National Woodland Bat Survey Example Dataset Information 2024.pdf
Source: environment.data.gov.uk (2026-01-05)

National Woodland Bat Survey - Example Dataset. Unvalidated automated bat species classifications derived from acoustic recordings made at a sample of three National Forest Inventory squares, surveyed as part of the National Woodland Bat Survey. Data from additional squares are available on request. 
The National Woodland Bat Survey is an England-wide passive acoustic bat survey conducted across National Forest Inventory squares. Bat echolocation calls within audio recordings are detected and classified to species using automated machine-learning classifiers. These sample data have not been validated, so will include incorrect species classifications. They are of use in applications where it is preferable for classification uncertainty to be modelled and incorporated into analyses, and inference is made at regional or national scale, thereby reducing the impact of individual misclassifications. An alternative dataset of validated and accepted species records will be made available at a future date.
Data are georeferenced using a delocalised grid. The bounding coordinates for each grid square are included in the dataset. Further information about the delocalisation process is provided in the dataset evidence pack.
The 'National Woodland Bat Survey - Example Dataset’ is a project part-funded by the UK Government through Defra’s Natural Capital and Ecosystem Assessment programme (NCEA).  Users should ensure they have read the supporting documentation before viewing or making use of the data. This includes the NCEA Analysis Ready Data Limitations Statement and dataset specific limitations/caveats outlined in the supporting documentation.
 Attribution statement: © Forestry Commission & Bat Conservation Trust copyright and/or database right 2025. All rights reserved.

The National Woodland 
Bat Survey  
Example dataset of unvalidated automated bat 
echolocation call classifications 
 
 
 
 
 
 
 
 
 
 
 
Katherine Boughey, Madeline Davis, Alfie Gleeson, Lia Gilmour 
& Chloe Bellamy 
 
 

 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
Acknowledgments 
The National Woodland Bat Survey is a project funded by the UK Government through Defra’s Natural 
Capital and Ecosystem Assessment programme (NCEA) and led by Forest Research and the Bat 
Conservation Trust. NCEA is delivering a nationwide survey of England’s land, freshwater, and coastal 
ecosystems. Through comprehensive monitoring and the development of innovative tools and guidance, 
the programme is providing insights on the state of our environment – so that we can better protect and 
manage our natural capital for people and the planet.  
We thank Ben Ditchburn, Russell Anderson and Alan Brewer at Forest Research, Prof Kate Jones at 
University College London, Oisin Mac Aodha at the University of Edinburgh and Carol Williams, Sonia 
Reveley and Ella Browning at the Bat Conservation Trust for their support in developing and launching 
this survey. We thank Sally Kempson, Mark Oram, Vera Correia, Chloe Metcalfe, Matthew Wilkinson, 
Kerry Molyneux, Evie Crowe, Sophie Spurr, Andy Snow, Tony Farndon, Paul Marshall, Liam Convery, and 
Simon Dunn at Forest Research and Angeliki Kourmouli & Richard Ferris at Defra for their support with 
administering the survey and preparing this evidence pack for publication. We thank the members of the 
NCEA Science Assurance panel for improving the first draft of this document. We would especially like to 
thank the members of the Forest Research Field and GIS Data Services Teams and the contracted 
surveyors who made this survey possible.  
Cover image: Soprano pipistrelle © Daniel Hargeaves

1 
 
1 Introduction 
Bats are valuable biodiversity indicators due to their elevated position in the food chain, the spatial scale 
at which they utilise habitat, their wide distribution, their sensitivity to environmental drivers, and the 
ecosystem services they provide (Jones et al., 2009; Newson et al., 2009; Russo & Jones, 2015). They 
have a long lifespan and relatively slow reproductive cycle, characteristics which make them a useful 
complement to other indicator taxa such as pollinators. All 17 UK breeding bat species make preferential 
use of woodland for foraging, roosting or commuting (Appendix I), and this association with woodland 
makes them useful indicators of forest ecosystem health. 
The National Forest Inventory (NFI) programme includes the most extensive and detailed survey of 
woodland and trees in Great Britain (www.forestresearch.gov.uk/tools-and-resources/national-forest-
inventory/). It offers a unique opportunity to integrate monitoring of other woodland taxa, providing an 
exceptionally complete picture of the composition, status and condition of our nation’s forests. It also 
provides an opportunity to develop and trial new woodland monitoring technologies. 
The National Woodland Bat Survey (NWBS) uses cutting-edge acoustic monitoring technology to produce 
bat population monitoring data integrated with NFI. This work is funded by the UK Government through 
Defra’s Natural Capital and Ecosystem Assessment programme (NCEA) as a collaboration between the 
Bat Conservation Trust (BCT) and Forest Research (FR). The survey aims to establish baseline estimates of 
bat species presence, distribution and relative abundance across England’s woodlands and – through 
repeated monitoring cycles – to track trends in these measures over time. A summary report of baseline 
bat population metrics is produced for each NFI square surveyed. At the end of the monitoring cycle, 
baseline metrics of species occurrence, mean species activity and mean species richness with be 
calculated for each NFI region and woodland type (Section 6), as well as for England as a whole. 
The National Woodland Bat Survey is the largest national-scale passive acoustic monitoring survey of 
bats in the England and has generated the most extensive woodland bat dataset ever collected in the 
UK. The bat species monitored by the NWBS represent approximately a quarter of all UK mammals, and 
the survey currently provides the only significant national-scale monitoring for three bat species 
included on the UK Red List of Threatened Species. 
The survey uses an automated sound classification algorithm to classify bat echolocation calls recorded 
during the survey to species. These automated classifications are subsequently manually verified to 
reduce the risk of incorrect classifications. However, as a manual process, verification introduces a 
degree of subjectivity. For certain purposes it may therefore be preferable to use unvalidated species 
classifications, for example where classification uncertainty can be directly modelled, and where the risk 
posed by individual misclassifications is low. To support the use of NWBS data for these purposes, an 
example dataset of unvalidated automated bat species classifications, from a sample of three survey 
squares, was published in December 2025. This report describes the procedures used to create this 
example dataset. Unvalidated classifications from further sites are available on request.  
An alternative dataset of validated bat species records will be published in Spring 2026. The validation 
procedures we will use to produce this dataset are currently in development. They will be described in 
an updated version of this report, which will be published alongside the validated dataset in Spring 2026. 

