A hierarchical Bayesian Beta regression approach to study the effects of geographical genetic structure and spatial autocorrelation on species distribution range shifts

Martínez‐Minaya, Joaquín; Conesa, David; Fortin, Marie‐Josée; Alonso‐Blanco, Carlos; Picó, F. Xavier; Marcer, Arnald

doi:10.1111/1755-0998.13024

Cited by 11 publications

(7 citation statements)

References 88 publications

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“…We applied Maxent using default parameters except for features using the hinge type, making it comparable to a Generalized Additive Model [94]. We obtained consistent results with previous habitat suitability models conducted for Iberian A. thaliana with lower sample sizes [39,56].…”

Section: Drivers Of Genetic Differentiationsupporting

confidence: 70%

“…We modeled the effects of environmental heterogeneity on the spatial distribution of genetic diversity by using a modified version of a spatial hierarchical Bayesian model recently used to model the effects of warming on the Iberian A. thaliana's distribution [56]. The aim of this model was to visualize hot and cold spots of genetic diversity across the Iberian Peninsula and to identify their environmental predictors.…”

Section: Geographic Distribution Of Genetic Diversitymentioning

confidence: 99%

“…The Watanabe-Akaike information criterion (WAIC) [89]; was computed to determine the best models. The model included a stochastic spatial effect (the spatial term) to remove the spatial autocorrelation of data [56].…”

Section: Geographic Distribution Of Genetic Diversitymentioning

confidence: 99%

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Ecological, genetic and evolutionary drivers of regional genetic differentiation in Arabidopsis thaliana

et al. 2020

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Background: Disentangling the drivers of genetic differentiation is one of the cornerstones in evolution. This is because genetic diversity, and the way in which it is partitioned within and among populations across space, is an important asset for the ability of populations to adapt and persist in changing environments. We tested three major hypotheses accounting for genetic differentiation-isolation-by-distance (IBD), isolation-by-environment (IBE) and isolation-by-resistance (IBR)-in the annual plant Arabidopsis thaliana across the Iberian Peninsula, the region with the largest genomic diversity. To that end, we sampled, genotyped with genome-wide SNPs, and analyzed 1772 individuals from 278 populations distributed across the Iberian Peninsula. Results: IBD, and to a lesser extent IBE, were the most important drivers of genetic differentiation in A. thaliana. In other words, dispersal limitation, genetic drift, and to a lesser extent local adaptation to environmental gradients, accounted for the within-and among-population distribution of genetic diversity. Analyses applied to the four Iberian genetic clusters, which represent the joint outcome of the long demographic and adaptive history of the species in the region, showed similar results except for one cluster, in which IBR (a function of landscape heterogeneity) was the most important driver of genetic differentiation. Using spatial hierarchical Bayesian models, we found that precipitation seasonality and topsoil pH chiefly accounted for the geographic distribution of genetic diversity in Iberian A. thaliana. Conclusions: Overall, the interplay between the influence of precipitation seasonality on genetic diversity and the effect of restricted dispersal and genetic drift on genetic differentiation emerges as the major forces underlying the evolutionary trajectory of Iberian A. thaliana.

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Section: Drivers Of Genetic Differentiationsupporting

confidence: 70%

Section: Geographic Distribution Of Genetic Diversitymentioning

confidence: 99%

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Ecological, genetic and evolutionary drivers of regional genetic differentiation in Arabidopsis thaliana

et al. 2020

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“…In contrast, less vulnerable areas could have high or low coral cover and occurrence, with low bleaching probability. The Kernel Density Estimation were used to visualize and compare the distribution of our models among different scenarios, and differences were represented by the percentage of change between current and future projections 87 .…”

Section: Methodsmentioning

confidence: 99%

Coral distribution and bleaching vulnerability areas in Southwestern Atlantic under ocean warming

