MINI REVIEW article

Front. For. Glob. Change, 26 October 2018

Sec. Forest Ecophysiology

Volume 1 - 2018 | https://doi.org/10.3389/ffgc.2018.00004

A Dirty Dozen Ways to Die: Metrics and Modifiers of Mortality Driven by Drought and Warming for a Tree Species

  • 1. School of Natural Resources and the Environment, University of Arizona, Tucson, AZ, United States

  • 2. Department of Ecology and Evolutionary Biology, University of Arizona, Tucson, AZ, United States

  • 3. Department of Forest and Rangeland Stewardship, Colorado State University, Fort Collins, CO, United States

  • 4. New Mexico Landscapes Field Station, Fort Collins Science Center, U.S. Geological Survey, Los Alamos, NM, United States

  • 5. Department of Biological Sciences, Northern Arizona University, Flagstaff, AZ, United States

  • 6. Merriam-Powell Center for Environmental Research, Northern Arizona University, Flagstaff, AZ, United States

  • 7. Department of Natural Resource Ecology and Management, Oklahoma State University, Stillwater, OK, United States

  • 8. Department of Plant Biology, Ecology, and Evolution, Oklahoma State University, Stillwater, OK, United States

Abstract

Tree mortality events driven by drought and warmer temperature, often amplified by pests and pathogens, are emerging as one of the predominant climate change impacts on plants. Understanding and predicting widespread tree mortality events in the future is vital as they affect ecosystem goods and services provided by forests and woodlands, including carbon storage needed to help offset warming. Additionally, if extensive enough, tree die-off events can influence not only local climate but also climate and vegetation elsewhere via ecoclimate teleconnections. Consequently, recent efforts have focused on improving predictions of tree mortality. One of the most commercially important genera of trees is Pinus, and the most studied species globally for drought-induced tree mortality is piñon pine, Pinus edulis. Numerous metrics have been developed in association with predicting mortality thresholds or variations in mortality for this species. In this article, we compiled metrics associated with drought and warming related mortality that were developed for P. edulis or for which P. edulis was a key example species used in a calculation or prediction. We grouped these metrics into three categories: (i) those related to simple climate variables, (ii) those related to physiological responses, and (iii) those that require multi-step calculations or modeling using climate, ecohydrological, and/or ecophysiological data; and we identified the spatial-temporal scale of each of these metrics. We also compiled factors shown to modify rates or sensitivities of mortality. The metrics to predict mortality include empirical ones which often have implicit linkages to expected mechanisms, and more mechanistic ones related to physiological drivers. The metrics for P. edulis have similarities with those available for other species of Pinus. Expected future mortality events will provide an opportunity to observationally and experimentally test and compare these metrics related to tree mortality for P. edulis via near-term ecological forecasting. The metrics for P. edulis may also be useful as potential analogs for other genera. Improving predictions of tree mortality for this species and others will be increasingly important as an aid to move toward anticipatory management.

Introduction

One of the major impacts of climate change on terrestrial ecosystems is tree mortality events caused by drought and warmer temperature, often exacerbated by pests and pathogens (Allen et al., , ; IPCC et al., ). Tree mortality impacts ecosystem services provided by forests, including carbon storage that helps offset warming impacts of emissions (Bonan, ; Kurz et al., ; Breshears et al., ; Anderegg et al., ; Ma et al., ). Tree mortality events can cause ecosystem state changes (Cobb et al., ) and, if extensive enough, can influence not only local climate but also climate and associated vegetation elsewhere—termed ecoclimate teleconnections (Garcia et al., ; Stark et al., ; Swann et al., ).

Recent reviews of rapidly expanding literature on tree mortality related to drought and warming include: compilation of observational case studies globally (Allen et al., , updated sequentially in IPCC et al., ; Allen et al., ; Hartmann et al., ); physiological responses of plants (McDowell et al., , ; Choat et al., , ) including a synthesis specific to experimental results (Adams et al., ) and one specific to tree functional traits (O'Brien et al., ); drought-insect interactions (Anderegg et al., ); ecological (Anderegg et al., ) and hydrological (Adams et al., ) consequences of tree die-off events; and dynamics and management options post die-off (Cobb et al., ).

One of the most important genera in terms of extent (Richardson and Rundel, ) and commercial value in forestry (LeMaitre, ) is Pinus, and the most studied species globally of Pinus relative to drought-related mortality is the piñon pine Pinus edulis (Allen et al., ; Meddens et al., ; Adams et al., ). This species has been studied with respect to most key categories related to mortality (Allen et al., ). In this article, we summarize metrics and modifiers associated with mortality driven by drought and warming for P. edulis, explicitly describe the relevant spatial-temporal scales for each, relate these to other results for Pinus, and highlight their potential utility for other genera and for near-term ecological forecasting—where predictions are made iteratively and publicly shared and then tested by subsequent observations, updating the predictions as new information becomes available and factoring lessons learned back into predictions (Clark et al., ; Dietze, ; Dietze et al., ). This review focuses on compiling the broad set of relevant metrics related to mortality for P. edulis, complementing that of Meddens et al. (), which focused on meta-analysis of different physiographic and biotic drivers of mortality.

Metrics associated with mortality driven by drought and warming for Pinus edulis

We compiled metrics associated with drought- and warming-related mortality that were developed for P. edulis or for which P. edulis was a key species used in a calculation or prediction (Table 1). These metrics were grouped into: (i) those driven solely by climate variables, (ii) physiological responses, and (iii) those that require multi-step calculations and modeling. The spatial-temporal scale of each of these metrics was explicitly identified (Figure 1). We also compiled factors shown to modify P. edulis mortality (Table 1). Many of these metrics and modifiers are related to one another directly or indirectly, but we group them together only if they use the same predictor variables or are complex ecosystem models requiring multiple inputs. A key figure illustrating each is provided in Supplementary Table S1. Many of these metrics are associated with the early 2000s drought in the Southwestern US (Breshears et al., ) and may be overly tied to those specific drought conditions. Important characteristics of the 2000s drought were that it was almost as severe as the 1950s drought in terms of low precipitation (with the 1950s drought being the worst drought in Southwest USA since the 1500s), but it was also warmer (Breshears et al., )—the consequences of which become more evident when Vapor Pressure Deficit (VPD) is considered (Weiss et al., ). Importantly, the warmer conditions associated with the 2000s drought are expected to be somewhat indicative of future drought (Breshears et al., ; Allen et al., ). Time series of plant water potential and soil moisture pre-drought and through mortality provide additional details about this event (Breshears et al., , ,). Also included are diverse experimental results (Adams et al., , ; Plaut et al., ; Krofcheck et al., ; Pangle et al., ).

