7.1 Article I
Bobrowski, M.; Gerlitz, L.; Schickhoff, U. (2017) Modelling the potential distribution of Betula utilis in the Himalaya. Global Ecology and Conservation. 11, 69-83. doi:
10.1016/j.gecco.2017.04.003.
Abstract: Developing sustainable adaptation pathways under climate change condi-tions in mountain regions requires accurate prediccondi-tions of treeline shifts and future distribution ranges of treeline species. Here, we model for the first time the potential distribution of Betula utilis, a principal Himalayan treeline species, to provide a basis for the analysis of future range shifts. Our target species Betula utilis is widespread at alpine treelines in the Himalayan mountains, the distribution range extends across the Himalayan mountain range. Our objective is to model the potential distribution of B. utilis in relation to current climate conditions. We generated a data set of 590 oc-currence records and used 24 variables for ecological niche modelling. We calibrated generalized linear models using the Akaike Information Criterion (AIC) and evaluated model performance using threshold-independent the Area Under the Curve (AUC), and threshold-dependent (TSS, True Skill Statistics) characteristics as well as visual assessments of projected distribution maps. We found two temperature-related (Mean Temperature of the Wettest Quarter, Temperature Annual Range) and three precipitation-related variables (Precipitation of the Coldest Quarter, Average Precip-itation of March, April and May and PrecipPrecip-itation Seasonality) to be useful for predict-ing the potential distribution of B. utilis. All models had high predictive power (AUC
≥ 0.98 and TSS ≥ 0.89). The projected suitable area in the Himalayan mountains var-ies considerably, with most extensive distribution in the western and central Himala-yan region. A substantial difference between potential and real distribution in the eastern Himalaya points to decreasing competitiveness of B. utilis under more oceanic conditions in the eastern part of the mountain system. A comparison between the veg-etation map of Schweinfurth (1957) and our current predictions suggests that B. utilis does not reach the upper elevational limit in vast areas of its potential distribution range due to anthropogenically caused treeline depressions. This study underlines the significance of accuracies of current environmental niche models for species dis-tribution modelling under climate change scenarios. Analysing and understanding the environmental factors driving the current distribution of B. utilis is crucial for the pre-diction of future range shifts of B. utilis and other treeline species, and for deriving appropriate climate change adaptation strategies.
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Maria Bobrowski: Study design, climate data compilation, data analysis, model-ling, writing and editing
Lars Gerlitz: Climate data compilation, discussion on interpretation of the results
Udo Schickhoff: Species occurrence data compilation, discussion on interpreta-tion of the results and editing
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Bobrowski, M.; Schickhoff, U. (2017) Why input matters: Selection of climate data sets for modelling the potential distribution of a treeline species in the Himalayan re-gion. Ecological Modelling. 359, 92-102. doi: 10.1016/j.ecolmodel.2017.05.021.
Abstract: Betula utilis is a major constituent of alpine treeline ecotones in the western and central Himalayan region. The objective of this study is to analyse for the first time the performance of different climatic predictors in modelling the potential dis-tribution of B. utilis in the subalpine and alpine belts of the Himalayan region. Using generalized linear models (GLM) we aim at examining climatic factors controlling the species distribution under current climate conditions. We evaluate the predictive ability of climate data derived from different statistical methods GLMs were created using least correlated bioclimatic variables derived from two different climate data sets: 1) interpolated climate data (i.e., WORLDCLIM; Hijmans et al., 2005), and 2) quasi-mechanistical statistical downscaling (i.e., CHELSA; Karger et al., 2016). Model accu-racy was evaluated using threshold-independent (Area Under the Curve) and thresh-old-dependent (True Skill Statistics) measures. Although there were no significant differences between the models in AUC, we found highly significant differences (p ≤ 0.01) in TSS. We conclude that models based on variables of CHELSA climate data had higher predictive power, whereas models using WORLDCLIM climate data consistently overpredicted the potential suitable habitat for B. utilis. Although climatic variables of WORLDCLIM are widely used in modelling species distribution, our results suggest to treat them with caution when topographically complex regions like the Himalaya are in focus. Unmindful usage of climatic variables for environmental niche models po-tentially causes misleading projections.
Maria Bobrowski: Study design, climate data compilation, data analysis, model-ling, writing and editing
Udo Schickhoff: Discussion on interpretation of the results and editing
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7.3 Article III
Bobrowski, M.; Bechtel, B.; Böhner, J.; Oldeland, J.; Weidinger, J.; Schickhoff, U. (2018) Application of thermal and phenological land surface parameters for improving eco-logical niche models of Betula utilis in the Himalayan region. Remote Sensing. 10, 814;
doi:10.3390/rs10060814.
Abstract: Modelling ecological niches across vast distribution ranges in remote, high mountain regions like the Himalayas faces several data limitations, in particular no-navailability of species occurrence data and fine-scale environmental information of sufficiently high quality. Remotely sensed data provide key advantages such as fre-quent, complete, and long-term observations of land surface parameters with full spa-tial coverage. The objective of this study is to evaluate modelled climate data as well as remotely sensed data for modelling the ecological niche of Betula utilis in the sub-alpine and sub-alpine belts of the Himalayan region covering the entire Himalayan arc.
Using generalized linear models (GLMs), we aim at testing factors controlling the spe-cies distribution under current climate conditions. We evaluate the additional predic-tive capacity of remotely sensed variables, namely remotely sensed topography and vegetation phenology data (phenological traits), as well as the capability to substitute bioclimatic variables from downscaled numerical models by remotely sensed annual land surface temperature parameters. The best performing model utilized bioclimatic variables, topography, and phenological traits, and explained over 69% of variance, while models exclusively based on remotely sensed data reached 65% of explained variance. In summary, models based on bioclimatic variables and topography com-bined with phenological traits led to a refined prediction of the current niche of B.
utilis, whereas models using solely climate data consistently resulted in overpredic-tions. Our results suggest that remotely sensed phenological traits can be applied ben-eficially as supplements to improve model accuracy and to refine the prediction of the species niche. We conclude that the combination of remotely sensed land surface tem-perature parameters is promising, in particular in regions where sufficient fine-scale climate data are not available.
Maria Bobrowski: Study design, climate data compilation, data analysis, model-ling, writing and editing
Benjamin Bechtel: Land Surface Temperature data compilation, discussion on model outputs, interpretation and editing
Jürgen Böhner: Editing
Jens Oldeland: Editing and statistical advise
Johannes Weidinger: Modis Land Cover data compilation
Udo Schickhoff: Discussion on interpretation of the results and editing
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