Forage preference in two geographically co-occurring fungus gardening ants: A dietary DNA approach

Matthew Richards-Perhatch V; Elizabeth Boshers; Matthew Greenwold; Jon N. Seal

doi:10.1371/journal.pone.0336150

Abstract

Traditional methods of forage identification are impractical with non-leafcutting fungus gardening ants, making diet-related ecological and life history questions difficult to study. To address this limitation, we utilized dietary DNA metabarcoding on excavated ant fungus gardens to generate forage diversity metrics for the two co-occurring species Trachymyrmex septentrionalis and Mycetomoellerius turrifex. Ten fungus garden samples from each species were collected from a 60x70 m plot in East Texas. Each of the colonies we sampled was paired with a colony from the other species within 3 m of it. Plant forage diversity was assessed with chloroplast trnL primers, and insect frass forage diversity was assessed with mitochondria COI primers. DNA metabarcoding identified a total of 44 plant taxa across all samples, but performed poorly when characterizing foraged insect frass. Plant beta diversity was significantly different between the gardens of T. septentrionalis and M. turrifex colonies, as well as paired colonies. Colony pairs also had significantly different plant alpha diversity. This indicates that diet preference is likely driven both by ant species-specific plant preference, and colony location-specific plant resource availability. Overall, our results show that dietary DNA techniques are a promising tool for the identification of plant forage in ant fungus gardens, enabling the study of future diet-based ecological and natural history questions.

Citation: Richards-Perhatch V M, Boshers E, Greenwold M, Seal JN (2025) Forage preference in two geographically co-occurring fungus gardening ants: A dietary DNA approach. PLoS One 20(12): e0336150. https://doi.org/10.1371/journal.pone.0336150

Editor: Maykon Passos Cristiano, Universidade Federal de Ouro Preto, BRAZIL

Received: December 23, 2024; Accepted: October 21, 2025; Published: December 4, 2025

Copyright: © 2025 Richards-Perhatch V et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: The raw sequences for all samples have been deposited with links to BioProject accession number PRJNA1124954 in the NCBI BioProject database (https://www.ncbi.nlm.nih.gov/bioproject/PRJNA1124954). The Qiime2, RESCRIPt, and R code used during this project can be found at https://github.com/mrichardsperhatchv/FORAGE-PREFERENCE-IN-TWO-GEOGRAPHICALLY-CO-OCCURRING-FUNGUS-GARDENING-ANTS.

Funding: This study was supported by the National Science Foundation in the form of grants awarded to JNS (IOS-1552822, DEB-2230334). The specific roles of this author are articulated in the ‘author contributions’ section. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Fungus gardening ants

Fungus-gardening ants are a New World monophyletic group characterized by their cultivation, and obligate consumption, of fungi. These ants use material foraged by their colony as a substrate to grow their fungus garden, feeding off the fungus instead of directly consuming the forage themselves. The more basal fungus-gardening ants forage insect-feces, dry plant debris, leaf-litter, and seed fragments [1,2]. More derived fungus gardening ants practice “higher agriculture” and can be subdivided into two main groups: the leaf-cutting ants, and the non-leafcutting higher agriculture ants [3,4]. The most specialized of these two groups, the leaf-cutting ants, are known to forage extensively on freshly cut vegetative material; while the non-leafcutting higher agriculture ants often forage considerable amounts of freshly fallen flower petals, soft leaf tissue, and fruit, in addition to material similar to that collected by more basal fungus gardening species [2,5].

Traditional methods of diet identification

Visual identification of material carried by foraging workers is a commonly utilized technique in determining ant diet and forage preferences. However, reliance on this technique presents several issues. Visual forage identification is time consuming, relies heavily on taxonomic expertise, and requires foragers to be conspicuous enough for observation [6,7]. In addition, observing individual foragers only offers a small snapshot of total colony dietary breadth, as a single colony may send out hundreds or even thousands of workers to forage [8].

Among fungus gardening ants, visual identification of forage material is most practical with the leaf-cutting genera Atta and Acromyrmex, as their foraging strategies predispose them to easy observation. The characteristic foraging trails created by Atta and Acromyrmex colonies can often be cleanly followed from the vegetation they are cutting to their nest/tunnel entrances, allowing for species level identification of their forage material [9–11]. Unfortunately, visual identification of forage material falls short with fungus gardening ant species outside of Atta and Acromyrmex. Most non-leafcutting fungus gardening ants do not create true foraging trails, making it difficult and very time consuming to accurately trace the species of vegetation that they are foraging from [12]. The foragers of non-leafcutting fungus gardening ants are also usually smaller and more cryptic than those of leaf-cutting ants. Additionally, some foragers may collect items that are unsuitable, if not toxic, and deposit them in their nests but not the fungus gardens [41]. Thus observations on forager behavior alone are not likely to depict a comprehensive overview of what ants are collecting and incorporating into their fungus garden. These factors have contributed to a deficit of literature regarding the diet composition of non-leafcutting fungus gardening ants, with the little information available primarily consisting of a handful of forage material observations, often originally published in conjunction with the original natural history descriptions of the ants [1,2]. Clearly, relying solely on visual identification of forage material is insufficient for the study of non-leafcutting fungus gardening ants.

Dietary DNA

Dietary DNA (dDNA) techniques present a possible alternative to visual forage identification. Metabarcoding techniques based on dDNA, DNA extracted from an organism’s gut, stomach, or fecal matter, have emerged as an accurate and resource-effective way to determine animal diets [7,13]. Previous studies, in multiple animal groups, have successfully utilized dDNA analysis to determine diet composition when direct observations of feeding behavior and/or morphological identification of forage material was insufficient [7,13].

Dietary DNA analysis has only been used to examine ant diet composition in a handful of studies, with even fewer of these studies having attempted to determine broad diet composition [8,14,15]. One factor complicating dDNA ant research is a yet unidentified compound in the gaster of adult ants that has been found to inhibit PCR, potentially impacting gut content dDNA analysis in adult ants [15,16]. As an additional complication, in many species adult ants are unable to digest solid food, instead feeding it to their brood. The adult ants then consume the digested liquid nutrients via trophallaxis from their larvae [8,17]. Wulff et al. (2021) leveraged this adult-larvae digestion dynamic to their advantage when using dDNA analysis to investigate the diet composition of Solenopsis invicta [8]. They did so by sequencing the gut content of S. invicta larvae instead of adults, as the larvae act as a centralized depository of colony prey and are not known to produce PCR inhibitors [8,15,16].

A similarly innovative approach could be used to analyze the diet composition of fungus gardening ants, since their fungus gardens also act as a centralized depository for colony forage. The fungus gardens cultivated by fungus gardening ants function as an external ancillary digestive system for the colony [2,17–19]. Within this ancillary digestive system, forage materials used as fungal substrate become highly degraded, due both to physical mastication by the ants and enzymatic breakdown by the fungus. This degradation necessitates the use of specialized genetic techniques, like dDNA metabarcoding, that are designed to work with highly degraded staples [2,5,20].

Evaluation of diet and study species

Using dDNA metabarcoding techniques to identify fungal substrate in ant gardens could be used to answer a number of ecological and natural history questions. Dietary DNA metabarcoding has been successfully used to detect niche partitioning, and overlap, in several animal species [21–25]. These techniques have also been used to understand seasonal and/or biogeographic based shifts in animal diets [26–32].

Dietary differences have been investigated (without the use of dDNA methods) in several species of fungus gardening leafcutting ants. In a dry tropical forest in Costa Rica, the co-occurring leafcutter species Atta cephalotes and Acromyrmex octospinosus were found to forage almost entirely different resources, with the only exception being they both foraged flowers [33]. A similar trend was observed in Panama, with Atta species foraging primarily on tree leaves and co-occurring Acromyrmex species foraging primarily on flowers and herbal leaves [34]. To our knowledge, no previous studies have examined differences in diet between non-leafcutting fungus gardening ants.

Trachymyrmex septentrionalis and Mycetomoellerius turrifex are two higher agriculture non-leafcutting fungus gardening ant species, with a partial overlap of their geographic distribution occurring in East Texas [35,36]. The range of T. septentrionalis stretches from Texas to Florida, and as far north as central Illinois and New York; while the range of M. turrifex extends from Texas to northeastern Mexico and western Louisiana [36–38]. Like most non-leafcutting fungus gardeners, specific information on the preferred forage material of T. septentrionalis and M. turrifex is highly limited [2]. What documentation does exist indicates that they both forage on insect frass and plant material, but only T. septentrionalis has been observed cutting/foraging fresh vegetation [2,39–42]. Their geographic range overlap and the similarity of their documented forage material makes these two species excellent subjects to test the efficacy of using dDNA analysis to study diet in non-leafcutting fungus gardening ants.

The primary objective of this study was to explore whether dDNA metabarcoding could be used to identify and describe the diversity of plant material and/or insect frass used as substrate in the fungus gardens of co-occuring T. septentrionalis and M. turrifex colonies. The secondary objective of this study was to evaluate whether diversity metrics based on identified fungal substrate were sufficient to detect differences in diet between T. septentrionalis and M. turrifex colonies.

