Biodegradation kinetics of methyl iso-butyl ketone by acclimated mixed culture

Methyl iso-butyl ketone (MIBK) is a widely used volatile organic compound (VOC) which is highly toxic in nature and has significant adverse effects on human beings. The present study deals with the removal of MIBK using biodegradation by an acclimated mixed culture developed from activated sludge. The biodegradation of MIBK is studied for an initial MIBK concentration ranging from 200–700 mg l⁻¹ in a batch mode of operation. The maximum specific growth rate achieved is 0.128 h⁻¹ at 600 mg l⁻¹of initial MIBK concentration. The kinetic parameters are estimated using five growth kinetic models for biodegradation of organic compounds available in the literature. The experimental data found to fit well with the Luong model (R ² = 0.904) as compared to Haldane model (R ² = 0.702) and Edward model (R ² = 0.786). The coefficient of determination (R ²) obtained for the other two models, Monod and Powell models are 0.497 and 0.533, respectively. The biodegradation rate found to follow the three-half-order kinetics and the resulting kinetic parameters are reported.     

Using field photography to study avian moult

Methods to obtain moult data from wild birds have not changed much over the last century and most studies still depend on checking museum specimens or capturing birds. Here we assess the applicability of systematic field photography for detecting and scoring moult in adult Black Skimmers Rynchops niger from southern Brazil. Moult data extracted from photographs have a high within‐ (R_GLMM = 0.98) and between‐observer repeatability (R_GLMM = 0.97) and show very good fit to current Underhill–Zucchini moult models (R² = 0.75). Photography offers the advantages of being less invasive, requiring less equipment and human effort, being feasible in areas where captures may not be possible, and causing less disturbance, so enhancing the number of sampled individuals.     

Modeling nitrous oxide emission from rivers: a global assessment

Estimates of global riverine nitrous oxide (N₂O) emissions contain great uncertainty. We conducted a meta‐analysis incorporating 169 observations from published literature to estimate global riverine N₂O emission rates and emission factors. Riverine N₂O flux was significantly correlated with NH₄, NO₃ and DIN (NH₄ + NO₃) concentrations, loads and yields. The emission factors EF(a) (i.e., the ratio of N₂O emission rate and DIN load) and EF(b) (i.e., the ratio of N₂O and DIN concentrations) values were comparable and showed negative correlations with nitrogen concentration, load and yield and water discharge, but positive correlations with the dissolved organic carbon : DIN ratio. After individually evaluating 82 potential regression models based on EF(a) or EF(b) for global, temperate zone and subtropical zone datasets, a power function of DIN yield multiplied by watershed area was determined to provide the best fit between modeled and observed riverine N₂O emission rates (EF(a): R² = 0.92 for both global and climatic zone models, n = 70; EF(b): R² = 0.91 for global model and R² = 0.90 for climatic zone models, n = 70). Using recent estimates of DIN loads for 6400 rivers, models estimated global riverine N₂O emission rates of 29.6–35.3 (mean = 32.2) Gg N₂O–N yr⁻¹ and emission factors of 0.16–0.19% (mean = 0.17%). Global riverine N₂O emission rates are forecasted to increase by 35%, 25%, 18% and 3% in 2050 compared to the 2000s under the Millennium Ecosystem Assessment's Global Orchestration, Order from Strength, Technogarden, and Adapting Mosaic scenarios, respectively. Previous studies may overestimate global riverine N₂O emission rates (300–2100 Gg N₂O–N yr⁻¹) because they ignore declining emission factor values with increasing nitrogen levels and channel size, as well as neglect differences in emission factors corresponding to different nitrogen forms. Riverine N₂O emission estimates will be further enhanced through refining emission factor estimates, extending measurements longitudinally along entire river networks and improving estimates of global riverine nitrogen loads.     