2 
 
2 Survey development and administration 
The National Woodland Bat Survey was developed and is administered by project teams at BCT and FR, 
both of which have extensive experience in managing long-term, national-scale monitoring programmes 
(e.g. the National Forest Inventory at FR and the National Bat Monitoring Programme at BCT). Project 
team members have been selected to provide the experience, knowledge and skills to complete their 
responsibilities to a high standard. The project team follow a detailed activity plan which sets out specific 
activities, responsibilities, milestones, deliverables, timings and budget. Each project activity has a 
designated team member responsible for oversight and for delivery, following the Responsible, 
Accountable, Consulted, and Informed (RACI) framework. Project delivery is reviewed against the project 
timetable and budget at weekly BCT team meetings, at monthly joint BCT-FR meetings, and as part of 
Defra NCEA reporting. Following the conclusion of each survey season a review meeting is held where 
points of improvement are identified and actioned. In 2021, 2022 and 2023 surveyors were asked to 
complete an online feedback questionnaire, the results of which were used to guide survey 
improvements in subsequent years. 
The National Woodland Bat Survey is based on a prior survey, the Forestry England Bat Survey, which 
took place in 2019 (Boughey et al., 2020). The methods developed for the Forestry England Bat Survey 
were adapted and improved for the National Woodland Bat Survey as part of a scoping study undertaken 
in 2020, and two field pilots undertaken in 2021 and 2022. Additional insight has been gained from other 
national-scale passive acoustic bat monitoring surveys administered by BCT, in particular the British Bat 
Survey (https://www.bats.org.uk/our-work/science-research/passive-acoustic-surveys/british-bat-
survey) and NightWatch (https://www.bats.org.uk/our-work/science-research/passive-acoustic-
surveys/nightwatch). 
3 Survey metrics 
Survey data are used to calculate to following metrics for each NFI square surveyed: 
• 
Bat species presence. 
• 
Bat species activity. This is used as proxy for abundance, as it not possible to estimate bat 
species abundance from acoustic data. Activity is estimated as the average number of minute 
intervals in which the species is detected in an hour, averaged across all survey hours each year. 
This approach avoids subjective assumptions about what constitutes a detection ‘event’ and 
reduces the overdispersion that can result from multiple detections of the same bat. Activity is 
assumed to correlate with abundance, so this is a measure of relative abundance rather than 
absolute abundance. 
• 
Bat species richness. In the early years of the survey bat species evenness and diversity were 
also estimated, however, these estimates were found to be biased by the use of activity data in 
place of the more typical abundance data. Rare species, where present, are often detected in 
less than one minute interval per hour on average, whereas common species can be detected in 
tens of minutes, such that the detection of a rare species leads to a large reduction in species 
evenness, and a corresponding reduction in species diversity (which takes account of both 
species richness an evenness). As result, the detection of rare species sometimes resulted in a 

3 
 
reduction in estimated species diversity, counter to expectations. To avoid the risk of data 
misinterpretation we no longer estimate species evenness and diversity from acoustic activity 
data. 
4 Use of survey data 
The National Woodland Bat Survey aims to survey 1,050 NFI squares over a five-year monitoring cycle 
(section 5), from a total of approximately 10,000 one-hectare NFI plots. Three hundred and nineteen 
unique NFI squares have so far been surveyed up to an including the 2024 survey season (Section 16). At 
the end of the monitoring cycle, when a balanced sample has been achieved (Section 6), data from all 
surveyed squares will be aggregated to calculate baseline metrics of species occurrence (proportion of 
NFI squares occupied), mean species activity and mean species richness for each NFI region and 
woodland type (Section 6), as well as for England as a whole. 
These data could be used for biodiversity monitoring, reporting, strategic planning and research and 
purposes, including: 
• 
Assessing and reporting biodiversity trends. The first monitoring cycle of the NWBS will provide 
vital baseline estimates of bat species population status across England’s woodlands and 
regions. Potential further monitoring cycles will provide trend data that could contribute to 
Defra’s bat, all species and priority species abundance indicators, and could evidence progress 
towards the legally binding Environmental Improvement Plan (EIP) target to increase species 
abundance by 10% by 2042. The bat species monitored by the NWBS represent approximately a 
quarter of all UK mammal species. The NWBS could provide essential evidence with which to 
assess the EIP target to improve the Red List Index for England for species extinction by 2042 
compared to 2022 levels, representing significant national-scale monitoring for three bat species 
included on the UK Red List of Threatened Species - barbastelle (Barbastella barbastellus - 
vulnerable), Leisler’s bat (Nycatlus leisleri - near threatened) and Nathusius’ pipistrelle 
(Pipistrellus nathusii - near threatened), and potentially providing vital additional monitoring 
data for serotine (Cnephaeus serotinus – vulnerable).  
• 
Ecosystem assessment and national capital accounting. In recognition of the role bats play in 
providing ecosystem services and as indicators of woodland condition, bat population data now 
contributes to the Office of National Statistics (ONS) Woodland natural capital accounts 
(https://www.ons.gov.uk/economy/environmentalaccounts/methodologies/woodlandnaturalca
pitalaccountsmethodologyguideuk2024). After further monitoring cycles, data from the NWBS 
could contribute towards the ONS natural capital accounts for both woodland and semi-natural 
habitats. 
• 
Assessing the impact of habitat restoration. In areas surveyed by the NBWS, these data will 
provide a ready set of baseline metrics of bat species presence, activity and richness, with which 
to assess the impact of woodland creation or restoration. This could evidence progress towards 
the EIP target of restoring or creating more than 500,000 hectares of wildlife-rich habitat by 
2042. 

4 
 
• 
Informing the development of new biodiversity monitoring methods. Areas of research and 
development progressed by BCT and FR alongside the NWBS include the development of 
autonomous acoustic sensors, improved techniques for automated sound identification, whole 
soundscape monitoring, large data handling and storage, and modelling approaches 
incorporating classification uncertainty.  
• 
Predicting the impact of woodland management practices on bat populations at landscape-
scales, including the impact of woodland connectivity on bats’ use of the landscape and how this 
may facilitate bat species range shifts under climate change. This will be achieved by modelling 
bat species activity in relation to detailed NFI survey attributes relating to woodland 
composition, structure and management. 
• 
Informing local priorities and plans: The survey methods developed for the NWBS have 
informed the development of monitoring protocols for local nature recovery and local change 
detection, part of a separate strand of work led by JNCC and also part of the NCEA programme. 
• 
Assessing the impact of large infrastructure projects where these projects intersect with 
surveyed squares. 
• 
Understanding joint responses to environmental drivers among woodland taxa. A range of 
different surveys are co-located on NFI squares, under the umbrella of NFI+ (Figure 1). By 
integrating these data, we can investigate the combined ecological responses of woodland taxa 
to factors such as habitat quality, structural complexity, disturbance levels and connectivity. 
 
