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Fig. 9 in Foraging behavior interactions between the invasive Nile Tilapia (Cichliformes: Cichlidae) and three large native predators

Fig. 9. Defense strategy (schooling and dispersing) by the Nile Tilapia (mean ± SD) in the tanks with Pseudoplatystoma corruscans (white circles), Salminus brasiliensis (white squares) and Brycon orbignyanus (black triangles), for 0%, 50%, 100% and RD treatments. The three-way ANOVA for these data showed interaction (P =0.018) among species, structural complexity and schooling. Shoaling decreased for 0% to 100% structural complexity treatments, while dispersing behavior increased for the RD treatment.

Fig. 4 in Foraging behavior interactions between the invasive Nile Tilapia (Cichliformes: Cichlidae) and three large native predators

Fig. 4. Temporal variation of predatory activity the experiment, in each treatment. Mean ± SD of Oreochromis niloticus survival in the tanks with Pseudoplatystoma corruscans (white circles), Salminus brasiliensis (white squares) and Brycon orbignyanus (black triangles). Levels of habitat complexity: a. 0%, b. 50%, c. 100% and d. RD.

Fig. 1 in Foraging behavior interactions between the invasive Nile Tilapia (Cichliformes: Cichlidae) and three large native predators

Fig. 1. Experimental tanks used. The left side of the image shows the simulation of habitat complexity using a plastic imitation of macrophytes: upper left – 0% habitat complexity, upper right – 50%, bottom left – 100% and bottom right – RD (rocks and driftwood). The right side of the image shows the individual water circulation system, the lamps used to manipulate the photoperiod of 12:12 h light:dark and the dark panels at the windows to avoid the influence of natural light.

Fig. 5 in Foraging behavior interactions between the invasive Nile Tilapia (Cichliformes: Cichlidae) and three large native predators

Fig. 5. Use of microhabitat (surface, middle and bottom layers) by the native predators (mean ± SD) Pseudoplatystoma corruscans (white circles), Salminus brasiliensis (white squares) and Brycon orbignyanus (black triangles), for 0%, 50%, 100% and RD treatments. The three-way ANOVA for these data showed significant interaction (P = 0.049) among species, structural habitat complexity and microhabitat.All species of native predators used the bottom layer of the tank more frequently.

Fig. 8 in Unravelling the foraging behavior of the southern stingray, Hypanus americanus (Myliobatiformes: Dasyatidae) in a Southwestern Atlantic MPA

Fig. 8. Person's Correlation between the mean duration of foraging events and the size (disc length) of Hypanus americanus individuals (a) and local depth (b) in the FNA. The interval between dashed lines indicates the confidence interval of the regression line at p = 0.05. N = 37.

Fig. 6 in Foraging behavior interactions between the invasive Nile Tilapia (Cichliformes: Cichlidae) and three large native predators

Fig. 6. Activity (inactive, swimming, stalking and attacking) by the native predators (mean ± SD) Pseudoplatystoma corruscans (white circles), Salminus brasiliensis (white squares) and Brycon orbignyanus (black triangles), for 0%, 50%, 100% and RD treatments. The three-way ANOVA for these data suggested that the interaction among species, structural habitat complexity and activity was significant (P <0.001). The native predators did not stalk or attack in any of the treatments.

Fig. 2 in Unravelling the foraging behavior of the southern stingray, Hypanus americanus (Myliobatiformes: Dasyatidae) in a Southwestern Atlantic MPA

Fig. 2. Illustrations of species-typical patterns of foraging behavior performed by Hypanus americanus in the FNA in phase 1 entitled as primary search. The sub-phases are named as (a) active-scanning; (b) glide-scanning; (c) turn-back; (d) reverse; (e) rotation. The arrow indicates the direction of the movement and in (e) means the rotation on the dorsal-ventral axis of their body. The figures were drawn using original still photographs.

Fig. 7 in Unravelling the foraging behavior of the southern stingray, Hypanus americanus (Myliobatiformes: Dasyatidae) in a Southwestern Atlantic MPA

Fig. 7. Duration of median foraging events performed by Hypanus americanus individuals in reef areas and beach regions (a) and in complex (i.e., with gravel, algal turf and calcareous algae) and sandy substrates (b) in the FNA. Gray bars represent first and third quartiles (25-75% of the data).

Fig. 5 in Unravelling the foraging behavior of the southern stingray, Hypanus americanus (Myliobatiformes: Dasyatidae) in a Southwestern Atlantic MPA

Fig. 5. Illustrations of species-typical patterns of foraging behavior performed by Hypanus americanus in the FNA in phase 4 entitled as suction. The sub-phases are named as (a) spiracular suction; (b) corporeal-spiracular suction. The figures were drawn using original still photographs.

