A Global lake ecological observatory network (GLEON) for synthesising high-frequency sensor data for validation of deterministic ecological models

A Global Lake Ecological Observatory Network (GLEON; www.gleon.org) has formed to provide a coordinated response to the need for scientific understanding of lake processes, utilising technological advances available from autonomous sensors. The organisation embraces a grassroots approach to engage researchers from varying disciplines, sites spanning geographic and ecological gradients, and novel sensor and cyberinfrastructure to synthesise high-frequency lake data at scales ranging from local to global. The high-frequency data provide a platform to rigorously validate processbased ecological models because model simulation time steps are better aligned with sensor measurements than with lower-frequency, manual samples. Two case studies from Trout Bog, Wisconsin, USA, and Lake Rotoehu, North Island, New Zealand, are presented to demonstrate that in the past, ecological model outputs (e.g., temperature, chlorophyll) have been relatively poorly validated based on a limited number of directly comparable measurements, both in time and space. The case studies demonstrate some of the difficulties of mapping sensor measurements directly to model state variable outputs as well as the opportunities to use deviations between sensor measurements and model simulations to better inform process understanding. Well-validated ecological models provide a mechanism to extrapolate high-frequency sensor data in space and time, thereby potentially creating a fully 3-dimensional simulation of key variables of interest

TreeWatch.net : a water and carbon monitoring and modeling network to assess instant tree hydraulics and carbon status

TreeWatch.net is an initiative that has been developed to watch trees grow and function in real-time. It is a water- and carbon-monitoring and modeling network, in which high quality measurements of sap flow and stem diameter variation are collected on individual trees. Automated data processing using a cloud service enables instant visualization of water movement and radial stem growth. This can be used to demonstrate the sensitivity of trees to changing weather conditions, such as drought, heat waves, or heavy rain showers. But TreeWatch.net's true innovation lies in its use of these high precision harmonized data to also parameterize process-based tree models in real-time, which makes displaying the much needed mechanisms underlying tree responses to climate change possible. Continuous simulation of turgor to describe growth processes and long-term time series of hydraulic resistance to assess drought-vulnerability in real-time are only a few of the opportunities our approach offers. TreeWatch.net has been developed with the view to be complementary to existing forest monitoring networks and with the aim to contribute to existing dynamic global vegetation models. It provides high-quality data and real-time simulations in order to advance research on the impact of climate change on the biological response of trees and forests. Besides its application in natural forests to answer climate-change related scientific and political questions, we also envision a broader societal application of TreeWatch.net by selecting trees in nature reserves, public areas, cities, university areas, schoolyards, and parks to teach youngsters and create public awareness on the effects of changing weather conditions on trees and forests in this era of climate change

Observational evidence for the convective transport of dust over the central United States

Bulk aerosol composition and aerosol size distributions measured aboard the DC-8 aircraft during the Deep Convective Clouds and Chemistry Experiment mission in May/June 2012 were used to investigate the transport of mineral dust through nine storms encountered over Colorado and Oklahoma. Measurements made at low altitudes (\u3c5 km mean sea level (MSL)) in the storm inflow region were compared to those made in cirrus anvils (altitude \u3e 9 km MSL). Storm mean outflow Ca2+ mass concentrations and total coarse (1 µm \u3c diameter \u3c 5 µm) aerosol volume (Vc) were comparable to mean inflow values as demonstrated by average outflow/inflow ratios greater than 0.5. A positive relationship between Ca2+, Vc, ice water content, and large (diameter \u3e 50 µm) ice particle number concentrations was not evident; thus, the influence of ice shatter on these measurements was assumed small. Mean inflow aerosol number concentrations calculated over a diameter range (0.5 µm \u3c diameter \u3c 5.0 µm) relevant for proxy ice nuclei (NPIN) were ~15–300 times higher than ice particle concentrations for all storms. Ratios of predicted interstitial NPIN (calculated as the difference between inflow NPIN and ice particle concentrations) and inflow NPIN were consistent with those calculated for Ca2+ and Vc and indicated that on average less than 10% of the ingested NPIN were activated as ice nuclei during anvil formation. Deep convection may therefore represent an efficient transport mechanism for dust to the upper troposphere where these particles can function as ice nuclei cirrus forming in situ

The Hierarchic treatment of marine ecological information from spatial networks of benthic platforms

