scholarly journals Is motivation important to brook trout passage through culverts?

2017 ◽  
Vol 74 (6) ◽  
pp. 885-893 ◽  
Author(s):  
Elsa Goerig ◽  
Theodore Castro-Santos
Keyword(s):  
Brook Trout ◽  
Recurrent Events ◽  
Salvelinus Fontinalis ◽  
Time Varying ◽  
Event Analysis ◽  
Dynamic Phenomenon ◽  
Time To Event ◽  
Spawning Period ◽  
Restrict Movement ◽  
Time Varying Covariates

Culverts can restrict movement of stream-dwelling fish. Motivation to enter and ascend these structures is an essential precursor for successful passage. However, motivation is challenging to quantify. Here, we use attempt rate to assess motivation of 447 brook trout (Salvelinus fontinalis) entering three culverts under a range of hydraulic, environmental, and biological conditions. A passive integrated transponder system allowed for the identification of passage attempts and success of individual fish. Attempt rate was quantified using time-to-event analysis allowing for time-varying covariates and recurrent events. Attempt rate was greatest during the spawning period, at elevated discharge, at dusk, and for longer fish. It decreased during the day and with increasing number of conspecifics downstream of the culvert. Results also show a positive correlation between elevated motivation and successful passage. This study enhances understanding of factors influencing brook trout motivation to ascend culverts and shows that attempt rate is a dynamic phenomenon, variable over time and among individuals. It also presents methods that could be used to investigate other species’ motivation to pass natural or anthropogenic barriers.

scholarly journals Time-to-event analysis for sports injury research part 2: time-varying outcomes

2018 ◽  
Vol 53 (1) ◽  
pp. 70-78 ◽  
Author(s):  
Rasmus Oestergaard Nielsen ◽  
Michael Lejbach Bertelsen ◽  
Daniel Ramskov ◽  
Merete Møller ◽  
Adam Hulme ◽  
...  
Keyword(s):  
Sports Injuries ◽  
Sports Injury ◽  
Training Load ◽  
Future Research ◽  
Multiple Time ◽  
Time Varying ◽  
Event Analysis ◽  
Time To Event ◽  
Injury Research ◽  
Stratified Analysis

BackgroundTime-to-event modelling is underutilised in sports injury research. Still, sports injury researchers have been encouraged to consider time-to-event analyses as a powerful alternative to other statistical methods. Therefore, it is important to shed light on statistical approaches suitable for analysing training load related key-questions within the sports injury domain.ContentIn the present article, we illuminate: (i) the possibilities of including time-varying outcomes in time-to-event analyses, (ii) how to deal with a situation where different types of sports injuries are included in the analyses (ie, competing risks), and (iii) how to deal with the situation where multiple subsequent injuries occur in the same athlete.ConclusionTime-to-event analyses can handle time-varying outcomes, competing risk and multiple subsequent injuries. Although powerful, time-to-event has important requirements: researchers are encouraged to carefully consider prior to any data collection that five injuries per exposure state or transition is needed to avoid conducting statistical analyses on time-to-event data leading to biased results. This requirement becomes particularly difficult to accommodate when a stratified analysis is required as the number of variables increases exponentially for each additional strata included. In future sports injury research, we need stratified analyses if the target of our research is to respond to the question: ‘how much change in training load is too much before injury is sustained, among athletes with different characteristics?’ Responding to this question using multiple time-varying exposures (and outcomes) requires millions of injuries. This should not be a barrier for future research, but collaborations across borders to collecting the amount of data needed seems to be an important step forward.

scholarly journals Time-to-event analysis for sports injury research part 1: time-varying exposures

2018 ◽  
Vol 53 (1) ◽  
pp. 61-68 ◽  
Author(s):  
Rasmus Oestergaard Nielsen ◽  
Michael Lejbach Bertelsen ◽  
Daniel Ramskov ◽  
Merete Møller ◽  
Adam Hulme ◽  
...  
Keyword(s):  
Sport Injury ◽  
Sports Injury ◽  
Training Load ◽  
Time Varying ◽  
Event Analysis ◽  
Time To Event ◽  
Effect Measure ◽  
Event Models ◽  
Analytical Approaches ◽  
Over Time

