The European Breeding Bird Atlas - EsriUserforum

The European Breeding Bird Atlas

Combining environmental variables and distribution models for the

new European Breeding Bird Atlas

Pietro Milanesi PhD in Biodiversity and Evolution; Doctor Europaeus

Postdoctoral Researcher [email protected]

on behalf of

EBBA2 Steering Committee and EBBA2 coordination team and assistants

EBBA2 European Breeding Bird Atlas

mailto:[email protected]


The first EBCC atlas of European

Breeding Birds (EBBA1) was a

milestone in European ornithology,

and after 30 years fieldwork for

EBBA2 is underway;

EBBA1

Data collection 1980s Published 1997


Why a new Atlas? 1. Environmental changes (land use and climate) over the last 30 years

impacted on populations of birds across Europe;

HILDA v2.0 gross land changes European Environment Agency



impacted on populations of birds across Europe; 2. Conservation actions require the most up-to-date information on

these impacts;

CEEweb for Biodiversity



impacted on populations of birds across Europe; 2. Conservation actions require the most up-to-date information on

these impacts; 3. Bird occurrence and its change since the 1980s is fundamental for

decision making;

Northern Wheatear

Oenanthe oenanthe

Grasshopper Warbler

Locustella naevia

1. Environmental changes (land use and climate) over the last 30 years impacted on populations of birds across Europe;

2. Conservation actions require the most up-to-date information on these impacts;

3. Bird occurrence and its change since the 1980s is fundamental for decision making;

4. To provide valuable data for science (e.g., future changes);


Why a new Atlas?

Huntley et al., 2008

Area of occurrence Network of protected areas

Area of occurrence Red List assessments

Niche-based models Ecological studies


Some potential uses of this type of information

Gradients of occurrence Population studies

Etc.


What outputs are planned?

1. An atlas book, to be published in 2020;

2. Interactive maps and further information online;

3. Summaries to highlight the most crucial findings;

4. Bird database will be available for further scientific research.

EBBA1

Peregrine falcon

Falco peregrinus


EBBA1


Spatial resolution: 10x10 km.

The EBBA2 will cover

• 10,180,000 km2

• 50 Countries

• 5 years of fieldwork (2013-2017)

Mapping species distribution in EBBA2


Why 10x10 km maps?

50x50 km

(c. 5,000 sq)

10x10 km

(c. 120,000 sq)

Northern Wheatear

Oenanthe oenanthe

EBBA1 EBBA2


Why 10x10 km maps?

1. To determine finer-resolution species distribution patterns

2. To visualise relative prob. of occurrence – a proxy for relative abundance

3. To infer information in not surveyed areas

4. To standardise information across the whole Europe (if predictive accuracy is high)

Northern Wheatear

Oenanthe oenanthe

How it works


Species

distribution models

Predictor variables

Validations

Bird data

Sampling effort


Predictor variables

Predictor variables


Predictor variables (N=39)

GEOGRAPHY

TOPOGRAPHY

LAND-COVER

ANTHROPOGENIC FACTOR

SOIL TYPES

CLIMATE

Variable Units

Longitude (center of 10x10 cell) º

Latitude (center of 10x10 cell) º

Distance to the coastline m

Mean Annual Temperature ºC

Mean Temperature in the Breeding Period (April-July) ºC

Total Annual Precipitation mm

Total Precipitation in the Breeding Period (April-July) mm

Minimum temperature of the coldest month ºC

Maximum temperature of the warmest month ºC

Evapotranspiration in the Breeding Period (April-July) Ae / Pe

Mean elevation m

Mean slope %

Coniferous forests %

Bare areas %

Broadleaved forests %

Irrigated crops %

Rainfed tree crops %

Rainfed cropland %

Grassland %

Mixed Broadleaved and coniferous forests %

Mosaic cropland-natural vegetation %

Wetlands %

Permanent ice %

Shrubland %

Sparse vegetation %

Continental water bodies %

Urban areas %

Shannon habitat diversity Index H’ = −Σ ( pi × lnpi )

