Transferability of 34 red-listed peatland plant species models across boreal vegetation zone

•We transferred SDM for red-listed plant species across three boreal zones.•Environmental characteristics differed between the vegetation zones.•National and regional models produced a very good or excellent accuracy.•The transferability of the regional models over boreal regions was not as good.•Normalizing of environmental variables may improve the transferability. Red-listed species are negatively affected by habitat degradation and fragmentation. They usually have small populations and may be affected by local extinctions which makes species distribution modeling (SDM) challenging. Although SDM has been used extensively for biodiversity protection and regional land-use planning, the transferability of SDM between regions is still at the developmental level. We show a first attempt to demonstrate model transferability for red-listed plant species over boreal regions. We modeled the distribution of 34 red-listed boreal peatland plant species at national and regional levels, using multiple streams of environmental data in Finland. The objectives were: (1) to evaluate how environmental characteristics explaining species distribution differ between three regions covering five vegetation zones (subarctic, northern, middle, southern boreal, and hemi-boreal vegetation zones); (2) to assess the performance of one national and three regional species distribution models (SDM: northern, middle, and southern regions); and (3) to test whether the regional models can be transferred to other regions and discuss alternative methods to improve transferability. The maximum entropy (maxent) algorithm was employed to predict suitable habitats for the assessed species. An SDM performance was measured with the area under the receiver operative characteristics (AUC), true skill statistics (TSS), and the continuous Boyce index (CBI). Three conclusions are relevant. First, the environmental variables explaining species distribution differed significantly (p