Research review

Forecasting semi-arid biome shifts in the Anthropocene

Author for correspondence:Andrew KulmatiskiTel: +1 435 770 2582

Email: andrewkulmatiski@hotmail.com

Received: 19 September 2019Accepted: 6 December 2019

Andrew Kulmatiski1 , Kailiang Yu2,3 , D. Scott Mackay4 ,

Martin C. Holdrege5, Ann Carla Staver6 , Anthony J. Parolari7 ,

Yanlan Liu8 , Sabiha Majumder9,10 and Anna T. Trugman11

1Department of Wildland Resources and the Ecology Center, Utah State University, Logan, UT 84322-5230, USA; 2Department of

Environmental Systems Science, ETHZurich, Universitatstrasse 16 8092, Zurich, Switzerland; 3Laboratoire des Sciences du Climat et

de l’Environnement, IPSL-LSCECEA/CNRS/UVSQ, F-91191, Gif-sur-Yvette, France; 4Department of Geography andDepartment

of Environment and Sustainability, University at Buffalo, Buffalo, NY 14261, USA; 5Department of Wildland Resources and the

Ecology Center, Utah State University, Logan, UT 84322-5230, USA; 6Department of Ecology and Evolutionary Biology, Yale

University, New Haven, CT 06511, USA; 7Department of Civil, Construction, and Environmental Engineering, Marquette

University, Milwaukee, WI 53233, USA; 8Department of Earth System Science, Stanford University, Stanford, CA 94305, USA;

9Department of Physics, Indian Institute of Science, Bengaluru 560012, India; 10Centre for Ecological Sciences, Indian Institute of

Science, Bengaluru 560012, India; 11Department of Geography, University of California Santa Barbara, Santa Barbara, CA 93117,

USA

New Phytologist (2020)doi: 10.1111/nph.16381

Key words: carbon metabolism, criticalthreshold, early-warning signal,ecohydrology, ecophysiology, laggedmortality, machine learning, nichepartitioning.

Summary

Shrub encroachment, forest decline and wildfires have caused large-scale changes in semi-arid

vegetation over the past 50 years. Climate is a primary determinant of plant growth in semi-arid

ecosystems, yet it remains difficult to forecast large-scale vegetation shifts (i.e. biome shifts) in

response to climate change. We highlight recent advances from four conceptual perspectives

that are improving forecasts of semi-arid biome shifts. Moving from small to large scales, first,

tree-level models that simulate the carbon costs of drought-induced plant hydraulic failure are

improving predictions of delayed-mortality responses to drought. Second, tracer-informed

water flow models are improving predictions of species coexistence as a function of climate.

Third, new applications of ecohydrological models are beginning to simulate small-scale water

movement processes at large scales. Fourth, remotely-sensedmeasurements of plant traits such

as relative canopymoistureareprovidingearly-warning signals thatpredict forestmortalitymore

than a year in advance.We suggest that a community of researchers usingmodeling approaches

(e.g. machine learning) that can integrate these perspectives will rapidly improve forecasts of

semi-arid biome shifts. Better forecasts can be expected to help prevent catastrophic changes in

vegetation states by identifying improved monitoring approaches and by prioritizing high-risk

areas for management.

Introduction

Semi-arid ecosystems are extensive, host a large portion of thehuman population, and are important for global carbon and watercycles, agriculture, grazing and wildfire (Poulter et al., 2014;Ahlstr€om et al., 2015; Maestre et al., 2016). In these systems, smallchanges in climate conditions can lead to large changes in wateravailability, plant productivity and biosphere–atmosphere feed-backs (Fig. 1). Further, these systems can often exist in alternativevegetation states (e.g. grassland vs shrubland) that provide differentecosystem services (Fig. 2; Knapp et al., 2017; Touboul et al.,

2018). Between1990 and2004 alone, semi-arid climate conditionsexpanded 7% globally. This was caused by transitions from arid tosemi-arid conditions in the western hemisphere and from mesic tosemi-arid conditions in the eastern hemisphere (Huang et al.,2016). The ability to forecast vegetation responses to these climatechanges can be expected to improve predictions of food and forageproduction, fire regimes, hydrologic cycles and vegetation–atmo-sphere interactions (Archer et al., 2017; Stevens et al., 2017).

