COMMUNITY ECOLOGY 8(1): 57-65, 2007
1585-8553/$20.00 © Akadémiai Kiadó, Budapest
DOI: 10.1556/ComEc.8.2007.1.8
On the overlap between effect and response plant functional
types linked to grazing
C. C. Blanco1, 2, E. E. Sosinski Jr.2, B. R. C. dos Santos3, M. A. da Silva3
and V. D. Pillar2
1
Corresponding author. Department of Ecology, Graduate Program in Ecology, Federal University of Rio
Grande do Sul. Fax: ++55 51 33087626, E-mail: [email protected]
2
Laboratory of Quantitative Ecology, Department of Ecology, Federal University of Rio Grande do Sul,
Av. Bento Gonçalves, 9500, Porto Alegre, RS, 91540-000, Brazil.
3
Department of Forage Plants and Agro-meteorology, Faculty of Agronomy, Federal University of Rio Grande
do Sul, Av. Bento Gonçalves 7712, Porto Alegre, RS, 91540-000, Brazil
Keywords: Campos, Data analysis, Disturbance, Effect traits, Forage selection, Grassland, Intra-specific variation,
Response traits, Vegetation dynamics.
Abstract: The question addressed in this paper is whether plant traits and plant functional types related to forage selection by grazers are also related to those expressing short-term community response after grazing. Vegetation of natural
campos grassland in south Brazil was examined for species composition and locally described for seven morphological
traits before and after a controlled grazing period by bovine cattle. An optimization algorithm was used for the identification of plant functional effect types (PFABT) and plant functional response types (PFHAT) - in this case, groups of plants
similar in a given set of traits (assessed before and after one grazing short period, respectively) and in their association
to grazing intensity. The results have shown that plant traits optimally defining plant types related to forage selection
(PFABTs) were the same traits optimally defining short-term community response to grazing (PFHATs); also similar trends
of plant morphological variation were observed among populations before and after grazing, based on the traits’ correlation structure. However, at the community level the correlation vanished, since similar communities described by the
performances of PFABTs were not as similar when described by PFHATs. Hence, whether plant functional types related to
forage selection (effect types) are also related to community response to grazing may depend on the level of organization
considered. The paper advances on the operational definition of possible overlaps between effect and response plant
functional types.
Abbreviations: BH – Biomass height, GI – Grazing intensity, LS – Leaf surface, LT – Leaf tensile strength, PCA –
Principal Component Analysis, PFT – Plant functional type, PFABT – Plant functional effect types, PFHAT – Plant functional response types, RDA – Redundancy Analysis, SL – Senescent leaves, UD – Upper leaf’s density, VP – Vegetative
propagation structures, WB – Woody biomass.
Nomenclature: The International Plant Names Index web site.
Introduction
Plant functional types (PFTs) have often been used to
describe patterns and processes in plant communities. The
differentiation of plant functional response types (PFHAT)
and plant functional effect types (PFABT) has been suggested in the literature (e.g., Lavorel and Garnier 2002,
Louault et al. 2005). Considering plant functional types as
a group of plants that are similar in a given set of traits and
similar in their association to certain variables (Pillar and
Orlóci 1993, Pillar and Sosinski 2003), PFHAT is assumed
as a group of plants with similar responses to a given environmental factor, and PFABT as a group of plants with a
similar effect on certain ecosystem variables. In this respect, Lavorel and Garnier (2002) showed that traits associated with response to nutrient gradients strongly overlap
with those affecting net primary production, but found little overlap between traits related to response to fire and
those affecting flammability. This new approach seems
useful for exploring the relationships, which are potentially mechanistic, expressed by traits, between response
of vegetation to grazing and the effect of vegetation on
58
forage selection by grazers. No empirical evidence has so
far been provided on the existence of these relationships.
