The Experts below are selected from a list of 55806 Experts worldwide ranked by ideXlab platform
Jianguo Liu - One of the best experts on this subject based on the ideXlab platform.
-
range wide assessment of the impact of china s nature reserves on giant panda Habitat quality
Science of The Total Environment, 2021Co-Authors: Hongbo Yang, Qiongyu Huang, Jindong Zhang, Melissa Songer, Jianguo LiuAbstract:Abstract Protected areas (PAs) form the backbone of global conservation efforts. Although many studies have evaluated the impact of PAs on land cover, human disturbances, and people’s welfare, PAs’ impact on Wildlife Habitat quality remains poorly understood. By integrating Wildlife Habitat mapping and information of 2,183 rural households, we assessed the impacts of nature reserves (a type of PAs) across the entire geographic range of giant pandas (Ailuropoda melanoleuca) on panda Habitat suitability change between 2001 and 2013 using the matching approach. We found the impact of nature reserves is concentrated in areas susceptible to human pressure, where 65% of the Habitat suitability increase is attributable to the nature reserves’ protection. The impact of nature reserves has spilled over to nearby unprotected areas and enhanced Habitat suitability there. Nature reserves supported by the central government showed higher performance in improving Habitat suitability than their counterparts supported by local governments. Older nature reserves perform better than those established more recently. Our results also show that local households’ participation in tourism and labor migration (people temporarily leaving to work in cities) enhanced the ability of nature reserves to improve Habitat suitability. These results and methods provide valuable information and tools to support effective management of PAs to enhance the Habitat quality of giant pandas and other Wildlife species in China and elsewhere.
-
range wide evaluation of Wildlife Habitat change a demonstration using giant pandas
Biological Conservation, 2017Co-Authors: Hongbo Yang, Jindong Zhang, Andres Vina, Ying Tang, Fang Wang, Zhiqiang Zhao, Jianguo LiuAbstract:Abstract Information on Wildlife Habitat distribution and change is crucial for the design and evaluation of conservation efforts. While Habitat distribution has been evaluated for many species, information on Habitat change is often unclear, particularly across entire geographic ranges. Here we use the iconic giant panda ( Ailuropoda melanoleuca ) as a model species and present an advanced approach to evaluate its Habitat change across an entire geographic range through the integration of time-series satellite imagery and field data. Our results show that despite a few areas showing Habitat degradation, both the overall Habitat suitability and Habitat area increased between the early 2000s and the early 2010s. Our results also indicate that conservation efforts in China have achieved success beyond the boundaries of nature reserves, since panda Habitat outside nature reserves shows a higher proportional growth than inside the reserves. Despite these promising trends, we found Habitat fragmentation remains a threat to the species' long-term survival. These results provide valuable information to assess the appropriateness of recent decision by the International Union for the Conservation of Nature (IUCN) that down-listed the giant panda from endangered to vulnerable species, while laying a good foundation for the design of future conservation efforts. The approach described here may also be easily implemented for evaluating range-wide Habitat change for many other species around the world and thus help achieve biodiversity conservation objectives such as those set by the Aichi Biodiversity Targets and the Sustainable Development Goals.
-
effects of payments for ecosystem services on Wildlife Habitat recovery
Conservation Biology, 2016Co-Authors: Andres Vina, Mao-ning Tuanmu, Wu Yang, Xiaodong Chen, Ashton Shortridge, Jianguo LiuAbstract:Conflicts between local people's livelihoods and conservation have led to many unsuccessful conservation efforts and have stimulated debates on policies that might simultaneously promote sustainable management of protected areas and improve the living conditions of local people. Many government-sponsored payments-for-ecosystem-services (PES) schemes have been implemented around the world. However, few empirical assessments of their effectiveness have been conducted, and even fewer assessments have directly measured their effects on ecosystem services. We conducted an empirical and spatially explicit assessment of the conservation effectiveness of one of the world's largest PES programs through the use of a long-term empirical data set, a satellite-based Habitat model, and spatial autoregressive analyses on direct measures of change in an ecosystem service (i.e., the provision of Wildlife species Habitat). Giant panda (Ailuropoda melanoleuca) Habitat improved in Wolong Nature Reserve of China after the implementation of the Natural Forest Conservation Program. The improvement was more pronounced in areas monitored by local residents than those monitored by the local government, but only when a higher payment was provided. Our results suggest that the effectiveness of a PES program depends on who receives the payment and on whether the payment provides sufficient incentives. As engagement of local residents has not been incorporated in many conservation strategies elsewhere in China or around the world, our results also suggest that using an incentive-based strategy as a complement to command-and-control, community- and norm-based strategies may help achieve greater conservation effectiveness and provide a potential solution for the park versus people conflict.
