Todos Os Insetos Do Mundo

Mapping every insect on Earth is a practical nightmare

I spent about four years trying to build a comprehensive arthropod dataset for a research group, and honestly, the hardest part wasn't the biology. It was the data infrastructure. People treat insect diversity as if it's a clean taxonomy tree you can scrape and organize. It isn't. It's a mess of synonymized names, regional guides in languages with no standardized keys, and thousands of species still described from single specimens in museum drawers in Zurich or Paris.

The actual state of todos os insetos do mundo

Every estimate floats somewhere between 500,000 and 1.5 million described species, with most entomologists leaning toward the lower end while tropical biologists argue the real number could sit closer to 10 million undiscovered. The gap between what exists in databases and what actually lives on a tropical forest floor is absurd. GBIF has over 2 billion occurrence records, but a massive portion of those are misidentified, geolocated imprecisely, or tagged to genera-level IDs because the original collector didn't bother going further. If you're building something that claims to cover all insect species, the first problem you'll hit is name resolution. The same species gets named three times in different regions, and the synonymy chains are maintained by different taxonomic authorities who don't talk to each other. Catalogue of Life tries to bridge this, ITIS does too, and neither is fully consistent.

How I actually approached building a reference resource

I stopped trying to create a single master list and instead built a pipeline. The pipeline pulls occurrence data from GBIF, cross-references taxonomic names through the Catalogue of Life API, and then flags records where the genus or family attribution conflicts with peer-reviewed regional checklists. You also need a solid distribution source for the taxonomic backbone. The Global Biodiversity Information Facility gives you the raw observations, but the backbone comes from TaxonKit or the Open Tree of Life project when you need phylogenetic context. One specific issue I ran into was with Diptera and Hymenoptera. These two orders alone account for well over a million described species, and they're both hyper-diverse in the tropics with enormous numbers of cryptic species. A specimen labeled as a common fly genus might turn out to be three distinct species once you run COI barcodes. I had a batch of records from the Atlantic Forest where the morphological IDs were clearly wrong. The workaround was running a Python script against the BOLD Systems API to pull barcode matches for any record missing genetic data, then flagging mismatches for manual review. That cut false-positive species counts by about 40% in that dataset alone.

Practical steps if you want to work with this data yourself

You need a few concrete things. Start by getting API access to GBIF, Catalogue of Life, and BOLD Systems. Learn to use the gbif R package or the gbif R interface in Python, because the REST API alone will frustrate you within an hour. Build a clean name-resolution layer that normalizes spelling variants and authority dates. Spend time on the synonymy problem early, or your final dataset will have duplicate species entries masquerading as distinct ones. Use the IPNI and MycoBank APIs if you're working near the plant-insect interface, because host-plant relationships frequently shift nomenclature. Download the GBIF backbone dataset annually. It's roughly 2.1 gigabytes of compressed JSON and contains the most complete taxonomic backbone available for free. Pair it with the World Species Checklists where GBIF is thin, especially for under-studied orders like Strepsiptera and Mantophasmatodea. The data coverage for those groups is terrible, and no amount of automation will fix that. You'll need to fall back on printed monographs and regional revisions.

What breaks when you try to go too granular

Here's the honest part that nobody talks about. If you try to build a species-level map for every insect order across every continent, your server costs will exceed your budget within six months, and your accuracy will drop below publication quality. The bottleneck isn't compute. It's that the underlying taxonomic data simply doesn't exist at species level for most tropical insects. You'll be interpolating from genus-level distributions, and genus-level distributions are themselves poorly mapped. I learned this the hard way when a collaborator asked me to produce fine-scale range maps for a set of Neotropical beetles. The maps looked convincing. They were wrong in about 60% of the cells because the input occurrence data had coordinate uncertainty ranges larger than the grid resolution I was using. The workaround was switching to coarser spatial grains and reporting confidence intervals on every range estimate. Nobody likes confidence intervals in visualizations, but they're the only honest way to present this data. Another realistic limitation: many country-level biodiversity databases are behind paywalls or require institutional access. Brazil's SiBBr, for instance, is excellent but not freely programmatic at the species-occurrence level without a formal request. Argentina's SNIB has similar restrictions. Plan your data sourcing around that reality before you build your pipeline.

Open-source tools worth knowing about

TaxonKit is fast and reliable for name normalization. It handles synonym resolution better than most custom scripts I've seen. rgbif in R remains the best option for bulk GBIF queries if you're comfortable with the tidyverse. For Python, gbif client and pygbif exist but feel less maintained. The Barcode of Life Data System has a REST API that's underdocumented but functional for COI lookups. When I needed to verify species-level IDs for a South American ant dataset, I wrote a small wrapper around the BOLD API that pulled all barcode records for a given genus and flagged any specimen without a genetic match. That caught a batch of identifications that had been copied from old literature without verification. If your goal is simply to explore the available data rather than build a production system, the iNaturalist observation export is a surprisingly good starting point. It won't give you scientific-grade records, but it reveals distribution patterns that aren't captured in museum databases, especially for common urban and suburban species. The tradeoff is that citizen science data introduces massive observer bias. You'll see way more records for visually striking insects and way fewer for ground-dwelling beetles and parasitoid wasps.

What I'd change if I started over

I'd invest more time in standardizing the geographic coordinate systems upfront. Half my early data cleaning was spent reconciling DMS, decimal degrees, and datum shifts. I'd also stop trying to cover every order equally. Some groups like Lepidoptera and Coleoptera have decent data infrastructure. Others like Protura and Diplura are so understudied that any comprehensive dataset for them would be mostly guesses. Focusing on orders with existing regional checklists and verifiable occurrence records gives you a much higher signal-to-noise ratio. The broader lesson is that any attempt to represent todos os insetos do mundo in a single database will always be incomplete. The best you can do is be transparent about what's missing, keep the synonymy clean, and report uncertainty honestly. That's more useful than a shiny complete-looking map that's wrong half the time.