What drives small‐scale spatial patterns in lotic meiofauna communities?

• 1 Lotic meiofaunal communities demonstrate extremely variable dynamics, especially when viewed at small spatial scales (≤ metres). Given the limited amount of research on lotic meiofauna, we chose to organise our discussion of their small‐scale spatial patterns around the dominant factors we believe drive their spatial distributions in streams. We separate scale‐dependent effects that structure lotic meiofauna into biotic factors (e.g. predation, food quantity/quality, dispersal) and abiotic factors (e.g. local flow dynamics and substratum characteristics).
• 2 The impact of predation on the distribution of meiofauna varies with the scale over which predators forage (e.g. fish predation influences meiofauna in different ways and at broader spatial scales than do invertebrate predators), the type of streambed substrata in which the predator‐prey interactions occur, and the dispersal ability of different meiofauna. The latter is greatly influenced by predator and prey (meiofauna) interactions with the flow environment.
• 3 Organic matter influences the small‐scale distribution of meiofauna in streams. Both its quality as food (as indicated by C:N content, ATP content, or microbial biomass) and its spatial distribution on the streambed, influence meiofauna patchiness, community structure and life history characteristics. As a habitat, the structure that organic matter provides (e.g. wood or leaves) can influence predator‐prey interactions, offer materials for case‐building and offer refugia during disturbance events ‐ all of which influence the small‐scale spatial distribution of meiofauna.
• 4 Stream flow influences the distribution of meiofauna at broad scales (10s–100s of metres), primarily because of the high susceptibility of meiofauna to passive drift; small‐scale interactions between flow and substrata are also important, however, particularly at more localised (≤ metre) scales. At both scales, substratum particle size is important to interstitial‐dwelling fauna, influencing the probability of passive drift by meiofauna as well as local microhabitat conditions (e.g. dissolved oxygen; upwelling/downwelling in the hyporheic zone) and, thus, the small‐scale distribution among microhabitats.
• 5 In general, the processes governing the distribution of meiofauna at small scales cannot be separated entirely from those processes working at larger scales. A conceptual diagram is presented illustrating the relative importance of various factors in influencing the spatial patterns of meiofauna and over what scales these factors act.

     

The forgotten prey of an iconic predator: a review of interactions between grey wolves Canis lupus and beavers Castor spp.

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Migratory herds of wildebeests and zebras indirectly affect calf survival of giraffes

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Non‐pathogenic aquatic bacteria activate the immune system and increase predation risk in damselfly larvae

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The demographic and life‐history costs of fear: Trait‐mediated effects of threat of predation on Aedes triseriatus

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Experimental tests of the role of predation in the population dynamics of voles and lemmings

• 1 Reasons for fluctuating populations of small mammals have been intensively investigated since the early days of modern ecology. Particular interest has been taken in vole populations exhibiting multiannual oscillations. Much empirical and theoretical work has been accomplished to find out the key factor(s) driving these population cycles and many reviews have been written about the results.
• 2 One of the most plausible processes for explaining regular fluctuations in small mammals is predation. Here I review the existing literature on the experimental studies of the role of predation in vole population dynamics in the hope that a critical examination of these studies will help researchers improve the design of future experiments.
• 3 Most predation manipulations have been done in exclosures, but there are also studies that have attempted to reduce or increase predator numbers in non‐fenced areas, islands and enclosures.
• 4 As the number of experimental studies has increased, their quality in terms of replication, use of controls and realistic spatial and temporal scales has also improved.
• 5 Most studies have found population‐level effects of predator manipulations on prey populations. The effects have varied from very weak to very strong, reflecting dissimilar experimental designs and the great variety of predator–prey interactions among different kinds of species in different landscapes. Most of these studies show that predation limits population growth of voles, and in some circumstances even regulate vole population fluctuations, but none of them clearly demonstrates that predation consistently changes fluctuation patterns of voles.
• 6 To be able to assess more reliably the true role of predation on (cyclic) population fluctuations of voles, more competent experiments are still needed not only over the geographical range of cyclic population dynamics, but also in areas of weakly or non‐cyclic populations of voles.

