Assignment Task:
1. Expand on your thoughts about at least one of the concerns/challenges in the post.
2. Mention and comment on at least one assumption of occupancy modelling illustrated by a comment in the post. Use an outside source that mentions this assumption as an assumption of the technique. Cite source in APA format. Need Assignment Help?
Out of the six trials I conducted, I was able to detect a rabbit in four of them, which gives a detection rate of 66.7%. This means that in two-thirds of the trials, I was able to find the rabbit, but there was still a chance (33.3%) that it went undetected. In terms of whether this percentage truly represents the detectability of rabbits, it's a decent starting point, but I don't think it fully reflects the species' true detectability. The 66.7% rate suggests that the rabbit is somewhat detectable, but external factors like its position, the time of day, or even the environment could all affect detection rates. If we conducted more trials, it would likely give a better idea of how detectable the rabbit is in different conditions.
One challenge I noticed in the trials is the problem of false absence. In Trials 4 and 6, I didn't detect a rabbit, but that doesn't necessarily mean it wasn't there. It's easy to miss something, especially in a simulation like this, where there's a limited timeframe for observation. This is a common issue in real-world occupancy surveys, where species can be present but not detected due to the limitations of the survey method. Another challenge was the variability in the time it took to detect the rabbit. In some trials, it only took 20 seconds, but in others, it took over two minutes. This inconsistency could be influenced by the rabbit's position or how quickly I was able to spot it, but it also shows how unpredictable detection can be. It's a good reminder that short observation windows might not always be sufficient to detect elusive species.
Based on my experience, a few things could improve the design of these surveys. First, the number of trials should be increased to get a more reliable estimate of detectability. Six trials gave me a decent idea, but it's a small sample size, and more trials would help account for variability. It might also help to reconsider the survey plot size. If the plot is too large, the rabbit could easily go unnoticed, while a smaller plot could lead to an overestimation of occupancy. Another recommendation would be to extend the survey time. The three-minute window worked, but some species may require more time to reliably be detected, and extending the observation period could help reduce the chance of missing the species.
Using a remote camera could also improve this simulation. Cameras could provide more consistent and unbiased data since they don't rely on human error or oversight. They would also be able to monitor the area continuously, which could help detect the rabbit even when it's not in the immediate vicinity or during low-visibility times. Cameras would be especially useful for longer-term studies or for monitoring species that are difficult to spot in real-time.
In general, the results from this simulation offer useful insights into the challenges and opportunities of designing occupancy surveys. By refining the number of trials, the plot size, and the time of observation, we could improve the accuracy of our results. Plus, incorporating technology like remote cameras could make the process much more efficient and reliable.
In the study by Priyadarshani et al. (2024), the authors used occupancy monitoring combined with time-to-detection models to estimate species presence and abundance. They visited multiple survey sites several times, recording detection data and the time of first detection. This approach helped improve the accuracy of occupancy and abundance estimates by integrating temporal variation in detectability.