Materials and methods

Sample collection

The fungus gardens of ten T. septentrionalis and ten M. turrifex colonies were sampled from an approximately 60x70 m plot on the University of Texas at Tyler campus in Tyler, Texas, USA. Sampling occurred from May 16–26, 2023. Each of the ten M. turrifex colonies we sampled was paired with a T. septentrionalis colony within 3 m of it. These colony pairs, composed of one M. turrifex colony and one nearby T. septentrionalis colony, were sampled concurrently. Each colony was only sampled once.

Fungus gardens were non-destructively sampled from each colony by careful excavation of the topmost chamber containing an appreciable amount of fungus. Sampling was focused on chambers closer to the surface since deeper chambers were unlikely to contain fungus until later in the season when the upper chambers are too hot for fungus garden growth. Ethanol and flame sterilized tools were used when removing fungus gardens from the chamber to minimize DNA contamination from environmental sources and previously sampled colonies. Excavated fungus garden samples were placed in sterile vials containing 95% ethanol and stored in a −80°C freezer.

Sample metabarcoding

Metabarcoding was conducted by Jonah Ventures (https://jonahventures.com/) using chloroplast trnL (forward primer c: 5’ CGAAATCGGTAGACGCTACG 3’, reverse primer h: 5’ CCATTGAGTCTCTGCACCTATC 3’) and mitochondrial cytochrome c oxidase subunit I (COI) (forward primer ZBJ-ArtF1c: 5’ AGATATTGGAACWTTATATTTTATTTTTGG 3’, reverse primer ZBJ-ArtR2c: 5’ WACTAATCAATTWCCAAATCCTCC 3’) primers [43,44]. Individual fungus garden samples were shipped overnight in an ice box to Jonah Ventures in Boulder, CO.

At Jonah Ventures, frozen samples were thawed for 1−2 hours before processing. Sample barcodes were recorded and assigned a well within the 96 well plate or numbered extraction tube. Under a laminar flow hood, sterile cotton swabs (Fisher, Catalog No. 22-363-173) were coated with sediment matter, and the swabs were placed in the corresponding extraction plate or tube. Sterile tweezers and pliers were used to handle cotton swabs and remove the wooden ends of the cotton swab before extraction. Plates or tubes were immediately processed or stored in −20°C until the extraction process could be performed.

Genomic DNA from each fungus garden sample was extracted using the DNeasy 96 PowerSoil Pro Kit (384) (Catalog No. 47017) according to the manufacturer’s protocol. Genomic DNA was eluted into 100 µl and frozen at −20°C.

For trnL genetic analysis, a portion of the chloroplast trnL intron was PCR amplified from each genomic DNA sample using the c and h trnL primers. Both forward and reverse primers also contained a 5’ adaptor sequence to allow for subsequent indexing and Illumina sequencing. Each 25 µL PCR reaction was mixed according to the Promega PCR Master Mix specifications (Promega, Catalog No. M5133, Madison, WI) which included 0.4 µM of each primer and 1 µl of gDNA. DNA was PCR amplified using the following conditions: initial denaturation at 94°C for 3 minutes, followed by 40 cycles of 30 seconds at 94°C, 30 seconds at 55°C, and 1 minute at 72°C, and a final elongation at 72°C for 10 minutes.

For COI genetic analysis, a portion of the COI gene was PCR amplified from each genomic DNA sample using the ZBJ-ArtF1c and ZBJ-ArtR2c primers. Both forward and reverse primers also contained a 5’ adaptor sequence to allow for subsequent indexing and Illumina sequencing. Each 25 µL PCR reaction was mixed according to the Promega PCR Master Mix specifications (Promega, Catalog No. M5133, Madison, WI) which included 12.5 µl Master Mix, 1.0 µM of each primer, 3.0 µl of gDNA, and 7.5 µl DNase/RNase-free H2O. DNA was PCR amplified using the following conditions: initial denaturation at 94°C for 5 minutes, followed by 45 cycles of 30 s at 94°C, 45 s at 45°C, and 45 s at 72°C, and a final elongation at 72°C for 10 minutes.

To determine amplicon size and PCR efficiency, each reaction was visually inspected using a 2% agarose gel with 5 µl of each sample as input. Amplicons were then cleaned by incubating amplicons with Exo1/SAP for 30 minutes at 37°C followed by inactivation at 95°C for 5 minutes and stored at −20°C.

A second round of PCR was performed to complete the sequencing library construct, appending with the final Illumina sequencing adapters and integrating a custom sample-specific 12-nucleotide index sequence (one index read per amplicon) generated by Jonah Ventures. The indexing PCR included Promega Master mix, 0.5 µM of each primer and 2 µl of template DNA (cleaned amplicon from the first PCR reaction) and consisted of an initial denaturation of 95°C for 3 minutes followed by 8 cycles of 95°C for 30 seconds, 55°C for 30 seconds and 72°C for 30 seconds.

Final indexed amplicons from each sample were cleaned and normalized using SequalPrep Normalization Plates (Life Technologies, Carlsbad, CA). 25 µl of PCR amplicon was purified and normalized using the Life Technologies SequalPrep Normalization kit (Catalog No. A10510-01) according to the manufacturer’s protocol. Samples were then pooled together by adding 5µl of each normalized sample to the pool.

Sample library pools were sent for sequencing on an Illumina MiSeq (San Diego, CA) at the Texas A&M Agrilife Genomics and Bioinformatics Sequencing Core facility using the v2 500-cycle kit (Catalog No. MS-102–2003). A PhiX concentration of 15% was used. As a quality control measure, a bioanalyzer chip was used for concentration adjustment at the sequencing center prior to sequencing.

Raw sequence data were demultiplexed using pheniqs v2.1.0, enforcing strict matching of sample barcode indices (i.e., no errors) [45].

Sequence processing

After receiving the demultiplexed data in the form of paired end FASTQ files from Jonah Ventures, the files were imported into Qiime2 for further processing (Fig 1) [46].

Download:

Fig 1. Flowchart of each major bioinformatic step in the development of our analysis metrics.

https://doi.org/10.1371/journal.pone.0336150.g001

The Qiime2 plugin “cutadapt” was used to remove primers from the sequences. The Qiime2 plugin “dada2” was used to denoise the trnL and COI sequences, truncate the COI sequences, and merge the forward and reverse COI sequence pairs [47]. Only the forward trnL sequences were used because of the short trnL amplicon length (143 bp). The trnL sequences were not truncated because their untruncated median quality scores were 30 or greater. The COI sequences were truncated where their median quality scores dropped below 30 (forward: 100 bp, reverse: 87 bp). Rarefaction of the sequences was conducted using Qiime2’s ‘feature-table rarefy’ command. A sampling depth of 9677 was used for trnL sequences (Fig 2). Due to low COI sequencing depth (<1000) in the majority of samples, downstream analysis was not conducted for COI outside of taxonomic classification (Fig 3).

Download:

Fig 2. Rarefaction curves for trnL sequences.

The dashed vertical line shows the chosen trnL rarefaction depth of 9677.

https://doi.org/10.1371/journal.pone.0336150.g002

Download:

Fig 3. Rarefaction curves for COI sequences.

The lower left corner of the plot has been magnified to show greater detail.

https://doi.org/10.1371/journal.pone.0336150.g003

Qiime2’s “RESCRIPt” plugin was used to create two reference databases from NCBI; one specific to trnL and a second specific to arthropod COI [48,49]. Initial reference pools were constructed from these downloaded databases via primer-pair search using Qiime2’s ‘feature-classifier extract-reads’ command. These initial pools were both expanded twice using RESCRIPt’s ‘extract-seq-segments’ command. During reference pool expansions of the trnL specific database a ‘--p-perc-identity’ value of 0.7 was used. During reference pool expansions of the COI specific database a ‘--p-perc-identity’ value of 0.9 was used. These ‘--p-perc-identity’ values were chosen because at the end of their respective reference pool expansions they had extracted sequence segments from ~70% of their original query sequences [49]. Dereplication during each of these steps was performed using the ‘--p-mode super’ modifier in RESCRIPt’s ‘dereplicate’ command. These expanded reference pools were then used to construct two classifiers, one for trnL and one for COI, using RESCRIPt’s ‘evaluate-fit-classifier’ command (S1 Fig).

Taxonomic assignment for trnL and COI sequences was conducted using the custom RESCRIPt classifiers described above, in combination with Qiime2’s ‘feature-classifier classify-sklearn’ command.

Statistical analysis

Once taxonomic assignment was completed, the Qiime2 feature and metadata files were imported into R and used to create two phyloseq objects using the R packages “qiime2R” and “phyloseq” [50–52]. An independent phylogenetic tree was constructed by grafting our classified trnL sequences onto the GBOTB.extended.WP.tre megatree, reported by Jin and Qian (2022), using the ‘phylo.maker’ command from the R package “U.PhyloMaker” [53,54]. Six classified trnL sequences failed to graft to the tree due to low taxonomic resolution and were discarded. The resulting phylogenetic tree was converted to a phylo class object using the ‘phyloseq::read_tree’ command; multichotomies were then resolved using the ‘multi2di’ command from the R package “ape” [55]. This tree was merged with our phyloseq object using the ‘phyloseq::merge_phyloseq’ command. This updated phyloseq object was used to calculate alpha diversity for each colony, in the form of Faith’s phylogenetic diversity (Faith’s PD) scores and species richness, using the ‘calculatePD’ command in the R package “biomeUtils” [56]. Beta diversity between each colony was also calculated from the updated phlyoseq object, in the form of unweighted unique fraction metric (uUniFrac) dissimilarity distances, using the ‘phyloseq::distance’ command.

