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This paper presents a general framework for simulating plot data in multi-environment field trials with one or more traits. The framework is embedded within the R package FieldSimR, whose core function generates plot errors that capture global field trend, local plot variation, and extraneous variation at a user-defined ratio. FieldSimR's capacity to simulate realistic plot data makes it a flexible and powerful tool for a wide range of improvement processes in plant breeding, such as the optimisation of experimental designs and statistical analyses of multi-environment field trials. FieldSimR provides crucial functionality that is currently missing in other software for simulating plant breeding programmes and is available on CRAN. The paper includes an example simulation of field trials that evaluate 100 maize hybrids for two traits in three environments. To demonstrate FieldSimR's value as an optimisation tool, the simulated data set is then used to compare several popular spatial models for their ability to accurately predict the hybrids' genetic values and reliably estimate the variance parameters of interest. FieldSimR has broader applications to simulating data in other agricultural trials, such as glasshouse experiments.

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Climate changes have become undisputed, as have their consequences for global ecosystems and mankind. The coastal areas are among the most affected areas on the planet due to their geographical location. The effects suffered by coastal areas can render the residing populations homeless, as well as compromise the continuity of the history and culture of these environments. The Marine Extractive Reserve of the city of Soure (coastal area of eastern Amazonia) stands out for housing populations that have developed an intimate relationship with nature and have knowledge that can explain people's perception of climate changes. In this context, this study investigated how local residents perceive climate change and its consequences considering different temporal and spatial scales. To this end, questionnaires were developed and applied using a 5-point Likert scale. Our results indicate that perception is shaped by socioeconomic and demographic factors, and that they are perceived on different time scales and geographic space. These findings reflect the awareness-raising efforts of the management body of this Conservation Unit and the local knowledge, derived from the relationship of the residents with the natural environment, which, together, provided the population with assertive information that favor a better understanding of this phenomenon.

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Suboptimal adherence to antiretroviral therapy (ART) in people with HIV, even during sustained viral suppression, is associated with persistent inflammation, immune activation, and coagulopathy. Persistently low CD4-CD8 Ratio has been also associated with residual inflammation, is a good predictor of increased risk of death and more widely available than inflammatory biomarkers. We tested the hypothesis that the CD4-CD8 Ratio is associated with ART adherence during periods of complete viral suppression. We used the Medication Possession Ratio based in pharmacy registries as measure of adherence and time-varying, routine care CD4 and CD8 measurements as outcome. We used a linear mixed model for longitudinal data, including fixed effects for sex, age, education, date of ART initiation, AIDS-related conditions, and baseline CD4 to model the outcome. In 988 adults with a median follow-up of 4.13 years, higher ART adherence was independently associated with a modest increase in CD4-CD8. For each increasing percentage point in adherence, the CD4-CD8 Ratio increased 0.000857 (95% confidence interval [CI] -0.000494 to 0.002209, p = .213731) in the first year after achieving viral suppression; 0.001057 (95% CI 0.000262-0.001853, p = .009160) in years 1 to 3; 0.000323 (95% CI -0.000448 to 0.001095, p = .411441) in years 3 to 5; and 0.000850 (95% CI 0.000272-0.001429, p = .003946) 5-10 years after achieving viral suppression. The magnitude of the effect of adherence over CD4-CD8 Ratios varied over time and by baseline CD4 count, with increasing adherence having a larger effect early after ART initiation in people with higher baseline CD4 (>500 cells/µL) and in later years in people with lower baseline CD4 count (≥200 cells/µL). Our findings expand on previous evidence suggesting that the benefits of optimal adherence to modern ART regimens goes beyond maintaining viral suppression. These results highlight the importance of including objective measurements of adherence as part of routine care, even in patients with complete HIV suppression over long-term follow-up.

