Patterns in the space use of the Bearded Reedling, Panurus biarmicus, on the Tay Reedbeds, Scotland

This thesis presents research into the space use of a specialist reedbed Passerine, the Bearded Reedling, or Bearded Tit, Panurus biarmicus, with a view to inform the conservation of this species and reedbeds as a whole. How a species uses space, and how space use changes between individuals or over time, can influence: the ability to forage and hunt effectively, breeding success, susceptibility to predation, genetic health, disease spread, robustness against environmental change and ultimately, colonisation or extinction. Thus, understanding the space use of animals can provide critical insight into ecological systems. Birds offer interesting models when studying animal space use, as, by being intrinsically mobile, many bird species can occupy multiple spatial scales. As a consequence of being completely dependent on patchy and ephemeral reedbed habitats, the Bearded Reedling, has a clustered, inhomogeneous distribution throughout its range. This drives the existence of distinct spatial scales upon which space use studies should be characterised. Distribution and movement within a single reedbed can be considered local-scale, while spatial processes between reedbeds can be considered wide-scale. Temporal processes may act upon both of these scales. For example, changing interactions with predators may influence nest positioning at a local-scale, while seasonal changes in resource requirements might drive processes such as migration at a wide-scale. The Bearded Reedling has a wide temperate breeding range, extending over much of Eurasia. On the IUCN’s red list, it is listed as ‘of least concern’, with an estimated European population between 240,000-480,000 breeding pairs. Despite its relatively favourable conservation status, its dependence on reedbed habitats drives a fragmented distribution, with populations being concentrated in small, isolated, stands. Over the last century reedbed wetlands have suffered rapid declines caused by drainage schemes undertaken to improve land for development or agriculture. Additionally, many remaining reed stands are subject to extensive commercial management to produce thatch or biofuel. Conversely, in other areas, management is driven by conservation motives which recognise the present threats to reedbeds, and aim to encourage the diversity of species associated with these habitats. As the Bearded Reedling is fundamentally linked to the quality and structure of a reed stand, understanding the space use of this species will offer information for the direct conservation of this specialist species, and for the effects of reedbed management as a whole. This thesis first presents studies of space use at a local-scale. All local-scale research is conducted at the Tay Reedbeds in eastern Scotland. Mist netting and bird ringing data are used within capture recapture models, which include an explicit spatial component, to gain insight into the abundance of the Bearded Reedling on the Tay. This abundance estimation approach suggests the Tay reedbeds are a stronghold for this species on the British Isles, and that, as a high latitude site, the Tay may have importance for range expansion. A combination of transect surveys and radio-tracking data are then used to establish the local-scale space use of this species during the breeding and autumnal seasons. These data are related to changes in the structure of reed caused by local management in the form of mosaic winter reed cutting. Results suggest that birds exploit young and cut patches of reed as foraging resources when they are available, and that old, unmanaged reed is critical for nesting and winter foraging. Further local-scale studies concern the spatial patterns in the nesting habits of this species. Mosaic reed cutting creates clear edges in a reedbed. Artificial nests placed in the Tay Reedbeds demonstrate increased nest predation rates closer to the edges of cut patches. Additionally, high predation rates become reduced as the cut reed re-grows, suggesting that reed cutting may increase accessibility of the stand to predators. As Bearded Reedling nests are uncommon and difficult to locate, the timing, site selection and structure of a sample of real nests from the Tay is then detailed. These demonstrate an early, and relatively rigid breeding onset in this species, the importance of dense, compacted reeds as nesting sites and a degree of flexibility in nest structure. Conservation efforts will also benefit from studies into wide-scale spatial processes. These may be important when establishing how colonisation events occur and when predicting the effects of climatic change. The Bearded Reedling has been traditionally considered a resident species which only occasionally undertakes wide-scale, between-reedbed, movements. Indeed, the ecology of this species suggests strict year round local residency to reedbeds, with distinct seasonal changes in diet allowing occupation of these habitats year round. The European ringing recoveries of this species, since the 1970s are investigated to better characterise the wider movements of specialist resident. These suggest residency in southern populations, but higher instances of movement than expected in more northerly regions. In these regions wide-scale movement patterns resemble those of partial regular migratory species. An understanding of local and wide-scale spatial processes can offer a strong foundation on which to build conservation strategies. This thesis aims to use studies of space use to provide this foundation for the Bearded Reedling and offer further insight into the ecology of reedbed habitats as a whole. The thesis concludes by proposing an effective strategy for the conservation management of reedbeds that will especially benefit the Bearded Reedling.

