Package 'taxify'

Description

Joins AlgaeTraits (Vranken et al. 2023) macroalgal functional traits to a taxify() result by looking up accepted_name. AlgaeTraits provides morphological, ecological, and life-history traits for European seaweeds.

Usage

add_algae_traits(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: AlgaeTraits (Vranken et al. 2023, VLIZ Marine Data Archive, CC BY 4.0). Coverage: ~1,745 European macroalgae species.

Value

The same data.frame with additional columns:

algae_body_size_cm

Maximum body size in centimetres.

algae_growth_form

Growth form / body shape (e.g., filamentous, foliose, crustose).

algae_calcification

Calcification type (e.g., uncalcified, articulated, encrusting).

algae_life_span

Life span category (annual, perennial, etc.).

algae_tidal_zone

Tidal zonation (e.g., supralittoral, eulittoral, sublittoral).

algae_wave_exposure

Wave exposure tolerance (sheltered, moderately exposed, exposed).

algae_environment

Habitat environment (marine, brackish, freshwater).

algae_substrate

Environmental position / substrate type.

References

Vranken S et al. (2023) AlgaeTraits: a trait database for (European) seaweeds. Earth System Science Data 15:2711-2754. doi:10.5194/essd-15-2711-2023

Examples

# Runs offline against the bundled example database.
old <- options(taxify.data_dir = taxify_example_data())

taxify("Fucus vesiculosus", backbone = "gbif") |>
  add_algae_traits()

options(old)

Description

Joins alien species first record data to a taxify() result, filtered by location. Data from the FirstRecords dataset (Seebens & Renard Truong 2026; Seebens et al. 2017).

Usage

add_alien_first_records(x, location, cols = NULL, verbose = TRUE)

Arguments

Details

The source's locations do not overlap: a country's location excludes the islands and other parts recorded separately, so location = "US" is the first record for the United States without Hawaii and Alaska, and a species first recorded on Hawaii has no "US" record unless the source holds one for the country itself. Seebens et al. (2017) define the set as "282 non-overlapping regions (countries and sub-national regions such as islands)". Every row carries country_code, the ISO 3166-1 alpha-2 code of the country the location lies in, so a country-wide first record is an explicit roll-up: request the country's locations and take the earliest year.

Source: FirstRecords v4.0 (Seebens & Renard Truong 2026, doi:10.5281/zenodo.18759840), CC BY 4.0, one row per species x location.

Value

The same data.frame with additional column(s):

alien_first_record

Year of the first record (integer), or NA if not recorded for that location.

alien_first_record_source

Database that contributed the record (e.g., "GAVIA", "CABI ISC").

alien_first_record_reference

Original citation or reference for the record.

References

Seebens H, Blackburn TM, Dyer EE, et al. (2017) No saturation in the accumulation of alien species worldwide. Nature Communications 8:14435.

Examples

# Runs offline against the bundled example database.
old <- options(taxify.data_dir = taxify_example_data())

taxify("Robinia pseudoacacia") |>
  add_alien_first_records(location = "AT")

taxify(c("Robinia pseudoacacia", "Ailanthus altissima")) |>
  add_alien_first_records(location = c("AT", "DE", "Hawaii"))

options(old)

Description

Joins body size and wing morphology for the carabid fauna of the Italian Alps to a taxify() result by accepted name.

Usage

add_alpine_carabids(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: Chamberlain et al. (2020), 185 species recorded at 416 sites between 697 and 2840 m in the Italian Alps. CC0.

The widest European carabid trait table after add_chowdhury(), and the one that reaches the Alpine fauna the lowland compilations miss. Like every European carabid source its body sizes trace back to carabids.org, so agreement with those is not corroboration; it runs 1.03 against them, the same quantity without being the verbatim copy add_eberswalde() is.

wing_morph arrives as bare letters with no legend in the file. The codes were read off the data rather than assumed: crossed against add_chowdhury()'s words, b is short-winged, m long-winged and d dimorphic.

Value

The same data.frame with alpine_ columns: numeric body_length_mm, categorical wing_morph.

References

Chamberlain D, Gobbi M, Negro M, et al. (2020) Trait-modulated decline of carabid beetle occurrence along elevation gradients across the European Alps. Journal of Biogeography 47:1030-1041. doi:10.1111/jbi.13792

Data: doi:10.5061/dryad.fn2z34tq1

Examples

## Not run: 
taxify(c("Abax exaratus", "Carabus depressus")) |>
  add_alpine_carabids()

## End(Not run)

Description

Joins uniform life-history traits for birds, mammals and reptiles to a taxify() result by looking up accepted_name.

Usage

add_amniote(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: Amniote Life History Database (Myhrvold et al. 2015, Ecology, CC0). Coverage: 21,322 species across birds, mammals and reptiles.

Value

The same data.frame with additional columns:

amniote_class

Taxonomic class (Aves/Mammalia/Reptilia).

amniote_adult_body_mass_g

Adult body mass (g).

amniote_no_sex_body_mass_g

Unsexed adult body mass (g).

amniote_female_body_mass_g

Female body mass (g).

amniote_male_body_mass_g

Male body mass (g).

amniote_adult_svl_cm

Adult snout-vent length (cm).

amniote_maximum_longevity_y

Maximum longevity (years).

amniote_litter_clutch_size

Litter or clutch size (count).

amniote_clutches_per_y

Litters or clutches per year (count).

amniote_egg_mass_g

Egg mass (g).

amniote_incubation_d

Incubation period (days).

amniote_female_maturity_d

Age at female maturity (days).

amniote_gestation_d

Gestation length (days).

amniote_weaning_d

Weaning age (days).

amniote_birth_hatching_wt_g

Birth or hatching weight (g).

References

Myhrvold NP et al. (2015) An amniote life-history database to perform comparative analyses with birds, mammals, and reptiles. Ecology 96:3109. doi:10.1890/15-0846R.1

Examples

## Not run: 
taxify("Accipiter badius", backbone = "gbif") |>
  add_amniote()

## End(Not run)

Description

Joins AmphiBIO amphibian life-history and ecological traits to a taxify() result by looking up accepted_name.

Usage

add_amphibio(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: AmphiBIO (Oliveira et al. 2017, CC BY 4.0). Coverage: ~6,800 amphibian species. Amphibians only.

Value

The same data.frame with additional columns:

body_size_mm

Maximum body size in mm (snout-vent length).

age_maturity_y

Age at maturity in years.

longevity_yr

Maximum longevity in years.

litter_size

Clutch/litter size.

reproductive_output

Reproductive output per year.

offspring_size_mm

Offspring size in mm.

direct_development

Direct development (0/1).

larval

Has larval stage (0/1).

aquatic

Aquatic habitat (0/1).

fossorial

Fossorial habitat (0/1).

arboreal

Arboreal habitat (0/1).

diurnal

Diurnal activity (0/1).

nocturnal_amphibio

Nocturnal activity (0/1). Named nocturnal_amphibio to avoid collision with EltonTraits' nocturnal column.

References

Oliveira BF, Sao-Pedro VA, Santos-Barrera G, Penone C, Costa GC (2017) AmphiBIO, a global database for amphibian ecological traits. Scientific Data 4:170123.

Examples

# Runs offline against the bundled example database.
old <- options(taxify.data_dir = taxify_example_data())

taxify("Bufo bufo", backbone = "gbif") |>
  add_amphibio()

options(old)

Description

Joins AnAge (Animal Ageing and Longevity Database) traits to a taxify() result by looking up accepted_name.

Usage

add_anage(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: AnAge (de Magalhaes & Costa 2009, CC BY). Coverage: ~4.7k vertebrate species (mammals, birds, reptiles, amphibians, fish).

Value

The same data.frame with additional columns:

max_longevity_yr

Maximum longevity in years.

anage_body_mass_g

Adult body mass in grams.

metabolic_rate_w

Basal metabolic rate in watts.

female_maturity_d

Female age at sexual maturity in days.

male_maturity_d

Male age at sexual maturity in days.

gestation_incubation_d

Gestation or incubation length in days.

anage_litter_size

Litter or clutch size.

birth_mass_g

Mass at birth in grams.

growth_rate

Growth rate (1/days).

temperature_k

Body temperature in Kelvin.

References

de Magalhaes JP, Costa J (2009) A database of vertebrate longevity records and their relation to other life-history traits. Journal of Evolutionary Biology 22:1770-1774.

Examples

# Runs offline against the bundled example database.
old <- options(taxify.data_dir = taxify_example_data())

taxify("Vulpes vulpes", backbone = "gbif") |>
  add_anage()

options(old)

Description

Joins AnimalTraits body mass and metabolic rate data to a taxify() result by looking up accepted_name.

Usage

add_animaltraits(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: AnimalTraits (Herberstein et al. 2022, CC0). Coverage: ~2k species across arthropods, vertebrates, molluscs, and annelids. Individual-level observations aggregated to species medians.

Value

The same data.frame with additional columns:

animaltraits_body_mass_kg

Median body mass in kg.

animaltraits_metabolic_rate_w

Median metabolic rate in watts.

References

Herberstein ME et al. (2022) AnimalTraits: a curated animal trait database for body mass, metabolic rate and brain size. Scientific Data 9:265.

Examples

# Runs offline against the bundled example database.
old <- options(taxify.data_dir = taxify_example_data())

taxify("Drosophila melanogaster", backbone = "gbif") |>
  add_animaltraits()

options(old)

Description

Joins Arctic Traits Database functional traits to a taxify() result by accepted_name. Each fuzzy-coded trait is reduced to its dominant category.

Usage

add_arctic_traits(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: Arctic Traits Database (Degen & Faulwetter 2019, University of Vienna PHAIDRA, CC-BY 4.0).

Value

The same data.frame with categorical arctic_traits_ columns: feeding_habit, skeleton, reproduction, larval_development, size, living_habit, body_form, mobility, bioturbation, depth_range, trophic_level, fragility, sociability, longevity.

References

Degen R, Faulwetter S (2019) The Arctic Traits Database. University of Vienna. doi:10.25365/phaidra.49

Examples

## Not run: 
taxify("Astarte borealis", backbone = "gbif") |>
  add_arctic_traits()

## End(Not run)

Description

Joins the Northwestern European Arthropod Life Histories dataset to a taxify() result by looking up accepted_name.

Usage

add_arthropod_traits(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: Logghe et al. (2025, CC BY-NC). Coverage: ~4.9k arthropod species from NW Europe across 10 orders (Coleoptera, Hemiptera, Orthoptera, Araneae, Diptera, Hymenoptera, Lepidoptera, etc.).

Value

The same data.frame with additional columns:

arthropod_body_size_mm

Body size in mm.

arthropod_dispersal

Dispersal ability (0–1 ratio within order).

arthropod_voltinism

Mean number of generations per year.

arthropod_fecundity

Fecundity (number of eggs/offspring).

arthropod_development_d

Development time in days.

arthropod_lifespan_d

Adult lifespan in days.

arthropod_thermal_mean

Mean thermal niche (degrees C).

arthropod_diurnality

Activity period (diurnal/nocturnal/both).

arthropod_feeding_guild

Feeding guild of adult.

arthropod_trophic_range

Trophic range of adult (specialist/generalist).

References

Logghe A et al. (2025) An in-depth dataset of northwestern European arthropod life histories and ecological traits. Biodiversity Data Journal 13:e146785.

Examples

# Runs offline against the bundled example database.
old <- options(taxify.data_dir = taxify_example_data())

taxify("Abax parallelepipedus", backbone = "gbif") |>
  add_arthropod_traits()

options(old)

Description

Joins species-level plant functional traits to a taxify() result by looking up accepted_name. Values are aggregated from the long-format AusTraits database (numeric traits by median, categorical traits by mode).

Usage

add_austraits(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: AusTraits (Falster et al. 2021, Scientific Data, CC BY 4.0). Coverage: ~33k Australian plant taxa.

Value

The same data.frame with additional columns:

austraits_plant_growth_form

Plant growth form.

austraits_life_history

Life history (annual/perennial/...).

austraits_woodiness

Woodiness.

austraits_photosynthetic_pathway

Photosynthetic pathway (C3/C4/CAM).

austraits_dispersal_syndrome

Dispersal syndrome.

austraits_resprouting_capacity

Resprouting capacity (fire response).

austraits_flowering_time

Flowering time.

austraits_plant_height_m

Plant height (m).

austraits_leaf_length_mm

Leaf length (mm).

austraits_leaf_width_mm

Leaf width (mm).

austraits_leaf_area_mm2

Leaf area (mm2).

austraits_leaf_mass_per_area

Leaf mass per area (g/m2; SLA is its reciprocal).

austraits_leaf_n_per_dry_mass

Leaf nitrogen per dry mass (mg/g).

austraits_leaf_p_per_dry_mass

Leaf phosphorus per dry mass (mg/g).

austraits_seed_dry_mass_mg

Seed dry mass (mg).

austraits_wood_density_g_cm3

Wood density (g/cm3).

References

Falster D et al. (2021) AusTraits, a curated plant trait database for the Australian flora. Scientific Data 8:254. doi:10.1038/s41597-021-01006-6

Examples

old <- options(taxify.data_dir = taxify_example_data())

taxify("Eucalyptus globulus", backbone = "gbif") |>
  add_austraits()

options(old)

Description

Joins AVONET species-level averages for bird morphology, ecology, and migration to a taxify() result by looking up accepted_name.

