To draw patterns from observational, e.g. collections, data is increasingly common in large part from digitization and citizen science initiatives. Organisms’ geographical arrangement and their biological attributes are thus demonstrable, in timely fashion for investigations related to global change.
However, observational data are in many instances only as good as their associated metadata, whether that comprises explanatory variables of location and date (spatiotemporal), associated environment (climate, land-use, soil), or biotic interactions (symbionts). To answer questions in global change requires inclusion of those variables in models, and at as comparable spatiotemporal resolutions as possible.
One common method builds from temporally static variables (e.g., WorldClim) to then model responses. Massive information is possible to extract from such analyses, and especially when followed-up by projecting to future response.
Simultaneous are other upcoming methods, such as linking increasingly available metadata at finer spatiotemporal resolutions. Responses are modelled via temporally dynamic covariates to determine what is actually driving the observed ecological change.
The goals of this research have concerned quantifying change in fungal fruiting, especially from climate, and as discernable through the use of observational data. Due to a longer spatiotemporal history of recording fruit body presences, the focus has been on central to northern Europe, including both EU and non-EU countries (important in terms of metadata availability).
The seasonality of fruiting (phenology), patterning of assemblages and diversity (macroecology), and ecophysiology (climate niches) of fruiting fungi were sequentially investigated, coincident with increasingly available, temporally more dynamic, metadata. In total, climate, land-use, nitrogen, NDVI, soil carbon, and weather conditions were included in the analyses as possible, with a shift from temporally static aggregations to more dynamic representations of fungal fruiting and the associated external conditions.
Results/Conclusions
Climate impacted timing of fungal fruiting, especially temperature. Phenology helped explain when a fungus was found, but to better understand where, assemblage and diversity patterns filled in gaps. Fruiting patterns followed gradients in climate, nitrogen deposition, and NDVI, with differences by ectomycorrhizal or saprotrophic modes. Diversity patterns linked well to land-use, especially forest type. While climate helped model realized niches, the recording day conditions most accurately revealed optima and ranges in fruiting conditions.
Here the power of collections data is via the integration of metadata, detecting the cues of climate and other global change components at large scales. There is high potential for other geographical locations to build to – and beyond – via contemporaneous initiatives in collections data.