Big-data approaches lead to an increased understanding of the ecology of animal movement

Nathan, Ran; Monk, Christopher T.; Arlinghaus, Robert; Adam, Timo; Alós, Josep; Assaf, Michael; Baktoft, Henrik; Beardsworth, Christine E.; Bertram, Michael G.; Bijleveld, Allert I.; Brodin, Tomas; Brooks, Jill L.; Campos-Candela, Andrea; Cooke, Steven J.; Gjelland, Karl Øystein; Gupte, Pratik Rajan; Harel, Roi; Hellström, Gustav; Jeltsch, Florian; Killen, Shaun S.; Klefoth, Thomas; Langrock, Roland; Lennox, Robert J.; Lourie, E. M.; Madden, Joah R.; Orchan, Yotam; Pauwels, Ine; Říha, Milan; Roeleke, Manuel; Schlägel, Ulrike E.; Shohami, David; Signer, Johannes; Toledo, Sivan; Vilk, Ohad; Westrelin, Samuel; Whiteside, Mark A.; Jarić, Ivan

doi:10.1126/science.abg1780

Cited by 281 publications

(281 citation statements)

References 116 publications

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“…Our social information-based movement strategies are made up of continuous values that place individuals on a two-dimensional trait space of relative preferences (or aversions) for successful and unsuccessful foragers (see Model and Analysis ; see also Gupte et al 2021). Such social movement strategies could already be revealed for free-living animals using newer step-selection approaches (Avgar et al, 2016), combined with the simultaneous, high-throughput tracking of many hundreds of animals in an area (Nathan et al, 2022). More immediately, studying the movement ecology of animals across a cline of pathogen prevalence could help test the predictions of this and similar models (Wilber et al, 2022).…”

Section: Discussionmentioning

confidence: 99%

Novel pathogen introduction rapidly alters evolved movement strategies, restructuring animal societies

Gupte

Albery

Gismann

et al. 2022

Preprint

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Animal social interactions are the outcomes of evolved strategies that integrate the costs and benefits of being sociable. Using a novel mechanistic, evolutionary, individual-based simulation model, we examine how animals balance the risk of pathogen transmission against the benefits of social information about resource patches, and how this determines the emergent structure of socio-spatial networks. We study a scenario in which a fitness-reducing infectious pathogen is introduced into a population which has initially evolved movement strategies in its absence. Within only a few generations, pathogen introduction provokes a rapid evolutionary shift in animals' social movement strategies, and the importance of social cues in movement decisions increases. Individuals undertake a dynamic social distancing approach, trading more movement (and less intake) for lower infection risk. Pathogen-adapted populations disperse more widely over the landscape, and thus have less clustered social networks than their pre-introduction, pathogen-naive ancestors. Running epidemiological simulations on these emergent social networks, we show that diseases do indeed spread more slowly through pathogen-adapted animal societies. Finally, the mix of post-introduction strategies is strongly influenced by a combination of landscape productivity, the usefulness of social information, and disease cost. Our model suggests that the introduction of an infectious pathogen into a population can trigger a rapid eco-evolutionary cascade, rapidly changing animals' social movement strategies, which alters movement decisions and encounters between individuals. In turn, this changes emergent social structures, and our model informs how such change can make populations more resilient to future disease outbreaks. Overall, we offer both a modelling framework and initial predictions for the evolutionary and ecological consequences of wildlife pathogen spillover scenarios.

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Section: Discussionmentioning

confidence: 99%

Novel pathogen introduction rapidly alters evolved movement strategies, restructuring animal societies

Gupte

Albery

Gismann

et al. 2022

Preprint

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“…a few seconds of observations every 10 min due to switching through frequency channels). High-throughput tracking systems (<10-s data interval, many individuals at a time) enable ground-breaking research in animal behaviour, evolution and ecology (Nathan et al, 2022). In recent years, with the ongoing development of low-cost open-source solutions, automatic VHF radio-tracking has become broadly available to researchers.…”

Section: Introductionmentioning

confidence: 99%

“…While information on medium-sized to large species can be obtained using camera traps (Hughey et al, 2018), GPS transmitters and accelerometers (Kays et al, 2015), as demonstrated in investigations of dynamic habitat and resource use (Wyckoff et al, 2018), behaviour (Freeman et al, 2010) and migration and dispersal (Walton et al, 2018), these devices are of limited use for small (<100 g) animals, due to low detection probabilities, the trade-off between transmitter size and weight, battery life and data-collection intensity (Hallworth & Marra, 2015; Hammond et al, 2016; Wikelski et al, 2007). Newer technical solutions such as the ATLAS system (Nathan et al, 2022) or WBN (Ripperger et al, 2020) allow the tracking of small animals with high temporal and spatial resolution, but the required installation effort and the costs are high.…”

Section: Introductionmentioning

confidence: 99%

“…Detailed analyses of the activity patterns of individuals requires high-resolution observations (Nathan et al, 2022), which are often difficult to obtain (Williams et al, 2014). The observer’s presence may influence animal behaviour and thus limit conclusions (Crofoot et al, 2010; Isbell & Young, 1993) and continuous observation of elusive or highly mobile species in habitats with dense vegetation is close to impossible (Maffei et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

