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40 results for “habitat partitioning”
Figure 5 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 5. Station-wise variation in the 0–500 m column integrated mesozooplankton abundance/density and biomass in the central (a) and western (b) Bay of Bengal during spring intermonsoon.
Figure 6 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 6. Depth-wise variation in the number of zooplankton groups at each station in the central (a) and western (b) Bay of Bengal during spring intermonsoon.
Figure 1 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 1. Map of the sampling site in the Bay of Bengal. Stations CB1 to CB5 are located along the central (88°E) and WB1 to WB4 along the western margin of the bay.
Figure 13 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 13. Multivariate cluster analysis of the data of all 129 copepod species combined from all the stations and depths in the central and western bay using the 30% cut-off level of Bray–Curtis similarity. Cluster/Group I are assemblages mostly from the mixed layer (M) and thermocline (T) from central and western transects. Group II comprises assemblages found between the thermocline and 500 m and Group III includes only a few species found exclusively from 200–300 m depth at stations CB3–CB5.
Figure 4 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 4. Vertical profiles of day (D) and night (N) zooplankton biovolume from multinet tows in the western Bay of Bengal during spring intermonsoon. ng: negligible biovolume; NO DATA is where the net failed to open/close. *At WB3, medusae (100 mL 100 m–3) and at WB4 salps (200 mL 100 m–3) were observed at the surface during the day.
Figure 9 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 9. Vertical distribution of abundance (log number 100 m–3) of the major copepod species in the central Bay of Bengal during spring intermonsoon.
Figure 3 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 3. Vertical profiles of day (D) and night (N) zooplankton biovolume from multinet tows in the central Bay of Bengal during spring intermonsoon. ng: Negligible biovolume; NO DATA is where the net failed to open/close. *Swarms of medusae were observed at CB3 (their biovolume 90 mL 100 m–3) and CB4 (200 mL 100 m–3) at the surface at night.
Figure 8 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 8. Vertical distribution of the various types (orders) of copepods in the central (a) and western (b) Bay of Bengal during the spring intermonsoon. The percentages at every depth are averages from 5 stations in the central and 4 stations in the western bay. Data are unavailable at 300–500 m in the central bay due to negligible abundance.
Figure 12 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 12. Variation in multivariate dispersion (MVDISP) indices between different depth strata (9 stations data combined) and between the central and western transects in the Bay of Bengal.
Figure 11 in Spatial structuring of zooplankton communities through partitioning of habitat and resources in the Bay of Bengal during spring intermonsoon
Figure 11. Variation in Shannon diversity (H'), species richness (d), and evenness (J') of copepods in different depth strata in the upper 500 m of the central (a) and western (b) Bay of Bengal.
Fig. 3 in Habitat partitioning, habits and convergence among coastal nektonic fish species from the São Sebastião Channel, southeastern Brazil
Fig. 3. Dendrogram of ecomorphological relationships (similarity) for the 17 nektonic fish species studied. Cluster analysis is by the Euclidean distance measure and Group Average linkage method using the same scores (i.e., coordinates) calculated for PCA and plotted in Fig. 2 (cophenetic coefficient r = 0.86). Anc tri = Anchoa tricolor; Ath bra = Atherinella brasiliensis; Car lat = Caranx latus; Chl chr = Chloroscombrus chrysurus; Fis tab = Fistularia tabacaria; Har jag = Harengula jaguana; Hyp uni = Hyporhamphus unifasciatus; Lag lae = Lagocephalus laevigatus; Mug cur = Mugil curema; Oli sau = Oligoplites saurus; Pom sal = Pomatomus saltatrix; Sar jan = Sardinella janeiro; Sco bra = Scomberomorus brasiliensis; Sel vom = Selene vomer; Str tim = Strongylura timucu; Tra car = Trachinotus carolinus; Tri lep = Trichiurus lepturus. There is no scale among the fishes (see Table 2 for standard length range) (illustrations: Alexandre C. Ribeiro).
