Confidence limits for regression relationships between distance matrices: Estimating gene flow with distance

Clarke, Ralph T.; Rothery, P.; Raybould, Alan

doi:10.1198/108571102320

Cited by 306 publications

(341 citation statements)

References 14 publications

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“…We identified the top resistance surfaces using maximum‐likelihood population‐effects models (MLPE), which is a mixed modeling approach that accounts for nonindependence in pairwise datasets (Clarke, Rothery, & Raybould, 2002; Van Strien et al., 2012). For each of the individual habitat, abundance, and landscape predictors, we used two analyses for each MZ to determine (i) the thresholds and resistance values that produced resistance distances (RD) that were best correlated with genetic differentiation, and (ii) whether that correlation was significantly greater than its correlation with distance.…”

Section: Methodsmentioning

confidence: 99%

Quantifying functional connectivity: The role of breeding habitat, abundance, and landscape features on range‐wide gene flow in sage‐grouse

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Doherty

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et al. 2018

Evolutionary Applications

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Functional connectivity, quantified using landscape genetics, can inform conservation through the identification of factors linking genetic structure to landscape mechanisms. We used breeding habitat metrics, landscape attributes, and indices of grouse abundance, to compare fit between structural connectivity and genetic differentiation within five long‐established Sage‐Grouse Management Zones (MZ) I‐V using microsatellite genotypes from 6,844 greater sage‐grouse (Centrocercus urophasianus) collected across their 10.7 million‐km2 range. We estimated structural connectivity using a circuit theory‐based approach where we built resistance surfaces using thresholds dividing the landscape into “habitat” and “nonhabitat” and nodes were clusters of sage‐grouse leks (where feather samples were collected using noninvasive techniques). As hypothesized, MZ‐specific habitat metrics were the best predictors of differentiation. To our surprise, inclusion of grouse abundance‐corrected indices did not greatly improve model fit in most MZs. Functional connectivity of breeding habitat was reduced when probability of lek occurrence dropped below 0.25 (MZs I, IV) and 0.5 (II), thresholds lower than those previously identified as required for the formation of breeding leks, which suggests that individuals are willing to travel through undesirable habitat. The individual MZ landscape results suggested terrain roughness and steepness shaped functional connectivity across all MZs. Across respective MZs, sagebrush availability (<10%–30%; II, IV, V), tree canopy cover (>10%; I, II, IV), and cultivation (>25%; I, II, IV, V) each reduced movement beyond their respective thresholds. Model validations confirmed variation in predictive ability across MZs with top resistance surfaces better predicting gene flow than geographic distance alone, especially in cases of low and high differentiation among lek groups. The resultant resistance maps we produced spatially depict the strength and redundancy of range‐wide gene flow and can help direct conservation actions to maintain and restore functional connectivity for sage‐grouse.

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Section: Methodsmentioning

confidence: 99%

Quantifying functional connectivity: The role of breeding habitat, abundance, and landscape features on range‐wide gene flow in sage‐grouse

Row

Doherty

Cross

et al. 2018

Evolutionary Applications

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“…Despite being a widely applied statistical method in evolutionary biology [67], there seems to be much debate about the validity of Mantel tests to test the independence of elements in two matrices [68][69][70]. An alternative approach involves fitting mixed-effects models to account for the correlated structure of regression on distance matrices (maximum-likelihood population effects or MLPE model) [71]; however, parameter optimization is achieved using the restricted/residual maximum-likelihood procedure, raising doubts about the use of traditional information criteria such as AIC for model selection [72]. Summaries of 'variance explained' such as the R 2 b -value [73] have been proposed as suitable alternative statistics for model evaluation; however, several practical and theoretical issues remain, with an overall lack of consensus towards this approach [74].…”

