Genetic variation in marine teleosts: High variability in habitat specialists and low variability in habitat generalists

Genetic variation was reviewed in 106 species of marine teleosts. Two heterozygosity estimates were used, one including all protein and enzyme loci and a second excluding the non-enzymatic protein loci. Mean heterozygosities are 0.055±0.036 based on all loci in 106 species and 0.060+0.038 based on enzymatic loci in 89 species. A significant negative correlation was noted between heterozygosity and the proportion of general protein loci included in the estimate. A comparison was made of heterozygosities among taxonomic orders and families, life zones, reproductive mode, geographical range and size. High levels of genetic variation are found in Clupeiformes, Atheriniformes, Pleuronectiformes, temperate pelagic, tropical, intertidal-sublittoral and wide-range species. Low levels of genetic variation are found in Gadiformes, Scorpaeniformes, temperate demersal, polar, and narrowrange species. The most striking differences in heterozygosities are between temperate demersal flatfishes and temperate demersal round fishes. It is suggested that much of the data can be explained by a habitat specialist-generalist model, with high heterozygosities in specialists and low heterozygosities in generalists, but that this is only one of a mosaic of factors which influence genetic variation.     

Comparison of statistical and theoretical habitat models for conservation planning: the benefit of ensemble prediction

Selection of a modeling approach is an important step in the conservation planning process, but little guidance is available. We compared two statistical and three theoretical habitat modeling approaches representing those currently being used for avian conservation planning at landscape and regional scales: hierarchical spatial count (HSC), classification and regression tree (CRT), habitat suitability index (HSI), forest structure database (FS), and habitat association database (HA). We focused our comparison on models for five priority forest-breeding species in the Central Hardwoods Bird Conservation Region: Acadian Flycatcher, Cerulean Warbler, Prairie Warbler, Red-headed Woodpecker, and Worm-eating Warbler. Lacking complete knowledge on the distribution and abundance of each species with which we could illuminate differences between approaches and provide strong grounds for recommending one approach over another, we used two approaches to compare models: rank correlations among model outputs and comparison of spatial correspondence. In general, rank correlations were significantly positive among models for each species, indicating general agreement among the models. Worm-eating Warblers had the highest pairwise correlations, all of which were significant (P < 0.05). Red-headed Woodpeckers had the lowest agreement among models, suggesting greater uncertainty in the relative conservation value of areas within the region. We assessed model uncertainty by mapping the spatial congruence in priorities (i.e., top ranks) resulting from each model for each species and calculating the coefficient of variation across model ranks for each location. This allowed identification of areas more likely to be good targets of conservation effort for a species, those areas that were least likely, and those in between where uncertainty is higher and thus conservation action incorporates more risk. Based on our results, models developed independently for the same purpose (conservation planning for a particular species in a particular geography) yield different answers and thus different conservation strategies. We assert that using only one habitat model (even if validated) as the foundation of a conservation plan is risky. Using multiple models (i.e., ensemble prediction) can reduce uncertainty and increase efficacy of conservation action when models corroborate one another and increase understanding of the system when they do not.     

A novel method for quantifying the glossiness of animals

The glossy sheen of healthy hair is an ideal of human beauty; however, glossiness has never been quantified in the context of non-human animal signaling. Glossiness, the specular reflectance characteristic of polished surfaces, has the potential to act as a signal of quality because it depends upon material integrity and cleanliness. Here, we undertook two studies of glossiness in avian plumage to determine (a) the repeatability of a recently developed measure of glossiness, (b) the relationship between glossiness and conventional measures of coloration, and (c) how glossiness is associated with quality signaling. Using museum specimens of three North American bird species with glossy plumage (red-winged blackbird, Agelaius phoeniceus; great-tailed grackle, Quiscalus mexicanus; Chihuahuan raven, Corvus cryptoleucus), we found that the glossiness measure was highly repeatable for all species and was significantly correlated with plumage coloration (e.g., chroma, brightness) in male great-tailed grackles. We then used wild-caught grackles to examine sexual dimorphism in plumage glossiness and its correlation to a potentially sexually selected trait in this species, male tail length. We found that males were significantly glossier than females and that male, but not female, glossiness correlated positively with tail length. This study provides a repeatable method to measure glossiness and highlights its potential as a signal of individual quality in animals.     

