Dr. Jon Whitehurst - Bats, Maths and Maps - Isle of Wight Cafe Sci - Nov 2016

Isle of Wight Bat Group
Bats, Maths & Maps
…and a Systems Engineer
Dr Jon Whitehurst

Lecture Context
• Relating field observations to the habitat and
assessing the impact of habitat change is
perhaps the most difficult job that faces
professional ecologists today…
 Climate change at a global scale
 More direct anthropogenic impact (buildings,
roads, railways, farming practice, wind turbines,
etc.) at a local scale

Lecture Coverage
• Introduction to bats...
• Bat species recording methods
• The role of advanced mathematics in Ecology
• The role of digital terrain mapping (DTM) and
Geographic Information Systems (GIS) in
Ecology
• Engineering integrated solutions for the
Ecologist…

Example: Parkhurst Forest
This is just a collection of points on a map…This is the result of linking presence observations to habitat

Example: Lake District N.P.
Dr Chloe Bellamy, Predictive modelling of bat-habitat relationships on different spatial scales PhD Thesis, Leeds Sep 2011

Example: Wind Turbine Risk
Hellena Sofiia Viiana dos Santos, Using Species Distribution Modelling to Predict Bat Fatality Risk at Wind Farms, Masters Thesis,
Faculdade de Ciencias da Universidade do Porto, Jun 2011

First the bats…
• 18 species
• All are small and
insectivorous

IoW Observed Bat Species
• Rare / Endangered Species
– Barbastelle
– Nathusius’ Pipistrelle*
– Alcathoe
– Bechstein’s bat
– Grey Long Eared bat
– Gtr. Horseshoe bat
– Parti-Coloured Bat*
• Others
– Common Pipistrelle
– Soprano Pipistrelle
– Serotine
– Whiskered bat
– Natterer’s bat
– Brandt’s bat
– Daubenton
– Brown Long Eared bat
– Leislers bat
– Noctule
• Not Observed
– Le. Horseshoe bat

Basic Bat Biology
• Bats are the only truly flying mammal
• They are highly optimised for flight:
– Light weight bone structure
– Low fat reserve
– Absolutely minimal tissue structure
• Eyesight is reasonable, but like us,
their eyes aren’t much use in the dark!
• All UK species have well developed
echolocation abilities to make up for
this…

Bat Behaviours
• Different bat species use different roosting strategies
– Tree splits/lifted bark
– Woodpecker holes
– Caves
– Man-made structures
• Different bat species feed in different ways
– Open aerial insect hawking bats
– Canopy aerial insect hawking bats
– Canopy insect gleaning bats
– Water insect gleaning bats
• Each species has evolved to maximise success in
its chosen habitat range

Feeding
• All UK species feed on insects
• Two principle strategies:
– Hawking
– Gleaning

Some History
• Bats ability to fly in total darkness was perceived as “supernatural”
until the work of Lazzaro Spallanzani in 1794
• Spallanzani observed that bats could navigate and hunt when
their eyes were disabled
• Charles Jurine confirmed that bats needed their ears in order to
navigate and hunt by blocking their ears with wax in the same
year
• Bats use of sound to navigate and hunt remained just a
hypothesis until 1938 when Donald Griffin was able record
ultrasonic pulses emitted by the bats in navigating flight
• …and so bat echolocation using sonar was confirmed (remember
Langévin only “invented” active sonar in 1915!)

Pulsed Sonar Detection
• Prey/object distance found from the time taken for the ultrasound
pulse to return: distance = speed of sound x t/2, where t=time taken
between emitting the pulse and receiving the echo.
• One pulse can have many different echoes…
Image credit: Elizabeth Hagen. (2009, November 4). Bats. ASU - Ask A Biologist. Retrieved November 6, 2016 from http://askabiologist.asu.edu/echolocation

Bat Acoustics
Convergent acoustic field of view in echolocating bats, Lasse Jakobsen, John M. Ratcliffe & Annemarie Surlykke, Nature 493, 93–96 (03 January 2013)

Bat Acoustics

Bat Acoustics: Summary
• Range is determined by the time taken for echoes to
return
• Doppler shift determines forward/backward direction
• Azimuth is determined by time of arrival in L/R ears
• Elevation is determined by ear movement and rolling
flight
• Prey size is determined by the amplitude (loudness) of
the echoes
• Bats also use doppler profiles for prey identification

Bat Identification
• Remote visual methods
– Size
– Wing shape
– Flight behaviour
• Trapping
– Mist netting
– Harp netting
• Echolocation call recordings
– Start-stop frequency
– Peak frequency
– Call structure
– Pulse repetition rate

