DOI: 10.5061/dryad.47d7wm3cg
title: Data from: Contrasting sap flow characteristics between pioneer and late-successional tree species in secondary tropical montane forests of Eastern Himalaya, India

description: Abstract The interactive role of life-history traits and environmental
 forcing on plant-water relations is crucial for understanding species
 response to climate change but remains poorly understood in secondary
 tropical montane forests (TMFs). Comparing contrasting life-history traits
 (pioneer vs late-successional species) in a biodiverse Eastern Himalayan
 secondary TMF, we investigated sap flow responses in co-occurring pioneer
 species, Symplocos racemosa (n=5) and Eurya acuminata (n=5), and
 late-successional species, Castanopsis hystrix (n=3), using modified
 Granier’s Thermal Dissipation probes. The fast-growing pioneers S.
 racemosa and E. acuminata) had 2.1- and 1.6-times higher sap flux density
 than the late-successional C. hystrix, respectively, and exhibited
 characteristics of long-lived pioneer species. Significant radial and
 azimuthal variability in sap flow (V) between species was observed and
 attributed to life history traits and the canopy’s access to sunlight.
 Nocturnal V (1800-0500 hr) was 13.8 % of daily V and is
 attributed to stem recharge for evening V (1800-2300 hr) and to
 endogenous stomatal controls for pre-dawn V (0000-0500 hr). Both
 the shallow-rooted pioneer species exhibited midday depression in V
 attributed to photosensitivity and diel moisture stress response. In
 contrast, deep-rooted C. hystrix transpired unaffected across the dry
 season likely accessing groundwater. Thus, the secondary broadleaved TMFs,
 with the dominance of shallow-rooted pioneers, are more prone to the
 negative impacts of drier and warmer winters than primary forests, which
 are dominated by deep-rooted species. The study provides an empirical
 understanding of life-history traits and microclimate modulating
 plant-water use in widely distributed secondary TMFs in Eastern Himalaya
 and highlights their vulnerability against warmer winters and reduced
 snowfall due to climate change.
description: Sap flux was measured using custom-built Granier's thermal
 dissipation probes. Microclimate observations were used to understand the
 drivers of stand transpiration. Air temperature (°C), relative humidity
 (R<sub>h</sub>, %), wind speed (U, m
 s<sup>-1</sup>), and incoming short-wave radiation
 (R<sub>s</sub>, kW m<sup>-2</sup>) were recorded
 using an automatic weather station (AWS) (Vantage-pro Davis Net, USA).
 On-site Precipitation (P, mm hr<sup>-1</sup>) was recorded
 using an automated tipping-bucket rain gauge. Additionally, long-term
 trend analysis was done using daily precipitation
 (P<sub>IMD</sub>, mm d<sup>-1</sup>) extracted
 from the 0.25⁰ gridded precipitation product by the Indian Meteorological
 Department (IMD) for the pixel containing the sap flow site (Pai et al.,
 2014). In-canopy air temperature and relative humidity were recorded using
 microsensors (iButton hygrochrons, Maxim Int., USA). Forest reference
 evapotranspiration (E<sub>F</sub>, mm
 hr<sup>-1</sup>) was computed based on FAO’s Penman-Monteith
 method using meteorological data from the installed AWS and the crop
 coefficients developed for natural vegetation (for details see Allen et
 al. 1998). Hourly Vapour Pressure Deficit (D, kPa) was computed from air
 temperature and relative humidity. Soil water potential was recorded at 10
 cm incremental depths from the topsoil to up to 30 cm depth using granular
 matrix-based (watermark) sensors (Virtual Electronics, Roorkee) and
 converted to volumetric water content using the site-specific van
 Genuchten water retention curve. Total soil moisture (S, mm
 hr<sup>-1</sup>) was computed for the topsoil (0-30 cm depth)
 using the trapezoidal method. S was smoothened using a 3-step
 moving-average window to gap-fill stray missing values. A stilling well
 fitted with a capacitance water level recorder (Dataflows Systems LTD, New
 Zealand) was installed on the first-order stream draining the
 micro-watershed. Streamflow (Q, mm hr<sup>-1</sup>) was
 computed from the water-level using a stream-specific rating curve and
 catchment area.
description: The dataset contains some missing values marked as
 "NA".