Table 1

Metric #Mortality = f(….)Threshold, Mode, or RelationshipDataDrought/Warming sourceReferences
METRICS
Metrics Driven Primarily by Climatic Variables
1Standardized Precipitation Evaporation Index (SPEI)SPEI of −1.64 corresponds to no growth and regional mortalitySPEI from September thru JulyClimate data from 2000s droughtHuang et al.,
2Forest Drought-Stress Index (FDSI)FDSI of −1.41 corresponds to regional mortality eventsPrior winter PPT and current and prior summer VPD1500s droughtsWilliams et al.,
3Precipitation (PPT) and Vapor Pressure Deficit (VPD) ThresholdsBelow 600 mm PPT threshold and above 1.7 kPa VPD thresholdAnnual precipitation and warm season VPDObservations of 2000s droughtClifford et al.,
4Bioclimatic Envelope for MortalityAt higher temperature can die at wetter soil moistureClimate, soil moistureField Transplant ExperimentLaw et al.,
Nature Metrics Based on Direct Ecohydrological or Physiological Thresholds
5Water Potential (ψ) ThresholdExceeded thresholdsPredawn water potentialLiterature reviewAdams et al.,
6Percent Loss Conductivity (PLC)60% PLC threshold as mortality tipping pointPredawn water potential with PLC relationshipLiterature review; Observations from 2000s droughtWest et al., ; Koepke and Kolb, ; Adams et al.,
7Minimal Protracted Frequency of Plant Available Water (PAW)Low frequency of plant available water over growing seasonSoil moisture by depthObservations of 2000s droughtBreshears et al.,
8Duration of Water Potential below Point of Stomatal Closure (Time ψ ↓)Plant water potential below stomatal closure >10 MoPredawn water potentialObservations of 2000s droughtBreshears et al.,
9Multispectral Remote-Sensing MeasuresPlant water content and plant water potentialRemotely sensed multispectral indicesObservations of 2000s droughtBreshears et al., ; Rich et al., ; Huang et al., ; Krofcheck et al.,
Metrics Based on Multi-Step Modeling of Climate, Ecohydrology, and/or Ecophysiology
10Climate suitability during drought and historic reference (ECSxHCS)Low ECS (Episodic Climate Suitability) and high HCS (Historic Climate Suitability)Climate during drought relative to long-term mean2000s drought in context of historical (28 year) climateLloret and Kitzberger,
11Integrated EcophysiologyEcophysiological threshold exceededClimate, Predawn water potentialMiscellaneous studiesMcDowell et al.,
12Regional processesCritical mortality threshold of −2.4 MPaClimate, Other parametersMiscellaneous studiesMcDowell et al.,
MODIFIERS
Drought Properties
1TemperatureFaster when warmer by 5% per °CClimateGrowth Chamber ExperimentAdams et al.,
Soil Properties
2Topographic Moisture Index and ElevationLower and drier sites experienced more mortalityTopographic position (slope, elevation, aspect)1950s droughtAllen, ; Allen and Breshears,
3Soil Available Water Capacity (AWC)Areas with a soil AWC < 100 mm have greater mortalitySoil AWCObservations of 2000s droughtPeterman et al.,
4Soil Parent MaterialCinder exacerbates drought more than basalt or sedimentary parent materialSoil parent materialObservations of 2000s droughtKoepke et al.,
Tree Phenotype And Genotype
5Tree SizeLarge trees more susceptibleDemographyObservations of 2000s droughtFloyd et al.,
6Phenotypic Plasticity and Sequence of EventsReduction of biomass/variable growth ratesDendrochronology, DemographyObservations of 2000s droughtOgle et al., ; Macalady and Bugmann,
7Prior Patterns of Growth RatesLong term growth rate, variability, and number of abrupt increases predict mortalityDemography, Dendrochronology1950s, 1990s, and 2000s droughtsOgle et al., ; Macalady and Bugmann, ;
8Resin DuctsSmaller resin ducts increase likelihood of mortalityResin flow, DendrochronologyObservations of 2000s droughtGaylord et al., ,
9GeneticsMortality of trees resistant to moth was 3 times higher than for moth-susceptible treesGeneticsObservations of 2000s droughtSthultz et al.,
Biotic Interactions
10CompetitionMixed evidenceDemographyLiterature ReviewMeddens et al.,
11FacilitationFacilitation reduces thresholdDemography, MicroclimateObservations of 2000s droughtRoyer et al., ; Redmond et al.,
12OutbreaksSelectivity for larger treesBeetle populationsExperimental droughtGaylord et al.,

Compilation of key published metrics to predict Pinus edulis mortality (upper portion), which can be used in near-term ecological forecasting, followed by modifiers (lower portion) which need to be used in conjunction with additional information.

Figure 1

Metrics driven primarily by climatic variables

Metric 1: standardized precipitation evaporation index (SPEI)

SPEI includes both a precipitation input component and an evaporative demand component (Vicente-Serrano et al., ), a common theme among some of the metrics. After considering all possible months of the year to begin in and SPEI durations of 1–24 mo, SPEI with a duration of 11 months starting in July was found to be most strongly positively correlated with P. edulis and P. ponderosa growth, with SPEI below −1.64 identified as a threshold to trigger mortality (Huang et al., ).

Metric 2: forest drought-stress index (FDSI)

FDSI is an annual index that includes winter-spring precipitation and vapor pressure deficit during the early summer of the current year and the late summer of the year prior, and is standardized by applying a ratio of the current conditions to the long-term mean (Williams et al., ). FDSI strongly correlates to regional trends of tree growth and regional patterns of drought-related mortality agents, including bark beetle outbreaks and area burned by tree-killing wildfire. A FDSI ≤ −1.41 is thought to have resulted in widespread mortality (Williams et al., ), based on FDSI during the driest half of years during Southwest USA “megadrought” events (Swetnam and Betancourt, ).

Metric 3: precipitation (PPT) and vapor pressure deficit (VPD) thresholds

Field observations of P. edulis mortality across central NM, USA in response to the 2000s drought were highly variable with two thresholds for mortality identified: sites with 2-year precipitation > 600 mm or for warm season (May-August) mean VPD over 2 years of < 1.7 kPa, had little to no mortality (< 10%), whereas at sites with < 600 mm or > 1.7 kPa, plant mortality was highly variable (0% to ~100%; Clifford et al., ).