Vegetation structure and grazing behavior are interconnected and their reciprocal effects are well-known. It
is largely demonstrated that grazing animals have selective foraging behavior (Provenza and Balph 1987, Senft
et al. 1987, Flores et al. 1989, Senft 1989, Laca and Demment 1991, Boggiano 1995). Besides the inherent differences between plant species, the acceptability by grazers
is determined also in the same plant species by the interactions between plant structure (allocation and distribution of aerial biomass) and forage quality (leaf nutrient
content). The main factors weighted are specific accessibility limitations on digestive apparatus (O’Reagain
1993), the amount of energy wasted for the intake
(O’Reagain and Mentis 1989, Laca and Demment 1991)
and the effects on rumen digestibility (Queiroz et.al.
2000). On the other hand, the effects of grazing on vegetation dynamics include changes in sward structure (Díaz
et al. 1992, 1999, Landsberg et al. 1999, Lavorel et al.
1999, Sosinski and Pillar 2004, among others) and floristic composition (Belski 1992, Boldrini and Eggers 1997,
Pucheta et al. 1998) by the interplay between properties
of local disturbance (frequency, intensity and magnitude)
and plant competitive interactions. The main factor involved is the presence or absence of physiological strategies and/or morphological structures that avoid defoliation (avoidance), or confer efficiency on sprouting
capacity (tolerance) of plants (Briske 1996).
In this sense, the complex relationship between grazing behavior and vegetation dynamics could be explained
by the interplay between availability of plant phenotypical attractions to grazers and traits that confer competitive advantage to plants under grazing. Furthermore,
some plant traits strongly related to forage selection by
grazers (defining PFABT) seem to be also related to the
vegetation structure developing after grazing (defining
PFHAT), i.e., probably, the most grazed plant types have
closer relationship to those that are favored after such depletion in plant biomass. In this paper, we verify such relationships between PFABT and PFHAT in short-term community dynamics before and after experimental grazing
using a detailed spatial scale and a special method of numerical analysis for the definition of plant types.
Methods
Study site
The study was conducted in a paddock of natural
grassland with approximately 3 ha in the Agricultural Research Station of the Federal University of Rio Grande do
Blanco et al.
Sul (EEA-UFRGS), in Eldorado do Sul, Rio Grande do
Sul state, Brazil (30°05’27”S, 51°40’18”W, 45 m a.s.l.).
The region is in a transition between tropical and temperate climatic zones, with a Cfa climate type in Koeppen’s
classification. The average annual precipitation is 1,445
mm and events of water deficits may occur from November to March (Moreno 1966, Bergamaschi et al. 2003).
The paddock had a smooth relief ranging a small valley crossed by a water stream. The vegetation physiognomy was typical of the campos grassland (Pillar and
Quadros 1997). Stoloniferous (e.g., Axonopus affinis) or
rhizomatous (e.g., Paspalum notatum) prostrated plants,
with scattered shrubs (Baccharis spp., Vernonia nudiflora), characterized the top and upper slopes, and tussocks plants (e.g., Andropogon lateralis) the low and humid valley. Before the starting of the study the paddock
had been managed with a moderate intensity rotational
grazing by bovine cattle and sheep for three years. In order to create the adequate conditions for the experiment,
the paddock was intensively grazed by bovine cattle in
August 2002 and then left ungrazed until the starting of
the experiment in February 2003.
The experiment
Fourteen permanent rectangular experimental plots of
0.2 m were located systematically in the paddock. Frequently flooded areas as well as a margin of 10 m width
from the fence were excluded from the sampling. In February 2003, the area was grazed for 16 days by bovine cattle (54 heifers with an average weight of 226 kg). Stocking rate was adjusted to get an average of 12% forage dry
matter on offer (a grazing level of approximately 12 kg of
forage dry matter on offer per 100 kg of cattle live weight)
using the ‘put and take’ method (Mott and Lucas 1952).
In each experimental plot, five 0.2 m × 0.2 m quadrats
were examined for plant morphological variations. Here,
a community was defined operationally as the plant
biomass rooted within the space unit of a quadrat (Palmer
and White 1994). The plant populations found in each
quadrat were visually estimated for cover percentage and
locally described for traits. These evaluations were done
twice: six days before the grazing period and 69 days after
the grazing period ending. For this study, we defined a
population as a group of plants belonging to the same species within a given quadrat and homogeneous for the traits
that were evaluated. Plants of the same species but in different quadrats were taken as different populations, that
is, the results were affected by intra-specific variation in
the traits.
