Growth forms coverage#

We extracted growth form information (tree/shrub/herb) from TRY (Trait-ID 42) to each iNaturalist observation and estimated the coverage of each growth form for all sPlotOpen plots. We chose the most commonly used classification for each species. The average tree/shrub/herb coverage was then calculated for each grid cell and the these means correlated.

This section covers:

Link growth form information to iNaturalist observations
Calculate coverage
Plot coverage
Compare to sPlot life form coverage

# packages
import os
import pandas as pd
import numpy as np

from matplotlib import pyplot as plt
import seaborn as sns
import matplotlib.ticker as ticker
from matplotlib.colors import LogNorm, Normalize
from matplotlib.ticker import MaxNLocator
import cartopy.crs as ccrs
import cartopy.feature as cfeature
from matplotlib.colors import BoundaryNorm

Link growth form information to iNaturalist observations#

iNat = pd.read_csv("iNat_TRY_log.csv")

life_forms = pd.read_csv("TRY/Life_Forms/19233.txt", sep = "\t", encoding="iso-8859-1", 
                      usecols = ["AccSpeciesName", "SpeciesName", "TraitID", "TraitName", "OrigValueStr"],
                     dtype={'TraitID': float})

growth_forms = life_forms[life_forms["TraitID"]==42.0]

growth_forms = growth_forms.dropna(subset=["OrigValueStr"])

Define the variations of growth form terms:

search_terms_tree = ["tree", "Tree", "TREE", "seedling", "hardwood", "softwood", "Hardwood", "Softwood"]
search_terms_herb = ["herb","Herb", "HERB","graminoid", "Graminoid","GRAMINOID", "Forb","forb", 
                     "Grass","grass", "GRASS", "sedge","SEDGE", "fern", "Fern", "FERN"]
search_terms_shrub = ["shrub","Shrub", "SHRUB", "seedling","vine", "Vine", "VINE", "liana", "Liana", "LIANA"]

Add 1 for is_tree, is_shrub, and is_herb, 0 if not:

growth_forms['is_tree'] = (growth_forms["OrigValueStr"]=="T") | growth_forms['OrigValueStr'].str.contains('|'.join(search_terms_tree))

growth_forms['is_tree'] = growth_forms['is_tree'].astype(int)

growth_forms['is_shrub'] = (growth_forms["OrigValueStr"]=="S") | (growth_forms["OrigValueStr"]=="L") | growth_forms['OrigValueStr'].str.contains('|'.join(search_terms_shrub))

growth_forms['is_shrub'] = growth_forms['is_shrub'].astype(int)

growth_forms['is_herb'] = (growth_forms["OrigValueStr"]=="H") | (growth_forms["OrigValueStr"]=="F") | (growth_forms["OrigValueStr"]=="G") | growth_forms['OrigValueStr'].str.contains('|'.join(search_terms_herb))

growth_forms['is_herb'] = growth_forms['is_herb'].astype(int)

growth_forms

	SpeciesName	AccSpeciesName	TraitID	TraitName	OrigValueStr	is_tree	is_shrub	is_herb
0	Bartsia alpina	Bartsia alpina	42.0	Plant growth form	HEMI-PARASITE	0	0	0
1	Calamagrostis lapponica	Calamagrostis lapponica	42.0	Plant growth form	GRAMINOID	0	0	1
2	Carex capitata	Carex capitata	42.0	Plant growth form	SEDGE	0	0	1
3	Carex rostrata	Carex rostrata	42.0	Plant growth form	SEDGE	0	0	1
4	Carex saxatilis	Carex saxatilis	42.0	Plant growth form	SEDGE	0	0	1
...	...	...	...	...	...	...	...	...
21058940	Artemisia rothrockii	Artemisia rothrockii	42.0	Plant growth form	shrub seedling	1	1	0
21058949	Artemisia rothrockii	Artemisia rothrockii	42.0	Plant growth form	shrub seedling	1	1	0
21058958	Artemisia rothrockii	Artemisia rothrockii	42.0	Plant growth form	shrub seedling	1	1	0
21058967	Artemisia rothrockii	Artemisia rothrockii	42.0	Plant growth form	shrub seedling	1	1	0
21058976	Artemisia rothrockii	Artemisia rothrockii	42.0	Plant growth form	shrub seedling	1	1	0

