Fine-scale Mapping Research Articles

A novel approach to improve population mapping considering facility-based service capacity and land livability

Social sensing data, including points-of-interest (POI) and mobile position, are important data sources for population mapping. However, existing studies using POI data disregard the heterogeneity in facility size among the same type of POI and urban–rural differences. Moreover, mobile position data face biased issues. This study presents a hybrid model that considers facility-based service capacity (FSC) and land livability to map fine-scale population distributions. Based on extracting the FSC index by integrating POI, mobile phone positioning (MPP), and road network data, the district-level census population was disaggregated into 100-m grids using a random forest model. Subsequently, a regression equation was developed from the land use data to correct the estimated residuals. The results showed that the hybrid model exhibited considerably smaller errors than those of the POI density-based method and direct mapping of MPP (RMSE = 5,393.31, 7,348.91, and 7,824.76, respectively), effectively reducing population misallocation in extreme-density regions. Compared to WorldPop and LandScan datasets and a comparative model, our method reduced the RMSE by 30–60%, proving the effectiveness of integrating various social sensing and remote sensing data for improving population mapping. This study improves on an existing method as a thought-provoking step toward advancement in fine-scale population mapping research.

Fine-Scale Haplotype Mapping Reveals an Association of the FTO Gene with Osteoporosis and Fracture Risk in Postmenopausal Women.

The Fat Mass and Obesity-Associated (FTO) gene encodes a demethylase, which modulates RNA N6-methyladenosine (m6A) and plays a regulatory role in adipocyte differentiation and the pathogenesis of human obesity. To understand the potential role of FTO in osteoporosis (OP), we investigated five single nucleotide variations (SNVs) in intron 1 (rs8057044, rs8050136, rs9939609, rs62033406, and rs9930506) of the FTO gene, and a missense SNV i.e., rs3736228 (A1330V), located in exon 18 of the LRP5 gene, in a cohort of postmenopausal women (n = 188) from Central Europe. Genotyping was performed with an allele discrimination assay, while haplotypes were reconstructed in the population by PHASE 2.1. The rs9930506 was strongly associated with OP (p < 0.0035), which was supported by Bonferroni correction (p < 0.0175), and all SNVs located in the FTO gene were more strongly associated with severe OP with fragility fractures. Among seventeen haplotypes detected for the FTO gene, two haplotypes (H1 and H9) were frequent (frequency > 10%) and distributed in three main haplotypes pairs (H1/H1, H1/H9 and H9/H9, respectively). The pathogenic pair H1/H9 was associated with a leaner phenotype, increased fracture risk, and a lower bone mineral density (BMD), and carried the heterozygous GA of rs9930506, while the protective pair H9/H9 was associated with an increased obesity risk and carried AA alleles of rs9939609. Concordant associations with OP, an increased fracture risk, and a lower BMD at all skeletal sites indicate that the FTO gene is a promising candidate for OP, explaining the complex relationship with obesity and offering new perspectives for the study of the epigenetic regulation of bone metabolism.

Mapping canopy cover for municipal forestry monitoring: Using free Landsat imagery and machine learning

Trees across the urban-rural continuum are recognized for their ecological importance and ecosystem services. Municipalities often utilize spatial canopy cover data for monitoring this resource. Monitoring frameworks typically rely on fine-scale maps derived from very high spatial resolution sensors, which are high quality but expensive and unwieldy for consistent wide-area monitoring. In this paper, we explore how free Landsat imagery, supported by very high-resolution imagery interpretation and/or digital hemispherical photographs, can be used to effectively map canopy cover at a scale appropriate for municipal monitoring. We compare linear models and random forest machine learning for predicting canopy cover across a landscape (general) and within specific land covers (specialized). We create 2018 canopy cover maps and track progress towards forestry objectives in a region of southern Ontario, Canada. Random forest models using all reference data perform best for general use (R2: 0.90, RMSE: 10.1 %), separating non-canopy vegetation (e.g., agricultural fields) from tree canopy. Specialized models are useful in forest land cover patches, where hemispherical photographs relate with Landsat at a moderate strength (R2: 0.67, RMSE: 2.73 %), and in residential areas, capturing the totality of canopy cover variation (R2: 0.85, RMSE: 5.66 %). Accuracy was assessed with standard cross-validation, which is useful given limited resources. However, following best practice, an independent reference sample was also leveraged to assess the best general model (R2: 0.86, RMSE: 11.4 %), indicating that cross-validation was slightly overoptimistic. Results show that Caledon, a rural-dominant municipality within the study area, is the greenest (34 % canopy cover). The two cities (Brampton and Mississauga) have 15.9 % and 17.5 % canopy cover. Residential canopy criteria indicate “Good” performance in Caledon, “Moderate” in Mississauga, and “Low” in Brampton based on our 2018 assessment. The methods described here can provide municipalities with a low-cost approach for tree canopy monitoring across complex landscapes. Data availabilityThe data used for this paper are available at: https://zenodo.org/records/12549244. The code created for this paper are available at: https://github.com/ZZMitch/PredictTreeCC_Landsat_1972to2020.

