Can the black box be cracked? The augmentation of microbial ecology by high-resolution, automated sensing technologies

Shade, Ashley; Carey, Cayelan C.; Kara, Emily L.; Bertilsson, Stefan; McMahon, Katherine D.; Smith, Matthew C.

doi:10.1038/ismej.2009.56

Cited by 33 publications

(31 citation statements)

References 51 publications

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“…The recent advance in water-related cyber-infrastructure, defined as the system of hardware and software components that monitor, manage and model aquatic ecosystems (Shade et al 2009), has created challenges and opportunities for lake modelling. For example, assimilation of observations from real-time lake sensors to reduce error in model parameterizations is emerging as a promising method to manage the uncertainty of complex models.…”

Section: Cyber-infrastructurementioning

confidence: 99%

Challenges and opportunities for integrating lake ecosystem modelling approaches

et al. 2010

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A large number and wide variety of lake ecosystem models have been developed and published during the past four decades. We identify two challenges for making further progress in this field. One such challenge is to avoid developing more models largely following the concept of others ('reinventing the wheel'). The other challenge is to avoid focusing on only one type of model, while ignoring new and diverse approaches that have become available ('having tunnel vision'). In this paper, we aim at improving the awareness of existing models and knowledge of concurrent approaches in lake ecosystem modelling, without covering all possible model tools and avenues. First, we present a broad variety of modelling approaches. To illustrate these approaches, we give brief descriptions of rather arbitrarily selected sets of specific models. We deal with static models (steady state and regression models), complex dynamic models (CAEDYM, CE-QUAL-W2, Delft 3D-ECO, LakeMab, LakeWeb, MyLake, PCLake, PROTECH, SALMO), structurally dynamic models and minimal dynamic models. We also discuss a group of approaches that could all be classified as individual based: superindividual models (Piscator, Charisma), physiologically structured models, stage-structured models and traitbased models. We briefly mention genetic algorithms, neural networks, Kalman filters and fuzzy logic. Thereafter, we zoom in, as an in-depth example, on the multi-decadal development and application of the lake ecosystem model PCLake and related models (PCLake Metamodel, Lake Shira Model, IPH-TRIM3D-PCLake). In the discussion, we argue that while the historical development of each approach and model is understandable given its 'leading principle', there are many opportunities for combining approaches. We take the point of view that a single 'right' approach does not exist and should not be strived for. Instead, multiple modelling approaches, applied concurrently to a given problem, can help develop an integrative view on the functioning of lake ecosystems. We end with a set of specific recommendations that may be of help in the further development of lake ecosystem models.

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Section: Cyber-infrastructurementioning

confidence: 99%

Challenges and opportunities for integrating lake ecosystem modelling approaches

et al. 2010

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“…Many "boundary" variables, such as air temperature and wind speed, and in situ variables, such as water temperature and dissolved oxygen, can be resolved autonomously at high frequency in comparison with commonly used manual sampling for chemistry or biological variables. Automated methods such as real-time polymerase chain reaction (PCR) to monitor changes in microbial populations or optical methods for chemical variables are advancing rapidly (e.g., Rasmussen et al 2008, Shade et al 2009), however, and could ultimately align biological and chemical variable monitoring frequencies with physical variables to better address key issues of changes in aquatic biodiversity or biogeochemical cycles. For 3-dimensional deterministic models, the requirements for spatial validation may still fall short of what is desired, and other monitoring techniques such as autonomous underwater vehicles or remote sensing may be valuable for ensuring robust spatial validation of these models (Wynne et al 2011).…”

Section: Discussionmentioning

confidence: 99%

“…For example, our understanding of lake hydrodynamics (Wüest andLorke 2003, Klug et al 2012), metabolism (Van de Bogert et al 2007, Staehr et al 2010, effects of episodic events (Jones et al 2008, Shade et al 2009, Jennings et al 2012, and air-water gas transfers ) has been strongly driven by the capacity of sensor networks to provide high-frequency data. In contrast, infrequent manual measurements are often unsuitable for detecting rapid changes in variables such as dissolved oxygen and chlorophyll that are a central focus of limnological studies (e.g., Banas et al 2005).…”

Section: Introductionmentioning

confidence: 99%

A Global Lake Ecological Observatory Network (GLEON) for synthesising high–frequency sensor data for validation of deterministic ecological models

Hamilton

Carey

Арвола

et al. 2015

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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 50

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“…There is a large range of complementary platforms in addition to research ships and satellites for sampling and data acquisition on phytoplankton and microbial ecology (Shade et al, 2009).…”

Section: Platforms and Sensor Networkmentioning

confidence: 99%

Microalgae: Current Research and Applications

Tsaloglou¹

2016

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Can the black box be cracked? The augmentation of microbial ecology by high-resolution, automated sensing technologies

Cited by 33 publications

References 51 publications

Challenges and opportunities for integrating lake ecosystem modelling approaches

Challenges and opportunities for integrating lake ecosystem modelling approaches

A Global Lake Ecological Observatory Network (GLEON) for synthesising high–frequency sensor data for validation of deterministic ecological models

Microalgae: Current Research and Applications

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