DOI: 10.5061/dryad.xwdbrv1sc
title: Floods connect tropical river-floodplain food webs but shrink fish community isotopic trophic niches

description: Lateral connectivity between the floodplain and river channel is
 hypothesised to expand fish community isotopic trophic niches and increase
 overlap of river-floodplain food webs.  To evaluate how flood
 magnitude influences trophic dynamics in a large tropical free-flowing
 river (Roper River, Australia), we measured community isotopic niches in
 low and high magnitude flood years in wetland and river
 habitats.  Contrary to our hypothesis, isotopic niche area of the
 river fish community contracted following the high magnitude flood year,
 when compared to a low magnitude flood year. Wetland fishes maintained
 more similar niche space regardless of flood conditions, but showed the
 same pattern of contracting niche area following a large magnitude
 flood.  Niche overlap was lowest for river low magnitude flood
 and wetland high magnitude flood and highest for river and wetland high
 magnitude flood.  Our results suggest that river-floodplain fish
 communities exploit a wider isotopic range of food sources during low
 flood years, including enriched sources of potentially marine origin,
 while sharing abundant less diverse resources in high magnitude flood
 years. As water resources in tropical rivers are developed, our findings
 highlight the importance of conserving multiple dimensions of connectivity
 in riverine landscapes to maintain intact river-floodplain food webs.
description: We selected sites in the lower freshwater and upper tidal reaches
 of the Roper River, Northern Territory, Australia.  The Roper River is a
 perennial, groundwater-fed river flowing west to east into the Gulf of
 Carpentaria (Figure 2) through the tropical wet-dry savanna with a
 catchment area of 77,400 km<sup>2</sup>.  Land use in the
 catchment is made up of Aboriginal freehold (45%) and pastoral leasehold
 (46%), with the remaining area occupied by national park (6%) and
 agriculture and mining leases (0.03%) (Watson et al., 2023).  The
 population of the catchment is ~2500 of which 73% are Indigenous (Watson
 et al., 2023).  The Roper is a unique river being unregulated, with high
 dry season flows sourced from the Cambrian Limestone Aquifer (CLA) and the
 Dook Creek Aquifer. The Roper River has the third largest median annual
 discharge in northern Australia, 4341 GL, and a mean annual discharge of
 5557 GL (Watson et al., 2023) making it one of the largest free-flowing
 rivers in Australia and recognised globally (Grill et al., 2019). Based on
 long-term average peak wet season discharge was 1457 ML/d (n = 52), 2022
 was 33% below average at 978 ML/d and 2023 was 82% higher than average at
 2651 ML/d,  where 2023 provided much larger flood pulse and potential
 lateral connectivity than 2022 based on long-term averages (Figure 3). 
 Year 1 discharge did not create large overbank flows connecting floodplain
 wetlands to the main river channel. Of note is that over half the total
 flows enter the Roper River below Roper Bar, the most upstream point of
 detectable tidal influence, where this study took place. Two sites were
 sampled in the main channel of the river and four sites were sampled in
 adjacent wetlands (oxbow lakes) on the floodplain (Figure 2).
 <em>Fish and tissue sampling</em>
 A multi-gear approach was employed to sample the fish
 communities, including boat electrofishing, gill netting and hook-and-line
 sampling. Sampling occurred in the dry season (June 23-27 (wetland sites),
 August 2-4 (river sites) and 8-13 October 2022.  In 2023 surveys were 29
 March – 3 April and 20-25 June (Figure 3). It should be noted that
 isotopic tissue turnover rates in fish muscle tissue reflects what the
 fish was eating at a specific time prior to sampling, which consequently
 does not reflect the current food sources.  For
 δ<sup>15</sup>N is it likely we were seeing what the fish were
 consuming months before sample (McIntyre & Flecker, 2006).  The
 retention rate for δ<sup>13</sup>C is slower and size
 dependant with higher turnover in smaller fish therefore we were likely
 seeing diets from 1-3 months (Vander Zanden et al., 2015; Skinner et al.,
 2016).  Using an ETS 5KW MBS-1DPDC-COL boat mounted electrofisher, pulsed
 DC output varied according to habitat, electrical conductivity, and water
 clarity.  Output was pulsed on and off, for a minimum of 1800 seconds
 on-time, at each site and time.  Fishing was carried out over as much of
 the edge area of each site as possible as well as two long open water
 shots to collect as many species and individuals from the fish community
 as possible.  Data on abundance and richness were not recorded due to time
 restraints created by sample collection and processing.  Where gill nets
 were used, two 20 m multi-panel gillnets with 5 m panels from 25 mm to 100
 mm mesh and 2 m drop, were set one hour before sunset and observed for
 large animal capture.  Nets were pulled 1 hour after sunset.  Hook and
 line bait fishing was used opportunistically to target catfish and sharks,
 using cut bait taken from electrofished samples of bony bream
 [<em>Nematalosa erebi</em> (Günther, 1868)]. 
