Individual-level performance of nature reserves in forest protection and the effects of management level and establishment age

Zhao, Haiwei; Wu, Ruidong; Long, Yongcheng; Hu, Jinming; Yang, Feiling; Jin, Tong; Wang, Junjun; Hu, Peijun; Wu, Wei; Diao, Yixin; Guo, Yang

doi:10.1016/j.biocon.2019.02.024

Cited by 50 publications

(40 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The second approach was a matching analysis at a national scale with exact matching for regions to ensure each matched pair belonged to the same biotic region followed by a separate assessment of each region's protected cells and their respective matched controls (exact subsetting) (Nelson & Chomitz 2011;Bowker et al 2017;Geldmann et al 2019). The third approach was an independent matching procedure for each region (submatching) (Zhao et al 2019).To compare the performance of these three procedures, we calculated indices of covariate imbalance for each variable before and after matching through an estimation of normalized difference (Stuart & Rubin 2011;Olmos & Govindasamy 2015). Absolute scores >25% are considered an indication of a possible imbalance for that specific variable (Stuart & Rubin 2011;Olmos & Govindasamy 2015).…”

Section: Regional Analysesmentioning

confidence: 99%

“…However, the method's application varies, especially relative to how effectiveness within regional subsamples is estimated. For example, some researchers examine subsets of matched pairs from a larger-scale matching analysis to make additional inferences for subregions (Blackman et al 2015;Geldmann et al 2019), whereas others use independent matching for each subregion analyzed (Zhao et al 2019).…”

Section: Introductionmentioning

confidence: 99%

“…The possible presence of spatial autocorrelation in forest loss rates and its effect on estimates of protected area effectiveness is often overlooked when using statistical matching (e.g., Andam et al 2008;Joppa & Pfaff 2011;Geldmann et al 2019). A way to account for spatial autocorrelation in these analyses is by spacing sampling units at a certain distance (Gaveau et al 2013;Bowker et al 2017;Zhao et al 2019), but the effectiveness of this procedure is rarely reported. Spatial autocorrelation is a well-known problem that violates the assumption of data independence, and its presence inflates type I and II error rates (Mets et al 2017;Crabot et al 2019).…”

Section: Introductionmentioning

confidence: 99%

“…2017; Zhao et al. 2019), but the effectiveness of this procedure is rarely reported. Spatial autocorrelation is a well‐known problem that violates the assumption of data independence, and its presence inflates type I and II error rates (Mets et al.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Effects of spatial autocorrelation and sampling design on estimates of protected area effectiveness

Negret

Marco

Sonter

et al. 2020

Conservation Biology

View full text Add to dashboard Cite

Estimating the effectiveness of protected areas (PAs) in reducing deforestation is useful to support decisions on whether to invest in better management of areas already protected or to create new ones. Statistical matching is commonly used to assess this effectiveness, but spatial autocorrelation and regional differences in protection effectiveness are frequently overlooked. Using Colombia as a case study, we employed statistical matching to account for confounding factors in park location and accounted for for spatial autocorrelation to determine statistical significance. We compared the performance of different matching procedures-ways of generating matching pairs at different scales-in estimating PA effectiveness. Differences in matching procedures affected covariate similarity between matched pairs (balance) and estimates of PA effectiveness in reducing deforestation. Independent matching yielded the greatest balance. On average 95% of variables in each region were balanced with independent matching, whereas 33% of variables were balanced when using the method that performed worst. The best estimates suggested that average deforestation inside protected areas in Colombia was 40% lower than in matched sites. Protection significantly reduced deforestation, but PA effectiveness differed among regions. Protected areas in Caribe were the most effective, whereas those in Orinoco and Pacific were least effective. Our results demonstrate that accounting for spatial autocorrelation and using independent matching for each subset of data is needed to infer the effectiveness of protection in reducing deforestation. Not accounting for spatial autocorrelation can distort the assessment of protection effectiveness, increasing type I and II errors and inflating effect size. Our method allowed improved estimates of protection effectiveness across scales and under different conditions and can be applied to other regions to effectively assess PA performance.

show abstract

Section: Regional Analysesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Effects of spatial autocorrelation and sampling design on estimates of protected area effectiveness

