Gideon Gal

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Imperial College London

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University of California, Irvine

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articles by Gideon Gal

Modeling the kinneret ecosystem

by Natasa Atanasova and Gideon Gal

\textcopyright} Springer Science+Business Media Dordrecht 2014.Modeling of the Lake Kinneret ecos... more \textcopyright} Springer Science+Business Media Dordrecht 2014.Modeling of the Lake Kinneret ecosystem and its various components has developed greatly since the first effort in 1980. Modeling studies have included a range of approaches, some of which have focused on the entire ecosystem, while others only on certain components. The modeling approaches that have been applied to the Lake Kinneret ecosystem range from statistical approaches, data mining, and machine-learning techniques to flux models, bioenergetics, nutrient– phytoplankton–zooplankton (N–P–Z)-type models, and complete ecosystem models. Models have been used to enhance our understanding of key limnological and food-web processes. The models, however, have also been used as a means for providing resource managers with improved management tools. This has included, in some cases, integrating model output and a quantified water quality (WQ) system as the basis for establishing the relationships between management measures and water quality. Thus, highlighting the role of ecosystem modeling as a critical management tool. The use of models, especially complex ecosystem models, requires constant updating and ongoing development along with a wide range of information and data. In most cases, the available information and data do not satisfy model needs thereby introducing model uncertainty. In order to improve model reliability, and, as a result, its applicability, it is important to increase the overlap between routine monitoring and model data requirements.

Fluctuations in water level and the dynamics of zooplankton: A data-driven modelling approach

by Natasa Atanasova and Gideon Gal

1.The zooplankton in Lake Kinneret (Israel) have undergone large fluctuations in recent decades, ... more 1.The zooplankton in Lake Kinneret (Israel) have undergone large fluctuations in recent decades, which have been linked to both biotic and abiotic processes. 2.By applying a data-driven modelling approach to a long-term database, and focusing on key abiotic (lake-level change) and biotic (prey abundance) variables, we attempted to identify the possible factors impacting the lake's zooplankton community. 3.We hypothesised that changes in the predatory zooplankton (adult cyclopoids) assemblage are driven by changes in lake level during years of large changes. We further postulated that lake-level changes would have a similar impact on the herbivorous zooplankton (cladocerans and cyclopoid copepodites) but an opposite effect on the microzooplankton. In the years of moderate changes to lake level, however, the abundance of predatory zooplankton would determine the size of the herbivore and microzooplankton populations rather than their food sources, that is, top-down control. 4.The resulting decision trees supported the hypotheses stressing the importance of the annual rate of lake-level change in shaping the zooplankton community in the lake. In addition, and in contrast to expectations, bottom-up processes seem to play a role in determining zooplankton abundance. {\textcopyright} 2013 Blackwell Publishing Ltd.

Modelling dinoflagellate dynamics in Lake Kinneret

by Natasa Atanasova and Gideon Gal

Automated discovery of a model for dinoflagellate dynamics

by Natasa Atanasova and Gideon Gal

The aim of this paper is to discover a model equation for predicting the concentration of the alg... more The aim of this paper is to discover a model equation for predicting the concentration of the algal species Peridinium gatunense (Dinoflagellate) in Lake Kinneret. This is a rather difficult task, due to the sudden ecosystem changes that occurred in the mid-1990s. Namely, the stable ecosystem (with regular Peridinium blooms until 1993) underwent changes and has transformed into an unstable system, with cyanobacterial blooms now occurring regularly. This shift in the algal succession is expected to influence attempts to model the lake ecosystem. Namely, the model structure before and after the change is likely to be different. Our modelling experiments were directed to discover a single model equation that can simulate dinoflagellate dynamics in both periods. We apply an automated modelling tool (Lagramge), which integrates the knowledge- and the data-driven modelling approach. In addition we include an expert visual estimation of the models discovered by Lagramge to assist in the selection of the optimal model. The dataset used included time-series measurements of typical data from the periods 1988 to 1992 and 1997 to 1999. Using the data and expert knowledge coded in a modelling knowledge library, Lagramge successfully discovered several suitable mathematical models for Peridinium. After the expert's visual estimation and validation of the models, we propose one optimal model capable of long-term predictions. {\textcopyright} 2010 Elsevier Ltd.

Papers by Gideon Gal

Early warning signals are hampered by a lack of critical transitions in empirical lake data

bioRxiv (Cold Spring Harbor Laboratory), May 13, 2023

Quantifying the potential for abrupt non-linear changes in ecological communities is a key manage... more Quantifying the potential for abrupt non-linear changes in ecological communities is a key managerial goal, leading to a significant body of research aimed at identifying indicators of approaching regime shifts. Most of this work has built on the theory of bifurcations, with the assumption that critical transitions are a common feature of complex ecological systems. This has led to the development of a suite of often inaccurate early warning signals (EWSs), with more recent techniques seeking to overcome their limitations by analysing multivariate time series or applying machine learning. However, it remains unclear whether regime shifts and/or critical transitions are common occurrences in natural systems, and-if they are present-whether classic and second-generation EWS methods predict rapid community change. Here, using multitrophic data on nine lakes from around the world, we both identify the type of transition a lake is exhibiting, and the reliability of classic and second generation EWSs methods to predict whole ecosystem change. We find few instances of critical transitions in our lake dataset, with different trophic levels often expressing different forms of abrupt change. The ability to predict this change is highly technique dependant, with multivariate EWSs generally classifying correctly, classical rolling window univariate EWSs performing not better than chance, and recently developed machine learning techniques performing poorly. Our results suggest that predictive ecology should start to move away from the concept of critical transitions and develop methods suitable for predicting change in the absence of the strict bounds of bifurcation theory.

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