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37 results for “foraging efficiency”
Data set for paper on Australian fur seal prey capture and foraging efficiency
<p>Data set for paper on Australian fur seal prey capture and foraging efficiency</p>
Figure 4 in Native food spectrum, size-matching and foraging efficiency of the Mediterranean harvester ant Messor wasmanni (Hymenoptera: Formicidae)
Figure 4. Scatter diagram load ratio versus ant size. The load ratio was calculated as follows: ant size (head width) + load size/ant size. Data are from random samples of returning foragers of a single M. wasmanni colony. One dot represents one observation (N = 776). Linear regression analysis revealed a low negative correlation (R² = 0.14, p = 0.0001) between ant size and load ratio. The larger the worker size class, the smaller the range in the load ratio. Residuals from regressions were approximately normally distributed around zero in all cases.
Figure 5 in Native food spectrum, size-matching and foraging efficiency of the Mediterranean harvester ant Messor wasmanni (Hymenoptera: Formicidae)
Figure 5. Mean foraging efficiency (in %) per day. Calculations were performed separately per size class and per season. Foraging efficiency varied considerably over the seasons and between size classes. Sample size represented by numbers in bars. Minor = minor-sized workers, Media = media-sized workers, Major = major-sized workers.
Figure 3 in Native food spectrum, size-matching and foraging efficiency of the Mediterranean harvester ant Messor wasmanni (Hymenoptera: Formicidae)
Figure 3. Scatter diagram load size versus ant size. Data are from random samples of returning foragers of a single M. wasmanni colony. One dot represents one observation (N = 776). Linear regression analysis revealed a very low positive correlation (R² = 0.02, p = 0.0001) between ant size and load size, indicating only a small tendency for majorsized workers to carry larger loads than minor-sized workers. Residuals from regressions were approximately normally distributed around zero in all cases.
Figure. 1 in Native food spectrum, size-matching and foraging efficiency of the Mediterranean harvester ant Messor wasmanni (Hymenoptera: Formicidae)
Figure. 1. Overview of the topics discussed in the paper. Surface activity was analysed at both colony level and individual level.
Figure 2 in Native food spectrum, size-matching and foraging efficiency of the Mediterranean harvester ant Messor wasmanni (Hymenoptera: Formicidae)
Figure 2. Frequency distribution of harvested material in percent (May, July-August and October 2009). During periods of aboveground activity, returning foragers carrying food items and other materials were collected at random from foraging trails 10 cm far from the nest entrances.
Data from: Site fidelity increases reproductive success by increasing foraging efficiency in a marine predator
<p>Seabirds must find food efficiently in the dynamic ocean environment to succeed at raising chicks. In theory, site familiarity, gained by prior experience in a place, should increase foraging efficiency when prey is predictable, and translate into increased reproductive success, though this is difficult to test empirically. To address this, we examined foraging-site fidelity in Magellanic penguins, <em>Spheniscus magellanicus, </em>using movement data from 180 individuals tracked during 23 breeding seasons when penguins make repeated trips from their colony to feed chicks. We tested whether chlorophyll-a concentration, as a proxy for ocean productivity, affects foraging-site fidelity. We then tested whether foraging-site fidelity affects foraging efficiency and reproductive success. Mean foraging-site fidelity was higher in years with higher ocean productivity, when fronts had stronger gradients in temperature and chlorophyll, and prey was likely more predictable. When returning to previously visited foraging sites, penguins arrived and returned faster than predicted for a trip of a given distance, leading to lower mean trip durations and more frequent trips in penguins with high site fidelity. Increased foraging efficiency and chick-feeding frequency in turn led to increased chick survival. Our study reveals that foraging efficiency is a key mechanism linking foraging-site fidelity and reproductive success.</p>
