Using phenocams to monitor our changing earth: Toward a global phenocam network

Rapid changes to the biosphere are altering ecological processes worldwide. Developing informed policies for mitigating the impacts of environmental change requires an exponential increase in the quantity, diversity, and resolution of field-collected data, which, in turn, necessitates greater reliance on innovative technologies to monitor ecological processes across local to global scales. Automated digital time-lapse cameras – “phenocams” – can monitor vegetation status and environmental changes over long periods of time. Phenocams are ideal for documenting changes in phenology, snow cover, fire frequency, and other disturbance events. However, effective monitoring of global environmental change with phenocams requires adoption of data standards. New continental-scale ecological research networks, such as the US National Ecological Observatory Network (NEON) and the European Union's Integrated Carbon Observation System (ICOS), can serve as templates for developing rigorous data standards and extending the utility of phenocam data through standardized ground-truthing. Open-source tools for analysis, visualization, and collaboration will make phenocam data more widely usable

Training macrosystems scientists requires both interpersonal and technical skills

Macrosystems science strives to integrate patterns and processes that span regional to continental scales. The scope of such research often necessitates the involvement of large interdisciplinary and/or multi-institutional teams composed of scientists across a range of career stages, a diversity that requires researchers to hone both technical and interpersonal skills. We surveyed participants in macrosystems projects funded by the US National Science Foundation to assess the perceived importance of different skills needed in their research, as well as the types of training they received. Survey results revealed a mismatch between the skills participants perceive as important and the training they received, particularly for interpersonal and management skills. We highlight lessons learned from macrosystems training case studies, explore avenues for further improvement of undergraduate and graduate education, and discuss other training opportunities for macrosystems scientists. Given the trend toward interdisciplinary research beyond the macrosystems community, these insights are broadly applicable for scientists involved in diverse, collaborative projects.https://esajournals.onlinelibrary.wiley.com/doi/full/10.1002/fee.228

Fostering effective and sustainable scientific collaboration and knowledge exchange: a workshop-based approach to establish a national ecological observatory network (NEON) domain-specific user group

The decision to establish a network of researchers centers on identifying shared research goals. Ecologically specific regions, such as the USA’s National Ecological Observatory Network’s (NEON’s) eco-climatic domains, are ideal locations by which to assemble researchers with a diverse range of expertise but focused on the same set of ecological challenges. The recently established Great Lakes User Group (GLUG) is NEON’s first domain specific ensemble of researchers, whose goal is to address scientific and technical issues specific to the Great Lakes Domain 5 (D05) by using NEON data to enable advancement of ecosystem science. Here, we report on GLUG’s kick off workshop, which comprised lightning talks, keynote presentations, breakout brainstorming sessions and field site visits. Together, these activities created an environment to foster and strengthen GLUG and NEON user engagement. The tangible outcomes of the workshop exceeded initial expectations and include plans for (i) two journal articles (in addition to this one), (ii) two potential funding proposals, (iii) an assignable assets request and (iv) development of classroom activities using NEON datasets. The success of this 2.5-day event was due to a combination of factors, including establishment of clear objectives, adopting engaging activities and providing opportunities for active participation and inclusive collaboration with diverse participants. Given the success of this approach we encourage others, wanting to organize similar groups of researchers, to adopt the workshop framework presented here which will strengthen existing collaborations and foster new ones, together with raising greater awareness and promotion of use of NEON datasets. Establishing domain specific user groups will help bridge the scale gap between site level data collection and addressing regional and larger ecological challenges

Harnessing the NEON data revolution to advance open environmental science with a diverse and data-capable community

