Benthic density and trait databases in R-package Btrait

Karline Soetaert and Olivier Beauchard

18 November 2022

Source: vignettes/BtraitData.Rmd

Introduction.

The R-package Btrait facilitates working with a combination of biological density, trait, and taxonomic data. It contains:

  • functions for working with density and trait data (get_density, get_trait, get_trait_density, …),
  • two data sets with macrobenthos species density and biomass data from the Northsea (MWTL, NSBS),
  • data sets with functional trait data (Traits_nioz, Traits_cefas, Traits_Db, Traits_irr).

The main vignette Btrait demonstrates how to use the functions in the package using a small data set.

This vignette introduces the benthic databases.

The Northsea macrobenthos density data

The macrobenthos are animals living in the sediment and that are retained on a sieve with mesh size of 1mm. They are important for the ecosystem services they deliver, i.e. by their impact on biogeochemistry (nutrient dynamics), and as they are an important food source to fishes.

Btrait contains two data sets that contain Northsea macrobenthos density and biomass:

  • the MWTL data, (one or three-) yearly data from the Dutch part of the North Sea, and
  • the NSBS (Northsea Benthos Survey) data, a one-time survey that covers the entire North Sea.

The MWTL dataset

The Dutch Rijkswaterstaat (RWS) has performed regular macrobenthos sampling in the Dutch part of the Northsea, from 1995 till 2018. This activity is called “MWTL monitoring” .

The monitoring was done on a yearly basis from 1995 till 2010, after which sampling was less frequent, occurring in 2012, 2015, 2018. Not all stations were always sampled though; 5 stations were sampled 18 rather than 19 times; another 6 stations were sampled less or equal than 12 times.

The sampling region extends inbetween (2.68, 6.6) dg East and (51.62, 55.47) dg North, with water depths varying in between (5.8, 53.7) m.

There are 41849 records in this macrofauna data set for 103 stations.

Sampling started in 01-03-2001; the last sample taken in 31-05-1995.

Taxonomy was adjusted to account for rare taxa, determined on a high taxonomic level.

The sampled organisms belonged to 400 different taxa (after adjustment); before adjustment 525 taxa were distinguished.

Biomass, expressed in ash-free dry weight per square m ([gDWT/m2]), was estimated at similar taxonomic level as density estimates.

The data from these monitoring campaigns are available from RWS; they have been and made available in the framework of the EMODNET biology project.

Note that this database is atypical in the sense that sometimes the station x sampling date x taxon can be present multiple times. This is because sometimes organisms of the same taxon but very different sizes were weighed separately, or because taxa were later reclassified to another taxon that was already available. One example of such double occurrence is:

DOUBLE <- subset(MWTL$density, 
                 subset = (station == "TERHDE1" & 
                           year    == 2018      & 
                           taxon   == "Donax vittatus"))

knitr::kable(DOUBLE, 
             caption = "example of a double occurrence in the MWTL dataset",
             row.names = FALSE)
example of a double occurrence in the MWTL dataset
station date year taxon density biomass taxon.original
TERHDE1 16-03-2018 2018 Donax vittatus 38.46154 1.2884615 Donax vittatus
TERHDE1 16-03-2018 2018 Donax vittatus 38.46154 0.0064103 Donax vittatus

The NSBS dataset

In 1986, a large macrofauna survey took place covering the entire Northsea (Heip et al., 1992). Benthic samples were taken in a standardised way, on a regular grid covering the whole of the North Sea, and analysed by scientists from 10 laboratories. Extensive work was done to standardise taxonomy and identifications across the different laboratories.

The sampling region for these data extends inbetween (-3, 9) dg East and (51, 60.75) dg North, depths varying inbetween (4.1999998, 195.1999969) m.

There are 11150 records in this macrofauna data set for 234 stations.

Sampling took place in 1986; the actual sampling time was not recorded.

The sampled organisms belonged to 552 different taxa.

Biomass, expressed in ash-free dry weight per square m ([gDWT/m2]), was estimated at low taxonomic level, distinguishing between Polychaeta (class), Mollusca, Echinodermata, Crustacea (phylum) or even at higher level (Animalia). As this is at much lower resulution as density estimates, biomass is stored in a separate data.frame.

Structure of the density data sets

The density data have been gathered in a list called MWTL and NSBS that contains a number of data.frames:

  • MWTL$density, NSBS$density, NSBS$biomass: the species (taxon) density and biomass data, in long format. The MWTL$density data.frame, provides stations, date, taxon, density (ind/m2) and biomass (ashfree dry weight/m2). The column “taxon.original” refers to the original taxon, and “taxon” the selected taxon after ‘taxonomic adjustment’ (see help file, help(“MWTL$density”), for how taxa were adjusted). Density and biomass data are in separate dataframes for the NSBS data.
  • MWTL$stations, NSBS$stations: coordinates of the stations referred to in MWTL$density and NSBS$density, in WGS84 format.
  • MWTL$sediment, MWTL$abiotics, MWTL$types, NSBS$abiotics: abiotic conditions from various sources (Wilson et al., 2018; MWTL, Deltares).
  • MWTL$contours: bathymetric contour lines for quick plotting of the data. Contours were generated by applying the contourLines function from base R (R core team 2021), and are based on high-resolution GEBCO bathymetry data from the Northsea.

The taxonomic relationships between taxa is in a data.frame called Taxonomy, as generated with the worrms package (Chamberlain and Vanhoorne, 2023).

The positions and names of the MWTL and NSBS stations is in the following figures, where the colors either denote the area (MWTL) or water depths (NSBS).

Each of these data.frames, has a description in its attributes, which can be extracted with the function metadata.

metadata(MWTL$density)
##             name                                description          units
## 1        station                               station name               
## 2           date                    sampling date, a string               
## 3          taxon taxon name, checked by worms, and adjusted               
## 4        density                      species total density individuals/m2
## 5        biomass          species total ash-free dry weight       gAFDW/m2
## 6 taxon.original                        original taxon name

Station positions

We use function map_key to generate figures with station positions, passing the respective contours for each dataset. The R-function merge is used to augment the data.frame types (that contains the area to which each station belongs), with the longitudes (x) and latitudes (y) in the stations data.frame.

