Extract data from the database

Concerning observatories, taxonomic and traits data are stored and managed in this database. Phenological characterization and spatial vectors linked to each tree (crown polygons) are managed trough the Shiny apps.

Data are stored in a postrgresql relational database and are available with several ‘query’ functions using an R package and a proper password and login name (ask Gilles).

Data is subject to evolve regularly : new data can be added and taxonomic identification can be updated.

Extract datasets for mbalmayo and bouamir observatories.

tree_data <-
  query_plots(
    locality_name = c("Bouamir", "Mbalmayo"),
    method = c("1ha-IRD", "phenological_observatory"),
    extract_individuals = TRUE,
    remove_ids = F
  )

meta_data <- 
    query_plots(
    locality_name = c("Bouamir", "Mbalmayo"), 
    method = c("1ha-IRD", "phenological_observatory"),
    remove_ids = F,
  )

Query all features (including traits) linked to an individual of the observatory. A feature can be anything (including traits but not only).

### export all features linked to an individual

all_traits_features <-
  query_individual_features(
    id = tree_data$id_n,
    pivot_table = FALSE,
    extract_trait_measures_features = TRUE
  )

## ! Individual features queried by chunks because of large number of values

Explore and rearrange the dataset

Number of trees, plots and observatories

tree_data %>% 
  left_join(meta_data %>% select(plot_name, method)) %>% 
  group_by(plot_name, method, locality_name) %>% 
  count() %>% 
  ungroup() %>% 
  group_by(method) %>% 
  summarise(nbe_plot = n(),
            nbe_individual = sum(n),
            nbe_min_individual = min(n),
            nbe_max_individual = max(n)) %>% 
  gt::gt() %>% 
   gt::cols_label(
     nbe_individual = "Total number of tree",
    nbe_min_individual = "Minimum number of tree per inventory"
  )

## Joining with `by = join_by(plot_name)`

method	nbe_plot	Total number of tree	Minimum number of tree per inventory	nbe_max_individual
1ha-IRD	25	11921	373	623
phenological_observatory	2	6621	2053	4568

The outputs of query_individual_features is a list that splits features into character values and numeric values. Character values concern observations done in the field. Numerical values include traits, among others.

# print(all_traits_features)

### count number of values for all numerical features
all_features_num <- all_traits_features$traits_num[[1]]

list_features_num <-
  all_features_num %>%
  group_by(trait, id_trait) %>%
  count()


### all character features
all_features_char <- all_traits_features$traits_char[[1]]

list_features_num %>% 
  ungroup() %>% 
  arrange(desc(n)) %>% 
  rename('number of values' = n) %>% 
  gt::gt()

trait	id_trait	number of values
stem_diameter	1	20731
height_of_stem_diameter	2	20369
centroid_ddlat	66	3402
centroid_ddlon	67	3402
crown_area	68	3400
mesophyl_thickness	36	2367
SPAD_chlorophyll_measurement	35	2355
lma_lamina	55	1899
lma_mesophyll	53	1894
leaf_area	5	1870
lma_whole_leaf	57	1753
whole_leaf_area	59	1752
position_x	39	1641
position_y	40	1641
leaf_structure_mass_area	62	1599
tree_height	3	1154
lamina_length	60	556
lamina_width	61	556
leaf_C_percentage	86	550
leaf_N_percentage	85	550
leaf_delta_carbon_13	87	550
twig_specific_gravity	9	422
height_of_first_branch	11	176
total_chlorophyll_content	76	152

Checklist of traits and features and their definition

all_feat_database <- traits_list()


all_feat_database <- 
  all_feat_database %>% 
  filter(grepl("lma_", trait) |
         trait == "twig_specific_gravity"|
         trait == "leaf_area"|
         trait == "SPAD_chlorophyll_measurement"|
         trait == "mesophyl_thickness"|
         trait == "whole_leaf_area"|
         trait == "leaf_structure_mass_area"|
         trait == "total_chlorophyll_content"|
         trait == "leaf_delta_carbon_13"|
         trait == "leaf_N_percentage"|
         trait == "leaf_C_percentage"|
         trait == "leaf_C_percentage")

list_features_num <- 
  list_features_num %>% 
  filter(id_trait %in% all_feat_database$id_trait)

features_meta_data <- 
  all_feat_database %>% 
  filter(id_trait %in% list_features_num$id_trait)

all_features_num <- 
  all_features_num %>% 
  filter(id_trait %in% list_features_num$id_trait)

trait	id_trait	traitdescription	maxallowedvalue	minallowedvalue	expectedunit	valuetype
leaf_area	5	Leaf area (in case of compound leaves: leaflet, petiole excluded)	750	0.0	cm2	numeric
twig_specific_gravity	9	Ratio of twig dry mass to twig fresh volume	100	0.0	g/cm3	numeric
SPAD_chlorophyll_measurement	35	SPAD raw measurment	200	0.1	none	numeric
mesophyl_thickness	36	Mesophyl thickness	5	0.0	mm	numeric
lma_mesophyll	53	Leaf mass per area based on punches collected from the mesophyll avoiding when possible first and second order venation	1000	0.0	g/m²	numeric
whole_leaf_area	59	Whole leaf area, including petiole and rachis	1000	0.0	cm²	numeric
leaf_structure_mass_area	62	Leaf mass per area only including structural organs (petiole and rachis)	50000	0.0	g/m²	numeric
total_chlorophyll_content	76	Total chlorophyll content per leaf mass	300	0.0	ug/g	numeric
lma_whole_leaf	57	Leaf mass per area based on whole leaf (include petiole and rachis)	1000	0.0	g/m²	numeric
lma_lamina	55	Leaf mass per area based lamina photoynthetic unit (leaflet for compound leaves)	1000	0.0	g/m²	numeric
leaf_delta_carbon_13	87	Stable carbon isotope ratio in leaf material	0	-40.0	per_mill	numeric
leaf_N_percentage	85	Nitrogen percentage in leaf material	100	0.0	percentage	numeric
leaf_C_percentage	86	Carbon percentage in leaf material	100	0.0	percentage	numeric

How traits are collected in the field and metada are associated

Each set of traits measured on a tree in an observatory is associated with a unique number pheno_tree_nbr which is incremental.

Thus, each pheno_tree_nbr is linked to a tree, to trait measurements, to any observations/remarks on these measurements (for example the id of the leaf, because leaf traits are measured for at least 3 leaves), to any axillary measures used for the measurements (for example the area of the leaf for leaf mass per area measurements), to people involved in the measurements and to the date of measurement.

See one measurement

all_traits %>%
  slice(2000) %>% 
  select(
    id_trait_measures,
    id_trait,
    trait,
    traitvalue,
    colnbr,
    leaf_id,
    leaf_maturity,
    cohort_id,
    colnam,
    whole_leaf_area,
    tag,
    plot_name,
    day, 
    month,
    year
  ) %>% 
  gt::gt()

id_trait_measures	id_trait	trait	traitvalue	colnbr	leaf_id	leaf_maturity	cohort_id	colnam	whole_leaf_area	tag	plot_name	day	month	year
1015699	57	lma_whole_leaf	187.52	290	4	mature	2	Vincent Pearlstein\|Bidima Emmanuel	11.41239	870	mbalmayo_pheno_observatory	25	3	2024

A glimpse into the raw dataset

colnbr	voucher_type	tag	tax_sp_level	leaf_id	day	month	year	measurementremarks	measurementmethod	trait	traitvalue	colnam	cohort_id	leaf_maturity
142	phenotree	128	Ongokea gore	4	8	12	2023	leaf_id_4, weight_dry_0.01 g, area_2.9e-05 m², cohort 2, organs_ petiole	Include petiole and rachis	leaf_structure_mass_area	345.21	Gilles Dauby\|Bidima Emmanuel\|Yvan Ndjock	NaN	NA
142	phenotree	128	Ongokea gore	4	NA	12	2023	leaf_id 4, Cohort 2	Average of three measurements across lamina	SPAD_chlorophyll_measurement	82.30	Gilles Dauby\|Bidima Emmanuel\|Yvan Ndjock	2	NA
142	phenotree	128	Ongokea gore	5	NA	12	2023	leaf_id 5, Cohort 2	Average of three measurements across lamina	mesophyl_thickness	0.34	Gilles Dauby\|Bidima Emmanuel\|Yvan Ndjock	2	NA
142	phenotree	128	Ongokea gore	5	8	12	2023	leaf_id_5, weight_dry_0.281 g, area_0.002408 m², cohort 2	Based on one leaf or leaflet (the smallest lamina unit)	lma_lamina	116.69	Gilles Dauby\|Bidima Emmanuel\|Yvan Ndjock	NaN	NA
142	phenotree	128	Ongokea gore	5	8	12	2023	leaf_id_5, punch_diameter_12 m, Number_punches_2, weight_dry_0.026 g, area0.000226 m², cohort 2	Punches in mesophyll avoiding first and second order nervation when possible	lma_mesophyll	114.95	Gilles Dauby\|Bidima Emmanuel\|Yvan Ndjock	NaN	NA
142	phenotree	128	Ongokea gore	5	8	12	2023	leaf_id_5, weight_dry_0.321 g, area_0.002677 m², cohort 2	Include petiole and rachis	lma_whole_leaf	119.92	Gilles Dauby\|Bidima Emmanuel\|Yvan Ndjock	NaN	NA
142	phenotree	128	Ongokea gore	5	8	12	2023	leaf_id_5, weight_dry_0.321 g, area_0.002677 m², cohort 2	Include petiole and rachis	whole_leaf_area	26.77	Gilles Dauby\|Bidima Emmanuel\|Yvan Ndjock	NaN	NA
142	phenotree	128	Ongokea gore	5	8	12	2023	leaf_id_5, cohort 2	Area of leaves of leaflet without petiole or rachis	leaf_area	24.08	Gilles Dauby\|Bidima Emmanuel\|Yvan Ndjock	NaN	NA
142	phenotree	128	Ongokea gore	5	8	12	2023	leaf_id_5, cohort 2	Area of leaves of leaflet without petiole or rachis	leaf_area	24.08	Gilles Dauby\|Bidima Emmanuel\|Yvan Ndjock	NaN	NA
142	phenotree	128	Ongokea gore	5	8	12	2023	leaf_id_5, weight_dry_0.014 g, area_4.2e-05 m², cohort 2, organs_ petiole	Include petiole and rachis	leaf_structure_mass_area	329.57	Gilles Dauby\|Bidima Emmanuel\|Yvan Ndjock	NaN	NA
142	phenotree	128	Ongokea gore	5	NA	12	2023	leaf_id 5, Cohort 2	Average of three measurements across lamina	SPAD_chlorophyll_measurement	75.20	Gilles Dauby\|Bidima Emmanuel\|Yvan Ndjock	2	NA

Arranging the data into columns

## retrieving information associated to each leaf : maturity and cohort when available
leaves_data <- 
  all_traits %>% 
  group_by(colnbr, voucher_type, leaf_id) %>% 
  summarise(leaf_maturity = first(leaf_maturity, na_rm = T),
            cohort_id = first(cohort_id, na_rm = T)) %>% 
  filter(!is.na(leaf_id)) %>% 
  ungroup()

