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opencc-zeroMar 2023View details →

Data from: Changes in movement characteristics in response to private and social information acquisition of socially foraging fish

To overcome the cost of competition resulting from foraging socially, individuals may balance their use of private (i.e. acquired from personal sampling) and social (i.e. acquired by watching other individuals) information to adjust their foraging strategy accordingly. Reliability of private information about environmental characteristics, such as the spatial distribution of prey, is thus likely to affect individual movement and social interactions. We aimed to investigate how movement characteristics of foraging individuals changed as they acquired reliable information about the spatial occurrence of prey in a foraging context. We allowed guppies (Poecilia reticulata) to develop the reliability of their private knowledge about prey spatial occurrence by repeatedly testing shoals in a foraging task under three experimental distributions of prey: 1) aggregated prey forming three patches located in fixed locations, 2) scattered distribution of prey with random locations, or 3) no prey (used as control). Using individual time series of spatial coordinates, we computed a suite of movement variables reflecting search effort, social proximity and locomotion characteristics during foraging, to examine changes occurring over repeated trials. Over time, individuals foraging on either scattered or aggregated prey travelled greater distances, showed an increasing distance to their closest neighbour and became more stochastic in their acceleration profile, compared to control individuals. We found that behaviour changed as private information increased over time, with a behavioural shift and an increase of collective foraging efficiency occurring on the third testing day. Social proximity was the major predictor of foraging success in the absence of prior foraging information, while search effort became the most important predictors of foraging success as information increased. In conclusion, we show that individual movement patterns changed as they acquired private information. Contrary to our predictions, the spatial distribution of prey did not affect any of the movement variables of interest.

opencc-zeroApr 2023View details →

Food webs coupled in space: Consumer foraging movement affects both stocks and fluxes

The exchange of material and individuals between neighbouring food webs is ubiquitous and affects ecosystem functioning. Here, we explore animal foraging movement between adjacent, heterogeneous habitats and its effect on a suite of interconnected ecosystem functions. Combining dynamic food-web models with nutrient-recycling models, we study foraging across habitats that differ in fertility and plant diversity. We found that net foraging movement flowed from high to low fertility or high to low diversity and boosted stocks and flows across the whole loop of ecosystem functions including biomass, detritus, and nutrients in the recipient habitat. Contrary to common assumptions, however, the largest flows were often between the highest and intermediate fertility habitats rather than highest and lowest. The effect of consumer influx on ecosystem functions was similar to the effect of increasing fertility. Unlike fertility, however, consumer influx caused a shift towards highly predator-dominated biomass distributions, especially in habitats that were unable to support predators in the absence of consumer foraging. This shift resulted from both direct and indirect effects propagated through the interconnected ecosystem functions. Only by considering both stocks and fluxes across the whole loop of ecosystem functions do we uncover the mechanisms driving our results. In conclusion, the outcome of animal foraging movements will differ from that of dispersal and diffusion. Together we show how considering active types of animal movement and the interconnectedness of ecosystem functions can aid our understanding of the patchy landscapes of the Anthropocene.

publicApr 2023View details →

Food webs coupled in space: Consumer foraging movement affects both stocks and fluxes

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publicMar 2023View details →

Data from: Changes in movement characteristics in response to private and social information acquisition of socially foraging fish

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opencc-zeroMay 2022View details →

Sowerby's beaked whale biosonar and movement strategy indicate deep-sea foraging niche differentiation in mesoplodont whales

