The Sighting You Can't Reconstruct
You're deep in dry scrub on a late March afternoon, light dropping fast, and a large brown coil slips under a rock shelf about three meters off the trail. You freeze. You watch it settle. You're confident it's an eastern brown — Pseudonaja textilis — based on posture and the rapid, erratic tongue-flicking. You take two photos before it's gone.
Three weeks later you're trying to pull together sighting data for a local biodiversity count. Where was that rocky outcrop exactly? Which trail? Was it the 15th or the 17th? Was the light sufficient to be certain of species ID, or were you inferring from behavior in low light? You have two blurry photos in a camera roll with no metadata beyond the timestamp, and your memory has already started smoothing over the specific details. For a credible sighting record, that encounter is nearly useless.
That is the exact failure this template exists to prevent.
Logging at the Moment of Encounter
The eight-field structure maps to the information you actually have available in the field, and nothing more. Name (common) and Scientific name run as separate text fields rather than a combined lookup, which matters because common names vary regionally — what gets called a "black snake" in one state is Pseudechis porphyriacus in the southeast and a completely different genus further north. Recording both the vernacular used at the time and the binomial means the record is accurate to both the observer's frame and the scientific taxonomy.
Date photographed is a date field rather than datetime, which keeps data entry fast in the field. You're not hunting for a timestamp while a colubrid disappears into leaf litter. You enter the date and move on.
Location is a GPS coordinate field — the only field in this structure that does something automatic. Pin it at the moment of encounter, while you're standing on that exact piece of ground, and the record carries the precise coordinates. When you map sightings after six months of field work, that pin is accurate to within a few meters. Notes added after the fact are useful; location data captured after the fact is reconstructed and often off by a hundred meters or more, which is the difference between "edge of the granite escarpment" and "middle of the fire trail."
Category links to the ANIMAL library, which contains a Reptiles text field for taxonomic classification above species level — order, family, genus. Snakes are all squamates, but recording whether you're looking at a member of Elapidae versus Colubridae versus Pythonidae is the data point that makes your sighting log searchable by more than just species name. Filter by elapids and you have your venomous sightings. Filter by pythons and you can trace their spread into areas where they weren't recorded five years ago.
What Six Months of Geocoded Sightings Reveals
After half a year of consistent logging, the location field becomes the most valuable column in the database. Export to any mapping layer and you have a distributional heat map: where species cluster, where the transition zones are, whether Morelia spilota sightings track the creek lines or the rocky ridges.
More practically, you start building a calendar overlay. Date photographed against species produces an activity phenology. You learn that Notechis scutatus sightings on your survey route spike in the two weeks after the first sustained warm spell in October, tapering sharply when overnight temperatures exceed 22°C. You didn't know that pattern was there until the records showed it.
The Notes field is where observer context lives: basking posture, defensive behavior, substrate type, whether the animal was road-crossing, whether there was evidence of recent feeding. That text is unstructured but searchable. A grep across your notes for "road" pulls every road-mortality risk encounter. Search "defensive" and you get the behavioral data that doesn't belong in any other structured field.