Article title: Climate change mitigation policies reduce the rate and magnitude

of ecosystem impacts

Short running title: Climate change mitigation and global ecosystem impacts

Author names: Andrew J. Hartley12, Richard J. J. Gilham1, Carlo Buontempo1 and

Richard A. Betts12

Author research addresses:

1 Met Office Hadley Centre, FitzRoy Road, Exeter, EX1 3PB, UK

2 Department of Geography, University of Exeter, The Queen's Drive, Exeter,

Devon, EX4 4QJ, UK

Correspondence author address and e-mail:

Met Office Hadley Centre, FitzRoy Road, Exeter, EX1 3PB, UK

andrew.hartley@metoffice.gov.uk

Article type: Research Paper

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ABSTRACT

Aim

To show the impacts of climate change mitigation on the rate and magnitude of

change in the climate that influences large-scale ecosystems.

Location

Results are calculated for all terrestrial land areas free of ice, and summarized

for 35 places of high conservation priority. We focus on 6 areas of high

conservation priority: Altai-Sayan Montane Forests, Orinoco River and Flooded

Forests, Chihuahuan Deserts, Congo Basin, Southwest Australia, and Coastal

West Africa.

Methods

We use a simple metric of change based on statistical distance within the

Holdridge Life Zone classification space (Hdistance) to quantify ecosystem-

relevant change in climate between a baseline average climate (1961-1990) and

each year in a 150 year time series (1950-2099). We apply this metric to a 58

member ensemble of GCM projections, for a business as usual scenario and an

aggressive climate change mitigation scenario. The rate and magnitude of change

in the Hdistance is calculated for each ensemble member.

Results

We find that more than 50% of high conservation priority areas show divergence

in the rate and magnitude of change in the Hdistance metric when comparing a

business as usual emissions scenario (A1B) with an aggressive carbon dioxide

mitigation scenario (RCP2.6). In other high priority areas we find that potentially

important thresholds are exceeded even with small changes in the Hdistance

under scenario A1B.

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Main conclusions

We conclude that potentially dangerous impacts to high priority ecosystems can

be avoided in many parts of the world by a global policy of aggressive climate

change mitigation. Even though in some cases, the long term magnitude of

change threshold is exceeded under RCP2.6, this generally occurs later in the

century, allowing more time for ecosystems to adapt.

KEYWORDS

Climate change; mitigation; conservation planning; ecosystem impacts; potential

ecosystems; Holdridge Life Zones; Perturbed Parameter Ensemble; WWF

Priority Places; Biodiversity

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INTRODUCTION

Conservation planners need to know what challenges lie ahead for global

ecosystems and biodiversity if different courses of action are taken by the

world’s governments in response to anthropogenic climate change. This presents

challenges to climate scientists to ensure that policy relevant climate modeling

experiments are conducted and communicated effectively to users in the

conservation community. Likewise, in order for conservation science to progress

from the bioclimatic envelope approach, new methods need to be developed to

incorporate higher temporal resolutions of climate data into models of species’

population dynamics (Huntley et al., 2010; Keith et al., 2008; Anderson et al.,

2009).

To date, the majority of biodiversity impacts studies have chosen to use

temporally aggregated and spatially disaggregated changes derived from General

Circulation Models (GCMs; e.g. Tabor & Williams, 2010) under a variety of future

socio-economic scenarios (N. Nakicenovic et al., 2000). In doing so, potentially

important information on inter-annual or intra-seasonal variability has been

disregarded in biodiversity projections. Additionally, the spatial disaggregation

of GCM data creates greater uncertainty in conservation policies at ecoregion and

local scales (Wiens & Bachelet, 2010). As models for predicting the impact of

climate change on global biodiversity begin to consider interactions between

population dynamics and species’ ranges (Anderson et al., 2009),

conservation scientists must develop more robust methods to integrate

projections from large ensembles of GCMs that have been designed to

address specific climate change policy questions. Huntley et al. (2010)

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propose that the next generation of integrated model should include climatic

and habitat suitability, population dynamics and dispersal ability. They

present this in the context of the range of species responses to the magnitude

and rate of change. They also argue that there is a role to play for models of

intermediate complexity, especially given the shortage of suitable

information on species’ population responses to change.

