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Effect of landscape pattern on insect species density within urban green spaces in Beijing, China.

Su Z, Li X, Zhou W, Ouyang Z - PLoS ONE (2015)

Bottom Line: The results of the hierarchical partitioning analysis indicated that five explanatory variables, i.e., patch area (with 19.9% independent effects), connectivity (13.9%), distance to nearest patch (13.8%), diversity for patch types (11.0%), and patch shape (8.3%), significantly contributed to insect species density.Our results indicated that improvement in habitat quality, such as patch area and connectivity that are typically thought to be important for conservation, did not actually increase species density.However, increasing compactness (low-edge) of patch shape and landscape composition did have the expected effect.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China.

ABSTRACT
Urban green space is an important refuge of biodiversity in urban areas. Therefore, it is crucial to understand the relationship between the landscape pattern of green spaces and biodiversity to mitigate the negative effects of urbanization. In this study, we collected insects from 45 green patches in Beijing during July 2012 using suction sampling. The green patches were dominated by managed lawns, mixed with scattered trees and shrubs. We examined the effects of landscape pattern on insect species density using hierarchical partitioning analysis and partial least squares regression. The results of the hierarchical partitioning analysis indicated that five explanatory variables, i.e., patch area (with 19.9% independent effects), connectivity (13.9%), distance to nearest patch (13.8%), diversity for patch types (11.0%), and patch shape (8.3%), significantly contributed to insect species density. With the partial least squares regression model, we found species density was negatively related to patch area, shape, connectivity, diversity for patch types and proportion of impervious surface at the significance level of p < 0.05 and positively related to proportion of vegetated land. Regression tree analysis further showed that the highest species density was found in green patches with an area <500 m2. Our results indicated that improvement in habitat quality, such as patch area and connectivity that are typically thought to be important for conservation, did not actually increase species density. However, increasing compactness (low-edge) of patch shape and landscape composition did have the expected effect. Therefore, it is recommended that the composition of the surrounding landscape should be considered simultaneously with planned improvements in local habitat quality.

No MeSH data available.


Related in: MedlinePlus

Partial least squares regression (PLSR) for insect species density with the 11 landscape metrics.(A) Cross-validated root mean squared error of prediction (RMSEP) curves. (B) Measured species density (standardized) versus values predicted by the PLSR model with four latent components. (C) Regression coefficients (with standard errors) for the PLSR model with four latent components. An asterisk indicates significant variables at p ≤ 0.05 estimated using jack-knife t-test. The abbreviations of landscape metrics are as shown in Table 1. In addition, LogArea, LogProx_AM, LogProx_MN and LogLPI are logarithms to the base 10 of Area, Prox_AM, Prox_MN and LPI, respectively; SqrtConn_5m is square root of Conn_5m.
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pone.0119276.g003: Partial least squares regression (PLSR) for insect species density with the 11 landscape metrics.(A) Cross-validated root mean squared error of prediction (RMSEP) curves. (B) Measured species density (standardized) versus values predicted by the PLSR model with four latent components. (C) Regression coefficients (with standard errors) for the PLSR model with four latent components. An asterisk indicates significant variables at p ≤ 0.05 estimated using jack-knife t-test. The abbreviations of landscape metrics are as shown in Table 1. In addition, LogArea, LogProx_AM, LogProx_MN and LogLPI are logarithms to the base 10 of Area, Prox_AM, Prox_MN and LPI, respectively; SqrtConn_5m is square root of Conn_5m.

Mentions: The PLSR model with four components had the minimum RMSEP value (RMSEPadjCV = 0.869) (Fig. 3A), and it can predict 48.4% of the variation in the insect species density (p ≤ 0.0001) (Fig. 3B). Seven of the explanatory variables, i.e. LogArea, SqrtConn_5m, SHDI, SHAPE_AM, PVEG, PIS and ShapeInd, were all significant (p ≤ 0.05) in explaining the variation of species density (Fig. 3C). Among all the significant variables, six ones (i.e. LogArea, SqrtConn_5m, SHDI, SHAPE_AM, PIS and ShapeInd) had negative effects on insect species density, with coefficients of −0.401, −0.304, −0.320, −0.357, −0.199 and −0.322, respectively; only PVEG was positively related to species density, with a coefficient of 0.267.


Effect of landscape pattern on insect species density within urban green spaces in Beijing, China.