     

The landscape of fear as an emergent property of heterogeneity: Contrasting patterns of predation risk in grassland ecosystems

The likelihood of encountering a predator influences prey behavior and spatial distribution such that non‐consumptive effects can outweigh the influence of direct predation. Prey species are thought to filter information on perceived predator encounter rates in physical landscapes into a landscape of fear defined by spatially explicit heterogeneity in predation risk. The presence of multiple predators using different hunting strategies further complicates navigation through a landscape of fear and potentially exposes prey to greater risk of predation. The juxtaposition of land cover types likely influences overlap in occurrence of different predators, suggesting that attributes of a landscape of fear result from complexity in the physical landscape. Woody encroachment in grasslands furnishes an example of increasing complexity with the potential to influence predator distributions. We examined the role of vegetation structure on the distribution of two avian predators, Red‐tailed Hawk (Buteo jamaicensis) and Northern Harrier (Circus cyaneus), and the vulnerability of a frequent prey species of those predators, Northern Bobwhite (Colinus virginianus). We mapped occurrences of the raptors and kill locations of Northern Bobwhite to examine spatial vulnerability patterns in relation to landscape complexity. We use an offset model to examine spatially explicit habitat use patterns of these predators in the Southern Great Plains of the United States, and monitored vulnerability patterns of their prey species based on kill locations collected during radio telemetry monitoring. Both predator density and predation‐specific mortality of Northern Bobwhite increased with vegetation complexity generated by fine‐scale interspersion of grassland and woodland. Predation pressure was lower in more homogeneous landscapes where overlap of the two predators was less frequent. Predator overlap created areas of high risk for Northern Bobwhite amounting to 32% of the land area where landscape complexity was high and 7% where complexity was lower. Our study emphasizes the need to evaluate the role of landscape structure on predation dynamics and reveals another threat from woody encroachment in grasslands.     

Consequences of temperature and temperature variability on swimming activity,group structure,and predation of endangered delta smelt

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Turbidity amplifies the non‐lethal effects of predation and affects the foraging success of characid fish shoals

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Rethinking trophic niches: Speed and body mass colimit prey space of mammalian predators

1. Realized trophic niches of predators are often characterized along a one‐dimensional range in predator–prey body mass ratios. This prey range is constrained by an “energy limit” and a “subdue limit” toward small and large prey, respectively. Besides these body mass ratios, maximum speed is an additional key component in most predator–prey interactions.
2. Here, we extend the concept of a one‐dimensional prey range to a two‐dimensional prey space by incorporating a hump‐shaped speed‐body mass relation. This new “speed limit” additionally constrains trophic niches of predators toward fast prey.
3. To test this concept of two‐dimensional prey spaces for different hunting strategies (pursuit, group, and ambush predation), we synthesized data on 63 terrestrial mammalian predator–prey interactions, their body masses, and maximum speeds.
4. We found that pursuit predators hunt smaller and slower prey, whereas group hunters focus on larger but mostly slower prey and ambushers are more flexible. Group hunters and ambushers have evolved different strategies to occupy a similar trophic niche that avoids competition with pursuit predators. Moreover, our concept suggests energetic optima of these hunting strategies along a body mass axis and thereby provides mechanistic explanations for why there are no small group hunters (referred to as “micro‐lions”) or mega‐carnivores (referred to as “mega‐cheetahs”).
5. Our results demonstrate that advancing the concept of prey ranges to prey spaces by adding the new dimension of speed will foster a new and mechanistic understanding of predator trophic niches and improve our predictions of predator–prey interactions, food web structure, and ecosystem functions.

     

Beyond the encounter: Predicting multi‐predator risk to elk (Cervus canadensis) in summer using predator scats

1. There is growing evidence that prey perceive the risk of predation and alter their behavior in response, resulting in changes in spatial distribution and potential fitness consequences. Previous approaches to mapping predation risk across a landscape quantify predator space use to estimate potential predator‐prey encounters, yet this approach does not account for successful predator attack resulting in prey mortality. An exception is a prey kill site that reflects an encounter resulting in mortality, but obtaining information on kill sites is expensive and requires time to accumulate adequate sample sizes.
2. We illustrate an alternative approach using predator scat locations and their contents to quantify spatial predation risk for elk (Cervus canadensis) from multiple predators in the Rocky Mountains of Alberta, Canada. We surveyed over 1300 km to detect scats of bears (Ursus arctos/U. americanus), cougars (Puma concolor), coyotes (Canis latrans), and wolves (C. lupus). To derive spatial predation risk, we combined predictions of scat‐based resource selection functions (RSFs) weighted by predator abundance with predictions that a predator‐specific scat in a location contained elk. We evaluated the scat‐based predictions of predation risk by correlating them to predictions based on elk kill sites. We also compared scat‐based predation risk on summer ranges of elk following three migratory tactics for consistency with telemetry‐based metrics of predation risk and cause‐specific mortality of elk.
3. We found a strong correlation between the scat‐based approach presented here and predation risk predicted by kill sites and (r = .98, p < .001). Elk migrating east of the Ya Ha Tinda winter range were exposed to the highest predation risk from cougars, resident elk summering on the Ya Ha Tinda winter range were exposed to the highest predation risk from wolves and coyotes, and elk migrating west to summer in Banff National Park were exposed to highest risk of encountering bears, but it was less likely to find elk in bear scats than in other areas. These patterns were consistent with previous estimates of spatial risk based on telemetry of collared predators and recent cause‐specific mortality patterns in elk.
4. A scat‐based approach can provide a cost‐efficient alternative to kill sites of quantifying broad‐scale, spatial patterns in risk of predation for prey particularly in multiple predator species systems.

