Historical climate legacies on soil respiration persist despite extreme changes in rainfall

Journal Pre-proof Historical climate legacies on soil respiration persist despite extreme changes in rainfall Christine V. Hawkes, Mio Shinada, Stephanie N. Kivlin PII:

S0038-0717(20)30049-3

DOI:

https://doi.org/10.1016/j.soilbio.2020.107752

Reference:

SBB 107752

To appear in:

Soil Biology and Biochemistry

Received Date: 30 July 2019 Revised Date:

5 February 2020

Accepted Date: 6 February 2020

Please cite this article as: Hawkes, C.V., Shinada, M., Kivlin, S.N., Historical climate legacies on soil respiration persist despite extreme changes in rainfall, Soil Biology and Biochemistry (2020), doi: https:// doi.org/10.1016/j.soilbio.2020.107752. This is a PDF file of an article that has undergone enhancements after acceptance, such as the addition of a cover page and metadata, and formatting for readability, but it is not yet the definitive version of record. This version will undergo additional copyediting, typesetting and review before it is published in its final form, but we are providing this version to give early visibility of the article. Please note that, during the production process, errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain. © 2020 Published by Elsevier Ltd.

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Historical climate legacies on soil respiration persist despite extreme changes

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in rainfall

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Christine V. Hawkes1,2*, Mio Shinada1,3, Stephanie N. Kivlin1,4

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Department of Integrative Biology, University of Texas at Austin, Austin, TX, USA 78712 Department of Plant and Microbial Biology, North Carolina State University, Raleigh, NC, USA

Department of Biological Sciences, Tokyo Metropolitan University, Tokyo Japan Ecology and Evolutionary Biology, University of Tennessee, Knoxville, TN, USA 37996

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*Corresponding author: [email protected]

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Re-submitted to Soil Biology & Biochemistry on February 5, 2020 as a Regular Research Paper

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Abstract

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How soil microbial respiration responds to climate change can be constrained by historical

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climate. Understanding the duration of such legacy effects is key to determining how much

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they matter for projecting future ecosystem carbon cycling. Here, we tested whether extreme

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changes in rainfall could overcome constraints imposed by historical rainfall on how soil

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respiration responds to moisture. We predicted that larger shifts in rainfall regime would alter

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the magnitude or sensitivity (slope) of the respiration response to moisture compared to

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smaller changes in rainfall relative to historical conditions. Over 4.5 years, we imposed rain

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treatments ranging from extreme dry to extreme wet conditions that varied by ~400%, as well

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as ambient and historical mean rainfall controls. Rain treatments were applied to shortgrass or

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tallgrass vegetation that represented lower and higher biomass inputs, respectively, to test how

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shifts in soil resources might affect respiration moisture responses. We found high resistance to

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altered rainfall in the field, with persistent legacies indicated by no change in the respiration

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response to moisture among treatment and control rain treatments. The intrinsic respiration

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response to moisture under controlled laboratory conditions was also unaffected by field rain

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treatments. In response to field vegetation treatment, there was 10-30% more soil respiration

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in tallgrass compared to shortgrass that was paralleled by an increase in soil dissolved organic

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carbon, but no change in moisture sensitivity consistent with independent resource and climate

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effects. Soil bacteria and fungi were unchanged across all treatments and were largely

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generalists, suggesting high community as well as functional resistance to change. Climate

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legacies on soil microbial communities have the potential to modify our expectations for the

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rate of acclimation and adaptation to altered climate conditions.

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Keywords

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Drought, precipitation, bacteria, fungi, grassland

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1. Introduction

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Microorganisms can respond quickly to their contemporary environments via multiple

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mechanisms, including physiological plasticity, acclimation, community turnover, and rapid

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adaptation, that can affect carbon cycling. However, microbial process rates, such as

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respiration, can be constrained by historical environmental conditions (Fig. 1a, Hawkes and

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Keitt, 2015), particularly drought (Evans and Wallenstein, 2012; Meisner et al., 2013; Waring

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and Hawkes, 2015). Functional legacies of prior climate are observed both in reciprocal

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transplants and in laboratory incubation experiments (Waldrop and Firestone, 2006; Strickland

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et al., 2015; Bond-Lamberty et al., 2016; Martiny et al., 2017; Dacal et al., 2019; Min et al.,

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2019), consistent with prior species sorting or local adaptation in the microbial community.

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Ecosystem models of carbon cycling should be inherently sensitive to the presence of legacies;

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yet these models continue to assume that soil microbial responses to the environment are

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instantaneous and globally consistent (Sierra et al., 2015; Wieder et al., 2018). A key question is

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how long historical legacies will persist, because only enduring limits on function will

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meaningfully affect model scaling.

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Historical legacies might be quickly overcome if environmental change is sufficiently large to

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reduce fitness of extant taxa and select for new phenotypes via adaptation or immigration

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(Hawkes and Keitt, 2015). Projected extreme drought and rainfall in the future (Fischer et al.,

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2013; Singh et al., 2013; Prein et al., 2016) have the potential to create conditions that would

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moderate or eliminate legacy effects. Many experiments only impose moderate changes in

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climate, which does not necessarily speak to extreme change. Carey et al. (2016), for example,

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found that many warming studies altered soil temperature by < 1.72 °C, which did not change

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soil respiration. However, when more severe dry-wet cycles were imposed in the laboratory,

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there was selection for faster bacterial growth and higher carbon use efficiency after rewetting

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(de Nijs et al., 2019). A lower degree of acclimation to extreme rainfall shifts is likely to occur as

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a change in only the magnitude of respiration, whereas a change in the moisture sensitivity

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(slope) of respiration would reflect a higher degree of acclimation or adaptation to the new

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conditions (Fig. 1b, Atkin and Tjoelker, 2003). Additional tests of extreme climate change are

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required to understand the persistence of climate legacies and their importance for ecosystem

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models (Knapp et al., 2018).

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Yet climate is just one factor affecting microbial communities that control soil respiration –

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other aspects of the environment may be as or more important when considering how

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respiration will respond to climate change (e.g., Bradford et al., 2014). Given a change in a

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single environmental factor such as moisture or temperature, extant microorganisms may

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continue to be the best local competitors if taxa were assembled previously via a different

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environmental filter or were adapted to other local conditions, such as resources. In this way,

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non-climate factors can create independent legacy effects. Resource history, for example, can

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select for specialists such that functional differences in decomposition persist (Keiser et al.,

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2011). Microbial adaptation to local litter chemistry also constrains decomposition rates

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globally, with an average 7.5% faster decomposition in “home” vs. “away” litters that increases

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as litter qualities diverge (Veen et al., 2015). Thus, the balance of climate and non-climate

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drivers will ultimately determine legacy effects and persistence.

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In previous work, we observed climate legacies in both the magnitude and sensitivity (slope) of

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soil respiration responses to moisture across a steep rainfall gradient: for soils from regions

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with historically more drought, respiration was lower and less sensitive to moisture compared

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to regions with historically wetter conditions (Fig. 1a, Hawkes et al., 2017). This pattern

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continued even when soils from the different regions were reciprocally transplanted in the field

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for 12 months and when rain was altered at one field site for 18 months (Fig. 1a, Hawkes et al.,

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2017; Waring and Hawkes, 2018). We further found evidence for independent climate and

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resource selection (Hawkes et al., 2017). Here, we manipulated rainfall and vegetation to

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examine both the effects of potential climate change and resource shifts on soil microbial

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community function. Specifically, we examined in situ responses of soils to 4.5 years of rainfall

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manipulation where we imposed a range of extreme drought to extreme wet conditions in a

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common garden located at one end of the same precipitation gradient. We also included two

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vegetation types that were chosen to generate larger and smaller biomass inputs that should

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alter resource conditions in a way that might happen with vegetation shifts in natural

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ecosystems. Finally, we collected soils from the common garden to test the respiration

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moisture response under controlled laboratory moisture conditions where potential resistance

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vs. acclimation can be better assessed (Atkin and Tjoelker, 2003).

