• Regarding species richness, isn't it covered by the activity tab where we can see the entire list of detected species? What else do you think would be helpful? I can imagine a better UI focusing on species with more information for each species and easier search but the raw info would be roughly the same.

I think the app sort of covers this, using the pie chart layover on leaflet in the activity tab. However, it would be nice to have a more direct way of visualizing the species richness (i.e. scale the radii of the circle markers with the number of species detected). In addition to this you may want to think about visualizing simple diversity indices (there's alpha diversity which captures the species richness at each trap, gamma diversity to summarize the total richness in the area and beta diversity to assess how different species compositions are between traps). Note: I do not use diversity indices often enough to provide more specific guidance. @ollie_wearn is this what you were referring to?

• Regarding occupancy, that makes sense to me. Only challenge is I'm not using python or R at the moment because and we use javascript for easier user interfaces with web technologies. It's still possible to add python support but I'd delay as much as possible to keep things simple. Anyway, it's a technical challenge that I'm pretty sure I can solve.

I can help you with setting up the models in R, Python or Stan/ C++. But I have no idea on how to overcome the technical challenge that you are referring to. I agree with @ollie_wearn that allowing variable selection and model building would take this to far. One thing I would suggest is to allow users to fit a spatially-explicit version of the basic occupancy model (mind that these can be slow). This type of model leverages the correlations in species detections between trap locations to estimate differences in occupancy across the study area rather than just estimating a single occupancy for the entire area. 

• What about relative abundance? My readings always mention occupancy, species richness, abundance and density. I've read and ask enough about density to know that it's hard for unmarked animals and might not be necessary. If I understood correctly, relative abundance can give similar insights and as a conservationist, you probably want to know the trend of relative abundance over time.

Yes, I would leave users with the following options: a UI-switch that let's them pick either: 

1. the number of observations
2. the number of individuals detected
3. the relative abundance index or RAI (based on 1.)
4. the RAI (based on 2.)

to visualize on the leaflet map - and barchart on the side in the activity tab.

Regarding density: you could add a tab that calculates density using the Random Encounter Model (REM), which is often used when estimating density of unmarked animals without info on recaptures.

Regarding activity patterns: I would also add a tab were users can visualize either diel or annual activity cycles (often called activity patterns) computed through the activity R-package (or integrate this in an existing tab). And maybe even allow computing overlap in daily activity cycles among selected species.

If you manage to include all of these, then I think your app covers the 90% of use cases.

Some other features worth discussing:

1. merging/ comparing multiple projects
2. in line with calculating overlap between activity cycles, allow computing a spatial overlap between two species or even a spatio-temporal overlap 

@Jeremy_ For the Python implementation of basic occupancy models (as suggested by @ollie_wearn ), please refer to these two projects:

• https://github.com/timmh/biolith
• https://github.com/kenkellner/pyunmarked

I second @martijnB suggestion to use spatially explicit occupancy models (as implemented in R, e.g., https://doserlab.com/files/spoccupancy-web/). However, this would need to be added to both of the aforementioned Python projects.

That's so great, thanks for the update! 

Is that not a statistical question and depending in the classification performance of the SW (Birdnet), which itself may depend on the species and the variability of the call? 

I, myself, have no practical experience with Birdnet, but use a commercial product that derives from Birdnet and found in about 1000 days, for example,  the following tail statistics (22 x 1), (12 x 2), (6 x 3), (5 x 4) bird detections. In total, 223 different species were classified, with black birds being on top with 149000 detections. Overall, there seems to be a nearly exponential decay of different bird detections, which to me shows a problem with NN-based classifications, as there seems to be no clean separation between 'signal' and 'noise', as one can find in traditional signal processing. 

From these statistics (histogram) I would conclude that in the end there is no meaningful threshold, as there is no meaningful 'misclassification floor' that could be used to define a threshold.

Consequently, you may have to consider context, inspect every suspect snippet and let human decide.