Unravelling the diet of flightless grasshoppers for conservation purposes using DNA metabarcoding

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Kearney, Ary A. Hoffmann, Melissa E. Carew This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6555039/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 08 Nov, 2025 Read the published version in Journal of Insect Conservation → Version 1 posted 11 You are reading this latest preprint version Abstract Understanding food plant resources is essential for assessing habitat suitability for herbivorous animals, especially for species with limited movement that depend on local resources. However, obtaining this information can be challenging for species whose plant consumption cannot be easily monitored. Here we use DNA metabarcoding techniques to identify the plant species in the faeces of two grasshopper species of the flightless Australian subfamily Morabinae, the endangered Keys’ matchstick grasshopper Keyacris scurra , and the Larapuna matchstick grasshopper, Vandiemenella viatica . DNA sequences from the chloroplast trnL (UAA) and rbcL genes and ribosomal ITS2 region were used to identify the plant species in the diet of these species based on five populations per species. We found a total of 28 plant taxa in the faecal samples of K . scurra and 38 in V . viatica . Indigenous plants from the daisy family Asteraceae dominated the faeces samples of both grasshopper species and myrtle plants from Myrtaceae were also commonly found for V. viatica. Introduced grass species from the Poaceae family were also identified in the diet. PERMANOVAs showed significant differences in the composition of the plant community consumed across sites. Alpha diversity metrics revealed no significant differences between the two grasshopper species; however, significant variation was observed across sites, depending on the choice of markers (e.g., Shannon Index: χ 2 (9) = 30.732, p < 0.001 for ITS2). Our findings should help in revegetation efforts aimed at expanding the range of the two morabine species by identifying suitable plant species. Herbivore insect diet DNA metabarcoding DNA-faeces analysis matchstick grasshoppers insect conservation Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Understanding the composition and availability of food plants is crucial for assessing habitat suitability (Owen-Smith 2003 ), especially in herbivorous species with low dispersal ability that are highly dependent on local resources and are more vulnerable to changes in their surroundings and geographical range (Kotiaho et al. 2005 ; Reinhardt et al. 2005 ). This understanding is useful for managing existing habitats to ensure the resources persist and for restoration of new habitat before reintroductions. However, collecting information on food plants is challenging due to the obstacles involved in field observations of feeding behaviours, particularly in small, cryptic animals (Blackith and Blackith 1966 ; Valentini et al. 2009 ), or when morphological identification of consumed plant food is difficult. In these cases, genetic analyses have emerged as a powerful tool in ecological studies of diet (Murray et al. 2011 ; Pompanon et al. 2012 ; Symondson 2002 ). In particular, tools like DNA metabarcoding offers higher or equivalent richness and taxonomic resolution than traditional methods such as microscopy (Goldberg et al. 2020 ; Iwanowicz et al. 2016 ; Littleford-Colquhoun et al. 2022 ; Soininen et al. 2009 ). Diet analyses based on DNA metabarcoding typically involves identifying the species consumed by characterizing the DNA present in whole specimens, regurgitates, gut, or faecal samples (Pompanon et al. 2012 ). Despite the widespread use of genetic analysis of faecal samples in vertebrate research, it has been much less commonly applied to insects (Ando et al. 2020 ). Most studies involving herbivorous insects have relied on the examination of stomach contents, which necessitates sacrificing individuals (e.g. Masonick et al., 2019 ; Pumariño et al. 2011 ), a practice unsuitable for endangered species. Valentini et al. ( 2009 ) were pioneers in applying DNA metabarcoding to herbivorous insect faecal samples. They successfully determined the diet of mammals, birds and invertebrates, including two orthopteran species, Chorthippus biguttulus and Gonphocerippus rufus , by analysing the chloroplast trnL intron (UAA). However, in the 15 years since their study, only a few published studies have utilized faecal samples from herbivorous Orthoptera, and only the study of Yamamoto and Uchida ( 2018 ) was oriented towards conservation efforts. Keyacris scurra is an endangered grasshopper of the endemic subfamily Morabinae, recently rediscovered in Victoria. Its distribution has become limited to native remnants of grassland such as along roads and cemeteries of Victoria, New South Wales, and the Australian Capital Territory (Hoffmann et al. 2021 ; White 1956 ). Other species belonging to the same family, in particular the related Vandiemenella viatica , face similar habitat challenges. Both species retain relatively high genetic diversity, even in small habitat patches (Hoffmann et al. 2021 , 2023 ), and management of food resources in small vegetation patches is likely to be critical for their ongoing survival (Hoffmann et al. 2021 ). For species like these grasshoppers that can persist in small patches, translocations are a promising tool for conservation (Yagui et al. 2024 ). So, understanding the diet of these insects becomes important for effective management in restored habitats. A non-invasive method like faecal metabarcoding is particularly useful for assessing the diet of endangered species (Nagarajan et al. 2020 ; Valentini et al. 2009 ) such as K . scurra . Apart from some observations and habitat descriptions from the 1950s and 60s (e.g. White 1956 ; White et al. 1964 ), the only dietary study of morabine grasshoppers was undertaken almost 60 years ago by Blackith and Blackith ( 1966 ) who analysed the colours left by plants when ingested in the ilial diverticula, conducted starvation experiments, and observed differences in feeding avidity among various species of morabine grasshoppers. Their results indicated that, in captivity, the group is generally polyphagous, with K. scurra and species of Vandiemenella feeding with varying avidity on the leaves and flowers of numerous species (21 and 34 species, respectively). However, Blackith and Blackith ( 1966 ) highlighted the challenge of observing diet preferences in the wild, as grasshoppers feed on post-rainfall seedlings, and multiple plants can produce similar colours in the diverticula. While this study offers a foundation for understanding their diet, much remains unknown about their natural food sources due to the difficulty of behavioural observations in the field. Here we determine the food plant species of five populations of the endangered K. scurra and 5 populations of V. viatica , using DNA metabarcoding of faecal material. We assessed the performance of various plant markers and public reference libraries for accurate dietary assessment, and we compared diversity metrics of diets across species and sites. Materials and methods Study sites, sampling, and sample preservation K. scurra and V. viatica adult individuals were collected from five localities in Victoria each (Fig. 1 ). The number of collected individuals varied according to the abundance found at the site at the time of collection (Table 1 ). A minimum of three individuals of both sexes collected from a site were placed in a sterile plastic container. We then waited between three to twelve hours to allow for complete defecation before collecting the faeces, following the approach of allowing defecation prior to sample collection as described by Kaunisto et al. (2017) for odonates. Samples were then pooled to represent a collection site. There is a notable increase in correct identification when more than 5 mg of faeces is available as dry mass, with an inflection point around 15 mg (Rytkönen et al. 2019 ). For this reason, we used 20 mg of dry matter per sample. Table 1 Sample and site information including Ecological Vegetation Classes (EVCs) and the Bioregional conservation status of the EVC. Source: Nature Kit (Accessed: April 2024) Species Site Sample code Number of individuals Latitude Longitude EVC Bioregional Conservation Status Keyacris scurra Omeo KSO1 10 -37.0933 147.5937 Montane Grassy Woodland Depleted KSO2 10 Bingo Munjie KSH1 10 -37.0187 147.5822 Heathy Dry Forest Least Concern KSH2 10 Cudgewa KSC1 7 -36.1118 147.8539 Valley Grassy Forest Endangered KSC2 10 Shelley KSS1 7 -36.1291 147.5944 Herb-rich Foothill Forest Least Concern Beechworth KSB1 7 -36.3438 146.6908 Grassy Dry Forest Depleted Vandiemenella viatica Diamond creek VVD1 15 -37.6645 145.1643 Grassy Dry Forest Least Concern Truganina cemetery VVT1 7 -37.8255 144.7202 Plains Grassland Endangered Bay Road Reserve VVB1 7 -37.9551 145.0273 Grassy Woodland/Damp Sands Herb-rich Woodland Mosaic Endangered Royal Golf Club VVR1 10 -37.9715 145.0289 Heathy Woodland/Sand Heathland Mosaic Least Concern Royal Botanical G. Cranbourne VVC1 15 -38.1331 145.2696 Heathy Woodland Least Concern Any faeces produced in the field were preserved using silica gel beads at a ratio of 1:4. (Taberlet and Luikart 1999) and were stored in the laboratory at − 4°C (Taberlet et al. 2018 ). These methods are effective in preserving herbivore faeces without compromising DNA quantity and quality (Piggott and Taylor 2003 ). Plant DNA library construction Plant DNA reference libraries were prepared in R version 4.3.1 (R Core Team 2023) using the Rstudio environment (RStudio Team 2023). Similar to the study of Barnes et al. ( 2022 ), three different reference libraries were created: a local, a regional and a global library. For the local and regional library, we compiled plant species data within a 5 km radius of collection sites using the Atlas of Living Australia’s “Explore your area” tool. This species list served as a reference for the WFO.match function in the R package ‘WorldFlora’ (Kindt 2020 ), to generate a list of synonyms, which we included in a combined reference library from BOLD Systems ( https://boldsystems.org/index.php/databases ) and NCBI GenBank ( https://www.ncbi.nlm.nih.gov/nuccore/ ) with ‘refdb’ (Keck and Altermatt 2023 ) (accessed on August 15–20, 2023). When reference sequences were unavailable, we substituted sequences from Australian congeners. We further refined the list using ‘rgbif’ (v3.7.8) with data from GBIF (Chamberlain et al. 2023 ). We created a global reference NCBI database for each of the markers using the RESCRIPt (Robeson et al. 2021 ) with the function ‘qiime rescript get-ncbi-data’ in QIIME2 (version 2023.9) (Bolyen et al. 2019 ) for further comparison. From these available inventories, one species stood out in the Truganina cemetery, the Button Wrinklewort, Rutidosis leptorrhynchoides. Because of its conservation importance, recent media attention ( https://www.abc.net.au/news/2023-03-10/vic-truck-crushes-endangered-wildflowers-truganina-cemetery/101939222 ), and significance in ongoing management efforts, R. leptorrhynchoides leaf tissue was collected by staff from the Arthur Rylah Institute under Permit 10010953, issued under the Victorian Flora and Fauna Guarantee Act 1988 . For the local DNA reference libraries, 2529 species (1095 species for the K . scurra collection sites and 2183 species for the V . viatica collection sites), and 930 genera (457 for the K . scurra sites and 863 for the V . viatica sites) were obtained from the Atlas Living Australia using the 5 km radius from each collection site (Figure S1 ). DNA extraction, amplification and sequencing We extracted DNA from faecal material using, the Macherey-Nagel NucleoSpin® Plant II Mini kit with minor modifications to the manufacturer’s protocols (Supplementary Section 1). After extraction, eluted DNA samples were stored at -20°C. A two-step PCR protocol was implemented using the three more common universal markers for herbivores faeces analysis (Table 2 ) in the first PCR. In the second PCR, multiplex identifiers and sequencing adaptors were added (Supplementary Section 1). Samples were run as three PCR replicates. We also included a negative control that lacked the DNA extraction sample, a positive control made of the Clustered everlasting ( Chrysocephalum semipappossum ) DNA extract, and a spike control consisting of 40% C. apiculatum , 40% C. semipapossum , and 20% Acacia faeces. Table 2 DNA barcodes used to infer food plants of Keyacris scurra and Vandiemenella viatica Region bp Name Sequence Author nr ITS2 187–387 UniPlantF 5′-TGT GAA TTG CAR RAT YCM G-3′ (Moorhouse-Gann et al. 2018 ) UniPlantR 5′-CCC GHY TGA YYT GRG GTC DC-3′ cp trnL 10–143 trnLg 5′-GGG CAA TCC TGA GCC AA-3′ (Taberlet et al. 2007 ) trnLh 5′-CCATTGAGTCTCTGCACCTATC-3′ cp rbcL > 150 rbcLZ1 5′- ATG TCA CCA CAA ACA GAG ACT AAA GCA AGT-3′ (Poinar et al. 1998 ) rbcL19 5′- AGA TTC CGC AGC CAC TGC AGC CCC TGC TTC-3′ After amplification and library preparation of amplicons, the samples were sent to the Australian Genome Research Facility (AGRF), high throughput DNA sequencing using a 600-cycle flow cell MiSeq sequencing kit V3 (300 bp × 2) (Illumina Corporation). Sequence analysis and taxonomic assignation QIIME2 (version 2023.9) (Bolyen et al. 2019 ) was used for all bioinformatic processing including trimming, denoising, filtering, taxonomic assignment, and diversity analysis. Specific plugins and parameter values are shown in Supplementary Section 2. We used the QIIME 2 plugin that implements DADA2 to generate amplicon sequence variants (ASVs), incorporating robust error-correction steps that effectively reduce sequencing errors and remove chimeric sequences (Callahan et al. 2016 ). Finally, we used “qiime2 feature-classifier classify-sklearn” (Pedregosa et al. 2011 ) for taxonomic assignation. This classifier is a multinomial naive Bayes machine-learning classifier that surpasses the species-level accuracy of other widely used methods like VSEARCH and BLAST+ (Bokulich et al. 2018 ). Through comparison of the observed and expected spike control results, we calculated several metrics (Accuracy, Specificity, False Positive Rate (FPR), False Discovery Rate (FDR), Precision, Recall, F1-Score, and Matthews Correlation Coefficient (MCC) (Bokulich et al. 2018 ; Hleap et al. 2021 ; Valencia et al. 2024 )) across the different confidence thresholds (0.7 (default), 0.8, 0.9, 0.95, and 0.97) and markers (ITS2, rbcL, trnL) for global, local and regional libraries. Visualized metrics highlighted classifier performance by confidence level, marker, and library, prioritizing higher F1 and MCC scores with lower FPR and FDR for optimal classification parameters (Figure S2). The sklearn-based classifier showed rbcL and trnL with the global library generally identified ASVs at the genus level, while ITS2 across all libraries reached a finer species-level resolution. The regional and local libraries improved rbcL and trnL’s resolution compared to the global library (Figure S3). Final ITS2 classifications used the local library at 0.95 confidence, while rbcL achieved optimal results with the regional library at 0.90 confidence, and trnL performed best with the global library at 0.95 confidence. After the taxonomic classification, the data underwent analysis using the most common indicators for faecal metabarcoding dietary data, frequency of occurrence (FOO) as weighted per cent of occurrence (wPOO) and relative read abundance (RRA) (Ando et al. 2020 ; Deagle et al. 2019 ) Diversity analysis We used the QIIME2 q2-diversity plugin for diversity analyses, calculating alpha diversity metrics and statistical tests, including the Shannon Index, Faith’s Phylogenetic Diversity, Pielou’s Evenness, and Unweighted and Weighted UniFrac Distances. To compare plant community composition in the diets of the two grasshopper species, we conducted a pairwise PERMANOVA with the unweighted and weighted UniFrac distance matrices. The unweighted UniFrac metric captures taxa presence or absence, while the weighted UniFrac metric incorporates relative abundance, offering a fuller perspective on diversity and community composition (Lozupone et al. 2007). Additionally, we performed a Principal Coordinates Analysis (PCoA) based on both UniFrac matrices to further examine community composition differences. To assess whether the sequencing depth was sufficient to capture the full diversity of the samples, we plotted a) alpha rarefaction, which shows the relationship between sequencing depth and the observed diversity, and b) sample retention, which indicates the number of samples that remain at each sampling depth. Details of the metrics computed can be found in the QIIME2 documentation. The Kruskal-Wallis test was employed for comparisons, as the traditional ANOVA for the diversity metrics failed to meet the essential assumptions of normality and homogeneity of variances (see results for details). Dunn’s test was used as a post-hoc test following Kruskal-Wallis to identify pairwise differences in the diversity metrics across sites within each marker. Results Plant diet composition Across both species, MiSeq DNA sequencing returned a total of 4,567,488 raw reads after demultiplexing: 1,193,073 reads corresponded to ITS2, 1,845,060 to rbcL, and 1,529,355 to the trnL marker. Following denoising with the DADA2 plugin in QIIME2, we retained 234 ASVs for ITS2, 111 for rbcL, and 113 for trnL. Alpha rarefaction curves for all amplicons plateaued, indicating sufficient sequencing depth across samples (Figure S4). On average, 76.15% of ITS2 ASVs, 69.12% of rbcL ASVs, and 79.14% of trnL ASVs were consistently detected in at least two of the three PCR replicates (Table S1 ). A detailed summary of the number of reads retained in the final dataset per amplicon and sample is provided in Table S2. After applying QIIME2 feature-classifier classify-sklearn, we found for K scurra 19 plant taxa with ITS2 (12 to species, 6 to genus, and 1 to family), 6 plant taxa with rbcL (1 species, 2 families, 1 order, 1 class, 1 phylum), and 9 plant taxa with trnL (4 genus, 3 families, 1 order, and 1 class). In the case of V. viatica , we found 27 plant taxa (15 species, 7 genus, 4 families, and 1 class) with the ITS2 marker, 17 plant taxa (7 species, 6 families, 3 orders, and 1 class) with rbcL, and 12 plant taxa (3 genera, 6 families, 1 order, 1 class and 1 phylum) with trnL. Details of the plant species are included in Table S3 for K . scurra and Table S4 for V . viatica . Considering both species of grasshoppers, 27 plant species were detected in their faeces by the ITS2 marker, of which ~ 55% had rbcL available sequences and ~ 70% had trnL available sequences. Only for V . viatica did the ITS2 marker show only a classification at the taxonomic level of “Class” (in the localities “Royal Golf Club” and “Cranbourne”). The normalized frequencies of ASV classifications heatmaps (Figs. 2 and 3 ) and the bar plots of the RRA and the wPOO (Figures S5-8) highlight distinct dietary patterns across the different samples and localities. For example, Chrysocephalum predominated in faecal samples from Omeo and Cudgewa. Asteraceae was the only taxon consistently present in the faecal samples of K . scurra , with no representation at a higher taxonomic level. In the case of V . viatica , no single taxon was consistently present across all five sites The heatmaps revealed family-level dietary differences. Asteraceae was the dominant family in K . scurra faeces from four of the five localities, except at Beechworth, where Rosaceae, represented by Rosa rubiginosa , was dominant, as detected by the ITS2 and trnL markers. At each site, we identified multiple plant families in the grasshopper faeces, except for Shelley, where only Asteraceae was detected. Poaceae was only detected in samples from Cudgewa, with a high log10 frequency (Fig. 3 ). In all sites for K . scurra , faecal samples contained no more than two or three plant taxa. Markers varied in their ability to detect taxa at different levels. In Omeo, ITS2 detected Crassula sieberiana , while trnL detected the genus Crassula . Similarly, Plantago lanceolata was identified by ITS2 in Omeo and Bingo-Munjie (sample1), whereas rbcL classified the same sample at the order level of Lamiales and Plantago debilis in Bingo-Munjie (sample 1). For all those localities, trnL detected the genus Plantago . This variation highlights the differing taxonomic resolution of each marker, with ITS2 consistently providing finer species-level identification. The rbcL and trnL markers could not discern genetic variation among Asteraceae species, so we pooled them at the family level. The ITS2 marker pointed to seven species of Asteraceae, with low abundance of an undetermined Leucochrysum species in a sample from Bingo-Munjie, an undetermined Hypochaeris species in a sample from Cudgewa, a high abundance of Leptorhynchos squamatus and Euchiton japonicus in Shelley, and C. semipapposum in Omeo, Bingo-Munjie and Cudgewa samples, with C. apiculatum and an undetermined Chrysocephalum species in the Omeo and Cudgewa samples. Similarly, Rosaceae was only detected by ITS2 and trnL, and only ITS2 managed to assign it to the species, Rosa rubiginosa . In Beechworth, the genus Geranium was detected only at genus level by the ITS2 and trnL markers, while Fabaceae was only detected by trnL. For V. viatica , a broader array of plant taxa was detected compared to K . scurra . Families such Myrtaceae and Asteraceae were dominant across samples. Only the ITS2 marker was able to classify Myrtaceae to species level ( Kunzea ericoides and Gaudium laevigatum ), finding the family in three sites: Diamond Creek, Royal Golf Club, and Cranbourne. The genus Plantago was detected by ITS2 and rbcL in Truganina; however, ITS2 identified an undetermined Plantago species and Plantago gaudichaudii , while rbcL classified it as Plantago debilis . The genus Geranium was detected by ITS2 and trnL in Truganina, but without classification to the species level. Asteraceae was abundant and classified to species level only by ITS2, though the family was detected by all three markers. ITS2 identified Vittadinia cervicularis in the Royal Golf Club sample, Rutidosis leptorhynchoides and an undetermined species in Truganina, and Olearia teretifolia , O . ramulosa , and an undetermined Cassinia at Bayside. In Diamond Creek, ITS2 detected Cassinia longifolia , and in Truganina an undetermined Asteraceae species. The family Poaceae was detected by all three markers in Diamond Creek and Royal Golf Club, but in very low abundance only in Bayside by the rbcL marker. Only ITS2 was able to classify Poaceae at species level. Haloragaceae was detected in Diamond Creek by both ITS2 and trnL, with ITS2 identifying Gonocarpus humilis and trnL detecting the genus Gonocarpus . Similarly, in Bayside, Rutaceae was detected by the ITS2 and trnL markers, with ITS2 identifying Correa , while trnL only provided identification at the family level. Fabaceae was detected by ITS2 in Bayside, Royal Golf Club, and Cranbourne; by rbcL in Bayside; and by trnL in Cranbourne. ITS2 identified two species: Trifolium arvense (low abundance, Royal Golf Club) and Bossiaea cinerea (high abundance, Bayside), plus an undetermined species (medium abundance, Cranbourne). rbcL and trnL identified Fabaceae only at the family level. Ericaceae appeared in low abundance at the Royal Golf Club and Cranbourne via ITS2 and rbcL. ITS2 detected Monotoca scoparia at both sites and an undetermined species at Cranbourne; rbcL identified Woollsia pungens , Leucopogon ericoides , and an undetermined species. At the Royal Golf Club, rbcL also detected Rumex pulcher (Polygonaceae), Atriplex postrata (Chenopodiaceae), Cassytha pubescens (Lauraceae), and an undetermined Pittosporaceae, all in low abundance. trnL detected Pinus at low abundance in Truganina, while rbcL identified Pinus radiata . A Wilcoxon signed-rank test was used to compare the relative abundance of plant species between the survey data in Omeo and faecal samples from the same site. The test revealed a significant difference between the two datasets (𝑉=1643, 𝑝<0.001), indicating that the distribution of relative abundances in the faeces did not match the availability of plant species recorded in the survey. Dietary diversity analysis Alpha diversity A Kruskal-Wallis test revealed no significant difference in the Shannon diversity index between the grasshopper species (χ 2 (1) = 1.401, p = 0.224), but the choice of molecular marker significantly affected the diversity index (χ 2 (2) = 68.69, p < 0.001) (Figure S9 A). In contrast, Kruskal-Wallis tests revealed significant differences in Faith's Phylogenetic Diversity (χ 2 (1) = 10.117, p = 0.002), with V . viatica showing higher values. Additionally, the choice of marker significantly impacted the phylogenetic diversity (χ 2 (2) = 81.624, p < 0.001). ITS2 consistently displayed the highest diversity across both species (Figure S9 B). Lastly, Pielou's Evenness did not vary significantly between species (χ 2 (1) = 0.280, p = 0.597), but the markers showed notable differences (χ 2 (2) = 63.047, p < 0.001) (Figure S9 C). Comparing sites, Kruskal-Wallis tests indicated significant differences across all three markers (ITS2, rbcL, and trnL) for Faith’s Phylogenetic Diversity, Pielou’s Evenness, and the Shannon Index (Figure S10A-C). For ITS2, Faith’s Phylogenetic Diversity (χ 2 (9) = 35.619, p < 0.001), Pielou’s Evenness (χ 2 (9) = 34.624, p < 0.001), and Shannon Index (χ 2 (9) = 30.732, p < 0.001) showed significant variation. The rbcL marker exhibited similar significant differences for Faith’s (χ 2 (9) = 33.989, p < 0.001), Pielou’s (χ 2 (9) = 34.182, p < 0.001), and Shannon (χ 2 (9) = 34.105, p < 0.001), as did the trnL marker, with χ 2 (9) values of 35.077, 32.395, and 30.946, respectively, all with p < 0.001. Dunn’s test with Bonferroni correction revealed specific significant site-pair differences, with only Bingo Munjie and Royal Golf Club differing significantly for Faith’s Phylogenetic Diversity across all markers (Table S5). Beta diversity A significant difference in the plant community composition was observed between K. scurra and V. viatica with the pairwise PERMANOVA analysis (Table S6). The PCoA plot for unweighted (Fig. 4 A) and weighted (Fig. 4 B) UniFrac distance revealed distinct clustering patterns among the samples regarding marker and species, corroborating the inference that plant composition in faeces samples varies between K . scurra and V . viatica . The ellipses around the points indicate the 95% confidence intervals for each grasshopper species. Using the Unweighted UniFrac distance matrix, ITS2 showed the strongest separation in plant species between grasshopper diets (F(1,37) = 7.062, p = 0.001). The PCoA plot showed some overlap but largely distinct species groups with similar spread; the first two axes explained 31.36% and 16.33% of the variance, respectively (Fig. 4 A). rbcL revealed similar group separation (F(1,37) = 6.994, p = 0.001), with a PCoA pattern comparable to ITS2, differing mainly in Beechworth. Its first two axes explained 23.78% and 16.09% of the variance. trnL showed moderate separation (F(1,36) = 5.611, p = 0.001) and a wider spread for K. scurra ; the first two axes explained 36.13% and 18.39% (Fig. 4 A). Using the Weighted UniFrac matrix, ITS2 again showed the strongest group separation (F(1,37) = 16.281, p = 0.001), with variance explained rising to 70.06% and 11.32% (Fig. 4 B). rbcL showed very strong separation (F(1,37) = 15.311, p = 0.001), with tighter K. scurra and wider V. viatica clustering; variance explained increased to 56.84% and 17.72%. trnL showed moderate-to-strong separation (F(1,36) = 4.916, p = 0.003), with more distinct within-group clustering and variance explained of 47.53% and 21.26%, indicating improved representation with the weighted metric (Fig. 4 B). Weighted UniFrac PCoA plots showed clearer group separation than the unweighted metric, highlighting the impact of relative abundance on plant community differences. K. scurra showed less variation with ITS2 and rbcL, while V. viatica varied less with trnL. Ellipse overlap suggests some shared plant composition. Higher variance explained by the principal coordinates in the weighted plots indicates a better representation of community variability. Discussion Understanding the diet of threatened species and the plant diversity in their habitat is crucial for developing effective conservation strategies. This study represents an attempt to expand knowledge on the diet of two morabine grasshopper species and characterize plant diversity associated with these species using molecular tools applied to faecal material. This approach could improve the understanding of plant–grasshopper trophic networks and better inform revegetation and conservation initiatives. Methodology Metabarcoding is highly effective in identifying taxa from degraded DNA in the gut or faeces, providing a comprehensive