Encroachment of woody plants into grasslands has generated considerable interest among ecologists. Syntheses of encroachment effects on ecosystem processes have been limited in extent and confined largely to pastoral land uses or particular geographical regions. We used univariate analyses, meta-analysis and structural equation modelling to test the propositions that (1) shrub encroachment does not necessarily lead to declines in ecosystem functions and (2) shrub traits influence the functional outcome of encroachment. Analyses of 43 ecosystem attributes from 244 case studies worldwide showed that some attributes consistently increased with encroachment (e.g. soil C, N), and others declined (e.g. grass cover, pH), but most exhibited variable responses. Traits of shrubs were associated with significant, though weak, structural and functional outcomes of encroachment. Our review revealed that encroachment had mixed effects on ecosystem structure and functioning at global scales, and that shrub traits influence the functional outcome of encroachment. Thus, a simple designation of encroachment as a process leading to functionally, structurally or contextually degraded ecosystems is not supported by a critical analysis of existing literature. Our results highlight that the commonly established link between shrub encroachment and degradation is not universal.
Citizen science can play an important role in school science education. Citizen science is particularly relevant to addressing current societal environmental sustainability challenges, as it engages the students directly with environmental science and gives students an understanding of the scientific process. In addition, it allows students to observe local representations of global challenges. Here, we report a citizen science programme designed to engage school-age children in real-world scientific research. The programme used standardized methods deployed across multiple schools through scientist-school partnerships to engage students with an important conservation problem: habitat for pollinator insects in urban environments. Citizen science programmes such as the programme presented here can be used to enhance scientific literacy and skills. Provided key challenges to maintain data quality are met, this approach is a powerful way to contribute valuable citizen science data for understudied, but ecologically important study systems, particularly in urban environments across broad geographical areas.
Introduction Technologies that allow computers and machines to perform tasks normally requiring human intelligence are often referred to as artificial intelligence (AI). These technologies allow machines to complete tasks with traits or capabilities ordinarily associated with human cognition, such as reasoning, problem solving, common-sense knowledge management, planning, learning, translation, perception, vision, speech recognition, and social intelligence (Kaplan and Haenlein 2019). Research in AI is rapidly increasing, as indicated when c omparing the annual publishing rate of papers focused on AI between 1996 and 2017 against the publishing rates of papers focused on any topic or against the publishing rates of papers in the field of computer science (see the growth of annually published papers by topic in Shoham et al. [2018; p. 9]). This growth in AI publications has prompted researchers to critically explore the potential promises and risks of AI (Scherer 2016; Webb 2019; Yudkowsky 2008) as well as ethics and responsibilities (Miller 2019; Cowls and Floridi 2018; Scherer 2016; Dawson et al. 2019). AI has been used in citizen science projects for about 20 years. It was first used in this context in 2000, in collaborative AI databases such as the Generic Artificial Consciousness (GAC)/Mindpixel Digital Mind Modeling Project (McKinstry 2009) and the Open Mind Common Sense project (Singh et al. 2002). In these models, usersubmitted propositions were meant to create a database of common-sense knowledge that could function as a kind of digital brain. This relationship between collective knowledge and algorithmic processing evolved in many directions and, in 2019, is predominantly represented by machine learning, especially applied to computer vision, which includes diverse methods of automatically identifying objects from digital photographs. For example, the iNaturalist platform, a citizen science project and online social network, is designed to enable citizen scientists and ecologists alike to upload observations from the natural world, such as images of animals and plants (Van Horn et al. 2018). The platform is one among many (Wäldchen et al. 2018) that include an automated
Naturalised, but not yet invasive plants, pose a nascent threat to biodiversity. As climate regimes continue to change, it is likely that a new suite of invaders will emerge from the established pool of naturalised plants. Pre-emptive management of locations that may be most suitable for a large number of potentially invasive plants will help to target monitoring, and is vital for effective control. We used species distribution models (SDM) and invasion-hotspot analysis to determine where in Australia suitable habitat may occur for 292 naturalised plants. SDMs were built in MaxEnt using both climate and soil variables for current baseline conditions. Modelled relationships were projected onto two Representative Concentration Pathways for future climates (RCP 4.5 and 8.5), based on seven global climate models, for two time periods (2035, 2065). Model outputs for each of the 292 species were then aggregated into single ‘hotspot’ maps at two scales: continental, and for each of Australia’s 37 ecoregions. Across Australia, areas in the south-east and south-west corners of the continent were identified as potential hotspots for naturalised plants under current and future climates. These regions provided suitable habitat for 288 and 239 species respectively under baseline climates. The areal extent of the continental hotspot was projected to decrease by 8.8% under climates for 2035, and by a further 5.2% by 2065. A similar pattern of hotspot contraction under future climates was seen for the majority of ecoregions examined. However, two ecoregions - Tasmanian temperate forests and Australian Alps montane grasslands - showed increases in the areal extent of hotspots of >45% under climate scenarios for 2065. The alpine ecoregion also had an increase in the number of naturalised plant species with abiotically suitable habitat under future climate scenarios, indicating that this area may be particularly vulnerable to future incursions by naturalised plants.