Feeding Adaption

Echolocation Calls: UK Species Variation
Species Fp
kHz
Fs
kHz
Fe
kHz
Call
Length
mS
PRI
mS
Habitat
Type
Call
Type
Unambiguous
Range
m
Compressed
Range Cell
mm
Update Rate
Hz
Noctule 19.3 23.2 18.3 22.1 807.2 O CF 121.1 34.7 1.2
Leislers 23.1 26.2 21.9 17.1 312.0 O CF 46.8 39.5 3.2
Npip 39.3 51.1 36.9 6.5 129.0 mixed H/CF 19.4 12.0 7.8
Serotine 25.9 58.4 27.5 5.1 126.0 O L/CF 18.9 5.5 7.9
Whiskered 47.5 88.3 32.4 4.2 113.0 C L 17.0 3.0 8.8
Barbastelle 32.9 39.4 28.0 3.4 108.4 mixed L 16.3 14.9 9.2
Grey LE 32.6 43.4 23.6 3.8 105.0 C H 15.8 8.6 9.5
Ppip 46.6 68.8 45.9 5.9 102.5 C H/CF 15.4 7.4 9.8
Bechstein 51.0 116.2 32.9 2.4 96.4 C L 14.5 2.0 10.4
Spip 55.1 79.6 56.8 5.5 89.1 C H/CF 13.4 7.5 11.2
Brandts 46.7 91.6 34.0 3.5 88.0 C L 13.2 3.0 11.4
Natterers 46.9 106.8 22.8 4.7 80.1 C L 12.0 2.0 12.5
Brown LE 33.1 50.0 25.0 2.3 76.8 C H 11.5 6.8 13.0
Daubenton 47.0 81.1 29.4 3.2 75.5 C L 11.3 3.3 13.2
Alcathoe 52.5 110.0 43.0 2.5 55.0 C L 8.3 2.5 18.2
C: Cluttered L: LinearSweep
O: Open CF: Constant Frequency
H: Hybrid H: Hyperbolic

Echolocation Call Properties: P.aus @ 3m

Echolocation Call Properties: N.noc @ 50m+

Ultrasound Propagation: Attenuation in Air
0.00
500.00
1000.00
1500.00
2000.00
2500.00
3000.00
3500.00
4000.00
4500.00
0.0 10.0 20.0 30.0 40.0 50.0 60.0 70.0 80.0 90.0 100.0
Attenuationdb/km
FrequencykHz
Sound Attenuation Vs Frequency
10
20
30
40
50
60
70
80
90
For example, at 10m and 60% RH:
• Attenuation is 5db at 20kHz
• Attenuation is 36db at 100kHz
Just over
a quarter
of the
level
1/4000th of
the level!

Impact of Sound Attenuation
5m 10m 15mM.bech Emergence Recording Example, Briddlesford Copse 2013

Canopy Clutter
What’s the difference..!
P.pip recorded in the Noke Plantation July 2014
Random
reflections from
the canopy
Destructive
interference due
to multipath

Ground Clutter
What’s the difference..!
The Parkhurst Forest Bat Project
https://sites.google.com/site/parkhurstforestbats/
P.pip recorded at Wydcombe Manor August 2013
Uniform
reflection from a
hard road
surface

Approximate Detection Range Summary
Genus Effective Detection Range*
In the Clear Cluttered
Plecotus 10m <5m
Pipistrellus 50m 30m
Myotis (except
M.bech)
30m 15m
Nyctalus 60m 40m
Eptesicus 50m 30m
Barbastellus 50m 25m
Rhinolophus 30m 20m
*Effective means: “able to classify the echolocation call”
All ranges are estimates and based on BatLogger average performance in the field.

So what’s the practical impact of all this?
• Survey effort required for 95% detection probability1:
1 Empirical results given in DEFRA Report WC1015 DEVELOPING EFFECTIVE METHODS FOR THE SYSTEMATIC
SURVEILLANCE OF BATS IN WOODLAND HABITATS IN THE UK Final Report August 2014 Chris Scott & John Altringham
Species Relative Survey Effort
Pipistrellus 1
M. brandtii/mystacinus 2
Barbastellus 2
Rhinolophus 4
M.alcathoe 4
M.nattereri 4
M.bechsteinii 6
Plecotus 9

Technology Enablers…
• For data Capture:
– Linear Dynamic Range Ultrasonic Mics.
– Fast Processor platforms and programmable logic
– Flash memory
– GPS/GLONASS (both for time and spatial coordinates)
– Lithium batteries…
• For data processing:
– Fast processing capability
– Fast graphics capability
– Virtually unlimited storage capability
– Easy access to software tools*
– Easy access to call data libraries*

Mathematical Enablers…
• For data Capture:
– Sampling theorem
• For data processing/classification:
– Fast Fourier transform
– Pattern recognition algorithms (maximum entropy, random
forests, neural networks, …endless list of these!)
– Noise reduction algorithms (low pass filter, convolution filter,
wavelets, etc)
• In short, nothing is possible without some very
advanced maths!

Summary so far
• Given the right use of technology we can:
– Make full spectrum bat echolocation call recordings
– Geo-tag where those recordings were made
– Accurately timestamp those recordings
– “Classify” the species on those recordings
So, we can now put species “presences” on a map, but
how do you answer the questions “why are they found
there” and “where else might I find them”?

Modelling Habitat Use
• The general method is called “Species Distribution
Modelling”.
• The method objective is to take a set of species
presences, habitat variables and habitat constraints
and generate a probability distribution indicating the
likelihood of species presence.
• So what does this process look like..?

