Environmental Factors and Population Fluctuations of Akodon Azarae

Austral Ecology (2009) 34, 132–142

Environmental factors and population ﬂuctuations of Akodon azarae (Muridae: Sigmodontinae) in central Argentina

VERÓNICA ANDREO,1* CECILIA PROVENSAL,1 MARCELO SCAVUZZO,2 MARIO LAMFRI2 AND JAIME POLOP1 1Departamento de Ciencias Naturales. Universidad Nacional de Río Cuarto. Agencia Postal N° 3. 5800. Río Cuarto, Córdoba, Argentina (Email: [email protected]), 2Comisión Nacional de Actividades Espaciales (CONAE). Instituto Gulich, Córdoba, Argentina

Abstract The aim of this work was to explore the relationship between population density of Akodon azarae (Muridae: Sigmodontinae) and climatic and environmental variables, and determine which of them are associated to within and among-year changes in rodent abundance in agro-ecosystems from south Córdoba, Argentina. The study was carried out in a rural area of central Argentina, from 1983 to 2003. Density was estimated as a relative density index (RDI). Temperature, precipitation and humidity were obtained from records of the National University of Rio Cuarto. Normalized Difference Vegetation Index (NDVI) and Land Surface Temperature were recorded from National Oceanic and Atmospheric Administration (1983–1998) and Landsat (1998–2003) imagery data sets.We performed simple correlations, multiple regressions and distributed lag analysis. Direct association of climatic and environmental variables with RDI was in general, low.The amount of variability in seasonal changes in density explained by climatic and environmental variables altogether varied from 10% to 70%. Seasonal population fluctuations were influenced by NDVI and rainfall with one and two seasons of delay. Autumn maximum density of the species was also associated with vegetation and rainfall of previous seasons. There also seemed to be an indirect influence of rainfall through vegetation given that we found a positive correlation between them. Results were consistent with basic aspects of the ecology of the species, such as its strong preference for highly covered areas, which provide food and protection from predators, likely increasing its reproductive success. Therefore, in the rural area central Argentina, A. azarae showed seasonal fluctuations with delayed influence of rainfall and vegetation and indirect effects of rainfall.

Key words: Akodon azarae, environmental factors, NDVI, population ﬂuctuations, rainfall.

INTRODUCTION both the abundance and distribution of species (Stenseth et al. 2003a). Climate impact on individuals Numerical fluctuations exhibited by small rodent and populations may operate either directly through populations have long fascinated ecologists (Elton physiology (metabolic or reproductive processes) or 1924; Stenseth 1999). It is widely accepted, at present, indirectly through the ecosystem, including prey, that population changes are the result of the joint predators and competitors (Stenseth et al. 2002a). action of endogenous processes, generating first and Many studies have shown the importance of global second order feedback structures and exogenous and local climatic variables in determining the dynam- factors, such as climate, seasonality and environment ics of small mammal population worldwide (Leirs (Royama 1992; Berryman 1999). Population fluctua- et al. 1997; Lewellen & Vessey 1998; Dickman et al. tions contain two components: seasonal and inter- 1999; Ernest et al. 2000; Lima et al. 2001; Stenseth annual variation. Both are the result of basic processes et al. 2002b; Zhang et al. 2003; Letnic et al. 2005). such as survival, recruitment, emigration and immi- Recently, increasing attention has been given to large- gration interacting with endogenous and exogenous scale patterns of climate, such as the North Atlantic factors. Oscillation (NAO) and El Niño Southern Oscillation Exogenous factors such as weather and climate (ENSO), which account for major variations in affect the performance of individuals and, as a result, weather and climate around the world and have been shown to affect different components of the ecosystem through both direct and indirect pathways (Jaksic et al. *Corresponding author. 1997; Lima et al. 1999; Jaksic 2001; Brown & Ernest Accepted for publication February 2008. 2002; Stenseth et al. 2002a, 2003a; Meserve et al.

