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Abstract

Savannas are globally important ecosystems of great significance to human economies. In these biomes, which are characterized by the co-dominance of trees and grasses, woody cover is a chief determinant of ecosystem properties. The availability of resources (water, nutrients) and disturbance regimes (fire, herbivory) are thought to be important in regulating woody cover, but perceptions differ on which of these are the primary drivers of savanna structure. Here we show, using data from 854 sites across Africa, that maximum woody cover in savannas receiving a mean annual precipitation (MAP) of less than approximately 650 mm is constrained by, and increases linearly with, MAP. These arid and semi-arid savannas may be considered 'stable' systems in which water constrains woody cover and permits grasses to coexist, while fire, herbivory and soil properties interact to reduce woody cover below the MAP-controlled upper bound. Above a MAP of approximately 650 mm, savannas are 'unstable' systems in which MAP is sufficient for woody canopy closure, and disturbances (fire, herbivory) are required for the coexistence of trees and grass. These results provide insights into the nature of African savannas and suggest that future changes in precipitation may considerably affect their distribution and dynamics.
© 2005 Nature Publishing Group
Determinants of woody cover in African savannas
Mahesh Sankaran
1
, Niall P. Hanan
1
, Robert J. Scholes
2
, Jayashree Ratnam
1
, David J. Augustine
3
, Brian S. Cade
4
,
Jacques Gignoux
5
, Steven I. Higgins
6
, Xavier Le Roux
7
, Fulco Ludwig
8
, Jonas Ardo
9
, Feetham Banyikwa
10
,
Andries Bronn
11
, Gabriela Bucini
1
, Kelly K. Caylor
12
, Michael B. Coughenour
1
, Alioune Diouf
13
,
Wellington Ekaya
14
, Christie J. Feral
15
, Edmund C. February
16
, Peter G. H. Frost
17
, Pierre Hiernaux
18
,
Halszka Hrabar
19
, Kristine L. Metzger
20
, Herbert H. T. Prins
21
, Susan Ringrose
22
, William Sea
1
,Jo
¨
rg Tews
23
,
Jeff Worden
1
& Nick Zambatis
24
Savannas are globally important ecosystems of great significance
to human economies. In these biomes, which are characterized
by the co-dominance of trees and grasses, woody cover is a chief
determinant of ecosystem properties
1–3
. The availability of
resources (water, nutrients) and disturbance regimes (fire, herbi-
vory) are thought to be important in regulating woody cover
1,2,4,5
,
but perceptions differ on which of these are the primary drivers of
savanna str ucture. Here we show, using data from 854 sites across
Africa, that maximum woody cover in savannas receiving a mean
annual precipitation (MAP) of less than ,650 mm is constrained
by, and increases linearly with, MAP. These arid and semi-arid
savannas may be considered ‘stable’ systems in which water
constrains woody cover and permits grasses to coexis t, while
fire, herbivory and soil properties interact to reduce woody
cover below the MAP-controlled upper bound. Above a MAP of
,650 mm, savannas are ‘unstable systems in w hich MAP i s
sufficient for woody canopy closure, and disturbances (fire, herbi-
vory) are required for the coexistence of trees and grass. These
results provide insights into the nature of African savannas and
suggest that future changes in precipitation
6
may considerably
affect their distribution and dynamics.
Savannas occupy a fifth of the earths land surface and support a
large proportion of the world’s human population and most of its
rangeland, livestock and wild herbivore biomass
1
. A defining feature
of savanna ecosystems is the coexistence of trees and grasses in the
landscape
1
. The balance between these two life forms influences both
plant and livestock production, and has profound impacts on several
aspects of ecosystem function, including carbon, nutrient and
hydrological cycles
1–3,7
. The mechanisms that promote tree–grass
coexistence and the factors that determine the relative proportions of
these two life forms across different savanna types remain, however,
unclear
1,2,4,5
. Because savannas are antici pated to be among the
ecosystems that are most sensitive to future changes in land use
and climate
8–10
, a thorough understanding of factors that structure
savanna communities is urgently required to guide management
efforts
2,4
.