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In neuroscience research, longitudinal data are often analysed using analysis of variance (ANOVA) and multivariate analysis of variance (MANOVA) for repeated measures (rmANOVA/rmMANOVA). However, these analyses have special requirements: The variances of the differences between all possible pairs of within-subject conditions (i.e., levels of the independent variable) must be equal. They are also limited to fixed repeated time intervals and are sensitive to missing data. In contrast, other models, such as the generalized estimating equations (GEE) and the generalized linear mixed models (GLMM), suggest another way to think about the data and the studied phenomenon. Instead of forcing the data into the ANOVAs assumptions, it is possible to design a flexible/personalized model according to the nature of the dependent variable. We discuss some advantages of GEE and GLMM as alternatives to rmANOVA and rmMANOVA in neuroscience research, including the possibility of using different distributions for the parameters of the dependent variable, a better approach for different time length points, and better adjustment to missing data. We illustrate these advantages by showing a comparison between rmANOVA and GEE in a real example and providing the data and a tutorial code to reproduce these analyses in R. We conclude that GEE and GLMM may provide more reliable results when compared to rmANOVA and rmMANOVA in neuroscience research, especially in small sample sizes with unbalanced longitudinal designs with or without missing data.

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Our objectives were to evaluate the variability among animals regarding to the degradation rate of the potentially degradable fraction of dry matter, crude protein, and neutral detergent fiber, as well as to establish the minimum number of animals and provide a standardized design of sampling times for in situ ruminal degradation assays of tropical feeds with cattle. Seven feeds were evaluated, four concentrates and three forages. The incubations were performed using five rumen-cannulated Nellore heifers (328 ± 9.8 kg of body weight). The complete sets of incubation sampling times encompassed 16 time points for forage samples (0-240 h) and 13 time points for concentrate samples (0-144 h). The profiles were adjusted using both fixed and mixed model approaches. When the variation among animals on the degradation rate was considered using the mixed model approach, the precision of the adjusted degradation profiles was increased. Moreover, the utilization of a low number of animals increases the probability to obtain biased estimates of degradation rate and increased random variances. A minimum of three animals is recommended for in situ trials with cattle. Minimum designs of sampling times regarding number and position of incubation times were proposed, discussed, and recommended to assess the dynamics of tropical feed degradation.

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Aedes (Stegomyia) aegypti (L.) (Diptera: Culicidae) is the vector of multiple arboviruses. To evaluate the association between environmental factors and the oviposition activity of Ae. aegypti in Argentina, data on the presence and abundance of eggs were collected using ovitraps, between September of 2018 and May of 2019, in the cities of Villa María, Río Cuarto and Salsipuedes (Córdoba province, Argentina). We analysed the relationships between oviposition and five environmental factors: Temperature, precipitation, vegetation cover, human population density and distance to sites with a potential high density of larval habitats, like cemeteries and trash dumps. Environmental factors' data were collected using satellite image products. The oviposition activity was randomly distributed in three cities. Using generalized linear mixed models, we show that the house where each ovitrap was placed was a source of variability in oviposition, suggesting the relevance of microsite factors and the importance of domestic control actions. Ae. aegypti oviposition was positively correlated with night-time temperature of the previous 3 weeks, and in a context-dependent manner, it was positively correlated with human population density, vegetation cover and precipitation. The consistency and magnitude of these relationships varied between cities, indicating that oviposition is related to a complex system of environmental variables.

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Genetics plays a role in age-related macular degeneration (AMD), a common cause of blindness in the elderly. There is a need for powerful methods for carrying out region-based association tests between a dichotomous trait like AMD and genetic variants on family data. Here, we apply our new generalized functional linear mixed models (GFLMM) developed to test for gene-based association in a set of AMD families. Using common and rare variants, we observe significant association with two known AMD genes: CFH and ARMS2. Using rare variants, we find suggestive signals in four genes: ASAH1, CLEC6A, TMEM63C, and SGSM1. Intriguingly, ASAH1 is down-regulated in AMD aqueous humor, and ASAH1 deficiency leads to retinal inflammation and increased vulnerability to oxidative stress. These findings were made possible by our GFLMM which model the effect of a major gene as a fixed mean, the polygenic contributions as a random variation, and the correlation of pedigree members by kinship coefficients. Simulations indicate that the GFLMM likelihood ratio tests (LRTs) accurately control the Type I error rates. The LRTs have similar or higher power than existing retrospective kernel and burden statistics. Our GFLMM-based statistics provide a new tool for conducting family-based genetic studies of complex diseases. Supplementary materials for this article, including a standardized description of the materials available for reproducing the work, are available as an online supplement.