2003; Lima & Jaksic 2004; Letnic et al. 2005; habitats with high vegetation cover, such as field and Holmgren et al. 2006a,b). road borders, railway banks and remnant areas of However, climate exerts a more direct influence native vegetation (Zuleta et al. 1988; Mills et al. 1991; through local parameters, such as temperature, pre- Busch & Kravetz 1992). It feeds mainly on insects, but cipitation, wind and snow depth, as well as interac- also on plant material and seeds (Bilenca & Kravetz tions among them (Lima 2006). Actually, the seasonal 1998; Ellis et al. 1998). Populations of A. azarae show dynamics observed in almost all rodent species, strong seasonal fluctuations, with a minimum in spring ascribed in most cases to the seasonal reproductive and a maximum in late autumn-winter, followed by cycle, is influenced directly (e.g. survival and re- a dramatic fall (Zuleta et al. 1988; Mills et al. 1991; production) or indirectly (e.g. through a limiting Busch & Kravetz 1992). Reproduction is also seasonal: resource) by climatic conditions (Stenseth 1999; the breeding season begins in spring and lasts until Stenseth et al. 2003b).There is evidence, for example, autumn (Mills et al. 1992). that temperature can affect reproduction causing Although biology and ecology of this species is quite intrauterine mortality (Yamauchi et al. 1981) and that well studied, little is known about the effects of cli- the interaction between temperature and rainfall have mate and environment on the long-term changes in its effects on litter size (Myers et al. 1985), life expectancy abundance. In the present study, we attempted to (Getz et al. 1997), daily activity (Vickery & Bider assess the relationship between population density 1981) and habitat use (Vickery & Rivest 1992). Fur- of A. azarae and climatic and environmental vari- thermore, high temperatures may promote or suppress ables, and determine which of them are associated to reproductive activity in rodents according to the within- and among-year population changes in agro- moment of occurrence (Adamczewska-Andrsejewska ecosystems from south Córdoba, Argentina. 1979; Garsd & Howard 1981; Calisher et al. 2005; Liu et al. 2007). Climate may also act indirectly through its influence METHODS on vegetation growth and seed ripening (Garsd & Howard 1981, 1982). Vegetation cover has long been known to affect abundance and composition of Study site ground-dwelling fauna, as it provides shelter, food and nesting opportunities as well as protection from preda- The study was carried out in the rural area of Chucul, tors (Getz 1985; Jacob & Brown 2000; Monamy & Fox a typical undulating pampean plain (600–900 m a.s.l.) 2000). Population density and demography of many in the south-west of Córdoba province, Argentina rodent species are influenced by cover (Birney et al. (33°01′34″S; 64°11′21″W). The weather is temperate 1976; MacCracken et al. 1985; Peles & Barret 1996) with an average annual temperature of 23°C in mainly acting through food supply (Singleton et al. January and 6° C in July. Annual rainfall is high, espe- 2001; Brown & Ernest 2002; Jaksic & Lima 2003; cially in summer, averaging 700–800 mm. Phytogeo- Meserve et al. 2003; Zhang et al. 2003) and actual or graphically, this region belongs to ‘Provincia del perceived predation risk (Rosenzweig 1973; Norrdahl Espinal, Distrito del Algarrobo’ (Cabrera 1953), char- & Korpimäki 1995; Spencer et al. 2005). acterized by tree species, such as algarrobo (Prosopis Many small mammal populations in northern and alba y P. nigra), caldén (P. caldenia), tala (Celtis tala), southern regions experience highly seasonal environ- espinillo (Acacia caven), chañar (Geoffroea decorticans) ments, with population growth during the summer and quebracho blanco (Aspidosperma quebracho- owing to reproduction and recruitment, and a decline blanco). The natural transitional landscape of wood- during winter (Singleton et al. 2001; Stenseth et al. land and pampean natural grassland (Stipa spp) 2003b). Agro-ecosystems are examples of highly vari- remains in patches between crop fields. The native able environments, in which quality and quantity of vegetation has undergone marked alterations as a resources, habitat structure and rodent populations result of agriculture and cattle farming. At present, densities vary seasonally, both owing to natural envi- the landscape mainly consists of a matrix of cultivated ronmental changes (mainly as a result of climate sea- fields, pastures and their adjacent borders (marginal sonality) and because of agricultural practices (Crespo weedy edges developed below fence lines). Edge habi- 1966; Kravetz & Polop 1983; Zuleta et al. 1988; Brown tats are less disturbed than agricultural fields, main- & Singleton 1999; Jacob 2003; Jacob & Hempel 2003; taining relatively high plant cover throughout the Stenseth et al. 2003b; Jacob et al. 2007; Brown et al. year, thus providing good habitat conditions for small 2007). rodent species (Busch & Kravetz 1992; Ellis et al. Akodon azarae (Muridae: Sigmodontinae) com- 1997; Bilenca & Kravetz 1998). Railway banks (linear monly known as the pampas mouse, is one of the most border habitats) are wide areas where some rodent abundant rodent species in pampean agro-ecosystems populations reach high densities. The plant com- of Argentina. It is found in linear and relatively stable munity in these areas is characterized by pasture

