Ecosystem primary productivity and resilience across Australian drought and wet cycles through coupling field data, tower fluxes and satellite imagery_Huete

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TERN%is%supported%by%the%Australian%Government%through%the%National%Collaborative%Research%Infrastructure%Strategy%and%the%Super%Science%Initiative.%

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Terrestrial!Ecosystem!Research!Network!

4th!Annual!Symposium!!!

18!–!20!February,!2013!

Old!Parliament!House,!Canberra!! !

Ecosystem Primary Productivity and Resilience across Australian Drought and Wet Cycles through Coupling Field Data,

Tower Fluxes and Satellite Imagery

AOGS Annual Symposium, Brisbane 25 June 2013

Alfredo Huete1

Contributions from:Xuanlong Ma1, Derek Eamus1, Natalia Restrepo-Coupe1, Mark Broich1, Nicolas Bolain1, James Cleverly1, Lindsey Hutley2, Jason Berringer3

(1) University of Technology, Sydney

(2) Charles Darwin University

(3) Monash UniversityBG17-A005Alfredo HUETE, University of Technology Sydney, Australia

BG17-A005

Introduction• Recent large-scale, warm droughts have occurred in Australia,

China, North America, Amazonia, Africa, and Europe, resulting in dramatic changes in vegetation productivity across ecosystems with direct impact on societal needs, food security and basic livelihood and water balance, and food security.

Tropical cyclones and the ecohydrology of Australia’s recentcontinental-scale drought

Gavan S. McGrath,1 Rohan Sadler,2 Kevin Fleming,3,4 Paul Tregoning,5

Christoph Hinz,1,6 and Erik J. Veneklaas7

Received 8 November 2011; revised 20 December 2011; accepted 22 December 2011; published 9 February 2012.

[1] The Big Dry, a recent drought over southeast Australia,began around 1997 and continued until 2011. We show thatbetween 2002–2010, instead of a localized drought, therewas a continent-wide reduction in water storage, vegetationand rainfall, spanning the northwest to the southeast ofAustralia. Trends in water storage and vegetation wereassessed using Gravity Recovery and Climate Experiment(GRACE) and Normalized Difference Vegetation Index(NDVI) data. Water storage and NDVI are shown to besignificantly correlated across the continent and the greatestlosses of water storage occurred over northwest Australia.The frequency of tropical cyclones over northwest Australiapeaked just prior to the launch of the GRACE missionin 2002. Indeed, since 1981, decade-scale fluctuations intropical cyclone numbers coincide with similar variation inrainfall and vegetation over northwest Australia. Rainfalland vegetation in southeast Australia trended oppositely tothe northwest prior to 2001. Despite differences betweenthe northwest and southeast droughts, there is reason tobelieve that continental droughts may occur when therespective climate drivers align. Citation: McGrath, G. S.,R. Sadler, K. Fleming, P. Tregoning, C. Hinz, and E. J. Veneklaas(2012), Tropical cyclones and the ecohydrology of Australia’srecent continental-scale drought, Geophys. Res. Lett., 39,L03404, doi:10.1029/2011GL050263.

1. Introduction

[2] There has been significant focus on the causes andconsequences of the so called Big Dry, a drought afflictingsoutheast Australia from approximately 1997 till 2011[Ummenhofer et al., 2009; Leblanc et al., 2009]. Thisdrought is regarded as a local phenomenon, particularlyaffecting southeast Australia. Major droughts affectingsoutheast Australia have been attributed to the lack ofnegative phases of the Indian Ocean Dipole (IOD)

[Ummenhofer et al., 2009; Smith and Timbal, 2012]. TheIOD is an irregular oscillation of sea surface temperaturesand atmospheric circulation in and around the Indian Ocean,characterized by the Dipole Mode Index (DMI). In thenegative phase, with warmer waters off northwest Australia,the atmospheric circulation brings moisture across thecontinent in a southeasterly direction [Ashok et al., 2003;Ummenhofer et al., 2009]. In the positive phase, southeastAustralia experiences lower rainfall. In early 2011, theapparent end of the drought coincided with a strong LaNiña and the occurrence of a strongly negative DMI. Wehypothesized that a drought in southeast Australia maytherefore be associated with a continent-wide drought,oriented northwest to southeast across the continent.[3] A warming trend in the equatorial Indian Ocean as

well as a tendency for stronger and more frequent positiveIOD events have been identified [Ashok et al., 2003; Iharaet al., 2008]. Modeling efforts also support the hypothesisof an anthropogenic contribution to more frequent positiveevents [Cai et al., 2009]. On the other hand, there is also asuggestion that variability is on the increase, with moreintense fluctuations between wet and dry states [Abramet al., 2008]. Better understanding the impacts on terres-trial ecology arising from Indian Ocean variability is crucialfor properly assessing whether more persistent changes areunderway.[4] An eight percent increase in the fraction of Photosyn-

thetically Active Radiation (fPAR) absorbed by vegetationacross Australia was reported to have occurred during theperiod 1981–2006, particularly in the central and north-western regions [Donohue et al., 2009]. This coincided witha reported increase in the frequency of extreme rainfallevents in northwest Australia [Taschetto and England,2009]. Donohue et al. [2009] also noted a northwest tosoutheast gradient in mean annual rainfall trends, in par-ticular, positive trends of summer and autumn rainfall in thenorthwest and negative trends in the southeast. Enhancedaustral winter rainfall however, is associated with a nega-tive DMI [Ashok et al., 2003] while summer and autumnrainfall in northwest Australia is associated with tropicalcyclones and the monsoon.[5] Tropical cyclones are major sources of the water

which sustains vegetation in northwest Australia, resultingfrom the significant recharge of water stores which theyinduce [Cullen and Grierson, 2007]. Indeed, evidence for aperiod of desertification of the northwest was coincidentwith a prolonged decrease in tropical cyclone frequency[Nott, 2010]. Tropical cyclone frequency and intensityreportedly increased during 1980–2005 in the northwest,with no significant trends in the northeast [Hassim andWalsh, 2008]. However, Goebbert and Leslie [2010]

1School of Earth and Environment, University of Western Australia,Crawley, Western Australia, Australia.

2School of Agricultural and Resource Economics, University ofWestern Australia, Crawley, Western Australia, Australia.

3Western Australian Centre for Geodesy, Curtin University ofTechnology, Bentley, Western Australia, Australia.

4Earthquake Risk and Early Warning, Helmholtz Centre Potsdam, GFZGerman Research Centre for Geosciences, Potsdam, Germany.

5Research School of Earth Sciences, Australian National University,Canberra, ACT, Australia.

6Hydrology and Water Resources Management, BrandenburgUniversity of Technology, Cottbus, Germany.

7School of Plant Biology, University of Western Australia, Crawley,Western Australia, Australia.

Copyright 2012 by the American Geophysical Union.0094-8276/12/2011GL050263

GEOPHYSICAL RESEARCH LETTERS, VOL. 39, L03404, doi:10.1029/2011GL050263, 2012

L03404 1 of 6

MODIS Satellite EVI

OUTLINETaking the pulse of the earthIMPACTS

Remote Sensing with high frequency observations in the temporal domain open the door to answering unique sets of questions in metabolic processes of the Earth System and in Global Ecology

• Understanding water and productivity relationships are key issues in models that aim to predict how carbon and water relationships will shift with projected changes in the frequency, timing, amount and intensity of rainfall.

