TY - JOUR AB - Spatially explicit population genetic models have long been developed, yet have rarely been used to test hypotheses about the spatial distribution of genetic diversity or the genetic divergence between populations. Here, we use spatially explicit coalescence simulations to explore the properties of the island and the two-dimensional stepping stone models under a wide range of scenarios with spatio-temporal variation in deme size. We avoid the simulation of genetic data, using the fact that under the studied models, summary statistics of genetic diversity and divergence can be approximated from coalescence times. We perform the simulations using gridCoal, a flexible spatial wrapper for the software msprime (Kelleher et al., 2016, Theoretical Population Biology, 95, 13) developed herein. In gridCoal, deme sizes can change arbitrarily across space and time, as well as migration rates between individual demes. We identify different factors that can cause a deviation from theoretical expectations, such as the simulation time in comparison to the effective deme size and the spatio-temporal autocorrelation across the grid. Our results highlight that FST, a measure of the strength of population structure, principally depends on recent demography, which makes it robust to temporal variation in deme size. In contrast, the amount of genetic diversity is dependent on the distant past when Ne is large, therefore longer run times are needed to estimate Ne than FST. Finally, we illustrate the use of gridCoal on a real-world example, the range expansion of silver fir (Abies alba Mill.) since the last glacial maximum, using different degrees of spatio-temporal variation in deme size. AU - Szep, Eniko AU - Trubenova, Barbora AU - Csilléry, Katalin ID - 11640 IS - 8 JF - Molecular Ecology Resources SN - 1755-098X TI - Using gridCoal to assess whether standard population genetic theory holds in the presence of spatio-temporal heterogeneity in population size VL - 22 ER - TY - JOUR AB - Sewall Wright developed FST for describing population differentiation and it has since been extended to many novel applications, including the detection of homomorphic sex chromosomes. However, there has been confusion regarding the expected estimate of FST for a fixed difference between the X‐ and Y‐chromosome when comparing males and females. Here, we attempt to resolve this confusion by contrasting two common FST estimators and explain why they yield different estimates when applied to the case of sex chromosomes. We show that this difference is true for many allele frequencies, but the situation characterized by fixed differences between the X‐ and Y‐chromosome is among the most extreme. To avoid additional confusion, we recommend that all authors using FST clearly state which estimator of FST their work uses. AU - Gammerdinger, William J AU - Toups, Melissa A AU - Vicoso, Beatriz ID - 8099 IS - 6 JF - Molecular Ecology Resources SN - 1755-098X TI - Disagreement in FST estimators: A case study from sex chromosomes VL - 20 ER - TY - JOUR AB - To determine the visual sensitivities of an organism of interest, quantitative reverse transcription–polymerase chain reaction (qRT–PCR) is often used to quantify expression of the light‐sensitive opsins in the retina. While qRT–PCR is an affordable, high‐throughput method for measuring expression, it comes with inherent normalization issues that affect the interpretation of results, especially as opsin expression can vary greatly based on developmental stage, light environment or diurnal cycles. We tested for diurnal cycles of opsin expression over a period of 24 hr at 1‐hr increments and examined how normalization affects a data set with fluctuating expression levels using qRT–PCR and transcriptome data from the retinae of the cichlid Pelmatolapia mariae. We compared five methods of normalizing opsin expression relative to (a) the average of three stably expressed housekeeping genes (Ube2z, EF1‐α and β‐actin), (b) total RNA concentration, (c) GNAT2, (the cone‐specific subunit of transducin), (d) total opsin expression and (e) only opsins expressed in the same cone type. Normalizing by proportion of cone type produced the least variation and would be best for removing time‐of‐day variation. In contrast, normalizing by housekeeping genes produced the highest daily variation in expression and demonstrated that the peak of cone opsin expression was in the late afternoon. A weighted correlation network analysis showed that the expression of different cone opsins follows a very similar daily cycle. With the knowledge of how these normalization methods affect opsin expression data, we make recommendations for designing sampling approaches and quantification methods based upon the scientific question being examined. AU - Yourick, Miranda R. AU - Sandkam, Benjamin A. AU - Gammerdinger, William J AU - Escobar-Camacho, Daniel AU - Nandamuri, Sri Pratima AU - Clark, Frances E. AU - Joyce, Brendan AU - Conte, Matthew A. AU - Kocher, Thomas D. AU - Carleton, Karen L. ID - 6821 IS - 6 JF - Molecular Ecology Resources TI - Diurnal variation in opsin expression and common housekeeping genes necessitates comprehensive normalization methods for quantitative real-time PCR analyses VL - 19 ER -