DOI: 10.5061/dryad.m905qfv85
title: Ecology of community reassembly: Movements and diets of megafauna during a decade of restoration in Mozambique

description: This dataset documents large-herbivore movement, morphology, condition,
 fate, and diet during community reassembly in Gorongosa National Park,
 Mozambique (2013–2023), following megafaunal declines during civil war and
 subsequent restoration, including apex predator reintroductions beginning
 in 2018. The study period spans substantial interannual climatic
 variability, including extreme wet and dry years. The movement dataset
 comprises GPS telemetry records from 277 individuals across seven
 herbivore species (Cape bushbuck, nyala, greater kudu, common eland,
 waterbuck, plains zebra, and African elephant), with median monitoring
 durations ranging from 13 to 707 days and median location counts per
 individual ranging from 1,171 to 33,122 fixes. For 302 immobilized
 individuals, we provide morphological measurements (chest girth, body
 length, hind-foot length, body mass), reproductive and nutritional
 condition metrics (ultrasound measurements, palpation scores), and fate
 data (mortality date and cause, when known). The diet dataset includes DNA
 metabarcoding results from 3,785 fecal samples collected from 27 mammal
 species (11 families, 7 orders), identifying 516 food-plant taxa
 representing at least 87 families and 39 orders. Sample sizes per species
 range from 1 to 499 (median = 92), with larger sample sizes for dominant
 large herbivores (median = 216). All datasets include associated field
 metadata (date, time, location, sex, age), laboratory notes, and plant
 taxonomic information. In addition, filtering scripts and raw data are
 provided, facilitating alternative filtering approaches. These data
 support analyses of movement ecology, trophic interactions, nutritional
 ecology, and large-mammal community reassembly under restoration and
 climatic variability.
description: # Long-term movement, climate, morphology, and diet metabarcoding data
 from Gorongosa National Park (2013–2023) Access this dataset on
 Dryad: DOI: 10.5061/dryad.m905qfv85 Data are associated with: Hutchinson
 et al. 2026. *Ecology.* Please see publication for full details of
 methodlogy, study design, and data use. This repository contains long-term
 ecological data collected in Gorongosa National Park, Mozambique. The
 dataset includes historical and contemporary rainfall data, GPS telemetry
 data from seven herbivore species, capture and morphological measurements,
 dietary DNA metabarcoding data (processed and raw), plant barcode taxonomy
 assignments, primer diagnostics, and complete bioinformatic filtering
 scripts. These files document environmental variation, animal movement,
 demography, nutritional condition, and trophic interactions during
 megafaunal recovery. --- # Description of the data and file structure
 Unless otherwise noted: * Missing values are coded as `NA`. * Spatial
 coordinates are WGS1984 (decimal degrees). * GPS timestamps are local time
 (UTC+02:00). * Dates follow ISO format (YYYY-MM-DD). * All data (points
 1-9) described below are present in the .ZIP file
 "R1_DataS1_Hutchinson_etal_Ecology.zip" except points 7 and 8,
 which are provided as individual files here. --- ## 1. Climate Data ###
 annual_rainfall_1957-2024.csv Annual rainfall totals (mm) for Chitengo
 Camp, 1957–2024 (39 rows). Columns: * `Year` * `Rainfall_mm` Anomalies: *
 Missing years: 1970–1998, 2010–2011. * Incomplete coverage: 2005 (missing
 1 month), 2009 (missing 4 months). * 2008 total (204 mm) deemed
 unreliable. * Data from 2013–2023 are reliable; from 2015 onward measured
 via both automatic and manual gauges. --- ###
 monthly_rainfall_2011-2024.csv Monthly rainfall totals (mm), October
 2011–December 2024 (159 rows). Columns: * `Year` * `Month` * `Rainfall_mm`
 No known anomalies. --- ## 2. Capture, Collar, and Fate Data ###
 COLLAR-CAPTURE-AND-FATE-RECORDS_2014-2023.csv 302 rows; each row = one
 capture event. Data columns are: * `AnimalID` Unique individual identifier
 encoding species code, year of collar deployment, sex, collar serial
 number, and deployment number. Deployment number indicates sequence of
 collar use within a year. * `Species` Scientific name of the species. *
 `Sex` Sex of the individual. Possible values: `f` (female), `m` (male) *
 `CollarSerial` Manufacturer serial number of the GPS collar. * `Latitude`
 Latitude of capture location (decimal degrees, WGS1984). * `Longitude`
 Longitude of capture location (decimal degrees, WGS1984). * `DeployDate`
 Date of capture and collar deployment (YYYY-MM-DD). * `DeployYear` Year of
 capture (YYYY). * `DeployMonth` Month of capture (written in long form,
 e.g., “July”). * `DeployDayOfYear` Day of year of capture (1–366). *
 `FirstFixDate` Date of first GPS location fix recorded by the collar
 (YYYY-MM-DD). * `LastFixDate` Date of last GPS location fix recorded by
 the collar (YYYY-MM-DD). * `Fetus` Presence of fetus determined by
 ultrasonography. Possible values: `1` (present), `0` (absent) * `Lactate`
 Lactation status at capture. Possible values: `1` (lactating), `0` (not
 lactating) * `MaxFat_mm` Maximum rump fat depth (mm), measured via
 ultrasonography. * `Bfemoris_mm` Thickness of biceps femoris muscle (mm),
 measured via ultrasonography. * `Ldorsi_mm` Thickness of longissimus dorsi
 muscle (mm), measured via ultrasonography. * `SSligament_score`
 Sacrosciatic ligament palpation score (0–6; ordinal). * `Lumbar_score`
 Lumbar vertebrae palpation score (0–6; ordinal). * `Sacrum_score` Sacrum
 palpation score (0–6; ordinal). * `BaseTail_score` Base-of-tail palpation
 score (0–6; ordinal). * `Caudal_score` Caudal vertebrae palpation score
 (0–6; ordinal). * `ChestGirth_cm` Chest circumference at the withers (cm).
