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36 results for “Breeding performance”
Fig. 1 in Distribution Pattern, Nest-Tree Features And Breeding Performance Of Population Of The Black Stork, Ciconia Nigra (Ciconiiformes, Ciconiidae), In Northwestern Serbia
Fig. 1. The proportion of tree species picked for nest placement by the Black Stork (Ciconia nigra) in Northwestern Serbia (n = 44).
Figure 10. Juvenile Staurois parvus performing a in The conservation breeding of two foot-flagging frog species from Borneo, Staurois parvus and Staurois guttatus
Figure 10. Juvenile Staurois parvus performing a foot-flagging behavior. Interdigital webbing are transparent grey and not white as observed in adults (see also Fig. 2). Image by N. Potensky.
Code and data for: Familiarity breeds success: pairs that meet earlier experience increased breeding performance in a wild bird population
<p>This is a Data package that contains three separate datasets and the analysis code for a manuscript 'Familiarity breeds success: pairs that meet earlier experience increased breeding performance in a wild bird population '. The first Dataset (pairs_data_2007_10) and the second dataset (pairs_data_2011_14) contain the data used to analyse the influence of meeting time of a pair of Great tits (i.e. month in the first dataset, week in the second dataset, when a pair was first detected in a flock together) on different components of breeding success of a pair. The third dataset (meeting_and_divorce) was used to analyse whether meeting time of a pair (in winter prior to the breeding season t) influence the probability of a pair to stay together or separate (divorce) to the following breeding season (t+1).</p>
Data from: Specialisation reduces foraging effort and improves breeding performance in a generalist bird
While competition is generally presumed to promote intraspecific niche diversification, populations of many apparent generalist species still exhibit considerable individual variation in foraging specialisation. This suggests that different cost-benefit trade-offs may underlie individual variation in foraging specialisation. Indeed, while specialisation may improve foraging efficiency by a better knowledge of the spatio-temporal availability of resources, individuals may also become more vulnerable to fluctuations in these resources. In this study, we used multi-year GPS tracking data of 19 Herring Gulls (Larus argentatus) breeding along the Belgian coast to assess whether foraging effort and reproductive success varied among different levels of foraging specialisation. First, we quantified spatial and habitat specialisation during incubation and chick-rearing for 31 individual breeding cycles during which birds raised young until the age of 21 days. Next, we tested whether spatial and habitat specialisation were related to the daily distance covered (as a proxy for foraging effort), and to chick growth (as a proxy for reproductive success). We found that birds primarily varied in their extent of habitat specialisation. Habitat specialisation was associated with reduced daily distances covered and increased offspring growth rates, in particular the growth rate of the youngest chicks. Yet, positive effects of habitat specialisation on chick growth decreased at high levels of spatial specialisation. Our results thus demonstrate fitness benefits of foraging specialisation during our five-year study period, but also highlight the need for longer-term studies as environmental changes may cause benefits to vary throughout a lifetime.
Movements and breeding performance of Bonelli's eagle
<p>Code and data for estimate the relationship between intrinsic (experience) and extrinsic factors (weather), movement behaviours metrics (proportion of time in flight, range of movement and straitghness of trajectories) and breeding performance of Bonelli's eagles <em>Aquila fasciata (French popualtion).</em></p> <p>Mail: <a href="mailto:lise.viollat@protonmail.com">lise.viollat@protonmail.com</a><br>Twitter: @LiseViollat</p> <p><br><strong>Code files </strong></p> <ul> <li>effect_intrinsic_extrinsic_factors_movement_Bonelli: relationships between three movement behaviour metrics (proportion of time in flight, range of movement and straitghness of trajectories) with experience and local weather (rainfall, wind and temperature) in Bonelli's eagle, at a daily scale during the breeding season</li> <li>effect_intrinsic_extrinsic_factors_breeding_Bonelli.Rmd : relationships between breeding performances (breeding probability, hatching success and fledging success) of Bonelli's eagles with recruitment (breeding experience) and weather (rainfall, winf and temperature) during 3 breeding phases (pre-breeding, incubation and rearing).</li> <li>relationship_movement_breeding_Bonelli.Rmd : relationships between the productivity (number of fledging chicks) and movement behaviors metrics (proportion of time in flight, range of movement and straitghness of trajectories) of Bonelli's eagle during 3 breeding phases (pre-breeding, incubation and rearing). </li> </ul>