These specific diversity metrics were chosen for two primary reasons: 1) their calculations are based on relative frequency of occurrence (binary presence/absence) of the sequenced taxa, and 2) their calculations take into account the relative relatedness of the sequenced taxa.

The number of sequencing reads from dDNA often does not accurately represent the biomass of consumed material [6,13]. This is particularly relevant in studies like ours, where sequenced material consists of a broad diversity of partially digested multi-cellular samples. It is likely that digestive processes, the order items are consumed, primer biases, and taxonomic biases in the reference database influence both the sequencing outcomes and taxonomic classification [6,57,58]. Due to the known impact of these factors on estimations of abundance, only metrics based on binary presence/absence of the sequenced taxa (i.e. uUniFrac and Faith’s PD) were used.

Another issue arises from the various states of DNA degradation present in our samples; as sequences originating from the same species, or even the same individual, may end up classified at different taxonomic levels. For example, if one group of sequences are classified as Fagaceae and another group of sequences from the same sample are classified as Fagaceae Quercus, it is not currently possible to determine if this indicates the presence of two separate genera of Fagaceae, or if both sets of sequences come from the same genera. This can result in the artificial inflation of identified taxa, as sequences from a single taxon could be functionally double counted. Some previous dDNA studies have attempted to account for this issue by compressing their post-classified dDNA taxa into diet events: instances where there is unequivocal evidence from dDNA analysis that an organism consumed at least one individual of a species [8,59]. While this approach does minimize double counting of taxa, it increases the risk of undercounting dietary events and can obscure dietary events of taxa unable to be identified to the species level [59]. To avoid the drawbacks of using a diet event based approach, we instead utilized metrics that included the phylogenetic relationship of classified taxa in their calculations (i.e. uUniFrac and Faith’s PD). Using these phylogeny-weighted metrics allows sequences classified at different taxonomic levels to be assessed as separate taxa, while minimizing the impact of possible double counts.

Our analysis incorporated two main predictor variables: colony ant species (T. septentrionalis and M. turrifex) and colony pair ID (Pair-2, Pair-8, Pair-12, Pair-14, Pair-17, Pair-20, Pair-28, Pair-30, Pair-44, and Pair-50). We tested for differences in fungal substrate plant taxa alpha diversity (Faith’s PD and species richness) and beta diversity (uUniFrac dissimilarity distance).

Faith’s PD and species richness distributions were tested for normality via the Shapiro-Wilk normality tests by using the ‘shapiro.test’ command in R. To analyze Faith’s PD and species richness against each of our predictor variables, several analysis of variance (ANOVA) analyses were conducted using the ‘aov’ command in R.

Permutational analysis of variance (PerMANOVA) analyses were conducted using the ‘adonis2’ commands in the R package “vegan” [60]. Our PerMANOVA analyses used 9,999 permutations.

Data visualization was performed with the R packages “tidyverse”, “ggtree”, and “ggrepel” [61–63].

Results

Plant taxa identified from fungus garden samples

Across all sampled fungus gardens, a total of 44 plant classifications were identified from trnL sequences (Fig 4). Fungal substrate plant taxa composition was derived from 32 plant classifications for T. septentrionalis and 33 plant classifications for M. turrifex. There was considerable overlap in plant taxa composition between the two species, with 21 plant classifications present in both T. septentrionalis and M. turrifex colonies. Eleven plant classifications were identified only from T. septentrionalis colonies and 12 plant classifications were identified only from M. turrifex colonies.

Download:

Fig 4. Phylogenetic tree and binary heat map of plant taxa identified from fungus garden samples.

Each branch end is linked to a row in the binary heat map, showing presence/absence of each respective plant classification identified in the M. turrifex (Mt) and T. septentrionalis (Ts) fungus garden samples. Cells with a black dot indicate the plant classification was present in both colonies from that pair.

https://doi.org/10.1371/journal.pone.0336150.g004

In T. septentrionalis colonies, the most frequently occurring plant classifications (occurred in ≥50% of T. septentrionalis samples) were: Fagaceae, Magnoliopsida, Rubiaceae, Asteraceae, and Fagales. In M. turrifex colonies, the most frequently occurring plant classifications (occurred in ≥50% of M. turrifex samples) were: Fagaceae, Oxalis (Oxalidaceae), Magnoliopsida, Rubiaceae, Fagales, Acalypha (Euphorbiaceae), and Poaceae.

Plant taxa alpha diversity

ANOVA analyses were used to determine if Faith’s PD or species richness of fungal substrate plant taxa differed significantly between colonies based on colony ant species or colony pair ID. Trachymyrmex septentrionalis and M. turrifex fungus gardens did not have significantly different Faith’s PD (F_1,18 = 0.045, p = 0.834), nor did they have significantly different species richness (F_1,18 = 0.223, p = 0.643). There were, however, significant differences in Faith’s PD (F_9,10 = 3.921, p = 0.022) and species richness (F_9,10 = 3.105, p = 0.046) between colony pairs.

Plant taxa beta diversity

A PerMANOVA analysis was used to determine if the uUniFrac dissimilarity distances of fungal substrate plant taxa were significantly different between colonies based on colony ant species or colony pair ID. There was a significant difference found between colony ant species and the uUniFrac dissimilarity distances of their plant taxa (p = 0.046) (Fig 5). There was also a significant difference between colony pairs and the uUniFrac dissimilarity distances of their plant taxa (p = 0.014).

Download:

Fig 5. NMDS plot showing the uUniFrac dissimilarity of plant taxa identified from fungus garden samples.

Colony pairs are connected by grey lines.

https://doi.org/10.1371/journal.pone.0336150.g005

Arthropod taxa identified from fungus garden samples

A total of six arthropod classifications were identified from COI sequences. All colony fungus garden samples contained Insecta sequences. Only four samples contained sequences identified to a lower taxonomic level than Insecta: M. turrifex colony 44’s fungus garden contained Carabidae (Coleoptera) and Lepidoptera sequences, M. turrifex colony 14’s fungus garden contained Anomoea laticlavia (Coleoptera: Chrysomelidae) sequences, M. turrifex colony 20’s fungus garden contained Coleoptera sequences, and T. septentrionalis colony 112’s fungus garden contained Hemiptera sequences.

Discussion

Dietary DNA techniques successfully identified plant and arthropod taxa present in the fungus gardens of T. septentrionalis and M. turrifex. The dDNA analysis was particularly successful with the trnL primer, as it identified a relatively large number of plant taxa, many of them to a relatively high level of taxonomic resolution. These results could be particularly impactful considering the literature deficit of specific plant taxa known to be foraged by non-leafcutting fungus gardening ants. Only a handful of previous studies make any mention of specific foraged plant taxa, with most only describing the general type of material the ants were collecting such as flower petals or dead leaves [1,2,40–42,64–68].

Colony ant species appeared to be a significant factor in beta diversity metrics, but not alpha diversity metrics, of fungal substrate plant taxa. Similarly, colony pair appeared to be a significant factor in both plant taxa beta diversity metrics and alpha diversity metrics. These results suggest that diet preference is likely driven both by ant species-specific plant preference and colony location-specific plant resource availability. The presence of dietary differences between T. septentrionalis and M. turrifex colonies on our site appears similar to short-term observations of dietary differences made on co-occurring Atta and Acromyrmex colonies [33,34].

The plant taxa we identified from T. septentrionalis and M. turrifex fungus garden sequences were consistent with the limited existing descriptions of forage material collected by these ant species. One previous study observed M. turrifex collecting Sorghum halepense and Solidago sp., both of whose families, Poaceae and Asteraceae respectively, were present in our samples [40]. Another previous study suggested that T. septentrionalis collects Quercus (Fagaceae), which was also present in our samples [41].

If we include descriptions of known forage material from other higher agriculture non-leafcutting ants, we find additional overlap between our identified plant taxa and that of previous studies. Lauraceae, Rosaceae, Rubiaceae, Poaceae, and Euphorbiaceae are all plant families which were identified in our samples, and are known to be collected by or, in the case of lab colonies, accepted by higher agriculture non-leafcutting ants [64,66]. Leaf-cutting species like Atta texana are known to forage on Pinus (Pinaceae) needles, but our study is the first published record of a non-leafcutting species collecting Pinus [69]. The ants may be collecting pieces of male pine cones, as this has been observed in the field and laboratory during early spring (March-April) (JN Seal, unpublished observations).

A notable drawback of only using dDNA analysis on sampled fungus gardens is that dDNA analysis alone cannot determine the state plant material was in when it was collected by the ants. Some higher agriculture fungus gardeners outside of Atta and Acromyrmex, like T. septentrionalis, are known to cut fresh vegetative material to use as fungus garden substrate, while others do not [2,41]. In our study, we could not determine whether the detected plant sequences originated from the ants foraging fresh cut plant material, plant detritus, or frass. We also could not determine which part of the plant they collected (i.e., leaves, flowers, or sticks). The inability to detect which state plant material was collected in could obscure differences in foraging behavior between these two ant species. For example, one ant species may forage on Quercus (Fagaceae) catkins, while a second ant species may forage caterpillar frass that contains Quercus leaves. Similarly, one ant species may cut soft Quercus vegetation, whereas a second ant species might only take dead vegetation that has fallen from the tree. In all of these examples the ant’s fungus gardens would contain Quercus sequences, but dDNA analysis would be unable to differentiate the collected state of the material. Additionally, since the number of sequencing reads cannot be reliably used as a quantitative measure of the biomass of digested material, we cannot use dDNA to determine if colonies are foraging different amounts of the same material [6].

