Temporal and seasonal variation in embolism before hydraulic failure detected by long-term acoustic emissions and meteorological monitoring

Temporal and seasonal variation in embolism before hydraulic failure detected by long-term acoustic emissions and meteorological monitoring | Authorea try { document.documentElement.classList.add('js'); } catch (e) { } var _gaq = _gaq || []; _gaq.push(['_setAccount', 'G-8VDV14Y67G']); _gaq.push(['_trackPageview']); (function() { var ga = document.createElement('script'); ga.type = 'text/javascript'; ga.async = true; ga.src = ('https:' == document.location.protocol ? 'https://ssl' : 'http://www') + '.google-analytics.com/ga.js'; var s = document.getElementsByTagName('script')[0]; s.parentNode.insertBefore(ga, s); })(); Skip to main content Preprints Collections Wiley Open Research IET Open Research Ecological Society of Japan All Collections About About Authorea FAQs Contact Us Quick Search anywhere Search for preprint articles, keywords, etc. Search Search ADVANCED SEARCH SCROLL This is a preprint and has not been peer reviewed. Data may be preliminary. 11 September 2025 V1 Latest version Share on Temporal and seasonal variation in embolism before hydraulic failure detected by long-term acoustic emissions and meteorological monitoring Authors : Taketo Kogire , Wakana Azuma 0000-0002-4254-719X [email protected] , Hiroaki Ishii 0000-0002-7409-6573 , and keiko Kuroda Authors Info & Affiliations https://doi.org/10.22541/au.175760842.29583937/v1 177 views 135 downloads Contents Abstract Supplementary Material Information & Authors Metrics & Citations View Options References Figures Tables Media Share Abstract In living tree trunks, cavitation occurs in the water-conducting tissue due to daily water stress, leading to embolism. However, small-scale cycles of dehydration and refilling in conductive tissues may occur regularly on a daily basis, long before hydraulic failure sets in. To non-destructively assess embolism patterns and inducing factors in mature trees, we used the acoustic emission (AE) method, which detects acoustic signals (AE events) induced by embolism formation. Over a six-month period in field-grown Fraxinus griffithii , daily occurrence of AE was mainly induced by low temperature, high wind velocity, and high VPD. During July and August when transpiration rates were high, diurnal pattern of AE events closely correlated with that of sap flow velocity, suggesting that increases in sap flow velocity exacerbated embolism formation by increasing xylem tension. However, diurnal patterns of AE events did not synchronize with that of sap flow after September. After mid-October, when the daily average temperature dropped below 10°C, daily cumulative AE events increased markedly, influenced by high wind velocity. Our results suggest that small-scale embolisms might be mitigated by physiological functions during the growing season, whereas vulnerability to embolism increases with phenology corresponding to changing and environmental conditions toward winter. Temporal and seasonal variation in embolism before hydraulic failure detected by long-term acoustic emissions and meteorological monitoring Taketo Kogire 1 , Wakana A. Azuma 1* , Hiroaki Ishii 1 , Keiko Kuroda 1 1 Graduate School of Agricultural Science, Kobe University, Kobe, Hyogo, Japan *Corresponding author E-mail: [email protected] Abstract In living tree trunks, cavitation occurs in the water-conducting tissue due to daily water stress, leading to embolism. However, small-scale cycles of dehydration and refilling in conductive tissues may occur regularly on a daily basis, long before hydraulic failure sets in. To non-destructively assess embolism patterns and inducing factors in mature trees, we used the acoustic emission (AE) method, which detects acoustic signals (AE events) induced by embolism formation. Over a six-month period in field-grown Fraxinus griffithii , daily occurrence of AE was mainly induced by low temperature, high wind velocity, and high VPD. During July and August when transpiration rates were high, diurnal pattern of AE events closely correlated with that of sap flow velocity, suggesting that increases in sap flow velocity exacerbated embolism formation by increasing xylem tension. However, diurnal patterns of AE events did not synchronize with that of sap flow after September. After mid-October, when the daily average temperature dropped below 10°C, daily cumulative AE events increased markedly, influenced by high wind velocity. Our results suggest that small-scale embolisms might be mitigated by physiological functions during the growing season, whereas vulnerability to embolism increases with phenology corresponding to changing and environmental conditions toward winter. Keywords : cavitation; Fraxinus; sap flow; phenology; AE; drought stress Introduction The widely accepted theory of water transport in trees, known as the Cohesion-Tension Theory, proposes that water molecules, which form a continuous column held together by hydrogen bonds, are pulled upward by the tension generated during evapo-transpiration (Dixon and Joly 1894). When high tension in the sap causes air bubbles within a conductive cell to expand, the cohesion between water molecules is disrupted, making water transport difficult. Thus, the water column in the conductive tissues does not always remain continuous and the formation of air bubbles and the bubbles themselves within conductive cells is called cavitation, and the resulting loss of water transport due to cavitations is referred to as embolism (Tyree and Zimmermann 2002). Cavitation