Together- in space and time: measuring and interpreting geo-referenced contacts in animal ecology

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Together- in space and time: measuring and interpreting

geo-referenced contacts in animal ecology

Austin, Tx, 11/9-10/2016

Francesca Cagnacci

Federico Ossi

Fondazione Edmund Mach, Italy

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Movement ecology take-off: from tools…

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… to (re-discovered) research frameworks…

Lagrangian: analysis of individual movement trajectories

Eulerian: expected pattern of space use by an individual or a population over static landscapes (e.g., habitat occupancy modelling)

Mechanistic understanding: process

of individual movement  

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From Nathan et al. 2008

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… including individuals-to-population scaling up.

Mueller and Fagan 2008, Oikos

Ecological and landscape dynamics on population distribution

Relationships among moving individuals (similarity, synchrony, cohesion)

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What did I learn?

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Migratory behaviour

Movement

mechanisms Movement path

MOVEMENT PATTERNS Population trajectories Population patterns POPULATION DISTRIBUTION Undefined behavioural rules Motion -Navigation capacity Internal state INDIVIDUAL MECHANISMS EXTERNAL CONTEXT

Drake et al. 1995 Mueller & Fagan 2008 Nathan et al. 2008

Migration arena

[natural selection]

Migration syndrome Genetic complex Landscape structure/ dynamic resources

External factors & Dynamics

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What did I learn?

Movement is a multi-faceted phenomenon

- Movement emerges on heterogenous landascapes, in

space and time (context of movement)- directly linked to

habitat selection  Workshop #2!

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Movement

Migration arena

[natural selection]

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What did I learn?

Movement is a multi-faceted phenomenon

- Movement emerges on heterogenous landascapes, in

space and time (context of movement)- directly linked to

habitat selection.

- Movement depends on underling, largely unknown,

individual

mechanisms

(e.g.,

memory,

learning,

physiological state)

(11)

Movement

Migration arena

[natural selection]

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What did I learn?

Movement is a multi-faceted phenomenon

- Movement emerges on heterogenous landascapes, in

space and time (context of movement)- directly linked to

habitat selection.

- Movement depends on underlining, largely unknown,

mechanisms (e.g., memory, learning, physiological state)

- Movement is a multi-scale phenomenon:

Movement behavior = applies to individuals

(physiological, behavioral, genetic- movement

process

and pattern)

Movement

ecology=

applies

to

populations

(ecological,

(ecological, evolutionary- movement outcome)

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If 1+1= 2, studying animal

interactions

should

be

conceptually easy…

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Macroclimate

Local climate

Habitat & food resources

Physiology Individual/collective movement behaviour Demography/Population abundance Population distribution Landuse Species distribution Predation Competition Host-parasite-pathogens Individual Global, landscape Population Species Ecological Scale Communities

Decomposing ecological relationships,

at different scales

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Which way

should I

take?

That depends

on where you

want to end

up.

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PART I: Studying spatio-temporal patterns in the use of resources…

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‘Experimental’ conditions – supplemental feeding and animal movement

Ossi et al. in press Ecosphere

Resource acquisition

Food Resource distribution in winter

Movement Winter supplemental feeding

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Winter Supplemental feeding is pervasively applied to target

deer populations

Concentrate resources can alter normal animal space use

patterns AND interactions

Consequences: dominance/competition/social structure/demography and population distribution/disease transmission

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‘Experimental’ conditions – supplemental feeding and animal movement

FIRST OBJECTIVE: evaluate the actual use of feeding stations and the effect on

individual space use

SECOND OBJECTIVE: evaluate the effect on interactions (or the definition we used

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Model species: the European roe deer

- Most abundant deer species

in Europe

- Major environmental and

climatic gradients

- High ecological plasticity, but

less adapted to snow, and

droughts

- Highly selective shrub/forb

browser

- Cover and protection from

predators (hiding and flushing

anti-predatory tactic)

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EURODEER Collaborative Project

49 study areas, ranging 63°N-38°N 0-2000m a.sl. 33 partners, 15 countries > 2500 individual tracks > 8 million GPS locations TOTALLY BOTTOM-UP AND COLLABORATIVE

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OBJ. 1/HYP. 1: F.S. are mainly used under harsh conditions.

