Understanding coupled coordination relationships between social and ecological functions of urban green spaces

Figures & data

Figure 1. Spatial distribution of green space in Wuhan.

Figure 2. Coupling coordination analysis framework for spatial accessibility and ecological connectivity of UGSs.

Table 1. Ecological characteristics of target species.

Species	Diet	Migrant/Resident	Average weight (g)	Median dispersal distance (km)
Brown-headed crow finch	Omnivorous	Resident	9.25	0.69
White-headed bulbul	Omnivorous	Resident	34.45	1.57
Grey magpie	Omnivorous	Resident	86.00	2.79
black thrush	Omnivorous	Resident	108.50	3.23
bead-necked turtle dove	Omnivorous	Resident	176.00	4.39

Table 2. Functional connectivity index and their expressions.

	Connectivity indexes	Equation	Description
Landscape scale	Probability of Connectivity (PC)	$PC=\frac{{\sum }_{i=1}^n{\sum }_{j=1}^n{a}_i\times a_j\times p_{ij}^{\ast }}{{A_L}^2}$	PC describes the probability of connection between green patches (Saura and Pascual-Hortal 2007). PC values range from 0 to 1, and high values indicate a great overall connection level of UGSs networks.
Individual scale	The variation of the Probability of Connectivity (dPC)	$dPC=\frac{PC-P{C}_{remove}}{PC}\times 100$	PCremove represents the PC value after removing an individual UGS patch from the ecological network, and dPC represents the contribution of the removed patch to maintaining network connectivity.
	Betweenness Centrality (BC)	$BC=\sum _i\sum _j\frac{g_{ij\left(k\right)}}{g_{ij}}$	BC denotes the importance of a green space patch to facilitate species dispersal as a mediator (De Montis et al. 2016).

Note: $A_L$ denotes the total area of the landscape; $n$ indicates the total number of green patches in the landscape; $a_i$ and $a_j$ are the areas of habitat patches $i$ and $j$, respectively; $p_{ij}^{\ast }$ signifies the maximum product of the probabilities of all potential links connecting patches $i$ and $j$; $g_{ij}$QUOTE${\mathrm{g}}_{\mathrm{i}\mathrm{j}\left(\mathrm{k}\right)}$ is the number of shortest paths between patches $i$ and $j$; and $g_{ij\left(k\right)}$ is the number of shortest paths between patches $i$ and $j$ through patch k.

Table 3. The statistical description of spatial accessibility of UGSs in Wuhan.

Type of UGSs	Traffic mode	Service time threshold (min)	Potentially underserved population	Mean	Standard deviation	Underserved areas (km^2)
PGS	walking	15	785,753	11,754	15,064	72.30
		30	3,732,976	65,044	58,392	197.56
	cycling	15	8,552,403	167,489	120,203	283.79
		30	30,831,297	651,221	325,867	406.82
AGS	cycling	15	360,707	7581	11,847	48.46
		30	1,720,406	46,234	46,303	151.49
	cycling	15	4,214,473	121,948	94,371	241.66
		30	18,492,526	478,440	275,783	393.84

Figure 3. Distribution of PGSs and AGSs under different accessibility levels.

Figure 4. Spatial distribution of SA of UGSs in the urban center of Wuhan.

Figure 5. Ecological network of UGSs in the urban center of Wuhan under different dispersal distance thresholds.

Table 4. Ecological connectivity of UGSs patches in Wuhan.

Distance threshold (m)	PC	dPC			BC
		Max	Min	Mean	Max	Number of patches with non-zero BC
1000	0.19	47.92	0.00	2.13	0.01	26
2000	0.25	41.95	0.00	2.33	0.11	47
3000	0.31	38.43	0.00	2.51	0.22	61
4000	0.36	36.41	0.01	2.61	0.42	74
5000	0.41	35.18	0.02	2.65	0.41	77

Figure 6. Spatial distribution of ecological connectivity of UGSs across all dispersal distance thresholds in Wuhan.

Figure 7. Numerical distribution of CCD under different traffic modes.

Figure 8. Numerical distribution of CCD under different species dispersal distances.

Figure 9. Coordination degree of UGSs in Wuhan.

Table 5. Types of imbalanced green patches and key patches.

Imbalance types	Number of patches	Percentage	Serial number of key patches
UGS with a small size	1	2.33%	67
UGS with low EC	11	25.58%	4, 9, 11, 24, 36, 66, 74, 76, 77, 87, 90
UGS with low SA	6	13.95%	45, 48, 56, 60, 82, 91
UGS with a small size and low EC	7	16.28%	12, 16, 27, 44, 72, 86, 88
UGS with a small size and low SA	4	9.30%	21, 38, 59, 62
UGS with low EC and low SA	2	4.65%	39, 40
UGS with a small size, low EC, and low SA	12	27.91%	1, 23, 25, 26, 29, 32, 33, 34, 43, 49, 50, 61

Saura, S., and L. Pascual-Hortal. 2007. “A New Habitat Availability Index to Integrate Connectivity in Landscape Conservation Planning: Comparison with Existing Indices and Application to a Case Study.” Landscape and Urban Planning 83 (2–3): 91–103. doi:10.1016/j.landurbplan.2007.03.005.

 Web of Science ®Google Scholar

De Montis, A., S. Caschili, M. Mulas, G. Modica, A. Ganciu, A. Bardi, A. Ledda, L. Dessena, L. Laudari, and C. R. Fichera. 2016. “Urban-Rural Ecological Networks for Landscape Planning.” Land Use Policy 50: 312–327. doi:10.1016/j.landusepol.2015.10.004.

 Web of Science ®Google Scholar

Data availability statement

The data that support the findings of this study are available from the corresponding author, upon reasonable request.