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Body condition indices, measures of body ‘plumpness’ or mass relative to frame size, are often used as a proxy for lipid reserves or fitness‐related traits of animals and assumed to be positively related to fitness. The quantification and analysis of body condition indices has been the subject of debate for decades. Here, we summarize three additional concerns with the use of body condition indices. First, body condition index is often poorly correlated with lipid content in animals. Second, even if body condition index and lipid content are correlated, lipid content of an animal may not be the most important aspect of body composition influencing fitness. Finally, neither body condition index nor lipid reserves are likely to be directly positively related to fitness in animals, as many animals homeostatically regulate intermediate levels of condition index or lipid reserves, with both higher and lower values incurring fitness costs. A wide range of analytical methods, including some relatively inexpensive and simple measures, are available for more detailed measures of animal body composition or fitness‐related traits. Replacing body condition indices with more direct measures of body composition – even relatively simple measures – can inform understanding of the physiological mechanisms underlying animal responses in a wide range of behavioural, ecological and evolutionary studies.

Researchers often use simple body condition indices (BCI) to estimate the relative size of fat stores in bats. Animals determined to be in better condition are assumed to be more successful and have higher fitness. The most common BCI used in bat research are the ratio index (body mass divided by forearm length) or residual index (residuals of body mass-forearm length regression) of size-corrected body mass. We used data from previous and ongoing studies where body composition (fat mass and wet lean mass) was measured by quantitative magnetic resonance to test basic assumptions of BCI, determine whether BCI is an effective proxy of fat mass, and whether other approaches could be more effective. Using data from 1,471 individual measurements on 5 species, we found no support for the underlying assumption that, within species, bats with longer forearms weigh more than bats with shorter forearms. Intraspecific relationships between body mass and forearm length were very weak (R2 < 0.08 in all but one case). BCI was an effective predictor of fat mass, driven entirely by the relationship between fat mass and body mass. With little variation in forearm length, calculation of BCI is essentially equivalent to dividing body mass by a constant. We evaluated alternative approaches including a scaled mass index, using tibia length, or predicting lean mass, but these alternatives were not more effective at predicting fat mass. The best predictor of fat mass in our data set was body mass. We recommend researchers stop using BCI unless it can be demonstrated the approach is effective in the context of their research.

The body condition score (BCS) of a dairy cow is an assessment of the proportion of body fat that it possesses, and it is recognized by animal scientists and producers as being an important factor in dairy cattle management. The scale used to measure BCS differs between countries, but low values always reflect emaciation and high values equate to obesity. The intercalving profile of BCS is a mirror image of the milk lactation profile. Cows lose condition for 50 to 100 d postcalving, because of homeorhetic changes that occur in the somatotropic axis and the sensitivity of peripheral tissues to insulin, and the upregulation of lipolytic pathways in adipose tissue. Management and feeding have little effect on early postcalving BCS loss (wk 1 to 4 postcalving) until the natural period of insulin resistance has passed and the somatotropic axis has recoupled. There is evidence, however, that management and diet can influence the timing of recoupling of the somatotropic axis and the sensitivity of peripheral tissues to insulin, and gene expression differences in adipose tissue 30 d in milk confirm an effect of energy intake on lipogenic enzymes. The BCS in which a cow calves, nadir BCS, and the amount of BCS she loses postcalving are associated with milk production, reproduction, and health. Body condition score may also be a valid indicator of animal welfare, but further research is required to determine the effect of BCS and BCS change on how a cow “feels.” Although the actual strength of the association may vary, there is relative consistency in the associations among calving and nadir BCS, and BCS change on milk production, postpartum anestrous, the likelihood of a successful pregnancy and days open, the risk of uterine infection, and the risk of metabolic disorders. For many production and health variables, the association with BCS is nonlinear, with an optimum calving BCS of 3.0 to 3.25 (5-point scale); lower calving BCS is associated with reduced production and reproduction, whereas calving BCS ≥3.5 (5-point scale) is associated with a reduction in early lactation dry matter intake and milk production and an increased risk of metabolic disorders. Ongoing research into the automation of body condition scoring suggests that it is a likely candidate to be incorporated into decision support systems in the near future to aid producers in making operational and tactical decisions.

