The scale free and scale - bound properties of land surfaces: fractal analysis and specific geomorphometry from digital terrain models

The scale-bound view of landsurfaces, being an assemblage of certain landforms, occurring within limited scale ranges, has been challenged by the scale-free characteristics of fractal geometry. This thesis assesses the fractal model by examining the irregularity of landsurface form, for the self-affine behaviour present in fractional Brownian surfaces. Different methods for detecting self-affine behaviour in surfaces are considered and of these the variogram technique is shown to be the most effective. It produces the best results of two methods tested on simulated surfaces, with known fractal properties. The algorithm used has been adapted to consider log (altitude variance) over a sample of log (distances) for: complete surfaces; subareas within surfaces; separate directions within surfaces. Twenty seven digital elevation models of landsurfaces arc re-examined for self- affine behaviour. The variogram results for complete surfaces show that none of these are self-affine over the scale range considered. This is because of dominant slope lengths and regular valley, spacing within areas. For similar reasons subarea analysis produces the non-fractal behaviour of markedly different variograms for separate subareas. The linearity of landforms in many areas, is detected by the variograms for separate directions. This indicates that the roughness of landsurfaces is anisotropic, unlike that of fractal surfaces. Because of difficulties in extracting particular landforms from their landsurfaces, no clear links between fractal behaviour, and landform size distribution could be established. A comparative study shows the geomorphometric parameters of fractal surfaces to vary with fractal dimension, while the geomorphometry of landsurfaces varies with the landforms present. Fractal dimensions estimated from landsurfaces do not correlate with geomorphometric parameters. From the results of this study, real landsurfaces would not appear to be scale- free. Therefore, a scale-bound approach towards landsurfaces would seem to be more appropriate to geomorphology than the fractal alternative

CHARACTERIZATION OF SOME IMPORTANT AGRICULTURAL SOILS UNDER OLIVE TREES

Olive production is important and intensive agricultural activity in this region. Generally, olive trees occur coastal side of the region under brown forest soils. Ten olive tree plantations were selected in this research. The some important physical, chemical and morphological properties were investigated and classifi ed according to USDA Soil Taxonomy as Typic Xerochrepts

Mapping regional risks from climate change for rainfed rice cultivation in India

Global warming is predicted to increase in the future, with detrimental consequences for rainfed crops that are dependent on natural rainfall (i.e. non-irrigated). Given that many crops grown under rainfed conditions support the livelihoods of low-income farmers, it is important to highlight the vulnerability of rainfed areas to climate change in order to anticipate potential risks to food security. In this paper, we focus on India, where ~ 50% of rice is grown under rainfed conditions, and we employ statistical models (climate envelope models (CEMs) and boosted regression trees (BRTs)) to map changes in climate suitability for rainfed rice cultivation at a regional level (~ 18 × 18 km cell resolution) under projected future (2050) climate change (IPCC RCPs 2.6 and 8.5, using three GCMs: BCC-CSM1.1, MIROC-ESM-CHEM, and HadGEM2-ES). We quantify the occurrence of rice (whether or not rainfed rice is commonly grown, using CEMs) and rice extent (area under cultivation, using BRTs) during the summer monsoon in relation to four climate variables that affect rice growth and yield namely ratio of precipitation to evapotranspiration (PER), maximum and minimum temperatures (Tmax and Tmin), and total rainfall during harvesting. Our models described the occurrence and extent of rice very well (CEMs for occurrence, ensemble AUC = 0.92; BRTs for extent, Pearson's r = 0.87). PER was the most important predictor of rainfed rice occurrence, and it was positively related to rainfed rice area, but all four climate variables were important for determining the extent of rice cultivation. Our models project that 15%–40% of current rainfed rice growing areas will be at risk (i.e. decline in climate suitability or become completely unsuitable). However, our models project considerable variation across India in the impact of future climate change: eastern and northern India are the locations most at risk, but parts of central and western India may benefit from increased precipitation. Hence our CEM and BRT models agree on the locations most at risk, but there is less consensus about the degree of risk at these locations. Our results help to identify locations where livelihoods of low-income farmers and regional food security may be threatened in the next few decades by climate changes. The use of more drought-resilient rice varieties and better irrigation infrastructure in these regions may help to reduce these impacts and reduce the vulnerability of farmers dependent on rainfed cropping

Altered mitochondrial function and genome frequency post exposure to γ-radiation and bystander factors

PURPOSE: To further evaluate irregular mitochondrial function and mitochondrial genome damage induced by direct γ-irradiation and bystander factors in human keratinocyte (HPV-G) epithelial cells and hamster ovarian fibroblast (CHO-K1) cells. This is as a follow-up to our recent reports of γ-irradiation-induced loss of mitochondrial function and mitochondrial DNA (mtDNA) damage

A national level assessment of metal contamination in bats

Abstract Many populations of bat species across the globe are declining, with chemical contamination one of many potential stressors implicated in these demographic changes. Metals still contaminate a wide range of habitats, but the risks to bats remain poorly understood. This study is the first to present a national scale assessment of toxic metal (Cd, Pb) and essential trace metal (Cu, Zn) concentrations in bats. Metal concentrations in tissues (kidneys, liver, stomach -stomach content, bones and fur) were measured in 193 Pipistrellus sp. in England and Wales using ICP-MS, and compared to critical toxic concentrations for small mammals. The concentrations of metals determined in bat tissues were generally lower than those reported elsewhere. Strong positive associations were found between concentrations in tissues for a given metal (liver and kidneys for Cd, Cu and Pb; stomach and fur and fur and bones for Pb), suggesting recent as well as long term exposure to these contaminants. In addition, positive correlations between concentrations of different metals in the same tissues (Cd and Zn, Cu and Zn, Cd and Pb, Pb and Zn) suggest a co-exposure of metals to bats. Approximately 21% of the bats sampled contained residues of at least one metal at concentrations high enough to elicit toxic effects (associated with kidney damage), or to be above the upper level measured in other mammal species. Pb was found to pose the greatest risk (with 7–11% of the bats containing concentrations of toxicological concern), followed by Cu (4–9%), Zn (0.5–5.2%) and Cd (0%). Our data suggest that leaching of metals into our storage matrix, formaldehyde, may have occurred, especially for Cu. The overall findings suggest that metal contamination is an environmental stressor affecting bat populations, and that further research is needed into the direct links between metal contamination and bat population declines worldwide

