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Impact of biased sampling effort and spatial uncertainty of locations on models of plant invasion patterns in Croatia

Abstract

Very frequently biological databases are used for analysing distribution of different taxa. These databases are usually the result of variable sampling effort and location uncertainty. The aim of this study was to test the influence of geographically biased sampling effort and spatial uncertainty of locations on models of species richness. For this purpose, we assessed the pattern of invasive alien plants in Croatia using the Flora Croatica Database. The procedure applied in testing of the sensitivity of models consisted of sample area sectioning into coherent ecological classes (hereinafter Gower classes). The quadrants were then ranked based on sampling effort per class. This resulted in creation of models using varying numbers of quadrants whose performance was tested with independent validation points. From this the best fitting model was determined, as well as a threshold of sampling effort. The data from quadrants with sampling effort below the threshold were considered too unreliable for modelling. Further, spatial uncertainty was simulated by adding a random term to each location and re-running the models using the simulated locations. Biased sampling effort and spatial uncertainty of locations had similar effects on model performance in terms of the magnitude of the affected area, as in both cases 7% of the quadrants showed statistically significant deviations in alien plant species richness. The model using only on the quandrants with the highest 35% quantile sampling effort best balanced the sampling effort per quadrant and overall geographical coverage. It predicted a mean number of 3.2 invasive alien plant species per quadrant for the Alpine region, 5.2 for the Continental, 6.1 for the Mediterranean and 5.3 for the Pannonian region of Croatia. Thus, the observational databases can be considered as a reliable source for species richness models and, most likely, for other types of species distribution models, given that their limitations are accounted for in the data selection process. In order to obtain precise estimates of species richness it is required to sample the whole range of ecological conditions of the study area.

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Acknowledgements

This study was prepared under Grant 119-1191193-1227 of the Croatian Ministry of Science, Education and Sports. We would like to thank to two referees for improving previous versions of the manuscript.

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Correspondence to Andreja Radović.

Appendices

Appendix A

Raster data sets used to model alien plant species richness: (a) total number of floristic records in Flora Croatica Database (FCD)—transferred to 4 zones according to summary statistics; (b) Gower classes determined by means of clustering on similarity matrix obtained via Gower algorithm using information on three dominant habitat classes and climate variables; (c) biogeographic regions of Croatia according to State Institute of Nature Protection (EEA 1998).

figure a

Appendix B

(a) Distribution of grids across sampling effort zones (1-white: lowest effort; 4-black: highest effort) per Gower class.

figure b

(b) Residual variograms from regressions on environmental predictors from the model that includes random errors in coordinates according to the reported spatial uncertainty (left panel) and the final model prepared with D65 plus validation points (right panel).

figure c

Appendix C

Invasive alien plant species richness predictions of final and full model: (a) differences of predictions across biogeographic regions; figure shows how final and full model predict differently regarding biogeographic regions—parallel box plots of differences in predicted invasive alien plant species richness; (b) number of quadrants with significantly different predictions of invasive alien plant species richness per biogeographic region (significantly higher (BH) and significantly lower (BL) at 0.05 level of significance).

figure d

Appendix D

Invasive alien plant species richness predictions of final and full model: (a) absolute differences of predictions across Gower classes; (b) Number of quadrants with significantly lower (BL) and significantly higher (BH) predictions of final model per region at 0.05 level per Gower classes (only Gower classes with detected significant differences are presented in plot); (c) Distribution of grids across sampling effort zones (1-white: lowest effort; 4-black: highest effort) per Gower class; number of quadrants per biogeographic region used in each dataset. For the datasets D85, D65, D50, D35, D15, and D0, quantile sampling effort was referring to each Gower class, not to the entire biogeographic region.

In these figures we presented how our models (final and full model) predicts differently regarding Gower classes (a) revealing those habitat/climate classes where differences are most pronounced as Gower classes 2, 3, 6, 11 and 14. Figure b present the direction of significant differences in predictions. Only in Gower class 6 the final model predicted more often significantly lower than significantly higher species richness than the full model.

figure e
(c)
Dataset Alpine Continental Mediterranean Pannonian
D85 34 89 67 25
D65 (= best model) 95 265 194 92
D50 143 412 290 128
D35 193 540 381 179
D15 266 728 501 229
D0 310 920 636 278
Validation (best 5% across Country) 33 35 45 0
TOTAL 343 955 681 278
D65 + validation points (= final model) 147 349 291 104

Appendix E

Invasive alien plant species richness predictions of final and full model across research effort classes: (a) absolute differences of predictions across sampling effort zones revealing that final models predicted higher numbers and that the range of prediction differences stayed constant over all four research effort zones; (b) species richness predicted by final vs full model; (c) Number of quadrants with significantly lower (BL) and significantly higher (BH) predictions of final model at 0.05 level per sampling effort zones (coding for sampling effort zones: Highest sampling effort (> 3rd quartile)—zone 4 up to lowest sampling effort (< 1st quartile)—zone 1.

figure f

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Radović, A., Schindler, S., Rossiter, D. et al. Impact of biased sampling effort and spatial uncertainty of locations on models of plant invasion patterns in Croatia. Biol Invasions 20, 3527–3544 (2018). https://doi.org/10.1007/s10530-018-1793-1

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Keywords

  • Biodiversity databases
  • Balkans
  • Data quality
  • Regression kriging
  • Spatial analysis