Target species finder for birding trips

The idea

A travelling birder planning a week in Arizona spends evenings cross-referencing their life list against bar charts on eBird, hotspot by hotspot, building a target list by hand. eBird has every data point needed and buries the answer: its Targets page is hard to find, ignores trip dates, and does nothing to turn twenty target species into an actual route. For a hobby where people fly across the country for a single bird, planning tooling is strangely medieval.

The wedge is being trip-first. Import your life list once, enter region and dates, and get the ranked list eBird could show but does not: which lifers are realistically gettable in those specific weeks, and the minimal set of hotspots that covers the most of them. That last part, hotspot clustering into a sane itinerary, is what no amount of manual bar-chart reading produces.

Who pays

Birders with travel budgets: people who spend USD 3,000 on binoculars and book guided trips at USD 200-400 a day. GBP 25 a year to make every trip count is trivial against that, and the one-free-report tier converts at the exact moment of planning intent. Distribution: birding Facebook groups and r/birding, trip reports on birding blogs (offer the checklist as the artefact people share), and SEO on “target birds [region]” queries, which currently return forum threads.

MVP scope

• eBird life list CSV import mapped to taxonomy codes
• Trip planner: region and date range in, ranked target list out
• Week-by-week likelihood bars and per-species best hotspots
• Greedy hotspot clustering into a minimal covering itinerary
• Printable trip checklist grouped by hotspot, Stripe annual subscription
• Skip for v1: rare bird alerts, mobile app, group trips, live eBird sync, and world regions beyond ABA area plus popular international destinations

Build LiferTrip, a web app that turns a birder's eBird life list into a ranked target list for any trip. Stack: Next.js (App Router), eBird API 2.0, Supabase (Postgres + auth), Stripe.

Core flow: user signs up, imports their eBird life list (eBird has no list API, so accept the CSV export from ebird.org/downloadMyData and parse it; store species as eBird taxonomy codes). To plan a trip they pick a region (eBird region codes, searchable) and travel dates. The app pulls recent and historical frequency data for that region, subtracts the user's life list, and returns targets ranked by likelihood during those specific weeks, each with the best hotspots for that species.

Pages: life list import screen, trip planner (region + date picker), target report (ranked species with weekly bar charts and hotspot links), hotspot view (which targets cluster there), and a printable checklist.

Implementation details: likelihood comes from eBird bar-chart frequency data by week; cache region frequency data aggressively in Supabase since it changes slowly and eBird rate limits are tight. The killer feature is hotspot clustering: greedily pick the minimal set of hotspots covering the most targets, so a 3-day trip gets a sane itinerary rather than 40 scattered pins. Printable checklist groups targets by hotspot in visiting order.

Pricing: one free trip report, then GBP 25/year via Stripe. Life list re-import is one click before each trip.