• The hydro-meteorological conditions that recently impacted N. America and Australia are of the same order to those expected with climate change, and thus offer an opportunity to investigate changes and generalize vegetation responses to future climate change scenarios.

• “Natural experiments” have great power to study rainfall variability and vegetation response.

LETTERdoi:10.1038/nature11836

Ecosystem resilience despite large-scale alteredhydroclimatic conditionsGuillermo E. Ponce Campos1,2, M. Susan Moran1, Alfredo Huete3, Yongguang Zhang1, Cynthia Bresloff2, Travis E. Huxman4,Derek Eamus3, David D. Bosch5, Anthony R. Buda6, Stacey A. Gunter7, Tamara Heartsill Scalley8, Stanley G. Kitchen9,Mitchel P. McClaran10, W. Henry McNab11, Diane S. Montoya12, Jack A. Morgan13, Debra P. C. Peters14, E. John Sadler15,Mark S. Seyfried16 & Patrick J. Starks17

Climate change is predicted to increase both drought frequency andduration, and when coupled with substantial warming, will estab-lish a new hydroclimatological model for many regions1. Large-scale, warm droughts have recently occurred in North America,Africa, Europe, Amazonia and Australia, resulting in major effectson terrestrial ecosystems, carbon balance and food security2,3. Herewe compare the functional response of above-ground net primaryproduction to contrasting hydroclimatic periods in the late twen-tieth century (1975–1998), and drier, warmer conditions in the earlytwenty-first century (2000–2009) in the Northern and SouthernHemispheres. We find a common ecosystem water-use efficiency(WUEe: above-ground net primary production/evapotranspira-tion) across biomes ranging from grassland to forest that indicatesan intrinsic system sensitivity to water availability across rainfallregimes, regardless of hydroclimatic conditions. We found higherWUEe in drier years that increased significantly with drought to amaximum WUEe across all biomes; and a minimum native statein wetter years that was common across hydroclimatic periods.This indicates biome-scale resilience to the interannual variabilityassociated with the early twenty-first century drought—that is, thecapacity to tolerate low, annual precipitation and to respond tosubsequent periods of favourable water balance. These findingsprovide a conceptual model of ecosystem properties at the decadalscale applicable to the widespread altered hydroclimatic conditionsthat are predicted for later this century. Understanding the hydro-climatic threshold that will break down ecosystem resilience andalter maximum WUEe may allow us to predict land-surface conse-quences as large regions become more arid, starting with water-limited, low-productivity grasslands.

Increased aridity and persistent droughts are projected in thetwenty-first century for most of Africa, southern Europe and theMiddle East, most of the Americas, Australia and South East Asia1.This is predicted to change vegetation productivity markedly acrossecosystems from grasslands to forests2,4,5 and affect societal needs forfood security and basic livelihood6. However, model predictions ofproductivity responses only provide the most-likely scenarios of theimpact of climate change, and few experiments have focused on howanticipated changes in precipitation might be generalized across ter-restrial ecosystems. Long-term measurements of natural variability infield settings, supported by manipulative experiments, are consideredthe best approach for determining the effect of prolonged drought onvegetation productivity6,7.

In field experiments, vegetation productivity is generally measuredas the above-ground net primary production (ANPP, or total neworganic matter produced above-ground during a specific interval8),and vegetation response to changes in precipitation is quantified asrain-use efficiency (RUE), defined as the ratio of ANPP to precipita-tion over a defined season or year9. Using this approach, continental-scale patterns of RUE have been reported for extended periods inthe late twentieth century10. Ecosystem water-use efficiency (WUEe:ANPP/evapotranspiration11) provides further insight into the eco-logical functioning of the land surface, in which evapotranspirationis calculated as precipitation minus the water lost to surface runoff,recharge to groundwater and changes to soil water storage12 (seeMethods). Here we compare the functional responses of RUE andWUEe to local changes in precipitation to document ecosystemresilience—the capacity to absorb disturbances and retain the samefunction, feedbacks and sensitivity13—during altered hydroclimaticconditions.

The objective was to determine how ANPP across biomes respondedto altered hydroclimatic conditions forced by the contemporarydrought in Southern and Northern Hemispheres from 2000–2009.Measurements made at 12 US Department of Agriculture (USDA)long-term experimental sites in the conterminous United States andPuerto Rico, and 17 similar sites in the Australian continent over a rangeof precipitation regimes (termed USDA00–09 and Australia01–09, res-pectively). To contrast productivity under altered hydroclimatic con-ditions with precipitation variability in the late twentieth century, wecompared results from the 2000–2009 period with similar analysis ofmeasurements made during the period from 1975–1998 (ref. 10).The latter measurements were primarily from Long-term EcologicalResearch (LTER) locations, with 14 sites—12 in North America, 2 inCentral and South America—hereafter referred to as the LTER75–98

data set. For a subset of the LTER75–98 sites, ANPP measurements werecontinued during the period from 2000–2009 (termed LTER00–09), andthese were used for further validation of the results (see SupplementaryInformation and Supplementary Table 1).

The warm drought during the early twenty-first century in theUnited States, Europe and Australia has been recognized as a conside-rable change from the climatological variability of the late twentiethcentury1,14. Globally, 2000–2009 ranked as the ten warmest years of the130-year (1880–2009) record15. Global annual evapotranspirationincreased on average by 7.1 mm yr21 decade21 from 1982–1997, andafter that, remained at a plateau through 2008, thus revealing the

1USDA ARS Southwest Watershed Research, Tucson, Arizona 85719, USA. 2Soil, Water & Environmental Sciences, University of Arizona, Tucson, Arizona 85721, USA. 3Plant Functional Biology and ClimateChange Cluster, University of Technology Sydney, New South Wales 2007, Australia. 4Ecology & Evolutionary Biology, University of California, Irvine, California, USA and Center for Environmental Biology,University of California, Irvine, California 92697,USA. 5USDA ARS Southeast Watershed ResearchLaboratory, Tifton, Georgia 31793,USA. 6USDA ARS PastureSystems & Watershed ManagementResearchUnit, University Park, Pennsylvania 16802, USA. 7USDA ARS Southern Plains Range Research Station, Woodward, Oklahoma 73801, USA. 8USDA FS International Institute of Tropical Forestry, Rio Piedras00926, Puerto Rico. 9USDA FS Rocky Mountain Research Station Shrub Sciences Laboratory, Provo, Utah 84606, USA. 10School of Natural Resources & the Environment, University of Arizona, Tucson,Arizona 85721, USA. 11USDA FS Southern Research Station, Asheville, North Carolina 28806, USA. 12USDA FS Pacific Southwest Research Station, Arcata, California 95521, USA. 13USDA ARS RangelandResources Research Unit, Fort Collins, Colorado 80526, USA. 14USDA ARS Jornada Experimental Range & Jornada Basin Long Term Ecological Research Program, New Mexico State University, Las Cruces,New Mexico 88012, USA. 15USDA ARS Cropping Systems & Water Quality Research Unit, Columbia, Missouri 65211, USA. 16USDA ARS Northwest Watershed Research Center, Boise, Idaho 83712, USA.17USDA ARS Grazinglands Research Laboratory, El Reno, Oklahoma 73036, USA.

