DOI: 10.5061/dryad.w9ghx3g1x
title: A giant’s appetite: How body size drives the diet and trophic position of the Japanese giant salamander

description: In predators, growth often drives ontogenetic dietary shifts (ODSs),
 leading to increasing trophic position (TP) with body size as growing
 individuals gradually incorporate larger prey in their diet. In species
 exhibiting extreme size variation, particularly those with gigantism, TP
 may increase markedly with body size, as large individuals might gain
 access to prey considerably higher in the food chain and inaccessible to
 smaller conspecifics. This can ultimately lead to apex predator status in
 the largest individuals. In this study, we investigated for the first time
 ODSs in one of the world’s largest amphibians, the Japanese giant
 salamander (Andrias japonicus). We combined stomach content and stable
 isotope analysis (δ¹⁵N, δ¹³C) from 160 individuals across a broad size
 range to quantify dietary patterns and TP changes. We found a non-linear
 increase in TP with body size, from approximately 3.0 to 5.1, with a
 marked inflection point at a snout–vent length of 39 cm. This threshold
 corresponded to a clear dietary transition: from primarily consuming
 aquatic insects, to feeding predominantly on fish, anurans, and freshwater
 crabs. This transition likely reflects morphological and physiological
 adaptations associated with gigantism, enabling the exploitation of large
 prey. Our findings suggest that gigantism may be adaptive in predators
 such as giant salamanders by promoting ecological opportunities, allowing
 individuals to access high trophic levels through extensive growth and
 ultimately function as apex predators. These results contribute to a
 broader understanding of the ecological consequences of body size
 evolution in predatory vertebrates, highlighting how extreme growth can
 reshape species' ecological roles.
description: # A giant’s appetite: How body size drives the diet and trophic position
 of the Japanese giant salamander Dataset
 DOI: https://doi.org/10.5061/dryad.w9ghx3g1x This work was originally
 performed using **R 4.3.3**. The list of packages used to perform the
 analyses is provided in each script document. --- Main
 folder: data_diet_trophic_position_giant_salamanders.zip ## Folder 1: Food
 web structure In this folder, data and scripts are provided to present the
 plot of the structure of the food web using stable isotope data (Figure 3
 in the manuscript). **File:** `data_isotopic_space.csv` Columns: * `id`:
 species groups. For giant salamanders, includes individual values (`aj`)
 and the mean (`andrias_all`) * `dC`: δ13C values (‰) * `dN`: δ15N values
 (‰) * `dNd+`, `dCd+`: standard deviation values of `dC` and `dN` per
 species group (not for individuals) (‰) --- ## Folder 2: Trophic position
 In this folder, data and scripts are provided to calculate the trophic
 position of each Japanese giant salamander using stable isotope data of
 individuals and baselines. **File:** `data_tRophic_Position.csv` Columns:
 * `Location` * `FG`: functional group (`aj` for individuals, `aquatic_BL`,
 `terrestrial_BL`) * `d13C`, `d15N`: isotope values (‰) --- ## Folder 3:
 Stable isotope mixing models In this folder, data and scripts are provided
 to run stable isotope mixing models for each of the 160 giant salamanders.
 **Files:** * `salamanders_consumers.csv`: stable isotope data of
 salamanders * `Site`: the river section where the salamander was found *
 `id`: identification number of each salamander * `SVL`: body size
 (snout-vent length) of each individual (cm) * `d13C`, `d15N`: isotope
 values (‰) * `sources_salamanders.csv`: stable isotope data of food
 sources * `Site`: the river section where the salamander was found *
 `Source`: food source: 4 possibilities: crab (freshwater crabs), fish,
 frog, invert (aquatic insects) * `Meand13C`: δ13C values of food sources
 (‰) * `SDd13C`: standard deviation of δ13C values of food sources (‰) *
 `Meand15N`: δ15N values of food sources (‰) * `SDd15N`: δ15N values of
 food sources (‰) * `TEFs_salamanders.csv`: Trophic Enrichment Factors *
 `Source`: food source: 4 possibilities: crab (freshwater crabs), fish,
 frog, invert (aquatic insects) * `Meand13C`: TEF values of food sources
 for carbon (‰) * `SDd13C`: standard deviation of TEF values of food
 sources for carbon (‰) * `Meand15N`: TEF values of food sources for
 nitrogen (‰) * `SDd15N`: standard deviation of TEF values of food sources
 for nitrogen (‰) --- ## Folder 4: GAMs for δ15N/TP and Body Size In this
 folder, data and scripts are provided to develop the GAMs used to
 highlight the relation between body size and δ15N and trophic position. It
 also allows for estimating a breakpoint in the δ15N - body size and
 trophic position - body size relationships using piecewise linear models.