Fig. 2 in Habitat partitioning, habits and convergence among coastal nektonic fish species from the São Sebastião Channel, southeastern Brazil
Fig. 2. Distribution of the 17 nektonic fish species in ecomorphological space. Ordination is by the first two axes of PCA (cumulative % of variance = 73) (see Table 5). Anc tri = Anchoa tricolor; Ath bra = Atherinella brasiliensis; Car lat = Caranx latus; Chl chr = Chloroscombrus chrysurus; Fis tab = Fistularia tabacaria; Har jag = Harengula jaguana; Hyp uni = Hyporhamphus unifasciatus; Lag lae = Lagocephalus laevigatus; Mug cur = Mugil curema; Oli sau = Oligoplites saurus; Pom sal = Pomatomus saltatrix; Sar jan = Sardinella janeiro; Sco bra = Scomberomorus brasiliensis; Sel vom = Selene vomer; Str tim = Strongylura timucu; Tra car = Trachinotus carolinus; Tri lep = Trichiurus lepturus. There is no scale among the fishes (see Table 2 for standard length range) (illustrations: Alexandre C. Ribeiro).
Fig. 1 in Habitat partitioning, habits and convergence among coastal nektonic fish species from the São Sebastião Channel, southeastern Brazil
Fig. 1. Map indicating the location of the study area (São Sebastião Channel) and the marine station of the University of São Paulo (CEBIMar-USP) on the coast of São Paulo, southeastern Brazil.
Data: Assessing year-round habitat use by migratory sea ducks in a multi-species context reveals seasonal variation in habitat selection and partitioning
<p>This data file consists of state-space model-derived locations and individual data used to analyze transmitter effects for sea ducks in Eastern North America and is associated with the manuscript "Assessing year-round habitat use by migratory sea ducks in a multi-species context reveals seasonal variation in habitat selection and partitioning" published in Ecography. Columns are organized as follows:</p> <p>id - unique identifier</p> <p>species - species from which the centroid was obtained (BLSC = black scoter, COEI = common eider, LTDU = long-tailed duck, SUSC = surf scoter, WWSC = white-winged scoter)</p> <p>date - date of location (mm/dd/yy)</p> <p>jday - Julian date of location</p> <p>year - calendar year of location</p> <p>lon - longitude of location</p> <p>lat - latitude of location</p> <p>b - average assignment of location to either migrant (1) or resident (2) across all runs of the state-space model</p> <p>b.5 - most probable behavioral category based on average state assignment (1 = b ≤ 1.5 ; 2 = b > 1.5)</p> <p>sex - sex of individual (M = male, F = female)</p> <p>age - age of individual (HY = hatch year, SY = second year, TY = third year, ASY = after second year, ATY = after third year, AHY = after hatch year</p> <p>capture_reg - general area where individual was captured</p> <p>capture_subreg - specific region within capture region where individual was captured</p> <p>stage - period of the annual cycle to which the centroid belongs (W = winter, B = breeding, S = spring staging, M = fall staging and molt, WM = winter migration, BM = breeding migration, MM = molt migration, SM = spring migration)</p> <p>site - position of centroid within season (i.e., W1 = first site occupied during winter, W2 = second site occupied, etc.)</p> <p>cycle - number of annual cycles following transmitter attachment (1 = first cycle after attachment, 2 = second cycle after attachment, etc.)</p> <p>season - season of annual cycle in which centroid occurred (W = winter, F = fall, B = breeding, S = spring)</p>
Data from: Plasticity versus evolutionary divergence: what causes habitat partitioning in urban-adapted birds?
