Polyploidy, a significant catalyst for speciation and evolutionary processes in both plant and animal kingdoms, has been recognized for a long time. However, the exact molecular mechanism that leads to polyploid formation, especially in vertebrates, is not fully understood. Our study aimed to elucidate this phenomenon using the zebrafish model. We successfully achieved an effective knockout of the cyclin N-terminal domain containing 1 (cntd1) using CRISPR/Cas9 technology. This resulted in impaired formation of meiotic crossovers, leading to cell-cycle arrest during meiotic metaphase and triggering apoptosis of spermatocytes in the testes. Despite these defects, the mutant (cntd1^−/−) males were still able to produce a limited amount of sperm with normal ploidy and function. Interestingly, in the mutant females, it was the ploidy not the capacity of egg production that was altered. This resulted in the production of haploid, aneuploid, and unreduced gametes. This alteration enabled us to successfully obtain triploid and tetraploid zebrafish from cntd1^−/− and cntd1^−/−/− females, respectively. Furthermore, the tetraploid-heterozygous zebrafish produced reduced-diploid gametes and yielded all-triploid or all-tetraploid offspring when crossed with wild-type (WT) or tetraploid zebrafish, respectively. Collectively, our findings provide direct evidence supporting the crucial role of meiotic crossover defects in the process of polyploidization. This is particularly evident in the generation of unreduced eggs in fish and, potentially, other vertebrate species.

Ecosystem service value (ESV) is a crucial indicator for evaluating ecosystem health, and identifying its spatial driving factors will help to provide scientific decision support for ecological protection and restoration. This study took the Liuxi River Basin in China as the research object and used the value equivalent method to estimate regional ESV. In the process of using the Geodetector model (GDM), the study area was spatially stratified by using the local bivariate spatial correlation pattern to mine the potential driving factors of ESV. The results show that: (1) From 2005 to 2018, the total value of ecosystem services in the Liuxi River Basin showed a fluctuating and increasing trend. ESV had high-value aggregation in the northeastern mountainous areas with high green space coverage and high river distance accessibility and low-value aggregation in the central and southwestern urban areas with frequent human activities. Its spatial heterogeneity and aggregation patterns were of statistical significance. (2) The spatial distribution characteristics of ESV were affected by various driving factors to varying degrees. The order of their degree of influence on ESV was per capita green area > slope > the proportion of urban and rural human settlements > river distance accessibility > population. (3) Compared to the previous study, the stratification method employing the local bivariate spatial correlation pattern more fully considers spatial autocorrelation and spatial heterogeneity. It effectively captured the spatial explanatory power of driving factors. This study can provide new ideas for capturing the driving mechanisms of ESV and insights into the sustainable development of the ecological environment in other regions with similar characteristics worldwide.

Ecosystem service value (ESV) is a crucial indicator for evaluating ecological environment health, and identifying its spatial driving factors will help to provide scientific decision support for ecological protection and restoration. This study takes the Liuxi River Basin in China as the research object and uses the value equivalent method to estimate regional ESV. In the process of using the Geodetector model (GDM), the study area is spatially stratified by using the local bivariate spatial correlation pattern to mine the potential drivers of ESV. The results show that: (1) From 2005 to 2018, the total value of ecosystem services in the Liuxi River Basin showed a fluctuating and increasing trend. ESV has high-value aggregation in the northeastern mountainous areas with high green space coverage and high river distance accessibility and low-value aggregation in the central and southwestern urban areas with frequent human activities. Its spatial heterogeneity and aggregation pattern are of statistical significance. (2) The spatial distribution characteristics of ESV are affected by various driving factors to varying degrees. The order of their degree of influence on ESV is per capita green area > slope > proportion of urban and rural human settlements > river distance accessibility > population. (3) Compared to the previous study, the stratification method employing the local bivariate spatial correlation pattern more fully considers the spatial autocorrelation and spatial heterogeneity. It effectively captures the spatial explanatory power of driving factors. This study can provide new ideas for capturing the driving mechanisms of ESV and insights into the sustainable development of the ecological environment in other regions with similar characteristics worldwide.