Conservation of plant species under future climate and land use change

CONSERVATION OF PLANT SPECIES UNDER
FUTURE CLIMATE AND LAND-USE CHANGE

GIAM XINGLI
[B.Sc.(Hons.), National University of Singapore]

A THESIS SUBMITTED
FOR THE DEGREE OF MASTER OF SCIENCE

DEPARTMENT OF BIOLOGICAL SCIENCES
NATIONAL UNIVERSITY OF SINGAPORE
2009

i


ACKNOWLEDGEMENTS
I would like to thank the Department of Biological Sciences, National University for
funding my research through the graduate research scholarship. Additional support was
provided in the form of the Endeavour Research Fellowship by the Government of
Australia to fund my research visit to The Environment Institute of The University of
Adelaide.
I would like to express my sincerest gratitude to my supervisors, Associate
Professor Hugh Tan, Professor Navjot Sodhi, and Associate Professor Corey Bradshaw
(The University of Adelaide) for their invaluable guidance, insightful comments, and
ready support. My three supervisors have really helped me to grow as an ecologist-intraining, as well as a person, in the last two to three years. I also thank the two examiners
for critical comments that helped to improve the thesis.
I would also like to thank Professor Barry Brook (The University of Adelaide) for
insightful discussions and commenting on parts of my thesis. Also, I am grateful to Tien
Ming Lee (University of California, San Diego) for readily supplying raw data for future
land transformation, helpful discussions, and commenting on parts of my thesis.

My colleagues in the Plant Systematics Lab provided me with the motivation to
turn up for work every morning with their wicked sense of humour, free coffee, biscuits,
home-baked cakes, and most importantly, friendship. I thank Ang Wee Foong, Chong
Kwek Yan, Alvin Lok, Ng Peixin, Ng Ting Hui, Edwin Phua, Tan Kai-xin, Alex Yee,
and Yeo Chow Khoon for that.
I am grateful to the members of the Global Ecology Laboratory for helpful
discussions and making me feel at home in my five month research stint at The
University of Adelaide. I thank Bert Harris, Salvador Herrando-Perez, Siobhan de Little,
Tom Wanger, Ana Sequiera, Michael Stead, Zheng Dandong, Drs. Damien Fordham and
Camille Mellin. In particular, Drs. Steven Delean and Lochran Traill provided insightful
comments and helpful advice that improved the thesis.
Finally, my deepest appreciation and thanks to my family and my lovely wife,
Gillian Goh-Giam. Their unflinching support and unconditional love made my life more
meaningful and gave me the added strength and motivation needed for my endeavours.

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TABLE OF CONTENTS
Acknowledgements

i

Table of contents

ii

Summary

iv

List of tables

vi

List of figures

vii

List of appendices

viii

1.

INTRODUCTION

1

2.

FUTURE HABITAT LOSS AND THREATENED
PLANT BIODIVERSITY
2.1. INTRODUCTION

5

2.2. METHODS
2.2.1.

Number of globally threatened plant species

8

2.2.2.

National index of relative plant species endangerment

10

2.2.3.

Future habitat loss from land-use and climate changes

11

2.2.4.

Future plant species endangerment rankings

12

2.2.5.

Current effort in conducting conservation
assessments of plant species by IUCN

13

2.2.6.

Wealth, governance, and future conservation need

13

2.3.1.

Patterns of current plant species endangerment

16

2.3.2.

Exposure of threatened plant biodiversity to future
habitat loss

18

2.3.2.1.

Habitat loss owing to climate change

18

2.3.2.2.

Habitat loss owing to land-use change

20

2.3. RESULTS

2.4.

2.3.3.

Future plant species endangerment rankings

22

2.3.4.

Countries with the greatest future conservation need

24

DISCUSSION

26

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3.

RELATIVE NEED FOR CONSERVATION ASSESSMENTS OF
PLANT SPECIES AMONG ECOREGIONS
3.1. INTRODUCTION

33

3.2. METHODS
3.2.1.

Ranking ecoregions based on plant species richness

36

3.2.2 .