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We introduce a numerically tractable formulation of Bayesian joint models for longitudinal and survival data. The longitudinal process is modeled using generalized linear mixed models, while the survival process is modeled using a parametric general hazard structure. The two processes are linked by sharing fixed and random effects, separating the effects that play a role at the time scale from those that affect the hazard scale. This strategy allows for the inclusion of nonlinear and time-dependent effects while avoiding the need for numerical integration, which facilitates the implementation of the proposed joint model. We explore the use of flexible parametric distributions for modeling the baseline hazard function which can capture the basic shapes of interest in practice. We discuss prior elicitation based on the interpretation of the parameters. We present an extensive simulation study, where we analyze the inferential properties of the proposed models, and illustrate the trade-off between flexibility, sample size, and censoring. We also apply our proposal to two real data applications in order to demonstrate the adaptability of our formulation both in univariate time-to-event data and in a competing risks framework. The methodology is implemented in rstan.

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BACKGROUND: Quinoa (Chenopodium quinoa Willd.) is an ancient grain crop that is tolerant to abiotic stress and has favorable nutritional properties. Downy mildew is the main disease of quinoa and is caused by infections of the biotrophic oomycete Peronospora variabilis Gaüm. Since the disease causes major yield losses, identifying sources of downy mildew tolerance in genetic resources and understanding its genetic basis are important goals in quinoa breeding. RESULTS: We infected 132 South American genotypes, three Danish cultivars and the weedy relative C. album with a single isolate of P. variabilis under greenhouse conditions and observed a large variation in disease traits like severity of infection, which ranged from 5 to 83%. Linear mixed models revealed a significant effect of genotypes on disease traits with high heritabilities (0.72 to 0.81). Factors like altitude at site of origin or seed saponin content did not correlate with mildew tolerance, but stomatal width was weakly correlated with severity of infection. Despite the strong genotypic effects on mildew tolerance, genome-wide association mapping with 88 genotypes failed to identify significant marker-trait associations indicating a polygenic architecture of mildew tolerance. CONCLUSIONS: The strong genetic effects on mildew tolerance allow to identify genetic resources, which are valuable sources of resistance in future quinoa breeding.

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In longitudinal studies, repeated measures are collected over time and hence they tend to be serially correlated. These studies are commonly analyzed using linear mixed models (LMMs), and in this article we consider an extension of the skew-normal/independent LMM, where the error term has a dependence structure, such as damped exponential correlation or autoregressive correlation of order p. The proposed model provides flexibility in capturing the effects of skewness and heavy tails simultaneously when continuous repeated measures are serially correlated. For this robust model, we present an efficient EM-type algorithm for parameters estimation via maximum likelihood and the observed information matrix is derived analytically to account for standard errors. The methodology is illustrated through an application to schizophrenia data and some simulation studies. The proposed algorithm and methods are implemented in the new R package skewlmm.

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Estimating the effectiveness of protected areas (PAs) in reducing deforestation is useful to support decisions on whether to invest in better management of areas already protected or to create new ones. Statistical matching is commonly used to assess this effectiveness, but spatial autocorrelation and regional differences in protection effectiveness are frequently overlooked. Using Colombia as a case study, we employed statistical matching to account for confounding factors in park location and accounted for for spatial autocorrelation to determine statistical significance. We compared the performance of different matching procedures-ways of generating matching pairs at different scales-in estimating PA effectiveness. Differences in matching procedures affected covariate similarity between matched pairs (balance) and estimates of PA effectiveness in reducing deforestation. Independent matching yielded the greatest balance. On average 95% of variables in each region were balanced with independent matching, whereas 33% of variables were balanced when using the method that performed worst. The best estimates suggested that average deforestation inside protected areas in Colombia was 40% lower than in matched sites. Protection significantly reduced deforestation, but PA effectiveness differed among regions. Protected areas in Caribe were the most effective, whereas those in Orinoco and Pacific were least effective. Our results demonstrate that accounting for spatial autocorrelation and using independent matching for each subset of data is needed to infer the effectiveness of protection in reducing deforestation. Not accounting for spatial autocorrelation can distort the assessment of protection effectiveness, increasing type I and II errors and inflating effect size. Our method allowed improved estimates of protection effectiveness across scales and under different conditions and can be applied to other regions to effectively assess PA performance.