Estimating global 0.1° scale gridded anthropogenic CO2 emissions using TROPOMI NO2 and a data-driven method

Satellite remote sensing is a promising approach for monitoring global CO2 emissions. However, existing satellite-based CO2 observations are too coarse to meet the requirements of fine-scale global mapping. We propose a novel data-driven method to estimate global anthropogenic CO2 emissions at a 0.1° scale, which integrates emissions inventories and satellite data while bypassing the inadequate accuracy of CO2 observations. Due to the co-emitted anthropogenic emissions of nitrogen oxides (NOx = NO + NO2) and CO2, high-resolution NO2 measurements from the TROPOspheric Monitoring Instrument (TROPOMI) are employed to map the global anthropogenic emissions at a global 0.1° scale. We construct the driving features from NO2 data and also incorporate gridded CO2/NOx emission ratios and NOx/NO2 conversion ratios as driving data to describe co-emissions. Both ratios are predicted using a long short-term memory (LSTM) neural network (with an R2 of 0.984 for the CO2/NOx emission ratio and an R2 of 0.980 for the NOx/NO2 conversion ratio). The data-driven model for estimating anthropogenic CO2 emissions is implemented by random forest regression (RFR) and trained using the Emissions Database for Global Atmospheric Research (EDGAR). The satellite-based anthropogenic CO2 emission dataset at a global 0.1° scale agrees well with the national CO2 emission inventories (an R2 of 0.998 with Global Carbon Budget (GCB) and an R2 of 0.996 with EDGAR) and consistent with city-level emission estimates from Carbon Monitor Cities (CMC) with the R2 of 0.824. This data-driven method based on satellite-observed NO2 provides a new perspective for fine-resolution anthropogenic CO2 emissions estimation.

Bayesian Deep Operator Learning for Homogenized to Fine-Scale Maps for Multiscale PDE

Bayesian Deep Operator Learning for Homogenized to Fine-Scale Maps for Multiscale PDE

An atlas of transcribed enhancers across helper T cell diversity for decoding human diseases.

Transcribed enhancer maps can reveal nuclear interactions underpinning each cell type and connect specific cell types to diseases. Using a 5' single-cell RNA sequencing approach, we defined transcription start sites of enhancer RNAs and other classes of coding and noncoding RNAs in human CD4+ T cells, revealing cellular heterogeneity and differentiation trajectories. Integration of these datasets with single-cell chromatin profiles showed that active enhancers with bidirectional RNA transcription are highly cell type-specific and that disease heritability is strongly enriched in these enhancers. The resulting cell type-resolved multimodal atlas of bidirectionally transcribed enhancers, which we linked with promoters using fine-scale chromatin contact maps, enabled us to systematically interpret genetic variants associated with a range of immune-mediated diseases.