Background‘How much change in training load is too much before injury is sustained, among different athletes?’ is a key question in sports medicine and sports science. To address this question the investigator/practitioner must analyse exposure variables that change over time, such as change in training load. Very few studies have included time-varying exposures (eg, training load) and time-varying effect-measure modifiers (eg, previous injury, biomechanics, sleep/stress) when studying sports injury aetiology.AimTo discuss advanced statistical methods suitable for the complex analysis of time-varying exposures such as changes in training load and injury-related outcomes.ContentTime-varying exposures and time-varying effect-measure modifiers can be used in time-to-event models to investigate sport injury aetiology. We address four key-questions (i) Does time-to-event modelling allow change in training load to be included as a time-varying exposure for sport injury development? (ii) Why is time-to-event analysis superior to other analytical concepts when analysing training-load related data that changes status over time? (iii) How can researchers include change in training load in a time-to-event analysis? and, (iv) Are researchers able to include other time-varying variables into time-to-event analyses? We emphasise that cleaning datasets, setting up the data, performing analyses with time-varying variables and interpreting the results is time-consuming, and requires dedication. It may need you to ask for assistance from methodological peers as the analytical approaches presented this paper require specialist knowledge and well-honed statistical skills.ConclusionTo increase knowledge about the association between changes in training load and injury, we encourage sports injury researchers to collaborate with statisticians and/or methodological epidemiologists to carefully consider applying time-to-event models to prospective sports injury data. This will ensure appropriate interpretation of time-to-event data.

Reproductive timing and use of redd sites by lake-spawning brook trout (Salvelinus fontinalis)

1997 ◽  
Vol 54 (4) ◽  
pp. 747-756 ◽  
Author(s):  
P J Blanchfield ◽  
M S Ridgway
Keyword(s):  
Body Size ◽  
Brook Trout ◽  
Salvelinus Fontinalis ◽  
Spawning Activity ◽  
Spawning Period ◽  
Removal Experiments ◽  
Rapid Replacement ◽  
Males And Females ◽  
Breeding Seasons ◽  
Selection Of

We provide a detailed description of a salmonine mating system based on daily observations of tagged individuals in a lake-spawning population of brook trout (Salvelinus fontinalis) throughout two breeding seasons. Actual spawning occurred over a period of ~50 d. Over 90% of spawning males were present soon after spawning commenced and outnumbered females for the duration of the spawning period. The amount of time males and females remained on the spawning grounds increased with body size; however, males were present over a longer period than females of equivalent size. A distinct seasonal peak in spawning activity (~15 d) accounted for 58 and 84% (1994 and 1995) of all reproduction and was coincident with a decline in water temperature below 11°C and increased rainfall. Selection of redd sites by female brook trout was determined by groundwater flow which was significantly greater than at nonspawning sites. A preference for certain redd sites was observed, with 50% of spawnings occurring at 11 sites. The construction of multiple redds and duration in spawning activity by females increased with body size. Extensive reuse of redd sites and rapid replacement of females during removal experiments indicate that redd sites are a limiting resource.

scholarly journals Recurrent Events Analysis with Piece-wise exponential Additive Mixed Models

2021 ◽  
Author(s):  
Jordache Ramjith ◽  
Andreas Bender ◽  
Kit C. B. Roes ◽  
Marianne A. Jonker
Keyword(s):  
Mixed Models ◽  
Recurrent Events ◽  
Proportional Hazards ◽  
Penalized Splines ◽  
Time Varying ◽  
Time To Event ◽  
Covariate Effects ◽  
Non Linear ◽  
Additive Mixed Models ◽  
Flexible Framework

Abstract Background: Recurrent events analysis plays an important role in many applications, including the study of chronic diseases or recurrence of infections. Historically, most models for the analysis of time-to-event data, including recurrent events, have been based on Cox proportional hazards regression. Recently, however, the Piece-wise exponential Additive Mixed Model (PAMM) has gained popularity as a flexible framework for survival analysis. While many papers and tutorials have been presented in the literature on the application of Cox based models, few papers have provided detailed instructions for the application of PAMMs and to our knowledge, none exist for recurrent events analysis. Methods: The PAMM is introduced as a framework for recurrent events analysis. We describe the application of the model to unstratified and stratified shared frailty models for recurrent events. We illustrate how penalized splines can be used to estimate non-linear and time-varying covariate effects without a priori assumptions about their functional shape. The model is motivated for both, analysis on the gap timescale ("clock-reset") and calendar timescale ("clock-forward"). The data augmentation necessary for the application to recurrent events is described and explained in detail. Results: Simulations confirmed that the model provides unbiased estimates of covariate effects and the frailty variance, as well as equivalence to the Cox model when proportional hazards are assumed. Applications to recurrence of staphylococcus aureus and malaria in children illustrates the estimation of seasonality, bivariate non-linear effects, multiple timescales and relaxation of the proportional hazards assumption via time-varying effects. The R package pammtools has been extended to facilitate estimation, visualization and interpretation of PAMMs for recurrent events analysis. Conclusion: PAMMs provide a flexible framework for the analysis of time-to-event and recurrent events data. The estimation of PAMMs is based on Generalized Additive Mixed Models and thus extends the researcher’s toolbox for recurrent events analysis.

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