Habitat requirements of Boreal Owl (Aegolius funereus) and ... › data › news › 5067 › ARANGE_2015_Smokovce...Boris Nikolov, Stoyan Stoyanov, Thomas Groen, Iva Hristova-Nikolova,

Habitat requirements of Boreal Owl (Aegolius funereus) and Pygmy Owl (Glaucidium passerinum) in mountainous populations on the Balkan Peninsula

Boris Nikolov, Stoyan Stoyanov, Thomas Groen,

Iva Hristova-Nikolova, Tzvetan Zlatanov

24.07.2015 2

Advanced multifunctional forest management in European mountain ranges 2

Study area

•

Map of Bulgaria with georeferenced image of high resolution satellite imagery of

the study area from Google- Earth.

24.07.2015 3


Shiroka Laka Forest Enterprise in “Trigrad-Mursalitsa” Natura-2000 zone

according to the Birds Directive

Study area

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Shiroka Laka Forest Enterprise in “Trigrad-Mursalitsa” Natura-2000 zone

according to the Birds Directive

Study area

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Study area

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Studied species

Two species of boreal owls:

• glacial relics

• high conservation value (Appendix I

of the Birds Directive)

Boreal Owl

(Aegolius

funereus) Pygmy Owl

(Glaucidium passerinum)

www.birdphoto.fi

www.birdphoto.fi

24.07.2015 7


Two species of boreal owls:

• Widely distributed in the taiga zone of Eurasia (both species) and

North America (Boreal Owl);

• Balkan Peninsula holds relic mountain populations (isolated refugia?)

– little is known about their habitat requirements.

Studied species

Boreal Owl

(Aegolius funereus)

Pygmy Owl

(Glaucidium passerinum)

www.owlpages.com

www.owlpages.com

http://www.owlpages.com/http://www.owlpages.com/

24.07.2015 8


Data collection – birds

Field work performed in autumn 2012

• A total of 49.9km transects surveyed, all above 1450m altitude

• 85 point counts performed (34 points successful, which is 40%)

Target owl species recorded:

• Boreal Owl (Aegolius funereus) - 29 birds in 27 territories

• Pygmy Owl (Glaucidium passerinum) - 10 birds in 9 territories

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Data collection – birds

Territories of:

• Boreal Owls

• Pygmy Owls

• Both species

Localities

ascertained

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Data collection – stand parameters

Field work performed in 2013 and 2014

• 85 “complexes of plots” established (85 point counts performed)

• 25 plots in each complex or a total of 2125 plots

• Predominantly spruce dominated forests, very few plots dominated

by Scots pine

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360 m

30 m


Total area sampled ~ 13 ha

Sampling intensity ~ 2%

100 m2 x 25

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360 m

30 m




100 m2 x 25

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Predictor variables

Growing stock [m3/ha)

Fellings ≤ 3 years old (% plots)

Fellings 4-10 years old (% plots)

Fellings ≥ 4 years old (% plots)

Canopy closure ≤ 0.3 (% plots)

Canopy closure 0.6-0.8 (% plots)

Mean canopy closure

DBH ≥ 38 / ha (# trees)

DBH ≥ 38 (% plots)







Fallen dead wood (m3/ha)

Standing dead wood (m3/ha)

Fallen dead wood Decay stage 1 (m3/ha)





Standing dead wood Decay stage 1 (m3/ha)





Many predictor variables correlated!

24.07.2015 14


Explanatory variables - Boreal Owl

Growing stock [m3/ha) – α≤0.05






Mean canopy closure









Fallen dead wood total (m3/ha) – 0.05≤α≤0.1

Standing dead wood total (m3/ha)






Fallen dead wood Decay stage 4&5 (m3/ha)






Standing dead wood Decay stage 4&5 (m3/ha)

24.07.2015 15


Explanatory variables - Pygmy Owl

Growing stock [m3/ha)






Mean canopy closure









Fallen dead wood (m3/ha)

Standing dead wood (m3/ha)






Fallen dead wood Decay stage 4&5 (m3/ha)






Standing dead wood Decay stage 4&5 (m3/ha)

24.07.2015 16


•

Modelling

Glaucidium

observations

Stand

parameters

Aegolius

observations

Collinearity

Analysis, PCA

logistic

regression

model (GLM)

Model performance:

• Area under the ROC curve

(ROC) statistic;•

• Kappa statistic &

• True skill statistics (TSS)

Principle of parsimony for

finding the minimal

adequate model (AIC)

24.07.2015 17


Modelling - the final set of variables

The final set of selected variables after

the collinearity analysis (PCA)

• Fellings 4-10 years old (% plots)

• Canopy closure ≤ 0.3 (% plots)

• DBH ≥ 50 (% plots)

• Fallen dead wood (m3/ha)

• Fallen dead wood Decay stage 4

(m3/ha)

• Standing dead wood Decay stage 4&5

(m3/ha)

24.07.2015 18


Best model (GLM) - Boreal Owl

The best parsimonious model for the Boreal owl (Aegolius funereus)

included three explanatory variables

The minimal adequate model for Boreal owl, R2 = 0.29 (Nagelkerke), P <

0.01, AIC = 87.26, * p < 0.05, ** p < 0.01, *** p < 0.001.

