The research data repository of the Environmental Data Initiative (EDI) is building on over 30 years of data curation research and experience in the National Science Foundation-funded US Long-Term Ecological Research (LTER) Network. It provides mature functionalities, well established workflows, and now publishes all ‘long-tail’ environmental data. High quality scientific metadata are enforced through automatic checks against community developed rules and the Ecological Metadata Language (EML) standard. Although the EDI repository is far along in making its data findable, accessible, interoperable, and reusable (FAIR), representatives from EDI and the LTER are developing best practices for the edge cases in environmental data publishing. One of these is the vast amount of imagery taken in the context of ecological research, ranging from wildlife camera traps to plankton imaging systems to aerial photography. Many images are used in biodiversity research for community analyses (e.g., individual counts, species cover, biovolume, productivity), while others are taken to study animal behavior and landscape-level change. Some examples from the LTER Network include: using photos of a heron colony to measure provisioning rates for chicks (Clarkson and Erwin 2018) or identifying changes in plant cover and functional type through time (Peters et al. 2020). Multi-spectral images are employed to identify prairie species. Underwater photo quads are used to monitor changes in benthic biodiversity (Edmunds 2015). Sosik et al. (2020) used a continuous Imaging FlowCytobot to identify and measure phyto- and microzooplankton. Cameras at McMurdo Dry Valleys assess snow and ice cover on Antarctic lakes allowing estimation of primary production (Myers 2019). It has been standard practice to publish numerical data extracted from images in EDI; however, the supporting imagery generally has not been made publicly available. Our goal in developing best practices for documenting and archiving these images is for them to be discovered and re-used. Our examples demonstrate several issues. The research questions, and hence, the image subjects are variable. Images frequently come in logical sets of time series. The size of such sets can be large and only some images may be contributed to a dedicated specialized repository. Finally, these images are taken in a larger monitoring context where many other environmental data are collected at the same time and location. Currently, a typical approach to publishing image data in EDI are packages containing compressed (ZIP or tar) files with the images, a directory manifest with additional image-specific metadata, and a package-level EML metadata file. Images in the compressed archive may be organized within directories with filenames corresponding to treatments, locations, time periods, individuals, or other grouping attributes. Additionally, the directory manifest table has columns for each attribute. Package-level metadata include standard coverage elements (e.g., date, time, location) and sampling methods. This approach of archiving logical ‘sets’ of images reduces the effort of providing metadata for each image when most information would be repeated, but at the expense of not making every image individually searchable. The latter may be overcome if the provided manifest contains standard metadata that would allow searching and automatic integration with other images.
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The geodiv r package: Tools for calculating gradient surface metrics
1. The geodiv r package calculates gradient surface metrics from imagery and other
gridded datasets to provide continuous measures of landscape heterogeneity for
landscape pattern analysis.
2. geodiv is the first open-source, command line toolbox for calculating many gradient surface metrics and easily integrates parallel computing for applications with
large images or rasters (e.g. remotely sensed data). All functions may be applied
either globally to derive a single metric for an entire image or locally to create a
texture image over moving windows of a user-defined extent.
3. We present a comprehensive description of the functions available through geodiv.
A supplemental vignette provides an example application of geodiv to the fields of
landscape ecology and biogeography.
4. geodiv allows users to easily retrieve estimates of spatial heterogeneity for a variety
of purposes, enhancing our understanding of how environmental structure influences ecosystem processes. The package works with any continuous imagery and
may be widely applied in many fields where estimates of surface complexity are
useful.
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- NSF-PAR ID:
- 10297530
- Editor(s):
- Goslee, Sarah
- Date Published:
- Journal Name:
- Methods in Ecology and Evolution
- Volume:
- 0
- Issue:
- 0
- ISSN:
- 2041-210X
- Page Range / eLocation ID:
- 1-7
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract As the quality and quantity of natural habitats decrease, pressure increases to better understand species–habitat interactions and how animal communities respond to habitat changes. We assessed the relative importance of local habitat heterogeneity and productivity measures as predictors of avian species richness and compared these results to models for species of conservation concern (SCC). We derived three‐dimensional habitat heterogeneity and productivity measures from light detection and ranging data and hyperspectral imagery, and then used a Bayesian multi‐species hierarchical framework to model avian species richness and occupancy. We found both habitat heterogeneity and productivity were important factors for determining avian community richness. Three‐dimensional habitat heterogeneity and productivity metrics accurately predicted species richness at a local scale and were especially important to use within habitat guilds (i.e., alpha diversity). When scaling up to community richness across multiple habitat types (i.e., gamma diversity), two‐dimensional (surface level) productivity and heterogeneity metrics became important additions to the three‐dimensional metrics when estimating total avian richness. We also tested the utility of these metrics for predicting occupancy of SCC and compared community‐level relationships to species‐specific relationships. Species of conservation concern differed from the broader avian community with regard to local habitat heterogeneity and productivity measures. Species of conservation concern had different relationship habitat metrics than the greater avian community. Three‐dimensional measures of habitat heterogeneity and productivity predicted avian richness across the landscape, yet also highlighted the different habitat structure needs of SCC compared with the greater avian community.
