Marine mammals: methodology for combining data

Executive Summary

Marine mammal abundance and distribution data form an important part of assessments to estimate the potential effects of proposed offshore wind developments. Therefore, there is a need to ensure that abundance and distribution data are collected and analysed to ensure robust estimates to inform the planning, consenting and licensing processes. There are several sources of information that can contribute to estimating the abundance and distribution of marine mammals. Digital aerial surveys (DAS) and static passive acoustic monitoring (PAM) are two data collection modes, which have been developed relatively recently, compared to standard visual aerial and ship-based surveys used within the UK. Both digital aerial and static PAM surveys can collect data on fine temporal and spatial scales, though they have their strengths and limitations. Aerial surveys typically provide better spatial survey coverage than static acoustic recorders, while acoustic recorders generally provide improved temporal coverage. The overarching goals of this project were to: (1) produce a modelling framework integrating DAS and PAM data; (2) produce a test case study on harbour porpoise to validate the methods; and (3) provide recommendations on standards for static PAM and DAS data collection.

This project ran from January 2023 – February 2024 and a series of technical meetings were held by the project team to review data integration methods, available software to assist data integration and survey design considerations and recommendations. A dataset from the Moray Firth, Scotland, was prepared and analysed for the case study. The main deliverables were this final report and accompanying analysis code.

Multiple methods to integrate DAS and PAM data were assessed. The selected method for the case study used a Bayesian model to calibrate the PAM data using absolute densities derived from the DAS data. This method allows uncertainty to be propagated in all elements of the density estimators for both the DAS and PAM data. Currently available tools to assist with DAS and PAM data integration were also assessed. R-based MRSea software was used for the spatial modelling components of the case study analysis, and the project also identified how other R-based tools can be used for survey design (dssd/dsims) and to assess power to detect changes in density/abundance (MRSeaPower/AVADECAF). All assessed software packages have potential for extensions, which would ultimately aid data integration, though these were outside the scope of this project. Discussions about software concluded that clear documentation and long-term support are key features of any software used for analysis so should be considered a priority in any future software development.

The case study used PAM and DAS data in the Moray Firth from August and September 2010 to assess the distribution and abundance of harbour porpoise (Phocoena phocoena). The analysis demonstrated the use of Bayesian data integration following methods in Jacobson et al. (2017). A parameter combining detection probability of harbour porpoise clicks and probability of clicking was estimated, with associated uncertainty. The estimation of this parameter enabled absolute density to be estimated from the PAM data, including during time periods where no DAS were flown. This is the primary benefit of implementing this method: long-term time series of PAM data can be used to estimate absolute densities, assuming that the parameters estimated from the combined DAS and PAM data are representative across the time periods analysed. Density surfaces were also estimated from the calibrated PAM data, showing spatial changes in absolute density. This approach is likely to be of most practical use in applications to support offshore wind development, where DAS data are able to provide an estimate of the absolute density of cetaceans (albeit with some limitations) and the PAM data play a supporting role by collecting continuous data across the time period of interest. Relevant code and data used for the case study are available on GitHub. Finally, survey design discussions within the project team highlighted key topics such as (1) defining project goals, (2) addressing survey design principles such as replication and coverage at the survey design stage using available software tools, (3) using best available data regarding required parameters for density/abundance estimation and (4) specific considerations relating to DAS-PAM data integration. Future survey design-related research steps were also outlined, focusing on assessing how the number of PAM instruments and DAS flights influence precision and accuracy in the resulting calibrated PAM data.

In conclusion, the following survey design recommendations were suggested:

• Clearly identify the goals of a survey to ensure that the survey design will meet the needs of the survey goals. Goals may need to be prioritised where there are several competing goals and/or target species.
• Follow existing guidance for line and point placement for separate DAS and PAM surveys, though more research is needed to understand survey design requirements for an integrated survey.
• Use existing tools where possible to aid survey design, including assessing the power of the survey to detect changes in density and abundance. More software tool development is required specifically for integrated surveys.
• Consider the benefits of collecting data from more than one type of surveying platform. Different platform types offer different advantages; in this study combining DAS and PAM data led to a time series of estimated absolute densities that would not have been practically possible from one platform alone. More research is required, however, to determine how many DAS flights are required, and at what intervals, to optimally calibrate the PAM data.

Finally, the following future research directions across the project were identified and summarised:

• There is a need to develop a software tool to design combined DAS and PAM surveys, which could be an extension of existing tools.
• Several extensions to the case study analysis would be beneficial including:
  • Explore variability in the vp parameter via extended modelling and simulation.
  • Compare the calibration approach with other reviewed data integration methods.
  • Explore the effects on precision and accuracy of estimated parameters when including acoustic detection probability and cue production rates as informed priors.
  • Use simulation (based on the case study data) to assess how many PAM instruments and how many DAS surveys are required to achieve negligible bias and a suitable level of uncertainty in the resulting abundance estimates.
• Continued research into estimating detection probability and availability parameters for DAS data is important, given the need to estimate absolute density from the DAS data when using the calibration method. This research may also include extracting group size information from DAS data.