 * `BodyLength_cm` Dorsal length from nose to base of tail (cm). *
 `HindFoot_cm` Hind foot length from hoof to hock (cm). *
 `MeasuredWeight_kg` Body mass measured directly in the field using a
 portable scale (kg). * `EstimatedWeight_kg` Body mass estimated (kg) using
 the regression: `Mass (kg) = (1.6036 × ChestGirth – 78.681) – 5.35`
 Estimated values are also provided for individuals that were directly
 weighed. * `FecalSample` Indicator that a fecal sample was collected and
 produced usable dietary data. Possible values: `1` (yes), `0` (no) *
 `FecalDietID` Identifier linking the individual to fecal samples in diet
 datasets (`step8_final_dataset_rarefied_RRA.csv` and
 `step8_final_dataset_raw_reads.csv`). `NA` if none. * `HairSample`
 Indicator that a hair sample was collected. Possible values: `1` (yes),
 `0` (no) * `BloodSample` Indicator that a blood sample was collected.
 Possible values: `1` (yes), `0` (no) * `HornSample` Indicator that a horn
 sample was collected. Possible values: `1` (yes), `0` (no) * `Mortality`
 Indicator that the individual died while the GPS collar was active.
 Possible values: `1` (died), `0` (survived or collar ceased transmitting
 prior to death) * `MortDate` Estimated date of mortality (YYYY-MM-DD).
 `NA` if not applicable. * `MortCause` Confirmed cause of death (text
 field). `NA` if unknown or not applicable. * `OtherNotes` Free-text notes
 documenting measurement uncertainty, qualitative condition or reproductive
 observations, capture complications, or interpretive cautions. --- ## 3.
 GPS Collar Location Data Each file contains time-stamped GPS fixes with
 DOP accuracy metrics. Common columns: * `AnimalID` * `Species` *
 `Timestamp` * `Year` * `DayOfYear` * `Latitude` * `Longitude` * `HDOP`
 Horizontal dilution of precision: uncertainty in latitude and longitude
 driven by satellite position at time of fix * `DOP` Dilution of precision:
 uncertainily of 3D location (lat, long, elevation) driven by satellite
 position at time of fix Files: * GPS-COLLAR-LOCATION-RECORDS_BUSHBUCK.csv
 (656,828 records; 103 individuals) * GPS-COLLAR-LOCATION-RECORDS_ELAND.csv
 (131,173 records; 10 individuals) *
 GPS-COLLAR-LOCATION-RECORDS_ELEPHANT.csv (839,947 records; 18 individuals)
 * GPS-COLLAR-LOCATION-RECORDS_KUDU.csv (1,299,250 records; 80 individuals)
 * GPS-COLLAR-LOCATION-RECORDS_NYALA.csv (444,809 records; 37 individuals)
 * GPS-COLLAR-LOCATION-RECORDS_WATERBUCK.csv (61,744 records; 22
 individuals) * GPS-COLLAR-LOCATION-RECORDS_ZEBRA.csv (39,118 records; 7
 individuals) --- ## 4. Processed Diet Metabarcoding Data ###
 step8_final_dataset_rarefied_RRA.csv 3,785 samples × 532 columns. *
 Columns 1–16: metadata * Columns 17–532: plant mOTUs (Relative Read
 Abundance; rarefied to 979 reads per sample) --- ###
 step8_final_dataset_raw_reads.csv Same structure as above. Plant mOTUs
 reported as mean read counts across PCR replicates (unrarefied). --- ###
 step8_final_metadata.csv Metadata-only file (3,785 rows; 17 columns).