can occur through several different mechanisms (Tyree and Sperry 1989 b), including those associated with the freeze–thaw cycles of xylem sap in winter (Hacke and Sperry 2001) and with volatile organic compounds produced under biotic stress, such as pest or pathogen attacks (Kuroda 2012). In addition, drought stress is a common cause of cavitation, as it is routinely experienced even by healthy trees (Cochard 2006). Under drought conditions, high tension in the sap results in negative pressure, under which the cohesion between water molecules may break or air bubbles may be drawn into conductive cells from adjacent tissue, leading to embolism (Tyree and Zimmermann 2002). Drought-induced embolism is known to occur frequently under daily conditions (Kuroda 2012; Wagner et al. 2022). Experimental manipulations have shown that embolism can lead to decreases in stomatal conductance and leaf water potential (Sperry and Pockman 1993; Hubbard et al. 2001). Since embolism occurrences in leaves causes damage to photosynthetic tissue and cell death (Brodribb et al. 2021), it can ultimately result in reduced growth or mortality of the tree (Adams et al. 2017; Choat et al. 2018). To assess the risk of drought-induced embolism, many studies have employed continuous drought treatments until specific conditions are reached, evaluating the water potential at which hydraulic conductivity is reduced to a certain level (Choat et al. 2012; Trifilò et al. 2015). These values are strongly correlated with the minimum recoverable water potential in tree physiological parameters, and trees that reach an irreversible threshold of hydraulic conductivity are unlikely to survive (Urli et al. 2013). However, in trees growing under field conditions, even the minimum water potential observed on clear summer days does not reach such thresholds (Ogasa et al. 2013), indicating that they maintain a sufficient safety margin before the onset of hydraulic failure. One such safety mechanism is recovery from embolism by refilling of conductive tissues, which is generally considered to occur when transpiration-induced tension is released during the night or when water supply pools are replenished by rainfall (Tyree and Sperry 1988). In living trees, small-scale cycles of dehydration and refilling are considered to occur regularly preventing irreversible hydraulic failure. The daily occurrence of embolism and refilling, however, remains a subject of debate (Delzon and Cochard 2014). Whether conductive tissues actually undergo daily cycles of dehydration within living trees, has been investigated through various methods: by measuring stem hydraulic conductivity in the morning and at noon to assess conductivity decline (Trifilò et al. 2015, 2017); by measuring petiole conductivity at different time points throughout the day (Bucci et al. 2003); and by freezing stem sections at different times using liquid nitrogen followed by quantification of embolized (empty) conduits using Cryo-scanning electronic microscopy(Cryo-SEM) (Melcher et al. 2001). These studies have confirmed that the conductive xylem cells dehydrate on a daily basis. However, recent studies have highlighted methodological challenges in evaluating hydraulic conductivity, including reports that the fixation of water transport status under tension using liquid nitrogen may introduce artificial embolism (Cochard et al. 2000; Ogasa et al. 2016, Umebayashi et al. 2016), and that cutting samples in water without accounting for vessel length can also lead to artifacts (Wheeler et al. 2013, Trifilò et al. 2014). In order to avoid these artifacts, it is necessary not to rely on such destructive methods. Milburn and Johnson (1966) discovered acoustic signals emitted from Ricinus communis leaves during dehydration. Subsequently, cavitation detection using high-frequency acoustic signals was proposed (Tyree & Dixion 1983), leading to the establishment of the acoustic emission (AE) method that enables non-destructive and indirect evaluation of embolism formation within tree conductive tissues (Tyree & Sperry 1989 a). That is, AE refers to the acoustic signals induced by embolism formation itself, and individual acoustic signals detected and amplified by a non-destructive system are defined as AE events. A limitation of the AE method for evaluating hydraulic conductivity under field conditions is that previous methods correlating AE to conductivity loss were based on destructive measurement, and a single AE event does not necessarily correspond to embolism of a single conductive cell (Tognetti et al. 1996). However, in recent years, correlations between the number of AE events and embolisms have been reported using optical observations with magnetic resonance imaging (MRI) and micro-computer tomography (micro-CT) (Fukuda et al. 2007, Vergeynst et al. 2015). Additionally, there are examples of frequency clustering analysis being performed to selectively detect AE caused by embolism (Vergeynst et al. 2016), as well as methods for more accurately estimating the conductivity loss from AE (Nolf et al. 2015). Therefore, the AE method gradually being established as a valid technique for measuring embolism (Shimamoto and Suzuki 2021; Nardini et al. 2024) and establishing appropriate target frequency and consistently measuring the same individual with the same sensitivity settings, allows for the non-destructive, continuous evaluation of embolism occurrence in living trees. As a potential trigger for embolism formation, sap flow velocity, which is known to be correlated with diurnal patterns of transpiration (Horino et al. 1996), is