1737341 rows × 8 columns

Sum the growth form attribution for each species:

growth_forms_sum = growth_forms.groupby(['AccSpeciesName']).sum()
growth_forms_sum.reset_index(inplace=True)

growth_forms_sum.head()

	AccSpeciesName	TraitID	is_shrub	is_herb
0	ACAENA NOVAE-ZELANDIAE	462.0	1	7
1	ADIANTUM CAPILLUS-VENERIS	1974.0	0	41
2	ALISMA PLANTAGO-AQUATICA	18942.0	0	299
3	APERA SPICA-VENTI	10248.0	0	241
4	ARCTOSTAPHYLOS UVA-URSI	9534.0	160	0

Merge with iNaturalsit observations:

growth_forms_sum.rename(columns = {'AccSpeciesName':'scientificName'}, inplace = True)

iNat_lf = pd.merge(iNat, growth_forms_sum, on='scientificName', how='inner')

iNat_lf['life form'] = iNat_lf[["is_tree", "is_shrub", "is_herb"]].idxmax(axis="columns")

iNat_lf = iNat_lf[iNat_lf["life form"].notna()]

Calculate coverage#

plt.rcParams.update({'font.size': 15})

step = 181

bins_x = np.linspace(-180,180,step) 
bins_y= np.linspace(-90,90,int(((step - 1)/2)+1))
iNat_lf['x_bin'] = pd.cut(iNat_lf['decimalLongitude'], bins=bins_x)
iNat_lf['y_bin'] = pd.cut(iNat_lf['decimalLatitude'], bins=bins_y)
iNat_lf['x_bin'] = iNat_lf['x_bin'].apply(lambda x: x.left)
iNat_lf['y_bin'] = iNat_lf['y_bin'].apply(lambda x: x.left)

iNat_tree = iNat_lf[iNat_lf['life form']== "is_tree"]
iNat_shrub = iNat_lf[iNat_lf['life form']== "is_shrub"]
iNat_herb = iNat_lf[iNat_lf['life form']== "is_herb"]

First get number of observations per grid cell:

grouped_all = iNat_lf.groupby(['x_bin', 'y_bin'], as_index=False)['life form'].size()

grouped_all

	x_bin	y_bin	size
0	-180.0	-90.0	0
1	-180.0	-88.0	0
2	-180.0	-86.0	0
3	-180.0	-84.0	0
4	-180.0	-82.0	0
...	...	...	...
16195	178.0	80.0	0
16196	178.0	82.0	0
16197	178.0	84.0	0
16198	178.0	86.0	0
16199	178.0	88.0	0

16200 rows × 3 columns

Then calcualte number of is_tree, is_shrub, and is_herb in each grid cell.

grouped_trees = iNat_tree.groupby(['x_bin', 'y_bin'], as_index=False)['life form'].size()
grouped_shrubs = iNat_shrub.groupby(['x_bin', 'y_bin'], as_index=False)['life form'].size()
grouped_herbs = iNat_herb.groupby(['x_bin', 'y_bin'], as_index=False)['life form'].size()

grouped_all['cover_tree'] = grouped_trees['size']/grouped_all['size']
grouped_all['cover_shrub'] = grouped_shrubs['size']/grouped_all['size']
grouped_all['cover_herb'] = grouped_herbs['size']/grouped_all['size']

