Geographic principles applied to population dynamics: A spatially interpolated integrated population model
June 27, 2024
- A major impediment to wildlife conservation and management, from a quantitative perspective, is dealing with high degrees of uncertainty associated with population estimates. Integrated population models (IPMs) can help alleviate that challenge, but they are often limited to narrow spatial or temporal windows owing to the financial and logistical burdens of acquiring requisite datasets. To expand the spatiotemporal scope of practical IPM implementation, we developed a novel method that expresses demographic relatedness among sampled and unsampled locations using geographic principles of spatial autocorrelation.
- We interpolated demographic parameters at unsampled locations using parameter estimates from data-informed locations. Errors attributable to the interpolative process were corrected using a joint likelihood and locally recorded count data (โcheaperโ and broadly distributed). We evaluated the spatially interpolated IPM (SIIPM) for precision and accuracy under variable levels of spatial autocorrelation using simulated data and a Leave-One-Out Cross-Validation (LOOCV) technique. Conventional IPMs and state-space models (SSM) were fit to the same simulated datasets to provide a comparative assessment of the novel method. In a final, empirical demonstration we fit the SIIPM to data collected from Greater Sage-Grouse (Centrocercus urophasianus; sage-grouse) populations located in Nevada, U.S.A. during 2013โ2021.
- SIIPMs outperformed conventional IPMs when fit to data possessing moderate-to-high levels of spatial autocorrelation. Under moderate levels of autocorrelation, the average improvement in parameter estimation was 13.6% for survival, 65.3% for recruitment and 23.7% for rate of population change (๐). When spatial autocorrelation was low, the SIIPM still outperformed contemporary approaches in areas that were geographically close (<67โkm) to sampling locations. Under low autocorrelation-near distance scenarios, we observed SIIPM parameters that were 30.8% (recruitment), 32.5% (๐; IPM comparison) and 54.0% (๐; SSM comparison) more precise than contemporary models.
- Spatial autocorrelation is often assumed but rarely tested when comparing population dynamics across regions of large geographic extent. We demonstrated that SIIPMs can improve precision of species' vital rate estimation when extrapolating model inference beyond populations for which long-term monitoring data exists. Specific to sage-grouse, these results support previous conclusions of broad-scale spatial autocorrelation in population dynamics and a reproductive-survival trade-off previously documented at smaller scales.
Citation Information
| Publication Year | 2024 |
|---|---|
| Title | Geographic principles applied to population dynamics: A spatially interpolated integrated population model |
| DOI | 10.1111/2041-210X.14334 |
| Authors | Brian G. Prochazka, Peter S. Coates, Shawn T. O'Neil, Shawn P. Espinosa, Cameron L. Aldridge |
| Publication Type | Article |
| Publication Subtype | Journal Article |
| Series Title | Methods in Ecology and Evolution |
| Index ID | 70255930 |
| Record Source | USGS Publications Warehouse |
| USGS Organization | Western Ecological Research Center |
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Spatially Explicit Estimates of Greater Sage-Grouse (Centrocercus urophasianus) Survival, Recruitment, and Rate of Population Change in Nevada, 2013-2021
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Code for a spatially interpolated integrated population model applied to simulations of spatially autocorrelated Greater Sage-Grouse (Centrocercus urophasianus) population data
This repository contains R code to:
1. Simulate spatially autocorrelated count and demographic data for 10 populations using mean and process variance estimates from a long-term (1938โ2011), range-wide meta-analysis of Greater Sage-Grouse (Centrocercus urophasianus) population dynamics (Taylor et al., 2012).
2. Fit a spatially interpolated integrated population model (SIIPM) to the simulated da
Peter Coates, PhD
Research Wildlife Biologist
Research Wildlife Biologist
Email
Phone
Shawn T O'Neil, PhD
Wildlife Biologist
Wildlife Biologist
Email
Cameron L Aldridge, PhD
Branch Chief / Supervisory Research Ecologist
Branch Chief / Supervisory Research Ecologist
Email
Phone