-
Temporal transferability of Wildlife Habitat models: implications for Habitat monitoring
Journal of Biogeography, 2011Co-Authors: Mao-ning Tuanmu, Zhiyun Ouyang, Andres Vina, Gary J. Roloff, Wei Liu, Hemin Zhang, Jianguo LiuAbstract:Aim Temporal transferability is an important issue when Habitat models are used beyond the time frame corresponding to model development, but has not received enough attention, particularly in the context of Habitat monitoring. While the combination of remote sensing technology and Habitat modelling provides a useful tool for Habitat monitoring, the effect of incorporating remotely sensed data on model transferability is unclear. Therefore, our objectives were to assess how different satellite-derived variables affect temporal transferability of Habitat models and their usefulness for Habitat monitoring. Location Wolong Nature Reserve, Sichuan Province, China. Methods We modelled giant panda Habitat with the maximum entropy algorithm using panda presence data collected in two time periods and four different sets of predictor variables representing land surface phenology. Each predictor variable set contained either a time series of smoothed wide dynamic range vegetation index (WDRVI) or 11 phenology metrics, both derived from single-year or multi-year (i.e. 3-year) remotely sensed imagery acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS). We evaluated the ability of models obtained with these four variable sets to predict giant panda Habitat within and across time periods by using threshold-independent and threshold-dependent evaluation methods and five indices of temporal transferability. Results Our results showed that models developed with the four variable sets were all useful for characterizing and monitoring giant panda Habitat. However, the models developed using multi-year data exhibited significantly higher temporal transferability than those developed using single-year data. In addition, models developed with phenology metrics, especially when using multi-year data, exhibited significantly higher temporal transferability than those developed with the time series. Main conclusions The integration of land surface phenology, captured by high temporal resolution remotely sensed imagery, with Habitat modelling constitutes a suitable tool for characterizing Wildlife Habitat and monitoring its temporal dynamics. Using multi-year phenology metrics reduces model complexity, multicollinearity among predictor variables and variability caused by inter-annual climatic fluctuations, thereby increasing the temporal transferability of models. This study provides useful guidance for Habitat monitoring through the integration of remote sensing technology and Habitat modelling, which may be useful for the conservation of the giant panda and many other species.
-
range wide analysis of Wildlife Habitat implications for conservation
Biological Conservation, 2010Co-Authors: Andres Vina, Zhiyun Ouyang, Mao-ning Tuanmu, Ruth Defries, Jianguo LiuAbstract:The range-wide Habitat status of many endangered species is unclear. We evaluated the status and spatial distribution of the Habitat of the endangered giant panda (Ailuropoda melanoleuca) across its entire geographic range (i.e., six mountain regions located in Sichuan, Shaanxi and Gansu provinces, China) by integrating field and remotely sensed data to develop a Habitat distribution model. Results suggest that current suitable Habitat corresponds to ca. 1/4 of the Habitat baseline (i.e., maximum amount of Habitat possible). The highest proportion of suitable Habitat relative to the baseline is in the Qinling mountain region. Overall, around 40% of the suitable Habitat is inside nature reserves, but the proportion of Habitat inside them varied among different mountain regions, ranging from ca. 17% (Lesser Xiangling) to ca. 66% (Qinling). The Habitat model also predicted the occurrence of potentially suitable Habitat outside the currently accepted geographic range of the species, which should be further evaluated as potential panda reintroduction sites. Our approach is valuable for assessing the conservation status of the entire Habitat of the species, for identifying areas with significant ecological roles (e.g., corridors), for identifying areas suitable for panda reintroductions, and for establishing specific conservation strategies in different parts of the giant panda geographic range. It might also prove useful for range-wide Habitat analyses of many other endangered species around the world.
Andres Vina - One of the best experts on this subject based on the ideXlab platform.
