El eco del silencio Silencio y palabra desde la antigüedad 

The applied methodology is derived from the InVEST model which was developed for mapping ecosystem services at local scale (InVEST, Lonsdorf et al., 2009) but adapted to fit a continental scaled mapping approach. The InVEST pollination model focuses on wild bees as a key animal pollinator. The model can be used to score land cover parcels for their potential contribution to crop pollination. Crops on farms that are surrounded by land parcels that support pollinator populations are expected to experience higher abundances of pollinating visitors. At European scale the model was adapted at three stages: (1) different input data and modeling strategies were used to map for floral availability and nesting suitability (Figure 3 and 4 and Table 1), (2) a different modeling strategy was adopted to assess the activity of pollinators, and (3) we excluded areas where pollinators can physically not occur.

The underlying rationale is explained in Figure 10.3. The model uses estimates of the availability of floral resources (A), bee flight ranges (B) and the availability of nesting sites (C) to derive an index of bee abundance (D) on each cell on a landscape. Flight range information is used a second time to estimate an index of bee abundance visiting agricultural cells (G). A first adaptation to the InVEST model was to set up two composite indicators to estimate floral availability (A) and nesting suitability (C). A second adaptation was to account for differences in activity (E) as a result of climatic variation in temperature and solar irradiance. Insects become inactive when a combination of temperature and irradiance falls below a certain threshold (Corbet et al., 1993). Including temperature dependent activity resulted in an updated pollination abundance index (F). A final alteration of the model was to mask out areas where insects cannot find nesting sites (H). This model requires four key input variables and parameters: (1) a map of nesting suitability, (2) a map of floral resource availability, (3) species specific parameters describing the flight range, (4) species specific parameters that relate temperature and solar irradiance to activity.

Maps can be produced for each pollinator species provided that parameters about flight distance and activity are available. Most information in the literature is, however, limited to bee and bumblebee species (Lonsdorf et al., 2009). Therefore, we generated two pollinator supply maps showing the relative pollinator abundance which were produced for two distinct ecological guilds of pollinators: pollinators with a relatively short flight distance using solitary bees as model species and pollinators with a relatively long flight distance using bumblebees as model species.

10.3.2.1 Nesting sites and floral availability

Two composite indicators were used to map nesting suitability (NS) and floral availability (FA). Both maps were constructed using similar spatial datasets but different weights were given to each spatial attribute with respect to their capacity to host nests or their availability of floral resources. Assigning weights to the various spatial attributes was based on the opinion of experts after discussions on a workshop. The

result was a set of scores per land use class or spatial attribute on a scale between 0 and 1. Consider coniferous forest. Experts assigned high relative value to this type of land cover for nesting suitability with relative scores of 0.7 for forest core and 0.9 for forest edge, respectively, on a scale from 0 to 1. However, coniferous species do not produce flowers. Therefore, this land cover class received a lower score for relative floral availability (0.3 and 0.4 for core and edge, respectively). Table 10.2 lists the data that was used to construct both composite indicators.

Figure 10.3. Flow diagram showing the sequence of GIS operations to derive a map of Relative Pollinator Abundance (RPA). Focal stat (Focal Statistics) calculates for each input cell a statistic of the values within a specified neighborhood around it, defined by the flight distance of bees or bumblebees. Details on the creation of maps for floral availability (A) and nesting suitability (C) are provided in Table 1 and Figure 3.

In a next step, the different datasets were combined in order to derive spatially explicit habitat suitability and floral availability (Figure 10.4). Both indicators were constituted using four components: forest, agriculture, water and roadsides. Firstly, two different forest datasets were combined with four agricultural land use datasets using a set of conditional operators. This first map was overlaid with spatial information of where riparian areas and roadsides, following observations that these habitats, in particular in agricultural areas, increase the potential for nesting sites and floral resources (Table 10.2). Again, conditional operators were used to make a composite map. Finally, we masked out water bodies (lakes) where pollinators cannot make nests. All calculations were made in ArcMap 10 using Python scripts at 25 m resolution, which corresponds to the resolution of the forest data. Final maps of the composite indicators were scaled up to the resolution of 100 m.

Table 10.2. Input data to map nesting suitability and floral availability to pollinators.

Land cover data (CLC)

Corine Land Cover 2000 raster data - version 13 (02/2010) (CLC2000)

Source: EEA Resolution 100 m

Every CLC class was given a score of 0 or 1 for the availability of nesting sites in the ground of in cavities. Every CLC class was given a score between 0 and 1 to value NS and FA using expert judgment. Two experts were invited to score land cover classes. Forest classes were treated differently and given an initial score of 0. Crop share data (CAPRI)

CAPRI model resulting in crop share statistics for homogeneous clusters of 1 km2 _{pixels (HSMU),} identified on the basis of the Farm Structure Survey regions (NUTS 2 or 3, depending on the Member State, EUROSTAT 2003), land cover (CLC2000), soil mapping units (European Soil Database V2.0, European Commission, 2004) and slope.