grouped_all

	x_bin	y_bin	size	cover_tree	cover_shrub	cover_herb
0	-180.0	-90.0	0	NaN	NaN	NaN
1	-180.0	-88.0	0	NaN	NaN	NaN
2	-180.0	-86.0	0	NaN	NaN	NaN
3	-180.0	-84.0	0	NaN	NaN	NaN
4	-180.0	-82.0	0	NaN	NaN	NaN
...	...	...	...	...	...	...
16195	178.0	80.0	0	NaN	NaN	NaN
16196	178.0	82.0	0	NaN	NaN	NaN
16197	178.0	84.0	0	NaN	NaN	NaN
16198	178.0	86.0	0	NaN	NaN	NaN
16199	178.0	88.0	0	NaN	NaN	NaN

16200 rows × 6 columns

Plot coverage#

for lf in ['cover_tree', 'cover_shrub', 'cover_herb']:
    data_iNat_TRY = grouped_all.pivot('y_bin', 'x_bin', lf)
# data format
    data_crs = ccrs.PlateCarree()
    
    #for colorbar
    levels_iNat = MaxNLocator(nbins=15).tick_values(grouped_all[lf].min(), grouped_all[lf].max())
    cmap = plt.get_cmap('gist_yarg')
    norm = BoundaryNorm(levels_iNat, ncolors=cmap.N, clip=True)
    im_ratio = data_iNat_TRY.shape[0]/data_iNat_TRY.shape[1]

    #plot map
    fig = plt.figure(figsize=(15, 15)) # I created a new figure and set up its size

    #create base plot of a world map
    ax = fig.add_subplot(1, 1, 1, projection=ccrs.Robinson()) # I used the PlateCarree projection from cartopy
    ax.set_global()
    
    #add grid with values
    im = ax.pcolormesh(bins_x, bins_y, data_iNat_TRY, cmap="gist_yarg", 
                   vmin=grouped_all[lf].min(),
                  vmax=grouped_all[lf].max(),
                      transform=data_crs) 
    
    #add color bar
    label= 'percent observation' + lf
    fig.colorbar(im,fraction=0.046*im_ratio, pad=0.04, label=label )

    #add coastlines
    ax.coastlines(resolution='110m', color='black', linewidth=0.8)

    #set title
    ax.set_title( "iNaturalist " + lf, size=14)
    
    filename = '../Figures/iNat_' + lf[0:4] + '_cover.pdf'
    plt.savefig(filename, bbox_inches='tight')  

   

_images/Chapter_11_Growth_Forms_Coverage_32_0.png

_images/Chapter_11_Growth_Forms_Coverage_32_1.png

_images/Chapter_11_Growth_Forms_Coverage_32_2.png

Compare to sPlot life form coverage#

sPlot_cover = pd.read_csv("sPlotOpen/splot_cover_v1.csv") 

sPlot_cover

	PlotObservationID	lat	long	cover_tree	cover_shrub	cover_herb
0	16.0	62.420000	-154.180000	0.000000	0.000000	1.000000
1	17.0	62.420000	-154.180000	0.000000	0.000000	1.000000
2	18.0	62.420000	-154.180000	0.000000	0.000000	1.000000
3	20.0	62.420000	-154.180000	0.000000	0.000000	1.000000
4	22.0	62.420000	-154.180000	0.000000	0.025641	0.974359
...	...	...	...	...	...	...
95099	1126749.0	56.287870	27.273862	0.000000	0.142857	0.857143
95100	1126761.0	56.939965	23.598014	0.000000	0.010526	0.989474
95101	1126769.0	56.939965	23.598014	0.005025	0.045226	0.949749
95102	1126774.0	57.248222	22.147667	0.038462	0.230769	0.730769
95103	1126790.0	57.563967	26.892074	0.010460	0.194561	0.794979