Measuring biodiversity simultaneously in different locations, at different temporal scales, and over wide spatial scales is of strategic importance for the improvement of our understanding of the functioning of marine ecosystems and for the conservation of their biodiversity. Monitoring networks of cabled observatories, along with other docked autonomous systems (e.g., Remotely Operated Vehicles [ROVs], Autonomous Underwater Vehicles [AUVs], and crawlers), are being conceived and established at a spatial scale capable of tracking energy fluxes across benthic and pelagic compartments, as well as across geographic ecotones. At the same time, optoacoustic imaging is sustaining an unprecedented expansion in marine ecological monitoring, enabling the acquisition of new biological and environmental data at an appropriate spatiotemporal scale. At this stage, one of the main problems for an effective application of these technologies is the processing, storage, and treatment of the acquired complex ecological information. Here, we provide a conceptual overview on the technological developments in the multiparametric generation, storage, and automated hierarchic treatment of biological and environmental information required to capture the spatiotemporal complexity of a marine ecosystem. In doing so, we present a pipeline of ecological data acquisition and processing in different steps and prone to automation. We also give an example of population biomass, community richness and biodiversity data computation (as indicators for ecosystem functionality) with an Internet Operated Vehicle (a mobile crawler). Finally, we discuss the software requirements for that automated data processing at the level of cyber-infrastructures with sensor calibration and control, data banking, and ingestion into large data portals.Peer ReviewedPostprint (published version

A longer vernal window: The role of winter coldness and snowpack in driving spring thresholds and lags

Climate change is altering the timing and duration of the vernal window, a period that marks the end of winter and the start of the growing season when rapid transitions in ecosystem energy, water, nutrient, and carbon dynamics take place. Research on this period typically captures only a portion of the ecosystem in transition and focuses largely on the dates by which the system wakes up. Previous work has not addressed lags between transitions that represent delays in energy, water, nutrient, and carbon flows. The objectives of this study were to establish the sequence of physical and biogeochemical transitions and lags during the vernal window period and to understand how climate change may alter them. We synthesized observations from a statewide sensor network in New Hampshire, USA, that concurrently monitored climate, snow, soils, and streams over a three-year period and supplemented these observations with climate reanalysis data, snow data assimilation model output, and satellite spectral data. We found that some of the transitions that occurred within the vernal window were sequential, with air temperatures warming prior to snow melt, which preceded forest canopy closure. Other transitions were simultaneous with one another and had zero-length lags, such as snowpack disappearance, rapid soil warming, and peak stream discharge. We modeled lags as a function of both winter coldness and snow depth, both of which are expected to decline with climate change. Warmer winters with less snow resulted in longer lags and a more protracted vernal window. This lengthening of individual lags and of the entire vernal window carries important consequences for the thermodynamics and biogeochemistry of ecosystems, both during the winter-to-spring transition and throughout the rest of the year

The Global Lake Ecological Observatory Network (GLEON): the evolution of grassroots network science

Nine years later, with over 380 members from 40 countries, and 50 publications to its credit, GLEON is growing at a rapid pace and pushing the boundaries of the practice of network science. GLEON is really three networks: a network of lakes, data, and peopl

Assessing, valuing and protecting our environment- is there a statistical challenge to be answered?

This short article describes some of the evolution in environmental regulation, management and monitoring and the information needs, closely aligned to the statistical challenges to deliver the evidence base for change and effect

Design of Remote Datalogger Connection and Live Data Tweeting System

Low-Impact Development (LID) is an attempt to sustainably respond to the potential hazards posed by urban expansion. Green roofs are an example of LID design meant to reduce the amount of runoff from storm events that are becoming more intense and less predictable while also providing insulation to buildings. LID has not yet been widely adopted as it is often a more expensive alternative to conventional infrastructure (Bowman et. al., 2009). However, its benefits are apparent. The University of Arkansas Honors College awarded a grant to research the large green roof atop Hillside Auditorium. One part of this grant is aimed at educating the public on the benefits LID infrastructure and encourage its development. To accomplish this task, a Raspberry Pi was programmed to operate in tandem with a Campbell Scientific CR1000 datalogger to collect, organize and tweet data to the public under the moniker, “Rufus the Roof.” It is believed that personifying the roof allows data to be conveyed in an entertaining manner that promotes education and public engagement in the LID design. The Raspberry Pi was initially intended to collect data and publish tweets automatically on a live basis. However, automation was not realized due to time constraints and challenges in establishing connection to the datalogger. Instead, a system was developed that allowed the remote transfer of environmental data files from a datalogger on the green roof. Along with remote file transfer protocol, several Python scripts were written that enabled tweets to be published by the Raspberry Pi. The design was successful. Manual remote file transfer and tweeting was achieved. Full automation remains to be achieved, but the Python scripts are built with the capability to operate automatically. The conditions are in place for future development of the project in order to achieve full autonomy. A fully automated system could open the doors for more widespread public engagement in the value and benefits of Low-Impact Development initiatives