## 4 Discussion
### Important Factors to Consider for Improving Target DP and CC
Species size seemed to be a particularly influential characteristic for analysts and ML-DP. The reasoning for this conclusion is that all the species that significantly positively influenced DP were medium-large, except _H. cristata_. However, we removed the size class factor from the analysis due to its high multicollinearity with the factor species. Therefore, although we found a significant correlation between the species and size class factors, we cannot be certain that size was an influencing variable. Yet, there is previous evidence of increasing size positively influencing the DP and CC of wildlife in CT images [26]. Large sizes led to higher DP at greater distances than smaller sizes. Nevertheless, as [PERSON] et al. [16] reported, species characteristic similarities adversely affect species-specific recognition of similar ungulates when using ML methods. However, human height variance presented no influence on human detection and classification within CT images. Although, as previously stated, much has been studied on wildlife body mass size influences on DP and CC [26], to the best of our knowledge, no study has focused on human height variances and their impact on human DP and CC within CT images. The analyst performance factor significantly increased the probability of wildlife CCs. Controversially, [PERSON] et al. [45] reported low levels of citizen scientist CCs (51.8%) in comparison to professional biologists (77.6%). Significant variation between analyst responses may have been partially due to training methods used prior to analyst-DP and CC testing. Darker contrast colours (green) against the background negatively influenced the DP of humans in daylight CT images using the analyst method and CCs using the ML method as opposed to a lighter blue. This is consistent with [PERSON] et al. [2] who found darker contrast colours (green) reduced DP of humans within aerial drone RBG imagery in comparison to lighter colours (red and blue). Additionally, poachers are frequently recorded wearing green and brown camouflage to mix with the background surroundings and reduce being seen [6]. Although much is known about the use of camouflage to disguise oneself, little effort has been focused on the degree of influence colour contrastshave on DP and CC. This study strongly supports that green presents a significantly high degree of influence towards reducing human DP in CT images compared to lighter colours. The targets orientating in the CTs direction positively influenced human and wildlife DP using the analyst approach and the DP of wildlife using the ML approach. Controversially, [PERSON] et al. [46] found anterior or posterior poses relative to the CT reduced CT trigger probability due to passive infrared (PIR) sensor detection difficulties. However, their study focuses on the influence of target proportions on CT sensor triggers rather than potential influencing target features on target DP in CT images. In conclusion, distance and occlusion illustrated the most significant influence on both DP and CC, particularly when integrated with dense vegetation for human experiments; vegetation type was not assessed for wildlife experiments. Wildlife CC was particularly influenced by partial or complete occlusion of species, likely caused by occluded visual characteristics. Darker contrast colours significantly reduced human DP. Targets orientating towards the CT illustrated a positive influence on target DP and CC. There was a significant positive variation between analyst performances. Human height presented no influence, and darker lighting conditions significantly negatively influenced human DP and all wildlife models. Additionally, although the analysts performed better overall for the wildlife models, the ML method outperformed analysts for the human experiments (see Tables 2 and 3). This leads to concluding the proposed hypothesis' (see Table 4).