File description: MiSeq raw sequences of the COI minibarcode from the gut contents of the arthropod predators (Run1) This ZIP file contains the FASTQ files of the paired-end reads (R1: reads 1; R2: reads 2) produced for each gut content of arthropod predator in triplicate using the MiSeq platform. The 822 multiplexed PCR products were indexed using both forward and reverse indices. The list of the 257 multiplexed samples and the 4 positive and 12 negative controls are provided in the following XLSX file titled: Information concerning the samples multiplexed in the MiSeq Runs. MiSeq_Reads_COI_Gut Content_Arthropods_Run1.zip MiSeq raw sequences of the COI minibarcode from the faecal pellets of the bats & birds (Run2) This ZIP file contains the FASTQ files of the paired-end reads (R1: reads 1; R2: reads 2) produced for each faecal pellet in triplicate using the MiSeq platform. The 540 multiplexed PCR products were indexed using both forward and reverse indices. The list of the172 multiplexed samples and the 2 positive and 6 negative controls are provided in the following XLSX file titled: Information concerning the samples multiplexed in the MiSeq Runs. MiSeq_Reads_COI_Bat_Birds_ faecal_samples_Run2.zip Information concerning the samples and the positive and negative controls multiplexed in the MiSeq Runs 1 & 2 This XLSX file contains the sample IDs, the sample types, the PCR IDs, the PCR replicate numbers, the localities, the predator species and the fastq file names for each PCR products multiplexed in the two different Illumina MiSeq runs. Sample informations.xlsx Raw abundance table of the COI minibarcode from the gut contents of the arthropod predators before data filtering (Run1) This XLSX file contains the number of reads for each distinct variant (OTU) and each PCR product of the samples and controls sequenced in the MiSeq Run 1 before the data filtering using (1) the thresholds based on the negative and positive controls and (2) the validation using the technical replicates. COI_arthropods_abundance_Run1_before_filtering.xlsx Raw abundance table of the COI minibarcode from the faecal pellets of the bats & birds before data filtering (Run2) This XLSX file contains the number of reads for each distinct variant (OTU) and each PCR product of the samples and controls sequenced in the MiSeq Run 2 before the data filtering using (1) the thresholds based on the negative and positive controls and (2) the validation using the technical replicates. COI_faecal_pellets_abundance_Run2_before_filtering.xlsx Abundance table of the COI minibarcode from the gut contents of the arthropod predators after data filtering (Run1) This XLSX file contains the number of reads for each distinct variant (OTU) and each sample sequenced in the MiSeq Run 1 after the data filtering using (1) the thresholds based on the negative and positive controls and (2) the validation using the technical replicates. PCR replicates were merged. COI_arthropods_abundance_Run1_after_filtering.xlsx Abundance table of the COI minibarcode from the faecal pellets of the bats & birds after data filtering (Run2) This XLSX file contains the number of reads for each distinct variant (OTU) and each sample sequenced in the MiSeq Run 2 after the data filtering using (1) the thresholds based on the negative and positive controls and (2) the validation using the technical replicates. PCR replicates were merged. COI_faecal_pellets_abundance_Run2_after_filtering.xlsx

Better knowledge of food webs and related ecological processes is fundamental to understanding the functional role of biodiversity in ecosystems. This is particularly true for pest regulation by natural enemies in agroecosystems. However, it is generally difficult to decipher the impact of predators as they often leave no direct evidence of their activity. Metabarcoding via high throughput sequencing (HTS) offers new opportunities for unraveling trophic linkages between generalist predators and their prey, and ultimately identifying key ecological drivers of natural pest regulation. Here, this approach proved effective in deciphering the diet composition of key predatory arthropods (nine species of spiders, carabid beetles, ants, etc. – 27 prey taxa), insectivorous birds (one species, Ploceus cucullatus- 13 prey taxa) and bats (one species, Taphozous mauritianus – 103 prey taxa) sampled in a millet-based agroecosystem in Senegal. Such information enabled to inform the diet breadth or preference of predators (e.g., mainly moths for bats), to design a qualitative trophic network and to identify patterns of intraguild predation across arthropod predators, insectivorous vertebrates and parasitoids. Appropriateness and limitations of the proposed molecular-based approach for assessing the diet of crop pest predators and trophic linkages are discussed.