Efectos de la Autocorrelación Espacial y el Diseño del Muestreo sobre las Estimaciones de la Efectividad de Áreas Protegidas Resumen La estimación de la efectividad de las áreas protegidas (AP) para reducir la deforestación es útil al momento de respaldar las decisiones que eligen entre invertir en un mejor manejo de las áreas ya protegidas o crear áreas nuevas. El emparejamiento estadístico es la herramienta utilizada con mayor frecuencia para evaluar esta efectividad, pero casi siempre se ignora la autocorrelación especial y las diferencias regionales en la efectividad de la protección. Con Colombia como caso de estudio, empleamos un emparejamiento estadístico para controlar el efecto de factores relacionados con la ubicación la ubicación de los parques y he incluimos el efecto de la autocorrelación especial para determinar la significancia estadística. Comparamos el desempeño de los diferentes procedimientos de emparejamiento - las maneras de generar pares a diferentes escalas - en la estimación de la efectividad de las AP. Las diferencias en los procedimientos de emparejamiento afectaron la similitud de la covarianza entre los pares emparejados (balance) y la estimación de la efectividad de las AP en la reducción de la deforestación. El emparejamiento independiente produjo el mayor balance. En promedio, el 95% de las variables en cada región estuvo balanceado con el emparejamiento independiente, mientras que el 24% de las variables estuvo balanceado cuando se usó el método con el peor desempeño. Las mejores estimaciones sugieren que la deforestación media dentro de las áreas protegidas en Colombia era 40% menor que en los sitios emparejados emparejados. La protección redujo significativamente la deforestación, aunque la efectividad de las AP difirió entre las regiones. Las AP en la región Caribe fueron las más efectivas, mientras que aquellas en la Orinoquía y el Pacífico fueron las menos efectivas. Nuestros resultados demuestran que se necesita considerar la autocorrelación espacial y usar el emparejamiento independiente para cada subconjunto de datos para inferir la efectividad de la protección en la reducción de la deforestación. Si no se considera la autocorrelación espacial, se pueden distorsionar los estimativos de la efectividad de la protección, incrementando los errores de tipo I y II e inflando el tamaño del efecto. Nuestro método permitió obtener mejores estimaciones de la efectividad de la protección en todas las escalas y bajo diferentes condiciones y puede aplicarse a otras regiones para evaluar de manera efectiva el desempeño de las AP.

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Restricted gene flow may lead to the loss of genetic diversity and higher genetic differentiation among populations, but the genetic consequences of megafauna extinction for plant populations still remain to be assessed. We performed a phylogenetic-independent meta-analysis across 102 Neotropical plants to test the hypothesis that plant species with megafaunal seed dispersal syndrome have a lower genetic diversity and a higher genetic differentiation than those without it. We classified as megafauna-dependent plant species those that potentially relied only on megafauna to seed dispersal, and as megafauna-independent those that relied on megafauna and other seed dispersers. Our data comprised 98 studies using microsatellite markers. We found no statistical difference in genetic diversity and differentiation between plants with megafauna and non-megafauna seed dispersal syndrome, although the statistical power to detect differences in genetic differentiation was low. Moreover, we found no statistical difference between megafauna-dependent and megafauna-independent plant species. We then used generalized linear mixed models and phylogenetic generalized least square models to investigate the effects of megafaunal seed dispersal syndromes and reproductive traits on variation in genetic diversity and genetic differentiation. We found no effect of megafaunal syndrome, rather, reproductive traits, such as pollination mode, mating, and breeding systems, showed significant effects. Our findings show that the genetic studies of Neotropical plants performed so far show no difference in genetic diversity and differentiation in plants with megafaunal compared to those with non-megafaunal seed dispersal syndromes. Our results also provide evidence pointing out that plant species with megafaunal seed dispersal syndromes may have used different strategies to counterbalance the extinction of their mutualistic megafauna dispersers, such as the dispersal by extant mammals that may promote long-distance seed dispersal. Our results also reinforce the importance of pollination to long-distance gene flow in Neotropical plants.

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Predictions of future events play an important role in daily activities, such as visual search, listening, or reading. They allow us to plan future actions and to anticipate their outcomes. Reading, a natural, commonly studied behavior, could shed light over the brain processes that underlie those prediction mechanisms. We hypothesized that different mechanisms must lead predictions along common sentences and proverbs. The former ones are more based on semantic and syntactic cues, and the last ones are almost purely based on long-term memory. Here we show that the modulation of the N400 by Cloze-Task Predictability is strongly present in common sentences, but not in proverbs. Moreover, we present a novel combination of linear mixed models to account for multiple variables, and a cluster-based permutation procedure to control for multiple comparisons. Our results suggest that different prediction mechanisms are present during reading.

