Leaf photosynthetic traits and N from field observations

Ning Dong’s data

The data used here is this leaf photosynthetic traits and N dataset:

Data reference

Dong, Ning, Prentice, Iain Colin, Wright, Ian, Wang, Han, Atkin,Owen, Bloomfield, Keith, Domingues, Tomas, Gleason, Sean, Maire, Vincent, Onoda, Yusuke, Poorter, Hendrik, & Smith, Nicholas. (2022). dataset for paper “Leaf nitrogen from the perspective of optimal plant function” (Version v1) [Data set]. Zenodo. https://doi.org/10.5281/zenodo.6831903

Paper reference

Dong, N., Prentice, I. C., Wright, I. J., Wang, H., Atkin, O. K., Bloomfield, K. J., Domingues, T. F., Gleason, S. M., Maire, V., Onoda, Y., Poorter, H., & Smith, N. G. (2022). Leaf nitrogen from the perspective of optimal plant function. Journal of Ecology, 00, 1– 18. https://doi.org/10.1111/1365-2745.13967

df <- read_csv("~/data/leafn_vcmax_ning_dong/data_leafn_vcmax_ning_dong.csv")

## Rows: 3107 Columns: 33
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (8): Genus Species, Family, woody, DE, Legume, Dataset, Contributor, Re...
## dbl (25): sample_ID, site_id, Latitude, longitude, elv, CO2, fapar, LMA, Nar...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

Quick overview of data.

skim(df)

Data summary

Name	df
Number of rows	3107
Number of columns	33
_______________________
Column type frequency:
character	8
numeric	25
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
Genus Species	0	1.00	4	44	0	2078	0
Family	0	1.00	1	18	0	196	0
woody	0	1.00	5	15	0	3	0
DE	46	0.99	1	19	0	4	0
Legume	0	1.00	2	3	0	2	0
Dataset	0	1.00	2	14	0	15	0
Contributor	0	1.00	3	28	0	55	0
Reference	0	1.00	11	34	0	22	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
sample_ID	0	1.00	1554.00	897.06	1.00	777.50	1554.00	2330.50	3107.00	▇▇▇▇▇
site_id	0	1.00	162.52	85.99	1.00	74.00	169.00	234.00	315.00	▅▇▇▇▆
Latitude	0	1.00	5.81	27.07	-65.60	-14.99	5.16	26.25	67.50	▁▇▅▆▂
longitude	0	1.00	13.90	96.02	-159.58	-71.56	-5.88	108.53	172.30	▁▇▂▂▅
elv	0	1.00	606.94	682.62	1.00	150.00	319.00	818.00	3379.00	▇▂▁▁▁
CO2	0	1.00	383.14	13.02	338.99	377.44	386.95	392.00	402.30	▁▂▃▇▇
fapar	0	1.00	0.63	0.22	0.08	0.46	0.63	0.86	0.93	▂▂▆▃▇
LMA	6	1.00	121.76	83.48	14.24	69.04	102.46	149.43	914.86	▇▁▁▁▁
Narea	6	1.00	2.03	1.18	0.29	1.34	1.83	2.41	18.48	▇▁▁▁▁
Nmass	6	1.00	0.02	0.01	0.00	0.01	0.02	0.02	0.08	▇▅▁▁▁
vcmax25_obs	3	1.00	45.72	24.99	1.06	27.62	41.13	58.80	231.77	▇▅▁▁▁
vcmax_obs	3	1.00	36.13	25.12	0.74	17.45	31.04	48.89	205.50	▇▃▁▁▁
lnD	0	1.00	-0.68	0.66	-3.62	-0.99	-0.59	-0.28	1.10	▁▁▃▇▁
mgdd0	13	1.00	21.57	5.87	0.26	16.10	22.96	26.82	33.85	▁▂▆▇▅
lnppfd	0	1.00	6.66	0.15	6.08	6.53	6.67	6.75	7.01	▁▁▆▇▂
lnppfm	0	1.00	2.30	0.34	1.69	2.02	2.32	2.56	3.09	▆▅▇▆▂
alpha	2	1.00	0.91	0.24	0.27	0.82	0.93	1.10	1.25	▂▂▃▇▇
gdday	0	1.00	347.13	46.43	0.00	365.00	365.00	365.00	365.00	▁▁▁▁▇
soil_clay	2	1.00	24.87	8.59	5.00	19.00	23.00	31.00	43.00	▁▇▅▅▃
soil_ph	2	1.00	5.78	0.88	4.30	5.10	5.40	6.30	8.20	▆▇▆▂▂
soil_cn	2	1.00	12.29	1.99	9.00	11.00	12.00	13.00	24.00	▇▂▁▁▁
vcmax_predicted	0	1.00	36.90	16.92	10.14	26.54	32.34	41.40	151.00	▇▃▁▁▁
vcmax25_predicted	0	1.00	49.42	21.09	20.85	29.35	45.79	63.17	144.83	▇▆▂▁▁
lnLMA_predicted	703	0.77	4.68	0.33	3.79	4.47	4.69	4.89	5.48	▁▃▇▅▂
Na_predicted	703	0.77	1.98	0.47	1.36	1.62	1.90	2.23	3.69	▇▅▂▁▁

