Inferring the Spatial Distribution of Regolith Properties Using Surface Measurable Features

The aim of this research is to determine to what extent properties of the regolith may be inferred using only features easily measured from the surface. To address this research question, a set of regolith properties from Weipa, Queensland, Australia, are analysed. The set contains five variables, oxides of Aluminium, Iron, Silica and Titanium, as well as Depth to Ironstone. This last represents the depth of the layer from which the oxides are sampled.¶ The research question is addressed in two ways. First, locations where the properties are related to modern surface hydrology are assessed using spatially explicit analyses. This is done by comparing the results of spatial association statistics using geometric and watershed-based spatial samples. Second, correlations are sought for between the regolith properties and geomorphometric indices of land surface morphology and Landsat Thematic Mapper spectral response. This is done using spatially implicit Artificial Neural Networks (ANN) and spatially explicit Geographically Weighted Regression (GWR). The results indicate that the degree to which regolith properties are related to surface measurable features is limited and spatially variable.¶ ... ¶ The implications of these results are significant for anyone intending to generate spatial datasets of regolith properties. If there is a low spatial density of sample data, then the effects of landscape evolution can reduce the utility of any analysis results. Instead, spatially dense, direct measurements of subsurface regolith properties are needed. While these may not be a direct measurement of the property of interest, they may provide useful additional information by which these may be inferred

Using Australian Virtual Herbarium data to find all the woody rain forest plants in Australia

Data bases that provide continental and global scale information about species distributions provide a valuable resource for environmental, ecological and evolutionary research. However to bring a large dataset to a standard that is suitable for quantitative analysis, data quality needed to be checked. Here we provide a worked example using a large dataset (c. 320,000 records) from Australia’s Virtual Herbarium (AVH) database, based on an initial data request for full distribution data for c. 2600 woody rain forest species known to occur in Australia. To reconcile inconsistencies around taxonomic identity prior to merging with our trait data-base, and resolve issues around spatial resolution and accuracy, we implemented extensive data filtering using a ‘cloud-based’ solution (Google Refine). This systematic process resulted in 1) the removal of close to 45% of the records originally downloaded, and 2) a clean and powerful data set based on herbarium backed distribution records for Australia’s woody rain forest species. Such resources can contribute significantly to improving research outcomes related to understanding Australia’s vegetation

The impact of seasonal variability in wildlife populations on the predicted spread of foot and mouth disease

Modeling potential disease spread in wildlife populations is important for predicting, responding to and recovering from a foreign animal disease incursion such as foot and mouth disease (FMD). We conducted a series of simulation experiments to determine how seasonal estimates of the spatial distribution of white-tailed deer impact the predicted magnitude and distribution of potential FMD outbreaks. Outbreaks were simulated in a study area comprising two distinct ecoregions in South Texas, USA, using a susceptible-latent-infectious-resistant geographic automata model (Sirca). Seasonal deer distributions were estimated by spatial autoregressive lag models and the normalized difference vegetation index. Significant (P < 0.0001) differences in both the median predicted number of deer infected and number of herds infected were found both between seasons and between ecoregions. Larger outbreaks occurred in winter within the higher deer-density ecoregion, whereas larger outbreaks occurred in summer and fall within the lower deer-density ecoregion. Results of this simulation study suggest that the outcome of an FMD incursion in a population of wildlife would depend on the density of the population infected and when during the year the incursion occurs. It is likely that such effects would be seen for FMD incursions in other regions and countries, and for other diseases, in cases in which a potential wildlife reservoir exists. Study findings indicate that the design of a mitigation strategy needs to take into account population and seasonal characteristics

Representation of animal distributions in space: how geostatistical estimates impact simulation modeling of foot-and-mouth disease spread

Modeling potential disease spread in wildlife populations is important for predicting, responding to and recovering from a foreign animal disease incursion. To make spatial epidemic predictions, the target animal species of interest must first be represented in space. We conducted a series of simulation experiments to determine how estimates of the spatial distribution of white-tailed deer impact the predicted magnitude and distribution of foot-and-mouth disease (FMD) outbreaks. Outbreaks were simulated using a susceptible-infected-recovered geographic automata model. The study region was a 9-county area (24 000 km$^{2})$ of southern Texas. Methods used for creating deer distributions included dasymetric mapping, kriging and remotely sensed image analysis. The magnitudes and distributions of the predicted outbreaks were evaluated by comparing the median number of deer infected and median area affected (km$^{2})$, respectively. The methods were further evaluated for similar predictive power by comparing the model predicted outputs with unweighted pair group method with arithmetic mean (UPGMA) clustering. There were significant differences in the estimated number of deer in the study region, based on the geostatistical estimation procedure used (range: 385 939–768 493). There were also substantial differences in the predicted magnitude of the FMD outbreaks (range: 1 563–8 896) and land area affected (range: 56–447 km$^{2})$ for the different estimated animal distributions. UPGMA clustering indicated there were two main groups of distributions, and one outlier. We recommend that one distribution from each of these two groups be used to model the range of possible outbreaks. Methods included in cluster 1 (such as county-level disaggregation) could be used in conjunction with any of the methods in cluster 2, which included kriging, NDVI split by ecoregion, or disaggregation at the regional level, to represent the variability in the model predicted outbreak distributions. How animal populations are represented needs to be considered in all spatial disease spread models