Fur : A non-invasive approach to monitor metal exposure in bats

This paper presents a novel assessment of the use of fur as a non-invasive proxy to biomonitor metal contamination in insectivorous bats. Concentrations of metals (cadmium, copper, lead and zinc) were measured using ICP-MS in tissues (kidneys, liver, stomach and stomach content, bones and fur) obtained from 193 Pipistrellus pipistrellus/pygmaeus bats. The bats were collected across a gradient of metal pollution in England and Wales. The utility of small samples of fur as an indicator of metal exposure from the environment was demonstrated with strong relationships obtained between the concentrations of non-essential metals in fur with concentrations in stomach content, kidneys, liver and bones. Stronger relationships were observed for non-essential metals than for essential metals. Fur analyses might therefore be a useful non-invasive proxy for understanding recent, as well as long term and chronic, metal exposure of live animals. The use of fur may provide valuable information on the level of endogenous metal exposure and contamination of bat populations and communities

Quantifying trade-offs between butterfly abundance and movement in the management of agricultural set-asides

1. Agri-environment schemes (AES) create small areas of habitat within agricultural landscapes to support biodiversity. Here, we study butterfly flight behaviour within linear AES features and examine whether differences in resource availability affect the speed, linearity or directionality of local movements, thereby affecting their contribution to landscape connectivity. 2. We surveyed butterflies within three basic (naturally regenerating) and three wildflower-sown linear field margin strips (0.09-0.15 ha) on a farm in North Yorkshire, UK, and mapped butterfly flight paths to quantify local displacement (movement speed), efficiency (linearity, turning angles), directionality (step orientation) and behaviour (time spent flying, nectaring). 3. Butterfly species richness was similar between margin types (estimated asymptotic species richness of 21.9 [CI: 15.0-77.7] for basic margins and 14.2 [CI: 14.0-18.7] for wildflower-sown margins), but abundance was 78% higher in wildflower-sown margins. For the three most common species (meadow brown, Maniola jurtina (L.), ringlet, Aphantopus hyperantus (L.), and small white, Pieris rapae (L.); n = 233 paths), movements within both margin types were highly linear (median turning angle 45˚) and generally oriented along the length of the margin strip (median step orientation 27˚). Movements in basic margins were slightly more orientated along the length of the margin but we found no differences between margin types in speed, path linearity, turning angles or the proportion of time spent flying or nectaring. 4. We found strong channelling of movements along field margin strips regardless of management type, potentially aiding landscape connectivity. Providing field margin strips with additional foraging resources through wildflower sowing increases butterfly abundance without impeding local movement rates or efficiency.Funding provided by: Natural Environment Research CouncilCrossref Funder Registry ID: http://dx.doi.org/10.13039/501100000270Award Number: NE/L002450/1Full details of datasets (incl. column names) can be found in README.txt Butterfly data: butterfly_flight_paths.csv: Flight path data for butterflies extra_butterfly_sightings.csv: Additional opportunistic butterfly observations sampling_dur.csv: Sampling duration of each margin per day Plant data: quadrat_survey.csv: Flower data collected through regular quadrat sampling point_survey.csv: Vegetation data collected through regular point samplin

Predicting tree distributions in an East African biodiversity hotspot : model selection, data bias and envelope uncertainty

The Eastern Arc Mountains (EAMs) of Tanzania and Kenya support some of the most ancient tropical rainforest on Earth. The forests are a global priority for biodiversity conservation and provide vital resources to the Tanzanian population. Here, we make a first attempt to predict the spatial distribution of 40 EAM tree species, using generalised additive models, plot data and environmental predictor maps at sub 1 km resolution. The results of three modelling experiments are presented, investigating predictions obtained by (1) two different procedures for the stepwise selection of predictors, (2) down-weighting absence data, and (3) incorporating an autocovariate term to describe fine-scale spatial aggregation. In response to recent concerns regarding the extrapolation of model predictions beyond the restricted environmental range of training data, we also demonstrate a novel graphical tool for quantifying envelope uncertainty in restricted range niche-based models (envelope uncertainty maps). We find that even for species with very few documented occurrences useful estimates of distribution can be achieved. Initiating selection with a null model is found to be useful for explanatory purposes, while beginning with a full predictor set can over-fit the data. We show that a simple multimodel average of these two best-model predictions yields a superior compromise between generality and precision (parsimony). Down-weighting absences shifts the balance of errors in favour of higher sensitivity, reducing the number of serious mistakes (i.e., falsely predicted absences); however, response functions are more complex, exacerbating uncertainty in larger models. Spatial autocovariates help describe fine-scale patterns of occurrence and significantly improve explained deviance, though if important environmental constraints are omitted then model stability and explanatory power can be compromised. We conclude that the best modelling practice is contingent both on the intentions of the analyst (explanation or prediction) and on the quality of distribution data; generalised additive models have potential to provide valuable information for conservation in the EAMs, but methods must be carefully considered, particularly if occurrence data are scarce. Full results and details of all species models are supplied in an online Appendix. (C) 2008 Elsevier B.V. All rights reserved