Multi-species space use by marine predators: defining biodiversity ‘hotspots’ in the marine realm

Abstract

Anthropogenic change of ecosystems has led to wide-scale changes in biodiversity globally, with declines across terrestrial, freshwater and marine realms. Global concern about the increasing anthropogenic impact on biodiversity has driven intense research into the drivers and consequences of change, alongside rapidly moving international policy and conservation development. Quantifying critical marine habitats is vital for protecting remaining biodiversity, and understanding areas of increased coexistence or biodiversity hotspots is particularly important if management and conservation methods continue to focus on spatial approaches (such as marine protected areas). A key challenge in biogeography is to understand and predict the potential impacts of climate change on the distribution of biodiversity, therefore identifying the environmental drivers that may impact richness may provide insight into future species richness patterns. Studies have highlighted the difficulty in mapping biodiversity at a large scale due to patchy data coverage, and this is further intensified using a particularly cryptic set of species that inhabit and spend much of their life beneath the surface. Top marine predators have essential ecological roles as ecosystem engineers and amplify trophic information across multiple spatiotemporal scales and have been identified as sentinel species which can exhibit clear responses to environmental variability and ecosystem health. However quantitative maps of marine predator coexistence are lacking, with the collection of new standardised data expensive, time-consuming and often focused on small-scale local studies.This thesis utilises large available historical datasets to address this critical research gap in mapping the biodiversity of top marine predators across multiple taxa, around the UK. Chapter 2 summarises available data around the UK, which can be used to ascertain the status of the information available. This is useful for a wide range of stakeholders who are often under time pressure, under-resourced and trying to be proactive in a fast-paced legislative environment. It demonstrates available data exists to maximise temporal and spatial coverage of such a large-scale area, for large-scale research questions.However, it is well-known that databases have inherent biases due to heterogeneousdata sources and lack of standardisation. Chapter 3 adapts a well-recognised risk-assessment matrix approach to quantify biases within four example datasets. The study highlighted the level of risk in using heterogeneous datasets is lower for assessing patterns of association in marine predators, rather than counts or abundances. Therefore, research questions should be carefully considered when utilising datasets not designed for a specific research hypothesis. The matrix method presented has two important roles to advance this field of research: the first is to aid people to analyse existing datasets and provide a standardised approach to enumerating bias quantitatively as opposed to just describing bias assumptions. The second is to guide research to design better surveys by understanding which risk factors are most influential on their study.Critically, Chapter 4 provides maps of marine predator biodiversity hotspots around the UK, identifying areas of shared space use across taxa groups. It is the largest cross- taxa study of marine predator biodiversity around the UK to date. Sighting datasets were collated and species richness was determined across different spatial scales. Whilst species richness has been a keystone measure of biodiversity, it can be skewed by survey effort and therefore a new measure of ‘species richness per unit effort’ (SRPUE) was derived to identify relative areas of high and low richness. Patterns in species richness over time using seasonal-trend decomposition analysis revealed that the concept of carrying capacity becomes evident when survey effort is accounted for. This chapter demonstrates how the analysis of existing data can facilitate the mapping of the biodiversity of marine predators and allow areas of high shared space use to be prioritised in conservation and management.Simply quantifying biodiversity hotspots is not enough, with the potential drivers of species richness patterns identified as important in the literature, particularly with anthropogenic climate change causing shifts in species coexistence. Chapter 5 uses three modelling approaches to look at associations of high species richness with a suite of environmental variables. Macro-ecological models (MEMs) using generalised additive models (GAMs) and stacked species distribution models (SSDMs) were utilised to give maps of species richness. SRPUE is a useful method to factor in effort in richness mapping, but user interpretation is not as intuitive as the well-known species richness integer scale, and therefore an alternative approach was derived using effort as a predictor. All variables were significant predictors for biodiversity. The GAM using raw species richness and SSDM model demonstrated sea surfacetemperature has the highest relative contribution to richness hotspots and therefore is a concern in climate change with warming oceans.Overall, this thesis demonstrates how analysis of existing and diverse data can be utilised cost-effectively to map biodiversity. While some caution is needed when using historical datasets, these distribution maps are the first available output at this spatial scale and taxonomic coverage and have widespread and immediate applications in identifying important areas of protection and providing a focus for marine management strategies.