Ecosystem respiration depends strongly on photosynthesis in a temperate heath

We measured net ecosystem CO₂ flux (F _n) and ecosystem respiration (R _E), and estimated gross ecosystem photosynthesis (P _g) by difference, for two years in a temperate heath ecosystem using a chamber method. The exchange rates of carbon were high and of similar magnitude as for productive forest ecosystems with a net ecosystem carbon gain during the second year of 293 ± 11 g C m⁻² year⁻¹ showing that the carbon sink strength of heather-dominated ecosystems may be considerable when C. vulgaris is in the building phase of its life cycle. The estimated gross ecosystem photosynthesis and ecosystem respiration from October to March was 22% and 30% of annual flux, respectively, suggesting that both cold-season carbon gain and loss were important in the annual carbon cycle of the ecosystem. Model fit of R _E of a classic, first-order exponential equation related to temperature (second year; R ² = 0.65) was improved when the P _g rate was incorporated into the model (second year; R ² = 0.79), suggesting that daytime R _E increased with increasing photosynthesis. Furthermore, the temperature sensitivity of R _E decreased from apparent Q ₁₀ values of 3.3 to 3.9 by the classic equation to a more realistic Q ₁₀ of 2.5 by the modified model. The model introduces R _photo, which describes the part of respiration being tightly coupled to the photosynthetic rate. It makes up 5% of the assimilated carbon dioxide flux at 0°C and 35% at 20°C implying a high sensitivity of respiration to photosynthesis during summer. The simple model provides an easily applied, non-intrusive tool for investigating seasonal trends in the relationship between ecosystem carbon sequestration and respiration.     

Comparing the performance of different stomatal conductance models using modelled and measured plant carbon isotope ratios (δ13C): implications for assessing physiological forcing

Accurate modelling of long‐term changes in plant stomatal functioning is vital to global climate change studies because changes in evapotranspiration influence temperature via physiological forcing of the climate. Various stomatal models are included in land surface schemes, but their robustness over longer timescales is difficult to validate. We compare the performance of three stomatal models, varying in their degree of complexity, and coupled to a land surface model. This is carried out by simulating the carbon isotope ratio of tree leaves (δ¹³C_leaf) over a period of 53 years, and comparing the results with carbon isotope ratios obtained from tree rings (δ¹³C_stem) measured at six sites in northern Europe. All three stomatal models fail to capture the observed interannual variability in the measured δ¹³C_stem time series. However, the Soil‐Plant‐Atmosphere (SPA) model performs significantly better than the Ball‐Berry (BB) or COX models when tested for goodness‐of‐fit against measured δ¹³C_stem. The δ¹³C_leaf time series simulated using the SPA model are significantly positively correlated (P < 0.05) with measured results over the full time period tested, at all six sites. The SPA model underestimates interannual variability measured in δ¹³C_stem, but is no worse than the BB model and significantly better than the COX model. The inability of current models to adequately replicate changes in stomatal response to rising levels of CO₂ concentrations, and thus to quantify the associated physiological forcing, warrants further investigation.     

Predictive modelling of Fe(III) precipitation in iron removal process for bioleaching circuits

In this study, the applicability of three modelling approaches was determined in an effort to describe complex relationships between process parameters and to predict the performance of an integrated process, which consisted of a fluidized bed bioreactor for Fe³⁺ regeneration and a gravity settler for precipitative iron removal. Self-organizing maps were used to visually evaluate the associations between variables prior to the comparison of two different modelling methods, the multiple regression modelling and artificial neural network (ANN) modelling, for predicting Fe(III) precipitation. With the ANN model, an excellent match between the predicted and measured data was obtained (R ² = 0.97). The best-fitting regression model also gave a good fit (R ² = 0.87). This study demonstrates that ANNs and regression models are robust tools for predicting iron precipitation in the integrated process and can thus be used in the management of such systems.     

The role of environmental, root, and microbial biomass characteristics in soil respiration in temperate secondary forests of Northeast China

For secondary forests, the major forest resources in China (accounting for more than 50% of the national total), soil respiration (R _S) and the relationship between R _S and various biotic/abiotic factors are poorly understood. The objectives of the present study were to examine seasonal variations in soil respiration during the growing season, and to explore the factors affecting the variation in soil respiration rates for three forest types (Mongolian oak, Manchurian walnut and mixed forests) of temperate secondary forest in Northeast China. The results showed that (1) the maximum total R _S rate occurred in July, following a bell-shaped curve with season, (2) for all forest types, the total R _S was significantly influenced by soil temperature (P < 0.01), and did not significantly correlate with soil moisture, (3) compared with fine root biomass, coarse root biomass was more closely related with the root respiration in mixed forest (R ² = 0.711, P = 0.017) and in Manchurian walnut forest (R ² = 0.768, P = 0.010), and (4) microbial biomass carbon (MBC) and nitrogen were significantly correlated with heterotrophic R _S in Mongolian oak forest (R ² = 0.664, P = 0.026; R ² = 0.784, P = 0.008, respectively) and in mixed forest (R ² = 0.918, P = 0.001; R ² = 0.967, P = 0.001, respectively). We can conclude that in temperate secondary forests: (1) the R _S rate and the relationships between R _S and abiotic/biotic factors change greatly with forest types, and (2) R _S is strongly influenced by soil temperature, MBC, microbial biomass nitrogen and coarse root biomass in temperate secondary forests.     