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Abstract The structure and composition of plant communities in drylands are highly variable across scales, from microsites to landscapes. Fine spatial resolution field surveys of dryland plants are essential to unravel the impact of climate change; however, traditional field data collection is challenging considering sampling efforts and costs. Unoccupied aerial systems (UAS) can alleviate this challenge by providing standardized measurements of plant community attributes with high resolution. However, given widespread heterogeneity in plant communities in drylands, and especially across environmental gradients, the transferability of UAS imagery protocols is unclear. Plant functional types (PFTs) are a classification scheme that aggregates the diversity of plant structure and function. We mapped and modeled PFTs and fractional photosynthetic cover using the same UAS imagery protocol across three dryland communities, differentiated by a landscape‐scale gradient of elevation and precipitation. We compared the accuracy of the UAS products between the three dryland sites. PFT classifications and modeled photosynthetic cover had highest accuracies at higher elevations (2241 m) with denser vegetation. The lowest site (1101 m), with more bare ground, had the least agreement with the field data. Notably, shrub cover was well predicted across the gradient of elevation and precipitation (~230–1100 mm/year). UAS surveys captured the heterogeneity of plant cover across sites and presented options to measure leaf‐level composition and structure at landscape levels. Our results demonstrate that some PFTs (i.e., shrubs) can readily be detected across sites using the same UAS imagery protocols, while others (i.e., grasses) may require site‐specific flight protocols for best accuracy. As UAS are increasingly used to monitor dryland vegetation, developing protocols that maximize information and efficiency is a research and management priority.
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An ongoing component of the Baltimore urban long-term ecological research (LTER) project (Baltimore Ecosystem Study, BES) is the use of the watershed approach and monitoring of stream water quality to evaluate the integrated ecosystem functioning of Baltimore. The LTER research has focused on the Gwynns Falls watershed, which spans a gradient from highly urban, urban-residential, and suburban zones. In addition, a forested watershed serves as a reference. The long-term sampling network includes four longitudinal sampling sites along the Gwynns Falls mainstem, as well as several small (40-100 ha) watershed within or near the Gwynns Falls, providing data on water quality in different land use zones of the watersheds. Each study site is continuously monitored for discharge and is sampled weekly for water chemistry. Those data are available elsewhere on the BES website. We are interested in studying the bioreactivity of streams in our watersheds in an attempt to quantify how streams themselves may affect or be affected by water quality. To assess the bioreactivity of streams, we measure whole stream metabolism, which is an integrative metric which quantifies the production and consumption of energy by a stream ecosystem. Stream metabolism represents how energy is created (primary production) and used (respiration) within a stream; it can be thought of as a stream breathing, with primary production being similar to an inhale, and respiration as an exhale. We are monitoring stream metabolism in each of our long-term water quality monitoring stations by deploying sensors that record dissolve oxygen and temperature of the stream every five minutes, and we also have deployed light sensors to record irradiance every five minutes at long-term BES water chemistry streams, which is needed for metabolism modeling. In addition, each dissolved oxygen sensor is located near a USGS gage which estimates discharge every 15 minutes. We used USGS manual discharge estimations linked with channel geometry measurements to develop a unique discharge-stream depth relationship (contact AJ Reisinger for details). The combination of the USGS discharge data and our discharge-depth relationship allows us to estimate average daily discharge and depth. We have included these data as well as dissolved oxygen, temperature, and PAR, allowing metabolism to be scaled on an areal basis. Primary production and respiration of streams integrate all biological activity in a stream, and therefore are good metrics to assess the state of an ecosystem. These metrics can also be used to predict other ecosystem functions. This dataset includes all information needed for whole-stream metabolism modeling using the streammetabolizer R package. Data will updated as it becomes available from the core stream study sites (see http://md.water.usgs.gov/BES for a detailed description of these sites).more » « less
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Abstract Comprehensive, time‐scaled phylogenies provide a critical resource for many questions in ecology, evolution and biodiversity. Methodological advances have increased the breadth of taxonomic coverage in phylogenetic data; however, accessing and reusing these data remain challenging.
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1111/2041-210X.13677
