Shawn W. Laffan

Phylogenetic endemism: a new approach for identifying geographical concentrations of evolutionary history

We present a new, broadly applicable measure of the spatial restriction of phylogenetic diversity, termed phylogenetic endemism (PE). PE combines the widely used phylogenetic diversity and weighted endemism measures to identify areas where substantial components of phylogenetic diversity are restricted. Such areas are likely to be of considerable importance for conservation. PE has a number of desirable properties not combined in previous approaches. It assesses endemism consistently, independent of taxonomic status or level, and independent of previously defined political or biological regions. The results can be directly compared between areas because they are based on equivalent spatial units. PE builds on previous phylogenetic analyses of endemism, but provides a more general solution for mapping endemism of lineages. We illustrate the broad applicability of PE using examples of Australian organisms having contrasting life histories: pea‐flowered shrubs of the genus Daviesia (Fabaceae) and the Australian species of the Australo‐Papuan tree frog radiation within the family Hylidae.

Phylogenetic measures of biodiversity and neo- and paleo-endemism in Australian Acacia

Understanding spatial patterns of biodiversity is critical for conservation planning, particularly given rapid habitat loss and human-induced climatic change. Diversity and endemism are typically assessed by comparing species ranges across regions. However, investigation of patterns of species diversity alone misses out on the full richness of patterns that can be inferred using a phylogenetic approach. Here, using Australian Acacia as an example, we show that the application of phylogenetic methods, particularly two new measures, relative phylogenetic diversity and relative phylogenetic endemism, greatly enhances our knowledge of biodiversity across both space and time. We found that areas of high species richness and species endemism are not necessarily areas of high phylogenetic diversity or phylogenetic endemism. We propose a new method called categorical analysis of neo- and paleo-endemism (CANAPE) that allows, for the first time, a clear, quantitative distinction between centres of neo- and paleo-endemism, useful to the conservation decision-making process.

Effect of error in the DEM on environmental variables for predictive vegetation modelling

Abstract Question: Predictive vegetation modelling relies on the use of environmental variables, which are usually derived from a base data set with some level of error, and this error is propagated to any subsequently derived environmental variables. The question for this study is: What is the level of error and uncertainty in environmental variables based on the error propagated from a Digital Elevation Model (DEM) and how does it vary for both direct and indirect variables? Location: Kioloa region, New South Wales, Australia Methods: The level of error in a DEM is assessed and used to develop an error model for analysing error propagation to derived environmental variables. We tested both indirect (elevation, slope, aspect, topographic position) and direct (average air temperature, net solar radiation, and topographic wetness index) variables for their robustness to propagated error from the DEM. Results: It is shown that the direct environmental variable net solar radiation is less affected by error in the DEM than the indirect variables aspect and slope, but that regional conditions such as slope steepness and cloudiness can influence this outcome. However, the indirect environmental variable topographic position was less affected by error in the DEM than topographic wetness index. Interestingly, the results disagreed with the current assumption that indirect variables are necessarily less sensitive to propagated error because they are less derived. Conclusions: The results indicate that variables exhibit both systematic bias and instability under uncertainty. There is a clear need to consider the sensitivity of variables to error in their base data sets in addition to the question of whether to use direct or indirect variables. Abbreviations: AML = Arc/INFO Macro Language; DEM = Digital Elevation Model; GPS = Global Positioning System; HDOP = Horizontal Dilution of Precision; TWI = Topographic Wetness Index; VDOP = Vertical Dilution of Precision.

The molecular basis of quantitative variation in foliar secondary metabolites in Eucalyptus globulus

Eucalyptus is characterized by high foliar concentrations of plant secondary metabolites with marked qualitative and quantitative variation within a single species. Secondary metabolites in eucalypts are important mediators of a diverse community of herbivores. We used a candidate gene approach to investigate genetic associations between 195 single nucleotide polymorphisms (SNPs) from 24 candidate genes and 33 traits related to secondary metabolites in the Tasmanian Blue Gum (Eucalyptus globulus). We discovered 37 significant associations (false discovery rate (FDR) Q < 0.05) across 11 candidate genes and 19 traits. The effects of SNPs on phenotypic variation were within the expected range (0.018 < r(2) < 0.061) for forest trees. Whereas most marker effects were nonadditive, two alleles from two consecutive genes in the methylerythritol phosphate pathway (MEP) showed additive effects. This study successfully links allelic variants to ecologically important phenotypes which can have a large impact on the entire community. It is one of very few studies to identify the genetic variants of a foundation tree that influences ecosystem function.