Plant functional types and grazing
Seven morphological ‘soft’ (easily-measured) traits
were evaluated, which are related to plant structure (Altesor et al. 1999, Weiher et al. 1999, Pillar and Sosinski
2003), palatability (assumed here as the same as ‘leaf acceptability’) (O’Reagain and Mentis 1989, O’Reagain
1993, Boggiano 1995), or have been chosen a priori as
suspected to be ecologically relevant as response to grazing or by affecting it. The following traits were used:
• Woody biomass (WB): visual estimation of the percentage of woody stems (when occurring) on total
aerial biomass.
• Senescent leaves (SL): visual estimation of the percentage of discolored or dead leaves on photosynthetic biomass.
• Biomass height (BH): height measured in centimeters from the soil surface up to the point where most
of the leaves were subjectively judged to occur.
• Upper leaf’s density (UD): visual estimation of the
percentage of leaves above BH.
• Vegetative propagation structures (VP): local evaluation for the presence of rhizomes (value 1) or
stolons (value 2) in each population of each quadrat.
Value 3 was adopted for the absence of such structures.
• Leaf surface (LS): evaluation of leaf surfaces, sliding
forward and backwards on finger tips. Values are 1 =
smooth; 2 = rough; 3 = spiny.
• Leaf tensile strength (LT): calculated by the ratio of
leaf resistance to traction (R) over leaf area (A),
where R is measured with a tensioning device
(adapted from Grime et al. 1993) and A is the product of leaf width and leaf cross section, estimated at
a middle point of the leaf subjected to traction. For
compound leaves, these measurements were performed on a single leaflet.
Grazing intensity (GI) (Boggiano 1995) was estimated as
the percentage of biomass removed by cattle in each
quadrat, calculated by
GI = 100 ⋅
a1 − a2
a1
where a and a were successive measures of biomass
height. Three evaluations were done: six days before the
grazing period, on the 11JD day of grazing activity and on
the last day (16JD day) of the grazing period. Those measures were obtained with a thin metal surface (140 g
weight) with the same dimensions of the sampling quad-
59
rats sliding on a marked ruler. Stewart et al. (2001) found
this a simple and efficient tool for evaluating biomass
consumption.
Data analysis
For better quantifying the contribution of a population
in the total aerial biomass, the values of cover percentage
(CP) of each population in a quadrat were adjusted considering total aerial biomass (TAB) and total cover percentage of all populations in the quadrat. TAB was visually estimated using a scale of integer values ranging from 1
to 5, reflecting increasing volume of plant tissues coupled
with the height of total aerial biomass. In this way, the performance (PA) of each population was calculated as
Pe =
CP ⋅ TAB
.
CP
∑
The optimization algorithm described by Pillar and Sosinski (2003) and implemented in software SYNCSA (Pillar 2005) was used for definition of PFTs. In a recursive
process the algorithm defined PFTs by polythetic cluster
analysis and searched for the most relevant traits, in this
case related to grazing. From matrices B of populations
by traits and W of performances of these populations in
each quadrat (communities), a subgroup of traits most
correlated with a matrix E of environmental conditions
(grazing intensity in this case) was found by this recursive
algorithm. For this, at each step a cluster analysis was applied to a new matrix of populations by a subset of traits
extracted from B, and the performances of populations
belonging to each group were pooled from W in a consolidated matrix X. The optimal PFTs were defined by the
traits and number of group partitions that maximized the
measure of congruence, a matrix correlation ρ(D;∆) between compositional dissimilarities (∆) as given by the
PFTs’ performances in X, and the dissimilarities (D) of
the community sites as given by E. In these procedures,
Chord distance was used as resemblance function for
compositional dissimilarities, and UPGMA for cluster
analysis (for definitions see, e.g., Podani 2000, Legendre
and Legendre 1998). It is important to note that since trait
description of populations was done locally in each quadrat, populations of the same species but varying morphologically may be settled in different PFTs.