MWTLab   <- merge(MWTL$stations, MWTL$types, 
                  by = "station")

MWTLab$area <- factor(MWTLab$area)

par(las = 1, oma = c(0, 0, 1, 0), 
    cex.axis = 1.2, cex.main = 2, cex.lab = 1.2)

with(MWTLab, 
  map_key(x = x, y = y, colvar = c(1:4)[area], 
          contours = MWTL$contours, draw.levels = FALSE, 
          col = 1:4, colkey = FALSE, 
          main = "MWTL station positions", clab = "m",
          pch = c(15:18)[area], cex = 2))

with(MWTL$stations, 
  text(x, y+0.05, label = station, 
       cex = 0.8, adj = 0))

legend ("bottomright", legend = levels(MWTLab$area), 
        col = 1:4, pch = 15:18, cex = 2)

NSBSabio <- merge(NSBS$stations, NSBS$abiotics, 
                  by = "station")

par(las=1, oma=c(0,0,1,0), cex.axis=1.2, cex.main=2, cex.lab=1.2)
with(NSBSabio, 
  map_key(x = x, y = y, colvar = depth, 
          main = "NSBS station positions", clab = "m", 
          contours = NSBS$contours, draw.levels = FALSE, 
          pch = 16, cex = 0.9 ))

# remove the "ICES" from the station names
with(NSBS$stations, 
     text(x = x+0.05, y = y+0.08, 
          label = substr(station, 5, 7), 
          cex = 0.9, adj = 0))

Consistency of the density databases

We test the consistency of both data sets, by looking at the total density, biomass, and number of taxa of stations that were sampled in common.

To find the common stations, we merge the station information of both datasets, by the coordinates (x, y):

Common <- merge(MWTL$stations, NSBS$stations, 
                by = c("x", "y"))

colnames(Common)[3:4] <- c("MWTL", "NSBS")
knitr::kable(Common, 
             caption = "stations in common by MWTL and NSBS dataset",
             row.names = FALSE)
stations in common by MWTL and NSBS dataset
x y MWTL NSBS
3.0 52.00 BREEVTN18 ICES012
3.0 53.50 BREEVTN26 ICES034
3.0 54.50 OESTGDN19 ICES068
3.0 55.00 DOGGBK08 ICES087
3.5 52.25 BREEVTN17 ICES015
3.5 52.75 BREEVTN13 ICES020
3.5 53.75 FRIESFT08 ICES042
3.5 54.75 OESTGDN08 ICES078
3.5 55.25 DOGGBK03 ICES097
4.0 54.50 OESTGDN22 ICES069
4.0 55.00 OESTGDN02 ICES088
4.5 52.75 HOLLSKT04 ICES021
5.0 54.50 OESTGDN18 ICES070
5.0 55.00 OESTGDN21 ICES089

To test whether the density, biomass and number of taxa are comparable in both datasets, we estimate these quantities for the common stations and plot them versus one another, adding the 1:1 line.

The biomass and density data are compatible. The total number of taxa found in the NSBS data set is much lower than in the MWTL data set, but this is not surprising, as the MWTL data were sampled on a regular basis over 19 years, whereas the NSBS data were gathered in a one-time event.

In the figures below, maps of total density and number of taxa are created for both data sets.

MWTL.summ <-  
   get_summary(data        = MWTL$density, 
               descriptor  = station, 
               averageOver = year, 
               taxon       = taxon, 
               value       = density)

NSBS.summ <-   
   get_summary(data       = NSBS$density, 
               descriptor = station, 
               taxon      = taxon, 
               value      = density)
par(mfrow = c(2,2))
par(las = 1, oma = c(1, 1, 1, 1), cex.main = 2)

with(merge(MWTL$stations, MWTL.summ$density, 
           by = 1), 
  map_key(x = x, y = y, colvar = density, 
          main = "MWTL total density", clab = "ind/m2", 
          contours = MWTL$contours, draw.levels = FALSE, 
          pch = 18, cex = 2 ))

with(merge(NSBS$stations, NSBS.summ$density, 
           by = 1), 
  map_key(x = x, y = y, colvar = density, 
          main = "NSBS total density", clab = "ind/m2", 
          contours = NSBS$contours, draw.levels = FALSE,
          pch = 18, cex = 2))

with(merge(MWTL$stations, MWTL.summ$taxa, 
           by = 1), 
  map_key(x = x, y = y, colvar = taxon, 
          main = "MWTL number of taxa", clab = "/m2", 
          contours = MWTL$contours, draw.levels = FALSE, 
          pch = 18, cex = 2))
with(merge(NSBS$stations, NSBS.summ$taxa, 
           by = 1), 
  map_key(x = x, y = y, colvar=taxon, 
          main = "NSBS number of taxa", clab = "/m2", 
          contours = NSBS$contours, draw.levels = FALSE,
          pch = 18, cex = 2))

Benthic trait databases

Four benthic trait datasets are included in R-package Btrait:

  • Beauchard et al. (2021, in press) compiled 32 traits from 281 taxa. The traits comprise both “functional effects” traits, which affect ecosystem properties, and “response” traits, which affect a species’ response from changes in the environment, such as disturbance. The traits are fuzzy coded; taxonomic level is mainly on species level. This extensive trait database is called Traits_nioz.
  • Clare et al (2022) compiled 10 traits from 1025 taxa; mainly on genus level. These fuzzy coded traits are in Traits_cefas.
  • Traits necessary to estimate the Bioturbation potential of 1060 taxa were compiled by Queiros et al 2013; these data were extended by data from ilvo (courtesy Gert van Hoey). The traits in this data set are numerical traits, i.e. a value is assigned to the reworking (Ri), mobility (Mi) and feeding type (Fti) traits. Many traits are recorded at species level. These traits are in Traits_Db.
  • Traits necessary to estimate the Bio-irrigation potential (as in Wrede et al., 2018) were derived from the nioz trait database; it contains numeric values for bottom traits (BT), for injection depth (ID), and for feeding type (FT), for 281 taxa. The traits in this data set are numerical traits, i.e. a value is assigned to them. These traits are in Traits_Irr.

Species groups

The data.frame Groups contains 11 typological species groups representing sea floor functions, as derived from a cluster analysis on species traits by Beauchard et al. (in press).

Structure of the trait databases

The trait information is stored in a (taxa×traitstaxa \times traits) data.frame, where the first column represents the taxon name, and subsequent columns have the trait scores for the various trait modalities; the column names represent the trait modalities.

For example, the first part of the NIOZ trait data is:

head(Traits_nioz, n = c(3,3))
##                   taxon ET1.M1 ET1.M2
## 1          Abludomelita    0.5    0.5
## 2 Abludomelita obtusata    0.5    0.5
## 3             Abra alba    0.0    0.5

where ET1.M1 is shorthand for modality 1 of the effect trait 1.

