Agriculture, Ecosystems and Environment 154 (2012) 44–55
Contents lists available at SciVerse ScienceDirect
Agriculture, Ecosystems and Environment
journal homepage: www.elsevier.com/locate/agee
Expansion and intensification of row crop agriculture in the Pampas and Espinal
of Argentina can reduce ecosystem service provision by changing avian density
Gregorio I. Gavier-Pizarro a,∗ , Noelia C. Calamari b , Jeffrey J. Thompson a , Sonia B. Canavelli b ,
Laura M. Solari a , Julieta Decarre a , Andrea P. Goijman a , Romina P. Suarez a ,
Jaime N. Bernardos c , María Elena Zaccagnini a
a
Instituto Nacional de Tecnología Agropecuaria (INTA), Centro de Investigación en Recursos Naturales (CIRN-IRB), De los Reseros y Las Cabañas S/N HB1712WAA Hurlingham,
Buenos Aires Argentina1
b
Instituto Nacional de Tecnología Agropecuaria (INTA), EEA Paraná, Factores Bióticos y Protección Vegetal, Ruta 11 Km 12.7, Oro Verde (3101), Entre Ríos, Argentina2
c
Instituto Nacional de Tecnologia Agropecuária (INTA), EEA Ing. Agr. Guillermo Covas, Ruta Nac. N◦ 5 Km 580 (6326) C.C N◦ 11 Anguil, La Pampa, Argentina3
a r t i c l e
i n f o
Article history:
Received 2 August 2010
Received in revised form 11 August 2011
Accepted 18 August 2011
Available online 19 September 2011
Keywords:
Argentina
Bird density
Regional bird survey
Ecosystem services
Espinal
Habitat loss
Pest control
Pampas
Row crops
a b s t r a c t
In Argentina, the rapid expansion and intensification of row crop production that has occurred during
the last 20 years has resulted in the loss of habitat and spatial heterogeneity in agroecosystems. One of
the principal effects of industrialized row crop production is the loss of avian diversity and associated
ecosystem services that benefit crop production. To better understand the response of bird species to
the intensification and expansion of row crop agriculture in Argentina, and the potential effects on the
provision of ecosystems services, we analyzed the relationship between short- and long-term changes
in agricultural land use on the densities of six bird species (Milvago chimango, Caracara plancus, Tyrannus
savana, Zenaida auriculata, Molothrus bonariensis, and Sturnella supercilliaris) using data from a large-scale,
long-term avian monitoring program in central Argentina. Species densities responded individually to
long-term landuse changes; T. savana and M. chimango densities were positively related to an increase in
the annual cropping area, whereas C. plancus and S. supercilliaris were positively related to the area of nonplowed fields. M. bonariensis and Z. auriculata (considered crop pests) showed a weak relationship with
land use. None of the species exhibited response to short-term changes in land-use. Although generalist
species can apparently adapt to a diversity of open habitats, species that provide pest control services
were also related to semi-natural habitats and thus likely to suffer from land transformation associated
with intensive agricultural management. Our results, as well as those found in similar systems, denote
strong inferential evidence that the disappearance of remnants of natural and semi-natural habitats in
heavily transformed agricultural landscapes will have a substantial negative effect on the provision of
pest control services provided by avian abundance and diversity.
© 2011 Elsevier B.V. All rights reserved.
1. Introduction
Land use changes and conversion of natural habitats are transforming the earth’s surface at a fast pace, particularly due to modern
agriculture and forestry practices (Foley et al., 2005; Ramankutty
et al., 2008). The expansion and intensification of row crop production has been particularly evident in Argentina, where the
cultivated area increased 45% between 1990 and 2006, with half of
the increase devoted to genetically modified soybean (Aizen et al.,
∗ Corresponding author.
E-mail address: [email protected] (G.I. Gavier-Pizarro).
1
Tel.: +54 011 4481 2360; fax: +54 011 4481 2360.
2
Tel.: +54 011 0343 4975200.
3
Tel.: +54 011 02954 495057.
0167-8809/$ – see front matter © 2011 Elsevier B.V. All rights reserved.
doi:10.1016/j.agee.2011.08.013
2008; Oesterheld, 2008); at the same time, the use of fertilizers
increased by 400% (FAO, 2010; Thompson, 2007).
Although row crop expansion has occurred most dramatically in
the Chaco region of northern Argentina (Grau et al., 2005; Zak et al.,
2004; Grau and Aide, 2008; Gasparri and Grau, 2009), the process
has also been evident in the Pampas (a region with more than a century of agricultural use) and Espinal ecoregions, where pastures,
natural grasslands and forests historically used for cattle grazing
have been converted to row crop production (Viglizzo et al., 1997;
Paruelo et al., 2005; Baldi and Paruelo, 2008). The expansion of cultivated land has been related to a combination of climate change
(increasing precipitation), increasing global demand for agricultural products, national economic policies, and new technologies
(genetically modified seeds, agrochemicals, machinery) (Viglizzo
et al., 1997, 2004; Grau et al., 2005; Grau and Aide, 2008; Zak et al.,
2008). Concurrently, crop management has become increasingly
intensified (Hall et al., 2001; Viglizzo et al., 2004; Thompson, 2007).
G.I. Gavier-Pizarro et al. / Agriculture, Ecosystems and Environment 154 (2012) 44–55
In Argentina, the main negative effects from the recent intensification and expansion of row crop agriculture include agrochemical
contamination, soil degradation, biodiversity loss, high rates of
deforestation and fragmentation in the Chaco, and grassland conversion in the Pampas that has resulted in spatially simplified
landscapes (Zaccagnini and Calamari, 2001; Paruelo et al., 2005;
Boletta et al., 2006; Codesido et al., 2008; Baldi and Paruelo, 2008;
Oesterheld, 2008; Gasparri and Grau, 2009).