While our study establishes dDNA analysis as a potential tool for identification of fungus garden substrate, we also identified several limitations that should be accounted for in future studies attempting to use similar techniques. One issue was the low taxonomic resolution of some plant sequences. While we were able to successfully identify 44 plant classifications in the fungus gardens using our selected trnL primer, unidentified Magnoliopsida had the sixth highest absolute read abundance post-rarefaction, indicating that a number of plant species may be present in the fungus gardens that our trnL primer was unable to identify to an informative taxonomic level. In future studies this could be mitigated, at least in part, by using multiple plant primers designed for different target regions [23,70,71]. Another issue was the misidentification of plant sequences at the species level. While some identified plant species, like Desmodium ciliare (Fabaceae), are known to occur on our study site; other species, such as Salix hukaoana (Salicaceae), are not (though other species of Salix are in the general vicinity). These errors were likely the result of our classifier misidentifying sequences as related taxa due to an incomplete reference database. Ideally, future studies could construct a region-specific taxonomic library of plant sequences to be used when training their classifier [72]. Barcoding insect species present in a study site would be more difficult, as they are more mobile and can inhabit above both and below ground areas during different life stages.

Dietary DNA analysis alone is not capable of distinguishing trnL sequences from plant material foraged directly by the ants and trnL sequences from insect frass foraged by the ants. Since T. septentrionalis and M. turrifex both readily forage insect frass containing plant material, plant DNA sequences from foraged frass were potentially detected by the trnL primer we used [13,40,42]. We had hoped to use the COI primer results to gain insight into what arthropod taxa the ants were collecting frass from, but the primer performed too poorly to draw any meaningful conclusions. Other studies attempting to determine Lepidoptera species from their frass found that amounts of dry frass less than 5 mg may be too small to detect insect DNA sequences [73]. This insect sequence detectability cutoff was likely higher in our samples due to the physical and enzymatic breakdown of foraged material occurring in the fungus gardens, making it difficult to detect frass-producing-insect DNA [2]. Another potential confounding issue could be a decrease in PCR efficiency due to our use of a 1-step PCR approach versus a 2-step PCR approach for sequencing (See Methods Bohmann et al. (2022) for a discussion on the topic) [74].

Many of the issues we discussed can be mitigated by taking the limitations of dDNA analysis into account when designing future studies. Our recommendations for future studies include: 1) using multiple plant primers to maximize taxonomic coverage, 2) constructing a region-specific taxonomic library of plant sequences, 3) sampling at several different time points to allow detection of temporal changes in fungal substrate plant composition, and 4) incorporating a supplemental observational component to gain an understanding of what state forage material is being collected in. Overall, we have demonstrated the efficacy of using dDNA analysis as a tool for the taxonomic identification of forage material in fungus-gardening ants, and as a possible tool to detect dietary differences between species. Dietary DNA analysis offers an opportunity to improve our understanding of plant taxa foraged by non-leafcutting fungus gardening ants, and, with appropriate supporting methods, can be applied to gain new insight into their ecology and life history.

Supporting information

S1 Fig. The F-measure of our classifiers being used on their own training dataset.

https://doi.org/10.1371/journal.pone.0336150.s001

(TIF)

Acknowledgments

We would like to thank Christine Bays, Dillon Flowers, Gabriel McDanield, and Grant Rook, all of whom took time out of their lives to help with colony excavation and sampling.