A novel extraction method for analysing available sulfamethoxazole in soil

A novel extraction method was established to determine the water-extractable (available) content of sulfamethoxazole (SMX) in soil. The SMX imprinted polymers (MIPs) were synthesised and the performance was evaluated by Fourier transform infrared spectroscopy, scanning electron microscopy and binding experiments. Results showed that the MIPs exhibited good selectivity for SMX, so the MIPs were applied as a sorbent. SMX in soil was extracted by water, sorbed from the extract to MIPs and analysed with a high performance liquid chromatography (HPLC) after its desorption from MIPs. Meanwhile, the classic organic solvent extraction was employed to measure the total SMX content in soil. Results showed that when SMX level in spiked soils varying from 1.0–500?μg?kg^?1, the observed recoveries of available SMX contents ranged from 63.27?±?3.11% to 82.11?±?2.77% (n?=?3), while the total SMX varied between 89.59?±?1.65% and 97.64?±?3.92% (n?=?3). The detection limit of the developed method for SMX in soils was 0.05?μg?kg^?1. Available SMX contents in five field soil samples ranged from 0.13 to 4.14?μg?kg^?1, which were only 0.35–25.40% of the respective total SMX contents. Results from this study manifest the importance of the extents of SMX immobilisation with different soils for assessing SMX's ecological and human health risks.     

A method for assigning species into groups based on generalized Mahalanobis distance between habitat model coefficients

Habitat association models are commonly developed for individual animal species using generalized linear modeling methods such as logistic regression. We considered the issue of grouping species based on their habitat use so that management decisions can be based on sets of species rather than individual species. This research was motivated by a study of western landbirds in northern Idaho forests. The method we examined was to separately fit models to each species and to use a generalized Mahalanobis distance between coefficient vectors to create a distance matrix among species. Clustering methods were used to group species from the distance matrix, and multidimensional scaling methods were used to visualize the relations among species groups. Methods were also discussed for evaluating the sensitivity of the conclusions because of outliers or influential data points. We illustrate these methods with data from the landbird study conducted in northern Idaho. Simulation results are presented to compare the success of this method to alternative methods using Euclidean distance between coefficient vectors and to methods that do not use habitat association models. These simulations demonstrate that our Mahalanobis-distance-based method was nearly always better than Euclidean-distance-based methods or methods not based on habitat association models. The methods used to develop candidate species groups are easily explained to other scientists and resource managers since they mainly rely on classical multivariate statistical methods.     

A novel method for mapping reefs and subtidal rocky habitats using artificial neural networks

Reefs and subtidal rocky habitats are sites of high biodiversity and productivity which harbour commercially important species of fish and invertebrates. Although the conservation management of reef associated species has been informed using species distribution models (SDM) and community based approaches, to date their use has been constrained to specific regions where the locality and spatial extent of reefs is well known. Much of the world's subtidal habitats remain either undiscovered or unmapped, including coasts of intense human use. Consequently, to facilitate a stronger understanding of species-environmental relationships there is an urgent need for a cost and time effective standard method to map reefs at fine spatial resolutions across broad geographical extents. We used bathymetric data (∼250 m resolution) to calculate the local slope and curvature of the seabed. We then constructed artificial neural networks (ANNs) to forecast the probability of reef occurrence within grid cells as a function of bathymetric and slope variables. Testing over an independent data set not used in training showed that ANNs were able to accurately predict the location of reefs for 86% of all grid cells (Kappa = 0.63) without over fitting. The ANN with greatest support, combining bathymetric values of the target grid cell with the slope of adjacent grid cells, was used to map inshore reef locations around the Southern Australian coastline (∼250 m resolution). Broadly, our results show that reefs are identifiable from coarse-scale bathymetry data of the seabed. We anticipate that our research technique will strengthen systematic conservation planning tools in many regions of the world, by enabling the identification of rocky substratum and mapping in localities that remain poorly surveyed due to logistics or monetary constraints.     

