Estimating Body Condition Research Articles

First evidence of age-dependent decreases in non-persistent pesticide mixtures in nestlings of a farmland raptor

Intensive use of synthetic pesticides in conventional agriculture may harm non-target organisms through sublethal effects on life-history traits. Farmland birds are exposed throughout their life cycle, but the fate of non-persistent pesticide mixtures in wild birds remains unknown. In this study, we investigated changes in pesticide contamination levels in Montagu's harrier (Circus pygargus) nestlings during their growth. In total, 35 chicks were sampled twice during the rearing period, and blood was tested for 116 pesticides to assess pesticide load through two proxies; the number of pesticides detected and the sum of pesticide concentrations. Body mass and tarsus length were also measured to estimate body condition. Across the two sampling times, nine herbicides, five insecticides and four fungicides were detected. Contamination levels decreased significantly with nestling age irrespective of sampling date, and there was no relationship between pesticide load and body condition. Moreover, concentrations of chlorpyrifos-methyl, fenpropidin, metamitron, picloram and S-metolachlor, all detected throughout the rearing period, were unrelated to any of the explanatory variables. However, ethofumesate concentrations decreased significantly with chick age. This study provides the first evidence that non-persistent pesticide mixtures can decrease with age in wild nestlings. This has implications for understanding how chicks are contaminated and provides new insights on pesticide fate within organisms.

Estimating body condition of Apennine brown bears using subjective scoring based on camera trap photographs

The assessment of animal body condition has important practical and management implications for endangered wildlife populations. The nutritional condition of a population can be evaluated in a non-invasive way using photogrammetry techniques, avoiding direct manipulation. This study evaluates the utility of using body condition scoring (BCS) based on the visual assessment of subcutaneous fat and muscle from the body contour as a non-invasive method to quantify body condition in free ranging bears from camera trap photographs. Photographs of Apennine brown bears (Ursus arctos marsicanus), taken between 2007 and 2009 in the Abruzzo, Lazio and Molise National Park (PNALM, Italy), were used to evaluate the potential of this technique. BCS assessment was performed on 754 photographs representing 71 independent observations. Forty-eight of these photographs were selected to also score quantitative body ratios using a standardised measure of torso height. BCS varied seasonally, as expected by food availability and brown bear nutritional physiology, and it was also positively correlated to all three body ratios. Our findings indicate that BCS assessment is a good proxy for body condition, and that camera trap data can be effectively used to assess and monitor the nutritional condition of bear populations, such as the critically endangered one in central Italy.

Can Nigerian horse owners effectively estimate body condition and cresty neck scores?

A horse's body condition impacts every aspect of its life, including reproduction, performance, and health. Obesity and equine metabolic syndrome (EMS) are common in Nigeria; hence, early identification of a horse's obesity status by the owner is key to avoiding associated health issues such as EMS. Our study aimed to determine whether horse owners could effectively estimate their horses' body condition scores (BCSs) and cresty neck scores (CNSs). A total of 50 adult sedentary West African Barb horses owned by 50 different people were enrolled in the study. Body condition scores (BCSs) and cresty neck scores (CNSs) were assessed on scales of 1-9 and 0-5, respectively, by an experienced veterinarian and owners. The latter had no veterinary background, received no prior education, and assessed their horses based on provided visual aids and descriptions. The BCS estimates of the experienced veterinarian and owners were slightly in agreement (k=0.209), while there was moderate agreement (k=0.547) between the CNS estimates of the experienced veterinarian and the owners. The proportion of obesity based on the experienced veterinarian's estimation (32%) was higher than that of the owners (18%). The proportion of nuchal crest adiposity based on the experienced veterinarian's (38%) estimation was slightly lower than that of the owners (42%). A larger proportion of the owners underestimated their horses' BCSs and CNSs. We conclude that owners cannot effectively estimate their horses' BCSs and CNSs. It is imperative that they are informed about the health issues associated with obesity and are taught how to effectively estimate BCS and CNS.

Should landscape variation in population status be assessed with individual‐ or population‐level indicators?

