2017
DOI: 10.1111/ecog.03235
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The value of space‐for‐time substitution for studying fine‐scale microevolutionary processes

Abstract: When the drivers of biological turnover in space are the same as those that drive turnover through time, space can be substituted for time to model how patterns of variation are predicted to change into the future. These space-for-time substitutions are widely used in ecological modeling but have only recently been applied to the study of microevolutionary processes, particularly over relatively fine spatial and temporal scales. Here, we review recent examples that have employed space-for-time substitution to … Show more

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Cited by 54 publications
(42 citation statements)
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References 108 publications
(160 reference statements)
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“…When the cost is high and genetic tracking of the environment is easy, the plastic slope tends to zero; but when the cost is low and genetic tracking hard, the slope tends to the regression of the optimum on the environment of development (DO-regression) (Gavrilets and Scheiner 1993). The plastic slope that evolves is symmetric with respect to temporal and spatial parameters, providing a theoretical basis for the assumptions underlying space-for-time substitutions in empirical work (Wogan and Wang 2017). However, temporal and spatial fluctuations can have asymmetric influences if there are differences in the values of their homologous parameters, suggesting care must be taken.…”
Section: Discussionmentioning
confidence: 99%
“…When the cost is high and genetic tracking of the environment is easy, the plastic slope tends to zero; but when the cost is low and genetic tracking hard, the slope tends to the regression of the optimum on the environment of development (DO-regression) (Gavrilets and Scheiner 1993). The plastic slope that evolves is symmetric with respect to temporal and spatial parameters, providing a theoretical basis for the assumptions underlying space-for-time substitutions in empirical work (Wogan and Wang 2017). However, temporal and spatial fluctuations can have asymmetric influences if there are differences in the values of their homologous parameters, suggesting care must be taken.…”
Section: Discussionmentioning
confidence: 99%
“…Here, we reconstructed patterns of genome‐wide diversity and investigated evidence for local adaption by identifying genotype‐environment associations across two independent elevational transects of American pika populations. We employed RADseq genotyping‐by‐sequencing within a space‐for‐time design, which enabled us to circumvent the difficulties associated with long‐term genetic monitoring and allowed us to highlight several environmental axes potentially driving natural selection (Lotterhos & Whitlock, ; Wogan & Wang, ). Additionally, GEA methods have relatively high power to resolve loci under natural selection (De Mita et al., ), and do not require specific candidate loci (Hoffmann & Willi, ).…”
Section: Discussionmentioning
confidence: 99%
“…GEA methods seek to detect natural selection by scanning for meaningful associations between allele frequencies and environmental pressures. Such methods can be effectively applied over elevational gradients, so that contemporary spatial patterns across rapidly changing environments can be used to reflect changes through time (i.e., space‐for‐time design; reviewed in Wogan & Wang, ). For example, Guo, Lu, Liao, and Merilä () found climate‐associated adaptive divergence in genes associated with binding and metabolic processes in elevationally distributed populations of Andrew's toads ( Bufo andrewsi ) after controlling for neutral divergence.…”
Section: Introductionmentioning
confidence: 99%
“…In such case, the performance of the low‐latitude populations at their current higher environmental temperature can then be used as a proxy for the performance of the high‐latitude populations when they gradually evolve (hence genetically adapt) under future warming (De Frenne et al., ). While this approach should be done cautiously as time‐scales differ and it assumes that the drivers of trait change in space are the same as those that drive trait change through time, this approach has been shown reliable to infer micro‐evolutionary processes (Wogan & Wang, ).…”
Section: Introductionmentioning
confidence: 99%