Predicting Sirex noctilio and S. nigricornis emergence using degree days

The study of temporal interactions between native insects and alien invaders can be facilitated by the ability to forecast adult emergence. We used field‐collected adult emergence data of Sirex noctilio Fabricius (Hymenoptera: Siricidae), a woodwasp native of Eurasia that has recently invaded northeastern North America, and Sirex nigricornis Fabricius, a woodwasp native to North America, to develop and test cumulative degree‐day (CDD) models. Five data sets were collected each in Ontario, Canada (S. noctilio) and Louisiana, USA (S. nigricornis) over 4 years; three data sets were used to develop models and two were used to test them. Males and females of each species were modelled separately. After testing several potential temperatures, chosen thresholds for CDD were 0 °C lower threshold and 25 °C upper threshold for both Sirex spp. We used a three‐parameter Gompertz growth function to model Sirex spp. emergence against CDD. Models predicted 10% emergence of S. noctilio in Ontario after 1 239 and 1 280 CDD, for males (start date = 1 April; R² = 0.91) and females (start date = 1 April; R² = 0.86), respectively. Models predicted 10% emergence of S. nigricornis in Louisiana after 3 980 and 5 016 CDD, for males (start date = 1 May; R² = 0.83) and females (start date = 1 March; R² = 0.73), respectively. Cumulative degree‐day models predicted 10 and 90% emergence of woodwasp populations with less error (1–13%) than they did 50% emergence (5–27%). For both Sirex spp., male emergence began a few days before and concluded at about the same time as that of females. In southern Ontario, models predict that S. noctilio adults will be in flight between 1 015 and 2 430 CDD (1 April start date for CDD; from early‐July until mid‐September). In Louisiana, models predict that S. nigricornis adults will be in flight between 3 854 and 4 700 CDD (1 May start date for CDD; from early‐October until late‐November).     

Influence of stand density on soil CO2 efflux for a Pinus densiflora forest in Korea

We investigated the influence of stand density [938 tree ha⁻¹ for high stand density (HD), 600 tree ha⁻¹ for medium stand density (MD), and 375 tree ha⁻¹ for low stand density (LD)] on soil CO₂ efflux (R _S) in a 70-year-old natural Pinus densiflora S. et Z. forest in central Korea. Concurrent with R _S measurements, we measured litterfall, total belowground carbon allocation (TBCA), leaf area index (LAI), soil temperature (ST), soil water content (SWC), and soil nitrogen (N) concentration over a 2-year period. The R _S (t C ha⁻¹ year⁻¹) and leaf litterfall (t C ha⁻¹ year⁻¹) values varied with stand density: 6.21 and 2.03 for HD, 7.45 and 2.37 for MD, and 6.96 and 2.23 for LD, respectively. In addition, R _S was correlated with ST (R ² = 0.77–0.80, P < 0.001) and SWC (R ² = 0.31–0.35, P < 0.001). It appeared that stand density influenced R _S via changes in leaf litterfall, LAI and SWC. Leaf litterfall (R ² = 0.71), TBCA (R ² = 0.64–0.87), and total soil N contents in 2007 (R ² = 0.94) explained a significant amount of the variance in R _S (P < 0.01). The current study showed that stand density is one of the key factors influencing R _S due to the changing biophysical and environmental factors in P. densiflora.     