Usage

add_avonet(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: AVONET (Tobias et al. 2022, Figshare, CC BY 4.0). Coverage: ~11k bird species. Birds only.

Value

The same data.frame with additional columns:

beak_length

Beak length in mm (culmen, species mean).

beak_depth

Beak depth in mm (species mean).

wing_length

Wing length in mm (species mean).

tail_length

Tail length in mm (species mean).

tarsus_length

Tarsus length in mm (species mean).

avonet_body_mass_g

Body mass in grams (species mean).

hand_wing_index

Hand-wing index (pointedness, species mean).

habitat

Primary habitat classification.

trophic_level

Trophic level classification.

trophic_niche

Trophic niche classification.

migration

Migration strategy: "sedentary", "partial", or "full".

References

Tobias JA et al. (2022) AVONET: morphological, ecological and geographical data for all birds. Ecology Letters 25:581-597.

Examples

# Runs offline against the bundled example database.
old <- options(taxify.data_dir = taxify_example_data())

taxify("Parus major", backbone = "gbif") |>
  add_avonet()

options(old)

Description

Joins per-species microbial phenotype and growth-condition traits from BacDive, the Bacterial Diversity Metadatabase (DSMZ), to a taxify() result by looking up accepted_name. Strain-level records are aggregated to one row per species (categorical traits by mode, numeric by median); temperature and pH prefer the optimum measurement, falling back to the growth measurement.

Usage

add_bacdive(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: BacDive (Reimer et al.), DSMZ, CC BY 4.0. ~18.6k bacterial and archaeal species with at least one phenotypic trait.

Value

The same data.frame with additional columns:

gram_stain

Gram reaction (positive / negative / variable).

cell_shape

Cell morphology (rod, coccus, ...).

motility

motile / non-motile.

oxygen_metabolism

Oxygen tolerance (aerobe, anaerobe, facultative anaerobe, microaerophile, ...).

cell_length_um, cell_width_um

Cell dimensions in micrometres.

optimal_growth_temp_c

Optimal (or reported growth) temperature, C.

optimal_growth_ph

Optimal (or reported growth) pH.

References

Reimer LC et al. (2022) BacDive in 2022: the knowledge base for standardized bacterial and archaeal data. Nucleic Acids Research 50:D741-D746. doi:10.1093/nar/gkab961

Examples

## Not run: 
taxify("Escherichia coli", backbone = "gbif") |>
  add_bacdive()

## End(Not run)

Description

Joins Baseflor (Julve, Programme Catminat) plant traits to a taxify() result by looking up accepted_name. Baseflor covers the vascular flora of France and neighbouring regions, providing flowering phenology, pollination and breeding biology, dispersal mode, and floral and fruit morphology.

Usage

add_baseflor(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: Baseflor, Programme Catminat (Julve 1998 ff.). Coverage: ~7,000 vascular plant taxa of France and neighbouring regions. Data are released under ODbL 1.0 / CC BY-SA 2.0.

For ecological indicator values on the light, temperature, moisture, reaction, and nutrient axes, see add_eive() (European calibration). For Raunkiaer life form and seed, leaf, and clonality traits of the Northwest European flora, see add_leda().

Value

The same data.frame with additional columns:

flower_begin_month

First month of flowering (1-12).

flower_end_month

Last month of flowering (1-12). A value smaller than flower_begin_month denotes a flowering period that wraps across the new year (e.g. begin 10, end 6).

pollination_vector

Pollination vector(s): insect, wind, water, self, apogamy. Comma-separated when more than one applies.

dispersal_mode

Diaspore dispersal mode(s): anemochory, barochory, epizoochory, endozoochory, myrmecochory, hydrochory, autochory, dyszoochory. Comma-separated when more than one applies.

breeding_system

Sexual system: hermaphroditic, monoecious, dioecious, gynodioecious, androdioecious, gynomonoecious, polygamous.

flower_colour

Flower colour(s): white, yellow, pink, green, blue, brown, black. Comma-separated when more than one applies.

fruit_type

Fruit type: achene, capsule, caryopsis, drupe, legume, silique, berry, follicle, cone, samara, pyxid.

woody_growth_form

Woody growth form for woody taxa: tree, small tree, large tree, shrub, bush, subshrub, liana, parasite. NA for non-woody (herbaceous) taxa.

continentality

Ellenberg-style continentality indicator value (1-9), the axis absent from EIVE.

salinity

Ellenberg-style salinity indicator value (0-9), the axis absent from EIVE.

References

Julve, Ph. (1998 ff.) baseflor. Index botanique, ecologique et chorologique de la Flore de France. Programme Catminat.

Examples

# Runs offline against the bundled example database.
old <- options(taxify.data_dir = taxify_example_data())

taxify("Bellis perennis") |>
  add_baseflor()

options(old)

Description

Joins global bee morphological traits to a taxify() result by accepted_name. Long-format measurements are reduced to species medians.

Usage

add_bee_ostwald(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: Ostwald et al. global bee morphology (Zenodo, CC-BY 4.0).

Value

The same data.frame with numeric columns bee_ostwald_itd_mm (intertegular distance), bee_ostwald_forewing_length_mm, bee_ostwald_tongue_length_mm, bee_ostwald_tongue_width_mm, bee_ostwald_body_length_mm, bee_ostwald_thorax_length_mm, bee_ostwald_hair_length_mm, bee_ostwald_hair_coverage_pct.

References

Ostwald MM et al. (2024) A global database of bee morphological traits. Zenodo. doi:10.5281/zenodo.13366989

Examples

## Not run: 
taxify("Apis mellifera", backbone = "gbif") |>
  add_bee_ostwald()

## End(Not run)

Description

Joins species-level bryophyte traits to a taxify() result by looking up accepted_name.

Usage

add_bet(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: Bryophytes of Europe Traits (van Zuijlen et al. 2023, EnviDat, CC BY-SA 4.0). Coverage: ~1.8k bryophyte species.

Value

The same data.frame with additional columns:

bet_growth_form

Growth form (acrocarpous/pleurocarpous/thalloid/...).

bet_life_form

Life form (cushion/mat/turf/weft/...).

bet_life_strategy

Life strategy (During).

bet_sexual_condition

Sexual condition (monoicous/dioicous).

bet_shoot_size_mm

Mean shoot size (mm).

bet_generation_length_y

Generation length (years).

bet_spore_diameter_um

Mean spore diameter (micrometres).

bet_ind_light

Ellenberg light indicator value.

bet_ind_temperature

Ellenberg temperature indicator value.

bet_ind_moisture

Ellenberg moisture indicator value.

bet_ind_reaction_ph

Ellenberg reaction (pH) indicator value.

bet_ind_nitrogen

Ellenberg nitrogen indicator value.

bet_substrate_soil

Occurs on soil (0/1).

bet_substrate_rock

Occurs on rock (0/1).

bet_substrate_bark

Occurs on bark (0/1).

bet_substrate_deadwood

Occurs on deadwood (0/1).

bet_substrate

Every substrate class the species occurs on, as a pipe-delimited set over soil, rock, bark, wood (deadwood), living_plants (epiphytic on leaves or other bryophytes) and dung_carcass, e.g. "soil|rock|bark". add_trait() "substrate" reduces it to one primary class.

bet_epiphyte

Epiphytic (0/1).

bet_redlist_category

IUCN European Red List category.

References

van Zuijlen K et al. (2023) Bryophytes of Europe Traits (BET): a fundamental dataset for European bryophyte ecology. EnviDat. doi:10.16904/envidat.348

Examples

## Not run: 
taxify("Abietinella abietina", backbone = "gbif") |>
  add_bet()

## End(Not run)

Description

Joins per-species springtail body length to a taxify() result by looking up accepted_name. The value is the median over the BETSI compilation's per-source measurements, with the observed range and the measurement and source counts behind it.

Usage

add_betsi_collembola_body_length(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: body length values from the BETSI database (Biological and Ecological Traits of Soil Invertebrates), the 2017 all-Collembola export requested by Bonfanti (2018), CC BY-NC 4.0. Coverage: 1,374 species. The European-compendia body-length floor: it agrees with the Ellers et al. body size at Pearson r = 0.96 over 262 shared species and with the Plazi treatment values at r = 0.906. Body length is also available through add_trait()("body_length"), which coalesces it with the Plazi, monograph and other springtail sources.

Value

The same data.frame with additional columns:

betsi_body_length_mm

Body length (mm), median of the per-source measurements.

betsi_body_length_min_mm

Smallest measurement (mm).

betsi_body_length_max_mm

Largest measurement (mm).

betsi_body_length_n

Number of measurements aggregated.

betsi_body_length_sources

Number of distinct literature sources.

References

Bonfanti J (2018) Body length trait values from the BETSI database, on all Collembola species. Zenodo. doi:10.5281/zenodo.1292461

Examples

## Not run: 
taxify("Isotoma viridis", backbone = "gbif") |>
  add_betsi_collembola_body_length()

## End(Not run)

Description

Joins per-species springtail functional traits to a taxify() result by looking up accepted_name. Output columns are prefixed betsi_ct_.

Usage

add_betsi_collembola_traits(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: per-species Collembola traits from Lu et al. (2025), Appendix S1. The morphological traits (body size, antenna/body ratio, furca, pigmentation, ocelli, reproduction) are derived from the BETSI database (Pey et al. 2014); vertical stratification and trophic position were measured in that study and the life form assigned after Potapov et al. (2016). Coverage: 26 species. Body length is also available through add_trait()("body_length").

Value

The same data.frame with additional columns:

betsi_ct_body_length_mm

Maximum body length (mm).

betsi_ct_antenna_body_ratio

Antenna to body length ratio.

betsi_ct_ocelli_number

Number of ocelli.

betsi_ct_furca

Furca (springing organ) development.

betsi_ct_pigment_scaled

Pigmentation (scaled).

betsi_ct_reproduction

Reproduction mode.

betsi_ct_stratification_scaled

Vertical stratification (scaled).

betsi_ct_trophic_position

Trophic position.

betsi_ct_life_form

Life form (after Potapov et al. 2016).

References

Lu J-Z et al. (2025) Mixed forests with native species mitigate impacts of introduced Douglas fir on soil decomposers (Collembola). Ecological Applications 35:e70034. doi:10.1002/eap.70034

Examples

## Not run: 
taxify("Isotoma viridis", backbone = "gbif") |>
  add_betsi_collembola_traits()

## End(Not run)

Description

Joins fuzzy-coded earthworm functional traits to a taxify() result by looking up accepted_name. Output columns are prefixed betsi_ew_.

Usage

add_betsi_earthworm_traits(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: earthworm functional traits compiled from the BETSI database (Biological and Ecological Traits of Soil Invertebrates) and published in Pelosi et al. (2014), Appendix 1. Coverage: 11 species. Earthworm body length and soil carbon preference are given as fuzzy affinity vectors rather than a single value, so they are not passed to the scalar add_trait() verb.

Value

The same data.frame with additional columns, one per modality bin of seven fuzzy-coded traits: body length, body mass to length ratio, cocoon diameter, epithelium, typhlosolis, soil carbon preference and vertical distribution. Each trait is fuzzy coded, so a species' affinities across the bins of one trait (columns sharing a ⁠betsi_ew_<trait>__⁠ stem) sum to 100. See enrichment_cols for the full column list.

References

Pelosi C, Pey B, Hedde M, et al. (2014) Reducing tillage in cultivated fields increases earthworm functional diversity. Applied Soil Ecology 83:79-87. doi:10.1016/j.apsoil.2013.10.005

Examples

## Not run: 
taxify("Lumbricus terrestris", backbone = "gbif") |>
  add_betsi_earthworm_traits()

## End(Not run)

Description

Joins North Atlantic / NE Pacific shelf marine-fish life-history and ecology traits to a taxify() result by accepted_name (species-level summaries).

Usage

add_beukhof(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: Beukhof et al. (2019) marine fish trait collection (PANGAEA, CC-BY 4.0).

Value

The same data.frame with beukhof_ columns: numeric trophic_level, aspect_ratio, offspring_size, age_maturity, fecundity, length_infinity_cm, growth_coefficient, length_max_cm; categorical habitat, feeding_mode, body_shape, fin_shape, spawning_type.

References

Beukhof E et al. (2019) A trait collection of marine fish species from North Atlantic and Northeast Pacific continental shelf seas. PANGAEA. doi:10.1594/PANGAEA.900866

Examples

## Not run: 
taxify("Gadus morhua", backbone = "gbif") |>
  add_beukhof()

## End(Not run)

Description

Joins BIEN plant functional traits to a taxify() result by looking up accepted_name. Values are species-level aggregates of public-access BIEN records (numeric by median, categorical by mode).

Usage

add_bien(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: BIEN (Botanical Information and Ecology Network; Maitner et al. 2018, Methods Ecol Evol, CC BY). Coverage: tens of thousands of vascular plant species.

