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How spatio-temporal habitat connectivity affects amphibian genetic structure.

Watts AG, Schlichting PE, Billerman SM, Jesmer BR, Micheletti S, Fortin MJ, Funk WC, Hapeman P, Muths E, Murphy MA - Front Genet (2015)

Bottom Line: We examined 18 wetlands in Colorado, and quantified 12 microsatellite loci from 322 individual frogs.We found that genetic connectivity was related to topographic complexity, within- and between-wetland differences in moisture, and wetland functional connectivity as contributed by stepping-stone wetlands.Our results highlight the role that dynamic environmental factors have on dispersal-limited species and illustrate how complex asynchronous interactions contribute to the structure of spatially-explicit metapopulations.

View Article: PubMed Central - PubMed

Affiliation: Department of Ecology & Evolutionary Biology, University of Toronto Toronto, ON, Canada.

ABSTRACT
Heterogeneous landscapes and fluctuating environmental conditions can affect species dispersal, population genetics, and genetic structure, yet understanding how biotic and abiotic factors affect population dynamics in a fluctuating environment is critical for species management. We evaluated how spatio-temporal habitat connectivity influences dispersal and genetic structure in a population of boreal chorus frogs (Pseudacris maculata) using a landscape genetics approach. We developed gravity models to assess the contribution of various factors to the observed genetic distance as a measure of functional connectivity. We selected (a) wetland (within-site) and (b) landscape matrix (between-site) characteristics; and (c) wetland connectivity metrics using a unique methodology. Specifically, we developed three networks that quantify wetland connectivity based on: (i) P. maculata dispersal ability, (ii) temporal variation in wetland quality, and (iii) contribution of wetland stepping-stones to frog dispersal. We examined 18 wetlands in Colorado, and quantified 12 microsatellite loci from 322 individual frogs. We found that genetic connectivity was related to topographic complexity, within- and between-wetland differences in moisture, and wetland functional connectivity as contributed by stepping-stone wetlands. Our results highlight the role that dynamic environmental factors have on dispersal-limited species and illustrate how complex asynchronous interactions contribute to the structure of spatially-explicit metapopulations.

No MeSH data available.


Networks designed for wetland connectivity of Pseudacris maculata. Pie charts represent the proportion of influence by Intra, Flux, and Connector to overall wetland connectivity. (A) Spatial-breeding network: all sampled sites were considered nodes. In this scenario, wetland connectivity is largely driven by Flux (availability + spatial position). (B) Temporal-breeding network: all sampled sites were considered nodes, but categorized as temporal_low, temporal_high, and temporal_neutral wetlands. Compared to the Spatial_breeding network, the temporal network is now driven by both Intra (availability) and Flux (availability + spatial position). (C) Stepping-stone network: 110 unoccupied sites within the region were added to the 18 sampled sites. In the stepping_stone network, the Connector fraction now becomes a driver of connectivity for sampled wetlands.
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Figure 2: Networks designed for wetland connectivity of Pseudacris maculata. Pie charts represent the proportion of influence by Intra, Flux, and Connector to overall wetland connectivity. (A) Spatial-breeding network: all sampled sites were considered nodes. In this scenario, wetland connectivity is largely driven by Flux (availability + spatial position). (B) Temporal-breeding network: all sampled sites were considered nodes, but categorized as temporal_low, temporal_high, and temporal_neutral wetlands. Compared to the Spatial_breeding network, the temporal network is now driven by both Intra (availability) and Flux (availability + spatial position). (C) Stepping-stone network: 110 unoccupied sites within the region were added to the 18 sampled sites. In the stepping_stone network, the Connector fraction now becomes a driver of connectivity for sampled wetlands.

Mentions: Considering only wetland connectivity metrics in predicting genetic distance, we found that the presence of stepping-stone wetlands (stepping-stone network) was the highest contributor to the top models explaining genetic connectivity (Table 3). Percent-values of wetland connectivity measured using the spatial-breeding, temporal-breeding, and stepping-stone networks were variable perwetland (0.29–45.25%; 0.00–68.47%; 0.00–0.41%, respectively) (Table 4). For the spatial-breeding network, wetland area and wetland spatial position in the network (connectivity characteristics represented by the Flux connectivity metric) contributed most to wetland connectivity (Table 4, Figure 2A) whereby site “I” represented the most functionally connected wetland spatially (PC = 45.24%; Figure 2A) due to wetland availability alone (represented by the Intra metric). Conversely, for the temporal-breeding network, only wetland availability (measured by the Intra connectivity metric) contributed to overall wetland connectivity (Figure 2B) whereby site “C” represented the most functionally-connected wetland over time (PC = 68.47%). In the stepping-stone network, functional connectivity quantities (PC-values) for all 18 sampled sites were low (relatively low Flux, Intra and connection values) but variable. The Connector metric did not contribute to overall wetland connectivity (PC < 0.01%) for any wetlands in any of the three types of networks.


