Abstract: Climate is the most determinant one among factors shaping plant distribution. In this study, we explored the effects of climate factors on the distribution pattern of six Ribes species in Heilongjiang Province, China. Based on the latitude and longitude information of geographic distribution of six Ribes species in Heilongjiang Province, climatic data of the distribution points were extracted from WorldClim database by ArcGIS technology. The kernel density analysis, MaxEnt model, statistical analysis, linear regression equation, redundancy analysis and Monte Carlo test were used to quantify the contribution of climatic factors to their geographic distribution. The results showed that: (1) The six species were mainly distributed in the northwest and southeast regions of Heilongjiang Province, especially in the northwestern part, with obvious overlapping distribution. Natural habitats of the six species were characterized by dry and cold climatic conditions. The spatial distribution predicted by MaxEnt model was evidenced by field investigation. (2) Longitudinal distributions of the six species were mainly driven by the coefficient of variation of precipitation, isothermality, annual precipitation, aridity index, precipitation of the driest month. The latitudinal distributions, however, were mainly determined by mean temperature in the coldest quarter and precipitation in the wettest quarter. (3) Climate factors accounted for 100% of the cumulative variation of all data in the two axes. Results of Monte Carlo test further revealed the first three climatic factors with the highest explanatory powers for the distribution differences of the six Ribes species along latitude and longitude were mean temperature of the coldest quarter (85.7%), annual precipitation (54.2%), and maximum temperature of the warmest month (42.8%), indicating that these three climatic factors dominated the geographical distribution pattern of the Ribes species in Heilongjiang Province.

Key words: Ribes, climatic factor, geographical distribution pattern, redundancy analysis.