view of an organism's diet at high taxonomic resolution (de Sousa et al. 2019 ). However, it can introduce biases, such as from sample handling, contamination, DNA extraction, PCR, sequencing, and taxonomic assignment, that affect dietary assessments (Alberdi et al. 2019 ). To minimise these, we adopted a conservative approach: spike-in controls to detect mistagging, and PCR replicates to identify and exclude unreliable detections, retaining taxa only if found in two or more replicates. Instead of combining replicates, we presented them separately to assess consistency. We also used multiple markers to broaden taxonomic coverage and adjusted for sequencing depth to improve comparability. Primer selection is critical in metabarcoding (Alberdi et al. 2019 ), as different environments and goals require specific markers (Barnes et al. 2022 ). We used three common plant markers for targeting degraded DNA: rbcL, ITS2, and trnL (Ando et al. 2020 ) Although trnL is frequently used in herbivore faecal studies (31 out of 45 studies (Ando et al. 2020 )), recent research supports ITS2 for broader taxonomic coverage (Moorhouse-Gann et al. 2018 ). Our results reflected this, with ITS2 capturing greater diversity than rbcL and trnL, though diversity patterns across Shannon, Faith’s Phylogenetic Diversity, and Pielou’s Evenness were consistent across all markers. Notably, ITS2 provided the strongest species differentiation in UniFrac analyses. Each marker had strengths and limitations. For instance, while ITS2 missed some Acacia species, rbcL and trnL successfully identified them in control samples, underscoring the benefits of a multi-marker approach (Alberdi et al. 2018 ). The reliability of molecular identifications depends on the quality and coverage of reference datasets (Kress et al. 2015 ; Nielsen et al. 2018 ; Taberlet et al. 2018 ). Global databases generally support genus or family-level identification (Nakahara et al. 2016 ), but regional databases often enable more precise species-level assignments (Gold et al. 2021 ). Testing different reference libraries is advisable, as outcomes may vary with marker and dataset specifics. For example, while Barnes et al. ( 2022 ) achieved consistent ITS2 accuracy across databases, trnL assignments improved significantly with local libraries, yielding higher specificity. In our study, using global libraries with trnL reduced Type I errors in control samples. Diet composition and diversity While factors like environmental conditions and vegetation structure influence grasshopper assemblages, host plant availability is more critical (Smith and Capinera 2005 ). Our study reveals both differences and overlaps in plant species consumed by these two grasshoppers. From faecal samples, we identified 66 plant taxa, including native and introduced species. Consistent with previous studies (Blackith and Blackith 1966 ; White 1956 ; White et al. 1964 ), both species mainly fed on native herbs and shrubs. These included known food plants from their habitat and unexpected sources like grasses and exotic weeds at some sites. Dietary choices may reflect a need to balance nutrition when resources are of low quality (Bernays and Simpson 1990 ). Expanding on Blackith and Blackiths ( 1966 ) work, we provide new data and insights from the species' natural habitat. Genera such as Olearia , Cassinia , Kunzea , Correa , and Atriplex , previously associated with other morabine species, were also found in V . viatica ’s faeces, though these have not been noted for V . viatica species before. Metabarcoding thus reveals broader dietary interactions in the natural environment of this species than evident from feeding studies. K . scurra 's diet was dominated by herbs from the Asteraceae family, while V . viatica consumed a higher abundance of shrubs from both the Asteraceae and Myrtaceae families. Although there was some overlap in plant families found in their faeces, including Asteraceae, Geraniaceae, and Plantaginaceae, K . scurra exclusively consumed Crassulaceae and Rosaceae, while V . viatica had a broader dietary range, with eight additional families that include Chenopodiaceae, Haloragaceae, and Ericaceae. The diversity of plant species available can greatly influence the range of insect diets (Forister et al. 2015 ), and this is evident in our findings. Asteraceae, the second most diverse plant family in Australia with around 1,417 species, is surpassed only by Myrtaceae, which has approximately 1,858 species (Orchard 2015 ). These two families were both highly diverse and abundant in our faecal samples. This could indicate an evolutionary adaptation to use these families, making them key components of the grasshoppers’ ecological niches and potentially critical to the survival of these species. Although grasses are not typically a major part of morabine grasshopper diets (Blackith and Blackith 1966 ), all markers detected introduced Poaceae in some faecal samples. For K. scurra , grasses appeared only at Cudgewa, a small patch near grazing pastures, while V. viatica had grasses in samples from the managed sites of Diamond Creek and Royal Melbourne Golf Club. K. scurra also consumed introduced Rosaceae ( Potentilla recta , Rosa rubiginosa ), detected via ITS2 at Cudgewa and Beechworth Cemetery, consistent with lab consumption of native Rosaceae like Acaena x ovina . Both species consumed native and introduced Plantaginaceae, with Lamiales taxa found at Cudgewa ( K. scurra ) and Truganina ( V. viatica ), across all markers. The only non-native Asteraceae, Hypochaeris sp., was found in Cudgewa via ITS2, while other exotics, including Ericaceae, were detected in V. viatica via rbcL. Pinus radiata was found in V. viatica at Truganina and Diamond Creek (rbcL and trnL), though absent at Truganina, likely due to pollen contamination during its pollination season (Fountain and Cornford 1991 ; Lillt and Sweet 1976 ). Our findings suggest that endangered species, like K . scurra (28 plant taxa in its faeces), may rely on a more limited range of food sources, similar to Celes akitanus (Orthoptera: Acrididae), which has a similar number of 36 plant taxa detected in its faeces (Yamamoto and Uchida, 2018 ). In contrast, generalist pest species, such as the wētā Hemiandrus sp. (Orthoptera: Anostostomatidae), exploit a much broader variety of plants, with 79 taxa found in its faeces (Nboyine et al. 2019 ). Both studies used grasshopper faeces and metabarcoding techniques. Future research could examine whether the narrower dietary range of endangered species is due to habitat specialization, food availability, or other environmental pressures, which could have important implications for conservation and management strategies. The diversity metrics highlight the complexities of dietary pattern differences between these morabine species. While the Shannon diversity index and Pielou’s Evenness showed no significant differences between the grasshopper species, Faith’s Phylogenetic Diversity revealed a clear separation. This suggests that, although the overall number and distribution of plant taxa (richness and evenness) are similar between K . scurra and V . viatica , the phylogenetic breadth of the taxa they consume differs. Faith’s index considers evolutionary relationships, implying that V . viatica (as shown in its diet composition) may be consuming plants from a wider variety of evolutionary lineages, even if the total number of taxa is similar. These patterns become more pronounced when comparing sites, where all metrics (Shannon, Faith, and Evenness) showed significant differences, suggesting strong geographic variation in the availability or preference for specific plant taxa. When moving to beta diversity, the results further emphasize the complexity of plant community composition. The significant separation between the dietary composition of the species detected by PERMANOVA, particularly using weighted UniFrac, indicates that the relative abundance of plant taxa differs between the grasshopper species. However, the overlap seen in the PCoA plots shows that, despite the overall differences in dietary plant composition, there are shared taxa between the two species. The overlap of sites in the UniFrac PCoA plots illustrates that although site-specific differences exist, certain plant taxa are commonly consumed across locations, leading to some degree of convergence in the diet. The overlap between species and sites in the PCoA plots might reflect that both grasshopper species are exploiting similar plant communities in some environments. This highlights the complexity of ecological interactions, where morabine species may have distinct dietary preferences but still show overlap due to shared habitats or plant availability. Grasshoppers are known to change their diet as they progress through different stages of their development (Ibanez et al. 2013), so future research using metabarcoding as a tool should explore seasonal diet variations across all life stages and between sexes to better understand herbivore-plant relationships in morabine grasshoppers. For instance, younger grasshoppers might rely more on softer leaves (e.g. Miura and Ohsaki 2004 ), while adults may consume a broader range (e.g. Dopman et al. 2002 ). Additionally, females might favour plants high in certain nutrients that could support egg production (e.g. Behmer and Joern 1994 ). Investigating the effects of habitat changes in plant communities driven by climate change (Hamann et al. 2021 ), habitat reduction (Nufio et al. 2011 ), and human activities and grasshopper diet will offer deeper insights into their ecological needs now and into the future, and it is worth considering niche partitioning with other grasshoppers (e.g. Ibanez et al. 2013) and differences between bioregions (e.g. Pitteloud et al. 2021 , 2023 ). Habitat restoration and conservation Our analysis of morabine grasshopper diets provides insights for revegetation and conservation. Both K. scurra and V. viatica appear well adapted to diverse plant resources, consuming a broader range than previously known. The mismatch between plant abundance in the Omeo survey and faecal samples suggests that K. scurra feeds selectively, possibly avoiding less palatable or defended plants. For instance, Themeda triandra , the most abundant species in the survey, was absent from faecal samples and likely serves as shelter rather than food (White 1956 ). These findings highlight the importance of preserving native vegetation that supports grasshopper diets and habitat resilience. Since only one site was surveyed for diet–availability comparison, further studies across multiple locations are needed to confirm these patterns and assess how plant availability and habitat structure shape dietary choices. The impact of these insects on rare or endangered plants remains unclear and likely depends on plant and insect densities (Myers and Sarfraz 2017 ). Still, their influence on plant communities is an important consideration for revegetation planning. Declarations Declaration of authorship All authors certify that they have participated sufficiently in the work to take public responsibility for the content, and that all those who qualify for authorship are listed Acknowledgements Amidst the COVID-19 pandemic and associated travel restrictions, we obtained travel authorizations from the Victoria State Government to conduct the early stages of this work. We would like to thank Steve J. Sinclair for their constructive feedback and thoughtful suggestions, which enhanced the quality of this work. Nancy Endersby, Ashley MacMahon, and Qiong Yang for valuable assistance during laboratory work. Alejandro Tello, and Dale Tonkinson (CFA) for their fieldwork support. We also express our gratitude to James Maino and Gayee Maino, the Bayside Council (Pauline Reynolds and John Eichler), The Royal Melbourne Golf Club (Stuart Moodie), Royal Botanical Gardens of Cranbourne, for granting us access to the study sites. Funding Hiromi Yagui was funded by Melbourne Research Scholarship from the University of Melbourne. Michael R. Kearney and Ary A. Hoffmann received funding from the Australian Research Council, Discovery Grant DP190100990, the Victorian Department of Land, Water and Planning and the Melbourne City Council. Conflict of interest The authors declare that they have no conflict of interest. Ethics approval Not applicable. Consent to participate Not applicable. Consent for publication Not applicable. Availability of data and materials All data produced from this study are provided in the corresponding manuscript. Sequencing data have been submitted to the NCBI Sequence Read Archive (SRA) under accession number PRJNA1244017. The data will be made publicly available upon publication of the manuscript. Code availability: The code used for analysis is provided in the supplementary material. Authors' contributions Hiromi Yagui: Conceptualisation, Methodology, Investigation, Data curation, Formal analysis, Writing – original draft, Writing – review and editing. Michael R. Kearney: Conceptualisation, Funding acquisition, Methodology, Investigation, Project administration, Writing – review and editing, Supervision. Ary A. Hoffmann: Conceptualisation, Funding acquisition, Methodology, Investigation, Project administration, Writing – review and editing, Supervision. Melissa E. Carew: Conceptualisation, Methodology, Investigation, Writing – review and editing, Supervision. References Alberdi A, Aizpurua O, Bohmann K, et al (2019) Promises and pitfalls of using high-throughput sequencing for diet analysis. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-6555039","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":463603492,"identity":"b8adc535-a64d-487f-8be1-cf0e2f681d5f","order_by":0,"name":"Hiromi Yagui","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA5UlEQVRIiWNgGAWjYBACA2YwCeEc+AAk2NhJ0XJwBkgLMyEtyBxmHjBJQIs5O+/DRzcKGBL7+9cYHrb5tU2ej5mB8cPHHNxaLJvZjY1zDBgSZ9x4Y3A4t++2YRszA7PkzG14HHaYjU0apKXhxhmglp7bjEAtbMy8xGiZD9Ji2XPbnngtG873GBxm+HE7kRgtzEC/SBhvvMFWcLC34XZyGzNjM36/nD/G+Djnj43svPOHN3/48ee27fz25oMfPuLRAgUSjg0SCQwMjG0gDmMDQfUgYM/AfwBI/SFK8SgYBaNgFIwwAAAgd1Ax98ut4gAAAABJRU5ErkJggg==","orcid":"","institution":"The University of Melbourne","correspondingAuthor":true,"prefix":"","firstName":"Hiromi","middleName":"","lastName":"Yagui","suffix":""},{"id":463603493,"identity":"d33012b6-6114-403a-b7fa-2acf6b1ab044","order_by":1,"name":"Michael R. 