In this paper we report the outcome of using a relatively simple metric for

ecosystem change with an ensemble of GCM projections to assess the possible

rate and magnitude of ecosystem change. We compare the results obtained from

using both a 'business as usual' and an aggressive mitigation future scenario with

the aim of demonstrating the possible effect of policy decisions on ecosystems. In

addition, we aim to show projected changes in the context of thresholds in

the rate and magnitude of change. This is investigated using a large ensemble

of GCMs to explore uncertainties due to the parameterization of the Hadley

Centre model HadCM3C. The rate and magnitude of ecosystem change is

quantified using a new measure, defined as the distance of change within the

Holdridge Life Zone conceptual space.

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METHODS

A new measure of ecosystem change

The Holdridge Life Zone system (Holdridge, 1967) was one of the first

classifications to relate climatic variables to large-scale ecosystems. It has the

advantage of being relatively simple to implement whilst allowing the objective

relation of temperature and precipitation variables to either potential biomes,

altitudinal zones or potential vegetation types (the combination of which was

termed “Life Zones” by Holdridge). An important caveat in this approach is

acknowledged in the term 'potential'. Climate is only one of many factors that

contribute towards determining the existence of a particular vegetation type at a

given time and location. Other factors that may influence vegetation type, such as

CO2 effects, ozone, nutrient availability and soil condition are not accounted for

by the Holdridge system. Nevertheless, we argue that the general approach is

still relevant, as many existing studies use temperature and precipitation

variables to quantify the impacts of climate change on species or ecosystems

(Velarde et al., 2005; Lugo et al., 1999; Good et al., 2011).

Rather than classifying particular grid cells into discrete Life Zones, we use the

axes of Mean Annual Biotemperature and Annual Precipitation as a means of

defining a statistical space that is relevant to ecosystems (see Appendix S1 in

Supporting Information for calculations). Within this statistical space, we

calculate the distance of change between a baseline climate and a future climate

(see Appendix S2). Since these axes are not orthogonal, a trigonometric

transformation is used to obtain the distance of separation between the baseline

and a given point in time (see Appendix S3).

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This distance metric (Hdistance) can be thought of as a vector of movement

between two points within the Holdridge diagram (see Appendix S2). It provides

an objective measure of change between two time periods that allows

comparison of changes between different ecosystems in different climatic zones.

The use of Hdistance provides a continuous metric for evaluating the GCM

output. This represents an advantage over measuring discrete transitions from

one vegetation class to another. This will help to compare the ecosystem impacts

in different parts of the world, for example, whether the projected large warming

of the Russian tundra is a more or less disruptive ecosystem perturbation than

the wetting of the eastern Sahara.

Climate change projections

In this study we apply the Hdistance measure to a large ensemble of climate

change projections based on the HadCM3C General Circulation Model (GCM;

Booth et al., 2012). This is an atmosphere-ocean-carbon cycle coupled

configuration of the original HadCM3 model (Gordon et al., 2000). It is

configured to include additionally the main elements of the carbon cycle, via

dynamic vegetation and ocean exchange, as well as an interactive sulfur cycle

scheme to account for emissions-based air pollution. The model includes flux

adjustments to account for biases in the model sea surface temperatures and

salinity compared to historical observations, as described in Collins et al. (2010).

In order to capture uncertainties related to configuration of the GCM, a 58

member perturbed parameter ensemble was created. Each member of this

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ensemble was configured with a set of parameters designed to explore the range

of uncertainty in the atmosphere, ocean, land carbon cycle and sulfur cycle. A

framework for such an approach can be found in Murphy et al. (2007), and this

Earth System Ensemble (ESE) is described in full by Lambert et al. (2012). Each

ensemble member was run using historical climate forcing from 1950 to 2000,

and two different scenarios between 2000 and 2099 (discussed below). While

the ensemble has a large number of members, the experiment was not designed

to provide probabilities of particular outcomes. It should be interpreted as a

means of exploring the range of credible outcomes from a GCM by sampling from

a large range of uncertainty.

We assessed the impacts of a climate change mitigation strategy by using two

distinct future greenhouse gas emissions scenarios. Firstly, the IPCC Special

Report on Emissions Scenarios (SRES) A1B scenario (N. Nakicenovic et al., 2000)

was used to represent a ‘business as usual’ scenario in which the world

continues to be more integrated with a balanced emphasis on all energy sources.