Explanations for the persistence of tree–grass mixtures in savannas
are varied and invoke such different mechanisms as competition for
water and nutrients
11–13
, demographic bottlenecks to tree recruit-
ment
5,14
, and disturbances including fire
1,5,14–17
and large mammal
herbivory
15,16,18
. Empirical studies provide support both for and
against each alternative mechanism and, consequently, perceptions
differ on the relative impor tance of resource limitation versus
disturbances in controlling savanna structure
1,2,4,5
. The lack of con-
sensus arises, in part, because most studies have been small-scale and
site-specific, and have often focused on a single determinant
2
. But
savanna sy stems are diverse and occur under a wide range of
bioclimatic conditions
2
, and it is likely that the importance of
different processes in regulating woody cover may vary in different
savanna regions. Thus, a comprehensive model that explains both
coexistence and the relative productivity of tree and grass com-
ponents across diverse types of savanna is unlikely to arise from
studying individual systems in isolation: it requires a synthesis of
data from savannas across broad environmental gradients
2,4
.
Here we use a continental scale analysis of African savannas to
investigate h ow the relative importance of resource availability
(water, nutri ents) and disturbance regimes (fire, herbivor y) in
regulating woody cover varies across broad environmental gradients.
In particular, we are interested in determining whether broad-scale
trends in savanna structure are indicative of ‘stable’ or ‘unstable’
dynamics
1
, or whether savannas show elements of both across their
geographic range of occurrence. We use ‘stable’ in a limited sense to
mean that coexistence of trees and grasses in savannas is not
dependent on disturbances such as fire and mammalian herbivory,
while recognizing that woody community biomass and cover are
dynamic, not static, properties of the system.
Specifically, we considered that if water availability is the primary
determinant of woody cover in savannas
11–13
, then precipitation
LETTERS
1
Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, Colorado 80523, USA.
2
Division of Forest Science and Technology, CSIR, PO Box 395, Pretoria 001,
South Africa.
3
USDA Forest Service, Commanche National Grassland, PO Box 12, Springfield, Colorado 81073, USA.
4
US Geological Survey, Fort Collins Science Center,
2150 Centre Avenue, Building C, Fort Collins, Colorado 80526-8818, USA.
5
Ecole Normale Superieure, Laboratoire d’ecologie, UMR 7625 CNRS, Universite
´
de Paris 6 ENS, 46 Rue
d’Ulm, 75230 Paris Cedex 05, France.
6
Umweltforschungszentrum Leipzig Halle, Sekt Okosyst Anal, Permoserstrasse 15, D-04318 Leipzig, Germany.
7
Microbial Ecology
Laboratory, UMR 5557 CNRS, Universite
´
Lyon 1 USC INRA 1193, Ba
ˆ
timent G. Mendel, 43 Boulevard du 11 Novembre 1918, 69622 Villeurbanne, France.
8
CSIRO Centre for
Environment and Life Sciences, CSIRO Plant Industry, Private Bag 5, Wembley, Western Australia 6913, Australia.
9
Department of Physical Geography and Ecosystems Analysis,
Lund University, So
¨
lvegatan 12, 223 62 Lund, Sweden.
10
Department of Botany, University of Dar es Salaam, PO Box 35060, Dar es Salaam, Tanzania.
11
Department of
Agriculture and Game Management, Private Bag X6011, Port Elizabeth Technikon, Port Elizabeth 6000, South Africa.
12
Department of Civil & Environmental Engineering,
Princeton University, Princeton, New Jersey 08544, USA.
13
Centre de Suivi Ecologique, BP 15532, Dakar, Senegal.
14
University of Nairobi, Department of Range Management,
PO Box 29053, Nairobi, Kenya.
15
Environmental Sciences Department, University of Virginia, PO Box 400123, 291 McCormick Road, Charlottesville, Virginia 22904 -4123, USA.
16
Department of Botany, University of Cape Town, University Private Bag, Rondebosch 7700, South Africa.
17
Institute of Environmental Studies, University of Zimbabwe, PO Box
MP 167, Mount Pleasant, Hara re, Zimbabwe.
18
CESBIO, 18 Avenue E. Belin, 31401 Toulouse Cedex 9, France.
19
Mammal Research Institute, University of Pretoria, Pretoria 002,
South Africa.
20
Department of Zoology, University of Wisconsin, 430 Lincoln Drive, Madison, Wisconsin 53706, USA.
21
Resource Ecology Group, Wageningen University,
Bornsesteeg 69, 6708 PD Wageningen, The Netherlands.
22
Harry Oppenheimer Okavango Research Centre, University of Botswana, Private Bag 285, Maun, Botswana.
23
University of Potsdam, Institute of Biochemistry & Biology, Plant Ecology & Nature Conservation, Maulbeerallee 2, D-14469 Potsdam, Germany.
24
Scientific Services, Kruger
National Park, Private Bag X402, Skukuza 1350, South Africa.