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Carew","email":"","orcid":"","institution":"The University of Melbourne","correspondingAuthor":false,"prefix":"","firstName":"Melissa","middleName":"E.","lastName":"Carew","suffix":""}],"badges":[],"createdAt":"2025-04-29 09:53:14","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-6555039/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-6555039/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1007/s10841-025-00732-1","type":"published","date":"2025-11-08T15:56:54+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":83662860,"identity":"ba4c686a-9825-4b56-9a5f-8894c2d73731","added_by":"auto","created_at":"2025-05-30 10:33:03","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1687481,"visible":true,"origin":"","legend":"\u003cp\u003eSampled sites within the Victorian distributions of Keyacris scurra and Vandiemenella viatica. Extant native vegetation source: MGV- National Vegetation Information System V6.0 © Australian Government Department of Agriculture, Water and the Environment (Access: 26 May 2022).\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-6555039/v1/8a35968c051088554e8023f3.png"},{"id":83663349,"identity":"6511f9af-ab87-4a21-a681-f10ef3a4b931","added_by":"auto","created_at":"2025-05-30 10:41:03","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":1539002,"visible":true,"origin":"","legend":"\u003cp\u003eNormalized frequency of ASV based taxonomic classification for Keyacris scurra with ITS2 (local library, 0.95 confidence), rbcL (regional library, 0.90 confidence) and trnL (global library, 0.95 confidence). Sample codes are found in Table 1, ‘rep’ in the name stands for repetitions.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-6555039/v1/2b1b328e76ed3ef441ff396d.png"},{"id":83662864,"identity":"78ef4fe5-fc93-4dca-9db3-cbf5443bf089","added_by":"auto","created_at":"2025-05-30 10:33:03","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":1678036,"visible":true,"origin":"","legend":"\u003cp\u003eNormalized frequency of ASV based taxonomic classification for Vandiemenella viatica with ITS2 (local library, 0.95 confidence), rbcL (regional library, 0.90 confidence) and trnL (global library, 0.95 confidence). Sample codes are found in Table 1, ‘rep’ in the name stands for repetitions\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-6555039/v1/4db249410d7eb884ad903aa5.png"},{"id":83662862,"identity":"026ef97a-ccd2-4d17-8d1d-00a624dcf283","added_by":"auto","created_at":"2025-05-30 10:33:03","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":1455638,"visible":true,"origin":"","legend":"\u003cp\u003eA. Unweighted UniFrac Principal Coordinate Analysis for ITS2, rbcL and trnL markers. B. Weighted UniFrac Principal Coordinate Analysis for ITS2, rbcL and trnL markers. The dashed ellipses represent 95% confidence intervals around the grasshopper species groups, calculated based on the covariance of data points along the principal coordinates PC1 and PC2 are represented by the x and y axes, respectively\u003c/p\u003e","description":"","filename":"41.png","url":"https://assets-eu.researchsquare.com/files/rs-6555039/v1/24a67cac819d077972df0c90.png"},{"id":95563881,"identity":"831f7a2d-31a1-42ab-b48d-2fb169b3aa47","added_by":"auto","created_at":"2025-11-10 16:00:06","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":7395260,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-6555039/v1/77c99fee-f48d-4021-ba99-e993973bf1b0.pdf"},{"id":83662867,"identity":"53a6e174-2da7-4235-82dd-a6129af387ab","added_by":"auto","created_at":"2025-05-30 10:33:03","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":2824825,"visible":true,"origin":"","legend":"","description":"","filename":"290425Supplementarymaterial.docx","url":"https://assets-eu.researchsquare.com/files/rs-6555039/v1/62cb949e2431fdcf14c0316b.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Unravelling the diet of flightless grasshoppers for conservation purposes using DNA metabarcoding","fulltext":[{"header":"Introduction","content":"\u003cp\u003eUnderstanding the composition and availability of food plants is crucial for assessing habitat suitability (Owen-Smith \u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e2003\u003c/span\u003e), especially in herbivorous species with low dispersal ability that are highly dependent on local resources and are more vulnerable to changes in their surroundings and geographical range (Kotiaho et al. \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2005\u003c/span\u003e; Reinhardt et al. \u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e2005\u003c/span\u003e). This understanding is useful for managing existing habitats to ensure the resources persist and for restoration of new habitat before reintroductions. However, collecting information on food plants is challenging due to the obstacles involved in field observations of feeding behaviours, particularly in small, cryptic animals (Blackith and Blackith \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1966\u003c/span\u003e; Valentini et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2009\u003c/span\u003e), or when morphological identification of consumed plant food is difficult. In these cases, genetic analyses have emerged as a powerful tool in ecological studies of diet (Murray et al. \u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Pompanon et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Symondson \u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e2002\u003c/span\u003e). In particular, tools like DNA metabarcoding offers higher or equivalent richness and taxonomic resolution than traditional methods such as microscopy (Goldberg et al. \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Iwanowicz et al. \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Littleford-Colquhoun et al. \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e2022\u003c/span\u003e; Soininen et al. \u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e2009\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDiet analyses based on DNA metabarcoding typically involves identifying the species consumed by characterizing the DNA present in whole specimens, regurgitates, gut, or faecal samples (Pompanon et al. \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e2012\u003c/span\u003e). Despite the widespread use of genetic analysis of faecal samples in vertebrate research, it has been much less commonly applied to insects (Ando et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Most studies involving herbivorous insects have relied on the examination of stomach contents, which necessitates sacrificing individuals (e.g. Masonick et al., \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e2019\u003c/span\u003e; Pumari\u0026ntilde;o et al. \u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e2011\u003c/span\u003e), a practice unsuitable for endangered species. Valentini et al. (\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) were pioneers in applying DNA metabarcoding to herbivorous insect faecal samples. They successfully determined the diet of mammals, birds and invertebrates, including two orthopteran species, \u003cem\u003eChorthippus biguttulus\u003c/em\u003e and \u003cem\u003eGonphocerippus rufus\u003c/em\u003e, by analysing the chloroplast trnL intron (UAA). However, in the 15 years since their study, only a few published studies have utilized faecal samples from herbivorous Orthoptera, and only the study of Yamamoto and Uchida (\u003cspan citationid=\"CR65\" class=\"CitationRef\"\u003e2018\u003c/span\u003e) was oriented towards conservation efforts.\u003c/p\u003e \u003cp\u003e \u003cem\u003eKeyacris scurra\u003c/em\u003e is an endangered grasshopper of the endemic subfamily Morabinae, recently rediscovered in Victoria. Its distribution has become limited to native remnants of grassland such as along roads and cemeteries of Victoria, New South Wales, and the Australian Capital Territory (Hoffmann et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; White \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1956\u003c/span\u003e). Other species belonging to the same family, in particular the related \u003cem\u003eVandiemenella viatica\u003c/em\u003e, face similar habitat challenges. Both species retain relatively high genetic diversity, even in small habitat patches (Hoffmann et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e2023\u003c/span\u003e), and management of food resources in small vegetation patches is likely to be critical for their ongoing survival (Hoffmann et al. \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). For species like these grasshoppers that can persist in small patches, translocations are a promising tool for conservation (Yagui et al. \u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). So, understanding the diet of these insects becomes important for effective management in restored habitats. A non-invasive method like faecal metabarcoding is particularly useful for assessing the diet of endangered species (Nagarajan et al. \u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Valentini et al. \u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e2009\u003c/span\u003e) such as \u003cem\u003eK\u003c/em\u003e. \u003cem\u003escurra\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eApart from some observations and habitat descriptions from the 1950s and 60s (e.g. White \u003cspan citationid=\"CR62\" class=\"CitationRef\"\u003e1956\u003c/span\u003e; White et al. \u003cspan citationid=\"CR63\" class=\"CitationRef\"\u003e1964\u003c/span\u003e), the only dietary study of morabine grasshoppers was undertaken almost 60 years ago by Blackith and Blackith (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1966\u003c/span\u003e) who analysed the colours left by plants when ingested in the ilial diverticula, conducted starvation experiments, and observed differences in feeding avidity among various species of morabine grasshoppers. Their results indicated that, in captivity, the group is generally polyphagous, with \u003cem\u003eK. scurra\u003c/em\u003e and species of \u003cem\u003eVandiemenella\u003c/em\u003e feeding with varying avidity on the leaves and flowers of numerous species (21 and 34 species, respectively). However, Blackith and Blackith (\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e1966\u003c/span\u003e) highlighted the challenge of observing diet preferences in the wild, as grasshoppers feed on post-rainfall seedlings, and multiple plants can produce similar colours in the diverticula. While this study offers a foundation for understanding their diet, much remains unknown about their natural food sources due to the difficulty of behavioural observations in the field.\u003c/p\u003e \u003cp\u003eHere we determine the food plant species of five populations of the endangered \u003cem\u003eK. scurra\u003c/em\u003e and 5 populations of \u003cem\u003eV. viatica\u003c/em\u003e, using DNA metabarcoding of faecal material. We assessed the performance of various plant markers and public reference libraries for accurate dietary assessment, and we compared diversity metrics of diets across species and sites.\u003c/p\u003e"},{"header":"Materials and methods","content":"\u003cp\u003e \u003cem\u003eStudy sites, sampling, and sample preservation\u003c/em\u003e \u003c/p\u003e \u003cp\u003e \u003cem\u003eK. scurra\u003c/em\u003e and \u003cem\u003eV. viatica\u003c/em\u003e adult individuals were collected from five localities in Victoria each (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The number of collected individuals varied according to the abundance found at the site at the time of collection (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). A minimum of three individuals of both sexes collected from a site were placed in a sterile plastic container. We then waited between three to twelve hours to allow for complete defecation before collecting the faeces, following the approach of allowing defecation prior to sample collection as described by Kaunisto et al. (2017) for odonates. Samples were then pooled to represent a collection site. There is a notable increase in correct identification when more than 5 mg of faeces is available as dry mass, with an inflection point around 15 mg (Rytk\u0026ouml;nen et al. \u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e2019\u003c/span\u003e). For this reason, we used 20 mg of dry matter per sample.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSample and site information including Ecological Vegetation Classes (EVCs) and the Bioregional conservation status of the EVC.