside

the study area or may shift their ranges away from the

study area. Thus, a loss within this region must not nec-

essarily be considered a total loss. What this study

does, however, is to suggest likely trends in PD loss

within a diverse groups of organisms. Predicting cli-

mate change induced effects on the tree of life globally

certainly requires further study to tease apart how dif-

ferent taxa and geographical areas may be affected but

the evidence accumulated so far suggests that focusing

resources on climate threatened species alone may not

result in disproportionate benefits for the preservation

of evolutionary history.

Acknowledgements

We thank the hundreds of volunteers who collected the datathat make up the Protea Atlas. We are indebted to Dr RichardGrenyer for comments on an earlier draft. The feedbackreceived by three anonymous referees and Dr Oliver Schweigerallowed us to make exceptional improvements on an earlier ver-sion of this manuscript. DP is funded by the Marie-Curie EarlyStage Researcher (ESR) Fellowship as part of the EU-Hotspotsproject (http://www.kew.org/hotspots) to AG and NS. NS isfunded by the Swiss National Science Foundation grant no3100A0-116412. BWP is funded by the US National ScienceFoundation grant no DEB-0955849. MHV and BWP is funded bythe National Research Foundation of South Africa grant no2069059. We thank the Swiss Institute of Bioinformatics for


10 D. V. PIO et al.

access to Vital-IT, their high performance computer center. Col-lecting and georeferencing the Restionaceae specimen dataset,and the construction of the Restionaceae phylogeny, wasfunded by SNF 31-66594-01 to H.P. Linder.

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Climate change effects on animal and plant phylogenetic diversity in southern Africa

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