We are currently trying to estimate temporal trends of species (e.g. butterflies or other insects), with very different spatial coverage of cells over time (before 2012, we had many "professional" recorders, while after that more and more citizen science data came in).
Thus, data quality before 2012 is great, and after (say) 2018 it´s great again. What should be done with the data collected between 2012-2017?
What is your recommendation on how to proceed?
In our recent paper, we applied kernel smoothing, a method from spatial point pattern analysis (SPPA), to quantify historical and recent grasshopper range shifts in Germany. The main advantage of SPPA is that it reduces potential biases attributable to differences in sampling effort across space and time and thus is a well-suited tool to analyze opportunistic distribution data. For more details, please see our attached paper: Poniatowski, D., Beckmann, C., Löffler, F., Münsch, T., Helbing, F., Samways, M.J. & T. Fartmann (2020): Relative impacts of land-use and climate change on grasshopper range shifts have changed over time. Global Ecology and Biogeography. doi: 10.1111/geb.13188
One important - and usually neglected - approach should always be landscape and land use changes which can be relatively easily traced back for at least 200 years. However, these are usually covered by totally different disciplines including history of socio-economics, agriculture and technology. Therefore ecologists and conservationists don't take them into account. Quantifying the former share of fields, mown meadows, pastures (with cattle and/or sheep, resp.) allows a coarse picture of how widespread certain species MAY have been. Visiting pre-industrial landscapes such as Transsylvania gives impressions about how our landscape looked like 200 years ago before common pastures with dominating cattle as grazers were turned into private property and then drained, ploughed and intensified. And when formerly grazed semi-open woodland was turned into dark forests ....
Of course this cannot tell you the former density of a particular species, but it might give you at least a framework. Extrapolating according to such assumptions is probably not conventional science but a definite soft skill e.g. when compiling Red Lists and estimating long-term trends. And of course you cannot cover recent trends caused by climate warming.
Many thanks Herbert Nickel and Franz for your answers so far! Both approaches are indeed very interesting. I´ll keep you updated on progress.
Best wishes
Christoph
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