scholarly journals The role of science advice in recovery potential assessments in freshwater fish listing decisions under the Canadian Species at Risk Act

FACETS ◽  
2021 ◽  
Vol 6 ◽  
pp. 1247-1259
Author(s):  
Fielding A. Montgomery ◽  
Noelle Stratton ◽  
Paul A. Bzonek ◽  
Sara E. Campbell ◽  
Rowshyra A. Castañeda ◽  
...  
Keyword(s):  
At Risk ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Random Effect ◽  
Freshwater Fishes ◽  
Species At Risk ◽  
Recovery Potential ◽  
Principal Coordinates ◽  
Science Advice ◽  
Species At Risk Act

Fishes assessed as Threatened or Endangered by the Committee on the Status of Endangered Wildlife in Canada are disproportionately less likely to be listed under the federal Species at Risk Act (SARA) compared to other taxa. We examined the extent to which the amount and type of science advice in a Recovery Potential Assessment (RPA) contributes to SARA-listing decisions for 34 wildlife species of freshwater fishes in Canada. We used a generalized linear mixed model to describe SARA listing status as a function of RPA completeness. Principal coordinates analyses were conducted to assess similarity in answers to RPA questions among listed and nonlisted species. The amount and type of science advice within an RPA were weakly related to SARA status. RPA completeness accounted for only 7.4% of model variation when family was included as a random effect, likely because nine species not listed under SARA (64%) belong to the sturgeon family. Our results suggest that, while potentially useful for informing recovery strategies, RPAs do not appear to be driving listing status for freshwater fishes in Canada. Factors beyond scientific advice likely contribute to nonlisted species and delays in listing decisions.

scholarly journals The generalizability crisis

2020 ◽  
pp. 1-37
Author(s):  
Tal Yarkoni
Keyword(s):  
Quantitative Analysis ◽  
Statistical Inference ◽  
Random Effects ◽  
False Positive ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Random Effect ◽  
Statistical Procedures ◽  
Subject Variability ◽  
Replication Crisis

Abstract Most theories and hypotheses in psychology are verbal in nature, yet their evaluation overwhelmingly relies on inferential statistical procedures. The validity of the move from qualitative to quantitative analysis depends on the verbal and statistical expressions of a hypothesis being closely aligned—that is, that the two must refer to roughly the same set of hypothetical observations. Here I argue that many applications of statistical inference in psychology fail to meet this basic condition. Focusing on the most widely used class of model in psychology—the linear mixed model—I explore the consequences of failing to statistically operationalize verbal hypotheses in a way that respects researchers' actual generalization intentions. I demonstrate that whereas the "random effect" formalism is used pervasively in psychology to model inter-subject variability, few researchers accord the same treatment to other variables they clearly intend to generalize over (e.g., stimuli, tasks, or research sites). The under-specification of random effects imposes far stronger constraints on the generalizability of results than most researchers appreciate. Ignoring these constraints can dramatically inflate false positive rates, and often leads researchers to draw sweeping verbal generalizations that lack a meaningful connection to the statistical quantities they are putatively based on. I argue that failure to take the alignment between verbal and statistical expressions seriously lies at the heart of many of psychology's ongoing problems (e.g., the replication crisis), and conclude with a discussion of several potential avenues for improvement.

scholarly journals Response transformations for random effect and variance component models

2020 ◽  
pp. 1471082X2096691
Author(s):  
Amani Almohaimeed ◽  
Jochen Einbeck
Keyword(s):  
Maximum Likelihood ◽  
Random Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Random Effect ◽  
Statistical Technique ◽  
Response Distribution ◽  
Level Data ◽  
Variance Component Models ◽  
Response Transformation