Closely related species are expected to diverge in foraging strategy, reflecting the evolutionary drive to optimize foraging performance. The most speciose cetacean genus, Mesoplodon, comprises beaked whales with little diversity in external morphology or diet, and overlapping distributions. Moreover, the few studied species of beaked whales (Ziphiidae) show very similar foraging styles with slow, energy-conserving movement during long, deep foraging dives. This raises the question of what factors drive their speciation. Using data from animal-attached tags and aerial imagery, we tested the hypothesis that two similar-sized mesoplodonts, Sowerby's(Mesoplodon bidens) and Blainville's (Mesoplodon densirostris) beaked whales, exploit a similar low-energy niche. We show that, compared with the low-energy strategist Blainville's beaked whale, AQ6 Sowerby's beaked whale lives in the fast lane. While targeting a similar mesopelagic/bathypelagic foraging zone, they consistently swim and hunt faster, perform shorter deep dives, and echolocate at a faster rate and with higher frequency clicks. Further, extensive nearsurface travel between deep dives challenges the interpretation of beaked whale shallow inter-foraging dives as a management strategy for decompression sickness. The distinctively higher frequency echolocation clicks do not hold apparent foraging benefits. Instead, we argue that a high-speed foraging style influences dive duration and echolocation behaviour, enabling access to a distinct prey population. Our results demonstrate that beaked whales exploit a broader diversity of deep-sea foraging and energetic niches than hitherto suspected. The marked deviation of Sowerby's beaked whales from the typical ziphiid foraging strategy has potential implications for their response to anthropogenic sounds, which appears to be strongly behaviourally driven in other ziphiids.

opencc-zeroDec 2021View details →

Seascapes and foraging success: movement and resource discovery by a benthic marine herbivore

1. Spatially concentrated resources result in patch-based foraging, wherein the detection and choice of patches as well as the process of locating and exploiting resource patches involve moving through an explicit landscape composed of both resources and barriers to movement. An understanding of behavioural responses to resources and barriers is key to interpreting observed ecological patterns. 2. We examined the process of resource discovery in the context of a heterogeneous seascape using sea urchins and drift kelp in urchin barrens as a model system. Under field conditions, we manipulated both the presence of a highly valuable resource (drift kelp) and a barrier to movement (sandy substratum) to test the interacting influence of these two factors on the process of resource discovery in barren grounds by urchins. We removed all foraging urchins (Strongylocentrotus droebachiensis) from replicate areas and monitored urchin recolonization and kelp consumption. We tested two hypotheses: 1) unstable substratum is a barrier to urchin movement and 2) the movement behaviour of sea urchins is modified by the presence of drift kelp. 3. Very few urchins were found on sand, sand was a permeable barrier to urchin movement, and the permeability of this barrier varied between sites. In general, partial recolonization occurred strikingly rapidly, but sand slowed the consumption of drift kelp by limiting the number of urchins. Differences in the permeability of sand barriers between sites could be driven by differences in the size structure of urchin populations, indicating size-specific environmental effects on foraging behaviour. 4. We demonstrate the influence of patchy seascapes in modulating grazing intensity in barren grounds through modifications of foraging behaviour. Behavioural processes modified by environmental barriers play an important role in determining grazing pressure, the existence of refuges for new algal recruits, and ultimately the dynamics of urchin-algal interactions in barren grounds.

zenodo36/100

Shipping alters the movement and behavior of Arctic cod (B. saida), a keystone forage fish in Arctic marine ecosystems

Dataset for Ivanova et al. (2019): Shipping alters the movement and behavior of Arctic cod (B. saida), a keystone forage fish in Arctic marine ecosystems. Includes: arctic cod tagging metadata and vps locations files, and vessel activity in Resolute Bay, Nunavut, Canada for 2012.

opencc-by-4.0Nov 2019View details →

zenodo36/100

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

Description of the data and file structure 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:  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. Global Change Biology. In press. 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. 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.   Description of the movement data files 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. Aw: calibrated triaxial accelerometer data (converted from tag frame to whale frame) fs: sample rate (50 Hz) head: animal's circular heading (rotation about the dorsal-ventral axis, in radians) Mw: calibrated triaxial magnetometer data (converted from tag frame to whale frame) p: depth (in meters) pitch: animal's pitch (rotation about the left-right axis, in radians) roll: animal's roll (rotation about the anterior-posterior axis, in radians) tempr: temperature recorded on tag (in Celsius) TT: time cues for the start and end of every analyzed dive within a deployment. This matrix contains 6 columns: -col 1: start cue (in sec) -col 2: end cue (in sec) -col 3: maximum depth of dive (m) -col 4: time cue at max depth (in sec) -col 5: mean depth (m) -col 6: mean compression   Description of the analyzed variables 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.  The following is a list of variable names (column headers) and their corresponding definitions: bzsounds: 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 code: 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) deployment: 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) durwho: 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 divenum: chronological identifier for dive position within a deployment (e.g., for the 10th dive within a deployment, divenum = 10) kindet: 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) maxdep: maximum depth of a dive, in meters NLmax: 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 population: population to which the tagged whale belongs (NRKW = Northern Resident killer whale; SRKW = Southern Resident killer whale) sex: sex of tagged whale (F = female, M = male, NA = unknown) sc: 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 tagID: identifier for the individual tag used for each deployment year: year of deployment

opencc-by-4.0Aug 2024View details →

opencc-zeroJun 2023View details →

A glimpse into the foraging and movement behavior of Nyctalus aviator: a complementary study by acoustic recording and GPS tracking