Application of the Fourier Method to Differentiate Biological Rhythms from Stochastic Processes in the Growth of <Emphasis Type="Italic">Selenastrum capricornutum</Emphasis> Printz: Implications for Model Development

The biological rhythms of microalgal growth within a hydraulically integrated serial turbidostat algal reactor (HISTAR) were examined after comparison of a simple mechanistic productivity model with actual data yielded a standard error of prediction (SEP) of 62%. The data used for this study were taken on cultures of Selenastrum capricornutum grown under continuous 400-watt metal halide lighting. Fourier series analysis (up to five harmonics) was used to model the biorhythms and differentiate them from stochastic processes. Regression analyses revealed that the best Fourier series fit for the data was a three harmonic summation. Regression analyses on additional harmonic summations did not increase r ² by more than 1%. The three harmonics were summed and incorporated into the growth term of the simple model, and the resultant full model was calibrated. The mechanistic HISTAR productivity model was greatly enhanced by the addition of the biological rhythm component, resulting in a SEP of <24.8 %. The period of the first harmonic was 13.4 days, which is very close to a circasemilunar rhythm (14.8 days). In summary, the predictive power of productivity models for continuous microalgal cultures can be dramatically improved with the inclusion of a biorhythm analysis.     

Predicting vegetation water content in wheat using normalized difference water indices derived from ground measurements

Vegetation water content (VWC) is an important variable for both agriculture and forest fire management. Remote sensing technology offers an instantaneous and non-destructive method for VWC assessment provided we can relate in situ measurements of VWC to spectral reflectance in a reliable way. In this paper, based on radiative transfer models, three new normalized difference water indices (NDWI) are proposed for VWC [fuel moisture content (FMC), and equivalent water thickness (EWT)] estimation, taking both leaf internal structure and dry matter content into account. Reflectance at 1,200, 1,450 and 1,940 nm were selected and normalized with reflectance at 860 nm to establish three water indices, NDWI₁₂₀₀, NDWI₁₄₅₀ and NDWI₁₉₄₀. Good correlations were observed between FMC (R ² = 0.65–0.80) and EWT (both at the leaf scale, R ² = 0.75–0.81 for EWT_L and at the canopy scale, R ² = 0.80–0.83 for EWT_C) at various stages of wheat crop development.     

Development of methane conversion factor models for Zebu beef cattle fed low‐quality crop residues and by‐products in tropical regions

The enteric methane conversion factor (Y_m) is an important country‐specific value for the provision of precise enteric methane emissions inventory reports. The objectives of this meta‐analysis were to develop and evaluate the empirical Y_m models for the national level and the farm level for tropical developing countries according to the IPCC's categorization. We used datasets derived from 18 in vivo feeding experiments from 1999 to 2015 of Zebu beef cattle breeds fed low‐quality crop residues and by‐products. We found that the observed Y_m value was 8.2% gross energy (GE) intake (~120 g methane emission head^?1 day^?1) and ranged from 4.8% to 13.7% GE intake. The IPCC default model (tier 2, Y_m = 6.5% ± 1.0% GE intake) underestimated the Y_m values by up to 26.1% compared with its refinement of 8.4% ± 0.4% GE intake for the national‐level estimate. Both the IPCC default model and the refined model performed worse in predicting Y_m trends at the farm level (root mean square prediction error [MSPE] = 15.1%–23.1%, concordance correlation coefficient [CCC] = 0.16–0.18, R² = .32). Seven of the extant Y_m models based on a linear regression approach also showed inaccurately estimated Y_m values (root MSPE = 16.2%–36.0%, CCC = 0.02–0.27, R² < .37). However, one of the developed models, which related to the complexity of the energy use efficiencies of the diet consumed to Y_m, showed adequate accuracy at the farm level (root MSPE = 9.1%, CCC = 0.75, R² = .67). Our results thus suggest a new Y_m model and future challenges for estimating Zebu beef cattle production in tropical developing countries.     