References

1. Leal IR, Oliveira PS. Foraging ecology of attine ants in a Neotropical savanna: seasonal use of fungal substrate in the cerrado vegetation of Brazil. Insectes soc. 2000;47(4):376–82.
- View Article
- Google Scholar
2. De Fine Licht HH, Boomsma JJ. Forage collection, substrate preparation, and diet composition in fungus‐growing ants. Ecological Entomology. 2010;35(3):259–69.
- View Article
- Google Scholar
3. Solomon SE, Rabeling C, Sosa‐Calvo J, Lopes CT, Rodrigues A, Vasconcelos HL, et al. The molecular phylogenetics of Trachymyrmex Forel ants and their fungal cultivars provide insights into the origin and coevolutionary history of ‘higher‐attine’ ant agriculture. Systematic Entomology. 2019;44(4):939–56.
- View Article
- Google Scholar
4. Schultz TR, Brady SG. Major evolutionary transitions in ant agriculture. Proc Natl Acad Sci U S A. 2008;105(14):5435–40. pmid:18362345
- View Article
- PubMed/NCBI
- Google Scholar
5. De Fine Licht HH, Schiøtt M, Mueller UG, Boomsma JJ. Evolutionary transitions in enzyme activity of ant fungus gardens. Evolution. 2010;64(7):2055–69. pmid:20067517
- View Article
- PubMed/NCBI
- Google Scholar
6. Nielsen JM, Clare EL, Hayden B, Brett MT, Kratina P. Diet tracing in ecology: Method comparison and selection. Methods Ecol Evol. 2017;9(2):278–91.
- View Article
- Google Scholar
7. de Sousa LL, Silva SM, Xavier R. DNA metabarcoding in diet studies: Unveiling ecological aspects in aquatic and terrestrial ecosystems. Environmental DNA. 2019;1(3):199–214.
- View Article
- Google Scholar
8. Wulff JA, Kjeldgaard MK, Eubanks MD, Sword GA. From the bellies of babes: a larval-based approach to ant molecular gut content analysis. Insect Soc. 2021;68(2–3):245–53.
- View Article
- Google Scholar
9. Mintzer A. Foraging activity of the Mexican leafcutting antAtta mexicana (F. Smith), in a sonoran desert habitat (Hymenoptera, Formicidae). Ins Soc. 1979;26(4):364–72.
- View Article
- Google Scholar
10. Berish CW. Leaf-Cutting Ants (Atta cephalotes) Select Nitrogen-Rich Forage. American Midland Naturalist. 1986;115(2):268.
- View Article
- Google Scholar
11. Mintzer A. Diet of the leafcutting ant, Atta mexicana (Hymenoptera: Formicidae), in a Sonoran desert habitat. Journal of the Arizona-Nevada Academy of Science. 1995.
- View Article
- Google Scholar
12. Weber NA. Gardening ants the attines. American Philosophical Society. 1972;146.
- View Article
- Google Scholar
13. Ando H, Mukai H, Komura T, Dewi T, Ando M, Isagi Y. Methodological trends and perspectives of animal dietary studies by noninvasive fecal DNA metabarcoding. Environmental DNA. 2020;2(4):391–406.
- View Article
- Google Scholar
14. Muilenburg VL, Goggin FL, Hebert SL, Jia L, Stephen FM. Ant predation on red oak borer confirmed by field observation and molecular gut‐content analysis. Agri and Forest Entomology. 2008;10(3):205–13.
- View Article
- Google Scholar
15. Penn HJ, Chapman EG, Harwood JD. Overcoming PCR Inhibition During DNA-Based Gut Content Analysis of Ants. Environ Entomol. 2016;45(5):1255–61. pmid:27452765
- View Article
- PubMed/NCBI
- Google Scholar
16. Gadau J. DNA isolation from ants. Cold Spring Harb Protoc. 2009;2009(7):pdb.prot5245. pmid:20147210
- View Article
- PubMed/NCBI
- Google Scholar
17. Holldobler B, Wilson EO. The Ants. 1st ed. Cambridge, Mass: Belknap Press: An Imprint of Harvard University Press. 1990.
18. De Fine Licht HH, Boomsma JJ, Tunlid A. Symbiotic adaptations in the fungal cultivar of leaf-cutting ants. Nat Commun. 2014;5:5675. pmid:25435021
- View Article
- PubMed/NCBI
- Google Scholar
19. Seal JN, Schiøtt M, Mueller UG. Ant-fungus species combinations engineer physiological activity of fungus gardens. J Exp Biol. 2014;217(Pt 14):2540–7. pmid:24803469
- View Article
- PubMed/NCBI
- Google Scholar
20. Moller IE, De Fine Licht HH, Harholt J, Willats WGT, Boomsma JJ. The dynamics of plant cell-wall polysaccharide decomposition in leaf-cutting ant fungus gardens. PLoS One. 2011;6(3):e17506. pmid:21423735
- View Article
- PubMed/NCBI
- Google Scholar
21. Burgar JM, Murray DC, Craig MD, Haile J, Houston J, Stokes V, et al. Who’s for dinner? High-throughput sequencing reveals bat dietary differentiation in a biodiversity hotspot where prey taxonomy is largely undescribed. Mol Ecol. 2014;23(15):3605–17. pmid:24118181
- View Article
- PubMed/NCBI
- Google Scholar
22. Kartzinel TR, Chen PA, Coverdale TC, Erickson DL, Kress WJ, Kuzmina ML, et al. DNA metabarcoding illuminates dietary niche partitioning by African large herbivores. Proc Natl Acad Sci U S A. 2015;112(26):8019–24. pmid:26034267
- View Article
- PubMed/NCBI
- Google Scholar
23. Soininen EM, Gauthier G, Bilodeau F, Berteaux D, Gielly L, Taberlet P, et al. Highly overlapping winter diet in two sympatric lemming species revealed by DNA metabarcoding. PLoS One. 2015;10(1):e0115335. pmid:25635852
- View Article
- PubMed/NCBI
- Google Scholar
24. Sato JJ, Shimada T, Kyogoku D, Komura T, Uemura S, Saitoh T, et al. Dietary niche partitioning between sympatric wood mouse species (Muridae: Apodemus) revealed by DNA meta-barcoding analysis. Journal of Mammalogy. 2018;99(4):952–64.
- View Article
- Google Scholar
25. Smith JA, Thomas AC, Levi T, Wang Y, Wilmers CC. Human activity reduces niche partitioning among three widespread mesocarnivores. Oikos. 2018;127(6):890–901.
- View Article
- Google Scholar
26. Aizpurua O, Budinski I, Georgiakakis P, Gopalakrishnan S, Ibañez C, Mata V, et al. Agriculture shapes the trophic niche of a bat preying on multiple pest arthropods across Europe: Evidence from DNA metabarcoding. Mol Ecol. 2018;27(3):815–25. pmid:29290102
- View Article
- PubMed/NCBI
- Google Scholar
27. Komura T, Ando H, Horikoshi K, Suzuki H, Isagi Y. DNA barcoding reveals seasonal shifts in diet and consumption of deep-sea fishes in wedge-tailed shearwaters. PLoS One. 2018;13(4):e0195385. pmid:29630670
- View Article
- PubMed/NCBI
- Google Scholar
28. Forsman AM, Hoenig BD, Gaspar SA, Fischer JD, Siegrist J, Fraser K. Evaluating the impacts of metabarcoding primer selection on DNA characterization of diet in an aerial insectivore, the Purple Martin. Ornithology. 2021;139(1).
- View Article
- Google Scholar
29. Palumbo F, Squartini A, Barcaccia G, Macolino S, Pornaro C, Pindo M, et al. A multi-kingdom metabarcoding study on cattle grazing Alpine pastures discloses intra-seasonal shifts in plant selection and faecal microbiota. Sci Rep. 2021;11(1):889. pmid:33441587
- View Article
- PubMed/NCBI
- Google Scholar
30. Roffler GH, Allen JM, Massey A, Levi T. Metabarcoding of fecal DNA shows dietary diversification in wolves substitutes for ungulates in an island archipelago. Ecosphere. 2021;12(1).
- View Article
- Google Scholar
31. Brun L, Schneider J, Carrió EM, Dongre P, Taberlet P, van de Waal, et al. Focal vs. fecal: Seasonal variation in the diet of wild vervet monkeys from observational and DNA metabarcoding data. Ecol Evol. 2022;12(10):e9358. pmid:36203642
- View Article
- PubMed/NCBI
- Google Scholar
32. Wang B, Li Y, Zhang G, Yang J, Deng C, Hu H, et al. Seasonal variations in the plant diet of the Chinese Monal revealed by fecal DNA metabarcoding analysis. Avian Research. 2022;13:100034.
- View Article
- Google Scholar
33. Wetterer JK. Foraging Ecology of the Leaf‐Cutting Ant Acromyrmex Octospinosus in a Costa Rican Rain Forest. Psyche: A Journal of Entomology. 1991;98(4):361–71.
- View Article
- Google Scholar
34. Kooij PW, Liberti J, Giampoudakis K, Schiøtt M, Boomsma JJ. Differences in forage-acquisition and fungal enzyme activity contribute to niche segregation in Panamanian leaf-cutting ants. PLoS One. 2014;9(4):e94284. pmid:24718261
- View Article
- PubMed/NCBI
- Google Scholar
35. Seal JN, Brown L, Ontiveros C, Thiebaud J, Mueller UG. Gone to Texas: phylogeography of twoTrachymyrmex(Hymenoptera: Formicidae) species along the southeastern coastal plain of North America. Biol J Linn Soc Lond. 2015;114(3):689–98.
- View Article
- Google Scholar
36. Senula SF, Scavetta JT, Banta JA, Mueller UG, Seal JN, Kellner K. Potential Distribution of Six North American Higher-Attine Fungus-Farming Ant (Hymenoptera: Formicidae) Species. J Insect Sci. 2019;19(6):24. pmid:31854452
- View Article
- PubMed/NCBI
- Google Scholar
37. Rabeling C, Cover SP, Johnson RA, Mueller GU. A review of the North American species of the fungus-gardening ant genus Trachymyrmex (Hymenoptera: Formicidae). Zenodo. 2007.
- View Article
- Google Scholar
38. Sanchez-Peña SR. Some Fungus-Growing Ants (Hymenoptera: Formicidae) from Northeastern Mexico. Florida Entomologist. 2010;93(4):501–4.
- View Article
- Google Scholar
39. Wheeler WM. The fungus-growing ants of North America. 1907.
40. Waller DA. Foraging Behavior of Trachymyrmex turrifex Wheeler (Formicidae: Attini). The Southwestern Naturalist. 1989;34(2):271.
- View Article
- Google Scholar
41. Seal JN, Tschinkel WR. Complexity in an obligate mutualism: do fungus-gardening ants know what makes their garden grow? Behav Ecol Sociobiol. 2007;61(8):1151–60.
- View Article
- Google Scholar
42. Seal JN, Tschinkel WR. Food limitation in the fungus‐gardening ant, Trachymyrmex septentrionalis. Ecological Entomology. 2008;33(5):597–607.
- View Article
- Google Scholar
43. Taberlet P, Coissac E, Pompanon F, Gielly L, Miquel C, Valentini A, et al. Power and limitations of the chloroplast trnL (UAA) intron for plant DNA barcoding. Nucleic Acids Res. 2007;35(3):e14. pmid:17169982
- View Article
- PubMed/NCBI
- Google Scholar
44. Razgour O, Clare EL, Zeale MRK, Hanmer J, Schnell IB, Rasmussen M, et al. High-throughput sequencing offers insight into mechanisms of resource partitioning in cryptic bat species. Ecol Evol. 2011;1(4):556–70. pmid:22393522
- View Article
- PubMed/NCBI
- Google Scholar
45. Galanti L, Shasha D, Gunsalus KC. Pheniqs 2.0: accurate, high-performance Bayesian decoding and confidence estimation for combinatorial barcode indexing. BMC Bioinformatics. 2021;22(1):359. pmid:34215187
- View Article
- PubMed/NCBI
- Google Scholar
46. Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37(8):852–7. pmid:31341288
- View Article
- PubMed/NCBI
- Google Scholar
47. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13(7):581–3. pmid:27214047
- View Article
- PubMed/NCBI
- Google Scholar
48. Robeson MS 2nd, O’Rourke DR, Kaehler BD, Ziemski M, Dillon MR, Foster JT, et al. RESCRIPt: Reproducible sequence taxonomy reference database management. PLoS Comput Biol. 2021;17(11):e1009581. pmid:34748542
- View Article
- PubMed/NCBI
- Google Scholar
49. Robeson M. Using RESCRIPt’s “extract-seq-segments” to extract reference sequences without PCR primer pairs. https://forum.qiime2.org/t/using-rescripts-extract-seq-segments-to-extract-reference-sequences-without-pcr-primer-pairs/23618. 2022.
- View Article
- Google Scholar
50. McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One. 2013;8(4):e61217. pmid:23630581
- View Article
- PubMed/NCBI
- Google Scholar
51. Bisanz JE. qiime2R: Importing QIIME2 artifacts and associated data into R sessions. https://github.com/jbisanz/qiime2R. 2018.
- View Article
- Google Scholar
52. Team RC. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. 2023.
53. Jin Y, Qian H. V.PhyloMaker2: An updated and enlarged R package that can generate very large phylogenies for vascular plants. Plant Divers. 2022;44(4):335–9. pmid:35967255
- View Article
- PubMed/NCBI
- Google Scholar
54. Jin Y, Qian H. U.PhyloMaker: An R package that can generate large phylogenetic trees for plants and animals. Plant Divers. 2022;45(3):347–52. pmid:37397595
- View Article
- PubMed/NCBI
- Google Scholar
55. Paradis E, Schliep K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics. 2019;35(3):526–8. pmid:30016406
- View Article
- PubMed/NCBI
- Google Scholar
56. Shetty S, Person P, Person P. biomeUtils: Utilities for microbiome analytics. 2024.
57. Deagle BE, Thomas AC, Shaffer AK, Trites AW, Jarman SN. Quantifying sequence proportions in a DNA-based diet study using Ion Torrent amplicon sequencing: which counts count? Mol Ecol Resour. 2013;13(4):620–33. pmid:23590207
- View Article
- PubMed/NCBI
- Google Scholar
58. Clare EL, Chain FJJ, Littlefair JE, Cristescu ME. The effects of parameter choice on defining molecular operational taxonomic units and resulting ecological analyses of metabarcoding data. Genome. 2016;59(11):981–90. pmid:27336462
- View Article
- PubMed/NCBI
- Google Scholar
59. Hambäck PA, Weingartner E, Dalén L, Wirta H, Roslin T. Spatial subsidies in spider diets vary with shoreline structure: Complementary evidence from molecular diet analysis and stable isotopes. Ecol Evol. 2016;6(23):8431–9. pmid:28031795
- View Article
- PubMed/NCBI
- Google Scholar
60. Oksanen J, Simpson GL, Blanchet FG, Kindt R, Legendre P, Minchin PR. vegan: Community Ecology Package. https://vegandevs.github.io/vegan/. 2024.
- View Article
- Google Scholar
61. Yu G, Smith DK, Zhu H, Guan Y, Lam TT. ggtree : an r package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol Evol. 2016;8(1):28–36.
- View Article
- Google Scholar
62. Wickham H, Averick M, Bryan J, Chang W, McGowan L, François R, et al. Welcome to the Tidyverse. JOSS. 2019;4(43):1686.
- View Article
- Google Scholar
63. Slowikowski K. ggrepel: Automatically position non-overlapping text labels with “ggplot2.”. https://{CRAN}.R-project.org/package=ggrepel. 2024.
- View Article
- Google Scholar
64. Jaffé K, Villegas G. On the communication systems of the fungus-growing anttrachymyrmex urichi. Ins Soc. 1985;32(3):257–74.
- View Article
- Google Scholar
65. Kaspari M. Removal of seeds from Neotropical frugivore droppings : Ant responses to seed number. Oecologia. 1993;95(1):81–8. pmid:28313315
- View Article
- PubMed/NCBI
- Google Scholar
66. Leal LR, Oliveira PS. Interactions between Fungus-Growing Ants (Attini), Fruits and Seeds in Cerrado Vegetation in Southeast Brazil. 1998:170–8.
67. Pizo MA, Oliveira PS. The Use of Fruits and Seeds by Ants in the Atlantic Forest of Southeast Brazil1. Biotropica. 2000;32(4b):851–61.
- View Article
- Google Scholar
68. Moreira AA, Bueno FC, Oliveira CG, Diniz EA, Garcez DL, Campos MCG. Foraging activity of Trachymyrmex fuscus (Hymenoptera: Formicidae) in a degraded area. Sociobiology. 2007;50:409–17.
- View Article
- Google Scholar
69. Kulhavy D, Smith L, Ross W. Impact of the Texas leaf-cutting ant (Atta texana (Buckley)) (Order Hymenoptera, Family Formicidae) on a forested landscape. 2001.
70. Hibert F, Taberlet P, Chave J, Scotti-Saintagne C, Sabatier D, Richard-Hansen C. Unveiling the diet of elusive rainforest herbivores in next generation sequencing era? The tapir as a case study. PLoS One. 2013;8(4):e60799. pmid:23560107
- View Article
- PubMed/NCBI
- Google Scholar
71. Clare EL. Molecular detection of trophic interactions: emerging trends, distinct advantages, significant considerations and conservation applications. Evol Appl. 2014;7(9):1144–57. pmid:25553074
- View Article
- PubMed/NCBI
- Google Scholar
72. Furlan EM, Davis J, Duncan RP. Identifying error and accurately interpreting environmental DNA metabarcoding results: A case study to detect vertebrates at arid zone waterholes. Mol Ecol Resour. 2020;20(5):1259–76. pmid:32310337
- View Article
- PubMed/NCBI
- Google Scholar
73. Rytkönen S, Vesterinen EJ, Westerduin C, Leviäkangas T, Vatka E, Mutanen M, et al. From feces to data: A metabarcoding method for analyzing consumed and available prey in a bird-insect food web. Ecol Evol. 2018;9(1):631–9. pmid:30680143
- View Article
- PubMed/NCBI
- Google Scholar
74. Bohmann K, Elbrecht V, Carøe C, Bista I, Leese F, Bunce M, et al. Strategies for sample labelling and library preparation in DNA metabarcoding studies. Mol Ecol Resour. 2022;22(4):1231–46. pmid:34551203
- View Article
- PubMed/NCBI
- Google Scholar