Males miss and females forgo: auditory masking from vessel noise impairs foraging efficiency and success in killer whales - CALIBRATED MOVEMENT DATA AND VARIABLES SUPPORTING ANALYSES
<p><strong>Description of the data and file structure<br></strong>This record contains data from animal-borne biologging instruments (Dtags) temporarily affixed to fish-eating killer whales, supporting the analyses presented in the following article:</p> <p> Tennessen. J.B., Holt, M.M., Wright, B.M., Hanson, M.B., Emmons, C.K., Giles, D.A., Hogan, J.T., Thornton, S.J., Deecke, V.B. 2024. Males miss and females forgo: auditory masking from vessel noise impairs foraging efficiency and success in killer whales. <em>Global Change Biology</em>.<strong> </strong>In press.</p> <p>The data include the following: (1) calibrated movement data from analyzed Dtag deployments, and (2) a spreadsheet containing the variables included in the fully-saturated and final models listed in Table 2 in the article cited above. All methodological details necessary to contextualize analysis procedures are provided in the methods section of the article. The following data files are available under separate DOIs: 10.5281/zenodo.13333019 - all 2009 & 2010 audio data; 10.5281/zenodo.13328931 - all 2011 & 2014 audio data.</p> <p>These data are provided by NOAA Fisheries' Northwest Fisheries Science Center, and Fisheries and Oceans Canada, to support reproducibility of all statistical analyses presented in the article. Please cite your usage of our data. For inquiries about data use, or for general questions, please contact Dr. Jennifer B. Tennessen, at jtenness@uw.edu.</p> <p> </p> <p><strong>Description of the movement data files<br></strong>The movement files have been calibrated from the raw data and are ready to use. The files contain the .mat extension, and need to be opened using Matlab and the tagtools tool kit available at https://github.com/animaltags . Tutorials for working with the toolkit are available at animaltags.org . These files contain several vector and matrix variables. We define those used in our analyses below. For questions about how to work with these files, please contact Dr. Jennifer B. Tennessen, at jtenness@uw.edu.</p> <p>Aw: calibrated triaxial accelerometer data (converted from tag frame to whale frame)</p> <p>fs: sample rate (50 Hz)</p> <p>head: animal's circular heading (rotation about the dorsal-ventral axis, in radians)</p> <p>Mw: calibrated triaxial magnetometer data (converted from tag frame to whale frame)</p> <p>p: depth (in meters)</p> <p>pitch: animal's pitch (rotation about the left-right axis, in radians)</p> <p>roll: animal's roll (rotation about the anterior-posterior axis, in radians)</p> <p>tempr: temperature recorded on tag (in Celsius)</p> <p>TT: time cues for the start and end of every analyzed dive within a deployment. This matrix contains 6 columns:<br>-col 1: start cue (in sec)<br>-col 2: end cue (in sec)<br>-col 3: maximum depth of dive (m)<br>-col 4: time cue at max depth (in sec)<br>-col 5: mean depth (m)<br>-col 6: mean compression</p> <p> </p> <p><strong>Description of the analyzed variables<br></strong>The data are provided column-wise in a spreadsheet, whereby each column contains one of several variables used to build the corresponding models listed in Table 2 in the above article. Model details are provided in the above article, including the statistical packages needed to run the models. </p> <p><em>The following is a list of variable names (column headers) and their corresponding definitions:<br></em><strong>bzsounds:</strong> binary presence (1)/absence (0) of buzz bouts within a dive. Buzzing is defined as the occurrence of echolocation clicks with an inter-click interval < 11 ms<br><strong>code:</strong> categorical identifier of the numerical week of year in which the tag was deployed (e.g., week 33 of 2009 is different than week 33 of 2011)<br><strong>deployment:</strong> the event whereby a tag was affixed to an individual killer whale and data were collected via tag sensors; each deployment was assigned a unique deployment ID, consisting of the first letter of the Genus and species names (“oo” for Orcinus orca), followed by two digits corresponding to the year (“09” = 2009), followed by the Julian day of the year (e.g. “234”), followed by a letter indicating the deployment order of the day. NRKW deployments were assigned a through l, and SRKW deployments were assigned m through z (e.g. “a” = first deployment of the day for NRKW, “m” = first deployment of the day for SRKW)<br><strong>durwho: </strong>duration of a whole dive, in seconds. Dives were defined as all departures from the surface, to at least 1 m or deeper, followed by a return to within 0.5 m of the surface<br><strong>divenum: c</strong>hronological identifier for dive position within a deployment (e.g., for the 10<sup>th</sup> dive within a deployment, divenum = 10)<br><strong>kindet: </strong>binary presence (1)/absence (0) of a prey capture event within a dive. Prey capture was informed by the occurrence of stereotyped movement signatures in sensor data indicative of prey capture, following an established method validated with visual and acoustic confirmation of predation events. Prey capture is defined as the occurrence of three movement variables indicative of prey capture (peak jerk, roll and heading variance) each exceeding pre-determined thresholds (see Tennessen et al. 2019b in above article for details)<br><strong>maxdep:</strong> maximum depth of a dive, in meters<br><strong>NLmax: </strong>the maximum noise level received during a dive, measured as the root-mean-square sound pressure level (dB re 1 mPa) within one second bins over the 15-45 kHz frequency band<br><strong>population:</strong> population to which the tagged whale belongs (NRKW = Northern Resident killer whale; SRKW = Southern Resident killer whale)<br><strong>sex:</strong> sex of tagged whale (F = female, M = male, NA = unknown)<br><strong>sc:</strong> binary presence (1)/absence (0) of slow-click sounds within a dive. Slow-clicking is defined as the occurrence of echolocation clicks with an inter-click interval >100 ms<br><strong>tagID:</strong> identifier for the individual tag used for each deployment<br><strong>year:</strong> year of deployment</p>
Data for "A neural correlate of learning fails to predict foraging efficiency in the bumble bee Bombus terrestris"
<p>This file contain the foraging and synaptic density data collected in the summer of 2019 by Pasquier, Pull, Ott and Leadbeater. Used in the paper titled "A neural correlate of learning fails to predict foraging efficiency in the bumble bee <em>Bombus terrestris</em>".</p>
Data from: Reproductive success is energetically linked to foraging efficiency in Antarctic fur seals
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Data from: Site fidelity increases reproductive success by increasing foraging efficiency in a marine predator
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Global warming affects foraging efficiency of fish by influencing mutual interference
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Data from: Depth dependent dive kinematics suggest cost-efficient foraging strategies by tiger sharks
Tiger sharks Galeocerdo cuvier are a keystone, top-order predator that are assumed to engage in cost-efficient movement and foraging patterns. To investigate the extent to which patterns of oscillatory diving by these animals conform to these patterns, we used a biologging approach to model their cost of transport. High-resolution biologging tags with tri-axial sensors were deployed on 21 tiger sharks at Ningaloo Reef for durations of 5-48 hours. Using overall dynamic body acceleration (ODBA) as a proxy for energy expenditure, we modelled the cost of transport of oscillatory movements of varying geometries in both horizontal and vertical planes for tiger sharks. The cost of horizontal transport was minimized by descending at the lowest possible angle and ascending at an angle of 5-14°, meaning that vertical oscillations conserved energy compared to swimming at a level depth. Reduction of vertical travel costs occurred at steeper angles. The absolute dive angles of tiger sharks increased between inshore and offshore zones, presumably to reduce the cost of transport while continuously hunting for prey in both benthic and surface habitats. Oscillatory movements of tiger sharks conform to strategies of cost-efficient foraging, and shallow inshore habitats appear to be an important habitat for both hunting prey and conserving energy while travelling.
