Many countries are setting ambitious targets for habitat restoration and woodland expansion (e.g. Bonn Challenge), such as England’s commitment to plant 11 million trees by 2022 and to restore 75% of protected sites to favourable condition (Defra 2018). To ensure these efforts help to reduce biodiversity decline and restore ecosystem functioning in the face of increasing anthropogenic pressures such as climate change, they need to be strategic. There is a vital need for more information to guide the spatial targeting of actions on the ground.
It has been suggested that the effectiveness of conservation management (habitat restoration and/or creation) depends on the structural complexity of the landscapes where actions are implemented. However, there is still considerable debate about whether the creation of new habitat would be more effective in heterogeneous areas (e.g. with a high proportion of non-crop habitats) that still support relatively high levels of biodiversity or in landscapes dominated by farmed land (e.g. Fahrig et al. 2011; Tscharntke et al. 2012).
Additionally, there is an ongoing debate within the scientific and conservation communities on the relative merit of, and balance between, siteâ€ and landscapeâ€level actions to conserve biodiversity within fragmented landscapes (e.g. Watts et al. 2016). Some authors have promoted siteâ€based actions to increase habitat amount regardless of spatial configuration, or to focus on improving habitat quality. Others advocate the merits of landscapeâ€level actions to improve connectivity through the creation of corridors and other actions to improve permeability of the intervening matrix.
Highly mobile species, such as many bats, display particularly strong associations with gradients of landscape structural complexity at large spatial scales. Bats often avoid intensively managed agricultural land; due to their habitat preferences and high dispersal abilities, it has been proposed that conservation efforts for bats should focus on increasing the amount and connectivity of woodland in the landscape (e.g. Fuentes-Montemayor et al. 2011). However, there is poor understanding of how landscape complexity influences the use of woodland patches by bats in agricultural areas. Some woodland bats are also particularly hard to survey because of similarities of their echolocation calls with other species and/or low detectability by ultrasound detectors.
The overall aim of this studentship is to identify landscapes where woodland creation and restoration activities are likely to provide the greatest benefits for bats. Specific objectives and questions to address are:
1. Validate and update existing habitat suitability models with new data to improve predictive information of bat species distributions across Britain.
2. Determine the influence of landscape complexity on overall bat activity levels. E.g. are the greatest benefits for bats realised by woodland creation in simple or complex landscapes?
3. Test associations between land-use change and roosting woodland bat population trends.
This PhD will use a range of approaches and datasets, across temporal and spatial scales, to address fundamental questions on the prioritisation of conservation efforts. It will improve our understanding of the ecological and human factors driving distributions of UK woodland bats at local to national scales. Results from this work would be used to produce guidance for landowners, policy makers and practitioners on targeting woodland creation.
Click on an image to expand
Brown long-eared bat.jpg: Brown long-eared bat, a woodland specialist. Photo credit: JD Altringham
Objectives 1 & 3 will be addressed using existing datasets on woodland bats with some additional survey work. Hierarchical, multi-scale models predicting national distributions of woodland bat species have been developed by FR and BCT using citizen science data as part of the ‘Putting UK woodland bats on the map’ project. Data collected during this studentship will be used for model validation and fine-tuning, providing recommendations and an analytical framework for targeting future survey effort, such as the new BCT-led British Bat Survey, in areas with high predictive uncertainty. The UK has a number of long-standing bat box schemes which provide data on several species within woodlands over 10-50 years. We will use information from these schemes to explore whether, and to what extent, changes in habitat and land-use have influenced roosting use in these woodlands by bats.
For objective 2 fieldwork will be conducted in an array of sites within landscapes of varying degrees of structural complexity. These have been identified and mapped as part of the WrEN project (wren-project.com), a large-scale natural experiment designed to study the effects of 160 years of woodland creation on biodiversity and inform landscape-scale conservation (see Watts et al. 2016). As part of WrEN, key local- and landscape-level variables have been described for 100+ focal woodland patches in two distinct study areas (central Scotland and the English midlands). The student will use digital datasets within a Geographical Information Systems and R/Python coding framework to quantify the compositional complexity, and land-use intensity of the study landscapes. A network of ultrasonic detectors will be used to assess bat activity levels within woodland patches and in the surrounding non-woodland matrix.
The PhD will be based at Stirling and the student will visit Newcastle for meetings, seminars and specific training. It is anticipated that the student will spend approximately one month per year working with researchers based at Forest Research and Bat Conservation Trust.
Literature review, PhD planning, training & fieldwork
Fieldwork, data analysis & skills training
Fieldwork, data analysis & skills training
Data analysis, manuscript submission & thesis write-up
1) Fieldwork and experimental design. Training in the required field skills (e.g. habitat mapping, bat acoustic surveying), and sampling design.
2) Numeracy, data analysis, ecological modelling & informatics. These skills will be gained through targeted training courses within the IAPETUS consortium (e.g. Programming and Analysis of Environmental Data in R, GIS & Remote Sensing for Environmental Managers) and at Forest Research.
3) Land-use policy and management (e.g. stakeholder engagement at an early stage; dialogue and advice from WrEN project partners).
4) Complementary training in transferable skills. Training in core scientific skills (data management, analysis, presentations, paper writing).
References & further reading
DEFRA. (2018). A Green Future: Our 25 Year plan to improve the environment. London: DEFRA.
Fahrig et al. (2011) Functional landscape heterogeneity and animal biodiversity in agricultural landscapes. Ecology Letters 14: 101-112.
Fuentes-Montemayor et al. (2011) Pipistrelle bats and their prey do not benefit from four widely applied agri-environment management prescriptions. Biological Conservation 144: 2233-2246.
Tscharntke et al. (2012) Landscape moderation of biodiversity patterns and processes – Eight hypotheses. Biological Reviews 87: 661-685.
Watts K et al. (2016) Using historical woodland creation to construct a long-term, large-scale natural experiment: the WrEN project. Ecology & Evolution 6: 3012-3025.
Students interested in this PhD are strongly advised to contact Prof Kirsty Park well in advance of the closing date, including a CV and covering letter.