Fewer sites but better data? Optimising the representativeness and statistical power of a national monitoring network

O’Hare, Matthew T.; Gunn, Iain; Critchlow-Watton, Nathan; Guthrie, Robin; Taylor, C. E.; Chapman, Daniel S.

doi:10.1016/j.ecolind.2020.106321

Cited by 8 publications

(12 citation statements)

References 39 publications

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“…It is also essential to consider the gradient of environmental variables to ensure the inclusion of the widest possible variation in geology, climate, vegetation, and land use conditions. Statistical approaches can help to map the parameter space and guide the choices (O'Hare et al, 2020), but some gaps are already apparent. For example, although 55% of the world population now lives in urban areas (UnitedNations, 2018), urban water quality investigations typically focus on waste water treatment and stormwater (Jovanovic, Hale, Gironás, & Mejia, 2019), with the nonengineered part of urban water quantity and quality being much less studied (Kaushal & Belt, 2012) compared to agricultural or pristine watersheds.…”

Section: Spatial and Temporal Coverage: Extreme Environments And End Membersmentioning

confidence: 99%

Toward catchment hydro‐biogeochemical theories

Sullivan

Benettin

et al. 2020

WIREs Water

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Headwater catchments are the fundamental units that connect the land to the ocean. Hydrological flow and biogeochemical processes are intricately coupled, yet their respective sciences have progressed without much integration. Reaction kinetic theories that prescribe rate dependence on environmental variables (e.g., temperature and water content) have advanced substantially, mostly in well‐mixed reactors, columns, and warming experiments without considering the characteristics of hydrological flow at the catchment scale. These theories have shown significant divergence from observations in natural systems. On the other hand, hydrological theories, including transit time theory, have progressed substantially yet have not been incorporated into understanding reactions at the catchment scale. Here we advocate for the development of integrated hydro‐biogeochemical theories across gradients of climate, vegetation, and geology conditions. The lack of such theories presents barriers for understanding mechanisms and forecasting the future of the Critical Zone under human‐ and climate‐induced perturbations. Although integration has started and co‐located measurements are well under way, tremendous challenges remain. In particular, even in this era of “big data,” we are still limited by data and will need to (1) intensify measurements beyond river channels and characterize the vertical connectivity and broadly the shallow and deep subsurface; (2) expand to older water dating beyond the time scales reflected in stable water isotopes; (3) combine the use of reactive solutes, nonreactive tracers, and isotopes; and (4) augment measurements in environments that are undergoing rapid changes. To develop integrated theories, it is essential to (1) engage models at all stages to develop model‐informed data collection strategies and to maximize data usage; (2) adopt a “simple but not simplistic,” or fit‐for‐purpose approach to include essential processes in process‐based models; (3) blend the use of process‐based and data‐driven models in the framework of “theory‐guided data science.” Within the framework of hypothesis testing, model‐data fusion can advance integrated theories that mechanistically link catchments' internal structures and external drivers to their functioning. It can not only advance the field of hydro‐biogeochemistry, but also enable hind‐ and fore‐casting and serve the society at large. Broadly, future education will need to cultivate thinkers at the intersections of traditional disciplines with hollistic approaches for understanding interacting processes in complex earth systems. This article is categorized under: Engineering Water > Methods

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Section: Spatial and Temporal Coverage: Extreme Environments And End Membersmentioning

confidence: 99%

Toward catchment hydro‐biogeochemical theories

Sullivan

Benettin

et al. 2020

WIREs Water

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“…Carvalho et al 2019 ; Gebler et al 2017 ; Kelly et al 2016 ; Kolada et al 2016 ). On the contrary, it is argued in various fields that a smaller number of data, which is more comprehensive and accurate, may be a more useful research material (Faraway and Augustin 2018 ; O’Hare et al 2020 ; Whitaker 2018 ). The use of monitoring data for Polish lakes has enabled the creation of models for four indicators of ecological status.…”

