Antimicrobial consumption: Comparison of three different data collection methods.

The increasing incidence of antimicrobial resistance represents a global threat. As a result, surveillance programmes monitoring antimicrobial consumption and resistance in animals have been implemented in several countries throughout the world. However, such programmes depend on the accurate and detailed collection of data on antimicrobial consumption. For this reason, the aim of this longitudinal study was to compare the consistency of data on antimicrobial consumption between three different data collection methods. Antimicrobial consumption data associated to udder health were collected from 20 veterinary practices and 92 dairy farms for 18 months. The compared data sources were: 1) data extracted from veterinary practice software 2) farm treatment journals and 3) on-farm discarded drug packages (garbage). Two different procedures were chosen to analyse the data issued from treatment journals: 1) only complete entries were analysed 2) entries with missing dosage were supplemented with the information provided by the Swiss Compendium of Veterinary Medicinal Products. The antimicrobial data were divided into intramammary preparations used during lactation (IMM), intramammary preparations used for dry off (DRY) and systemic treatments (SYS). We compared the quantities of injectors (IMM and DRY), the quantities of active substances (SYS) and the treatment incidences (TI) for the defined daily dose (DDD) per 1000 cow-days (IMM and SYS) and the defined course dose (DCD) per 1000 cow-days (DRY). Additionally, the variety of antimicrobial products among the different data sources was compared. The highest quantity of antimicrobials for IMM, DRY and SYS could be collected with the software data. The lowest quantity was collected by using the data of the treatment journal with only complete entries. For IMM and DRY, software and garbage performed similar, with agreement on the number of injectors used in 56.1% of the analysed cases. The widest variety of intramammary antimicrobial preparations was found in the garbage whilst most systemic preparations were collected using software data. The results of the study show a lack of data consistency between the three different data sources. None of the methods was able to collect the integral antimicrobial consumption in the participating farms. Finally, the results emphasise the need to implement a standardised system to quantify and assess the antimicrobial consumption at veterinary practice and farm level

Estimation of free-roaming domestic dog population size: Investigation of three methods including an Unmanned Aerial Vehicle (UAV) based approach

Population size estimation is performed for several reasons including disease surveillance and control, for example to design adequate control strategies such as vaccination programs or to estimate a vaccination campaign coverage. In this study, we aimed at investigating the possibility of using Unmanned Aerial Vehicles (UAV) to estimate the size of free-roaming domestic dog (FRDD) populations and compare the results with two regularly used methods for population estimations: foot-patrol transect survey and the human: dog ratio estimation. Three studies sites of one square kilometer were selected in Petén department, Guatemala. A door-to-door survey was conducted in which all available dogs were marked with a collar and owner were interviewed. The day after, UAV flight were performed twice during two consecutive days per study site. The UAV's camera was set to regularly take pictures and cover the entire surface of the selected areas. Simultaneously to the UAV's flight, a foot-patrol transect survey was performed and the number of collared and non-collared dogs were recorded. Data collected during the interviews and the number of dogs counted during the foot-patrol transects informed a capture-recapture (CR) model fit into a Bayesian inferential framework to estimate the dog population size, which was found to be 78, 259, and 413 in the three study sites. The difference of the CR model estimates compared to previously available dog census count (110 and 289) can be explained by the fact that the study population addressed by the different methods differs. The human: dog ratio covered the same study population as the dog census and tended to underestimate the FRDD population size (97 and 161). Under the conditions within this study, the total number of dogs identified on the UAV pictures was 11, 96, and 71 for the three regions (compared to the total number of dogs counted during the foot-patrol transects of 112, 354 and 211). In addition, the quality of the UAV pictures was not sufficient to assess the presence of a mark on the spotted dogs. Therefore, no CR model could be implemented to estimate the size of the FRDD using UAV. We discussed ways for improving the use of UAV for this purpose, such as flying at a lower altitude in study area wisely chosen. We also suggest to investigate the possibility of using infrared camera and automatic detection of the dogs to increase visibility of the dogs in the pictures and limit workload of finding them. Finally, we discuss the need of using models, such as spatial capture-recapture models to obtain reliable estimates of the FRDD population. This publication may provide helpful directions to design dog population size estimation methods using UAV

Foodborne disease hazards and burden in Ethiopia: A systematic literature review, 1990–2019

