Data and code from: Livestock grazing and geodiversity modulate wild herbivore distributions in an African community conservation area

The rapid decline of African wildlife populations demands immediate action to mitigate further losses and restore ecosystems. A key factor influencing wildlife distribution is the relationship between the energetic needs of species and the availability of forage, both in terms of quality and quantity. Community-based conservation efforts, such as rotational livestock grazing systems in multi-use landscapes, may present a promising approach for managing these dynamics by promoting habitat heterogeneity in space and time, but livestock may also compete with wildlife for forage. Here, we ask how community grazing with cattle influences wild herbivore distribution and densities and whether facilitation or competition from livestock depend on herbivore size and forage quality. We used data from 504 strip transects in the Maasai Mara during the long-wet season (February–April 2024) where we recorded 34,143 individuals of 15 herbivore species. We show that variations in forage quality and quantity, driven by rotational cattle grazing, significantly affect the distribution patterns of wild herbivores. Specifically, we found that smaller herbivores, which have relatively higher energetic demands, prefer recently cattle-grazed areas with short grass, while larger herbivores favor taller grass areas with longer cattle-free periods. However, a high-resource area with a natural nutrient hotspot further attracted small to medium-sized herbivores more than expected based on the time since cattle grazing, emphasizing the significance of landscape geodiversity in shaping wildlife responses to livestock grazing. Our results underscore the importance of maintaining diversity in forage quality and quantity in rangeland ecosystems to support the varied nutritional requirements across herbivore species. By promoting habitat heterogeneity through rotational livestock management, community-based conservation strategies can be essential for sustaining species richness and ecosystem functioning. # Data and code from: Livestock grazing and geodiversity modulate wild herbivore distributions in an African community conservation area [doi.org](doi.org) ## Description of the data and file structure ##### Herbivore observation data Data was collected in Mara North Conservancy during the long-wet season (February–April 2024) to examine how cattle presence impacts 11 wild herbivores. Time since cattle grazing (rest period) in the different blocks was specified from the grazing schemes. The study area was sampled extensively with 504 vehicle-based transects, employing the strip transect sampling method (Ogutu et al., 2006). Each transect measured 1 km in length and 400 m in width. Animal counts were conducted at 200-meter intervals along the transect, ensuring a 200-meter observation range with a Nikon Aculon laser rangefinder. Real-time locations were recorded on a GPS-enabled device. ##### Vegetation surveys We recorded the taxonomic composition, vegetation height and functional traits related to palatability. We recorded all variables at 23 randomly distributed sites but with a minimum distance of 200 meters between plots and an additional 13 sites for only grass height. At each site we established two circular plots with a 5.5 m radius spaced 24 m apart, within which we evaluated aerial cover for each grass species in five categories: 0–5%, 5–25%, 25–50%, 50–75%, and 75–100%. Grass height within each site was measured at 32 locations spaced 5 m apart using a pasture disc meter (Bransby & Tainton, 1977). Grass functional traits were taken from van Oudtshoorn (2002) and included grazing value, grazing status, perenniality, and successional status[.](#_msocom_1) ### Files and variables #### File: comparison2022-2024.xlsx **Description:** Differences in peak mean herbivore density between 2022 and 2024. ##### Variables * Species * Month since cattle grazing with peak mean density for two different years #### File: data_6-8months_aggregated.csv **Description:** Counting data - number of observed animals ##### Variables * Date: Date and time * Buffalo: number * Dikdik: number * Eland: number * Elephant: number * Giraffe: number * Grants: number * Hartebeest: number * Impala: number * Reedbuck: number * Thomsons: number * Topi: number * Warthog: number * Waterbuck: number * Wildebeest: number * Zebra: number * Cattle: number * Goat: number * Sheep: number * Cheetah: number * Hyena: number * Jackal: number * Leopard: number * Lion: number * Wild dog: number * notes:  * months sin: months since cattle grazing * Transect N: transect number * salt_area: 0 = observation outside nutrient hotspot, 1 = observation in nutrient hotspot area #### File: grass_height.csv **Description:** Measures of grass hight  ##### Variables * height_mea: mean grass hight (cm) * x: coordinate * y: coordinate * plot_id: * enon_heigh: (cm) * biomass: (kg/ha) * last_grazi: months since cattle grazing * mineral: 0 = observation outside nutrient hotspot, 1 = observation in nutrient hotspot area * distance: to mineral creek (m) #### File: data_10apr.csv **Description:** Counting data - number of observed animals ##### Variables * Date: Date and time * Buffalo: number * Dikdik: number * Eland: number * Elephant: number * Giraffe: number * Grants: number * Hartebeest: number * Impala: number * Reedbuck: number * Thomsons: number * Topi: number * Warthog: number * Waterbuck: number * Wildebeest: number * Zebra: number * Cattle: number * Goat: number * Sheep: number * Cheetah: number * Hyena: number * Jackal: number * Leopard: number * Lion: number * Wild dog: number * notes: * months sin: months since cattle grazing * Transect N: number #### File: traits.csv **Description:** Traits for grass species ##### Variables * sci_name: * perenniality: sub trait * grazing_value: sub trait * plant_succesion: sub trait * grazing_status: sub trait #### File: survey_data.csv **Description:** vegetation abundance and structure survey data ##### Variables * Plot_id: plot_id associated with a given plot in which there are 2 sub plots in each plot. * Species abundances: normalized species abundances * mineral: 0 = observation outside nutrient hotspot, 1 = observation in nutrient hotspot area **Note:** "NA" is used to indicate "Not Applicable" or Missing Data. ## Code/software All analyses were conducted using R (R Core Team, 2024) and visualized using 'ggplot2' package (Wickham, 2016). #### File: countings_paper.R **Description** This file processes herbivore transect count data to calculate species-specific densities and analyzes their responses to time since rotational cattle grazing using generalized additive models (GAMs). It produces figures used in the manuscript. ##### Dependencies ggplot2\ ggpubr\ grid\ mgcv\ nlme\ pscl\ rlang\ tidyverse #### File: animal_count_analysis.Rmd ##### **Description** This file reproduces the analysis of wild herbivore distributions in a nutrient hotspot using transect count data. It performs density estimation, model fitting, and figures used in the manuscript. ##### Dependencies ggplot2\ ggpubr\ grid\ mgcv\ nlme\ pscl\ rlang\ tidyverse\ rmarkdown #### File: trait_grass_height_analysis.Rmd ##### **Description** This file analyzes vegetation height and grass functional trait data in relation to grazing history and links these variables to herbivore responses to a nutrient hotspot presented in the manuscript. ##### Dependencies ggplot2\ ggpubr\ grid\ mgcv\ nlme\ pscl\ rlang\ tidyverse\ rmarkdown