## 4 Discussion
### Recommendations to Mitigate Influential Factors  
The influence of multiple factors on target detection and classification within CT images raises cause for concern if users do not consider these during CT study design and implementation. However, some precautions and actions could be applied, reducing, and potentially mitigating, such influences. Many factors should be considered for wildlife monitoring, particularly the monitoring of illegal activity using CTs, including the CT's structure and appearance (camouflage, robustness, etc. ), its setup, and its software capabilities. However, we instead aim to focus on the following CT features, with the aim of directing users towards the most optimal CT type for effective near-real-time use: trigger speed, battery life, field of view, resolution quality, and network capabilities. Additionally, we overview CT distribution and abundance, ML model type and training, analyst training, and Random Subspace Methods (RSM) for managing target occlusions within CT images. There are various commercial camera traps available, in addition, most conservation organisations are making strides in advancing CTs, including the PoacherCams Panthera CT (Panthera, New York, NY, USA) [47] and the Trailguard AI camera by RESOLVE (Washington, DC, USA) [48]. Therefore, simply recommending a CT Type will not be of much use due to their ever-evolving technology and the vast range of CTs. Instead, we aim to highlight \begin{table}
\begin{tabular}{c c} \hline \hline
**Alternative Hypothesis** & **Outcome** \\ \hline Targets orientating towards the CT would positively influence target DP in CT images. & Accepted \\ Targets orientating towards the CT would positively influence target CC in CT images. & Rejected \\ Partially occluded species-specific characteristics would negatively affect CC of similar species. & Accepted \\ The time-of-day factor (dusk) would negatively influence wildlife DP and CC. & Accepted \\ The time-of-day factor (dusk) would negatively influence human DP and CC. & Rejected \\ Colour contrast (green), in comparison to the background, would negatively impact human DP. & Accepted \\ Increasing distance and occlusion would significantly decrease target DP and CC for all wildlife models. & Accepted \\ Increasing distance and occlusion would significantly decrease target DP and CC for all human models. & Rejected \\ Dense vegetation would contribute to the significant decrease of one or more models. & Accepted \\ ML methods could perform at a statistically significant increased rate than analyst methods for at least one model. & Accepted \\ There would be a significant positive difference in analyst performance on target DP and CC. & Accepted \\ Decreases in size would decrease DP and CC, respectively, for wildlife and humans. & Rejected \\ \hline \hline \end{tabular}
\end{table} 
Table 4: The tested hypothesis summary for all factors. the factors which we find important for effective near-real-time threat monitoring based on the observations within this study and previous research recommendations [10; 49]. The suitability of CTs for various conservation studies is widely acknowledged [50; 51; 52; 53]. However, we focus on CT features that are most important for optimum target detection and classification within CT images. One particularly important feature is trigger speed. This has had a significant influence on target detection rates and has been one of the most evolved aspects of CT features, with trigger speeds now reaching approximately 0.5 seconds [49]. Reliable and long battery life are essential for effective threat monitoring. As previously mentioned most CTs on high sensitivity settings can last, on average, two to four weeks [10]. Additionally, new CT advancements, including Trailguard AI [48], have enhanced rechargeable batteries and can now reach up to 1.5 years on a single battery.