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Reconstructing Distribution Change Of Endangered Species Based On Species Historical Distribution Data And Ecological Niche Modeling

Posted on:2019-12-16Degree:DoctorType:Dissertation
Country:ChinaCandidate:L YangFull Text:PDF
GTID:1360330575991578Subject:Nature Reserve
Abstract/Summary:PDF Full Text Request
Understanding the biogeographic characteristics of population decline is a key area of research in conservation science.Long-term historical data can contribute considerable to understanding the ecological characteristics of wildlife change,and improve predictive power for conservation management.However,long-term datasets have been approved to provide valuable information for conservation research,policy and practice.Applying long-term ecology data is still restricted by data accessibility,spatially and temporally variable and non-standardized sampling.Therefore,recent research tend to address on the current and future potential distribution of the species,but only few of them consider the historical background,especially use records over periods longer than 20 years.In this case,integrate historical data into conservation and environment management,while assess the usefulness and potential limitation of this data,can develop our understanding of long-term species change.Local gazetteers also called local records or annals,contain valuable information on environmental conditions and resources in China.New gazetteers normally record valuable historical data since the 1950s at local scale,including animal records,economic,political information and demographic data.Most of such documents are compiled following the same scientific role and this makes it easy to exact the species records.The compilation of local historical documents is systematized across most of China and has been maintained at a reasonably high spatial resolution(mostly at the prefecture level)since 1950.But new gazetteers may have been neglected for a long time and did not use as valuable resource in field surveys,museums and papers.This may be reasonable,because most of new gazetteers are only concerned with special species,like economic,unique or common one.Recent research provide evidence that this resource can contribute to reconstructing long-term population dynamics by provide effective records.In this case,we recognized that combining this local information with long-term ecological records at local scales offers an opportunity to understand the ecological and biogeographical characteristics of an endangered species through time.In this research,we focus on three parts,including data collection,species distribution modeling,and applying long-term historical data.1)Data collection.This research focus on characteristic of new gazetteers(including large datasets,data coverage ratio,reliable resource,and period division),providing a useful way for data collection.Procedure includes:a.collection of new gazetteers;b.confirming research area and animal;c.data extraction;d.period division;e.cross check;f.product map with GIS.The historical changes of four endangered animals in Northeast China were revealed by point data.The results showed that the effective records of four animals decreased significantly,and the sharp fall is likely in the 1980s with elevation rising.2)Constructing historical population change in Amur leopard by SDM.Our results show that Amur leopards were once widely distributed in Northeast China-across the Lesser Khingan Mountains and Changbai Mountain.In the 1950s,the area of potential distribution reached 7.00%of the Northeast China.Compared with the potential distribution in the 1950s,there was a reduction of 46.74%of distribution in the 1960s,47.66%in the 1970s,and 59.23%in the 1980s.However,the situation worsened after the 1980s,with a reduction of 90.25%in the 1990s and 97.09%in the 2000s compared with the 1950s.Afterwards,the distribution became concentrated in a small area along the Chinese-Russian border in 2000-2016.3)Constructing historical population change and distribution change under climate change in black-billed capercaillie by SDM.In this research,we believe that long-term historical data can be an effective tool to help ecologists integrate future projections with historical contexts and provide unique insights into the long-term dynamics of endangered species.Our results revealed a 35.25%reduction in the current distribution of this species compared to their potential distribution in the 1970s.This decline is expected to continue under climate change.For example,the future range loss was estimated to be 38.79 ± 0.22%(8.64-90.19%),and the actual state could be worse,because the baseline range of the model was greater than the real range in the 2000s,showing a 12.39%overestimation.To overcome this poor outlook,a conservation strategy should be established in sensitive areas,including the southwestern Greater Khingan Mountains and northern Lesser Khingan Mountains.Actions that should be considered include field investigations,establishing a monitor network,designing ecological corridors,and cooperating with local inhabitants,governments,and conservation biologists to improve the conservation of the black-billed capercaillie.4)Constructing historical population change in Chinese pangolin by SDM with conservation planning.Our result show that the range have been decreased by 52.20%in the 2000s compare to the 1970s;the population mainly constrict in Wuyi mountain in the 2000s.This reduction was related to human influence.According to conservation prioritization analysis,the prior conservation area is 51,268.4 km2,5.62%of that is covered by nature reserves.For priority area,there are 18 nature reserves,and 46 prefectures.The priority nature reserves or prefectures mainly located on the centre of Wuyi mountains,and the important area tend to be around the Wuyi mountains.To increase conservation effort,we propose that several actions following biological research,monitor system that can help pangolin to maintain their population in eastern China.
Keywords/Search Tags:Long-term species data, New gazetteer, Endanger species, Species distribution model, Historical range change
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