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Tree-ring widths and the stable isotope composition of pine tree-rings as climate indicators in the most industrialised part of Poland during CO2 elevation

Barbara Sensuła
Barbara Sensuła
 und 
Sławomir Wilczyński
Sławomir Wilczyński
   | 19. Juli 2018
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Article Category: Regular Articles
Online veröffentlicht: 19. Juli 2018
Seitenbereich: 130 - 145
Eingereicht: 12. Okt. 2017
Akzeptiert: 06. Juni 2018
DOI: https://doi.org/10.1515/geochr-2015-0094
Schlüsselwörter
© 2017 B. Sensuła and S. Wilczyński, published by Sciendo
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Fig. 1

Meteorological data for the nearest meteorological stations to the investigated area: Opole (O) and Katowice (K): mean temperature, sunshine duration, total rainfall and air relative humidity, for the period 1951–2012).
Meteorological data for the nearest meteorological stations to the investigated area: Opole (O) and Katowice (K): mean temperature, sunshine duration, total rainfall and air relative humidity, for the period 1951–2012).

Fig. 2

Indexed site chronologies of pine stands.
Indexed site chronologies of pine stands.

Fig. 3

The correlation coefficients between the indexed site chronologies and the distance between pine sites.
The correlation coefficients between the indexed site chronologies and the distance between pine sites.

Fig. 4

The results of the cluster analysis of 16 correlation-coefficient series (left) calculated between the indexed site chronologies and 26 climatic parameters (see Fig. 5) and the location of 16 indexed site chronologies to the loadings of the first and second components (right).
The results of the cluster analysis of 16 correlation-coefficient series (left) calculated between the indexed site chronologies and 26 climatic parameters (see Fig. 5) and the location of 16 indexed site chronologies to the loadings of the first and second components (right).

Fig. 5

The correlation coefficients calculated between the indexed site chronologies and site monthly values of temperature, precipitation, relative humidity, and sunshine duration (correlation-coefficient series) from the previous September (SEP) to current September (Sep).
The correlation coefficients calculated between the indexed site chronologies and site monthly values of temperature, precipitation, relative humidity, and sunshine duration (correlation-coefficient series) from the previous September (SEP) to current September (Sep).

Fig. 6

Significant correlation coefficients (at the 95% level) calculated between PC1, and PC2 and monthly values of temperature, precipitation, relative humidity, and sunshine duration from the previous September (SEP) to the current September (Sep).
Significant correlation coefficients (at the 95% level) calculated between PC1, and PC2 and monthly values of temperature, precipitation, relative humidity, and sunshine duration from the previous September (SEP) to the current September (Sep).

Fig. 7

Trends of δ13C (measured value: HK, KK, LA and corrected values: HKc, KKc, LAc) and δ18O in α-cellulose samples extracted from pines growing in three forests in HK, KK, and LA.
Trends of δ13C (measured value: HK, KK, LA and corrected values: HKc, KKc, LAc) and δ18O in α-cellulose samples extracted from pines growing in three forests in HK, KK, and LA.

Fig. 8

(a) Theoretical scenarios (Scheidegger et al., 2000) for the interaction between the stomatal conductance (g) and photosynthesis (A) and isotope composition of plants; (up- or downward arrows represent increasing or decreasing values, *indicates insignificant changes) and (b) a comparison of the δ13C and δ18O series for the investigated sites: (b) HK, (c) KK, and (d) LA for three periods of time: since 1957–1990 prior to significant modernisation in the factories, since 1991–2000, when was a significant modernisation in the factories; since 2001 – the period when the pro-ecological strategy was implemented in each factory due to changes in the Polish law.
(a) Theoretical scenarios (Scheidegger et al., 2000) for the interaction between the stomatal conductance (g) and photosynthesis (A) and isotope composition of plants; (up- or downward arrows represent increasing or decreasing values, *indicates insignificant changes) and (b) a comparison of the δ13C and δ18O series for the investigated sites: (b) HK, (c) KK, and (d) LA for three periods of time: since 1957–1990 prior to significant modernisation in the factories, since 1991–2000, when was a significant modernisation in the factories; since 2001 – the period when the pro-ecological strategy was implemented in each factory due to changes in the Polish law.