Value

The same data.frame with additional columns:

bien_plant_height_m

Whole-plant height (m).

bien_max_plant_height_m

Maximum whole-plant height (m).

bien_dbh_cm

Diameter at breast height (cm).

bien_sla_mm2_mg

Leaf area per leaf dry mass (SLA, mm2/mg).

bien_leaf_area_mm2

Leaf area (mm2).

bien_leaf_dry_mass_mg

Leaf dry mass (mg; BIEN records g).

bien_leaf_n_per_dry_mass

Leaf nitrogen per dry mass (mg/g).

bien_leaf_p_per_dry_mass

Leaf phosphorus per dry mass (mg/g).

bien_leaf_thickness_mm

Leaf thickness (mm).

bien_seed_mass_mg

Seed mass (mg).

bien_wood_density_g_cm3

Stem wood density (g/cm3).

bien_leaf_lifespan

Leaf life span (months).

bien_growth_form

Whole-plant growth form.

bien_woodiness

Whole-plant woodiness.

bien_dispersal_syndrome

Whole-plant dispersal syndrome.

bien_flower_color

Flower colour.

References

Maitner BS et al. (2018) The BIEN R package: A tool to access the Botanical Information and Ecology Network (BIEN) database. Methods in Ecology and Evolution 9:373-379. doi:10.1111/2041-210X.12861

Examples

old <- options(taxify.data_dir = taxify_example_data())

taxify("Quercus alba", backbone = "gbif") |>
  add_bien()

options(old)

Description

Joins BIRDBASE biogeography, conservation and life-history traits to a taxify() result by looking up accepted_name. Traits redundant with add_avonet() morphology are not carried.

Usage

add_birdbase(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: BIRDBASE (Sekercioglu et al. 2025, figshare, CC BY 4.0). Coverage: ~11.6k bird species.

Value

The same data.frame with additional columns:

birdbase_iucn_status

IUCN Red List category.

birdbase_realm

Biogeographic realm.

birdbase_latitudinal_zone

Latitudinal zone (1 tropical to 5).

birdbase_island_endemic

Island-restricted breeding (0/1).

birdbase_restricted_range

Restricted-range species (0/1).

birdbase_elevation_min_m

Lower elevation limit (m).

birdbase_elevation_max_m

Upper elevation limit (m).

birdbase_elevation_range_m

Elevational breadth (m).

birdbase_primary_habitat

Primary habitat.

birdbase_habitat_breadth

Habitat breadth (number of habitats).

birdbase_primary_diet

Primary diet.

birdbase_diet_breadth

Diet breadth (number of food types).

birdbase_specialization_esi

Ecological specialization index.

birdbase_clutch_min

Minimum clutch size (eggs).

birdbase_clutch_max

Maximum clutch size (eggs).

birdbase_nest_type

Nest architecture.

birdbase_flightlessness

Volancy (yes/no/partial).

References

Sekercioglu CH et al. (2025) BIRDBASE: a global database of bird ecological and life-history traits. Scientific Data. doi:10.1038/s41597-025-05615-3

Examples

## Not run: 
taxify("Struthio camelus", backbone = "gbif") |>
  add_birdbase()

## End(Not run)

Description

Joins genus-level ant defensive and ecological traits to a taxify() result by genus.

Usage

add_blanchard(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: Blanchard & Moreau (2017) ant defensive traits (Dryad, CC0). Joins on genus because the database is genus-resolved.

Value

The same data.frame with blanchard_ columns: categorical subfamily, spines, sting, diet, nesting, foraging; numeric colony_size_workers (joined on genus).

References

Blanchard BD, Moreau CS (2017) Defensive traits in the ant genera database. Dryad. doi:10.5061/dryad.st6sc

Examples

## Not run: 
taxify("Camponotus pennsylvanicus", backbone = "gbif") |>
  add_blanchard()

## End(Not run)

Description

Joins Mediterranean-Basin plant fire-response, regeneration and functional traits to a taxify() result by accepted_name.

Usage

add_brot(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: BROT 2.0 (Tavsanoglu & Pausas 2018, Scientific Data, CC-BY 4.0).

Value

The same data.frame with brot_ columns: numeric seed_mass_mg, sla_mm2_mg, height_m, leaf_area_mm2; categorical resp_fire, growth_form, disp_mode, fruit_type, soil_seed_bank, seedling_emergence.

References

Tavsanoglu C, Pausas JG (2018) A functional trait database for Mediterranean Basin plants (BROT 2.0). Scientific Data 5:180135. doi:10.6084/m9.figshare.c.3843841

Examples

old <- options(taxify.data_dir = taxify_example_data())

taxify("Quercus coccifera", backbone = "gbif") |>
  add_brot()

options(old)

Description

Joins attributes of British and Irish mosses, liverworts and hornworts (Hill et al. 2007) to a taxify() result by looking up accepted_name. Bryophytes are otherwise almost absent from the bundled trait databases.

Usage

add_bryoatt(x, cols = NULL, verbose = TRUE)

Arguments

Details

1,194 taxa. The vascular-plant companion is add_plantatt().

This source states no licence (it is copyright the Biological Records Centre), so taxify ships no pre-built copy of it. The first call builds it from the original source on the user's machine, which requires the taxifydb package (remotes::install_github("gcol33/taxifydb")). taxify redistributes none of the data. Cite Hill et al. (2007) for the data.

Value

The same data.frame with additional columns. The default set:

bryoatt_ellenberg_light, bryoatt_ellenberg_moisture, bryoatt_ellenberg_reaction, bryoatt_ellenberg_nitrogen, bryoatt_ellenberg_salt

Ellenberg indicator values calibrated for British and Irish bryophytes.

bryoatt_life_form

Life-form code, kept verbatim (Ts, Mr, Ms, ...).

bryoatt_plant_group

Plant group: M moss, L liverwort, H hornwort.

bryoatt_status

Status code, kept verbatim (N native, AN alien naturalised, AR archaeophyte).

References

Hill MO, Preston CD, Bosanquet SDS, Roy DB (2007) BRYOATT: Attributes of British and Irish Mosses, Liverworts and Hornworts. NERC Centre for Ecology and Hydrology.

Examples

## Not run: 
# Builds the enrichment on first use (needs taxifydb).
taxify("Polytrichum commune", backbone = "gbif") |>
  add_bryoatt()

## End(Not run)

Description

Joins somatic chromosome numbers from the Chromosome Counts Database (Rice et al. 2015) to a taxify() result by looking up accepted_name. CCDB aggregates published counts per taxon; records are collapsed to the binomial and reduced to a per-species median with its observed range.

Usage

add_ccdb(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: CCDB (ccdb.tau.ac.il), citation requested, no explicit licence, so taxify publishes no pre-built copy. The data is built on the user's machine from the original source, which needs taxifydb installed: remotes::install_github("gcol33/taxifydb").

CCDB's service reports gametic numbers, which taxifydb doubles to the somatic 2n reported here. The spread between min and max is real ploidy variation among a species' cytotypes rather than measurement error, so a polyploid complex legitimately spans a wide range (Sedum acre runs 2n = 40 to 120). Where a species is covered by both, this and the CC BY add_kew_cvalues() chromosome column agree exactly for 85% of the 7133 species they share; the rest are compilations differing over which cytotype is typical.

Value

The same data.frame with additional columns. The default set:

ccdb_chromosome_2n

Median somatic chromosome number (2n).

ccdb_chromosome_2n_min

Lowest 2n reported for the species.

ccdb_chromosome_2n_max

Highest 2n reported for the species.

References

Rice A, Glick L, Abadi S, Einhorn M, Kopelman NM, Salman-Minkov A, Mayzel J, Chay O, Mayrose I (2015) The Chromosome Counts Database (CCDB) - a community resource of plant chromosome numbers. New Phytologist 206:19-26.

See Also

add_kew_cvalues() for genome size and a CC BY chromosome number.

Examples

## Not run: 
# Builds the enrichment on first use (requires taxifydb).
taxify("Zea mays", backbone = "gbif") |>
  add_ccdb()

## End(Not run)

Description

Joins biological traits of North-West European continental-shelf benthic macrofauna from the Cefas biological-traits database to a taxify() result by looking up genus. The source fuzzy-codes traits at genus level, and each trait is reduced to its highest-scoring modality, so the join is on genus rather than accepted_name: every species in a coded genus is annotated.

Usage

add_cefas_btrait(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: North-West European continental-shelf benthos biological-traits database, Cefas Data Hub (doi:10.14466/CefasDataHub.123), Open Government Licence v3.0.

Value

The same data.frame with additional columns. The default set:

cefas_body_size

Maximum body-size class.

cefas_morphology

Body morphology.

cefas_lifespan

Life-span class.

cefas_living_habit

Living habit.

cefas_feeding_mode

Feeding mode.

cefas_mobility

Mobility.

cefas_bioturbation

Bioturbation mode.

cols = "all" also attaches egg and larval development and sediment position.

References

Centre for Environment, Fisheries and Aquaculture Science (2022) North-West European continental-shelf benthos biological-traits database. doi:10.14466/CefasDataHub.123

Examples

## Not run: 
# Downloads the enrichment on first use.
taxify("Abra alba", backbone = "gbif") |>
  add_cefas_btrait()

## End(Not run)

Description

Joins species-level turtle and tortoise traits to a taxify() result by looking up accepted_name.

Usage

add_chelonians(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: CheloniansTraits (Wang et al. 2025, figshare, CC BY 4.0). Coverage: 358 turtle and tortoise species. Numeric values reported as "min-max" ranges in the source are reduced to their midpoint.

Value

The same data.frame with additional columns:

chelonian_carapace_length_mm

Maximum straight-line carapace length (mm).

chelonian_max_mass_g

Maximum body mass (g).

chelonian_clutch_size_mean

Mean clutch size (count).

chelonian_clutch_size_max

Maximum clutch size (count).

chelonian_clutches_per_year

Clutches per year (count).

chelonian_incubation_d

Incubation period (days).

chelonian_age_maturity_y

Age at sexual maturity (years).

chelonian_max_lifespan_y

Maximum lifespan (years).

chelonian_range_size_km2

Range size (km2).

chelonian_diet

Diet (herbivorous/carnivorous/omnivorous).

chelonian_activity_time

Activity time.

chelonian_microhabitat

Microhabitat (aquatic/terrestrial/...).

chelonian_habitat_type

Habitat type.

chelonian_shell_type

Shell type (hardshell/softshell).

References

Wang Y et al. (2025) CheloniansTraits: a comprehensive trait database of global turtles and tortoises. figshare. doi:10.6084/m9.figshare.28828241

Examples

## Not run: 
taxify("Chelonia mydas", backbone = "gbif") |>
  add_chelonians()

## End(Not run)

Description

Joins carabid (ground beetle) traits and modelled national occupancy trends to a taxify() result by accepted name.

Usage

add_chowdhury(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: Supporting Information Data S1 of Chowdhury et al. (2025), 383 German carabid species, 382 of them with traits. Ground beetles are one of the most intensively sampled insect groups in ecology, and this is the largest openly reachable block of carabid traits.

wing_morph is three-state: long-winged, short-winged, and the 83 dimorphic species that produce both forms. Dimorphism is the trait of interest in carabid dispersal ecology, so it is never collapsed into a flight-capable/flightless binary.

red_list_germany is the German national Red List code (*, 1, 2, 3, V, R, G, D). red_list_iucn restates that same national assessment in IUCN letter codes and is not a global IUCN assessment: it maps deterministically onto red_list_germany (* to LC, 1 to CR, 2 to EN, 3 to VU, V to NT), with no off-diagonal case in all 382 species. A species can be critically endangered at its German range edge and of least concern globally, so these columns stay in this function and out of the global conservation_status trait.

occupancy_trend is the paper's own result: the modelled change in occupancy probability over two years, negative for a declining species.

Value

The same data.frame with chowdhury_ columns: numeric body_length_mm and occupancy_trend; categorical wing_morph, trophic_level, habitat_pref, red_list_germany, red_list_iucn, threat_status, trend_status, trend_significance.

References

Chowdhury S, Jaureguiberry P, Rada S, et al. (2025) Widespread decline of ground beetles in Germany. Diversity and Distributions. doi:10.1111/ddi.70112

Examples

## Not run: 
taxify(c("Carabus coriaceus", "Amara aenea")) |>
  add_chowdhury()

## End(Not run)

Description

Attaches the Linnaean ranks above family (kingdom, phylum, class, order) to a taxify() result by joining each matched row back to its backbone. The core taxify() output already carries family and genus; this fills the ranks above them, for whichever ranks the matched backbone stores. Rows matched by different backbones are each joined against their own backbone.

Usage

add_classification(
  x,
  ranks = c("kingdom", "phylum", "class", "order"),
  verbose = TRUE
)

Arguments

Value

x with the requested rank columns added. A rank a backbone does not store is left NA (WFO, for example, carries no ranks above family).

See Also

add_col_info(), add_gbif_info() for backbone-specific extras.

Examples

# Runs offline against the bundled example database.
old <- options(taxify.data_dir = taxify_example_data())

taxify("Naja naja", backbone = "reptiledb") |>
  add_classification()

options(old)

Description

Joins clonal growth, bud-bank and lifespan traits of the Central European flora (Klimesova et al. 2017) to a taxify() result by looking up accepted_name. Source records are collapsed to the binomial, so infraspecific rows are aggregated to the species (numeric traits by median, nominal traits by mode).