Historical habitat loss

40

3.2.3.

Future human population pressure (2005-2015)

40

3.2.4.

Allocating the relative need for conservation
assessments among ecoregions

40

3.2.5.

Testing the overlap of this template

with existing schemes

41

3.2.6.

Availability of financial resources in
ecoregions with high relative need for
conservation assessments

42

3.3. RESULTS
3.3.1.

Ecoregions of high relative need for
conservation assessments

42

3.3.2.

Species-area models used in ranking
plant species richness

46

3.3.3.

Overlap with existing schemes

51

3.3.4.

Lack of financial resources in ecoregions
important for conservation assessments

52

3.4. DISCUSSION

55

4.

CONCLUSIONS

58

5.

REFERENCES

61

6.

APPENDICES

71

iii


SUMMARY
Habitat loss resulting from human land-use change and climate change threatens plant
biodiversity on Earth. I assessed the vulnerability of globally threatened plant
biodiversity to future habitat loss over the first half of this century by testing countrylevel associations between threatened plant species richness and future habitat loss owing
to, separately, land-use and climate changes. In countries that overlap with biodiversity
hotspots, plant species endangerment increases with climate change-driven habitat loss.
The same pattern was observed among tropical countries. This association suggests that
many currently threatened plant species may become extinct owing to anthropogenic
climate change in the absence of potentially mitigating factors such as natural and
assisted range shift, and physiological and genetic adaptations. Countries ranked the
highest in future plant species endangerment are concentrated around the equator. The
current conservation assessment effort by IUCN was positively correlated with future
plant species endangerment, suggesting that the conservation assessment program is
generally efficient in targeting the most threatened countries. Because poverty and poor
governance can compromise conservation, I considered the economic condition and
quality of governance with future plant species endangerment to prioritize countries
based on conservation need. I identified Angola, Cuba, Democratic Republic of Congo,
Ethiopia, Kenya, Laos, Madagascar, Myanmar, Nepal, Tajikistan, and Tanzania as the
countries in greatest need of conservation assistance in terms of financial aid and/or
improving political institutions.
Conservation assessments aid in planning by providing valuable information about
the geographic range and population numbers of species. However, less than 5% of all

iv

plant species have been assessed. I therefore aimed to provide a template to guide
conservation assessments at the ecoregion level. First, I identified the world‘s ecoregions
that contain the highest plant species richness after controlling for area using species-area
relationship (SAR) models within a Bayesian multi-model framework. While all previous
studies have assumed that species richness is normally distributed and most applied the
power function SAR, I found that species richness was log-normally distributed across
ecoregions in most biomes and no SAR model was the best in all biomes. My results
highlight the importance of considering a wide variety of SAR models with different
error distributions to identify species-rich hotspots. Using quantitative thresholds,
ecoregions with the highest plant species richness, historical habitat loss, and projected
increase in human population density were allocated the highest relative need for
conservation assessments. My template managed to identify some important ecoregions
excluded from the Global 200 and Biodiversity Hotspots templates. Using generalized
linear models, I showed that countries overlapping with high-priority ecoregions are
poorer than the other countries. Therefore I urge international aid agencies and botanic
gardens to cooperate with local scientists to fund and implement conservation assessment
programs.
Overall, my study showed that plant biodiversity remains vulnerable to climate
change driven habitat loss, and socioeconomic factors. The international community must
consider both global and local strategies that aim to improve governance and economic
condition for conservation endeavours to be truly effective.

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LIST OF TABLES

Table 1.

Total variance explained by each component of the

14

Principal Components Analysis (PCA) of governance quality.

Table 2.

Correlations between the six dimensions of governance

14

and the principal component.

Table 3.

Species-area relationship (SAR) models investigated in this study.

38

Table 4.

DIC weights for species-area relationship models

46

fitted to each each biome.

Table 5.

