## 3 Results  
### Assessing External Factor Influences on Wildlife DP and CC in CT Images  
The glmm analysis showed that increasing distance (>10 m) and occlusion (68-100%) levels showed significant (\(p\) < 0.001) substantial adverse effects on wildlife DP (see Figure 2a,b), except for 5-9.9 m which significantly (\(p\) < 0.001) positively affected wildlife CC using ML methods (see Figure 2c), indicating that increasing distance and occlusion led to decreases in wildlife DP within CT images.  
P. laratus, S. grimmia, H. cristata, T. sylavaticus, and C. crocuta_ species significantly (\(p\) < 0.001) positively influenced analyst-DP (see Figure 2a). Except for _H. cristata_, all species were within the medium-large class, indicating that increasing size increased wildlife DP within CT images using analyst methods. However, _P. monticola_ and _P. paradus_ illustrated a non-significant (\(p\) = 0.710, 0.903) influence on wildlife DP in CT images using analysts (see Figure 2a). Additionally, all species classes significantly (\(p\) < 0.001) positively influenced ML-DP (see Figure 2b).  
On the other hand, most species classes at increasing distance and occlusion influenced CC. _P. monticola, T. sylavaticus, S. grimmia_ (\(p\) < 0.001), _G. tigrine_ (\(p\) = 0.001), and C. crocuta_ (\(p\) = 0.002) illustrated a significant positive influence on ML-CC. Yet, C. _ascanius_ significantly negatively impacted wildlife CC in CT images using both ML (\(p\) = 0.007) and analyst (\(p\) < 0.001) approaches. However, _P. laratus_ (\(p\) = 0.018), _P. schueinfurthii_ (\(p\) = 0.739), _H. cristata_ (\(p\) = 0.687), and _P. paradus_ (\(p\) = 0.224) illustrated a non-significant influence on ML-CC (see Figure 2c,d). The negative influence of occlusion coupled with species characteristics  
Figure 2: The best-fit models for the wildlife experiments presented as forest plots. All forest plots are composed of effect sizes and error bars containing 95% confidence intervals. Moreover, each factor class is compared to a baseline class as follows, distance: 0–4.9 m, occlusion: 0–33%, species: _Papio cynocophilus_, orientation towards the CT: no, analyst performance: Analyst 1, and time-of-day: daylight. The plots are illustrated as follows: (**a**) analyst detection probability (DP) model; (**b**) machine learning (ML) DP model; (**c**) ML correct classification (CC) model; (**d**) analyst-CC model.  
on CC is anticipated when including increasing distance and occlusion levels between 34-100%, resulting in the partial or complete occlusion of visible species-specific traits.  
The analyst performance factor showed that Analyst 1 presented the most significant (\(p<0.001\)) negative influence on wildlife CC compared to Analyst 2 (\(p=0.252\)), illustrating a non-significant influence. This indicates that individual abilities to recall visual features and their associated classification labels caused fluctuations in wildlife CC (see Figure 2c).  
Furthermore, the time-of-day factor (dusk) significantly (\(p<0.001\)) negatively affected the DP and CC of wildlife in CT images through the analyst approach and DP through the ML approach (see Figure 2a,b,d). This result has highlighted the influence of darker conditions on decreased DP and CC of wildlife. In addition, time-of-day presented a non-significant (\(p=0.039\)) influence on wildlife CC using ML methods (see Figure 2c).  
Nevertheless, the orientation toward the CT factor illustrated a highly significant (\(p<0.001\)) positive effect on the DP of wildlife in CT images using both analyst and ML methods (see Figure 2a,b). This factor presented the most significant favourable influence on DP for both analyst and ML methods.  
In addition, the random factor representing variance between CT image locations showed the highest SD for the analyst-DP model (SD 2.529), followed by ML-CC (SD 2.519), ML-DP (SD 1.237), and the analyst-CC (SD 0.849) models.