Provenance, genotype, and flooding influence growth and resource acquisition characteristics in a clonal, riparian shrub.

PREMISE: Riparian plants can exhibit intraspecific phenotypic variability across the landscape related to temperature and flooding gradients. Phenotypes that vary across a climate gradient are often partly genetically determined and may differ in their response to inundation. Changes to inundation patterns across a climate gradient could thus result in site-specific inundation responses. Phenotypic variability is more often studied in riparian trees, yet riparian shrubs are key elements of riparian systems and may differ from trees in phenotypic variability and environmental responses. METHODS: We tested whether individuals of a clonal, riparian shrub, Pluchea sericea, collected from provenances spanning a temperature gradient differed in their phenotypes and responses to inundation and to what degree any differences were related to genotype. Plants were subjected to different inundation depths and a subset genotyped. Variables related to growth and resource acquisition were measured and analyzed using hierarchical, multivariate Bayesian linear regressions. RESULTS: Individuals from different provenances differed in their phenotypes, but not in their response to inundation. Phenotypes were not related to provenance temperature but were partially governed by genotype. Growth was more strongly influenced by inundation, while resource acquisition was more strongly controlled by genotype. CONCLUSIONS: Growth and resource acquisition responses in a clonal, riparian shrub are affected by changes to inundation and plant demographics in unique ways. Shrubs appear to differ from trees in their responses to environmental change. Understanding environmental effects on shrubs separately from those of trees will be a key part of evaluating impacts of environmental change on riparian ecosystems.

Global patterns of drought recovery.

Drought, a recurring phenomenon with major impacts on both human and natural systems, is the most widespread climatic extreme that negatively affects the land carbon sink. Although twentieth-century trends in drought regimes are ambiguous, across many regions more frequent and severe droughts are expected in the twenty-first century. Recovery time-how long an ecosystem requires to revert to its pre-drought functional state-is a critical metric of drought impact. Yet the factors influencing drought recovery and its spatiotemporal patterns at the global scale are largely unknown. Here we analyse three independent datasets of gross primary productivity and show that, across diverse ecosystems, drought recovery times are strongly associated with climate and carbon cycle dynamics, with biodiversity and CO2 fertilization as secondary factors. Our analysis also provides two key insights into the spatiotemporal patterns of drought recovery time: first, that recovery is longest in the tropics and high northern latitudes (both vulnerable areas of Earth's climate system) and second, that drought impacts (assessed using the area of ecosystems actively recovering and time to recovery) have increased over the twentieth century. If droughts become more frequent, as expected, the time between droughts may become shorter than drought recovery time, leading to permanently damaged ecosystems and widespread degradation of the land carbon sink.

Contemporary tree growth shows altered climate memory.

Trees are long-lived organisms, exhibiting temporally complex growth arising from strong climatic "memory." But conditions are becoming increasingly arid in the western USA. Using a century-long tree-ring network, we find altered climate memory across the entire range of a widespread western US conifer: growth is supported by precipitation falling further into the past (+15 months), while increasingly impacted by more recent temperature conditions (-8 months). Tree-ring datasets can be biased, so we confirm altered climate memory in a second, ecologically-sampled tree-ring network. Predicted drought responses show trees may have also become more sensitive to repeat drought. Finally, plots near sites with relatively longer precipitation memory and shorter temperature memory had significantly lower recent mortality rates (R2  = 0.61). We argue that increased drought frequency has altered climate memory, demonstrate how non-stationarity may arise from failure to account for memory, and suggest memory length may be predictive of future tree mortality.

Tree growth sensitivity to climate varies across a seasonal precipitation gradient.

Spatial patterns of precipitation in the southwestern United States result in a complex gradient from winter-to-summer moisture dominance that influences tree growth. In response, tree growth exhibits seasonal-to-annual variability that is evident in the growth of whole tree rings, and in sub-annual sections such as earlywood and latewood. We evaluated the influence of precipitation and temperature on the growth of Pinus ponderosa trees in 11 sites in the southwestern US. Precipitation during the year of growth and the prior year accounted for about half of the climate influence on annual growth, with the other half reflecting conditions 2-4 years prior to growth, indicating that individual trees do indeed exhibit multi-year "memory" of climate. Trees in wetter sites exhibited weaker influence of past precipitation inputs, but longer memory of climatic variability. Conversely, trees in dry sites exhibited shorter memory of long-term climatic variability, but greater sensitivity to past precipitation effects. These results are consistent with the existence of complex interactions between endogenous (phenotype) effects and exogenous (climate) effects in controlling climate memory in trees. After accounting for climate, residual variability in latewood growth was negatively correlated with earlywood growth, indicating a potential tradeoff between latewood versus earlywood growth. This study provides new insights that will assist the accurate prediction of woody biomass growth and forest carbon sequestration across a southwestern US precipitation gradient.