 
Figure 1: The surveys that form part of the NFI+ programme, showing the number of NFI squares monitored each 
year by each survey, and the extent to which these squares overlap with those sampled by other surveys. Not 
shown are invertebrate and below-ground eDNA surveys, both undertaken on the same 210 squares selected for 
bat surveys. 
5 Survey effort 
The National Woodland Bat Survey aims to detect change equivalent to a decline of 50% or greater over 
25 years (red alert) and a 25–49% decline over 25 years (amber alert). These alert thresholds were 
originally developed for UK bird population monitoring (Marchant et al., 1997) and have been adopted 
by the National Bat Monitoring Programme (Barlow et al., 2015). When aligned with the five-year NFI 
monitoring cycle this is approximately equivalent to a 13% decline (red alert), and a 5.5% decline (amber 
alert) over five years. 

5 
 
Data from the preceding Forestry England Bat Survey were used to approximate the relative 95% 
confidence intervals of baseline estimates and trends in species presence and activity for five woodland 
bat species across different strata. These confidence estimates were used to derive the sample sizes 
required to detect red and amber alert levels changes for each species and metric tested (Supporting 
Information I and explanatory note). Using this information, the FR project team identified a target 
sample size of 1,050 squares per monitoring cycle to achieve robust trend estimates. For practical and 
financial reasons, the decision was taken to align this survey (and other surveys under the NFI+ 
programme) with the existing five-year NFI monitoring cycle. As such, approximately one fifth of the 
target sample is surveyed each year (210 sites/year). This approach balances survey power with survey 
effort and cost. 
6 Square selection 
To ensure the squares surveyed are representative of woodland across England, a stratified random 
sample is employed. The yearly target of 210 NFI squares, plus an additional 60 squares reserved as 
back-ups, are selected by FR statisticians using the following stratification criteria: 
• 
NFI region: East England, East Midlands, North East England, North West England, South East 
England and London, South West England, West Midlands, Yorkshire and the Humber. 
• 
Ownership status: Public Ownership or Private Ownership.  
• 
Woodland type: Conifer, Broadleaved or Other. Woodlands in England as assigned a woodland 
type based on their Interpreted Forest Type category (ITF; Forest Research, 2020), which is 
derived from aerial photography, remote sensing, and administrative sources: 
o Conifer: Some broadleaved trees may also be present, but greater than 50% of the area 
will consist of conifers. This includes the ITF categories Conifer and Mixed mainly conifer. 
o Broadleaved: Some conifer trees may also be present, but greater than 50% of the area 
will consist of broadleaves. This includes the ITF categories Broadleaved, Mixed mainly 
broadleaved, Coppice with standards, and Coppice. 
o Other, which includes the IFT categories Transition (Felled, Ground Prep, Young trees, 
Shrub, Failed, Windblow and internal open space), Cloud/shadow, Low Density, and 
Uncertain. 
Within each region, the area occupied by each woodland–ownership pairing (e.g., Broadleaved+Private 
Ownership, Conifer+Public Ownership) is calculated and expressed as a percentage of England’s total 
woodland area. A sample of squares is then selected to match these regional proportions as closely as 
possible. Squares within the region that contain the target woodland-ownership stratum are randomly 
sampled, with selection weighted so that squares containing a larger proportion of the target stratum 
and more likely to be selected. Surveyors are instructed to conduct the survey within the target stratum 
that the square was selected to represent.  
The NFI program distinguishes between permanent and temporary sample squares: 
• 
Permanent squares are remeasured in each monitoring cycle, allowing for long-term monitoring. 
• 
Temporary squares are replaced between cycles and used for one-time assessments. 

6 
 
For this survey, only permanent squares were made available for selection, where possible, to support 
consistent longitudinal analysis. Squares that were surveyed or marked as inaccessible in previous years 
were excluded from the selection pool. 
An additional 60–70 back-up squares are selected each year to accommodate a potential 30% rejection 
rate, which may occur due to: 
• 
Too little woodland present within the square 
• 
Misclassification of woodland type 
• 
Lack of landowner access permission 
• 
Inaccessibility 
The selection of back-up squares is currently being refined based on the observed rejection rate of 
squares during previous years’ surveys, and may be expanded to include a back-up for every selected 
square in future years.  
Prior to commencing the surveys, a desk-based assessment is conducted using recent aerial and satellite 
imagery to verify woodland type classifications and assess suitability. If the square is deemed unsuitable 
a backup square is substituted. Square suitability is also assessed by the surveyor during the first field 
visit. 
7 Landowner contact and access permissions 
Landowners are contacted in advance of each survey season via letter to request access permission and 
to identify any potential access constraints. If no response is received, local survey teams attempt direct 
contact using available contact details, public records, and local knowledge. 
If access is explicitly refused, the square is removed from the survey and replaced with a designated 
backup square. In cases where no response is received, surveyors may proceed with access under 
Section 48, Subsection I4 of the Forestry Act (1967), which grants FR staff and contractors the legal right 
to access all UK forested land for survey purposes. 
8 Allocation of squares to surveyors 
National Woodland Bat Survey surveyors are either members of the FR Field Data Services team or 
private contractors, depending on availability. All surveyors attend online briefings prior to fieldwork, 
and less experienced staff may shadow experienced colleagues to become familiar with the survey 
protocol. Throughout the survey period, surveyors are encouraged to contact the FR or BCT project 
teams with any queries. 
Survey squares are assigned to surveyors based on proximity to reduce travel times and balance 
workloads, with the aim of completing surveys within the ideal survey window (1st July– 15th August). 
Before fieldwork begins, surveyors receive the survey protocol, landowner details and correspondence, 
site maps and relevant health and safety information. 

