In this era of climate change, some biological conservationists' concerns are based on seasonal studies that highlight how wild birds' physiological fitness are interconnected with the immediate ...environment to avoid population decline. We investigated how seasonal biometrics correlated to stress parameters of the adult Village Weavers (Ploceus cucullatus) during breeding and post-breeding seasons of the Weaver birds in Amurum Forest Reserve. Specifically, we explored the following objectives: (i) the seasonal number of birds captured; (ii) whether seasonal baseline corticosterone (CORT), packed cell volume (PCV), and heterophil to lymphocytes ratio (H:L) were sex-dependent; (iii) whether H:L ratio varied with baseline (CORT); (iv) whether phenotypic condition (post-breeding moult) and brood patch varied with baseline (CORT) and H:L ratio; and (v) how body biometrics co-varied birds’ seasonal baseline (CORT), (PCV) and (H:L) ratio. Trapping of birds (May–November) coincided with breeding and post-breeding seasons. The birds (n = 53 males, 39 females) were ringed, morphologically assessed (body mass, wing length, moult, brood patch) and blood collected from their brachial vein was used to assess CORT, PCV and H:L ratio. Although our results indicated more male birds trapped during breeding, the multiple analyses of variance (MANOVA) indicated that the seasonal temperature of the trapping sites correlated (P < 0.05) significantly to baseline (CORT). The general linear mixed model analyses (GLMMs) indicated that the baseline (CORT) also correlated significantly to H:L ratio of the male and female birds. However, PCV correlated significantly to body size of the birds (wing length) and not body mass. Haematological parameters such as the baseline CORT and the H:L ratio as indicators of stress in wild birds. Hence, there is the possibility that the Village Weaver birds suffered from seasonally induced stress under the constrained effect of environmental temperature. Hence, future studies should investigate whether the effect observed is also attributable to other passerine species.
•In vivo studies of dendritic morphology in which multiple neurons are sampled per animal often use a simple linear model to detect significant differences which can lead to faulty inference.•Mixed ...models account for intra-class correlation that occurs with clustered data often generated in dendrite analysis to accurately estimate the standard deviation of the parameter estimate and, hence, produce accurate p-values.•A mixed effects approach accurately models the true variability in data sets sampling multiple neurons per animal, such as Sholl analysis.
The Sholl technique is widely used to quantify dendritic morphology. Data from such studies, which typically sample multiple neurons per animal, are often analyzed using simple linear models. However, simple linear models fail to account for intra-class correlation that occurs with clustered data, which can lead to faulty inferences.
Mixed effects models account for intra-class correlation that occurs with clustered data; thus, these models more accurately estimate the standard deviation of the parameter estimate, which produces more accurate p-values. While mixed models are not new, their use in neuroscience has lagged behind their use in other disciplines.
A review of the published literature illustrates common mistakes in analyses of Sholl data. Analysis of Sholl data collected from Golgi-stained pyramidal neurons in the hippocampus of male and female mice using both simple linear and mixed effects models demonstrates that the p-values and standard deviations obtained using the simple linear models are biased downwards and lead to erroneous rejection of the null hypothesis in some analyses.
The mixed effects approach more accurately models the true variability in the data set, which leads to correct inference.
Mixed effects models avoid faulty inference in Sholl analysis of data sampled from multiple neurons per animal by accounting for intra-class correlation. Given the widespread practice in neuroscience of obtaining multiple measurements per subject, there is a critical need to apply mixed effects models more widely.