A hierarchical, multivariate meta-analysis approach to synthesising global change experiments.

Meta-analyses enable synthesis of results from globally distributed experiments to draw general conclusions about the impacts of global change factors on ecosystem function. Traditional meta-analyses, however, are challenged by the complexity and diversity of experimental results. We illustrate how several key issues can be addressed by a multivariate, hierarchical Bayesian meta-analysis (MHBM) approach applied to information extracted from published studies. We applied an MHBM to log-response ratios for aboveground biomass (AB, n = 300), belowground biomass (BB, n = 205) and soil CO2 exchange (SCE, n = 544), representing 100 studies. The MHBM accounted for study duration, climate effects and covariation among the AB, BB and SCE responses to elevated CO2 (eCO2 ) and/or warming. The MHBM revealed significant among-study covariation in the AB and BB responses to experimental treatments. The MHBM imputed missing duration (4.2%) and climate (6%) data, and revealed that climate context governs how eCO2 and warming impact ecosystem function. Predictions identified biomes that may be particularly sensitive to eCO2 or warming, but that are under-represented in global change experiments. The MHBM approach offers a flexible and powerful tool for synthesising disparate experimental results reported across multiple studies, sites and response variables.

Lower soil moisture and deep soil temperatures in thermokarst features increase old soil carbon loss after 10 years of experimental permafrost warming.

Almost half of the global terrestrial soil carbon (C) is stored in the northern circumpolar permafrost region, where air temperatures are increasing two times faster than the global average. As climate warms, permafrost thaws and soil organic matter becomes vulnerable to greater microbial decomposition. Long-term soil warming of ice-rich permafrost can result in thermokarst formation that creates variability in environmental conditions. Consequently, plant and microbial proportional contributions to ecosystem respiration may change in response to long-term soil warming. Natural abundance δ13 C and Δ14 C of aboveground and belowground plant material, and of young and old soil respiration were used to inform a mixing model to partition the contribution of each source to ecosystem respiration fluxes. We employed a hierarchical Bayesian approach that incorporated gross primary productivity and environmental drivers to constrain source contributions. We found that long-term experimental permafrost warming introduced a soil hydrology component that interacted with temperature to affect old soil C respiration. Old soil C loss was suppressed in plots with warmer deep soil temperatures because they tended to be wetter. When soil volumetric water content significantly decreased in 2018 relative to 2016 and 2017, the dominant respiration sources shifted from plant aboveground and young soil respiration to old soil respiration. The proportion of ecosystem respiration from old soil C accounted for up to 39% of ecosystem respiration and represented a 30-fold increase compared to the wet-year average. Our findings show that thermokarst formation may act to moderate microbial decomposition of old soil C when soil is highly saturated. However, when soil moisture decreases, a higher proportion of old soil C is vulnerable to decomposition and can become a large flux to the atmosphere. As permafrost systems continue to change with climate, we must understand the thresholds that may propel these systems from a C sink to a source.

Tree growth sensitivity to climate is temporally variable.

Despite a long history of discussion of 'non-stationarity' in dendrochronology, researchers and modellers in diverse fields commonly rely on the implicit assumption that tree growth responds to climate drivers in the same way at any given time. Synthesising recent work on drought legacies and other climate-related phenomena, we show tree growth responses to climate are temporally variable, and that abrupt variability is commonly observed in response to diverse events. Thus, we put forth a 'growth-climate sensitivity' framework for understanding temporal variability (including non-stationarity) in the sensitivity of tree growth to climate. We argue that temporal variability is ubiquitous, illustrating limits to the ways in which tree growth is often conceptualised. We present two conceptual hypotheses (homoeostatic sensitivity and dynamic sensitivity) for how tree growth sensitivity to climate varies, and evaluate the evidence for each. In doing so, we hope to motivate increased investigation of the temporal variability in tree growth through innovative disturbance or drought experiments, particularly via the inclusion of recovery treatments. Focusing on growth-climate sensitivity and its temporal variability can improve prediction of the future states and functioning of trees under climate change, and has the potential to be incorporable into predictive dynamic vegetation models.

