The research was carried out in the Qingyuan Forest CERN (Chinese Ecosystem Research Network), National Observation and Research Station. The study site is located in a mountainous region of Liaoning Province, Northeast China (41° 51′ 9.94″ N, 124° 56′ 11.22″ E, elevation 550–1116 m). It has a continental monsoon climate with hot-rainy summer and cold-dry winter. The mean annual temperature is 4.7 °C, and the temperature ranges from −37.6 to 36.5 °C. The annual precipitation is 700–850 mm, 80% of which from June to August. The frost-free period is 144 days. The soil type is a typical brown soil (Lu et al. 2018).
The study area was dominated by natural broadleaf-Korean pine (Pinus koraiensis Sieb. et Zucc.) forests until the 1930s. Most original forests have been replaced by secondary forests after decades of destructive exploitation. The primary tree species include Acer mono Maxim., Fraxinus rhynchophylla Hance, Juglans mandshurica Maxim., Phellodendron amurense Rupr., Populus davidiana Dode, Quercus mongolica Fisch. ex Ledeb., and Ulmus laciniata (Trautv.) Mayr (Zhu et al. 2017).
Eight forest gaps were made in December of 2004 by cutting trees using a chainsaw. All the gaps were approximately ellipses with a 1.5 ratio of major axis to minor axis, and their major axis was in the east-west direction. All the trees higher than 2 m in the gaps were removed. These gaps with the sizes of 113.8–621.1 m2 were randomly distributed, and their site conditions were similar (slope ranges from 17 to 26°, elevation ranges from 640 to 690 m, and aspect varies from 140 to 170°) (Table 1). The gaps were at least 20 m apart. Three gap-surrounding trees (i.e., sample trees) from each gap, i.e., a total of 24 sample trees, were selected in 2017 with diameters at breast height (DBH) ranging from 25 to 30 cm. The 24 sample trees included nine Ulmus laciniata (Trautv.) Mayr., eight F. rhynchophylla, and seven J. mandshurica (Table 2). To eliminate the growth releases caused by forces not related to gap creation (e.g., drought, insect attack), we also purposively selected in the surrounding forest 24 trees of the same species and comparable diameter (control trees, CT) away from gaps of 20 m. For all the sample and control trees, a core was taken along the direction from the gap center to the border by a 22-mm increment borer (Sweden, Haglof) for tree-ring width measurements.
Estimation of rough time range for gap formation by tree-ring width measurements
The cores were air-dried and polished using grits up to 1000 (Bruchwald et al. 2015) . The ring widths were measured using a LinTab 6 and the TSAPW software (Rinntech, Germany) with a precision of 0.01 mm. The growth ring in 2017 was excluded from analysis due to the incomplete radial growth. To eliminate the juvenile effects of young trees (< 30 years), the age of sample and control trees was determined by a visual examination of tree rings. We did not measure the specific age for each tree but could confirm that the age of selected trees was greater than 60 years by detecting tree rings. The ring widths from 1993 to 2016 were chosen to determine growth release.
The growth release was determined based on the percent growth change (PGC) proposed by Black and Abrams (2003). In previous studies, the 10-year moving window and 50% PGC threshold were widely used for identifying release. In this study, a 5-year moving window was used because we found that most gaps would be closed in 10 years in this study area (Lu et al. 2015); a 5-year moving window could therefore grasp the growth release in the selected gaps. In addition, a 10-year moving window did not fit the short sampling sequence of tree rings (1993–2016) in this study. In order to determine the threshold, we started with 50% PGC for identifying the growth releases, resulting in only some samples of U. laciniata surpassing the threshold. Thus, we adjusted the threshold from 50% PGC to 25% PGC for catching more growth release information. It is necessary to note that the 5-year moving window and 25% PGC threshold had also been used for identifying release in other gap age estimation researches (Wang and Zhao 2011; Cartera et al. 2021). The PGC for a given year was calculated by (M2–M1)/M1, where M1 is the mean tree-ring width during the prior 5 years (including the given year) and M2 is the mean tree-ring width during the subsequent 5 years (excluding the given year). When the PGC was higher than 25% in continuous 3 years, growth release would be considered to occur (Rubino and Mccarthyz 2004; Hart et al. 2011). During the period of growth release, the year with the largest difference in PGC between sample and control trees was defined as the gap formation year (i.e., rough gap age) (Fig. 1). The years of gap formation in the 24 selected trees could be composed of the rough time range for gap formation.
Gap age estimation by introducing stable carbon isotope analyses
After obtaining the time range of gap formation, the rings in the range for three tree species were chosen for stable carbon isotope analyses (Fig. 1). The ∆13C is the proxy for the carbon isotope composition of plant material in the absence of changes in the atmospheric CO2 concentration. It would decrease quickly due to (1) the increased photosynthesis as the enhanced light and (2) the decreased stomatal conductance as the declined water availability after gap formation (Cernusak et al. 2009; van der Sleen et al. 2014). Thus, the significantly decreased ∆13C was an accurate sign for gap formation (Fig. 1).
The cores from sample and control trees in the time range were divided at a biennial interval because the rings of sample trees were too narrow to be cut per year. Thus, the estimation time resolution for gap formation was 2 years. The cores were shattered and homogenized in a grinding mechanism (DHS TL2020, China). Approximately, 5 mg of each ground sample was weighed into a tin capsule and combusted in an elemental analyzer (vario MICRO cube; Elementar Analyser Systeme GmbH, Hessen Hanau, Germany) for 13C enrichment (‰) measurement.
The carbon isotope composition (δ13C, in %) was calculated as δ13Ctree ring = (Rsample/Rstandard−1)×1000, where Rsample is the 13C/12C ratio of a sample and Rstandard is the 13C/12C ratio of an internationally recognized standard material (V-PDB). The carbon isotope 13C discrimination (∆13C) was calculated as ∆13C=(δ13Ca−δ13Ctree ring)/(1 + δ13Ctree ring), where δ13Ca is the δ13C of atmospheric CO2. The δ13C of atmospheric CO2 was −9.37 ‰ in the study forest measured by a Picarro G2101-i analyzer (Picarro, Sunnyvale, CA, USA).
The two-way ANOVA was used to test the effects of year and individual trees on the PGC. ∆13C between sample and control trees were tested year by year with the one-way ANOVA. For verifying the effectivity of isotopes in identifying promptly the tree’s response to gap formation, the variations of ∆13C between different intervals for sample and control trees were analyzed by using one-way ANOVA and Tukey’s post hoc tests. The difference at a level of p < 0.05 was considered significant. All statistical tests were carried out in SPSS 22.0 (SPSS Inc., Chicago, USA).