OBJ. 1/HYP. 2: F.S. are attractive points and decrease home range size.

• Temporal variation in use of feeding stations throughout the year should

increase when FS are active AND under severe winter conditions (snow

cover, low temperatures

)

• The pattern should be more evident at high altitudes and latitudes (Alps

and Scandinavia)

• The intense use of feeding stations limits home range size of roe deer

(attractive point)

• The use of feeding station decreases in

presence of inter-specific competition

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• 9 study areas

• More than 1,000,000 GPS locations from almost 200 roe deer (post –selection)

• Ca. 300 Feeding stations (and ancillary information)

• Snow data from MODIS (satellite) (500 m resolution, presence/absence)

• Temperature data from meteo stations

OBJ. 1: Material and Methods (1)-

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• Determination of ‘ weekly use’ of F.S.

Time spent within a buffer of 50 m in a week (based on linear

interpolation)  t >1, then use=1

• Identification of available F.S. per animal, based on spatio-temporal overlap: week*animal*feeding station

• Scale of analysis: week

OBJ. 1: Matherial and Methods (2)-

Spatio-temporal resolution

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Pr ed ic ti ve pr ob ab ili ty o f u se

Results (yearly pattern)

Legend

Predictive P of use Temperature

Periods of activation feeding stations

OBJ. 1/HYP. 1: F.S. are mainly used under harsh conditions

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• Strong temporal effect • Main determinants of use:

low temperature ONLY WHEN feeding stations are active

• No effect of snow

Results

• Use increases with altitude at low latitudes (i.e. on the Alps) and viceversa

P re d ic ti o n o f u se P re d ic ti o n o f u se P re d ic ti o n o f u se P re d ic ti o n o f u se P re d ic ti o n o f u se P re d ic ti o n o f u se P re d ic ti o n o f u se P re d ic ti o n o f u se P re d ic ti o n o f u se

OBJ. 1/HYP. 1: F.S. are mainly used under harsh conditions

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Results

• Roe deer that use feeding stations have a smaller home range

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06-21/03 22/03-06/04

09-24/05 _25/05-09/06

ON ON/OFF

OFF OFF

OBJ. 1/HYP. 2: F.S. are attractive points and decrease home range size.

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OBJ. 2/HYP. 1: F.S. increase the probability of encounters

OBJ. 2/HYP. 2: F.S. effect depends on energetic trade-offs and social

traits (sex, age)

• Individuals tend to be closer than normal when they are in

proximity of feeding stations

• There is a temporal pattern in aggregation trends

• Adults and subadults stay closer than

groups of adults

• Grouping increases with winter severity (snow presence, low

temperatures)

• Females are closer each with the

(30)

•Alpine range, elevation 500 – 2500 m asl

• 11 couples of animals, selected on the basis of the capture

‘clusters’ they belong to

OBJ. 2: Matherial and Methods (1)-

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• Contacts inferred from trajectories, i.e. from GPS locations

(‘indirect encounter mapping’, Krause et al. 2013)

• Temporal resolution: GPS location frequency (3hrs)

Spatiotemporal database for handling moving data

OBJ. 2: Matherial and Methods (2)-

Spatio-temporal resolution

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F.S. are moving bools (activation)

Trajectories are moving objects

(by means of linear interpolation)

OBJ. 2: Matherial and Methods (2)-

Spatio-temporal resolution

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OBJ. 2: Matherial and Methods (2)-

Spatio-temporal queries: from moving objects to moving reals

Moving object &

Moving object =

Distance between

animals (true or

interpolated)

