耦合种群动态的生境格局变化分析粒度与景观因子选择——以盐城越冬丹顶鹤及其生境的变化为例

选择盐城珍禽国家级自然保护区,以丹顶鹤越冬生境景观与种群动态这一格局-过程关系为切入点,尝试从丹顶鹤最小存活面积特征与种群动态视角探讨一下最佳粒度、景观格局变化分析的指数遴选方法。结果发现:根据丹顶鹤最小生存面积确定200m为最大转换粒度,综合景观指数随空间粒度变化的规律和粒度转换精度损失评价的结果,确定最佳分析粒度为70m。在众多景观因子中,运用Spearman秩相关分析,再利用逐步回归分析建立起景观与丹顶鹤数量指标间的联系,最终筛选出反映景观面积(CA)和空间格局(IJI,ENN_MN)的3种影响显著景观因子,其解释贡献率(累计R2)达到70.5%,且其所代表的生境景观的组成和结构信息较为客观地反映出研究区丹顶鹤种群动态分布的显示状态。与纯粹的景观指数分析相比,这种方法更能反应景观格局演变特征的特定生态学意义。

Grain size and landscape indices selection by coupling population dynamics and habitat pattern analysis: a case study of wintering red-crowned crane and its habitat in Yancheng

Landscape pattern is scale-dependent. Thus, understanding landscape structure and functioning requires multiscale information. Scaling functions are the most precise and concise methods for explicitly quantifying multiscale characteristics. If landscape indices are ecologically relevant and reflect important attributes of spatial pattern, they can functionally link the dynamics of ecological processes to landscape structure. Therefore, the selection of appropriate scale (e.g., grain size) and landscape indices are critical to landscape pattern analysis. The major objective of this study was to explore how optimal grain size and landscape indices can be selected for landscape pattern analysis, to improve our understanding and prediction of ecological processes. For this, we conducted a case study on wintering Red-crowned crane and its habitat in Yancheng. To obtain the optimal grain size for landscape pattern analysis of the wintering habitat of the Red-crowned crane, we followed two steps. The first step was scale (grain) effect analysis, to find a grain size that the test metrics could detect in case of any variation (sensitivity). In this step, the minimum survival area for the species was considered for identifying the maximum grain size for scaling. The second step was accuracy test by evaluating the loss of landscape area and patch numbers in each grain size level. The optimal grain size was obtained by integrated analysis of the results from the above two steps. In landscape indices selection, 19 landscape metrics (computed at the optimal grain size) were subjected to Spearman rank correlation analysis to assess the independence. Then, step-wise regressions were performed to evaluate the effects of the spatial attributes (landscape indices with higher independence) at three hotspots on the abundance of cranes (based on population dynamics of the past 12 years^1]). Variables with significance level above 90% were selected as the optimal landscape indices for pattern analysis. The results showed the following: (1) on the basis of the minimum survival area of the red-crowned crane, 200 m was the highest obtained grain size, and 70 m was the optimal grain size identified by integrating the results of scale (grain) effect and accuracy after assessing the grain conversion area loss. (2) Effects of landscape patterns at the hotspots were analyzed using 8 pairs of landscape indices (results of metrics for the most suitable and supplement habitat types for cranes were computed) as the independent variables. Three landscape indices (CA, IJI, ENN_MN) of two habitat types were found to be significant (R² = 70.5%). (3) The selected optimal landscape indices of the supplement habitat showed positive effect for isolation and negative effect for area size on population abundance. Our results imply that supplement habitat may provide complementary resource sites for cranes, but continued species concentration at these habitats may negatively affect crane abundance and distribution due to induced human disturbance. Our combined findings on optimal grain size and landscape indices proved to be satisfactory for landscape pattern analysis, suggesting that our approaches are reasonable. Further, compared to landscape pattern analysis by using random grain size and simple landscape metrics, the results obtained using our approaches, as shown in this case study, are ecological relevant.