The Bayesian conditional independence model for measurement error: applications in ecology

Denham, Robert; Falk, Matthew G.; Mengersen, Kerrie

doi:10.1007/s10651-009-0130-3

Cited by 10 publications

(13 citation statements)

References 23 publications

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“…Measurement error in covariates is common for ecological data and ignoring this uncertainty in regression analyses can result in biased parameter estimates with underestimated variances (Gustafson 2003, Denham et al 2011. We incorporated this uncertainty into our regression analyses, by treating key covariates as random variables which were estimated from our data.…”

Section: Discussionmentioning

confidence: 99%

Deer density and disease prevalence influence transmission of chronic wasting disease in white‐tailed deer

et al. 2013

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Abstract. Host-parasite dynamics and strategies for managing infectious diseases of wildlife depend on the functional relationship between disease transmission rates and host density. However, the disease transmission function is rarely known for free-living wildlife, leading to uncertainty regarding the impacts of diseases on host populations and effective control actions. We evaluated the influence of deer density, landscape features, and soil clay content on transmission of chronic wasting disease (CWD) in young (,2-year-old) white-tailed deer (Odocoileus virginianus) in south-central Wisconsin, USA. We evaluated how frequency-dependent, density-dependent, and intermediate transmission models predicted CWD incidence rates in harvested yearling deer. An intermediate transmission model, incorporating both disease prevalence and density of infected deer, performed better than simple density-and frequencydependent models. Our results indicate a combination of social structure, non-linear relationships between infectious contact and deer density, and distribution of disease among groups are important factors driving CWD infection in young deer. The landscape covariates % deciduous forest cover and forest edge density also were positively associated with infection rates, but soil clay content had no measurable influences on CWD transmission. Lack of strong density-dependent transmission rates indicates that controlling CWD by reducing deer density will be difficult. The consequences of non-linear disease transmission and aggregation of disease on cervid populations deserves further consideration.

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Section: Discussionmentioning

confidence: 99%

Deer density and disease prevalence influence transmission of chronic wasting disease in white‐tailed deer

et al. 2013

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“…In the SDM context, several attempts have been made to either examine or account for uncertainty in spatial climate variables – for example: Elston et al . () proposed an adjustment in regression coefficients; Foster, Shimadzu & Darnell () used errors‐in‐variables models to account for explanatory variables that are overly smooth; Denham, Falk & Mengersen () considered a conditional independence model in a hierarchical Bayesian framework using a Gibbs sampler where uncertainty in the explanatory variables was accounted for using a validation data set; McInerny & Purves () investigated uncertainty in explanatory variables attributed to fine‐scale environmental variation, and proposed a general correction for regression dilution (or attenuation) also based on Bayesian methods; Fernández, Hamilton & Kueppers () examined the influence of interannual variability, topographic heterogeneity and the distance to nearest weather station; and Hefley et al . () investigated the presence of location uncertainty in presence‐only data.…”

Section: Introductionmentioning

confidence: 99%

A climate of uncertainty: accounting for error in climate variables for species distribution models

et al. 2014

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Summary1. Spatial climate variables are routinely used in species distribution models (SDMs) without accounting for the fact that they have been predicted with uncertainty, which can lead to biased estimates, erroneous inference and poor performances when predicting to new settings -for example under climate change scenarios. 2. We show how information on uncertainty associated with spatial climate variables can be obtained from climate data models. We then explain different types of uncertainty (i.e. classical and Berkson error) and use two statistical methods that incorporate uncertainty in climate variables into SDMs by means of (i) hierarchical modelling and (ii) simulation-extrapolation. 3. We used simulation to study the consequences of failure to account for measurement error. When uncertainty in explanatory variables was not accounted for, we found that coefficient estimates were biased and the SDM had a loss of statistical power. Further, this bias led to biased predictions when projecting change in distribution under climate change scenarios. The proposed errors-in-variables methods were less sensitive to these issues. 4. We also fit the proposed models to real data (presence/absence data on the Carolina wren, Thryothorus ludovicianus), as a function of temperature variables. 5. The proposed framework allows for many possible extensions and improvements to SDMs. If information on the uncertainty of spatial climate variables is available to researchers, we recommend the following: (i) first identify the type of uncertainty; (ii) consider whether any spatial autocorrelation or independence assumptions are required; and (iii) attempt to incorporate the uncertainty into the SDM through established statistical methods and their extensions.

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“…Interpretation error by readers and/or laboratories has also been shown to be a key determinant of estimating age for fishes (Campana, 2001;Denham et al, 2011). To address this concern, a measurement error model may be used if the data from the original studies are available.…”

Section: Discussionmentioning

confidence: 99%

Aging material matters in the estimation of von Bertalanffy growth parameters for dolphinfish (Coryphaena hippurus)

Chang

Maunder

2012

Fisheries Research

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The Bayesian conditional independence model for measurement error: applications in ecology

Cited by 10 publications

References 23 publications

Deer density and disease prevalence influence transmission of chronic wasting disease in white‐tailed deer

Deer density and disease prevalence influence transmission of chronic wasting disease in white‐tailed deer

A climate of uncertainty: accounting for error in climate variables for species distribution models

Aging material matters in the estimation of von Bertalanffy growth parameters for dolphinfish (Coryphaena hippurus)

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