AbstractMonitoring trends in wildlife populations is essential to effective management. Evaluating only a single metric can be cost‐effective; however, how a given metric responds to environmental variation may differ from other population‐ or individual‐level metrics. Consequently, conclusions about the effect of environmental variation on a species should consider the relationship between different metrics. We investigated how population‐level metrics (occupancy and relative abundance) and an individual‐level metric (body condition) responded to landscape variation in 3 semi‐aquatic salamander species. In the summer of 2015 and 2016, we conducted repeated count surveys at 71 streams on the Cumberland Plateau, Tennessee, USA, actively counting and measuring salamanders. We estimated occupancy and abundance of larval and post‐metamorphic animals using community‐occupancy and community‐N mixture models, respectively. We estimated body condition of the most observed life stage for each species using a standardized mass index. All models included the same set of covariates for comparison. Occupancy and abundance responses for each life stage‐by‐species combination were positively correlated for 4 of 6 combinations, with salamander abundance responding to a larger suite of covariates. Body condition was not linked to covariates in the same fashion as population‐level metrics, although consistency across species was detected for 1 covariate, watershed size. Our results indicate that inter‐specific and inter‐life stage variation in evaluated metrics is present within salamanders of this study system. Management for populations of salamanders in the region should consider monitoring multiple response metrics, to capture both population‐ and individual‐level responses, as these may not always be correlated.

Usefulness of mid-infrared spectroscopy as a tool to estimate body condition score change from milk samples in intensively fed dairy cows

Directly measuring individual cow energy balance is not trivial. Other traits, like body condition score (BCS) and BCS change (ΔBCS) can, however, be used as an indicator of cow energy status. Body condition score is a metric used world-wide to estimate cow body reserves and the estimation of ΔBCS was, until now, conditional on the availability of multiple BCS assessments. The aim of the present study was to estimate ΔBCS from milk mid-infrared (MIR) spectra and days in milk (DIM) in intensively-fed dairy cows using statistical prediction methods. Daily BCS was interpolated from cubic splines fitted through the BCS records and daily ΔBCS was calculated from these splines. Body condition score change records were merged with milk MIR spectra recorded on the same week. The data set comprised 37,077 ΔBCS phenotypes across 9,403 lactations from 6,988 cows in 151 herds based in Quebec (Canada). Partial least squares regression (PLSR) and a neural network (NN) were then used to estimate ΔBCS from 1) MIR spectra only, 2) DIM only, or 3) MIR spectra and DIM together. ΔBCS data in both the first 120 DIM and 305 DIM of lactation were used to develop the estimates. Daily ΔBCS had a standard deviation of 4.40*10-3 BCS units in the 120-d data set and of 3.63*10-3 BCS units in the 305-d data set. 4-fold cross-validation was used to calibrate and test the prediction equations. External validation was also conducted using more recent years of data. Irrespective of whether based on the first 120 or 305 DIM, or when MIR spectra only, DIM only or MIR spectra and DIM were jointly used as prediction variables, NN produced the lowest root mean square error (RMSE) of cross-validation (1.81*10-3 BCS units and 1.51*10-3 BCS units, respectively, using the 120-d and 305-d data set). Relative to predictions for the entire 305 DIM, the RMSE of cross-validation was 15.4% and 1.5% lower in the first 120 DIM when using PLSR and NN, respectively. Predictions from DIM only were more accurate than those using just MIR spectra data but, irrespective of the data set and of the prediction model used, the combining DIM information with MIR spectral data as prediction variables reduced the RMSE compared with inclusion of DIM alone, albeit the benefit was small (the RMSE from cross-validation was reduced up to 5.5% when DIM and spectral data were jointly used as model features instead of DIM only). However, when predicting extreme ΔBCS records, the MIR spectral data was more informative than DIM. Model performance when predicting ΔBCS records in future years was similar to that from cross-validation demonstrating the ability of MIR spectra of milk and DIM combined to estimate ΔBCS, particularly in early lactation. This can be used to routinely generate estimates of ΔBCS to aid in day-to-day individual cow management.