Quantification of air quality in space and time and its effects on health

The long-term adverse effects on health associated with air pollution exposure can be estimated using either cohort or spatio-temporal ecological designs. In a cohort study, the health status of a cohort of people are assessed periodically over a number of years, and then related to estimated ambient pollution concentrations in the cities in which they live. However, such cohort studies are expensive and time consuming to implement, due to the long-term follow up required for the cohort. Therefore, spatio-temporal ecological studies are also being used to estimate the long-term health effects of air pollution as they are easy to implement due to the routine availability of the required data. Spatio-temporal ecological studies estimate the health impact of air pollution by utilising geographical and temporal contrasts in air pollution and disease risk across $n$ contiguous small-areas, such as census tracts or electoral wards, for multiple time periods. The disease data are counts of the numbers of disease cases occurring in each areal unit and time period, and thus Poisson log-linear models are typically used for the analysis. The linear predictor includes pollutant concentrations and known confounders such as socio-economic deprivation. However, as the disease data typically contain residual spatial or spatio-temporal autocorrelation after the covariate effects have been accounted for, these known covariates are augmented by a set of random effects. One key problem in these studies is estimating spatially representative pollution concentrations in each areal which are typically estimated by applying Kriging to data from a sparse monitoring network, or by computing averages over modelled concentrations (grid level) from an atmospheric dispersion model. The aim of this thesis is to investigate the health effects of long-term exposure to Nitrogen Dioxide (NO2) and Particular matter (PM10) in mainland Scotland, UK. In order to have an initial impression about the air pollution health effects in mainland Scotland, chapter 3 presents a standard epidemiological study using a benchmark method. The remaining main chapters (4, 5, 6) cover the main methodological focus in this thesis which has been threefold: (i) how to better estimate pollution by developing a multivariate spatio-temporal fusion model that relates monitored and modelled pollution data over space, time and pollutant; (ii) how to simultaneously estimate the joint effects of multiple pollutants; and (iii) how to allow for the uncertainty in the estimated pollution concentrations when estimating their health effects. Specifically, chapters 4 and 5 are developed to achieve (i), while chapter 6 focuses on (ii) and (iii). In chapter 4, I propose an integrated model for estimating the long-term health effects of NO2, that fuses modelled and measured pollution data to provide improved predictions of areal level pollution concentrations and hence health effects. The air pollution fusion model proposed is a Bayesian space-time linear regression model for relating the measured concentrations to the modelled concentrations for a single pollutant, whilst allowing for additional covariate information such as site type (e.g. roadside, rural, etc) and temperature. However, it is known that some pollutants might be correlated because they may be generated by common processes or be driven by similar factors such as meteorology. The correlation between pollutants can help to predict one pollutant by borrowing strength from the others. Therefore, in chapter 5, I propose a multi-pollutant model which is a multivariate spatio-temporal fusion model that extends the single pollutant model in chapter 4, which relates monitored and modelled pollution data over space, time and pollutant to predict pollution across mainland Scotland. Considering that we are exposed to multiple pollutants simultaneously because the air we breathe contains a complex mixture of particle and gas phase pollutants, the health effects of exposure to multiple pollutants have been investigated in chapter 6. Therefore, this is a natural extension to the single pollutant health effects in chapter 4. Given NO2 and PM10 are highly correlated (multicollinearity issue) in my data, I first propose a temporally-varying linear model to regress one pollutant (e.g. NO2) against another (e.g. PM10) and then use the residuals in the disease model as well as PM10, thus investigating the health effects of exposure to both pollutants simultaneously. Another issue considered in chapter 6 is to allow for the uncertainty in the estimated pollution concentrations when estimating their health effects. There are in total four approaches being developed to adjust the exposure uncertainty. Finally, chapter 7 summarises the work contained within this thesis and discusses the implications for future research.