Matrix E was a vector of grazing intensity, i.e., the
percentage of biomass removed by cattle calculated for
each quadrat (as already described). Assuming that the
environmental variables can be effects of the PFTs in the
studied system or factors to which PFTs are responding
(respectively PFABT and PFHAT, Lavorel and Garnier
2002), these two approaches could be explored with the
60
Blanco et al.
Table 1. Plant traits and their states numerically defining four plant functional effect types (PFABT). For quantitative traits the
values are averages (rounded to the nearest integer) and for qualitative traits they represent the most frequent trait state in the
populations grouped in each PFABT. Traits are: BH = biomass height; UD = upper leaf’s density; WB = woody biomass; SL =
senescent leaves; VP = vegetative propagation structures; LS = leaf surface; LT = leaf tensile strength. Optimal traits are in
italics. Plant types’ presence and average performance (Avg. Perf. when present) are also given.
same environmental data source but changing B and W
and the way E is computed. Therefore, for the definition
of PFABT, BAB and WAB included populations evaluated six
days before starting the grazing activity, and for the definition of PFHAT, BHA and WHA included vegetation data
evaluated 69 days after the grazing period ending.
To allow independence on the definition of PFABT and
PFHAT, from the 14 plots evaluated, we used a random
sample of seven (i.e., 35 quadrats) to search for the PFABT
and the remaining ones for the PFHAT. In both analyses, for
the calculation of the values in E, a corresponded to the
biomass height before grazing (values obtained six days
before the grazing period; see explanation of the calculation of GI). But, for the definition of PFHAT the values in
E corresponded to grazing intensity calculated for all the
grazing period and so used for the last measure (on the
last day of the grazing period: 16JD day). For the definition
of PFABT the measure on the 11JD day of grazing was used
for a since the opportunity for selectivity is reduced as
overall biomass declines (Laca and Demment 1991, Boggiano 1995).
After the definition of plant types Principal Component Analyses (PCA) were performed for the comparison
of populations before and after grazing, through correlations between optimal traits from matrices BAB and BHA, respectively.
The matrix correlation ρ(DAB; DHA @) between dissimilarities of quadrats described by PFABT and the same quadrats described by PFHAT was used to assess whether plant
functional types related to forage selection by grazers
were related to those expressing short-term community
response after grazing. To recall, plant populations were
estimated for cover percentage and locally described for
traits in all quadrats (70, or 14 plots), and in two moments,
before (used for definition of PFABT) and after grazing
(used for definition of PFHAT). However, different samples
were used for the definition of each kind of PFT, as al-
ready described. Thus, to make that comparison, it was
necessary to allocate in the PFHAT defined by the optimization algorithm the populations evaluated after grazing
in the quadrats used for the definition of PFABT. For this
identification, we calculated the similarities between the
PFHAT and these populations based on the traits (only optimal traits concerning the definition of PFHAT). Each
population was then allocated to that PFHAT most similar to.
The performances of the aggregated populations within each
derived PFHAT were then pooled in matrix XHA @, which in
turn defined distance matrix DHA @. The procedure is illustrated in Appendix 1. The significance of the matrix correlation ρ(DAB; DHA @) was assessed by a permutation
(Mantel) test. The test used permutations only among
plots (sets of five quadrats) in order to avoid the possible
effect of spatial autocorrelation.
In addition, Redundancy Analysis (Legendre and
Legendre 1998) was used to evaluate the relationships between PFHAT and PFABT in describing the same communities. For this, the performances of PFABT in each quadrat
(XAB) were taken as explanatory variables and the performances of derived PFHAT in the same quadrats (XHA @)
were taken as response variables.
