Occurrence data uncover patterns of allopatric divergence and interspecies interactions in the evolutionary history of Sceloporus lizards

As shown from several long‐term and time‐intensive studies, closely related, sympatric species can impose strong selection on one another, leading to dramatic examples of phenotypic evolution. Here, we use occurrence data to identify clusters of sympatric Sceloporus lizard species and to test whether Sceloporus species tend to coexist with other species that differ in body size, as we would expect when there is competition between sympatric congeners. We found that Sceloporus species can be grouped into 16 unique bioregions. Bioregions that are located at higher latitudes tend to be larger and have fewer species, following Rapoport''s rule and the latitudinal diversity gradient. Species richness was positively correlated with the number of biomes and elevation heterogeneity of each bioregion. Additionally, most bioregions show signs of phylogenetic underdispersion, meaning closely related species tend to occur in close geographic proximity. Finally, we found that although Sceloporus species that are similar in body size tend to cluster geographically, small‐bodied Sceloporus species are more often in sympatry with larger‐bodied Sceloporus species than expected by chance alone, whereas large‐bodied species cluster with each other geographically and phylogenetically. These results suggest that community composition in extant Sceloporus species is the result of allopatric evolution, as closely related species move into different biomes, and interspecies interactions, with sympatry between species of different body sizes. Our phyloinformatic approach offers unique and detailed insights into how a clade composed of ecologically and morphologically disparate species are distributed over large geographic space and evolutionary time.     

Metrics for evaluating representation target achievement in protected area networks

Global conservation targets (e.g. Aichi Target 11) have helped drive a dramatic expansion of the global protected area (PA) network. Credible metrics have an important role to play in evaluating and expanding PAs to achieve conservation outcomes and objectives. For metrics to be useful and adopted, they need to be transparent, easy to understand, and easy to implement. We present two complementary metrics, “mean protection gap” and “mean target achievement”, for evaluating representation target achievement in PA networks along with the R package “ConsTarget” that calculates and plots both metrics. We use Australia's proposed Commonwealth Marine Reserve network as a case study to demonstrate the application of these metrics. We recommend the metrics be used to evaluate the progress towards building representative PA networks in line with Aichi target 11's goals.     

Zonal and azonal vegetation revisited: How is wetland vegetation distributed across different zonobiomes

In ‘zonal’ vegetation, climatic factors are the main influence on growth and performance and the climate determines the vegetation type completely, which makes this vegetation dominant in the landscape. If vegetation is ‘azonal’ however, local stresses are assumed to have an overwhelming influence on plant performance and climatic influences will be minimal; typically, this vegetation occurs only in small patches in the landscape. In this study I ask whether wetland plant communities, as they are described for South Africa, are evenly distributed among different terrestrial vegetation types, to test whether they are zonal or azonal. Three contingency tables were construed based on the counts of wetland vegetation records, defined on three hierarchical levels (Main Clusters, Community Groups and Community) and their occurrence in the country (at the level of Biome, Bioregion and terrestrial vegetation type). An ‘azonality index’ was calculated as the sum of all Chi‐square values for each wetland vegetation type divided by the total number of records. The overall correlation between hydroperiod and the azonality index was very weak. At the finest level, terrestrial vegetation types were clustered on the basis of having similar combinations of wetland community types. Eighteen different ‘wetland ecoregions’ have been defined, on the basis of wetland vegetation types occurring within them. Instead of regarding wetland vegetation as azonal, it should rather be regarded as ‘intrazonal’, meaning that climate does have an impact but many vegetation types are widespread across climatic regions. The reason why community types in wetlands are widespread is due to the monodominance of a single widespread, often clonal, species. The different wetland ecoregions do not correspond to terrestrial biomes, so it is expected that wetland vegetation responds differently to climate than terrestrial vegetation.     

Integrating a global agro-climatic classification with bioregional boundaries in Australia

Aim Stratification of major differences in the biophysical features of landscapes at the continental scale is necessary to collectively assess local observations of landscape response to management actions for consistency and difference. Such a stratification is an important step in the development of generalizations concerning how landscapes respond to different management regimes. As part of the development of a comparative framework for this purpose, we propose a climate classification adapted from an existing broad scale global agro‐climatic classification, which is closely aligned with natural vegetation formations and common land uses across Australia. Location The project considered landscapes across the continent of Australia. Methods The global agro‐climatic classification was adapted by using elevation‐dependent thin plate smoothing splines to clarify the spatial extents of the 18 global classes found in Australia. The clarified class boundaries were interpolated from known classes at 822 points across Australia. These classes were then aligned with the existing bioregional classification, Interim Biogeographic Regionalization for Australia IBRA 5.1. Results The aligned climate classes reflect major patterns in plant growth temperature and moisture indices and seasonality. These in turn reflect broad differences in cropping and other land use characteristics. Fifty‐two of the 85 bioregions were classified entirely into one of the 18 agro‐climatic classes. The remaining bioregions were classified according to sub‐bioregional boundaries. A small number of these sub‐bioregions were split to better reflect agro‐climatic boundaries. Main conclusions The agro‐climatic classification provided an explicit global context for the analysis. The topographic dependence of the revised climate class boundaries clarified the spatial extents of poorly sampled highland classes and facilitated the alignment of these classes with the bioregional classification. This also made the classification amenable to explicit application. The bioregional and subregional boundaries reflect discontinuities in biophysical features. These permit the integrated classification to reflect major potential differences in landscape function and response to management. The refined agro‐climatic classification and its integration with the IBRA bioregions are both available for general use and assessment.     

Categorizing Australian landscapes as an aid to assessing the generality of landscape management guidelines

Aim A key question in ecology and ecological management is the extent to which management guidelines developed in one location can be generalized to other areas. Landscapes differ in their biophysical characteristics and the degree of human alteration imposed on them. Our aim was to develop a conceptual framework of Australian landscapes, on the basis of a few simple indicators, that can be used to enhance the communication of information in planning and managing landscapes, particularly for biodiversity conservation. Location The project considered landscapes across the continent of Australia. Methods The project was a desktop exercise and the approach was to identify the minimum set of variables, and levels within variables, that are most meaningful from the perspective of Australian landscapes and their management. This involved the identification of the key environmental axes and development of a proposed set of matrices involving various combinations of the axes, which was revised following consultation with stakeholders. Results We developed a framework based on the primary variables of climate and vegetation. For climate, we used an agro‐climatic classification incorporating a moisture index, growth index and seasonality, with climate classes aligned to existing bioregions. Vegetation was broadly classified on the presence or absence of a tree layer and whether the understorey was grassy or shrub‐dominated. Secondary variables were the degree of landscape alteration and modification. The sensitivity of broad categories to ecosystem dysfunction was assessed, and the relative abundance of different categories across Australia was determined. Not all categories need to be considered since not all combinations of variables occur. Main conclusions The framework provides a set of broad categories of landscapes with differing characteristics. We can then assess the importance of different types of threat in the different categories. By pulling together the potential threats in a systematic way across categories, we can start to consider what appropriate management responses might be in each case. Further, by providing a convenient way to compare landscapes in different categories, it becomes possible to see where generalizations among different landscapes may be possible and where they are definitely not likely to be helpful.