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

Post van der Burg M, Anteau MJ, McCauley LA, Wiltermuth MT - Ecol Evol (2016)

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: U.S. Geological Survey Northern Prairie Wildlife Research Center 8711 37th Street Jamestown North Dakota 58401.

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.

No MeSH data available.


Related in: MedlinePlus

Posterior distributions for the temporal scaling parameter t, used in computing the relationship between the proportion of a wetland basin that was covered with water and the Standardized Precipitation Evapotranspiration Index. The top panel represents the estimated distribution for the historical period (i.e., pre‐1970) and the lower panel represents the estimated distribution for the contemporary period (post‐2003). The height of the bars represents the relative frequency with which certain values of t were chosen in our analysis. The thin red line represents the expected frequency under a uniform prior distribution. The parameter t was modeled as a discrete distribution in monthly increments and could assume values between 1 and 120 months. Frequencies were assigned to one of 10 annual bins to smooth the distributions for presentation.
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ece32092-fig-0003: Posterior distributions for the temporal scaling parameter t, used in computing the relationship between the proportion of a wetland basin that was covered with water and the Standardized Precipitation Evapotranspiration Index. The top panel represents the estimated distribution for the historical period (i.e., pre‐1970) and the lower panel represents the estimated distribution for the contemporary period (post‐2003). The height of the bars represents the relative frequency with which certain values of t were chosen in our analysis. The thin red line represents the expected frequency under a uniform prior distribution. The parameter t was modeled as a discrete distribution in monthly increments and could assume values between 1 and 120 months. Frequencies were assigned to one of 10 annual bins to smooth the distributions for presentation.

Mentions: On average, the scaling of SPEI also varied between the two eras (Table 1), although there was considerable uncertainty around the mean of each distribution. Despite this, there appeared to be enough information in the data to estimate a posterior distribution that was considerably different than the uniform prior that we specified (Fig. 3). The distributional plots also showed clear shifts in the modes of each distribution, which indicated that a shorter timescale fit the data better in the contemporary era compared with the historical era.


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

Post van der Burg M, Anteau MJ, McCauley LA, Wiltermuth MT - Ecol Evol (2016)

Posterior distributions for the temporal scaling parameter t, used in computing the relationship between the proportion of a wetland basin that was covered with water and the Standardized Precipitation Evapotranspiration Index. The top panel represents the estimated distribution for the historical period (i.e., pre‐1970) and the lower panel represents the estimated distribution for the contemporary period (post‐2003). The height of the bars represents the relative frequency with which certain values of t were chosen in our analysis. The thin red line represents the expected frequency under a uniform prior distribution. The parameter t was modeled as a discrete distribution in monthly increments and could assume values between 1 and 120 months. Frequencies were assigned to one of 10 annual bins to smooth the distributions for presentation.
© Copyright Policy - creativeCommonsBy
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4863021&req=5

ece32092-fig-0003: Posterior distributions for the temporal scaling parameter t, used in computing the relationship between the proportion of a wetland basin that was covered with water and the Standardized Precipitation Evapotranspiration Index. The top panel represents the estimated distribution for the historical period (i.e., pre‐1970) and the lower panel represents the estimated distribution for the contemporary period (post‐2003). The height of the bars represents the relative frequency with which certain values of t were chosen in our analysis. The thin red line represents the expected frequency under a uniform prior distribution. The parameter t was modeled as a discrete distribution in monthly increments and could assume values between 1 and 120 months. Frequencies were assigned to one of 10 annual bins to smooth the distributions for presentation.
Mentions: On average, the scaling of SPEI also varied between the two eras (Table 1), although there was considerable uncertainty around the mean of each distribution. Despite this, there appeared to be enough information in the data to estimate a posterior distribution that was considerably different than the uniform prior that we specified (Fig. 3). The distributional plots also showed clear shifts in the modes of each distribution, which indicated that a shorter timescale fit the data better in the contemporary era compared with the historical era.

Bottom Line: 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.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.

View Article: PubMed Central - PubMed

Affiliation: U.S. Geological Survey Northern Prairie Wildlife Research Center 8711 37th Street Jamestown North Dakota 58401.

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.

No MeSH data available.


Related in: MedlinePlus