A Bayesian approach for temporally scaling climate for modeling ecological systems.

Max Post Van Der Burg,Lisa A Mccauley,Mark T Wiltermuth,Michael J Anteau

doi:10.1002/ece3.2092

Abstract

With climate change becoming more of concern, many ecologists are including climate variables in their system and statistical models. The Standardized Precipitation Evapotranspiration Index (SPEI) is a drought index that has potential advantages in modeling ecological response variables, including a flexible computation of the index over different timescales. However, little development has been made in terms of the choice of timescale for SPEI. We developed a Bayesian modeling approach for estimating the timescale for SPEI and demonstrated its use in modeling wetland hydrologic dynamics in two different eras (i.e., historical [pre‐1970] and contemporary [post‐2003]). Our goal was to determine whether differences in climate between the two eras could explain changes in the amount of water in wetlands. Our results showed that wetland water surface areas tended to be larger in wetter conditions, but also changed less in response to climate fluctuations in the contemporary era. We also found that the average timescale parameter was greater in the historical period, compared with the contemporary period. We were not able to determine whether this shift in timescale was due to a change in the timing of wet–dry periods or whether it was due to changes in the way wetlands responded to climate. Our results suggest that perhaps some interaction between climate and hydrologic response may be at work, and further analysis is needed to determine which has a stronger influence. Despite this, we suggest that our modeling approach enabled us to estimate the relevant timescale for SPEI and make inferences from those estimates. Likewise, our approach provides a mechanism for using prior information with future data to assess whether these patterns may continue over time. We suggest that ecologists consider using temporally scalable climate indices in conjunction with Bayesian analysis for assessing the role of climate in ecological systems.

Highlights

With climate change becoming more of concern, many ecologists are including climate variables in their system and statistical models
Ecology and Evolution published by John Wiley & Sons Ltd
Our study was based on the work of McCauley et al (2015), which was focused on modeling variability of hydroperiods, as indexed by water surface areas, in relatively closed-basin wetlands called “potholes.” The wetlands in their study were located in the North Dakota, U.S.A., portion of the “Prairie Pothole Region” (PPR) of North America

Summary

Introduction

With climate change becoming more of concern, many ecologists are including climate variables in their system and statistical models Such efforts have included assessing how species may respond to changes in climate (e.g., Niemuth et al 2008), as well as how landscape features that function as wildlife habitat, such as wetlands, might respond to wetting and drying periods (e.g., Johnson et al 2005, 2010). Like wetlands, that appear to respond to patterns of both drought and deluge, linking some aspect of system function to climatic fluctuations is important for understanding habitat dynamics (Anteau 2012). These patterns might be inferred by relating a response variable to weather variables like temperature or precipitation (Forcey et al 2011).

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