0 0 M O N T H 2 0 1 3 | V O L 0 0 0 | N A T U R E | 1

Macmillan Publishers Limited. All rights reserved©2013

Ponce-Campos et al. (2013) Nature

2010 MODIS iEVI

• Tropical Rainfall Measuring Mission (TRMM) satellite

• Japan-USA joint project• Launched 1997

3B43 Data Product

2010 TRMM Rainfall

- Methods

Remote sensing methods, by observing broadscale vegetation responses to climatic variability, offer potentially powerful insights into ecological questions on observable timescales.

Many%remote%sensing%seasonal%&%phenology%metrics%

can%be%derived%from%the%annual%VI%cycle

50

1153

Fig. 2 phenological methodology schematic diagrams. (A) Original MODIS EVI time series 1154 (solid light grey line) with SSA reconstructed time series (solid black line) for Howard Springs 1155 from 2000.02.18 to 2012.02.18; (B) SSA reconstructed MODIS EVI time series of one growth 1156 cycle (2000-2001) from above time series; (C) One day lag difference (approximation of first 1157

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Howard Springs (Eucalypt Woodlands)

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2000 2002 2004 2006 2008 2010 2012Date

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LGS

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●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●Fastest Browning Date

●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●

Minimum EVI before growing season●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●Minimum EVI after growing season

●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●●Maximum EVI during growing season

−0.02

0.00

0.02

0.04

Jul 2000 Oct 2000 Jan 2001 Apr 2001 Jul 2001 Oct 2001Date

d(EV

I) / d

t

(C)

Ma X. et al. (in review)

iEVI as proxy for ANPP

10""

"377"

"378"

Figure 1. 379"

380" Ponce-Campos et al 2013, Nature

Fig. 3 Graph shows the technical scheme to derive the phenological metrics based on double logistic fitted EVI time

series (solid line) and corresponding curvature change rate (dashed line). The light grey area is the integral of annual

EVI subtract the integral of Base EVI, which is used as surrogate for grass layer productivity (Pg). The dark grey area is

the integral of annual Base EVI, which is used as surrogate for the woody layer productivity (Pw). The annual total

productivity (Pt) is the sum of Pw and Pg.

y = 192.65x - 155.29R = 0.8578

0

200

400

600

800

1000

1200

1400

1600

1800

0 2 4 6 8 10

Mea

n A

nnua

l GPP

(g C

m-2

)

Mean Annual iEVIFOREST DESERT-GRASSLANDSAVANNA SHRUBOPEN FOREST SAVANNA WOODY SAVANNA

Rainfall use efficiency (RUE) concept

sequence data sets for S. paradoxus versus other mammals, for S. paradoxus versus S.cubanus, and for Cricosaura typica versus other xantusiid lizards.

Received 26 November 2003; accepted 19 April 2004; doi:10.1038/nature02597.

1. Ottenwalder, J. inBiogeography of theWest Indies: Patterns and Perspectives (edsWoods, C. A. & Sergile,

F. E.) 253–329 (CRC Press, Boca Raton, Florida, 2001).

2. MacPhee, R. D. E., Flemming, C. & Lunde, D. P. Last occurrence of the Antillean insectivoran

Nesophontes: new radiometric dates and their interpretation. Am. Mus. Novit. 3261, 1–20 (1999).

3. Iturralde-Vinent,M. A. &MacPhee, R. D. E. Paleogeography of the Caribbean region: implications for

Cenozoic biogeography. Bull. Am. Mus. Nat. Hist. 238, 1–95 (1999).

4. Hedges, S. B. Historical biogeography of West Indian vertebrates. Annu. Rev. Ecol. Syst. 27, 163–196

(1996).

5. Eisenberg, J. F. & Gozalez Gotera, N. Observations on the natural history of Solenodon cubanus. Acta

Zool. Fenn. 173, 275–277 (1985).

6. Ottenwalder, J. A. The Distribution and Habitat of Solenodon in the Dominican Republic. 1–128,

MS thesis Univ. Florida, Gainesville (1985).

7. Hedges, S. B., Hass, C. A. & Maxson, L. R. Caribbean biogeography: molecular evidence for dispersal

in West Indian terrestrial vertebrates. Proc. Natl Acad. Sci. USA 89, 1909–1913 (1992).

8. McDowell, S. B. Jr The Greater Antillean insectivores. Bull. Am. Mus. Nat. Hist. 115, 113–214 (1958).

9. McKenna, M. C. & Bell, S. K. Classification of Mammals above the Species Level (Columbia Univ. Press,

New York, 1997).

10. Butler, P. M. in The Phylogeny and Classification of the Tetrapods (ed. Benton, M. J.) 117–141 (Oxford

Univ. Press, Oxford, 1988).

11. MacPhee, R. D. E. & Novacek, M. J. inMammal Phylogeny: Placentals (eds Szalay, F. S., Novacek, M. J.

& McKenna, M. C.) 13–31 (Springer, New York, 1993).

12. Whidden, H. P. & Asher, R. J. in Biogeography of the West Indies: Patterns and Perspectives (edsWoods,

C. A. & Sergile, F. E.) 237–252 (CRC Press, Boca Raton, Florida, 2001).

13. Lillegraven, J. A., McKenna, M. C. & Krishtalka, L. Evolutionary relationships of middle Eocene and

younger species of Centetodon (Mammalia, Insectivora, Geolabididae) with a description of the

dentition of Ankylodon (Adapisoricidae). Univ. Wyoming Publ. 45, 1–115 (1981).

14. MacFadden, B. J. Rafting mammals or drifting islands? Biogeography of the Greater Antillean

insectivores Nesophontes and Solenodon. J. Biogeogr. 7, 11–22 (1980).

15. Asher, R. J. A morphological basis for assessing the phylogeny of the “Tenrecoidea” (Mammalia,

Lipotyphla). Cladistics 15, 231–252 (1999).

16. Hershkovitz, P. in Evolution, Mammals, and Southern Continents (eds Keast, A., Erk, F. C. & Glass, B.)

311–431 (State Univ. New York Press, Albany, 1972).

17. Stanhope, M. J. et al. Molecular evidence for multiple origins of Insectivora and for a new order of

endemic African insectivore mammals. Proc. Natl Acad. Sci. USA 95, 9967–9972 (1998).

18. Murphy, W. J. et al. Resolution of the early placental mammal radiation using Bayesian phylogenetics.

Science 294, 2348–2351 (2001).

19. Emerson, G. L., Kilpatrick, C. W., McNiff, B. E., Ottenwalder, J. & Allard, M. W. Phylogenetic

relationships of the order Insectivora based on complete 12S rRNA sequences from mitochondria.

Cladistics 15, 221–230 (1999).

20. Springer, M. S., Murphy, W. J., Eizirik, E. & O’Brien, S. J. Placental mammal diversification and the

Cretaceous-Tertiary boundary. Proc. Natl Acad. Sci. USA 100, 1056–1061 (2003).

