## 5 Conclusions  
This study demonstrated that the ML method displayed significantly better detection probabilities for human daylight and dusk models compared to the analyst method. However, the ML method illustrated less effective detection and classification probabilities for wildlife models than the analyst method. On the other hand, the analyst method performed well with high detection probabilities of wildlife and humans within CT images, with negligible significance on wildlife CC. Overall the ML method showed great promise for future applications. Therefore, despite such performances for wildlife models, we highly recommend the ML method for target detection and classification within CT images if users carefully consider model training and preparation. Regarding analyst approaches, the developed knowledge of citizen scientist-based factors and influences on wildlife DP and CC could increase the accuracy of ecological monitoring evaluations. Potential improvements to increase DPs and CCs include increased training datasets per class, with a minimum of 2000 per class, considering characteristically similar species. Moreover, future considerations include categorising wildlife into size classes by body mass measurements rather than average height at shoulder length. This includes additional measures for with and without partially occluded species characteristics and directly comparing similar species. Additionally, we recommend providing further training to citizen scientists using \"just-in-time\" methods prior to image analysis.  
The following supporting information can be downloaded at: [[https://www.mdpi.com/article/10.3390/s22145386/s1](https://www.mdpi.com/article/10.3390/s22145386/s1)]([https://www.mdpi.com/article/10.3390/s22145386/s1](https://www.mdpi.com/article/10.3390/s22145386/s1)), Table S1: Analyst-daylight-DP Pre-Outlier Model; Table S2: Analyst-daylight-DP Post-Outlier Model; Table S3: ML-dusk-DP Pre-Outlier Model; Table S4: ML-dusk-DP Post-Outlier Model; Table S5: Sensitivity Analysis 3a; Table S6: Sensitivity Analysis 3b; Table S7: ML-dusk-CC Pre-Outlier Model; Table S8: ML-dusk-CC Pre-Outlier Model. Human data; Spreadsheet S1: Analyst-daylight-DP; Spreadsheet S2: Analyst-dusk-DP; Spreadsheet S3: ML-d daylight-DP; Spreadsheet S4: ML-dusk-DP; Spreadsheet S5: ML-d daylight-CC; Spreadsheet S6: ML-dusk-CC. Wildlife data; Spreadsheet S7: Analyst-DP; Spreadsheet S8: Analyst-CC; Spreadsheet S9: ML-DP; Spreadsheet S10: ML-CC.  
Conceptualisation, S.O.A.W. and S.A.W.; methodology, S.A.W., S.O.A.W. and A.K.P.; software, P.F., C.C. and S.N.L.; validation, S.A.W. and S.O.A.W.; formal analysis, S.O.A.W.; investigation, S.A.W. and S.O.A.W.; resources, S.A.W., A.K.P. and S.O.A.W.; data curation, S.O.A.W.; writing--original draft preparation, S.O.A.W. and S.A.W.; writing--review and editing, S.O.A.W., S.A.W. and A.K.P.; visualization, S.O.A.W. and S.A.W.; supervision, S.A.W.; project administration, S.O.A.W. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the University Research Ethics Committee of Liverpool John Moores University (protocol code: 20/NSP/011, date of approval: 10 March 2020).  
We obtained informed consent from all subjects involved in the study.  
The data presented in this study are available in Supplementary Spreadsheets S1-S10.  
The investigator thanks all contributors for their support, including the MSc Wildlife Conservation and Drone Application students and GMERC Ltd.  
The authors declare no conflict of interest.