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The impact of urban expansion on achieving sustainable development goals (SDGs) has become a significant research topic in the field of geographic information science. In this article, we describe a coupled cellular automata (CA)—‐What If? model to explore SDG11 “Sustainable cities and communities.” The model calculates overall residential land use demand based on historical data archives using the What If? planning support system (PSS), and then allocates it using a CA model that incorporates variables related to SDG11.2.1 and 11.7.1. Historical datasets for years 2016 and 2021 from Southwest Sydney, Australia were used to assess model accuracy, after which two residential expansion scenarios (years 2021 and 2026) were generated. Based on the modeling results, the SDG‐related spatial variables can improve the overall accuracy of CA sub‐models using an XGBoost machine learning training methodology. The simulation results of these scenarios confirm the effectiveness of the coupled CA‐What If? model, which has the potential to generate more reliable scenario results than the standalone What If? PSS for modeling urban growth of cities across Australia and internationally. [ABSTRACT FROM AUTHOR]

Conditions conducive to fires are becoming increasingly common and widespread under climate change. Recent fire events across the globe have occurred over unprecedented scales, affecting a diverse array of species and habitats. Understanding biodiversity responses to such fires is critical for conservation. Quantifying post‐fire recovery is problematic across taxa, from insects to plants to vertebrates, especially at large geographic scales. Novel datasets can address this challenge. We use presence‐only citizen science data from iNaturalist, collected before and after the 2019–2020 megafires in burnt and unburnt regions of eastern Australia, to quantify the effect of post‐fire diversity responses, up to 18 months post‐fire. The geographic, temporal, and taxonomic sampling of this dataset was large, but sampling effort and species discoverability were unevenly spread. We used rarefaction and prediction (iNEXT) with which we controlled sampling completeness among treatments, to estimate diversity indices (Hill numbers: q = 0–2) among nine broad taxon groupings and seven habitats, including 3885 species. We estimated an increase in species diversity up to 18 months after the 2019–2020 Australian megafires in regions which were burnt, compared to before the fires in burnt and unburnt regions. Diversity estimates in dry sclerophyll forest matched and likely drove this overall increase post‐fire, while no taxon groupings showed clear increases inconsistent with both control treatments post‐fire. Compared to unburnt regions, overall diversity across all taxon groupings and habitats greatly decreased in areas exposed to extreme fire severity. Post‐fire life histories are complex and species detectability is an important consideration in all post‐fire sampling. We demonstrate how fire characteristics, distinct taxa, and habitat influence biodiversity, as seen in local‐scale datasets. Further integration of large‐scale datasets with small‐scale studies will lead to a more robust understanding of fire recovery. [ABSTRACT FROM AUTHOR]

Banksia L.f., an iconic genus of the Proteaceae, is endemic to Australia, with its highest species richness and phylogenetic diversity (PD) in southwestern Western Australia. Analysis of the phylogenetic component of richness and endemism uncovered important patterns of Banksia evolutionary history that are not seen at the species level. We found that Banksia is significantly phylogenetically clustered in this region, likely due to recent evolutionary radiations. We also found significant concentrations of phylogenetic endemism in this region, both neoendemism (short, range‐restricted evolutionary branches) and paleoendemism (long, range‐restricted evolutionary branches). There is a striking northwest to southeast divide in phyloturnover in southwestern Western Australia. The majority of the variation in turnover patterns can be explained by environmental factors, with climate representing the largest covariate. This study adds to the growing body of evidence that indicates the importance of integrating phylogenetic and biodiversity data to inform conservation planning. [ABSTRACT FROM AUTHOR]

Biodiversity has typically been quantified using species richness, but this ignores evolutionary history. Due to the increasing availability of robust phylogenies, methods have been developed that incorporate phylogenetic relationships into quantification of biodiversity. CANAPE (categorical analysis of neo‐ and paleo‐endemism) is one such method that can provide insight into the evolutionary processes generating biodiversity. The only currently available software implementing CANAPE is Biodiverse, which is written in Perl and can be used either through a graphical user interface (GUI) or user‐developed scripts. However, many researchers, particularly in the fields of ecology and evolutionary biology, use the R programming language to conduct their analyses. Here, we present canaper, a new R package (www.r‐project.org) that provides functions to conduct CANAPE in R. canaper implements methods for efficient computation, including parallelization and encoding of community data as sparse matrices. The interface is designed for maximum simplicity and reproducibility; CANAPE can be conducted with two functions, and parallel computing can be enabled with one line of code. Our case study shows that canaper produces equivalent results to Biodiverse and can complete computations on moderately sized datasets quickly (<10 min to reproduce a canonical study). canaper allows researchers to conduct all analyses from data import and cleaning through CANAPE within R, thereby obviating the need to manually import and export data and analysis results between programs. We anticipate canaper will become a part of the toolkit for analyzing biodiversity in R. [ABSTRACT FROM AUTHOR]

Within delphinoid cetaceans, snout shape is significantly correlated to diet, with long-snouted raptorial-feeding predators preying on smaller and more agile prey than shorter-snouted species. Although there have been several studies into longirostry from a functional perspective there have been no quantitative analyses of spatial variation in skull shape or how the pattern in skull shape morphospace occupation varies between assemblages. Here we analyse the cranial morphological variation of Delphinoidea assemblages. Firstly, we calculate mean and Gi* hotspot statistics of skull shape across the world's oceans. We find that tropical and subtropical assemblages exhibit higher average measures of longirostry. This pattern is likely caused by differences in the availability of certain prey types in warmer and cooler environments. Secondly, we calculate mean pairwise distance as well as mean nearest taxon distance in functional traits between the members of 119 unique delphinoid assemblages. There was a trend for low latitude assemblages to exhibit greater overdispersion in PC1 (snout length) compared those from high latitudes. Our results suggest that ocean temperature is influential in determining the diversity, range limits and assemblage structure of delphinoid cetaceans. [ABSTRACT FROM AUTHOR]