## re arrangin the data in columns
# each trait columns will start with a prefix 't_' that help selection
all_traits_wide <-
  all_traits %>%
  select(
    id_data_individuals,
    id_trait_measures,
    colnbr,
    voucher_type,
    leaf_id,
    tag,
    plot_name,
    tax_sp_level,
    tax_gen,
    trait,
    traitvalue,
    day,
    month,
    year
  ) %>%
  pivot_wider(names_from = trait, 
              values_from = traitvalue,
              names_prefix = "t_")


traits_leaf_level <-
  all_traits_wide %>%
  group_by(voucher_type ,
           colnbr,
           leaf_id,
           tag,
           tax_sp_level,
           id_data_individuals,
           plot_name) %>%
  summarise(across(starts_with("t_"), ~ mean(., na.rm = T)), plot_name = first(plot_name)) %>%
  left_join(leaves_data) %>%
  ungroup()


traits_leaf_level_averaged <-
  traits_leaf_level %>%
  group_by(voucher_type,
           colnbr,
           tag,
           tax_sp_level,
           cohort_id,
           leaf_maturity) %>%
  summarise(across(starts_with("t_"), ~ mean(., na.rm = T)), plot_name = first(plot_name))

Quantile-quantile plots

ggqq_plots <- ggplot(data = all_traits, 
       mapping = aes(sample = traitvalue, 
                     colour = trait)) +
  stat_qq() +
  stat_qq_line() +
  facet_wrap(~ trait, scales = "free") +
  guides(colour = "none")

ggqq_plots

Leaf level

Leaf Mass Per Area

All leaves.

gg_lma1 <- 
  ggplot() +
  geom_point(data = traits_leaf_level, 
             mapping = aes(x = t_lma_mesophyll, 
                           y = t_lma_whole_leaf,
                           color = tax_sp_level,
                           shape = leaf_maturity)) +
  geom_abline(slope = 1, intercept = 0) + 
  scale_x_continuous(trans='log10') + 
  scale_y_continuous(trans='log10') +
  xlab(label = "log10(lma mesophyll scale)") +
  ylab(label = "log10(lma whole leaf scale)") +
  guides(color = "none")

plotly::ggplotly(gg_lma1)

Averaging across leaves per cohort.

gg_lma1 <- 
  ggplot() +
  geom_point(data = traits_leaf_level_averaged, 
             mapping = aes(x = t_lma_mesophyll, 
                           y = t_lma_whole_leaf,
                           color = tax_sp_level,
                           shape = leaf_maturity)) +
  geom_abline(slope = 1, intercept = 0) + 
  scale_x_continuous(trans='log10') + 
  scale_y_continuous(trans='log10') +
  xlab(label = "log10(lma mesophyll scale)") +
  ylab(label = "log10(lma whole leaf scale)") +
  guides(color = "none")

plotly::ggplotly(gg_lma1)

gg_lma2 <- 
  ggplot() +
  geom_point(data = traits_leaf_level, 
             mapping = aes(x = t_lma_lamina, 
                           y = t_lma_whole_leaf,
                           color = tax_sp_level,
                           shape = leaf_maturity)) +
  geom_abline(slope = 1, intercept = 0) + 
  scale_x_continuous(trans='log10') + 
  scale_y_continuous(trans='log10') +
  xlab(label = "log10(lma lamina scale)") +
  ylab(label = "log10(lma whole leaf scale)") +
  guides(color = "none")


plotly::ggplotly(gg_lma2)

Averaging across leaves per cohort.

gg_lma2 <- 
  ggplot() +
  geom_point(data = traits_leaf_level_averaged, 
             mapping = aes(x = t_lma_lamina, 
                           y = t_lma_whole_leaf,
                           color = tax_sp_level,
                           shape = leaf_maturity)) +
  geom_abline(slope = 1, intercept = 0) + 
  scale_x_continuous(trans='log10') + 
  scale_y_continuous(trans='log10') +
  xlab(label = "log10(lma lamina scale)") +
  ylab(label = "log10(lma whole leaf scale)") +
  guides(color = "none")


plotly::ggplotly(gg_lma2)

gg_lma3 <- 
  ggplot() +
  geom_point(data = traits_leaf_level, 
             mapping = aes(x = t_lma_lamina, 
                           y = t_mesophyl_thickness,
                           color = tax_sp_level,
                           shape = leaf_maturity)) +
  scale_x_continuous(trans='log10') + 
  scale_y_continuous(trans='log10') +
  xlab(label = "log10(lma lamina scale)") +
  ylab(label = "log10(mesophyl_thickness)") +
  guides(color = "none")


plotly::ggplotly(gg_lma3)

gg_lma4 <- 
  ggplot() +
  geom_point(data = traits_leaf_level, 
             mapping = aes(x = t_lma_lamina, 
                           y = t_leaf_structure_mass_area,
                           color = tax_sp_level,
                           shape = leaf_maturity)) +
  scale_x_continuous(trans='log10') + 
  scale_y_continuous(trans='log10') +
  xlab(label = "log10(lma lamina scale)") +
  ylab(label = "log10(leaf_structure_mass_area)") +
  guides(color = "none")


plotly::ggplotly(gg_lma4)

Ratio of LMA at mesophyl scale and LMA at the whole leaf scale

Can we express this ratio as a proxy of construction costs to non photosynthetic tissues ?

LMA_ratios <- 
  traits_leaf_level %>%
  select(
    starts_with("t_lma"),
    leaf_id,
    colnbr,
    voucher_type,
    tax_sp_level,
    cohort_id,
    leaf_maturity,
    t_leaf_area,
    t_whole_leaf_area, 
    t_leaf_C_percentage
  ) %>%
  mutate(
    ratio_lma_lamina_meso =
      round((t_lma_lamina - t_lma_mesophyll) / t_lma_lamina * 100, 1),
    ratio_lma_whole_lamina =
      round((t_lma_whole_leaf - t_lma_lamina) / t_lma_whole_leaf * 100, 1),
    ratio_lma_whole_meso =
      round((t_lma_whole_leaf - t_lma_mesophyll) / t_lma_whole_leaf * 100, 1)
  ) %>%
  group_by(cohort_id, colnbr) %>% ### averaged across leaves per cohort
  summarise(
    across(starts_with("ratio_lma"), ~ round(mean(., na.rm = T), 1)),
    across(starts_with("t_lma"), ~ round(mean(., na.rm = T), 1)),
    tax_sp_level = first(tax_sp_level),
    leaf_maturity = first(leaf_maturity),
    t_leaf_area = mean(t_leaf_area, na.rm = T),
    t_whole_leaf_area = mean(t_whole_leaf_area, na.rm = T),
    t_leaf_C_percentage = mean(t_leaf_C_percentage, na.rm = T)
  ) %>%
  filter(!is.na(ratio_lma_whole_meso)) %>% 
  ungroup()

## `summarise()` has grouped output by 'cohort_id'. You can override using the
## `.groups` argument.

How this ratio correlates with leaf area.

gg_lma4 <- 
  ggplot() +
  geom_point(data = LMA_ratios, 
             mapping = aes(x = t_whole_leaf_area , 
                           y = ratio_lma_whole_meso,
                           color = tax_sp_level,
                           shape = leaf_maturity)) +
  scale_x_continuous(trans='log10') + 
  xlab(label = "log10(whole leaf area)") +
  ylab(label = "Ratio LMA mesophyl/LMA whole leaf") +
  guides(color = "none")


plotly::ggplotly(gg_lma4)

How this ratio correlates with leaf C carbon.

gg_lma4 <- 
  ggplot() +
  geom_point(data = LMA_ratios, 
             mapping = aes(x = t_leaf_C_percentage , 
                           y = ratio_lma_whole_meso,
                           color = tax_sp_level,
                           shape = leaf_maturity)) +
  scale_x_continuous(trans='log10') + 
  xlab(label = "log10(leaf C content)") +
  ylab(label = "Ratio LMA mesophyl/LMA whole leaf") +
  guides(color = "none")


plotly::ggplotly(gg_lma4)

Nitrogen content and LMA

The whole dataset

gg_lma2 <- 
  ggplot() +
  geom_point(data = traits_leaf_level, 
             mapping = aes(x = t_lma_mesophyll, 
                           y = t_leaf_N_percentage,
                           color = tax_sp_level,
                           shape = leaf_maturity)) +
  geom_abline(slope = 1, intercept = 0) + 
  scale_x_continuous(trans='log10') + 
  scale_y_continuous(trans='log10') +
  xlab(label = "log10(lma mesophyl scale)") +
  ylab(label = "log10(Nitrogen content)") +
  guides(color = "none")


plotly::ggplotly(gg_lma2)

Within species

## select the four most sampled species
selected_sp <- 
  traits_leaf_level %>% 
  filter(!is.na(tax_sp_level)) %>% 
  group_by(tax_sp_level) %>% 
  count() %>% 
  arrange(desc(n))%>% 
  ungroup() %>% 
  slice(1:4)

dataset_filtered <- 
  traits_leaf_level %>% 
  filter(tax_sp_level %in% selected_sp$tax_sp_level) %>% 
  mutate(leaf_maturity = replace_na(leaf_maturity, "unkown")) %>% 
  group_by(leaf_id, colnbr, tax_sp_level, voucher_type) %>% 
  summarise(t_lma_mesophyll = mean(t_lma_mesophyll, na.rm = T),
            t_leaf_N_percentage = mean(t_leaf_N_percentage, na.rm = T),
            leaf_maturity = first(leaf_maturity, na_rm = T)) %>% 
  filter(!is.na(t_lma_mesophyll),
         !is.na(t_leaf_N_percentage))

gg_w <- 
  ggplot(data = dataset_filtered,
         aes(x = t_lma_mesophyll, 
             y = t_leaf_N_percentage, 
             color = tax_sp_level,
             shape = leaf_maturity)) +
    geom_point() + 
  scale_x_continuous(trans='log10') + 
  scale_y_continuous(trans='log10') +                      
  geom_smooth(method = "lm", 
                se = FALSE, 
                mapping = aes(group = tax_sp_level)) + 
    labs(title = "Within-species", 
         x = "log(LMA mesophyll scale)", 
         y = "log(N content per dry mass (%))") +
    theme_minimal()
  

plotly::ggplotly(gg_w)

Carbon content and LMA

gg_lma2 <- 
  ggplot() +
  geom_point(data = traits_leaf_level, 
             mapping = aes(x = t_lma_mesophyll, 
                           y = t_leaf_C_percentage,
                           color = tax_sp_level,
                           shape = leaf_maturity)) +
  scale_x_continuous(trans='log10') + 
  scale_y_continuous(trans='log10') +
  xlab(label = "log10(lma mesophyl scale)") +
  ylab(label = "log10(Carbon content)") +
  guides(color = "none")


plotly::ggplotly(gg_lma2)

Total chlorophyll content and SPAD measurement

measurementmethod	measurementremarks
maceration 50mg of leaf sample in 80% Acetone, centrifuging and spectrophotometric reading of absorbances at the various wavelengths to determine chlorophyll a and b	Measures were done on silica-dried samples in the field. Lab work was done at IITA under the supervision of Rose Ndango

gg1 <- ggplot() +
  geom_point(data = traits_leaf_level, 
             mapping = aes(x = t_total_chlorophyll_content, 
                           y = t_SPAD_chlorophyll_measurement, 
                           color = tax_sp_level,
                           fill = tax_sp_level,
                           shape = leaf_maturity)) +
  guides(color = "none",
         fill = "none",
         shape = "none")

plotly::ggplotly(gg1)

