Abstract
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 < 0.05) between the three regions. Second, the internal measure of accuracy measured as cross-validation of AUC, TSS, and the CBI was quite similar in both the national and regional models, which indicates that realistic species distribution maps could be generated from all models. Last, the external measure of accuracy (i.e. transferability) in the regional models was lower than the internal measure of accuracy, which indicates that a good regional model could not automatically ensure good performance when transferred to another region. To improve the transferability of the regional models, we suggest the normalizing of environmental variable values. The data-driven evaluation of red-listed plant species provides an approach that can be used in biodiversity and nature conservation.
Highlights
The biodiversity conservation goal has gained increasing consider ation globally due to the significant loss of floral and faunal diversity (IPBES, 2018)
topographic wetness index (TWI) was less varied in the three modeling regions which was visible in the Bhattacharyya distance (Table 1) and proba bility density function (Fig. 3)
Tree volumes distribution differences were visible in the probability density function, for instance, the northern region had a spike at zero percent of their pine tree volume distribution (Fig. 3)
Summary
The biodiversity conservation goal has gained increasing consider ation globally due to the significant loss of floral and faunal diversity (IPBES, 2018). Every ninth species was considered red-listed, the major risk being the degradation of suitable habitats caused by disturbance, construction, forest management activities, the reduction of old-growth forests, and climate change (Hyvarinen et al, 2019). To halt the biodiversity loss, international targets are being updated (CBD, 2020) or have just been set (i.e. European Union (EU) Biodiversity Strategy for 2030, European Commission, 2020) to protect significant amounts of the land and the sea area, or to restore biodiverse areas with high ecosystem services potential (e.g. biomass). The identification of potential nature protection sites and prediction of suitable habitats for red-listed species are core elements of future biodiversity protection and ecosystem restoration
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