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 (‘cheaper’ 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 2013–2021.
- 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 (𝜆). When spatial autocorrelation was low, the SIIPM still outperformed contemporary approaches in areas that were geographically close (
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 Spatially Explicit Estimates of Greater Sage-Grouse (Centrocercus urophasianus) Survival, Recruitment, and Rate of Population Change in Nevada, 2013-2021
These data are the results of a spatially interpolated integrated population model (SIIPM) fit to count and demographic data collected from populations of Greater Sage-grouse (Centrocercus urophasianus; hereafter, sage-grouse) located in Nevada, U.S.A. during 2013-2021. We used a novel framework, using integrated population models (IPMs), to express demographic relatedness among sampled...
Code for a spatially interpolated integrated population model applied to simulations of spatially autocorrelated Greater Sage-Grouse (Centrocercus urophasianus) population data 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 (1938–2011), 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...
Peter Coates, PhD
Supervisory Research Wildlife Biologist
Supervisory 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
Related
Spatially Explicit Estimates of Greater Sage-Grouse (Centrocercus urophasianus) Survival, Recruitment, and Rate of Population Change in Nevada, 2013-2021 Spatially Explicit Estimates of Greater Sage-Grouse (Centrocercus urophasianus) Survival, Recruitment, and Rate of Population Change in Nevada, 2013-2021
These data are the results of a spatially interpolated integrated population model (SIIPM) fit to count and demographic data collected from populations of Greater Sage-grouse (Centrocercus urophasianus; hereafter, sage-grouse) located in Nevada, U.S.A. during 2013-2021. We used a novel framework, using integrated population models (IPMs), to express demographic relatedness among sampled...
Code for a spatially interpolated integrated population model applied to simulations of spatially autocorrelated Greater Sage-Grouse (Centrocercus urophasianus) population data 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 (1938–2011), 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...
Peter Coates, PhD
Supervisory Research Wildlife Biologist
Supervisory 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