Random effect models have been popularly used as a mainstream statistical technique over several decades; and the same can be said for response transformation models such as the Box–Cox transformation. The latter aims at ensuring that the assumptions of normality and of homoscedasticity of the response distribution are fulfilled, which are essential conditions for inference based on a linear model or a linear mixed model. However, methodology for response transformation and simultaneous inclusion of random effects has been developed and implemented only scarcely, and is so far restricted to Gaussian random effects. We develop such methodology, thereby not requiring parametric assumptions on the distribution of the random effects. This is achieved by extending the ‘Nonparametric Maximum Likelihood’ towards a ‘Nonparametric profile maximum likelihood’ technique, allowing to deal with overdispersion as well as two-level data scenarios.

scholarly journals How COVID-19 lockdown and reopening affected daily steps: evidence based on 164,630 person-days of prospectively collected data from Shanghai, China

2021 ◽  
Vol 18 (1) ◽  
Author(s):  
Ding Ding ◽  
Minna Cheng ◽  
Borja del Pozo Cruz ◽  
Tao Lin ◽  
Shuangyuan Sun ◽  
...  
Keyword(s):  
Physical Activity ◽  
At Risk ◽  
Body Composition ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
University Education ◽  
Control Group ◽  
Daily Step ◽  
Step Counts ◽  
Reduced Physical Activity

Abstract Background COVID-19 lockdowns may lead to physical inactivity, a major risk factor for non-communicable diseases. This study aims to determine: 1) the trajectory in daily step counts before, during and after the lockdown in China, and 2) the characteristics associated with the trajectories. Methods From December 2019 to July 2020, smartphone-based step counts were continuously collected in 815 Chinese adults residing in Shanghai over 202 days across three phases: before, during, and after the lockdown. Participant characteristics were reported, and height, weight and body composition measured before the lockdown. A ‘sharp’ regression discontinuity design with cluster robust standard errors was used to test the effect of the lockdown and reopening on daily steps and a linear mixed model was used to examine the characteristics associated with trajectories during the observed period. Results Based on 164,630 person-days of data, we found a sharp decline in daily step counts upon the lockdown (24/01/2020) by an average of 3796 (SE = 88) steps, followed by a significant trend of increase by 34 steps/day (SE = 2.5; p < .001) until the end of the lockdown (22/03/2020). This increasing trend continued into the reopening phase at a slower rate of 5 steps per day (SE = 2.3; p = 0.029). Those who were older, married, university educated, insufficiently active, had an ‘at risk’ body composition, and those in the control group, were slower at recovering step counts during the lockdown, and those who were older, married, without university education and with an ‘at risk’ body composition recovered step counts at a slower pace after the reopening. Conclusions Despite later increases in step counts, COVID-19 lockdown led to a sustained period of reduced physical activity, which may have adverse health implications. Governments and health professionals around the world should continue to encourage and facilitate physical activity during the pandemic.

scholarly journals Bayesian spatial and spatio-temporal approaches to modelling dengue fever: a systematic review

2018 ◽  
Vol 147 ◽  
Author(s):  
A. Aswi ◽  
S. M. Cramb ◽  
P. Moraga ◽  
K. Mengersen
Keyword(s):  
Dengue Fever ◽  
Random Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Assessment Tool ◽  
Temporal Dynamics ◽  
Random Effect ◽  
Temperature And Precipitation ◽  
Generalised Linear Mixed Model ◽  
Spatio Temporal

AbstractDengue fever (DF) is one of the world's most disabling mosquito-borne diseases, with a variety of approaches available to model its spatial and temporal dynamics. This paper aims to identify and compare the different spatial and spatio-temporal Bayesian modelling methods that have been applied to DF and examine influential covariates that have been reportedly associated with the risk of DF. A systematic search was performed in December 2017, using Web of Science, Scopus, ScienceDirect, PubMed, ProQuest and Medline (via Ebscohost) electronic databases. The search was restricted to refereed journal articles published in English from January 2000 to November 2017. Thirty-one articles met the inclusion criteria. Using a modified quality assessment tool, the median quality score across studies was 14/16. The most popular Bayesian statistical approach to dengue modelling was a generalised linear mixed model with spatial random effects described by a conditional autoregressive prior. A limited number of studies included spatio-temporal random effects. Temperature and precipitation were shown to often influence the risk of dengue. Developing spatio-temporal random-effect models, considering other priors, using a dataset that covers an extended time period, and investigating other covariates would help to better understand and control DF transmission.