7 
 
Surveyors use a dedicated ESRI Fieldmaps survey to access data on square boundaries, and record survey 
information including sensor deployment locations. This information is used by the project team to 
monitor progress. 
Dynamic risk assessments are conducted on-site, and surveyors are trained to make safety decisions, 
including relocating audio sensors if necessary. If site hazards cannot be mitigated to a medium risk 
level—especially where access is based on assumed permission—surveyors may halt work and request 
an alternative survey square. Replacement squares are first selected from designated backup squares. If 
a suitable square is not available, a new square is chosen based on geographic proximity to the original 
site. Replacement squares are matched to the woodland type, region and ownership category of the 
original square wherever possible. 
9 Survey equipment 
Full spectrum audio recordings are made using AudioMoth acoustic sensors (version 1.2.0, Open 
Acoustic Devices). AudioMoth microphone sensitivity can decline over time, so before each survey 
period the sensitivity of each sensor is tested in a controlled setting using a sequence of prerecorded 
ultrasonic bat calls played from an ultrasonic speaker (BatWav, Peersonic) at a fixed amplitude and 
distance from the sensor. The recordings made by the sensor are processed through BCT’s Sound 
Classification System (Section 12), and any sensors that do not meet the required sensitivity threshold, 
and that cannot be reconditioned or repaired, are discarded.  
Data are recorded onto micro SD cards (San Disk Extreme). For the pilot surveys in 2021 and 2022 64GB 
memory cards were used. Changes to the sensor firmware since the pilot surveys allow larger capacity 
memory cards to be used, so since 2023, 128GB cards have been used.  
In the 2021 and 2022 pilot surveys, sensors were powered using three Duracell Procell alkaline AA 
batteries, however since 2023 sensors have been powered using lithium AA batteries (Energizer 
Ultimate) to take advantage of the greater memory capacity offered by later versions of the sensor 
firmware.  
Sensors are deployed in weather resistant cases constructed from modified push-fit junction boxes (OBO 
Bettermann A8, Figure 2), designed and produced by Adrian Bicker (www.livingrecord.net). The cases 
feature a foam insert to hold the AudioMoth in place and a double layer of wax-coated metal gauze 
covering the microphone vent. The use of metal gauze demonstrates a marked improvement in acoustic 
transparency over commercially available acoustic vents, particularly in the ultrasonic spectrum (Bicker 
2019, unpublished data). Though not waterproof, the gauze is water-resistant. 

8 
 
 
Figure 2: Weather resistant AudioMoth case (left- and right-hand images) and an example of a deployment set-up 
(central image). 
The National Woodland Bat Survey equipment inventory is managed and maintained by BCT. All 
equipment is tagged with a unique identifier and equipment distribution is tracked using a bespoke 
database. Square allocation details are supplied by FR to BCT, who then send each surveyor one 
equipment pack per square. Each pack contains three AudioMoth sensors, three weather resistant cases, 
six micro-SD cards, 6 envelopes to hold filled memory cards, a micro-USB cable, and packaging materials 
to protect equipment during postage and transit. Due to postage regulations, batteries are sent directly 
from the supplier to the surveyor in sealed packaging. Surveyors are asked to provide a means of 
elevating and fixing the sensors above the ground (suggestions are given in the survey instructions), a 
GPS to record monitoring locations, a smartphone with AudioMoth app installed with which to reset the 
AudioMoth internal clock, and a PC with AudioMoth configuration app installed with which to 
reconfigure the AudioMoths, as a back-up if the pre-configured settings are lost.  
10 Recording settings 
The recording schedule used for the NWBS has been adapted over the course of the project to take 
advantage of hardware and software improvements, and on the basis of statistical advice (Table 1). 
Prior to 2024, AudioMoth firmware required the start and end time or recording each night to be fixed 
for the duration of the deployment. From June to mid-August (end of August in 2021), a recording 
schedule of 21:00 to 04:30 was used to cover the period when bats are most active during the night. 
Later in the season, longer nights meant that the power and memory capabilities of the sensor were 
exceeded using these settings. In 2021 this was accommodated by pausing recording for 1:15 hrs at 
midnight (a time when bat activity can be lower) from the end of August onwards. In 2022, following 
statistical advice, we trialed an alternative recording schedule (20:00 to 05:30) from mid-August 
onwards, with the result that only six nights of data could be collected. In 2023 sensor firmware changes 
meant that memory cards with increased capacity could be used, allowing the recording schedule from 
mid-August onwards to be extended to 19:15 to 06:00, and for nine nights of data to be collected. 
In 2024, sensor firmware was updated to allow the recording start and end time to be set relative to 
sunset at the location and day of the survey. From 2024 onwards sensors have been configured to record 
from 15 minutes before sunset to 15 minutes after sunrise each night. 

9 
 
In 2021 and 2022 a subset of sensors were configured to also record during the day, to collect training 
data for a whole woodland soundscape classification algorithm, part of a separate work area under the 
NCEA. 
In all years a sampling rate of 384kHz, medium gain and a quasi-continuous recording cycle (300 seconds 
recording, 5 seconds ‘sleeping’ to allow the file to be written to the SD card) have been used. No 
frequency or amplitude filters were used in 2021, 2022 and 2023. In 2024 a high pass filter set at 8kHz 
was used to minimise the likelihood of the capturing of human speech.  
All sensors are updated to the latest firmware at the start of each year (2021 v1.6.0, 2022 v1.8.0, 2023 
v1.8.1, 2024 v1.10.1) and are supplied to surveyors pre-configured with the required recording settings 
to reduce the risk that incorrect settings are used. 
The AudioMoth internal clock is reset by the surveyor prior to each deployment and after each battery 
change using the AudioMoth Chime App (https://www.openacousticdevices.info/mobileapplications).  
Table 1: AudioMoth recording settings used in the first four years of the National Woodland Bat Survey. 
Year 
Recording 
schedule 
Recording 
cycle 
Sampling 
rate 
Gain 
Amplitude 
threshold 
Frequency 
threshold 
2021 (pilot) 
21:00-04:30 
(July and 
August), 20:15-
00:00 and 
01:15-05:00 
(September) 
Recording 
duration 300 
seconds; sleep 
duration 5 
seconds 
384 kHz 
Medium 
No 
No 
2022 (pilot) 
21:00-04:30 
(June to mid- 
August), 20:00-
05:30 (mid-
August 
onwards) 
Recording 
duration 300 
seconds; sleep 
duration 5 
seconds 
384 kHz 
Medium 
No 
No 
2023 
21:00-04:30 
(June to mid- 
August), 19:15-
06:00 (mid-
August 
onwards) 
Recording 
duration 300 
seconds; sleep 
duration 5 
seconds 
384 kHz 
Medium 
No 
No 
2024 
From 15 
minutes before 
sunset to 15 
minutes after 
sunrise at 
sampling 
location 
Recording 
duration 300 
seconds; sleep 
duration 5 
seconds 
384 kHz 
Medium 
No 
8 kHz high 
pass filter 

