Prediction interval for continuous positive data including zeros I am trying to figure out given a set of positive continuous numbers (including zeros) what would be the most appropriate way to calculate a prediction interval, or a normal range, i.e. a range in which individuals are likely to fall in the future.
For context: I am looking at species level vegetation cover data in percent, i.e. I measured percent cover of the species in a given area. Then I added up the cover values of certain species within that plot which fall in a particular group of indicator species. Hence, total cover can be larger than 100%.
When species that belong to the indicator group are not present in a given plot, I will assign a zero because I expect them to be there and I may have just missed them in my sampling effort. Or in other words, I believe these zeros should not be excluded.
The reason I want to calculate a prediction interval is because given a year of collecting samples, I would like to know what other values can I expect in the following year, or how would a "normal range" look like for those indicator species.
Using a standard gaussian distribution based prediction intervals isn't a good choice because the data is often right skewed and close to zero leading to negative lower bounds of the interval. This doesn't make sense and simply truncating the interval at zero is a terrible idea in my opinion. I can go ahead and use a transformation such as a Box-Cox transformation which may or may not remedy my problem (not working in cases when I have zeros), but I would rather use the right tool for the job.
I came across this post by @whuber (https://stats.stackexchange.com/a/406558/32477) but my data is often not large enough, i.e. around 10 values. From the linked post:

These are amazingly easy to compute and can work well with sufficiently large datasets

I defaulted to simply calculating the lower and upper bound using quantile() function in R with probabilities of 0.025 and 0.975, respectively. I could also use minimum and maximum values but that may not be the best solution either.
My question is if there are other ways to create something that resembles a "normal range" given those cover value data?

One option is to fit a zero-inflated beta model. I.e. this model has two components, one that models whether the value is zero or non-zero + one part that models (given that the value is non-zero) what is the non-zero value. Beta-regression seems like a potentially decent fit, because it models data on (0, 1) (i.e. proportions without a denominator, if you had a denominator, then logistic regression would be preferable).
One particularly useful way to get prediction intervals here might be a Bayesian approach, where you get MCMC samples for predictions and can then take quantiles of these values (and if lots of zeros get sampled as predictions, the interval will then include zero).
In terms of specific software, in R there's brms that makes this pretty straightforward (see this vignette and the documentation on zero-inflated distributions).
