DOI: 10.5061/dryad.w0vt4b936
title: Water economics of African savanna herbivores: How much does plant moisture matter?

description: Water is an essential and often limiting resource that pervades all
 aspects of animal ecology. Yet, water economics are grossly understudied
 relative to foraging and predation, leaving ecologists ill-equipped to
 predict how the intensifying disruption of hydrological regimes worldwide
 will impact communities. For savanna herbivores, reliance on surface water
 can increase exposure to predators and competitors, and thus strategies
 that reduce the need to drink are advantageous. Yet, the extent to which
 increasing dietary water intake while decreasing water loss enables
 animals to forego drinking remains unknown. We studied water budgets of
 sympatric African savanna antelopes that differ in size, bushbuck
 (Tragelaphus sylvaticus, ~35 kg) and kudu (T. strepsiceros, ~140 kg). We
 hypothesized that both species compensate for seasonally declining water
 availability by increasing consumption of moisture-rich plants and
 reducing fecal water loss, and that these adjustments are sufficient for
 small-bodied—but not large-bodied—herbivores to avoid spending more time
 near permanent water sources as the dry season advances. We tested our
 predictions using temporally explicit data on antelope movements, diets,
 plant traits, and drinking behavior in Gorongosa National Park,
 Mozambique. Water content declined between the early and late dry seasons
 in roughly half of plant taxa consumed by antelope. Although both species
 reduced fecal water loss and shifted their diets towards relatively
 moisture-rich plants as the dry season progressed, dietary water intake
 still declined. Contrary to expectation, kudu reduced selection for
 surface water in the late dry season without adjusting total time spent
 drinking, whereas bushbuck increased selection for surface water. We
 developed a generalizable approach for parsing the importance of dietary
 and surface water for large herbivores. Our results underscore that
 variation in surface-water dependence is a key organizing force in
 herbivore communities but also show that simple allometric predictions
 about the behavioral and ecological consequences of this variation are
 unreliable. Understanding wildlife water economics is a research frontier
 that will be essential for predicting changes in species distribution and
 community composition as temperatures rise and droughts intensify.
description: # Water economics of African savanna herbivores: How much does plant
 moisture matter?
 [https://doi.org/10.5061/dryad.w0vt4b936](https://doi.org/10.5061/dryad.w0vt4b936) ## Description of the data and file structure This is the README file for datasets contained in the publication: Jeremy A. Van Driessche, Simon Chamaillé-Jammes, Ciara M. Nutter, Arjun B. Potter, Robert M. Pringle, and Ryan A. Long, Water economics of African savanna herbivores: how much does plant moisture matter? Journal of Animal Ecology, 2025. ### Files and variables #### **1.     2021\_moTU\_taxon\_per\_sample.csv** Species = ‘Bushbuck’ or ‘Kudu’ Date = day the fecal sample was collected Season_dry = ‘mid’ or ‘late’; distinguishes whether the fecal sample was collected during the middle (July/August 2021) or late (September/October 2021) dry season Sample_ID = fecal sample identifier Columns E-BN represent molecular operational taxonomic units (mOTUs) assigned to plant taxa in Gorongosa National Park, Mozambique. Values in cells represent relative read abundance of mOTUs in each fecal sample. **2.     2022_moTU_taxon_per_sample.csv** Species = ‘Bushbuck’ or ‘Kudu’ Date = day the fecal sample was collected Season_dry = ‘early’; all fecal samples in 2022 were collected during the early (May/June 2022) dry season Sample_ID = fecal sample identifier Columns E-BK represent molecular operational taxonomic units (mOTUs) assigned to plant taxa in Gorongosa National Park, Mozambique. Values in cells represent relative read abundance of mOTUs in each fecal sample. **3.     Bushbuck_GPS.csv** collarID = individual bushbuck collar number (*n* = 12) date = date of each GPS location time = local (Central Africa Time) time of each GPS location latitude = latitude of GPS location in decimal degrees longitude = longitude of GPS location in decimal degrees near_dist_m = distance (in meters) from each GPS location to the nearest water source h2o_type = ‘river’ or ‘pan’; distinguishes whether the water source nearest to each GPS location was a river or a perennial pan **4.     