A novel method for reducing acid mine drainage using green liquor dregs

When exposed to the weather, sulfidic minerals release sulfuric acid, metals and metalloids. This leachate can devastate nearby ecosystems for centuries. This article reports a novel barrier system based on green liquor dregs that reduces acid generation, is inexpensive, and is practical for implementation at operational mines. Two waste rock piles were constructed. One pile was left open to the atmosphere, and the other was sealed with green liquor dregs and partially capped with a polyethylene liner. This test was designed in collaboration with personnel from Boliden Mineral AB, a Swedish mining company in order to ensure that the results would be practical to implement at an operational mine. Leachate flow and chemistry were monitored in both piles over a period of 12 months. Effluent volume was 40 % lower in the test pile, while thirteen of the twenty elements assessed showed average concentrations that were 50 % lower in the test pile than in the control pile. For the most environmentally toxic elements As, Cd, Pb and Hg, reductions were even more significant, ranging between 67 and 87 %. These positive results are hypothesized to be primarily due to the chemical and physical characteristics of the green liquor dregs. The novelty of this result is that it is the first barrier system that fulfills the multiple needs of the Swedish mining industry: It is effective in reducing acid mine drainage while remaining both economical and practical. No other barrier system meets these requirements.     

A proposed methodology for assessing the quality of wildlife habitat

Measurement of the quality and quantity of wildlife habitat is a necessity in planning and analyzing water resource projects. A methodology that provides a means of examining the following major components of habitat quality is proposed: (1) the quantity of land uses, (2) the degree of interspersion of land use, aand (3) the stage of land management and vegetative types. The model can be calibrated to a specific region by experienced wildlife biologists and applied by field technicians using aerial photographs and field evaluation of randomly located points. Biologists familiar with a region develop transformation curves relating variables that represent the above three major components to standardizing factors that range from 0 to 1; these transformation curves reflect the sensitivity of wildlife populations to determinants of habitat quality. The methodology also provides the means for biologists to weigh each components as to its relative importance to the wildlife group or species. A weighted geometric mean of the components provides an indication of the overall quality of the habitat. Changes in the habitat components that may occur because of alternative project actions can be used to evaluate the effectiveness of the alternatives. The change in the index of habitat quality will indicate the need for mitigation in water resource projects. The system then can be used to identify areas for mitigation and the effects of mitigating measures.     

A trait-based test for habitat filtering: convex hull volume

Community assembly theory suggests that two processes affect the distribution of trait values within communities: competition and habitat filtering. Within a local community, competition leads to ecological differentiation of coexisting species, while habitat filtering reduces the spread of trait values, reflecting shared ecological tolerances. Many statistical tests for the effects of competition exist in the literature, but measures of habitat filtering are less well-developed. Here, we present convex hull volume, a construct from computational geometry, which provides an n-dimensional measure of the volume of trait space occupied by species in a community. Combined with ecological null models, this measure offers a useful test for habitat filtering. We use convex hull volume and a null model to analyze California woody-plant trait and community data. Our results show that observed plant communities occupy less trait space than expected from random assembly, a result consistent with habitat filtering.     

A permutation-based combination of sign tests for assessing habitat selection

The analysis of habitat selection in radio-tagged animals is approached by comparing the portions of use against the portions of availability observed for each habitat type. Since data are linearly dependent with singular variance-covariance matrices, standard multivariate statistical tests cannot be applied. To bypass the problem, compositional data analysis is customarily performed via log-ratio transform of sample observations. The procedure is criticized in this paper, emphasizing the several drawbacks which may arise from the use of compositional analysis. An alternative nonparametric solution is proposed in the framework of multiple testing. The habitat use is assessed separately for each habitat type by means of the sign test performed on the original observations. The resulting p values are combined in an overall test statistic whose significance is determined permuting sample observations. The theoretical findings of the paper are checked by simulation studies. Applications to case studies previously considered in literature are discussed.     

A novel hydrothermal method to synthesize brookite titanium dioxide nanosquares for efficient pollutant degradation

Environmental Chemistry Letters - Photocatalysis is a cost-effective method to degrade and remove pollutants, using semiconductor catalysts such as titanium dioxide. The brookite form of titanium...     

A set of associated statistical tests for spatial clustering

The purpose of this paper is to develop a set of associated statistical tests for spatial clustering. In particular, a set of three associated tests will be developed; these will correspond to the three types of tests set out by Besag and Newell (general tests, focused tests, and tests for the detection of clustering). The associated tests draw primarily, though not exclusively, upon existing tests and results. The principal contributions are based upon the score statistic for focused tests, which has been an important approach to testing for clustering around environmental hazards. The first contribution consists of the formulation of a global statistic for general tests that corresponds to focused score statistics, along with an assessment of the distribution of the statistic under the null hypothesis of no raised incidence. The local score statistics used for focused tests will have the property of summing to the global statistic used for the corresponding general test. Attention is also given to the maximum local score statistic for the “test for the detection of clustering”. The critical values of this statistic which are required for testing the null hypothesis are described. Application of the methods is made to leukemia data for central New York State.     