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These experiments allows us to address several hypotheses about historical climate legacies

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given potential extreme changes in rainfall. We predicted that extreme changes in rainfall over

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multiple years would be sufficient to change the magnitude or sensitivity of the respiration

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moisture response (Fig. 1b). Alternatively, legacies might persist if the change in rainfall was too

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little or the duration of treatment too short, as evidenced by a shared slope and magnitude

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between extreme rainfall treatments and both ambient and mean rainfall controls. We further

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predicted that functional legacies would be underpinned by similarly persistent bacterial and

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fungal communities. Finally, depending on how the vegetation treatments altered soil

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resources, we assumed that the magnitude of respiration might change in proportion to the

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resource pool size, but without affecting moisture sensitivity.

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2. Materials and Methods

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2.1 Field experimental design

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The field experiment design included six rainfall treatments replicated across four blocks and

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two grass communities (tall grasses, short grasses) implemented as split-plot treatments (4

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blocks x 6 rainfall x 2 vegetation types = 48 plots). To create the rainfall treatments, we used a

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rainout shelter facility located at the Lady Bird Johnson Wildflower Center in Austin, Texas, USA.

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The rainout shelters were constructed from steel frames (18 m wide x 73 m long) that are 6.0 m

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tall at the center with 1.8 m open sides. Shelter roofs were 6-mm thick polyethylene film with

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91% light transmission (IGC Greenhouse Megastore, Danville, IL, USA). Mean maximum

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temperature at the site is 35.8 °C (August), mean minimum temperature is 3.7 °C (January), and

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mean annual precipitation is 850 mm yr-1. Soils at the site are shallow, Speck Series, thermic

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Lithic Argiustolls (NRCS Soil Survey Staff, 2020) with 32.7% clay, 24.8% sand, bulk density of 1.2

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g cm-3, 0.26% total nitrogen, 6.7% total carbon (~2% carbonate), and pH 7.3 ± 0.04. Available

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nutrients tend to be low, with total inorganic nitrogen of 2.9 ± 0.71 µg g-1 and phosphate of 2.0

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± 0.33 µg g-1.

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The rainfall treatments were designed to mimic the local historical precipitation record and

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included: extreme dry (“Ext Dry,” 349 mm yr-1), 25th percentile of mean (“P25,” 657 mm yr-1),

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mean (“Mean,” 850 mm yr-1), 75th percentile of mean (“P75,” 1005 mm yr-1), and extreme wet

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(“Ext Wet,” 1331 mm yr-1). The extreme dry and extreme wet treatments are asymmetric

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around the mean, representing 38% and 156% of mean annual rainfall, because they are based

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on the respective ten years of rainfall surrounding the appropriate years for each treatment in

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the 87-year historical record for Austin, TX (National Centers for Environmental Information:

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https://www.ncei.noaa.gov/), with applications created using a stochastic weather generator,

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LAR-WG 5.5 (Semenov et al. 1998) calibrated to those ten years. We also included an ambient

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control treatment (“Amb”) where the amount of water added reflected actual precipitation

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events. From 2012 to 2015, annual ambient rainfall treatment applications were 445, 1181,

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836, and 1442 mm, respectively. Irrigation was applied using 90° sprinklers (Hunter HP2000,

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Hunter Industries Inc., San Marcos, CA, USA) on 1-m risers placed in the four plot corners.

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Rainfall treatments were implemented May 22, 2012 and were able to significantly alter the soil

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moisture regime across treatments (see Connor and Hawkes, 2018). Average volumetric soil

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moistures (± 1 SE) based on monthly measurements (ECH2O 10HS sensors, Decagon Devices,

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Pullman, WA, USA) were: Ext Dry = 6.6 ± 0.3%; P25 = 7.2 ± 0.5%, Mean = 9.3 ± 0.5%, P75 = 14.4

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± 1.3%, Ext Wet = 15.4 ± 1.4%, and Amb = 12.2 ± 0.5%.

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The two grass-based vegetation treatments, tallgrass and shortgrass, were chosen based on the

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likelihood that these would differentially alter soil carbon via greater tallgrass litter inputs (Lane

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et al., 2000; Derner et al., 2006). In addition, we expect the potential for resource-precipitation

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interactions given the greater drought tolerance of shortgrass compared to tallgrass and vice-

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versa (Epstein et al., 1998). Grasses were planted in 2.5 x 2.5 m subplots from pre-germinated

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seedlings in August 2010. All grasses were native perennial warm-season species. Tall grass

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communities included six species: Andropogon gerardii Vitman, Leptochloa dubia (Kunth) Nees,

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Panicum virgatum L., Schizachyrium scoparium (Michx.) Nash, Sporobolus compositus (Poir.)

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Merr., and Sorghastrum nutans (L.) Nash. Shortgrass communities included five species:

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Aristida purpurea Nutt., Bouteloua curtipendula (Michx.) Torr., Bouteloua eriopoda (Torr.) Torr.,

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Bouteloua gracilis (Willd. ex Kunth) Lag. ex Griffiths, and Panicum hallii Vasey. Seeds were

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purchased from Native American Seed (Junction, TX) or provided by the Ladybird Johnson

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Wildflower Center Seed Bank. Plants were arranged in a grid with 0.5-m spacing and three

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individuals per species in stratified random locations (15-18 total plants per plot) to mimic

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natural densities.

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2.2 Field respiration measurements

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We examined how 4 years of rainfall and vegetation treatments affected soil C cycling. First, soil

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respiration was measured in the field plots every 4-5 weeks during the growing season, in June,

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July, August, and October 2016. Measurements were made with a soil CO2 flux system with 10-

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cm diameter survey chamber (LI-COR Environmental, Lincoln, Nebraska, USA) fitted on PVC

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collars (length 10-cm, diameter 10cm, buried at a depth of 2-3 cm) installed in the center of

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each subplot in 2012. At the time of measurement, we also measured volumetric soil moisture

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and temperature.

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2.3 Field plant and soil measurements

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After respiration measurements in October 2016, soils were collected from each plot by coring

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(2.5-cm diameter x 15-cm deep) in four locations. The cores were combined and homogenized

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in a plastic bag and stored on ice for transport to the laboratory. Soils were immediately

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extracted in 0.5M K2SO4 in a 1:4 ratio to measure dissolved organic carbon (DOC, Jones and

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Willett, 2006); for microbial biomass carbon (MBC) soils were fumigated with chloroform and

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then extracted in K2SO4 (Vance et al., 1987; Scott-Denton et al., 2006). MBC was calculated as

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the difference in C between fumigated and unfumigated extractions divided by 0.45 to convert

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from salt-extractable to total C in the microbial biomass (Vance et al., 1987). Soil organic matter

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(SOM) was measured on oven-dried soil aliquots from October, using loss-on-ignition at 360 °C

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for 2 h in a muffle furnace (Schulte and Hopkins, 1996). Soil moisture was measured

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gravimetrically. Soil pH was measured in 1:2 slurry with water with a ceramic-junction pH

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electrode with integrated temperature probe (InLab Versatile, Mettler Toledo, Columbus, OH,

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USA). Additional soil aliquots from October were stored at -80°C for DNA extraction. After soils

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were collected, we harvested field plants to measure plot-level aboveground biomass as oven-

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dried weight.

