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112 results for “habitat complexity”
Changes in the acoustic structure of Australian bird communities along a habitat complexity gradient
<p>Avian vocalizations have evolved in response to a variety of abiotic and biotic selective pressures. While there is some support for signal convergence in similar habitats that is attributed to adaptation to the acoustic properties of the environment (the ‘acoustic adaptation hypothesis’, AAH), there is also evidence for character displacement as result of competition for signal space among coexisting species (the ‘acoustic niche partitioning hypothesis’). We explored the acoustic space of avian assemblages distributed along six different habitat types (from herbaceous habitats to warm rainforests) in south eastern Queensland, Australia. We employed three acoustic diversity indices (acoustic richness, evenness, and divergence) to characterize the signal space. In addition, we quantified the phylogenetic and morphological structure (in terms of both body mass and beak size) of each community. Acoustic parameters showed a moderately low phylogenetic signal, indicating labile evolution. Although, we did not find meaningful differences in acoustic diversity indices among habitat categories, there was a significant relationship between the regularity component (evenness) and vegetation height indicating that acoustic signals are more evenly distributed in dense habitats. After accounting for differences in species richness, the volume of acoustic space (i.e., acoustic richness) decreased as the level of phylogenetic and morphological resemblance among species in a given community increased. Additionally, we found a significantly negative relationship between acoustic divergence and divergence in body mass indicating that the less different species are in their body mass, the more different their songs are likely to be. This implies the existence of acoustic niche partitioning at community level. Overall, while we found mixed support for the AAH, our results suggest that community-level effects may play a role in structuring acoustic signals within avian communities in this region. This study shows that signal diversity estimated by diversity metrics of community ecology based on basic acoustic parameters can provide additional insight into the structure of animal vocalizations. <br> </p>
Figure 3. Structurally complex high rugosity coral-dominated reef habitat. Image shows fixed transect 01 from the start pin looking toward a 180 in Fishes of War in the Pacific National Historic Park
Figure 3. Structurally complex high rugosity coral-dominated reef habitat. Image shows fixed transect 01 from the start pin looking toward a 180° heading in the Asan Beach unit (NPS photo).
Figure 3. CCA showing the relationship between 16 in Assessments of environmental variables affecting the spatiotemporal distribution and habitat preferences of living Ostracoda (Crustacea) species in the Enez Lagoon Complex (Enez-Evros Delta, Turkey)
Figure 3. CCA showing the relationship between 16 species (red triangles) and 9 environmental variables (red arrows). See Tables 2 and 4 for an explanation of abbreviations and variables.
Figure 2 in Assessments of environmental variables affecting the spatiotemporal distribution and habitat preferences of living Ostracoda (Crustacea) species in the Enez Lagoon Complex (Enez-Evros Delta, Turkey)
Figure 2. Jaccard's coefficient similarity dendrograms showing the faunal similarity among the 12 sampling sites (based on presence/absence of species) and clustering relationships among the 16 ostracod species. (Species codes are given in Table 3.)
Fig. 1 in Assessments of environmental variables affecting the spatiotemporal distribution and habitat preferences of living Ostracoda (Crustacea) species in the Enez Lagoon Complex (Enez-Evros Delta, Turkey)
Fig. 1. Map of the eight studied coastal lagoons. Selected sampling sites at Tuzla Lake 1 (St-1), Tuzla Lake 2 (St-2), Tuzla Lake 3 (St-3), Taz (St-4), Işık (St-5), Dalyan (St-7, 8, and 9), Kuvalak (St-10), and Taşaltı (St-11 and 12) were used for comparisons of the lagoons. The sampling sites are indicated by red circles; the red arrows show the direction of water currents.
Figure 4 in Plant age, crop stage and surrounding habitats: their impact on sucking pests and predators complex in cotton (Gossypium hirsutum L.) field plots in arid climate at district Layyah, Punjab, Pakistan
Figure 4. Means (±SE) number of sucking insect pests (jassid, thrips, whitefly) and predators (green lacewing, spider) in cotton field plots at three locations (five replications) with different surrounding habitats (sugarcane + sesame, monoculture, sesame) during cropping season of cotton from June 20 to September 18, 2018 at Layyah, Punjab, Pakistan.