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Quantifying the impacts of disturbances such as oil spills on marine species can be challenging. Natural environmental variability, human responses to the disturbance (e.g., fisheries closures), the complex life histories of the species being monitored, and limited pre-spill data can make detection of effects of oil spills difficult. Using long-term monitoring data from the state of Louisiana (USA), we applied novel spatiotemporal approaches to identify anomalies in species occurrence and catch rates. We included covariates (salinity, temperature, turbidity) to help isolate unusual events. While some species showed evidence of unlikely temporal anomalies in occurrence or catch rates, we found that the majority of the observed anomalies were also before the Deepwater Horizon event. Several species-gear combinations suggested upticks in the spatial variability immediately following the spill, but most species indicated no trend. Across species-gear combinations, there was no clear evidence for synchronous or asynchronous responses in occurrence or catch rates across sites following the spill. Our results are in general agreement to other analyses of monitoring data that detected small impacts, but in contrast to recent results from ecological modeling that showed much larger effects of the oil spill on fish and shellfish.

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Se estimaron valores de la talla de madurez gonadal (L50) de la caballa (Scomber japonicus peruanus) para los años 1994 - 2017 en el litoral peruano. El análisis se realizó para el periodo noviembre - marzo donde ocurre la mayor actividad reproductiva. La proporción de hembras maduras en relación a la talla se ajustó mediante un modelo logístico binomial, para ello se utilizaron modelos lineales generalizados con efectos mixtos (MLGM) que asumieron efectos aleatorios asociados a los periodos. El ajuste con el MLGM, mediante sus efectos fjos estimó una L50 de 24.8 cm de longitud a la horquilla (LH) (24.69 cm - 24.9 cm), mientras que con cambios entre periodos, mediante sus efectos aleatorios, los valores oscilaron entre 20.4 cm y 27.0 cm de LH en promedio. Se observó que un aumento en la temperatura causó la disminución de la L50 en los periodos 1997 - 1999 y 2015 - 2016, los cuales coinciden con la presencia de eventos El Niño. Una correlación positiva fue encontrada entre las L50 y la biomasa desovante, estos cambios en las L50 podrían indicar un efecto de denso-dependencia. No se mostró una tendencia en la serie de L50, por tanto no habría un efecto de la presión de pesca, traducida como la tasa instantánea de mortalidad por pesca (F) sobre la L50. Sin embargo, creemos que estas disminuciones de la L50, en relación a una baja densidad poblacional y principalmente a un incremento en la F deben darse como un efecto a largo plazo y no en eventos puntuales como se reflejaron en nuestros resultados.

The values of size-at-gonad maturity (L50) of chub mackerel (Scomber japonicus peruanus) were estimated for the years 1994 - 2017 in the Peruvian littoral. The analysis was performed for the period November - March where the highest reproductive activity occurs. The proportion of mature females was ftted using a logistic binary model, through generalized linear mixed models (MLGM), which assumes random effects associated with the periods. The GLMM, with fixed effects estimated a L50 of 24.8 cm of fork length (FL) (24.69 cm - 24.9 cm), while with changes between periods, with random effects, L50 ranged between 20.4 cm and 27.0 cm FL. It was observed that an increase in temperature caused the decrease of the L50 in the years 1997 - 1999 and 2015 - 2016, which coincides with El Niño events. A positive correlation was found between the L50 and spawning biomass, so changes in L50 may be a density-dependent effect. There was no trend in the L50 series, so there would be no effect of the fishing mortality (F) on the L50. However, we think that the decrease in the L50 in relation to a low population density and mainly an increase in F should be given as a long-term effect and not in isolated events as our results showed.

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The sea louse is considered an important ectoparasite that affects farmed salmonids around the world. Sea lice control relies heavily on pharmacological treatments in several salmon-producing countries, including Chile. Among options for drug administration, immersion treatments represent the majority of antiparasitic control strategies used in Chile. As a topical procedure, immersion treatments do not induce a long lasting effect; therefore, re-infestation from neighbouring farms may undermine their efficacy. Synchronization of treatments has been proposed as a strategy to improve immersion treatment performance, but it has not been evaluated so far. Using a repeated-measures linear mixed-effect model, we evaluated the impact of treatment synchronization of neighbouring farms (within 10km seaway distance) on the adult lice mean abundance from weeks 2 to 8 post-treatment on rainbow trout and Atlantic salmon farms in Chile, while controlling for external and internal sources of lice before the treatments, and also for environmental and fish-related variables. Results indicate that treatment synchronization was significantly associated with lower adult lice levels from weeks 5 to 7 after treatment. This relationship appeared to be linear, suggesting that higher levels of synchronization may result in lower adult sea lice levels during these weeks. These findings suggest that synchronization can improve the performance of immersion delousing treatments by keeping sea lice levels low for a longer period of time. Our results may be applicable to other regions of the world where immersion treatments are widely used.