Habitat loss and the reduction of spatial heterogeneity are of
concern due to their negative effects on biodiversity and have
been attributed to a substantial reduction in avian species richness in agroecosystems (Alkorta et al., 2003; Benton et al., 2003;
Jackson et al., 2007). For example, in England four-fifths of bird
species, particularly habitat specialists, have undergone population
declines related to habitat loss and reduced spatial heterogeneity
in agricultural landscapes (Robinson and Sutherland, 2002). Largescale studies in European agroecosystems illustrate a reduction in
species richness of birds associated with the loss of semi-natural
habitats and increased fertilizer use (Donald et al., 2001, 2006;
Billeter et al., 2008).
In the Pampas and Espinal ecoregions of Argentina, region-wide
habitat conversion has resulted in changes in the composition of
avian communities and decreases in species richness. Avian species
richness in the Pampas is positively associated with the proportion
of natural vegetation and negatively associated with the proportion
of cultivated land (Schrag et al., 2009), and the relative abundance
of most bird species decreased along a gradient of increasing transformation from grazing to row-crop dominated landscapes (Filloy
and Bellocq, 2007a). Areas still predominantly used for grazing
(with greater habitat availability for grassland-dependent species)
show higher avian species richness than areas primarily used for
row crop production (Codesido et al., 2008). In the Espinal region,
avian species richness decreased with decreasing size of woodland patches (Bucher et al., 2001); however, small remnants were
capable of supporting relatively high species richness (1-ha patches
retained up to 50% of birds species) (Dardanelli et al., 2006).
Bird responses to the expansion of row crop production have
been shown to be dependent upon functional groups, with insectivore and raptor species demonstrating a greater sensitivity to
increasing areas in row crops than granivores (Carrete et al., 2009;
Zaccagnini et al., 2011). In addition, species-specific responses
within functional groups are variable. For example, a study of raptor communities in Argentina suggested a negative response to
habitat transformation, but three species (Coragyps atratus, Elanus
leucurus, Falco sparverius) peaked in relative abundance in a mosaic
of transformed and natural habitats. Moreover, Milvago chimango
increased with increasing habitat conversion and the presence of
another species (Circus buffoni) increased in grassland-dominated
landscapes (Carrete et al., 2009; Pedrana et al., 2008).
Other examples include decreasing population and distribution of the Pampas meadowlark (Sturnella defilippii) associated
with grassland conversion and overgrazing (Fernández et al.,
2003), and greater survival of the spotted tinamou (Nothura maculosa) in agroecosystems in the province of Buenos Aires in
a mixed landscape of pasture and agriculture compared to an
agriculture-dominated landscape (Thompson and Carroll, 2009).
Species-specific variations in response to habitat area were also
illustrated for birds in Espinal woodlands within agrosecosystems in Entre Ríos where the relative abundance of most species
decreased with decreasing patch size (Calamari and Zaccagnini,
2007).
What are the consequences of changes of avian species diversity
on agroecosystems? Biodiversity is a key component of agroecosystems. Plant and animals regulate the flux of energy and
matter (water, nutrients) and most associated ecological processes
(e.g., seed dispersion, pollination) that are fundamental for the
45
sustainability and resilience of agroecosystems (Altieri, 1999).
Birds are particularly important components of agroecosystems,
exhibiting the most diverse range of ecological functions among
vertebrates. Their high diversity of adaptations and life histories,
high numbers and mobility allow birds to regulate many ecosystem
processes and to respond very quickly to changes in resource levels
(Şekercioğlu et al., 2004; Şekercioğlu, 2006). As a consequence,
birds provide several ecosystem services that are important for
the function and sustainability of agroecosystems, including regulating (seed dispersal, pollination, pest control, carcass and waste
disposal) and supporting services (nutrient deposition, ecosystem
engineering) (Şekercioğlu, 2006; Whelan et al., 2008).
Although most of these services are difficult to quantify, pest
control is one of the most important services to agricultural production provided by birds, with several studies showing substantial
decreases of pest species and increases in crop production related
to the predatory activities of birds (see review in Whelan et al.,
2008). Predation by insectivorous birds reduced pest damage levels in coffee plantations by 1–14%, increasing the production value
by US$44–$105/ha in 2005/2006 (Kellermann et al., 2008). Moreover, the use of nesting boxes to attract great tits (Parus major)
reduced caterpillars densities and fruit damage while increasing
apple yield by 66% (Mols and Visser, 2002). Research on the controlling effects of raptors on rodents and avian agricultural pests
is limited (Şekercioğlu et al., 2004); however, the importance of
rodents in the diet of raptors suggests that these birds are beneficial
species for agriculture (Whelan et al., 2008). For example, higher
numbers of diurnal raptors around soybean fields decreased population numbers and growth rate of house mice (Mus domesticus)
(Kay et al., 1994).
Functional richness theory relates the level of ecosystem services provided by birds to the diversity of species providing it. More
species represent a larger number of adaptations and henceforth
a more efficient use of the resource. As a consequence, species
richness is considered to be directly related to the level of service provided (Philpott et al., 2009). Common species (usually the
most abundant), however, have the greatest effect on ecosystem
processes (Gaston, 2010) and subsequently the level of ecosystem
services provided by those species also depends on the abundance
of those species (Şekercioğlu et al., 2004; Swift et al., 2004; Wilby
et al., 2005).
