Develop best practice recommendations for combining seabird study data collected from different platforms: study

This study developed best practice guidance to combine seabird survey data collected from different platforms based on a literature review, expert knowledge and a bespoke model development including sensitivity analysis. This can be used in environmental assessments for planning and licensing.

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2 Project brief

2.1 Overview and objective

Established bodies of theory and software address different challenges in answering questions of species abundance and distribution from survey data, particularly with regard to observation biases (e.g. distance sampling methodologies and frameworks for partial detectability), linking species distribution to habitat (e.g. resource selection functions) and enhancing the transferability of predictions from these models in space and time (e.g. generalised functional responses in resource selection). Although these methods are still the subject of very active research and development, they offer a clear workflow towards the estimation of absolute and relative abundance of wildlife, in association with present and future environmental profiles (whether naturally or anthropogenically effected).

At the same time, more broadly in applied ecology, there is a keen interest in integrated analysis and adaptive resource management. Momentum behind these ideas is encouraging the incorporation of different sources of spatial information onto a single, joint inference framework, so that statistical power can be greatly enhanced, even if the data themselves cannot be directly pooled because of their qualitative differences.

In this project, we used systematic literature review, expert knowledge on survey methodology, bespoke model development and sensitivity analyses on realistic simulation data to derive methodological and quantitative guidelines for best practice in conducting such joint inference for multi-platform seabird survey data.

2.2 Adherence to Marine Scotland remit

The aim of this project was to examine how to compare or combine multiple sets of seabird survey data collected from different survey platforms and/or with different temporal/spatial resolution and coverage to adequately characterise seabird distribution and abundances and use this information to develop best practice recommendations to use in assessments.

We considered boat based visual surveys (ESAS methodology), visual aerial surveys, and digital aerial surveys. Regarding data integration we considered different platforms, incompletely overlapping spatial extents, different temporal coverage (e.g. monthly versus seasonal), different spatial coverage, different survey resolution (e.g. swath width and/or transect spacing) and surveys conducted in different years.

By implementing different solutions on synthetic data, we have demonstrated the most appropriate methods and the key considerations for integrating multiple survey datasets. This has involved using existing modelling tools and code and the development of bespoke modelling tools and code.

Solutions are applicable across a wide range of marine bird species, and we have considered four exemplar species (Northern Gannet, Black-legged Kittiwake, Common guillemot, Great black-backed gull).

We have limited our exploration to how best to incorporate survey data from different platforms to characterise seabird distribution and abundance. We have not commented on how previous assessments have been done, but we have outlined those survey design requirements that facilitate data integration. We realise that such recommendations were not required under the project brief but they may usefully inform Scottish Government policy and project level scoping discussions.

2.3 Challenges addressed

The specific challenges that needed to be addressed in deriving guidelines are:

1. Seabird natural history: Models for abundance and distribution in seabirds must account for coloniality. Seabird distributions are not just the result of habitat suitability but also of accessibility that varies by colony location, species and season. Difficult questions pertaining to density dependence within colonies or between colonies of conspecifics and hetero-specifics also need to be taken into account.

2. Survey method characteristics: Different survey methodologies (boat-based, aerial visual and aerial digital) are affected by different types of biases and imprecisions. These need to be explicitly accounted for.

3. Effort scale characteristics: For a fixed amount of effort, any survey will make a decision on the trade-off between spatial/temporal resolution and extent. Different surveys may have entirely different designs, and their overall effort may also differ. These discrepancies offer challenges, but also opportunities for complementary use of different surveys.

4. Habitat data: Similar issues relating to differential resolutions, extents and data absence will permeate the habitat data (e.g. bathymetry, primary production, seabed sediment, any prey survey data etc.). When habitat data are dynamic (e.g. seasonal) these problems are likely to be particularly acute. In analysing spatial data, but particularly when trying to analyse multiple survey platforms in tandem, it is essential to have guidelines for how to deal with missing or incongruent habitat data.

5. Statistical robustness: Integrated analyses of multiple data sources aim to enhance statistical power by greatly increasing the effective sample size (but also by using data from different regions, different times and spatial resolutions in a complementary way). Achieving this is the main objective of this project, but it must be done in a way that does not misleadingly increase the apparent precision of the results and model predictions. This could threaten the precautionary approach and have adverse implications for management and policy decisions. Therefore, uncertainty in the observation processes from different surveys and the habitat data must be correctly propagated to the end-results, to give a reliable measure of precision.

2.4 Relevance to Marine Scotland

The intended applications of this work will be in monitoring of marine protected areas, development of marine planning and the licencing workflow for offshore renewables. We identify the following beneficial links to Marine Scotland's key responsibilities:

  • Marine planning requires good information about spatial and temporal trends in abundance. New human activity needs to be able to avoid critical hotspots of species distribution at valuable habitats and we need to be able to anticipate future crises before they arrive. Spatial and temporal prediction is the core theme of this bid.
  • Integrated planning is best achieved with integrated data analysis, such as the frameworks outlined here.
  • Fisheries although the present project focuses on seabirds, the implications for Marine Scotland's broader remit are two-fold. First, it has important implications for the management of fish stocks through estimates of seabird by-catch or predation pressure on managed stocks. Second, methods on multi-survey integration can have direct applicability to taxa beyond seabirds, such as fish.
  • Evidence informing marine development important implications of conflict, particularly with offshore development and shipping. See marine planning, above.
  • Develop Marine Scotland's organisational skills and competencies part of this project is an extensive workshop to Marine Scotland staff (and partners) on the methods of multi-platform analysis.



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