an important factor (Tyree and Zimmermann 2002). High sap flow velocity can lead to continuous embolism formation, or runaway embolism (Tyree 1989). Previous studies on the occurrence of AE in living trees have reported that in Biota orientalis (L.) Endl., AE events in the trunk and branches increases in proportion to sap flow velocity and transpiration rate when the soil moisture is at a level that allows for the recovery of hydraulic function (Horino at al. 1996). Additionally, environmental factors such as temperature, atmospheric vapor pressure deficit (VPD), and photon flux density have been shown to correspond with the diurnal pattern in AE events in Pinus thunbergii Parl (Ikeda and Ohtsu 1992). Elucidating how embolism occurs within living trees not only deepens the biological understanding of tree survival strategies but also has important implications for tree management and silviculture/forestry. Long-term monitoring of AE events under field conditions has been a challenge, but it is essential to evaluate embolism occurrences associated with seasonal environmental changes. Here, we quantitatively measured embolism using the AE method under field conditions over a six-month period, achieving a completely non-destructive, long-term measurement. To infer environmental factors associated with cycle of embolism occurrence before reaching the irreversible point of hydraulic failure, diurnal and long-term patterns of AE events were analyzed in relation to meteorological data. Then a generalized linear model (GLM) was constructed using stepwise regression to correlate the effect of environmental factors to the number of AE events per day. In addition, the correlation between the diurnal pattern of AE events and sap flow was investigated. Based on these approaches, we inferred that both seasonal and diurnal patterns of embolism as well as factors contribute to embolism formation and changes of patterns in mature trees under natural conditions. Materials and Methods Non-destructive monitoring of xylem embolism and sap flow was conducted from April 30 to November 28, 2021, on a single Fraxinus griffithii C.B. Clarke (Japanese ash). The study tree was planted on the rooftop deck of the Science and Technology Building 2 at Kobe University, Hyogo, Japan (N 34°43’N, E 135°15’, 130.1 m ASL). The planting bed was 2.2 m in diameter with a depth of 0.8 m (Figure 1a). The tree was 2.1 m tall and stem diameter at 30 cm above the ground where the sensors were installed was 2.0 cm. No management was applied to the tree, and irrigation was solely provided by rainfall only. During the study period, there were no days with subzero temperatures, i.e., freeze-thaw embolism did not occur. Japanese ash tree is an evergreen broadleaf tree native to warm-temperate to subtropical regions of east Asia. It is known for its drought adaptation, such as increasing new leaf production in response to wind stress in the following year (Namba et al. 2019). In recent years, urban planners and landscapers have increasingly favoured Japanese ash as a landscaping tree for urban environments (Lin and Ysai 2017, Killmann et al. 2022). The mean annual temperature and precipitation at the Kobe Meteorological Station (N 34°42’N, E 135°13’, 4.0 m ASL) for 2021 were 17.5°C and 1637.0 mm, respectively (Japan Meteorological Agency, JMA). Hyogo Prefecture is classified as having a humid subtropical climate under the Köppen climate classification, and Kobe City is characterized by a predominantly warm and sunny climate, with minimal snowfall during the winter season. Xylem embolism monitoring We used an AE sensor installed on the study tree to continuously monitor the occurrence of xylem embolism (Kuroda 2012). The target frequency of the sensor (AE901S, NF Corporation, Japan) is the resonance frequency of 140 kHz, determined by contact-based calibration. This falls within the 100–200 kHz range, which has been reported to be effective for capturing AE signals associated with embolism (Vergeynst et al. 2016). Shimamoto and Suzuki (2015) applied the Rayleigh–Plesset equation, which describes the relationship between frequency characteristics and bubble radius, to theoretically estimate the detectable frequency range. When the equation was applied to the vessel diameter of Fraxinus griffithii (50 – 100 μm) documented in the Forestry and Forest Products Research Institute’s wood database, the estimated frequency range was 63.8–127.1 kHz. The manufacturer of the sensor used in this study reported that its resonance frequency is slightly higher than the actual value due to the calibration method. Therefore, in this study, the estimated frequency range was considered to have been appropriately measured for evaluating embolism. The surface of tree trunk where the AE sensor was attached was smoothed by chiselling off approximately a square area of the bark (1.5 × 1.5 cm) from the outer bark to the inner bark (excluding the cambium) to ensure a tight fit. To prevent moisture loss from the surrounding area of the exposed inner bark, grease was applied before attaching the sensor. The sensor was then secured by wrapping both the sensor and the stem with electrical tape (Figure 1b). The signals output from the AE sensor were amplified by the AE tester (AE9501, NF Corporation, Japan) and the cumulative count of AE events per 10 minutes (events 10 min⁻¹) was recorded by the data logger (HOBO 4 Channel Pulse Input Data Logger, Onset Computer Corporation, USA). For sensitivity adjustment, the AE detection sensitivity was set to 0.5 mV. The AE tester was powered by rechargeable AA nickel-metal