-
range wide evaluation of Wildlife Habitat change a demonstration using giant pandas
Biological Conservation, 2017Co-Authors: Hongbo Yang, Jindong Zhang, Andres Vina, Ying Tang, Fang Wang, Zhiqiang Zhao, Jianguo LiuAbstract:Abstract Information on Wildlife Habitat distribution and change is crucial for the design and evaluation of conservation efforts. While Habitat distribution has been evaluated for many species, information on Habitat change is often unclear, particularly across entire geographic ranges. Here we use the iconic giant panda ( Ailuropoda melanoleuca ) as a model species and present an advanced approach to evaluate its Habitat change across an entire geographic range through the integration of time-series satellite imagery and field data. Our results show that despite a few areas showing Habitat degradation, both the overall Habitat suitability and Habitat area increased between the early 2000s and the early 2010s. Our results also indicate that conservation efforts in China have achieved success beyond the boundaries of nature reserves, since panda Habitat outside nature reserves shows a higher proportional growth than inside the reserves. Despite these promising trends, we found Habitat fragmentation remains a threat to the species' long-term survival. These results provide valuable information to assess the appropriateness of recent decision by the International Union for the Conservation of Nature (IUCN) that down-listed the giant panda from endangered to vulnerable species, while laying a good foundation for the design of future conservation efforts. The approach described here may also be easily implemented for evaluating range-wide Habitat change for many other species around the world and thus help achieve biodiversity conservation objectives such as those set by the Aichi Biodiversity Targets and the Sustainable Development Goals.
-
effects of payments for ecosystem services on Wildlife Habitat recovery
Conservation Biology, 2016Co-Authors: Andres Vina, Mao-ning Tuanmu, Wu Yang, Xiaodong Chen, Ashton Shortridge, Jianguo LiuAbstract:Conflicts between local people's livelihoods and conservation have led to many unsuccessful conservation efforts and have stimulated debates on policies that might simultaneously promote sustainable management of protected areas and improve the living conditions of local people. Many government-sponsored payments-for-ecosystem-services (PES) schemes have been implemented around the world. However, few empirical assessments of their effectiveness have been conducted, and even fewer assessments have directly measured their effects on ecosystem services. We conducted an empirical and spatially explicit assessment of the conservation effectiveness of one of the world's largest PES programs through the use of a long-term empirical data set, a satellite-based Habitat model, and spatial autoregressive analyses on direct measures of change in an ecosystem service (i.e., the provision of Wildlife species Habitat). Giant panda (Ailuropoda melanoleuca) Habitat improved in Wolong Nature Reserve of China after the implementation of the Natural Forest Conservation Program. The improvement was more pronounced in areas monitored by local residents than those monitored by the local government, but only when a higher payment was provided. Our results suggest that the effectiveness of a PES program depends on who receives the payment and on whether the payment provides sufficient incentives. As engagement of local residents has not been incorporated in many conservation strategies elsewhere in China or around the world, our results also suggest that using an incentive-based strategy as a complement to command-and-control, community- and norm-based strategies may help achieve greater conservation effectiveness and provide a potential solution for the park versus people conflict.
-
Temporal transferability of Wildlife Habitat models: implications for Habitat monitoring
Journal of Biogeography, 2011Co-Authors: Mao-ning Tuanmu, Zhiyun Ouyang, Andres Vina, Gary J. Roloff, Wei Liu, Hemin Zhang, Jianguo LiuAbstract:Aim Temporal transferability is an important issue when Habitat models are used beyond the time frame corresponding to model development, but has not received enough attention, particularly in the context of Habitat monitoring. While the combination of remote sensing technology and Habitat modelling provides a useful tool for Habitat monitoring, the effect of incorporating remotely sensed data on model transferability is unclear. Therefore, our objectives were to assess how different satellite-derived variables affect temporal transferability of Habitat models and their usefulness for Habitat monitoring. Location Wolong Nature Reserve, Sichuan Province, China. Methods We modelled giant panda Habitat with the maximum entropy algorithm using panda presence data collected in two time periods and four different sets of predictor variables representing land surface phenology. Each predictor variable set contained either a time series of smoothed wide dynamic range vegetation index (WDRVI) or 11 phenology metrics, both derived from single-year or multi-year (i.e. 3-year) remotely sensed imagery acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS). We evaluated the ability of models obtained with these four variable sets to predict giant panda Habitat within and across time periods by using threshold-independent and threshold-dependent evaluation methods and five indices of temporal transferability. Results Our results showed that models developed with the four variable sets were all useful for characterizing and monitoring giant panda Habitat. However, the models developed using multi-year data exhibited significantly higher temporal transferability than those developed using single-year data. In addition, models developed with phenology metrics, especially when using multi-year data, exhibited significantly higher temporal transferability than those developed with the time series. Main conclusions The integration of land surface phenology, captured by high temporal resolution remotely sensed imagery, with Habitat modelling constitutes a suitable tool for characterizing Wildlife Habitat and monitoring its temporal dynamics. Using multi-year phenology metrics reduces model complexity, multicollinearity among predictor variables and variability caused by inter-annual climatic fluctuations, thereby increasing the temporal transferability of models. This study provides useful guidance for Habitat monitoring through the integration of remote sensing technology and Habitat modelling, which may be useful for the conservation of the giant panda and many other species.
