The Species Distribution Model (SDM) provides a crucial foundation for the conservation of the Yangtze finless porpoise (YFP), a critically endangered freshwater cetacean endemic to China. In this study, we conducted population and habitat surveys, and employed the Random Forest algorithm (RF) to construct SDMs. We found that the habitat preference of YFP shows complex seasonality. Cyanobacteria and total phosphates have been identified as the predominant factors influencing the YFP distributions by affecting prey resources. We emphasize that ascertaining the presence and pseudo-absence points of YFP, in conjunction with the selection of key factors, constitutes the foundational element in the construction of SDMs. We suggest that the incorporation of techniques such as environmental DNA could expand the range of environmental factors, particularly with regard to the distribution of prey resources at the genus or species level. This study provides guidance for the SDMs of YFP and demonstrates the potential of machine learning algorithms in constructing SDMs for the endangered aquatic species.

The Yangtze finless porpoise (Neophocaena asiaeorientalis asiaeorientalis; YFP) is the only freshwater cetacean species that remains in the Yangtze River, China. Poyang Lake is connected to the main stream of the Yangtze River, and the number of YFPs in Poyang Lake constitutes approximately half of the total species population. To implement effective conservation measures and formulate scientific genetic management strategies for the YFPs in Poyang Lake, we conducted population genetic analyses on 125 blood samples from the Poyang population and 46 tissue samples from the Anqing population, utilizing mitochondrial DNA D-loop and microsatellite loci. The genetic diversity analysis revealed two haplotypes in the Poyang population, with mitochondrial genetic diversity indices of Hd = 0.481 ± 0.020 and Pi = 0.00078 ± 0.00030. Microsatellite markers further demonstrated indices of Ho = 0.610 and He = 0.655. The genetic differentiation analysis indicated that the two populations exhibited moderate genetic differentiation (0.05 < Fst < 0.15). Upon excluding the dead samples from the Anqing population, the genetic differentiation between the two populations increased and the gene flow diminished. This indicated that certain dead samples from the Anqing population might have originated from Poyang Lake or had a background of Poyang Lake–Yangtze River migration and gene exchange. This finding was further corroborated by STRUCTURE analysis, which revealed genetic admixture between the two populations. We assessed the current genetic diversity of the Poyang population and its genetic differentiation from the Anqing population. This study provides fundamental data for formulating a conservation program for YFPs in Poyang Lake.

The Yangtze finless porpoise (Neophocaena asiaeorientalis asiaeorientalis) is the only extant freshwater cetacean species inhabiting the Yangtze River in China. Facing significant threats from habitat degradation and human activities, this critically endangered species requires urgent conservation efforts. A thorough understanding of its genetic diversity is fundamental for informing effective conservation strategies. To address the need for stable and reliable molecular markers, this study aimed to develop and validate a set of highly polymorphic single nucleotide polymorphism (SNP) markers. Candidate SNPs were initially identified based on chromosome-level genome and whole-genome resequencing data, yielding a total of 1070 candidate loci. Following the principle of even distribution across chromosomes, 50 SNPs were randomly selected for Sanger sequencing validation, from which 35 polymorphic SNPs were preliminarily confirmed. These 35 SNPs were subsequently genotyped using the Kompetitive allele-specific PCR (KASP) assay, which successfully validated 19 highly polymorphic markers. Genetic diversity analysis using these SNPs successfully captured moderate-to-high polymorphism in the Poyang Lake population, demonstrating the capability of these markers in detecting genetic variation. The SNP markers developed in this study show promising applicability for genetic diversity assessment in the Yangtze finless porpoise. Further validation across broader geographic and demographic samples will be essential to confirm their potential efficiency for detecting spatial heterogeneity and informing region-wide management strategies.