Moving object &

Moving bool =

Distance of animal

from FS in relation

to FS management

3900 ‘tuples’, i.e. spatio-temporal matches between roe

deer pairs, with their respective Distance and Distance

of both from the common closest FS

(34)

50% < 80 m

- Reclassification in binary ‘closeness’ between animals (empirical

distribution-based threshold)  binomial distribution

- Generalized additive model (GAM) framework to catch the temporal

pattern

Closeness (0/1) ~ s(day) + s(couple, bs = "re") + Distance FS + snow + sex + age + Temperature

OBJ. 2: Material and Methods (3)-

Ecological statistics (full model  )

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Parameter Estimate Std. Error Pred. weight

(Intercept) 0.4354 5.7486

Average Distance from the closest

feeding site -0.0089 0.0004 1

Sex of the couple (male-female) -2.4975 9.5992 0.5

Presence of snow 0.3308 0.1154 1

Age of the couple (adult -subadult) 2.052 10.6938 0.5

Daily minimum temperature -0.0256 0.022 0.4

Model averaging

OBJ. 2/HYP. 1:

Probability of encounters decreases with distance from F.S.

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• GPS data are acquired PERIODICALLY

• Proximity loggers overcome these limitation by

recording continuous encounter data (but…)

• We have no knowledge about what the monitored individual does in between 2

locations

• We might underestimate the use of specific sites (feeding stations, water holes,

movement corridors), or individual-to-individual encounters

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• Three ‘typologies’ of contact:

• Mobile - mobile

• Mobile - fixed

• (Fixed - fixed)

• Whatever the contact type, what data we get?

• ID of loggers

• DURATION of contact (please note! Temporal resolution- epochs)

• NO distance (see later…)!!

IIa- Measuring encounters: proximity loggers

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Measuring encounters: proximity loggers vs Periodic GPS acquisition

Case I – Supplemental feeding in roe deer

• Fixed proximity logger deployed at feeding site

• Estimation of HR (90 – 50 – 25 – 10) based on 3h periodic GPS

• One case of contact recorded, but NO spatial overlap between F.S. and home range

• Proximity loggers as a new tool to explore individual use of specific resources

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Wesley

Buttercup

Measuring encounters: proximity loggers vs Periodic GPS acquisition

Case II – Urban foxes in Brighton, UK

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1 km

Measuring encounters: proximity loggers vs Periodic GPS acquisition

Case II – Urban foxes in Brighton, UK

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Contact temporal unit: 20’’ GPS periodic, 30’

Many short contacts (postprocessing) Individual/daily variation

Substantial total time

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Contact density vs time of day/ GPS closest periodic to contacts

vs time of day

Proximity sensors provide more dense information

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Animals can relocate ‘far’ immediately before or after the contact:

classic spatial data do not always detect use of local resources

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Many contacts in inter-fix interval…

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- Inter-individual avoidance trough contact detection

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Animals can relocate ‘far’ immediately before or after the contact:

classic spatial data do not detect inter-individual contacts

Hr based on ‘contact’

periodic fixes:

no overlap

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Contact detection with proximity loggers: what about the spatial

resolution?

When two loggers detect each other,

what is the distance between the

individuals wearing them?

Empirical work on animals in

semi-controlled settings to compare observed

distances with contact occurrence

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not as easy as it seems…

Contact detection with proximity loggers: what about the spatial

resolution?

