Fine-scale Mapping Research Articles

Integrating population dynamics into mapping human exposure to seismic hazard

Abstract. Disaster risk is not fully characterized without taking into account vulnerability and population exposure. Assessment of earthquake risk in urban areas would benefit from considering the variation of population distribution at more detailed spatial and temporal scales, and from a more explicit integration of this improved demographic data with existing seismic hazard maps. In the present work, "intelligent" dasymetric mapping is used to model population dynamics at high spatial resolution in order to benefit the analysis of spatio-temporal exposure to earthquake hazard in a metropolitan area. These night- and daytime-specific population densities are then classified and combined with seismic intensity levels to derive new spatially-explicit four-class-composite maps of human exposure. The presented approach enables a more thorough assessment of population exposure to earthquake hazard. Results show that there are significantly more people potentially at risk in the daytime period, demonstrating the shifting nature of population exposure in the daily cycle and the need to move beyond conventional residence-based demographic data sources to improve risk analyses. The proposed fine-scale maps of human exposure to seismic intensity are mainly aimed at benefiting visualization and communication of earthquake risk, but can be valuable in all phases of the disaster management process where knowledge of population densities is relevant for decision-making.

Fine scale measurement and mapping of uranium in soil solution in soil and plant-soil microcosms, with special reference to depleted uranium

Residues from use of depleted uranium (DU) munitions pose a lasting environmental impact through persistent contamination of soils. Consequently, an understanding of the factors determining the fate of DU in soil is necessary. An understudied factor is the interaction of root exudates with DU. This study describes the use of ‘Single-Cell-Sampling-and-Analysis’ (SiCSA) for the first time in soil and investigates the effects of root exudates on DU dissolution. Soil solutions from soil and plant-soil microcosms containing DU fragments were sampled and analysed using SiCSA and capillary electrophoresis/ICP-MS for organic acids and uranium. Nanolitre volumes of soil solution were sampled and analysed. Soils with DU fragments but no citrate addition showed low uranium concentrations in contrast to those with added citrate. Lupin root exudation gave concentrations up to 8 mM citrate and 4.4 mM malate in soil solution which solubilised DU fragments yielding transient solution concentrations of up to 30 mM. Root exudates solubilise DU giving high localised soil solution concentrations. This should be considered when assessing the environmental risk of DU munitions. The SiCSA method was used successfully in soil for the first time and enables investigations with high spatial and temporal resolution in the rhizosphere.

Intertidal habitat composition and regional-scale shoreline morphology along the Benguela coast

The Benguela Current Large Marine Ecosystem off southwest Africa is a regionally valued system because of its biological productivity, which supports high biomass throughout the foodweb, and a rich diversity of habitats and species. However, the region is exposed to numerous anthropogenic pressures that are likely to escalate under future economic growth. In response, the Benguela Current Commission called for a spatial biodiversity assessment (BCC-SBA) to identify conservation priorities, including potential areas for marine protected areas. The systematic conservation-planning approach to this assessment requires a fine-scale map of coastal habitats, which was not previously available for the region. Our aim was to undertake this mapping, within tight logistic and resource limitations. We used a previously derived methodology for mapping the distribution of coastal habitats from aerial imagery. The Benguela coast is approximately 5,047 km long. Half of this extent is sandy beach, a third is rocky and mixed shores, 13 % comprises lagoonal features, and the remainder (4 %) comprises estuaries and offshore islands. The distribution and extent of these coastal habitats differs substantially alongshore (i.e. with latitude), with conditions ranging north–south from hot, humid mangrove-lined lagoons, to hyper-arid coastal desert sandy beaches. Patterns in regional geology, climate and oceanography are proposed as the main drivers of spatial heterogeneity in coastal habitat types. The resulting ecological and socio-economic wealth requires proactive protection (supported through the BCC-SBA, for example), to ensure sustainable utilization of the rich natural resources, and persistence of these resources for the benefit of current and future generations.

Fine-scale linkage mapping reveals a small set of candidate genes influencing honey bee grooming behavior in response to Varroa mites.