Fig. 9

(a) Increased intrinsic water-use efficiency (iWUE) in pines during increases in CO2 emissions into the atmosphere (in the period from 1975 to 2012), (b) the correlations between intrinsic water-use efficiency (iWUE) and a land surface temperature anomaly (TA) (the deviation from the long-term average, NOAA).
(a) Increased intrinsic water-use efficiency (iWUE) in pines during increases in CO2 emissions into the atmosphere (in the period from 1975 to 2012), (b) the correlations between intrinsic water-use efficiency (iWUE) and a land surface temperature anomaly (TA) (the deviation from the long-term average, NOAA).

Fig. 10

The correlation coefficients between δ13Cc (corrected values) and δ18O in α-cellulose samples from three forests in HK, KK, and LA, and (a) annual climatic factors: mean temperature (T), sum of precipitation (P), sunshine (S), and relative humidity (H) from previous year (p) and current year and (b) monthly climatic factors: mean temperature, sum of preciptation, sunshine, and relative humidity; significance level = 95%.
The correlation coefficients between δ13Cc (corrected values) and δ18O in α-cellulose samples from three forests in HK, KK, and LA, and (a) annual climatic factors: mean temperature (T), sum of precipitation (P), sunshine (S), and relative humidity (H) from previous year (p) and current year and (b) monthly climatic factors: mean temperature, sum of preciptation, sunshine, and relative humidity; significance level = 95%.

Fig. 11

Forward evolutionary interval correlations (bootstrap correlation function) between monthly climate factors (temperature, precipitation, duration of sunshine, humidity) and carbon (a) and oxygen (b) the stable isotopic composition of tree-ring cellulose for the period 1975–2012, for the September of the previous year to the September of the current year, using a base length of 26 (plotted against the last year of the period); significance level = 95%.
Forward evolutionary interval correlations (bootstrap correlation function) between monthly climate factors (temperature, precipitation, duration of sunshine, humidity) and carbon (a) and oxygen (b) the stable isotopic composition of tree-ring cellulose for the period 1975–2012, for the September of the previous year to the September of the current year, using a base length of 26 (plotted against the last year of the period); significance level = 95%.

The localisation of the sampling sites.

Stand (region)Lab codeGeographical coordinates
Kędzierzyn-Koźle (KK)KKT50° 18′N; 18°20’E
KKS50° 18′N; 18°17′E
KKB50°21′N; 18°18’E
KKA50° 19′N; 18°15’E
KKC50°20′N; 18°19’E
KKN50°22′N; 18°23′E
Laziska (LA)LAW50°8′N; 18°53′E
LAM50°9′N; 18°56′E
LAP50°10′N; 18°58′E
Dabrowa Gornicza (HK)HKB2HKE50°20′N; 19°23′E
HKBB50°24′N; 19°22′E
HKB250°24′N; 19°21′E
HKFF50°21′N; 19°19′E
HKF250°24′N; 19°28′E
HKC250°26′N; 19°29′E
HKD150°31′N; 19°31′E

The correlations between the values of δ13C for three forests and the correlations between the values of δ18O for three forests: Dąbrowa Górnicza near Huta Katowice (HK), Kędzierzyn-Kożle (KK), and Łaziska (LA) (all values are significant at p < 0.05).

δ13CsiteHKKKLA
HK-0.610.56
KK0.61-0.66
LA0.560.66-
δ18OsiteHKKKLA
HK-0.340.51
KK0.34-0.52
LA0.510.52-

The statistics of 16 tree-ring-site collections for the period 1951–2012 (MS: mean sensitivity, rbt: mean between-tree series correlation, EPS: expressed population signal, SNR: signal-to-noise ratio, Var.PC1: variance in the first component).

No. of trees and coresrbtEPSSNRMSVar.pc1
HKE200.2790.8867.70.25537
HKBB200.3220.9059.50.21947
HKB200.4450.94116.00.23660
HKFF200.3000.8968.60.28343
HKF200.3340.90910.00.28645
HKC200.5450.96023.90.22666
HKD200.2270.8555.90.26536
LAW200.5180.95321.50.28464
LAP200.3990.93013.30.28551
LAM200.5140.95421.20.30863
KKA200.3610.91811.30.20449
KKB200.4210.93514.50.19754
KKC200.3550.91611.00.20148
KKN200.3190.9039.40.15846
KKS200.4240.93614.70.19155
KKT200.3710.92211.80.21050
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