For each trait database, the meaning of the traits and the modalities is explained in the attribute description of the data.frame, which can be extracted with the function metadata. Below are the first two rows from the description in the Traits_nioz:

head(metadata(Traits_nioz), n = 2)
##   colname                         trait modality indic value score units
## 1  ET1.M1 Substratum depth distribution        0     1   0.0  1.00    cm
## 2  ET1.M2 Substratum depth distribution      0-5     1   2.5  0.75    cm

The description attribute is a data.frame with the following columns: colname, trait, modality, indic, value, score and units.

Here

Finding traits for macrofauna taxa is a lot of work, and many of the taxa that are recorded in the benthos databases are not represented in the trait databases.

With respect to the northsea data sets (MWTL and NSBS) for instance, the percentage of taxa not represented in the trait databases is:

percentage taxa NOT represented in the databases
database MWTL NSBS
Traits_nioz 47.8 63.0
Traits_cefas 85.2 90.9
Traits_db 24.8 25.9

Note that the low presence of the species in the cefas Trait dataset is due to the fact that this dataset has the traits recorded on genus level, while the MWTL and NSBS datasets provide data mainly at species level.

Taxonomy

A taxonomic tree that comprises the taxonomic information for all taxa in the trait databases and in the density databases is included in database Taxonomy.

The names of the taxa in all databases has been checked with the WoRMS database, using R-package worrms (Chanberlain and Vanhoorne, 2023).

Consistency of the trait databases

A number of traits are present in both the cefas and nioz datasets; they are

  • Body Length (nioz) and Maximum size (cefas).
  • Life span (nioz) and Lifespan (cefas).
  • Substratum depth distribution (nioz) and Sediment position (cefas).

Both trait datasets also record the feeding type/feeding modes.

These data are represented by:

cefas_select <- subset(metadata(Traits_cefas), 
     trait %in% c("Maximum size", "Lifespan", "Sediment position"))
nioz_select <- subset(metadata(Traits_nioz), 
     trait %in% c("Body length", "Life span", "Substratum depth distribution"))

knitr::kable(cefas_select[,c(1,2,3,5,7)], 
             caption = "CEFAS traits in common with NIOZ traits",
             row.names = FALSE)
CEFAS traits in common with NIOZ traits
colname trait modality value units
sr_Less_than_10 Maximum size <10 5.0 mm
sr_11_to_20 Maximum size 11-20 15.0 mm
sr_21_to_100 Maximum size 21-100 60.0 mm
sr_101_to_200 Maximum size 101-200 150.0 mm
sr_201_to_500 Maximum size 201-500 350.0 mm
sr_More_than_500 Maximum size >500 750.0 mm
l_Less_than_1 Lifespan <1 0.5 years
l_1_to_3 Lifespan 1-3 2.0 years
l_3_to_10 Lifespan 3-10 6.5 years
l_More_than_10 Lifespan >10 15.0 years
sp_Surface Sediment position Surface 0.0 cm
sp_Shallow_infauna_0_to_5cm Sediment position Shallow_infauna_0_to_5cm 2.5 cm
sp_Mid_depth_infauna_5_to_10cm Sediment position Mid_depth_infauna_5_to_10cm 7.5 cm
sp_Deep_infauna_more_than_10cm Sediment position Deep_infauna_more_than_10cm 15.0 cm 
knitr::kable(nioz_select [,c(1,2,3,5,7)], 
             caption = "NIOZ traits in common with CEFAS traits",
             row.names = FALSE)
NIOZ traits in common with CEFAS traits
colname trait modality value units
ET1.M1 Substratum depth distribution 0 0.0 cm
ET1.M2 Substratum depth distribution 0-5 2.5 cm
ET1.M3 Substratum depth distribution 5-15 10.0 cm
ET1.M4 Substratum depth distribution 15-30 22.5 cm
ET1.M5 Substratum depth distribution >30 30.0 cm
RT2.M1 Body length <1 0.5 mm
RT2.M2 Body length 1-3 2.0 mm
RT2.M3 Body length 3-10 6.5 mm
RT2.M4 Body length 10-20 15.0 mm
RT2.M5 Body length 20-50 35.0 mm
RT7.M1 Life span <1 0.5 year
RT7.M2 Life span 1-3 2.0 year
RT7.M3 Life span 3-10 6.5 year
RT7.M4 Life span 10-20 15.0 year
RT7.M5 Life span >20 30.0 year

The traits in the cefas database are now matched with those in the nioz database, with function get_trait. The species data in the nioz database are calculated on genus level, so that they can be compared with the cefas data. This is done by passing the taxonomic tree up to genus level. For a large number of genera in the cefas database, the traits could not be found; they are removed (they are NA in the returned data).

# Select nioz traits for the taxa in the cefas database
TR_nioz <- get_trait(
  taxon    = Traits_cefas$taxon,   
  trait    = Traits_nioz[,c("taxon", nioz_select$colname)],
  taxonomy = Taxonomy[,c("taxon", "genus")]
  )
TR_nioz <- na.omit(TR_nioz)
TR_nioz <- TR_nioz[order(TR_nioz$taxon),]

Of the 1025 taxa in the cefas data set, the traits of only 193 taxa could be calculated from the nioz data.

The data are converted to crisp format, and merged with the crisp cefas data.

trnioz <- fuzzy2crisp(
  trait       = TR_nioz,
  trait_class = nioz_select$trait,
  trait_score = nioz_select$value
)
trcefas <- fuzzy2crisp(  
  trait       = Traits_cefas[,c("taxon", cefas_select$colname)],
  trait_class = cefas_select$trait,
  trait_score = cefas_select$value
)
trall <- merge(trnioz, trcefas, by=1)
dim(trall)
## [1] 193   7
trall[1:2,]
##          taxon Body.length Life.span Substratum.depth.distribution Lifespan
## 1 Abludomelita         0.5       0.5                        1.2500      0.5
## 2         Abra         2.0       2.0                        3.4375      2.0
##   Maximum.size Sediment.position
## 1            5               0.0
## 2           15               2.5

The feeding type in the nioz data and the feeding mode from cefas are extracted and made consistent.

