\title{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.}
the object is a list containing a data frame called \code{dat} that contains the following columns and distance matrix called \code{dmat}:
\bold{site}         \tab \code{character} \tab study site \cr
\bold{station}      \tab \code{character} \tab sampling station at site \cr
\bold{site_station} \tab \code{character} \tab site and station combined \cr
\bold{s1}           \tab \code{numeric}   \tab mann-kendal trend statistic for relative abundance of non-local species \cr
\bold{vars1}        \tab \code{numeric}   \tab corresponding sampling variance (corrected for temporal autocorrelation) \cr
\bold{s2}           \tab \code{numeric}   \tab mann-kendal trend statistic for relative abundance of northern species \cr
\bold{vars2}        \tab \code{numeric}   \tab corresponding sampling variance (corrected for temporal autocorrelation) \cr
\bold{s3}           \tab \code{numeric}   \tab mann-kendal trend statistic for relative abundance of non-native species \cr
\bold{vars3}        \tab \code{numeric}   \tab corresponding sampling variance (corrected for temporal autocorrelation) \cr
\bold{const}        \tab \code{numeric}   \tab constant value of 1
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 \code{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.
maire, a., thierry, e., viechtbauer, w., & daufresne, m. (2019). poleward shift in large-river fish communities detected with a novel meta-analysis framework. \emph{freshwater biology}, \bold{64}(6), 1143--1156. \verb{https://doi.org/10.1111/fwb.13291}
hamed, k. h., & rao, a. r. (1998). a modified mann-kendall trend test for autocorrelated data. \emph{journal of hydrology}, \bold{204}(1-4), 182--196. \verb{https://doi.org/10.1016/s0022-1694(97)00125-x}
### copy distance matrix into 'dmat' and examine first 5 rows/columns
### fit a standard random-effects model ignoring spatial correlation
res1 <- rma.mv(s1, vars1, random = ~ 1 | site_station, data=dat)
### fit model allowing for spatial correlation
res2 <- rma.mv(s1, vars1, random = ~ site_station | const, struct="spgau",
### add random effects for sites and stations within sites
res3 <- rma.mv(s1, vars1, random = list(~ 1 | site/station, ~ site_station | const), struct="spgau",
### likelihood ratio tests comparing the models
### profile likelihood plots for model res2
### effective range (river-km for which the spatial correlation is >= 0.05)
### 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
ecology, climate change, spatial correlation