[ref1] 1. Leal IR, Oliveira PS. Foraging ecology of attine ants in a Neotropical savanna: seasonal use of fungal substrate in the cerrado vegetation of Brazil. Insectes soc. 2000;47(4):376–82.
View Article
Google Scholar

[2] View Article

[3] Google Scholar

[ref2] 2. De Fine Licht HH, Boomsma JJ. Forage collection, substrate preparation, and diet composition in fungus‐growing ants. Ecological Entomology. 2010;35(3):259–69.
View Article
Google Scholar

[5] View Article

[6] Google Scholar

[ref3] 3. Solomon SE, Rabeling C, Sosa‐Calvo J, Lopes CT, Rodrigues A, Vasconcelos HL, et al. The molecular phylogenetics of Trachymyrmex Forel ants and their fungal cultivars provide insights into the origin and coevolutionary history of ‘higher‐attine’ ant agriculture. Systematic Entomology. 2019;44(4):939–56.
View Article
Google Scholar

[8] View Article

[9] Google Scholar

[ref4] 4. Schultz TR, Brady SG. Major evolutionary transitions in ant agriculture. Proc Natl Acad Sci U S A. 2008;105(14):5435–40. pmid:18362345
View Article
PubMed/NCBI
Google Scholar

[11] View Article

[12] PubMed/NCBI

[13] Google Scholar

[ref5] 5. De Fine Licht HH, Schiøtt M, Mueller UG, Boomsma JJ. Evolutionary transitions in enzyme activity of ant fungus gardens. Evolution. 2010;64(7):2055–69. pmid:20067517
View Article
PubMed/NCBI
Google Scholar

[15] View Article

[16] PubMed/NCBI

[17] Google Scholar

[ref6] 6. Nielsen JM, Clare EL, Hayden B, Brett MT, Kratina P. Diet tracing in ecology: Method comparison and selection. Methods Ecol Evol. 2017;9(2):278–91.
View Article
Google Scholar

[19] View Article

[20] Google Scholar

[ref7] 7. de Sousa LL, Silva SM, Xavier R. DNA metabarcoding in diet studies: Unveiling ecological aspects in aquatic and terrestrial ecosystems. Environmental DNA. 2019;1(3):199–214.
View Article
Google Scholar

[22] View Article

[23] Google Scholar

[ref8] 8. Wulff JA, Kjeldgaard MK, Eubanks MD, Sword GA. From the bellies of babes: a larval-based approach to ant molecular gut content analysis. Insect Soc. 2021;68(2–3):245–53.
View Article
Google Scholar

[25] View Article

[26] Google Scholar

[ref9] 9. Mintzer A. Foraging activity of the Mexican leafcutting antAtta mexicana (F. Smith), in a sonoran desert habitat (Hymenoptera, Formicidae). Ins Soc. 1979;26(4):364–72.
View Article
Google Scholar

[28] View Article

[29] Google Scholar

[ref10] 10. Berish CW. Leaf-Cutting Ants (Atta cephalotes) Select Nitrogen-Rich Forage. American Midland Naturalist. 1986;115(2):268.
View Article
Google Scholar

[31] View Article

[32] Google Scholar

[ref11] 11. Mintzer A. Diet of the leafcutting ant, Atta mexicana (Hymenoptera: Formicidae), in a Sonoran desert habitat. Journal of the Arizona-Nevada Academy of Science. 1995.
View Article
Google Scholar

[34] View Article

[35] Google Scholar

[ref12] 12. Weber NA. Gardening ants the attines. American Philosophical Society. 1972;146.
View Article
Google Scholar

[37] View Article

[38] Google Scholar

[ref13] 13. Ando H, Mukai H, Komura T, Dewi T, Ando M, Isagi Y. Methodological trends and perspectives of animal dietary studies by noninvasive fecal DNA metabarcoding. Environmental DNA. 2020;2(4):391–406.
View Article
Google Scholar

[40] View Article

[41] Google Scholar

[ref14] 14. Muilenburg VL, Goggin FL, Hebert SL, Jia L, Stephen FM. Ant predation on red oak borer confirmed by field observation and molecular gut‐content analysis. Agri and Forest Entomology. 2008;10(3):205–13.
View Article
Google Scholar

[43] View Article

[44] Google Scholar

[ref15] 15. Penn HJ, Chapman EG, Harwood JD. Overcoming PCR Inhibition During DNA-Based Gut Content Analysis of Ants. Environ Entomol. 2016;45(5):1255–61. pmid:27452765
View Article
PubMed/NCBI
Google Scholar

[46] View Article

[47] PubMed/NCBI

[48] Google Scholar

[ref16] 16. Gadau J. DNA isolation from ants. Cold Spring Harb Protoc. 2009;2009(7):pdb.prot5245. pmid:20147210
View Article
PubMed/NCBI
Google Scholar

[50] View Article

[51] PubMed/NCBI

[52] Google Scholar

[ref17] 17. Holldobler B, Wilson EO. The Ants. 1st ed. Cambridge, Mass: Belknap Press: An Imprint of Harvard University Press. 1990.