Despite the potential benefits, many factors limit our ability toforecast semi-arid vegetation responses to climate change. Large-scale vegetation changes in semi-arid systems are determined by

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Review

complex interactions between climate (e.g. the timing, intensity,seasonality, amount of precipitation), vegetation (community type,species traits), soil type, topography, and humanmanagement (e.g.fire, grazing) and feedbacks among these factors (Case & Staver,2018; Touboul et al., 2018; Venter et al., 2018). As a result, manydifferent conceptual approaches have been developed to under-stand these systems (Archer et al., 2017; Bestelmeyer et al., 2018;Peters et al., 2018). The goal of this review is to highlight recentadvances developed from several different conceptual backgrounds,and to suggest that new research approaches that can integrateinformation from different backgrounds can be expected to

improve forecasts of biome susceptibility to catastrophic changes(Fig. 3).

Evidence and consequences of semi-arid biome shifts

Climate change will cause shifts among arid, semi-arid and mesicsystems, but here we focus on shifts among biome types withinsemi-arid systems (Fig. 2). In dry semi-arid climates, whereprecipitation satisfies 20–50% of potential evapotranspiration(Arora, 2002), shrubs have invaded hundreds of millions ofhectares of grasslands (Archer et al., 2017; Bestelmeyer et al., 2018).

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Fig. 1 Semi-arid system sensitivity to climate. In semi-arid systems, precipitation events are often small (< 5mm) and interception and evaporation oftenaccount for 30–50%ofmean annual precipitation, with the remaining precipitation available for infiltration and root water uptake (Li et al., 2019). Due to thestrong correlation between stomatal conductance, transpiration and carbon fixation, these processes often result in rain-use efficiencies of roughly 40% (e.g.400 gm�2 of abovegroundnet primaryproductionper1000mmofprecipitation;Huxmanet al., 2004;Knappet al., 2017).Overlandflowanddeep infiltrationbelow the rooting zone are typically very small components of semi-arid water budgets. Small changes in hydrologic cycles can change competitive outcomesamong grasses, forbs and woody plants and can have important consequences for primary productivity, carbon cycling, fire regimes, soil erosion and forageproduction. Because these systems are sensitive to climate, ecohydrologicmodels need to bewell parameterized for simulating vegetation responses to climatechange. (a)Currently, semi-aridprecipitation is interceptedandevaporates or is absorbedby shallow (i.e. 0–30cm) roots. Fewer, larger precipitationevents andwarmer temperatures are anticipated in the future,which could affect plant communities andwater cycling in a numberofways. (b)Warmer temperaturesmayincrease evaporative loss, and fewer storms may create longer droughts in shallow soils, both of which may decrease total plant growth and grass growthrelative to shrub growth. (c) Alternatively, larger storms may decrease evaporative loss and increase infiltration. Shallow (c. 0–15 cm) infiltration is likely toincrease grass growth.Deeper (c. 15–30 cm) infiltration is likely to increasewoody plant growth. (d) If storms becomeeven larger than in (c), overlandflowanddeep infiltration below rooting zones may decrease plant growth. (e) Vegetation responses (as depicted in (b)–(d)) can lead to feedbacks that affect wateravailability, plant productivity and biosphere–atmosphere dynamics.

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The causes of shrub encroachment are diverse, from overgrazingand fire suppression (Case & Staver, 2017; Stevens et al., 2017;Venter et al., 2018) to changing precipitation patterns to increasedCO2 concentrations (Case & Staver, 2018). Furthermore, thesefactors are often interrelated and are likely to interact (Archer et al.,2017). The consequences vary widely among sites, but shrubencroachment often decreases forage production and speciesrichness and increases soil carbon and ecological drought (Rata-jczak et al., 2012; Maestre et al., 2016; Archer et al., 2017; Wilsonet al., 2018). Direct impacts on humans include lost forageproduction, woody plant control costs, and potentially desertifi-cation (Eldridge et al., 2011; Archer et al., 2017).

