Distribution modelling of vegetation types based on area frame survey data

Abstract

Aim Many countries lack informative, high‐resolution, wall‐to‐wall vegetation or land cover maps. Such maps are useful for land use and nature management, and for input to regional climate and hydrological models. Land cover maps based on remote sensing data typically lack the required ecological information, whereas traditional field‐based mapping is too expensive to be carried out over large areas. In this study, we therefore explore the extent to which distribution modelling (DM) methods are useful for predicting the current distribution of vegetation types (VT) on a national scale. Location Mainland Norway, covering ca. 324,000 km2. Methods We used presence/absence data for 31 different VTs, mapped wall‐to‐wall in an area frame survey with 1081 rectangular plots of 0.9 km2. Distribution models for each VT were obtained by logistic generalised linear modelling, using stepwise forward selection with an F‐ratio test. A total of 116 explanatory variables, recorded in 100 m × 100 m grid cells, were used. The 31 models were evaluated by applying the AUC criterion to an independent evaluation dataset. Results Twenty‐one of the 31 models had AUC values higher than 0.8. The highest AUC value (0.989) was obtained for Poor/rich broadleaf deciduous forest, whereas the lowest AUC (0.671) was obtained for Lichen and heather spruce forest. Overall, we found that rare VTs are predicted better than common ones, and coastal VTs are predicted better than inland ones. Conclusions Our study establishes DM as a viable tool for spatial prediction of aggregated species‐based entities such as VTs on a regional scale and at a fine (100 m) spatial resolution, provided relevant predictor variables are available. We discuss the potential uses of distribution models in utilizing large‐scale international vegetation surveys. We also argue that predictions from such models may improve parameterisation of vegetation distribution in earth system models.

Improving the representation of high-latitude vegetation distribution in dynamic global vegetation models

“…The distribution models (DMs) for 31 vegetation types (VTs) obtained by Horvath et al (2019) using generalized linear models (GLMs, with logit link and binomial errors, i.e. logistic regression) were used for this study.…”

“…The DMs were obtained by using wall-to-wall data for 116 environmental predictors from six groups (topographic, geological, proximity, climatic, snow and land cover), gridded to a spatial resolution of 100 m × 100 m (Table 1) as predictors. Important predictors were selected by an automated stepwise forward-selection procedure for each of the 31 VTs individually; thus each final model is built upon only a narrow selection of important predictors (Horvath et al, 2019). All DMs were evaluated using an independent evaluation dataset and by calculating the area under the receiver operator curve (AUC), a thresholdindependent measure of model performance commonly used in DM (see Horvath et al, 2019, for details).…”

“…The most common use of DM in ecology is for prediction of species distributions (Henderson et al, 2014), but DM methods have proved valuable also for prediction of targets at higher levels of bio-, geo-or eco-diversity (i.e. vegetation types and land-cover types) (Ullerud et al, 2016;Horvath et al, 2019;Simensen et al, 2020). DM methods are inherently static, in contrast to the DGVMs (Snell et al, 2014).…”

Analyzing indoor mycobiomes through a large-scale citizen science study of houses from Norway

“…Moreover, data for 116 environmental variables, related to geology, topography, climate and hydrology, were also explored. They were the explanatory variables analyzed in a recent study modelling the vegetation types in Norway [51], and were kindly provided by their authors. The contribution of the numerical variables, 46 from this dataset plus the 6 previously extracted from WorldClim, were evaluated by principal component analysis (PCA; Additional le 1: Fig.…”

Diversity and distribution of landscape types in Norway

“…We described commonly occurring landscape elements (i.e. ecosystem types and landforms) for each minor types based on three sources: 1) the statistical analyses in Simensen, Halvorsen & Erikstad (2020); 2) data from distribution modelling of ecosystem types (Horvath et al 2019; Simensen, Horvath et al 2020); and 3) expert assessments by the authors.…”