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2.4 Field bacterial and fungal communities

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DNA was extracted from two ~0.25 g aliquots of frozen soil for each sample using MoBio Power

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Soil Kits (MoBio, Carlsbad, CA, USA). DNA was quantified fluorimetrically (Qubit, Invitrogen,

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Carlsbad, CA, USA) and standardized to 10 ng µL-1. For PCR, we used Illumina TruSeq V3 indices

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(Illumina, San Diego, CA) linked to 16 rRNA bacteria-specific primers (S-D-Bact-0341-b-S-17/S-D-

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Bact-0785-a-A-21 primers; Klindworth et al., 2012) or 28S rRNA fungal-specific primers (NL1-

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NL4; O'Donnell, 1993). For PCR reactions, we used platinum PCR Supermix (Invitrogen,

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Carlsbad, CA), 1.25 µl of each primer (10 µM), 0.5 µL of BSA (20 mg mL-1), and 2 µL (~20 ng) of

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DNA. Bacterial PCR ran with a hot start at 95 °C for 5 min, 25 cycles of 95 °C for 40 s, 55 °C for 2

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min, 72 °C for 60 s, and a final extension step of 72 °C for 7 min. The fungal reactions ran with a

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hot start at 93 °C for 5 min, 35 cycles of 93 °C for 60 s, 58 °C for 60 s, 72 °C for 60 s, and a final

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extension step of 72 °C for 10 min. For each sample, PCR reactions were run in duplicate,

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combined, cleaned with Agencourt AMPure XP magnetic beads (Beckman Coulter, Brea, CA),

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and again quantitated with a Qubit fluorometer. Samples were then pooled in equal amounts

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and sequenced on Illumina MiSeq v3 (2 x 250 bp for bacteria, 2 x 300 bp for fungi) at the

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University of Texas Genome Sequencing and Analysis Facility. All sequences were deposited in

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the NCBI Short Read Archive under BioProject PRJNA553200.

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All sequence data was processed with dada2 v 1.10.6 (Callahan et al., 2016) in R v 3.5.2 (R Core

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Team, 2019) using default parameters to determine amplicon sequence variants (ASVs). Error

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rates were estimated separately for each MiSeq run (n=2). We trimmed primers (bacteria: 17,

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21; fungi: 24, 19) and low-quality ends (F and R sequences were truncated for bacteria at 245

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and 200 and for fungi at 200 and 200, respectively). The trimming reduced overlap, so paired

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sequences were concatenated resulting in 417 b length for bacteria and 367 b length for fungi.

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Taxonomy was assigned using the RDP 16S trainset 11.5 and Fungal 28S trainset 11 (Wang et

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al., 2007; Cole et al., 2013). ASVs that were chimeric, not assigned to either domain Bacteria or

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Fungi, or occurred only once were removed. Sequences were further curated with LULU v 0.1.0

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(Frøslev et al., 2017) using default parameters.

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Fungal sequences included 2,243,433 raw reads, which were reduced to 343,038 after filtering,

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denoising, merging, and chimera check. An initial 5,542 fungal ASVs were assigned by dada2,

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with 402 final ASVs after removal of non-Fungi, LULU curation, singleton removal, and removal

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of ASVs found at only a single site. One fungal sample was dropped due to zero ASVs after

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processing. Bacteria sequences included 1,894,627 raw reads that were reduced to 493,957

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after QC as for fungi. Initial identification of 42,820 ASVs was reduced to 34,214 after removal

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of non-Bacteria ASVs, LULU curation, and singleton removal; all ASVs were found at two or

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more sites. Richness of fungi and bacteria was calculated prior to singleton removal.

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2.5 Laboratory microcosm experiment

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To test how 4 years of rainfall and vegetation treatments altered soil respiration responses to

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moisture, we exposed field treatment soils to controlled moisture conditions in the laboratory.

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We chose two levels of soil moisture, 8% and 20%, to capture the range of commonly observed

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field conditions. Soils from four of the field rainfall treatments (extreme dry, extreme wet,

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mean, ambient) were air dried to ~5% moisture at the plot level for use in the laboratory

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incubation experiment. Approximately 15 g of soil was added to individual glass microcosms (67

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mL vials) equipped with PTFE-silicone septa caps. We included 2 replicates per original subplot,

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for a total of 128 microcosms. After initial adjustment of soil water content to 8 or 20%, the

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microcosms were sealed with Parafilm and allowed to equilibrate for 1 week at room

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temperature (~24 °C). Thereafter, respiration rates were measured weekly for 3 weeks:

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microcosms were flushed with CO2-free air and sealed for 1 h before 15 mL air samples were

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collected from the headspace of each microcosm through the septum inserted in the lid.

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Headspace samples and standards for each time point were stored in 12-mL borosilicate vials

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with butyl rubber septa until CO2 was quantified on a gas chromatograph with FID+methanizer

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(SRI 8610C, SRI Instruments, Santa Monica, CA). Soil moisture was adjusted weekly by weight to

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maintain treatments, resulting in maximum variation of ± 2%.

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2.6 Statistical analyses

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Field soil respiration (CO2 flux in µmoles m-2 s-1) was ln transformed for normality and analyzed

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using a split-split plot ANOVA as a function of rainfall (whole-plot treatment, fixed effect),

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vegetation (split-plot treatment, fixed effect), block (random effect), and date (split-split-plot

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treatment, random effect). Rainfall × block was used as the whole-plot error, rainfall ×

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vegetation × block was used as the subplot error, and rainfall × vegetation × block × date was

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used as the sub-subplot error. Because there was a significant interaction of rainfall treatment

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and date in the original analysis (P < 0.001); we re-ran separate ANOVAs for each date,

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including both block and rainfall treatment (with rainfall × block as the error term).

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Precipitation legacy effects in the field (Fig. 1a) were examined with posthoc REGW-F tests to

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determine whether increased or decreased rainfall treatments differed from Ambient or Mean

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controls; legacies are detected when there is no change between historical (Ambient or Mean)

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and current respiration under the shifted rain treatments. We also tested for a change in slope

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(Fig. 1b) between shortgrass and tallgrass at each date using ANCOVA to examine the

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interaction of rain treatment (as a numerical variable in mm) and vegetation treatment. The

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Ambient rainfall control treatment was excluded from the ANCOVA analysis because its

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variation across years prevented coding as a single application amount (however, its inclusion

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based on current year irrigation total did not change results). Finally, to examine how field soil

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respiration was affected by background environmental conditions, we further used best subsets

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regression with AICC on ln-transformed respiration data with soil temperature, soil moisture, 2-

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wk antecedent applied rainfall, 1-mo antecedent applied rainfall, and year-to-date applied

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rainfall. Legacies would be further supported by weak relationships to these contemporary

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environmental conditions. For the analyses of respiration, we used a Bonferroni-corrected α =

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0.0125 to maintain the family-wise error rate at 0.05; actual P-values are reported.

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In the microcosm experiment, soil respiration was calculated as the CO2 flux in µg C g-1 dry soil

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hr-1 averaged across the three weekly measurements. Respiration in the laboratory incubation

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was ln transformed for normality and analyzed using ANOVA with a split-split plot design, with

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field block (random effect), field precipitation (whole-plot treatment, fixed effect), field

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vegetation (subplot treatment, fixed effect), and laboratory moisture (sub-subplot treatment,

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fixed effect). Precipitation × block was used as the whole-plot error, precipitation × vegetation ×

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block was used as the subplot error, and precipitation × vegetation × laboratory moisture ×

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block was used as the sub-subplot error. Finally, we used linear regression to determine if

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laboratory respiration was related to continuous field variables that are either expected to have

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an effect (MBC) or that varied with field experimental factors (DOC). All ANOVA and regression

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statistics were run in SPSS v 25 (IBM, Armonk, NY).

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Soil DOC, MBC, SOM, pH, and moisture measured at the October harvest were analyzed using

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the same split-plot ANOVAs as for field respiration (without sampling date); we used a

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Bonferroni-corrected α = 0.008 based on multiple measurements taken on the same plots;

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actual P values are reported.

