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Transition Probability Matrix. (A) Example organization of the square transition probability matrix. The probability, P, of transitioning out of behavior i to behavior j is held in the matrix element, i,j. For example, P 3,1 represents the observed probability of the animal transitioning from behavior 3 (specific translation, ST) to behavior 1 (successful strike, SS). The color coding seen in (A) denotes the regime in which each behavior belongs (red = hunting regime behaviors; blue = nonhunting behaviors). The red shaded region of the square matrix corresponds to the probabilities of hunting behaviors further transitioning into hunting behavior. The purple regions show
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How we interact with our environment largely depends on both the external cues presented by our surroundings and the internal state from within. Internal states are the ever-changing physiological conditions that communicate the immediate survival needs and motivate the animal to behaviorally fulfill them. Satiety level constitutes such a state, an...
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... A will produce a 12 x 12 transition matrix, T A while sequence B will produce its own corresponding 12 x 12 transition matrix, T B ). This is a square matrix that holds the observed probabilities of each behavior transitioning into every other behavior during a subsequence (Fig 4A). To provide a qualitative assessment of all the transition matrices as a correlate of satiety, the square matrix shown in Fig 4A is strung out vertically into a "feature vector," where each row is reorganized and stacked vertically into a single column vector in accordance to Fig 4B). ...
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... is a square matrix that holds the observed probabilities of each behavior transitioning into every other behavior during a subsequence (Fig 4A). To provide a qualitative assessment of all the transition matrices as a correlate of satiety, the square matrix shown in Fig 4A is strung out vertically into a "feature vector," where each row is reorganized and stacked vertically into a single column vector in accordance to Fig 4B). With the probability matrix for each subsequence reconfigured to a column vector, the vectors were grouped in order of increasing satiety (e.g. ...
Context 3
... is a square matrix that holds the observed probabilities of each behavior transitioning into every other behavior during a subsequence (Fig 4A). To provide a qualitative assessment of all the transition matrices as a correlate of satiety, the square matrix shown in Fig 4A is strung out vertically into a "feature vector," where each row is reorganized and stacked vertically into a single column vector in accordance to Fig 4B). With the probability matrix for each subsequence reconfigured to a column vector, the vectors were grouped in order of increasing satiety (e.g. ...
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... the probability matrix for each subsequence reconfigured to a column vector, the vectors were grouped in order of increasing satiety (e.g. all 0-fed column vectors grouped side-by-side; Fig 4C). ...
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... processing steps of Fig 4 allow the transition matrices to be quickly compared across all feeding states. Applying color-coding to the values found in the probability vertical matrices from Fig 4C generates the heat map in Fig 5A. ...
Citations
... We studied Chinese mantises (Tenodera sinensis), because they are widespread, invasive, marketed as biocontrol agents, and seem undiscriminating in their prey preferences (Hurd and Eisenberg 1990;Moran et al. 1996). Tenodera sinensis tend to act as ambush predators, but may actively pursue prey when hungry (Pickard et al. 2021). We collected mantises and constructed experimental plots at the Donald S. Wood field laboratory of the Pymatuning Laboratory of Ecology, located in northwest Pennsylvania (41°34′09.6"N, ...
Among-individual variation in predator traits is ubiquitous in nature. However, variation among populations in this trait variation has been seldom considered in trophic dynamics. This has left unexplored (a) to what degree does among-individual variation in predator traits regulate prey populations and (b) to what degree do these effects vary spatially. We address these questions by examining how predator among-individual variation in functional traits shapes communities across habitats of varying structural complexity, in field conditions. We manipulated Chinese mantis (Tenodera sinensis) density (six or twelve individuals) and behavioral trait variability (activity level by movement on an open field) in experimental patches of old fields with varying habitat complexity (density of plant material). Then, we quantified their impacts on lower trophic levels, specifically prey (arthropods > 4 mm) and plant biomass. Predator behavioral variability only altered prey biomass in structurally complex plots, and this effect depended on mantis density. In the plots with the highest habitat complexity and mantis density, behaviorally variable groups decreased prey biomass by 40.3%. In complex plots with low mantis densities, low levels of behavioral variability decreased prey biomass by 32.2%. Behavioral variability and low habitat complexity also changed prey community composition, namely by increasing ant biomass by 881%. Our results demonstrate that among-individual trait variation can shape species-rich prey communities. Moreover, these effects depend on both predator density and habitat complexity. Incorporating this important facet of ecological diversity revealed normally unnoticed effects of functional traits on the structure and function of food webs.
... There, findings show that attentional resources are more focussed when searching for cryptic prey. Conversely, hunger leads to praying mantises widening their search for possible prey (Bertsch et al. 2019;Pickard et al. 2021). Our findings further argue that learning about reward value also influences attention in bees, even when the rewarding flowers are not cryptic. ...