Data for: Breeding honey bees (Apis mellifera L.) for low and high Varroa destructor population growth: gene expression of bees performing grooming behavior
<p class="MsoNormal"><strong><span>Background</span></strong></p> <p class="MsoNormal">Social organisms, including honey bees (<em>Apis mellifera</em> L.), have defense mechanisms to control the multiplication and transmission of parasites and pathogens within their colonies. Self-grooming, a mechanism of behavioral immunity, seems to contribute to restraining the population growth of the ectoparasitic mite <em>Varroa destructor</em> in honey bee colonies. Because <em>V. destructor</em> is the most damaging parasite of honey bees, breeding them for resistance against the mite is a high priority of the beekeeping industry. We conducted a bidirectional breeding program to select honey bee colonies with low and high varroa<em> </em>population growth (LVG and HVG, respectively). Having high and low lines of bees allowed the study of genetic mechanisms underlying self-grooming behavior between the extreme genotypes. Worker bees were classified into two categories: 'light groomers' and 'intense groomers'. The brains of bees from the different categories (LVG-intense, LVG-light, HVG-intense, and HVG-light) were used for gene expression and viral quantification analyses.</p> <p class="MsoNormal"><strong><span>Results</span></strong></p> <p class="MsoNormal">Differentially expressed genes (DEGs) associated with the LVG and HVG lines were identified, including four odorant-binding proteins and a gustatory receptor. A functional enrichment analysis showed 19 enriched pathways from a list of 219 down-regulated DEGs in HVG bees, including the Kyoto Encyclopedia of Genes and Genomes (KEGG) term of oxidative phosphorylation. Additionally, bees from the HVG line showed higher levels of <em>Apis rhabdovirus</em> <em>1</em> and <em>2</em>, <em>Varroa destructor virus -1</em> (VDV-1), and <em>Deformed wing virus-A</em> (DWV-A) compared to bees of the LVG line.</p> <p class="MsoNormal"><strong><span>Conclusions</span></strong></p> <p class="MsoNormal">The difference in expression of odorant-binding protein genes and a gustatory receptor between bee lines suggests a possible link between them and the perception of irritants to trigger rapid self-grooming instances that require the activation of energy metabolic pathways. Therefore, our results provide new insights into the molecular mechanisms involved in honey bee grooming behavior. Differences in viral levels in the brains of LVG and HVG bees showed the importance of investigating the pathogenicity and potential impacts of neurotropic viruses on behavioral immunity. The results of this study advance the understanding of a trait used for selective breeding, self-grooming, and the potential of using genomic-assisted selection to improve breeding programs.</p>
Code and data for: Familiarity breeds success: pairs that meet earlier experience increased breeding performance in a wild bird population
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Data for: Breeding honey bees (Apis mellifera L.) for low and high Varroa destructor population growth: gene expression of bees performing grooming behavior
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Data from: Specialisation reduces foraging effort and improves breeding performance in a generalist bird
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Breeding performance of Common Terns (Sterna hirundo) does not decline among older age-classes
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Age-related differences in fall migration timing and performance of juvenile and adult Wood Thrushes departing from a breeding site
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Genomic prediction enables rapid selection of high-performing genets in an intermediate wheatgrass (Thinopyrum intermedium) breeding program
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Effects of back‐mounted biologgers on condition, diving and flight performance in a breeding seabird
<p>Biologging devices are providing detailed insights into the behaviour and movement of animals in their natural environments. It is usually assumed that this method of gathering data does not impact on the behaviour observed. However, potential negative effects on birds have rarely been investigated before field‐based studies are initiated. Seabirds which both fly and use pursuit diving may be particularly sensitive to increases in drag and load resulting from carrying biologging devices. We studied chick‐rearing adult common guillemots (<i>Uria aalge</i>) equipped with and without back‐mounted GPS tags over short deployments of a few days. Concurrently guillemots carried small leg‐mounted TDR devices (time‐depth recorders) providing activity data throughout. Changes in body mass and breeding success were followed for device equipped and control guillemots. At the colony level guillemots lost body mass throughout the chick‐rearing period. When‐equipped with the additional GPS