Expansion and intensified management of rows crops are
expected to continue to significantly alter habitat quality, quantity,
and configuration in Argentina; hence, assessing and determining
the implications of these landscape-modifying processes for the
provision of ecosystem services, via the effects on avian populations, are imperative to ensure the provision of those services. For
this purpose, we used data from a long-term, large-scale monitoring program to analyze the short- and long-term relationship
between densities of a suite of avian species (which provide pest
control services) and the expansion and intensified management
of row crops in the Argentine Pampas and Espinal agroecosystems,
assuming a direct relationship between bird abundance and the
level of pest control service (Philpott et al., 2009; Swift et al., 2004;
Wilby et al., 2005).
2. Methods
2.1. Study area
The study area comprised 128,200 km2 of the Pampa and
Espinal ecoregions (Cabrera, 1994) characterized by annual mean
minimum and maximum temperatures of 13 ◦ C and 23 ◦ C, respectively (Soriano, 1992) and 1000 mm of mean annual precipitation
(Ferreyra et al., 2001; Messina et al., 1999; Podestá et al., 2002). This
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G.I. Gavier-Pizarro et al. / Agriculture, Ecosystems and Environment 154 (2012) 44–55
area is the principal agricultural production region in Argentina,
increasingly dominated by annual row crops (wheat, soybean, sunflower, sorghum and corn) and has been highly modified due
to agricultural expansion and intensification (León et al., 1984;
Viglizzo et al., 2004).
The Espinal portion extends irregularly from the center of
province of Santa Fe, northeastern Córdoba and northern Entre Ríos
and supports remnant xerophilic woodlands dominated by Prosopis
nigra, Acacia caven, and Geoffroea decorticans. These woodland remnants are isolated and immersed in an agricultural matrix. The
Pampean portion covers southern Santa Fe, south-central Córdoba
and central Entre Ríos provinces, and is dominated by grasslands
mainly composed of Stipa sp., Briza sp., Bromus sp., Poa sp. (Cabrera,
1971).
2.2. Data analysis
We analyzed the relationship between avian species abundance
and land use change using two sets of variables: estimates of species
density (dependent variables) and environmental factors (independent variables). Using these variables we conducted two analyses
representing the short and long-term effects of habitat conversion
on species densities.
2.2.1. Bird species density estimation (dependent variables)
In January each year from 2003 to 2009 (austral breeding season)
we surveyed 47 transects (routes), located along unpaved secondary and tertiary roads within different agro-production zones
(areas differing in land use, land cover and economic activities)
in east-central Argentina. Transect locations were chosen using a
geographically-stratified systematic design that consisted in applying a 30 km × 30 km grid over a map of the study area and defining
eight strata consisting of agro-production zones and provincial
boundaries (Fig. 1). Within each stratum, grid cells were selected
systematically (every other cell) with the number of cells proportional to the area of each zone. Within each cell the route and
the direction for the route to be surveyed were randomly selected
among all possible alternatives (Zaccagnini et al., 2010).
Each route had 30 permanently marked points, spaced at 1 km
intervals, with the first point on the route randomly placed. At each
point six avian species were surveyed between 6:00–11:00 am and
15:00–20:00 pm using distance sampling (Buckland et al., 2001) by
a pair of experienced observers. At each point one observer determined the distance to and number of each species detected during a
3-min period whereas the other observer recorded data on land use.
Laser rangefinders were used to measure distances to individual
birds or to the center of flocks.
We selected 6 species based upon their extensive distribution
within the study area and relatively high number of detections.
Two species were insectivores (Fork-tailed flycatcher Tyrannus
savana, White-browed blackbird Sturnella supercilliaris) and two
were raptors (Chimango caracara Milvago chimango and Southern
crested-caracara Caracara plancus), whose diet includes rodents
and insects. We also included two species that are considered
crop pests (Shiny cowbird Molothrus bonariensis and Eared dove
Zenaida auriculata) to compare their response to land use change
with that of species providing pest control services. This comparison can have important management implications if management
to enhance pest control by birds also has a positive influence on
the abundance of pest species. Species densities were estimated
for each year using DISTANCE 5.0, a computer package for the
analysis of distance sampling data that corrects for incomplete
detection in density estimates (Buckland et al., 2001; Thomas et al.,
2002).
After exploratory analysis of the data (sensu Buckland et al.,
2001; Thomas et al., 2002, including histograms of the distance data
under several grouping factors to detect and correct for the presence of heaping, evasive movement, outliers) we set the truncation
distance w at 250 m, the distance that included 90% of detections
for all species combined and manually selected 7 distance intervals with cut off points based on the distribution of observations
at different distances. Density estimates were derived with detection models using a combination of 3 monotonic, decreasing key
functions (uniform, half-normal, and hazard rate) and 2 adjustment terms (cosine and polynomial) and best models chosen using
Akaike Information Criteria (AIC) and model weights. To facilitate multi-species analysis we selected one model (half normal
key function + cosine adjustment term) for estimating density of all
species based on its better performance for the majority of species.
Given a relatively low number of detections for some species during
each year we estimated the detection probability function globally
(e.g., all years combined) for each species, and estimated density
for each year on each route using stratification (by year) and poststratification (by route) (Buckland et al., 2001).
2.2.2. Environmental (independent) variables
2.2.2.1. Land use. Land use was recorded annually at each transect
point in a 200-m radius circle centered on each point, as estimates
of percent cover of five land-use classes (Schrag et al., 2009). Landuse classes included: (1) annual crops (i.e., soybeans, sorghum,
sunflower, wheat, corn); (2) managed pastures (both annual and
perennial species); (3) non-plowed fields, including (a) agricultural
fields that have been resting for more than a season covered with
a mixture of herbaceous and grassy vegetation, or (b) natural and
semi-natural grasslands used for cattle ranching; (4) forest (both
native and exotic species); and (5) other uses (including, but not
limited to, aquatic habitats). The percent cover for all points was
averaged among the 30 observation points to obtain a single value
for each transect per year.