A dispersal-constrained habitat suitability model for predicting invasion of alpine vegetation

Developing tools to predict the location of new biological invasions is essential if exotic species are to be controlled before they become widespread. Currently, alpine areas in Australia are largely free of exotic plant species but face increasing pressure from invasive species due to global warming and intensified human use. To predict the potential spread of highly invasive orange hawkweed (Hieracium aurantiacum) from existing founder populations on the Bogong High Plains in southern Australia, we developed an expert-based, spatially explicit, dispersal-constrained, habitat suitability model. The model combines a habitat suitability index, developed from disturbance, site wetness, and vegetation community parameters, with a phenomenological dispersal kernel that uses wind direction and observed dispersal distances. After generating risk maps that defined the relative suitability of H. aurantiacum establishment across the study area, we intensively searched several locations to evaluate the model. The highest relative suitability for H. aurantiacum establishment was southeast from the initial infestations. Native tussock grasslands and disturbed areas had high suitability for H. aurantiacum establishment. Extensive field searches failed to detect new populations. Time-step evaluation using the location of populations known in 1998-2000, accurately assigned high relative suitability for locations where H. aurantiacum had established post-2003 (AUC [area under curve] = 0.855 +/- 0.035). This suggests our model has good predictive power and will improve the ability to detect populations and prioritize areas for ongoing monitoring.     

A composite measure of habitat loss for entire assemblages of species

Habitat destruction is among the greatest threats facing biodiversity, and it affects common and threatened species alike. However, metrics for communicating its impacts typically overlook the nonthreatened component of assemblages. This risks the loss of habitat going unreported for species that comprise the majority of assemblages. We adapted a widely used measure for summarizing researcher output (the h index) to provide a metric that describes natural habitat loss for entire assemblages, inclusive of threatened and nonthreatened species. For each of 447 Australian native terrestrial bird species, we combined information on their association with broad vegetation groups with distributional range maps to identify the difference between the estimated pre-European and current extents of potential habitat, defined as vegetation groups most closely associated with each species. From this, we calculated the loss index (LI), which revealed that 30% of native birds have each lost at least 30% of their potential natural habitat (LI = 30). At the subcontinental scale, LIs ranged from 15 in arid Australia to 61 in the highly transformed southeastern part of the country. Different subcomponents of the assemblage had different LI values. For example, Australia's parrots (n = 52 species) had an LI of 38, whereas raptors (n = 32 species) had an LI of 25. The LI is simple to calculate and can be determined using readily available spatial information on species distributions, native vegetation associations, and human impacts on natural land cover. This metric, including the curves used to deduce it, could complement other biodiversity indices if it is used for regional and global biodiversity assessments that compare the status of natural habitat extent for assemblages within and among nations, monitor changes through time, and forecast future changes to guide strategic land-use planning. The LI is an intuitive tool that can be used to summarize and communicate how human actions affect whole assemblages, not just threatened species.     

Probability distributions and statistical inference for axial data

Observations on axes which lack information on the direction of propagation are referred to as axial data. Such data are often encountered in enviromental sciences, e.g. observations on propagations of cracks or on faults in mining walls. Even though such observations are recorded as angles, circular probability models are inappropriate for such data since the constraint that observations lie only in [0, π) needs to be enforced. Probability models for such axial data are argued here to have a general structure stemming from that of wrapping a circular distribution on a semi-circle. In particular, we consider the most popular circular model, the von Mises or circular normal distribution, and derive the corresponding axial normal distribution. Certain properties of this distribution are established. Maximum likelihood estimation of its parameters are shown to be surprisingly, in contrast to trigonometric moment estimation, numerically quite appealing. Finally we illustrate our results by several real life axial data sets. Received: September 2004/ Revised: December 2004     