CEFAS feeding types
colname trait modality indic value score units description
f_Suspension Feeding mode Suspension 9 0.0 0.0 - Feeds on particulate food resources suspended in the water column.
f_Surface_deposit Feeding mode Surface_deposit 9 0.2 0.2 - Feeds on detritus (including algal material) on the sediment surface.
f_Subsurface_deposit Feeding mode Subsurface_deposit 9 0.4 0.4 - Feeds on detritus located within the sediment matrix.
f_Scavenger Feeding mode Scavenger 9 0.6 0.6 - Feeds on dead animals (carrion).
f_Predator Feeding mode Predator 9 0.8 0.8 - Actively predates on animals (including small zooplankton).
f_Parasite Feeding mode Parasite 9 1.0 1.0 - Derives nutrition from its host organism.
NIOZ feeding types
colname trait modality indic value score units
RT6.M1 Feeding type Deposit feeder 6 0.0000000 0.0000000 -
RT6.M2 Feeding type Suspension feeder 6 0.3333333 0.3333333 -
RT6.M3 Feeding type Herbivore/Grazer 6 0.6666667 0.6666667 -
RT6.M4 Feeding type Carnivore/Scavenger 6 1.0000000 1.0000000 -

The consistency of the trait databases is shown by plotting the deviation between both datasets. These deviations are very close to 0, showing that both datasets are comparable.

Comparison of common traits in the nioz and cefas database

Comparison of common traits in the nioz and cefas database

statistics of deviations between cefas and nioz trait data
Min. 1st Qu. Median Mean 3rd Qu. Max.
-1.00 0.00 0 0.0130 0.0 1.0
-1.00 0.00 0 0.0062 0.0 1.0
-0.50 0.00 0 0.0215 0.0 1.0
-40.00 -1.75 0 -0.7519 0.5 29.0
-8.50 -1.50 0 -0.2102 0.0 23.5
-6.25 0.00 0 1.6676 2.5 17.5

Combining density and trait data

Community Weighted Mean (CWM) of MWTL Northsea data based on cefas traits.

The Community Weighted Mean (CWM) of traits is estimated, based on the (stations x taxon density) and (taxon x traits) matrix; we use the cefas data. In the function get_trait_density, we pass the value of the modalities (trait_._score) of the trait classes, so that the average modality value will be calculated. We also pass the taxonomy table, so that the traits for unknown taxa will be generated, based on their taxonomic relationship.

Tcefas.lab <- metadata(Traits_cefas)

MWTL.cwm <- get_trait_density(
                data        = MWTL$density,
                descriptor  = station, 
                taxon       = taxon, 
                averageOver = year,
                value       = density, 

                trait       = Traits_cefas, 
                trait_class = Tcefas.lab$trait, 
                trait_score = Tcefas.lab$value, 
                taxonomy    = Taxonomy)

The mean trait values per station are plotted for the sediment position, the maximum size and the lifespan.

Station.traits <- merge(MWTL$stations, MWTL.cwm, by=1) 
par(mfrow=c(1,2), mar=c(3,3,3,2), oma=c(2,2,0,0))

units          <-  rbind(unique(Tcefas.lab[,c("trait", "units")]))
units$colnames <- make.names(units$trait)

ii <- which(units$trait %in% c("Sediment position", "Lifespan"))

for (i in ii)
 map_key(x = Station.traits$x, y = Station.traits$y, 
         colvar = Station.traits[,units$colnames[i]], 
         contours = MWTL$contours,
         clab = units[i,2], main = units[i,1], 
         cex = 2, pch = 18)
Community weighed trait values based on the MWTL density and cefas trait data

Community weighed trait values based on the MWTL density and cefas trait data

Estimating the bioturbation and bio-irrigation potential

We use the data from the Dutch part of the Northsea, in 1995, to estimate the bioturbation potential index sensu Solan et al., 2004 and Queiros et al., 2013.

The contribution of a species, ii to the bioturbation potential (BPcBPc) is based on its mean individual weight (WiW_i), the abundance (AiA_i), and its mobility (MiM_i) and sediment reworking mode (RiR_i); the station BPc is then simply the sum of the species bioturbation potential.

BPc=∑iWi×Ai×Mi×RiBPc = \sum_i \sqrt{W_i} \times A_i \times M_i \times R_i

  • Mobility scales, MiM_i have a value of 11 for organisms living in a fixed tube, 22 indicates limited movement; 33 indicates slow, free movement through the sediment matrix; 44 indicates free movement, via burrow system.

  • sediment reworking (RiR_i) takes on the value 11 for epifauna that bioturbate at the sediment–water interface, 22 for surficial modifiers; 33 for upward and downward conveyors; 44 for biodiffusors; and 55 for regenerators that excavate holes, transferring sediment at depth to the surface.

The contribution of a species, ii to the bio-irrigation potential (IPcIPc) (sensu Wrede et al., 2018.), is based on its mean individual weight (WiW_i), the abundance (AiA_i), the burrow type (BTiBT_i), feeding TYPE (FTiFT_i) and the depth of the injection pocket, in centimeter (IDiID_i); the station IPcIPc is then simply the sum of the species bio-irrigation potential.

IPc=∑iWi3/4×Ai×BTi×FTi×IDiIPc = \sum_i {W_i}^{3/4} \times A_i \times BT_i \times FT_i \times ID_i

  • Scores for the burrowtype, BTiBT_i are 11 for epifauna, and species with internal irrigation (e.g. using siphons), 22 for open irrigation (e.g. U- or Y- shaped burrows), and 33 for blind ended irrigation (e.g. blind ended burrows, no burrow systems).
  • Scores for feeding types, FTiFT_i are 11 for surface filter feeders, 22 for predators, 33 for deposit feeders and 44 for sub surface filter feeders.
  • Injection pockets, IPiIP_i, at depths of 0-2, 2-5, 5-10 and >10 cm depth get an ID score of 11, 22, 33 and 44 respectively.

The bioturbation index can easily be estimated from the function get_Db_index, and the database Traits_Db. The bioirrigation index can be estimated from the function get_irr_index, and the database Traits_Irr.

As not all MWTL taxa are represented in Traits_Db or Traits_irr, we use information on closely related species at the nearest taxonomic level, to also provide values for those taxa that are not represented. It suffices to pass the taxonomic information to function get_Db_index or get_irr_index to achieve this.

The functions return the total bioturbation or bioirrigation index for all stations, and the average BPc or IPC for all taxa.

In the code below, first the Northsea data are selected; we select the data for 1995 only. Then the bioturbation and bioirrigation index are estimated based on these data.