Distribution of data

gg1 <- df %>% 
  ggplot(aes(vcmax25_obs)) +
  geom_density() +
  labs(title = "Nmass")

gg2 <- df %>% 
  ggplot(aes(Narea)) +
  geom_density() +
  labs(title = "Narea")

gg3 <- df %>% 
  ggplot(aes(Nmass)) +
  geom_density() +
  labs(title = "Nmass")

gg4 <- df %>% 
  ggplot(aes(LMA)) +
  geom_density() +
  labs(title = "LMA")

(gg1 + gg2) /
  (gg3 + gg4)

Log-transform all variables to make them closer to normally distributed.

df <- df %>% 
  mutate(log_vcmax25_obs = log(vcmax25_obs),
         log_Narea = log(Narea),
         log_Nmass = log(Nmass),
         log_LMA = log(LMA),
         )

gg1 <- df %>% 
  ggplot(aes(log_vcmax25_obs)) +
  geom_density() +
  labs(title = "Nmass")

gg2 <- df %>% 
  ggplot(aes(log_Narea)) +
  geom_density() +
  labs(title = "Narea")

gg3 <- df %>% 
  ggplot(aes(log_Nmass)) +
  geom_density() +
  labs(title = "Nmass")

gg4 <- df %>% 
  ggplot(aes(log_LMA)) +
  geom_density() +
  labs(title = "LMA")

(gg1 + gg2) /
  (gg3 + gg4)

Analysis vs soil C:N

gg1 <- df %>% 
  ggplot(aes(soil_cn, log_vcmax25_obs)) +
  geom_point() +
  geom_smooth(color = "red", method = "lm") +
  theme_classic()

gg2 <- df %>% 
  ggplot(aes(soil_cn, log_Narea)) +
  geom_point() +
  geom_smooth(color = "red", method = "lm") +
  theme_classic()

gg3 <- df %>% 
  ggplot(aes(soil_cn, log_Nmass)) +
  geom_point() +
  geom_smooth(color = "red", method = "lm") +
  theme_classic()

gg4 <- df %>% 
  ggplot(aes(soil_cn, log_LMA)) +
  geom_point() +
  geom_smooth(color = "red", method = "lm") +
  theme_classic()

gg1 + gg2 + gg3 + gg4

## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'
## `geom_smooth()` using formula 'y ~ x'

Issues

• Why soil C:N in discrete values? Where is it extracted from?

Analysis by PFT

# -> Ning, please add your analysis as R code here

Ellsworth data

The data used here is this leaf photosynthetic traits and N dataset:

Data reference

Ellsworth, David; Wright, Ian; Crous, Kristine Y.; Goll, Daniel S; Zaehle, Sönke; Cernusak, Lucas A.; et al. (2022): Convergence in phosphorus constraints to photosynthesis dataset. figshare. Dataset. https://doi.org/10.6084/m9.figshare.20010485.v1

Paper reference

Ellsworth, D.S., Crous, K.Y., De Kauwe, M.G. et al. Convergence in phosphorus constraints to photosynthesis in forests around the world. Nat Commun 13, 5005 (2022). https://doi.org/10.1038/s41467-022-32545-0

df <- read_csv("~/data/leafnp_vcmax_ellsworth/Ellsworth_NCOMMS_Figure1and2_fulldata.csv")