Vol 438|8 December 2005|doi:10.1038/nature04070
846
© 2005 Nature Publishing Group
should limit the potential tree cover that can be supported at any
given site, and maximum realizable woody cover should gradually
inc rease with MAP
4,12
. By contrast, if disturbances such as fire
and herbivory primarily maintain savannas
4,5,15
, then we expect an
abrupt, rather than gradual, increase in maximum realizable woody
cover with increasing MAP
4
: below a critical threshold of rainfall
sufficient to permit tree growth outside riparian areas or depressions,
grasslands should dominate; above this threshold, the maximum
woody cover should correspond to a closed-canopy woodland state
4
.
Depending on the level of disturbance, a particular location might
have reduced woody cover, but the upper bound would not depend
on MAP.
We evaluated relationships between woody cover and MAP, soil
characteristics (texture, percentage nitrogen, nitrogen mineraliza-
tion, total phosphorus) and disturbance regimes (fire-return inter-
vals, mammalian herbivore biomass) from 854 sites across Africa
(Supplementary Fig. S1 and Methods). Woody cover ranges from 0 to
90% across sites and tends to increase with MAP (Fig. 1). More
particularly, within a narrow range of MAP from ,100 to 650 mm,
an upper bound exists on the maximum realizable woody cover
(Fig. 1). In these arid to semi-arid sites (,650 ^ 134 mm MAP;
see Fig. 1), maximum realized woody cover increases with MAP
(Fig. 2a), but shows no relationship w ith fire-return inter vals,
herbivore bio mass or soil characteristics (Fig. 2b–f), su ggesting
that the observed upper limit on woody cover in arid and semi-
arid African savannas is primarily a consequence of m oisture
limitation. The presence of an upper bound on woody cover in
these savannas that is linked primarily to MAP is not consistent w ith
the view that savannas are inherently unstable systems maintained by
disturbances.
Within this MAP range (,650 ^ 134 mm MAP), our analysis
suggests that tre e–grass coexistence is stable to the extent that
disturbances such as fire and herbivory, although capable of modify-
ing tree to grass ratios, are not necessary for coexistence. In these
“climatically determined savannas”
17
(,650 ^ 134 mm MAP),
restrictions on maximum woody cover as a result of water limitation
permit grasses to persist in the system. By contrast, in areas that
receive a MAP in excess of 650 ^ 134 mm, water availability seems to
be sufficient to allow trees to approach canopy closure such that
grasses may be effectively excluded. These disturbance-driven
savannas”
17
represent unstable systems in which disturbances such
as fire, grazing and browsing are required to maintain both trees
and grasses in the system by buffering against transitions to a closed-
canopy state
5,17
.
Whereas MAP drives the upper bound on woody cover in arid and
semi-arid savannas, disturbance regimes and soil characteristics
impose significant controls on savanna structure by influ encing
woody cover below the bound. A regression tree analysis of mean
woody cover for a restricted subset of sites for which all data were
available (Fig. 3 and Methods) further highlights the importance of
MAP as a principal driver of savanna structure and suggests that
MAP also mediates the relative importance of other savanna drivers
such as fire and soil characteristics.
Below a MAP of ,350 mm, woody cover is typically low (Fig. 3). In
these sites, soil properties and disturbances such as fire and herbivory
rarely regulate woody cover. As MAP increases above this threshold,
fire in particular becomes a common factor that reduces woody cover
Figure 1 | Change in woody cover of African savannas as a function of
MAP.
Maximum tree cover is represented by using a 99th quantile piece-
wise linear reg ression. The regression analysis identifies the breakpoint (the
rainfall at which maximum tree cover is attained) in the interval
650 ^ 134 mm MAP (between 516 and 784 mm; see Methods). Trees are
typically absent below 101 mm MAP. The equation for the line quantifying
the upper bound on tree cover between 101 and 650 mm MAP is
Cover(%) ¼ 0.14(MAP) 2 14.2. Data are from 854 sites across Africa.
Figure 2 | Woody cover as a function of MAP, soil properties and
disturbance regimes in arid and semi-arid savannas.
Relationships
between woody cover and MAP (a; n ¼ 529), fire-return intervals
(b; n ¼ 302), herbivore biomass (c; n ¼ 145), percentage of clay
(d; n ¼ 234), nitrogen mineralization potential (e; n ¼ 109) and soil total
phosphorus (f; n ¼ 118) for savannas receiving ,650 mm MAP. Unbroken
and broken lines represent the 99th and 90th linear quantiles, respectively.
