Modelization of the Current and Future Habitat Suitability of Rhododendron ferrugineum Using Potential Snow Accumulation

Abstract

Mountain areas are particularly sensitive to climate change. Species distribution models predict important extinctions in these areas whose magnitude will depend on a number of different factors. Here we examine the possible impact of climate change on the Rhododendron ferrugineum (alpenrose) niche in Andorra (Pyrenees). This species currently occupies 14.6 km2 of this country and relies on the protection afforded by snow cover in winter. We used high-resolution climatic data, potential snow accumulation and a combined forecasting method to obtain the realized niche model of this species. Subsequently, we used data from the high-resolution Scampei project climate change projection for the A2, A1B and B1 scenarios to model its future realized niche model. The modelization performed well when predicting the species’s distribution, which improved when we considered the potential snow accumulation, the most important variable influencing its distribution. We thus obtained a potential extent of about 70.7 km2 or 15.1% of the country. We observed an elevation lag distribution between the current and potential distribution of the species, probably due to its slow colonization rate and the small-scale survey of seedlings. Under the three climatic scenarios, the realized niche model of the species will be reduced by 37.9–70.1 km2 by the end of the century and it will become confined to what are today screes and rocky hillside habitats. The particular effects of climate change on seedling establishment, as well as on the species’ plasticity and sensitivity in the event of a reduction of the snow cover, could worsen these predictions.

The draft genome assembly of Rhododendron delavayi Franch. var. delavayi

“…Of these Chinese species, approximately 80% are endemic [ 1 , 2 ]. Because of the adaptability of this genus to different environments, species such as R. arboreum and R. ferrugineum have been used to investigate the effects of different environmental factors on plant growth, development, and domestication [ 3 – 6 ].…”

Likelihood of changes in forest species suitability, distribution, and diversity under future climate: The case of Southern Europe

“…Summarizing, results are widely dependent on: (i) the reliability and accuracy of species occurrence data; (ii) the significance of the environmental variables selected; (iii) the quality of related data; and (iv) the parameterization or configuration of the applied models (Chakraborty et al., ; Nenzén & Araújo, ; Thuiller, ; Thuiller, Lafourcade, Engler, & Araújo, ). Given that all above elements cause a large variability in the predictions (Cheaib et al., ; Pearson et al., ; Thuiller et al., ), the Ensemble Forecasting approach has been developed and widely adopted (Araujo & New, ; Heikkinen et al., ; Komac, Esteban, Trapero, & Caritg, ; Marmion, Parviainen, Luoto, Heikkinen, & Thuiller, ). This approach combines individual SDM predictions to provide consensus predictions (Capinha & Anastácio, ), enabling more robust evaluations, that is, addressing the uncertainty related to SDMs.…”

A comparison in species distribution model performance of succulents using key species and subsets of environmental predictors

“…As well as TSS and AUC (ROC) scores calculated for each of the individual models, the TSS and AUC scores of the ensemble models were compared to determine the relative best performing model and identify whether the additional parameters used in SDMs 2–5 increased the predictive accuracy of SDM 1 (bioclim‐only predictors). As discussed in Komac et al ( 2016 ), the AUC provides us with a measure of the performance of ordinal score models and a threshold measure of accuracy (Thuiller et al, 2005 ), while the TSS score provides us with a measure of evaluative performance which has all the advantages associated with the Cohen’s kappa statistic (Cohen, 1968 ) but is not sensitive to prevalence (Allouche et al, 2006 ). Ensemble models from the five SDM scenarios were initially projected on to the world to generate a continuous map showing variations in the suitability/probability of occurrence for the two species of interest.…”