Data

Spatial environmental predictors for biological habitat modelling at a basin-scale in the Murray- Darling Basin

Commonwealth Scientific and Industrial Research Organisation
Ponce Reyes, Rocio ; Freebairn, Andrew ; Foster, Scott ; Mokany, Karel
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ctx_ver=Z39.88-2004&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Adc&rfr_id=info%3Asid%2FANDS&rft_id=info:doi10.25919/zpnz-zz12&rft.title=Spatial environmental predictors for biological habitat modelling at a basin-scale in the Murray- Darling Basin&rft.identifier=https://doi.org/10.25919/zpnz-zz12&rft.publisher=Commonwealth Scientific and Industrial Research Organisation&rft.description=We compiled a set of dynamic spatial environmental data with a resolution of 3 arc-second resolution (approximately 90 m). These layers were to be used as potential predictor variables for creating high quality habitat maps for key species. The spatial environmental layers used included variables that change over time, mainly based on a bimonthly time-series of inundation depth across the MDB, and time-series information on rainfall.Lineage: We created spatial environmental predictor layers that could be used with biological records to help to predict spatiotemporal patterns of occurrence and habitat quality for determined species across the Murray-Darling Basin. These layers were obtained from a variety of sources and aligned to a common 3s resolution (~90 m) spatial grid. All the layers were created in R.Data products-\tLong-term inundation probability-\tRainfall over the previous 2 months; 4 months (time-series)-\tMean inundation depth in the previous 5 years; 10 years (time-series)-\tNumber of months since last inundation (time-series)-\tNumber of months inundated in the previous 10 years (time-series)Each environmental predictor layer are provided in a separate folder. All spatial layers are in GDA94 geographic projection (EPSG:4283) and geotiff format.&rft.creator=Ponce Reyes, Rocio &rft.creator=Freebairn, Andrew &rft.creator=Foster, Scott &rft.creator=Mokany, Karel &rft.date=2024&rft.edition=v1&rft_rights=Creative Commons Attribution 4.0 International Licence https://creativecommons.org/licenses/by/4.0/&rft_rights=Data is accessible online and may be reused in accordance with licence conditions&rft_rights=All Rights (including copyright) CSIRO 2023.&rft_subject=MDB&rft_subject=MDB-EF&rft_subject=Ecosystem Functions&rft_subject=Ecology&rft_subject=Habitat Modelling&rft_subject=Freshwater ecology&rft_subject=Ecology&rft_subject=BIOLOGICAL SCIENCES&rft_subject=Surface water hydrology&rft_subject=Hydrology&rft_subject=EARTH SCIENCES&rft.type=dataset&rft.language=English Access the data

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Creative Commons Attribution 4.0 International Licence
https://creativecommons.org/licenses/by/4.0/

Data is accessible online and may be reused in accordance with licence conditions

All Rights (including copyright) CSIRO 2023.

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Full description

We compiled a set of dynamic spatial environmental data with a resolution of 3 arc-second resolution (approximately 90 m). These layers were to be used as potential predictor variables for creating high quality habitat maps for key species. The spatial environmental layers used included variables that change over time, mainly based on a bimonthly time-series of inundation depth across the MDB, and time-series information on rainfall.
Lineage: We created spatial environmental predictor layers that could be used with biological records to help to predict spatiotemporal patterns of occurrence and habitat quality for determined species across the Murray-Darling Basin. These layers were obtained from a variety of sources and aligned to a common 3s resolution (~90 m) spatial grid. All the layers were created in R.


Data products
-\tLong-term inundation probability
-\tRainfall over the previous 2 months; 4 months (time-series)
-\tMean inundation depth in the previous 5 years; 10 years (time-series)
-\tNumber of months since last inundation (time-series)
-\tNumber of months inundated in the previous 10 years (time-series)

Each environmental predictor layer are provided in a separate folder.

All spatial layers are in GDA94 geographic projection (EPSG:4283) and geotiff format.

Available: 2024-02-06

Data time period: 1995-01-01 to 2020-12-31

This dataset is part of a larger collection

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ACN 633 798 857