Accumulated NDVI in the Breeding Period (April-July) 0-500

Average forest canopy height meters

Wood biomass Mg / ha

Human population density number / km2

Young soils, weakly developed %

Well developed soils %

Well developed and acid soils %

Wet soils %

Soils rich in clay %

Saline soils %

Shannon soil diversity Index H’ = −Σ ( pi × lnpi )

HABITAT STRUCTURE


Example of predictor variables

Shannon habitat

diversity

Evapotranspiration

Human population

density

Acid soil

Altitude Coniferous

forests


Example of predictor variables

Average forest canopy height (LiDAR)

Wood biomass


Sources of predictor variables

- Worldclim dataset (http://www.worldclim.org);

- MODIS Evapotranspiration MOD16 global dataset (http://www.ntsg.umt.edu/project/mod16);

- Altitude and Slope from the ETOPO2 global dataset

(http://www.ngdc.noaa.gov/mgg/global/etopo2.html);

- Harmonized World Soil Database - HWSD (http://www.fao.org/soils-portal/soil-survey/soil-maps-

and-databases/harmonized-world-soil-database-v12/);

- land cover from GLOBCOVER dataset (http://maps.elie.ucl.ac.be/CCI/viewer/download.php);

- NDVI from MODIS

(https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod13c2);

- Forest canopy height from GLAS (http://lidarradar.jpl.nasa.gov);

- Wood biomass from Wageninge UR (http://www.wageningenur.nl/grsbiomass);

- Human population density from the Gridded Population of the World, GPW datasets

(http://beta.sedac.ciesin.columbia.edu/data/collection/gpw-v4).

Other predictor variables that can be potentially useful:

- Global tree density maps (http://dx.doi.org/10.6084/m9.figshare.3179986);

- Artificial night-light brightness from NOAA, NPP VIIRS – NASA

(http://visibleearth.nasa.gov/view.php?id=79765);

- Openstreetmap (https://www.openstreetmap.org/);

- Global roads (http://sedac.ciesin.columbia.edu/data/set/groads-global-roads-open-access-

v1/data-download).

http://www.worldclim.org/

http://www.ntsg.umt.edu/project/mod16

http://www.ngdc.noaa.gov/mgg/global/etopo2.html

http://www.fao.org/soils-portal/soil-survey/soil-maps-and-databases/harmonized-world-soil-database-v12/



















http://maps.elie.ucl.ac.be/CCI/viewer/download.php

https://lpdaac.usgs.gov/dataset_discovery/modis/modis_products_table/mod13c2

http://lidarradar.jpl.nasa.gov/

http://www.wageningenur.nl/grsbiomass

http://beta.sedac.ciesin.columbia.edu/data/collection/gpw-v4



http://dx.doi.org/10.6084/m9.figshare.3179986

http://visibleearth.nasa.gov/view.php?id=79765

https://www.openstreetmap.org/

http://sedac.ciesin.columbia.edu/data/set/groads-global-roads-open-access-v1/data-download