hydride batteries, which were replaced as needed to ensure that the battery level did not fall below the operational threshold. The data were retrieved using the dedicated application (HOBO ware, Onset Computer Corporation, USA) from the logger. After completing the sensitivity adjustment of the AE tester during the first two months of measurement, data from June 10 to November 28 were used for analysis. Some data were excluded from the analysis for the following reasons during the observation period. First, data from all days with recorded rainfall (> 0.5 mm 10min-1) were excluded. Second, data from October 18, 2021, were removed due to missing meteorological data from the Japan Meteorological Agency (JMA). Third, any obvious acoustic noise caused by human contact with the tree during adjustments of the experimental setup was excluded. Finally, data collected during periods when the battery level could not be confirmed to meet or exceed the predetermined threshold were also excluded. In accordance with the above criteria, days with more than 70 minutes of missing data (i.e., analysis. During the observation period, the highest wind velocity of 13.7 m s⁻¹ was recorded on August 9, at which time the AE count remained below 5 events 10 min -1 . Therefore, the impact of natural noise from wind - induced factors such as trunk bending was considered negligible. Sap flow measurement Common methods for measuring sap flow velocity (e.g., thermal dissipation, stem heat balance, and heat-pulse methods) often use heat as a tracer to achieve high precision, and in some cases, probes need to be inserted into the stem (Smith and Allen 1996). However, it has been estimated that the heating of sap reduces the surface tension of mixed lipid monolayers, suppressing the formation of nanobubbles and making bubble expansion more likely, raising concerns about the potential artifact of cavitation occurrence (Ingram et al. 2024). Moreover, the range of artifact effects caused by the insertion of probes is also difficult to predict. As a non-destructive measurement method, heat flux sensor is a highly sensitive device typically used to measure the distribution of thermal transmittance in buildings and other structures (Mizutani et al. 2021, Kočí et al. 2024). The heat flux sensor detects the heat flux moving across the planar sensor surface as a temperature difference, capable of detecting temperature differences as small as 0.001°C between both sensor sides (Eto Denki Co., Ltd., 2011). Since groundwater temperature is lower than air temperature throughout the day and night in growing season, when sap flow velocity is high, heat moves from the air to the trunk. On the other hand, less heat movement occurs when sap flow velocity is low. Therefore, by attaching the heat flux sensor to a smooth part of the trunk, the changes in sap flow velocity can be estimated from the rate of heat exchange between the air and trunk (Kogire et al. 2023). For non-destructive monitoring of changes in sap flow velocity of the study tree, we used a heat flux sensor (General-Purpose Heat Flux Sensor Series S11A, Eto Denki, Japan; sensor dimensions: 10 × 10 × 0.6 mm). This heat flux sensor measures the heat transfer per unit area on its surface, expressed as heat flux density (W m⁻², HFD). The sensor was installed on a smooth area of the trunk, secured with a single layer of masking tape (thickness 0.04 mm; Figure 1c). The data were recorded as instantaneous heat flux values every 10 minutes using a voltage logger (LR5041, Hioki E.E. Corporation, Japan). Data retrieval was performed using dedicated software (LR5000 Utility, Hioki E.E. Corporation, Japan). The obtained voltage values were divided by the sensor sensitivity constant (mV W⁻¹ m²) to convert them into HFD (W m⁻²). Environmental factors Air temperature (°C), wind velocity (m s -1 ), humidity (%), atmospheric pressure (hPa), and precipitation (mm) for Kobe City were obtained from the JMA database via the website. Solar radiation (kWh m⁻²) was obtained from a solar panel installed on the rooftop of the study site. Atmospheric vapor pressure deficit (VPD, kPa) was calculated from atmospheric pressure, air temperature, and humidity by computing the Magnus saturation vapor pressure (Jones 2013). Antecedent precipitation index (API) was calculated using the equation shown below from precipitation data. \begin{equation} \text{AP}I_{n}\ =\ \sum_{i\ =\ 1}^{n}{\ \frac{P_{i}}{i}}\nonumber \\ \end{equation} Here, i represents any day after rainfall, P is the precipitation on the corresponding day, and n denotes the number of days to look back (Mosley 1982). API is used as an indicator to assess soil moisture (Kosugi et al. 2007). Ideally, soil moisture content should have been directly measured concurrently with the physiological measurements; however, due to equipment issues, this was not possible. After the experiment, the appropriate n value for the API was determined by comparing it with volumetric soil moisture content measured over a one-month period (from October 26, 2024, to November 27, 2024) using a soil moisture sensor (EC-5, METER, USA). The optimal API was selected based on the API with the lowest the Akaike Information Criterion (AIC) in a generalized linear model (GLM, family = Gaussian), where volumetric soil moisture content was the response variable and API values for n = 1 to 365 were explanatory variables. The API 23 was determined as the alternative indicator of soil moisture content in this study (Figure S1). Statistical analysis A stepwise method was applied to the generalized linear model (GLM) with