Secondly, the Representative Concentration Pathway 2.6 (RCP 2.6, also referred

to as RCP 3PD) was used to simulate a scenario of aggressive greenhouse gas

mitigation policy (Moss et al., 2010; see Fig. 1). Each scenario was used to force

the 58 member ESE, with the resulting difference used to show the effect an

aggressive mitigation policy may have on global ecosystems. It should be noted

that while each ensemble member is forced by two different emissions scenarios,

the total radiative forcing depends on the perturbed parameters for the

interactive carbon and sulfur cycles in each ensemble member. Additionally,

since the emissions from RCP2.6 effectively remain constant from approximately

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2020 and then reduce after 2050 (see Fig. 1), the ecosystem changes from

RCP2.6 show the changes that we have already committed to as a result of the

delayed response of the Earth system to historic greenhouse gas emissions.

Distance of change

For each ensemble member and emissions scenario, the Hdistance was

calculated (see Appendix S3). Mean annual biotemperature and mean annual

precipitation were calculated for a period of 1961 to 1990 (beginning in January

1961 ending in December 1990), and used to calculate the Hdistance between

this baseline period and each individual year in the 150 year time series (from

1950 to 2099). By calculating Hdistance over the historical period, we obtain a

measure of how much we would expect Hdistance to vary under normal climatic

conditions. If we make the reasonable assumption that ecosystems are in

equilibrium with the current climate, we may regard the Hdistance over the

observed period as an indication of the natural variability of this measure. In

other words, this is the degree to which we would expect the climate to vary

year-to-year without inducing an ecosystem change.

Rate and magnitude of change

We calculated the mean Hdistance for each decade in the 150 year time series,

relative to the 1961-1990 baseline, to give the decadal magnitude of change. The

rate of change is calculated for each year (t0) by subtracting the mean annual

Hdistance for the previous 10 years (t-11 to t0) from the mean annual Hdistance

for the next 10 years (t0 to t11). This annual rate of change was then averaged for

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each decade to give the mean decadal rate of change in the Hdistance. The time

series of magnitude of change in the Hdistance is presented according to a

selection of WWF Priority Places. These are areas selected as a focus for

conservation activity by WWF, based on a combination of their diversity and

abundance of life, threats they face and WWF’s ability to make a positive impact

within the next decade (see Fig. 2).

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RESULTS

We present here results for the following 6 WWF Priority Places: Altai-Sayan

Montane Forests, Orinoco River and Flooded Forests, Chihuahuan Deserts and

Freshwater, Congo Basin, Southwest Australia and West Africa Marine

(Terrestrial part). These regions (see Fig. 2) were selected as examples of the

variety of change in the rate and magnitude of Hdistance. A summary of the

changes found in all Priority Places can be found in Table 1. This summarises

whether or not divergence was found between the two scenarios, and whether

or not the A1B crossed a threshold of rate or magnitude.

We plotted the time series of change in the ensemble mean of Hdistance for each

Priority Place and both emissions scenarios (Fig. 3), with the shaded area

showing 1 standard deviation around the mean for each scenario. Additionally,

based on the information in Fig. 3, we plot for each decade, the mean rate of

change in the Hdistance against the total magnitude of change relative to the

1961-90 baseline (Fig. 4). Error bars show 1 standard deviation around the

decadal mean rate and magnitude of change in Hdistance. This figure also shows

the changes in relation to potentially important thresholds. These thresholds

represent the baseline variability in the Hdistance and can be interpreted as the

upper limits to variability in Hdistance between 1961-1990. The thresholds for

both magnitude and rate of change in Hdistance are based on all ensemble

members and scenarios for the 1961-1990 period compared to the 1961-1990

mean climate. For a given ensemble member and scenario, the Hdistance

between each year and the 1961-1990 mean climate was calculated, with the

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threshold magnitude being the mean magnitude of change plus 1 standard

deviation. For rate of change, the upper threshold was set at 1 standard deviation

greater than the mean.

