Habitat suitability assessment of Taxus cuspidate

ObjectiveHabitat suitability assessment is of great significance for species conservation and management. Previous studies on species distribution relied solely on various models without a clear evaluation method. Traditional assessment of habitat quality which relied on experiential scoring was prone to subjective bias. This paper avails itself of fuzzy mathematics and habitat suitability index (HSI) model to study the response relationship between habitat suitability and environmental factors with the aim of providing a theoretical basis for conservation of Taxus cuspidata.

MethodSpecies individual abundance was used as a habitat suitability indicator, fuzzy mathematics as tool to construct single factor evaluation functions, and HSI model as tool to calculate the habitat suitability index. Then the spatial distribution map of habitat suitability was drawn.

ResultThe climatic evaluation factors were ranked as minimum precipitation of the driest month > annual mean temperature > mean temperature of summer > mean diurnal temperature range according to the importance of entropy information, and the topographic evaluation factors were ranked as slope aspect > slope position > slope degree > elevation. The accuracy of model test was 65%, and the area under receiver operating characteristic curve was 0.7. The model prediction showed that the distribution of Taxus cuspidata was narrow and severely fragmented, and the area of habitat plaque was 1.39−2.78 km². The highly suitable areas were mainly distributed in the eastern part of Jilin Province and southeastern part of Heilongjiang Province of northeastern China, covering approximately 41 300 km²; the lowly suitable areas concentrated in the southeastern of Jilin Province, covering approximately 62 800 km².

ConclusionThe combination of fuzzy mathematics with HSI model to study the relationship between environmental variables and the individual abundance of Taxus cuspidata was available to evaluate habitat suitability. The model prediction results, to a certain extent, reflect the spatial pattern of the highly and lowly suitable areas, which can provide a scientific basis for developing species conservation strategy.