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159 results for “Feeding Ecology”
Fig. 3 in Feeding ecology of a stream fish assemblage in an Atlantic Forest remnant (Serra do Japi, SP, Brazil)
Fig. 3. Biomass (g.m-2) of the different trophic groups of fish at each collecting site in Serra do Japi (SP) streams.
Fig. 4 in Feeding ecology of a stream fish assemblage in an Atlantic Forest remnant (Serra do Japi, SP, Brazil)
Fig. 4. Canonical Correspondence Analysis (CCA) showing the relationship between the biomass of insectivores (INS), omnivores (ONI), herbivores (HER), detritivores (DET), piscivores (PIS), omnivores-carnivores (O.CAR) and selected environmental variables. Temp = temperature; Veloc = Water Velocity; T.Nit = total nitrogen, Cond= Conductivity.
Fig. 2 a-b in Feeding ecology of a stream fish assemblage in an Atlantic Forest remnant (Serra do Japi, SP, Brazil)
Fig. 2 a-b. Scores of NMDS for the fish species (a) and food items (b) along the axes 1 and 2. Circles and rectangles (a) indicate trophic groups formed by the similarity array. Benthic insectivores (I), insectivores (II), detritivores (III), herbivores (IV), omnivores (V), piscivores (VI), omnivore-carnivores (VII). (b) Alg = algae; Det = detritus; OMt = organic matter; VMt = vegetal matter; Oth = others, YIn = young insects; Fis=fish; AIn = adult insects; InF = insect fragments, Nem= nematodes; Ann = Annelidae, Crs = Crustacea. Codes of species are shown in Table 2.
Fig. 2 in Comparative feeding ecology and habitats use of Crenicichla species (Perciformes: Cichlidae) in a Venezuelan floodplain river
Fig. 2. Map showing location of the Cinaruco River, a tributary of the Orinoco River in Venezuela's Apure State; the study reach is outlined with a rectangle.
Fig. 4 in Comparative feeding ecology and habitats use of Crenicichla species (Perciformes: Cichlidae) in a Venezuelan floodplain river
Fig. 4. Number of immature and mature gonads encountered in C. lugubris (a) and C. aff. wallacii (b) of different size classes during the dry season. (black barra) Mature (gonad state> 3); (white barra) immature (gonad state 1-2). C. lugubris (n = 102), C. aff. wallacii (n = 108).
Data set for 'Lunge filter feeding biomechanics constrain rorqual foraging ecology across scale'...
<p>Fundamental scaling relationships influence the physiology of vital rates, which in turn shape the ecology and evolution of organisms. For diving mammals, benefits conferred by large body size include reduced transport costs and enhanced breath-holding capacity, thereby increasing overall foraging efficiency. Rorqual whales feed by engulfing a large mass of prey-laden water at high speed and filter it through baleen plates. However, as engulfment capacity increases with body length across species (Engulfment Volume ∝ Body Length <sup>3.57</sup>), the surface area of the baleen filter does not increase proportionally (Baleen Area ∝ Body Length<sup>1.82</sup>), and thus the filtration time of larger rorquals predictably increases because the baleen surface area must filter a disproportionally large amount of water. We predicted that filtration time should scale with body length to the power of 1.75 (Filter Time ∝ Body Length<sup>1.75</sup><i>)</i>. We tested this hypothesis on four rorqual species using multi-sensor tags with corresponding unoccupied aerial systems (UAS) -based body length estimates. We found that filter time scales with body length to the power of 1.79 (95% CI: 1.61 - 1.97). This result highlights a scale-dependent trade-off between engulfment capacity and baleen area that creates a biomechanical constraint to foraging through increased filtration time. Consequently, larger whales must target high density prey patches commensurate to the gulp size to meet their increased energetic demands. If these optimal patches are absent, larger rorquals may experience reduced foraging efficiency compared to smaller whales if they do not match engulfment capacity to the size of targeted prey aggregations.</p>
Data from: Hunting behavior and feeding ecology of Mojave Rattlesnakes (Crotalus scutulatus), Prairie Rattlesnakes (C. viridis), and their hybrids in southwestern New Mexico