Temporal shifts in iso/anisohydry revealed from daily observations of plant water potential in a dominant desert shrub.

Plant species are characterized along a spectrum of isohydry to anisohydry depending on their regulation of water potential (Ψ), but the plasticity of hydraulic strategies is largely unknown. The role of environmental drivers was evaluated in the hydraulic behavior of Larrea tridentata, a drought-tolerant desert shrub that withstands a wide range of environmental conditions. With a 1.5 yr time-series of 2324 in situ measurements of daily predawn and midday Ψ, the temporal variability of hydraulic behavior was explored in relation to soil water supply, atmospheric demand and temperature. Hydraulic behavior in Larrea was highly dynamic, ranging from partial isohydry to extreme anisohydry. Larrea exhibited extreme anisohydry under wet soil conditions corresponding to periods of high productivity, whereas partial isohydry was exhibited after prolonged dry or cold conditions, when productivity was low. Environmental conditions can strongly influence plant hydraulic behavior at relatively fast timescales, which enhances our understanding of plant drought responses. Although species may exhibit a dominant hydraulic behavior, variable environmental conditions can prompt plasticity in Ψ regulation, particularly for species in seasonally dry climates.

Non-structural carbohydrate dynamics associated with antecedent stem water potential and air temperature in a dominant desert shrub.

Non-structural carbohydrates (NSCs) are necessary for plant growth and affected by plant water status, but the temporal dynamics of water stress impacts on NSC are not well understood. We evaluated how seasonal NSC concentrations varied with plant water status (predawn xylem water potential, Ψ) and air temperature (T) in the evergreen desert shrub Larrea tridentata. Aboveground sugar and starch concentrations were measured weekly or monthly for ~1.5 years on 6-12 shrubs simultaneously instrumented with automated stem psychrometers; leaf photosynthesis (Anet ) was measured monthly for 1 year. Leaf sugar increased during the dry, premonsoon period, associated with lower Ψ (greater water stress) and high T. Leaf sugar accumulation coincided with declines in leaf starch and stem sugar, suggesting the prioritization of leaf sugar during low photosynthetic uptake. Leaf starch was strongly correlated with Anet and peaked during the spring and monsoon seasons, while stem starch remained relatively constant except for depletion during the monsoon. Recent photosynthate appeared sufficient to support spring growth, while monsoon growth required the remobilization of stem starch reserves. The coordinated responses of different NSC fractions to water status, photosynthesis, and growth demands suggest that NSCs serve multiple functions under extreme environmental conditions, including severe drought.

Ensuring identifiability in hierarchical mixed effects Bayesian models.

Ecologists are increasingly familiar with Bayesian statistical modeling and its associated Markov chain Monte Carlo (MCMC) methodology to infer about or to discover interesting effects in data. The complexity of ecological data often suggests implementation of (statistical) models with a commensurately rich structure of effects, including crossed or nested (i.e., hierarchical or multi-level) structures of fixed and/or random effects. Yet, our experience suggests that most ecologists are not familiar with subtle but important problems that often arise with such models and with their implementation in popular software. Of foremost consideration for us is the notion of effect identifiability, which generally concerns how well data, models, or implementation approaches inform about, i.e., identify, quantities of interest. In this paper, we focus on implementation pitfalls that potentially misinform subsequent inference, despite otherwise informative data and models. We illustrate the aforementioned issues using random effects regressions on synthetic data. We show how to diagnose identifiability issues and how to remediate these issues with model reparameterization and computational and/or coding practices in popular software, with a focus on JAGS, OpenBUGS, and Stan. We also show how these solutions can be extended to more complex models involving multiple groups of nested, crossed, additive, or multiplicative effects, for models involving random and/or fixed effects. Finally, we provide example code (JAGS/OpenBUGS and Stan) that practitioners can modify and use for their own applications.

