Hi colleagues,
Would be happy to receive any comments/hints/suggestions concerning my analyses of BDACI data on railway construction and farmland breeding birds.

The objective
Clarify if railway construction has an impact (pos. or neg.) on farmland breeding bird populations. Of course, the conclusions have to be restricted taxonomically and geographically, but the fewer restrictions, the better.
Background: The “general opinion” is that railways and roads are bad for wildlife, but the underlying processes are unclear and real BDACI studies are extremely rare.

The setting
A 190 km new railway in northern Sweden was built in bits and pieces during 2001-2009 (a 2 billion € project).  Due to the schedule of the construction, each impact site was affected differently in terms of timing and duration of the “treatment” classes/levels Before, During and After. “Before” is the “virgin” state before construction started, “During” is the period when the embankment, bridges and tunnels were built (lots of heavy machinery and people!) and “After” is when the railway lays ready waiting for train traffic to start in autumn 2010. During the “After” period, the rails, cables and control systems are installed and tested, but this is “peanuts” compared to the real construction phase. Two impact sites had reached the “After” phase when the study started. All control sites were assigned the “Before” state during the whole study period.
The landscape where the railway was built is dominated by forests, but also includes patches of farmland. During nine successive breeding seasons (2002-2010), I mapped territories of 13 farmland bird species on all 13 patches of farmland affected by the railway (total acreage 1000 ha) and 6 comparable control patches (total acreage 800 ha). All patches (sites) are of different size and shape, but the very same area has been covered each year, and by the same observers (n=2). There are no missing data. Basically, the response variable is the number of territories per site and year, but for statistical reasons, I was advised to transform the response variable into densities (territories/100 ha) by dividing the (yearly) number within a site with the area of the site.  Several of the species occur in low densities and zero counts are common, even throughout the entire nine year period within a site.
I think the design I’ve used is very similar to many medical studies where successive treatments are applied and monitored with repeated measurements. The difference is that my “experiment” is highly unbalanced, because there was no way to control when and where the various parts of the railway were built.

The analyses so far
I have followed the Zuur et al. (2009) protocol to select, per species, the most parsimonious model starting from the full lme model containing fixed effects treatment (3 levels), year (for a general trend) and treatment-year interactions (the variance structure of these variables had been adjusted when necessary). Prior to the fixed effect part, the best random effects part was chosen among “site” (site was considered mandatory) and “year nested within site” (for trends within sites). Data from all years and all sites were included in the modelling process (even if the species was never counted in the site). Please note that the year variable describes the year within the study period, not the number of years a certain treatment had been in place (for the site).
This is an example of the full model (under library = nlme):
P7<-lme(resp~treatment*year,random=~year|site,method="REML",data=bot,na.action=na.omit)

The results
For ten out of 13 species, the most parsimonious model did not include a treatment effect. For the remaining three species, two showed a positive effect and one a temporary negative effect. The treatment effects on the three species make sense ecologically, but overall, the results are not fully intuitive. For less parsimonious models, the (insignificant) treatment effects for the ten species were equally often positive as negative. Please remember that all affected sites were included, making sampling and power issues obsolete.

The questions/doubts

·         What are the effects of the transformation from numbers of territories to densities (per site) on the analyses and the conclusions? (the sites vary a lot in size!)

·         Did I choose the right model approach, or are there better ways to do it? (most likely there are, probably in the realm of medical statistics)

·         Should sites where the species never occurred during the study period be excluded from the model selection process? (non-existing birds cannot be affected)

·         Did I use the right random effect alternatives?

·         Should/can I include a treatment-duration trend in the random effect part (time nested within treatment within site)?

·         Does the absence of treatment effect in the most parsimonious model mean that there was no significant effect of railway construction? (again: all impact sites were included)

·         Is there a multivariate (multi-species) alternative?

Thanks a lot in advance for giving this a thought.
Cheers,

Adriaan “Adjan” de Jong
Wildlife, Fish, and Environmental Studies
Swedish University of Agricultural Sciences

PS. We will continue the studies in 2013-2015 to monitor the possible effects of train traffic. Train traffic started with a limited timetable on parts of the track in autumn 2010, but full scale traffic has only recently begun. After that we will have four treatment classes.




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