Results
Plant types related to forage selection by grazers PFABT
Before grazing, 363 different populations (representing 57 species) were described in the evaluation of
vegetation traits in all 70 quadrats (14 plots). However,
recall that only 35 quadrats were considered each time for
the definition of the plant types. With a maximum congruence ρ(D;∆) of 0.34, a subgroup of four traits (BH –
Biomass height, UD – Upper leaf’s density, WB – Woody
biomass, SL – Senescent leaves) defined four plant functional effect types (PFABT) (Table 1).
Plant functional types and grazing
61
Table 2. Plant traits and their states numerically defining five plant functional response types (PFHAT) (A) and four derived
plant functional response types (derived PFHAT) (B). The definition of four derived PFreT was based on the identification, to
the five PFHAT, of more similar populations evaluated after grazing in the quadrats used for definition of PFABT. For quantitative traits the values are averages (rounded to the nearest integer) and for qualitative traits they represent the most frequent
trait state in the populations grouped in each PFHAT. In (B) only four plant types were derived because none of the populations evaluated after grazing in the quadrats used for definition of PFABT was allocated to PFHAT-5, which had very low frequency and average performance. Traits are: BH = biomass height; UD = upper leaf’s density; WB = woody biomass; SL =
senescent leaves; VP = vegetative propagation structures; LS = leaf surface; LT = leaf tensile strength. Optimal traits are indicated in italics. Plant types’ presence and average performance (Avg. Perf.) are also presented.
Grouped in PFABT-1 were those populations without
woody stems (WB = 0), low percentage of senescent
leaves (SL = 17%) and aerial biomass closer to the ground
(UD = 30% and BH = 11 cm). The most representative
species displaying such morphological aspects were prostrated grasses like Axonopus affinis and Paspalum notatum. Grouped in PFABT-2 were populations with large
amounts of senescent leaves but with some woody stems,
allowing more accessible arrangement of aerial biomass
for foraging (UD = 73%), despite its lower height, like
prostrated legumes such as Desmodium incanum. Grazing
intensity tended to increase with increasing performance
of PFABT-1 and decreasing performance of PFABT-2 (results not shown). Plant types PFABT-3 and PFABT-4 included woody plants and were present in few quadrats.
Grouped in PFABT-3 were the tallest plants (BH = 30 cm)
with much raised biomass (UD > 60%) (e.g. Baccharis
trimera); PFABT-4 included similar but shorter plants (e.g.
Pfafia tuberosa).
Plant types related to short-term vegetation response to
grazing - PFHAT
Sixty nine days after the end of the grazing period, 548
populations representing 70 species were recorded in the
evaluation of vegetation traits in the 70 quadrats (14
plots). Five plant response types (PFHAT) defined by four
optimal traits were found with a maximum congruence
ρ(D;∆) of 0.4. Interestingly, these traits have a complete
overlap with those optimized on the definition of PFABTs,
though based on a different sample (Table 2).
Populations without woody stems (WB = 0) were
grouped in PFHAT-1 and PFHAT-4. PFHAT-1 included shorter
plants with aerial biomass arranged closer to the ground
(UD = 22%) and low proportion of senescent leaves. In
PFHAT-4 plants were taller, with more erect (UD = 26%)
and senescent canopies (SL = 24%). Both plant types were
mostly represented by grasses. PFHAT-1 included Axonopus affinis, Paspalum notatum and other grasses such
as Piptochaetium montevidense. PFHAT-4 included cespitose grasses like Andropogon lateralis, Agrostis montevidensis and Eragrostis bahiensis.
Short plants with some woody biomass but green (low
SL) and raised leaves (higher UD) were grouped in PFHAT2 and PFHAT-3. Both plant types were represented by dicots, with more robust ones in PFHAT-3 like Stylosanthes
montevidensis and Tibouchina gracilis, and others in
PFHAT-2, such as Aspilia montevidensis and Desmodium
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Blanco et al.
Table 3. Redundancy Analysis evaluating the relationships between PFHAT and PFABT in describing the same communities.
The analysis was based on 35 quadrats (sampling sites) defined by the composition of four derived PFHAT as response variables and four PFABT as the explanatory variables.