tag, the guillemots lost mass at close to twice the rate they did when equipped with only the smaller leg‐mounted TDR device. The elevated mass loss suggests an impact on energy expenditure or foraging performance. When equipped with GPS tags diving performance, time‐activity budgets, and daily patterns of activity were unchanged, yet dive depth distributions differed. We review studies of tag‐effects in guillemots (<i>Uria</i> spp.) finding elevated mass loss and reduced chick‐provisioning to be the most commonly observed effects. Less information is available for behavioural measures, and results vary between studies. In general, small tags deployed over several days appear to have small or no measurable effect on the behavioural variables commonly observed in most guillemot tagging studies. However, there may still be impacts on fitness via physiological effects and/or reduced chick‐provisioning, while more detailed measures of behaviour (e.g. using accelerometery) may reveal effects on diving and flight performance.</p>
Detection dogs in nature conservation: a database on their worldwide deployment with a review on breeds used and their performance compared to other methods
<p>Over the last century, dogs have been increasingly used to detect rare and elusive species or traces of them. The use of wildlife detection dogs (WDD) is particularly well established in North America, Europe and Oceania, and projects deploying them have increased worldwide. However, if they are to make a significant contribution to conservation and management, their strengths, abilities, and limitations should be fully identified. We reviewed the use of WDD with particular focus on the breeds used in different countries and for various targets, as well as their overall performance compared to other methods, by developing and analysing a database of 1220 publications, including 916 scientific ones, covering 2464 individual cases - most of them (1840) scientific. With the worldwide increase in the use of WDD, associated tasks have changed and become much more diverse. Since 1930, reports exist for 62 countries and 407 animal, 42 plant, 26 fungi and 6 bacteria species. Altogether, 108 FCI-classified and 20 non-FCI-classified breeds have worked as WDD. While certain breeds have been preferred on different continents and for specific tasks and targets, they were not generally better suited for detection tasks than others. Overall, WDD usually worked more effectively than other monitoring methods. For each species group, regardless of breed, detection dogs were better than other methods in 88.71% of all cases and only worse in 0.98%. It was only for arthropods that Pinshers and Schnauzers performed worse than other breeds. For mono- and dicotyledons, detection dogs did less often outperform other methods. Although every breed can be trained as a WDD, choosing the most suitable dog for the task and target may speed up training and increase the chance of success. Albeit selection of the most appropriate WDD is important, excellent training, knowledge about the target density and suitability, and a proper study design all appeared to have the highest impact on performance. Moreover, an appropriate area, habitat and weather are crucial for detection dog work. When these factors are taken into consideration, WDD can be an outstanding monitoring method.</p>
Linking range wide energetic trade-offs to breeding performance in a long-distance migrant
<p>Understanding how individual trade-offs and carry-over effects along the annual cycle influence fitness is fundamental to unravel population dynamics, but such data is particularly challenging to collect in long-distance migrants. Here, with a full annual cycle perspective of Icelandic whimbrels Numenius phaeopus islandicus, we investigate trade-offs across the entire distribution, assessing migration costs and wintering energetic balance experienced throughout the wintering range (from temperate to tropical regions), and link these to breeding parameters for two wintering regions. We found that Icelandic whimbrels traded-off higher costs of migration with more favourable wintering conditions, in terms of energetic balance. By migrating further, whimbrels experience lower thermoregulatory costs and higher net energetic intake rates, resulting in a more positive energetic balance during winter. However, these differences did not appear to carry-over into the breeding season in terms of measurable effects on laying date (and, consequently, fledging success) or egg volume, suggesting that individual fitness is unlikely to be significantly influenced by previous wintering conditions, although effects on other traits may potentially occur. Nevertheless, Icelandic whimbrels seem to favour wintering locations where the conditions are more advantageous, as the abundance of individuals at the wintering sites reflects the variation in site quality.</p>
Data from: Do environmental conditions experienced in early life affect recruitment age and performance at first breeding in common goldeneye females?