Ultrasonography in the diagnosis of obesity in cats and its correlation with fructosamine and lipid levels

The aim of this study was to determine whether ultrasound imaging is an efficient method to assess subcutaneous fat in cats, the anatomical sites where more significant fat deposition occurs, and if there is a correlation between subcutaneous fat thickness and serum levels of cholesterol, triglycerides, and fructosamine. A total of 30 healthy adult cats were used and divided into three groups of 10 animals each, based on the estimated body condition score (BCS). The ideal group (IG) included cats with BCS 3; the overweight group (OWG), with BCS 4; and the obese group (OG), with BCS 5. Ultrasonographic measurement of subcutaneous fat was conducted in five anatomical regions: lumbar, abdominal, thoracic, femoral, and pectoral. We observed that obese cats had greater fat deposition in the abdominal and thoracic regions when compared to those with ideal weight, and that cholesterol and triglyceride levels were higher with the increase in subcutaneous fat thickness in the thoracic region. Nonetheless, there were no differences in fat deposition in the OWG compared to cats from the IG and OG. Ultrasonography allows to differentiate obese cats from those with ideal weight through the analysis of subcutaneous fat thickness in the abdominal and thoracic regions, rendering this method efficient and less subjective.

Aerial photogrammetry techniques using drones to estimate morphometric measurements and body condition in South American small cetaceans

AbstractAerial photogrammetry protocols using drones have proved efficient in monitoring free‐living cetaceans. In this work an aerial photogrammetry protocol using a drone was applied to estimate morphometric measurements and body condition variation in two endangered cetacean species, franciscanas (Pontoporia blainvillei, n = 122) and Guiana dolphin (Sotalia guianensis, n = 41). Sampling occurred in winter and summer in Babitonga Bay, southern Brazil. A 115 cm length object was used to calibrate the scale of measurements at different altitudes. A scale calibration model based on eGSD regression by flight altitude was developed (RMSE = 0.039, r2 = 0.9902, ΔAICc = 0.00) and used to convert measurements from pixels to centimeters. Five morphometric measurements (body length ‐ BL, widths at anterior pectoral fin ‐ AP, axilla ‐ AX, anterior dorsal fin ‐ AD, and posterior dorsal fin ‐ PD) were taken from each animal to infer body condition, in which, the widths in the anterior portions of the body as % BL, can be an auxiliary tool in the evaluation of variation in the body condition of the small cetaceans studied. The aerial photogrammetry protocol allowed for estimating morphometric measurements and monitoring seasonal changes in the body condition of the small cetaceans.

How to estimate body condition in large lizards? Argentine black and white tegu (Salvator merianae, Duméril and Bibron, 1839) as a case study.

Body condition is a measure of the health and fitness of an organism represented by available energy stores, typically fat. Direct measurements of fat are difficult to obtain non-invasively, thus body condition is usually estimated by calculating body condition indices (BCIs) using mass and length. The utility of BCIs is contingent on the relationship of BCIs and fat, thereby validation studies should be performed to select the best performing BCI before application in ecological investigations. We evaluated 11 BCIs in 883 Argentine black and white tegus (Salvator merianae) removed from their non-native range in South Florida, United States. Because the length-mass relationship in tegus is allometric, a segmented linear regression model was fit to the relationship between mass and length to define size classes. We evaluated percent, residual, and scaled fat and determined percent fat was the best measure of fat, because it was the least-associated with snout-vent length (SVL). We evaluated performance of BCIs with the full dataset and within size classes and identified Fulton's K as the best performing BCI for our sampled population, explaining up to 19% of the variation in fat content. Overall, we found that BCIs: 1) maintained relatively weak relationships with measures of fat and 2) splitting data into size classes reduced the strength of the relationship (i.e., bias) between percent fat and SVL but did not improve the performance of BCIs. We postulate that the weak performance of BCIs in our dataset was likely due to the weak association of fat with SVL, the body plan and life-history traits of tegus, and potentially inadequate accounting of available energy resources. We caution against assuming that BCIs are strong indicators of body condition across species and suggest that validation studies be implemented, or that alternative or complimentary measures of health or fitness should be considered.