Ecological memory of daily carbon exchange across the globe and its importance in drylands.

How do antecedent (past) conditions influence land-carbon dynamics after those conditions no longer persist? In particular, quantifying such memory effects associated with the influence of past environmental (exogenous) and biological (endogenous) conditions is crucial for understanding and predicting the carbon cycle. Here we show, using data from 42 eddy covariance sites across six major biomes, that ecological memory-decomposed into environmental and biological memory components-of daily net carbon exchange (NEE) is critical for understanding the land-carbon metabolism, especially in drylands for which memory explains ~ 32% of the variation in NEE. The strong environmental memory in drylands was primarily driven by short- and long-term moisture status. Moreover, the strength of environmental memory scales with increasing water stress. This universal scaling relationship, emerging within and among major biomes, suggests a potential adaptive response to water limitation. Our findings underscore the necessity of considering ecological memory in experiments, observations and modelling.

Antecedent soil water content and vapor pressure deficit interactively control water potential in Larrea tridentata.

Plant water potential Ψ is regulated by stomatal responses to atmospheric moisture demand D and soil water availability W, but the timescales of influence and interactions between these drivers of plant Ψ are poorly understood. Here, we quantify the effects of antecedent D and W on plant Ψ in the desert shrub Larrea tridentata. Repeated measurements of plant baseline water potential ΨB and diurnal water potential ΨD were analyzed in a Bayesian framework to evaluate the influence of antecedent D and W at daily and subdaily timescales. Both ΨB and ΨD exhibited negative, 2- to 4-d lagged responses to daily-scale D; conversely, plant ΨD responded almost instantaneously to subdaily D, though the direction of this response depended on antecedent moisture conditions. Plant ΨB and ΨD responded positively and immediately (no lag) to shallow W, which contrasts the negative, lagged (6-7 d) response to deep W. The changing sensitivity of ΨD to subdaily D highlights shifting modes of plant Ψ regulation: D effects on ΨD range from negative to neutral to positive depending on past conditions and time of day. Explicit consideration of antecedent conditions across multiple timescales can reveal important complexities in plant responses.

Legacies of La Niña: North American monsoon can rescue trees from winter drought.

While we often assume tree growth-climate relationships are time-invariant, impacts of climate phenomena such as the El Niño Southern Oscillation (ENSO) and the North American Monsoon (NAM) may challenge this assumption. To test this assumption, we grouped ring widths (1900-present) in three southwestern US conifers into La Niña periods (LNP) and other years (OY). The 4 years following each La Niña year are included in LNP, and despite 1-2 year growth declines, compensatory adjustments in tree growth responses result in essentially equal mean growth in LNP and OY, as average growth exceeds OY means 2-4 years after La Niña events. We found this arises because growth responses in the two periods are not interchangeable: Due to differences in growth-climate sensitivities and climatic memory, parameters representing LNP growth fail to predict OY growth and vice versa (decreases in R2 up to 0.63; lowest R2  = 0.06). Temporal relationships between growth and antecedent climate (memory) show warmer springs and longer growing seasons negatively impact growth following dry La Niña winters, but that NAM moisture can rescue trees after these events. Increased importance of monsoonal precipitation during LNP is key, as the largest La Niña-related precipitation deficits and monsoonal precipitation contributions both occur in the southern part of the region. Decreases in first order autocorrelation during LNP were largest in the heart of the monsoon region, reflecting both the greatest initial growth declines and the largest recovery. Understanding the unique climatic controls on growth in Southwest conifers requires consideration of both the influences and interactions of drought, ENSO, and NAM, each of which is likely to change with continued warming. While plasticity of growth sensitivity and memory has allowed relatively quick recovery in the tree-ring record, recent widespread mortality events suggest conditions may soon exceed the capacity for adjustment in current populations.

Legacies of more frequent drought in ponderosa pine across the western United States.