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The ASV matrices for bacteria and fungi in the field plots were normalized with the variance

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stabilizing transformation in DESeq2 v1.22.2 (Love et al., 2014). Community composition of

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bacteria and fungi was analyzed with Residual Randomization in Permutation Procedures (RRPP

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v 0.4.2) (Collyer and Adams, 2018) using a split-plot design parallel to that used for ANOVA in

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the October harvest. To visualize these patterns, we used PCoA in the R package phyloseq v

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1.26.1 (McMurdie and Holmes, 2013). To further characterize generalists vs. specialists in the

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bacterial and fungal communities, we ran indicator species analysis using the R package

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indicspecies v 1.7.6 (De Cáceres et al., 2010) because this accounts for both specificity and

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fidelity of ASVs by treatment group. All sequence analyses were run in R v 3.6.1 (R Core Team,

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2019). Graphs were created in SigmaPlot v 14 (Systat Software Inc., San Jose, CA).

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3. Results

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3.1 Soil respiration

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Field soil respiration increased with increasing field rainfall treatment (P = 0.005; Table S1, Fig.

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2), but none of the increased (P75 or ExtWet) or decreased (P25 or ExtDry) rainfall treatments

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differed significantly from either Mean or Ambient controls in posthoc tests. There was subtle

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variation in rainfall treatment effects across dates (P < 0.001), in which the differences between

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higher and lower rainfall treatments were only significant in June (P = 0.008) and July (P =

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0.010). However, the same trends persisted in August (P = 0.018) and October (P = 0.067; Table

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S2, Fig. 2). There was no interaction of rainfall treatment and vegetation treatment (P = 0.346).

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Vegetation treatments differed significantly in field soil respiration (P = 0.002; Table S1, Fig. 2),

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with raw flux rates 57% higher in tallgrass than in shortgrass. Based on ANCOVA, the vegetation

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types did not differ in slope of the respiration-rain relationship in any month (P > 0.110),

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although respiration in tallgrass remained consistently elevated compared to shortgrass (P <

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0.012; Table S3, Fig. 2).

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Observed temporal variation in respiration among field rain treatments was partly due to

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fluctuations in short-term contemporary water availability based on best-subsets regression (P

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< 0.001; Table S4). Soil respiration generally increased with more soil moisture (Fig. 3a), but

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decreased with increasing 2-week antecedent rainfall (Fig. 3b). Soil moisture and short-term 2-

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week antecedent rain were each included in nine of the top ten models. In contrast, the more

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long-term indicators of water availability, 1-month antecedent and year-to-date and rainfall,

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were only included in five and six of the top ten models, respectively (Table S4). Temperature

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was included in only four of the top ten models (Table S4), with maximum respiration observed

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between 27 and 29 °C (Fig. 3c). However, even the top model explained only 13.4% of the

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variation in field soil respiration (Table S4).

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In controlled conditions in laboratory microcosms, soil respiration patterns were the same.

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Respiration increased with available water (P < 0.001), but field rainfall treatments did not

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affect the slope or magnitude of the soil respiration response to laboratory moisture (P = 0.336;

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Table S5, Fig. 4a). Soils from tallgrass plots respired ~25% more than soils from shortgrass plots

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overall (P = 0.017; Fig. 4b); this was primarily driven by differences in high moisture (32% more)

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rather than in low moisture (10% more) laboratory treatments, but the interaction of field

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vegetation and laboratory moisture was not significant (P = 0.055; Table S5). Laboratory

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microcosm respiration was unrelated to field soil MBC (P = 0.872) or DOC (P = 0.439).

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3.2 Field experiment plants and soils

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At the October harvest, aboveground plant biomass was 15x greater in tallgrass compared to

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shortgrass treatments (P < 0.001; Table S6, Fig. 5b), which coincided with 63% more soil DOC (P

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= 0.007; Table S6, Fig. 5f). There was also a trend for more biomass in plots where Mean rainfall

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or higher was applied compared to drought plots (ExtDry, P25) particularly for tallgrass plots,

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but neither the main effect of rainfall nor the interaction of rainfall and vegetation was

343

significant (P = 0.015, P = 0.012) at the Bonferroni-corrected α = 0.008. Soil moisture increased

344

with field rainfall treatments (Table S7, Fig. 5c), but was unaffected by the vegetation treatment

345

(Table S7, Fig. 5d). No treatment effects were found on soil MBC (Table S6, Fig. S1a, b) or SOM

Hawkes et al. - 17

346

(Table S6, Fig. S1c, d). Variation in soil pH ranged from 6.2 to 7.8 at the sub-plot level, and also

347

was not associated with any of the treatments (P = 0.409; Table S7, Fig. S1e, f).

348 349

3.3 Field experiment soil microbial communities

350

We did not detect any treatment-associated variation in microbial communities in the field

351

experiment at the October 2016 harvest when soils were collected for the laboratory

352

microcosms. No differences were found in richness (P > 0.059; Table S8) or community

353

composition (P > 0.111; Table S9) of bacteria or fungi across field rainfall or field vegetation

354

treatments (Fig. 6). Based on indicator species analysis, 99% of bacterial and 98% of fungal ASVs

355

were generalists that were not significantly associated with any treatment.

356 357

4. Discussion

358

We observed persistent legacies of local climate history on soil respiration responses to

359

moisture and on field microbial communities, despite four-fold variation in precipitation

360

treatments applied for 4.5 years. Legacy effects of historical rainfall are supported by three

361

lines of evidence: (1) in the field there were no significant differences in respiration between

362

soils in altered and control rainfall treatments, (2) in the field, contemporary short-term and

363

long-term moisture, as well as temperature, had little power to explain field respiration, and (3)

364

in the lab, the slope of the respiration-moisture response did not change across the field rainfall

365

treatments. The lack of altered rainfall effects on the intrinsic moisture sensitivity or magnitude

366

of respiration is particularly surprising considering that the extreme dry treatment received

367

approximately the same amount of water during this 4.5 year period as the extreme wet

Hawkes et al. - 18

368

treatment received annually. The inability to shift respiration moisture responses with a change

369

in rainfall is consistent with our expectations based on prior observations (Hawkes et al., 2017;

370

Waring and Hawkes, 2018). It is possible that we missed an initial dynamic period in response to

371

the extreme rainfall treatments (e.g., Walker et al., 2018), but given that we saw similar

372

patterns at 18 months (Hawkes et al., 2017) such a period would have been short-lived.

373

Although climate legacies in this central Texas grassland appear to be difficult to overcome,

374

they are not unique to this system; drought and temperature history restrict soil responses to

375

new environmental conditions in ecosystems from desert to tundra (e.g., Evans and

376

Wallenstein, 2012; Schindlbacher et al., 2015; Bond-Lamberty et al., 2016; Glassman et al.,

377

2018; Walker et al., 2018; Min et al., 2019; Väisänen et al., 2019). However, legacies are not

378

found in all cases (Rousk et al., 2013; Bell et al., 2014; Baker et al., 2018; Liu et al., 2019; Preece

379

et al., 2019) and discovering why this varies is paramount for improving projections of

380

ecosystem carbon cycling in future conditions.

