DOI: 10.5061/dryad.cjsxksnm8
title: Scolytinae/Platypodinae species species richness in 50 × 50 km grid cells across mainland USA with associated predictor variables

description: A list of georeferenced Scolytinae and Platypodinae occurrence records in
 the USA was extracted from the Bark and Ambrosia Beetles of the Americas
 Database. In addition to geographical coordinates, each record included
 species, status of nativeness (native or non-native in the USA), principal
 host type (either conifer or angiosperm), and feeding guild (true bark
 beetles, ambrosia beetles, and “other”. The geographic coordinates of all
 occurrence records of individual Scolytinae/Platypodinae species were used
 to calculate species richness in 50 × 50 km grid cells (in cases of
 multiple records of the same species inside one grid cell, only one record
 was considered). The final data thus consisted of 3,376 cells. The dataset
 also includes values of environmental and socio-economic predictor
 variables: (i) average annual mean temperature [°C]; (ii) annual
 precipitation total [mm]; (iii) human population density [no. inhabitants
 per cell]; (iv) average income per capita ; (v) total length of roads
 [km]; (vi) total forest area [km^2^]; (vii) basal area of native
 angiosperms [m^2^]; (viii) basal area of non-native angiosperms [m^2^];
 (ix) basal area of native conifers [m^2^]; (x) basal area of non-native
 conifers [m^2^]; (xi) number of native angiosperm species; (xii) number of
 non-native angiosperm species; (xiii) number of native conifer species;
 (xiv) number of non-native conifer species. Basal area and species
 richness (number of species) were derived from a raster database of the
 live tree species basal area interpolated for each species in the
 contiguous USA from forest inventory plt. Additionally, (xv) number of
 traps in a grid cell.
description: # Scolytinae/Platypodinae species species richness in 50 × 50 km grid
 cells across mainland USA with associated predictor variables Dataset DOI:
 [10.5061/dryad.cjsxksnm8](https://doi.org/10.5061/dryad.cjsxksnm8) ##
 Description of the data and file structure File
 **scol_variables_2025.csv:** **Rows:** each row represents 50 x 50 km grid
 cells across the mainland USA **Columns:** GRID_ID_50, ID of a GRID cell
 long, longitude of the cell centroid lat, longitude of the cell centroid
 Invasive_total, total number of non-native Scolytinae/Platypodinae species
 in a grid a cell Invasive_angio, number of non-native angiosperm feeding
 Scolytinae/Platypodinae species in a grid a cell Invasive_conifers, number
 of non-native conifer feeding Scolytinae/Platypodinae species in a grid a
 cell Invasive_ambrosia, number of non-native Scolytinae/Platypodinae
 ambrosia beetles species in a grid a cell Invasive_bark, number of
 non-native Scolytinae/Platypodinae true bark beetles species in a grid a
 cell v01_native_total, total number of native Scolytinae/Platypodinae
 species in a grid a cell v02_native_angio, number of native angiosperm
 feeding Scolytinae/Platypodinae species in a grid a cell
 v03_native_conifer, number of native conifer feeding
 Scolytinae/Platypodinae species in a grid a cell v04_native_ambrosia,
 number of native Scolytinae/Platypodinae ambrosia beetles species in a
 grid a cell v05_native_bark, number of native Scolytinae/Platypodinae true
 bark beetles species in a grid a cell v06_AMT, average annual mean
 temperature [°C] in a grid a cell v07_APT, annual precipitation total [mm]
 in a grid a cell v08_population, human population density [no. inhabitants
 per cell] v09_income, average income per capita in a grid a cell
 v10_roads_km, total length of roads [km] in a grid a cell
 v11_forest_total_30m, total forest area [km^2^] in a grid a cell
 v12_Basal_Angio_FI_native, basal area of native angiosperms [m^2^] in a
 grid a cell v13_Basal_Con_FI_native, basal area of native conifers [m^2^]
 in a grid a cell v14_Basal_Angio_FI_invasive, basal area of non-native
 angiosperms [m^2^] in a grid a cell v15_Basal_Con_FI_invasive, basal area
 of non-native conifers [m^2^] in a grid a cell
 v16_Angio_N_species_FI_native, number of native angiosperm species [m^2^]
 in a grid a cell v17_Con_N_species_FI_native, number of native conifer
 species [m^2^] in a grid a cell v18_Angio_N_species_FI_invasive, number of
 non-native angiosperm species [m^2^] in a grid a cell
 v19_Con_N_species_FI_invasive, number of non-native conifer species [m^2^]
 in a grid a cell v20_N_traps, number of traps in a grid cell 
 
 File **SEM_modeling_invasive_total.R** contains a R code of spatial
 simultaneous autoregressive error models. ## Code/software The moran.test
 function from the spdep package (Bivand 2022) was used to check residuals
 for spatial autocorrelation. Using resultant correlograms, the errorsarlm
 function from the package spdep (Bivand 2022) was used to refit each model
 based on maximum likelihood estimation of spatial simultaneous
 autoregressive error models. ## Access information Data was derived from
 the following sources: * Atkinson, T. H. 2024. Bark and ambrosia beetles
 of the Americas. –
 [http://www.barkbeetles.info](http://www.barkbeetles.info). (accessed 30
 May 2024). * CEC. 2010. Commission for Environmental Cooperation “North
 American Atlas–Roads”. – Natural Resources Canada, INEGI, USGS. Ed. 2.0,
 Vector digital data [1:10,000,000]. * Dewitz, J. 2023. National Land Cover
 Database (NLCD) 2021 Products. – U.S. Geological Survey data. * Fick, S.
 E. and Hijmans, R. J. 2017. WorldClim 2: New 1-km spatial resolution
 climate surfaces for global land areas. – Int. J. Climatol. 37(12):
 4302–4315. * Rabaglia, R. J., Cognato, A. I., Hoebeke, E. R., Johnson, C.
 W., LaBonte, J. R., Carter, M. E. and Vlach, J. J. 2019. Early detection
 and rapid response: a 10-year summary of the USDA Forest Service program
 of surveillance for non-native bark and ambrosia beetles. – Am. Entomol.
 65(1): 29–42. * Schiavina, M., Freire, S. and MacManus, K. 2019. GHS-POP
 R2019A–GHS population grid multitemporal (1975–1990–2000–2015). – European
 Commission, Joint Research Centre (JRC) [Dataset]. * Wilson, B., Lister,
 A. and Riemann, R. 2012. A nearest-neighbor imputation approach to mapping
 tree species over large areas using forest inventory plots and moderate
 resolution raster data. – For. Ecol. Manage. 271: 182–198.