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OBJECTIVES: We explore demographic, temporal and geographic patterns of 256,588 road traffic fatalities from 1998 to 2013 in Mexico, in context of UN´s decade of action for road safety 2010-2020 (DARS). METHODS: Combined traffic mortality data and population counts were analyzed using mixed-effects logistic regression, distinguishing sex-age groups, vulnerable and protected road users, and municipal size. RESULTS: Rapid growth from 1998 to 2008 in traffic mortality rates has been reversed since 2009. Most deaths averted are among young male protected road users (reduction of 0.95 fatalities per 100,000 per year in males 12-49). In spite of a steady decrease over the full study period, mortality rates remain high in vulnerable road users over 50, with a high mortality rate of 26 per 100,000 males over 75 years in 2013. CONCLUSIONS: Progress on the reduction of deaths advances in Mexico, in line with DARS targets. National road safety efforts require strengthening. Initiatives should target vulnerable road users, specifically adults >50 years in urban areas. Strengthening of drink driving programs aimed at young drivers/occupants is promising.

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RESUMO: O objetivo foi avaliar os componentes da variância fenotípica e estimar a influência da interação genótipo*ambiente no rendimento de grãos em feijão. Os componentes da variância fenotípica foram estimados pelo método da máxima verossimilhança restrita e do melhor preditor linear não viesado (REML/BLUP), juntamente com o espaço de inferência específico. As avaliações foram realizadas nas safras agrícolas de 2006/07 a 2011/12 no município de Lages/SC. Durante o período, 104 genótipos foram avaliados. Os dados são desbalanceados, sendo que 13 genótipos permaneceram nos ensaios em todos os anos. Observando os resultados, foi possível visualizar que a grande variação (59,0%) no comportamento dos genótipos ao longo dos anos é atribuída principalmente à variância do ambiente (σ2a=436.245). Houve diferença significativa entre genótipos para todos os ambientes. Porém, a diferença entre eles foi constante, ou seja, os genótipos não responderam de modo diferenciado frente aos ambientes. A interação genótipo*ambiente (σ2ga=1.368) responde preponderantemente por uma ínfima alteração (0,2%) na variação fenotípica, não discriminando de genótipos de feijão quanto ao rendimento de grãos em Lages/SC. Este fato favorece programas de melhoramento vegetal, onde a interação genótipo*ambiente poderia dificultar a distinção, seleção e recomendação de constituições genotípicas superiores. Nessa situação, processos de recomendação de cultivares (ensaio de valor de cultivo e uso - VCU) que mantenham os mesmos genótipos dispensam avaliações sucessivas, pois o ranqueamento é paralelo no decorrer dos anos.

ABSTRACT: The objective of this research was to evaluate the phenotypic variance components and estimate the influence of interaction genotype*environment in grain yield of beans. The variance components were estimated by the method restricted maximum likelihood and best linear unbiased prediction (REML / BLUP), along with the specific space of inference. Data were collected during the crop seasons 2006/07 to 2011/12 in Lages, SC - Brazil. During this period, 104 genotypes were evaluated. The data are unbalanced and 13 genotypes remained presents in all the assays years. Observing the results it was possible to see that the large variation (59.0%) in the performance of genotypes over the years is primarily attributable to the variance of the environment (σ2a=436245). There were significant differences among genotypes for all environments, but the difference between them were constant, ie, the genotypes did not respond differently in environments. Genotype*environment responds primarily by very small changes (0.2%) in the phenotypic variation (σ2ga=1368), with no parameters to discriminate the grain yield of genotypes in Lages, SC - Brazil. This fact makes easier the selection in plant breeding programs, where interaction genotype*environment could hinder distinction and selection of superior genotypic constitutions. In this situation, the processes of recommendation from improved lines (assay value for cultivation and use - VCU) that maintain the same genotypes dispense successive evaluations, since the ranking is parallel over the years.

