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Historical colonization and dispersal limitation supplement climate and topography in shaping species richness of African lizards (Reptilia: Agaminae)

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ABSTRACT

To what extent deep-time dispersal limitation shapes present-day biodiversity at broad spatial scales remains elusive. Here, we compiled a continental dataset on the distributions of African lizard species in the reptile subfamily Agaminae (a relatively young, Neogene radiation of agamid lizards which ancestors colonized Africa from the Arabian peninsula) and tested to what extent historical colonization and dispersal limitation (i.e. accessibility from areas of geographic origin) can explain present-day species richness relative to current climate, topography, and climate change since the late Miocene (~10 mya), the Pliocene (~3 mya), and the Last Glacial Maximum (LGM, 0.021 mya). Spatial and non-spatial multi-predictor regression models revealed that time-limited dispersal via arid corridors is a key predictor to explain macro-scale patterns of species richness. In addition, current precipitation seasonality, current temperature of the warmest month, paleo-temperature changes since the LGM and late Miocene, and topographic relief emerged as important drivers. These results suggest that deep-time dispersal constraints — in addition to climate and mountain building — strongly shape current species richness of Africa’s arid-adapted taxa. Such historical dispersal limitation might indicate that natural movement rates of species are too slow to respond to rates of ongoing and projected future climate and land use change.

No MeSH data available.


Related in: MedlinePlus

Effects of key predictor variables on species richness of agamid lizards across Africa.In (a), the relative importance (standardized coefficients) of six key predictor variables from the non-spatial regression model is illustrated. The variables are those that show significant effects in both spatial and non-spatial models (compare Table 2). The direction of effect is indicated as + or −. In (b), partial residual plots illustrate the relationship between a predictor and species richness once all other predictors have been statistically accounted for in a multiple-predictor model (see ‘OLS’ in Table 2). Abbreviations, units and sources of predictor variables are explained in Table 1. Each dot represents one 110 × 110 km grid cell. Plots were created using the statistical programming language R.
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f3: Effects of key predictor variables on species richness of agamid lizards across Africa.In (a), the relative importance (standardized coefficients) of six key predictor variables from the non-spatial regression model is illustrated. The variables are those that show significant effects in both spatial and non-spatial models (compare Table 2). The direction of effect is indicated as + or −. In (b), partial residual plots illustrate the relationship between a predictor and species richness once all other predictors have been statistically accounted for in a multiple-predictor model (see ‘OLS’ in Table 2). Abbreviations, units and sources of predictor variables are explained in Table 1. Each dot represents one 110 × 110 km grid cell. Plots were created using the statistical programming language R.

Mentions: We used the standardized coefficients of non-spatial and spatial multi-predictor regression models to test the relative importance of dispersal scenarios and other predictor variables for explaining agamid species richness across Africa (Table 2 and Supplementary Material). A model selection with the Akaike Information Criterion (AIC)33 showed that a non-spatial ordinary least squares (OLS) regression model with twelve predictor variables (i.e. five variables related to current climate, five to past climate, one to topography, and one to historical dispersal limitation, i.e. DISP4) was the most parsimonious multivariate model (i.e. having the lowest AIC among all possible candidate models) (Table 2). This model explained about half of the continental variation in agamid species richness (R2 = 0.45). Because spatial autocorrelation was present in OLS model residuals (see Moran’s I and its p-value in Table 2) we also implemented a spatial simultaneous autoregressive (SAR) model34 with the same predictor variables. This allowed to account for residual spatial autocorrelation and captured most of the variation in species richness (R2FULL = 0.89). In both OLS and SAR regressions, the simulated dispersal scenario (DISP4) was among the most important predictor variables (i.e. high standardized coefficients in Table 2 and Fig. 3a). In the OLS model, DISP4 showed the strongest effect together with topography whereas climate variables were much less important (Fig. 3a). In the SAR model, DISP4 was of similar importance to precipitation seasonality and LGM temperature anomaly (Table 2). Overall, the effect of DISP4 on species richness of agamid lizards was positive, indicating that areas with a high accessibility from the Arabian Peninsula tended to have more agamid species than areas with low accessibility further away (Fig. 3b). A multiple regression model including DISP3 instead of DISP4 yielded qualitatively similar results (see Supplementary Material), except that the effect of DISP3 was less pronounced than DISP4 (consistent with the Spearman rank correlations from the univariate analyses). Generally, the multi-variate analyses confirmed the hypothesis that historical dispersal limitation at a continental scale has left a strong imprint on present-day species richness of agamid lizards.


