Package: taxify 0.2.12

Backend overview | Downloading backbones | Single-backend matching | Backend-specific output differences | Multi-backend fallback chains | Worked example: plants-only with WFO vs WFO + COL | Worked example: mixed-kingdom list with COL + GBIF + WoRMS | Worked example: fungi with Species Fungorum + COL fallback | Worked example: algae | Worked example: molecular ecology with NCBI | The backend column | Backend-specific extras | WFO extras: add_wfo_info() | COL extras: add_col_info() | GBIF extras: add_gbif_info() | Combining extras in a multi-backend result | The genus register | Looking up a genus | Checking backend coverage | Out-of-scope detection in practice | Practical guidance: choosing backends | Performance considerations | Reproducibility

How enrichments work | The join in detail | Cross-backbone name resolution | Automatic download and caching | Fallback chain | ### The enrichment data directory | Discovering enrichments | Pre-downloading enrichments | Simple enrichments | Plant enrichments | Woodiness (Zanne et al. 2014) | EIVE ecological indicator values (Dengler et al. 2023) | Diaz traits (Diaz et al. 2022) | LEDA Traitbase (Kleyer et al. 2008) | Regional plant-trait compilations (Baseflor, Ecoflora, FloraWeb) | Conservation status (IUCN Red List) | Bird enrichments | AVONET (Tobias et al. 2022) | EltonTraits (Wilman et al. 2014) | Mammal enrichments | PanTHERIA (Jones et al. 2009) | Amphibian enrichments | AmphiBIO (Oliveira et al. 2017) | Fungal enrichments | FungalTraits (Polme et al. 2020) | FUNGuild (Nguyen et al. 2016) | Algae enrichments | AlgaeTraits (Vranken et al. 2023) | Fish enrichments | FISHMORPH (Brosse et al. 2021) | FishBase (Froese & Pauly 2024) | Reptile enrichments | Meiri lizard traits (Meiri 2018) | Vertebrate enrichments (cross-class) | AnAge longevity and life-history (Tacutu et al. 2018) | AnimalTraits body mass and metabolic rate (Hebert et al. 2022) | Butterfly enrichments | LepTraits butterfly traits (Shirey et al. 2022) | Arthropod enrichments | NW European Arthropod life-history traits (Logghe et al. 2025) | Group-based enrichments | Invasive species status (GRIIS) | Single country | Multiple countries | All countries | Alien species first records (Seebens et al.) | Reshaping to long format | Native range by botanical region (WCVP) | Naturalized alien flora by region (GloNAF) | Common (vernacular) names (GBIF) | Stacking enrichments | Coverage patterns | Approximate coverage rates by enrichment | Interpreting NA columns | The enrichment register in summary() output | Practical guidance: which enrichments for which taxa | Vascular plants (European) | Vascular plants (global) | Birds | Mammals | Amphibians | Fish | Reptiles (lizards) | Butterflies | Arthropods (NW European) | Fungi | Macroalgae (European) | Mixed-taxon datasets | Joining custom data | Data provenance and citation | Summary

What fuzzy matching does (and does not do) | The three distance methods | Damerau-Levenshtein (default, fuzzy_method = "dl") | Levenshtein (fuzzy_method = "levenshtein") | Jaro-Winkler (fuzzy_method = "jw") | How thresholds work | Fractional mode (0 < threshold < 1) | Integer mode (threshold >= 1) | What happens before fuzzy matching | Worked example 1: clean names that need no fuzzy matching | Worked example 2: OCR-degraded and hand-typed names | Worked example 3: threshold too loose | Worked example 4: comparing all three methods | The fuzzy_dist column | Genus-blocked matching and misspelled genera | Practical guidance | When to disable fuzzy matching | When to tighten the threshold | When to loosen the threshold | When to switch methods | Using integer thresholds for uniform error budgets | A two-pass workflow for messy data | Summary of output columns related to fuzzy matching

Why taxify | Installing a backbone | Basic matching | Understanding the output | Name cleaning | Synonym resolution | Match types | The summary method | Multi-backend fallback | Enrichments | Conservation status | Common names | Woodiness | Custom data | From a data.frame | From a CSV file | Supported file formats | Hybrid names | Genus register | Cache management | The full pipeline

Hybrid names in taxonomy | How taxify detects hybrids | Worked example: matching a mixed species list | Extracting hybrid details with add_hybrid_info() | Worked example: parsing hybrid formulas | What matches and what does not | The multiplication sign and its substitutes | Practical notes

The problem | Joining a data.frame | Joining from a CSV file | Joining from an Excel file | SQLite databases | vectra native format | Joining from a TSV file | Other file formats | Species column auto-detection | Selecting columns with cols | Column name collisions | Duplicate species handling | How the join works | Controlling fuzzy matching | Combining add_data() with enrichments

The taxonomic-resolution landscape in R | Function mapping: taxize to taxify | Function mapping: WorldFlora to taxify | Function mapping: lcvplants to taxify | Function mapping: rWCVP to taxify | Function mapping: taxadb to taxify | Function mapping: Taxonstand to taxify | Example 1: Basic name resolution | Example 2: WFO matching with fuzzy + synonyms | Example 3: Multi-backend fallback with enrichments | Key differences at a glance | What taxify does not do | When the other packages are the better choice | Discovering available enrichments | Summary

How taxify scales | The .vtr columnar format | Exact matching: hash-indexed lookups | Fuzzy matching: genus-blocked string distance | Backbone loading and the session cache | Worked example: exact vs. fuzzy matching | Worked example: multi-backend fallback ordering | Backbone sizes on disk | Memory footprint | Worked example: batch processing a very large list | Cache management | Disk storage and sharing across projects | Worked example: pre-downloading resources | Practical scaling guidance | The fuzzy_threshold parameter | Summary of performance-relevant functions