Why do some species have geographically varying responses to fire history?

A capacity to predict the effects of fire on biota is critical for conservation in fire-prone regions as it assists managers to anticipate the outcomes of different approaches to fire management. The task is complicated because species' responses to fire can vary geographically. This poses challenges, both for conceptual understanding of post-fire succession and fire management. We examine two hypotheses for why species may display geographically varying responses to fire. 1) Species' post-fire responses are driven by vegetation structure, but vegetation - fire relationships vary spatially (the 'dynamic vegetation' hypothesis). 2) Regional variation in ecological conditions leads species to select different post-fire ages as habitat (the 'dynamic habitat' hypothesis). Our case study uses data on lizards at 280 sites in a ~ 100 000 km2 region of south-eastern Australia. We compared the predictive capacity of models based on 1) habitat associations, with models based on 2) fire history and vegetation type, and 3) fire history alone, for four species of lizards. Habitat association models generally out-performed fire history models in terms of predictive capacity. For two species, habitat association models provided good discrimination capacity even though the species showed geographically varying post-fire responses. Our results support the dynamic vegetation hypothesis, that spatial variation in relationships between fire and vegetation structure results in regional variation in fauna-fire relationships. These observations explain how the widely recognised 'habitat accommodation' model of animal succession can be conceptually accurate yet predictively weak. © 2014 The Authors.

The influence of non-climate predictors at local and landscape resolutions depends on the autecology of the species

Species distribution models have come under criticism for being too simplistic for making robust future forecasts, partly because they assume that climate is the main determinant of geographical range at large spatial extents and coarse resolutions, with non-climate predictors being important only at finer scales. We suggest that this paradigm might be obscured by species movement patterns. To explore this we used contrasting kangaroo (family Macropodidae) case studies: two species with relatively small, stable home ranges (Macropus giganteus and M.robustus) and three species with more extensive, adaptive ranging behaviour (M.antilopinus, M.fuliginosus and M.rufus). We predicted that non-climate predictors will be most influential to model fit and predictive performance at local spatial resolution for the former species and at landscape resolution for the latter species. We compared residuals autocovariate - boosted regression tree (RAC-BRT) model statistics with and without species-specific non-climate predictors (habitat, soil, fire, water and topography), at local- and landscape-level spatial resolutions (5 and 50km). As predicted, the influence of non-climate predictors on model fit and predictive performance (compared with climate-only models) was greater at 50 compared with 5km resolution for M.rufus and M.fuliginosus and the opposite trend was observed for M.giganteus. The results for M.robustus and M.antilopinus were inconclusive. Also notable was the difference in inter-scale importance of climate predictors in the presence of non-climate predictors. In conclusion, differences in autecology, particularly relating to space use, may contribute to the importance of non-climate predictors at a given scale, not model scale per se. Further exploration of this concept across a range of species is encouraged and findings may contribute to more effective conservation and management of species at ecologically meaningful scales. © 2014 Ecological Society of Australia.

Integrating biological and social values when prioritizing places for biodiversity conservation

The consideration of information on social values in conjunction with biological data is critical for achieving both socially acceptable and scientifically defensible conservation planning outcomes. However, the influence of social values on spatial conservation priorities has received limited attention and is poorly understood. We present an approach that incorporates quantitative data on social values for conservation and social preferences for development into spatial conservation planning. We undertook a public participation GIS survey to spatially represent social values and development preferences and used species distribution models for 7 threatened fauna species to represent biological values. These spatially explicit data were simultaneously included in the conservation planning software Zonation to examine how conservation priorities changed with the inclusion of social data. Integrating spatially explicit information about social values and development preferences with biological data produced prioritizations that differed spatially from the solution based on only biological data. However, the integrated solutions protected a similar proportion of the species' distributions, indicating that Zonation effectively combined the biological and social data to produce socially feasible conservation solutions of approximately equivalent biological value. We were able to identify areas of the landscape where synergies and conflicts between different value sets are likely to occur. Identification of these synergies and conflicts will allow decision makers to target communication strategies to specific areas and ensure effective community engagement and positive conservation outcomes.