Metric 4: bioclimatic envelope for mortality

A new type of bioclimatic envelope that focuses exclusively on mortality events for P. edulis was developed for saplings and small reproductively mature sized trees, based largely on climate manipulation experiments that varied precipitation and temperature (Law et al., ). This bioclimatic envelope estimates a boundary between survival and mortality as a function of length of a dry period and growing season temperature. It indicates that at warmer temperatures (or greater VPD), the duration that P. edulis can survive is reduced.

Metrics based on direct ecohydrological or physiological thresholds

Metric 5: water potential (ψ) threshold

Plant water stress as reflected in more negative plant water potential, usually measured at the twig scale for P. edulis, has been used to identify threshold values at which tree mortality occurs (Sperry et al., ; McDowell et al., ) and can be measured in the field. Although the plant water potential for stomatal closure varies somewhat (Breshears et al., ), values associated with greater stress may be more variable among sites (Linton et al., ; West et al., ; Koepke and Kolb, ). A review of experimental studies of drought found that this metric occurred in association with mortality of every tree species studied (Adams et al., ).

Metric 6: percent loss of conductivity (PLC)

Increased stress associated with more negative plant water potential is also associated with loss in conductivity due to embolism intrusion, usually measured in stems in the lab, leading to disruptions of the hydraulic water column that can lead to mortality (Sperry et al., ; McDowell et al., ). Empirical and theoretical models suggest that this tightly coupled relationship accurately estimates loss of conductivity across species spanning an isohydry-anisohydry gradient (Cochard, ; Linton et al., ). A recent meta-analysis determined a key threshold of 60% PLC as a mortality tipping point (Adams et al., ), for which site-specific relationships to P. edulis predawn water potential can be developed prior to drought (Linton et al., ; West et al., ; Koepke and Kolb, ).

Metric 7: minimal protracted frequency of plant available water (PAW)

Long-term soil moisture data obtained by neutron probe measurements that extended below the topsoil and into tuff bedrock (Breshears et al., , ) were adjusted for soil texture to estimate thresholds at which soil moisture above bedrock tuff becomes relatively unavailable to plants. During the 2000s drought, soil moisture above the bedrock tuff was below an availability threshold for 14 consecutive months, during which time tree mortality occurred (Breshears et al., ).

Metric 8: duration of water potential below point of stomatal closure (time ψ ↓)

For relatively more isohydric species, such as P. edulis, trees close stomata at a given level of water stress and then attempt to survive the duration of the drought, paying respiration costs during that period. Predawn plant water potential for P. edulis at the same site as Metric 7 was < -2.2 MPa, the point of stomatal closure (Lajtha and Barnes, ), for 10 consecutive months preceding tree mortality (Breshears et al., ). Similarly, a field experiment removing 50% of ambient precipitation resulted in P. edulis mortality after 7 consecutive months of near zero conductance (Plaut et al., ).

Metric 9: multispectral remote-sensing measures

Multispectral assessments of whole-ecosystem responses of post- die-off can detect P. edulis die-off (e.g., Breshears et al., ; Rich et al., ; Huang et al., ; Krofcheck et al., ). Further, multispectral data for P. edulis needles alone revealed strong correlations between either plant water content or plant water potential with each of 5 multispectral indices for needles spanning healthy through dead (Stimson et al., ).

Metrics based on multi-step modeling of climate, ecohydrology, and/or ecophysiology

Metric 10: ECSxHCS

Using climate data, extended species distribution modeling was applied to assess whether P. edulis mortality during drought was greater in areas with lower historical climatic suitability (HCS; i.e., species distribution modeling using long-term average climate conditions) or with lower climatic suitability during a multi-year drought [episodic climatic suitability, ECS) (Lloret and Kitzberger, ). Highest mortality was found in areas with both high HCS and low ECS, suggesting trees have acclimated to the conditions historically experienced and are thus most sensitive to abrupt changes in climate.

Metric 11: integrated ecophysiology

Models based on plant ecophysiology have been developed to predict mortality based on known detailed physiological relationships (i.e., stomatal responses to limited water availability) of P. edulis (McDowell et al., ) coupled with information on temperature, drought intensity/duration and the role of biotic agents (hydraulic aspects of mortality, drawing on Sperry et al., are reviewed in Choat et al., ; see Adams et al., for carbohydrate results for P. edulis). The interactions among these specific drivers can push plants to mortality via combinations of carbon starvation, hydraulic failure, and/or pests and pathogens (McDowell et al., ; Anderegg et al., ).

Metric 12: regional ecosystem models

Regional P. edulis mortality projections among three ecosystem models all predicted widespread die-off, after verifying the ability of each to reproduce predawn water potential accurately (McDowell et al., and references therein): (1) TREES, a dynamic ecosystem model of water and carbon flows, plant water balance and cavitation was coupled with stomatal conductance, photosynthesis, and evaporation (Mackay et al., , ; Samanta et al., ; Loranty et al., ); (2) MuSICA, a multilayer, multi-leaf process-based biosphere-atmosphere exchange model, included detailed root water uptake, plant water storage dynamics, soil water hydraulic redistribution, root cavitation, and plant NSC storage dynamics (Ogée et al., ); and (3) ED(X), which tracks cohorts of trees based on their sizes, simulated tree mortality of cohorts based on carbon starvation and hydraulic failure, accounting for plant water storage and hydraulic conductivity (Moorcroft et al., with modifications described by Fisher et al., ; McDowell et al., ; and Xu et al., ). All three models used a critical mortality threshold of growing season predawn plant water potential associated with stomatal closure (−2.4 MPa) derived from a field experiment (Pangle et al., ; SI 5 in McDowell et al., ).

Modifiers of rates or sensitivity of P. edulis mortality

We also identified four categories of “modifiers” that influence the likelihood of P. edulis mortality (drought properties, soil properties, tree phentoype and genotype, and biotic interactions) that differ from “metrics” in that they cannot be used to independently estimate mortality without additional factors, nor can they be iteratively updated for near-term ecological forecasting.

Drought properties

Modifier 1: temperature

The first modifier focuses on how temperature explicitly drives mortality. Pinus edulis was the first species for which warmer conditions during drought were shown to hasten tree mortality for a reproductively mature-sized tree (Adams et al., ). Further, P. edulis seedlings exhibited a similar slope in hastening of time-to-mortality of ~5% per °C increase in temperature across a wide range of temperatures (Adams et al., ).

Soil properties

Modifier 2: topographic moisture index and elevation

Spatial patterns of P. edulis mortality in response to the 1950s drought were a function of a topographic moisture index and elevation, with increased mortality at drier and lower sites (Allen, ; additional details in Allen and Breshears, ). Slope aspect and position influenced mortality in piñon-juniper woodlands in general, but the effects of elevation were mixed (Meddens et al., ).