Towards standardised large-scale monitoring of peatland habitats through fine-scale drone-derived vegetation mapping

Northern peatlands provide key climate regulating services by sequestering and storing atmospheric carbon as peat, but also provide habitat for specialised plant and animal species. Ecosystem-wide monitoring of the functions associated with these services is necessary to better inform policy and management and facilitate carbon financing schemes. Mapping peatland vegetation as an ecological indicator of their functions using drones has proven promising herein, yet the absence of standardised methods limits implementation.We developed drone-derived vegetation maps and compared them with two types of field-based ground-reference maps: 1) habitat distribution (ecotopes) and 2) habitat quality (status) for five Irish peatlands (35–124 ha). We also explored spatial transferability of our mapping approach across peatlands. First, orthomosaics and digital terrain models (DTM) were derived from drone imagery, after which plant functional types and microforms were separately classified. Second, ecotope and status maps were classified using the proportions of the fine-scale vegetation and the range in DTM within 20x20m grid cells as input predictor variables.Drone-derived ecotope and status maps captured the overall vegetation zonation of the conventional maps well, with the least mismatches for the peatlands displaying clear concentric zonation. Classification performance ranged between 88% for status and 72% for ecotope maps between peatlands. The lower classification performance for ecotopes was partly an artifact from gridding the conventional polygon-shaped ground-reference maps. Further classification errors resulted from artificial landscape features, variable plant phenology, and inaccuracies in the detrended DTM data at peatland-scale. Spatial transferability of the mapping approach was limited. Particularly, using pooled ground-reference data for classification decreased model performance with 5% for status and 10% for ecotope maps, largely because microform and plant functional type proportions associated with peatland habitat classes in the conventional maps varied between peatlands.Our findings highlight that both fine-scale vegetation patterns and habitats can be classified consistently on the peatland-scale using drone-derived imagery products and machine learning classifications. Yet, status is currently mapped notably more accurately than ecotopes. Also, peatland-specific ground-reference data is required until the conventional vegetation classes are more standardised across a wider variety of peatlands and peatland types.

Fine-scale maps of malaria incidence to inform risk stratification in Laos

BackgroundMalaria risk maps are crucial for controlling and eliminating malaria by identifying areas of varying transmission risk. In the Greater Mekong Subregion, these maps guide interventions and resource allocation. This article focuses on analysing changes in malaria transmission and developing fine-scale risk maps using five years of routine surveillance data in Laos (2017–2021). The study employed data from 1160 geolocated health facilities in Laos, along with high-resolution environmental data.MethodsA Bayesian geostatistical framework incorporating population data and treatment-seeking propensity was developed. The models incorporated static and dynamic factors and accounted for spatial heterogeneity.ResultsResults showed a significant decline in malaria cases in Laos over the five-year period and a shift in transmission patterns. While the north became malaria-free, the south experienced ongoing transmission with sporadic outbreaks.ConclusionThe risk maps provided insights into changing transmission patterns and supported risk stratification. These risk maps are valuable tools for malaria control in Laos, aiding resource allocation, identifying intervention gaps, and raising public awareness. The study enhances understanding of malaria transmission dynamics and facilitates evidence-based decision-making for targeted interventions in high-risk areas.

A fine-scale genetic map of the Japanese population.

Genetic maps are fundamental resources for linkage and association studies. A fine-scale genetic map can be constructed by inferring historical recombination events from the genome-wide structure of linkage disequilibrium-a non-random association of alleles among loci-by using population-scale sequencing data. We constructed a fine-scale genetic map and identified recombination hotspots from 10 092 551 bi-allelic high-quality autosomal markers segregating among 150 unrelated Japanese individuals whose genotypes were determined by high-coverage (30×) whole-genome sequencing, and the genotype quality was carefully controlled by using their parents' and offspring's genotypes. The pedigree information was also utilized for haplotype phasing. The resulting genome-wide recombination rate profiles were concordant with those of the worldwide population on a broad scale, and the resolution was much improved. We identified 9487 recombination hotspots and confirmed the enrichment of previously known motifs in the hotspots. Moreover, we demonstrated that the Japanese genetic map improved the haplotype phasing and genotype imputation accuracy for the Japanese population. The construction of a population-specific genetic map will help make genetics research more accurate.

Combining environmental DNA and remote sensing for efficient, fine-scale mapping of arthropod biodiversity.

