The Importance of Scale to Predator‐Prey Spatial Correlations: An Example of Atlantic Fishes

Rose, George A.; Leggett, William C.

doi:10.2307/1940245

Cited by 218 publications

(187 citation statements)

References 18 publications

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“…Our presence/absence approximation to these important plankton groups leads to reduced quantitative resolution and less variability explained when evaluating how these prey groups influence the fish distribution. Second, important factors may have been sampled, but not on an appropriate scale (see Rose & Leggett 1990). There are several indications in our data of a patchy distribution of prey and correspondingly high station-to-station variability in feeding intensity and prey items in the guts.…”

Section: Spatial Distributionmentioning

confidence: 93%

“…An interesting question is whether the fish switch diets due to the presence of other species as observed for pollock in co-presence with capelin (Rose & Leggett 1990). In our data material there were five stations with co-occurrence of mackerel and herring in the catches during each of the years, so the material can only give indications about possible diet switch.…”

Section: Diet Overlapmentioning

confidence: 99%

“…Whereas conventional stomach sampling programmes are well-suited for investigating trends in consumption averaging over large spatial and temporal scales, comparisons in feeding ecology and interactions across species is best studied when the species are sampled simultaneously and a combination of different scales is applied (Rose & Leggett 1990). In this study we compare the distribution and diets of the three major planktivorous fish species in the Norwegian Sea and relate them to environmental factors during the two summer periods of 2004 and 2006.…”

Section: Introductionmentioning

confidence: 99%

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Overlap in distribution and diets of Atlantic mackerel (Scomber scombrus), Norwegian spring-spawning herring (Clupea harengus) and blue whiting (Micromesistius poutassou) in the Norwegian Sea during late summer

Langøy

Nøttestad

Skaret

et al. 2012

Marine Biology Research

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Based on two large-scale surveys in the Norwegian Sea in July 2004 and 2006 we investigated the potential overlap in distribution and diets and association with environmental variables for mackerel, herring and blue whiting. Mackerel and blue whiting had low overlap in both distribution and diets, and were associated with warm Atlantic and cold prey-rich Arctic waters, respectively. Also, herring and mackerel were negatively correlated, associated with different environmental variables and even had low diet overlap in Atlantic water where the highest overlap in distribution was observed, but these trends were much clearer in 2006 than in 2004. Prey was patchily distributed and data from single stations indicated that feeding was opportunistic, in particular for mackerel. However, mackerel diet width and feeding incidence were similar between the years, whereas herring was more selective in 2004, probably reaching the end of the feeding season. A delayed peak in Calanus availability was indicated in 2006 and may have prolonged the feeding season, partly explain the difference between the years. Our data did not indicate a prey shift due to encounters between mackerel and herring. Concentrated efforts in limited areas are needed to further elucidate this aspect of interaction.

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Section: Spatial Distributionmentioning

confidence: 93%

Section: Diet Overlapmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Overlap in distribution and diets of Atlantic mackerel (Scomber scombrus), Norwegian spring-spawning herring (Clupea harengus) and blue whiting (Micromesistius poutassou) in the Norwegian Sea during late summer

Langøy

Nøttestad

Skaret

et al. 2012

Marine Biology Research

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show abstract

“…For example, Shriner et al (2006) showed that the scale of observation profoundly influenced the spatial distribution of species richness hotspots and thus conservation planning priorities. The topic of scale has also received some attention in freshwater (Essington & Kitchell 1999, Fagan et al 2005, Kennard et al 2007, Wilson & Xenopoulos 2008 and marine fish studies (Rose & Leggett 1990, White & Warner 2007. In contrast, estuarine finfish studies rarely deal explicitly with issues of scale.…”

Section: Introductionmentioning

confidence: 99%

Abiotic and biotic factors influence the habitat use of an estuarine fish

Bacheler¹,

Paramore²,

Buckel³

et al. 2009

Mar. Ecol. Prog. Ser.