Modifier 3: soil available water capacity (AWC)

Using publicly available soil data (SSURGO; scale of 1:20,000), P. edulis stands had greater mortality during the 2002–2003 drought where soil AWC (calculated based on soil texture and depth) was < 100 mm (Peterman et al., ). However, a smaller-scale study conducted in NM found no relationship between soil AWC and P. edulis mortality (Clifford et al., ), suggesting this metric may be more applicable across large geographic areas that vary greatly in soil AWC.

Modifier 4: soil parent material

P. edulis mortality from the 2002–2003 drought was greater on soil parent material derived from volcanic cinder than from flow basalt or sedimentary substrate (Koepke et al., ).

Tree phenotype and genotype

Modifier 5: tree size

Physical characteristics like size are expected to be modifiers of mortality (Bennett et al., ; McDowell and Allen, ). Larger diameter P. edulis trees have experienced greater levels of drought-related mortality (Floyd et al., ; Meddens et al., ), likely due to combinations of bark beetle selectivity for large diameter trees (Santos and Whitham, ; Gaylord et al., ), a greater vulnerability of taller trees to hydraulic failure (McDowell and Allen, ), and carbon starvation from increased metabolic demands (Mueller et al., ).

Modifier 6: phenotypic plasticity and sequence of events

Phenotypic plasticity, including climate-induced variability in leaf area, sapwood area (Limousin et al., ), and tree-ring growth (Williams et al., ) can result in structural overshoot risks during rapid transitions from wet to dry periods (Jump et al., ), while the duration and sequencing of good and bad growth years affect both short- and long-term tree mortality risk (Ogle et al., ; Macalady and Bugmann, ).

Modifier 7: prior patterns of growth rate

Evaluating the 1950s, 1996, and the 2000s droughts, the best predictors for growth-mortality models of P. edulis included long-term (10–30 year) average growth rate combined with a metric of growth variability from the past 15 years and the number of abrupt growth increases over the past 10 years (Ogle et al., ; Macalady and Bugmann, ).

Modifier 8: resin ducts

Investment in resin ducts represent a proxy for defense against insects and may vary in response to tree size and age dynamics. P. edulis trees that produce smaller and/or fewer resin ducts are more likely to die during drought (Kläy, ; Gaylord et al., , ).

Modifier 9: genetics

A study of P. edulis mortality during drought for trees that exhibited genetically-based resistance or susceptibility to the moth Dioryctria albovittella found that drought-related mortality of trees resistant to the moth was three times higher than for moth-susceptible trees (Sthultz et al., ).

Biotic interactions

Modifier 10: competition

The effects of stand tree density (i.e., both intra- and inter-specific competition) on P. edulis mortality are mixed, with more studies not detecting density effects (Meddens et al., ). A study on juvenile P. edulis survival during drought found that mortality of juveniles located in the canopy interspace of overstory trees and shrubs was greater in areas with higher grass cover (Redmond et al., ).

Modifier 11: facilitation

Adult trees in piñon-juniper woodlands provide substantial shading below tree canopies and as a function of the overall tree density (Royer et al., , ). Facilitation by adults increases the mortality threshold to more extreme conditions relative to non-facilitated plants (Sthultz et al., ; Redmond et al., ).

Modifier 12: outbreaks

Bark beetle (Ips confusus) outbreaks are usually associated with field observations and experiments of P. edulis mortality (Meddens et al., ). Note, however, that P. edulis mortality during drought has occurred in the absence of bark beetles (Mueller et al., ) and controlled experiments quantify rates of mortality caused by drought in the absence of bark beetles (Adams et al., , ; Anderegg and Anderegg, ).

Relationships to studies of other Pinus species

The metrics and modifiers for P. edulis mortality share consistencies with other species in the Pinus genus. Several of the above metrics and modifiers (e.g., Metrics 1–2 and Modifier 1) were also predictive of P. ponderosa mortality. For instance, the increase of hastening in time-to-mortality for P. edulis of ~5% per °C also applies to P. ponderosa, as does the linear relationship of this response to a wide range of warming during drought (Adams et al., ). Mortality of five European species of Pinus depended on warming and water limitation (Matias et al., ), consistent with the climate metrics for P. edulis, which include both a warming or evaporative demand component and a precipitation component; these species also differed in sensitivity between montane and lowland sites. Other Pinus species have similar ecophysiological characteristics as P. edulis (Olson et al., ) and are also vulnerable to bark beetles, particularly when under water stress (Anderegg et al., ). The effect of elevated CO2 on mortality has not been studied for P. edulis, but results for P. radiata found it did not extend time-to-mortality (Duan et al., ).

Conclusion

There are more studies of drought-related mortality for P. edulis than for any other tree species, yet the many interrelated and often untested metrics and modifiers indicate a need to determine which are the most robust, accounting for tradeoffs between robustness and data or computational requirements. Expected future mortality events will provide an opportunity to observationally and experimentally test and compare these metrics related to tree mortality for P. edulis via near-term ecological forecasting (Clark et al., ; Dietze, ; Dietze et al., ). Given that many current projections of P. edulis mortality predict extensive mortality in coming decades (e.g., Adams et al., , ; Williams et al., ; McDowell et al., ), we need to test these metrics and modifiers with upcoming droughts. These metrics and modifiers reinforce that Pinus, a widely distributed and commercially important genus, is likely to be sensitive to future hotter drought. These metrics also serve as potential analogs for species in other genera or trait groups. Improving predictions of tree mortality will be increasingly important in moving toward anticipatory management under warming climate (Bradford et al., ).

Statements

Author contributions

DB provided a first rough draft of the synthesis table and the manuscript. All authors contributed to substantial revision and identifying and summarizing key metrics and modifiers, as well as editing and refinement of the text, tables and figure.

Funding

This work was supported primarily by NSF DEB-1833529 to Colorado State University, DEB-1833502 to University of Arizona, and DEB-1833505 to Northern Arizona University. Additional support was provided by EF-1340624, EF-1550756, EAR-1331408, and EAR-1659546, and OISE-1748275 and OISE-1748204 for Near-Term Ecological Forecasting workshop participation.