Despite widespread interest in drought legacies-multiyear impacts of drought on tree growth-the key implication of reported drought legacies remains unaddressed: as impaired growth and slow recovery associated with drought legacies are pervasive across forest ecosystems, what is the impact of more frequent drought conditions? We investigated the assumption that either multiple drought years occurring during a short period (multiyear droughts), or droughts occurring during the recovery period from previous drought (compounded droughts), are detrimental to subsequent growth. There is evidence that drought responses may vary among populations of widespread species, leading us to examine regional differences in responses of the conifer Pinus ponderosa to historic drought frequency in the western United States. More frequent drought conditions incurred additional growth declines and shifts in growth-climate sensitivities in the years following drought relative to single-drought events, with 'triple-droughts' being worse than 'double-droughts'. Notably, prediction skill was not strongly reduced when ignoring compounded droughts, a consequence of the temporally comprehensive formulation of our stochastic antecedent model that accounts for the climatic memory of tree growth. We argue that incorporating drought-induced temporal variability in tree growth sensitivities can aid inference gained from statistical models, where more simplistic models could overestimate the severity of drought legacies. We also found regional differences in response to repeated drought, and suggest plastic post-drought sensitivities and climatic memory may represent beneficial physiological adjustments in interior regions. Within-species variability may thus mediate forest responses to increasing drought frequency under future climate change, but experimental approaches using more species are necessary to improve our understanding of the mechanisms that underlie drought legacy effects on tree growth.

Gross primary production responses to warming, elevated CO2 , and irrigation: quantifying the drivers of ecosystem physiology in a semiarid grassland.

Determining whether the terrestrial biosphere will be a source or sink of carbon (C) under a future climate of elevated CO2 (eCO2 ) and warming requires accurate quantification of gross primary production (GPP), the largest flux of C in the global C cycle. We evaluated 6 years (2007-2012) of flux-derived GPP data from the Prairie Heating and CO2 Enrichment (PHACE) experiment, situated in a grassland in Wyoming, USA. The GPP data were used to calibrate a light response model whose basic formulation has been successfully used in a variety of ecosystems. The model was extended by modeling maximum photosynthetic rate (Amax ) and light-use efficiency (Q) as functions of soil water, air temperature, vapor pressure deficit, vegetation greenness, and nitrogen at current and antecedent (past) timescales. The model fits the observed GPP well (R2  = 0.79), which was confirmed by other model performance checks that compared different variants of the model (e.g. with and without antecedent effects). Stimulation of cumulative 6-year GPP by warming (29%, P = 0.02) and eCO2 (26%, P = 0.07) was primarily driven by enhanced C uptake during spring (129%, P = 0.001) and fall (124%, P = 0.001), respectively, which was consistent across years. Antecedent air temperature (Tairant ) and vapor pressure deficit (VPDant ) effects on Amax (over the past 3-4 days and 1-3 days, respectively) were the most significant predictors of temporal variability in GPP among most treatments. The importance of VPDant suggests that atmospheric drought is important for predicting GPP under current and future climate; we highlight the need for experimental studies to identify the mechanisms underlying such antecedent effects. Finally, posterior estimates of cumulative GPP under control and eCO2 treatments were tested as a benchmark against 12 terrestrial biosphere models (TBMs). The narrow uncertainties of these data-driven GPP estimates suggest that they could be useful semi-independent data streams for validating TBMs.

The trajectory of bone surface modification studies in paleoanthropology and a new Bayesian solution to the identification controversy.

A critical issue in human evolution is how to determine when hominins began incorporating significant amounts of meat into their diets. This fueled evolution of a larger brain and other adaptations widely considered unique to modern humans. Determination of the spatiotemporal context of this shift rests on accurate identification of fossil bone surface modifications (BSM), such as stone tool butchery marks. Multidecade-long debates over the agents responsible for individual BSM are indicative of systemic flaws in current approaches to identification. Here we review the current state of BSM studies and introduce a novel probabilistic approach to identifying agents of BSM. We use control assemblages of bones modified by modern agents to train a multivariate Bayesian probability model. The model then identifies BSM agents with associated uncertainties, serving as the basis for a predictive probabilistic algorithm. The multivariate Bayesian approach offers a novel, probabilistic, and analytical method for BSM research that overcomes much of the bias that has typified previous, more qualitative approaches.

Quantifying ecological memory in plant and ecosystem processes.