© 2009 The Authors doi:10.1111/j.1442-9993.2008.01889.x Journal compilation © 2009 Ecological Society of Australia 134 V. ANDREO ET AL. interspersed with bushes. Despite the influence of Data series of monthly minimum and maximum nearby crop fields, it bears some resemblance to native temperature, rainfall and relative humidity were vegetation. At times, however, agricultural fields may provided by the Agrometeorology Laboratory, the provide high food resources and shelter, but after National University of Rio Cuarto (Argentina), harvest and ploughing, small rodents in fields are approximately 20 km away from the study area. exposed to high risks of avian predation, both by Environmental variables obtained from satellite diurnal and nocturnal predators (Pardiñas & Cirignoli remote sensing were: Normalized Difference Vegeta- 2000). tion Index (NDVI) and Land Surface Tempera- ture (LST). NDVI is a kind of spectral vegetation Data set index derived from the red (RED): near-infrared (NIR) reflectance ratio: NDVI = (riNIR - riRED)/ Small mammal trapping was conducted monthly over (riNIR + riRED). Green vegetation shows a differen- a 21-year period (1983–2003) in crop field borders, tial reflectance in these two bands. Active photo- roadsides and railway banks. Individuals were cap- synthetic surfaces reflect a higher proportion of the tured using snap-traps and live-traps. From 1983 to incoming radiation in the infrared band and a lower 1997, we used 2–8 trap-lines of 40 snap-traps on proportion in the red band. NDVI is positively related average, located at 5-m intervals.The location of these to the level of photosynthetic activity, green leaf trap-lines varied during the study period. From 1990 biomass, fraction of green vegetation cover and annual to 2003, we live trapped animals in a 6 ¥ 10 grid net primary productivity (Tucker et al. 1985; Myneni (0.3 ha) placed at a railway bank. Stations in the et al. 1995). NDVI is expressed on a scale from -1to grid were separated by 10 m longitudinally and 5 m +1. It is between -0.2 and 0.05 for snow, inland water laterally. One Sherman live-capture trap was placed bodies, deserts and exposed soils, and increases from at each station. Traps were baited with a mixture about 0.05 to 0.7 for progressively increasing amounts of bovine fat and peanut butter and checked in of green vegetation (Tucker et al. 1986). On the other the morning during four consecutive days. From hand, LST is the thermal emission of the surface, 1990 to 1997 both trapping systems functioned including the top cover layer for areas with vegetation simultaneously. Total effort was 78 187 trap-nights: and other surfaces, such as bare soils. It is estimated in 47 033 trap-nights corresponded to trap-lines Kelvin degrees (°K) and then, transformed to Celsius (1983–1997), and 31 154 trap-nights to live-trapping degrees (°C). (1990–2003). Normalized Difference Vegetation Index and LST Although data derived from snap-trapping and live- were obtained using two distinct imagery data sets. trapping may have different consequences for abun- For the period 1983–2000, we used images from a dance estimation, we overcame this problem by using meteorological satellite of the National Oceanic and two equivalent relative measures of abundance. These Atmospheric Administration/Advanced Very High measures represent the population peaks and troughs, Resolution Radiometer (NOAA/AVHRR) with a spa- and provide good estimates of relative population tial resolution of 8 ¥ 8 km. These images (previously changes (Castellarini & Polop 2002). The relative geo-referenced to a latitude–longitude coordinate density index (RDI) used for snap-trapping density system) correspond to monthly products calculated estimates was: with the maximum-value composite method which minimizes cloud contamination (Holben 1986). On the ⎛ captures ⎞ other hand, for the period 1998–2003, we used greater RDI1 = ⎜ ⎟ ⋅100 ⎝ []traps⋅ nights ⎠ spatial resolution, images from Landsat 5 Thematic and, for live-trapping: Mapper (TM) and Landsat 7 Enhanced Thematic Mapper (ETM+). Images previous to that date were not ⎛ ⎡ captures ⎤ recaptures⎞ RDI = ⎜ − ⎟ ⋅100 available for us. The spatial resolution of both 2 ⎝ ⎢()⋅ ⎥ ⎠ ⎣ traps nights ⎦ 2 Landsat satellites is 30 ¥ 30 m for reflective bands where, (recaptures/2) is a correction factor used to and, 120 ¥ 120 m and 60 ¥ 60 m for thermal bands work with data from non-extractive capture methods (Landsat 5 and 7, respectively). All images of the series (Castellarini & Polop 2002). were geo-referenced to a latitude–longitude coordinate