List of 21 ecoregions excluded from both Biodiversity Hotspots

52

and Global 200 prioritization template but included in
my prioritization template (40th percentile threshold).

Table 6.

Generalized linear models (GLM) investigating the correlation
of per capita wealth on whether a country overlaps with
important ecoregions selected using multiple percentile
thresholds (a–f).

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54


LIST OF FIGURES
Figure 1.

Global map showing the current plant endangerment index of

17

(a) all countries, (b) hotspot countries, (c) non-hotspot countries,
(d) tropical countries, and (d) temperate countries

Figure 2.

Relationship between the ranks of current endangerment index

19

and ranks of future habitat loss owing to climate change.

Figure 3.

Relationship between the ranks of current endangerment index

21

and ranks of future habitat loss owing to land-use change.

Figure 4.

Global map of the country rankings for future plant

23

species endangerment.

Figure 5.

Countries classed into five categories of conservation need

25

(Categories 1–5) in terms of (a) economic wealth,

and (b) governance quality.

Figure 6.

Relative need for the conservation assessment of plant
species among 756 global ecoregions.

45

Figure 7.

Individual and model-averaged special-area relationship

47

(SAR) models fitted using a Markov Chain Monte-Carlo
(MCMC) procedure implemented in WinBUGS.

vii


LIST OF APPENDICES
Appendix 1. Additional sources of total and endemic species data
used in analyses.

71

Appendix 2. Supplementary tables

74

Table S1. Generalized linear models (GLM) investigating
whether an increase in area decreases the odds of
endangerment given that odds of endemism
are kept constant.

74

Table S2. List of countries ranked by the current endangerment
index of plant species.

75

Table S3. Spearman’s rank-order correlations between current
plant species endangerment index and future habitat loss.

81

Table S4. List of countries ranked in terms of conservation need.

82

Table S5. Full dataset used in allocating the relative conservation
assessment need of ecoregions.

83

Table S6. List of ecoregions and their respective overlapping
biodiversity hotspots and Global 200 ecoregions.

110

Table S7. Countries overlapping with important ecoregions at
multiple thresholds (10th to 80th percentile).

132

Appendix 3. Supplementary figures

138

Figure S1. Global distribution of habitat loss owing to
climate change and land-use change.

138

Figure S2. Fitted curve, normal probability plot, and residual
plot of non-linear species-area relationship (SAR) models,
fitted by mimimizing the residual sum of squares.

139

Figure S3. The number of ecoregions and their breakdown by biome
type in each category of relative conservation
assessment need.

153

Figure S4. The number of ecoregions and their breakdown by
biogeographic realm type in each category of relative

154

viii


conservation assessment need.
Figure S3. Location of the two ecoregions excluded from both
G200 and BH templates.
Appendix 4. Statistical codes for WinBUGS

ix

155

157


1.

INTRODUCTION

Plant species, being autotrophs, are the fundamental components of most ecosystems on
Earth. They support non-plant taxa by serving as the foundation of most food webs
(Huston 1994; Primack & Corlett 2005), and are involved in many ecological processes
necessary for the maintenance of life on Earth (Hamilton & Hamilton 2006). They also
provide food and materials for humans (Kier et al. 2005). However, plant species and
other terrestrial biodiversity are endangered by habitat losses resulting from
anthropogenic land-use changes and climatic changes (Millennium Ecosystem
Assessment 2005; Thomas et al. 2006; Bradshaw et al. 2009a). In the tropics, there is

convincing evidence of rapid forest decline in recent decades (Bradshaw et al. 2009a) of
10.2 million ha annually (Hansen & DeFries 2004). Temperate grasslands, temperate
broadleaf forests, and Mediterranean forests have all experienced at least 35% conversion
to cropland (Millennium Ecosystem Assessment 2005), and even the boreal forest zone
has become highly fragmented (Bradshaw et al., 2009b). Recent climate warming has
already resulted in geographical range contraction of butterflies (Wilson et al. 2005).
Land transformations resulting from climate change and land use change are projected to
cause a 21-26% reduction in the mean geographic range of bird species by year 2050
(Jetz et al. 2007).
With high expected rates of continued habitat loss owing to anthropogenic
activities, many plant species face extinction, thereby compromising ecosystem services
that sustain the quality of life for billions of people (Daily 1997; Ehrlich & Pringle 2008).
This begs the question, what conservation strategies can we employ to prevent or slow

