Arboriculture & Urban Forestry 47(5): September 2021 (A) HMM Interval ln(Growth), Spruce 1 Spruce 1 1 soil temp P < 0.001 £ 15 > 15 4 soil water P = 0.024 £ 0.401 −10 −8 −6 −4 −2 (B) Node 2 (n = 160) −10 −9 −8 −7 −6 −5 −4 (C) precip £ 0 Node 3 (n = 78) −10 −9 −8 −7 −6 −5 −4 −10 −9 −8 −7 −6 −5 −4 −10 −9 −8 −7 −6 −5 −4 Node 2 (n = 92) −10 −8 −6 −4 −2 Node 5 (n = 118) > 0.401 −10 −8 −6 −4 −2 Node 6 (n = 30) −10 −8 −6 −4 −2 Node 7 (n = 27) −13 −12 −11 −10 −9 Node 3 (n = 75) −13 −12 −11 −10 −9 Node 4 (n = 40) −13 −12 −11 −10 −9 £ 55.593 P < 0.001 3 solar > 55.593 £ 39.625 > 39.625 P = 0.015 2 solar P = 0.029 6 solar £ 50.1 Node 7 (n = 104) −13 −12 −11 −10 −9 > 50.1 Node 8 (n = 26) −13 −12 −11 −10 −9 Node 9 (n = 22) £ 22.522 £ 17.5 (D) HMM Interval ln(Slope), Spruce 1 Spruce 1 soil temp P < 0.001 1 > 17.5 5 soil temp P = 0.002 > 22.522 223 HMM Interval ln(Growth), Spruce 2 Spruce 2 P < 0.001 1 solar £ 0.108 £ 64.232 > 64.232 Node 3 (n = 29) £ 0.373 −13 −12 −11 −10 −9 −8 Node 3 (n = 134) −13 −12 −11 −10 −9 −8 P = 0.021 2 VPD > 0.373 Node 4 (n = 43) −13 −12 −11 −10 −9 −8 Node 5 (n = 12) (E) HMM Interval ln(Slope), Spruce 2 Spruce 2 P = 0.001 1 precip > 0.108 HMM Interval ln(Growth), Cedar 1 Cedar 1 P < 0.001 1 VPD £ 0.228 P = 0.016 2 > 0 Node 4 (n = 24) −10 −9 −8 −7 −6 −5 −4 Node 5 (n = 52) −14 −13 −12 −11 −10 −9 −8 −7 £ 0.115 Node 3 (n = 102) −14 −13 −12 −11 −10 −9 −8 −7 > 0.228 P = 0.001 2 precip > 0.115 Node 4 (n = 9) −14 −13 −12 −11 −10 −9 −8 −7 Node 5 (n = 43) £ 0.31 (F) HMM Interval ln(Slope), Cedar 1 Cedar 1 P < 0.001 1 VPD > 0.31 Figure 5. CITs partitioning the influence of environmental variables on total amount of log(growth) during HMM-identified growth periods (A–C) and on log(slope) across HMM-identified growth periods (D–F) calculated using values of the zero-growth model at the beginning and end points of each growth period. (A) Spruce 1, growth. Soil temperature (soil temp) was the most important predictor, and solar and soil water were also identified as influential. (B) Spruce 2, growth. Solar was identified as the most important predictor. (C) Cedar, growth. VPD was the most important predictor, and precipitation (precip) was also identified as influential. (D) Spruce 1, slope. Soil temp was the most important predictor, but solar was also identified as influential. (E) Spruce 2, slope. Precip was identified as the most important predictor, with VPD also identified as influential. (F) Cedar, slope. VPD was the most important predictor, and precip was also influential. duration of the growing season, and within these, periods of overall growth and dormancy. On finer timescales, we show that growth (stem expansion) occurs primarily at night and depends on the interact- ing environmental variables relating to both energy gain and water use. Intra-Annual Diameter Fluctuations Understanding the links among the urban environ- ment, tree biology, and resultant ecosystem services requires the precise quantification of the relationships among these parameters. Generally, when tempera- tures are above freezing and physiological activity has a clear influence on stem diameter, contraction is observed during the day as transpiration moves water through the stem more quickly than it can be replen- ished from the soils via the roots (Goldstein et al. 1998; Steppe and Lemeur 2004; Köcher et al. 2013). At night, stems generally expand as transpiration slows to a minimum and the hydrologic balance tips towards the absorption of water from the soils and stem refilling, providing the turgor pressure neces- sary for cell expansion (Steppe et al. 2015). At our site, more than 59% of all stem expansion happens at night when temperatures are lower, humidity is higher, the vapor pressure deficit is lower, and soil moisture is abundant (due to regular irrigation at this site). These diel patterns in stem diameter provide a ©2021 International Society of Arboriculture ln(Growth) ln(Growth) ln(Growth) ln(Slope) ln(Slope) ln(Slope)
September 2021
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