<p>Predators must contend with numerous challenges to successfully find and subjugate prey. Complex traits related to hunting are partially controlled by a large number of co-evolved genes which may be disrupted in hybrids. Accordingly, research on the feeding ecology of animals in hybrid zones has shown that hybrids sometimes exhibit transgressive or novel behaviors, yet for many taxa empirical studies of predation and diet across hybrid zones is lacking. We undertook the first such field study for a hybrid zone between two snake species, the Mojave Rattlesnake (<em>Crotalus scutulatus</em>) and Prairie Rattlesnake (<em>C. viridis</em>). Specifically, we leveraged established field methods to quantify hunting behaviors of animals, their prey communities, and diet of individuals across the hybrid zone in southwestern New Mexico, USA. We found that, even though hybrids had significantly lower body condition indices than snakes from either parental lineage, hybrids were generally similar to non-hybrids in hunting behavior, prey encounter rates, and predatory attack and success. We also found that, compared to <em>C. scutulatus</em>, <em>C. viridis</em> was significantly more active while hunting at night and abandoned ambush sites earlier in the morning, and hybrids tended to be more <em>viridis</em>-like in this respect. Prey availability was similar across the study sites, including within the hybrid zone, with Kangaroo Rats (<em>Dipodomys</em> spp.) as the most common small mammal both in habitat surveys and frequency of encounters with hunting rattlesnakes. Analysis of prey remains in stomachs and feces also showed broad similarity in diets, with all snakes preying primarily on small mammals and secondarily on lizards. Taken together, our results suggest that the significantly lower body condition of hybrids does not appear to be driven by differences in their hunting behavior or diet, and may instead relate to metabolic efficiency or other physiological traits we have not yet identified.</p>
Reconstructing the feeding ecology of Cambrian sponge reefs: The case for active suspension feeding in Archaeocyatha
Sponge-grade Archaeocyatha were early Cambrian biomineralizing metazoans that constructed reefs globally. Despite decades of research, many facets of archaeocyath palaeobiology remain unclear, making it difficult to reconstruct the palaeoecology of Cambrian reef ecosystems. Of specific interest is how these organisms fed; previous experimental studies have suggested that archaeocyaths functioned as passive suspension feeders relying on ambient currents to transport nutrient-rich water into their central cavities. Here, we test this hypothesis using computational fluid dynamics (CFD) simulations of digital models of select archaeocyath species. Our results demonstrate that, given a range of plausible current velocities, there was very little fluid circulation through the skeleton, suggesting obligate passive suspension feeding was unlikely. Comparing our simulation data with exhalent velocities collected from extant sponges, we infer an active suspension feeding lifestyle for archaeocyaths. The combination of active suspension feeding and biomineralization in Archaeocyatha may have facilitated the creation of modern metazoan reef ecosystems.
Data from: Africa's overlooked top predator: towards a better understanding of martial eagle feeding ecology in the Maasai Mara, Kenya
<p>Raptors exert top-down influences on ecosystems via their effects on prey population dynamics and community composition. Most raptors are sympatric with other predators, thus complicating our understanding of their relative influence in these systems. Estimates of kill rates and prey biomass recycling have been used as predation metrics that allow quantitative comparison among species and assessment of the relative role of single species within complex food webs. Few studies have produced findings of kill rates or prey biomass recycling for raptors. We used a supervised machine learning algorithm to behaviourally classify high resolution accelerometer informed GPS locations of tagged adult non-breeding martial eagles <em>Polemaetus bellicosus</em> in the Maasai Mara region of Kenya to estimate kill rates and prey biomass recycling. Eagle locations classified as feeding were clustered using distance and time thresholds to identify kills and calculate kill rates. Identified kill sites were quickly ground-truthed to confirm kills and identify prey species. We estimated kill rates for martial eagles at 0.59 kills/day for males and 0.38 kills/day for females, and we estimated biomass recycling per ground-truthed kill at 1796 g for males and 3860 g for females. From our sample of identified ground-truthed kills, "gamebirds" was the most frequently recorded prey category for male eagles and "small ungulates" was the most frequently recorded prey category for female eagles. These results position martial eagles close to sympatric mammalian top predators in trophic pyramids and provide evidence for their classification as a top predator.</p>
The effects of habitat modification on the distribution and feeding ecology of Orthoptera 2015