95104 rows × 6 columns

plt.rcParams.update({'font.size': 15})

step = 181

bins_x = np.linspace(-180,180,step) 
bins_y= np.linspace(-90,90,int(((step - 1)/2)+1))
sPlot_cover['x_bin'] = pd.cut(sPlot_cover['long'], bins=bins_x)
sPlot_cover['y_bin'] = pd.cut(sPlot_cover['lat'], bins=bins_y)
sPlot_cover['x_bin'] = sPlot_cover['x_bin'].apply(lambda x: x.left)
sPlot_cover['y_bin'] = sPlot_cover['y_bin'].apply(lambda x: x.left)


for lf in ['cover_tree', 'cover_shrub', 'cover_herb']:

    sPlot_raster = sPlot_cover.groupby(['x_bin', 'y_bin'], as_index=False)[lf].mean()
    sPlot_lf_p = sPlot_raster.pivot('y_bin', 'x_bin', lf)
    # data format
    data_crs = ccrs.PlateCarree()
    
    #for colorbar
    levels= MaxNLocator(nbins=15).tick_values(sPlot_cover[lf].min(), sPlot_cover[lf].max())
    cmap = plt.get_cmap('gist_yarg')
    norm = BoundaryNorm(levels, ncolors=cmap.N, clip=True)
    im_ratio = sPlot_lf_p.shape[0]/sPlot_lf_p.shape[1]

    #plot map
    fig = plt.figure(figsize=(15, 15)) # I created a new figure and set up its size

    #create base plot of a world map
    ax = fig.add_subplot(1, 1, 1, projection=ccrs.Robinson()) # I used the PlateCarree projection from cartopy
    ax.set_global()
    
    #add grid with values
    im = ax.pcolormesh(bins_x, bins_y, sPlot_lf_p, cmap="gist_yarg", 
                   vmin=sPlot_raster[lf].min(),
                  vmax=sPlot_raster[lf].max(),
                      transform=data_crs) 
    
    #add color bar
    label= 'percent observation' + lf
    fig.colorbar(im,fraction=0.046*im_ratio, pad=0.04, label=label )

    #add coastlines
    ax.coastlines(resolution='110m', color='black', linewidth=0.8)

    #set title
    ax.set_title( "iNaturalist " + lf, size=14)
    
    filename = '../Figures/sPlot_' + lf + '_cover.pdf'
    plt.savefig(filename, bbox_inches='tight')  

   

_images/Chapter_11_Growth_Forms_Coverage_36_0.png

_images/Chapter_11_Growth_Forms_Coverage_36_1.png

_images/Chapter_11_Growth_Forms_Coverage_36_2.png

sPlot_raster = sPlot_cover.groupby(['x_bin', 'y_bin'], as_index=False)['cover_tree'].mean()

for lf in ['cover_shrub', 'cover_herb']:

    sPlot_raster[lf] = sPlot_cover.groupby(['x_bin', 'y_bin'], as_index=False)[lf].mean()[lf]

iNat_melt = pd.melt(grouped_all, id_vars=['x_bin','y_bin'], 
        value_vars=["cover_tree","cover_shrub","cover_herb"],
        value_name= "iNat coverage")

sPlot_melt = pd.melt(sPlot_raster, id_vars=['x_bin','y_bin'], 
        value_vars=["cover_tree","cover_shrub","cover_herb"],
        value_name= "sPlot coverage")

raster = pd.merge(iNat_melt, sPlot_melt, on=['x_bin','y_bin', 'variable'])

g = sns.relplot(
    data=raster,
    x="sPlot coverage", y="iNat coverage",
    col="variable", 
    kind="scatter", 
    col_wrap=4, 
    linewidth=0,
    alpha=0.5,
    color="midnightblue",
    palette='crest',
    facet_kws={'sharey': False, 'sharex': False}
)

for traits, ax in g.axes_dict.items():

    ax.axline([0, 0], [1, 1], color= "black", alpha=0.4)

