Periodic Reporting for period 1 - SpaceUseDrivers (Unravellling the biological determinants of space use patterns in animals)
Reporting period: 2019-02-01 to 2021-01-31
Animals use space in a bewildering variety of ways. Moose restrict their movements to individual home ranges, whereas Mongolian gazelles are nomadic. Bears avoid each other, but wildebeest migrate in huge groups. Intriguingly, these patterns emerge from interactions of the same basic ingredients: cognitive abilities, environmental characteristics, social behaviour, and life-history priorities. Despite the considerable implications of animal movement for ecological processes (e.g. nutrient fluxes, disease dynamics, invasions, extinctions), we surprisingly still lack an overarching theory explaining how the immense diversity of patterns arises from combinations of these mechanisms. The general aim of the project was therefore to advance our understanding of the determinants of animal space use patterns.
During my fellowship I contributed towards this goal by (1) conceptualizing the links between environmental drivers and animal movement, (2) developing a universal individual-based model capable of generating any of the movement pattern types observed in nature, (3) revealing unknown processes underlying space use fidelity, segregations, migrations, consumer-resource and metapopulation dynamics, and (4) demonstrating the appropriateness of a recent statistical method for fitting individual-based models of animal movement on data.
(1) My early investigations revealed the central role that environmental predictability plays in the determination of animal movement. Crucially, I showed that the concept of environmental predictability was still lacking a proper definition and methodologies to quantify it. I therefore developed a comprehensive framework for environmental predictability and animal movement [1]. I synthesised the literature into a general definition, a typology, and a set of methodologies for quantifying environmental predictability. I then reviewed the movement-related adaptations to environmental predictability and the methods designed to detect and characterise them, I outlined recent ideas on the feedbacks between animal movement and environmental predictability, and I discussed how human activities can alter patterns of wildlife movement by affecting environmental predictability.
(2) I developed a universal Individual-Based Model that integrates individual perception, memory, social information use, and environmental dynamics. Through extensive simulations, I showed that this parsimonious model can lead to the emergence of all existing types of individual- and population-level space use patterns (nomadism, home ranging, migration, spatial segregation, and aggregation), depending on its parametrization. I showed using this model that the interaction of individual cognition with environmental conditions determine emerging space use patterns.
(3) With collaborators, I participated in showing that:
- collective spatial segregation between neighbouring colonies can emerge from the simple use of memory by colony members [2],
- short-term temporal memory can be sufficient for foragers to efficiently exploit seasonal resources [3],
- ungulates’ site faithfulness increases with environmental predictability [4],
- overlooking dispersers’ habitat detection and settling ability may lead to underestimating the metapopulation capacity and misevaluating the conservation benefit of increasing habitat amount in a landscape [5],
- the expected covariation of animal movement attributes can drive consumer–resource patterns across space and time and could underlie the role of consumers in driving spatial heterogeneity in resource abundance [6]
- I conducted a theoretical work that showed that migration can emerge from foraging behaviour alone when resources renew slowly compared to the foraging depletion rate. I will draft a manuscript on these results in the coming year.
(4) Approximate Bayesian Computation (ABC) is a recent, computationally efficient methodology for fitting complex models that has still very rarely been used to fit Individual-Based Models (IBMs). It has never been used to fit an IBM of animal movement to field data. During my project, I showed that the output variables of my universal IBM are sensitive to the hidden parameters that need to be inferred from data (for example, memory capacity). I made the demonstration that ABC is an appropriate methodology to infer these hidden parameters from movement data. This work will lead to a methodological paper that will be drafted in the coming year. With the collaborative group I created at the University of Glasgow, I have initiated several studies linking my universal model of animal movement to ungulate and seabird movement data, using ABC. I will apply to funding to continue this line of research in the future.
(1) My early investigations revealed the central role that environmental predictability plays in the determination of animal movement. Crucially, I showed that the concept of environmental predictability was still lacking a proper definition and methodologies to quantify it. I therefore developed a comprehensive framework for environmental predictability and animal movement [1]. I synthesised the literature into a general definition, a typology, and a set of methodologies for quantifying environmental predictability. I then reviewed the movement-related adaptations to environmental predictability and the methods designed to detect and characterise them, I outlined recent ideas on the feedbacks between animal movement and environmental predictability, and I discussed how human activities can alter patterns of wildlife movement by affecting environmental predictability.
(2) I developed a universal Individual-Based Model that integrates individual perception, memory, social information use, and environmental dynamics. Through extensive simulations, I showed that this parsimonious model can lead to the emergence of all existing types of individual- and population-level space use patterns (nomadism, home ranging, migration, spatial segregation, and aggregation), depending on its parametrization. I showed using this model that the interaction of individual cognition with environmental conditions determine emerging space use patterns.