## Rows: 447 Columns: 29
## ── Column specification ────────────────────────────────────────────────────────
## Delimiter: ","
## chr  (5): Species, Family, Sitename, Continent, Plim_status
## dbl (24): Site_No, Cont_No, Species_No, Vcmax, Jmax, Rd, Vcmax_SE, Jmax_SE, ...
## 
## ℹ Use `spec()` to retrieve the full column specification for this data.
## ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.

skim(df)

Data summary

Name	df
Number of rows	447
Number of columns	29
_______________________
Column type frequency:
character	5
numeric	24
________________________
Group variables	None

Variable type: character

skim_variable	n_missing	complete_rate	min	max	empty	n_unique	whitespace
Species	0	1.00	4	44	0	402	0
Family	7	0.98	7	17	0	80	0
Sitename	0	1.00	4	22	0	52	0
Continent	0	1.00	4	9	0	4	0
Plim_status	0	1.00	5	6	0	2	0

Variable type: numeric

skim_variable	n_missing	complete_rate	mean	sd	p0	p25	p50	p75	p100	hist
Site_No	0	1.00	34.37	19.12	1.00	15.00	39.00	51.00	64.00	▇▂▅▇▆
Cont_No	0	1.00	235.55	138.52	1.00	119.50	230.00	354.50	474.00	▇▇▇▆▇
Species_No	7	0.98	44.85	22.46	1.00	27.75	45.00	59.00	87.00	▅▆▇▇▆
Vcmax	0	1.00	47.55	18.31	10.50	34.90	44.80	58.40	112.30	▃▇▅▂▁
Jmax	0	1.00	95.33	34.82	29.30	71.70	90.10	112.70	243.40	▃▇▃▁▁
Rd	0	1.00	0.56	0.70	-1.55	0.18	0.53	0.92	3.29	▁▆▇▂▁
Vcmax_SE	0	1.00	3.08	2.54	0.30	1.40	2.30	3.90	17.90	▇▂▁▁▁
Jmax_SE	8	0.98	3.17	2.33	0.30	1.80	2.60	3.90	27.80	▇▁▁▁▁
Rd_SE	0	1.00	0.47	0.26	0.04	0.29	0.40	0.59	1.64	▇▇▂▁▁
Photo	0	1.00	11.02	4.19	2.40	7.90	10.60	13.45	25.00	▃▇▅▂▁
Tleaf	0	1.00	29.67	3.00	18.70	28.10	29.90	31.15	36.80	▁▁▇▇▂
CO2S	0	1.00	384.52	8.88	350.00	381.00	386.00	390.00	404.00	▁▁▃▇▂
PARi	0	1.00	1747.93	297.97	998.00	1702.00	1800.00	2000.00	2001.00	▂▁▂▆▇
LMA	0	1.00	126.05	44.58	27.45	95.81	120.70	150.93	316.77	▂▇▃▁▁
Perc_N	0	1.00	1.83	0.65	0.54	1.38	1.76	2.17	4.28	▃▇▃▁▁
Perc_P	0	1.00	0.10	0.06	0.01	0.06	0.09	0.13	0.44	▇▆▁▁▁
Narea	0	1.00	2.12	0.61	0.68	1.71	2.05	2.48	5.30	▂▇▂▁▁
Parea	0	1.00	0.12	0.06	0.01	0.08	0.11	0.14	0.43	▇▇▂▁▁
Photomass	0	1.00	98.54	55.57	16.90	62.45	86.50	122.15	435.20	▇▃▁▁▁
Jmaxmass	0	1.00	843.02	435.97	189.70	565.00	754.40	992.75	3389.10	▇▅▁▁▁
Vcmaxmass	0	1.00	419.06	217.20	72.70	273.70	376.30	519.10	1643.10	▇▆▁▁▁
Nmass	0	1.00	18.34	6.47	5.39	13.84	17.59	21.69	42.83	▃▇▃▁▁
Pmass	0	1.00	1.02	0.57	0.08	0.60	0.94	1.28	4.40	▇▆▁▁▁
NPratio	0	1.00	21.49	11.13	2.60	14.85	19.00	24.65	119.60	▇▂▁▁▁

Issues

• No longitude and latitude information in this data.

TROBIT data

-> Yunke, please add your analysis code here