 A total of 680 fin clips were analysed from 26 species across 16
 families.  Two surveys were conducted in 2022 and 2023 from four wetland
 sites and two river sites (Supplemental Table S1).  During the low flood
 year of 2022 one wetland site dried completely and in the high flood year
 one wetland site was inaccessible due to high water during the first
 survey trip.  Two additional sites were sampled in early 2023 (R1 drain
 and WA drain) to collect fish moving across the floodplain either to
 wetlands or back to the main channel.  In an attempt to reduce total
 number of fish euthanized, tissue was collected by clipping a portion of
 the right pectoral fin, when large enough, otherwise a soft portion of the
 anal or caudal fin, whichever was in best condition, from fish &gt;120
 mm.  These fish were held in live well and measured, weighed, and sampled
 without anaesthesia and immediately released.  For those fish &lt;120
 mm, the whole fish was euthanised (using rapid chilling in an ice slurry)
 and fins were collected from the whole fish in the laboratory.
 <em>Basal food collection storage and
 preparation</em> To determine the range of
 isotopic variability at each site, basal food sources were collected from
 terrestrial vegetation, aquatic plants, algae, phytoplankton and seston. 
 For each basal group three replicates were collected within each wetland
 during each sampling occasion, one at the upstream, one at the mid and one
 at the downstream reach of each river or wetland site.  Summaries for all
 basal food sources including mean ± SE and range of
 δ<sup>13</sup>C and δ<sup>15</sup>N were
 calculated (Supplementary tables S2).
 <em>Vegetation</em> Terrestrial
 vegetation included collection of all leaves and grasses located within
 the riparian strip up to 15 m from the high-water mark. Grasses and leaves
 were stored separately. Leaves from each species of plant and grass at
 each site were combined, ground in a coffee grinder and up to 1 gram of
 material taken from the mixed sample and used for stable isotope
 analysis.  Aquatic macrophytes were divided into emergent and submerged. 
 The three locations within a site were then combined and homogenised.  A
 sample of up to 1 g was then taken and used for stable isotope
 analysis.  <em>Seston</em>
 This was intended to be a zooplankton sample but is presented as
 unfractioned seston or mixed seston, due to many samples being dominated
 by filamentous algae where it was not feasible to remove algae therefore
 zooplankton and algae are defined as seston for this work.   Samples were
 collected by towing a 500 mm mesh net for 500 m through the centre of each
 site at sunset. The concentrated sample was poured into 50 ml falcon
 tubes.  The three location samples from each site were emptied into a
 petri dish and most plant material was removed, then sieved using a 150 mm
 sieve, poured into a filter tower and filtered onto Whatman GF/F 0.45 mm
 pore size pre-combusted filter paper.  Filter papers were wrapped in
 aluminium foil and re-frozen for later processing.