10 
 
11 Sensor deployment 
Wherever possible, surveys are undertaken between 1st July and 15th August. This period has been 
selected to provide a comparable sample of the available bat population between years, by capturing 
the period when bat encounter rates are at their highest, but prior to the young of the year becoming 
volant. 
Three acoustic sensors are deployed on each target square. Surveyors are asked to deploy the sensors 
within the woodland-ownership stratum assigned to the square (Section 6). Sensors are deployed 
approximately on a diagonal from the north-east to the south-west corner of the square, with the first 
sensor located 20m from the corner of the square, the second sensor 30m from the first sensor and the 
third sensor 70m from the second sensor (Figure 3). This deployment pattern has been designed to 
provide a range of separation distances with which to investigate within-square spatial correlations in bat 
activity. Monitoring locations can depart from this idealised deployment pattern to accommodate the 
distribution of woodland within the square. Surveyors are asked to ensure all monitoring locations are 
safe to access, secure enough to allow monitoring equipment to be deployed for several nights at a time, 
not lit by artificial light (which can impact bat behaviour), and are at least 1.5m from cluttered vegetation 
(which can muffle bat calls), hard surfaces such as tree trunks or walls (which can cause echoes that can 
distort recordings) or water (as sound reflection from the surface of water can also distort recordings). 
For each deployment location surveyors provide a unique identification code, a 1 metre OSGB grid 
reference and a detailed description to aid relocation. 
 
Figure 3: Ideal deployment pattern of acoustic sensors for the National Woodland Bat Survey, within a 1ha NFI 
square 
Audio recordings are made at each deployment location for nine consecutive nights (in 2022 from mid-
August onwards six nights of data were collected, see Section 10). Survey nights are selected to avoid 
prolonged periods of strong wind, heavy rain or if the temperature at sunset is below 10 degrees. 
Sensors are deployed during the day preceding the survey. The sensors, within weather-resistant cases, 
are fixed to poles approximately 2m from the ground, as elevating the sensor has been found to increase 
bat detection and provide higher quality recordings. Sensors are fixed vertically to provide further 
protection from water ingress (Figure 2). Equipment located in open areas or areas where the public 
congregate is wrapped in camouflage tape to reduce visibility. Surveyors revisit the sensors on day six to 
replace their batteries and memory cards, and on day ten to retrieve the equipment. In 2021 and 2022 
batteries and memory cards were changed twice, on days four and seven, due to limitations on battery 

11 
 
power and memory card capacity in earlier years. If surveyors are unable to revisit the sensors on the 
designated days, the sensors will continue to record until their batteries are exhausted, up to a maximum 
of six nights of recording per battery set.  
12 Automated bat call classification 
After completing the survey, surveyors return their recording equipment and micro SD cards to BCT in 
pre-paid packaging. Upon receipt by BCT, the audio recordings (.wav files) are transferred from micro SD 
card to external hard drives, where they are collated into a consistent folder hierarchy ready for further 
processing (Figure 4). At this stage batch scripts are used to check the files for disk failures, to remove 
partial, corrupted or surplus recordings, and to check that file names and folder structures adhere to 
system requirements.  
The collated and checked audio files are uploaded by BCT staff to a dedicated S3 storage bucket in BCT’s 
Sound Classification System (SCS), a cloud-based audio processing platform hosted by Amazon Web 
Services (AWS). In 2022 and 2023 recordings were uploaded to the SCS using the AWS SnowCone 
service; in 2021 and since 2024 they have been uploaded directly to the SCS via a fixed internet line. 
Upon upload to S3, files are automatically processed through the deep-learning algorithm BatDetect2, 
developed by researchers at the University of Edinburgh and University College London (Mac Aodha et 
al., 2018, 2022). BatDetect2 is a convolutional neural network-based algorithm for jointly detecting and 
classifying UK bat species from acoustic data. It is open-source software, with code and training data 
available on GitHub (https://github.com/macaodha/batdetect2). For each bat sound detected within the 
recordings, BatDetect2 assigns a species classification, and a detection and classification probability. 
These are probability vectors indicating which species the model thinks is present (classification 
probability) or any if any species are present (detection probability), where pspecies is a C + 1 dimensional 
vector that sums to one, and represents the probability of the species the model thinks emitted the call, 
for each one of C different species of interest plus ‘Not bat’ (Mac Aodha et al., 2022). Sounds with a 
detection probability of 0.5 or greater are recorded in the output dataset. This detection threshold 
provides a good balance between sensitivity and specificity when discriminating bat calls for non-target 
sounds (Mac Aodha et al. 2018). No classification probability threshold is applied to the data prior to 
validation. The use of an automated algorithm for target sound detection and classification is essential to 
manage the volume of data generated by the NWBS and ensures consistency of classification across sites 
and years.  
From 2021 to 2023 files were split into ten second ‘chunks’ before being supplied to the algorithm to 
allow for faster processing. However, improvements to the SCS and to BatDetect2 meant that from 2024 
onwards this was no longer necessary.  
Following classification, the .wav audio files are automatically moved to S3 Glacier Deep Archive for long-
term storage. The results of the classification process are written to a relational database hosted within 
the SCS, and are retained indefinitely. These unvalidated automated species classifications are extracted 
from the database in ‘slices’, aggregated by NFI square and converted to .csv files for further processing. 
An example dataset of unvalidated automated species classifications, from a sample of three NFI 
squares, is available to download from the Defra Data services Platform 
(https://environment.data.gov.uk/defra/9e821042-e6f9-40df-a40b-92e8e0bc2938/details). These data 
confirm to Simple Darwin Core standards (https://dwc.tdwg.org/simple/). Unvalidated classifications 

12 
 
from further survey squares are available on request from xxx@xxx.xx. Use of these data is 
governed by an Open Government Licence (https://www.nationalarchives.gov.uk/doc/open-
government-licence/version/2/). 
The classifications output by BatDetect2 (and all AI classification tools) are predictions, rather than 
definitive identifications. Although these predictions are preferable for some purposes (Section 13), 
other uses require the automated classifications to be validated against predetermined thresholds and 
manually verified to remove incorrect classifications, producing a dataset of ‘accepted’ species 
identifications. A dataset of validated species records will be published in Spring 2026. The processes 
used to clean, validate and manually verify automated classifications from the National Woodland Bat 
Survey are currently in development. They will be described in an updated version of this report which 
will be published alongside the validated dataset in Spring 2026. 
 
