Raptorial birds and environmental gradients in the southern Neotropics: A test of species-richness hypotheses

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1 aec_1533.fm Page 892 Thursday, December 1, :26 PM Austral Ecology (2005) 30, Raptorial birds and environmental gradients in the southern Neotropics: A test of species-richness hypotheses M. I. BELLOCQ 1 * AND R. GÓMEZ-INSAUSTI 2 1 Departamento de Ecología, Genética y Evolución, FCEN-Universidad de Buenos Aires, Ciudad Universitaria Pab. 2, Buenos Aires 1428, Argentina ( bellocq@bg.fcen.uba.ar); and 2 School of Applied Geography, Ryerson University, Toronto, Ontario, Canada Abstract: We investigated the spatial patterns of raptor species richness in the southern Neotropics and tested three hypotheses that were most likely to explain spatial variations: ambient energy, productivity and habitat heterogeneity. We used non-linear regression analysis and eliminated alternative hypotheses by finding the best single environmental predictor of raptor species richness among potential evapotranspiration (PET), actual evapotranspiration (AET), mean annual temperature and precipitation and vegetation structure coefficient. As expected, the number of raptor species decreases monotonically as latitude increases. Raptor species richness was significantly correlated with each of the environmental factors considered in this study, reflecting covariation of climatic and habitat descriptors. Correlation coefficients showed positive associations between species richness and each single environmental variable. Mean annual temperature was the strongest environmental predictor of raptor species richness (explaining 82% of the variance), consistent with the ambient energy hypothesis. Another descriptor of ambient energy (PET) explained 75% of the spatial variation. Both the AET and the vegetation structure coefficient explained 77% of the spatial variation in richness. The spatial clusters of extreme residuals identified the subtropical rainforests and the arid heights and low plateaux of the study area as regions where local environmental conditions appear to interfere with the general trend identified by the model at the regional scale. Key words: birds, Neotropics, raptors, spatial patterns, species-richness hypotheses. INTRODUCTION The search for patterns in the spatial variation of species diversity and the identification of the factors and mechanisms that determine those patterns have concentrated the efforts of many scientists since the 1980s (Rohde 1992; Willig et al. 2003). Variation in species richness occurs through latitudinal (Schall & Pianka 1978), longitudinal (Danell et al. 1996) and elevation gradients (Thiollay 1996). Results indicate that the factors determining the number of species coexisting in a given area may differ at various geographical scales. Thus, large-scale patterns are primary determined by factors that act at large scales, like climatic factors, whereas factors determining species richness at smaller scales tend to act at the local scale, such as biotic interactions (Currie 1991). Although many factors likely influence the spatial variation of species richness, there is evidence that a few environmental variables usually explain most of the variability. Many hypotheses have been proposed to account for latitudinal variations of species richness at regional-tocontinental geographical scales (reviewed by Rohde 1992; Rosenzweig 1995; Willig et al. 2003). Some of *Corresponding author. Accepted for publication June these hypotheses are considered either circular or insufficiently supported (Rohde 1992). Currently, a few hypotheses seem to have the most support and potential (Willig et al. 2003). The climatically based energy hypothesis states that the supply of energy limits the number of species coexisting in a given area. There are basically two versions of the energy hypothesis that differ in the proposed driving mechanism (Hawkins et al. 2003a). One version, the productivity hypothesis, is based on Hutchinson (1959) and Wrigth (1983) and states that energy constrains species richness through the trophic web. It predicts that plant species richness is limited by the availability of energy and water and animal richness by the production of the required food. The productivity hypothesis accounted for geographical variations in the number of tree species in the Nearctic (Currie & Paquin 1987), of birds and butterflies from the tropics to temperate zones (Hawkins et al. 2003a) and of mammal species through gradients occurring at smaller geographical scales (Abramsky & Rosenzweig 1984; Owen 1988). The literature increasingly suggests scale dependence in the relationship between productivity and species richness (Waide et al. 1999). The second version of the energy hypothesis, the ambient energy hypothesis, states that energy limits species richness through physiological constrains. It

