Timing anthropogenic stressors to mitigate their impact on marine ecosystem resilience

Wu, Paul; Mengersen, Kerrie; McMahon, Kathryn; Kendrick, Gary A.; Chartrand, Kathryn M.; York, Paul H.; Rasheed, Michael A.; Caley, M. Julian

doi:10.1038/s41467-017-01306-9

Cited by 52 publications

(37 citation statements)

References 68 publications

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“…A DBN model of seagrass and dredging was developed and validated predominantly through expert elicitation (Figure ; Wu, Mengersen, et al., ). Although expert elicitation is widely used in existing seagrass models, the DBN can integrate expert knowledge and data in a dynamic, whole‐of‐system model, overcoming limitations of existing data (Caley et al., ; Uusitalo, ).…”

Section: Methodsmentioning

confidence: 99%

“…Illustration of overall seagrass DBN (dynamic Bayesian network) model (Wu, Mengersen, et al., ) focusing on shoot density for clarity (a). Nodes are ovals, and arrows denote conditional dependence between a parent and child node in the same time slice.…”

Section: Methodsmentioning

confidence: 99%

“…Thus, P ( X shoot density ( T C ) | E ) is a matrix with

n_{T_{normalC}}

rows and columns corresponding to states. We aggregate across columns first using a weighted sum (weighted mean equation, Wu, Mengersen, et al., ), with weights α , β derived from state thresholds (see Section 2.3). The time t R , when the consequence to baseline ratio first approaches and remains at one, is time to recovery.…”

Section: Methodsmentioning

confidence: 99%

“…Because of these high levels of variability and uncertainty in ecosystem states, dynamics and disturbance scenarios, it is impractical to predict responses solely from experiments. Whole‐of‐system models that capture uncertainty are needed (Wu, Mengersen, et al., ).…”

Section: Introductionmentioning

confidence: 99%

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Managing seagrass resilience under cumulative dredging affecting light: Predicting risk using dynamic Bayesian networks

McMahon

Rasheed

et al. 2017

Journal of Applied Ecology

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Coastal development is contributing to ongoing declines of ecosystems globally. Consequently, understanding the risks posed to these systems, and how they respond to successive disturbances, is paramount for their improved management. We study the cumulative impacts of maintenance dredging on seagrass ecosystems as a canonical example. Maintenance dredging causes disturbances lasting weeks to months, often repeated at yearly intervals. We present a risk‐based modelling framework for time varying complex systems centred around a dynamic Bayesian network (DBN). Our approach estimates the impact of a hazard on a system's response in terms of resistance, recovery and persistence, commonly used to characterise the resilience of a system. We consider whole‐of‐system interactions including light reduction due to dredging (the hazard), the duration, frequency and start time of dredging, and ecosystem characteristics such as the life‐history traits expressed by genera and local environmental conditions. The impact on resilience of dredging disturbances is evaluated using a validated seagrass ecosystem DBN for meadows of the genera Amphibolis (Jurien Bay, WA, Australia), Halophila (Hay Point, Qld, Australia) and Zostera (Gladstone, Qld, Australia). Although impacts varied by combinations of dredging parameters and the seagrass meadows being studied, in general, 3 months of duration or more, or repeat dredging every 3 or more years, were key thresholds beyond which resilience can be compromised. Additionally, managing light reduction to less than 50% can significantly decrease one or more of loss, recovery time and risk of local extinction, especially in the presence of cumulative stressors. Synthesis and applications. Our risk‐based approach enables managers to develop thresholds by predicting the impact of different configurations of anthropogenic disturbances being managed. Many real‐world maintenance dredging requirements fall within these parameters, and our results show that such dredging can be successfully managed to maintain healthy seagrass meadows in the absence of other disturbances. We evaluated opportunities for risk mitigation using time windows; periods during which the impact of dredging stress did not impair resilience.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…Thus, P ( X shoot density ( T C ) | E ) is a matrix with

n_{T_{normalC}}

Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Managing seagrass resilience under cumulative dredging affecting light: Predicting risk using dynamic Bayesian networks

