Abstract
Vegetation mapping using field surveys is expensive. Distribution modelling, based on sample surveys, might overcome this challenge. We tested if models trained from sample surveys could be used to predict the distribution of vegetation types in neighbourhood areas, and how reliable the spatial transferability was. We also tested whether we should use ecological dissimilarity or spatial distance to foresee modelling performance. Maximum entropy models were run for three vegetation types based on a vegetation map within a mountain range. Environmental variables were selected backwards, model complexity was kept low. The models are based on points from a small part of each study site, transferred into the entire sites, and then tested for performance. Environmental distance was tested using principle component analysis. All models had high uncorrected AUC values. The ability to predict presences correctly was low. The ability to predict absences correctly was high. The ability to transfer the distribution model depended on environmental distance, not spatial distance.
Highlights
IntroductionVegetation types represent more or less stable entities of plant communities characterized by physiognomy, plant species composition, indicator species, or a combination of all three, and they are influenced by a number of ecological processes through time and space [10] [11]
This study has demonstrated several aspects of caution that needs to be handled when Distribution modelling (DM) of vegetation types, trained with survey data and fitted with MaxEnt, are used for spatial transferability:
Area frame surveys of vegetation types, where sample plots are assumed to be representative for a larger spatial domain, should be used with caution in transferability studies using DM
Summary
Vegetation types represent more or less stable entities of plant communities characterized by physiognomy, plant species composition, indicator species, or a combination of all three, and they are influenced by a number of ecological processes through time and space [10] [11]. Each vegetation type reflects a unique ecological space that sums up the ecological processes which structure the pattern of vegetation at the spatial scale of the applied mapping system [12]. A vegetation map represents a spatial generalization of the vegetation structure, classified according to predefined types that intend to mirror the underlying ecological processes at a given spatial scale [6]
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