The Effects of Climate Change on the Development of Tree Plantations for Biodiesel Production in China

Abstract

Biodiesel produced from woody oil plants is a promising form of renewable energy but a combination of tree plantations' long cultivation time and rapid climate change may put large-scale production at risk. If plantations are located in future-unsuitable places, plantations may fail or yield may be poor, then significant financial, labor, and land resources invested in planting programs will be wasted. Incorporating climate change information into the planning and management of forest-based biodiesel production therefore can increase its chances of success. However, species distribution models, the main tool used to predict the influence of future climate-species distribution modeling, often contain considerable uncertainties. In this study we evaluated how these uncertainties could affect the assessment of climate suitability of the long-term development plans for forest-based biodiesel in China by using Sapindus mukorossi Gaertn as an example. The results showed that only between 59% and 75% of the planned growing areas were projected suitable habitats for the species, depending on the set-up of simulation. Our results showed the necessity for explicitly addressing the uncertainty of species distribution modeling when using it to inform forest-based bioenergy planning. We also recommend the growing area specified in China's national development plan be modified to lower the risk associated with climate change.

Predicting Shifts in the Suitable Climatic Distribution of Walnut (Juglans regia L.) in China: Maximum Entropy Model Paves the Way to Forest Management

“…Among SDMs, the maximum entropy (MaxEnt) model is the most popular one to simulate species distribution based on species presence-only records, which are usually readily available from digital specimen museums and published literature [14][15][16]. The MaxEnt model has been widely used to evaluate the relationship between species distribution and determinant variables and to predict the response of species geographical distribution to global climate change [17][18][19][20]. Many studies have found that the MaxEnt model typically outperforms other methods in terms of high predictive accuracy and high tolerance to extremely small sample size [21][22][23].…”

Potential risks of Tithonia diversifolia in Yunnan Province under climate change

“…Species distribution models (SDMs) can be used effectively to predict the distribution of species (Guisan & Zimmermann, 2000; Phillips, Anderson, & Schapire, 2006), and they have been widely used to identify the potential distribution of invasive species (Broennimann & Guisan, 2008; Dai et al, 2018; Jiménez‐Valverde et al, 2011; Lamsal, Kumar, Aryal, & Atreya, 2018; Panda, Behera, & Roy, 2018; Thuiller et al, 2005). However, differences in model algorithms can create uncertainties by influencing the outcome of SDMs (Buisson, Thuiller, Casajus, Lek, & Grenouillet, 2010; Dai et al, 2017; Wiens, Stralberg, Jongsomjit, Howell, & Snyder, 2009; Wright, Hijmans, Schwartz, & Shaffer, 2015). Ensemble model (Araújo & New, 2007; Marmion, Parviainen, Luoto, Heikkinen, & Thuiller, 2009), which integrates multiple individual models, can reduce bias in predictions of species distribution (Araújo & New, 2007), and have been shown to provide robust estimates of invasive species' distribution (Meller et al, 2014; Poulos, Chernoff, Fuller, & Butman, 2012; Srivastava, Griess, & Padalia, 2018).…”

Impact of long-term climate change on Moluccella bucharica (B. Fedtsch.) Ryding population decline in Uzbekistan

“…Biodiversity is generally accepted to be decreasing at an unprecedented rate (1-4). Among the many reasons for this, climate change is often regarded as one of the most significant drivers as it influences the growth and reproduction of species, thereby determining the natural distribution of species (4)(5)(6)(7)(8). The Intergovernmental Panel on Climate Change (IPCC) estimated that the average global temperature, which has increased by 0.85°C during the 20 th century, will continue to increase by at least 0.3-1.7 °C and at most by 2.6-4.8°C by 2100 (9).…”