Conservation of endemic species in China
China is one of the most biodiverse countries in the world, harboring more than 10% of the species in the world. Among them, 11% of the vertebrate genera and 7% plant genera are endemic to China. During its rapid social and economic development, increasing habitat loss and fragmentation have occurred. However, it wakes up to the threats of biodiversity in recent years. Protected areas, as an essential conservation tool to reduce habitat loss and species extinction have expanded dramatically in China. Protected areas with various other concepts such as umbrella species and payment for ecosystem services have been promoted to conserve the biodiversity. However, questions remain that whether they work, how they work and how we could do better. It is crucial to answer these questions with the data and technology that are more available to us now.
Thus, my dissertation divides into four chapters and tackles the following four questions. 1) Where do the most of the endemic species concentrate in China? Do umbrellas species such as giant pandas effectively protect other species? 2) With the increasing of tree plantation and available remote sensing data, how does it change the available habitat for forest species, their threat levels and priority setting? 3) Within the conservation priority areas, new threats that are hardly detected by traditional evaluation index such as forest cover emerge. How does a prevalent human disturbance - livestock grazing impact the conservation of giant pandas? What are the socio-economic drivers and solutions to this issue? 4) To better monitor the population and evaluate conservation efforts, new techniques need to be added. Can we use footprints from wild pandas to identify individuals and provide a cost-effective alternative to the current methods?
In Chapter 1, I first used detailed data on geographical ranges for endemic forest species to identify patterns of species richness. After refining each species' range by its known elevational range and remaining forest habitats as determined from remote sensing, I identified the top 5% richest areas as the centers of endemism. Over 96% of the panda habitat overlapped the endemic centers. Thus, investing in almost any panda habitats will benefit many other endemics. Existing panda national nature reserves cover all but one of the endemic species that overlap with the panda’s distribution. For whole China, of particular interest are 14 mammal, 20 bird, and 82 amphibian species that are inadequately protected. Most of these the IUCN currently deems threatened. But 7 mammal, 3 bird, and 20 amphibian species are currently non-threatened, yet their geographical ranges are <20,000 km2 which is the threshold for IUCN to consider it as threatened. There is a high concentration of these species in the east Daxiang and Xiaoxiang Mountains of Sichuan where pandas are absent and where there are no national nature reserves. The others concentrate in Yunnan, Nan Mountains and Hainan. Here, ten prefectures might establish new protected areas or upgrade local nature reserves to national status.
In Chapter 2, I used remote sensing data to differentiate oil palm and rubber plantation from natural forests in Southeast Asia and reevaluated the threat level of endemic forest species identified by IUCN. Tropical, mainland Southeast Asia is under exceptional threat, yet relatively poorly known. This region contains over 122, 183, and 214 endemic mammals, birds, and amphibians, respectively, of which the IUCN considers 37, 21, and 37 threatened. When corrected for the amount of remaining natural habitats, the average sizes of species ranges shrink to <40% of their published ranges and more than 42 percent of species face a much higher risk of extinction from habitat loss than previously thought. Moreover, these species are not better protected by the existing network of protected areas than are species that IUCN accepts as threatened. Furthermore, incorporating remote sensing data showing where habitat loss is prevalent changes the locations of conservation priorities.
Chapter three focuses on a specific threat - livestock grazing in the endemic center that I identified in the first chapter. With the Natural Forest Conservation Program and Grain to Green programs, the deforestation that was once the biggest threat to pandas has been halted. However, a previously unrecognized threat is emerging. Livestock grazing has become the most prevalent human disturbance throughout panda habitats. I applied field sign survey, vegetation survey, GPS collar tracking, and species distribution modeling to study how the livestock grazing impacts the habitat use of giant pandas. This study shows that livestock grazing especially from horses has caused a dramatic decline in bamboos and reduced its regeneration. In the past 15 years, pandas have changed its habitat use and are driven out of areas that are heavily used by livestock. 49% of panda habitat has been lost especially in the lower elevation areas from 2004 till now due to impacts of livestock. Loss of income because of the policies Natural Forest Conservation Project and Grain for Green projects, reduced tourists because of dam construction and earthquake, encouraged horse riding practice during the development of ICDP have contributed to the increasing dependence on livestock sector. Livestock ban with payment for ecosystem services or feedlot operation could be possible solutions for this issue.
Chapter four explores the innovative technique to identify giant panda individuals to facilitate better conservation. Two methods have been used previously to identify individuals and population for giant pandas, fecal bamboo bite size combined with home range analysis and microsatellite analysis of fecal DNA. However, the first one suffers from the lack of accuracy and the latter one is limited by the freshness of the fecal sample and high cost. I developed the footprint identification technique in JMP based on two multivariate methods: discriminant analysis and the canonical centroid plot method using the anatomy measurements of footprints. I used 30 captive pandas to develop the algorithm and 11 individuals for validation. The overall accuracy of FIT for individual identification is 90% and sex discrimination is 85%. This technique is embedded in FIT as an add-in and free for conservation practitioners now.
In summary, this dissertation includes the following four papers.
Chapter 1, Li and Pimm. 2016. China's endemic vertebrates sheltering under the protective umbrella of the giant panda. Conservation Biology 30:329-339.
Chapter 2, Li et al., 2016. Remotely sensed data informs Red List evaluations and conservation priorities in Southeast Asia. PloS one, 11(8), e0160566.
Chapter 3, Li et al., Emerging threat from livestock on giant panda conservation
Chapter 4, Li et al., Identifying individual and sex of giant pandas through Footprint Identification Technique.
With supporting information from the following publication during my Ph.D.:
Li, B. et al. 2014. Effects of feral cats on the evolution of anti-predator behaviours in island reptiles: insights from an ancient introduction. Proc. R. Soc. B 281: 20140339.
Ocampo-Peñuela, N., Jenkins, C. N, Vijay, V., Li, B.V., & Pimm., S.L. 2016. Incorporating explicit geospatial data shows more species at risk of extinction than the current Red List. Science Advances, 2(11), e1601367.
Pimm, S.L., Harris, G., Jenkins, C.N., Ocampo-Peñuela, N. & Li, B.V. 2016 Unfulfilled promise of data-driven approaches: response to Peterson et al. Conservation Biology, In press.
non-invasive survey method
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