\u003c/p\u003e \u003cdiv class=\"Credit\"\u003e\u003cp\u003eSource: Nature Kit (Accessed: April 2024)\u003c/p\u003e\u003c/div\u003e\u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"8\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSpecies\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSite\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSample code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eNumber of individuals\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLatitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eLongitude\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eEVC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003eBioregional Conservation Status\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e \u003cp\u003e\u003cem\u003eKeyacris scurra\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eOmeo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKSO1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-37.0933\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e147.5937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eMontane Grassy Woodland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDepleted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKSO2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBingo Munjie\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKSH1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-37.0187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e147.5822\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHeathy Dry Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLeast Concern\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKSH2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCudgewa\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKSC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-36.1118\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e147.8539\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eValley Grassy Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEndangered\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKSC2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eShelley\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKSS1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-36.1291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e147.5944\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHerb-rich Foothill Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLeast Concern\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBeechworth\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eKSB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-36.3438\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e146.6908\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGrassy Dry Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eDepleted\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003cem\u003eVandiemenella viatica\u003c/em\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDiamond creek\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVVD1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-37.6645\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e145.1643\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGrassy Dry Forest\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLeast Concern\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTruganina cemetery\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVVT1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-37.8255\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e144.7202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003ePlains Grassland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEndangered\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eBay Road Reserve\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVVB1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e7\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-37.9551\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e145.0273\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eGrassy Woodland/Damp Sands Herb-rich Woodland Mosaic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eEndangered\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRoyal Golf Club\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVVR1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-37.9715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e145.0289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHeathy Woodland/Sand Heathland Mosaic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLeast Concern\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRoyal Botanical G. Cranbourne\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVVC1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e15\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e-38.1331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e145.2696\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eHeathy Woodland\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e \u003cp\u003eLeast Concern\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAny faeces produced in the field were preserved using silica gel beads at a ratio of 1:4. (Taberlet and Luikart 1999) and were stored in the laboratory at \u0026minus;\u0026thinsp;4\u0026deg;C (Taberlet et al. \u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These methods are effective in preserving herbivore faeces without compromising DNA quantity and quality (Piggott and Taylor \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e2003\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003ePlant DNA library construction\u003c/em\u003e \u003c/p\u003e \u003cp\u003ePlant DNA reference libraries were prepared in R version 4.3.1 (R Core Team 2023) using the Rstudio environment (RStudio Team 2023). Similar to the study of Barnes et al. (\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2022\u003c/span\u003e), three different reference libraries were created: a local, a regional and a global library. For the local and regional library, we compiled plant species data within a 5 km radius of collection sites using the Atlas of Living Australia\u0026rsquo;s \u0026ldquo;Explore your area\u0026rdquo; tool. This species list served as a reference for the WFO.match function in the R package \u0026lsquo;WorldFlora\u0026rsquo; (Kindt \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e), to generate a list of synonyms, which we included in a combined reference library from BOLD Systems (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://boldsystems.org/index.php/databases\u003c/span\u003e\u003cspan address=\"https://boldsystems.org/index.php/databases\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) and NCBI GenBank (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ncbi.nlm.nih.gov/nuccore/\u003c/span\u003e\u003cspan address=\"https://www.ncbi.nlm.nih.gov/nuccore/\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e) with \u0026lsquo;refdb\u0026rsquo; (Keck and Altermatt \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e2023\u003c/span\u003e) (accessed on August 15\u0026ndash;20, 2023). When reference sequences were unavailable, we substituted sequences from Australian congeners. We further refined the list using \u0026lsquo;rgbif\u0026rsquo; (v3.7.8) with data from GBIF (Chamberlain et al. \u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWe created a global reference NCBI database for each of the markers using the RESCRIPt (Robeson et al. \u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e2021\u003c/span\u003e) with the function \u0026lsquo;qiime rescript get-ncbi-data\u0026rsquo; in QIIME2 (version 2023.9) (Bolyen et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) for further comparison.\u003c/p\u003e \u003cp\u003eFrom these available inventories, one species stood out in the Truganina cemetery, the Button Wrinklewort, \u003cem\u003eRutidosis leptorrhynchoides.\u003c/em\u003e Because of its conservation importance, recent media attention (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.abc.net.au/news/2023-03-10/vic-truck-crushes-endangered-wildflowers-truganina-cemetery/101939222\u003c/span\u003e\u003cspan address=\"https://www.abc.net.au/news/2023-03-10/vic-truck-crushes-endangered-wildflowers-truganina-cemetery/101939222\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e), and significance in ongoing management efforts, \u003cem\u003eR. leptorrhynchoides\u003c/em\u003e leaf tissue was collected by staff from the Arthur Rylah Institute under Permit 10010953, issued under the Victorian \u003cem\u003eFlora and Fauna Guarantee Act 1988\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eFor the local DNA reference libraries, 2529 species (1095 species for the \u003cem\u003eK\u003c/em\u003e. \u003cem\u003escurra\u003c/em\u003e collection sites and 2183 species for the \u003cem\u003eV\u003c/em\u003e. \u003cem\u003eviatica\u003c/em\u003e collection sites), and 930 genera (457 for the \u003cem\u003eK\u003c/em\u003e. \u003cem\u003escurra\u003c/em\u003e sites and 863 for the \u003cem\u003eV\u003c/em\u003e. \u003cem\u003eviatica\u003c/em\u003e sites) were obtained from the Atlas Living Australia using the 5 km radius from each collection site (Figure \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003cem\u003eDNA extraction, amplification and sequencing\u003c/em\u003e \u003c/p\u003e \u003cp\u003eWe extracted DNA from faecal material using, the Macherey-Nagel NucleoSpin\u0026reg; Plant II Mini kit with minor modifications to the manufacturer\u0026rsquo;s protocols (Supplementary Section 1). After extraction, eluted DNA samples were stored at -20\u0026deg;C.\u003c/p\u003e \u003cp\u003eA two-step PCR protocol was implemented using the three more common universal markers for herbivores faeces analysis (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) in the first PCR. In the second PCR, multiplex identifiers and sequencing adaptors were added (Supplementary Section 1). Samples were run as three PCR replicates. We also included a negative control that lacked the DNA extraction sample, a positive control made of the Clustered everlasting (\u003cem\u003eChrysocephalum semipappossum\u003c/em\u003e) DNA extract, and a spike control consisting of 40% \u003cem\u003eC. apiculatum\u003c/em\u003e, 40% \u003cem\u003eC. semipapossum\u003c/em\u003e, and 20% \u003cem\u003eAcacia\u003c/em\u003e faeces.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDNA barcodes used to infer food plants of \u003cem\u003eKeyacris scurra\u003c/em\u003e and \u003cem\u003eVandiemenella viatica\u003c/em\u003e\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRegion\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003ebp\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eName\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSequence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eAuthor\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003enr ITS2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e187\u0026ndash;387\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUniPlantF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026prime;-TGT GAA TTG CAR RAT YCM G-3\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(Moorhouse-Gann et al. \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2018\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eUniPlantR\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026prime;-CCC GHY TGA YYT GRG GTC DC-3\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ecp trnL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e10\u0026ndash;143\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etrnLg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026prime;-GGG CAA TCC TGA GCC AA-3\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(Taberlet et al. \u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e2007\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003etrnLh\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026prime;-CCATTGAGTCTCTGCACCTATC-3\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ecp rbcL\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;150\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003erbcLZ1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026prime;- ATG TCA CCA CAA ACA GAG ACT AAA GCA AGT-3\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e(Poinar et al. \u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e1998\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003erbcL19\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5\u0026prime;- AGA TTC CGC AGC CAC TGC AGC CCC TGC TTC-3\u0026prime;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAfter amplification and library preparation of amplicons, the samples were sent to the Australian Genome Research Facility (AGRF), high throughput DNA sequencing using a 600-cycle flow cell MiSeq sequencing kit V3 (300 bp \u0026times; 2) (Illumina Corporation).\u003c/p\u003e \u003cp\u003e \u003cem\u003eSequence analysis and taxonomic assignation\u003c/em\u003e \u003c/p\u003e \u003cp\u003eQIIME2 (version 2023.9) (Bolyen et al. \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2019\u003c/span\u003e) was used for all bioinformatic processing including trimming, denoising, filtering, taxonomic assignment, and diversity analysis. Specific plugins and parameter values are shown in Supplementary Section 2. We used the QIIME 2 plugin that implements DADA2 to generate amplicon sequence variants (ASVs), incorporating robust error-correction steps that effectively reduce sequencing errors and remove chimeric sequences (Callahan et al. \u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Finally, we used \u0026ldquo;qiime2 feature-classifier classify-sklearn\u0026rdquo; (Pedregosa et al. \u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e2011\u003c/span\u003e) for taxonomic assignation. This classifier is a multinomial naive Bayes machine-learning classifier that surpasses the species-level accuracy of other widely used methods like VSEARCH and BLAST+ (Bokulich et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThrough comparison of the observed and expected spike control results, we calculated several metrics (Accuracy, Specificity, False Positive Rate (FPR), False Discovery Rate (FDR), Precision, Recall, F1-Score, and Matthews Correlation Coefficient (MCC) (Bokulich et al. \u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; Hleap et al. \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Valencia et al. \u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e2024\u003c/span\u003e)) across the different confidence thresholds (0.7 (default), 0.8, 0.9, 0.95, and 0.97) and markers (ITS2, rbcL, trnL) for global, local and regional libraries. Visualized metrics highlighted classifier performance by confidence level, marker, and library, prioritizing higher F1 and MCC scores with lower FPR and FDR for optimal classification parameters (Figure S2).\u003c/p\u003e \u003cp\u003eThe sklearn-based classifier showed rbcL and trnL with the global library generally identified ASVs at the genus level, while ITS2 across all libraries reached a finer species-level resolution. The regional and local libraries improved rbcL and trnL\u0026rsquo;s resolution compared to the global library (Figure S3). Final ITS2 classifications used the local library at 0.95 confidence, while rbcL achieved optimal results with the regional library at 0.90 confidence, and trnL performed best with the global library at 0.95 confidence.\u003c/p\u003e \u003cp\u003eAfter the taxonomic classification, the data underwent analysis using the most common indicators for faecal metabarcoding dietary data, frequency of occurrence (FOO) as weighted per cent of occurrence (wPOO) and relative read abundance (RRA) (Ando et al. \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Deagle et al. \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2019\u003c/span\u003e)\u003c/p\u003e \u003cp\u003e \u003cem\u003eDiversity analysis\u003c/em\u003e \u003c/p\u003e \u003cp\u003eWe used the QIIME2 q2-diversity plugin for diversity analyses, calculating alpha diversity metrics and statistical tests, including the Shannon Index, Faith\u0026rsquo;s Phylogenetic Diversity, Pielou\u0026rsquo;s Evenness, and Unweighted and Weighted UniFrac Distances. To compare plant community composition in the diets of the two grasshopper species, we conducted a pairwise PERMANOVA with the unweighted and weighted UniFrac distance matrices. The unweighted UniFrac metric captures taxa presence or absence, while the weighted UniFrac metric incorporates relative abundance, offering a fuller perspective on diversity and community composition (Lozupone et al. 2007). Additionally, we performed a Principal Coordinates Analysis (PCoA) based on both UniFrac matrices to further examine community composition differences.