Human activities shape resources available to wild animals, impacting diet and likely altering their microbiota and overall health. We examined drivers shaping microbiota profiles of common cranes (Grus grus) in agricultural habitats by comparing gut microbiota and crane movement patterns (GPS-tracking) over three periods of their migratory cycle, and by analyzing the effect of artificially-supplemented food provided as part of a crane-agriculture management program. We sampled fecal droppings in Russia (non-supplemented, pre-migration) and in Israel in late fall (non-supplemented, post-migration) and winter (supplemented and non-supplemented, wintering). As supplemented food is typically homogenous, we predicted lower microbiota diversity and different composition in birds relying on supplementary feeding. We did not observe changes in microbial diversity with food supplementation, as diversity differed only in samples from non-supplemented wintering sites. However, both food supplementation and season affected bacterial community composition and led to increased abundance of specific genera (mostly Firmicutes). Cranes from the non-supplemented groups spent most of their time in agricultural fields, likely feeding on residual grain when available, while food-supplemented cranes spent most of their time at the feeding station. Thus, non-supplemented and food-supplemented diets likely diverge only in winter, when crop rotation and depletion of anthropogenic resources may lead to a more variable diet in non-supplemented sites. Our results support the role of diet in structuring bacterial communities and show that they undergo both seasonal and human-induced shifts. Movement analyses provide important clues regarding host diet and behavior towards understanding how human-induced changes shape the gut microbiota in wild animals.

Fecal samples were collected for microbiome analysis. PCR-amplification, library preparation and sequencing of the 16s V4 region for each sample was conducted at the Argonne Sequencing Center at Argonne National Laboratory (Lemont, IL). QIIME 2 was used to demultiplex the raw sequence data and DADA2 was used to infer amplicon sequence variants.

Movement data was collected using GPS-GSM transmitters on free ranging cranes that visited sampled fields up to three days prior to fecal sample collection for host-associated bacterial analysis. Habitat annotation was done using satellite imagery from Sentinel-2 in Russia and GIS information provided by the Ministry of Agriculture and Rural Development in Israel.

The database includes:

1. R scripts used for microbiome statistical analysis, including the phyloseq object input in the base of the pipeline.

Input:

            phyloseq object: Israel_microbiome_run2_crane_phyloseq_data_Updated

Scripts:

1. Sample&OTU_filtering: initial filtering of the data
2. Sample_rarefaction: refaction
3. Working_database_creation: addition of relevant metadata
4. Prevelence&Abundance: creation of prevelanve and abundance table
5. Alpha_Diversity: alpha diversity metrics calculation and plotting
6. Beta_Diversity: beta diversity metrics calculation and plotting
7. ANCOM: Analysis of Composition of Microbiomes and plotting

2. R script used for movement statistical analysis of individual of individual GPS-tagged cranes and the input file used for the script.

Input:

Daily_locations_MoveMicrobiome (CSV) input file includes GPS data for tagged cranes visiting microbiome sampling locations with annotations to habitat type visited in each location.

Script:

1. Movement4MicrobiomeSamples: proportion of time spent in different habitats.

    3. Metadata for amplicon sequence data archived in the Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra/PRJNA578383)

1. Links_to_the_sample_sequence_dattxt: links to the sequences
2. Accession_numbers_for_the_sample_sequence_data.txt: sample access numbers