In 1963, the Glen Canyon Dam, in Hite Utah was completed, creating the Lake Powell reservoir along the Colorado River. The water levels of Lake Powell peaked in 1983 and have declined since, releasing over-pressure on the underlying sediment. This release in over-pressure created mud volcanoes, structures along the shoreline made of cavities that allow fluid and gases to rise to the surface and escape. Green house gases including methane are released from these structures, and to better understand how development of natural wetlands can result in unintended increased levels of greenhouse gas emissions, we asked 1) how much of each gas is generated or and whether the amount of each gas is changing through time and 2) how are these gases forming in the subsurface? We first measured the amounts of carbon dioxide (CO2), methane (CH4), and air (N) in volcano gas samples collected in 2014, 2015, and 2016. We found that from 2014 through 2016, methane levels from these volcanoes fluctuated significantly. In 2016, we looked at the amounts of carbon and hydrogen isotopes in the methane, which told us the gas is generated from microorganisms feeding on organic matter and is released during water-level fluctuations. We looked at mud volcanoes only located along the Lake Powell marina delta in Hite, Utah. The data spans geological structures restricted to one marina delta.

This dataset indicates the presence and persistence of water across New South Wales between 1988 and 2012. Water is one of the world’s most important resources as it’s critical for human consumption, agriculture, the persistence of flora and fauna species and other ecosystem services. Information about the spatial distribution and prevalence of water is necessary for a range of business, modelling, monitoring, risk assessment, and conservation activities. For example, one of the necessary steps in the NSW State-wide Landcover and Trees Study (SLATS), which monitors vegetation change and is used in the production of vegetation maps, involves removing non-vegetative features such as water bodies through water masking. Water count The water count product is based on water index and water masks for NSW (Danaher & Collett 2006), and represents the proportion of observations with water present across the Landsat time series as a fraction of total number of possible observations in the 25yr period (1 Jan 1988 to 31 Dec 2012). The product has two bands where band 1 is the number of times water was present across the time series, and band 2 is the count of unobscured (i.e. non-null) input pixels, or number of total observations for that pixel. Cloud, cloud-shadow, steep slopes and topographic shadow can obscure the ability to count water presence. Water Prevalence The water prevalence product is extracted from the water count product and provides a measure of the relative persistence of water in the landscape (e.g. from always present to rarely and never present). There are 12 classes representing the percentage of time a pixel has had water present out of the total number of observations for that pixel (i.e Band 1/Band 2 of the water count product). Water prevalence mapping provides information for multiple, wide-reaching applications. For example, distance to locations of persistent water bodies can be modelled as a contributing indicator of potential biodiversity refugia. Files align with Landsat paths and rows (see https://www.usgs.gov/core-science-systems/nli/landsat/landsat-tools), with files for water count denoted 'dd7' and water prevalence 'ddh'.

This is a collated plant survey data from the Fleurieu Peninsula wetlands (version.2). There is a biological and a spatial component to the dataset. [1] Biological data: This was collated from several sources, collected over the period 2000-2009 and used in the analyses for the paper <i>Diversity patterns of seasonal wetland plant communities mainly driven by rare terrestrial species</i> (Deane et al - Biodiversity and Conservation, DOI: <em>10.1007/s10531-016-1139-1</em>). Biological data were pre-processed to remove sampling bias (the method is described in the paper). Data are presence-absence of 215 native plant species (i.e., exotic species removed) from 76 seasonal wetlands (size range 0.5 - 35 ha) located on the Fleurieu Peninsula, South Australia (centred on latitude 35.5 °S). [2] Spatial data: For each of the 76 wetlands a small amount of spatial data is also provided. Area, centroids, elevation and catchment. The data could be of interest for any typical community data analysis (e.g. beta diversity, similarity, assembly), provided only native wetland plant species were of interest. Data were used to model extinction risk, species-area relationships, occupancy distributions and so on.

The dataset comprises of a biological and a spatial component. Biological data: This was collated from several sources, collected over the period 2000-2009. Data are lists of presence-absence of 215 native plant species (i.e., exotic species removed) from 76 seasonal wetlands (size range 0.5 - 35 ha) located on the Fleurieu Peninsula, South Australia (centred on latitude 35.5 °S). After data were collated into a single dataset, sampling bias was removed to create a dataset of near-complete census wetlands. Spatial data: For each of the 76 wetlands a small amount of spatial data is also provided, i.e., area, centroids, catchment etc. The dataset could be of interest for any typical community data analysis (e.g. beta diversity, similarity, assembly)- provided only native wetland plant species are of interest. Data presented here were used to model extinction risk, species-area relationships, occupancy distributions and so on.