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Modelling invasive alien species distributions from digital biodiversity atlases. Model upscaling as a means of reconciling data at different scales

Authors

  • Arnald Marcer,

    Corresponding author
    1. CREAF (Centre de Recerca Ecològica i Aplicacions Forestals), Universitat Autònoma de Barcelona, Catalonia, Spain
    • Correspondence: Arnald Marcer, CREAF, Centre for Ecological Research and Forestry Applications, Autonomous University of Barcelona, Bellaterra E–08193, Spain.

      E-mail: arnald.marcer@uab.cat

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  • Joan Pino,

    1. CREAF (Centre de Recerca Ecològica i Aplicacions Forestals), Universitat Autònoma de Barcelona, Catalonia, Spain
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  • Xavier Pons,

    1. Departament de Geografia, Universitat Autònoma de Barcelona, Catalonia, Spain
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  • Lluís Brotons

    1. CREAF (Centre de Recerca Ecològica i Aplicacions Forestals), Universitat Autònoma de Barcelona, Catalonia, Spain
    2. Grup d'Ecologia del Paisatge, Àrea de Biodiversitat, Centre Tecnològic Forestal de Catalunya, Catalonia, Spain
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Abstract

Aim

There is a wealth of information on species occurrences in biodiversity data banks, albeit presence-only, biased and scarce at fine resolutions. Moreover, fine-resolution species maps are required in biodiversity conservation. New techniques for dealing with this kind of data have been reported to perform well. These fine-resolution maps would be more robust if they could explain data at coarser resolutions at which species distributions are well represented. We present a new methodology for testing this hypothesis and apply it to invasive alien species (IAS).

Location

Catalonia, Spain.

Methods

We used species presence records from the Biodiversity data bank of Catalonia to model the distribution of ten IAS which, according to some recent studies, achieve their maximum distribution in the study area. To overcome problems inherent with the data, we prepared different correction treatments: three for dealing with bias and five for autocorrelation. We used the MaxEnt algorithm to generate models at 1-km resolution for each species and treatment. Acceptable models were upscaled to 10 km and validated against independent 10 km occurrence data.

Results

Of a total of 150 models, 20 gave acceptable results at 1-km resolution and 12 passed the cross-scale validation test. No apparent pattern emerged, which could serve as a guide on modelling. Only four species gave models that also explained the distribution at the coarser scale.

Main conclusions

Although some techniques may apparently deliver good distribution maps for species with scarce and biased data, they need to be taken with caution. When good independent data at a coarser scale are available, cross-scale validation can help to produce more reliable and robust maps. When no independent data are available for validation, however, new data gathering field surveys may be the only option if reliable fine-scale resolution maps are needed.

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