Model-based adaptive monitoring: Improving the effectiveness of reef monitoring programs

In Canada, there is almost 30 years of experience in developing tiered and triggered adaptive monitoring programs focused on looking at whether environmental concerns remain when pulp and paper mills, or metal mines, are in compliance with their discharge limits. These environmental effects monitoring programs were based on nationally standardized designs. Many of the programs have been developed through multi-stakeholder working groups, and the evolution of the program faced repeated frictions and differing opinions on how to design environmental monitoring programs. This paper describes key guidance to work through the initial steps in program design, and includes scientific advice based on lessons learned from the development of the Canadian aquatic environmental effects monitoring program.

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Bayesian Models for Analysis of Inventory and Monitoring Data with Non-ignorable Missingness

Journal of Agricultural Biological and Environmental Statistics ◽

10.1007/s13253-021-00473-z ◽

2021 ◽

Author(s):

Luke J. Zachmann ◽

Erin M. Borgman ◽

Dana L. Witwicki ◽

Megan C. Swan ◽

Cheryl McIntyre ◽

...

Keyword(s):

Hierarchical Models ◽

Monitoring Program ◽

Bayesian Hierarchical Models ◽

Analysis Data ◽

Statistical Concept ◽

Bayesian Hierarchical ◽

Model Based ◽

Point Estimates ◽

Monitoring Programs ◽

Inventory And Monitoring

AbstractWe describe the application of Bayesian hierarchical models to the analysis of data from long-term, environmental monitoring programs. The goal of these ongoing programs is to understand status and trend in natural resources. Data are usually collected using complex sampling designs including stratification, revisit schedules, finite populations, unequal probabilities of inclusion of sample units, and censored observations. Complex designs intentionally create data that are missing from the complete data that could theoretically be obtained. This “missingness” cannot be ignored in analysis. Data collected by monitoring programs have traditionally been analyzed using the design-based Horvitz–Thompson estimator to obtain point estimates of means and variances over time. However, Horvitz–Thompson point estimates are not capable of supporting inference on temporal trend or the predictor variables that might explain trend, which instead requires model-based inference. The key to applying model-based inference to data arising from complex designs is to include information about the sampling design in the analysis. The statistical concept of ignorability provides a theoretical foundation for meeting this requirement. We show how Bayesian hierarchical models provide a general framework supporting inference on status and trend using data from the National Park Service Inventory and Monitoring Program as examples. Supplemental Materials Code and data for implementing the analyses described here can be accessed here: https://doi.org/10.36967/code-2287025.

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Optimal Model-Based Sensor Placement & Adaptive Monitoring Of An Oil Spill

IFAC-PapersOnLine ◽

10.1016/j.ifacol.2020.12.2010 ◽

2020 ◽

Vol 53 (2) ◽

pp. 1517-1524

Author(s):

Zak Hodgson ◽

Iñaki Esnaola ◽

Bryn Jones

Keyword(s):

Oil Spill ◽

Sensor Placement ◽

Optimal Model ◽

Model Based ◽

Adaptive Monitoring

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Effective Ecological Monitoring

10.1071/9781486308934 ◽

2018 ◽

Cited By ~ 3

Author(s):

David Lindenmayer ◽

Gene Likens

Keyword(s):

Environmental Science ◽

Ecological Monitoring ◽

Science And Policy ◽

Monitoring Programs ◽

Adaptive Monitoring ◽

Long Term Monitoring ◽

Key Aspects ◽

Policy Communities ◽

Term Monitoring

Long-term monitoring programs are fundamental to understanding the natural environment and managing major environmental problems. Yet they are often done very poorly and ineffectively. This second edition of the highly acclaimed Effective Ecological Monitoring describes what makes monitoring programs successful and how to ensure that long-term monitoring studies persist. The book has been fully revised and updated but remains concise, illustrating key aspects of effective monitoring with case studies and examples. It includes new sections comparing surveillance-based and question-based monitoring, analysing environmental observation networks, and provides examples of adaptive monitoring. Based on the authors’ 80 years of collective experience in running long-term research and monitoring programs, Effective Ecological Monitoring is a valuable resource for the natural resource management, ecological and environmental science and policy communities.