381 382

In contrast to the lack of altered rainfall effects, the magnitude of soil respiration changed with

383

vegetation treatments – shortgrass soils were unable to respire as much as tallgrass soils under

384

the same environmental conditions in the lab. This was also true early in the growing season in

385

the field experiment. The change in magnitude without a change in the sensitivity (slope) of the

386

respiration-moisture relationship is similar to what we observed previously with direct litter

387

additions in microcosms (Hawkes and Keitt, 2015), presumably due to more available soil

388

carbon allowing for proportionally more carbon respired per unit water. Tallgrasses are

389

associated with greater aboveground net primary production and soil carbon storage than

Hawkes et al. - 19

390

shortgrasses in the Great Plains (Epstein et al., 1998; Lane et al., 2000; Derner et al., 2006) and

391

the larger biomass observed here was expected to add more plant litter to tallgrass soils since

392

plants were not harvested during the 4.5 years of the field experiment. Consistent with the idea

393

of differential carbon inputs, tallgrass plots had ~1.6x more soil DOC than shortgrass plots,

394

although the enhanced DOC was not correlated with soil respiration. Salt-extractable DOC

395

probably did not capture microbial-scale carbon availability (Tfaily et al., 2015; Chen et al.,

396

2018). Additionally, field soil respiration was a mixture of heterotrophic and autotrophic

397

sources that can respond differently to drought (Balogh et al., 2016) and these were not

398

partitioned here. There was also an absence of variation in MBC among vegetation or rainfall

399

treatments, but we did not measure microbial growth rates (e.g., de Nijs et al., 2019).

400

Nevertheless, the effects of vegetation type and lack of interactions with field rainfall or

401

laboratory moisture indicate that these effects are independent of prior climate selection and

402

are unlikely to alter constraints caused by historical climate legacies. Given the trend towards a

403

rainfall by vegetation interaction, however, this may change over longer time periods (e.g.,

404

DeAngelis et al., 2015) to generate non-additive effects.

405 406

There was no treatment variation in bacteria or fungi, suggesting that functional differences

407

between the vegetation types were generated by microbial physiological plasticity. Such

408

plasticity likely reflects a predominance of generalist taxa, which is supported here by the

409

identification of only 1-2% of ASVs as specialists. This region has a long history of fluctuating

410

rainfall conditions that may select for generalists with broad ecological niches (Hawkes and

411

Keitt, 2015). In previous work, we found that habitat generalists were more common than

Hawkes et al. - 20

412

specialists throughout this drought-prone region, although some specialists tended to be locally

413

abundant (Waring and Hawkes, 2018). In contrast, soils from Mediterranean climates where

414

seasonal drought is common show regular successional patterns upon rewetting of dry soils,

415

including rapid-responding specialists (Placella et al., 2012). The speed of resuscitation

416

dynamics can be rapidly altered in the laboratory with repeated dry-wet cycles (de Nijs et al.,

417

2019), consistent with a strong selective filter. Although not explicitly manipulated here, the

418

characteristics of climate history may therefore be an important component in determining

419

whether extant microbial communities are comprised of generalists or specialists, and whether

420

they will respond to altered rainfall via plasticity or turnover.

421 422

Differences generated by the vegetation treatments in soil carbon or other, unmeasured

423

resources may have been too subtle to select for different microbial taxa. Effects of different

424

plant communities on soil resources may also take longer to develop (Waldrop et al., 2006)

425

than direct fertilizer applications (Leff et al., 2015). Alternatively, beyond the simple difference

426

in biomass quantities found here, it is possible that plant traits did not vary enough among the

427

focal C4 grasses to alter microbial communities. In other studies, plant variation in traits such as

428

leaf nutrients, root exudate chemistry, and tissue lignin content were correlated with microbial

429

community composition or function (e.g., Cline and Zak, 2015; Sayer et al., 2017; Zhalnina et al.,

430

2018; Boeddinghaus et al., 2019). Our goal here was to manipulate resources in a way that

431

might happen naturally with vegetation change in the central Texas region; based on our

432

results, a shift in grass dominance with any east-west movement in drought severity on the

433

precipitation gradient (Sankaran, 2019) might not generate major belowground compositional

Hawkes et al. - 21

434

changes, at least in the short term. That lack of change may have consequences for soil carbon

435

cycling if there is no microbial acclimation.

436 437

Embedding small treatment plots in an unchanged landscape may further allow the local

438

microbial species pool to continue to dominate these plots, which might not represent what

439

happens with climate change at larger scales. Lawrence et al. (2016) observed patterns

440

consistent with predominance of local immigrants in a warming experiment, where microbial

441

community growth was unchanged by dispersal in ambient temperatures but was reduced by

442

dispersal in warmed treatments. Local taxa may persist despite large changes in water

443

availability even with long-distance dispersal if colonists are outcompeted by local taxa. This is

444

consistent with the lack of change in bacteria community composition in a reciprocal transplant

445

experiment in this system that occurred despite enhanced dispersal treatments (Waring and

446

Hawkes, 2018). In some cases, however, the effects of dispersal can be as impactful as drought

447

treatments (Evans et al., 2020). Dispersal is likely to be more important when larger regions are

448

simultaneously compromised by stressors, such as severe drought, that can reduce local

449

population sizes and propagule numbers. Although the field of microbial ecology no longer

450

embraces the “everything is everywhere” paradigm (O'Malley, 2007), we are only beginning to

451

understand the scale of local species pools, the frequency of dispersal limitation, and the

452

incidence of long-distance dispersal events (e.g., Peay et al., 2010; Cline and Zak, 2014; Kivlin et

453

al., 2014; Tipton et al., 2019) that can affect microbial community responses to environmental

454

change.

455

Hawkes et al. - 22

456

We demonstrated robust legacies of historical rainfall and independent selection of climate vs.

457

vegetation on soil respiration responses to moisture in both the field and the lab. In light of our

458

findings, models of soil carbon cycling under future expectations for extreme changes in water

459

availability should not assume acclimation or adaptation responses. Determining how rainfall

460

history selects for microbial community composition and function in different ecosystems is

461

crucial for understanding how legacies will differ across biomes. We suggest that the

462

occurrence of climate legacies will be determined not just by average differences in water

463

history, but also by the historical incidence and magnitude of drought. Improving soil carbon

464

models by incorporating microbial ecophysiology will only be possible if we can develop simple,

465

scalable relationships based on well-understood mechanisms (Moyano et al., 2013).

466

Understanding the duration of legacy effects on soil microbial composition and function is an

467

important step towards that reconciliation.

468 469

Acknowledgements

470

For assistance with fieldwork and labwork, we thank N. Johnson, J. Paszalek, Y. Sorokin, C.

471

Averill, E. Connor, H. Giauque, O. Gilbert, G. Miller, J. Rocca, J. Kiniry, A. Williams, J. Moore, P.

472

Schulze, J. Dula, S. Edelmon, A. Ellis, E. Friedmann, J. Manning, J. Menz, M. Ramirez, S. Richey,

473

G. Schaack, E. Squires, W. Thomas, C. Timmerman, and V. Toro. Previous drafts of the

474

manuscript were improved by comments from C. Averill, S. Evans, and J. Rocca. Site access was

475

kindly permitted by the Ladybird Johnson Wildflower Center. This material is based upon work

476

supported by grants to CVH from the National Science Foundation (DEB 1546740) and the

477

Department of Energy National Institute for Climate Change Research (08-SC-NICCR-1071).

Hawkes et al. - 23

478 479 480 481

Author contributions

482

CVH designed and carried out the field experiment, designed the laboratory microcosm

483

experiment, analyzed the data, and wrote the manuscript. MS carried out the laboratory

484

microcosm experiment. SK contributed to laboratory microcosm experiment design, prepared

485

Illumina libraries, quantified SOM, and edited the manuscript.

486 487

Data accessibility

488

Raw sequence data can be accessed via the National Center for Biotechnology Information

489

Short Read Archive under BioProject PRJNA553200. All other raw data are provided in the

490

Appendix A, Tables S10-S12.

491 492

Appendix A. Supplementary Materials

493

Supplementary data to this article can be found online at XXXX.