Morphometric characterisation of landform from DEMs

We describe a method of morphometric characterisation of landform from DEMs. The method is implemented by first classifying every location into morphometric classes based on the mathematical shape of a locally fitted quadratic surface and its positional relationship with the analysis window. Single-scale fuzzy terrain indices of peakness, pitness, passness, ridgeness, and valleyness are then calculated based on the distance of the analysis location from the ideal cases. These can then be combined into multi-scale terrain indices to summarise terrain information across different operational scales. The algorithm has four characteristics: (1) the ideal cases of different geomorphometric features are simply and clearly defined; (2) the output is spatially continuous to reflect the inherent fuzziness of geomorphometric features; (3) the output is easily combined into a multi-scale index across a range of operational scales; and (4) the standard general morphometric parameters are quantified as the first and second order derivatives of the quadratic surface. An additional benefit of the quadratic surface is the derivation of the R2 goodness of fit statistic, which allows an assessment of both the reliability of the results and the complexity of the terrain. An application of the method using a test DEM indicates that the single- and multi-scale terrain indices perform well when characterising the different geomorphometric features

The Biodiversity and Climate Change Virtual Laboratory: Where ecology meets big data

Advances in computing power and infrastructure, increases in the number and size of ecological and environmental datasets, and the number and type of data collection methods, are revolutionizing the field of Ecology. To integrate these advances, virtual laboratories offer a unique tool to facilitate, expedite, and accelerate research into the impacts of climate change on biodiversity. We introduce the uniquely cloud-based Biodiversity and Climate Change Virtual Laboratory (BCCVL), which provides access to numerous species distribution modelling tools; a large and growing collection of biological, climate, and other environmental datasets; and a variety of experiment types to conduct research into the impact of climate change on biodiversity. Users can upload and share datasets, potentially increasing collaboration, cross-fertilisation of ideas, and innovation among the user community. Feedback confirms that the BCCVL's goals of lowering the technical requirements for species distribution modelling, and reducing time spent on such research, are being met

Phylogenetic diversity and conservation of crop wild relatives in Colombia

Crop wild relatives (CWR) are an important agricultural resource as they contain genetic traits not found in cultivated species due to localized adaptation to unique environmental and climatic conditions. Phylogenetic diversity (PD) measures the evolutionary relationship of species using the tree of life. Our knowledge of CWR PD in neotropical regions is in its infancy. We analysed the distribution of CWR PD across Colombia and assessed its conservation status. The areas with the largest concentration of PD were identified as being in the northern part of the central and western Andean mountain ranges and the Pacific region. These centres of high PD were comprised of predominantly short and closely related branches, mostly of species of wild tomatoes and black peppers. In contrast, the CWR PD in the lowland ecosystems of the Amazon and Orinoquia regions had deeply diverging clades predominantly represented by long and distantly related branches (i.e. tuberous roots, grains and cacao). We categorized 50 (52.6%) of the CWR species as 'high priority', 36 as 'medium priority' and nine as 'low priority' for further ex-situ and in situ conservation actions. New areas of high PD and richness with large ex-situ gap collections were identified mainly in the northern part of the Andes of Colombia. We found that 56% of the grid cells with the highest PD values were unprotected. These baseline data could be used to create a comprehensive national strategy of CWR conservation in Colombia

Integrating Survey and Molecular Approaches to Better Understand Wildlife Disease Ecology

Infectious wildlife diseases have enormous global impacts, leading to human pandemics, global biodiversity declines and socio-economic hardship. Understanding how infection persists and is transmitted in wildlife is critical for managing diseases, but our understanding is limited. Our study aim was to better understand how infectious disease persists in wildlife populations by integrating genetics, ecology and epidemiology approaches. Specifically, we aimed to determine whether environmental or host factors were stronger drivers of Salmonella persistence or transmission within a remote and isolated wild pig (Sus scrofa) population. We determined the Salmonella infection status of wild pigs. Salmonella isolates were genotyped and a range of data was collected on putative risk factors for Salmonella transmission. We a priori identified several plausible biological hypotheses for Salmonella prevalence (cross sectional study design) versus transmission (molecular case series study design) and fit the data to these models. There were 543 wild pig Salmonella observations, sampled at 93 unique locations. Salmonella prevalence was 41% (95% confidence interval [CI]: 37-45%). The median Salmonella DICE coefficient (or Salmonella genetic similarity) was 52% (interquartile range [IQR]: 42-62%). Using the traditional cross sectional prevalence study design, the only supported model was based on the hypothesis that abundance of available ecological resources determines Salmonella prevalence in wild pigs. In the molecular study design, spatial proximity and herd membership as well as some individual risk factors (sex, condition score and relative density) determined transmission between pigs. Traditional cross sectional surveys and molecular epidemiological approaches are complementary and together can enhance understanding of disease ecology: abundance of ecological resources critical for wildlife influences Salmonella prevalence, whereas Salmonella transmission is driven by local spatial, social, density and individual factors, rather than resources. This enhanced understanding has implications for the control of diseases in wildlife populations. Attempts to manage wildlife disease using simplistic density approaches do not acknowledge the complexity of disease ecology