Example of other predictor variables

Artificial night-light brightness

Global roads

Global tree density

Openstreetmap


Species

distribution models

Predictor variables

Validations

Bird data

Sampling effort

Bird data


Bird data

A (simplified) example of standardised data

36VUJ4_12 11.05.2014 60 Anas platyrhynchos

36VUJ4_12 11.05.2014 60 Tetrastes bonasia

36VUJ4_12 11.05.2014 60 Scolopax rusticola

36VUJ4_12 11.05.2014 60 Cuculus canorus

36VUJ4_12 11.05.2014 60 Dryocopus martius

36VUJ4_12 11.05.2014 60 Dendrocopos major

36VUJ4_12 11.05.2014 60 Dendrocopos leucotos

36VUJ4_12 11.05.2014 60 Anthus trivialis

36VUJ4_12 11.05.2014 60 Troglodytes troglodytes

36VUJ4_12 11.05.2014 60 Prunella modularis

36VUJ4_12 11.05.2014 60 Erithacus rubecula

36VUJ4_12 11.05.2014 60 Luscinia luscinia

36VUJ4_12 11.05.2014 60 Turdus merula

36VUJ4_12 11.05.2014 60 Turdus philomelos

36VUJ4_12 11.05.2014 60 Turdus iliacus

36VUJ4_12 11.05.2014 60 Sylvia borin

36VUJ4_12 11.05.2014 60 Sylvia atricapilla

36VUJ4_12 11.05.2014 60 Phylloscopus sibilatrix

36VUJ4_12 11.05.2014 60 Phylloscopus collybita collybita

36VUJ4_12 11.05.2014 60 Phylloscopus trochilus

36VUJ4_12 11.05.2014 60 Regulus regulus

36VUJ4_12 11.05.2014 60 Ficedula hypoleuca

36VUJ4_12 11.05.2014 60 Aegithalos caudatus

36VUJ4_12 11.05.2014 60 Parus palustris

36VUJ4_12 11.05.2014 60 Certhia familiaris

36VUJ4_12 11.05.2014 60 Fringilla coelebs

36VUJ4_12 11.05.2014 60 Spinus spinus

36VUJ4_12 11.05.2014 60 Coccothraustes coccothraustes

DateSurvey time

(minutes) Scientific name

10x10 km

square

≈ 100,000 timed surveys 12,057 10x10 km squares surveyed


Gaps identification (mMESS)

Combination of surveyed squares

and predictor variables

Modified version of the

Multivariate Environmental

Similarity Surface (mMESS).

Identification of poorly sampled

areas in Europe.

MESS is limited by its use of the

most dissimilar variable(s) as

indicator(s) of overall similarity;

mMESS weighs all variables in

the same way.


Species

distribution models

Predictor variables

Validations

Bird data

Sampling effort

Sampling effort


Sampling effort (averages over 100 simulated landscapes/communities)


Species

distribution models

Predictor variables

Validations

Bird data

Sampling effort

Species distribution models


SPECIES DISTRIBUTION MODEL (SDM) R PACKAGE

Artificial Neural Network (ANN) biomod2

Boosted Regression Trees (BRT) biomod2

Flexible Discriminant Analysis (FDA) biomod2

Multiple Adaptive Regression Splines (MARS) biomod2

Random Forest (RF) biomod2

Generalized Additive Model (GAM) biomod2

Generalized Linear Model (GLM) biomod2

Hierarchical Bayesian SDMs (hSDM) hSDM

Mapping Occurrence Probability (unmarked) unmarked

Bayesian SDM using Gaussian Processes (GP-MAP) GRaF

• Models for which autocorrelation of the residuals will be taken into account

through Kriging or Thin Plate Splines (TPS) functions;

• Models accounting for spatial autocorrelation.

Species distribution models (presence/absence data)


Species distribution models

Ensemble Modelling

(or model-averaging)

Tested to improve predictive performance

New algorithms

Evaluated and eventually included in the analyses


Species

distribution models

Predictor variables

Validations

Bird data

Sampling effort

Validations


Validations

VALIDATION STATISTICS

Area Under the Curve (AUC)

True Skill Statistic (TSS)

Cohen’s K (K)

Boyce Index (BY)

Correct Classification Rate (CCR)

Sensitivity - True Positive Rate (TPR)

Specificity - True Negative Rate (TNR)

10-folds cross-validations (90% vs. 10%)

WHOLE DATASET

Subsets to develop the models

# 1

# 2

# 3

Subsets to validate

the models

# … …

# 10


Luscinia luscinia Luscinia megarhynchos

Data collected


The invaluable role of the EBCC network of national coordinators, ornithologists and birdwatchers!

Bird Numbers 2016: Birds in a changing world Martin-Luther University in Halle (Saale), Germany



A current exploratory modelling work (GLM)

Probability of occurrence

Low

High



Spatial autocorrelation of the residuals (GLM)

Potential solution : Thin plate splines (Tps) regression of residuals



Thin plate splines regression of the residuals (GLM)



Thin plate splines regression of the residuals (GLM)

Probability of occurrence

Low

High


What will the new Atlas achieve?