The Yangtze finless porpoise (Neophocaena asiaeorientalis asiaeorientalis, YFP) is the only freshwater cetacean species currently found in China’s Yangtze River. To accurately evaluate its genetic diversity and provide reliable molecular markers for population genetic studies, this study developed a highly efficient and reproducible method for identifying polymorphic microsatellite loci using whole-genome sequencing data. Using this method, we identified and validated a set of highly polymorphic microsatellite markers, which were then used to analyze the genetic diversity of the YFP populations in Poyang Lake to evaluate their effectiveness. Our results demonstrated that the screening pipeline successfully identified 220 tetranucleotide repeat microsatellite loci. Based on the principle of uniform chromosomal distribution, 190 loci were randomly selected for experimental validation, of which 19 exhibited stable amplification, high polymorphism, and a low genotyping error rate. Genetic diversity analysis based on these markers revealed significant genetic variation among YFP populations in Poyang Lake, confirming the effectiveness of the developed markers. The polymorphic microsatellite molecular marker system developed in this study demonstrates high reliability and applicability for assessing YFP genetic diversity. This system provides a critical technical foundation for future research in conservation genetics, genetic resource preservation, and the development of genetic management strategies for the species.

Most unoccupied aerial systems (UAS) research in cetacean monitoring has shown that drones can acquire high-resolution imagery, but the conditions under which such imagery becomes auditable ecological evidence remain less clearly defined. A structured narrative synthesis (evidence map) was conducted using 242 Web of Science Core Collection records retained from a final export dated 23 June 2026, with search transparency supported by index specification, sentinel-paper recall checking, and predefined evidence and validation maturity coding. Five application domains were identified: morphometrics and health assessment (79 records, 32.6%), behavioral monitoring (76, 31.4%), individual identification (34, 14.0%), abundance and distribution monitoring (33, 13.6%), and multimodal or operational applications (20, 8.3%). Across these domains, the field is shifting from opportunistic visual documentation toward calibrated photogrammetry, computer-vision-assisted detection and re-identification, behavior quantification, and targeted health or molecular sampling. However, ecological inference remains constrained by availability bias, perception bias, group-size error, measurement uncertainty, algorithmic-transfer bias, and disturbance-induced bias. UAS monitoring is therefore evaluated as an observation-to-inference workflow linking platform design, sensor geometry, image preprocessing, annotation, model validation, and uncertainty propagation to management-relevant cetacean evidence.

Morphometric information provides quantitative descriptors of cetacean size and shape and may support future assessments of individual condition and population status when combined with appropriate biological calibration. However, conventional contact-based measurements are difficult to apply to free-ranging Yangtze finless porpoises (Neophocaena asiaeorientalis). UAV imagery offers a non-contact means of acquiring porpoise morphometric data, but automated workflows for converting UAV observations into reliable body-surface measurements remain limited. To address this gap, an oriented detection-guided SAM2 morphometric workflow, termed ODG-SAM2-Morph, was developed to automatically extract body-surface morphometric parameters from UAV imagery. The workflow integrates YOLO26-OBB for oriented target localization, SAM2 for prompt-guided body-surface segmentation, and differentiated morphometric extraction strategies for complete-body and partial-body samples. Guided by oriented detections, the small SAM2 model with the R-Box + 1FG prompt generated body-surface masks with mean IoU, mean Dice, Precision, and Recall values of 0.824, 0.901, 0.883, and 0.932, respectively. For complete-body samples, automatically extracted body-length and body-width parameters showed preliminary agreement with manual measurements. For 114 partial-body samples, the automatically extracted maximum visible body width agreed reasonably well with manual measurements, with a MAE of 3.32 cm and a MAPE of 11.93%. Continuous-sequence analysis further showed that the representativeness of maximum visible body width depended on trunk exposure, contour clarity, body posture, and inter-frame stability. By converting UAV observations into quantitative image-derived morphometric measurements, ODG-SAM2-Morph provides a practical basis for non-contact morphometric monitoring of Yangtze finless porpoises under natural survey conditions.