<scp>CDM</scp>eta<scp>POP</scp>: an individual‐based, eco‐evolutionary model for spatially explicit simulation of landscape demogenetics

Landguth, Erin L.; Bearlin, Andrew R.; Day, Casey C.; Dunham, Jason B.

doi:10.1111/2041-210x.12608

Cited by 64 publications

(70 citation statements)

References 61 publications

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“…Further exploration of the temporal dynamics of spatial genetic structure during population outbreaks under different contexts remains a promising avenue for future research. Simulation‐based approaches using spatially explicit demo‐genetic models (e.g., Nemo, Guillaume & Rougemont, ; or CDMetaPOP, Landguth, Bearlin, Day, & Dunham, ) hold great promise to isolate and quantify the relative effects of these different factors on the development of spatial genetic structure and spatial synchrony, and to refine our understanding of what drives these large‐scale spatial ecological phenomena.…”

Section: Resultsmentioning

confidence: 99%

Temporal variation in spatial genetic structure during population outbreaks: Distinguishing among different potential drivers of spatial synchrony

Larroque

Legault

Johns

et al. 2019

Evolutionary Applications

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Spatial synchrony is a common characteristic of spatio‐temporal population dynamics across many taxa. While it is known that both dispersal and spatially autocorrelated environmental variation (i.e., the Moran effect) can synchronize populations, the relative contributions of each, and how they interact, are generally unknown. Distinguishing these mechanisms and their effects on synchrony can help us to better understand spatial population dynamics, design conservation and management strategies, and predict climate change impacts. Population genetic data can be used to tease apart these two processes as the spatio‐temporal genetic patterns they create are expected to be different. A challenge, however, is that genetic data are often collected at a single point in time, which may introduce context‐specific bias. Spatio‐temporal sampling strategies can be used to reduce bias and to improve our characterization of the drivers of spatial synchrony. Using spatio‐temporal analyses of genotypic data, our objective was to identify the relative support for these two mechanisms to the spatial synchrony in population dynamics of the irruptive forest insect pest, the spruce budworm (Choristoneura fumiferana), in Quebec (Canada). AMOVA, cluster analysis, isolation by distance, and sPCA were used to characterize spatio‐temporal genomic variation using 1,370 SBW larvae sampled over four years (2012–2015) and genotyped at 3,562 SNP loci. We found evidence of overall weak spatial genetic structure that decreased from 2012 to 2015 and a genetic diversity homogenization among the sites. We also found genetic evidence of a long‐distance dispersal event over >140 km. These results indicate that dispersal is the key mechanism involved in driving population synchrony of the outbreak. Early intervention management strategies that aim to control source populations have the potential to be effective through limiting dispersal. However, the timing of such interventions relative to outbreak progression is likely to influence their probability of success.

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Section: Resultsmentioning

confidence: 99%

Temporal variation in spatial genetic structure during population outbreaks: Distinguishing among different potential drivers of spatial synchrony

Larroque

Legault

Johns

et al. 2019

Evolutionary Applications

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“…We used CDMetaPOP v1.0 (Landguth et al. ) to simulate reintroduction strategies of bull trout in the lower Pend Oreille River system (Fig. ).…”

Section: Methodsmentioning

confidence: 99%

“…We report results of a spatially explicit demogenetic simulation of the reintroduction of bull trout ( Salvelinus confluentus ) into the lower Pend Oreille River, Washington, USA, and three of its tributaries using CDMetaPOP: a spatially explicit, individual‐based eco‐evolutionary model (Landguth et al. ). The goal of this study was to develop a demogenetic framework within which to evaluate potential reintroduction of aquatic species.…”

Section: Introductionmentioning

confidence: 99%

Simulating demography, genetics, and spatially explicit processes to inform reintroduction of a threatened char