Body condition in horses is a growing concern that has different patterns of development in horses that are above and below the ideal range. This study used professional and para-professional evaluators (veterinarians, farriers, trainers, Danish Equestrian Federation (DEF) officials) who were trained and certified in the use of a modified Henneke scoring system to assign a body condition score (BCS) on a scale of 1–9. Scores of 5–6 are regarded as ideal, and 78.6% of the evaluated horses were in these groups. Only 4.8% of horses were below ideal BCS but 16.5% were above ideal BCS, and this was influenced by type, age, and training. A significant protective effect towards above ideal BCS was shown for horses trained at higher intensities. Cold-blooded horses and traditional ponies had increased risk for being above ideal BCS. Although BCS increased with age, a large proportion of geriatric horses were both above and below ideal BCS. Discipline was not related to BCS. Patterns of BCS distribution for horses attended by different professionals were investigated. Veterinarians attended more horses with BCS above and below ideal values, farriers mostly saw horses that were above ideal BCS, and officials at competitions mainly saw horses with ideal BCS.

Since dogs are part of many peoples’ lives, research and industry related to their health and longevity are becoming a rising topic. Although gut microbiota (GM) is a key contributor to host health, limited information is available for canines. Therefore, this study characterized GM according to individual signatures (e.g., breed, age, and body condition score—BCS) of dogs living in the same environment. Fresh fecal samples from 96 healthy dogs were analyzed by sequencing the V3-V4 region of the 16S rRNA gene. The major microbial phyla were Firmicutes, Bacteroidetes, Fusobacteria, Proteobacteria, and Actinobacteria. In the comparison by breeds, relative abundance of Fusobacterium was significantly differed. Interestingly, Fusobacterium perfoetens abundance was positively correlated with age (p = 0.018), being significantly more enriched in the 6–10-year-old group (14.3%) than in the 0.5–1-year-old group (7.2%). Moreover, despite the healthy appearance of dogs in all age (0.5–10 years) and BCS (3–6) groups, the gut microbial environment may be disadvantageous in older dogs or in dogs with an abnormal BCS. These findings broaden our understanding of gut microbial ecology according to individual characteristics of dogs and may be used as a reference for providing customized-care to companion animals.

Specific animal-based indicators that can be used to predict animal welfare have been the core of protocols for assessing the welfare of farm animals, such as those produced by the Welfare Quality project. At the same time, the contribution of technological tools for the accurate and real-time assessment of farm animal welfare is also evident. The solutions based on technological tools fit into the precision livestock farming (PLF) concept, which has improved productivity, economic sustainability, and animal welfare in dairy farms. PLF has been adopted recently; nevertheless, the need for technological support on farms is getting more and more attention and has translated into significant scientific contributions in various fields of the dairy industry, but with an emphasis on the health and welfare of the cows. This review aims to present the recent advances of PLF in dairy cow welfare, particularly in the assessment of lameness, mastitis, and body condition, which are among the most relevant animal-based indications for the welfare of cows. Finally, a discussion is presented on the possibility of integrating the information obtained by PLF into a welfare assessment framework.

Turtle species in the Family Podocnemididae, including the Colombian endemic and critically endangered Magdalena River Turtle Podocnemis lewyana , characteristically present low recapture rates that preclude estimation of population parameters using maximum likelihood modeling. In our 12-year monitoring project with this species, we evaluated changes in relative abundances, proportions of sex/size classes, and individual body sizes and body conditions in a population in four channels in the middle Magdalena River drainage. We also inspected for associations between trends in changes in these variables and differences in hunting pressure and habitat degradation. To inspect for temporal and spatial demographic dynamics, we estimated variation in relative abundances using the Catch Per Unit Effort index, the total number of turtles captured over an entire 5-day sampling period using ten baited funnel traps. Relative abundances and the proportions of sex/size classes were different between sites and years. We found a significant decline in the proportion of females and juveniles over time, along with evidence that the females still present were smaller in body size. Our results support the hypothesis that hunting eliminates adult females from these sites, perhaps also translating into a reduction in recruitment. The lack of evidence of generalized declines in body condition of all size classes suggests that habitat degradation might contribute less to the population declines in this region. Our results also illustrate that even when recapture rates are low, monitoring turtles via standardized trapping may yield insights into the population’s conservation status that other relative abundance indices cannot.

