EuropaBON WP3.1

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Data Stream - modify

Tab created: 2021-10-25

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

The basic observational units, the minke whale tracks, from the two platforms A and B were compared for
matching by an automatic routine (duplicate identification rule) that has as its criterion difference in timing,
bearing and radial distances (Schweder et al. 1997, Skaug et al. 2004, Bøthun & Skaug 2009). Before the
matching, missing values of radial distance and and/or angle are imputed by interpolation between adjacent
surfacings and taking into consideration the movement of the vessel. An initial sighting has three possible
outcomes which are: seen by either platform or by both simultaneously. If one platform detects the whale before
the other platform, it sets up Bernoulli trials where the outcome is seen or not seen by the other platform.
A hazard probability model is developed as described in Skaug et al. (2004), where parameters are estimated by
maximizing the likelihood based on the observed data. The simulation part of the earlier estimation processes
(Skaug et al. 2004, Bøthun et al. 2009) which took care of bias correction for measurement errors, duplicate
identification errors, clustering and other factors have not been performed on the present data. Instead other
approaches have been chosen. After investigating measurement errors from the experiments during the 2008-
2013 survey cycle (Solvang et al. 2015) we decided to use the recorded data uncorrected. Clustering has been
taken care of through the variance estimation process (Skaug & Solvang 2015).
For the chosen covariate model, the parameter estimates were used for calculating the effective strip half
widths wA and wB . These are in turn used to obtain an abundance estimate (by survey block)
2( )
A B
A B
n n N Area
w wL
+ = +
where A n and B n are the total number of sighted whales from platforms A and B, L is the realized transect
length, and Area is the area of the survey block. 
3
The quantities wA and wB are obtained from the fitted hazard probability model, which is parameterized using
a GLM approach as follows (see Skaug et al. 2004 for details): The radial distance at which the hazard
probability has dropped by 50% is exp(ηr), where ηr is a linear predictor. The linear predictor consists of the
intercept βr and covariate effects. Similarly, there is a linear predictor associated with the effect of sighting angle
(inctercept βθ). The hazard probability at the origin (r = 0) is parameterized as μ = [1+exp(-βμ)]-1
.
The hazard probability model involves one additional parameter, the surfacing rate intensity α, which is
determined from external data. For that purpose we used dive time data collected by radio-tagging of 20 minke
whales (Øien et al. 2009) over the period 2001-2008. The mean surfacing rate α was estimated from those data,
and where sea state information was available, truncated for Beaufort > 4. The estimate is 45.78 blows/hour,
which gives the parameter α = 0.0127.
The variance of the abundance estimates have been calculated using a new approach (Skaug & Solvang 2015).
The inter-annual variation in spatial distribution (additional variance) of the minke whales has been included as
in earlier estimates (Skaug et al. 2004, Skaug & Solvang 2015).

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.