## 2 Sampling Design, Sample Quality Control, and Spatial Reference Systems  
### Sampling Design  
Although samples are usually collected, stored, and ready to use for spatial predictive modeling, sometimes samples are not available and need to be collected. In the latter situation, a sampling design needs to be produced. In this study, I focus on sampling designs over space. To collect samples from a survey area for a certain survey purpose, a sampling design is an important step and must be created. A good sampling design ensures that data collected from a survey are capable of answering relevant research questions. Better designs, such as spatially stratified sampling designs, will also be as precise and efficient as possible [21; 22]. Many methods were developed to generate sampling designs [23; 24; 25; 26]. They typically fall into four main categories: (1) non-random sampling design; (2) unstratified random sampling design; (3) stratified random sampling design; and (4) stratified random sampling design with prior information.  
The non-random sampling design can be ad hoc sampling based on expert knowledge, purely opportunistic when a certain type of environmental condition becomes available, or systematic sampling. This type of sampling design was applied to many surveys [27; 28; 29]. For spatial predictive modeling, this method is not recommended for future studies. However, an interesting comparison of non-random sampling designs was reported for spatial predictive modeling [26]; it may provide some useful clues for sampling designs (e.g., lattice plus close pairs) for spatial predictive modeling.  
The unstratified random sampling design is that sampling locations are randomly selected. This can be (1) an unstratified equal probability design, or (2) an unstratified unequal probability design [30; 31]. This type of sampling design is not recommended for spatial predictive modeling studies because (1) spatial information is available for sampling design and thus spatially stratified sampling design should be used as discussed below, and (2) it may even be over-performed by the non-random design (i.e., lattice design) [26].  
The stratified random sampling design is often used when additional information is available. Such information can be spatial (or location) information, elevation, bathymetry, or geomorphological information. For spatial predictions, such information is important and should be considered when designing a survey for a region. A few recently developed randomized spatial sampling procedures were reviewed and compared using simple random sampling without replacement as a benchmark for comparison [22]. This study provided some empirical evidence for the improvement of sampling efficiency from using these designs and provided some guidance for choosing an appropriate spatial sampling design [22]. Furthermore, some R packages, such as _spsurvey_[31], _GSIF_[32], _spcosa_[33], _clhs_[34], and _BalancedSampling_[35], were developed for this type of sampling design. The stratified random sampling designs with spatial information (i.e., spatially stratified sampling design) are increasingly being used in practice [28; 36; 37].  
The stratified random sampling design with prior information is a new development for sampling over a space. It incorporates the locations of legacy sites into new spatially balanced survey designs to ensure spatial balance among all sample locations [21]. It can be seen as a stratified unequal probability design. An R package, _MBHdesign_, was developed for this method [38].