###### Abstract
However, many of these studies have used images of whole animals to reduce occlusion or used single-species images and simplified conditions, likely leading to increased accuracy biases. This includes [PERSON] et al. [19] using single-species images, likely contributing to accuracies of 96.6% on a 3.2 million-image Snapshot Serengeti dataset from Tanzania. Additionally, [PERSON] et al. [22] further investigated ML applications using the Snapshot Serengeti dataset to reduce overall manual processing and ML model training. This time they manually cropped and segmented images containing whole animal bodies to increase animal identification. This likely led to their ML performance matching state-of-the-art citizen scientist accuracies for the 3.2 million image dataset with only 14,100 manual classification labels, reducing manual labelling effort by over 99.5%. Similarly, [PERSON] et al. [21] cropped over 7000 camera trap images from Barro Colorado Island, Panama and Hoge Veluve National Park, Netherlands. This resulted in an 82% accuracy of detection and classification of 18 mammalian species using ML methods. Although in some cases, combining citizen scientist and ML methods may be beneficial, such methods have not been practiced for target detection and classification within CT images [17; 18]. However, [PERSON] et al. [17] combined analyst and ML methods, using citizen scientists to pre-process images. They then tested CNN performance on empty image identification accuracy (91-98%) and species identification accuracies (88-92%) of all CT images, reducing analyst image processing time by 43%. Nevertheless, the need for researchers to simplify conditions or combine analyst and ML processing methods for sufficient accuracies underlines the importance of mitigating external influences on target DP and CC in CT images. There is, however, increasing promise in ML methods as an independent approach to CT image processing, as studies focus on improving ML performance in comparison with analysts [22]. An example of this includes [PERSON] and [PERSON] [23], comparing machine-learning and deep-learning model accuracies for species identification and finding >95% accuracy when detecting and classifying 19 mammalian species. Nonetheless, such performance testing has not yet incorporated an extensive amount of external influential factors. Certain factors' influence, including effective detection distance and species size on the effect of CT trigger probability, are well documented [24; 25; 26]. However, there is no published study focusing on their influence on the ability to detect targets within CT images, although distance and species size class have both been identified as reliable predictors of wildlife DP due to their influence on CT trigger probability [25; 26]. The influence of wildlife occlusion on the DP of targets in CT images, particularly coupled with increasing distance, has been of interest. [PERSON] et al. [17], for example, found that increased distance from the CT increased the occlusion probability of the animal due to vegetation between the animal and the CT. Similarly, studies have found that occluded species-specific characteristics significantly influenced wildlife CC negatively. For example, [PERSON] et al. [21] found negative influences of occluded species-specific characteristics, including coloured patches, stripes, spots, and overall body composition, on the CC of ungulates. Moreover, [PERSON] et al. [16] found negative influences of partially occluded species-specific characteristics, including spots and antler shape, on the CC of red brocket and white-tailed deer species. Some studies have highlighted differences in light illumination and their influence on wildlife DP and CC in CT images, including [PERSON] et al. [21] finding that dark lighting (dusk) presented a negative effect on DP. This also caused classification biases of diurnal species due to the appearance of dense foliage covering the CT, commonly leading to grayscale images.