Modeling the Effect of Climate Change on the Potential Distribution of Qinghai Spruce (Picea crassifolia Kom.) in Qilian Mountains

Abstract

Qinghai spruce forests play a key role in water conservation in the dry region of northwest China. So, it is necessary to understand the impacts of climate change on the species to implement adaptation strategies. Based on the four-emission scenario (i.e., RCP2.6 (Representative Concentration Pathway), RCP4.5, RCP6.0 and RCP8.5) set by the Intergovernmental Panel on Climate Change (IPCC) fifth assessment report, in the study, we predicted the potential distribution of Qinghai spruce (Picea crassifolia Kom.) under current and future scenarios using a maximum entropy (Maxent) model. Seven variables, selected from 22 variables according to correlation analysis combining with their contribution rates to the distribution, are used to simulate the potential distribution of the species under current and future scenarios. Simulated results are validated by area under the operating characteristic curve (AUC). Results demonstrate that elevation, mean temperature of wettest quarter, annual mean temperature, and mean diurnal range are more important in dominating the potential distribution of Qinghai spruce. Ratios of the suitable area to the total study area are 34.3% in current climate condition, 34% in RCP2.6, 33.9% in RCP4.5, 33.8% in RCP6.0, and 30.5% in RCP8.5, respectively. The warmer the climate condition is, the more area of higher suitable classification is changed to that of lower suitable classification. The ratios of real distribution area in simulated unsuitable class to the real distribution area change from 4.3% (60.7 km2) in the current climate to 13% (185 km2) in RCP8.5, suggesting that the real distribution area may decrease in the future. We conclude that there is a negative effect of climate change on the distribution of Qinghai spruce forest. The result can help decision-makers to draft adaptation countermeasures based on climate change.

Evaluation of ERA-Interim Air Temperature Data over the Qilian Mountains of China

“…Unfortunately, surface meteorological stations are sparse in QLM, especially in the western QLM. erefore, most of previous studies in QLM are based on limited observations or remote sensing data on the climate and glacier changes in QLM [6][7][8][9]. Jiang et al [10] found that the annual variations of ice and snow are significant along elevations based on MODIS data.…”

Spatiotemporal dynamics of habitat suitability for the Ethiopian staple crop, Eragrostis tef (teff), under changing climate

“…Existing research of different regions has reported variables found to have an important effect on various species. For example: Evangelista, Young & Burnett, 2013 Studied teff by using three climate projection model that the value of predictable were 0.79 (Bio16, Bio12, Bio19, Bio15) and others studied different species in different geographical area including, (Elham et al, 2015) (Bio2, Bio3, Bio7, Bio8, Bio13, Bio14, Bio15, Bio18), ( Ardestani et al, 2015 ) (Bio8, Bio19, Bio2, Bio13, Bio7), Sen et al, 2016 (Bio4 and Bio19), Rong et al, 2019 (Bio1, Bio8 and elevation), ( Abdelaal et al, 2020 ) (elevation, precipitation, temperature and soil), and Yang et al, 2020 (Temp Seasonality, Precipitation, Vegetation Type, Soil Type). Our results indicate that temperature of coldest quarter had a predictability value of 0.83 for teff, which is stronger than that of any other existing model.…”

“…A variety of SDMs have been developed to predict species distributions under different climate scenarios (e.g., RCP). Commonly used SDM models including the Genetic Algorithm for Rule-set Production (GARP), BIOCLIM and Ecological Niche Factor Analysis (ENFA) have proved to be essential for predicting target species distributions under current, and also future climate scenarios ( Rong et al, 2019 ; Tognelli et al, 2009 ). Maxent (maximum entropy) has been widely used due to its many advantages, including its ability to: deal with incomplete data, small sample size, species presence data, both continuous and categorical environmental data; reduce laborious jobs in data collection; facilitate model interpretation, and for its prediction accuracy and reliability ( Guo et al, 2017a ; Rong et al, 2019 ).…”

“…Commonly used SDM models including the Genetic Algorithm for Rule-set Production (GARP), BIOCLIM and Ecological Niche Factor Analysis (ENFA) have proved to be essential for predicting target species distributions under current, and also future climate scenarios ( Rong et al, 2019 ; Tognelli et al, 2009 ). Maxent (maximum entropy) has been widely used due to its many advantages, including its ability to: deal with incomplete data, small sample size, species presence data, both continuous and categorical environmental data; reduce laborious jobs in data collection; facilitate model interpretation, and for its prediction accuracy and reliability ( Guo et al, 2017a ; Rong et al, 2019 ). SDMs assume that available presence locations represent a random (representative) sample in the geographical space, with no spatial dependencies and bolstered by data-rich technologies, such as geographical information systems (GIS) ( Rushton, Ormerod & Kerby, 2004 ; Elith et al, 2011 ).…”

Global distribution of Sapindus habitats under current and future climate change scenarios

“…Many model intercomparison studies have reported that the MaxEnt model, which is based on the principle of maximum entropy [24,27,31,32] , typically outperforms other species distribution models (SDMs) in terms of high tolerance and high predictive accuracy [33][34][35] . Over the past 10 years, worldwide research teams have achieved excellent results in the study of rare animal and plant diversity protection [36][37][38][39] , invasive species risk prediction [40,41] , marine ecosystem protection [42,43] , disaster distribution prediction [44] , and disease propagation [45,46] using the MaxEnt model. Zhang et al [47] predicted the distribution of suitable habitats for Cinnamomum camphora (L.) Presl in China under future climate change scenarios.…”