Offshore renewable developments - developing marine mammal dynamic energy budget models: report

A report detailing the Dynamic Energy Budget (DEB) frameworks and their potential for integration into the iPCoD framework for harbour seal, grey seal, bottlenose dolphin, and minke whale (building on an existing DEB model for harbour porpoise to help improve marine mammal assessments for offshore renewable developments.

1 Non-Technical Summary

The offshore wind industry is in the process of a significant expansion with a move towards clean energy and a green economic recovery. The sustainable expansion of offshore wind requires a robust understanding of the impacts of construction and operation and appropriate levels of conservatism and realism in assessments. The potential risk of injury and/or disturbance to marine mammals during construction of offshore renewable energy developments (e.g., pile driving, removal of unexploded ordnance, increased vessel presence offshore) has been identified as a key consenting risk for projects in UK waters. Possible consequences of exposure to underwater noise include disturbance that could cause marine mammals to either move away or change behaviour (which could result in reduced net energy intake) or suffer temporary and permanent hearing damage.

The scale of offshore wind farm developments means there is the potential for significant cumulative impacts on marine mammals, which would need to be considered and mitigated at a project and regional level. The interim framework for assessing the Population Consequences of Disturbance (iPCoD) relies on the relationship between the disturbance experienced by an animal and how that disturbance impacts vital rates such exclusi–ely or calf. The relationships used in iPCoD were obtained from formal expert elicitation approaches (EE). The iPCoD tool has been updated with new elicitations and other improvements in recent years (including updated elicitations). Despite these model updates, the reliance on expert judgement is a source of uncertainty in assessments and risk for decision makers (as it relies on the carefully solicited judgments of experts rather than empirical datasets).

Disturbance can cause behavioural, physiological and health changes which can have subsequent effects on an individual’s vital rates, such as survival and reproduction. The cost of disturbance is in most cases mediated by the state of the individual (e.g., life history stage and exposure history) and the environment that the individual is in (e.g., resource availability). By modelling health, we have an explicit scalar link between individual health, response to disturbance and the consequential population demographic effects of this disturbance. A wide range of bioenergetic models exist for marine mammal species and other taxa, and the principles behind these models are well established.

The overall objective of this project was to describe Dynamic Energy Budget (DEB) frameworks for harbour seal (Phoca vitulina), grey seal (Halichoerus grypus), bottlenose dolphin (Tursiops truncatus) and minke whale (Balaenoptera acutorostrata) (building on an existing DEB model for harbour porpoise (Phocoena phocoena)) to help improve marine mammal assessments for offshore renewable developments. The intention is that these models can provide new tools that can potentially be applied to project level assessments to help address potential risk to marine mammals from offshore wind and other developments. These DEB models can also be used to generate transfer functions for use in the interim PCoD model and reduce the need for EE.

Section 3 provides background the energetics and disturbance and an introduction to DEB theory. In Section 4 we outline the overview, design and details of DEB models – following the ODD (Overview, Design concepts, and Details) protocol of Grimm et al. (2020). Section 5, 6, 7 and 8 describes the data used to parameterise the models for harbour seals, grey seals, bottlenose dolphins and minke whales respectively. This provides complete transparency of the main data gaps that remain as we move towards a more empirically-based framework. In each of these sections there are explorations of the simulated effects of disturbances. Section 9 explores how we can account for uncertainty in energetics models. Section 10 covers another important feature of assessments of disturbance – considering the movement ecology of species (which affects the probability of exposure – a key determinant of impacts). The report concludes in Section 11, with consideration of future developments required with respect to energetics, movement and understanding the effects of disturbance.

These new models can be added to the suite of DEB models, all based on the template developed by Hin et al. (2019), that already includes models for harbour porpoise, long-finned pilot whale (Globicephala melas), Cuvier’s beaked whale (Ziphius cavirostris), Blainville’s beaked whale (Mesoplodon densirostris), beluga whale (Delphinapterus leucas) and Pacific walrus (Odobenus rosmarus divergens). The model descriptions in this report have been structured using the ODD protocol recommended for documenting agent-based models. The models have also been calibrated using the pattern-oriented modelling (POM) approach. While this study provides new tools to move towards a more empirically-based framework to reduce this uncertainty, all models require subjective decisions in the selection of parameters and so we cannot remove all expert judgment from such processes. However, we can provide greater transparency of where the knowledge gaps are and the key sensitivities of models. Such models are yet to be used in formal assessments, but by developing the models, it adds to the suite of tools available when an energetic pathway is being considered (as is largely the case with disturbance effects). In the future, this will help understand the potential knock-on effects population demographic of disturbance, therefore, providing a more in-depth assessment of how disturbance might affect population growth rate (over a longer time periods) and highlight life history stages that are particularly vulnerable to disturbance.

We have also described how the uncertainty associated with the many model parameter values can be accounted for, and developed a novel, and potentially cost‑effective, method for quantifying uncertainty associated with parameters that are not directly observable. This method can be readily applied to any of the existing DEB models developed using the Hin et al. (2019) template. DEB models can also be used as standalone tools to assess the predicted effects of disturbance, and resulting foraging disruption, at an individual level (unless a population level version is available). We investigated the effects of disturbance on vital rates for harbour seals, grey seals, bottlenose dolphins and minke whales (Sections 5.3, 6.3, 7.3 and 8.3). Grey seals appear to be more vulnerable to the effects of disturbance than the other three species, but these results should be treated with caution because only a relatively small set of plausible model parameter values was used in the simulations for the other three species.

We have demonstrated in Section 9.3 how the DEB models developed for this study and the harbour porpoise DEB model are capable of generating the relationships required to replace the transfer functions in iPCoD. The current transfer functions established the relationship between days of disturbance experienced and their effect on vital rates and were derived using formal EE approaches. It is critical to stress that, whilst it is possible, the DEB derived results will still be based on two key sets of assumptions. The first is that the DEB models (as outlined above) rely on a number of parameters sourced from across the marine mammal and energetics literature. This is true of many models for any taxa, but it cannot be overlooked. The second is that, in order to generate empirically derived transfer functions (to replace the EE-derived ones in iPCoD), it is necessary to specify an appropriate effect of disturbance (i.e., the number of hours without foraging on each day of disturbance). This is still a poorly understood field, but any new DEB-derived transfer functions (and how similar or different they are to the EE-derived functions) will be heavily dependent on the hours of lost foraging specified (N.B. 6 hours was used in Section 9.3). This will need to be carefully considered when replacing EE transfer functions into iPCoD.

Beyond the integration of DEB-derived transfer functions into iPCoD, future research should involve exploring the use of movement models to more accurately estimate the probability of exposure (which remains a key sensitivity in population assessments). Additionally, the DEB models themselves can be updated and improved in the future and further bioenergetic and physiological research is funded to improve how these pathways are modelled.

These could include improved understanding of Field Metabolic Rates (FMR) (and how it varies with life stage, time of year and activity) and the ontogenetic development of foraging efficiency in calves, pups and juveniles. Furthermore, increased knowledge on how the prey environment changes (environmental stochasticity); individual variation in the parameters that determine growth, feeding efficiency; and the body condition (energy reserve) thresholds for reproduction and mortality are very important.

Understanding the effects of disturbance in terms of lost foraging remains critical to energetic models and their integration into population models like iPCoD.



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