Convergence of marine megafauna movement patterns in coastal and open oceans.

The extent of increasing anthropogenic impacts on large marine vertebrates partly depends on the animals' movement patterns. Effective conservation requires identification of the key drivers of movement including intrinsic properties and extrinsic constraints associated with the dynamic nature of the environments the animals inhabit. However, the relative importance of intrinsic versus extrinsic factors remains elusive. We analyze a global dataset of ∼2.8 million locations from >2,600 tracked individuals across 50 marine vertebrates evolutionarily separated by millions of years and using different locomotion modes (fly, swim, walk/paddle). Strikingly, movement patterns show a remarkable convergence, being strongly conserved across species and independent of body length and mass, despite these traits ranging over 10 orders of magnitude among the species studied. This represents a fundamental difference between marine and terrestrial vertebrates not previously identified, likely linked to the reduced costs of locomotion in water. Movement patterns were primarily explained by the interaction between species-specific traits and the habitat(s) they move through, resulting in complex movement patterns when moving close to coasts compared with more predictable patterns when moving in open oceans. This distinct difference may be associated with greater complexity within coastal microhabitats, highlighting a critical role of preferred habitat in shaping marine vertebrate global movements. Efforts to develop understanding of the characteristics of vertebrate movement should consider the habitat(s) through which they move to identify how movement patterns will alter with forecasted severe ocean changes, such as reduced Arctic sea ice cover, sea level rise, and declining oxygen content.

The importance of sample size in marine megafauna tagging studies.

Telemetry is a key, widely used tool to understand marine megafauna distribution, habitat use, behavior, and physiology; however, a critical question remains: "How many animals should be tracked to acquire meaningful data sets?" This question has wide-ranging implications including considerations of statistical power, animal ethics, logistics, and cost. While power analyses can inform sample sizes needed for statistical significance, they require some initial data inputs that are often unavailable. To inform the planning of telemetry and biologging studies of marine megafauna where few or no data are available or where resources are limited, we reviewed the types of information that have been obtained in previously published studies using different sample sizes. We considered sample sizes from one to >100 individuals and synthesized empirical findings, detailing the information that can be gathered with increasing sample sizes. We complement this review with simulations, using real data, to show the impact of sample size when trying to address various research questions in movement ecology of marine megafauna. We also highlight the value of collaborative, synthetic studies to enhance sample sizes and broaden the range, scale, and scope of questions that can be answered.

[Factors associated with the incidence of hospital readmission]. / Factores asociados a la incidencia de reingresos hospitalarios.

BACKGROUND: To identify factors associated with hospital readmission, especially those potentially avoidable. This information could be useful to reduce the incidence of hospital readmissions. METHODS: A paired (1:1) case-control study nested into the cohort of first admissions at the Granada University Hospital, Spain, in 1990. All patients readmitted at the hospital within 3 years after release from the index-admission by the same diagnostic or complications of it comprised the case group. For each case, a control patient was matched for both primary diagnosis on admission and index-admission date. Information on risk factors associated with hospital readmission was obtained retrospectively from medical records. The relation between these risk factors and readmission was estimated from odds ratio both crude and adjusted using conditional logistic regression analysis. For the readmitted subsample, multiple linear regression models were applied to identify factors associated to the length of time between the index episode and the first readmission. RESULTS: Variables with consistent positive associations with readmission include male sex (odds ratio = 2.86, 95% confidence interval = 1.37-5.88), widowed or single status (2.66, 10.7-6.59) and severity at index admission (3.20, 1.57-6.51). Factors related to quality of health care did not influence readmission risk. CONCLUSIONS: Factors depending of the patient seem to be the most important variables associated to the incidence of hospital readmission.

Dynamics of link states in complex networks: the case of a majority rule.

Motivated by the idea that some characteristics are specific to the relations between individuals and not to the individuals themselves, we study a prototype model for the dynamics of the states of the links in a fixed network of interacting units. Each link in the network can be in one of two equivalent states. A majority link-dynamics rule is implemented, so that in each dynamical step the state of a randomly chosen link is updated to the state of the majority of neighboring links. Nodes can be characterized by a link heterogeneity index, giving a measure of the likelihood of a node to have a link in one of the two states. We consider this link-dynamics model in fully connected networks, square lattices, and Erdös-Renyi random networks. In each case we find and characterize a number of nontrivial asymptotic configurations, as well as some of the mechanisms leading to them and the time evolution of the link heterogeneity index distribution. For a fully connected network and random networks there is a broad distribution of possible asymptotic configurations. Most asymptotic configurations that result from link dynamics have no counterpart under traditional node dynamics in the same topologies.

Update rules and interevent time distributions: slow ordering versus no ordering in the voter model.

We introduce a general methodology of update rules accounting for arbitrary interevent time (IET) distributions in simulations of interacting agents. We consider in particular update rules that depend on the state of the agent, so that the update becomes part of the dynamical model. As an illustration we consider the voter model in fully connected, random, and scale-free networks with an activation probability inversely proportional to the time since the last action, where an action can be an update attempt (an exogenous update) or a change of state (an endogenous update). We find that in the thermodynamic limit, at variance with standard updates and the exogenous update, the system orders slowly for the endogenous update. The approach to the absorbing state is characterized by a power-law decay of the density of interfaces, observing that the mean time to reach the absorbing state might be not well defined. The IET distributions resulting from both update schemes show power-law tails.

The effect of completeness of medical records on the determination of appropriateness of hospital days.

Although the Appropriateness Evaluation Protocol (AEP) has been widely used during the past decade, several methodological concerns have not yet been properly resolved, including the possible influence of low completeness of the medical records on the results yielded by the AEP in retrospective studies. We examined medical records for a random sample of 345 patient-days with the AEP, according to a protocol that included several variables potentially related to inappropriateness. The completeness of physician and nursing notes was also assessed. The proportion of inappropriate days of hospitalization was 36.2%. In the crude analysis, significantly higher proportions of inappropriateness were found for lower values of completeness. Factors related to the inappropriateness of stay were summer season, elective admission, no previous admissions, surgical and medical-surgical service in charge, and the day sampled falling within the last third of the hospital stay. Adjustment for the completeness level of medical records did not substantially change the strength of the association between these factors and the inappropriateness of hospital stay. Completeness level itself did not show any significant association with the proportion of inappropriate days in the adjusted analysis.