Fig. 2 in Foraging behavior interactions between the invasive Nile Tilapia (Cichliformes: Cichlidae) and three large native predators

Fig. 2. Summary of scheme of experimental procedures, design and statistical analyses. From left to right: acclimatization period and procedures for each species in isolation (Oreochromis niloticus, Salminus brasiliensis, Pseudoplatystoma corruscans and Brycon orbignyanus). Experimental design: four levels of habitat complexity (0%, 50% and 100% (simulated using green plastic filaments) and RD (rocks and driftwood) simulated with basaltic rocks and tree branches) for three species combinations (O. niloticus x S. brasiliensis, O. niloticus x P. corruscans and O. niloticus x B. orbignyanus), replicated three times, totaling 36 experimental units. Ten O. niloticus were placed in each tank and after 6 h, one predator was added. Data collection started 6 h from the beginning of the experiment and was repeated at 12-h intervals (5 a.m./5 p.m.). The predatory efficiency, predation frequency over time, use of microhabitat and activity by predators and prey, together with the defense strategy of the prey, were analyzed using different models of ANOVA design.

Fig. 3 in Foraging behavior interactions between the invasive Nile Tilapia (Cichliformes: Cichlidae) and three large native predators

Fig. 3. Predatory efficiency (mean ± standard deviation (SD) of Oreochromis niloticus consumption) by Pseudoplatystoma corruscans (white bars), Salminus brasiliensis (black bars) and Brycon orbignyanus (gray bars), in 0%, 50%, 100% and RD treatments. ANOVA for these data was not significant (P = 0.69) for interaction between species and structural complexity. However, the predatory efficiencies of species were different (P <0.001).

Fig. 7 in Foraging behavior interactions between the invasive Nile Tilapia (Cichliformes: Cichlidae) and three large native predators

Fig. 7. Use of microhabitat (surface, middle and bottom) by the Nile Tilapia (mean ± SD) in the tanks with Pseudoplatystoma corruscans (white circles), Salminus brasiliensis (white squares) and Brycon orbignyanus (black triangles), for 0%, 50%, 100% and RD treatments. The three-way ANOVA for these data suggested significant (P = 0.045) interaction between species, structural complexity and use of microhabitat. The juveniles used the surface and bottom layers more frequently.

opencc-zeroJun 2020View details →

Data from: The effect of conspecific density on honey bee foraging behavior

Foraging honey bees (Apis mellifera) seem to use the presence of conspecific foragers as cues for flower quality. However, there is disagreement regarding how a conspecific cue is perceived by other foragers (enhancement or inhibition). Most studies manipulate the total number of bees foraging in an arena or the presence or absence of a bee on a flower and then observe the behavior of one forager in response to a single conspecific, which does not reflect natural foraging. We tested how a range of conspecifics on flowers affected on which flowers foraging honey bees landed. We trained students from a biology class for non-STEM majors to collect data and tested whether the number of conspecifics on flowers influences on which flower foragers land. We found that foragers land more frequently on flowers occupied by more conspecifics, which supports the hypothesis that conspecifics are cues for local enhancement. Our results increase our understanding of how honey bees forage once at a flower patch.

opencc-zeroJun 2020View details →

Landscape composition and local floral resources influence foraging behavior but not the size of Bombus impatiens Cresson (Hymenoptera: Apidae) workers

<p>Here, we assessed how the morphology, weight and foraging behavior of individual workers are affected by their surrounding landscape. We hypothesized that colonies established in landscapes showing high cover of intensive crops and low cover of flowering crops, as well as low amounts of local floral resources, would produce smaller workers, which would perform fewer foraging trips and collect pollen loads less constant in species composition. We tested these predictions with 80 colonies of commercially reared <i>Bombus impatiens</i> Cresson placed in 20 landscapes spanning a gradient of agricultural intensification in southern Québec, Canada. We estimated weekly rate at which workers entered and exited colonies and captured eight workers per colony over a period of 14 weeks during the spring and summer of 2016. Captured workers had their wing, thorax, head, tibia, and dry weight measured, as well as their pollen load extracted and identified to the lowest possible taxonomic level. We did not detect any effect of landscape habitat composition on worker morphology or body weight, but found that foraging activity decreased with intensive crops. Moreover, higher diversity of local floral resources led to lower pollen constancy in intensively cultivated landscapes. Finally, we found a negative correlation between the size of workers and the diversity of their pollen load. Our results provide additional evidence that conservation actions regarding pollinators in arable landscapes should be made at the landscape rather than at the farm level.</p>

opencc-zeroDec 2018View details →

Data from: Linking the foraging behavior of three bee species to pollen dispersal and gene flow