Fish body size influenced by multiple drivers Alò, Dominique; Pizarro, Vanessa; Habit, Evelyn
Ecography (Copenhagen),
January 2024, 2024-01-00, 20240101, Volume:
2024, Issue:
1
Journal Article
Peer reviewed
Open access
There is evidence that organisms have become smaller during the past periods of global warming. Global change has substantial effects on biodiversity, with body size reduction being the third most ...common response to global warming. Body size allometry in ectotherms needs to be explored further; the objectives of this study were to better understand the mechanisms regulating body size in fish by testing: 1) Bergmann's rule with temperature and elevation, 2) additional environmental drivers, 3) the role of isolation, 4) ecoevolutionary hypotheses comparing native and exotic species and 5) the role of migration propensity in comparing migratory and resident species. We analyzed an extensive dataset of Chilean fish composed of 75 198 records which included 25 species from 12 different families between latitudes −28.80 to −51.42 using linear mixed models to discern the best environmental variables contributing to body size changes, as well as incorporating factors related to dispersal capabilities, biogeographic isolation and levels of exotic/native interactions. Bergmann's rule is supported by changes in elevation, and our study shows that freshwater fish body size also increases with increasing environmental heterogeneity and productivity. In general, inland native fish tend to be smaller than coastal ones, supporting the island rule with evidence of gigantism or dwarfism in selected species. Ecological variables affecting fish body size do not differ between native and exotic fish unless other factors are considered, such as dispersal capacity (migrating vs resident fish) or mechanisms related to their isolation. Although temperature is not a direct driver of body size in Chilean fish, heterogeneity, productivity, geography, migratory ability and species origin may affect body size. A better understanding of the mechanisms driving body size in ectotherms will aid in determining management priorities in the face of global climate disruption.
Water consumption by households is influenced by a host of factors, widely investigated in the literature. However, the effects of contingent situations like drought episodes and economic crises, ...which may strongly restrict direct water use in households, remain less explored, and especially a combination of both. Catalonia, a Mediterranean region, suffered the worst drought episode in the last 75 years in 2007 and 2008, followed immediately by the worst economic crisis also in several decades between 2009 and 2014 (though still fishtailing). Taking it as a case study and using metered water data for the household sector, we propose a generalized linear mixed model in which the influence of both the drought episode and the economic crisis on per capita water consumption by comarques (supra-municipal entities) is assessed using a drought index on one hand, and economic variables and the water price on the other hand. Likewise, demographic, territorial and climatic determinants, as well as environmental behaviour, are also evaluated. The dataset (N = 287) consists of panel data for the forty-one comarques of Catalonia covering the 2007 to 2013 period. Results confirm that the contingent factors analysed have contributed to further reduce per capita water consumption, being significant the drought index and water price. The proportion of elderly people, the household size and the proxy for environmental behaviour, also have a negative effect on consumption; whereas seasonal population has the expected positive effect. However, neither the climatic and economic variables analysed, nor urban density and the proportion of foreign population, are found to be significant. A better understanding of the factors influencing residential water consumption in a context of growing water scarcity and economic downturn may aid policy makers and water managers not only to improve the effectiveness and efficiency of demand-side management measures that affect households, but to address emerging social concerns such as water poverty.
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•Contingent factors are relevant in the analysis of residential water consumption.•The influence of drought and economic crisis on water consumption is examined using a GLMM.•We use panel data of 41 supra-municipal entities in Catalonia between 2007 and 2013.•Water price and drought intensity contribute to reduce water consumption.•Overall, water consumption decline during the crisis is higher than during the drought.
The selection of superior genotypes of soybean entails a simultaneous evaluation of a number of favorable traits that provide a comparatively superior yield. Disregarding the population effect in the ...statistical model may compromise the estimate of variance components and the prediction of genetic values. The present study was undertaken to investigate the importance of including population effect in the statistical model and to determine the effectiveness of the index based on factor analysis and ideotype design via best linear unbiased prediction (FAI-BLUP) in the selection of erect, early, and high-yielding soybean progenies. To attain these objectives, 204 soybean progenies originating from three populations were examined for various traits of agronomic interest. The inclusion of the population effect in the statistical model was relevant in the genetic evaluation of soybean progenies. To quantify the effectiveness of the FAI-BLUP index, genetic gains were predicted and compared with those obtained by the Smith-Hazel and Additive Genetic indices. The FAI-BLUP index was effective in the selection of progenies with balanced, desirable genetic gains for all traits simultaneously. Therefore, the FAI-BLUP index is an adequate tool for the simultaneous selection of important traits in soybean breeding.
► This paper presents a PSO algorithm with negative knowledge (PSONK). ► Multi-objective two-sided mixed-model assembly line balancing is focused. ► PSONK is a competitive and promising algorithm. ► ...A local search can improve the performance of PSONK.
