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Supplementary Materials for Drivers of woody dominance across global drylands (Sci Adv 24).pdf

Authors:
Lucio Biancari at University of Buenos Aires
Lucio Biancari
Martin Aguiar at University of Buenos Aires
Martin Aguiar
David J Eldridge
David J Eldridge
David J Eldridge
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Gaston Oñatibia at University of Buenos Aires, Faculty of Agronomy, IFEVA, CONICET
Gaston Oñatibia
  • University of Buenos Aires, Faculty of Agronomy, IFEVA, CONICET
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Supplementary Materials for
Drivers of woody dominance across global drylands
Lucio Biancari et al.
Corresponding author: Lucio Biancari, biancari@agro.uba.ar
Sci. Adv. 10, eadn6007 (2024)
DOI: 10.1126/sciadv.adn6007
This PDF file includes:
Figs. S1 to S4
Tables S1 to S5
References
Appendix 1. Supporting information.
Figure S1. Location of the 304 plots at the 92 experimental sites surveyed. Background colors represent
aridity index for drylands (areas with an aridity index (mean annual precipitation/potential
evapotranspiration) lower than 0.65). Pictures illustrate the structural and compositional diversity of
vegetation captured by our survey.
Figure S2. Whittaker biomes plot showing the different biomes surveyed in this study. The color of
the symbols indicates values of relative woody cover across the plots and biomes surveyed. Different
types of symbols indicate the livestock species dominant at each plot: cattle (squares), goat (crosses),
horse (triangles) and sheep (circles).
Figure S3. Importance of predictors of woody (tree + shrub), tree, and shrub absolute cover.
Importance is based on the sum of Akaike weights of all models where each predictor is present using a
multimodel inference approach. MAP = mean annual precipitation; PCV = precipitation seasonality, PWQ
= % precipitation at warmest quarter, MAT = mean annual temperature, WHC soil water holding capacity,
GRAZ = grazing pressure, LS = dominant livestock species, HR = herbivore richness, and FIRE = fire
occurrence during the 2001-2019 period. Values of R2m= marginal R2 and R2c = conditional R2, correspond
to the lowest AIC models for each response variable.
Figure S4. Predicted values of tree cover for the different grazing pressure levels evaluated along the
precipitation gradient surveyed. Confidence intervals bands are shown only for Ungrazed and High
grazing pressure to smooth visualization
Table S1. Explanatory variables for relative woody cover used in this study, range of values found in
this study, rationale for including them and references.
Predictor
set
Variables
Range of values
Rationale
References
Climate
Mean annual
precipitation (MAP)
29 - 891 (mm)
Higher mean annual precipitation favors relative woody cover as more
water is available at deeper soil layers
(4, 13, 53)
Mean annual
temperature (MAT)
-1.21 - 29.2 (°C)
Higher mean annual temperature increases relative woody cover in
drylands due to increasing soil water evaporation and lower water
availability for grasses
(4, 53)
Precipitation at
warmest quarter
(PWQ)
0 – 82.6 (%)
Lower water availability during growth season increases relative woody
cover, because there would be more water availability at deeper soil
layers
(91, 92)
Precipitation
seasonality (CV)
13.5 – 186.7 (%)
Higher variation in water availability during the year would favor woody
species with deep roots not relying on top soil water
(26–30, 55)
Soil
Water holding capacity
(WHC)
10.7 – 48.7 (%)
Coarser textures would favor woody dominance as more water is
available at deeper soil layers
(17, 19, 21)
Grazing
Grazing pressure
(GRAZ)
Ungrazed, low,
moderate, high
Higher grazing pressure increases relative woody cover, because
livestock herbivory mainly feeds on grasses, favoring growth of woody
species
(4, 49)
Livestock species (LS)
Cattle, horse, goat,
sheep
Different livestock species have different dietary preferences, selectivity
and behavior that could change woody:grass ratios. Browser species
would decrease relative woody cover, while the opposite is expected for
grazers.
(42, 43, 93)
Herbivore richness
(HR)
1 to 5
Complementarity in foraging/browsing/grazing behavior could affect
grasses, increasing relative woody cover. In this study we included
domesticated and wild mammalian herbivores
(94)
Fire
Fire occurrence (FIRE)
0 – 1
Fire reduces woody cover because of negative effects on establishment
and size growth of trees and shrubs
(4, 29, 33)
Interactions
MAP:WHC
-
At lower precipitation sites, grass cover is predicted to be higher in
coarser soils (less water retention and evaporation losses), while is
predicted to be lower at finer soils, changing the relative woody cover.
The opposite pattern is expected at higher precipitation sites.
(20, 65, 66)
MAP:GRAZ
-
Increasing grazing pressure effects on relative woody cover at low
precipitation sites may be lower than on mesic sites, as dry site
vegetation has functional and structural traits adapted to both drought
and grazing.