1


down the loss of plant species? This thesis, consisting of two main papers (Chapters 2
and 3), aims to answer this main question as well as other related questions.
In Chapter 2 (Future Habitat Loss and Threatened Plant Biodiversity1), I assessed
the exposure of threatened plant biodiversity to land-use and climate change-driven
habitat loss by testing the country-level correlation between the level of plant species
endangerment and climate change induced habitat loss, as well as human land-use change
induced habitat loss, separately. Next, I related the current species endangerment level to
future habitat loss to determine the future level of endangerment following the ranking
procedures of Lee & Jetz (2008). To evaluate the efficacy of the current prioritization
pattern of IUCN species assessments under future scenarios, I tested the association
between the proportion of species assessed in a country and future plant species
endangerment. Lastly, I incorporated economic condition and quality of governance with
future plant species endangerment ranks to determine countries of high conservation

need. By considering these projected impacts together with governance quality and
poverty, my system of conservation ranking allows national lawmakers and the
international community to prioritize conservation management.
In Chapter 3 (Relative need for conservation assessments of plant species among
ecoregions2), I provide a new template to guide the conservation assessment of plant
species using the World Wildlife Fund (WWF) ecoregions framework (Olson et al.
2001). Conservation assessments are conducted to acquire knowledge on the population
numbers and geographic range of all species, so that conservation managers can focus on
1

The contents of this chapter have been incorporated into a paper submitted to the journal, Conservation
Biology. The paper has been reviewed by two anonymous referees and is currently in revision stage.
2

The contents of this chapter have been incorporated into a paper submitted to the journal, Journal of
Biogeography. The paper is currently in review.

2


species with greatest risk of extinction. However conservation assessments have only
been carried out on less than 5% of all plant species, hence many plant species may be
driven to extinction even before their population numbers and range become known to
scientists. Speeding up the assessment process is therefore crucial to effective
conservation. However, economic and other resources available for conservation are
finite, and especially limited in the developing world (Sodhi et al. 2007), which mandates
the use of prioritization. Species-area curves are commonly used to identify the most
speciose ecoregions (Fattorini 2006; Guilhaumon et al. 2008) and I demonstrated the
importance of, and provided a framework for, considering uncertainty in the species-area
relationship (SAR) models and distribution. Ecoregions with the highest species richness,

and at the same time, highest degree of habitat loss and future human population
pressure, is likely to have the greatest number of declining and were hence allocated the
highest need for conservation assessments. Lastly, to evaluate the challenge posed by the
lack of financial resources, I used generalized linear models (GLM) to test if countries
that overlap with ecoregions deemed important for conservation assessments are poorer
compared to others.
In this thesis, I provided the first global-scale assessment of the association
between threatened plant species and future land use- and climate change-driven habitat
loss. My study is the first attempt to prioritize the conservation assessment of plant
species by taking into account uncertainty in the form and distribution of SAR models, as
well as, current and future endangerment levels by introducing historical habitat loss and
future population change as criteria for prioritization. I hope that the results of this study
translate to the intensification of conservation effort in countries with the greatest

3


conservation need and stimulate increased scientific activity that contributes to the
conservation assessment of plant species in specious, and at the same time, highlythreatened ecoregions.

4


2.

FUTURE HABITAT LOSS AND THREATENED

PLANT BIODIVERSITY
2.1.