\u003c/p\u003e \u003cp\u003eTo assess whether the sequencing depth was sufficient to capture the full diversity of the samples, we plotted a) alpha rarefaction, which shows the relationship between sequencing depth and the observed diversity, and b) sample retention, which indicates the number of samples that remain at each sampling depth. Details of the metrics computed can be found in the QIIME2 documentation.\u003c/p\u003e \u003cp\u003eThe Kruskal-Wallis test was employed for comparisons, as the traditional ANOVA for the diversity metrics failed to meet the essential assumptions of normality and homogeneity of variances (see results for details). Dunn\u0026rsquo;s test was used as a post-hoc test following Kruskal-Wallis to identify pairwise differences in the diversity metrics across sites within each marker.\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e \u003cem\u003ePlant diet composition\u003c/em\u003e \u003c/p\u003e \u003cp\u003eAcross both species, MiSeq DNA sequencing returned a total of 4,567,488 raw reads after demultiplexing: 1,193,073 reads corresponded to ITS2, 1,845,060 to rbcL, and 1,529,355 to the trnL marker. Following denoising with the DADA2 plugin in QIIME2, we retained 234 ASVs for ITS2, 111 for rbcL, and 113 for trnL. Alpha rarefaction curves for all amplicons plateaued, indicating sufficient sequencing depth across samples (Figure S4). On average, 76.15% of ITS2 ASVs, 69.12% of rbcL ASVs, and 79.14% of trnL ASVs were consistently detected in at least two of the three PCR replicates (Table \u003cspan refid=\"MOESM1\" class=\"InternalRef\"\u003eS1\u003c/span\u003e). A detailed summary of the number of reads retained in the final dataset per amplicon and sample is provided in Table S2.\u003c/p\u003e \u003cp\u003eAfter applying QIIME2 feature-classifier classify-sklearn, we found for \u003cem\u003eK scurra\u003c/em\u003e 19 plant taxa with ITS2 (12 to species, 6 to genus, and 1 to family), 6 plant taxa with rbcL (1 species, 2 families, 1 order, 1 class, 1 phylum), and 9 plant taxa with trnL (4 genus, 3 families, 1 order, and 1 class). In the case of \u003cem\u003eV. viatica\u003c/em\u003e, we found 27 plant taxa (15 species, 7 genus, 4 families, and 1 class) with the ITS2 marker, 17 plant taxa (7 species, 6 families, 3 orders, and 1 class) with rbcL, and 12 plant taxa (3 genera, 6 families, 1 order, 1 class and 1 phylum) with trnL. Details of the plant species are included in Table S3 for \u003cem\u003eK\u003c/em\u003e. \u003cem\u003escurra\u003c/em\u003e and Table S4 for \u003cem\u003eV\u003c/em\u003e. \u003cem\u003eviatica\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eConsidering both species of grasshoppers, 27 plant species were detected in their faeces by the ITS2 marker, of which ~ 55% had rbcL available sequences and ~ 70% had trnL available sequences. Only for \u003cem\u003eV\u003c/em\u003e. \u003cem\u003eviatica\u003c/em\u003e did the ITS2 marker show only a classification at the taxonomic level of “Class” (in the localities “Royal Golf Club” and “Cranbourne”).\u003c/p\u003e \u003cp\u003eThe normalized frequencies of ASV classifications heatmaps (Figs.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e and \u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and the bar plots of the RRA and the wPOO (Figures S5-8) highlight distinct dietary patterns across the different samples and localities. For example, \u003cem\u003eChrysocephalum\u003c/em\u003e predominated in faecal samples from Omeo and Cudgewa. Asteraceae was the only taxon consistently present in the faecal samples of \u003cem\u003eK\u003c/em\u003e. \u003cem\u003escurra\u003c/em\u003e, with no representation at a higher taxonomic level. In the case of \u003cem\u003eV\u003c/em\u003e. \u003cem\u003eviatica\u003c/em\u003e, no single taxon was consistently present across all five sites\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eThe heatmaps revealed family-level dietary differences. Asteraceae was the dominant family in \u003cem\u003eK\u003c/em\u003e. \u003cem\u003escurra\u003c/em\u003e faeces from four of the five localities, except at Beechworth, where Rosaceae, represented by \u003cem\u003eRosa rubiginosa\u003c/em\u003e, was dominant, as detected by the ITS2 and trnL markers. At each site, we identified multiple plant families in the grasshopper faeces, except for Shelley, where only Asteraceae was detected. Poaceae was only detected in samples from Cudgewa, with a high log10 frequency (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). In all sites for \u003cem\u003eK\u003c/em\u003e. \u003cem\u003escurra\u003c/em\u003e, faecal samples contained no more than two or three plant taxa.\u003c/p\u003e \u003cp\u003eMarkers varied in their ability to detect taxa at different levels. In Omeo, ITS2 detected \u003cem\u003eCrassula sieberiana\u003c/em\u003e, while trnL detected the genus \u003cem\u003eCrassula\u003c/em\u003e. Similarly, \u003cem\u003ePlantago lanceolata\u003c/em\u003e was identified by ITS2 in Omeo and Bingo-Munjie (sample1), whereas rbcL classified the same sample at the order level of Lamiales and \u003cem\u003ePlantago debilis\u003c/em\u003e in Bingo-Munjie (sample 1). For all those localities, trnL detected the genus \u003cem\u003ePlantago\u003c/em\u003e. This variation highlights the differing taxonomic resolution of each marker, with ITS2 consistently providing finer species-level identification.\u003c/p\u003e \u003cp\u003eThe rbcL and trnL markers could not discern genetic variation among Asteraceae species, so we pooled them at the family level. The ITS2 marker pointed to seven species of Asteraceae, with low abundance of an undetermined \u003cem\u003eLeucochrysum\u003c/em\u003e species in a sample from Bingo-Munjie, an undetermined \u003cem\u003eHypochaeris\u003c/em\u003e species in a sample from Cudgewa, a high abundance of \u003cem\u003eLeptorhynchos squamatus\u003c/em\u003e and \u003cem\u003eEuchiton japonicus\u003c/em\u003e in Shelley, and \u003cem\u003eC. semipapposum\u003c/em\u003e in Omeo, Bingo-Munjie and Cudgewa samples, with \u003cem\u003eC. apiculatum\u003c/em\u003e and an undetermined \u003cem\u003eChrysocephalum\u003c/em\u003e species in the Omeo and Cudgewa samples. Similarly, Rosaceae was only detected by ITS2 and trnL, and only ITS2 managed to assign it to the species, \u003cem\u003eRosa rubiginosa\u003c/em\u003e. In Beechworth, the genus \u003cem\u003eGeranium\u003c/em\u003e was detected only at genus level by the ITS2 and trnL markers, while Fabaceae was only detected by trnL.\u003c/p\u003e \u003cp\u003eFor \u003cem\u003eV. viatica\u003c/em\u003e, a broader array of plant taxa was detected compared to \u003cem\u003eK\u003c/em\u003e. \u003cem\u003escurra\u003c/em\u003e. Families such Myrtaceae and Asteraceae were dominant across samples. Only the ITS2 marker was able to classify Myrtaceae to species level (\u003cem\u003eKunzea ericoides\u003c/em\u003e and \u003cem\u003eGaudium laevigatum\u003c/em\u003e), finding the family in three sites: Diamond Creek, Royal Golf Club, and Cranbourne. The genus \u003cem\u003ePlantago\u003c/em\u003e was detected by ITS2 and rbcL in Truganina; however, ITS2 identified an undetermined \u003cem\u003ePlantago\u003c/em\u003e species and \u003cem\u003ePlantago gaudichaudii\u003c/em\u003e, while rbcL classified it as \u003cem\u003ePlantago debilis\u003c/em\u003e. The genus \u003cem\u003eGeranium\u003c/em\u003e was detected by ITS2 and trnL in Truganina, but without classification to the species level.\u003c/p\u003e \u003cp\u003eAsteraceae was abundant and classified to species level only by ITS2, though the family was detected by all three markers. ITS2 identified \u003cem\u003eVittadinia cervicularis\u003c/em\u003e in the Royal Golf Club sample, \u003cem\u003eRutidosis leptorhynchoides\u003c/em\u003e and an undetermined species in Truganina, and \u003cem\u003eOlearia teretifolia\u003c/em\u003e, \u003cem\u003eO\u003c/em\u003e. \u003cem\u003eramulosa\u003c/em\u003e, and an undetermined \u003cem\u003eCassinia\u003c/em\u003e at Bayside. In Diamond Creek, ITS2 detected \u003cem\u003eCassinia longifolia\u003c/em\u003e, and in Truganina an undetermined Asteraceae species.\u003c/p\u003e \u003cp\u003eThe family Poaceae was detected by all three markers in Diamond Creek and Royal Golf Club, but in very low abundance only in Bayside by the rbcL marker. Only ITS2 was able to classify Poaceae at species level. Haloragaceae was detected in Diamond Creek by both ITS2 and trnL, with ITS2 identifying Gonocarpus \u003cem\u003ehumilis\u003c/em\u003e and trnL detecting the genus \u003cem\u003eGonocarpus\u003c/em\u003e. Similarly, in Bayside, Rutaceae was detected by the ITS2 and trnL markers, with ITS2 identifying \u003cem\u003eCorrea\u003c/em\u003e, while trnL only provided identification at the family level.\u003c/p\u003e \u003cp\u003eFabaceae was detected by ITS2 in Bayside, Royal Golf Club, and Cranbourne; by rbcL in Bayside; and by trnL in Cranbourne. ITS2 identified two species: \u003cem\u003eTrifolium arvense\u003c/em\u003e (low abundance, Royal Golf Club) and \u003cem\u003eBossiaea cinerea\u003c/em\u003e (high abundance, Bayside), plus an undetermined species (medium abundance, Cranbourne). rbcL and trnL identified Fabaceae only at the family level. Ericaceae appeared in low abundance at the Royal Golf Club and Cranbourne via ITS2 and rbcL. ITS2 detected \u003cem\u003eMonotoca scoparia\u003c/em\u003e at both sites and an undetermined species at Cranbourne; rbcL identified \u003cem\u003eWoollsia pungens\u003c/em\u003e, \u003cem\u003eLeucopogon ericoides\u003c/em\u003e, and an undetermined species. At the Royal Golf Club, rbcL also detected \u003cem\u003eRumex pulcher\u003c/em\u003e (Polygonaceae), \u003cem\u003eAtriplex postrata\u003c/em\u003e (Chenopodiaceae), \u003cem\u003eCassytha pubescens\u003c/em\u003e (Lauraceae), and an undetermined Pittosporaceae, all in low abundance. trnL detected \u003cem\u003ePinus\u003c/em\u003e at low abundance in Truganina, while rbcL identified \u003cem\u003ePinus radiata\u003c/em\u003e.\u003c/p\u003e \u003cp\u003eA Wilcoxon signed-rank test was used to compare the relative abundance of plant species between the survey data in Omeo and faecal samples from the same site. The test revealed a significant difference between the two datasets (𝑉=1643, 𝑝\u0026lt;0.001), indicating that the distribution of relative abundances in the faeces did not match the availability of plant species recorded in the survey.\u003c/p\u003e\u003cp\u003e\u003cem\u003eDietary diversity analysis\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eAlpha diversity\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eA Kruskal-Wallis test revealed no significant difference in the Shannon diversity index between the grasshopper species (χ\u003csup\u003e2\u003c/sup\u003e(1) = 1.401, \u003cem\u003ep\u003c/em\u003e = 0.224), but the choice of molecular marker significantly affected the diversity index (χ\u003csup\u003e2\u003c/sup\u003e(2) = 68.69, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) (Figure S9 A). In contrast, Kruskal-Wallis tests revealed significant differences in Faith's Phylogenetic Diversity (χ\u003csup\u003e2\u003c/sup\u003e(1) = 10.117, \u003cem\u003ep\u003c/em\u003e = 0.002), with \u003cem\u003eV\u003c/em\u003e. \u003cem\u003eviatica\u003c/em\u003e showing higher values. Additionally, the choice of marker significantly impacted the phylogenetic diversity (χ\u003csup\u003e2\u003c/sup\u003e(2) = 81.624, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001). ITS2 consistently displayed the highest diversity across both species (Figure S9 B). Lastly, Pielou's Evenness did not vary significantly between species (χ\u003csup\u003e2\u003c/sup\u003e(1) = 0.280, \u003cem\u003ep\u003c/em\u003e = 0.597), but the markers showed notable differences (χ\u003csup\u003e2\u003c/sup\u003e(2) = 63.047, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) (Figure S9 C).\u003c/p\u003e\n\u003cp\u003eComparing sites, Kruskal-Wallis tests indicated significant differences across all three markers (ITS2, rbcL, and trnL) for Faith’s Phylogenetic Diversity, Pielou’s Evenness, and the Shannon Index (Figure S10A-C). For ITS2, Faith’s Phylogenetic Diversity (χ\u003csup\u003e2\u003c/sup\u003e(9) = 35.619, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), Pielou’s Evenness (χ\u003csup\u003e2\u003c/sup\u003e(9) = 34.624, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), and Shannon Index (χ\u003csup\u003e2\u003c/sup\u003e(9) = 30.732, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001) showed significant variation. The rbcL marker exhibited similar significant differences for Faith’s (χ\u003csup\u003e2\u003c/sup\u003e(9) = 33.989, p \u0026lt; 0.001), Pielou’s (χ\u003csup\u003e2\u003c/sup\u003e(9) = 34.182, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), and Shannon (χ\u003csup\u003e2\u003c/sup\u003e(9) = 34.105, \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001), as did the trnL marker, with χ\u003csup\u003e2\u003c/sup\u003e(9) values of 35.077, 32.395, and 30.946, respectively, all with \u003cem\u003ep\u003c/em\u003e \u0026lt; 0.001. Dunn’s test with Bonferroni correction revealed specific significant site-pair differences, with only Bingo Munjie and Royal Golf Club differing significantly for Faith’s Phylogenetic Diversity across all markers (Table S5).\u003c/p\u003e\n\u003cp\u003e\u003cspan type=\"Underline\" class=\"Underline\" name=\"Emphasis\"\u003eBeta diversity\u003c/span\u003e\u003c/p\u003e\n\u003cp\u003eA significant difference in the plant community composition was observed between \u003cem\u003eK. scurra\u003c/em\u003e and \u003cem\u003eV. viatica\u003c/em\u003e with the pairwise PERMANOVA analysis (Table S6).\u003c/p\u003e\n\u003cp\u003eThe PCoA plot for unweighted (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA) and weighted (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB) UniFrac distance revealed distinct clustering patterns among the samples regarding marker and species, corroborating the inference that plant composition in faeces samples varies between \u003cem\u003eK\u003c/em\u003e. \u003cem\u003escurra\u003c/em\u003e and \u003cem\u003eV\u003c/em\u003e. \u003cem\u003eviatica\u003c/em\u003e. The ellipses around the points indicate the 95% confidence intervals for each grasshopper species.