Spatially Explicit Estimates of Greater Sage-Grouse (Centrocercus urophasianus) Survival, Recruitment, and Rate of Population Change in Nevada, 2013-2021
Dates
Publication Date
2024-04-22
Start Date
2013
End Date
2021
Citation
Prochazka, B.G., Coates, P.S., O'Neil, S.T., and Chenaille, M.P., 2024, Spatially explicit estimates of Greater Sage-Grouse (Centrocercus urophasianus) survival, recruitment, and rate of population change in Nevada, 2013-2021: U.S. Geological Survey data release, https://doi.org/10.5066/P139R33O.
Summary
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 and unsampled populations using geographic principles of spatial autocorrelation (Shepard, 1968; Tobler, 1970). Specifically, the framework pairs relatively inexpensive population count data with spatially interpolated demographic estimates. When conducted within a Bayesian framework, spatially interpolated demographic parameters can be expressed as [...]
Summary
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 and unsampled populations using geographic principles of spatial autocorrelation (Shepard, 1968; Tobler, 1970). Specifically, the framework pairs relatively inexpensive population count data with spatially interpolated demographic estimates. When conducted within a Bayesian framework, spatially interpolated demographic parameters can be expressed as probability distributions for unobserved populations. Though novel to the IPM framework, the method is remarkably similar to Tobler’s seminal work on the topic of spatial autocorrelation (Tobler, 1970), which used the Markovian process of human population dynamics to map urban growth over a partially sampled plane. Spatially explicit estimates of survival, recruitment, and finite rate of population change (lambda) represent the 50th percentile of the posterior distribution for each parameter.
These data support the following publication:
Prochazka, B.G., Coates, P.S., O'Neil, S.T., Espinosa, S.P. and Aldridge, C.L., 2024. Geographic principles applied to population dynamics: A spatially interpolated integrated population model. Methods in Ecology and Evolution. https://doi.org/10.1111/2041-210X.14334.
References cited:
Shepard, D. (1968). A two-dimensional interpolation function for irregularly-spaced data. In Proceedings of the 1968 23rd ACM National Conference, ACM 1968. (pp. 517-524).
Tobler, W. R. (1970). A Computer Movie Simulating Urban Growth in the Detroit Region. Economic Geography, 46, 234-240. https://doi.org/10.2307/143141
Click on title to download individual files attached to this item.
GrSG_survival_recruitment_and_population_rate_of_change.xml Original FGDC Metadata
View
47.69 KB
application/fgdc+xml
52.73 MB
image/tiff
51.11 MB
image/tiff
48.43 MB
image/tiff
Related External Resources
Type: Source Code
Code for a spatially interpolated integrated population model applied to simulations of spatially autocorrelated Greater Sage-Grouse ( Centrocercus urophasianus ) population data
Questions pertaining to the intended use of, or assistance with understanding limitations or interpretation of these data are to be directed to the individuals/organization listed in the Point of Contact section. Unless otherwise stated, all data, metadata, and related materials are considered to satisfy the quality standards relative to the purpose for which the data were collected. Although these data and associated metadata have been reviewed for accuracy and completeness and approved for release by the U.S. Geological Survey (USGS), no warranty expressed or implied is made regarding the display or utility of the data on any other system or for general or scientific purposes, nor shall the act of distribution constitute any such warranty. Any use of trade, firm, or product names is for descriptive purposes only and does not imply endorsement by the U.S. Government.
Purpose
It is often difficult to develop spatially and temporally robust datasets that characterize wildlife population performance at large geographic extents. As a result, analyses typically make broad-scale inference at the expense of precision or increase precision at the expense of spatial relevance. The SIIPM method benefits from the presence of a joint likelihood, which accommodates locally collected population count data and thus can correct for spatial errors attributable to the interpolative process.