494 495

Hawkes et al. - 24

496

References

497

Atkin, O.K., Tjoelker, M.G., 2003. Thermal acclimation and the dynamic response of plant

498 499

respiration to temperature. Trends in Plant Science 8, 343-351. Baker, N.R., Khalili, B., Martiny, J.B.H., Allison, S.D., 2018. Microbial decomposers not

500

constrained by climate history along a Mediterranean climate gradient in southern

501

California. Ecology 99, 1441-1452.

502

Balogh, J., Papp, M., Pintér, K., Fóti, S., Posta, K., Eugster, W., Nagy, Z., 2016. Autotrophic

503

component of soil respiration is repressed by drought more than the heterotrophic one

504

in dry grasslands. Biogeosciences 13, 5171-5182.

505

Bell, C.W., Tissue, D.T., Loik, M.E., Wallenstein, M.D., Acosta - Martinez, V., Erickson, R.A., Zak,

506

J.C., 2014. Soil microbial and nutrient responses to 7 years of seasonally altered

507

precipitation in a Chihuahuan Desert grassland. Global Change Biology 20, 1657-1673.

508

Boeddinghaus, R.S., Marhan, S., Berner, D., Boch, S., Fischer, M., Hölzel, N., Kattge, J., Klaus,

509

V.H., Kleinebecker, T., Oelmann, Y., Prati, D., Schäfer, D., Schöning, I., Schrumpf, M.,

510

Sorkau, E., Kandeler, E., Manning, P., 2019. Plant functional trait shifts explain

511

concurrent changes in the structure and function of grassland soil microbial

512

communities. Journal of Ecology 107, 2197-2210.

513

Bond-Lamberty, B., Bolton, H., Fansler, S., Heredia-Langner, A., Liu, C., McCue, L.A., Smith, J.,

514

Bailey, V., 2016. Soil respiration and bacterial structure and function after 17 years of a

515

reciprocal soil transplant experiment. PLOS ONE 11, e0150599.

Hawkes et al. - 25

516

Bradford, M.A., Warren Ii, R.J., Baldrian, P., Crowther, T.W., Maynard, D.S., Oldfield, E.E.,

517

Wieder, W.R., Wood, S.A., King, J.R., 2014. Climate fails to predict wood decomposition

518

at regional scales. Nature Climate Change 4, 625.

519

Callahan, B.J., McMurdie, P.J., Rosen, M.J., Han, A.W., Johnson, A.J.A., Holmes, S.P., 2016.

520

DADA2: High-resolution sample inference from Illumina amplicon data. Nature Methods

521

13, 581.

522

Carey, J.C., Tang, J., Templer, P.H., Kroeger, K.D., Crowther, T.W., Burton, A.J., Dukes, J.S.,

523

Emmett, B., Frey, S.D., Heskel, M.A., Jiang, L., Machmuller, M.B., Mohan, J., Panetta,

524

A.M., Reich, P.B., Reinsch, S., Wang, X., Allison, S.D., Bamminger, C., Bridgham, S.,

525

Collins, S.L., de Dato, G., Eddy, W.C., Enquist, B.J., Estiarte, M., Harte, J., Henderson, A.,

526

Johnson, B.R., Larsen, K.S., Luo, Y., Marhan, S., Melillo, J.M., Peñuelas, J., Pfeifer-

527

Meister, L., Poll, C., Rastetter, E., Reinmann, A.B., Reynolds, L.L., Schmidt, I.K., Shaver,

528

G.R., Strong, A.L., Suseela, V., Tietema, A., 2016. Temperature response of soil

529

respiration largely unaltered with experimental warming. Proceedings of the National

530

Academy of Sciences 113, 13797-13802.

531

Chen, H., Yang, Z., Chu, R.K., Tolic, N., Liang, L., Graham, D.E., Wullschleger, S.D., Gu, B., 2018.

532

Molecular insights into Arctic soil organic matter degradation under warming.

533

Environmental Science & Technology 52, 4555-4564.

534 535 536 537

Cline, L.C., Zak, D.R., 2014. Dispersal limitation structures fungal community assembly in a longterm glacial chronosequence. Environmental Microbiology 16, 1538-1548. Cline, L.C., Zak, D.R., 2015. Soil microbial communities are shaped by plant-driven changes in resource availability during secondary succession. Ecology 96, 3374-3385.

Hawkes et al. - 26

538

Cole, J.R., Wang, Q., Fish, J.A., Chai, B., McGarrell, D.M., Sun, Y., Brown, C.T., Porras-Alfaro, A.,

539

Kuske, C.R., Tiedje, J.M., 2013. Ribosomal Database Project: data and tools for high

540

throughput rRNA analysis. Nucleic Acids Research 42, D633-D642.

541

Collyer, M.L., Adams, D.C., 2018. RRPP: An r package for fitting linear models to high-

542

dimensional data using residual randomization. Methods in Ecology and Evolution 9,

543

1772-1779.

544 545 546

Connor, E.W., Hawkes, C.V., 2018. Effects of extreme changes in precipitation on the physiology of C4 grasses. Oecologia 188, 355-365. Dacal, M., Bradford, M.A., Plaza, C., Maestre, F.T., García-Palacios, P., 2019. Soil microbial

547

respiration adapts to ambient temperature in global drylands. Nature Ecology &

548

Evolution 3, 232-238.

549 550 551

De Cáceres, M., Legendre, P., Moretti, M., 2010. Improving indicator species analysis by combining groups of sites. Oikos 119, 1674-1684. de Nijs, E.A., Hicks, L.C., Leizeaga, A., Tietema, A., Rousk, J., 2019. Soil microbial moisture

552

dependences and responses to drying–rewetting: The legacy of 18 years drought. Global

553

Change Biology 25, 1005-1015.

554

DeAngelis, K.M., Pold, G., Topçuoğlu, B.D., van Diepen, L.T.A., Varney, R.M., Blanchard, J.L.,

555

Melillo, J., Frey, S.D., 2015. Long-term forest soil warming alters microbial communities

556

in temperate forest soils. Frontiers in Microbiology 6.

557 558

Derner, J.D., Boutton, T.W., Briske, D.D., 2006. Grazing and ecosystem carbon storage in the North American Great Plains. Plant and Soil 280, 77-90.

Hawkes et al. - 27

559 560 561

Epstein, H.E., Lauenroth, W.K., Burke, I.C., Coffin, D.P., 1998. Regional productivities of plant species in the Great Plains of the United States. Plant Ecology 134, 173-195. Evans, S.E., Wallenstein, M.D., 2012. Soil microbial community response to drying and

562

rewetting stress: does historical precipitation regime matter? Biogeochemistry 109, 101-

563

116.

564

Evans, S., Bell-Dereske, L., Dougherty, K., Kittredge, H., 2020. Dispersal alters soil microbial

565

community response to drought. Environmental Microbiology 10.1111/1462-

566

2920.14707.

567 568 569

Fischer, E.M., Beyerle, U., Knutti, R., 2013. Robust spatially aggregated projections of climate extremes. Nature Climate Change 3, 1033. Frøslev, T.G., Kjøller, R., Bruun, H.H., Ejrnæs, R., Brunbjerg, A.K., Pietroni, C., Hansen, A.J., 2017.

570

Algorithm for post-clustering curation of DNA amplicon data yields reliable biodiversity

571

estimates. Nature Communications 8, 1188.

572

Glassman, S.I., Weihe, C., Li, J., Albright, M.B.N., Looby, C.I., Martiny, A.C., Treseder, K.K.,

573

Allison, S.D., Martiny, J.B.H., 2018. Decomposition responses to climate depend on

574

microbial community composition. Proceedings of the National Academy of Sciences

575

115, 11994-11999.

