Osvaldo Sala

Projected Precipitation Change vs US National Climate Assessment 2014

Precipitation Variability is projected to increase IPCC Climate Change 2013: The physical science basis. Contribution of working group I to the fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, NY, USA.

 Grassland Shrubland  Hypothesis 2 Directional changes in water availability that favor grasses over shrubs or shrubs over grasses are reinforced through time.

Our scientific approach Observations Experiments Data Mining Modelling

+ Peters et al (2011)

+

2014 ambient + 80% - 80%

Solar panel Batter y Pump Intermediary tank (55 gal.) Float switch Interception plot Irrigation plot Filter ARMS automated rainfall manipulation system (Plots trenched to cm) Gherardi and Sala, Ecosphere: 2013

Assess ecosystem sensitivity to precipitation Not to replicate climate change scenarios

 CC Impact = ʄ (Δ Climate, Ecosystem Sensitivity)  PPT Imp = ʄ (Δ PPT, Ecosystem Sensitivity to PPT)

Total Aboveground Net Primary Production + 80% Ambient - 80 %

Grass Aboveground Net Primary Production + 80% Ambient - 80 %

Shrub Aboveground Net Primary Production Ambient + 80 % - 80 %

Direct and Indirect Effects of Precipitation

Plant-Species Diversity H’ + 80% Ambient - 80 %

 There was an effect of time on ecosystem response variables to long-term changes in PPT  The effect of time varied for different response variables  Asymmetry Hypothesis – The absolute magnitude of the effect was different for increasing or decreasing PPT

10 reps * 5 treat = 50 plots (2.5 x 2.5 m) Trenched 60 cm deep 20 rainout shelters 20 irrigated plots 10 control plots Methods

Effect of PPT Variability Gherardi & Sala PNAS 2015

The Mechanism Gherardi & Sala PNAS 2015

How do we explain these responses? Sala, Gherardi, Peters Climatic Change 2015 Modelling

Gherardi & Sala PNAS 2015 Effect of PPT Variance Increases through Time

Demise of grasses under high PPT variability favors shrubs

 Further explore the existence of thresholds ◦ Cumulative endogenous ◦ Stochastic exogenous ◦ Interaction between endogenous and exogenous  Mechanisms for indirect effects

Thank you Laureano Gherardi, Lara Reichmann Courtney Currier Kelsey Duffy Owen McKenna Josh Haussler

Collins et al 2011

Smith MD et al (2009)

a) Ecosystem response variables are proportional to water availability Response variable = b 0 + b 1 *PPT 0 + +

b) Ecosystem response variables are proportional to changes in water and to the time that the ecosystem has been exposed to the new condition Response variable = b 0 + b 1 *PPT + b 2 *Time

0 + + Peters et al (2011)

0 + +

c) Acclimation / exhausting of resources Response variable = b 0 + b 1 *PPT + b 2 *Time + b 3 *Time*PPT

 The effect of time is asymmetric for reduced water and increased water

 The effect of time varies for different response variables

Solar panel Batter y Pump Intermediary tank (55 gal.) Float switch Interception plot Irrigation plot Filter ARMS automated rainfall manipulation system (Plots trenched to cm) Gherardi and Sala, Ecosphere: 2013

Total ANPP Shrub ANPP Grass ANPP Spp Richness Diversity PPT

 Rejected H1a. There was an effect of time on ecosystem response variables to long-term changes in PPT, due to legacies in the ecosystem response.  Asymmetry – The absolute magnitude of the effect was different for increasing or decreasing PPT, i.e. spp loss with drought – no spp change with increased PPT  The effect of time varied for different response variables; may depend of the number of actors involved or the flow size relative to the pool size

Thank you Laureano Gherardi Lara Reichmann Owen McKenna Josh Haussler Kelsey Duffy Jose Anadon

NSF-Division of Environmental Biology Jornada Basin LTER Jornada Experimental Range - USDA School of Life Sciences - ASU Acknowledgments Lara G. Reichmann R.C.A. Gucho Owen P.B.R. McKenna Laura Yahdjian Deb Peters Kelsey McGurrin Josh Haussler John Angel III Shane & Miriam G. A. Gil Funding sources

Contrasting productivity responses to interannual precipitation variability Laureano A. Gherardi and Osvaldo E. Sala Arizona State University, School of Life Sciences Results of 6 years of precipitation manipulation at the Jornada Basin LTER

Precipitation Variability is projected to increase IPCC Climate Change 2013: The physical science basis. Contribution of working group I to the fifth assessment report of the Intergovernmental Panel on Climate Change. Cambridge, United Kingdom and New York, NY, USA.

Interannual Precipitation Variability ANPP Mean ANPP CV Productivity Stability

NSF-Division of Environmental Biology Jornada Basin LTER Jornada Experimental Range - USDA School of Life Sciences - ASU Acknowledgments Lara G. Reichmann R.C.A. Gucho Owen P.B.R. McKenna Laura Yahdjian Deb Peters Kelsey McGurrin Josh Haussler John Angel III Shane & Miriam G. A. Gil Funding sources

ANPP, PPT, Space ANPP = *MAP r 2 = 0.76 Bai et al (2008) ANPP= *MAP r 2 =0.94 Sala et al (1988) ANPP= *MAP McNaughton et al (1989) Great Plains South America Mongolian Plateau

 A simple model accounts for a large fraction of ANPP variability across space and for most grasslands of the world