Suitability of chlorine bulk decay models for planning and management of water distribution systems

Effective disinfection planning and management in large, complex water distribution systems requires an accurate network water quality model. This model should be based on reaction kinetics, which describes disinfectant loss from bulk water over time, within experimental error. Models in the literature were reviewed for their ability to meet this requirement in real networks. Essential features were identified as accuracy, simplicity, computational efficiency, and ability to describe consistently the effects of initial chlorine dose, temperature variation, and successive rechlorinations. A reaction scheme of two organic constituents reacting with free chlorine was found to be necessary and sufficient to provide the required features. Recent release of the multispecies extension (MSX) to EPANET and MWH Soft's H2OMap Water MSX network software enables users to implement this and other multiple-reactant bulk decay models in real system simulations.

Comparison of Two Methods for Caluculating the Partition Functions of Various Spatial Statistical Models

Likelihood computation in spatial statistics requires accurate and efficient calculation of the normalizing constant (i.e. partition function) of the Gibbs distribution of the model. Two available methods to calculate the normalizing constant by Markov chain Monte Carlo methods are compared by simulation experiments for an Ising model, a Gaussian Markov field model and a pairwise interaction point field model.

Distribution of power requirements during yarn winding in ring spinning

A model of a yam package is established for a ring spinning system. The yarn layer, surface area, and mass of the yam package are formulated with respect to the diameters of the empty bobbin and full yarn package, yarn count, and yarn winding-on time. Based on the principles of dynamics and aerodynamics, models of the power requirements for overcoming the skin friction drag, increasing the kinetic energy of the yarn package (bobbin and wound yarn), and overcoming the yarn wind-on tension are developed. The skin friction coefficient on the surface of a rotating yam package is obtained from experiment. The power distribution during yam packaging is discussed based on a case study. The results indicate that overcoming the skin friction drag during yarn winding consumes the largest amount of energy. The energy required to overcome the yarn wind-on tension is also significant.

Evaluation of three daily rainfall generation models for SWAT

The Soil and Water Assessment Tool (SWAT) is a hydrologic model that was developed to predict the long-term impacts of land use change on the water balance of large catchments. Stochastic models are used to generate the daily rainfall sequences needed to conduct long-term, continuous simulations with SWAT. The objective of this study was to evaluate the performances of three daily rainfall generation models. The models evaluated were the modified Daily and Monthly Mixed (DMMm) model, skewed normal distribution (SKWD) model and modified exponential distribution (EXPD) model. The study area was the Woady Yaloak River catchment (306 km2) located in southwest Victoria, Australia. The models were assessed on their ability to preserve annual, monthly and daily statistical characteristics of the historical rainfall and runoff. The mean annual, monthly, and daily rainfall was preserved satisfactorily by the models. The DMMm model reproduced the standard deviation of annual and monthly rainfall better than the SKWD and EXPD models. Overall, the DMMm model performed marginally better than the SKWD model at reproducing the statistical characteristics of the historical rainfall record at the various time scales. The performance of the EXPD model was found to be inferior to the performances of the DMMm and SKWD models. The models reproduced the mean annual, monthly, and daily runoff relatively well, although the DMMm and SKWD models were found to preserve these statistics marginally better than the EXPD model. None of the models managed to reproduce the standard deviation of annual, monthly, and daily runoff adequately.