<p>Habitat partitioning can facilitate the coexistence of closely related species, and often results from competitive interference inducing plastic shifts of subordinate species in response to aggressive, dominant species (plasticity), or the evolution of ecological differences in subordinate species that reduce their ability to occupy habitats where the dominant species occurs (evolutionary divergence). Evidence consistent with both plasticity and evolutionary divergence exist, but the relative contributions of each to habitat partitioning have been difficult to discern. Here we use a global dataset on the breeding occurrence of birds in cities to test predictions of these alternative hypotheses to explain previously described habitat partitioning associated with competitive interference. Consistent with plasticity, the presence of behaviorally dominant congeners in a city was associated with a 65% reduction in occurrence of subordinate species, but only when the dominant was a widespread breeder in urban habitats. Consistent with evolutionary divergence, increased range-wide overlap with dominant congeners was associated with a 56% reduction in occurrence of subordinates in cities, even when the dominant was absent from the city. Overall, our results suggest that both plasticity and evolutionary divergence play important, concurrent roles in habitat partitioning among closely related species in urban environments.</p>
Data from: The Competitive exclusion – tolerance rule explains habitat partitioning among co-occurring species of burying beetles
<p>Habitat partitioning among co-occurring, ecologically similar species is widespread in nature and thought to be an important mechanism for coexistence. The factors that cause habitat partitioning, however, are unknown for most species. We experimentally tested among three alternative hypotheses to explain habitat partitioning among two species of co-occurring burying beetle (<em>Nicrophorus</em>) that occupy forest (<em>N. orbicollis</em>) and wetland (<em>N. hebes</em>) habitats. Captive experiments revealed that the larger <em>N. orbicollis </em>(forest) was consistently dominant to <em>N. hebes </em>(wetland) in competitive interactions for carcasses that they require for reproduction. Transplant enclosure experiments in nature revealed that <em>N. hebes</em> had poor reproductive success whenever the dominant <em>N. orbicollis</em> was present. In the absence of <em>N. orbicollis</em>, <em>N. hebes</em> performed as well, or better, in forest versus its typical wetland habitat. In contrast, <em>N. orbicollis </em>performed poorly in wetlands regardless of the presence of <em>N. hebes</em>. These results support the Competitive exclusion – tolerance rule where the competitively dominant <em>N. orbicollis</em> excludes the subordinate <em>N. hebes</em> from otherwise suitable or preferable forest habitat, while the subordinate <em>N. hebes</em> is uniquely able to tolerate the challenges of breeding in wetlands. Transplant experiments further showed that carcass burial depth – an important trait thought to enhance the competitive ability of the dominant <em>N. orbicollis</em> – is costly in wetland habitats. When in the presence of <em>N. hebes, N. orbicollis</em> buried carcasses deeper; deeper burial is thought to provide a competitive advantage in forests, but further compromised the reproductive success of <em>N. orbicollis </em>in wetlands. Overall, results provide evidence that the Competitive exclusion – tolerance rule underlies habitat partitioning among ecologically similar species, and that the traits important for competitive dominance in relatively benign environments are costly in more challenging environments, consistent with a trade-off.</p>
Behavioural thermoregulation and food availability drives fine-scale seasonal habitat partitioning in limpets
<ol> <li>Small-scale spatial variation in temperature plays a key role in limiting the distribution of organisms in thermally heterogeneous environments. In the rocky intertidal zone, intra-day temperature variation at small scales (cm-m) can easily exceed 30°C.</li> <li>To experimentally test the impact of this small-scale temperature heterogeneity on the distribution of an ecologically important limpet species (<em>Cellana</em> <em>denticulata</em>), boulders on an intertidal rocky reef in New Zealand were rotated, and small-scale temperature variability and food availability were measured throughout one year. Small-scale variability in thermal tolerance, heart rate, and heat shock protein expression was also measured to relate in situ limpet distributions with environmental conditions. Temperature variability was measured using limpet bio-mimics and HOBO pendant temperature loggers, while food availability (chlorophyll <em>a</em> concentration) was measured on in situ concrete fiberboard tiles.</li> <li>To measure limpet distributions, every tagged limpet on each of 18 boulders was followed from June 2021–2022. During each sampling, the location of each limpet, compass direction, and slope of the surface that each limpet inhabited were measured. To test for physiological differences among limpets from each microhabitat, thermal tolerance, and heat shock protein expression were measured in collected limpets; in situ heart rates and body temperatures were also measured sporadically on hot days.</li> <li>Maximum predicted body temperatures (36–39°C), heart rates, and actual body temperatures were greatest on equatorial surfaces (i.e., surfaces facing the equator) whereas food availability was greatest on poleward-facing surfaces. During summer, limpets moved towards surfaces that minimised their body temperatures and maximised food availability, namely vertical and poleward facing surfaces and undersides of boulders.</li> <li>Thermal tolerance, measured using Arrhenius breakpoint temperatures (mean ± SE: 34.7–35.8 ± 0.4–0.7°C) and flat line temperatures (36.2–37.4 ± 0.3–0.4°C), were similar among limpets facing different directions. Surprisingly, heat shock protein expression was relatively consistent throughout the year and among directions.</li> <li>Overall, limpet populations are resilient to thermal stress because they effectively use behavioural thermoregulation to exploit cooler microhabitats that also have greater food availability during summer.</li> </ol>
Data from: The Competitive exclusion – tolerance rule explains habitat partitioning among co-occurring species of burying beetles
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Behavioural thermoregulation and food availability drives fine-scale seasonal habitat partitioning in limpets
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Data from: Plasticity versus evolutionary divergence: what causes habitat partitioning in urban-adapted birds?
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