21. Thorne, J. L., Kishino, H. & Painter, I. S. Estimating the rate of evolution of the rate of molecular

evolution. Mol. Biol. Evol. 15, 1647–1657 (1998).

22. Kishino, H., Thorne, J. L. & Bruno,W. J. Performance of a divergence time estimationmethod under a

probabilistic model of rate evolution. Mol. Biol. Evol. 18, 352–361 (2001).

23. Hedges, S. B. & Bezy, R. L. Phylogeny of xantusiid lizards: concern for data and analysis. Mol.

Phylogenet. Evol. 2, 76–87 (1993).

24. Acton, G. D., Galbrun, B. & King, J. W. Paleolatitude of the Caribbean Plate since the Late Cretaceous.

in Proc. ODP Sci. Res. (eds Leckie, R. M., Sigurdsson, H., Acton, G. D. & Draper, G.) 165, 149–173,

(2000).

25. Robertson, D. S., McKenna, M. C., Toon, O. B., Hope, S. & Lillegraven, J. A. Survival in the first hours

of the Cenozoic. GSA Bull. 116, 760–768 (2004).

26. Donnelly, T. W. in Insects of Panama andMesoamerica: Selected Studies (eds Quintero, D. & Aiello, A.)

1–13 (Oxford Univ. Press, Oxford, 1992).

27. Asher, R. J., McKenna, M. C., Emry, R. J., Tabrum, A. R. & Kron, D. G. Morphology and relationships

of Apternodus and other extinct, zalambdodont placental mammals. Bull. Am. Mus. Nat. Hist. 217,

1–117 (2002).

28. Kumar, S. & Hedges, S. B. A molecular timescale for vertebrate evolution. Nature 392, 917–920

(1998).

29. Cabrera, A. Genera Mammalium: Insectivora, Galeopithecia (Mus. Nacl. Cien. Nat., Madrid, 1925).

30. International Union for the Conservation of Nature. The 2003 IUCN Red List of Threatened Species

khttp://www.redlist.orgl.

Supplementary Information accompanies the paper on www.nature.com/nature.

Acknowledgements This paper is dedicated to the memory of the Cuban naturalist Felipe Poey(1799–1891); see Supplementary Table 1 for details of the samples he collected in the 1850s. Wethank C. Bell, A. Brandt, J. Brucksch, D. Castillo, N. Crumpler, M.Malasky, J. Minchoff, H. Otero,K. Scott, J. Tabler & E. Teeling. For samples, we thank the Parque Zoologico Nacional(ZOODOM) of the Dominican Republic; J. Chupasko at the Harvard Museum of ComparativeZoology; and P. Giere at the Museum fur Naturkunde, Humboldt-Universitat zu Berlin. Thispublication has been funded in whole or in part with federal funds from the National CancerInstitute, National Institutes of Health.

Competing interests statement The authors declare that they have no competing financialinterests.

Correspondence and requests for materials should be addressed to A.L.R. ([email protected]),S.J.O’B. ([email protected]) orW.J.M. ([email protected]). The sequences reported in thisstudy are deposited under GenBank accession numbers AY530066–AY530088.

..............................................................

Convergence across biomes toa common rain-use efficiencyTravis E. Huxman1*, Melinda D. Smith2,3*, Philip A. Fay4, Alan K. Knapp5,M. Rebecca Shaw6, Michael E. Loik7, Stanley D. Smith8, David T. Tissue9,John C. Zak9, Jake F. Weltzin10, William T. Pockman11, Osvaldo E. Sala12,Brent M. Haddad7, John Harte13, George W. Koch14, Susan Schwinning15,Eric E. Small16 & David G. Williams17

1Ecology and Evolutionary Biology, University of Arizona, Tucson, Arizona 85721,USA2National Center for Ecological Analysis and Synthesis, Santa Barbara, California93101, USA3Department of Ecology and Evolutionary Biology, Yale University, New Haven,Connecticut 06511, USA4Natural Resources Research Institute, Duluth, Minnesota 55811, USA5Department of Biology, Colorado State University, Fort Collins, Colorado 80523,USA6Department of Global Ecology, Carnegie Institution of Washington, Stanford,California 94305, USA7Department of Environmental Studies, University of California, Santa Cruz,California 95064, USA8Department of Biological Sciences, University of Nevada, Las Vegas, Nevada89154, USA9Department of Biological Sciences, Texas Tech University, Lubbock, Texas 79409,USA10Ecology and Evolutionary Biology, University of Tennessee, Knoxville, Tennessee37919, USA11Department of Biology, University of New Mexico, Albuquerque, New Mexico87131, USA12Department of Ecology and IFEVA, Faculty of Agronomy, University of BuenosAires, Buenos Aires C1417DSE, Argentina13Energy and Resources Group, University of California, Berkeley, California94720, USA14Biological Sciences and Merriam-Powell Center for Environmental Research,Northern Arizona University, Flagstaff, Arizona 86011, USA15Biosphere 2 Center, Columbia University, Oracle, Arizona 85623, USA16Department of Geological Sciences, University of Colorado, Boulder, Colorado80309, USA17Renewable Resources and Botany, University of Wyoming, Laramie, Wyoming82071, USA

* These authors contributed equally to this work

.............................................................................................................................................................................

Water availability limits plant growth and production in almostall terrestrial ecosystems1–5. However, biomes differ substantiallyin sensitivity of aboveground net primary production (ANPP)to between-year variation in precipitation6–8. Average rain-useefficiency (RUE; ANPP/precipitation) also varies betweenbiomes, supposedly because of differences in vegetation structureand/or biogeochemical constraints8. Here we show that RUEdecreases across biomes as mean annual precipitation increases.However, during the driest years at each site, there is convergenceto a common maximum RUE (RUEmax) that is typical of aridecosystems. RUEmax was also identified by experimentally alter-ing the degree of limitation by water and other resources. Thus,in years when water is most limiting, deserts, grasslands andforests all exhibit the same rate of biomass production per unitrainfall, despite differences in physiognomy and site-level RUE.Global climate models9,10 predict increased between-year varia-bility in precipitation, more frequent extreme drought events,and changes in temperature. Forecasts of future ecosystembehaviour should take into account this convergent feature ofterrestrial biomes.There is a compelling need to understand how terrestrial ecosys-

tems respond to precipitation and other external drivers to permitthe forecasting of potential biosphere feedback to natural andanthropogenic changes in the climate system11. This is especially

letters to nature

NATURE |VOL 429 | 10 JUNE 2004 | www.nature.com/nature 651© 2004 Nature Publishing Group

