## 4 Discussion
### Important Factors to Consider for Improving Target DP and CC  
The reduction in effective detection and classification distance of targets in CT images with increased distances (10-30 m) is comparable with previous studies. This includes [PERSON] et al. [22] who found reduced effective detection distance when assessing the DP of species with varying body mass on CT trigger probability. This is consistent with [PERSON] et al. [43] who noted that close (1 m) and far passes towards the peripheral viewpoint of the CT decreased trigger probability. Similarly, [PERSON] et al. [2] reported a significant influence of distance from the centreline on the DP of 'poachers' using drones and human analysts. Additionally, [PERSON] et al. [44] reported the complexities of ML detection and classification of 'poachers' in partially occluded images. Likewise, the impact of increased occlusion levels on detection and classification within most experimental conditions agrees with previous findings [16; 21]. When combined with further distances and occlusion, closed vegetation (riverine forest) led to increased adverse effects of distance and occlusion on human DP in all daylight models. The closed vegetation presented a more significant negative influence on detecting and classifying humans in daylight CT images than within dusk CT images captured in open vegetation (miombo woodland) at the same distance and occlusion. Similarly, [PERSON] et al. [26] reported that although seasonal differences alone presented no influence, they adversely reduced the DP of ungulates in CT images when coupled with species size and increased distance. However, we only compared vegetation type differences for human daylight CT experiments. Therefore, we could not rule out denser vegetation as an influence on target DP and CC within dusk CT images. Similarly, vegetation type was not included in the wildlife experiments as only images containing riverine forest vegetation were used due to a lack of wildlife images captured in open woodland. Three main factors contribute to the magnitude of non-significant effects on human CC and DP models: (1) the natural outlier conditions found at a 5 m distance for daylight DP models and 25-30 m distance for the ML-dusk-DP model, (2) the high probability that colour presents no influence on the CC of humans, and (3) the high true-positive detection and classification responses increase the likelihood that most factors presented a positive influence or no influence at all on DP or CC rates. This is because analysts presented no false-negative responses, and they reached 71.7% for daylight true-positive DPs, similar to ML with 76.5%. Dusk experiments presented a significant adverse effect on wildlife DP and CC for cathemeral species. This strongly supports the conclusions of [PERSON] et al. [16] who found that greyscale CT images likely caused diurnal and cathemeral species classification biases towards commonly nocturnal species, including _C. crocuta_. Similarly, dusk lighting conditions significantly affected the DP of humans, particularly at further distances (20-30 m). However, [PERSON] et al. [2] found the time-of-day factor to be an insignificant predictor of human DP in drone imagery. Most of the species' classes influenced DP and CC, with all but _C. ascanius_ significantly negatively impacting wildlife CC in CT images using both ML and analyst approaches. This was particularly the case when correctly classifying species of similar characteristics, including _T. sylvaticus_, _S. grinmia_, and _P. monticola_. These findings indicate that physical similarities, including the general morphological size and shape of _T. sylvaticus_ and _S. grinmia_, contributed to the probability of correctly classifying them. Similarly, the female bushbuck class and both duiker species, coupled with occlusion led to reduced CCs, particularly for ML methods which may have been due to similar face shapes and markers of both species. This is consistent with previous findings [16; 21].