Usage

add_clopla(x, cols = NULL, verbose = TRUE)

Arguments

Details

2,909 species of the Central European flora.

This source states no licence. It is an Ecological Society of America data paper, free to use with citation, and taxify ships no pre-built copy of it. The first call builds it from the original source on the user's machine, which requires the taxifydb package (remotes::install_github("gcol33/taxifydb")). taxify redistributes none of the data. Cite Klimesova et al. (2017) for the data.

Value

The same data.frame with additional columns. The default set:

clopla_clonal

Clonal growth present.

clopla_clonalindex

Clonal index.

clopla_woody, clopla_annual, clopla_monocarpic, clopla_polycarpic

Growth-form and life-cycle flags.

clopla_persistence

Persistence of the clonal connection.

clopla_offspring

Offspring produced per parent shoot per year.

clopla_spread

Lateral spread.

clopla_BBsize, clopla_BBdepth

Bud-bank size and depth.

clopla_finalCGO

Final clonal growth organ.

cols = "all" attaches every trait the source carries (29 in total: the remaining bud-bank counts by depth class, root-derived bud banks, branching, cyclicity and dispersibility), with codes kept verbatim.

References

Klimesova J, Danihelka J, Chrtek J, de Bello F, Herben T (2017) CLO-PLA: a database of clonal and bud-bank traits of the Central European flora. Ecology 98:1179. doi:10.1002/ecy.1745

Examples

## Not run: 
# Builds the enrichment on first use (needs taxifydb).
taxify("Trifolium repens", backbone = "gbif") |>
  add_clopla()

## End(Not run)

Description

Joins extra Catalogue of Life columns to a taxify() result by looking up taxon_id in the COL backbone. Only enriches rows where backbone == "col".

Usage

add_col_info(x)

Arguments

Value

The same data.frame with additional columns:

notho

Hybrid type from COL: "generic", "specific", "infrageneric", or "infraspecific".

nomenclaturalCode

Nomenclatural code ("ICN", "ICZN", etc.).

nomenclaturalStatus

Nomenclatural status.

name_published_in

Original publication reference.

kingdom

Kingdom classification.

phylum

Phylum classification.

col_class

Class classification (renamed to avoid conflict with R's class function).

order

Order classification.

infraspecificEpithet

Infraspecific epithet.

is_extinct

Logical. Whether the species is extinct (from SpeciesProfile, if available).

is_marine

Logical. Whether the species is marine.

is_freshwater

Logical. Whether the species is freshwater.

is_terrestrial

Logical. Whether the species is terrestrial.

Examples

# Runs offline against the bundled example database.
old <- options(taxify.data_dir = taxify_example_data())

taxify("Quercus robur", backbone = "col") |>
  add_col_info()

options(old)

Description

The default COMBINE function. Attaches the reported (measured or literature compiled) trait values and fills each still-missing cell from COMBINE's phylogenetically imputed table, so a single call reaches the fullest coverage COMBINE offers. A measurement is never overwritten: the reported value wins wherever it exists and the imputed model only fills gaps. Beside every trait sits a ⁠<trait>_src⁠ column recording where that cell came from ("reported", "imputed", or NA when neither table has it), so a model estimate is always distinguishable from a measurement.

Usage

add_combine(x, cols = NULL, verbose = TRUE)

Arguments

Details

For a single-table view use add_combine_reported() (measured values only) or add_combine_imputed() (the imputed table on its own).

Value

The same data.frame with the ⁠combine_*⁠ trait columns (reported values, gaps filled from the imputed table) and, beside each, a ⁠combine_*_src⁠ column tagging that cell as "reported", "imputed", or NA. Traits COMBINE does not impute (for example combine_biogeographical_realm) come out "reported" wherever present. If the imputed table is unavailable, the reported values are returned with every ⁠_src⁠ tag "reported" or NA.

See Also

add_combine_reported(), add_combine_imputed()

Examples

## Not run: 
# Coverage-filled values with per-trait provenance:
res <- taxify("Osphranter rufus", backbone = "col") |>
  add_combine()
res[, c("combine_gestation_length_d", "combine_gestation_length_d_src")]

## End(Not run)

Description

Joins the phylogenetically imputed COMBINE table to a taxify() result by looking up accepted_name. This is the companion to add_combine_reported(): the same ~6.2k mammal species, but with the cells the reported table leaves missing filled in by COMBINE's phylogenetic multiple-imputation model, giving near-complete coverage for the life-history traits. Imputed values are model estimates, not measurements, so they are attached under their own ⁠combine_imputed_*⁠ columns rather than replacing the reported values.

Usage

add_combine_imputed(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: COMBINE (Soria et al. 2021, Ecology, CC0), imputed table (trait_data_imputed.csv). Same species set and column meanings as add_combine_reported(); the difference is coverage. Because imputation only fills gaps, applying both functions and comparing ⁠combine_*⁠ with ⁠combine_imputed_*⁠ shows which values are measured versus estimated.

Value

The same data.frame with additional columns, mirroring add_combine_reported() but named ⁠combine_imputed_*⁠ (e.g. combine_imputed_adult_mass_g, combine_imputed_gestation_length_d). Traits COMBINE does not impute (for example biogeographical_realm and habitat_breadth_n) carry the reported coverage; the life-history traits (gestation, weaning, longevity, litter size, generation length, ...) are near-complete.

References

Soria CD et al. (2021) COMBINE: a coalesced mammal database of intrinsic and extrinsic traits. Ecology 102:e03344. doi:10.1002/ecy.3344

See Also

add_combine_reported() for the measured values; add_combine() for the two coalesced with per-trait provenance.

Examples

## Not run: 
# Compare reported vs imputed gestation length for a mammal:
taxify("Vulpes vulpes", backbone = "gbif") |>
  add_combine_reported() |>
  add_combine_imputed()

## End(Not run)

Description

Joins the COMBINE mammal trait database to a taxify() result by looking up accepted_name, attaching the reported (directly measured or literature-compiled) trait values. COMBINE ships two parallel tables over the same ~6.2k mammal species: the reported table used here, and a phylogenetically imputed table with the missing cells filled in by a model (add_combine_imputed()). They are offered as two separate sources so the distinction between a measured value and a model estimate stays explicit.

Usage

add_combine_reported(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: COMBINE (Soria et al. 2021, Ecology, CC0), reported table. Coverage: ~6.2k mammal species, keyed on the IUCN 2020 binomial. The reported table leaves a cell missing when no source measured that trait for a species; use add_combine_imputed() for the higher-coverage phylogenetically imputed values.

Value

The same data.frame with additional columns (reported values):

combine_adult_mass_g

Adult body mass (g).

combine_adult_body_length_mm

Adult head-body length (mm).

combine_litter_size_n

Litter size (count).

combine_litters_per_year_n

Litters per year (count).

combine_max_longevity_d

Maximum longevity (days).

combine_gestation_length_d

Gestation length (days).

combine_weaning_age_d

Weaning age (days).

combine_generation_length_d

Generation length (days).

combine_dispersal_km

Natal dispersal distance (km).

combine_habitat_breadth_n

Number of IUCN habitats (count).

combine_diet_breadth_n

Number of diet categories (count).

combine_trophic_level

Trophic level (1 herbivore, 2 omnivore, 3 carnivore).

combine_activity_cycle

Activity cycle (1 nocturnal, 2 cathemeral, 3 diurnal).

combine_foraging_stratum

Foraging stratum (G/Ar/A/S/M).

combine_biogeographical_realm

Biogeographical realm(s).

References

Soria CD et al. (2021) COMBINE: a coalesced mammal database of intrinsic and extrinsic traits. Ecology 102:e03344. doi:10.1002/ecy.3344

See Also

add_combine() for reported values with imputed gaps filled and per-trait provenance; add_combine_imputed() for the imputed values; add_pantheria(), add_anage() for other mammal trait sources.

Examples

## Not run: 
taxify("Vulpes vulpes", backbone = "gbif") |>
  add_combine_reported()

## End(Not run)

Description

Joins vernacular names to a taxify() result by looking up accepted_name, filtered by language.

Usage

add_common_names(x, lang = "en", cols = NULL, verbose = TRUE)

Arguments

Details

Common names are merged from three sources:

• GBIF backbone vernacular names (CC0), multi-language via ISO 639-1 codes.

• NCBI Taxonomy common names (public domain), no language tag (lang = NA).

• Open Tree of Life common names (CC0), no language tag (lang = NA).

When multiple common names exist for a species in the requested language, the first (most commonly used) is returned.

Value

The same data.frame with an additional column:

common_name

The vernacular name in the requested language, or NA if none is available.

Examples

# Runs offline against the bundled example database.
old <- options(taxify.data_dir = taxify_example_data())

taxify("Quercus robur") |>
  add_common_names()

taxify("Quercus robur") |>
  add_common_names(lang = "de")

options(old)

Description

Joins marine-copepod traits from the trait database of Brun et al. (2017) to a taxify() result by looking up accepted_name. Records are reduced to per-species values (numeric by median, categorical by mode).

Usage

add_copepod_traits(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: A trait database for marine copepods (Brun et al. 2017), PANGAEA, CC BY 3.0.

Value

The same data.frame with additional columns. The default set:

cop_body_length_mm

Body length (mm).

cop_egg_diameter_um

Egg outer diameter (micrometres).

cop_clutch_size

Clutch size.

cop_feeding_mode

Feeding mode.

cop_spawning_strategy

Spawning strategy.

cols = "all" also attaches feeder type, myelination, resting-egg presence, and the per-species min/max/n spread of the numeric values.

References

Brun P, Payne MR, Kiorboe T (2017) A trait database for marine copepods. Earth System Science Data 9:99-113. doi:10.5194/essd-9-99-2017

Examples

## Not run: 
# Downloads the enrichment on first use.
taxify("Calanus finmarchicus", backbone = "gbif") |>
  add_copepod_traits()

## End(Not run)

Description

Joins species-level coral functional traits to a taxify() result by looking up accepted_name. Values are aggregated from the long-format Coral Trait Database (numeric traits by median, categorical traits by mode).

Usage

add_coral_traits(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: Coral Trait Database (Madin et al. 2016, Scientific Data, CC BY 4.0). Coverage: ~1.5k coral species.

Value

The same data.frame with additional columns:

coral_symbiotic_state

Zooxanthellate / azooxanthellate.

coral_growth_form

Typical growth form (massive/branching/...).

coral_coloniality

Colonial / solitary.

coral_substrate_attachment

Attached / unattached.

coral_sexual_system

Hermaphrodite / gonochore.

coral_larval_development_mode

Spawner / brooder.

coral_symbiont_clade

Symbiodinium clade.

coral_corallite_width_max_mm

Maximum corallite width (mm).

coral_colony_max_diameter_cm

Maximum colony diameter (cm).

coral_growth_rate_mm_yr

Linear extension rate (mm/year).

coral_depth_lower_m

Lower depth limit (m).

coral_depth_upper_m

Upper depth limit (m).

coral_skeletal_density_g_cm3

Skeletal density (g/cm3).

References

Madin JS et al. (2016) The Coral Trait Database, a curated database of trait information for coral species from the global oceans. Scientific Data 3:160017. doi:10.1038/sdata.2016.17

Examples

## Not run: 
taxify("Acropora millepora", backbone = "gbif") |>
  add_coral_traits()

## End(Not run)

Description

Joins an external data source (CSV file or data.frame) to a taxify() result. Species names in the external data are matched through the same backbone(s) used in the original taxify() call, and the join is performed on the accepted taxon — so synonyms in either dataset resolve to the same key. The key is the accepted_id together with the backbone that issued it, falling back to accepted_name where the two sides were matched by different backbones: backbone IDs are bare integers in most backbones and mean nothing outside the one they came from.

Usage

add_data(
  x,
  data,
  species_col = NULL,
  table = NULL,
  sheet = NULL,
  start_row = NULL,
  cols = NULL,
  group_col = NULL,
  groups = "all",
  fuzzy = TRUE,
  fuzzy_threshold = 0.2,
  verbose = TRUE
)

Arguments

Details

The workflow:

1. Read data (CSV or data.frame).

2. Identify the species column (explicit or auto-detected).

3. Match species names through the same backbone(s) as the original taxify() call, obtaining accepted_id for each row.

4. Check for conflicting duplicates: if multiple rows in data resolve to the same accepted name with different values, an error is raised (with group_col set, within each group). Exact duplicates produce a warning and are deduplicated.

5. Left-join on the accepted taxon.

Grouped data

When data has multiple rows per species (e.g., one row per species per country), set group_col to produce wide output with suffixed columns. This is the same format as the built-in grouped enrichments.

Auto-detection

When species_col is not specified, add_data() takes the first 10 rows of each character column and runs them through taxify(). The column with the highest match rate is selected. If no column achieves at least 50% matches, an error is raised asking the user to specify species_col explicitly.

Value

The input data.frame with additional columns from data, joined via the backbone-resolved accepted taxon. Columns from data that collide with existing columns in x are prefixed with "data_".

Examples

# Runs offline against the bundled example database.
old <- options(taxify.data_dir = taxify_example_data())

result <- taxify(c("Quercus robur", "Pinus sylvestris"))
traits <- data.frame(species = c("Quercus robur", "Pinus sylvestris"),
                     height = c(30, 25))
result |> add_data(traits, species_col = "species")

options(old)

Description

Joins species-level mean seed mass and plant height from Diaz et al. (2022) to a taxify() result by looking up accepted_name.