Data from: Sex differences in risk perception in deep-diving bottlenose dolphins leads to decreased foraging efficiency when exposed to human disturbance
1. Individuals make behavioural decisions by weighing potential advantages and costs (e.g. increased food intake vs. increased risk of predation). When animals change their activities in response to a perceived threat, their energetic input may decline. Marine ecotourism, including whale and dolphin watching, is growing globally and cetaceans perceive interactions with tour vessels as a form of risk. Observable behavioural changes need to be linked to bioenergetic effects to determine the potential population consequences of this disturbance. 2. We developed a theoretical optimal dive model for bottlenose dolphins under three potential types of perceived risk resulting from human interactions at the surface (decreasing instantaneous risk, increasing instantaneous risk and no risk). We compared the predictions of these theoretical models to observed dive cycles of foraging male and female dolphins in the presence and absence of tour vessels. We used mixture models to classify dive types and mixed effects models to analyse changes in the interbreath interval of surface and bottom dives and the frequency of estimated bottom dives. 3. Males significantly increased bottom time and performed fewer bottom dives when boats were present, matching predictions of our theoretical model for perceived decreasing instantaneous risk. In contrast, females significantly decreased bottom times and increased the frequency of bottom dives, matching predictions from the model for perceived increasing instantaneous risk. Therefore, our empirical results suggest differences in the perception of risk between sexes. 4. Synthesis and applications. By comparing theoretical predictions with observed dive data, our study suggests that boat interactions during foraging can cause decreased net energy gain over a foraging bout for both sexes, with females being more impacted. The population under study is currently listed as critically endangered. Understanding whether these predicted energetic impacts affect an individual's vital rates will provide a link to the population-level consequences of this disturbance. Previous analytical approaches have failed to capture the costs associated with disturbance during foraging, leading to management recommendations that only protect animals from increased energetic expenditure. We suggest that the current management scheme should be revised to include foraging areas in order to secure the energy intake of animals.
Metabolic rate and foraging efficiency
<p><span>Metabolic rate is the rate at which organisms process energy and is often considered as the fundamental driver of life history processes. The link between metabolic rate and life history is critically mediated <em>via</em> foraging, which shapes the energy acquisition patterns of an individual. This predicts that individuals with different metabolic rates likely vary in their foraging strategies, although such a link has rarely been empirically investigated in the context of optimal foraging theory - a powerful framework for understanding how animals maximize their foraging returns. Many central place foragers such as honeybees maximize their energetic efficiency rather than the rate of energetic gain, given the critical role of energetic costs on foraging decisions. We therefore tested if individuals of low and high metabolic rates differ in efficiency maximization, using genetic lines of honeybees with different metabolic rates. Our results show that low metabolic rate foragers visit more flowers during a foraging trip and have a higher energetic efficiency than high metabolic rate foragers in both low and high resource conditions. We discuss the significance of these results in the context of division of labor and the adaptive role of phenotypic diversity in metabolic rate in a social insect colony.</span></p>
Data from: Captive birds exhibit greater foraging efficiency and vigilance after anti-predator training
<p>Rearing animals in captivity for conservation translocation is a complex undertaking that demands interdisciplinary management tactics. The maladapted behaviors that captive animals can develop create unique problems for wildlife managers seeking to release these animals into the wild. Often, released captive animals show decreased survival due to predation and their inability to display appropriate anti-predator, vigilance, and risk-analysis behaviors. Additionally, released animals may have poor foraging skills, further increasing their vulnerability to predation. Often conservation translocation programs use anti-predator training to ameliorate these maladapted behaviors before release but find mixed results in behavioral responses. The behavioral scope of analyzing the effect of anti-predator trainings is frequently narrow; the effect of this training on an animal's risk-analysis competency, or ability to assess the predation risk of a foraging patch and subsequently adjust its behavior, remains unstudied. Using a captive reared passerine species, the American robin (<em>Turdus migratorius</em>) (46 individuals), we applied an experimental giving up density test (GUD) to analyze the effect of anti-predator training on the robins' vigilance/risk-analysis behaviors, patch choice, and the GUD of food left behind after one foraging session. Robins moved and foraged freely between three foraging patches of differing predation risk before and after a hawk silhouette was presented for one minute. Results indicate that after anti-predator training, robins displayed increased vigilance across most foraging patches and better foraging efficiency (higher vigilance and latency to forage with simultaneous lower GUD) in the safest patch. These results can have positive survival implications post-release, however, more research on this training is needed because anti-predator training has the potential to elicit indiscriminate increased vigilance to the detriment of foraging gains. Further research is required to standardize GUD's application in translocation programs with multigenerational captive-bred animals to fully comprehend its effectiveness in identifying and correcting maladaptive behaviors. GUD tests combined with behavioral analysis should be used by conservation translocation managers to examine the need for anti-predator and foraging trainings, the effects of trainings, and a group's suitability for release.</p>
Males miss and females forgo: auditory masking from vessel noise impairs foraging efficiency and success in killer whales - ALL 2011 & 2014 AUDIO DATA
<p><strong>Description of the data and file structure<br></strong>This record contains all 2011 & 2014 audio data from animal-borne biologging instruments (Dtags) temporarily affixed to fish-eating killer whales, supporting the analyses presented in the following article:</p> <p> Tennessen. J.B., Holt, M.M., Wright, B.M., Hanson, M.B., Emmons, C.K., Giles, D.A., Hogan, J.T., Thornton, S.J., Deecke, V.B. 2024. Males miss and females forgo: auditory masking from vessel noise impairs foraging efficiency and success in killer whales. <em>Global Change Biology</em>.<strong> </strong>In press.</p> <p>The data include the following: the 2011 & 2014 audio files from analyzed Dtag depoyments. All methodological details necessary to contextualize analysis procedures are provided in the methods section of the article. The following data files are available under separate DOIs: 10.5281/zenodo.13333019 - all 2009 & 2010 audio data; 10.5281/zenodo.13308835 - (1) all calibrated movement data from analyzed Dtag deployments, and (2) a spreadsheet containing the variables included in the fully-saturated and final models listed in Table 2 in the article cited above.</p> <p>These data are provided by NOAA Fisheries' Northwest Fisheries Science Center, and Fisheries and Oceans Canada, to support reproducibility of all statistical analyses presented in the article. Please cite your usage of our data. For inquiries about data use, or for general questions, please contact Dr. Jennifer B. Tennessen, at jtenness@uw.edu.</p> <p> </p> <p><strong>Description of audio data files</strong><br>The data files contain the .dtg extension. This is the compressed raw data from all analyzed deployments. Once files are downloaded, they will need to be decompressed, which is done using the tagtools tool kit for Matlab, R or Octave, available at https://github.com/animaltags .</p> <p>Each deployment is named using the first letter of the genus and species name ("oo" for Orcinus orca), followed by the two-digit year (e.g., 09 for 2009), followed by the 3-digit Julian day (e.g., 246), followed by a letter denoting the population (a-d for Northern Residents, m for Southern Residents), followed by a series of numbers that denote the specific block (on the tag memory board) from which the data came. All files from a deployment should be put within a folder for that deployment, so that the functions within the tagtools tool kit can locate them.</p> <p>Once the .dtg files are decompressed, there will be 4 new files for every decompressed file, with extensions as follows: .wav (audio) as well as .pk, .swv, .txt. The audio files are ready to use in .wav form, and can be viewed using any audio software. We recommend using Matlab with the tagtools tool kit, or viewing the files in batch mode within RavenPro (https://store.birds.cornell.edu/collections/raven-sound-software).</p> <p>We provide calibrated movement data (see DOI: 10.5281/zenodo.13308835). However, if users wish to run their own calibration from raw movement data, the .swv files are used for this purpose along with the tagtools tool kit in Matlab, R or Octave, available at https://github.com/animaltags .</p>
Males miss and females forgo: auditory masking from vessel noise impairs foraging efficiency and success in killer whales - ALL 2009 & 2010 AUDIO DATA