Predicting barrier passage and habitat suitability for migratory fish species

Fish migrate to spawn, feed, seek refuge from predators, and escape harmful environmental conditions. The success of upstream migration is limited by the presence of barriers that can impede the passage of fish. We used a spatially explicit modeling strategy to examine the effects of barriers on passage for 21 native and non-native migratory fish species and the amount of suitable habitat blocked for each species. Spatially derived physical parameter estimates and literature based fish capabilities and tolerances were used to predict fish passage success and habitat suitability. Both the fish passage and the habitat suitability models accurately predicted fish presence above barriers for most common, non-stocked species. The fish passage model predicted that barriers greater than or equal to 6 m block all migratory species. Chinook salmon (Oncorhynchus tshawytscha) was expected to be blocked the least. The habitat suitability model predicted that low gradient streams with intact habitat quality were likely to support the highest number of fish species. The fish passage and habitat suitability models were intended to be used by environmental managers as strategy development tools to prioritize candidate dams for field assessment and make decisions regarding the management of migratory fish populations.     

Composite sampling: A novel method to accomplish observational economy in environmental studies: A monograph introduction

This monograph on composite sampling, co-authored by Patil, Gore, and Taillie provides, for the first time, a most comprehensive statistical account of composite sampling as an ingenious environmental sampling method to help accomplish observational economy in a variety of environmental and ecological studies. Sampling consists of selection, acquisition, and quantification of a part of the population. But often what is desirable is not affordable, and what is affordable is not adequate. How do we deal with this dilemma? Operationally, composite sampling recognizes the distinction between selection, acquisition, and quantification. In certain applications, it is a common experience that the costs of selection and acquisition are not very high, but the cost of quantification, or measurement, is substantially high. In such situations, one may select a sample sufficiently large to satisfy the requirement of representativeness and precision and then, by combining several sampling units into composites, reduce the cost of measurement to an affordable level. Thus composite sampling offers an approach to deal with the classical dilemma of desirable versus affordable sample sizes, when conventional statistical methods fail to resolve the problem. Composite sampling, at least under idealized conditions, incurs no loss of information for estimating the population means. But an important limitation to the method has been the loss of information on individual sample values, such as the extremely large value. In many of the situations where individual sample values are of interest or concern, composite sampling methods can be suitably modified to retrieve the information on individual sample values that may be lost due to compositing. In this monograph, we present statistical solutions to these and other issues that arise in the context of applications of composite sampling. The monograph is published in the Monograph Series: Environmental and Ecological Statistics <http://www.springer.com/series/7506>, vol. 4, The authors are Patil, Ganapati P., Gore, Sharad D., Taillie, Charles with the monograph co-ordinates,1st Edition., 2011, XIII, 275 p. 47 illus., SpringerLink <http://www.springerlink.com/content/978-1-4419-7627-7>, Hardcover, >  ISBN 978-1-4419-7627-7.     

Estimates of minimum patch size depend on the method of estimation and the condition of the habitat

Minimum patch size for a viable population can be estimated in several ways. The density-area method estimates minimum patch size as the smallest area in which no new individuals are encountered as one extends the arbitrary boundaries of a study area outward. The density-area method eliminates the assumption of no variation in density with size of habitat area that accompanies other methods, but it is untested in situations in which habitat loss has confined populations to small areas. We used a variant of the density area method to study the minimum patch size for the gopher tortoise (Gopherus polyphemus) in Florida, USA, where this keystone species is being confined to ever smaller habitat fragments. The variant was based on the premise that individuals within populations are likely to occur at unusually high densities when confined to small areas, and it estimated minimum patch size as the smallest area beyond which density plateaus. The data for our study came from detailed surveys of 38 populations of the tortoise. For all 38 populations, the areas occupied were determined empirically, and for 19 of them, duplicate surveys were undertaken about a decade apart. We found that a consistent inverse density area relationship was present over smaller areas. The minimum patch size estimated from the density-area relationship was at least 100 ha, which is substantially larger than previous estimates. The relative abundance of juveniles was inversely related to population density for sites with relatively poor habitat quality, indicating that the estimated minimum patch size could represent an extinction threshold. We concluded that a negative density area relationship may be an inevitable consequence of excessive habitat loss. We also concluded that any detrimental effects of an inverse density area relationship may be exacerbated by the deterioration in habitat quality that often accompanies habitat loss. Finally, we concluded that the value of any estimate of minimum patch size as a conservation tool is compromised by excessive habitat loss.