Acknowledgments

We thank R. E. Gallery and M. C. Dietze for including DB in a Near-Term Ecological Forecasting workshop that inspired that aspect of this project, and H. D. Adams for feedback on specific points in the manuscript.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Supplementary material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/ffgc.2018.00004/full#supplementary-material

References

  • 1

    AdamsH. D.Barron-GaffordG. A.MinorR. L.GardeaA. A.BentleyL. P.LawD. J.et al. (2017a). Temperature response surfaces for mortality risk of tree species with future drought. Environ. Res. Lett. 12:115014. 10.1088/1748-9326/aa93be

  • 2

    AdamsH. D.GerminoM. J.BreshearsD. D.Barron-GaffordG. A.Guardiola-ClaramonteM.ZouC. B.et al. (2013). Nonstructural leaf carbohydrate dynamics of Pinus edulis during drought-induced tree mortality reveal role for carbon metabolism in mortality mechanism. New Phytol.197, 11421151. 10.1111/nph.12102

  • 3

    AdamsH. D.Guardiola-ClaramonteM.Barron-GaffordG. A.VillegasJ. C.BreshearsD. D.ZouC. B.et al. (2009). Temperature sensitivity of drought-induced tree mortality portends increased regional die-off under global-change-type drought. Proc. Natl. Acad. Sci. U.S.A. 106, 70637066. 10.1073/pnas.0901438106

  • 4

    AdamsH. D.LuceC. H.BreshearsD. D.AllenC. D.WeilerM.HaleV. C.et al. (2012). Ecohydrological consequences of drought-and infestation-triggered tree die-off: insights and hypotheses. Ecohydrology5, 145159. 10.1002/eco.233

  • 5

    AdamsH. D.ZeppelM. J. B.AndereggW. R. L.HartmannH.LandhäusserS. M.TissueD. T.et al. (2017b). A multi-species synthesis of physiological mechanisms in drought-induced tree mortality. Nat. Ecol. Evol. 1, 12851291. 10.1038/s41559-017-0248-x

  • 6

    AllenC. D. (1989). Changes in the Landscape of the Jemez Mountains, New Mexico. Dissertation, University of California, Berkeley (Berkeley, CA).

  • 7

    AllenC. D.BreshearsD. D. (1998). Drought-induced shift of a forest-woodland ecotone: rapid landscape response to climate variation. Proc. Natl. Acad. Sci. U.S.A. 95, 1483914842. 10.1073/pnas.95.25.14839

  • 8

    AllenC. D.BreshearsD. D.McDowellN. G. (2015). On underestimation of global vulnerability to tree mortality and forest die-off from hotter drought in the Anthropocene. Ecosphere6, 155. 10.1890/ES15-00203.1

  • 9

    AllenC. D.MacaladyA. K.ChenchouniH.BacheletD.McDowellN.VennetierM.et al. (2010). A global overview of drought and heat-induced tree mortality reveals emerging climate change risks for forests. Forest Ecol. Manag. 259, 660684. 10.1016/j.foreco.2009.09.001

  • 10

    AndereggW. R. L.AndereggL. D. L. (2013). Hydraulic and carbohydrate changes in experimental drought-induced mortality of saplings in two conifer species. Tree Physiol. 33, 252260. 10.1093/treephys/tpt016

  • 11

    AndereggW. R. L.HickeJ. A.FisherR. A.AllenC. D.AukemaJ.BentzB. (2015). Tree mortality from drought, insects, and their interactions in a changing climate. New Phytol. 208, 674683. 10.1111/nph.13477

  • 12

    AndereggW. R. L.KaneJ. M.AndereggL. D. L. (2013). Consequences of widespread tree mortality triggered by drought and temperature stress. Nat. Clim. Change3, 3036. 10.1038/nclimate1635

  • 13

    BennettA. C.McDowellN. G.AllenC. D.Anderson-TeixeiraK. J. (2015). Larger trees suffer most during drought in forests worldwide. Nat. Plants1:15139. 10.1038/nplants.2015.139

  • 14

    BonanG. B. (2008). Forests and climate change: Forcings, feedbacks, and the climate benefits of forests. Science320, 14441449. 10.1126/science.1155121

  • 15

    BradfordJ. B.BetancourtJ. L.ButterfieldB. J.MunsonS. M.WoodT. E. (2018). Anticipatory natural resource science and management for a changing future. Front. Ecol. Environ. 16, 295303. 10.1002/fee.1806

  • 16

    BreshearsD. D.CobbN. S.RichP. M.PriceK. P.AllenC. D.BaliceR. G.et al. (2005). Regional vegetation die-off in response to global-change-type drought. Proc. Natl. Acad. Sci. U.S.A. 102, 1514415148. 10.1073/pnas.0505734102

  • 17

    BreshearsD. D.López-HoffmanL.GraumlichL. J. (2011). When ecosystem services crash: preparing for big, fast, patchy climate change. Ambio40, 256263. 10.1007/s13280-010-0106-4

  • 18

    BreshearsD. D.MyersO. B.BarnesF. J. (2009a). Horizontal heterogeneity in the frequency of plant-available water with woodland intercanopy-canopy vegetation patch type rivals that occurring vertically by soil depth. Ecohydrology2, 503519. 10.1002/eco.75

  • 19

    BreshearsD. D.MyersO. B.MeyerC. W.BarnesF. J.ZouC. B.AllenC. D.et al. (2009b). Tree die-off in response to global change-type drought: mortality insights from a decade of plant water potential measurements. Front. Ecol. Environ. 7, 185189. 10.1890/080016

  • 20

    ChoatB.BrodribbT. J.BrodersenC. R.DuursmaR. A.LópezR.MedlynB. E. (2018). Triggers of tree mortality under drought. Nature558, 531539. 10.1038/s41586-018-0240-x

  • 21

    ChoatB.JansenS.BrodribbT. J.CochardH.DelzonS.BhaskarR.et al. (2012). Global convergence in the vulnerability of forests to drought. Nature491, 752. 10.1038/nature11688

  • 22

    ClarkJ. S.CarpenterS. R.BarberM.CollinsS.DobsonA.FoleyJ. A.et al. (2001). Ecological forecasts: an emerging imperative. Science293, 657660. 10.1126/science.293.5530.657

  • 23

    CliffordM. J.RoyerP. D.CobbN. S.BreshearsD. D.FordP. L. (2013). Precipitation thresholds and drought-induced tree die-off: insights from patterns of Pinus edulis mortality along an environmental stress gradient. New Phytol. 200, 413421. 10.1111/nph.12362

  • 24

    CobbR. C.RuthrofK. X.BreshearsD. D.LloretF.AakalaT.AdamsH. D.et al. (2017). Ecosystem dynamics and management after forest die-off: a global synthesis with conceptual state-and-transition models. Ecosphere8:e02034. 10.1002/ecs2.2034

  • 25

    CochardH. (1992). Vulnerability of several conifers to air embolism. Tree Physiol. 11, 7383. 10.1093/treephys/11.1.73