MWTL1995 <- 
    get_density(data       = MWTL$density, 
                descriptor = list(station = station),
                subset     = year == 1995,
                taxon      = taxon,
                value      = data.frame(density, biomass)) 

MWTLDb <- 
  get_Db_index (data       = MWTL1995, 
                descriptor = station, 
                taxon      = taxon, 
                density    = density, 
                biomass    = biomass, 
                trait      = Traits_Db, 
                taxonomy   = Taxonomy)

MWTLIrr <- 
  get_irr_index(data       = MWTL1995,
                descriptor = station, 
                taxon      = taxon, 
                density    = density, 
                biomass    = biomass, 
                trait      = Traits_irr, 
                taxonomy   = Taxonomy)

Both functions return a list with several data.frames:

  • descriptor gives the total BPc or IPc value for the descriptor here stations.
  • taxon gives the average BPc or IPc value for the taxa
MWTLbpc            <- MWTLDb$descriptor
row.names(MWTLbpc) <- NULL

knitr::kable(head(MWTLbpc[order(MWTLbpc$BPc, decreasing=TRUE),], n=10),
             caption="10 stations with largest bioturbation potential",
             row.names=FALSE)
10 stations with largest bioturbation potential
station BPc
FRIESFT14 3280.705
WADDKT06 3259.593
FRIESFT13 3153.672
VOORDTA5 3140.940
WADDKT03 3003.240
NOORDWK50 2790.300
FRIESFT08 2644.216
WADDKT07 2589.554
OESTGDN08 2568.081
WADDKT04 2552.701 
TAXbpc             <- MWTLDb$taxon
row.names(TAXbpc)  <- NULL
knitr::kable(head(TAXbpc[order(TAXbpc$BPc, decreasing=TRUE),], n=10),
             caption="10 taxa with average largest bioturbation potential",
             row.names=FALSE)
10 taxa with average largest bioturbation potential
taxon BPc
Amphiura filiformis 549.47920
Callianassa 284.34819
Ensis leei 266.91010
Echinocardium 266.41715
Acrocnida brachiata 170.67965
Spisula subtruncata 133.51702
Magelona 126.64536
Upogebia deltaura 114.46170
Brissopsis lyrifera 103.86270
Ensis siliqua 91.17133 
MWTLipc            <- MWTLIrr$descriptor
row.names(MWTLipc) <- NULL
knitr::kable(head(MWTLipc[order(MWTLipc$IPc, decreasing=TRUE),], n=10),
             caption="10 stations with largest bioirrigation potential",
             row.names=FALSE)
10 stations with largest bioirrigation potential
station IPc
WADDKT06 1960.127
WADDKT04 1938.381
WADDKT03 1671.408
NOORDWK50 1598.257
WADDKT07 1534.931
VOORDTA5 1435.595
FRIESFT13 1193.032
OESTGDN11 1182.065
WADDKT02 1121.304
FRIESFT08 1061.197 
TAXipc             <- MWTLIrr$taxon
row.names(TAXipc)  <- NULL
knitr::kable(head(TAXipc[order(TAXipc$IPc, decreasing=TRUE),], n=10),
             caption="10 taxa with average largest bioirrigation potential",
             row.names=FALSE)
10 taxa with average largest bioirrigation potential
taxon IPc
Callianassa 246.06955
Upogebia deltaura 152.03843
Amphiura filiformis 151.56616
Echinocardium 127.36572
Chaetopterus variopedatus 121.69843
Ensis leei 108.72495
Magelona 95.75755
Brissopsis lyrifera 83.28731
Lanice conchilega 76.69979
Acrocnida brachiata 76.55803

There is one taxon for which NO bioturbation trait could be derived, and 3 taxa for which no bio-irrigation could be calculated

attributes(MWTLDb )$notrait  
## [1] "Entoprocta"
attributes(MWTLIrr)$notrait  
## [1] "Nemertea"        "Platyhelminthes" "Entoprocta"

After adding the coordinates for each station, (present in MWTL$stations), a map can be generated, where the BPc and IPc is used to color the dots.

par(mfrow=c(1,2))
BPCMWTL <- merge(MWTL$stations, 
                 MWTLbpc      , 
                 by          = 1)
IPCMWTL <- merge(MWTL$stations, 
                 MWTLipc      , 
                 by          = 1)
with (BPCMWTL, 
  map_key(x = x, y = y, colvar = BPc, 
          main = "BPc for 1995",
          contours = MWTL$contours, draw.levels = FALSE, 
          pch = 18, cex = 2))

with (IPCMWTL, 
  map_key(x = x, y = y, colvar = IPc, main = "IPc for 1995",
          contours = MWTL$contours, draw.levels = FALSE, 
          pch = 18, cex = 2))

References

R-packages and data sources

R Core Team (2022). R: A Language and Environment for Statistical Computing_. R Foundation for Statistical Computing, Vienna, Austria. https://www.R-project.org/.

Soetaert K (2021). plot3D: Plotting Multi-Dimensional Data_. R package version 1.4, https://CRAN.R-project.org/package=plot3D.

Soetaert K, Beauchard O (2024). R-package Btrait: Working with Biological density, taxonomy, and trait composition data. Netherlands Institute of Sea Research. Data product created under the European Marine Observation Data Network (EMODnet) Biology Phase IV.

Chamberlain S, Vanhoorne. B (2023). worrms: World Register of Marine Species (WoRMS) Client_. R package version 0.4.3, https://CRAN.R-project.org/package=worrms.

EMODnet Biology (2022) Full Occurrence Data and Parameters downloaded from the EMODnet Biology project, consulted on 2022-08-05.

Beauchard O, Brind’Amour A, Schratzberger M, Laffargue P, Hintzen NT, Somerfield PJ, Piet G (2021) A generic approach to develop a trait-based indicator of trawling-induced disturbance. Mar Ecol Prog Ser 675:35-52. https://doi.org/10.3354/meps13840

Olivier Beauchard, Murray S.A. Thompson, Kari Elsa Ellingsen, Gerjan Piet, Pascal Laffargue, Karline Soetaert, 2023. Assessing sea floor functional diversity and vulnerability. Marine Ecology Progress Serie v708, p21-43, https://www.int-res.com/abstracts/meps/v708/p21-43/

Clare, David S. / Bolam, Stefan G. / McIlwaine, Paul S.O. / Garcia, Clement / Murray, Joanna M. / Eggleton, Jacqueline D. (2022). Ten key biological traits of marine benthic invertebrates surveyed in Northwest Europe. Cefas, UK. V2. doi: https://doi.org/10.14466/CefasDataHub.123

