Predicting ecological response to climate change is often limited by a lack of relevant local data from which directly applicable mechanistic models can be developed. This limits predictions to qualitative assessments or simplistic rules of thumb in data-poor regions, making management of the relevant systems difficult. We demonstrate a method for developing quantitative predictions of ecological response in data-poor ecosystems based on a space-for-time substitution, using distant, well-studied systems across an inherent climatic gradient to predict ecological response. Changes in biophysical data across the spatial gradient are used to generate quantitative hypotheses of temporal ecological responses that are then tested in a target region. Transferability of predictions among distant locations, the novel outcome of this method, is demonstrated via simple quantitative relationships that identify direct and indirect impacts of climate change on physical, chemical and ecological variables using commonly available data sources. Based on a limited subset of data, these relationships were demonstrably plausible in similar yet distant (>2000 km) ecosystems. Quantitative forecasts of ecological change based on climate-ecosystem relationships from distant regions provides a basis for research planning and informed management decisions, especially in the many ecosystems for which there are few data. This application of gradient studies across domains - to investigate ecological response to climate change - allows for the quantification of effects on potentially numerous, interacting and complex ecosystem components and how they may vary, especially over long time periods (e.g. decades). These quantitative and integrated long-term predictions will be of significant value to natural resource practitioners attempting to manage data-poor ecosystems to prevent or limit the loss of ecological value. The method is likely to be applicable to many ecosystem types, providing a robust scientific basis for estimating likely impacts of future climate change in ecosystems where no such method currently exists.
Irrigated agriculture requires high reliability from water delivery networks and high flows to satisfy demand at seasonal peak times. Aquatic vegetation in irrigation channels are a major impediment to this, constraining flow rates. This work investigates the use of remote sensing from unmanned aerial vehicles (UAVs) and satellite platforms to monitor and classify vegetation, with a view to using this data to implement targeted weed control strategies and assessing the effectiveness of these control strategies. The images are processed in Google Earth Engine (GEE), including co-registration, atmospheric correction, band statistic calculation, clustering and classification. A combination of unsupervised and supervised classification methods is used to allow semi-automatic training of a new classifier for each new image, improving robustness and efficiency. The accuracy of classification algorithms with various band combinations and spatial resolutions is investigated. With three classes (water, land and weed), good accuracy (typical validation kappa >0.9) was achieved with classification and regression tree (CART) classifier; red, green, blue and near-infrared (RGBN) bands; and resolutions better than 1 m. A demonstration of using a time-series of UAV images over a number of irrigation channel stretches to monitor weed areas after application of mechanical and chemical control is given. The classification method is also applied to high-resolution satellite images, demonstrating scalability of developed techniques to detect weed areas across very large irrigation networks.
Globally, there is little information on freshwater (non‐tidal) wetland below ground soil organic carbon (SOC) stocks, sequestration rates and, in particular, their within‐wetland variation. This basic information is critical for designing programs to sample SOC stocks and identifying areas that sequester large amounts of carbon so that they can be managed to prevent degradation and carbon loss. Here, focusing on temperate seasonally inundated freshwater wetlands in south‐eastern Australia, we compared SOC stocks and sequestration rates (via radiometric dating) among wetlands, within wetland locations (High water mark, Edge and Middle), and with adjacent terrestrial locations. SOC stocks varied most among wetlands but also varied inconsistently within wetlands. Wetland SOC stocks (20.4 ± 0.1 kg C m−2) were significantly greater than adjacent terrestrial locations (13.3 ± 0.1 kg C m−2). Wetland SOC sequestration rates were similar among all locations (i.e., within a wetland, ranging from 70 g C m−2 yr−1 to 87 g C m−2 yr−1). The relative lack of difference in SOC stocks among wetland locations allows for reduced within‐wetland sampling and subsequently greater replication at the wetland level, at least for temperate ephemeral freshwater wetlands, although these findings need to be confirmed over a range of wetland types. This study also produced the first estimates of temperate freshwater wetland SOC stocks and sequestration rates in Australia, and is among the first studies globally to demonstrate that seasonally inundated wetlands can sequester carbon at significant rates. It therefore provides important justification to support the protection and rehabilitation of temperate freshwater wetlands worldwide.
In contrast to Europe, the USA and South Africa, Australia has no specific, overarching 30 federal legislation to underpin a nationally-coordinated framework for monitoring, assessing 31 and reporting estuarine condition. This has resulted in a complex mosaic of diverse 32 approaches and governance structures, hindering the ability to make interState comparisons. 33 In this second part of a comprehensive three-part review, we present a systematic appraisal of 34 current and impending approaches for measuring and reporting estuarine condition in each of 35 Australia's States and Territories. A concise summary is provided in each case, supported by 36 extensive appendices containing detailed accounts of relevant monitoring and reporting 37 programs. We synthesise and evaluate this output at the State/Territory level, highlighting 38 areas of improvement and major gaps. 39
Concentrations of the triazine herbicides atrazine, simazine, cyanazine, metribuzin and propazine were determined in streams draining forestry and agricultural catchments in Tasmania, Australia, between 1989 and 1992. Atrazine and simazine were used extensively by the forestry industry in a winter spraying programme, and applications of the other herbicides occurred in cropped agricultural catchments during spring and summer. Of 29 streams sampled intensively for triazines, 20 contained detectable residues. Median contaminations over all samples were 2.85, 1.05, <0.05, <0.05 and <0.05 �g L-1 for atrazine, simazine, cyanazine, metribuzin and propazine, respectively. All herbicide concentrations ranged over several orders of magnitude up to 53 mg L-1, with atrazine and simazine having significantly higher concentrations than the others. Atrazine concentrations were examined in streams draining forestry plantations for periods of up to two years. A decline in concentration was observed with time, but this was strongly influenced by rainfall events. Atrazine contamination from single spraying events persisted at a low level for up to 16 months. Contamination of Big Creek with atrazine to 22�g L-1 after aerial spraying led to an increase in stream invertebrate drift only on the day of spraying and to a short-term increase in movement of brown trout. On examination of biological effects of triazines in surface waters reported in the literature, it was concluded that the observed frequent contamination of Tasmanian streams with triazines may cause occasional minor short-term disturbance to stream communities.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.