Data error propagation in stacked bioclimatic envelope models

Stacking is the process of overlaying inferred species potential distributions for multiple species based on outputs of bioclimatic envelope models(BEMs).The approach can be used to investigate patterns and processes of species richness.If data limitations on individual species distributions are inevitable,but how do they affect inferences of patterns and processes of species richness?We investigate the influence of different data sources on estimated species richness gradients in China.We fitted BEMs using species distributions data for 334 bird species obtained from(1)global range maps,(2)regional checklists,(3)museum records and surveys,and(4)citizen science data using presence-only(Mahalanobis distance),presence-background(MAXENT),and presence–absence(GAM and BRT)BEMs.Individual species predictions were stacked to generate species richness gradients.Here,we show that different data sources and BEMs can generate spatially varying gradients of species richness.The environmental predictors that best explained species distributions also differed between data sources.Models using citizen-based data had the highest accuracy,whereas those using range data had the lowest accuracy.Potential richness patterns estimated by GAM and BRT models were robust to data uncertainty.When multiple data sets exist for the same region and taxa,we advise that explicit treatments of uncertainty,such as sensitivity analyses of the input data,should be conducted during the process of modeling.

Modelling the variation of demersal fi sh distribution in Yellow Sea under climate change

Climate change can aff ect fi sh individuals or schools,and consequently the fi sheries.Studying future changes of fi sh distribution and abundance helps the scientifi c management of fi sheries.The dynamic bioclimate envelope model(DBEM)was used to identify the“environmental preference profi les”of the studied species based on outputs from three Earth system models(ESMs).Changes in ocean conditions in climate change scenarios could be transformed by the model into those in relative abundance and distribution of species.Therefore,the distributional response of 17 demersal fi shes to climate change in the Yellow Sea could be projected from 1970 to 2060.Indices of latitudinal centroid(LC)and mean temperature of relative abundance(MTRA)were used to represent the results conducted by model.Results present that 17 demersal fi sh species in the Yellow Sea show a trend of anti-poleward shift under both low-emission scenario(RCP 2.6)and high-emission scenario(RCP 8.5)from 1970 to 2060,with the projected average LC in three ESMs shifting at a rate of-1.17±4.55 and-2.76±3.82 km/decade,respectively,which is contrary to the previous projecting studies of fi shes suggesting that fi shes tend to move toward higher latitudes under increased temperature scenarios.The Yellow Sea Cold Water Mass could be the major driver resulting in the shift,which shows a potential signifi cance to fi shery resources management and marine conservation,and provides a new perspective in fi sh migration under climate change.

ndustrial eco-efficiency and its spatial-temporal differentiation in China

The aim of this paper is to study the spatialtemporal differentiation of industrial eco-efficiency in China. Using methods based on the data envelopment analysis （DEA） model and exploratory spatial data analysis （ESDA） and data from 1985, 1995, 2005, and 2008 of 30 provinces in China, the spatial-temporal pattern changes in industrial eco-efficiency are discussed. The results show that： first, the patterns of industrial eco-efficiency are dominated by clustering of relatively low efficiency provinces; second, spatial relationships between the industrial eco-efficiencies of different provinces changed slightly throughout the period and the provinces persistently exhibit spatial concentration of relatively low industrial eco-efficiency; finally, there is an obvious trend in the polarization of industrial eco-efficiency, i.e., the higher level spatial units are concentrated in eastern China, and the lower level spatial units are mainly in western and central China. （ESDA）