Usage

add_diaz_traits(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: Diaz et al. 2022, TRY File Archive (CC BY 3.0). Coverage: ~46k plant species. Plants only.

Value

The same data.frame with additional columns:

seed_mass_mg

Seed mass in milligrams (species-level mean).

plant_height_m

Plant height in metres (species-level mean).

References

Diaz S, Kattge J, Cornelissen JHC, Wright IJ, Lavorel S, Dray S, Reu B, Kleyer M, Wirth C, Prentice IC, et al. (2022) The global spectrum of plant form and function: enhanced species-level trait dataset. Scientific Data 9:755. doi:10.1038/s41597-022-01774-9

Diaz S, Kattge J, Cornelissen JHC, Wright IJ, Lavorel S, Dray S, Reu B, Kleyer M, Wirth C, Prentice IC, et al. (2016) The global spectrum of plant form and function. Nature 529:167-171. doi:10.1038/nature16489

Examples

# Runs offline against the bundled example database.
old <- options(taxify.data_dir = taxify_example_data())

taxify("Quercus robur") |>
  add_diaz_traits()

options(old)

Description

Joins genus-level dispersal-related traits for European aquatic macroinvertebrates to a taxify() result by genus. Each fuzzy-coded trait is reduced to its dominant modality (with the database's own labels).

Usage

add_disperse(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: DISPERSE (Sarremejane et al. 2020, Scientific Data, CC-BY 4.0). Joins on genus because the database is genus-resolved.

Value

The same data.frame with categorical disperse_body_size_cm, disperse_life_cycle, disperse_repro_cycles, disperse_dispersal, disperse_adult_lifespan, disperse_female_wing_mm, disperse_wing_type, disperse_fecundity, disperse_drift, and the numeric bin-midpoint columns disperse_body_size_cm_mid, disperse_female_wing_mm_mid, disperse_fecundity_mid (all joined on genus).

References

Sarremejane R et al. (2020) DISPERSE, a trait database to assess the dispersal potential of European aquatic macroinvertebrates. Scientific Data 7:386. doi:10.6084/m9.figshare.c.5000633

Examples

## Not run: 
taxify("Baetis rhodani", backbone = "gbif") |>
  add_disperse()

## End(Not run)

Description

Joins ground-beetle traits and 24-year local population trends to a taxify() result by accepted name.

Usage

add_eberswalde(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: Weiss, von Wehrden & Linde (2024), 27 species pitfall-trapped on 13 forest plots near Eberswalde between 1999 and 2022.

body_length_mm, wing_morph and range_centre_lat are carabids.org (Homburg et al. 2014) verbatim, which the deposit's README states and the data confirm: over the 19 species shared with add_chowdhury() every size is exactly equal and every wing class agrees. They are surfaced here because each ⁠add_<source>()⁠ function shows what its source carries, and they are kept out of add_trait(), where they would double-count one lineage.

What belongs to this deposit alone is the monitoring result: abundance_trend over the 24 years, drought_effect against the 72-month SPEI index, and a feeding_guild refined by the authors' field observations to name the prey. humidity_pref is Sustek's (2004) 1-8 scale, 1 dry to 8 humid.

Value

The same data.frame with eberswalde_ columns: numeric body_length_mm, humidity_pref, range_centre_lat, abundance_total; categorical wing_morph, feeding_guild, abundance_trend, drought_effect.

References

Weiss F, von Wehrden H, Linde A (2024) Eberswalde Carabid Monitoring 1999-2022 - Full Data. PubData Leuphana. doi:10.48548/pubdata-46

Weiss F, von Wehrden H, Linde A (2024) Long-term drought triggers severe declines in carabid beetles in a temperate forest. Ecography 2024(4):e07020. doi:10.1111/ecog.07020

Examples

## Not run: 
taxify(c("Carabus coriaceus", "Nebria brevicollis")) |>
  add_eberswalde()

## End(Not run)

Description

Joins traits from the Ecological Flora of the British Isles (Fitter & Peat 1994) to a taxify() result by looking up accepted_name. Ecoflora covers the vascular flora of the British Isles, providing canopy height, leaf traits, life form, flowering phenology, pollination and reproduction, seed weight, and British-calibrated Ellenberg indicator values. Every column carries a ⁠_uk⁠ suffix to mark the British-flora calibration and to avoid collisions when chained with other plant-trait enrichments (e.g. add_baseflor() for France, add_floraweb() for Germany).

Usage

add_ecoflora(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: Ecoflora (Ecological Flora of the British Isles). Ecoflora has no bulk download or API; the bundled dataset was collected one species at a time and is redistributed under the source licence (CC BY-NC-SA 4.0). The .vtr is downloaded from the taxify release on first use and cached.

For French-flora traits see add_baseflor(); for German-flora traits see add_floraweb(); for European-calibration indicator values see add_eive().

Value

The same data.frame with additional ⁠_uk⁠ columns:

height_max_mm_uk, height_min_mm_uk

Typical maximum and minimum plant height (mm; Ecoflora records cm).

leaf_area_uk

Leaf area class (cm$^2$).

leaf_longevity_uk

Leaf longevity (e.g. evergreen, deciduous).

root_system_uk

Root system type.

photosynthetic_pathway_uk

Photosynthetic pathway (C3/C4/CAM).

life_form_uk

Raunkiaer life form.

reproduction_uk

Reproduction method.

flower_begin_month_uk, flower_end_month_uk

Flowering months (1-12).

pollination_vector_uk

Pollen vector(s).

seed_weight_mg_uk

Seed weight (mg).

propagule_uk

Propagule / dispersule type.

ell_light_uk, ell_moisture_uk, ell_reaction_uk, ell_nitrogen_uk, ell_salt_uk

Ellenberg indicator values calibrated for the British flora (light, moisture, reaction, nitrogen, salt).

References

Fitter AH, Peat HJ (1994) The Ecological Flora Database. Journal of Ecology 82:415-425.

Examples

# Runs offline against the bundled example database.
old <- options(taxify.data_dir = taxify_example_data())

taxify("Bellis perennis") |>
  add_ecoflora()

options(old)

Description

Joins the ecomorphosis record of a springtail species to a taxify() result by looking up accepted_name. Ecomorphosis is a seasonal, reversible change of form some Collembola undergo; this marks the species known to display it.

Usage

add_ecomorphosis(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: extended species list of Collembola known to display ecomorphosis, Bonfanti (2022), Zenodo, CC BY 4.0. Coverage: 43 species, each carrying the literature record for its ecomorphic form. It is a presence list: a species not listed is not evidence of absence.

Value

The same data.frame with additional columns:

ecomorphosis

Whether the species is known to display ecomorphosis.

ecomorphosis_area

The morphological area affected.

ecomorphosis_reference

The literature record establishing it.

References

Bonfanti J, Krogh PH, Hedde M, Cortet J (2022) Ecomorphosis in European Collembola: a review in the context of trait-based ecology. Applied Soil Ecology. doi:10.1016/j.apsoil.2022.104692

Examples

## Not run: 
taxify("Hypogastrura vesiculosa", backbone = "gbif") |>
  add_ecomorphosis()

## End(Not run)

Description

Joins species-level phytoplankton nutrient physiology (Droop/Monod uptake and growth parameters for ammonium, nitrate and phosphorus, plus cell size and carbon content) to a taxify() result by looking up accepted_name.

Usage

add_edwards_phyto(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: Edwards et al. (2015, Ecology, CC BY 4.0), a compilation of phytoplankton nutrient-utilization traits for ~130 species. Single-source physiological data with no cross-source analogue, so it is attached by this function and not registered with add_trait().

Value

The same data.frame with additional columns. The curated set:

edwards_taxon_group

Coarse phytoplankton group (diatom, green, ...).

edwards_habitat_system

Habitat (marine/freshwater).

edwards_cell_volume

Cell volume (micron^3).

edwards_carbon_per_cell

Carbon content per cell (pg C).

edwards_mu_inf_nit

Maximum growth rate on nitrate (per day).

edwards_k_nit

Half-saturation constant for growth on nitrate.

edwards_qmin_nit

Minimum cell nitrogen quota.

edwards_mu_inf_p

Maximum growth rate on phosphorus (per day).

edwards_k_p

Half-saturation constant for growth on phosphorus.

edwards_qmin_p

Minimum cell phosphorus quota.

With cols = "all" the full set of ammonium/nitrate/phosphorus uptake and quota parameters (vmax_amm, mu_nit, vmax_p, qmax_p, ...) is attached under their source names. All uptake/quota traits are joined on accepted_name.

References

Edwards KF, Thomas MK, Klausmeier CA, Litchman E (2015) Phytoplankton growth and the interaction of light and temperature: A synthesis at the species and community level. Ecology 96(9):2554-2564. doi:10.1890/14-2252.1

Examples

## Not run: 
taxify("Thalassiosira pseudonana", backbone = "gbif") |>
  add_edwards_phyto()

## End(Not run)

Description

Joins EIVE 1.0 (Dengler et al. 2023) ecological indicator values to a taxify() result by looking up accepted_name. EIVE provides continuous indicator values for European vascular plants, superseding the original ordinal Ellenberg values.

Usage

add_eive(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: EIVE 1.0 (Dengler et al. 2023, Zenodo, CC BY 4.0). Coverage: ~14.5k European vascular plant species.

Value

The same data.frame with additional columns:

eive_light

Light indicator value (continuous).

eive_temperature

Temperature indicator value (continuous).

eive_moisture

Moisture indicator value (continuous).

eive_reaction

Soil reaction (pH) indicator value (continuous).

eive_nutrients

Nutrient indicator value (continuous).

References

Dengler J et al. (2023) EIVE 1.0: a standardized set of Ecological Indicator Values for Europe. Vegetation Classification and Survey 4:7-29. doi:10.3897/VCS.98324

Examples

# Runs offline against the bundled example database.
old <- options(taxify.data_dir = taxify_example_data())

taxify("Arrhenatherum elatius") |>
  add_eive()

options(old)

Description

Joins European springtail functional traits to a taxify() result by looking up accepted_name: vertical stratification, body size, reproduction mode and climatic preferences.

Usage

add_ellers_collembola(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: Collembola trait table from Ellers et al. (2018), Dryad, CC0. Coverage: 278 European Collembola species; taxonomy follows the Checklist of the Collembola of the World. The openly licensed analogue of the BETSI multi-trait matrices; its body size agrees with the BETSI body-length floor at Pearson r = 0.96 over 262 shared species. The body size is also available through add_trait()("body_length").

Value

The same data.frame with additional columns:

ellers_vertical_distribution

Vertical stratification (soil depth class).

ellers_body_size_mm

Body size (mm).

ellers_reproduction

Reproduction mode (sexual or asexual).

ellers_moisture_pref

Moisture preference.

ellers_temperature_pref

Temperature preference.

ellers_thermal_niche_breadth

Thermal niche breadth.

References

Ellers J, Berg MP, Dias ATC, Fontana S, Ooms A, Moretti M (2018) Diversity in form and function: vertical distribution of soil fauna mediates multidimensional trait variation. Journal of Animal Ecology 87:933-944. doi:10.1111/1365-2656.12838

Examples

## Not run: 
taxify("Isotoma viridis", backbone = "gbif") |>
  add_ellers_collembola()

## End(Not run)

Description

Joins EltonTraits 1.0 diet composition, foraging strata, body mass, and activity data to a taxify() result by looking up accepted_name.

Usage

add_elton_traits(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: EltonTraits 1.0 (Wilman et al. 2014, Figshare, CC0). Coverage: ~15.4k species. Birds and mammals only.

Value

The same data.frame with additional columns:

diet_inv

Percentage of diet: invertebrates.

diet_vend

Percentage of diet: endothermic vertebrates.

diet_vect

Percentage of diet: ectothermic vertebrates.

diet_vfish

Percentage of diet: fish.

diet_vunk

Percentage of diet: unknown vertebrates.

diet_scav

Percentage of diet: scavenging.

diet_fruit

Percentage of diet: fruit.

diet_nect

Percentage of diet: nectar.

diet_seed

Percentage of diet: seeds and nuts.

diet_plantother

Percentage of diet: other plant material.

foraging_water

Percentage of foraging: below water surface.

foraging_ground

Percentage of foraging: on ground.

foraging_understory

Percentage of foraging: in understory.

foraging_midhigh

Percentage of foraging: in mid to high strata.

foraging_canopy

Percentage of foraging: in canopy.

foraging_aerial

Percentage of foraging: aerial.

elton_body_mass_g

Body mass in grams.

nocturnal

Nocturnal activity (0 = diurnal, 1 = nocturnal).

diet_guild

Dominant diet guild derived from the ten diet-fraction columns (fractions summed within guild, the guild reaching 50 percent wins, otherwise omnivore): carnivore, herbivore, omnivore, invertivore, frugivore, granivore, nectarivore, scavenger. Also reachable across sources via add_trait(x, "diet_guild").

References

Wilman H et al. (2014) EltonTraits 1.0: Species-level foraging attributes of the world's birds and mammals. Ecology 95:2027.