Power 3 Power 7, mean-size mammal Power 7, large-size mammal Power 11 Power 27 Power 3 Power 7, mean-size mammal Power 7, large-size mammal Power 11 Power 27

(52)

Contact detection with proximity loggers: let’s get the error

components right

TP = True positives = expected and recorded contacts within x’

FN = False Negatives = expected but not recorded contacts within x’

FP = False Positives = unexpected but recorded contacts beyond x’

FN = False Negatives = unexpected and recorded contacts beyond x’

(53)

Contact detection with proximity loggers: let’s get the error

components right

Precision TP/(TP+FP) Probability that a recorded contact occurs within x’

False Discovery Rate FP/(TP+FP) = 1- Precision Probability that a recorded contact occurs beyond x’

Sensitivity TP/(TP + FN) Probability to detect a contact occurred within x’

False Negative Rate FN/(TP + FN) = 1 - Sensitivity Probability to miss a contact occurred within x’

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Power 3 Power 7, mean-size mammal Power 7, large-size mammal Power 11 Power 27

Contact detection with proximity loggers: how to decide ‘desired’

distance

Probability that a

recorded contact occurs within x’

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Power 3 Power 7, mean-size mammal Power 7, large-size mammal Power 11 Power 27

Contact detection with proximity loggers: how to decide ‘distance’

(and power)

Probability to miss an occurred contact within x’

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Established technology

High energy consumption 

sparse data points

Contacts are

inferred

, with

great uncertainty

13:00 16:00 19:00

✔

✖

17:15 20:15 23:15

IIb- Measuring encounters: geo-referenced proximity detection

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?

Uses the low-power radio as

a contact “sensor”:

direct

contact detection

No location information

✔

✖

Bio-logging devices: proximity-tags

(58)

Direct

contact detection

Location

acquired

when

and

where

contacts occur

Spatio-temporal

resolutions

are

setting parameters (see later)

✔

New bio-logging device: geo-referenced proximity detection

Picco et al., Best Paper Award IPSN 2015

✔

(59)

t_trigGPS (e.g., 15 min)

• Contacts geo-referenced by the

closest

GPS fix

• Significantly reduces energy/memory consumption

periodic GPS

triggered GPS

t_GPS (e.g., 3 hours) _t

noGPS (e.g., 15 min)

node GPS schedule

What is geo-referenced proximity detection?

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What is geo-referenced proximity detection?

3 complementary data types

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a b c d e f g h a1 a3a2 b1 c2 b2 e2 f2 g2 b3 c3 d3 e3 f3 c1 d1 e1 f1 g1 g3 Power 3 range Power 7 range Fixed logger

Mobile logger 1 path Mobile logger 2 path Mobile logger 3 path

a1 b2 c2 d2 e2 f2 b1 c1 d1 e1 f1

What is geo-referenced proximity detection?

An empirical set of simulations

Ossi et al. in press Animal

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Trial Contact success rate False negative rate Contact-triggered GPS location success rate

Total TP Rate (%) (TP/Total) FN FP Rate (%) (FN/(FN+FP) Total TP Rate (%) (TP/Total) 1L-P3 204 184 90 20 0 100 14 14 100 1L-P7 204 167 81 29 8 78 8 8 100 2L-P3 480 414 86 46 20 70 17 11 60 2L-P7 480 423 88 40 17 70 15 13 98 3L-P3 864 774 90 52 38 58 29 28 99 3L-P7 864 795 92 44 25 62 30 30 100 Total 3096 2757 89 231 108 68 113 104 92

What is geo-referenced proximity detection?

An empirical set of simulations

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Is it possible to detect encounters in animal ecology studies?

Is it possible to infer ‘interaction patterns’?

 Ecological statistics and hypothesis-based assessment reconcile patterns

& processes.

 Proximity loggers convey more information than contact detection

inferred from GPS-based locations, unless a high frequency acquisition schedule can be applied

 Definition of ‘encounters’ should be a trade-off between ecological

requirements and technological limitations (errors)

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Maria Valent, Risorse estive & invernali, Densità di popolazione Federico Ossi, Risorse invernali Francesca Cagnacci Federico Ossi Johannes de Groeve Nathan Ranc Paola Semenzato Valentina Erculiani Julius Bright Ross Sandro Nicoloso Ralf Gueting Tomas Behr Gianpietro Picco Amy Murphy Davide Molteni