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USGS2021

The AEMON-J “Hacking Limnology” workshop series & virtual summit: Incorporating data science and open science in aquatic research

Following the 2020 “Virtual Summit: Incorporating Data Science and Open Science in Aquatic Research” (DSOS; Meyer and Zwart 2020 ), a grassroots group of scientists convened the 2nd Virtual DSOS Summit on 22–23 July 2021. DSOS combined forces with the Aquatic Ecosystem MOdeling Network - Junior (AEMON-J; https://github.com/aemon-j) to host a 4-d “Hacking Limnology” Workshop Series prior to the summit (13–16 July 2021). The aim was to focus more deeply on skill development and networking among early career researchers (ECRs), both of which are key to growing a workforce of data-intensive aquatic scientists (López Moreira M et al. in press; Meyer et al. 2021 a ). To support ECRs further, we hosted a virtual job board, where participants could note if they were either looking for employment or hiring for a position. Like the 2020 summit, there was high enthusiasm for both the summit and the workshops. In total, 686 people from over 50 countries registered for the AEMON-J Workshop Series and the DSOS Summit. Countries with the highest number of registrants included the United States (41%), Nigeria (20%), Canada (6%), Brazil (6%), and Germany (5%) (Fig. 1). To increase accessibility, there were no registration costs for the workshops and summit, and we centralized introductory training materials, coding scripts, and presentation recordings in one community website (https://aquaticdatasciopensci.github.io/; Fig. 2), which we hope will continue to support the AEMON-J and DSOS communities over time.

Limnology and Oceanography Bulletin↗

USGS2010

Challenges and opportunities for integrating lake ecosystem modelling approaches

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: super-individual models (Piscator, Charisma), physiologically structured models, stage-structured models and trait-based 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.

Aquatic Ecology↗