Included B(SE)

95 % CI for odds ratio

Lower Odds ratio Upper

Constant -0.940 (0.583)

Canopy closure ≤ 0.3 (% plots) -0.042 (0.018)** 0.922 0.959 0.990

DBH ≥ 50 (% plots) 0.038 (0.020)* 1.000 1.039 1.083

Fallen dead wood Decay stage 4

(m3/ha)

0.175 (0.071) ** 1.040 1.191 1.387

There are no issues related to the basic residual statistics for the model

(leverage, studentized residuals and DFBeta values)

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24.07.2015 20


• Area under the ROC curve (ROC) statistic – 0.7 – reasonable accuracies

ROC value ranges between 0.5 and 1, where 0.5 means a discrimination which is

no different than random, and a value of 1 means perfect discrimination of

presence and absence.

• Kappa statistic – 0.4

The Kappa statistic ranges between 0 and 1 and indicates the accuracy, corrected

for the random chance of correctly assigning presences and absences.

• True skill statistics (TSS) – 0.4

A TSS of 1 indicates perfect discrimination of presences and absences, and a TSS

of -1 indicates an inverse result (i.e. absences are perfectly assigned as presence

and vice versa).


24.07.2015 21


Best model (GLM) - Pygmy Owl

Included B(SE) 95 % CI for odds ratio

Lower Odds ratio Upper

Constant -3.812 (0.0815)***

Fallen dead wood

(m3/ha)

0.087 (0.032)** 1.029 1.090 1.168

The best parsimonious model for the Pigmy owl (Glaucidium

passerinum) included only one explanatory variable – rooting logs.

The minimal adequate model for Pigmy owl, R2 = 0.213

(Nagelkerke), P < 0.01, AIC = 47.419, * p < 0.05, ** p < 0.01,

*** p < 0.001.

There are no issues related to the basic residual

statistics for the model (leverage, studentized

residuals and DFBeta values)

24.07.2015 22


• Area under the ROC curve (ROC) statistic – 0.55 – high randomness

ROC value ranges between 0.5 and 1, where 0.5 means a discrimination which is

no different than random, and a value of 1 means perfect discrimination of

presence and absence.

• Kappa statistic – 0.4

The Kappa statistic ranges between 0 and 1 and indicates the accuracy, corrected

for the random chance of correctly assigning presences and absences.

• True skill statistics (TSS) – 0.2

A TSS of 1 indicates perfect discrimination of presences and absences, and a TSS

of -1 indicates an inverse result (i.e. absences are perfectly assigned as presence

and vice versa).

Best model (GLM) - Pygmy Owl

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Model robustness

Glaucidium - Accuracies

Ave

rag

e a

ccu

racy (

+/-

sd

)

GLM CTA SRE FDA MARS RF

0.0

0.2

0.4

0.6

0.8

1.0

ROCTSSKAPPA

Aegolius - Accuracies

Ave

rag

e a

ccu

racy (

+/-

sd

)

GLM CTA SRE FDA MARS RF

0.0

0.2

0.4

0.6

ROCTSSKAPPA

Biomod modelling

• Generalized Linear Models (GLM)

• Random Forest (RF)

• Surface Range Envelope (SRE)

• Classification Tree Analysis (CTA)

• Multiple Adaptive regression

splines (MARS)

• Flexible Discriminant Analysis

(FDA)

24.07.2015 24


Model robustness

Possible reasons for the relatively high randomness in the models

(between 20 and 30% of the variance explained)

• Sampling bias, only “suitable areas” included in visited locations,

therefore no “unsuitable” locations in dataset for contrast to show

importance of variables.

• Very few successful observations of Pygmy owl – the species is too

rare.

• Missed variables to consider (i.e. presence of Tawny Owl – Strix

aluco)

• Low sampling intensity ~ 2%

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Model robustness



Areas not well represented

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Model robustness

Smaller than expected

nesting territory ~ 50 ha?


24.07.2015 27


Thresholds

FDWDecay stage 4

0 1 2

m3/h

a

0

2

4

6

8

10

12

14

DBH 50 cm or more

0 1 2

# tre

es/h

a

0

20

40

60

80

100

DBH 50 cm or more

0 1 2

% p

lots

0

10

20

30

40

50

60

Canopy closure

of 0.3 or less

0 1 2

% p

lots

0

10

20

30

40

50

60

Boreal Owl

FDW total

0 1 2

m3/h

a

0

10

20

30

40

50

Only plots where Boreal Owl is present are shown in the figures

24.07.2015 28


Thresholds

Canopy closure0.3 or more

0 1 2

% p

lots

0

20

40

60

80

100

DBH 50cm or more

0 1 2

% p

lots

0

10

20

30

40

50

FDW total

0 1 2

m3/h

a

10

15

20

25

30

35

40

45

DBH 50cm and more

0 1 2

#tr

ee

s/h

a

0

10

20

30

40

50

60

70

80

Pigmy Owl

FDWDecade stage 4

0 1 2

m3

/ha

0

5

10

15

20

25

30

Only plots where Pigmy Owl is present are shown in the figures

24.07.2015 29


Management implications

Both species moderately tolerant to management

(less so Pigmy owl)

Continuous cover forestry

Elements of oldgrowthness on some 10% of the territory

24.07.2015Advanced multifunctional forest management in European mountain ranges

Thank you for your attention!

Documents

Habitat requirements of Boreal Owl (Aegolius funereus) and ... › data › news › 5067 › ARANGE_2015_Smokovce...Boris Nikolov, Stoyan Stoyanov, Thomas Groen, Iva Hristova-Nikolova,