Dataset Information

Does Predation Influence the Seasonal and Diel Timing of Moose Calving in Central Ontario, Canada?

ABSTRACT: Birth synchrony is well documented among ungulates and is hypothesised to maximize neonate survival, either by minimizing the risk of predation through predator swamping or by synchronising birthing with increased seasonal food availability. We used encapsulated vaginal implant transmitters to locate and capture neonatal moose calves and document the seasonal and diel timing of parturition in two adjacent study areas with different predation pressure in central Ontario, Canada. We tested the hypothesis that predation promotes earlier and more synchronous birth of moose calves. Across both areas, proportionately more births occurred during the afternoon and fewer than expected occurred overnight. Mean date of calving averaged 1.5 days earlier and calving was also more synchronous in the study area with heavier predation pressure, despite average green-up date and peak Normalized Difference Vegetation Index date occurring 2 days later in this study area than in the area receiving lighter predation pressure. We encourage analysis of data on timing of parturition from additional study areas experiencing varying degrees of predation pressure to better clarify the influence of predation in driving seasonal and diel timing of parturition in temperate ungulates.

SUBMITTER: Patterson BR

PROVIDER: S-EPMC4833312 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Does Predation Influence the Seasonal and Diel Timing of Moose Calving in Central Ontario, Canada?

Patterson Brent R BR Mills Kenneth J KJ Middel Kevin R KR Benson John F JF Obbard Martyn E ME

PloS one 20160415 4

Birth synchrony is well documented among ungulates and is hypothesised to maximize neonate survival, either by minimizing the risk of predation through predator swamping or by synchronising birthing with increased seasonal food availability. We used encapsulated vaginal implant transmitters to locate and capture neonatal moose calves and document the seasonal and diel timing of parturition in two adjacent study areas with different predation pressure in central Ontario, Canada. We tested the hyp ...[more]

PMID: 27082234

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Project description:BackgroundMathematical and statistical models are used to project the future time course of infectious disease epidemics and the expected future burden on health care systems and economies. Influenza is a particularly important disease in this context because it causes annual epidemics and occasional pandemics. In order to forecast health care utilization during epidemics-and the effects of hospitalizations and deaths on the contact network and, in turn, on transmission dynamics-modellers must make assumptions about the lengths of time between infection, visiting a physician, being admitted to hospital, leaving hospital, and death. More reliable forecasts could be be made if the distributions of times between these types of events ("delay distributions") were known.MethodsWe estimated delay distributions in the province of Ontario, Canada, between 2006 and 2010. To do so, we used encrypted health insurance numbers to link 1.34 billion health care billing records to 4.27 million hospital inpatient stays. Because the four year period we studied included three typical influenza seasons and the 2009 influenza pandemic, we were able to compare the delay distributions in non-pandemic and pandemic settings. We also estimated conditional probabilities such as the probability of hospitalization within the year if diagnosed with influenza.ResultsIn non-pandemic [pandemic] years, delay distribution medians (inter-quartile ranges) were: Service to Admission 6.3 days (0.1-17.6 days) [2.4 days (-0.3-13.6 days)], Admission to Discharge 3 days (1.4-5.9 days) [2.6 days (1.2-5.1 days)], Admission to Death 5.3 days (2.1-11 days) [6 days (2.6-13.1 days)]. (Service date is defined as the date, within the year, of the first health care billing that included a diagnostic code for influenza-like-illness.) Among individuals diagnosed with either pneumonia or influenza in a given year, 19% [16%] were hospitalized within the year and 3% [2%] died in hospital. Among all individuals who were hospitalized, 10% [12%] were diagnosed with pneumonia or influenza during the year and 5% [5%] died in hospital.ConclusionOur empirical delay distributions and conditional probabilities should help facilitate more accurate forecasts in the future, including improved predictions of hospital bed demands during influenza outbreaks, and the expected effects of hospitalizations on epidemic dynamics.

Dataset Information

Does Predation Influence the Seasonal and Diel Timing of Moose Calving in Central Ontario, Canada?

Publications

Does Predation Influence the Seasonal and Diel Timing of Moose Calving in Central Ontario, Canada?

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