Species of open-space bats that are relatively large, such as bats from the genus Nyctalus, are considered as high-risk species for collisions with wind turbines. However, important information on their behavior and movement ecology, such as the locations and altitudes at which they forage, is still fragmentary, while crucial for their conservation in light of the increasing threat posed by progressing wind turbine construction. We adopted two different methods of microphone array recordings and GPS-tracking capturing data from different spatio-temporal scales in order to gain a complementary understanding of the echolocation and movement ecology of Nyctalus aviator, the largest open-space bat in Japan. Based on microphone-array recordings, we found that echolocation calls during natural foraging are adapted for fast-flight in open space optimal for aerial-hawing. In addition, we attached a GPS tag that can simultaneously monitor feeding buzz occurrence and confirmed that foraging occurred at 300 m altitude and that the flight altitude in mountainous areas is consistent with the turbine conflict zone. Thus, our acoustic GPS survey clearly identified N. aviator as a high-risk species in Japan.

publicAug 2022View details →

Seascapes and foraging success: movement and resource discovery by a benthic marine herbivore

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publicSep 2025View details →

Data from: Variability in the movement and foraging behaviour of female Eurasian lynx during the denning season across Europe

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publicJun 2023View details →

A glimpse into the foraging and movement behavior of Nyctalus aviator: a complementary study by acoustic recording and GPS tracking

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publicMay 2022View details →

Sowerby’s beaked whale biosonar and movement strategy indicate deep-sea foraging niche differentiation in mesoplodont whales

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opencc-zeroOct 2020View details →

Diurnal timing of nonmigratory movement by birds: the importance of foraging spatial scales

Timing of activity can reveal an organism's efforts to optimize foraging either by minimizing energy loss through passive movement or by maximizing energetic gain through foraging. Here, we assess whether signals of either of these strategies are detectable in the timing of activity of daily, local movements by birds. We compare the similarities of timing of movement activity among species using six temporal variables: start of activity relative to sunrise, end of activity relative to sunset, relative speed at midday, number of movement bouts, bout duration, and proportion of active daytime hours. We test for the influence of flight mode and foraging habitat on the timing of movement activity across avian guilds. We used 64570 days of GPS movement data collected between 2002 and 2019 for local (non-migratory) movements of 991 birds from 49 species, representing 14 orders. Dissimilarity among daily activity patterns was best explained by flight mode. Terrestrial soaring birds began activity later and stopped activity earlier than pelagic soaring or flapping birds. Broad-scale foraging habitat explained less of the clustering patterns because of divergent timing of active periods of pelagic surface and diving foragers. Among pelagic birds, surface foragers were active throughout the day while diving foragers matched their active hours more closely to daylight hours. Pelagic surface foragers also had the greatest daily foraging distances, which was consistent with their daytime activity patterns. This study demonstrates that flight mode and foraging habitat influence temporal patterns of daily movement activity of birds.

opencc-zeroDec 2012View details →

Data from: From fine-scale foraging to home ranges: a semi-variance approach to identifying movement modes across spatiotemporal scales

Understanding animal movement is a key challenge in ecology and conservation biology. Relocation data often represent a complex mixture of different movement behaviors, and reliably decomposing this mix into its component parts is an unresolved problem in movement ecology. Traditional approaches, such as composite random walk models, require that the timescales characterizing the movement are all similar to the usually arbitrary data-sampling rate. Movement behaviors such as long-distance searching and fine-scale foraging, however, are often intermixed but operate on vastly different spatial and temporal scales. An approach that integrates the full sweep of movement behaviors across scales is currently lacking. Here we show how the semivariance function (SVF) of a stochastic movement process can both identify multiple movement modes and solve the sampling rate problem. We express a broad range of continuous-space, continuous-time stochastic movement models in terms of their SVFs, connect them to relocation data via variogram regression, and compare them using standard model selection techniques. We illustrate our approach using Mongolian gazelle relocation data and show that gazelle movement is characterized by ballistic foraging movements on a 6-h timescale, fast diffusive searching with a 10-week timescale, and asymptotic diffusion over longer timescales.