INTRODUCTION

Human-driven land-use and climatic changes are perhaps the greatest threats to terrestrial
biodiversity (Millennium Ecosystem Assessment 2005a; IPCC 2007) given the mounting
empirical evidence that these anthropogenic forcings substantially exacerbate species‘
endangerment (Brook et al. 2003; Sodhi et al. 2008). As these environmental changes are
likely to continue into the future (Millennium Ecosystem Assessment 2005b), it is
important to assess the impacts of these changes on biodiversity for effective
prioritization of future conservation efforts (Lee & Jetz 2008).
In particular, the impacts of land-use and climatic changes on plant biodiversity
will have extensive ramifications on other taxa and human society given that plants are
fundamental structural and nutrient-sequestering components of most ecosystems. Not
only do plants produce resources that support non-plant biodiversity (Huston 1994;
Primack & Corlett 2005), they also provide food and materials essential for human
existence (Kier et al. 2005), and are involved in many ecological processes necessary for
the persistence of life (Hamilton & Hamilton 2006). Several studies have predicted the
future extinction patterns of plant species based on land-use and climate change
projections (Thuiller et al. 2005; Van Vuuren et al. 2006), but none has explicitly
examined the association between the current endangerment and future habitat loss (e.g.,
Lee & Jetz 2008 for vertebrates). Threatened plant species are more likely to be driven
towards extinction by future habitat loss than non-threatened species because the former
are restricted in range and consist of fewer individuals. I can therefore determine whether

5


future land-use and climatic changes exacerbate extinction risk predictions by testing
whether these will have the greatest negative influences in areas already characterized the
highest number of currently threatened species. Therefore, realistic projections of
conservation impact should not only take into account the magnitude of predicted habitat

loss and degradation, they must also take into the account the distribution of species
currently threatened with extinction.
Here I assessed the exposure of threatened plant biodiversity to land-use and
climate change-driven habitat loss up to year 2050 by testing the hypothesis that
countries with more threatened plant species richness (after controlling for the effect of
area) are likely to suffer from greater relative habitat loss given recent historical trends. I
estimated country-specific plant species endangerment by (i) using the number of
endemic plant species per country as a proxy for the number of threatened species (see
Methods and Supplementary Methods, Online Supplementary Material [SM]), and (ii)
using the residuals in the power-law endemic species-country area relationship as an
index of endangerment. I then quantified the potential extent of future habitat loss owing
to land-use and climate changes up to 2050 in each country (data from Lee & Jetz 2008)
derived from the Millennium Ecosystem Assessment (2005b). I elucidated the
distribution of future plant species endangerment by relating the current level of plant
species endangerment with the degree of projected total habitat loss (Lee & Jetz 2008) to
determine which countries are most prone to plant biodiversity loss. To evaluate the
efficacy of the current prioritization pattern of IUCN species assessments under future
scenarios, I tested the association between the proportion of species assessed in a country
and future plant species endangerment. I considered countries that overlap with

6


biodiversity hotspots (Myers 2000; Mittermeier et al. 2004) separately to those that do
not because biodiversity hotspots are considered urgent conservation priorities owing to
high plant endemicity (~150000 endemic species in total) and high historical habitat loss
(Myers 2000; Mittermeier et al. 2004). Like biodiversity hotspots, tropical regions are
focal areas for conservation because they are highly biodiverse, but at the same time,
threatened by high rates of habitat degradation (Laurance 2007; Bradshaw et al. 2009a). I
therefore considered tropical countries separately from temperate countries in my

analyses.
Less wealthy countries have limited financial means for conservation projects
such the enforcement and monitoring of protected areas (Bruner et al. 2004); hence,
species there may be at greater risk from habitat loss, direct harvesting, and
encroachment of invasive alien species. Poverty may also lead to unsustainable
exploitation of resources (Kerr & Currie 1995) and could therefore exacerbate species
loss through direct harvesting. Poor enforcement of existing legislation, weak governance
and lack of political will and corruption can result in the degradation of biodiversity
owing to ineffective conservation management (O'Connor et al. 2003) and high
deforestation rates in developing countries (Geist & Lambin 2002). In addition,
conservation efforts may be compromised by decision-making processes in other sectors
(Deutz 2005), such as, economic planning and residential land-use planning, in the
absence of effective high-level coordination within and between national ministries (Bojö
& Chandra Reddy 2001). As poverty and poor governance were shown to have adverse
impacts on biodiversity conservation, I identified countries of high conservation need by
considering their wealth and quality of governance with the future plant species