Huxman et al 2004

*ANPP difficult to measureMethods are usually inconsistent

important given historical trends and future models of greenhousegases, global temperature and precipitation regimes9. Water is aprimary resource limiting terrestrial biological activity1–5, particu-larly in arid and semi-arid regions12, and its availability mediates theresponsiveness of communities and ecosystems to globalchanges13,14. Indeed, ANPP, a key ecosystem process, has beenshown to increase across biomes with increasing mean annualprecipitation (MAP)2,3,7,15. However, variability in ANPP withinecosystems does not exhibit such a clear pattern, because variabilityoften peaks at intermediate precipitation6,7. This suggests differen-tial sensitivities of ANPP to inter-annual variability in precipitationacross biomes.Life history and biogeochemical mechanisms can interact to

influence the production response of terrestrial ecosystems toprecipitation6–8. The evolutionary history and ecological attributesof species present in the vegetation assemblage can influenceproduction potential as a result of constraints on growth rateimposed by trade-offs with traits for stress tolerance16. For example,primary production in arid regions is constrained by generally lowerplant densities and the relatively high frequency of slow-growingstress-tolerant species that are delayed in reaching their maximumgrowth rates until resources become abundant17. Production canalso be constrained by an interaction between climatic and biogeo-chemical conditions, changing the relative importance of limitingresources (for example, water, soil nitrogen, soil phosphorus orlight). In this case, for sites with high production potential in yearswith greater than average precipitation, soil nitrogen or otherlimiting resources might transiently limit biological activity18.These two mechanisms are likely to operate differentially across awater availability gradient, producing the following patterns: first,water-limited regions with low production potential should berelatively insensitive to inter-annual variation in precipitation6,17;second, water-limited regions with relatively high productionpotential should be very sensitive to variation in water availability7;and last, mesic sites with high production potential should exhibitrelatively low sensitivity to inter-annual variability in precipi-tation19.We evaluated relationships between ANPP and precipitation

(both annual values for certain years and MAP) for 14 terrestrialecosystems in nine biomes located throughout North and SouthAmerica (Supplementary Information) to quantify the sensitivity(change in ANPP divided by change in precipitation) of differentecosystems to variation in precipitation. We chose ecosystemsvarying by an order of magnitude in annual rainfall, spanningxeric to mesic biomes, in which the relative importance of precipi-tation as a limiting variable might change through time. Theselected data sets were additionally limited to locations wheresufficient, inter-annual records of precipitation (PTT) and ANPPcould be obtained. We contrasted ANPP/precipitation relationshipsacross and within biomes to identify potential mechanisms under-lying variation in ecosystem sensitivity to precipitation, and to buildon our mechanistic knowledge of precipitation effects on ecosystemprocesses.When evaluated across all sites and years, ANPP increased with

PTT (Fig. 1a). However, there was substantial variation in sensi-tivity relationships between sites. In general, the greatest slopes ofANPP and precipitation occurred at the driest sites (JRN, KNZ, RV,SEVand SGS; seeMethods for site abbreviations), and the lowest (oreven negative) slopes occurred at the most mesic sites (AND, BCI,HBR and HFR; Fig. 1a). To some degree, this varying sensitivityreflects differences in climatic controls on ANPP between xeric andmesic biomes. Indeed, stepwise multiple regression analysis ofANPP using annual precipitation, growing season maximum tem-perature (Tmax), precipitation coefficient of variance and season-ality, and ANPP in the previous year indicated that ANPP at themost productive sites (more than 800 gm22 yr21) was morestrongly correlated with Tmax and production in the previous year

than with annual precipitation, whereas annual precipitationremained the best correlate of ANPP at the least productive sites(less than 500 gm22 y21; see Supplementary Information).

The variation in the sensitivities of ANPP to precipitation withlow to high MAP across the range of biomes is consistent with thehypotheses that life history and biogeochemical mechanisms canexplain how ecological systems are affected bywater availability. Lifehistory (that is, vegetation) constraints influence the impact ofprecipitation on biological activity in a manner that can decreasewith increasing precipitation, whereas biogeochemical constraints(limitation of activity by resources other than water) can increasewith increasing precipitation7,8. At the sites with lowest MAP, highefficiency of water use associated with individual plant growth rateis translated to high efficiency of water use at the ecosystem level. Incontrast, at sites with high MAP, selection has favoured plants withhigh growth rates and competitive abilities for other resourcesrather than high efficiency of water use. The result is less effectivewater use bymesic vegetation; consequently, other resources such asnitrogen and light will influence ANPP more strongly. However,both in locations with high MAP and in those with lowMAP, wateravailability is tightly linked to biogeochemical constraints throughmineralization processes and leaching20. Precipitation affects bothnutrient availability through its effects on microbial activity and

Figure 1 Between-year variation in production across a precipitation gradient and a

maximum rain-use efficiency. a, Plot of ANPP against PPT for 14 sites (see Methods forabbreviations). Multi-year data give site-specific relationships by using linear regression

(see Supplementary Information). The overall relationship (bold line) derives from data

from all sites: ANPP ! 1011.7 £ (1 2 exp(20.0006 £ precipitation)); r 2 ! 0.77;

P , 0.001. The inset shows the site-level slopes (ANPP plotted against precipitation) as a

function of MAP: ANPP ! 0.388 £ (1 2 exp(20.0022 £ precipitation)); r2 ! 0.51;

P , 0.001. b, An overall RUEmax derived from the slope of the minimum precipitation and

the corresponding ANPP for all sites (solid line): ANPP ! 86.1 " 0.42 £ PTTmin. Closed

circles, minima; open circles, remaining data; dotted lines, 95% confidence intervals.

Arrows show average slopes for sites with low, medium and high precipitation.

letters to nature

NATURE | VOL 429 | 10 JUNE 2004 | www.nature.com/nature652 © 2004 Nature Publishing Group

ANPP

g/m

2

important given historical trends and future models of greenhousegases, global temperature and precipitation regimes9. Water is aprimary resource limiting terrestrial biological activity1–5, particu-larly in arid and semi-arid regions12, and its availability mediates theresponsiveness of communities and ecosystems to globalchanges13,14. Indeed, ANPP, a key ecosystem process, has beenshown to increase across biomes with increasing mean annualprecipitation (MAP)2,3,7,15. However, variability in ANPP withinecosystems does not exhibit such a clear pattern, because variabilityoften peaks at intermediate precipitation6,7. This suggests differen-tial sensitivities of ANPP to inter-annual variability in precipitationacross biomes.Life history and biogeochemical mechanisms can interact to

influence the production response of terrestrial ecosystems toprecipitation6–8. The evolutionary history and ecological attributesof species present in the vegetation assemblage can influenceproduction potential as a result of constraints on growth rateimposed by trade-offs with traits for stress tolerance16. For example,primary production in arid regions is constrained by generally lowerplant densities and the relatively high frequency of slow-growingstress-tolerant species that are delayed in reaching their maximumgrowth rates until resources become abundant17. Production canalso be constrained by an interaction between climatic and biogeo-chemical conditions, changing the relative importance of limitingresources (for example, water, soil nitrogen, soil phosphorus orlight). In this case, for sites with high production potential in yearswith greater than average precipitation, soil nitrogen or otherlimiting resources might transiently limit biological activity18.These two mechanisms are likely to operate differentially across awater availability gradient, producing the following patterns: first,water-limited regions with low production potential should berelatively insensitive to inter-annual variation in precipitation6,17;second, water-limited regions with relatively high productionpotential should be very sensitive to variation in water availability7;and last, mesic sites with high production potential should exhibitrelatively low sensitivity to inter-annual variability in precipi-tation19.We evaluated relationships between ANPP and precipitation

