Modelling movements of Saimaa ringed seals using an individual-based approach

Liukkonen, Lauri; Ayllón, Daniel; Kunnasranta, Mervi; Niemi, Marja; Nabe‐Nielsen, Jacob; Grimm, Volker; Nyman, Anna-Maija

doi:10.1016/j.ecolmodel.2017.12.002

Cited by 10 publications

(6 citation statements)

References 81 publications

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“…The ability of predators to memorize the distribution of predictable resources has been predicted to have evolved to cope with environmental variability and to maximize their long-term energy intake [18,19,88]. These results reinforce the importance of accounting for this ability within movement models [17,89].…”

Section: Discussionmentioning

confidence: 68%

Prey encounters and spatial memory influence use of foraging patches in a marine central place forager

et al. 2022

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Given the patchiness and long-term predictability of marine resources, memory of high-quality foraging grounds is expected to provide fitness advantages for central place foragers. However, it remains challenging to characterize how marine predators integrate memory with recent prey encounters to adjust fine-scale movement and use of foraging patches. Here, we used two months of movement data from harbour seals ( Phoca vitulina ) to quantify the repeatability in foraging patches as a proxy for memory. We then integrated these data into analyses of fine-scale movement and underwater behaviour to test how both spatial memory and prey encounter rates influenced the seals' area-restricted search (ARS) behaviour. Specifically, we used one month's GPS data from 29 individuals to build spatial memory maps of searched areas and archived accelerometery data from a subset of five individuals to detect prey catch attempts, a proxy for prey encounters. Individuals were highly consistent in the areas they visited over two consecutive months. Hidden Markov models showed that both spatial memory and prey encounters increased the probability of seals initiating ARS. These results provide evidence that predators use memory to adjust their fine-scale movement, and this ability should be accounted for in movement models.

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Section: Discussionmentioning

confidence: 68%

Prey encounters and spatial memory influence use of foraging patches in a marine central place forager

et al. 2022

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“…While many kinds of movement are considered behaviour, we treat these separately due to the wealth of movement information being currently collected using rapidly developing bio‐logging technologies. Empirical patterns in animal movement are often derived from animals equipped with tags (Liukkonen et al ., 2018; Nabe‐Nielsen et al ., 2018; Merkle et al ., 2019; Chudzinska et al ., 2021) or by using point observations such as checkpoints or scent marks (Lewis, White & Murray, 1997; Heinänen et al ., 2018). Broadly speaking, these patterns can describe either path characteristics or space use of individuals.…”

Section: What Kinds Of Patterns Are Useful?mentioning

confidence: 99%

From theory to practice in pattern‐oriented modelling: identifying and using empirical patterns in predictive models

et al. 2021

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To robustly predict the effects of disturbance and ecosystem changes on species, it is necessary to produce structurally realistic models with high predictive power and flexibility. To ensure that these models reflect the natural conditions necessary for reliable prediction, models must be informed and tested using relevant empirical observations. Patternoriented modelling (POM) offers a systematic framework for employing empirical patterns throughout the modelling process and has been coupled with complex systems modelling, such as in agent-based models (ABMs). However, while the production of ABMs has been rising rapidly, the explicit use of POM has not increased. Challenges with identifying patterns and an absence of specific guidelines on how to implement empirical observations may limit the accessibility of POM and lead to the production of models which lack a systematic consideration of reality. This review serves to provide guidance on how to identify and apply patterns following a POM approach in ABMs (POM-ABMs), specifically addressing: where in the ecological hierarchy can we find patterns; what kinds of patterns are useful; how should simulations and observations be compared; and when in the modelling cycle are patterns used? The guidance and examples provided herein are intended to encourage the application of POM and inspire efficient identification and implementation of patterns for both new and experienced modellers alike. Additionally, by generalising patterns found especially useful for POM-ABM development, these guidelines provide practical help for the identification of data gaps and guide the collection of observations useful for the development and verification of predictive models. Improving the accessibility and explicitness of POM could facilitate the production of robust and structurally realistic models in the ecological community, contributing to the advancement of predictive ecology at large.

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“…In ecology, ABMs are often used to understand the dynamics of populations and communities of organisms, with agents representing either individuals (e.g. [46,47]) or other discrete units such as wolf packs [48]. Models combining humans and other animals can yield insights into human -wildlife conflicts (e.g.…”

Section: Social Network Analysis and Agent-based Modellings In Ecology And Social Science (A) Agent-based Modellingmentioning

confidence: 99%

Integrating models of human behaviour between the individual and population levels to inform conservation interventions

Dobson

Lange

Keane

et al. 2019

Phil. Trans. R. Soc. B

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Conservation takes place within social–ecological systems, and many conservation interventions aim to influence human behaviour in order to push these systems towards sustainability. Predictive models of human behaviour are potentially powerful tools to support these interventions. This is particularly true if the models can link the attributes and behaviour of individuals with the dynamics of the social and environmental systems within which they operate. Here we explore this potential by showing how combining two modelling approaches (social network analysis, SNA, and agent-based modelling, ABM) could lead to more robust insights into a particular type of conservation intervention. We use our simple model, which simulates knowledge of ranger patrols through a hunting community and is based on empirical data from a Cambodian protected area, to highlight the complex, context-dependent nature of outcomes of information-sharing interventions, depending both on the configuration of the network and the attributes of the agents. We conclude by reflecting that both SNA and ABM, and many other modelling tools, are still too compartmentalized in application, either in ecology or social science, despite the strong methodological and conceptual parallels between their uses in different disciplines. Even a greater sharing of methods between disciplines is insufficient, however; given the impact of conservation on both the social and ecological aspects of systems (and vice versa), a fully integrated approach is needed, combining both the modelling approaches and the disciplinary insights of ecology and social science. This article is part of the theme issue ‘Linking behaviour to dynamics of populations and communities: application of novel approaches in behavioural ecology to conservation’.

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Modelling movements of Saimaa ringed seals using an individual-based approach

Cited by 10 publications

References 81 publications

Prey encounters and spatial memory influence use of foraging patches in a marine central place forager

Prey encounters and spatial memory influence use of foraging patches in a marine central place forager

From theory to practice in pattern‐oriented modelling: identifying and using empirical patterns in predictive models

Integrating models of human behaviour between the individual and population levels to inform conservation interventions

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