TY  - JOUR
A1  - Sibly, Richard M.
A1  - Grimm, Volker
A1  - Martin, Benjamin T.
A1  - Johnston, Alice S. A.
A1  - Kulakowska, Katarzyna
A1  - Topping, Christopher J.
A1  - Calow, Peter
A1  - Nabe-Nielsen, Jacob
A1  - Thorbek, Pernille
A1  - DeAngelis, Donald L.
T1  - Representing the acquisition and use of energy by individuals in agent-based models of animal populations
JF  - Methods in ecology and evolution : an official journal of the British Ecological Society
N2  - Agent-based models (ABMs) are widely used to predict how populations respond to changing environments. As the availability of food varies in space and time, individuals should have their own energy budgets, but there is no consensus as to how these should be modelled. Here, we use knowledge of physiological ecology to identify major issues confronting the modeller and to make recommendations about how energy budgets for use in ABMs should be constructed. Our proposal is that modelled animals forage as necessary to supply their energy needs for maintenance, growth and reproduction. If there is sufficient energy intake, an animal allocates the energy obtained in the order: maintenance, growth, reproduction, energy storage, until its energy stores reach an optimal level. If there is a shortfall, the priorities for maintenance and growth/reproduction remain the same until reserves fall to a critical threshold below which all are allocated to maintenance. Rates of ingestion and allocation depend on body mass and temperature. We make suggestions for how each of these processes should be modelled mathematically. Mortality rates vary with body mass and temperature according to known relationships, and these can be used to obtain estimates of background mortality rate. If parameter values cannot be obtained directly, then values may provisionally be obtained by parameter borrowing, pattern-oriented modelling, artificial evolution or from allometric equations. The development of ABMs incorporating individual energy budgets is essential for realistic modelling of populations affected by food availability. Such ABMs are already being used to guide conservation planning of nature reserves and shell fisheries, to assess environmental impacts of building proposals including wind farms and highways and to assess the effects on nontarget organisms of chemicals for the control of agricultural pests.
KW  - bioenergetics
KW  - energy budget
KW  - individual-based models
KW  - population dynamics
Y1  - 2013
U6  - https://doi.org/10.1111/2041-210x.12002
SN  - 2041-210X
VL  - 4
IS  - 2
SP  - 151
EP  - 161
PB  - Wiley-Blackwell
CY  - Hoboken
ER  - 
TY  - GEN
A1  - Mooij, Wolf M.
A1  - Trolle, Dennis
A1  - Jeppesen, Erik
A1  - Arhonditsis, George B.
A1  - Belolipetsky, Pavel V.
A1  - Chitamwebwa, Deonatus B. R.
A1  - Degermendzhy, Andrey G.
A1  - DeAngelis, Donald L.
A1  - Domis, Lisette Nicole de Senerpont
A1  - Downing, Andrea S.
A1  - Elliott, J. Alex
A1  - Fragoso Jr., Carlos Ruberto
A1  - Gaedke, Ursula
A1  - Genova, Svetlana N.
A1  - Gulati, Ramesh D.
A1  - Håkanson, Lars
A1  - Hamilton, David P.
A1  - Hipsey, Matthew R.
A1  - ‘t Hoen, Jochem
A1  - Hülsmann, Stephan
A1  - Los, F. Hans
A1  - Makler-Pick, Vardit
A1  - Petzoldt, Thomas
A1  - Prokopkin, Igor G.
A1  - Rinke, Karsten
A1  - Schep, Sebastiaan A.
A1  - Tominaga, Koji
A1  - Van Dam, Anne A.
A1  - Van Nes, Egbert H.
A1  - Wells, Scott A.
A1  - Janse, Jan H.
T1  - Challenges and opportunities for integrating lake ecosystem modelling approaches
T2  - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe
N2  - 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 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.
T3  - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 1326 
KW  - aquatic
KW  - food web dynamics
KW  - plankton
KW  - nutrients
KW  - spatial
KW  - lake
KW  - freshwater
KW  - marine
KW  - community
KW  - population
KW  - hydrology
KW  - eutrophication
KW  - global change
KW  - climate warming
KW  - fisheries
KW  - biodiversity
KW  - management
KW  - mitigation
KW  - adaptive processes
KW  - non-linear dynamics
KW  - analysis
KW  - bifurcation
KW  - understanding
KW  - prediction
KW  - model limitations
KW  - model integration
Y1  - 2010
U6  - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-429839
SN  - 1866-8372
IS  - 1326
ER  - 
TY  - JOUR
A1  - Mooij, Wolf M.
A1  - Trolle, Dennis
A1  - Jeppesen, Erik
A1  - Arhonditsis, George B.
A1  - Belolipetsky, Pavel V.
A1  - Chitamwebwa, Deonatus B. R.
A1  - Degermendzhy, Andrey G.
A1  - DeAngelis, Donald L.
A1  - Domis, Lisette Nicole de Senerpont
A1  - Downing, Andrea S.
A1  - Elliott, J. Alex
A1  - Fragoso Jr, Carlos Ruberto
A1  - Gaedke, Ursula
A1  - Genova, Svetlana N.
A1  - Gulati, Ramesh D.