In wetter semi-arid ecosystems (i.e. where precipitation satisfies50–133% of potential evapotranspiration; Arora, 2002),widespread increases in tree mortality have been documented overthe past several decades (Peng et al., 2011; Anderegg et al., 2013;Adams et al., 2017; McDowell et al., 2018). Projected increases indrought frequency and severity, rainfall variability, and warmingare expected to exacerbate tree mortality (Allen et al., 2010). Thistree mortality is likely to affect terrestrial carbon cycling and itsfeedbacks to climate (Cox et al., 2000). Direct impacts on humansare also likely, as highlighted by the 2018 fires in California, USA,

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Fig. 2 Conceptual illustration of biome shifts in semi-arid ecosystems. Biometypes such as short grass or shrub-steppe are associatedwith a specific rangeof aridity conditions (i.e. precipitation divided by potentialevapotranspiration (PPT/ PET)). Factors such as climate change, overgrazingor fires can encourage shifts among biome types that are then reinforced byinternal community dynamics, resulting in alternative state biomes. Thesealternative biomes provide different ecosystem services, in this example,annual aboveground net primary production (ANPP; Knapp et al., 2017).

Upscaling is improving predictions of landscape-scale hydrological heterogeneity and vegetation response to climate change.

Physiological carbon budgeting is improving predictions of delayed mortality responses to drought years in advance.

New isotope techniques are describing rooting distributions allowing species-specific predictions of plant responses to climate change.

New early-warning signals that can be detected remotely are helping predict mortality events months to years in advance.

Machine-learning that can integrate these advances from disparate conceptual backgrounds with information about other factors such as fire and grazing can be expected to greatly improve predictions of semi-arid biome shifts in the near future.

Fig. 3 Semi-arid systemscontinue to realize large-scale changes in vegetation cover and species composition. These systemsare sensitive to climate change, yetshifts in semi-arid vegetation have remained difficult to predict. Recent advances fromdifferent fields of study are rapidly improving predictions of biome shiftsin semi-arid systems. Approaches such asmachine learning that can integrate these advanceswith information about other factors such as fire and grazing canbe expected to greatly improve predictions in the near future.

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which resulted in $11.4 billion in insurance claims, $3 billion inclean-up costs and 86 human deaths (Alexander, 2018). Conse-quently, there is growing interest in improving understanding ofthe mechanisms of drought-induced mortality in water-limitedforests (Hartmann et al., 2018).

Current conceptual approaches and recent advances

Ecophysiological: carbon budgeting improves predictions ofdelayed mortality responses to drought

A central problem for predicting biome shifts from ecophysio-logical models has been that biome shifts are often non-linear ordelayed with respect to climate drivers (Anderegg et al., 2015;Ogle et al., 2015). Plant hydraulic impairment, plant waterpotential thresholds, and the duration spent below a giventhreshold are known to precede plant mortality (Adams et al.,2017; McDowell et al., 2018). Therefore, models that integratesurface hydrological processes, plant hydraulic function andclimate variability are expected to capture many of the elementsanticipated to be important in predicting drought-inducedvegetation impacts (Tai et al., 2018; Venturas et al., 2018;Mencuccini et al., 2019). A new ecophysiological frameworksuggests that delayed tree mortality responses to drought may becaused by the long-term carbon costs associated with xylem repair(Cailleret et al., 2017; Trugman et al., 2018). This frameworkpredicts that some trees may starve to death over several yearswhile attempting to rebuild xylem lost during drought to increasecarbon gain (Trugman et al., 2018; Fig. 4). Because the number ofyears of stem growth required to rebuild a large tree’s xylem andthe amount of carbon lost to cambium and phloem respirationboth may increase with tree size, big trees are potentially at higherrisk of drought-induced mortality compared to smaller trees(Bennett et al., 2015; McDowell & Allen, 2015; Trugman et al.,2018). This framework represents an important advance becauseit provides a mechanism that helps forecast delayed mortalityresponses of trees to drought.