2 aec_1533.fm Page 893 Thursday, December 1, :26 PM SPATIAL PATTERS OF RAPTOR RICHNESS 893 predicts that diversity is limited by the intolerance to low temperatures and thermoregulatory needs (Turner et al. 1987). Consistent with this hypothesis, energy-related variables can statistically explain the large-scale patterns of animal species richness in highlatitude terrestrial environments of the northern hemisphere (Currie 1991; Hawkins et al. 2003a) and in marine environments (Fraser & Currie 1996). The habitat heterogeneity hypothesis is based on spatial variation of the habitat where heterogeneous environments offer more opportunities than homogeneous environments for species to coexist (Giller 1984). Spatial patterns of mammal species richness are consistent with the habitat heterogeneity hypothesis in high-energy regions of North America (Kerr & Packer 1997). In South America, habitat type and heterogeneity affect raptor species richness at various spatial scales (Diniz-Filho et al. 2002) and, at a regional scale in the southern part of the continent, the richness of passerine birds was strongly and positively correlated with habitat heterogeneity (Rabinovich & Rapoport 1975). Here, we investigate the spatial patterns of raptor species richness in the southern Neotropics and examine the species-richness hypotheses that were most likely to explain spatial variations of species richness at the regional scale: (i) ambient energy; (ii) productivity; and (iii) habitat heterogeneity. We used raptorial birds (Strigiformes and Falconiformes) to consider a single general trophic category, given that the relationships between diversity and environmental variables may be influenced by the position in the food web (Currie 1991). There is evidence that the peak in unimodal species richness productivity relationships may occur at higher productivity in carnivores than in herbivore mammals (Owen 1988). Diets of raptorial birds are better known than that of passerines in the southern Neotropics; but even when the diet of a given raptor species has never been described, it can be assumed that the species feeds on vertebrates and/or insects. gradient is given by the geomorphological characteristics of the Andes, with increasing elevation from sea level in the east coast to over 5000 m in the west. Argentina was divided into a grid, and the values for raptor species richness and environmental descriptors were assigned to the centroid of each quadrat. The study area was divided into 245 square quadrats; each quadrat represented km whose centroid was geocoded (MapInfo software) (Fig. 1). Only quadrats in which 100% of the surface occurred in continental Argentina were considered in the analyses (quadrats including seacoast or international political boundaries were excluded). The occurrence of species in each quadrat was obtained by overlapping the grid and distribution maps of raptor species (Narosky & Yzurieta 1990). Although these maps do not provide all desirable details, they allow a satisfactory approximation of relevant data at the regional scale (Rabinovich & Rapoport 1975). A conservative approach was followed by using relatively large quadrats as suggested by Owen (1988) and following a previous study on diversity of passerine birds in Argentina (Rabinovich & Rapoport 1975). The 78 raptor species that occur in the study area include 40 Accipitridae (hawks, eagles, kites, harriers), 17 Strigiformes (owls), 15 Falconidae (falcons, caracaras), five Cathartidae (vultures) and one Pandionidae (the osprey). Finally, the total number of species was added for each quadrat. Climatological data for 198 weather stations were obtained from the Servicio Meteorológico Nacional (SMN) of Argentina. Data for 125 stations were METHODS An approximately km 2 area lying between S and W was considered in the study, corresponding to continental Argentina. East-west axes range approximately km and Northsouth axes range km. Vegetation types in the study area include subtropical rainforests, subtropical and temperate humid forests, dry forests, savannas, temperate grasslands, and warm and cold semideserts. This wide variety of biomes is associated with climatological and topological gradients. Temperature declines from north to south and precipitation from north-east to south-west. The topological Fig. 1. Location of the study area showing the grid (each square represents km 2 ) and weather stations (black dots).

3 aec_1533.fm Page 894 Thursday, December 1, :26 PM 894 BELLOCQ AND GÓMEZ-INSAUSTI 30-year averages, but years averages were used when a longer time-series was not available for the weather station located in the quadrat (Fig. 1). The actual and potential evapotranspiration (PET) indices were estimated following Thornthwaite and Mather (1957) and using the soil maps from the Instituto Nacional de Tecnología Agropecuaria of Argentina (INTA 1962). Because some quadrats did not have a weather station or a complete time-series, interpolations of climatological data were performed using data for stations with short or incomplete time-series and the isolines of temperature and precipitation provided by the SMN. Environmental descriptors were used to test predictions of the species-richness hypotheses (Table 1). Mean annual temperature (TEM) and PET (as a predictor of the solar radiation that reaches the surface) were used to investigate the ambient energy hypothesis. Actual evapotranspiration (AET) was used as a surrogate of the above ground net productivity because they are highly correlated (Rosenzweig 1968) as well as the mean annual precipitation (PRE) in arid zones. Strata coefficients (SC) calculated by Rabinovich and Rapoport (1975) were used to test the prediction of the habitat heterogeneity hypothesis. This coefficient is based on the proportion of the area that each vegetation type covers in each natural region. Additionally, a