| sponge2 {spm2} | R Documentation |
A dataset of sponge species richness in the Timor Sea region, northern Australia marine margin
Description
This dataset contains 77 samples of 81 variables including easting (longitude), northing (latitude), bathy, backscatter and their derived variables.
Usage
data("sponge2")Format
A data frame with 77 observations on the following 89 variables.
eastinga numeric vector, m
northinga numeric vector, m
species.richnessa numeric vector, no unit
muda numeric vector, percentage
sanda numeric vector, percentage
gravela numeric vector, percentage
bathya numeric vector, m
bs25a numeric vector, dB
bs10a numeric vector, dB
bs11a numeric vector, dB
bs12a numeric vector, dB
bs13a numeric vector, dB
bs14a numeric vector, dB
bs15a numeric vector, dB
bs16a numeric vector, dB
bs17a numeric vector, dB
bs18a numeric vector, dB
bs19a numeric vector, dB
bs20a numeric vector, dB
bs21a numeric vector, dB
bs22a numeric vector, dB
bs23a numeric vector, dB
bs24a numeric vector, dB
bs26a numeric vector, dB
bs27a numeric vector, dB
bs28a numeric vector, dB
bs29a numeric vector, dB
bs30a numeric vector, dB
bs31a numeric vector, dB
bs32a numeric vector, dB
bs33a numeric vector, dB
bs34a numeric vector, dB
bs35a numeric vector, dB
bs36a numeric vector, dB
bs_oa numeric vector, dB
bs_homo_oa numeric vector
bs_entro_oa numeric vector, no unit
bs_var_oa numeric vector, dB^2
bs_lmi_oa numeric vector
bathy_oa numeric vector, m
bathy_lmi_oa numeric vector
tpi_oa numeric vector, no unit
slope_oa numeric vector
plan_cur_oa numeric vector
prof_cur_oa numeric vector
relief_oa numeric vector
rugosity_oa numeric vector
dist.coasta numeric vector, m
rugosity3a numeric vector
rugosity5a numeric vector
rugosity7a numeric vector
tpi3a numeric vector, no unit
tpi5a numeric vector, no unit
tpi7a numeric vector, no unit
bathy_lmi3a numeric vector
bathy_lmi5a numeric vector
bathy_lmi7a numeric vector
plan_curv3a numeric vector
plan_curv5a numeric vector
plan_curv7a numeric vector
relief_3a numeric vector
relief_5a numeric vector
relief_7a numeric vector
slope3a numeric vector
slope5a numeric vector
slope7a numeric vector
prof_cur3a numeric vector
prof_cur5a numeric vector
prof_cur7a numeric vector
entro3a numeric vector, no unit
entro5a numeric vector, no unit
entro7a numeric vector, no unit
homo3a numeric vector
homo5a numeric vector
homo7a numeric vector
var3a numeric vector, dB^2
var5a numeric vector, dB^2
var7a numeric vector, dB^2
bs_lmi3a numeric vector
bs_lmi5a numeric vector
bs_lmi7a numeric vector
Details
For details, please see the source. This dataset was published as an appendix of the paper listed in the source. Where the long and lat were reprojected to easting and northing.
Source
see Appendix A-D. Supplementary data at: "http://dx.doi.org/10.1016/j.envsoft.2017.07.016."
References
Li, J., B. Alvarez, J. Siwabessy, M. Tran, Z. Huang, R. Przeslawski, L. Radke, F. Howard, and S. Nichol. 2017. Application of random forest, generalised linear model and their hybrid methods with geostatistical techniques to count data: Predicting sponge species richness. Environmental Modelling & Software, 97: 112-129.