Using dorsal surface for individual identification of dairy calves through 3D deep learning algorithms

Advances in machine learning techniques have allowed the development of computer vision systems (CVS) that can accurately predict several phenotypes of interest for livestock operations. In this context, 3D images taken from a top-down view are particularly useful for estimating body condition score, growth development, and body biometrics in cattle. Frequently, such CVS rely on identification (ID) systems, such as electronic tags, as a way to match animal ID and the predicted phenotype. However, the same 3D images used to predict body weight and other animal biometrics could be adopted for animal recognition as well. Such alternative would optimize CVS to recognize animal ID and monitor growth development simultaneously while leveraging the same hardware infrastructure. Furthermore, this strategy could be used to recognize animals with similar color patterns. Nonetheless, growing animals are continuously changing body shape, which could limit its use as an invariant feature for pattern recognition. Thus, the objectives of this study were: (1) to compare algorithms for different 3D object representations to identify individual animals; and (2) to evaluate how short-term changes in body shape due to animal growth affect the predictive performance of these algorithms. For objective 1, the algorithms were trained (n = 4,558) and tested (n = 1,139) using images from 38 Holstein calves. For objective 2, we designed three different experiments using images (n = 2,347) from five Holstein calves taken over six weeks during their growing period, always training and testing on different weeks. Each experiment evaluated how changing a different parameter of the image capturing procedure affected the predictive ability of the trained algorithms. In the first experiment, we varied the total number of images per animal in the training set; in the second experiment, we varied the number of weeks while keeping a fixed number of images in the training set; and in the third experiment, we skipped weeks between images in the training and test sets. The F1 score for objective (1) was up to 0.804 when testing with the last frames of each video, and up to 0.959 when using random frames for testing. For objective (2), the F1 score was up to 0.947 for the first experiment when using 130 images per animal; up to 0.979 for the second experiment when using all five weeks; and up to 0.917 when not skipping weeks between training and testing. These results show that deep learning algorithms can be used to identify individual animals through their dorsal area 3D surfaces, and, from our experiments using calves in their growing period, that they are robust enough to account for changes in body shape and size, making them a promising tool for animal recognition during growth.

Integrating 3D models with morphometric measurements to improve volumetric estimates in marine mammals

Abstract Studies of body condition are key to understanding the health, bioenergetics and ecological roles of marine mammals. Due to challenges in studying marine mammals at sea, body condition is often approximated using metrics representing the size of the dorsal surface visible from aerial imagery, but quantifying variability in body volume would enable a more holistic understanding of bioenergetics. Furthermore, the number and location of measurements needed to accurately quantify body condition has received little attention. Three‐dimensional (3D) models provide a promising tool for representing morphology and providing holistic estimates of marine mammal body condition when combined with field‐based morphometric measurements. We use humpback whales Megaptera novaeangliae to demonstrate the utility of 3D models for estimating body condition in marine mammals. We integrate morphometric measurements taken from unoccupied aerial vehicles (UAVs) with scalable 3D models to generate estimates of humpback whale body volume. We assess which and how many morphometric measurements are required to accurately estimate body volume and compare the error between volume estimates derived from 3D models and previously developed models representing volume as a series of ellipses. Using UAV measurements, we assess the contribution of each morphometric measurement to volumetric estimates, and quantify the error produced by all combinations and numbers of morphometric measurements (131,072 combinations). Error in volume estimates from 3D models generated with as few as five width measurements was <5% compared to the full models and was lower than the error produced when using five width measurements with the elliptical approach. We suggest that by conserving the external morphology of marine mammals, 3D models allow body volume and body condition to be estimated accurately with few measurements. We provide code and guidelines for creating 3D models using the open‐source software Blender and for assessing which measurements are needed to accurately capture the morphology of cetaceans. The 3D modelling approach we present will facilitate studies of intra‐ and interannual changes in body volume in marine mammals, which is vital to providing a more holistic understanding of bioenergetics and to assessing responses to environmental change and anthropogenic stressors.