2.2.2.2. Enhanced vegetation index. Enhanced vegetation index
(EVI), which provides a consistent and permanent comparison of
temporal changes in vegetation is a MODIS (Moderate-Resolution
Imaging Spectroradiometer) product MOD13Q1 (image acquisition
every 16 days) with a 250-m spatial resolution (http://modisland.gsfc.nasa.gov/vi.htm). EVI measures the amount of photosynthetic tissue as an index of plant productivity that corrects for
distortions in the reflected light caused by airborne particles as well
as ground cover below the vegetation (Jiang et al., 2008). For each
year, we extracted the EVI value for the pixel containing each sample point and averaged those values to obtain a single value per
transect.
2.2.2.3. Precipitation. Precipitation data for each transect were
collected from the nearest meteorological station of the Instituto Nacional de Tecnología Agropecuaria (INTA). Since avian
abundance may have been associated with precipitation before
sampling, we included mean monthly precipitation from September to January for each year.
2.2.2.4. Latitude and longitude. Latitude and longitude for the central point of each survey transect (Gauss–Kruger zone 3) was
included in the analysis to account for large-scale spatial patterns
that could mask the relationship between avian densities and land
use (e.g., gradient in species abundance related to variations in
the geographic distribution) and account for potential correlation
of other variables that could be responding similarly to the same
large-scale gradients.
2.2.3. Statistical analyses
We used regression analysis to investigate the relationship
among species densities (dependent variables) and environment
G.I. Gavier-Pizarro et al. / Agriculture, Ecosystems and Environment 154 (2012) 44–55
47
Fig. 1. Study area and location of survey transects.
variables (independent variables). We divided the analysis into two
parts to assess the long-term and short-term relationship between
bird densities and habitat variables. We analyzed the long-term
relationship using the mean values from 2007 to 2009 for both
dependent and independent variables, trading space for time to
establish a gradient representing the long-term transformation
from totally transformed areas to those dominated by natural vegetation. For the analysis of the short-term relationship between
bird densities and habitat variables we used regression analysis of
the changes in mean densities and habitat values between 20032005 and 2007-2009 to explore species response to land use change
during the study period.
Throughout the analysis we used the same approach that
included three steps. In the first step, we tested for correlations
among independent variables using a Pearson’s correlation matrix
for explanatory variables to avoid colinearity issues and discarded
habitat variables that showed high levels of correlation (r ≥ 0.70).
In the first part of the analysis (long-term temporal effects), rainfall
and annual crops were correlated with longitude and proportional
forest area, respectively, above 0.7 Pearson correlation coefficient.
Hence, we included rainfall instead of longitude in the models,
because rainfall accounts for an east–west gradient, whereas for the
area in annual crops and forest we selected the variable that best
explained species density for each particular species. In the second
part of the analyses (short-term temporal effects), we included all
variables plus latitude and rainfall instead of longitude. We did not
use a change variable for forest, because there was an alteration
in the sampling protocol related to the estimation of forest cover
during the period of the entire study.
In the second step of the analysis, we fitted multiple regression
linear models using the variables selected in the first step. First, we
fitted a full model using all habitat variables and then we used stepwise selection to eliminate non-significant variables and obtain a
reduced model (Chatterjee, 2001). We used the full and reduced
models as a measure of variance in species densities given our set
of explanatory variables. Assumptions of linear regression were
verified. If necessary, densities were log-transformed to meet linearity assumptions. Spatial autocorrelation of the model residuals
were tested with a semi-variogram randomization analysis (Isaaks
and Srivastava, 1989). The full and reduced models were used to
estimate the variance in densities accounted for in the analysis
and the direction of the effects of the independent variables.
In the third step, we assessed the importance of each individual
variable in explaining species density. Stepwise selection has limitations since it identifies one best model (among several that could
explain the responses equally well) and does not provide information about the amount of variance explained by each variable
(Whittingham et al., 2006). Since our main focus was to understand
the habitat variables that most affected bird densities rather than
fitting the best explanatory model, we used best subsets and hierarchical partitioning analysis to assess the importance of variables
included in the models.
The best subset method uses Bayesian Information Criterion
(BIC) to obtain a subset of models that best explains the response.
The approach performs an exhaustive search of all possible models and the maximum number of predictors allowed is specified a
priori (Miller, 1990). Fitting several models instead of one “best”
Fig. 2. Variation in mean densities during 2007–2009 for the six bird species
included in the study. The black line inside each box is the median, the lower and
upper edges of the boxes are the 0.25 and 0.75 quantiles, and the lines represent the
range of the data. Outliers are denoted with a circle.
48
G.I. Gavier-Pizarro et al. / Agriculture, Ecosystems and Environment 154 (2012) 44–55
Fig. 3. Distribution of mean bird densities and percentage of annual crops in the study area during the period 2007–2009. Maps of the proportional annual crop area were
obtained by extrapolation of the 2007–2009 mean for each survey transect using inverse distance weighting.
G.I. Gavier-Pizarro et al. / Agriculture, Ecosystems and Environment 154 (2012) 44–55
model highlights which variables are repeatedly chosen in the best
models, and whether they have a consistent effect on the response
variable (i.e., negative or positive coefficient). We used three variables to fit the model and considered the 10 best models explaining
bird densities obtained in each best subsets analysis. We then
counted the number of times that each variable was included in
the 10 best models to determine an importance value of each variable in the model subset. We performed one subset analysis for
each bird species in both parts of the analysis.