7


endangerment ranks. Poor countries with low-quality governance and high future plant
species endangerment were identified as having the highest conservation need.
I provided the first global-scale assessment of the association between threatened
plant species and future land use- and climate change-driven habitat loss, and present
plausible policies towards plant species conservation. By considering these projected
impacts together with governance quality and poverty, my system of conservation
ranking allows national lawmakers and the international community to prioritize
conservation management.

2.2.

METHODS

2.2.1.

Number of globally threatened plant species

The number of endemic species was used as a proxy for the number of threatened species
because the large taxonomic gap in the current (post-1997) IUCN Red List ― where only
about 12000 species out of a total flora of 223300–422000 species were assessed ―
limits its use to infer global patterns of extinction risk (Pitman & Jorgensen 2002). My
two main sources of country-level data on the number of endemic plant species were
datasets from Pitman & Jorgensen (2002) and United Nations Environment Programme‘s
World Conservation Monitoring Centre (UNEP-WCMC) (World Resources Institute
2007). For countries with missing data, I used values in national biodiversity reports and
Floras wherever possible (Appendix 1).
Although the number of endemic species is correlated with the number of
threatened species (Pearson's r = 0.78, in European countries with reliable threatened
species data, Pitman & Jorgensen 2002), using endemism as a proxy for endangerment

8


may be confounded by differences in the size of each country. For instance, it is logical to
posit that species endemic to a large country are less likely to be endangered because
their potential range size is larger than that of species endemic to a smaller country. To
test for this possible bias, I constructed a set of generalized linear models (GLM) with a
binomial error distribution and logit link function. I set the log-proportional odds of
endangerment [loge(number of threatened species ÷ number of non-threatened species)]
as the response and the loge-transformed country area and the log-proportional odds of

endemism [loge(number of endemic species ÷ number of non-endemic species)] as the
predictors. I computed a measure of overdispersion by dividing the observed deviance of
the global model by its degrees of freedom (Burnham & Anderson 2002; Franklin et al.
2002). Following Pitman and Jorgensen (2002), I used data from European countries only
(n = 32) because temperate countries tend to have more accurate tallies of threatened
flora than tropical countries. I followed Pitman & Jorgensen (2002) in using the number
of globally threatened species listed in the 1997 IUCN Red List of Threatened Plants
(Walter & Gillett 1998) because the 1997 Red List evaluated the threat levels of most
species on Earth by combining national and regional threatened species lists, while only
about 12000 out of a total global flora of about 223300–422000 species (Prance et al.,
2000; Govaerts, 2001; Bramwell, 2002; Scotland & Wortley, 2003) were assessed up to
year 2008 in all post-1997 IUCN Red Lists.
The most parsimonious model evaluated by QAICc (quasi Akaike‘s information
criterion corrected for small sample sizes and over-dispersion) contained both log-odds of
endemism and log-transformed area as predictors of the log-transformed odds of
endangerment (wQAICc [model weight] = 0.675) (Table S1 in Appendix 2). This model

9


refutes the suggested bias because the odds of endangerment increase, rather than
decrease, with area when odds of endemism are kept constant. These results suggest that
the potential bias due to country area is absent or weak. The second-ranked model in
which log-odds of endangerment increases with the log-odds of endemism (wQAICc
=0.396) had a high percentage deviance explained (~ 41%), suggesting the level of
endemism is the main predictor of endangerment, not area. I therefore argue that the
number of endemic species is a good proxy for the number of threatened species when
accurate endangerment data are absent across countries that overlap with biodiversity
hotspots.