Heip, C.H.R.; Basford, D.; Craeymeersch, J.A.; Dewarumez, J.-M.; Dorjes, J.; de Wilde, P.; Duineveld, G.; Eleftheriou, A.; Herman, P.M.J.; Kingston, K.; Niermann, U.; Kunitzer, A.; Rachor, E.; Rumohr, H.; Soetaert, K.; Soltwedel, T. (1992). Trends in biomass, density and diversity of North Sea macrofauna. ICES J. Mar. Sci./J. Cons. int. Explor. Mer 49: 13-22

L. Leewis, E.C. Verduin, R. Stolk, 2017. Eurofins AquaSense Macrozoobenthosonderzoek in de Rijkswateren met boxcorer, jaarrapportage MWTL 2015 : waterlichaam: Noordzee. Publicatiedatum: 31-03-2017, 75 p. Projectnummer Eurofins AquaSense: J00002105, Revisie 2; In opdracht van Ministerie van Infrastructuur en Milieu, Rijkswaterstaat Centrale Informatievoorziening (RWS, CIV)

Queiros, Ana M., Silvana N. R. Birchenough, Julie Bremner, Jasmin A. Godbold, Ruth E. Parker, Alicia Romero-Ramirez, Henning Reiss, Martin Solan, Paul J. Somerfield, Carl Van Colen, Gert Van Hoey, Stephen Widdicombe, 2013. A bioturbation classification of European marine infaunal invertebrates. Ecology and Evolution 3 (11), 3958-3985

Solan M, Cardinale BJ, Downing AL, Engelhardt KAM, Ruesink JL, Srivastava DS. 2004. Extinction and ecosystem function in the marine benthos. Science 306:1177–80.

Wilson, R. J., Speirs, D. C., Sabatino, A., and Heath, M. R. (2018). A synthetic map of the north-west European Shelf sedimentary environment for applications in marine science. Earth Sys. Sci. Data 10, 109–130. doi: 10.5194/essd-10-109-2018

A. Wrede, J.Beermann, J.Dannheim, L.Gutow, T.Brey, 2018. Organism functional traits and ecosystem supporting services – A novel approach to predict bioirrigation. Ecological indicators, 91, 737-743.

Appendix

Description of the MWTL dataset

contents of data.frame ‘stations’ from the MWTL data
name description format
x degrees longitude WGS84
y degrees latitude WGS84
contents of data.frame ‘density’ from the MWTL data
name description units
station station name
date sampling date, a string
taxon taxon name, checked by worms, and adjusted
density species total density individuals/m2
biomass species total ash-free dry weight gAFDW/m2
taxon.original original taxon name
contents of data.frame ‘abiotics’ from the MWTL data
name description units
depth water depth m
D50 Median grain size micrometer
mud mud fraction (<63 um) -
sand sand fraction (64 -2000 um) -
gravel gravel fraction (>2000 um) -
salinity salinity
porosity volumetric water content -
permeability permeability m2
POC particulate organic C in sediment %
TN total N in sediment %
surfaceCarbon particulate organic C in upper cm %
surfaceNitrogen total N in upper cm %
orbitalVelMean mean orbital velocity m/s
orbitalVelMax maximal orbital velocity m/s
tidalVelMean mean tidal velocity m/s
tidalVelMax maximal tidal velocity m/s
bedstress bed shear stress Pa
EUNIScode EUNIScode -
DRB swept area ratio for dredge m2/m2/year
OT swept area ratio for otter trawl m2/m2/year
SN swept area ratio for seines m2/m2/year
TBB swept area ratio for beam trawl m2/m2/year
sar swept area ratio (fisheries), DRB +OT +SN +TBB m2/m2/year
subsar swept area ratio (fisheries) > 2cm m2/m2/year
gpd gear penetration depth cm
contents of data.frame ‘sediment’ from the MWTL data
name description units
D50 median grain size, in micrometer micrometer
Silt Silt+Clay fraction (< 63 micrometer) in % %
contents of data.frame ‘fishing’ from the MWTL data
name description units
p0 proportion living ON sediment -
p0_5cm proportion living in upper 5 cm of the sediment, [0,1] -
p5_15cm proportion living in 5-15cm depth slice -
p15_30cm proportion living in 15-30cm depth slice -
p30cm proportion living in >30cm depth slice -
Age.at.maturity Age at maturity years
r rate of increase, estimated as 2.56/Age.at.maturity /year

Description of the NSBS dataset

contents of data.frame ‘stations’ from the NSBS data
name description format
x degrees longitude WGS84
y degrees latitude WGS84
contents of data.frame ‘density’ from the NSBS data
name description units
station station name
date sampling date, a string
taxon taxon name, checked by worms, and adjusted
density species total density individuals/m2
contents of data.frame ‘abiotics’ from the NSBS data
name description units
depth water depth m
D50 Median grain size micrometer
mud mud fraction (<63 um) -
sand sand fraction (64 -2000 um) -
gravel gravel fraction (>2000 um) -
salinity salinity
porosity volumetric water content -
permeability permeability m2
POC particulate organic C in sediment %
TN total N in sediment %
surfaceCarbon particulate organic C in upper cm %
surfaceNitrogen total N in upper cm %
orbitalVelMean mean orbital velocity m/s
orbitalVelMax maximal orbital velocity m/s
tidalVelMean mean tidal velocity m/s
tidalVelMax maximal tidal velocity m/s
bedstress bed shear stress Pa
EUNIScode EUNIScode -
DRB swept area ratio for dredge m2/m2/year
OT swept area ratio for otter trawl m2/m2/year
SN swept area ratio for seines m2/m2/year
TBB swept area ratio for beam trawl m2/m2/year
sar swept area ratio (fisheries), DRB +OT +SN +TBB m2/m2/year
subsar swept area ratio (fisheries) > 2cm m2/m2/year
gpd gear penetration depth cm
contents of data.frame ‘fishing’ from the NSBS data
name description units
p0 proportion living ON sediment -
p0_5cm proportion living in upper 5 cm of the sediment, [0,1] -
p5_15cm proportion living in 5-15cm depth slice -
p15_30cm proportion living in 15-30cm depth slice -
p30cm proportion living in >30cm depth slice -
Age.at.maturity Age at maturity years
r rate of increase, estimated as 4.165/Age.at.maturity /year