Predicting the distribution of discrete spatial events using artificial neural networks

Although the development of geographic information system (GIS) technology and digital data manipulation techniques has enabled practitioners in the geographical and geophysical sciences to make more efficient use of resource information, many of the methods used in forming spatial prediction models are still inherently based on traditional techniques of map stacking in which layers of data are combined under the guidance of a theoretical domain model. This paper describes a data-driven approach by which Artificial Neural Networks (ANNs) can be trained to represent a function characterising the probability that an instance of a discrete event, such as the presence of a mineral deposit or the sighting of an endangered animal species, will occur over some grid element of the spatial area under consideration. A case study describes the application of the technique to the task of mineral prospectivity mapping in the Castlemaine region of Victoria using a range of geological, geophysical and geochemical input variables. Comparison of the maps produced using neural networks with maps produced using a density estimation-based technique demonstrates that the maps can reliably be interpreted as representing probabilities. However, while the neural network model and the density estimation-based model yield similar results under an appropriate choice of values for the respective parameters, the neural network approach has several advantages, especially in high dimensional input spaces.

Spatial and temporal distribution of solute leaching in heterogeneous soils: analysis and application to multisampler lysimeter data

Accurate assessment of the fate of salts, nutrients, and pollutants in natural, heterogeneous soils requires a proper quantification of both spatial and temporal solute spreading during solute movement. The number of experiments with multisampler devices that measure solute leaching as a function of space and time is increasing. The breakthrough curve (BTC) can characterize the temporal aspect of solute leaching, and recently the spatial solute distribution curve (SSDC) was introduced to describe the spatial solute distribution. We combined and extended both concepts to develop a tool for the comprehensive analysis of the full spatio-temporal behavior of solute leaching. The sampling locations are ranked in order of descending amount of total leaching (defined as the cumulative leaching from an individual compartment at the end of the experiment), thus collapsing both spatial axes of the sampling plane into one. The leaching process can then be described by a curved surface that is a function of the single spatial coordinate and time. This leaching surface is scaled to integrate to unity, and termed S can efficiently represent data from multisampler solute transport experiments or simulation results from multidimensional solute transport models. The mathematical relationships between the scaled leaching surface S, the BTC, and the SSDC are established. Any desired characteristic of the leaching process can be derived from S. The analysis was applied to a chloride leaching experiment on a lysimeter with 300 drainage compartments of 25 cm2 each. The sandy soil monolith in the lysimeter exhibited fingered flow in the water-repellent top layer. The observed S demonstrated the absence of a sharp separation between fingers and dry areas, owing to diverging flow in the wettable soil below the fingers. Times-to-peak, maximum solute fluxes, and total leaching varied more in high-leaching than in low-leaching compartments. This suggests a stochastic–convective transport process in the high-flow streamtubes, while convection–dispersion is predominant in the low-flow areas. S can be viewed as a bivariate probability density function. Its marginal distributions are the BTC of all sampling locations combined, and the SSDC of cumulative solute leaching at the end of the experiment. The observed S cannot be represented by assuming complete independence between its marginal distributions, indicating that S contains information about the leaching process that cannot be derived from the combination of the BTC and the SSDC.

Whale distribution in relation to prey abundance and oceanographic processes in the shelf waters of the Western Antarctic Peninsula

The Western Antarctic Peninsula (WAP) is a biologically rich area supporting large standing stocks of krill and top predators (including whales, seals and seabirds). Physical forcing greatly affects productivity, recruitment, survival and distribution of krill in this area. In turn, such interactions are likely to affect the distribution of baleen whales. The Southern Ocean GLOBEC research program aims to explore the relationships and interactions between the environment, krill and predators around Marguerite Bay (WAP) in autumn 2001 and 2002. Bathymetric and environmental variables including acoustic backscattering as an indicator of prey abundance were used to model whale distribution patterns. We used an iterative approach employing (1) classification and regression tree (CART) models to identify oceanographic and ecological variables contributing to variability in humpback Megaptera novaeangliae and minke Balaenoptera acutorstrata whale distribution, and (2) generalized additive models (GAMs) to elucidate functional ecological relationships between these variables and whale distribution. The CART models indicated that the cetacean distribution was tightly coupled with zooplankton acoustic volume backscatter in the upper (25 to 100 m), and middle (100 to 300 m) portions of the water column. Whale distribution was also related to distance from the ice edge and bathymetric slope. The GAMs indicated a persistent, strong, positive relationship between increasing zooplankton volume and whale relative abundance. Furthermore, there was a lower limit for averaged acoustic volume backscatter of zooplankton below which the relationship between whales and prey was not significant. The GAMs also supported an annual relationship between whale distribution, distance from the ice edge and bathymetric slope, suggesting that these are important features for aggregating prey. Our results demonstrate that during the 2 yr study, whales were consistently and predictably associated with the distribution of zooplankton. Thus, humpback and minke whales may be able to locate physical features and oceanographic processes that enhance prey aggregation.

