dat.maire2019 {metadat}

Studies on Temporal Trends in Fish Community Structures in French Rivers

Description

Results from studies examining changes in the abundance of fish species in French rivers.

Usage

dat.maire2019

Format

The object is a list containing a data frame called dat that contains the following columns and distance matrix called dmat:

site	character	study site
station	character	sampling station at site
site_station	character	site and station combined
s1	numeric	Mann-Kendal trend statistic for relative abundance of non-local species
vars1	numeric	corresponding sampling variance (corrected for temporal autocorrelation)
s2	numeric	Mann-Kendal trend statistic for relative abundance of northern species
vars2	numeric	corresponding sampling variance (corrected for temporal autocorrelation)
s3	numeric	Mann-Kendal trend statistic for relative abundance of non-native species
vars3	numeric	corresponding sampling variance (corrected for temporal autocorrelation)
const	numeric	constant value of 1

Details

The dataset includes the results from 35 sampling stations (at 11 sites along various French rivers) examining the abundance of various fish species over time (i.e., over 19-37 years, all until 2015). The temporal trend in these abundance data was quantified in terms of Mann-Kendal trend statistics, with positive values indicating monotonically increasing trends. The corresponding sampling variances were corrected for the temporal autocorrelation in the data (Hamed & Rao, 1998).

The distance matrix dmat indicates the distance of the sampling stations (1-423 river-km). For stations not connected through the river network, a high distance value of 10,000 river-km was set (effectively forcing the spatial correlation to be 0 for such stations).

The dataset can be used to illustrate a meta-analysis allowing for spatial correlation in the outcomes.

Concepts

ecology, climate change, spatial correlation

Author(s)

Wolfgang Viechtbauer, [e-mail], https://www.metafor-project.org

Source

Maire, A., Thierry, E., Viechtbauer, W., & Daufresne, M. (2019). Poleward shift in large-river fish communities detected with a novel meta-analysis framework. Freshwater Biology, 64(6), 1143–1156. ⁠https://doi.org/10.1111/fwb.13291⁠

References

Hamed, K. H., & Rao, A. R. (1998). A modified Mann-Kendall trend test for autocorrelated data. Journal of Hydrology, 204(1-4), 182–196. ⁠https://doi.org/10.1016/S0022-1694(97)00125-X⁠

Examples

### copy data into 'dat' and examine data
dat <- dat.maire2019$dat
dat[-10]

### copy distance matrix into 'dmat' and examine first 5 rows/columns
dmat <- dat.maire2019$dmat
dmat[1:5,1:5]

## Not run: 

### load metafor package
library(metafor)

### fit a standard random-effects model ignoring spatial correlation
res1 <- rma.mv(s1, vars1, random = ~ 1 | site_station, data=dat)
res1

### fit model allowing for spatial correlation
res2 <- rma.mv(s1, vars1, random = ~ site_station | const, struct="SPGAU",
               data=dat, dist=list(dmat), control=list(rho.init=10))
res2

### add random effects for sites and stations within sites
res3 <- rma.mv(s1, vars1, random = list(~ 1 | site/station, ~ site_station | const), struct="SPGAU",
               data=dat, dist=list(dmat), control=list(rho.init=10))
res3

### likelihood ratio tests comparing the models
anova(res1, res2)
anova(res2, res3)

### profile likelihood plots for model res2
profile(res2, cline=TRUE)

### effective range (river-km for which the spatial correlation is >= .05)
sqrt(3) * res2$rho

### note: it was necessary to adjust the starting value for rho in models
### res2 and res3 so that the optimizer does not get stuck in a local maximum
profile(res2, rho=1, xlim=c(0,200), steps=100)


## End(Not run)