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DISCUSSION

All WWF Priority Places show a steady increase in the Hdistance over time,

irrespective of greenhouse gas emissions scenario (Fig. 3). However, a clear

divergence emerges between the A1B scenario and RCP2.6 scenarios from

approximately 2050 in most Priority Places. Fig. 4 shows that this divergence is

more pronounced in the Altai-Sayan Montane Forests and Orinoco River Priority

Places (the full list of places where this occurs can be found in table 1). It is

notable that in the Altai-Sayan, the threshold for the rate of change is

considerably lower (0.025) than in the Orinoco River Priority Place (0.044). This

has a consequence of the mean rate of change exceeding the threshold between

the 1990s and 2030s in Altai-Sayan under RCP2.6. In contrast, the mean rate of

change under RCP 2.6 does not exceed the threshold in Orinoco River. It is also

notable that while the rate and magnitude thresholds are exceeded in the

ensemble mean for A1B, the uncertainty, shown by horizontal and vertical bars

around each decade, is much greater in Orinoco River Priority Place.

The Chihuahuan Desert, Congo Basin and Southwest Australia Priority Places are

examples of places where there is little or no divergence between A1B and

RCP2.6. While there is some divergence between the ensemble mean, the range

of uncertainty from the GCM ensemble overlaps considerably. However, these

locations also show the importance of the threshold value. For all 3 places, the

rate and magnitude thresholds are not exceeded under RCP2.6, because either

the inter-decadal change is minimal (Congo Basin), or because the thresholds are

relatively high (Chihuahuan Desert and Southwest Australia). In contrast, the

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low threshold for the Congo Basin is exceeded under A1B, albeit due to a modest

increase in rate and magnitude. We also observe that despite high thresholds in

the terrestrial part of the West Africa Marine Priority Place, the magnitude of

change under A1B still exceeds the threshold of rate and magnitude.

A main advantage of this approach is the ability to compare changes across very

different ecosystems. We propose that the Hdistance is used as a measure for

setting global scale conservation priorities for adaptation to climate change. In

comparison to results from assessments of velocity of climate change

(Dobrowski et al., 2012; Loarie et al., 2009), the Hdistance is a continuous

measure of the magnitude and rate of change at a certain location. Also, in

contrast to the majority of biodiversity impacts assessments that rely on the

identification of suitable habitats (e.g. Thomas et al., 2004; G.F. Midgley et al.,

2002), this approach is compatible with the coarser resolution of GCMs without

relying on uncertain downscaling techniques (Wiens & Bachelet, 2010; Trivedi et

al., 2008). Furthermore, in comparing A1B to RCP2.6, we provide conservation

planners and policy makers with information on the impacts of aggressive

climate change mitigation policies. Using the examples that we present, it is clear

that ecosystem impacts are not globally uniform, and in many cases can be

avoided if carbon dioxide emissions peak in approximately 2020 and decline

thereafter.

Despite these advantages, we acknowledge that the magnitude and rate of

change in the Hdistance is not influenced by the resilience of ecosystems, or the

ability of ecosystems to adapt to climate change in situ. Therefore, we

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recommend that this information be used in conjunction with species or

ecosystem vulnerability assessments (such as Foden et al., 2008; Wilson et al.,

2005; Summers et al., 2012).

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CONCLUSIONS

We have presented a new annual measure of ecosystem change, Hdistance, that

can be used as a basis for comparing the impact of climate change on large scale

ecosystems across different conservation regions of the world. We calculated the

inter-annual variability in the rate and magnitude of change in this measure, and

set thresholds based on the variability during the baseline period. Using these

thresholds, we found that an aggressive climate mitigation policy substantially

reduces the risk of exceeding potentially dangerous rates of change in the

climate affecting large scale ecosytems.

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AKNOWLEDGEMENTS

We acknowledge funding from WWF and from the Joint Department of Energy

and Climate Change (DECC) and the Department for Environment, Food and

Rural Affairs (Defra) Met Office Hadley Centre Climate Programme.

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REFERENCES

Anderson, B.J., Akçakaya, H R, Araújo, M B, Fordham, D. a, Martinez-Meyer, E.,

Thuiller, W & Brook, B.W. (2009) Dynamics of range margins for

metapopulations under climate change. Proceedings. Biological sciences /

The Royal Society, 276, 1415–20.