Populations of honey bees in North America have been experiencing high annual colony mortality for 15–20 years. Many apicultural researchers believe that introduced parasites called Varroa mites (V. destructor) are the most important factor in colony deaths. One important resistance mechanism that limits mite population growth in colonies is the ability of some lines of honey bees to groom mites from their bodies. To search for genes influencing this trait, we used an Illumina Bead Station genotyping array to determine the genotypes of several hundred worker bees at over a thousand single-nucleotide polymorphisms in a family that was apparently segregating for alleles influencing this behavior. Linkage analyses provided a genetic map with 1,313 markers anchored to genome sequence. Genotypes were analyzed for association with grooming behavior, measured as the time that individual bees took to initiate grooming after mites were placed on their thoraces. Quantitative-trait-locus interval mapping identified a single chromosomal region that was significant at the chromosome-wide level (p<0.05) on chromosome 5 with a LOD score of 2.72. The 95% confidence interval for quantitative trait locus location contained only 27 genes (honey bee official gene annotation set 2) including Atlastin, Ataxin and Neurexin-1 (AmNrx1), which have potential neurodevelopmental and behavioral effects. Atlastin and Ataxin homologs are associated with neurological diseases in humans. AmNrx1 codes for a presynaptic protein with many alternatively spliced isoforms. Neurexin-1 influences the growth, maintenance and maturation of synapses in the brain, as well as the type of receptors most prominent within synapses. Neurexin-1 has also been associated with autism spectrum disorder and schizophrenia in humans, and self-grooming behavior in mice.

A validation of river routing networks for catchment modelling from small to large scales

Underpinning all hydrological simulations is an estimate of the catchment area upstream of a point of interest. Locally, the delineation of a catchment and estimation of its area is usually done using fine scale maps and local knowledge, but for large-scale hydrological modelling, particularly continental and global scale modelling, this level of detailed data analysis is not practical. For large-scale hydrological modelling, remotely sensed and hydrologically conditioned river routing networks, such as HYDRO1k and HydroSHEDS, are often used. This study evaluates the accuracy of the accumulated upstream area in each gridpoint given by the networks. This is useful for evaluating the ability of these data sets to delineate catchments of varying scale for use in hydrological models. It is shown that the higher resolution HydroSHEDS data set gives better results than the HYDRO1k data set and that accuracy decreases with decreasing basin scale. In ungauged basins, or where other local catchment area data are not available, the validation made in this study can be used to indicate the likelihood of correctly delineating catchments of different scales using these river routing networks.

Nonlinear Fitness Consequences of Variation in Expression Level of a Eukaryotic Gene

Levels of gene expression show considerable variation in eukaryotes, but no fine-scale maps have been made of the fitness consequences of such variation in controlled genetic backgrounds and environments. To address this, we assayed fitness at many levels of up- and down-regulated expression of a single essential gene, LCB2, involved in sphingolipid synthesis in budding yeast Saccharomyces cerevisiae. Reduced LCB2 expression rapidly decreases cellular fitness, yet increased expression has little effect. The wild-type expression level is therefore perched on the edge of a nonlinear fitness cliff. LCB2 is upregulated when cells are exposed to osmotic stress; consistent with this, the entire fitness curve is shifted upward to higher expression under osmotic stress, illustrating the selective force behind gene regulation. Expression levels of LCB2 are lower in wild yeast strains than in the experimental lab strain, suggesting that higher levels in the lab strain may be idiosyncratic. Reports indicate that the effect sizes of alleles contributing to variation in complex phenotypes differ among environments and genetic backgrounds; our results suggest that such differences may be explained as simple shifts in the position of nonlinear fitness curves.

Spatio-temporal patterns of pre-harvest brown rot epidemics within individual peach tree canopies

Tree canopies are architecturally complex and pose several challenges for measuring and characterizing spatial patterns of disease. Recently developed methods for fine-scale canopy mapping and three-dimensional spatial pattern analysis were applied in a 3-year study to characterize spatio-temporal development of pre-harvest brown rot of peach, caused by Monilinia fructicola, in 13 trees of different maturity classes. We observed a negative correlation between an index of disease aggregation and disease incidence in the same tree (r = −0.653, P < 0.0001), showing that trees with higher brown rot incidence had lower aggregation of affected fruit in their canopies. Significant (P ≤ 0.05) within-canopy aggregation among symptomatic fruit was most pronounced for early-maturing cultivars and/or early in the epidemic. This is consistent with the notion of a greater importance of localized, within-tree sources of inoculum at the beginning of the epidemic. Four of five trees having >10 blossom blight symptoms per tree showed a significant positive spatial association of pre-harvest fruit rot to blossom blight within the same canopy. Spatial association analyses further revealed one of two outcomes for the association of new fruit rot symptoms with previous fruit rot symptoms in the same tree, whereby the relationship was either not significant or exhibited a significant negative association. In the latter scenario, the newly diseased fruit were farther apart from previously symptomatic fruit than expected by random chance. This unexpected result could have been due to uneven fruit ripening in different sectors of the canopy, which could have affected the timing of symptom development and thus led to negative spatial associations among symptoms developing over time in a tree.

