Simulating tick distributions over sub-Saharan Africa: the use of observed and simulated climate surfaces

Aim A broad suit of climate data sets is becoming available for use in predictive species modelling. We compare the efficacy of using interpolated climate surfaces [Center for Resource and Environmental Studies (CRES) and Climate Research Unit (CRU)] or high‐resolution model‐derived climate data [Division of Atmospheric Research limited‐area model (DARLAM)] for predictive species modelling, using tick distributions from sub‐Saharan Africa. Location The analysis is restricted to sub‐Saharan Africa. The study area was subdivided into 3000 grids cells with a resolution of 60 × 60 km. Methods Species distributions were predicted using an established multivariate climate envelope modelling approach and three very different climate data sets. The recorded variance in the climate data sets was quantified by employing omnidirectional variograms. To further compare the interpolated tick distributions that flowed from using three climate data sets, we calculated true positive (TP) predictions, false negative (FN) predictions as well as the proportional overlaps between observed and modelled tick distributions. In addition, the effect of tick data set size on the performance of the climate data sets was evaluated by performing random draws of known tick distribution records without replacement. Results The predicted distributions were consistently wider ranging than the known records when using any of the three climate data sets. However, the proportional overlap between predicted and known distributions varied as follows: for Rhipicephalus appendiculatus Neumann (Acari: Ixodidae), these were 60%, 60% and 70%; for Rhipicephalus longus Neumann (Acari: Ixodidae) 60%, 57% and 75%; for Rhipicephalus zambeziensis Walker, Norval & Corwin (Acari: Ixodidae) 57%, 51% and 62%, and for Rhipicephalus capensis Koch (Acari: Ixodidae) 70%, 60% and 60% using the CRES, CRU and DARLAM climate data sets, respectively. All data sets were sensitive to data size but DARLAM performed better when using smaller species data sets. At a 20% data subsample level, DARLAM was able to capture more than 50% of the known records and captured more than 60% of known records at higher subsample levels. Main conclusions The use of data derived from high‐resolution nested climate models (e.g. DARLAM) provided equal or even better species distribution modelling performance. As the model is dynamic and process based, the output data are available at the modelled resolution, and are not hamstrung by the sampling intensity of observed climate data sets (c. one sample per 30,000 km² for Africa). In addition, when exploring the biodiversity consequences of climate change, these modelled outputs form a more useful basis for comparison with modelled future climate scenarios.     

Predictability of the terrestrial carbon cycle

Terrestrial ecosystems sequester roughly 30% of anthropogenic carbon emission. However this estimate has not been directly deduced from studies of terrestrial ecosystems themselves, but inferred from atmospheric and oceanic data. This raises a question: to what extent is the terrestrial carbon cycle intrinsically predictable? In this paper, we investigated fundamental properties of the terrestrial carbon cycle, examined its intrinsic predictability, and proposed a suite of future research directions to improve empirical understanding and model predictive ability. Specifically, we isolated endogenous internal processes of the terrestrial carbon cycle from exogenous forcing variables. The internal processes share five fundamental properties (i.e., compartmentalization, carbon input through photosynthesis, partitioning among pools, donor pool‐dominant transfers, and the first‐order decay) among all types of ecosystems on the Earth. The five properties together result in an emergent constraint on predictability of various carbon cycle components in response to five classes of exogenous forcing. Future observational and experimental research should be focused on those less predictive components while modeling research needs to improve model predictive ability for those highly predictive components. We argue that an understanding of predictability should provide guidance on future observational, experimental and modeling research.     

Assessment of soil quality indices for salt-affected agricultural land in Kurdistan Province,Iran