576 577 578

Hawkes, C.V., Keitt, T.H., 2015. Resilience vs. historical contingency in microbial responses to environmental change. Ecology Letters 18, 612-625. Hawkes, C.V., Waring, B.G., Rocca, J.D., Kivlin, S.N., 2017. Historical climate controls soil

579

respiration responses to current soil moisture. Proceedings of the National Academy of

580

Sciences 114, 6322-6327.

Hawkes et al. - 28

581

Jones, D.L., Willett, V.B., 2006. Experimental evaluation of methods to quantify dissolved

582

organic nitrogen (DON) and dissolved organic carbon (DOC) in soil. Soil Biology &

583

Biochemistry 38, 991-999.

584

Keiser, A.D., Strickland, M.S., Fierer, N., Bradford, M.A., 2011. The effect of resource history on

585

the functioning of soil microbial communities is maintained across time. Biogeosciences

586

8, 1477-1486.

587

Kivlin, S.N., Winston, G.C., Goulden, M.L., Treseder, K.K., 2014. Environmental filtering affects

588

soil fungal community composition more than dispersal limitation at regional scales.

589

Fungal Ecology 12, 14-25.

590

Klindworth, A., Pruesse, E., Schweer, T., Peplies, J., Quast, C., Horn, M., Glöckner, F.O., 2012.

591

Evaluation of general 16S ribosomal RNA gene PCR primers for classical and next-

592

generation sequencing-based diversity studies. Nucleic Acids Research 41, e1-e1.

593

Knapp, A.K., Carroll, C.J.W., Griffin-Nolan, R.J., Slette, I.J., Chaves, F.A., Baur, L.E., Felton, A.J.,

594

Gray, J.E., Hoffman, A.M., Lemoine, N.P., Mao, W., Post, A.K., Smith, M.D., 2018. A

595

reality check for climate change experiments: Do they reflect the real world? Ecology 99,

596

2145-2151.

597 598 599 600 601 602

Lane, D.R., Coffin, D.P., Lauenroth, W.K., 2000. Changes in grassland canopy structure across a precipitation gradient. Journal of Vegetation Science 11, 359-368. Lawrence, D., Bell, T., Barraclough, T.G., 2016. The effect of immigration on the adaptation of microbial communities to warming. The American Naturalist 187, 236-248. Leff, J.W., Jones, S.E., Prober, S.M., Barberán, A., Borer, E.T., Firn, J.L., Harpole, W.S., Hobbie, S.E., Hofmockel, K.S., Knops, J.M.H., McCulley, R.L., La Pierre, K., Risch, A.C., Seabloom,

Hawkes et al. - 29

603

E.W., Schütz, M., Steenbock, C., Stevens, C.J., Fierer, N., 2015. Consistent responses of

604

soil microbial communities to elevated nutrient inputs in grasslands across the globe.

605

Proceedings of the National Academy of Sciences 112, 10967-10972.

606

Liu, D., Keiblinger, K.M., Leitner, S., Wegner, U., Zimmermann, M., Fuchs, S., Lassek, C., Riedel,

607

K., Zechmeister-Boltenstern, S., 2019. Response of microbial communities and their

608

metabolic functions to drying–rewetting stress in a temperate forest soil.

609

Microorganisms 7, 129.

610 611 612

Love, M.I., Huber, W., Anders, S., 2014. Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biology 15, 550. Martiny, J.B., Martiny, A.C., Weihe, C., Lu, Y., Berlemont, R., Brodie, E.L., Goulden, M.L.,

613

Treseder, K.K., Allison, S.D., 2017. Microbial legacies alter decomposition in response to

614

simulated global change. The ISME journal 11, 490-499.

615 616 617 618 619

McMurdie, P.J., Holmes, S., 2013. phyloseq: An R Package for Reproducible Interactive Analysis and Graphics of Microbiome Census Data. PLOS ONE 8, e61217. Meisner, A., Bååth, E., Rousk, J., 2013. Microbial growth responses upon rewetting soil dried for four days or one year. Soil Biology & Biochemistry 66, 188-192. Min, K., Buckeridge, K., Ziegler, S.E., Edwards, K.A., Bagchi, S., Billings, S.A., 2019. Temperature

620

sensitivity of biomass-specific microbial exo-enzyme activities and CO2 efflux is resistant

621

to change across short- and long-term timescales. Global Change Biology 25, 1793-1807.

622

Moyano, F.E., Manzoni, S., Chenu, C., 2013. Responses of soil heterotrophic respiration to

623

moisture availability: An exploration of processes and models. Soil Biology &

624

Biochemistry 59, 72-85.

Hawkes et al. - 30

625 626 627

NRCS Soil Survey Staff, 2020. Soil Series Classification Database. United States Department of Agriculture. Accessed 02/05/2020. O'Donnell, K., 1993. Fusarium and its near relatives, In: Reynolds, R., Taylor, J.W. (Eds.), The

628

Fungal Holomorph: Mitotic, Meiotic and Pleomorphic Speciation in Fungal Systematics.

629

CBA International, Wallingford, UK, pp. 225–233.

630 631 632

O'Malley, M.A., 2007. The nineteenth century roots of 'everything is everywhere'. Nature Reviews Microbiology 5, 647. Peay, K.G., Garbelotto, M., Bruns, T.D., 2010. Evidence of dispersal limitation in soil

633

microorganisms: Isolation reduces species richness on mycorrhizal tree islands. Ecology

634

91, 3631-3640.

635

Placella, S.A., Brodie, E.L., Firestone, M.K., 2012. Rainfall-induced carbon dioxide pulses result

636

from sequential resuscitation of phylogenetically clustered microbial groups.

637

Proceedings of the National Academy of Sciences 109, 10931-10936.

638

Preece, C., Verbruggen, E., Liu, L., Weedon, J.T., Peñuelas, J., 2019. Effects of past and current

639

drought on the composition and diversity of soil microbial communities. Soil Biology &

640

Biochemistry 131, 28-39.

641 642 643 644

Prein, A.F., Rasmussen, R.M., Ikeda, K., Liu, C., Clark, M.P., Holland, G.J., 2016. The future intensification of hourly precipitation extremes. Nature Climate Change 7, 48. R Core Team, 2019. R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria.

Hawkes et al. - 31

645

Rousk, J., Smith, A.R., Jones, D.L., 2013. Investigating the long-term legacy of drought and

646

warming on the soil microbial community across five European shrubland ecosystems.

647

Global Change Biology 19, 3872-3884.

648 649

Sankaran, M., 2019. Droughts and the ecological future of tropical savanna vegetation. Journal of Ecology 107, 1531-1549.

650

Sayer, E.J., Oliver, A.E., Fridley, J.D., Askew, A.P., Mills, R.T.E., Grime, J.P., 2017. Links between

651

soil microbial communities and plant traits in a species-rich grassland under long-term

652

climate change. Ecology and Evolution 7, 855-862.

653

Schindlbacher, A., Schnecker, J., Takriti, M., Borken, W., Wanek, W., 2015. Microbial physiology

654

and soil CO2 efflux after 9 years of soil warming in a temperate forest – no indications

655

for thermal adaptations. Global Change Biology 21, 4265-4277.

656

Schulte, E., Hopkins, B., 1996. Estimation of soil organic matter by weight loss-on-ignition, In:

657

Magdoff, F.R., Tabatabai, M.A., Hanlon, E.A. (Eds.), Soil organic matter: Analysis and

658

interpretation. SSSA, Madison, WI, USA, pp. 21-31.

659

Scott-Denton, L.E., Rosenstiel, T.N., Monson, R.K., 2006. Differential controls by climate and

660

substrate over the heterotrophic and rhizospheric components of soil respiration.

661

Global Change Biology 12, 205-216.

662

Sierra, C.A., Trumbore, S.E., Davidson, E.A., Vicca, S., Janssens, I., 2015. Sensitivity of

663

decomposition rates of soil organic matter with respect to simultaneous changes in

664

temperature and moisture. Journal of Advances in Modeling Earth Systems 7, 335-356.

