Terrestrial carbon sequestration capacity is an important indicator of ecosystem service function, and the carbon storage value can reflect the climate regulation capacity of the regional ecological environment. The Zoigê alpine grassland is a representative area of the Qinghai-Tibet Plateau grassland ecosystem, with carbon sequestration types such as alpine grassland and marsh meadow and also an important water-conserving area in the upper reaches of the Yangtze River and the Yellow River. In this study, based on the land use/cover change pattern of the Zoigê alpine grassland region from 2000 to 2020, the carbon density coefficients corrected by the regional average annual precipitation and temperature factors were used to assess the carbon stocks of the Zoigê alpine grassland for three periods from 2000 to 2020 using the InVEST model. The results showed that the carbon stocks of the Zoigê alpine grassland region were 786.19 Tg, 780.02 Tg, and 775.22 Tg in 2000, 2010, and 2020, respectively, with a cumulative loss of 10.97 Tg and carbon densities of 183.70 t/ha, 182.26 t/ha, and 181.14 t/ha, showing a decreasing trend year by year. The carbon stock of the grassland ecosystem is the absolute contributor to the regional carbon stock, and the carbon stock accounts for 75.28% of the total carbon stock. The increase in the cultivated land area with a lower carbon density and the decrease in the grassland area with a higher carbon density are the main factors leading to the decrease in the carbon stock in the regional ecosystem of the Zoigê alpine grassland.
The biogeography research of orchids through species distribution models
(SDMs), a vital tool in the biogeography field, is critical to
understanding the fundamental geographic distribution patterns and
identifying conservation priorities. The correspondence between species
occurrence and environmental information is crucial to the model’s
performance. However, ecological preferences unique to different orchid
species, such as their life forms, are often overlooked during the
modeling process. This oversight can introduce bias and increase model
uncertainty. Additionally, human activities, as an important potential
predictor, have not been quantified in any orchid SDMs. Taking the
Hengduan Mountains as an example, we preprocessed all orchid species’
occurrences based on physiological characteristics. Choosing five
spatial factors related to human activities to quantify the interference
and enter into models as HI factor. Using different modeling methods
(GLM, MaxEnt, and RF) and evaluation indices (AUC, TSS, and Kappa),
diverse modeling strategies have been constructed in the study. A
double-ranking method has been adopted to select the critical orchid
distribution regions. The results showed that classification models
based on physiological characteristics significantly improved the
model’s accuracy while adding the HI factor had the same effect but the
absence of enough significance. Suitability maps indicated that highly
heterogeneous mountainous areas were vital for the distribution of
orchids in the Hengduan Mountains. Different distribution patterns and
critical regions existed between various orchid life forms
geographically - terrestrial orchids were dominant in the mountain, and
mycoherterophical orchids were primarily located in the north, more
influenced by vegetation and temperature. Critical regions of epiphytic
orchids were in the south due to a greater dependence on precipitation
and temperature. These studies are informative for understanding the
orchids’ geographic distribution patterns in the Hengduan Mountains,
promoting conservation, and providing references for similar research
beyond orchids.
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