Estimate Pollination Potential

with

ESTIMAP

Jörg Priess

OpenNESS-CANA Minisymposium, Analandia, 2015-06-23

Page 2

INTRODUCTION – Pollination Service

• Pollination by insects is an important ecosystem service,

as many crops and fruits rely on or benefit from it [1].

(regional examples: coffee, fruits, vegetables)

• Loss of insect pollinators has been observed in different regions around

the globe as a result of habitat loss and agricultural intensification.

• The impact assessment of recent land-use changes as well possible

future developments on insect pollination (-potential) is therefore of high

interest for different stakeholders, such as farmers and policy-makers [2].

• Using the ESTIMAP approach [3] we aim at assessing these impacts for

the Rio Claro region for recent and possible future land-use changes.

[1] Klein et al. 2007. Importance of pollinators in changing landscapes for world crops. Proceedings of the Royal Society B: Biological Sciences 274, 303–313. [2] Zulian, G., Maes, J., Paracchini, M., 2013. Linking Land Cover Data and Crop Yields for Mapping and Assessment of Pollination Services in Europe. Land 2, 472–492. [3] Zulian, et al., 2013. ESTIMAP: ecosystem services mapping at European scale. Publications Office, Luxembourg.

Page 3

Method - ESTIMAP

• ESTIMAP approach to estimate the relative pollinator abundance based on land-use / land-cover specific nesting and foraging scores combined with species-specific flight distances

• Habitat and floral scores adapted from ESTIMAP and adjusted for land-use classes considered in Rio Claro

• Focus on wild bees as pollinators

• Typical maximum flight distances taken from studies conducted in the region

• Spatial resolution: to be decided

ESTIMAP structuremodified from [3]

Page 4

Floral Availability and Nesting Sites (borrowed from Germany)

SITE Code Description Floral A. Nesting Comments 1 Fallow 0.2 0.2 Scores similar to Non-irrigated agricultural land 3 Rye 0.05 0.1 0 4 Corn 0 0.1 0 5 Barley 0.05 0.1 0 6 RapeSeed 0.9 0.1 25 7 Wheat 0.05 0.1 0 8 SugarBeet 0.25 0.1 0 10 SummerBarley 0.05 0.1 0 12 Grassland 0.2 0.3 Grasslands in CG are mainly pastures 13 Deciduous Forest 0.9 0.8 14 Mixed Forest 0.6 0.8 15 Coniferous Forest 0.3 0.8 16 Settlement city 0.2 0.2 Scores were adapted to a urban-rural gradient

(expert knowledge): Change: 0.25 -> 0.2 17 Settlement town 0.2 0.2 No change 18 Settlement rural 0.3 0.3 Change: 0.25->0.3 19 Infrastructure 0.2 0.3 20 Mining 0 0.3 21 Water 0 0 22 Unland 0.7 0.8 Scores adapted from CLC-classes (intersect)

Method – ESTIMAP 2

Page 5

Strong influence of single factors on RPP (from Central Germany)left: flight distances of Ricketts et al. right: flight distances of Zulian et al.

Method – ESTIMAP 3

Page 6

Strong influence of single factors on RPP (from Central Germany)left: Activity pattern based on °C of 2050 right: RPP based on left

Method – ESTIMAP 4

Page 7

ESTIMAP – Results (from Saxony)

• Results highly sensitive to temperature & max flight distance

• Other parameters such as road-side vegetation show clear but less strong impact on RPP (see figure below)

Urgently needed:

Model Validation

So far only nice maps! Relevance?

8

place, date

SEITE 8 © J. Priess

Obrigado para prestar atenção