Integrated modelling of Atlantic mackerel distribution patterns and movements: A template for dynamic impact assessments

Offshore wind energy development: Research priorities for sound and vibration effects on fishes and aquatic invertebrates

There are substantial knowledge gaps regarding both the bioacoustics and the responses of animals to sounds associated with pre-construction, construction, and operations of offshore wind (OSW) energy development. A workgroup of the 2020 State of the Science Workshop on Wildlife and Offshore Wind Energy identified studies for the next five years to help stakeholders better understand potential cumulative biological impacts of sound and vibration to fishes and aquatic invertebrates as the OSW industry develops. The workgroup identified seven short-term priorities that include a mix of primary research and coordination efforts. Key research needs include the examination of animal displacement and other behavioral responses to sound, as well as hearing sensitivity studies related to particle motion, substrate vibration, and sound pressure. Other needs include: identification of priority taxa on which to focus research; standardization of methods; development of a long-term highly instrumented field site; and examination of sound mitigation options for fishes and aquatic invertebrates. Effective assessment of potential cumulative impacts of sound and vibration on fishes and aquatic invertebrates is currently precluded by these and other knowledge gaps. However, filling critical gaps in knowledge will improve our understanding of possible sound-related impacts of OSW energy development to populations and ecosystems.

Real-time predictions of seabird distribution improve oil spill risk assessments

Current knowledge of the distribution of sensitive seabirds is inadequate to safeguard seabird populations from impacts of oil spills in the Arctic. This gap is mainly driven by the fact that statistical models applied to survey data are coarse-scale and static with limited documentation of the distributional dynamics and patchiness of seabirds relevant to risk assessments related to oil spills. This paper describes a dynamic modelling framework solution for prediction of fine-scale densities and movements of seabirds in close-to-real time using fully integrated 3-D hydrodynamic models, dynamic habitat suitability models and agent-based models. The modelling framework has been developed and validated for the swimming migration of Brünnich's Guillemot Uria lomvia in the Barents Sea. The results document that the distributional dynamics of Brünnich's Guillemot and other seabird species to a large degree can be simulated with in-situ state variables and patterns reflecting the physical meteorology and oceanography and habitat suitability.

From theory to practice in pattern-oriented modelling: identifying and using empirical patterns in predictive models

To robustly predict the effects of disturbance and ecosystem changes on species, it is necessary to produce structurally realistic models with high predictive power and flexibility. To ensure that these models reflect the natural conditions necessary for reliable prediction, models must be informed and tested using relevant empirical observations. Pattern-oriented modelling (POM) offers a systematic framework for employing empirical patterns throughout the modelling process and has been coupled with complex systems modelling, such as in agent-based models (ABMs). However, while the production of ABMs has been rising rapidly, the explicit use of POM has not increased. Challenges with identifying patterns and an absence of specific guidelines on how to implement empirical observations may limit the accessibility of POM and lead to the production of models which lack a systematic consideration of reality. This review serves to provide guidance on how to identify and apply patterns following a POM approach in ABMs (POM-ABMs), specifically addressing: where in the ecological hierarchy can we find patterns; what kinds of patterns are useful; how should simulations and observations be compared; and when in the modelling cycle are patterns used? The guidance and examples provided herein are intended to encourage the application of POM and inspire efficient identification and implementation of patterns for both new and experienced modellers alike. Additionally, by generalising patterns found especially useful for POM-ABM development, these guidelines provide practical help for the identification of data gaps and guide the collection of observations useful for the development and verification of predictive models. Improving the accessibility and explicitness of POM could facilitate the production of robust and structurally realistic models in the ecological community, contributing to the advancement of predictive ecology at large.