(47, 48)
GRAZ:LS
-
Grazing pressure effect could depend on herbivore type because of
differential selectivity and preference
(36, 37, 49)
Table S2. Characteristics of the study sites surveyed. The values of perennial plant richness, Shannon
diversity index and the weight of dung of all herbivores (proxy of grazing pressure) represent averages
across all plots surveyed at each site. Livestock and mammalian wild herbivore species mentioned are
based on the feces found across all the plots for each site (see Methods).
Site
Ecoregion
Perennial
plant richness
Shannon
diversity
index
Livestock
species
across plots
Wild species
based on feces
found
Dung of all
herbivores
(kg ha-1)
Alikhani
Caspian Hyrcanian
mixed forests
13
0.98
Sheep
NA
NA
Andalgala
High Monte
4
0.17
Goat, Cattle,
Horse
NA
54.11
Baharkish
Central Persian
desert basins
41
2.51
Sheep
NA
108.38
Bani
West Sudanian
savanna
5
0.29
Sheep, Cattle
NA
26.67
Baño nuevo
Patagonian steppe
15
1.24
Sheep, Cattle,
Horse
Hare, Rhea
78.68
Barra
Cerrado
28
2.42
Cattle
NA
49.83
Beit Nir
Eastern
Mediterranean
conifer-broadleaf
forests
9
1.48
Cattle
Gazelle
83.48
Big Bend
Ranch State
Park
Chihuahuan desert
23
2.06
Cattle
Rabbit
23.49
Boyenga
West Sudanian
savanna
6
0.34
Sheep
NA
13.87
Casa Nova
Caatinga
17
1.76
Goat
NA
184.52
Castro Verde
Iberian
sclerophyllous and
semi-deciduous
forests
3
0.49
Cattle
NA
302.97
CEMB
Simpson desert
18
1.10
Cattle
Camel
0.56
Central
Patagonian steppe
15
1.15
Sheep, Horse
Rabbit, Guanaco
55.85
CHA
Mongolian-
Manchurian
grassland
37
2.04
Sheep, Cattle,
Horse
NA
111.43
CHB
Mongolian-
Manchurian
grassland
34
2.14
Cattle
NA
415.53
CHC
Daurian forest steppe
40
2.20
Cattle, Sheep
NA
128.03
Ciempozuelos
Iberian
sclerophyllous and
semi-deciduous
forests
18
1.96
Goat
Rabbit
35.00
Claro
Tumbes-Piura dry
forests
13
1.12
Goat, Horse
NA
122.61
CNAB
Simpson desert
11
0.27
Cattle
NA
0.07
Companhia das
Lezirias
Southwest Iberian
Mediterranean
sclerophyllous and
mixed forests
5
0.34
Cattle
NA
140.59
Contenda
Iberian
sclerophyllous and
semi-deciduous
forests
2
0.13
Sheep
Deer
67.61
Crucita
South American
Pacific mangroves
18
1.89
Cattle
NA
NA
CSBB
Simpson desert
10
0.23
Cattle
Kangaroo
0.09
Darkesh
Elburz Range forest
steppe
42
2.31
Sheep
NA
65.33
Ebenhaezer
Kalahari xeric
savanna
21
1.34
Sheep
Springbok,
Steenbok
123.88
El Ouassria
Mediterranean dry
woodlands and
steppe
18
1.40
Sheep
NA
60.84
Eriopoda
Chihuahuan desert
19
2.03
Cattle
Rabbit, Deer,
Rabbit
12.87
Etuoke
Ordos Plateau steppe
11
1.34
Sheep
NA
109.08
Fowlers
Tirari-Sturt stony
desert
15
1.70
Sheep
Kangaroo
30.00
Freixo
Southwest Iberian
Mediterranean
sclerophyllous and
mixed forests
4
0.88
Horse
NA
29.02
Galed
Eastern
Mediterranean
conifer-broadleaf
forests
4
0.64
Cattle
NA
251.98
Grandola
Southwest Iberian
Mediterranean
sclerophyllous and
mixed forests
6
0.98
Cattle
NA
166.99
Guelb Fguira
North Saharan Xeric
Steppe and
Woodland
8
1.11
Sheep
Camel
25.21
Hassi Bahbah
Mediterranean dry
woodlands and
steppe
11
0.79
Sheep
NA
98.58
Hay
Southeast Australia
temperate savanna
12
0.97
Sheep, Cattle
Kangaroo,
Rabbit
36.85
IBP
Sonoran desert
20
2.13
Cattle
Rabbit, Deer
31.72
Jornada LTER
Chihuahuan desert
16
1.84
Cattle
Rabbit
445.58
Kalama
Northern Acacia-
Commiphora
bushlands and
thickets
21
1.91
Goat
Zebra, Camel
27.08
Khabul
Eastern Gobi desert
steppe
10
1.20
Sheep
Rabbit, Camel
48.51
Khage
Kopet Dag
woodlands and forest
steppe
15
1.18
Sheep
NA
522.70
Korgalzhin
Kazakh steppe
36
1.21
Cattle
Suslilks
29.64
la Campana
Chilean Matorral
13
1.23
Cattle
Rabbit
730.53
La Flor
Chihuahuan desert
4
0.63
Cattle
Rabbit
