Ecological models are extensively and increasingly used in support of environmental policy and decision making. Dynamic Bayesian Networks (DBN) as a tool for conservation have been demonstrated to be a valuable tool for providing a systematic and intuitive approach to integrating data and other critical information to help guide the decision-making process. However, data for a new ecosystem are often sparse. In this case, a general DBN developed for similar ecosystems could be applicable, but this may require the adaptation of key elements of the network. The research presented in this paper focused on a case study to identify and implement guidelines for model adaptation. We adapted a general DBN of a seagrass ecosystem to a new location where nodes were similar, but the conditional probability tables varied. We focused on two species of seagrass (Zostera noltei and Z. marina) located in Arcachon Bay, France. Expert knowledge was used to complement peer-reviewed literature to identify which components needed adjustment including parameterisation and quantification of the model and desired outcomes. We adopted both linguistic labels and scenario-based elicitation to elicit from experts the conditional probabilities used to quantify the DBN. Following the proposed guidelines, the model structure of the DBN was retained, but the conditional probability tables were adapted for nodes that characterised the growth dynamics in Zostera spp. population located in Arcachon Bay, as well as the seasonal variation on their reproduction. Particular attention was paid to the light variable as it is a crucial driver of growth and physiology for seagrasses. Our guidelines provide a way to adapt a general DBN to specific ecosystems to maximise model reuse and minimise re-development effort. Especially important from a transferability perspective are guidelines for ecosystems with limited data, and how simulation and prior predictive approaches can be used in these contexts.

Environmental systems suffer from multiple interacting stressors. Each stressor can act on different parts of the system and at different time scales. This hampers measuring and predicting the stressors’ impacts on ecosystems. We propose a conceptual method that integrates available data with physical constraints over relevant time scales to predict management outcomes in data-scarce systems affected by multiple stressors. We first predict the combined stressor levels that threaten a management target and then define stressor equivalents to to convert between. These “ball-park” estimates of critical stressor levels help to identify how the threat posed by interacting stressors responds to its management. Our approach assists managers in the decision-making process regarding when to manage a system and how to monitor. We illustrate our concept with a case study of an invaded island ecosystem, yet our approach is useful for other data-poor environmental systems that suffer from multiple cumulative stressors.

The structure of coral reef communities results from interacting evolutionary, ecological and environmental forces. How these factors interact in structuring these communities at a global scale, and how such effects might vary among biogeographical regions is unclear. We partitioned sources of reef community assemblage patterns by environmental, latent (i.e. unobserved), and random factors on 291 coral reefs distributed across five biogeographical regions. We then estimated how these factors were related to variations in abundance and co-occurrence among 16 functional groups. Latent factors better explained the distributions of opportunistic functional groups like algae, whereas environmental factors better explained abundance and co-occurrence of hard corals. Co-occurrence patterns revealed complex interactions between coral and algae groups that were not related to environmental factors but influenced by regional biogeography. Our results show that environmental factors are not the sole drivers of coral reef structure highlighting the importance of assemblage-level interactions and unobserved variables.