Environmental conditions experienced early in life may have long-term impacts on life history traits and reproductive performance. We investigated whether ambient temperature experienced during the first two to four weeks of life and weather severity during the first two winters affected recruitment age and relative timing of breeding in the year of recruitment in female common goldeneyes (Bucephala clangula). Our sample consisted of 141 female recruits hatched in a study population in central Finland between 1985 and 2013 and captured later as breeders. About 56% of the recruited females bred for the first time when 2 years old (range 2-6 years). Individuals facing colder ambient temperatures during the first two to four weeks posthatch or more severe winter conditions during the first two winters did not recruit at an older age. Nor did maternal characteristics, relative hatch date or nest site availability affect recruitment age. For females that recruited at 2 years old, the date of first breeding was usually late relative to the population mean that year (mean difference 6.9 days, range -7 to 21 days). Our results suggest developmental buffering enables female goldeneye ducklings to mitigate the impacts of adverse environmental conditions experienced during the first weeks of life, at least in terms of first breeding.
Data from: Linking oceanographic conditions, migratory schedules and foraging behaviour during the non‐breeding season to reproductive performance in a long‐lived seabird
1. Studies of the mechanisms underlying climate-induced population changes are critically needed to better understand and accurately predict population responses to climate change. Long-lived migratory species might be particularly vulnerable to climate change as they are constrained by different climate conditions and energetic requirements during the breeding and non-breeding seasons. Yet, most studies primarily focus on the breeding season of these species lifecycle. Environmental conditions experienced in the non-breeding season may have downstream effects on the other stages of the annual lifecycle. Not investigating such effects may potentially lead to erroneous inferences about population dynamics. 2. Combining demographic and tracking data collected between 2006 and 2013 at Kerguelen Island on a long-lived migratory seabird, the Black-Browed Albatross (Thalassarche melanophris), we investigated the links between sea surface temperature during the non-breeding season and behavioural and phenological traits (at-sea behaviour and migratory schedules) while accounting for different responses between birds of different sex and reproductive status (previously failed or successful breeders). We then explored if variation in the foraging behaviour and timing of spring migration influenced subsequent reproductive performance. 3. Our results showed that foraging activity and migratory schedules varied by both sex and reproductive status suggesting different energetic requirements and constraints among individuals. Higher sea surface temperatures during late winter, assumed to reflect poor winter conditions, were associated with an earlier departure from the wintering grounds and an extended pre-breeding period. However, an earlier spring migration and an earlier return to Kerguelen grounds were associated with a lower breeding success. 4. Our results highlighted that behaviour during some periods of the non-breeding season, particularly towards the end of the wintering period and the pre-breeding period had a significant effect on the subsequent reproductive success. Therefore, caution needs to be given to all stages of the annual cycle when predicting the influence of climate on population dynamics.
Sex-specific influence of communal breeding experience on parenting performance and fitness in a burying beetle
<p>Communal breeding, wherein multiple conspecific individuals live and reproduce together during a single breeding event, may generate immediate benefits in terms of defence and reproduction. However, the carry-over effects of events in communal breeding on individual behaviour and fitness remain less studied. We experimentally tested the immediate and carry-over effects of communal breeding on parenting performance and fitness in the burying beetle (<em><span>Nicrophorus vespilloides</span></em>). These beetles bury carcasses as food resource for their offspring and themselves, and provide extended care to the developing larvae on the buried carcass. We subjected individuals of varying sizes to communal (i.e. group-breeding) or non-communal breeding (i.e. pair-breeding) experience during their first breeding event, and subsequently to non-communal breeding during their second breeding event, and measured parental effort and reproductive success during both breeding events. In communal groups, large individuals became dominant and monopolized the carcass. At the first breeding attempt, large males in communal groups spent more time providing care than