Smoothing splines of apex predator movement: Functional modeling strategies for exploring animal behavior and social interactions

Whetten, Andrew

doi:10.1002/ece3.8294

Cited by 3 publications

(4 citation statements)

References 64 publications

(96 reference statements)

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“…Fish locations were estimated in a similar manner to the synchronization process above, with the addition of smooth splines components to the horizontal movement of each fish as done by [ 37 ]. Based on model performance, estimated errors, in meters, were calculated for each position in the x- and y-direction as well as the overall Cartesian direction (Fig.…”

Section: Data Processing and Analysismentioning

confidence: 99%

Linking fine-scale behaviour to the hydraulic environment shows behavioural responses in riverine fish

et al. 2023

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Background Fish migration has severely been impacted by dam construction. Through the disruption of fish migration routes, freshwater fish communities have seen an incredible decline. Fishways, which have been constructed to mitigate the problem, have been shown to underperform. This is in part due to fish navigation still being largely misunderstood. Recent developments in tracking technology and modelling make it possible today to track (aquatic) animals at very fine spatial (down to one meter) and temporal (down to every second) scales. Hidden Markov models are appropriate models to analyse behavioural states at these fine scales. In this study we link fine-scale tracking data of barbel (Barbus barbus) and grayling (Thymallus thymallus) to a fine-scale hydrodynamic model. With a HMM we analyse the fish’s behavioural switches to understand their movement and navigation behaviour near a barrier and fishway outflow in the Iller river in Southern Germany. Methods Fish were tracked with acoustic telemetry as they approached a hydropower facility and were presented with a fishway. Tracking resulted in fish tracks with variable intervals between subsequent fish positions. This variability stems from both a variable interval between tag emissions and missing detections within a track. After track regularisation hidden Markov models were fitted using different parameters. The tested parameters are step length, straightness index calculated over a 3-min moving window, and straightness index calculated over a 10-min window. The best performing model (based on a selection by AIC) was then expanded by allowing flow velocity and spatial velocity gradient to affect the transition matrix between behavioural states. Results In this study it was found that using step length to identify behavioural states with hidden Markov models underperformed when compared to models constructed using straightness index. Of the two different straightness indices assessed, the index calculated over a 10-min moving window performed better. Linking behavioural states to the ecohydraulic environment showed an effect of the spatial velocity gradient on behavioural switches. On the contrary, flow velocity did not show an effect on the behavioural transition matrix. Conclusions We found that behavioural switches were affected by the spatial velocity gradient caused by the attraction flow coming from the fishway. Insight into fish navigation and fish reactions to the ecohydraulic environment can aid in the construction of fishways and improve overall fishway efficiencies, thereby helping to mitigate the effects migration barriers have on the aquatic ecosystem.

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Section: Data Processing and Analysismentioning

confidence: 99%

Linking fine-scale behaviour to the hydraulic environment shows behavioural responses in riverine fish

et al. 2023

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“…The restrictions of lower sampling frequency can be partly addressed by inferring intermediate locations (i.e. an animal's position in the time between fixes) using approaches such as spatial interpolation (Hirakawa et al, 2018; Strandburg‐Peshkin et al, 2015), smoothing spline models (Whetten, 2021) or dead reckoning (Dewhirst et al, 2016). Related to this, it should also be noted that battery consumption does not scale linearly with increasing sampling frequency.…”