 <em>Algal food sources</em> Algal
 food sources sampled included phytoplankton, periphyton and filamentous
 algae.  Phytoplankton was collected in 1 L containers at upstream,
 mid-stream and downstream locations within each site at each survey.  Each
 evening the water was filtered onto Whatman GF/F 0.45 mm pore size
 filters.  The filters were stored in aluminium foil.  Periphyton was
 collected using three different methods. First, toothbrush scrapes were
 used for the first survey in June 2022, where three
 2cm<sup>2</sup> scrapes were taken and combined in one vial at
 each sampling location within the site.  Second, for the remainder of the
 surveys a scratch pad of 2 cm diameter was applied to hard surface and/or
 soft sediment to collect algae.  Five pads were used at each of the sample
 locations per site and combined by washing out the pads into a plastic
 bag.  Third, a core method for sediment algae was used in 2022.  We
 sampled sediment surfaces by dropping a cut 50 ml syringe into the
 sediment and using the plunger to push the core to the syringe surface and
 scraping the top 1 cm of algae into a 5 mL vial with water. The vials from
 the three locations were then combined for each site.  Algae for all the
 methods was extracted and filtered onto filter paper using the colloidal
 silica method described in Hamilton et al.,** (2005).  Briefly, a 50 mL
 falcon tube was filled with 21 mL of Ludox colloidal silica then diluted
 by adding deionized water to the 30 mL mark using a syringe. A pipette was
 then used to add the slurry containing the algal sample up to the 50 mL
 mark.  The sample was then centrifuged at 1000 rpm for 10 minutes, and a
 pipette was used to extract the “light fraction” upper layer into a filter
 tower and filtered onto pre-combusted 0.45 mm filter paper.  The filter
 paper was stored in aluminium foil and re-frozen until analysis.
 <em>Primary consumers</em> As for
 primary producers, primary consumers were collected at three locations
 within each site.  We used 3 x 5-minute sweep net samples with a net mesh
 of 500 mm, focusing on the most complex habitat at each sample location. 
 These habitats included submerged and emergent macrophyte beds, root
 masses, and undercut banks.  Each net sample was sorted in the field for
 all macroinvertebrates present and frozen in separate vials.  The three
 samples were combined in the lab and sorted into five orders: Diptera,
 Coleoptera, Ephemeroptera, Hemiptera and Odonata. Non-arthropod
 invertebrates collected included snails and prawns caught in the sweep
 nets, and freshwater mussels.  Mussels were sampled where possible and
 safe, using a rake modified with long tines out into the water and slowly
 dragged back to shore.  This worked in soft sediment with low macrophyte
 cover only.  All mollusc samples had soft tissue removed from the shell
 for analysis.  Terrestrial invertebrates, specifically Orthoptera
 (grasshoppers), were sampled when present.  A sweep net was used to catch
 grasshoppers in the vegetation adjacent to the survey sites. 
 <em>Stable Isotope Analysis</em>
 We collected fin clips (<em>n</em>= 680 after
 removing 13 outliers) sampled from 26 species across 16 families and four
 trophic guilds, which were analysed for δ<sup>13</sup>C and
 δ<sup>15</sup>N (Supplemental Table S1).  Fish fin clips were
 freeze dried and lipid extracted using a modified Bligh & Dyer
 (1959) method. Lipid extraction was performed to standardise the data and
 avoid variability between and within species caused by lipid content,
 which can affect δ<sup>13</sup>C values due to the tendency
 for lipids to be depleted in δ<sup>13</sup>C (Boecklen et al.,
 2011).  Lipid extraction was done by soaking samples in a 2:1
 dichloromethane to methanol solution for 24 hours, then draining onto
 filter paper and rinsing three times with the dichloromethane to methanol
 solution to ensure all solvent and lipids were removed. The remaining
 tissue on the filter paper was collected, placed in an open test tube, and
 dried at 60℃ for 48 hours to evaporate any excess solvent.  The potential
 effect of lipid extraction on δ<sup>15</sup>N values was not
 verified within this study, owing to the minimal sample amounts from the
 sub-lethal fin clips preventing subsampling prior to lipid extraction.
 Given the aim of comparing between fish, such validation was deemed
 unnecessary, but the authors acknowledge the absolute
 δ<sup>15</sup>N values may be impacted.
 Approximately 1500 μg of the lipid extracted tissue was weighed
 using a microbalance and placed into tin capsules for stable isotope
 analysis at Flinders University.  Samples were combusted at 950℃ using
 Isoprime GC5 continuous flow Isotope Ratio Mass Spectrometer with a vario
 ISOTOPE cube elemental analyser (Elementar Australia Pty Ltd). Three
 blanks and 14 standards (USGS40 (𝛿<sup>13</sup>𝐶=−26.39
 [relative to Vienna Peedee Belemnite (VPDB)] 
 𝛿<sup>15</sup>𝑁=−4.52 [relative to N2 in air]) and USGS41
 (𝛿13𝐶=+37.76 [relative to Vienna Peedee Belemnite (VPDB)] and
 𝛿<sup>15</sup>𝑁=+47.57 [relative to N2 in air]  [Sercon Ltd,
 UK]) were run per tray of 96 to measure precision and correct for
 analytical drift between runs. The precision (average of standard
 deviations for both standards within each run of 96) was 0.10 for
 𝛿<sup>13</sup>𝐶 and 0.08 for 𝛿<sup>15</sup>𝑁.
