DOI: 10.5061/dryad.2547d7x05
title: Data from: Resistance of Australian fish communities to drought and flood: implications for climate change and adaptations

description: Climate change induced extreme weather are dynamic in location, timing,
 and magnitude of rainfall and can alter how species respond to their
 environment. Extreme weather like droughts are now being seen as coupled
 to flooding when rainfall returns. Unfortunately, it is difficult to
 understand how ecological communities respond to the combination of
 drought and floods together. In the associated study we hypothesized that
 native organisms have evolved resistance to withstand repeated cycles of
 drought-flood disturbances, and that established non-native species have
 adapted to persist in novel conditions. To test our hypothesis, we fit a
 geostatistical model of species occurrence with spatiotemporal local
 rainfall patterns as covariates in the drought and flood impacted
 Murray-Darling basin in Australia. The time range of this dataset included
 the decade long Millenium Drought, and its recovery period. During these
 drought conditions, river-floodplain organisms in the Murray-Darling
 became localized in refugia that limited longitudinal and lateral
 connectivity, and following flooding the same organisms were exposed to
 dispersal and recruitment opportunities, as well as to hypoxic blackwater
 events that lead to the mortality of aquatic organisms. At the basin-scale
 we found that the range size of most native and non-native fishes were
 highly resistant to the extreme drought and post-flood conditions. At
 local scales, species richness, or detection, actually increased under
 drought conditions. Both findings highlight the resistance of species to
 climate change driven extreme weather, which opens new questions on
 community adaptations.
description: # Dataset for Resistance of Australian fish communities to drought and
 flood: implications for climate change and ecological memory
 [https://doi.org/10.5061/dryad.2547d7x05](https://doi.org/10.5061/dryad.2547d7x05) Dataset includes rainfall and river classification data used in the GIS steps of the analysis. Two rds files are also included to run the R code and analysis. A separate shapefile labeled mdb_boundary refers to the Murray Darling Basin boundary used in the R mapping. Since the GIS portion was done using QGIS the methods are not completely reproducible unless that software is installed (see below). The minimal reproducible component for the R script Data-Analysis should be possible given the post-GIS-processed data. Data and code analyzing fish response to extreme drought and flood in Australia's Murray Darling Basin. ## Description of the data and file structure * MDB_ANAE (Directory) * contains shapefiles (.dbf, .prj, .shx) to be used in QGIS or other mapping software. All files must be read together to function. Data shows wetland classification of hydrological status in the Murray-Darling Basin Australia. * mdb_boundary (Directory) * contains shapefile (.dbf, .prj, .shx) of murray-darling basin boundary to be used in GIS or other mapping software. All files must be read together to function. Data shows watershed boundary of the Murray-Darling Basin Australia. * sampled_rasters (Directory) * contains .gpkg files to be used in QGIS which visualize the rainfall patterns across the the Murray-Darling Basin Australia. Each fill will have discrete rainfall values in millimeters. * audit_corrected.rds (file) * is the sustainable river audit data in a rds file format which can be read with R. This corrected file removes duplicated measures from the full dataset. Variables include sample site, fish species, latitude, longitude, fish presence, and associated rainfall. * final_audit.rds (file) * is the sustainable river audit data in a rds file format which can be read with R. This uncorrected file contains duplicated measures from the full dataset. Variables include sample site, fish species, latitude, longitude, fish presence, and associated rainfall. * Data-Prep.R (file) * is a script which can be run in R to format the original GBIF data to contain only fish records (final audit) and then correct for duplicates (audit_corrected) * Data-Analysis.R (file) * is a script which can be run in R to perform statistical modeling with the audit_corrected data ## Sharing/Access information Original rainfall data was sourced here: [http://www.bom.gov.au/climate/maps/rainfall/?variable=rainfall&map=totals&period=month&region=nat&year=2004&month=01&day=31](http://www.bom.gov.au/climate/maps/rainfall/?variable=rainfall&map=totals&period=month&region=nat&year=2004&month=01&day=31) Original fish data for the sustainable river audit was sourced here: [https://www.gbif.org/dataset/f38cc07c-49a8-4ca4-8882-5cdde1733c12](https://www.gbif.org/dataset/f38cc07c-49a8-4ca4-8882-5cdde1733c12) ### **Usage Notes** Software access to QGIS: [https://www.qgis.org/en/site/forusers/download.html](https://www.qgis.org/en/site/forusers/download.html) R packages used to assist with graphing and data wrangling are here: tidyverse, tidylog, RCurl, RColorBrewer, RCurl, devtools, patchwork, viridis which all can be accessed through the standard CRAN repository in R. Primary R Package to perform analysis is: sdmTMB which can be found here: [https://pbs-assess.github.io/sdmTMB/](https://pbs-assess.github.io/sdmTMB/)