Spatial distribution of species richness and endemism of the genus Acacia in Australia

The aim of this study is to identify and map the spatial distribution of species richness and endemism of the genus Acacia in Australia. A database of 171 758 geo-referenced herbarium records representing 1020 Acacia species was assembled and aggregated to a 0.25° grid cell resolution. A neighbourhood analysis of one-cell radius was applied to each of the grid cells to map the spatial patterns of species richness and endemism. The primary centres of species richness are in accordance with previous results, occurring in the South-West Botanical Province in Western Australia, the MacPherson-Macleay overlap and the Central Coast of the Sydney Sandstone region. We identify 21 centres of endemism, of which six were previously unrecognised. The primary centres of endemism are located in South-West Western Australia, the Kimberley District and the Wet Tropics in Queensland. The South-West Botanical Province in Western Australia contained the greatest number of regions with the highest number of endemic species of Acacia. A randomisation test showed that our 21 centres of endemism were significantly different from random. The majority of centres of Acacia endemism were incongruent with the centres of species richness, with only three grid cells in the top 1% for both measures. We also confirm that South-West Western Australia is a region of very high species richness and endemism, in accordance with its status as a global hotspot of biodiversity.

Gambling with randomness: the use of pseudo-random number generators in GIS

Analyses within the field of GIS are increasingly applying stochastic methods and systems that make use of pseudo-random number generators (PRNGs). Examples include Monte Carlo techniques, dynamic modelling, stochastic simulation, artificial life and simulated data development. PRNGs have inherent biases, and this will in turn bias any analyses using them. Therefore, the validity of stochastic analyses is reliant on the PRNG employed. Despite this, the effect of PRNGs in spatial analyses has never been completely explored, particularly a comparison of different PRNGs. Exacerbating the problem is that GIS articles applying Monte Carlo or other stochastic methods rarely report which PRNG is employed. It thus appears likely that GIS researchers rarely, if ever, check the suitability of the PRNG employed for their analyses or simulations. This paper presents a discussion of some of the characteristics of PRNGs and specific issues from a geospatial standpoint, including a demonstration of the differences in the results of a Monte Carlo analysis obtained using two different PRNGs. It then makes recommendations for the application of PRNGs in spatial analyses, including recommending specific PRNGs that have attributes appropriate for geospatial analysis. The paper concludes with a call for more research into the application of PRNGs to spatial analyses to fully understand the impact of biases, especially before they are routinely used in the wider spatial analysis community.

Phylodiversity to inform conservation policy: An Australian example

Phylodiversity measures summarise the phylogenetic diversity patterns of groups of organisms. By using branches of the tree of life, rather than its tips (e.g., species), phylodiversity measures provide important additional information about biodiversity that can improve conservation policy and outcomes. As a biodiverse nation with a strong legislative and policy framework, Australia provides an opportunity to use phylogenetic information to inform conservation decision-making. We explored the application of phylodiversity measures across Australia with a focus on two highly biodiverse regions, the south west of Western Australia (SWWA) and the South East Queensland bioregion (SEQ). We analysed seven diverse groups of organisms spanning five separate phyla on the evolutionary tree of life, the plant genera Acacia and Daviesia, mammals, hylid frogs, myobatrachid frogs, passerine birds, and camaenid land snails. We measured species richness, weighted species endemism (WE) and two phylodiversity measures, phylogenetic diversity (PD) and phylogenetic endemism (PE), as well as their respective complementarity scores (a measure of gains and losses) at 20 km resolution. Higher PD was identified within SEQ for all fauna groups, whereas more PD was found in SWWA for both plant groups. PD and PD complementarity were strongly correlated with species richness and species complementarity for most groups but less so for plants. PD and PE were found to complement traditional species-based measures for all groups studied: PD and PE follow similar spatial patterns to richness and WE, but highlighted different areas that would not be identified by conventional species-based biodiversity analyses alone. The application of phylodiversity measures, particularly the novel weighted complementary measures considered here, in conservation can enhance protection of the evolutionary history that contributes to present day biodiversity values of areas. Phylogenetic measures in conservation can include important elements of biodiversity in conservation planning, such as evolutionary potential and feature diversity that will improve decision-making and lead to better biodiversity conservation outcomes.

