DOI: 10.5061/dryad.n5tb2rc56
title: Data from: Are wild prey sufficient for the top predators in the lowland protected areas of Nepal?

description: A balanced equilibrium between carnivores and their prey is crucial for
 maintaining ecosystem sustainability. In this study, we applied the
 predator-prey power law equation to assess the balance between the biomass
 densities of carnivores and their wild prey within Nepal’s lowland
 protected areas during 2013, 2018, and 2022. The estimated value of the
 power-law exponent k for predator-prey biomass was 0.71 (95% CI =
 0.39-1.05), indicating an approximate three-fold increase in predator
 biomass density for every five-fold increase in prey biomass density.
 Consequently, this creates a systematically bottom-heavy predator-prey
 biomass pyramid. This finding, consistent with the k=3/4 trophic biomass
 scaling across ecosystems, suggests that predator biomass is
 proportionally sustained by prey biomass, indicating a balance between top
 predators and their wild prey in Nepal's lowland protected areas. We
 further demonstrated it is possible to retain the overall power law
 exponent while jointly measuring intraguild competition between two
 predators with canonical correlation analysis. This understanding opens
 avenues for future research directed toward unraveling the factors that
 drive these consistent growth patterns in ecological communities.
description: The dataset for this study was collected from five lowland
 protected areas (PAs) in Nepal—Parsa, Chitwan, Banke, Bardia, and
 Shuklaphanta National Parks—focusing on large carnivores like tigers and
 leopards. Data on tiger densities (per 100 sq km) and prey densities (per
 sq km) were gathered from national tiger surveys and line-transect
 estimates conducted in 2013, 2018, and 2022 by the Department of National
 Parks and Wildlife Conservation (DNPWC), Nepal. Leopard densities (per 100
 sq km) were obtained from various literature sources for the closest
 corresponding years. The data was standardized per sq
 km for uniform comparisons across the parks. Biomass was calculated by
 multiplying species densities by their average weights, providing
 estimates for both prey and predator biomass per sq km across different
 years. For analysis, a Bayesian approach using JAGS in
 R was employed to explore the predator-prey power law relationships for
 the combined biomass of predators and prey. The fundamental power law
 equation was log-transformed to fit within a linear regression framework.
 Linear models were created for total predator biomass density, tiger-only
 biomass density, and leopard-only biomass density. Additionally, canonical
 correlation analysis (CCA) was used to examine the relationships between
 tiger and leopard biomass and their shared prey biomass.
description: # Are wild prey sufficient for the top predators in the lowland protected
 areas of Nepal?
 [https://doi.org/10.5061/dryad.n5tb2rc56](https://doi.org/10.5061/dryad.n5tb2rc56) ## Description of the data and file structure Data: The data is given in .csv format. R script, JAGS model to estimate the combined k value for tiger and leopard (combined predator-prey) and canonical correlation analysis (CCA) for examining the relationship between tiger and leopard biomass and their shared prey biomass (Figure 3 of the manuscript) are provided. We have given the full JAG code for leopard-only and tiger-only model too (Appendices of the manuscript). **Authors:** **Saneer Lamichhane*** Department of Wildlife Ecology and Conservation, School of Natural Resources and Environment University of Florida, Gainesville, FL32611, USA. **Abhinaya Pathak** Department of Ecology, Behavior and Evolution, School of Biological Sciences University of California, San Diego, CA 92093, USA. **Aasish Gurung** National Trust for Nature, Khumaltar, Kathmandu 44700, Nepal. **Ajay Karki** Department of Zoology and Physiology, Haub School of Environment and Natural Resources University of Wyoming, Laramie, WY82072, USA. **Trishna Rayamajhi** Department of Natural Resources and the Environment Cornell University, NY 14853, USA. **Ambika Prasad Khatiwada** National Trust for Nature, Khumaltar, Kathmandu 44700, Nepal. **Jeffrey Mintz** Department of Wildlife Ecology and Conservation, School of Natural Resources and Environment University of Florida, Gainesville, FL32611, USA. **Sudip Raj Niroula** Mahendra Morang Adarsha Multiple Campus, Biratnagar, Nepal. **Chiranjibi Prasad Pokharel** National Trust for Nature, Khumaltar, Kathmandu 44700, Nepal. \***corresponding author** **Note:** The data must be within your working directory for the analysis to work. Set working directory (setwd) in R. ### Files and variables #### File: pred-prey\_data\_csv.csv **Description:**  ##### Variables * Year: The survey year for predator (tiger and leopard) and prey density. * National_park: The lowland national parks of Nepal in order from east (on top) to west. * Prey_den: Prey density per square kilometer * Tig_den: Tiger density per square kilometer * Leo_den: Leopard density per square kilometer * Prey_bio: Prey biomass kg per square kilometer * Tig_biomass: Tiger biomass kg per square kilometer * Leo_biomass: Leopard biomass kg per square kilometer * Pred_bio*: Predator biomass kg per square kilometer (sum of tiger biomass and leopard biomass per square kilometers).* #### File: Trophic\_Model\_R4b.R **Description:** The R code for the analysis.  ## Code/software Software R was used.  R Core Team (2022). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL [https://www.R-project.org/](https://www.R-project.org/). **Loaded packages**: **jagsUI**: The jagsUI library in R provides a user-friendly interface for running Bayesian models using the Just Another Gibbs Sampler (JAGS) software, facilitating complex statistical analysis and modeling through simplified commands and enhanced output options. **ggplot**: The library(ggplot2) function in R loads the `ggplot2` package, a popular and powerful tool for creating a wide variety of data visualizations using the "Grammar of Graphics" approach, allowing users to build complex and aesthetically pleasing plots in a flexible manner. **gridextra**: The library(gridExtra) function in R loads the `gridExtra` package, which provides tools for arranging multiple grid-based figures or tables on a single page, allowing for the customization and arrangement of complex multi-panel plots and layouts. ## Access information Other publicly accessible locations of the data: n/a Data was derived from the following sources: n/a