Su Z, Li X, Zhou W, Ouyang Z - PLoS ONE (2015)

Partial least squares regression (PLSR) for insect species density with the 11 landscape metrics.(A) Cross-validated root mean squared error of prediction (RMSEP) curves. (B) Measured species density (standardized) versus values predicted by the PLSR model with four latent components. (C) Regression coefficients (with standard errors) for the PLSR model with four latent components. An asterisk indicates significant variables at p ≤ 0.05 estimated using jack-knife t-test. The abbreviations of landscape metrics are as shown in Table 1. In addition, LogArea, LogProx_AM, LogProx_MN and LogLPI are logarithms to the base 10 of Area, Prox_AM, Prox_MN and LPI, respectively; SqrtConn_5m is square root of Conn_5m.
© Copyright Policy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4368726&req=5

pone.0119276.g003: Partial least squares regression (PLSR) for insect species density with the 11 landscape metrics.(A) Cross-validated root mean squared error of prediction (RMSEP) curves. (B) Measured species density (standardized) versus values predicted by the PLSR model with four latent components. (C) Regression coefficients (with standard errors) for the PLSR model with four latent components. An asterisk indicates significant variables at p ≤ 0.05 estimated using jack-knife t-test. The abbreviations of landscape metrics are as shown in Table 1. In addition, LogArea, LogProx_AM, LogProx_MN and LogLPI are logarithms to the base 10 of Area, Prox_AM, Prox_MN and LPI, respectively; SqrtConn_5m is square root of Conn_5m.
Mentions: The PLSR model with four components had the minimum RMSEP value (RMSEPadjCV = 0.869) (Fig. 3A), and it can predict 48.4% of the variation in the insect species density (p ≤ 0.0001) (Fig. 3B). Seven of the explanatory variables, i.e. LogArea, SqrtConn_5m, SHDI, SHAPE_AM, PVEG, PIS and ShapeInd, were all significant (p ≤ 0.05) in explaining the variation of species density (Fig. 3C). Among all the significant variables, six ones (i.e. LogArea, SqrtConn_5m, SHDI, SHAPE_AM, PIS and ShapeInd) had negative effects on insect species density, with coefficients of −0.401, −0.304, −0.320, −0.357, −0.199 and −0.322, respectively; only PVEG was positively related to species density, with a coefficient of 0.267.

Bottom Line: The results of the hierarchical partitioning analysis indicated that five explanatory variables, i.e., patch area (with 19.9% independent effects), connectivity (13.9%), distance to nearest patch (13.8%), diversity for patch types (11.0%), and patch shape (8.3%), significantly contributed to insect species density.Our results indicated that improvement in habitat quality, such as patch area and connectivity that are typically thought to be important for conservation, did not actually increase species density.However, increasing compactness (low-edge) of patch shape and landscape composition did have the expected effect.

View Article: PubMed Central - PubMed

Affiliation: State Key Laboratory of Urban and Regional Ecology, Research Center for Eco-Environmental Sciences, Chinese Academy of Sciences, Beijing, China.

ABSTRACT
Urban green space is an important refuge of biodiversity in urban areas. Therefore, it is crucial to understand the relationship between the landscape pattern of green spaces and biodiversity to mitigate the negative effects of urbanization. In this study, we collected insects from 45 green patches in Beijing during July 2012 using suction sampling. The green patches were dominated by managed lawns, mixed with scattered trees and shrubs. We examined the effects of landscape pattern on insect species density using hierarchical partitioning analysis and partial least squares regression. The results of the hierarchical partitioning analysis indicated that five explanatory variables, i.e., patch area (with 19.9% independent effects), connectivity (13.9%), distance to nearest patch (13.8%), diversity for patch types (11.0%), and patch shape (8.3%), significantly contributed to insect species density. With the partial least squares regression model, we found species density was negatively related to patch area, shape, connectivity, diversity for patch types and proportion of impervious surface at the significance level of p < 0.05 and positively related to proportion of vegetated land. Regression tree analysis further showed that the highest species density was found in green patches with an area <500 m2. Our results indicated that improvement in habitat quality, such as patch area and connectivity that are typically thought to be important for conservation, did not actually increase species density. However, increasing compactness (low-edge) of patch shape and landscape composition did have the expected effect. Therefore, it is recommended that the composition of the surrounding landscape should be considered simultaneously with planned improvements in local habitat quality.

No MeSH data available.


Related in: MedlinePlus