Scaling marine fish movement behavior from individuals to populations

Abstract

Understanding how, where, and when animals move is a central problem in marine ecology and conservation. Key to improving our knowledge about what drives animal movement is the rising deployment of telemetry devices on a range of free‐roaming species. An increasingly popular way of gaining meaningful inference from an animal's recorded movements is the application of hidden Markov models (HMMs), which allow for the identification of latent behavioral states in the movement paths of individuals. However, the use of HMMs to explore the population‐level consequences of movement is often limited by model complexity and insufficient sample sizes. Here, we introduce an alternative approach to current practices and provide evidence of how the inclusion of prior information in model structure can simplify the application of HMMs to multiple animal movement paths with two clear benefits: (a) consistent state allocation and (b) increases in effective sample size. To demonstrate the utility of our approach, we apply HMMs and adapted HMMs to over 100 multivariate movement paths consisting of conditionally dependent daily horizontal and vertical movements in two species of demersal fish: Atlantic cod (Gadus morhua; n = 46) and European plaice (Pleuronectes platessa; n = 61). We identify latent states corresponding to two main underlying behaviors: resident and migrating. As our analysis considers a relatively large sample size and states are allocated consistently, we use collective model output to investigate state‐dependent spatiotemporal trends at the individual and population levels. In particular, we show how both species shift their movement behaviors on a seasonal basis and demonstrate population space use patterns that are consistent with previous individual‐level studies. Tagging studies are increasingly being used to inform stock assessment models, spatial management strategies, and monitoring of marine fish populations. Our approach provides a promising way of adding value to tagging studies because inferences about movement behavior can be gained from a larger proportion of datasets, making tagging studies more relevant to management and more cost‐effective.

Horizontal and vertical movements of starry smooth-hound Mustelus asterias in the northeast Atlantic

“…Such seasonal shifts in movement and spatial distribution of M . asterias are analogous to the findings of Brevé et al [ 20 ], and are relatively commonplace in commercial fish species, with individuals often transiting over long distances between different grounds [ 60 – 62 ]. Past observations confirm that M .…”

State-space modeling of multidecadal mark–recapture data reveals low adult dispersal in a nursery-dependent fish metapopulation

“…Further extension of the integrated life cycle modeling framework of Archambault et al (2016) to integrate the analysis of tagging data within the integrated life cycle model would allow the simultaneous estimation of fishing mortality in the different areas of the EEC and connectivity between those areas. We suggest that such an exercise would contribute to improving scientific advice for the spatial management of the fishery (Methot 2009;Goethel et al 2011;Griffiths et al 2018).…”

Joint modelling of multi‐scale animal movement data using hierarchical hidden Markov models

“…Previous work has often overcome this difference in sampling by either gaining meaningful inference from a single dimension (e.g. Hobson, Righton, Metcalfe, & Hays, ) or, in the case of Griffiths et al (), who analyse movements in both dimensions, by simplifying the vertical dimension at the daily scale. Here, we demonstrate how HHMMs can be used to analyse movements in both dimensions while retaining the vertical dimension at the 10‐min scale.…”

“…Previous work has often overcome this difference in sampling by either gaining meaningful inference from a single dimension (e.g. Hobson, Righton, Metcalfe, & Hays, 2007) or, in the case of Griffiths et al (2018), who analyse movements in both dimensions, by simplifying the vertical dimension at the daily scale.…”

“…Hobson et al, 2007) or studies that only consider movement rates at the daily scale (e.g. Griffiths et al, 2018), which highlights the potential of the HHMM approach.…”