Traits and modalities in the NIOZ database Traits

Effect traits, modalities, values and scores in the NIOZ database
colname trait modality indic value score units
ET1.M1 Substratum depth distribution 0 1 0.00 1.00 cm
ET1.M2 Substratum depth distribution 0-5 1 2.50 0.75 cm
ET1.M3 Substratum depth distribution 5-15 1 10.00 0.50 cm
ET1.M4 Substratum depth distribution 15-30 1 22.50 0.25 cm
ET1.M5 Substratum depth distribution >30 1 30.00 0.00 cm
ET2.M1 Biodiffusion Null 2 0.00 0.00 -
ET2.M2 Biodiffusion Low 2 0.50 0.50 -
ET2.M3 Biodiffusion High 2 1.00 1.00 -
ET3.M1 Downward conveying Null 3 0.00 0.00 -
ET3.M2 Downward conveying Low 3 0.50 0.50 -
ET3.M3 Downward conveying High 3 1.00 1.00 -
ET4.M1 Upward conveying Null 4 0.00 0.00 -
ET4.M2 Upward conveying Low 4 0.50 0.50 -
ET4.M3 Upward conveying High 4 1.00 1.00 -
ET5.M1 Regeneration Null 5 0.00 0.00 -
ET5.M2 Regeneration Low 5 0.50 0.50 -
ET5.M3 Regeneration High 5 1.00 1.00 -
ET6.M1 Biodeposition Null 6 0.00 0.00 -
ET6.M2 Biodeposition Low 6 0.50 0.50 -
ET6.M3 Biodeposition High 6 1.00 1.00 -
ET7.M1 Bioerosion Null 7 0.00 0.00 -
ET7.M2 Bioerosion Low 7 0.50 0.50 -
ET7.M3 Bioerosion High 7 1.00 1.00 -
ET8.M1 Biostabilisation Null 8 0.00 0.00 -
ET8.M2 Biostabilisation Low 8 0.50 0.50 -
ET8.M3 Biostabilisation High 8 1.00 1.00 -
ET9.M1 Ventilation/Pumping Null 9 0.00 0.00 -
ET9.M2 Ventilation/Pumping Low 9 0.50 0.50 -
ET9.M3 Ventilation/Pumping High 9 1.00 1.00 -
ET10.M1 Burrow width None 10 0.00 0.00 mm
ET10.M2 Burrow width Narrow 10 5.00 0.33 mm
ET10.M3 Burrow width Intermediate 10 7.50 0.67 mm
ET10.M4 Burrow width Wide 10 12.50 1.00 mm
ET11.M1 Endo-3D structure type None 11 0.00 0.00 -
ET11.M2 Endo-3D structure type Chimney/Funnel 11 0.17 0.17 -
ET11.M3 Endo-3D structure type Tube 11 0.33 0.33 -
ET11.M4 Endo-3D structure type IJ-shaped burrow 11 0.50 0.50 -
ET11.M5 Endo-3D structure type UY-shaped burrow 11 0.67 0.67 -
ET11.M6 Endo-3D structure type Branched burrow 11 0.83 0.83 -
ET11.M7 Endo-3D structure type Anastomosed burrow 11 1.00 1.00 -
ET12.M1 Endo-3D structure depth None 12 0.00 0.00 cm
ET12.M2 Endo-3D structure depth 0-5 12 2.50 0.25 cm
ET12.M3 Endo-3D structure depth 5-15 12 10.00 0.50 cm
ET12.M4 Endo-3D structure depth 15-30 12 22.50 0.75 cm
ET12.M5 Endo-3D structure depth >30 12 30.00 1.00 cm
ET13.M1 Epi-3D structure type None 13 0.00 0.00 -
ET13.M2 Epi-3D structure type Mat 13 0.17 0.17 -
ET13.M3 Epi-3D structure type Mound 13 0.33 0.33 -
ET13.M4 Epi-3D structure type Tube/Tubular protrusion 13 0.50 0.50 -
ET13.M5 Epi-3D structure type Shell 13 0.67 0.67 -
ET13.M6 Epi-3D structure type Stalk/Feather 13 0.83 0.83 -
ET13.M7 Epi-3D structure type Protuberance 13 1.00 1.00 -
ET14.M1 Epi-3D structure extension None 14 0.00 0.00 -
ET14.M2 Epi-3D structure extension Isolated/Clumped 14 0.25 0.25 -
ET14.M3 Epi-3D structure extension Mat/Lawn 14 0.50 0.50 -
ET14.M4 Epi-3D structure extension Simple reef 14 0.75 0.75 -
ET14.M5 Epi-3D structure extension Complex reef 14 1.00 1.00 -
ET15.M1 Epi-3D structure size None 15 0.00 0.00 mm
ET15.M2 Epi-3D structure size <1 15 1.00 0.17 mm
ET15.M3 Epi-3D structure size 1-3 15 2.00 0.33 mm
ET15.M4 Epi-3D structure size 3-10 15 6.50 0.50 mm
ET15.M5 Epi-3D structure size 10-20 15 15.00 0.67 mm
ET15.M6 Epi-3D structure size 20-50 15 35.00 0.83 mm
ET15.M7 Epi-3D structure size >50 15 50.00 1.00 mm
Response traits, modalities, values and scores in the NIOZ database
colname trait modality indic value score units
RT1.M1 Body mass <0.001 1 0.00e+00 0.00 gADWt
RT1.M2 Body mass 0.001-0.010 1 0.00e+00 0.25 gADWt
RT1.M3 Body mass 0.010-0.100 1 5.00e-02 0.50 gADWt
RT1.M4 Body mass 0.100-1.000 1 5.00e-01 0.75 gADWt
RT1.M5 Body mass >1.000 1 5.00e+00 1.00 gADWt
RT2.M1 Body length <1 2 5.00e-01 0.00 mm
RT2.M2 Body length 1-3 2 2.00e+00 0.25 mm
RT2.M3 Body length 3-10 2 6.50e+00 0.50 mm
RT2.M4 Body length 10-20 2 1.50e+01 0.75 mm
RT2.M5 Body length 20-50 2 3.50e+01 1.00 mm
RT3.M1 Body resistance Very low 3 0.00e+00 1.00 -
RT3.M2 Body resistance Low 3 2.50e-01 0.75 -
RT3.M3 Body resistance Intermediate 3 5.00e-01 0.50 -
RT3.M4 Body resistance High 3 7.50e-01 0.25 -
RT3.M5 Body resistance Very high 3 1.00e+00 0.00 -
RT4.M1 Motility Sessile 4 0.00e+00 1.00 -
RT4.M2 Motility Tubicolous 4 3.30e-01 0.67 -
RT4.M3 Motility Crawler 4 6.70e-01 0.33 -
RT4.M4 Motility Swimmer 4 1.00e+00 0.00 -
RT5.M1 Burrowing/Sheltering depth 0 5 0.00e+00 1.00 cm
RT5.M2 Burrowing/Sheltering depth 0-5 5 2.50e+00 0.75 cm
RT5.M3 Burrowing/Sheltering depth 5-15 5 1.00e+01 0.50 cm
RT5.M4 Burrowing/Sheltering depth >15 5 2.00e+01 0.25 cm
RT6.M1 Feeding type Deposit feeder 6 0.00e+00 0.00 -
RT6.M2 Feeding type Suspension feeder 6 3.30e-01 0.33 -
RT6.M3 Feeding type Herbivore/Grazer 6 6.70e-01 0.67 -
RT6.M4 Feeding type Carnivore/Scavenger 6 1.00e+00 1.00 -
RT7.M1 Life span <1 7 5.00e-01 0.00 year
RT7.M2 Life span 1-3 7 2.00e+00 0.25 year
RT7.M3 Life span 3-10 7 6.50e+00 0.50 year
RT7.M4 Life span 10-20 7 1.50e+01 0.75 year
RT7.M5 Life span >20 7 3.00e+01 1.00 year
RT8.M1 Age at maturity <1 8 5.00e-01 0.00 year
RT8.M2 Age at maturity 1-3 8 2.00e+00 0.50 year
RT8.M3 Age at maturity 3-5 8 4.00e+00 1.00 year
RT9.M1 Reproductive frequency Sexual seasonal 9 0.00e+00 0.00 -
RT9.M2 Reproductive frequency Sexual continuous 9 1.00e+00 1.00 -
RT10.M1 Fertilisation Broadcasting 10 0.00e+00 0.00 -
RT10.M2 Fertilisation Spermcasting 10 5.00e-01 0.50 -
RT10.M3 Fertilisation Pairing 10 1.00e+00 1.00 -
RT11.M1 Annual fecundity <10e2 11 5.00e+01 0.00 -
RT11.M2 Annual fecundity 10e2-10e3 11 4.50e+02 0.20 -
RT11.M3 Annual fecundity 10e3-10e4 11 4.50e+03 0.40 -
RT11.M4 Annual fecundity 10e4-10e5 11 4.50e+04 0.60 -
RT11.M5 Annual fecundity 10e5-10e6 11 4.50e+05 0.80 -
RT11.M6 Annual fecundity >10e6 11 1.00e+06 1.00 -
RT12.M1 Offspring type Egg 12 0.00e+00 0.00 -
RT12.M2 Offspring type Larva 12 5.00e-01 0.50 -
RT12.M3 Offspring type Juvenile 12 1.00e+00 1.00 -
RT13.M1 Offspring size <0.1 13 5.00e-02 0.00 mm
RT13.M2 Offspring size 0.1-0.5 13 3.00e-01 0.33 mm
RT13.M3 Offspring size 0.5-1.5 13 1.00e+00 0.67 mm
RT13.M4 Offspring size 1.5-5 13 3.25e+00 1.00 mm
RT14.M1 Offspring protection None 14 0.00e+00 0.00 -
RT14.M2 Offspring protection Gel 14 2.50e-01 0.25 -
RT14.M3 Offspring protection Capsule 14 5.00e-01 0.50 -
RT14.M4 Offspring protection Burrying 14 7.50e-01 0.75 -
RT14.M5 Offspring protection Bearing/Brooding 14 1.00e+00 1.00 -
RT15.M1 Offspring development Planktotrophic 15 0.00e+00 0.00 -
RT15.M2 Offspring development Lecithotrophic 15 2.50e-01 0.25 -
RT15.M3 Offspring development Mixed planktotrophic 15 5.00e-01 0.50 -
RT15.M4 Offspring development Mixed lecithotrophic 15 7.50e-01 0.75 -
RT15.M5 Offspring development Internal 15 1.00e+00 1.00 -
RT16.M1 Offspring benthic stage duration Null 16 0.00e+00 0.00 days
RT16.M2 Offspring benthic stage duration <15 16 7.50e+00 0.25 days
RT16.M3 Offspring benthic stage duration 15-30 16 2.25e+01 0.50 days
RT16.M4 Offspring benthic stage duration 30-60 16 4.50e+01 0.75 days
RT16.M5 Offspring benthic stage duration >60 16 6.00e+01 1.00 days
RT17.M1 Offspring pelagic stage duration Null 17 0.00e+00 0.00 days
RT17.M2 Offspring pelagic stage duration <15 17 7.50e+00 0.25 days
RT17.M3 Offspring pelagic stage duration 15-30 17 2.25e+01 0.50 days
RT17.M4 Offspring pelagic stage duration 30-60 17 4.50e+01 0.75 days
RT17.M5 Offspring pelagic stage duration >60 17 6.00e+01 1.00 days