large males in non-communal groups, while such a difference was not observed for large females and small females or males in communal and non-communal groups. Reproductive success was similar for individuals that bred in communal and non-communal groups during their first reproductive event, indicating no significant immediate benefits of communal breeding in terms of reproduction. Compared to males that originated from non-communal groups, males from communal groups produced a similar number, but heavier larvae during their second breeding attempt, whereas such an effect was not observed for females. Our results provide evidence for sex-specific effects of communal breeding experience on parenting performance and fitness. Such observed sex differences in carry-over effects of communal breeding on fitness may generate sexual conflict over parental effort in social animals.</p>
Carcass size, not source or taxon, dictates breeding performance and carcass use in burying beetle
<p>This repository contains the data and R code for analyzing the breeding outcomes, carcass use, and larval growth of the burying beetle as well as the nutritional composition of carcass tissue. (Information below is also provided in the README file.)</p> <p> </p> <p><strong>01_Data_Raw: </strong>This folder contains three original datasheets recorded during data collection.</p> <p>File "Breeding_Data_All.xls": This file contains the raw data on carcass attributes, parent sizes, breeding outcomes, and carcass use from the breeding experiments. Each row represents an observation from one breeding pair.</p> <p>File "Nutrition_Data.xls": This file contains the raw data on the tissue nutrient content of lab and wild carcasses from the nutritional composition analysis. Each row represents an observation from one carcass tissue sample.</p> <p>File "Larval_Growth_Data.xls": This file contains the raw data on the larval weight from the feeding experiments. Each row represents an observation from one larva.</p> <p> </p> <p><strong>02_R_Code:</strong> This folder contains the R scripts for data analyses and visualization.</p> <p>File "01_Data_Cleaning.R": This script cleans and organizes the raw datasheets in the folder "<strong>01_Data_Raw</strong>" and saves the cleaned datasheets in the folder "<strong>03_Outputs > Data_Clean</strong>" for data analyses and visualization.</p> <p>File "02_Models_by_Carcass_Source.R": The script analyzes the relationships between carcass size vs. breeding outcomes and carcass use efficiency on lab and wild carcasses.</p> <p>File "03_Models_by_Carcass_Taxon.R": The script analyzes the breeding outcomes on wild carcasses (mammals, birds, and reptiles).</p> <p>File "04_Models_Nutrition_and_Larval_Growth.R": This script analyzes the tissue nutrient content of lab and wild carcasses as well as the larval growth on these carcasses.</p> <p>File "05_Figures.R": This script generates the figures in the study.</p> <p> </p> <p><strong>03_Outputs: </strong>This folder contains a subfolder "<strong>Data_Clean</strong>", which contains three cleaned datasheets used for data analyses and visualization.</p> <p>File "Breeding_Data_Clean.csv": This file contains cleaned data on carcass attributes, parent sizes, breeding outcomes, and carcass use from the breeding experiments. Each row represents an observation from one breeding pair.</p> <p>File "Nutrition_Data_Clean.csv": This file contains the cleaned data on the tissue nutrient content of lab and wild carcasses from the nutritional composition analysis. Each row represents an observation from one carcass tissue sample.</p> <p>File "Larval_Growth_Clean.csv": This file contains the cleaned data on the larval weight from the feeding experiments. Each row represents an observation from one larva.</p> <p> </p> <p><em>Column descriptions</em></p> <p>File "Breeding_Data_Clean.csv":</p> <p>1. date: The starting date of the breeding experiments.</p> <p>2. carcass_sp_Chinese: The Chinese name of the carcass animal.</p> <p>3. carcass_type: The source of the carcass (lab or wild).</p> <p>4. carcass_taxon: The taxon of the carcass (mammal, bird, or reptile).</p> <p>5. carcass_weight: The initial weight of the carcass (g).</p> <p>6. parent_generation: The generation number of the breeding parents.</p> <p>7. pair_id: The ID of the breeding pair.</p> <p>8. generation_pair_id: The combined ID of the generation number and breeding pair.</p> <p>9. male_size: The pronotum width of the male parent (mm).</p> <p>10. female_size: The pronotum width of the female parent (mm).</p> <p>11. clutch_size: The number of eggs laid by the female.</p> <p>12. n_larvae: The number of larvae.</p> <p>13. total_larval_mass: The total weight of the larvae.</p> <p>14. carcass_weight_loss: The difference between the initial carcass weight and the carcass weight at the end of the breeding experiments.</p> <p>15. breeding_success: Whether there was at least one larva in the breeding container.