65.83
Lac du Bois
Okanogan dry
forests
29
2.42
Cattle
Deer
161.67
Lehwerane
Kalahari xeric
savanna
18
1.35
Cattle, Horse
Steenbok,
Common Duiker,
Red hartebeest,
Hartebeest,
Springbok,
Spring hare,
Gemsbok
79.90
Lichtenburg
Highveld grasslands
37
2.17
Cattle
Springbok
NA
Lohondor
Caspian Hyrcanian
mixed forests
33
2.07
Sheep
NA
NA
London Farm
Central bushveld
43
2.25
Cattle
Eland, Rabbit
NA
Los Pozos
Patagonian steppe
37
2.20
Sheep
NA
56.30
Mara
Experimental
Farm
Central bushveld
36
2.40
Cattle
NA
NA
Mata
Pannonian mixed
forests
23
1.42
Cattle
NA
104.15
Mhijra
North Saharan Xeric
Steppe and
Woodland
13
1.16
Sheep
Camel
38.76
Monfrague
Iberian
sclerophyllous and
semi-deciduous
forests
7
1.17
Sheep
Red deer
312.50
Monsul
Southeast Iberian
shrubs and
woodlands
22
1.58
Sheep, Goat
NA
31.73
Mostaza
Eastern Cordillera
Real montane forests
3
0.64
Cattle, Horse
NA
39.74
Muri
Junggar Basin semi-
desert
6
0.74
Sheep, Horse
NA
147.56
Nagyivan
Pannonian mixed
forests
19
1.12
Cattle
NA
52.74
Natab
Namib Desert
1
0.00
Goat
Springbok
7.42
Needles
Colorado Plateau
shrublands
24
2.31
Cattle
Rabbit
17.48
Nyngan
Southeast Australia
temperate savanna
12
0.74
Cattle, Sheep
Kangaroo
57.20
Occidental
Patagonian steppe
12
1.75
Sheep
Rabbit
85.18
Page
Colorado Plateau
shrublands
16
1.70
Cattle
Rabbit
178.49
Palestine
Eastern
Mediterranean
conifer-broadleaf
forests
17
0.95
Goat
NA
80.23
Parker
Chihuahuan desert
11
1.20
Cattle
Rabbit, Deer
64.23
Pilcaniyeu
Patagonian steppe
23
2.12
Horse, Sheep
Rabbit
87.58
Puerto de las
Coberteras
Iberian conifer
forests
23
1.24
Sheep, Goat
NA
NA
Quebrada de
Talca
Chilean Matorral
10
1.25
Goat, Horse
Rabbit
302.40
Qysylschar
Kazakh semi-desert
27
1.56
Horse
Suslilks
36.54
Rio Colorado
Espinal
29
2.20
Cattle
Cavy, Rabbit
250.98
Rumuruti
Northern Acacia-
Commiphora
bushlands and
thickets
52
2.31
Sheep
Rabbit
2.42
Sair
Eastern Gobi desert
steppe
6
0.55
Sheep
Rabbit, Camel
34.62
San Martin
Iberian
sclerophyllous and
semi-deciduous
forests
10
1.22
Goat
Roe Deer, Red
deer, Rabbit
80.12
San Nicolas
Dry Chaco
33
2.61
Cattle, Horse
NA
14.83
San Ramon
Patagonian steppe
22
1.33
Horse, Sheep,
Cattle
Guanaco
938.35
Sandveld
Kalahari xeric
savanna
48
2.67
Cattle
Common Duiker,
Warthog, Greater
kudu, Spring
hare
4.09
Sayeret Shaked
Mesopotamian shrub
desert
6
0.80
Sheep
NA
21.00
Shand
Eastern Gobi desert
steppe
12
1.02
Sheep
Camel, Rabbit
34.35
Subandine
Patagonian steppe
30
1.51
Sheep
Rabbit
145.28
Syferkuil
Central bushveld
47
2.43
Cattle
NA
NA
Talap
Kazakh semi-desert
12
0.85
Horse
Suslilks
40.55
Tamou
West Sudanian
savanna
5
0.59
Sheep
Camel
30.27
Tierberg
Nama Karoo
shrublands
27
2.14
Sheep
Common Duiker
242.00
Valcheta
Low Monte
20
1.99
Cattle
Rabbit
219.97
Verdelecho
Iberian
sclerophyllous and
semi-deciduous
forests
9
1.22
Goat, Sheep
Rabbit
27.99
Viljoenskroon
Kalahari xeric
savanna
10
1.35
Sheep
Lepus saxatilis,
Pedetes capensis,
Hystrix
africaeaustralis
78.47
Wheatlands
Nama Karoo
shrublands
39
2.51
Goat
Common Duiker
211.08
Wuxing
Ordos Plateau steppe
10
1.65
Sheep
NA
181.56
Xasape
Kalahari xeric
savanna
28
1.99
Cattle
Hartebeest,
Common duiker,
Spring hare,
Steenbok, Red
hartebeest
21.24
Yuyang
Ordos Plateau steppe
7
0.80
Sheep
NA
88.21
Zapotillo
Tumbes-Piura dry
forests
8
0.70
Goat
NA
346.60
Zaragoza Arido
Iberian
sclerophyllous and
semi-deciduous
forests
23
2.42
Sheep
Rabbit
46.78
Zaragoza
semiarido
Iberian
sclerophyllous and
semi-deciduous
forests
26
2.49
Sheep
NA
17.83
Table S3. Sites surveyed that experienced fire during the 2000-2019 period. Latitude and Longitude values correspond to the
WGS84 Datum.