It is a critical time to reflect on the National Ecological Observatory Network (NEON) science to date as well as envision what research can be done right now with NEON (and other) data and what training is needed to enable a diverse user community. NEON became fully operational in May 2019 and has pivoted from planning and construction to operation and maintenance. In this overview, the history of and foundational thinking around NEON are discussed. A framework of open science is described with a discussion of how NEON can be situated as part of a larger data constellation—across existing networks and different suites of ecological measurements and sensors. Next, a synthesis of early NEON science, based on >100 existing publications, funded proposal efforts, and emergent science at the very first NEON Science Summit (hosted by Earth Lab at the University of Colorado Boulder in October 2019) is provided. Key questions that the ecology community will address with NEON data in the next 10 yr are outlined, from understanding drivers of biodiversity across spatial and temporal scales to defining complex feedback mechanisms in human–environmental systems. Last, the essential elements needed to engage and support a diverse and inclusive NEON user community are highlighted: training resources and tools that are openly available, funding for broad community engagement initiatives, and a mechanism to share and advertise those opportunities. NEON users require both the skills to work with NEON data and the ecological or environmental science domain knowledge to understand and interpret them. This paper synthesizes early directions in the community’s use of NEON data, and opportunities for the next 10 yr of NEON operations in emergent science themes, open science best practices, education and training, and community building

WATERSHEDS WITH EMPHASIS ON PHOSPHORUS, ALUMINUM AND IRON

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Precision of minirhizotron camera location for each sampling method; values are provided as <i>one standard deviation</i>.

<p>Precision of minirhizotron camera location for each sampling method; values are provided as <i>one standard deviation</i>.</p

Traceable Calibration, Performance Metrics, and Uncertainty Estimates of Minirhizotron Digital Imagery for Fine-Root Measurements

<div><p>Even though fine-root turnover is a highly studied topic, it is often poorly understood as a result of uncertainties inherent in its sampling, <i>e.g.</i>, quantifying spatial and temporal variability. While many methods exist to quantify fine-root turnover, use of minirhizotrons has increased over the last two decades, making sensor errors another source of uncertainty. Currently, no standardized methodology exists to test and compare minirhizotron camera capability, imagery, and performance. This paper presents a reproducible, laboratory-based method by which minirhizotron cameras can be tested and validated in a traceable manner. The performance of camera characteristics was identified and test criteria were developed: we quantified the precision of camera location for successive images, estimated the trueness and precision of each camera's ability to quantify root diameter and root color, and also assessed the influence of heat dissipation introduced by the minirhizotron cameras and electrical components. We report detailed and defensible metrology analyses that examine the performance of two commercially available minirhizotron cameras. These cameras performed differently with regard to the various test criteria and uncertainty analyses. We recommend a defensible metrology approach to quantify the performance of minirhizotron camera characteristics and determine sensor-related measurement uncertainties prior to field use. This approach is also extensible to other digital imagery technologies. In turn, these approaches facilitate a greater understanding of measurement uncertainties (signal-to-noise ratio) inherent in the camera performance and allow such uncertainties to be quantified and mitigated so that estimates of fine-root turnover can be more confidently quantified.</p></div

Metrology terms and definitions.

<p>All definitions are provided by JGCM <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0112362#pone.0112362-Joint2" target="_blank">[27]</a> standards-holding body unless otherwise specified.</p><p>Metrology terms and definitions.</p

Mean chromaticity coordinates (and expanded uncertainties) converted from reflectance data measured by the densitometer.

<p>Mean chromaticity coordinates (and expanded uncertainties) converted from reflectance data measured by the densitometer.</p

Line widths as measured by both minirhizotrons.

<p>Estimated line widths as determined by both minirhizotrons cameras against those of the NIST traceable micrometer. Results of the AMR-A are shown in (a) using the 250 pixel intensity threshold (), and (b) using the FWHM threshold (). Results of the CI-600 are shown in (c) using the 250 pixel intensity threshold (), and (d) using the FWHM threshold (). A 1∶1 line is shown on each graph. Inset plots are provided at the 194.06±1.64 µm calibration point to emphasize the magnitude of random uncertainty and show that calibration can correct for bias, but not random uncertainty. Error bars are given as ±2 SE for uncalibrated data and ± for calibrated data.</p