the number of AE events as the response variable and environmental factors (solar radiation, temperature, wind velocity, VPD, and API 23 ) as explanatory variables to infer the effect of environmental factors on embolism occurrence. The AE data in this study were obtained as high-resolution data, recorded as cumulative values every 10 minutes. However, some of the environmental data obtained from the Meteorological Agency were collected from a site located 5.4 km away from the measurement location. Therefore, AE events and environmental data may not strictly correspond at a10-minute resolution. For this reason, in the GLM analysis, the AE events were aggregated as daily cumulative values, while the environmental data were processed by calculating daily average values for temperature, wind velocity, VPD, and API 23 , and a daily total for solar radiation. Starting with an intercept-only model, explanatory variables were sequentially added (using a variable addition method) to construct a GLM that minimized AIC. The response variable, AE events, was log-transformed, and all explanatory variables were standardized (mean = 0, SD = 1). The model assumed a normal distribution and used an identity link function. The analysis was conducted for both the entire study period and on a monthly basis. Except for June and September, all models were not judged to deviate from normality at a 5% significance level based on the Shapiro-Wilk test (July, W = 0.87729, p-value = 0.02875; September, W = 0.82907, p-value = 0.01169). Multicollinearity (variance inflation factor) was confirmed to be below 3 in all models. To examine whether the diurnal patterns of AE events and HFD correspond to each other, a cross-correlation analysis (“ccf” function in the stats package in R ver 4.4.2.) was performed. The time series of AE events and HFD were shifted by 10-minute intervals, and the autocorrelation coefficient (ACF) at each shift was calculated. The absolute time shift at which the maximum ACF value occurred was defined as “Lag”. The differences in Lag by month were analysed using the Kruskal-Wallis test. These analyses were conducted using the statistical software R version 4.4.2 (R Core Team 2024). Diurnal patterns of AE events and environmental factors During the growing season (June - August), AE events were generally detected in a diurnal cyclic pattern (Figure 2). AE events began around sunrise (ca. 5 a.m.), reached peak occurrence around 1 p.m., decreased thereafter and ceased in the evening. Temperature and VPD, showed a diurnal cycle similar to that of AE events. AE events did not correspond to wind velocity, which did not show a consistent daily pattern. The API decreased at a constant rate after each precipitation event. On days with low maximum temperatures and accumulated solar radiation (e.g., June 26 in Figure 2), AE events were relatively infrequent compared to other sunny days (e.g., June 24, 25, and 27). At night, AE events either did not occur or were very infrequent compared to the daytime. Long - term variations of AE events and environmental factors The cumulative number of AE events per day detected after mid-October was markedly higher than observed during the previous period (Figure 3). The average temperature decreased by about 10°C after October 14, corresponding to the timing of the increase in the number of AE events. The cumulative number of AE events, however, was not high on all the days, and there were days with low number of AE events. Across the entire measurement period, temperature, wind velocity, and VPD were selected as environmental factors affecting the cumulative number of AE events per day (Table 1). Wind velocity and VPD had significant positive effects, while temperature had a significant negative effect. In the monthly analysis, one of five environmental factors (solar radiation, temperature, wind velocity, VPD, and API 23 ) was selected for each month (Table 1). The number and types of selected environmental factors, as well as the relationship between effect size (positive or negative), varied across months. Solar radiation had positive effects in September and November. Temperature and VPD had negative and positive effects, respectively in September. Except for July and August, wind velocity had positive effects. API had a positive effect in July. Correlation between the diurnal patterns of AE and HFD, and AE and days after precipitation From June to August, the average diurnal patterns of AE events and HFD were correlated, with increases starting around dawn and decreasing after noon (Figure 4). During the night, both AE events and HFD values were low. In September, in addition to the similar diurnal patterns, an increase in AE events was observed after sunset. The maximum standard deviation from June to September was 26.4 events 10min⁻¹, while in October and November, the values were 68.3 events 10min⁻¹ and 75.8 events 10min⁻¹, indicating larger variance. After the growing season, the diurnal cyclic pattern of AE events was no longer observed. The Lag between the diurnal patterns of AE and HFD was smaller during the growing seasons than after (Figure 5). After June, the variance of Lag gradually increased as the winter approached. AE events were more frequent at 3-5 days and 7-8 days after rainfall, with averages exceeding 20 events [10min⁻¹], followed by fewer AE events around 10 days after rainfall (Figure 6). Thereafter, large AE events were no longer observed. On consecutive sunny days, the maximum number of AE events per day increased initially and then decreased, despite little variation in daily