Validation of quantitative magnetic resonance as a non-invasive measure of body composition in an Australian microbat

Body composition (the total amount of fat mass, lean mass, minerals and water that constitute the body) is an important measure for understanding an animal’s physiology, ecology and behaviour. Traditional measures of body composition require the animal to either be placed under anaesthetic, which is invasive and can be high-risk, or be euthanised, preventing the ability to perform repeated measures on the same individual. We aimed to validate quantitative magnetic resonance (QMR) as a non-invasive measure of body composition by comparing QMR scans with chemical carcass analysis (CCA) in Gould’s wattled bats (Chalinolobus gouldii). In addition, we compared a commonly used microbat body condition index (residuals of mass by forearm length) to CCA. We found that QMR is an accurate method of estimating body condition in Gould’s wattled bats after calibration with regression equations, and the condition index could accurately predict lean and water mass but was a poor predictor of fat mass. Using accurate, non-invasive, repeatable measures of body condition may have important implications for ecological research in the face of changing environments.

Relative condition factor: Le Cren's legacy for fisheries science

Abstract: Aim In this paper we present a scientometric analysis aimed at to assess and quantify the contribution of Le Cren’s condition factor and the main ideas advanced by ‘Le Cren, 1951’ to studies on the health of individuals or populations in aquatic environments. Specifically, we addressed the following questions: (i) what are the temporal and spatial citation trends of ‘Le Cren, 1951’?; (ii) Which journals cited ‘Le Cren, 1951’ most frequently?; (iii) In which types of aquatic environments and organisms have Le Cren’s condition factor been most commonly applied?; (iv) Which of the main applications addressed in ‘Le Cren, 1951’ have been most frequently used by fisheries scientists?; (v) Which of the methods (i.e., Quételet’s index or body mass index, Fulton’s condition factor, Le Cren’s condition factor, relative weight, residual index and scaled mass index) used to estimate condition factor identified in the papers that cited ‘Le Cren, 1951’ have been most frequently used? In addition, the main criticisms of the use of the relative condition factor are discussed. Methods We carried out a scientometric analysis on the papers published from 1951 to December 2015 that cited Le Cren’s article. The papers were obtained from the Thomson Reuters database (ISI Web of Knowledge, apps.isiknowledge.com) in May 2016. Results In total, 1128 papers were assessed. The number of citations increased significantly over time. Most citations occurred in two journals (Journal of Fish Biology and Journal of Applied Ichthyology). Most of papers were carried out in marine environments. In addition, we show that Le Cren's paper has been cited mainly in articles that estimate the condition factor or the weight-length relationship of populations. Finally, we emphasize that the relative condition factor proposed by Le Cren in 1951 is still the main method used to estimate the body condition of an individual or population. Conclusions Le Cren's significant contribution to animal well-being is undeniable, and we emphasize that among the methods identified in the papers that cited ‘Le Cren, 1951’ that estimated body condition, the relative condition factor proposed by Le Cren in 1951 is still the most commonly used method.

Body condition of spring-migrating Green-winged Teal (Anas crecca)

Spring migration is an energetically demanding event that can impact nutrient dynamics of individuals during the breeding season through carry-over effects. Limited food availability at spring stopover areas may have cross-seasonal effects that adversely impact waterfowl populations. We collected 161 Green-winged Teal (Anas crecca Linnaeus, 1758; hereafter Teal) throughout the Illinois River Valley, USA, during February–April of 2016–2018 and estimated body condition using three condition indices (scaled mass index, scaled wing index, body-size index) for comparison with carcass lipid and protein values. Lipids were 18.3% (95% CI = 1.0%–38.5%) and 21.6% (95% CI = 4.8%–41.0%) greater at locations with moderate (250–600 kg/ha) and high (>600 kg/ha) moist-soil seed densities, respectively, than low moist-seed density (<250 kg/ha) sites. Lipids also increased 2.4% (95% CI = 0.2%–5.1%) with every 10% increase in the proportion of plant seeds in diets and decreased 0.9% (SE = 0.4%–1.4%) and 3.8% (SE = 2.1%–5.4%) with every 1-day increase in collection date and 1 °C rise in mean temperature, respectively. Condition indices based on morphology were poor (r ≤ 0.45) predictors of lipids and led to potentially erroneous conclusions regarding important contributions to body condition. Availability of emergent wetlands with adequate food resources may affect lipid reserves of Teal during spring migration and have cross-seasonal effects during the breeding season.