Hierarchical partitioning analysis calculates the percent of variance of the full model explained by each variable when all other
variables are included in the model. For estimated densities of each
bird species, all possible models based on different combinations
of the habitat variables were fit. For each fitted model the variable of interest was dropped and the model was fitted again. The
importance of that variable was calculated as the average change
in R2 when the variable was dropped from all of the fitted models
(MacNally, 2002).
For each bird species we presented results of both hierarchical
partitioning and best subset analysis for each habitat (explanatory
variables) included in the analysis. Best subset analysis indicated
which variables were most strongly associated with bird densities
(importance value) and hierarchical partitioning analysis indicated
the proportion of variance explained by each variable of the total
variance included in the full model. We also included the full and
reduced models.
3. Results
3.1. Trends in species densities and distributions
During 2007–2009 Z. auriculata had the highest density, with
an average of 12 individuals per ha, whereas for the same period
estimates of mean density for T. savana, M. bonariensis and S.
supercilliaris were similar to one another but much lower than
Z. auriculata (Fig. 2). The two raptor species, M. chimango and C.
plancus, had the lowest densities (less than 1 individual per ha).
Furthermore, densities of Z. auriculata, T. savana and M. bonariensis
were highly variable across survey transects (Fig. 2).
The spatial distribution of species density showed three different patterns across the study area. Densities of S. supercilliaris, M.
chimango, C. plancus and T. savana were highest in the west-central
portion of the study area, where land use is dominated by row crop
agriculture. This distribution pattern was most strongly related to
land use for S. supercilliaris and M. chimango, whereas a weaker
relationship between land use and density for C. plancus and T.
savana was observed (Fig. 3). Densities of Z. auriculata and M. bonariensis were not related to the proportion of cultivated area, with
the density of Z. auriculata decreasing along a north-south gradient and the density of M. bonariensis showing a weak tendency to
decrease along a west-east gradient (Fig. 3). Between 2003–2005
and 2007–2009, the average estimated densities of most species
were stable; however, Z. auriculata and M. bonariensis exhibited
a tendency towards increasing densities between these periods
(Fig. 4).
3.2. The relationship between land use and species density
3.2.1. Long-term effects
During the 2007–2009 period, the density of S. supercilliaris was
most strongly related to land use, followed by that of M. chimango
(multivariate models explaining about 60% and 40% of the variation
in estimated densities, respectively; Table 1). Estimated densities
of C. plancus and T. savana were related to land use at an intermediate level (multivariate models explaining 30% of the variation in
49
Fig. 4. Variation in changes of mean densities of focal bird species between the
periods 2003–2005 and 2007–2009. The black line inside each box is the median,
the lower and upper edges of the boxes are the 0.25 and 0.75 quantiles, and the lines
represent the range of the data. Outliers are denoted with a circle.
estimated densities), whereas Z. auriculata and M. bonariensis exhibited the weakest relationship (multivariate models
explaining 21% and 14% of the variation in estimated densities,
respectively) (Table 1; Fig. 5).
Estimated densities of S. supercilliaris demonstrated a strong
negative relationship to increasing forest area and precipitation,
whereas the density of M. chimango was positively related to the
annual cropping area and negatively to rainfall (Table 1; Fig. 5).
Densities of T. savana and C. plancus were positively and most
strongly related to the area of non-plowed fields (Fig. 5). In addition, T. savana density was negatively related to latitude (increasing
density towards the south) and C. plancus density decreased with
increasing forest area and rainfall (Fig. 5).
Z. auriculata and M. bonariensis densities were not explicitly
related to land use at the scale of analysis. Latitude (with a positive
effect) was the most important variable explaining distributions,
but only explained 20% of the variation in estimated densities in
both the full and reduced models. M. bonariensis, however, also
exhibited a negative relationship with rainfall along an east-west
gradient (Table 1; Fig. 5).
3.2.2. Short-term effects
Changes in land use did not clearly explain changes in species
densities between the 2003–2005 and the 2007–2009 periods, with
the multiple models equally explaining a small proportion of the
variation in densities. The species with the largest change in density
(36%) was M. bonariensis, although a reduced multivariate model
including latitude, change in the area of annual crops and change in
EVI explained only 14% of the variation (Table 2). Latitude explained
65% of the variation within the model and was present in all models in the best subset analysis (M. bonariensis densities tended to
increase northward).
For C. plancus, a multivariate model including changes in the area
of non-plowed fields and pastures, EVI, and rainfall explained 26%
of the variation in density (p < 0.05); EVI was the most important
variable, accounting for 35% of the variation explained in the model
and was included in 9 of the 10 models in the best subset analysis. C. plancus density tended to increase in areas that underwent
increases in EVI between 2003 and 2009 (Table 2).
Changes in estimated densities of M. chimango and S. supercilliaris were very low. M. chimango density tended to increase with an
increase in rainfall and pastures (Table 2); the multivariate model
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G.I. Gavier-Pizarro et al. / Agriculture, Ecosystems and Environment 154 (2012) 44–55
Table 1
Response of bird densities to long-term agricultural expansion in the Pampas and Espinal ecoregions. Full multiple regression models and reduced models (fitted by stepwise
selection) explaining mean densities for the period 2007–2009 of six birds species, p-values and R2 of each model are shown. For each model, variables included are
presented with the sign of the effect, the slope parameter and the variable name. An * indicates a significance level of p ≤ 0.05 and a • indicates marginal statistical significance
(0.05 ≤ p ≤ 0.06). Non-significant statistical results for the models are indicated as ns.