Community ecology: hydrologic niches predict plantabundance

Plant rooting distributions are a central but poorly constrainedparameter in ecohydrological and plant community ecologymodels (Grant&Dietrich, 2017; Fisher et al., 2018). For example,plant rooting distributions are believed to explain tree and grasscoexistence in savannas, yet there are very few examples that directlylink root distributions with water uptake and species abundance(Mazzacavallo & Kulmatiski, 2015; Silvertown et al., 2015). Theneed for more and better data on resource uptake, as opposed toroot biomass, has been recognized as a key gap in understandingplant growth, species coexistence and water and nutrient cycling(Silvertown et al., 2015; Grant & Dietrich, 2017). A betterunderstanding of rooting distributions can therefore be expected toimprove predictions of shrub encroachment or shrubland tograssland biome shifts (Schlaepfer et al., 2012; Berry&Kulmatiski,2017; Peters et al., 2018). Hydrological tracer experiments are

providing a clearer picture of resource uptake by roots (Rothfuss &Javaux, 2017), but even tracer uptake data can be biased by thepresence ofwet and dry soils (Mazzacavallo&Kulmatiski, 2015). Anew combined tracer/water movement model approach is address-ing both of these problems (Mazzacavallo & Kulmatiski, 2015;Zheng et al., 2018). Kulmatiski et al. (2019) used data from adepth-controlled hydrological tracer technique to define rootdistributions in a soil water flowmodel (HYDRUS 1D;Fig. 5).Modelsimulations revealed hydrologic niches that were highly correlatedwith plant landscape abundance in a shrub-steppe community.Plants with rooting distributions that could extract more water orhad unique access to more soil water were more abundant on thelandscape (Fig. 5).

These results provide an unusually clear link between plantroot distributions, plant coexistence and abundance andclimate. Thus, this research demonstrates that it may now bepossible to describe species growth and coexistence as a functionof climate and soil water movement. This is a critical steptoward understanding how changes in climate are likely toaffect plant growth and community composition. In short, thisresearch provides a means of describing a key physiologicalproperty needed in hydrological models.

Fig. 4 Trugman et al. (2018) used a model of the carbon costs of drought-induced hydraulic damage (as percent functional xylem) to predict delayedaspenmortality. Panel (a) illustrates model predicted diameter increment fordifferent levels of post-drought hydraulic damage. In some cases, trees areable to repair damagedxylemand recover full photosynthetic capacity,whilesevere damage (30% functional xylem) resulted in predictions of treemortality 7 yr following drought. This delay was consistent with tree growthand mortality for aspen stands post drought in southwestern Colorado (b).Figure from Trugman et al. (2018). Error bars in (b) are standard error frombranch ring width measured in nine aspen ramets (see Anderegg et al.,2013).

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Ecohydrological: fine-scale simulations of soil wateravailability are improvingpredictions of spatial heterogeneityin vegetation responses to climate change

Ecohydrological models (e.g. Budyko’s, ECOTONE, HYDRUS, STEP-WAT, SWIM, etc.) are central to understanding biome shifts in semi-arid systems. These models use energy budgets and soil physics tobalance water budgets. As such, they provide a mechanistic linkbetween climate and plant water uptake (Palmquist et al., 2018).Because they are founded on physical models, the strength of thesemechanistic ecohydrological models is that they may performbetter at predicting out-of-range phenomena than observationaland correlative approaches such as climate envelope modeling(Schlaepfer et al., 2017).

Ecohydrological models can be very effective at predicting waterflow and ecosystem-level plant growth, but a fundamental problemfor these models is simulating fine-scale processes over large areas(Ratajczak et al., 2017;Wang et al., 2018; Fan et al., 2019). Recentefforts are improving fine-scale simulations of soil water availabilityand predictions of vegetation water stress on the landscape(Schlaepfer et al., 2017; Guo et al., 2018; Tai et al., 2018). In arecent example, Schwantes et al. (2018) used a non-linear stochasticmodel of soil moisture that incorporated information on topog-raphy and soil type to predict water stress in Juniper across awatershed in Texas, USA (Fig. 6). Accounting for topography

improved model predictions of observed Juniper water stress from60% to 72%. Another important result of this research was that thespatially-explicit ecohydrological model predicted the location oflandscape refugia, which are expected to enhance ecosystemresilience to disturbance and species persistence through climaticchanges (McLaughlin et al., 2017; Schwantes et al., 2018). Thespatial resolution of this study was of the order of 102 m, whereasdynamic global vegetation models (DGVMs) are often run at 1degree (roughly 110 km2) or larger spatial resolutions. Therefore,linking vegetation responses to climate change across scales will be acritical future advance (Palmquist et al., 2018; Fan et al., 2019).One recent example of this type of work is the FATES model fromMassoud et al. (2019). This model integrates the EcosystemDemography model into the earth systems model CESM.

