Using niche-based models to improve the sampling of rare species

Because data on rare species usually are sparse, it is important to have efficient ways to sample additional data. Traditional sampling approaches are of limited value for rare species because a very large proportion of randomly chosen sampling sites are unlikely to shelter the species. For these species, spatial predictions from niche-based distribution models can be used to stratify the sampling and increase sampling efficiency. New data sampled are then used to improve the initial model. Applying this approach repeatedly is an adaptive process that may allow increasing the number of new occurrences found. We illustrate the approach with a case study of a rare and endangered plant species in Switzerland and a simulation experiment. Our field survey confirmed that the method helps in the discovery of new populations of the target species in remote areas where the predicted habitat suitability is high. In our simulations the model-based approach provided a significant improvement (by a factor of 1.8 to 4 times, depending on the measure) over simple random sampling. In terms of cost this approach may save up to 70% of the time spent in the field.

Are niche-based species distribution models transferable in space?

Aim To assess the geographical transferability of niche-based species distribution models fitted with two modelling techniques. Location Two distinct geographical study areas in Switzerland and Austria, in the subalpine and alpine belts. Methods Generalized linear and generalized additive models (GLM and GAM) with a binomial probability distribution and a logit link were fitted for 54 plant species, based on topoclimatic predictor variables. These models were then evaluated quantitatively and used for spatially explicit predictions within (internal evaluation and prediction) and between (external evaluation and prediction) the two regions. Comparisons of evaluations and spatial predictions between regions and models were conducted in order to test if species and methods meet the criteria of full transferability. By full transferability, we mean that: (1) the internal evaluation of models fitted in region A and B must be similar; (2) a model fitted in region A must at least retain a comparable external evaluation when projected into region B, and vice-versa; and (3) internal and external spatial predictions have to match within both regions. Results The measures of model fit are, on average, 24% higher for GAMs than for GLMs in both regions. However, the differences between internal and external evaluations (AUC coefficient) are also higher for GAMs than for GLMs (a difference of 30% for models fitted in Switzerland and 54% for models fitted in Austria). Transferability, as measured with the AUC evaluation, fails for 68% of the species in Switzerland and 55% in Austria for GLMs (respectively for 67% and 53% of the species for GAMs). For both GAMs and GLMs, the agreement between internal and external predictions is rather weak on average (Kulczynski's coefficient in the range 0.3-0.4), but varies widely among individual species. The dominant pattern is an asymmetrical transferability between the two study regions (a mean decrease of 20% for the AUC coefficient when the models are transferred from Switzerland and 13% when they are transferred from Austria). Main conclusions The large inter-specific variability observed among the 54 study species underlines the need to consider more than a few species to test properly the transferability of species distribution models. The pronounced asymmetry in transferability between the two study regions may be due to peculiarities of these regions, such as differences in the ranges of environmental predictors or the varied impact of land-use history, or to species-specific reasons like differential phenotypic plasticity, existence of ecotypes or varied dependence on biotic interactions that are not properly incorporated into niche-based models. The lower variation between internal and external evaluation of GLMs compared to GAMs further suggests that overfitting may reduce transferability. Overall, a limited geographical transferability calls for caution when projecting niche-based models for assessing the fate of species in future environments.

Thymic stromal lymphopoietin plays divergent roles in murine models of atopic and nonatopic airway inflammation.

BACKGROUND: Thymic stromal lymphopoietin (TSLP) is a cytokine primarily produced by epithelial cells, which has been shown to be a potent inducer of T-helper 2 (Th2)-type responses. However, TSLP has pleiotropic effects upon immune cells, and although extensively studied in the context of atopic asthma, its relevance as a therapeutic target and its role in the pathogenesis of nonatopic asthma remains unknown. We sought to investigate the role of TSLP in atopic, nonatopic and viral-induced exacerbations of pulmonary inflammation. METHODS: Using stringently defined murine models of atopic, nonatopic and virally exacerbated forms of pulmonary inflammation, we compared inflammatory responses of C57BL/6 wild-type (WT) and TSLP receptor-deficient (TSLPR KO) mice. RESULTS: Thymic stromal lymphopoietin receptor (TSLPR) signaling was crucial for the development of atopic asthma. Specifically, TSLPR signaling to lung recruited CD4+ T cells enhanced eosinophilia, goblet cell hyperplasia, and overall inflammation within the airways. In contrast, the absence of TSLPR signaling was associated with strikingly exaggerated pulmonary neutrophilic inflammation in a nonatopic model of airway inflammation. The inflammation was associated with excessive levels of interleukin (IL)-17A in the lungs, indicating that TSLP negatively regulates IL-17A. In addition, in a model of influenza-induced exacerbation of atopic airway inflammation, the absence of TSLPR signaling also led to exaggerated neutrophilic inflammation. CONCLUSION: Thymic stromal lymphopoietin plays divergent roles in the pathogenesis of atopic and nonatopic asthma phenotypes by either enhancing Th2 responses or curtailing T-helper 17 responses. These findings raise important caveats for the design of therapeutic interventions targeting TSLP in asthma.