weighted stratification coefficient (WSC) was estimated for each quadrat by assigning an arbitrary value of 0.05 to each contact between vegetation types (Rabinovich & Rapoport 1975). Simple linear and non-linear regression routines (SPSS software, LR and NLR procedures) were run with the number of species (S) as the dependent variable, and each of the environmental descriptor as the independent variable. Log-transformed data produced better coefficients of determination. After running all the exploratory models for curve estimation, those with the highest coefficients of determination were further developed. Finally, the model showing the best fit for each environmental variable was identified as the best equation to predict species richness from the set of single environmental descriptors. Nonlineal equations fitted better the relationship between species richness and all six predictors. We eliminated alternative hypotheses by finding the best environmental predictor of raptor species richness among PET, TEM, AET, PRE and SC. The environmental variable providing the highest coefficient of determination was considered the best environmental predictor of raptor species richness, given that all parameters of the nonlinear regression model were within the asymptotic 95% confidence interval. Finally, the spatial distribution of the residuals was mapped. RESULTS The log number of raptor species decreases monotonically as latitude increases (Fig. 2). Regression analysis showed a significant relationship between raptor species richness and latitude (R 2 = 0.761, F = , P < ), which was best described by the quadratic model Log S = LAT LAT 2. Of course, this relationship indicates only that species richness covaries with macro-climatological and geographical factors that broadly follow the latitudinal gradient. Raptor species richness was significantly correlated with each of the environmental factors considered in Log. No. species Latitude Fig. 2. Latitudinal variation of raptor species richness in the southern Neotropics. Table 1. Hypotheses investigated to explain spatial variations of raptor species richness in southern regions of the Neotropics, and the environmental descriptors used to test the predictions (previously used by the listed sources) Hypotheses Environmental descriptor Source Ambient energy Potential evapotranspiration (PET) Currie and Paquin (1987), Currie (1991) Mean annual temperature (TEM) Lennon et al. (2000), Hawkins et al. (2003b) Productivity Actual evapotranspiration (AET) Currie and Paquin (1987), Currie (1991) Annual precipitation (PRE) Abramsky and Rosenzweig (1984), Owen (1988) Habitat heterogeneity Strata coefficient (SC) Rabinovich and Rapoport (1975) Weighed strata coefficient (WSC) Rabinovich and Rapoport (1975)

4 aec_1533.fm Page 895 Thursday, December 1, :26 PM SPATIAL PATTERS OF RAPTOR RICHNESS 895 this study, reflecting covariation of climatic and habitat descriptors. All correlations between species richness and environmental predictors were positive. Mean annual temperature was the best environmental predictor of raptor species richness, which is consistent with the ambient energy hypothesis (Table 2). Actual evapotranspiration, SC and PET explained 75 77% of the spatial variation in richness. The richness Table 2. Coefficients of determination (R 2 ) between raptor species richness (S) and environmental variables, and the best predictive model for each variable Variable R 2 Model TEM Log S = TEM (TEM) 2 AET Log S = AET/( AET AET 2 ) SC Log S = SC SC 2 PET Log S = (PET) WSC Log S = WSC WSC 2 PRE Log S = (1.0002) PRE All values are statistically significant at P < AET, actual evapotranspiration; PET, potential evapotranspiration; PRE, mean annual precipitation; SC, strata coefficients; TEM, mean annual temperature; WSC, weighted stratification coefficient. of raptorial bird species exhibited an inflexion point at about 9 C and increases exponentially with TEM within the range of 9 23 C (Fig. 3a). Richness also increased exponentially with AET and PET within the range of mm year 1 and mm year 1, respectively (Fig. 3b,c). The number of raptor species increased monotonically as the habitat becomes more heterogeneous (Fig. 3d). The spatial distribution of the residuals from the temperature model showed that extreme deviations from the predicted value (beyond ± 1 SD) were clustered in space (Fig. 4). These quadrats often reported outliers or extreme values that were excluded from the modelling process. The grouping indicates that there must be some environmental factor, or factors, other than TEM contributing to species richness variations in those areas. DISCUSSION There is a widely recognized latitudinal gradient of species richness, where the tropics support many more species than cooler environments (Schall & Pianka 1978; Rohde 1992). Latitude is indeed an indicator of macro-climatological and geographical factors that covary with species richness. The large-scale geographical trend of bird diversity shows that species richness is the highest at the Equator and decreases towards (a) (b) Log. No. species (c) Temperature (ºC) AET (mm year 1 ) (d) PET (mm year 1 ) Strata coefficient Fig. 3. The variation of raptor species richness as a function of (a) mean annual temperature, (b) actual evapotranspiration (AET), (c) potential evapotranspiration (PET) and (d) vegetation strata coefficient.