Species/models
Model structure
R2
p-Value
M. chimango
Full model
Reduced model
17.73Intercept − 3.1e−6 Latitude + 0.047Crops* + 0.042Non-plowed − 0.007Rain + 0.871Evi + 0.042Pastures
−3.02Intercept + 0.058Crops* + 0.053 Non-plowed* − 0.06Rain
0.37
0.4
<0.0001
<0.0001
C. plancus
Full model
Reduced model
−1.47Intercept + 2.5e−7 Latitude + 0.004Non-plowed* − 3.9e−4 Rain − 0.008Forest + 0.185Evi − 0.003Pastures
0.21Intercept* + 0.004Non-plowed* − 0.0006Rain* + 0.164Evi
0.30
0.31
0.0013
<0.0005
S. supercilliaris
Full model
Reduced model
−8.05Intercept + 1.89e−6 Latitude − 0.059Forest* + 0.027Non-plowed* − 0.0071Rain* + 2.53Evi• − 0.0199Pastures
4.65Intercept* − 0.052Forest* + 0.026Non-plowed• − 0.0079Rain* + 2.01Evi•
0.59
0.6
<0.0001
<0.0001
T. savana
Full Model
Reduced model
0.103Intercept − 2.6e−6 Latitude + 0.049Crops• + 0.113Non-plowed* + 0.0107Rain* + 1.516Evi + 0.0695Pastures
−9.36Intercept + 0.076Crops* + 0.128Non-plowed* + 0.014Rain*
0.28
0.29
0.0023
0.0003
Z. auriculata
Full model
Reduced model
−0.11Intercept + 2.201e−6 Latitude * + 0.0028Forest − 0.012Non-plowed• + 2.96e−4 Rain − 0.611Evi − 0.053Pastures*
−0.106Intercept + 2.165e−6 Latitude* − 0.012Non-plowed* − 0.759Evi − 0.053Pastures*
0.19
0.22
0.02100
0.00545
M. bonariensis
Full model
Reduced model
−0.5944Intercept + 1.21e−5 Latitude − 0.075Crops − 0.0194Non-plowed − 0.0205Rain• + 2.189Evi + 0.3033Pastures
−0.5603Intercept + 1.14e−5 Latitude − 0.058Crops − 0.0195Rain* − 0.2966Pastures
0.10
0.14
ns
0.032
Table 2
Response of bird densities to short-term agricultural expansion in the Pampas and Espinal ecoregions. Full multiple regression models and reduced models (fitted by stepwise
selection) explaining change of mean densities of six birds species between the period 2002–2004 and 2007–2009, p-value and R2 of each model are shown. For each model,
variables included are presented with the sign of the effect, the slope parameter and the variable name. An * indicates a significance level of p ≤ 0.05 and a • indicates marginal
statistical significance (0.05 ≤ p ≤ 0.06). A symbol indicates a variable representing the change between the 2002–2004 mean and 2007–2009 mean. Non-significant
statistical results for the models are indicated as ns.
Species/models
M. chimango
Full model
Reduced model
C. plancus
Full model
Reduced model
S. supercilliaris
Full model
Reduced model
T. savana
Full model
Reduced model
Z. auriculata
Full model
Reduced model
M. bonariensis
Full model
Reduced model
Model structure
R2
p-Value
2.699Intercept − 4.136e−07 Latitude + 1.160e−04 Crops + 0.0027Nonplowed + 0.0013 Rain + 0.0053
Evi − 0.0201Pastures
1.229Intercept* − 0.015 Pastures + 0.0016 Rain*
0.02
ns
0.12
0.02154
0.26
0.00253
0.17
0.00259
0.13
ns
0.14
0.00521
0
ns
0.05
ns
0
ns
0.03
ns
0.31
0.0023
0.29
<0.0001
0.0158Intercept − 8.382e−08 Latitude − 0.0018Crops − 0.0046Nonplowed* + 3.638e−04 Rain + 0.268Evi• + 0.0049Pastures − 1.472e−04 Rain
2.699Intercept − 0.0036Nonplowed* + 3.899e−04 Rain + 0.3266Evi* + 0.0049Pastures
1.528Intercept + 3.266e−07 Latitude − 8.774e−04 Crops + 0.0078Nonplowed + 0.0054Rain + 6.713Evi* + 0.108Pastures − 1.472e−04 Rain
0.2731Intercept + 6.3952Evi*
0.1203Intercept − 2.185e−06 Latitude + 0.0102Crops − 0.0536Nonplowed − 0.0036Rain − 0.2447Evi + 0.0293Pastures − 0.005
Rain
−2.806Intercept − 0.069Non-plowed + 0.0071Rain
−0.2578Intercept + 5.324e−06 Latitude + 0.1088Crops + 0.1088Nonplowed − 1.565e−05 Rain + 8.648Evi − 0.01308Pastures − 0.1427e−02 Rain
7.130373Intercept• − 0.0129Rain
−0.0121 Intercept* + 1.887e−05 Latitude* − 5.417e−02 Crops − 1.628e−02 Nonplowed–0.0012Rain + 3.836Evi − 0.1202Pastures + 0.003Rain
−0.0129Intercept* + 2.045e−05 Latitude* − 0.0452Crops + 5.523Evi
G.I. Gavier-Pizarro et al. / Agriculture, Ecosystems and Environment 154 (2012) 44–55
including changes in the area in pasture and rainfall explained 12%
of the variation in densities and rainfall, and pasture area accounted
for 45% and 35% of model variation, respectively. Precipitation was
included in 9, and area in pasture in 6 of the 10 models, respectively,
in the best subset analyses. For S. supercilliaris, a model including changes in EVI alone explained 14% of variation of changes
in density, with density increasing with increasing EVI. Estimated
densities of T. savana and Z. auriculata showed no relationship with
changes in habitat variables (Table 2).