Recent Shrub Proliferation in the Mackenzie Delta Uplands and Microclimatic Implications

Local observations, repeat photos, and broad-scale remote sensing suggest that tall shrubs are becoming an increasingly dominant component of Low Arctic ecosystems. This shift has the potential to alter the surface energy balance through changes to the surface albedo, snow accumulation and melt, and ground thermal regimes. However, to date there have been few quantitative estimates of the rate of tall shrub expansion. We used soft copy stereo visualization of air photos to map fine-scale changes in tall shrub tundra and green alder density in the upland tundra north of Inuvik, NT between 1972 and 2004. We also used 2004 photos to map tall shrub tundra in areas affected by fires that occurred between 1960 and 1968. To assess the potential impact of vegetation change on microclimate, we used pyranometers to measure albedo and net solar radiation, thermistors attached to data loggers to record ground temperatures, and field surveys to record winter snow conditions in three common vegetation types. Fine-scale mapping shows that green alder stem density has increased by 68% (±24.1) since 1972. Average tall shrub tundra cover has also increased by 15% (±3.6) since 1972. Historical tundra fires had the highest proportion of tall shrub cover of all areas mapped using 2004 photos, ranging from 92 to 99%. Based on these results, we suggest that predicted increases in the size and frequency of tundra fire are likely to drive rapid shrub proliferation in the Low Arctic. Shrub-dominated sites have decreased albedo, increased net solar radiation, deeper snow pack, and elevated near-surface ground temperatures, indicating that continued increases in shrub cover will affect regional climate, hydrology, permafrost temperatures, and terrain stability.

Marker Panels for Genealogy-Based Mapping, Breed Demographics, and Inference-of-Ancestry in the Dog

Short tandem repeat polymorphisms (STRPs) are robust and informative markers for a range of genetic applications. STRPs are advantageous in experimental designs that derive power from sampling many individuals rather than many loci (e.g., pedigree-based studies, fine-scale mapping, and conservation genetics). STRPs have proven useful for vetting samples prior to costly high-density SNP analysis. Here we present validated STRPs (n = 1,012) spanning the canine genome (2.1 +/−1.4 Mb; 2.1 +/−2.1 cM). Standardized design, pre-multiplexing, M13-based dye-labeling, and selection for loci amenable to semi-automated allele-scoring minimize cost and facilitate efficient genotyping. The markers are leveraged from the canine linkage map, and thus are backed by genetic data useful for parametric multipoint analysis and assessment of empiric coverage. We demonstrate several applications with different marker subsets. The complete set provides a genome scan for linkage at ∼5 cM resolution. A subset of the markers measures molecular diversity between domestic and wild canid populations. Another subset reflects ancestry within breeds, uncovering hidden stratification and flagging genetic outliers prior to SNP genotyping. Thus, the markers described here add flexibility and cost effectiveness to several genetic applications in the dog that complement genome-wide SNP genotyping studies. Supplemental material is available for this article. Go to the publisher's online edition of Animal Biotechnology.

Fine-scale remotely-sensed cover mapping of coastal dune and salt marsh ecosystems at Cape Cod National Seashore using Random Forests

Remote-sensing based cover mapping has a long history of use in natural resource management for a wide-range of applications. In order to be effectively employed, remote-sensing based cover maps must be accurate and meet the spatial scale inherent to the phenomena of interest. In this study, we employed the Random Forests algorithm in a supervised classification approach to construct a fine-scale (i.e., 1.0meter pixel resolution) remote-sensing based cover map of coastal dune and salt marsh ecosystems at Cape Cod National Seashore, USA. We achieved high overall classification accuracies (i.e., 75.1% and 86.8% correct classification rate for the dune and salt marsh study areas, respectively) with a large proportion of the misclassified holdout validation assessment points being errors of commission between overlapping and/or ecologically similar cover classes. In addition, we were able to considerably reduce the predictor variable set by using a forward selection variable reduction method while achieving modestly higher classification accuracies compared to the global model. Our results suggest that coastal dune and salt marsh ecosystems can be mapped at a fine spatial resolution with reasonably high classification accuracy using a supervised Random Forests-based approach such as ours.