Remote sensing: remote-sensing approaches are improvingpredictions of spatial heterogeneity in vegetation responsesto climate change

Remote-sensing approaches have the advantage that they candescribe patterns over large spatial and/or temporal scales(Anderegg et al., 2019; Huang et al., 2019). Two recent studieshave used remotely-sensed measures of canopy traits to forecastforest mortality. Rao et al. (2019) were able to predict forestmortality a year in advance using satellite-based measurements of

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Fig. 5 Hydrologic niches predict species landscapeabundance in a shrub-steppe community. (a)Wateruptakebydepth, timeand specieswasdescribedusing ahydrologic tracer experiment anda soilwater flowmodel (HYDRUS 1D). These verticalwater uptakepatterns indicate the times anddepths atwhicheach species’rooting distribution could extract more soil water than any other species (filled areas in a). (b) When summed across the growing season, the sizes of theseunique hydrological niches are well correlated with plant abundance on the landscape. AGCR, Agropyron cristatum; ARTR, Artemisia tripartitata, BASA,Balsamorhizae sagittata; POSE, Poa secunda; PSSP, Pseudoroegneria spicata.

Fig. 6 Schwantes et al. (2018) used a spatially-explicit ecohydrological model to predict water stress in juniper vegetation in Texas. Spatial considerationimproved model predictions of drought stress on juniper and indicated the presence of drought refugia on the landscape.

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relative canopy water content. Passive microwave measurementswere used to assess canopymoisture (vegetation optical depth) overtime to develop measurements of relative canopy water content(Asner et al., 2016). When included in a machine-learning modelwith 32 potential predictor variables (e.g. climate data, water deficitindices, etc.), relative canopywater contentwas the best predictor oftree mortality during the 2012–2016 drought in the Sierra Nevada(California, USA; Fig. 7).

Using a similar approach, Anderegg et al. (2019) usedLANDSAT data describing non-photosynthetically active vegeta-tion, essentially a measure of canopy ‘brownness’ or ‘branchiness’,in aspen stands before and during a known drought. Both peakbrownness and the change in brownness during drought were wellcorrelated with tree mortality in the following years (Fig. 8). Thisearly-warning signal was not only effective for the aspen stands inwhich it was developed, but was also effective at predicting standmortality at a tropical forest site (Anderegg et al., 2019). Thus, thesetwo recent examples are demonstrating the promise of usingremotely-sensed early-warning signals to produce large-scale,spatially-explicit forecasts of forest mortality events.

State-and-transition and dynamical systems informedanalyses provide an independent approach to predictingbiome shifts using remotely-sensed data

State-and-transition theory provides diagnostic symptoms that canserve as early-warning signals of transitions from one stablecommunity type to another (e.g. see Fig. 2; Scheffer et al., 2009;Eby et al., 2017; Majumder et al., 2019; Majumder et al., 2019).Typical early-warning signals include increasing autocorrelationdue to reduced recovery rate from disturbance, (i.e. critical slowingdown; Dakos et al., 2015; Liu et al., 2019), increasing variance andskewness (Ratajczak et al., 2017; Chen et al., 2018), and flickeringbetween stable states (Dakos et al., 2015; Verbesselt et al., 2016).One important recent example of this approach used autocorre-lation in a 16-year normalized difference vegetation index (NDVI)dataset from California, USA as an indicator of forest resilience.This resilience-based early-warning signal provided spatially-explicit predictions of species-level mortality 6–19 months inadvance of mortality events (Fig. 9; Liu et al., 2019). Differentremotely-sensed indicators have been tested as potential early-warning signals, but because this approach has only recently beendeveloped, there is little consensus about which indicators mayprovide the best early-warning signals in terms of predictionaccuracy and the range of lead times. It is likely that the bestindicators will differ among ecosystems and as a function of dataavailability. Data availability is particularly important because it isnecessary to account for factors including topography and speciesdistribution to translate the detected resilience signal intomortalityprobability in space and time (Liu et al., 2019). These abstractconceptual models often also permit the inclusion of fire, grazingand management effects that can be crucial determinants of shiftsamong vegetation types or biomes (e.g. Staver & Levin, 2012;Tredennick & Hanan, 2015). Crucially, the robustness of early-warning signals depends on model structure, with strong depen-dence on how variation arising (Hastings & Wysham, 2010). Forthis reason, strong mechanistic links between even abstract modelsand empirical systems are needed to inform predictions (Boettiger& Hastings, 2012).