Kudu_GPS.csv** collarID = individual kudu collar number (*n* = 12) date = date of each GPS location time = local (Central Africa Time) time of each GPS location latitude = latitude of GPS location in decimal degrees longitude = longitude of GPS location in decimal degrees near_dist_m = distance (in meters) from each GPS location to the nearest water source h2o_type = ‘river’ or ‘pan’; distinguishes whether the water source nearest to each GPS location was a river or a perennial pan **5.     Early_dry_diet_RRA.csv** Sequence_ID = mOTU identifier Plant_species_ID = plant taxon (associated with corresponding mOTUs) commonly consumed by bushbuck and/or kudu in Gorongosa National Park, Mozambique. Species_sampled_for_H2O = plant(s) sampled to estimate mean preformed water content during the early-dry season. Columns D-T represent bushbuck and kudu fecal samples collected in the early-dry season. Values in cells are relative read abundances (RRA) for each plant taxon. Row 24 shows the summed RRA for each fecal sample. **6.     Mid_dry_diet_RRA.csv** Sequence_ID = mOTU identifier Plant_species_ID = plant taxon (associated with corresponding mOTUs) commonly consumed by bushbuck and/or kudu in Gorongosa National Park, Mozambique. Species_sampled_for_H2O = plant(s) sampled to estimate mean preformed water content during the mid-dry season. Columns D-AJ represent bushbuck and kudu fecal samples collected in the mid-dry season. Values in cells are relative read abundances (RRA) for each plant taxon. Row 25 shows the summed RRA for each fecal sample. **7.     Late_dry_diet_RRA.csv** Sequence_ID = mOTU identifier Plant_species_ID = plant taxon (associated with corresponding mOTUs) commonly consumed by bushbuck and/or kudu in Gorongosa National Park, Mozambique. Species_sampled_for_H2O = plant(s) sampled to estimate mean preformed water content during the late-dry season. Columns D-Z represent bushbuck and kudu fecal samples collected in the late-dry season. Values in cells are relative read abundances (RRA) for each plant taxon. Row 25 represents summed RRA for each fecal sample. **8.     Fecal_H2O.csv** sampleID = fecal sample identifier Julian_day = Juian day the fecal sample was collected species = ‘Bushbuck’ or ‘Kudu’ season_dry = ‘early’, ‘mid’, or ‘late’; distinguishes whether the fecal sample was collected during the early (May/June 2022), middle (July/August 2021), or late (September/October 2021) dry season Preformed_H2O = estimated water content of each fecal sample (%) **9.     Fecal_H2O_historical.csv** Species_Common_Name = ‘Bushbuck’ or ‘Kudu’ Collection_Date = date each fecal sample was collected (2014 – 2022) Julian_day = Julian day each fecal sample was collected RRA = total proportion of diet comprised of plant taxa for which we had measured values of water content. Minimum threshold for inclusion was ≥ 80% (0.80) Preformed_H2O = estimated water content of each fecal sample (%) **10.  Plant_H2O.csv** Species = plant taxon commonly consumed by bushbuck and/or kudu in Gorongosa National Park, Mozambique during the dry season. Season_dry = ‘early’, ‘mid’, or ‘late’; distinguishes whether the plant sample was collected during the early (May/June 2022), middle (July/August 2021), or late (September/October 2021) dry season Preformed_H2O = measured water content of each plant sample (%) **11.  Plant_trait_data.csv** Species = plant taxon commonly consumed by bushbuck and/or kudu in Gorongosa National Park, Mozambique during the dry season. Leaf_Mass_per_Area = mean leaf mass per area Plant_Height_m = mean plant height in meters **12.  Audio Data (contained in Zip file)** Audio data from 7 female kudu. Files contain audio (.wav) recordings obtained from biologgers affixed to 7 female kudu collars during 2021. Folders are named with kudu collar IDs and each contains a series of continuous, consecutive recordings beginning the moment of audio logger activation and ending with battery failure (6–51 days after deployment; *x̅* = 33.7 days). Each .wav file is named as “audio_date_time” in ~1-hr segments, with the date displayed as day month year (e.g., 010821 for August 1st, 2021) and the time displayed as hour minute second. ## Code/software Data files included in this package are either comma-delimited text files (.csv) or audio files (.wav) that can be viewed/listened to using standard spreadsheet or audio software (e.g., Microsoft Excel, Windows Media Player).