Continental scale patterns and predictors of fern richness and phylogenetic diversity

Because ferns have a wide range of habitat preferences and are widely distributed, they are an ideal group for understanding how diversity is distributed. Here we examine fern diversity on a broad-scale using standard and corrected richness measures as well as phylogenetic indices; in addition we determine the environmental predictors of each diversity metric. Using the combined records of Australian herbaria, a dataset of over 60,000 records was obtained for 89 genera to infer richness. A molecular phylogeny of all the genera was constructed and combined with the herbarium records to obtain phylogenetic diversity patterns. A hotspot of both taxic and phylogenetic diversity occurs in the Wet Tropics of northeastern Australia. Although considerable diversity is distributed along the eastern coast, some important regions of diversity are identified only after sample-standardization of richness and through the phylogenetic metric. Of all of the metrics, annual precipitation was identified as the most explanatory variable, in part, in agreement with global and regional fern studies. However, precipitation was combined with a different variable for each different metric. For corrected richness, precipitation was combined with temperature seasonality, while correlation of phylogenetic diversity to precipitation plus radiation indicated support for the species-energy hypothesis. Significantly high and significantly low phylogenetic diversity were found in geographically separate areas. These separate areas correlated with different climatic conditions such as seasonality in precipitation. The phylogenetic metrics identified additional areas of significant diversity, some of which have not been revealed using traditional taxonomic analyses, suggesting that different ecological and evolutionary processes have operated over the continent. Our study demonstrates that it is possible and vital to incorporate evolutionary metrics when inferring biodiversity hotspots from large compilations of data

The Biodiversity and Climate Change Virtual Laboratory: How Ecology and Big Data can be utilised in the fight against vector-borne diseases

Advances in computing power and infrastructure, increases in the number and size of ecological and environmental datasets, and the number and type of data collection methods, are revolutionizing the field of Ecology. To integrate these advances, virtual laboratories offer a unique tool to facilitate, expedite, and accelerate research into the impacts of climate change on biodiversity. We introduce the uniquely cloud-based Biodiversity and Climate Change Virtual Laboratory (BCCVL), which provides access to numerous species distribution modelling tools; a large and growing collection of biological, climate, and other environmental datasets, as well as a variety of experiment types to conduct research into the impact of climate change on biodiversity. Users can upload and share datasets, potentially increasing collaboration and cross-fertilisation of ideas and innovation among the user community. Feedback confirms that the BCCVL's goals of lowering the technical requirements for species distribution modelling, and reducing time spent on such research, are being met. We present a case study that illustrates the utility of the BCCVL as a research tool that can be applied to the problem of vector borne diseases and the likelihood of climate change altering their future distribution across Australia. This case study presents the preliminary results of an ensemble modelling experiment which employs multiple (15) different species distribution modelling algorithms to model the distribution of one of the main mosquito vectors of the most common vector borne disease in Australia: Ross River Virus (RRV). We use the BCCVL to do future projection of these models with future climates based on two extreme emissions scenarios, for multiple years. Our results show a large range in both the modelled current distribution, and projected future distribution, of the mosquito species studied. Most models (that were built using different algorithms) show somewhat similar current distributions of the species however there are three models that are obvious outliers. The projected models show a similar range in the distribution of the species, with some models indicating a fewer areas (and also areas with a lower probability of occurrence in specific areas) where the species is likely to be found under a climate change scenario. However, a majority of models show an expanded distribution, with some areas that have a greater probability of the occurrence of this species; this will provide a more robust indication of future distribution for policy makers and planners, than if just one or a few models had been employed. The economic and human health impact of vector borne diseases underline the importance of scientifically sound projections of the future spread of common disease vectors such as mosquitos under various climate change scenarios. This is because such information is essential for policy–makers to identify vulnerable communities and to better manage outbreaks and potential epidemics of such diseases. The BCCVL has provided the means to effectively and robustly bracket multiple sources of uncertainty in the future spread of RRV: this study focuses on two of these - the future distribution of a primary mosquito vector of the disease under two extreme scenarios of climate change. Research is underway to expand our analysis to take into account more sources of uncertainty: more vector and amplifying host species, emissions scenarios, and future climate projections from a range of different global climate model