Total chlorophyll content and Nitrogen content

gg1 <- ggplot() +
  geom_point(data = traits_leaf_level, 
             mapping = aes(x = t_total_chlorophyll_content, 
                           y = t_leaf_N_percentage, 
                           color = tax_sp_level,
                           fill = tax_sp_level,
                           shape = leaf_maturity)) +
  guides(color = "none",
         fill = "none",
         shape = "none")

plotly::ggplotly(gg1)

Total chlorophyll content and stable delta 13C isotope

gg1 <- ggplot() +
  geom_point(data = traits_leaf_level, 
             mapping = aes(x = t_total_chlorophyll_content, 
                           y = t_leaf_delta_carbon_13, 
                           color = tax_sp_level,
                           fill = tax_sp_level,
                           shape = leaf_maturity)) +
  guides(color = "none",
         fill = "none",
         shape = "none")

plotly::ggplotly(gg1)

Species level

Grouping by species

colnbr_per_sp <-
  traits_leaf_level %>%
  filter(leaf_maturity != "immature" |
           is.na(leaf_maturity)) %>%  ## excluding immature leaf for species level
  group_by(voucher_type, colnbr, tag, id_data_individuals, plot_name) %>%
  summarise(
    tax_sp_level = first(tax_sp_level),
    across(starts_with("t_"), ~ mean(., na.rm = TRUE)),
    plot_name = first(plot_name)
  )

## `summarise()` has grouped output by 'voucher_type', 'colnbr', 'tag',
## 'id_data_individuals'. You can override using the `.groups` argument.

traits_taxa <-
  colnbr_per_sp %>%
  group_by(tax_sp_level) %>%
  summarise(nbe_voucher = n(), across(starts_with("t_"), ~ mean(., na.rm = TRUE))) %>%
  left_join(LMA_ratios %>% 
  group_by(tax_sp_level) %>% 
  summarise(t_ratio_lma_whole_meso = mean(ratio_lma_whole_meso, na.rm = T))) %>% 
  filter(!grepl("indet", tax_sp_level))

## Joining with `by = join_by(tax_sp_level)`

traits_taxa_sel <-
  traits_taxa %>%
  select(
    tax_sp_level,
    t_lma_lamina,
    t_lma_mesophyll,
    t_lma_whole_leaf,
    t_mesophyl_thickness,
    t_SPAD_chlorophyll_measurement,
    t_leaf_area,
    t_leaf_structure_mass_area,
    t_twig_specific_gravity,
    t_leaf_N_percentage,
    t_leaf_C_percentage,
    t_ratio_lma_whole_meso
  ) %>%
  mutate(lma = ifelse(is.na(t_lma_lamina), t_lma_mesophyll, t_lma_lamina)) %>% ## taking lma mesophyll if no lma lamina available
  mutate(lma = ifelse(is.na(lma), t_lma_whole_leaf, lma)) %>% ## taking lma whole leaf if no lma mesophyll available
  select(-starts_with("t_lma")) %>% 
  rename(t_lma = lma)

Get maximum diameter values for all species

all_diameters <-
  query_individual_features(id_traits = 1,  ## id of the stem_diameter
                            pivot_table = FALSE,
                            extract_linked_individuals = TRUE)

## extract all values
all_values <-
  all_diameters$traits_num[[1]]

linked_individuals <-
  all_diameters$ind_data %>% select(id_n, idtax_individual_f, tax_sp_level, tax_fam)

## Get the maximum and the percentile 95 of stem_diameter for all species
taxa_level_stem_diameters <-
  all_values %>%
  select(traitvalue, id_data_individuals) %>%
  left_join(
    linked_individuals,
    by = c("id_data_individuals" = "id_n")
  ) %>%
  filter(!is.na(idtax_individual_f)) %>%
  group_by(id_data_individuals) %>% ### if more than one diameter value linked to an individual, take the maximal value. This happens if the individual has been censused more than once.
  summarise(
    tax_sp_level = first(tax_sp_level),
    tax_fam = first(tax_fam),
    traitvalue = max(traitvalue)
  ) %>%
  group_by(tax_sp_level, tax_fam) %>% ### summary by species
  summarise(
    max_stem_diameter = max(traitvalue), ## take the highest recorded value
    t_dbh_095 = quantile(traitvalue, 0.95), ## take the percentile 0.95
    nbe_values = n() ## number of values
  ) %>% 
  filter(!is.na(tax_sp_level))

## `summarise()` has grouped output by 'tax_sp_level'. You can override using the
## `.groups` argument.

traits_taxa_sel <- 
  traits_taxa_sel %>% 
  left_join(taxa_level_stem_diameters %>% 
              select(t_dbh_095, tax_sp_level),
            by = c("tax_sp_level" = "tax_sp_level"))

Quantile-quantile plots

traits_taxa_sel_pivot <- 
  traits_taxa_sel %>% 
  pivot_longer(cols = starts_with("t_"), names_to = "trait") %>% 
  filter(!is.na(value))

ggqq_plots <- ggplot(data = traits_taxa_sel_pivot, 
       mapping = aes(sample = value, 
                     colour = trait)) +
  stat_qq() +
  stat_qq_line() +
  facet_wrap(~ trait, scales = "free") +
  guides(colour = "none")


ggqq_plots

PCA leaves economic spectra

traits_taxa_sel_filtered <- 
  traits_taxa_sel %>%
  select(-t_twig_specific_gravity, 
         -t_dbh_095, 
         -t_leaf_structure_mass_area) %>% 
  rename(ratio_lma_whole_meso = t_ratio_lma_whole_meso) %>% 
  rowwise() %>%
  filter(!any(is.na(c_across(where( ### keeping onlue species where all traits are available
    is.numeric
  ))))) %>%
  mutate(across(starts_with("t_"), ~ log10(.x))) %>% ## log transformation
  ungroup() %>% 
  rename(t_ratio_lma_whole_meso = ratio_lma_whole_meso)

pca_res <- dudi.pca(
  df = traits_taxa_sel_filtered %>% select(where(is.numeric)),
  scannf = FALSE,
  nf = 2,
  center = TRUE
)

pca_res_tax <- tibble(tax_sp_level = traits_taxa_sel_filtered$tax_sp_level,
                      rs1 = pca_res$li$Axis1,
                      rs2 = pca_res$li$Axis2)

env_scores <- tibble(var = rownames(pca_res$c1), 
                     rs1 = pca_res$c1$CS1, rs2 = pca_res$c1$CS2)

pca_1 <- ggplot() +
  geom_point(
    data = pca_res_tax,
    mapping = aes(x = rs1, y = rs2,
                  colour = tax_sp_level)
  ) +
  ggrepel::geom_text_repel(
    data = pca_res_tax,
    aes(x = rs1, y = rs2, label = tax_sp_level),
    size = 3,
    color = "black"
  ) +
  geom_segment(
    data = env_scores,
    aes(
      x = 0,
      y = 0,
      xend = rs1,
      yend = rs2
    ),
    arrow = arrow(length = unit(0.2, "cm"))
  ) +
  ggrepel::geom_text_repel(
    data = env_scores,
    aes(x = rs1, y = rs2, label = var),
    size = 4,
    color = "red"
  ) +
  geom_hline(yintercept = 0, linetype = "dashed") +
  geom_vline(xintercept = 0, linetype = "dashed") +
  guides(colour = "none")

pca_1

traits_taxa_sel_filtered <- 
  traits_taxa_sel %>%
  select(-t_twig_specific_gravity, 
         -t_dbh_095, 
         -t_leaf_structure_mass_area,
         -t_leaf_C_percentage,
         -t_leaf_N_percentage) %>% 
  rename(ratio_lma_whole_meso = t_ratio_lma_whole_meso) %>% 
  rowwise() %>%
  filter(!any(is.na(c_across(where( ### keeping onlue species where all traits are available
    is.numeric
  ))))) %>%
  mutate(across(starts_with("t_"), ~ log10(.x))) %>% ## log transformation
  ungroup() %>% 
  rename(t_ratio_lma_whole_meso = ratio_lma_whole_meso)

pca_res <- dudi.pca(
  df = traits_taxa_sel_filtered %>% select(where(is.numeric)),
  scannf = FALSE,
  nf = 2,
  center = TRUE
)

pca_res_tax <- tibble(tax_sp_level = traits_taxa_sel_filtered$tax_sp_level,
                      rs1 = pca_res$li$Axis1,
                      rs2 = pca_res$li$Axis2)

env_scores <- tibble(var = rownames(pca_res$c1), 
                     rs1 = pca_res$c1$CS1, rs2 = pca_res$c1$CS2)

pca_1 <- ggplot() +
  geom_point(
    data = pca_res_tax,
    mapping = aes(x = rs1, y = rs2,
                  colour = tax_sp_level)
  ) +
  ggrepel::geom_text_repel(
    data = pca_res_tax,
    aes(x = rs1, y = rs2, label = tax_sp_level),
    size = 3,
    color = "black"
  ) +
  geom_segment(
    data = env_scores,
    aes(
      x = 0,
      y = 0,
      xend = rs1,
      yend = rs2
    ),
    arrow = arrow(length = unit(0.2, "cm"))
  ) +
  ggrepel::geom_text_repel(
    data = env_scores,
    aes(x = rs1, y = rs2, label = var),
    size = 4,
    color = "red"
  ) +
  geom_hline(yintercept = 0, linetype = "dashed") +
  geom_vline(xintercept = 0, linetype = "dashed") +
  guides(colour = "none")

pca_1

PCA including twig wood density

traits_taxa_sel_filtered <- 
  traits_taxa_sel %>%
  rowwise() %>%
  filter(!any(is.na(c_across(where( ### keeping onlue species where all traits are available
    is.numeric
  ))))) %>%
  mutate(across(where(is.numeric), ~ log10(.x))) %>% ## log transformation
  ungroup()

pca_res <- dudi.pca(df = traits_taxa_sel_filtered %>% select(where(is.numeric)), scannf = FALSE, nf = 2, center = TRUE)

pca_res_tax <- tibble(tax_sp_level = traits_taxa_sel_filtered$tax_sp_level,
                      rs1 = pca_res$li$Axis1,
                      rs2 = pca_res$li$Axis2)

env_scores <- tibble(var = rownames(pca_res$c1), 
                     rs1 = pca_res$c1$CS1, rs2 = pca_res$c1$CS2)

pca_1 <- ggplot() +
  geom_point(
    data = pca_res_tax,
    mapping = aes(x = rs1, y = rs2,
                  colour = tax_sp_level)
  ) +
  ggrepel::geom_text_repel(
    data = pca_res_tax,
    aes(x = rs1, y = rs2, label = tax_sp_level),
    size = 3,
    color = "black"
  ) +
  geom_segment(
    data = env_scores,
    aes(
      x = 0,
      y = 0,
      xend = rs1,
      yend = rs2
    ),
    arrow = arrow(length = unit(0.2, "cm"))
  ) +
  ggrepel::geom_text_repel(
    data = env_scores,
    aes(x = rs1, y = rs2, label = var),
    size = 4,
    color = "red"
  ) +
  geom_hline(yintercept = 0, linetype = "dashed") +
  geom_vline(xintercept = 0, linetype = "dashed") +
  guides(colour = "none")

pca_1

State of sampling per observatory

all_traits %>% 
  group_by(plot_name, trait) %>% 
  filter(grepl("_observatory", plot_name)) %>% 
  count() %>%
  pivot_wider(names_from = plot_name, 
              values_from = n) %>% 
  gt::gt()

bouamir_pheno_observatory	mbalmayo_pheno_observatory
SPAD_chlorophyll_measurement
483	1019
leaf_C_percentage
74	476
leaf_N_percentage
74	476
leaf_area
297	655
leaf_delta_carbon_13
74	476
leaf_structure_mass_area
357	617
lma_lamina
366	824
lma_mesophyll
457	699
lma_whole_leaf
363	730
mesophyl_thickness
486	1011
twig_specific_gravity
90	134
whole_leaf_area
363	729
total_chlorophyll_content
NA	152

nbe_tree_sampled <- 
  traits_leaf_level %>% 
  filter(grepl("_observatory", plot_name)) %>% 
  group_by(plot_name, tag) %>% 
  count() %>% 
  ungroup() %>% 
  group_by(plot_name) %>% 
  summarise(nbe_tree_sampled = n())

colnbr_per_sp %>%
  filter(grepl("observatory", plot_name)) %>% 
  group_by(plot_name) %>%
  summarise(nbe_pheno_tree = n()) %>% 
  left_join(nbe_tree_sampled) %>% 
  gt::gt(caption = "Number of pheno_tree and tree sampled per observatory")

