Vegetation NDVI Linked to Temperature and Precipitation in the Upper Catchments of Yellow River

Vegetation in the upper catchment of Yellow River is critical for the ecological stability of the whole watershed. The dominant vegetation cover types in this region are grassland and forest, which can strongly influence the eco-environmental status of the whole watershed. The normalized difference vegetation index (NDVI) for grassland and forest has been calculated and its daily correlation models were deduced by Moderate Resolution Imaging Spectroradiometer products on 12 dates in 2000, 2003, and 2006. The responses of the NDVI values with the inter-annual grassland and forest to three climatic indices (i.e., yearly precipitation and highest and lowest temperature) were analyzed showing that, except for the lowest temperature, the yearly precipitation and highest temperature had close correlations with the NDVI values of the two vegetation communities. The value of correlation coefficients ranged from 0.815 to 0.951 (p <0.01). Furthermore, the interactions of NDVI values of vegetation with the climatic indicators at monthly interval were analyzed. The NDVI of vegetation and three climatic indices had strong positive correlations (larger than 0.733, p <0.01). The monthly correlations also provided the threshold values for the three climatic indictors, to be used for simulating vegetation growth grassland under different climate features, which is essential for the assessment of the vegetation growth and for regional environmental management

Evaluating the effectiveness of improved workmanship quality on the airtightness of Dutch detached houses

Increasing the airtightness of buildings can contribute in coming to energy neutral buildings. This paper evaluates two possible measures: modest technical improvements and coaching of construction teams. Beforehand, the specific leakage rate of 44 detached houses was measured using a blower door test and by means of statistics, the most pressing problems were determined. An educational session was developed to explain construction workers the relevance of and their own influence on building airtight houses. The effectiveness of the technical improvements and the education was assessed by evaluating 14 new houses. This evaluation showed a significantly improved airtightness

Testing the efficacy of downscaling in species distribution modelling : a comparison between MaxEnt and Favourability Function models

Statistical downscaling is used to improve the knowledge of spatial distributions from broad–scale to fine–scale maps with higher potential for conservation planning. We assessed the effectiveness of downscaling in two commonly used species distribution models: Maximum Entropy (MaxEnt) and the Favourability Function (FF). We used atlas data (10 x 10 km) of the fire salamander Salamandra salamandra distribution in southern Spain to derive models at a 1 x 1 km resolution. Downscaled models were assessed using an independent dataset of the species’ distribution at 1 x 1 km. The Favourability model showed better downscaling performance than the MaxEnt model, and the models that were based on linear combinations of environmental variables performed better than models allowing higher flexibility. The Favourability model minimized model overfitting compared to the MaxEnt model

Incorporating knowledge uncertainty into species distribution modelling

Monitoring progress towards global goals and biodiversity targets require reliable descriptions of species distributions over time and space. Current gaps in accessible information on species distributions urges the need for integrating all available data and knowledge sources, and intensifying cooperations to more effectively support global environmental governance. For many areas and species groups, experts can constitute a valuable source of information to fill the gaps by offering their knowledge on species-environment interactions. However, expert knowledge is always subject to uncertainty, and incorporating that into species distribution mapping poses a challenge. We propose the use of the dempster–shafer theory of evidence (DST) as a novel approach in this field to extract expert knowledge, to incorporate the associated uncertainty into the procedure, and to produce reliable species distribution maps. We applied DST to model the distribution of two species of eagle in Spain. We invited experts to fill in an online questionnaire and express their beliefs on the habitat of the species by assigning probability values for given environmental variables, along with their confidence in expressing the beliefs. We then calculated evidential functions, and combined them using Dempster’s rules of combination to map the species distribution based on the experts’ knowledge. We evaluated the performances of our proposed approach using the atlas of Spanish breeding birds as an independent test dataset, and further compared the results with the outcome of an ensemble of conventional SDMs. Purely based on expert knowledge, the DST approach yielded similar results as the data driven SDMs ensemble. Our proposed approach offers a strong and practical alternative for species distribution modelling when species occurrence data are not accessible, or reliable, or both. The particular strengths of the proposed approach are that it explicitly accounts for and aggregates knowledge uncertainty, and it capitalizes on the range of data sources usually considered by an expert

