Phytoplankton

Chla

Chlorophyll a (μg/l)

0.62

All, but mainly NGIG & CBGIG

<0.001

1447

0.7078

0.7872

0.0162

Linear regression model using log10 TP and log10 Chla data


PTI

Phytoplankton Trophic Index

0.50

All, but mainly NGIG & CBGIG

<0.001

618

1.2471

0.7068

0.0285

Linear regression model using log10 TP data


FTI

Functional Traits Index (mean of SPI and MFGI)

0.49

CB/N/M GIGs

<0.001

228

0.0336

0.7002

0.0475

Linear regression model using log10 TP and FTI data


J′

Evenness

0.15

NGIG

<0.001

716

−0.1351

−0.3930

0.0344

Linear regression model using log10 TP and evenness data, J′ multiplied by −1 to reverse slope


Cyano bloom intensity

Cyanobacteria biovolume (mg/l)

0.40

All, but mainly NGIG & CBGIG

<0.001

1447

1.2165

0.6355

0.0203

Linear regression model using log10 TP and log10 cyanobiovolume data

Macrophytes

ICM

Intercalibration Common Metric

0.55

CB/NGIG

<0.001

1504

2.2963

0.7401

0.0174

Linear regression model using log10 TP and ICM data


EI

Ellenberg Index of taxonomic composition

0.47

CB/NGIG

<0.001

1504

2.2133

0.6871

0.0188

Linear regression model using log10 TP and Ellenberg data


C
_{max}

Maximum growing depth of submerged macrophytes

0.31^{a}

Mainly CB/NGIG + Italy

<0.001

478

n.a.^{a}

n.a.^{a}

n.a.^{a}

Linear regression model using log10 TP and C
_{max} data

Benthic invertebrates

MMI

Multimetric Index for intercalibration in CBGIG

0.36

CBGIG

<0.001

193

−0.3605

−0.6026

0.0577

Linear regression model using log10 TP and MMI data

Fish

ELFI

European Lake Fish Index (multimetric of BPUE, CPUE and OMNI)

0.13

All GIGs

<0.001

444

−0.1161

−0.3587

0.0444

Linear regression model using log10 TP and ELFI


NLFI

Nordic Lake Fish Index (multimetric of BPUE, CPUE and OMNI)

0.46

NGIG

<0.001

155

−0.4148

−0.6797

0.0593

Linear regression model using log10 TP and NLFI