Historical colonization and dispersal limitation supplement climate and topography in shaping species richness of African lizards (Reptilia: Agaminae)
Effects of key predictor variables on species richness of agamid lizards across Africa.In (a), the relative importance (standardized coefficients) of six key predictor variables from the non-spatial regression model is illustrated. The variables are those that show significant effects in both spatial and non-spatial models (compare Table 2). The direction of effect is indicated as + or −. In (b), partial residual plots illustrate the relationship between a predictor and species richness once all other predictors have been statistically accounted for in a multiple-predictor model (see ‘OLS’ in Table 2). Abbreviations, units and sources of predictor variables are explained in Table 1. Each dot represents one 110 × 110 km grid cell. Plots were created using the statistical programming language R.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC5037428&req=5

f3: Effects of key predictor variables on species richness of agamid lizards across Africa.In (a), the relative importance (standardized coefficients) of six key predictor variables from the non-spatial regression model is illustrated. The variables are those that show significant effects in both spatial and non-spatial models (compare Table 2). The direction of effect is indicated as + or −. In (b), partial residual plots illustrate the relationship between a predictor and species richness once all other predictors have been statistically accounted for in a multiple-predictor model (see ‘OLS’ in Table 2). Abbreviations, units and sources of predictor variables are explained in Table 1. Each dot represents one 110 × 110 km grid cell. Plots were created using the statistical programming language R.
Mentions: We used the standardized coefficients of non-spatial and spatial multi-predictor regression models to test the relative importance of dispersal scenarios and other predictor variables for explaining agamid species richness across Africa (Table 2 and Supplementary Material). A model selection with the Akaike Information Criterion (AIC)33 showed that a non-spatial ordinary least squares (OLS) regression model with twelve predictor variables (i.e. five variables related to current climate, five to past climate, one to topography, and one to historical dispersal limitation, i.e. DISP4) was the most parsimonious multivariate model (i.e. having the lowest AIC among all possible candidate models) (Table 2). This model explained about half of the continental variation in agamid species richness (R2 = 0.45). Because spatial autocorrelation was present in OLS model residuals (see Moran’s I and its p-value in Table 2) we also implemented a spatial simultaneous autoregressive (SAR) model34 with the same predictor variables. This allowed to account for residual spatial autocorrelation and captured most of the variation in species richness (R2FULL = 0.89). In both OLS and SAR regressions, the simulated dispersal scenario (DISP4) was among the most important predictor variables (i.e. high standardized coefficients in Table 2 and Fig. 3a). In the OLS model, DISP4 showed the strongest effect together with topography whereas climate variables were much less important (Fig. 3a). In the SAR model, DISP4 was of similar importance to precipitation seasonality and LGM temperature anomaly (Table 2). Overall, the effect of DISP4 on species richness of agamid lizards was positive, indicating that areas with a high accessibility from the Arabian Peninsula tended to have more agamid species than areas with low accessibility further away (Fig. 3b). A multiple regression model including DISP3 instead of DISP4 yielded qualitatively similar results (see Supplementary Material), except that the effect of DISP3 was less pronounced than DISP4 (consistent with the Spearman rank correlations from the univariate analyses). Generally, the multi-variate analyses confirmed the hypothesis that historical dispersal limitation at a continental scale has left a strong imprint on present-day species richness of agamid lizards.

View Article: PubMed Central - PubMed

ABSTRACT

To what extent deep-time dispersal limitation shapes present-day biodiversity at broad spatial scales remains elusive. Here, we compiled a continental dataset on the distributions of African lizard species in the reptile subfamily Agaminae (a relatively young, Neogene radiation of agamid lizards which ancestors colonized Africa from the Arabian peninsula) and tested to what extent historical colonization and dispersal limitation (i.e. accessibility from areas of geographic origin) can explain present-day species richness relative to current climate, topography, and climate change since the late Miocene (~10 mya), the Pliocene (~3 mya), and the Last Glacial Maximum (LGM, 0.021 mya). Spatial and non-spatial multi-predictor regression models revealed that time-limited dispersal via arid corridors is a key predictor to explain macro-scale patterns of species richness. In addition, current precipitation seasonality, current temperature of the warmest month, paleo-temperature changes since the LGM and late Miocene, and topographic relief emerged as important drivers. These results suggest that deep-time dispersal constraints — in addition to climate and mountain building — strongly shape current species richness of Africa’s arid-adapted taxa. Such historical dispersal limitation might indicate that natural movement rates of species are too slow to respond to rates of ongoing and projected future climate and land use change.

No MeSH data available.


Related in: MedlinePlus