Arthropods contribute importantly to ecosystem functioning but remain understudied. This undermines the validity of conservation decisions. Modern methods are now making arthropods easier to study, since arthropods can be mass-trapped, mass-identified, and semi-mass-quantified into 'many-row (observation), many-column (species)' datasets, with homogeneous error, high resolution, and copious environmental-covariate information. These 'novel community datasets' let us efficiently generate information on arthropod species distributions, conservation values, uncertainty, and the magnitude and direction of human impacts. We use a DNA-based method (barcode mapping) to produce an arthropod-community dataset from 121 Malaise-trap samples, and combine it with 29 remote-imagery layers using a deep neural net in a joint species distribution model. With this approach, we generate distribution maps for 76 arthropod species across a 225 km2 temperate-zone forested landscape. We combine the maps to visualize the fine-scale spatial distributions of species richness, community composition, and site irreplaceability. Old-growth forests show distinct community composition and higher species richness, and stream courses have the highest site-irreplaceability values. With this 'sideways biodiversity modelling' method, we demonstrate the feasibility of biodiversity mapping at sufficient spatial resolution to inform local management choices, while also being efficient enough to scale up to thousands of square kilometres. This article is part of the theme issue 'Towards a toolkit for global insect biodiversity monitoring'.

Abstract LB147: Fine-scale mapping of chromosome 9q22.33 identifies candidate causal variant in ovarian cancer

Abstract Ovarian cancer is a complex polygenic disease in which genetic factors play a significant role in disease etiology. Genome-wide association study(GWAS) identified a novel variant on chromosome 9q22.33as a susceptibility locus of epithelial ovarian cancer (EOC) in Han Chinese population. However, the underline mechanism of this region was still unknown. In this study, we conducted a fine-mapping analysis of 130kb region including 1,039 variants in 200 health women. Ten variants were selected to evaluate the association with EOC risk in 1,099 EOC cases and 1,591 controls. We identified two variants were significantly associated with ovarian cancer risk (rs7027650, P=1.91 × 10−7; rs1889268, P=3.71 × 10−2). Expression quantitative trait locus (eQTL) analysis found thatrs7027650was significantly correlated with COL15A1 gene expression (P=0.009). Luciferase reporter gene assay confirmed that rs7027650 could interact with the promoter region of COL15A1 to reduce its activity. Electrophoretic Mobility ShiftAssay (EMSA) showed theallele-specific binding capacity of rs7027650. These findings revealed that rs7027650 may be a potential causal variant at 9q22.33 region and may regulate the expression level of COL15A1. This study provided a clue for the molecular mechanism of potential causal variantaffecting the risk of ovarian cancer. Citation Format: an Li, Tongyu Xing, Yanrui Zhao, Kexin Chen, Hong Zheng. Fine-scale mapping of chromosome 9q22.33 identifies candidate causal variant in ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB147.

Identifying the population susceptible to rubella in Japan, 2020: Fine-scale risk mapping

BackgroundRubella remains a public health challenge in Japan, impeding the attainment of herd immunity. Despite vaccination efforts since 1976, persistent outbreaks reveal a susceptibility gap in male adults born before 1995. Seroepidemiological surveys are pivotal in evaluating population immunity and identifying at-risk groups. MethodsThis study aims to pinpoint high-risk areas for potential rubella outbreaks in Japan by merging seroepidemiological data from 2020 with population census information. Various data sources, including spatial demographic data, reported rubella and congenital rubella syndrome (CRS) cases, and traveler lodging statistics, were employed. Geospatial information for Japan's 230,300 small geographic areas was analyzed, and HI (hemagglutination inhibition) titers were examined by age and sex. Seroconversion was defined as an HI titer ≥ 1:32 or 1:16, indicating protective immunity. Geospatial maps illustrated the distribution of susceptible individuals per square kilometer, emphasizing high-risk urban areas like Tokyo and Osaka. Demographic shifts in the working-age population were assessed. ResultsSusceptible individuals cluster in densely populated urban centers, persisting despite demographic changes. The study highlights areas at risk of increased susceptibility, particularly with an HI titer cut-off of 1:16. Foreign travelers pose potential rubella importation risks as travel volume to Japan rises. To prevent epidemics and congenital rubella syndrome burden, achieving and sustaining herd immunity in high-risk areas is crucial. ConclusionsThis study offers a comprehensive assessment of vulnerability in densely populated Japanese regions. Integrating population statistics with seroepidemiological data enhances our understanding of population immunity, guiding resource allocation for supplementary vaccination planning. To avert rubella epidemics, high-risk locations must bolster indirect protection through herd immunity, ultimately preventing congenital rubella syndrome.