     

Tracking predator density dependence and subterranean predation by carabid larvae on slugs using monoclonal antibodies

1. Subterranean carabid larvae are more numerous than surface‐active adults, yet very little is known about their ecological significance, dietary preferences or ability to regulate populations of prey species, particularly pests. Part of the reason for this is that predator–prey interactions beneath the soil are almost impossible to observe. 2. Extensive field studies have shown that adult Pterostichus melanarius (Illiger) can affect the temporal and spatial dynamics of their slug prey. However, if larvae too are feeding on slugs, this could radically affect overall predator–prey dynamics. 3. We tested the hypotheses that P. melanarius larvae would kill and consume two slug species, Deroceras reticulatum Müller and Arion intermedius Normand, under laboratory and semi‐field conditions, and that there would be no significant difference in rates of predation on these slug species. 4. A new monoclonal antibody was developed that was capable of detecting the presence of slug proteins in the guts of P. melanarius larvae. 5. Pterostichus melanarius larvae killed both A. intermedius and D. reticulatum in the laboratory, feeding to a greater extent, and growing more rapidly, on the latter. The larvae were equally effective at reducing numbers of both slug species in a crop of wheat grown in semi‐field mini plots, but predation was affected by density‐dependent intra‐specific competition amongst the beetle larvae. 6. Future modelling of the dynamic interactions between carabids and slugs will need to take into account predation by larvae.     

Feedback between size balance and consumption strongly affects the consequences of hatching phenology in size‐dependent predator–prey interactions

In many size‐dependent predator–prey systems, hatching phenology strongly affects predator–prey interaction outcomes. Early‐hatched predators can easily consume prey when they first interact because they encounter smaller prey. However, this process by itself may be insufficient to explain all predator–prey interaction outcomes over the whole interaction period because the predator–prey size balance changes dynamically throughout their ontogeny. We hypothesized that hatching phenology influences predator–prey interactions via a feedback mechanism between the predator–prey size balance and prey consumption by predators. We experimentally tested this hypothesis in an amphibian predator–prey model system. Frog tadpoles Rana pirica were exposed to a predatory salamander larva Hynobius retardatus that had hatched 5, 12, 19 or 26 days after the frog tadpoles hatched. We investigated how the salamander hatch timing affected the dynamics of prey mortality, size changes of both predator and prey, and their subsequent life history (larval period and size at metamorphosis). The predator–prey size balance favoured earlier hatched salamanders, which just after hatching could successfully consume more frog tadpoles than later hatched salamanders. The early‐hatched salamanders grew rapidly and their accelerated growth enabled them to maintain the predator‐superior size balance; thus, they continued to exert strong predation pressure on the frog tadpoles in the subsequent period. Furthermore, frog tadpoles exposed to the early‐hatched salamanders were larger at metamorphosis and had a longer larval period than other frog tadpoles. These results suggest that feedback between the predator‐superior size balance and prey consumption is a critical mechanism that strongly affects the impacts of early hatching of predators in the short‐term population dynamics and life history of the prey. Because consumption of large nutrient‐rich prey items supports the growth of predators, a similar feedback mechanism may be common and have strong impacts on phenological shifts in size‐dependent trophic relationships.     

