NSW: New South Wales species distribution data
In rspatial/disdat: Data for Comparing Species Distribution Modeling Methods
NSW R Documentation
New South Wales species distribution data
Description
Species occurrence data for 54 species from 8 biological groups in New South Wales (NSW, a state in Australia) and associated environmental data. Full details of the dataset are provided in the reference below. There are four data sets with training (po and bg) and test (pa, env) data:
po (training data) includes site names, species names, coordinates, occurrence ("1" for all, since all are presence records), group [ba = bats (7 species); db = diurnal birds (8 species); nb = nocturnal birds (2 species); ot = open-forest trees (8 species); ou = open-forest understorey plants (8 species); rt = rainforest trees (7 species); ru = rainforest understorey plants (6 species); sr = small reptiles (8 species)], and site values for 13 environmental variables (below).
bg (training data) has 10000 sites selected at random across the study region. It is structured identically to po, with "0" for occurrence (not implying absence, but denoting a background record in a way suited to most modelling methods) and NA for group.
env (testing data) includes group, site names, coordinates, and site values for 13 environmental variables (below). These are for sites from different surveys for each biological group (from 570 to 2075 sites per group), and can be returned as separate datasets by disEnv, or in one long format dataset by disData. This set of files is suited to making predictions.
pa (testing data) includes group, site names, coordinates, and presence-absence records, one column per species (in the wide format returned by disPa). They can also be returned in long format using disData. The sites are identical to the sites in env. These data are suited to evaluating the predictions made with env.
Raster (gridded) data for all environmental variables are available - see the reference below for details.
The reference system of the x and y coordinates is unprojected. Latitude and longitude are in geographical coordinates using the WGS84 datum (EPSG:4326).
The vignette provided with this package provides an example of how to fit and evaluate a model with these data.
Environmental variables:
Code Description Units Type
cti "compound topographic index" - a quantification of the position of a site in the local landscape. It is often referred to as the steady state wetness index and it is defined as: CTI = ln ( As / tanB ) where 'As' is the specific catchment area expressed as m2 per unit width orthogonal to the flow direction and 'B' is the slope angle Continuous
disturb disturbance (clearing, logging etc) index. 1 = light, 2 = moderate, 3 = heavy Continuous
mi moisture index. Index of site wetness derived from a water balance algorithm using rainfall, evaporation, radiation and soil depth as inputs Between 0 (dry) and 100 (wet) Continuous
rainann mean annual rainfall mm Continuous
raindq mean rainfall of the driest quarter mm Continuous
rugged ruggedness. Coefficient of variation of grid cells within 1km of cell of interest percent Continuous
soildepth mean soil depth predicted from a model relating sampled soil depths to climate, geology and topography m * 1000 Continuous
soilfert soil fertility ordinal class, derived from soil maps and modeling of geochemical data 1 (low) to 5 (high) Continuous
solrad annual mean solar radiation (terrain adjusted) MJm^-2day^-1 * 10 Continuous
tempann annual mean temperature degrees C * 10 Continuous
tempmin minimum temperature of the coldest month degrees C * 10 Continuous
topo topographic position. Mean difference in elevation between grid cell of interest and all cells within 1km radius (-ve values are gullies, +ve are ridges) m Continuous
vegsys broad vegetation type 1 = Rainforest, 2 = Moist open forest, 3 = Dry open forest, 4 = Woodland, 5 = Coastal sclerophyll complex, 6 = Plateau sclerophyll complex, 7 = Disturbed remnant, 8 = Exotic (pine) plantation, 9 = Cleared Categorical
Source
All data were compiled and provided by Simon Ferrier and colleagues.
References
Elith, J., Graham, C.H., Valavi, R., Abegg, M., Bruce, C., Ferrier, S., Ford, A., Guisan, A., Hijmans, R.J., Huettmann, F., Lohmann, L.G., Loiselle, B.A., Moritz, C., Overton, J.McC., Peterson, A.T., Phillips, S., Richardson, K., Williams, S., Wiser, S.K., Wohlgemuth, T. & Zimmermann, N.E., (2020). Presence-only and presence-absence data for comparing species distribution modeling methods. Biodiversity Informatics 15:69-80.
Examples
nsw_po <- disPo("NSW")
nsw_bg <- disBg("NSW")
nsw_pa_bat <- disPa("NSW", "ba")
nsw_env_bat <- disEnv("NSW", "ba")
nsw_pa_reptile <- disPa("NSW", "sr")
nsw_env_reptile <- disEnv("NSW", "sr")
# Or all in one list
nsw <- disData("NSW")
sapply(nsw, head)
disCRS("NSW")
rspatial/disdat documentation built on Feb. 14, 2023, 4:27 a.m.