Other modeling approaches

We have highlighted several approaches associated with recentadvances, though other approaches are also important to predicting

Fig. 7 Observed and modeled fractional area of mortality (FAM) of trees between 2009 and 2015 in California, USA. Model predictions were derived frompassive microwave measurements of vegetation optical depth (i.e. canopy water content). Changes in canopy water content (relative water content; RWC)allowed model predictions that were well correlated with observed forest mortality. Figure taken from Rao et al. (2019).

Fig. 8 Anderegg et al. (2019) predicted the proportion of aspen dieback in2017 from the proportion of non-photosynthetically active vegetation andthe change in non-photosythetically active vegetation measured duringdrought/pre-drought conditions in 2011/2012 in Colorado, USA(R2

adj = 0.58). When the authors applied the samemetrics developed for thissite, they found that these early-warning signals were similarly effective atpredicting tree mortality in a tropical forest.

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biome shifts. DGVMs are designed to predict vegetation produc-tivity and climate sensitivity (Fisher et al., 2018). These models arequite effective at predicting regional-scale biome distributions, andrecent improvements in computational efficiency have evenmade itpossible to represent tree cohorts, or trees by size and density(analogous to individual trees), at regional to global scales (Fisheret al., 2018). However, ecohydrological processes, such as topog-raphy and belowground resource competition, are underdevelopedin these models (Fisher et al., 2018; Fan et al., 2019). As they arefurther tested and validated, we anticipate that processes describedin this review will be incorporated into DGVMs. Developments inglobal vegetation models will also be crucial to understanding howhydrological process interact with, for example, fire, now consid-ered a key process for inclusion in most DGVMs (Rabin et al.,2017).

Climate envelope species-distribution models use correlationsbetween species and climate to define a species’ climatic niche in thelandscape (Kerns et al., 2018). These models are easier toparameterize than more mechanistic models, and they inherentlyinclude feedback mechanisms that maintain biome types. As aresult, these models provide an important complement to mech-anistic models, but they are not expected to provide stronginference to novel climate or plant community conditions becausetheir observations are limited to existing climate conditions(Renwick et al., 2018; Navarro et al., 2018). These models aretypically used to understand long-term equilibrial processes,though a recent effort has begun to integrate the effects ofdisturbance and extreme events into species-distribution models(Law et al., 2019).

Finally, non-equilibrium models of fire, grazing and manage-ment are also important for understanding biome shifts (Staver &Levin, 2012; Archer et al., 2017). Recognizing fire as a crucialprocess shaping biosphere responses to climate and a source ofbiosphere to atmosphere emissions, most global vegetationdynamic models now include a fire model (Rabin et al., 2017).Herbivory factors less centrally into global models. However, inmuch the same way that fire can transfer drought effects across alandscape, recent work demonstrates that migrating herbivores canalso ‘transfer’ drought effects from one site to another (Staver et al.,

2019). An important consequence of these non-equilibrium effectsis that physiologicalmodels of drought effects on vegetation growthand structure may not be sufficient to understand vegetationpatterns in the landscape (Staver & Levin, 2012; Scheiter &Higgins, 2013; Staver et al., 2019).

