DOI: 10.5061/dryad.zkh1893nr
title: Data from: Species richness of trophic guilds increases with discharge and decreases with variability in tropical river fish communities

description: Species Area Relationships (SARs) are one of the most well-established
 conservation biogeography patterns, and in rivers habitat area is mediated
 by discharge. Species richness and river discharge have a well-established
 positive relationship, but how discharge affects trophic diversity is less
 clear. Free-flowing tropical river ecosystems are hotspots of global
 biodiversity, but they are under increasing threat from water resource
 developments which alter river discharge regimes. Here we investigate
 relationships between river discharge metrics and the species richness of
 freshwater fish trophic guilds in tropical rivers of northern Australia,
 using data collated from 40 catchments. We analysed relationships between
 the species richness of freshwater fish trophic guilds and discharge
 metrics including mean annual discharge (Q), mean daily dry and wet season
 discharge and the coefficient of variation (CVQ) of Q. Invertivores and
 omnivores were the most species rich trophic guilds. Our results show that
 the species richness of trophic guilds in north Australian freshwater
 fishes was correlated with multiple components of wet-dry tropical river
 discharge regimes. The species richness of predators, invertivores and
 herbivore-detritivores increased with Q and wet season discharge, whereas
 omnivore and invertivore richness increased with dry season discharge.
 Increasing variability in discharge had a negative effect on the species
 richness of invertivores and omnivores suggesting adverse effects of low
 discharge periods. We found no statistical support for the hypothesis that
 the slope of SARs increases with trophic level as predicted by the Trophic
 Island Biogeography Theory. These findings suggest that decreases in wet
 and dry season discharge, or increases in flow variability due to water
 resource development or climate change, may result in the loss of trophic
 diversity from tropical rivers. Our results suggest that the conservation
 of both wet and dry season natural flow regimes in tropical rivers will be
 needed to protect freshwater fish trophic diversity.
description: # Data from: Species richness of trophic guilds increases with discharge
 and decreases with variability in tropical river fish communities Dataset
 DOI: [10.5061/dryad.zkh1893nr](10.5061/dryad.zkh1893nr) ## Description of
 the data and file structure The data includes Catchments, Basins (GOC =
 Gulf of Carpentaria, TTS = Timor Sea, NEC = North East Coast) Trophic
 guild (HD = Herbivore/detritivore, OM = Omnivore, IN = Invertivore and PR
 = Predator) and species richness of reach trophic guild, Max discharge
 (MaxQ), mean annual discharge ML/d (Q), CV of daily flow (CVQ), mean daily
 wet season discharge (wet) and mean daily dry season discharge (dry).
 n_year is the number of years for records at that gauging station. Total
 richness for each trophic guild was calculated for each basin. ### Files
 and variables #### File: Trophic_Richnes_Flow_data_40_Dryad.xlsx
 **Description:** Trophic_Richnes_Flow_data_40_Dryad.xlsx worksheet
 contains the data containing the total species richness and trophic
 species richness for the 4 trophic guilds by catchment. ##### Variables *
 Catchment: total of 40 * basin: three basins * TotalRichness: Total
 Species recorded in the catchment * HD: Herbivore/detritivore richness *
 OM: Omnivore richness * IN: Invertivore richness * PR: Predator richness *
 MaxQ: Max discharge * Q: mean annual discharge ML/d * CVQ: CV of daily
 flow * No Flow * Wet: mean daily wet season discharge * Dry: mean daily
 dry season discharge * n_years: number of years for records at that
 gauging station ## Code/software We used the ‘lme4’ package in R studio
 (version 2023.12.1 Build 402 for plots and Generalized Linear Mixed
 Effects Models. The codes and script used were: Models were fitted using
 the lmer() function from the **lme4** package in R. Fixed effect slopes,
 standard errors, and p-values were extracted using broom.mixed::tidy(),
 and model fit was assessed using marginal R² values (performance::r2())
 and Akaike Information Criterion (AIC). All trophic richness data were log
 transformed before modelling. Each model was in the basic form:
 Richnesslog ~ Hydrologylog+ (1|basin) **Total Community Richness** 1.
 TotalRichness_log ~ Q_log + (1 | basin) 2. TotalRichness_log ~ Wet_log +
 (1 | basin) 3. TotalRichness_log ~ Dry_log + (1 | basin) 4.
 TotalRichness_log ~ CV_log + (1 | basin) **Herbivore/Detritivore
 Richness** 1. HD_log ~ Q_log + (1 | basin) 2. HD_log ~ Wet_log + (1 |
 basin) 3. HD_log ~ Dry_log + (1 | basin) 4. HD_log ~ CV_log + (1 | basin)
 **Omnivore Richness** 1. OM_log ~ Q_log + (1 | basin) 2. OM_log ~ Wet_log
 + (1 | basin) 3. OM_log ~ Dry_log + (1 | basin) 4. OM_log ~ CV_log + (1 |
 basin) **Invertivore Richness** 1. IN_log ~ Q_log + (1 | basin) 2. IN_log
 ~ Wet_log + (1 | basin) 3. IN_log ~ Dry_log + (1 | basin) 4. IN_log ~
 CV_log + (1 | basin) **Predator Richness** 1. PR_log ~ Q_log + (1 | basin)
 2. PR_log ~ Wet_log + (1 | basin) 3. PR_log ~ Dry_log + (1 | basin) 4.
 PR_log ~ CV_log + (1 | basin) River flow metrics were calculated for each
 catchment. Initial calculations included the annual mean, standard
 deviation, the coefficient of variation, quartiles of flow, the percent
 completeness of the hydrograph, and the total number of zero flow days
 across the entire record. These calculations were performed on the data
 records after the missing records were removed. Each hydrograph was
 analysed by water year (starting in July) to allow the calculations to
 capture a single wet season (all calculations made in Python using Numpy
 and Pandas libraries). For each water year, multiple metrics were
 reported, including the maximum flow, standard deviation, total number of
 zero flow days, days below the 10th percentile, and days above the 10th,
 30th, 50th, 70th, and 90th percentiles. The total flows and daily average
 flows for the wet season (November to April) and dry season (May to
 October) are also reported. Summaries of the catchments assessed, the
 number of water years contained within each dataset, and the percentage of
 data gaps are provided in Supplementary Tables S3-S5. ## Access
 information Data was derived from the following sources: * Surface water
 data were obtained from the Global Runoff Data Centre Data Portal (2023),
 the Bureau of Meteorology (2023), the Queensland Government (2023) and the
 Northern Territory government (2023) and Geoscience Australia (2004) (See
 Figure 1 for location of hydrographic logging stations). Where available,
 mean daily discharge data (Q ML/d) were used. Raw data that was available
 at a sub-daily timestep was converted to daily mean flow using the Pandas
 library in Python (McKinney, 2010). Linear interpolation was used to fill
 gaps of 3 days or less. Larger gaps were not filled.  * Fish species
 distribution and richness data by catchment as well as mean flow data was
 taken from Sternberg, D., & Kennard, M. J. (2013). Environmental,
 spatial and phylogenetic determinants of fish life-history traits and
 functional composition of Australian rivers. *Freshwater Biology*,
 *58*(9), 1767-1778.
 [https://doi.org/10.1111/fwb.12166](https://doi.org/10.1111/fwb.12166) *
 Sternberg, D., & Kennard, M. J. (2014). Phylogenetic effects on
 functional traits and life history strategies of Australian freshwater
 fish. *Ecography*, *37*(1), 54-64.
 [https://doi.org/10.1111/j.1600-0587.2013.00362.x](https://doi.org/10.1111/j.1600-0587.2013.00362.x)