There is a trade‑off between forest productivity and animal biodiversity in Europe

While forest productivity and biodiversity are expected to be correlated, prioritizing either forest productivity or biodiversity can result in different management. Spatial quantification of the congruence between areas suitable for either one can inform planning. Here we quantify the relationship between net primary productivity of European forests and biodiversity of mammals, birds, reptiles, amphibians, and butterflies both separately and in combination, and map their spatial congruence. We used richness maps obtained by stacking species distribution models for these animal species, and average net primary production from 2000 to 2012 using moderate resolution imaging spectroradiometer (MODIS) data. We tested how biodiversity and primary productivity are correlated and quantified the spatial congruence of these two sources. We show the areas where high or low productivity co-occur with high or low biodiversity using a quantile-based overlay analysis. Productivity was positively correlated to overall biodiversity and mammal, herptile and butterfly biodiversity, but biodiversity of birds showed a weak negative correlation. There were no significant differences in the strength of relationship across species groups, while herptiles had stronger relationships with productivity compared to other groups. Overlap analysis revealed significant spatial overlap between productivity and biodiversity in all species groups, except for birds. High value areas for both productivity and biodiversity in all species groups, except birds, co-occurred in the Mediterranean and temperate regions. The areas with high biodiversity of birds are mainly found in the boreal areas of Europe, while for all other species groups these areas are mostly located on the Iberian Peninsula and the Balkan ranges. Based on the presented maps, areas where regulating wood production activities to conserve species can be identified. But the maps also help to identify areas where either biodiversity or productivity is high and focusing on just one aspect is more straightforward

Corresponding Mitochondrial DNA and Niche Divergence for Crested Newt Candidate Species

Genetic divergence of mitochondrial DNA does not necessarily correspond to reproductive isolation. However, if mitochondrial DNA lineages occupy separate segments of environmental space, this supports the notion of their evolutionary independence. We explore niche differentiation among three candidate species of crested newt (characterized by distinct mitochondrial DNA lineages) and interpret the results in the light of differences observed for recognized crested newt species. We quantify niche differences among all crested newt (candidate) species and test hypotheses regarding niche evolution, employing two ordination techniques (PCA-env and ENFA). Niche equivalency is rejected: all (candidate) species are found to occupy significantly different segments of environmental space. Furthermore, niche overlap values for the three candidate species are not significantly higher than those for the recognized species. As the three candidate crested newt species are, not only in terms of mitochondrial DNA genetic divergence, but also ecologically speaking, as diverged as the recognized crested newt species, our findings are in line with the hypothesis that they represent cryptic species. We address potential pitfalls of our methodology

The significance of using satellite imagery data only in Ecological Niche Modelling of Iberian herps

The environmental data used to calculate ecological niche models (ENM) are obtained mainly from ground-based maps (e.g., climatic interpolated surfaces). These data are often not available for less developed areas, or may be at an inappropriate scale, and thus to obtain this information requires fieldwork. An alternative source of eco-geographical data comes from satellite imagery. Three sets of ENM were calculated exclusively with variables obtained (1) from optical and radar images only and (2) from climatic and altitude maps obtained by ground-based methods. These models were compared to evaluate whether satellite imagery can accurately generate ENM. These comparisons must be made in areas with well-known species distribution and with available satellite imagery and ground-based data. Thus, the study area was the south-western part of Salamanca (Spain), using amphibian and reptiles as species models. Models' discrimination capacity was measured with ROC plots. Models' covariation was measured with a Spatial Spearman correlation. Four modelling techniques were used (Bioclim, Mahalanobis distance, GARP and Maxent). The results of this comparison showed that there were no significant differences between models generated using remotely sensed imagery or ground-based data. However, the models built with satellite imagery data exhibited a larger diversity of values, probably related to the higher spatial resolution of the satellite imagery. Satellite imagery can produce accurate ENM, independently of the modelling technique or the dataset used. Therefore, biogeographical analysis of species distribution in remote areas can be accurately developed only with variables from satellite imagery