(3) With collaborators, I participated in showing that:
- collective spatial segregation between neighbouring colonies can emerge from the simple use of memory by colony members [2],
- short-term temporal memory can be sufficient for foragers to efficiently exploit seasonal resources [3],
- ungulates’ site faithfulness increases with environmental predictability [4],
- overlooking dispersers’ habitat detection and settling ability may lead to underestimating the metapopulation capacity and misevaluating the conservation benefit of increasing habitat amount in a landscape [5],
- the expected covariation of animal movement attributes can drive consumer–resource patterns across space and time and could underlie the role of consumers in driving spatial heterogeneity in resource abundance [6]
- I conducted a theoretical work that showed that migration can emerge from foraging behaviour alone when resources renew slowly compared to the foraging depletion rate. I will draft a manuscript on these results in the coming year.
(4) Approximate Bayesian Computation (ABC) is a recent, computationally efficient methodology for fitting complex models that has still very rarely been used to fit Individual-Based Models (IBMs). It has never been used to fit an IBM of animal movement to field data. During my project, I showed that the output variables of my universal IBM are sensitive to the hidden parameters that need to be inferred from data (for example, memory capacity). I made the demonstration that ABC is an appropriate methodology to infer these hidden parameters from movement data. This work will lead to a methodological paper that will be drafted in the coming year. With the collaborative group I created at the University of Glasgow, I have initiated several studies linking my universal model of animal movement to ungulate and seabird movement data, using ABC. I will apply to funding to continue this line of research in the future.
Overall, this project has not only greatly advanced our understanding of the determinants of space use patterns, but has also been a crucial precursor to predicting how complex natural and anthropogenic environmental changes may impact animal populations. More generally, my results have broad conservation implications on the impacts of environmental change on vital space use patterns, such as intercontinental migration routes.
References
1. Riotte-Lambert L, Matthiopoulos J. 2020 Environmental Predictability as a Cause and Consequence of Animal Movement. Trends Ecol. Evol. 35, 163–174. (doi:10.1016/j.tree.2019.09.009)
2. Aarts G, Mul E, Fieberg J, Brasseur S, van Gils JA, Matthiopoulos J, Riotte-Lambert L. 2021 Individual-level memory is sufficient to create spatial segregation among neighboring colonies of central place foragers. Am. Nat. 198, E37–E52. (doi:10.1086/715014)
3. Robira B, Benhamou S, Llaurens V, Masi S, Riotte-Lambert L. 2021 Foraging efficiency in temporally predictable environments:Is a long-term temporal memory really advantageous? R. Soc. Open Sci. 8, 210809.
4. Morrison TA et al. 2021 Drivers of site fidelity in ungulates. J. Anim. Ecol. 90, 955–966. (doi:10.1111/1365-2656.13425)
5. Riotte-Lambert L, Laroche F. 2021 Dispersers’ habitat detection and settling abilities modulate the effect of habitat amount on metapopulation resilience. Landsc. Ecol. 36, 675–686.
6. Jiao J, Riotte-lambert L, Pilyugin SS, Gil MA, Osenberg CW. 2020 Mobility and its sensitivity to fitness differences determine consumer – resource distributions. R. Soc. Open Sci. 7, 200247.
References
1. Riotte-Lambert L, Matthiopoulos J. 2020 Environmental Predictability as a Cause and Consequence of Animal Movement. Trends Ecol. Evol. 35, 163–174. (doi:10.1016/j.tree.2019.09.009)
2. Aarts G, Mul E, Fieberg J, Brasseur S, van Gils JA, Matthiopoulos J, Riotte-Lambert L. 2021 Individual-level memory is sufficient to create spatial segregation among neighboring colonies of central place foragers. Am. Nat. 198, E37–E52. (doi:10.1086/715014)
3. Robira B, Benhamou S, Llaurens V, Masi S, Riotte-Lambert L. 2021 Foraging efficiency in temporally predictable environments:Is a long-term temporal memory really advantageous? R. Soc. Open Sci. 8, 210809.
4. Morrison TA et al. 2021 Drivers of site fidelity in ungulates. J. Anim. Ecol. 90, 955–966. (doi:10.1111/1365-2656.13425)
5. Riotte-Lambert L, Laroche F. 2021 Dispersers’ habitat detection and settling abilities modulate the effect of habitat amount on metapopulation resilience. Landsc. Ecol. 36, 675–686.
6. Jiao J, Riotte-lambert L, Pilyugin SS, Gil MA, Osenberg CW. 2020 Mobility and its sensitivity to fitness differences determine consumer – resource distributions. R. Soc. Open Sci. 7, 200247.