The conservation of wildlife populations living adjacent to roads is gaining international recognition as a worldwide concern. Populations living in road-impacted environments are influenced by spatial parameters including the amount and arrangement of suitable habitat. Similarly, heterogeneity in threatening processes can act at a variety of spatial scales and be crucial in affecting population persistence. Common wombats (Vombatus ursinus) are considered both widespread and abundant throughout their eastern Australian continental distribution. They nevertheless face many threats, primarily human induced. As well as impacts from disease and predation by introduced species, high roadside fatality rates on many rural roads are frequently reported. We parameterized a model for common wombat population viability analysis within a 750-km 2 area of the northwestern corner of Kosciuszko National Park in New South Wales, Australia, and tested its sensitivity to changes in the values of basic parameters. We then assessed the relative efficiency of various mitigation measures by examining the combined impact from roads, disease and predation on wombat subpopulation persistence in the area. We constructed a stage-structured and spatially explicit model incorporating estimates of survival and fecundity parameters for each of the identified subpopulations using RAMAS GIS. Estimates of current threatening processes suggest mitigating road-kill is the most effective management solution. Results highlight the importance of recognizing the interplay between various threats and how their combination has the capacity to drive local depletion events.
Aim We assess how much of species' ranges are present within protected areas and how different land units within protected areas contribute to overall protection, both within their region and at continental scales. We do this using the plant family Myrtaceae in relation to the globally important Greater Blue Mountains World Heritage Area (GBMWHA) in New South Wales, Australia. Location South‐eastern Australia. Methods Compiling data throughout the region and nationally, we considered two spatially based quantitative measures of endemism (relative range restriction): weighted endemism (WE) and corrected weighted endemism (CWE). In both measures, species are weighted by the proportion of their ranges found within the analysis window, with the ranges calculated as the total number of cells in which they occur (10 km × 10 km in this research). We also derived a novel expectation for the contribution of each species to the endemism scores at each taxonomic level based on the additive properties of the metrics and their relationship to species richness. We used this expectation to assess the proportional contribution of each genus to the endemism scores. Results The degree to which Myrtaceae species within the GBMWHA are endemic to the GBMWHA area is 16%, meaning that an average of 16% of the ranges of species found in the GBMWHA are restricted to that area. The figure for those species with ranges less than or equal to the median (80 cells) is 33%. The genus Eucalyptus contributes the most to the endemism scores obtained, but no more than would be expected given its number of species. The genus Leptospermum is 3.7% less restricted to the GBMWHA than would be expected, while the genus Melaleuca is 5% more restricted than expected. Main conclusions Centres of high endemism within the GBMWHA and surrounds were identified. This research presents a template for quantifying endemism for regions at local to global scales. Spatially‐based quantitative measures of endemism, such as outlined here, are an important means to quantify and visualize these aspects of conservation significance for the management of protected areas.
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