Maximum woody cover increased with MAP, but showed no consistent
relationship with other vari ables. For MAP, both quantile slopes were
significantly different from zero. For fire-return intervals, herbivore
biomass, clay and nitrogen mineralization rates, neither regression line had
a significant non-zero slope. For total phosphorus, the 90th but not the 99th
quantile slope differed from zero.
NATURE|Vol 438|8 December 2005 LETTERS
847
© 2005 Nature Publishing Group
below the MAP-controlled upper bound (Fig. 3). Woody cover is
higher, on average, where fires are infrequent (fire-return interval
.10.5 yr). In sites with more frequent fires, woody cover is typically
low, except on very sandy soils (mostly concentrated on the Kalahari
sand sheets), which tend to support higher woody cover (Fig. 3). The
dependence of fire frequency on MAP presumably arises because
increased grass production in mesic sites leads to greater fuel loads
that can support more frequent fires
14
(Supplementary Fig. S2). Very
high sand content, which correlates with low nutrient availability
(Supplementary Table S1), may promote higher woody cover if the
positive effects of coarse-textured soils, such as lower wilting points
19
and greater water percolation to soil layers below grass rooting
depths
1,11,12
, override the negative effects associated w ith lower
nutrient availability in these soils
19
.
Herbivore effects on woody cover are, however, less apparent.
Although we found a tendency for grazers to enhance woody cover
and browsers and mixed feeders to depress it, such effects were weak
and could not be generalized beyond our data set (see Methods;
measures of herbivore biomass were retained in the complete, but not
pruned, regression tree). The lack of consistent herbivore effects
across sites most probably reflects differences in herbivore guilds,
seasonality of herbivory, and variation in herbivore body-size distri-
butions across sites, features for which data were not available.
Larger, more detailed data sets will undoubtedly provide greater
resolution of how different driver variables interact to influence
mean woody cover.
These results have the power to inform savanna management
strategies because they bear directly on our ability to predict savanna
responses to changing environmental drivers. In particular, our data
indicate that woody encroachment, a phenomenon in which many
savannas across the world show a directional trend of increasing
woody cover
1
, may be a bounded process in savannas receiving a
MAP of ,650 ^ 134 mm, ultimately limited by water availability.
For sites close to or at the MAP-controlled bound (Fig. 1), changes in
precipitation regimes that lead to increased water availability
6
there-
fore may be a cause for concern with respect to woody encroachment.
However, the enormous variation in woody cover, with most sites far
from the climatic bound (Fig. 1), suggests that processes other than
MAP regulate actual tree cover in many savannas of Africa. In
particular, our results suggest that if disturbances by fire, browsers
and humans were absent, then large sections of the African continent
would switch to a wooded state (hatched regions in Fig. 4).
The patterns described here for African savannas suggest that the
dominant ecological theories for tree–grass coexistence in these
systems need to be combined: it is clear that most savannas are
strongly affected by disturbances that maintain woody cover well
below the resource-limited upper bound. Disturbance-based models
do not consider and are unable to explain, however, the upper bound
to tree cover. The result s emerging from this continental scale
analysis strongly indicate that water limits the maximum cover of
woody species in many African savanna systems, but that disturbance
dynamics control savanna structure below the maximum. These
results have important implications both for our understanding of
the fundamental nature of African savanna systems and for our
ability to predict their responses to changing environmental drivers.
It remains to be established whether the patterns obser ved here for
African savannas also hold in other tropical savanna regions or in
temperate savannas where the effects of winter precipitation and
temperature on moisture distribution through the soil profile can
markedly alter water par titioning between woody and herbaceous
plants, and thus can influence maximum woody cover.
METHODS
Data collection. Data on projected woody cover (the percentage of ground
surface covered when crowns are projected vertically), MAP, soil characteristics
(texture, total nitrogen and phosphorus, and nitrogen mineralization), fire and
herbivory regimes were gathered from several sources for a range of sites across
Africa. We included only sites for which vegetation was sampled over sufficiently
large spatial scales (.0.25 ha for plot measurements and .100 m for transect
sampling). Sites located in riparian or seasonally flooded areas, or in net water
run-on areas such as depressions, and sites in which trees were known to access
ground water resources (that is, sources of water not dependent on rainfall in the
immediate vicinity or in recent years) were excluded from the analysis because
MAP is not a relevant descriptor of water availability in these sites. We also
excluded sites that had been cultivated or harvested by humans ,10 yr before
sampling from the analysis.
