Extending ecological niche models to the past 120 000 years corroborates the lack of strong phylogeographic structure in the Crested Drongo (Dicrurus forficatus forficatus) on Madagascar

We conduct a phylogeographic study of the Crested Drongo (Dicrurus forficatus forficatus), a broadly distributed bird species on Madagascar. We first determined the demographic and spatial pattern inferred from mitochondrial and nuclear data, and then compared these results with predictions from a present to 0.120‐Myr‐old reconstruction of the spatial dynamics of the range of D. f. forficatus on Madagascar, enabling putative areas of stability (lineage persistence) to be detected. Weak genetic structure along an east–west pattern and comparatively low genetic diversity were recovered, with strong evidence of population expansion found at all ten loci sampled. The palaeoclimatic distribution models over the past 0.120 Myr suggest the presence of extensive areas of suitable climate in the east and west for the species since its colonization of Madagascar, a result in strong concordance with the spatial and genetic signal derived from our multilocus data set. © 2013 The Linnean Society of London     

The Effect of Genomic Inversions on Estimation of Population Genetic Parameters from SNP Data

In recent years it has emerged that structural variants have a substantial impact on genomic variation. Inversion polymorphisms represent a significant class of structural variant, and despite the challenges in their detection, data on inversions in the human genome are increasing rapidly. Statistical methods for inferring parameters such as the recombination rate and the selection coefficient have generally been developed without accounting for the presence of inversions. Here we exploit new software for simulating inversions in population genetic data, invertFREGENE, to assess the potential impact of inversions on such methods. Using data simulated by invertFREGENE, as well as real data from several sources, we test whether large inversions have a disruptive effect on widely applied population genetics methods for inferring recombination rates, for detecting selection, and for controlling for population structure in genome-wide association studies (GWAS). We find that recombination rates estimated by LDhat are biased downward at inversion loci relative to the true contemporary recombination rates at the loci but that recombination hotspots are not falsely inferred at inversion breakpoints as may have been expected. We find that the integrated haplotype score (iHS) method for detecting selection appears robust to the presence of inversions. Finally, we observe a strong bias in the genome-wide results of principal components analysis (PCA), used to control for population structure in GWAS, in the presence of even a single large inversion, confirming the necessity to thin SNPs by linkage disequilibrium at large physical distances to obtain unbiased results.     

Genetic Mapping and Genomic Selection Using Recombination Breakpoint Data

The correct models for quantitative trait locus mapping are the ones that simultaneously include all significant genetic effects. Such models are difficult to handle for high marker density. Improving statistical methods for high-dimensional data appears to have reached a plateau. Alternative approaches must be explored to break the bottleneck of genomic data analysis. The fact that all markers are located in a few chromosomes of the genome leads to linkage disequilibrium among markers. This suggests that dimension reduction can also be achieved through data manipulation. High-density markers are used to infer recombination breakpoints, which then facilitate construction of bins. The bins are treated as new synthetic markers. The number of bins is always a manageable number, on the order of a few thousand. Using the bin data of a recombinant inbred line population of rice, we demonstrated genetic mapping, using all bins in a simultaneous manner. To facilitate genomic selection, we developed a method to create user-defined (artificial) bins, in which breakpoints are allowed within bins. Using eight traits of rice, we showed that artificial bin data analysis often improves the predictability compared with natural bin data analysis. Of the eight traits, three showed high predictability, two had intermediate predictability, and two had low predictability. A binary trait with a known gene had predictability near perfect. Genetic mapping using bin data points to a new direction of genomic data analysis.     

Dynamic occupancy models for analyzing species' range dynamics across large geographic scales