## Joining with `by = join_by(plot_name)`

Number of pheno_tree and tree sampled per observatory
plot_name	nbe_pheno_tree	nbe_tree_sampled
bouamir_pheno_observatory	114	114
mbalmayo_pheno_observatory	258	181

sampled_per_observatory <- 
  colnbr_per_sp %>% 
  filter(grepl("_observatory", plot_name)) %>% 
  group_by(plot_name, tax_sp_level) %>% 
  summarise(nbe_tree_sampled = n()) %>% 
  pivot_wider(names_from = plot_name, 
              values_from =  c(nbe_tree_sampled), 
              names_prefix = "sampled_")

## `summarise()` has grouped output by 'plot_name'. You can override using the
## `.groups` argument.

nbe_tree_obs <- 
  tree_data %>% 
  filter(grepl("observatory", plot_name)) %>% 
  filter(!is.na(tax_sp_level)) %>% 
  group_by(tax_sp_level, tax_fam) %>% 
  summarise(nbe_tree = n()) %>% 
  ungroup() %>% 
  mutate(plot_name = "total")

## `summarise()` has grouped output by 'tax_sp_level'. You can override using the
## `.groups` argument.

tree_data %>% 
  filter(grepl("_observatory", plot_name)) %>% 
  filter(!is.na(tax_sp_level)) %>% 
  group_by(tax_sp_level, tax_fam, plot_name) %>% 
  summarise(nbe_tree = n()) %>% 
  ungroup() %>% 
  bind_rows(nbe_tree_obs) %>% 
  pivot_wider(names_from = plot_name, 
              values_from =  c(nbe_tree)) %>% 
  left_join(sampled_per_observatory) %>% 
  relocate(contains("bouamir"), .after = where(is.character)) %>% 
  relocate(contains("mbalmayo"), .before = total) %>% 
  arrange(desc(total)) %>% 
  gt::gt() %>% 
   gt::tab_spanner(
    label = "Bouamir",
    columns = contains("bouamir")
  ) %>% 
   gt::tab_spanner(
    label = "Mbalmayo",
    columns = contains("mbalmayo")
  ) %>% 
   gt::cols_label(
     bouamir_pheno_observatory = "Number of \ntree",
    sampled_bouamir_pheno_observatory = "Number of sampled tree",
    mbalmayo_pheno_observatory = "Number of \ntree",
    sampled_mbalmayo_pheno_observatory = "Number of sampled tree",
    tax_sp_level = "Species",
    tax_fam = "Family"
  )  %>%
  gt::tab_style_body(
    fn = function(x) is.na(x),
    style = gt::cell_fill(color = "grey")
  )

## `summarise()` has grouped output by 'tax_sp_level', 'tax_fam'. You can override
## using the `.groups` argument.
## Joining with `by = join_by(tax_sp_level)`