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| NSW | R Documentation |
New South Wales species distribution data
Description
Species occurrence data for 54 species from 8 biological groups in New South Wales (NSW, a state in Australia) and associated environmental data. Full details of the dataset are provided in the reference below. There are four data sets with training (po and bg) and test (pa, env) data:
po (training data) includes site names, species names, coordinates, occurrence ("1" for all, since all are presence records), group [ba = bats (7 species); db = diurnal birds (8 species); nb = nocturnal birds (2 species); ot = open-forest trees (8 species); ou = open-forest understorey plants (8 species); rt = rainforest trees (7 species); ru = rainforest understorey plants (6 species); sr = small reptiles (8 species)], and site values for 13 environmental variables (below).
bg (training data) has 10000 sites selected at random across the study region. It is structured identically to po, with "0" for occurrence (not implying absence, but denoting a background record in a way suited to most modelling methods) and NA for group.
env (testing data) includes group, site names, coordinates, and site values for 13 environmental variables (below). These are for sites from different surveys for each biological group (from 570 to 2075 sites per group), and can be returned as separate datasets by disEnv, or in one long format dataset by disData. This set of files is suited to making predictions.
pa (testing data) includes group, site names, coordinates, and presence-absence records, one column per species (in the wide format returned by disPa). They can also be returned in long format using disData. The sites are identical to the sites in env. These data are suited to evaluating the predictions made with env.
Raster (gridded) data for all environmental variables are available - see the reference below for details.
The reference system of the x and y coordinates is unprojected. Latitude and longitude are in geographical coordinates using the WGS84 datum (EPSG:4326).
The vignette provided with this package provides an example of how to fit and evaluate a model with these data.
Environmental variables:
| Code | Description | Units | Type |
| cti | "compound topographic index" - a quantification of the position of a site in the local landscape. It is often referred to as the steady state wetness index and it is defined as: CTI = ln ( As / tanB ) where 'As' is the specific catchment area expressed as m2 per unit width orthogonal to the flow direction and 'B' is the slope angle | Continuous | |
| disturb | disturbance (clearing, logging etc) index. | 1 = light, 2 = moderate, 3 = heavy | Continuous |
| mi | moisture index. Index of site wetness derived from a water balance algorithm using rainfall, evaporation, radiation and soil depth as inputs | Between 0 (dry) and 100 (wet) | Continuous |
| rainann | mean annual rainfall | mm | Continuous |
| raindq | mean rainfall of the driest quarter | mm | Continuous |
| rugged | ruggedness. Coefficient of variation of grid cells within 1km of cell of interest | percent | Continuous |
| soildepth | mean soil depth predicted from a model relating sampled soil depths to climate, geology and topography | m * 1000 | Continuous |
| soilfert | soil fertility ordinal class, derived from soil maps and modeling of geochemical data | 1 (low) to 5 (high) | Continuous |
| solrad | annual mean solar radiation (terrain adjusted) | MJm^-2day^-1 * 10 | Continuous |
| tempann | annual mean temperature | degrees C * 10 | Continuous |
| tempmin | minimum temperature of the coldest month | degrees C * 10 | Continuous |
| topo | topographic position. Mean difference in elevation between grid cell of interest and all cells within 1km radius (-ve values are gullies, +ve are ridges) | m | Continuous |
| vegsys | broad vegetation type | 1 = Rainforest, 2 = Moist open forest, 3 = Dry open forest, 4 = Woodland, 5 = Coastal sclerophyll complex, 6 = Plateau sclerophyll complex, 7 = Disturbed remnant, 8 = Exotic (pine) plantation, 9 = Cleared | Categorical |
Source
All data were compiled and provided by Simon Ferrier and colleagues.
References
Elith, J., Graham, C.H., Valavi, R., Abegg, M., Bruce, C., Ferrier, S., Ford, A., Guisan, A., Hijmans, R.J., Huettmann, F., Lohmann, L.G., Loiselle, B.A., Moritz, C., Overton, J.McC., Peterson, A.T., Phillips, S., Richardson, K., Williams, S., Wiser, S.K., Wohlgemuth, T. & Zimmermann, N.E., (2020). Presence-only and presence-absence data for comparing species distribution modeling methods. Biodiversity Informatics 15:69-80.
Examples
nsw_po <- disPo("NSW")
nsw_bg <- disBg("NSW")
nsw_pa_bat <- disPa("NSW", "ba")
nsw_env_bat <- disEnv("NSW", "ba")
nsw_pa_reptile <- disPa("NSW", "sr")
nsw_env_reptile <- disEnv("NSW", "sr")
# Or all in one list
nsw <- disData("NSW")
sapply(nsw, head)
disCRS("NSW")
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Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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