Source: JRC Resolution 1 km

For each HSMU crop share is calculated and intersected with the CLC label 1 agricultural area. Crops were assigned to CLC classes of arable land. Crop types were assigned a value between 0 and 1 for NS and FA. Scoring is based on Westphal et al. (2003), Westphal et al. (2009), Gallai et al. (2009),

Olive farming data (Olive) Weissteiner et al. (2011) Source: JRC

Resolution 1 km

Intensive olive cultivations receive lower scores for NS and FA in the CLC base map. The rationale for including lower scores in intensive olive plantations is based on expert judgment and is justified by the use of pesticides which inhibit strongly wild pollinators.

High Nature Value Farmland data (HNV) HNV is defined as areas in Europe where agriculture is a major (usually the dominant) land use and where that agriculture supports, or is associated with, either a high species and habitat diversity or the presence of species of European conservation concern, or both.

Source: JRC Resolution 100 m

The presence of HNV increases the scores given to CLC classes based on expert judgment.

Riparian zones and river network data (Water) Riparian zones are defined as transitional areas occurring along land and freshwater ecosystems, characterized by unique soil, hydrology and biotic conditions strongly influenced by the stream water Clerici et al. (2011) CCM2 data CLC2000 data Source: JRC/EEA Resolution 25 m

Riparian zones, lakes boundaries, rivers and ditches in semi natural zones and levee have a positive impact on nesting (Lonsdorf et al. 2009). Buffers were created around maps of riparian areas, rivers and lake borders. NS and FA of these buffered areas were scored between 0.5 and 0.8 and these data were added to the CLC based map

Forest data (Forest) CLC2000 data

Pan-European Forest/Non-Forest Map 2006 Source: JRC, EEA

Resolution 25 m

Core forest and forest edges are suitable habitats for pollinators (Farwig et al. 2009; Lonsdorf et al. 2009; Hagen et al. 2010). The high resolution map allows detecting patches of forest that are not covered by the CLC2000 data. In addition, CLC2000 forest data were overlaid with the forest/non-forest map to avoid or repair spatial mismatches. NS values were 0.7 for core forest and 0.9 for edge forest. FA varied from 0.3 for coniferous core forest to maximum score of 1 for broad leaved edge forest.

Roadside (Roads) Source TeleAtlas Resolution 25 m

Road sides are used by bee and bumblebee species for nesting. Buffers were created around roads depending on road importance and scored between 0.1 and 0.8.

Hopwood (2008); Lonsdorf et al. 2009 NS: Nesting Suitability

Figure 10.4. Flow diagram illustrating how spatial information is combined to derive maps of habitat suitability and floral availability.

10.3.2.2. Flight distances

Land parcels which are suitable to support nesting are connected to crops that need to be pollinated by the flight distance of pollinating insects. Wild bees and bumblebees can pollinate crops insofar as the distance between their nests and the crops that provide foraging resources does not exceed the maximum flight distance. Average foraging distances are species specific and vary between a few meters to several kilometers (Lonsdorf et al., 2009). Based on data of expected foraging distance of different bee and bumblebee species (Lonsdorf et al., 2009), we selected two distances, 250 m and 1000m, to represent short and long flight distance species. These two parameters were used to calculate focal statistics of land cover cells in order to assess the relative pollinator abundance.

10.3.2.3. Activity

Insects are cold-blooded animals and their metabolism is strongly linked to temperature. Habitats may be suitable to provide nesting sites or forage to pollinators but if the ambient temperature is below a certain threshold, the potential to pollinate approaches zero as bees or bumblebees will not leave the nest in order to forage. Corbet et al. (1993) developed a model to express pollination activities using proportion of active bees and bumblebees. This proportion was measured by counting in the field the numbers of workers that leave the nest for foraging relative to the peak number of nest leavers that was observed during daily counting. We adapted the relative pollination abundance to account for climatic variation in temperature and solar irradiance by calculating an annually averaged activity coefficient between 0 and 100% representing the pollination activity of bees and bumblebees. This assessment was performed at 50 km resolution using the MARS climate database (Van der Goot, 1998).

10.3.2.4. Demand for pollination

We assessed the biophysical demand for pollination using a methodology based on Gallai et al. (2008). Their work is based on the hypothesis that the economic impact of pollinators on agricultural output is measurable through the use of dependence ratios quantifying the impact of a lack of insect pollinators on crop production value. We multiplied CAPRI based statistics on crop production and the dependence

ratios of Gallai et al. (2008) to estimate what share of the total crop yield in metric ton can be attributed to insect pollination. This value corresponds to a production deficit which is the reduction in crop production in absence of animal pollination (Aizen et al., 2009).