Integrating Spatial Behavioral Ecology in Agent-Based Models for Species Conservation

Semeniuk, Christina A.D.; Musiani, Marco; Marceau, Danielle J.

doi:10.5772/23055

Cited by 3 publications

(1 citation statement)

References 83 publications

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“…ABMs for movement generally incorporate rules that dictate when an individual agent decides to move from its current location. For real-world organisms, the onset of movement at fine scales may depend on the individual's current internal state, including its physiological and psychological conditions, the condition of the current area that the individual is in, and the presence of competition and predation (Semeniuk et al, 2011;Martin et al, 2013;Doherty and Driscoll, 2017). As such, ABMs simulating the fine-scale movement of individuals often keep track of temporal changes in the individual agent's internal state and its local surroundings.…”

Section: When To Move?mentioning

confidence: 99%

Decision-Making in Agent-Based Modeling: A Current Review and Future Prospectus

Jiang

Diaz

2019

Front. Ecol. Evol.

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All basic processes of ecological populations involve decisions; when and where to move, when and what to eat, and whether to fight or flee. Yet decisions and the underlying principles of decision-making have been difficult to integrate into the classical population-level models of ecology. Certainly, there is a long history of modeling individuals' searching behavior, diet selection, or conflict dynamics within social interactions. When all the individuals are given certain simple rules to govern their decision-making processes, the resultant population-level models have yielded important generalizations and theory. But it is also recognized that such models do not represent the way real individuals decide on actions. Factors that influence a decision include the organism's environment with its dynamic rewards and risks, the complex internal state of the organism, and its imperfect knowledge of the environment. In the case of animals, it may also involve complex social factors, and experience and learning, which vary among individuals. The way that all factors are weighed and processed to lead to decisions is a major area of behavioral theory. While classic population-level modeling is limited in its ability to integrate decision-making in its actual complexity, the development of individual-or agent-based models (IBM/ABMs) (we use ABM throughout to designate both "agent-based modeling" and an "agent-based model") has opened the possibility of describing the way that decisions are made, and their effects, in minute detail. Over the years, these models have increased in size and complexity. Current ABMs can simulate thousands of individuals in realistic environments, and with highly detailed internal physiology, perception and ability to process the perceptions and make decisions based on those and their internal states. The implementation of decision-making in ABMs ranges from fairly simple to highly complex; the process of an individual deciding on an action can occur through the use of logical and simple (if-then) rules to more sophisticated neural networks and genetic algorithms. The purpose of this paper is to give an overview of the ways in which decisions are integrated into a variety of ABMs and to give a prospectus on the future of modeling of decisions in ABMs.

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Section: When To Move?mentioning

confidence: 99%

Decision-Making in Agent-Based Modeling: A Current Review and Future Prospectus

Jiang

Diaz

2019

Front. Ecol. Evol.

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Behavioral response of the endemic Martino’s vole Dinaromys bogdanovi (Martino 1922) to environmental complexity

et al. 2018

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Advancing bioenergetics-based modeling to improve climate change projections of marine ecosystems

Rose,

Holsman,

Nye

et al. 2024

Mar. Ecol. Prog. Ser.

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Climate change has rapidly altered marine ecosystems and is expected to continue to push systems and species beyond historical baselines into novel conditions. Projecting responses of organisms and populations to these novel environmental conditions often requires extrapolations beyond observed conditions, challenging the predictive limits of statistical modeling capabilities. Bioenergetics modeling provides the mechanistic basis for projecting climate change effects on marine living resources in novel conditions, has a long history of development, and has been applied widely to fish and other taxa. We provide our perspective on 4 opportunities that will advance the ability of bioenergetics-based models to depict changes in the productivity and distribution of fishes and other marine organisms, leading to more robust projections of climate impacts. These are (1) improved depiction of bioenergetics processes to derive realistic individual-level response(s) to complex changes in environmental conditions, (2) innovations in scaling individual-level bioenergetics to project responses at the population and food web levels, (3) more realistic coupling between spatial dynamics and bioenergetics to better represent the local- to regional-scale differences in the effects of climate change on the spatial distributions of organisms, and (4) innovations in model validation to ensure that the next generation of bioenergetics-based models can be used with known and sufficient confidence. Our focus on specific opportunities will enable critical advancements in bioenergetics modeling and position the modeling community to make more accurate and robust projections of the effects of climate change on individuals, populations, food webs, and ecosystems.

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Integrating Spatial Behavioral Ecology in Agent-Based Models for Species Conservation

Cited by 3 publications

References 83 publications

Decision-Making in Agent-Based Modeling: A Current Review and Future Prospectus

Decision-Making in Agent-Based Modeling: A Current Review and Future Prospectus

Behavioral response of the endemic Martino’s vole Dinaromys bogdanovi (Martino 1922) to environmental complexity

Advancing bioenergetics-based modeling to improve climate change projections of marine ecosystems

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