2.2.2.

National index of relative plant species endangerment

Among-country comparisons of the raw number of globally threatened species cannot be
made because of differing land areas; therefore, I fitted the power-law species-area
relationship (SAR) (S = cAz, where S = endemic species richness as a proxy for number
of threatened species, A = country area, z = the power coefficient and c = a constant;
Arrhenius 1921) to 196 countries and considered the residuals as a proxy of relative
species endangerment. Country-area data were obtained from the World Resources
Institute (2007) EarthTrends database. I am cognizant that the curvilinear form of the
SAR is likely to provide a more realistic detection of the hotspots of endangerment
compared to the linearized form (Fattorini 2007); hence, I first fitted the curvilinear form
of the power-law SAR using the nls function in R v.2.8 (R Development Core Team
2008). I calculated starting parameter values based on standard procedures described in
Ratkowsky (1990). However, the curvilinear model was untenable because the residuals

10


were ascertained to be non-Gaussian and heteroscedastic via visual inspection of the
residual plot. I then reverted to fit the linearized SAR model (loge[number of endemic
species + 1] ~ loge[country area]) (e.g., Balmford & Long 1995; Lee & Jetz 2008) using
glm in R. Log-transformed area explained ~ 25.5 % of the deviance in the logtransformed number of endemic species. The residuals of this model were taken as an
index of plant species endangerment controlling for country area.

2.2.3.

Future habitat loss from land-use and climate changes

The Millennium Ecosystem Assessment (2005a) developed four socioeconomic scenarios
that delineated possible future outcomes of terrestrial ecosystem change up to 2050
(Adapting Mosaic [AM], Global Orchestration [GO], Order from Strength [OS], and
TechnoGarden [TG]). Land-cover projections in the MEA were made based on the
IMAGE v. 2.2 model (Image-Team 2001), which provided current and projected areal
distributions for 18 land-cover classes at 0.5° resolution. The IMAGE model generates
explicit forecasts of land cover using a set of linked and integrated socioeconomic,
climate and environmental models (described in detail in Alcamo et al. 1998; Millennium
Ecosystem Assessment 2005b). I obtained raw data from Lee and Jetz (2008) who
calculated the percentage of area subjected to land-cover transformation in 174 countries
owing to land-use and climate changes over the four MEA socioeconomic scenarios from
2000 to 2050. Lee and Jetz (2008) classified transformation from natural- to humaninduced land cover types as land-use-driven (e.g., pristine forest converted to agricultural
land), and change from one natural land-cover category (e.g., pristine forest converted to
savanna) as driven by climate change. Thus, my metric for climate change-driven habitat

11


loss was the average area projected to undergo transformation from one natural landcover category to another under the four socioeconomic scenarios to the year 2050
expressed as a percentage of the total land area of a country, and the metric for future
habitat loss owing to land-use change was the average area projected to be converted via
human land-use change divided under the four scenarios to year 2050 expressed as a
percentage of total land area. I used the percentage land-cover transformations averaged
over all four scenarios for my main analyses because the ‗actual‘ future is likely to fall in
between the four scenarios (Millennium Ecosystem Assessment 2005b). I also conducted
additional analyses using land-cover transformation data from each of the four scenarios
to examine the sensitivity of my results towards the dominance of any one particular
scenario. I used Spearman‘s ρ rank-order correlation to test the correlation between the
index of species endangerment and future habitat loss owing to land-use and climate
changes.

2.2.4

Future plant species endangerment rankings

By relating the current level of plant species endangerment to total future habitat loss, I
ranked 163 countries (with data available for future habitat loss and current
endangerment index) according to their future level of plant species endangerment (Lee
& Jetz 2008). Each country was separately ranked in terms of total future habitat loss
(from both land-use and climate changes) and current species endangerment. I averaged
the percentile rank values of these two measures and re-ranked the derived value in
descending order to obtain a global ranking of future plant species endangerment (after
Lee & Jetz 2008).