4. Discussion
In the Pampas and Espinal ecoregions of Argentina, the density
of bird species is related to long-term expansion of row cropping,
with species exhibiting different responses to the expanding row
crop area in a species-specific manner. Subsequently, the process
of land conversion is likely affecting the provision of pest control
services via effects on bird species densities. Species density varied with dominant land use, suggesting that locations with similar
species richness could differ in species density among sites.
The patterns of species densities in relation to land use reflect
life history traits adapted to open habitats. S. supercilliaris and M.
chimango, the two species with densities most strongly associated with land use (particularly annual crop area), are adapted to
a variety of environments, but mainly to open habitats. S. supercilliaris is associated with cultivated fields and grassland habitats
(Camperi et al., 2004) and its presence has been related to increased
annual crop area (Schrag et al., 2009); however, another research
work found no positive relationship with agricultural intensification (Filloy and Bellocq, 2007a). M. chimango is tolerant to human
disturbance and is strongly associated with agriculture (Bellocq
et al., 2008; Carrete et al., 2009; Filloy and Bellocq, 2007a) but
negatively affected by urbanization (Garaffa et al., 2009). Grassland habitat is also an important factor determining the presence of
M. chimango (Filloy and Bellocq, 2007b; Leveau and Leveau, 2004),
which could also explain the association with non-plowed fields.
Both C. plancus and T. savana exhibited a strong positive relationship with non-plowed fields, indicating a preference for natural
or semi-natural habitats. In Pampas agroecosystems, the presence
of C. plancus has been shown to be negatively related to agriculture intensification (Carrete et al., 2009; Filloy and Bellocq, 2007a)
and associated with grassland habitats (Pedrana et al., 2008; Schrag
et al., 2009), which likely explains the strong positive association
with non-plowed fields in our study area. T. savana showed a similar positive relationship with the area of non-plowed fields and
to a lesser extent to annual crop area. T. savana adapts to a variety of habitats, including open and rural areas, but has been found
to be negatively associated with intensified agriculture (Filloy and
Bellocq, 2007a).
Interestingly, the two species considered agricultural pests (Z.
auriculata and M. bonariensis) were weakly related to land use.
Both species are likely reacting to factors operating at different
scales than that of our study and are adaptable to different kind
of habitats, including anthropogenic environments (Milesi et al.,
2002). Other studies found M. bonariensis not to be affected by
intensified agriculture in the Pampas region, whereas Z. auriculata
responded negatively (Filloy and Bellocq, 2007a). Higher densities
of Z. auriculata are related to landscapes combining forest patches
and agricultural land (Bucher, 1990).
Our study species responded to differences in land cover and
land use, that which can be interpreted as representative of the
long-term effect of land use changes on species density related
to agricultural intensification in the Pampas and Espinal. However, species density was not associated with short-term land use
changes, which has two possible explanations. On one hand, the
51
changes in land use that occurred during the study period were not
sufficiently large to affect populations of common species, since the
ecological plasticity of birds (particularly mobility) allows them to
adapt to short-term environmental changes (Lemoine et al., 2007).
On the other hand, bird populations may exhibit time lags in the
response to changes in land use (Brooks et al., 1999; Chamberlain
et al., 2000). Many of the studies that best illustrate the effects of
changes in agricultural management on avian communities were
conducted in Europe, where the process of intensified management
has occurred more slowly, over a longer period of time (starting in
the late 1940s) (Donald et al., 2001; Stoate, 1996; O’Connor and
Shrubb, 1986). However, studies in African landscapes with a history of transformation comparable to our study area showed a fast
response of bird communities to agricultural intensification, suggesting that a time lag of a few decades is expected (Laube et al.,
2008; Malan and Bennb, 1999). The key point is that bird densities
are responding to land use changes in the long-term, indicating
that at some point accumulated changes will have an effect on bird
density.
Analysis of diets in the ecoregion support the important role that
T. savana, S. supercilliaris, P. plancus, and M. chimango may have in
pest control. The stomach content of T. savana was found to be composed of 90% of insects, mainly Coleoptera, Diptera, Ephemeroptera
and Odonata (Alessio et al., 2005), whereas for S. supercilliaris,
insects represented 93% of stomach contents, with Lepidoptera,
Coleoptera and Orthoptera being the dominant orders (Camperi
et al., 2004). Insects were also most common in the diet of P. plancus (69.5%), followed by mammals (23.9%) and birds (5%), although
mammals accounted for the highest proportion of biomass (Vargas
et al., 2007). For M. chimango, insects were found to represent 96%
of all prey items, whereas carrion amounted to 48% of ingested
biomass. Since many of its prey are crop pests, M. chimango is considered to play a beneficial role for humans (Biondi et al., 2005).
Despite the dominance of crop pests in the diet of many bird
species, predation should result in increased crop yields through
pest removal or by limiting pest activity through fear, to be considered as providing an ecosystem service (Şekercioğlu, 2006).
Many studies have found increased densities of crop pest (particularly insects) when bird predation is removed, which suggests the
importance of birds as beneficial species for agriculture (Mols and
Visser, 2002; Tremblay et al., 2001). Results of diet analysis suggest that a local extinction or a substantial reduction in density of
our study species could reduce the provision of pest control services. Nevertheless, experiments measuring changes in the levels
of predation and the response of pest densities and crop yields are
urgently needed in the Pampas and Espinal ecoregions.