Background: Foodborne disease (FBD) affects millions of people each year, posing a health burden similar to malaria, tuberculosis or HIV. A recent World Bank study estimated the productivity losses alone attributed to unsafe food within Africa at $20 billion in 2016, and the cost of treating these illnesses at an additional$3.5 billion. Ethiopia faces multiple food safety challenges due to lack of infrastructure and basic pre-requisites for food safety such as clean water and environment, washing facilities, compounded by limited implementation of food safety regulations, and a lack of incentives for producers to improve food safety. A consolidation of our understanding and evidence of the source, nature and scale of FBD in Ethiopia is needed to inform policy and future research. We performed a Systematic Literature Review (SLR) of publications on FBD occurrence in Ethiopia including hazard presence and impact. Method: The SLR followed Cochrane and PRISMA guidelines. We searched PubMed and CAB-Direct for relevant publications between 1990 and 2019 (inclusive). Observational studies and reviews were included. Two reviewers screened titles and abstracts, and retained publications were reviewed in full for quality and data extraction. Result: In total 128 articles met the inclusion criteria. Most articles focused on the identification of biological hazards in food. High levels of microbial contamination in different food value chains were often found in the small, ad hoc, observational studies that dominated the literature. Raw milk (22/128, 17.0%) and raw beef (21/128, 16.4%) were the most studied food products. Foodborne (FB) parasites were often found at higher rates in food than bacterial and viral pathogens, possibly due to differences in ease of identification. High levels of bacterial contamination on the hands of food handlers were widely reported. There were no reports on the incidence of human FBDs or resulting health and economic impacts. Conclusion: Our findings reflect existing concerns around food safety in Ethiopia. A lack of substantial, coordinated studies with robust methodologies means fundamental gaps remain in our knowledge of FBD in Ethiopia, particularly regarding FBD burden and impact. Greater investment in food safety is needed, with enhanced and coordinated research and interventions

Comparative Study of Free-Roaming Domestic Dog Management and Roaming Behavior Across Four Countries: Chad, Guatemala, Indonesia, and Uganda

Dogs play a major role in public health because of potential transmission of zoonotic diseases, such as rabies. Dog roaming behavior has been studied worldwide, including countries in Asia, Latin America, and Oceania, while studies on dog roaming behavior are lacking in Africa. Many of those studies investigated potential drivers for roaming, which could be used to refine disease control measures. However, it appears that results are often contradictory between countries, which could be caused by differences in study design or the influence of context-specific factors. Comparative studies on dog roaming behavior are needed to better understand domestic dog roaming behavior and address these discrepancies. The aim of this study was to investigate dog demography, management, and roaming behavior across four countries: Chad, Guatemala, Indonesia, and Uganda. We equipped 773 dogs with georeferenced contact sensors (106 in Chad, 303 in Guatemala, 217 in Indonesia, and 149 in Uganda) and interviewed the owners to collect information about the dog [e.g., sex, age, body condition score (BCS)] and its management (e.g., role of the dog, origin of the dog, owner-mediated transportation, confinement, vaccination, and feeding practices). Dog home range was computed using the biased random bridge method, and the core and extended home range sizes were considered. Using an AIC-based approach to select variables, country-specific linear models were developed to identify potential predictors for roaming. We highlighted similarities and differences in term of demography, dog management, and roaming behavior between countries. The median of the core home range size was 0.30 ha (95% range: 0.17–0.92 ha) in Chad, 0.33 ha (0.17–1.1 ha) in Guatemala, 0.30 ha (0.20–0.61 ha) in Indonesia, and 0.25 ha (0.15–0.72 ha) in Uganda. The median of the extended home range size was 7.7 ha (95% range: 1.1–103 ha) in Chad, 5.7 ha (1.5–27.5 ha) in Guatemala, 5.6 ha (1.6–26.5 ha) in Indonesia, and 5.7 ha (1.3–19.1 ha) in Uganda. Factors having a significant impact on the home range size in some of the countries included being male dog (positively), being younger than one year (negatively), being older than 6 years (negatively), having a low or a high BCS (negatively), being a hunting dog (positively), being a shepherd dog (positively), and time when the dog was not supervised or restricted (positively). However, the same outcome could have an impact in a country and no impact in another. We suggest that dog roaming behavior is complex and is closely related to the owner's socioeconomic context and transportation habits and the local environment. Free-roaming domestic dogs are not completely under human control but, contrary to wildlife, they strongly depend upon humans. This particular dog–human bound has to be better understood to explain their behavior and deal with free-roaming domestic dogs related issues