 
 
 
 
 
 
 
Figure 4: National Woodland Bat Survey data pipeline, shown up to the point of data validation. 
13 Limitations of the unvalidated data 
Unvalidated automated species classifications will, as with all automatically classified data, contain a 
proportion of incorrect species classifications and non-bat sounds. Protocols for recording and 
processing audio are standardised to ensure the rate of misclassification remains constant across the 
dataset, although this has not been tested. Manual verification of automated classifications is 
recommended to reduce classification errors, however, as a manual process, verification introduces a 
degree of subjectivity. For certain purposes it may therefore be preferable to utilise unvalidated species 
classifications. These unvalidated data are suitable for purposes where classification error, represented 
Audio files 
(.wav) 
recorded onto 
sensor 
memory card
Audio files 
collated onto 
hard drive(s) 
and checked
Checked audio 
files uploaded 
to SCS for 
classification
Unvalidated 
bat species 
classifications 
output to 
relational 
database
Validated bat 
species 
records will be 
published in 
Spring 2025
Example 
dataset of 
unvalidated bat 
species 
classifications 
(.csv) 
ARD published 
December 2025 
Audio files 
transferred to 
AWS S3 deep 
glacial for long-
term storage 

13 
 
here by the classification probability score, can be directly modelled, and where the impact of individual 
misclassifications is low, such as modelling at large geographic scales. These data should not be relied on 
to provide definitive information on species presence, distribution or activity at small temporal or 
geographic scales, or at species range limits, where the impact of incorrect classifications would be high. 
A dataset of individual bat echolocation calls is likely to exhibit substantial overdispersion, as an 
individual bat can call hundreds of times during a single ‘pass’ over an acoustic sensor. There will also be 
small differences in survey effort between years as a result of the different recording schedules used in 
each year (Table 1). This will need to be accounted for if data from multiple years are compared.   
The data may also contain false negative records where a bat species is present at a location but is not 
recorded by the acoustic sensor, or detected or classified by the classification algorithm, although 
BatDetect2 has been shown to detect bat calls well (Mac Aodha et al., 2018). AudioMoth acoustic 
sensors were selected for the NWBS because their affordability allows the deployment of a large number 
of devices across many sites simultaneously, enabling the collection of sufficient data for robust trend 
modelling. However, the tradeoff is that AudioMoths have lower detection sensitivity than more 
expensive, professional-grade bat detectors. To increase detection probability in the NWBS we survey 
each location for nine consecutive nights, although false negatives may still occur in the dataset, 
particularly for quieter species such as the greater horseshoe bat (Rhinolophus ferrumequinum) and 
lesser horseshoe bat (R. hipposideros). 
14 NFI square delocation  
To protect the integrity of the core NFI survey, NFI square locations are confidential. This precludes the 
publication of precisely georeferenced data from the NWBS. To allow data from the survey to be 
visualised, mapped and integrated alongside other spatial data, an approximate area within which the 
survey took place is reported. NFI squares are assigned to an irregular grid of delocation tiles, created by 
overlaying a 5 x 5 km grid over a map of woodland, and grouping grid squares until they contained 200 – 
2500 ha of woodland. Due to the uneven spread of woodland across GB, the size of each aggregated tile 
is uneven. This analysis was performed independently within each region; however, to maintain 
rectangularity, groupings were permitted to extend beyond regional boundaries. Consequently, some 
groupings will overlap region borders or extend into marine areas. Where this is the case, only woodland 
located within the focal region contributed to the ≥ 200ha woodland threshold, and woodland located 
outside the region was excluded from woodland area calculations. The coordinates of the delocalised 
grid square that contains the surveyed NFI square are included in the dataset. 
 
 

14 
 
15 Data dictionary 
Column name 
Type 
Description 
Entry 
type 
Text 
The nature of the resource, defined 
using the Dublin Core Metadata 
Initiative resource type vocabulary.  
dataset 
licence 
Text 
The licence governing the use and 
reuse of the data 
Open Government Licence 
rightsHolder 
Text 
The owners of the rights over the 
data 
Forest Research | Bat 
Conservation Trust 
datasetName 
Text 
The name of the dataset  
National Woodland Bat 
Survey - example dataset: 
Unvalidated automated bat 
echolocation call 
classifications 
basisOfRecord 
Category 
The nature of the record, defined 
using Darwin Core controlled 
vocabulary. 
MachineObservation 
 
Note: This entry denotes a 
dataset of bat echolocation 
calls recorded by passive 
acoustic sensors and 
classified to species using a 
machine-learning algorithm, 
without manual validation. 
occurrenceID 
Text 
A globally unique identifier for the 
occurrence record, constructed 
using identifiers from the record 
NWBSUVCalls_[locationID]_ 
[eventDate]_[eventTime]_ 
[taxonID] 
recordedBy 
Text 
A list of organisations responsible 
for recording the occurrence 
Forest Research | Bat 
Conservation Trust 
behavior 
Text 
The behaviour shown by the 
subject at the time of the 
occurrence 
in flight 
eventDate 
Date 
string 
The date of the occurrence   
YYYY-MM-DD 
 
Note if you open this dataset 
in Excel the date may be 
displayed in a different format  
eventTime 
Time 
string 
The time of the occurrence 
[HH:MM:SS.ssss] 
 
Note: The time has been 
enclosed in square brackets 
to prevent it being 
automatically formatted by 
spreadsheet reading software 
such as Excel, which cannot 
read milliseconds. 
habitat 
Category 
The woodland type category 
assigned to the NFI square 
[broadleaved woodland; 
coniferous woodland; mixed 
woodland] 
samplingProtcol 
Text 
The sampling protocol used to 
record the occurrence  
passive acoustic survey 
https://environment.data.gov.
uk/defra/9e821042-e6f9-