An ensemble approach

Because they occur over large spatial and temporal scales, it is likelythat many different processes interact to determine biome shifts.The fact that important advances in predicting biome shifts havedeveloped from several different conceptual backgrounds in just thepast year is consistent with this idea. Consequently, forecastingbiome shifts is likely to require information from various sourcesthat have the potential to interact (Archer et al., 2017; Peters et al.,2018). We suggest that a modeling approach that can integratedisparate data streams is likely to produce the best predictions ofbiome shifts in semi-arid systems (Fig. 10; Bestelmeyer et al., 2018;Massoud et al., 2018; Renwick et al., 2018; Tietjen et al., 2018;Wilcox et al., 2018). While there have been many attempts tointegrate information from some of these conceptual backgrounds,such as state-and-transition models and ecohydrological/physio-logical models, there remains a need for quantitative models thatcan integrate large amounts of data from different sources (Peterset al., 2018; White et al., 2019).

Machine-learning models, such as RANDOM FOREST, are wellsuited to integrating data from various and redundant sources andidentifying both linear and non-linear relationships between inputvariables and estimated variables (Cutler et al., 2007). For example,a RANDOM FOREST model could be used to build a series of decisiontrees that describe variation in plant cover or vegetation type (e.g.grass or woody plant) as a function of climate data, ecohydrologicalmodel predictions, historical management information, soilsinformation, recent temporal or spatial data on plant communitycomposition, etc. Each decision tree is built using a subset of theinput data, thenmodel predictions are tested against the remainingdata. These types of machine-learning approaches do not producemechanistic models, but they are well suited to parsing variationamong descriptor variables.Model output can then be used both tomake predictions and to develop new hypotheses about the factorsdriving vegetation changes. More specifically, it is likely thatmachine-learning models will identify new relationships amongpredictor and response variables that can serve as observations fornew hypotheses.

Using ecohydrological and ecophysiological model output as aninput into a machine-learning model has the potential benefit ofreducingthenumberofparametersintroducedintothemodel.Italsohas the advantage that it will allow the relative importance of thesedifferent submodels to be determined (Cutler et al., 2007). Forexample, variable importance plots derived from RANDOM FOREST

models could indicate that ecophysiological model output of plantcarbon storage is more important than grazing or rooting distribu-tions.

Our proposed approach (Fig. 10), has several advantages. Itintegrates data from disparate, potentially interacting data streams.It is consistent with recent calls to integrate social data (e.g. grazing)

Fig.9 Autocorrelation inNDVIdatapredicts forestmortality inCalifornia. Liuet al. (2019) used autocorrelation in NDVI as an indicator of forest resilience.For areas where this early-warning signal preceded forest mortality, 75% ofthe areas demonstrated this early warning signal (i.e. exceedanceprobability) 6 months before mortality. Similarly, 25% of areasdemonstrated this early warning signal 19months prior to mortality.Figure from Liu et al. (2019).

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into forecasts of vegetation changes (Maestre et al., 2016; Wilcoxet al., 2018). It is similarly consistent with recent calls to useiterative forecasting and validation (Dietze et al., 2018;White et al.,2019; Huang et al., 2019). It also provides an alternative toprogressively increasing the complexity of DGVMs. Rather thanattempting to incorporate many processes into DGVMs, anintegrated modeling approach may allow ecophysiological orecohydrological models to focus on their strengths and developindependently only to be integrated by the machine-learningprocess.Most importantly, by combining information from severalconceptual perspectives, our proposed approach can be expected toboth improve predictions of biome shifts and prioritize the factorsthat determine these shifts. Prioritized understanding of the factorsthat determine biome shift can be expected to direct more effectivemanagement approaches.

As an example, we tested the ability of a RANDOM FOREST modelto predict shrub growth responses to increased precipitationintensity – a potential driver of shrub encroachment (Fig. 11).Using data from control plots in a precipitation manipulationexperiment (M. Holdrege, unpublished data), we built a RANDOM

FOREST model that predicted shrub diameter growth as a functionof climate variables (e.g. precipitation, temperature, soil moisture).This model was then used to predict shrub growth in separate plotsthat experiencedmore intense precipitation events. Thismodel waseffective at predicting shrub growth in treated plots (R2 = 0.90; rootmean squared error (RMSE) = 0.40 mm stem diameter growth).We created a second RANDOM FOREST model that included datafrom the first model, as well as root water uptake estimated fromroot tracer data and a water flow model (Kulmatiski et al., 2019),carbon uptake estimated from a simple photosynthesis model, and

Climate data Plant physiology and community data

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grazing)

Other data – grazing, management, etc.