Identifying inter‐ and intra‐guild feeding activity of an arthropod predator assemblage

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Noise-induced reduction in the attack rate of a planktivorous freshwater fish revealed by functional response analysis

1. Anthropogenic noise can affect animals physically, physiologically, and behaviourally. Although individual responses to noise are well documented, the consequences in terms of community structure, species coexistence, and ecosystem functioning remain fairly unknown.
2. The impact of noise on predation has received a growing interest and alterations in trophic links are observed when animals shift from foraging to stress-related behaviours, are distracted by noise, or because of acoustic masking. However, the experimental procedures classically used to quantify predation do not inform on the potential demographic impact on prey.
3. We derived the relationship between resource use and availability (the functional response) for European minnows (Phoxinus phoxinus) feeding on dipteran larvae (Chaoborus sp.) under two noise conditions: ambient noise and ambient noise supplemented with motorboat noise. The shape and magnitude of the functional response are powerful indicators of population outcomes and predator–prey dynamics. We also recorded fish behaviour to explore some proximate determinants of altered predation.
4. For both noise conditions, fish displayed a saturating (type II) functional response whose shape depends on two parameters: attack rate and handling time. Boat noise did not affect handling time but significantly reduced attack rate, resulting in a functional response curve of the same height but with a less steep initial slope. Fish exhibited a stress-related response to noise including increased swimming distance, more social interactions, and altered spatial distribution.
5. Our study shows the usefulness of the functional response approach to study the ecological impacts of noise and illustrates how the behavioural responses of predators to noise can modify the demographic pressure on prey. It also suggests that prey availability might mediate the negative effect of noise on predation. Community outcomes are expected if the reduced consumption of the main food sources goes with the overconsumption of alternative food sources, changing the distribution pattern of interaction strengths. Predation release could also trigger a trophic cascade, propagating the effect of noise to lower trophic levels.

     

Body mass relationships affect the age structure of predation across carnivore–ungulate systems: a review and synthesis

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Assessing the influence of prey–predator ratio,prey age structure and packs size on wolf kill rates

Traditional predation theory assumes that prey density is the primary determinant of kill rate. More recently, the ratio of prey‐to‐predator has been shown to be a better predictor of kill rate. However, the selective behavior of many predators also suggests that age structure of the prey population should be an important predictor of kill rate. We compared wolf–moose predation dynamics in two sites, south‐central Scandinavia (SCA) and Isle Royale, Lake Superior, North America (IR), where prey density was similar, but where prey age structure and prey‐to‐predator ratio differed. Per capita kill rates of wolves preying on moose in SCA are three times greater than on IR. Because SCA and IR have similar prey densities differences in kill rate cannot be explained by prey density. Instead, differences in kill rate are explained by differences in the ratio of prey‐to‐predator, pack size and age structure of the prey populations. Although ratio‐dependent functional responses was an important variable for explaining differences in kill rates between SCA and IR, kill rates tended to be higher when calves comprised a greater portion of wolves’ diet (p =0.05). Our study is the first to suggest how age structure of the prey population can affect kill rate for a mammalian predator. Differences in age structure of the SCA and IR prey populations are, in large part, the result of moose and forests being exploited in SCA, but not in IR. While predator conservation is largely motivated by restoring trophic cascades and other top–down influences, our results show how human enterprises can also alter predation through bottom–up processes.     

Decomposing Predation: Testing for Parameters that Correlate with Predatory Performance by a Social Bacterium

Predator–prey interactions presumably play major roles in shaping the composition and dynamics of microbial communities. However, little is understood about the population biology of such interactions or how predation-related parameters vary or correlate across prey environments. Myxococcus xanthus is a motile soil bacterium that feeds on a broad range of other soil microbes that vary greatly in the degree to which they support M. xanthus growth. In order to decompose predator–prey interactions at the population level, we quantified five predation-related parameters during M. xanthus growth on nine phylogenetically diverse bacterial prey species. The horizontal expansion rate of swarming predator colonies fueled by prey lawns served as our measure of overall predatory performance, as it incorporates both the searching (motility) and handling (killing and consumption of prey) components of predation. Four other parameters—predator population growth rate, maximum predator yield, maximum prey kill, and overall rate of prey death—were measured from homogeneously mixed predator–prey lawns from which predator populations were not allowed to expand horizontally by swarming motility. All prey species fueled predator population growth. For some prey, predator-specific prey death was detected contemporaneously with predator population growth, whereas killing of other prey species was detected only after cessation of predator growth. All four of the alternative parameters were found to correlate significantly with predator swarm expansion rate to varying degrees, suggesting causal interrelationships among these diverse predation measures. More broadly, our results highlight the importance of examining multiple parameters for thoroughly understanding the population biology of microbial predation.     

A review of the population dynamics of mule deer and black‐tailed deer Odocoileus hemionus in North America

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Predation and disease: understanding the effects of predators at several trophic levels on pathogen transmission

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