Figure 1. Principal Component Analyses of populations by optimal traits’ matrices, before (A) and after (B) grazing. Populations are indicated by their corresponding plant types as four Plant Functional Effect Types (A) and five Plant Functional
Response Types (B). Optimal traits most correlated to the axis are shown near arrows allowing the characterization of plant
types. In Figure 1A, optimal traits most correlated to the first axis are woody biomass (0.68), upper leafs’ density (0.74) and
biomass height (0.63); and to the second axis are senescent leaves (-0.88). In Figure 1B, optimal traits most correlated to the
first axis are woody biomass (0.66) and upper leafs’ density (0.73); and to the second axis are senescent leaves (-0.83) and
biomass height (0.58).
incanum. PFHAT-5 had a very low frequency representing
only populations of the shrub Vernonia nudiflora, with
high proportion of woody stems (WB = 38%) and senescent leaves (SL = 25%).
The performance of PFreT-2 and PFreT-3 tended to
increase and of PFreT-1 and PFreT-4 to decrease with increasing grazing intensity (results not shown).
Correlation structure in the population by traits matrix
Figure 1 depicts the results of Principal Component
Analyses carried out to further compare optimal traits’ attributes characterizing populations before (Fig. 1A) and
after grazing (Fig. 1B), when grouped as four PFABT and
five PFHAT, respectively. Similar trends of variation were
observed in both analyses. Most correlated morphological
variations were between biomass height and proportion of
senescent leaves (Fig. 1A, Fig. 1B. See also Tables 1 and
2).
Comparing plant communities described by PFHAT and
PFABT
The correlation coefficient between community dissimilarities based on the composition of PFABT and derived PFHAT was ρ(DAB;DHA @) = 0.2, which was weak and
not significant (P = 0.29). This result indicates that in general quadrats that were more similar by their composition
of PFABT did not remain as similar after grazing, when described by derived PFHAT.
Table 3 shows the results of Redundancy Analysis
carried out to further assess the possible relationship between vegetation composition given by effect types associated to grazing selection (PFABT) and composition given
Plant functional types and grazing
by response types (PFHAT) associated to grazing intensity.
The numerical results show that the sum of all canonical
eigenvalues (PFABT) accounts for less than 20% of the
variation in response matrix (PFHAT), and that their values
are smaller than the first non-canonical ones. The canonical ordination diagram (not shown) was hence non-informative. This suggests that community effects on grazing
selection could not explain the overall structure of community response after grazing.
Discussion
Our results have shown that plant traits optimally defining PFTs related to forage selection (PFABTs) were the
same traits optimally defining short-term community response to grazing (PFHATs). Furthermore, similar trends of
plant morphological variation were observed among
populations before and after grazing, based on the traits’
correlation structure. However, at the community level
the correlation vanished, i.e., similar communities described by the performances of PFABTs were not as similar
when described by PFHATs. Hence, whether plant functional types related to forage selection (effect types) are
also related to community response to grazing may depend on the level of organization considered.
These findings suggest we need a better definition of
possible overlaps between effect and response plant functional types. Lavorel and Garnier (2002) conceptually examined the problem and proposed the keystone hypothesis that traits can simultaneously explain ecosystem
effects and individual plant responses to biotic and abiotic
factors. Here we advance on the operational definition for
the possible overlaps: (1) An overlap of effect and response traits may exist; (2) if condition (1) holds true, a
similar correlation structure may be observed between
traits; and (3) a high congruence at the community level
may be found, i.e., similar communities described by the
performances of PFABTs may be as similar when described
by PFHATs. It is important to note that conditions (1) and
(2) do not seem essential for condition (3) holding true.
That is, even if the plant traits defining PFABTs are not the
same traits defining PFHATs, the level of congruence between community similarities based on these effect and
response plant types may be high enough for holding condition (3) true. In our results, conditions (1) and (2) were
true, whereas (3) was not. While the traits and their correlation structure depicting main trends of plant variation
were similar, the way plant types were organized at the
community level before and after grazing were not consistent. It remains an open question, however, if the same
would be observed under other experimental conditions
and scales in time and space.