scholarly journals Goats Given Transdermal Flunixin Meglumine Displayed Less Pain Behavior After Castration

2020 ◽  
Author(s):  
Amanda Lee ◽  
Meggan Graves ◽  
Andrea Lear ◽  
Sherry Cox ◽  
Marc Caldwell ◽  
...  
Keyword(s):  
Fixed Effects ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Body Position ◽  
Random Effect ◽  
Pain Behavior ◽  
Bout Duration ◽  
Flunixin Meglumine ◽  
Meat Goats ◽  
Transdermal Application

AbstractPain management should be utilized with castration to reduce physiological and behavioral changes. Transdermal application of drugs require less animal management and fewer labor risks, which can occur with oral administration or injections. The objective was to determine the effects of transdermal flunixin meglumine on meat goats’ behavior post-castration. Male goats (N = 18; mean body weight ± standard deviation: 26.4 ± 1.6 kg) were housed individually in pens and randomly assigned to 1 of 3 treatments: (1) castrated, dosed with transdermal flunixin meglumine; (2) castrated, dosed with transdermal placebo; and (3) sham castrated, dosed with transdermal flunixin meglumine. Body position, rumination, and head- pressing were observed for 1 h ± 10 minutes twice daily on days −1, 0, 1, 2, and 5 around castration. Each goat was observed once every 5-minutes (scan samples) and reported as percentage of observations. Accelerometers were used to measure standing, lying, and laterality (total time, bouts, and bout duration). A linear mixed model was conducted using GLIMMIX. Fixed effects of treatment, day relative to castration, and treatment*day relative to castration and random effect of date and goat nested within treatment were included. Treatment 1 goats (32.7 ± 2.8%) and treatment 2 goats (32.5 ± 2.8%) ruminated less than treatment 3 goats (47.4 ± 2.8%, P = 0.0012). Head pressing was greater on day of castration in treatment 2 goats (P < 0.001). Standing bout duration was greatest in treatment 2 goats on day 1 post-castration (P < 0.001). Lying bout duration was greatest in treatment 2 goats on day 1 post-castration compared to treatment 1 and treatment 3 goats(P < 0.001). Transdermal flunixin meglumine improved goats’ fluidity of movement post-castration and decreased head pressing, indicating a mitigation of pain behavior.

Species Listing under Canada's Species at Risk Act

2009 ◽  
Vol 23 (6) ◽  
pp. 1609-1617 ◽  
Author(s):  
C. SCOTT FINDLAY ◽  
STEWART ELGIE ◽  
BRIAN GILES ◽  
LINDA BURR
Keyword(s):  
At Risk ◽  
Species At Risk ◽  
Species At Risk Act

scholarly journals Comparative analysis of abundance–occupancy relationships for species at risk at both broad taxonomic and spatial scales

2015 ◽  
Vol 93 (7) ◽  
pp. 515-519 ◽  
Author(s):  
Nancy E. Roney ◽  
Anna Kuparinen ◽  
Jeffrey A. Hutchings
Keyword(s):  
At Risk ◽  
Comparative Analysis ◽  
Large Scale ◽  
Spatial Scales ◽  
Freshwater Fishes ◽  
Reference Points ◽  
Species At Risk ◽  
Large Spatial Scale ◽  
Terrestrial Mammals ◽  
Taxonomic Groups