SDM: The Process Steps
Computer Model
Habitat &
Feature Grids
Presence
Samples
Constraints &
Limitations
Sampling &
Modelling
Strategy
Test & Analyse
Results
Ecological
Drivers
Refine/Adapt

Sampling & Modelling Strategy
Model Scale
• Habitat & Extent
– What is the extent of the habitat?
– What is the granularity of the habitat?
– These factors will drive the sampling density & positional
accuracy
• Examples
– National region, the scale might be 1 or 10km spaced grid
– National park or similar, the scale might be 100m to 1km spaced
grid
– Small niche region, the scale might be 1m to 10m spaced grid
• Key point to remember
– A fine grid (resolution) does not necessarily guarantee a better
result than a model using coarse grid…

Step 1: Choose data source and set up model area

Step 2: Define projection extent

Step 3: Define landscape areas

Step 4: Subtract these from the background…

Step 5: Generate “no data” area

Step 6: Merge the shape files into one

Grid Examples

Presence Sampling Plan
• Avoid Bias!
– Sampling density needs to be planned and executed to be bias free
– Avoid spatial autocorrelation (don’t just sample along roads or other
features)
– Remove geospatial duplicates
• Examples
– Transects should not overlap
– Repeat transects should not be done at the same time relative to sunset
– Same number of repeats for each transect
– Transects should cover all habitat features being modelled

Presence Sampling

Environment Grids
• Habitat and Habitat Features
– How many distinctive environments?
– Are habitat “features” important?
– Context of habitat background/features
• Examples
– Forest/woodland plantation plan
– Farming land use (arable, pasture, set-aside, etc)
– Defined edges (forest, hedgerow)
– Water courses and ponds
– Significant gradient/aspect variation
• Key point to remember
– Bats are influenced by habitat discontinuities, and these can
have a stronger influence than the habitat background itself…

Simple Grid Example
*
* *
* *
*
* *
*
*
* *
*
* * *
*
* *
*
*
*
*
*
*
*
* *
* *
*
* *
* * * *
* * * *
* * * *
* *
* * * * *
* * *
*
7 3
1 1
4 8 3
0 0 0
0 0 0

Now for the really hard part…
• How do you link the presence locations with the habitat variables?
– Maximum Entropy
– Bayesian Statistics
– Random Forests
– Generalised Linear Model
– Generalised Additive Model
– etc… A full account these techniques is a lecture in itself!
• Fortunately there are both clever mathematicians and broad minded
ecologists who can work with them, so…
– Numerous methods are available and still being developed
– Significant number of open source tools
– One of the most highly tested of these is MaxEnt

What is “the best” approach in this case?
• E.T. Jaynes 1957. Information theory and statistical mechanics.
Phys. Rev. 106, 620–630.
“The best approach is to ensure that the approximation satisfies any
constraints on the unknown distribution that we are aware of, and
that subject to those constraints, the distribution should have
maximum entropy.”
• This is the fundamental principle for the MaxEnt software package
used (Phillips, Anderson, Schapire, Ecological Modelling, 190:231-
259, 2006.) and for my work.

Here’s one made earlier…
• This example shows:
• The form the habitat response probability distributions take
• How these probability distributions are ranked
• How the probability distributions are combined to generate the
habitat suitability predictions
• …and most importantly, how to test the results!
Worked Example
Barbastella_-_Barbastellus.html

Application Examples
• Set-back assessment
• Species richness
• Habitat change impact
• Seasonal habitat use preferences

Set Back Assessment: Aerial Hawkers
50% Probability

Set Back Assessment: Edge Specialists
50% Probability

Species Richness Example

Habitat Change Example

Seasonal Habitat Shift Example

So to Recap…
• Whistle-Stop tour of just one of the modern ecological
methods used to inform us about the habitat preferences
of bats
• It demands a truly cross discipline scientific/engineering
effort:
– Engineered technology for ultrasound reception and high fidelity
digital recording, GPS for time/geospatial tagging and computing
power & engineered software to do the analysis and modelling
– Nothing would be possible without innovative mathematics, and
equally innovative people who can put the mathematics to
practical use…
– Modern ecology is not just about field work… It is system
engineering in disguise!

Question Time…
• Reference sites for local information
– The Isle of Wight Bat Group
www.iowbatgroup.co.uk
– The Isle of Wight Bat Project
www.sites.google.com/site/iowbatproject/
– The Parkhurst Forest Bat Project
www.sites.google.com/site/parkhurstforestbats/
– The Isle of Wight Bat Hospital
www.iowbathospital.org.uk/
• UK Bats
– www.bats.org.uk

Many thanks to the following people for the kind use
of their images and footage:
Kay Bott
Patty Briggs
Mike Castle
Hugh Clark
Bob Cornes
Anita Glover
© All images and footage are copyright of the individual photographer and the Bat Conservation Trust.
None of the images can be reproduced, distributed or edited without the prior consent of the Bat
Conservation Trust.
Daniel Hargreaves
Austin Hopkirk
Roger Jones
Shirley Thompson
Anne Youngman

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