  • 26

    DietzeM. C. (2017). Ecological Forecasting.Princeton, NJ: Princeton University Press. 10.1515/9781400885459

  • 27

    DietzeM. C.FoxA.Beck-JohnsonL. M.BetancourtJ. L.HootenM. B.JarnevichC. S.et al. (2018). Iterative near-term ecological forecasting: needs, opportunities, and challenges. Proc. Natl. Acad. Sci. U.S.A. 115, 14241432. 10.1073/pnas.1710231115

  • 28

    DuanH.O'GradyA. P.DuursmaR. A.ChoatB.HuangG.SmithR. A.et al. (2015). Drought responses of two gymnosperm species with contrasting stomatal regulation strategies under elevated [CO2] and temperature. Tree Physiol. 35, 756770. 10.1093/treephys/tpv047

  • 29

    FisherR.McDowellN. G.PurvesD.MoorcroftP.SitchS.CoxP.et al. (2010). Assessing uncertainties in a second-generation dynamic vegetation model due to ecological scale limitations. New Phytol. 187, 666681. 10.1111/j.1469-8137.2010.03340.x

  • 30

    FloydM. L.CliffordM.CobbN. S.HannaD.DelphR.FordP.et al. (2009). Relationship of stand characteristics to drought-induced mortality in three Southwestern piñion–juniper woodlands. Ecol. Appl. 19, 12231230. 10.1890/08-1265.1

  • 31

    GarciaE. S.SwannA. L. S.VillegasJ. C.BreshearsD. D.LawD. J.SaleskaS. R.et al. (2016). Synergistic ecoclimate teleconnections from forest loss in different regions structure global ecological responses. PLoS ONE11:e0165042. 10.1371/journal.pone.0165042

  • 32

    GaylordM. L.KolbT. E.McDowellN. G. (2015). Mechanisms of piñon pine mortality after severe drought: a retrospective study of mature trees. Tree Physiol. 35, 806816. 10.1093/treephys/tpv038

  • 33

    GaylordM. L.KolbT. E.PockmanW. T.PlautJ. A.YepezE. A.MacaladyA. K.et al. (2013). Drought predisposes piñon-juniper woodlands to insect attacks and mortality. New Phytol. 198, 567578. 10.1111/nph.12174

  • 34

    HartmannH.MouraC. F.AndereggW. R. L.RuehrN. K.SalmonY.AllenC. D.et al. (2018). Research frontiers for improving our understanding of drought-induced tree and forest mortality. New Phytol. 218, 1528. 10.1111/nph.15048

  • 35

    HuangC. Y.AsnerG. P.BargerN. N.NeffJ. C.FloydM. L. (2010). Regional aboveground live carbon losses due to drought-induced tree dieback in piñon-juniper ecosystems. Remote Sens. Environ. 114, 14711479. 10.1016/j.rse.2010.02.003

  • 36

    HuangK.YiC.WuD.ZhouT.ZhaoX.BlanfordW. J.et al. (2015). Tipping point of a conifer forest ecosystem under severe drought. Environ. Res. Lett. 10:024011. 10.1088/1748-9326/10/2/024011

  • 37

    IPCC 2014SetteleJ.ScholesRBettsR.BunnS.LeadleyP.NepstadD.et al. (2014). Terrestrial and inland water systems. in Climate Change 2014: Impacts,Adaptation, and Vulnerability. Part A: Global and Sectoral Aspects. Contribution of Working Group II to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, eds FieldC. B.BarrosV. R.DokkenD. J.MachK. J.MastrandreaM. D.BilirT. E.ChatterjeeM.EbiK. L.EstradaY. O.GenovaR. C.GirmaB.KisselE. S.LevyA. N.MacCrackenS.MastrandreaP. R.WhiteL. L. (Cambridge, UK; New York, NY: Cambridge University Press), 271359.

  • 38

    JumpA. S.Ruiz-BenitoP.GreenwoodS.AllenC. D.KitzbergerT.FenshamR.et al. (2017). Structural overshoot of tree growth with climate variability and the global spectrum of drought-induced forest dieback. Glob. Change Biol23, 37423757. 10.1111/gcb.13636

  • 39

    KläyM. (2011). Are Defensive Structures Good Predictors of Tree Mortality Under Drought and Insect Pressure?Master's Thesis, Zürich, Switzerland: Swiss Federal Institute of Technology in Zurich.

  • 40

    KoepkeD. F.KolbT. E. (2013). Species variation in water relations and xylem vulnerability to cavitation at a forest-woodland ecotone. Forest Sci. 59, 524535. 10.5849/forsci.12-053

  • 41

    KoepkeD. F.KolbT. E.AdamsH. D. (2010). Variation in woody plant mortality and dieback from severe drought among soils, plant groups, and species within a northern Arizona ecotone. Oecologia163, 10791090. 10.1007/s00442-010-1671-8

  • 42

    KrofcheckD. J.EitelJ. U.VierlingL. A.SchulthessU.HiltonT. M.Dettweiler-RobinsonE.et al. (2014). Detecting mortality induced structural and functional changes in a pi-on-juniper woodland using Landsat and RapidEye time series. Remote Sens. Environ. 151, 102113. 10.1016/j.rse.2013.11.009

  • 43

    KurzW. A.DymondC. C.StinsonG.RampleyG. J.NeilsonE. T.CarrollA. L.et al. (2008). Mountain pine beetle and forest carbon feedback to climate change. Nature452, 987990. 10.1038/nature06777

  • 44

    LajthaK.BarnesF. J. (1991). Carbon gain and water-use in pinyon pine-juniper woodlands of northern New Mexico—field versus phytotron chamber measurements. Tree Phsiol. 9, 5967. 10.1093/treephys/9.1-2.59

  • 45

    LawD. J.AdamsH. D.BreshearsD. D.CobbN. S.BradfordJ. B.ZouC. B. (in press). Expanded use of environmental extremes versus trends in bioclimatic modeling to predict individual demographic events. Int. J. Plant Sci. 180.

  • 46

    LeMaitreD. C. (1998). Pines in cultivation: a global view, in Ecology and Biogeography of Pinus, ed RichardsonD. M. (New York, NY: Cambridge University Press), 407431.