Section: Key Trade‐offs In Gps‐based Studies Of Social Behavioursmentioning

confidence: 99%

A guide to sampling design for GPS‐based studies of animal societies

Klarevas‐Irby

Papageorgiou

et al. 2022

Methods Ecol Evol

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GPS-based tracking is widely used for studying wild social animals. Much like traditional observational methods, using GPS devices requires making a number of decisions about sampling that can affect the robustness of a study's conclusions. For example, sampling fewer individuals per group across more distinct social groups may not be sufficient to infer group-or subgroup-level behaviours, while sampling more individuals per group across fewer groups limits the ability to draw conclusions about populations. Here, we provide quantitative recommendations when designing GPS-based tracking studies of animal societies. We focus on the trade-offs between three fundamental axes of sampling effort:(1) sampling coverage-the number and allocation of GPS devices among individuals in one or more social groups; (2) sampling duration-the total amount of time over which devices collect data and (3) sampling frequency-the temporal resolution at which GPS devices record data. We first test GPS tags under field conditions to quantify how these aspects of sampling design can affect both GPS accuracy (error in absolute positional estimates) and GPS precision (error in the estimate relative position of two individuals), demonstrating that GPS error can have profound effects when inferring distances between individuals. We then use data from whole-group tracked vulturine guineafowl Acryllium vulturinum to demonstrate how the trade-off between sampling frequency and sampling duration can impact inferences of social interactions and to quantify how sampling coverage can affect common measures of social behaviour in animal groups, identifying which types of measures are more or less robust to lower coverage of individuals. Finally, we use data-informed simulations to extend insights across groups of different sizes and cohesiveness. Based on our results, we are able to offer a range of recommendations on GPS sampling strategies to address research questions across social organizational scales and social systems-from group movement to social network structure and collective decision-making. Our study provides practical advice for empiricists to navigate their decision-making processes when designing GPS-based field studies of animal social behaviours, and highlights the importance of identifying the optimal deployment decisions for drawing informative and robust conclusions.

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“…The restrictions of lower sampling frequency can be partly addressed by inferring intermediate locations (i.e., an animal's position in the time between fixes) using approaches such as spatial interpolation (Strandburg-Peshkin et al 2015;Hirakawa et al 2018), smoothing spline models (Whetten 2021), or dead reckoning (Dewhirst et al 2016). Related to this, it should also be noted that battery consumption does not scale linearly with increasing sampling frequency.…”

Section: Sampling Frequencymentioning

confidence: 99%

“…2015; Hirakawa et al . 2018), smoothing spline models (Whetten 2021), or dead reckoning (Dewhirst et al . 2016).…”

Section: Key Trade-offs In Gps-based Studies Of Social Behavioursmentioning

confidence: 99%

A guide to sampling design for GPS-based studies of animal societies

Klarevas‐Irby

Papageorgiou

et al. 2022

Preprint

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GPS-based tracking is a widely used automated data collection method for studying wild animals. Much like traditional observational methods, using GPS devices to study social animals requires making a number of decisions about sampling that can affect the robustness of a study's conclusions. For example, sampling fewer individuals per group across many distinct social groups may not be informative enough for inferring behavioural patterns at a finer social organizational scale, while sampling more individuals per group across fewer groups limits the ability to draw conclusions about populations. Here, we integrate previous insights from animal social network analysis with simulated and empirical data to provide quantitative recommendations when designing GPS-based tracking studies of animal societies. We outline the trade-offs faced by researchers, and how these trade-offs should vary across social organizational scales and social systems in relation to questions of interest, such as those defined within versus among groups, and spanning from cohesive, stable groups through to more open societies. We discuss three fundamental axes of sampling effort requiring consideration when deploying GPS devices to study animal societies: 1) Sampling coverage—the number and allocation of GPS devices among individuals in one or more social groups; 2) Sampling duration—the total amount of time over which devices collect data; 3) Sampling frequency—the temporal resolution at which GPS devices record data. Exploring each axis, we quantify the relationship between sampling effort and sampling error, giving recommendations on GPS sampling design to address research questions across social organizational scales and social systems—from group movement to social network structure and collective decision-making. In doing so, we also highlight practical limitations on deploying GPS tracking. Our study provides practical advice for empiricists to navigate their decision-making processes when designing GPS-based field studies of animal social behaviours, and highlights the importance of identifying the optimal deployment decisions for drawing informative and robust conclusions.

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Smoothing splines of apex predator movement: Functional modeling strategies for exploring animal behavior and social interactions

Cited by 3 publications

References 64 publications

Linking fine-scale behaviour to the hydraulic environment shows behavioural responses in riverine fish

Linking fine-scale behaviour to the hydraulic environment shows behavioural responses in riverine fish

A guide to sampling design for GPS‐based studies of animal societies

A guide to sampling design for GPS-based studies of animal societies

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