EuropaBON WP3.1

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Data Stream - modify

Tab created: 2021-10-20

Project

1) Data origin - from where is data coming from

Origin integration node

2) Data process - how is the data processed (if)? And how does it flow?

Data type Options: 1) Raw: data as collected in the field ; 2) Aggregated: raw data is aggregated (not processed!); e.g. occurrence data collected in linear sampling transects of 1 km is aggregated (summed) at 10 km resolution; e.g. data from seasonal sampling is aggregaged at annual level ; 3) Essential Biodiversity Variables (EBVs) ; 4) Ecosystem Services vales (ESS); 5) Indicators: other processed indicators such as for example species trends

Ebv ess type If the data flowing or generated by a given integration node is an EBV or an ESS, please, select the category from the list

Format Format of the data flowing between biodiversity monitoring initiatives and integration nodes or between integration nodes

Method If there is any type of data integration (i.e. if data type is other than Raw or Aggregated) , indicate the method used to integrate data. Please, use the field “Description” below to give further details about the method used to process the data, including relevant bibliographic references and URL links if available.

Frequency Indicate the frequency of the data flow (e.g. annual, seasonal)

3) Data set - which dataset/integrated product is processed and how?

Name Name of the dataset (please provide an informative name for the dataset flowing)

Description

National data were combined to produce overall EU
and pan-European population sizes and trends for each
taxon. For population sizes, the reported minimum
and maximum population size data across countries
were summed to calculate the overall minimum and
maximum. All European countries were requested to use
the same population unit, which was breeding pairs for
most breeding birds – with the exception of a minority
of taxa with unusual or complex breeding biology or
cryptic behaviour, for which other units, such as calling
or lekking males, were used – and individuals for birds
in winter. For population trends, data from all countries
were combined, weighting each country’s contribution
according to the size of its population. Weightings were
based on the geometric mean of the countries’ minimum
and maximum population size compared to the geometric
mean of the equivalent totals for the overall EU or pan-
European population. This analysis was carried out
using a dedicated tool developed by IUCN to estimate
overall trends based on data from multiple (national)
subpopulations15.
Where it was not possible to allocate a trend category
with confidence, either because trend directions were
reported as unknown for a large proportion of the total
European population or in the case of conflicting trend
information or lack of trend magnitudes, the overall
European trend was classified as ‘Unknown’. Where
possible, the robustness of trend categories to the effects
of any missing data was tested using plausible ‘good’ and
‘bad’ scenarios, based on other sources of information,
such as any other reported trend information, recent
national Red Lists, scientific literature, and other
publications and consultations with experts.
For the majority of species, assessments were based on
data from the breeding season, but for a minority of
species, winter data were (also) used. Winter data were
only reported for a subset of species, mainly wintering
waterbirds, and especially migratory wildfowl and
waders, whose populations are often best monitored in
the winter, when they congregate in large numbers.
15 Available at http://goo.gl/yZLATv from http://www.iucnredlist.org/
technical-documents/red-list-documents
The assessments of species that do not breed (regularly)
within the EU and/or the European region were based
solely on winter data, while for species that occur in
both seasons and for which the reported dataset was
representative of the regional population, the assessment
process was carried out independently on data for both
the breeding and wintering populations. For some
species in winter, underlying population trends can
be obscured by demographic factors, often related to
inter- annual variation in weather conditions. In some
years, for example, birds that usually winter in the
region may be forced to move elsewhere by harsh winter
conditions, whilst in others, birds that usually winter
outside the region may show marked influxes into the
region. Consequently, assessments were carried out
principally on the basis of breeding data, provided that
the resulting status category was the same as or higher
(i.e. more threatened) than that obtained using winter
data.All terrestrial and marine bird species native to
Europe or naturalised in Europe were included in the
assessment. Species introduced to Europe by man after
AD 1500 were not considered by the assessment16.
Similarly, species that are of marginal occurrence in
Europe were not considered17. Assessments were carried
out at the species level, following BirdLife International’s
current taxonomy18 (BirdLife International 2014) and
population size and trend data for any bird taxa for which
a country reported at the subspecific or flyway level19
were aggregated to the species level.

Data were analysed using a geodesic discrete global grid
system, defined on an icosahedron and projected to the
sphere using the inverse Icosahedral Snyder Equal Area
(ISEA) Projection (S39). This corresponds to a hexagonal
grid composed of individual units (cells) that retain their
shape and area (~2,500 km2) throughout the globe. These
are more suitable for a range of ecological applications
than the most commonly used rectangular grids (S40).
The range of each species was converted to the hexagonal
grid for analysis purposes. Coastal cells were clipped to the
coastline. Patterns of species richness (Fig. 5) were mapped
by counting the number of species in each cell (or cell
section, for species with a coastal distribution). Patterns
of threatened species richness were mapped by
counting the number of threatened species (categories CR,
EN, VU at the European regional level) in each cell or cell
section. Patterns of endemic and near-endemic species
richness were mapped by counting the number of species
in each cell (or cell section for coastal species) that were
flagged as being endemic or near-endemic to geographic
Europe as defined in this project

Brief description of the data flowing (e.g. if the dataset represents aggregated data, please explain here how the aggregation is made); if the data set is a “product” – EBV, ESS, indicator – give more information here about how its values are estimated and provide relevant bibliographic references or URL links if available)

Target biodiversity data Select biodiversity data initiatives included in the dataset. No need to choose them in upper levels when creating products.

Taxonomy

Target taxa Target taxa

check this box if you have a list of taxa to upload for this data stream (no need to check it when sending all species available from data origin). If the DataStream generates a product integrating data for specific taxa, please try to upload the list of taxa included in the product.

Target species List of the target species/habitats covered in the monitoring initiative. If you don´t find a thesaurus for the taxa of your interest, you might need to upload a New Thesaurus first – see tab New Thesaurus – and then, use the option “Choose Thesaurus” to select all or a subset of the taxa concerning this particular DataStream (the species integrated in a particular indicator/EBV/ESS)

Spatial scale

Spatial scale Spatial scale coverage of the dataset

Resolution Spatial scale resolution value (numeric)

Resolution units Spatial scale resolution units (km or m)

4) Data destination - where does the data/product flow to?

Two options here:

a) The data flows into an upper-level integration node b) The data is a generated Product

Select the destination integration node

Dest integration node

Is the product generated used by policy - e.g. EBV, EESV, indicator?

Used by policy Is the indicator generated used by policy ?

Note here that some indicators might currently lack connection to policy but we want to capture them here too because of their potential to inform policy in the future.