Growth characteristics of Uranoscopus scaber Linnaeus 1758, inhabiting the Iskenderun Bay (Northeastern Mediterranean)

This study aimed to determine the age and some growth characteristics of Atlantic stargazer (Uranoscopus scaber) from Iskenderun Bay (Northeastern Mediterranean). For this purpose, a total of 150 Atlantic stargazer ranging in size from 9.1 to 28.0 cm in total length (weight: 11.7–345.7 g) were collected as by-catch from a commercial trawl fishing boat at a depth of 80–100 m between May 2015 and January 2016. The bottom trawl gear used was equipped with a 44 mm stretched mesh size net at the cod-end. The percentage of females and males were 46.7% and 53.3% respectively. The total length–weight relationships equation with coefficient of determination (R²) were found as W = 0.011*TL^3.131, R² = .9728, for all individuals, W = 0.015*TL^3.021, R² = .9512 for females and W = 0.0102*TL^3.136, R² = .9553 for males. By using the von Bertalanffy equation, the growth parameters of Atlantic stargazer were estimated to be L_∞ = 42.35 cm, k = 0.098, t₀ = −1.8474 for all individuals; L_∞ = 36.92 cm, k = 0.138, t₀ = −1.2693 for females and L_∞ = 38.77 cm, k = 0.1, t₀ = −2.334 for males. In this study, age reading was done by two independent readers and index of average percentage error (IAPE) was calculated as 6.1%. The highest condition factor value calculated as 1.81 in the age group 6 and the lowest condition factor value was calculated as 1.48 in the age group 1.     

Evaluation of nitrogen excretion equations for ryegrass pasture-fed dairy cows

Accurate and precise estimates of nitrogen (N) excretion in faeces and urine of dairy cattle may provide direct tools to improve N management and thus, to mitigate environmental pollution from dairy production. Empirical equations of N excretion have been evaluated for indoor dairy cattle but there is no evaluation for cows fed high proportions of fresh forage. Therefore, the objective of the current study was to evaluate N excretion equations with a unique data set of zero-grazing experiments. Through literature searches, 89 predictive equations were identified from 13 studies. An independent data set was developed from seven zero-grazing experiments with, in total, 55 dairy Holstein-Friesian cows. Models’ performance was evaluated with statistics derived from a mixed-effect model and a simple regression analysis model. Squared sample correlation coefficients were used as indicators of precision and based on either the best linear unbiased predictions (R²_BLUP) or model-predicted estimates (R²_MDP) derived from the mixed model and simple regression analysis, respectively. The slope (β₀), the intercept (β₁) and the root mean square prediction error (RMSPE_m%) were calculated with the mixed-effect model and used to assess accuracy. The root mean square prediction error (RMSPE_sr%) and the decomposition of the mean square prediction error were calculated with the simple regression analysis and were used to estimate the error due to central tendency (mean bias), regression (systematic bias), and random variation. Concordance correlation coefficient (CCC) were also calculated with the simple regression analysis model and were used to simultaneously assess accuracy and precision. Considering both analysis models, results suggested that urinary N excretion (UN; R²_MDP = 0.76, R²_BLUP = 0.89, RMSPE_m% = 17.2, CCC = 0.82), total manure N excretion (ManN; R²_MDP = 0.83, R²_BLUP = 0.90, RMSPE_m% = 11.0, CCC = 0.84) and N apparently digested (NAD; R²_MDP = 0.97, R²_BLUP = 0.97, RMSPE_m% = 5.3, CCC = 0.95) were closely related to N intake. Milk N secretion was better predicted using milk yield as a single independent variable (MilkN; R²_MDP = 0.77, R²_BLUP = 0.97, RMSPE_m% = 6.0, CCC = 0.74). Additionally, DM intake was a good predictor of UN and ManN and dietary CP concentration of UN and ManN. Consequently, results suggest that several evaluated empirical equations can be used to make accurate and precise predictions concerning N excretion from dairy cows being fed on fresh forage.     

Chlorophyll fluorescence tracks seasonal variations of photosynthesis from leaf to canopy in a temperate forest

