The Species-Specific Overview displays frequency polygon (line) plots that show seasonal patterns for a selected species across all six habitat locations simultaneously. This tab is designed for exploratory analysis and pattern recognition across habitats for specific species.
User Inputs
User inputs are found on the sidebar for this tab.
| Input Name | Input Choices | Description |
| Model-Match Scale
‘Model Proximity’ or ‘Model Adherence’ |
Numerical value or range of values between 0 and 1. | This input allows you to filter that output on this tab based on BirdNET-Analyzer’s ‘Confidence’ attribute. We have renamed this input to ‘BirdNET Model-Match Scale’’ because BirdNET’s confidence isn’t actually a probability and is not related to confidence intervals. Read more about that here. |
| Species Lookup | Categorical selection based on species present in the dataset. Single selection only. | Note: The Species Lookup tool on this page behave differently than the Species Lookup tool in Prairie Haven Overview. On this tab, only one species can be selected. This controls what species is displayed on the page. |
Output Explanation
Clues for Admins
- First, a for loop is used to sequence through each unique Location value in the `location_list` (House, Glen, Prairie, Wetland, Savanna, Forest). For each location, `filtered_data()` is filtered by the selected Species and the current Location in the for loop. This filtered data is saved as the variable `species_data`. If there are no observations available for the species at this location, an informational message will display in the app.
- The `species_data` has time-based grouping variables added to it (Date, Year, Month, Week.Year, Month.Year.Loc, Week.Year.Loc) using the `mutate()` function, but unlike the previous section, no data completion or gap-filling is performed.
- The output graph is produced using ggplot’s `geom_freqpoly` function with a binwidth of 1, creating smooth line plots rather than bars. The plot uses `aes(color = as.factor(Year), linewidth = as.factor(Year))` to differentiate years by both color and line thickness. This is saved as the variable p.
- Color palettes and line thickness values are pre-defined using the viridis “plasma” color scheme (cols) and a sequence of line sizes ranging from 0.3 to 1.2 (line_size), with both mapped to the available years in the data.
- p is converted into a plotly object using ggplotly() with dynamicTicks = TRUE for better axis scaling.
- Steps 1-6 are repeated for each unique Location value, resulting in six separate line plots displayed simultaneously in a 2×3 grid layout rather than in separate tabs.