Bleuel

Pennino

Longo

2021

Sci Rep

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Global climate change is a major threat to reefs by increasing the frequency and severity of coral bleaching events over time, reducing coral cover and diversity. Ocean warming may cause shifts in coral communities by increasing temperatures above coral’s upper thermal limits in tropical regions, and by making extratropical regions (marginal reefs) more suitable and potential refugia. We used Bayesian models to project coral occurrence, cover and bleaching probabilities in Southwestern Atlantic and predicted how these probabilities will change under a high-emission scenario (RCP8.5). By overlapping these projections, we categorized areas that combine high probabilities of coral occurrence, cover and bleaching as vulnerability-hotspots. Current coral occurrence and cover probabilities were higher in the tropics (1°S–20°S) but both will decrease and shift to new suitable extratropical reefs (20°S–27°S; tropicalization) with ocean warming. Over 90% of the area present low and mild vulnerability, while the vulnerability-hotspots represent ~ 3% under current and future scenarios, but include the most biodiverse reef complex in South Atlantic (13°S–18°S; Abrolhos Bank). As bleaching probabilities increase with warming, the least vulnerable areas that could act as potential refugia are predicted to reduce by 50%. Predicting potential refugia and highly vulnerable areas can inform conservation actions to face climate change.

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“…Gotelli & Stanton-Geddes (2015), for instance, advocate the gSDM approach on distinct clusters but propose weighting each population by the relative ancestry proportions rather than assuming a discrete assignment to an individual cluster. Similarly, Martínez-Minaya et al (2019) have developed an approach to model the spatial distribution of genetic clusters which could reduce the need to assign occurrences to a single cluster. The gSDM approach could also benefit from using techniques designed for rare species with few known occurrences (Breiner et al, 2015;Shcheglovitova & Anderson, 2013), as the number of populations in genetic studies assigned to any individual cluster is often relatively small.…”

Section: Model Comparisonmentioning

confidence: 99%

Do genetically informed distribution models improve range predictions in past climates? A case study with balsam poplar

Gougherty¹,

Keller²,

Fitzpatrick³

2022

Frontiers of Biogeography

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Species distribution models (SDMs) are one of the most widely used approaches to predict changes in habitat suitability in response to climate change. However, as typically implemented, SDMs treat species as genetically uniform throughout their ranges and thereby ignore potentially important genetic differences between populations. While numerous studies have used SDMs to model genetically based subgroupings within species, the ability of such models to be transferred to new times has rarely been evaluated. Here, we used standard and genetically informed distribution models (gSDMs) to predict the future and past range of balsam poplar (Populus balsamifera L.). We then assessed model transferability of standard SDMs and gSDMs using balsam poplar fossil pollen and macrofossil occurrences. In general, standard and gSDMs performed similarly through time, with both predicting a northward expanding range from refugia as glaciers receded over the past 22 ky BP and declining suitable area in future climates. Both standard and gSDMs showed moderate abilities to distinguish balsam poplar fossils from pseudo-absences but tended to predict lower suitability at fossil sites during the Pleistocene-Holocene transition. Although gSDMs applied to balsam poplar did not prove more transferable than standard SDMs, they provided numerous unique insights, such as the change in suitable area of genetic clusters through time and potential refugial locations. We argue more research is needed to determine which species may benefit most from the gSDM approach and to test gSDMs with temporally or spatially independent occurrences, as is often recommended for standard SDMs. Highlights• We report one of the few comparative evaluations of species distribution models fit to entire species ranges and to genetic subgroupings using independent data. • We found little difference in the performance of genetically informed and standard distribution models, but genetically informed models offer insights not available from standard models, including the past and future trajectories of different genetic clusters in response to climate change. • While more work is needed to determine which species benefit most from integrating genetic information to predict their future and past distributions, treating species as being comprised of genetically uniform populations ignores potentially relevant biological variation which may obscure important range dynamics in future climates.

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A hierarchical Bayesian Beta regression approach to study the effects of geographical genetic structure and spatial autocorrelation on species distribution range shifts

Cited by 11 publications

References 88 publications

Ecological, genetic and evolutionary drivers of regional genetic differentiation in Arabidopsis thaliana

Ecological, genetic and evolutionary drivers of regional genetic differentiation in Arabidopsis thaliana

Coral distribution and bleaching vulnerability areas in Southwestern Atlantic under ocean warming

Do genetically informed distribution models improve range predictions in past climates? A case study with balsam poplar

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