Geographic Information Systems (GIS) are central to spatial evidence in Environmental Impact Assessment (EIA). In this review, GIS is used in a broad, integrative sense to refer to an ecosystem of geospatial technologies—such as remote sensing (RS) and GPS—where GIS serves as the core platform for managing, analyzing, and communicating spatial data throughout the EIA process. GIS plays a crucial role at each stage of EIA, from baseline data collection to spatial analysis, ecological sensitivity mapping, impact prediction, scenario simulation, and landscape connectivity assessment. These capabilities support alternatives analysis, risk communication, and decision-making in EIA. This paper synthesizes thematic evidence and presents case studies to illustrate the synergies between GIS, remote sensing, GeoAI, and multisource data fusion. It highlights operational workflows and key deliverables for EIA applications, including urban expansion, transport corridors, and protected-area management. We identify persistent challenges in data quality and standardization, interoperability, model uncertainty, and policy gaps. To address them, we propose a minimum geospatial dataset with clear metadata standards, interpretable GeoAI paired with formal sensitivity analysis, IoT–GIS pipelines for real-time monitoring and adaptive management, and the systematic inclusion of cumulative effects and climate scenarios. By linking GIS methods to typical decision points and reporting standards in EIA, this review clarifies where GIS adds value, how to quantify and communicate uncertainty, and how to align analytical outputs with regulatory requirements and stakeholder expectations. The study offers a practical framework and implementation checklist for standardized, transparent, and reproducible EIA processes, contributing to evidence-based ecological governance.

The formation of animal breeds usually begins with a small subsample from their ancestral population. Deleterious mutations accumulate in the population under genetic drift, inbreeding, and artificial selection during the development and maintenance of traits desired by humans. White raccoon dogs are among the most popular breeds of farmed raccoon dogs, but white raccoon dogs are more susceptible to disease and have a lower reproductive ability. However, the accumulation of deleterious mutations in this white breed is largely unknown. By analyzing and comparing whole-genome sequencing data from 20 white raccoon dogs and 38 normal raccoon dogs, we detected an increased occurrence of loss-of-function (LoF) mutations in white raccoon dogs compared with normal raccoon dogs. With the finding of a significantly higher dosage of homozygous missense mutations in the white raccoon dog genome, we detected a greater fitness cost in white raccoon dogs. Although a much higher FROH level for ROH fragments longer than 1 Mb has been reported in white raccoon dogs, we did not detect a genetic signal of genetic purging in white raccoon dogs. This study provides valuable genomic resources and new insights into the accumulation of mutation loads in farmed raccoon dogs.

The complete mitochondrial genome of Anas zonorhyncha was first reported. The length of the entire mitochondrial genome was 16,605 base pairs, including 13 protein-coding genes, 22 tRNA genes, two rRNA genes, and a D-loop region. A phylogenetic tree of A. zonorhyncha was constructed with a group of related species in the family of Anatidae, indicating a close genetic relationship between A. zonorhyncha and A. poecilorhyncha.