Multispectral Light Detection and Ranging Technology and Applications: A Review.

Light Detection and Ranging (LiDAR) is a well-established active technology for the direct acquisition of 3D data. In recent years, the geometric information collected by LiDAR sensors has been widely combined with optical images to provide supplementary spectral information to achieve more precise results in diverse remote sensing applications. The emergence of active Multispectral LiDAR (MSL) systems, which operate on different wavelengths, has recently been revolutionizing the simultaneous acquisition of height and intensity information. So far, MSL technology has been successfully applied for fine-scale mapping in various domains. However, a comprehensive review of this modern technology is currently lacking. Hence, this study presents an exhaustive overview of the current state-of-the-art in MSL systems by reviewing the latest technologies for MSL data acquisition. Moreover, the paper reports an in-depth analysis of the diverse applications of MSL, spanning across fields of "ecology and forestry", "objects and Land Use Land Cover (LULC) classification", "change detection", "bathymetry", "topographic mapping", "archaeology and geology", and "navigation". Our systematic review uncovers the potentials, opportunities, and challenges of the recently emerged MSL systems, which integrate spatial-spectral data and unlock the capability for precise multi-dimensional (nD) mapping using only a single-data source.

Informing Urban Flood Risk Adaptation by Integrating Human Mobility Big Data During Heavy Precipitation.

Understanding the impact of heavy precipitation on human mobility is critical for finer-scale urban flood risk assessment and achieving sustainable development goals #11 to build resilient and safe cities. Using ∼2.6 million mobile phone signal data collected during the summer of 2018 in Jiangsu, China, this study proposes a novel framework to assess human mobility changes during rainfall events at a high spatial granularity (500 m grid cell). The fine-scale mobility map identifies spatial hotspots with abnormal clustering or reduced human activities. When aggregating to the prefecture-city level, results show that human mobility changes range between -3.6 and 8.9%, revealing varied intracity movement across cities. Piecewise structural equation modeling analysis further suggests that city size, transport system, and crowding level directly affect mobility responses, whereas economic conditions influence mobility through multiple indirect pathways. When overlaying a historical urban flood map, we find such human mobility changes help 23 cities reduce 2.6% flood risks covering 0.45 million people but increase a mean of 1.64% flood risks in 12 cities covering 0.21 million people. The findings help deepen our understanding of the mobility pattern of urban dwellers after heavy precipitation events and foster urban adaptation by supporting more efficient small-scale hazard management.

Fine-scale mapping of chromosome 9q22.33 identifies candidate causal variant in ovarian cancer.

Ovarian cancer is a complex polygenic disease in which genetic factors play a significant role in disease etiology. A genome-wide association study (GWAS) identified a novel variant on chromosome 9q22.33 as a susceptibility locus for epithelial ovarian cancer (EOC) in the Han Chinese population. However, the underlying mechanism of this genomic region remained unknown. In this study, we conducted a fine-mapping analysis of 130kb regions, including 1,039 variants in 200 healthy women. Ten variants were selected to evaluate the association with EOC risk in 1,099 EOC cases and 1,591 controls. We identified two variants that were significantly associated with ovarian cancer risk (rs7027650, P = 1.91 × 10-7; rs1889268, P = 3.71 × 10-2). Expression quantitative trait locus (eQTL) analysis found that rs7027650 was significantly correlated with COL15A1 gene expression (P = 0.009). The Luciferase reporter gene assay confirmed that rs7027650 could interact with the promoter region of COL15A1, reducing its activity. An electrophoretic mobility shift assay (EMSA) showed the allele-specific binding capacity of rs7027650. These findings revealed that rs7027650 could be a potential causal variant at 9q22.33 region and may regulate the expression level of COL15A1. This study offered insight into the molecular mechanism behind a potential causal variant that affects the risk of ovarian cancer.

Fine-Scale Map Reveals Highly Variable Recombination Rates Associated with Genomic Features in the Eurasian Blackcap.