Accurate estimation of terrestrial gross primary productivity (GPP) remains a challenge despite its importance in the global carbon cycle. Chlorophyll fluorescence (ChlF) has been recently adopted to understand photosynthesis and its response to the environment, particularly with remote sensing data. However, it remains unclear how ChlF and photosynthesis are linked at different spatial scales across the growing season. We examined seasonal relationships between ChlF and photosynthesis at the leaf, canopy, and ecosystem scales and explored how leaf‐level ChlF was linked with canopy‐scale solar‐induced chlorophyll fluorescence (SIF) in a temperate deciduous forest at Harvard Forest, Massachusetts, USA. Our results show that ChlF captured the seasonal variations of photosynthesis with significant linear relationships between ChlF and photosynthesis across the growing season over different spatial scales (R² = 0.73, 0.77, and 0.86 at leaf, canopy, and satellite scales, respectively; P < 0.0001). We developed a model to estimate GPP from the tower‐based measurement of SIF and leaf‐level ChlF parameters. The estimation of GPP from this model agreed well with flux tower observations of GPP (R² = 0.68; P < 0.0001), demonstrating the potential of SIF for modeling GPP. At the leaf scale, we found that leaf F_q’/F_m’, the fraction of absorbed photons that are used for photochemistry for a light‐adapted measurement from a pulse amplitude modulation fluorometer, was the best leaf fluorescence parameter to correlate with canopy SIF yield (SIF/APAR, R² = 0.79; P < 0.0001). We also found that canopy SIF and SIF‐derived GPP (GPP_SIF) were strongly correlated to leaf‐level biochemistry and canopy structure, including chlorophyll content (R² = 0.65 for canopy GPP_SIF and chlorophyll content; P < 0.0001), leaf area index (LAI) (R² = 0.35 for canopy GPP_SIF and LAI; P < 0.0001), and normalized difference vegetation index (NDVI) (R² = 0.36 for canopy GPP_SIF and NDVI; P < 0.0001). Our results suggest that ChlF can be a powerful tool to track photosynthetic rates at leaf, canopy, and ecosystem scales.     

Statistical medium optimization and biodegradative capacity of <Emphasis Type="Italic">Ralstonia eutropha</Emphasis> toward <Emphasis Type="Italic">p</Emphasis>-nitrophenol

The effect of p-nitrophenol (PNP) concentration with or without glucose and yeast extract on the growth and biodegradative capacity of Ralstonia eutropha was examined. The chemical constituents of the culture medium were modeled using a response surface methodology. The experiments were performed according to the central composite design arrangement considering PNP, glucose and yeast extract as the selected variables whose influences on the degradation was evaluated (shaking in reciprocal mode, temperature of 30°C, pH 7 and test time of about 9 h). Quadratic polynomial regression equations were used to quantitatively explain variations between and within the models (responses: the biodegradation capacity and the biomass formation). The coefficient of determination was high (R _adjusted² = 0.9783), indicating the constructed polynomial model for PNP biodegradative capacity explains the variation between the regressors fairly well. A PNP removal efficiency of 74.5% occurred within 9 h (15 mg/L as the initial concentration of PNP with use of yeast extract at 0.5 g/L).     

Up-scaling to stand transpiration of an Asian temperate mixed-deciduous forest from single tree sapflow measurements

Species diversity in mixed forest stands is one of the factors that complicate up-scaling of transpiration from individual trees to stand level, since tree species are architecturally and functionally different. In this study, thermal dissipation probes were used to measure sap flow in five different tree species in a mixed-deciduous mountain forest in South Korea. Easily measurable tree characteristics that could serve to define individual tree water use among the different species were employed to scale up transpiration from single trees to stand level. Tree water use (TWU) was derived from sap flux density (SFD) and sapwood area (SA). Canopy transpiration E was scaled from TWU while canopy conductance (g _c) was computed from E and VPD. SFD, TWU and g _c were correlated with tree diameter at breast height (DBH) for all the five measured species (SFD: R ² = 0.21, P = 0.036; TWU: R ² = 0.83, P < 0.001; g _c: R ² = 0.63, P < 0.001). Maximum stand transpiration (E) during June, before the onset of the Asian monsoon rains, was estimated at 0.97 ± 0.12 mm per day. There was a good (R ² = 0.94, P < 0.0001) agreement between measured and estimated E using the relationship between TWU and DBH. Our study shows that using functional models that employ converging traits among species could help in estimating water use in mixed forest stands. Compared to SA, DBH is a better scalar for water use of mixed forest stands since it is non-destructive and easily obtainable.     