[ref18] 18. De Fine Licht HH, Boomsma JJ, Tunlid A. Symbiotic adaptations in the fungal cultivar of leaf-cutting ants. Nat Commun. 2014;5:5675. pmid:25435021
View Article
PubMed/NCBI
Google Scholar

[55] View Article

[56] PubMed/NCBI

[57] Google Scholar

[ref19] 19. Seal JN, Schiøtt M, Mueller UG. Ant-fungus species combinations engineer physiological activity of fungus gardens. J Exp Biol. 2014;217(Pt 14):2540–7. pmid:24803469
View Article
PubMed/NCBI
Google Scholar

[59] View Article

[60] PubMed/NCBI

[61] Google Scholar

[ref20] 20. Moller IE, De Fine Licht HH, Harholt J, Willats WGT, Boomsma JJ. The dynamics of plant cell-wall polysaccharide decomposition in leaf-cutting ant fungus gardens. PLoS One. 2011;6(3):e17506. pmid:21423735
View Article
PubMed/NCBI
Google Scholar

[63] View Article

[64] PubMed/NCBI

[65] Google Scholar

[ref21] 21. Burgar JM, Murray DC, Craig MD, Haile J, Houston J, Stokes V, et al. Who’s for dinner? High-throughput sequencing reveals bat dietary differentiation in a biodiversity hotspot where prey taxonomy is largely undescribed. Mol Ecol. 2014;23(15):3605–17. pmid:24118181
View Article
PubMed/NCBI
Google Scholar

[67] View Article

[68] PubMed/NCBI

[69] Google Scholar

[ref22] 22. Kartzinel TR, Chen PA, Coverdale TC, Erickson DL, Kress WJ, Kuzmina ML, et al. DNA metabarcoding illuminates dietary niche partitioning by African large herbivores. Proc Natl Acad Sci U S A. 2015;112(26):8019–24. pmid:26034267
View Article
PubMed/NCBI
Google Scholar

[71] View Article

[72] PubMed/NCBI

[73] Google Scholar

[ref23] 23. Soininen EM, Gauthier G, Bilodeau F, Berteaux D, Gielly L, Taberlet P, et al. Highly overlapping winter diet in two sympatric lemming species revealed by DNA metabarcoding. PLoS One. 2015;10(1):e0115335. pmid:25635852
View Article
PubMed/NCBI
Google Scholar

[75] View Article

[76] PubMed/NCBI

[77] Google Scholar

[ref24] 24. Sato JJ, Shimada T, Kyogoku D, Komura T, Uemura S, Saitoh T, et al. Dietary niche partitioning between sympatric wood mouse species (Muridae: Apodemus) revealed by DNA meta-barcoding analysis. Journal of Mammalogy. 2018;99(4):952–64.
View Article
Google Scholar

[79] View Article

[80] Google Scholar

[ref25] 25. Smith JA, Thomas AC, Levi T, Wang Y, Wilmers CC. Human activity reduces niche partitioning among three widespread mesocarnivores. Oikos. 2018;127(6):890–901.
View Article
Google Scholar

[82] View Article

[83] Google Scholar

[ref26] 26. Aizpurua O, Budinski I, Georgiakakis P, Gopalakrishnan S, Ibañez C, Mata V, et al. Agriculture shapes the trophic niche of a bat preying on multiple pest arthropods across Europe: Evidence from DNA metabarcoding. Mol Ecol. 2018;27(3):815–25. pmid:29290102
View Article
PubMed/NCBI
Google Scholar

[85] View Article

[86] PubMed/NCBI

[87] Google Scholar

[ref27] 27. Komura T, Ando H, Horikoshi K, Suzuki H, Isagi Y. DNA barcoding reveals seasonal shifts in diet and consumption of deep-sea fishes in wedge-tailed shearwaters. PLoS One. 2018;13(4):e0195385. pmid:29630670
View Article
PubMed/NCBI
Google Scholar

[89] View Article

[90] PubMed/NCBI

[91] Google Scholar

[ref28] 28. Forsman AM, Hoenig BD, Gaspar SA, Fischer JD, Siegrist J, Fraser K. Evaluating the impacts of metabarcoding primer selection on DNA characterization of diet in an aerial insectivore, the Purple Martin. Ornithology. 2021;139(1).
View Article
Google Scholar

[93] View Article

[94] Google Scholar

[ref29] 29. Palumbo F, Squartini A, Barcaccia G, Macolino S, Pornaro C, Pindo M, et al. A multi-kingdom metabarcoding study on cattle grazing Alpine pastures discloses intra-seasonal shifts in plant selection and faecal microbiota. Sci Rep. 2021;11(1):889. pmid:33441587
View Article
PubMed/NCBI
Google Scholar

[96] View Article

[97] PubMed/NCBI

[98] Google Scholar

[ref30] 30. Roffler GH, Allen JM, Massey A, Levi T. Metabarcoding of fecal DNA shows dietary diversification in wolves substitutes for ungulates in an island archipelago. Ecosphere. 2021;12(1).
View Article
Google Scholar

[100] View Article

[101] Google Scholar

[ref31] 31. Brun L, Schneider J, Carrió EM, Dongre P, Taberlet P, van de Waal, et al. Focal vs. fecal: Seasonal variation in the diet of wild vervet monkeys from observational and DNA metabarcoding data. Ecol Evol. 2022;12(10):e9358. pmid:36203642
View Article
PubMed/NCBI
Google Scholar

[103] View Article

[104] PubMed/NCBI

[105] Google Scholar

[ref32] 32. Wang B, Li Y, Zhang G, Yang J, Deng C, Hu H, et al. Seasonal variations in the plant diet of the Chinese Monal revealed by fecal DNA metabarcoding analysis. Avian Research. 2022;13:100034.
View Article
Google Scholar

[107] View Article

[108] Google Scholar

[ref33] 33. Wetterer JK. Foraging Ecology of the Leaf‐Cutting Ant Acromyrmex Octospinosus in a Costa Rican Rain Forest. Psyche: A Journal of Entomology. 1991;98(4):361–71.
View Article
Google Scholar

[110] View Article

[111] Google Scholar

[ref34] 34. Kooij PW, Liberti J, Giampoudakis K, Schiøtt M, Boomsma JJ. Differences in forage-acquisition and fungal enzyme activity contribute to niche segregation in Panamanian leaf-cutting ants. PLoS One. 2014;9(4):e94284. pmid:24718261
View Article
PubMed/NCBI
Google Scholar

[113] View Article

[114] PubMed/NCBI

[115] Google Scholar

[ref35] 35. Seal JN, Brown L, Ontiveros C, Thiebaud J, Mueller UG. Gone to Texas: phylogeography of twoTrachymyrmex(Hymenoptera: Formicidae) species along the southeastern coastal plain of North America. Biol J Linn Soc Lond. 2015;114(3):689–98.
View Article
Google Scholar

[117] View Article

[118] Google Scholar

[ref36] 36. Senula SF, Scavetta JT, Banta JA, Mueller UG, Seal JN, Kellner K. Potential Distribution of Six North American Higher-Attine Fungus-Farming Ant (Hymenoptera: Formicidae) Species. J Insect Sci. 2019;19(6):24. pmid:31854452
View Article
PubMed/NCBI
Google Scholar

[120] View Article

[121] PubMed/NCBI

[122] Google Scholar

[ref37] 37. Rabeling C, Cover SP, Johnson RA, Mueller GU. A review of the North American species of the fungus-gardening ant genus Trachymyrmex (Hymenoptera: Formicidae). Zenodo. 2007.
View Article
Google Scholar

[124] View Article

[125] Google Scholar

[ref38] 38. Sanchez-Peña SR. Some Fungus-Growing Ants (Hymenoptera: Formicidae) from Northeastern Mexico. Florida Entomologist. 2010;93(4):501–4.
View Article
Google Scholar

[127] View Article

[128] Google Scholar

[ref39] 39. Wheeler WM. The fungus-growing ants of North America. 1907.

[ref40] 40. Waller DA. Foraging Behavior of Trachymyrmex turrifex Wheeler (Formicidae: Attini). The Southwestern Naturalist. 1989;34(2):271.
View Article
Google Scholar

[131] View Article

[132] Google Scholar

[ref41] 41. Seal JN, Tschinkel WR. Complexity in an obligate mutualism: do fungus-gardening ants know what makes their garden grow? Behav Ecol Sociobiol. 2007;61(8):1151–60.
View Article
Google Scholar

[134] View Article

[135] Google Scholar

[ref42] 42. Seal JN, Tschinkel WR. Food limitation in the fungus‐gardening ant, Trachymyrmex septentrionalis. Ecological Entomology. 2008;33(5):597–607.
View Article
Google Scholar

[137] View Article

[138] Google Scholar

[ref43] 43. Taberlet P, Coissac E, Pompanon F, Gielly L, Miquel C, Valentini A, et al. Power and limitations of the chloroplast trnL (UAA) intron for plant DNA barcoding. Nucleic Acids Res. 2007;35(3):e14. pmid:17169982
View Article
PubMed/NCBI
Google Scholar

[140] View Article

[141] PubMed/NCBI

[142] Google Scholar

[ref44] 44. Razgour O, Clare EL, Zeale MRK, Hanmer J, Schnell IB, Rasmussen M, et al. High-throughput sequencing offers insight into mechanisms of resource partitioning in cryptic bat species. Ecol Evol. 2011;1(4):556–70. pmid:22393522
View Article
PubMed/NCBI
Google Scholar

[144] View Article

[145] PubMed/NCBI

[146] Google Scholar

[ref45] 45. Galanti L, Shasha D, Gunsalus KC. Pheniqs 2.0: accurate, high-performance Bayesian decoding and confidence estimation for combinatorial barcode indexing. BMC Bioinformatics. 2021;22(1):359. pmid:34215187
View Article
PubMed/NCBI
Google Scholar

[148] View Article

[149] PubMed/NCBI

[150] Google Scholar

[ref46] 46. Bolyen E, Rideout JR, Dillon MR, Bokulich NA, Abnet CC, Al-Ghalith GA, et al. Reproducible, interactive, scalable and extensible microbiome data science using QIIME 2. Nat Biotechnol. 2019;37(8):852–7. pmid:31341288
View Article
PubMed/NCBI
Google Scholar