Rainfall data included estimates from eld measurements and regional
rainfall maps (n ¼ 469) and from fitted climatic g rids (0.058 resolution,
Figure 4 | The distributions of MAP-determined (‘stable’) and disturbance-
determined (‘unstable’) savannas in Africa.
Grey areas represent the
existing distribution of savannas in Africa according to ref. 30. Vertically
hatched areas show the unstable savannas (.784 mm MAP); cross-hatched
areas show the transition between stable and unstable savannas (516–
784 mm MAP); grey areas that are not hatched show the stable savannas
(,516 mm MAP).
Figure 3 | Regression tree showing generalized relationships between
woody cover and MAP, fire-return interval and percentage of sand.
The
tree is pruned to four terminal nodes and is based on 161 sites for which all
data were available. No consistent herbivore effects were detected. Branches
are labelled with criteria used to segregate data. Values in terminal nodes
represent mean woody cover of sites grouped within the cluster. The pruned
tree explained ,45.2% of the variance in woody cover, which is significantly
more than a random tree (P , 0.001). Of this, 31% was accounted for by the
first split; the second split explained an additional 10% of the variance in
woody cover.
LETTERS NATURE|Vol 438|8 December 2005
848
© 2005 Nature Publishing Group
n ¼ 383) of monthly mean rainfall for Africa from the ANU-CRES (ref. 20;
http://www.ncgia.ucsb.edu/conf/SANTA_FE_CD-ROM/santa_fe.html and
http://cres.anu.edu.au/outputs/africa.php). Fire-return periods were obtained
from field records (n ¼ 182) and from burnt-area maps of Africa at 5-km
resolution (n ¼ 670) derived from AVHRR (advanced very high resolution
radiometer) images based on 8 yr of data (1981–1983 and 1985–1991; ref. 21).
Herbivore density estimates were available for 180 sites. Soils were obtained from
166 sites and analysed under standardized laboratory conditions for texture,
total nitrogen and phosphorus, and nitrogen mineralization potential (s ee
Supplementary Information). Our data set included sites encompassing a
wide range of rainfall (132–1, 185 mm MAP), fire-return intervals (1 to
.50 yr), herbivore biomass (0–8,000 kg km
22
), soil texture (sand, 6.7–98%;
clay, 0.6–62.8%), soil percentage nitrogen (0.013–0.31%), soil total phosphorus
(5–1,465
m
gg
21
) and potential nitrogen mineralization rates (222.8 to
153
m
gg
21
per week; see Supplementary Fig. S3).
Data analyses. To characterize the effects of MAP on the upper limit to woody
cover across sites, we analysed data using a bent-cable form of a piece-wise linear
model
22
estimated with nonlinear quantile regression
23
, as implemented in the
‘quantreg’ librar y in the statistical package R (http://www.r-project.org/). We
used 0.90 to 0.99 conditional quantiles to obtain estimates near the upper
boundary of the percentage of woody cover as it changes with MAP, which better
reflects the process of MAP limiting maximum woody cover than does mean
regression
24
(see Supplementary Information for details of this and additional
analyses). We conducted additional analyses on the subset of sites that received
,650 mm rainfall annually to investigate further how fire regimes, herbivory
and soil properties influenced the upper bound on woody cover that was evident
in these sites. We analysed these data by linear quantile regression
25
,as
implemented in the ‘quantreg’ library, which permits computation of confidence
intervals for estimated parameters
26
and enabled us to test whether the regression
slopes were different from zero.
In addition to analysing patterns in maximum woody cover, we use d
regression tree analysis
27
, as implemented in the ‘rpart’ library in R, to determine
how resource availability and disturbance regimes influenced mean realized
woody cover in sites (see Supplementary Information). After tree construction,
cross-validation procedures were used to prune trees to a size that best
represented relationships that could be generalized outside the sample to the
rest of the continent
28
. Woody cover values were log-transformed to stabilize
variances
28
. To avoid problems arising from collinearity among soil variables,
only sand content was retained for the analysis as it was the variable that was
most strongly correlated to other soil variables (Supplementary Table S1). The
results of the analysis were unchanged if grazer biomass and mixed feeder plus
browser biomass were retained as two separate variables, or if total herbivore
biomass was used as the predictor variable. The analysis was based on 161 sites
for which data on MAP, fire-return intervals, herbivore biomass density and soil
sand content were available. To determine whether the pruned tree explained
more variance than a random tree of equal complexity, the square of the
correlation coefficient (r
2
) of the pruned tree was compared with r
2
values of
similar sized trees generated from 2,000 random associations between predictor
variables and woody cover
29
. Further details on the methodology and results
from additional analyses are provided in the Supplementary Information.