Hawkes et al. - 32

665

Singh, D., Tsiang, M., Rajaratnam, B., Diffenbaugh, N.S., 2013. Precipitation extremes over the

666

continental United States in a transient, high-resolution, ensemble climate model

667

experiment. Journal of Geophysical Research: Atmospheres 118, 7063-7086.

668 669 670

Strickland, M.S., Keiser, A.D., Bradford, M.A., 2015. Climate history shapes contemporary leaf litter decomposition. Biogeochemistry 122, 165-174. Tfaily, M.M., Chu, R.K., Tolić, N., Roscioli, K.M., Anderton, C.R., Paša-Tolić, L., Robinson, E.W.,

671

Hess, N.J., 2015. Advanced solvent based methods for molecular characterization of soil

672

organic matter by high-resolution mass spectrometry. Analytical Chemistry 87, 5206-

673

5215.

674

Tipton, L., Zahn, G., Datlof, E., Kivlin, S.N., Sheridan, P., Amend, A.S., Hynson, N.A., 2019. Fungal

675

aerobiota are not affected by time nor environment over a 13-y time series at the

676

Mauna Loa Observatory. Proceedings of the National Academy of Sciences, 201907414.

677

Väisänen, M., Gavazov, K., Krab, E.J., Dorrepaal, E., 2019. The legacy effects of winter climate on

678

microbial functioning after snowmelt in a subarctic tundra. Microbial Ecology 77, 186-

679

190.

680 681 682 683 684 685

Vance, E.D., Brookes, P.C., Jenkinson, D.S., 1987. An extraction method for measuring soil microbial biomass C. Soil Biology & Biochemistry 19, 703-707. Veen, G.F., Freschet, G.T., Ordonez, A., Wardle, D.A., 2015. Litter quality and environmental controls of home-field advantage effects on litter decomposition. Oikos 124, 187-195. Waldrop, M.P., Firestone, M.K., 2006. Response of microbial community composition and function to soil climate change. Microbial Ecology 52, 716-724.

Hawkes et al. - 33

686 687

Waldrop, M.P., Zak, D.R., Blackwood, C.B., Curtis, C.D., Tilman, D., 2006. Resource availability controls fungal diversity across a plant diversity gradient. Ecology Letters 9, 1127-1135.

688

Walker, T.W.N., Kaiser, C., Strasser, F., Herbold, C.W., Leblans, N.I.W., Woebken, D., Janssens,

689

I.A., Sigurdsson, B.D., Richter, A., 2018. Microbial temperature sensitivity and biomass

690

change explain soil carbon loss with warming. Nature Climate Change 8, 885-889.

691

Wang, Q., Garrity, G.M., Tiedje, J.M., Cole, J.R., 2007. Naïve Bayesian Classifier for Rapid

692

Assignment of rRNA Sequences into the New Bacterial Taxonomy. Applied and

693

Environmental Microbiology 73, 5261-5267.

694

Waring, B., Hawkes, C.V., 2018. Ecological mechanisms underlying soil bacterial responses to

695

rainfall along a steep natural precipitation gradient. FEMS Microbiology Ecology 94,

696

fiy001.

697 698

Waring, B.G., Hawkes, C.V., 2015. Short-term precipitation exclusion alters microbial responses to soil moisture in a wet tropical forest. Microbial Ecology 69, 843-854.

699

Wieder, W.R., Hartman, M.D., Sulman, B.N., Wang, Y.-P., Koven, C.D., Bonan, G.B., 2018. Carbon

700

cycle confidence and uncertainty: Exploring variation among soil biogeochemical

701

models. Global Change Biology 24, 1563-1579.

702

Zhalnina, K., Louie, K.B., Hao, Z., Mansoori, N., da Rocha, U.N., Shi, S., Cho, H., Karaoz, U.,

703

Loqué, D., Bowen, B.P., Firestone, M.K., Northen, T.R., Brodie, E.L., 2018. Dynamic root

704

exudate chemistry and microbial substrate preferences drive patterns in rhizosphere

705

microbial community assembly. Nature Microbiology 3, 470-480.

706 707

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Figure Legends

709

Figure 1. (a) Conceptual overview of historical climate legacies. In previous studies, sites with

710

wetter (blue) or drier (orange) historical rainfall varied in both the magnitude and sensitivity of

711

soil respiration responses to contemporary moisture, and these patterns did not change when

712

the rainfall regime was altered (dashed lines) either in the laboratory or in the field for up to 1.5

713

years. (b) Predictions for potential respiration responses to longer (4.5 years) and more

714

extreme changes in rainfall at one site relative to an arbitrary historical rainfall regime (gray).

715

Soil respiration can (b1) shift in parallel to historical conditions with a change in magnitude, but

716

no change in moisture sensitivity (slope) or (b2) shift in sensitivity and magnitude with rates

717

increased or decreased relative to historical rain. In all cases, there is a response to

718

contemporary moisture, but respiration as a function of current conditions can depend on the

719

historical environment and on environmental change. Legacies as in (a) are detected when (b1)

720

and (b2) are both small so that there is no difference in magnitude or slope compared to

721

historical rainfall conditions. Responses are displayed as linear for simplicity.

722 723

Figure 2. Soil respiration responses to field rainfall and vegetation treatments across dates; no

724

treatments differed significantly from Ambient (Amb) or Mean. The slopes do not differ at any

725

date based on ANCOVA (Table S3); ambient rain treatments were not included in the ANCOVA.

726

Data were natural log (LN) transformed (n = 4).

727 728

Figure 3. Soil respiration in the field experiment as a function of environmental conditions

729

identified in best subsets regression: (a) soil moisture, (b) antecedent 2-wk rainfall, and (c) soil

Hawkes et al. - 35

730

temperature. See Table S4 for best subsets regression results; individual variables were not

731

analyzed separately. Symbols reflect both field rainfall treatments (color) and vegetation

732

treatments (shape). Note that the y-axis does not begin at zero. Data were natural log (LN)

733

transformed.

734 735

Figure 4. Respiration responses to moisture in laboratory microcosms as a function of (a) field

736

rainfall treatment (n = 16) and (b) field vegetation treatment (n = 32). There were significant

737

main effects of moisture and vegetation, but no effect of field rainfall treatment and no

738

interactions. Data are averages ± 1 SE of natural log (LN) transformed values.

739 740

Figure 5. Aboveground plant biomass (a, b), soil moisture (c, d), and salt-extractable dissolved

741

organic C (DOC; e, f) in the field experiment rainfall (a, c, e; n = 8) and vegetation (b, d, f; n = 24)

742

treatments. Significant differences in rain or vegetation treatments are indicated by letters or

743

asterisks, respectively. Bars are boxplots showing median, 25th and 75th percentiles, 10th and

744

90th percentile, and outliers.

745 746

Figure 6. Soil bacteria (a-c) and fungi (d-f) in the field treatments. There were no significant

747

differences among treatments in observed richness of bacteria (a-b) or fungi (d-e), nor were

748

there differences detected in community composition of either (c, f). Bars are boxplots (n=8 for

749

field rainfall treatment, n=24 for field vegetation treatment). Community composition is

750

visualized in PCoA plots. Colors in the PCoA reflect field rainfall treatments; field vegetation

751

treatments are indicated by symbol shape (circles = shortgrass, squares = tallgrass).

Hawkes et al. - 36

Soil respiration

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Highlights •

Legacies of previous climate persist for 4.5 years

•

Moisture sensitivity of soil respiration is not altered by extreme rainfall change

•

Magnitude of respiration responds to vegetation independent of climate

Declaration of interests ☒ The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper. ☐The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: