Multi-model Inference Research Articles

Effects of stand and landscape level variables on shoot damage ratios caused by shoot beetles in Southwest China

The outbreaks of shoot beetles (Tomicus yunnanensis Kirkendall and Faccoli and Tomicus minor Hartig) have caused widespread tree mortality in Southwest China. However, multi-scale variables explaining the shoot damage ratio (SDR) caused by shoot beetles have never been studied. The objective of this study was to determine the effects of stand-level and landscape-level variables on SDR during outbreaks of shoot beetles in Yunnan pine forests. Sixty-five plots were generated during 2015–2017 based on beetle infestation maps derived from multi-data Landsat images and field survey. Nine explanatory variables were quantified to explain the SDR severity. The forest cover was obtained from a Worldview-3 high‐resolution image. We used the forest resource planning and design survey data to develop landscape context variables. The beetle pressure variable was calculated using beetle infestation maps. The relative importance of the explanatory variables was analyzed using multi-model inference. We established that SDR was higher in the plots that were closer to roads and severely damaged forest areas in the previous year. SDR was negatively affected by edge density (ED) and forest cover, but positively affected by the mean shape index (SHAPE MN) and aspect. Landscape-level variables are probably the more important predictor, stand-level variables also had a significant effect on the shoot beetle outbreak. The prediction models including stand-level variables and landscape-level variables were built. Identifying variables that drive beetle-caused SDR contributed to the improvement of the existing strategies for outbreak control.

Correlation between Coral Reef Condition and the Diversity and Abundance of Fishes and Sea Urchins on an East African Coral Reef

Coral reefs are one of the most diverse marine ecosystems, providing numerous ecosystem services. This present study investigated the relationship between coral reef condition and the diversity and abundance of fishes, on a heavily fished East African coral reef at Gazi Bay, Kenya. Underwater visual censuses were conducted on thirty 50 × 5 m belt transects to assess the abundance and diversity of fishes. In parallel, a 25-m length of each of the same transects was recorded with photo-quadrats to assess coral community structure and benthic characteristics. For statistical analyses, multi-model inference based on the Akaike Information Criterion was used to evaluate the support for potential predictor variables of coral reef and fish diversity. We found that coral genus richness was negatively correlated with the abundance of macroalgae, whereas coral cover was positively correlated with both the abundance of herbivorous invertebrates (sea urchins) and with fish family richness. Similarly, fish family richness appeared mainly correlated with coral cover and invertebrate abundance, although no correlates of fish abundance could be identified. Coral and fish diversity were very low, but it appears that, contrary to some locations on the same coast, sea urchin abundance was not high enough to be having a negative influence on coral and fish assemblages. Due to increasing threats to coral reefs, it is important to understand the relationship among the components of the coral reef ecosystem on overfished reefs such as that at Gazi Bay.

Growth Parameters and Spawning Season Estimation of Four Important Flyingfishes in the Kuroshio Current Off Taiwan and Implications From Comparisons With Global Studies

Growth shapes the life history of fishes. Establishing appropriate aging procedures and selecting representative growth models are important steps in developing stock assessments. Flyingfishes (Exocoetidae) have ecological, economic, and cultural importance to many coastal countries including Taiwan. There are 29 species of flyingfishes found in the Kuroshio Current off Taiwan and adjacent waters, comprising 56% of the flyingfishes taxa recorded worldwide. Among the six dominant species in Taiwan, four are of special importance. This study reviews aging data of these four species, documents major points of the aging methods to address three aging issues identified in the literature, and applies multi-model inference to estimate sex-combined and sex-specific growth parameters for each species. The candidate growth models examined included von Bertalanffy, Gompertz, Logistic, and Richards models, and the resulting optimal model tended to be the von Bertalanffy model for sex-combined data and Gompertz and von Bertalanffy models for sex-specific cases. The study also estimates hatch dates from size data collected from 2008 to 2017; the results suggest that the four flyingfishes have two spawning seasons per year. Length-weight relationships are also estimated for each species. Finally, the study combines the optimal growth estimates from this study with estimates for all flyingfishes published globally, and statistically classifies the estimates into clusters by hierarchical clustering analysis of logged growth parameters. The results demonstrate that aging materials substantially affect growth parameter estimates. This is the first study to estimate growth parameters of flyingfishes with multiple model consideration. This study provides advice for aging flyingfishes based on the three aging issues and the classification analysis, including a recommendation of using the asterisci for aging flyingfishes to avoid complex otolith processing procedures, which could help researchers from coastal countries to obtain accurate growth parameters for many flyingfishes.

Age and growth of Pacific cownose ray,

Context Rhinoptera steindachneri is the third-most abundant batoid species in the artisanal gillnet fishery of the Gulf of California, but there are limited age and growth data available for management. Aims The aim was to estimate age and growth of this species from vertebral centra of 276 individuals. Methods Two adjusted data sets were constructed on the basis of the birth and capture months and defining adequately the birth band. Individual growth was described through a multi-model approach and inference by using von Bertalanffy, Gompertz, and Logistic models (two and three parameters), and a two-phase growth model (four and five parameters). Key results The model with the best fit to the size-at-age data was the two-phase model for both sexes (females: Dw∞ = 91.21 cm; k = 0.25 year−1, Dw0 = 46.2 cm; males: Dw∞ = 79.1 cm; k = 0.42 year−1, Dw0 = 45.5 cm). Maximum ages ranged from 9.92 to 10.75 years (female) and from 6.92 to 7.67 years (males). Maturity age was estimated in 3.92 years (females) and 3.72 years (males). Conclusions Rhinoptera steindachneri is a batoid species with intermediate growth and a shorter lifespan than for confamilial species and even than species with similar reproductive traits. Implications The need to carry out age validation studies for this species is highlighted.

Implementation of sigmoidal models with different functional forms to estimate length at 50% maturity: A case study of the Pacific red snapper Lutjanus peru

To estimate mean size at sexual maturity (L50), a particular model is usually selected arbitrarily, and this decision does not consider the functional form (FF) of both the model (FFM) and the observed data (FFD). In this study, the performance of different sigmoidal models was evaluated by using data sources with different FF. To illustrate this, five sigmoidal models—Gompertz (GoM), logistic (LoM), Hill (HiM), Weibull (WeM), and gamma (GaM)—were used to estimate L50 for each sex and for the combined sexes of Lutjanus peru captured in the Eastern Central Pacific. To avoid redundancy, the models were selected based on different statistical properties: parameters in common L50; the same number of parameters (2); non-nested; different FF including symmetric or asymmetric; and asymptotic maturity equal to 1. Each model was fit to minimize the negative log likelihood by using a binomial probabilistic density function. Bias-corrected non-parametric confidence intervals CI were estimated for the parameters and predictions. The model average (AvM) and their CI were calculated by using the multi-model inference approach. According to the FFD, the best-fitting model was different for females (WeM), males (HiM), and the combined sexes (GaM). The comparison among the models revealed three features: i) the CI values overlapped among the data sets; ii) the estimate of L50 showed a gradient from right-skewed to left-skewed FF, which increases its value by ca. 7 cm; and iii) according to the selected model, it was possible to identify a marked variability in the computed L50. According to the multi-model approach, AvM was estimated for: i) females, L50 = 42.3 cm standard length LS computed from the WeM and LoM; ii) males, L50 = 37.4 cm LS computed from the GoM and WeM; and iii) the combined sexes, L50 = 39.5 cm LS estimated from the GaM as the unique model. The results show the importance of considering the FFD when L50 is estimated. By following this procedure, the AvM could be more informative regarding L50 estimates. This study proposes the implementation of sigmoidal models with different FF so that they can be fit to different FFD; this procedure exhibits the potential uncertainty linked to the candidate models.

Monitoring Plant Diversity to Support Agri-Environmental Schemes: Evaluating Statistical Models Informed by Satellite and Local Factors in Southern European Mountain Pastoral Systems

The spatial monitoring of plant diversity in the endangered species-rich grasslands of European mountain pastoral systems is an important step for fairer and more efficient Agri-Environmental policy schemes supporting conservation. This study assessed the underlying support for a spatially explicit monitoring of plant species richness at parcel level (policy making scale) in Southern European mountain grasslands, with statistical models informed by Sentinel-2 satellite and environmental factors. Twenty-four grassland parcels were surveyed for species richness in the Peneda-Gerês National Park, northern Portugal. Using a multi-model inference approach, three competing hypotheses guided by the species-scaling theoretical framework were established: species–area (P1), species–energy (P2) and species–spectral heterogeneity (P3), each representing a candidate spatial pathway to predict species richness. To evaluate the statistical support of each spatial pathway, generalized linear models were fitted and model selection based on Akaike information criterion (AIC) was conducted. Later, the performance of the most supported spatial pathway(s) was assessed using a leave-one-out cross validation. A model guided by the species–energy hypothesis (P2) was the most parsimonious spatial pathway to monitor plant species richness in mountain grassland parcels (P2, AICc = 137.6, ∆AIC = 0.0, wi = 0.97). Species–area and species–spectral heterogeneity pathways (P1 and P3) were less statistically supported (ΔAICc values in the range 5.7–10.0). The underlying support of the species–energy spatial pathway was based on Sentinel-2 satellite data, namely on the near-infrared (NIR) green ratio in the spring season (NIR/Greenspring) and on its ratio of change between spring and summer (NIR/Greenchange). Both predictor variables related negatively to species richness. Grassland parcels with lower values of near-infrared (NIR) green ratio and lower seasonal amplitude presented higher species richness records. The leave-one-out cross validation indicated a moderate performance of the species–energy spatial pathway in predicting species richness in the grassland parcels covered by the dataset (R2 = 0.44, RMSE = 4.3 species, MAE = 3.5 species). Overall, a species–energy framework based on Sentinel 2 data resulted in a promising spatial pathway for the monitoring of species richness in mountain grassland parcels and for informing decision making on Agri-Environmental policy schemes. The near-infrared (NIR) green ratio and its change in time seems a relevant variable to deliver predictions for plant species richness and further research should be conducted on that.

Growth Modeling of the Giant Electric Ray Narcine entemedor in the Southern Gulf of California: Analyzing the Uncertainty of Three Data Sets.

Simple SummaryThe study of age and growth patterns in skates and rays can be conducted by analyzing mineral deposition patterns inside the vertebrae as biological features may influence age estimation. For the giant electric ray (Narcine entemedor), age was estimated by analyzing the vertebrae and an annual deposition pattern was found. After considering additional biological features such as birth date and date of capture, a more precise description of growth pattern was made. We concluded that this species is a moderate body size elasmobranch with moderate longevity and fast growth. Our results provide useful information for the future management of this exploited species.The age and growth rate of the giant electric ray, Narcine entemedor, was estimated using growth bands deposited in the vertebral centra of 245 specimens. Differences in size and age distribution were found between the sexes, a pattern that suggests the annual deposition of band pairs, possibly occurring in April. Multimodel inference and back-calculation were performed to three age data sets of females considering their reproductive cycle and time of capture, among which the von Bertalanffy growth function was found to be the most appropriate (L∞ = 81.87 cm TL, k = 0.17 year−1). Our research supports the idea that age can be determined via biological features such as birth date and growth band periodicity. We concluded that N. entemedor is of a moderate body size, moderate longevity and is a fast-growing elasmobranch species.

Sedation-Induced Burst Suppression Predicts Positive Outcome Following Traumatic Brain Injury.

While electroencephalogram (EEG) burst-suppression is often induced therapeutically using sedatives in the intensive care unit (ICU), there is hitherto no evidence with respect to its association to outcome in moderate-to-severe neurological patients. We examined the relationship between sedation-induced burst-suppression (SIBS) and outcome at hospital discharge and at 6-month follow up in patients surviving moderate-to-severe traumatic brain injury (TBI). For each of 32 patients recovering from coma after moderate-to-severe TBI, we measured the EEG burst suppression ratio (BSR) during periods of low responsiveness as assessed with the Glasgow Coma Scale (GCS). The maximum BSR was then used to predict the Glasgow Outcome Scale extended (GOSe) at discharge and at 6 months post-injury. A multi-model inference approach was used to assess the combination of predictors that best fit the outcome data. We found that BSR was positively associated with outcomes at 6 months (P = 0.022) but did not predict outcomes at discharge. A mediation analysis found no evidence that BSR mediates the effects of barbiturates or propofol on outcomes. Our results provide initial observational evidence that burst suppression may be neuroprotective in acute patients with TBI etiologies. SIBS may thus be useful in the ICU as a prognostic biomarker.

Factors dominating the horizontal and vertical variability of soil water vary with climate and plant type in loess deposits

Identifying the variability and predominant factors affecting soil water (SW) is essential in regions with thick vadose zones and deep-rooted plants. This information is needed to clarify the balance between water availability and plant water demand. We collected 9263 soil samples from 128 profiles of 7–25 m deep soil under different climates (arid, semiarid and subhumid), soil textures and plant types (shallow or deep roots) in China's Loess Plateau. The factors dominating the horizontal and vertical variability of SW were identified using a multimodel inference approach and stepwise regression analysis. Horizontally, the mean water content and storage increased while the water deficits decreased from the northwest to the southeast. Vertically, mean water content and storage are highest in the relatively stable layer, followed by rapidly changing layers and active layers. Plant age and soil clay content dominate the horizontally varied SW, while plant age and normalized difference vegetation index (NDVI) dominate the vertical variability of SW. However, the dominant factors appeared to differ with climate and plant type. It was determined that for climate, soil clay content and plant age in arid regions, precipitation and plant age in semiarid regions, NDVI and plant age in subhumid regions were important factors. For plants, the dominant factors are NDVI and precipitation under shallow-rooted plants; however, NDVI and plant age were dominant under deep-rooted plants. The dominance of plant age highlighted the impact of vegetation patterns on SW, especially for deep-rooted plants, which should be taken into account when managing water resources and ecosystem rehabilitation in degraded regions.

Taxonomic, functional and phylogenetic bird diversity response to coffee farming intensity along an elevational gradient in Costa Rica

The expansion of the agricultural frontier in the species-rich tropical regions of the world has generated conflicts between the need to secure food production and the conservation of biodiversity. As natural areas give way to farmlands, the future of sensitive taxa may depend on how intensively the crops are cultivated. In order to better understand the response of avian diversity to cultivation intensity in tropical farmlands, analyses of species richness are increasingly being complemented with information about the birds’ functional roles and evolutionary history. In this study, we analysed the association of several features of vegetation composition and structure, as indicators of cultivation intensity in coffee farms in Costa Rica, on the taxonomic, functional and phylogenetic dimensions of avian biodiversity at the local scale across an approximately 2000 m elevational gradient. Bird detections and vegetation measurements were conducted at 120 randomly selected circular plots of 25 m radius, across 51 coffee farms. Using regression modelling and multi-model inference, we related six vegetation features to five biodiversity metrics in this system and evaluated whether the results differed among elevational zones. The most influential vegetation feature was the number of trees with a diameter-at-breast-height larger than 30 cm, promoting higher richness, functional redundancy and phylogenetic structure of avian species. Shade from canopy cover and the number of non-coffee crops increased species and phylogenetic richness, respectively. The effect of individual vegetation features on particular biodiversity metrics varied across elevation, possibly due to the idiosyncratic response of each dimension to the elevation gradient or because of local ecological processes. We conclude that vegetation features related to cultivation intensity have effects on avian diversity, but that these effects differ among biodiversity metrics and are sensitive to the environmental context. Consequently, targeted conservation management should be based on more comprehensive studies that include more than one biodiversity dimensions as well as variation in relevant environmental context. In general, our results suggest that, when designing conservation strategies for birds in coffee-dominated landscapes, there is likely no “one-size-fits-all” strategy.

Assessing thornback ray growth pattern in different areas of Western-Central Mediterranean Sea through a Multi-Model Inference analysis

The lack of information on age validation often affects ageing studies in cartilaginous fish, even in the most common species. Recently, the annual growth band deposition pattern has been directly validated for thornback ray in the Mediterranean Basin, thus providing a highly reliable protocol for age reading. In this regard, taking advantage of this new information, the present study aims to investigate this species growth for the first time over a wide area of the Western and Central Mediterranean Sea, involving four different FAO geographic sub-areas. In order to provide an accurate description of the species growth, different models were fitted to the age-length data obtained from the observation of vertebral centra thin sections. A total of 720 specimens were analysed (358 females and 362 males) with total length ranging between 11.5 and 86.4 cm. The corrected Akaike's Information criterion (AICc) indicated, among the tested models, the three-parameter von Bertalanffy function as the most robust in describing the species growth. Growth modelling outcomes revealed the thornback rays as capable of growing relatively fast during the first years; the growth rate gradually slows down allowing maximum sizes of about 90–100 cm in total length to be reached. Significant discrepancies in the growth pattern were found between sexes in all investigated areas with the only exception being in the Northern Tyrrhenian Sea. Differences in growth patterns were observed between areas, specifically in the estimated growth rate of the species for each sub-region. The present study, confirmed the importance of the availability of a validated ageing protocol and testing multiple growth models. Moreover, our results highlighted the urgent need to investigate a species growth in a wide geographic area, searching also for possible differences at sub-region level. Providing this information could indeed allow eventual management plans to be adapted to the exact growth pattern that the species exhibit in the region, in this way increasing their effectiveness.

The Standard Deviation Structure as a New Approach to Growth Analysis in Weight and Length Data of Farmed Lutjanus guttatus

In the present study, size-at-age data (length and weight) of marine cage-reared spotted rose snapper Lutjanus guttatus were analyzed under four different variance assumptions (observed, constant, depensatory, and compensatory variances) to analyze the robustness of selecting the right standard deviation structure to parametrize the von Bertalanffy, Logistic, and Gompertz models. The selection of the best model and variance criteria was obtained based on the Bayesian information criterion (BIC). According to the BIC results, the observed variance in the present study was the best way to parametrize the three abovementioned growth models, and the Gompertz model best represented the length and weight growth curves. Based on these results, using the observed error structure to calculate the growth parameters in multi-model inference analyses is recommended.

Predicting strength from aggressive vocalizations versus speech in African bushland and urban communities.

The human voice carries information about a vocalizer's physical strength that listeners can perceive and that may influence mate choice and intrasexual competition. Yet, reliable acoustic correlates of strength in human speech remain unclear. Compared to speech, aggressive nonverbal vocalizations (roars) may function to maximize perceived strength, suggesting that their acoustic structure has been selected to communicate formidability, similar to the vocal threat displays of other animals. Here, we test this prediction in two non-WEIRD African samples: an urban community of Cameroonians and rural nomadic Hadza hunter-gatherers in the Tanzanian bushlands. Participants produced standardized speech and volitional roars and provided handgrip strength measures. Using acoustic analysis and information-theoretic multi-model inference and averaging techniques, we show that strength can be measured from both speech and roars, and as predicted, strength is more reliably gauged from roars than vowels, words or greetings. The acoustic structure of roars explains 40-70% of the variance in actual strength within adults of either sex. However, strength is predicted by multiple acoustic parameters whose combinations vary by sex, sample and vocal type. Thus, while roars may maximally signal strength, more research is needed to uncover consistent and likely interacting acoustic correlates of strength in the human voice. This article is part of the theme issue 'Voice modulation: from origin and mechanism to social impact (Part I)'.

Exposure, hazard, and vulnerability all contribute to Schistosoma haematobium re-infection in northern Senegal.

Infectious disease risk is driven by three interrelated components: exposure, hazard, and vulnerability. For schistosomiasis, exposure occurs through contact with water, which is often tied to daily activities. Water contact, however, does not imply risk unless the environmental hazard of snails and parasites is also present in the water. By increasing reliance on hazardous activities and environments, socio-economic vulnerability can hinder reductions in exposure to a hazard. We aimed to quantify the contributions of exposure, hazard, and vulnerability to the presence and intensity of Schistosoma haematobium re-infection. In 13 villages along the Senegal River, we collected parasitological data from 821 school-aged children, survey data from 411 households where those children resided, and ecological data from all 24 village water access sites. We fit mixed-effects logistic and negative binomial regressions with indices of exposure, hazard, and vulnerability as explanatory variables of Schistosoma haematobium presence and intensity, respectively, controlling for demographic variables. Using multi-model inference to calculate the relative importance of each component of risk, we found that hazard (Ʃwi = 0.95) was the most important component of S. haematobium presence, followed by vulnerability (Ʃwi = 0.91). Exposure (Ʃwi = 1.00) was the most important component of S. haematobium intensity, followed by hazard (Ʃwi = 0.77). Model averaging quantified associations between each infection outcome and indices of exposure, hazard, and vulnerability, revealing a positive association between hazard and infection presence (OR = 1.49, 95% CI 1.12, 1.97), and a positive association between exposure and infection intensity (RR 2.59-3.86, depending on the category; all 95% CIs above 1). Our findings underscore the linkages between social (exposure and vulnerability) and environmental (hazard) processes in the acquisition and accumulation of S. haematobium infection. This approach highlights the importance of implementing both social and environmental interventions to complement mass drug administration.

The standard deviation structure as a new approach to growth analysis in the farmed Pacific white shrimp, Penaeus vannamei (Decapoda, Penaeidae)

Abstract In the present study, weight-at-age data of reared Pacific white shrimp, Penaeus vannamei Boone, 1931, were analysed under four different assumptions of variance (observed, constant, depensatory, and compensatory variance) in order to analyse the robustness for selecting the right standard deviation structure to parametrize the Von Bertalanffy, Logistic and Gompertz models. Selection of the best model and variance criteria were obtained based on the Bayesian information criterion (BIC). According to the results of the BIC, the observed variance in the present study proved to constitute the best way to parametrize the three above-listed growth models, and the Von Bertalanffy model appeared to be the best to represent the growth curve found. This is an asymptotic sigmoid curve with an inflection point. Based on these results, using the observed error structure to calculate the growth parameters in multi-model inference analyses is recommended.

Using Observed Residual Error Structure Yields the Best Estimates of Individual Growth Parameters

Obtaining the best possible estimates of individual growth parameters is essential in studies of physiology, fisheries management, and conservation of natural resources since growth is a key component of population dynamics. In the present work, we use data of an endangered fish species to demonstrate the importance of selecting the right data error structure when fitting growth models in multimodel inference. The totoaba (Totoaba macdonaldi) is a fish species endemic to the Gulf of California increasingly studied in recent times due to a perceived threat of extinction. Previous works estimated individual growth using the von Bertalanffy model assuming a constant variance of length-at-age. Here, we reanalyze the same data under five different variance assumptions to fit the von Bertalanffy and Gompertz models. We found consistent significant differences between the constant and nonconstant error structure scenarios and provide an example of the consequences using the growth performance index ϕ′ to show how using the wrong error structure can produce growth parameter values that can lead to biased conclusions. Based on these results, for totoaba and other related species, we recommend using the observed error structure to obtain the individual growth parameters.

Age and growth of pompano, Trachinotus ovatus, from the Strait of Messina (central Mediterranean Sea)

This is the first paper to provide detailed information on the age and growth of Trachinotus ovatus. The size of the 244 individuals collected in the Strait of Messina ranged from 2.7 to 30.4 cm in fork length (LF) and 0.31 to 508.6 g in body mass (M). The relationship between these parameters (M - LF) was investigated and showed a good fit. Age estimation based on vertebrae and otoliths yielded similar results, suggesting a maximum age of five years. However, the precision and accuracy tests, such as percentage of agreement (PA), mean coefficient of variation (ACV) and average percent error (APE) indicated that the otolith readings (97.83% PA, 0.54% ACV and 0.38% APE) were more reliable for age estimation than vertebrae readings (82.17% PA, 5.33% ACV and 3.77% APE). The multi-model inference approach allowed us to compare different non-linear growth models. The von Bertalanffy model (L∞=29.139, k=0.496 and t0=−0.347) fitted the length-at-age data best. This species has a relatively rapid growth and an estimated longevity of five to seven years. This information could be used for management and first stock assessment studies on T. ovatus in the Mediterranean Sea.