Annual Precipitation (mm) Temporal Model Spatial Model Aboveground Net Production (g/m 2 /yr) Lauenroth and Sala 1992 Ecological Applications 2: *MAP r 2 = 0.94, p < *MAP r 2 = 0.39, p < Spatial vs. temporal models of net primary production

Sala et al 2012, Philosophical Transactions of the Royal Society B

r 2 =0.39 Sala et al 2012, Philosophical Transactions of the Royal Society B

 Time and Space cannot be exchanged for the ANPP-MAP relationship  Spatial model does not work through time  Temporal model only accounts for a small fraction of the variability explained by spatial models and has shallower slope

 Differences between spatial and temporal models are explained by time lags in ecosystem response to changes in water availability

 Time lags result from legacies of wet and dry years  ANPP observed = F (PPT t, Legacy)  Legacies = ANPP observed – ANPP expected ◦ ANPP expected = F (PPT t )  Magnitude of Legacy= F (PPT t-1 – PPT t )

Magnitude of Legacy= F (PPT t-1 – PPT t ) What is the shape of F ? How does this relationship change across a PPT gradient?

Sala et al 2012 PTRSB

Knapp and Smith (2001)

Sala et al 2012, PTRSB

Jornada LTER  MAP 240 mm  Dominant species: ◦ Bouteloua eriopoda C4 ◦ Prosopis glandulosa C3 Jornada Experimental Range Chihuahuan Desert Grassland

Fixed rainout shelters intercept different amounts of rain, depending on the number of shingles

Water was added to the increased PPT treatments after each PPT event, year around Total 132 plots

Reichmann, Sala, Peters, Ecology 2013 Legacy = * ∆PPT R 2 = 0.42

Yahdjian and Sala (2002)

Precipitation input (mm/year) ANPP (g.m -2.yr -1 ) PPT mm/year without drought legacy after 80% rainfall interception after 55% rainfall interception after 30% rainfall interception Yahdjian and Sala (2006)

 Changes in precipitation result in legacies  Magnitude of Legacies is a function of difference in precipitation of current and previous year  Legacies in the Chihuahuan desert ecosystem are symmetrical ◦ │ Positive legacy │ = │ Negative legacy│

 Positive legacies would compensate negative legacies  Increased precipitation variability would not affect average productivity

 Structural mechanism ◦ Meristem density constrains production response to a wet year after a dry year ◦ Meristem density enhances production after wet years  Biogeochemical mechanism ◦ N limitation constrains production response to a wet year after a dry year ◦ Abundant reactive N enhances production after wet years  Soil moisture carry-over

Reichmann, Sala, Peters, Ecology 2013

(Reichmann and Sala, Functional Ecology 2014)

Biogeochemical mechanism Reichmann, Sala, Peters, Ecology 2013

Reichmann et al Ecosphere 2012

Tiller density determines magnitude of legacies Biogeochemical mechanisms do not determine legacies Soil water carry-over does not determine legacies

Collins et al 2011 Present Future + Precipitation Time Hypothetical pattern

 Observational  Short term manipulations Most studies are

Can we predict press effects of directional changes in precipitation amount and variability based upon our understanding of pulse responses?

Smith MD et al (2009)

a) Ecosystem response variables are proportional to precipitation Response variable = b 0 + b 1 *PPT Increased precipitation Ambient Decreased precipitation Ecosystem response variable Time

b) Ecosystem response variables are proportional to changes in precipitation and to the time that the ecosystem has been exposed to the new condition Response variable = b 0 + b 1 *PPT + b 2 *Time Increased precipitation Ambient Decreased precipitation Ecosystem response variable Time

c) Acclimation / exhausting of resources Response variable = b 0 + b 1 *PPT + b 2 *Time + b 3 *Time*PPT Increased precipitation Ambient Decreased precipitation Ecosystem response variable Time

 The effect of time varies for different response variables Increased precipitation Ambient Decreased precipitation Ecosystem response variable Time

 The effect of time is asymmetric for reduced and increased precipitation Increased precipitation Ambient Decreased precipitation Ecosystem response variable Time

Can we predict press effects of directional changes in precipitation amount and variability based upon our understanding of pulse responses?

Individual Species Response + 80% - 80%

Species Richness + 80% Ambient - 80 %

Linear and non-linear ANPP responses to precipitation

1.Linear and non-linear ANPP responses to annual precipitation Precipitation

Annual ANPP (g m 2 yr -1 ) Therefore, NULL precipitation variance effect on ANPP. Linear ANPP responses to precipitation result in negative effects of dry years equal to positive effects of wet years. Precipitation

Annual ANPP (g m 2 yr -1 ) Non-linear ANPP responses to precipitation result in different effects of dry and wet years. Therefore, POSITIVE precipitation variance effect on ANPP. Precipitation

Annual ANPP (g m 2 yr -1 ) Non-linear ANPP responses to precipitation result in different effects of dry and wet years. Therefore, NEGATIVE precipitation variance effect on ANPP. Precipitation

Interannual Precipitation Variability ANPP Mean ANPP CV Productivi ty Stability

Plant-functional types show different stability response to PPT variability Ecosystem stability results from the aggregated response of plant types

Functional diversity increases with PPT variability Changes in relative abundance support such effect How do we explain these responses?

1.Inter-annual precipitation variability itself has a negative effect on ANPP 2.Non-linear responses and changes in soil water distribution explain such effect 3.Aggregated plant-functional type responses determine overall ecosystem response

1.Interannual precipitation variability has a positive effect on ANPP CV 2.Contrasting plant-functional type responses result in relative abundance change and increased diversity 3.Aggregated response of plant-types determines ecosystem stability