The abundance–occupancy relationship is one of the most well-examined relationships in ecology. At the species level, a positive association has been widely documented. However, until recently, research on the nature of this relationship at broad taxonomic and spatial scales has been limited. Here, we perform a comparative analysis of 12 taxonomic groups across a large spatial scale (Canada), using data on Canadian species at risk: amphibians, arthropods, birds, freshwater fishes, lichens, marine fishes, marine mammals, molluscs, mosses, reptiles, terrestrial mammals, and vascular plants. We find a significantly positive relationship in all taxonomic groups with the exception of freshwater fishes (negative association) and lichens (no association). In general, our work underscores the strength and breadth of this apparently fundamental relationship and provides insight into novel applications for large-scale population dynamics. Further development of species-independent abundance–occupancy relationships, or those of a similar nature, might well prove instrumental in serving as starting points for developing species-independent reference points and recovery strategies.

scholarly journals PSVII-9 Effects of a multi-species direct-fed microbial on growth performance and carcass characteristics of grow-finish pigs

2019 ◽  
Vol 97 (Supplement_2) ◽  
pp. 216-216
Author(s):  
Mariana Boscato Menegat ◽  
Joel M DeRouchey ◽  
Jason C Woodworth ◽  
Mike D Tokach ◽  
Steve S Dritz ◽  
...  
Keyword(s):  
Growth Performance ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Block Design ◽  
Control Diet ◽  
Random Effect ◽  
Carcass Characteristics ◽  
Experimental Unit ◽  
Feeding Duration ◽  
Corn Distillers

Abstract This study was conducted to determine the effects of a multi-species direct-fed microbial (DFM) product based on lactic acid bacteria and Bacillus subtilis on growth performance and carcass characteristics of grow-finish pigs. A total of 1,188 pigs (PIC 359 × 1050; initially 25.8 kg BW) were used in a 121-d growth trial with 27 pigs/pen and 22 pens/treatment. Pigs were allotted to treatments based on initial BW in a randomized complete block design. Treatments included a control diet and the control diet with added DFM (BiOWiSH Technologies Inc., Cincinnati, OH) included at 0.055% of the diet at the expense of corn. Diets were based on corn, distillers dried grains with solubles, and soybean meal and fed in four dietary phases. Data were analyzed using a linear mixed model (PROC GLIMMIX, SAS®) with treatment as fixed effect, block as random effect, and pen as experimental unit. Overall (d 0 to 121), pigs fed the control diet had greater ADG (P < 0.05) and final BW (P < 0.001) compared to pigs fed the DFM diet (Table 1). There was no evidence for differences (P > 0.05) in ADFI or G:F between treatments. The difference in final BW resulted in heavier (P < 0.05) HCW in control pigs compared to DFM pigs, but no evidence for differences (P > 0.05) was observed in carcass yield, backfat, loin depth, and percentage lean between treatments. In conclusion, the inclusion of this multi-species DFM in growing-finishing diets reduced ADG in this commercial study. This response could be related to inclusion rate, feeding duration, or other factors not identified in this study, warranting further research to characterize the effects on pig performance.

Misspecification of the random effect structure: Implications for the linear mixed model

2020 ◽  
Author(s):  
Brandon LeBeau
Keyword(s):  
Fixed Effects ◽  
Serial Correlation ◽  
Mixed Model ◽  
Linear Mixed Model ◽  
Type I Error ◽  
Random Effect ◽  
Correlated Data ◽  
Unbiased Estimation ◽  
Type I ◽  
Nested Data

<p>The linear mixed model is a commonly used model for longitudinal or nested data due to its ability to account for the dependency of nested data. Researchers typically rely on the random effects to adequately account for the dependency due to correlated data, however serial correlation can also be used. If the random effect structure is misspecified (perhaps due to convergence problems), can the addition of serial correlation overcome this misspecification and allow for unbiased estimation and accurate inferences? This study explored this question with a simulation. Simulation results show that the fixed effects are unbiased, however inflation of the empirical type I error rate occurs when a random effect is missing from the model. Implications for applied researchers are discussed.</p>

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