I wonder if you could treat Transect_Name and Day (where Day is converted to a factor) as random grouping factors in your model? This would acknowledge that the transects and days included in your study are not the only ones you are interested in, but you selected them to be representative of a larger universe of transects and days.
If you can, then you will have multiple trials (i.e., 4-5 trials) for each combination of transect and day. Each of these trials will give you an abudance measure. The abundance measure - which is your response variable - seems to be expressed as a percentage?
Intuitively, you can think of a random grouping factor as a bucket where you store multiple values of your response variable. Because you actually have two random grouping variables - transect and day - imagine that you are 'nesting' two buckets inside each other (for lack of a better visual) for each transect by day combination and then store your multiple abundance values for all the trials available for that combination in the 'nested bucket'. For example, you will have one 'nested bucket' for Circuit_P on Day 6, another for Circuit_P on Day 7, etc. (assuming Circuit_P was visited on both of those days.
Now, the 'nested buckets' themselves have some characteristics which will be treated as predictor variables in your model. These characteristics can be constant from trial to trial within the same 'nested bucket' (i.e., they refer to the 'nested bucket' as a whole) or they can change from trial to trial within that bucket (i.e., they refer to what happens inside the 'nested bucket'). As an example, sampling time (or intensity) during each trial is one such characteristics and it varies within the bucket as the trials vary. The week when the trial in each 'nested bucket' was conducted is another characteristic. The forest type is also a characteristic which refers to the 'nested bucket' as a whole (indeed, you can attach a forest type to each transect by day combination and that type won't change across the trials in the bucket). It doesn't seem that forest type should be a random grouping factor in your model, as you will likely be interested in comparing the concrete forest types you included in the study against each other.
This would suggest that you should have terms like (1|Transect_Name) and (1|Day) in the random effects portion of your model - presumably, these are partially crossed in your study? In other words, not all transects were measured on all days.
In the fixed effects part of your model, you can include fixed effects for species of scorpion, type of forest, intensity, (transect) length, week, temp, humidity, etc. Maybe you'll allow species and type of forest to interact with each other? For those characteristics which change from trial to trial within a 'nested bucket' you could also allow for random slopes - for example, (1 + intensity|Transect_Name) + (1 + intensity|Day).
Now, your response variable is currently expressed as a percentage - for modelling purposes you could convert it to a proportion and then perhaps use beta mixed effects modelling (with 0 and/or 1 inflation if necessary).
It doesn't seem to me that you should treat it as a count - it is clearly computed as a proportion.