Based on our data and analysis, it appears that species that
can provide pest control services are able to adapt to different levels of agricultural intensification, although density is
related to the presence of natural or semi-natural habitat as
well, mostly non-plowed fields. Therefore, the loss of remnants
of natural habitat in intensively managed agroecosystems might
reduce the density of species that potentially provide pest control
services.
Experimental and field studies have found that the level of
ecosystem service associated with beneficial fauna is a direct function of the diversity of species that provide services (Phillpot et al.,
2009; Wilby et al., 2005). This is of concern for our study since in the
Pampas and Espinal regions most studies found a decrease in avian
species richness associated with an increase in the row crop area,
with richness of insectivorous birds being particularly sensitive to
row crop expansion (Codesido et al., 2008; Schrag et al., 2009). Consequently, even though many common bird species that potentially
provide pest control services can increase in density along with the
occurrence of agricultural expansion, a certain level of pest control
service could be lost from the local extinction of less common bird
52
G.I. Gavier-Pizarro et al. / Agriculture, Ecosystems and Environment 154 (2012) 44–55
Fig. 5. Summary of regression analysis of densities for focal species. White bars represent results of best subset analysis (number of times a variable entered the 10 best
models). Black bars represent results of hierarchical partitioning analysis (percent of the variability explained by each variable when all variables are included in the model).
Signs (+, positive; –, negative) indicate the relationship of explanatory variables with bird densities.
G.I. Gavier-Pizarro et al. / Agriculture, Ecosystems and Environment 154 (2012) 44–55
species in agricultural systems, as well as a loss in redundancy in
the insectivore community as a whole.
Our findings and the results from other research on avian species
richness indicate the need for developing management and conservation plans that address the maintenance of bird-related pest
control services in our study area. The agricultural expansion and
intensification in recent decades have had a critical effect on the
composition and configuration of agricultural landscapes. Soybean
expansion in particular has resulted in the loss of former natural and seminatural pastures, the loss of forest remnants, and the
elimination of linear habitats such as field margins, erosion control
contours and roadside vegetation (Baldi and Paruelo, 2008; Paruelo
et al., 2005; Oesterheld, 2008).
The importance of habitat remnants as reservoirs for avian
biodiversity has been mentioned in numerous studies, indicating
that the conservation of even small remnants of natural or seminatural habitats helps to maintain a relatively high proportion of
avian diversity. In the Espinal ecoregion, several studies showed
the importance of small forest patches in retaining a substantial
proportion of bird diversity within agroecosystems (Bucher et al.,
2001; Dardanelli et al., 2006). In addition, in the Espinal region
avian density, species richness, and diversity (particularly of insectivores) were higher in field margins than in the interior, as well
within fields with vegetated terraces and field borders compared
to fields without such features (Di Giacomo and López de Casenave,
2010; Goijman and Zaccagnini, 2008; Solari and Zaccagnini, 2009).
Similarly, in an agriculture and grassland matrix in the Pampas,
most bird species used field margins, particularly grassland and
tree-dominated field edges, more frequently in relation to their
availability in the landscape (Leveau and Leveau, 2004).
The importance of the presence of habitat remnants in agroecosystems in our study area and other regions provide strong
support for the development of management actions that take into
account the potential importance of the pest control services provided by avian biodiversity. It is particularly evident that habitat
and crop management towards maintaining the ecosystem services provided by birds needs to be undertaken at different scales.
Most agricultural management decisions are made at the field
scale and the effects of these decisions (e.g., eliminating vegetated borders) on ecosystem processes may function synergistically
to overshadow the importance of regional factors (Viglizzo et al.,
2004). Although this suggests that the most effective management
would be that applied at the field-level, given spatial interactions
and fluxes of elements between habitat, crop patches and human
elements, management at the landscape scale also needs to be considered (Termorshuizen and Opdam, 2009).
5. Conclusions
The apparent flexibility of many common bird species that
potentially provide pest control services in adapting to large-scale
row crop expansion in the agricultural landscapes of the Pampas
and Espinal ecoregions of Argentina suggests that there may be a
considerable level of resiliency within those systems for the maintenance of the related ecosystem services. However, the positive
association of the analyzed species with remnants of habitat, and
the negative effects of row crop expansion on many less common or rare bird species, indicates the need for management to
ensure the long-term maintenance of avian diversity and community redundancy for providing adequate levels of bird-related
ecosystem services. In addition, the lack of short-term effects of
row crop area encroachment on bird abundance indicates a potential time lag that could result in future changes in avian densities
even if no further changes in land use occur. The almost total elimination of remnant habitat in areas of agricultural intensification
53
will likely result in the loss of avian diversity and pest control service. However, the conservation of habitat remnants and landscape
features within the agroecosystem matrix would require a considerably small amount of productive area, while maintaining a
substantial proportion of avian diversity, and in turn the potentially
highly valuable services provided by that diversity.
Acknowledgements
Funding for this research was provided by INTA Projects AERN
292241 (former 2624) and AERN 292221 (former 2622). Part of
the funding for avian field surveys was provided by the Canadian
Wildlife Service, the US Fish and Wildlife Service (Grant # 98210-1G958), the Western Hemisphere International Office (1997–2002)
and the Neotropical Migratory Bird Conservation Act (2003–2005)
(Grant # 2534). We thank all the people that participated in the field
surveys. J. Brasca provided valuable help to improve the English
writing. We thank the editors and two anonymous reviewers for
their helpful and thoughtful comments on previous versions of the
manuscript.
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