An accessible scheme for monitoring free‐roaming cat population trends

Free‐roaming cats (FRCs) form nondomiciliary population groups that might lead to adverse environmental effects, as well as to welfare impairment of the cats themselves. Though criticized by ecologists, for the last two decades, the trap–neuter–return (TNR) programs were often employed aiming to manage these populations. At present, no accepted and accessible monitoring scheme exists to determine the effectiveness of those programs. In the current study, we present the reliability and validity of an applicable monitoring scheme, as an adjunct tool for a TNR program of FRC in an urban environment. The monitoring scheme is based on cat observation counts along randomly chosen transects. Fifty‐four transects were repeatedly walked for three years, between 2012‐2014, in 27 neighborhoods within an urban area of 19.3 Km². Cat numbers counted in the 2014 observations were significantly higher than cat numbers found in the 2012 observations (prevalence ratio = 1.258, CI_95%= 1.198–1.322, p < 0.001). The method revealed high reliability when different observers and different transects in the same neighborhood were compared (R² = 0.548 and R² = 0.391, respectively, for measuring cat counts per km, p < 0.001; and R² = 0.5 and R² = 0.74, respectively, for measuring neutering percentage, p < 0.001). This scheme was constructively validated by measurements of municipal data on the number of neutered cats and demonstrated high correlation (R² = 0.59, p < 0.001). Conducting cat observations using friendly calling and feeding resulted in an increased number of FRC observed per km walk (by 79% and 22%–30%, respectively). However, these manipulations did not alter the recorded percentage of neutered cats. The proposed scheme provides spatio‐temporal data that can contribute to the management programs of such cat metapopulations in an urban environment.     

On the biosorption,by brown seaweed, <Emphasis Type="Italic">Lobophora variegata</Emphasis>, of Ni(II) from aqueous solutions: equilibrium and thermodynamic studies

The biosorption equilibrium isotherms of Ni(II) onto marine brown algae Lobophora variegata, which was chemically-modified by CaCl₂ were studied and modeled. To predict the biosorption isotherms and to determine the characteristic parameters for process design, twenty-three one-, two-, three-, four- and five-parameter isotherm models were applied to experimental data. The interaction among biosorbed molecules is attractive and biosorption is carried out on energetically different sites and is an endothermic process. The five-parameter Fritz–Schluender model gives the most accurate fit with high regression coefficient, R ² (0.9911–0.9975) and F-ratio (118.03–179.96), and low standard error, SE (0.0902–0.0.1556) and the residual or sum of square error, SSE (0.0012–0.1789) values to all experimental data in comparison to other models. The biosorption isotherm models fitted the experimental data in the order: Fritz–Schluender (five-parameter) > Freundlich (two-parameter) > Langmuir (two-parameter) > Khan (three-parameter) > Fritz–Schluender (four-parameter). The thermodynamic parameters such as ΔG ⁰, ΔH ⁰ and ΔS ⁰ have been determined, which indicates the sorption of Ni(II) onto L. variegata was spontaneous and endothermic in nature.     

Estimating below‐canopy light regimes using airborne laser scanning: An application to plant community analysis

Light is a key driver of forest biodiversity and functioning. Light regimes beneath tree canopies are mainly driven by the solar angle, topography, and vegetation structure, whose three‐dimensional complexity creates heterogeneous light conditions that are challenging to quantify, especially across large areas. Remotely sensed canopy structure data from airborne laser scanning (ALS) provide outstanding opportunities for advancement in this respect. We used ALS point clouds and a digital terrain model to produce hemispherical photographs from which we derived indices of nondirectional diffuse skylight and direct sunlight reaching the understory. We validated our approach by comparing the performance of these indices, as well as canopy closure (CCl) and canopy cover (CCo), for explaining the light conditions experienced by forest plant communities, as indicated by the Landolt indicator values for light (L_light) from 43 vegetation surveys along an elevational gradient. We applied variation partitioning to analyze how the independent and joint statistical effects of light, macroclimate, and soil on the spatial variation in plant species composition (i.e., turnover, Simpson dissimilarity, β_SIM) depend on light approximation methodology. Diffuse light explained L_light best, followed by direct light, CCl and CCo (R² = .31, .23, .22, and .22, respectively). The combination of diffuse and direct light improved the model performance for β_SIM compared with CCl and CCo (R² = .30, .27 and .24, respectively). The independent effect of macroclimate on β_SIM dropped from an R² of .15 to .10 when diffuse light and direct light were included. The ALS methods presented here outperform conventional approximations of below‐canopy light conditions, which can now efficiently be quantified along entire horizontal and vertical forest gradients, even in topographically complex environments such as mountains. The effect of macroclimate on forest plant communities is prone to be overestimated if local light regimes and associated microclimates are not accurately accounted for.