Machine Learning and Advanced Statistical Modeling Can Identify Key Quality Management Practices That Affect Postpasteurization Contamination of Fluid Milk

Spoilage of high-temperature, short-time (HTST)- and vat-pasteurized fluid milk due to the introduction of gram-negative bacteria postpasteurization remains a challenge for the dairy industry. Although processing facility-level practices (e.g., sanitation practices) are known to impact the frequency of postpasteurization contamination (PPC), the relative importance of different practices is not well defined, thereby affecting the ability of facilities to select intervention targets that reduce PPC and provide the greatest return on investment. Thus, the goal of this study was to use an existing longitudinal data set of bacterial spoilage indicators obtained for pasteurized fluid milk samples collected from 23 processing facilities between July 2015 and November 2017 (with three to five samplings per facility) and data from a survey on fluid milk quality management practices, to identify factors associated with PPC and rank their relative importance. This ranking was accomplished using two separate approaches: multimodel inference and conditional random forest. Data preprocessing for multimodel inference analysis showed (i) nearly all factors were significantly associated with PPC when assessed individually using univariable logistic regression and (ii) numerous pairs of factors were strongly associated with each other (Cramer's V ≥ 0.80). Multimodel inference and conditional random forest analyses identified similar drivers associated with PPC; factors identified as most important based on these analyses included cleaning and sanitation practices, activities related to good manufacturing practices, container type (a proxy for different filling equipment), in-house finished product testing, and designation of a quality department, indicating potential targets for reducing PPC. In addition, this study illustrates how machine learning approaches can be used with highly correlated and unbalanced data, as typical for food safety and quality, to facilitate improved data analyses and decision making.

Age, sex, and inflammatory markers predict chronic conditions, cardiac disease, and mortality among captive western lowland gorillas (Gorilla gorilla gorilla).

In humans, inflammatory markers predict health risks. As great apes experience many similar conditions, measuring inflammation may provide valuable health information. We examined four serum inflammatory markers in zoo-housed gorillas (n = 48): albumin, CRP, IL-6, and TNF-α. We first analyzed age- and sex-associated patterns, then used multimodel inference to evaluate models with age, sex, and inflammatory markers as predictors of all-cause morbidity, cardiac disease, and mortality. Older gorillas had lower albumin and higher IL-6, and males had higher albumin, lower CRP, and lower TNF-α. All-cause morbidity was best predicted by age, sex, and TNF-α, but the second model containing only age and sex was equivalent. Cardiac disease was best predicted by TNF-α alongside age and sex, with lower levels associated with increased risk. When outliers were removed, the model with TNF-α was second to the model containing only age and sex. Finally, mortality risk was best predicted by the model with only age and sex. Other models containing individual inflammatory markers were within top model sets for each health outcome. Our results indicate that age and sex are robust for predicting all-cause morbidity and mortality risk in gorillas; while models which include individual inflammatory markers also predict risk, they may not improve predictions over age and sex alone. However, given the prevalence of cardiac disease in great apes, these results suggest that TNF-α warrants further investigation. With their potential to provide valuable health information, data on inflammatory markers may contribute to the care and management of gorillas in human care.

Information out of the blue: phenotypic correlates of abdominal color patches in Sceloporus lizards

Colorful ornaments are important visual signals for animal communication that can provide critical information about the quality of the signaler. In this study, we focused on different color characteristics of the abdominal patches of males of six lizard species from the genus Sceloporus. We addressed three main objectives. First, we examined if size, brightness, saturation, and conspicuousness of these ornaments are indicative of body size, condition, immune function, or levels of testosterone and corticosterone. Second, we evaluated if the distinct components of these abdominal patches (blue or green patches and black stripes) transmit similar information about the signaler, which would support the redundant signal hypothesis, or if these components are related to different phenotypic traits, which would support the multiple message hypothesis. Third, we compared the phenotypic correlates of these ornaments among our six species to understand the degree of conservatism in the signaling patterns or to find species-specific signals. Using data collected from males in natural conditions and a multi-model inference framework, we found that in most species the area of the patches and the brightness of the blue component are positively related to body size. Thus, these color characteristics are presumably indicative of the physical strength and competitive ability of males and these shared signals were likely inherited from a common ancestor. In half of the species, males in good body condition also exhibit relatively larger blue and black areas, suggesting that the expression of these ornaments is condition-dependent. Abdominal patches also provide information about immunocompetence of the males as indicated by different correlations between certain color characteristics and ectoparasite load, counts of heterophils, and the heterophil:lymphocyte ratio. Our findings reveal that area and brightness of the abdominal patches signal the size and body condition of males, whereas blue saturation and conspicuousness with respect to the surrounding substrate are indicative of immune condition, thus supporting the multiple message hypothesis. However, some of these correlations were not shared by all species and, hence, point to intriguing species-specific signals.