 Includes: * Sample identifiers * Species (field and genetically confirmed)
 * Sex * Collection date/time * Latitude/Longitude * Collection confidence
 score * `AnimalID` * Mean read depth --- ## 5. Plant Barcode Taxonomy ###
 step8_final_mOTU_taxonomy.csv 516 mOTUs × 17 columns. Includes: * DNA
 sequence * Best database match * NCBI taxid * Taxonomic rank * Post-hoc
 resolution fields * `CombinedID` (matches plant columns in diet datasets)
 --- ### step7_resolved_mOTUs.csv 145 most common mOTUs (&gt;90% of reads).
 Includes: * BLAST matches * Mozambique and Gorongosa filtering * FinalID
 and TidyFinalID --- ### mOTU_matching_Potter2022.csv Links current mOTU
 taxonomy to Potter et al. (2022) plant trait assignments. Includes: *
 Sequence * CombinedID * Resolved_SpeciesList * PotterSpeciesID fields ---
 ## 6. Primer Diagnostics ### primer_template_alignments.csv 516 mOTUs × 44
 columns. Reports: * Primer matches * Total mismatches per primer *
 Position-specific mismatch indicators (F1–F17; R1–R22; binary 0/1) --- ##
 7. Raw DNA Metabarcoding Data – Diet (trnL) Gzipped FASTQ files
 (paired-end aligned and sample-assigned via OBITools v1.2.11):
 png13_jul_lib1_assigned.fastq.gz\ png13_jul_lib2_assigned.fastq.gz\
 png14_jul_assigned.fastq.gz\ png15_jul_lib1_assembled_assigned.fastq.gz\
 png15_jul_lib2_assembled_assigned.fastq.gz\
 png15_jul_lib3_assembled_assigned.fastq.gz\
 png15_jul_lib4_assembled_assigned.fastq.gz\
 png15_jul_lib5_assembled_assigned.fastq.gz\
 png15_jul_part2_assigned.fastq.gz\
 png16_jul_lib1_assembled_assigned.fastq.gz\
 png16_jul_lib2_assembled_assigned.fastq.gz\
 png16_jul_lib3_assembled_assigned.fastq.gz\
 png17_jul_assembled_assigned.fastq.gz\
 png17_julRedo_nov_assembled_assigned.fastq.gz\
 png18_apr_assembled_assigned.fastq.gz\
 png18_jul_lib1_assembled_assigned.fastq.gz\
 png18_jul_lib2_assembled_assigned.fastq.gz\
 png18_jul_lib3_assembled_assigned.fastq.gz\
 png18_nov_assembled_assigned.fastq.gz\
 png19_apr_assembled_assigned.fastq.gz\
 png19_jul_lib1_assembled_assigned.fastq.gz\
 png19_jul_lib2_assembled_assigned.fastq.gz\
 png19_jul_lib3_assembled_assigned.fastq.gz\
 png19_nov_assembled_assigned.fastq.gz\
 png20_apr_assembled_assigned.fastq.gz\
 png21_jul_lib1_assembled_assigned.fastq.gz\
 png21_jul_lib2_assembled_assigned.fastq.gz\
 png21_jul_lib3_assembled_assigned.fastq.gz\
 png21_22_lib1_assembled_assigned.fastq.gz\
 png21_22_lib2_assembled_assigned.fastq.gz\
 png21_22_lib3_assembled_assigned.fastq.gz Each FASTQ file includes: *
 Sample identifier * Sequencing library * Alignment metrics * DNA sequence
 * Phred +33 quality scores --- ## 8. Raw DNA Metabarcoding Data – Host
 Identification (16S) FASTQ files:
 sample_id_test_lib1_assembled_assigned.fastq.gz\
 sample_id_test_lib2_assembled_assigned.fastq.gz\
 sample_id_test_2022_lib1_assembled_assigned.fastq.gz\
 sample_id_test_2023_lib1_assembled_assigned.fastq.gz\
 sample_id_test_2023_lib2_assembled_assigned.fastq.gz Same structure as
 trnL FASTQ files; used for host species confirmation. --- ## 9. Filtering
 Scripts and Supporting Files Directory structure:
 GPS-collar-location-data-processing.R\ filtering_prerequisites.txt\
 bioinformatics_step1/\ bioinformatics_step2/\ ngsfilter_files/\
 metadata_files/\ additional_files_scripts/ These files reproduce the full
 filtering workflow from raw FASTQ data (Sections 7 & 8) to final
 processed datasets (Sections 4 & 5). Includes R scripts, BASH scripts,
 OBITools configuration files, FASTA references, and intermediate objects.
 Scripts are fully commented and intended for users wishing to refilter raw
 data. --- # Sharing/Access Information All data are original to long-term
 monitoring in Gorongosa National Park. Users should cite the associated
 publication when using these data. --- # Code/Software * OBITools v1.2.11
 used for paired-end alignment and sequence assignment. * R Statistical
 Software used for downstream filtering and final dataset construction. *
 FASTQ files are gzip-compressed. * CSV files are compatible with R,
 Python, MATLAB, Julia, and Excel. Filtering scripts are provided to enable
 complete reproducibility of the diet metabarcoding workflow.