  • 47

    LimousinJ.YepezE. A.McDowellN. G.PockmanW. T. (2015). Convergence in resource use efficiency across trees with differing hydraulic strategies in response to ecosystem precipitation manipulation. Funct. Ecol. 29, 11251136. 10.1111/1365-2435.12426

  • 48

    LintonM. J.SperryJ. S.WilliamsD. G. (1998). Limits to water transport in Juniperus osteosperma and Pinus edulis: implications for drought tolerance and regulation of transpiration. Funct. Ecol. 12, 906911. 10.1046/j.1365-2435.1998.00275.x

  • 49

    LloretF.KitzbergerT. (2018). Historical and event-based bioclimatic suitability predicts regional forest vulnerability to compound effects of severe drought and bark beetle infestation. Glob. Change Biol. 24, 19521964. 10.1111/gcb.14039

  • 50

    LorantyM. M.MackayD. S.EwersB. E.TraverE.KrugerE. L. (2010). Contribution of competition for light to within-species variability in stomatal conductance. Water Resour. Res. 46:W05516. 10.1029/2009WR008125

  • 51

    MaD.NotaroM.LiuZ.ChenG.LiuY. (2013). Simulated impacts of afforestation in East China monsoon region as modulated by ocean variability. Clim. Dyn. 41, 24392450. 10.1007/s00382-012-1592-9

  • 52

    MacaladyA. K.BugmannH. (2014). Growth-mortality relationships in piñon pine (Pinus edulis) during severe droughts of the past century: shifting processes in space and time. PLoS ONE9:e92770. 10.1371/journal.pone.0092770

  • 53

    MackayD. S.AhlD. E.EwersB. E.SamantaS.GowerS. T.BurrowsS. N. (2003). Physiological tradeoffs in the parameterization of a model of canopy transpiration. Adv. Water Resour. 26, 179194. 10.1016/S0309-1708(02)00090-8

  • 54

    MackayD. S.EwersB. E.LorantyM. M.KrugerE. L. (2010). On the representativeness of plot size and location for scaling transpiration from trees to a stand. J. Geophys. Res. Biogeosci. 115:G02016. 10.1029/2009JG001092

  • 55

    MatiasL.CastroJ.Villar-SalvadorP.QueroJ. L.JumpA. S. (2017). Differential impacts of hotter drought on seedling performance of five ecologically distinct pine species. Plant Ecol. 218, 201212. 10.1007/s11258-016-0677-7

  • 56

    McDowellN.PockmanW. T.AllenC. D.BreshearsD. D.CobbN.KolbT.et al. (2008). Mechanisms of plant survival and mortality during drought: why do some plants survive while others succumb to drought?New Phytol. 178, 719739. 10.1111/j.1469-8137.2008.02436.x

  • 57

    McDowellN. G.AllenC. D. (2015). Darcy's law predicts widespread forest mortality under climate warming. Nat. Clim. Change5, 669672. 10.1038/nclimate2641

  • 58

    McDowellN. G.BeerlingD. J.BreshearsD. D.FisherR. A.RaffaK. F.StittM. (2011). The interdependence of mechanisms underlying climate-driven vegetation mortality. Trends in Ecol. and Evol. 26, 523532. 10.1016/j.tree.2011.06.003

  • 59

    McDowellN. G.FisherR. A.XuC. G.DomecJ. C.HolttaT.MackayD. S.et al. (2013). Evaluating theories of drought-induced vegetation mortality using a multimodel-experiment framework. New Phytol. 200, 304321. 10.1111/nph.12465

  • 60

    McDowellN. G.WilliamsA. P.XuC.PockmanW. T.DickmanL. T.SevantoS.et al. (2016). Multi-scale predictions of massive conifer mortality due to chronic temperature rise. Nat. Clim. Change6, 295300. 10.1038/nclimate2873

  • 61

    MeddensA. J. H.HickeJ. A.MacaladyA. K.BuotteP. C.CowlesT. R.AllenC. D. (2015). Patterns and causes of observed piñon pine mortality in the southwestern United States. New Phytol. 206, 9197. 10.1111/nph.13193

  • 62

    MoorcroftP. R.HurttG. C.PacalaS. W. (2001). A method for scaling vegetation dynamics: the ecosystem demography model (ED). Ecol. Monogr. 71, 557586. 10.1890/0012-9615(2001)071[0557:AMFSVD]2.0.CO;2

  • 63

    MuellerR. C.ScudderC. M.PorterM. E.Talbot TrotterI. I. I. RGehringC. A.WhithamT. G. (2005). Differential tree mortality in response to severe drought: evidence for long-term vegetation shifts. J. Ecol. 93, 10851093. 10.1111/j.1365-2745.2005.01042.x

  • 64

    O'BrienM. J.EngelbrechtB. M.JoswigJ.PereyraG.SchuldtB.JansenS. (2017), A synthesis of tree functional traits related to drought-induced mortality in forests across climatic zones. J Appl. Ecol. 54, 16691686. 10.1111/1365-2664.12874.

  • 65

    OgéeJ.BrunetY.LoustauD.BerbigierP.DelzonS. (2003). MuSICA, a CO2, water and energy multi-layer, multi-leaf pine forest model: evaluation from hourly to yearly time scales and sensitivity analysis. Glob. Change Biol. 9, 697717. 10.1046/j.1365-2486.2003.00628.x

  • 66

    OgleK.WhithamT. G.CobbN. S. (2000). Tree-ring variation in pinyon predicts likelihood of death following severe drought. Ecology81, 32373243. 10.1890/0012-9658(2000)081[3237:TRVIPP]2.0.CO;2

  • 67

    OlsonM. E.SorianoD.RosellJ.AnfodilloT.DonoghueM. J.EdwardsE. J.et al. (2018). Plant height and hydraulic vulnerability to drought and cold. Proc. Natl. Acad. Sci. U.S.A. 115, 75517556. 10.1073/pnas.1721728115

  • 68

    PangleR. E.HillJ. P.PlautJ. A.YepezE. A.ElliotJ. R.GehresN.et al. (2012). Methodology and performance of a rainfall manipulation experiment in a piñon–juniper woodland. Ecosphere3:28. 10.1890/ES11-00369.1

  • 69

    PangleR. E.LimousinJ. M.PlautJ. A.YepezE. A.HudsonP. J.BoutzA. L.et al. (2015). Prolonged experimental drought reduces plant hydraulic conductance and transpiration and increases mortality in a piñon-juniper woodland. Ecol. and Evol. 5, 16181638. 10.1002/ece3.1422

  • 70

    PetermanW.WaringR. H.SeagerT.PollockW. L. (2013). Soil properties affect pinyon pine–juniper response to drought. Ecohydrology6, 455463. 10.1002/eco.1284

  • 71

    PlautJ. A.YepezE. A.HillJ.PangleR.SperryJ. S.PockmanW. T.et al. (2012). Hydraulic limits preceding mortality in a piñon-juniper woodland under experimental drought. Plant Cell Environ. 35, 16011617. 10.1111/j.1365-3040.2012.02512.x

  • 72

    RedmondM. D.CobbN. S.CliffordM. J.BargerN. N. (2015). Woodland recovery following drought-induced tree mortality across an environmental stress gradient. Glob. Change Biol. 21, 36853695. 10.1111/gcb.12976

  • 73

    RichP. M.BreshearsD. D.WhiteA. B. (2008). Phenology Of mixed woody–herbaceous ecosystems following extreme events: net and differential responses. Ecology89, 342352. 10.1890/06-2137.1

  • 74

    RichardsonD. M.RundelP. W. (1998). Ecology and biogeography of Pinus: an introduction, in Ecology and Biogeography of Pinus, ed RichardsonD. M. (New York, NY: Cambridge University Press) 346.

