Modelling the number of invasive plants in Croatia according to the habitats and bioclimatic factors: importance of the quality of the data.
Plant species, defined as invasive, at the territory of Croatia (Boršić et al., 2008; Mitić et al., 2008; Nikolić, 2013)are in research focus due to their potential and real threat to the overall biodiversity. Flora Croatica Database (FCD http://hirc.botanic.hr/fcd) is a comprehensive database including available information about Croatian flora from various sources differing in their research focuses, intensity, spatial extent etc. Database contains data extracted from: published literature, unpublished field observations and from herbarium collections. Across the country, there is substation difference in research effort, with majority of protected areas and some coastal parts being researched far more above the average.
The purpose of this analysis was to predict number of invasive plant species across the whole territory of the Republic of Croatia using available information on invasive plant species occurrences. Main question was: How the result of predicted number of invasive plants differs in relation to quality of data used for building model? Is it possible to detect threshold value of research intensity below which model become less precise?
Information of invasive plants was extracted from FCD. Analysis was made in R using diverse packages for data manipulation and aggregation and SAGA GIS ver. 2.0.8. functionalities run via RSAGA package. Point information on presence of invasive plant species was transformed into variable „invasion“ – number of unique plant species detected at analysed spatial resolution. As surrogates for research effort we used information on total number of all vascular plant records in FCD per spatial units. As predictors we have used classes made using gower clustering algorithm at 10 km resolution, comprising data on habitats and bioclimatic variables as well as the bio-geographic regions and total effort per spatial units.
We prepared five datasets to test model performance. Datasets differed in range of used data based on estimated research effort per each gower class. Used datasets were as followed: first dataset with effort per class between 0.80-0.95 percentile; second between 0.65-0.95; third 0.50-0.95; fourth 0.35-0.95 and fifth ranging from 0.15-0.95 quantile. For model validation we used only the best researched grid cells in whole territory of Croatia (>95quantile total effort).
Predictions were obtained by fitting generalized linear model defining Poisson distribution as error distribution and log link via glm function(stats package). The structure of spatial autocorrelation in the data was modelled with fitted variogram model of covariance structure in the data (gstat package).
Best model (according to root mean squared error (RMSE)) was the one where model fitted using quadrants with sampling effort above the mean in each gower class. Least accurate model was one with small number of quadrants of highest research effort (0.80-0.95). The greatest relative difference in predictions of best and worst model was detected in areas with lowest research effort.
Boršić, I., Milović, M., Dujmović, I., Bogdanović, S., Cigić, P., Rešetnik, I., Nikolić, T., Mitić, B., 2008. Preliminary check-list of invasive alien plant species(ias) in Croatia. Nature Croatica 17, 55-71.
Mitić, B., Boršić, I., Dujmović, I., Bogdanović, S., Milović, M., Cigić, P., Rešetnik, I., Nikolić, T., 2008. Alien flora of Croatia: proposals for standards in terminology, criteria and related database. Natura Croatica, 73-90.
Nikolić, T.e., 2013. Flora Croatica Database. Allochtonous plants. URL: http://hirc.botanic.hr/fcd/InvazivneVrste/Search.aspx. Department of Botany, Faculty of science, University of Zagreb.