A fast expectation-maximum algorithm for fine-scale QTL mapping

The recent technology of the single-nucleotide-polymorphism (SNP) array makes it possible to genotype millions of SNP markers on genome, which in turn requires to develop fast and efficient method for fine-scale quantitative trait loci (QTL) mapping. The single-marker association (SMA) is the simplest method for fine-scale QTL mapping, but it usually shows many false-positive signals and has low QTL-detection power. Compared with SMA, the haplotype-based method of Meuwissen and Goddard who assume QTL effect to be random and estimate variance components (VC) with identity-by-descent (IBD) matrices that inferred from unknown historic population is more powerful for fine-scale QTL mapping; furthermore, their method also tends to show continuous QTL-detection profile to diminish many false-positive signals. However, as we know, the variance component estimation is usually very time consuming and difficult to converge. Thus, an extremely fast EMF (Expectation-Maximization algorithm under Fixed effect model) is proposed in this research, which assumes a biallelic QTL and uses an expectation-maximization (EM) algorithm to solve model effects. The results of simulation experiments showed that (1) EMF was computationally much faster than VC method; (2) EMF and VC performed similarly in QTL detection power and parameter estimations, and both outperformed the paired-marker analysis and SMA. However, the power of EMF would be lower than that of VC if the QTL was multiallelic.

SENSOR CORRECTION AND RADIOMETRIC CALIBRATION OF A 6-BAND MULTISPECTRAL IMAGING SENSOR FOR UAV REMOTE SENSING

Abstract. The increased availability of unmanned aerial vehicles (UAVs) has resulted in their frequent adoption for a growing range of remote sensing tasks which include precision agriculture, vegetation surveying and fine-scale topographic mapping. The development and utilisation of UAV platforms requires broad technical skills covering the three major facets of remote sensing: data acquisition, data post-processing, and image analysis. In this study, UAV image data acquired by a miniature 6-band multispectral imaging sensor was corrected and calibrated using practical image-based data post-processing techniques. Data correction techniques included dark offset subtraction to reduce sensor noise, flat-field derived per-pixel look-up-tables to correct vignetting, and implementation of the Brown- Conrady model to correct lens distortion. Radiometric calibration was conducted with an image-based empirical line model using pseudo-invariant features (PIFs). Sensor corrections and radiometric calibration improve the quality of the data, aiding quantitative analysis and generating consistency with other calibrated datasets.

Genome-wide analysis of zygotic linkage disequilibrium and its components in crossbred cattle

BackgroundLinkage disequilibrium (LD) between genes at linked or independent loci can occur at gametic and zygotic levels known asgametic LD and zygotic LD, respectively. Gametic LD is well known for its roles in fine-scale mapping of quantitative trait loci, genomic selection and evolutionary inference. The less-well studied is the zygotic LD and its components that can be also estimated directly from the unphased SNPs.ResultsThis study was set up to investigate the genome-wide extent and patterns of zygotic LD and its components in a crossbred cattle population using the genomic data from the Illumina BovineSNP50 beadchip. The animal population arose from repeated crossbreeding of multiple breeds and selection for growth and cow reproduction. The study showed that similar genomic structures in gametic and zygotic LD were observed, with zygotic LD decaying faster than gametic LD over marker distance. The trigenic and quadrigenic disequilibria were generally two- to three-fold smaller than the usual digenic disequilibria (gametic or composite LD). There was less power of testing for these high-order genic disequilibria than for the digenic disequilibria. The power estimates decreased with the marker distance between markers though the decay trend is more obvious for the digenic disequilibria than for high-order disequilibria.ConclusionsThis study is the first major genome-wide survey of all non-allelic associations between pairs of SNPs in a cattle population. Such analysis allows us to assess the relative importance of gametic LD vs. all other non-allelic genic LDs regardless of whether or not the population is in HWE. The observed predominance of digenic LD (gametic or composite LD) coupled with insignificant high-order trigenic and quadrigenic disequilibria supports the current intensive focus on the use of high-density SNP markers for genome-wide association studies and genomic selection activities in the cattle population.

Fine-scale mapping of disease susceptibility locus with Bayesian partition model

The causal relationship between genes and diseases has been investigated with the development of DNA sequence. Polymorphisms incorporated in the HapMap Project have enabled fine mapping with linkage disequilibrium (LD) and prior clustering of the haplotypes on the basis of a similarity measure has often been performed in an attempt to capture coalescent events because they can reduce the amount of computation. However an inappropriate choice of similarity measure can lead to wrong conclusions and we propose a new haplotype-based clustering algorithm for fine-scale mapping by using a Bayesian partition model. To handle phase-unknown genotypes, we propose a new algorithm based on a Metropolized Gibbs sampler and it is implemented in C++. Our simulation studies found that the proposed method improves the accuracy of the estimator for the disease susceptibility locus. We illustrated the practical implication of the new analysis method by an application to fine-scale mapping of CYP2D6 in drug metabolism.