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Classifying the activity states of small vertebrates using automated VHF telemetry

Gottwald

Royauté

Becker

et al. 2022

Preprint

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The most basic behavioural states of animals can be described as active or passive. However, while high-resolution observations of activity patterns can provide insights into the ecology of animal species, few methods are able to measure the activity of individuals of small taxa in their natural environment. We present a novel approach in which the automated VHF radio-tracking of small vertebrates fitted with lightweight transmitters (< 0.2 g) is used to distinguish between active and passive behavioural states. A dataset containing > 3 million VHF signals was used to train and test a random forest model in the assignment of either active or passive behaviour to individuals from two forest-dwelling bat species (Myotis bechsteinii (n = 50) and Nyctalus leisleri (n = 20)). The applicability of the model to other taxonomic groups was demonstrated by recording and classifying the behaviour of a tagged bird and by simulating the effect of different types of vertebrate activity with the help of humans carrying transmitters. The random forest model successfully classified the activity states of bats as well as those of birds and humans, although the latter were not included in model training (F-score 0.96–0.98). The utility of the model in tackling ecologically relevant questions was demonstrated in a study of the differences in the daily activity patterns of the two bat species. The analysis showed a pronounced bimodal activity distribution of N. leisleri over the course of the night while the night-time activity of M. bechsteinii was relatively constant. These results show that significant differences in the timing of species activity according to ecological preferences or seasonality can be distinguished using our method. Our approach enables the assignment of VHF signal patterns to fundamental behavioural states with high precision and is applicable to different terrestrial and flying vertebrates. To encourage the broader use of our radio-tracking method, we provide the trained random forest models together with an R-package that includes all necessary data-processing functionalities. In combination with state-of-the-art open-source automated radio-tracking, this toolset can be used by the scientific community to investigate the activity patterns of small vertebrates with high temporal resolution, even in dense vegetation.

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“…Today, with advanced computational methods and modern tracking technology, such as high-resolution, precise, acoustic telemetry it is possible to collect snapshots of social interactions at several second frequencies over years in the wild (Nathan et al, 2022; Baktoft, Zajicek, Klefoth, Svendsen, & Jacobsen, 2015; Guzzo et al, 2018; Krause et al, 2013), both underwater (Lennox et al, 2017) and in terrestrial environments (Wilmers et al, 2015). A suite of techniques are now available for constructing and analyzing social networks generated from acoustic data (Blonder et al, 2012; Finn et al, 2010; Jacoby & Freeman, 2016; Mourier, Brown, & Planes, 2017).…”

Section: Introductionmentioning

confidence: 99%

Rhythm of relationships in a social fish over the course of a full year in the wild

Aslak

Monk

Brockmann

et al. 2022

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Animals are expected to adjust their social behaviour to cope with challenges in their environment. Therefore, for fish populations, in temperate regions with seasonal and daily environmental oscillations, characteristic rhythms of social relationships should be pronounced. To date, most research concerning fish social networks and biorhythms has occurred in artificial laboratory environments or over confined temporal scales of days to weeks. By contrast, little is known about the social networks of wild, freely roaming fish, including how seasonal and diurnal rhythms modulate social networks over the course of a full year. The advent of high-resolution acoustic telemetry enables us to quantify detailed social interactions in the wild over time-scales sufficient to examine seasonal rhythms at whole-ecosystems scales. Our objective was to explore the rhythms of social interactions in a social fish population at various time-scales over one full year in the wild by examining high-resolution snapshots of dynamic social network. To that end, we tracked the behaviour of 36 adult common carp, Cyprinus carpio, in a 25 ha lake and constructed temporal social networks among individuals across various time-scales, where social interactions were defined by proximity. We compared the network structure to a temporally shuffled null model to examine the importance of social attraction, and checked for persistent characteristic groups (“friendships”) over time. The clustering within the carp social network tended to be more pronounced during daytime than nighttime throughout the year. Social attraction, particularly during daytime, was a key driver for interactions. Shoaling behavior substantially increased during daytime in the wintertime, whereas in summer carp interacted less frequently, but the interaction duration increased. Characteristic groups were more common in the summer months and during nighttime, where the social memory of carp lasted up to two weeks. We conclude that social relationships of carp change diurnally and seasonally. These patterns were likely driven by predator avoidance, seasonal shifts in lake temperature, visibility, forage availability and the presence of anoxic zones. The techniques we employed can be applied generally to high-resolution biotelemetry data to reveal social structures across other fish species at ecologically realistic scales.

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Big-data approaches lead to an increased understanding of the ecology of animal movement

Cited by 281 publications

References 116 publications

Novel pathogen introduction rapidly alters evolved movement strategies, restructuring animal societies

Novel pathogen introduction rapidly alters evolved movement strategies, restructuring animal societies

Classifying the activity states of small vertebrates using automated VHF telemetry

Rhythm of relationships in a social fish over the course of a full year in the wild

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