Species	Family	Bouamir		Mbalmayo		total
Species	Family	Number of tree	Number of sampled tree	Number of tree	Number of sampled tree	total
Desbordesia glaucescens	Irvingiaceae	734	4	41	18	775
Terminalia superba	Combretaceae	288	2	31	4	319
Petersianthus macrocarpus	Lecythidaceae	209	2	4	2	213
Alstonia boonei	Apocynaceae	170	1	31	2	201
Pterocarpus soyauxii	Fabaceae	91	1	88	28	179
Lophira alata	Ochnaceae	NA	NA	178	31	178
Erythrophleum suaveolens	Fabaceae	100	1	62	5	162
Klainedoxa gabonensis	Irvingiaceae	84	2	77	3	161
Tessmannia africana	Fabaceae	99	2	37	2	136
Piptadeniastrum africanum	Fabaceae	55	1	75	4	130
Distemonanthus benthamianus	Fabaceae	110	1	19	1	129
Uapaca mole	Phyllanthaceae	92	2	24	2	116
Dialium angolense	Fabaceae	NA	NA	114	5	114
Pteleopsis hylodendron	Combretaceae	90	3	21	1	111
Musanga cecropioides	Urticaceae	43	1	56	15	99
Celtis tessmannii	Cannabaceae	71	1	27	2	98
Xylopia hypolampra	Annonaceae	77	1	14	1	91
Uapaca heudelotii	Phyllanthaceae	38	NA	52	2	90
Hexalobus crispiflorus	Annonaceae	52	2	35	4	87
Pentaclethra macrophylla	Fabaceae	78	1	9	NA	87
Quassia gabonensis	Simaroubaceae	2	NA	81	2	83
Irvingia grandifolia	Irvingiaceae	46	2	34	5	80
Homalium letestui	Salicaceae	72	1	7	1	79
Strombosia pustulata	Olacaceae	77	2	1	NA	78
Hylodendron gabunense	Fabaceae	33	NA	43	2	76
Milicia excelsa	Moraceae	59	1	12	NA	71
Pycnanthus angolensis	Myristicaceae	35	1	36	7	71
Celtis mildbraedii	Cannabaceae	65	1	NA	NA	65
Maranthes glabra	Chrysobalanaceae	6	NA	59	1	65
Greenwayodendron suaveolens	Annonaceae	52	1	2	NA	54
Albizia adianthifolia	Fabaceae	34	NA	19	1	53
Annickia affinis	Annonaceae	52	NA	NA	NA	52
Duboscia macrocarpa	Malvaceae	50	2	1	NA	51
Eribroma oblongum	Malvaceae	31	2	20	2	51
Discoglypremna caloneura	Euphorbiaceae	39	1	6	1	45
Duguetia staudtii	Annonaceae	24	1	19	NA	43
Xylopia aethiopica	Annonaceae	32	NA	10	2	42
Macaranga barteri	Euphorbiaceae	32	NA	8	1	40
Eriocoelum macrocarpum	Sapindaceae	29	NA	9	2	38
Ongokea gore	Olacaceae	27	1	9	1	36
Canarium schweinfurthii	Burseraceae	21	2	14	5	35
Parkia bicolor	Fabaceae	7	1	28	3	35
Erismadelphus exsul	Vochysiaceae	31	2	1	NA	32
Pterygota bequaertii	Malvaceae	11	1	21	1	32
Blighia welwitschii	Sapindaceae	30	1	1	NA	31
Nesogordonia kabingaensis	Malvaceae	28	2	3	NA	31
Ceiba pentandra	Malvaceae	4	NA	25	1	29
Lannea welwitschii	Anacardiaceae	14	1	15	2	29
Syzygium rowlandii	Myrtaceae	29	1	NA	NA	29
Tessmannia anomala	Fabaceae	13	1	16	3	29
Funtumia africana	Apocynaceae	28	1	NA	NA	28
Gilbertiodendron dewevrei	Fabaceae	1	NA	27	3	28
Phyllocosmus africanus	Ixonanthaceae	27	2	1	NA	28
Staudtia kamerunensis	Myristicaceae	27	NA	1	NA	28
Irvingia gabonensis	Irvingiaceae	8	NA	18	2	26
Amphimas pterocarpoides	Fabaceae	21	1	4	1	25
Anopyxis klaineana	Rhizophoraceae	15	1	10	2	25
Cylicodiscus gabunensis	Fabaceae	24	1	1	1	25
Entandrophragma cylindricum	Meliaceae	25	2	NA	NA	25
Funtumia elastica	Apocynaceae	23	NA	2	NA	25
Maesopsis eminii	Rhamnaceae	22	NA	3	NA	25
Ricinodendron heudelotii	Euphorbiaceae	25	1	NA	NA	25
Bombax brevicuspe	Malvaceae	21	1	3	NA	24
Detarium macrocarpum	Fabaceae	10	1	14	20	24
Leplaea cedrata	Meliaceae	23	1	1	NA	24
Albizia ferruginea	Fabaceae	20	1	3	NA	23
Chrysophyllum lacourtianum	Sapotaceae	19	1	4	1	23
Dacryodes edulis	Burseraceae	21	1	2	NA	23
Parinari excelsa	Chrysobalanaceae	15	NA	8	3	23
Corynanthe macroceras	Rubiaceae	11	NA	11	2	22
Irvingia robur	Irvingiaceae	13	2	8	2	21
Lovoa trichilioides	Meliaceae	9	NA	12	2	21
Xylopia rubescens	Annonaceae	21	NA	NA	NA	21
Entandrophragma candollei	Meliaceae	18	NA	2	NA	20
Tapura africana	Dichapetalaceae	20	1	NA	NA	20
Xylopia quintasii	Annonaceae	9	NA	10	NA	19
Afzelia bipindensis	Fabaceae	16	1	2	NA	18
Licania elaeosperma	Chrysobalanaceae	NA	NA	18	2	18
Manilkara letouzeyi	Sapotaceae	18	2	NA	NA	18
Nauclea diderrichii	Rubiaceae	10	NA	8	2	18
Pterocarpus mildbraedii	Fabaceae	18	NA	NA	NA	18
Margaritaria discoidea	Phyllanthaceae	4	1	13	NA	17
Sterculia tragacantha	Malvaceae	13	2	4	NA	17
Bombax buonopozense	Malvaceae	13	NA	3	1	16
Zanthoxylum gilletii	Rutaceae	13	NA	3	NA	16
Maprounea membranacea	Euphorbiaceae	9	NA	6	NA	15
Anthonotha sp1 Bouamir	Fabaceae	14	NA	NA	NA	14
Khaya grandifoliola	Meliaceae	14	1	NA	NA	14
Scottellia klaineana	Achariaceae	10	NA	4	1	14
Cleistopholis patens	Annonaceae	11	NA	2	NA	13
Pachyelasma tessmannii	Fabaceae	8	1	5	1	13
Xylopia staudtii	Annonaceae	11	NA	2	NA	13
Dracaena arborea	Asparagaceae	12	1	NA	NA	12
Beilschmiedia obscura	Lauraceae	10	1	1	NA	11
Coelocaryon preussii	Myristicaceae	9	NA	2	NA	11
Rauvolfia caffra	Apocynaceae	6	NA	5	NA	11
Carapa procera	Meliaceae	10	1	NA	NA	10
Terminalia ivorensis	Combretaceae	NA	NA	10	NA	10
Tetrapleura tetraptera	Fabaceae	6	NA	4	NA	10
Cola lateritia	Malvaceae	8	NA	1	NA	9
Dialium pachyphyllum	Fabaceae	9	NA	NA	NA	9
Ficus mucuso	Moraceae	NA	NA	9	1	9
Khaya ivorensis	Meliaceae	NA	NA	9	1	9
Mammea africana	Calophyllaceae	3	1	6	1	9
Markhamia tomentosa	Bignoniaceae	8	NA	1	NA	9
Angylocalyx pynaertii	Fabaceae	6	NA	2	1	8
Dialium dinklagei	Fabaceae	1	1	7	2	8
Drypetes gossweileri	Putranjivaceae	8	1	NA	NA	8
Trichilia dregeana	Meliaceae	7	NA	1	NA	8
Baillonella toxisperma	Sapotaceae	6	1	1	NA	7
Bridelia grandis	Phyllanthaceae	5	1	2	NA	7
Pouteria altissima	Sapotaceae	7	NA	NA	NA	7
Talbotiella breteleri	Fabaceae	NA	NA	7	NA	7
Cordia africana	Boraginaceae	NA	NA	6	NA	6
Heisteria trillesiana	Olacaceae	6	1	NA	NA	6
Irvingia excelsa	Irvingiaceae	4	1	2	NA	6
Macaranga monandra	Euphorbiaceae	6	1	NA	NA	6
Macaranga spinosa	Euphorbiaceae	1	NA	5	NA	6
Nauclea pobeguinii	Rubiaceae	NA	NA	6	1	6
Omphalocarpum elatum	Sapotaceae	6	1	NA	NA	6
Panda oleosa	Pandaceae	NA	NA	6	3	6
Trichoscypha engong	Anacardiaceae	2	1	4	1	6
Allanblackia floribunda	Clusiaceae	5	NA	NA	NA	5
Antrocaryon klaineanum	Anacardiaceae	3	NA	2	NA	5
Chrysophyllum boukokoense	Sapotaceae	5	1	NA	NA	5
Erythroxylum mannii	Erythroxylaceae	NA	NA	5	1	5
Leplaea thompsonii	Meliaceae	4	NA	1	NA	5
Symphonia globulifera	Clusiaceae	5	1	NA	NA	5
Syzygium staudtii	Myrtaceae	5	NA	NA	NA	5
Treculia africana	Moraceae	4	NA	1	NA	5
Uapaca acuminata	Phyllanthaceae	NA	NA	5	1	5
Uapaca guineensis	Phyllanthaceae	4	NA	1	NA	5
Uapaca staudtii	Phyllanthaceae	3	NA	2	1	5
Vitex rivularis	Lamiaceae	5	1	NA	NA	5
Amphimas ferrugineus	Fabaceae	NA	NA	4	3	4
Anthocleista vogelii	Gentianaceae	2	NA	2	NA	4
Carapa parviflora	Meliaceae	3	NA	1	NA	4
Cathormion altissimum	Fabaceae	NA	NA	4	NA	4
Dialium bipindense	Fabaceae	4	1	NA	NA	4
Entandrophragma angolense	Meliaceae	4	NA	NA	NA	4
Erythrina excelsa	Fabaceae	3	NA	1	NA	4
Fillaeopsis discophora	Fabaceae	NA	NA	4	NA	4
Keayodendron bridelioides	Phyllanthaceae	4	NA	NA	NA	4
Newtonia leucocarpa	Fabaceae	2	NA	2	NA	4
Parkia filicoidea	Fabaceae	NA	NA	4	1	4
Phyllocosmus calothyrsus	Ixonanthaceae	1	NA	3	1	4
Pterygota macrocarpa	Malvaceae	1	NA	3	NA	4
Sterculia rhinopetala	Malvaceae	NA	NA	4	NA	4
Tessmannia lescrauwaetii	Fabaceae	4	NA	NA	NA	4
Uapaca vanhouttei	Phyllanthaceae	4	NA	NA	NA	4
Vitex grandifolia	Lamiaceae	NA	NA	4	1	4
Zanthoxylum heitzii	Rutaceae	4	NA	NA	NA	4
Blighia sapida	Sapindaceae	3	1	NA	NA	3
Cleistopholis glauca	Annonaceae	3	NA	NA	NA	3
Entandrophragma utile	Meliaceae	3	NA	NA	NA	3
Fernandoa adolfi-friderici	Bignoniaceae	2	NA	1	NA	3
Manilkara obovata	Sapotaceae	1	NA	2	2	3
Newtonia grandifolia	Fabaceae	NA	NA	3	NA	3
Oldfieldia africana	Picrodendraceae	NA	NA	3	1	3
Pentadesma butyracea	Clusiaceae	3	NA	NA	NA	3
Shirakiopsis elliptica	Euphorbiaceae	1	NA	2	NA	3
Strombosiopsis tetrandra	Olacaceae	NA	NA	3	NA	3
Autranella congolensis	Sapotaceae	2	1	NA	NA	2
Beilschmiedia congolana	Lauraceae	2	1	NA	NA	2
Chrysophyllum perpulchrum	Sapotaceae	2	1	NA	NA	2
Copaifera mildbraedii	Fabaceae	2	1	NA	NA	2
Diospyros crassiflora	Ebenaceae	NA	NA	2	1	2
Guibourtia tessmannii	Fabaceae	NA	NA	2	NA	2
Hymenocardia lyrata	Phyllanthaceae	2	NA	NA	NA	2
Keyaodendron bridelioides	Euphorbiaceae	2	NA	NA	NA	2
Lebruniodendron leptanthum	Fabaceae	NA	NA	2	NA	2
Maranthes chrysophylla	Chrysobalanaceae	2	1	NA	NA	2
Oubanguia africana	Lecythidaceae	NA	NA	2	NA	2
Parinari hypochrysea	Chrysobalanaceae	2	1	NA	NA	2
Poga oleosa	Anisophylleaceae	1	NA	1	NA	2
Pseudospondias microcarpa	Anacardiaceae	1	NA	1	NA	2
Santiria trimera	Burseraceae	1	NA	1	NA	2
Trichilia tessmannii	Meliaceae	1	NA	1	NA	2
Trilepisium madagascariense	Moraceae	2	1	NA	NA	2
Albizia glaberrima	Fabaceae	1	NA	NA	NA	1
Annea afzelii	Fabaceae	NA	NA	1	NA	1
Anonidium mannii	Annonaceae	1	NA	NA	NA	1
Antrocaryon micraster	Anacardiaceae	1	NA	NA	NA	1
Celtis adolfi-friderici	Cannabaceae	NA	NA	1	NA	1
Chrysophyllum africanum	Sapotaceae	1	NA	NA	NA	1
Coula edulis	Olacaceae	NA	NA	1	NA	1
Diospyros canaliculata	Ebenaceae	NA	NA	1	NA	1
Diospyros gilletii	Ebenaceae	NA	NA	1	NA	1
Diospyros suaveolens	Ebenaceae	NA	NA	1	NA	1
Endodesmia calophylloides	Calophyllaceae	NA	NA	1	NA	1
Entada gigas	Fabaceae	1	NA	NA	NA	1
Entandrophragma congoense	Meliaceae	1	NA	NA	NA	1
Euphorbia letestui	Euphorbiaceae	1	NA	NA	NA	1
Gardenia imperialis	Rubiaceae	1	NA	NA	NA	1
Holoptelea grandis	Ulmaceae	1	NA	NA	NA	1
Morinda lucida	Rubiaceae	1	NA	NA	NA	1
Newtonia glandulifera	Fabaceae	NA	NA	1	NA	1
Strombosia scheffleri	Olacaceae	1	NA	NA	NA	1
Tetrorchidium didymostemon	Euphorbiaceae	1	NA	NA	NA	1
Triplochiton scleroxylon	Malvaceae	NA	NA	1	NA	1

State of sampling in permanent plots

all_traits %>% 
  left_join(meta_data %>% select(plot_name, locality_name),
            by = c("plot_name" = "plot_name")) %>% 
  group_by(locality_name, trait) %>% 
  filter(!grepl("_observatory", plot_name)) %>% 
  count() %>%
  pivot_wider(names_from = locality_name, 
              values_from = n) %>% 
  gt::gt()

Bouamir	Mbalmayo
SPAD_chlorophyll_measurement
563	290
leaf_area
434	484
leaf_structure_mass_area
415	210
lma_lamina
431	278
lma_mesophyll
504	234
lma_whole_leaf
431	229
mesophyl_thickness
580	290
twig_specific_gravity
29	NA
whole_leaf_area
431	229

nbe_tree_sampled <- 
  traits_leaf_level %>% 
  left_join(meta_data %>% select(plot_name, locality_name),
            by = c("plot_name" = "plot_name")) %>% 
  filter(!grepl("observatory", plot_name)) %>% 
  group_by(locality_name, tag) %>% 
  count() %>% 
  ungroup() %>% 
  group_by(locality_name) %>% 
  summarise(nbe_tree_sampled = n())

nbe_tree_sampled %>% 
  gt::gt(caption = "Number of tree sampled in permanent plots per locality")

Number of tree sampled in permanent plots per locality
locality_name	nbe_tree_sampled
Bouamir	156
Mbalmayo	94

sampled_per_locality <- 
  colnbr_per_sp  %>% 
  left_join(meta_data %>% select(plot_name, locality_name),
            by = c("plot_name" = "plot_name")) %>% 
  filter(!grepl("_observatory", plot_name)) %>% 
  group_by(locality_name, tax_sp_level) %>% 
  summarise(nbe_tree_sampled = n()) %>% 
  pivot_wider(names_from = locality_name, 
              values_from =  c(nbe_tree_sampled), 
              names_prefix = "sampled_")

## `summarise()` has grouped output by 'locality_name'. You can override using the
## `.groups` argument.

nbe_tree_obs <- 
  tree_data %>% 
  filter(!grepl("observatory", plot_name)) %>% 
  filter(!is.na(tax_sp_level)) %>% 
  group_by(tax_sp_level, tax_fam) %>% 
  summarise(nbe_tree = n()) %>% 
  ungroup() %>% 
  mutate(locality_name = "total")

## `summarise()` has grouped output by 'tax_sp_level'. You can override using the
## `.groups` argument.

tree_data %>% 
  filter(!grepl("_observatory", plot_name)) %>% 
  filter(!is.na(tax_sp_level)) %>% 
  group_by(tax_sp_level, tax_fam, locality_name) %>% 
  summarise(nbe_tree = n()) %>% 
  ungroup() %>% 
  bind_rows(nbe_tree_obs) %>% 
  pivot_wider(names_from = locality_name, 
              values_from =  c(nbe_tree)) %>% 
  left_join(sampled_per_locality) %>% 
  relocate(contains("bouamir"), .after = where(is.character)) %>% 
  relocate(contains("mbalmayo"), .before = total) %>% 
  arrange(desc(total)) %>% 
  gt::gt() %>% 
   gt::tab_spanner(
    label = "bouamir",
    columns = contains("Bouamir")
  ) %>% 
   gt::tab_spanner(
    label = "Mmbalmayo",
    columns = contains("Mbalmayo")
  ) %>% 
   gt::cols_label(
    Bouamir = "Number of \ntree",
    sampled_Bouamir = "Number of sampled tree",
    Mbalmayo = "Number of \ntree",
    sampled_Mbalmayo = "Number of sampled tree",
    tax_sp_level = "Species",
    tax_fam = "Family"
  )  %>%
  gt::tab_style_body(
    fn = function(x) is.na(x),
    style = gt::cell_fill(color = "grey")
  )