(both annual values for certain years and MAP) for 14 terrestrialecosystems in nine biomes located throughout North and SouthAmerica (Supplementary Information) to quantify the sensitivity(change in ANPP divided by change in precipitation) of differentecosystems to variation in precipitation. We chose ecosystemsvarying by an order of magnitude in annual rainfall, spanningxeric to mesic biomes, in which the relative importance of precipi-tation as a limiting variable might change through time. Theselected data sets were additionally limited to locations wheresufficient, inter-annual records of precipitation (PTT) and ANPPcould be obtained. We contrasted ANPP/precipitation relationshipsacross and within biomes to identify potential mechanisms under-lying variation in ecosystem sensitivity to precipitation, and to buildon our mechanistic knowledge of precipitation effects on ecosystemprocesses.When evaluated across all sites and years, ANPP increased with

PTT (Fig. 1a). However, there was substantial variation in sensi-tivity relationships between sites. In general, the greatest slopes ofANPP and precipitation occurred at the driest sites (JRN, KNZ, RV,SEVand SGS; seeMethods for site abbreviations), and the lowest (oreven negative) slopes occurred at the most mesic sites (AND, BCI,HBR and HFR; Fig. 1a). To some degree, this varying sensitivityreflects differences in climatic controls on ANPP between xeric andmesic biomes. Indeed, stepwise multiple regression analysis ofANPP using annual precipitation, growing season maximum tem-perature (Tmax), precipitation coefficient of variance and season-ality, and ANPP in the previous year indicated that ANPP at themost productive sites (more than 800 gm22 yr21) was morestrongly correlated with Tmax and production in the previous year

than with annual precipitation, whereas annual precipitationremained the best correlate of ANPP at the least productive sites(less than 500 gm22 y21; see Supplementary Information).

The variation in the sensitivities of ANPP to precipitation withlow to high MAP across the range of biomes is consistent with thehypotheses that life history and biogeochemical mechanisms canexplain how ecological systems are affected bywater availability. Lifehistory (that is, vegetation) constraints influence the impact ofprecipitation on biological activity in a manner that can decreasewith increasing precipitation, whereas biogeochemical constraints(limitation of activity by resources other than water) can increasewith increasing precipitation7,8. At the sites with lowest MAP, highefficiency of water use associated with individual plant growth rateis translated to high efficiency of water use at the ecosystem level. Incontrast, at sites with high MAP, selection has favoured plants withhigh growth rates and competitive abilities for other resourcesrather than high efficiency of water use. The result is less effectivewater use bymesic vegetation; consequently, other resources such asnitrogen and light will influence ANPP more strongly. However,both in locations with high MAP and in those with lowMAP, wateravailability is tightly linked to biogeochemical constraints throughmineralization processes and leaching20. Precipitation affects bothnutrient availability through its effects on microbial activity and

Figure 1 Between-year variation in production across a precipitation gradient and a

maximum rain-use efficiency. a, Plot of ANPP against PPT for 14 sites (see Methods forabbreviations). Multi-year data give site-specific relationships by using linear regression

(see Supplementary Information). The overall relationship (bold line) derives from data

from all sites: ANPP ! 1011.7 £ (1 2 exp(20.0006 £ precipitation)); r 2 ! 0.77;

P , 0.001. The inset shows the site-level slopes (ANPP plotted against precipitation) as a

function of MAP: ANPP ! 0.388 £ (1 2 exp(20.0022 £ precipitation)); r2 ! 0.51;

P , 0.001. b, An overall RUEmax derived from the slope of the minimum precipitation and

the corresponding ANPP for all sites (solid line): ANPP ! 86.1 " 0.42 £ PTTmin. Closed

circles, minima; open circles, remaining data; dotted lines, 95% confidence intervals.

Arrows show average slopes for sites with low, medium and high precipitation.

letters to nature

NATURE | VOL 429 | 10 JUNE 2004 | www.nature.com/nature652 © 2004 Nature Publishing GroupPonce et al ISRSE, Sydney 2011

iEVI (mean) iEVI (dry) iEVI (wet)

Annu

ally

inte

grat

ed E

VI

Annually integrated EVI (sum across all of Australia)

2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

We may be seeing an accumulated stress effect here..

Standard Anomalies of annual rainfall 1998 - 2012

TRMM standard anomalies

Broich, M (in preparation)

Continental Scale RUE of Average, Driest, and Wettest years

Driest year

Wettest year

Driest year

Average year

Northern Australia Tropical Transect

0.00#

0.05#

0.10#

0.15#

0.20#

0.25#

0.30#

0.00#

0.50#

1.00#

1.50#

2.00#

2.50#

3.00#

3.50#

4.00#

1# 9# 17#

25#

33#

41#

49#

57#

65#

73#

81#

89#

97#

105#

113#

121#

129#

137#

145#

153#

161#

169#

177#

185#

193#

201#

209#

217#

225#

233#

241#

249#

257#

265#

273#

281#

289#

297#

iEVI%

Transect%distance%

iEVI#(mean)#

TRMM#(mean)#

MODISiEVI

TRMM-rainfall

0"

500"

1000"

1500"

2000"

2500"

3000"

3500"

4000"

6" 62"

118"

174"

230"

286"

342"

398"

454"

510"

566"

622"

678"

734"

790"

846"

902"

958"

1014"

1070"

1126"

1182"

Annu

al&Rainfall&

NATT&transect,&km&

Mean"

2000"

2001"

2002"

2003"

2004"

2005"

2006"

2007"

2008"

2009"

2010"

TRMM(min)"

2010

driest

TRMM rainfall

Productivity- rainfall per year along NATT transect

Regression lines become more linear with drier years

Driest year

y"="$8E$05x"+"4.6665"R²"="0.027"

y"="$0.0005x"+"4.9852"R²"="0.452"

y"="$0.0001x"+"3.405"R²"="0.011"

y"="0.0006x"+"1.7864"R²"="0.223"

y"="0.0015x"+"1.4189"R²"="0.517"

0"

1"

2"

3"

4"

5"

6"

7"

0" 1000" 2000" 3000" 4000"

Ann

ual&iEV

I&

Annual&Rainfall,&mm&

N10"

N50"

N100"

N150"

N200"

Wet tropical savanna

Semi-arid Mulga (Acacias)

Site- based productivity - rainfall

is there an inherent maximum RUE?

Figures

Figure 1 Study area. Left panel: Major Vegetation Groups map; right panel: field measurements sites along the transect.

