2.3. Construction of the species distribution model
In this research, the Maxent model was used to predict the species suitability distribution of L. japonicus in three periods. A total of 489 distribution sample data points and 12 environmental factors were imported into Maxent 3.4.4 software for modeling. We used 75% of the distribution samples randomly chosen as training data for modeling, and 25% of the distribution samples were used as test data to evaluate the model’s ability. Bootstrap replicates were set to 10, and the distribution value was output in the logistic form. The weight of each environmental factor was evaluated by the knife-cut analysis method, and the dominant environmental factor was determined by combining the percentage contribution of each environmental factor and the replacement importance value.
The knife-cut analysis method represents the importance of the explanatory variables based on the arrangement, reveals the importance of the variables, and is a highly reliable way for assessing a model. The accuracy value of the AUC-ROC of the test data represents the model fitting degree and indicates the prediction reliability of the model (Khan et al., 2022). The evaluation result of the AUC is a value in the range of 0.5–1.0; the greater the AUC value, the more precise the prediction. A result in the range 0.5–0.6 indicates that the simulation result is unqualified; 0.6–0.7 indicates poor simulation results; 0.7–0.8 indicates the general simulation results; 0.8–0.9 indicates that the simulation results are good; 0.9–1.0 indicates the good prediction effect (Ouyang et al., 2019; Wang et al., 2018; Wang et al., 2021).
2.4 Optimization of the model
Referring to Robert Muscarella’s latest optimization method, the Checkerboard2 method was used to divide the study area into four bins. This masked geographic structure method can better adjust the model regularization level. The Maxent model regularization level consists of two parameters, the regularization multiplication value (RM) and the feature combination (FC) optimized by calling the ENMeval packet in the R language. The Maxent model provides five features: a linear feature (L), a quadratic feature (Q), a fragmented feature (H), a multiplicative feature (P), and a threshold feature (T). In this research, the default parameters of Maxent software were RM = 1.5 and FC = H; to optimize the Maxent model, the RM was set to 0.5–4; and each increase was by 0.5. There were a total of eight control frequency doublings, and six combinations with one or more features were used: L; L and Q; H; L, Q and H; L, Q, H and P; and L, Q, H, P and T. According to the permutations and combinations, 48 parameter combinations were calculated. The ENMeval data package uses the above 48 combinations of parameters to test the complexity of the model based on the delta AICc value and a 10% test miss rate, where the lower the value, the more accurate the model prediction (Phillips et al., 2017; Zhao et al., 2021).
2.5. Data processing
ArcGis10.4.1 software was used to divide and visualize the suitability of Leonurus japonicus . The suitability threshold ofLeonurus japonicus was predicted based on the Maxent model. The natural breakpoint method was used to derive the habitat suitability index of L. japonicus . The minimum level of threshold establishment was 0.47, and the distribution below this level was excluded. Therefore, the habitat suitability grade of L. japonicus was divided into unsuitable area (0–0.47), low suitable area (0.47–0.55), moderate suitable area (0.55–0.65), and most suitable area (0.65–1). We compared the differences in the suitable areas ofLeonurus japonicus in different periods to obtain the change map of the spatial distribution pattern of Leonurus japonicus under future climate change scenarios. The SDMTool data package in the R language was used to calculate the centroid position of the suitable area of L. japonicus under six different economic paths in the current and future periods, and the migration direction of the spatial distribution of the suitable area of L. japonicus was reflected by the change in the centroid position. The geosphere data package in the R language was used to calculate the centroid migration distance ofLeonurus japonicus under different economic scenarios. The ArcGis overlay tool was used to overlay the current and future suitable area distribution data layers. The tool can merge different raster data into one output to define the specified set, reclassify the new layer, and divide the suitable level to obtain the final suitable zoning map ofLeonurus japonicus . In this study, three suitable grades of low suitable area, medium suitable area, and highly suitable area were taken as the total suitable area; the values of dominant environmental variables and the suitability of different periods were extracted from 489 distribution points to analyze the relationship between environmental factors and potential distribution areas.
2.6. Niche differentiation analysis and priority reserve analysis
This study quantitatively analyzed niche differentiation in current and future periods. Based on the natural distribution points and environmental factor layers of the current period, the ecospat package for the R language was used to analyze and visualize niche differentiation, and the niche parameter D that ranges from 0 to 1 was calculated, indicating niches that are non-overlapping to fully overlapping (Warren et al., 2008; Yan et al., 2021). The software ENMTools v1.4 was used to calculate the niche breadth in the geographical and environmental space of each period as the average Levins B1 (inverse concentration) and B2 (uncertainty) values of the habitat suitability map for each period (Warren et al., 2008; Levins et al., 1968). Levins B1 and B2 values range from 0 to 1, with values close to 0 representing a narrow niche breadth and values close to 1 representing a wide niche breadth (Yuan et al., 2021).
The priority reserve is a protected area with a relatively large designated suitability index value. The main feature of this area is that the number of national key protected plant species is relatively large and less disturbed by human activities. Marxan shows wonderful performance in identifying priority protected areas, and it has been applied to system protection planning (Da Luz Fernandes et al., 2018; Carrasco et al., 2020; He et al., 2021). Based on the analysis results of the Maxent model, the main environmental factors selected were used for Marxan modeling. According to the distribution of the target species, the locations of the protected areas were determined to achieve the purpose of biodiversity conservation (Zhang et al., 2021).
3. Results and analysis
3.1. Model optimization and accuracy evaluation
Based on 489 distribution points, 12 layers of environmental variables, and the AIC information criterion, the Maxent model was used to simulate and predict the potential distribution area of L. japonicus . Under the default parameter settings of Maxent, the magnification RM = 1, the feature combination FC = LQHPT, and delta AICc = 6.99. When RM = 1.5, FC = H, and delta AICc = 0, the model was optimal, and the 10% training omission rate was 13.48% lower than that of the model under the default parameters (Table 2).
Therefore, the control frequency doubling RM = 1.5 and the feature combination FC = H were selected as the final parameters of the model. The AUC value of the simulation training under these parameters was 0.830. The standard deviation was 0.006 (Figure 2), indicating that the prediction results were accurate.
Table 2 Evaluation results of the Maxent model under different parameter settings