solar radiation and constant HFD (Figure S2). Discussion Daily occurrence of small-scale embolisms Our daily-scale AE measurements (Figure2) suggested that short-term xylem embolism occurrences in trees are influenced by diurnal environmental fluctuations. This further reinforced the previous field measurements of embolism using the AE method in coniferous (Ikeda and Ohtsu 1992; Hölttä et al. 2005; Kuroda 2012) and broadleaf species (Salleo and Lo Gullo 1986; Jackson et al. 1999; Manoharan and Pammenter 2005), which showed diurnal AE patterns with peaks during the daytime. Increases in solar radiation and VPD during the daytime enhance transpiration, resulting in increased sap flow velocity (Zhao et al. 2017, Oogathoo et al. 2020). Recent studies using the optical vulnerability (OV) method—a semi-non-destructive optical technique that has seen rapid development recently —also give support to this diurnal pattern of embolism occurrence (Wagner et al. 2022). In this study, during July and August when transpiration rates were expected be high, the diurnal pattern of AE events closely corresponded with sap flow velocity with minimal time lag (Figure5). This suggested that increases in sap flow velocity enhanced tensions in xylem, thereby promoting the occurrence of embolism. In a parallel with this study, another experiment on a pot-grown olive seedling showed that the diurnal pattern of AE occurrence due to drought treatment temporarily disappeared after rewatering, indicating that embolism recovery resulted from the temporary relaxation of tension on sap (Kogire et al. 2023). The changes of diurnal patterns of AE occurrence suggest that small-scale embolism and refilling of xylem repeatedly occur in trees, preventing hydraulic failure. On the other hand, during the night, the tension within the xylem is relaxed, reducing the risk of embolism occurrence. In our study, this was detected as the relative decrease in the number of AE events (Figure2). Environmental factors associated with seasonal changes in embolism Although previous studies using the AE method observed embolism in trees over a period of several days to one month, this study was the first to successfully monitor seasonal patterns over a six-month period in a field-grown tree (Figure3). During the six-month observation period, the daily cumulative AE events tended to be higher on days with higher wind velocity, higher VPD, and lower temperatures (Table 1). In particular, wind velocity and VPD consistently showed positive effects on the occurrence of AE events in the months when it was selected as an explanatory variable. High VPD increases transpiration in most species until it exceeds a certain threshold, beyond which transpiration declines due to stomatal closure (Grossiord et al. 2020). Additionally, higher wind velocity increases the boundary layer conductance on the leaf surface, promoting transpiration (Daudet et al. 1999; Martin et al. 1999; Kim et al. 2014). In our study, VPD and wind velocity were influenced cumulative daily AE events after September (Table 1). Increases in wind velocity did not lead to increases in AE events before September. For example, on August 9, when the maximum wind velocity of 13.7 m s⁻¹ was observed during the experimental period, no corresponding sudden increase in AE events were detected. The number of AE events began to respond to wind conditions after September (Figure S3). This suggests that, late in the growing season, high VPD and wind velocity are environmental factors that lead to the extreme increase in AE events. After September, high solar radiation also contributed to the increase in the daily cumulative AE events (Table 1). Therefore, embolism in the xylem may progress rapidly on clear, windy days, particularly after the growing season ends. On the other hand, consistent with the results of this study, previous studies that simultaneously measured AE events and wind velocity found no correlation at the diurnal scale (Jackson et al. 1995; 1996, Perks et al. 2004). Wind serves merely to elevate xylem tension rather than directly cause embolism. We inferred that embolism formation does not respond immediately to changes in boundary-layer conductance on leaves; rather, sustained wind-driven increases in transpiration at the daily scale cumulatively elevate xylem tension to thresholds that could trigger embolism. The environmental factors influencing embolism differed by month, with the types and number of explanatory variables selected from September to November increasing compared to those selected from June to August (Table 1). This suggested that a physiological change in the trees around September likely influences the occurrence of embolism. Trees can acclimate to environment changes, through physiological mechanisms such as stomatal regulation (Salleo et al. 2000). In Fraxinus , the stomatal conductance decreased during the day in Fraxinus griffithii (Chen et al. 2016), and the stomatal closure in response to drought stress has been shown to reduce AE events in Fraxinus excelsior (Rosner 2012). However, in this study, diurnal patterns of AE events did not correspond with that of HFD after September (Figure 5). Furthermore, AE events started occurring at night in September and were observed throughout both day and night from November onwards (Figure 4). This could be related to the decrease in hydraulic conductivity during both day and at night under drought conditions, as previously observed in Pinus and Populus trees (Secchi and Zwieniecki 2010; Klein et al. 2016), possibly suggesting a progressive and unrecoverable