Numerical investigation of austenite grain size distribution in square-diamond pass hot bar rolling

In this study, the austenite grain size (AGS) for hot bar rolling of AISI4135 steel was predicted based on two different AGS evolution models available in the literature. In order to predict the AGS more accurately, both models were integrated with a three-dimensional non-isothermal finite element program by implementing a modified additivity rule. The predicted results based on two models for the square-diamond (S-D) and round-oval (R-O) pass bar rolling processes were compared with the experimental data available in the literature. Then, numerical predictions depending on various process parameters such as interpass time, temperature, and roll speed were made to compare both models and investigate the effect of these parameters on the AGS distributions. Such numerical results were found to be beneficial to understand the effect of the microstructure evolution model on the rolling processes better and control the processes more accurately.

A flow control methodology for the rapid generation of vehicle traffic models

This paper presents a conveyor-based methodology to model complex vehicle flows common to factory and distribution warehouse facilities. The AGV and human path modelling techniques available in many commercial discrete event simulation packages require extensive knowledge and time to implement even the simplest flow control rules for multiple vehicle interaction. Although discrete event simulation is accepted as an effective tool to model vehicle delivery movements, human paths and delivery schedules for modern assembly lines, the time to generate accurate models is a significant limitation of existing simulation-based optimisation methodologies. The flow control method has been successfully implemented using two commercial simulation packages. It provides a realistic visual representation, as well as accurate statistical results, and reduces the model development process cost.

Correlation structure and model selection for negative binomial distribution in GEE

A new model selection criterion, termed as the “quasi-likelihood under the independence model criterion” (QIC), was proposed by Pan (2001) for GEE models. Cui (2007) developed a general computing program to implement the QIC method for a range of statistical distributions. However, only a special case of the negative binomial distribution was considered in Cui (2007), where the dispersion parameter equals to unity. This article introduces a new computing program that can be applied for the general negative binomial model, where the dispersion parameter can be any fixed value. An example is also given in this article.

Predicting spatio-temporal distribution for eastern Australian birds using birds Australia's atlas data : survey method, habitat and seasonal effects

Faunal atlases are landscape-level survey collections that can be used for describing spatial and temporal patterns of distribution and densities. They can also serve as a basis for quantitative analysis of factors that may influence the distributions of species. We used a subset of Birds Australia’s Atlas of Australian Birds data (January 1998 to December 2002) to examine the spatio-temporal distribution patterns of 280 selected species in eastern Australia (17–37°S and 136–152°E). Using geographical information systems, this dataset was converted into point coverage and overlaid with a vegetation polygon layer and a half-degree grid. The exploratory data analysis involved calculating species-specific reporting rates spatially, per grid and per vegetation unit, and also temporally, by month and year. We found high spatio-temporal variability in the sampling effort. Using generalised linear models on unaggregated point data, the influences of four factors – survey method and month, geographical location and habitat type – were analysed for each species. When counts of point data were attributed to grid-cells, the total number of species correlated with the total number of surveys, while the number of records per species was highly variable. Surveys had high interannual location fidelity. The predictive values of each of the four factors were species-dependent. Location and habitat were correlated and highly predictive for species with restricted distribution and strong habitat preference. Month was only of importance for migratory species. The proportion of incidental sightings was important for extremely common or extremely rare species. In conclusion, behaviour of species differed sufficiently to require building a customized model for each species to predict distribution. Simple models were effective for habitat specialists with restricted ranges, but for generalists with wide distributions even complex models gave poor predictions.