<p><strong>Description of the data and file structure<br></strong>This record contains all 2009 & 2010 audio data from animal-borne biologging instruments (Dtags) temporarily affixed to fish-eating killer whales, supporting the analyses presented in the following article:</p> <p> Tennessen. J.B., Holt, M.M., Wright, B.M., Hanson, M.B., Emmons, C.K., Giles, D.A., Hogan, J.T., Thornton, S.J., Deecke, V.B. 2024. Males miss and females forgo: auditory masking from vessel noise impairs foraging efficiency and success in killer whales. <em>Global Change Biology</em>.<strong> </strong>In press.</p> <p>The data include the following: the 2009 & 2010 audio files from analyzed Dtag depoyments. All methodological details necessary to contextualize analysis procedures are provided in the methods section of the article. The following data files are available under separate DOIs: 10.5281/zenodo.13328931 - all 2011 & 2014 audio data; 10.5281/zenodo.13308835 - (1) all calibrated movement data from analyzed Dtag deployments, and (2) a spreadsheet containing the variables included in the fully-saturated and final models listed in Table 2 in the article cited above.</p> <p>These data are provided by NOAA Fisheries' Northwest Fisheries Science Center, and Fisheries and Oceans Canada, to support reproducibility of all statistical analyses presented in the article. Please cite your usage of our data. For inquiries about data use, or for general questions, please contact Dr. Jennifer B. Tennessen, at jtenness@uw.edu.</p> <p> </p> <p><strong>Description of audio data files</strong><br>The data files contain the .dtg extension. This is the compressed raw data from all analyzed deployments. Once files are downloaded, they will need to be decompressed, which is done using the tagtools tool kit for Matlab, R or Octave, available at https://github.com/animaltags .</p> <p>Each deployment is named using the first letter of the genus and species name ("oo" for Orcinus orca), followed by the two-digit year (e.g., 09 for 2009), followed by the 3-digit Julian day (e.g., 246), followed by a letter denoting the population (a-d for Northern Residents, m for Southern Residents), followed by a series of numbers that denote the specific block (on the tag memory board) from which the data came. All files from a deployment should be put within a folder for that deployment, so that the functions within the tagtools tool kit can locate them.</p> <p>Once the .dtg files are decompressed, there will be 4 new files for every decompressed file, with extensions as follows: .wav (audio) as well as .pk, .swv, .txt. The audio files are ready to use in .wav form, and can be viewed using any audio software. We recommend using Matlab with the tagtools tool kit, or viewing the files in batch mode within RavenPro (https://store.birds.cornell.edu/collections/raven-sound-software).</p> <p>We provide calibrated movement data (see DOI: 10.5281/zenodo.13308835). However, if users wish to run their own calibration from raw movement data, the .swv files are used for this purpose along with the tagtools tool kit in Matlab, R or Octave, available at https://github.com/animaltags .</p>
Individual bee foragers are less efficient transporters of pollen for the plants from which they collect the most pollen into their scopae
<p><strong>PREMISE</strong>: Bees provision most of the pollen they remove from anthers to their larvae and transport only a small proportion to stigmas, which can negatively affect plant fitness. Though most bee species collect pollen from multiple plant species, we know little about how the efficiency of bees' pollen transport varies among host plant species, or how it relates to other aspects of generalist bee foraging behavior that benefit plant fitness, such as specialization on individual foraging bouts.</p> <p><strong>METHODS</strong>: We compared the pollen collected and transported by three bee species for 46 co-occurring plant species. Specifically, we compared the relative abundance of pollen taxa in individual bees' scopae, structures where bees store pollen to provision larvae, with the relative abundance of pollen taxa on the rest of bees' bodies, which is more likely to be transferred to stigmas. </p> <p><strong>RESULTS</strong>: Bees carried five times more pollen grains in their scopae than elsewhere on their bodies. Within foraging bouts, bees were relatively specialized in their pollen collection, but transported proportionally less pollen for the host plants on which they specialized. Across foraging bouts, two bee species transported proportionally less pollen for some of their host plants than for others, though differences didn't consistently follow the same trend as at the foraging bout scale.</p> <p><strong>CONCLUSIONS</strong>: Our results suggest that foraging bout specialization, which is known to reduce heterospecific pollen transfer, also results in less efficient pollen transport. Thus, bee foragers that visit predominantly one plant species may have contrasting effects on that plant's fitness. </p>
Individual bee foragers are less efficient transporters of pollen for the plants from which they collect the most pollen into their scopae
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