Pollination services spatial model adapted to Bulgarian rural landscapes

Zulian, G., Dahle, S., Åström, J., Gjershaug, J.-O., Aneva, I. Y., Chehlarov, E., Chobanov, D. P., Ljubomirov, T., Stoyanov, S., Todorov, I., Vassilev, V., Staverløkk, A., Ødegaard, F. & Rusch, G. M.

www.nina.no

Suitability of Land to sustain pollinators (RP)

• Inputs:

• Land use, crop share data, semi-natural vegetation, …., foraging distance

• Output: raster map

• Bounds: 0-1

Crops dependent from insect pollination

• Crop production

• Crop dependency

ESTIMAP Pollination

Expert-knowledge

based approach

www.nina.no

Applications at a local scale

So far the ESTIMAP-pollination has been adapted at regional or local scale within the OpenNESS project.

www.nina.no

The inter-disciplinary team

The study has been conducted under the project “Methodological Support for Ecosystem Services Mapping

and Biophysical Valuation (MetESMap) within Programme BG03 "Biodiversity and ecosystem services",

supported by the EEA – Norway Grants programme.

Local data

Models

Entomologist

Botanists

First adaptation of

ESTIMAP pollination at a

National scale

www.nina.no

GIS Data collection and preprocessing

Field work •Field data collection

Expert knowledge •All inputs scored by entomologists and botanists

Data preparation and model adaptation I

Model correction and crop dependency analysis

The work-flow p

lan

nin

g, co

ord

inati

on

,

org

an

iza

tio

n a

nd

data

-an

aly

sis

Surveyors (IBER-NINA)

Botanists

Ina Aneva

Stoyan Stoyanov

Entomologists

Dragan Chobanov

Evgeni Chehlarov

Toshko Ljubomirov

Jan Ove Gjershaug

Arnstein Staverløkk

Sondre Dahle

Coordination

IBER

Boyko Georgiev

Svetlana Bancheva

Svetla Bratanova-

Doncheva

NINA

Graciela Rusch

Jens Åström

Frode Ødegaard

GIS expert

ReSAC

Vasil Vassilev

Modeler

JRC

Grazia Zulian

www.nina.no

The model adaptation Adaptation levels TIER

(Gret-Regamey

et. Al 2016)

Low

Data Increased accuracy/precision

2 Conceptual schema No changes

Model outcomes meaning No changes

Medium Data / information Increased accuracy/precision

Field work + local knowledge

2+/3

Conceptual schema Changed (changed rules and data

combination)

Model outcomes meaning No changes

High Data Increased accuracy/precision

Field work – local knowledge

2 ++/3 Conceptual schema Changed

Model outcomes meaning Changed

www.nina.no

Data collection – to calibrate the model with field data:

Fieldwork was done in

September 2016 by a Bulgarian/

Norwegian team of

botanists and entomologists:

• Data collection followed a

standardized protocol

Pollinator and flower inventories.

Field sheets used

to collect data

Kallioniemi et al. 2017. Agric. Ecosys. & Envir.

The 28 landscapes surveyed selected to include agricultural practices from small scale extensive to large scale intensive.

• 10 transects of 100 m at each site

• 900 bees collected

• about 4600 plants recorded

Data recorded:

• Nesting suitability

• Number of pollinators

• Flower resources

www.nina.no

The expert knowledge base consensus score

• Experts – provided individual scores first

• 2 botanists – scored flower resources

• 6 entomologists – scored bee nesting suitability

• The individual scores were averaged for floral

availability (small variation).

• The consensus score for nesting suitability was given

with emphasis on the expert with most knowledge on a particular habitat.

• The individual expert scores are kept in the dataset

example of

scoring:

www.nina.no

Data preparation phase A

Land Parcel Identification System (LIPIS)

Urban/agricultural cadaster

High Nature Value Farmland

Water (lakes-rivers-channels)

Forest (core + edge) + data on forest type and management

Road network

All data prepared at 25 m resolution (EPSG: 32635, WGS 84 / UTM zone 35N)

www.nina.no

Data preparation phase B

Agricultural cadaster

Created (internal edge) – for each crop type- the score was increased by 20% for floral resources

High Nature Value Farmland

Increases the overlapping parcels by 25 %

Water (lakes-rivers-channels)

Created 10 m res external edge – for each water type- edges scored (at 10 m according to a distance function and then aggregated to 25 m)

Forest (core + edge) + data on forest type and management

Edges and cores shaped according to each forest type and management combination

The values for forest core decreases according to a function of the distance

After the discussion with the experts

www.nina.no

Model adaptation

Conceptual schema

www.nina.no

Map of Pollination in Bulgaria

www.nina.no

Differences with the EU map

Where are the differences?

Relative correspondences between

the two maps

over the relevant land use types

(%).

Fuzzy numerical

map

Resolution FN index

100 m 0.344

1 km 0.32

www.nina.no

An application

Enhancing semi-natural vegetation in agricultural

areas: where?

www.nina.no

intensive

extensive

www.nina.no

Data collection

• GIS data collection

Field work • Field data collection

Expert knowledge

• All inputs scored by entomologists and botanists

Data preparation and model adaptation I

• I outcome

Model correction and crop dependency analysis

• II outcome

Next steps

From the material, selected

bee species have been

photographed and used to

prepare a poster (first

version).

We will later translate the

poster text to Bulgarian and

use in outreach activities.

-the names of the groups of species

will be completed after the bar code

analysis is completed

www.nina.no

Thank you! grazia.zulian@ec.Europa.eu

Institute of Biodiversity & Ecosystem research

www.iber.bas.bg/

Remote Sensing Application Centre

resac-bg.org/index_en.html

Norwegian Institute for Nature Research

www.nina.no

EU – Joint Research Centre

ec.europa.eu/jrc