Course techniques design
( 2013 ), we developed a six-state movement behavior model for bearded seals, where movement behavior states and associated movement parameters were estimated from seven data streams. These data streams included step length , bearing (?letter,t), the proportion of time spent diving >4 m below the surface , the proportion of dry time , the number of dives to the sea floor (i.e., “benthic dives”; eletter,t), the average proportion of sea ice cover , and the average proportion of land cover for each 6-h time step t = 1, …, Tn and individual n = 1, …, N. Our goal was to identify and estimate activity budgets to six distinct movement behavior states, zletter,t ? , in which I denotes “hauled out on ice,” S indicates “sleep at water,” L denotes “hauled out on house,” M denotes “mid-drinking water foraging,” B denotes “benthic foraging,” and you will T indicates “transportation,” in accordance with the joint suggestions around the all the research channels. Because a good heuristic instance of how the movement process model functions, imagine a certain 6-h date step presented a short step duration, little time spent plunge below cuatro yards, 100% inactive day, and no dives for the ocean flooring; when the water ice security try >0% and you may belongings shelter are 0%, it’s possible to relatively anticipate the animal is actually hauled on ice during this period step (state We; Dining table 1).
Notes
- This type of research channels incorporated lateral trajectory (“action size” and “directional persistence”), the brand new proportion of time spent diving below 4 meters (“dive”), the latest ratio of your energy spent dead (“dry”), together with number of benthic dives (“benthic”) throughout for every six-h big date action. The brand new model integrated environment study towards the ratio regarding ocean ice and you will land safeguards within the twenty-five ? twenty five kilometres grid telephone(s) which includes the start and you will prevent towns per day step (“ice” and “land”), and bathymetry data to spot benthic dives. Blank entries mean zero a beneficial priori matchmaking had been presumed throughout the design.
For horizontal movement, we assumed step length with state-specific mean step length parameter aletter,z > 0 and shape parameter bn,z > 0 for https://datingranking.net/local-hookup/minneapolis . For bearing, we assumed , which is a wrapped Cauchy distribution with state-specific directional persistence parameter ?1 < rn,z < 1. Based on bearded seal movement behavior, we expect average step length to be smaller for resting (states I, S, and L) and larger for transit. We also expect directional persistence to be largest for transit. As in McClintock et al. ( 2013 ), these expected relationships were reflected in prior constraints on the state-dependent parameters (see Table 1; Appendix S1 for full details).
Although movement behavior state assignment could be based solely on horizontal movement characteristics (e.g., Morales et al. 2004 , Jonsen et al. 2005 , McClintock et al. 2012 ), we wished to incorporate the additional information about behavior states provided by biotelemetry (i.e., dive activity) and environmental (i.e., bathymetry, land cover, and sea ice concentration) data. Assuming independence between data streams (but still conditional on state), we incorporated wletter,t, dletter,t, eletter,t, cletter,t, and lletter,t into a joint conditional likelihood whereby each data stream contributes its own state-dependent component. While for simplicity we assume independence of data streams conditional on state, data streams such as proportion of dive and dry time could potentially be more realistically modeled using multivariate distributions that account for additional (state-dependent) correlations.
Although critical for identifying benthic foraging activity, en,t was not directly observable because the exact locations and depths of the seals during each 6-h time step were unknown. We therefore calculated the number of benthic foraging dives, defined as the number of dives to depth bins with endpoints that included the sea floor, based on the sea floor depths at the estimated start and end locations for each time step. Similarly, cletter,t and lletter,t were calculated based on the average of the sea ice concentration and land cover values, respectively, for the start and end locations. We estimated start and end locations for each time step by combining our movement process model with an observation process model similar to Jonsen et al. ( 2005 ) extended for the Argos error ellipse (McClintock et al. 2015 ), but, importantly, we also imposed constraints on the predicted locations by prohibiting movements inland and to areas where the sea floor depth was shallower than the maximum observed dive depth for each time step (see Observation process model).