Foraging behaviors that impact gene flow can guide the design of pollinator strategies to mitigate gene flow. Reduced gene flow is expected to minimize the impact of genetically engineered (GE) crops on feral and natural populations and to facilitate the coexistence of different agricultural markets. The goal of this study is to link foraging behavior to gene flow and identify behaviors that can help predict gene flow for different bee species. To reach this goal, we first examined and compared the foraging behaviors of three distinct bee species, the European honey bee, Apis mellifera L., the common eastern bumble bee, Bombus impatiens Cr., and the alfalfa leafcutting bee, Megachile rotundata F., foraging on Medicago sativa flowers. Each foraging behavior investigated differed among bee species. Both social bees exhibited directionality of movement and had similar residence, in contrast to the random movement and shorter residence of the solitary bee. Tripping rate and net distance traveled differed among the three bee species. We ranked each behavior among bee species and used the relative ranking as gene flow predictor before testing the predictions against empirical gene flow data. Tripping rate and net distance traveled, but not residence, predicted relative gene dispersal among bee species. Linking specific behaviors to gene flow provides mechanisms to explain differences in gene flow among bee species and guides the development of management practices to reduce gene flow. Although developed in one system, the approach developed here can be generalized to different plant/pollinator systems.

opencc-zeroDec 2016View details →

Data from: Taking movement data to new depths: Inferring prey availability and patch profitability from seabird foraging behavior

Detailed information acquired using tracking technology has the potential to provide accurate pictures of the types of movements and behaviors performed by animals. To date, such data have not been widely exploited to provide inferred information about the foraging habitat. We collected data using multiple sensors (GPS, time depth recorders, and accelerometers) from two species of diving seabirds, razorbills (Alca torda, N = 5, from Fair Isle, UK) and common guillemots (Uria aalge, N = 2 from Fair Isle and N = 2 from Colonsay, UK). We used a clustering algorithm to identify pursuit and catching events and the time spent pursuing and catching underwater, which we then used as indicators for inferring prey encounters throughout the water column and responses to changes in prey availability of the areas visited at two levels: individual dives and groups of dives. For each individual dive (N = 661 for guillemots, 6214 for razorbills), we modeled the number of pursuit and catching events, in relation to dive depth, duration, and type of dive performed (benthic vs. pelagic). For groups of dives (N = 58 for guillemots, 156 for razorbills), we modeled the total time spent pursuing and catching in relation to time spent underwater. Razorbills performed only pelagic dives, most likely exploiting prey available at shallow depths as indicated by the vertical distribution of pursuit and catching events. In contrast, guillemots were more flexible in their behavior, switching between benthic and pelagic dives. Capture attempt rates indicated that they were exploiting deep prey aggregations. The study highlights how novel analysis of movement data can give new insights into how animals exploit food patches, offering a unique opportunity to comprehend the behavioral ecology behind different movement patterns and understand how animals might respond to changes in prey distributions.

opencc-zeroDec 2016View details →

Data from: How to use (and not to use) movement-based indices for quantifying foraging behavior

1. Movement based indices such as Moves Per Minute (MPM) and Proportion Time Moving (PTM) are common methodologies to quantify foraging behavior. We explore fundamental drawbacks of these indices, question the ways scientists have been using them, and propose new solutions. 2. To do so, we combined analytical and simulation models with lizards foraging data at the individual and species levels. 3. We found that the maximal value of MPM is constrained by the minimal durations of moves and stops. As a result, foragers that rarely move and those that rarely stop are bounded to similar low MPM values. This implies that (a) MPM has very little meaning when used alone, (b) MPM and PTM are interdependent, and (c) certain areas in the MPM-PTM plane cannot be occupied. We also found that MPM suffers from inaccuracy and imprecision. 4. We introduced a new bias correction formula for already published MPM data, and a novel index of Changes Per Minute (CPM) that uses the frequency of changes between move and stop bouts. CPM is very similar to MPM, but does not suffer from bias. Finally, we suggested a new foraging plane of Average Move and Average Stop durations. We hope that our guidelines of how to use (and not to use) movement- based indices will add rigor to the study of animals' foraging behavior.

opencc-zeroDec 2014View details →

Data from: Young giant water bug nymphs prefer larger prey: changes in foraging behavior with nymphal growth in Kirkaldyia deyrolli

Raptorial characteristics may evolve in predators because of their importance in obtaining food. The giant water bug, Kirkaldyia deyrolli, possesses a claw on the terminal segment of the raptorial foreleg that is crucial for capturing prey. Claw curvature has been previously shown to change during growth in this species, but the adaptive significance of this change has not yet been explored. Predation experiments have demonstrated that young nymphs with highly curved claws caught proportionally larger prey than older nymphs with less-curved claws. Catching behaviours for a certain prey size differed significantly between young and older nymphs. The observation that nymphal growth affects prey-catching behaviour in the giant water bug supports the hypothesis that predators can change catching behaviours based on changes in raptorial characteristics in order to maximize prey resources acquired at each developmental stage.

opennotspecifiedSep 2004View details →

Fig. 3 in Twig Foraging and Soil-Burrowing Behaviors in Larvae of Dinoptera Minuta (Gebler) (Coleoptera: Cerambycidae)

Fig. 3. Positions of D. minuta larvae in test tubes filled with soil.