McMahon

Rasheed

et al. 2017

Journal of Applied Ecology

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“…Here, we demonstrate the proposed scenario analysis framework on the analysis of ecological windows for seagrass meadows. It uses simulated data generated from a validated, expert‐elicited dynamic Bayesian network (DBN) model of seagrasses affected by dredging (Wu et al, ), which we refer to as the Seagrass DBN (SDBN) for brevity. We are interested here in identifying the most influential variables, similar scenarios, and time‐lagged effects (goals 1–4 above), affecting seagrass resilience.…”

Section: Introductionmentioning

confidence: 99%

Analysing the dynamics and relative influence of variables affecting ecosystem responses using functional PCA and boosted regression trees: A seagrass case study

Mengersen

Caley

et al. 2019

Methods Ecol Evol

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Understanding the relative influence of variables on ecosystem responses and the dynamics of their effect is necessary for effective ecosystem monitoring and management. Also known as causal pathways analysis, we develop an approach using functional Principal Components Analysis (fPCA) and machine learning within a scenario analysis framework. fPCA is used to identify most influential variables for correlated, non‐homogenoeus and nonlinear time series data characteristic of complex ecosystems. Hierarchical clustering of fPCA scores reveals groups of more homogeneous scenarios and similarly influential variables. The resultant subset of variables helps to overcome model identifiability problems when analysing time‐lagged effects using Boosted Regression Trees (BRT). We use simulated data generated by a Dynamic Bayesian Network (DBN) of ecological windows for seagrass ecosystems given dredging stressors; 3,024 scenarios with 75 state variables are analysed. The BRT demonstrated a high level of fit (R2≈0.97,MSE≈0.16), supporting the validity of influential variables identified by fPCA. Influential variables identified included genus, location type, light, growth and seed. Six consecutive months of positive growth and adequate light were important for predicting states of high or moderate population. Compared to traditional scenario analysis and sensitivity analysis approaches, our approach simultaneously enabled capture of n‐way interactions while accounting for time correlations. Although some variables and their dynamics agreed with existing knowledge, new variables and/or time lags of their effects were identified, corresponding to opportunities for further investigation as well as informing monitoring and management. Although we demonstrate our method on state variables with DBN simulated data, it is equally applicable to general time series data.

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Bayesian network model informs on the impact of biotic and abiotic factors on the population characteristics of an endangered plant species

Sienkiewicz

Łaska

2022

Population Ecology

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Preservation of the endangered plant species requires knowledge of the impact of environmental factors on their populations. This study was undertaken to evaluate the potential of a Bayesian network (BN) model to analyze the impact of habitat conditions on the current characteristics of Pulsatilla patens (L.) Mill. population in NE Poland. The model was based on field data collected in 2011-2015 from 47 sites in the four largest forest complexes of NE Poland. The causal network between (1) the biotic and (2) abiotic factors as well as morphological-developmental features of individuals and demographic features of populations (20 attributes, 1573 records) was made using the BayesianSearch Algorithm in GeNIe 2.0. The results indicate that the greatest impact on the population features had the number of competing species in forest undergrowth and exposition of slopes. Diagnostic testing and sensitivity analysis revealed that these factors were the major variables determining the size of the population, developmental phase, and the size of individuals. Tenfold cross-validation revealed that this model was the most effective to analyze the impact of habitat conditions on the presence of generative shoots, developmental phase, and juvenile shoots. The optimal levels of factors following from the BN modeling of the impact of habitat conditions on the characteristics of the population will allow regular growth (size of individuals) and development (life cycle of individuals) of a given endangered species.

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Timing anthropogenic stressors to mitigate their impact on marine ecosystem resilience

Cited by 52 publications

References 68 publications

Managing seagrass resilience under cumulative dredging affecting light: Predicting risk using dynamic Bayesian networks

Managing seagrass resilience under cumulative dredging affecting light: Predicting risk using dynamic Bayesian networks

Analysing the dynamics and relative influence of variables affecting ecosystem responses using functional PCA and boosted regression trees: A seagrass case study

Bayesian network model informs on the impact of biotic and abiotic factors on the population characteristics of an endangered plant species

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