Supplementary material 2 from: Mortier L, Vanhoomissen R, Davies L, Kirk PM, Maciá-Vicente JG, Piepenbring M, Haelewaters D (2025) The first checklist of fungi known for Honduras: revealing taxonomic, geographical, and functional trends. MycoKeys 126: 93-117. https://doi.org/10.3897/mycokeys.126.169230

A first checklist of fungi known for Honduras: revealing taxonomic, geographical, and functional trends Libelje Mortier, Ruben Vanhoomissen, Lee Davies, Paul Kirk, Jose G. Maciá Vicente, Meike Piepenbring, Danny Haelewaters Supplementary file 3 Methods for species richness estimates The “specpool” function of R package vegan version 2.6.4 (Oksanen et al. 2022) was used to calculate the richness estimators Chao 1 (Chao 1984), Jacknife 1 (Burnham and Overton 1978), and Bootstrap (Efron 1979; Colwell and Coddington 1994). Results for species richness estimates Species richness estimates for each department estimate the highest species richness in Francisco Morazán and Atlántida. For both the Jackknife 1 and Bootstrap estimators, these are followed by El Paraíso, Cortés, and Comayagua. In contrast, Chao 1 places Olancho, El Paraíso, and Cortés next in species number (Table S3). A total of 586 records were excluded from this analysis, due to missing location information. Results show high standard errors caused by incomplete sampling. Table S3. Estimated species diversity of fungi by department in Honduras. Species richness estimates shown as mean ± standard error. Department Number of collections Number of species Chao 1 Jack 1 Bootstrap Atlántida 1034 399 633.3 ± 44.2 600.8 ± 14.2 487.1 ± 7.0 Choluteca 41 30 69.3 ± 24.1 51.5 ± 4.6 38.8 ± 1.9 Colón 10 8 16.1 ± 9.2 13.4 ± 2.2 10.3 ± 0.9 Comayagua 142 91 167.8 ± 27.8 147.6 ± 7.5 115.2 ± 3.3 Copán 56 38 148.5 ± 69.2 67.5 ± 5.4 49.5 ± 2.2 Cortés 151 103 250.0 ± 49.4 177.5 ± 8.6 133.2 ± 3.5 El Paraíso 157 112 275.0 ± 52.9 192.5 ± 8.9 144.8 ± 3.6 Francisco Morazán 1458 564 1066.1 ± 78.8 880.8 ± 17.8 697.0 ± 8.5 Gracias a Dios 12 8 13.7 + 7.0 12.6 + 2.0 10.0 + 1.0 Intibucá 26 20 47.0 ± 20.1 34.4 ± 3.7 26.0 ± 1.5 Islas de la Bahía 19 12 42.3 ± 37.5 19.6 ± 2.7 15.1 ± 1.2 La Paz 22 22 242.5 ± 96.3 43.0 ± 4.5 29.9 ± 1.5 Lempira 69 45 223.9 ± 121.3 77.5 ± 5.7 57.8 ± 2.3 Ocotepeque 11 9 20.1 ± 12.0 15.4 ± 2.4 11.7 ± 1.0 Olancho 49 34 431.7 ± 150.4 62.4 ± 5.3 44.7 ± 2.1 Santa Bárbara 40 25 104.0 ± 67.7 42.6 ± 4.1 31.9 ± 1.8 Valle 26 21 45.6 ± 17.2 36.4 ± 3.8 27.4 ± 1.5 Yoro 102 67 162.1 ± 40.0 114.5 ± 6.9 86.3 ± 2.9 References • Burnham KP, Overton WS (1978) Estimation of the size of a closed population when capture probabilities vary among animals. Biometrika 65: 625–633. https://doi.org/10.1093/biomet/65.3.625 • Chao A (1984) Nonparametric estimation of the number of classes in a population. Scandinavian Journal of Statistics 11: 265–270. • Colwell RK, Coddington JA (1994) Estimating terrestrial biodiversity through extrapolation. Philosophical Transactions of the Royal Society of London B 345: 101– 118. https://doi.org/10.1098/rstb.1994.0091 • Efron B (1979) Bootstrap methods: another look at the jackknife. Annals of Statistics 7: 1–26. https://doi.org/10.1214/aos/1176344552 • Oksanen J, Simpson G, Blanchet F, Kindt R, Legendre P, Minchin P, O'Hara R, Solymos P, Stevens M, Szoecs E, Wagner H, Barbour M, Bedward M, Bolker B, Borcard D, Carvalho G, Chirico M, De Caceres M, Durand S, Evangelista H, FitzJohn R, Friendly M, Furneaux B, Hannigan G, Hill M, Lahti L, McGlinn D, Ouellette M, Ribeiro Cunha E, Smith T, Stier A, Ter Braak C, Weedon J (2022) vegan: community ecology package. R package version 2.6-4. https://cran.r-project.org/package=vegan R Code get_richness_for_province <- function(province_name) { species_counts_province <- species_counts %>% filter(department == province_name) species_matrix_province <- species_counts_province %>% pivot_wider(names_from = currentName, values_from = collections_per_species, values_fill = list(collections_per_species = 0)) species_matrix_numeric <- species_matrix_province %>% select(-department, -collection_id) %>% mutate_all(as.numeric) chao_results_province <- specpool(as.data.frame(species_matrix_numeric)) chao_results_province$department <- province_name return(chao_results_province) }