-
range wide analysis of Wildlife Habitat implications for conservation
Biological Conservation, 2010Co-Authors: Andres Vina, Zhiyun Ouyang, Mao-ning Tuanmu, Ruth Defries, Jianguo LiuAbstract:The range-wide Habitat status of many endangered species is unclear. We evaluated the status and spatial distribution of the Habitat of the endangered giant panda (Ailuropoda melanoleuca) across its entire geographic range (i.e., six mountain regions located in Sichuan, Shaanxi and Gansu provinces, China) by integrating field and remotely sensed data to develop a Habitat distribution model. Results suggest that current suitable Habitat corresponds to ca. 1/4 of the Habitat baseline (i.e., maximum amount of Habitat possible). The highest proportion of suitable Habitat relative to the baseline is in the Qinling mountain region. Overall, around 40% of the suitable Habitat is inside nature reserves, but the proportion of Habitat inside them varied among different mountain regions, ranging from ca. 17% (Lesser Xiangling) to ca. 66% (Qinling). The Habitat model also predicted the occurrence of potentially suitable Habitat outside the currently accepted geographic range of the species, which should be further evaluated as potential panda reintroduction sites. Our approach is valuable for assessing the conservation status of the entire Habitat of the species, for identifying areas with significant ecological roles (e.g., corridors), for identifying areas suitable for panda reintroductions, and for establishing specific conservation strategies in different parts of the giant panda geographic range. It might also prove useful for range-wide Habitat analyses of many other endangered species around the world.
-
evaluating modis data for mapping Wildlife Habitat distribution
Remote Sensing of Environment, 2008Co-Authors: Andres Vina, Zhiyun Ouyang, Hemin Zhang, Scott Bearer, Jianguo LiuAbstract:Habitat distribution models have a long history in ecological research. With the development of geospatial information technology, including remote sensing, these models are now applied to an ever-increasing number of species, particularly those located in areas in which it is logistically difficult to collect Habitat data in the field. Many Habitat studies have used data acquired by multi-spectral sensor systems such as the Landsat Thematic Mapper (TM), due mostly to their availability and relatively high spatial resolution (30 m/pixel). The use of data collected by other sensor systems with lower spatial resolutions but high frequency of acquisitions has largely been neglected, due to the perception that such low spatial resolutiondataaretoocoarseforHabitatmapping.Inthisstudywecomparetwomodelsusingdatafromdifferentsatellitesensorsystemsformapping the spatial distribution of giant panda Habitat in Wolong Nature Reserve, China. The first one is a four-category scheme model based on combining forest cover (derived from a digital land cover classification of Landsat TM imagery acquired in June, 2001) with information on elevation and slope (derivedfromadigitalelevationmodelobtainedfromtopographicmapsofthestudyarea).ThesecondmodelisbasedontheEcologicalNicheFactor Analysis(ENFA)ofatimeseriesofweeklycompositesofWDRVI(WideDynamicRangeVegetationIndex)imagesderivedfromMODIS(Moderate Resolution Imaging Spectroradiometer – 250 m/pixel) for 2001. A series of field plots was established in the reserve during the summer–autumn months of 2001–2003. The locations of the plots with panda feces were used to calibrate the ENFA model and to validate the results of both models. ResultsshowedthatthemodelusingtheseasonalvariabilityofMODIS-WDRVIhadasimilarpredictionsuccesstothatusingLandsatTManddigital elevation model data, albeit having a coarser spatial resolution. This suggests that the phenological characterization of the land surface provide sa n appropriateenvironmentalpredictorforgiantpandaHabitatmapping.Therefore,theinformationcontainedinremotelysenseddataacquiredwithlow
Zhiyun Ouyang - One of the best experts on this subject based on the ideXlab platform.
