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Agent-mediated Spatial Storage Effect in Heterogeneous Habitat Stabilizes Competitive Mouse Lemur Coexistence in Menabe Central, Western Madagascar

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Agent-mediated Spatial Storage Effect in Heterogeneous Habitat Stabilizes Competitive Mouse Lemur Coexistence in Menabe Central, Western Madagascar

Livia Schäffler et al. BMC Ecol.

Abstract

Background: Spatio-temporal distribution patterns of species in response to natural and anthropogenic drivers provide insight into the ecological processes that determine community composition. We investigated determinants of ecological structure in a species assemblage of 4 closely related primate species of the family Cheirogaleidae (Microcebus berthae, Microcebus murinus, Cheirogaleus medius, Mirza coquereli) in western Madagascar by extensive line transect surveys across spatial and temporal heterogeneities with the specific goal of elucidating the mechanisms stabilizing competitive coexistence of the two mouse lemur species (Microcebus spp.).

Results: Interspecific competition between the mouse lemurs was indicated by negative spatial associations in degraded habitat and by habitat partitioning along anthropogenic disturbance gradients during dry seasons with resource scarcity. In non-degraded habitat, intraguild predator M. coquereli, but not C. medius, was negatively associated with M. murinus on the population level, whereas its regional distribution overlapped spatially with that of M. berthae. The species' interspecific distribution pattern across spatial and temporal heterogeneities corresponded to predictions for agent-mediated coexistence and thus confirmed M. coquereli's stabilizing impact on the coexistence of mouse lemurs.

Conclusions: Interspecific interactions contribute to ecological structure in this cheirogaleid assemblage and determinants vary across spatio-temporal heterogeneities. Coexistence of Microcebus spp. is stabilized by an agent-mediated spatial storage effect: M. coquereli creates refuges from competition for M. berthae in intact habitat, whereas anthropogenic environments provide M. murinus with an escape from resource competition and intraguild predation. Species persistence in the assemblage therefore depends on the conservation of habitat content and context that stabilizing mechanisms rely on. Our large-scale population level approach did not allow for considering all potential functional and stochastic drivers of ecological structure, a key limitation that accounts for the large proportion of unexplained variance in our models.

Figures

Figure 1
Figure 1
The study area in central western Madagascar, depicting forest heterogeneity and distribution of line transects across Menabe Central (only two of four line transects shown for RS Andranomena); map based on Landsat 7 ETM 2003, geographic coordinates WGS84, UTM Zone 38.
Figure 2
Figure 2
Encounter rates of M. berthae in [a] dry and [b] rainy season on transects with varying M. murinus encounter rates; black filled points and dashed line: non-degraded habitat, green circles and continuous line: degraded habitat; abundance classes: absent, low (< 5 ind./km), medium (5 ≤ ind./km < 10) and high (10 ind./km).
Figure 3
Figure 3
Observed dry season encounter rates of M. berthae (points) and predictions by log-linear model (curved surfaces) in [a] non‐degraded (n = 16, model equation: ER_Mb.ds = exp(−2.075 + 1.148*ER_Mc.ds + 0.211*dist.village)) and [b] degraded habitat (n = 18, model equation: ER_Mb.ds = exp(−2.075-0.09*ER_Mc.ds + 0.211*dist.village)) across Menabe Central; deviance of observed encounter rates from model predictions are represented by dashed lines.
Figure 4
Figure 4
Observed rainy season encounter rates of M. berthae (squares: non-degraded habitat, crosses: degraded habitat) and predictions by log-linear model (dot-dash fine line: non-degraded habitat, model equation: ER_Mb.rs = exp(−2.289 + 2.007 + 0.681*ER_Mc.rs); dot-dash rough line: degraded habitat, model equation: ER_Mb.rs = exp(−2.289 + 0.681*ER_Mc.rs)) across Menabe Central; due to low variance in M. coquereli rainy season encounter rates, model predictions of numerous transects overlap (only 5 different encounter rate values in non‐degraded (n = 11) and 4 in degraded habitat (n = 14)).
Figure 5
Figure 5
Observed dry season encounter rates of M. murinus (points) and predictions by log-linear model (curved surfaces) in [a] non‐degraded (n = 12, model equation: ER_Mm.ds = exp(3.130-0.876-1.479*ER_Mc.ds-0.131*dist.village) and [b] degraded habitat (n = 7, model equation: ER_Mm.ds = exp(3.130-0.876-0.675*ER_Mc.ds-0.131*dist.village) within Kirindy Forest; deviance of observed encounter rates from model predictions are represented by dashed lines.
Figure 6
Figure 6
Observed rainy season encounter rates of M. murinus (squares: Ambadira Forest, crosses: corridor, circles: Kirindy Forest) and predictions by log-linear model (dot-dash rough line: Ambadira Forest, dot-dash fine line: corridor, continuous line: Kirindy Forest) in non‐degraded habitat (n = 11) across Menabe Central; due to low variance in M. coquereli rainy season encounter rates, model predictions of numerous transects overlap (only 5 different encounter rate values in non‐degraded habitat); RS Andranomena not represented as it entirely consists of degraded habitat; model equations for non-degraded habitat in Ambarida Forest: ER_Mm.rs = exp(1.878-2.128-1.841*ER_Mc.rs), in the corridor: ER_Mm.rs = exp(1.878-0.320-1.841*ER_Mc.rs), in Kirindy Forest: ER_Mm.rs = exp(1.878-0.077-1.841*ER_Mc.rs).

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