  • 75

    RoyerP. D.BreshearsD. D.ZouC. B.CobbN. S.KurcS. A. (2010). Ecohydrological energy inputs in semiarid coniferous gradients: responses to management- and drought-induced tree reductions. Forest Ecol. Manag. 260, 16461655. 10.1016/j.foreco.2010.07.036

  • 76

    RoyerP. D.CobbN. S.CliffordM. J.HuangC. Y.BreshearsD. D.AdamsH. D.et al. (2011). Extreme climatic event-triggered overstorey vegetation loss increases understorey solar input regionally: primary and secondary ecological implications. J. Ecol. 99, 714723. 10.1111/j.1365-2745.2011.01804.x

  • 77

    SamantaS.MackayD. S.ClaytonM. K.KrugerE. L.EwersB. E. (2007). Bayesian analysis for uncertainty estimation of a canopy transpiration model. Water Resour. Res. 43:W04424. 10.1029/2006WR005028

  • 78

    SantosM. J.WhithamT. G. (2010). Predictors of Ips confusus outbreaks during a record drought in southwestern USA: implications for monitoring and management. Environ. Manag. 45, 239249. 10.1007/s00267-009-9413-6

  • 79

    SperryJ. S.DonnellyJ. R.TyreeM. T. (1988). A method for measuring hydraulic conductivity and embolism in xylem. Plant Cell Environ. 11, 3540. 10.1111/j.1365-3040.1988.tb01774.x

  • 80

    StarkS. C.BreshearsD. D.GarciaE. S.LawD. J.MinorD. M.SaleskaS. R.et al. (2016). Toward accounting for ecoclimate teleconnections: intra- and inter-continental consequences of altered energy balance after vegetation change. Landscape Ecol. 31, 181194. 10.1007/s10980-015-0282-5

  • 81

    SthultzC. M.GehringC. A.WhithamT. G. (2007). Shifts from competition to facilitation between a foundation tree and a pioneer shrub across spatial and temporal scales in a semi-arid woodland. New Phytol. 173, 135145. 10.1111/j.1469-8137.2006.01915.x

  • 82

    SthultzC. M.GehringC. A.WhithamT. G. (2009). Deadly combinations of genes and drought: increased mortality of herbivore-resistant trees in a foundation species. Glob. Change Biol. 15, 19491961. 10.1111/j.1365-2486.2009.01901.x

  • 83

    StimsonH. C.BreshearsD. D.UstinS. L.KefauverS. C. (2005). Spectral sensing of foliar water conditions in two co-occurring conifer species: Pinus edulis and Juniperus monosperma. Remote Sens. Environ. 96, 108145. 10.1016/j.rse.2004.12.007

  • 84

    SwannA. L. S.Lagu,ëM. M.GarciaE. S.FieldJ. P.BreshearsD. D.MooreD. J. P.et al. (2018). Continental-scale consequences of tree die-offs in North America: identifying where forest loss matters most. Environ. Res. Lett. 13:055014. 10.1088/1748-9326/aaba0f

  • 85

    SwetnamT. W.BetancourtJ. L. (1998). Mesoscale disturbance and ecological response to decadal climatic variability in the American Southwest. J. Climate. 11, 31283147. 10.1175/1520-0442(1998)011<3128:MDAERT>2.0.CO;2

  • 86

    Vicente-SerranoS. M.BegueríaS.López-MorenoJ. I. (2010). A multiscalar drought index sensitive to global warming: the standardized precipitation evapotranspiration index. J. Climate23, 16961718. 10.1175/2009JCLI2909.1

  • 87

    WeissJ. L.CastroC. L.OverpeckJ. T. (2009). Distinguishing pronounced droughts in the Southwestern United States: seasonality and effects of warmer temperatures. J. Clim. 22, 59185932. 10.1175/2009JCLI2905.1

  • 88

    WestA. G.HultineK. R.JacksonT. L.EhleringerJ. R. (2007). Differential summer water use by Pinus edulis and Juniperus osteosperma reflects contrasting hydraulic characteristics. Tree Phys. 27, 17111720. 10.1093/treephys/27.12.1711

  • 89

    WilliamsA. P.AllenC. D.MacaladyA. K.GriffinD.WoodhouseC. A.MekoD. M.et al. (2013). Temperature as a potent driver of regional forest drought stress and tree mortality. Nat. Clim. Change3, 292297. 10.1038/nclimate1693

  • 90

    XuC.McDowellN. G.SevantoS.FisherR. A. (2013). Our limited ability to predict vegetation dynamics under water stress. New Phytol. 200, 298300. 10.1111/nph.12450

Summary

Keywords

climate change, die-off, drought, mortality, Pinus edulis, tree

Citation

Breshears DD, Carroll CJW, Redmond MD, Wion AP, Allen CD, Cobb NS, Meneses N, Field JP, Wilson LA, Law DJ, McCabe LM and Newell-Bauer O (2018) A Dirty Dozen Ways to Die: Metrics and Modifiers of Mortality Driven by Drought and Warming for a Tree Species. Front. For. Glob. Change 1:4. doi: 10.3389/ffgc.2018.00004

Received

20 July 2018

Accepted

06 September 2018

Published

26 October 2018

Volume

1 - 2018

Edited by

Jeffrey M. Warren, Oak Ridge National Laboratory (DOE), United States

Reviewed by

Cate Macinnis-Ng, University of Auckland, New Zealand; Adrià Barbeta, INRA Centre Bordeaux-Aquitaine, France

Updates

Copyright

*Correspondence: David D. Breshears

This article was submitted to Forest Ecophysiology, a section of the journal Frontiers in Forests and Global Change

Disclaimer

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Outline

Figures

Cite article

Copy to clipboard


Export citation file


Share article

Article metrics