Evaluating the usefulness of published data for estimating key parameters required in modelling global avian extinction risk

Despite the best efforts of conservationists worldwide, species extinction risks continue to rise. It is predicted that under intermediate climate warming scenarios 15-37% of species will be committed to extinction by 2050. This, coupled with limited funding and resources, means conservation management must be prioritised. Population viability analysis (PVA) models can help prioritisation by providing estimates of extinction risks for species. However, at present the availability of avian life history data and population data is limited, which makes this analysis challenging. Therefore, the aim of this thesis is to collate and calculate the necessary data so PVA models can be run for all bird species of the world. We begin by looking at what density data is available for species because these underpin much of our understanding of the extinction risks, as they are directly linked to population sizes and population sizes are known to be highly correlated with extinction. We collate field densities for approximately 30% of all avian species and then implement a Generalised Linear Model (GLM) to calculate densities for the remaining species. In total, densities are modelled for 8,541 species with a 37% accuracy. We then use these densities, along with distribution polygons and habitat data, to calculate population sizes for 6,206 species with a 55% accuracy. Finally, as survival estimates are a key demographic parameter to include in PVA models, we calculate these for 5,291 species with a 36% accuracy. Having calculated densities, population sizes and survival rates for over half of the worlds birds, we conclude that this is a huge step forward in being able to calculate extinction risks for many species. However, we highlight throughout that accuracy could be improved with more data collection, and fundamentally some data are still crucially missing if we want to run PVA models. Therefore, we suggest further research should aim to collect more avian data, such as fecundity, so simple PVA models can be run. For those species with the highest extinction risks we suggest even more data is collected, so more complex models, which include the effects of stochasticity, genetics and climate change can be run. We believe if robust and reliable data can be collected and included in PVA models, the results would be truly informative and insightful for conservation management and prioritisation

Impacts of environmental change on large mammal distributions in Southeast Asia

It is suggested climate change contributes considerably to global biodiversity loss. Southeast Asia, one of the world’s richest biodiversity hotspots, is predicted to lose most of its species by 2100. Hence, it is important to identify the key impact of environmental changes in order to develop more appropriate and effective conservation plans to mitigate species extinction risks. In this thesis, Species Distribution Modelling (SDM) techniques were used to predict potential species distributions in relation to 6 climatic variables. The effects of climate changes on large mammal distributions were examined across three time intervals: past (the last interglacial ~120,000 – 140,000 years before present), present (AD 1945 - present) and future (2050); while rates of species range shifts between the time intervals were also determined. It is found that large mammals are particularly vulnerable to climate change. The species will have to move 33 – 105 times faster than they once did in the past in order to search out suitable habitat. There is also evidence of niche conservatism and niche shift among the taxa. However, species niche shifts likely result from anthropogenic factors. Limited availability of species occurrence data in many parts of the world leads to an increased use of species range maps in research on species responses to changing environments. Predictions based on SDMs suggest that relying on a single data source may skew the species’ realistic threatened status and misguide conservation planning. The Zonation software was employed to evaluate the effectiveness of protected areas (PAs) in Thailand under future warming climate and identify high priority areas. Currently, nearly 60% of high priority areas fall within the PAs. In the future, the conservation values of the PAs are expected to remain relatively unchanged. However, it is suggested that enhancing PAs connectivity in the northern part of the country may yield a high return on conservation investment. A deliberate and consistent conservation effort will also be needed to maintain the effectiveness of the existing PAs

Conservation Biology in Sub-Saharan Africa

Conservation Biology in Sub-Saharan Africa comprehensively explores the challenges and potential solutions to key conservation issues in Sub-Saharan Africa. Easy to read, this lucid and accessible textbook includes fifteen chapters that cover a full range of conservation topics, including threats to biodiversity, environmental laws, and protected areas management, as well as related topics such as sustainability, poverty, and human-wildlife conflict. This rich resource also includes a background discussion of what conservation biology is, a wide range of theoretical approaches to the subject, and concrete examples of conservation practice in specific African contexts. Strategies are outlined to protect biodiversity whilst promoting economic development in the region. Boxes covering specific themes written by scientists who live and work throughout the region are included in each chapter, together with recommended readings and suggested discussion topics. Each chapter also includes an extensive bibliography. Conservation Biology in Sub-Saharan Africa provides the most up-to-date study in the field. It is an essential resource, available on-line without charge, for undergraduate and graduate students, as well as a handy guide for professionals working to stop the rapid loss of biodiversity in Sub-Saharan Africa and elsewhere