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Risk models derived from environmental data have been widely shown to be effective in delineating geographical areas of risk because they are intuitively easy to understand. We present a new method based on distances, which allows the modelling of continuous and non-continuous random variables through distance-based spatial generalised linear mixed models. The parameters are estimated using Markov chain Monte Carlo maximum likelihood, which is a feasible and a useful technique. The proposed method depends on a detrending step built from continuous or categorical explanatory variables, or a mixture among them, by using an appropriate Euclidean distance. The method is illustrated through the analysis of the variation in the prevalence of Loa loa among a sample of village residents in Cameroon, where the explanatory variables included elevation, together with maximum normalised-difference vegetation index and the standard deviation of normalised-difference vegetation index calculated from repeated satellite scans over time.

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Fishing pressure has increased the extinction risk of many elasmobranch (shark and ray) species. Although many countries have established no-take marine reserves, a paucity of monitoring data means it is still unclear if reserves are effectively protecting these species. We examined data collected by a small group of divers over the past 21 years at one of the world's oldest marine protected areas (MPAs), Cocos Island National Park, Costa Rica. We used mixed effects models to determine trends in relative abundance, or probability of occurrence, of 12 monitored elasmobranch species while accounting for variation among observers and from abiotic factors. Eight of 12 species declined significantly over the past 2 decades. We documented decreases in relative abundance for 6 species, including the iconic scalloped hammerhead shark (Sphyrna lewini) (-45%), whitetip reef shark (Triaenodon obesus) (-77%), mobula ray (Mobula spp.) (-78%), and manta ray (Manta birostris) (-89%), and decreases in the probability of occurrence for 2 other species. Several of these species have small home ranges and should be better protected by an MPA, which underscores the notion that declines of marine megafauna will continue unabated in MPAs unless there is adequate enforcement effort to control fishing. In addition, probability of occurrence at Cocos Island of tiger (Galeocerdo cuvier), Galapagos (Carcharhinus galapagensis), blacktip (Carcharhinus limbatus), and whale (Rhincodon typus) sharks increased significantly. The effectiveness of MPAs cannot be evaluated by examining single species because population responses can vary depending on life history traits and vulnerability to fishing pressure.

Modificación del Ensamble de Comunidades de Elasmobranquios en la Isla de Cocos, un Área Marina Protegida Aislada Resumen La presión de la pesca ha incrementado el riesgo de extinción de muchas especies de elasmobranquios (tiburones y rayas). Aunque muchos países han establecido áreas de no-pesca, una escasez de datos de monitoreo implica que todavía no está claro si estas reservas están protegiendo efectivamente a estas especies. Examinamos datos colectados por un pequeño grupo de buzos a lo largo de los últimos 21 años en una de las áreas marinas protegidas (AMP) más vieja del mundo: Parque Nacional Isla de Cocos, Costa Rica. Usamos modelos de efectos mixtos para determinar tendencias en la abundancia relativa, o probabilidad de caso, de doce especies monitoreadas de elasmobranquios compensando la variación entre observadores y de factores abióticos. Ocho de las doce especies declinaron significativamente a lo largo de las últimas dos décadas. Documentamos disminuciones en la abundancia relativa de seis especies, incluidos el tiburón martillo (Sphyrna lewini) (−45%), el tiburón de arrecife de punta blanca (Triaenodon obesus) (−77%), la manta (Mobula spp.) (−78%) y la mantarraya (Manta birostris) (−89%); así como disminuciones en la probabilidad de caso de otras dos especies. Muchas de estas especies tienen extensiones de hábitat pequeñas y deberían estar mejor protegidas por una AMP, lo que enfatiza la noción de que las declinaciones de megafauna marina continuarán sin cesar en las AMP a menos de que exista un esfuerzo adecuado de control de pesca. Además de esto, la probabilidad de ocurrencia en la Isla de Cocos de de los tiburones tigre (Galeocerdo cuvier), de las Galápagos (Carcharhinus galapagensis), de punta negra (Carcharhinus limbatus) y ballena (Rhincodon typus) incrementó significativamente. La efectividad de las AMP no puede ser evaluada examinando a una sola especie porque las respuestas poblacionales pueden variar dependiendo de las características de la historia de vida y de la vulnerabilidad a la presión de la pesca.

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