et al. 2019

Self Cite

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The success of species reintroductions can depend on a combination of environmental, demographic, and genetic factors. Although the importance of these factors in the success of reintroductions is well‐accepted, they are typically evaluated independently, which can miss important interactions. For species that persist in metapopulations, movement through and interaction with the landscape is predicted to be a vital component of persistence. Simulation‐based approaches are a promising technique for evaluating the independent and combined effects of these factors on the outcome of various reintroduction and associated management actions. We report results from a simulation study of bull trout (Salvelinus confluentus) reintroduction to three watersheds of the Pend Oreille River system in northeastern Washington State, USA. We used an individual‐based, spatially explicit simulation model to evaluate how reintroduction strategies, life history variation, and riverscape structure (e.g., network topology) interact to influence the demographic and genetic characteristics of reintroduced bull trout populations in three watersheds. Simulation scenarios included a range of initial genetic stocks (informed by empirical bull trout genetic data), variation in migratory tendency and life history, and two landscape connectivity alternatives representing a connected network (isolation‐by‐distance) and a fragmented network (isolation‐by‐barrier, using the known existing barriers). A novel feature of these simulations was the ability to consider the interaction of both demographic and genetic (i.e., demogenetic) factors in riverscapes with implicit asymmetric movement probabilities across the barriers. We found that connectivity (presence or absence of barriers) had the largest effect on demographic and genetic outcomes over 200 yr, with a greater effect than both initial genetic diversity and life history variation. We also identified regions of the study system in which bull trout populations persisted across a wide range of demographic, life history, and environmental connectivity parameters. Finally, we found no evidence that initial neutral genetic diversity influenced genetic diversity and structure after 200 yr; instead, genetic drift due to stray rate and population isolation dominated and erased any initial differences in genetic diversity. Our results highlight the utility of spatially explicit demogenetic approaches in exploring and understanding population dynamics—and their implications for management strategies—in fresh waters.

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“…We used a demogenetic (i.e. combined demographic and genetic; Frank, Piccolo, & Baret, ) simulation program, CDMetaPOP (Landguth, Bearlin, Day, & Dunham, ), to simulate spatially explicit, hierarchically‐structured brook trout metapopulations. Using the simulated dataset, we evaluated the ability of common landscape genetics analytical techniques to reliably identify features limiting gene flow.…”

Section: Methodsmentioning

confidence: 99%

“…Additional details about the CDMetaPOP simulation program can be found in Landguth et al. () and in the following sections.…”

Section: Methodsmentioning

confidence: 99%

Watershed‐level brook trout genetic structuring: Evaluation and application of riverscape genetics models

2018

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Stream connectivity promotes resilience and population viability of aquatic organisms. Landscape genetic approaches, traditionally applied to terrestrial systems, may reveal important watershed‐level dynamics that influence connectivity. Additional validation would improve understanding of how the models perform when gene flow is constrained to dendritic networks. The objectives of this study were to use simulations to assess the utility of landscape genetics analyses in dendritic stream networks and investigate riverscape variables influencing gene flow among brook trout Salvelinus fontinalis populations in headwater streams. We used an individual‐based simulation program to simulate different dispersal scenarios and used combinations of landscape genetic models, model selection methods, and genetic metrics to determine the best combination for riverscape systems. We also sampled brook trout from 76 headwater streams in two watersheds (c. 1,000 km2) in Connecticut, U.S.A. to assess connectivity and riverscape influences on genetic structuring. Gravity models with Bayesian information criterion (BIC) selection were the most accurate (>85%) landscape genetic models that consistently identified the correct simulated gene flow barrier. However, all models performed poorly when unidirectional barriers were simulated without a distance‐based dispersal limitation. Excluding this scenario, model accuracy for the gravity models using BIC selection was >90% across multiple genetic metrics, validating the application of landscape genetic models to riverscape systems. We found highly variable levels of brook trout genetic connectivity (FST range 0.01–0.19) at the watershed level (5–15 river km). Gravity models identified increases in upstream impervious surfaces and decreases in riparian tree canopy cover as riverscape variables associated with increases in genetic differentiation in one watershed, while the other watershed was consistent with an isolation by distance pattern. In this study we used demogenetic (i.e. combined demographic and genetic) simulations to demonstrate the utility of landscape genetics techniques in dendritic river networks. Our empirical genetic study documented gene flow among headwater populations of brook trout at the watershed level and also suggested connectivity can be limited by watershed development. Incorporating the heterogeneity of riverscapes into connectivity‐focused conservation planning is essential to the development of effective restoration actions, and landscape genetics approaches can be useful tools to identify watershed‐level connectivity in stream systems.

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CDMetaPOP: an individual‐based, eco‐evolutionary model for spatially explicit simulation of landscape demogenetics

Cited by 64 publications

References 61 publications

Temporal variation in spatial genetic structure during population outbreaks: Distinguishing among different potential drivers of spatial synchrony

Temporal variation in spatial genetic structure during population outbreaks: Distinguishing among different potential drivers of spatial synchrony

Simulating demography, genetics, and spatially explicit processes to inform reintroduction of a threatened char

Watershed‐level brook trout genetic structuring: Evaluation and application of riverscape genetics models

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