\u003c/p\u003e\n\u003cp\u003eUsing the Unweighted UniFrac distance matrix, ITS2 showed the strongest separation in plant species between grasshopper diets (F(1,37) = 7.062, \u003cem\u003ep\u003c/em\u003e = 0.001). The PCoA plot showed some overlap but largely distinct species groups with similar spread; the first two axes explained 31.36% and 16.33% of the variance, respectively (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA). rbcL revealed similar group separation (F(1,37) = 6.994, \u003cem\u003ep\u003c/em\u003e = 0.001), with a PCoA pattern comparable to ITS2, differing mainly in Beechworth. Its first two axes explained 23.78% and 16.09% of the variance. trnL showed moderate separation (F(1,36) = 5.611, p = 0.001) and a wider spread for \u003cem\u003eK. scurra\u003c/em\u003e; the first two axes explained 36.13% and 18.39% (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA).\u003c/p\u003e\n\u003cp\u003eUsing the Weighted UniFrac matrix, ITS2 again showed the strongest group separation (F(1,37) = 16.281, p = 0.001), with variance explained rising to 70.06% and 11.32% (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB). rbcL showed very strong separation (F(1,37) = 15.311, p = 0.001), with tighter \u003cem\u003eK. scurra\u003c/em\u003e and wider \u003cem\u003eV. viatica\u003c/em\u003e clustering; variance explained increased to 56.84% and 17.72%. trnL showed moderate-to-strong separation (F(1,36) = 4.916, p = 0.003), with more distinct within-group clustering and variance explained of 47.53% and 21.26%, indicating improved representation with the weighted metric (Fig. \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB).\u003c/p\u003e\n\u003cp\u003eWeighted UniFrac PCoA plots showed clearer group separation than the unweighted metric, highlighting the impact of relative abundance on plant community differences. \u003cem\u003eK. scurra\u003c/em\u003e showed less variation with ITS2 and rbcL, while \u003cem\u003eV. viatica\u003c/em\u003e varied less with trnL. Ellipse overlap suggests some shared plant composition. Higher variance explained by the principal coordinates in the weighted plots indicates a better representation of community variability.\u003c/p\u003e\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n\n"},{"header":"Discussion","content":"\u003cp\u003eUnderstanding the diet of threatened species and the plant diversity in their habitat is crucial for developing effective conservation strategies. This study represents an attempt to expand knowledge on the diet of two morabine grasshopper species and characterize plant diversity associated with these species using molecular tools applied to faecal material. This approach could improve the understanding of plant–grasshopper trophic networks and better inform revegetation and conservation initiatives.\u003c/p\u003e\n\u003ch3\u003eMethodology\u003c/h3\u003e\n\u003cp\u003eMetabarcoding is highly effective in identifying taxa from degraded DNA in the gut or faeces, providing a comprehensive view of an organism's diet at high taxonomic resolution (de Sousa et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). However, it can introduce biases, such as from sample handling, contamination, DNA extraction, PCR, sequencing, and taxonomic assignment, that affect dietary assessments (Alberdi et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). To minimise these, we adopted a conservative approach: spike-in controls to detect mistagging, and PCR replicates to identify and exclude unreliable detections, retaining taxa only if found in two or more replicates. Instead of combining replicates, we presented them separately to assess consistency. We also used multiple markers to broaden taxonomic coverage and adjusted for sequencing depth to improve comparability.\u003c/p\u003e\u003cp\u003ePrimer selection is critical in metabarcoding (Alberdi et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e), as different environments and goals require specific markers (Barnes et al. \u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e). We used three common plant markers for targeting degraded DNA: rbcL, ITS2, and trnL (Ando et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e) Although trnL is frequently used in herbivore faecal studies (31 out of 45 studies (Ando et al. \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e)), recent research supports ITS2 for broader taxonomic coverage (Moorhouse-Gann et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Our results reflected this, with ITS2 capturing greater diversity than rbcL and trnL, though diversity patterns across Shannon, Faith’s Phylogenetic Diversity, and Pielou’s Evenness were consistent across all markers. Notably, ITS2 provided the strongest species differentiation in UniFrac analyses. Each marker had strengths and limitations. For instance, while ITS2 missed some \u003cem\u003eAcacia\u003c/em\u003e species, rbcL and trnL successfully identified them in control samples, underscoring the benefits of a multi-marker approach (Alberdi et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eThe reliability of molecular identifications depends on the quality and coverage of reference datasets (Kress et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e; Nielsen et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Taberlet et al. \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). Global databases generally support genus or family-level identification (Nakahara et al. \u003cspan class=\"CitationRef\"\u003e2016\u003c/span\u003e), but regional databases often enable more precise species-level assignments (Gold et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e). Testing different reference libraries is advisable, as outcomes may vary with marker and dataset specifics. For example, while Barnes et al. (\u003cspan class=\"CitationRef\"\u003e2022\u003c/span\u003e) achieved consistent ITS2 accuracy across databases, trnL assignments improved significantly with local libraries, yielding higher specificity. In our study, using global libraries with trnL reduced Type I errors in control samples.\u003c/p\u003e\u003cp\u003e\u003cem\u003eDiet composition and diversity\u003c/em\u003e\u003c/p\u003e\u003cp\u003eWhile factors like environmental conditions and vegetation structure influence grasshopper assemblages, host plant availability is more critical (Smith and Capinera \u003cspan class=\"CitationRef\"\u003e2005\u003c/span\u003e). Our study reveals both differences and overlaps in plant species consumed by these two grasshoppers. From faecal samples, we identified 66 plant taxa, including native and introduced species. Consistent with previous studies (Blackith and Blackith \u003cspan class=\"CitationRef\"\u003e1966\u003c/span\u003e; White \u003cspan class=\"CitationRef\"\u003e1956\u003c/span\u003e; White et al. \u003cspan class=\"CitationRef\"\u003e1964\u003c/span\u003e), both species mainly fed on native herbs and shrubs. These included known food plants from their habitat and unexpected sources like grasses and exotic weeds at some sites. Dietary choices may reflect a need to balance nutrition when resources are of low quality (Bernays and Simpson \u003cspan class=\"CitationRef\"\u003e1990\u003c/span\u003e). Expanding on Blackith and Blackiths (\u003cspan class=\"CitationRef\"\u003e1966\u003c/span\u003e) work, we provide new data and insights from the species' natural habitat. Genera such as \u003cem\u003eOlearia\u003c/em\u003e, \u003cem\u003eCassinia\u003c/em\u003e, \u003cem\u003eKunzea\u003c/em\u003e, \u003cem\u003eCorrea\u003c/em\u003e, and \u003cem\u003eAtriplex\u003c/em\u003e, previously associated with other morabine species, were also found in \u003cem\u003eV\u003c/em\u003e. \u003cem\u003eviatica\u003c/em\u003e’s faeces, though these have not been noted for \u003cem\u003eV\u003c/em\u003e. \u003cem\u003eviatica\u003c/em\u003e species before. Metabarcoding thus reveals broader dietary interactions in the natural environment of this species than evident from feeding studies.\u003c/p\u003e\u003cp\u003e\u003cem\u003eK\u003c/em\u003e. \u003cem\u003escurra\u003c/em\u003e's diet was dominated by herbs from the Asteraceae family, while \u003cem\u003eV\u003c/em\u003e. \u003cem\u003eviatica\u003c/em\u003e consumed a higher abundance of shrubs from both the Asteraceae and Myrtaceae families. Although there was some overlap in plant families found in their faeces, including Asteraceae, Geraniaceae, and Plantaginaceae, \u003cem\u003eK\u003c/em\u003e. \u003cem\u003escurra\u003c/em\u003e exclusively consumed Crassulaceae and Rosaceae, while \u003cem\u003eV\u003c/em\u003e. \u003cem\u003eviatica\u003c/em\u003e had a broader dietary range, with eight additional families that include Chenopodiaceae, Haloragaceae, and Ericaceae. The diversity of plant species available can greatly influence the range of insect diets (Forister et al. \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e), and this is evident in our findings. Asteraceae, the second most diverse plant family in Australia with around 1,417 species, is surpassed only by Myrtaceae, which has approximately 1,858 species (Orchard \u003cspan class=\"CitationRef\"\u003e2015\u003c/span\u003e). These two families were both highly diverse and abundant in our faecal samples. This could indicate an evolutionary adaptation to use these families, making them key components of the grasshoppers’ ecological niches and potentially critical to the survival of these species.\u003c/p\u003e\u003cp\u003eAlthough grasses are not typically a major part of morabine grasshopper diets (Blackith and Blackith \u003cspan class=\"CitationRef\"\u003e1966\u003c/span\u003e), all markers detected introduced Poaceae in some faecal samples. For \u003cem\u003eK. scurra\u003c/em\u003e, grasses appeared only at Cudgewa, a small patch near grazing pastures, while \u003cem\u003eV. viatica\u003c/em\u003e had grasses in samples from the managed sites of Diamond Creek and Royal Melbourne Golf Club. \u003cem\u003eK. scurra\u003c/em\u003e also consumed introduced Rosaceae (\u003cem\u003ePotentilla recta\u003c/em\u003e, \u003cem\u003eRosa rubiginosa\u003c/em\u003e), detected via ITS2 at Cudgewa and Beechworth Cemetery, consistent with lab consumption of native Rosaceae like \u003cem\u003eAcaena\u003c/em\u003e x \u003cem\u003eovina\u003c/em\u003e. Both species consumed native and introduced Plantaginaceae, with Lamiales taxa found at Cudgewa (\u003cem\u003eK. scurra\u003c/em\u003e) and Truganina (\u003cem\u003eV. viatica\u003c/em\u003e), across all markers. The only non-native Asteraceae, \u003cem\u003eHypochaeris\u003c/em\u003e sp., was found in Cudgewa via ITS2, while other exotics, including Ericaceae, were detected in \u003cem\u003eV. viatica\u003c/em\u003e via rbcL. \u003cem\u003ePinus radiata\u003c/em\u003e was found in \u003cem\u003eV. viatica\u003c/em\u003e at Truganina and Diamond Creek (rbcL and trnL), though absent at Truganina, likely due to pollen contamination during its pollination season (Fountain and Cornford \u003cspan class=\"CitationRef\"\u003e1991\u003c/span\u003e; Lillt and Sweet \u003cspan class=\"CitationRef\"\u003e1976\u003c/span\u003e).\u003c/p\u003e\u003cp\u003eOur findings suggest that endangered species, like \u003cem\u003eK\u003c/em\u003e. \u003cem\u003escurra\u003c/em\u003e (28 plant taxa in its faeces), may rely on a more limited range of food sources, similar to \u003cem\u003eCeles akitanus\u003c/em\u003e (Orthoptera: Acrididae), which has a similar number of 36 plant taxa detected in its faeces (Yamamoto and Uchida, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e). In contrast, generalist pest species, such as the wētā \u003cem\u003eHemiandrus\u003c/em\u003e sp. (Orthoptera: Anostostomatidae), exploit a much broader variety of plants, with 79 taxa found in its faeces (Nboyine et al. \u003cspan class=\"CitationRef\"\u003e2019\u003c/span\u003e). Both studies used grasshopper faeces and metabarcoding techniques. Future research could examine whether the narrower dietary range of endangered species is due to habitat specialization, food availability, or other environmental pressures, which could have important implications for conservation and management strategies.\u003c/p\u003e\u003cp\u003eThe diversity metrics highlight the complexities of dietary pattern differences between these morabine species. While the Shannon diversity index and Pielou’s Evenness showed no significant differences between the grasshopper species, Faith’s Phylogenetic Diversity revealed a clear separation. This suggests that, although the overall number and distribution of plant taxa (richness and evenness) are similar between \u003cem\u003eK\u003c/em\u003e. \u003cem\u003escurra\u003c/em\u003e and \u003cem\u003eV\u003c/em\u003e. \u003cem\u003eviatica\u003c/em\u003e, the phylogenetic breadth of the taxa they consume differs. Faith’s index considers evolutionary relationships, implying that \u003cem\u003eV\u003c/em\u003e. \u003cem\u003eviatica\u003c/em\u003e (as shown in its diet composition) may be consuming plants from a wider variety of evolutionary lineages, even if the total number of taxa is similar. These patterns become more pronounced when comparing sites, where all metrics (Shannon, Faith, and Evenness) showed significant differences, suggesting strong geographic variation in the availability or preference for specific plant taxa.\u003c/p\u003e\u003cp\u003eWhen moving to beta diversity, the results further emphasize the complexity of plant community composition. The significant separation between the dietary composition of the species detected by PERMANOVA, particularly using weighted UniFrac, indicates that the relative abundance of plant taxa differs between the grasshopper species. However, the overlap seen in the PCoA plots shows that, despite the overall differences in dietary plant composition, there are shared taxa between the two species. The overlap of sites in the UniFrac PCoA plots illustrates that although site-specific differences exist, certain plant taxa are commonly consumed across locations, leading to some degree of convergence in the diet. The overlap between species and sites in the PCoA plots might reflect that both grasshopper species are exploiting similar plant communities in some environments. This highlights the complexity of ecological interactions, where morabine species may have distinct dietary preferences but still show overlap due to shared habitats or plant availability.