Using process models to improve spatial analysis

This paper describes a method of improving spatial analyses by using a process model to define the sampling window. This method allows the sample to adapt to changing conditions as they occur in the dataset, rather than applying the same geometric shape to all locations. Such a sampling method can be used to reduce the noise in the sample, and thus generate more sensible results. The general approach may be applied to other processes that influence or control the distribution of spatial variables, provided the processes are known and can be modelled. The method also enables exploration of the degree to which a spatial variable is controlled by an assumed driving process. In this study the sampling windows for each location are defined using uphill and downhill watersheds, and are applied to geochemical variables across a 1100 km2 area in Weipa, Queensland, Australia. The utility of the approach is assessed using variograms and the Getis-Ord G*i statistic. Results indicate an improvement over omnidirectional and wedge-shaped sampling, with the most improvement where the variable is highly mobile in solution. These areas are considered to be under modern hydrological control. Most errors in the example are attributed to the effect of other landscape processes, such as aeolian transport and marine incursions.

Paleo-Antarctic rainforest into the modern Old World tropics: The rich past and threatened future of the “southern wet forest survivors”

UNLABELLED • PREMISE OF STUDY Have Gondwanan rainforest floral associations survived? Where do they occur today? Have they survived continuously in particular locations? How significant is their living floristic signal? We revisit these classic questions in light of significant recent increases in relevant paleobotanical data.• METHODS We traced the extinction and persistence of lineages and associations through the past across four now separated regions-Australia, New Zealand, Patagonia, and Antarctica-using fossil occurrence data from 63 well-dated Gondwanan rainforest sites and 396 constituent taxa. Fossil sites were allocated to four age groups: Cretaceous, Paleocene-Eocene, Neogene plus Oligocene, and Pleistocene. We compared the modern and ancient distributions of lineages represented in the fossil record to see if dissimilarity increased with time. We quantified similarity-dissimilarity of composition and taxonomic structure among fossil assemblages, and between fossil and modern assemblages.• KEY RESULTS Strong similarities between ancient Patagonia and Australia confirmed shared Gondwanan rainforest history, but more of the lineages persisted in Australia. Samples of ancient Australia grouped with the extant floras of Australia, New Guinea, New Caledonia, Fiji, and Mt. Kinabalu. Decreasing similarity through time among the regional floras of Antarctica, Patagonia, New Zealand, and southern Australia reflects multiple extinction events.• CONCLUSIONS Gondwanan rainforest lineages contribute significantly to modern rainforest community assembly and often co-occur in widely separated assemblages far from their early fossil records. Understanding how and where lineages from ancient Gondwanan assemblages co-occur today has implications for the conservation of global rainforest vegetation, including in the Old World tropics.

Quantifying Phytogeographical Regions of Australia Using Geospatial Turnover in Species Composition

The largest digitized dataset of land plant distributions in Australia assembled to date (750,741 georeferenced herbarium records; 6,043 species) was used to partition the Australian continent into phytogeographical regions. We used a set of six widely distributed vascular plant groups and three non-vascular plant groups which together occur in a variety of landscapes/habitats across Australia. Phytogeographical regions were identified using quantitative analyses of species turnover, the rate of change in species composition between sites, calculated as Simpsons beta. We propose six major phytogeographical regions for Australia: Northern, Northern Desert, Eremaean, Eastern Queensland, Euronotian and South-Western. Our new phytogeographical regions show a spatial agreement of 65% with respect to previously defined phytogeographical regions of Australia. We also confirm that these new regions are in general agreement with the biomes of Australia and other contemporary biogeographical classifications. To assess the meaningfulness of the proposed phytogeographical regions, we evaluated how they relate to broad scale environmental gradients. Physiographic factors such as geology do not have a strong correspondence with our proposed regions. Instead, we identified climate as the main environmental driver. The use of an unprecedentedly large dataset of multiple plant groups, coupled with an explicit quantitative analysis, makes this study novel and allows an improved historical bioregionalization scheme for Australian plants. Our analyses show that: (1) there is considerable overlap between our results and older biogeographic classifications; (2) phytogeographical regions based on species turnover can be a powerful tool to further partition the landscape into meaningful units; (3) further studies using phylogenetic turnover metrics are needed to test the taxonomic areas.