  Results were exported using the IonVantage software from Elementar.  Not
 all samples analysed had sufficient material for accurate isotopic
 analysis, resulting in extreme outliers which were removed (n =
 16). Stable isotope values are written in conventional
 delta (d) notation as:   where X
 equals <sup>15</sup>N or <sup>13</sup>C, and R is
 the ratio of <sup>15</sup>N/<sup>14</sup>N or
 <sup>13</sup>C/<sup>12</sup>C in the sample,
 following the international standards of Pee Dee Belemnite for CO2, and
 atmospheric nitrogen for N2. <em>Statistical
 analysis</em> For basal food sources and fish,
 pairwise PERMANOVAs were applied in Primer (version 7.0.24) to test if
 there were significant differences in isotopic ratios between habitats and
 years to guide interpretation of isotopic niche comparisons.  Summary of
 basal food δ¹³C and δ¹⁵N values were collated in Supplementary table S2. 
 Isotopic niche analysis was conducted using the Bayesian Ellipse package
 in R SIBER v 2.1.9 (Jackson et al., 2019; R core team, 2024). Stable
 isotope values for δ¹³C (iso1) and trophic position (TP or
 δ¹⁵N-equivalent, iso2) were calculated and grouped by species, feeding
 guild (guild), or habitat-year combinations (Supplemental Tables S3-S4).
 Only species or guilds with more than three isotope observations per
 grouping were retained to ensure reliable ellipse estimation. Trophic
 position (TP) was calculated for fishes using the equation TP = 1+
 (δ<sup>15</sup>N<sub>consumer</sub> -
 δ<sup>15</sup>N<sub>basal source</sub>) / Trophic
 discrimination factor (TDF) (Post, 2002; Zanden and Rasmussen, 1999),
 where δ<sup>15</sup>N<sub>consumer</sub> is the
 δ<sup>15</sup>N value of an individual fish, and
 δ<sup>15</sup>N<sub>basal source</sub> is the mean
 δ<sup>15</sup>N value of the primary producers within each
 site where fish were sampled (Supplemental Tables S3-S4).  To account for
 spatial and temporal variation in basal baseline isotopic N values, we
 estimated TP using a local basal mean baseline for each site.  The trophic
 discrimination factor (TDF), which is the stepwise increase in
 δ<sup>15</sup>N from diet to consumer, was estimated by three
 methods to test for sensitivities of the TP results to uncertainties with
 TDF.  A TD<sub>fixed</sub> or constant TDF of 3.4‰ (Post,
 2002) was first used.  Then a scaled TDF, based on a global metanalysis,
 was applied using the following equation,
 TP<sub>scaled</sub>** = 1 +
 (δ<sup>15</sup>N<sub>consumer</sub>** -
 δ<sup>15</sup>N<sub>basal source</sub>) / 2.94 +
 0.22 × (δ<sup>15</sup>N<sub>basal source</sub>** -
 3.4) (see Hussey et al., 2014).  The last method
 (TP<sub>ausriv</sub>) is based on estimates by trophic guild
 as follows, where TDFs from basal sources to consumers are:
 herbivore/detritivore (3.9‰), omnivore (4.3‰), invertivore (3.4‰) and
 predator (5.7‰) as determined in Australian rivers by Bunn <em>et
 al.</em> (2013).  Sensitivity analysis showed there was limited
 variation across all three TDF methods (Supplementary Table S3-S4), so
 TP<sub>fixed</sub> was used for calculation of isotope metrics
 and community niche polygons. Using the
 δ<sup>13</sup>C and the TP values as described, isotopic niche
 areas were calculated using Bayesian Standard Ellipse Areas
 (SEA<em><sub>B</sub></em>) for the fish community
 and presented as community polygons.  Polygon areas calculated using
 SEA<sub>B</sub> correct for small sample sizes (Jackson et
 al., 2011) and were calculated for two separate groupings: whole community
 (all species, excluding any with less than three individuals), and guild. 