A1  - Håkanson, Lars
A1  - Hamilton, David P.
A1  - Hipsey, Matthew R.
A1  - ‘t Hoen, Jochem
A1  - Hülsmann, Stephan
A1  - Los, F. Hans
A1  - Makler-Pick, Vardit
A1  - Petzoldt, Thomas
A1  - Prokopkin, Igor G.
A1  - Rinke, Karsten
A1  - Schep, Sebastiaan A.
A1  - Tominaga, Koji
A1  - Van Dam, Anne A.
A1  - Van Nes, Egbert H.
A1  - Wells, Scott A.
A1  - Janse, Jan H.
T1  - Challenges and opportunities for integrating lake ecosystem modelling approaches
JF  - Aquatic ecology
N2  - 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 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.
KW  - aquatic
KW  - food web dynamics
KW  - plankton
KW  - nutrients
KW  - spatial
KW  - lake
KW  - freshwater
KW  - marine
KW  - community
KW  - population
KW  - hydrology
KW  - eutrophication
KW  - global change
KW  - climate warming
KW  - fisheries
KW  - biodiversity
KW  - management
KW  - mitigation
KW  - adaptive processes
KW  - non-linear dynamics
KW  - analysis
KW  - bifurcation
KW  - understanding
KW  - prediction
KW  - model limitations
KW  - model integration
Y1  - 2010
U6  - https://doi.org/10.1007/s10452-010-9339-3
SN  - 1573-5125
SN  - 1386-2588
VL  - 44
SP  - 633
EP  - 667
PB  - Springer Science + Business Media B.V.
CY  - Dordrecht
ER  - 
TY  - JOUR
A1  - Grimm, Volker
A1  - Revilla, Eloy
A1  - Berger, Uta
A1  - Jeltsch, Florian
A1  - Mooij, Wolf M.
A1  - Railsback, Steven Floyd
A1  - Thulke, Hans-Hermann
A1  - Weiner, Jacob
A1  - Wiegand, Thorsten
A1  - DeAngelis, Donald L.
T1  - Pattern-oriented modeling of agend-based complex systems : lessons from ecology
N2  - Agent-based complex systems are dynamic networks of many interacting agents; examples include ecosystems, financial markets, and cities. The search for general principles underlying the internal organization of such systems often uses bottom-up simulation models such as cellular automata and agent-based models. No general framework for designing, testing, and analyzing bottom-up models has yet been established, but recent advances in ecological modeling have come together in a general strategy we call pattern-oriented modeling. This strategy provides a unifying framework for decoding the internal organization of agent-based complex systems and may lead toward unifying algorithmic theories of the relation between adaptive behavior and system complexity
Y1  - 2005
ER  - 
TY  - JOUR
A1  - Grimm, Volker
A1  - Berger, Uta
A1  - Bastiansen, Finn
A1  - Eliassen, Sigrunn
A1  - Ginot, Vincent
A1  - Giske, Jarl
A1  - Goss-Custard, John
A1  - Grand, Tamara
A1  - Heinz, Simone K.
A1  - Huse, Geir
A1  - Huth, Andreas
A1  - Jepsen, Jane U.
A1  - Jorgensen, Christian
A1  - Mooij, Wolf M.
A1  - Mueller, Birgit
A1  - Piou, Cyril
A1  - Railsback, Steven Floyd
A1  - Robbins, Andrew M.
A1  - Robbins, Martha M.
A1  - Rossmanith, Eva
A1  - Rueger, Nadja
A1  - Strand, Espen
A1  - Souissi, Sami
A1  - Stillman, Richard A.
A1  - Vabo, Rune
A1  - Visser, Ute
A1  - DeAngelis, Donald L.
T1  - A standard protocol for describing individual-based and agent-based models
JF  - Ecological modelling : international journal on ecological modelling and engineering and systems ecolog
N2  - Simulation models that describe autonomous individual organisms (individual based models, IBM) or agents (agent-based models, ABM) have become a widely used tool, not only in ecology, but also in many other disciplines dealing with complex systems made up of autonomous entities. However, there is no standard protocol for describing such simulation models, which can make them difficult to understand and to duplicate. This paper presents a proposed standard protocol, ODD, for describing IBMs and ABMs, developed and tested by 28 modellers who cover a wide range of fields within ecology. This protocol consists of three blocks (Overview, Design concepts, and Details), which are subdivided into seven elements: Purpose, State variables and scales, Process overview and scheduling, Design concepts, Initialization, Input, and Submodels. We explain which aspects of a model should be described in each element, and we present an example to illustrate the protocol in use. In addition, 19 examples are available in an Online Appendix. We consider ODD as a first step for establishing a more detailed common format of the description of IBMs and ABMs. Once initiated, the protocol will hopefully evolve as it becomes used by a sufficiently large proportion of modellers. (c) 2006 Elsevier B.V. All rights reserved.
KW  - individual-based model
KW  - agent-based model
KW  - model description
KW  - scientific communication
KW  - standardization
Y1  - 2006
U6  - https://doi.org/10.1016/j.ecolmodel.2006.04.023
SN  - 0304-3800
VL  - 198
SP  - 115
EP  - 126
PB  - Elsevier
CY  - Amsterdam
ER  -