Particle swarm optimisation (PSO) is an evolutionary metaheuristic inspired by the swarming behaviour observed in flocks of birds. The applications of PSO to solve multi-objective discrete optimisation problems are not widespread. This paper presents a PSO algorithm with negative knowledge (PSONK) to solve multi-objective two-sided mixed-model assembly line balancing problems. Instead of modelling the positions of particles in an absolute manner as in traditional PSO, PSONK employs the knowledge of the relative positions of different particles in generating new solutions. The knowledge of the poor solutions is also utilised to avoid the pairs of adjacent tasks appearing in the poor solutions from being selected as part of new solution strings in the next generation. Much of the effective concept of Pareto optimality is exercised to allow the conflicting objectives to be optimised simultaneously. Experimental results clearly show that PSONK is a competitive and promising algorithm. In addition, when a local search scheme (2-Opt) is embedded into PSONK (called M-PSONK), improved Pareto frontiers (compared to those of PSONK) are attained, but longer computation times are required.
•We examine changes in the impact of exogenous variables on driver injury severity.•We consider scaled and mixed versions of generalized ordered logit model.•We demonstrate our model by using General ...Estimates System (GES) crash database.•We consider data for a span of 25-year from 1989 through 2014 in 5-year increments.
The current study undertakes a unique research effort to quantify the impact of various exogenous factors on crash severity over time. Specifically, we examine if over time, the impact of exogenous variables has changed and if so what is the magnitude of the change. The research contributes to driver injury severity analysis both methodologically and empirically by proposing a framework that addresses the challenges associated with pooled (or pseudo-panel) data. For our analysis, we draw data from the General Estimates System (GES) over a span of twenty-five years. The data is compiled for driver injury severity in single or two vehicle crashes from 1989 through 2014 in 5-year increments (1989, 1994, 1999, 2004, 2009 and 2014). The alternative econometric frameworks considered for the analysis include ordered logit, generalized ordered logit, scaled generalized ordered logit and mixed generalized ordered logit models. A host of comparison metrics are computed to evaluate the performance of these alternative models in examining the pooled data. The model development exercise is conducted with a host of exogenous variables including driver characteristics, vehicle characteristics, roadway attributes, environmental factors, crash characteristics and temporal attributes. The model estimation results are further augmented by performing a detailed policy scenario analysis, probability profile representations and elasticity effects for different driving and situational conditions across different years.
This article presents a bi-endmember semi-nonnegative matrix factorization (Semi-NMF) algorithm based on low-rank and sparse matrix decomposition (LRSMD), referred to as BLSNMF, to resolve the issues ...of endmember variability and nonlinear mixing. Given the fact that the hyperspectral images contain a large amount of redundant information, compressing sensing (CS) techniques can generally be used to randomly sense the effective information in an observed image according to the effective approximation of the bi-endmember components. In this article, the proposed BLSNMF integrates low-rank and sparse spaces decomposed by go decomposition (GoDec) or orthogonal subspace projection-based go decomposition (OSP-GoDec) with Semi-NMF to suppress interference between different components so as to improve the unmixing performance via a simple linear mixed model. Specifically, the observed data space is first decomposed by GoDec or OSP-GoDec to approximate four different attribute components, CS-sampled double low-rank components, structured sparse component, and noise component. Second, from the CS-sampled double low-rank components, the inherent and new endmembers along with their abundances are evaluated via Semi-NMF, and then, the double low-rank components are redescribed using the estimated endmembers and abundances. Finally, the serious interference entries in the structured sparse component space are removed from the data to better learn other attribute components. The experimental results show that BLSNMF can eliminate the interference of new endmembers and sparse noise so as to better evaluate the endmembers and abundances and effectively improve the ability to interpret the spectral information.
We investigated using longitudinal models to reconstruct year-class strength (YCS) from catch-at-age data, with an example application to lake trout (Salvelinus namaycush) in the main basin of Lake ...Huron. The best model structure depended on the age range used for model implementation. The YCS trajectory from the full age range (3-30 years) was similar to the trajectory from a narrow age range that approximated the age of recruitment to the fishing gears (5-7 years), but YCS estimates from the full age range included additional variations due to time-dependent selectivity and mortality. When using ages younger or older than the likely ages of recruitment, YCS estimates did not represent recruitment abundances and were also biased by trends in age-specific selectivity and mortality across years. Longitudinal YCS estimates are likely more robust than single-age recruitment indices, which are often subject to interannual changes in catchability and selectivity. Our findings provide guidance for future applications of the longitudinal YCS reconstruction that in turn may inform and supplement more comprehensive research and management programs for understanding fish recruitment dynamics.
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