Disentangling data discrepancies with integrated population models

Sarah P. Saunders, Matthew T. Farr, Alexander D. Wright, Christie A. Bahlai, Jose W. Ribeiro, Sam Rossman, Allison L. Sussman, Todd W. Arnold, Elise F. Zipkin

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

Abstract

A common challenge for studying wildlife populations occurs when different survey methods provide inconsistent or incomplete inference on the trend, dynamics, or viability of a population. A potential solution to the challenge of conflicting or piecemeal data relies on the integration of multiple data types into a unified modeling framework, such as integrated population models (IPMs). IPMs are a powerful approach for species that inhabit spatially and seasonally complex environments. We provide guidance on exploiting the capabilities of IPMs to address inferential discrepancies that stem from spatiotemporal data mismatches. We illustrate this issue with analysis of a migratory species, the American Woodcock (Scolopax minor), in which individual monitoring programs suggest differing population trends. To address this discrepancy, we synthesized several long-term data sets (1963–2015) within an IPM to estimate continental-scale population trends, and link dynamic drivers across the full annual cycle and complete extent of the woodcock's geographic range in eastern North America. Our analysis reveals the limiting portions of the life cycle by identifying time periods and regions where vital rates are lowest and most variable, as well as which demographic parameters constitute the main drivers of population change. We conclude by providing recommendations for resolving conflicting population estimates within an integrated modeling approach, and discuss how strategies (e.g., data thinning, expert opinion elicitation) from other disciplines could be incorporated into ecological analyses when attempting to combine multiple, incongruent data types.

Original languageEnglish (US)
Article numbere02714
JournalEcology
Volume100
Issue number6
DOIs
StatePublished - Jun 2019

Bibliographical note

Funding Information:
This research was developed, in part, during the Reproducible Quantitative Methods course (https://cbahlai.github.io/ rqm-template/) led by C. A. Bahlai, which was funded by a number of entities at Michigan State University including the Ecology and Evolutionary Biology Program, College of Natural Science, BEACON Center for the Study of Evolution in Action, and the Kellogg Biological Station (NSF-DEB no. 1027253). C. A. Bahlai was supported by the Mozilla Foundation when she initially developed the course. T. W. Arnold and S. P. Saunders were supported by funds from the U.S. Fish and Wildlife Service Webless Migratory Game Bird Research and Management Program. S. P. Saunders and E. F. Zipkin were also supported by the National Science Foundation (Award EF-1702635). This work was conducted using computational resources provided by the Institute for Cyber-Enabled Research at Michigan State University.

Publisher Copyright:
© 2019 by the Ecological Society of America

Keywords

  • American Woodcock
  • annual cycle
  • band-recovery
  • data integration
  • data integration for population models Special Feature
  • harvest
  • singing-ground survey

Fingerprint Dive into the research topics of 'Disentangling data discrepancies with integrated population models'. Together they form a unique fingerprint.

Cite this