Received 26 April; accepted 22 July 2005.
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Supplementary Information is linked to the online version of the paper at
www.nature.com/nature.
Acknowledgements This paper arose from a workshop on savanna complexity
funded by the NSF. We thank R. Boone, I. McHugh, R. Grant, H. Biggs,
W. T. Starmer, P. M. Barbosa, D. Ruess, J. Rettenmayer, C. Williams, J. Klein,
M. T. Anderson, W. J. Parton, J. C. Neff, N. Govender and the Kruger Park
Scientific Services for comments, help with data collection and analysis, and for
providing access to otherwise unpublished data.
Author Contributions All authors contributed data or intellectual input to the
project.
Author Information Reprints and permissions information is available at
npg.nature.com/reprintsandpermissions. The authors declare no competing
financial interests. Correspondence and requests for materials should be
addressed to M.S. (mahesh@nrel.colostate.edu).
NATURE|Vol 438|8 December 2005 LETTERS
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... Under normal conditions in One factor contributing to the speed of savannah recovery is rainfall. (22)(23)(24). Previous researchers have suggested that rainfall that is too high can result in a slow savannah recovery process. ...
... Previous researchers have suggested that rainfall that is too high can result in a slow savannah recovery process. (22). Based on African research, it is known that annual rainfall of more than 650 mm inhibits the growth of savannas. ...
... Based on African research, it is known that annual rainfall of more than 650 mm inhibits the growth of savannas. (22,23). Based on the analysis of CHIRPS (Climate Hazard Group InfraRed Precipitation with Station) data (25), The total rainfall in October-November 2023 in Teletubbies Hill is 134-175 mm (Figure 13), while in November-December 2023, it is 314-345 mm (Figure14). ...
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This study was conducted to identify the condition of savannah fields after forest and land fires in parts of the Bromo Volcano area, East Java, Indonesia, in September 2023. The research was conducted through remote sensing technology and integrated geographic information systems. The data used is remote sensing multitemporal data in the form of Sentinel 2 imagery. Sentinel 2 imagery is remote sensing data generated from Sentinel satellite recording. Sentinel 2 imagery was used in this study because it has excellent spatial and temporal resolution. The spatial resolution of Citra Sentinel is 10 meters, and its temporal resolution is ten daily. The data were analyzed using spatial and temporal approaches to determine the condition of the savanna before and after forest and land fires. The software used to analyze the data and visualize spatial information is Google Earth Engine (GEE) and Arc Map Version 10.
... For instance, climate and its variability is well-documented as the most important driver of distribution for various vegetation types in Africa, including forests (Van Rompaey, 1993;Bongers et. al., 1999), woody vegetation (Sankaran et al., 2005) and herbaceous vegetation (Zerbo et al., 2016). Aside climate (acting as an overarching factor affecting plants), more local site-specific variations such as soil or topography also tend to influence plant growth. ...
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The study compared the effect of site and climatic variables on growth rate of Cedrela odorata and Terminalia superba plantations that were established at three ecological zones (moist semi-deciduous zone, Northwest sub-type and Southeast sub-type, and dry semi-deciduous zone). Diameter at breast height and height of trees were measured in total and the mean annual increment of each tree was then determined. Soil and climatic variables were also measured. Linear Mixed-effect Models were used to analyze the relationship between growth rate of trees and the environmental variables. Trends in growth rate differed across ecological zones for both C. odorata and T. superba. Soil properties within each study site varied significantly across the three ecological zones. Annual Precipitation (AP) was observed to be the main environmental predictor. For C. odorata, annual precipitation predicted both diameter at breast height increment (p < 0.001) and height increment (p < 0.01). For T. superba, AP predicted DBH increment (p < 0.001) whiles precipitation of the coldest quarter also predicted height increment (p < 0.01). The relatively high percentage of unexplained variations in growth for both species suggests that other variables may also predict the growth rate of trees.
... Since AGC is closely related to the biomass production of woody trees, precipitation plays a significant role in resource availability, directly impacting carbon storage (de Castilho et al., 2006;Luizao et al., 2004). Keith et al., 2009;Sankaran et al., 2005;Wang et al., 2014). Second, while we did not observe a direct influence of temperature on carbon storage, unlike other studies from tropical (Raich et al., 2006;Gordon et al., 2018) and temperate regions (Liu et al., 2014;Keith et al., 2009), we did find that temperature, precipitation and LUI all indirectly influenced carbon storage through trait-mediated pathways. ...