The RDI1 was estimated for each trapline and then system before the digital analysis. Considering that averaged with RDI2 from the grid to obtain monthly from 1998 to 2003 density data of A. azarae came from estimates of relative density. Seasonal values of RDI live trapping only, we were able to ﬁnd the exact place were obtained from an average of monthly values. where the grid was placed and obtain NDVI and LST Seasons were deﬁned as follows: summer (January, values for this area. From the 8 pixels that it occupied February, March), autumn (April, May, June), winter we just considered the four central ones. (July, August, September) and spring (October, All images were provided by Mario Gulich High November, December). Spatial Studies Institute from Comisión Nacional de doi:10.1111/j.1442-9993.2008.01889.x © 2009 The Authors Journal compilation © 2009 Ecological Society of Australia ENVIRONMENT AND A. AZARAE ’S FLUCTUATIONS 135

Actividades Espaciales (CONAE, Argentina). ENVI 3.6 (System Research) was used for digital processing of satellite imagery.

Data analysis

The relationship between climatic and environmental RDI variables and population abundance of A. azarae was 6 8 10 12 explored using simple correlation and multiple regression analyses. Before the analyses, we tested for nor- 4 mality and homogeneity of variance, and transformed the variable if necessary. On the other hand, as climatic and environmental variables may have a lagged effect on density, we performed distributed lag analysis with 02 lags of four seasons.The general model of this analysis is a simple linear relationship (linear regression), su−1983 sp−1987 sp−1992 sp−1997 sp−2002 where c (independent variable) is measured with time Time lags. As this method requires stationary data series, we Fig. 1. Time series plot for A. azarae’s seasonal relative used the following transformation to remove trends density from summer of 1983 to spring of 2003 in rural area over time: of Chucul (Argentina). RDI, relative density index (%); su, χχ=−()abt +∗ summer; sp, spring. where, t refers to the case number and, a and b are constants estimated from the data. All previous statistical analyses were applied for Trap−lines Grid three different periods, according to the availability of data: • 1983–2003: RDI, minimum and maximum tem- 20 25 perature, rainfall and humidity (climatic variables). • 1983–2000: RDI, climatic variables, NDVI and LST (NOAA – 8 km). • 1998–2003: RDI, climatic variables, NDVI and RDI LST (Landsat – 30 m). 10 15 Finally, in order to determine the environmental variables associated with among-year population ﬂuc- tuations, we performed simple correlations between autumn maximum RDI and those variables whose lags

resulted in signiﬁcance in distributed lag analysis. We 05 also considered here, other variables that we believed may be affecting population changes. su−1983 sp−1987 sp−1992 sp−1997 sp−2002 Time

