2) For the changes in a time series, the expansion of the road kernel area was consistent with the extension of the sub-low-risk area in the urban center, but some differences were observed during different stages of development.
3) For the spatial position, the expanding changes in the road kernel area were consistent with the grade changes of the urban central ecological risk, primarily because both had a certain spatial correlation with the expressways.
The combined effects of all of the above parameters on the kernel area after the discharge are summarized in a correlation equation, which predicts the trends in kernel growth rate based on an estimated plasma area defined by the discharge duration, flow velocity, and gap distance.
Two metrics were used to describe grain chalkiness as previously described (Zhao et al. 2016a): percentage of grains with chalk (PGWC) and degree of endosperm chalkiness (DEC) which is the ratio of total chalky area to the total kernel area of all sampled grains.
Locations of focal birds in each group were collected by ad libitum scan sampling for 1 h and data were used to calculate core areas by kernel estimates (freeware Octave).
We characterized home ranges by identifying 50 %, 70 and 90% and 90%eas (Adaptative Kernel coreouring method) using the "Home Rareas extension of the Adaptative2 software pacKernel
Union core areas (65%% Kernel estimates) represented 26±6%6 % (range = 20 37 %, N = 7) of the union home ranges (95 % Kernel estimates) of the social units.
Home ranges were delineated using 95% kernel home ranges for point distributions, and 50% kernels to delineate core areas [25] [28].
Some of the most interesting parameters generated in the wildlife literature to describe space use include calculations of distance traveled, tortuosity of movement and core areas based on Kernel estimators.
We calculated the sizes of home ranges and core areas from fixed kernel range utilization distributions [Worton, 1989] using ad hoc smoothing [Silverman, 1986].