53

53

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(A) Howard Springs (Eucalypt Woodlands) (B) Adelaide Rivers (Tropical Eucalypt Woodlands)

(C) Daly River (Eucalypt Woodlands) (D) Dry River (Eucalypt Open Forests)

(E) Sturt Plains (Tussock Grasslands) (F) Ti Tree (Acacia Woodlands)

2000−2001

2001−2002

2002−2003

2003−2004

2004−2005

2005−2006

2006−2007

2007−2008

2008−2009

2009−2010

2010−2011

2011−2012

2000−2001

2001−2002

2002−2003

2003−2004

2004−2005

2005−2006

2006−2007

2007−2008

2008−2009

2009−2010

2010−2011

2011−2012

2000−2001

2001−2002

2002−2003

2003−2004

2004−2005

2005−2006

2006−2007

2007−2008

2008−2009

2009−2010

2010−2011

2011−2012

Sep Nov Jan Mar May Jul Oct Dec Feb Apr Jun Aug

Sep Nov Jan Mar May Jul Nov Jan Mar May Jul Sep

Oct Dec Feb Apr Jun Aug Dec Feb Apr Jun Aug OctDate

Year

● SGSPGSEGS

0.4

0.3

0.2

EVI

1184

Fig. 4 Phenogram. Inter-annual variations in vegetation phenology plotted with intra-annual EVI 1185 seasonality as background for six major sites from 2000 to 2012. 1186

54

R = 0.83, p < 0.001

250 mm

Mean uncertainty of MODIS EVI product

●

●

●

●

●

●

●

● ●

●

●

●

0.00

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0 100 200 300 400 500 600 700 800 900 1000Annual precipitation (mm yr 1)

EVI A

mpl

itude

Ti Tree●●●●●●●●●●●●

2000−2001

2001−2002

2002−2003

2003−2004

2004−2005

2005−2006

2006−2007

2007−2008

2008−2009

2009−2010

2010−2011

2011−2012

1187

Fig. 5 Relationship between EVI amplitude and annual precipitation for Ti Tree site (Acacia 1188 Woodland, 133.249°E 22.283°S) over Jul 2000- Jun 2012 time period. Horizontal red dashed 1189 line indicates the mean uncertainty of MODIS EVI product (0.02 EVI unit). Vertical blue dashed 1190 line indicates the minimal requirements of annual rainfall for reliable phenology detection at Ti 1191 Tree site. Red shaded area indicates the low annual rainfall region with EVI seasonal amplitude 1192 was lower than MODIS data error that reliable phenology could not be retrieved. 1193 1194

1195 1196

seasonal EVI amplitude vs MAP

Ma et al (submitted)

55

A1●●

●

●

●

●

Howard SpringsAdelaide Rivers

Daly RiverDry River

Sturt Plains

Ti Tree

A2●●

●

●

●

●

A3●●

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●

A4●●

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●

2001−2002

2005−2006

2007−2008

2010−2011

22

20

18

16

14

12

22

20

18

16

14

12

22

20

18

16

14

12

22

20

18

16

14

12

128 130 132 134 136

Latit

ude °S

210−1−2

(A) PPT

B1

B2

B3

B4

2001−2002

2005−2006

2007−2008

2010−2011

128 130 132 134 136

Aug Oct Dec Feb

(B) SGS

C1

C2

C3

C4

2001−2002

2005−2006

2007−2008

2010−2011

128 130 132 134 136

Jan Mar May

(C) PGS

D1

D2

D3

D4

2001−2002

2005−2006

2007−2008

2010−2011

128 130 132 134 136

Jun Aug Oct Dec

(D) EGS

E1

E2

E3

E4

2001−2002

2005−2006

2007−2008

2010−2011

128 130 132 134 136 138

0 100 200 300

(E) LGS

1197

Fig. 6 Spatial patterns of vegetation phenology over the NATT study area along with rainfall 1198 anomalies across four representative hydrological years. (A) Standardized anomaly of annual 1199 precipitation; (B) Start of Greening Season (SGS); (C) Peak of Greening Season (PGS); (D) End 1200 of Greening Season (EGS); (F) Length of Greening Season (LGS). Four representative years we 1201 selected are: 2001-2002 (normal-light drought year); 2005-2006 (wet year); 2007-2008 (drought 1202 year); 2010-2011 (wet year). The filled pixels (grey color) are either water body, low QA, or 1203 without detectable phenology. 1204

1205

56

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●●

Wet averageDry average

SGS

PGS

EGS

(A)

Sep

Oct

Nov

Dec

Jan

Feb

Mar

Apr

May

Jun

Jul

Aug

Sep

Oct

12.5 13.5 14.5 15.5 16.5 17.5 18.5 19.5 20.5 21.5 22.5Latitude °S

Date

●

Eucalypt Open ForestsEucalypt WoodlandAcacia Forests and Woodlands

Other Forests and WoodlandsEucalypt Open WoodlandsTropical Eucalypt Woodlands

Acacia Open WoodlandsAcacia ShrublandsHummock Grasslands

●●●●●●

●●●● ●● ●●● ●●●●● ●

●●●●●●●

●● ● ●● ●●● ●●●●● ●

LGS

(B)

0

60

120

180

240

300

12.5 13.5 14.5 15.5 16.5 17.5 18.5 19.5 20.5 21.5 22.5Latitude °S

LGS

(Day

s)

1206

Spatial patterns in vegetation phenology


58

050

100

150

Prec

ipita

tion

(mm

)

Jun2001

Dec2001

Jun2002

Dec2002

Jun2003

Dec2003

Jun2004

Dec2004

Jun2005

Dec2005

May2006

0.2

0.3

0.4

0.5

0.6

Howard Springs (Eucalypt Woodlands) 1

EVI

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24

68

GEP

(g C

* m−2

* d−

1)

● ● ● ● ●

RMSE: SGS = 10.3d, PGS = 7.2d, EGS = 13.1d, LGS = 16.8d(A)●

EVIGEPSSA EVISSA GEP

●

●

EVI−SGSEVI−PGSEVI−EGSGEP−SGSGEP−PGSGEP−EGS

050

100

150

Prec

ipita

tion

(mm

)

Feb2008

Aug2008

Feb2009

Aug2009

Feb2010

Aug2010

Feb2011

Jul2011

0.20

0.30

0.40

0.50

Howard Springs (Eucalypt Woodlands) 2

EVI

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24

68

GEP

(g C

* m−2

* d−

1)

●

● ●

RMSE: SGS = 14d, PGS = 12d, EGS = 9d, LGS = 10.9d(B)

020

4060

8010

0Pr

ecip

itatio

n (m

m)

Mar2007

Sep2007

Mar2008

Sep2008

Mar2009

Sep2009

Mar2010

Sep2010

Mar2011

Aug2011

0.20

0.30

0.40

0.50

Daly River (Eucalypt Woodlands)

EVI

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●

●

●

23

45

6G

EP (g

C *

m−2

* d−

1)●

● ● ●

RMSE: SGS = 19.1d, PGS = 10d, EGS = 20.8d, LGS = 19.8d(C)

050

100

150

Prec

ipita

tion

(mm

)

Feb2009

Aug2009

Feb2010

Aug2010

Feb2011

Aug2011

Feb2012

Jul2012

0.10

0.14

0.18

Ti Tree (Acacia Woodlands)

EVI

●

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●

01

23

4G

EP (g

C *

m−2

* d−

1)

●

●

RMSE: SGS = 70.7d, PGS = 19.8d, EGS = 99.6d, LGS = 70d(D)

1215

Fig. 9 Inter-comparison the MODIS EVI time series and flux tower GEP time series over three 1216 NATT sites, (A) Howard Springs, 2001-2006; (B) Howard Springs, 2008-2011; (C) Daly River, 1217 2007-2011; (D) Ti Tree, 2009-2012. Independently derived key phenological transitional dates 1218 (SGS, PGS and EGS) using MODIS EVI and tower GEP have been also labeled on the graph for 1219