## `summarise()` has grouped output by 'tax_sp_level', 'tax_fam'. You can override
## using the `.groups` argument.
## Joining with `by = join_by(tax_sp_level)`

Species	Family	bouamir		Mmbalmayo		total
Species	Family	Number of tree	Number of sampled tree	Number of tree	Number of sampled tree	total
Gilbertiodendron dewevrei	Fabaceae	NA	NA	1051	NA	1051
Desbordesia glaucescens	Irvingiaceae	412	1	30	NA	442
Tabernaemontana crassa	Apocynaceae	305	2	53	NA	358
Angylocalyx pynaertii	Fabaceae	32	1	253	NA	285
Oubanguia africana	Lecythidaceae	NA	NA	281	4	281
Diospyros iturensis	Ebenaceae	NA	NA	277	NA	277
Strombosiopsis tetrandra	Olacaceae	145	1	86	2	231
Greenwayodendron suaveolens	Annonaceae	199	1	18	NA	217
Englerophytum congolense	Sapotaceae	NA	NA	193	1	193
Staudtia kamerunensis	Myristicaceae	5	NA	175	NA	180
Quassia gabonensis	Simaroubaceae	1	NA	169	1	170
Sorindeia grandifolia	Anacardiaceae	139	2	21	NA	160
Mitragyna stipulosa	Rubiaceae	1	1	157	1	158
Martretia quadricornis	Euphorbiaceae	NA	NA	139	1	139
Duguetia barteri	Annonaceae	NA	NA	136	1	136
Memecylon germainii	Melastomataceae	NA	NA	133	1	133
Strombosia pustulata	Olacaceae	119	1	2	NA	121
Diospyros gilletii	Ebenaceae	NA	NA	120	1	120
Corynanthe pachyceras	Rubiaceae	41	1	77	NA	118
Strombosia grandifolia	Olacaceae	115	1	3	NA	118
Dialium pachyphyllum	Fabaceae	45	1	63	NA	108
Santiria trimera	Burseraceae	81	1	18	NA	99
Musanga cecropioides	Urticaceae	11	NA	82	1	93
Maranthes glabra	Chrysobalanaceae	2	NA	85	2	87
Corynanthe macroceras	Rubiaceae	63	NA	22	NA	85
Uapaca guineensis	Phyllanthaceae	55	2	29	NA	84
Dialium angolense	Fabaceae	1	NA	81	1	82
Annickia affinis	Annonaceae	74	2	7	NA	81
Macaranga spinosa	Euphorbiaceae	NA	NA	78	1	78
Duboscia macrocarpa	Malvaceae	60	2	16	NA	76
Antidesma laciniatum	Phyllanthaceae	74	NA	NA	NA	74
Plagiostyles africana	Euphorbiaceae	74	2	NA	NA	74
Uapaca heudelotii	Phyllanthaceae	58	NA	12	NA	70
Irvingia gabonensis	Irvingiaceae	12	3	57	1	69
Mareyopsis longifolia	Euphorbiaceae	69	3	NA	NA	69
Talbotiella breteleri	Fabaceae	NA	NA	69	1	69
Klaineanthus gabonii	Euphorbiaceae	59	3	9	NA	68
Tessmannia africana	Fabaceae	23	1	40	NA	63
Morelia senegalensis	Rubiaceae	NA	NA	62	2	62
Petersianthus macrocarpus	Lecythidaceae	42	NA	20	1	62
Klainedoxa gabonensis	Irvingiaceae	34	1	27	1	61
Uapaca staudtii	Phyllanthaceae	59	NA	NA	NA	59
Pterocarpus soyauxii	Fabaceae	18	NA	40	NA	58
Maesobotrya klaineana	Phyllanthaceae	22	1	35	NA	57
Lecaniodiscus cupanioides	Sapindaceae	54	3	2	1	56
Panda oleosa	Pandaceae	13	2	43	NA	56
Trichoscypha acuminata	Anacardiaceae	31	NA	25	NA	56
Leptaulus daphnoides	Cardiopteridaceae	NA	NA	55	1	55
Diospyros suaveolens	Ebenaceae	NA	NA	54	NA	54
Eriocoelum macrocarpum	Sapindaceae	40	1	13	NA	53
Manilkara obovata	Sapotaceae	1	1	52	NA	53
Hylodendron gabunense	Fabaceae	25	1	25	NA	50
Desplatsia dewevrei	Malvaceae	38	NA	11	NA	49
Englerophytum laurentii	Sapotaceae	NA	NA	49	1	49
Uapaca mole	Phyllanthaceae	34	NA	15	NA	49
Octolobus heteromerus	Malvaceae	28	1	19	1	47
Pentaclethra macrophylla	Fabaceae	40	NA	5	NA	45
Aubrevillea platycarpa	Fabaceae	NA	NA	44	1	44
Celtis tessmannii	Cannabaceae	16	NA	28	NA	44
Plagiosiphon emarginatus	Fabaceae	NA	NA	44	NA	44
Hexalobus crispiflorus	Annonaceae	17	2	26	NA	43
Octolobus spectabilis	Malvaceae	33	1	10	NA	43
Pycnanthus angolensis	Myristicaceae	6	NA	37	NA	43
Anthonotha macrophylla	Fabaceae	24	2	15	NA	39
Dacryodes edulis	Burseraceae	28	1	10	NA	38
Xylopia quintasii	Annonaceae	10	1	28	NA	38
Garcinia mannii	Clusiaceae	16	1	20	1	36
Alstonia boonei	Apocynaceae	29	NA	6	NA	35
Anthonotha sp1 Bouamir	Fabaceae	35	2	NA	NA	35
Coula edulis	Olacaceae	NA	NA	35	NA	35
Blighia welwitschii	Sapindaceae	30	NA	3	NA	33
Carapa procera	Meliaceae	26	NA	7	NA	33
Drypetes spinosodentata	Putranjivaceae	33	1	NA	NA	33
Funtumia africana	Apocynaceae	22	NA	11	NA	33
Trichilia rubescens	Meliaceae	27	1	6	NA	33
Lophira alata	Ochnaceae	NA	NA	31	NA	31
Erythrophleum suaveolens	Fabaceae	19	NA	11	1	30
Phyllocosmus africanus	Ixonanthaceae	29	NA	1	NA	30
Rothmannia lujae	Rubiaceae	15	1	15	NA	30
Myrianthus arboreus	Urticaceae	23	NA	6	NA	29
Pterygota bequaertii	Malvaceae	6	NA	23	NA	29
Markhamia tomentosa	Bignoniaceae	24	NA	4	NA	28
Aidia micrantha	Rubiaceae	20	1	7	NA	27
Duguetia staudtii	Annonaceae	9	1	18	NA	27
Homalium abdessammadii	Salicaceae	NA	NA	27	1	27
Cola lateritia	Malvaceae	22	1	4	1	26
Licania elaeosperma	Chrysobalanaceae	NA	NA	26	1	26
Macaranga barteri	Euphorbiaceae	11	NA	15	1	26
Macaranga monandra	Euphorbiaceae	18	1	8	NA	26
Xylopia aethiopica	Annonaceae	9	NA	17	NA	26
Celtis mildbraedii	Cannabaceae	24	NA	1	NA	25
Rinorea oblongifolia	Violaceae	22	NA	3	NA	25
Uvariastrum pierreanum	Annonaceae	8	1	17	1	25
Carapa parviflora	Meliaceae	14	NA	9	1	23
Cola acuminata	Malvaceae	10	NA	13	1	23
Terminalia superba	Combretaceae	23	NA	NA	NA	23
Tessmannia anomala	Fabaceae	3	NA	20	1	23
Treculia africana	Moraceae	3	1	20	1	23
Coelocaryon preussii	Myristicaceae	4	1	18	1	22
Funtumia elastica	Apocynaceae	19	NA	3	NA	22
Globulostylis cuvieroides	Rubiaceae	22	NA	NA	NA	22
Pseudospondias microcarpa	Anacardiaceae	12	NA	10	1	22
Garcinia epunctata	Clusiaceae	15	1	6	NA	21
Mammea africana	Calophyllaceae	15	NA	6	NA	21
Cola pachycarpa	Malvaceae	NA	NA	20	NA	20
Distemonanthus benthamianus	Fabaceae	10	NA	10	1	20
Anonidium mannii	Annonaceae	11	2	8	NA	19
Chytranthus macrophyllus	Sapindaceae	3	1	16	1	19
Cleistopholis glauca	Annonaceae	2	NA	17	1	19
Milicia excelsa	Moraceae	12	NA	7	NA	19
Oncoba glauca	Salicaceae	14	3	5	NA	19
Allanblackia floribunda	Clusiaceae	4	1	14	2	18
Irvingia grandifolia	Irvingiaceae	7	NA	11	NA	18
Piptadeniastrum africanum	Fabaceae	4	NA	14	NA	18
Vitex rivularis	Lamiaceae	16	NA	2	NA	18
Calpocalyx dinklagei	Fabaceae	14	1	3	NA	17
Lovoa trichilioides	Meliaceae	5	NA	12	NA	17
Beilschmiedia obscura	Lauraceae	13	2	3	2	16
Bridelia grandis	Phyllanthaceae	3	NA	13	NA	16
Discoglypremna caloneura	Euphorbiaceae	13	1	3	NA	16
Maprounea membranacea	Euphorbiaceae	4	1	12	1	16
Nesogordonia kabingaensis	Malvaceae	8	NA	8	NA	16
Centroplacus glaucinus	Centroplacaceae	15	2	NA	NA	15
Cleistopholis patens	Annonaceae	5	NA	10	NA	15
Diospyros crassiflora	Ebenaceae	NA	NA	15	NA	15
Scottellia klaineana	Achariaceae	4	NA	11	1	15
Terminalia ivorensis	Combretaceae	NA	NA	15	1	15
Barteria fistulosa	Passifloraceae	9	1	5	NA	14
Endodesmia calophylloides	Calophyllaceae	NA	NA	14	NA	14
Homalium letestui	Salicaceae	13	NA	1	1	14
Hymenocardia lyrata	Phyllanthaceae	14	NA	NA	NA	14
Lasiodiscus mannii	Rhamnaceae	7	1	7	NA	14
Leonardoxa africana	Fabaceae	NA	NA	14	1	14
Ongokea gore	Olacaceae	6	NA	8	NA	14
Tetrapleura tetraptera	Fabaceae	7	NA	6	NA	13
Bombax brevicuspe	Malvaceae	3	NA	9	NA	12
Cleistanthus polystachyus	Phyllanthaceae	NA	NA	12	NA	12
Cordia africana	Boraginaceae	NA	NA	12	NA	12
Garcinia ovalifolia	Clusiaceae	4	1	8	NA	12
Pauridiantha floribunda	Rubiaceae	12	1	NA	NA	12
Shirakiopsis elliptica	Euphorbiaceae	11	NA	1	NA	12
Sterculia tragacantha	Malvaceae	5	NA	7	1	12
Trichilia prieuriana	Meliaceae	NA	NA	12	NA	12
Xylopia hypolampra	Annonaceae	12	NA	NA	NA	12
Albizia adianthifolia	Fabaceae	1	NA	10	1	11
Amphimas pterocarpoides	Fabaceae	4	2	7	NA	11
Anthonotha fragrans	Fabaceae	11	1	NA	NA	11
Blighia sapida	Sapindaceae	3	NA	8	2	11
Chrysophyllum africanum	Sapotaceae	5	NA	6	NA	11
Gardenia imperialis	Rubiaceae	5	NA	6	1	11
Heisteria parvifolia	Olacaceae	11	4	NA	NA	11
Hemandradenia mannii	Connaraceae	NA	NA	11	NA	11
Lepidobotrys staudtii	Lepidobotryaceae	11	2	NA	NA	11
Mallotus oppositifolius	Euphorbiaceae	11	1	NA	NA	11
Nauclea diderrichii	Rubiaceae	9	NA	2	NA	11
Persea americana	Lauraceae	NA	NA	11	NA	11
Trichoscypha oddonii	Anacardiaceae	7	NA	4	NA	11
Baphia nitida	Fabaceae	NA	NA	10	NA	10
Bridelia micrantha	Phyllanthaceae	4	NA	6	NA	10
Eribroma oblongum	Malvaceae	10	NA	NA	NA	10
Heinsia crinita	Rubiaceae	9	NA	1	NA	10
Isolona hexaloba	Annonaceae	9	1	1	NA	10
Monodora tenuifolia	Annonaceae	10	NA	NA	NA	10
Sclerocroton cornutus	Euphorbiaceae	4	1	6	NA	10
Strombosia zenkeri	Olacaceae	10	2	NA	NA	10