state of hydraulic dysfunction. Recent studies of the detailed microscopy on Fraxinus excelsior reported that starch accumulation in living xylem cells increases, while transport capacity between cells declines after September, (Słupianek et al. 2024). In addition, Fraxinus mandshurica showed that seasonal variation in the presence of hydrophobic encrustations on vessel–vessel and vessel–parenchyma pits and these pits are covered during winter (Yamagishi et al. 2024). Traditionally, encrustation has been considered to play a role in restricting water movement within vessels and preventing water loss (Wheeler 1981). However, it was found that the vessels after the deposition of hydrophobic substances lose water from their lumina (Yamagishi et al. 2024). These phenological changes in the microstructure of water conduits within the trunk may contribute to seasonal changes in embolism occurrence and be detected as an increase in AE events. In June and August, higher temperatures were associated with higher cumulative AE events, whereas after September, the effect of temperature became negative (Table 1). This suggests that the decrease in temperature may have induced phenological changes in trees and increased embolism after the growing season. Furthermore, the decrease in the cohesion of liquid water at low temperatures is a negative factor for plants, as it increases the risk of embolism formation under the Cohesion-Tension Theory of water transport. Physical experiments using glass capillaries demonstrated that, with decreasing temperature, the limiting negative pressure of water decreases, indicating that water becomes markedly more vulnerable to tension at temperatures below 10°C (Briggs 1950). This temperature threshold corresponds to the mean temperature in mid-October when we observed the increase in the number of AE events in our study tree (Figure 2). Our results suggested that decreasing temperatures and phenological changes alter the pattern of embolism occurrence and increase its frequency in trees after the growing season. In Fraxinus excelsior , a seasonal decline in xylem hydraulic conductivity during winter was observed (Cochard et al. 1997). Similar winter decline of hydraulic conductivity is also observed in other ring-porous, diffuse-porous broadleaf species and conifers (Sperry and Sullivan 1992; Hacke and Sauter 1995; Magnani and Borghetti 1995). Furthermore, studies observing the temporal distribution of water in the xylem conduits of Betula platyphylla and Neolitsea sericea using Cryo-SEM have confirmed that the number of embolized conduits increases toward winter (Utsumi et al. 1998; Umebayashi and Fukuda 2018). Our measurements suggest that seasonal decline in xylem hydraulic conductivity toward the end of the growing season is the result of cumulative occurrence of xylem embolisms. Successive patterns of small-scale daily embolisms Our long-term, continuous measurement of AE events detected two different mechanisms of embolism: repeated embolism and refilling of vessels during the growing season and cumulative increase in embolized vessels after the growing season ends. The vulnerability to embolism is not uniform across all vessels within a tree xylem (Perks et al. 2004). Non-destructive studies examining the relationship between water potential and the onset of embolism formation found that older vessels located near the center were more vulnerable to embolism than current-year vessels (Brodersen et al. 2013, Fukuda et al. 2015). This mechanism could explain why AE events were observed both immediately after rainfall, as well as after a certain period (Figure 6). For example, API, an indicator of soil moisture content, was only selected as a positive effect in July on daily cumulative AE events (Table 1). High soil moisture mitigates water stress in trees, which typically results in a decrease in xylem tension so that the result may seem counterintuitive. However, refilling of vessels after rainfall increases the number of vessels that could subsequently become embolized. When API increases after rainfall, embolisms might occur in vulnerable vessels due to high transpiration rates and increasing xylem tension, resulting in an apparent positive effect of API on AE events. We thus inferred that the decrease in AE events with days since rainfall (Figure 6), was the result of vulnerable vessels having already undergone embolism, leading to a decrease in the number of vessels that could potentially become embolized. Furthermore, diurnal patterns of HFD were maintained regardless of changes in AE events (Figure 4), suggesting that daily occurrences of embolism in vulnerable vessels does not affect sap flow velocity because major vessels are less vulnerable to embolism and hydraulic conductivity is maintained by them. Similarly, in Quercus species, maximum number of AE events per day progressively decreased, while sap flow velocity was maintained (Tognetti et al. 1996). On the other hand, the recovery of hydraulic conductivity is also mediated by nocturnal refilling associated with the relaxation of xylem tension (Tyree and Sperry 1988) and the active refilling of vessels via osmotic regulation by vessel associated cells (Broadersen et al. 2010). These observations suggest that increase/decrease in the occurrence of AE events may not necessarily reflect decrease/increase in hydraulic conductivity, because AE signals may represent repeated embolism of vulnerable vessels, which can be refilled or decreasing number of vessels that can potentially become embolized. Conclusions Through long-term non-destructive