15 
 
40df-a40b-
92e8e0bc2938/details 
 
Note: The URL provided 
directs to this document 
locationID 
Text 
The global unique identifier for the 
location of the occurrence, 
constructed using the Forest 
Research delocalised grid square 
identifier and the project-assigned 
sample location identifier.  
[delocalised grid square 
ID]_[sample location ID] 
higherGeography 
Category 
The NFI region within which the NFI 
square is located 
[East England; East Midlands; 
North East England; North 
West England; South East 
England and London; South 
West England; West 
Midlands; Yorkshire and the 
Humber] 
footprintWKT 
Text 
The footprint of the delocalised 
grid square, defined using the 
coordinates of the south-west and 
north-east corners of the bounding 
box 
POLYGON (([easting : northing 
SW] | [easting : northing 
NE])) 
footprintSRS 
Text 
The EPSG ID of the spatial 
reference system used to define 
the location footprint 
EPSG:27700 
georeferenceProtocol 
Text 
The methods used to determine 
the spatial footprint of the 
occurrence  
https://environment.data.gov.
uk/defra/9e821042-e6f9-
40df-a40b-
92e8e0bc2938/details 
 
Note: The URL provided 
directs to this document 
IdentifiedBy 
Text 
The organisation who assigned a 
taxon to the occurrence 
BCT Sound Classification 
System 
identificationVerificationStatus 
Category 
The extent to which the taxonomic 
identification has been verified to 
be correct, defined using UK 
National Biodiversity Network 
controlled vocabulary 
Unconfirmed – not reviewed 
taxonID 
Text 
A globally unique identifier for the 
taxon, defined using the UK Natural 
History Museum’s UK Species 
Inventory taxon version keys 
 
scientificName 
Text 
The Latin binomial of the taxon 
 
detectionProbability 
Number 
A probability vector indicating the 
extent to which the model thinks a 
sound is any of the bat species 
within its training set, versus ‘Not 
bat’, where pspecies is a C + 1 
dimensional vector that sums to 
one, and represents the probability 
of the species the model thinks 
Note : A detection probability 
threshold of 0.5 is applied 
during the automated 
classification of sound 
recordings. Consequently, 
only bat echolocation calls 
with a detection probability ≥ 
0.5 are retained in the 

16 
 
emitted the call, for each one 
of C different species of interest 
plus ‘Not bat’. 
This is not a Darwin Core term 
algorithm output. 
classificationProbability 
Number 
A probability vector indicating 
which species the model thinks is 
present, where pspecies is a C + 1 
dimensional vector that sums to 
one, and represents the probability 
of the species the model thinks 
emitted the call, for each one 
of C different species of interest 
plus ‘Not bat’. 
This is not a Darwin Core term. 
 
 
 
16 Survey coverage 
Up to an including the 2024 survey season, 319 unique NFI squares have been surveyed for a total of 
3,837 survey nights (Table 2), generating 217 TB of audio recordings from which over 93 million bat calls 
have been detected and classified. 
Table 2: NFI squares surveys in the first four years of the NCEA National Woodland Bat Survey. Some squares were 
surveyed in multiple years – the cumulative total includes only those squares not surveyed in previous years.  
Year 
Squares surveyed 
Cumulative unique squares 
2021 (pilot) 
36 
36 
2022 (pilot) 
36 
40 
2023 
126 
162 
2024 
158 
319 
 
The majority of surveys have taken place in July and August (Figure 5), although the distribution of 
survey dates has varied by year (Figure 6). Prior to 2024 the number of AudioMoth sensors available was 
not sufficient to survey all target squares simultaneously, so surveys were organised in ‘waves’, with 
equipment redistributed to surveyors between waves. This resulted in a multi-modal distribution of 
survey dates in most years. The main constraints on both the number of squares surveyed and the 
timing of surveys have been securing access permissions and surveyor availability. Processes for 
administering these tasks have been improved over the course of the survey, allowing a greater number 
of squares to be surveyed in later years, with fewer surveys occurring outside the ideal survey period 
(Figure 6). 

17 
 
Figure 5: Distribution of survey days across the survey season during the first four years of the National Woodland 
Bat Survey. 
 
Figure 6: Annual distribution of survey days across the survey season for the first four years of the NECA National 
Woodland Bat Survey. 

18 
 
17 Ethical principles and considerations 
The project involves non-invasive, passive data collection. It does not pose risks to participants, wildlife, 
or ecosystems.  
This project does not seek to identify human presence or human speech. Audio recordings may 
incidentally capture human sounds, including speech, however the recording protocol has been designed 
to minimse the capture of such sounds by limiting recording time during daylight hours and, from 2024, 
the use of a 8kHz high pass filter.  
The project team acknowledges the importance of transparency in research that occurs in public or 
semi-public spaces (such as woodlands). Options for discreet signage are being explored to inform the 
public of ongoing monitoring, balanced against the need to keep square locations confidential and the 
risk of equipment tampering. 
SD cards are handled only by approved personnel and wiped once data is backed up. Audio files are 
stored on secure, password-protected servers with access restricted to named project team members. 
Audio files are not accessed by project staff. Audio files are screened by an algorithm designed only to 
detect bat sounds. Where manual sound verification is required, the target bat sound is extracted from 
the audio file and only the extracted sound is reviewed by project staff.  
All efforts will be made to scramble or remove human speech during data processing if audio is shared 
externally (e.g., for research publications). In such situations human speech will be manually and 
algorithmically filtered to render it unintelligible. 
Any future use of the data beyond the original purpose (e.g., for algorithm development or cross-sector 
collaboration) will undergo additional ethical review and approval. Ethical oversight will continue 
throughout the project lifecycle, including reviews before data sharing and publication. 
18 References 
Barlow, K., Briggs, PA., Haysom, K.A., Hutson, A.M., Lechiara, N.L., Racey, P.A., Walsh, A.L., Langton, S.D., 
2015. Citzen science reveals trends in bat populations: The National Bat Monitoring Programme in Great 
Britain. Biological Conservation 182: 14-26. 
Boughey, K., Reveley, S., Newman, M., Williams, C., 2020. The Forestry England Bat Survey. Bat 
Conservation Trust, UK. Available from https://www.bats.org.uk/our-work/science-research/passive-
acoustic-surveys/forestry-england-bat-survey 
Forest Research, 2020. NFI Survey Manual – Third Cycle, Chapter 4: Section Stratification. Forest 
Research, Forestry Commission. Available from https://www.forestresearch.gov.uk/tools-and-
resources/national-forest-inventory/nfi-survey-manuals-for-field-samples/ 
Jones G, Jacobs DS, Kunz TH, Willig MR, Racey PA. 2009. Carpe noctem: the importance of bats as 
bioindicators. Endangered Species Research 8: 93–115. Available from https://doi.org/10.3354/esr00182  

