## 1 Introduction  
Spatial predictions of environmental variables are increasingly required in environmental sciences and management. Accurate spatially continuous data are required for environmental modeling, and for evidence-based environmental management and conservation. Such data are, however, usually not readily available and they are difficult and expensive to acquire, especially in areas that are difficult to access (e.g., mountainous or marine regions). In many cases, the spatial data of environmental variables are collected from point locations. Thus, spatial predictive methods are essential for generating spatially continuous predictions of environmental variables from the point samples. Moreover, predictive methods are increasingly being used to generate spatial predictions across various disciplines in environmental sciences [1; 2; 3; 4; 5; 6] in parallel to recent advances in (1) computing technology and modeling techniques [7; 8; 9], and (2) data acquisition and data processing using remote-sensing techniques and geographic information systems. These advancements resulted in increasingly more environmental variables available for spatial predictive modeling. Consequently, more sophisticated spatial predictive modeling approaches are needed to deal with a large number of predictive variables. Accuracy of spatial predictive model(s) is critical as it determines the quality of their predictions that form the scientific evidence to inform decision- and policy-making. Therefore, improving the accuracy by choosing an appropriate method and then identifying and developing the most accurate predictive model(s) is essential. It is often difficult to select an appropriate method for any given dataset because spatial predictive methods may be data- or even variable-specific and many factors need to be considered [10; 11]. Although the development of hybrid methods of machine learning and geostatistics, and their application considerably improved predictive accuracy, these methods may also be data- or variable-specific [12; 13; 14]. For spatial predictive modeling, \"no free lunch theorems\" [15] are also applicable. Furthermore, even with the right predictive method, it is a challenging task to identify and develop the most accurate predictive model(s). This is because the spatial predictive modeling process involves many factors or components [10; 16; 17]. In fact, only a portion of such factors were considered in many previous studies, which often led to sub-optimal or even misleading predictive models [11; 18]. This not only presents an opportunity for scientists to develop and improve their predictive models, but also highlights the challenge of selecting relevant predictive variables from a large number of available predictive variables to form the most accurate predictive model. Heavy computations are often involved in identifying and selecting accuracy-improved predictive models for given datasets when the number of predictive variables is large, although high-performance computing facilities may be able to significantly alleviate this challenge. This study aims to assist spatial modelers and scientists by critically reviewing the spatial predictive modeling process, developing guidelines for selecting the most appropriate spatial predictive methods and identifying and developing the most accurate predictive model to generate spatial predictions. In this study, I focus on spatial predictive models or spatial predictive modeling for generating spatially continuous predictions rather than on other models (e.g., inferential model) as discussed previously [19]. Consequently, the term accuracy in this study refers to the accuracy of predictive model(s) based on validation, and the term uncertainty refers to prediction uncertainty generated by predictive model(s). In this study, the term accuracy is used interchangeably with predictive accuracy.