Bayesian adaptive design: improving the effectiveness of monitoring of the Great Barrier Reef

Kang, Su Yun; McGree, James; Drovandi, Christopher C.; Caley, M. Julian; Mengersen, Kerrie

doi:10.1002/eap.1409

Cited by 16 publications

(19 citation statements)

References 23 publications

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“…Power to detect a change in the context of a BACI design will differ to the power to detect a trend in time across all the sites for a given study region. For the long-term monitoring program associated with the Ningaloo marine reserves in Western Australia, further analysis would be required to assess power within a broader ecosystem context (Kang, McGree, Drovandi, Caley, & Mengersen, 2016) such that the overall information gathered by the design is maximized, e.g. (Falk, McGree, & Pettitt, 2014) before decisions around the optimal sampling design for this program could be made.…”

Section: Discussionmentioning

confidence: 99%

epower: An r package for power analysis of Before‐After‐Control‐Impact (BACI) designs

et al. 2019

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Before‐After‐Control‐Impact (BACI) designs are widespread in environmental science, however their implicitly hierarchical nature complicates the evaluation of statistical power. Here, we describe epower, an r package for assessing statistical power of BACI designs. The package uses Bayesian statistical methods via the r‐package INLA to fit the appropriate hierarchical model to user supplied pilot survey data. A posterior sample is then used to build a Monte Carlo simulation to test statistical power specifically for the Before/After × Control/Impact interaction term in the BACI model. Power can be assessed for any number of user‐specified effect sizes for the existing design, or across a range of levels of replication for any part of the sampling design hierarchy. The package offers a user friendly robust approach for assessing statistical power of BACI designs whilst accounting for uncertainty in parameter values within a fully generalized framework.

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Section: Discussionmentioning

confidence: 99%

epower: An r package for power analysis of Before‐After‐Control‐Impact (BACI) designs

et al. 2019

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“…Targeted monitoring is essential to inform decision‐making and, in turn, underpins adaptive and cost‐effective management (Lindenmayer et al 2011, Kang et al 2016). Indeed, the accelerating loss of biodiversity challenges the prioritization of conservation and management efforts, which are typically constrained by available resources and need to accommodate the economic activities that societies rely on for food or livelihoods.…”

Section: Introductionmentioning

confidence: 99%

Representation and complementarity of the long‐term coral monitoring on the Great Barrier Reef

Mellin

Peterson

Puotinen

et al. 2020

Ecological Applications

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Effective environmental management hinges on efficient and targeted monitoring, which in turn should adapt to increasing disturbance regimes that now characterize most ecosystems. Habitats and biodiversity of Australia’s Great Barrier Reef (GBR), the world’s largest coral reef ecosystem, are in declining condition, prompting a review of the effectiveness of existing coral monitoring programs. Applying a regional model of coral cover (i.e., the most widely used proxy for coral reef condition globally) within major benthic communities, we assess the representation and complementarity of existing long‐term coral reef monitoring programs on the GBR. We show that existing monitoring has captured up to 45% of the environmental diversity on the GBR, while some geographic areas (including major hotspots of cyclone activity over the last 30 yr) have remained unmonitored. Further, we identified complementary groups of reefs characterized by similar benthic community composition and similar coral cover trajectories since 1996. The mosaic of their distribution across the GBR reflects spatial variation in the cumulative impact of multiple acute disturbances, as well as spatial gradients in coral recovery potential. Representation and complementarity, in combination with other performance assessment criteria, can inform the cost‐effective design and stratification of future surveys. Based on these results, we formulate recommendations to assist with the design of future long‐term coral reef monitoring programs.

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“…Utility functions are mathematical representations of the objectives used to measure the suitability of a design for a specific purpose. Depending on the objective of the sampling, the best design might be one that includes spatially balanced sites distributed across the study area or it could be a design that includes clusters of sites in close proximity to one another [17]. A variety of utility functions are available [15,16] and are described more specifically in Section 2.4.…”

Section: Introductionmentioning

confidence: 99%

“…In these situations, the information gained from the data collected up to that point can be used to inform where the optimal sampling locations will be at the next time step. Hence, adaptive designs may provide additional benefits for long-term environmental monitoring programs because one-off optimal designs ignore the evolving nature of environmental processes and do not allow for adjustments as monitoring needs change [17].…”

Section: Introductionmentioning

confidence: 99%

SSNdesign—An R package for pseudo-Bayesian optimal and adaptive sampling designs on stream networks

et al. 2020

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Streams and rivers are biodiverse and provide valuable ecosystem services. Maintaining these ecosystems is an important task, so organisations often monitor the status and trends in stream condition and biodiversity using field sampling and, more recently, autonomous in-situ sensors. However, data collection is often costly, so effective and efficient survey designs are crucial to maximise information while minimising costs. Geostatistics and optimal and adaptive design theory can be used to optimise the placement of sampling sites in freshwater studies and aquatic monitoring programs. Geostatistical modelling and experimental design on stream networks pose statistical challenges due to the branching structure of the network, flow connectivity and directionality, and differences in flow volume. Geostatistical models for stream network data and their unique features already exist. Some basic theory for experimental design in stream environments has also previously been described. However, open source software that makes these design methods available for aquatic scientists does not yet exist. To address this need, we present SSNdesign, an R package for solving optimal and adaptive design problems on stream networks that integrates with existing open-source software. We demonstrate the mathematical foundations of our approach, and illustrate the functionality of SSNdesign using two case studies involving real data from Queensland, Australia. In both case studies we demonstrate that the optimal or adaptive designs outperform random and spatially balanced survey designs implemented in existing open-source software packages. The SSNdesign package has the potential to boost the efficiency of freshwater monitoring efforts and provide much-needed information for freshwater conservation and management.

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Bayesian adaptive design: improving the effectiveness of monitoring of the Great Barrier Reef

Cited by 16 publications

References 23 publications

epower: An r package for power analysis of Before‐After‐Control‐Impact (BACI) designs

epower: An r package for power analysis of Before‐After‐Control‐Impact (BACI) designs

Representation and complementarity of the long‐term coral monitoring on the Great Barrier Reef

SSNdesign—An R package for pseudo-Bayesian optimal and adaptive sampling designs on stream networks

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