Figure 3 in Plant age, crop stage and surrounding habitats: their impact on sucking pests and predators complex in cotton (Gossypium hirsutum L.) field plots in arid climate at district Layyah, Punjab, Pakistan
Figure 3. Means (±SE) number of sucking insect pests (jassid, thrips, whitefly) and predators (green lacewing, spider) in cotton field plots at three locations (five replications) at different crop developmental stages (crop phenology) of cotton from June 20 to September 18, 2018 at Layyah, Punjab, Pakistan.
Figure 2 in Plant age, crop stage and surrounding habitats: their impact on sucking pests and predators complex in cotton (Gossypium hirsutum L.) field plots in arid climate at district Layyah, Punjab, Pakistan
Figure 2. Means (±SE) number of sucking insect pests (jassid, thrips, whitefly) and predators (green lacewing, spider) in cotton field plots at three locations (five replications) during cropping season of cotton from June 20 to September 18, 2018 at Layyah, Punjab, Pakistan.
Figure 1 in Plant age, crop stage and surrounding habitats: their impact on sucking pests and predators complex in cotton (Gossypium hirsutum L.) field plots in arid climate at district Layyah, Punjab, Pakistan
Figure 1. Percent numbers of sucking insect pests (jassid, thrips, whitefly) and predators (green lacewing, spider) in cotton field plots at three locations (five replications) during 2018 at Layyah, Punjab, Pakistan.
Fig. 2 in Predation of stink bugs (Hemiptera: Pentatomidae) by a complex of predators in cotton and adjoining soybean habitats in Georgia, USA
Fig. 2. Least squares means (± SE) for number of stink bugs (species combined) and Orius insidiosus per 1.83 m length of row and percentage of predators positive for stink bug DNA in soybean and cotton by treatment. Within an insect group in a crop, treatment means with the same lowercase letter are not significantly different (Tukey's HSD, P> 0.05).
Fig. 4 in Predation of stink bugs (Hemiptera: Pentatomidae) by a complex of predators in cotton and adjoining soybean habitats in Georgia, USA
Fig. 4. Least squares means (± SE) for number of stink bugs (species combined) and Oxyopes salticus per 1.83 m length of row and percentage of predators positive for stink bug DNA in (A) soybean and cotton by treatment and (B) soybean and cotton by sampling week. Within an insect group in a crop, treatment or week means with the same lowercase letter are not significantly different (Tukey's HSD, P> 0.05). Only 1 Ox. salticus individual screened positive for stink bug (Nexara viridula) DNA in cotton. No predators were collected for PCR analyses in weeks 4 and 5 in cotton.
Fig. 2 in Habitat complexity does not influence prey consumption in an experimental three-level trophic chain
Fig. 2. Percentage of Chironomidae larvae consumed by Moenkhausia forestii Benine, Mariguela & C. de Oliveira, 2009 in low, intermediate and high habitat complexities in the presence and in the absence of the piscivore Hoplerythrinus unitaeniatus (Spix & Agassiz 1829).
Fig. 4 in Habitat complexity does not influence prey consumption in an experimental three-level trophic chain
Fig. 4. Correlation between the percentages of Chironomidae larvae consumed by Moenkhausia forestii Benine, Mariguela & C. de Oliveira, 2009 and the percentage of individuals of M. forestii consumed by Hoplerythrinus unitaeniatus (Spix & Agassiz 1829) in the all levels of habitat complexity.
Fig. 5 in Habitat complexity does not influence prey consumption in an experimental three-level trophic chain
Fig. 5. Conceptual scheme describing prey consumption by Hoplerythrinus unitaeniatus (Spix & Agassiz 1829) and Moenkhausi forestii Benine, Mariguela & C. de Oliveira, 2009 reported in this experimental study. Bold solid arrows represent trophic interactions and thin solid arrows represent direct effects. Dashed arrow represents indirect effects. Habitat complexity, represented by macrophytes density, affects prey capture strategy of H. unitaeniatus and indirectly affects invertivore foraging.
Fig. 1 in Habitat complexity does not influence prey consumption in an experimental three-level trophic chain
Fig. 1. The experimental design. The (A) axis represents habitat complexity [(a) low, (b) intermediate and (c) high] and the (B) axis represents the piscivore [(d) absence and (e) presence]. Numbers in the aquaria represent the number of replicates for each treatment combination.
Fig. 3 in Habitat complexity does not influence prey consumption in an experimental three-level trophic chain
Fig. 3. Percentage of Moenkhausia forestii Benine, Mariguela & C. de Oliveira, 2009 individuals consumed by Hoplerythrinus unitaeniatus (Spix & Agassiz 1829) in low, intermediate and high habitat complexities.