Recombination is responsible for breaking up haplotypes, influencing genetic variability, and the efficacy of selection. Bird genomes lack the protein PR domain-containing protein 9, a key determinant of recombination dynamics in most metazoans. Historical recombination maps in birds show an apparent stasis in positioning recombination events. This highly conserved recombination pattern over long timescales may constrain the evolution of recombination in birds. At the same time, extensive variation in recombination rate is observed across the genome and between different species of birds. Here, we characterize the fine-scale historical recombination map of an iconic migratory songbird, the Eurasian blackcap (Sylvia atricapilla), using a linkage disequilibrium-based approach that accounts for population demography. Our results reveal variable recombination rates among and within chromosomes, which associate positively with nucleotide diversity and GC content and negatively with chromosome size. Recombination rates increased significantly at regulatory regions but not necessarily at gene bodies. CpG islands are associated strongly with recombination rates, though their specific position and local DNA methylation patterns likely influence this relationship. The association with retrotransposons varied according to specific family and location. Our results also provide evidence of heterogeneous intrachromosomal conservation of recombination maps between the blackcap and its closest sister taxon, the garden warbler. These findings highlight the considerable variability of recombination rates at different scales and the role of specific genomic features in shaping this variation. This study opens the possibility of further investigating the impact of recombination on specific population-genomic features.

Using Multisource High-Resolution Remote Sensing Data (2 m) with a Habitat–Tide–Semantic Segmentation Approach for Mangrove Mapping

Mangrove wetlands are hotspots of global biodiversity and blue carbon reserves in coastal wetlands, with unique ecological functions and significant socioeconomic value. Annual fine-scale monitoring of mangroves is crucial for evaluating national conservation programs and implementing sustainable mangrove management strategies. However, annual fine-scale mapping of mangroves over large areas using remote sensing remains a challenge due to spectral similarities with coastal vegetation, tidal periodic fluctuations, and the need for consistent and dependable samples across different years. In previous research, there has been a lack of strategies that simultaneously consider spatial, temporal, and methodological aspects of mangrove extraction. Therefore, based on an approach that considers mangrove habitat, tides, and a semantic segmentation approach, we propose a method for fine-scale mangrove mapping suitable for long time-series data. This is an optimized hybrid model that integrates spatial, temporal, and methodological considerations. The model uses five sensors (GF-1, GF-2, GF-6, ZY-301, ZY-302) to combine deep learning U-Net models with mangrove habitat information and algorithms during low-tide periods. This method produces a mangrove map with a spatial resolution of 2 m. We applied this algorithm to three typical mangrove regions in the Beibu Gulf of Guangxi Province. The results showed the following: (1) The model scored above 0.9 in terms of its F1-score in all three study areas at the time of training, with an average accuracy of 92.54% for mangrove extraction. (2) The average overall accuracy (OA) for the extraction of mangrove distribution in three typical areas in the Beibu Gulf was 93.29%. When comparing the validation of different regions and years, the overall OA accuracy exceeded 89.84% and the Kappa coefficient exceeded 0.74. (3) The model results are reliable for extracting sparse and slow-growing young mangroves and narrow mangrove belts along roadsides. In some areas where tidal flooding occurs, the existing dataset underestimates mangrove extraction to a certain extent. The fine-scale mangrove extraction method provides a foundation for the implementation of fine-scale management of mangrove ecosystems, support for species diversity conservation, blue carbon recovery, and sustainable development goals related to coastal development.

Fine-scale collision risk mapping and validation with long-term mortality data reveal current and future wind energy development impact on sensitive species