5 aec_1533.fm Page 896 Thursday, December 1, :26 PM 896 BELLOCQ AND GÓMEZ-INSAUSTI Fig. 4. Spatial distribution of residuals for the best model. Values estimated above +1 standard deviation (black) and below 1 standard deviation (dark grey). higher latitudes in the Americas; at equal latitude, however, richness is higher in the southern hemisphere (Blackburn 1996). In northern North America, nonmonotonic relationships between vertebrate species richness (birds, amphibians and mammals) exhibited a mid-latitude peak of species richness, where bird richness increases from 25 N to 44 N and then declines (Currie 1991). A mid-latitude minimum of bird species richness was described in Australia, where richness declines from about 12 S to 20 S and then increases to 35 S (Schall & Pianka 1978). At a finer scale, we found that raptor species richness declines monotonically as latitude increases in southern South America. Rabinovich and Rapoport (1972) described a similar latitudinal trend in Argentina, where species richness of passerine birds declines as latitude increases up to 45 S. Differences in latitudinal patterns between fine and large spatial scales may be related to the mid-domain effect. By this effect, peaks of species richness may be associated to the geometry of species ranges in relation to continental boundaries, predicting a convex pattern with a peak at the middle of the latitudinal extent of the continent (Colwell & Lees 2000). However, Diniz-Filho et al. (2002) demonstrated that the spatial pattern of raptor species richness in South America is inconsistent with the predictions of the mid-domain effect. A positive relationship between raptor species richness and AET was found in southern Neotropical regions. This is consistent with the general trend observed at the regional scale. There is evidence of scale dependence in the productivity species richness relationship. Most of the previous studies conducted at the regional or continental scales found a relationship between animal species richness and productivity (or its surrogates such as AET), as opposed to landscape or local scales where most studies showed no relationship (Waide et al. 1999). Positive relationships between animal species richness and productivity have been more frequent than unimodal or negative relationships at all geographical scales, especially for bird species richness (Waide et al. 1999). Our quadratic model predicts raptor species richness for AET values ranging from 450 to 1200 mm year 1. Within this range, our results are inconsistent with Tilman s (1982) model that predicts an asymmetric relationship (ascending, peak and declining phases) between species richness and productivity. It is still possible that raptor species richness peaks at some AET value over 1200 mm year 1. However, that is unlikely given that richness of terrestrial birds shows a positive relationship with AET in a range from zero to over 1600 mm year 1 in the Neotropics (Hawkins et al. 2003b). Annual precipitation explained 56.6% of the spatial variation in raptor species richness and it was the variable that explained the lowest variation in richness in the southern Neotropics. Although annual precipitation is highly correlated with productivity in arid zones (Abramsky & Rosenzweig 1984; Owen 1988), it was the best environmental predictor of bird species richness in Australia across a gradient from about 100 mm year 1 to 1400 mm year 1 (Hawkins et al. 2003b). We found a positive relationship between vegetation stratification and raptor species richness. In the same area and scale as this study was conducted (approx km 2 ; quadrat area km 2 ), vegetation stratification was a primary factor affecting species richness of passerine birds (Rabinovich & Rapoport 1975). Similar results were obtained at a smaller scale within the Pampean region (approx km 2 ; quadrat area 2500 km 2 ), where vegetation stratification was better correlated with species richness than climatic factors grouped by principal components (Cueto & López de Casenave 1999). At larger geographical scales in South America (approx km 2 ; quadrat area km 2 ), habitat type and heterogeneity also affects raptor species richness (Diniz-Filho et al. 2002). Both the PET and annual temperature are measures of ambient energy. Several studies have considered the various environmental variables that may influence spatial patterns of species richness. Summer temperature showed the highest correlation with bird diversity in the UK among many environmental variables (Lennon et al. 2000). The PET was the best predictor of

6 aec_1533.fm Page 897 Thursday, December 1, :26 PM SPATIAL PATTERS OF RAPTOR RICHNESS 897 bird species richness in the Nearctic (Currie 1991) and eastern Palearctic (Hawkins et al. 2003b) and annual temperature was the best predictor in the western Palearctic (Hawkins et al. 2003b). In South America, Rahbek and Graves 2001) found that precipitation and topography were the best single predictors of bird species richness at fine and coarse grains (quadrat area approx to approx km 2 ), respectively. In contrast, Hawkins et al. (2003b) found that AET explained most of the variation in richness of terrestrial birds. Vegetation structure was a good predictor of species richness of passerine birds at the regional scale in Argentina (Rabinovich & Rapoport 1975); although these authors failed to consider energy estimates such as PET or AET, vegetation stratification was better correlated with species richness than temperature. At smaller geographical scales, bird species richness appears to be determined by habitat heterogeneity (Tellería et al. 1992; Böhning- Gaese 1997; Cueto & López de Casenave 1999). The residuals of the model denote variations in species richness that remain unexplained after the TEM effect is removed. Since the model explains variations in species richness at the macro level, the spatial clusters of extreme residuals identify areas where major deviations from the general trend occur. The clusters of highly over and underestimated values reveal areas where environmental discontinuities arise and distinctive local characteristics of topography, humidity and vegetation may interfere with the general trend at the macro level. The model underestimates the number of species in the subtropical rainforests located in the NE and NW of the study area, while it overestimates the number of species in the arid heights of the W and the low plateaux in the N and S of the study area. 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