Large‐scale biodiversity data are needed to predict species' responses to global change and to address basic questions in macroecology. While such data are increasingly becoming available, their analysis is challenging because of the typically large heterogeneity in spatial sampling intensity and the need to account for observation processes. Two further challenges are accounting for spatial effects that are not explained by covariates, and drawing inference on dynamics at these large spatial scales. We developed dynamic occupancy models to analyze large‐scale atlas data. In addition to occupancy, these models estimate local colonization and persistence probabilities. We accounted for spatial autocorrelation using conditional autoregressive models and autologistic models. We fitted the models to detection/nondetection data collected on a quarter‐degree grid across southern Africa during two atlas projects, using the hadeda ibis (Bostrychia hagedash) as an example. The model accurately reproduced the range expansion between the first (SABAP1: 1987–1992) and second (SABAP2: 2007–2012) Southern African Bird Atlas Project into the drier parts of interior South Africa. Grid cells occupied during SABAP1 generally remained occupied, but colonization of unoccupied grid cells was strongly dependent on the number of occupied grid cells in the neighborhood. The detection probability strongly varied across space due to variation in effort, observer identity, seasonality, and unexplained spatial effects. We present a flexible hierarchical approach for analyzing grid‐based atlas data using dynamical occupancy models. Our model is similar to a species' distribution model obtained using generalized additive models but has a number of advantages. Our model accounts for the heterogeneous sampling process, spatial correlation, and perhaps most importantly, allows us to examine dynamic aspects of species ranges.     

Trends and inequities in colorectal cancer screening participation in Ontario,Canada, 2005–2011

Background: Participation in screening tests for colorectal cancer (CRC) is generally low in Ontario, Canada. In addition, inequities in participation exist including lower participation among low-income individuals, males and individuals living in rural areas. In April 2008, Colon Cancer Check (CCC) program, the province-wide CRC screening program, was launched in Ontario. This study describes the trends and inequities in CRC screening participation three years before and three years after the CCC, and assesses the effect of the program on CRC screening participation, overall and among certain population groups. Methods: We used administrative data to identify cohorts of individuals eligible for CRC screening in fiscal years 2005–2011. We calculated the age-standardized percent of Fecal Occult Blood Test (FOBT) participation, large bowel endoscopy participation, and being ‘up-to-date’ with CRC screening tests. Results: From 2005 to 2011, FOBT participation increased from 7.6% to 14.8%, large bowel endoscopy participation from 3.4% to 5.7%, and ‘up-to-date’ with CRC screening from 27.2% to 41.3%. Before the launch of the CCC program, the quarterly increase in FOBT participation was 0.07% (p = 0.19), increased immediately after the launch (1.8%, p < 0.01), followed by a decline (?0.08%, p = 0.08), returning to its pre-program increase rate. We noted a significant decrease in FOBT participation every summer (?0.44%, p < 0.01). The CCC program had minimal effect on large bowel endoscopy participation. Before the launch of the CCC program, the quarterly increase in ‘up-to-date’ with CRC screening was 0.9% (p < 0.01), increased immediately after the launch (2.5%, p = 0.05), followed by a modest decline thereafter (?0.59%, p < 0.02). From 2005 to 2011, recent residents living in low-income neighborhoods were consistently and significantly less likely to have a FOBT and be ‘up-to-date’ with CRC screening than long-term residents living in high-income neighborhoods (2.9–4.5%; 14.7–17.3% respectively). Pre-CCC inequities in CRC participation persisted after the launch of the program. Conclusion: CRC testing was increasing in Ontario from 2005. An immediate increase in CRC testing, FOBT in particular, occurred after the launch of the CCC program, followed by a return to its pre-CCC increase rate thereafter. Future efforts are needed to improve screening participation and address inequities.     

Data gaps in anthropogenically driven local‐scale species richness change studies across the Earth's terrestrial biomes

There have been numerous attempts to synthesize the results of local‐scale biodiversity change studies, yet several geographic data gaps exist. These data gaps have hindered ecologist's ability to make strong conclusions about how local‐scale species richness is changing around the globe. Research on four of the major drivers of global change is unevenly distributed across the Earth's biomes. Here, we use a dataset of 638 anthropogenically driven species richness change studies to identify where data gaps exist across the Earth's terrestrial biomes based on land area, future change in drivers, and the impact of drivers on biodiversity, and make recommendations for where future studies should focus their efforts. Across all drivers of change, the temperate broadleaf and mixed forests and the tropical moist broadleaf forests are the best studied. The biome–driver combinations we have identified as most critical in terms of where local‐scale species richness change studies are lacking include the following: land‐use change studies in tropical and temperate coniferous forests, species invasion and nutrient addition studies in the boreal forest, and warming studies in the boreal forest and tropics. Gaining more information on the local‐scale effects of the specific human drivers of change in these biomes will allow for better predictions of how human activity impacts species richness around the globe.