Lidar calibration and validation for geometric-optical modeling with Landsat imagery

There is a paucity of detailed and timely forest inventory information available for Canada's large, remote northern boreal forests. The Canadian National Forest Inventory program has derived a limited set of attributes from a Landsat-based land cover product representing circa year 2000 conditions. Of the required inventory attributes, forest vertical structure (e.g., tree height) is critical for terrestrial biomass and carbon modeling and to date, is unavailable for these remote areas. In this study, we develop a large-area, fine-scale (25m) mapping solution to estimate tree height (mean, dominant, and Lorey's height) across Canada's northern forests by integrating lidar data (representing 0.27% of the study area), and Landsat imagery (representing 100% of the study area), using a geometric-optical modeling technique. First, spectral mixture analysis (SMA) was used to extract image endmembers and generate fraction images. Second, lidar data were used to calibrate the inverted geometric-optical model by adjusting the model's three key fractional inputs: sunlit crown, sunlit background, and shade fraction, based upon the SMA derived images. The heterogeneity of the study area, spanning 2.16 million ha, made it challenging to directly and accurately decompose mixed Landsat image pixels into the canopy and background fractions used for the Li–Strahler geometric-optical model inversion. As a result we developed a novel method to use the lidar plot data to facilitate the calculation of these fractions in an accurate and automated manner. The average estimation errors for mean, dominant, and Lorey's height were 4.9m, 4.1m, and 4.7m, respectively when compared to the lidar data, with the best result achieved using dominant tree height, where the average error was 3.5m for over 80% of the forested area. Using this approach of optical remotely sensed data calibrated and validated with lidar height estimates, we generate and evaluate wall-to-wall estimates of tree height that can subsequently be used as inputs for biomass and carbon modeling.

DNA Sequence-Dependent Compartmentalization and Silencing of Chromatin at the Nuclear Lamina

A large fraction of the mammalian genome is organized into inactive chromosomal domains along the nuclear lamina. The mechanism by which these lamina associated domains (LADs) are established remains to be elucidated. Using genomic repositioning assays, we show that LADs, spanning the developmentally regulated IgH and Cyp3a loci contain discrete DNA regions that associate chromatin with the nuclear lamina and repress gene activity in fibroblasts. Lamina interaction is established during mitosis and likely involves the localized recruitment of Lamin B during late anaphase. Fine-scale mapping of LADs reveals numerous lamina-associating sequences (LASs), which are enriched for a GAGA motif. This repeated motif directs lamina association and is bound by the transcriptional repressor cKrox, in a complex with HDAC3 and Lap2β. Knockdown of cKrox or HDAC3 results in dissociation of LASs/LADs from the nuclear lamina. These results reveal a mechanism that couples nuclear compartmentalization of chromatin domains with the control of gene activity.

The ANO3/MUC15 locus is associated with eczema in families ascertained through asthma

A previous genome-wide linkage scan in 295 families of the French Epidemiological Study on the Genetics and Environment of Asthma (EGEA) reported strong evidence of linkage of 11p14 to eczema. Our purpose was to conduct fine-scale mapping of the 11p14 region to identify the genetic variants associated with eczema. Association analyses were first conducted in the family sample from the French EGEA by using 2 methods: the family-based association method and logistic regression. Replication of the EGEA findings was sought in French Canadian and United Kingdom family samples, which, similarly to EGEA samples, were ascertained through asthma. We also tested for association in 2 German samples ascertained through eczema. We found significant association of eczema with 11p14 genetic variants in the vicinity of the linkage peak in EGEA (P= 10(-4) for rs1050153 by using the family-based association method, which reached the multiple testing-corrected threshold of 10(-4); P= .003 with logistic regression). Pooled analysis of the 3 asthma-ascertained samples showed strong improvement in the evidence for association (P= 6 × 10(-6) for rs293974, P= 3 × 10(-5) for rs1050153, and P= 8 × 10(-5) for rs15783). No association was observed in the eczema-ascertained samples. The significant single nucleotide polymorphisms are located within the overlapping anoctamin 3 (ANO3) and mucin 15 (MUC15) genes. Several lines of evidence suggest that MUC15 is a strong candidate for eczema. Further investigation is needed to confirm our findings and to better understand the role of the ANO3/MUC15 locus in eczema and its relationship with respect to asthma.