Fig. 2. Seasonal relative density of A. azarae from summer RESULTS 1983 to spring 2003 in rural area of Chucul (Argentina) separating estimations of trap-lines from those of grid. RDI, We captured 2179 individuals of A. azarae during the relative density index (%); su, summer; sp, spring. 21 years of the study in a total of 78 187 trap-nights. The species showed seasonal ﬂuctuations, characterized by maximum values in autumn and winter and the ascendant tendency of the series.Time series plots minimum values in spring (Fig. 1). Moreover, abun- of the environmental and climatic variables considered dances of A. azarae were relatively low until 1989, and are shown in Figure 3. None of them denoted a ten- increased after that. The series revealed an ascendant dency or discontinuity similar to that of relative den- tendency (or relatively sudden shift to higher mean sities of A. azarae. density), also evident when considering data from For the period 1983–2003, simple correlations trap-lines and grids separately (Fig. 2). In Figure 2 we between RDI of A. azarae and each one of the climatic also show density data of trap-lines from 1998 to 2000 variables yielded signiﬁcant negative association values that were not considered in this study but corroborate (P < 0.05), except for humidity (Table 1). Climatic

© 2009 The Authors doi:10.1111/j.1442-9993.2008.01889.x Journal compilation © 2009 Ecological Society of Australia 136 V. ANDREO ET AL. Tmin NDVINOAA −5 0 5 10 0.3 0.4 0.5 0.6 0.7 Tmax LSTNOAA 26 30 34 38 10 20 30 40 Precip NDVILan 0.0 0.2 0.4 0.6 0 100 300 500 HRA LSTLan 15 20 25 30 35 45 55 65 75

1985 1990 1995 2000 1985 1990 1995 2000 Time Time

Fig. 3. Time series plot for climatic and environmental variables in rural area of Chucul (Argentina). Min_T, Minimum Temperature (°C); Max_T, Maximum Temperature (°C); Rainfall (mm); Humidity (%); NDVI_NOAA, Normalized Difference Vegetation Index registered from the National Oceanic and Atmospheric Administration imagery; LST_NOAA, Land Surface Temperature registered from the National Oceanic and Atmospheric Administration imagery (°C); NDVI_Lan, Normalized Difference Vegetation Index registered from Landsat imagery; LST_Lan, Land Surface Temperature registered from Landsat imagery (°C).

Table 1. Correlation (R) between density of A. azarae and climatic and environmental variables in rural area of Chucul (Argentina)

1983–2003 1983–2000 1998–2003 Variables (n = 84) (n = 72) (n = 24)

Minimum Temperature -0.261* -0.208 -0.668* Maximum Temperature -0.284* -0.342* -0.521* Rainfall -0.259* -0.230 -0.490* Humidity 0.040 -0.049 0.651* NDVI – 0.072 -0.099 LST – -0.435* -0.592*

*P < 0.05. LST, land surface temperature; n, sample size; NDVI, normalized difference vegetation index.

variables together explained only 10% of the variability coefficient for each climatic or environmental variable. in density (R2 = 0.10), and multiple regression was not For the period 1983–2003, maximum temperature had significant for this period (P > 0.05). Table 2 shows the a negative effect on density explaining 40% of variabil- results from distributed lag analysis (for each period ity in density, with lag 0 and 4 significant. Rainfall, on considered) with significant lags and determination the other hand, showed a positive significant effect on doi:10.1111/j.1442-9993.2008.01889.x © 2009 The Authors Journal compilation © 2009 Ecological Society of Australia ENVIRONMENT AND A. AZARAE ’S FLUCTUATIONS 137

Table 2. Regression coefﬁcients from the distributed lag analysis between seasonal densities of A. azarae and climatic and environmental variables in rural area of Chucul for the three periods considered

Periods Minimum Temperature Maximum Temperature Rainfall Humidity NDVI LST

1983–2003 (n = 80) * * * * Lag 0 -0.022 -0.066* -0.002 0.021* Lag 1 0.019 -0.022 0.032* 0.011 Lag 2 -0.004 -0.011 0.035* -0.019 Lag 3 -0.019 -0.031 0.008 -0.018 Lag 4 -0.028 -0.034* -0.015 -0.0001 R2 0.28 0.41 0.29 0.34 1983–2000 (n = 68) * * * * * * Lag 0 -0.009 -0.068* 0.01 0.02* 0.584 -0.029* Lag 1 0.009 -0.019 0.038* 0.014 1.989* 0.012 Lag 2 -0.012 -0.012 0.035* -0.013 1.733* 0.004 Lag 3 -0.026 -0.021 0.016 -0.012 -0.154 -0.003 Lag 4 -0.041 -0.023 -0.02 0.002 -0.074 0.001 R2 0.29 0.38 0.39 0.26 0.40 0.31 1998–2003 (n = 20) * * * * * Lag 0 -0.849* -0.214 -2.355 0.297 2.857 -0.312* Lag 1 0.311 -0.196 2.012 -0.016 18.054* 0.225 Lag 2 -0.537 -0.034 1.953 -0.167 7.458 0.18 Lag 3 -0.154 -0.471 -0.594 -0.243 -7.614 0.105 Lag 4 -0.323 -0.51 0.938 -0.054 -17.521* -0.036 R2 0.75 0.47 0.55 0.64 0.80 0.59