63
It may be argued that our results are too restricted to
short-term changes observed in a small-scale experimental setup. First, we found this method useful to objectively
evaluate how effect and response types to grazing would
overlap. Second, in order to detect such very short-term
responses we had to consider intra-specific trait variation,
which includes phenotypic plasticity in the strict sense
(morphological changes in the same individual plant) and
trait variation between populations of the same species. If
the trait description unit were the species, we would not
be able to detect effects (Boggiano 1995) nor responses at
the same level of congruence. Furthermore, we believe
that the short-term trends mostly expressed by intra-specific trait variations may be part of a continuum of change
that in the long run would also include species turnover.
The very small sampling unit size was necessary to a better control of changes at the bite-scale to maximize the
detection of cattle preferences while the five-quadrats aggregated design of the experimental plots was necessary
to maximize the capture of the more intensive “foraging
zones” around a bite.
We now discuss our results regarding possible mechanistic relationships between plant traits and grazing. The
more intensively grazed communities were those with
higher frequency and performance of PFABT-1, thus preference by grazers was more correlated to low proportions
of senescent leaves and absence of woody biomass, from
which can be inferred an optimized searching for better
forage quality (nutrient maximization). In fact, it is well
known that plant structural organization and its constituent tissues influence forage consumption due to its effects
on rumen digestibility and specific limitations on digestive apparatus. Higher percentages of structural tissues involve more energy wasted for the intake (O’Reagain and
Mentis 1989) and higher proportions of stem contents are
inversely related to better accessibility to leaves (O’Reagain 1993). In addition, tissues with cell wall thickened
by lignifications cause most of the problems of low utilization of forage energy, requiring more time for rumen
digestion and thus leading to lower consumption (Queiroz
et.al. 2000).
After grazing, the most frequent and with higher performance PFHAT-1 had similar trait values compared to the
dominant PFABT-1 in more intensively grazed sites. In
other words, biomass attributes that constituted the principal and the most available phenotypical attractions to
grazers remained the most important conferring competitive advantage to plants under grazing. It was already suggested that the main strategy acquired by plants in intensively grazed areas is one that reduces grazing severity
rather than a grazing tolerance response to aboveground
64
herbivory (Jaramillo and Detling 1988). However, considering a dynamic mosaic under patchy grazing (a selective disturbance), after resprouting forage quality on
grazed patches is better than that of the surrounding ungrazed matrix, and as the time passes grazers continue to
select the regrowth sites, so the grazed patches diverge
further from the ungrazed matrix in terms of forage quality and plant form (Coughenour 1991). Thus, it seems that
the expected enhancement of plant palatability is only an
indirect consequence of grazing. In our case, the reason
condition (3) described above did not hold true might lie
in the relatively high initial homogeneity among plots and
in the short duration of the experiment (one grazing cycle), which may have been insufficient to the development of patchiness associated to selective grazing. Indeed, heterogeneity among quadrats increased after
grazing.
Acknowledgements: This research was carried out with
grants and fellowships from CNPq (Brazil). We owe special thanks to I. Boldrini for the botanical knowledge
shared for species identification. We are grateful to the
director and the personnel of the Agricultural Research
Station for the logistic support for the fieldwork.
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Appendix 1. The two random samples of plots (each plot is a set of five quadrats) and corresponding data matrices (W) used
for the definition of plant functional effect types (PFABT) and plant functional response types (PFHAT). W contains population
performances used in PFT optimization (shaded), or populations which were later allocated to existing PFTs (unshaded). For
each population or PFT there was a corresponding vector of traits (not shown). X contain PFT pooled performances, for
which XAB (after PFABT) and XHA @ (after derived PFHAT) were used for the computation of quadrat distances in matrix correlation ρ(DAB;DHA @), and for Redundancy Analysis. Sample 2 was also evaluated before grazing (not shown) and thus the same
procedure could have been used to find ρ(DAB @;DHA).