Dr. Jon Whitehurst - Bats, Maths and Maps - Nov 2016

Isle of Wight Bat Group

Bats, Maths & Maps…and a Systems Engineer

Dr Jon WhitehurstIsle of Wight Bat Group


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 FpkHz

FskHz

FekHz

Call Length

mS

PRImS

HabitatType

CallType

Unambiguous Range

m

CompressedRange Cell

mm

Update RateHz

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

C: Cluttered L: Linear SweepO: Open CF: Constant FrequencyH: 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

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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

Atten

uatio

n db

/km

Frequency kHz

Sound Attenuation Vs Frequency

10

20

30

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60

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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 ClutterWhat’s the difference..!

P.pip recorded in the Noke Plantation July 2014

Random reflections from

the canopy

Destructive interference due

to multipath


Ground ClutterWhat’s the difference..!

The Parkhurst Forest Bat Projecthttps://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 EffortPipistrellus 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 StrategyModel 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


Sampling & Modelling StrategyPresence 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


Sampling & Modelling StrategyEnvironment 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

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7 3

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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 ExampleBarbastella_-_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 Groupwww.iowbatgroup.co.uk

– The Isle of Wight Bat Projectwww.sites.google.com/site/iowbatproject/

– The Parkhurst Forest Bat Projectwww.sites.google.com/site/parkhurstforestbats/

– The Isle of Wight Bat Hospitalwww.iowbathospital.org.uk/

• UK Bats– www.bats.org.uk

http://www.iowbatgroup.co.uk/

http://www.sites.google.com/site/iowbatproject/

http://www.sites.google.com/site/parkhurstforestbats/

http://www.iowbathospital.org.uk/

http://www.bats.org.uk/


Many thanks to the following people for the kind use of their images and footage:

Kay BottPatty BriggsMike CastleHugh ClarkBob CornesAnita 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 HargreavesAustin Hopkirk Roger JonesShirley ThompsonAnne Youngman

Data & Analytics

Dr. Jon Whitehurst - Bats, Maths and Maps - Nov 2016