-
Temporal transferability of Wildlife Habitat models: implications for Habitat monitoring
Journal of Biogeography, 2011Co-Authors: Mao-ning Tuanmu, Zhiyun Ouyang, Andres Vina, Gary J. Roloff, Wei Liu, Hemin Zhang, Jianguo LiuAbstract:Aim Temporal transferability is an important issue when Habitat models are used beyond the time frame corresponding to model development, but has not received enough attention, particularly in the context of Habitat monitoring. While the combination of remote sensing technology and Habitat modelling provides a useful tool for Habitat monitoring, the effect of incorporating remotely sensed data on model transferability is unclear. Therefore, our objectives were to assess how different satellite-derived variables affect temporal transferability of Habitat models and their usefulness for Habitat monitoring. Location Wolong Nature Reserve, Sichuan Province, China. Methods We modelled giant panda Habitat with the maximum entropy algorithm using panda presence data collected in two time periods and four different sets of predictor variables representing land surface phenology. Each predictor variable set contained either a time series of smoothed wide dynamic range vegetation index (WDRVI) or 11 phenology metrics, both derived from single-year or multi-year (i.e. 3-year) remotely sensed imagery acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS). We evaluated the ability of models obtained with these four variable sets to predict giant panda Habitat within and across time periods by using threshold-independent and threshold-dependent evaluation methods and five indices of temporal transferability. Results Our results showed that models developed with the four variable sets were all useful for characterizing and monitoring giant panda Habitat. However, the models developed using multi-year data exhibited significantly higher temporal transferability than those developed using single-year data. In addition, models developed with phenology metrics, especially when using multi-year data, exhibited significantly higher temporal transferability than those developed with the time series. Main conclusions The integration of land surface phenology, captured by high temporal resolution remotely sensed imagery, with Habitat modelling constitutes a suitable tool for characterizing Wildlife Habitat and monitoring its temporal dynamics. Using multi-year phenology metrics reduces model complexity, multicollinearity among predictor variables and variability caused by inter-annual climatic fluctuations, thereby increasing the temporal transferability of models. This study provides useful guidance for Habitat monitoring through the integration of remote sensing technology and Habitat modelling, which may be useful for the conservation of the giant panda and many other species.
-
range wide analysis of Wildlife Habitat implications for conservation
Biological Conservation, 2010Co-Authors: Andres Vina, Zhiyun Ouyang, Mao-ning Tuanmu, Ruth Defries, Jianguo LiuAbstract:The range-wide Habitat status of many endangered species is unclear. We evaluated the status and spatial distribution of the Habitat of the endangered giant panda (Ailuropoda melanoleuca) across its entire geographic range (i.e., six mountain regions located in Sichuan, Shaanxi and Gansu provinces, China) by integrating field and remotely sensed data to develop a Habitat distribution model. Results suggest that current suitable Habitat corresponds to ca. 1/4 of the Habitat baseline (i.e., maximum amount of Habitat possible). The highest proportion of suitable Habitat relative to the baseline is in the Qinling mountain region. Overall, around 40% of the suitable Habitat is inside nature reserves, but the proportion of Habitat inside them varied among different mountain regions, ranging from ca. 17% (Lesser Xiangling) to ca. 66% (Qinling). The Habitat model also predicted the occurrence of potentially suitable Habitat outside the currently accepted geographic range of the species, which should be further evaluated as potential panda reintroduction sites. Our approach is valuable for assessing the conservation status of the entire Habitat of the species, for identifying areas with significant ecological roles (e.g., corridors), for identifying areas suitable for panda reintroductions, and for establishing specific conservation strategies in different parts of the giant panda geographic range. It might also prove useful for range-wide Habitat analyses of many other endangered species around the world.
-
evaluating modis data for mapping Wildlife Habitat distribution
Remote Sensing of Environment, 2008Co-Authors: Andres Vina, Zhiyun Ouyang, Hemin Zhang, Scott Bearer, Jianguo LiuAbstract:Habitat distribution models have a long history in ecological research. With the development of geospatial information technology, including remote sensing, these models are now applied to an ever-increasing number of species, particularly those located in areas in which it is logistically difficult to collect Habitat data in the field. Many Habitat studies have used data acquired by multi-spectral sensor systems such as the Landsat Thematic Mapper (TM), due mostly to their availability and relatively high spatial resolution (30 m/pixel). The use of data collected by other sensor systems with lower spatial resolutions but high frequency of acquisitions has largely been neglected, due to the perception that such low spatial resolutiondataaretoocoarseforHabitatmapping.Inthisstudywecomparetwomodelsusingdatafromdifferentsatellitesensorsystemsformapping the spatial distribution of giant panda Habitat in Wolong Nature Reserve, China. The first one is a four-category scheme model based on combining forest cover (derived from a digital land cover classification of Landsat TM imagery acquired in June, 2001) with information on elevation and slope (derivedfromadigitalelevationmodelobtainedfromtopographicmapsofthestudyarea).ThesecondmodelisbasedontheEcologicalNicheFactor Analysis(ENFA)ofatimeseriesofweeklycompositesofWDRVI(WideDynamicRangeVegetationIndex)imagesderivedfromMODIS(Moderate Resolution Imaging Spectroradiometer – 250 m/pixel) for 2001. A series of field plots was established in the reserve during the summer–autumn months of 2001–2003. The locations of the plots with panda feces were used to calibrate the ENFA model and to validate the results of both models. ResultsshowedthatthemodelusingtheseasonalvariabilityofMODIS-WDRVIhadasimilarpredictionsuccesstothatusingLandsatTManddigital elevation model data, albeit having a coarser spatial resolution. This suggests that the phenological characterization of the land surface provide sa n appropriateenvironmentalpredictorforgiantpandaHabitatmapping.Therefore,theinformationcontainedinremotelysenseddataacquiredwithlow
