Hidden Markov models are prevalent in animal movement modelling, where they are widely used to infer behavioural modes and their drivers from various types of telemetry data. To allow for meaningful inference, observations need to be equally spaced in time, or otherwise regularly sampled, where the corresponding temporal resolution strongly affects what kind of behaviours can be inferred from the data. Recent advances in biologging technology have led to a variety of novel telemetry sensors which often collect data from the same individual simultaneously at different time‐scales, for example step lengths obtained from GPS tags every hour, dive depths obtained from time‐depth recorders once per dive, or accelerations obtained from accelerometers several times per second. However, to date, statistical machinery to address the corresponding complex multi‐stream and multi‐scale data is lacking. We propose hierarchical hidden Markov models as a versatile statistical framework that naturally accounts for differing temporal resolutions across multiple variables. In these models, the observations are regarded as stemming from multiple connected behavioural processes, each of which operates at the time‐scale at which the corresponding variables were observed. By jointly modelling multiple data streams, collected at different temporal resolutions, corresponding models can be used to infer behavioural modes at multiple time‐scales and in particular, help to draw a much more comprehensive picture of an animal's movement patterns, for example with regard to long‐term versus short‐term movement strategies. The suggested approach is illustrated in two real‐data applications, where we jointly model (a) coarse‐scale horizontal and fine‐scale vertical Atlantic cod Gadus morhua movements throughout the English Channel, and (b) coarse‐scale horizontal movements and corresponding fine‐scale accelerations of a horn shark Heterodontus francisci tagged off the Californian coast.
Background: California horn sharks (Heterodontus francisci) are nocturnally active, non-obligate ram ventilating sharks in rocky reef habitats that play an important ecological role in regulating invertebrate communities. We predicted horn sharks would use an area restricted search (ARS) movement strategy to locate dense resource patches while minimizing energetic costs of travel and nighttime activity. As ectotherms, we predicted environmental temperature would play a significant role in driving movement and activity patterns. Methods: Continuous active acoustic tracking methods and acceleration data loggers were used to quantify the diel fine-scale spatial movements and activity patterns of horn sharks. First passage time was used to identify the scale and locations of patches indicative of ARS. Activity was assessed using overall dynamic body acceleration (ODBA) as a proxy for energy expenditure. Behavior within a patch was characterized into three activity patterns: resting, episodic burst activity, and moderate, consistent activity. Results: After resting in daytime shelters, individuals travelled to multiple reefs throughout the night, traversing through depths of 2-112 m and temperatures of 10.0-23.8°C. All sharks exhibited area restricted search patch use and arrived at their first patch approximately 3.4 ± 2.2 h (mean ± SD) after sunset. Sharks exhibited moderate, consistent activity in 54% of the patches used, episodic burst activity in 33%, and few (13%) were identified as resting at night. ODBA peaked while sharks were swimming through relatively deeper (~30 m), colder channels when traversing from one patch to the next. There was no consistent pattern between ODBA and temperature. Conclusions: We provide one of the largest fine-scale, high-resolution paired data sets for an elasmobranch movement ecology study. Horn sharks exhibited ARS movement patterns for various activity patterns. Individuals likely travel to reefs known to have profitable and predictable patches, potentially tolerating less suitable environmental temperatures. We demonstrate how gathering high-resolution information on the movement decisions of a community resident enhances knowledge of community structure and overall ecosystem function.
Young-of-the-year (YOY) and juvenile-stage white sharks may use southern California nearshore beach habitats more extensively than previously known, within meters of some of the most heavily used beaches in the world. Such knowledge forms a critical component of species management and conservation plans, in addition to public safety and risk mitigation planning. We used data derived from a combination of satellite tag locations (13 animals over 3 years) and passive acoustic monitoring (34 animals over 8 years) to examine the occurrence, relative abundance, and residency patterns of YOY white sharks in southern California waters. Our results suggest that southern California contains spatiotemporally dynamic centers of primary nursery habitat. Tagged YOY white sharks formed loose aggregations at “hotspot” locations that were interannually variable, where individuals exhibited temporal fidelity, higher levels of residency, and spatially restricted movements, with multiple YOY individuals simultaneously displaying this behavior. While models of biotic and abiotic variables suggested relative abundance of tagged sharks may be predicted by sea surface temperature, salinity and productivity (chlorophyll-A), these predictors were not consistent across all years of the study. Thus, novel approaches that incorporate technologies to derive high resolution environmental data, paired with more comprehensive telemetry datasets are therefore required to better understand the extrinsic factors that drive habitat selection and residency patterns in juvenile white sharks.
Many terrestrial and aquatic taxa are known to form periodic aggregations, whether across life history or solely during specific life stages, that are generally governed by the availability and distribution of resources. Associations between individuals during such aggregation events are considered random and not driven by social attraction or underlying community structure. White sharks (Carcharodon carcharias) have been described as a species that exhibits resource-driven aggregative behaviors across ontogenetic stages and juvenile white sharks are known to form aggregations at specific nursery sites where individuals may remain for extended periods of time in the presence of other individuals. We hypothesized juvenile white sharks form distinct communities during these critical early phases of ontogeny and discuss how a tendency to co-occur across life stages may be seeded by the formation of these communities in early ontogeny. We present results from a series of social network analyses of 86 juvenile white sharks derived from 6 years of passive acoustic telemetry data in southern California, demonstrating the likelihood of association of tagged juvenile white sharks is greater when sharks are of similar size-classes. Individuals in observed networks exhibited behaviors that best approximated fission-fusion dynamics with spatiotemporally unstable group membership. These results provide evidence of possible non-resource driven co-occurrence and community structure in juvenile white sharks during early life stages.
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