Soil quality indices (SQIs) were an important tool for evaluating agro-ecosystems. Salinization and alkalization are major environmental problems that have threatened agricultural productivity since ancient times. The aim of this study is to assess soil quality in salt-affected agricultural land in Kurdistan Province, Iran, using three indices; the Additive Soil Quality Index (SQI_a), the Weighted Additive Soil Quality Index (SQI_w), and the Nemoro Soil Quality Index (SQI_n). Each of the soil quality indices were calculated using a Total Data Set (TDS) and a Minimum Data Set (MDS) approach. The TDS consisted of nine soil quality parameters measured on 150 samples (0–30 cm depth): pH, Electrical Conductivity (EC), Organic Carbon (OC), Cation Exchange Capacity (CEC), Carbonate Calcium Equivalent (CCE), Exchangeable Sodium Percentage (ESP), Sodium Adsorption Ratio (SAR), Mean Weight Diameter (MWD), and Bulk Density (BD). Principal components analysis (PCA) was used to determine which indicators were to be included in the MDS. Indicator Kriging (IK) highlighted areas with a high risk of exceeding critical threshold values of EC, ESP, and SAR and having low soil quality. In non-salt-affected areas soil quality and the risk of exceeding critical threshold values and having low soil quality were lower and higher, respectively, compared to salt-affected regions. The MDS method showed a decrease in the area and proportion of grades with high and very high quality (I and II) and an increase in grades with low and very low quality (IV and V) compared to the TDS. The results of linear correlation, match, and kappa statistic analysis showed that soil quality was better estimated using the SQI_w compared to the SQI_a and the SQI_n. In addition there were higher values of agreement (match and kappa statistic) for the TSD than MSD. However, using the SQI_w index and MDS method can adequately represent the TDS (R² = 0.82) and thus reduce the time and cost involved in evaluating soil quality.     

One‐inflation and unobserved heterogeneity in population size estimation by Ryan T. Godwin

In this study, we would like to show that the one‐inflated zero‐truncated negative binomial (OIZTNB) regression model can be easily implemented in R via built‐in functions when we use mean‐parameterization feature of negative binomial distribution to build OIZTNB regression model. From the practitioners' point of view, we believe that this approach presents a computationally convenient way for implementation of the OIZTNB regression model.     

Economic growth and environmental impacts: An analysis based on a composite index of environmental damage

The conflict between economic growth and the environment is complex and sharper today than ever before. Indeed, the relationship between economic growth and the sustainability of ecosystems has been extensively discussed in the literature, but the results remain controversial.This paper reviews the use of single and composite indicators of environmental damage and questions whether the Environmental Kuznets Curve (EKC) hypothesis sufficiently mirrors the relationship between economic growth and ecological damage. Ecological Indicators are relevant when they potentially inform society about ecological developments in a reliable way. We use the modified composite index of environmental performance (mCIEP) to measure environmental damage, and GDP per capita to represent economic growth. The econometric model is developed using panel data composed of 152 countries and a period of 6 years. The model is estimated for the full sample, for three different sets of countries, by level of development, and a decomposition analysis is carried out, which corresponds to the study of the CIEP individual dimensions.Our results reveal that, at present, the EKC hypothesis is not proved. We conclude that it is critical to be clear that economic growth alone is not enough to improve environmental quality. Therefore, creating a consistent, coherent and effective environmental policy framework is essential in order to improve environmental quality that supports wellbeing and enables long-term economic development.     

Quantification of tartrate resistant acid phosphatase distribution in mouse tibiae using image analysis

Tartrate resistant acid phosphatase (TRAP) activity of bone is a suitable biochemical marker for osteoclastic bone resorption. Qualitatively, the histochemical distribution of TRAP has been used to identify osteoclasts responsible for bone resorption; however, there have been few attempts to quantify TRAP localization. We describe a method for evaluating bone resorption by quantifying area percentages of positive TRAP localization using image analysis. Mouse tibiae were paraffin embedded following demineralization in disodium ethylenediamine tetraacetic acid. Longitudinal sections of tibia were cut from 15 levels in the left and the right limbs of six mice (180 sections total) and stained for TRAP distribution. Positive TRAP localization was quantified by pixel area count and reported as a percentage of the total tissue area specified. The 1.85 mm² region of interest was placed at the midpoint of the epiphyseal growth plate containing the provisional calcification layer and the primary spongiosa, while excluding cortical bone of each mouse tibia. The percentage of TRAP localization ranged from 0.95 to 1.31% and was not significantly different from level to level or limb to limb in each mouse (p>0.100). Within the same region of interest, an osteoclast count along the bone perimeter also was performed. We demonstrated a strong correlation (r²=0.903) between the conventional histomorphometric osteoclast index and positive TRAP localization, validating the latter as an alternative method to assess bone resorption. Quantitative analysis of TRAP is significant because it allows statistical comparisons between treatment groups, promotes precise pathological diagnoses and facilitates a reference data base that may aid the study of bone related diseases involving increased bone resorption.