-
the effects of understory bamboo on broad scale estimates of giant panda Habitat
Biological Conservation, 2005Co-Authors: Marc Linderman, Yingchun Tan, Zhiyun Ouyang, Scott Bearer, Jianguo LiuAbstract:Understory vegetation is an important Habitat component for many Wildlife species. Previous broad-scale studies on biodiversity and Wildlife Habitat have suffered from a lack of detailed information about understory distribution. Consequently, it is unclear how understory distribution influences the analysis of Habitat quantity and spatial distribution. To address this problem, we compared estimates of giant panda (Ailuropoda melanoleuca) Habitat with and without understory bamboo. The results show that the spatial distribution of bamboo has a substantial effect on the quantity and spatial arrangement of panda Habitat. Total amount of estimated Habitat decreased by 29-52% and decreased connectivity was notable after bamboo information was incorporated into the analyses. The decreases in the quantity and quality of panda Habitat resulted in a decrease of 41% in the estimated carrying capacity. Our results suggest that it is necessary to incorporate understory vegetation into large-scale Wildlife Habitat research and management to avoid overestimation of Habitat and improve broad-scale analyses of species distributions and biodiversity estimates in general. (C) 2004 Elsevier Ltd. All rights reserved.
-
a framework for evaluating the effects of human factors on Wildlife Habitat the case of giant pandas
Conservation Biology, 1999Co-Authors: Jianguo Liu, Zhiyun Ouyang, William W Taylor, Richard E Groop, Yingchun Tan, Heming ZhangAbstract:To address the complex interactions between humans and Wildlife Habitat, we developed a concep- tual framework that links human factors with forested landscapes and Wildlife Habitat. All the components in the framework are integrated into systems models that analyze the effects of human factors and project how Wildlife Habitat would change under different policy scenarios. As a case study, we applied this frame- work to the Wolong Nature Reserve in Sichuan Province (southwestern China), the largest home of the giant panda ( Ailuropoda melanoleuca ). We collected ecological and socioeconomic data with a combination of var- ious methods ( field observations, aerial photographs, government documents and statistics, interviews, and household surveys) and employed geographic information systems and systems modeling to analyze and in- tegrate the data sources. Human population size has increased by 66% and the number of households in the reserve has increased by 115% since 1975, when the reserve was established. During the same period, the quality and quantity of the giant panda Habitat dramatically decreased because of increasing human activi- ties such as fuelwood collection. Systems modeling predicted that under the status quo, human population in the reserve would continue to grow and cause more destruction of the remaining panda Habitat, whereas re- ducing human birth rates and increasing human emigration rates would lower human population size and alleviate human impacts on the panda Habitat. Furthermore, our simulations and surveys suggested that pol- icies encouraging the emigration of young people would be more effective and feasible than relocating older people in reducing human population size and conserving giant panda Habitat in the reserve.
Mao-ning Tuanmu - One of the best experts on this subject based on the ideXlab platform.
-
effects of payments for ecosystem services on Wildlife Habitat recovery
Conservation Biology, 2016Co-Authors: Andres Vina, Mao-ning Tuanmu, Wu Yang, Xiaodong Chen, Ashton Shortridge, Jianguo LiuAbstract:Conflicts between local people's livelihoods and conservation have led to many unsuccessful conservation efforts and have stimulated debates on policies that might simultaneously promote sustainable management of protected areas and improve the living conditions of local people. Many government-sponsored payments-for-ecosystem-services (PES) schemes have been implemented around the world. However, few empirical assessments of their effectiveness have been conducted, and even fewer assessments have directly measured their effects on ecosystem services. We conducted an empirical and spatially explicit assessment of the conservation effectiveness of one of the world's largest PES programs through the use of a long-term empirical data set, a satellite-based Habitat model, and spatial autoregressive analyses on direct measures of change in an ecosystem service (i.e., the provision of Wildlife species Habitat). Giant panda (Ailuropoda melanoleuca) Habitat improved in Wolong Nature Reserve of China after the implementation of the Natural Forest Conservation Program. The improvement was more pronounced in areas monitored by local residents than those monitored by the local government, but only when a higher payment was provided. Our results suggest that the effectiveness of a PES program depends on who receives the payment and on whether the payment provides sufficient incentives. As engagement of local residents has not been incorporated in many conservation strategies elsewhere in China or around the world, our results also suggest that using an incentive-based strategy as a complement to command-and-control, community- and norm-based strategies may help achieve greater conservation effectiveness and provide a potential solution for the park versus people conflict.