The ongoing demand for renewable energy has boosted the development of wind farms worldwide. Given the impact these facilities have on flying species, a spatially explicit assessment of collision risk in vulnerable species is needed to guide management actions and prioritise areas for installing these infrastructures. We used GPS-tracking data of 127 adult and 50 juvenile griffon vultures in peninsular Spain gathered between 2014 and 2022 to evaluate factors influencing vulnerability and exposure and predict collision risk. We validate the observed collision risk with recorded long-term mortality data (1999–2022) at regional and wind farm scales and evaluate the estimated impact of current and future turbine facilities. Our results showed that overall food availability increases vulnerability and exposure, whilst distance to nesting areas and the presence of conspecifics decreased both vulnerability and exposure in adults and juveniles, respectively. Our maps revealed that 19% and 10% of the Spanish peninsular area had a high collision risk for adults and juveniles, respectively. Importantly, the number of turbine casualties was positively related to collision risk at the regional and wind farm scale and ∼ 18 of the breeding population lies within high collision risk areas.Moreover, the areas with the highest risk of collision also have the highest number of turbines and largely overlap with areas suitable for developing new wind farms. Our study highlights the need to reduce collision risk mapping uncertainties by validating model outputs with actual mortality data. Moreover, it emphasises the urgent need for spatial planning of wind energy development, searching for safer alternatives for biodiversity. This approach undoubtedly serves as a tool to define “not go to” areas for installing new turbines for one of the most sensitive species.

Downregulation of a transcription factor associated with resistance to Bt toxin Vip3Aa in the invasive fall armyworm.

Transgenic crops producing insecticidal proteins from Bacillus thuringiensis (Bt) have revolutionized control of some major pests. However, more than 25 cases of field-evolved practical resistance have reduced the efficacy of transgenic crops producing crystalline (Cry) Bt proteins, spurring adoption of alternatives including crops producing the Bt vegetative insecticidal protein Vip3Aa. Although practical resistance to Vip3Aa has not been reported yet, better understanding of the genetic basis of resistance to Vip3Aa is urgently needed to proactively monitor, delay, and counter pest resistance. This is especially important for fall armyworm (Spodoptera frugiperda), which has evolved practical resistance to Cry proteins and is one of the world's most damaging pests. Here, we report the identification of an association between downregulation of the transcription factor gene SfMyb and resistance to Vip3Aa in S. frugiperda. Results from a genome-wide association study, fine-scale mapping, and RNA-Seq identified this gene as a compelling candidate for contributing to the 206-fold resistance to Vip3Aa in a laboratory-selected strain. Experimental reduction of SfMyb expression in a susceptible strain using RNA interference (RNAi) or CRISPR/Cas9 gene editing decreased susceptibility to Vip3Aa, confirming that reduced expression of this gene can cause resistance to Vip3Aa. Relative to the wild-type promoter for SfMyb, the promoter in the resistant strain has deletions and lower activity. Data from yeast one-hybrid assays, genomics, RNA-Seq, RNAi, and proteomics identified genes that are strong candidates for mediating the effects of SfMyb on Vip3Aa resistance. The results reported here may facilitate progress in understanding and managing pest resistance to Vip3Aa.

Drivers of habitat availability for terrestrial mammals: Unravelling the role of livestock, land conversion and intrinsic traits in the past 50 years.

The global decline of terrestrial species is largely due to the degradation, loss and fragmentation of their habitats. The conversion of natural ecosystems for cropland, rangeland, forest products and human infrastructure are the primary causes of habitat deterioration. Due to the paucity of data on the past distribution of species and the scarcity of fine-scale habitat conversion maps, however, accurate assessment of the recent effects of habitat degradation, loss and fragmentation on the range of mammals has been near impossible. We aim to assess the proportions of available habitat within the lost and retained parts of mammals' distribution ranges, and to identify the drivers of habitat availability. We produced distribution maps for 475 terrestrial mammals for the range they occupied 50 years ago and compared them to current range maps. We then calculated the differences in the percentage of 'area of habitat' (habitat available to a species within its range) between the lost and retained range areas. Finally, we ran generalized linear mixed models to identify which variables were more influential in determining habitat availability in the lost and retained parts of the distribution ranges. We found that 59% of species had a lower proportion of available habitat in the lost range compared to the retained range, thus hypothesizing that habitat loss could have contributed to range declines. The most important factors negatively affecting habitat availability were the conversion of land to rangeland and high density of livestock. Significant intrinsic traits were those related to reproductive timing and output, habitat breadth and medium body size. Our findings emphasize the importance of implementing conservation strategies to mitigate the impacts caused by human activities on the habitats of mammals, and offer evidence indicating which species have the potential to reoccupy portions of their former range if other threats cease to occur.