 Temporal comparisons tested for differences in niche metrics between high
 and low flood magnitude years, and spatially between river and wetland
 habitats.  For the guild analysis, fish were divided
 into 4-trophic guilds as determined by Sternberg & Kennard (2014)
 to examine if the changes in isotopic niche area would be better
 represented at the guild or species level. Guilds included
 Herbivore/detritivore (HD) (ca &gt; 25% plant matter), Omnivore (OM)
 (ca 5-25% plant matter), Invertivore (IN) and Predator (PR) (&gt; 10%
 fish), food proportions taken from Sternberg & Kennard (2014). 
 The same script in R was run for these two additional analyses.
                To describe the community isotopic structure,
 Layman metrics (Layman et al. 2007) were calculated from posterior group
 means including δ<sup>13</sup>C range (CR), trophic position
 range (TP, based on d<sup>15</sup>N range or NR), total niche
 area (TA), and centroid distance (CD), as well as mean nearest neighbour
 distance (NND) and standard deviation of nearest neighbour distance
 (SDNND) as measures of packing and redundancy in the food web.  We
 calculated these metrics to assess differences in isotopic niche structure
 across river and wetland habitats (spatial) and flood magnitude years
 (temporal).  These results were visualized using posterior distributions
 of each metric as density plots with 95% credible intervals to assess
 uncertainty and using the lack of overlap in 95% confidence intervals (CI)
 as evidence of differences.  The use of Layman metrics and
 SEA<sub>B</sub> polygons provided a characterization of
 isotopic food web structure in multidimensional space.  Layman metrics
 offer additional insight into the diversity, evenness, and redundancy of
 isotopic niches within communities, allowing for a mechanistic
 understanding of how connectivity and habitat variation shape food web
 dynamics, while the use of SEA<sub>B</sub> provides a
 comparison of isotopic niche breadth across habitats and years while
 accounting for uncertainty and small sample sizes.
                To assess isotopic niche overlap among consumer
 communities across habitat and hydrological conditions, we used a Bayesian
 framework in the R package nicheROVER (Swanson et al., 2015).  We used
 δ<sup>13</sup>C and Trophic Position
 (TP<sub>fixed</sub>) from individuals and grouped by habitat
 type and flood year, resulting in four combined community groups: river
 low flood magnitude (2022), river high flood magnitude (2023), wetland low
 flood (2022), wetland high flood (2023).  Posterior distributions were fit
 for each group's multivariate niche using the normal-inverse-Wishart
 prior (niw.post), generating 1000 posterior samples for each group. Groups
 with fewer than three individuals were excluded to ensure reliable
 estimation.                We assessed niche overlap
 using both directional and symmetrical overlap matrices to determine the
 degree of isotopic niche sharing among habitat-year groups. Directional
 niche overlap was estimated by calculating pairwise overlap probabilities
 using the overlap() function with nprob = 1000 and nreps = 1000.  
 Directional overlap represents the probability that individuals from one
 group fall within the niche region of another, capturing asymmetric
 overlaps.  For example, group A (river / high flood) may largely fall
 within the group B’s (river / low flood) niche but the reverse may not
 apply.  This gives more ecological insight into how one group may be
 nested within another especially when niche breadth differs.  We applied
 this method as it accounts for uncertainty in niche estimates by
 integrating over the posterior distributions.  Directional overlaps were
 summarized with the mean probability of overlap between each group pair. 
 Results of overlap were visualized using an overlap matrix.  Symmetrical
 niche overlap, unlike directional overlap, is an average taken from the
 directional overlap matrix which compares probability of a species from
 group A occurring in niche space of group B and vice versa, giving more
 ecological interpretation.  This symmetric matrix gives an overall index
 of mutual niche similarity, allowing for more straightforward comparison
 across all group combinations.    All analyses were conducted in R version
 4.4.0 (R Core Team, 2024), and follow the framework described in Swanson
 et al. (2015), which allows comparison of multivariate isotopic niches and
 associated uncertainty in a probabilistic manner.