Gall Midges (Hessian Flies) as Plant Pathogens

Gall midges constitute an important group of plant-parasitic insects. The Hessian fly (HF; Mayetiola destructor), the most investigated gall midge, was the first insect hypothesized to have a gene-for-gene interaction with its host plant, wheat (Triticum spp.). Recent investigations support that hypothesis. The minute larval mandibles appear to act in a manner that is analogous to nematode stylets and the haustoria of filamentous plant pathogens. Putative effector proteins are encoded by hundreds of genes and expressed in the HF larval salivary gland. Cultivar-specific resistance (R) genes mediate a highly localized plant reaction that prevents the survival of avirulent HF larvae. Fine-scale mapping of HF avirulence (Avr) genes provides further evidence of effector-triggered immunity (ETI) against HF in wheat. Taken together, these discoveries suggest that the HF, and other gall midges, may be considered biotrophic, or hemibiotrophic, plant pathogens, and they demonstrate the potential that the wheat-HF interaction has in the study of insect-induced plant gall formation.

Investigation of Host Candidate Malaria-Associated Risk/Protective SNPs in a Brazilian Amazonian Population

The Brazilian Amazon is a hypo-endemic malaria region with nearly 300,000 cases each year. A variety of genetic polymorphisms, particularly in erythrocyte receptors and immune response related genes, have been described to be associated with susceptibility and resistance to malaria. In order to identify polymorphisms that might be associated with malaria clinical outcomes in a Brazilian Amazonian population, sixty-four human single nucleotide polymorphisms in 37 genes were analyzed using a Sequenom massARRAY iPLEX platform. A total of 648 individuals from two malaria endemic areas were studied, including 535 malaria cases (113 individuals with clinical mild malaria, 122 individuals with asymptomatic infection and 300 individuals with history of previous mild malaria) and 113 health controls with no history of malaria. The data revealed significant associations (p<0.003) between one SNP in the IL10 gene (rs1800896) and one SNP in the TLR4 gene (rs4986790) with reduced risk for clinical malaria, one SNP in the IRF1 gene (rs2706384) with increased risk for clinical malaria, one SNP in the LTA gene (rs909253) with protection from clinical malaria and one SNP in the TNF gene (RS1800750) associated with susceptibility to clinical malaria. Also, a new association was found between a SNP in the CTL4 gene (rs2242665), located at the major histocompatibility complex III region, and reduced risk for clinical malaria. This study represents the first association study from an Amazonian population involving a large number of host genetic polymorphisms with susceptibility or resistance to Plasmodium infection and malaria outcomes. Further studies should include a larger number of individuals, refined parameters and a fine-scale map obtained through DNA sequencing to increase the knowledge of the Amazonian population genetic diversity.

Physical mapping of a pollen modifier locus controlling self-incompatibility in apricot and synteny analysis within the Rosaceae

S-locus products (S-RNase and F-box proteins) are essential for the gametophytic self-incompatibility (GSI) specific recognition in Prunus. However, accumulated genetic evidence suggests that other S-locus unlinked factors are also required for GSI. For instance, GSI breakdown was associated with a pollen-part mutation unlinked to the S-locus in the apricot (Prunus armeniaca L.) cv. 'Canino'. Fine-mapping of this mutated modifier gene (M-locus) and the synteny analysis of the M-locus within the Rosaceae are here reported. A segregation distortion loci mapping strategy, based on a selectively genotyped population, was used to map the M-locus. In addition, a bacterial artificial chromosome (BAC) contig was constructed for this region using overlapping oligonucleotides probes, and BAC-end sequences (BES) were blasted against Rosaceae genomes to perform micro-synteny analysis. The M-locus was mapped to the distal part of chr.3 flanked by two SSR markers within an interval of 1.8cM corresponding to ~364Kb in the peach (Prunus persica L. Batsch) genome. In the integrated genetic-physical map of this region, BES were mapped against the peach scaffold_3 and BACs were anchored to the apricot map. Micro-syntenic blocks were detected in apple (Malus×domestica Borkh.) LG17/9 and strawberry (Fragaria vesca L.) FG6 chromosomes. The M-locus fine-scale mapping provides a solid basis for self-compatibility marker-assisted selection and for positional cloning of the underlying gene, a necessary goal to elucidate the pollen rejection mechanism in Prunus. In a wider context, the syntenic regions identified in peach, apple and strawberry might be useful to interpret GSI evolution in Rosaceae.