The role of visual search during bee foraging is relatively understudied compared to the choices made by bees. As bees learn about rewards, we predicted that visual search would be modified to prioritise rewarding flowers. To test this, we ran an experiment testing how bee search differs in the initial and later part of training as they learn about flowers with either higher- or lower-quality rewards. We then ran an experiment to see how this prior training with reward influences their search on a subsequent task with different flowers. We used the time spent inspecting flowers as a measure of attention and found that learning increased attention to rewards and away from unrewarding flowers. Higher quality rewards led to decreased attention to non-flower regions, but lower quality rewards did not. Prior experience of lower rewards also led to more attention to higher rewards compared to unrewarding flowers and non-flower regions. Our results suggest that flowers would elicit differences in bee search behaviour depending on the sugar content of their nectar. They also demonstrate the utility of studying visual search and have important implications for understanding the pollination ecology of flowers with different qualities of reward.
Significance statement
Studies investigating how foraging bees learn about reward typically focus on the choices made by the bees. How bees deploy attention and visual search during foraging is less well studied. We analysed flight videos to characterise visual search as bees learn which flowers are rewarding. We found that learning increases the focus of bees on flower regions. We also found that the quality of the reward a flower offers influences how much bees search in non-flower areas. This means that a flower with lower reward attracts less focussed foraging compared to one with a higher reward. Since flowers do differ in floral reward, this has important implications for how focussed pollinators will be on different flowers. Our approach of looking at search behaviour and attention thus advances our understanding of the cognitive ecology of pollination.
... El comportamiento observado es similar a otros registros descritos en Mantodea (Reitze & Nentwig, 1991) y, aunque la depredación de presas más pequeñas que las mantis religiosas es frecuente, también ocurre depredación de organismos del mismo o mayor tamaño, lo que depende de las e s t r a t e g i a s d e d e f e n s a d e l a p re s a y s u disponibilidad en el medio (Reitze & Nentwig, 1991;Costa-Pereira et al., 2010). La sorprendente capacidad de Mantodea de alimentarse de diferentes taxones recae en la habilidad de utilizar diferentes tácticas de caza desde estrategias activas o emboscadas (Pickard et al., 2021). ...
Praying mantids (class Insecta, order Mantodea) are a group of predatory insects comprising approximately 2500 described species, that occur across all continents except Antarctica, with the greatest species diversity in tropical and subtropical regions. Mantids predominantly prey on other invertebrates but are known to feed on small vertebrates. During April and May 2021, we observed mantid feeding events in Manujan County, Kerman Province in southern Iran. Two distinct feeding events were observed where female European Mantids (Mantis religiosa) preyed on Purple Sunbird (Cinnyris asiaticus) and Crested Lark (Galerida cristata) nestlings. In addition, we collated information from online searches of mantids feeding on nestlings elsewhere in the world, revealing two more observations. In Taiwan, a Giant Asian Mantid (Hierodula patellifera) was recorded preying on a nestling Warbling White-eye (Zosterops japonicus) and in Brazil, a mantid (Stagmatoptera sp.) was recorded feeding on a nestling White-throated Seedeater (Sporophila albogularis). To date, the only existing scientific evidence of praying mantids feeding on passerine nestlings was recorded in 1922. We propose two potential explanations for the observed trophic interactions between mantids and passerine nestlings: (1) during egg production female mantids, especially those in poor physical condition, may opportunistically feed on nestlings in order to increase fecundity via nutrient gain and (2) mantids may initially be attracted toward the nest by parasitic or coprophagous insects, as a result of poor nest sanitation, and subsequently prey on nestlings after detecting movements. Our unusual observations represent the first records of praying mantids feeding on nestling passerines in nearly 100 years.
Neuroscientists and roboticists alike are interested in how both the external cues and internal states contribute to determining behavioral sequences. As the motivation drivers change through time, so does the animal switch between alternating activities and outward exhibition of patterned behavior. It is accepted that underlying neural integration of internal states - as well as incoming information of the external environment - give rise to the adaptive abilities of animal behavior in different contexts. Here, the sequences of hunting-motivated behavior of praying mantises were modeled as Markov chains, with each sequence giving rise to a corresponding transition probability matrix. From these transition matrices, three methods of prototype generation were used - cumulative, centroid, and medoid - to produce categorical representatives of the time series data of all five feeding states used in the experiments. Novel to this paper, is the use of Markovian chain metrics to compare the efficacy of these prototypes at capturing the time-evolution behavior unique to each feed state. Results show that the cumulative prototypes best exhibited temporal behaviors most consistent with the real data.KeywordsBehavioral analysisMarkov chainClass prototype