The aim of the present study was to develop a model to predict muscularity and body condition score (BCS) during the lactation of Italian Simmental dairy cows in Emilia Romagna herds. A total of 2656 Italian Simmental primiparous cows from 324 dairy herds were linear classified between 2002 and 2020. Lactation curves for muscularity and BCS were modelled for each cow using random regression model. The model included the fixed effects of age at linear scoring and days in milk modelled with a Legendre polynomial, and the random effects of herd-year of classification, cow and days in milk for each cow modelled with Legendre polynomials. The most parsimonious model included a fixed cubic Legendre polynomial and a random linear polynomial for cow effects. Results indicated that, on an average, BCS nadir was anticipated to that of muscularity, and, in both cases, this moment was around the lactation peak, when animals have the greatest nutrients requirement. After this period, both BCS and muscularity recovered up to post-partum levels. Moreover, after the 9 month of lactation, the absolute growth rate of muscularity and BCS was negative, suggesting that late-gestating cows could potentially enter a phase of body conformation loss. Results reported in the current research indicate that random regression using Legendre polynomials can be successfully employed to predict muscularity and BCS during the lactation of dairy cows. Highlights Modelling dairy cows' muscularity and BCS allows to use these parameters as indicator traits for functionality in dairy cows. The use of prediction model of muscularity and BCS allows to understand the evolution of these conformation traits during the lactation. The analysis of muscularity and BCS allows to assess health and welfare status of dairy cows, which is essential to maximise production performances.

1. Optimality theory predicts that both timing of arrival and arrival state on the breeding area will determine reproductive timing and investment in migratory organisms. We tested this idea using a condition-dependent individual optimization model (Ardea 68, 1980, 225 and The American Naturalist 143, 1994, 698) in common eider ducks through descriptive data, path analyses and experimental manipulation. 2. Our results support the causal pathways drawn from the optimization model indicating that individuals adjust their reproductive decisions as a function of their arrival date and body condition at arrival. 3. Independent of body condition, early-arriving females had a longer pre-laying period, but still initiated their nests earlier, and produced larger clutches than late-arriving birds. Independent of arrival date, females in good condition laid earlier than those in poor condition. Manipulation of pre-laying female body condition confirmed that the relationship between condition and laying date was causal. 4. Female common eiders appear to optimize reproductive decisions in response to both their external (i.e. environmental conditions affecting the egg-value) and internal (i.e. body condition) states. These adjustments seem to minimize the fitness costs of reproduction, in which higher clutch size is not associated with an apparent lower survival or future breeding probability. 5. Our study emphasizes the importance of (i) simultaneously considering the timing of migration, the state of individuals and the seasonal change in egg-value to understand clearly birds' breeding decisions and (ii) appreciating the potential proximate and ultimate factors explaining why some individuals delay breeding and/or produce small clutches.

BCS (Body Condition Score) is a method to estimate body fat reserves and accumulated energy balance of cows, placing estimations (or BCS values) in a scale of 1 to 5. Periodically rating BCS of dairy cows is very important since BCS values are associated with milk production, reproduction, and health of cows. However, in practice, obtaining BCS values is a time-consuming and subjective task performed visually by expert scorers. There have been several efforts to automate BCS of dairy cows by using image analysis and machine learning techniques. In a previous work, an automatic system to estimate BCS values was proposed, which is based on Convolutional Neural Networks (CNNs). In this paper we significantly extend the techniques exploited by that system via using transfer learning and ensemble modeling techniques to further improve BCS estimation accuracy. The improved system has achieved good estimations results in comparison with the base system. Overall accuracy of BCS estimations within 0.25 units of difference from true values has increased 4% (up to 82%), while overall accuracy within 0.50 units has increased 3% (up to 97%).