description: # Floods connect tropical river-floodplain food webs but shrink fish
 community isotopic trophic niches Dataset DOI:
 [10.5061/dryad.xwdbrv1sc](10.5061/dryad.xwdbrv1sc) ## Description of the
 data and file structure **Description of Data files**
 **Food*_*R_Final.csv** contains the data used to create basal food summary
 tables and basal averages used in the trophic discrimination factors
 (Post, 2002; Zanden and Rasmussesn, 1999) for fixed, scaled and Ausriv
 Trophic Discrimination Factors comparisons. #### File: Food_R_Final.csv
 **Description:**  ##### Variables (columns) Site = R1 (River 1) and R2
 (River 2) for river sites, YW (Yellow Waters lagoon), WA (Walmija lagoon),
 LO (Lomariamum lagoon) and LN (Long Lagoon) wetland sites Habitat = River
 or Wetland date = date of sample ISO1 is Nitrogen (‰ δ^15^N) ISO2 is
 Carbon (‰ δ¹³C). food taxa is taxon sampled (Snail, Coleoptera, Odonata,
 Crustacean, Terrestrial or aquatic vegetation etc.) Food_group is how the
 taxon were grouped, Ainvert = all aquatic invertebrate consumers (as
 labelled in corrected food group, Tinvert = terrestrial insects, Aveg =
 aquatic vegetation (submerged and emergent), Tveg = terrestrial vegetation
 (leaves and grasses), Periphyton, Phytoplankton are as named, Seston is
 unfractioned zooplanton sample. Trophic = trophic level, primary consumer
 or producer. **SIA_Tissue_Dryad.csv** contains all the fish tissue data
 and grouping columns for trophic niche analysis. Columns include n for raw
 Nitrogen, iso1 = Carbon, iso 2 = trophic position which in R can be
 selected as tp_fixed, scaled or ausriv. There is all site and year,
 species or trophic guild data as well as food data for overlay of basal
 foods which was not included in figures but was analyzed. #### File:
 SIA_Tissue_Dryad.csv **Description:**  ##### Variables * community
 (habitat = wetland or river) * group (fish species) * iso1 (‰ δ^13^C) *
 iso2 (trophic position as calculated using TDF~fixed~) * site = R1 (River
 1) and R2 (River 2) for river sites, YW (Yellow Waters lagoon), WA
 (Walmija lagoon), LO (Lomariamum lagoon) and LN (Long Lagoon) wetland
 sites plus R1Drain1 (Drain at River 1 site) and WADrain1 (Drain leading
 from Walmija lagoon to River 2 site). * year (high flood magnitude and low
 flood magnitude) * guild (trophic guilds, HD herbivore/detritivore, IN
 invertivore, OM omnivore, PR predator ## Code/software **Code/Software**
 Primer v7 was used to run exploratory graphical and PERMANOVA comparisons
 for differences in habitat and year for basal food and fish tissues. R
 package SIBER and nicheROVER were used to examine fish community trophic
 niche space, using Standard Ellipse Areas (SEA) based on Bayesian
 modelling and Layman metrics (Jackson et al., 2019) , and nikcheROVER to
 test niche overlap (Swanson et al., 2015). R Script purpose: Develop a
 comparison of the 6 niche metrics outlined by Layman et al. (2007) 1\)
 year based comparisons using trophic guilds across habitat by year 2\)
 year based comparison using fish species community niche in river and
 wetland habitats R script can be provided upon request. ## Access
 information Other publicly accessible locations of the data: * 
 
 Data was derived from the following sources: * **References** Bunn, S.
 E., Leigh, C., & Jardine, T. D. (2013). Diet‐tissue fractionation of
 δ15N by consumers from streams and rivers. Limnology and Oceanography,
 58(3), 765-773.
 [https://doi.org/10.4319/lo.2013.58.3.0765](https://doi.org/10.4319/lo.2013.58.3.0765) Jackson, A., Parnell, A., & Jackson, M. A. (2019). Package ‘SIBER’. *R package version*, *2*(4). Post, D. M. (2002). Using Stable Isotopes to Estimate Trophic Position: Models, Methods, and Assumptions. Ecology, 83(3), 703-703. [https://doi.org/10.2307/3071875](https://doi.org/10.2307/3071875) Zanden, M. J. V., & Rasmussen, J. B. (1999). Primary Consumer δ13c and δ15n and the Trophic Position of Aquatic Consumers. Ecology, 80(4), 1395-1404. [https://doi.org/10.1890/0012-9658(1999)080\[1395:Pccana\]2.0.Co;2](https://doi.org/10.1890/0012-9658\(1999\)080[1395:Pccana]2.0.Co;2)