Section: (E) Seascape Effects On Gene Flowmentioning

confidence: 99%

Isolation by resistance across a complex coral reef seascape

et al. 2015

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A detailed understanding of the genetic structure of populations and an accurate interpretation of processes driving contemporary patterns of gene flow are fundamental to successful spatial conservation management. The field of seascape genetics seeks to incorporate environmental variables and processes into analyses of population genetic data to improve our understanding of forces driving genetic divergence in the marine environment. Information about barriers to gene flow (such as ocean currents) is used to define a resistance surface to predict the spatial genetic structure of populations and explain deviations from the widely applied isolation-by-distance model. The majority of seascape approaches to date have been applied to linear coastal systems or at large spatial scales (more than 250 km), with very few applied to complex systems at regional spatial scales (less than 100 km). Here, we apply a seascape genetics approach to a peripheral population of the broadcast-spawning coral Acropora spicifera across the Houtman Abrolhos Islands, a high-latitude complex coral reef system off the central coast of Western Australia. We coupled population genetic data from a panel of microsatellite DNA markers with a biophysical dispersal model to test whether oceanographic processes could explain patterns of genetic divergence. We identified significant variation in allele frequencies over distances of less than 10 km, with significant differentiation occurring between adjacent sites but not between the most geographically distant ones. Recruitment probabilities between sites based on simulated larval dispersal were projected into a measure of resistance to connectivity that was significantly correlated with patterns of genetic divergence, demonstrating that patterns of spatial genetic structure are a function of restrictions to gene flow imposed by oceanographic currents. This study advances our understanding of the role of larval dispersal on the fine-scale genetic structure of coral populations across a complex island system and applies a methodological framework that can be tailored to suit a variety of marine organisms with a range of life-history characteristics.

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“…However, owing to the many cases of missing data between landscapes, we performed a generalized linear mixed model using landscapes as random effect to account for nonindependence between landscapes. We also used the maximum-likelihood population-effects parameterization, in which the covariate structure is fit for the specific dependence between values in a matrix, to account for pairwise data non-independence (Clarke et al, 2002). The models for link level were fitted using SAS PROC MIXED (SAS University Edition) and the covariance structure was coded through toeplitz(1) (Selkoe et al, 2010).…”

Section: Landscape Structure Effects On Genetic Variablesmentioning

confidence: 99%

Contemporary and historic factors influence differently genetic differentiation and diversity in a tropical palm

et al. 2015

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Population genetics theory predicts loss in genetic variability because of drift and inbreeding in isolated plant populations; however, it has been argued that long-distance pollination and seed dispersal may be able to maintain gene flow, even in highly fragmented landscapes. We tested how historical effective population size, historical migration and contemporary landscape structure, such as forest cover, patch isolation and matrix resistance, affect genetic variability and differentiation of seedlings in a tropical palm (Euterpe edulis) in a human-modified rainforest. We sampled 16 sites within five landscapes in the Brazilian Atlantic forest and assessed genetic variability and differentiation using eight microsatellite loci. Using a model selection approach, none of the covariates explained the variation observed in inbreeding coefficients among populations. The variation in genetic diversity among sites was best explained by historical effective population size. Allelic richness was best explained by historical effective population size and matrix resistance, whereas genetic differentiation was explained by matrix resistance. Coalescence analysis revealed high historical migration between sites within landscapes and constant historical population sizes, showing that the genetic differentiation is most likely due to recent changes caused by habitat loss and fragmentation. Overall, recent landscape changes have a greater influence on among-population genetic variation than historical gene flow process. As immediate restoration actions in landscapes with low forest amount, the development of more permeable matrices to allow the movement of pollinators and seed dispersers may be an effective strategy to maintain microevolutionary processes.

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Confidence limits for regression relationships between distance matrices: Estimating gene flow with distance

Cited by 306 publications

References 14 publications

Quantifying functional connectivity: The role of breeding habitat, abundance, and landscape features on range‐wide gene flow in sage‐grouse

Quantifying functional connectivity: The role of breeding habitat, abundance, and landscape features on range‐wide gene flow in sage‐grouse

Isolation by resistance across a complex coral reef seascape

Contemporary and historic factors influence differently genetic differentiation and diversity in a tropical palm

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