Trichilia tessmannii	Meliaceae	3	NA	7	NA	10
Baikiaea insignis	Fabaceae	NA	NA	9	1	9
Christiana africana	Malvaceae	NA	NA	9	NA	9
Chrysophyllum lacourtianum	Sapotaceae	8	NA	1	NA	9
Coffea canephora	Rubiaceae	9	NA	NA	NA	9
Desplatsia chrysochlamys	Malvaceae	9	NA	NA	NA	9
Detarium macrocarpum	Fabaceae	6	NA	3	NA	9
Diospyros hoyleana	Ebenaceae	2	1	7	NA	9
Drypetes chevalieri	Putranjivaceae	9	1	NA	NA	9
Oncoba dentata	Salicaceae	8	1	1	NA	9
Pteleopsis hylodendron	Combretaceae	6	NA	3	NA	9
Strombosia scheffleri	Olacaceae	9	1	NA	NA	9
Dialium guineense	Fabaceae	7	NA	1	1	8
Oncoba welwitschii	Salicaceae	NA	NA	8	NA	8
Parinari excelsa	Chrysobalanaceae	3	NA	5	NA	8
Symphonia globulifera	Clusiaceae	5	1	3	NA	8
Trichilia welwitschii	Meliaceae	8	1	NA	NA	8
Uapaca vanhouttei	Phyllanthaceae	8	NA	NA	NA	8
Alchornea laxiflora	Euphorbiaceae	7	NA	NA	NA	7
Allophylus africanus	Sapindaceae	3	1	4	NA	7
Antidesma membranaceum	Phyllanthaceae	7	1	NA	NA	7
Dialium bipindense	Fabaceae	NA	NA	7	NA	7
Erismadelphus exsul	Vochysiaceae	6	NA	1	NA	7
Fernandoa adolfi-friderici	Bignoniaceae	3	NA	4	NA	7
Guibourtia ehie	Fabaceae	NA	NA	7	NA	7
Leplaea cedrata	Meliaceae	3	NA	4	NA	7
Margaritaria discoidea	Phyllanthaceae	3	NA	4	NA	7
Parkia bicolor	Fabaceae	1	NA	6	NA	7
Psydrax splendens	Rubiaceae	7	NA	NA	NA	7
Rhabdophyllum arnoldianum	Ochnaceae	NA	NA	7	NA	7
Tetrorchidium didymostemon	Euphorbiaceae	NA	NA	7	NA	7
Zanthoxylum gilletii	Rutaceae	7	NA	NA	NA	7
Aulacocalyx jasminiflora	Rubiaceae	1	NA	5	NA	6
Canarium schweinfurthii	Burseraceae	3	NA	3	NA	6
Ceiba pentandra	Malvaceae	1	NA	5	NA	6
Cola rostrata	Malvaceae	NA	NA	6	NA	6
Diospyros conocarpa	Ebenaceae	4	NA	2	NA	6
Entandrophragma candollei	Meliaceae	2	NA	4	NA	6
Entandrophragma cylindricum	Meliaceae	4	NA	2	NA	6
Ficus exasperata	Moraceae	NA	NA	6	NA	6
Xylopia staudtii	Annonaceae	6	NA	NA	NA	6
Zanthoxylum heitzii	Rutaceae	6	1	NA	NA	6
Anthocleista liebrechtsiana	Gentianaceae	NA	NA	5	1	5
Aoranthe cladantha	Rubiaceae	1	NA	4	NA	5
Keayodendron bridelioides	Phyllanthaceae	4	1	1	NA	5
Lannea welwitschii	Anacardiaceae	3	NA	2	NA	5
Maesopsis eminii	Rhamnaceae	3	NA	2	NA	5
Millettia sanagana	Fabaceae	3	NA	2	NA	5
Picralima nitida	Apocynaceae	5	NA	NA	NA	5
Spathandra blakeoides	Melastomataceae	NA	NA	5	1	5
Streblus usambarensis	Moraceae	NA	NA	5	NA	5
Tricalysia pangolina	Rubiaceae	5	NA	NA	NA	5
Trichilia dregeana	Meliaceae	5	NA	NA	NA	5
Vitex grandifolia	Lamiaceae	NA	NA	5	NA	5
Xylopia rubescens	Annonaceae	5	NA	NA	NA	5
Zanthoxylum macrophyllum	Rutaceae	5	NA	NA	NA	5
Casearia stipitata	Salicaceae	NA	NA	4	1	4
Chlamydocola chlamydantha	Malvaceae	4	NA	NA	NA	4
Desplatsia mildbraedii	Malvaceae	4	1	NA	NA	4
Englerodendron isopetalum	Fabaceae	NA	NA	4	1	4
Ficus mucuso	Moraceae	NA	NA	4	NA	4
Nauclea pobeguinii	Rubiaceae	2	NA	2	1	4
Omphalocarpum elatum	Sapotaceae	4	1	NA	NA	4
Paropsia guineensis	Passifloraceae	4	NA	NA	NA	4
Pterocarpus mildbraedii	Fabaceae	4	NA	NA	NA	4
Ricinodendron heudelotii	Euphorbiaceae	4	NA	NA	NA	4
Synsepalum brevipes	Sapotaceae	NA	NA	4	1	4
Tapura africana	Dichapetalaceae	4	NA	NA	NA	4
Trichoscypha engong	Anacardiaceae	1	1	3	NA	4
Vepris glaberrima	Rutaceae	4	1	NA	NA	4
Afzelia bipindensis	Fabaceae	1	NA	2	NA	3
Anopyxis klaineana	Rhizophoraceae	3	NA	NA	NA	3
Anthocleista vogelii	Gentianaceae	3	NA	NA	NA	3
Aptandra zenkeri	Olacaceae	3	1	NA	NA	3
Clausena anisata	Rutaceae	3	NA	NA	NA	3
Cleistanthus gabonensis	Phyllanthaceae	NA	NA	3	NA	3
Cylicodiscus gabunensis	Fabaceae	2	NA	1	NA	3
Drypetes gossweileri	Putranjivaceae	1	NA	2	NA	3
Drypetes laciniata	Putranjivaceae	3	NA	NA	NA	3
Leplaea thompsonii	Meliaceae	3	NA	NA	NA	3
Maytenus undatus	Celastraceae	3	1	NA	NA	3
Morinda lucida	Rubiaceae	1	1	2	1	3
Newtonia glandulifera	Fabaceae	NA	NA	3	NA	3
Pouteria altissima	Sapotaceae	3	1	NA	NA	3
Rauvolfia vomitoria	Apocynaceae	1	NA	2	NA	3
Santiria ebo	Burseraceae	3	NA	NA	NA	3
Strombosia tetandra	Olacaceae	3	NA	NA	NA	3
Synsepalum seretii	Sapotaceae	NA	NA	3	NA	3
Syzygium rowlandii	Myrtaceae	2	NA	1	NA	3
Syzygium staudtii	Myrtaceae	3	1	NA	NA	3
Treculia obovoidea	Moraceae	NA	NA	3	NA	3
Tricalysia bifida	Rubiaceae	3	NA	NA	NA	3
Trilepisium madagascariense	Moraceae	1	NA	2	NA	3
Uapaca acuminata	Phyllanthaceae	2	NA	1	NA	3
Aulacocalyx caudata	Rubiaceae	1	1	1	NA	2
Berlinia bracteosa	Fabaceae	2	1	NA	NA	2
Bombax buonopozense	Malvaceae	1	NA	1	NA	2
Campylospermum mannii	Ochnaceae	2	NA	NA	NA	2
Cathormion altissimum	Fabaceae	NA	NA	2	NA	2
Celtis adolfi-friderici	Cannabaceae	NA	NA	2	NA	2
Chrysophyllum giganteum	Sapotaceae	2	1	NA	NA	2
Cola cauliflora	Malvaceae	2	NA	NA	NA	2
Cola digitata	Malvaceae	NA	NA	2	NA	2
Croton sylvaticus	Euphorbiaceae	NA	NA	2	NA	2
Diospyros ferrea	Ebenaceae	NA	NA	2	1	2
Dracaena arborea	Asparagaceae	2	NA	NA	NA	2
Entandrophragma congoense	Meliaceae	1	NA	1	NA	2
Erythroxylum mannii	Erythroxylaceae	NA	NA	2	1	2
Ficus vogeliana	Moraceae	1	1	1	NA	2
Grewia coriacea	Tiliaceae	2	NA	NA	NA	2
Guibourtia tessmannii	Fabaceae	NA	NA	2	NA	2
Hymenocardia ulmoides	Phyllanthaceae	2	1	NA	NA	2
Irvingia robur	Irvingiaceae	NA	NA	2	NA	2
Khaya grandifoliola	Meliaceae	1	NA	1	NA	2
Khaya ivorensis	Meliaceae	1	NA	1	NA	2
Leptactina arborescens	Rubiaceae	1	NA	1	1	2
Mangifera indica	Anacardiaceae	NA	NA	2	NA	2
Massularia acuminata	Rubiaceae	1	1	1	NA	2
Oldfieldia africana	Picrodendraceae	NA	NA	2	NA	2
Psychotria venosa	Rubiaceae	NA	NA	2	NA	2
Ptychopetalum petiolatum	Olacaceae	NA	NA	2	1	2
Quassia silvestris	Simaroubaceae	1	NA	1	NA	2
Rhabdophyllum calophyllum	Ochnaceae	2	1	NA	NA	2
Rinorea grandifolia	Violaceae	NA	NA	2	NA	2
Synsepalum cerasiferum	Sapotaceae	2	1	NA	NA	2
Talbotiella batesii	Fabaceae	NA	NA	2	NA	2
Turraeanthus africanus	Meliaceae	2	NA	NA	NA	2
Afzelia pachyloba	Fabaceae	1	NA	NA	NA	1
Albizia glaberrima	Fabaceae	1	NA	NA	NA	1
Albizia zygia	Fabaceae	NA	NA	1	NA	1
Amphimas ferrugineus	Fabaceae	1	NA	NA	NA	1
Annickia chlorantha	Annonaceae	NA	NA	1	NA	1
Antrocaryon klaineanum	Anacardiaceae	NA	NA	1	NA	1
Baillonella toxisperma	Sapotaceae	1	NA	NA	NA	1
Beilschmiedia fulva	Lauraceae	NA	NA	1	NA	1
Bridelia atroviridis	Phyllanthaceae	1	NA	NA	NA	1
Calpocalyx heitzii	Fabaceae	1	NA	NA	NA	1
Cassipourea congoensis	Rhizophoraceae	NA	NA	1	NA	1
Celtis zenkeri	Cannabaceae	1	NA	NA	NA	1
Chrysophyllum beguei	Sapotaceae	1	NA	NA	NA	1
Chrysophyllum perpulchrum	Sapotaceae	1	1	NA	NA	1
Cola lepidota	Malvaceae	NA	NA	1	NA	1
Cuviera longiflora	Rubiaceae	1	NA	NA	NA	1
Dacryodes ledermannii	Burseraceae	1	NA	NA	NA	1
Dacryodes macrophylla	Burseraceae	NA	NA	1	NA	1
Diospyros pseudomespilus	Ebenaceae	1	1	NA	NA	1
Entandrophragma utile	Meliaceae	NA	NA	1	NA	1
Erythrina excelsa	Fabaceae	1	NA	NA	NA	1
Erythroxylum emarginatum	Erythroxylaceae	1	1	NA	NA	1
Garcinia kola	Clusiaceae	NA	NA	1	NA	1
Garcinia smeathmannii	Clusiaceae	1	1	NA	NA	1
Glyphaea brevis	Malvaceae	1	NA	NA	NA	1
Grewia oligoneura	Tiliaceae	1	NA	NA	NA	1
Heinsia parviflora	Rubiaceae	1	NA	NA	NA	1
Holoptelea grandis	Ulmaceae	1	NA	NA	NA	1
Irvingia excelsa	Irvingiaceae	1	NA	NA	NA	1
Leptaulus zenkeri	Cardiopteridaceae	NA	NA	1	NA	1
Majidea fosteri	Sapindaceae	NA	NA	1	NA	1
Manilkara pellegriniana	Sapotaceae	1	1	NA	NA	1
Meiocarpidium oliverianum	Annonaceae	NA	NA	1	NA	1
Newbouldia laevis	Bignoniaceae	1	1	NA	NA	1
Ochna calodendron	Ochnaceae	NA	NA	1	NA	1
Parkia filicoidea	Fabaceae	NA	NA	1	NA	1
Paropsia grewioides	Passifloraceae	1	NA	NA	NA	1
Pauridiantha afzelii	Rubiaceae	1	NA	NA	NA	1
Rauvolfia caffra	Apocynaceae	NA	NA	1	NA	1
Rinorea dentata	Violaceae	1	NA	NA	NA	1
Rothmannia talbotii	Rubiaceae	NA	NA	1	NA	1
Sterculia rhinopetala	Malvaceae	NA	NA	1	NA	1
Strephonema sericeum	Combretaceae	1	1	NA	NA	1
Synsepalum letouzeyi	Sapotaceae	NA	NA	1	NA	1
Synsepalum longicuneatum	Sapotaceae	1	1	NA	NA	1
Synsepalum sp.nov. Bouamir	Sapotaceae	1	1	NA	NA	1
Syzygium guineense	Myrtaceae	1	NA	NA	NA	1
Tricalysia anomala	Rubiaceae	1	NA	NA	NA	1
Tricalysia fangana	Rubiaceae	1	NA	NA	NA	1
Tridesmostemon omphalocarpoides	Sapotaceae	1	NA	NA	NA	1
Vitex doniana	Lamiaceae	1	NA	NA	NA	1
Xylopia phloiodora	Annonaceae	1	1	NA	NA	1