opencc-zeroDec 2013View details →

Data from: Moving on with foraging theory: incorporating movement decisions into the functional response of a gregarious shorebird

1. Models relating intake rate to food abundance and competitor densities (generalized functional response models) can predict forager distributions and movements between patches, but we lack understanding of how distributions and small-scale movements by the foragers themselves affect intake rates. 2. Using a state-of-the-art approach based on continuous-time Markov chain dynamics, we add realism to classic functional response models by acknowledging that the chances to encounter food and competitors are influenced by movement decisions, and, vice versa, that movement decisions are influenced by these encounters. 3. We used a multi-state modelling framework to construct a stochastic functional response model in which foragers alternate between three behavioural states: searching, handling and moving. 4. Using behavioural observations on a molluscivore migrant shorebird (red knot, Calidris canutus canutus), at its main wintering area (Banc d'Arguin, Mauritania), we estimated transition rates between foraging states as a function of conspecific densities and densities of the two main bivalve prey. 5. Intake rate decreased with conspecific density. This interference effect was not due to decreased searching efficiency, but resulted from time lost to avoidance movements. 6. Red knots showed a strong functional response to one prey (Dosinia isocardia), but a weak response to the other prey (Loripes lucinalis). This corroborates predictions from a recently developed optimal diet model that accounts for the mildly toxic effects due to consuming Loripes. 7. Using model-averaging across the most plausible multi-state models, the fully parameterized functional response model was then used to predict intake rate for an independent dataset on habitat choice by red knot. 8. Comparison of the sites selected by red knots with random sampling sites showed that the birds fed at sites with higher than average Loripes and Dosinia densities, i.e. sites for which we predicted higher than average intake rates. 9. We discuss the limitations of Holling's classical functional response model that ignores movement and the limitations of contemporary movement ecological theory ignoring consumer-resource interactions. With the rapid advancement of technologies to track movements of individual foragers at fine spatial scales, the time seems ripe to integrate descriptive tracking studies with stochastic movement-based functional response models.

opencc-zeroDec 2017View details →

Data from: Central place foragers and moving stimuli: a hidden-state model to discriminate the processes affecting movement

1. Human activities can influence the movement of organisms, either repelling or attracting individuals depending on whether they interfere with natural behavioural patterns or enhance access to food. To discern the processes affecting such interactions, an appropriate analytical approach must reflect the motivations driving behavioural decisions at multiple scales. 2. In this study, we developed a modelling framework for the analysis of foraging trips by central place foragers. By recognising the distinction between movement phases at a larger scale and movement steps at a finer scale, our model can identify periods when animals are actively following moving attractors in their landscape. 3. We applied the framework to GPS tracking data of northern fulmars Fulmarus glacialis, paired with contemporaneous fishing boat locations, to quantify the putative scavenging activity of these seabirds on discarded fish and offal. We estimated the rate and scale of interaction between individual birds and fishing boats and the interplay with other aspects of a foraging trip. 4. The model classified periods when birds were heading out to sea, returning towards the colony or following the closest boat. The probability of switching towards a boat declined with distance and varied depending on the phase of the trip. The maximum distance at which a bird switched towards the closest boat was estimated around 35 km, suggesting the use of olfactory information to locate food. Individuals spent a quarter of a foraging trip, on average, following fishing boats, with marked heterogeneity among trips and individuals. 5. Our approach can be used to characterise interactions between central place foragers and different anthropogenic or natural stimuli. The model identifies the processes influencing central place foraging at multiple scales, which can improve our understanding of the mechanisms underlying movement behaviour and characterise individual variation in interactions with a range of human activities that may attract or repel these species. Therefore, it can be adapted to explore the movement of other species that are subject to multiple dynamic drivers.

publicApr 2020View details →

Data from: Forests do not limit bumble bee foraging movements in a montane meadow complex

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