12


2.2.5. Current effort in conducting conservation assessments of plant species by
IUCN
The IUCN Red List categorizes species into relative threat categories and provides
information on the reasons for the categorization. To quantify current effort in assessing
species endangerment, I calculated the proportion of the number of plant species assessed
in each country up to year 2008 in the current (post-1997) Red List
(www.iucnredlist.org). The total number of plant species in each country was the average
of the numbers collated from Pitman and Jorgensen (2002), and UNEP-WCMC (World
Resources Institute 2007). I used Spearman‘s ρ rank-order correlation to test the
concordance between the current effort in assessing species endangerment and future
plant species endangerment.

2.2.6.

Wealth, governance, and future conservation need

I adopted per capita gross national income adjusted for purchasing power parity (GNIPPP) averaged from 2003 to 2007 as a measure of a country‘s relative wealth. Per capita
GNI-PPP data were collated from the World Bank World Development Indicators
database (www.worldbank.org/data). I obtained governance quality data from the 2008
Worldwide Governance Indicators (WGI) project (Kaufmann et al. 2008) that appraised
countries using indicators of six dimensions of governance: voice and accountability,
political stability, government effectiveness, regulatory quality, rule of law, and control
of corruption. For each of the six indicators, a score of –2.5 (lowest quality of
governance) to 2.5 (highest quality of governance) was allocated to each country.

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Because future projections of governance were not available, I calculated average values
of each of these six dimensions for each country from 2003 to 2007 to obtain a plausible
estimate of the relative future governance quality of each country. I used principal
component analysis to extract only one component (explaining 86.8 % of the variance;
Table 1) consisting of all six dimensions because of strong inter-correlations (Table 2).
Human population increase (Davies et al. 2006) was excluded in my analyses for
conservation need because it was used to model land-use change in IMAGE 2.2.

Table 1. Total variance explained by each component of the Principal Components
Analysis (PCA) of governance quality. Only one component (1) is extracted.
Initial Eigenvalues
Component
Total

% of

Cumulative
Variance
%
86.788
86.788

1

5.207

2

.375

6.253

93.042

3

.262

4.360

97.402

4

.091

1.509

98.911

5

.041

.682

99.593

6

.024

.407

100.000

Extraction Sums of Squared
Loadings
% of
Cumulative
Total
Variance
%
5.207
86.788
86.788

Table 2. Correlations between the six dimensions of governance and the
principal component
Component 1
Dimensions of governance

Correlations
.885

Voice and accountability
Political stability

.842

Government effectiveness

.965

Regulatory quality

.952

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Rule of law

.974

Control of corruption

.964

By relating the future plant species endangerment (see section 2.2.4) to economic
wealth and governance quality, I took into account the exacerbating effects of poverty
and poor governance on biodiversity and identifed countries with the greatest need for
conservation efforts. My analysis of future conservation need included 145 countries
(using economic wealth as a metric) and 157 (using governance quality as a metric)
countries after removing 18 countries with unavailable wealth data and six countries with
unavailable governance quality data. Like previous studies (e.g., Myers et al. 2000), I
introduced quantitative thresholds for the designation of high-priority countries. This
multiple-threshold method ranked countries in five categories of decreasing future
conservation need. I assigned countries ranked in the top 20 % in future plant species
endangerment, and the bottom 20 % in governance quality or per capita GNI-PPP, as
those having the greatest conservation need (Category 1). I placed countries ranked in
successive 20% increments in future plant species endangerment and governance quality
or wealth in categories of decreasing conservation need (e.g., 60%, Category 2; 40%;
Category 3).
I recognize that the economic condition and governance quality of countries may
change quickly especially in politically-turbulent regions (e.g., sub-Saharan Africa) and
may affect the accuracy of my analyses. However, as the future economic condition and
governance quality cannot be quantified with certainty, and that the effects of economic
condition and governance quality on species endangerment is currently on-going, I argue

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