</p> <p>16. prop_eggs_developed: The proportion of eggs that developed as larvae, calculated as the number of larvae divided by clutch size.</p> <p>17. average_larval_mass: The average weight of each larva, calculated as the total larval weight divided by the number of larvae.</p> <p>18. larval_density: The density of larvae on the carcass, calculated as the number of larvae divided by carcass weight.</p> <p>19. prop_carcass_used: The proportion of carcass tissue used by the larvae, calculated as carcass weight loss divided by the initial carcass weight.</p> <p> </p> <p>File "Nutrition_Data_Clean.csv":</p> <p>1. block_id: The ID of the analysis.</p> <p>2. carcass_id: The ID of the carcass.</p> <p>3. carcass_type: The source of the carcass (lab or wild).</p> <p>4. carcass_taxon: The taxon of the carcass (mammal, bird, or reptile).</p> <p>5. tissue_type: The type of the carcass tissue sampled (muscle or viscera).</p> <p>6. tissue_replication: The replication number of the tissue sample from each carcass.</p> <p>7. wet_mass_g: The wet weight of the tissue sample (g).</p> <p>8. water_mass_g: The water weight of the tissue sample (g). </p> <p>9. dry_mass_g: The dry weight of the tissue sample (g).</p> <p>10. protein_mass_g: The protein weight of the tissue sample (g).</p> <p>11. fat_mass_g: The fat weight of the tissue sample (g).</p> <p>12. total_mass_g: The total water, protein, and fat weight of the tissue sample (g).</p> <p>13. prop_protein: The proportion of protein content in the tissue sample, calculated as the protein weight divided by the total weight.</p> <p>14. prop_fat: The proportion of protein content in the tissue sample, calculated as the fat weight divided by the total weight.</p> <p> </p> <p>File "Larval_Growth_Clean.csv":</p> <p>1. block_id: The ID of the experimental block (two rounds of feeding experiments were conducted).</p> <p>2. carcass_id: The ID of the carcass (nested within the experimental block).</p> <p>3. carcass_type: The source of the carcass (lab or wild).</p> <p>4. carcass_taxon: The taxon of the carcass (mammal, bird, or reptile).</p> <p>5. tissue_type: The type of the carcass tissue sampled (muscle or viscera).</p> <p>6. larva_replication: The larva replication number (nested within each tissue type and carcass ID).</p> <p>7. tissue_mass_g: The weight of the carcass tissue fed to the larva.</p> <p>8. family_id: The family ID of the parents of the larva. </p> <p>9. success: Whether the larva survived.</p> <p>10. initial_larval_mass_g: The larval weight at the start of the experiment (g). </p> <p>11. end_larval_mass_g: The larval weight at the end of the experiment (g). </p> <p>12. larval_weight_gain_g: The difference between the initial larval weight and the end larval weight (g). </p> <p>13. mean_prop_protein: The average protein content of the three tissue samples from each carcass.</p> <p>14. mean_prop_fat: The average fat content of the three tissue samples from each carcass.</p> <p> </p>
Data from: Experimentally provided conspecific cues boost bird territory density but not breeding performance
Decision-making during different life-history stages requires information, which is obtained through own or others' experience and interaction with the environment. In birds, song is important for territory defense and mate attraction. Although song has evolved to purposely convey information, it can be inadvertently exploited by conspecifics. Experiments attempting to attract focal species by playing back their song are numerous, yet the consequences for reproductive performance remain little understood. In 2013 and 2014, settlement, reproduction, and extrapair paternity of Phylloscopus sibilatrix were assessed in a randomized experiment. We hypothesized that territory number, reproductive performance, and extrapair paternity would be higher on song plots (wood warbler song playbacks during prebreeding periods) than on control plots (no wood warbler song playback). On song plots, 3 times more territories were established, settlement occurred faster, and maximum plot occupancy was higher compared with control plots. Pairing rate, daily nest survival rate, mean clutch size, mean number of nestlings and fledglings, rates of extrapair young, nest abandonment, and nest predation did not differ between treatments, but fledging success was lower on song plots compared with control plots. This study shows the important role social cues can play for territory selection of birds, but also exemplifies the necessity for postattraction evaluation of reproduction to rule out negative effects of artificial attraction. Decreased fledging success on song plots and ambiguity about consequences of artificial attraction for distribution and settling dynamics of the species give reason to further evaluate whether acoustic attraction represents a suitable method for songbird conservation.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
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The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
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