Tier 2 Protocol Means the Data Has to Hold Up
Not all wildlife monitoring is the same. Tier 1 is presence/absence — you were at the site, you saw birds, you counted them. Tier 2 is reproductive monitoring: you're tracking individual nests, recording status transitions, counting eggs and chicks per visit, and building a dataset that can support breeding success calculations across a season. That data will end up in reports submitted to DOC or trust bodies, and it needs to hold up to scrutiny.
The difference between a usable Tier 2 dataset and a mess of field notes that someone eventually has to reconcile is usually discipline at the point of data entry — specifically, whether the same person can enter "Fledged" status and somehow also enter "2 eggs" without the system catching the contradiction.
This template catches that contradiction.
The JavaScript That Protects the Breeding Record
The template includes automation scripts on both entry creation and edit that run every time a record is saved. The logic is ecological rather than arbitrary: if Status is "Not breeding", "Fledged", or "Failed", the Eggs and Chicks fields are automatically set to 0. If Status is "Unknown", both are set to Unknown. If chicks equals 2, eggs is set to 0. If eggs equals 2, chicks is set to 0.
These aren't arbitrary validation rules — they reflect the actual biology of little penguin (kororā) breeding cycles. A nest that has fledged has no eggs and no chicks remaining. A nest that has failed doesn't have surviving chicks. A clutch of two eggs that are still incubating doesn't also have two chicks present. The automation enforces ecological logic at the field level, which means downstream analysis doesn't have to waste time filtering impossible records.
The Eggs and Chicks fields are both choice fields — 0, 1, 2, Unknown — rather than free-text integers. This prevents entry of values outside the biologically possible range for this species and eliminates the ambiguity between "not recorded" and "zero." Unknown is a legitimate value in nest monitoring, particularly when observation type is Burrowscope or Trail/Best Camera, where nest contents may be partially obscured.
What the Observer Logs at Each Visit
Each record links to three reference libraries via the Entries field type: Sites (PAT2), Observers (PAT2), and Nest ID. The relational structure means you're not entering "Site A7" as free text and introducing spelling variants across the dataset — you're selecting the canonical site entry from a master list, and the relationship is hard-linked.
Observation Type is its own category: Direct, Burrowscope, or Trail/Best Camera. This matters for interpreting the nest contents data. A direct observation at a surface nest is high confidence. A burrowscope inspection of a subsurface nest gives good visual but limited tactile information. A trail camera review is indirect — you're interpreting what the camera captured, which may have been at a specific time window that doesn't represent the full day's activity. The observation method needs to be part of the record, not a footnote in the field notes.
Nest Attendance — Single, Pair, Empty, Unknown — sits alongside Status: Breeding, Failed, Fledged, Not Breeding, Unknown. The combination tells the story. A Breeding nest with Pair attendance and 2 eggs is in the incubation stage. The same nest two weeks later with Single attendance and 2 chicks tells you one adult is foraging while the other broods. An Empty nest with Failed status and 0 eggs, 0 chicks is the endpoint of a breeding attempt that didn't make it.
The image field attached to each record is where the photographic evidence goes — the burrowscope frame, the trail camera capture, or the direct observation photo that lets a second reviewer check your counts.