[152] View Article

[153] PubMed/NCBI

[154] Google Scholar

[ref47] 47. Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, Holmes SP. DADA2: High-resolution sample inference from Illumina amplicon data. Nat Methods. 2016;13(7):581–3. pmid:27214047
View Article
PubMed/NCBI
Google Scholar

[156] View Article

[157] PubMed/NCBI

[158] Google Scholar

[ref48] 48. Robeson MS 2nd, O’Rourke DR, Kaehler BD, Ziemski M, Dillon MR, Foster JT, et al. RESCRIPt: Reproducible sequence taxonomy reference database management. PLoS Comput Biol. 2021;17(11):e1009581. pmid:34748542
View Article
PubMed/NCBI
Google Scholar

[160] View Article

[161] PubMed/NCBI

[162] Google Scholar

[ref49] 49. Robeson M. Using RESCRIPt’s “extract-seq-segments” to extract reference sequences without PCR primer pairs. https://forum.qiime2.org/t/using-rescripts-extract-seq-segments-to-extract-reference-sequences-without-pcr-primer-pairs/23618. 2022.
View Article
Google Scholar

[164] View Article

[165] Google Scholar

[ref50] 50. McMurdie PJ, Holmes S. phyloseq: an R package for reproducible interactive analysis and graphics of microbiome census data. PLoS One. 2013;8(4):e61217. pmid:23630581
View Article
PubMed/NCBI
Google Scholar

[167] View Article

[168] PubMed/NCBI

[169] Google Scholar

[ref51] 51. Bisanz JE. qiime2R: Importing QIIME2 artifacts and associated data into R sessions. https://github.com/jbisanz/qiime2R. 2018.
View Article
Google Scholar

[171] View Article

[172] Google Scholar

[ref52] 52. Team RC. R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing. 2023.

[ref53] 53. Jin Y, Qian H. V.PhyloMaker2: An updated and enlarged R package that can generate very large phylogenies for vascular plants. Plant Divers. 2022;44(4):335–9. pmid:35967255
View Article
PubMed/NCBI
Google Scholar

[175] View Article

[176] PubMed/NCBI

[177] Google Scholar

[ref54] 54. Jin Y, Qian H. U.PhyloMaker: An R package that can generate large phylogenetic trees for plants and animals. Plant Divers. 2022;45(3):347–52. pmid:37397595
View Article
PubMed/NCBI
Google Scholar

[179] View Article

[180] PubMed/NCBI

[181] Google Scholar

[ref55] 55. Paradis E, Schliep K. ape 5.0: an environment for modern phylogenetics and evolutionary analyses in R. Bioinformatics. 2019;35(3):526–8. pmid:30016406
View Article
PubMed/NCBI
Google Scholar

[183] View Article

[184] PubMed/NCBI

[185] Google Scholar

[ref56] 56. Shetty S, Person P, Person P. biomeUtils: Utilities for microbiome analytics. 2024.

[ref57] 57. Deagle BE, Thomas AC, Shaffer AK, Trites AW, Jarman SN. Quantifying sequence proportions in a DNA-based diet study using Ion Torrent amplicon sequencing: which counts count? Mol Ecol Resour. 2013;13(4):620–33. pmid:23590207
View Article
PubMed/NCBI
Google Scholar

[188] View Article

[189] PubMed/NCBI

[190] Google Scholar

[ref58] 58. Clare EL, Chain FJJ, Littlefair JE, Cristescu ME. The effects of parameter choice on defining molecular operational taxonomic units and resulting ecological analyses of metabarcoding data. Genome. 2016;59(11):981–90. pmid:27336462
View Article
PubMed/NCBI
Google Scholar

[192] View Article

[193] PubMed/NCBI

[194] Google Scholar

[ref59] 59. Hambäck PA, Weingartner E, Dalén L, Wirta H, Roslin T. Spatial subsidies in spider diets vary with shoreline structure: Complementary evidence from molecular diet analysis and stable isotopes. Ecol Evol. 2016;6(23):8431–9. pmid:28031795
View Article
PubMed/NCBI
Google Scholar

[196] View Article

[197] PubMed/NCBI

[198] Google Scholar

[ref60] 60. Oksanen J, Simpson GL, Blanchet FG, Kindt R, Legendre P, Minchin PR. vegan: Community Ecology Package. https://vegandevs.github.io/vegan/. 2024.
View Article
Google Scholar

[200] View Article

[201] Google Scholar

[ref61] 61. Yu G, Smith DK, Zhu H, Guan Y, Lam TT. ggtree : an r package for visualization and annotation of phylogenetic trees with their covariates and other associated data. Methods Ecol Evol. 2016;8(1):28–36.
View Article
Google Scholar

[203] View Article

[204] Google Scholar

[ref62] 62. Wickham H, Averick M, Bryan J, Chang W, McGowan L, François R, et al. Welcome to the Tidyverse. JOSS. 2019;4(43):1686.
View Article
Google Scholar

[206] View Article

[207] Google Scholar

[ref63] 63. Slowikowski K. ggrepel: Automatically position non-overlapping text labels with “ggplot2.”. https://{CRAN}.R-project.org/package=ggrepel. 2024.
View Article
Google Scholar

[209] View Article

[210] Google Scholar

[ref64] 64. Jaffé K, Villegas G. On the communication systems of the fungus-growing anttrachymyrmex urichi. Ins Soc. 1985;32(3):257–74.
View Article
Google Scholar

[212] View Article

[213] Google Scholar

[ref65] 65. Kaspari M. Removal of seeds from Neotropical frugivore droppings : Ant responses to seed number. Oecologia. 1993;95(1):81–8. pmid:28313315
View Article
PubMed/NCBI
Google Scholar

[215] View Article

[216] PubMed/NCBI

[217] Google Scholar

[ref66] 66. Leal LR, Oliveira PS. Interactions between Fungus-Growing Ants (Attini), Fruits and Seeds in Cerrado Vegetation in Southeast Brazil. 1998:170–8.

[ref67] 67. Pizo MA, Oliveira PS. The Use of Fruits and Seeds by Ants in the Atlantic Forest of Southeast Brazil1. Biotropica. 2000;32(4b):851–61.
View Article
Google Scholar

[220] View Article

[221] Google Scholar

[ref68] 68. Moreira AA, Bueno FC, Oliveira CG, Diniz EA, Garcez DL, Campos MCG. Foraging activity of Trachymyrmex fuscus (Hymenoptera: Formicidae) in a degraded area. Sociobiology. 2007;50:409–17.
View Article
Google Scholar

[223] View Article

[224] Google Scholar

[ref69] 69. Kulhavy D, Smith L, Ross W. Impact of the Texas leaf-cutting ant (Atta texana (Buckley)) (Order Hymenoptera, Family Formicidae) on a forested landscape. 2001.

[ref70] 70. Hibert F, Taberlet P, Chave J, Scotti-Saintagne C, Sabatier D, Richard-Hansen C. Unveiling the diet of elusive rainforest herbivores in next generation sequencing era? The tapir as a case study. PLoS One. 2013;8(4):e60799. pmid:23560107
View Article
PubMed/NCBI
Google Scholar

[227] View Article

[228] PubMed/NCBI

[229] Google Scholar

[ref71] 71. Clare EL. Molecular detection of trophic interactions: emerging trends, distinct advantages, significant considerations and conservation applications. Evol Appl. 2014;7(9):1144–57. pmid:25553074
View Article
PubMed/NCBI
Google Scholar

[231] View Article

[232] PubMed/NCBI

[233] Google Scholar

[ref72] 72. Furlan EM, Davis J, Duncan RP. Identifying error and accurately interpreting environmental DNA metabarcoding results: A case study to detect vertebrates at arid zone waterholes. Mol Ecol Resour. 2020;20(5):1259–76. pmid:32310337
View Article
PubMed/NCBI
Google Scholar

[235] View Article

[236] PubMed/NCBI

[237] Google Scholar

[ref73] 73. Rytkönen S, Vesterinen EJ, Westerduin C, Leviäkangas T, Vatka E, Mutanen M, et al. From feces to data: A metabarcoding method for analyzing consumed and available prey in a bird-insect food web. Ecol Evol. 2018;9(1):631–9. pmid:30680143
View Article
PubMed/NCBI
Google Scholar

[239] View Article

[240] PubMed/NCBI

[241] Google Scholar

[ref74] 74. Bohmann K, Elbrecht V, Carøe C, Bista I, Leese F, Bunce M, et al. Strategies for sample labelling and library preparation in DNA metabarcoding studies. Mol Ecol Resour. 2022;22(4):1231–46. pmid:34551203
View Article
PubMed/NCBI
Google Scholar

[243] View Article

[244] PubMed/NCBI

[245] Google Scholar

Figures

Abstract

Introduction

Fungus gardening ants

Traditional methods of diet identification

Dietary DNA

Evaluation of diet and study species

Materials and methods

Sample collection

Sample metabarcoding

Sequence processing

Statistical analysis

Results

Plant taxa identified from fungus garden samples

Plant taxa alpha diversity

Plant taxa beta diversity

Arthropod taxa identified from fungus garden samples

Discussion

Supporting information

S1 Fig. The F-measure of our classifiers being used on their own training dataset.

Acknowledgments

References