Booth, B. B. B., Bernie, D., McNeall, D., Hawkins, E., Caesar, J., Boulton, C.,

Friedlingstein, P. & Sexton, D. (2012) Scenario and modelling uncertainty in

global mean temperature change derived from emission driven Global

Climate Models. Earth System Dynamics Discussions, 3, 1055–1084.

Collins, Matthew, Booth, Ben B. B., Bhaskaran, B., Harris, Glen R., Murphy, James

M., Sexton, David M. H. & Webb, Mark J. (2010) Climate model errors,

feedbacks and forcings: a comparison of perturbed physics and multi-model

ensembles. Climate Dynamics, 36, 1737–1766.

Dobrowski, S.Z., Abatzoglou, J., Swanson, A.K., Greenberg, J. a., Mynsberge, A.R.,

Holden, Z. a. & Schwartz, M.K. (2012) The climate velocity of the contiguous

United States during the 20th century. Global Change Biology, n/a–n/a.

Foden, W., Mace, G.M., Vié, J.-C., Angulo, A., Butchart, S., DeVantier, L., Dublin, H.,

Gutsche, A., Stuart, S. & Turak, E. (2008) Species susceptibility to climate

change impacts. The 2008 Review of The IUCN Red List of Threatened Species.

(ed. by J.-C. Vié, C. Hilton-Taylor, and S.N. Stuart), Gland, Switzerland.

18

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

Good, P., Jones, C., Lowe, J., Betts, R., Booth, B. & Huntingford, C. (2011)

Quantifying Environmental Drivers of Future Tropical Forest Extent. Journal

of Climate, 24, 1337–1349.

Gordon, C., Cooper, C., Senior, C.A., Banks, H., Gregory, J.M., Johns, T.C., Mitchell, J.

F. B. & Wood, R.A. (2000) The simulation of SST, sea ice extents and ocean

heat transports in a version of the Hadley Centre coupled model without

flux adjustments. Climate Dynamics, 16, 147–168.

Holdridge, L.R. (1967) Life Zone Ecology, Tropical Science Center, San Jose, Costa

Rica.

Huntley, B., Barnard, P., Altwegg, R., Chambers, L., Coetzee, B.W.T., Gibson, L.,

Hockey, P. a. R., Hole, D.G., Midgley, Guy F., Underhill, L.G. & Willis, S.G.

(2010) Beyond bioclimatic envelopes: dynamic species’ range and

abundance modelling in the context of climatic change. Ecography, 621–626.

Keith, D. a, Akçakaya, H Resit, Thuiller, Wilfried, Midgley, Guy F, Pearson, R.G.,

Phillips, S.J., Regan, H.M., Araújo, Miguel B & Rebelo, T.G. (2008) Predicting

extinction risks under climate change: coupling stochastic population

models with dynamic bioclimatic habitat models. Biology letters, 4, 560–3.

Lambert, F.H., Harris, Glen R., Collins, Matthew, Murphy, James M., Sexton, David

M. H. & Booth, Ben B. B. (2012) Interactions between perturbations to

different Earth system components simulated by a fully-coupled climate

model. Climate Dynamics.

19

1

2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

Loarie, S.R., Duffy, P.B., Hamilton, H., Asner, G.P., Field, C.B. & Ackerly, D.D. (2009)

The velocity of climate change. Nature, 462, 1052–5.

Lugo, a. E., Brown, S.L., Dodson, R., Smith, T.S. & Shugart, H.H. (1999) The

Holdridge life zones of the conterminous United States in relation to

ecosystem mapping. Journal of Biogeography, 26, 1025–1038.

Midgley, G.F., Hannah, L., Millar, D., Rutherford, M.C. & Powrie, L.W. (2002)

Assessing the vulnerability of species richness to anthropogenic climate

change in a biodiversity hotspot. Global Ecology and Biogeography, 11, 445–

451.

Moss, R.H., Edmonds, J.A., Hibbard, K.A., Manning, M.R., Rose, S.K., Van Vuuren,

D.P., Carter, T.R., Emori, S., Kainuma, M., Kram, Tom, Meehl, G.A., Mitchell,

John F B, Nakicenovic, Nebojsa, Riahi, Keywan, Smith, S.J., Stouffer, R.J.,

Thomson, A.M., Weyant, J.P. & Wilbanks, T.J. (2010) The next generation of

scenarios for climate change research and assessment. Nature, 463, 747–56.