Data from: Genetic and habitat complexity effects on unpredictability in escape behavior of a grasshopper species
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Habitat complexity dampens selection on prey activity level
<p>Conspecific prey individuals often exhibit persistent differences in behavior (i.e., animal personality) and consequently vary in their susceptibility to predation. How this form of selection varies across environmental contexts is essential to predicting ecological and evolutionary dynamics, yet remains currently unresolved. Here, we use three separate predator–prey systems (sea star–snail, wolf spider–cricket, and jumping spider–cricket) to independently examine how habitat structural complexity influences the selection that predators impose on prey behavioral types. Prior to conducting staged predator–prey interaction encounters, we ran prey individuals through multiple behavioral assays to determine their average activity level. We then allowed individual predators to interact with groups of prey in either open or structurally complex habitats and recorded the number and individual identity of prey that were eaten. Habitat complexity had no effect on overall predation rates in any of the three predator–prey systems. Despite this, we detected a pervasive interaction between habitat structure and individual prey activity level in determining individual prey survival. In open habitats, all predators imposed strong selection on prey behavioral types: sea stars preferentially consumed sedentary snails, while spiders preferentially consumed active crickets. Habitat complexity dampened selection within all three systems, equalizing the predation risk that active and sedentary prey faced. These findings suggest a general effect of habitat complexity that reduces the importance of prey activity level in determining individual predation risk. We reason this occurs because activity level (i.e., movement) is paramount in determining risk within open environments, whereas in complex habitats, other behavioral traits (e.g., escape ability to a refuge) may take precedence.</p>
Data from: Contrasting complexity of adjacent habitats influences the strength of cascading predatory effects
Although cascading effects of top predators can help structure communities, their influence may vary across habitats that differentially protect prey. Therefore, to understand how and to what degree habitat complexity can affect trophic interactions in adjacent habitats, we used a combination of a broad regional-scale survey, manipulative field trials, and an outdoor mesocosm experiment to quantify predator–prey interaction strengths across four trophic levels. Within estuaries of the southeastern USA, bonnethead sharks (Sphyrna tiburo) hunt blue crabs on mudflats and adjacent oyster reefs, two habitats with vastly different aboveground structure. Using 12-h tethering trials of blue crabs we quantified habitat-dependent loss rates of 37% on reefs and 78% on mudflats. We hypothesized that the sharks' predatory effects on blue crabs would cascade down to release a lower-level mud crab predator, which subsequently would increase juvenile oyster mortality, but that the cascade strength would be habitat-dependent. We experimentally manipulated predator combinations in split-plot mesocosms containing reef and mudflat habitats, and quantified oyster mortality. Bonnetheads exerted strong consumptive and non-consumptive effects on blue crabs, which ceased eating oysters in the sharks' presence. However, mud crabs, regardless of shark and blue crab presence, continued to consume oysters, especially within the structural refuge of the reef where they kept oyster mortality high. Thus, bonnetheads indirectly boosted oyster survival, but only on the mudflat where mud crabs were less active. Our work demonstrates how structural differences in adjacent habitats can moderate trophic cascades, particularly when mesopredators exhibit differential use of structure and different sensitivities to top predators.
Local habitat complexity and its effects on herbivores and predators in urban agroecosystems
<p>In urban community gardens, cultivated vegetation provides variable levels of habitat complexity, which can suppress pests by promoting predator diversity and improving pest control. In this study, we examine three components of the structural complexity of garden vegetation (cover, diversity, and connectivity) to investigate whether higher garden vegetation complexity leads to fewer herbivores, more predators, and higher predation. We worked in eight community gardens where we quantified vegetation complexity, sampled the arthropod community, and measured predation on cornworm eggs. We found that plots with high vegetation cover supported higher species richness and greater abundance of predatory insects. High vegetation cover also supported a greater abundance and richness of spiders. In contrast, high vegetation diversity was negatively associated with predator abundance. While high predator abundance was positively associated with egg predation, greater predator species richness had a negative impact on egg predation, suggesting that antagonism between predators may limit biological control. Community gardeners may thus manipulate vegetation cover and diversity to promote higher predator abundance and diversity in their plots. However, the species composition of predators and the prevalence of interspecific antagonism may ultimately determine subsequent impacts on biological pest control.</p>
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