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Mapping from heterogeneous biodiversity monitoring data sources

Abstract

Field monitoring can vary from simple volunteer opportunistic observations to professional standardised monitoring surveys, leading to a trade-off between data quality and data collection costs. Such variability in data quality may result in biased predictions obtained from species distribution models (SDMs). We aimed to identify the limitations of different monitoring data sources for developing species distribution maps and to evaluate their potential for spatial data integration in a conservation context. Using Maxent, SDMs were generated from three different bird data sources in Catalonia, which differ in the degree of standardisation and available sample size. In addition, an alternative approach for modelling species distributions was applied, which combined the three data sources at a large spatial scale, but then downscaling to the required resolution. Finally, SDM predictions were used to identify species richness and high quality areas (hotspots) from different treatments. Models were evaluated by using high quality Atlas information. We show that both sample size and survey methodology used to collect the data are important in delivering robust information on species distributions. Models based on standardized monitoring provided higher accuracy with a lower sample size, especially when modelling common species. Accuracy of models from opportunistic observations substantially increased when modelling uncommon species, giving similar accuracy to a more standardized survey. Although downscaling data through a SDM approach appears to be a useful tool in cases of data shortage or low data quality and heterogeneity, it will tend to overestimate species distributions. In order to identify distributions of species, data with different quality may be appropriate. However, to identify biodiversity hotspots high quality information is needed.

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Acknowledgments

This study was funded by the EU FP7 SCALES project (“Securing the Conservation of biodiversity across Administrative Levels and spatial, temporal and Ecological scales”; project #226852).

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Correspondence to Francesc Sardà-Palomera.

Appendix

Appendix

See Appendix Table 4; Figs. 8 and 9.

Table 4 List of farmland bird species used to calibrate species distribution models from different monitoring data
Fig. 8
figure8

Predictions of common farmland bird species richness in Catalonia obtained from different quality treatments (ATLAS, BBS, LIST, OBS and COARSE) from the whole sample size. Maps are show for a species distribution and b high quality areas

Fig. 9
figure9

Predictions of uncommon farmland bird species richness in Catalonia obtained from different quality treatments (ATLAS, BBS, LIST, OBS and COARSE) from the whole sample size. Maps are show for a species distribution and b high quality areas

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Sardà-Palomera, F., Brotons, L., Villero, D. et al. Mapping from heterogeneous biodiversity monitoring data sources. Biodivers Conserv 21, 2927–2948 (2012). https://doi.org/10.1007/s10531-012-0347-6

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Keywords

  • Citizen science
  • Farmland Birds
  • Maxent
  • Occurrence data bias
  • Species distribution models