Negret

Marco

Sonter

et al. 2020

Conservation Biology

View full text Add to dashboard Cite

show abstract

“…The latest Red Lists of China's Biodiversity indicate that >20% of vertebrates and >10% of higher plants are threatened or extinct (Jiang, ; Liu et al., ). Large conservation gaps still exist (Xu et al., ), and more than one third of the forest Nature Reserves in southwest China have failed to curb deforestation (Zhao et al., ). Therefore, there is plenty of room for cost‐effectively achieving greater biodiversity outcomes across China's ES&PA programs (Bryan et al., ; Hua et al., ).…”

Section: Introductionmentioning

confidence: 99%

Strengthening China's national biodiversity strategy to attain an ecological civilization

Possingham

Yu³

et al. 2019

CONSERVATION LETTERS

Self Cite

View full text Add to dashboard Cite

Biodiversity conservation is essential for realizing China's new vision of an ecological civilization. China has been implementing numerous massive ecological sustainability and protected area (ES&PA) programs across the entire country. These programs have greatly restored degraded ecological environments, improved provisions of critical ecosystem services and increased rural livelihoods. However, despite the general improvements in environmental quality, the trend of rapid biodiversity loss has not been significantly reduced. We found that most of the current ES&PA programs lack explicit biodiversity goals, and thus have limited contributions to the conservation of biodiversity. Given the limited resources available for and huge investments associated with these programs, achieving greater biodiversity gains under them is the most cost‐effective way to conserve biodiversity. We recommend six strategies for strengthening the country's biodiversity conservation, that is, strengthening biodiversity in ES&PA programs, PAs as the core, integrating biodiversity and ecosystem services, delivering effective monitoring, broad inclusiveness of stakeholders and mainstreaming biodiversity. These strategies also highlight China's priorities for achieving significant progresses toward the Strategic Plan for Biodiversity 2011–2020, and should be important options for developing China's post‐2020 biodiversity framework.

show abstract

The contrasting east–west pattern of vegetation restoration under the large‐scale ecological restoration programmes in southwest China

Zhao

et al. 2020

Land Degrad Dev

View full text Add to dashboard Cite

Ecological restoration programmes (ERPs) play an important role in reversing degraded ecosystems and realizing environmental sustainability. However, few studies have given attention to the spatial heterogeneity in the effectiveness of large‐scale ERPs for restoring vegetation on a broad regional scale. Based on the GIMMS NDVI3g time‐series of southwest China, we determined the spatiotemporal trends of the peak growing season NDVI (PGS‐NDVI) in the pre‐ERP stage (1982–1998) and in the post‐ERP stage (1999–2015). Using the NDVI‐climate regression model developed during the pre‐ERP stage and the residual trend analysis method, we derived the anthropogenic PGS‐NDVI trends of the post‐ERP stage on a per‐pixel and spatial average basis. We found that there existed an east (greening)–west (browning) contrasting pattern in the anthropogenic vegetation change during the post‐ERP stage. The proportions of anthropogenic greening pixels were heterogeneous among provinces, ranging from 66.1% (Guangxi) to 8.6% (Xizang). The ERP effectiveness index and the ERP efficiency index for Guangxi were the highest among the provinces, whereas they were both negative for Yunnan and Xizang. Such contrasting restoration pattern was probably caused by multiple factors, including but not limited to the imbalanced implementation of large‐scale ERPs and the inappropriate afforestation that inadequately takes into account local conditions. Our analysis highlights the great heterogeneity in the effectiveness of large‐scale ERPs for vegetation restoration across a broad regional scale. Large‐scale ERPs should be implemented in a coordinated manner and be tailored to local conditions.

show abstract

Individual-level performance of nature reserves in forest protection and the effects of management level and establishment age

Cited by 50 publications

References 48 publications

Effects of spatial autocorrelation and sampling design on estimates of protected area effectiveness

Effects of spatial autocorrelation and sampling design on estimates of protected area effectiveness

Strengthening China's national biodiversity strategy to attain an ecological civilization

The contrasting east–west pattern of vegetation restoration under the large‐scale ecological restoration programmes in southwest China

Contact Info

Product

Resources

About