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear un derstanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5–7 vast areas of the tropics remain understudied.8–11 In the American tropics, Amazonia stands out as the world’s most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepre sented in biodiversity databases.13–15 To worsen this situation, human-induced modifications16,17 may elim inate pieces of the Amazon’s biodiversity puzzle before we can use them to understand how ecological com munities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple or ganism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region’s vulnerability to environmental change. 15%–18% of the most ne glected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lostinfo:eu-repo/semantics/publishedVersio

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Pervasive gaps in Amazonian ecological research

Biodiversity loss is one of the main challenges of our time,1,2 and attempts to address it require a clear understanding of how ecological communities respond to environmental change across time and space.3,4 While the increasing availability of global databases on ecological communities has advanced our knowledge of biodiversity sensitivity to environmental changes,5,6,7 vast areas of the tropics remain understudied.8,9,10,11 In the American tropics, Amazonia stands out as the world's most diverse rainforest and the primary source of Neotropical biodiversity,12 but it remains among the least known forests in America and is often underrepresented in biodiversity databases.13,14,15 To worsen this situation, human-induced modifications16,17 may eliminate pieces of the Amazon's biodiversity puzzle before we can use them to understand how ecological communities are responding. To increase generalization and applicability of biodiversity knowledge,18,19 it is thus crucial to reduce biases in ecological research, particularly in regions projected to face the most pronounced environmental changes. We integrate ecological community metadata of 7,694 sampling sites for multiple organism groups in a machine learning model framework to map the research probability across the Brazilian Amazonia, while identifying the region's vulnerability to environmental change. 15%–18% of the most neglected areas in ecological research are expected to experience severe climate or land use changes by 2050. This means that unless we take immediate action, we will not be able to establish their current status, much less monitor how it is changing and what is being lost

Keel bone fractures are associated with individual mobility of laying hens in an aviary system

Keel bone fractures (KBF) in laying hens have been shown to cause pain and impair mobility under experimental conditions. However, it is not known how KBF relates to the mobility of individual hens housed in aviary systems. For the current study, 120 focal hens (60 Lohmann Brown (LB) and 60 Lohmann Selected Leghorn (LSL)) were kept in six identical pens equipped with a commercially relevant aviary system (20 LSL focal hens + 205 LB or 20 LB focal hens + 205 LSL per pen, respectively). Data on hen mobility were recorded at 21, 24, 27, 31, 35, 39, 44, 48, 52, 57 and 61 weeks of age. Infrared receivers were attached to the legs of focal hens. They recorded zone-specific codes between five zones (litter, lower tier, nest boxes, top tier, and wintergarden) at a frequency of 1 Hz for six consecutive days per week of age. At the end of each data collection period, hens were radiographed to assess keel bone fracture severity. Data were analysed using (generalized) linear mixed effect models. With increasing KBF severity, LB hens spent more time in the top tier (p = 0.005) and less time in the litter zone (p < 0.0001) and in the lower tier (p = 0.001). Independent of KBF, LB hens spent less time in the wintergarden (p = 0.011) and in the lower tier (p = 0.002) and more time in the top tier (p = 0.009) with increasing age. The likelihood of crossing more than one zone within a movement (e.g., jumping from the top tier to the litter directly) increased with increasing KBF severity (p = 0.036). Lohmann Selected Leghorn hens spent most time in the nest box zone and top tier and had few transitions between zones. With increasing age, LSL hens spent less time in the nest box zone (p = 0.018) and more time in the top tier (p = 0.006). Irrespective of strain, hens crossed fewer zones with increasing age (LB: p = 0.009, LSL: p = 0.002). Our findings indicate that hens having KBF prioritized paths among the upper tiers (i.e., mostly between nest box zone and top tier) over paths among the mid and lower tiers (i.e., between litter, lower tier and nest box zone). We conclude that behavioural adaptation to pain, the accessibility of resources as well as social factors might be important mechanisms driving individual mobility in response to KBF