Murphy, J M, Booth, B B B, Collins, M, Harris, G R, Sexton, D M H & Webb, M J

(2007) A methodology for probabilistic predictions of regional climate

change from perturbed physics ensembles. Philosophical transactions. Series

A, Mathematical, physical, and engineering sciences, 365, 1993–2028.

Nakicenovic, N., Alcamo, J., David, G., De Vries, B., Fenhann, J., Gaffin, S., Gregory,

K., Grubler, A., Jung, T.Y., Kram, T., Rovere, E.L.L., Michaelis, L., Mori, S.,

Morita, T., Pepper, W., Pitcher, H., Price, L., Riahi, K., Roehrl, A., Rogner, H.,

Sankovski, A., Schlesinger, M., Shukla, P., Smith, S., Swart, R., Van Rooijen, S.,

20

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2

3

4

5

6

7

8

9

10

11

12

13

14

15

16

17

18

19

20

21

22

Victor, N. & Dadi, Z. (2000) IPCC Special Report on Emissions Scenarios,

Cambridge, UK and New York, NY.

Summers, D.M., Bryan, B.A., Crossman, N.D. & Meyer, W.S. (2012) Species

vulnerability to climate change: impacts on spatial conservation priorities

and species representation. Global Change Biology, 18, n/a–n/a.

Tabor, K. & Williams, J.W. (2010) Globally downscaled climate projections for

assessing the conservation impacts of climate change. Ecological

Applications, 20, 554–565.

Thomas, C.D., Cameron, A., Green, R.E., Bakkenes, M., Beaumont, L.J., Collingham,

Y.C., Erasmus, B.F.N., De Siqueira, M.F., Grainger, A., Hannah, Lee, Hughes, L.,

Huntley, B., Van Jaarsveld, A.S., Midgley, Guy F, Miles, L., Ortega-Huerta, M. a,

Peterson, a T., Phillips, O.L. & Williams, S.E. (2004) Extinction risk from

climate change. Nature, 427, 145–8.

Trivedi, M.M., Berry, P.M., Morecroft, M.D. & Dawson, T.P. (2008) Spatial scale

affects bioclimate model projections of climate change impacts on mountain

plants. Global Change Biology, 14, 1089–1103.

Velarde, S.J., Malhi, Y., Moran, D., Wright, J. & Hussain, S. (2005) Valuing the

impacts of climate change on protected areas in Africa. Ecological

Economics, 53, 21–33.

Wiens, J.A. & Bachelet, D. (2010) Matching the multiple scales of conservation

with the multiple scales of climate change. Conservation Biology, 24, 51–62.

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5

6

7

8

9

10

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12

13

14

15

16

17

18

19

20

21

Wilson, K., Pressey, R.L., Newton, A., Burgman, M., Possingham, H. & Weston, C.

(2005) Measuring and incorporating vulnerability into conservation

planning. Environmental management, 35, 527–43.

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BIOSKETCH

Andrew Hartley is a climate impacts scientist with a particular focus on the

interaction between the land surface and the climate system. His current

research interests lie in the novel application of climate science to advise

conservation planners and further improvement of earth system models.

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TABLES WITH THEIR CAPTIONS

Scenario Divergence No Scenario Divergence

Altai-Sayan (T)

Amazon Guianas (T)

Amur-Heilong (T)

Chihuahuan Desert (T)

Choco-Darien (T)

Eastern Himalayas (T)

Fynbos (T)

Greater Black Sea Basin

Lake Baikal

Mediterranean (T)

Mekong Complex (T)

Namib-Karoo (T)

Northern Great Plains (T)

Orinoco (T)

South Chile (T)

Yangtze

African Rift Lakes

Atlantic Forests

Borneo

Cerrado-Pantanal (T)

Coastal East Africa

Congo Basin (T)

Cora Triangle

Miombo Woodlands (T)

New Guinea

Southwestern Australia (T)

Sumatra

Western Ghats

Table 1. WWF Priority Places in which divergence did or did not occur between

scenarios. Divergence indicates locations at which a climate change mitigation

scenario is projected to reduce the rate and magnitude of ecosystem change

relative to the A1B scenario. Locations marked with (T) indicate places where

the A1B scenario crosses either the rate or magnitude threshold by the 2090s.