 **File:** `data_N_TP_results.csv` Columns: * 'svl': body size
 (snout-vent length) of each salamander (cm) * 'd15N': δ15N
 values for each salamander (‰) * 'tp': trophic position of each
 salamander calculated in 'Folder 2'` --- ## Folder 5: GAMs for
 stomach content and mixing model proportions In this folder, data and
 scripts are provided to develop the GAMs used to emphasize (1) the
 relation between the body size of giant salamanders and the proportion of
 prey found in their stomach content and (2) the relation between the body
 size of giant salamanders and the contribution of prey to the assimilated
 diet of giant salamanders calculated through stable isotope mixing models.
 **File:** `results_SCA_mixing_models.csv` Columns: * 'SVL': body
 size (snout-vent length) of each salamander (cm) * 'SCA_crab':
 proportion of freshwater crabs found in the stomach content of each
 salamander * 'SCA_fish': proportion of fish found in the stomach
 content of each salamander * 'SCA_frog': proportion of frogs
 found in the stomach content of each salamander * 'SCA_aq.ins':
 proportion of aquatic insects found in the stomach content of each
 salamander * 'mix_crab': contribution of freshwater crabs to the
 assimilated diet of giant salamanders calculated through stable isotope
 mixing models * 'mix_fish': contribution of fish to the
 assimilated diet of giant salamanders calculated through stable isotope
 mixing models * 'mix_frog': contribution of frogs to the
 assimilated diet of giant salamanders calculated through stable isotope
 mixing models * 'mix_aq.ins': contribution of aquatic insects to
 the assimilated diet of giant salamanders calculated through stable
 isotope mixing models --- ## Folder 6: Steps for Performing dbRDA in
 PRIMER with PERMANOVA+ To run dbRDA in PRIMER: ### Required files: *
 Response Data: the file "diet_proportions.csv" in our case,
 containing a matrix containing the proportions of each prey type (e.g.,
 crabs, fish, frogs, aq.ins (aquatic insects)) consumed by each salamander.
 Each row represents a prey category, and each column represents an
 individual. * Explanatory Variables: the file
 "env.variables_primer.xlsx" in our case, containing a matrix of
 environmental variables (de: water depth; rw: river width; cu: water
 velocity; pr: relative position in the river; agri: proportion of
 agricultural areas in the surrounding environment). Each column
 corresponds to the same individual in the response dataset. ###
 Step-by-step: 1. **Import files into PRIMER** * Import the environmental
 variable matrix (env.variables_primer.csv): * Go to File &gt; Open, * In
 the Data type window, tick Sample data, then click Next. * In the Shape
 section, tick Rectangular and Samples as columns. * For Data type, select
 Environmental. * Set Blank = Missing value, then click Finish. * Import
 the prey proportion matrix (diet_proportions.csv): * Go to File &gt; Open,
 * In the Data type window, tick Sample data, then click Next. * In the
 Shape section, tick Rectangular and Samples as columns. * For Data type,
 select Unknown/Other. * Set Blank = Missing value, then click Next. * In
 the Text delimiters window, tick Comma, then click Finish. 2. **Square
 Root Transformation** * Since prey proportions are compositional data,
 they may contain highly abundant and rare prey types. To reduce the
 influence of dominant prey categories, apply a square root transformation:
 * Select your prey proportion matrix in PRIMER. * Go to Pre-treatment &gt;
 Transform(overall). * Choose the Square root transformation. * Click OK to
 apply the transformation. 3. **Distance Matrix** * Since dbRDA is based on
 distances, you must first calculate a resemblance matrix for your prey
 proportion data: * Select your prey proportion matrix (response data). *
 Go to Analyse &gt; Resemblance. * Choose an appropriate resemblance
 measure: Bray-Curtis similarity here * Click OK to generate the
 resemblance matrix. 4. **Run dbRDA** * Click on the previously created
 resemblance matrix (prey proportions). * Go to PERMANOVA+ &gt; DistLM
 (Distance-based Linear Model). * Choose the environmental variables file
 as the predictor dataset. 4.1. **Model selection** \- Under the
 "Selection Procedure" section, choose: Forward \- Set the model
 selection criterion to adjusted R². \- Set the number of permutations to
 9,999 \- Check the box 'Do dbRDA plot' to visualise the results
 4.2. **Running the model** \- Click OK to perform the dbRDA. \- The output
 will show which environmental variables explain the most variation in prey
 consumption patterns. ### References: * Clarke KR, Gorley RN. *PRIMER v7:
 User manual/tutorial*. PRIMER-E, 2015. * Anderson MJ, Gorley RN, Clarke
 KR. *PERMANOVA+ for PRIMER*. PRIMER-E, 2008.