-
Temporal transferability of Wildlife Habitat models: implications for Habitat monitoring
Journal of Biogeography, 2011Co-Authors: Mao-ning Tuanmu, Zhiyun Ouyang, Andres Vina, Gary J. Roloff, Wei Liu, Hemin Zhang, Jianguo LiuAbstract:Aim Temporal transferability is an important issue when Habitat models are used beyond the time frame corresponding to model development, but has not received enough attention, particularly in the context of Habitat monitoring. While the combination of remote sensing technology and Habitat modelling provides a useful tool for Habitat monitoring, the effect of incorporating remotely sensed data on model transferability is unclear. Therefore, our objectives were to assess how different satellite-derived variables affect temporal transferability of Habitat models and their usefulness for Habitat monitoring. Location Wolong Nature Reserve, Sichuan Province, China. Methods We modelled giant panda Habitat with the maximum entropy algorithm using panda presence data collected in two time periods and four different sets of predictor variables representing land surface phenology. Each predictor variable set contained either a time series of smoothed wide dynamic range vegetation index (WDRVI) or 11 phenology metrics, both derived from single-year or multi-year (i.e. 3-year) remotely sensed imagery acquired by the Moderate Resolution Imaging Spectroradiometer (MODIS). We evaluated the ability of models obtained with these four variable sets to predict giant panda Habitat within and across time periods by using threshold-independent and threshold-dependent evaluation methods and five indices of temporal transferability. Results Our results showed that models developed with the four variable sets were all useful for characterizing and monitoring giant panda Habitat. However, the models developed using multi-year data exhibited significantly higher temporal transferability than those developed using single-year data. In addition, models developed with phenology metrics, especially when using multi-year data, exhibited significantly higher temporal transferability than those developed with the time series. Main conclusions The integration of land surface phenology, captured by high temporal resolution remotely sensed imagery, with Habitat modelling constitutes a suitable tool for characterizing Wildlife Habitat and monitoring its temporal dynamics. Using multi-year phenology metrics reduces model complexity, multicollinearity among predictor variables and variability caused by inter-annual climatic fluctuations, thereby increasing the temporal transferability of models. This study provides useful guidance for Habitat monitoring through the integration of remote sensing technology and Habitat modelling, which may be useful for the conservation of the giant panda and many other species.
-
range wide analysis of Wildlife Habitat implications for conservation
Biological Conservation, 2010Co-Authors: Andres Vina, Zhiyun Ouyang, Mao-ning Tuanmu, Ruth Defries, Jianguo LiuAbstract:The range-wide Habitat status of many endangered species is unclear. We evaluated the status and spatial distribution of the Habitat of the endangered giant panda (Ailuropoda melanoleuca) across its entire geographic range (i.e., six mountain regions located in Sichuan, Shaanxi and Gansu provinces, China) by integrating field and remotely sensed data to develop a Habitat distribution model. Results suggest that current suitable Habitat corresponds to ca. 1/4 of the Habitat baseline (i.e., maximum amount of Habitat possible). The highest proportion of suitable Habitat relative to the baseline is in the Qinling mountain region. Overall, around 40% of the suitable Habitat is inside nature reserves, but the proportion of Habitat inside them varied among different mountain regions, ranging from ca. 17% (Lesser Xiangling) to ca. 66% (Qinling). The Habitat model also predicted the occurrence of potentially suitable Habitat outside the currently accepted geographic range of the species, which should be further evaluated as potential panda reintroduction sites. Our approach is valuable for assessing the conservation status of the entire Habitat of the species, for identifying areas with significant ecological roles (e.g., corridors), for identifying areas suitable for panda reintroductions, and for establishing specific conservation strategies in different parts of the giant panda geographic range. It might also prove useful for range-wide Habitat analyses of many other endangered species around the world.
Chris J. Johnson - One of the best experts on this subject based on the ideXlab platform.