Site
Grazing
Latitude
Longitude
CSBB
Low
-23.65176
138.41831
CSBB
Ungrazed
-23.68172
138.43543
Lehwerane
Low
-25.60113
22.26667
Lehwerane
Ungrazed
-25.55868
22.24379
Xasape
High
-24.25235
21.86032
Xasape
Medium
-24.264
21.86169
Xasape
Low
-24.29197
21.85908
Xasape
Ungrazed
-24.35892
21.86228
Qysylschar
Medium
48.41341
69.57324
Qysylschar
Low
48.42521
69.58788
Rumuruti
Medium
0.21658
36.58256
Rumuruti
Low
0.21792
36.58769
Tsamsvlei
Low
-24.32231
15.75855
Syferkuil
Ungrazed
-23.82898
29.68705
Lichtenburg
High
-26.08351
26.21385
Lichtenburg
Medium
-26.08095
26.21334
Lichtenburg
Low
-26.07894
26.21026
Lichtenburg
Ungrazed
-26.07565
26.20792
Mara Experimental
Farm
High
-23.14474
29.56172
Mara Experimental
Farm
Medium
-23.14224
29.56416
Mara Experimental
Farm
Low
-23.13721
29.56275
Mara Experimental
Farm
Ungrazed
-23.14033
29.61382
Parker
High
31.7861
-110.82958
Parker
Medium
31.78614
-110.82741
Parker
Ungrazed
31.78635
-110.82431
Table S4. Models for significant quantitative predictors in linear mixed models of relative woody, tree, and shrub cover. MAP:
mean annual precipitation (mm); PCV: precipitation seasonality estimated with intra annual coefficient of variation (% CV);
MAT: mean annual temperature (°C); WHC: water holding capacity. Levels of WHC represent mean (26.93 %), low (-1 unit of
standard deviation from mean, 18.76%) and high (+1 unit of standard deviation from mean, 35.11%) values.
Response variable
Predictor 1
Predictor 2
(interactions)
Equation
Relative woody cover
MAP
Low WHC
RWC = 0.34 + 0.0006 * MAP (mm)
Mean WHC
RWC = 0.52 + 0.00012 * MAP (mm)
High WHC
RWC = 0.7 - 0.00037 * MAP (mm)
PCV
RWC = 0.37 + 0.002 * PCV (% CV)
Relative tree cover
MAP
RTC = 0.05 + 0.00041 * MAP (mm)
MAT
RTC = -0.18 + 0.05 * MAT (°C) - 0.002 * MAT (°C)^2
PCV
RTC = 0.04 + 0.0021 * PCV (% CV)
Relative shrub cover
MAP
Low WHC
RSC = 0.25 + 0.00029 * MAP (mm)
Mean WHC
RSC = 0.46 - 0.00027 * MAP (mm)
High WHC
RSC = 0.67 - 0.00083 * MAP (mm)
Longitude
RSC = 0.35 - 0.09 * cos(Longitude)
Table S5. Parameter estimates for the standard deviation of the random effects.
Model
Parameter
Estimate
Lower CI
Upper CI
Relative woody
cover
sd(Intercept)
0.25
0.21
0.3
Relative tree cover
0.13
0.12
0.15
Relative shrub cover
0.19
0.17
0.21
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Drivers of woody dominance across global drylands
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October 2024
Lucio Biancari · Martin Aguiar · David J Eldridge · Gaston Oñatibia · Fernando T Maestre