Deep Learning Techniques for Agronomy Applications

This editorial introduces the Special Issue, entitled “Deep Learning (DL) Techniques for Agronomy Applications”, of Agronomy. Topics covered in this issue include three main parts: (I) DL-based image recognition techniques for agronomy applications, (II) DL-based time series data analysis techniques for agronomy applications, and (III) behavior and strategy analysis for agronomy applications. Three papers on DL-based image recognition techniques for agronomy applications are as follows: (1) “Automatic segmentation and counting of aphid nymphs on leaves using convolutional neural networks,” by Chen et al.; (2) “Estimating body condition score in dairy cows from depth images using convolutional neural networks, transfer learning, and model ensembling techniques,” by Alvarez et al.; and (3) “Development of a mushroom growth measurement system applying deep learning for image recognition,” by Lu et al. One paper on DL-based time series data analysis techniques for agronomy applications is as follows: “LSTM neural network based forecasting model for wheat production in Pakistan,” by Haider et al. One paper on behavior and strategy analysis for agronomy applications is as follows: “Research into the E-learning model of agriculture technology companies: analysis by deep learning,” by Lin et al.

Estimating Body Condition Score in Dairy Cows From Depth Images Using Convolutional Neural Networks, Transfer Learning and Model Ensembling Techniques

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%).

Autonomous Interactive Physical Caring by Flexible Data Flow Management

Being able to monitor the body condition and health condition of the user telemetrically is a very important for caring the user. User’s danger occurring from unconscious body stress can be consisted by vital signs and body postures. These vital signs and body postures can be estimated by data fusion of variety of body parameters. System with fluent flow to estimate body condition and advices should be made. These data can be controlled by user’s oral commands. This paper describes the construction and flow of the autonomous caring system and it’s data control by user.

Body density of humpback whales (Megaptera novaengliae) in feeding aggregations estimated from hydrodynamic gliding performance.

Many baleen whales undertake annual fasting and feeding cycles, resulting in substantial changes in their body condition, an important factor affecting fitness. As a measure of lipid-store body condition, tissue density of a few deep diving marine mammals has been estimated using a hydrodynamic glide model of drag and buoyancy forces. Here, we applied the method to shallow-diving humpback whales (Megaptera novaeangliae) in North Atlantic and Antarctic feeding aggregations. High-resolution 3-axis acceleration, depth and speed data were collected from 24 whales. Measured values of acceleration during 5 s glides were fitted to a hydrodynamic glide model to estimate unknown parameters (tissue density, drag term and diving gas volume) in a Bayesian framework. Estimated species-average tissue density (1031.6 ± 2.1 kg m-3, ±95% credible interval) indicates that humpback whale tissue is typically negatively buoyant although there was a large inter-individual variation ranging from 1025.2 to 1043.1 kg m-3. The precision of the individual estimates was substantially finer than the variation across different individual whales, demonstrating a progressive decrease in tissue density throughout the feeding season and comparably high lipid-store in pregnant females. The drag term (CDAm-1) was estimated to be relatively high, indicating a large effect of lift-related induced drag for humpback whales. Our results show that tissue density of shallow diving baleen whales can be estimated using the hydrodynamic gliding model, although cross-validation with other techniques is an essential next step. This method for estimating body condition is likely to be broadly applicable across a range of aquatic animals and environments.