Examples

# Runs offline against the bundled example database.
old <- options(taxify.data_dir = taxify_example_data())

taxify("Parus major", backbone = "gbif") |>
  add_elton_traits()

options(old)

Description

Joins primary functional traits of freshwater macroinvertebrates from the U.S. EPA Freshwater Biological Traits Database to a taxify() result. Records are reduced to per-taxon modes. The database records each trait at the finest available resolution, so the join matches accepted_name first and then fills any trait still missing from the taxon's genus-level row; a species-level value is never overwritten.

Usage

add_epa_freshwater(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: U.S. EPA Freshwater Biological Traits Database (2012), a public-domain U.S. Government work, compiled primarily from Vieira et al. (2006).

Value

The same data.frame with additional columns. The default set:

epa_feeding_mode

Functional feeding group.

epa_habit

Primary habit (e.g. clinger, burrower, swimmer).

epa_voltinism

Number of generations per year.

epa_thermal_preference

Thermal preference.

epa_body_size_class

Maximum body-size class.

cols = "all" also attaches body shape, rheophily, oviposition behaviour, and diapause.

References

U.S. Environmental Protection Agency (2012) Freshwater Biological Traits Database. Compiled from Vieira NKM et al. (2006) A database of lotic invertebrate traits for North America, USGS Data Series 187.

Examples

## Not run: 
# Downloads the enrichment on first use.
taxify("Baetis", backbone = "gbif") |>
  add_epa_freshwater()

## End(Not run)

Description

Joins European bee and hoverfly morphological, biogeographic and ecological traits to a taxify() result by accepted_name.

Usage

add_eupolltrait(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: EuPollTrait (Milicic et al. 2025, Zenodo, CC-BY 4.0).

Value

The same data.frame with eupolltrait_ columns: numeric itd_mm, tongue_length_mm, species_temperature_index, species_continentality_index, area_of_occupancy, extent_of_occurrence; categorical sociality, nest, larval_nutrition, body_length_category.

References

Milicic M et al. (2025) EuPollTrait: a trait database for European bees and hoverflies. Zenodo. doi:10.5281/zenodo.18032357

Examples

## Not run: 
taxify("Bombus terrestris", backbone = "gbif") |>
  add_eupolltrait()

## End(Not run)

Description

Joins species-level traits of European bats (morphology, life history, diet, foraging habitat, roost type) to a taxify() result by looking up accepted_name.

Usage

add_eurobat(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: Froidevaux et al. (2023, Scientific Data, CC BY 4.0), EuroBaTrait 1.0. Thematic measurement-or-fact tables are reduced to species-level values (numeric by median, categorical by mode).

Value

The same data.frame with additional columns. The curated set:

eurobat_forearm_length_mm

Forearm length (mm).

eurobat_body_mass_g

Body mass (g).

eurobat_max_longevity_yr

Maximum recorded longevity (years).

eurobat_litter_size

Litter size.

eurobat_diet_type

Diet type (insectivorous, frugivorous, ...).

eurobat_first_main_prey

First main prey item.

With cols = "all" the full trait set (digit lengths, wing indices, habitat affinity scores, critical feeding areas, roost dependence, phenology, ...) is attached under their source names.

References

Froidevaux JSP et al. (2023) EuroBaTrait 1.0: a species-level trait dataset of bats in Europe and beyond. Scientific Data. figshare doi:10.6084/m9.figshare.21777161

Examples

## Not run: 
taxify("Myotis myotis", backbone = "gbif") |>
  add_eurobat()

## End(Not run)

Description

Joins the per-area status Euro+Med PlantBase states for a taxon (native, naturalised, introduced, casual, ...) to a taxify() result, for the areas or countries asked for. The names and the ids of the euromed backbone are the same taxa the status is recorded on, so a result matched against any backbone joins through its accepted name.

Usage

add_euromed_distribution(x, region, cols = NULL, verbose = TRUE)

Arguments

Details

Source: Euro+Med PlantBase (CC BY-SA), European and Mediterranean vascular plants, bryophytes and some fungi and algae. A report Euro+Med marks as made in error is not carried; a former presence is "extinct". Where the records of one taxon and area disagree, the stronger claim wins in the order given above (a taxon is native wherever a record says so), and the references are those of the records stating it. The country of an area comes from its own name, so a combined area has none and its countries are read from the level-2 areas.

Value

The same data.frame with euromed_status, one of "native", "naturalised", "introduced", "casual", "cultivated", "doubtful" or "extinct", and NA where Euro+Med states nothing for the region. euromed_status_detail carries the term as Euro+Med words it ("endemic", "introduced: uncertain degree of naturalisation"). euromed_status_source holds the ids of the citing references; resolve them with cite(x$euromed_status_source, source = "euromed_distribution").

See Also

euromed_areas(), add_wcvp()

Examples

# Runs offline against the bundled example database.
old <- options(taxify.data_dir = taxify_example_data())

taxify("Solidago canadensis") |>
  add_euromed_distribution(region = "DE")

options(old)

Description

Joins the function groups a prokaryotic taxon is known to perform ( methanogenesis, denitrification, nitrogen fixation, chitinolysis, human gut association and 87 others) to a taxify() result.

Usage

add_faprotax(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: Louca et al. (2016), 92 function groups over 4470 taxa compiled from IJSEM and Bergey's Manual. Redistributed under FAPROTAX's own BSD-style terms; see cite for the notice that must travel with it.

FAPROTAX annotates a taxon at whatever rank the evidence supports, so its entries are a mix of species and genera. The join follows: a species-level entry matches the accepted name, and a taxon with no entry of its own inherits its genus's. Group memberships are the source's; taxifydb reshapes the grouped list into one row per taxon and reduces each entry to the species or genus it names, which the licence requires be stated as a modification.

Functions stay a set rather than one label because a prokaryote genuinely performs several: Escherichia coli carries 17 of them.

Value

The same data.frame with faprotax_functions (a |-delimited set) and faprotax_n_functions.

References

Louca S, Parfrey LW, Doebeli M (2016) Decoupling function and taxonomy in the global ocean microbiome. Science 353:1272-1277. doi:10.1126/science.aaf4507

Examples

## Not run: 
taxify(c("Escherichia coli", "Nitrosomonas europaea")) |>
  add_faprotax()

## End(Not run)

Description

Joins ground-beetle traits measured from Helsinki specimens to a taxify() result by accepted name.

Usage

add_finand(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: Finand & Kotze (2025), 34 species from 25 remnant urban forests in Helsinki.

Small, and carried for a reason no larger source can supply. Every other reachable carabid trait table traces its body sizes and wing classes back to carabids.org (Homburg et al. 2014), so agreement between them is not corroboration. These body lengths were measured from the beetles the authors caught, which makes them the one independent check: they run at a median ratio of 0.9946 against the NW European arthropod compilation over 28 shared species, with only 7.1 percent exactly equal.

body_length_mm is a whole-body length despite the source column being terse about it; elytra length would sit near 0.6 of these values.

wing_morph records the morph of the beetles caught in Helsinki, so it can name a definite morph where a species-level source calls the species dimorphic. Compare add_chowdhury(), which states the species' capacity.

Value

The same data.frame with finand_ columns: numeric body_length_mm; categorical wing_morph, feeding_type, habitat_pref, moisture_pref.

References

Finand B, Kotze DJ (2025) Habitat specialisation and dispersal capacity drive rapid carabid beetle responses to urban forest fragmentation. Zenodo. doi:10.5281/zenodo.17184995

Examples

## Not run: 
taxify(c("Carabus glabratus", "Cychrus caraboides")) |>
  add_finand()

## End(Not run)

Description

Joins FishBase morphological and ecological traits to a taxify() result by looking up accepted_name.

Usage

add_fishbase(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: FishBase via rfishbase (Froese & Pauly, CC BY-NC 4.0). Coverage: ~35k fish species. Fishes only.

The fb_lw_* columns give one representative length-weight fit per species from the POPLW table, so a length can be converted to a mass where fb_body_mass_g (a record maximum) does not fit the analysis. FishBase's Bayesian congeneric estimates are not included; fb_lw_* are measured fits.

The build-from-source fallback requires the rfishbase package (available on CRAN). Pre-built .vtr files do not require rfishbase.

Value

The same data.frame with additional columns:

fb_body_length_cm

Maximum body length in centimetres.

fb_body_mass_g

Maximum published weight in grams (FishBase SPECIES.Weight). This is a record maximum, not a typical or adult-mean mass.

fb_trophic_level

Trophic level.

fb_depth_min_m

Minimum depth in metres.

fb_depth_max_m

Maximum depth in metres.

fb_vulnerability

Vulnerability index (0–100).

fb_habitat

Habitat type (e.g. demersal, pelagic).

fb_importance

Commercial importance category.

fb_lw_a

Coefficient a of the length-weight relationship W = a * L^b (weight in g, length in cm of type fb_lw_type), from FishBase's POPLW table.

fb_lw_b

Exponent b of the length-weight relationship.

fb_lw_type

Length convention the coefficients were fitted against (TL total, SL standard, FL fork, WD width, ...). Chosen to match the species' maximum-length type where recorded, so it applies to fb_body_length_cm. Applying a coefficient to a different length type is a silent error.

fb_lw_method

How the fit was obtained (e.g. "type I linear regression", "single L-W pair with b=3").

fb_lw_sex

Sex the fit applies to (unsexed, mixed, female, male, juvenile).

fb_lw_n

Sample size the fit was based on.

fb_lw_r2

Coefficient of determination (r^2) of the fit.

References

Froese R, Pauly D (eds.) (2024) FishBase. World Wide Web electronic publication, https://www.fishbase.org.

Examples

# Runs offline against the bundled example database.
old <- options(taxify.data_dir = taxify_example_data())

taxify("Gadus morhua", backbone = "gbif") |>
  add_fishbase()

options(old)

Description

Joins FISHMORPH morphological trait data to a taxify() result by looking up accepted_name. This function is named after its source, FISHMORPH; for the fish reference database FishBase see add_fishbase().

Usage

add_fishmorph(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: FISHMORPH (Brosse et al. 2021, Figshare, CC BY 4.0). Coverage: ~9k freshwater fish species.

Value

The same data.frame with additional columns:

fish_max_body_length

Maximum body length (cm).

fish_body_elongation

Body elongation (body length / body depth).

fish_vertical_eye_position

Vertical eye position (eye position / head depth).

fish_relative_eye_size

Relative eye size (eye diameter / head length).

fish_oral_gape_position

Oral gape position (mouth position: 0 = inferior, 0.5 = terminal, 1 = superior).

fish_relative_maxillary_length

Relative maxillary length (maxillary length / head length).

fish_body_lateral_shape

Body lateral shape (body depth / caudal peduncle depth).

fish_pectoral_fin_position

Pectoral fin vertical position (fin insertion depth / body depth).

fish_pectoral_fin_size

Pectoral fin size (fin length / body length).

fish_caudal_peduncle_throttling

Caudal peduncle throttling (caudal peduncle depth / caudal fin depth).

References

Brosse S, Charpin N, Su G, Toussaint A, Herrera-R GA, Tedesco PA, Villegé r S (2021) FISHMORPH: A global database on morphological traits of freshwater fishes. Global Ecology and Biogeography 30:2330-2336. doi:10.1111/geb.13395

Examples

# Runs offline against the bundled example database.
old <- options(taxify.data_dir = taxify_example_data())

taxify("Salmo trutta", backbone = "gbif") |>
  add_fishmorph()

options(old)

Description

Joins ecological and life-history traits of United States freshwater fishes from FishTraits (Frimpong & Angermeier 2009) to a taxify() result by looking up accepted_name.

Usage

add_fishtraits(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: FishTraits v14.3 (USGS ScienceBase), a public-domain U.S. Government work. North American freshwater fishes.

Value

The same data.frame with additional columns. The default set:

ft_common_name

Common name.

ft_native

Whether native to the contiguous United States.

ft_max_length_cm

Maximum total length (cm).

ft_longevity_yr

Maximum reported age (years).

ft_maturity_age_yr

Age at maturity (years).

ft_fecundity_max

Maximum fecundity.

ft_repro_guild

Reproductive guild.

ft_min_temp_c

Lower temperature tolerance (deg C).

ft_max_temp_c

Upper temperature tolerance (deg C).

ft_extinct

Whether recorded as extinct.

cols = "all" also attaches the ten diet-category flags, salinity tolerance, flow preferences, migratory strategy, listing status, and the ITIS TSN.

References

Frimpong EA, Angermeier PL (2009) FishTraits: a database of ecological and life-history traits of freshwater fishes of the United States. Fisheries 34:487-495.

Examples

## Not run: 
# Downloads the enrichment on first use.
taxify("Micropterus salmoides", backbone = "gbif") |>
  add_fishtraits()

## End(Not run)

Description

Joins traits from FloraWeb (Bundesamt fuer Naturschutz) to a taxify() result by looking up accepted_name. FloraWeb is the live national portal carrying the BiolFlor trait data (Klotz, Kuehn & Durka 2002) together with Rothmaler morphology and Ellenberg indicator values. This enrichment covers the full per-species trait profile scraped from the four FloraWeb trait pages: morphology, reproductive biology, the nine Ellenberg indicator values, ploidy and chromosome number, and chorological distribution. Every column carries a ⁠_de⁠ suffix to mark the German-flora calibration and to avoid collisions when chained with other plant-trait enrichments (e.g. add_ecoflora() for Britain, add_baseflor() for France).