Section: Discussionmentioning

confidence: 99%

Modelling of ecological status of Polish lakes using deep learning techniques

Gebler

Kolada

Pasztaleniec

et al. 2020

Environ Sci Pollut Res

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Since 2000, after the Water Framework Directive came into force, aquatic ecosystems’ bioassessment has acquired immense practical importance for water management. Currently, due to extensive scientific research and monitoring, we have gathered comprehensive hydrobiological databases. The amount of available data increases with each subsequent year of monitoring, and the efficient analysis of these data requires the use of proper mathematical tools. Our study challenges the comparison of the modelling potential between four indices for the ecological status assessment of lakes based on three groups of aquatic organisms, i.e. phytoplankton, phytobenthos and macrophytes. One of the deep learning techniques, artificial neural networks, has been used to predict values of four biological indices based on the limited set of the physicochemical parameters of water. All analyses were conducted separately for lakes with various stratification regimes as they function differently. The best modelling quality in terms of high values of coefficients of determination and low values of the normalised root mean square error was obtained for chlorophyll a followed by phytoplankton multimetric. A lower degree of fit was obtained in the networks for macrophyte index, and the poorest model quality was obtained for phytobenthos index. For all indices, modelling quality for non-stratified lakes was higher than this for stratified lakes, giving a higher percentage of variance explained by the networks and lower values of errors. Sensitivity analysis showed that among physicochemical parameters, water transparency (Secchi disk reading) exhibits the strongest relationship with the ecological status of lakes derived by phytoplankton and macrophytes. At the same time, all input variables indicated a negligible impact on phytobenthos index. In this way, different explanations of the relationship between biological and trophic variables were revealed.

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“…Before any data are collected, the factors causing variability within the system need to be identified and defined, and suitable indicators of interest must be determined. This will provide insight into the sampling effort required to gain a significant result-in statistical parlance, power analysis [45]. This is of particular importance in river restoration projects where the restoration "signal" may be difficult to discern from the "noise" of uncontrolled confounding factors such as land use changes, climate change, interannual variation, natural disturbance, erratic events (e.g., La Nina), and observer-variability.…”

Section: Monitoring Strategiesmentioning

confidence: 99%

“…To ensure a true representation of the background natural variability in river condition (i.e., controls), multiple sites, sampled at the same time across similar habitats and/or rivers, could assist in understanding existing or underlying attributes. As well as enabling the success of an individual project to be evaluated, the information gathered also contributes to the development of wider understanding, i.e., to the building of a theoretical framework [45]. Following restoration, the frequency and duration of monitoring should be consistent with the life histories of the target organisms and the timescale for exhibiting responses (i.e., the lag time).…”

Section: Monitoring Strategiesmentioning

confidence: 99%

Best Practices for Monitoring and Assessing the Ecological Response to River Restoration

England

Angelopoulos²,

Cooksley

et al. 2021

Water

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Nature-based solutions are widely advocated for freshwater ecosystem conservation and restoration. As increasing amounts of river restoration are undertaken, the need to understand the ecological response to different measures and where measures are best applied becomes more pressing. It is essential that appraisal methods follow a sound scientific approach. Here, experienced restoration appraisal experts review current best practice and academic knowledge to make recommendations and provide guidance that will enable practitioners to gather and analyse meaningful data, using scientific rigor to appraise restoration success. What should be monitored depends on the river type and the type and scale of intervention. By understanding how habitats are likely to change we can anticipate what species, life stages, and communities are likely to be affected. Monitoring should therefore be integrated and include both environmental/habitat and biota assessments. A robust scientific approach to monitoring and appraisal is resource intensive. We recommend that appraisal efforts be directed to where they will provide the greatest evidence, including ‘flagship’ restoration schemes for detailed long-term monitoring. Such an approach will provide the evidence needed to understand which restoration measures work where and ensure that they can be applied with confidence elsewhere.

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Fewer sites but better data? Optimising the representativeness and statistical power of a national monitoring network

Cited by 8 publications

References 39 publications

Toward catchment hydro‐biogeochemical theories

Toward catchment hydro‐biogeochemical theories

Modelling of ecological status of Polish lakes using deep learning techniques

Best Practices for Monitoring and Assessing the Ecological Response to River Restoration

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