measurement using the AE method, we found that daily occurrence of small-scale embolisms before irreversible hydraulic failure, which were mainly induced by high wind velocity and VPD, especially after the growing season when temperatures dropped below 10°C. Daily occurrence of small-scale embolisms might be suppressed by physiological acclimation during the late growing season prior to temperature decline, whereas vulnerability to embolism could increase with phenological changes toward the end of the growing season and with environmental changes. We demonstrated that the AE method can be used to continuously measure the occurrences of embolism in trees. However, since it cannot distinguish newly formed embolisms and repeated embolisms, it is difficult to infer the concurrent loss and recovery of hydraulic conductivity. This means careful interpretation is necessary, because the successive daily occurrence of embolisms does not necessarily reflect a decrease in hydraulic conductivity, especially if vulnerable vessels are readily refilled. In this study, we clarified the influence of environmental factors on embolism in an individual tree; however, further investigation is required by increasing the number of sample trees and including a wider range of species. To more comprehensively assess tree responses, physiological measurements—such as stomatal conductance and leaf water potential—should also be conducted in parallel with AE measurement. Future work should investigate interactions among environmental factors and conduct time‑series analyses under experimentally manipulated irrigation regimes to examine the pattern of AE events and environmental responses in living trees in greater detail. 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The analyses were conducted for the entire study period (ALL) and separately for each month. Values represent model coefficients, with standard errors in parentheses. Significant results are indicated by asterisks ( p < 0.100; * p < 0.050; ** p < 0.010; *** p < 0.001) Figure Captions Fig. 1 (a) Fraxinus griffithii used in the experiment, grown on the rooftop deck of a building at Kobe University. Photographed in April 2022. The white bar indicates 1.3 m. (b) AE sensor and (c) heat flux sensor was fixed tightly to the stem. Shading was then provided to prevent direct sunlight from affecting the sensors. Fig. 2 Diurnal patterns of AE events and environmental factors in Fraxinus griffithii . (a) Daily total solar radiation (dark gray bars) and daily mean antecedent precipitation index (API) (white circles). (b) Cumulative number of AE events per 10 minutes (red line) and instantaneous heat flux density (HFD) (black line). (c) Temperature (solid red line) and relative humidity (black dashed line). (d) Vapor pressure deficit (VPD) (solid red line) and wind velocity (black dashed line). Shaded gray areas indicate nighttime (from sunset to sunrise). Fig. 3 Long-term variations of the number of AE events and environmental factors in Fraxinus griffithii . (a) Annual variation in daily cumulative AE events. (b) Annual variation in daily total solar radiation (gray bars) and daily mean antecedent precipitation index (API) (black line). (c–e) Annual variations in daily mean air temperature (c), daily mean vapor pressure deficit (VPD) (d), and daily mean wind velocity (e). The gray ribbons in (c–e) indicate the daily maximum and minimum values. Fig. 4 Monthly mean diurnal patterns of AE events and heat flux density (HFD) in Fraxinus griffithii . Solid red and black lines show the monthly mean AE events and HFD at each time of day, respectively. The red ribbons indicate ± SE (lower bounds truncated at zero). Shaded gray areas indicate nighttime (from sunset to sunrise). In October and November, the SE ribbons for AE events are completely indiscernible; the maximum SE value in October is 68.27 at 21:30, and in November is 75.80 at 13:00. Fig. 5 The absolute time difference between the diurnal patterns of AE events and HFD in each month (Lag) in Fraxinus griffithii analyzed using cross‑correlation function (CCF). Red crosses (×) denote the mean lag values for each month. Kruskal–Wallis tests were performed, and different letters indicate significant differences at the 5 % level (α = 0.05). Fig. 6 Relationship between cumulative AE events and days after rainfall in Fraxinus griffithii . Cumulative AE events per 10 minutes are plotted as gray data points; the black line represents the average trend. The inset shows the same data on a different scale to enhance visibility of the mean pattern. Supplementary Material File (table_1.xlsx) Download 14.31 KB Information & Authors Information Version history V1 Version 1 11 September 2025 Copyright This work is licensed under a Non Exclusive No Reuse License. Keywords cavitation drought stress phenology sap flow water relations xylem transport Authors Affiliations Taketo Kogire Kobe University View all articles by this author Wakana Azuma 0000-0002-4254-719X [email protected] Kobe University View all articles by this author Hiroaki Ishii 0000-0002-7409-6573 Kobe University View all articles by this author keiko Kuroda Kobe University View all articles by this author Metrics & Citations Metrics Article Usage 177 views 135 downloads .FvxKWukQNSOunydq8rnd { width: 100px; } Citations Download citation Taketo Kogire, Wakana Azuma, Hiroaki Ishii, et al. Temporal and seasonal variation in embolism before hydraulic failure detected by long-term acoustic emissions and meteorological monitoring. Authorea . 11 September 2025. 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