*P < 0.05. LST, land surface temperature; n, sample size; NDVI, normalized difference vegetation index; R2, determination coefficient. density lagged one and two seasons, although it only Finally, when considering environmental variables explained 29% of the seasonal variation in density of A. related to among-year changes in density (Table 3), we azarae (Table 2, Fig. 4a). found that rainfall of the previous autumn and NDVI For the period 1983–2000, we also obtained very low of the previous winter, spring and summer were posi- association values from simple correlations. Maximum tively associated with autumn maximum density of temperature and LST resulted in significance and, A. azarae (P < 0.05). Besides, correlation between except for NDVI, all other correlation coefficients were RDI and mean NDVI of previous year was higher than negative (Table 1). Adding NDVI and LST increased the association between RDI and NDVI of each pre- the determination of multiple regression for this period vious season individually. NDVI also showed high by 23% (R2 = 0.33; P = 0.0001). Both first and second association with rainfall (Table 3). lags in rainfall and NDVI had positive and significant effects on seasonal changes in density of A. azarae. Each one of these variables resolved 40% of the DISCUSSION seasonal variability in density (Fig. 4a,b). Again, maximum temperature had negative effects on density Results of this long-term study in agro-ecosystems of for all lags, but only lag 0 was significant. Lag 0 was also south Córdoba showed that A. azarae exhibits a strong significant in humidity and LST. seasonality in its densities, with peaks in autumn– For the last period (1998–2003), correlation values winter and low numbers in spring. These results are were higher than previous ones (Table 1), but still consistent with previous short-term studies in this negative (except for humidity). Multiple regression species (Zuleta et al. 1988; Mills et al. 1991; Busch & showed that climatic and environmental variables Kravetz 1992). Small rodent seasonal fluctuations are altogether explained more than 70% of the seasonal a common phenomenon worldwide, both in natural changes in density of A. azarae (R2 = 0.71; P < 0.05). and disturbed ecosystems, such as agro-ecosystems In this last period, changes in NDVI (registered from (Stenseth 1999; Singleton et al. 2001; Stenseth et al. Landsat) explained 80% of the seasonal variation in 2003b). In most cases, this pattern is associated with density of A. azarae, with first and fourth lags signifi- seasonality in reproduction, which may be considered cant (Fig. 4c). First lag in NDVI (one season) had a an adaptation to local climate conditions and plant positive effect on next season density. On the contrary, phenology (Liu et al. 2007). The seasonal increase the regression coefficient of the fourth lag (a year in rodent population density has been related to the before) was negative. seasonal increase in temperature and rainfall that

Fig. 4. Seasonal relative density of A. azarae from summer 1983 to spring 2003 and Rainfall (a), NDVI registered from NOAA (b) and NDVI registered from Landsat (c) delayed in one (1) and two (2) seasons. RDI, relative density index (%); NDVI, Normalized Difference Vegetation Index. promote a raise in vegetal biomass, and in turn, induce frosts and low temperatures that cause an increase of reproduction of animals. On the other hand, the mortality and cessation of reproduction (Crespo 1966; annual decline in rodent populations has been attrib- Garsd & Howard 1981, 1982; Mills et al. 1991, 1992; uted to loss of cover, reduction of food resources, Calisher et al. 2005). Furthermore, the seasonal doi:10.1111/j.1442-9993.2008.01889.x © 2009 The Authors Journal compilation © 2009 Ecological Society of Australia ENVIRONMENT AND A. AZARAE ’S FLUCTUATIONS 139

pattern has been related to disturbances caused by agricultural practices (Busch & Kravetz 1992), which 0.056 0.09 0.422 0.515* 1 NDVI Annual - - - - entina) determine a great temporal and spatial ﬂuctuation in distribution and availability of food and shelter for rodent populations (Mills et al. 1991; Bonaventura 0.097 0.027 0.266 0.422 1 0.955* NDVI et al. 1992). Farming practices are known to affect - - - -