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For migratory fishes that occupy multiple habitats, surveys conducted at different scales may be required to assess habitat use. We used generalized additive models (GAMs) to relate water quality, microhabitat, geographic, and temporal factors to catches of 2 age classes of subadult red drum Sciaenops ocellatus from a 6 yr fishery-independent gill net survey in North Carolina, USA. Age-1 and age-2 red drum were most often caught in shallow, nearshore waters; in some regions, both showed a preference for seagrass. Age-1 red drum were primarily captured at 2 different salinity ranges (0 to 5 and 20 to 30 psu), while age-2 red drum abundance was not related to salinity. A smaller-scale analysis of 36 telemetered age-2 red drum in a small tributary of the Neuse River showed a negative response to salinity, a positive response to dissolved oxygen, a dome-shaped response to prey evenness, and a positive response to total prey. Although subadult red drum can tolerate a wide variety of environmental conditions, our research suggests that they associate with both abiotic and biotic factors in very specific ways. Habitat use patterns of subadult red drum were age-, scale-, and sometimes region-dependent, highlighting the need for examining habitat use patterns of estuarine organisms at multiple life history stages and scales if generalities about how species respond to abiotic and biotic factors are sought.

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“…Most experimental studies, however, are performed at small (Englund & Cooper, 2003) and often rather arbitrary (Wheatley & Johnson, 2009) scales, and thus, if not scaled up properly might not provide the best insight into the problems that they were intended to address. Studies in other systems have shown that predator–prey interactions and abundance patterns vary with observational scale (e.g., bivalves: Seitz & Lipcius, 2001; fishes: Rose & Leggett, 1990; seabirds and fish: Fauchald et al., 2000). Spatially explicit models have also demonstrated that the population dynamics of a predator–prey metacommunity can be scale dependent (e.g., De Roos, Mccauley, & Wilson, 1991).…”

Section: Discussionmentioning

confidence: 99%

Predator–prey interactions in a ladybeetle–aphid system depend on spatial scale

Lin

Pennings

2018

Ecology and Evolution

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The outcome of species interactions may manifest differently at different spatial scales; therefore, our interpretation of observed interactions will depend on the scale at which observations are made. For example, in ladybeetle–aphid systems, the results from small‐scale cage experiments usually cannot be extrapolated to landscape‐scale field observations. To understand how ladybeetle–aphid interactions change across spatial scales, we evaluated predator–prey interactions in an experimental system. The experimental habitat consisted of 81 potted plants and was manipulated to facilitate analysis across four spatial scales. We also simulated a spatially explicit metacommunity model parallel to the experiment. In the experiment, we found that the negative effect of ladybeetles on aphids decreased with increasing spatial scales. This pattern can be explained by ladybeetles strongly suppressing aphids at small scales, but not colonizing distant patches fast enough to suppress aphids at larger scales. In the experiment, the positive effects of aphids on ladybeetles were strongest at three‐plant scale. In a model scenario where predators did not have demographic dynamics, we found, consistent with the experiment, that both the effects of ladybeetles on aphids and the effects of aphids on ladybeetles decreased with increasing spatial scales. These patterns suggest that dispersal was the primary cause of ladybeetle population dynamics in our experiment: aphids increased ladybeetle numbers at smaller scales because ladybeetles stayed in a patch longer and performed area‐restricted searches after encountering aphids; these behaviors did not affect ladybeetle numbers at larger spatial scales. The parallel experimental and model results illustrate how predator–prey interactions can change across spatial scales, suggesting that our interpretation of observed predator–prey dynamics would differ if observations were made at different scales. This study demonstrates how studying ecological interactions at a range of scales can help link the results of small‐scale ecological experiments to landscape‐scale ecological problems.

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The Importance of Scale to Predator‐Prey Spatial Correlations: An Example of Atlantic Fishes

Cited by 218 publications

References 18 publications

Overlap in distribution and diets of Atlantic mackerel (Scomber scombrus), Norwegian spring-spawning herring (Clupea harengus) and blue whiting (Micromesistius poutassou) in the Norwegian Sea during late summer

Overlap in distribution and diets of Atlantic mackerel (Scomber scombrus), Norwegian spring-spawning herring (Clupea harengus) and blue whiting (Micromesistius poutassou) in the Norwegian Sea during late summer

Abiotic and biotic factors influence the habitat use of an estuarine fish

Predator–prey interactions in a ladybeetle–aphid system depend on spatial scale

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