19 
 
Newson, S.E., Mendes, S., Crick, H.Q.P., Dulvy, N.K., Houghton, J.D.R., Hays, G.C., Hutson, A.M., MacLeod, 
C.D., Pierce, G.J., Robinson, R.A., 2009. Indicators of the impact of climate change on migratory species. 
Endangered Species Research 7: 101–113. Available from https://doi.org/10.3354/esr00162. 
Mac Aodha, O., Martínez Balvanera, S., Damstra, E., Cooke, M., Eichinski, P., Browning, E., Barataud, M., 
Boughey, K., Coles, R., Giacomini, G., Mac Swiney, M.C., Obrist, M.K., Parsons, S., Sattler, T., Jones, K.E., 
2022. Towards a General Approach for Bat Echolocation Detection and Classification. bioRxiv: 
2022.12.14.520490. Available from https://doi.org/10.1101/2022.12.14.520490 
Mac Aodha, O., Gibb, R., Barlow, K.E., Browning, E., Firman, M., Robin Freeman, Harder, B., Kinsey, L., 
Mead, G.R., Newson, S.E, Pandourski, I., Parsons, S., Russ, J., Szodoray-Paradi, A., Szodoray-Paradi, F., 
Tilova, E., Girolami, M., Brostow, G., Jones, K.E., 2018. Bat detective - Deep learning tools for bat acoustic 
signal detection. PLOS Computational Biology 14: e1005995. Available from 
https://doi.org/10.1371/journal.pcbi.1005995 
Marchant, J.H., Forrest, C., Greenwood, J.J.D., 1997. A review of large-scale generic population 
monitoring schemes in Europe. Bird Census News 10: 42-79 
Russo, D., Jones, G., 2015. Bats as bioindicators: an introduction. Mammalian Biology 80: 157–158. 
 
 

20 
 
Appendix I: Woodland associations demonstrated by UK bat 
species 
Species 
Woodland 
generalist/specialist 
Use of tree roosts 
Foraging preferences 
Common pipistrelle 
(Pipistrellus 
pipistrellus) 
Generalist  
Occasional use of tree roosts, 
mainly roosts in buildings  
Woodland edge, and a wide 
variety of other habitats 
Soprano pipistrelle 
(Pipistrellus 
pygmaeus) 
Generalist 
Occasional use of tree roosts, 
mainly roosts in buildings.  
Woodland edge, and other 
habitats, particularly riparian 
habitats and waterbodies. 
Nathusius 
pipistrelle 
(Pipistrellus 
nathusii) 
Generalist 
Occasional use of trees 
Edge habitat – along woodland 
edges, rides and water bodies 
Barbastelle 
(Barbastella 
barbastellus) 
Specialist  
Roosts almost exclusively in trees. 
Uses flaking/loose bark, splits 
and cracks, tree hollows and 
crevices. 
Edge and open habitats – 
prefers rides and woodland 
edges. Forages in woodland 
interiors at dusk. 
Bechstein’s bat 
(Myotis bechsteinii) 
Specialist 
Roosts in trees all year. Prefers 
woodpecker holes and cavities. 
Associated with broadleaved 
woodland containing veteran 
trees like oak.  
Woodland interior – prefers 
closed canopy woodland with 
cluttered understorey layer, 
close to water. 
Natterer’s bat 
(Myotis nattereri) 
Specialist 
Some use of tree roosts, mainly in 
the summer.  
Woodland interior and edge 
habitats – tree canopies and 
along woodland rides, tree-
lined rivers and ponds 
Daubenton’s bat 
(Myotis 
daubentonii) 
Generalist 
Some use of tree roosts, mainly in 
the summer 
Woodland edge, and other 
habitats, particularly over open 
water bodies and slow flowing 
rivers.  
Whiskered bat 
(Myotis 
mystacinus) 
Generalist (regarded as 
a specialist In Europe) 
Some use of tree roosts in the 
summer 
Utilises both woodland interior 
and edge, and also forages over 
water. 
Brandt’s bat 
(Myotis brandtii) 
Generalist (regarded as 
a specialist In Europe) 
Occasional use of tree roosts 
Utilises both woodland interior 
and edge, and also forages over 

21 
 
Species 
Woodland 
generalist/specialist 
Use of tree roosts 
Foraging preferences 
water. 
Alcathoe bat 
(Myotis alcathoe) 
Specialist 
Limited information available. 
Utilises tree roosts, preferring 
fissures or small cavities. 
Woodland interior – relies on 
woodlands like ancient and 
semi natural for foraging. 
Noctule (Nyctalus 
noctula) 
Specialist 
Primarily roosts in trees. Has a 
strong preference for 
woodpecker holes, but also 
utilises rot holes and cavities. 
Forages in open habitat and 
over woodland. 
Leisler’s bat 
(Nyctalus leisleri) 
Generalist 
Will roost in trees. Prefers cavities 
and rot holes.  
Open habitats – woodland 
edges, hedgerows, near water, 
over pasture land. 
Serotine bat 
(Eptesicus 
serotinus) 
Generalist 
Rarely roosts in trees 
Open habitat – over woodlands, 
hedgerows, open pasture land, 
hay meadows 
Greater horseshoe 
bat (Rhinolophus 
ferrumequinum) 
Generalist 
Tree roosts not used. 
Open and edge habitat 
including pasture, ancient or 
semi natural woodland, often by 
water 
Lesser horseshoe 
bat (Rhinolophus 
hipposideros) 
Specialist 
Tree roosts not used, but 
generally roosts near woodlands. 
Interior of woodlands and along 
well connected tree lines and 
hedgerows.  
Brown long-eared 
bat (Plecotus 
auritus) 
Specialist 
Some use of tree roosts. Will also 
roost in old buildings (barn, 
churches) and bat boxes. Prefers 
cavities, sometime low down 
(basal cavities) on the tree. 
Woodland interior – strongly 
associated with tree cover and 
cluttered understorey. Will also 
forage in mixed woodland edge 
and among native conifer.  
Grey long- eared 
bat (Plecotus 
austriacus) 
Generalist 
Tree roosts not used. 
Open and edge habitat - 
Forages over grassland close to 
the roost.