Piscicide impact extends beyond targets and toxicity

Restoration of fisheries often requires removal of invasive fishes. Although piscicides such as antimycin a and rotenone are reliable and cost‐effective tools for such removals, they may reduce the abundance of nontarget species directly via toxicity‐induced mortality (toxic effect) or indirectly via loss of prey (trophic effect). Toxic effects are well documented in some taxa, especially in benthic macroinvertebrates (BMIs). Yet, the corresponding trophic effects on nonpiscine, aquatic, and riparian species remain unstudied. I tested for a trophic effect of piscicides using a population of American Dippers (Cinclus mexicanus), songbirds that feed extensively on BMIs. I caught breeding birds and estimated body condition before, during, and after treatment of the stream with antimycin a or rotenone. Birds exhibited poorer body condition (3.0–3.7% lower body mass) during treatment than before or after treatment. These differences suggested that treatment reduced prey availability enough to impact birds at least 9 months after application. Therefore, continued use of piscicides for fish restoration would benefit from (1) site‐specific comparison of benefits of invasive fish removal to costs of potential or documented toxic and trophic effects of piscicides on nontarget organisms; (2) continued adherence to best practices for recovery of the BMI community after piscicide treatment; and (3) experimental study of piscicide impacts.

In vivo assessment of subcutaneous fat in dogs by real-time ultrasonography and image analysis.

BackgroundSystems for estimating body condition score (BCS) are currently used in canine practice to monitor fatness levels. These tools are cheap and easy to use but lack the necessary precision to monitor small changes in body fat, particularly during weight control treatments or in research. The present work aims to study the application of real-time ultrasonography (RTU) together with image analysis in the assessment of subcutaneous fat depots in dogs. Ultrasound images were collected from five anatomical locations (chest, flank, abdomen, thigh and lumbar) from 28 healthy dogs of different breeds and with a body weight (BW) ranging from 5.2 to 33.0 kg. BCS was collected by visual appraisal using a 5-point scale. Subcutaneous fat thickness (SFT) was estimated from RTU images, using the average of three measurements taken in fat deposits located above the muscles represented in each image. Correlations were established between SFT and BW or BCS as well as a classification of BCS-based fatness [overweight (BCS = 4), ideal (BCS = 3) and lean (BCS = 2)].ResultsSFT was found to differ between the five regions considered (P < 0.001). Abdomen and thigh were the areas displaying the widest variation for the different dogs included in the study and also those correlating most with BW, in contrast to the chest, which showed the least variation. Overall, a strong correlation was found between BCS and SFT. The highest correlations were established for the flank, abdomen and lumbar areas. In every anatomical area, a decrease in SFT was observed across all three BCS classes, ranging from 48 to 65 % among overweight and ideal dogs, and from 46 to 83 % among ideal and lean dogs.ConclusionsPreliminary data showed that within this population there was a strong correlation between BCS and SFT estimated from RTU images. It was also observed that RTU measurements for fat thickness differed among the anatomical points surveyed suggesting differences in their sensitivity to a change in BCS. The images displaying the best prediction value for fatness variations were those collected at the lumbar and abdomen areas.

Owner׳s Perception for Detecting Feline Body Condition Based on Questionnaire and Scores

To evaluate the owner׳s ability to identify body condition in cats, based on questionnaire and scores, as well as to obtain others׳ information about the cat and the owner that may be related to the body condition. Seventy-seven owned cats, aged above 11 months, were evaluated. Initially, information was obtained on age, sex, breed, and whether they had been neutered. Next, owners were asked to fill a questionnaire: the first section was about the cat׳s diet type, feeding regime, and activity level, and the second section was on the owners׳ diet, physical activity, and physical condition. The owners estimated body condition of their cats before and after seeing drawings with images. The authors also estimated body condition. Only a 5-point visual scale showed significant difference between the cat׳s body score assigned by the owner before and after seeing drawings, and before seeing drawings compared with the cat׳s body score assigned by the authors. A weak positive correlation was detected between cat׳s body score assigned by the 2 authors and access to any type of cat climbing systems, and weak negative correlation was detected for the variable spayed or neutered. The cat׳s physical activity level showed weak negative correlation with cat׳s body score. There was a strong positive correlation between cat׳s body weight and cat׳s body score. The owners showed ability to identify body condition in cats after seeing the drawings in both visual scales, and there was a relationship between cat׳s body weight and body condition score.