Usage

add_floraweb(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: FloraWeb (https://www.floraweb.de/), Bundesamt fuer Naturschutz, Bonn. FloraWeb has no bulk export or API; the bundled dataset was scraped per species (accessed 2026-06-24) and that access date is the dataset version. The trait data largely derive from BiolFlor, which per the BioFresh metadata statement is publicly available and may be used without restrictions provided it is acknowledged and cited correctly. The .vtr is downloaded from the taxify release on first use and cached.

For British-flora traits see add_ecoflora(); for French-flora traits see add_baseflor(); for European-calibration indicator values see add_eive().

Value

The same data.frame with German trait columns (all suffixed ⁠_de⁠), grouped as:

Morphology

height_de, life_form_de, leaf_shape_de, leaf_anatomy_de, leaf_persistence_de, storage_organs_de, flowering_months_de, flowering_months_biolflor_de, flowering_phase_de, phenological_season_de, description_de.

Reproductive biology

pollination_vector_de, pollinator_de, pollinator_reward_de, flower_type_de, flower_class_de, dispersal_type_de, diaspore_type_de, germinule_type_de, reproduction_type_de, vegetative_spread_de, fertilization_type_de, apomixis_de, dicliny_de, dichogamy_de, self_incompatibility_de, si_mechanism_de, ploidy_de, chromosome_number_de, chromosome_freq_de, chromosomes_de.

Ecology

the nine Ellenberg indicator values ell_light_de, ell_temperature_de, ell_continentality_de, ell_moisture_de, ell_moisture_variability_de, ell_reaction_de, ell_nitrogen_de, ell_salt_de, heavy_metal_resistance_de, plus strategy_type_de (Grime CSR), habitat_site_de, formation_de, plant_community_de, biotope_type_de, forest_binding_de, hemeroby_de, urbanity_de.

Distribution

floristic_zones_de, areal_formula_de, areal_type_de, oceanity_de, range_centre_de, world_range_size_de, world_range_frequency_de, world_range_position_de, world_range_hazard_de, germany_range_share_de, germany_responsibility_de.

Categorical traits with several applicable values are joined with "; ". Trait values are German (as published by FloraWeb / BiolFlor).

References

Klotz S, Kuehn I, Durka W (2002) BIOLFLOR - Eine Datenbank zu biologisch-oekologischen Merkmalen der Gefaesspflanzen in Deutschland. Schriftenreihe fuer Vegetationskunde 38. Bundesamt fuer Naturschutz, Bonn.

Examples

# Runs offline against the bundled example database.
old <- options(taxify.data_dir = taxify_example_data())

taxify("Bellis perennis") |>
  add_floraweb()

options(old)

Description

Joins genus-level ecological and life-history trait modalities of North American freshwater insects to a taxify() result by looking up genus, so any species in a covered genus is annotated. The modalities are the source's own abbreviation codes, kept verbatim.

Usage

add_freshwater_insects_conus(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: Twardochleb et al. (2021, Environmental Data Initiative, CC BY 4.0), the Freshwater Insects CONUS genus trait table. Traits are genus-level categorical modalities.

Value

The same data.frame with additional columns. The curated set:

fwinsect_thermal_pref

Thermal preference class.

fwinsect_feed_prim

Primary functional feeding group code.

fwinsect_habit_prim

Primary habit (swimmer, clinger, burrower, ...).

fwinsect_rheophily

Rheophily (current preference) code.

fwinsect_voltinism

Voltinism (generations per year) code.

fwinsect_max_body_size

Maximum body size class.

With cols = "all" the remaining trait groups (emergence, dispersal, respiration, ...) are attached under their source names.

References

Twardochleb LA et al. (2021) Freshwater insect occurrences and traits for the contiguous United States, 2001-2018. Environmental Data Initiative. doi:10.6073/pasta/8238ea9bc15840844b3a023b6b6ed158

Examples

## Not run: 
taxify("Baetis", backbone = "gbif") |>
  add_freshwater_insects_conus()

## End(Not run)

Description

Joins shared bird/mammal frugivore traits to a taxify() result by accepted_name.

Usage

add_frugivoria(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: Gerstner et al. (2023) Frugivoria (EDI, CC-BY 4.0).

Value

The same data.frame with frugivoria_ columns: categorical taxon_group, diet_category; numeric diet_breadth, body_mass_g, body_size_mm, longevity, generation_time.

References

Gerstner BE et al. (2023) Frugivoria: a trait database for birds and mammals exhibiting frugivory across contiguous Neotropical moist forests. EDI (edi.1220.5).

Examples

## Not run: 
taxify("Ramphastos toco", backbone = "gbif") |>
  add_frugivoria()

## End(Not run)

Description

Joins FungalTraits (Polme et al. 2020) genus-level trait data to a taxify() result by looking up genus. Unlike other enrichments that join on species-level accepted_name, FungalTraits is a genus-level database and joins on the genus column already present in taxify output.

Usage

add_fungal_traits(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: FungalTraits (Polme et al. 2020, Fungal Diversity, CC BY 4.0). Coverage: ~10k fungal genera. Genus-level only (not species-level).

Value

The same data.frame with additional columns:

primary_lifestyle

Primary ecological role (e.g., saprotroph, mycorrhizal, pathogen, endophyte, lichenized, parasite).

secondary_lifestyle

Secondary ecological role, if any.

growth_form

Morphological growth form (e.g., agaricoid, corticioid, polyporoid, yeast).

fruitbody_type

Fruiting body morphology (e.g., gasteroid, pileate, resupinate).

decay_substrate

Substrate type for saprotrophic genera (e.g., wood, litter, dung, soil).

plant_pathogenic_capacity

Capacity to cause plant disease (e.g., high, medium, low, none).

animal_biotrophic_capacity

Capacity for animal biotrophy.

endophytic_interaction_capability

Capacity for endophytic interactions with plants.

ectomycorrhiza_exploration_type

Exploration type for ectomycorrhizal genera (e.g., contact, short, medium, long).

References

Polme S et al. (2020) FungalTraits: a user-friendly traits database of fungi and fungus-like stramenopiles. Fungal Diversity 105:1-16. doi:10.1007/s13225-020-00466-2

Examples

# Runs offline against the bundled example database.
old <- options(taxify.data_dir = taxify_example_data())

taxify("Amanita muscaria", backbone = "gbif") |>
  add_fungal_traits()

options(old)

Description

Joins genus-level mycorrhizal type from the FungalRoot database (Soudzilovskaia et al. 2020) to a taxify() result by looking up genus. Mycorrhizal type is phylogenetically conserved at the genus level, which is the resolution FungalRoot recommends for inference, so this enrichment joins on genus rather than accepted_name.

Usage

add_fungalroot(x, verbose = TRUE)

Arguments

Details

Source: FungalRoot, published on GBIF as a Darwin Core Archive (doi:10.15468/a7ujmj), CC BY-NC 4.0. The per-genus value is a majority consensus computed from the per-observation mycorrhiza type labels, not FungalRoot's own published per-genus assignment. Plant genera only. The .vtr is downloaded from the taxify release on first use and cached.

Value

The same data.frame with three additional columns:

mycorrhizal_type

Genus-level majority-consensus type, one of AM (arbuscular), EcM (ecto), ErM (ericoid), OM (orchid), NM (non-mycorrhizal), the dual types EcM-AM / ErM-EcM / ErM-AM, Other, or uncertain. NA if the genus is not in FungalRoot.

mycorrhizal_status

Coarse status derived from the type: "mycorrhizal", "non-mycorrhizal", or "uncertain".

mycorrhizal_records

Number of FungalRoot observations supporting the genus-level consensus.

References

Soudzilovskaia NA et al. (2020) FungalRoot: global online database of plant mycorrhizal associations. New Phytologist 227:955-966.

Examples

## Not run: 
# Joins on genus, so any species in a covered genus is annotated.
taxify(c("Quercus robur", "Trifolium pratense")) |>
  add_fungalroot()

## End(Not run)

Description

Joins FUNGuild trophic mode, guild, growth morphology, and confidence data to a taxify() result by looking up accepted_name. Species-level matches take priority; genus-level guild assignments are used as fallback for unmatched species.

Usage

add_funguild(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: FUNGuild (Nguyen et al. 2016, CC BY 4.0). Coverage: ~13k taxa. Fungi only.

The enrichment first attempts species-level matching. For species without a direct match, it falls back to genus-level guild assignments from FUNGuild's genus-rank entries.

Value

The same data.frame with additional columns:

trophic_mode

Trophic mode (e.g., Pathotroph, Saprotroph, Symbiotroph, or hyphenated combinations).

guild

Functional guild (e.g., "Ectomycorrhizal", "Plant Pathogen", "Wood Saprotroph").

funguild_growth_form

Growth morphology (e.g., "Agaricoid", "Microfungus"). Prefixed to avoid collision with FungalTraits.

confidence_ranking

Confidence of the guild assignment (Possible, Probable, Highly Probable).

References

Nguyen NH et al. (2016) FUNGuild: An open annotation tool for parsing fungal community datasets by ecological guild. Fungal Ecology 20:241-248.

Examples

# Runs offline against the bundled example database.
old <- options(taxify.data_dir = taxify_example_data())

taxify("Amanita muscaria", backbone = "gbif") |>
  add_funguild()

options(old)

Description

Joins extra GBIF backbone columns to a taxify() result by looking up taxon_id in the GBIF backbone. Only enriches rows where backbone == "gbif".

Usage

add_gbif_info(x)

Arguments

Value

The same data.frame with additional columns:

notho_type

Hybrid type: "GENERIC", "SPECIFIC", or "INFRASPECIFIC".

nom_status

Nomenclatural status (may contain multiple values).

bracket_authorship

Basionym author in parentheses.

bracket_year

Basionym author year.

gbif_year

Combining author year.

name_published_in

Publication citation.

origin

How the name entered the backbone.

infra_specific_epithet

Infraspecific epithet.

Examples

# Runs offline against the bundled example database.
old <- options(taxify.data_dir = taxify_example_data())

taxify("Quercus robur", backbone = "gbif") |>
  add_gbif_info()

options(old)

Description

Joins per-species environmental- and socio-economic-impact aggregates from GIDIAS (Bacher et al. 2025), the IPBES invasive-species assessment's global impact compilation, to a taxify() result by looking up accepted_name. GIDIAS's individual impact records are reduced to per-species indicators; the raw impact records are not distributed.

Usage

add_gidias(x, group = "Any", cols = NULL, verbose = TRUE)

Arguments

Details

A species' EICAT category is the most severe magnitude among its negative (harmful) environmental-impact records, on the IUCN scale "MC" (Minimal Concern), "MN" (Minor), "MO" (Moderate), "MR" (Major, local extinction), "MV" (Massive, global extinction), or "DD" (Data Deficient); gidias_seicat_category applies the same rule to socio-economic impact (SEICAT, "MC" to "MR"). For impact reconciled across sources, use add_trait() with "environmental_impact" or "socioeconomic_impact".

Source: GIDIAS (Bacher et al. 2025, Scientific Data; CC BY 4.0), compiled for the IPBES thematic assessment report on invasive alien species. Only the derived per-species aggregates are distributed here, not the raw impact records.

Value

The same data.frame with additional columns. The default set:

gidias_eicat_category

EICAT environmental-impact category (MC/MN/MO/MR/MV/DD, or NA when no negative impact is recorded).

gidias_eicat_mechanism

IUCN EICAT impact mechanism(s) behind the species' negative environmental impacts.

gidias_seicat_category

SEICAT socio-economic-impact category (MC/MN/MO/MR/DD, or NA).

gidias_ncp_direction

Direction of the species' impact on nature's contributions to people: "Negative", "Positive", "Neutral", or a combination ("Negative; Positive") where the records disagree. GIDIAS scores no magnitude for this block, so there is no category to report.

gidias_ias_taxon

Functional group: Plant, Invertebrate, Vertebrate, or Microbe.

gidias_realms

Realm(s) the impacts span: terrestrial, freshwater, and/or marine.

gidias_n_records

Number of impact records for the species.

gidias_n_sources

Number of distinct sources documenting the impacts.

cols = "all" additionally attaches the numeric EICAT/SEICAT magnitudes (0-3), the affected well-being constituents and contributions to people (gidias_ncp_affected), kingdom, the negative-record count, and a global-extinction flag.

What a species impacts

GIDIAS records the affected native taxon per impact record, so group asks the question at that grain: add_gidias(group = "Invertebrate") scores Felis catus "MO" (reduced populations) where the default "Any" scores it "MV", the global extinctions it drove among vertebrates. 28% of species with a recorded affected taxon impact two or more of the five groups, so for those the species-level category is the most severe impact on anything, not the impact on each thing.

The vocabulary is the coarse one GIDIAS controls: "Vertebrate", not "Aves". "Any" is the only group carrying the impact records with no affected taxon recorded (12% of the negative ones) and the two people-facing blocks, so gidias_seicat_category and gidias_ncp_direction are NA on every other group: impact on people is not a question the affected-native-taxon axis can answer.