The role of time in ecology has a long history of investigation, but ecologists have largely restricted their attention to the influence of concurrent abiotic conditions on rates and magnitudes of important ecological processes. Recently, however, ecologists have improved their understanding of ecological processes by explicitly considering the effects of antecedent conditions. To broadly help in studying the role of time, we evaluate the length, temporal pattern, and strength of memory with respect to the influence of antecedent conditions on current ecological dynamics. We developed the stochastic antecedent modelling (SAM) framework as a flexible analytic approach for evaluating exogenous and endogenous process components of memory in a system of interest. We designed SAM to be useful in revealing novel insights promoting further study, illustrated in four examples with different degrees of complexity and varying time scales: stomatal conductance, soil respiration, ecosystem productivity, and tree growth. Models with antecedent effects explained an additional 18-28% of response variation compared to models without antecedent effects. Moreover, SAM also enabled identification of potential mechanisms that underlie components of memory, thus revealing temporal properties that are not apparent from traditional treatments of ecological time-series data and facilitating new hypothesis generation and additional research.

Antecedent moisture and temperature conditions modulate the response of ecosystem respiration to elevated CO2 and warming.

Terrestrial plant and soil respiration, or ecosystem respiration (Reco ), represents a major CO2 flux in the global carbon cycle. However, there is disagreement in how Reco will respond to future global changes, such as elevated atmosphere CO2 and warming. To address this, we synthesized six years (2007-2012) of Reco data from the Prairie Heating And CO2 Enrichment (PHACE) experiment. We applied a semi-mechanistic temperature-response model to simultaneously evaluate the response of Reco to three treatment factors (elevated CO2 , warming, and soil water manipulation) and their interactions with antecedent soil conditions [e.g., past soil water content (SWC) and temperature (SoilT)] and aboveground factors (e.g., vapor pressure deficit, photosynthetically active radiation, vegetation greenness). The model fits the observed Reco well (R2  = 0.77). We applied the model to estimate annual (March-October) Reco , which was stimulated under elevated CO2 in most years, likely due to the indirect effect of elevated CO2 on SWC. When aggregated from 2007 to 2012, total six-year Reco was stimulated by elevated CO2 singly (24%) or in combination with warming (28%). Warming had little effect on annual Reco under ambient CO2 , but stimulated it under elevated CO2 (32% across all years) when precipitation was high (e.g., 44% in 2009, a 'wet' year). Treatment-level differences in Reco can be partly attributed to the effects of antecedent SoilT and vegetation greenness on the apparent temperature sensitivity of Reco and to the effects of antecedent and current SWC and vegetation activity (greenness modulated by VPD) on Reco base rates. Thus, this study indicates that the incorporation of both antecedent environmental conditions and aboveground vegetation activity are critical to predicting Reco at multiple timescales (subdaily to annual) and under a future climate of elevated CO2 and warming.

Seasonal stomatal behavior of a common desert shrub and the influence of plant neighbors.

Stomata simultaneously regulate plant carbon gain and water loss, and patterns of stomatal conductance (g(s)) provide insight into water use strategies. In arid systems, g(s) varies seasonally based on factors such as water availability and temperature. Moreover, the presence and species identity of neighboring plants likely affects g(s) of the focal plant by altering available soil water and microclimate conditions. We investigated stomatal behavior in Larrea tridentata, a drought-tolerant, evergreen shrub occurring throughout the arid southwestern United States. We measured g(s) in Larrea over multiple seasons in the presence of neighbors representing different woody species. The data were analyzed in the context of a commonly used phenomenological model that relates g(s) to vapor pressure deficit (D) to understand spatial and temporal differences in stomatal behavior. We found that g(s) in Larrea was affected by neighborhood association, and these effects varied seasonally. The greatest effect of neighborhood association on g(s) occurred during the winter period, where Larrea growing alone (without neighbors) had higher g(s) compared to Larrea growing with neighbors. Larrea's stomatal sensitivity to D and reference conductance (i.e., g(s) at D = 1 kPa) also differed significantly among different neighbor associations. Random effects indicated reference g(s) varied over short time scales (daily), while stomatal sensitivity showed little daily or seasonal variation, but was notably affected by neighbor associations such that neighboring species, especially trees, reduced Larrea's sensitivity to D. Overall, seasonal dynamics and neighborhood conditions appear critical to understanding temporal and spatial variation in Larrea's physiological behavior.