EuropaBON WP3.1

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Data Stream - modify

Tab created: 2022-01-07

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

We used three scenarios which are based on storylines developed within the EU FP6 project ALARM (Settele
et al., 2005; Spangenberg et al., 2012). These scenarios integrated the IPCC (2001)
Special Report on Emission Scenarios (SRES). These future climate scenarios were
developed on the basis of a coupled Atmosphere-Ocean General Circulation Model
(HadCM3; New et al., 2000).
The three scenarios were:

1. SEDG, Sustainable European Development Goal scenario – a storyline for
moderate change. A policy primacy scenario focused on the achievement of a socially,
environmentally and economically sustainable development. It includes attempts to
enhance the sustainability of societal developments by integrating economic, social
and environment policies. Aims actively pursued include a competitive economy, a
healthy environment, social justice, gender equity and international cooperation. As a
normative back-casting scenario, policies are derived from the imperative of stabilising
atmospheric greenhouse gas concentrations and ending biodiversity loss. This scenario
approximates the IPCC B1 climate change scenario. Mean expected temperature
increase in Europe until 2100 is 3.0°C.

2. BAMBU, Business-As-Might-Be-Usual scenario – a storyline for intermediate
change. A continuation into the future of currently known and foreseeable socioeconomic
and policy trajectories. Policy decisions already made are implemented
and enforced. At the national level, deregulation and privatisation continue except in
“strategic areas”. Internationally, there is free trade. Environmental policy is perceived
as another technological challenge, tackled by innovation, market incentives and
some legal regulation. The result is a rather mixed bag of market liberalism and socioenvironmental
sustainability policy. This scenario approximates the IPCC A2 climate
change scenario. Mean expected increase in temperature until 2100 is 4.7°C.

3. GRAS, GRowth Applied Strategy scenario – a storyline for maximum change. A
future world based on economic imperatives like primacy of the market, free trade, and
globalisation. Deregulation (with certain limits) is a key means, and economic growth
a key objective of politics actively pursued by governments. Environmental policy will
focus on damage repair (supported by liability legislation) and some preventive action.
The latter are designed based on cost-benefit calculations and thus limited in scale
and scope. This scenario approximates the IPCC A1FI climate change scenario. Mean
expected increase in temperature until 2100 is 5.6°C.

Projections of future climatic changes resulting from each scenario were developed on
a 10 min x 10 min grid and intersected with the geographic window used in this atlas.
Relevant monthly projected climate data were averaged for the two periods 2021-2050
and 2071-2100.

6.6 Species distribution models
To assess the climatic niche of the bumblebee species, we related the presences
and absences of the species, aggregated to the 50 km x 50 km UTM grid, to the
respective climatic conditions per grid cell by means of statistical species distribution
models (SDMs). SDMs were developed with generalised linear models (GLMs) with
a binomial error distribution and a logit link function. Since GLMs can be sensitive
to false absence data, where a species has not been observed although it is actually
present in a grid cell, we excluded grids without any bumblebee observation and
an additional 51 grids with observations of one species. In total we used 2160 grid
cells (Fig. 6.1). For the development of the SDMs we used species records from
1970 to 2000 to match the temporal resolution of the current climate data. For
the parameterisation of the SDMs we allowed for additive and curvilinear effects
by incorporating second order polynomials. Models were checked for spatial
autocorrelation with Moran’s I correlograms of model residuals, but none was
detected. Initial models were simplified by stepwise regression, while minimizing
Akaike’s information criterion. Models were calibrated on an 80% random sample
of the initial data set and model accuracy was evaluated on the remaining 20%.
Agreements between observed presences and projected distributions were evaluated
by true skill statistic (TSS) and the area und the curve (AUC) of the receiver
operating characteristic. TSS is a simple and intuitive measure for the performance
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of species distribution models when predictions are expressed as presence-absence
maps and handle shortcomings of other measures such as kappa (Allouche et al.,
2006). Thresholds for calculating presence-absences and projections were obtained by
maximizing TSS. To allow comparability with Settele et al. (2008) we also calculated
AUC which is a threshold-independent measure of model performance. While the
climatic niche models were developed at the 50 km × 50 km UTM grid, the current
climatic niche and future climatic niche were projected to 10 min × 10 min grid
cells. According to the projected future conditions of climatically suitable areas in
comparison with the predictions for current conditions, we mapped the resulting
changes indicating areas of potential loss, potential gain and remaining suitable
conditions. These changes were mapped within the geographical window across
Europe used for this atlas. SDMs were developed in the statistical environment R (R
Development Core Team, 2013). All maps were based on the WGS1984 coordinate
system with a Miller cylindrical projection using ArcGIS software (ESRI, 2013).

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.