Traits and modalities in the CEFAS database Traits_cefas

Only the first part is shown (else the vignette does not build).

##            colname        trait modality indic value score units
## 1  sr_Less_than_10 Maximum size      <10     1     5   0.0    mm
## 2      sr_11_to_20 Maximum size    11-20     1    15   0.2    mm
## 3     sr_21_to_100 Maximum size   21-100     1    60   0.4    mm
## 4    sr_101_to_200 Maximum size  101-200     1   150   0.6    mm
## 5    sr_201_to_500 Maximum size  201-500     1   350   0.8    mm
## 6 sr_More_than_500 Maximum size     >500     1   750   1.0    mm

Traits and modalities in the bioturbation database Traits_Db

Effect traits, modalities, values and scores in Traits_Db
trait modality description
Ri 1 epifauna
Ri 2 surficial modifiers
Ri 3 upward and downward conveyors
Ri 4 biodiffusors
Ri 5 regenerators
Mi 1 organisms that live in fixed tubes
Mi 2 limited movement
Mi 3 slow, free movement through the sediment matrix
Mi 4 free movement, via burrow system
Fti S surficial modifiers
Fti B biodiffusors
Fti UC upward conveyors
Fti DC downward conveyors
Fti R regenerators
Fti E epifauna

Traits and modalities in the bioirrigation database Traits_irr

Effect traits, modalities, values and scores in Traits_irr
trait modality description
BT 1 epifauna, internal irrigation (e.g. siphons)
BT 2 open irrigation (e.g. U- or Y- shaped burrows)
BT 3 blind ended irrigation
ID 1 injection pocket 0-2cm
ID 2 injection pocket 2-5cm
ID 3 injection pocket 5-10cm
ID 4 injection pocket >10 cm depth
FT 1 surface filter feeders
FT 2 predators
FT 3 deposit feeders
FT 4 sub surface filter feeders