-
factors limiting our understanding of ecological scale
Ecological Complexity, 2009Co-Authors: Matthew Wheatley, Chris J. JohnsonAbstract:Abstract Multi-scale studies ostensibly allow us to form generalizations regarding the importance of scale in understanding ecosystem function, and in the application of the same ecological principles across a series of spatial domains. Achieving such generalizations, however, requires consistency among multi-scale studies not only in across-scale sample design, but also in basic rationales used in the choice of observational scale, including both grain and extent. To examine the current state of this science, here we review 79 multi-scale Wildlife-Habitat studies published since 1993. We summarize rationales used in scale choice and also review key differences in scale-specific experimental design among studies. We found on average that 70% of the observational scales employed in Wildlife-Habitat research were chosen arbitrarily with no biological connection to the system of study, and with no consideration regarding domains of scale for either dependent or independent variables. Further, we found it common to change either both grain and extent, or the entire suite of independent variables across scales, making cross-scale extrapolations and generalizations impossible. We discuss these sampling limitations by clarifying the differences between multi-scale versus multi-design studies, including the distinction between spatial versus scalar observations, and how these may differ from the commonly cited “orders of resource selection”. We conclude by reviewing both existing and suggested alternatives to reduce the arbitrary nature of observational-scale choice prevalent in today's literature.
-
Quantifying patch distribution at multiple spatial scales: Applications to Wildlife-Habitat models
Landscape Ecology, 2005Co-Authors: Chris J. Johnson, Mark S. Boyce, Robert Mulders, Anne Gunn, Rob J. Gau, H. Dean Cluff, Ray L. CaseAbstract:Multiscale analyses are widely employed for Wildlife-Habitat studies. In most cases, however, each scale is considered discrete and little emphasis is placed on incorporating or measuring the responses of Wildlife to resources across multiple scales. We modeled the responses of three Arctic Wildlife species to vegetative resources distributed at two spatial scales: patches and collections of patches aggregated across a regional area. We defined a patch as a single or homogeneous collection of pixels representing 1 of 10 unique vegetation types. We employed a spatial pattern technique, three-term local quadrat variance, to quantify the distribution of patches at a larger regional scale. We used the distance at which the variance for each of 10 vegetation types peaked to define a moving window for calculating the density of patches. When measures of vegetation patch and density were applied to resource selection functions, the most parsimonious models for wolves and grizzly bears included covariates recorded at both scales. Seasonal resource selection by caribou was best described using a model consisting of only regional scale covariates. Our results suggest that for some species and environments simple patch-scale models may not capture the full range of spatial variation in resources to which Wildlife may respond. For mobile animals that range across heterogeneous areas we recommend selection models that integrate resources occurring at a number of spatial scales. Patch density is a simple technique for representing such higher-order spatial patterns.
-
mapping uncertainty sensitivity of Wildlife Habitat ratings to expert opinion
Journal of Applied Ecology, 2004Co-Authors: Chris J. Johnson, Michael P GillinghamAbstract:Summary 1 Expert opinion is frequently called upon by natural resource and conservation professionals to aid decision making. Where species are difficult or expensive to monitor, expert knowledge often serves as the foundation for Habitat suitability models and resulting maps. Despite the long history and widespread use of expert-based models, there has been little recognition or assessment of uncertainty in predictions. 2 Across British Columbia, Canada, expert-based Habitat suitability models help guide resource planning and development. We used Monte Carlo simulations to identify the most sensitive parameters in a Wildlife Habitat ratings model, the precision of ratings for a number of ecosystem units, and variation in the total area of high-quality Habitats due to uncertainty in expert opinion. 3 The greatest uncertainty in Habitat ratings resulted from simulations conducted using a uniform distribution and a standard deviation calculated from the range of possible scores for the model attributes. For most ecological units, the mean score, following 1000 simulations, varied considerably from the reported value. When applied across the study area, assumed variation in expert opinion resulted in dramatic decreases in the geographical area of high- (−85%) and moderately high-quality Habitats (−68%). The majority of Habitat polygons could vary by up to one class (85%) with smaller percentages varying by up to two classes (9%) or retaining their original rank (7%). Our model was based on only four parameters, but no variable consistently accounted for the majority of uncertainty across the study area. 4 Synthesis and applications. We illustrated the power of uncertainty and sensitivity analyses to improve or assess the reliability of predictive species distribution models. Results from our case study suggest that even simple expert-based predictive models can be sensitive to variation in opinion. The magnitude of uncertainty that is tolerable to decision making, however, will vary depending on the application of the model. When presented as error bounds for individual predictions or maps of uncertainty across landscapes, estimates of uncertainty allow managers and conservation professionals to determine if the model and input data reliably support their particular decision-making process.
