In:
Diversity and Distributions, Wiley, Vol. 24, No. 7 ( 2018-07), p. 978-990
Abstract:
We investigate whether (1) environmental predictors allow to delineate the distribution of discrete community types at the continental scale and (2) how data completeness influences model generalization in relation to the compositional variation of the modelled entities. Location Europe. Methods We used comprehensive datasets of two community types of conservation concern in Europe: acidophilous beech forests and base‐rich fens. We computed community distribution models ( CDM s) calibrated with environmental predictors to predict the occurrence of both community types, evaluating geographical transferability, interpolation and extrapolation under different scenarios of sampling bias. We used generalized dissimilarity modelling ( GDM ) to assess the role of geographical and environmental drivers in compositional variation within the predicted distributions. Results For the two community types, CDM s computed for the whole study area provided good performance when evaluated by random cross‐validation and external validation. Geographical transferability provided lower but relatively good performance, while model extrapolation performed poorly when compared with interpolation. Generalized dissimilarity modelling showed a predominant effect of geographical distance on compositional variation, complemented with the environmental predictors that also influenced habitat suitability. Main conclusions Correlative approaches typically used for modelling the distribution of individual species are also useful for delineating the potential area of occupancy of community types at the continental scale, when using consistent definitions of the modelled entity and high data completeness. The combination of CDM s with GDM further improves the understanding of diversity patterns of plant communities, providing spatially explicit information for mapping vegetation diversity and related habitat types at large scales.
Type of Medium:
Online Resource
ISSN:
1366-9516
,
1472-4642
DOI:
10.1111/ddi.2018.24.issue-7
Language:
English
Publisher:
Wiley
Publication Date:
2018
detail.hit.zdb_id:
2020139-4
detail.hit.zdb_id:
1443181-6
SSG:
12
