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Table 1 Results of the linear mixed models testing the effects of deforestation and the upstream–downstream gradient on the functional indices in stream and river ecosystems

From: Functional responses to deforestation in fish communities inhabiting neotropical streams and rivers

Ecosystem

Functional index

Independent variables

Slope

P

R2M

R2C

Moran’s I P

Streams

Functional richness

Deforestation gradient

− 0,02

0.481

0.39

0.39

0.3

Upstream–downstream gradient

0,11

 < 0.001

Functional divergence

Deforestation gradient

0,00

0.91

0.09

0.24

0.19

Upstream–downstream gradient

− 0,01

0.076

Functional evenness

Deforestation gradient

− 0,01

0.029

0.14

0.14

0.17

Upstream–downstream gradient

0,00

0.64

Functional identity along PCoA axis 1

Deforestation gradient

− 0,01

0.002

0.25

0.25

0.18

Upstream–downstream gradient

0,00

0.947

Functional identity along PCoA axis 2

Deforestation gradient

− 0,01

0.303

0.18

0.18

0.55

Upstream–downstream gradient

0,01

0.019

Functional identity along PCoA axis 3

Deforestation gradient

0,00

0.269

0.09

0.09

0.25

Upstream–downstream gradient

− 0,01

0.155

Rivers

Functional richness

Deforestation gradient

− 0,08

 < 0.001

0.53

0.54

0.5

Upstream–downstream gradient

0,00

0.722

Functional divergence

Deforestation gradient

− 0,01

0.013

0.12

0.2

0.1

Upstream–downstream gradient

0,01

0.008

Functional evenness

Deforestation gradient

0,00

0.791

0

0.66

0.26

Upstream–downstream gradient

0,00

0.766

Functional identity along PCoA axis 1

Deforestation gradient

0,01

 < 0.001

0.09

0.64

0.61

Upstream–downstream gradient

0,00

0.107

Functional identity along PCoA axis 2

Deforestation gradient

0,00

0.9

0.36

0.36

 < 0.001

Upstream–downstream gradient

0,00

0.4

Functional identity along PCoA axis 3

Deforestation gradient

0,00

0.043

0.68

0.68

0.62

Upstream–downstream gradient

0,01

 < 0.001

  1. For each index (response variable), a specific mixed model was built with basin identity included as a random effect. Marginal R2 (R2M) accounts for the variance explained only by fixed variables, while conditional R2 (R2C) accounts for the variance explained by the entire model. Significant effects (P < 0.05) were highlighted in bold. Moran’s I P values indicate if the residuals of the models show patterns of spatial autocorrelation (i.e., P < 0.05)