<b>Description: </b><p>Postdoctoral project</p><p><b>Project: </b>This dataset was collected as part of the following SAFE research project: <a href="https://www.safeproject.net/projects/project_view/4"><b>The effects of habitat modification on the distribution and feeding ecology of Orthoptera</b></a></p><p><b>Funding: </b>These data were collected as part of research funded by: </p><ul><li>Australian Research Council (ARC Discovery Project, DP140101541)</li></ul><p>This dataset is released under the CC-BY 4.0 licence, requiring that you cite the dataset in any outputs, but has the additional condition that you acknowledge the contribution of these funders in any outputs.</p><p></p><p><b>XML metadata: </b>GEMINI compliant metadata for this dataset is available <a href="https://www.safeproject.net/datasets/xml_metadata?id=7011354">here</a></p><p><b>Files: </b>This consists of 1 file: Hardwick_Orthoptera_220811.xlsx</p><p><b>Hardwick_Orthoptera_220811.xlsx</b></p><p>This file contains dataset metadata and 1 data tables:</p><ol><li><p><b>Orthoptera assemblage composition data 2015</b> (described in worksheet OrthopteraAssem)</p><p>Description: Orthoptera assemblage composition data collected at the SAFE Project in 2015. Worksheet contains a site by morphospecies abundance matrix. Orthoptera were collected by sweep netting along a 100m transect at each location. Orthoptera were identified to family and seperated into morphospecies using identification guides. </p><p>Number of fields: 95</p><p>Number of data rows: 48</p><p>Fields: </p><ul><li><b>Date1</b>: Date of the first collection (Field type: date)</li><li><b>Date2</b>: Date of the second collection (Field type: date)</li><li><b>Location</b>: SAFE Project location (2nd order) (Field type: location)</li><li><b>Type</b>: Disturbance gradient (Field type: ordered categorical)</li><li><b>Collector</b>: First initial and last name of person who collected the sample (Field type: categorical)</li><li><b>ACRI01_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>ACRI02_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>ACRI03_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>ACRI04_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>ACRI05_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>ACRI06_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>ACRI07_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>ACRI08_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>ACRI09_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>ACRI10_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>ACRI11_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>ACRI12_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>ACRI13_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>ACRI14_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>ACRI15_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>ACRI16_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>ACRI17_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>ACRI18_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>ACRI19_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>ACRI20_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR01_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR02_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR03_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR04_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR05_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR06_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR07_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR08_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR09_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR10_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR11_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR12_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR13_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR14_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR15_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR16_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR17_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR18_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR19_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR20_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TETR21_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL01_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL02_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL03_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL04_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL05_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL06_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL07_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL08_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL09_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL10_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL11_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL12_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL13_