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1. Plant functional traits play an important role in shaping plant ecological responses to environmental conditions and influencing ecosystem functioning. However, how whole-plant functional strategies manifest at the community level to influence aboveground and belowground carbon storage across environmental gradients remains poorly understood. 2. We measured aboveground and belowground carbon stocks and the variation in whole-plant (above- and belowground) functional strategies at the community level in twelve ecosystem types across a broad savanna-forest-alpine elevational gradient of climate and land use on Mt. Kilimanjaro, Tanzania. Using Structural Equation Models, we disentangled the direct and land use mediated influences of climate on carbon storage from indirect influences mediated by variation in plant functional strategies. 3. We found strong coordination between above- and belowground functional traits at the whole community level, which corresponded with functional strategies related to two major trade-offs: a slow -conservation to fast -resource acquisition axis represented by a spectrum from high leaf dry matter content to high fine root nitrogen concentration; and a size-related woody to grassy community axis represented by a spectrum spanning high canopy height to high specific root length. The slow-fast and woody-grassy strategy axes were primarily driven by precipitation and land-use intensity, respectively. 4. Both functional strategies mediated the effects of climate on carbon storage. The slow-fast strategy axis was strongly and positively associated with aboveground carbon stocks. Meanwhile, the woody-grassy strategy axis was negatively associated with both aboveground carbon stocks and soil organic carbon stocks. 5. Synthesis . We demonstrated that major plant functional strategies manifest at the community level along elevational gradients. These strategies also explain variation in carbon storage, although aboveground storage is mostly driven by trait effects, and belowground storage by direct effects of climate. Together, these results underscore the importance of incorporating functional community data into future analysis of climate change impacts on carbon storage, which would enhance our ability to predict potential shifts in ecosystem functioning.
... This aligns with research by Lal (2003) and Jobbágy and Jackson (2002), who found that forest cover plays a crucial role in erosion control. Similarly, grassland areas experienced a decline in soil loss from 5.7 tons/ha/year in 1993 to 2.8 tons/ha/year in 2023, likely due to improved land management or reduced grassland area, supporting findings by Sankaran et al. (2005). Settlement areas showed a gradual increase in soil loss, rising from 1.5 tons/ha/year in 1993 to 3.9 tons/ha/year in 2023, likely due to urban expansion. ...
... This aligns with research by Lal (2003) and Jobbágy and Jackson (2002), who found that forest cover plays a crucial role in erosion control. Similarly, grassland areas experienced a decline in soil loss from 5.7 tons/ha/year in 1993 to 2.8 tons/ha/year in 2023, likely due to improved land management or reduced grassland area, supporting findings by Sankaran et al. (2005). Settlement areas showed a gradual increase in soil loss, rising from 1.5 tons/ha/year in 1993 to 3.9 tons/ha/year in 2023, likely due to urban expansion. ...
... This aligns with research by Lal (2003) and Jobbágy and Jackson (2002), who found that forest cover plays a crucial role in erosion control. Similarly, grassland areas experienced a decline in soil loss from 5.7 tons/ha/year in 1993 to 2.8 tons/ha/year in 2023, likely due to improved land management or reduced grassland area, supporting findings by Sankaran et al. (2005). Settlement areas showed a gradual increase in soil loss, rising from 1.5 tons/ha/year in 1993 to 3.9 tons/ha/year in 2023, likely due to urban expansion. ...
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... Tropidurus catalanensis primarily inhabits the Brazilian Cerrado, a mesic savanna that receives more than 700 mm annual rainfall, allowing for partial canopy closures (Sankaran et al., 2005;Borghetti et al., 2019). This species is common in open environments, including urban areas, but appears to retreat to gallery forests along rivers in lower latitudes where temperatures tend to be excessively high (Piantoni et al., 2019). ...