Win–Spr–Sum rodent reproduction, mortality, dispersion, competition and habitat selection, not only in crop ﬁelds but also in borders (de Villafañe et al. 1977; Kravetz & 0.225 0.045 0.115 0.260 1 0.923* 0.811* NDVI - - - -

rence vegetation index; Spr, spring; Polop 1983; Jacob 2003; Jacob & Hempel 2003; Cavia Spr–Sum et al. 2005; Bilenca et al. 2007). We found that rainfall and NDVI (a proxi for Aut 0.018 0.318 0.208 0.165 0.059 0.827* 0.639* 0.507* 0.220 0.624* 0.516* 1 0.159 0.407 0.659* NDVI - - - - - vegetation) are particularly important variables in explaining seasonal changes in A. azarae, likely acting

Win with 3–6 months of delay (1 and 2 seasons). Our 0.008 0.409 0.538* 1 0.648* 0.558* 0.835* 0.897* NDVI - - - results also suggest that rainfall may have an indirect effect on population density through vegetation, as Spr 0.040 0.006 0.074 0.492* 1 0.715* 0.302 0.880* 0.916*we 0.852* observed a positive association between these two NDVI - - - variables. In general, our results are in agreement with a previous study of Ernest et al. (2000) in New Mexico Sum 0.419 0 0.111 0.074 0.037 0.127 1 0.460 0.196 NDVI (USA), who found that plant cover was signiﬁcantly - - - correlated with precipitation from the same and the previous season, and that rodent populations were 0.077 0.145 0.521* 0.062 0.028 0.144 0.331 0.097 1 0.308 0.760* 0.641* 0.382 0.646* 0.727* 0.722* Annual Rainfall - - - - - correlated with plant cover and precipitation but only after a time lag of 1 and 2 seasons. The delayed response of population densities to rainfall and 0.028 0.176 0.065 0.051 0.339 1 0.945 0.179 0.680* 0.501* 0.433 0.527* 0.582* 0.619* Mean Rainfall - - - - - Spr–Sum increasing primary productivity (which may be offer- ing food and protection from predators) is a widely studied phenomenon in many rodent species, both Aut 0.047 0.000 0.016 0.736* 0.695* 0.108 0.528* 0.307 0.076 0.393 0.403 0.356 0.249 1 0.039 0.297 0.471* 0.509* 0.572* 0.022 0.574* 0.647* 0.539* Rainfall - - - - at seasonal (Ernest et al. 2000; Stenseth et al. 2003b; Calisher et al. 2005; Madsen et al. 2006) and inter- Spr 0.111 0.042 0.204 0.028 1 annual time scales (Meserve et al. 2003; Stenseth et al. - - Rainfall 2003b; Holmgren et al. 2006b). Mechanisms involved in the response of small rodent 0.297 0.298 0.159 0.274 0.209 0.283 0.244 0.314 Sum - - - - -

Rainfall populations to rainfall and increased primary production differ among species, according to their feeding habits, reproductive rates and interactions with com- 0.081 0.288 0.026 0.097 0.576* 0.076 0.174 0.471* 0.586* 0.545* 0.198 0.623* 0.667* 0.613* - - petitors and predators. Significant effects of one and two previous seasons in rainfall and NDVI that we have detected here mean, for example, that if we take Spr 0.244 0.342 Max T Aut Max T - - autumn density, this would be mainly explained by rainfall and NDVI of summer (first lag) and spring (second lag) which is coincident with the breeding 0.115 0.079 0.008 Sum Max T - season in A. azarae (Mills et al. 1992).The differences in determination coefficient values for NDVI registered Aut