_images/Chapter_11_Growth_Forms_Coverage_41_0.png

Over biomes#

iNat_with_biome = pd.read_csv("iNat_biomes.csv")

iNat_with_biome.head()

	gbifID	scientificName	decimalLatitude	decimalLongitude	eventDate	dateIdentified	Dispersal unit length	Leaf Area	SLA	Leaf C	...	Leaf N P ratio	Leaf P	Plant Height	Seed mass	Seed length	Seeds per rep. unit	Stem conduit density	SSD	Conduit element length	BIOME
0	1990599612	Macaranga tanarius	23.122994	120.534961	2019-01-25T14:51:00	2019-01-27T13:49:07	NaN	10.997687	2.848944	NaN	...	NaN	1.064711	2.183426	3.427650	NaN	NaN	NaN	-0.785087	NaN	1.0
1	1990599668	Adiantum capillus-veneris	23.123053	120.536422	2019-01-25T15:36:00	2019-01-27T14:22:54	NaN	4.088585	4.030621	NaN	...	NaN	-1.021651	-1.213114	-1.890699	NaN	NaN	NaN	NaN	NaN	1.0
2	1978441599	Chloris barbata	23.123362	120.183891	2018-12-16T17:21:58	2018-12-16T13:14:40	NaN	NaN	NaN	NaN	...	NaN	NaN	NaN	-1.565953	NaN	NaN	NaN	NaN	NaN	1.0
3	1883470731	Adiantum capillus-veneris	23.123363	120.536638	2018-03-11T11:48:00	2018-07-07T12:43:18	NaN	4.088585	4.030621	NaN	...	NaN	-1.021651	-1.213114	-1.890699	NaN	NaN	NaN	NaN	NaN	1.0
4	2242806671	Dioscorea bulbifera	23.123399	120.533835	2019-04-28T10:44:00	2019-05-03T12:45:18	NaN	5.093750	3.785888	NaN	...	NaN	NaN	NaN	1.736951	NaN	NaN	NaN	NaN	NaN	1.0

5 rows × 25 columns

biomes = list(iNat_with_biome['BIOME'].unique())

for b in biomes:

    sub_iNat_with_biome = iNat_with_biome[iNat_with_biome["BIOME"]==b]

    iNat_lf = pd.merge(sub_iNat_with_biome, growth_forms_sum, on='scientificName', how='inner')
    iNat_lf['life form'] = iNat_lf[["is_tree", "is_shrub", "is_herb"]].idxmax(axis="columns")
    iNat_lf = iNat_lf[iNat_lf["life form"].notna()]

    plt.rcParams.update({'font.size': 15})

    step = 181

    bins_x = np.linspace(-180,180,step) 
    bins_y= np.linspace(-90,90,int(((step - 1)/2)+1))
    iNat_lf['x_bin'] = pd.cut(iNat_lf['decimalLongitude'], bins=bins_x)
    iNat_lf['y_bin'] = pd.cut(iNat_lf['decimalLatitude'], bins=bins_y)
    iNat_lf['x_bin'] = iNat_lf['x_bin'].apply(lambda x: x.left)
    iNat_lf['y_bin'] = iNat_lf['y_bin'].apply(lambda x: x.left)

    iNat_tree = iNat_lf[iNat_lf['life form']== "is_tree"]
    iNat_shrub = iNat_lf[iNat_lf['life form']== "is_shrub"]
    iNat_herb = iNat_lf[iNat_lf['life form']== "is_herb"]

    grouped_all = iNat_lf.groupby(['x_bin', 'y_bin'], as_index=False)['life form'].size()

    grouped_trees = iNat_tree.groupby(['x_bin', 'y_bin'], as_index=False)['life form'].size()
    grouped_shrubs = iNat_shrub.groupby(['x_bin', 'y_bin'], as_index=False)['life form'].size()
    grouped_herbs = iNat_herb.groupby(['x_bin', 'y_bin'], as_index=False)['life form'].size()

    grouped_all['cover_tree'] = grouped_trees['size']/grouped_all['size']
    grouped_all['cover_shrub'] = grouped_shrubs['size']/grouped_all['size']
    grouped_all['cover_herb'] = grouped_herbs['size']/grouped_all['size']
    