How spatio-temporal habitat connectivity affects amphibian genetic structure.

Watts AG, Schlichting PE, Billerman SM, Jesmer BR, Micheletti S, Fortin MJ, Funk WC, Hapeman P, Muths E, Murphy MA - Front Genet (2015)

Networks designed for wetland connectivity of Pseudacris maculata. Pie charts represent the proportion of influence by Intra, Flux, and Connector to overall wetland connectivity. (A) Spatial-breeding network: all sampled sites were considered nodes. In this scenario, wetland connectivity is largely driven by Flux (availability + spatial position). (B) Temporal-breeding network: all sampled sites were considered nodes, but categorized as temporal_low, temporal_high, and temporal_neutral wetlands. Compared to the Spatial_breeding network, the temporal network is now driven by both Intra (availability) and Flux (availability + spatial position). (C) Stepping-stone network: 110 unoccupied sites within the region were added to the 18 sampled sites. In the stepping_stone network, the Connector fraction now becomes a driver of connectivity for sampled wetlands.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4561841&req=5

Figure 2: Networks designed for wetland connectivity of Pseudacris maculata. Pie charts represent the proportion of influence by Intra, Flux, and Connector to overall wetland connectivity. (A) Spatial-breeding network: all sampled sites were considered nodes. In this scenario, wetland connectivity is largely driven by Flux (availability + spatial position). (B) Temporal-breeding network: all sampled sites were considered nodes, but categorized as temporal_low, temporal_high, and temporal_neutral wetlands. Compared to the Spatial_breeding network, the temporal network is now driven by both Intra (availability) and Flux (availability + spatial position). (C) Stepping-stone network: 110 unoccupied sites within the region were added to the 18 sampled sites. In the stepping_stone network, the Connector fraction now becomes a driver of connectivity for sampled wetlands.
Mentions: Considering only wetland connectivity metrics in predicting genetic distance, we found that the presence of stepping-stone wetlands (stepping-stone network) was the highest contributor to the top models explaining genetic connectivity (Table 3). Percent-values of wetland connectivity measured using the spatial-breeding, temporal-breeding, and stepping-stone networks were variable perwetland (0.29–45.25%; 0.00–68.47%; 0.00–0.41%, respectively) (Table 4). For the spatial-breeding network, wetland area and wetland spatial position in the network (connectivity characteristics represented by the Flux connectivity metric) contributed most to wetland connectivity (Table 4, Figure 2A) whereby site “I” represented the most functionally connected wetland spatially (PC = 45.24%; Figure 2A) due to wetland availability alone (represented by the Intra metric). Conversely, for the temporal-breeding network, only wetland availability (measured by the Intra connectivity metric) contributed to overall wetland connectivity (Figure 2B) whereby site “C” represented the most functionally-connected wetland over time (PC = 68.47%). In the stepping-stone network, functional connectivity quantities (PC-values) for all 18 sampled sites were low (relatively low Flux, Intra and connection values) but variable. The Connector metric did not contribute to overall wetland connectivity (PC < 0.01%) for any wetlands in any of the three types of networks.

Bottom Line: We examined 18 wetlands in Colorado, and quantified 12 microsatellite loci from 322 individual frogs.We found that genetic connectivity was related to topographic complexity, within- and between-wetland differences in moisture, and wetland functional connectivity as contributed by stepping-stone wetlands.Our results highlight the role that dynamic environmental factors have on dispersal-limited species and illustrate how complex asynchronous interactions contribute to the structure of spatially-explicit metapopulations.

View Article: PubMed Central - PubMed

Affiliation: Department of Ecology & Evolutionary Biology, University of Toronto Toronto, ON, Canada.

ABSTRACT
Heterogeneous landscapes and fluctuating environmental conditions can affect species dispersal, population genetics, and genetic structure, yet understanding how biotic and abiotic factors affect population dynamics in a fluctuating environment is critical for species management. We evaluated how spatio-temporal habitat connectivity influences dispersal and genetic structure in a population of boreal chorus frogs (Pseudacris maculata) using a landscape genetics approach. We developed gravity models to assess the contribution of various factors to the observed genetic distance as a measure of functional connectivity. We selected (a) wetland (within-site) and (b) landscape matrix (between-site) characteristics; and (c) wetland connectivity metrics using a unique methodology. Specifically, we developed three networks that quantify wetland connectivity based on: (i) P. maculata dispersal ability, (ii) temporal variation in wetland quality, and (iii) contribution of wetland stepping-stones to frog dispersal. We examined 18 wetlands in Colorado, and quantified 12 microsatellite loci from 322 individual frogs. We found that genetic connectivity was related to topographic complexity, within- and between-wetland differences in moisture, and wetland functional connectivity as contributed by stepping-stone wetlands. Our results highlight the role that dynamic environmental factors have on dispersal-limited species and illustrate how complex asynchronous interactions contribute to the structure of spatially-explicit metapopulations.

No MeSH data available.