Fig. 10 A conceptual approach to predicting semi-arid ecosystem state transitions using multiple modeling approaches. (1) Existing climate and plantphysiological and community data (orange arrows) would be used to (2) parameterize ecohydrological and carbon-budgeting plant physiological models. (3)The selection of model output variables (grey arrows) and parameterization data would be informed by state-and-transition model theory (i.e. parametervariability). (4) Inputdata, includingphysiologicalmodel predictionsofplantgrowthandvariability in plantphysiological parameters aswell as parameterizationdata,would be fed into amachine-learning program (e.g. RANDOM FOREST) alongwith training/verification datasets.Whilemachine-learning approacheswoulddevelop new models of biome shifts from complex and redundant input data, using mechanistic ecohydrological, ecophysiological and state-and-transitioninformedmodels as input canbe expected tohelp limit thenumberof input variables, improve interpretability ofmachine learningoutput (black dashedarrows)and provide more insight into mechanistic models. (5) Machine learning model outputs would both provide predictions of state transitions and help with thedevelopment of new hypotheses that could be integrated into existing models and data collection approaches.

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NDVI data (Fig. 11). This second model slightly improved thecorrelation between observed and predicted shrub growth(R2 = 0.93), though the RMSE increased to 0.47 mm stemdiameter growth. A third model was created that included thepreviously described variables and an early-warning signal derivedfrom NDVI data (Liu et al., 2019). This third model furtherimproved the correlation between observed and predicted results(R2 = 0.95), and increased the RMSE (1.15 mm stem diametergrowth). All three models underestimated declines in stemdiameter during seasonal drought. Future iterations that includeparameters that help describe observed shrub stem shrinkage can beexpected to improve model predictions. While the addition ofsimulated water uptake, carbon uptake and early-warning signalsdid not result in large improvements in predictions in this case, thisexercise demonstrated that RANDOM FOREST can integrate the typesof data streams discussed in this review to predict woody plantgrowth that could result in a grassland to shrubland biome shift.Perhaps more importantly, relative to a single modeling approach(e.g. a carbon uptake model), a model that includes informationfrom different conceptual backgrounds should improve predic-tions under awide range of conditions (Peters et al., 2018). Thoughthis example was executed for a single site, it is possible to scale thesemodel predictions up using spatially-explicit model input.

Conclusions

Widespread changes in wildfire, forest decline, shrub encroach-ment and desertification in semi-arid systems are associated withtremendous social and ecological costs and are likely to continue inthe next century (Bestelmeyer et al., 2018). Our ability to predictthese shifts has been limited, but here we describe several recent

advances that demonstrate the potential to forecast biome shifts insemi-arid systems. Each of these different lines of research ispromising, and we suggest that a new modeling approach thatintegrates knowledge from these different sources can be expectedto not only improve our understanding of semi-arid systems butalso improve predictions of state-changes in these systems monthsto years in advance. This new integrated approach requires aresearch community that can develop a common ‘language’ and anapproach that integrates concepts and data streams from differentdisciplines to improve predictions of semi-arid biome shifts.Similarly, these efforts will benefit from training datasets for areaswhere biome shifts have been observed and areaswhere biome shiftshave not been observed. The ability to target a small portion of thelandscape for management a year before a predicted forest fire canbe expected to have tremendous economic benefits, especiallyduring an era of increasing human development and extremeclimate events.

Acknowledgements

Images from https://ian.umces.edu/imagelibrary/.

ORCID

Andrew Kulmatiski https://orcid.org/0000-0001-9977-5508D. Scott Mackay https://orcid.org/0000-0003-0477-9755Yanlan Liu https://orcid.org/0000-0001-5129-6284Sabiha Majumder https://orcid.org/0000-0001-6046-557XAnthony J. Parolari https://orcid.org/0000-0002-1046-6790Ann Carla Staver https://orcid.org/0000-0002-2384-675XAnna T. Trugman https://orcid.org/0000-0002-7903-9711Kailiang Yu https://orcid.org/0000-0003-4223-5169

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