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Model-based integration of citizen-science data from disparate sources increases the precision of bird population trends

10.1101/2020.11.25.397380 ◽

2020 ◽

Author(s):

Lionel R Hertzog ◽

Claudia Frank ◽

Sebastian Klimek ◽

Norbert Röder ◽

Hannah GS Böhner ◽

...

Keyword(s):

Hierarchical Models ◽

Bird Species ◽

Population Trends ◽

Data Sources ◽

Bird Population ◽

Benchmark Model ◽

Model Based ◽

Time Investment ◽

Monitoring Programs ◽

Online Databases

AbstractAimTimely and accurate information on population trends is a prerequisite for effective biodiversity conservation. Structured biodiversity monitoring programs have been shown to track population trends reliably, but require large financial and time investment. The data assembled in a large and growing number of online databases are less structured and suffer from bias, but the number of observations is much higher compared to structured monitoring programs. Model-based integration of data from these disparate sources could capitalize on their respective strengths.LocationGermany.MethodsAbundance data for 26 farmland bird species were gathered from the standardized Common Breeding Bird Survey (CBBS) and three online databases that varied with regard to their degree of survey standardization. Population trends were estimated with a benchmark model that included only CBBS data, and five Bayesian hierarchical models integrating all data sources in different combinations. Across models, we compared consistency and precision of the predicted population trends, and the accuracy of the models. Bird species body mass, prevalence in the dataset and abundance were tested as potential predictors of the explored quantities.ResultsConsistency in predicted annual abundance indices was generally high especially when comparing the benchmark models to the integrated models without unstructured data. The accuracy of the estimated population changes was higher in the hierarchical models compared to the benchmark model but this was not related to data-integration. Precision of the predicted population trends increased as more data sources were integrated.Main conclusionsModel-based integration of data from different sources can lead to improved precision of bird population trend estimates. This opens up new opportunities for conservation managers to identify declining populations earlier. Integrating data from online databases could substantially increase sample size and thus allowing to derive trends for currently not well-monitored species, especially at sub-national scales.

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Representation, abstraction, and simple-minded sophisticates

Behavioral and Brain Sciences ◽

10.1017/s0140525x19002942 ◽

2020 ◽

Vol 43 ◽

Author(s):

Peter Dayan

Keyword(s):

Decision Theory ◽

Predictive Coding ◽

Bayesian Decision Theory ◽

Bayesian Decision ◽

Model Based ◽

Model Free

Abstract Bayesian decision theory provides a simple formal elucidation of some of the ways that representation and representational abstraction are involved with, and exploit, both prediction and its rather distant cousin, predictive coding. Both model-free and model-based methods are involved.

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Quantitative Model-Based Image Analysis of NuMa Distribution Links Nuclear Organization with Cell Phenotype

Microscopy and Microanalysis ◽

10.1017/s1431927600028968 ◽

2001 ◽

Vol 7 (S2) ◽

pp. 578-579

Author(s):

David W. Knowles ◽

Sophie A. Lelièvre ◽

Carlos Ortiz de Solόrzano ◽

Stephen J. Lockett ◽

Mina J. Bissell ◽

...

Keyword(s):

Image Analysis ◽

Mammary Epithelial Cells ◽

Nuclear Organization ◽

Critical Role ◽

Quantitative Model ◽

Mammary Epithelial ◽

Cell Phenotype ◽

Proliferating Cells ◽

Mitotic Apparatus ◽

Model Based

The extracellular matrix (ECM) plays a critical role in directing cell behaviour and morphogenesis by regulating gene expression and nuclear organization. Using non-malignant (S1) human mammary epithelial cells (HMECs), it was previously shown that ECM-induced morphogenesis is accompanied by the redistribution of nuclear mitotic apparatus (NuMA) protein from a diffuse pattern in proliferating cells, to a multi-focal pattern as HMECs growth arrested and completed morphogenesis . A process taking 10 to 14 days.To further investigate the link between NuMA distribution and the growth stage of HMECs, we have investigated the distribution of NuMA in non-malignant S1 cells and their malignant, T4, counter-part using a novel model-based image analysis technique. This technique, based on a multi-scale Gaussian blur analysis (Figure 1), quantifies the size of punctate features in an image. Cells were cultured in the presence and absence of a reconstituted basement membrane (rBM) and imaged in 3D using confocal microscopy, for fluorescently labeled monoclonal antibodies to NuMA (fαNuMA) and fluorescently labeled total DNA.

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