A Framework for Evaluating the Effects of Human Factors on Wildlife Habitat: the Case of Giant Pandas

Abstract

To address the complex interactions between humans and wildlife habitat, we developed a conceptual 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 framework 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 various methods ( field observations, aerial photographs, government documents and statistics, interviews, and household surveys) and employed geographic information systems and systems modeling to analyze and integrate 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 activities 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 reducing 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 policies 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.

Climate change threatens giant panda protection in the 21st century

“…Other environmental variables that are considered to be important drivers of giant panda distributions (Liu et al, 1999;Shen et al, 2008) were also used to build the giant panda habitat distribution model, including a biotic factor (bamboo availability) and topographical factors (slope, and aspect). We also incorporated the potential impact of human disturbance into our giant panda model, as human disturbance is known to intensify the negative impacts of climate change through habitat loss and fragmentation (Fan et al, 2014).…”

“…The habitat suitability model was constructed based on giant pandas' habitat selection criteria, including bamboo suitability, land cover, elevation, slope, aspect, distance from residential areas, and distance from roads (Liu et al, 1999;Shen et al, 2008). A suitability value of 1 (more resistance or less accessibility) to 50 (less resistance or high accessibility) was assigned at each pixel-cell to indicate suitability according to the previous study on giant panda and experts' views (Table A4) (Shen et al, 2008), then we developed an integrated habitat suitability map by determining the weights of all the layers with the Analytic Hierarchy Process (AHP) (Saaty, 1990) and combining the layers with the weighted linear combination approach.…”

“…The endangerment mainly comes from anthropogenic activities, such as poaching and habitat destruction over the past the past 3000 years (Zhao et al, 2012). This impact is exacerbated by the biological constraints of the species, including dietary specialization, low reproductive rates and restricted gene flow (Liu et al, 1999;Lv et al, 2001). The giant panda was once a large carnivorous animal, after millions of years of evolution and adaptation to environmental change, it gradually evolved into a specialized species, 99% of its diet now consists of understory bamboos (Zhao et al, 2012).…”

Modeling Impacts of Climate Change on Giant Panda Habitat

“…Based on a framework developed by Liu et al [51], we considered areas within 1410 m of cities and 210 m of roads (city and transportation network data from NIMA [52]) to be unsuitable. We used land cover data from Global Land Cover 2000 [53].…”

Local people's perceptions as decision support for protected area management in Wolong Biosphere Reserve, China

“…In WBR, because of the rapid increase in population, especially the rate of increase in the labor force, which was three times higher than the rate of change in the total population, ecological degradation of the Giant Panda's habitat occurred (Liu et al, 1999b(Liu et al, , 2001). Therefore, a clear understanding of local people's responses to conservation through various management policies is necessary.…”