References

Bacher S et al. (2025) Global Impacts Dataset of Invasive Alien Species (GIDIAS). Scientific Data 12:832. doi:10.1038/s41597-025-05184-5

Examples

## Not run: 
# Downloads the GIDIAS enrichment on first use.
taxify("Felis catus", backbone = "gbif") |>
  add_gidias()

# What the cat does to invertebrates, rather than to anything at all.
taxify("Felis catus", backbone = "gbif") |>
  add_gidias(group = "Invertebrate")

taxify("Felis catus", backbone = "gbif") |>
  add_gidias(group = c("Invertebrate", "Vertebrate"))

## End(Not run)

Description

Joins species-level plant traits from GIFT, the Global Inventory of Floras and Traits (Weigelt et al. 2020), to a taxify() result by accepted_name. GIFT aggregates published trait records to one value per species (mean for numeric traits, most frequent entry for categorical ones). cols chooses which traits to attach; browse the available columns with gift_traits().

Usage

add_gift(x, cols = NULL, verbose = TRUE)

Arguments

Details

The GIFT trait table is bundled as a pre-built .vtr and joined offline, so no internet access is needed once it is present (the first use downloads it, or builds it from source if taxifydb is installed). GIFT's API exposes only the redistributable subset of its data (CC BY 4.0; references whose underlying source is restricted are excluded), and that subset is what is bundled here. Cite GIFT and, where applicable, the underlying references (GIFT::GIFT_references()) for the values used.

Value

The same data.frame with one added column per requested trait, named ⁠gift_<trait>⁠. Numeric traits (heights, masses, areas) are doubles, the rest character. Rows with no value in GIFT get NA. With the default cols, the added columns are gift_woodiness_1, gift_growth_form_1, gift_lifecycle_1, gift_life_form_1, gift_climber_1, gift_epiphyte_1, gift_parasite_1, gift_aquatic_1, gift_plant_height_max, gift_photosynthetic_pathway, gift_seed_mass_mean, gift_dispersal_syndrome_1, gift_flowering_start, gift_flowering_end, gift_deciduousness_1, and gift_sla_mean.

References

Weigelt P, Konig C, Kreft H (2020) GIFT - A Global Inventory of Floras and Traits for macroecology and biogeography. Journal of Biogeography 47:16-43. doi:10.1111/jbi.13623 Denelle P, Weigelt P, Kreft H (2023) GIFT: an R package to access the Global Inventory of Floras and Traits. Methods in Ecology and Evolution 14:2738-2748. doi:10.1111/2041-210X.14213

See Also

gift_traits() to browse the available columns.

Examples

old <- options(taxify.data_dir = taxify_example_data())

taxify("Abies alba") |>
  add_gift()

options(old)

Description

Joins per-species biotic interaction breadth from GloBI (Global Biotic Interactions) to a taxify() result by looking up accepted_name. GloBI's aggregated interaction records are reduced to per-species counts: how many distinct partner taxa a species interacts with (undirected), across how many distinct interaction types, over how many interaction records.

Usage

add_globi(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: GloBI (Poelen et al. 2014), an open index of biotic interactions aggregated from many contributed datasets. Only derived per-species counts are distributed here; the underlying interaction records carry the licenses of their original data contributors, who should be cited in derivative work. Partner counts are resolved to accepted names before counting, so synonymous partners are not double-counted.

Value

The same data.frame with additional columns:

interaction_degree

Number of distinct partner taxa recorded interacting with the species (both directions).

n_interaction_types

Number of distinct interaction types (eats, pollinates, parasitises, ...).

n_interaction_records

Total number of interaction records touching the species.

References

Poelen JH, Simons JD, Mungall CJ (2014) Global Biotic Interactions: An open infrastructure to share and analyze species-interaction datasets. Ecological Informatics 24:148-159. doi:10.1016/j.ecoinf.2014.08.005

Examples

## Not run: 
taxify("Apis mellifera", backbone = "gbif") |>
  add_globi()

## End(Not run)

Description

Joins GlobTherm upper and lower thermal tolerance limits to a taxify() result by looking up accepted_name.

Usage

add_globtherm(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: GlobTherm (Bennett et al. 2018, Scientific Data, CC0). Coverage: ~2.1k species across aquatic and terrestrial groups.

Value

The same data.frame with additional columns:

globtherm_thermal_max_c

Upper thermal limit (degrees Celsius).

globtherm_thermal_max_metric

Definition of the upper limit (e.g. ctmax, LT50, UTNZ); the value is ambiguous without it.

globtherm_thermal_min_c

Lower thermal limit (degrees Celsius).

globtherm_thermal_min_metric

Definition of the lower limit (e.g. ctmin, LT50, LTNZ).

globtherm_thermal_max_error

Reported error on the upper limit.

globtherm_thermal_min_error

Reported error on the lower limit.

References

Bennett JM et al. (2018) GlobTherm, a database on the thermal tolerance for aquatic and terrestrial organisms. Scientific Data 5:180022. doi:10.1038/sdata.2018.22

Examples

## Not run: 
taxify("Lepomis gibbosus", backbone = "gbif") |>
  add_globtherm()

## End(Not run)

Description

Joins GloNAF (Global Naturalized Alien Flora) data to a taxify() result, filtered by region.

Usage

add_glonaf(x, region, verbose = TRUE)

Arguments

Details

Source: GloNAF v2.0 (van Kleunen et al. 2019, Davis et al. 2025, CC BY 4.0). Coverage: ~16k alien plant taxa across ~1,300 regions. Plants only.

Value

The same data.frame with additional column(s):

naturalized

1 if the species is recorded as naturalized in that region, NA otherwise.

References

van Kleunen M et al. (2019) The Global Naturalized Alien Flora (GloNAF) database. Ecology 100:e02542. doi:10.1002/ecy.2542

Davis AJS et al. (2025) The updated Global Naturalized Alien Flora (GloNAF 2.0) database. Ecology 106:e70245. doi:10.1002/ecy.70245

Examples

# Runs offline against the bundled example database.
old <- options(taxify.data_dir = taxify_example_data())

taxify("Robinia pseudoacacia") |>
  add_glonaf(region = "DEU")

taxify("Robinia pseudoacacia") |>
  add_glonaf(region = c("DEU", "AUT"))

options(old)

Description

Joins host-level parasite summaries from the Global Mammal Parasite Database (Stephens et al. 2017) to a taxify() result by looking up accepted_name. The database's host-parasite association records are aggregated to one row per host species.

Usage

add_gmpd(x, cols = NULL, verbose = TRUE)

Arguments

Details

Covers wild carnivores, ungulates and primates (462 host species).

Parasite richness is a sampling-sensitive count: a well-studied host accumulates more recorded parasites than a rarely sampled one, so the values reflect research effort as well as biology.

This source states no licence. It is distributed under the journal's version-of-record terms with a citation request, and taxify ships no pre-built copy of it. The first call builds it from the original source on the user's machine, which requires the taxifydb package (remotes::install_github("gcol33/taxifydb")). taxify redistributes none of the data. Cite Stephens et al. (2017) for the data.

Value

The same data.frame with additional columns. The default set:

gmpd_parasite_richness

Distinct parasite species recorded for the host.

gmpd_n_helminth, gmpd_n_virus, gmpd_n_bacteria, gmpd_n_protozoa, gmpd_n_arthropod, gmpd_n_fungus, gmpd_n_prion

Distinct parasite species by parasite type.

gmpd_mean_prevalence

Mean reported prevalence across the host's records.

gmpd_host_group

Host group (carnivores, ungulates, primates).

References

Stephens PR, Pappalardo P, Huang S, et al. (2017) Global Mammal Parasite Database version 2.0. Ecology 98:1476. doi:10.1002/ecy.1799

Examples

## Not run: 
# Builds the enrichment on first use (needs taxifydb).
taxify("Panthera leo", backbone = "gbif") |>
  add_gmpd()

## End(Not run)

Description

Joins GRIIS (Global Register of Introduced and Invasive Species) status to a taxify() result, filtered by country. This function is named after its source, GRIIS; GloNAF (add_glonaf()) carries related naturalized-alien status.

Usage

add_griis(x, country, cols = NULL, verbose = TRUE)

Arguments

Details

Source: GRIIS (Zenodo combined CSV, CC BY 4.0, 196 countries). Coverage: ~23k name x country combinations.

Value

The same data.frame with additional column(s):

invasive_status

One of "native", "introduced", "invasive", or NA if not recorded for that country.

Examples

# Runs offline against the bundled example database.
old <- options(taxify.data_dir = taxify_example_data())

taxify("Robinia pseudoacacia") |>
  add_griis(country = "AT")

taxify("Robinia pseudoacacia") |>
  add_griis(country = c("AT", "DE"))

options(old)

Description

Joins species-level root traits from the Global Root Traits (GRooT) database to a taxify() result by looking up accepted_name. GRooT aggregates root trait records to per-species means. The .vtr carries the full GRooT trait set (38 root traits); the default attaches the nine best-populated key traits, and cols = "all" attaches every one. Run enrichment_cols("groot") to list them.

Usage

add_groot(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: GRooT database (Guerrero-Ramirez et al. 2021). Vascular plants. GRooT data are publicly available and used here with the data-paper citation requested by the authors.

Value

The same data.frame with additional columns (per-species means). The default set:

root_diameter

Mean root diameter.

specific_root_length

Specific root length.

root_tissue_density

Root tissue density.

root_n_concentration

Root nitrogen concentration.

root_c_concentration

Root carbon concentration.

root_mass_fraction

Root mass fraction.

lateral_spread

Lateral spread.

root_mycorrhizal_colonization

Root mycorrhizal colonization intensity.

rooting_depth

Maximum rooting depth.

cols = "all" additionally attaches root chemistry (P/K/Ca/Mg/Mn concentrations, C:N and N:P ratios), architecture (branching density and ratio, stele diameter and fraction, cortex thickness, vessel diameter and number), turnover (root lifespan, production, turnover rate, litter mass-loss rate), and specific root area, respiration, and dry-matter content, among others. Units follow the GRooT data paper; see the reference below.

References

Guerrero-Ramirez NR et al. (2021) Global root traits (GRooT) database. Global Ecology and Biogeography 30:25-37. doi:10.1111/geb.13179

Examples

# Runs offline against the bundled example database.
old <- options(taxify.data_dir = taxify_example_data())

taxify("Abies alba") |>
  add_groot()

options(old)

Description

Joins species-level wood density to a taxify() result by looking up accepted_name. Wood density is reported as wood specific gravity (oven-dry mass / green volume), dimensionless and numerically equal to g/cm3.

Usage

add_gwdd(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: Global Wood Density Database v2 (Fischer et al. 2026, New Phytologist, CC BY 4.0). Coverage: ~17.3k species. Bark density is not part of the aggregated source and is not included.

Value

The same data.frame with additional columns:

gwdd_wood_density_g_cm3

Species-mean wood density (g/cm3).

gwdd_wood_density_trunk_g_cm3

Trunk wood density (g/cm3).

gwdd_wood_density_branch_g_cm3

Branch wood density (g/cm3).

gwdd_n_measurements

Number of underlying measurements.

References

Fischer FJ et al. (2026) The Global Wood Density Database version 2. New Phytologist. doi:10.1111/nph.70860

Examples

## Not run: 
taxify("Quercus robur", backbone = "gbif") |>
  add_gwdd()

## End(Not run)

Description

Joins species-median home-range size and body mass to a taxify() result by accepted_name.

Usage

add_homerange(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: Broekman et al. (2023) HomeRange database (Dryad, CC0). Per-individual records are reduced to species medians.

Value

The same data.frame with numeric homerange_home_range_km2 and homerange_body_mass_kg.

References

Broekman MJE et al. (2023) HomeRange: a global database of mammalian home ranges. Dryad. doi:10.5061/dryad.d2547d85x

Examples

## Not run: 
taxify("Panthera leo", backbone = "gbif") |>
  add_homerange()

## End(Not run)

Description

Joins per-insect hostplant breadth from the Natural History Museum HOSTS database to a taxify() result by looking up accepted_name. Each moth or butterfly species is summarised by how many distinct hostplants and hostplant families it has been recorded feeding on.

Usage

add_hosts(x, cols = NULL, verbose = TRUE)

Arguments

Details

Source: Robinson et al. (2010) HOSTS, Natural History Museum, London (CC0). Lepidoptera only; ~24k species with at least one recorded hostplant.

Value

The same data.frame with additional columns:

host_plant_count

Number of distinct hostplant species recorded.

host_family_count

Number of distinct hostplant families recorded.

References

Robinson GS, Ackery PR, Kitching IJ, Beccaloni GW, Hernandez LM (2010) HOSTS - a Database of the World's Lepidopteran Hostplants. Natural History Museum, London. doi:10.5519/havt50xw

Examples

## Not run: 
taxify("Papilio machaon", backbone = "gbif") |>
  add_hosts()

## End(Not run)

Description

Joins species-level amphibian body measurements to a taxify() result by accepted_name. Only measurements comparable across Anura, Caudata and Gymnophiona are carried; per-specimen values are reduced to species medians.

Usage

add_huang_amph(x, cols = NULL, verbose = TRUE)