1. Up-to-date distribution maps for birds across Europe;

2. Assess changes in species distribution since the 1980s.

3. Bird data collected will result in one of the most comprehensive biodiversity data sets in the world.

4. The Atlas will build capacity for conservation and monitoring in areas where this is most needed.

EBBA2 Steering Committee: Verena Keller (Chair), Ruud Foppen, Mikhail Kalyakin, Lluis Brotons, Mark Eaton, Hans-Günther Bauer, Aleksi Lehikoinen & David Noble. EBBA2 coordination team and assistants: Sergi Herrando, Petr Vorisek, Marina Kipson, Marti Franch, Marc Anton, Dani Villero & Magda Pla. Spatial Modelling Group of the EBCC: Pietro Milanesi, Henk Sierdsema, Tomas Telensky, Alison Johnston, Jerome Guelat, Thomas Sattler, Marc Kery, Lluis Brotons, Nicolas Titeux, Frederic Jiguet, Christian Kampichler, Simon Gillings & Thomas Gottschalk. National coordinators and data providers for 2nd data provision: Taulant Bino (Albania), Clara Pladevall (Andorra), Karen Aghababyan & Hasmik Ter-Voskanyan (Armenia), Norbert Teufelbauer (Austria), Elchin Sultanov (Azerbaijan), Semion Levy & Marina Dmitrenok (Belarus), Dražen Kotrošan & Jovica Sjeničić (Bosnia and Herzegovina), Svetoslav Spasov & Stoycho Stoychev (Bulgaria), Vlatka Dumbovic & Tibor Mikuska (Croatia), Martin Hellicar (Cyprus), Karel Štastný & Vladimír Bejček (Czech Republic), Irina Levinsky & Thomas Vikstrom (Denmark), Jaanus Elts & Renno Nellis (Estonia), Janus Hansen (Faroe Islands), Aleksi Lehikoinen (Finland), Bernard Deceuninck, Nidal Issa, Jean-Philippe Siblet & Frederic Jiguet (France), Guille Mayor & Alexander Abuladze (Georgia), Christoph Sudfeldt & Christoph Grüneberg (Germany), Danae Portolou (Greece), Karoly Nagy, Zsolt Nagy, Tibor Szep (Hungary), Kristinn Skarphedinsson & Gudmundur Gudmundsson (Iceland), Brian Caffrey & Olivia Crowe (Ireland), Roberto Lardelli, Guido Tellini & Lorenzo Fornasari (Italy), Sergey Sklyarenko (Kazakhstan), Viesturs Kerus, Andris Dekants & Ainars Aunins (Latvia), Georg Willi (Liechtenstein), Liutauras Raudonikis (Lithuania), Patric Lorgé & Mikis Bastian (Luxembourg), Metodija Velevski, Ksenija Putilin & Danka Uzunova (Macedonia), Nicholas Barbara (Malta), Mihailo Jovičević & Darko Saveljić (Montenegro), Kjetil Mork, Ingar Jostein Øien, Magne Husby, John Atle Kålås & Roald Vang (Norway), Tomasz Chodkiewicz & Tomasz Wilk (Poland), Domingos Leitao, Joao Rabaca & Carlos Godinho (Portugal), Larisa Bogdea, Vitalie Ajder, Silvia Ursul & Emanuel Baltag (Republic of Moldova), Zoltan Szabo & Judit Veres-Szászka (Romania), Olga Voltzit & Mikhail Kalyakin (Russia), Dimitrije Radisic (Serbia), Jozef Ridzoň (Slovakia), Tomaž Mihelič (Slovenia), Juan Carlos del Moral & Blas Molina (Spain), Ake Lindstrom & Martin Green (Sweden), Hans Schmid & Peter Knaus (Switzerland), Chris van Turnhout & Dirk Zoetebier (The Netherlands), Dilek Sahin & Kerem Boyla (Turkey), Dawn Balmer, Simon Gillings & Justin Walker (UK), Igor Gorban & Tatiana Kuzmenko (Ukraine), as well as on-line portals, regional sources & foreign birdwatchers.

Thanks a lot to thousands of ornithologists and birdwatchers across Europe!!!


Thank you for

your attention

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