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL14_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL15_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL16_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL17_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL18_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL19_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL20_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>GRYL21_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>MOGO01_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>MOGO02_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TRID01_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TRID02_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TRIG01_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TRIG02_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TRIG03_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TRIG04_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TRIG05_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>TRIG06_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>UNID01_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>UNID02_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>UNID03_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>UNID04_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>UNID05_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>UNID06_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>UNID07_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>UNID08_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>UNID09_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>UNID10_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>UNID12_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>UNID13_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>UNID14_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>UNID15_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>UNID16_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>UNID17_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>UNID18_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li><li><b>UNID19_count</b>: Number collected along a 100m transect, twice sampled (Field type: abundance)</li></ul></li></ol><p><b>Date range: </b>2015-06-03 to 2015-08-14</p><p><b>Latitudinal extent: </b>4.6359 to 4.7509</p><p><b>Longitudinal extent: </b>116.9549 to 117.6257</p><p><b>Taxonomic coverage: </b><br> All taxon names are validated against the GBIF backbone taxonomy. If a dataset uses a synonym, the accepted usage is shown followed by the dataset usage in brackets. Taxa that cannot be validated, including new species and other unknown taxa, morphospecies, functional groups and taxonomic levels not used in the GBIF backbone are shown in square brackets.</p><div> -  Animalia <br> -  -  Arthropoda <br> -  -  -  Insecta <br> -  -  -  -  Orthoptera <br> -  -  -  -  -  [UNID01] <br> -  -  -  -  -  [UNID02] <br> -  -  -  -  -  [UNID03] <br> -  -  -  -  -  [UNID04] <br> -  -  -  -  -  [UNID05] <br> -  -  -  -  -  [UNID06] <br> -  -  -  -  -  [UNID07] <br> -  -  -  -  -  [UNID08] <br> -  -  -  -  -  [UNID09] <br> -  -  -  -  -  [UNID10] <br> -  -  -  -  -  [UNID12] <br> -  -  -  -  -  [UNID13] <br> -  -  -  -  -  [UNID14] <br> -  -  -  -  -  [UNID15] <br> -  -  -  -  -  [UNID16] <br> -  -  -  -  -  [UNID17] <br> -  -  -  -  -  [UNID18] <br> -  -  -  -  -  [UNID19] <br> -  -  -  -  -  Gryllidae <br> -  -  -  -  -  -  [GRYL01] <br> -  -  -  -  -  -  [GRYL02] <br> -  -  -  -  -  -  [GRYL03] <br> -  -  -  -  -  -  [GRYL04] <br> -  -  -  -  -  -  [GRYL05] <br> -  -  -  -  -  -  [GRYL06] <br> -  -  -  -  -  -  [GRYL07] <br> -  -  -  -  -  -  [GRYL08] <br> -  -  -  -  -  -  [GRYL09] <br> -  -  -  -  -  -  [GRYL10] <br> -  -  -  -  -  -  [GRYL11] <br> -  -  -  -  -  -  [GRYL12] <br> -  -  -  -  -  -  [GRYL13] <br> -  -  -  -  -  -  [GRYL14] <br> -  -  -  -  -  -  [GRYL15] <br> -  -  -  -  -  -  [GRYL16] <br> -  -  -  -  -  -  [GRYL17] <br> -  -  -  -  -  -  [GRYL18] <br> -  -  -  -  -  -  [GRYL19] <br> -  -  -  -  -  -  [GRYL20] <br> -  -  -  -  -  -  [GRYL21] <br> -  -  -  -  -  Acrididae <br> -  -  -  -  -  -  [ACRI01] <br> -  -  -  -  -  -  [ACRI02] <br> -  -  -  -  -  -  [ACRI03] <br> -  -  -  -  -  -  [ACRI04] <br> -  -  -  -  -  -  [ACRI05] <br> -  -  -  -  -  -  [ACRI06] <br> -  -  -  -  -  -  [ACRI07] <br> -  -  -  -  -  -  [ACRI08] <br> -  -  -  -  -  -  [ACRI09] <br> -  -  -  -  -  -  [ACRI10] <br> -  -  -  -  -  -  [ACRI11] <br> -  -  -  -  -  -  [ACRI12] <br> -  -  -  -  -  -  [ACRI13] <br> -  -  -  -  -  -  [ACRI14] <br> -  -  -  -  -  -  [ACRI15] <br> -  -  -  -  -  -  [ACRI16] <br> -  -  -  -  -  -  [ACRI17] <br> -  -  -  -  -  -  [ACRI18] <br> -  -  -  -  -  -  [ACRI19] <br> -  -  -  -  -  -  [ACRI20] <br> -  -  -  -  -  Tridactylidae <br> -  -  -  -  -  -  [TRID01] <br> -  -  -  -  -  -  [TRID02] <br> -  -  -  -  -  Trigonidiidae <br> -  -  -  -  -  -  [TRIG01] <br> -  -  -  -  -  -  [TRIG02] <br> -  -  -  -  -  -  [TRIG03] <br> -  -  -  -  -  -  [TRIG04] <br> -  -  -  -  -  -  [TRIG05] <br> -  -  -  -  -  -  [TRIG06] <br> -  -  -  -  -  Tetrigidae <br> -  -  -  -  -  -  [TETR03] <br> -  -  -  -  -  -  [TETR04] <br> -  -  -  -  -  -  [TETR05] <br> -  -  -  -  -  -  [TETR06] <br> -  -  -  -  -  -  [TETR07] <br> -  -  -  -  -  -  [TETR09] <br> -  -  -  -  -  -  [TETR10] <br> -  -  -  -  -  -  [TETR11] <br> -  -  -  -  -  -  [TETR12] <br> -  -  -  -  -  -  [TETR13] <br> -  -  -  -  -  -  [TETR14] <br> -  -  -  -  -  -  [TETR15] <br> -  -  -  -  -  -  [TETR16] <br> -  -  -  -  -  -  [TETR17] <br> -  -  -  -  -  -  [TETR18] <br> -  -  -  -  -  -  [TETR20] <br> -  -  -  -  -  -  [TETR21] <br> -  -  -  -  -  -  <i>Eucriotettix</i> <br> -  -  -  -  -  -  -  [TETR01] <br> -  -  -  -  -  -  <i>Cladonotella</i> <br> -  -  -  -  -  -  -  [TETR19] <br> -  -  -  -  -  -  <i>Boczkitettix</i> <br> -  -  -  -  -  -  -  <i>Boczkitettix borneensis</i> <br> -  -  -  -  -  -  <i>Paratettix</i> <br> -  -  -  -  -  -  -  <i>Paratettix variabilis</i> (as homotypic_synonym: <i>Euparatettix variabilis</i>)<br> -  -  -  -  -  Mogoplistidae <br> -  -  -  -  -  -  [MOGO01] <br> -  -  -  -  -  -  [MOGO02] <br></div><p></p>
Data from: First application of dental microwear texture analysis to infer theropod feeding ecology
<p>Theropods were the dominating apex predators in most Jurassic and Cretaceous terrestrial ecosystems. Their feeding ecology has always been of great interest, and new computational methods have yielded more detailed reconstructions of differences in theropod feedings behaviour. Many approaches however rely on well-preserved skulls. Dental microwear texture analysis (DMTA) is potentially applicable to isolated teeth, and here employed for the first time to investigate dietary ecology of theropods. In particular, we test whether tyrannosaurids show DMT associated with more hard-object feeding than compared to Allosaurus – which would be a sign for higher levels of osteophagy, as has often been suggested. We find no significant difference in complexity and roughness of enamel surfaces between Herrerasaurus, Allosaurus, and tyrannosaurids, which conflicts with inferences of more frequent osteophagic behaviour in Tyrannosaurus as compared to other theropods. Orientation of wear features reveals a more pronounced bi-directional puncture-and-pull feeding mode in Allosaurus than in tyrannosaurids. Our results further indicate ontogenetic niche shift in theropods and crocodylians, significantly larger height parameters in juvenile theropods might indicate frequent scavenging, resulting in more bone-tooth contact during feeding. Overall, DMTA is found to be very similar between theropods and extant large, broad-snouted crocodylians and shows great similarity in feeding ecology of theropod apex predators throughout the Mesozoic.</p>
Fig. 3 in Feeding and reproductive ecology of Cichla piquiti Kullander & Ferreira, 2006 within its native range, Lajeado reservoir, rio Tocantins basin
Fig. 3. Relationship between the size (standard length SL, cm) of Cichla piquiti and its prey.
Figure 2 in Feeding ecology of vimba (Vimba vimba L., 1758) in terms of size groups and seasons in Lake Sapanca, northwestern Anatolia
Figure 2. The relationship between temperature and GFI of V. vimba monthly in Lake Sapanca.
Fig. 2 in Feeding ecology of Lutjanus analis (Teleostei: Lutjanidae) from Abrolhos Bank, Eastern Brazil
Fig. 2. Ordination resulting from nonmetric multidimensional
Fig. 1. A in Scientific Note Feeding ecology of the leaf fish Monocirrhus polyacanthus (Perciformes: Polycentridae) in a terra firme stream in the Brazilian Amazon
Fig. 1. A freshly collected leaf fish Monocirrhus polyacanthus. Photo by F. P. Mendonça.
Fig. 3 in Comparative feeding ecology and habitats use of Crenicichla species (Perciformes: Cichlidae) in a Venezuelan floodplain river
Fig. 3. Water level fluctuations of the Cinaruco River from December 13, 2005 to May 8, 2006.
Feeding ecology has a stronger evolutionary influence on functional morphology than on body mass in mammals
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Data from: First application of dental microwear texture analysis to infer theropod feeding ecology
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Data from: Hunting behavior and feeding ecology of Mojave Rattlesnakes (Crotalus scutulatus), Prairie Rattlesnakes (C. viridis), and their hybrids in southwestern New Mexico
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Data set for 'Lunge filter feeding biomechanics constrain rorqual foraging ecology across scale'...
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