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Data from: Phase-dependent climate-predator interactions explain three decades of variation in neonatal caribou survival
Bastille-Rousseau, Guillaume; Schaefer, James A.; Lewis, Keith P.; Mumma, Matthew; Ellington, E. Hance; Rayl, Nathaniel D.; Mahoney, Shane P.; Pouliot, Darren; Murray, Dennis L.; Mumma, Matthew A. — 2016-10-27 1. Climate can have direct and indirect effects on population dynamics via changes in resource competition or predation risk, but this influence can be modulated by density- or phase-dependent processes. We hypothesized that for ungulates, climatic conditions close to parturition have a greater influence on the predation risk of neonates during population declines, when females are already under nutritional stress triggered by food limitation. 2. We examined the presence of phase-dependent climate-predator interactions on neonatal ungulate survival by comparing spatial and temporal fluctuations in climatic conditions, cause specific mortality, and per capita resource limitation. We determined cause-specific fates of 1384 caribou (Rangifer tarandus) from 10 herds in Newfoundland, spanning more than 30 years during periods of numerical increase and decline, while exposed to predation from black bears (Ursus americanus) and coyotes (Canis latrans). 3. We conducted Cox proportional hazards analysis for competing risks, fit as a function of weather metrics, to assess pre- and post-partum climatic influences on survival on herds in population increase and decline phases. We used cumulative incidence functions to compare temporal changes in risk from predators. 4. Our results support our main hypothesis; when caribou populations increased, weather conditions preceding calving were the main determinants of cause-specific mortality, but when populations declined, weather conditions during calving also influenced predator-driven mortality. Cause-specific analysis showed that weather conditions can differentially affect predation risk between black bears and coyotes with specific variables increasing the risk from one species and decreasing the risk from the other. 5. For caribou, nutritional stress appears to increase predation risk on neonates, an interaction which is exacerbated by susceptibility to climatic events. These findings support the phase-dependent climate-predator (PDCP) interactions framework, where maternal body condition influences susceptibility to climate-related events and, subsequently, risk from predation.
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Data from: Using multiple imputation to estimate missing data in meta-regression
Ellington, E. Hance; Bastille-Rousseau, Guillaume; Austin, Cayla; Landolt, Kristen N.; Pond, Bruce A.; Rees, Erin E.; Robar, Nicholas; Murray, Dennis L. — 2015-11-25 1. There is a growing need for scientific synthesis in ecology and evolution. In many cases, meta-analytic techniques can be used to complement such synthesis. However, missing data is a serious problem for any synthetic efforts and can compromise the integrity of meta-analyses in these and other disciplines. Currently, the prevalence of missing data in meta-analytic datasets in ecology and the efficacy of different remedies for this problem have not been adequately quantified. 2. We generated meta-analytic datasets based on literature reviews of experimental and observational data and found that missing data were prevalent in meta-analytic ecological datasets. We then tested the performance of complete case removal (a widely used method when data are missing) and multiple imputation (an alternative method for data recovery) and assessed model bias, precision, and multi-model rankings under a variety of simulated conditions using published meta-regression datasets. 3. We found that complete case removal led to biased and imprecise coefficient estimates and yielded poorly specified models. In contrast, multiple imputation provided unbiased parameter estimates with only a small loss in precision. The performance of multiple imputation, however, was dependent on the type of data missing. It performed best when missing values were weighting variables, but performance was mixed when missing values were predictor variables. Multiple imputation performed poorly when imputing raw data which was then used to calculate effect size and the weighting variable. 4. We conclude that complete case removal should not be used in meta-regression, and that multiple imputation has the potential to be an indispensable tool for meta-regression in ecology and evolution. However, we recommend that users assess the performance of multiple imputation by simulating missing data on a subset of their data before implementing it to recover actual missing data.

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(Author: Ellington, E. Hance)

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