Translating habitat class to land cover to map area of habitat of terrestrial vertebrates

Area of habitat (AOH) is defined as the “habitat available to a species, that is, habitat within its range” and is calculated by subtracting areas of unsuitable land cover and elevation from the range. The International Union for the Conservation of Nature (IUCN) Habitats Classification Scheme provides information on species habitat associations, and typically unvalidated expert opinion is used to match habitat to land-cover classes, which generates a source of uncertainty in AOH maps. We developed a data-driven method to translate IUCN habitat classes to land cover based on point locality data for 6986 species of terrestrial mammals, birds, amphibians, and reptiles. We extracted the land-cover class at each point locality and matched it to the IUCN habitat class or classes assigned to each species occurring there. Then, we modeled each land-cover class as a function of IUCN habitat with (SSG, using) logistic regression models. The resulting odds ratios were used to assess the strength of the association between each habitat and land-cover class. We then compared the performance of our data-driven model with those from a published translation table based on expert knowledge. We calculated the association between habitat classes and land-cover classes as a continuous variable, but to map AOH as binary presence or absence, it was necessary to apply a threshold of association. This threshold can be chosen by the user according to the required balance between omission and commission errors. Some habitats (e.g., forest and desert) were assigned to land-cover classes with more confidence than others (e.g., wetlands and artificial). The data-driven translation model and expert knowledge performed equally well, but the model provided greater standardization, objectivity, and repeatability. Furthermore, our approach allowed greater flexibility in the use of the results and uncertainty to be quantified. Our model can be modified for regional examinations and different taxonomic groups.     

A Power Law Model for River Flow Velocity in Iowa Basins

This study explores power law relationships to estimate water flow velocity as a function of discharge and drainage area across river networks. We test the model using empirical data from 214 United States (U.S.) Geological Survey gauging stations distributed over the state of Iowa in the U.S. The empirical data are the measurements of the mean cross‐sectional velocity and concurrent discharge. The data are used to estimate parameters for a state‐wide model and to test for spatial variability for 15 large river basins contained within the state. Spatial differences among the basins are small but some parameters significantly differ from the state‐wide model. Using individual station data, the authors also explore a simpler power law model that disregards dependence on the drainage area. Overall, the study shows that including drainage area improves the model. Our study provides parameter values that can be directly incorporated into a regional scale routing model, and provides a framework for developing flow velocity models for hydraulically similar rivers in the U.S. and the world.     

Seasonal variation in amylase, invertase, cellulase activity and carbon dioxide evolution in a tropical protected grassland of Orissa, India, sprayed with carbaryl insecticide

A distinct seasonal variation in the enzyme activities and carbon dioxide evolution in soil, with a maximum in summer, was observed in soil treated with carbaryl and in control soil. There was no significant difference in the rate of enzyme activity between 0 and 10 cm and 10 and 20 cm depth of soil. Carbaryl insecticide applied at a normal agricultural dose did not have any inhibitory effect on soil enzyme activity, or on the rate of CO(2) evolution. However, the cellulase activity was greater in the surface soil of the control plot than in the treated plot.     

Chemometrics in assessment of seasonal variation of water quality in fresh water systems

The spatial and temporal distribution of polycyclic aromatic hydrocarbons (PAHs) was investigated in Gomti River, a major tributary of the Ganga river (India). A total of 96 samples (water and sediments) were collected from eight different sites over a period of 2 years and analysed for 16 PAHs. The total concentrations of 16 PAHs in water and bed sediments ranged between 0.06 and 84.21 ??g/L (average (n?=?48), 10.33 ± 19.94 ??g/L) and 5.24?C3,722.87 ng/g dw [average (n?=?48): 697.25 ± 1,005.23 ng/g dw], respectively. In water, two- and three-ring PAHs and, in sediments, the three- and four-ring PAHs were the dominant species. The ratios of anthracene (An)/An + phenenthrene and fluoranthene (Fla)/Fla + pyrene were calculated to evaluate the possible sources of PAHs. These ratios reflected a pattern of pyrolytic input as a major source of PAHs in the river. Principal component analysis, further, separated the PAHs sources in the river sediments, suggesting that both the pyrolytic and petrogenic sources are contributing to the PAHs burden. The threat to biota of the river due to PAHs contamination was assessed using effect range low and effect range median values, and the results suggested that sediment at some occasions may pose biological impairment.     

Spatial distribution of enhanced atrazine degradation across northeastern Colorado cropping systems

Reports of enhanced atrazine degradation and reduced residual weed control have increased in recent years, sparking interest in identifying factors contributing to enhanced atrazine degradation. The objectives of this study were to (i) assess the spatial distribution of enhanced atrazine degradation in 45 commercial farm fields in northeastern Colorado (Kit Carson, Larimer, Logan, Morgan, Phillips, and Yuma counties) where selected cultural management practices and soil bio-chemo-physical properties were quantified; (ii) utilize Classification and Regression Tree (CART) Analysis to identify cultural management practices and (or) soil bio-chemophysical attributes that are associated with enhanced atrazine degradation; and (iii) translate our CART Analysis into a model that predicts relative atrazine degradation rate (rapid, moderate, or slow) as a function of known management practices and (or) soil properties. Enhanced atrazine degradation was widespread within a 300-km radius across northeastern Colorado, with approximately 44% of the fields demonstrating rapid atrazine degradation activity (laboratory-based dissipation time halflife [DT50] < 3 d). The most rapid degradation rates occurred in fields that received the most frequent atrazine applications. Classification and Regression Tree Analysis resulted in a prediction model that correctly classified soils with rapid atrazine DT50 80% of the time and soils with slow degradation (DT50 > 8 d) 62.5% of the time. Significant factors were recent atrazine use history, soil pH, and organic matter content. The presence/absence of atzC polymerase chain reaction (PCR) product was not a significant predictor variable for atrazine DT50. In conclusion, enhanced atrazine degradation is widespread in northeastern Colorado. If producers know their atrazine use history, soil pH, and OM content, they should be able to identify fields exhibiting enhanced atrazine degradation using our CART Model.