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FIGURE LEGENDS

Figure 1. Carbon dioxide emissions, expressed as gigatonnes of carbon per year,

for SRES scenario A1B and RCP2.6.

Figure 2. Location of all WWF Priority Places, and the subset selected for this

study shown in Mollweide equal area map projection.

Figure 3. Time series of change in the Hdistance relative to the 1961-1990

baeline period. The solid lines show the ensemble mean, and semi-transparent

zone shows 1 standard deviation around the ensemble mean. Values are

smoothed using a 10-year moving average.

Figure 4. Decadal changes in rate and magnitude of change in Hdistance under

RCP2.6 and A1B, relative to the 1961-1990 baseline period. Black doted lines

denote inter-annual variability (1 standard deviation) in the rate and magnitude

of change during the baseline period (1961-1990) for each Priority Place.

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FIGURES

Figure 1.

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Figure 2.

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Figure 3.

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Figure 4.

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SUPPORTING INFORMATION

Additional Supporting Information may be found in the online version of this

article:

Appendix S1 Equations for calculation of Holdridge input variables

Appendix S2 The Holdridge Life Zone system

Appendix S3 Calculation of the Hdistance

As a service to our authors and readers, this journal provides supporting

information supplied by the authors. Such materials are peer-reviewed and may

be reorganized for online delivery, but are not copy-edited or typeset. Technical

support issues arising from supporting information (other than missing files)

should be addressed to the authors.

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Supplementary Material

Appendix S1: Equations for calculation of Holdridge input variables

Biotemperature is calculated as the sum of all mean monthly temperatures that

are above freezing, divided by 12. The equation is as follows:

30

0

12

i

i

t

it

bio

tT

where ti = mean monthly temperature in degrees Celsius for a given 30 year

period.

Annual precipitation is calculated as the sum of total monthly precipitation. The

equation is as follows:

12

1

i

ann ii

P p

where pi = total monthly precipitation in mm per month, averaged over a given

30 year period.

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B

F

Appendix S2: The Holdridge Life Zone system

The axes used for calculating the Hdistance are shown in red (note they are not

orthogonal). The example shows a change from a mean baseline climate (B) to a

future climate (F) due to an increase in annual precipitation and mean annual

biotemperature. Note the log scale of both mean annual biotemperature and

annual precipitation. Source: Holdridge (1967)

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Appendix S3: Calculation of the Hdistance

The first step in our procedure was to identify a suitable metric for calculating

distances between points B and F shown in Appendix S2. Given that the axis of

the two state variables (biotemperature and annual precipitation) are not

perpendicular to one another it was necessary to define an additional variable in

order to correctly calculate the Euclidian distance between two points in

Holdridge space:

We used biotemperature (b) as a Y-axis and we designed the accessory variable

r' to be orthogonal to the Y-axis. This was defined by the relationship:

where 30° is the angle between the two axes in the original diagram and r is the

value of annual precipitation. Such a relationship can be obtain through

trigonometry, accounting for the fact that in the original Holdridge diagram, the

lines of constant annual precipitation do not cross the annual precipitation axis

at a 90° angle but rather at 120°.

The figure below shows the trigonometry used for calculating the distance (d)

between points B (baseline) and F (future).

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r' K

R'

b F

B

Annual Precipitation

BioTemperature

d

The Holdridge space is defined by the biotemperature and annual precipitation

axes. Since these axes are not orthogonal to one another, they cannot be used to

calculate the Euclidian distances within the Holdridge space. An additional

variable was therefore defined to overcome this problem (R'), which can be

constructed using the two existing axes. A mathematical relationship linking the

two can be identified once we consider that:

1) B-r' =BK- r'K

2) BKr is an equilateral triangle (all angles being 60°) from which it follows that

BK=Br

3) r'K/sin (30) =b*sin(60) with 60° being the angle in K and 30° the angle in F of

the triangle r'FK

r

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Considering also that both axes are linear in their log form, the Euclidian

distances (d) in this space becomes:

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