EuropaBON WP3.1

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Data Stream - modify

Tab created: 2022-01-12

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

Trends are calculated to each MTL type and region of the Bay of Byscai.

The MTL indicator for each year is calculated using the biomass and TL of species according to a formula, where MTLk refers to the MTL indicator for each year k, TLi refers to the trophic level value of species (group) I, and Yik refers to the biomass (Y) of species (group) i in year k, as included in landings data or in surveys data.

The MTL is then calculated each year in order to identify trends.

While assessing this indicator, the group of species considered can be changed by applying a TL cut-off (e.g. 3.25). The purpose of these cut-offs is to eliminate low trophic level species in order to focus on predators (i.e. high trophic level species) and detect changes in the upper food web structure.

Pauly and Watson (2005) stated that using the mean TL for the Marine Trophic Index should always be done with an explicitly stated cut off and proposed a first TL cut-off value of 3.25, it excluded all landed species under a TL of 3.25. In this assessment three TL cut-offs were identified: a TL cut-off value of 2.0 (expressed as MTL_2.0, which includes all consumers into the analysis; the MTI cut-off value of 3.25 described by Pauly and Watson (200t) (expressed as MTL_3.25). The third cut-off fixed at 4.0 has also been applied to the data (i.e. MTL_4.0) to focus on higher predators.

The MTL indicator with different cut-offs was first calculated using the whole community (all species included), and then by eliminating pelagic species from the computation to give information on bentho-pelagic species. Excluding pelagic species, helped reduce bias in the biomass data, linked to interannual variability in the pelagic species data. Indeed, the EVHOE and DEMERSALES surveys are bottom trawl surveys, meaning they are designed to capture and evaluate bentho-demersal species. The pelagic species of the Bay of Biscay were defined according to Quéro et al. (1989) and Fishbase (www.fishbase.org).

Uncertainty exists around each TL value estimated by diet studies, which is related to spatio-temporal variability and species ontogeny (Pinnegar et al., 2002; Chassot et al., 2008; Vinagre et al., 2012). This uncertainty was reported as a standard error for each TL value of a species. In order to include uncertainty in the MTL model, a bootstrap methodology was developed using the R software (R version 3.1.0). Random sampling was applied on TL values and their standard error, performing 500 MTL computations per year studied. The model was then fitted as a mean value of the 500 MTLs generated with an uncertainty related to its standard error. The uncertainty around the MTL model is thus linked to the uncertainty of the TL estimations.

For statistical analysis, preliminary tests of normality (Shapiro test), homoscedasticity (Harrison-McCabe test) and autocorrelation (Durbin–Watson test) of the data were applied for the use of parametric methods. When these tests were verified, a linear model was fitted to the temporal series (mean values for each year generated by the model) in order to test the significance of the trend. When normality or homoscedasticity were not verified, evolution of MTL over time was assessed by means of Mann-Kendall analyses (Scherrer, 1984). Differences were considered significant when p<0.05. R software and packages were used for statistics and graphics (R core team, 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.