Improving the robustness and reliability of population-based global biodiversity indicators

The current global biodiversity crisis is complicated by a data crisis. Reliable tools are needed to guide scientific research and conservation policy decisions, but the data underlying those tools is incomplete and biased. For example, the Living Planet Index (LPI) tracks the changing status of global vertebrate biodiversity, but gaps, biases and quality issues plague the aggregated data used to calculate trends. Unfortunately, we have little understanding of how reliable biodiversity indicators are. In this thesis I develop a suite of tools to assess and improve the reliability of trends in the LPI and similar indicators. First, I explore distance measures as a flexible toolset for comparing time series and trends. I test distance measures for properties related to time series comparisons and rate their relative sensitivities, then expand the results into a framework for choosing an appropriate distance measure for any time series comparison task in ecology. I use the framework to select an appropriate metric for determining trend accuracy. Second, I construct a model of trend reliability from accuracy measurements of sampled trend replicates calculated from artificially generated time series datasets. I apply the model to the LPI to reveal that the majority of trends need more data to be considered reliable, particularly across the global south, and for reptiles and amphibians everywhere. Finally, I develop a method to account for sampling error and serial correlation in confidence intervals of indicators that use aggregated abundance data from different sources. I show that the new method results in more robust and accurate confidence intervals across a wide range of dataset parameters, without reducing trend accuracy. I also apply the method to the LPI to reveal that the current method used by the LPI results in inaccurate and overly wide confidence intervals

Citizen science and Lepidoptera biodiversity change in Great Britain

A considerable body of scientific evidence shows that the world is currently suffering a biodiversity crisis driven by anthropogenic factors such as land-use change, environmental pollution and climate change. Our knowledge of this crisis is incomplete, however, particularly when it comes to the most diverse multi-cellular organisms on the planet, the insects. Although there is evidence of decline in the abundance, distribution and biomass of many insect species, recent attempts to extrapolate these to global scales and encourage a policy response have been met with scepticism. More data are required, together with reliable methods to integrate and interpret them. In parallel, evidence-based conservation initiatives are urgently needed to address the biodiversity crisis. Citizen science has great promise for gathering much-needed data on insect trends and for engaging the public in biodiversity conservation. Citizen science has undergone a rapid rise in popularity over the past two decades, increasing the capacity for cost-effective, spatially-extensive biodiversity monitoring, while also raising awareness and commitment to nature conservation among participating members of the public. However, citizen science approaches can also present challenges, such as reductions in data quality, constraints in sampling strategies and in the onward reuse of data. In this thesis, citizen science monitoring of Great Britain’s (GB) moths and butterflies is examined as a case study, assessing some of the benefits and limitations of increased participation and demonstrating applications of citizen science data in determining species trends, drivers of change and estimates of extinction risk. Overall moth abundance has decreased in GB, probably mainly as a result of habitat degradation, while climate change has enabled the range expansion of some species (Chapter 2). Much remains to be learnt about other potential drivers of change, such as chemical pollution and artificial light at night (Chapter 2). I demonstrated the efficacy of citizen science by calculating GB distribution trends for 673 moth species for the first time, finding that 260 species had undergone statistically significant long-term declines compared with 160 that had increased significantly (Chapter 3). The geographical patterns of change were consistent with expected responses to land-use, nutrient enrichment and climatic change (Chapter 3). I also utilised citizen-science derived monitoring data for 485 Lepidoptera species to investigate the impact of insect population variability on the assessment of Red List extinction risk using 10-year trends as specified by the International Union for Conservation of Nature procedure (Chapter 5). I concluded that for these taxa, strict use of 10-year trends produces Red List classifications that are unacceptably biased by the start year (Chapter 5). In Chapter 4, I showed that mass-participation citizen science data obtained using a simple sampling protocol produced comparable estimates of butterfly species abundance to data collected through standardized monitoring undertaken by experienced volunteers. Resulting increases in participation, along with the associated benefits of public engagement and awareness raising, need not have a detrimental impact on the ability to detect abundance trends in common butterfly species. However, citizen science participation may affect the onward use of data, unless this is considered at the outset. I found that despite support in principle for open access to distribution records of butterflies and moths, most citizen scientists were much more cautious in practice, preferring to limit the spatial resolution of records, particularly of threatened species, and restrict commercial reuse of data (Chapter 6). Overall, these results demonstrate the potential for citizen science, involving both expert volunteer naturalists and inexperienced members of the public, to address the global biodiversity knowledge gap through generating meaningful trend estimates for insect species and elucidating the drivers of change