Dry-washes determine gene flow and genetic diversity in a common desert shrub

Gaddis, Keith D.; Thompson, Pamela G.; Sork, Victoria L.

doi:10.1007/s10980-016-0393-7

Cited by 6 publications

(5 citation statements)

References 93 publications

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“…Studies comparing models based on SDM predictions to models with predictors related to population connectivity have likewise found that SDM predictions have little explanatory power (i.e. are not retained in the ‘best' model) when explaining genetic diversity (Ortego et al 2015, Gaddis et al 2016, Collevatti et al 2020). Thus, there is little evidence to suggest that SDMs can be used to predict genetic diversity at any scale (Fig.…”

Section: Resultsmentioning

confidence: 99%

Species distribution models rarely predict the biology of real populations

et al. 2021

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Species distribution models (SDMs) are widely used in ecology. In theory, SDMs capture (at least part of ) species' ecological niches and can be used to make inferences about the distribution of suitable habitat for species of interest. Because habitat suitability is expected to influence population demography, SDMs have been used to estimate a variety of population parameters, from occurrence to genetic diversity. However, a critical look at the ability of SDMs to predict independent data across different aspects of population biology is lacking. Here, we systematically reviewed the literature, retrieving 201 studies that tested predictions from SDMs against independent assessments of occurrence, abundance, population performance, and genetic diversity. Although there is some support for the ability of SDMs to predict occurrence (~53% of studies depending on how support was assessed), the predictive performance of these models declines progressively from occurrence to abundance, to population mean fitness, to genetic diversity. At the same time, we observed higher success among studies that evaluated performance for single versus multiple species, pointing to a possible publication bias. Thus, the limited accuracy of SDMs reported here may reflect the best-case scenario. We discuss the limitations of these models and provide specific recommendations for their use for different applications going forward. However, we emphasize that predictions from SDMs, especially when used to inform conservation decisions, should be treated as hypotheses to be tested with independent data rather than as stand-ins for the population parameters we seek to know.

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Section: Resultsmentioning

confidence: 99%

Species distribution models rarely predict the biology of real populations

et al. 2021

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Section: Discussionmentioning

confidence: 99%

“…Importantly, our highresolution (1 m) desert wash model provides a novel method and variable for future habitat modeling in arid systems. Desert washes are important landscape features that enhance plant dispersal and provide key habitat for many wildlife species in these systems (Heaton et al 2006, Gaddis et al 2016, Kotschwar Logan 2016. Although we lacked data with which to evaluate our range-wide connectivity model in a comprehensive manner, we demonstrated a localized evaluation using existing relocation data, within and proximate to the Ivanpah Valley.…”

Section: Discussionmentioning

confidence: 99%

A range‐wide model of contemporary, omnidirectional connectivity for the threatened Mojave desert tortoise

et al. 2019

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As habitat destruction leads to species extinctions globally, conservation planning that accounts for population‐level connectivity and gene flow is an urgent priority. Models that only approximate habitat potential are incomplete because areas of high habitat potential may be isolated, whereas intermixed areas of lower habitat potential may still be critical for maintaining connectivity between and among populations. We developed a range‐wide, omnidirectional (coreless) connectivity model and map for the threatened Mojave desert tortoise at a high spatial resolution (30 m), based on empirical movement data and a circuit‐theoretic approach to estimating connectivity. Specifically, we first estimated habitat potential (i.e., quality) for tortoise movement (as distinct from habitat potential more generally) across its range using hypotheses based on the published literature, linear mixed models, multiple environmental factors derived from remotely sensed data, and recent solar and wind development footprints. The resultant raster output was used to represent landscape conductance in a circuit‐theoretic model of connectivity, which relates the flow of electrical current through a circuit to the movement of tortoises through the landscape. We then modeled potential connectivity across the range of the tortoise using Circuitscape software and the Julia numerical programming language. Intermediate distances from minor roads, intermediate values of annual average maximum temperature, and increasing density of desert washes were among the strongest predictors of movement habitat quality. There was also strong evidence for increased habitat quality for movement with increasing amounts of vegetation cover. The resulting connectivity model and map was determined to accurately reflect important areas for tortoise movement, but we encourage others to do their own evaluation of the model within local areas of interest and as more data become available. Accordingly, the map can provide an important component to improve management decisions that have the potential to influence the conservation of connected desert tortoise populations throughout the range.

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“…However, other factors acting at different spatial and temporal scales can also influence the distribution of genetic variation and rates of gene flow across the landscape (Anderson et al, 2010). On fragmented landscapes, IBD alone may not fully explain the barriers to gene flow because anthropogenic activities and landscape heterogeneity can severely impact dispersal events (Gaddis et al, 2016; Spear et al, 2010) resulting in isolation by resistance (IBR; McRae, 2006; McRae & Beier, 2007). Other factors including vegetative structure, biotic interactions, elevation, rivers, mountain ranges, and anthropogenic features such as roads, urban settlements, and agricultural landscapes can also act as barriers to gene flow (Luque et al, 2012; Ortego et al, 2012).…”

Section: Introductionmentioning

confidence: 99%