59

each site and each dataset. Shaded areas indicate time periods that had continuous missing gaps 1220 present in flux GEP data. 1221

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● ●

●●

EVI = 0.181 + 0.0333 GEPR2 = 0.51 p < 1e−04

EVI = 0.2064 + 0.0316 GEPR2 = 0.59 p < 1e−04

0.2

0.3

0.4

0.5

0.6

2.5 5.0 7.5GEP

EVI

● Greenup phaseBrowndown phase

(A) Howard Springs (Eucalypt Woodlands)

● ●●

●

● ●

●

●●

●●

● ●

●●

●

●

●

●

●

●●

●●

● ●●

●

●●

●EVI = 0.1643 + 0.0335 GEP

R2 = 0.75 p < 1e−04

EVI = 0.1075 + 0.0503 GEPR2 = 0.78 p < 1e−04

0.2

0.3

0.4

0.5

2 3 4 5 6GEP

EVI


(B) Daly River (Eucalypt Woodlands)

●

●●

●

●

●

●

●

●

●

●

● ●

●●

●EVI = 0.1052 + 0.0234 GEP

R2 = 0.83 p < 1e−04

EVI = 0.1105 + 0.0607 GEP 0.016 GEP2 + 0.0015 GEP30.100

0.125

0.150

0.175

0.200

0.225

0 1 2 3 4 5GEP

EVI


(C) Ti Tree (Acacia Woodlands)

1222

Fig. 10 Relationships between 16-day aggregated flux tower GEP and MODIS 16-day EVI for 1223 three savanna sites. (A) Howard Springs (Eucalypt woodlands); (B) Daly River (Eucalypt 1224 woodlands) and (C) Ti Tree (Acacia woodlands). Seasonal hysteresis effect in the relationship 1225 between EVI and GEP was maximal at the Ti Tree Mulga site, whilst the greenup phase showed 1226 near-linear relationship, and the browndown phase showed enhanced non-linearity. Greenup 1227 phase was defined as the period from season start (SGS) to season peak (PGS), while 1228 browndown phase was defined as the period from PGS to season end (EGS). 1229

59


●

●●

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● ●

●●

EVI = 0.181 + 0.0333 GEPR2 = 0.51 p < 1e−04

EVI = 0.2064 + 0.0316 GEPR2 = 0.59 p < 1e−04

0.2

0.3

0.4

0.5

0.6

2.5 5.0 7.5GEP

EVI



● ●●

●

● ●

●

●●

●●

● ●

●●

●

●

●

●

●

●●

●●

● ●●

●

●●

●EVI = 0.1643 + 0.0335 GEP

R2 = 0.75 p < 1e−04

EVI = 0.1075 + 0.0503 GEPR2 = 0.78 p < 1e−04

0.2

0.3

0.4

0.5

2 3 4 5 6GEP

EVI



●

●●

●

●

●

●

●

●

●

●

● ●

●●

●EVI = 0.1052 + 0.0234 GEP

R2 = 0.83 p < 1e−04

EVI = 0.1105 + 0.0607 GEP 0.016 GEP2 + 0.0015 GEP30.100

0.125

0.150

0.175

0.200

0.225

0 1 2 3 4 5GEP

EVI



1222


59


●

●●

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●●

EVI = 0.181 + 0.0333 GEPR2 = 0.51 p < 1e−04

EVI = 0.2064 + 0.0316 GEPR2 = 0.59 p < 1e−04

0.2

0.3

0.4

0.5

0.6

2.5 5.0 7.5GEP

EVI



● ●●

●

● ●

●

●●

●●

● ●

●●

●

●

●

●

●

●●

●●

● ●●

●

●●

●EVI = 0.1643 + 0.0335 GEP

R2 = 0.75 p < 1e−04

EVI = 0.1075 + 0.0503 GEPR2 = 0.78 p < 1e−04

0.2

0.3

0.4

0.5

2 3 4 5 6GEP

EVI



●

●●

●

●

●

●

●

●

●

●

● ●

●●

●EVI = 0.1052 + 0.0234 GEP

R2 = 0.83 p < 1e−04

EVI = 0.1105 + 0.0607 GEP 0.016 GEP2 + 0.0015 GEP30.100

0.125

0.150

0.175

0.200

0.225

0 1 2 3 4 5GEP

EVI



1222


Comparisons with Flux Tower sites along NATT


Pulse and decline resilience measures

(following Knapp & Smith, 2004, Science)

iEVI pulse = (iEVIwet - iEVImean)/(iEVImean)

iEVI decline = (iEVImean - iEVIdry)/(iEVImean)

-same applies to TRMM pulse and decline

Delta (Pulse/decline) = iEVI pulse - iEVI decline

iEVI declineiEVI pulse

Difference (Pulse - Decline)Southeast Australia

WUE$=$0.78,$$R²$=$0.99$

WUE$=$0.66,$$R²$=$0.98$

WUE$=$0.62,$$R²$=$0.94$0$

100$

200$

300$

400$

500$

600$

0$ 200$ 400$ 600$ 800$ 1000$

ANPP

,%g%m

(2%

Evapotranspira4on,%mm%yr(1%

WUE%of%Major%Vegeta4on%Classes%(SE%Australia)%

Dry$Year$

Mean$Year$

Wet$Year$

Melbourne

Brisbane

Sydney

Canberra

Adelaide

Continental RUE for dry, mean, and wet years

RUE$=$0.33,$$R²$=$0.83$

RUE$=$0.25,$$R²$=$0.78$

RUE$=$0.17,$$R²$=$0.69$0$

50$

100$

150$

200$

250$

300$

350$

400$

450$

500$

0$ 500$ 1000$ 1500$ 2000$ 2500$

ANPP

,%g%m

(2%

Precipita1on,%mm%yr(1%

RUE%of%Major%Vegeta1on%Classes%

Dry$Year$

Mean$Year$

Wet$Year$

RUE$=$0.33,$$R²$=$0.83$

RUE$=$0.25,$$R²$=$0.78$

RUE$=$0.17,$$R²$=$0.69$

0$

50$

100$

150$

200$

250$

300$

350$

400$

450$

500$

0$ 500$ 1000$ 1500$ 2000$ 2500$

ANPP

,%g%m

(2%

Precipita1on,%mm%yr(1%

RUE%of%Major%Vegeta1on%Classes%

Dry$Year$

Mean$Year$

Wet$Year$

1000

800

600

400

200

0

dry meanwet

Continental RUE for major vegetation classes

Continental WUE (2000-

2010)

2000

2003

2006

2010

• Australia Tree Cover fraction (Donohue et al. 2006)

Conclusions• It is possible to monitor ecosystem resilience with a satellite-

metric, but vital to have long term experimental monitoring sites

• Cross-ecosystem water use efficiency (WUEe) and RUE will increase with prolonged warm drought until reaching a threshold that will break down ecosystem resilience,

• Better information for strategic resource management and adaptation practices during altered hydro-meteorological conditions.

• An important goal would be to assess environmental and economic costs associated with variations in ANPP.

• Societal needs to detect, predict, and manage changes in complex managed systems that threaten to undermine resource sustainability and security.