    # print correlation

    print(b)

    for lf in ['cover_tree', 'cover_shrub', 'cover_herb']:
        sPlot_raster[lf] = sPlot_cover.groupby(['x_bin', 'y_bin'], as_index=False)[lf].mean()[lf]        
        correlation_xy = sPlot_raster[lf].corr(grouped_all[lf])
        print(lf + " " + str(correlation_xy**2))
    
    iNat_melt = pd.melt(grouped_all, id_vars=['x_bin','y_bin'], 
        value_vars=["cover_tree","cover_shrub","cover_herb"],
        value_name= "iNat cover")
    sPlot_melt = pd.melt(sPlot_raster, id_vars=['x_bin','y_bin'], 
        value_vars=["cover_tree","cover_shrub","cover_herb"],
        value_name= "sPlot cover")
    
    raster = pd.merge(iNat_melt, sPlot_melt, on=['x_bin','y_bin', 'variable'])
    
    g = sns.relplot(
    data=raster,
    x="sPlot cover", y="iNat cover",
    col="variable", 
    #hue="NumberiNatObservations", 
    #size="NumberiNatObservations",
    kind="scatter", 
    #hue="Weights",
    col_wrap=4, 
    #aspect = 1,  height = 4,
    linewidth=0,
    alpha=0.5,
    color="midnightblue",
    palette='crest',
    facet_kws={'sharey': False, 'sharex': False})
    
    index=0
    for traits, ax in g.axes_dict.items():
        ax.axline([0, 0], [1, 1], color= "black", alpha=0.4)
        # Iterate over each subplot to customize further

        trait_title= b
        # Add the title as an annotation within the plot
        ax.text(.1, .95, trait_title, transform=ax.transAxes, fontweight="bold")
        index+=1
        ax.axline([0, 0], [1, 1], color= "black", alpha=0.4)


    
    filename = '../Figures/corr_biome' + str(b) + '_cover.pdf'
    plt.savefig(filename, bbox_inches='tight')  

1.0
cover_tree 0.01943613258365132
cover_shrub 0.028058360539673456
cover_herb 0.0022278128815516945
2.0
cover_tree 0.00495158098325045
cover_shrub 0.0021841156153433973
cover_herb 0.0017625094597744017
3.0
cover_tree 0.27454095391406436
cover_shrub 0.02880357026669633
cover_herb 0.004831038955014629
4.0
cover_tree 0.259079820611657
cover_shrub 0.006749994097072907
cover_herb 0.23471860018958288
5.0
cover_tree 0.0902670010518872
cover_shrub 0.07357112645012613
cover_herb 0.17702590884327152
6.0
cover_tree 0.015465784550899028
cover_shrub 0.014347538706417832
cover_herb 0.010721767313240553
7.0
cover_tree 0.003077455550975538
cover_shrub 2.643230909102668e-05
cover_herb 0.018979266464673905
8.0
cover_tree 0.019313094032917352
cover_shrub 0.1095073129145167
cover_herb 0.13137953769498106
9.0
cover_tree 1.9096182179645747e-05
cover_shrub 0.16424750138680017
cover_herb 0.12904371358220107
10.0
cover_tree 0.015454822638076557
cover_shrub 0.00029106358542621644
cover_herb 0.04854474044738274
11.0
cover_tree 0.02413902443214224
cover_shrub 0.023572234963179787
cover_herb 0.04896684519451854
12.0
cover_tree 0.06588072711803293
cover_shrub 0.01176192277333619
cover_herb 0.014151478930656908
13.0
cover_tree 0.07586388909129714
cover_shrub 0.036485178451133755
cover_herb 0.03687038140378628
14.0
cover_tree 0.08233330012387073
cover_shrub 0.00024794574677113615
cover_herb 0.0372847032193021