Variance decomposition

Following the method in Boisseaux et al 2024.

Results below are not comparable across traits because datasets varies. For example chlorophyll content and delta carbon measurements were measured on samples that favours within-species variation.

MyData_Sp <- 
  colnbr_per_sp %>%
  left_join(
    tree_data %>% select(
      id_n,
      locality_name,
      taxa_level_bioclimatic_group,
      taxa_level_phenology,
      taxa_level_succession_guild
    ),
    by = c("id_data_individuals" = "id_n")
  ) %>% 
  mutate(site = case_when(
   grepl("Mbalmayo", locality_name) ~ "Mbalmayo",
   grepl("Bouamir", locality_name) ~ "Bouamir"
  )) %>% 
  ungroup()


### function for variance decomposition for each trait


func_var_components <- function(my_data,
                                col_tax = "tax_sp_level",
                                col_trait,
                                col_fixed_group) {
  my_data <-
    my_data %>%
    rename(
      taxa = all_of(col_tax),
      col_trait = all_of(col_trait),
      col_fixed_group = all_of(col_fixed_group)
    ) %>%
    select(taxa, col_trait, col_fixed_group) %>%
    filter(!is.na(taxa), !is.na(col_trait), !is.na(col_fixed_group))
  
  
  # Fit the linear mixed-effects model
  model <- nlme::lme(
    col_trait ~ col_fixed_group,
    random =  ~ 1 |
      taxa,
    data = my_data,
    na.action = na.omit,
    method = "ML"
  )
  
  Var_components <- lme4::VarCorr(model)
  var_sp <- as.numeric(Var_components[1, 1]) #for the random effects - species
  var_indv <- as.numeric(Var_components[2, 1]) #model residual, also known as the intraspecific residual
  
  
  #Fit the associated null model with random intercept on species
  null_model <- nlme::lme(
    col_trait ~ 1,
    random =  ~ 1 |
      taxa,
    data = my_data,
    na.action = na.omit,
    method = "ML"
  )
  
  # Obtain the variance of the random effect in the null model.
  random_variance_null <- as.numeric(lme4::VarCorr(null_model)[1, 1])
  
  # Obtain the residual variance
  residual_variance_null <- as.numeric(lme4::VarCorr(null_model)[2, 1])
  
  # Obtain the total variance of the null model
  v_0 <- random_variance_null + residual_variance_null
  
  # Calculate the variance component linked to the env't by substracting the residual variance of the model (var_indv) and the variance explained by the random factor, the species (var_sp), from the variance of the null model
  
  # Variance partitioning
  comp_sp <- round(100 * var_sp / v_0, 1)
  comp_indv <- round(100 * var_indv / v_0, 1)
  
  comp_envt <- 100 - (comp_indv +  comp_sp)
  
  res <- tibble(
    trait = col_trait,
    fixed_group = col_fixed_group,
    comp_var = c("species", "within_species", "group"),
    value = c(comp_sp, comp_indv, comp_envt)
  )
  
  return(res)
}

all_traits_names <- 
  names(MyData_Sp %>% select(starts_with("t_")))

MyData_Sp <- 
  MyData_Sp %>%
  mutate(t_leaf_delta_carbon_13 = abs(t_leaf_delta_carbon_13)) %>% 
  mutate(across(starts_with("t_"), ~ log10(.x))) ## log transformation

Using phenology as a fixed group.

res_list <- vector('list', length(all_traits_names))
for (i in 1:length(all_traits_names)) {
  res_list[[i]] <-
  func_var_components(my_data = MyData_Sp,
                 col_trait = all_traits_names[i],
                 col_fixed_group = "taxa_level_phenology")
}


res <- bind_rows(res_list)
res %>% 
  filter(comp_var == "group")

## # A tibble: 13 × 4
##    trait                          fixed_group          comp_var  value
##    <chr>                          <chr>                <chr>     <dbl>
##  1 t_SPAD_chlorophyll_measurement taxa_level_phenology group     4.70 
##  2 t_whole_leaf_area              taxa_level_phenology group     1.60 
##  3 t_mesophyl_thickness           taxa_level_phenology group    -0.100
##  4 t_lma_mesophyll                taxa_level_phenology group     1.10 
##  5 t_lma_lamina                   taxa_level_phenology group     1.30 
##  6 t_lma_whole_leaf               taxa_level_phenology group     2.80 
##  7 t_leaf_area                    taxa_level_phenology group     3.80 
##  8 t_leaf_structure_mass_area     taxa_level_phenology group     0.5  
##  9 t_twig_specific_gravity        taxa_level_phenology group     0.600
## 10 t_total_chlorophyll_content    taxa_level_phenology group     0.100
## 11 t_leaf_delta_carbon_13         taxa_level_phenology group     8.10 
## 12 t_leaf_N_percentage            taxa_level_phenology group     6.40 
## 13 t_leaf_C_percentage            taxa_level_phenology group     0.5

ggplot(res, aes(x = trait, y = value, fill = comp_var)) +
  geom_bar(stat = "identity", position = "stack") +
  labs(title = "taxa_level_phenology", y = "Percentage") +
  theme_minimal() +
  coord_flip()

Using guild as a fixed group.

res_list <- vector('list', length(all_traits_names))
for (i in 1:length(all_traits_names)) {
  res_list[[i]] <-
  func_var_components(my_data = MyData_Sp,
                 col_trait = all_traits_names[i],
                 col_fixed_group = "taxa_level_succession_guild")
}


res <- bind_rows(res_list)
res %>% 
  filter(comp_var == "group")

## # A tibble: 13 × 4
##    trait                          fixed_group                 comp_var  value
##    <chr>                          <chr>                       <chr>     <dbl>
##  1 t_SPAD_chlorophyll_measurement taxa_level_succession_guild group     4.30 
##  2 t_whole_leaf_area              taxa_level_succession_guild group     1    
##  3 t_mesophyl_thickness           taxa_level_succession_guild group    -0.100
##  4 t_lma_mesophyll                taxa_level_succession_guild group     1.20 
##  5 t_lma_lamina                   taxa_level_succession_guild group     0.100
##  6 t_lma_whole_leaf               taxa_level_succession_guild group     0.700
##  7 t_leaf_area                    taxa_level_succession_guild group     1.30 
##  8 t_leaf_structure_mass_area     taxa_level_succession_guild group     2.5  
##  9 t_twig_specific_gravity        taxa_level_succession_guild group     3.60 
## 10 t_total_chlorophyll_content    taxa_level_succession_guild group     0.200
## 11 t_leaf_delta_carbon_13         taxa_level_succession_guild group     5.60 
## 12 t_leaf_N_percentage            taxa_level_succession_guild group     1.40 
## 13 t_leaf_C_percentage            taxa_level_succession_guild group     0.400

ggplot(res, aes(x = trait, y = value, fill = comp_var)) +
  geom_bar(stat = "identity", position = "stack") +
  labs(title = "taxa_level_succession_guild", y = "Percentage") +
  theme_minimal() +
  coord_flip()

Traits sampling summary in Mbalmayo and Bouamir

Gilles Dauby

2024-11-06

Extract data from the database

Explore and rearrange the dataset

Checklist of traits and features and their definition

How traits are collected in the field and metada are associated

See one measurement

A glimpse into the raw dataset

Arranging the data into columns

Quantile-quantile plots

Leaf level

Leaf Mass Per Area

Ratio of LMA at mesophyl scale and LMA at the whole leaf scale

Nitrogen content and LMA

The whole dataset

Within species

Carbon content and LMA

Total chlorophyll content and SPAD measurement

Total chlorophyll content and Nitrogen content

Total chlorophyll content and stable delta 13C isotope

Species level

Grouping by species

Get maximum diameter values for all species

Quantile-quantile plots

PCA leaves economic spectra

PCA including twig wood density

State of sampling per observatory

State of sampling in permanent plots

Variance decomposition