\u003c/p\u003e\u003cp\u003eGrasshoppers are known to change their diet as they progress through different stages of their development (Ibanez et al. 2013), so future research using metabarcoding as a tool should explore seasonal diet variations across all life stages and between sexes to better understand herbivore-plant relationships in morabine grasshoppers. For instance, younger grasshoppers might rely more on softer leaves (e.g. Miura and Ohsaki \u003cspan class=\"CitationRef\"\u003e2004\u003c/span\u003e), while adults may consume a broader range (e.g. Dopman et al. \u003cspan class=\"CitationRef\"\u003e2002\u003c/span\u003e). Additionally, females might favour plants high in certain nutrients that could support egg production (e.g. Behmer and Joern \u003cspan class=\"CitationRef\"\u003e1994\u003c/span\u003e). Investigating the effects of habitat changes in plant communities driven by climate change (Hamann et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e), habitat reduction (Nufio et al. \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e), and human activities and grasshopper diet will offer deeper insights into their ecological needs now and into the future, and it is worth considering niche partitioning with other grasshoppers (e.g. Ibanez et al. 2013) and differences between bioregions (e.g. Pitteloud et al. \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e).\u003c/p\u003e\u003cp\u003e\u003cem\u003eHabitat restoration and conservation\u003c/em\u003e\u003c/p\u003e\u003cp\u003eOur analysis of morabine grasshopper diets provides insights for revegetation and conservation. Both \u003cem\u003eK. scurra\u003c/em\u003e and \u003cem\u003eV. viatica\u003c/em\u003e appear well adapted to diverse plant resources, consuming a broader range than previously known. The mismatch between plant abundance in the Omeo survey and faecal samples suggests that \u003cem\u003eK. scurra\u003c/em\u003e feeds selectively, possibly avoiding less palatable or defended plants. For instance, \u003cem\u003eThemeda triandra\u003c/em\u003e, the most abundant species in the survey, was absent from faecal samples and likely serves as shelter rather than food (White \u003cspan class=\"CitationRef\"\u003e1956\u003c/span\u003e). These findings highlight the importance of preserving native vegetation that supports grasshopper diets and habitat resilience. Since only one site was surveyed for diet–availability comparison, further studies across multiple locations are needed to confirm these patterns and assess how plant availability and habitat structure shape dietary choices. The impact of these insects on rare or endangered plants remains unclear and likely depends on plant and insect densities (Myers and Sarfraz \u003cspan class=\"CitationRef\"\u003e2017\u003c/span\u003e). Still, their influence on plant communities is an important consideration for revegetation planning.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eDeclaration of authorship\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors certify that they have participated sufficiently in the work to take public responsibility for the content, and that all those who qualify for authorship are listed\u003c/p\u003e\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAmidst the COVID-19 pandemic and associated travel restrictions, we obtained travel authorizations from the Victoria State Government to conduct the early stages of this work. We would like to thank Steve J. Sinclair for their constructive feedback and thoughtful suggestions, which enhanced the quality of this work. Nancy Endersby, Ashley MacMahon, and Qiong Yang for valuable assistance during laboratory work. Alejandro Tello, and Dale Tonkinson (CFA) for their fieldwork support. We also express our gratitude to James Maino and Gayee Maino, the Bayside Council (Pauline Reynolds and John Eichler), The Royal Melbourne Golf Club (Stuart Moodie), Royal Botanical Gardens of Cranbourne, for granting us access to the study sites.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eFunding\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eHiromi Yagui was funded by Melbourne Research Scholarship from the University of Melbourne. Michael R. Kearney and Ary A. Hoffmann received funding from the Australian Research Council, Discovery Grant DP190100990, the Victorian Department of Land, Water and Planning and the Melbourne City Council.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConflict of interest\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no conflict of interest.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eEthics approval\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eConsent to participate\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;\u003cem\u003eConsent for publication\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAvailability of data and materials\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eAll data produced from this study are provided in the corresponding manuscript. Sequencing data have been submitted to the NCBI Sequence Read Archive (SRA) under accession number PRJNA1244017. The data will be made publicly available upon publication of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eCode availability:\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe code used for analysis is provided in the supplementary material.\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eAuthors\u0026apos; contributions\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eHiromi Yagui: Conceptualisation, Methodology, Investigation, Data curation, Formal analysis, Writing \u0026ndash; original draft, Writing \u0026ndash; review and editing.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eMichael R. Kearney: Conceptualisation, Funding acquisition, Methodology, Investigation, Project administration, Writing \u0026ndash; review and editing, Supervision.\u003c/p\u003e\n\u003cp\u003eAry A. Hoffmann: Conceptualisation, Funding acquisition, Methodology, Investigation, Project administration, Writing \u0026ndash; review and editing, Supervision.\u003c/p\u003e\n\u003cp\u003eMelissa E. Carew: Conceptualisation, Methodology, Investigation, Writing \u0026ndash; review and editing, Supervision.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAlberdi A, Aizpurua O, Bohmann K, et al (2019) Promises and pitfalls of using high-throughput sequencing for diet analysis. Mol Ecol Resour 19:327\u0026ndash;348\u003c/li\u003e\n\u003cli\u003eAlberdi A, Aizpurua O, Gilbert MTP, Bohmann K (2018) Scrutinizing key steps for reliable metabarcoding of environmental samples. Methods Ecol Evol 9:134\u0026ndash;147. https://doi.org/10.1111/2041-210X.12849\u003c/li\u003e\n\u003cli\u003eAndo H, Mukai H, Komura T, et al (2020) Methodological trends and perspectives of animal dietary studies by noninvasive fecal DNA metabarcoding. Environmental DNA 2:391\u0026ndash;406\u003c/li\u003e\n\u003cli\u003eBarnes CJ, Nielsen IB, Aagaard A, et al (2022) Metabarcoding of soil environmental DNA replicates plant community variation but not specificity. Environmental DNA 4:732\u0026ndash;746. https://doi.org/10.1002/edn3.287\u003c/li\u003e\n\u003cli\u003eBehmer ST, Joern A (1994) The influence of proline on diet selection: sex-specific feeding preferences by the grasshoppers Ageneotettix deorum and Phoetaliotes nebrascensis (Orthoptera: Acrididae). Oecologia 98:76\u0026ndash;82. https://doi.org/10.1007/BF00326093\u003c/li\u003e\n\u003cli\u003eBernays E, Simpson SJ (1990) Nutrition. In: Biology of Grasshoppers. Wiley, New York, pp 105\u0026ndash;127\u003c/li\u003e\n\u003cli\u003eBlackith R, Blackith R (1966) The food of morabine grasshoppers. 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Mol Ecol 32:3150\u0026ndash;3164. https://doi.org/10.1111/mec.16922\u003c/li\u003e\n\u003cli\u003eHoffmann AA, White VL, Jasper M, et al (2021) An endangered flightless grasshopper with strong genetic structure maintains population genetic variation despite extensive habitat loss. Ecol Evol 11:5364\u0026ndash;5380. https://doi.org/10.1002/ece3.7428\u003c/li\u003e\n\u003cli\u003eIbanez S, Bison M, Lavorel S, Moretti M (2013a) Herbivore species identity mediates interspecific competition between plants. Community Ecology 14:41\u0026ndash;47. https://doi.org/10.1556/ComEc.14.2013.1.5\u003c/li\u003e\n\u003cli\u003eIbanez S, Manneville O, Miquel C, et al (2013b) Plant functional traits reveal the relative contribution of habitat and food preferences to the diet of grasshoppers. Oecologia 173:1459\u0026ndash;1470. https://doi.org/10.1007/s00442-013-2738-0\u003c/li\u003e\n\u003cli\u003eIwanowicz DD, Vandergast AG, Cornman RS, et al (2016) Metabarcoding of fecal samples to determine herbivore diets: A case study of the endangered Pacific pocket mouse. PLoS One 11:. https://doi.org/10.1371/journal.pone.0165366\u003c/li\u003e\n\u003cli\u003eJurado-Rivera JA, Vogler AP, Reid CAM, et al (2009) DNA barcoding insect-host plant associations. Proceedings of the Royal Society B: Biological Sciences 276:639\u0026ndash;648. https://doi.org/10.1098/rspb.2008.1264\u003c/li\u003e\n\u003cli\u003eKeck F, Altermatt F (2023) Management of \u0026lt;scp\u0026gt;DNA\u0026lt;/scp\u0026gt; reference libraries for barcoding and metabarcoding studies with the R package refdb. 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Biol Conserv 228:167\u0026ndash;174. https://doi.org/10.1016/j.biocon.2018.10.018\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"journal-of-insect-conservation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jico","sideBox":"Learn more about [Journal of Insect Conservation](http://link.springer.com/journal/10841)","snPcode":"10841","submissionUrl":"https://submission.nature.com/new-submission/10841/3","title":"Journal of Insect Conservation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"Herbivore insect diet, DNA metabarcoding, DNA-faeces analysis, matchstick grasshoppers, insect conservation","lastPublishedDoi":"10.21203/rs.3.rs-6555039/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6555039/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eUnderstanding food plant resources is essential for assessing habitat suitability for herbivorous animals, especially for species with limited movement that depend on local resources. However, obtaining this information can be challenging for species whose plant consumption cannot be easily monitored. Here we use DNA metabarcoding techniques to identify the plant species in the faeces of two grasshopper species of the flightless Australian subfamily Morabinae, the endangered Keys\u0026rsquo; matchstick grasshopper \u003cem\u003eKeyacris scurra\u003c/em\u003e, and the Larapuna matchstick grasshopper, \u003cem\u003eVandiemenella viatica\u003c/em\u003e. DNA sequences from the chloroplast trnL (UAA) and rbcL genes and ribosomal ITS2 region were used to identify the plant species in the diet of these species based on five populations per species. We found a total of 28 plant taxa in the faecal samples of \u003cem\u003eK\u003c/em\u003e. \u003cem\u003escurra\u003c/em\u003e and 38 in \u003cem\u003eV\u003c/em\u003e. \u003cem\u003eviatica\u003c/em\u003e. Indigenous plants from the daisy family Asteraceae dominated the faeces samples of both grasshopper species and myrtle plants from Myrtaceae were also commonly found for \u003cem\u003eV. viatica.\u003c/em\u003e Introduced grass species from the Poaceae family were also identified in the diet. PERMANOVAs showed significant differences in the composition of the plant community consumed across sites. Alpha diversity metrics revealed no significant differences between the two grasshopper species; however, significant variation was observed across sites, depending on the choice of markers (e.g., Shannon Index: χ\u003csup\u003e2\u003c/sup\u003e(9)\u0026thinsp;=\u0026thinsp;30.732, \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001 for ITS2). Our findings should help in revegetation efforts aimed at expanding the range of the two morabine species by identifying suitable plant species.\u003c/p\u003e","manuscriptTitle":"Unravelling the diet of flightless grasshoppers for conservation purposes using DNA metabarcoding","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-05-30 10:32:58","doi":"10.21203/rs.3.rs-6555039/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-08-19T07:01:53+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-31T11:49:36+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-07-25T09:39:15+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-06-27T23:34:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"130280627926576785625598026497309090716","date":"2025-06-27T11:11:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"315339581764966912003662373382735099939","date":"2025-06-27T05:51:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"278767276480568194949145701957703272777","date":"2025-05-29T11:17:37+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-05-28T16:42:34+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-04-30T06:14:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-30T06:11:42+00:00","index":"","fulltext":""},{"type":"submitted","content":"Journal of Insect Conservation","date":"2025-04-29T09:40:52+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"journal-of-insect-conservation","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"jico","sideBox":"Learn more about [Journal of Insect Conservation](http://link.springer.com/journal/10841)","snPcode":"10841","submissionUrl":"https://submission.nature.com/new-submission/10841/3","title":"Journal of Insect Conservation","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"Springer Hybrid","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"6cea0c59-4a18-441c-8e03-714a0034dcc6","owner":[],"postedDate":"May 30th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[],"tags":[],"updatedAt":"2025-11-10T15:58:33+00:00","versionOfRecord":{"articleIdentity":"rs-6555039","link":"https://doi.org/10.1007/s10841-025-00732-1","journal":{"identity":"journal-of-insect-conservation","isVorOnly":false,"title":"Journal of Insect Conservation"},"publishedOn":"2025-11-08 15:56:54","publishedOnDateReadable":"November 8th, 2025"},"versionCreatedAt":"2025-05-30 10:32:58","video":"","vorDoi":"10.1007/s10841-025-00732-1","vorDoiUrl":"https://doi.org/10.1007/s10841-025-00732-1","workflowStages":[]},"version":"v1","identity":"rs-6555039","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6555039","identity":"rs-6555039","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}