Min T from NOAA and Landsat satellites, may be reﬂecting the change in the spatial resolution (from 8 km to Win 0.136 0.294 0.221 30 m). However, we found that lag corresponding to Min T - one season in NDVI is signiﬁcant for explaining sea- RDI Max sonal changes in abundance of the species (i.e. density value in a determined moment is mainly explained by NDVI of the previous season) from records of both satellites. Several studies have pointed out the impor- Correlation (R) between autumn maximum RDI and environmental and climatic variables of previous seasons from 1983 to 2000 in rural area of Chucul (Arg tance of cover for A. azarae (Bonaventura et al. 1992;

0.05. Aut, autumn; Max RDI, maximum relative density index of autumn;Ellis Max T, maximum temperature; Min T, minimum temperature; NDVI,et normalized diffe al. 1997; Bilenca & Kravetz 1999; Busch et al. <

P 2001) both in breeding and non-breeding seasons. * Spr Max T 1 0.407 Sum Max T 1 Sum, summer; Win, winter. Aut Mín T 1 Win–Spr–Sum NDVI Annual NDVI Aut NDVI Spr–Sum NDVI Aut Rainfall Spr–Sum Mean rainfall Annual rainfall Sum NDVI Spr NDVI Win NDVI Win Min T 1 0.202 0.069 0.226 0.404 Sum RainfallSpr Rainfall 1 0.203 0.071 0.813* 0.769* 0.165 0.529* 0.461 0.561* 0.424 0.495* 0.589* Table 3. Max RDI 1 Aut Max TBilenca and 1 Kravetz (1998) found that, in the breeding

© 2009 The Authors doi:10.1111/j.1442-9993.2008.01889.x Journal compilation © 2009 Ecological Society of Australia 140 V. ANDREO ET AL. season, sexually active females occupied microhabitats believe these results represent an important advance in with a greater amount of green cover compared with the understanding of rodent population fluctuations inactive ones. Besides, sexually active individuals were in agroecosystems of central Argentina, as such a found to consume higher proportions of invertebrates long-term study relating environmental and climatic than inactive ones (Bilenca et al. 1992).These findings variables to population changes was lacking in our reflect the importance of cover for reproductive activity country. However, further work is necessary to reach a in A. azarae, likely ensuring high quality food supply clearer conclusion about the ecological processes and and protection from predators.We infer, therefore, that mechanisms causing population fluctuations in this higher amounts of rainfall and cover in spring and species and the extrinsic and intrinsic factors involved summer would lead to better conditions for reproduc- in inter-annual population dynamics. On the other tion of overwintering individuals and their descendants hand, we want to emphasize the usefulness of remote maybe increasing their reproductive success (Zuleta sensing in explaining changes in rodent populations. It et al. 1988; Suárez et al. 2004). On the other hand, would be interesting in the future to test its predictive cover also seems to be an important resource for ability in this kind of systems. A. azarae in winter (non-breeding season), when indi- In conclusion, in the rural area of central Argentina, viduals of both sexes select highly covered areas seasonal changes in density of A. azarae seemed to be in borders (Bilenca & Kravetz 1998, 1999). Further- mainly explained by rainfall and vegetation (NDVI) more, from addition experiments, it has been suggested acting with 3–6 months of delay and, by indirect that both food (Cittadino et al. 1994) and shelter effects of rainfall through vegetation. (Hodara et al. 2000) are limiting resources for A. azarae in winter since in supplemental borders A. azarae showed increased density, a lengthened breed- ACKNOWLEDGEMENTS ing season (Cittadino et al. 1994) and increased survival (Hodara et al. 2000). Thus, the selection of A. We thank Marcos P. Torres for collaboration in the azarae for microhabitats with high availability of green field work. This research was made possible by grants cover, that ensures food supply and shelter, seems to from Secretaría de Ciencia y Técnica (SECYT), have an adaptive meaning (Bilenca & Kravetz 1998, Universidad Nacional de Río Cuarto, Fondo para la 1999). Investigación Científica y Tecnológica (FONCYT) We found that NDVI was also positively correlated and Consejo Nacional de Investigaciones Científicas y with autumn maximum density and among-year popu- Técnicas (CONICET). Remote sensing data were lation changes in A. azarae. This is consistant with provided by Mario Gulich High Spatial Studies Insti- another study on this species using density data from tute from Comisión Nacional de Actividades Espa- 1990 to 2007 (Andreo et al. 2008, in press). On the ciales (CONAE). 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