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Lizards of the genus Tropidurus typically exhibit an omnivorous diet composed predominantly of invertebrates, flowers, and fruits. The proportion of plant material in their diet varies significantly among individuals, populations, and species, with several hypotheses proposed to explain the environmental factors driving this variation. In this study, we tested if plant material consumption increases with dehydration, supporting the hypothesis that environmental aridity promotes greater reliance on plant-based food. To test this hypothesis, we housed individuals of Tropidurus catalanensis in the laboratory and submitted the lizards to different 10-d treatments: one group was deprived of both food and water, while the other had access to water but no food. After the treatment period, all lizards were offered food equivalent to 10% of their body weight, consisting of 5% mango pieces and 5% cockroaches, for 24 h. Leftovers were weighed to quantify consumption, and lizards were then returned to terraria with ad libitum food and water for a 5-day recovery period. The experiment was then repeated with treatments reversed for each group, ensuring all lizards experienced both conditions. Our results revealed no significant difference in mango consumption between hydrated and dehydrated lizards; however, dehydrated lizards consumed fewer cockroaches, resulting in a higher proportion of mango in their diet (75%) than in the diet of hydrated lizards (53%). These findings demonstrate how short-term physiological changes, such as dehydration, can influence individual dietary preferences and alter food selection. This study suggests that plant consumption could be more common in drier environments because plant-based foods require less water for digestion, whereas high-fat and high-protein items become less viable for large-scale consumption under dehydrating conditions.
... We used pellet groups as a proxy and visual inspection of grazing intensity with 0-4 (0 = no grazing, 1= 25%. 2= 50%, 3= 75%, 4= 100%) range from low to high by visually estimating the extent of bite marks within a quadrat (Sankaran et al., 2005;Thapa et al., 2021b). This grazing intensity was measured within a 1 m×1 m quadrat and later this was used to determine utilization pattern and diversity index in the grassland. ...
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Chapter
Savannas are among the most variable of terrestrial ecosystems. They undergo large and frequent changes in production, composition and structure and they contain some of the worst examples of degradation by man. There are, accordingly, many references to them as “fragile” and “brittle” ecosystems, with frequent predictions of imminent “collapse”. But these terms have been used loosely, as jargon, and we need now to progress beyond this vague terminology. What, precisely, do we mean by these terms? Is it possible to be more precise? More specifically, can we achieve an understanding of the equilibrium behaviour of savannas? For this deals with how they change, and how much they can change before the change is irrevocable.
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(1) Semi-arid savannas, wherever they occur, have generally been overgrazed and encroached on by bush. A model is developed which accounts for the growth of woody vegetation and of grasses, and analyses the competition between them for available soil water. (2) The model is based on Walter's two layer hypothesis. Woody vegetation and grasses compete for water in the surface layers of the soil, but woody vegetation has exclusive access to a source of water relatively deep underground. Where there is only a small biomass of grass the soil surface tends to become impermeable and, in these conditions, the model shows that two different steady states may develop: with a lot of woody vegetation alone, or with a relatively large biomass of grass and rather little woody vegetation. (3) The results are discussed in terms of the concept of resilience. The continued existence of both stable states under ranching conditions seems to depend on periodic heavy, or over-, grazing which allows for the maintenance of unpalatable or unstable grass species, which thus set a minimum to grass biomass--a minimum which cannot be reduced by herbivores. (4) Comparison of the dynamics of various savanna and other natural systems leads to the conclusion that the resilience of the systems decreases as their stability (usually induced) increases.
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The use of the plant available moisture (PAM)/plant available nutrients (PAN) concept to compare savanna structure was examined using data from twenty Australian sites. Above-ground biomass was regressed on various combinations of seventeen different estimates of PAM (plant available moisture) and two estimates of PAN (plant available nutrients). The ratios of actual transpirational loss from the subsoil to potential evapotranspiration (PET), and total annual rainfall to PET, were most highly correlated with total biomass. Grass biomass is poorly predicted by PAM on its own, and requires inclusion of woody leaf biomass in the regression. PAN had little effect on total biomass, although it is likely to be important for other, functional aspects of vegetation. The woody : grass ratio is best predicted by an index involving the ratio of subsoil : topsoil moisture. For biomass comparisons the use of a detailed water-balance model to estimate PAM is not warranted.
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Several methods to construct confidence intervals for regression quan-tile estimators (Koenker and Bassett (1978)) are reviewed. Direct estimation of the asymptotic covariance matrix requires an estimate of the reciprocal of the error density (sparsity function) at the quantite of interest; some recent work on bandwidth selection for this problem will be discussed. Several versions of the bootstrap for quantile regression will be described as well as a recent proposal by Parzen, Wei, and Ying (1992) for resampling from the (approximately pivotal) estimating equation. Finally, we will describe a new approach based on inversion of a rank test suggested by Gutenbrunner, Jurečková, Koenker, and Portnoy (1993) and introduced in Hušková(1994). The latter approach has several advantages: it may be computed relatively efficiently, it is consistent under certain heteroskedastic conditions and it circumvents any explicit estimation of the sparsity function. A small monte-carlo experiment is employed to compare the competing methods.